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 of Energy has recently completed the following documents: "Draft Report on 

the Potential Impact of Alternative Efficiency Levels for Room Air Conditioners," "Draft Report 

on the Potential Impact 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 engineering and economic 

analyses of the United States room 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 environment and utility companies. 

The Department has initiated a comprehensive process improvement effort to examine issues 

surrounding the appliance standards program including those relating.to the planning and 

prioritization process, 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 analytical methods 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, 1000 Independence 

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- 81 19 regarding Residential Cooking Products or Kathi Epping at (202)586-7425 

regarding Room Air Conditioners . 

~ichbel J.~cdbe, Director 

0fic6 of Codes and Standards 

Building Technology, State and 

Community Programs 

Energy Efficiency and Renewable Energy 

Attachments: 

@ Printed with soy ink on recycled paper 

EE-RM-93-801-RAC COMMENT 01

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 of Energy has recently completed the following documents: "Draft Report on 

the Potential Impact of Alternative Efficiency Levels for Room Air Conditioners," "Draft Report 

on the Potential Impact of Alternative efficiency Levels for Residential Cooking Products," and 

"General Methodology for the Analysis of Alternative Efficiency Levels for Room Air 

Conditioners and Residential Cooking Products." The reports include engineering and economic 

analyses of the United States room 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 environment and utility companies. 

The Department has initiated a comprehensive process improvement effort to examine issues 

surrounding the appliance standards program including those relating to the planning and 

prioritization process, data collection and analysis, and decision making criteria. As part of this 

effort, the Department 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 analytical methods used. 

If you have any comments, please send them to: Ms. Sandy Beall, Office of Energy Efficiency 

and Renewable Energy, Mail Station EE-431, U.S. Department of Energy, 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 of Energy 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 
GENERAL METHODOLOGY FOR THE ANALYSIS OF 

ALTI5RNATIVE 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 of Codes and Standards 

 EE-RM-93-801-RAC COMMENT 01

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 of Codes 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 according to statutorily required economic and .technical criteria. 

As -part of the Department's. overall regulatory reform effort, we have initiated a 

-comprehensive review of the appliance energy efficiency rulemaking process. Throughout this 

process ilmprovement, the Department has solicited comments from stakeholders through 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 

alternative efficiency levels for the room air conditioner and 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 Document pubiished 

in November 1993, upon which the ~epartment's Notice of Proposed Rulemaking (NOPR) on 

March 4, 1994 for room air conditioners and 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 

 EE-RM-93-801-RAC COMMENT 01

PREFACE 

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 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 levels for 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 of Energy's Office of Energy Efficiency and Renewable Energy by the Energy Analysis 

Program at the Lawrence Berkeley National Laboratory. 

TItis study is an extensively revised version of the 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 of the analyses. 

Volume 1: General Methodology Page pol 

I 
. TABLE OF 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 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 LB'NL Residential Energy Model (LBNL-REM) ........:....... 3-8 

3.3.3 LBNL Manufacturer 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 Sensitivity Analysis ............................... 3-22 

3.3.4 Appliance Standards Environmental and Utility Model (ASEUM) . 3-27 

3.3.5 Life-Cycle Cost Analysis. payback Period, and 

Cost of Conserved Energy ................................. 3-30 

3.3.6 ~nvironmental Analysis .. .' ................................ 3-30 

3.4 SENSITIVITY ANALYSIS ........................ : ............. 3-32 

CHAPTER 4 . INDUSTRY PROFILE FOR LAWRENCE BERKELEY NATION& 

a LABORATORY MANUFACTURER IMPACT ANALYSIS' ....... 4-1 . 4.1 DEMAND .................................................... 4-1 

4.2 SUPPLY ... 1 ..................................................... b-6 ... 

4.3 :PRICES ............................. / .......................... 4-14 

CH APTE. R 5 . DEVELOPMENT OF BASE CASE FORECASTS ............... 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 Residential Energy Prices ......... ; ; ....................... 5-3 

5.2 SE~SITNITY ANALYSIS OF THE BASE CASE ....... ; ...,....... 5-5 

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

APPEND.IX A . UNCERTAINTY ANALYSIS FOR ENERGY EFFICIENCY ..... A- 1 

Volume.1: Table of Contents:Ceneral Methodology i 

 EE-RM-93-801-RAC COMMENT 01

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 of Conserved 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 of Contents:General Methodology i 
APPENDIX B . FORECASTING MODELS ................................ : B- 1 

B . 1 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 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 Since Previous Technical Support'Document ............ B-6 

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

APPENDIX C . LAWRENCE BERKEFY NATIONAL LABORATORY . . 

MANUFA(31TURER ANALYSIS MODEL ..................... C- 1 

C . 1 , . OVERVIEW OF THE 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.Theory and Assumptions .......................... C-3 

C.2.2 . Structural Assumptions .................................... 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~ 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 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 Generating Assumptions ....................... C-32 

C.2.4.2 Control Factors in the Control . . Panel .................. C-33 

C.2.4.3 Engineering Data .................. ; ............... C-34 

ii .. Volume I: ~able'of ~ontents:~eneral Methodology . . 

 EE-RM-93-801-RAC COMMENT 01

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 of Impact .. ; .................................... 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-i 3 

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'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:Gfmeral Methodology 
I 

. . 

. 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 Economic Analysis 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 Industry NPV Module .... ; .. C-41 

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

APPENDK ID . MAINTENANCE COSTS FOR INTERMllTENT IGNITION 

DEVICES IN GAS RANGES AND OVENS .................. D-1 

D . 1 ANALYSIS APPROACH ....................................... D- 1 

D.2 GENERAL DESCRIPTION OF INTERMlTTENT 

IGNITIONDEVICES(IIDS) ................................... D-1 

D.3 lvIAINTENANCE COSTS FOR IID COMPONENTS ................. D-2 

D.4 l&UN"IENANCE LIFETIME DISTRIBUTION AND PRESENT 

'VALUE OF MAINTENANCE COST ............................ D-2 

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

E . 1 1NT.RODUCIION ................................................ E- 1- 

E.2 AVQIDED COST: BACKGROUND AND 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 

ENVIRONMENTAL ASSESSMENT 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 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 . FESULTS .................................................. EA-7 

3.1 ROOM AIR CONDITIONERS ........................... EA-8 

3.1.1 Sulfur Dioxide and Nitrogen Oxide ~rnissions ........... 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- 10 

. . 

Volume I: ;Fable of Contents:General Methodology iii 

 EE-RM-93-801-RAC COMMENT 01

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 of the 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 of Capacity Savings .................................. E-S 

E.4.2 Amount of Capacity Savings .~ .............................. E-7 

EA.3 A voided Transmission and Distribution Capital Costs .. : ........ E-I0 

ENVIRONMENTAL ASSESSMENT 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 oj Contents:General Methodology iii 
Table 3.1 

Table 3.2 

Table 3.3 . 

Table 3.4 

Table 4.1 

Table 4.2 

-Table 4.3 

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 1 S.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 . 

Acquisitions of Appliance Manufacturers ................. : .. 4-12 r 

Occupied Households Projections (millions) ................... 5-2 

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

~is~osable personal Income (1 9h$) ........................... 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 of Thermal IntegrityICost Curves by ~ouse Type ...... B-3 

Percent Share of Each Class of Room 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..and Ovens ........... 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 for Mean Lifetime of IID Units ..................... 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 NOx Power Plant Emissions ............... EA-4 

Electricity Generation. Emissions Data. and Emissions 

Rates for SO2 and NOx at Fossil Fuel-Burning Power Plants ... EA-5 

Ernissiori Rates for SO2 and 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:GeneraL Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 

Table 5.3 

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 of Product Classes ...... : .... ' .... :. 3-1 

'Sensitivity of ROE 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 of Market Controlled by the Largest Producers of 

, Each Product ... -.................................... ' ... 4-7, 

Acquisitions of Appliance 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 of Thermal mtegrity/Cost Curves by House Type ...... B-3 

Percent Share of Each Class of Room Air Conditioners .......... B-4 

Percent Share of Each Class of Electric Cooktops " ......•........ B-4 

Percent Share of Each Class of Electric and Gas Ovens ........... B-4 

Survival Function of Room 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 of Capacity Need by ~RC 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. 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,- 1 1. 

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,-2 1. 

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 

Volume I: Table of Conlents:General Methodology . . 

 EE-RM-93-801-RAC COMMENT 01

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 of Pollutants 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 Room 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 Conventional Ovens, Level O.ne . . .. 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 ... Et\-25 

Redqction 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 Three:; ..... EA-28 

Reduction of ~ollutants for Microwave Ovens, Level Four . . . .. EA-29 

Reduction of Pollutants for Microwave Ovens~ Level Five· ~ .... E~-30 

Volume 1: Table ojCon'en's:General Methodology 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 

Activities in the LBNGREM .............................. 3-10 

Conceptual Approach to the Manufacturer Analysis Model ....... 3-16 

How Alternative Efficiency Levels Cause a Change in ROE 

andNPV ............................................. 3-20 

Kitchen Range Shipments and Housing. Starts .................. 4-3 

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

Room Air Conditioner Shipments and 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 Conditioner Firm 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 

Volume 1: Tdle of Contents:General Methodology 

 EE-RM-93-801-RAC COMMENT 01

• 

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 of Appliance Alternative 

Efficiency Levels ... . . : . . . . .'. . . . . . . . . . . .. . . . . . . . . . . . . . ~ . 3-4 

Activities in the 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 Group PPI ............. ',' ... '. . . .. . . . . . . . . . . . . . . 4-15 

National CPI and PPI Compared to Room Air 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 of Fixed Costs ........ ' .................. C-5 

MAM Modules and Primary Routs of Data 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:General Methodology 
- [F 

. . 'i , .. 

'CHAPTER 1. INTRODUCTION 

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

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

The legislation requires the Department of ~ner~~@O~) to consider new or amended standards for 

these andl,other types of products at specified times. DOE.is currently analyzing alternative energy 

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 womic impact 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 Consumer Anulysis, 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 Impact Analysis, which provides an integrated framework for assessihg the costs 

- and benefits of implementing 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 

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 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 ' 

lowefficiency units currently being manufactured: The analysis identifies a series of design options 

to improve energy efficiency and estimates the factory costs to produce them. Design options are 

added individually or in combination to the baseline unit to evaluate units that might be produced 

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

consumer 

Volume I:- General Methodology 

 EE-RM-93-801-RAC COMMENT 01

CHAPTER 1. INTRODUCTION 

( 

. , 

/ 

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

conservation standards for 12 of the 13 types of co1.lSumer products specificaiIy covered by the Act. 

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

thes,e andt·other types of products 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 (of the energy and economic impacts of the 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 Consumer Analysis, which forecasts appliance sales, efficiencies, energy use, and ' 

consumer expenditures. 

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

to the alternative efficiency levels. Their responses are quantified by changes in 

several financial perfonnance. measures fOI: a prototypical finn. 

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

. and benefits of implementing 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-Benefit Analysis that collects the results of all 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 of design 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: General Methodology Page 1-/ 
prices, and life-cycle cost curves are constructed for each class. The payback periods associated with 

each incremental change in efficiency are also calculated. 

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

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

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

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

present value of the alternative efficiency levels are computed by discounting the differences in the 

time streams of these expenditures in the "with" and "without" efficiency level cases. 

The Manufacturer Impact Analysis estimates the overall impact of imposing new 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 of the 

i,ndustry as 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 examines the 

possibility of price-cutting while the industry is adjusting to a lower sales volume. 

The Life-Cycle Cost Analysis evaluates 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 costs represent the marginal value of lower fuel consumption as well 

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

lead to 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 not just 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 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of new appliances by class, as well 

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

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

present value of the alternative efficiency levels are computed by discounting the differences in the 

time streams of these expenditures in the "with" and "without" efficiency level cases. 

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

efficiency levels on manufacturers. The analysis examines long-run impacts on.l)profitability, .2) 

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

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

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

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

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

The Industry Impact Analysis analyzes two short-run impacts as well. First, the ability of the 

. 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, if implementing new efficiency 

levels result in decreased sales for the particular industry being analyzed, the analysis examines the 

possibility of price':"cutting while the industry is adjusting to a lower sales volume. . 

The Life-Cycle Cost Analysis evaluates 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 costs represent the marginal value of lower fuel consumption as well 

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

lead to deferring the construction of new generating capacity. Because fuel costs and the need for 

additional capacity are region-specific, the Utility Analysis is conducted on a regional basis. 

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 not just 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 PURPOSE AND SCOPE 

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

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

Conservzition Amendments of 1988 (P.L. 100-357), provides energy conservation standards for 12 

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

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

The assessment of the alternative energy efficiency levels for the two types of products2 ' 

presented in this document is designed to evaluate their economic impacts according to the criteria 

in the Act. It includes an engineering analysis of the cost and performance of design options to 

improve the efficiency of the products; forecasts of.the number and average efficiency of products 

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

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

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 THE DOCUMENT 

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 !of each 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) dishwaslhersi 7) clothes washers; 8) clothes dryers; 9) direct heating equipment; 10) kitchen ranges and ovens; 1 1) 

pbol, heaters; 12) television sets; and 13) fluorescent lamp ballaists. 

The 2 types of products analyzed in this rulemaking are: 1) room air conditioners; and 2) kitchen ranges and ovens 

(including :microwave ovens). 

Volume I: General Methodology Page 2-1 

 EE-RM-93-801-RAC COMMENT 01

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 

of the 13 types of consumer productsl co:vered by the Act, and authorizes the Secretary of Energy 

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

The assessment of the 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 of the products; forecasts of the number and average efficiency of products 

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

determina~on of change in investment, revenues, and costS. to manufacturers ot the products; a 

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

an a$sessment of the environmental impacts of the 8Iternative efficiency levels. 

2.2 STRUCTURE OF THE DOCUMENT 

This report consists of two 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 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. 

containin.g analysis results for the product of interest. 

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 of products 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: outlines the structure of the document. 

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

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

their data requirements, and their outputs; and identifies the primary assumptions of 

the analysis. This chapter also provides an overview of the methodology employed 

to examine the sensitivity of the results to changes in the key assumptions, 

parameters, and exogenous forecasts used in the analysis. ' 

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

structure. 

Chapter 5 Development of Base Case ~orkasts: 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 of maximum technologically feasible energy 

efficiency. 

Appendix B Overview of the forecasting model: structure of the LBNL Residential Energy Model 

(LBNLREM) program. The principal components of the model are discussed and 

the component interrelationship is explained. Iq volume 2. Appendices - B describe . and define the inputs to the model specific to each respective end use. 

Appendix C Overview of Manufacturer Analysis Model (LBNLMAM): outlines how an industry 

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

on appliance manufacturers of imposing efficiency levels by product are quantified; 

defines the results of the analysis in tek of changes in gross rnargin, retum on sales, 

return on equity, return on assets, total assets,'and labor requirements; and presents 

the method for conducting a sensitivity analysis of the results. 

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

appliance type. 

Appendix E Electric utility impact modeling. 

. Page 2-2 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Chapter 1 

Chapter 2 

. Chapter 3 

Chapter 4 

Chapter 5 

V~lume 1 contains five· chapters and five appendice~: . 

Executive Summary: presents an overview of the analysis. 

Introduction: outlines the structure of the document. 

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

components of the 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 of maximum technologically feasible energy 

efficiency. 

Appendix B Overview of the forecasting model: structure of the LBNL Residential Energy Model 

(LBNL-REM) program. The principal components of the 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 of the manufacturing finns affected is constructed; describes how the effects 

on appliance manufacturers of imposing efficiency levels by product are quantified; 

defines the results of the analysis in terins of changes in gross margin, return on sales, 

return on eqUity, return on assets, total assets,'and labor requirements; and presents 

the method for conducting a sensitivity analysis of the results. 

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

appliance type. 

Appendix E Electric utility impact modeling. 

Page 2-2 Volume 1: General Methodology 
Environmental ~ssessment: Quantifies impactsof 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 ;md B) is product-specific and each Pg is 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 Alternative Egciency Levels: discusses the choice df . . 

efficiency levels to be analyzed and the projected impacts of each efficiency level. 

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

efficiencies, purchase costs, and operating costs of new and replacement appliances; 

and the net present benefit of the alternative efficiency levels: 

Chapter.4 ' - Life-Cycle Costs and Payback Periods: describes the effects of imposed efficiency 

levels on individual purchasers 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 Efficiency Levels on ~~~~~~~~~~: describes the analysis 

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

Chapter 6 Impact of Alternative Efficiency 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 databases used in the economic analysis of .tach product. 

Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of energy consumption. efficiency 

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

of new regulations. 

Projected National Impacts of Alternative EffIciency Levels: discusses the choice of 

efficiency levels to be analyzed and the projected impacts of each efficiency level. 

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

efficiencies, purchase costs, and operating costs of new and replacement appliances; 

and the net present benefit of the alternative efficiency levels~ 

. Life-Cycle Costs and Payback Periods: describes the effects of imposed efficiency 

levels 'on individual purchasers and users of appliances. It compares the life-cycle 

cost of appliances and 'other measures of ,consumer impact with arid without' 

implementing new efficiency levels. 

Impacts of Alternative Efficiency Levels on Manufacturers: describes the analysis 

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

Impact of Alternative 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 of fossil 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 a breakdown 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: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Appendix A Energy Use and Cost Data: provides a breakdown of the 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 of covered products. The appendix shows the structure of the model, 

the data sources used, and detailed outputs of base 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 . 

relationship between 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 

segregated product 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, encrgyconserving designs are ordered by cost-effectiveness; then combinations of energysaving design options are used to generate the relationships that .are inputs 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 feasible efficiencies is based on information gathered from the trade associations, 

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

Notice of Proposed Rulemaking (ANOPR) and the Notice of Proposed Rulemaking (NOPR). Energy 

use data have been obtained from AHAM (Association of Home Appliance Manufacturers), GAMA 

(Gas Appliance Manufacturers Association), lTC (Federal Trade Commissiori), manufacturers, and 

simulation ~odels. Sources of factory cost data include manufacturers, suppliers, and the ANOPR 

and NOPR comments. The cost estimates are combined with the efficiency estimates to generate the 

cost-efficiency relationships. , \ 

The relationships between manufacturer costs and efficiency that are presented 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 

Appendix D of this volume and Chapter 1 of Parts A and B of this report). The manufacturer analysis 

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

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

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

Table 3.1 Product Types and N,umber,of Product Classes 

. . 

Product ?'= Number of Classes 

~oom Air Conditioners 14 

Kitchen Ranges and Ovens 8 

Volume I: General Methodology Page 3-1 

 EE-RM-93-801-RAC COMMENT 01

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 of classes of ~h product type are shown in Tabl~ 3.1. Within each 

class, the energy-conserving designs are ordered by cost-effectiveness; then combinations of energysaving d(~sign options are used to generate the relationships that ,are inputs 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 feasible efficiencies is based on information gathered from the trade associations. 

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

Notice of Proposed Rulemaking (ANOPR) and the Notice of Proposed Rulemaking (NOPR). Energy 

use data have been obtained from AHAM (Association of Home Appliance Manufacturers), GAMA 

(Gas ApI)liance Manufacturers Association), FfC (Federal Trade Commission). manufacturers. and 

simulation n;todels. Sources of factory 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. ' 

The relationships 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 of this volume and Chapter 1 of Parts A and B of this 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 'of Product 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 chapter briefly describes how the economic analysis of alternative efficiency levels was 

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

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

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

how these components fulfdl the seven legislative requixements listed in the Introduction. This 

section discusses the interrelationships among the components that ensure consistency throughout 

the analysis. 

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

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

imposition of 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; 

Consumer Impact Models; 

Manufacmr Impact ~odels; 

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 performed on the economic impacts and 

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

determine their range of variation. 

3.2 OVERVIEW OF ANALYTIC FRAMEWORK 

The impact of implementing 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 effciency levels. For each product analyzed, these pro~ections are fmt made 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 differences between 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 significance of the differences, a sensitivity analysis is 

performed on the key parameters and assumptions. 

Page 3-2 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

This chapter briefly describes how the economic analysis of alternative 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 of the models. The models utilized in the analysis 

are: 

• Engineering Cost and Performance Models; 

• Consumer Impact Models; 

• Manufacturer Impact Models; 

• Utility Impact Model. 

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

discussed in the product-specific 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 of variation. 

3.2 OVERVIEW OF ANALYTIC FRAMEWORK 

The impact of implementing 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 then repeated imposing the· alternative efficiency levels as discussed in Chapter 3 

of Parts A and B. The differences between 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 significance of the 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 Engineering Analysis, which establishes technical feasibility and product attributes 

including costs of design options to improve appliance efficiency; 

A Consumer Analysis at two levels:' national aggregate impacts, and impacts on 

individuals. The national aggregate impacts include 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 Manufacturer dnalysis, which provides an estimate of kufacturers' response to the 

alternative efficiency levels. Their response is quantified by changes in several measures 

of financial performance for a finn; 

An Industry Impact Analysis, which shows financial and competitive impacts on the 

appliance industry; 

A Utility Analysis that measures the impacts of the altered energy-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.power plant; and 

A l?egulatory impact Analysis. which collects the results of all the analyses into the net 

benefits and costs from a national perspective. 

I' 

A simplified diagram of the analysis is 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 Engineering Analysis, then the 

legislation requires 'that no modified standard be prescribed for that product type. If the appliance 

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

economic, and impact analysis is done. For each of the products, the analysis is perfomed for a base 

case plus several alternative efficiency levels; The'selection of efficiency levels by class is described 

in Chapter 3 of each product-specific Part. 

Volume I: General Methedology 

 EE-RM-93-801-RAC COMMENT 01

. The economic. iI;t1pact analysis is performed in seven major areas: 

• An Engineering Analysis, which establishes technical feasibility and product attributes 

including costs of design options to improve appliance efficiency; 

.. 

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

• 

individuals. The national aggregate impacts include 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 Manufacturer Analysis, which provides an estimate of manufacturers' response to the 

alternative efficiency levels. Their response is quantified by changes in several measures 

of fmancial performance for a frrm; 

• An Industry Impact Analysis. which shows financial and competitive impacts on the 

appliance industry; 

• A Utility Analysis that measures the impacts of the altered energy-consumption patterns 

on electric utilities; 

• An Environmental Effects Analysis, which estimates changes in emissions of carbon. 

dlioxide, sulfur oxides, and nitrogen oxides, because of ~uced energy consumption in 

, the home and at the·power plant; and 

• A RegUlatory Impact Analysis,whichcollects the results of all the analyses into the net 

benefits and costs from a national perspective. 

A simplified diagram of the analysis is shown in Figure 3.1. Each type of analysis is 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. If the appliance 

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

economic, and impact analysis is done. For each of the 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 of each product-specific Part. 

. Volume 1: General Methildology Page 3-3 
Mmufretu 

Analysis 

* * 

Manufacturer Cost vs. Efficiency 

Retail Price vs. Efficiency 

Manufacturer Impacts 

Life-Cycle Costs 

National Energy 

Net Present Value to Society . 

Consumer Payback 

Generating Capacity 

Emissions (CO,, NO,, SO,) 

Figure 3.1 Analwc Framework for the Analysis of Appliance Alternative Efficiency 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 determine financial impacts on typical firms within the industry. The consumer analysis 

also forecasts energy savings and consumer expenditures for the purchase and operation of the 

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

impacts. Changes in sales, revenues, investments, and marginal costs of utilities are calculated from 

the energy savings in the Utility Analysis. . . 

. . 

Three time frames, are considered by the analysis. First, the analysis of consumer and utility 

impacts extends over a time frame consistent with the. life of the products and includes the time 

required to approach market saturation. This time frame extends to 2030 and the new efficiency 

levels come into effect in 1929. Second, the Manufacturer Analysis is performed for a typical year 

Page 3-4 Volume I: General Metltodology 

 EE-RM-93-801-RAC COMMENT 01

~ • 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 ~ • Consumer Payback 

y Utility 

I ADaJYliJ • • Generating Capacity 

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

Figure 3.1 Analytic Framework for the Analysis of Appliance 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 of new and replacement appliances. These data are used as inputs to the Manufacturer 

Analysis to determine financial impacts on typical firms within the industry. The consumer analysis 

also forecasts energy savings and consumer expenditures for the purchase and operation of the 

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

impacts. Changes in sales, revenues, investments, and marginal costs of utilities are calculated from 

the energy savings in the Utility Analysis. 

Three time frames are considered by the analysis. First, the analysis of consumer and utility 

impacts extends over a time frame consistent with the. life of the products and includes the time 

required to approach market saturation. This time frame extends to 2030 and the 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 
afler the i,mplementation of new efficiency levels. Third, the Engineering Analysis examines the 

technical feasibility of improving the efficiency of appliances before the efficiency levels come into 

effect-within the next three years. 

3.3 MODELS, DATA, AND ASSUMPTIONS, 

33.1 Engineering Cost and Performance Models ' 

The Engineering Analysis provides information on efficiencies, manufacturing costs, and other 

appliance characteristics for use 'in other components of the analysis. Appliance features that provide 

utility to the consumer are incorporated into the analysis through the creation of appliance classes. 

Classes are a subset of appliance types. For example, a water heater is an appliance type, but a 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.s performed in seven steps: 1) select applisince classes; 2) select baseline units; 3) select 

design options within each class; 4) determine maximum technically feasible designs; 5) calculate 

the efficiency improvement provided by each design option; 6) develop cost estimates; 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 alternative energy efficiency levels apply. DOE differentiates classes by 

the type of energy used (oil, natural gas, or electricity), and capacity or performance-related features 

that provide utility to the consumer and affect efficiency. For specific appliances, classes & 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~etiurr: has been specified are omitted. The appliance classes covered by the analysis are listed 

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 idspecific 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 I: General Methodology Page 3-5 ' 

 EE-RM-93-801-RAC COMMENT 01

· . 

after the implementatiori of new efficiency levels. Third, the Engineering Analysis examines the' 

, technical feasibility of improving the efqciency of appliances 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 of the analysis. Appliance features that provide 

utility to the consumer are incorporated into.the analysis through the creation of appliance classes. 

Classes are a subset of appliance types. For example, a water heater is an appliance type, but a gas-

'fired storage water heater is an appliance class. The Engineering Analysis develops cost and 

efficiency data for a set of design options within each appliance class. These data are the output of 

the engineering performance and cost models discussed later in this section. The Engineering 

Analysis is performed in seven steps: 1) select appliance classes; 2) select baseline units; 3) select 

design options within each class; 4) determine maximum technically feasible designs; 5) 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 of energy used (oil, natural gas, or electricity), 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 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 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 or that are present in prototypes are considered. They are selected after discussions with 

experts and an extensive literature review. The efficiency improvement and manufacturer cost of 

design options added to the baseline unit are calculated. 

Maximum Technologically Feasible Designs 

For each product class, a maximum technologically feasible design optian or combination of 

design options is identified. This option, or combination of options, results in the highest energy 

efficiency for each product class. The maximum technologically feasible efficiency level is one that 

can be reached by the addition of design options, both commercially feasible and in prototypes, to 

the baseline units. This design must be possible to assemble, but not necessarily to manufacture in 

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

For each of the product classes, the efficiency levels corresponding to various design options 

are determined from manufacturer data and from engineering calculations. 

Cost Estimates 

The manufacturer cost data are obtained through a lengthy process that included meetings and 

tours at manufacturing facilities, submission of formal requests to manufacturers for costing data, 

and review of the data received. Estimates of manufacturer cost are also received in response to the 

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 the Analysis 

Justifications for the most important assumptions of the Engineering Analysis are listed below. 

Page 3-6 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Design Options 

The Engineering Analysis identifies individual or combinations of design options with a 

potential for improving energy efficiency. Design options that are commercially available at the 

present time or that are present in prototypes are considered. They are selected after discussions with 

experts and an extensive literature review. The efficiency improvement and manufacturer cost of 

'design options added to the baseline unit are calculated. 

Maximum Technologically Feasible Designs 

For each product class. a maximum technologically feasible design option or combination of 

design options is identified. This option. or combination of options. resolts in the highest energy 

efficiency for each product ciass. The maximum technologically feasible efficiency level is one that 

can be reached by the addition of design options. 'both commercially feasible and in prototypes. to 

the baseline units. This design must be possible to assemble. but not necessarily to manufacture in 

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

Efficiency Calculation 

For each of the product classes, the efficiency levels corresponding to various design options 

are determined from manufacturer data and from engineering calculations. 

Cost 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 of the data received. Estimates of manufacturer cost are also received in response to the 

ANOPR and NOPR. In each product-specific Part. Appendix A contains detailed incremental cost 

data dis aggregated 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 of the appliance. Additional installation costs required for some 

designs are included in the purchase price. Additional maintenance costs associated with specific 

design options are also estimated. The price-efficiency relationships are a fundamental input to the 

, Consumer Analysis. . 

Assumptions in the Analysis 

Justifications for the most important assumptions of the Engineering Analysis are listed below. 

Page 3-6 Volume 1: General Methodology 

•. 
.. 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 fraction of total 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 starting point to which 

usage estimates, unless otherwise noted. design options are added to create higher 

efficiency units. The selection of baseline units 

. . is based on studies of products on the market in 

- 1990 and consultations with manufacturers. 

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 . . 

employed to detepnine unit 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. 

Volume I: General Metltodology 

 EE-RM-93-801-RAC COMMENT 01

, AssumptionIndustry production processes can be characterized in terms of medium 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 of total shipments for most of the 

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 of baseline units 

is based on studies of products on the market in 

, 1990 and consultations with manufacturers. 

As part of .the Consumer Product Efficiency 

Standards program, DOE was required to 

establish test procedures to determine average 

consumer utilization and energy usage for ·the 

'covered products. Test procedures for each 

product type have been promulgated and were 

employed to 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 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 infOmiation from industry sources and pub,lished data . 

. Volume I: General Methodology 'Page 3-7 
Outpirts from the Engineering Analysis 

For each combination of design options considered in the analysis, the models and data 

provide: 

energy efficiency (expressed as the DOE energy factor' andlor unit 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 characteristics such as appliance lifetimes, installation costs, 

and maintenance costs. 

Validation of the Results 

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

the others, engineering calculations based on physical principles are performed. 

33.2 LBNL ~esidentia~ Energy Model (LBNEREM) 

Purpose 

The LBNLREM models the appliance purchase 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 measurement of energy efficiency derived from the DOE test procedure for each product. 

Page 3-8 Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

It 

OutpUts from the Engineering Analysis 

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 of the 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 of existing 

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 of energy efficiency derived from the DOE test procedure for each product. 

Page 3-8 Volume 1: General Methodology 
Historical Development 

Early energy-demand modeling focused on engineering estimates or on the relationship 

between energy consumption and economic growth. In the 1970s. Oak Ridge National Laboratory 

(ORNL) developed the first model to integrate these two important aspects of energy demand, the 

Engineering-Economic Model of Residential Energy Use (1). 

The OWL 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 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 the Model * 

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 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, 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 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 of the five types of activities is 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 1-199 1) are from Census data; housing 

starts andl stocks (1992-2010) are from Annual Energy Outlook 1995 (5). The historical housing 

stocks (198 1-1991) are obtained by interpolating between 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 

 EE-RM-93-801-RAC COMMENT 01 , . 

Historical Development 

Early energy-demand modeling focused on engineering estimates or on the relationship 

between €!Oergy consumption and economic growth. In the 1970s, Oak Ridge National Laboratory 

(ORNL) developed the first model to integrate thes~ two important aspects of energy demand, the 

E!lgineerilng-Economic Model of Residenti3J 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 the Model 

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 of the 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 of the 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 and Housing (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 Choice Algorithm 

Historiciil efficiency data are available for selected years through 1992 for room air 

conditioners and kitchen ranges and ovens (depending on availability of 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 slowly than 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 efficiency change. 

Page 3-10 Volume I: General Methodology ' 

 EE-RM-93-801-RAC COMMENT 01

. 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 Choice Algorithm 

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 of data). After 1992, efficiency 

improvements are projected as a function of designs available (technological change) and of 

electricity, oil, or gas prices. If energy prices increase, the life-cycle cost of more efficient designs 

will increase more slowly than that of less efficient designs, making the more efficient designs more 

attractive. When the life-cycle cost of a more efficient design falls below the life-cycle cost of the 

current average design, then the more efficient design is projected to be purchased. However, if 

energy prices decline, the model projects no· further efficiency chang~. 

Page 3-10 Volume 1: General Methodology 
Thermal Zniegri@ 

The projection of investment in thermal..integrity measures in new houses is based on a' . lifecycle: cost calculation analogous to that done for equipment efficien~ies.~ Engineering estimates 

of the inc:remental costs of thermal integrity measunsare used together with current fuel prices and 

a market discount rate for each house type and heating fuel type. Secondary impacts of appliance 

efficiency changes on energy consumed to heat or cool houses are not estimated in the current 

analysis. . , 

Modeling Alte~tive Efiiency Levels 

The LBNLREM projects the average efficiency of new products, purchased each year, taking 

account of existing federal regulations. A distribution of unit energy consumptions (UEC) is 

construct;ed amund the projected average unit energy consumption for each class, based on relative 

efficiency distributions previously observed in the' marketplace; -Federal energy efficiency levels 

would eliminate at least part of the distribution. A new distribution is constructed in which ail units 

below the alternative efficiency levels are increased in efficiency to meet the new efficiency levels.. 

The new shipment-weighted average efficiency then characterizes the efficiency of 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 

improvements that may occur as a result of implementing new efficiency levels, and to calculate the 

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 surveys of appliance holdings. The fraction of each product 

that retin:s each year is based on the number of years since purchase for each 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 of potential purchasers of an appliance in new homes is 'equal to the number of 

nkw homes constructed each year. The number of potential in existing houses is equal 

to the numbex of retiring appliances, plus a small fraction of those households that did not'previo~sl~ 

own the !product. 

-. 

The .equipment efficiency and thermal integrity decisions are not solved simultaneously, but recursively. The 

previous year's thermal integrity is assumed in projecting this year's quipment'efficiency; then this year's equipment 

efficiency is used to calculate this year's thennal integrity. 

' For example, a cohort consists of all '8-year-old gas-fired water-heaters. Up to 30 coh.0rt.s. one for each year of 

purchase, iue tracked. 

Volume I: Geneml MethodologV , ' Page3-11 , 

 EE-RM-93-801-RAC COMMENT 01

Thermal Integrity 

The! projection of investment in thennalintegrity measures in new houses is based on a; 

life-cyc1e: cost calculation analogous to that done for equipment efficiencies.3 Engineering estimates , . 

of the inc:remental costs of thennal integrity measures are used together with current fuel prices and 

a market discount rate for each house type and heating fuel type. ,Secondary impacts of appliance 

efficiency changes on energy consumed to heat or cool houses are not estimated in the current . 

analysis. . 

Modeling Altemotive Efficiency Levels 

The! LBNL-REM projects the average efficiency of new 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 of the 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 of new 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 of implementing 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 of appliance holdings. The fraction of each product 

that retin~s each year 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 of potential purchasers in existing houses is equal 

to the number of retiring appliances, plus a small fraction of those 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 of all 'S-year-old gas-fired water-heaters. Up to 30 coh9rts, one for each year of 

purchase, nre tracked. 

Volume 1: General Methodology Page 3-11 
Calculation of 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 or not 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 expected to lower operating 

expenses and increase equipment prices. The percentage changes in these quantities are used, 

together with 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 Cakulations - ' 

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 intermediate results. For 

example, national electricity consumption for 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 Consumer Analysis assumes that decisions on the purchase and use of appliances depend 

on operating expenses, household income, and appliance prices. Manufacturers are projected to 

respond to the demand for more efficient products by incorporating technologically feasible and 

costeffective design options in new units. Forecasts of population growth, housing starts, personal 

income, and energy prices from published sources are utilized. Justifications for the most important 

assumptions in the Consumer Analysis are listed below. 

. Page 3-12 Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Calculation of 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 or not is modeled, and the fraction of the 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 of households 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, 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 Consumer Analysis assumes that decisions on the purchase and use of appliances depend 

on operating expenses, household income, and appliance prices. Manufacturers are projected to 

respond to the demand for more efficient products by incorporating technologically feasible and 

cost-effective design options ~n new units. Forecasts of population growth, housing starts, personal 

income, and energy prices from published sources are utilized: Justifications for the most important 

assumptions in the Consumer Analysis are listed below. . 

. Page 3-12 Volume 1: General Methodology 
Assumption 

1 

Justification 1 

' . Occupitd households will increase from 93 The Annual Energy Outlook 1995 projects annual 

, million in 11990 to 131 million in 2030. . growth of 1.0%. 0.6%. and 0.3% for single family, 

multifamily, and mobile 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 

$66,400 in 2030. 

, Residential electricity prices are .projected to 

increase from 8.40 cents/kWh in 1992 to 

8.76 cerlts/kWh 2010. Residential natural 

gas prirzes. are projected to increase from 

$5.871 Wtu in 1992 to $6.9UMMBtu in 

20 10. Residential distillate prices are 

projected to increase from $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. 

Engineering designs that 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 

The Annual 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. 

The Annual Energy Outlook 1995 pr~jects~~rowth 

in real disposable personal income for the nation 

as 2.0% per year from 1993 to 2010. 

The Annual Energy Outlook 1995 projects annual 

growth of 0.3% for residential electriciiy prices 

from 1993 to 2010. 

Research on equipment sales foi competipg 

alternatives and on historical efficiency choices 

,indicates that 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. 

Volume I: General Metlzodobgy Page 3-13 

 EE-RM-93-801-RAC COMMENT 01

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 

$66,400 in 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 of energy efficiency. 

Volume 1: General Methodology 

Justification 

The Annual 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. 

The Annual 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. ' 

The Annual Energy Outlook 1995 projects 'growth 

, in real, disposable personal, income for' the nation 

as 2.0% per year from 1993 to 2010. 

The Annual 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 

of historical 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 of possible energy efficiencies of new equipment from the 

Engineering Analysis. The purchase price of these products is derived from the factory cost supplied 

by the Engineering Analysis and adjusted for manufacturer and dealer markups in the Manufacturer 

Analysis. Historical housing stocks and starts are from U.S. Department of Commerce, Bureau of 

the Census. Projected energy prices, household incomes, and housing stocks and starts are from U.S. 

Department of Energy, Annual Energy Outlook 1995. 

Model Outputs - 

. The principal outputs from the.LBNL-REM for each year are: 

National energy consumption by end use and fuel; 

Per-unit equipment price and operating expense by product; 

. Total residential energy consumption by fuel; 

Projected annual shipments of residential appliances; and 

Differences in these quantities between a base case and kach efficiency level case. I 

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 evaluated for each 

regulated product. 

Cost-Benefit Analysis 

1 

The costs and benefits of the alternative efficiency levels from a national perspective are' 

quantified by calculating a net present value. The NPV is the sum of discounted savings in operating 

expenses minus the sum of discounted increases in equipment prices. 

DOE has determined that 7% real is the appropriate discount rate for calculating societal NPV.. 

The-NPV of alternative efficiency levels is also calculated at consumer discount rates of 4% and' 10% 

as' sensitivity analyses. 

 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 appliances differently. 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 

. '\ 

Volume I: General Methodology 

-. , 

 EE-RM-93-801-RAC COMMENT 01

!Jato Sources 

The LBNL-REM takes the range of possible energy efficiencies of new equipment from the 

. Engineering Analysis. The purchase price of these products is derived from the factory cost supplied 

by the Engineering Analysis and adjusted for manufacturer and dealer markups in the Manufacturer 

Analysis. Historical housing stocks and starts are from U.S. Department of Commerce, Bureau of 

the Census. Projected energy prices, household incomes, and housing stocks and starts are from U.S. 

Department of Energy, 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 by product; 

-. Total reSIdential energy consumption by fuel; 

- Projected annual shipments of residential appliances; and 

- Differences in these quantities between a base case and each 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-Benefit Analysis 

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 of discounted savings in operating 

expenses minus the sum of discounted increases in equipment prices. 

DOE has determined that 7% real is the appropriate discount rate for calculating societal NPV. 

TheNPV of alternative efficiency levels is also calculated at consumer discount rates of 4% 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 net present benefit of efficiency 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. For example, 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: General Methodology 
are ~alc~liWi as the net ksult.of implementing new 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 implementing new efficiency levels caused decreased . . 

, . .purchase *of a product, this would appear as an economic benefit, 'namely less money spent' on 

purchasing and use of the appliance? and if implknienting new 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 hlanufacturer Impact Analysis estimates both the short- and long-run impacts of alternative 

efficiency levels on profitability (return on equity) and many other variables for each industry under 

consideration. All computations used in this analysis are carried out by the Manufacturer Analysis 

Model (LE%NLcMAM). The LBNLMAM is a spreadsheet composed of thirteen 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 

Appliance Mmufacturers Association (GAMA), and the Airconditioning and Refrigeration Institute 

(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 for each by explicitly evaluating the standard em inthe 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 

future purchases of the product. Then, no money would be spent on purchasing the product, or on operating expenses, 

and the value of the savings would equal the amount of money that would have been spent without implementing the 

efficiency levels. Clearly, this would be a misrepresentation of the NPV of alternative efficiency levels. 

Volume I: Genc~ral Methodology ~&d 3-15 

 EE-RM-93-801-RAC COMMENT 01

are calculated as the net result- of implementing 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: If the NPV was calculated without nonn~izing to 

shipments, the results would be erroneous: if implementing new efficiency levels caused decreased . 

, ,.purchase -of a product, this would appear as an economic benefit, 'namely less money spent' on 

, purchasing and use of the appliance,S and if implementing 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) . . /-

Conceptual A.pproach 

The Manufacturer Impact Analysis estimates both the short- and long-run impacts of alternative' 

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). The LBNL-MAM is a spreadsheet composed of thirteen modules. It consists 

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 of Home 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 values for the above-mentioned deScriptive variables, the LBNL-MAM also computes 

standarde:~rs for each by, explicitly evaluating the standard errors in the estimates of input ,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 of the prod\.lCl 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 misrepresentation of the NPV ofaltemative efficiency levels. ' 

Volume J: 4'Jenera/ Methodology 
Variable 

Markup 

GRIM 

NPV 

Nat Intoma I I 

Figure 33 Conceptual Approach to the Manufacturer Analysis Model 

ROE is the primary measure of profitability, although gross margin, return on assets, and return 

on sales are also reported. Changes in assets and revenue are the measures of growth (positive or 

negative). Because both of the industries anal* are in an historical process of consolidation, any 

decrease in ROE will tend to hasten this process; thus, ROE also provides an indication of the impact 

of efficiency levels 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 determined by 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 alternative efficiency levels. To accomplish this, the model first estimates how much 

Page 3-16 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of profitability, although gross margin, return on assets, and return 

on sales are ~so reported; Changes in assets and revenue are the measures of growth (positive or 

negative). Because both of the industries analyzed are in an historical process of consolidation, any 

decrease in ROE will tend to hasten this process; thus, ROE also provides an indication of the impact 

of efficiency levels 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 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 price is used to calculate the other measures of impact. 

Description of the LBN~MAM 

Although LBNL-MAM examines many. aspects of an '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 for the product.) Then, it estimates how this 

price increase and the accompanying reduction of operating expense influence demand. Finally, 

given the: new price and level of demand, it computes a new level of profit. These calculations use 

estimated long-run costs. The LBNLMAM makes an additional calculation to correct for any 

short-run1 difficulties the industry may have in achieving its long-run markup and also estimates the 

impact of alternative effi~ienc~levels on industry net 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 increase infixed costs will not be passed on'via a price 

increase wrcept when it is sufficient to cause some firms to leave the industry? This result is hived 

fmm the assumption that firms act individually to aaximize profits and from the definition of fixed 

costs as those that are not proportional to output. The mathematical derivation is given in Appendix , 

C of this volume. . . 

. . 

A second influence on profits is the change in purchases resulting from both the change in price 

and the change in operating expense. These tend to offset eakh other because price increases 'lower 

quantity demanded, and increased efficiency increases demand (by lowering operatinggexpense). The 

LBNLNW takes these effects into account through the use of two demand elasticities which play 

an important role in determining the outcome and are therefore examined more closely in the 

sensitivity analysis. 

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 equilibrium to 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 and passed 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.increase their 

markup over variable costs, thus, indirectly covering their increased fixed costs. 

Volume 1: General Methodology Page 3-1 7 

 EE-RM-93-801-RAC COMMENT 01

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 consumer and, thus, increases demand for the product.) Then, it estimates how this 

price inc:rease 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 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 of alternative 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 be passed on'via a price 

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 of fixed 

costs as those that are not proportional to output. The mathematical derivation is given in Appendix 

C of this volume. ' , 

A second influence on profits is the change in 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 operating expense ). The, 

, LBNL-MAM takes these effects into account through the use of two demand elasticities which play 

an iInport8lllt role in determining the outcome and are therefore examined more closely in the 

sensitivity analysis. " ' 

Assumptions in' the Analysis 

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 of more detailed assumptions. " 

The long-run costs. The LBNL-MAM requires input of long-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:, General Methodology 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 fm that is described by industry-average values. 

Each one is modeled as if it produced only products of the industry being analyzed. This procedure 

is justified by the observation that red firms generally are organized into autonomous divisions, each 

of which produces a different type of appliance and one of which generally corresponds to the 

industry being modeled. Also, these divisions are generally run on a profit-and-loss basis similar 

to a firm. It is not feasible to analyze interdivisional interactions within real firms. 

Aggregation. Generally, it has been impossible to obtain data on crosselasticities of 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 Bciency levels are implemented in the model. A change in energy efficiency 

levels affects the inputs to the model in three distinct ways. 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 fmt evaluated from engineering data, and then 

amortized by taking into account the life of the investment, the date at which it is made, tax laws, 

and the'appropriate cost of funds. An additional, and sometimes larger, investment takes place as 

the old inventory is replaced with more expensive new units. The model assumes that the ratio of 

inventory to revenue remains unchanged, in this way both changes in quantity sold and unit value 

are taken into account. A third form of investment tracked by the LBNLMAM is the change in the 

demand for cash that accompanies a change in revenues. 

- 

Financial inputs. Several simplifying assumptions are used in the process of generating inputs 

for the 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 and analysis of short-run impacts are also discussed. 

Page3-18 . Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

decrease on average with quantity pr04uced, 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 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 if it produced only productS. of the industry being analyzed. This procedure 

is justifIed by the 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 of the 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 of products. 

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 of stricter 

, 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 of new plants and ,equipment. This cost is fIrst evaluated from engineering data, and then' 

amortized by taking into ae<:ount the life of the inves~entt the date at which it is made, tax laws, 

and the 'appropriate cost of funds. ,An additional, and sometimes larger, investment takes place as 

the old inventory is replaced with more expensive new units. The model assumes that the ratio of 

inventory to revenue remains unchanged; in this way both changes in quantity sold and unit value ' 

are taken into account A third form of investment tracked by the 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 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 and I analysis of short-run impacts are also discussed. 

Page 3-18 ' Volume 1: General Methodology 
Additionally, the various types of sensitivity analysis performed on the model's results are discussed. 

This analysis is crucial for understanding the accuracy and reliability of the model's predictions, and 

includes Ithe 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-un Impact Analysis 

The llea~t of the model is the long-nm impact analysis. It assumes that enough' time has passed 

for any changes in demand to have been accommodated by changes in the industry's productive 

capacity. Thus, in the long-run, capacity, or capital stock, is variable. Because capital is sometimes 

thought to be the only fucd cost, it might seem that fixed costs would be zero in the long run. This 

is not the case. For the purpose of price determination, whenever there are returns to scale, there are, 

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 increase has been determined, it and the operating expense decrease (obtained 

from engineering data) are used with the price elasticity of demand and the discount' rate to 

determint: the change in sales. These mechanisms,-are presented in'Figure 3.4, which displays the 

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 appliance more desirable, thereby shifting the 

demand curve out (e.g., an increase in production costs changes the marginal cost curve). Together, 

these determine price and the number of units shipped (quantity). Price and quantity, together with 

production costs; determine net income (profit), and these, together with changes in capital stock (not 

shown), determine ROE. 

The ISNLMAM simulates the implementation of alternative efficiency 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 average of 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 changes with a change in either price or operating ' 

expense. This change in markup can have an important impact on ROE. 

Volume I: General Methodology Page 3-1 9 

 EE-RM-93-801-RAC COMMENT 01 , \, 

Additionnlly. the vaPous types of sensitivity analysis perfonned on the model's results are discussed. 

This analysis is crucial for understanding the accuracy and reliability of the 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 of the 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, or capital 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. For the purpose of price detennination~ whenever there are returns 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 fixed costs. Design costs would typically fall into this 

category. See Appe~dix 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 

manufactl;Jrers are able to pass on is determined by the nature o.f the cost, i.e., whether it is fixed or 

variable, the priCe elasticity of demand, and the consumer discount rate as seen by the price-setting 

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 of units 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 of alternative efficiency levels as follows. For 

each product, a set of engineering 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 of the two costs and the energy usage for 

the relevant engineering designs. The weights used correspond to the relative numbers of shipments 

for each 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 or operating 

expense. This change in markup can have an important impact on ROE. 

Volume 1: General Methodology Page 3-19 
Figure 3.4 How Alternative Eficiency Levels 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 definition of variable costs. The manufacturer markup 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 from the economic markup7 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. 

Demand * Curve A 

Typical results of an analysis of an alterantive efficiency level at a particular level 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-of these numbers is a standard error. 

The errors might be as follows: 4% on price, 1% on sales, 4% on revenue, 10% on net income, and 

1.5% on ROE. To interpret the prediction for ROE, one could then conclude (by checking a table 

of the normal distribution) that there is about a 69% chance that ROE will increase, and a 31% 

chance that it will decrease. One could also conclude that the chance of profits declining by more 

than 2% is only 2.3%. 

ROE 

+ Quantity 

' For a thorough description of how markups ire derived and used in LB'NL-MAM, see Appendix C of this volume. 

Markup 

Page 3-20 

NPV 

Volume 1: General Methodology 

Price - W xzziz 

Costs 

> 

J 

 EE-RM-93-801-RAC COMMENT 01

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 of varial>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 of an analysis of an 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 'Of these numbers is a standard error. 

The errors might be as follows: 4% on price, 1 % on sales, 4% on revenue, 10% on net income, and 

1.5% on ROE. To interpret the prediction for ROE, one could then conclude (by 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 of profits 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, 

approximakly three years. During this time, some change in capacity may occur, and demand, apart 

from the change induced by implementing new efficiency levels, will change. Demand for both 

types of appliance is projected to grow by approximately 2% during the three years, 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 ignod the 

- ability of the industry to actively or passively adjust its capacity relative to demand. ~hk estimated 

short-run effect is what would happen if the industry converted, as a result of efficiency levels being 

implemerlted, all of its present capacity, and if the demand curve did not shift except as an indirect . 

consequence of imposed efficiency levels. 

The short-run analysis is based on the analogy between a change in demand caused by imposed 

efficiency levels and one caused by the business cycle. The business cycle periodically presents the 

industry with a fairly sharp decrease in demand that is much pater than the decrease predicted for 

any of the: efficiency levels. This demand shortfall seems to present all of the opportunities for price 

competition that would accompany a shortfall resulting from a change in efficiency levels. Data for 

the last few business cycles (18 years) have been analyzed to determine the ratio between the decline 

in demand (industry-wide) and the induced decline in price. 

If, for example, the long-run prediction is for a decline in demand of 2%. then, using the above 

figure, tht: short-run price could be predicted to be 0.06% (0.03 x 2%) lower than the long-run price. 

Actually, this is just a 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 short run, the actual fall in demand will be less than the 

long-run prediction of 296, and the actual fall in price will be greater than 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:-run prices, profit levels, and other variables will gradually, over a period of several years, 

approach their long-run values. How long this will take depends on the 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. 

Volume 1: General Methodology Page 3-21 

 EE-RM-93-801-RAC COMMENT 01

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 of appliance is projected to grow by approximately 2% during the three 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 of the industry to actively or passfvely adjust its capacity relative to demand. The estimated 

short-run effect is what would happen if the industry converted. as a result of efficiency levels being 

implemented. all of its present capacity. and if the demand curve did not shift except as an indirect 

consequence of imposed efficiency levels. 

The short-run analysis is based on the analogy between a change in demand caused by imposed 

efficiency levels and one Caused by the business cycle. The business cycle periodically presents the 

industry with a fairly ·sharp decrease in demand that is much greater than the decrease predicted for 

any of the: efficiency levels. This demand shortfall seems-to present all of the opportunities for price 

competition that would accompany a shortfall resulting from a change in efficiency levels. Data for 

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. the long-run prediction is for a decline in demand of 2%. 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 is just a first approximation. and the model does better. as follows: the short-run price 

response- factor (SRPR) of 0.03 means that the short-run price will be 0.06% lower than predicted 

with the long-run price formula if the actual decline in demand i~ 2%. This is different from the 

long-run prediction of a 2% decline. because the short-run price fall will keep the long-run demand . 

fall from fully materializing. Thus. in the short run. the actual fall in demand will be less than the 

long-run prediction of 2%. and the actual fall in price will be greater than the long-run prediction. 

but not by a full 0.06%. Short-run demand and price must be determined simultaneously, which· 

occurs in the short-run module of LBNL-MAM.· -

ShoI1:-run prices, profit levels, and other variables will gradually. over a period of several years, 

approach their long-run values. How long this will take depends on the industry, on the fact that the 

implemeJltati~n of new 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: General Methodology 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 efficiency levels 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 Regulatory Impact Model (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 separate module within the LBNLMAM and generates the industry net present value 

analysis given price, shipments, and cost inputs from LBNLMAM. For a detailed explanation 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 questions need to be answered. First, how do& 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 of a change of one standard error 

(S.E.) in the input variable. This analysis is presented only for the impact of input variables on ROE; 

this standardizes and simplifies the process. The results are displayed in a sensitivity chart, an 

example of which is given and explained below (Table 3.2). 

Association of Home Appliance Manufacturers, 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 Volume I:. General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of trade 

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 of efficiency levels on iridustry net present value 

(NPV). Theoretically. NPV provides an alternate measure of impact that is identical to industry 

RC?E that 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 of these uncertainties on the accuracy of the model's predictions is analyzed. 

Two types of questions need to be answered. First, how does a particular input variable contribute 

to the uncertainty of the outputs? . Second, how uncertain is'the estimate of a particular output 

variable? . 

Sensitivity charts 

The model's sensitivity to inputs is measured by the impact of a change of one standard error 

(S.E.) in the input variable. This analysis is presented only for the impact of input variables on ROE; 

this standardizes and simplifies the process. The results are displayed in a sensitivity chart, an 

example of which is given and explained below (Table 3.2). 

8 Association of Home 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 Sensitivity of ROE ta 1 S.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's real equity cost of capital 

4. ' EP = fds economic profit 

5. FCA = percentage of a fmls costs, 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 alternative efficiency levels 

8: ' dVC.N = increasein variable 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. These two variables & dollar values rather than penentages, 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 fmt set to its normal value. Next, one at a time, 

each is h~creased in absolute value 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. 

. . 

Volume I: General Methodology Page 3-23 

 EE-RM-93-801-RAC COMMENT 01

. Table.3.2 Sensitivity of ROE 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 of ROE to the control panel inputs defined below: 

1. ' IPE = industry price elasticity 

, 2. . RD = consumer discount rate 

3. BCC = fmn's real equity cost of capital 

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 of one-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: General Methodology Page 3-23 
Since each variable has its own standard error, the sensitivity reported in the table measures both 

how sensitive the model is to a change in the variable, and how uncertain the variable's value is. 

Note that the change in profit is simply the difference between long-run ROE and base case ROE. , 

Note also that, as expected, increasing the-absolute values of some variables increases ROE and, for 

other variables, decreases ROE. 

The comparison of the various inputs' contributions to the uncertainty in ROE focuses attention 

on the parts of the model that should be examined most closely to determine their accuracy and on 

the parts where improvement in the certainty of input variables would have the greatest payoff. 

Because of the differences in the sensitivities among input variables, it will generally be found that 

one to three of the inputs will dominate the model's uncertainty, in the sense that perfecting all the 

rest of the inputs would make only a negligible difference to the model's accuracy: 

Alternative Scenarios 

For each appliance class and hypothetical efficiency level, hundreds of different scenarios are 

run for the Monte Carlo analysis described in a following section. However, a few scenarios that 

involve different demand elasticities are singled out for special attention. 

If the reader wishes to estimate some other scenario not reported here, he or she can often simply 

use the sensitivity charts. To do this, first look up how sensitive ROE is to a one standard deviation 

change in the control variables that are changed by the proposed scenario. Then estimate by how 

many standard deviations the desired scenario variables differ 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. 

Uncertainty of Outputs (Monte Carlo Analysis) 

Output uncertainty is most directly addressed by the Monte Carlo anaiysis. -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 of price, shipments, revenue, net income, long-run ROE, and short-run 

ROE. Next, new values of the input variables are drawn from the same distribution. The model is 

run again and the new outputs recorded. This cycle can be repeated as many as 400 times (or more). 

After a sufficient number of runs, the mean and standard deviations of each output variable are 

computed. 

The standard deviations of the Monte Carlo output variables 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 objective as the standard error of a regression coefficient. In short, if the uncertainty 

in the inputs is misperceived, the estimate of uncertainty in the outputs cannot be relied upon. 

Page 3-24 . Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of some variables increases ROE and, for 

other variables, decreases ROE. 

, 

The comparison of the various inputs' contributions to the uncertainty in ROE focuses attention 

on the parts of the 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 of the differences in the sensitivities among input variables, it will generally be found that 

one t~ three of the inputs will dominate the model's uncertainty, in the sense that perfecting all the 

rest of the inputs would make only a negligible difference to the model's accuracy: 

Alternative Scenarios 

For each appliance class and hypothetical efficiency level, hundreds of different scenarios are 

run for the Monte Carlo analysis described in a following section.· However, a few scenarios that 

involve different demand elasticities are singled out for special attention. ' 

If the reader wishes to estimate some other scenario not reported here, he or she can often simply 

use the sensitivity charts. To do this, first look up how ·sensitive ROE is to a one 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 of interest. 

Uncertainty of Outputs (Monte Carlo Analysis) 

. . Output uncertainty is most directly addressed by the Monte Carlo analysis. -This analysis assigns 

an uncertainty to each of the nine control-panel input variables and then chooses a value for each 

based on this uncertainty. The model is then solved using these randomly chosen variables. All of 

the important outputs are tabulated on the Monte· Carlo page of the model. These outputs are 

changes from the base case of price, shipments, revenue, net income,long-run ROE, and·short-run 

ROE. Next, new values of the input 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 (or more). 

After a sufficient. number of runs, the mean and standard deviations of each output variable are 

computed. 

The standard deviations of the Monte Carlo output variables are the best estimate of the true 

uncertainty of the model's predictions. However, this ·does not mean they are absolutely reliable 

because they are based on estimates of the uncertainty of the inputs, and these are often not based 

on something as objective as the standard error of a regression coefficient. In short, if the uncertainty 

in the inputs is misperceived, the estimate of uncertainty in the outputs cannot be relied upon. 

Page 3-24 Volume 1: General Methodology 
~onethel.ess, the standard errors generated by the Monte Cailo procedure are useful for the 

interpretation of the LBNGMAM outputs. 

Assumptwnr in the ~na&sis 

lustifications for the most important assumptions of the sensitivity Analysis 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 elasticity will 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 elasticity either increases or decreases with 

cost; thus, constancy is the base assumption. 

Costs are assumed to have two 'compo- A linear cost function approximates any 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 previous justification. . 

function of sales. . . . . 

The debt-toequity ratio remains constant Firms attempt to maintain a relatively constant 

while the fm finances any investment overall debt-to-equity ratio, which they believe 

necessary to meet any imposed efficiency is optimal: Although specific new expenditures . 

levels. . '(srich 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. 

Volume I: Gened Methodology f' Page 3-25 , 

 EE-RM-93-801-RAC COMMENT 01

Nonetheless, the standard errors generated by the Monte Carlo procedure are useful for the 

interpreultion of the LBNL-MAM outputs. ' 

Assumptf,ons in the Analysis 

Justifications for the most important assumptions of the Sensitivity Analysis are listed below. 

• 

• 

• 

• 

• 

Assumption 

Finns maximize revenues minus economic 

costs (R-EC), where EC includes the cost 

of equity 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 of sales. 

The: debt-to-'equity ratio remains cOJlstant 

while Ilhefmn finances any investment 

necessary to meet any imposed efficiency 

levels. , 

Volume 1: Gen.eral Methodology 

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 linear cost 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 previous justification. 

Finns attempt to maintain a relatively constant 

overall debt-to-equity ratio, which they believe 

is optimal.' Although specific new expenditures . 

(such as those induced by 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 industry informants 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 industry by 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 

implementing new 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 of the 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 also produces a simplified income statement. As part of 

its algorithm, it calculates the markup from manufacturers' costs to consumer purchase price. The 

purchase price is used in the calculation of 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 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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) of capital' 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 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. 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 of price-efficiency curves,:the)ife-cycle-cost curves, and 

the calculation of payback periods, and throughout the LBNL-REM. 

All of these values are estimates that necessarily reflect some uncertainty. Estimates of these 

uncertainties and identification of their sources comprise another set of model outputs. Under each 

estimate of one of the five main output variables, the output table displays that variable's change 

Page 3-26 Volume I: General Methodology 
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 .the output variables. 

Datu Sources . . 

The 1BNLMA.M uses data that characterize both a particular industry and typical fms within 

that industry. Estimates of data are based on information from five general soums: 1) the 

Engineering Analysis, 2) the Consumer Analysis. 3) industry consultants, 4) public financid data, 

and 5) industry profiles. A detailed discuuion of model inputs appears in Appendix C of each. 

product-s pecific Part. . . 

3.3.4 Appliance Standards Environmental 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 lost revenues, 

ASEUM provides marginal electricity costs to be used in evaluating the societal benefits of 

albrnative-energy efficiency levels. ASEUM also quantifies impacts on the electric utility industry's 

need for new generating capacity. Appendix E of this volume contains more information on.the 

model and ,data used 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. The sum 

of the two is usually called avoided cost. The fuei cost element measures variable production costs 

avoided by reduced electrical demands, valued at marginal input fuel costs. The capacity cost 

measures the value of reduced 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 

combustion 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. 

ASETJM calculates avoided energy costs based on a disaggregation of 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. NO attempt 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 utility natural 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 forecast period. 

. The a.voided-capacity cost calculation in the model is based on a conservation load factor (CLF) 

for the eni:rgy savings attributable to the efficiency level, as well as the cost of a combustion turbine. 

Volume I: General Methodo0logy Page 3-27 

 EE-RM-93-801-RAC COMMENT 01

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 

of uncertainty 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 of this volume contains more informationon.the· 

model and·data used to perform these calculations.' 

ASElJM adopts the standard industry convention that the financial value of electricity savings . 

to an elecltric utility can be broken down into fuel cost savings and capacity cost savings. The sum 

of the 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 of reduced 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 of the 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 of the 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 of a combustion turbine. 

Volume 1: General Methodo'logy Page 3-27 
A CLF is defined as the average load savings of a conservation measure (in kW) divided by its peakbad 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 CLF is used to characterize the peak demand savings of a conservation measure. It is used 

to convert the capacity value of the alternative efficiency levels into a per kwh value described 

above. :She analysis assumes that the load shapes of appliances do not change as these appliances 

are mad(: .more efficient. . 

The NERC forecasts of capacity requikments for each region are used to account for regional 

variations in reserve margin. If NERC predicts an adequate reserve margin in a region for a given 

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 reductions and avoided 

costs. Revenue reduction is calculated by multipljing the change in electricity consumption by 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 calculations include 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 

appliance per MMBtu of source energy. These marginal costs are used to calculate societal costs and 

benefits (of implementing new efficiency levels. . . 

Assumptions in the Analysis - , 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' 

substance of NERC forecasts. 

, Page 3-28 . ' Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

A CLF i:; defined as the average load savings of a 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 of a 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 of appliances do not change 8$ these appliances 

are mad,c~more efficient. . ' . 

The NERC forecasts of capacity requirements for each region are used to account for regional 

variations in reserve margin. If NERC predicts an adequate reserve margin in a region for ~ 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 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 

. appliance per MMBtu of source energy. These margmal costs are used to calculate soci~tal costs and 

benefits lof implementing new efficiency levels. ' 

Assumptions in the Analysis 

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~ 9f NERC forecasts. 

Page 3-28 Volume 1: General Methodology 
Assumption , Justification 

 he analysis calculates the kriod during 

whch oil and gas or coal are the marginal fuels 

for c=lectricity.generation based on a simplified 

LDC for 'each NERC region. It therefore 

,assumes that this LDC accurately reflects the 

distribution of fuels on the margin. 

Oil and gas fractions (OGFS) for each NERC 

-region remain constant throughout the analysis 

period. ' 

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 WC is 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 

utilities to,meet peak loads; hence, oil and gas 

are assumed to be at the top of the LDC. The 

other fuel assumed to appear on 'the margin 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 

averaging NERC forecasts of the total amount 

of oil and gas generation for the yew 1990 to 

1995. There are no forecasts for the 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 same heat rate. generating units. . . 

Volume I: tfencml Methodology Page 3-29 

 EE-RM-93-801-RAC COMMENT 01

Assumption 

• The, analysis calculates the period during 

which oil and gas or coal 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 of fuels 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 peak loads; bence. oil and gas 

are assumed to be at the top of the 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 of the total amount 

of oil 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 of this 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 of changes 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 compaied to 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 

appliance classes analyzed. The LCC is the sum of the purchase price and the operating expense 

discounted over the lifetime of the appliance. It is calculated at the average efficiency for each class 

in the year efficiency levels are imposed using real consumer discount tates of 2%. 696, 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 

LBNLREM. Energy prices & taken from Annual Energy Outlook 1995 and appliance usage are 

taken from the results of the LBNL-REM. 

. The lifecycle cost 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 additional investment 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 denominator of 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 each product-specific Part. 

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 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-REM are 

multiplied by emissions factors to estimate net emissions reductions. 

Page 3-30 Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Assumptions 

• The NERC forecasts of changes in adjusted 

reserve margin are accurate. 

.. 

Justification 

The NERC forecasts are widely accepted. 

They are the only foreca$ts of adjusted 

. reserve margin at the regional level. 

3.3.5 Life-Cycle Cost Analysis, Payback Period, and Cost of Conserved Energy 

One measure. of the effect of implementing 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 of the purchase price and the operating expense 

. discounted over the lifetime of the appliance. It is calculated at the average efficiency for each class 

in the year efficiency levels are imposed using real consumer discount tales of 2%; 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 of the IBNlrREM. . . 

The llfe-cycle cost analysis also examines the payback periods (PBPs) and the cost of conserved 

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 of the increase in purchase price between the base and efficiency level 

cases to the decrease in annual operating expenditures. Both the numerator ~d denominator of this 

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 of the appliance, divid.ed by the annual ener~ savings. . 

Details of these calculations may be found in Chapters 4 of each 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 
- 

The main environmental effects of electricity generation and distribution on air and water quality 

result frolm emissions of sulfur dioxide (SOJ, nitrogen oxides (NO,), and carbon dioxide (CO ,). 

With the alternative efficiency levels lessening the need for electricity generation, 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 

emissions reported should be interpreted as reduced demand for emissions. Physical emissions of 

SO, will most likely not change, although the price of emission allowances should be lower than the 

case absent the imposition of new efficiency levels., 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, effects of power generation, such as water pollution, landuse effects, etc., are not covered in the analysis of appliance energy efficiency levels. The net effect 

of an appliance energy efficiency level on such environmental problems are just too smdl to be 

measurable. 

The multipliers used in this report are derived from a report that accompanied the 1991 NES (6). 

Estimarion of muitipliers over future periods is not as straightforward as it may seem. Our 

reasonat~lexpectation is of improved emission controls over time &d the effectiveness of these 

improvements must be forecast. In addition, future restrictions on emissions of greenhouse gases 

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 as the provisions of the Clean Air Act 

become effective. The factor for NO, falls, but less dramatically, while the CO, emissions, which 

have no cunrently known feasible abatement strategy, fall modestly. 

Table 3.3 Emissions Factors for Power Generation 

. Clearly, the approach ,used in 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 .fraction of time that a certain fuel .is a 

' 

marginal 'fuel will not be the same as its share of overall generation. -For residential air conditioning 

and coolcing, this is a reasonable assumption. Generation will be reduced at all times of the year.and , 

coal and: gas will both be marginal for some of the time. 

Volume I: ~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 

 EE-RM-93-801-RAC COMMENT 01

The main environmental effects of electricity generation and distribution on air and water quality 

result from emissions of sulfur dioxide (SOJ, nitrogen oxides (NOli)' and carbon dioxide (CO 2)' 

With the alternative efficiency levels lessening the·need for electricity generation, in gener3J, power 

plantemissions would be reduced. However, the case of S02 is complicated by the emissions' cap 

provisio:n oj[ the 1991 Clean Air Act Amendments. In the case of this 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 of emission' allowances should be lower than the 

case absc~nt the imposition of new efficiency leve,ls. , The ASEUM model also estimates the effect 

of implementing new efficiency.levels on the three emissions resulting from in-house combustion. 

The numerous other, but more minor, effects Qf power generation, such as water pollution,landuse effects, etc., are not covered in the analysis of appliance energy efficiency levels .. The net effect 

ofan appliance energy efficiency level on such environmental problems arejust too smail to be 

. measurahle. . 

The multipliers 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 of greenhouse gases 

could have a major.impact on CO2 emissions. Table 3.3 shows'some sample emission factors. As 

is clear, lhe forecast emission factor for S02 falls dramatically as the provisions of the 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 g. /kWh . 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 of overall generation .. For residential air conditioning 

and cooking, this is a reasonable assumption. Generation will be reduced at all times of the year .and . 

coal and: gas will both be marginal for some of the time. . . 

Volume i: General Methodology . Page 3·31 
.Table 3.4 shows the emissions factors used for in-house emissions, which are assumed fixed 

over the forecast period. 

- 

Table3.4 Emissions F'actors for In-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 

energy efficiency levels. This makes it possible to .place bounds on the uncertainty in the results of 

the analysis and to gain an unherstanding of which variables are most important in producing these 

results. Sensitivity analyses are developed in a series of distinct steps. For each computer model 

in the analysis, critical input-parameters are identified and reasonable ranges of variation are 

Year - 

2000 

determined; The sensitivity-of the model to changes in the value of each important parameter is then 

estimated.by running the model for.the base case and efficiency level cases. The significance of the 

results is assessed in terms of the magnitude of change in each model's outputs. 

Sensitivity runs are made with the 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 efficiency levels. 

- 

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-linear model, the effects of these input uncertainties may not be additive. A 

Monte Carlo analysis addresses this problem by computing standard errors, which are displayed in 

the output table. For each control variable, the Monte Carlo section randomly picks a value near the 

best estimate of its true value. As a result, the outputs of Monte Carlo vary from run to run. After 

finishing a batch of runs (typically 100 to 400), the LBNL-MAM calculates means and standard 

.deviations for the five main output variables. . 

SO2 

The results of the sensitivity analyses are examined to extract two types of information. First, 

the base case runs of the model are analyzed to determine the sensitivity of the forecasts to 

exogenous variables and assumptions. Second, the sensitivity analysis of the impacts of 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 

.Page 3-32 Volume 1: General Methodology 

Oil 

' 134.6 

NOx 

Gas 

45.1 

'CO2 

Oil 

55.9 

Gas 

4729 1 

Oil 

72485 

 EE-RM-93-801-RAC COMMENT 01

\. 

. 

, '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 of which variables are most important in producing these' 

results. Sensitivity an_alyses are developed in a series of distinct steps. For each computer model 

in the analysis, critical input parameters are identified and reasonable ranges of variation are 

determined; The sensitivity .of the model to changes in the value of each important parameter is ,then 

estimated by ruiming the model for the base case and efficiency level cases. The significance of the 

results is assessed in tenns of the magnitude of change 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 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 efficiency levels. 

Sensitivity analysis of the results of the 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 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. 

The results of the sensitivity analyses are examined to extract two ~s of information. 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 of the impacts of alternative 

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 of imposed 

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 sensitivity analysis is in variables and assumptions that affect ' , 

the two cases differently. The sensitivity analyses of the impacts of efficiency levels a& discussed 

in Chapters 6 of each product-specific Part. 

Volume I: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of the impacts of efficiency levels are discussed 

in Chapters 6 of each product -specific Part. 

;. 

Volume I: General Methodology 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 1-7 1. 

3. U.S. Department of Energy (US. DOE). 1988. Energy Conservation Progrcun for 

Consumer Products: Advance Notice of Proposd Rulemaking and Request for Public 

Comments Regarding Energy Conservation Standarak for Dishwashers, Clothes Washers, 

and Clothes Dryers," Washington, D.C. Federal Register, vol. 53, no. 96, 17712-17721. 

4. U.S.,Department of,Comrnerce, Bureau of the Census. 1983. 1980 Census of Population 

and Housing. Washington, 'D.C. PC8O-A1. 

5. U.S. DOE, Energy PC80- 1 -A 1 Information Administration. 1995. Annual Energy Outlook 

1995 with Projections to 201 0, 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 199 1 National Energy 

Strategy. Washington, D.C. SWNESl90-01. 

Page 3-34 Volume I: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

REFERENCES 

1. E. Hurst and J. 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. 41-71. 

3. . U.S. Department of Energy (U.S. DOE). 1988. Energy Conservation Program for 

Consumer Products: Advance Notice of Propo"sd Rulemaking and Request for 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 of Commerce, Bureau of the Census. 1983. 1980 Census of Population 

and Housing. Washington, D.C. PC80-A1. -

5. U.S. DOp, Energy PC80-1-Allnfonnation Administration. 1995. Annual Energy 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 for National 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 PROFILE FOR 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 

description of market shares followed by a discussion of mergexs and acquisitions in these industries, 

and finally a comparison of consumer and producer price indices. 

4.1 DEMAND ., 

Units Shipped and Housing Starts 

/ 

One of the primary components driving the demand of most appliances is housing starts. 

Although the changes in shipments of appliances are not entirely the result of changes in housing 

starts, housing starts can be used to predict shipments of most appliances for two reasons. Hrst, 

almost every new house requires a new set of appliances, so fluctuations in housing starts cause 

fluctuatio~ls in shipments of most common appliances. (In fact, most of the fluctuation in shipments 

of producrs being analyzedcan be attributed to the fluctuations in housing starts.) Second, housing 

starts are a good indicator of the strength of the economy as a whole. A healthy new-housing market 

is likely to correspond to a healthy economy, and a healthy ecbnomy encourages replacement and 

discretionary purchases of appliances. Therefore, there is generally a strong correlation between 

new-housing starts and appliance shipments. 

Fi,gures 4.1 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 strong demand 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) .in predicting 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 1960s to 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 united states 1994, U.S. Department of Commerce, 1994, p. 730. 

Shipments data for most products are from "Statistical Review," Appliance, April 1980, April 1990, and April 1993.~ 

. . 

Volume I: ' Gerperul Methodology . . page 4-1 

 EE-RM-93-801-RAC COMMENT 01

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 of the anal~ products. It then presents a brief 

description of marketshares followed by a discussion of mergers and acquisitions in these industries, 

and finally a comparison of consumer and producer price indices. 

4.1 DEMAND 

Units Sh~pped and Housing Starts 

. .. J. . . .' . . 

. Oltle of the primary components driving the demand of most appliances is housing starts. 

Although the changes in shipments of appliances are not entirely the result of changes in housing 

starts, housing startS can be used to predict shipments of most appliances for two reasons. first, 

almost every new house requires a new set of appliances, so fluctuations in housing starts cause 

fluctuatiolls in shipments of most common appliances. (In fact, most of the fluctuation in shipments 

ofproduct:s beiIig analyzed can be attributed to the fluctuations in housing starts.) Second, housing 

starts are ugood indic~tor of the strength of the economy as a whole. A healthy new-housing market 

is likely to correspond to a 'healthy economy, and a healthy economy encourages replacement and 

discretionary purchases of appliances. Therefore, there is generally. a strong correhltio~ between 

new-housing starts and appliance shipments. . 

Figures 4.1 through 4.31 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 

sltipments, of the various products covered by the National Appliance Energy Conservation Act of 

1987 (NAECA). Further, some appliances have strong demand in the replacement market or in the 

aftennarkl~t, and thus would be less correlated with new housing starts. Examples of such 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 of financiaI impacts on manufacturers. 

The explosive growth in microwave ovens is displayed in Figure 4.2. After a slow period 

of growth in the late 1960s to early 1970s, the popularity of microwave 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," Siatistical Abstract of the Unlted States 1994, U.S. Depanment of Commerce, 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 

1973 to over 85% in 1993 (1). 

I 

The sales of room air conditioners have little correlation with housing starts. We suspect that 

this is because room air conditioners are bought primarily in the aftermarket and have a strong 

seasonal component 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 saturation rates for the various analyzed appliances in selected years during the past 

20 years. Note that the data do not reflect the fact that some homes have more than one appliance 

of a particular type. In general, if most homes own an appliance but few homes have more than one 

unit, we expect that the appliance will have a relatively low elasticity of demand because the 

appliance is usually considered a necessity. This observation corresponds with the relatively low 

price elasticity of ranges and ovens and the higher price elasticity of 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 3 1 

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 

 EE-RM-93-801-RAC COMMENT 01

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 of room 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 of homes that own a particular appliance. Tables 4.1 

and 4.2 list the saturation rates for the various analyzed appliances in selected years during the past 

20 years. Note that the data do not reflect the fact that some hpmes have more than one 'appliance 

of a particular type. ID general, if most 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: General MethOdology 
s: Statlstlcal Abstract of the Unltrd Stater 1994. 

Appllanec "Statistical Review". (Var. Yeara) 

ge' Shipments and Housing Starts , 

. . 

. . 

ogy Page 43 

701'7.2 124 176 178 180 I82 I84 186 188 190 192 

 71 73 75 .77 79 81. 83 85 87 89 91 . 

Year 

Housing Starts 

---- Electric Ranges 

................ as Ranges 

I I I I I I I I I I I 

 EE-RM-93-801-RAC COMMENT 01

-""-" ..... -----.---.-..... -.--..... -.:---..... -...... -.... -:---" --' .. ----- --

;' ..... 

/' ........ 

--' ..... -.. -.1.\,.- ...... --.. -....... -.. 

/ \ 7/', / 

. / I \ I 

I \ '. . . I 

....... -''-''-r --.......... -\-----c-/--... - .... -:'-.. \._ ..... -: .. ----.-,.-.-- _. __ ._----------

.I \.1 \ r 

/............. \ ,I ',I .' 

. ... -·.-.. -.. -.. -... -.. -.. -.. ... 

'.,J ............. . 

---.-.-... ---... ----""-"--' ... --.--/;0'.-----.----.. -.---- .-----

.. ' 

 73 

Housing Starts 

Electric Ranges 

Gas Ranges 

77 79 

e" Statl.tlcal Ab.t,.act Statu 1994, 

Applla1fce "Statlstlcel Review", (Va •. Yea.s) 

ge and Housing Starts 

ogy 

81 

Year 

91 

Page 4-J 
Housing Starts 

Microwave Ovens 

I 

: Statlrtlcal Abrtraet a/ the Unlted Stater 1991. 

AppNaner "Slstistlcal Review", (Vat. Yean) 

ven Shipments and ~ous'in~ starts 

Year 

Volume I: Geneml Methodology ' 

 EE-RM-93-801-RAC COMMENT 01

--------------------r----------------

Housing Starts 

---- Microwave Ovens 

----.-.-... -.----------.----.. --

, _._--_._----'._-, . I 

I 

r-- I 

/' I 

/' I 

/ ............ I 

._._._ .. _-_._-------------------------/ " , / 

I 

I 

, I , I 

/ 

.' ._.--_ ..... _ ........... _-/.. _. - ... --_._- .-._ .. _ .... _-._._._-.-....... _ .. __ ...... _ ........ .. 

/-...,' 

,/ 

--

_/ 

....... 

------

: S'a"."c,1I ,U."acl all". U"".d Slalu 1994. 

Applia"CI "Slatistical Review· .. (Var. Yean) 

./ 

Year 

I 

90 92 

89 91 

ven Shipments and Housing Starts 

Volume 1: General Methodology. 
nditioner Shipments and Housing Starts 

\ iOiising Siaris 

gy ' 

Page 4-5 

i 

\ ---- RACs 

\ / \ ,'' . \ -. . I \ 

; \ \ 

f-------+-- 

/' '\ ,\ /. \ 

\/r \ I \ 

.. . v - / \: 

\ 

/ / 

* \' \J ' LA 

\ 1. 

\ I 

., \/ .V_ --- -- .- - 

' 

, .. 

I 

 71 73 75 77 79 81 83 85 87 89 91 

 Statlrtleal Ab#tracr ofthe Unlred Stater 1994. , , Year - Appliance "Statistical Review". (Var. Years) 

 EE-RM-93-801-RAC COMMENT 01

"-

" Housing Starts \ 

---- RACs \ /\ 

________ 

\/. \ . / \. 

\ / \ 

. \ . I" . ,/ \ 

\ /" / \ 

\ I' /\ / \ 

\ / 'v/ \ / \ 

\./ \ / . \.-

\ I 

+-----------------

I 

78 

77 

 Stat'.tlcal Ab."'act o/t", U"It,d Stat,. 1994, 

Applla"c, "Statiltlcal Review", (Vat. Years) 

\ I 

___ 

.. 

___ 

. 

___ 

.. 

____ .Y-._. __ . __ . _. ______ .. _____ . _. __ ._ . 

/. ' 

79 81 83 85 87 89 91 

'Year 

nditioner Shipments and Housing Starts 

gy Page 4-5 

I . 
Table 4.2 Market Saturations* 

Product 1981 (%) 1984 (56) 1987 (9%) 

Electric Stove ' - 54/29 54R9.5 . . 57/77 

Electric Oven 52/74 49/7 3 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 3011 6 

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 concentration is the extent to which market share is controlled by the largest firms 

in an industry; Table 4.3 shows the market concentration for the products according to the degree 

of the market controlled by the two, four, and five largest manufacturers in each industry. 

All of the products show a high level of market concentration. The market concentrations 

of the top five producers are at least 80%. The largest producer controls from 40% to more than 50% 

of the market for each of these products. Market concentration plays a part in the analpis because 

it may indicate the existence of market powef' in a particular industry. Firms with the largest market 

shares may have some market power, a factor that affects the markups used by these fums both 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: Consumption and Expeditures, 1984, October 1986, DOEEIA- 

.0314(84), U.S. Department of Energy; and Household Consumption and 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 

facing a demand curve that is not infinitely elastic. This means that if price is raised by some small amount, sales will 

not fall to zero, and if price is lowered by some small amount, sales will not become essentially infinite. 

Page 4-6 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

. . Table 4.2 Market Saturations* 

1981 (%) 1984 (%) 

.54129 54n9.5 

Electric Oven 52n4 49n3 57ns 

Gas Stove . 4612] 4Sn.0 . 2~7n.l 

Gas Oven 40IJ7 42118 4112] 

17n.3 34/51 

32117 30IJ9 

Source: RECS, 1981; 1984, 1987.2 

·Initial figure is for the stock of dwellings, ~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 market concentration for the products according to the deg~e . 

of the market controlled by the two, four, and five largest manufacturers in each industry, 

All of the products show a high level of market concentration. The market concentrations 

of the top five producers are at least 80%. The largest producer controls from 40% to more than 50% 

of the market for each of these Proclucts. Market concentration plays, a part in the analysis because 

it may indicate the existence of market powe~ in a particular industry. 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 and Expeiulitures. 1984. October 1986, DOElEIA-

,0314(84); U.S. Department of Energy; and Household Consumption and Expenditures. 1987. October 1989. DOElEIA032I1i/(87). U.S. Department of Energy. 

_ 3 Market power is the ability to 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 if price is raised by some small amollnt, sales will 

not fall tozer-o, and if price 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 - 

Number of 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 manufacturer market 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 

 EE-RM-93-801-RAC COMMENT 01

Table 4.3 Percent of Market Controlled by the Largest Producers of Each Product 

Product . 

Ranges (E~ectric) 

.Ranges (G~) 

Microwavt~ Ovens 

Room Air Conditioners 

Source: AppUaflce, September 1994. 

TopTwo(%) 

57 

49 

46 

47 

Number or Finns - TopFour(%) Top Five (%) 

89 95 

93 '96 

74 82 

. 76 82 

Figures 4A to 4.7 give a picture of the manufacturer market shares for each of the 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 Caloric brand name. 

GE.includes what was Roper. 

Source: Appliance, september 1994 

Figure 4.4 Electric Range Firm Market Shares: 1993. 

Page 4-8 Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Electric Range Firm Market Shares 

I Whirlpool.18% 1 

. I 

1 May tag 14% 1 

Maytag owns Jenn-Air. Hardwick and Magic Chef brand 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 45 Gas Range Firm Market Shares: 1993. . . 

Volume 1: General Methodology 

. 

Page 4-9 . 

 EE-RM-93-801-RAC COMMENT 01 J . 

Gas Range Firm Market Shares . .' . -

I May tag 24% I 

I Electrolux 24% 1 

I Others 1 % I 

I Peerless 3% I 

I Raytheon 20% r 

I Brown 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 

Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

, Microwave Oven Firm Market Shares 

I' San:tsung 20%, I 

1 Sharp 26% I 

I Others 2% I 

Matsushita 16% I Raytheon 2% I 

1 Sanyo 7% I I Goldstar 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: General Methodology 
RAC Firm Marke't Shares 

I Matsushita 8% 1 

Source: A,ppliance, September 1994. 

Figure 4.7 Room Air Conditioner Firm Market Share: 1993. 

Volume I: Ger~eral Methodology 

. . 

Page 4- 1 ? 

 EE-RM-93-801-RAC COMMENT 01

RAC Firm Market Shares 

,I Electrolux 21 % I I Fedders 25% I 

lathers 8% I 

I Whii'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 and Acquisitions 

The appliance-manufacturing industry has had a continuing history of consolidation. 

Mergers and acquisitions have two pupses: First, they produce large corporations with the 

financial resources and stability to be successful in an increasingly competitive market. Second, 

mergers and acquisitions meaq manufacturers can have a complete line of home appliances for 

product diversification and can therefore offer a complete set of appliances to consumers, an 

important feature in the builder market. There is increasing worldwide competition in the major 

appliance market, so mergers or acquisitions are likely to continue. Table 4.4 is a partial list of 

'major acquisitions of appliance manufacturers. 

Table 4.4 Acquisitions of Appliance Manufacturers 

Buyer Purchase Year 

Whirlpool Heil-Quaker 1964 

Raytheon Co. Amana Refrigeration. Inc. 1965 

White Consolidated Inc. Franklin Appliance Div. (Studebaker Cop.) 1967 

White Consolidated Inc. Kelvinator Appliance 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 Consolidated Inc. 

Caloric Corp. (Raytheon) 

White ~onsolidakl Inc. 

White Consolidated Inc. 

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 

Page 4-12 Volume I: General Methodology . . 

 EE-RM-93-801-RAC COMMENT 01

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 of home appliances for 

product diversification and can therefore offer a complete set of appliances to consumers, an 

important feature in the builder market. There is 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 of appliance manufacturers. . 

Table 4.4 Acquisitions of Appliance 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 Acquisitions of ~pplianee Manuf&&rs Con't 

Buyer Purchase Year . , 

Dart & haft ,Hobart Corp. . . 1981 

Magic chef. Inc. . 

Maytag Go. 

Maytag C.D. , 

Revco . 

Hardwick Stove , , , ' 

Jenn-Air (CarrierIUnited Techn) , . 

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 

(Allegheny Intl 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 &nufacturing facility (Whirlpool) 1986 

White Corrsolidated Ind. (Electrolux) Design & Manufacturing Corp. 

Thornson :LA. (France) GURCA consumer 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: General Methodology Page 4-13 

 EE-RM-93-801-RAC COMMENT 01

. Table 4.4 Acquisitions of Appliance 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 slowly than aggregate indexes measuring 

consumer and producer prices. The appliance industry has a long history of price stability even 

through fluctuations in the business cycle. ' Despite increased energy and component prices 

during the 1970s and 1980s, the industry has, through increased productivity, kept price increases 

lower than increases in the Consumer Price Index (CPI)." In recent years, the industry has made 

a strong push toward automation to keep costs down, spending hundreds of millions of dollars to 

automate production facilities. 

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 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 indicates the relative increase or decrease in the price index of a product 

grouping, using the PPI for all commodities as the yardstick. The subsequent figures and their 

products are 

Figure 4.8: 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 consumer and producer prke indices are' from a personal communication with the San Francisco, CA office 

of the U.S. Bureau of Labor-Statistics, May 1995. 

Page '4- 14 Volume 1: General Methodology \ 

 EE-RM-93-801-RAC COMMENT 01

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 of price 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 of dollars 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 indicates the relative increase or decrease in the price index of a product 

grouping, using the -PPI for all commodities as the yardstick. -The subsequent figures and their 

products are 

• ' Figure ,4.8: 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 of Labor· Statistics. May 1995. 

Page 4-14 Volume 1: General Methodology 

-r 
Year 

PPI 

-- - CPI 

....,.. -HH Cooking Eqmt 

 and CPI Compared to Household Cooking Equipment Product Gmup PPI 

- - -- .AP 

. . 

I. 

gy Page 4-15 

 EE-RM-93-801-RAC COMMENT 01

.. •....... .. .. --... -- .. ---

. 

__ . _ .... ______ ._. __ .__ .' ______ _-... ......... ... 

.................. :: . ..:.:.:.--:".":":":.-::....... . ... . 

......... ....... . 

_._---'--

-

----_._---

.. 

__ ._ .... _ .. _--_ .. _------_.-

.. ' ,/ 

,/ '---7---.--------'-------.---.--.------.-.------------------

--

,/ 

// I. 

...... ---.-... _------------_._._ ... _---_._._-----_._----_ ... _-------_._-,----

.. -.--.--.. ---.--.. .... -... --.---.---.---.---.-.:'------

PPI 

-- CPI 

HH Cooking Eqmt 

78 80 '82 

. . . 

-- -_._--_ ... _-------_._._-----_._-------'--

, r .. 

84 86 889092 

Year 

 and CPI to Household Cooking-Equipment Product Gr:oup P'PI 

gy Page 4·15 
and PPI Compared to Room Air Conditioners 

Volume I: Geneml Methodology 

PPI 

---- CPI 

.............. RAC (3585) 

I 

79 81 

I I 

83 1 85 87 89 93 

80 82 84 86 88 . 90 92 

Year 

 EE-RM-93-801-RAC COMMENT 01

............ 

• 

__ 

F •••••• :.:- •• 

__ ", 

---- --:-:.::-:- -:::'.::: . . 

--

. . . . . . . . ... 

._-_._ .... -._._-_. __ ... _----_ ........... __ ._-.. _--_ ..... _ .. __ .. __ ....... _._., .... ----.. _-_ .. _ ... _._ .. -...• ---.--

80 

PPI 

CPI 

RAC (3585) 

-·-T-· 

84 86 88 90 92 

Year 

and PPI Compared to Room Air Conditioners 

Volume I: General Methodology 
...... HH Appliances I 

jl# , .'* ;. .: . . 

Year 

-- CPI 

PI and PPI Compared to All Household Appliances 

. , 

ogy 

I 

. . 

Page 4-17 

 EE-RM-93-801-RAC COMMENT 01

.--.-- ... --- .. ....... _- --- .... _ .. _----_._--: .. -.. __ ._--_ .. - .,_ .. _ ... _------ .. '--'-"- ----.---.----

1_-- 

. . _ " __ 

--'.---"--'-'._'--. - - - - - = 

__ 

--

--

--

--

---

---

-.---- 

............................................................................. . 

 ••••••• __ • _____________ .. 

-------.-.---.. - .. -.. -.. --.-...... -.--.---------.---.. -_ .. -. -.-.--.. ----.----- 

__ 

'- -

_____ : __ .. _-' ____ ... __ ._.:. _____ .. ___ .._.: .. __ . ____ .. -._' .. _ .. ______ : ___ ... _____ . _____________ -____ . 

PPt 

-- CPt 

HH Appliances 

I 

86 I 

87 

I 

88 

.. 

I 

·1 90 

89 

Year 

PI and PPI Compared to All Household Appliances 

ogy 

92 

91 93 

Page 4-17 
REFERENCE 

1. "A Portrait of the U.S. Appliance Industry 1994,"Appliance, September, 1994, p. 70. . - 

Page 4-18 . Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

REFERENCE 

1. "A Portrait of the U.S. Appliance Industry 1994," Appliance, September, 1994, p. 70. 

Page. 4-18 . Volume 1: General Methodology 
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 care projections. These base case projections are 

compared to projections of conditions that would be likely to prevail if alternative efficiency 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 aggregate residential energy consumption 

by fuel type., ! 

5.1 ECONOIMIC AND DEMOGRAPHIC DATA: RESIDENTIAL 

The CBNL-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 equipment price, initial market shares, elasticities for market shares, efficiency choices, 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 Annual Energy Outlook 1995. (1). The 

stock of households is interpolated in years 198 1 to 199 1. 

Volume I: Gerteral Methodology Page 5-1 

 EE-RM-93-801-RAC COMMENT 01

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 case projections. These base case projections are 

compared, to projections of conditions that would be likely to prevail if alternative efficiency levels 

were enacted (see Chapter 3). The difference between the two projections is defined as the impact 

of implementing 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, unit energy consumption, 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 of each 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 of households is interpolated in years 1981 to 1991. 

Volume]: General Methodology PageS-] 
Table 5.1 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 

1 10.7 

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& (201 1-2030) 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 

 EE-RM-93-801-RAC COMMENT 01

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: , DOFJEIA AIIIUllJI Energy Outlook 1995 .. 

U.S. Department of Commerce, Bureau of Census (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 of Census, 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 201 1-2030, the projection 

is extended, assuming a linear increase in disposable personal income, with the annual increase being 

the average of the increases from 1993 to 2010. Annual Energy Outlook I994 (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 199 1 are obtained by 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 of Current Business. 

'(1 990) DOEJEIA ~&ual Energy Outlook 1994. ' (1992-2010) DO= Annual Energy Outlook 1995. 

(201 1-2030) LBNL extrapolation. 

Total 

.. (Billions) . 

5.1.4 Resiidential Energy Prices , . 

Per Oc'cupied Househol'd 

(Thousands) 

Past energy prices (1980-1988) are from the DOE/EIA's Monthly Energy Review (6). Energy 

prices for 1989 and 1990 fromDOE/EIA's Annual Energy Outlook 1994. Energy prices for 1992 . ' 

' to 20 10 are obtained from DOWEIA's Annul Energy Outlook 1995. Extrapolation of 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 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 I: General Methodology . . . Page 5-3 

 EE-RM-93-801-RAC COMMENT 01

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 of the increases from 1993 to 2010. Annual Energy Outlook 1994 (5) provides the figure 

for 1990 :and Annual 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:St energy prices (1980-1988) are from the DOFJEIA' s Monthly Energy Review (6). Energy 

. prices for 1989 and 1990 are from DOElEIA 's Annual Energy Outlook 1994. Energy prices for 1992 

to 2010 are obtained from DOElEIA's Annual Energy Outlook 1995. Extrapolation of energy 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: .General Methodology . Page 5-3 
These multipliers capture regional differences in residential rates and appliance holdings, as 

well as effects of rate structure. (For example, if electric heat is common in areas with lower 

residential rates, then the effective rate for electric space heating is lower than the average residential 

rate. Also, if the block structure offers declining rates for incnased 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 

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 12 Btu/kWh. 

Source: (1980-1988) DOE Monthly Energy Review. 

(1989-1990) DOEIEIA Annual Energy Outlook 1994. - 

(1991) interpolated by LBNL: 

(1 992-2010) DOE/EIA Annual Energy 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 

Other End Uses 1.04 1.1 1 

Source: LBNL derived from 1990 R~CS. 

I I 

Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

, . 

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) DOE Monthly Energy Review. 

(1989-1990) DOFJEIAAnnual Energy Outlook 1994. 

(1991) interpolated by LBNL: 

(1992-2010) DOFJEIAAnnuai Energy 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 THE BASE CASE 

5.2.1 Resiidential Sector sensitivity Cases . . 

 hes sensitivity cases for the 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 for that 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, 

electricity 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 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 sensitivity cases are presented in Sections 2.3 (base Case sensitivity) and 

3.6 (alternative efficiency level impah) of each product-specific Part of this report. 

. . 

Volume I: Geneml ~ethodolo~~ Page 5-5 

 EE-RM-93-801-RAC COMMENT 01

5.2 SENSITMTY ANALYSIS OF THE 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 Annual Energy Outlook 1995 forecast. 

• High Equipment Efficiency. Assume continuing future improvement in appliance 

efficienCies at a rate of 2% per year. ' 

• , Market Discount Rates ,Decline. Assume 'that market 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 of these sensitivity cases are presented in Sections 2.3 (base case sensitivity) and 

3.6 (altemative efficiency level impacts) of each 'product-specific Part of this report. 

Volume 1: General Meihodology 'Page 5-5 
REFERENCES 

1. U.S. Department of Energy (U.S. DOE), Energy Information Administration. 1995. Annual 

Energy Outlook 1995 with Projections to 2010. Washington, D.C. DOEEIA-0383(95), 

January. 

2. U.S. Department of Commerce, Bureau of the Census. 1983.1980 Census of Population 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 1994 with 

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-032 1(90), February. 

Page .5-6 . Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

REFERENCES 

1. U.S. Department of Energy (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 of Commerce, Bureau of the Census. 1983. 1980 Census of Population imd 

Housing. Washington, D.C. PC80-1-A. . . 

3. U.S. Department of Commerce, B~reau of the 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. 

Page 5-6 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 oder to understand the effects that the uncertainty of input values had on the calculation 

of energy savings, a risk-analysis program (1) was used to quantify the uncertainty. The risk-analysis 

program (See Figure A.1.) was an add-in to the spreadsheets used to generate the cost/efficiency 

tables shown in this report. 1t allowed the inputs to the calculations to be specified by an assumed 

distribution of values with associated probabilities. After the probabilities were specified, the 

energy-savings spreadsheet was recalculated several hund times, randomly choosing 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 for purposes of this study, therefore no distribution 

was applied to any of the inputs on the baseline models. The uncertainty of the inputs for combined 

or stacked1 design options included (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 example of this would be applying heat traps to a gas-fired storage water heater-and , . 

then applying a submerged combustion chamber: The heat traps reduce the standby loss, while the 

submerged combustion chamamber increases themxovery efficiency. In these cases, the uncertainties 

.from previous Zlesign options were used in.the calculations, even though that part of the calculation 

was not being changed by the current design option. 

The.results of the uncertainty analysis were reported only-for the maximum technologically 

feasible design options. These results include the uncertainties of all the design options that were 

applied before the maximum technologically feasible design option. The upper and lower values 

reported for the uncertainty of the efficiency indicator span the middle 95% of all values resulting .. 

. from the 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 data in the input fdes specified the properties and operating conditions of a room . . 

air conditioner. me risk-analysis, program was 'used - to generate several hundred simulation model 

input files. The distribution of values in the input files that were usedto,specify changes from the 

baseline 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 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. ,The standard deviation of-the distribution was then specified as 

Volume I:' Ge~eral Methodology Page A-1 . 

 EE-RM-93-801-RAC COMMENT 01

APPEND][x A. UNCERTAINTY ANALYSIS FOR ENERGY EFFICIENCY 

Many of the inputs to the calculations of energy savings for each design option have some 

degree of uncertainty associated with them. This un~rtainty of the inputs affects the level of 

confidence in the estimate of energy .savings for design options. As design options are combined, 

the effects of the uncertainty on the inputs are also combined, so that the uncertainty in the energy 

savings becomes larger and larger. 

In order to understand the effects that th.euncertainty of input values had or:t the calculation 

of energy 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 inputs to' 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 of input values chosen 

matched the ']probabilities specified in the input distribution. 

The basel~ne waS assumed to be exact for purpOses of this study, therefore no distribution 

was applif~ tlO any of the inputs on the baseline models. The uncertainty. of the inputs for combined 

or stacked! design options included (i.e., were applied on top of) the uncertainties of previous design 

options. Often different design options affected different parts. of the ~nergy ~onsumption 

.calculations. An example of this would be applying heat traps to a gas-fired storage water heater·and 

then applying a submerged combustion chamber: The heat traps reduce the standby loss, while the 

submerged combustion chamber increases therecoveI)' efficiency. In these cases, the uncertainties 

'from previous liesign options were used in .the calculations, even though that part of the calculation 

was not being changed by the current design option. 

Tberesults of the uncertainty analysis were reported only' for the maximum technologically 

. feasible design options. These results include the uncertainties of all the design options that were 

applied before the maximum technologically feasible design option. The upper and lower v8Iues 

reported for the uncertainty of the efficiency indicator span the middle 95% of all values resulting 

from the uncertainty analysis. 

For room air conditioners, the method was modified slightly to accommodate the use of a 

simulation model. An input 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 of a room 

air conditioner. 'The risk-analysis program was 'usedto generate several hundred simulation model 

input files. The distribution of values 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 of uncertainty 

on the inputs. ' . 

Most of the input values were assumed to have a normal distribution, with the selected value 

being the mean ~f the di~tribution~ ,The standard deviation of the distribution was then specified as 

Volume 1:' General Methodology Pa.geA-1 · 

r 
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 ~e mean. This truncation 

excludes the smallest 2.28% and the largest 2.28% of the possible values in a norma1 distribution. 

This was done to eliminate potential outliers that could unnaturally skew the results. 

Figure kl Schematic of @RISK Uncettainty Analysis for Spreadsheet Calculations 

Page A-2 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

the difference between the largest and/or the smallest values considered possible and the mean. The 

distributions were typically truncated at two standard deviations from the mean. This truncation 

excludes the smallest 2.28% and the largest 2.28%· of the possible values in a normal distribution. 

This·was done to eliminate potential outliers that could 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 

 EE-RM-93-801-RAC COMMENT 01

REFERENCES 

1. @RISK, Palisade Corporation, Newfield, NY, 1992. 

I 

; 

., Volume 1.: General Methodology . PageA-3 
APPENDIX B. FORECASTING MODELS 

. . 

B.l LBNL RESIDENTIAL ENERGY MODEL , ;' 

B.l.l Overview 

The ILBNL Residential Energy Model (LBNLREM) provides projections of the important 

characteristics of the residential appliance market. The LBNLREM utilizes a database of significant 

determinanti of the current residential appliance market, as well as parameters characterizing market 

decisions that will affect the energy consumption of future appliances. This appendix describes-the 

LBNLREM, the types of data it uses, the calculations it performs, and the results the LBNLREM 

provides. 

. 'I'he'JLBNLREM projects.numbers of households by house type, energy-related building shell 

characteristics, average energy efficiency of appliances, fraction of households owning each . . 

appliance:, usage behavior, and turnover of appliinces. These characteristics Are projected for each 

yek fronn 1980 to 2030. . . . , 

The ILBNLREM combines these factors to project energy consumption and expenditures by 

fuel type: for each end use, and monetary expenditures for equipment (both replacement and new % 

installations) and for fuels. The results are reported in several formats, for convenience, including: ' 

annual energy consumption and expenditures, annual equipment shipments, cumulative energy. 

consumption and expenditures over a period (e-g., from policy implementation date'to the end of the 

projection period), 'and net present values of equipment and energy expenditures, discounted and . . . - 

summed overtime. . . . 0' 

'Ilne basic equation in 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 ,TI is the, 

thermal integrity factor (for space conditioning only). 

EIouseholds respond to changes in operating expenses in three different ways. 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 improving the efficiency of their 

equipment (e.g., purchasing a water heater Gith more jacket insulation '(see B.1.4, "Equipment . , 

Volume 1: General Methodology . , pa'ge B-1 

 EE-RM-93-801-RAC COMMENT 01

APPENDIX B. FORECASTING MODELS 

B.I LBNL RESIDENTIAL ENERGY MODEL 

B.I.I Overview 

lhe LBNL Residential Energy Model (LBNL-REM) provides projections of the important 

characteri~tics of the residential appliance ~et. The LBNL-REM utilizes a database of signifi.cant 

determinantS of the current residential appliance market, as well as parameters characterizing market· 

decisions that will affect the energy consumption of future appliances. This appendix describes-the 

LBNL-REM, the types of data it uses, the calculations itperforms, and the results the LBNL-REM 

provides. 

lhelLBNL-REM projects numbers of households by house type, energy-related building shell 

characteristics, average energy efficiency of appliances, fraction of households owning each 

appliancc~, usage behavior, and turnover of appliances. 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 of the 

projection period)~ . and net present values of equipment and energy expenditures, discounted and 

summed overtime. . .. lhe basic 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 tIt 

ikm = STOKC m • C ikm •. EU ikm • U it • TI 

" . ttl (B.I) 

where STOKe is the stock of occupied housing units, C is the' saturation (percent of households with 

this equipment), EU is the unit energy consumption', U is the usage behavior factor, and TI is' the. 

thermal integrity factor (for space conditioning only). 

. Households respond to changes in operating expenses in three different ways. In the short 

. run (see B.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 of their 

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 equipmentjfuel choice (market share) elasticity (E,): 

B.1.2 Housing 

The model performs calculations for three house types, (i) single-family homes, (ii) multifamily homes, and (iii) mobile homes. Inputs for housing information includes annual projections 

of occupied households and housing starts. 

B.1.2.1, Occupied Households 

The number and type of occupied households for the base year 41980) are from the 0.1% 

Public Use Microsample (PUMS) of the 1980 Decennial Census. Projection of occupied households 

by house type from 1992 to 20 10 is 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 for past (1980-1989) housing starts by house type come from the Bureau of Census' 

Housing Starts (2). Projections (1 990-20 10) come from the llnnrcal Energy Outlook 1995. Housing 

starts in later years (20 1 1-2030) 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 performancdcost tradeoff for 

structures is: 

. . 

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

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

TBETA are parameters. 

Table B. 1 shows values of the parameters TINF/TIN@ TALFA, and TBETA. 

Page B-2. . Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

." 

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 (Ell)' a technical efficiency 

elasticity (Ee), 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 

of occupied households and housing starts. 

B.I.2.1 , Occupied Households 

o 

The number and type of occupied households for the base year (1980) are from the 0.1 % 

Public Use MicroSample (PUMS) of the 1980 Decennial Census. Projection of occupied households 

by house tyPe from 1992 to" 2010 is obtained from DOFJEIA's Annual 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 of 0.5%, 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 of Census' 

HoUsing Starts (2)." ProjeCtions (1990-2010) come from the Annual Energy 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 TIN is the thermal pe~ormance (relative heating energy normalized to 1980 existing houses); 

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

TBETA are parameters. 

"Table B.l shows values of the parameters 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 corresponding to a set 

of building.shel1 characteristics (e.g., ceiling insulation, floor insulation, glazing, floor insulation, 

infiltration rate), associated with an incremental.cost (0. For space heating, it is calculated from 

the relationship each year to minimize the life-cycle cost, given current energy prices, using a 

discount rate consistent with historical market tradeoffs. The thermal 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 the building shell characteristics is 

through internal loads generated by these products. Those internal loads interact with spaceconditio,ning requirements. . . 

8 TBETA 

267 17 

6067 

993 1 

Single-Family 

Multi-Family 

Mobile Homes 

B.1.4 Equipment Efficiency 

The LBNLREM pmjects the avkge efficiency or unit energy consumption, for a new. unit 

purchased in each future year. The.method involves characterization of 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 of the range of designs available-hm manufactunrs, equipment prices 

(set by 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 average ekciencies 

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:arket Shares ' 

T'he number of potential pu~hasers is calculated for two markets: housing starts and potential 

replacement/retrofit in existing housing. Total product sales for each year are determined by applying 

market share elasticities to projected equipment price, operating expense, and income to determine 

the fraction of potential purchasers who will actually purchase the product. The market share 

elasticities include cross-price variables, where appropriate, to capture inter-fuel competition, e.g., 

the choice between electric or gas cooktop. 

Volume I: Gt'neml Methodology Page 8-3 

 EE-RM-93-801-RAC COMMENT 01

!able B.I Parameters of Thermallntegrity/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 

of building'shell characteristics (e.g., ceiling insulation, floor insulation, glazing, floor insulation, 

infiltration rate), associated with an incrementaL cost (C). For space heating, it is calculated from 

the relationship each year to minimize the life-cycle cost, given current energy prices, using a 

discount rate consistent with historical 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 of these 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 of historical market behavior, 

in the fc)rm of market discount rate. The' observed average efficiency of the unit purchased is 

charactedzed as a function of the range of designs 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 of units 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 ' 

'1be number of potential 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. The percent 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.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'B of each'product-specific Part. 

. . 

56% 

44% 

24% 

76% 

Page B-4 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 

report. The percent 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 of Each Class of Room 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 of Each Class of Electric Cooktops 

coil element 85% 

smooth element 15% 

Table B.2c Percent Share of Each Class of Electric 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 of each'product-specific Part. 

PageB-4 Volume 1: General Methodology 

'\ 
. - 

Chapter 2 in each product-specific Part shows average equipment lifetimes. These lifetimes . , 

are assu:med to be constant over the projection period. The retirement functions (the percent of ' 

. ' products which survives, as a function of years after purchase) are shown in Table B.3. . . 

Table B.3 Survival Finetion MRoom AIC, Cooktops, and Ovens 

The LBNLREM produces annual projections of: 

a number of houses in stock by house type; . . 

housing starts by house type; 

thermal integrity factors for stock and new houses by house type; 

average unit energy consumption of new units sold (by class and by end 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 consumption by housing type. by fuel, and by end use; .. total equipment expenditures by end use; and 

total fuel expenditures. . . 

'.. 

Vilurne I: General hiethodology Page B-5 

 EE-RM-93-801-RAC COMMENT 01

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 of years 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 of houses 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 of new units sold (by class and by end uself':lel); 

• average unit equipment price of new units sold (by class and by end use/fuel); 

• 'fraction of existing ho~ses having each end use; 

• fraction of new 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': General Methodology 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 equipment expenditures; and 

discounted total (fuel and equipment) expenditures. 

The LBNLREM produces cumulative values for new units purchased in a period (1998- 

2030): 

discounted he1 expenditures by end use and fuel type; 

discounted equipment expenditures by end use and fuel type; ' 

discounted total (fuel and equipment) expenditures by end use and fuel type; 

units installed by end use and fuel type; and 

total discounted lifecycle expenditures per unit by end use and fuel type. 

Product types usually conespond to end uses. . 

B.1.9 Changes SincePrevious ~echnical Support Document - 

The principal changes since the previous Technical Support Document in 1993 are: 

(1) The set of clases being 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 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 of room air conditioners have been updated. 

(6) The average equipment lifetime of room air conditioners has been re-estimated. 

(7) A consistent set of market discount rates is used in calculating market 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 engineering data. The input listing for products in 

the current proposed rule is reproduced in Appendix B of each product-specific Part. 

Page B-6 Volume I: .General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 fuel type; 

• 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 of clases being analf1.ed has changed for room air conditioners: certain nODlouvers classes have been removed. • 

(2) . The set of design 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 of room air conditioners have been updated. 

(6) The average equipment lifetime of room air conditioners has been re-estimated. 

(7) A consistent set. of market 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 of each 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 with Pr&ctions to 2010. Washington: D.C. DOE~EIA-0383(95). January. 

2. US. Department of ~ommerce. Bureau of the Census. 1990. Housing Smm. Washington, 

D.C. July. . 

Volume 1: ~Gen~eral Metliodology Page B-7 

 EE-RM-93-801-RAC COMMENT 01

REFERENCES 

1. U.S. Department of Energy, Energy Information Administration. 1995. Annual Energy' 

Outlook 1995 with Projections to 2010. Washington,' D.C. DOFJEIA-0383(95). Janu~. 

·2. U.S. Department of Commerce, Bureau of the 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 spreadsheet all the caldulations necessary to determine the idpact of 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& incorporate new 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. 

Through this panel, the model provides the user not only with the best estimates of financial impacts, 

but also with the uncertainty of these estimates and a sensitivity analysis attributing that uncertainty 

to certain input variables. 

C.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 single typical 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 generally create 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 difficult to find data on-single-fum price elasticities, ,the LBNLMAM infers 

them fkom the fm's fixed costs and return on equity (ROE). The markup is over variable cost and 

not fixed; hence it must be greater than one if a fm with fixed costs is to recover its fixed costs and . . 

turn aprofit. , 

The industry has two elasticities of intexest: its price elkticity and its operating cost elasticity. 

., . 

Volume 1: General ~ethodolo~~ Page C-1 

 EE-RM-93-801-RAC COMMENT 01

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 model is crucial inputs. 

Through lhis panel, the model provides the user not only with the best estimates of financial impacts, 

but also with the uncertainty of these estimates and a sensitivity analysis attributing that uncertainty 

to certain input variables. 

C.I.2 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 of a single typical f1llIl, 3) the industry's price elasticity of demand, 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 .of costs 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 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 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 if a f1llIl with fixed costs js to recover its fixed costs and 

~a~~ , ' 

The industry has two elasticities of interest: its price elasticity and its operating cost elasticity. 

Volume 1: General Methodology Page C-l 
Although individual firms must calculate according to their own elasticities of demand, it is 

ultimately the industry elkticities that determine the level of demand experienced by 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 fums change the prices and operating costs of their appliances by 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 DNLMAM computes three primary and several secondary measures of impact. The 

three primary measures, ROE, industry net present value, and net income are p-nted because they 

can each move in opposite directions. For instance, if net income increases,'but assets and thus 

equity increase. by a greater percentage, ROE will decline. If the decline is large, the net effect will 

be viewed as negative, but if a small decrease in a healthy ROE is accompanied by a large increase 

in net income, the net effect might be viewed as positive. 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 

How Alternative-Emiency Levels Are Implemented in the Model 

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 induced by new efficiency levels is the purchase of new plants 

and equipment. Thisxost is fvst evaluated from engineering data and then amortized by taking into ' 

account the life of the investment, the timing of the expenditures, tax laws, and the cost of funds. 

An additional (and sometimes larger) investment 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 elasticities and consumer market discount rates, coupled 

Page C-2 Vohe I: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01, ' 

Althougb indlvidual flIlIlS ,must calculate according to their own elasticities of demand, it is 

ultimately the industry elaSticities that determine the level of demand experienced by 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 of their appliances by the 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 of impact. 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 greater percentage,' ROE will decline .. H the decline is large, the net effect will 

be viewed as negative, but if a 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 

of changes In the primary inputs. 

C.l.4 Methodology 

How Alternative 'Efficiencyuvels Are Implemented in the Model 

Generally,. a change in efficiency levels affects the model in tl1r'ee distinct ways. 

hnpleinentation of new efficiency levels require additional investment, raise production costs, and 

affect revenue. ' 

The most obvious inv~tment induced by new efficiency levels is the purchase of new plants 

and equipment. This,cost is first evaluated from engineering data and then amortized by taking into -

account the .life of the investment,the timing of the expenditureS, tax laws, and the cost of funds. 

An additional (and sometimes 'larger) investment is"made as old inventory is replaceq with more 

expensive new units. The model assumes that previous inventory ratios are maintained. A third 

form of investment tracked by LBNlJ;MAM is the change in the transactions demand for cash -that 

. accompanies a change in revenu~s.. . -

Increased costs of production 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 by price elasticities and consumer market discount rates, coupled 

Page C-2 Volume 1: General Methodology 
with the changes induced by imposed efficiency levels in price and operating costs. 

. , . . 2jpiccrl-year Approach . . . , 

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 :level case. 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 entrj into or exit 

from the industry. 

Use of Prc~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 representative of the 

industry. In many cases, this fm must 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 consistent with data for the portion of the industry 

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

profitability ratios, relative costs of complying with new efficiency levels, 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 charge higher markups 

on produc.ts that 'have desirable characteristics. Products without ,such desirable features are 

'generally lbought in larger quantities at lower prices by consumers who are more prick-conscious, 

and thus the markups for these products are lower. The per-unit profits made by manufacturers for 

these diffe'rent products may differ significantly. .. 0 

C.2 STRUCTURE OF THE LBNL-MAM .. 

C.2.1 Ec.onomic Theory and Assumptions 

Th~e basic question that LBNLMAM must answer is: how much of the cost increases caused 

by new efficiency levels can be passed through to consumers? If less than the normal pass-through 

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

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

underitancling of the relevant economics will be useful. 

In txo~lomics, costs are often divided into fixed and marginal components where fixed costs 

are just whiat their name suggests and marginal costs are the costs of producing an additional unit of 

Volume I:. General Methodology Page C-3 

 EE-RM-93-801-RAC COMMENT 01

with the changes induced" by imposed efficiency levels in price and operating costs. 

"Typical-ye'ar Approach 

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 

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 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 of the 

industry. In many cases, this fum must be thought of as representing an autonomous division of a 

larger firm. Prototypical fmns are defined by parameters that are important for determinilig the 

impacts of alternative efficiency levels and are consistent with data for the portion of the industry 

tl)ey represent Important paramet~rs used in the model include the cost structure of the firms, . 

profitability ratios, relative costs of complying 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 higher markups 

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 

Tbe basic question that LBNL-MAM must answer is: how much of the cost increases caused 

by new efficiency levels can be passed through to consumers? If less 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 of the relevant economics will be useful. 

In c~onomics, costs .are often divided into fixed and marginal components where fixed costs 

. are just what their name suggests and marginal costs are the costs of producing 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. under kperfect competition (i.e., where the market is characterized by a downwardsloping demand curve) price 'quais marginal cost times a markup. 

.3. In neither case. do fixed costs affect the calculation of price.' .,. 

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 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 difference between cost and 

revenue, it chooses the optimal output Q*. This choice is clearly independent of the shift in fixed 

cost, but once output is determined, the demand curve will dictate the price at which that output can 

be sold. A change in fixed cost has no effect on the demand curve and thus no effect on price. 

The separation of costs into fixed and variable must be done with an eye to the time horizon 

under consideration, because most (if not all) costs that are fured in the short run become variable 

in the long run. Three distinct time horizons are useful: the short run, the long run, and the very long 

run. In the short run, the productive capacity of the finns in the industry remains fured. In the long 

run, capacity may be adjusted, but the number of firms remains 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 elasticity of demand, may change. Thus, in the very long run, markup may change. 

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

is a markup over short-run marginal cost. Since in the long run, 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 interpretations may be given to this mathematical result. he 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 of demand. 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: 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's elasticity of demand. Conversely. if fixed costs are more than covered, positive 

economic profits will induce entry and elasticity and markup over marginal cost will be reduced. 

Page C-4 Volume 1: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

output. Three points are true in general of these:-cost distinctions: -

1. Under perfect competition, price equals marginal cost. 

, 

2. Under imperfect 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 of price.' 

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. If price 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 cost curve)? . Since the fIrm wishes to maximize ,the difference between cost and 

revenue, it chooses the optimal output Q* .. This choice is dearly independent of the shift in fixed 

cost, but once output is determined, the demand curve will dictate the price at which that output can 

be sold. A change in fIxed cost has no effeCt on the demand curve and thus no effect on price. 

The separation of costs into fixed and variable must be done with an eye to the time horizon 

under consideration, because mos! (if not 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 of the fums in the industry remains flXed. In the long 

run, capacity may be adjusted, but the number of firms 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 of demand, may change. Thus, in the very long run, markup may change. 

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

is a markup over short-run marginal cost. Since in the long run, capital is considered a marginal 

cost, how can the two pricing rules be reconciled? If a fIrm fmds 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 

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 of demand. 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: 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 ea~h firm's elaSticity of demand. 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: General Methodology 
Q* Output (Q) 

Figure Ct,l Profit Maximization and Fixed Costs 

Volume 1: General Methodology Page C-5 

 EE-RM-93-801-RAC COMMENT 01

.---. 

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 . J 

i' 

· . 

· 

· .. · 

· 

· .. 

· 

· 

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. 

Theory and the 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; eachproducing fm does not face an infiote elasticity of demand because a small 

increase in 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-manufacturing industry ' 

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. These relationships are part 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 exception to this disclaimer is the discussion on oligopsony power and the 

"effective" number of firms. There is no generally accepted theory on how to model a market 

characterized by significant market concentration on both the seller's and the buyer's side. As a 

result', we developed an approach that is discussed in greater detail below. . Derivation of Markup 

To begin the derivation of the markup equation, assume that firms maximize economic profit, 

where economic profit is revenue minus economic cost, and economic cost includes the cost of 

equity and all taxes. Economic costs (C) are assumed to be linear functions of output (Q) and 

revenues (R): 

Although cost is written as a linear function of output, this is not essential; a non-linear cost function 

Page C-6 Volume I: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

, 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 of the 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 of the model. 

Theory and the 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 of sales '(because the increase in price would 

induCe entry of new 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 

of fixed and variable costs, and as facing a demand curve with finite elasticity. 

. . The next section derives the equations relating markup, fixed costs, variable cost, and 

economic profit. These relationships are part 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 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 

To begin the derivation of the markup equation~ assume that firms maximize economic profit, 

-, where economic profit is revenue minus economic cost, and economic cost includes the cost of 

equity and all taxes. Economic costs (C) are assumed to be linear functions-of output (Q) and 

revenues (R): 

(C.I) 

Although cost is written as a linear function of output, this is not essential; 8: nort-linear cost function 

Page C-6 Volume 1: General Methodology 
wodd give a mathematically equivalent, but more difficult to exikss, result? , 

Next, make the standard economics assumption that firms maxi& eonom@ profit (R-C). 

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

will ftnd h economic profit to be zero. If, however, a fum canearn 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. This gives the following 

first-order condition: 

This equation completes the description of the supply-side assumptions. The demand side 

is described by an elasticity of demand which may vary, but which at the market equilibrium takes 

the value: e. Elasticity of demand is the percentage change in demand, caused by a I %.change in 

price P, and consequently is 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 /dQ by 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 differ only in that the two 

types of co:jt ( A, and P, ) will be replaced by C - Q (dC I dQ ) and dC I dR, respectively. 

Volume 1: General Methodology Page C-7 

 EE-RM-93-801-RAC COMMENT 01

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

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 of the supply-side assumptions. The demand side 

is described by 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 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 If we 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) will be replaced by C - Q (dC I dQ ) and dC I dR. respectively. , . 

Volume 1: General Methodology PageC-7, 
Substituting for R' in the first order condition yields: 

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: 

Note that because the own-price elasticity (e) is negative, p is greater than 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. 10 toprovide 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 of p which is used in the calibration stage of 

the LBNL-MAM to estimate y from Eq. C. 1 : 

Page C-8 Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

" 

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

then J.l is undefined, since the firm makes more 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 by J.l and fixed costs'are' 

not passed on at all. 

It is pow easy to derive another representation of J.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: General Methodology 
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 EP is economic profit, is used 

twice. The new form of the equation is: 

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

that fixed 'costs are large relative to economic2 profit, 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. . ' 

Oligopsony Power and the "Eflective Number of Finns" 

From an evaldation'of the 'malyzed 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 fuaher complicated by the .fact 

that the demand side of the market is also heavily concentrated due to the presence of large retailers 

Volume I: General Methodology , Page C-9 

 EE-RM-93-801-RAC COMMENT 01

(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 of the long-run module uses a simple variation of this 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 of fixed cost relative to revenue. 

Oligopsony Power and the "Effective Number 0/ Firms" 

From an evaluation ·of the ·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 of the market is also. heavily concentrat~ due to the presence of large retailers 

Volume 1: General Methodology Page C-9' 
who have a great deal of market power? In other words, we have an oligopoly facing 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 of large 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 and Montgomery Ward alone has been estimated to be about 30% for various products. One 

indicaticw of this oligopsony power is that if theoretical markups based on the actual number of firms 

in the irtdustry are estimated, we find that the markups are much too large relative to what we 

observe in the market. 

We address both the problems of oligopsony and of "over-estimated" markups by assuming 

that the low observed markup is the result of oligopsony power and is an indication of the strength 

of that power. Since a firm's markup is directly related to the elasticity of the 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 industj elasticity 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 fms from 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 firms to reflect the impact of the oligopsony. The 

effect of this is to reduce the market power of the individual firm to a&- with our observation of 

markup. Although the reduced market power is a consequence of specifying too many firms, and 

not of mtdeling oligopsony, we believe that it provides a good first-order approximation to the actual 

market structure and provides a way around the intractable problem of modeling a market with 

imperfectly competitive demand and supply sides. 

We now show how markup is computed from economic profit and fixed costs, and how the 

"effective" number of 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, "State of the AW to the 

Depanmeni: of Energy on Notice of Proposed Rulemaking on Energy Efficiency Standards for Clothes Dishwashers, Clothes 

washers and Clothes Dryers." CE-&-88-10]. October 10. 1989. . . . 

Page C-I0 Volume I: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

· . 

who have a great deal of market 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~ of oligopsony, we go on to show how ,,:,e incorporate this into the model. 

The rise of large 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 retail share 

of Sears and Montgomery Ward alone has been estimated to be about 30% for various products. One 

indication of this oligopsony power is that if theoretical markups based on the actual number of fmns 

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 of oligopsony and of "over-estimated" lI].arkups by assuming 

that the low Observed markUp is the result of oligopsony power and is an indication of the strength 

of that power. Since a firm's markup is directly related to the elasticity of the 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 of finns. 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 of finns rather than the actual number of finns to reflect the impact of the oligopsony. The 

effect of this is to reduce the market power of the individual firm· to a~ with our observation of 

markup. Although the reduced market power is a consequence of specifying too many fmns, and 

not of m(xleIing oligopsony, we believe that it provides a good first-order approximation to the actual 

market structure and provides a way around the intractable problem of modeling a market with 

imperfectly competitive demand and supply sides. 

We now show how markup is computed from economic profit and 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 of demand, 

the in~ustry elasticity of demand, and 

number of firms. 

(c. 19) 

3 The i1ncreasing concentration on the demand side of the market also agrees with comments by the home appliance 

industry during the comment for these and other appliances. For instance, see AHAM. "State of the AlHAM to the 

Depanment of Energy on Notice of Proposed 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: General Methodology 
We can solve for NF if the two elasticities are known We have & estimate of ei and can estimate . 

e as follo\vs. From Eq. C.11, we know that: I 

We can fuld the markup, p, from Eq. C. 12 if we know margin4 cost fi,. Marginal cost can be found 

from the fib sturn on equity, its fured costs, and the following price ideatity: xlQ=P-P, +PdQ , ' 

where Po is fixed cost and x: denotes eionomic profit, which is equal to revenue minus cost. (This 

is just a restatement of Eq. C. 13.) Noting that revenue,' R, equals P Q, we have: 

This gives. a formula for markup p based on our most easily observed variables. Solving Eqs. 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 

of fms observed. - 

Tc) suma'rize, we first look at a typical fumls.profit and 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. From these consideratio@, we find its markup. The markup indicates a firm's 

industry dern'and elasticity and the effective number of Ti in the industry. We use it in subsequent 

calculations. 

The 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 one just calculated and modified 

in keeping with the assumption that demand elasticity with 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 

 EE-RM-93-801-RAC COMMENT 01

. We can solve for NF if the 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) 

We can fmd tl'ne markup, J.l, from Eq. C.I2 if we 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 

is just a n:statement ofEq. C.13.) Noting that revenue, R, equals PQ, we have: 

p. p. R 

J.l - - = = 

PI P0 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 of fInns. NF: 

(C.22) 

We fInd that given two elasticities estimated from actual data. NF is higher than the actual number 

of fIrms 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 standards are . 

unmodified. These calculations use the markup that is based on the one just calculated and modifIed 

in keeping with the assumption that demand elasticity with respect to life-cycle cost is constant. The 

modified markup is calculated as in Eq. C.S but from a modified price elasticity. The new price 

Volume 1: (;eneral Methodology . Page C-ll 
elasticity is obtained as follows. First, the original price and operating cost elasticities are used to 

determine a lifecycle cost 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 elasticity between 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 

amortization procedure, uses an intermediate value for the lifetime of new capital. This procedure 

takes a one-time capital expense and converts it to an annual expense, taking into account the cost 

of debt and equity, the tax laws, the lifetime of the capital, and the age of any capital being replaced. 

Once the cost of capital has been annualized, it must be divided into fixed and vdable parts. 

The fixed part is then added to Po while the remainder is divided by Q and added to PI. 

Since there are undoubtedly some -economies of scale when parts are purchased, the 

LBNL-MAM provides for a division of the cost of parts and labor between the fixed and variable 

categories. However, the'savings generated by buying an additional 100,000 units above-an initial 

million, for example, will generally be so small that the cost of parts and labor will be referred to as 

a variable cost and thus can be thought of as being added dkctly to PI. 

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 operatingcost elasticities. Revenue is now just R = P Q, and economic 

income is just R-C. Assets are those of the calibration case plus the new capital resulting from 1990 

NAECA standards, and equity is computed from the debt-toequity ratio. This calculation of equity 

is the last essential calculation for the base case. 

Page C-I2 Volume I: ~eneral Methodology 

 EE-RM-93-801-RAC COMMENT 01

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, ff is 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 of demand 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 of life-cyc1e cost. the price elasticity increases. 

·"(C.24) 

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 econoinic difference between these two is their expected ·life, the LB~MAM. in its 

amortization procedure, uses an intermediate value for the lifetime of new capitaL This procedure 

takes a one-time capital expense and converts it to an annual expense, taking into account the cost 

of debt and equity, the tax laws, the lifetime of the capital, and the age of any capital being replaced. 

Once the cost of capital has been annualized, it must be divided into fixed and 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 of the cost of parts 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 of parts and labor will be referred to as 

a variable cost and thus cari be thought of as 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 now just R = P Q, and economic 

income is just R-C. Assets are those of the calibration case plus the new capital resulting from 1990 

NAECA standards, and equity is computed from the debt-to-equity ratio. This calculation of equity 

is the last essential calculation for the base case. 

Page C-12 . Volume 1: General Methodology 
The Long-nur Aniilys&: ~hn&d ~~ernative Emiency Lcvek . 8. 

. . . 

The analysis of the long-run effect of a change in efficiency levels from their 1990 NAECA 

level is essentially the same as the analysis of the base case. New costs are divided between fixed 

and variable categories hi the same way and added to the base case costs. From there,' the calculation 

proceeds in exactly ,the skne way as above. 

The cost structure. Costs are assumed to have two components: fixed and proportional to 

quantity. 

7ike asset structure. Assets are assumed to have the same two components. 

D,emQnd elasticity. The.demand elasticity with respect to life-cycle cost experienced by a 

single fmn is assumed to be independent of price and operating cost over the relevant region. This 

means that if the industry raises price, the percentage change in demand caused by a 1 % change in 

price remains the same as before the price increae. . . 

Oj7timizing behavior. A firm is assumed to maxi- revenue minus economic cost (R-EC). 

Debt-to-equizy ratio. The debt-toquity ratio is assumed to remain constant kdile the firm 

finances any investment necessary to'meet new efficiency levels. It is generally'assumed that fvms 

have somr: Dm ratio that they consider optimal, and.there is no reason for new efficiency levels to 

change this figure. 

. Anwrfization of capital costs. It is assumed that the one-time capital cost is to be amortized 

over its life (typically seven years), that its cost is the weighted average after-tax cost of capital, ahd 

that depreciation is straight-line over the tax life. 

Structure of cost resulting from changes alternative efiency levils. The cost of capital 

is assumecl 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: General Methodology 

. 

Page C-13 

 EE-RM-93-801-RAC COMMENT 01

The Long-run Anillysls: Changed AltenuJtive Efficiency Levels 

1be analysis of the long-run effect of a change in efficiency levels from their 1990 NAECA 

level is essentially the same as the ~alysis of the base case. New costs are divided: between fixed 

and variable categories in'the same way and added to the base case costs. From there, the calculation 

proceeds in exactly the same way as above. 

C.2.2 Structural Assumptions 

The cost 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 'of price 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 of capital. and 

that depreciation is straight-line over the tax life. 

St,.ucture of cost resulting/rom changes in alternative efficiency levels. The cost of capital 

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: <1encrol Methodology Page C-13 
C.23 Description of Module. 

The LBNLMAM is a Lotus 1-2-3 spreadsheet that is organized into 9 sheets, each of which 

is subdivided into a nuniber of modules or "pages." A flowchart depicting the main linkages 

between the most important modules appears in Figure C.2, and a brief description of each is given 

below. Starting with subsection C.2.3.1, each panel is given a full description. 

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 and assets 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 of new efficiency levels. 

4. Model and demand (long-run). This module does most of the economic calculations. 

Its calibration phase estimates the markup over variable cost which is used to estimate 

new prices once 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 computed by the long-yun model in 

order to take into account the possible short-run effects of excess capacity. An estimate 

of the responsiveness of price to changes in demand during the business cycle is 

combined with the change in demand predicted by the long-run model. 

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 change in ROE in tile long and short runs. 

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 functionality is exactly 

that of the externally developed Government Regulatory Impact Model (GRIM). See C.2.6 

for more details. 

Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

C.2.3 Description of Modules 

The LBNL-MAM is a Lotus 1-2-3 spreadsheet that is organized into 9 sheets, each of which 

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 of eaCh is given 

below. Starting with subsection C.2.3.I, each pane~ is given a full description. 

A. Primary Page. 

I; Control panel. This module displays nine key variables and their control factors, plus 

a summary of crucial 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 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 and assets 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 of new efficiency levels. 

4. Model and demand (long-run). This module does most of the 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 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 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 

(A I). 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 

of the variables. 

B. Industry Net Present Value. This module estimates the impacts of imposing new efficiency 

levels using an industry net present value approach. This niodule's functionalitY is exactly 

that of the 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 diffexent ways the two models treat costs. This module aligns inputs for 

{the two models using cost comparison factors. See C2.6 for more details. ' 

D. Program. LBNLMAM Primary Programming. The program flow is controlled through a 

series of menus. The program performs the calculations for the various outputs of the model, 

inclu.ding efficiency 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 due to 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 

standard errors from the Control Panel (Al) and Monte Carlo module (A6), and shortrun variables and percent changes from the Short-Run Model (AS). 

3. Charts of Sensitivity to Control Panel Variables. This module produces two charts. The 

first shows how profit is affected by a one-standarddeviation 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 of sensitivity to Model 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 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 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. Cbst Inputs 

1. Engineering cost input matrices. he 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 for the products being analyzed. 

2. Alternative Eficiency level distribution matrices. This module weights the costs given 

by the engineering inputs module in proportion to the number of products of a 

Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of menus. The program perfonns the calculations for the various outputs of the 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, of the fmancial impact of alternative efficienc levels on . 

the fIrm. In addition, a summary of the financial impacts due ~o 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 

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. This module 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 ~NLREM: . 

F. GRIM Outputs. This module displays the reSUlts. from the industry net present value 

module/GRIM (B). . 

G. Other Inputs' 

1" CoSt page 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 for the 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: Ge1leral MethodolOgy 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,cy levels. 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 estimate of manufacturer prices. 

I. Engineering Data. Raw engineering inputs are included on a separate sheet. These values are 

processed by,the- Cost Inputs (Gl) sheet before being passed to the rest of the model. . 

J. General Notes. 

C.2.3.1 Control Panel 

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

the user to easily adjust them. These variables are described below. The input values are the best 

available estimates of the relevant variables. 

The control panel has one control for each input variable. The codtrols, 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 cxytrol value of 1 actually changes 

an input value by an amount sufficient to make it just as unlikely as a point that is one standard 

deviation from the mean using a normal distribution. Consider an input value that is known to be 

strictly positive and have a mean of 5 and a standard deviation of 3. Setting its control to -2, which 

would otherwise change its value to -1 (an impossibility) actually only changes it to some small but 

positive value. This value would be just as unlikely as a point -2 in a normal distribution. . 

The Variation column shows either the standard deviation of an input variable if the variable 

is normally distributed, or the coefficient of variation (S.DJmean) if the input variable is non-normal. 

Page C-16 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of manufacnirer prices. 

I. Engineering Data. Raw engineering inputs are included on a separate sheet. These values are 

processed by the Cost Inputs (Gl) sheet before being passed to the rest of the model. 

J. General Notes. 

C.2.3.1 Control Panel 

The control.panel displays the ten most important input variables of the model and allows 

the user to easily adjust them. These variables are described below. The input values are the best 

available estimates of the relevant variables. 

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

labeled 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 it just as unlikely as a point that is one standard 

deviation from the mean using a normal distribution. Consider an input value that is known to be 

strictly positive and have a mean-of 5 and a standard deviation of 3. Setting its control to -2, which 

would otherwise change its value to -1 (an impossibility) actually only changes it to some small but 

positive value. This value would be just as unlikely as a point -2 in a normal distribution. 

The Variation column shows either the standard deviation of an input variable if the variable 

is normally distributed, or the coefficient of variation (S.D.lmean) if.the input variable is non-normal. 

Page C-16 Volume 1: General Methodology 
MAM'Modules and Primary Routes of Data Flow 

Engineering 1 ;Yr I-. . 

1X Cost 1 

Inputs f 

(~3) 1 

Volume I: 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 

 EE-RM-93-801-RAC COMMENT 01

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 of Data 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 givk the output 

values in the base case. The second column assumes some particular change in 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 efficiency levels). 

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 elasticity of demand (IPE), 

The consumer discount rate (RD), 

The market cost of capital financed through equity (ECC), - . 

Economic profit: the indus'try's ROE-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 withestimates of their standard errors, 

running the model, and recording the six output variables. This procedure is repeated up to 400 . times. Afterwards, the mean and standard deviations of the 400 computed values of each output 

variable are computed and displayed near the top of the Monte Carlo module. The top row, labeled 

Value, is the most recently computed random set of outputs. 

The first four output variables are the percent change resulting from 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 Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 about 2(03) . 

. 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 (Le .• the estimated output values 

for the period immediately after the imposition of the 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 of demand (IPE), 

2. The consumer discount rate (RD), 

3. The market cost of capital financed through equity (BCC). 

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

5. The long-run fixed part of costs and assetS (FCA). 

6. The long-run fixed part of one-time costs and assets (FIX), 

7. One-time capital costs resulting from the change in efficiency levels (CC.N), 

8. Variable cost of goods sold after the change in efficiency levels (dVC.N) 

9. The elasticity curve parameter of the 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 of their standard errors, 

running the model, and recording the six output variables. This procedure is repeated up to 400 

times. Afterwards, the mean and standard deviations of the 400 computed values of each output 

variable are computed and displayed near the top of the Monte Carlo module. The top row. labeled 

Value. is the most recently computed random set of outputs. ' 

The 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 

standard form, along with several key financial ratios ,and figures that highlight the firm's financial 

Page C·18 Volume 1: General Methodology 
position. The income statement and financial ratios 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 kity seen 'by comparing the two energy 

efficiency scenarios. To highlight the results, a final column in Fe Accounting Summary indicates 

the percei~tage change from the base case to the alternative efficiency level case. 

A second table at the bottom of the Accounting summary page lists 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 markup is computed, and for that reason 

they are reported separately in the economic analysis. 

Table'C.l Accounting Module 

Eaniing!; Before Interest and Taxes. EBIT = Revenue'- Total Expenses , 

Total ~1;:~enses Does not indude interest 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 , 

 EE-RM-93-801-RAC COMMENT 01

position. The income statement and financial ratios 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 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 at the bottom of the Accounting Summary page 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 of Goods 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 van able 

cost, change in unit energy consumption, maintenance cost, and installation cost for each design 

Volume 1: I']enerol Methodology 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 the previous engineering design. The figures arc in 1990 

dollars. 

2. Additional WC above 0 cost (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 engineering design option. 

4. Annual Unit Energy Costs per Year These engineering input data are the energy use per year 

measured in 1990 dollars. 

5. Total Annual Operating Costs (WS.E): These costs are the sum of cumulative maintenance 

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 translates ihe amortized 

capital costs into total one-time per firm capital costs. 

9. Total Capital and 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 as inputs to the rest of the model. The Long-Run module calculates 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. 

1 1. 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 

 EE-RM-93-801-RAC COMMENT 01

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 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 of the 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 Costs per Year: These engineering input data are the energy use per year 

measured in 1990 dollarS. 

5, Total Annual Operating Costs (KWS.E): These costs are the sum of cumulative 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 translates The 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 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 as inputs to the rest of the 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 Standards Levee ("REM") Module . - 

The Standards Levels module provides the same information as the Engineering Inputs 

module except that instead of listing the cost dnd 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, ' 

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 of engineering designs, then the "Standards Level Distribution . 

Module" appropriately averages the 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 of this module allows the user to detemiine.what alternative efficiency levels 

will be analyzed in a particular run. The module is used interactively by the user, who can choose 

"levels" LEV.B and LEV.N; which correspond to the Base case and Long-Run case, respectively. 

The bottom numbers in the "LevIn" matrix detennine whether these levels will refer to "alternative 

efficiency levels" or "engineering designs.': The'top num&rs specify the exact level or design. 

The values in the "LevIn" matrix determine what values are placed in the bottom two 

matrices when the model is run. For example if in "LevIn," LEV.B = 4 and. 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 1 would cause all of these 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, it provides the Long-Run Model'module with several 

inputs; thiese are averages. Second, it uses many of the data from the input pages and processes these 

data into prices, quantities sold, and revenue. These intermediate output data are then used in the 

financial and economic summaries to give output results, which are 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 alternative efficiency 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 average price (P.B), the fm's total 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: General Methodology Pagcl C-21 

 EE-RM-93-801-RAC COMMENT 01

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 of listing 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 of engineering 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 of engineering options are determined by the LBNlrREM. 

1be top section of this 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 of these 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, it provides the Long-Run Moder module with several 

inputs~ tbese are averages. Second, it uses many of the data from the input pages and processes these 

data into prices, quantities sold, and revenue. These intermediate output data are then used in the 

financial and economic summaries to give output results, which are used- for the analysis of the 

impact of 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: General Methodology Page C-2l 
Table C.2 Equations Used in the Costs, Sales, and Revenue Module 

" 1tem Calibration Case Formula or Definition 

Size - Prototypical firm market share ' 

Industry Shipments Annual industry shipments for a product class 

Relative Shipments (Q%) Product class market share 

(product class units sold/total units sold) 

Shipments (Q: 1) Product class shipments for a prototypical fm 

Price (P.l) Unit Variable Cost (UVC) input markup (MU.0) 

Revenue (R. 1) , Price Shipments. 

, Markup (M. 1) 1 + (P. 1 -. BB)/AA 

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 1996 UEU1990 UEC 

Weighted Operating Cost Ratio Operating Cost Ratio Relative Shipments 

Percent Change in Operating Cost C-~td Operating Cost Ratio KWHR - 1 

Page C-22 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

/ 

, " 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 of per 

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 

Laass Wtd Operating Cost Ratio" KWHR - I 

Volume 1: General Methodology 
markups for each of the product classes, and baseline unit variable costs to calculate the prototypical 

firm's shipments for each product class, its cost of goods sold, and its revenue. Also calculated are 

a weighted-average unit variable cost, selling price, and markup. '~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 for each of the product classes. The model then uses known price elasticities and discount 

rates to calculate changes in 'shipments for each of the alternative efficiency 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,markup listed in the panel is used to calculate the baseline model prices, 

- but it is nc~t.used in calculating the manufacturer pricesfor the alternative efficiency levels. In these - 

cases, LB1NL.MA.M uses an econdmic markup to determine prices.' 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, pricesat different efficiency levels do not exactly 

reflect the manufacturer cost times the manufacturer markup. 

C.23.7 The Financial Module 

The Financial module lists the financial ckcteristics if the prbtotypical fum being 

analyzed. Some of the inputs are used to calculate intermediate outputs of financial characteristics 

of the firm. The financial inputs and intermediate outputs are then used elsewhere in,the model 

when the impacts of . . new efficiency levels are calculated. Table C.3 gives 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 to revenue. For instance, the historical 

ratio between depreciable assets and revenues for ihe 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 analys$, the base case is 

identical to ithe calibration case. . . 

. 

For a thorough description of how maikups are derived and used in LBNL-MAM, see C.2.1. . . . 

Volume 1: General Methodblogy Page C-23 

 EE-RM-93-801-RAC COMMENT 01

markups i4Jr each of the product Classes, and baseline unit variable costs to calculate the prototypical 

firm's shipments for each product class, its cost of goods sold, and its revenue. Also calculated are 

a weighted-average unit variable cost, selling price, and markup. Markup is calculated from data on 

the ratio between the highest and lowest markups (ratio) and the typical markup (mid). 

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 of the product classes. The model then uses known price elasticities and discount 

rates to calculate changes in shipments for each of the 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 of the inputs are used to calculate intermediate outputs of fmancial 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.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 of the inputs are listed as ratios p~portional to revenue. 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 

Item Definition 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) 

- A complication may occur if a value changes in proportion to revenue between 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 another portion 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 Cost Amortization Module 

Crucial to the manufacturer impact analysis are the capital costs that the industry will incur 

because of the change in imposed efficiency levels. This module takes as input from the Engineering 

Analysis the estimate of the total value of the one-time capital costs and reduces.them to annual 

. expenses. To annualize (or "amortize") these costs, the module takes into account: the expected life 

of the new capital, the tax life of the investment, the weighted average cost of capital, and the timing 

of the investment relative to any capital that is being replaced. 

I The amortized capital costs are calculated for the change from the calibration case to 

whatever efficiency 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 expenditures for the four years previous to 

1993; these expenditures are assumed to'occur at the middle of each year. They are assumed to be 

financed by debt and equity in the firm's normal debt-to-equity ratio. , - 

Tax benefits will accrue over the tax life of the capital. Some part 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 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Table C.3 Financial Module 

Item 

DR [ER] 

DRR 

ECC 

PECC 

WACC 

ATWACC 

ROE 

Definition 

Depreciation 

Equity cost of capital 

Pre-taxECC 

Weighted average CC 

After-tax W ACC 

.Return on Equity (expected ROE for a 

prototypical firm) 

Formula 

Debt [Equity] l(Debt+Equity) 

Average depreciation rate· 

depreciable assets (expressed as a 

percent of revenue) 

ECC/(l - Tax Rate) 

DR • Interest rate + ER • PECC 

WACC· (1 - Tax Rate) 

A TW ACC + Economic Profit 

A complication may occur if a value changes in proportion to revenue between 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 another portion of the value will not change 

with revenues because of the 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 of the change in imposed efficiency levels. This module takes as input from the Engineering 

Analysis the estimate of the total value of the one-time capital costs and reduces them to annual 

expenses. To annualize (or "amortize") these costs, the module takes into account: the expected life 

of the new capital, the tax life of the investment, the weighted average cost of capital, and the timing 

of the investment relative to any capital that is being replaced. 

The amortized 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) of the 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 of each year. They are assumed to be 

financed by debt and equity in the firm's normal debt-to-equity ratio. -

Tax benefits will accrue over the tax life of the capital. Some part 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 Volume 1: General Methodology 

.. 
existing capital; the remainder is assumed to replace inadequate existing equipment. The replaced 

equipment is assumed to have had the same potential lifetime (L) as the replacing equipment and is 

assigned a certain age. If the new equipment replaces old equipment that would soon have needed 

replacing ;anyway, savings are included at the time ,the natural replacement would .have occurred. 

Because these savings are not tax-related, they reduce initial capital cost but are not calculated under 

tax effects. . , 

The computed annualized cost tells what conitant rcal dollar payment would. need to be made 

each year in order that at the end of the capital's useful life all of the issued debt could have been paid 

, off, and all of the issued equity could have been repurchased." Since imposed efficiency levels sre 

assumed to last indefinitely, new capital will need to be purchased at the end of this cycle; but of . 

, course the next cycle starts with a clean slate and repeats the same process we have just described. 

This process.can repeat indefinitely with no change in amortized payments. 

TELS procedure overestimates the burden to the: fm because, since industry is constantly 

improving the efficiency of product lines, those lines would undoubtedly be much nearer to meeting 

the implenlented efficiency levels by the beginning of the second cycle. Thus, much of the capital 

equipment re-installed at the end of the first capital's lifetime would have been a normal expense by 

this date (around 2001). For the interested reader; the details of the calculation method are given in 

Table C.4. . 

Table C.4 should be read in conjunction with the one-~ime Cost Amortization module. Note 

that the caJculations are all done with continuous interest ,and discount rates, which ,makes it 

necessary to convert ATWACC to 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 stream of 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 amortization factor CCLF and . 

the capital c0s.t leveliiation tax factor CCLTF. CCCLF includes the initial cost (normalized to 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. These two 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. investment muit start before 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 depreciation of 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. 

. . 

Volume I: General Methodology Page C-25 

 EE-RM-93-801-RAC COMMENT 01

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. If the new equipment replaces old equipment that would soon have needed 

replacing ;anyway, savings are included at the time the natural replacement would 'have occurred. 

Because these savings are not tax-related, they reduce initial capital cost but are not calculated under 

tax effects. ' 

The computed annualized cost tells what constant real dollar payment would, need to be made 

each year in order that at the end of the capital's useful life all of the issued debt could have been paid 

off, and all of the issued equity could have been repurchased.6 Since imposed efficiency levels are 

assumed tt:> last indefinitely, new capital will need to be purchased at the end of this cycle; but of ' 

course the next cycle starts with a clean slate and repeats the same process we have just described. 

This process can repeat indefinitely with no change in amortized payments. 

This procedure ov~restimates the burden to the fum because, since industry is constantly 

improving the efficiency of product lines, those lines would undoubtedly be much nearer to meetin~ 

the implement~ efficiency levels by the beginning of the second cycle. Thus, much of the capital 

equipment re-installed at the end of the first capital's lifetime would have been a normal expense by 

this date (around 2(01). For the interested reader~ the details of the calculation method are given in 

TableC.4. 

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

that the caJculations are all done with continuous interest and discount rates, which, makes it 

necessary to convert ATWACC to 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 $Y received t years 

in'the future is y. exp(-rt), where r is the discolint rate and exp denotes exponentiation. For a 

constant stream of income at $Y per year for L years, the present value is Y-' (1 - exp( -rL))/r. 

The: most important output of this module are the capital cost amortization factor CCLF and 

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 of its nonnallife if it 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 of the capital. CCLF is calculated to ~e into account 

the fact that investment must start before the implementation of new efficiency levels. The capital 

cost levelh~ation tax factor CCLTF similarly levelizes the present value of the tax benefit from 

straight-lin(~ depreciation of the initial cost, the tax benefit from the one-time depreciation of the old 

capital when it is scrapped, and the loss of tax benefit from the old capItal after it has been scrapped. 

The formula for these equations are shown in T~ble C.4: . 

6 Of cour.;e tax benefits are counted toward eliminating debt and equity. 

Volume 1: G.meral Methodology 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 

..........- P EPlacementL --....... ............. .....- .-----. 

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 tax benefit 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: General me tho do log^ 

 EE-RM-93-801-RAC COMMENT 01

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 

. ___ ._._. _. __ ~Iace!!!.~nt~{.. ____ _ ._--_ .... _-

RTL Remaining tax life max (TL - AGE, 0) 

ATR 

EMRT 

EMRL 

BN 

BEN 

Continuous after-tax weighted average cost of capital* In(1 + A1WACC) 

e·ATR·

TL 

e·ATR·

L 

Tax benefit rate Tm.. 

Tax benefit of existing 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 of capital 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 of tax benefit on existing capi~1 BEN· (l - e·ATR.RTL)/ATR • 

LEC2 Loss of tax benefit on discounted existing capital in DIS· BEN· (I - e·ATR'TL)lATR 

the future 

oen* 

LTB* 

CCLTF 

Loss of tax benefit from existing capital 

IX depreciation of existing capital. 

CC levelization tax factor . 

-(LEC I + 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 of exposition. 

Page C-26 Volume I: General Methodology 
Because capital depreciates, its average value is lawer than its initial value. In order to derive 

the factor AAF, by which the initial value should be multiplied to arrive at the average value, it is 

necessary to account for the exact path by which the capital depreciates. Since the value of a piece 

of capital is, at any time, simply the present value of its future income stream, the value of the 

depreciated capital as a function of time can be found from the stream of income that the capita( will 

generate. If that stream has a constant flow for a period of L 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 L years. Because of discounting, a unit of capital now should weigh more heavily 

than a unit of capital later. This is properly taken into account by requiring the following 

equivalence: 

PV(DF(t)) = PV(AAV) ' . 

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

to DF(t). *This equation can be solved for AA 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 factor we 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 the bottom of the One-Time Cost Amortization module are found the capital costs for the' 

base and long-run levels, and its coefficient of variation. he three factors just derived, CCLF, 

CCLTF, md MF, can be applied to. CC (for either the base case or long-run case), to derive the 

amortized outputs LCC, LCC. T and 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 a typical fum's behavior. It proceeds in three stages. The calibration stage 

uses data on present costs and profit to estimate the fm's maikup. The second stage estimates the 

industry's cun-ent situation. The third estimates a typical fim;'s situation with more stringent 

efficiency levels. A detailed discussion of the economic assumptions and 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 

Volume I: C;enesal Metlrodo1og.y Page C-27 

 EE-RM-93-801-RAC COMMENT 01

Because capital depreciates, its average value is lower than its initial value: In order to derive 

the factor AAF, by which the initial value should be multiplied to arrive at the average value, it is 

necessary to account for the exact path by which the capital depreciates. Since the value of a piece" 

of capital" is, at any time, simply the present value of its future income stream, the value of the 

depreciated capital as a function of time can be found from the stream of income that the capital will 

" generate. If that stream has a constant flow for a period of L years, then the depreciated value of a 

unit of capital is: 

l-e -r(L-t) 

DF(t)=--- l-e,-rL 

Now if !:but capital is held for a period of L years, it is logical to ask what the average value of the 

asset is oVI~r the L years. Because of discounting, a unit of capital now should weigh more heavily 

than a unit of capital later. This is properly taken into account by requiring the following 

equivalence: " 

PV(DF(t» = PV(AA V) 

where PV() is the L-year presel)t valu~ operator, and AA V is the average asset value corresponding 

to DF(t). This equation can be solved for AA V, to fmd the following: 

1 

~ AAV=" 1_[ L -rL] 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 multiply AA V 

by the fraction of one-time capital that is add-on rather than replacement capital, %NC. -

At the bottom of the 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. T and 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 of the 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 cost that is fixed or proportional to sales, mpectively. 

The suffixes - .B and .N refer to the base case and the alternative efficiency levels, respectively. 

A combined suffix such as .BF has the natural interpretation (i.e., a fixed variable in the base case). 

The most important variable names in the Long-Run Model module m A for assets, TC for total cost 

excluding taxes and the cost of equity, EC for economic cost (including taxes and equity), EI for 

economic income, and mu1 for markup above marginal cost, p - 1 . 

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

using the following basic 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 

obtained by observation. Fixed and Variable Costs must be deduced from accounting data and from 

information on economies of scale in the industry. 

Assets and costs in.the calibration stage are both represented as proportions of revenue, so 

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 fwd 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 (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 .of economic 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 estimate marginal economic cost (EC.Q) directly and use this value to finish , 

our analysis. However, because costs cannot be measured accurately enough, there could be 

considerable error in 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: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

used with variables that measure an asset or a cost that is fIXed or proportion alto 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 are A 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 of capital, 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 of revenue, so 

finn size is irrelevant. This means. for instance, that fIXed .and marginal costs are not given as· 

dollars and dollars per unit, but mstead. total fixed and total marginal costs are described as a 

fraction of revenue. . -

Depreciable assets are divided between fIXed and marginal according to the input parameter 

FCA, the "fixed part of costs and assets." 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 of fixed (total) costs in revenues. All other costs are marginal, and their amount is 

deduced from the finn's rate .of economic profit. 

Because the costs so far are tax-deductible, but revenue is taxable. economic fixed costs are 

. given by the following 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 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 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 is the ratio of total assets to revenue. 

Now the markup is easily computed using the formula given at the beginning of this section 

on 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 the current 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 their base case 

counterparts. 

Most base case variables are the same as the calibration case unless we are analyzing the 

impact of any possible interim efficiency levels. In that case,,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 

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

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

parameter FIX. 

In addition, an adjustment is made for the changing level of working capital as revenue 

changes. The following working 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 for A.B, EC.BQ, and TC.B, respectively. 

TIE alternative eficiency level case. For the case of changesin efficiency levels, new values 

of dVC (the clhange in variable cost), LA, and LCC are used. The formulas contain several terms not 

included in the 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!e remaining 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 

 EE-RM-93-801-RAC COMMENT 01

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 of economic. profit, and A: R is the ratio of total assets to revenue. ' 

Nf)W the markup is easily computed using the fonnula given at the beginning of this section 

on calibr2ttion and the model is ready to analyze the base level of alternative efficiency levels. 

nil! base case. The base caSe is an an8Iysis of what 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 of changes'in efficiency levels, new yalues 

of dVe (the clhangein variable 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 ROE are known because they are inputs or have 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 -cycle cost)'0 

CC.B 1X depreciation = XID.B = - 

L 

1X interest = XlLB = LA.B.DR.1-LTC 

1X equity cost = ' XIE.B = LAB .ER .ECC 

1X tax benefit = X1T.B = Lw.TE3 

Page C-30 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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 of the 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.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 vkiables 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 

decline in demand for its product, which will usually cause increased price competition within the 

industry, with a consequent fall in price. By regressing-price on quantity sold and on a time trend, 

we &n estimate the industry's "short-run irice response to 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 at the new lower price than at the previous higher price. Now take'the 

new estimate of the level of (and change in) demand resulting from 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 of price and quantity 

would thi.s be: true. 

Once price and quantity are known, all &her variables are easily computed using procedures 

that have already been described. . . . . 

Volume . . I: General Methodology . Page C-31 

 EE-RM-93-801-RAC COMMENT 01

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 - T AX.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. By regressing 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 

of the 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 of the level of (and change in) demand resulting from new efficiency levels and repeat 

the calculations. After five or fewer repetitions of these 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 of price 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: General Methodology . PageC·31 
C.23.11 Sensitivity Charts Module 

I This module produces a chart which shows how profit is affected by 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 Present Value Module - 

The LBNLMAM includes the current version of the Government Regulatory Impact Model 

(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 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 averages of subjective estimates made by industry representatives. 

This section describes the various origins of LBNL-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 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 

 EE-RM-93-801-RAC COMMENT 01

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 of the 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 of the 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 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·ofthe necessary input data are not publicly available and so have been estimated from 

availa~le data or estimated from average! of subjective estimates made by industry representatives. 

This section describes the various origins of LBNL-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 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 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: General Methodology 
. . 

.. ' 

discussio~os with industxy representatives. ~hk timing of capital ,iosts come from the Engineering . , 

' . Analysis. . 

 id and marginal costs and assets. 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 specific values 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 ' 

Industry price elasticity. Industry price elasticity 'measures the way a change in the average 

industry !price for a product will affect the industry's shipments. For example, if the price of a 

product increases 4% and shipments drop 2% as a result, the price elasticity is-0.5. Because . . 

imposed efficiency levels will cause the prices of products to increase, this input is -obviously crucial . 

in determining the impact of new efficiency levels on manufacturers. 

CIonsmer discount rate. he consumer market discount rate is a measure of how much 

consumers value future operating cost savings from energy-efficient design options. Becaus'e new 

efficiency levels mandate more efficient products that will have lower operating costs, this input will 

affect consumer demand and is therefore important in determining the impact of new efficiency 

levels on manufacturers. 

&pity cost of capital. This is the rate of return expected by 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. 

Economic profit. E&omic profit is the profit earned by a. firm above its expected return on 

equity. Irk other words, it is the profit eamed after all costs are taken into account, including the cost 

of equity (the market rate of return). .A fum makes economic profit only when it has some degree 

of markel: power. 

Long-run jixed part of costs and a.&ts. This figure is the portion of all costs and assets that 

is fixed over time, that is, which does not vary with quantity sold or with revenue. It is expressed 

as a percent of the base case costs and assets. From an economic standpoint, this figure is important ' 

for determining the impact of new efficiency levels on manufacturers. 

Lmg-runjkedpart of one-time capital cost. - This figure is the portion of one-time capital 

costs that is fixed in ,the long run. Although all capital costs are normally considered fixed in the 

short run., this portion of the capital costs is spent regardless -of the output capacity of the tooling 

purchased. Tbe other portion of one-time capital costs varies proportionally with the output capacity 

of the new equipment. 

Volume 1: General Methodology ' Page C-33 

 EE-RM-93-801-RAC COMMENT 01

" . 

discussiolllS with industry representatives. The timing of capital costs come from the Engineering 

Analysis. . . 

FlXed and marginai costs and assets. Some costs increase in proportion to shipments, and 

some are fixed. Industry representatives indicate that the bulk of costs and assets are proportional 

to 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 . 

. Industry price elasticity. Industry price elasticity measures the way a change in the average 

industry ]price for a product will affect the industry's shipments. For example, if the price of a 

product increases 4% and shipments drop 2% as a result, the price elasticity is ~O.5 .. Because 

. imposed I~fficiency levels will cause the prices of products to increase, this input is obviously crucial 

in detennining the impact of new 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 demand and is therefore important in determining the impact of pew efficiency 

. levels on IIUlXlufacturers . 

. rAluity cost of capital. This is the rate of return expected by equity 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 

of return 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. 

Economic profit. 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 

of equity (the market rate of return): A fmn makes ~nomic profit only when it has some degree 

of market power. . . . . 

Long-run fixed part of costs and assets. This figure is the portion of all costs and aSsets that 

is fixed ove~ time, that is, which does not vary with quantity sold or with revenue.· It is expressed 

as a perce:nt of the base case costs and assets. From an economic ~tandpoint, this figure is important 

for determining the impact of new efficiency levels on manufacturers. . 

Long-run fixed part of one-time capital cost . . This figure is the portion of one-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 of the output capacity of the tooling 

purchased. The other portion. of one-time capital costs varies proportionally with the output capacity 

of the new equipment. 

Volume 1: General Methodowgy Page C-JJ 
*. 

One-rime capital coifs. 'some of 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 aspecific efficiency level being analyzed; this number 

covers all products in a product type and is an input from thk 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 cost includes raw materials, direct labor, 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.demand curve. Changing the value of ro permits specifications other than constant 

demand elasticity (to = 0.) 

Short-run price response to demand (SRPR). When industry demand falls by Y%, price will, 

in the short run, fall by SRPR Y%. This response is expected to die out within a few years, and Y% 

should be less than 20% when SRPR is used. 

C.2.43 Engineering Data 

Incremental writ variable cost. The incremental unit variable costs are the variable costs 

associated with design options that increase a product's energy efficiency. This input differs from 

the variable cost increase input for the Control Panel (Section C.2.3.1) in that it lists incremental 

variable costs for each design option for each product class of the product type being analyzed, rather 

than a single shipments-weighted average number. The increased variable cost includes 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 foteach product class at each engineering design 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 efficiency level) being analyzed. 

Installation costs. The cost,(in dollars) of installing each product class- for each design level 

-is provided intabular form. 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 Volume I: Geneel Methodology 

 EE-RM-93-801-RAC COMMENT 01

One-time capital costs. Some of the 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 cost includes 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-run price 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 input differs 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 of the 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 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 fonn of a table which lists annual maintenance 

.costs for each 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 of a 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) of installing each product class for each design level 

·is provided in'tabular fonn. InsuiIlation 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 Volume 1: General Methodology 
Capital costs. Some of the design options used to m&t.the increased efficiencies mandated 

by the alternative efficiency levelirequire additional capital investment in the form of retoqling, 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 table listing . ' 

the total capid expenditure for the design options used tonxch each of the-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 xesulting hm imposed efficiency levels generally vary with 

the size of'the manufacturer. If more than one size of manufacturer is being analyzed for a product 

type, then input data for all' sizes being analyzed are included. . / 

C.2.4.4 Costs, Sales, and Revenues 

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 ihd product price elasticities and market 

discount rates which are based on forecasts provid by the LBNLREM. 

' Product daw market shares. Each of the classes within a product type has a "market share" 

of the shipments for that product type. 'For exarhple, 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 shipments by product class. 

Mtznufacturer market share. Each of the prototypical firms in the analysis is assigned la 

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 todetermine the firm's selling 

price for ;a particular product. It is an increase over,the unit.variable cost of that product, and it 

covers overhead, capital costs, and profit. In some industries, manufacturers use different markups 

on different product classes. ' The markup input lists the markup used for each class in the product ' 

type being analyzed. 

Initial prices. This input is the baseline manufacturer's selling price per unit for each product 

class. Added to the baseline unit price are the incremental costs of ieaching the'higher efficiency 

levels required by meeting new implemented efficiency levels. 

Energy price. 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). 

Volume I: ~Geneml Methodology Page C-35 

 EE-RM-93-801-RAC COMMENT 01

Capital costs. Some of the design options used to meet, the increased efficiencies mandated 

by the alternative efficiency levels require additional capital investment in the form of ret()()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 of the '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 of the manufacturer. If more than one size of manufacturer 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 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 and product price elasticities and market 

discount rates which are based on forecasts provided, by the LBNL-REM. 

Product class market shares. Each of the classes within a product type has a "market share" 

of the shipments for that product type. 'For example, 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. ' 

Initial prices. 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 of reaching 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: .General Methodology Page,C-35 

, I 
C.2.45 Financial Inputs 

Rates offinancial costs: 

~frer-t'ar 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 of corporate bonds with a Moody's 

rating comparable to ratings in the industry being analyzed. 

Interest lost on cash. Companies hold a portion of their current assets in the form of cash 

in bank 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 of depreciation. This figwe is the rate of depreciation on fixed assets for a typical fm 

in the industry. It is obtained by assuming straight-line deP&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. 

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 of their current assets in the form of cash in bank accounts; 

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

input is expressed as a percent of total revenue. ' ' . 

Inventory and receivables. This figure represents the inventory and receivables assets, 

expressed as a percent of total revenue. Inventory includes raw materials owned by the company, 

work in process, and finished goods that have not yet been shipped. Receivables refers to goods that 

have already been sold and shipped by the fm for which the firm has not yet been paid, but is 

instead owed money. 

Depreciable assets. This figure represents the net depreciable, or fixed, assets of the firm, 

expressed as a percent of revenue. Depreciable assets are tooling, equipment, production facilities, 

and other goods that have an original lifetime if longer than one year and thus are depreciated over 

several years rather than expensed in ~ne'~ear. / 

Page C-36 Volume 1: General Methodology 

/ EE-RM-93-801-RAC COMMENT 01

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 of corporate bonds with a Moody's 

rating comparable to ratings in the industry being analyzed. . 

Interest lost on cash. Companies hold a portion of their current assets in the form of cash 

in bank 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 of the capital used to form the ass~t. Thus. the cash account 

costs the company money but the cost is lower than for any of the c~mp3nY's other assets. 

Rate of depreciation. This figure is the rate of depreciation on fixed assets for a tYPical firm . , 

in the industry .. It is obtained by assuming straight-line depreciation 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 .. 

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 of their current assets in the form of cash in bank accounts; 

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

input is expressed as a percent of total revenue. 

Inventory and receivables. This figure represents the inventory and receivables assets, 

expressed as a percent of total revenue. Inventory includes raw materials owned by the company, 

work in process,-and finished goods that have not yet been shipped. Receivables refers to goods that 

have already been sold and shipped by the firm for which the firm has not yet been paid, but is 

instead owed money. 

Depreciable assets. This figure represents the net depreciable, or fixed, assets of the firm, 

expressed as a percent of revenue. Depreciable assets are tooling, equipment, production facilities, 

and other goods that have an original lifetime of longer than one year and thus are depreciated over 

several years rather than expensed in one·year. 

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 of all costs Md depreciable assets. This input is explained in the Control Panel 

section of' C.2.3.1. . 

Fixed par;! of one-time capital cost. This input is explained in the Control Panel section of C.2.3.1. 

Other fimmcial inputs: .. 

Economic profit. 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 factor of the fm and affects how much money a fum is able to borrow and 

at what interest rate, so it is important in the analysis of the impacts of new efficiency levels. 

Mtzrkup on a typical model. This figure is the markup used by a fm in determining the 

manufactimr's selling price. It is the markup used on the unit variable cost of a typical product in 

the industry being analyzed. 

Ratw sf highest to lowest markup. 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. 

- 

Costs and assets of one-time investment: 

One-time capital cost's life. This input is the pmducti\;e life of the equipment purchased 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-time capital 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 of new.capital is additional rather than for replacement. - 

Age ofold (repfuced) capital. This input gives the age, in years, of the old capital that gets 

replaced hause of the new efficiency levels. Under new efficiency levels, some old equipment will 

get scrappc:d before the end of its productive life and replaced by new equipment. Depending on the 

Volume I: Genemi Methodology Page C-37 

. . 

. . 

 EE-RM-93-801-RAC COMMENT 01

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" of the company. 

Fixed and variable costs: 

Fited part of all costs and depr:eciable assets. This input is explained in the Control Panel 

section of C.2.3.t. . '. 

, Fixed par.r of one·time capital cost. This input is explained in the Control Panel section of C.2.3.1. . . 

Other Jinttncial inputs: 

Economic profit. This input is explained in the Control Panel section of C.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 of the fmn and affects how much money a firm is able to borrow and 

at what interest rate, so it is important in the analysis of the impacts of new efficiency levels. . , 

MClrkup on a typical model. This figure is the markup used by a f1Illl in determining the 

manufacturer's selling price. It is the markup used on the unit variable cost of a typical product in 

the industry being analyzed. 

Ratio of highest to lOwest markup. In some industries, manufacturers use different markups 

on different product classes. If such a situation exists in the industry being analyzed, this ratio 

indicates the range of markups used. 

Costs and assets of one-time investment: 

Onte-time capital cost's life. This input is the productive life of the 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 of the 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 of new. capital is additional rather than for replacement. 

Age of old (replaced) capital. This input gives the age, in years, of the old capital that gets 

replaced bc~use of the new efficiency levels. Under new efficiency levels, some old equipment will 

get scrappc~d before the end of its productive life ~d replaced by new equipment. Depending on the 

Volume 1: (,eneral Methodology PageC-37 
age of the replaced capital, tax and/or economic 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 ,that the 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 

alternative efficiency levels on a few key variables: shipments, price, revenue, net income, and return 

on equity. 

For each of the five variables listed above, the Control Panel output table lists the base case 

number, the new long-run number, and the percentage change between the two. The table also lists 

the percentage changes for these 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 alterantive efficiency 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-run numbers are the model's estimates of impacts on manufacturers 

in the short run, before they have time to find a new optimum. 

Shipments. Shipments are the 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 a product type. Shipments data are calculated on the Costs, Sales, and Revenues page. 

Price. Price is the shipments-weighted average of the manufacturer's selling prices for the 

different classes within the product type being anal@. Price data are calculated on the Costs, 

Sales, and Revenues page. 

Revenue. Revenue is the amount of money received by the manufacturer for the products it 

has sold. It is the sum of the revenue for each of the product classes being sold by the manufacturer 

and it is expressed in millions of dollars. It can also be calculated by multiplying hie annual 

shipments times the shipments-weighted average price. Revenue data are calculated on the Costs, 

Sales, and Revenues page. 

Page C-38 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

age of the replaced capital, tax andlor economic consequences are factored into the calculations of 

the cost of the 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 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. '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. 

For each of the five variables listed above, the Control Panel output table lists the base case 

number, the new long-run number, and the percentage change between the two. The table also lists 

the percentage changes for these variables from the most recent previous . run for comparison 

purposes. The last column in the output table lists the short-run results for each of the variables. 

The base case values refer to the current state of the 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 of impacts on manufacturers 

in the short run, before they have time to find a new optimum. . 

Shipments. Shipments are the total-units sold by a prototypical 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 of the 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 of money received by the manufacturer for the products it 

has sold. It is the sum of the revenue for each of the product classes being sold by the manufacturer 

and it is expressed in millions of dollars. It can also be calculated by multiplying 'the annual 

shipments times the shipments-weighted average price. Revenue data are calculated on the Costs, 

Sales, and Revenues page. 

Page e-38 Volume 1: General Methodology 
. . 

Nt!t income. Net income is the after-tax profit made by the'company. express& in millions 

of dollars. Net income and the associated return-on-equity are 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 simply because 

of a fm's sine, and ROE adjusts for different-sized companies by 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. 

Gross margin. Gross margin shows, on a percentage basis, how much revenue remains after 

the variable cost of goods sold is accounted for: GM ='(R-VCGS)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, .and receivables; depreciable assets; and in the case of alternative efficiency levels, 

the one-tirne assets acquired to meet the new efficiency levels. 

~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's asse'ts. The returns are divided by the total assets of the fm to calculate return h assets. , . s 

~&,uity. Equity is the portion of a fm's assets owned by the stockholders of the firm. It is 

the difference between a firm's assets and its liabilities. In this model it is calculated by multiplying 

the equity percentage obtained from the debt-toequity ratio times the total assets figure. - 

Refurn on equity (ROE). See the 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 on in 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 

Volume I: Geneid Methodolog Page C739 

 EE-RM-93-801-RAC COMMENT 01

- . 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 of a 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 

of a 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 of dollars rather than millions. 

Gross margin. Gross margin shows. on a percentage basis. how much revenue. remains after 

the variable cost of goods sold is accounted for: GM =·(R-VCGS)/R. 

Return on sales. Return on sales is net income as a percentage of revenue. 

Total. assets. Total assets is the sum of all of the prototypical finn's assets. This includes 

cash, inventory •. and receivables; depreciable assets; and in the case of alternative 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 of bow productively a firm's assets are being used. The interest paid 

on debt instruments and the. net income that belongs to equity· shareholders· are the returns on the 

firm's assets. The returns are divided by the total assets of the firm to calculate return on assets. . - . 

-Equity. Equity is the portion of a finn's assets owned by the stockholders of the firm. It is 

the difference between a firm's assets and its liabilities. In this model it is calculated by multiplying 

. the equity percentage obtained from the debt-to-equity ratio times the total assets figure .. 

Return on equity (ROE). See the Control Panel section above for a description of ROE: 

C.~.5.3 Economic Analysis Outputs . 

The:: economic analysis outputs give a clear and concise picture of what 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: Gieneral Methodology Page C~39 
looked at from an ecohomic point of view rather than an accounting one. 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 estimates of their true values. As a result, the outputs vary from run 

to run, and the model can calculate means and standard deviations for the key measures of impact. 

It is then possible to bound the estimates of impacts and attribute a degree of certainty to them. 

The Monte Carlo Outputs section is a table that lists for each variable the value of the current 

iteration, the mean of all the iterations done in the current run, and the standard deviation 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, change in net income, and percentage point change in ROE for both 

the long-run analysis and the short-run analysis. See the Control Panel section (Section C.2.3.1) for 

a more complete description of each of these outputs. 

C.25.5 Retail Price Calculation 

One important role of LBNL-MAM is to provide estimates of retail prices of appliances at 

different efficiency levels for the 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 

manufacturer prices are derived from base manufacturer costs, engineering production costs, and 

appropriate manufacturer markups for each product. The base manufacturer cost is derived from 

current retail price data for appropriate product classes. 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' selling prices for each product class at each 

'efficiency level. These manufacturers prices are then multiplied by an estimate of the 

manufacturer-to-retail markup appropriate for the product class. This gives the estimated retail price. 

Page C-40 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

. iooked at from an economic point of view 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 of their true values. .As a result, the outputs vary from run 

to run, and the model can calculate means and standard deviations for the key measures of impact. 

. It is then possible to bound the estimates of impacts and attribute a degree.of certainty to them. 

The Monte Carlo Outputs section is a table that lists fox: each variable the 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 th~ iterations done in the curreJ.1t run. 

The measures of impacts are percentage change in shipmentS, percentage change in price, 

percentage change in revenue, change in net income, and percentage point change in ROE for both 

the long-run analysis and the short':'run analysis. See the Control Panel section (Section C.2.3.1) for 

a more complete description of each of these outputs. 

C.leS.S Retail Price Calculation 

One important role of LBNL-MAM is to provide estimates of retail 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 of the Costs, Sales, an~ Revenue module (Section C.2.3.6). 

The model generates a matrix of manufacturers' 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 associations7 contracted with the Arthur D. Little 

consulting finm 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). 'he latest version of this model is Version 1.2 and was released in April 1993. Since NPV 

is theoretically identical to ROE as an impact measure, the U.S. Department of 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'S functionality 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'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 induary based 

on those cash flows. On the other hand, 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 

approaches recognize the same costs. 

There are several categories of costs that *GRIM calculates as fixed ratios of revenues. 

Hence there are several categories of costs that are higher in GRIM than in 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: 

' Thi Association of Home Appliance Manufrtww. Gas Appliance Manufacturers Association. and Airsonditioni~ 

and Refrigeration Institute. . . . . . . 

. . Volume I: G;eneml Methodology Page C-41 

 EE-RM-93-801-RAC COMMENT 01

. . C.2.6 De'velopment and Integration of the 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 of this 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 of Energy decided to 

include thl! GRIM as part of the analysis. Hence the entire GRIM Version 1.2 model is integrated 

into the UBNL-MAM. . 

As a result, if the 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 of the GRIM's functionality 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 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 of costs that ·GRIM calculates as fixed ratios of revenues . 

Hence there are several categories of costs that are higher in GRIM than in the ~NLMAM. 

In the absence of an alternate solution. the lack of a 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 of Home Appliance Manufacturers. Gas Appliance Manufacturers Association. and Air-conditioning 

and Refrigeration Institute. 

Volume 1: G;eneral Methodology Page C-41 
1. Since the cost structure of GRIM is different from that of LBNLMAM, 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 of the 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 absence of GRIM-st);le cost inputs from the industry, we take 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 the LBNLMAM. 

iii. Using this cost input table, the LBNLMAM is run to estimate price and shipments for the 

alternative efficiency level case. 

iv. The price and shipments estimated by LBNLMAM are then input to the GRIM module, and 

the GRIM module is run to generate its results. Because several of the cost assumptions in 

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 

incorporated back into the LBNLMAM costs via "cost comparison factors" (defined below). 

The LBNLMAM is then run again to generate new price and shipments estim~tes based on 

these revised costs. 

vi. This iterative process is rep! until the cost differences between the major cost categories 

used in the GRIM module and that of the LBNLMAM run that generated the Gputs 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 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 assumptions resulted 

in a lower capital maintenanceexpense, the inkal GRIM cost assumption would be adjusted by a 

cost comparison factor. 

. Page C-42 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

'i. ' Since the cost structure of GRIM is different from that of LBNlrMAM, 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 of the 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 of the two modules.) , 

ii. In the absence of GRIM-stYle cost inputs from the industry, we take 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 to estimate 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 of the 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 of the 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 of the 

two mode~ to properly align and adjust their costs. For instance~ if the 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: General Methodology 
In this section, we discuss each of the Comparison Factors 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 

.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 leivel of capital expenditure is a fixed percentage 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.M and GRIM, which we add to LCC.N. Further, we need to take GLCC.N %to account 

for tax effixts and new assets. Since LCC.TN and LAN (the tax effects of one-time capital cost and . 

assets duo: to one-time capital costs, respectively) are obtained by applying levelization factors to 

CC.N (unlevelired capital costs), the adjustment to these variables is made by "unlevelizing" the Lost 

comparison factor GLCC.N and adding it to CC.N before the appropriate levelization factor is 

applied. . . 

. - 

Verne I: General Methodology .. . 

Page C-43 

 EE-RM-93-801-RAC COMMENT 01

· In this section, we discuss each of the 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 of capital expenditure is a fUed percentage of revenues, 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 of one-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 

' .. 
ACTORS MA M/GRIM Cost Iteration Detail 

-- GRIM COSTS 

- Manufacturin costs CGS+Labor+Mat.+OH+Dep. 7' Fixed and variable &- -- costs after new - Capital mstsfCCf .~L(CE~~T~~CO")+(CE~~ . erncon, - Working capital, [AR CC51 for each year standards 

distributions only. [No operating costs.] 

Also need to be scaled to industry- level costs 

'I - SGA/R&D, CC7+(R)ffD%, where CC7 = total annual SGA 

GRIM Cash - Taxes, Pre-tax n F.2, where F22 = fax rate. flow & NPV. - 

modules. 

GRIM costs are reported on an 

nputs: 

d new 

sign options 

n costs): 

 (labor, OH, 

apital) 

ts 

s distribution 

modules 

MAMlQRiM Thl8 proerr8 8Rould be done lor at 

Switch Cost Factoq Iea8t 8avm ItrnUoni (I.,., prfcr Set cost factors = 1 - Levellzed ca Ital costs end quantity and coat adju8tment8 need to be done rt laart 8even when off else use cost - Wo&ing facton when rnnning - ~anulactu&~ costs times to ~IOW~OnVeWenCe. GRIM. - SGAlOther costs 

- Taxes 

and GRIM Integration Overview 

Volume I: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

ACTORS 

R {CC.8L 

, % of R, (CC101 

ital, % {CCS} 

ndlture, %of R, fCC 11 J 

C3! 

 {CC4} 

MAM/GRIM Cost Iteration Detail 

cost-,§npu--

nd Cost Factor -

Fixed and variable ---

costs after new 

GRIM COSTS 

- Manufacturing costs - Capital costs (CCf 'Rl+(CE38TempCon)+(CE39 ·rempCon) 

standards . 

distributions only. 

[No operating costs.] 

Also need to be 

scaled to industrylevel costs 

..--.. : 

nputs: 

d new 

ign options 

 costs): 

 (labor, OH, 

apital) 

s 

s distribution 

p,a 

- Worfdng capital, {41R • CCS} for each year 

- SGAIR&D, CC7+(R)-RD%, where CC7 = total annual SGA 

GRIM Cash - Taxes, pre-tax" • F22, where F22 = tax rate 

now & NPV· f--------. 

modules 1 

j GRIM costs are reported on an I " Industry level and must bslevellzed 

L 

_____ ,;. NPV (' O,"erencer to per firm level. \ MAM rep rts costs on per firm level. 

MAM price '--_-C/o Both are en subsequently fonnatlon & 1---" ROE l converts to per unit. demand func .--____ ....J 

MAMCOSTS 

- Capital costs [LCC.B and LCC. 

. Manufacturing costs [VCGS) . . i - WorkIng capital, TC.B.'R:R.(1- R)-I; This Is Base WC 

::. variable.-. (p. TC+TaleSHVCGS+LCC.+ WC+ Taxes) 

L<>-[-··---·---·----r Adjuster _____ ._J I' 

MAMIGRIM Thl. proce .. ahould be done for at 

Switch Cost Factors 'ea.t .even iteration. (I.e., price Set cost factors = 1 - levelized capital costs and quantity and co.t adju.tment. 

when off else use cost - Working capital . need to be done at le .. t .. ven 

factors when running - Manufacturing costs time. to altowconvergence. 

GRIM. - SGAlOther costs - Taxes 

nd GRIM Integration Overview 

Volume 1: General Methodology 
Yalor YAY Coal Out~ub - The firsf 4 CWf Cat8 d8S fbw @'redly brt0 the 

1X C~phl c08- mellBno8r. 1~18"Catepty tor the ~emr Ls the OMER COSTS category whfch is 

celwleted as the ditfemnce betnwn 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 

+ 

Adjuster and Diierenmr 

(Cost tador generation) I 

Cost Factors Affeded Variables 

for 8s IMlIy 

netations as 

------- +[GI J Y 

-0' 

P.n(10r)l*hIm 

hput to ORlY for 

OanandFuncbonAlOorfim cuh f%w .naly.b Of WItm 

(MODEL 

Adjuster 

'7 

and DHferanoer 

Cash-florX;osls 

from GRIM (Cost fador generation) 

Figure C.4 Cost Comparison Factors and Their Relationships 

Volume 1: Gene& Methoddogy Page C45. . 

 EE-RM-93-801-RAC COMMENT 01

Irandi 

neer 

Cash-f1owCos1 - from GAl M 

s 

Major MAM Coat Output. 

Cost factors 

GLCC.N 

Olllor Coote 

The first 4 cost categories flow dif8C1ly Into /he 

difference,. The fifth category for /he dllferenoe, 

Is /he OTHER COSTS category whJch is 

csJcuIBted as the dlfferenoe between /he filSt 4 

cost categories aM the total costs (mctuding 

taxes) calculated by MAM. 

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 Iterations as necessary 

P .... OWlllclltl .. Input ID GRill for 

WOIk. c.p. 

wfEC.WC.An, IC.TC.EF.WC 

MAM PrIce FonnaIIon and cull now .... 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 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 it.in the Manufacturing Costs calculation. 

The baseline per unit costs of materials, 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.' Since shipments will vary when other adjustments are 

made, overhead costs 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 a result of other cost adjustments, an adjustment for working capital is needed. Hence we 

define a variable, WC. EC, which is the sum of the per unit differences in 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 efficiency level 

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- 

'eficiency level case. 

Page C-46 Volume I: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

Manufacturing Costs: 

Manufacturing 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 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 of materials, 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 of revenues. Since revenues are likely to 

change as a result of other cost adjustments, an adjustment for working capital is needed. Hence we 

defme a variable. WC.EC. which is the sum of the per unit differences in 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.EC,·Q. WCAN= 1 10 

ATR 

where (WC.ECj • Qio ) 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. WC.AN is then added to new efficiency level 

assets, A.N. 

The total "accounting" cost of working capital is: 

WC.TC=WCAN·(I-ER)-; 

8 The GRIM Overhead formula is OH = OlIt,+ (ORr Q.N), where OHb 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: General Methodology 
where ER is the equity ratio and i is the intuest rate. ~owevir, as we mentioned earlier, to facilitate 

convergence with GRIM, we assume (1 - ER) = 1. WC.TC is added to total costs in the alternative 

efficiency level case, TC. N. 

Finally, from an accounting cost standpoint, WC.TC will 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.M contains 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 fixed percentage of revenue and 

thus are vzuiable costs also. Since they are both relatively close in size, both are variable costs, and . ' 

.both are 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.N is shipments. These accounting costs are 

important ,in the deterinination of financial variables Such as Net Income, Taxes, and ROE. 

Economic costs are defined as: 

.9 This is 1-use in MAM, even' fixed costs such as capital costs are considered to have a variable portion in the long 

fun. 

Volume I: Gene& Methodology Page C-47 

 EE-RM-93-801-RAC COMMENT 01

where ER is the equity ratio and i is 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 of working 

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) and SGAIR&D (GRIM): 

MAM contains Other Variable Costs which are the variable portion of fixed 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 of revenue 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 of revenue. 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 of costs, and Q.N is shipments. These accounting costs are 

important in the detertnination of fmanciaI 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: General Methodology PageC-47 
where EC.N is total knomic costs, EC.NF is the total fixed part of economic costs, EC.NQ is the 

per unit variable part of economic costs, and Q.N is shipments. Economic costs.are important for 

the determination of price. 

, 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'MAM Other 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 fomiula for'a factor that,does the following: 

where OTHER- is MAM Other Variable 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. Wk scale each 

of the per unit values to firm values by multiplying by 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 annualized costs 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: 

Conversion costs: 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. 

Working capital: 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: Generol Methodology 

 EE-RM-93-801-RAC COMMENT 01

where EC.N is total economic costs, EC.NF is the total fixed part of economic costs, EC.NQ is the 

per unit variable part of economic costs, and Q.N is shipments. Economic costs ,are important for 

the d~termination of price. 

, 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 of the 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 Other Variable Costs, TC.N is total costs, e is the adjustment factor, and 

SGAgrim 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 per unit values for SGAgrim , OTH~R_, and TC.N. We scale each. 

of the per unit values to firm values by multiplying by Q.N and then 'calculating e. (For successive 

iterations, e is the sum of the individual calculations.) 

Cost Levelization 

Most costs in the GRIM are annualized costs and are thus in the same format as those of the 

LBNL-MAM.However, there are two types of costs 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 

following procedures: 

I. If they an alternative efficiency level costs incurred in years prior to new efticiency levels, 

future value them to the fmt year that new efficiency levels, take affect. This may be 

accomplished by the following formula'where the total future value C,, is: 

11. 'Ib derive the formula for levelized costs (L) from the future valued costs (C,, ), we perform 

tlie following: ... 

Volume 1: Geneml Methodology 

 EE-RM-93-801-RAC COMMENT 01

. To conVI!rt these "one-times~' costs into continuous, annualized costs, we "levelize" them via the 

following procedures: . 

I. If they 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)-Co

' 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 characterized for this analysis =.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 MC, , 'the present 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 

is calculated using the so-called capital recovery factor. CRF. as MC, = CRF - MC, . The capital 

recovery factor is calculated as. 

CRF = d 

1' - (1 +4)" 

National Appliance Enerb Conservation Act (NAECA), Public Law 100- 12, March 17,1987. where 

n is the appliance lifetime, and d is the discount rate. . . 

D.2 GENERAL DESCRIPTION OF INTERMITTENT IGNITION DEVICES-(IIDs) 

The cost analysis here was generated specifically for 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 syitems rather than m>s. Although electric g1o;bar ignitioa devices are a type of hot 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 electrical power for as long as the 

burner remains in operation while almost all other HSI systemdraw power only to 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 applicable to typical 

HSI devices, either. The main difference between HSI and IID devices is the actual ignitor 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 assumption is made here that HSI and spark ignitors 

Volume 1: General Methodology Page D-1 

 EE-RM-93-801-RAC COMMENT 01

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 of the maintenance cost, Mc;,v, is 

first derived. The value of MCpv 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), Public Law 100-12, March 17, 1987. where 

n is the ap:pliance lifetime, and d is the discount rate. 

D.2 GENERAL DESCRIPTION OF INTERMITTENT IGNITION DEVICES-(llDs) 

The c:ost analysis here was generated specifically for two types of pilotless 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 of hot 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 assumption in this analysis is,that HSI 

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, IID systems were considered for this analysis ti consist 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,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 wereconsidered separately in terms of maintenance costs. 

D.3 MAINTENANCE COSTS FOR ID COMPONENTS 

The maintenance costs for sensors &d control modules were obtained directly from a survey 

of equipment maintenance 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 of a 

defective part. The sensor and control module costs have been added above due to the unavailability 

of separate lifetime information for sensors. Both sensor and control module were assigned the same 

lifetime as the control module. 

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 

data by the R.S. Means Company (2). 

D.4 MAINTENANCE LIFETIME DISTRIBUTION 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. 

The presentvalue of maintenance cost for IIDs was characterized in this analysis as the average 

present value for all.possible failure times. In order to achieve such an average, a probability density 

Page 0-2 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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' of the system type, no systems were considered for this analysis to consist 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. 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 of maintenance costs. 

D.3 MAINTENANCE COSTS FOR un COMPONENTS 

The maintenance costs for sensors.and control modules were obtained directly from a survey 

of equipment 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 of a 

defective part. The sensor and control module costs have been added abOve due to the unavailability 

of separate 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 of location 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 if based on experience from the past. 

The, present value of maintenance cost for nos was characterized in this analysis as the average 

present value for all possible failure times. In order to. achieve such an average, a probability density 

,PageD-2 Volume 1: General Methodology 
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 .sensors as well. Convers@ons with 

service 01-gktions indicated 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 14 years. 

The average control module failure rate 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 

detedned to'be on the order of 5% of total yearly sales. 

Table D.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 

. maintenance cost ($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 .18 years for gas cooktops and ovens. It 

does not include the cost 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 maintenance cost (6% discount rate) derived from . this . value is 

MC, = MC,, CRF = $81 . .092 = $7.45 / year 

Volume 1: General Methodology 

. . 

Page 0-3 

 EE-RM-93-801-RAC COMMENT 01

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 of operation 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 of 5% of total yearly sales. 

Table 0.1 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 

. maintenance cost ($1), a discount rate of 6%, appliance installation in year 0, ami maintenance cost 

for an indilvidual year equal to $1 times the probability of failure 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 of failures 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.Value of Summation of Present 

Purchase Units Failing (F) Discount Factor (f) Maintenance ($1 -Ef) Value of Maintenance 

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: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

Table D.I Calculations for Mean Lifetime of lID Units 

Year After Yearly Fraction of Present Value Average PresentValue of Summation of Present 

Purchase Units FailinS ~ Discount Factor m Maintenance ~$I'F'n Value of Maintenance 

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. "Kindling Appliance Fires." Appliance, December; 1990, pp. 20-26. 

2. It. S. Means Company, Inc. 1990. Means Mechanical Cost Data. Kingston, MA. 

Volume I: ' General Methodology Page D-5 

 EE-RM-93-801-RAC COMMENT 01

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-S 
e 

APPENDIX E ELECTRIC UTILITY IMPACT MODELING 

.. - 

E.l INTRODUCTION 

IRis appendix summarizes the methods used to calculate utility avoided & for the energy 

savings [that result from the imposition of appliance energy efficiency levels. 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 and capacity savings of the 

efficiency levels are explained. The goal of this analysis is to calculate the peak and capacity savings 

and the ztvoided costs, in dollars per million Btu ($/MMBtu) of electricity saved, where the energy 

is expressed as primary energy.' Avoided costs contain both the marginal fuel and marginal &pacity 

value of each kwh saved by the alternative efficiency levels in each year of the period analyzed. 

E.2 AVOIDED COST: BACKGROUND AND CONVENTIONS 

The analysis adopts the standard utility conventibn that the value of electricity savings, 

commonly called aioided 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 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 is intended to measure the benefit of reduced system load during 

peak periods. Since electricity cannot normally be 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>y a lower peak demand is to estimate the lowest possible .cost of maintaining the cheapest 

generato]: available on hand year-around simply to generate the last kW demanded at the peak hour. 

By convt:ntion, this hypothetical generator is a lowcost gas turbine, and its capital cost per kW is 

called thc: gas turbine proxy. In addition to the generating capacity itself, lowering peak demand will 

also deliver a saving in the need for gak transmission 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 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 vqxy substantially. For the purposes of calculating 

electric utility avoided costs, heating appliances are defined as electric heat pumps in heating mode 

and electric resistance heat, cooling appliances are defmed as room and central air-conditioning plus 

heat pumps in cooling mode, and baseload appliances are defined as all other appliances affected 

by new efficiency levels, including all residential refrigeration. 

Volume I: General Methodology Page E-1 , 

 EE-RM-93-801-RAC COMMENT 01

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 of appliance 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 of the 

efficiency le.vels are explained. The goal of this analysis is to calculate the peak and capacity savings 

and the avoided costs, in dollars per million Btu ($lMMBtu) of electricity saved, where the energy 

is expre!:sed as primary energy . Avoided costs contain both the marginal fuel and marginal Capaei~ 

value of each kWh saved by the al~emative efficiency levels in each year of the period analyzed. 

E.2 A VOIDED 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 lower peak demand is to estimate the lowest possible cost of maintaining the cheapest 

generator available on hand year-around simply to 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 turbine proxy. 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 of load shape modifications is measured by the marginal cost of electricity, 

which depends on the variable cost of operating the power plants that will change their output in 

response to a change in load. This variable cost consists primarily of the cost of fuel for these 

marginal units. U.S. utilities are characterized by a substantial difference among the prices of fuels 

used to generate electricity on the margin. Marginal resources of a utility can be either more 

expensive oil and gas br less expensive non-oil-and-gas resounvs, typically coal or economy . . purchase of power generated by coal. Very low variable-mst nuclear oi nurof-river hydro resounrs 

are, usually base loaded and thus only rarely contribute to marginal energy costs. Even though the - . relative cost of natural gas as-a generation fuel has decreased in recent years, and seasonal price ., fluctuations often make it cheaper than coal bought under long-term contract, this oil and gas fraction 

approach is still valid because gas-frred resources are more likely 'to be 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 calculate the oil-and-gas fraction on the margin, OGF,,,, the total amount of oil- and gasgenerated electricity is divided by the total 'forecasted amount of electricity generation from NERC 

data (1) to obtain the total oil and gas fraction, OGF,. OGF, can then be related to OGF, with a 

simplified loadduration curve (LDC) for each 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 fraction of total generation 

from oil and gas (OGF,). Calculating the 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 

 EE-RM-93-801-RAC COMMENT 01

E.3 ENERGY COST SAVINGS 

, The energy value of load shape modifications is ineasured by the marginal cost of electricity, 

which depends on the variable cost of operating the power plants that will change their output in 

response to a change in load. This variable cost consists primarily of the cost of fuel for these 

marginal units. ,U.S. utilities are characterized by a substantial difference among the prices of fuels 

used to generate electricity on the margin. Marginal resources of ~ 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 or run-of-river hydro resources 

are,usually base loaded and thus only rarely contribute to marginal energy costs. Even though the 

relative cost of natural gas as-a generation fuel has decreased in recent years, and seasonal price' 

fluctuations often make it cheaper than 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 of each marginal fuel weighted by the fraction of time 

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,= OGFm ' Natural GasPrice + NOGFm • Coal Price +Variable O&M 

To calculate th,e oil-and-gas fraction on the margin, OGFm, the total amount of oil- and gasgenerated electricity is divided by the total 'forecastecI amount of electricity generation from NERC 

data (I) to obtain the total oil and gas fraction. 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 

OGFm• The non-oil-and-gasfraction (NOGFm) is simply 1 - OGFm• 

The purpose of the 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 of the triangle at the top of theLOC represents the fraction of total 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 Simplified Load-Duration Curve. 

The appropriate value for A = tan dais determined empirically: when OGF, 2 0.4, OGF, = 1 .O. The 

value of A. 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 I: General Methodology Page E-3 

 EE-RM-93-801-RAC COMMENT 01

~ 

~ 

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 of a year) 

! 

1.0. 

Figure E.l Simplified Load-Duration Curve. 

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

value of A. 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-average marginal 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 

analysis period. 

For cooling energy savings, the appropriate marginal energy cost (MEC) will exceed the 

annual average MEC because the OGF, is typically higher in summer than in winter. The annual 

average OGF, is approximately 52% when calculated using the energy savings fractions for heating, 

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 

0.6 1 

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 

Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

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

the above fonnulas, and a national weighted-average marginal cost for heating and baseload energy 

savings is calculated using the regional fuel price multipliers and energy savings 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 OGFm 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 A vg. . 0.52 

The calculation of MECs 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: General Methodology 
, where: 

i denotes a particular NERC region, ' 

ESF, represents the energy savings fraction for region i, and 

1.06 adjusts for transmission and'distribution losses. 

Tlhe ESF for region, i is the sum of .products of t-he heating, cooling, and baseload energy ' 

savings fntctions with 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 for investor-owned utilities 1990 is 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 (variable OM as a percent of -total OM) to the total O&M (O.5@kWh). This 

calculatio~n results in a variable O&M cost of 0.33$/kWh (in 1990 dollars). 

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 , 

available I: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 

combusticln turbine (6). This investment adds reliability to the system even if it never operates, since 

' it is ready to prevent outages if another generating unit fails. If the utility chooses to build another 

type of plant with higher capital costs, such as a baseload coal unit, it must be basing this choice on 

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 displace generators with higher 

. variable cost,per kwh. The additional capital cost of a coal 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 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 units on the margin and dwindling adjusted reserves. Such a utility would 

need capacity to preserve the system's reliability at 'an acceptable level. It would also benefit from 

installing a baseload unit that would reduce oil and gas consumption at the margin. This reduction 

Volume 1: General Methodology . Page ES 

 EE-RM-93-801-RAC COMMENT 01

· where: 

i denotes a particular NERC region, 

ESF1 represents 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 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 . 

available to meet load increases. For utility systems with excess capacity, this value may be equalto or close: 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 it never operates, since 

it is ready to prevent outages if another-generating unit fails. If the utility chooses to build another 

type of pblDt 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 per kWh. The additional capital cost of a 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 fuel costs 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 a baseload plant come on line when the present value of the 

energy-related capital cost of the plant is just equal to the present value of the 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 DOES interpretation of NERC data (8): 

1. If the NERC region is forecast to have an adjusted reserve margin larger'than 5% of 

forecasted peak demand, there is no reliability value for capacity.' 

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 of revenue 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 of disadvantage 

or ROD (10.0%). a fixed charge rate (13.0%): a turbine lifetime of 25 years, and the assumption of 

straight-line depreciation. 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 inflation rate of 5% 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. 

'The estimates of ROD and fixed charge rate have been calculated based on current interest rates and the corporate 

tax rate (34%) adopted in the 1986 tax revisions. The standard utility capital structure from EPRI is used (45% debt, 

10% preferred stock! and 10% common stock). Nominal costs of capital are 10% for debt and preferred stock and 13.4% 

for common stock. 

Page E-6 Volume I: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

in "fuel costs 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 a baseload plant come on line when the present value of the 

energy-related' capital cost of the plant is just equal to the present value of the 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 of NERC data (8): 

I. If the NERC region is forecast to have an adjusted reserye margin larger'than 5% of 

forecasted peak demand, there is no reliability value for capacity. 1 

2. If the NERC region is forecast to have a capacity deficiency (operating adjusted 

reserves of less 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 of reductions in peak load caused by cogenerators and other small power producers (9). 

First, the present value of revenue 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 of disadvantage 

or ROD (10.0%), a fixed charge rate (13.0%),2 a turbine lifetime of 25 years, and the assumption of 

straight-line depreciation. The calculation is repeated for each year of the analysis assuming no real 

capital cost escaiation 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 inflation rate of 5% 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 of ROD and fixed charge rate have been calculated based on current interest rates and the corporate 

tax rate (34%) adopted in the 1986 tax revisions.· The standard utility capital structure from EPRI is used (45% debt, 

10% preferred stock~ and 10% common stock). Nominal costs of capital are 10% for debt and preferred stock and 13.4% 

for common stock. 

PageE.6 Volume 1: General Methodology 
, . 

ROD, as the original stream. A stream of costs that escalates at the inflation .rate is also constant in 

real terms, so this technique levelizes the costs to some constant dollar value ($47.07 per kWIyr a '. . in 1990 clollars). This cog is then converted to dollars per kWh of electricity savings by.the method 

detailed in the next section. 

E.4.2 - A,mount of Capacity Savings - 

Am analysis of the load shape impacts of improved efficiencies for residential appliances 

must consider both the magnitude and timing of the impacts relative to periods of high system loads 

-*because :residential class load shape changes only have reliability value for the system when they 

coincide with times of high system loads. This relationship is approximated from modeling runs of 

the LBNlL Hourly and Peak Demand Model (10) and from two LBNL utility case studies (1 1),;(12). 

'I'hene are large but varying degrees of coincidence between residential peak loads and system 

peak loacis. The &e studies show that the average change in the 250 highest residential hourly loads 

is a reasonable measure of the system-level capacity value of appliance efficiency 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. The peak savings for residential appliances calculated using the LBNL Residential 

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 denominator by 8760 hours gives: 

Annual Energy Savings Peak Load Savings = , CLF 8760 

CLF . 8760 = Annual Energy Savings 

Peak Load Savings 

Volume I: General Methodology . Page E-7 

 EE-RM-93-801-RAC COMMENT 01

ROO, as the original stream. A stream of costs that escalates at the inflation .rate is also constant in 

real tenns, so this technique levelizes the costs to some constant dollar value ($47.07 per kW Iyr a 

in 1990 dollars). This co~t is then converted to dollars per kWh of electricity 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 of the impacts relative to periods of high system loads 

.' because :residential class load shape changes only have reliability value for the system when they 

coincide with times of high system loads. This relationship is approximated from modeling runs of 

the LBNlL. Hourly and Peak Demand Model (10) and from two LBNL utility case studies (11),.(12~. 

lhere are large but varying degrees of coincidence between residential peak loads and system 

peak loads. The case studies show that the average change in the 250 highest residential hourly loads 

is a reasonable measure of the system-level capacity value of appliance 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 peak savings 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 1 kW of peak load savings. 

The conservation load factors used for all products with 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 

Electric Water Heater 

Refrigerator 

Television 

Volume 1: General Methodology 

Conservation Load Factor 

0.46 

0.14 

-- 

-- 

- 

0.89 

0.15 . 

- 0.57 

. 0.63 

0.63 

0.76 

0.89 

0.89 

0.95 

 EE-RM-93-801-RAC COMMENT 01

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 1 kW of peak load savings. 

The conservation 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 

Clothes washer " . 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: 

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

Capacity Value = value of 1 kW of peak 'load savings in 1990 $/kW/yr, . 

0.3 = energy conversion efficiency including T&D losses, 

CLFSmelawi = conservation load factor for baseload appliances, 

1.2 = reserve margin adjustment needk for adequate reliability, 

xEsf'~mi!! = sum of baseload ~nergy Saving Factors for regions that .need capacity 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 future years, but this analysis does not account for the value of such deferrals.. 

Table E.3 Sums of Baseload Energy Savings Fractions for NERC Regio'ns , . 

That Need Capacity I I 

. . 

Volume I: General Methodology 

Year 

. 

1998 

. 1999 

2000 

Page E-9 . . 

Baseload . 

ESF 

. 

0.596 

1.000 

1 .ooo 

Regions 

Needing 

Capacity 

6 

, . ,9 

a 9 

 EE-RM-93-801-RAC COMMENT 01

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 year j, 

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 of baseload Energy Saving Factors for regions that need capacity in 

year j. . 

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 of Baseload 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 for capital costs of the T&D system that can 

, be avoided by reductions in electricity use. There are a wide range of estimates of such costs, 

depending on the particular characteristics of the utility system and of the consewation programs and 

policies that lead to such reductions in electricity use. Krause et al. (14) review studies on this topic 

and settle on a range for T&D savings of from $10 to $40kWlyr. Krause et al. also cite reasons why 

this range is probably too low and suggest that further work will probably raise these values. For 

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

more than $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 substantial uncertainties 

in applying such estimates (which are derived at the level of individual utilities) in national analyses. 

Page E-I 0 Volume 1: General Methodology 

 EE-RM-93-801-RAC COMMENT 01

E.4.3 A voided Transmission and Distribution Capital Costs 

Recently, utility planners have begun to account for capital costs of the 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 of the utility system and of the 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 of from $10 to $4OIkW /yr. Krause et aL also cite reasons why 

this range is probably too low and suggest that further work will probably raise these values. For 

example, they Cite marginal T &0 cost estimates of nine different utilities that (on average) totaled 

more than $1 OOIkW /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 of the substantial uncertainties 

in applying such estimates (which are derived at the level 'of individual utilities).in nationai analyses. 

Page E-I0, Volume 1: General Methodology 
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: Electric Power Supply and Demand for the 

Contiguous United States, 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. Pdo Alto, 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 Warming Risks 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 

 EE-RM-93-801-RAC COMMENT 01

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-Technical Assessment 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 of Energy. 1990. Staff Report: Electric Power Supply and Demand 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 of Energy, 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~ of Load 

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 of the New England Region. Lawrence Berkeley 

uLboratory LBL-30797 (vols. I and m. November. 1992. Berkeley, CA. 

Volume 1: ·General Methodology Page £·11 
DOE/EA-XXX 

EW.CR(3NMENTAL ASSESSMENT FOR ALTERNATIVE EFFICIENCY 

LEVELS FOR ROOM AIR CONDITIONERS 

AND KITCHEN RANGES AND OVENS 

. . . . 

1. INT.RODUCTION AND NEED FOR PROPOSED ACTION 

This Environmental ~sseisment (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 on ~nvimnmental Quality, and Title 40, . 

' Code of :Federal Rkgulations, 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 Act'and 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 of household appliances: room air conditioners and kitchen : 

ranges mid ovens. Each measure of possible environmental change is an altemative action, and it 

is compared 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-fueiled 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 altkmative efficiency levels will generallyd~crease air 

pollution by decreasing future energy demand; 'A major benefit of lower energy use is the reduced 

need to elnit sulfur dioxide, SO,. However, the Clean Air Act Amendments (1990) placed a national . . 

. 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 of the new efficiency levels and are listed by weight of NO, and CO,, respectively. 

NO, also contributes to acid de'&sition and is a pncursor to urban photochemical smog, as well as . 

being directly harmful if breathed. CO, emissions 'from fossil-fuel burning is considered an 

environmental hazard because it contributes to the ''greenhouse effect" by trapping heat energy from 

the earth that is emitted as infrared radiation. The greenhouse effect is expected to gradually raise 

the mean global temperature. 

., . 

Although the quantity of raw materials used per appliance will remain relatively constant, 

in most scenarios increased initial cost is expected to slightly deaease the number of appliances sold; 

resulting in small decbes in raw materials used. The main effect of the.appliance production 

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 

Volume I: Environmental Assessment Page EA-I 

 EE-RM-93-801-RAC COMMENT 01

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 on Environmentai 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 of household appliances: I'<?0m air conditioners 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 of lower 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 of nitrogen oxides (NO,) and carbon dioxide emissions (C02) will actually 

occur as n result of the new efficiency levels and are listed by weight of NOx 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 it contributes 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 of raw materials used per appliance will remain relatively constant, 

in most scenarios increased initial cost is expected to slightly decrease the number of appliances 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\'ironmental Assessment PageEA ... 1 
steel production is not included in the estimates for net SO, dsrrascs nsulting from design changes . . in these products. 

The focus of the environmental analysis is on just two air pollutants, 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 international concern, 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% of lead emissions, and insignificant 

amounts of particulate', carbon monoxide (CO), and volatile organic compounds (VOCs) (2). Inhouse combustion is an important contributor to 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 accompanied by 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 quality of 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 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 

some homes, thereby decreasing the impact of combustion on indoor air quality. Indoor air problems 

are usually due to a combination of factors, including a tight house envelope, insufficient ventilation 

for cooking 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 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 ESTIMATING ENVIRONMkNTAL IMPACTS 

The greatest impacts of implementing the new efficiency levels would be a reduction in 

electricity demand growth. The main environmental effects of power plants on air and water quality 

result from emissions of SO,, NO,, and CO,. With the alternative efficiency levels lessening the 

need for electricity generation, power plant emissions would 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-I0 fugitive dust definition. 

. .I Page EA-2 Volume 1: Environmental Assessment 

 EE-RM-93-801-RAC COMMENT 01

steel production is not included in the estimates for net S02 decreases resulting from design changes 

in these products. 

. . 

The fOCus of the environmental analysis is on just two air pollutants, 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 of NOx' Additionally, since the greenhouse 

effect is of such major international concern, the effect on emissions of CO2 is also reported. . 

Of the six criteria pOllutants, power. generation ,contributes major shares (70% and 32%, 

respectively) of total U.S. S02 and NOx emissions, but only 2% of lead emissions, and insignificant 

amounts of particulate I , 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 of acid rain formation would help improve the quality of wetlands and fish and 

wildlife as well as aid in the preserVation of historical and archaeological sites. Reductions in NOx 

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. 

some homes, thereby decreasing the impact of combustion on indoor air quality. Indoor air problems 

are usually. due to a combination of factors, including a tight house envelope, insufficient ventilation 

for cooking· appliances, presence of sources 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 of power plantS on air and water quality 

result from emissions of S02' NOx, and CO2, With the alternative efficiency levels lessening the 

need for electricity generation, power plant emissions would be reduced. 'A second source of these 

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: Environmental Assessment 
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'of appliances may 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. home is . 

heated with oil, the effect would be to raise.S02 emissions. 

. . 

2.1 Baselline Emissions 

Ici the Service Report that accompanies the 1991 National Energy Strategy m), the impact 

on power plant emissions as a result of revisions to Title V of the 1990 Clean ,Air Act Amendments, 

P. L. 101-549, are estimated (4). The emission foht that appears in that report is presented below 

as Table 11. 

NO, 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 NO,, 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 emissions reduction 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 'a iesult of the appliance efficiency levels, 

and that qpproach is adopted here. 

Tkr c&e of SO2 emissions is more complex. The legislation calls for2S0 emissions 

reductions in.two phases. .In the first phase of the reductions (beginning December 3 1, 

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 ehissions by 

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 3 1,2000). the options 

available to electric utilities for maintaining the 8.1 Mt SO2 emissions cap will broaden with the 

expected introduction. of new, advanced generating technologies. However, during this period 

utilities &e less able to reduce emissions by changing the way they utilize their plants; Since most 

plants will: 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: Environmentul Assessment Page EA-3 

 EE-RM-93-801-RAC COMMENT 01

Note iliat the effect of implementing 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. higher room . 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 of the home. For example, more efficient cooking appliances 

could lower the heat released in-house, increasing th~ load on the heating system. If the home js 

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 of revisions to Title V of the 1990 Clean Air 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 of the 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 asa result of the appliance efficiency levels, 

and that approach is adopted here. 

The case of S02 emissions is more complex. _ The legislation calls for2 SO 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 of the Clean 

Air Act. 'irhe major options are 1) to decrease their use of high-emission units and increase the use· 

of their 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 of complying 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 of the 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!!ntal Assessment PageEA-3 
The adoption or non-adoption of the efficiency level for room 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, 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 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, emissions reductions by weight are simply estimated 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 distribution of benefits 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 can be evaluated. (The report does not go beyond this explanation in defining 

the differences between the twocases.) 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 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-burning power plants in futm years, 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 SO2 and NO,. 

Page EA-4 

co2 

I 06 short tons 

2233 

2506 

3219 

3964 

4804 

Volume I: 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 

 EE-RM-93-801-RAC COMMENT 01

The adoption or ~oil-adoption of the 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 if the 

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 of benefits 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 of permitted 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 (f short 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 Sulfur Dioxide and Nitrogen Oxide Emissions 

In ~rder to capture the effects of cleaner-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 of these 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: Environmental Assessment 
Table EA-2. Electricity Generation, Emissions Data; and Emissions Rates for SO2 and NO, , 

' 

at Fossil Fuel-Burning Power Plants , 

Year I 109kWh 

ricity Generation Energy I use 

Emissions Rates 

Oil 

~o~k~h 

Tlhe calculated emissions rate data listed in Table 2 represents the average SO2 and 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 according to power plant type (i.e., coal, oil, gas). To obtain emission 

rate values, the amount of emissions was divided by 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 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 sounx was summed and then divided by the assumed efficiency of fossil fuel-burning power 

plants (301%), which includes transmission and distribution losses. This fossil fuel-burning power 

plant effkiency is consistent with 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 

interpolation was used to derive emission rates and, in turn, the corresponding abated emissions. 

Total 

~wrds 

Table 3 presents the emission factors (rates) that were used for SO, and NO, for in-house gas 

and oil combustion. The values for reduction of SO2 and 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 of those for residential gas space heaters and water heaters. Emission factors for oil in-house 

combustion were assumed to equal those associated ~5th a residential #2 oil boiler. 

Volume I: ~nvironmental hsessment Page EA-5 

 EE-RM-93-801-RAC COMMENT 01

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 109 kWh l~kWh 109 kWh Quads 1Cf short 1Cf short 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 NOx 

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 power plant type (Le., coal, oil, gas). To obtain emission . 

rate values, the amount of emissions was divided by the total energy use of fossil 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 of fossil fuel-1?uming power 

plants, (30%). which includes transmission and distribution losses. This fossil fuel-buming power' 

plant efficiency is consistent with that used by the LBNL Residential Energy Model (LBNL-REM). 

The amount of S02 and NOli emissions abated for any particular year is determined by 

multiplying 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 

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 of S02 and NOx emissions 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:f those 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: Environmental Assessment 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 Service Report 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 supplied by the report. 

Table EA4. Electricity Generation Data, Emissions Data, and Emissions Rates for CO,, at 

Fossil Fuel-Burning Power 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 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 199 1 NES were mistakenly reported 

as tons of carbon emitted. David Streets was one of authors at Argonne who contributed to the report. 

Page EA-6 Volume 1: Environmental Assessment 

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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-3. Emission Rates for S02 and NO. ·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 CO2 were 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 CO2 emission 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 of CO2 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 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 of carbon emitted. David Streets was one of authors at Argonne who contributed to the report. 

PageEA-6 Volume 1: Environmental Assessment 
- 

.I ' 

Talble 5 presents,the emission 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 combustion was 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. 

Table EA-5. Emission Rates for CO, from In-House Combustion . 

. . 

' CO, Gas Emission. . ' . CO, Oil Emission 

1 O-' tondQuud lO-' tons/Quad 

55.000 84.300 I 

3: RESULTS . 

The following results in Tables 6 through 25 indicate the potential changes in amounts of 

emitted CO,, SO, andN0, resulting horn 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 of the three emissions (i.e., CO,, SO,, and NO,) 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 

from in-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 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 of efficiency levels that are analyzed for a particular appliance depends 

on the nurnber and type of technologies that were considered for it. 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. 

Volume I: Environmental Assessment Page EA-7 

 EE-RM-93-801-RAC COMMENT 01

J 

Talble 5 presents ,the emission factors (rates) that were used for CO2 for in-house gas and oil 

combustion. The values for the reduction of CO2 emissions 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 

CO2 Gas Emission, 

J(j tons/Quad 

55,000 

, CO2 Oil 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,andNOx resulting 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 of the three emissions (Le., CO2, S02' and NOx) 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 of efficiency levels that are analyzed for a particular appliance dePends 

. on the number and type of technologies that were considered for it. For a detailed explanation of the 

specific tc~hnologies considered for each appliance. please refer to the appropriate sections ·and 

appendices of each product-specific Part in this ~port. 

Volume 1: Environmental Assessment PageEA-7 
3.1 ROOM AIR CONDITIONERS' 

3.1.1 Sulfur Dioxide and Nitrogen Oxide Emissions 

Sulfur dioxide emissions would be decreased by a cumulative total of up to 33 kt (37 

thousand short tons) 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 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 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 5 design changes to room air conditioners would result in an estimated decrease in NO, 

emissions of 5 1 kt (56 thousand short tons) 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 cumulative reduction in COzemissions from level 5 design changes is 5 1 Mt (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. Alternative efficiency 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 16 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 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 

Page EA-8 Volume 1: Environmed Assessment 

 EE-RM-93-801-RAC COMMENT 01

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% of the S02 emissions 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 S02 emissions 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 design changes to room air conditioners 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% of the NOx emissions 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 1 through 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 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 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% of the S02emissions~estimated to come from the residential sector in that year .. ~ discussed 

fage EA-8 Volume 1: Environmimtal Assessment . 
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 

of 65 kt (72 thousand short tons) between 2000 and 2030. N0,decreases would repment 0.02% and . . 

O.lO%'of the NO, emissions estimated to come from the residential sector in the years 2000 and 

2030, respectively. 

* Convenrknal Ovens 

. . 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.al sector in that year. In the year 2030, decreases in SO, emissions will represent about 

0.43% of the S0,emissions estimated to come from 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 a postponement. 

Level 5 design changes to conventional ovens 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 sector in 

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 short tons) behueen 2000 and 2030 in the level .5 scenario. In the year 2000, decreases in 

sulfur dioxide will represent about 0.02% of the SO2 emissions 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 sector in 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 a postponement. I 

Levef 5 design changes to microwave ovens would result in an estimated decrease in NO, 

emission:; of 48 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. 

. , 'Volume 1: Environmental Assessment 

.- . Page EA-9 

 EE-RM-93-801-RAC COMMENT 01

earlier. the possible reductions of S02emissions 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 

of 65 kt (72 thousand short tons) between 2000 and 2030. NOxdecreases would represent 0.02% and 

0.10%'o,f the NOx emissions 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 S02 emissions 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 % of the NOx emissions 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 S02 emissions will, represent about 

0.08% of the S02 emissions estimated to cOme from the resIdential sector in that year. As discussed 

above, the possible reductions of S02 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' 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 of 48 kt (53 thousand short tons) between 2000 and 2030. NO" decreases would represent 

0.02% and 0.07% of the NOx emissions estimated to come froIp the residential sector in the years 

2000 and 2030. respectively. 

'Volume 1: Environmental Assessment PageEA-9 
33.2 Carbon Diokide Emissions 

Cooktops 

The cumulative 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. power plant 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. 

Conventional Ovens 

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. power plant 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 in 2030. 

Page EZL-10 .Volume I: Environmental Assessment 

 EE-RM-93-801-RAC COMMENT 01

3.3.2 Carbon DioXide Emissions 

Cooktops 

The cumulative reduction in CO2 emissions from level 5 design changes is 36 Mt (39 million 

. short tons) of CO2, For the year 2000, the estimated cq reduction is 0.16 Mt of cq or about 

0.01 % of estimated U.S. pOwer plant CO2 emissions in 2000. For the year 2030, the estimated CO2 

reduction is 1.47 Mt of CO2 or about 0.09% of estimated U.S. power plant CO2 emissions in 2030. 

ConventionalOvertS 

The cumulative reduction in CO2 emissions 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% of estinlated U.S. power plant CO2 emissions m 2000. For the year 2030, the estimated 

CO2 reduction is 6.77 Mt of CQz or about 0.38% of estimated U.S. power plant C~ emissions in 

2030. . 

. Microwave Ovens 

. The cumulative reduction in CO 2 emissions 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 % of estimated U.S. power plant C02e~ssions in 2000. For the year 2030, the estimated CO2 

reduction is 1.02 Mt of CO2 or about 0.06% of estimated U.S. power plant CO 2 emissions in-2030. 

PageEA-10 ·Volume 1: Environmental Assessment 
- 

Table EA-6. ~eduction of Pollutants for Room Air Conditioners, Level One a 

, . 

. . 

Cum~llative SO2 reduction (kt): 59 (short tons): 65 .'000 

NOx 

Volume I: Environmental Assessment Page EA-?I 

Curnl~lati ve NOx reduction (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-dative C02 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 

0.3 1 

0.7 1 

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 

0.1 1 

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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-6. Reduction of Pollutants 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 

Cumlliati ve 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 CO2 reduction (Mt): 30 (short tons): 33 000 000 

Volume 1: Environmental A~sessment PageEA-H 
Table EA-7. Reduction of Pollutants for 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 

(short tons): 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: Environmental Assessment 

. , 

Reduction 

Total Reduction in as a of 

Emissions 1 Total I 

1.85 1 2.03 1' 0.11 I 

(short tons): 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 

 EE-RM-93-801-RAC COMMENT 01

Table EA .. '. Reduction of PollutantS 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 S02 reduction (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: EnvironmenltJl Assessment 
Tiable EA-8. Reduction of Pollubnts for RobmAir Conditioners, Level Three 

SO2 . 

Abated from Power I Abated from in House Plants 

thousand thousand 

kt . short'tons kt short tons 

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 I Abated 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 4.3 1 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 

1.1 1 1.22 0.04 

2.94 3.24 0.1 1 

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 . 

(short tons): 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): 2.5 1 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,. 

2.0 1 

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 

 EE-RM-93-801-RAC COMMENT 01

Tablte EA·8. Reduction ofPollutBnts for Room Air 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 S02 reduction (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: Environmental Assessment PageEA.13 
~nble EA-9. Reduction of Pollutants for Room Air Conditioners, Level Four 

SO, 

Abated from Power I Abated from In House 

I I kt I I kt 1 thousand 

short tons short tons 

Year 

2000 

2005 

2010 

2015 

2020 

- 2025 

2030 II 

Cumulative SO2 reduction (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 Volume I: Environmental ~ssessmknt 

(short tons): 156 000 

thousand 

short tons 

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 

0.2 1 

0.05 

0.13 

0.19 

0.20 

0.20 

0.20 

0,20 . 0.24 

0.24 

 EE-RM-93-801-RAC COMMENT 01

Table EA-9. Reduction of Pollutants 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 CO2 reduction (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): 3 3 

-- 

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 

0.8 1 

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 Page EA-15 

Year ni 

C02 

Cum~ulalive NOx reduction (kt): 5 1 (short tons): 56 000 

Abated from Power 

Plants 

- 

Abated from In House 

. 

Sear 

' 

2005 

2010 

201 5 

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 

Cumulative C02 reduction (Mt): 5 1 . . (short tons): 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 

short tons 

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 In House 

Mt.,', ' 

0.3 1 

0.93 

1.57 

1.66 

1.74 

1.83 ' 

1.93 

uction 

da% of 

Total 

Residential 

. Emissions 

0.04 

0.09 

0.1 1 

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 

 EE-RM-93-801-RAC COMMENT 01

TllbleEA-lO. Reduction of Pollutants 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 S02 reduction (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 

2010 0.00 . 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 CO2 reductIon (Mt): 51 (short tons): 56000 000 

Volume 1: Environmental Assessment PageEA-1S 
Table EA-11. Reduction of Pollutants for 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 I a % of Total 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 NOx reduction (kt): 0 (short tons): 0000 

2000 

Page EA-I6 

kt 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

Abated from Power 

Plants 

Volume I: Environmental Assessment 

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 

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 

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 

' 

 EE-RM-93-801-RAC COMMENT 01

Table EA-11. Reduction of Pollutants for Cooktops, Level One 

SOl 

Year Abated from Power -Abated from In House Total Reduction in 

Reduction as Plants Emissions 

a % of 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 tops): 0000 

NOx 

Year Abated from Power Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total 

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 % 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 C~ reduction (Mt): o (short tons): 0 000 000 

PageEA-16 Volume 1: Environmental Assessment 
Table EA-12. Reduction of Pollutants for Cooktop$, Level Two 

Abated from Power Year . Plants 

thousand ' kt short tons 

0.14 0.16 

0.5 1 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 SOz reduction (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 

1, Cumulative NOx reduction (kt): 9 (short tons): 10 000 

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 

0.5 1 

0.24 

0.04 

0.00 

Abated from Power 

Plants Abated from In House 

Mt 

.' 

0.172404 

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 

0.1 1 

0.4 1 

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 

0.0419 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 

0.0 1 

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 

0.5 1 

0.27 

0.05 

0.00 

 EE-RM-93-801-RAC COMMENT 01

Table EA-12. Reduction of Pollutants for CooktOp~, Level Two 

Year· Abated from Power Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total 

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 S02 reduction (kt): 11 (short tons): 12000 

NO" 

Year Abated from Power Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total 

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 % of Total 

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: .Environmental Assessment 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 

Cumulative NOx reduction (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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-~. Reduction of Pollutants for Cooktops, Level Three 

Year . Abated from Power 

Abated from In House Total Reduction in Reduction as Plants Emissions 

a % of Total 

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 S02 reduCtIon (kt): 5 . (short tons): 6000 

NOx 

, Abated from Power Total Reduction in Year Plants Abated from In House Emissions 

Reduction as 

a % of Total 

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 % of Total 

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 Pollutants for Cooktops, Level Four 

, . NOx 

so2 

Volume 1: Ensironmentul Assessment 

Cumulative S& reduction (kt): 13 . (short tons): 14'000 

Year 'F 

2005 

2010 . 

201 5 

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 ' 

Cumulative NOx reduction (kt): 18 (short tons): 20 - 

& 

2010 

2015 . 

202 5 

2030 

Reduction as 

a % of Total 

Residential 

Emissions 

0.00 

0.02 

0.03 

0.04 

0.03 , 

0.02 

0.02 

- - 

Abated from Power Yeiu Abated from In House 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:ative C02 reduction (Mt): 1 1 (short tons): 12 000 000 . ' 

thousand 

short tons 

-0.01 

-0.04 ' 

-0.06 

-0.10 

-0.09 

. -0.10 

-0.09 

thousand 

short tons 

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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-14. Reduction of Pollutants for Cooktops, Level Four 

SOz -

Year Abated from Power Abated nom In House Total Reduction in Reduction as Plants Emissions 'a % of Total 

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(h reduction (kt): 13 . (short tons): 14000 

NOx 

Yenr Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions 

a % of Total 

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 % of Total 

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 CO2 reductIon (Mt): II (short tons): 12000 000. 

Volume 1: Ent'ironmental Assessment PageEA.19 
Table EA-15. Reductionof Pollutants for Cooktops,Level Five 

L 

Year 

2000 

2005 

2010 

2015 

2020 

2025 

2030 

NOx 

Page EA-20 Volume 1: Environmentnl Assessment 

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 NOx reduction (kt): 65 - (short tons): 72 000 

Cumulative C02 reduction Wt): 36 (short tons): 39 000 000 

thousand 

short tons 

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 of Total 

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 

Reduction as 

a 8 of 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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-lS. Reduction ·of Pollutants for Cooktops, . Level Five 

Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions 

fl % of Total 

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 S02 reduction (kt):' 67 (short tons): 73000 

NOx 

Year Abated from Power . Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total 

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 NOx reduction (kt): 65 (short tons): 72000 

Year Abated from Power Abated from In House Total Reduction in Reduction as 

PlantS Emissions 

a % of Total 

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: Environmental Assessment 

\ 
Table EA-16. Reduction of Pollutants for ~onventidnnl .ovens, Level One 

SO2 

NOx 

Abated from Power Total Reduction in Abated from In House Reduction as Plants Emissions 

a % of Total 

thousand thousand kt kt thousand Residential . short tons 

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 

21030 ' 0.38 0.4 1 -0.02 403 0.35 0.39 . 0.02 

Cunlulanive NOx reduction (kt): 1 1 I (short tons): 13000 , 

Abated Power 'tear Abated from In House 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 Page EA-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 

 EE-RM-93-801-RAC COMMENT 01

Table EA·16. Reduction of Pollutants for Conventional.Ovens, Level One 

Year 

Abated from Power Abated from In House 

Total Reduction in Reduction as 

Plants Emissions 

a % of Totill 

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 % of Total 

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 % of Total ' 

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 CO2 reduction (Mt): 6 (short tons): 7 000 000 

Volume 1,' Environmental Assessment PageEA.21 
Table EA-17. Reduction of Pollutants for 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 

Cumulative NOx reduction (kt): 23 

- 

Abated from Power 

Plants 

- 

Abated from In House 

2000 

2005 

2010 

2015 

2020 

2025 

2030 

I Year I Abated Power I Abated 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 

shm tons 

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 

201 0 

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 

0.4 1 

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: Environmental Assessmeni 

 EE-RM-93-801-RAC COMMENT 01

Table EA-17. Reduction of Pollutants for Conventional Ovens, Level Two 

Year Abated from Power Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total - 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 S02 reduction (kt): 25 (short tons): . 28 000 

NOx 

Year 

Abated from Power Abated from In House Total Reduction in - Reduction as 

Plants Emissions 

a % of Total 

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 % of Total 

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 CO2 reduction (Mt): 13 (short tons): 14000000 

PageEA-22 Volume 1: Environmental Assessment 
. . 

Table EA-18.' Reduction of Pollutants for ~onventionsl Ovens, Level Thm 

. . . so2 

Abated from Power Total Reduction in Reduction as 

,Plants Emissions 

a 8 of Total . 

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 0.0 1 

0.46 0.5 1 -0.09 -Oil0 0.37 0.4 1 0.01 

20:15 ' 0.56 0.61 -0.15 -0.16 0.4 1 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 0.0 1 

2030 0.3 1 0.35 -0.15 -0.16 '0.17 0.18 0.0 1 

Cumullative SO2 reduction (kt): 9 (short tons): 10 000 

NOx 

201 0 

201 5 

20;!0 

20;!5 

20510 

Volume I: i~nvbonm'enhl ~ssessk Page EA-23 

0; 

co2 

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 0.3 1 

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 

0.3 1 

0.33 

Mt 

0.02 

0.11 

0.20 

0.28 

0.3 1 

0.32 

' 0.35 

Reduction as 

a 8 of Total 

Residential 

Emissions 

0.00 

0.01 

0.02 

0.03 

0.04 

0.04 

thousand 

short tons 

0.02 

0.12 

0.23 

0.3 1 

0.34 

0.36 

Total Reduction in 

Emissions 

Reduction as 

a 9% of Total 

Residential 

Emissions 

. 0.00 

0.0 1 

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 

 EE-RM-93-801-RAC COMMENT 01

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 % of Total 

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 % of Total 

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 % of Total 

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: Environmental Assessment PageEA-23 
Table EA-19. Reduction of Pollutants for 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 

1 1.26 

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 

1 1.06 

8.85 

6.95 

Total Reduction in 

Emissions 

kt 

-0.03 

kt 

1.45 

Cumulative NOx 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 

1 1.06 

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 

Page EA-24 

I 

Volume I: ~nviron~ental ~sseisment 

Cumulative C02 reduction (Mt): 129 (short tons): 142 000 000 

Year 

2000 

2005 

201 0 

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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-19. Reduction of Pollutants for Conventional Ovens, Level Four 

Year Abated from Power Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total 

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 S02 reduction (kt): 247 (short tons): 273000 

NOx 

Year Abated from Power Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total 

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 % of Total 

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: Environmental Assessment 
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 

Cummlative NOx reduc 

thousand 

short tons 

1.57 

5.46 

9:10 

1 1.35 

10.91 

9.58 

- 8.44 

In (kt): 

I 1 Abated fmm Power 

Pian ts 

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): 

\ 

Volume I: Environmental Assessmeit Page EA-25 

 EE-RM-93-801-RAC COMMENT 01

TaUde EA-20. Reduction of Pollutants for Conventional Ovens, Level Five , .' . 

S02 . 

Year Abated' fi:om Power Abated from In House Total Reduction in Reduction as 

Plants Emissions 

a % of Total 

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 % of Total 

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 % of Total 

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 CO2 reduction (Mt): 133 (short tons): 147 000 000 

Volume J: Environmental Assessment PageEA-25 
- 

Table EA-21. Reduction of Pollutants for M.ic,mw&e Ovens, Level One 

NOx 

Year 

2000 

2005 

2010 

201 5 

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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-ll. Reduction of Pollutants 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 CO2 redllctlon (Mt): o (short tons): 0000 000 

PageEA-26 Volume 1: Environmental Assessment 
TnbleEA-22. Reduction of Pollutants for ~iemwrbe Ovens, Level Two . . 

's ,~ 

2005 

2015 

2020 

2025 

2030 : 

Volume I: Environmental Assessment 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 

0.00 Plants , 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 

 EE-RM-93-801-RAC COMMENT 01

TableEA-22. Reduction of Pollutants 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 CO2 reduction (Mt): o (short tons): 0 000 000 

Volume 1: Environmental Assessment PageEA.27 ' 
Table EA-23. Reduction of Pollutants for 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 (short tons): 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 NOx reduction (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; 

(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 

Cumulative COz reduction (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 

Volume I: 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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-23. Reduction of Pollutants 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 S02 reductlon (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: Environmental Assessment 
. , 

Table EA-24. Reduction of Pollutants for Microwave Ovens, Level Four . 

SO, 

- .--- 

uctlon Abated from Power Abated from In House Tota~ Reduction in ya of 

Plants Emissions Total 

thousand thousand kt kt thousand Residential 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 

Cumulative NOx reduction (kt): 0 (short tons): 0000 

, 

. 

Volume 1: EnvironmenUrl Assessment . . Page EA-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 Abated from In House % of ' 

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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-24. Reduction of Pollutants 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 S02 reduction (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: Environmental Assessment Page El·29 
Table EA-25. Reduction of Pollutants for Microwave Ovens, Level Five 

SO, 

-Abated horn 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 

201 5 

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 

Cumulative S4 reduction (kt): 53 (short tons): 58 000 

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 

Cumulative NOx reduction (kt): 48 

2000 

Emissions Total C 

1.50 

2.04 

1.84 

1.67 

1.47 

1.28 

Page EA-30 Volume I: Environmental Assessment 

kt 

0.47 

kt 

-0.01 

Year 

2000 

2005 

2010 

2015 

2020 

2025 

2030 

thousand 

short tons 

0.52 

thousand 

short tons 

-0.02 

(short tons): 53 000 

1.65 

2.25 

2.03 

1.84 

1.62 

1.41 

Cumulative C02 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 from ln House 

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 

 EE-RM-93-801-RAC COMMENT 01

Table EA-25. Reduction of Pollutants 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 Total Reduction 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 CO2 reduction (Mt): 25 (short tons): 28000 000' 

Page EA-.30 Volume 1: Environmental Assessment 
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 of the 

U'nited States: 1994. ('1 14th Edition). 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 Service Report to the 1991 National Energy Strategy, Washington, D.C. SRlNESl90- 

0;1. 

5. 1. KMmey, 1990. Comparative Analysis of Monetary Estimutes of Extemal ~nbironmental , ' 

Costs Associated with Combustion of Fossil Fuels. Lawrence'Berkeley National Laboratory, 

Berkeley, CA. LBL-283 13. 

6. Ibid. 

Volume I: .Environmental Assessment 

. . 

. .. Page EA-31 

 EE-RM-93-801-RAC COMMENT 01

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. Statistical Abstract 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/or National 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 

Costs Associated with Combustion of Fossil Fuels. Lawrence Berkeley ,National Laboratory, 

B.~rkeley, CA. LBL-28313. ' 

6. Ibid. 

Volume 1: .Environmental Assessment Page EA-31 

. ~ 'l .'