# TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

RESIDENT IAL MICROWAVE OVEN S

– STAND

- BY POWER

May 20 13

U.S. Department of Energy Assistant Secretary Office of Energy Efficiency and Renewable Energy Building Technologies Program Appliances and Commercial Equipment Standards Washington, DC 20585This Document was prepared for the Department of Energy and by staff mem bers of Navigant Consulting, Inc.

and Ernest Orlando Lawrence Berkeley National Laboratory1

- i CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUM ENT

................................ ................................ ................. 1

- 1

1.2 SUMMARY OF NATIONAL BENEFITS

................................ ................................ ..... 1

- 1

1.3 OVERVIEW OF STANDARD S FOR MICROWAVE OVEN STANDBY

POWER CONSUMPTION

................................ ................................ .............................. 1

- 5

1.4 PROCESS FOR ESTABLIS HING ENERGY CONERVATION S TANDARDS

.......... 1

- 5

1.4.1 Process Rule

................................ ................................ ................................ ..................... 1

- 6

1.4.2 Procedures, Analyses, and Stakeholder Involvement

................................ ...................... 1

- 7

1.5 STRUCTURE OF THE DOC UMENT

................................ ................................ .......... 1

- 10

TABLE

Table 1.2.1 Annualized Benefits and Costs of New Standards for Microwave Oven Standby Mode and Off Mode for Products Sold 2016

- 2045

............................... 1

- 4

Table 1.4.1 Analyses Performed Under the Process Rule

................................ ...................... 1

- 71-1 CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUM ENT

The U.S. Department of Energy (DOE) has issued a final rulemaking regarding new energy efficiency standards for the electrical consumption of r esidential microwave oven s when in standby (non operating) mode. This document, termed a technical support document (TSD)

, is

a stand

- alone report that describ es the technical analyses performed and results obtained when examining the standby power consum ed by microwave oven s and e valuating the effects of efficiency standards on consumers, manufacturers, and the nation as a whole. This TSD was prepared in support of DOE’s final rule for the standby power consumption of residential

microwave ovens

.

1.2 SUMMARY OF NATIONAL BENEFITS

DOE’s analyses indicate that the new standards for microwave oven standby power w ill save a significant amount of en ergy

— an estimated 0. 4 8 quadrillion (10 15 ) British thermal units ( quads ) during 30 years ( 201 6

– 204 5 ). According to the Energy Information Adm inistration’s Annual Energy Outlook 201 2 ( AEO 201 2 ), total residential energy consumption is projected to be

20.8 quads in 201 6 . The amount of energy the standards will save throughout 30 years is equivalent to 2 percent of projected household energy use.

The cumulative national net present value (NPV) of the standards in 2011$, in terms of

t otal consumer costs and savings for products shipped in 201 6

– 204 5

, ranges from $ 1. 53 billion (at a 7

- percent discount rate) to $ 3. 38 billion (at a 3

- percent discount r ate). a The NPV is the estimated total value of future operating cost savings during the analysis period, minus the estimated increased product costs, discounted to 201 3 . The industry net present value (INPV) is

the sum of the discounted cash flows to the i ndustry from the base year through the end of the analysis period ( 201 3

– 204 5 ). Using a real discount rate of 8.0 percent, DOE estimates that the
INPV for manufacturers of all microwave ovens in the base case is $1. 4 billion in 2011 $

. DOE
expects that standards will cause manufacturers to lose up to 7 percent of their INPV, or approximately $ 96.6 million during the analysis period . Using a 7

- percent discount rate, the NPV of consumer costs and savings from today’s standards w ill amount to 16 to 38 times the total estimated industry losses. Using a 3

- percent discount rate, the NPV w ill amount to 35 to 8 4 times the total estimated industry losses.

The projected economic impacts of the new standards on individual consumers are positive. For microwav e

- only ovens and countertop convection microwave ovens ( P roduct C lass 1), for example, the estimated average life

- cycle cost (LCC) savings in 2011 $ a re $ 1 1 , and 88

a The discount rates DOE uses ( 7 percent and 3 percent ) follow guidance from the White House Office of

Management and Budget (OMB ; Circular A

- 4, section E, September 17, 2003).1-2 percent of consumers of P roduct C lass 1 units w ill experience positive economic effe cts. For

built

- in and over

- the

- range convection microwave ovens ( P roduct C lass 2), the estimated average LCC savings in 2011 $ are $ 1 2

. All consumers of P roduct C lass 2 unit s w ill experience positive economic effe cts.

In addition, the new standards w ill h ave significant positive environmental benefits. The energy savings projected from the standards w ill result in cumulative reductions in greenhouse gas emission s of 38.11 million metric tons b of carbon dioxide (CO 2 ) during the analysis period ( 201 6

– 204 5 )

. During th at period, the standards w ill result in an emissions reduction of 32.67

thousand tons of nitrogen oxides (NO X ) and 0.095 tons of mercury (Hg). c DOE estimates that the present monetary value of the reduction in CO 2 emissions is between $ 255 million and $3

, 615 million, expressed in 20 11 $. DOE also estimates that the present monetary value of the reduction

in NO X emissions, expressed in 20 11 $, is $ 21.8 million at a 7

- percent discount rate and $ 44.5

million at a 3

- percent discount rate. d

The benefits and costs of today’s standards also can be expressed using values t hat are

annualized throughout a 30

- year period. The annualized monetary values are the sum of (1) the

annualized national economic value of the benefits from utilizing products that meet the standards (consisting primarily of operating cost savings from using less energy minus increases in product purchase costs, which is another way of representing consumer NPV), and (2) the monetary value of the benefits of emission r eductions, including reductions in CO 2 emission s

. e

The value of the CO 2 reductions per metric ton of CO 2 , otherwise known as the social cost of

carbon (SCC), is calculated using a range of values developed by a recent Federal interagency process. The monet ary costs and benefits of cumulative emissions reductions are reported in

201 1 $ to permit straightforwa rd comparisons with the other costs and benefits. The derivation of

the values for the SCC is discussed in appendix 16A

.

Although combining the values of operating cost savings and CO 2 reductions provides a useful perspective, two issues should be considered. First, the national operating savings are monetary savings that U.S. consumer s experience as a result of market transactions, whereas

CO 2 reductions reflect a global valuation . Second, the operating cost savings and CO 2 savings

b Results for NO X and Hg are presented in short tons. A metric ton is equivalent to 1.1 short tons.

c DOE calculates emissions reduction s relative to the AEO 2012 Reference case, which generally represents current

legislation and environmental regulations, including recent government actions, for which implementing regulations were available as of December 31, 201 1.

d D OE has decided to await further guidance regarding consistent valuation and reporting of Hg emissions before it

monetizes Hg in its rulemakings

.

e DOE used a two

- step calculation process to convert the time

- series of costs and benefits into annualized va lues.

First, DOE calculated a present value in 201 3 , the year used for discounting the NPV of total consumer costs and savings, for the time

- series of costs and benefits using discount rates of 3 and 7 percent for all costs and benefits except for the valu e of CO 2 reductions. For the latter, DOE used a range of discount rates, as shown in Table

1.2.1 . From the present value, DOE then calculated the fixed annual payment over a 30

- year period ( 201 6 through

204 5 ) that yields the same present value. The fixed annual payment is the annualized value. Although DOE calculated annualized values, this does not imply that the time

- series of cost and benefits from which the annualized values were determined is a steady stream o f payments

.1-3 are assessed using different methods that incorporate different time frames for analysis. The national operating cost savings are measured for the lifetime of microw ave ovens shipped during

201 6

– 204 5 . The SCC values, on the other hand, reflect the present value of all future climate

related impacts resulting from the emission of one ton of CO 2 in each year. Tho se impacts

continue well beyond 2 100

.

Table 1.2.1 shows the annualized values for the new standards, expressed in 20 11 $. The results under the primary estimate are as follows. Using a 7

- percent discount rate for benefits and

costs other than CO 2 reductions, the cost of the standards promulgated in today’s final rule is
$ 58.4 million per year in increased product costs

. For CO 2 reductions, DOE used a 3

- percent discount rate and a value of $ 4 1 .1 /ton in 2011 $ for the SCC in 201 6

. T otal annualized benefits
are $ 1 74 million in red uced product operating costs, $58.4 million in CO 2 reductions, and $1. 6 4

million in reduced NO X emissions. In this case, the net benefit amounts to $ 1 75 million per year. Using a 3

- per cent discount rate for all benefits and costs and a value of $4 1 .1 /ton for the SCC in

201 6 , the standards established in today’s final rule result in $ 66.4 million per year in increased product costs . T he annualized benefits are $ 2 34 millio n in reduced ope rating costs, $58.4 million

in CO 2 reductions, and $2. 20 million in reduced NO X emissions. T he net benefit amounts to $ 2 28 million per year.1-4 Table 1.2.1 Annualized Benefits and Costs of New Standards for Microwave Oven Standby Mode and Off Mode for Products Sold 201 6

- 204 5

Discount Rate Primary

Estimate* Low Estimate* High

Estimate*

Benefits ( Million s 2011$ /year )

Operating Cost Savings 7% 174 162 191

3% 234 215 261

CO 2 Reduction at $12.6 /t** 5% 15.8 14.7 17.4

CO 2 Reduction at $4 1 .1 /t** 3% 58.4 54.1 64.5

CO 2 Reduction at $63.2 /t** 2.5% 87.4 80.9 96.7

CO 2 Reduction at $119.1 /t** 3% 179 166 198

NO X Reduction at $2,567/ton** 7% 1.64 1.54 1.79

3% 2.20 2.05 2.42

Total Benefits† 7% plus CO 2
range 191 to 354 178 to 329 210 to 391

7% 234 218 258

3% 294 271 328

3% plus CO 2
range 252 to 415 232 to 383 281 to 462

Costs

Incremental Product Costs 7% 58.4 59.6 57.5

3% 66.4 67.8 64.3

Net Benefits/Costs

Total† 7% plus CO 2
range 133 to 296 119 to 270 153 to 334

7% 175 158 200

3% 228 203 264

3% plus CO 2
range 185 to 349 164 to 315 217 to 398

* This table presents the annualized costs and benefits associated with microwave ovens shipped in 201 6

– 204 5

.

These results include benefits to consumers which accrue after 201 6 from the microwave ovens purchased from

201 6

– 204 5 . Costs incurred by manufacturers, some of which may be incurred prior to 201 6 in preparation for the rule, are not directly included, but are indirectly included as part of incremental equipment costs. The Primary, Low Benefits, and High Benefits Estimates utilize projections of energy prices and housing starts from the AEO

2012 Reference case, Low Estimate, and High Estimate, respectively. In addition, incremental product costs reflect a medium decline rate for projected product price trends in the Primary Estimate, constant product price s in

the Low Benefits Estimate, and a high decline rate for projected product price trends in the High Benefits

Estima te

. The methods used to derive projected price trends are explained in c hapter 8 and a ppendix 8C.1-5 † The CO 2 values represent global monetized values of the SCC, in 2011$ , in 2016 under sever al scenarios. The

values of $12.6, $4 1 .1, and $63.2 per metric ton are the averages of SCC distributions calculated using 5%, 3%, and 2.5% discount rates, respectively. T he value of $119.1 /t represents the 95 th percentile of the SCC distribution calculated using a 3% discount rate. The SCC time series used by DOE incorpo rate an escalation factor . The value

for NO X (in 2011$) is the average of the low and high values used in DOE’s analysis

. ‡ Total Benefits for both the 3

- percent and 7

- percent cases are derived using the series corresponding to SCC value

of $ 2,567 /t. In the rows labeled “7% plus CO 2 range” and “3% plus CO 2 range,” the operating cost and NO X benefits are calculated using the labeled discount rate, and those values are added to the full range of CO 2 values

.

1.3 OVERVIEW OF STANDARD S FOR MICROWAVE OVEN STANDBY

POWER CONSUMPTION

The Energy Policy and Conservation Act of 1975 (42 U.S.C. 6291

– 6309 ; EPCA ) established an energy conservation program for major household appliances. An a mendment to

EPCA gave DOE the authority to regulate the energy efficiency of severa l products, including

residential microwave ovens . The amendments to EPCA in the National Appliance Energy Conservation Act of 1987 ( 42 U.S.C. 6291

– 6309; NAECA) prescribed energy conservation standards for cooking products, as well as stipulating requirements for determining whether

th o se standards should be amended.

Following NAECA direct ives, DOE issued a notice of proposed rulemaking (NOPR) on March 4, 1994, proposing performance standards for eight gas and electric residential cooking produc ts, including microwave ovens operating in active mode . 59 FR 10464. Based on the July 15, 1996, Procedures for Consideration of New or Revised Energy Conservation Standards for

Consumer Products (the so

- called Process Rule ), DOE refined its analysis of co oking products.

61 FR 36974. DOE issued a final rule on September 8, 1998, that covered the active mode performance of electric cooking products, including microwave ovens. 63 FR 48038. f

1.4 PROCESS FOR ESTABLISHING ENERGY CONERVATION S TANDARDS In the November 15, 2007, advance notice of proposed rulemaking (ANOP R) regarding energy conservation standards for kitchen ranges and ovens and commercial clothes washers, DOE concluded that energy consumption by microwave ovens in the standby (nonoperating) mode represents a significant fraction of microwave ovens’ energy use, and that a standard regulating such energy consumption likely would provide significant energy savings. 72 FR 64432, 64441

– 42. The Energy Independence and Security Act of 2007 (EISA 2007; Pub. L. 110

– 140) direct s DOE to incorporate standby and off mo de energy use into any final rule establishing or revising a standard for a covered product adopted after July 1, 2010. (42 U.S.C. 6295(gg)(3)) DOE

therefore has amended standards that prescribe the maximum electricity consumed when the

microwave oven is i n standby mode. This TSD supports DOE’s final rule pertaining to the energy consumption associated with microwave oven standby mode and off mode power.

W hen DOE evaluat es a new or amended energy conservation standard under EPCA , it

must consider seven factors to the greatest extent practicable : (42 U.S.C. 6295(o)(2)(B)(i))

f The 1998 final rule maintained a “no standard” standard for electric cooking products, including microwave ovens.1-6 1. the economic impact of the standard on the manufacturers and consumers of the affected product;

2. the savings in operat ing costs throug hout the estimated average lifetime of the product compared to any increases in purchase price or maintenance cost s ; 3. the total projected amount of energy savings likely to result directly from the imposition of the standard;

4. any lessening of the utility or performance of the product likely to result from the imposition of the standard;

5. the impact of any lessening of competition, as determined in writing by the Attorney General, that is likely to result from the imposition of the standard;

6. the need for national energy conservation; and

7. other factors the Secretary of Energy considers relevant.

Other statutory requirements are set forth in 42 U.S.C. 6295(o)(1)

– (2)(A), and (2)(B)(ii)

– (iii) and (3)

– (4). Before DOE determines whether to ado pt a n energy conservation standard, it must solicit comments on the proposed standard. (42 U.S.C. 6 295 ( m )( 2 )(B)) Any new or amended standard must be designed to achieve significant additional conservation of energy and must be technologically feasible and economically justified. (42 U.S.C. 6295 ( o )( 2 )(A)) To

determine whether a standard is economic ally justifi ed , DOE must review comments on the

propos ed standard and conclude that the benefits of the standard exceed its burdens to the greatest extent practica ble, weighing the seven factors listed above. (42 U.S.C. 6295(o)(2)(B)(i))

1.4.1 Process Rule

In conducting rulemakings for both test procedures and energy conservation standards for appliances

, DOE employ s the procedures set forth in section 1.2 of the 1996 Process Rule . DOE applied those procedures to the extent they were appropriate to developing energy conservation standards for microwave oven standby power. The Process Rule enhances opportunities for receiving public input and improving analytic approache s. The Process Rule sets forth policies and guidelines for providing early public review and consultation with interested parties regarding design options, proposed standards, and the final standard. Design options are identified and screened based on (1) technological feasibility; (2) practicability to manufacture, install, and service; (3) adverse impacts on product utility or availability; and (4) adverse impacts on health or safety (Process Rule at section 4(a)(4)). 61 FR 36982. The Process Rule also

re quires DOE to evaluate uncertainty and variability in analytical results through the use of scenario or probability analysis.

The Process Rule calls for experts and interested parties to review DOE's analyses, including (1) the qualitative and quantitative analytical methods used; (2) the economic, engineering, and life

- cycle cost analyses; and (3) impacts on manufacturers and consumers, national energy savings, the economy, and the environment. See a ppendix A to subpart C of t itle 10 of the Code of Federal Regulations part 430 (10 CFR p art 430).1-7 1.4.2 Procedures, Analyses

, and Stakeholder Involvement

DOE considers stakeholder participation vital to a balanced discussion of the information required for a standards rulemaking and to the adoption of energy conservation standards that are, to the extent practicable, consensus

- based. DOE encourages the participation and interaction

of all stakeholders by providing formal public notifications via the Federal Register and by designating comment periods at each major step in a standards rulemaking. DOE publishes a formal public notice of the ini tial f ramework d ocument

, then an ANOPR, which is designed to publicly vet the models and tools used in the rule making and to facilitate public participation before the proposed standards are evaluated in detail. Next DOE publishes in the Federal Register a NOPR, which presents a discussion of comments received in response to the ANOPR; analys e s of the impacts of st andards on consumers, manufacturers, and the nation; DOE’s weighting of those impacts; and the proposed standards. Lastly, DOE publishes in the Federal Register the final rule, which includes a discussion of comments received in response to the NOPR; the r evised analysis of the impacts of standards; DOE’s weighting of those impacts; the

standard ( s ) DOE is adopt ing ; and the effective date ( s ) of the standard ( s )

.

Table 1.4.1 lists the analyses that are performed at each step of a standards rulemaking

. Following guidance in the Process Rule, a nalyses are reported, examined, revised, and expanded throughout the pro cess.

Table 1.4.1 Analyses Performed Under the Process Rule

ANOPR NOPR Final Rule

Market and technology assessment Revised ANOPR analyses plus: Revised NOPR analyses

Screening analysis
Life

- cycle cost analysis for

identified sub

- groups Engineering analysis

Markups for determining product price Manufacturer impact analysis

Energy (and water) use determination Employment impact analysis

Life

- cycle cost and payback period analysis Utility impact analysis

Shipments analysis Environmental assessment

National impact analysis Regulatory impact analysis

Preliminary manufacturer impact analysis

On March 27 , 2006, DOE published in the Federal Register the Rulemaking Framework for Commercial Clothes Washers and Residential Dishwashers, Dehumidifiers, and Cooking

Products (the f ramework d ocument ) , which described the procedural and analytical approaches DOE anticipated using to evaluate the develop ment of ene rgy conservation standards for all the

named products. This document is available at :1-8 www.regulations.gov/#!documentDetail;D=EERE

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. At that time

, DOE also

announced in the Federal Register the date, time, and place of a public meeting at which the

framework document would be discussed.

DOE held the public meeting on April 27, 2006, to discuss procedural and analytical approaches to the rulemakin g and to inform and facilitate stakeholder involvement in the rulemaking process. The analytical framework presented at the public meeting described the various analyses to be performed , such as the engineering analysis and the LCC and payback period (PBP) analysis; the methods proposed for conducting the analyses; and the relationships among the analyses.

During the April 2006 meeting, stakeholders commented about vari ous issues related to the rulemaking for all considered products . Comments relevant to the rulemaking for microwave oven standby power concerned:

1. current DOE test procedure ; 2. definition of baseline unit s ; 3. number of product classes;

4. consideration of limiting standby power as a design option for all considered products;

5. technology options for improving efficiency of all considered products;

6. type of approach to the engineering analysis;

7. efficiency levels to consider for all considered products;

8. proposed approaches for specifying typical annual energy ( and water ) consump tion for all considered products;

9. potential data sources for characterizing variability in annual energy (and water) consumption;

10. typical distribution channels and markups;

11. data sources for retail prices;

12. type of approach to employ for the LCC and PBP analys is ; 13. variability of forecasted energy prices;

14. relationship of repair, maintenance, and installation cost s to product efficiency;

15. product lifetimes;

16. development of consumer discount rates;

17. effects of purchase price on product shipments; and

18. identification of consumer sub

- groups.

Written s takeholder comments

, submitted during the comment period for the framework document, elaborated on issues raised at the public meeting. DOE worked with its contractors to provide that the analyses would add ress the identified issues.

I n preparation for developing the ANOPR

, DOE organized and held interviews with

manufacturers of microwave ovens. The companies DOE selected rang ed from small to large manufacturers, includ ing both members of the Association o f Home Appliance Manufacturers1-9 (AHAM) and non

- AHAM member companies. T he interviews were intended to achieve four objectives :

1. Solicit feedback on the draft engineering analysis (including methodology, production costs, manufacturing processes, and findi ngs)

. 2. Solicit feedback on the preliminary manufacturer impact analysis

. 3. Provide an opportunity, early in the rulemaking process, for manufacturers to express specific concerns to DOE

. 4. Foster cooperation between manufacturers and DOE.

S ix general topics related to the preliminary engineering analysis and manufacturer impact analysis were discussed during the interviews , including:

1. key issues identified by DOE and/or the manu facturer ; 2. projected shipments ; 3. capital conversion costs ; 4. produc t mix and profitability ;
5. market shares and indust ry consolidation ; and

6. cumulative regulatory burden.

To meet the objectives of the Process Rule, DOE developed spreadsheet models for the LCC, PBP, and national impact analyses for microwave oven standby po wer

. DOE developed an LCC spreadsheet that calculates the LCC and PBP at various energy efficiency levels. DOE also developed a national impact analysis spreadsheet that calculates the national energy savings (NES) and national net present values at variou s energy efficiency levels. This spreadsheet

includes a model that forecasts the effe cts of energy efficiency standards at various levels on product shipments. All the spreadsheets are available on the DOE website. Spreadsheets specific to cooking products, including microwave ovens, are found at : www.regulations.gov/#!documentDetail;D=EERE

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.

On November 15, 2007, DOE published in the Federal Register an Advance Notice of Proposed Rulemaking for Residential Dishwashers, Dehumidifiers, and Cooking Products, and
Commercial Clothes Washers, which described potential new energy conservation standards for

the named product types and announced a pub lic meeting to receive comments. This document is

available at : www.regulations.gov/#!documentDetail;D=EERE

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.

DOE held a public meeting on Decembe r 13, 2007, to provide stakeholders the opportunity to comment on the ANOPR and the analytical framework, models, and tools ( e.g. , LCC and NES spreadsheets) that DOE used to evaluate the effe cts of energy conservation standards; the results of preliminary analyses; and the candidate energy efficiency levels.

After stakeholders commented on the ANOPR at the public meeting and in writing during the subsequent comment period , DOE again interviewed manufacturers of microwave

ovens to assist with developing the manufacturer impact analysis for the NOPR. This second1-10 round of interviews addressed 13 topics, some of which visited a gain and in greater detail the six topics discussed during initial interviews. The 13 topics were:

1. key issues identified by DOE and /or the manufacturer ; 2. overview of company and organizational characteristics;

3. company financial parameters;

4. breakdown of production cost s ;
5. projected shipment s and market shares;

6. product mixes;

7. conversion costs;

8. markups and profitability;

9. cumulative regulatory burden;

10. exports, foreign competition, and outsourcing;

11. assessment of direct impacts on employment;

12. market consolidation; and

13. baseline products and alternative design options.

DOE issued a NOPR for microwave oven standby and off mode on October 17, 2008

, and held a public meeting on November 14, 2008. This document is available at: www.regulations.gov/#!documentDetail;D=EERE

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. DOE incorporated the information gathered at the interviews , as well as during the public meeting and comment period, into its engineering analysis ( chapter 5 of the NOPR TSD ) and preliminary manufacturer impact analysis ( c hapter 13 of th e NOPR TSD ). In addition, the Department conducted all the analyses

list ed in Table 1.4.1 that are required for the NOPR phase of a rulemaking. Taking into

consi deration the seven factors stipulated in 42 U.S.C. 6 295 ( o )( 2 )(B)(i), which are listed in

section 1.4 , and comments received from interested parties, the Department analyzed four trial

standard levels f or microwave oven standby power

. DOE subsequently issued a supplemental notice of proposed rulemaking (SNOPR) for microwave oven standby mode and off mode standards on February 14, 2012. This document is available at : www.regulations.gov/#!documentDetail;D=EERE

- 2011

- BT

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. The SNOPR TSD

was issued October 2011 , and the public meeting was held March 14, 2 012.

This TSD for the Department’s final rule for microwave oven standby power outlines the analytical method s used in this rulemaking and the results of the analyses performed

.

1.5 STRUCTURE OF THE DOC UMENT

Thi s TSD comprises 17 chapters and 1 7 appendices.

Chapter 1 Introduction : provides an overview of the appliance standards program and how it applies to this rulemaking ; outlines the structure of the

document.

Chapter 2 Analytical Framework : describes the rulemaking process.1-11 Chapter 3 Market and Technology Assessment : characterizes the market for

microwave ovens and the technologies available for decreasing

microwave oven standby power consumption

.

Chapter 4 Screening Analysis : determines which design options are viable for consideration in the engineering analysis.

Chapter 5 Engineering Analysis : discusses the methods used to develop the relationship between increased manufacturer price and increased efficiency.

Chapter 6 Markups for Product Price Determination : d e sc rib es the methods used

to establish markups for converting manufacturer costs to consumer retail prices

.

Chapter 7 Energy Use Analysis : discusses the pro cess used for generating estimates

of energy use for microwave ovens as a functio n of standard levels.

Chapter 8 Life

- Cycle Cost and Payback Period Analysis : discusses the effects of

standards on individual consumers and users of microwave ovens; compares the LCC and PBP of products with and without higher efficiency standards.

Chap ter 9 Shipments Analysis : d escribes the methods used for forecasting product shipments with and without higher efficiency standards, including how product purchase decisions are economically influenced and how DOE

uses econometric equations to model this relationship

.

Chapter 10 National Impact Analysis : d escribes the methods used for forecasting national energy consumption and national economic impacts based on annual product shipments and estimates of future product energy efficiency distribution s in the absence and presence of amended energy

conservation standards

.

Chapter 1 1 Consumer Sub

- G roup Analysis : discusses the effects of standards on

different sub

- groups of consumers and compares the LCC and PBP for

tho se consumers of products in the ab sence and presence of higher efficiency standards

.

Chapter 1 2 Manufacturer Impact Analysis : discusses the effects of standards on the finances and profitability of product manufacturers.

Chapter 1 3 Employment Impact Analysis : discusses the effects of standards on national employment.1-12 Chapter 1 4 Utility Impact Analysis : discusses the effects of standards on electric

utilities.

Chapter 1 5 Emissions Analysis : d e sc rib es the effects of standards on emissions of

carbon dioxide ( CO 2 ) , nitrogen oxides (NO X ), and mercury

.

Chapter 1 6 Monetization of Benefits of Emission s Reductions : discusses the basis for

estimat ing the monetary values assigned to the reduced emissions of

CO 2 and other pollutants that are expected to result from each trial

standard level

.

Chapter 1 7 Regulatory Impact Analysis : discusses the effects of non

- regulatory alternatives to efficiency standards.

Appendix 5A Data Submittal from the Association of Home Appliance Manufacturers

Appendix 5B Regression Methodology for Testing Microwav e Oven Standby Power

Appendix 5C Microwave Oven Display Configurations

Appendix 5D Microwave Oven Design Pathways

Appendix 7A Variables and Values from Residential Energy Consumption Survey

Appendix 8A User Instructions for the Life

- Cycle Cost Analysis Spreadsheet

Appendix 8B Uncertainty and Variabil i ty

Appendix 8C Estimation of Product Price Trends for Microwave Ovens

Appendix 8 D Distributions Used for Discount Rates

Appendix 9A Relative Price Elasticity of Demand For Appliances

Appendix 10 A User Instructions for the National Impacts Analysis Spreadsheet

Appendix 1 0B National Energy Savings a nd Net Present Value Using Alternative

Economic Growth Scenarios

Appendix 1 0C National Net Present Value and Emissions Reductio ns using Alternative

Product Price Forecasts

Appendix 12A Interview Guides for Manufacturer Impact Analysis1-13 Appendix 12B Overview of Governmen t Regulatory Impact Model

Appendix 16 A Social Cost of Carbon for Regulatory Impact Analysis Under Executive

Or der 12866

Appendix 16B Technical Update Of The Social Cost Of Carbon For Regulatory Impact Analysis Under Executive Order 12866

Appendix 17A Supporting Material for Regulatory Impact Analysis2

- i CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION

................................ ................................ ................................ ........... 2

- 1

2.2 MARKET AND TECHNOLOG Y ASSESSMENT

................................ ........................ 2

- 5

2.2.1 Market Assessment

................................ ................................ ................................ .......... 2

- 5

2.2.2 Technology Assessment

................................ ................................ ................................ ... 2

- 5

2.3 SCREENING ANALYSIS

................................ ................................ .............................. 2

- 5

2.4 ENGINEERING ANALYSIS

................................ ................................ .......................... 2

- 6

2.4.1 Manufacturing Cost Assessment

................................ ................................ ...................... 2

- 6

2.5 MARKUPS ANALYSIS

................................ ................................ ................................ . 2

- 6

2.6 ENERGY USE ANALYSIS

................................ ................................ ............................ 2

- 7

2.7 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

................................ ..... 2

- 7

2.8 SHIPMENTS ANALYSIS

................................ ................................ .............................. 2

- 8

2.9 NATIONAL IMPACT ANAL YSIS

................................ ................................ ................ 2

- 8

2.9.1 National Energy Savings

................................ ................................ ................................ .. 2

- 9

2. 9.2 Net Present Value

................................ ................................ ................................ ............ 2

- 9

2.9.3 Forecasted Efficiencies

................................ ................................ ................................ .. 2

- 10

2.10 CONSUMER SUB

- GROUP ANALYSIS

................................ ................................ ..... 2

- 10

2.11 MANUFACTURER IMPACT ANALYSIS

................................ ................................ .. 2

- 10

2.12 EMPLOYMENT IMPACT AN ALYSIS

................................ ................................ ....... 2

- 11

2.13 UTILITY IMPACT ANALY SIS

................................ ................................ ................... 2

- 11

2.14 EMISSIONS ANALYSIS

................................ ................................ .............................. 2

- 12

2.15 MONETIZING CARBON DI OXIDE AND OTHER EMIS SIONS

REDUCTIONS

................................ ................................ ................................ .............. 2

- 14

2.16 REGULATORY IMPACT AN ALYSIS

................................ ................................ ....... 2

- 15

FIGURE

Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Process

................................ ..... 2

- 22-1 CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section 6295(o)(2)(A) of 42 U.S.C. requires the U.S. Department of Energy (DOE) to
establish energy conservation standards that are technologically feasible and economically justified and w ill result in significant energy conservation

. This c hapter descri bes the general analytical framework that DOE uses in developing such standards, as well as aspects specific to the analysis of microwave oven standby power

. T he analytical framework summarizes the methodologies, analytical tools, and relation ships among the various analyses that are part of a standards rulemaking

.

Figure 2.1.1 summarizes the analytical components of the standards

- setting process

. The

column labeled Approaches lists the methodologies DOE uses to perform the various steps and analyses in the process. The primary focus of the figure is the column labeled Analyse s

. The

columns labeled Key I nputs and Key O utputs show how the analyses fit into the rulemaking process and how the y relate to each othe r

. Key inputs are the types of data and information that the analyses require

. Some key inputs can be found in public databases; other inputs DOE

collects from interested parties or expert s having specialized knowledge

. Key outputs are the analytical resul ts that feed directly into the standards

- setting process

. L ines with arrows connecting analyses show the types of information that feed from one analysis to another.2-2 Figure 2.1.1 Flow Diagram of Analys e s for the Rulemaking Process Utility Impact

Analysis

Notice of Proposed Rulemaking (NOPR)

Final Rule Approaches Key Inputs Analyses Key Outputs

Market and Technology

Assessment

Screening Analysis

Engineering Analysis

Markups for

Product Price

Determination Life

- Cycle Cost and Payback Period Analysis

National Impact Analysis Energy Use

Revise ANOPR Analyses

Life

- Cycle Cost

Sub

- Group Analysis

Manufacturer Impact Analysis Shipments Analysis

Employment Impact Analysis

Revise Analyses • Product Classes

• Technology Options

• Industry Cash Flow

• Sub

- Group Cash

- Flow

• Direct Employment Impacts

• Competitive Impacts

• Cumulative Regulatory Burden • Cost

- Efficiency Relationship

• Life

- Cycle Costs

• Payback Periods

• National Energy Savings

• Net Present Values

• National Employment Impacts

• Revised Results • Characterize Industry

• Market Segmentation

• Non

- Regulatory Programs Framework Document

• Efficiency

- Level Approach

• Design Option Approach

• Energy Prices

• Installation Costs

• Maintenance & Repair Costs

• Accounting Approach

• Backcast and Forecast

• Market Saturation

• Stakeholder Comments

• Demographics

• Manufacturer Interviews

• GRIM Analysis

• Emission Rates

• National Energy Savings

• Stakeholder Comments • Identify Firms/Products

• Historical Shipments

• Define Distribution Channels

• Economic Census Data Analysis

• Retail Price Collection and Analysis

• Manufacturer Prices

• Average Costs

• Manufacturer Financial Data Design Options Product Classes

Retail Prices Design Energy/ Use

Advance Notice of Proposed Rulemaking (ANOPR) • Efficiency/Performance • Manufacturing Cost

Candidate Standard

Levels • Analysis of Energy Use

Data

Environmental

Assessment

Regulatory Impact Analysis Technology Options

• Emission Estimates • Utility Load Factors

• National Energy Savings

• Non

- Regulatory
Alternatives • NEMS

- BT

• ImSET

• NEMS

- BT • Design Options

• Energy Price Forecasts

• Site

- to

- Source Factors • Energy

- Efficiency Levels

Direct Employment Impacts

• National Energy Savings

• National Product Costs

• National Operating Costs • Utility Impacts • Analysis of Market Data

• Analysis of Product Data

• Life

- Cycle Costs

• Payback Periods • Installation Costs

• Maint Costs

• Repair Costs • UEC

• Energy Prices • Annual Energy Use (UEC)

Preliminary Manufacturer Impact Analysis • Manufacturer Prices

• Average Costs • Conversion Capital Expenditures

• Direct Employment Impacts

• National Energy Savings

• Net Present Values • Trial Standard Levels ( TSLs )

TSLs • Product Prototypes

Energy

- Efficiency

Levels

• Department of Justice Review Utility Impact Analysis

Notice of Proposed Rulemaking (NOPR)

Final Rule Approaches Key Inputs Analyses Key Outputs

Market and Technology

Assessment

Screening Analysis

Engineering Analysis

Markups for

Product Price

Determination Life

- Cycle Cost and Payback Period Analysis

National Impact Analysis Energy Use

Revise ANOPR Analyses

Life

- Cycle Cost

Sub

- Group Analysis

Manufacturer Impact Analysis Shipments Analysis

Employment Impact Analysis

Revise Analyses • Product Classes

• Technology Options

• Industry Cash Flow

• Sub

- Group Cash

- Flow

• Direct Employment Impacts

• Competitive Impacts

• Cumulative Regulatory Burden • Cost

- Efficiency Relationship

• Life

- Cycle Costs

• Payback Periods

• National Energy Savings

• Net Present Values

• National Employment Impacts

• Revised Results • Characterize Industry

• Market Segmentation

• Non

- Regulatory Programs Framework Document

• Efficiency

- Level Approach

• Design Option Approach

• Energy Prices

• Installation Costs

• Maintenance & Repair Costs

• Accounting Approach

• Backcast and Forecast

• Market Saturation

• Stakeholder Comments

• Demographics

• Manufacturer Interviews

• GRIM Analysis

• Emission Rates

• National Energy Savings

• Stakeholder Comments • Identify Firms/Products

• Historical Shipments

• Define Distribution Channels

• Economic Census Data Analysis

• Retail Price Collection and Analysis

• Manufacturer Prices

• Average Costs

• Manufacturer Financial Data Design Options Product Classes

Retail Prices Design Energy/ Use

Advance Notice of Proposed Rulemaking (ANOPR) • Efficiency/Performance • Manufacturing Cost

Candidate Standard

Levels • Analysis of Energy Use

Data

Environmental

Assessment

Regulatory Impact Analysis Technology Options

• Emission Estimates • Utility Load Factors

• National Energy Savings

• Non

- Regulatory
Alternatives • NEMS

- BT

• ImSET

• NEMS

- BT • Design Options

• Energy Price Forecasts

• Site

- to

- Source Factors • Energy

- Efficiency Levels

Direct Employment Impacts

• National Energy Savings

• National Product Costs

• National Operating Costs • Utility Impacts • Analysis of Market Data

• Analysis of Product Data

• Life

- Cycle Costs

• Payback Periods • Installation Costs

• Maint Costs

• Repair Costs • UEC

• Energy Prices • Annual Energy Use (UEC)

Preliminary Manufacturer Impact Analysis • Manufacturer Prices

• Average Costs • Conversion Capital Expenditures

• Direct Employment Impacts

• National Energy Savings

• Net Present Values • Trial Standard Levels ( TSLs )

TSLs • Product Prototypes

Energy

- Efficiency

Levels

• Department of Justice Review2-3 The following analyses are performed for the advance notice of proposed rulemaking (ANOPR) stage of a standards rulemaking (now called the Preliminary Analysis phase) and updated for the notice of proposed rulemaking (NOPR)

, and final rule

. Periodically

, and for this rulemaking

, a s upplemental notice of proposed rulemaking (SNOPR) is required

,

**•** A market and technology assessment to characterize the relevant produc t markets and technology options, including prototype designs.

**•** A screening analysis to review each te chnology option and d etermine whether it is technologically feasible; is practical to manufacture, install, and service ; would adversely affect product utility or availability ; or would have adverse impacts on health and safety.

**•** An engineering analysis to develop cost

- efficien cy relationships for product designs that passed the screening analysis ; the manufacturer ’s production cost s (material, labor, and factory overhead) for achieving increased efficien cy are evaluated

.

**•** An energy (and water) use analysis to determine the annual energy use of the considered product (s) at specific efficiency levels

.

**•** A markups analysis to convert the manufacturer costs estimated from the engineering analysis to consumer prices, which are then used in the life

- cycle cost (LCC) and payback period analysi s (PBP) and in the manufacturer impact analysis.

**•** A life

- cycle cost and payback period analysis to calculate, at the c onsumer level, the discounted savings in operating costs ( minus maintenance and repair costs) through out the estimated average life of the covered product ( s ) , compared to any

increase in the installed cost of the product ( s ) likely to result directly from the imposition of the standard.

**•** A shipments analysis to forecast product shipments, which then are used to calculate the national impacts of standards on energy consumption and costs, net present value (NPV), and future manufacturer cash flows.

**•** A national impact analysis to assess the aggregate impacts nationwide of the NPV of

total consumer LCC and nat ional energy savings (NES).

**•** A preliminary manufacturer impact analysis to begin examin ing the financial effects of potential standards on manufacturers.

The Energy Policy and Conservation Act of 1975 (42 U.S.C. 6291

– 6309; EPCA) established an energy conservation program for major household appliances. The amendments to EPCA in the National Appliance Energy Conservation Act of 1987 (42 U.S.C. 6291

– 6309;

NAE CA) prescribed energy conservation standards for cooking products, as well as stipulating requirements for determining whether those standards should be amended. Following NAECA directives, DOE issued a NOPR on March 4, 1994, proposing active

- mode performa nce standards for eight gas and electric residential cooking products, including microwave ovens. 59

FR 10464. DOE issued a final rule for electric cooking products (including microwave ovens) in

1998. a

a The 1998 final rule mai ntained a “no standard” standard for electric cooking products, including microwave ovens. 63 FR 48038. DOE has continued to assess standards an d the need for any amendments.

I n the November 15, 2007, ANOPR regarding energy conservation standards for kitchen ranges2-4 and ovens and commercial clothes washers, DOE concluded that energy consumption by microwave ovens in the standby (nonoperating) mode represents a significant fraction of microwave ovens’ energy use, and that a standard regulating such energy consumption likely would provide significant energy savings. 72 FR 64432, 64441

– 42. The Energy Independence and Security Act of 2007 (EISA 2007; Pub. L. 110

– 140) also directs DOE to incorporate standby and off mode energy use into any final rule establishing or revising a standard for a covered product adopted after July 1, 2010. (42 U.S.C. 6295(gg)(3)) DOE therefore amended standards t o prescribe th e maximum electricity consumed when the microwave oven is in standby mode. The analyses DOE performed in the NOPR stage for microwave oven s tandby power , which we re updated for the SNOPR and final rule, are listed below.

**•** A consumer sub

- group analysis to evaluate variations in c onsumer characteristics that might cause a standard to affe ct the LCC for particular consumer sub

- populations differently than for the overall population.

**•** A manufacturer impact analysis to estimate the financial impact s of stand ards on

manufacturers and to calculate effe cts on competition, employment, and manufacturing capacity.

**•** An employment impact analysis to assess the aggregate effe cts of standards on national employment.

**•** A utility impact analysis to estimate the effects of proposed standards on the generation capacity and electricity generation of electric utilities

.

**•** A n emissions analysis to estimate the effects of amended energy conservation

standards on emissions of carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), nitrogen oxides (NO X ), and mercury (Hg )

.

**•** A monetization of emission reduction benefits resulting from reduced emissions

associated with potential amended standards

.

**•** A regulatory impact analysis to evaluate alternatives to proposed amended en ergy conservation standards that could achieve substantially the same regulatory goal

T he analyses conducted at the NOPR stage were discussed in the NOPR technical support document (TSD) for this rulemaking

. After publication of the NOPR, DOE received

comments from interested parties that indicated that further analyses were warranted for microwave ovens as to standby power. As a result, DOE continued the rulemaking for

microwave ovens. A final rule for conventional cooking products (cooktops and ovens) and for microwave oven energy factor ( operating power ) was published on April 8, 2009. 74 FR 16040. Updated analyses for the final rule for conventional cooking products and microwave oven energy factor are presented in the final rule TSD for those produc ts.

DOE issued a supplemental notice of proposed rulemaking (SNOPR) for microwave oven standby mode and off mode standards on February 14, 2012

. 77 FR 8526

. The SNOPR

TSD was issued in October 2011, and the public meeting held March 14, 2012. This final rule

TSD contains details of the revised NOPR and SNOPR analyses , outlined above, conducted for microwave oven standby power consumption.2-5 2.2 MARKET AND TECHNOLOG Y ASSESSMENT

The market and technology assessment characterizes the relevant product markets and technology options, inclu ding prototype designs, for c onsidered products. Chapter 3 of this TSD describes in detail the market and technology assessm ent for microwave oven standby power.

2.2.1 Market Assessment

When initiating a standards rulemaking, DOE devel ops information on the present and past industry structure and market characteristics for the products being studied

. This activity assesses the industry and products , both quantitatively and qualitatively, based on publicly

available information

. F or micr owave ovens , DOE addresse d (1) manufacturer market share and characteristics; (2 ) current regulatory and non regulatory initiatives for improving product efficiency; and (3) trends in product characteristics and retail markets

. This information serves as

re source material throughout the rulemaking.

DOE reviewed product literature and interviewed manufacturers to develop an overall picture of the market for microwave ovens in the United States

. Industry publications and trade journals, government agencies, and trade organizations provided the bulk of the inform ation, including information regarding (1) manufacturers and their market share s , (2) shipments by capacity

, an d (3) market saturation

. The appropriate sections of this TSD , particularly chapter 3,

des cribe the resulting information as DOE used it in the analysis.

DOE used the most reliable and accurate data available at the time of each analysis performed for this rulemaking

. All data are made available for public review

.

2.2.2 Technology Assessment

DOE typicall y uses information relating to current and past technology options and prototype designs to determine what technologies manufacturers use to attain higher performance levels

. In consultation with interested parties

, DOE develops a list of technolog ies

for consideration

. Initially, the list encompass es all th e technologies DOE believes are technologically feasible. DOE developed a list of technologically feasible design options for microwave oven standby power through consultation with manufact urers of components and

systems and from trade publications and technical papers

.

2.3 SCREENING ANALYSIS

As described in section 2. 2 .2 , DOE develops an initial list of efficiency enhancement options from the technologies identified as technologically feasible

. The n DOE , in consultation

with interested parties, reviews the list to determine whether th o se options (1) are technologically feasible; (2) are practicable to manufacture, install, and service; (3) have an adverse impact on product utility or availability; or (4) have adverse impacts on health and safety. In addition, DOE

remove s from the list any technolo gy options for which energy consumption data are lacking as well as any for which the energy consumption c an not be measured adequately by DOE test2-6 procedures

. Chapter 4 of this TSD describes the screening analysis for microwave oven standby

power. In the e ngineering analysis, DOE further considers those efficiency enhancement options

that were not eliminated in the screening analysis

2.4 ENGINEERING ANALYSIS

In the engineering analysis (chapter 5 of this TSD ) , DOE evaluates a range of product efficiency levels and their associated manufacturing costs in order to establish the relationship

between the cost and the efficiency of microwave ovens

. This relationship serves as the basis for

cost/benefit calculations related to individual consumers, manufacturers, and the nation

. C hapter

5 discusses the product classes DOE analyzed, the representative baseline units, the incremental efficiency levels, the methodology DOE used to develop manufacturing costs, the methodology it used to develop the energy co nsumption model, the cost

- efficiency curves, and the effect of efficiency improvements on microwave ovens

.

The purpose of the engineering analysis is to estimate the incremental production costs

associated with increasing product efficiency above the level of the baseline model in each product class. The engineering analysis generally considers technologies not eliminated in the screening analysis, although certain technologies were not analyzed because they offered negligible incremental improvements to efficienc y. DOE considers the remain ing technologies, designated de sign options, in developing cost

- efficiency curves, which subsequently are used for the LCC and PBP analysi s. For each product class , DOE selected efficiency levels and obtained

incremental cost dat a at each level

.

2.4.1 Manufacturing Cost Assessment

The manufacturing cost assessment , which is part of the engineering analysis, estimate d

the manufa cturing costs associated with reducing microwave oven standby power

. This approach involved the disassembly o f units, an alysis of the materials and manufacturing processes, and development of a spreadsheet cost model based on a clear and consistent manufacturing cost assessment methodology

. DOE buil t a detailed cost assessment model that accurately predicts manufacturing cost s . The model reports th os e costs in aggregated form to maintain confidentiality of the data

. DOE obtain ed input from interested parties on the manufacturing cost

estimates and ass umptions to confirm their accuracy.

2.5 MARKUPS ANALYSIS

DOE uses manufacturer

- to

- customer markups to convert the manufacturer selling price estimates from the engineering analysis to customer prices, which are then used in the LCC and PBP analysis and in th e manufacturer impact analysis. Retail prices are necessary for the baseline efficiency level and all other efficie ncy levels under consideration (see chapter 6 of this

TSD )

. To develop markups

, DOE identif ied distribution channels ( i.e. , how the products are distributed from the manufacturer to the consumer )

. After identifying appropriate distribution2-7 channels, DOE utilized econom ic census data from the U.S. Census Bureau and input from the industry to define how products are marked up from the manufacture r to the consumer

.

2.6 ENERGY USE ANALYSIS

The energy use analysis (chapter 7 of this TSD ) assesse s the energy

- saving potential of

various levels of microwave oven standby power

. As part of the energy use analysis

, DOE ma k e s certain engineering assumptions regarding product application, including how the product is operated and under what conditions. DOE also develop ed a test procedure to measure the

efficiencies of microwave oven standby power. On March 9, 2011 , DOE publishe d in the Federal Register a n interim final rule establishing a mic rowave oven standby and off mode test procedure

. 76 FR 12825 . SNOPR s for the t est p rocedure were published on November 23, 2011 (76 FR 72332) and May 16, 2012 ( 77 FR 28805 ). DOE subsequently published a final rule amending the microwave oven standby and off mode test procedure on January 18, 2013

. 78 FR

4015

.

2.7 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

New or amended energy conservation standards for products result in a c hange in operating cost s

— usually a decrease

— and a change in consumer price

— usually an increase. DOE analyzed the net effect of new standards on consumers (chapter 8 of this TSD ) by evaluating the net LCC using the cost

- efficiency relationship derived in th e engineering analysis, as well as the energy costs derived from the energy use analysis . Inputs to the LCC calculation include the installed cost to the consumer (consumer price plus installation cost); operating expenses (energy expenses and maintenance costs); the lifetime of the appliance; and a discount

rate.

Given that installed cost typically increases while operating cost typically decreases

under standards

, there come s a t ime in the life of a product that operates at a higher

- than

- baseline

effici ency when the net operating cost benefit (in dollars) from the time of purchase is equal to

the incremental firs t cost of purchasing the higher

- efficiency product

. The length of time required for a product to reach this cost

- equivalence point is known as t he payback period.

To estimate the LCC and PBP f or microwave oven standby power , DOE had to

determine several input values, including retail prices; electricity prices; discount rate s ; maintenance, service, and installation costs; and product lifetimes. DOE us ed typical values to

reflect conditions in the field for product life, product retail price, and energy consumption. DOE reviewed the Energy Information Administration (EIA)’s energy price data to establish electricity prices for residential consumer s. DOE used EIA’s Annual Energy Outlook 201 2 ( AEO

201 2 ) as the default source of projections of future energy prices for its LCC and PBP analysis

.

To forecast product costs, DOE developed price factors based on learning rates calculated

for manufacturing electric cooking equipment, including microwave ovens. The learning rates2-8 were calculated using the historic al Producer Price Index for electric cooking products

, along

with historic al shipments data that indicate cumulative production of th e products

.

DOE developed discount rates for residential consumers based on estimates of the interest or finance cost to purchase residential appliances. Following financial theory, the finance

cost of rais ing funds can be interpreted as (1) the financial cost of any debt incurred to purchase products, pri mari ly interest charges on debt; or (2) the opportunity cost of any equity used to purchase products, pri mari ly interest earnings on household equity.

DOE considered expected changes to maintenance, repair, and installation costs for the

microwave ovens covered in this rulemaking. Typically, small incremental changes in product efficiency incur no, or only small, changes in repair and maintenance costs c ompared to products having baseline efficiency. P roducts having efficiencies that are significantly greater than the

baseline are more likely to incur increased repair and maintenance costs because such products

are more likely to incorporate technologies that are new to the industry and not widely available.

DOE used information from various literature sources ( e.g. , Appliance M agazine ) and input from manufacturers and other stakeholders to establish average product lifetimes for use in

the LCC and sub sequent analyses.

2.8 SHIPMENTS ANALYSIS

Forecasts of s hipment s are required to calculate the national impacts of standards on energy consumption, NPV, and future manufacturer cash flows. DOE us ed historical data as the

basis for projecting future shipments o f microwave ovens (chapter 9 of this TSD ) . In projecting shipments, DOE accounted for two market segments: (1) new construction, and (2) existing residential buildings ( i.e. , replacing failed products) . To estimate the impacts of standards on product shipments, DOE accounted for the combined effects of changes in purchase price, annual operating cost, and household income on the consumer purchase decision.

2.9 NATIONAL IMPACT ANAL YSIS

The national impact analysis , described in chapte r 10 of this TSD

, assesses the NPV of

total consumer costs and benefits . DOE determined both the NPV and the national energy savings ( NES ) for the efficiency level s established for both product classes of microwave ovens

. To make the analysis more accessib le and transparent to all stakeholders, DOE prepared an NES

s preadsheet m odel in Microsoft Excel to forecast energy savings and the national economic costs and savings resulting from ne w standards. The NES s preadsheet m odel does not use probability

distrib utions for inputs or outputs. To assess the effe ct of input uncertainty on the NES and NPV results, DOE can conduct sensitivity analyses by running scenarios on input variables that are of

interest to stakeholders.2-9 2.9.1 National Energy Savings

T he inputs fo r determining NES are (1) annual energy consumption per unit

, (2) shipments

, (3) produc t stock

, (4) national energy consumption

, and (5) site

- to

- source conversion

factors. DOE calculated the national energy consumption by multiplying the number of units, or stock, of products (by vintage) by the unit energy consumption (also by vintage ). b DOE calculated national annual energy sa vings as the difference between national energy consumption in the base case (without new efficiency standards) and in each higher

- efficiency standards case. The analysis included estimated energy savings by fuel type us ed for generating electricity. DOE

estimate s energy consumption an d savings based o n site energy, then convert s the electricity consumption and savings in to source energy. Cumulative energy saving s are the sum of the

annual NES throughout the forecast period

.

The stock of products depend s on annual shi pments and the lifetime of the pro duct . DOE projected shipments under the base

- case efficiency and for vari ous standards

- case efficiency scenarios and product efficiency trends. DOE determined that projected shipments were lower under the higher efficiency cases than under base

- case effici ency

. The projections indicated that higher installed costs would cause some consumer s to forego produc t purchases. As a result,

DOE used the projection of shipments under each higher efficiency standards case and, in turn, the resulting stock of produc t under th at cas e, to determine the NES. Calculating the NES in this way avoids including savings that result from displaced shipments.

2.9.2 Net Present Value

The inputs for determining NPV are (1) total annual installed cost

, (2) total annual operating cost sa vings

, (3) discount factor

, (4) present value of costs

, and (5) present value of savings. DOE calculated NPV as the difference between the present value of operating cost savings and the present value of in creased total installed costs. It calculated net s avings each year as the difference between total operating cost savings (including savings in electricity, repair,

and maintenance cost s ) and increases in total installed costs ( produc t price). DOE calculated savings over the life of the produc t, accountin g for differences in yearly energy rates, and used a discount factor to discount future costs and savings to the present.

DOE calculated increases in total installed costs as the product of the difference in the

total installed cost between the base case and standards case and the annual shipments in the

standards case. Because costs of the more efficient produc t s purchased in the standards case are higher than those of produc t s bought in the base case, price increases appear as negative values

in the NPV analysis.

DOE expressed operating cost savings as decreases in operating costs associated with the

lower energy consumption of produc t s bought in the standards case compared to the base efficiency case. Total operating cost savings are the product of savings per unit and the number of units of each vintage surviving in a particular year.

b Vintage represents the age of the product

.2-10 2.9.3 Forecasted Efficiencies

Several of the inputs for determi ning NES ( e.g. , annual energy consumption per unit) an d NPV ( e.g. , total annual installed cost and total annual operating cost savings) depend on the efficiency of the produc t. Thus, DOE forecasted efficiencies for the base case and standards

case s

. The forecasted efficiencies specify the annual average shipm ent

- weighted produc t
efficiencies for future years

.

DOE developed product efficiencies for the base case based on product efficiencies in

201 6 . In other words, DOE determined the distribution of product efficiencies currently in the marketplace to develop a shipment

- weighted efficiency for 201 6 . As described in chapter 1 0

, DOE assumed that forecasted efficiencies remain frozen at the 201 6 efficiency level until the end

of the forecast period ( 2045 ).

DOE developed standards

- case forecasts (forecasts of efficiency trends after standards take effect) based on a roll

- up scenario . Under a roll

- up scenario, the efficiencies of products that

operate at performance levels below a prospective standard are assumed to move, or roll up, to

meet the minimum performance level a llowed under the new standard. The distribution of products at efficiency levels above the new minimum standard levels is unaffected ( that is , tho s e products remain at their pre

- standard performance level s)

. The roll

- up scenario dictates how

DOE determine s efficiency distributions in the first y ear a new standard takes effect

. As it did for the base case, DOE assumed that forecasted efficiencies under each standards case remain frozen

at the efficiency level in 201 6 u ntil the end of the forecast period.

2.10 CONSUMER SUB

- GROUP ANALYSIS

The consumer sub

- group analysis evaluates economic impacts of standards on identifiable groups of consumer s who might be disproportionately affected by new or amended energy efficiency stan dards

. DOE evaluate s the effects on particular sub

- groups of consumer s in part by using the LCC s preadsheet m odel to analyze the LCC and PBP for th o se particular

c onsu mers

.

In the case of microwave ovens, DOE analyzed two sub

- groups: (1) low

- income

households

, and (2) households occupied by senior citizens. Chapter 11 of this TSD describes the

sub

- group analysis for consumers of microwave ovens.

2.11 MANUFACTURER IMPACT ANALYSIS

The manufacturer impact analysis (MIA) assesses the impacts of new energy ef ficiency

standards on manufacturers of microwave ovens

. Potential impacts include financial effects, both quantitative and qualitative

, that might l ead to changes in the manufacturing practices for the products

. DOE identif ies th o se potential impacts throu gh interviews with manufacturers and

other interested parties

.2-11 As described in chapter 12 of this TSD

, DOE conduct ed the MIA for microwave standby power consumption in three phases, and further tailor ed the analytical framework based on

stakeholder comments

. In Phase I, DOE created a profile of the industry and conducted a preliminary MIA to identify important issu es that require d consideration

. In Phase II, an industry cash flow model and an interview q uestionnaire were prepared to guide subsequent discussions

. In Phase III, manufacturers were interviewed, and the impacts of standards were assessed both quantitatively and qualitatively

. DOE assessed i ndustry and sub

- group cash flow and NPV through use of the Government Regulatory Impact Model

. E ffe cts on competition, manufacturing capacity, employment, and cumulative regulatory burden were assessed based on

feedback and discussions during manufacturer interview s

.

2.12 EMPLOYMENT IMPACT AN ALYSIS

Energy conserv ation standards can affe ct employment both directly and indirectly. Direct employment effe cts are changes

, resulting from the imposition of new standards

, in the number of employees at the plants that produce covered products and at affiliated dis tribution and service companies. DOE evaluate s direct employment impacts in the MIA

. Indirect employment impacts may result if the imposition of standards causes expenditures to shift between goods (the substitution effect) and /or create changes in income and overall expenditure levels (the income effect)

.

As discussed in chapter 13 of this TSD

, DOE investigate d indirect employment impacts

for microwave oven standby power standards using the Impact of Sector Energ y Technologies (ImSET) model developed by Pacific Northwes t National Laboratory (PNNL)

. PNNL developed

the ImSET model for DOE ’s Office of P lanning, Budget, and Analysis. The model estimates the employment and income effects of energy

- saving technologies in buildings, industry, and transportation

. In comparison with simple economic multiplier approaches, ImSET allows for more complete and automated analysis of the economic effe cts of energy conservation

investments.

2.13 UTILITY IMPACT ANALY SIS

The utility impact analysis (chapter 14 of this TSD ) asses s es the effect s of higher efficiency standards on electr ic utility industries. To carry out this analysis, DOE use d a variant

of the EIA’s National Energy Modeling System (NEMS). NEMS is a large, multi sectoral, partial

- equilibrium model of the U .S. energy sector that EIA has developed throughout the past decade, primarily to help in preparing the AEO

. NEMS , which is a vailable in the public domain, produces a widely recognized baseline energy forecast for the United States , currently through

20 40 . T yp ical NEMS outputs include forecasts of electricity sales, electricity price, and avoided electric generating capacity.2-12 DOE uses a variant of NEMS known as NEMS

- BT to provide key inputs to its analysis. c

2.14 EMISSIONS ANALYSIS DOE conduct ed the utility impact analysis as a scen ario departing from the latest AEO reference

case. In other words, DOE model ed the energy savings from amended energy conservation standards using NEMS

- BT to generate forecasts that deviate from the AEO reference case.

In the emissions a nalysis, DOE estimate s the reduction in power sector emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ) and mercury (Hg) from potential energy conservation standards for the considered products. In addition, DOE will estimate emissions impacts in production activities (extracting, processing, and transporting fuels) that provide the energy inputs to power plants. These are referred to as “upstream” emissions. Together, these emissions account for the full

- fuel

- cycle (FFC). In accordance with DOE’s FFC Statement of Policy (76 FR 51282 (Aug. 18, 2011)), the FFC analysis includes impacts on emissions of me thane and nitrous oxide, both of which are recognized as greenhouse

gases.

DOE conduct ed the emissions analysis using emissions factors derived from data in

EIA’s Annual Energy Outlook 2012 , supplemen ted by data from other sources. EIA prepares the

Ann u al Energy Outlook using NEMS. Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions.

SO 2 emissions from affected electric generating units (EGUs) are subject to nationwide and regional emiss ions cap and trading programs. Title IV of the Clean Air Act sets an annual emissions cap on SO 2 for affected EGUs in the 48 contiguous states and the District of Columbia (D.C.). SO 2 emissions from 28 eastern states and D.C. were also limited under the Cl ean Air Interstate Rule (CAIR), which created an allowance

- based trading program that operates along with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR was remanded to EPA by the U.S. Court of Appeals for the District of Co lumbia Circuit (D.C. Circuit), but it remains in effect. See North Carolina v. EPA , 550 F.3d 1176 (D.C. Cir. 2008);

North Carolina v. EPA , 531 F.3d 896 (D.C. Cir. 2008). On July 6, 2011 EPA issued a replacement for CAIR, the Cross

- State Air Pollution Rule (CSAPR; also known as the Transport

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration

documentation. A useful summary is National Energy Modeling System: An Overview 2000 , DOE/EIA

0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model without any modification to code or data. Because DOE's analysis entails minor code modifications, and the model is run under various policy scenarios that are variations on EIA assumptions, DOE refers to t he model as

NEMS

- BT (BT stands for DOE’s Building Technologies Program, under whose aegis this work is performed).

NEMS

- BT previously was called NEMS

- BRS.2-13 Rule). 76 FR 48208 (August 8, 2011). The AEO 2012 NEMS assumes the implementation of

the CSAPR. d

The attainment of emissions caps is typically flexible among EGUs and is enforced through the use of emissions allowanc es and tradable permits. Under existing EPA regulations, any excess SO 2 emissions allowances resulting from the lower electricity demand caused by the adoption of an efficiency standard could be used to permit offsetting increases in SO 2 emissions by any r egulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions covered by the existing cap

- and

- trade system, but it concluded that no reductions in power sector emissions would occur for SO 2 as a result of standards.

Beginning in 2015, however, SO 2 emissions will fall as a result of the Mercury and Air Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 2011. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for HCl as a surrogate for acid gas hazardous air pollutants (HAP), and also established a standard for

SO 2 (a non

- HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. The

same controls are used to reduce HAP and non

- HAP acid gas; thus, SO 2 emissions will be reduced as a result of the control technologies installed on coal

- fired power plants to comply with the MATS requirements for acid gas. AEO 2012 assumes that, in order to continue operati ng, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce

SO 2 emissions. Under the MATS, NEMS shows a reduction in SO 2 emissions when electricity demand decreases (e.g., as a result of energy efficiency standards). Emissions will be far below the cap that would be established by CSAPR, so it is unlikely that excess SO 2 emissions allowances resulting from the lower electricity deman d would be needed or used to permit offsetting increases in SO 2 emissions by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce SO 2 emissions in 2015 and beyond.

CSAPR established a cap on NO X emissions in eastern States and the District of Columbia. Energy conservation standards are expected to have little or no physical effect on these emissions in those States covered by CSAPR because excess NO

x emissions allowances resulting from the lower electricity demand could be used to permit offsetting increases in NO

x emissions. However, standards would be expected to reduce NO

x emissions in the States not affected by CSAPR. Therefore, DOE estimates NO X emissions reductions from potential

standards in the States where emissions ar e not capped.

d On December 30, 2011, the D.C. Circuit stayed the new rules while a panel of judges reviews them, and told EPA

to continue administering CAIR (see EME Homer City Generation v. EPA , No. 11

- 1302, Slip Op. at *2 (D.C. Cir. Dec. 30, 2011)). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City
Generation, LP v. EPA

, No. 11

- 1302

, 2012 WL 3570721 at *24 (D.C. Cir. Aug. 21, 2012). The court again ordered EPA to continue administering CAIR. AEO 2012 had been finalized prior to this decision, however. DOE understands that CAIR and CSAPR are similar with respect to their ef fect on emissions impacts of energy efficiency

standards.2-14 The MATS limit mercury emissions from power plants, but they do not include emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. For this rulemaking, DOE estimated mercury emissions reductions u sing emissions factors based on

AEO 2012 , which incorporates the MATS.

With regard to the impact of standards on particulate matter (PM) , t he great majority of

ambient PM associated with power plants is in the form of secondary sulfates, which are produced at a significant distance from power plants by complex atmospheric chemical reactions that often involve the gaseous emissions of power plants, mainly SO 2 and NO

x . The monetary

benefits that DOE estima tes for reductions in SO 2 and NO

x emissions resulting from standards are in fact primarily related to the health benefits of reduced indirect PM. Power plants may also

emit particulates from the smoke stack. These dir ect, or primary, PM emissions can be di fficult to quantify, and DOE is not able to quantify them at this time.

2.15 MONETIZING CARBON DI OXIDE AND OTHER EMIS SIONS

REDUCTIONS

DOE estimate s the monetary benefits likely to result from the reduced emissions of CO 2 and NO X that are expected to result from each of the standard levels considered

.

In order to estimate the monetary value of benefits resulting from reduced CO 2 emissions, DOE use d the most current values for the social cost of carbon (SCC) developed and/or agreed to by an interagency work group . The SCC is intended to serve as a monetary measure of the incremental damage resulting from greenhouse gas (GHG) emissions, including, but not limited to, net loss of agricultural productivity, human health effects, property damage

from sea level rise, and changes in ecosystem services . W ith full regard for the limits of both quantification and monetization, the SCC can be used to provide estimates of the social benefits

of reductions in GHG emissions.

At the time of this analysis , the most recen t interagency estimates of the potential global benefits resulting from reduced CO 2 emissions in 20 1 6 , expressed in 2011 $, were $ 12.6 , $ 4 1 .1

, $ 63.2 , and $ 119.1 per metric ton avoided

. e T ho se values increase in real terms over time. Additionally, the intera gency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although DOE give s preference to consideration of the global benefits of reduced CO 2 emissions. To calculate a present value of the stream of monetary values, DOE discount ed each of the four SCC values using the discount rate that was used to obtain the SCC value in that case.

e Technical Update for Social Cost of Carbon for Regulatory Impact Analys is Under Executive Order 12866

. Interagency Working Group on Social Cost of Carbon, United States Government , April 2013.2-15 DOE recognizes that scientific and economic knowledge continues to evolve rapidly as to
the contribution of CO 2 and other GHG to changes in the future global climate and the potential resulting damages to the world economy

. Thus, these values are subject to change.

DOE also estimate d the potential monetary benefit of reduced NO X emissions resulting

from the standard levels it consider ed for microwave oven standby power

. A vailable estimates suggest a wide range of monetary values for NO X emissions , from $ 45 5 to $4, 6 79 per ton ( in

2011 $). f In accordance with guidance f rom the U.S. Office of Management and Budget (OMB)

, DOE conduct ed two calculations of the monetary benefits derived based on each of the economic

values for NO X , one using a real discount rate of 3 percent and another using a real discount rate of 7 percent

. g

DOE is investigating appropriate valuation of Hg and SO 2 emissions. DOE has not

monetize d estimates of SO 2 and Hg reduction in this rulemaking

.

2.16 REGULATORY IMPACT AN ALYSIS

Pursuant to Executive Order 12866 ( “Regulatory Planning and Review,” October 4,

1993

. 58 F R 51735 )

, DOE prepare d a regula tory impact analysis (RIA), which was subject to review under the Executive Order by the Office of Information and Regulatory Affairs at the

Of fice of Management and Budget. The RIA , described in chapter 17 of this TSD, evalu ated the ability of non

- regulatory alternatives to standards to achieve significant energy savings at a reasonable cost, and compared the effectiveness of each to the effectiveness of the new

standards.

DOE recognizes that voluntary or other non

- regulatory efforts by manufacturers, utilities, and other interested parties can result in substantial improvements to energy efficiency or

red uctions in energy consumption. DOE consider ed the likely effects of non

- regulatory initiatives on product energy u se, consumer utility, and LCC. Although DOE base d its

assessment on the documented effe cts of similar initiatives to date, it also consider ed information regarding the effects current initiative s might have in the future.

f F or additional information , refer to U.S. Office of Management and Budget (OMB)

– Office of Information and

Regulatory Affairs

. 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded
Mandates on State, Local, and Tribal Enti ties . Washington, D.C

.
g OMB. Circular A

- 4: Regulatory Analysis. September 17, 2003 . Please see:

www.whitehouse.gov/omb/circulars_a004_a

- 4

. ( A ccessed May 201 3 .)3

- i CHAPTER 3

. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CO NTENTS

3.1 INTRODUCTION

................................ ................................ ................................ ........... 3

- 1

3.2 PRODUCT DEFINITIONS

................................ ................................ ............................. 3

- 1

3. 3 PRODUCT CLASSES

................................ ................................ ................................ ..... 3

- 1

3.4 PRODUCT TEST PROCEDU RES

................................ ................................ ................. 3

- 4

3.5 MANUFACTURER TRADE G ROUPS

................................ ................................ ......... 3

- 8

3.6 MANUFACTURER INFORMA TION

................................ ................................ ........... 3

- 9

3. 6.1 Manufacturers and Market Shares

................................ ................................ ................... 3

- 9

3.6.2 Mergers and Acquisitions

................................ ................................ .............................. 3

- 10

3.6.3 Small Business Impacts

................................ ................................ ................................ . 3

- 12

3.6.4 Distribution Channels

................................ ................................ ................................ .... 3

- 12

3.7 REGULATORY PROGRAMS

................................ ................................ ..................... 3

- 13

3.7.1 Federal Energy Conservation Standards

................................ ................................ ........ 3

- 13

3.7.2 Energy Independence and Security Act of 2007

................................ ............................ 3

- 14

3.7.3 Foreign Standby Power Regulatory Program s

................................ ............................... 3

- 15

3.8 VOLUNTARY PROGRAMS

................................ ................................ ........................ 3

- 16

3.9 HISTORICAL SHIPMENTS

................................ ................................ ......................... 3

- 17

3.9.1 New Home Starts

................................ ................................ ................................ ........... 3

- 17

3.9.2 Unit Shipments

................................ ................................ ................................ ............... 3

- 18

3.9.3 Value of Shipments

................................ ................................ ................................ ........ 3

- 19

3.9.4 Imports and Exports

................................ ................................ ................................ ....... 3

- 21

3.10 MARKET SATURATION

................................ ................................ ............................ 3

- 22

3.11 PRODUCT RETAIL PRICE S

................................ ................................ ....................... 3

- 23

3.12 INDUSTRY COST STRUCT URE

................................ ................................ ................ 3

- 23

3.13 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES

.......................... 3

- 27

3.14 TECHNOLOGY ASSESSMEN T

................................ ................................ .................. 3

- 30

3.14 .1 Product Operations and Components

................................ ................................ ............. 3

- 30

3.14.2 Technology Options

................................ ................................ ................................ ....... 3

- 31

3.14.3 Energy Efficiency

................................ ................................ ................................ .......... 3

- 35

LIST OF TABLES

Table 3.3.1 Microwave Oven Product Classes

................................ ................................ ............ 3

- 4 Table 3.6.1 Major and Other Microwave Oven Manufacturers

................................ .................. 3

- 9 Table 3.6.2 Core Appliance Manufacturers

................................ ................................ .............. 3

- 10 Table 3.7.1 EU Ecodesign Requireme nts for Standby and Off Mode

................................ ...... 3

- 16

Table 3.8.1 FEMP Recommendations for Microwave Ovens

................................ .................. 3

- 17 Table 3.9.1 Industry Shipments of Microwave Ovens (Domestic and Import in Thousands of Units)

................................ ................................ ........................... 3

- 19 Table 3.9.2 Household Appliance Manufa cturing Statistics by Year

................................ ....... 3

- 203

- ii Table 3.9.3 Disposition of Previous Appliance ( Percentage )

................................ .................. 3

- 20 Table 3.9.4 Household Cooking Ap pliance Manufacturing Statistics by Year

........................ 3

- 21

Table 3.9.5 2008

– 2009 Imports of Microwave Ovens

................................ ............................. 3

- 22

Table 3.9.6 2008

– 2009 Exports of Microwave Ovens

................................ ............................ 3

- 2 2 Table 3.10.1 Appliance Saturation (Number in Millions) and Percentage of U.S.

Households with Product

................................ ................................ ................... 3

- 23 Table 3.11.1 Residential Appliance Retail Prices

................................ ................................ ..... 3

- 23 Table 3.12.1 Household Appliance Industry Employment and Earnings

................................ . 3

- 24

Table 3.12.2 Household C ooking Appliance Industry Employment and Earnings

.................. 3

- 25 Table 3.12.3 Household Appliance Industry Census Data

................................ ....................... 3

- 26 Table 3.12.4 Household Cooking Appliance Industry Census Data

................................ ........ 3

- 27 Table 3.13.1 Household Appliance Industry Cens us Data

................................ ....................... 3

- 28 Table 3.13.2 Household Cooking Appliance Industry Census Data

................................ ........ 3

- 29 Table 3.13.3 Full Production Capacity Utilization Rates

................................ ......................... 3

- 30 Table 3.14.1 Technology Options fo r Microwave Oven

– Standby Power

.............................. 3

- 31

LIST OF FIGURE S

Figure 3.6

. 1 2008 Market Shares for the Domestic Microwave Oven Market

......................... 3

- 10 Figure 3.6

. 2 2008 Core Appliance Market Shares

................................ ................................ .... 3

- 11 Figure 3.9

. 1 New Privately Owned Single

- Family and Multi

- Family Housing Unit Starts

in the United States from 1998

– 2009

................................ ............................... 3

- 18 Figure 3.14

. 1 AHAM and CBEEDAC Microwave Oven Standby Power versus Rated Output Power ,

................................ ................................ ................................ ..... 3

- 36 Figure 3.14

. 2 Microwave Oven Standby Pow er Consumption from the FEMP Database

....... 3

- 373-1 CHAPTER 3. MARKET AND TECHNOLOG Y ASSESSMENT

3.1 INTRODUCTION

This chapter provides a profile of the residential microwave ovens in the United States

. The U.S. Department of Energy (DOE) developed the market and technology assessment presented in this chapter primarily from publicly available information

. This assessment is helpful in identifying the major manufacturers and their product characteristics, which form the basis for the engineering and the life

- cycle cost (LCC) analyses

. Present and past industry structure and industry financial information h elp DOE in the process of conducting the manufacturer impact analysis.

3.2 PRODUCT DEFINITIONS

DOE defines “ microwave oven ” under the Energy Policy and Conservation Act (EPCA)

of 1975 , Pub. L. 94

- 163, (42 United States Code ( U.S.C. ) 6291

– 6309) as “ a class of k itchen ranges and ovens comprised of household cooking appliance s consisting of a compartment designed to cook or heat food by means of microwave energy

, including microwave ovens with or without thermal elements designed for surface browning of food and convection microwave

ovens

. ” (10 Code of Federal Regulations ( CFR ) 430.2)

3.3 PRODUCT CLASSES

DOE separate d microwave ovens into product classes

. When evaluating and establishing energy conservation standard s, DOE divides covered products into different produ ct class es using the following criteria: (1) type of energy used, and (2) capacity or other performance

related features such as those that provide utility to the consumer or others deemed appropriate by the Secretary that would justify the establishment o f a separate energy conservation standard

. (42 U.S.C. 6295 (q))

In the notice of proposed rulemaking (NOPR) published on October 17, 2008 (hereafter the “October 2008 NOPR”) , DOE proposed a single product class for microwave ovens. DOE

stated t his produc t class encompass es microwave ovens with and without browning elements, but does not include microwave ovens that incorporate convection systems. 73 FR 62034, 62048.

DOE reassessed what products would be considered microwave ovens under the regulatory de finition, and whether multiple product classes would be appropriate. As discussed

in the microwave oven test procedure final rule which was published in the Federal Register on March 9, 2011 (hereafter, the “ March 2011 TP Interim Final Rule ”) , DOE stated that microwave

ovens with or without thermal elements would be considered covered products under the3-2 definition provided in 10 CFR 430.2. In addition, DOE stated that combination microwave ovens ( i.e. , microwave ovens that incorporate convecti on features and any other means of cooking in a single compartment , which DOE later designated as convection microwave ovens for clarity ) would also be considered covered products under the regulatory definition in 10 CFR 430.2 because they are capable of cooking or heating food by means of microwave energy. DOE stated, based on its preliminary analysis, it believes that the general standby mode and off mode operation for convection microwave ovens does not differ from that of microwave

- only units and

micro wave ovens with thermal elements only. The standby mode operation for convection microwave ovens, as with other types of microwave ovens, consists of an energized display with a clock. As a result, DOE amended the microwave oven test procedure to require t hat the same standby mode and off mode testing methods be used for all microwave ovens. 76 FR 12825,

12828

– 30 (Mar

. 9, 2011)

.

In order to determine whether specific types of microwave ovens should be separated into different product classes, DOE investig ated whether there are any performance related features that would justify the establishment of a separate energy conservation standard. As discussed in the October 2008 NOPR, DOE tested a sample of 32 countertop microwave

- only units and measured standby p ower ranging from 1.2 watts (W) to 5.8 W. 73 FR 62034, 62042 (Oct. 17, 2008). None of these units was capable of operation in off mode, nor is DOE aware of any other current microwave ovens capable of such operation. DOE noted that standby power consumption

for microwave

- only units largely depended on the presence of a cooking sensor, the display technology, the power supply and control board, and implementation of a power

- down feature. With regards to display technologies, DOE noted that microwav e

- only units incorporated Light Emitting Diode (LED) displays, Liquid Crystal Displays (LCDs), and Vacuum Fluorescent Displays (VFDs).

For the supplemental NOPR (SNOPR) published in the Federal Register on February 14, 2012 (77 FR 8526) (hereafter the “ Fe bruary 2012 SNOPR ” )

, DOE conducted a survey of over

the

- range microwave

- only units available on the U.S. market. DOE determined that the display technologies used are similar to those used in countertop microwave

- only units ( i.e. , LED displays, LCDs, and V FDs). DOE’s market survey revealed that multiple over

- the

- range microwave

- only units incorporate both LED displays and LCDs. DOE research suggests that LED displays and LCDs in both countertop and over

- the

- range microwave

- only products offer acceptable con sumer utility features, including brightness, viewing angle, and ability to display complex characters. DOE also conducted in

- store standby mode testing on a limited sample of

over

- the

- range microwave

- only units which showed similar standby power consumpti on as countertop microwave

- only units. For these reasons, DOE tentatively conclude d that over

- the

range microwave

- only units would not warrant a separate product class according to the criteria put forth by EPCA. (42 U.S.C. 6295(q)) DOE understands that ov er

- the

- range microwave

- only units may have additional components that are energized during active mode operation ( i.e.

, exhaust fan motors). However, DOE’s testing showed that the presence of such features did not increase the standby power consumption to warrant establishing a separate product class. 77 FR 8526, 8536 (Feb. 14, 2012).3-3 For the February 2012 SNOPR, DOE also conducted standby power testing on a sample of 13 representative convection microwave ovens, including 5 countertop convection microwave ovens, 6 over

- the

- range convection microwave ovens, and 2 built

- in convection microwave ovens. DOE’s testing showed that the countertop convection microwave ovens use similar display technologies as countertop microwave

- only units ( i.e. , LED disp lays, LCDs, and VFDs), and had standby power consumption ranging from 1.2 W to 4.7 W, which is similar to the standby power consumption for countertop microwave

- only units. As a result, DOE tentatively

concluded in the February 2012 SNOPR that countertop c onvection microwave ovens would not warrant a product class separate from microwave

- only ovens. Id

.

DOE’s testing of built

- in and over

- the

- range convection microwave ovens showed that the standby power consumption for these products ranged from 4.1 W to 8.8 W, which is higher than the standby power consumption for other microwave oven product types ( i.e. , countertop
microwave

- only, over

- the

- range microwave

- only, and countertop convection microwave ovens).

DOE’s reve rse

- engineering analysis suggested that the additional features in built

- in and over

the

- range convection microwave ovens required to handle the thermal loads associated with their installation and to provide consumer utility , such as additional exhaust fa n motors, convection fan motors and heaters, and additional lights, require a significant number of additional relays on the control board, and thus require a larger power supply for the control of such relays. While the relays themselves do not consume po wer in standby mode , they increase the total power supply requirements of the control board and thus increase the standby losses of the power supply. As a result, DOE tentatively determined in the February 2012 SNOPR that a separate product class

should be established for built

- in and over

- the

- range convection microwave ovens. DOE recognized that built

- in and over

- the

- range microwave

- only units may similarly require some additional relays for exhaust fans and lights, and that countertop convection microwave ovens would require some additional relays for convection fans and heaters. However, DOE’s product testing and reverse

- engineering analyses indicated that these product types use similar

- sized power supplies as those found in countertop microwave

- only uni ts, and as a result would not warrant a separate product class from countertop microwave

- only units

. Id

.

DOE notes that over

- the

- range microwave ovens must operate under conditions that are harsher than countertop microwave ovens are typically exposed to, in terms of elevated temperatures and humidity levels. For the components that are associated with standby mode and off mode energy use, these conditions have the most effect on the displays. Under long

- term exposure, displays may degrade in illuminance o ver time, resulting in a consumer’s perception of reduced brightness. As discussed further in chapter 5 of this technical support document (TSD), DOE conducted accelerated lifetime testing of different microwave oven display types by subjecting a limited s ample of microwave ovens to high temperatures and humidity levels for an extended period of operation in standby mode. The results of this testing demonstrated that the illuminance of each display tended to decrease over time to varying degrees, but did no t reveal any correlation between display type and rate of illuminance reduction. In addition, DOE observed in its test sample a unit with an LED display that exhibited illuminance, a significant element of consumer utility, that was comparable to that of t he VFD on another unit. Based on this lifetime testing, and the existence of multiple over

- the

- range microwave oven models on the market with each type of display technology, DOE does not have information that would suggest3-4 over

- the

- range microwave ovens w ould require certain display technologies ( i.e., VFD) that have inherently higher power consumption than other display types that provide similar consumer utility. In addition, DOE is not aware of, nor did commenters provide information on, different

stand by power consumption that would be associated with controls that have the same functionality but different material selection.

As discussed in chapter 5 of this TSD , DOE also examined more closely in its final rule analysis whether combinations of design options are available that would allow over

- the

- range microwave

- only ovens to meet the same standby power levels as countertop microwave ovens. From its analysis, DOE concluded that design pathways exist for all over

- the

- range microwave

only ovens with LED displays and LCDs to meet a 1.0 W standard, so that none of these would

warrant classification separate from countertop microwave

- only ovens on the basis of energy use characteristics in standby mode. DOE further concludes that the range of these display technologies allows manufacturers to design over

- the

- range products with comparable consumer utility and durability of the display as for over

- the

- range microwave oven s with VFDs.

Therefore, DOE based its analysis on the following two product classes :

Ta ble 3.3

. 1 Microwave Oven Product Classes

Product Class

1. Microwave

- Only Ovens and Countertop Convection Microwave Ovens

2. Built

- in and Over

- the

- Range Convection Microwave Ovens

DOE determined that separate product classes for the purposes of setting energy conservation standards addressing standby mode and off mode energy use are warranted on the basis of different standby power performance. DOE did not evaluate whether the same product class distinction would also be appropriate for any active mode energy use standards because DOE eliminated the regulatory provisions establishing the cooking efficiency test procedure for

microwave ovens in a final rule published in the Federal Re gister on July 22, 2010 (hereafter, the “July 2010 TP Final Rule”)

. 75 FR 42579

. If DOE adopts amendments to the microwave oven test procedure to include provisions for measuring active mode cooking efficiency, DOE may reevaluate these product classes as p art of a future microwave oven energy conservation standards rulemaking. At that time, DOE may consider dividing countertop convection

microwave ovens and over

- the

- range/built

- in microwave

- only units into separate product classes to account for the energy performance of heating components other than the microwave portion.

3.4 PRODUCT TEST PROCEDU RES

DOE originally established t est procedures for microwave ovens to determine energy efficiency and annual energy use as the basis for representation and determinati on of compliance with energy conservation standards

. DOE established test procedures for microwave ovens through the rulemaking process

.3-5 DOE’s test procedures for microwave ovens initially appeared at appendix I to subpart B

of Title 10 of the CFR. Th ose test procedure s w ere part of a May 10, 1978 , final rule that first

established test procedures for conventional ranges, cooktops, and ovens (including microwave ovens). 43 FR 20108. DOE later revised its test procedures for cooking products to measure the ir efficiency and energy use more accurately

, publishing a final rule on October 3, 1997

. 62 FR

51976

. The revised microwave oven test procedure incorporated portions of the International

El ectrotechnical Commission (IEC) Standard 705

- 1998 and Amendment 2

- 1993 (IEC Standard 705), Methods for Measuring the Performance of Microwave Ovens for Households and Similar Purposes

. It measure d microwave oven cooking efficiency, but did not address energy use in the standby mode or off modes. Id.

Section 310 of the Energy Independence and Security Act of 200 7 ( EISA 2007 ) (Pub. L.

No. 110

- 140) amends Section 325 of EPCA to require that that the test procedures for microwave ovens be amended to include measurement of standby mode and off mode power , taking into consideration the most current version of IEC Standard 62301 Household electrical appliances

– Measurement of standby power (IEC Standard 62301) and IEC S tandard 62087 Methods of measurement for the power consumption of audio, video and relat ed equipment

. a If technically infeasible, DOE must prescribe a separate standby mode and off mode energy use test procedure

. EPCA, as amended by the EISA 2007 , also states that the final rule for this test procedure shall be published no later than March 31, 2011. ( 42 U.S.C. 6295(gg))

DOE published a test procedure NOPR on October 17, 2008 (hereafter referred to as the

“ October 2008 TP NOPR ” ), in which it proposed to incorporat e provisions from IEC Standard

62301 First Edition into the DOE microwave o ven test procedure, as well as language to clarify application of these provisions for measuring standby mode and off mode power in microwave ovens. The October 2008 TP NOPR also proposed correcting a technical error in the calculation

of microwave test co oking energy output. 73 FR 62134 (Oct. 17, 2008)

. DOE held a public meeting on the proposed rulemaking on November 14 , 2008 . DOE received comments from interested parties both in written responses to the October 2008 NOPR and at the November

2008 Public Meeting.

After considering comments from interested parties and additional information, DOE

issued a supplemental NOPR ( SNOPR ) on July 22, 2010 (hereafter referred to as the July 2010 TP SNOPR) for the test procedure for measuring microwave oven standby mode and off mode power consumption. 75 FR 42612 (Jul

. 22, 2010). In that SNOPR, DOE proposed adopting definitions of modes based on relevant provisions from IEC Standard 62301 Second Edition, Committee Draft for Vote (IEC Standard 62301 CDV), as well as language to clarify application of those provisions for measuring microwave oven standby mode and off mode power consumption. Id

.

In response to the October 2008 TP NOPR, DOE received comments from interested

a IEC 62087 do es not cover any products for this rulemaking, and therefore was not considered.3-6 parties regarding the accuracy and repeatabi lity of the existing DOE microwave oven test procedure for measuring cooking efficiency . Because of issues DOE identified with using its existing microwave oven test procedure, including the large test

- to

- test variation in cooking efficiency measurements, and because DOE is unaware of any test procedures that have been developed that address the concerns with the DOE microwave oven cooking efficiency test procedure raised by these interested parties, DOE published the July 2010 TP Final Rule repealing the p rovisions in the existing microwave oven test procedure relating to the measurement of cooking efficiency and energy factor (EF). 75 FR 42579 (Jul

. 22, 2010)

. Therefore, the requirement to integrate energy consumption in standby mode and off mode into

an o verall energy descriptor does not apply. DOE also published a notice in the Federal Register on July 22, 2010, announcing a public meeting to consider developing a new test procedure for active mode energy consumption of microwave ovens, and DOE will consi der the statutory requirement to integrate the test procedures for standby and off mode as any active mode test procedures are developed. 75 FR 42611.

DOE held a public meeting on September 16, 2010, to receive oral comments on the July 2010 TP SNOPR, and also sought and received written comments. DOE also invited inputs on microwave active mode test procedures for a potential new test procedure rulemaking

. After consideration of these comments, DOE published the March 2011 TP Interim Final Rule addressing microwave oven standby mode and off mode power. 76 FR 12825 ( Mar

. 9, 2011 )

. In that final rule, DOE adopted the provisions for the measurement of standby mode and off mode power use for both products that were pro posed in the July 2010 TP SNOPR. In addition, DOE also adopted provisions to define the test duration for cases in which the measured power is not stable ( i.e. , varies over a cycle), recognizing that the power consumption of microwave oven displays can vary based on the displayed clock t ime

. 76 FR 12825, 12837

– 40 (Mar. 9, 2011)

. DOE provided a 180

- day comment period on the March 2011 TP Interim Final Rule, during which it received several comments on potential improvements to the microwave oven test procedure recently adopted.

Based upo n the public comment, however, DOE decided to further analyze the update d

version of IEC Standard 62301 , Edition 2.0 2011

- 01 (IEC Standard 62301 (Second Edition)), for the microwave oven test procedure. DOE reviewed this latest version of the IEC standard, which was issued on January 27, 2011, and believes that it improves some measurements of standby mode and off mode energy use. Accordingly, DOE published a second test procedure SNOPR on November 23, 2011 (hereafter referred to as the November 2011 TP SNO PR), proposing to incorporate certain provisions of IEC Standard 62301 (Second Edition), along with clarifying language, into the DOE test procedures for microwave ovens adopted in the March 2011 TP Interim Final Rule. 76 FR 72331 (Nov. 23, 2011).

In the course of reviewing comments on the November 2011 TP SNOPR, DOE determined that an additional test procedure SNOPR would be necessary before moving to a final rule. DOE subsequently published the additional test procedure SNOPR on May 16, 2012 (hereafter the “ May 2012 TP SNOPR ” ), to address comments received on the November 2011 TP SNOPR regarding coverage of additional microwave oven product types in the DOE test procedure, and in particular products combining a microwave oven with other appliance3-7 functio nality. 77 FR 28805. Comments on this topic and other topics received in response to both the November 2011 TP SNOPR and the May 2012 TP SNOPR were addressed in a final rule published January 18, 2013 (hereafter referred to as the January 201 3 TP Final Rul e. 78 FR

4015.

The January 201 3 TP Final Rule amended the microwave oven test procedure to incorporate by reference certain provisions of IEC Standard 62301 (Second Edition) for the measurement of standby mode and off mode energy use. DOE also determined that microwave

ovens combined wit h other appliance functionality are covered under the definition of “microwave oven” at 10 CFR 430.2, but due to a lack of data and information, did not adopt provisions in the microwave oven test procedure to measure the standby mode and off mode

energy u se of the microwave component. 78 FR 4015, 4017

– 18 (Jan. 18, 2013)

.

In the November 2011 TP SNOPR, DOE examined IEC Standard 62301 (Second Edition), and determined that it improves the power measurement of the First Edition by adding specifications for t he power supply and power

- measuring instruments. DOE also examined the new testing methodology provided in the Second Edition under different scenarios of power consumption stability, and determined that, although in some cases the testing burden and compl exity would be increased as compared to IEC Standard 62301 (First Edition), the added burden was outweighed in most cases by the improved accuracy and representativeness of the resulting power consumption measurement. In the narrow case of microwave ovens with power consumption that varies as a function of the time displayed, DOE concluded that the application of the test methodology from IEC Standard 62301 (Second Edition) would cause manufacturers to incur significant burden that would not be warranted by any potential improved accuracy of the test method. 76 FR 72332, 72339

– 42 (Nov. 23, 2011). For those products only, DOE maintained the use of IEC Standard 62301 (First Edition) in the January 201 3 TP Final Rule for measuring standby mode power, including methodology in which the clock display is set to 3:23 prior to a 10

- minute stabilization period and a 10

- minute measurement period. Standby mode and off mode power consumption for all other microwave ovens is measured according to the methodology of IEC St andard 62301 (Second Edition). 78 FR 4015, 4019

– 21 (Jan. 18, 2013)

.

Interested parties commented that they support incorporation by reference of IEC Standard 62301 (Second Edition) in the DOE microwave oven test procedure, but stated that

DOE cannot dete rmine appropriate standard levels in this rulemaking without testing based on the final test procedure to be used to determine compliance. DOE reviewed its testing that it had conducted in support of various stages of the microwave oven test procedure rule making, and

determined that there were six microwave oven models that had been tested at different times according to both the First and Second Editions of IEC Standard 62301. In order to supplement this sample, DOE additionally tested eight more microwave ovens as part of its final rule analysis so that a comparison could be made between the standby power consumption

measurements obtained with the First Edition and Second Edition for various installation configurations, display types, and manufacturers/bra nds. Table 3.4

. 1 presents the results of the comparison between testing to the First Edition and the Second Edition, which showed results for the two methodologies va rying by no more than 5.5 percent, which DOE concludes demonstrates close enough agreement that manufacturers could apply the same design option3-8 pathways to achieve the varying standby power levels when measuring according to IEC Standard 62301 (Second Edi tion) as DOE’s analysis identified based on testing to IEC Standard 62301 (First Edition).

Table 3.4

. 1 Comparison of Standby Power Measurements According to IEC Standard 62301 (First Edition) and IEC Standard 62301 (Second Edition)

Configuration Display * Standby Power ( W ),

First Edition Standby Power ( W ), Second

Edition %

Difference Countertop Microwave

- Only Backlit LCD 3.84 3.66

- 4.7 Countertop Microwave

- Only Backlit LCD 2.18 2.18

- 0.3 Countertop Microwave

- Only Backlit LCD 3.81 3.78

- 1.0 Countertop Microwave

- Only LED 1.06 1.07 0.3 Countertop Microwave

- Only LED 1.76 1.77 0.8 Countertop Microwave

- Only LED 1.27 1.27

- 0.4 Countertop Microwave

- Only VFD 3.44 3.42

- 0.6 Countertop Microwave

- Only VFD 3.14 3.12

- 0.7 Countertop Convection LED 1.20 1.24 3.2 Countertop Convection VFD 4.14 4.13

- 0.1 Countertop Convection VFD 3.23 3.05

- 5.5

Over

- the

- Range Microwave

- Only VFD 1.66 1.67 0.4

Over

- the

- Range Microwave

- Only LED 0.78 0.78 0.0

Over

- the

- Range Convection VFD 4.50 4.48

- 0.4
* LCD = Liquid Crystal Display, LED = Light Emitting Diode, VFD = Vacuum Fluorescent Display

3.5 MANUFACTURER TRADE G ROUPS

DOE recognizes the importance of trade groups in disseminating information and promoting the interests of the industry that they support

. To gain insight into the residential

microwave oven industr y , DOE researched various associations available to manufacturers, suppliers, and users of such equipment

. DOE also used the member lists of these groups in the

con struction of an exhaustive database containing domestic manufacturers.

DOE identified the Association of Home Appliance Manufacturers (AHAM) as a trade group that support s the microwave oven industry

, AHAM

, b

b For more information

, visit : formed in 1967, aims to e nh ance the

value of the home appliance industry through l eadership, public education and advocacy

. AHAM provides services to its members including government relations ; certification programs for room air conditioners, dehumidifiers and room air clean ers ; an active communicat ions program ; and

technical services and research

. In addition, AHAM conducts other market and consumer research studies and publishes a biennial Fact Book

. AHAM also develops and maintains technical standards for various appliances to provide uniform, rep eatable procedures for measuring specific product characteristics and performance features

.

www.aham.org

.3-9 3.6 MANUFACTURER INFORMA TION

The following section details information regarding domestic manufacturers of

residential microwave ovens , including estimated market shar es (section 3.6.1 ), industry mergers and acquisitions (section 3.6.2 ), potential small business impacts (section 3.6.3 ), and product distribution channels (section 3.6.4 )

.

3.6.1 Manufacturers and Market Shares

Using publicly available data ( e.g. Appliance Magazine and market assessments done by third parties), DOE estimates the market shares for domestic manufacturers of each of the products contained in this standards rulemaking

. Manufacturers may offer multiple brand names

. Some of the brand names come from indep endent appliance manufacturers which have been acquired over time, and domestic manufacturers may put their brand on a product manufactured

overseas

. Companies included in this analysis may also be off

- shore manufacturers that maintain a significant domest ic presence via a U.S. entity.

The microwave oven market differs from the rest of the domestic cooking product market in that many of the manufacturers are foreign

- owned companies with manufacturing facilities outside of the United States, and many of th e domestic appliance manufacturers rebrand foreign

manufactured products

. Table 3.6.1 illustrates the 2008 market shares for the U.S. microwave

oven ma rket

. Microwave oven manufacturers with facilities inside the United States include Whirlpool

, the Sharp Electronics Corporation (Sharp) , Sub

- Zero, Summit

, and Viking

. Figure

3.6

. 1 lists microwave manufacturers active in the domestic market.

Table 3.6

. 1 Major and Other Microwave Oven Manufacturers

Major Manufacturers Other Manufacturers

LG Electronics, Inc. (LG) Daewoo Electronics Company, Ltd. (Daewoo) Sharp Panasonic Corporation of America (Panasonic) Samsung Electronics America, Inc. (Samsung) Whirlpool

Guangdong Galanz Enterprise Co., Ltd. (Galanz)

GD Midea Microwave Oven Mfg. Co. , Ltd. ( Midea ) Sanyo

Sub

- Zero

Summit Viking3-10 Figure 3.6

. 1 200 8 Market Shares for the Domestic Microwave Oven Market 1

3.6.2 Mergers and Acquisitions

Due to mergers and acquisitions, the home appliance industry continues to consolidate

. While this phenomenon varies from product to product within the industry, the large market shares of a few companies provide evidence in support of this characterization.

According to the January 20 10 issue of Appliance Magazine

, three manufacturers comprise 85 percent of the core appliance market share

. “ Core appliances ” include dishwashers, clothes dryers, freezers, ranges, refrigerators, and clothes washers

. Table 3.6

. 2 lists these core appliance manufacturers, and Figure 3.6

. 2 illustrates the breakdown of 2008 market shares in the

core appliance category.

Table 3.6

. 2 Core Appliance Manufacturers

Core Appliance Manufacturers Whirlpool

GE Frigidaire3-11 Figure 3.6

. 2 2008 Core Appliance Market Shares 2

On August 22, 2005, Whirlpool, headquartered in Benton Harbor, Michigan, and Maytag, based in Newton, Iowa, announced plans to merge in a deal worth $2.7 billion. 3 Maytag shareholders approved the merge r on December 22, 2005

. Shortly after announcing the merger, Whirlpool submitted a pre

- merger notification to the U.S. Department of Justice (DOJ)

. The DOJ Antitrust Division initiated an investigation, scheduled to end February 27, 2006, into the effects of the merger, including potenti al lessening of competition or the creation of a monopoly

. Following this initial review, the DOJ asked for additional materials from each company and extended the review to March 30, 2006

.

Opponents of the merger asserted that the combined companies wou ld control as much as 70 percent of the residential laundry market and as much as 50 percent of the residential

dishwasher market. 4 Whirlpool claimed that their large potential residential laundry market share was skewed because the company produces washi ng machines for Sears, which sells them

under their Kenmore in

- house brand

. Whirlpool went on to say that they must periodically bid with other manufacturers to keep the Kenmore contract and that Sears controls the pricing of the

Kenmore units. 5

In early January 2006, U.S. Senator Tom Harkin and U.S. Representative Leonard Boswell, both of Iowa, called upon the DOJ to block the merger, claiming it would give Whirlpool an unfair advantage in the home appliance industry

. The Congressmen wrote, that if3-12 the DO J does not block the deal, the agency should at least “ require that Whirlpool divest the washer and dryer portions of Maytag to a viable purchaser who will have the financial capability and desire to continue to operate that business .” 6

On March 29, 2006, DOJ closed its investigation and approved the merger

. DOJ claims “that the proposed transaction is not likely to reduce competition substantially

. The combination of strong rival suppliers with the ability to expand sales significantly and large cost savi ngs and other efficiencies that Whirlpool appears likely to achieve indicates that this transaction is not likely to harm consumer welfare.” 7

The DOJ Antitrust Division focused its investigation on residential laundry, although it considered impacts acros s all products offered by the two companies

. DOJ determined that the merger would not give Whirlpool excessive market power in the sale of its products and that any attempt to raise prices would likely be unsuccessful

. In support of this claim, DOJ noted t hat (1) other U.S. brands, including Kenmore, GE, and Frigidaire, are well established; (2) foreign manufacturers, including LG and Samsung, are gaining market share; (3) existing U.S. manufacturers are below production capacity; (4) the large home applian ce retailers have alternatives available to resist price increase attempts; and (5) Whirlpool and Maytag substantiated large cost savings and other efficiencies that would benefit consumers. 8

Whirlpool and Maytag completed the merger on March 31, 2006

. This large merger follows several other mergers and acquisitions in the home appliance industry

. For example, Maytag acquired Jenn

- Air Corporation (Jenn

- Air) in 1982, Magic Chef, Inc. (Magic Chef) in 1986, and Amana Appliances (Amana) in 2001

. Whirlpool ac quired the KitchenAid division of Hobart Corporation (KitchenAid) in 1986

. White Consolidated Industries (WCI) acquired the Frigidaire division of General Motors Corporation in 1979, and AB Electrolux acquired WCI (and therefore Frigidaire) in 1986.

3.6.3 Small Business Impacts

DOE considers the possibility of small businesses being impacted by the promulgation of energy conservation standards for residential cooking products

. At this time, DOE is not aware of any small microwave oven manufacturers, defined by t he Small Business Association (SBA) as having 750 employees or fewer, who produce products that fall under this rulemaking and

who therefore would be impacted by a minimum efficiency standard

. DOE estimates that there is

one small business which manufactur es a product which combines a microwave oven with other appliance functionality. However, because DOE is not amending energy conservation standards at this time for the microwave oven portion of such combined products, this manufacturer would not be impact ed. For further information, see chapter 12 of the TSD.

3.6.4 Distribution Channels

Understanding the distribution channels of products covered by this rulemaking is an important facet of the market assessment

. DOE gathered information regarding the distribution3-13 channels for residential cooking products from publicly available sources

. This section contains distribution channel information for residential appliances.

The distribution chain for residential applia nces, including microwave ovens , differs from commercial products, as the majority of consumers purchase their appliances directly from

retailers

. These retailers include : (1) home improvement, appliance, and department stores; (2) Internet retailers; (3) membership warehouse clubs; and (4) kitchen remodelers

. The AHAM

Fact Book 2005 reports that home improvement stores claim nearly one out of every four dollars spent on appliances. 9

Home appliance retailers generally obtain products directly from manufact urers

. The

AHAM Fact Book 2003 shows that over 93 percent of residential appliances are distributed from the manufacturer directly to a retailer. 10

3.7 REGULATORY PROGRAMS

The following section details current regulatory programs mandating energy conservation standards for residential cooking products

. Section 3.7.1 discusses Federal energy conservation

standards and section 3.7.2 reviews upcoming standards under EISA 2007

. In addition, section

3.7.3 review s foreign standby power regulatory programs that may impact the companies servicing the North American market

.

3.7.1 Federal Energy Conservation Standards

The National Appliance Energy Con servation Act of 1987 (NAECA) (42 U.S.C. 6291

- 6309)

, which amended EPCA, established prescriptive standards for kitchen ranges and ovens, but no standards were established for microwave ovens. (42 U.S.C. 6295(h)) The NAECA

amendments also required DOE to c onduct two cycles of rulemakings to determine whether to

revise the standard. (42 U.S.C. 6295 (h)(1)

– (2)) DOE undertook the first cycle of these rulemakings and issued a final rule on September 8, 1998 in which DOE found that no amended standards were jus tified for electric cooking products, including microwave ovens. 63 FR 48038.

DOE initiated the second cycle of energy conservation standards rulemakings for cooking products by publishing a framework document covering, in part, microwave ovens, and givi ng notice of a public meeting and the availability of the document. 71 FR 15059 (Mar

. 27, 2006). In its subsequent advance notice of proposed rulemaking (ANOPR) concerning energy conservation

standards for commercial clothes washers and residential dishwas hers, dehumidifiers, and cooking products, including microwave ovens, DOE determined that energy consumption by microwave ovens in the standby mode represents a significant portion of microwave oven energy use, and that a standard regulating such energy co nsumption would likely have significant energy savings. 72 FR 64432, 64441

– 42 (Nov. 15, 2007)

. DOE published a NOPR for the appliance standards rulemaking on October 17, 2008, in which it tentatively concluded that a standard for microwave oven standby mode and off mode energy consumption would be technologically3-14 feasible and economically justified. 73 FR 62034. DOE received responses to the NOPR from interested parties regarding the harmonization of standards and test procedures with those of other countries and international agencies. As a result of these comments, DOE decided to

consider the revised v ersion of IEC Standard 62301 ( i.e. , IEC Standard 62301 Second Edition) in the development of energy conservation standards for the standby mode and off mode power consumption of microwave ovens. In a final rule published on April 8, 2009 (74 FR 16040),

DOE established amended standards for gas cooking products, but again found that no active mode cooking efficiency standards were justified for electric cooking products, including microwave ovens. This rulemaking completed the second cycle of rulemakings req uired by the

NAECA amendments to EPCA

. (42 U.S.C. 6295(h)(2))

3.7.2 Energy Independence and Security Act of 200 7

On December 19, 2007

, the President signed into law EISA 2007 , which contains

numerous amendments to EPCA

. Section 310 of EISA 2007 amends section 3 25 of EPCA to require any final rule establishing or revising a standard for a covered product, adopted after July 10, 2010, shall incorporate standby mode and off mode energy use into a single amended or new

standard. (42 U.S.C. 6295(gg)(3)(A)) If not fea sible, the Secretary shall prescribe within the final rule a separate standard for standby mode and off mode energy consumption, if justified. (42 U.S.C. 6295(gg)(3)(B))

Off mode is defined by EPCA as “the condition in which an energy

- using product

– (I ) is connected to a main power source; and (II) is not providing any st andby or active mode function.” (42 U.S.C. 6295(gg)(1)(A)(ii)) Active mode refers to the main (cooking) function, while standby is defined by EPCA as “the condition in which an energy

- using product (I) is connected to a main power source; and (II) offers 1 or more of the following user

- oriented or protective functions: (aa) To facilitate the activation or deactivation of other functions (including active mode) by remote switch (i ncluding remote control), internal sensor, or timer

. (bb) Continuous functions, including information or status displays (including cloc ks) or sensor

- based

functions.” ( Id .; 42 U.S.C. 6295(gg)(1)(A)(iii))

On November 15, 2007 DOE published an advanced n otice of proposed rulemaking (ANOPR) (hereafter the “ November 2007 ANOPR ”) in which it stated that it was considering including standby power in the energy conservation standards and intend ed to initiate amendment of its test procedure to measure microwave oven standby power because : 1) energy conservation in standby mode represents a significant proportion of microwave oven annual energy consumption ; and 2) the range of standby power among microwave ovens currently on the market suggests that the likely im pact of a standard would be significant

. 72 FR 64432. Such a test procedure change is a prerequisite to incorporate a standby power requirement as part of the energy conservation standard for microwave ovens. c Data obtained by DOE during testing of

a repr esentative sample of microwave ovens and from AHAM show standby power ranging from3-15 1.1 to 5.8 W

. At the Federal Energy Management Program ( FEMP )

- defined limit of 2 W, the resulting 17.2 kWh of annual standby energy use represents 13 percent of the estimate d annual cooking energy consumption for a baseline microwave oven

. DOE wa s unaware of any

microwave oven s currently on the market in the United States that consume energy in off

- mode, differentiable from standby mode

. Therefore, DOE proposed for the purposes of the October

2008 NOPR to put forth standards for microwave oven standby power only, but stated it was open to consideration of off

- mode standards

. More recently, DOE determined

, based on a survey of products avai lable on the U.S. market

, that four countertop microwave

- only models produced by two manufacturers have electromechanical rotary dial controls and no displays, and which, therefore, are capable of operation in off mode. Because these units represent less t han 1 percent of the models in Product Class 1 and because their power consumption is already low due to the lack of a display, any energy savings associated with off mode energy conservation standards for

microwave ovens would be trivial. For these reason s, DOE did not consider standards for

microwave oven off mode in this rulemaking

.

As discussed above in section 3.4 , the test procedure rulemaking to incorporate methods for measuring standby mode and off mode energy consumption for microwave ovens was initiated in parallel with the current rulemaking. Because DOE repealed the provisions in the existing microwave oven test procedure relating to the measurement of cooking efficiency and EF, the requirement to integrate energy consumption in standby mode and off mode into an overall energy descriptor does not apply. The absence of active mode provisions results in a de facto separate energy use descriptor for microwa ve oven standby mode and off mode energy use. Accordingly, DOE is amend ing energy conservation standards for microwave ovens based on these separate energy use descriptors

.

3.7.3 Foreign Standby Power Regulatory Programs

The International Energy Agency (IEA) has raise d awareness of standby power through publications, international conferences, and policy advice to governments

. In 1999, the IEA developed the “1

- Watt Plan

, ” which proposed reducing standby power internationally in

electronic devices and which adv ocates that all countries harmonize energy policies and adopt the same definition and test procedure

. The IEA has advocated a 1

- W requirement for all consumer electrical products (unless specifically excluded) in standby mode

. The IEA also stated

that IEC Standard 62301 provides an internationally sanctioned definition and test procedure for standby power

, which is now widely specified and used. d

A number of countries have implemented regulatory approaches to standby power in

microwave ovens

. Australia h as announced plans to implement a mandatory 1

- W requirement for all consumer electrical products by 2013 , including microwave oven s. 11 The Korea Energy Management Corporation developed their “ e

- standby ” program , which established a mandatory standby power limit of 1 W for microwave ovens that became effective in 2010

. 12

d For more information

, visit : In accordance

www.iea.org/

.3-16 with Japan’s Top Runner Program, e Japanese appliance manufacturers made a voluntary declaration to reduce standby power consumption of microwave ovens without a timer as close

to 0 W as possible and that of microwave ovens with a timer to 1 W or lower. 13

The European Union (EU) passed the Ecodesign Directive in 2005 which calls for an implementing measure to reduce standby power for a specified list of energy using products,

w hich include microwave ovens

. On December 17 , 2008, the Commission of the European Communities published Commission Regulation 1275/2008 implementing Ecodesign requirements for standby and off mode electric power consumption. Table 3. 7

. 1 presents the criteria for standby and off mode power for microwave ovens , which are well in line with the

IEA 1

- W plan. 14

Table 3.7

. 1 EU Ecodesign R equirements for S tandby and O ff M ode

Mode Maximum Power ( W ) Tier 1 ( December 17, 2009 ) Off (no function) 1.0 W Standby (only reactivation function) 1.0 W Standby (information or status display plus reactivation function 2.0 W Tier 2 ( December 17, 2012 ) Off (no function) 0.5 W Standby (only reactivation function) 0.5 W Standby (information or status display plus reactivation function 1.0 W

3.8 VOLUNTARY PROGRAMS

DOE reviewed voluntary programs promoting energy efficient residential cooking products in the U nited States

. DOE noted that FEMP establish es voluntary energy conservation

standards for microwave ovens

.

DOE’s FEMP f works to reduce the cost and environmental impact of the Federal government by advancing energy efficiency and water conservation, promoting the use of distributed and renewable energy, and improving utility management decisions at Federal sites

. FEMP helps Federal buyers identify and purchase energy efficient equipment, including

microwave o vens.

Executive Order 13221 Energy Efficient Standby Power Devices, signed July 31, 2001, requires that Federal agencies purchase commercially available products with low standby

e Japan’s Energy Conservation Act uses a “top runner” method to set energy efficiency targets for residential, commercial, and transportation sector equipment. Target values for future products are set based on the level of the most energy efficient products on the market at the time of the value setting process. For more information, visit : http://www.eccj.or.jp/index_e.html (Accessed May 2013.)

f For more information

, visit : www.eere.energy.gov/femp

.3-17 power

. 66 FR 40571

. FEMP

, therefore

, issues standby power recommendations f or microwave

ovens

. Table 3.8

. 1 presents the microwave oven recommendations.

Table 3.8

. 1 FEMP Recommendations for Micr owave Ovens

Microwave Oven Classification Standby Power ( W )*

Standard Microwave Oven ≤ 2

* Based on test procedures specified in IEC Standard 62301

3.9 HISTORICAL SHIPMENTS

Awareness of annual product shipment trends is an important aspect of the market assessment and in the development of the standards rulemaking

. DOE reviewed data collected by the U.S. Census Bureau and AHAM to evaluate residential appliance product shipment trends and the value of these shipments , which were used during the shipments a nalysis ( c hapter 9 of

this TSD

. )

3.9.1 New Home Starts

Trends in new home starts may directly affect shipments of certain home appliances

. While there is certainly both a replacement and remodeling market for some appliances including microwave ovens , these pr oducts are also fixtures in virtually all new homes

.

Figure 3.9

. 1 presents the number of new single

- family and multi

- family housing units

started in the U nited States from 1998

– 201 1

. Over the 5

- year period from 2000

– 2005, single

family home starts increased 39.4 percent, to 1,716,000 units annually

. However, between 2005

and 201 1 , single

- family home starts decreased 7 4.9 percent, to 4 30

, 6 00 units annua lly

. Multi

family unit starts have remained relatively flat between 1998 and 2005 , hovering around 350,000 units annually. However, between 2005 and 20 1 1 , multi

- family unit starts decreased by 49.4 percent, to around 1 78

, 2 00 units annually. 153-18 Figure 3.9

. 1 New Privately Owned Single

- Family and Multi

- Family Housing Unit Starts in

the United States from 1998

– 20 1 1 16

3.9.2 Unit Shipments

Appliance Magazine provides annual unit shipments for residential appliances

, including

microwave ovens

. Table 3.9

. 1 presents annual shipments of microwave ovens

. The shipments of microwave ovens increased 90.2 percent from 1995 to 2004, to about 15.5 million units in 2004. However, between 2004 and 201 1 , shipments of microwave ovens decreased by 38.7 percent, to

9.5 million units in 20 11

. 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

1998 2000 2002 2004 2006 2008 2010Housing Units Started (thousands of units)

Single

- Family Units Multi

- Family Units3-19 Table 3.9

. 1 Industry Shipments of Microwave Ovens (Domestic and Import in Thousands of Units) 17

Year Microwave Ovens

2011 9,522

2010 9,334

2009 9,626

2008 11,340

2007 11,851

2006 13, 687

2005 13,86 0

2004 15,526

2003 14,274

2002 13,311

2001 13,446

2000 12,644

1999 11,422

1998 10,365

1997 8,883

1996 8,771

1995 8,162

3.9.3 Value of Shipments

Table 3.9

. 2 provides the value of shipments for the household appliance industry from

1994

– 2011 based upon data from the U.S. Census Bureau ’s Annual Survey of Manufacturers ( ASM )

. g The ASM expresses all dollar values in nominal dollars ; i.e.

, 2011 data are expressed in

2011 dollars, and 2010 data are expressed in 2010 dollars

. Using the Gross Domestic Product Implicit Price Deflator, DOE converted each year’s value of shipments to 2011 dollars

. In

consta nt 2011 dollars, the value of shipments has declined by approximately 45 percent over the

18

- year period.

g Available online at : www.census.gov/manufacturing/asm/index.html

.3-20 Table 3.9

. 2 Household Appliance Manufacturing Statistics by Year 18

Year Value of Shipments in

Nominal Dollars ( $1000 ) Value of Shipments in 20 11 Dollars ( $1000 )

2011 17,969,800 17,969,800

2010 18,019,808 18,373,128

2009 18,707,942 19,454,109

2008 21,504,780 22,567,556

2007 23,639,716 25,350,157

2006 24,162,513 26,673,372

2005 24,620,117 28,063,733

2004 23,989,141 28,257,210

2003 23,026,196 27,892,529

2002 22,269,541 27,556,375

2001 22,298,897 28,039,541

2000 23,414,965 30,108,200

1999 22,372,219 29,390,353

1998 22,471,878 29,955,233

1997 21,394,445 28,841,448

1996 22,156,900 30,396,671

1995 22,391,300 31,302,947

1994 22,793,900 32,529,651

The increase in shipment volumes combined with an overall decrease in constant

- dollar shipment values indicates that the U.S. appliance industry is very competitive.

According to data presented in the AHAM Fact Book 2003 , many old appliances are still being used after consumers purchase new units of same product

. Table 3.9.3 presents the various methods by which consumers dispose of their older appliances.

Table 3.9

. 3 Disposition of Previous Appliance ( Percentage ) 19

Product Kept It Left with

Previous

Home Sold /

Gave Away Recycling Facility Left at

Curb for Disposal Retailer

Took Away

Microwave Ovens 17 11 32 12 23 4

The value of s hipments of residential cooking products , a subset of the household appliance industry as defined in the ASM , ha s fluctuated over the last 18 years when viewed in

2011 real dollars

. Overall, the real value of shipments in 2011 was roughly 30 percent lower than

the value in 1994

. Table 3.9

. 4 provides the industry value of shipments for household cooking appliance manufacturing.3-21 Table 3.9

. 4 Household Cooking Appliance Manufacturing Statistics by Year 20

Year Value of Shipments in

Nominal Dollars ( $1000 ) Value of Shipments in 20 11 Dollars ( $1000 )

2011 3,817,189 3,817,189

2010 3,719,380 3,792,307

2009 3,835,185 3,988,152

2008 4,261,482 4,472,086

2007 4,786,768 5,133,112

2006 4,864,268 5,369,741

2005 5,114,677 5,830,067

2004 4,798,227 5,651,912

2003 4,691,713 5,683,255

2002 4,327,308 5,354,620

2001 3,773,442 4,744,880

2000 4,178,958 5,373,525

1999 3,915,499 5,143,786

1998 3,849,167 5,130,977

1997 3,520,635 4,746,102

1996 3,564,700 4,890,351

1995 3,788,200 5,295,888

1994 3,813,900 5,442,896

3.9.4 Imports and Exports

There is a large market for the import and export of home appliances

. Each month AHAM publishes import and export data for certain home appliances

. These data are released by the U.S. Census Bureau and aggregated by a third party

. On the whole, major appliance unit imports decreased 9.7 percent in 200 9 as compared to 200 8

. Major appliance unit exports

decreased 17.5 percent over the same period.

Table 3.9

. 5 shows selected import data from AHAM’s Import/Export Trade Report

– December 200 9. 21 For microwave ovens less than or equal to 22.5 inches in width, both the

number and value of units imported decreased by more than 17 percent from 2008 to 2009. For

microwave ovens 22.5 inches to 31.0 inches in width, both the number and value of units imported increased by more than 14 percent from 200 8 to 200 9

. For microwave ovens greater than 31.0 inches in width, both the number and value of units imported decreased by more than 10 percent

. Overall, the value of major appliance imports decreased 9.2 percent from 2008 to

2009

.3-22 Table 3.9

. 5 200 8

– 200 9 Imports of Microwave Ovens 22

Appliance Description Jan.

– Dec. 200 9 Jan.

– Dec. 200 8 % Change Jan.

– Dec. 200 9 Jan.

– Dec. 200 8 % Change Units $ Mil (Nominal)

Microwave ovens ≤
22.5 inches 3,776,772 4,567,709

- 17.3 130.042 165.033

- 21.2

Microwave ovens >

22.5

- 31.0 inches 3,263,577 2,392,880 36.4 135.885 118.232 14.9

Microwave ovens >

31.0 inches 7,442,569 8,378,,434

- 11.2 610.732 679.688

- 10.1

Table 3.9.6 shows selected export data from AHAM’s Import/Export Trade Report

– December 200 9

. 23 For the 1

- year period from 200 8 to 200 9

, both the number and value of unit exports of microwave oven expor ts decreased by more than 16 percent

. Overall, the total value of exports decreased 18.9 percent.

Table 3.9

. 6 2008

– 2009 Exports of Microwave Ovens 24

Appliance Description Jan.

– Dec. 200 9 Jan.

– Dec. 200 8 % Change Jan.

– Dec. 200 9 Jan.

– Dec. 200 8 % Change Units $ Mil (Nominal)

Microwave ovens 235,883 294,660

- 19.9 33.479 40.083

- 16.5

3.10 MARKET SATURATION

AHAM’s Fact Book 2005 and Appliance Magazine’s annual Portrait of the Appliance Industry presents the market saturation for microwave ovens

. As shown in Table 3.10

. 1 , the percentage of households with microwave ovens has decreased slightly since 2001.3-23 Table 3.10

. 1 Microwave Oven Saturation (Percentage of U.S. Households with Product) 25

Year Saturation ( % )

2008 91.0

2007 90.0

2006 90.0

2005 95.0

2004 95.5

2003 95.0

2002 95.0

2001 95.7

2000 95.6

1999 93.1

1990 82.7

1982 25.6

1970 Negligible

3.11 PRODUCT RETAIL PRICE S

Table 3.11.1 presents the average retail prices (in nominal dollars) of microwave ovens and

the C onsumer P rice I ndex (CPI) of each year (1982

– 84 = 100)

. Prices of microwave ovens have

decreased since 1980

.

Table 3.11

. 1 Residential Appliance Retail Prices 26

Product Average Retail Prices ( Nominal $ ) Percent Change

1980 1994 2002 2005 1980

– 2005

Microwave Ovens 450 239 145 99

- 78.0%

Consumer Price Index * 82.4 148.2 179.9 195.3 137.0%

* U.S. Department of Labor, Bureau of Labor Statistics, Consumer Price Index: U.S. city average

. 1982

– 84 = 100.

3.12 INDUSTRY COST STRUCT URE

DOE developed the household appliance industry cost structure from publicly available

information from the ASM

, ( Table 3.12.1 and Table 3.12.2 ) and the U.S. Securities and Exchange Commission (SEC) 10

- K reports filed by publicly owned manufacturers

. Table 3.12.1
presents the home appliance industry employment levels and earnings from 1994

– 2011

. The statistics illustrate a steady decline in the number of production and non

- production workers in the industry

.3-24 Because the ASM expresses all dollar values in constant 2011 dollars, th e following table shows that as industry employment levels decline, the industry payroll is also decreasing

. The percent decrease in total industry employees tracks closely with the percent decrease in payroll for all employees

.

Table 3.12

. 1 Household Appliance Industry Employment and Earnings 27

Year Production Workers ( ‘000 ) All Employees ( ‘000 ) Payroll for All Employees ( 20 11 $ Mil )

2011 36.0 42.4 1,775.6

2010 38.3 45.0 1,924.4

2009 41.1 48.6 2,016.0

2008 48.0 56.4 2,359.9

2007 56.2 65.5 2,664.6

2006 60.0 69.7 2,918.8

2005 64.3 76.9 3,076.7

2004 69.4 83.7 3,408.7

2003 71.0 85.3 3,494.9

2002 73.5 88.8 3,704.1

2001 76.6 92.6 3,880.8

2000 82.7 99.5 4,227.6

1999 82.3 97.9 4,089.6

1998 82.4 99.5 4,118.4

1997 79.8 97.2 3,932.1

1996 87.1 108.1 4,214.1

1995 88.9 110.8 4,283.9

1994 90.4 110.4 4,269.2

Table 3.12 .2 presents the employments levels and earnings for just the household laundry portion of the appliance industry from 1994

– 2011 . Statistics for both employment levels and payroll show a steady f luctuation from 1994

– 2002, but both have declined since that time.

P ayroll and employment levels again show a positive correlation.3-25 Table 3.12

. 2 Household Cooking Appliance Industry Employment and Earnings 28

Year Production Workers ( ‘000 ) All Employees ( ‘000 ) Payroll for All Employees ( 20 11 $ Mil )

2011 8.8 9.9 366.2

2010 8.7 9.8 353.3

2009 8.7 10.0 367.1

2008 10.4 11.8 427.6

2007 13.1 15.5 499.6

2006 12.7 14.4 485.4

2005 14.3 16.4 590.5

2004 14.4 17.0 607.4

2003 14.5 17.3 595.1

2002 14.7 18.0 616.2

2001 14.8 17.5 620.7

2000 15.3 18.0 658.6

1999 16.4 19.1 729.5

1998 15.3 18.2 697.3

1997 14.4 17.5 643.8

1996 14.7 18.1 709.8

1995 15.2 19.2 705.0

1994 15.0 18.6 701.0

Table 3.12 .3 presents the costs of materials and industry payroll as a percentage of value of shipments from 1994

– 2011

. The cost of materials as a per centage of value of shipments has fluctuated slightly over the 18

- year period

. DOE notes that fluctuations in raw material costs are common from year to year

. The cost of payroll for production workers as a percentage of value of shipments has declined sin ce 2000

. Similarly, the cost of total payroll as a percentage of value of shipments has declined since 2000

.3-26 Table 3.12

. 3 Household Appliance Industry Census Data 29

Year Cost of Materials as

a P ercent age of

V alue of S hipments ( % ) Cost of Payroll for

P roduction W orkers as

a P ercent age of V alue of

S hipments ( % ) Cost of Total Payroll ( P roduction + A dmin.)

as a P ercent age of V alue

of S hipments ( % )

2011 55.7 7.3 9.9

2010 56.1 7.7 10.5

2009 54.0 7.5 10.4

2008 59.3 7.7 10.5

2007 57.8 8.0 10.5

2006 57.9 8.4 10.9

2005 56.9 8.3 11.0

2004 57. 2 9. 0 12. 1

2003 5 5.9 9.2 12.5

2002 5 4.9 9.7 13.4

2001 56.8 10.1 13.8

2000 55.8 10.2 14.0

1999 54.7 10.2 13.9

1998 56.0 10.0 13.7

1997 52.8 9.9 13.6

1996 56.8 9.9 13.9

1995 5 7

. 7 9. 4 1 3.7

1994 55.5 9.2 13.1

Table 3.12 .4 shows the cost of materials and industry payroll as a percentage of value of shipments for the household cooking appliance industry from 1994

– 2011 . DOE observed a decline in the cost of payro ll as a percentage of value of shipments from 199 9

– 2011

. During that same time period , the cost of materials as a percentage of value of shipments has increased.3-27 Table 3.12

. 4 Household Cooking Appliance Industry Census Data 30

Year Cost of Materials as

a P ercent age of

V alue of S hipments ( % ) Cost of Payroll for

P roduction W orkers as

a P ercent age of V alue of

S hipments ( % ) Cost of Total Payroll ( P roduction + A dmin.)

as a P ercent age of V alue

of S hipments ( % )

2011 59.2 7.1 9.6

2010 62.4 6.9 9.3

2009 60.7 6.8 9.2

2008 70.5 7.4 9.6

2007 64.3 7.4 9.7

2006 66.8 7.0 9.0

2005 67.9 8.0 10.1

2004 67.3 8.1 10.7

2003 63.2 7.5 10.5

2002 64.6 8.0 11.5

2001 63.3 9.3 13.1

2000 57.1 8.5 12.3

1999 58.4 10.1 14.2

1998 57.8 9.3 13.6

1997 51.0 9.4 13.6

1996 61.5 9.3 14.5

1995 53.6 8.4 13.3

1994 52.2 8.5 12.9

A detailed financial analysis of each of the products covered by this rulemaking is presented in the manufacturer impact analysis

. ( See c hapter 1 2 of this TSD

. ) This analysis identifies key financial inputs including cost of capital, working capital, depreciation, and capital expenditures.

3.13 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES

Table 3.13.1 and Table 3.13 .2 show the year

- end inventory for the household appliance and household cooking appliance industr ies , respectively , according to the ASM

. Both in dollars and as a percentage of value of shipme nts, the end

- of

- year inventory for the industry has

declined since 1995

. These data illustrate a general trend of domestic manufacturers retaining

less of their inventories over time since 1994.3-28 Table 3.13

. 1 Household Appliance Industry Census Data 31

Year End

- of

- Year Inventory ( 20 11 $ Mil ) End

- of

- Year Inventory as a

P ercent age of V alue

of S hipments ( % )

2011 1,666.6 9.3

2010 1,768.8 9.6

2009 1,650.9 8.5

2008 1,942.6 8.6

2007 2,141.8 8.4

2006 1,864.0 7.0

2005 1,861.9 6.6

2004 2,058.7 7.3

2003 2,063.6 7.4

2002 2,218.9 8.1

2001 2,466.7 8.8

2000 2,783.4 9.2

1999 2,773.5 9.4

1998 2,990.3 10.0

1997 2,874.1 10.0

1996 3,154.1 10.4

1995 3,483.7 11.1

1994 3,610.8 11.13-29 Table 3.13

. 2 Household Cooking Appliance Industry Census Data 32

Year End

- of

- Year Inventory ( 20 11 $ Mil ) End

- of

- Year Inventory as a

P ercent age of V alue

of S hipments ( % )

2011 324.2 8.5

2010 309.7 8.2

2009 307.9 7.7

2008 407.3 9.1

2007 597.5 11.6

2006 425.5 7.9

2005 492.9 8.5

2004 483.3 8.6

2003 472.7 8.3

2002 533.9 10.0

2001 420.3 8.9

2000 474.6 8.8

1999 533.6 10.4

1998 635.0 12.4

1997 549.2 11.6

1996 630.5 12.9

1995 749.2 14.1

1994 764.2 14.0

DOE obtained full production capacity utilization rates from the U.S. Census Bureau, Survey of Plant Capacity from 1994

– 200 6

. Table 3.13.2 presents ut ilization rates for various sectors of the household appliance industry

. Full production capacity is defined as the maximum level of production an establishment could attain under normal operating conditions

. In the Survey of Plant Capacity report, the ful l production utilization rate is a ratio of the actual level of operations to the full production level

. The full production capacity utilization rate for household appliances in aggregate, along with the rates for cooking appliances and household laundry appliances, show a decrease in utilization from 1994

– 200 6 , although trends in subsets of that time period have fluctuated.3-30 Table 3.13

. 3 Full Production Capacity Utilization Rates 33

Year Rates ( % )

Household Appliances Cooking Appliances

2006 77 65

2005 74 57

2004 76 61

2003 78 57

2002 72 56

2001 70 50

2000 70 55

1999 75 59

1998 73 66

1997 73 61

1996 76 66

1995 79 66

1994 82 72

3.14 TECHNOLOGY ASSESSMEN T

This section provides a technology assessment for residential microwave ovens

. Contained in this technology assessment are details about product characteristics and operation (section 3.14.1 ), an examination of possible technological improvements (section 3.14.2 )

, and a characterization of the product efficiency levels currently commercially available (section

3.14.3 ).

3.14.1 Product Operations and Components

In preparation for the screening and engineering analyses, DOE prepared a brief description of the characteristics and operation of microwave ovens

. Th is description provide s a basis for understanding the technologies used to improve product efficiency.

A microwave oven is an appliance which heats foods and beverages through the conversion of electric energy to electromagnetic (microwave) energy

. The food load is placed inside the oven where it is subjected to the microwave energy

. In the portion of the

el ectromagnetic spectrum where microwave ovens operate, typically 2.45 gigahertz, water, fats, and sugars have high absorptivity and thus food heats readily

. Glass, ceramic, and paper have low absorptivity at these wavelengths, while metals reflect the micro waves

. Therefore microwave ovens efficiently heat foods while the cavity and utensils remain cool.

The microwave energy is generated by an electrically powered magnetron

. Line voltage is converted to the high voltage required by the magnetron through the use of a high

- voltage

transformer

. The microwaves are directed from the magnetron into the cavity via a metal duct, or waveguide

. To produce a more uniform distribution of energy in the cavity, one or more sets of3-31 rotating metal blades may be inserted in t he waveguide to alter the microwave patterns temporally

. Additionally, a turntable or translating tray may be provided in the cavity to move the load and thus promote even input energy distribution to the food

. Some microwave ovens are equipped with electr ically powered resistive or halogen heating elements for surface browning

, and in the case of convection microwave ovens, an additional fan for forced air distribution to enhance cooking performance

.

To achieve reduced power levels for cooking processes s uch as defrosting, the magnetron is typically cycled at an appropriate duty cycle which results in a time

- averaged power at the

desired level

. Electronic controls may include a clock /timer function as well as a humidity or infrared sensor for detecting whe n the desired level of cooking is achieved

. Standby power consumption associated with these electronics , as well as the power supply, may be several

watts

. In addition, a light is typically provided that is turned on when the door is opened or when

the mic rowave oven is in operation

.

Installation configurations include countertop , built

- in, and over

- the

- range

. Over

- the

range microwave ovens may include a ventilation fan that turns on automatically when the temperature from the cooktop below it rises ; however

, such an option is not required for

certification under the Underwriters Laboratories (UL) S tandard UL 923

- Edition 5, Standard for Microwave Cooking Appliances

.

3.14.2 Technology Options

In order to gain a deeper understanding of the technological imp rovements used to increase the efficiency of microwave ovens , DOE identified several possible technologies and examined the most common improvements used in today’s market. Because DOE only address ed energy conservation standards for standby mode and off m ode in this rulemaking, the following discusses only technology options associated with reducing standby mode and off mode energy consumption.

For microwave oven standby power , DOE investigated technology options for decreasing standby power

. DOE identifi ed lower

- power display technologies , cooking sensors with no standby power requirement, improved power supplies

, improved control boards, and automatic

power

- down from research

, literature review , and reverse

- engineering conducted as part of the

microwave oven analysis discussed in chapter 5 of this TSD

.

Table 3.14

. 1 Technology Options for Microwave Oven

– Standby Power

1. Lower

- power display technologies 2. Cooking sensors with no standby power requirement

3. Improved p ower suppl y and control board options

4 . Automatic power

- down

Lower

- power display technologies3-32 Microwave oven s are generally constructed using one of the following display types to
show clock time and other parameters : (1) VFD ; (2) backlit and non

- backlit LCD ; and (3) LED display

. Within the 32

- unit sample that DOE examined as part of the supplemental microwave oven analysis

, microwave ovens equipped with VFDs consumed the most power on average, followed by units featu ring backlit LCDs, LED display s , and non

- backlit LCD s

. Data gathered by DOE show that standby power levels between 1.1 W and 1.4 W have been achieved in

microwave oven s using either LED displays or LCDs, and standby power as low as 1.7 W has

been achieved using a backlit LCD

. The lowest standby power observed for a microwave oven

with a VFD was 2.5 W

. One display manufacturer stated to DOE that backlit LCDs can consume up to 5 0 percent less energy than VFDs in sizes applicable to microwave oven s , depending on the number of LED backlights used to light the display

. The manufacturer also stated that power requirements for LED displays are generally lower than for VFDs, but the difference depends considerably on the number of diode segments being lit

.

The di splay format on each microwave oven can be characterized as either numeric, alphanumeric, or dot matrix

. Whereas numeric displays can only show time, alphanumeric displays can also display letters

, though usually in simplified format

. Dot matrix displays c an show various fonts, letters, and symbols via matrices of pixels, making them the most flexible display technology

. In the reverse

- engineering analysis DOE conducted as part of chapter 5 of this TSD, DOE observed that LED displays found in microwave oven s generally contain four

numeric characters and a row of small words

. LCDs and VFDs allow for more flexibility in the numbers and words being displayed by using alphanumeric characters without increasing the power requirements

. LED displays could conceivab ly achieve this, but would require more diode segments, increasing the cost of the display

. However, this would not a ffect standby power, assuming that in standby mode only the clock time is being displayed

.

Cooking sensors with no standby power requirem ent

C ooking sensors detect that a desired cooking state of the food load has been reached and

then signal the controller to shut off the microwave oven , thereby preventing excess energy consumption in addition to optimizing food quality

. During product teardowns (see chapter 5 of

this TSD ) , DOE found that the most common identifiable cooking sensors a re absolute humidity

sensor s, which infer the cooking state of the food load from the amount of water vapor being released into the cavity

. This sensor tech nology requires standby power in t he range of 1

– 2 W to maintain a resistive heating element, whose conductivity changes in response to the presence of

water vapor or other gases , at operating temperature

. If the power to the sensor is switched off

and back on , warm

- up times in excess of two minutes may be required

. Standby testing by DOE

and AHAM revealed no microwave oven s with cooking sensors that consume less than 2 W in standby mode

. One Japanese microwave oven manufacturer stated during discussions wit h DOE that they are unaware of any humidity sensors that d o not require standby power to stay warm.

DOE has conducted research of cooking sensor technologies and has identified cooking sensors with zero standby power requirements and little to no warm

- up time

. During the reverse

engineering analysis for microwave ovens ( discussed in more detail in c hapter 5 of this TSD )

, DOE observed that microwave ovens from one manufacturer use a piezoelectric steam sensor3-33 which req uires zero power in standby mode

. This sensor technology functions by detecting a

burst of steam created from moisture in the food as it cooks

. Low frequency (approximately 10 hertz) components of the output signal of the device correlate with the amount o f hot steam

emitted from the load

. In addition to steam sensors, DOE has identified infrared and weight

sensors with little to no warm

- up time that do not consume standby power and that have been applied in microwave oven s currently on the Japanese market

. Infrared cooking sensors detect the temperature of the load in the field of view of the sens ing element

. These sensors may require multiple elements or a motor to rotate the sensor in order to expand the field of view to accurately measure the temperature of the food load depending on where it is placed in the oven cavity

. Weight sensors determine the cooking state based upon the reduction in weight of the food load, which results from moisture evaporating from the load

. DOE has also identified

relative hu midity sensors as a type of zero

- standby power s ensor that can be applied in a

microwave oven , but is unaware of any microwave oven s on the market that use this type of

sensor

.

Lastly, DOE is aware of an absolute humidity sensor capable of short warm

- up times (5

– 10 seconds) that requires no standby power . Based on DOE’s discussions with the manufacturer

of the zero

- standby power absolute humidity sensor, only the microwave oven controls would

need to be modified to add transistors to de

- energize the cook ing sensor while in standby mode, and the added cost of components would be approximately $0.10. DOE also notes that the sensor manufacturer supplies absolute humidity sensors (all of which are capable of short warm

- up times) to multiple microwave oven man ufacturers that comprise a significant portion of the U.S.

market , and one manufacturer indicated that it is currently using this zero

- standby power cooking

sensor in its microwave ovens. Manufacturers also noted that the reliability of these zero

- standby power absolute humidity sensors has not been an issue. One manufacturer noted that the reliability is expected to be improved compared to previous sensor types because the zero

standby absolute humidity sensors are only energized during the cooking cycle, whereas the previous sensors are energized continuously for the lifetime of the product.

Improved p ower supply and control board options

A potential area for standby power improvements is the power supplies on the control

board

. Multiple improvement p aths with varying risk to manufacturers are available, including the selective upgrading of power supply components to boost efficiency, the reduction of peak power demand through the use of lower

- power components, and the transition to switching

power sup plies.

Typically, microwave ovens incorporate unregulated plus regulated control board power supplies (also referred to as a linear power supply)

. The unregulated portion consists of a small transformer, a bridge rectifier, and an electrolytic capacitor

. Voltage regulators then step down the voltage(s) to the level(s) required by the control logic, display, and cooking sensor

. This approach results in a rugged power supply which is reliable, but typically ha s an efficiency of

about 40 percent

.3-34 According to power supply topology experts that DOE consulted, the quality of the transformer core material, diode type , capacitor quality, and voltage regulator selection could cut

no

- load standby power for the power supply in half and boost conversion efficiency from 40 to 70 percent. For a typical 5 W

- output microwave

- only unit power supply, that no

- load power consumption improvement is approximately 0.4 W

. DOE notes that other sources indicate that optimized transformers may reduce no

- load losses by approximately 0.6

– 0.7 W. 34

DOE also notes that switching from low

- cost electromechanical relays (used to drive components such as heaters, motors, lights, etc.) to low operating power electromechanical relays or solid state relays capable of driving the sa me loads as the low

- cost electromechanical

rela ys would decrease the total power requirements of the control board and thus decrease the standby losses of the power supply. As a result the overall standby power consumption of the

microwave oven would also decrease.

Switching power supplies offer the highest conversion efficiencies of up to 75 percent for switch mode power supply designs for appliance applications for power supply sizes similar to

those of microwave ovens h and lowest no

- load standby losses (0.2 W or less)

, though at a higher cost, higher part count, and greater complexity

. DOE is unaware of any switching power supply designs for application in microwave ovens that can achieve efficiencies above 75 percent

. S witching power supplies greater co mplexity may also result in lower overall reliability and take greater care to implement

. For example, a mong other issues, a switching power supply can be prone to causing electromagnetic interference

. DOE notes that there already are some premium

microwav e ovens on the U

. S

. market that incorporate switching power supplies

. DOE research suggests that the component prices for switch mode power supplies and traditional linear power supplies are currently nearly equivalent. However, DOE concludes that the indu stry will likely transition to switch mode power supplies as it gains more experience with them, causing switching component prices to fall further as volumes increase. Additionally, the adoption of switch mode power supplies would facilitate standardized control boards for world

- wide use, thereby reducing testing and development costs.

Automatic power

- down

Manufacturers could also meet very low ( i.e. , less than 1 W) standby power levels according to the definition of “standby mode” in the DOE test procedure by incorporat ing an automatic function that turns off most power

- consuming components once a period of inactivity has elapsed

. Such a low

- consumption state could be user

- selectable on demand, or could be the

default condition in w hich the microwave oven is shipped such that the consumer would be required to opt in to maintaining the display, cooking sensor, or other utility feature during standby

. DOE has determined that some microwave oven suppliers to the U.S. market have already taken such approaches to meet prescriptive standby power standards in other markets such as Japan. DOE understands that such a technology option would likely require a relay

-

h Information on design and efficiencies o f switch mode power supplies is available from Power Integrations at : www.powerint.com/en/applications/major

- appliances

. (Accessed May 2013.)3-35 driven disconnect between line power and the power filtration board typically inc orporated in microwave ovens. The automatic power

- down module would feature a 1.5 W switch mode power supply that can respond to a simple switch signal to power up and enable microwave oven operation via a relay on the power filtration board. If the existi ng door switches do not suffice, a n additional door switch could provide the necessary signal to enable the power supply, whose power consumption is otherwise nearly 0 W. Thus, the microwave oven would power up, enabling a light to be energized, with a del ay short enough to be perceived as instantaneous when the consumer opens the door

.

3.14.3 Energy Efficiency

In preparation for the screening and engineering analyses, DOE gathered data on the energy efficiency of microwave ovens currently available in the marketp lace

. These data were taken from databases maintained by a variety of regulatory agencies

. While this section is not intended to provide a complete characterization of the energy efficiency of all appliances currently available and in use, it does provide an overview of the energy efficiency of microwave

ovens covered by this rulemaking.

Microwave ovens consume energy in standby mode while powering the clock and

electronic controls

. AHAM recently provided data on microwave oven standby power for a sample o f microwave ovens available in the U.S. market

. 35 Figure 3.14

. 1 displays AHAM

measured microwave oven standby power data as a function of rated cooking output power

. Figure 3.14

. 1 also contains standby power measurements made by the Canadian Building Energy

End

- Use Data and Analysis Center (CBEEDAC) at the University of Alberta

. The data show a wide range of standby power, but there is no correlation b etween standby power and rated cooking output power. The data did not indicate the product type ( i.e. , microwave only,

convection ) or the installation configuration ( i.e. , countertop, over

- the

- range, built

- in).3-36 Figure 3.14

. 1 AHAM and CBEEDAC Microwave Oven Standby Power versus Rated Output Power 36

, 37

Until recently, FEMP publishe d a database of microwave oven s, sorted by standby power, although this database wa s also not completely representativ e of microwave ovens

available in the U.S. market

. Figure 3.14

. 2 displays the number of microwave oven models available with different standby power levels as listed in the FEMP standby power database as of May 2011

. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

600 800 1000 1200 1400 1600

Rated Output Power (W)Standby Power Consumption (W)

AHAM Data CBEEDAC Data3-37 Figure 3.14

. 2 Microwave Oven Standby Power Consumption from the FEMP Database 38

FEMP did not specify microwave oven rated cooking output power

. Therefore, it is not possible to make a direct comparison between the FEMP microwave oven standby power data

and the AHAM microwave oven test data

. However, the distribution s of standby power in the

FEMP database and the AHAM data are similar

. No residential microwave ovens consume less

than 1 W of standby power, some microwave ovens consume less than 2 W of standby power, but the large majority of microwave ovens consume between 2.1 and 3 W of standby power. 0 26 56

33

0 10 20 30 40 50 60

0 to 1 W 1.1 to 2 W 2.1 to 3 W 3.1 W or greaterNumber of Models

Standby Power3-38 REFERENCES

1 “U.S. Appliance Industry: Market Share, Life Expectancy & Replacement Market, and

Saturation Levels.” Appliance Market Research Report

, Appliance Magazine

, January 2010

.

2 Ibid.

3 Maytag. 2005. “Whirlpool Corporation and Maytag Corporation Sign Defin itive Merger

Agreement.” Maytag Press Release , August 22, 2005.

4 P. Hussmann. 2006. “Justice to Extend Maytag

- Whirlpool Merger Review.” Newton Daily

News Online , February 14, 2006.

5 W. Ryberg. 2006. “Harkin, Boswell want Maytag sale blocked.” Des Moines Register

,

January 13, 2006.

6 Ibid.

7 U.S. Department of Justice (U.S. DOJ). 2006. Department of Justice Antitrust Division

Statement on the Closing of its Investigation of Whirlpool’s Acquisition of Maytag , March 29,

2006.

8 Ibid.

9 A ssociation of Home Appliance Manufacturers (AHAM) Fact Book 2005.

10 AHAM Fact Book 2003

.

11 Tiele, Simone. 2008. Australia’s Approach to Standby Power. Department of the

Environment, Water, Heritage and the Arts, Australia, April 2, 2008. Available o nline at: www.energyrating.gov.au/wp

content/uploads/Energy_Rating_Documents/Library/Standby_Power/Standby_Pow er/2008

- standby

- holt.pdf

. ( Accessed May 201 3

. )

12 Korea’s Energy Standards & Labeling

–

Market Transformation

. Ministry of Knowledge Economy and Korea Energy Management Corporation. Available online at: www.kemco.or.kr/new_eng/pg02/pg02100101.asp . (Accessed May 2013 .)

13 Japan’s Top Runner Program . 2005. Final Report by Microwave Oven Evaluation Standard

Subcommittee

. Prepared by Japan’s Energy Efficiency Standards Subcommittee of the Advisory Committee for Natural Resources and Energy. November 2005. Available online at www.eccj.or.jp/top_runner/pdf/tr_microwaveoven .pdf . (Accessed May 2013 .)

14 Commission of the European Communities. 2008. Commission Regulation (EC) No

1275/2008 implementing Directive 2005/32/EC of the European Parliament and of the Council3-39 with regard to ecodesign requirements for standby and off mode electric power consumption of electrical and electronic household and office equipment. December 17, 2008. Available

online at : http://eur

lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:339:0045:0052:en:PDF . (Accessed May 2013 )

15 U.S. Census Bureau. 20 12 . “Table 3

. New Privately Owned Housing Units Started.” New

Residential Construction

, October 20 12

. Available online at: www.census.gov/construction/nrc/pdf/newresconst.pdf . (Accessed May 2013 .)

16 Ibid.

17 “2011 Full

- Year Appliance Industry Shipment Statistics.” Market Research

, Appliance

Magazine

, March 2012, and AHAM Fact Book 2005. Op. cit.

18 U.S. Census Bureau

. Annual Survey of Manufacturers (ASM): Value of Product Shipments:

Value of Shipments in Product Classes . 1994

– 2011, issued annually

.

19 AHAM Fact Book 2003.

20 U.S. Census Bureau. 200 9

. Statistics.

21 AHAM. Import/Export Trade Report

– December 200 9 . 2010.

22 Ibid

.

23 Ibid

.

24 Ibid

.

25 AHAM Fact Book 2005 and “The Share

- of

- Market Picture” Appliance Magazine , September

2007

– 2009.

26 AHAM Fact Book 2005

.

27 U.S. Census Bureau. 20 11

. “ Statistics for Industry Groups and Industries.” Annual Survey of

Manufactures ( ASM ), November 20 12

. Available online at: www.census.gov/manufacturing/asm/ . (Accessed May 2 013 .)

28 Ibid.

29 Ibid.

30 Ibid.3-40 31 Ibid.

32 Ibid.

33 U.S. Census Bureau, Survey of Plant Capacity: 2006 to 199 6

.

34 N. Nielsen. “Loss Optimizing Low Power 50 Hz Transformers Intended for AC/DC Standby Power Supplies.” Applied Power Electronics Conference and Exposition, 2004. IEEE , pp. 420

– 425, September 9, 2004.

35 AHAM. 2006. AHAM Test Data on Microwave Ovens . October 23, 2006. Docket Number

EE

- 2006

- STD

- 0127, Comment Number 17, November 30, 2006.

36 Ibid.

37 N. Fish, D. White, and D. Ryan. 2006. Standby Power Energy Use of Common Household Appliances . Canadian Building Energy End

- Use Data and Analysis Center

, University of Alberta, Canada

, July 2006.

38 FEMP Microwave S tandby P ower D atabase. Available online at http://www1.eere.energy.gov/femp/technologies/standby_power.aspx . (Accessed May 2013 .)4

- i CHAPTER 4

. SCREENING ANALYSIS

TABLE OF CO NTENTS

4.1 INTRODUCTION

................................ ................................ ................................ ........... 4

- 1

4.2 SCREENED

- OUT TECHNOLOGY OPTIO NS

................................ ............................. 4

- 1

4.3 REMAINING DESIGN OPT IONS

................................ ................................ ................. 4

- 4

LIST OF TABLES

Table 4.2.1 Retained Design Options for Microwave Ovens

– Standby Power

....................... 4

- 44-1 CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter details the screening analysis conducted by the U.S. Department of Energy (DOE) of the technology options identified in the market and technology assessment for

residential microwave oven s (chapter 3 of this technical support document (TSD))

. In the market and technology assessment, DOE presented an initial list of technologies that can be use d to
reduce the standby power consumption of microwave ovens. The goal of the screening analysis is to identify any technology options that will be elimin ated from further consideration in the rulemaking analyses.

T he candidate technology options are assessed based on DOE ’s analysis as well as inputs

from interested parties including manufacturers, trade organizations, and energy efficiency

advocates. Techn ology options that are judged to be viable approaches for improving standby consumption are retained as inputs to the subsequent e ngineering a nalysis , and are designated as design options

. Technology options that are not incorporated in commercial products or in working prototypes, or that fail to meet certain criteria

, as to practi ca bility to manufacture, install and service, as to impacts on product utility or availability, or as to health or safety will be

eliminated from consideration in accordance with Energy Conservation Program for Consumer Products: Procedures for Consideration of New or Revised Energy Conservation Standards for

Consumer Products

. 61 FR at 36974 (July 15, 1996)

. The rationale for either screening out or retaining each technology option is detailed in the following sections.

4.2 SCREENED

- OUT TECHNOLOGY OPTIO NS

The following section detail s the specific technology options that were screened out prior to the engineering analysis, along with the rationale for elimination.

The technolog ies identified in the market and technology assessment were evaluated pursuant to the criteria set out in the Energy Policy and Conservation Act ( EPCA )

. ( 42 United States Code ( U.S.C. ) 6 291

- 63 09 ) EPCA establishes criteria for prescribing new or amended

sta ndards designed to achieve the maximum improvement in energy efficiency. Furthermore, EPCA directs the Secretary of Energy to determine whether a standard is technologically feasible and economically justified. (42 U.S.C. 6295(o)(2)(A)(B)) In view of the E PCA requirements for determining whether a standard is technologically feasible and economically justified, a ppendix A to subpart C of Title 10 Code of Federal Regulations p art 430 (10 CFR p art 430), Procedures, Interpretations and Policies for Considerati on of New or Revised Energy Conservation Standards for Consumer Products (the Process Rule), sets forth procedures to guide DOE in the consideration and promulgation of new or revised product energy conservation standards under

EPCA

. These procedures elabo rate on the statutory criteria provided in 42 U.S.C. 6 295 and in part eliminate problematic technologies early in the process of revising an energy conservation4-2 standard

. Under the guidelines, DOE eliminates from consideration technologies that present

una cceptable problems with respect to the following four factors:

(1) Technological feasibility

. If it is determined that a technology has not been incorporated in commercial products or in working prototypes, then that technology will not be

considered further.

(2) Practicability to manufacture, install, and service

. If it is determined that mass production of a technology in commercial products and reliable installation and servicing of the technology could not be achieved on the scale necessary to s erve the relevant market at the time of the effective date of the standard, then that technology will not be considered further.

(3) Impacts on product utility to consumers

. If a technology is determined to have significant adverse impact on the utility of the product to significant subgroups of consumers, or

result s in the unavailability of any covered product type with performance characteristics (including reliability), features, size, capacities, and volumes that are substantially the same as product s generally available in the United States at the time, it will not be considered further

.

(4) Safety of technologies

. If it is determined that a technology will have significant adverse impacts on health or safety, it will not be considered further.

After reviewing the technology options to reduce standby power consumption of microwave against the screening criteria

, DOE did not eliminate any technology options from

consideration in its standby power rulemaking analysis for microwave ovens

.

For lo wer

- power display technologies, DOE understands that the consumer utility of a microwave oven display is associated with its brightness, viewing angle, and ability to display complex characters

. I nterviews DOE conducted with display manufacturers revealed that vacuum fluorescent displays ( VFDs ) can achieve higher brightness levels, wider viewing angles, and higher contrast than backlit liquid crystal displays ( LCDs )

. Display man ufacturers also stated that light

- emitting diode (LED) displays have largely com parable performance to VFDs in terms of brightness and viewing angle

. A VFD manufacturer mentioned that, while VFD technologies with efficiencies comparable to backlit LCDs do exist, such displays are substantially more expensive than the ones commonly fou nd in microwave ovens today

.

Multiple manufacturers of cooking products stated in interviews the need to differentiate their cooking appliance lines from those of their competitors with (among other features ) coordinated displays and user interfaces

. Manufacturers noted that LCD displays (backlit or not) do not work well in appliances that get very hot, such as ovens, due to thermal limitations in the technology

. Manufacturers also opposed switching entirely to LED

- based displays since it could

make it harder for them to differentiate their products, particularly in a market as commoditized

as microwave ovens

. Lastly, manufacturers noted that larger, more complex, and more colorful displays are usually associated with premium appliances, which will thus have a harder time4-3 achieving the same standby power consumption as units with smaller, dimmer, and simpler displays.

DOE notes that the current rulemaking does not seek to regulate the standby power consumption of conventional cooking appliances

. DOE a lso notes that not all high

- end appliance manufacturers use the same display technology across all cooking appliances that they

manufacture

. For example, at least one manufacturer uses a backlit LCD in its microwave oven, with the backlighting LEDs color

- c oordinated with the VFDs found in its ovens

.

DOE recognizes that certain microwave ovens, such as over

- the

- range microwave ovens

, may be exposed to higher temperature and humidity conditions than other installation conditions.

As discussed in c hapter 5 o f this TSD, DOE conducted accelerated lifetime testing of different microwave oven display types by subjecting a limited sample of microwave ovens to high temperatures and humidity levels for an extended period of operation in standby mode. The

results of this testing demonstrated that the illuminance of each display tended to decrease over time to varying degrees, but did not reveal any correlation between display type and rate of illuminance reduction. In addition, DOE observed in its test sample a unit w ith an LED display that exhibited illuminance, a significant element of consumer utility, that was comparable to that of the VFD on another unit. Based on this lifetime testing, and the existence of multiple over

- the

range microwave oven models on the mark et with each type of display technology, DOE does not have information that would suggest over

- the

- range microwave ovens would require certain display technologies ( i.e. , VFD) that have inherently higher power consumption than other display types that prov ide similar consumer utility.

For these reasons, DOE believes that lower

- power display technologies which maintain consumer utility can be incorporated in microwave ovens, and meet all of the screening criteria.

Similarly, t he number of different senso r technologies available on the market that do not require standby power suggests that the utility of a cooking sensor can be maintained with zero standby power

. Since improved power supplies and control board options also have no consumer utility impacts and have been incorporated in products currently available on the market, both of these technology options meet the screening criteria.

For automatic power

- down , DOE interviewed the Japanese Electrical Manufacturers’ Association (JEMA)

. These discussions revealed that there are a large number of microwave

oven s in the Japanese market that implement this feature

. For a power

- down function, DOE believes that the entire microwave oven can be switched off, with a dedicated, low power controller waiting for a c ontrols input, door opening, etc. before re

- animating the rest of the

microwave

. This controller then subsequently detects a period of inactivity before switching the rest of the microwave oven off again

. Power consumption could be reduced to well below 1 W, though at a reduction in consumer utility since displays, night lights, etc. would remain off until

a consumer interacts with the microwave oven

. DOE has determined , however, that control strategies are available to allow manufacturers to make design tr adeoffs between incorporating standby

- power

- consuming features such as displays or cooking sensors and including a function4-4 to turn power off to these components during standby

. Therefore, this technology option was retained for further analysis.

4.3 REMAINING DESIGN OPTIONS

The following sections list the technology options for each product covered by this rulemaking that were retained by DOE and subsequently designated as design options

. Each of these technologies will be evaluated further in the subsequent engineering analysis.

Table 4.3.1 Retained Design Options for Microwave Oven s

– Standby Power

1. Lower

- power display technologies

2. Cooking sensors with no standby power requirement

3. Improved power supply and control board options

4. Automatic power

- down5

- i CHAPTER 5

. ENGINEERING ANALYSIS

TABLE OF CO NTENTS

5.1 INTRODUCTION

................................ ................................ ................................ ........... 5

- 1

5.2 PRODUCT CLASSES ANAL YZED

................................ ................................ .............. 5

- 1

5.3 EFFICIENCY LEVELS

................................ ................................ ................................ ... 5

- 2

5.3.1 Baseline Units

................................ ................................ ................................ .................. 5

- 2

5.3.2 Incremental Standby Power Levels

................................ ................................ ................. 5

- 3

5.4 METHODOLOGY OVERVIEW

................................ ................................ .................... 5

- 9

5.4.1 AHAM Data Submittal

................................ ................................ ................................ .. 5

- 10

5.4.2 Manufacturer Interviews

................................ ................................ ................................ 5

- 10

5.4.3 Product Teardowns

................................ ................................ ................................ ........ 5

- 10

5.4.3.1 Selection of Units

................................ ................................ ......................... 5

- 11

5.4.3.2 Generation of Bill of Materials

................................ ................................ .... 5

- 11

5.4.3.3 Cost Structure of the Spreadsheet Models

................................ ................... 5

- 12

5.4.4 Product Testing

................................ ................................ ................................ .............. 5

- 13

5.5 ANALYSIS AND RESULTS

................................ ................................ ........................ 5

- 13

5.5.1 AHAM Data

................................ ................................ ................................ ................... 5

- 13

5.5.2 Product Testing and Teardowns

................................ ................................ ..................... 5

- 15

5.5.2.1 General Microwave Oven Construction

................................ ...................... 5

- 16

5.5.2.2 Test Units

................................ ................................ ................................ ..... 5

- 17

5.5.2.3 Micr owave

- Only Ovens: Product Testing and Teardowns

.......................... 5

- 19

5.5.2.4 Countertop Convection Microwave Ovens: Product Testing and

Teardowns

................................ ................................ ................................ .... 5

- 26

5.5.2.5 Built

- In and Over

- the

- Range Convection Microwave Ovens: Product Testing and Teardowns

................................ ................................ ................ 5

- 28

5.5.2.6 Over

- the

- Range Microwave Oven Display Accelerated Lifetime Testing 5

- 30

5.5.3 Incremental Cost Data

................................ ................................ ................................ .... 5

- 35

5.5.3.1 Incremental Cost Data for Microwave

- Only Ove ns and Countertop

Convection Microwave Ovens

................................ ................................ ..... 5

- 35

5.5.3.2 Incremental Cost Data for Built

- In and Over

- the

- Range Convection

Microwave Ovens

................................ ................................ ........................ 5

- 37

LIST OF TABLES

Table 5.3.1 Standby Power Levels for Microwave

- Only Ovens and Countertop

Convection Microwave Ovens

................................ ................................ ............. 5

- 4

Table 5.3.2 Standby Power Levels for Built

- In and Over

- the

- Range Convection

Microwave Ovens

................................ ................................ ................................ 5

- 4

T able 5.4.1 Engineering Analysis Methods

................................ ................................ .......... 5

- 105

- ii Table 5.5.1 Microwave Oven Standby Power Comparison

................................ .................. 5

- 15

Table 5.5.2 Microwave Oven Features in DOE Sample

................................ ....................... 5

- 18

Table 5.5.3 DOE Microwave

- Only Oven Standby Power (Table 1 of 4)

............................. 5

- 20

Table 5.5.4 DOE Microwave

- Only Oven Standby Power (Table 2 of 4)

............................. 5

- 20

Table 5.5.5 DOE Microwave

- Only Oven Standby Power (Table 3 of 4)

............................. 5

- 20

Table 5.5.6 DOE Microwave

- O nly Oven Standby Power (Table 4 of 4)

............................. 5

- 21

Table 5.5.7 DOE Countertop Convection Microwave Oven Standby Power

....................... 5

- 27

Table 5.5.8 Control Power Supply Transformer Standby Power Test Results

..................... 5

- 28

Table 5.5.9 DOE Built

- In and Over

- the

- Range Convection Microwave Oven Standby

Power

................................ ................................ ................................ ................. 5

- 29

Table 5.5.10 Incremental Manufacturing Costs for Product Class 1 Microwave Oven Standby Power

................................ ................................ ................................ ... 5

- 37

Table 5.5.11 Incremental Manufacturing Costs for Product Class 2 Microwave Oven Standby Power

................................ ................................ ................................ ... 5

- 38

LIST OF FIGURE S

Figure 5.4.1 Manufacturing Cost Assessment Stages

................................ ............................ 5

- 12 Figure 5.5.1 AHAM Microwave Oven Standby Power

................................ ......................... 5

- 14 Figure 5.5.2 Countertop Microwave Oven Standby Power Consumption as a Function of Rated Output Power in DOE Reverse

- Engineering Sample

......................... 5

- 22 Figure 5.5.3 Built

- In Microwave Oven Standby Power Consumption as a Function of Rated Output Power in DOE Reverse

- Engineering Sample

.............................. 5

- 23 Figure 5.5.4 AHAM Microwave Oven Standby Power Consumption as a Function of Rated Output Power

................................ ................................ ........................... 5

- 24 Figure 5.5.5 DOE Standby Power Consumption as a Function of Display Size for Countertop Convection Microwave Ovens

................................ ........................ 5

- 27 Figure 5.5.6 DOE Standby Power Consumption as a Function of Display Size for

Built

- In and Over

- the

- Range Convection Microwave Ovens

........................... 5

- 30 Figure 5.5.7 67.5 °C, 90

- percent RH, Illuminance at Start of 10

- Minute Measurement

Periods

................................ ................................ ................................ ................ 5

- 32 Figure 5.5.8 67.5 °C, 90

- percent RH, Illuminance at End of 10

- Minute Measurement

Periods

................................ ................................ ................................ ................ 5

- 33 Figure 5.5.9 82.5 °C, 90

- percent RH, Illuminance at Start of 10

- Minute Mea surement
Periods

................................ ................................ ................................ ................ 5

- 34 Figure 5.5.10 82.5 °C, 90

- percent RH, Illuminance at End of 10

- Minute Measurement

Periods

................................ ................................ ................................ ................ 5

- 355-1 CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

After conducting the screening analysis, the U.S. Department of Energy (DOE) performed an engineering analysis based on the remaining design options

. The engineering analysis consists of estimating the standby energy consumption and costs of products at various levels of increased efficiency

. This section provides an overview of the engineer ing analysis (section 5.1 ), discusses proposed product classes (section 5.2 ), establishes baseline unit specification s (section 5.3.1 ), discusses incremental efficiency levels (section 5.3.2 ), explains the methodology used during data gathering ( 5.4 ) and discusses the analysis and results (section 5.5 )

The primary inputs to the engineering analysis are baseline information from the market and technology assessment ( c hapter 3 of this technical sup port document ( TSD ) ) and technology options from the screening analysis ( c hapter 4

. ) Additional inputs include laboratory testing and

reverse

- engineering of representative microwave ovens

. The primary output of the engineering analysis is a set of cost

- ef ficiency curves

. In the subsequent markups analysis ( c hapter 6 ), DOE determined customer ( i.e.

, product purchaser) prices by applying distribution markups, sales tax and contractor markups

. After applying these markups, they serve as the input to the build ing

energy

- use and end

- use load characterization ( c hapter 7 ) and the life

- cycle cost (LCC) and payback period (PBP) analyses ( c hapter 8).

DOE typically structures its engineering analysis around one of three methodologies

. These are: (1) the design

- option approach, which calculates the incremental costs of adding specific design options to a baseline model that will improve its efficiency ( i.e. , lower its energy use in standby mode and off mode) ; (2) the efficiency

- level approach, which calculates the

relat ive costs of achieving increases in energy efficiency levels ( in this case , levels of reduced standby power) , without regard to the particular design options used to achieve such increases; and/or (3) the reverse engineering or cost

- assessment approach, wh ich involves a “bottom

- up” manufacturing cost assessment based on a detailed bill of materials (BOM) derived from teardowns of the product being analyzed

. Deciding which methodology to use for the engineering analysis depends on the product, the design opt ions under study, and any historical

data that DOE can draw on.

5.2 PRODUCT CLASSES ANAL YZED

DOE separated residential microwave ovens into product classes

. Because DOE formulated a separate energy conservation standard for standby power consumption for each

p roduct class, the criteria for separation into different classes are (1) type of energy used (natural gas or electricity), and (2) capacity or other performance

- related features such as those that provide utility to the consumer, or others deemed appropria te by the Secretary that would justify the establishment of a separate energy conservation standard

. (42 U.S.C

. 6295 (q))5-2 For the reasons discussed in chapter 3 of this TSD , DOE has analyzed for the purposes of

this final rule the following two product classes for microwave ovens:

**•** Product C lass 1: Microwave

- only ovens and countertop convection microwave ovens

**•** Product C lass 2: Built

- in and over

- the

- range convection microwave ovens

5.3 EFFICIENCY LEVELS

5.3.1 Baseline Units

DOE selected baseline units as reference points for each product class, against which DOE measured changes resulting from energy conservation standards

. The baseline unit in each product class represents the basic characteristics of equipment in that class

. Typically, a baseline

unit is a unit th at just meets current required energy conservation standards and provides basic consumer utility

.

DOE used the baseline units in the engineering analysis and the life

- cycle

- cost and payback

- period analysis

. To determine energy savings and changes in price , DOE compared each higher energy efficiency design option with the baseline unit

.

The identification of baseline units required establishing the baseline standby power

level

. For microwave oven standby power , energy conservation standard levels are expressed as

a maximum s tandby power consumption, in w atts (W)

. As discussed in chapter 3 of this TSD,

DOE determined based on a survey of products available on the U.S. market that four countertop

microwave

- only m odels produced by two manufacturers have electromechanical rotary dial controls and no displays, and which, therefore, are capable of operation in off mode. Because these units represent less than 1 percent of the models in product class 1 and because thei r power consumption is already low due to the lack of a display, any energy savings associated with off mode energy conservation standards for microwave ovens would be trivial. For these reasons,

DOE did not consider standards for microwave oven off mode. Based on product testing (see

section 5.6.1.3 )

, DOE selected a baseline standby power level which was the highest typical standby power for a microwave oven to provide full consumer utility for each product class

. For the purpose of this rulemaking , DOE determined consume r utility to be the ability to display complex characters, brightness , and viewing angle of the display as well as the ability to automatically control the cooking operation ( i.e.

, through the use of a cooking sensor)

. For the

built

- in an d over

- the

- range convection microwave ovens product class, consumer utility also included a sufficiently sized power supply capable of operating additional features required to handle the thermal loads associated with their installation and to provide cons umer utility, such

as additional exhaust fan motors, convection fan motors and heaters, and additional lights. For both product classes, the baseline power supply used a conventional linear power supply with an efficiency of approximately 40 percent or les s

. DOE selected a baseline standby power level of

4.0 W for the microwave

- only oven s and countertop convection microwave ovens product class

.5-3 For the built

- in and over

- the

- range convection microwave ovens product class, DOE selected a baseline standby powe r level of 4.5 W. As discussed in section 5.5.1.2 , DOE noted that the built

in convection microwave ovens in its test sample consumed more standby power than the over

the

- range convection microwave ovens. However, DOE notes that built

- in and over

- the

- range convection microwave ovens have essentially the same features ( i.e. , exhaust fans and convection heaters). DOE also noted that the two built

- in convection microwave ovens in its test sample were high

- end units with large displays and a larg er number of cooking settings and options. As a result, DOE believes that the standby power level of 4.5 W for the built

- in and

over

- the

- range convection microwave ovens product class provides full consumer utility and that the higher levels observed in DO E’s test sample for built

- in convection microwave ovens are not appropriate for establishing the baseline standby power levels.

5.3.2 Incremental Standby Power Levels

For the product classes presented in section 5.2 , DOE analyzed several standby power

levels and obtained incremental cost data at each of these levels

. Table 5.3 .1 and Table 5.3

. 2 provide standby power levels and the reference source of each level. DOE conducted the engineering analysis for microwave oven standby power for the two product class es DOE

identified for mic rowave ovens

. To analyze the cost

- energy use relationship for microwave oven standby power , DOE defined standby power levels expressed as a maximum average standby power, in W

. For the reasons noted above in section 5.3.1 , the standard levels do not include off mode power.

For the microwave

- only ovens and countertop convection microwave ovens product class, t he standby power levels were derived from review of the Federal Energy Management Program (F EMP ) procur ement efficiency recommendation , the International Energy Agency’s (IEA) One

- Watt program to lower standby power consumption below 1 W for various electrical appliances , and the current maximum microwave oven standby technology ( i.e. lowest standby

power ) that DOE believes is or could be commercially available at the time the energy

conservation standards become effective. T he max

- tech microwave

- only oven and countertop

convection microwave oven standby power l evel of 0.02 W corresponds to a unit equipped with a default automatic power

- down function that shuts off certain power

- consuming components after a specified period of user inactivity. The standby power at max

- tech was obtained from a

microwave oven curre ntly on the market in Korea which incorporates such a feature. DOE also added a standby power level as a gap

- fill between the FEMP Procurement Efficiency

Recommendation and IEA One

- Watt Program levels

. Table 5.3.1 provide s the microwave

- only ovens and countertop convection microwave ovens product class standby power levels and the

reference source for each level that DOE has analyzed

.5-4 Table 5.3

. 1 Standby Power Levels for Microwave

- Only Ovens and Countertop

Convection Microwave Ovens

Standby

Power Level Standby Power Level Source Standby Power (W)

Baseline Baseline 4.0

1 FEMP Procurement Efficiency Recommendation 2.0

2 Gap Fill 1.5

3 IEA 1

- Watt Program 1.0

4 Max

- Tech 0.02

For the built

- in and over

- the

- range convection microwave oven product class

, standby

power consumption is higher than the levels from the reference sources used for microwave

- only ovens and countertop convection microwave ovens. Therefore, the standby power levels were determined based on reverse

- engineering analyses on a representative sample o f built

- in and

over

- the

- range convection microwave ovens to analyze the various components that contribute to the standby power consumption of the unit. DOE also measure d the standby power consumed by these components individually. Note that , depending on the specific microwave oven design, manufacturers may choose different design options to reach the standby power levels.

Table 5.3

. 2 presents the standby power levels that DOE analyzed for built

- in and over

the

- range convection microwave ovens.

Table 5.3

. 2 Standby Power Levels for Built

- In and Over

- the

- Range Convection

Microwave Ovens

Standby

Level Standby Power Level Source Standby Power (W)

Baseline Baseline 4. 5

1 Zero W Cooking Sensor 3.7

2 Switch Mode Power Supply 2.7

3 Improved Relay Power Supply Design 2.2

4 Max

- Tech (Automatic Power

- Down) 0.04

In its analysis , DOE observed that the absolute humidity cooking sensor s used in these

convection microwave ovens on average consume 0.9 W of line power. DOE research suggests

that e ven microwave ovens without such sensors which use linear power supplies consume a

minimum of about 1 W as long as they a re connected to line power

. S everal factors influence the inherent efficiency of a linear power supply , which typically consist of a transformer, a r ectifier, a linear regulator

, and multiple smoothing capacitors.

The transformer in a typical microwave oven steps line voltage (120 volts AC ( VAC ) ) down to lower voltages, such as 24, 18, or 12VAC. Even when the transformer has no loads attached to it, th e device consumes power. Transformer design i nfluences these no

- load losses;

low

- loss transformers (LLTX) are available but are larger and more expensive to manufacture

.5-5 For the purpose of the final r ule, DOE considered the use of a n LLTX to reduce standby power requirements associated with linear power supplies.

Once a transformer has stepped down the line voltage to a lower AC voltage, the AC is typically rectified using a full

- wave bridge rectifier consisting of four diodes. The di odes used in

the rectification circuit have a typically minor effect on linear power supply efficiency as a

func ti on of the voltage drop that they impose. High efficiency rectifiers ( using Schottky diodes

, for example) feature lower voltage losses than sta ndard ones, though at a higher cost.

The rectified signal is direct current (DC) but exhibits significant ripple. To address the ripple, linear power supplies typically feature one or more capacitors, which smooth the DC voltage

. However, as the voltage b eing delivered by the transformer is not actively regulated, the resultant DC power is considered unregulated pow er, meaning that its voltage will fluctuate significantly as a function of the load attached to the transformer

. The type of capacitor used wil l also have a minor impact on power supply efficiency.

The next component in a linear power suppl y is the linear regulator, a n integrated circuit

which receives the unregulated voltage and produces a lower, regulated voltage. This regulated voltage output usually features at least one capacitor for further smoothing and to help with step transistions in power demand. The linear regulator works by monitoring the output voltage and adjusting its internal resistance to maintain it at a constant level

.

The greater the difference between the unregulated input voltage and the regulated output voltage, the more heat a linear regulator will produce and the less efficient its operation

. For example, several microwave ovens with linear power supplies in the DOE re verse engineering sample incorporated a 24 VDC unregulated power supply and a 5 VDC regulated power supply. If the circuit board attached to the regulated power supply consumes 1/2 ampere (A) of current, then the linear regulator would dissipate about 9 .5 W of heat while delivering 2. 5 W of power to

the control board . Even if the transformer, rectifier, and capacitors were 100

- percent efficient, such a linear power supply would be about 20

- percent efficient over all due to the difference between the unregula ted and regulated voltage.

Most microwave ovens torn down by DOE contained at least one linear regulator to

achieve the required number of regulated bus voltages. DOE research suggests that quantifying the impact of changing the unregulated voltage levels is difficult because other factors, such as

the losses in the transformer core , may overshadow the efficiency gains in “ downstream ” linear regulator circuit s

. However, a lower voltage unregulated power supply may provide an advantage when considering the e fficiency of the linear power supply as part of a comprehensive design strategy.

DOE research also suggests that the no

- load losses in a linear power supply are a function of the power supply capacity. Thus, to the ex tent that the power requirements of th e microwave oven control circuitry can be minimized, the size of the power supply can be reduced and hence standby losses reduced

. In addition to the display and the microcontroller, key microwave oven control components that consume significant power provided by the power supply include the5-6 relays that control the operation of components inside the microwave oven, such as the

magnetron. All microwave ovens reverse

- engineered by DOE contained electromechanical relays. These relays are typically activate d with unregulated power with the use of Darlington transistor arrays

.

DOE research suggests that e lectromechanical r elays are offered in a wide range of efficiencies, meaning that the coil power needed to activate a relay for a given power, voltage,

and/ or current capacity varies considerably. Solid state relays are another option, usually offering the lowest activation energy requirements though at higher cost. For the final rule cost analysis, DOE considered three relay types: a low efficiency, low

- cost electromechanical relay ; a high efficiency, high

- cost electromechanical relay ; and a solid state relay. These relays were chosen on the basis of relays currently offered on the market that have capabilities equivalent to the relays observed in the DOE rev erse

- engineering sample.

Another option to improve power supply efficiency and reduce standby power consumption is to substitute a switch

- mode power supply (SMPS) in place of a linear power supply. An SMPS convert s power at efficiencies approaching 74

- 76 p ercent and typically offers very low standby losses. DOE reverse

- engineering activities in multiple appliance categories have noted an increasing percentage of appliances equipped with an SMPS. DOE research suggests that the adoption of SMPS s is driven by factors including conversion efficiency, standby power considerations, increasing experience in implementation

, and decreasing SMPS
component costs

.

Since SMPS s can be designed for a wide range of input voltages ( i.e.

, 90

– 260 VAC), they allow the possib ility of designing one control board for world

- wide use versus designing one for each major market. This in turn could reduce the costs to the manufacturer by allowing

them to produce one reference design that only has to be certified once for worldwide us e

.

One inherent advantage that SMPS s typically offer is much lower standby power requirements because they do not feature a large , always

- on transformer with its inherent core losses like a linear power supply. Instead, a microprocessor is used to control a switching transistor that feeds just enough power into the ” downstream ” circuits to maintain the desired voltage. As a result, the standby losses associated with the SMPS itself are minimized. Additionally, some SMPS designs offer built

- in “ zero

- standby ” deep

- sleep states that can be started and stopped by a microprocessor or a low

- cost tactile switch.

The DOE sample of microwave oven s included two models with an identi cal SMPS. DOE supplemented the data it gathered through its reverse

- engineering effor ts by using reference designs published by SMPS component suppliers that have been certified by testing agencies. The DOE reference SMPS designs were designed to be drop

- in replacements for the current generation of linear power supplies, i.e. they feature two output voltages. The higher voltage (12 VDC) is not tightly regulated, reflecting current standard practice for unregulated power supplies for use with electromechanical relays

, while t he lower voltage (5 VDC) is tightly regulated,

which also reflects current practices regarding the power supplied to microprocessors.5-7 DOE observes that these reference SMPS s can be adapted to produce other voltages, i.e.

24 VDC and 3 .3 VDC, for example. The required changes in terms of circuitry and compon ents

are minor

, and DOE research suggests that the cost of doing so would be essentially zero.

M icrowave oven manufacturers also have the option of adopting electromechanical relays which are compatible with the output voltages provided by certified SMPS d esigns. Such relays are usually offered with multiple control voltages in the same exterior case, allowing manufacturers

to select drop

- in replacements.

For the final rule, DOE considered 18 separate design pathways that were a function of

the microwave oven type and the standby power measurements obtained during testing of 44

microwave ovens . For example, microwave ovens with a cooking sensor could implement

different design pathway s than units without a cooking sensor. Design pathways also differed by

d isplay type as the standby power measurements of microwaves with vacuum fluorescent displays ( VFD s) differed significantly from those using light emitting diode ( LED ) displays, for example.

In order to properly account for all potential energy savings, DOE conducted a market survey of all microwave oven type s on the market as of October 2012

. DOE classified these microwave oven types on the basis of:

**•** D isplay type (LED, liquid crystal displays ( LCD ) , LCD with backlighting, VFD,

or no display)

**•** Co oking sensor (present or not)

**•** Inverter power supply for the magnetron (present or not)

**•** Convection or other alternative cooking system ( present or not )

**•** Installation l ocation ( i.e.

, countertop or built

- in)

In total, the DOE sample contained 540 stock

- keepi ng units (SKUs) , of which 459 were

c la ssified as belonging to P roduct C lass 1 and 81 belonging to P roduct C lass 2. For Product

Class 1

, the SKUs were distributed as follows by display type : 41

- percent LED, 2

- percent LCD,

20

- percent b acklit LCD, 36

- percent VFD, and 1

- percent no display. For Product Class 2 , SKUs

were distributed as follows: 11

- percent LED, 4

- percent LCD, 16

- percent b acklit LCD, and 69

- percent VFD.

The cost s of achieving the various standby power levels were calculated on the weighted

basis of the market share of each microwave oven type as a function of its percentage of the total

number of SKUs in each product class . For example, 69 percent of the total cost of achieving the the standby power levels for Product Class 2 was de rived from the individual design pathways associated with the various types of VFD

- equipped, built

- in microwave oven s currently on the

market. Each design pathway considered the measured average standby power consumption for a given microwave type.5-8 Some d esign pathways were estimated, as not all microwave types were measured in representative quantities during the reverse engineering process. For example, the presence of an inverter drive for the magnetron add s an average of 0.9 W to the standby power cons umption of a microwave oven. Similarly, convection microwave ovens consume about 1 W of standby

power more than comparable microwave

- only ovens. In order to ensure that a given power supply option would be adequ a te for existing product designs, DOE investigated the actual power requi rements of the components inside the microwave oven s in its reverse

- engineering sample

.

Internal power draws under standby conditions were estimated on the basis of a 40

- percent efficient conventional linear power supply, which is representative of many units on the market featuring 12 VDC unregulated power and 5 VDC regulated power. The baseline standby

power consumption is driven in part by the power supply and also the display, sensor, and other

features

. Power draws for the various devices attached to the power supply such as cooking sensors, relays, etc. were also noted.

The design pathways, by which manufacturers could implement various combinations of design options to meet given power levels

, were then modeled. For every design pathway

, DOE verified that the power supply chosen could meet the power demands placed on it, i.e. baseline power, a cooking sensor (as applicable), and the simultaneous activation of up to four relays.

Some m icrowave ovens may c ontain more relays

, but a review of operating conditions suggest that four relays is the maximum number of relays that are activated simultaneously at any given

time.

For microwave

- only oven s, t hese relay s typically consist of one high

- power relay (16+ A) for the magnetron power supply and three lower

- capacity relays (1

– 3 A) to activate other

features such the interior light, rotating turntable, etc. For the purpose of this analysis, DOE

evaluated three types of relays of varying efficiency. These three efficiencies were reflected in the power required to activate each relay. Based on the 44 microwave ovens disassembled as part

of DOE’s reverse

- engineering, low efficiency, low

- cost relays were considered the default relay option.

Each design path for a given microwave oven type consisted of a starting configuration at
baseline eff i ciency

, and then each layering of design options that a manufacturer could choose to reduce standby power. For every standby power level, up to 18 different design o ption

combination s were considered, such as a 5 W conventional linear power supply with low

- cost relays, a 5 W SMPS with high

- efficiency relays, and so on. Where multiple approaches could be taken to meet a given standby power level, t he most cost

- effectiv e solution which could still meet all applicable power demands was then selected.

For example, the countertop LED

- equipped microwave

- only oven s in the DOE reverse

engineering sample that featured no cooking sensor or inverter drew an average of 1.5 W stand by power. As a result, such microwave ovens are expected to require no changes to their power supply until standby power is reduced below 1.5 W. Based on a review of available options,

DOE identified a change to a 5 W SMPS to meet the 1 .0 W standby power level. No additional5-9 changes were deemed necessary , as this SMPS has enough spare power to drive the four relays

discussed above. To meet the max

- tech standby power level , the deep

- sleep option for automatic

power

- down was selected.

The deep

- sleep module consists of a dedicated 1.5 W power supply and microchip that monitor the door switch of a microwave oven. Once a consumer opens the door, one of the door

microswitch es activates the power supply, which in turn connects the rest of the microwave to line po wer using a high

- amperage

- rated relay . Thus, whenever the microwave is in deep

- sleep mode, the power filtration board and any downstream components are disconnected from line power and hence cannot consume any power. DOE is aware of other potential approac hes to enabling automatic power

- down , but the above approach has been tested and verified by the component manufacturers to meet the maximum power threshold set by standby level ( SL ) 4.

Due to the added circuitry, wiring, and the extra door microswitch, t he deep

- sleep module adds significant ly more cost compared to other standby power design options. For example,

SMPS s in the 5 W power range have only marginally higher costs than conventional linear power supplies. As a result of current part prices, high efficiency linear power supplies employing an LLTX do not appear to be cost competitive with SMPS

, and hence were not considered viable pathways in the final rule analysis

.

Most microwave oven types could meet S L 1 with conventional power supplies as long as applicable models incorporated zero

- standby power cooking sensors . The only exception were

VFD

- equipped units

, which have inherently higher standby power requirements associated with their displays. Such units typically incorporated SMPS power supplies at the baseline

. Further,

n one of the VFD

- equipped microwaves could meet the standby power limits of either S L 3 or

S L 4 without the use of automatic power

- down, and a subset could not meet S L 2 except with that design option

.

5.4 METHODOLOGY OVERVIE W

In order to develop the cost

- efficiency relationships f or microwave ovens , DOE performed detailed product teardowns and reverse

- engineering analyses on a sample of product models spanning a range of standby power levels and product features

. DOE also con ducted

detailed standby power testing to gain insights into energy performance in standby mode and to evaluate individual components contributing to standby power consumption. AHAM also submitted data to DOE on standby power testing that it conducted

. DOE supplemented this information by conducting interviews with manufacturers of microwave ovens. Table 5.4

. 1 below shows which methods DOE used for each product class

.5-10 Table 5.4

. 1 Engineering Analysis Methods

Method Microwave

- Only Ovens and Countertop Convection

Microwave Ovens Built

- In and Over

- the

Range Convection

Microwave Ovens

AHAM Data √

Product Teardown √ √

Product Testing √ √

Manufacturer

Interviews √

5.4.1 AHAM Data Submittal

In support of this rulemaking effort, AHAM submitted data regarding standby power in microwave ovens. The AHAM submittal contained general descriptions of components related to standby power as well as data from standby power testing. The data provided to DOE by AHAM

are contained in a ppendix 5 A.

5.4.2 Manufacturer Interviews

DO E conducted confidential interviews with microwave ovens manufacturers to develop a deeper understanding of the various combinations of technologies used to reduce standby power consumption , and their associated manufacturing costs

. Sample questions asked during

the follow

- up interviews , which were also conducted in support of the manufacturer impact analysis, are contained in a ppendix 12 B

.

During the interviews, DOE also gathered information about the capital expenditures required to reduce the standby po wer consumption of the baseline units to various standby power levels ( i.e. , conversion capital expenditures by standby power level)

. The interviews provided information about the size and the nature of the capital investments

. DOE also requested

informati on about the depreciation method used to expense the conversion capital.

5.4.3 Product Teardowns

Other than obtaining detailed manufacturing costs directly from a manufacturer, the most accurate method for determining the production cost of a piece of equipment is to disassemble the equipment piece

- by

- piece and estimate the material and labor cost of each component using a process commonly called a physical teardown

. A supplementary method, called a catalog teardown, uses published manufacturer catalogs and suppl ementary component data to estimate the major physical differences between a piece of equipment that has been physically disassembled and another piece of similar equipment

. DOE performed physical teardown analysis on microwave ovens for this rulemaking

. T he teardown methodology is described in

detail in section 5.4.3.1 through section 5.4.3.3

.5-11 5.4.3.1 Selection of Units

During the process of selecting units for teardown, DOE consider s three main questions:

 What standby power levels should be captured in the teardown analysis?

 Are there units on the market that capture all potential standby power levels and design options?

 Which of the available units are most representative?

In responding to the preceding questions, DOE adopt s the following criteria for selecting units for the teardown analysis:

 T he selected products should span the full range of standby power levels for each product class under consideration;

 W ithin each product class, if possible, the selected products should come from the same manufacturer and be within the same product series;

 T he selected products should primarily come from manufacturer s with large market share in that product class, altho ugh the highest efficiency products were chosen irrespective of manufacturer; and

 T he selected products should have non

- standby power

- related features that are the same as, or similar to, features of other products in the same class and at the same standby power level

.

5.4.3.2 Generation of Bill of Materials

The end result of each teardown is typically a structured bill of materials (BOM)

. The process is discussed here in the context of the general methodology used for teardown analysis, although it should be noted a more limited approach was used for microwave ovens due to the substantial uniformity in design and construction among the sample units

. Structured BOMs describe each equipment part and its relationship to the other parts, in the estimated order of

assem bly

. The BOMs describe each fabrication and assembly operation in detail, including the type of equipment needed ( e.g. , stamping presses, injection molding machines, spot

- welders, etc.) and the process cycle times

. The result is a thorough and explicit mod el of the production

process

.

The BOMs incorporate all materials, components, and fasteners

, classified as either raw materials or purchased parts and assemblies

. The classification into raw materials or purchased parts is based on DOE’s previous industry experience, recent information in trade publications, and discussions with high

- and low

- volume original equipment manufacturers (OEMs).

For purchased parts, the purchase price is an estimate based on volume

- variable price quotations and detailed discussions with suppliers

. For fabricated parts, the price of intermediate materials ( e.g. , tube, sheet metal) and the cost of transforming them into finished parts are a n estimate based on current industry pricing.5-12 The cost of raw materials is determined using prices for copper, steel and aluminum from

the American Metals Market. 1

5.4.3.3 Cost Structure of the Spreadsheet Models The price of steel drastically increased in 2005, and the price of

copper has increased st eadily since 2004

. Because DOE is using a 5

- year average in material prices from 200 7

– 20 11 , these price increases are normalized, which better represents long

- term material prices.

The manufacturing cost assessment methodology used is a detailed, component

- focused technique for rigorously calculating the manufacturing cost of a product (direct materials, direct labor and some overhead

costs

. )

Figure 5.4

. 1 shows the three major steps in generating the manufacturing cost.

Figure 5.4

. 1 Ma nufacturing Cost Assessment Stages

The first step in the manufacturing cost assessment is the creation of a complete and structured BOM from the disassembly of the units selected for teardown

. The units are dismantled, and each part is characterized according to weight, manufacturing processes used, dimensions, material

, and quantity

. The BOM inco rporates all materials, components, and fasteners with estimates of raw material costs and purchased part costs

. Assumptions on the sourcing of parts and in

- house fabrication are based on industry experience, information in trade publications

, and discussi ons with manufacturers

. Interviews and plant visits are also conducted with manufacturers to ensure accuracy on methodology and pricing.

Following the development of a detailed BOM, the major manufacturing processes are identified and developed for the spr eadsheet model

. These processes are listed in Table 5.4

. 2

.

Table 5.4

. 2 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality Control Fixturing Washing Adhesive Bonding Inspecting & Testing Stamping/Pressing Powder Coating Spot Welding Brake Forming De

- burring Seam Welding

Cut ting and Shearing Polishing Insulating

Fabrication process cycle times are estimated and entered into the BOM

. For this analysis, $24.00 per hour was used as the average fully

- burdened labor rate based on typical5-13 annual wages and benefits of industry employees

. In the final step of the cost ass essment, assembly times and associated direct labor costs are estimated

. Once the cost estimate for each

teardown unit is finalized, a detailed summary is prepared for relevant components, subassemblies and processes

. The BOM thus details all aspects of unit costs

.

Design options used in units subject to teardown are noted in the summary sheet of each

cost model and are cost

- estimated individually

. Thus, various implementations of design options can be accommodated, ranging from assemblies that are entir ely purchased to units that are made entirely from raw materials

. Hybrid assemblies, consisting of purchased parts and parts made on

site are thus also accommodated

.

5.4.4 Product Testing

DOE conducted product testing on microwave ovens to develop a better unde rstanding of the potential standby power improvements associated with various design options. As part of this testing, DOE measured the power consumption of components contributing to standby power individually to further evaluate the strategies manufactur ers may take to reduce standby power

.

5.5 ANALYSIS AND RESULTS

5.5.1 AHAM Data

AHAM provided data on microwave oven standby power for a sample of microwave

ovens currently available in the U.S. market. 2 Standby power was tested in accordance with the

International Electrotechnical Commission ( IEC ) Standard 62301

- 2005, Household electrical appliances

– Measurement of standby power (IEC Standard 62301 ( First Edition ) )

.

Figure 5.5

. 1 displays AHAM

- measured microwave oven standby power data as a

function of rated cooking output power

. Unlike efficiency, these data show a wide range of standby power, but there is no correla tion between standby power and cooking output power

. Standby power ranged from 1.5 to 5.8 W , with 9 of the 21 microwave ovens meeting the FEMP

recommendation of 2.0 W or less

. The average standby power measured was 2.9 W

. AHAM did not provide any informati on with which DOE could correlate standby power with other features such as cooking sensors or displays

. The data did not indicate the product type ( i.e. , microwave only, convection ) or the installation configuration ( i.e. , countertop, over

- the

- range, buil t

- in).

DOE notes that m icrowave oven standby power consumption is impacted significantly by the digital clock display, with more complex graphical displays drawing more power

. According to AHAM, the three major types of displays used in microwave ovens are (1) LED displays, (2) VFDs, and (3) LCDs

. Typically, LCDs without back

- lighting use the least amount of energy

while VFDs use the most

. Energy consumed by LCDs would depend on whether back

- lighting is present or not, and if present, the number of LEDs used for back

- lighting

. For LED displays, the number of segments, color, and operating temperature affect energy consumption

. The size of the5-14 display (number of digits and icons) also affects the energy consumed by the display

. For VFDs,

the size of the di splay is a major factor in energy consumption. 3 Standby power consumption may also be affected by the electronic controls required for features such as cooking sensors

, along with associated power supplies

.

Figure 5.5

. 1 AHAM Microwave Oven Standby Power 4

Table 5.5

. 1 compares the AHAM

- measured microwave oven standby power data with data from other studies that have examined standby power in the United States and Canada

. It can be seen that average standby power has remained relatively constant since 1997 even though

minimum and maximum levels have varied by a greater amount

. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

600 700 800 900 1000 1100 1200 1300 1400

Rated Output Power (W)Standby Power (W)5-15 Table 5.5

. 1 Microwave Oven Standby Power Comparison

Source Microwave Oven Standby Po wer

Min ( W ) Average ( W ) Max ( W ) Std Dev ( W ) # Samples

AHAM 2006 5 1.5 2.9 5.8 0.8 21

CBEEDAC 2006 6 0.4 1.9 3.3 0.8 19
CREEDAC 2001 (New Stock) 7 0.8 3.8 12.6

- 4
CREEDAC 2001 (Existing Stock) 8 0.0 2.1 7.3

- 64

LBNL 2008 9 1.4 3.1 4.9

- 18

Florida Solar Energy Center 1998 10

- 3.0

- - 25

LBNL 1997 11

- 3.1

- - -

DOE recognize s that annual energy consumption can be significantly affected by standby power in real

- world usage

. For example, a 1

- W increase in standby power corresponds to an

increase of 8.76 kilowatt

- hours ( kWh ) in annual energy consumption

. Prior to it being repealed in a final rule published on July 22, 2010 ( 75 FR 42579), t he microwave oven test procedure

assume d an annual useful cooking energy output of 79.8 kWh

. For a baseline microwave oven that operates at 55.7 percent efficiency, annual energy consumption not including standby power

would be 143.3 kWh

. Therefore each W of standby power included in the annual energy consumption metric would increase it by 6 percent for a baseline microwave oven

. Since the highest measured standby power was 5.8 W , the increment of 3.8 W over the FEMP

recommended level of 2.0 W would represent a 23

- percent increase in annual energy consumption for a baseline microwave oven.

5.5.2 Product Testing and Teardowns

Section 310 of the Energy Independence and Security Act of 200 7 ( EISA 2007 ) (Pub. L.

No. 110

- 140) amends Section 325 of the EPCA to require DOE to incorporate standby mode and off mode energy use into a single amended or new standard, if feasible, for covered products, including microwave ovens, for any final rule establishing or revising a standard adopted after July 1, 2010

. If such a single standard is not feasible, DOE shall prescribe a separat e standard for standby mode and off mode energy consumption, if justified

. (42 U.S.C. 6295(gg)(3)) As discussed in chapter 3 of this TSD, DOE published a final rule on July 22, 2010 repealing the provisions in the existing microwave oven test procedure rel ating to the measurement of cooking efficiency and energy factor (EF). 75 FR 42579. DOE notes that the absence of active mode testing provisions in the microwave oven test procedure results in a de facto separate energy use descriptor for microwave oven st andby mode and off mode energy use. Therefore, DOE conducted microwave oven standby power testing to supplement the AHAM

- supplied data

, investigate d design options

, and develop ed a relationship between incremental manufacturing cost and standby power level s.

As discussed in chapter 3 of this TSD, i n the Ma r ch 2011 TP Interim Final Rule , DOE adopted definitions of standby, off, and active mode based on the provisions in IEC Standard5-16 62301 Second Edition

, Final Draft International Standard (IEC Standard 62301 FDIS )

. 76 FR 12825, 12834

– 37 (Mar

. 9, 2011) . DOE defines “standby mode” as the condition in which an

energy

- using product is connected to a mains power source and offers one or more of the following user oriented or protective functions which may persist for an indefinite time

– (I) a remote switch (including a remote control), internal sensor, or timer to facilitate the

ac tivation of other modes (including activation or deactivation of active mode) ; and (II) c ontinuous functions, including information or status displays (including clocks) or

sensor

- based functions. 76 FR 12825, 12836 (Mar. 9, 2011)

.

DOE defines “off mode ” as the condition in which an energy

- using product is connected to a mains power source , is not providing any active or standby mode function, and may persist

for an indefinite time

. Off mode would also include an indicator that shows the user only that t he product is in the off position. Id

.

DOE defines “active mode,” as the condition in which an energy

- using product is connected to a main power source

, has been activated

, and provides 1 or more main functions. Delay start mode is a one off user initiate d short duration function that is associated with an

active mode

. Id

.

DOE considers “main functions” for a microwave oven to be those operations in which the magnetron and/or thermal element is energized for at least a portion of the time for purposes

of heating, cooking, and/or defrosting the load

. According to the se definitions, standby mode would thus comprise the conditions in which the microwave oven is plugged in and consuming power for features including displays, cooking sensors, and sensors to rea ctivate the microwave

oven from a low

- power state after a period of user inactivity

. For reasons discussed later in this section, DOE does not believe that any microwave ovens currently for sale in the United States are capable of operation in off mode.

5.5.2.1 G eneral Microwave Oven Construction

All microwave ovens examined by DOE contain a metal cavity that is mechanically

attached to the front and rear chassis frames . Each of these pieces had been formed by stamp ing, joined, and then the assembly is painted . To this frame, a bottom panel is attached. This bottom panel covers the turntable motor and also acts as an electrical ground for the magnetron power supply and the magnetron. The inner cavity occupies, on average, about 70 percent of the width and height of the microwave oven. The remaining space is used for cooling channels, the magnetron, the magnetron power supply, the cooling fan, the microwave oven controller, and other miscellaneous components.

All of the microwave ovens examined have a door that is h inged on the left side of the unit. Beneath the right edge of the door, a latch mechanism with three switches ensures that the microwave oven cannot operate its magnetron without the door being closed. To the right of the door, the user interface frame typ ically features a foil

- based button array under a flexible plastic5-17 cover, along with the display

. The display may be either attached directly to the user interface

frame or integrated into the main control board for the microwave oven. The main control boa rd typically consists of a single

- sided printed circuit board ( PCB ) which features a small alternating current to direct current ( AC

- DC ) power supply, several relays, and control logic . All power coming into the microwave oven is filtered through a separat e power filtration board to which the power cord is attached.

In addition to the aforementioned components, typical microwave ovens contain a magnetron power supply featuring a microwave oven transformer, a high voltage diode, and a high

- voltage aluminum electrolytic capacitor. A single

- speed fan keeps the magnetron and microwave oven transformer cool during operation, while a turntable may rotate the food load inside the cavity while the microwave oven is operating. A single incandescent light bulb (20 W and higher) typically provides cavity illumination during operation and when the door is open. More sophisticated microwave ovens may contain cooking sensors, larger and multi

- colored displays with more complex graphics, and/or inverter

- based magnetron pow er supplies. Such

inverter

- equipped microwave oven s also feature larger cooling fans and ducts to help dissipate the heat generated by the inverter power supply. Convection microwave ovens additionally incorporate thermal heating elements and a fan to circ ulate the heated air within the cavity.

5.5.2.2 Test Units

DOE conducted standby power testing and reverse engineering analyses on three sets of

microwave ovens , 32 in the first set, an additional 12 in the second set

. DOE noted key parameters for each test uni t, presented in Table 5.5

. 2

. Since manufacturers are not required to report standby power for microwave ovens, DOE could not select its sample test units on the

basis of rated standby power consumption. Instead, DOE selected its sample microwave ovens to cover a wide range of cavity sizes and output power, as well as other features such as display

type, magnetron power supply type, and presence of cooking sensors. DOE w as unable to identify any microwave oven models with an automatic power

- down feature. Within the test sample, DOE observed significant standby power differences which it believes are attributable to specific design options or design option combinations.5-18 T able 5.5

. 2 Microwav e Oven Features in DOE S ample

Installation

Location Rated

Output

Power Display

Type Cooking

Sensor? Inverter

Used? Combination

Oven? Power
Supply

Type Avg.
Standby

Countertop 800 LCD

- - - Linear 1.38
Countertop 800 LCD

- - - Linear 1.40
Countertop 1100 LCD

- - - Linear 1.31

Built-In 1200 LCD-BL Yes

- Yes SMPS 4.19

Built-In 1200 LCD-BL Yes

- Yes SMPS 4.37
Countertop 900 LCD-BL Yes

- Yes Linear 2.36
Countertop 1200 LCD-BL Yes

- - Linear 3.52
Countertop 1200 LCD-BL Yes

- - Linear 3.41
Countertop 1200 LCD-BL Yes

- - Linear 3.88
Countertop 1100 LCD-BL Yes

- - Linear 3.10
Countertop 1100 LCD-BL Yes Yes Yes Linear 3.55
Countertop 1200 LCD-BL Yes Yes

- Linear 2.59
Countertop 1300 LCD-BL Yes Yes

- Linear 2.09
Countertop 1300 LCD-BL Yes Yes

- Linear 2.82
Countertop 1250 LCD-BL Yes Yes

- Linear 2.82
Countertop 1100 LCD-BL

- - - Linear 1.65
Countertop 1100 LCD-BL

- - - Linear 1.66
Countertop 1200 LCD-BL

- - - Linear 1.70
Countertop 1100 LCD-BL

- - - Linear 1.72
Countertop 1000 LED Yes

- Yes Linear 1.20
Countertop 1000 LED

- - Yes Linear 1.23
Countertop 700 LED

- - - Linear 1.77
Countertop 700 LED

- - - Linear 1.74
Countertop 800 LED

- - - Linear 1.51
Countertop 1100 LED

- - - Linear 1.55
Countertop 1100 LED

- - - Linear 1.40
Countertop 1100 LED

- - - Linear 1.65
Countertop 900 LED

- - - Linear 1.49
Countertop 700 LED

- - - Linear 1.07
Countertop 700 LED

- - - Linear 1.28
Countertop 1000 LED

- - - Linear 1.75
Countertop 1000 LED

- - - Linear 1.26

Built-In 975 VFD Yes

- Yes Linear 8.16

Built-In 975 VFD Yes

- Yes Linear 8.76
Countertop 900 VFD Yes

- Yes Linear 3.23
Countertop 1000 VFD Yes

- Yes Linear 4.68

Built-In 1100 VFD Yes

- Yes Linear 4.50

Built-In 1100 VFD Yes

- Yes Linear 4.59

Built-In 1000 VFD Yes

- Yes Linear 4.14

Built-In 900 VFD Yes

- Yes Linear 6.65
Countertop 800 VFD Yes

- - Linear 5.28
Countertop 1100 VFD Yes

- - Linear 3.08
Countertop 1150 VFD Yes

- - Linear 3.05
Countertop 1000 VFD

- - Yes Linear 2.485-19 Three different display technologies were observed to be incorporated in the sample units: 1) LED; 2) LCD; and 3) VFD. Most LCDs with backlights (LCD

- BL) used LEDs for

illumination

, though other technologies such as electroluminescent emitters can be used also.

Backlit LCDs allow a microwave oven user to read the display both at night and in daylight. For the purpose of Table 5.5

. 2 , all units that are fixed installations ( i.e. built

- in or over

- the

- range) are

identified as “ built

- in ,” while units that can be moved by hand are considered “ countertop. ”

DOE notes that many premium countertop units are available with optional “ trim kits ” for fixed installation. However, as these trim kits are optional add

- on purchases , these

microwave ovens are considered to be countertop units for the fina l rule

. The availability of such

trim kits

, however

, underscores the sometimes subtle differences between units that are designated for built

- in installation and the more common countertop units. Standby testing also suggested similar power profiles, with the average standby power difference between otherwise

similar built

- in and countertop microwave

- only units being 0.3 W. However, the difference was significantly higher f or similar built

- in and countertop convection microwave ovens

— about 3 W.

5.5.2.3 Microwave

- Only Ovens : Product Testing and Teardowns

For its first round of testing and teardowns , DOE examined 32 microwave

- only ovens in

support of its standby power investigation

. DOE also analyzed IEC Standard 62301 (First Edition) to determine its suitability for measuring standby power in microwave ovens

. DOE found the results produced by IEC Standard 62301 (First Edition) to be fairly consistent for

many microwave oven s

. However, for a few microwave oven s in the test sample, the standby power consumption vari ed by more than 5 percent over the course of 5 minutes, a threshold

above which IEC Standard 62301 (First Edition) requires a longer 12

- hour standby test duration

. Investigating the source of these variations in power consumption, DOE determined that stand by power can vary significantly with clock time, depending on the display type

.

DOE tested a sub

- sample of microwave oven s over 12

- hour periods and subsequently developed an alternate 10

- minute standby power test that achieved results for average power

co nsumption that were within 1 to 2 percent of the 12

- hour test results (see a ppendix 5B )

. Using

the most consistent data from : (1) the IEC Standard 62301 5

- minute test, (2) the regression analysis as described in a ppendix 5B, and /or (3) 12

- hour tests, stand by power for each

microwave oven in the sub

- sample was characterized

, t he minimum, maximum, and average input powers were recorded, and from the average standby power, an annual energy consumption

in kWh was calculated

. Table 5.5

. 3 through Table 5.5

. 6 s ummarize these results.5-20 Table 5.5

. 3 DOE Microwave

- Only Oven Standby Power (Table 1 of 4)

Table 5.5

. 4 DOE Microwave

- Only Oven Standby Power (Table 2 of 4)

Table 5.5

. 5 DOE Microwave

- Only Oven Standby Power (Table 3 of 4) DOE1 DOE2 DOE3 DOE4 DOE5 DOE6 DOE7 DOE8

700.0 700.0 700.0 700.0 800.0 800.0 800.0 800.0

0.7 0.7 0.7 0.7 1.0 1.0 0.8 0.8

- - - - Y

- - - Type LED LED LED LED VFD LED LCD LCD

Characters #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 A, 1 x 7 #, 1 x 4 #, 1 x 4 #, 1 x 4
# of LEDs for backlighting N/A N/A N/A N/A N/A N/A None None

Size 1.50" x

0.43" 1.50" x

0.43" 1.45" x

0.70" 1.90" x

0.83" 2.25" x

0.75" 1.50" x

0.80" 1.87" x

0.83" 1.87" x

0.83"
Min (W) 1.47 1.49 1.14 0.96 5.28 1.33 1.37 1.38
Average (W) 1.74 1.77 1.28 1.07 5.28 1.51 1.38 1.40
Max (W) 1.85 1.90 1.35 1.14 5.53 1.60 1.41 1.41
Annual (kWh) 15.22 15.47 11.21 9.40 46.28 13.22 12.12 12.28

- - - - - DOE5

- - DOE Unit Number

Stated Volume (ft 3 )

Unit is Similar toDisplayStandby Cooking Sensor Rated Output Power (W)

DOE9 DOE10 DOE11 DOE12 DOE13 DOE14 DOE15 DOE16

900.0 1,000.0 1,000.0 1,000.0 1,000.0 1,100.0 1,100.0 1,100.0

0.9 1.1 1.1 1.0 0.0 1.1 1.1 1.8

- - - - - - Y Y
Type LED LED LED LED VFD LED LCD VFD

Characters #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 A, 1 x 6 A, 1 x 7
# of LEDs for backlighting N/A N/A N/A N/A N/A N/A 5 N/A

Size 2.00" x

0.65" 1.85" x

1.10" 1.75" x

0.85" 1.75" x

0.70" 2.20" x

0.65" 2.00" x

0.65" 2.53" x

0.95" 2.25" x

0.75"
Min (W) 1.26 1.09 1.11 1.42 2.46 1.16 3.08 3.08
Average (W) 1.49 1.23 1.26 1.75 2.48 1.40 3.10 3.08
Max (W) 1.58 1.30 1.33 1.88 2.49 1.49 3.12 3.31
Annual (kWh) 13.09 10.73 11.01 15.32 21.71 12.26 27.14 26.95

- - - - - - DOE14

- DOE Unit Number
Rated Output Power (W)
Stated Volume (ft 3 ) Cooking SensorDisplayStandby

Unit is Similar to

DOE17 DOE18 DOE19 DOE20 DOE21 DOE22 DOE23 DOE24

1,100.0 1,100.0 1,100.0 1,100.0 1,100.0 1,100.0 1,150.0 1,200.0

1.0 1.4 1.0 1.3 1.1 1.0 1.4 2.2

- - - - - - Y Y
Type LCD LCD LCD LED LED LCD VFD LCD

Characters #, 1 x 4 A, 1 x 5 #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 A, 1 x 7 A, 1 x 6
# of LEDs for backlighting 3 4 None N/A N/A 3 N/A 10

Size 1.83" x

0.80" 2.03" x

0.81" 1.87" x

0.83" 1.50" x

0.80" 1.47" x

0.80" 2.00" x

0.83" 2.25" x

0.75" 2.05" x

1.10"
Min (W) 1.64 1.64 1.30 1.46 1.32 1.70 3.05 2.57
Average (W) 1.65 1.66 1.31 1.65 1.55 1.72 3.05 2.59
Max (W) 1.67 1.67 1.34 1.73 1.66 1.73 3.18 2.60
Annual (kWh) 14.48 14.51 11.48 14.44 13.57 15.02 26.68 22.66

- - DOE17

- - DOE17 DOE16 DOE30 DOE Unit Number
Rated Output Power (W)
Stated Volume (ft 3 ) Cooking Sensor

Unit is Similar toDisplayStandby5-21 Table 5.5

. 6 DOE Microwave

- Only Oven Standby Power (Table 4 of 4)

In the course of examining the 32 microwave

- only oven s in th is round of test sample s , DOE noted that all logic boards used linear power supplie s. In the second round of 12 samples,

reverse

- engineered in 2012, all but two units featured linear power supplies for the logic boards. The remaining two units featured the same SMPS power supply. Even though the two units were largely identical, the measured standby powered varied slightly . DOE research suggests that this

difference was likely due to the higher input voltage (230 V) of one unit versus the other (115 V). The unit with the higher in put voltage also featured a slightly higher standby power consumption

, reflecting the losses incurred in the power supply when converting from line power to board power.

None of the microwave oven s reverse

- engineered by DOE feature d an on/off switch or

electromechanical controls

. The October 2012 DOE survey of 540 microwave ovens suggests that a single countertop model is offered with electromechanical controls and no display , thus likely consuming only minimal power while not in use

. Although DOE did not test this unit, the

off

- mode power consumption is believed to be minimal . Past DOE research suggests that the power filtration board typically found in a countertop microwave oven will consume some power

on its own.

Such power fil tration boards ensure that microwaves do no interfere with domestic

power, and typically consist of multiple capacitors, a dual

- mode choke, a fuse, and other

components mounted on a printed circuit board . Th e board is typically connected permanently to

the incoming line power

, and all power used inside the microwave oven goes through it . Since the capacitors on these filtration boards are charged with potentially lethal voltages, safety regulations require that the capacitors are drained to safe voltages wi thin about a second of the oven being disconnected from line power. The bleed resistors commonly used for this task typically consume more power than the max

- tech SL 4 limits.

DOE is aware of an integrated circuit (IC) offered for the express purpose of draining filtration capacitors upon line power disconnect

, while consuming less than 5 milliwatts ( mW ) of standby power. This approach consists of a single IC mounted to the underside of the printed circuit board that a standard filtration capacitor is mo unted on. The IC monitors line voltage and DOE25 DOE26 DOE27 DOE28 DOE29 DOE30 DOE31 DOE32

1,200.0 1,200.0 1,200.0 1,200.0 1,200.0 1,250.0 1,300.0 1,300.0

1.2 2.2 1.2 2.0 2.0 2.2 1.2 1.2

- Y Y Y Y Y Y Y
Type LCD LCD LCD LCD LCD LCD LCD LCD

Characters #, 1 x 4 A, 1 x 6 A, 1 x 7 D, 2 x 8 A, 1 x 7 A, 1 x 6 #, 1 x 4 A, 1 x 6
# of LEDs for backlighting 5 10 4 6 4 4 10 10

Size 1.85" x

0.83" 2.05" x

1.10" 2.03" x

0.78" 2.45" x

1.17" 2.03" x

0.78" 2.05" x

1.10" 1.75" x

0.80" 2.05" x

1.10"
Min (W) 1.68 2.57 3.50 3.86 3.38 2.79 2.08 2.81
Average (W) 1.70 2.59 3.52 3.88 3.41 2.82 2.09 2.82
Max (W) 1.71 2.60 3.60 3.93 3.46 2.86 2.10 2.86
Annual (kWh) 14.86 22.66 30.84 34.02 29.90 24.69 18.28 24.72

DOE17 DOE30 DOE25

- - DOE31

- DOE31 Rated Output Power (W)

Unit is Similar to Stated Volume (ft 3 ) Cooking SensorDisplayStandby DOE Unit Number5-22 keeps the bleed resistors disconnected unless the line power is absent. However, as the solution appears to be proprietary, DOE did not consider it as a potential implementation path to reach S L 4. Instead, DOE re searched a separate, deep

- sleep module that could disconnect the microwave oven from line power upstream of the filtration board.

All 539 other microwave oven s surveyed in October 2012 incorporated a n electronic display

. Thus, virtually all sampled microwave oven s currently available on the U

. S

. market

consume standby power while waiting for user input

.

The average standby power, disaggregated by display type and the presence of a cooking

sensor as shown in Figure 5.5

. 2

, exhibits a relatively wide range of values

. These measured data correlate well with the standby power measurements that AHAM submitted to DOE (see Figure

5. 5.4 )

. In the following figures

, the legend key includes designations for the presence of a cooking sensor (CS), backlighting (B L ), an inverter drive for the magnetron (I) and alternative

co oking systems (Combo)

.

Figure 5.5

. 2 C ounter t op M icrowave Oven Standby Power Consumption as a Function of Rated Output Power in DOE R everse

- E ngineering S ample 0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

600 800 1000 1200 1400Average Standby (W)

Rated Output Power (W) LCD

LCD

- BL

- CS

Combo

LCD

- BL

- CS

LCD

- BL

- CS

- I

Combo

LCD

- BL

- CS

- I

LCD

- BL

LED

- CS

- Combo

LED

- Combo

LED5-23 0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6 6.5

7 7.5

8 8.5

9 9.5

600 700 800 900 1000 1100 1200 1300Average Standby (W)

Rated Output Power (W) LCD-BL-CS-Combo

VFD-CS-Combo

Figure 5.5

. 3 Built

- In Microwave Oven Standby Power Consumption as a Function of Rated Output Power in DOE Reverse

- Engineering Sample

While AHAM did not identify specific models in its data submittal, and thus DOE c ould

not derive information on display type, the data do es note which microwave oven s contain cooking sensors

. Like the microwave oven s in the DOE test sample, the microwave ovens with cooking sensors in the AHAM sample, ha ve on average, higher standby pow er consumption than

microwave oven s without cooking sensors

.5-24 Figure 5.5

. 4 AHAM Microwave Oven Standby Power Consumption a s a Function of Rated Output Power 12

Based on these standby p ower observations, DOE conclude s that standby power is a

function of features including the display, the presence of a cooking sensor, and controller power requirements

. The following sections discuss the impact of each of these features on overall standby power consumption.

Impact of Display Type on Standby Power

DOE noted three different types of displays among the 44 microwave oven s in its

reverse

- engineering sample

. The sampled display capabilities ranged from only being able to display numbers, to displaying alphanumeric characters, to incorporating dot

- matrix

- based displays which allow the display of multiple fonts, symbols, and , in some cases, graphi cs

. Di splay power requirements depend on the fundamental technology as well as the size of the display, the brightness, and the number and range of characters it can display

. See a ppendix 5C for a further discussion of display technologies

.

1. Light Emitting Diode (LED)

LED displays operate by individually lighting up each element in the display to create a

character or number o r to illuminate a symbol

. DOE confirmed that the power consumption of such displays varies depending on how many elements (and hence diodes) are active at any given

time

. Thus, LED

- display

- equipped microwave oven s demonstrate the greatest variation in

stan dby power consumption over the course of a day as the clock cycles. 1 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

650 750 850 950 1050 1150 1250 1350

Rated Output Power (W)Standby Power (W) No Sensor

Sensor5-25 The span between the maximum and minimum standby power consumption, based on the number of elements illuminated, range s from 0.2 to 0.4 W in the DOE test sample

. DOE found

no apparent correlation between display size and standby power consumption

. DOE recognizes that diode energy efficiency characteristics may be an important variable, but one for which

DOE was not able to ascertain any information

.

2. Vacuum

- Fluo rescent Display (VFD)

VFDs typically require more power than LCD

- or LED

- based displays and offer high brightness levels and wide viewing angles

. T hree of the VFD models with cooking sensors in the

first set of DOE reverse

- engineering samples had a feature that allow s the display t o be switched

off , but selecting this option decreased standby power consumption by only 0.1 to 0.25 W

. According to the IEC Standard 62301 test procedure, standby power would be measured with the displays powered off, resulting in a constant average value over the 12

- hour period

. DOE also

measured the variations in standby power with the display s on during the 12

- hour cycle for these

units

.

3. Liquid Crystal Display (LCD), with or without backlighting

An LCD without backlighting requires some ambient light t o allow a user to read it because it relies on reflected light to create a visible contrast

. LCDs without backlighting are frequently found on entry

- level microwave oven s, whereas more expensive units tend to have backlit displays

. LCDs with backlighting c an be read regardless of ambient lighting conditions

. The backlighting typically consists of multiple LEDs mounted behind the LCD display on the

same circuit board , though other illumination options exist, such as electroluminescent layers

sandwiched behin d the LCD display

. T he standby power consumption for microwave ovens with

LCDs in the DOE sample remains nearly constant over time (since the bars showing variation during 12

- hour tests are virtually indistinguishable from the symbols denoting the average values.)

Based on its research, DOE conclude s that certain display technologies such as VFDs inherently consume more power, on average, than compara b ly

- sized displays using other illumination technologies

. Standby power is also a function of the size and the complexity of the display ; more complex and larger displays typically consume more power than smaller, simpler displays.

Impact of a Cooking Sensor on Standby Power

DOE noted from its testing that microwave oven s with cooking sensors typ ically consume more power than comparable models that do not feature a cooking sensor

. From its

reverse

- engineering activities, DOE identified two different types of cooking sensors generally

used in microwave oven s today : (1) absolute humidity sensors; an d (2) piezo

- electric steam

sensors

. However, DOE is also aware of other approaches to measuring humidity, such as capacitance

- based sensors that are commonly use d in other appliance types, such as

dehumidifiers.5-26 A literature review of identifiable absolute humidity cooking sensors in the reverse

engineering sample indicates that such sensors typically supply power to a resistive heating element whose conductivity changes in response to the presence of water vapor or other gases associated with food cooking processes

. Based on its standby power observations between otherwise identical microwave ovens and information provided by sensor manufacturers, DOE

concludes that the resistive elements in such cooking sensors are typically maintained at operating tempera ture during standby mode because warmup times (which can be several minutes) are longer than typical microwave oven cooking times.

The specification sheet for one representative absolute humidity cooking sensor with standby power notes that the sensor req uires approximately 0.9 W to remain energized and ready for use. Based on a typical linear power supply efficiency of about 40 percent, the required microwave oven input power associated with the absolute humidity cooking sensor is thus approximately 2.3 W . Other microwave

- only ovens in the DOE sample use absolute humidity sensors whose power requirements appear comparable to those for the absolute humidity sensor

referenced above.

DOE research suggests that suppliers of such absolute humidity cooking senso rs are now offering versions of their sensors that feature no standby power consumption due to an internal redesign. DOE research suggests that s uch zero

- standby absolute humidity sensors are being offered at the same price as existing humidity sensors tha t do consume standby power. The implementation cost to the microwave oven manufacturer is likely zero

, as the sensor can be switched on and off with the help of a single transistor which may already be available on the main printed circuit board.

Testing performed on the steam sensors used in a number of microwave

- only ovens in the DOE sample revealed that these sensors do not consume power in standby mode

. For further discussion of steam cooking sensors, refer to chapter 3 of this TSD

. Past DOE reverse

en gineering efforts in other appliance categories also identified multiple manufacturers of capacitance

- based humidity sensors which require no standby power, have response times of less than 5 seconds, and can measure relative humidity to within less than 2

- percent resolution. Thus,

DOE concludes that manufacturers have multiple zero

- standby power cooking sensor choices

available.

5.5.2.4 Countertop Convection Microwave Oven s: Product Testing and

Teardow ns

DOE also conducted standby power testing on 5 countertop c onvection microwave ovens within the DOE sample , presented below in Table 5.5

. 7

. Testing showed standby power levels ranging from 1.2 W to 4.7 W , with an average of 3. 0 W . As discussed above in section 5.2 , these standby power levels are similar to the standby power levels for microwave

- only ovens. DOE

also noted that the counter top convection microwave ovens used similar display types as microwave

- only ovens ( i.e. , LED displays, LCDs, and VFDs).5-27 Table 5.5

. 7 DOE Countertop Convection Microwave Oven Standby Power

DOE Countertop

Combo MWO

Test Unit Rated Microwave Cooking Output
Power, W Display Type Display Size, in. 2 Cooking

Sensor # of

Heaters Average Standby Power, W

DOE CTC1 1,000 LED 2.25

- 2 1.20

DOE CTC2 900 LCD w/

LED Backlight 3.75

- 2 2.36

DOE CTC3 900 VFD 3.00 Yes 1 3.23

DOE CTC4 1,100 LCD w/

LED Backlight 2.66 Yes 1 3.55

DOE CTC5 1,000 VFD 10.86 Yes 1 4.68

Figure 5.5

. 5 illustrates the measured standby power consumption as a function of display size for countertop convection microwave ovens. Similar to microwave

- only ovens, DOE observed that standby power te nds to increase as display size increases.

Figure 5.5

. 5 DOE Standby Power Consumption as a Function of Display Size for Countertop Convection Microwave Ovens

DOE also conducted product teardowns and standby power testing of individual components contributing to the total standby power consumption. DOE noted that the cooking5-28 sensors used in these models were similar to those used in microwave

- only ovens. DOE als o investigated the requirements for the size of the controls power suppl y for countertop convection

microwave ovens. DOE noted that the capacity of a conventional linear power supply is strongly

correlated to the size of the transformer used to step down t he line input voltage to the unregulated voltage levels . DOE isolated the power supply transformers in the controls these

countertop convection microwave ovens and measured the standby power consumption. DOE similarly measured the power supply transformer standby power consumption for a limited

number of the microwave

- only ovens in its test sample.

The results from this testing, presented below in Table 5.5

. 8 , show t hat the size of the

controls power supplies ( i.e. , the standby losses associated with the power supply transformers) were similar for both countertop convection microwave ovens and microwave

- only ovens. In addition, as presented above in Table 5.5

. 7 , the total standby power consumption for countertop
convection microwave ovens is similar to that for microwave

- only ovens. For these reasons,

DOE believes that the size s of the power suppl ies for countertop microwave ovens are similar to

those for microwave

- only ovens. As a result

, DOE did not establish a separate product class for

countertop convection microwave ovens

.

Table 5.5

. 8 Control Power Supply Transformer Standby Power Test Results

DOE Countertop Combo

MWO Test Unit Transformer Standby Power, W DOE Microwave

Only Test Unit Transformer Standby Power, W

DOE CTC1 0.57 DOE28 1.44

DOE CTC2 0.95 DOE29 0.89

DOE CTC3 0.93 DOE30 1.11

DOE CTC4 1.81

DOE CTC5 1.08

5.5.2.5 Built

- In and Over

- the

- Range Convection Microwave Oven s: Product

Testing and Teardow ns

As discussed above in section 5.2 , DOE proposed to analyze a separate product class for

built

- in and over

- the

- range convection microwave ovens. DOE conducted standby power testing on a sample of six over

- the

- range convection microwave ovens and two built

- in convection

microwave ovens. The results from this testin g are shown below in Table 5.5

. 9 . Testing showed standby power levels rangin g from 4.1 W to 8.8 W. DOE noted that all of the units in its test sample had a cooking sensor and either a LCD touch screen with backlighting or a VFD

. DOE

noted that the built

- in convection microwave ovens in its test sample consumed more standby

power t han the over

- the

- range convection microwave ovens. However, DOE also noted that these two units were high er

- end units with larger displays and a larger number of cooking settings and options.5-29 Table 5.5

. 9 DOE Built

- In and Over

- the

- Range Convection Microwave Oven Standby

Power

DOE Built

In/Over

- the

Range Combo

MWO Test Unit Installation Rated

Microwave Cooking Output
Power, W Display Type Display

Size

, in. 2 Cooking

Sensor # of

Heaters Average Standby
Power,

W

DOE OTRC1 Over

- the

Range 1,200 LCD Touch w/ Backlight 13.66 Y 4 4.19

DOE OTRC2 Over

- the

Range 1,200 LCD Touch w/ Backlight 13.66 Y 4 4.37

DOE OTRC3 Over

- the

Range 1,100 VFD 3.91 Y 4 4.50

DOE OTRC4 Over

- the

Range 1,100 VFD 3.91 Y 1 4.59

DOE OTRC5 Over

- the

Range 1,000 VFD 7.75 Y 1 4.14

DOE OTRC6 Over

- the

Range 900 VFD 4.00 Y 4 6.65

DOE BC1 Built

- In 975 VFD 7.39 Y 4 8.16

DOE BC2 Built

- In 950 VFD 7.39 Y 4 8.76

As discussed above for microwave

- only ovens, display power requirements depend on the fundamental technology as well as the size of the display, the brightness, and the number and range of characters it can display. Figure 5.5

. 6 illustrates the measured standby power consumption as a function of display size for built

- in and over

- the

- range convection microwave

ovens

.5-30 Figure 5.5

. 6 DOE Standby Power Consumption as a Function of Display Size for Built

- In

and Over

- the

- Range Convection Microwave Ovens

5.5.2.6 Over

- the

- Range Microwave Oven Display Accelerated Lifetime

Testing

DOE evaluated the reliability and co nsumer utility of LED displays, LCDs, and VFDs in

over

- the

- range environments. Display manufacturers indicated that most LED displays and VFDs have maximum operating temperatures of 85 degrees Celsius (°C), while most LCDs have maximum operating temperatur es of 70 °C. DOE also noted that display reliability testing is generally conducted at 90 percent relative humidity (RH). According to display manufacturers, the rated lifetime ( i.e. , the time at which the display brightness will have decreased by 50

perce nt) for most LED displays is approximately 50,000 hours, whereas the lifetime for VFDs is between 35,000 and 50,000 hours. Display manufacturers also noted that LED displays and VFDs can achieve similar levels of brightness. For LCDs with LED backlighting, display manufacturers stated that the lifetime of approximately 50,000 hours is based on the LED backlights, because the LED backlighting will fail before the LCD itself as long as the display is operated within the rated temperature and humidity conditio ns. According to display manufacturers, if LED displays, LCDs, and VFDs are operated below their maximum rated operating temperature and humidity, the lifetime would not be affected.5-31 To further investigate reliability under the conditions experienced in over

- the

- range installations, DOE conducted testing on a sample of over

- the

- range microwave ovens with different display types. DOE selected 2 LED display, 2 LCD, and 3 VFD over

- the

- range microwave oven models from different manufacturers for testing. For each model, DOE purchased two identical units to evaluate the reliability under two separate temperature and humidity conditions. Prior to the start of testing, the illuminance for each display was measured

from a fixed distance under dark room conditions. In order to obtain consistent and comparable measurements, each clock display was set to 12:00 prior to the illuminance measurements. Because some displays may dim after a period of user inactivity, the illuminance for each unit was measured again after a period of 10 minutes of inactivity.

The microwave ovens were then operated in standby mode in an environmental chamber

for twelve 20

- hour periods at 82.5 ± 2.5 °C and 90 ± 5 percent RH for one set of the 7 microwave oven models, and for twelve 20

- hour p eriods at 67.5 ± 2.5 °C and 90 ± 5 percent RH

for the other set of 7 microwave ovens. The temperature conditions were selected based on the maximum rated operating conditions for the different display types, as discussed above. After each 20 hour period at elevated temperature and humidity, the environmental chamber and microwave ovens were cooled to ambient room temperature (23 ± 5°C), at which point the illuminance of each display was measured before and after a 10

- minute period of inactivity using

the sa me method described above. Each set of microwave ovens was exposed to the elevated temperature and humidity conditions for a total of 12 cycles of 20 hours (240 total hours), with illuminance measured twice at ambient room conditions after each cycle. In a ddition, the power and current draw were measured throughout the 20

- hour cycle and the 10

- minute illuminance measurement periods.

Figure 5.5 .7 and Figure 5.5 .8 show the illuminance measurements for the 67.5 °C and 90 percent RH testing. Figure 5.5 .9 and Figure 5.5 .10 show the results for the 82.5 °C and 90 percent RH testing

. The tes t results showed that display illuminance tended to degrade over time at these elevated conditions for most of the units tested, but the data did not reveal a correlation between the rate of degradation and display type. In particular, different VFDs in DO E’s test sample degraded both more and less rapidly than the LED displays under both set of temperature/humidity conditions, including an LED display with illuminance comparable to the

VFDs. DOE notes that one of the models with a backlit LCD failed after 20 hours at 82.5 °C and

60 hours at 67.5 °C. a However, DOE also notes that the other backlit LCD model had similar illuminance levels as two of the VFD models and showed little to no degradation. The power and current measurements similarly did not show an y trends indicating a correlation between the rate of degradation and display type

. Based on these test data, DOE concludes that all display types

can be used in over

- the

- range microwave oven applications without a loss in consumer utility.

a Failed LCD unit not presented in graphs.5-32 Figure 5.5

. 7 67.5 °C, 90

- percent RH, Illuminance at Start of 10

- M inute M easurement

P eriods b

b “LCD” unit presented in graph was only available for 40 hours of exposure to the 67.5 °C, 90

- percent RH

environmental conditions. 0.00 0.05 0.10 0.15 0.20 0.25 0.30

0 50 100 150 200 250 300Display Illuminance (lux)

Time at Elevated Conditions (hours) LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 35-33 Figure 5.5

. 8 67.5 °C, 90

- p ercent RH, Illuminance at End of 10

- M inute M easurement

P eriods c

c “LCD” unit presented in graph was only available for 40 hours of exposure to the 67.5 °C, 90

- percent RH

environmental conditions. 0.00 0.05 0.10 0.15 0.20 0.25 0.30

0 50 100 150 200 250 300Display Illuminance (lux)

Time at Elevated Conditions (hours) LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 35-34 Figure 5.5

. 9 82.5 °C, 90

- percent RH, Illuminance at Start of 10

- M inute M easurement

P eriods 0.00 0.05 0.10 0.15 0.20 0.25 0.30

0 50 100 150 200 250Display Illuminance (lux)

Time at Elevated Conditions (hours) LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 35-35 Figure 5.5

. 10 82.5 °C, 90

- percent RH, Illuminance at End of 10

- M inute Measurement

Periods

5.5.3 Incremental Cost Data

5.5.3.1 Incremental Cost Data for Microwave

- Only Ovens and Countertop

Convection Microwave Ovens

From the standby power testing and reverse

- engineering, DOE observed correlations between (1) specific components and technologies, or combinations thereof, and (2) measured standby power. Costs for each of the components and technologies were estimated by DOE using quotes obtained from suppliers, interviews with manufacturers, interviews with subject matter experts, research and literature review, and numerical modeling. Preliminary incremental manufacturing costs associated with the standby power levels w ere then obtained by considering combinations of these components as well as other technology options identified to reduce standby power. Manufacturer interviews were also conducted to obtain greater insight into the design strategies to improve efficiency and the associated costs. As discussed above in section

5.2 , DOE believes the standby power levels and characteristics of power supply designs ( i.e.

, 0.00 0.05 0.10 0.15 0.20 0.25 0.30

0 50 100 150 200 250Display Illuminance (lux)

Time at Elevated Conditions (hours) LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 35-36 size of the p ower supply) associated with microwave

- only countertop and over

- the

- range units as well as countertop convection microwave ovens are similar. As a result, DOE proposed to analyze microwave

- only ovens and countertop convection microwave ovens as a single pr oduct class. Therefore, DOE developed a single cost

- energy use relationship for this product class.

The design options DOE considered in the engineering analysis include various display technologies, cooking sensors with no standby power requirement, high er

- efficiency controller power supplies and relays, and improved control strategies. For details on the microwave oven standby design options, refer to chapter 3 of this TSD. DOE selected the design option(s) it believed manufactures would most likely impl ement to achieve a given standby power level for a given display technology.

The incremental cost to improve the standby power efficiency for each display technology was weighted by the estimated percentage of shipments based on a market survey of

SKUs t hat DOE conducted in October 2012. For example, DOE research suggests that 21 percent of all Product Class 1 SKUs are represented by countertop microwave

- only ovens that incorporate an LED display, no cooking sensor, and a standard magnetron power supply. Thus, 21 percent of the incremental cost to reach each standby power level for Product Class 1 is based on the design option pathway estimate for that particular type of microwave oven. The design pathways for other microwave oven types were calculated and included in the weighted average costs for both product classes. In total, 25 different types of microwave ovens were identified in the market survey, and up to 18 design pathways were investigated for each.

Every design pathway is unique, with test dat a and DOE estimates used to determine the power consumption and incremental manufacturing cost at each standby power level

. A ppendix

5D presents the details of the design pathways, including the design options and product

- based

share of each microwave oven type

. Although these likely pathways vary , the product type weighting suggest s generalized trends of power supply and microwave oven design changes as a function of standby power level for Product Class 1

.

For SL 1, DOE estimates that manufacturers would transition to cooking sensors with no standby power requirements for all units that include such a feature. In general, industry could continue using linear power supplies, though minor internal changes (such as lowering the unregulated voltage levels) ma y be implemented by manufacturers to further reduce standby power. Some pathways require switch

- mode power supplies even at efficiency level 1, particularly microwave ovens with VFD and backlit LCD displays.

To achieve SL 2, most product types would require the transition to SMPSs, as linear power supplies would likely no longer meet the standby power requirements. However, LED and

LCD

- equipped countertop microwave

- only ovens with or without cooking sensors, which

represent approx imately 30 percent of available models, could continue to use linear power supplies.

To transition from SL 2 to SL 3, all products would need incorporate SMPSs, with the exception of VFD

- and most backlit LCD

- equipped microwave ovens, for which the power5-37 consumption is too high to meet SL 3 even with am SMPS. Such products would either have to transition to an automatic power

- down function or switch to lower

- power display technologie s. For the final rule, DOE did not consider a switch to a different displ ay type. However, DOE research suggests that manufacturers could convert current microwave ovens featuring VFD displays to lower

- power technologies such as LEDs without the loss in consumer utility that would be associated with automatic power

- down. At SL 3, inverter

- based units would also require higher

- efficiency relays.

DOE research suggests that no microwave oven on the market in the United States can

attain the max

- tech SL 4 without the use of automatic power

- down. Therefore, the max

- tech

incremental cost reflects the cost of transitioning 100 percent of microwave ovens in the marketplace to a deep

- sleep module. Since the deep

- sleep module disconnects all power inside the microwave oven, there is no need for a highly efficient power supply. Thus, the automatic

power

- down design option includes the use of a conventional linear power supply. This approach minimizes the cost of the power supply and is a strategy employed by manufacturers of other products that DOE has reverse engineered.

Table 5.5

. 10 Incremental Manufacturing Costs for Product Class 1 Microwave Oven Standby Power

Standby Power

Level Standby Power, W Typical Design Options Incremental Cost, 2011$

Baseline 4.0

– $ 0

1 2.0 Zero

- standby sensor $0.26

2 1.5 SL 1 + Switch

- mode power supply (except countertop

microwave

- only LED and LCD) $0.38

3 1.0 SL 2 + Swit ch

- mode power supply (non

- VFD) or Auto power

- down (VFD) $3.28

4 0.02 Auto power

- down (all) $6.23

5.5.3.2 Incremental Cost Data for Built

- In and Over

- the

- Range Convection

Microwave Ovens

DOE conducted standby power testing and reverse engineering analyses on the 8 built

- in

and over

- the

- range convection microwave ovens in its test sample in order to develop the cost

efficiency relationships. DOE used a design

- option approach to determine the incremental manufacturing costs of convection microwave ovens for each standby power level. As discussed

ab ove for the microwave

- only oven and counte rtop convection micro wave oven product class, DOE observed correlations between specific components and technologies, or combinations thereof, and measured standby power. Costs for each of the components and technologies used at each standby power level were estimated by DOE u sing quotes obtained from various component suppliers as well as interviews with manufacturers.

For SL 1, DOE believes that, similar to the microwave

- only oven and countertop

convection microwave oven product class, manufacturers would likely have to ut ilize a cooking5-38 sensor with no standby power requirements. DOE estimated that the manufacturing cost of a

zero

- standby cooking sensor would be the same as that of the cooking sensor with high standby

power. In addition, all units other than non

- backlit LCD

- equipped models would require changing the conventional linear power supply to an SMPS. DOE used reverse engineering to determine the cost of the components used in a design of a switch mode power supply capable of delivering the same output power as the baseline conventional linear power supply.

DOE believes that no additional changes other than implementing an SMPS for non

backlit LCD

- equipped units would be required to achieve SL 2. Therefore, there is little

difference in the incremental costs for SL 1 and SL 2.

To move from SL 2 to SL 3, DOE determined that backlit LCD

- and VFD

- equipped microwave ovens would require an automatic power

- down function

. DOE estimated the manufacturing cost for this design option based on the cost of the components needed to design an automatic power

- down function that uses a transformerless power supply for the microprocessor chip along with a conventional linear power supply that is activated when the unit goes into active mode. DOE also estimated the costs associa ted with a door limit switch and Triac to control power through the transformer. DOE further estimated that LED

- and LCD

equipped models would need no additional design changes.

At SL 4, all models would be required to implement the automatic power

- down feature.

The results of this analysis for built

- in and over

- the

- range convection microwave ovens

are summarized in Table 5.5

. 11

.

Table 5.5

. 11 Incremental Manufacturing Costs for Product Class 2 Microwave Oven Standby Power

Standby Power

Level Standby Power, W Typical Design Options Incremental Cost, 2011$

Baseline 4.5

– $ 0

1 3.7 Zero

- standby sensor + Switch

- mode power supply (except LCD) $0.06

2 2.7 SL 1 + Switch

- mode power supply (LCD) $0.08

3 2.2 SL 2 (LED and LCD) or Auto power

- down (backlit LCD, VFD) $5.01

4 0.04 Auto power

- down (all) $5.865-39 REFERENCES

1 American Metals Market . Available online at www.amm.com/

. (Accessed May 2013. )

2 Association of Home Appliance Manufacturers (AHAM). 2006. AHAM Test Data on

Microwave Ovens . October 23, 2006

. Docket No. EE

- 2006

- STD

- 0127, Comment Number

17.

3 Ibid.

4 Ibid

.

5 Ibid

.

6 Fish, N. et al . 2006. Standby Power Energy Use of Common Household Appliances

. Canadian Building Energy End

- Use Data and Analysis Centre, CBEEDAC 2006

- RP

- 04

- DRAFT, July 2006.

7 Aulenback, A. et al . 2001. Stand

- By Power Requirements for Household Appliances

. Canadian Residential Energy End

- Use Data and Analysis Centre, CREEDAC

- 2001

- 05

- 04, May 2001.

8 Ibid

.

9 Lawrence Berkeley National Laboratory (LBNL). 2008

. Low

- Power Mode Energy Consumption in California Homes

. Report CEC

- 500

- 2008

- 035 prepared for California Energy Commission, September 2008. Available online at www.energy.ca.gov/2008publications/CEC

- 500

- 2008

- 035/CEC

- 500

- 2008

- 035.PDF

. (Accessed May 2013. )

10 Floyd, D. and Webber, C. 1998. “Leaking Electricity: Individual Field Measurement of

Consumer Electronics

. ” Proceedings of American Council for an Energy

- Efficient Economy

, Florida Solar Energy Center, August 1998.

11 Meier, A. and Huber, W. 1997. Results from the Investigations on Leaking Electricity in the

USA . Lawrence Berkeley National Laboratory, Berkel ey, CA, LBNL

- 40909, 1997.6

- i CHAPTER 6. MARKUPS FOR PRODUCT PRICE DETERMINATION

TABLE OF CO NTENTS

6.1 INTRODUCTION

................................ ................................ ................................ ........... 6

- 1

6.1.1 Distributio n Channels

................................ ................................ ................................ ...... 6

- 1

6.1.2 Procedure for Calculating Markups

................................ ................................ ................. 6

- 1

6.2 MANUFACTURER MARKUPS

................................ ................................ .................... 6

- 1

6.3 RETAILER MARKUPS

................................ ................................ ................................ .. 6

- 2

6.3.1 Approach to Calculating Retailer Markups

................................ ................................ ..... 6

- 2

6.3.1.1 Baseline Retailer Ma rkup

................................ ................................ .............. 6

- 3

6.3.1.2 Incremental Retailer Markups

................................ ................................ ........ 6

- 4

6.4 SALES TAXES

................................ ................................ ................................ ............... 6

- 8

6.5 SUMMARY OF MARKUPS

................................ ................................ .......................... 6

- 9

LIST OF TABLES

Table 6.2.1 Major Appliance Manufacturers: Gross Margins and Markups

.......................... 6

- 2

Table 6.3.1 Data for Calculating Baseline Markup: Electronics and Appliance Stores (2002)

................................ ................................ ................................ ................... 6

- 3

Table 6.3.2 Data for Calculating Incremental Markup: Electroni cs and Appliance Stores (2002)

................................ ................................ ................................ ........ 6

- 6

Table 6.3.3 Electronics and Appliance Stores: Concentration by Four Large Firms

............. 6

- 8

Table 6.4 .1 Average Sales Tax Rates by Census Division and Large State

........................... 6

- 9

Table 6.5.1 Summary of Markups

................................ ................................ .......................... 6

- 96-1 CHAPTER 6. MARKUPS FOR DETERMINING PRODUCT PRICE S

6.1 INTRODUCTION

To analy ze the effects of standards on consumer costs , DOE must determi ne the cost to

the consumer of both baseline products and more efficient units. For products having higher

than

- baselin e efficiency, DOE estimated consumer prices b y applying appropriate markup s to the incremental manufacturing costs estimated in the engineering analys is.

6.1.1 Distribution Channels

The appropriate markups for determining consumer product prices depend on the type of

distributio n channels through which products move from manufacturers to purchasers

. At each point in the distribution channel, companies mark up the price of products to cover their business costs and profit margin s

.

Data from the Associatio n of Home Appliance Manufacturers (AHAM) 1

6.1.2 Procedure for Calculating Markup s indicate that an overwhelming majority of residential appliances are sold through retail outlets. Because DOE is not aware of any other distribution channel that plays a significant role in the distribution of

resi dential microwave ove ns, DOE assumed that all microwaves are purchased by consumers

from retail outlets.

At each point in the distribution channel, companies mark up the price of a product to
cover their business costs and profit margin s. In company income statements, gross margin is the difference between the company ’s revenue and the company ’s cost of goods sold ( CGS ). The gross margin includes the expenses of companies in the distribution channel

, including overhead

c osts (sales, general, and administration); research and development and interest exp enses; depreciation and taxes ; and company profit

. To cover costs and to contribute positively to company cash flow, the price of a p roduct must include a markup. Products command lower or higher markups depending on company expenses associated with the product and the degree of market competition. In developing markups for manufacturers and retailers, DOE obtained data about the revenue, CGS, and expenses of firms that sell residential microwave ovens.

6.2 MANUFACTURER MARKUP S

DOE uses manufacturer markups to transform a manufacturer’s production costs in to a manufacturer sales price. Using the CGS and gross margin , DOE calculated the manufacturer markup ( MU MFG ) using the fol lowing equation

.

MFG MFG MFG

MFG CGS GM CGS MU +

=6-2 Where:

MU MFG = m anufacturer markup,

CGS MFG = m a nufacturer’s CGS or manufacturer production cost ( MPC) , and

GM MFG = m anufacturer’s gross margin.

The manufacturer’s CGS (or MPC) plus its GM equals the manufact urer selling price ( MSP ).

DOE developed an average manufacturer markup by examining the annual Securities and Exchange Commission (SEC) 10

- K reports filed by four publicly traded manufacturers engaged primarily in manufacturing appliance s and having a co mbined product range that includes

residential cooking products. 2 The four manufacturers represent a nearly 50

- percent mark et share for major appliances. Because the companies typically are diversified, producing a wide range of appliances, DOE assumed tha t an industry average markup was representative for the

manufacture of cooking products. DOE evaluated m arkups for 2002

– 2005

.

Table 6.2.1 lists the average corporate gross margin for 2002

– 2005 , and corresponding markups, fo r each of the four manufacturers. The average markup value based on the four companies is 1.26, which is the value that DOE used.

Table 6.2.1 Major Appliance Manufacturer s: Gross Margins and Markups

Mfr

. A Mfr

. B Mfr

. C Mfr

. D

Average net revenues ( million ) 372 280 4

, 770 12,682

Corporate gross margin ( %

) 15 28 16 22

Markup (%) 1.18 1.39 1.19 1.28

Source: SEC 10

- K reports (2002

– 2005)

.

6.3 RETAILER MARKUP S

Th e following sections describe DOE’s approach to calculating retailer markups and the development of baseline and incremental markups.

6.3.1 Approach to Calculating Retailer Markups

DOE based the retailer markups for residential microwave ov ens on financial data for electronics and appliance store category from the 2002 U.S. Census Business Expenditure Survey (BES)

. 3 DOE organized the financial data into statements that break down cost

comp onents incurred by firms in that category. DOE assume d that the income statements faithfully represent the various average costs incurred by firms that sell home appliances. Although electronics and appliance stores handle m any co mmodity lines, the data for the sector provide the most accurate available indi cation of expenses for selling home appliances.6-3 The BES data provided for electronics and appliance stores contain only total sales and detailed operating expenses. In order to construct a complete data set to estimate markups, DOE

needed to estimate CGS and GM

. The 1997 Business Expenses Survey provides total sales, gross margin

, and detailed operating expenses for the category of household appliance stores. The

CGS and GM account for a pproximately 70 percent and 30 percent of total sales

, respectively.

DOE found that GM as percent of sales was roughly constant in this sector from 1993 to 2007. a
Therefore, DOE assumed that the fractions of CGS and GM as percent of sales in 2002 are the

same as in 1997. Based on this assumption, DOE calculated the CGS

, gro ss margin

, and net profit for electronics and appliance stores in the 2002 BES

. b

6.3.1.1 Baseline Retailer Markup The baseline markup relates the manufacturer sales price of baseline products to the retailer sales price. DOE considers baseline models to be produ cts sold under current market conditions ( i.e. , without new energy efficiency standards). DOE used the following equation to calculate the baseline markup ( MU BASE ) for retailers as an average markup

.

RTL RTL RTL

BASE CGS GM CGS MU **+**

**=**

W here:

MU BASE = b aselin e retailer markup,

CGS RTL = r etailer’s cost of goods sold,

GM RTL = r etailer’s gross margin,

Table 6.3.1 shows DOE’s calculation of the baseline retailer markup for residential

microwave ovens

.

Table 6.3.1 Data for Calculating Baseli ne Markup: Electronics and Appliance Stores (2002) Type of Business Item Amount $1,000

Sales 83,896,811

Cost of goods sold (CGS) 57,888,800

Gross margin (GM) 26,008,011

Baseline markup = (CGS + GM)/CGS 1.45

Source s : U.S. Census

. 2002 Business Expens es Survey (for s ales) and 1997 Business Expenses Survey (for percentages of CGS and GM)

.

a U

. S . Census. Annual Retail Trade Report: Electronics and Appliance Stores Sales and Gross Margin

. 2007

.

Gross margin as a percent of sales ranges between 25 percent and 33 per cent during this period, with an average of 30 percent and no discernible upward or downward trend.

b The full version of 2002 Business Expenses Survey is available at: www.census.gov/prod/ec02/ be0200i1.pdf

. ( A ccessed May 2013 )6-4 6.3.1.2 Incremental Retailer Markup s Incremental markups are coefficients that relate the change in the manufacturer sales price of higher

- efficiency mo dels to the change in the retailer sales price. DOE considers higher

efficiency models to be products sold under market conditions affected by new or amended efficiency standards. Under new or amended standards

, the retailer faces an increase in CGS for

a particular product.

Unfortunately, there is limited empirical evidence regarding appliance retailer markup practices when a product increases in cost (due to increas ed efficiency or other factors) . DOE

understands that r eal

- world markup practices will va ry depending on the market conditions

retailers face , on the magnitude of the change in CGS

, and on any associated changes in retail costs. Pricing in retail stores may also involve rules of thumb that are difficult to identify and to incorporate into DOE’ s analysis.

Given the uncertainty about actual markup practices in appliance retailing, DOE ’s approach refl ects the following key concepts.

1. C hanges in the efficiency of goods sold are not expected to increase economic profits. Thus, DOE calculates ma rkups/gross margins to allow cost recovery for retail companies in the distribution chain (including changes in the cost of capital) without changes in company profit margins.

2. Efficiency improvements affe ct only some distribution costs. DOE sets markups a nd retail prices to cover the distribution costs that are expected to change with efficiency

but not those that are not expected to change with efficiency.

The incremental markup approach is described in more detail in Dale et al

. (2004). 4

To estimat e incremental retailer markups, DOE divides retailers’ operating expenses into two categories: (1) t hose that do not change when CGS increases because of amended efficiency standards (fixed ), and (2) t hose that incr ease proportionately with CGS (variable ). DOE defines fixed costs to include labor and occupancy expenses

, because th o se costs are not likely to

increase as a result of a rise in CGS

. All other expenses, as well as net profit, are assumed to change in proportion to CGS. Although some of th os e oth er expenses may not scale with CGS,

DOE take s a more conserv ative position and includes them as variable costs. c

DOE us ed the following equation to calculate the incremental markup ( MU INCR ) for

retailers

.

RTL RTL RTL

INCR CGS VC CGS MU **+**

**=**

c Note that under DOE’s approach, a component that has a high fixed cost yields a low incremental markup.6-5 W here:

MU INCR = increme ntal retailer markup,

CGS RTL = ret ailer’s cost of goods sold, and

VC RTL = ret ailer’s variable costs.

Table 6.3.2 shows the breakdown of operating expenses based on the 2002 BES data.

DOE estimate s that the incremental markup for retailers of microwave ovens is 1.17

.6-6 Table 6.3.2 Data for Calculating Incremental Markup: Electronics and Appliance Stores (2002)

Amount $1,000

Sales 83,896,811 Cost of Goods Sold (CGS) 57,888,800 Gross Margin (GM) 26,008,011 Labor & Occupancy Expenses (Fixed ) Annual payroll 10,267,605 Employer costs for fringe benefit 1 1,407,970 Contract labor costs including temporary help 160,094 Purchased utilities, total 427,809 Cost of purchased repair and maintenance services 308,78 9 Cost of purchased management consulting , administrative, and other professional services 300,548

Purchased communication services 400,598 Lease and rental payments 2,655,286 Taxes and license fees ( predominantly income taxes) 385,538

Subtotal: 16,31 4,237 Other Operating Expenses & Profit (Variable ) Expensed co mputer

- related supplies 86,751 Cost of purchased packaging and containers 41,866 Other materials and supplies not for resale 611,361 Cost of purchased transportation, shipping

, and warehous ing services 500,233 Cost of purchased printing services 285,012 Cost of purchased advertising and promotional services 1,840,898 Cost of purchased legal services 90,020 Cost of purchased accounting, auditing, and bookkeeping services 86,292 Cost of p urchased custom

- coded original software (expensed)

, including adapt at ion of off

- the

- shelf software 18,944 Cost of system support design and services

, including web design 35,748

Cost of insurance 393,201 Cost of data processing and other purchased compu ter services, except

communications 41,056 Depreciation and amortization charges 1,229,110 Commissions paid 106,061 Other operating expenses 2,929,906

Cost of contract work 21,955 Net profit before taxes 1,375,360

Subtotal: 9,693,774

Incremental Mar kup = (CGS + total other operating expenses and profit )/CGS 1.17

Source: U.S. Census

. 2002 Business Expenses Survey

.6-7 By dividing expenses into fixed and variable components, DOE’s calculation of incremental markup s as sumes that retailers cover cost cha nges without changing profit margins. Although retailers may be able to obtain higher profits for a time, DOE’s approach assumes that competition in the appliance retail market, combined with relatively inelastic demand ( i.e., the demand is not expected to decrease significantly with a relatively small increase in price), will exert a counterbalancing downward pressure on retailer profit ma rgins.

To measure the degree of competition in appliance retailing, DOE estimated the four

- firm

concentration ratio (FFCR) of major appliance sales in three retail c ategories : electronics and appliance stores, building materials and supplies dealers, and general merchandise stores

. The FFCR represents the market share of the four largest firms in each sector

. G enerally, an FFCR of

less than 40 percent indicates that a sector is not concentrated

, and an FFCR of more than 70

percent indicates that a sector is highly concentrated. d

, e

The FFCR of the sub

- sector of appliance sales within each channel is equal to the sector FFCR times the percent of total sales within each channel accounted for by major appliances. As

shown in Table 6.3.3 , the results indicate that appliance sales in electronics and appliance stores, household appliance stores, b uilding materials and supplies dealers, and general merchandise

stores have a FFCR well under the 40

- percent threshold. Moreover, the electronics and appliance

stores sector includes a sub

- sector of household appliance stores . Because this sub

- sector

conta ins many stores, it has a FFCR of only 16.8 percent

.

d University of Maryland University College. AMBA 607 course materials . (Last accessed August 18, 2012.)

< http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html >

e Quick MBA. Economics

— industry concentration

. (Last accessed August 18, 2012.)

< http://www.quickmba.com/econ/micro/indcon.shtml >6-8 Table 6.3.3 E lectronics and Appliance Stores: Concentration by Four Large Firms

Sector Four

- Firm

Concentration Ratio

% of s ector s ales Percent of S ales Accounted for by Major Appliances % Four

- Firm

Concentration Ratio

% of m ajor a ppliance

s ales

Electronics and appliance stores 44.3 39.4 17.5

S ub

- sector of

h ousehold appliance stores 16.8

- -

Building material s and supplies dealers 36.7 15.8 5.8

General merchandise

stores 65.6 35.4 23.2

Sources: U.S. Economi c Census

. Establishment and Firm Size: Concentration by Largest Firms for the United

States

. 2002

. U.S. Economic Census

. Product Lines: Kings of Business by Broad Product Line

. 2002.

*Note: It is assumed here that sales of major appliance are distributed u niformly within all firms in each sector.

6.4 SALES TAXES

S ales tax comprise s state and local sales taxes that are applied to the consumer produc t price. The sales tax is a multiplicative factor that increases the consumer produc t price. DOE

derived state an d local taxes from data provided by the Sales Tax Clearinghouse

. 5

T able 6.4.1 DOE derived population

- weighted average tax values for each Census division and large S tate, as well as the U.S. popula tion

- weighted average tax of 7. 1 percent ( ).6-9 Table 6.4.1 Average Sales Tax Rates by Census Division and Large State

Census Division/State Population Tax Rate %

New England 14,492,360 5.64

Mid Atlantic 21,564,041 6.62

East North Central 46,519,084 6.85

West North Central 20,639,751 7.06

South Atlantic 41,167,090 6.26

East South Central 18,553,961 7.93

West South Central 11,304,323 8.46

Mountain 22,373,411 6.80

Pacific 12,799,425 5.25

New York State 19,465,197 8.45

California 37,691,912 8.20

Texas 25,674,681 8.00

Florida 19,057,542 6.65

U.S. Average 7.11

6.5 SUMMARY OF MARKUPS

Table 6.5.1 summarizes the markups at each stage in the distribution channel for

microwave ovens plus the average sales tax.

Table 6.5.1 Summary of Markups

Markup Baseline Incremental

Manufacturer 1.26

Retailer 1.45 1.17

Sales t ax 0

. 07 1

Total 1.96 1.586-10 REFERENCES

1 Association of Home Appliance Manufacturers. AHAM 2003 Fact Book

. 2003. AHAM: Washington, D

. C. p. 25.

2 Security Exchange Commission. SEC 10

- K Reports

. Various dates, 2002

– 2005. Security Exchange Commission. www.sec.gov/

. (Accessed May 2013.)

3 U.S. Census Bureau. 2002 Economic Census, Business Expenses Survey, Retail Trade, Household Appliance Stores

. 2 002. U.S. Census Bureau : Washington, D

. C. www.census.gov/econ/bes/ . < www.census.gov/prod/ec02/be0200i1.pdf

. (Accessed May

2013 .)

4 Dale, L., D. Millstein, K . Coughlin, R. Van Buskirk, G

. Rosen quist, A

. Lekov, and S

. Bhuyan

. An Analysis of Price Determination and Markups in the Air

- Conditioning and Heating Equipment Industry

. January 2004 . Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL

- 52791

. 5 Sales Tax Clearinghouse, I nc. State Sales Tax Rates Along with Combined Average City

a nd County Rates

. thestc.com/STrates.stm

. (Accessed May 2013.)7

- i CHAPTER 7

. ENERGY USE ANALYSIS

TABLE OF CO NTENTS

7.1 INTRODUCTION

................................ ................................ ................................ ........... 7

- 1

7.2 CONSUMER SAMPLE

................................ ................................ ................................ ... 7

- 1

7.3 STANDBY POWER CONSUM PTION

................................ ................................ .......... 7

- 1

7.3.1 Annual Energy Consumption by Efficiency Level

................................ .......................... 7

- 3

7.3.2 Variability of Annual Energy Consumption

................................ ................................ .... 7

- 4

LIST OF TABLES

Table 7.2.1 Statistics Regarding RECS 2009 Sample

................................ ............................. 7

- 1

Table 7.3.1 Number of Meals Cooked per Day Based on RECS 2009 Sample

...................... 7

- 3

Table 7.3.2 RECS 2009 Microwave Oven Use Percentage

................................ .................... 7

- 3

Table 7.3.3 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Annual Energy Consumption by Standby Power Level

................................ ...... 7

- 4

Table 7.3.4 Bu ilt

- In and Over

- the

- Range Convection Microwave Ovens: Annual Energy Consumption by Standby Power Level

................................ ................... 7

- 4

LIST OF FIGURE S

Figure 7.3.2 Range of Ann ual Energy Use for Microwave

- Only Ovens and Countertop

Convection Microwave Ovens

................................ ................................ ............. 7

- 5 Figure 7.3.3 Range of Annual Energy Use for Built

- In and Over

- the

- Range Convection

Ovens

................................ ................................ ................................ ................... 7

- 57-1 CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

To carry out the life

- cycle cost (LCC) and payback period (PBP) analysi s described in chapter 8, the U.S. Department of Energy ( DOE ) must determine the energy savings t hat

con sumers will experience from more efficient products. This chapter describes how DOE determined the annual standby energy consumption of microwave ovens at various efficiency

levels

.

The engineering analysis

, described in chapter 5

, reports energy use bas ed on the DOE test procedure. This test provides standardized results that serve as the basis for comparing the performance of a ppliances under uniform conditions. Actual field usage of course varies depending on the conditions under which an appliance is operated.

To establish a reasonable range of energy consumption for microwave ovens used in the field, DOE primarily use d data from the Energy Information Administration (EIA)’s 200 9 Residential Energy Consumption Survey ( RECS 200 9 ). 1 RECS is a national sample survey of housing units that collects statistical information on the consumption of and expenditures for energy in housing units

, along with data on energy

- related characteristics of the housing units

and o ccupants. RECS 200 9 , which collected data on 12,083 housing units

, was constructed by EIA to be a national representation of the household population in the United States.

7.2 CONSUMER SAMPLE

F or the microwave oven sample

, DOE used t he subset of RECS 200 9 reco rds that met the

criterion that the household had a microwave oven. Because RECS 200 9 provides no means of determining the type of microwave oven in a given household, DOE use d the same sample for

both product classes. The RECS reported saturation of micro wave ovens in 200 9 was 88 percent.

Table 7.2.1 summarizes the data DOE on microwave oven ownership obtained from REC 200 9

.

Table 7.2.1 Statistics Regarding RECS 2009 Sample

Product

Class Selection Criterion No. of

Records No. of U.S. Hou seholds Represented million

1 and 2 Has m icrowave = Yes 11,616 10 9.0

7.3 STANDBY POWER CONSUM PTION

To estimate the annual energy use associated with standby power ( E

SP ) for both product classes, DOE used the following expression , in which the standby power i s multiplied by the number of hours in a year the mi crowave oven is in standby mode.7-2 SP SP SP Hours P E **× =** 

Where:

P

SP = standby power (watts), and

Hours SP = hours in a year the microwave oven is in standby mode ( hours ).

The annual standby hours equal the total hours in a year less the number of hours the

microwav e oven is in active operation. DOE used the following expression to determine the hours of active operation ( Hours ACTIVE )

.

RECS NOPR ACTIVE Use Hours Hours **× =** 

Where:

Hours NOPR = average microwa ve operating hours from the notice of proposed rulemaking ( NOPR ) , and

Use RECS = relative usage factor.

In the 2008 NOPR for cooking products, DOE determined the average hours of operation for microwaves ( Hours NOPR ) to be 71 hours per year. 2 DOE has no reason to believe that this number has changed.

For each household, RECS 2009 provides information on the frequency of microwave oven usage and the number of hot meals prepared per day. DOE used the followi ng expression to calculate the RECS usage factor ( Use RECS ) for each household in the sample

.



## ( **)**

AVG RECS RECS RECS Use Freq Meals Use / **× =** 

Where:

Meals RECS = RECS reported household hot meals per day, Freq RECS = RECS reported microwave use frequency, and

Use AVG = w eighted average usage factor.

The weighted average usage factor was calculated u sing the following expression.



## ∑ **∑ ×**  **×**

**=**

RECS RECS AVG AVG

AVG Weight Weight Freq Meals Use

Where:

Meals AVG = a verage household hot meals per day, Freq AVG = a verage microwave use frequency, and7-3 Weight = RECS househo ld weight.

The RECS 2009 data for Meals RECS and Freq RECS fall into o ne of the bins shown in Table

7.3.1 and Table 7.3.2 , respectively. For each question, DOE assumed a uniform or triangular distribution within the boundaries of each bin and randomly assigned a value from within the appropriate range to each sample household. For calculatin g the average usage factor ( Use AVG )

, DOE used the average value from the corresp onding bin

.

Table 7.3.1 Number of Meals Cooked per Day Based on RECS 2009 Sa mple

Bin Frequency of Microwave

Oven Use Meals per D ay
Min

. Max

. 0 Doesn't cook/ n ever cooks 0 .0 0.0

1 Three or more times a day 3.0 4.0

2 T wo times a day 1.0 3.0

3 Once a day 0.5 1.5

4 A few times each week 0.2 0.8

5 About once a week 0.1 0.2

6 Less than once a week 0.0 0.1

Table 7.3.2 RECS 2009 Microwave Oven Use Percentage

Bin Amount of Food Cooked in

Microwave Use Percentage %

Min

. Max

. 1 Most or all 90 100

2 About half 25 75

3 Som e or very little 20 30

4 Used only for snacks, defrosting, or reheating food 5 25

7.3.1 Annual Energy Consumption by Efficiency Level

Table 7.3.3 and Table 7.3.4 present the average annual energy con sumption by efficiency level for both product classes.7-4 Table 7.3.3 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Annual Energy Consumption by Standby Power Level

Efficiency

Level Standby

Power W Energy Use kWh/yr

Baseline 4 .00 34.76

1 2 .00 17.38

2 1.5 0 13.03

3 1 .00 8.69

4 0.02 0.17

Table 7.3.4 Built

- In and Over

- the

- Range Convection Microwave Ovens: Annual Energy Consumption by Standby Power Level

Efficiency

Level Standby

Power W Energy Use kWh/yr

Baseline 4.50 39.10

1 3.70 32.15

2 2.70 23.46

3 2.20 19 .12

4 0.04 0.35

7.3.2 Variability of Annual Energy Consumption

Figure 7.3.1 and Figure 7.3.2 show the range of average annual energy consumption by efficiency level for both product classes. Because va riation among households in use of

microwave ovens for cooking has a minor effect on the number of standby hours, the range in standby energy use is very small.7-5 0 5 10 15 20 25 30 35 40

Level 0 Level 1 Level 2 Level 3 Level 4Annual Energy Use (kWh)

Efficiency Level Microwave and MWO

- Convection CT Average Annual Energy Use Average; Median; Box 25%

- 75%; Whisker 5%

- 95%

median

average

Figure 7.3.2 Range of Annual Energy Use for Microwave

- Only Ovens and Countertop Convection Microwave Oven s

0 5 10 15 20 25 30 35 40 45

Level 0 Level 1 Level 2 Level 3 Level 4Annual Energy Use (kWh)

Efficiency Level Built

- In and Over

- the

- Range Convection Ovens Average Annual Energy Use Average; Median; Box 25%

- 75%; Whisker 5%

- 95%

median

average

Figure 7.3.3 Range of Annual Energy Use for Built

- In and Over

- the

- Range

Convection Ovens7-6 REFERENCES

1. U.S. Department of Energy (DOE)

– En ergy Information Administration (EIA). Residential Energy Consumption Survey: Preliminary 200 9 Public Use Microd ata File

. October 2011

. EIA: Washington, D

. C. www.eia.gov/consumption/residential/data/2009/index.cfm#microdata

. (Accessed May

2013 .)

2. DOE

– Energy Efficien cy and Renewable Energy

. Energy Conservation Program for

Consumer Products: Notice of Proposed Rulemaking for Residential Dishwashers, Dehumidifiers, and Cooking Products, and Commercial Clothes Washers

. Federal Register

, October 17, 2008 . Vol. 73 , no. 202 , pp. 62034

– 62134. www1.eere.energy.gov/buildings/appliance_standards/residential/pdfs/home_appl_nopr_ fr.pdf

. (Accessed May 2013.)8-i CHAPTER 8. LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYS I S

TABLE OF CONTENTS

8.1 INTRODUCTION

................................ ................................ ................................ ........... 8

- 1

8.1.1 General Approach to Analysis

................................ ................................ ......................... 8

- 1

8.1.2 Overview of Inputs to Analysis

................................ ................................ ....................... 8

- 3

8.2 INPUTS TO LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

................ 8

- 7

8.2.1 Inputs to Total Installed Cost

................................ ................................ ........................... 8

- 8

8.2.1.2 Forecasting Future Product Prices

................................ ................................ . 8

- 9

8.2.1.3 Baseline Manufacturer Cost

................................ ................................ ......... 8

- 11

8.2.1.4 Standard

- Level Manufacturer Cost Increases

................................ .............. 8

- 11

8.2.1.5 Overall Markup

................................ ................................ ............................ 8

- 12

8.2.1.6 Installation Cost

................................ ................................ ........................... 8

- 12

8.2. 1.7 Total Installed Cost

................................ ................................ ...................... 8

- 12

8.2.2 Inputs to Operating Cost

................................ ................................ ................................ 8

- 13

8.2.2.2 Annual Energy Consumption

................................ ................................ ....... 8

- 14

8.2.2.3 Residential Electricity Prices

................................ ................................ ....... 8

- 15

8.2.2.4 Energy Price Trends

................................ ................................ ..................... 8

- 17

8.2.2.5 Repair and Maintenance Costs

................................ ................................ ..... 8

- 17

8.2.2.6 Product Lifetime

................................ ................................ .......................... 8

- 17

8.2.3 Discount Rates

................................ ................................ ................................ ............... 8

- 19

8.2.4 Comp liance Date of Standard

................................ ................................ ........................ 8

- 23

8.2.5 Product Energy Efficiency in the Base Case

................................ ................................ . 8

- 23

8.3 RESULTS OF LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

........... 8

- 24

8.3.1 Summary of Results

................................ ................................ ................................ ....... 8

- 24

8.3.1. 2 Distributions of Impacts

................................ ................................ ............... 8

- 25

8.3.1.3 Range of Impacts

................................ ................................ ......................... 8

- 28

8.4 REBUTTABLE PAYBACK P ERIOD

................................ ................................ .......... 8

- 32

8.4.1 Inputs

................................ ................................ ................................ .............................. 8

- 32

8.4.2 Results

................................ ................................ ................................ ............................ 8

- 32

LIST OF TABLES

Table 8.1.1 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Summary of Inputs to LCC a nd PBP Input Summary

................................ ......... 8

- 6

Table 8.1.2 Built

- In and Over the Range Convection Microwave Ovens: Summary of Inputs to LCC and PBP

................................ ................................ ........................ 8

- 7

Table 8.2.1 Inputs to Total Installed Cost

................................ ................................ ............... 8

- 8

Table 8.2.2 Baseline Manufacturer Costs

................................ ................................ ............. 8

- 11

Table 8.2.3 Standard

- Level Manufacturer Cost Increases

................................ .................... 8

- 12

Table 8.2.4 Microwave Ovens: Overall Markups

................................ ................................ . 8

- 12

Table 8.2.5 Total Installed Costs

................................ ................................ .......................... 8

- 13

Table 8.2.6 Inputs to Operating Cost

................................ ................................ .................... 8

- 138-ii Table 8.2.7 Annual Energy Consumption

................................ ................................ ............. 8

- 15

Table 8.2.8 Average Residential Electricity Prices

................................ ............................... 8

- 16

Table 8.2.9 Lifetime Parameters

................................ ................................ ........................... 8

- 19

Table 8.2.10 Types of Household Debt and Equity by Percentage Shares

............................. 8

- 20

Table 8.2.11 Average Nominal Interest Rates for Househ old Debt

................................ ....... 8

- 21

Table 8.2.12 Average Real Effective Interest Rates for Household Debt

.............................. 8

- 21

Table 8.2.13 Average Nominal and Real Interest Rates for Household Equity

..................... 8

- 22

Table 8.2.14 Average Interest on Household Debt and Equity

................................ .............. 8

- 22

Table 8.2.15 Microwave

- Only and Countertop Convection Microwave Ovens: Base

Case Market Shares

................................ ................................ ............................ 8

- 23

Table 8.2.16 Built

- In and Over

- the

- Range Convection Microwave Ovens: Base

- Case

Market Shares

................................ ................................ ................................ .... 8

- 24

Table 8.3.1 Microwave

- Only and Countertop Convection Microwave Ovens: LCC and

PBP Results

................................ ................................ ................................ ........ 8

- 25

Table 8.3.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: LCC and

PBP Results

................................ ................................ ................................ ........ 8

- 25

Table 8.4.1 Rebuttable Payback Periods

................................ ................................ ............... 8

- 33

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for Determining LCC and PBP

................................ .... 8

- 4 Figure 8.2.1 Residential Electricity Price Trends

................................ ................................ ... 8

- 17 Figure 8.2.2 Weibull Function for MWO Lifetime

................................ ................................ 8

- 19 Figure 8.3.1 Microwave

- Only and Countertop Convection Microwave Ovens: Base

Case LCC Distribution

................................ ................................ ....................... 8

- 26 Figure 8.3.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: Base

- Case

LCC Distribution

................................ ................................ ............................... 8

- 26 Figure 8.3.3 Microwave

- Only and Countertop Convection Microwave Ovens: LCC Savings Distribution for TSL 3

................................ ................................ .......... 8

- 27 Figure 8.3.4 Built

- In and Over

- the

- Range Convection Microwave Ovens: LCC Savings

Distribution for TSL 3

................................ ................................ ....................... 8

- 27 Figure 8.3.5 Microwave

- Only and Countertop Convection Microwave Ovens: PBP

Distribution for TSL 3

................................ ................................ ....................... 8

- 28 Figure 8.3.6 Built

- In and Over

- the

- Range Convection Microwave Ovens: PBP

Distribution for TSL 3

................................ ................................ ....................... 8

- 28 Figure 8.3.7 Microwave

- Only an d Countertop Convection Microwave Ovens: Range of Average LCC Savings

................................ ................................ ........................ 8

- 29 Figure 8.3.8 Built

- In and Over the Range Convection Micr owave Ovens: Range of Average LCC Savings

................................ ................................ ........................ 8

- 30 Figure 8.3.9 Microwave

- Only and Countertop Convection Microwave Ovens: Range of Average Payback Period

................................ ................................ .................... 8

- 31 Figure 8.3.10 Built

- In and Over the Range Convection Microwave Ovens: Range of Average Payback Period

................................ ................................ .................... 8

- 318-1 CHAPTER 8. LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSI S

8.1 INTRODUCTION

This chapter describes the U.S. Department of Energy ’s (DOE)’s methodology for analyzing the economic impacts of new energy efficiency standards on individual consumers.

The effect of standards for microwave oven standby power on individual consumers includes a change in operating expense (usually decreased) and a change in purchase price (usually increased). This chapter descr ibes three metrics DOE used to determine the effect of standards

o n individual consumers.

**•** Life

- cycle cost (LCC) is the total consumer expense over the life of an appliance, including purchase price and operating costs (including energy expenditures). DOE discounts future operating costs to the time of purchase, and sums them over the

lifetime of the product

.

**•** Payback period (PBP) measures the amount of time it takes a consumer to recover the assumed higher purchase price of more energy efficient products t hrough lower operating costs.

**•** Rebuttable payback period is a special case of the PBP. Where as LCC and PBP are

estimated given a range of inputs that reflect field conditions, rebuttable payback period is based on laboratory conditions , specifically inputs to DOE ’s test procedure.

Inputs to the LCC and PBP calculations are discussed in section 8.2 of this chapter. Results for the LCC and PBP are presented in sec tion 8.3

. The rebuttable PBP is discussed in

section 8.4

. Key variables and calculations are presented for each metric. DOE performed the

calculations discussed here us i ng a series of Microsoft Excel spreadshe ets

, which are accessible

on the Internet : ( www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products.html ) Details and instructions for using the spreadsheets are present ed in a ppendix 8 A.

8.1.1 General Approach to Analysi s

DOE calculates l ife

- cycle cost (LCC), the total consumer expense over the life of an appliance, by the following equation.



## ( **)** 

## ∑

**= +**  **+ =**  N

t t t

r OC IC LCC

1 1

Where:

LCC = l ife

- cycle cost in dollars,

IC = t otal installed cost in dollars, ∑ = s um over the appliance lifetime, from year 1 to year N,

N = l ifetime of the appliance in years,8-2 OC = o perating cost in dollars,

r = d iscount rate, and

t = y ear for which operating cost is being determined.

Numerically, the payback period (PBP) , defined above, is the ratio of the increase in purchase cost ( i.e. , from a less energy efficient design to a more efficient design) to the decrease in annual operating expenditures. This type of ca lculation results in what is termed a simple payback period, because it d oes not take into account changes in operating expense s over time or the time value of money . That is, the calculation is done at an effective discount rate of zero percent. The equation for PBP is:

OC IC PBP **∆ ∆** 

=

Where:

Δ

IC = d ifference in total installed cost between the more energy efficient design and the baseline design, and

Δ OC = difference in annual operating expenses.

Payback periods are expressed in years. Payback periods greater than the life of the product in dicate that the increased total installed cost is not recovered through reduced operating

expenses.

Recognizing that several inputs to the determination of consumer LCC and PBP are either variable or uncertain, DOE conducted the LCC and PBP analys i s by modeling both the uncertainty and variability of the inputs using Monte Carlo simulation and probability

distributions. A ppendix 8 B provides a detailed explanation of Monte Carlo simulation and the use of probability distributions

. DOE used using Microsoft Excel spreadsheets combined with Crystal Ball (a commercially available add

- in program) to develop LCC and PBP spreadsheet

models that incorporate both Monte Carlo simulation and probability distributions.

In addition to using probability dist ributions to characteriz e several of the inputs to the analysi s , DOE developed a sample of individual households that use microwave ovens

. By developing household samples, DOE was able to perform the LCC and PBP calculations for each

household to account f or the variability in energy consumption and/or energy price associated

with a range of household s

.

As described in chapter 7 , DOE used the DOE Energy Information Administration ’s (EIA ’s ) 200 9 Reside ntial Energy Consumption Survey ( RECS 2009 ) to develop household samples for microwave

- only and convection microwave ovens. 1 The EIA constructed RECS

200 9, which consists of 12,083 housing units , to be a national representation of the household population in the United States. DOE used the subset of RECS 200 9 records in which the

household had a microwave oven

. Because RECS 2009 provides no means of determining the type of microwave oven in a given hous ehold, DOE used the same sample for both product8-3 classes. DOE used RECS to establish the variability of annua l appliance use and of energy pric es

. DOE was able to assign unique annual hours of operation to each household in the sample. The variability a mong households in annual appliance use and/or energy pricing contributes to the range of LCCs and PBPs calculat ed for any particular trial standard level ( T SL)

.

DOE displays the LCC and PBP results as distribu tions of impacts compared to baseline

conditions. Results , which are presented at the end of this chapter

, are based on 10,000 samples per Monte Carlo simulation run. To illustrate the implications of the analysi s , DOE generated a frequency chart that depicts the variation in LCC and PBP for each T SL considered.

8.1.2 Overview of Inputs to Analysis

DOE categorizes inputs to the LCC and PBP analysis as (1) inputs for establishing the purchase expense, otherwise known as the total installed cost, and (2) inputs for calculating the operating cost. The primary inputs for establishi ng the total inst alled cost are listed below.

**•** Baseline manufacturer cost : The costs incurred by the manufacturer to produce products that meet current minimum efficiency standards.

**•** Standard

- level manufacturer cost increases : The change in manufacturer cost
associated wit h producing products that meet a given T SL

.

**•** Markups and sales tax : The increases associated with converting the manufacturer

cost to a consumer product cost

.

**•** Installation cost : The cost to the consumer of installing the product

. The installation cost represents all costs required to install the product other than the marked

- up

consumer product cost. The installation cost includes labor, overhead, and any miscellaneous materials and parts

. Thus, the total installed cost equals the consumer product cost plus the installation cost.

**•** Learning rate: The cost reduction factor associated with economies of scale and technology learning.

The primary inputs for cal culating the operating cost are listed below.

**•** Product energy consumption : The on

- site energy use associated with operating the product

.

**•** Product efficiency : The product energy consumption associated with standard

- level products ( i.e.

, products having efficiencies greater than those of baseline products ).

**•** Energy prices : T he prices consumers pay for energy ( e.g. , electricity or natural gas).

**•** Energy price trends : DOE used the EIA ’s Annual Energy Outlook 20 1 2 ( AEO20 1 2 ) 2

**•** Repair and maintenance costs : Repair costs are associated with repairing or replacing components that have failed. Maintenance costs are associated with maintaining the operation of the product

. to forecast energy prices f or the results presented in this chapter

.

**•** Lifetime : The age at which the product is retired from service.8-4 **•** Discount rate : The rate at which DOE discounts future expenditures to establish their present value.

The data inputs to PBP are the total installed cost of the product to the consumer for each energy efficie ncy level and the annual (first

- year) operating expenditures for each T SL . T he inputs to total installed cost are the product cost plus the insta llation cost. The inputs to operating costs are the annual energy cost, the annual repair cost, and the annual maintenance cost. The PBP uses the same inputs as the LCC analysis, except P BP does not require energy price trends

or discount rates. Because the PBP is what is termed a simple payback, the required energy price is only for the year in which a new energy conservation standard take s effect. The energy price

DOE used in the PBP cal culation was the price projected for that year. Discount rates are also not required for the simple PBP calculation.

Figure 8.1.1 depicts the relationships among the inputs to the calculation of the LCC and

PBP. In the figure, the yellow boxes indicate inputs, the green boxes indicate intermediate outputs, and the blue boxes indicate the final outputs (the LCC and PBP).

Figure 8.1.1 Flow Diagram of Inputs for Determining LCC and PBP Baseline

Manufacturer

Cost

Std

- Level

Manufacturer

Cost

Manufacturer Markup

Retailer or

Distributor

Markup

Sales Tax Installation

Cost

Energy Consumption Product

Price

Lifetime Operating Expense Annual Energy Expense

Lifetime Repair and

Maintenance

Cost

Discount Rate

Energy Price

Trends Annual Operating Expense Payback

Period Life

- Cycle

Cost

Energy Prices Total Installed

Cost8-5 Table 8.1.1 and Table 8.1.2 summarize the input values that DOE used to calculate the

LCC and PBP for each micr owave oven product clas s

. DOE used single

- point values to
characterize all of the inputs to total cost, but characterized several of the inputs to operating cost using probability distributions that capture the input’s uncertainty and/or variability. For t hose inputs characterized with probability distributions, the values provided in the tables are average or typical values.8-6 Table 8.1.1 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Summary of Inputs to LCC and PBP Input Summary

Input Efficiency

Level Average or Typical

Value Characterization

2016 Baseline

manufacturer cost
2011$ Baseline 4 W* standby = $1 20.35 Single

- point value

2016 Standard

level manufacturer

cost increase 2011$ TSL 1 2 W standby = $0. 26 Single

- point value

TSL 2 1.5 W standby = $0. 38 Single

- point value

TSL 3 1 W standby = $ 3.28 Single

- point value

TSL 4 0.02 W standby = $ 6.23 Single

- point value

Retailer markup All Baseline = 1.45 Single

- point value Incremental = 1.17

Manufacturer markup All 1.2 98 Single

- point value

Sales tax All 7. 1 % Variability based on region

Annual energy use

kWh Baseline 34.7 6 Variability based on usage

TSL 1 17.38 Variability based on usage

TSL 2 13.03 Variability based on usage

TSL 3 8.69 Variability based on usage

TSL 4 0.17 Variability based on usage

201 6 average energy prices
2011$ All 12 cents/kWh† Variability based on region

Repair and

maintenance costs All No cost increase with efficiency No cost increase with efficiency

Installation costs All No cost increase with efficiency No cost increase with efficiency

Lifetime All 10. 9 1 years Weibull distribution

Discount rate All 4.9% Custom distribution

Energy price trend All AEO2012 reference case Single

- point values

* W = watts.

† kWh = kilowatt

- hour.8-7 Table 8.1.2 Built

- In and Over the Range Convection Microwave Ovens: Summary of Inputs to LCC and PBP

Input Efficiency

Level Average or Typical Value Characterization

2016 Baseline

manufacturer cost
2011$ Baseline 4.5 W * standby = $ 260.41 Single

- point value

2016 Standard

level manufacturer

cost increase 2011$ T SL 1 3.7 W standby = $ 0. 0 6 Single

- point value

T SL 2 2.7 W standby = $ 0.0 8 Single

- point value

T SL 3 2.2 W standby = $ 5.01 Single

- point value

T SL 4 0.04 W standby = $ 5. 86 Single

- point value

Retailer markup All Baseline = 1.45 Single

- point value Incremental = 1.17

Manufacturer markup All 1.2 98 Single

- point value

Sales tax All 7. 1 % Variability based on region

Annual energy use

kWh Baseline 39.10 Variability based on usage

T SL 1 32.15 Variability based on usage

T SL 2 23.46 Variability based on usage

T SL 3 19.1 2 Variability based on usage

T SL 4 0.35 Variability based on usage

201 6 average energy prices
2011$ All 12 c ents /kWh † Variability based on region

Repair and

maintenance costs All No cost increase with efficiency No cost increase with efficiency

Installation costs All No cost increase with efficiency No cost increase with efficiency

Lifetime All 10. 9 1 years Weibull distribution

Discount rate All 4. 9 % Custom distribution

Energy price trend All AEO 201 2 reference case Single

- point values

* W = watts.

† kWh = kilowatt

- hour.

8.2 INPUTS TO LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

Because D OE gathered most of its data for the LCC and PBP analysis in 201 2, DOE expresses dollar values in 2011$

.8-8 8.2.1 Inputs to Total Installed Cost

DOE uses the following equation to define the total installed co st.



### INST CPC IC **+ =** 

W here:

C

P C = consumer p roduct cost ( i.e.

, consumer cost for the product only), and

INST = i nstallation cost or the consumer cost to install the product

.

The product cost is based on how the consumer purchases the product . As discussed in chapter 6 , DOE defined markups and sales taxes for co nverting manufacturing costs into

consumer product cost s

. Table 8.2.1 summarizes the inputs for determining total installed cost.

Table 8.2.1 Inputs to Total Installed Cost

Baseline manufacturer cost

Standard

- level manufacturer cost

Distribution chain markups

Sales tax ( replacement applications)

Installation cost

The baseline manufacturer cost is the cost incurred by the manufacturer to produce products meeting existing minimum efficiency standards. Standard

- level manuf acturer cost

increases are the change in manufa cturer cost associated with producing products at a standard

level. Markups and sales tax convert the manufacturer cost to a consumer product cost. The

i nstallation cost is the cost to the consumer of installing the product and represents all costs required to install the product other than the marked

- up consumer product cost

. The installation cost includes labor, overhead, and any miscellaneous materials and parts

.

DOE calculated the total installed cost for baseline products based on the following equation.

BASE BASE OVERALL MFG BASE BASE BASE


### INST MU COST INST CPC IC

**+ ×**  **= +**  **=**

_

W here:

IC BASE = b aseline total installed cost

, CPC BASE = c onsumer product cost for baseline models,

INST BASE = b aseline installation cost,

COST MFG = m anufacturer cost for baseline models, and8-9 MU OVERALL_BASE = b aseline overall markup (product of manufacturer markup, baseline retailer or distributor markup, and sales tax).

DOE calculated the total installed cost for standard

- level products based on the following equation

.



## ( **) (**  **) (**  **) (**  **)** 

# ( **)**

STD INCR OVERALL MFG BASE STD STD BASE BASE STD BASE STD BASE STD STD STD

INST MU COST IC INST CPC INST CPC INST INST CPC CPC INST CPC IC

**∆ +**  **× ∆**  **+ =**  **∆ +**  **∆ +**  **+ =**  **∆ +**  **+ ∆**  **+ =**  **+ =** 

_

W here:

IC STD = s tandard

- level total installed cost

, CPC STD = c onsumer product cost for standard

- level models,

INST STD = s tandard

- level installation cost,

CPC BASE = c onsumer product cost for baseline models,

Δ CPC STD = c hange in product cost for standard

- level models,

INST BASE = b aseline installation cost,

Δ

INST STD = c hange in installation cost for standard

- level models,

IC BASE = b aseline total installed cost

, Δ COST MFG = c hange in manufacturer cost for standard

- level models, and

MU OVERALL_INCR = overall i ncremental markup (product of manufacturer markup, incremental retailer or distributor markup, and sales tax).

The re st of this section provides information about each of the above input variables that

DOE used to calculate the total installed cost for microwave oven s

.

8.2.1.2 Forecasting Future Product Prices

H istorical price data for certain appliances and equipment that have been subject to

energy conservation standards indicate that the assumption of constant real prices and costs may

often overestimate long

- term trends in appliance and equipment prices. Economic literature and historical data sugg est that the real costs of t he products may trend downward over time in

response to “learning” or “expe rience” curves. A draft paper, Using the Experience Curve Approach f or Appliance Price Forecasting, posted on the DOE web site at www1.eere.energy.gov/buildings/appliance_standards/pdfs/experience_curve_appliance_price_f orecasting_3

- 16

- 11.pdf , summarizes the currently available data and literature that are relevant to price forecasts for selected appliances and equipment.

In light of the data and DOE’s aim to improve the accuracy and robustness of its analyses, DOE has decided to project future product prices by incorporating learning over time,

consis tent with the analysis in the available literature

. DOE us ed this approach to derive learning

rates to forecast future prices of microwave ovens at the considered efficiency levels.8-10 An exte nsive literature discusses the learning or experience curve phenomenon, typically based on observations in the manufacturing sector. Based on the experience curve approach , the real cost of production is related to the cumulative production , or experience , with a product. To explain the empirical relationshi p, the theory of technology learning is used to substantiate a decline in the cost of producing a given product as firms accumulate experience with the technology. A common functional relationship for model ing the evolution of production costs is :

Y = aX - b

W here :

a = the initial price (or cost),

b = a positive constant known as the learning rate parameter,

X = cumulative production, and

Y = price as a function of cumulative production

.

Thus, as experience (production) accumulates, the cost of producing the next unit decreases. The percentage reduction in cost that occurs with each doubling of cumulative production is known as the learning rate ( LR ), and is given by:

LR = 1

– 2 - b

In typical learning curve formulations, the LR parameter is derived using two historical

data series: product price (or cost)

, and cumulative production, which is a function of shipments throughout a long period

.

To derive a n LR parameter for microwave ovens, DOE obtained historical Producer Price

I ndex (PPI) data for electric cooking products from the Bureau of Labor Statistics . DOE

understands that electric cooking products encompass much more than microwave ovens; however, because no PPI data specific to microwave ovens were available, DOE used PP I data for electric cooking products as representative of microwave ovens. Additionally, shipments of microwave oven have become a significant part of the overall shipments of electric household cooking product since 1975. For electric cooking products , DO E used PPI data spanning 1969

– 2011

. An i nflation

- adjusted price ind ex for electric cooking products was calculated by dividing the PPI series by the Consumer Price “all items” index for the same years

.

DOE assembled a time

- series of annual shipments for 19 69

– 2009 for electric household cooking products (including microwave ovens, cooktops, and r anges ) , an d for 1972

– 2009 for

microwave ovens from data submittals from AHAM, AHAM Fact Books , and Appliance Magazine

. The shipments data were used to estimate cumulative shipments (production ).

To estimate a learning rate parameter, a least

- squares power

- law fit was performed on the unified price ind ex versus cumulative shipments

. The form of the fitting equation is:

P(X) = P

o X

- b

,8-11 where the two parameters, b (the learning rate parameter) and P

o (the price or cost of the first unit of production), are obtained by fitting the model to the data.

Fitting the model to microwave oven shipments, he parameter values obtained are:

P

o = 13

. 08

− 1

. 54 + 1

. 74 (95% confidence) , and

b = 0.406 ± 0.024 (95% confidence)

.

T he estimated learning rate (defined as the fractional reduction in price expected from each doubling of cumulative production) is 1 0.1 ±1.7% (95

- percent confidence). For the LCC and PBP analysis, DOE used the central value, along with the shipments forecast for 2011

– 2016, to estimate the price of microwave ovens in 2016.

DOE’s derivation of learning rates for microwave ovens is described in further detail in appendix 8C

.

8.2.1.3 Baseline Manufacturer Cost DOE developed the baseline manufacturer costs for both product classes of microwave

ovens as described in the e ngineering a nalysis (described in chapter 5 )

. B aseline manufacturer

costs are shown in Table 8.2.2

.

Table 8.2.2 Baseline Manufacturer Cost s

Product Class Standby Power

W M anufacturer

Cost 2011$

Microwave

- only and countertop

convection microwave ovens 4.00 1 15

. 92

Built

- in and over

- the

- range convection microwave ovens 2. 50 25 0

. 83

8.2.1.4 Standard

- Level Manufacturer Cost Increases

DOE used a combination of cost data submitted by AHAM and a reverse

- engineering analysis to develop manufacturer cost increases associated with increases in microwave oven efficiency

. Refer to chapter 5 for details. Table 8.2.3 pre sents the standard

- l evel manufacturer

cost increase for each considered efficiency level

.8-12 Table 8.2.3 Standard

- Level Manufacturer Cost Increases

Microwave

- Only and Countertop

Convection Microwave Ovens Built

- In and Over

- the

- Range

Convection Microwave Ovens Standby

Power W Manufacturer Cost

Increase 2011$ Standby

Power W Manufacturer Cost

Increase 2011$

2.00 0.2 5 3.70 0. 0 5

1.50 0. 3 7 2.70 0.0 7

1.00 3. 16 2.20 4.82

0.02 6. 00 0.04 5. 65

8.2.1.5 Overall Markup The overall markup is the value determined by multiplying the manufacturer and retailer markups and the sales tax together to arrive at a single markup v alue. Table 8.2.4 shows the overall baseline and incremental markups for microwave ovens

. Refer to chapter 6 for details.

Table 8.2.4 Microwave Ovens : Overall Markups

Markup Baseline Incremental

Manufacturer 1.2 98

Retailer 1.45 1.17

Sales t ax 0.07 1

Total 2.02 1. 63

8.2.1.6 Installation Cost

DOE determined that any installation costs would not be affe cted by increased efficiency

levels. A similar finding was made in the previous rulemaking conducted for microwave ovens

. 3

8.2.1.7 Total Installed Cost

T otal installed cost is the sum of the consumer product cost and installation cost. Table

8.2.5 present s the total installed costs for the t wo product classes of microwave ovens

. Prices and costs are presented at the baseline level and each energy efficiency level.8-13 Table 8.2.5 Total Installed Costs

Microwave

- Only and Countertop Convection

Microwave Ovens Built

- In and Over

- the

- Range

Convection Microwave Ovens

Standby

Power W Total Installed

Cost 2011$ Standby

Power W Total Installed

Cost 2011$

4.00 23 2 .6 8 4.50 50 3

. 47

2.00 23 3 .0 9 3.70 50 3.56

1.50 233 .2 7 2.70 50 3.59

1.00 237

. 80 2.20 51 1.28

0.02 24 2

. 40 0.04 51 2.62

8.2.2 Inputs to Operating Cost

DOE defines the operating cost ( OC ) by the following equation:



### MC RC EC OC **+ +**  **=** W here :

EC = e nergy expenditure associated with operating the product

, RC = r epair cost associated with component failure, and

MC = c ost for maintaining product operation.

Table 8.2.6 shows the inputs for determining the annual operating costs and their discounted value over the product lifetime

.

Table 8.2.6 Inputs to Operating Cost

Annual energy consumption

Energy prices and price trends

Repair and maintenance costs

The annual energy consumption is the site energy use associated with operating the product

. A nnual energy consumption var ies with product efficiency. Energy prices are the prices paid by consumers for energy ( e. g. , electricity or natural gas). Multiplying the annual energy consumption by the energy price yields the annual energy cost. Repair costs are associated with repairing or replacing components that have failed. Maintenance costs are associated with maintaining the operation of the product

. DOE used energy price trends to forecast energy prices into the future and, along with the product lifetime and discount rate, to establish the present
value of lifetime energy costs.

DOE ca lculated the annual operating cost for baseline products based on the following equation.8-14 BASE BASE ENERGY BASE BASE MC RC PRICE AEC OC **+ +**  **× =**  )

(

W here:

O C BASE = o perating cost for baseline product

, AE C BASE = a nnual energy consumption for baseline product

, PRICE ENERGY = e nergy price,

RC BASE = r epair cost associated with component failure for baseline product, and

MC BASE = c ost for maintaining operation of baseline product,

DOE calculated the annual operating cost for standard

- level products based on the following equation.

STD STD ENERGY STD STD 

### MC RC PRICE AEC OC **+ +**  **× =**  )

(

W here:

O C STD = o perating cost for standard

- level product

, AE C STD = a nnual energy consumption for standard

- level product

, PRICE ENERGY = e nergy price,

RC STD = r epair cost associated with component failure for standa rd

- level product

, and

MC STD = c ost for maintaining operation of standard

- level product

.

The re st of this section provides information about each of the above input variables that DOE used to calculate the operating costs for both product classes of microwave ove ns

.

8.2.2.2 Annual Energy Consumption

As described in chapter 7 and in section 8.1.1, from RECS 200 9 DOE developed a sample

of individual households that use microwave ovens

. By developing a household sample, DOE was able to perform the LCC and PBP calculations for each household to account for the variability in both energy use and energy price associated with each household. T he annual

energy consumption values in Ta ble 8.2.7 are averages. DOE captured the variability in energy consumption when it conducted its LCC and PBP analysi s

. Table 8.2.7 provide s the average annual energy consumption by efficiency level for each product class

.8-15 Table 8.2.7 Annual Energy Consumption

Standard

Level Microwave

- Only and Countertop

Convection Microwave Ovens Built

- In and Over

- the

- Range

Convection Microwave Ovens

Standby

Power W Average Energy Consumptio n kWh/year Standby

Power W Average Energy Consumptio n kWh/year

Baseline 4.00 34. 76 4.50 39.10

1 2.00 17. 38 3.70 32.15

2 1.50 13.03 2.70 23.46

3 1.00 8. 69 2.20 19.12

4 0.02 0. 17 0.04 0.35

8.2.2.3 Residential Electricity Prices DOE derived electricity prices for each of 27 RECS Reportable Domain categories

. Using th es e data , DOE analyzed the variability of electricity prices at the regional level

.

DOE used data from EIA Form 861 4 to estimate electricity prices for residential

consumers in each of the 27 geographic areas

. Tho se data, published annually, include annual electricity sales in kilowatt hours (kWh) ; revenues from electricity sales ; and num ber of consumers in the residential, com mercial, and industrial sectors for every utility that serves final

consumers

. DOE calculated a verage residential electricity prices in two steps.

1. For each utility, an average residential price was estimated by divi ding residential revenues by residential sales.

2. An average regional price was calculated, weighting each utility that has customers in a region by the number of residential consumers served in that region.

Table 8.2.8 shows the average residential electricity price calculated for each geographic region.8-16 Table 8.2.8 Average Residential Electricity Prices

Geographic Area Average Price
2011$ /kW h

Connecticut, Maine, New Hampshire, Rhode Island, Vermont $0.137

Massachusetts $0.138

New York $0.179

New Jersey $0.169

Pennsylvania $0.122

Illinois $0.120

Indiana, Ohio $0.097

Michi g an $0.129

Wisconsin $0.131

Iowa, Minnesota, North Dakota,

South Dakota $0.106

Kansas, Nebraska $0.099

Missouri $0.094

Virginia $0.108

Delaware, DC, Maryland, West Virginia $0.128

Ge o rgia $0.104

North Carolina, South Carolina $0.106

Flor id a $0.118

Alabama, Kentucky, Mississippi $0.101

Tennessee $0.095

Arkansas, Louisiana, Oklahoma $0.093

Texas $0.120

Colorado $0.114

Idaho, Montana, Utah, Wyoming $0.090

Arizona $0.113

Nevada, New Mexico $0.120

California $0.153

Alaska, Hawaii, Oregon, Washington $0.109

Source: EIA Form 861 f or 201 0

.8-17 8.2.2.4 Energy Price Trends

DOE used EIA’s price fore casts to estimate future trends in electricity prices

. To arrive at prices in future years, it multiplied the average prices list ed in Table 8.2.8 b y the forecast of annual average price changes in EIA’s AEO 20 1 2 r eference case

. 2 To estimate the trend after

203 5 , DOE followed past guidelines that EIA provided to the Federal Energy Management Program

, using the average rate of chang e during 2020

– 2035.

DOE calculated LCC and PBP based on three separate projections from the AEO2012 : reference case, low economic growt h, and high economic growth. Tho se three cases reflect the uncertainty regarding economic growth during the forecast period. Figure 8.2.1 shows the three projected trends in residential electricity prices based on the three AEO2012 cases

.

Figure 8.2.1 Residential Electricity Price Trends

8.2.2.5 R epair and Maintenance Costs

DOE included no changes in repair or maintenance costs for microwave ovens that exceed baseline efficiency.

8.2.2.6 Product Lifetime

RECS 1 records the presence of various appliances in each household . That data,

combined with data from the U.S. Census’s American Housing Survey, 5 which surveys all housing including vacant and second homes, enabled DOE to estimate the stock of microwaves

in all households . By combining the results of both surveys with the known hist ory of appliance8-18 shipm ents (collected from Appliance M agazine or directly from manufacturer trade associations), DOE estimated the percentage of appliances of a given age still in operation. This

surv ival function, which DOE assumes has the form of a cumul ative Weibull distribution, provides an average and a median appliance lifetime.

The Weibull distribution is a probability distribution commonly used to measure failure

rates. a Its form is similar to that of an exponential distribution, which models a fixed failure rate, except that a Weibull distribution allows for a failure rate that changes through time in a particular fashion. The cumulative Weibull distribution takes the form:



# e x



### x P **β**

**α**

**θ ** 

** **  ** **  ****

**−**

**−**



### = ) ( for x > θ and

P(x) = 1 for x ≤ θ

. Where:

P(x) = probability that the appliance is still in use at age x ;

x = appliance age ;

α = scale parameter, which would be the decay length in an exponential

distribution ; β = shape parameter, which determines the way in which the failure rate changes through time ; and

θ = delay parameter, which allows for a delay before any failures occur.

When β = 1, the failure rate is constant over time, giving the distribution the form of a cumulative exponential distribution. In the case of appliances, β commonly is greater than 1, reflecting an increasing failure rate as appliances age. Figure shows the Weibu ll retirement

function for microwave ovens. The results of DOE’s analysis are shown in Table 8.2.9

.

a For reference on the Weibull distribution, see sections 1.3.6.6.8 and 8.4. 1.3 of the NIST/SEMATECH e

- Handbook

of Statistical Methods . < www.itl.nist.gov/div898/handbook/ > (Last accessed August 21, 2012.)8-19 Figure 8.2.2 Weibull Function for MWO Lifetime

Table 8.2.9 Lifetime Parameters

Product Weibull Parameters
Average

years Alpha (scale) Beta (shape)

Microwave oven 10.91 4.75 10.28

8.2.3 Discount Rates

The d iscount rate is the rate at which DOE discounts future expenditures to establish their present value. DOE derived discount rates for the LCC and PBP analyses from estimates of the cost to finance the purchase of the considered products. Following financial theory, the financial cost of raising funds to purchase products can be interpreted as: (1) the financial cost of any debt incurred to purchase a product, or (2) the opportunity cost of any equity used to purchase a product.

Discount Rates for Residential Consumers
The purchase of products installed in new homes entails different finance costs for consumers than does the purchase of appliances bought directly from retailers. For microwave and convection ovens, however, DOE believes that few products are installed by builders in new

homes. Households use vari ous methods to finance the purchase of major appliances. In principle, one could estimate the interest rates on the actual financing methods used to purchase appliances. However, the shares of different financing methods in total appliance purchases are

unknown.8-20 DOE’s approach involved identifying all possible debt or asset classes that might be used to purchase /lease the considered products , including household assets that might be affected indirectly. b DOE exc luded debt from primary mortgages and the equity of assets considered

non

- liquid (such as retirement accounts), because those are unlikely to be used to purchase products . DOE estimated the average percentage shares of the various types of debt and equity in the average U.S. household using data from the Federal Reserve Board’s Survey of Consumer Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 2004, and 2007. 6 Table 8.2.10 shows the average percentages of each considered type of debt or equity. DOE derived the mean percentages of each source of financing throughout the 7 years surveyed.

Table 8.2.10 Types of Household Debt and Equity by Percentage Shares

Type of Debt or Equity 1989 % 1992 % 1995 % 1998 % 2001 % 2004 % 2007 % Mean %

Home equity loan 4.3 4.5 2.7 2.8 2.8 4.4 4.6 3.7

Credit card 1.6 2.1 2.6 2.2 1.7 2.0 2.4 2.1

Other installment loan 2.8 1.7 1.4 1.7 1.1 1.3 1.1 1.6

Other residential loan 4.4 6.9 5.2 4.3 3.1 5.8 7.1 5.3

Other line of credit 1.1 0.6 0.4 0.2 0.3 0.5 0.3 0.5

Checking account 5.8 4.7 4.9 3.9 3.6 4.2 3.4 4.4

Savings or money market

account 19.2 18.8 14.0 12.8 14.2 15.1 13.0 15.3

Certificate of deposit 14.5 11.7 9.4 7.0 5.4 5.9 6.5 8.6

Savings bond 2.2 1.7 2.2 1.1 1.2 0.9 0.7 1.4

Bonds 13.8 12.3 10.5 7.0 7.9 8.4 6.7 9.5

Stocks 22.4 24.0 25.9 36.9 37.5 28.0 28.6 29.0

Mutual funds 8.0 11.1 20.9 20.1 21.3 23.4 25.5 18.6

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source s : Federal Reserve Board. Survey of Consumer Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 2004, and

2007

.

DOE estimated interest or return rates associated with each type of equity and debt. The source for interest rates for loans, credit cards, and l ines of credit was the Federal Reserve

Board’s SCF for 1989, 1992, 1995, 1998, 2001, 2004, and 2007. Table 8.2.11 shows the average nominal rates in each year and the inflation factors used to calculate real rates. DOE calculated effective interest rates for home equity loans in a similar manner as for mortgage rates, because

interest on both such loans is tax deduct ible. Table 8.2.12 shows the average effective real rates in each year and the mean rate across years. Because the interest rates for each type of household

debt re flect economic conditions throughout numerous years, they are expected to be representative of rates that may be in effect in 20 18

.

b An indirect effect would arise if a household sold assets in order to pay off a loan or credit card debt that might

have been used to finance the appliance purchase.8-21 Table 8.2.11 Average Nominal Interest Rates for Household Debt

Type of Debt 1989 % 1992 % 1995 % 1998 % 2001 % 2004 % 2007 % Mean %

Home equity loan 11.5 9.6 9.6 9.8 8.7 5.7 7.9 9.0

Credit card*

- - 14.2 14.5 14.2 11.7 12.6 3.4

Other installment loan 9.0 7.8 9.3 7.8 8.7 7.4 10.4 8.6

Other residential loan 8.8 7.6 7.7 7.7 7.5 6.0 6.3 7.4

Other line of credit 14.8 12.7 12.4 11.9 14.7 8.8 12.7 12.6

Inflation rate 4.82 3.01 2.83 1.56 2.85 2.66 2.85

Source s : Federal Reserve Board. Survey of Consumer Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 2004, and

2007.

* No data on interest rate s available for credit cards in 1989 or 1992.

Table 8.2.12 Average Real Effective Interest Rates for Household Debt

Type of Debt 1989 % 1992 % 1995 % 1998 % 2001 % 2004 % 2007 % Mean %

Home equity loan 3.8 4.3 4.4 5.8 3.8 1.9 2.1 3.0

Credit card*

- - 11.0 12.7 11.1 9.1 3.3 3.9

Other installment loan 4.9 5.8 7.0 6.6 6.1 5.4 9.7 10.7

Other residential loan 4.0 4.7 4.8 6.0 4.6 3.3 3.4 4.4

Other line of credit 9.6 9.4 9.3 10.2 11.7 6.0 9.7 9.4

Source s : Federal Reserve Board. Survey of Consumer Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 2004, and 2007.

* No data on interest rates available for credit cards in 1989 or 1992.

No similar rate data are available from the SCF for classes of assets, so the Department

derived that information from national historical data. The interest rates associated with

certificates of deposit, 7 savings bonds, 8 and bonds (AAA corporate bonds) 9 were collected from

Federal Reserve Board time

- s eries data for 1977

– 20 11

. DOE assumed rates on checking accounts to be zero. Rates on savings and money market accounts came from Cost of Savings Index data covering 1984

– 200 9

. 10 The rates for stocks are the annual returns on the Standard and

Poor’s 500 for 1977

– 200 9

. 11

Table 8.2.13 Rates for mutual funds are a weighted average of the stock rates (two

- thirds weight) and the bond rates (one

- third w eight) in each year for 1977

– 2011 . DOE adjusted the nominal rates to real rates using the annual inflation rate for each year. Average

nominal and real interest rates for the classes of household assets are listed in

. Because the interest and return rates for each type of ass et reflect economic conditions throughout numerous years, they are expected to be representative of rat es that may be in effect

in 2018

.8-22 Table 8.2.13 Average Nominal and Real Interest Rates for Household Equity

Type of Equity Average

Nominal Rate

% Average Real

Rate

%

Checking account

- 0.0

Savings and money market accounts 5. 1 2. 1

Certificate of deposit 6. 1 2. 0

Savings bond 7. 4 3.3

Bonds 8. 2 4. 0

Stocks 11. 8 7. 6

Mutual funds 10. 4 6.1

Table 8.2.14 summarizes the mean real effective rates of each type of equity or debt.

DOE determined the average percentage of each type of debt and asset using SCF data for 1989, 1992, 1995, 1998, 2001, 2004, and 2007. Each year of SCF data provides the percentages of debts and assets for U.S. households. DOE averaged those percentages for the 7 years of survey

data to arrive at the percentages shown in Table 8.2.14 . The average rate across all types of household debt and equity, weighted by the percentages of each type, is 5.0 percent

.

Table 8.2.14 Average Interest on Household Debt and Equity

Type of Debt or Equity Average P ercentage of Household Debt plus Equity

% * Mean Effective Real Rate

% **

Home equity loan 3.7 3.9

Credit card 2.1 10.7

Other installment loan 1.6 6.0

Other residential loan 5.3 4.4

Other line of credit 0.5 8.8

Checking account 4.4 0.0

Savings and money market account 15.3 2. 1

Certificate of deposit 8.6 2. 0

Savings bond 1.4 3.3

Bonds 9.5 4. 0

Stocks 29.0 7. 6

Mutual funds 18.6 6.1

Total/weighted

- average discount rate 100.0 5.0

* Not including primary mortgage or retirement accounts.

** Adjusted for inflation and, for home equity loans, tax deduction of interest.

DOE developed a normal probability distribution of interest rates for each asset type by using the mean value and standard deviation from the distribution. To account for variation8-23 among households, DOE sampled a rate for each household from the distributions for the appropriate asset class.

8.2.4 Compliance Date of Standard

The compliance date is the future date when manufacturers must com ply with a new or amended standard. The compliance date of the new energy efficiency sta ndards for microwave ovens manufactured in, or importe d into, the United States is April 1, 20 16

. DOE calculated the LCC for all consumers as if each would purchase a new product in 201 6

.

8.2.5 Product Energy Efficienc y in the Base Case

To estimate the percentage of consumers who would be affected by a standard at any of the potential efficiency level s , in its LCC analysis DOE considered the projected distribution of

efficiencies for product s that consumers purchase under the base case (the case without new or amended energy efficiency standards). DOE refers to this distribution of product energy efficiencies a s the bas e

- case efficiency distribution. Using the projected distribution of efficiencies for each product class , DOE randomly assigned a product efficiency to each sam ple household. The energy efficiency distributions that DOE used in the LCC analysis are described

below. For this final rule

, DOE used the efficiency distributions calculated in the previous microwave oven rulemaking. 12

Table 8.2.15 and Table 8.2.16 present recent market shares of the efficiency levels for

microwave

- only and conv ection microwave ovens. Because there is no evidence of a change in average efficiency in recent years, DOE assumed that the shares in 2014 are the same

.

Table 8.2.15 Microwave

- Only and Countertop Convection Microwave Ovens: Base

- Case

Market Shares

Standard

Level Standby

Power W Market

Share %

Baseline 4.00 46.2

1 2.00 34.6

2 1.50 19.2

3 1.00 0

4 0.02 08-24 Table 8.2.16 Built

- In and O ver

- the

- Range Convection Microwave Ovens: Base

- Case

Market Shares

Standard

Level Standby

Power W Market

Share %

Baseline 4.50 100

1 3.70 0

2 2.70 0

3 2.20 0

4 0.04 0

8.3 RESULTS OF LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

This section presents the results of the LCC and PBP analysis for microwave

- only and

convection microwave ovens

. A s discussed in section 8.1.1

, DOE’s approach to the LCC analysis relied on developing samples of consumers who use each of the product classe s. DOE

also used probability distributions to characterize the uncertainty in many of the inputs to the analysis. DOE used Monte Carlo simulation to perform the LCC calculations for the consumers in the sample. For each set of sample consumers using the product in each product class, DOE calculated the average LCC and LCC savings and the median and average PBP for each trial

standard lev el ( TSL).

DOE calculated LCC savings and PBPs relative to the base

- case products that it assigned

to the sample households

. F or some consumers DOE assigned a base

- case product that is more

efficient than some of the T SLs . For that reason, the average LCC impacts are not equal to the

difference be tween the LCC of a specific standard level and the LCC of the baseline product

LCC and PBP calculations were performed 10,000 times on the sample of consumers established for each product class

. Each LCC and PBP calculation was performed on a single

cons umer selected from the sample. A consumer was selected based on its weight ( i.e.

, how representativ e that particular consumer wa s of other consumer s in the distribution ). Each LCC and PBP calculation also sampled from the probability distributions that DOE developed to characterize many of the inputs to the analysis.

Based on the Monte Carlo simulations, for each T SL DOE calculated the share of consumers experiencing a net LCC benefit, a net LCC cost, and no effe ct. DOE considered a

consumer to receive no effe ct at a given standard level if DOE assigned it a base

- case product having the same or higher efficiency than the standard level

. T he following sections present figures that illustrate the range of LCC and PBP effe cts among sample consumers

.

8.3.1 Summary o f Results

Table 8.3.1 and Table 8.3.2 show the LCC and PBP results by T SL for each microwave

oven product class. The T SL s correspond to the energy efficiency levels included in the8-25 engineering analysis. With regard to the PBPs , D OE determined the median values by excluding

th ose users not affe cted by the standard.

Table 8.3.1 Microwave

- Only and Countertop Convection Microwave Ovens : LCC and

PBP Results

T SL Standby

Power

W Life

- Cycle Cost 2011$ Life

- Cycle Cost Savings

Median

Pa yback

Period

years Average

Installed

Cost Average

Lifetime Operating

Cost* Average

LCC Average Savings

2011$ % Users Having

Net

Cost No Impact Net

Benefit

Baseline 4.00 $ 234 $ 3 5 $ 26 9 NA 0 100 % 0 % NA

1 2.00 $ 234 $ 1 8 $ 25 2 $ 8 0 54 % 46 % 0.2

2 1.50 $ 234 $ 1 3 $ 24 7 $ 1 1 0 19 % 81 % 0.3

3 1.00 $ 23 9 $ 9 $ 24 8 $ 11 12 % 0 % 88 % 3.5

4 0.02 $ 243 $ 0 $ 244 $ 1 5 5 % 0 % 95 % 3.5

* Discounted

Table 8.3.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: LCC and PBP

Results

TSL Standby

Power

W Life

- Cycle Cost 2011$ Life

- Cycle Cost Savings

Median Payback

Period

years Average

Installed

Cost Average

Lifetime Operating

Cost* Average

LCC Average Savings

2011$ % Users Having

Net

Cost No Impact Net

Benefit

Baseli ne 4.50 $ 50 6 $ 40 $ 54 5 NA 0 % 100 % 0 % NA

1 3.70 $ 50 6 $ 33 $ 53 8 $ 7 0 % 0 % 100 % 0. 1

2 2.70 $ 50 6 $ 2 4 $ 52 9 $ 1 6 0 % 0 % 100 % 0 .1

3 2.20 $ 513 $ 1 9 $ 53 3 $ 1 2 0 % 0 % 100 % 3. 3

4 0.04 $ 51 5 $ 0 $ 515 $ 30 0 % 0 % 100 % 2. 0

* Discounted

8.3.1.2 Distributions of Impacts The figures in this section show the distribution of LCCs in the base case for each product class. Also presented for one standard level are figures showing the distribution of LCC impacts and the distribution of PBPs. The figures are presented as frequency charts that show the distribution of LCCs, LCC impacts, and PBPs with their corresponding probability of

occurrence. DOE generated t he figures for the distributions from a Monte Carlo simulation run based on 10,000 sample s.

Base

- Case LCC Distributions

. Figure 8.3.1 and Figure 8.3.2 show the base

- case LCC

distributions f or microwave

- only and convection microwave ovens

.8-26 Figure 8.3.1 Microwave

- Only and Countertop Convection Microwave

Ovens : Base

- Case LCC Distribution

Figure 8.3.2 Built

- In and Over

- the

- Range Convection Microwave Ovens:

Base

- Case LCC Distribution

Standard

- Level Distribution of Impacts

. Figure 8.3.3 and Figure 8.3.4 are example s of frequency chart s showing the distribution of LCC savings for microwave

- only and convection

microwave ovens at T SL 3 . DOE can generate frequency chart s similar to those shown for every

T SL

.8-27 Figure 8.3.3 Microwave

- Only and Countertop Convection Microwave Ovens: LCC Savings Distribution for T SL 3

Figure 8.3.4 Built

- In and O ver

- the

- Range Convection Microwave Ovens : LCC Savings Distribution for TSL 3

Figure 8.3.5 and Figure 8.3.6 are example s of frequency chart s showing the distribution

of PBPs for microwave

- o nly and convection microwave ovens at T SL 3

. DOE can generate frequency chart s similar to those shown for every standard level.8-28 Figure 8.3.5 Microwave

- Only and Countertop Convection Microwave

Ovens: PBP Distribution for T SL 3

Figure 8.3.6 Built

- In and Over

- the

- Range Convection Microwave Ovens:

PBP Dist ribution for T SL 3

8.3.1.3 Range of Impacts Figure 8.3.7 and Figure 8.3.8 show the range of LCC savings for the T SL s considered for

microwave

- only and convection microwave ovens

. For each standard level, the top and the8-29 bottom of the box indicate the 75 th and 25 th percentiles, respectively. The bar at the middle of the box indicates the median; 50 percent of the households have lifecycle cost savings above this

value. The “ whiskers ” at the bottom and the top of the box indicate the 5 th and 95 th percentiles.

The smal l box shows the average LCC savings for each standard level.

Figure 8.3.7 Microwave

- Only and Countertop Convection

Microwave Ovens : Range of Average LCC Savings8-30 Figure 8.3.8 Built

- In and Over the Range Convection Microwave

Ovens: Ra nge of Average LCC Savings

Figure 8.3.9 and Figure 8.3.10 show the range of PBPs for all T SL s consi dered for

microwave

- only and convection microwave ovens . For each efficiency level, the top and the bottom of the box in the figure indicate the 75 th and 25 th percentiles, respectively. The bar at the middle of the box indicates the median: 50 percent of the households have a payback period

above this value. The horizontal lines above and below each box indicate the 95 th and 5 th
percentiles, respectively. The s mall box indicates the average PBP for each T SL

.8-31 Figure 8.3.9 Microwave

- Only and Countertop Convection

Microwave Ovens : Range of Average Payback

Period

Figure 8.3.10 Built

- In and Over the Range Convection Microwave Ovens: Range of Average Payback Period8-32 8.4 REBUTTABLE PAYBACK PERIOD

DOE presents rebuttable PBPs to provide the legally established rebuttable presumption that an energy efficiency standard is economically justified if the additional product costs

attributed to the standard are less than three times the value of the first

- year energy cost savings. (42 U.S.C. §6295 (o)(2)(B)(iii))

The basic equation for rebuttable PBP is the same as that shown for the PBP in section

8.1.1

. Unlike the analyses described in section 8.2 , however, the rebuttable PBP is not based o n the use of household samples and probability distributions. T he rebuttable PBP is based not on
distributions but on discrete single

- point values. For example, wh ereas DOE uses a probability distribution of regional energy prices in the distributional PBP analysis, it uses only the national

average energy price from the probability distribution to determine the rebuttable PBP.

Other than the use of single

- point values, the most notable difference between the

distribution PBP and the rebuttable PBP is the latter’s reliance on the DOE test procedure to

deter mine a product’s annual energy consumption. T he following sections identify the differences, if any, between the annu al energy consumptions determined for the distribution PBP

and the rebuttable PBP.

D OE based the annual energy consumption values that it used to determine the rebuttable

PBP for microwave ovens on the number of operating hours per year specified in previous microwave rulemaking.

8.4.1 Inputs

Inputs for the rebuttable PBP differ from the distribution PBP in that the calculation uses discrete values, rather than distributions. Note that for the calculation of distribution PBP, because inputs for the determination of total installed cost were based on single

- point values,

on ly the variability and/or uncertainty in the inputs for determining operating cost contributed to variability in the distribution PBPs. The following summarizes the single

- po int values that DOE

used in determin ing the rebuttable PBP :

**•** Manufacturing costs, markups, sales taxes, and installation costs were all based on the single

- point values used in the distributional LCC and PBP analys i s

.

**•** Energy prices we re based on national average values for the year that new standards

will take effect.

**•** An average discou nt rate or lifetime is not required in the rebuttable PBP calculation.

**•** The effective date of th e standard is assumed to be 201 4

.

8.4.2 Results

DOE calculated rebuttable PBPs for each standard level relative to the distribution of product energy efficiencies estimated for the base case. In other words, DOE did not determine the rebuttable PBP relative to the baseline energy efficiency level, but relative to the distribution of product energy efficiencies DOE determined for the base case ( i. e. , the case without new8-33 energy conservation standards). Table 8.4.1 presents the rebuttable PBPs for microwave

- only

and convection microwave ovens.

Table 8.4.1 Rebuttable Pay back Periods

Microwave

- Only and Countertop

Convection

Microwave Ovens Built

- In and Over

the

- Range

Convection

Microwave Ovens

Standby

Power W PBP

years Standby

Power W PBP

years 2.00 0.2 3.70 0. 1

1.50 0. 3 2.70 1.1

1.00 3.5 2.20 3. 3

0.02 3.5 0.04 2.08-34 REFERENCES

1 U.S. Department of Energy (DOE)

– Energy Information A dministration (EIA). Residential Energy Consumption Survey: 2009 RECS Survey Data

— Public Use Microdata File. October 2011. EIA: Washington, D.C. www.eia.gov/consumption/residential/data/2009/#microdata

. (Accessed May 2013.)

2 DOE

– EIA. Annual Energy Outlook 2012 with Projections to 2035. June 25, 2012. Washington, D.C. www.eia.gov/forecasts/aeo/

. ( A ccessed May 2013 .)

3 DOE

– Energy Efficiency and Renewable Energy. Energy Conservation Program for Consumer Products: Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment, Residential Dishwashe rs, Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. 2009. Washington, D.C. www.regulations.gov/#!documentDetail;D=EERE

- 2006

- STD

- 0127

- 0004

. ( A ccessed May 2013 .)

4 DOE

– EIA. EIA Form 861: Customer Count Data by Utility. 2010. www.eia.doe.gov/cneaf/electricity/page/eia861.html

. ( A ccessed May 2013 .)

5 U.S. Census Bureau

– Housing and Household Economic Statistics Division. American Housing Survey National Data. 2009. HUD. www.huduser.org/portal/datasets/ahs/ahsdata09.html

. ( A ccessed May 2013 .)

6 The Federal Reserve Board. Survey of Consumer Finances. 1989, 1992, 1995, 1998, 2001, 2004, 2007. www.federalreserve.gov/pubs/oss/oss2/scfindex.html . (Accessed May 2013.)

7 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, In strument: CDs (secondary market), Maturity: 6

- month, Frequency: Annual, Description: Average rate on 6

- month negotiable certificates of deposit (secondary market), quoted on an investment basis, 2011. Washington, D.C. www.federalreserve.gov/releases/H15/data.htm . (Accessed May 2013.)

8 The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: State and local bonds, Maturity: 20

- ye ar, Frequency: Monthly, Description: Bond buyer go 20

- bond municipal bond index, 2011. Washington,

D.C. www.federalreserve.gov/releases/H15/data.htm . (Accessed May 2013.)

9 The Federal Re serve Board, Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: Corporate bonds/Moody's Seasoned AAA, Frequency: Annual, Description: Moody's yield on seasoned corporate bonds

- all industrie s, AAA, 2011. Washington, D.C. www.federalreserve.gov/releases/H15/data.htm . (Accessed May 2013.)8-35 10 Mortgage

- X

- Mortgage Information Service, Cost of Savings Ind ex (COSI) Index History, 2011. mortgage

- x.com/general/indexes/default.asp . (Accessed May 2013.)

11 Damodaran Online Data Page, Historical Returns on Stocks, Bonds and Bills

- United States, 2011. Damodaran. pages.stern.nyu.edu/~adamodar/

. (Accessed May 2013.)

12 DOE

– Energy Efficiency and Renewable Energy. Energy Conservation Program for Consumer Products: Technical Support Document: Energy Efficiency Program for

Consumer P roducts and Commercial and Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. Chapter 8 Life

Cycle Cost and Payback Period Analysis. 2009. Washington, D.C. www1.eere.energy.gov/buildings/appliance_standards/residential/pdfs/home_appliances _tsd/chapter_8.pdf . (Accessed May 2013.)9-i CHAPTER 9

. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9 -1

9.2 METHODOLOGY BEHIND SHIPMENTS MODEL METHODOLOGY .................... 9 -1

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments ........................................................................................................ 9 -3

9.3.2 Markets and Model Calibration ....................................................................................... 9 -4

9.3.2.1 New Construction .......................................................................................... 9-5

9. 3.2.2 Replacements ................................................................................................. 9 -6

9.3.3 Base-Case Shipments ....................................................................................................... 9 -7

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SHIPMENTS ............................. 9-8

9.5 AFFECTED STOCK ..................................................................................................... 9 -11

9.6 EFFECT OF TRIAL STANDARD LEVEL ON SHIPMENTS FORECAST .............. 9 -11

LIST OF TABLES

Table 9.3.1 Microwave Ovens: Historical Shipments, Domestic plus Imports ...................... 9 -4

Table 9.3.2 Microwave Ovens: Historical Market Saturations ............................................... 9 -6

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change ................. 9 -10

LIST OF FIGURES

Figure 9.3.1 Microwave Ovens: Survival and Retirement Functions ...................................... 9 -7 Figure 9.3.2 Microwave Ovens: Historical and Base-Case Shipments Forecast ..................... 9 -7 Figure 9.3.3 Microwave Ovens: Disaggregated Shipments Forecast for Base Case ............... 9 -8 Figure 9.6.1 Shipments Forecasts for Base Case and TSL 4 ................................................. 9 -129

- 1 CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future pr oduct shipments are a necessary input to calculations of the national energy savings (NES) and net present value (NPV), as well as to the manufacturer impact analysis. This chapter describes the data and methods the U.S. Department of Energy (DOE) used to forecast annual product shipments and presents results for microwave ovens being considered for this standards rulemaking . Because DOE did not have shipment data for both product classes, total shipments of microwaves were estimated and then separated by

market share.

To estimate shipments for microwaves, DOE used a shipments model that is calibrated

with hi storical shipments data. The shipments model estimates shipments to specific market segments, the results for which are then aggregated to estimate total product shipments. To estimate the impacts of potential standard levels on product shipments, the shipments model

a ccounts for the combined effects of changes in purchase price, annual operating cost, and household income on the consumer purchase decision.

The shipments model was developed as a part of the national impacts analysis (NIA)

spre adsheet. Appendix 10A discusses how to access the NIA spreadsheet and provides basic

inst ructions for its use.

The rest of this chapter explains the shipments models in more detail. Section 9.2 presents the methodology behind the shipments model; section 9.3 describes the data inputs and model calibration; section 9.4 discusses impacts on shipments from changes in product purchase

pr ice; section 9.5 discusses the affected stock; and section 9.6 presents the shipments forecast for the maximum trial standard level (TSL) that DOE considered.

9.2 M ETHODOLOGY BEHIND SHIPMENTS MODEL METHODOLOGY

DOE developed a national stock model for estimating annual shipments for this standards

rule making. The model considers market segmentation as a distinct input to the shipments

for ecast. As represented by the following equation, the two primary market segments for microwave ovens are installations in newly constructed homes and replacements.

Where:

Ship p (j) = total shipments of product p in year j

, Rpl p (j) = units of product p retired and replaced in year j

, and

NI p (j) = number of new installations of product p in year j

.9

- 2

DOE’s shipments model takes an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected construction trends. The models estimate shipments due to replacements using sales in previous years and assumptions about the lifetime of microwave ovens. Estimated sales attributable to replacements in a given

y ear therefore are equal to the total stock of the appliance minus the sum of the appliances sold in previous years that remain in the stock. As described in chapter 8, section 8.2.2.6, DOE

de termined the useful service life of microwave ovens. DOE then estimated how long the appliance is likely to remain in stock. The following equation represents how DOE estimated replacement shipments.

Where:

Stock p (j

- 1) = total stock of in-service appliances in year j -1

, prob Rtr (age) = probability that an appliance of a particular age will be retired, and

N = start year for when the model begins its stock accounting (start year is

specific to each product based on available historical shipments data).

Stock accounting takes product shipments, a retirement function, and initial in-service product stock as inputs to estimate the age distribution of in-service product stocks for all years.

The age distribution of in-service product stocks is a key input to both the NES and NPV calculations—the operating costs for any year depend on the age distribution of the stock. The

de pendence of operating cost on the equipment age distribution occurs under a standards case scenario that produces increasing efficiency over time, where by older, less efficient units may have higher operating costs, while younger, more-efficient units will have lower operating costs.

I n the case of an early replacement scenario, retirements due to early replacement will depend on the age of the units that are subject to early replacement.

DOE estimated replacements using product retirement functions that it developed based

on product lifetimes. DOE based the retirement function on a Weibull distribution for the product lifetime. The shipments model assumes that no units are retired below a minimum product lifetime and all units are retired before exceeding a maximum product lifetime. The models determine the probability of retirement at a certain age for all products using a Weibull equation:

for x > θ and

P(x) = 1 for x ≤ θ

Where:

P(x) = probability that the appliance is still in use at age x ;

x = appliance age;

α = scale parameter, which would be the decay length in an exponential

dist ribution;9

- 3

β = shape parameter, which determines the way in which the failure rate changes

through time; and

θ = delay parameter, which allows for a delay before any failures occur.

The retirement probability is the difference in the survival function from one year to

another.

DOE calculated total in-service stock of a product by integrating historical shipments

da ta starting from a specific year. The start year depends on the historical data available for the

pr odu ct. As units are added to the in-service stock, some of the older ones retire and exit the stock. To estimate future shipments, DOE developed a series of equations that define the dynamics and accounting of in-service stocks. For new units, the equation is:

Where:

Stock(j, age) = the population of in-service units of a particular age,
j = year for which the in-service stock is being estimated, and Ship (j) = n umber of units purchased in year j

.

The above equation states that the number of one-year-old units is simply equal to the number of new units purchased the previous year. The following equation describes the accounting of the existing in-service stock of units:

In the above equation, as the year is incremented from j to j+1 , the age is also

incremented from age to age+1 . With time, a fraction of the in-service stock is removed, that fraction being determined by a retirement probability function, p rob Rtr (age) , which is described

in section 9.3. Most replacements are made when a product wears out and fails. Over time, some

of the units will be retired and removed from the stock, triggering the shipment of a new unit. Because the products considered in this rulemaking are common appliances, replacements

c onstitute the majority of shipments.

9.3 DATA INPUTS AND MODEL CALIBRATION

The sections below describe the data inputs and market segments considered for

microwaves.

9.3.1 Historical Shipments

DOE used data on historical shipments (domestic shipments plus imports) to calibrate its shipments model for microwave ovens. DOE’s sources for historical shipments data were (1)

AH AM Fact Books for 2000, 1 2003

, 2 2005

, 3 and 2009 4 covering 1989−2009, and (2) Appliance9

- 4

Magazine 5 for 1981−1988. a Table 9.3.1 summarizes the historical data on microwave oven shipments.

Table 9.3.1 Microwave Ovens: Historical Shipments, Domestic plus Imports

Year Shipment s

millions Year Shipments

millions

1980 3.41 1995 8.16

1981 4.18 1996 8.77

1982 3.85 1997 9.14

1983 5.60 1998 10.37

1984 8.63 1999 11.42

1985 10.28 2000 12.64

1986 11.76 2001 13.45

1987 11.91 2002 13.31

1988 10.38 2003 14.27

1989 9.78 2004 15.53

1990 7.69 2005 13.86

1991 6.62 2006 13.69

1992 7.19 2007 11.85

1993 7.24 2008 11.34

1994 8.57 2009 9.63

Source for 1981

– 1988: Statistical Review. Appliance Magazine . 2009.

Sourc e for 1989

– 2009: AHAM Fact Books 2000, 2003, 2005, and 2009.

9.3.2 Markets and Model Calibration

The market for microwave ovens is comprised primarily of replacement units for products that have been retired from service. Total microwave oven shipments are represented by

the f ollowing equation:

Where:

Ship MWO (j) = total shipments of microwave ovens in year j

, Rpl MWO (j) = replacement shipments in year j

, NI MWO (j) = shipments to new households in year j , and

The sections below discuss these markets in further detail.

a Shipments estimates from Appliance Magazine included exports. Therefore, DOE reduced total shipments by 12.3 percent (the average percentage of exports for 1989–1993 based on the 5-year average difference between AHAM

Fact Book 2000 and Appliance Magazine ) to estimate total domestic shipments plus imports.9

- 5

9.3.2.1 New Construction DOE estimated shipments to new construction using two inputs: new housing forecasts and market saturation data. New housing includes newly constructed single- and multi-family units, referred to as new housing completions, and mobile home placements. For new housing

c ompletions and mobile home placements, DOE used actual data through 2009, and adopted the projections from the DOE Energy Information Administration’s (EIA ’s ) Annual Energy Outlook

20 1 2 ( AEO20 1 2 ) for 2009– 2035. 6

Table 9.3.2 presents historical market saturations of microwave ovens based on various sources, including AHAM ’s 2009 Fact Book

, 4 Appliance Magazine

, 5 NFO WorldGroup, 7 and EIA’s RECS for 1993, 8 1997, 9 2001, 10 2005

, 11 and 2009. 12 The table presents market saturations

for the overall housing stock, new households only, and households that have two or more

mi crowave ovens.

Because the forecast of shipments for the new housing market depends on the saturation of microwave ovens in new housing, DOE focused its attention on the market saturations for new housing as well as the saturations of housing having multiple microwave ovens. According to

R ECS, microwave oven saturation in new housing for 1993, 1997, 2001, 2005, and 2009 was 92 percent, 87.5 percent, 93.3 percent, 90.3 percent, and 100 percent, respectively. According to NFO, households having more than one microwave oven represented 3.7 percent, 4.9 percent, and 4.1 percent of the housing market for 1990, 1996, respectively. DOE incremented the saturation levels for new housing estimated by RECS by the amounts provided by NFO to estimate new housing saturations. As a result, DOE determined the effective saturation of microwave ovens in new housing to be 95.7 percent, 92.4 percent, 97.4 percent, and 94.6 percent

for 1993, 1997, 2001, and 2005 respectively. Because the 2009 RECS survey did not include a large enough sample of new homes with microwaves, DOE decided to retain the previous

saturation of 94.6 percent to estimate shipments to new construction for future years.9

- 6

Table 9.3.2 Microwave Ovens: Historical Market Saturations

Year Overall Household % New

Households % Households with

≥ 2 O vens %

AHAM* Appl † NFO ‡ REC S § RECS § NFO ‡

1978 7.2

1982 25.6

1983 33.3

1987 65.9

1990 82.7% 81.1 3.9

1991 85.2

1992 84.5

1993 85.5 84.1 92.0

1994 88.8

1995 88.9

1996 90.6 88.7 4.9

1997 91.0 83.0 87.5

1998 91.3

1999 93.1

2000 95.6

2001 90.7 95.7 91.8 86.1 93.3 4.1

2002 95.0

2003 95.0

2004 95.5

2005 89.3 87.9 90.3

2006

2007

2008

2009 95.9 100

Source s : * AHAM 2005 Fact Book

. † The Saturation Picture

. Appliance Magazine

. September 2009

. ‡ NFO World Group

, 2001

. § DOE

- EIA

. RECS, 1993, 1997, 2001, 2005 , and 2009

.

9.3.2.2 Replacements DOE used an accounting method that tracks the total stock of units by vintage to

de termine shipments to the replacement market. DOE estimated a stock of microwave ovens by

vint age by integrating historical shipments starting from 1980. Over time, some units are retired

a nd removed from the stock, triggering the shipment of a replacement unit. Depending on the

vint age, a certain percentage of each type of unit will fail and need to be replaced. To determine

whe n a microwave oven fails, DOE used a product survival function based on a lifetime distribution having an average value of 10.91 years. For a more complete discussion of

mi crowave lifetimes, refer to section 8.2.2.5 of chapter 8. Figure 9.3.1 shows the survival and retirement function that DOE used to estimate replacement shipments.9

- 7

Figure 9.3.1 Microwave Ovens: Survival and Retirement Functions

9.3.3 Base-Case Shipments

Figure 9.3.2 shows the forecasted shipments in the base case ( i.e. , the case without new energy efficiency standards) and the historical shipments DOE used to calibrate the forecast.

Figure 9.3.2 Microwave Ovens: Historical and Base-Case Shipments

Forecast9

- 8

Figure 9.3.3 presents forecasted shipments of microwave ovens in the base case ; shipm ents are disaggregated into those due to retirements and those going to new housing. Note

that replacement shipments comprise 85 percent to 92 percent of total microwave oven shipments during 2006

– 2035. The percentage of total shipments represented by replacements is high because the microwave oven market is fairly mature.

Figure 9.3.3 Microwave Ovens: Disaggregated Shipments Forecast for Base

Case

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SHIPMENTS

Economic theory suggests that, all else being equal, an increase in the price of a good

lea d s to a decrease in demand for it. Because DOE projects that appliance standards often result in an increase in the price of the product, DOE conducted a literature review and an analysis of appliance price and efficiency data to estimate the effects on product shipments from increases in product price. DOE also considered the decreases in operating costs from higher energy efficiency and changes over time in household in come. Appendix 9A explains the method DOE

used to quantify the effects of the above variables.

In the literature, DOE found only a few studies of appliance markets that are relevant to

thi s rulemaking analysis and identified no studies that use time-series data of produc t pr ices and shipments after 1980. The information that can be summarized from the literature suggests that

the demand for appliances is price-inelastic. Other information in the literature suggests that

a ppliances are a normal good, such that rising incomes increase the demand for appliances.9

- 9

Finally, the literature suggests that consumers use relatively high implicit discount rates b when comparing appliance prices and operating costs.

DOE found insufficient data on product purchase price and operating cost to perform a thoroug h analysis of dynamic changes in the appliance market. Rather, it used purchase price and efficiency data specific to residential refrigerators, clothes washers, and room air conditioners during 1980

– 2002 to evaluate broad market trends and conduct simple regression analyses. Th e data indicate that there has been an increase in appliance shipments and a decrease in appliance

purc hase price and operating costs during the period. Household income also increased during

thi s time. To simplify the analysis, DOE combined the available economic information into one

va riable, termed the relative price , and used this variable in an analysis of market trends, as well as to conduct a regression analysis. The re lative price is defined using the following expression.

Where:

RP = relative price,

TP = total price,

Income = household income,

PP = appliance purchase price, and

PVOC = present value of operating cost.

In the above equation, DOE used an implicit discount rate of 37 percent to determine the present value of operating costs.

DOE’s analysis of market trends sugg ests that the relative price elasticity of demand for the three appliances is relatively inelastic (that is, less than 1.0). DOE’s regression analysis suggests that the relative price elasticity of demand, averaged for the three appliances, is -0.34.

Thus a relative price increase of 10 percent results in a shipments decrease of 3.4 percent. Note that, because the relative price elasticity incorporates the effects of three factors (purchase price,

ope rating cost, and household income), the effect of any single factor is mitigated by changes to
the other two.

The relative price elasticity of -0.34 is consistent with estimates in the literature. Nevertheless, DOE stress es that the measure is based on a small data set subject to simple

statis tical analysis. More important ly , the me asure is based on an assumption that economic variables, including purchase price, operating costs, and household income, explain most of the

tre nd in appliances per household in the United States since 1980. Changes in appliance quality

a nd consumer preferences may have occurred during that period, but DOE did not account for

them in t his analysis. Despite these uncertainties, DOE believes that its estimate of the relative

b A high implicit discount rate with regard to operating costs means that consumers do not put much economic value on the operating cost savings realized from more efficient appliances. Consumers are much more concerned with the higher purchase prices.9

- 10

price elasticity of demand provides a reasonable assessment of the impact that purchase price, operating cost, and household income have on product shipments.

Because DOE’s forecasts of shipments and national impacts due to standards consider

30 -year period, it needed to consider how the relative price elasticity is affected once a new

standard takes effect. DOE considered the relative price elasticity provided above to be a shortter m value. It was unable to identify sources specific to household durable goods, such as appliances, to indicate how short- and long-term price elasticities differ. To estimate how the relative price elasticity changes over time, therefore, DOE relied on a study pertaining to

automobiles. 13

, 14 Th at study show ed that the automobile price elasticity of demand changes following a change in purchase price. With increasing years after the purchase price change, the price elasticity becomes more inelastic until it reaches a terminal value around the tenth year after the price change. Table 9.4.1 shows the relative change in the price elasticity of demand for automobiles over time. DOE developed a time-series of relative price elasticities for home appliances based on the relative change in the automobile price elasticity of demand. For years

not shown in Table 9.4.1 , DOE performed a linear interpolation to obtain the relative price elasticity.

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change

Change in e last icity

r elative to first year Number of Years After Price Change

1 2 3 5 10 20

1.00 0.78 0.63 0.4 4 0.35 0.33

Relative price elasticity

- 0.34

- 0.26

- 0.21

- 0.16

- 0.12

- 0.11

Based on the following equation, DOE estimated standards case shipments by incorporating the impact of the relative price into the base-case shipments forecast. Note that in the equation below, the relative price and the relative price elasticity are functions of the year, because both change with time.

Where:

Ship STD_p (j) = total shipments of product p in year j under the standards case, Rpl BASE_p (j) = u nits of product p retired and replaced in year j under the base case,

NI BASE_p (j) = n umber of new home installations of product p in year j under the base

case,
M BASE_p (j) = first-time owners market M of product p in year j under the base case,

e RP ( j)= relative price elasticity in year j (equals -0.34 for year 1), and

Δ RP(j)= change in relative price due to a standard level in year j

.9

- 11

9.5 AFFECTED STOCK

The affected stock is the in-service stock of a product that is affected by a standard level.

I n addition to the forecast of product shipments under both the base case and the standards case, the affected stock (which represents the difference in the appliance stock between the base case

a nd the standards case) is a key output of DOE’s shipments models. The affected stock quantifies the effect that new product shipments have on the appliance stock because of a standard level. Therefore, the affected stock consists of those in-service units that are purchased in or after the year the standard takes effect, as described by the following equation.

Where:

Aff Stock

p (j) = affected stock of units of product p of all vintages that are operational in

year j

, Ship p (j) = shipments of product p in year j

, Stock p (j) = stock of units of product p of all vintages that are operational in year j

, age = age of the units (years), and Std_yr = effective date of the standard.

As noted for the above equation, to calculate the affected stock, DOE must define the

e ffective date of the standard. For the NES and NPV results presented in chapter 10, DOE assumed that new energy efficiency standards will become effective in 2016. Thus, all

a ppliances purchased starting in 2016 are affected by the standard level.

9.6 EFFECT OF TRIAL STANDARD LEVEL ON SHIPMENTS FORECAST

Th is section compares the base-case shipments for eca st with the forecast for the maximum trial standard level (TSL) that DOE considered for microwave oven standby power.

F or all TSLs, shipments are forecasted to decrease—the effects from the increase in product purchase prices offsets the effects from decreased operating costs, resulting in a net decrease in shipments.

Figure 9.6.1 shows the effects on shipments during 201 6

– 204 5 that would arise from the

max imum TSL DOE considered for microwave oven standby power for both product classes (T SL 4). In Figure 9.6.1

, the difference between the shipments forecast for the base case and the

TSL illustrat es the annual shipments reduction caused by the TSL. The other TSLs have a smaller effect on shipments than does TSL 4.9

- 12 Figure 9.6.1 Shipments Forecasts for Base Case and TSL 49

- 13

REFERENCES

1 Association of Home Appliance Manufacturers (AHAM). AHAM 2000 Fact Book . 2000. AHAM: Washington, D.C.

2 AHAM. AHAM 2003 Fact Book . 2003. AHAM: Washington, D.C.

3 AHAM. AHAM 2005 Major Appliance Fact Book. June 2006. AHAM: Washington, D.C.

4 AHAM

. AHAM 2009 Fact Book . 2009. AHAM: Washington, D.C.

5 The Life Expectancy/Replacement Picture

. Appliance Magazine. 2009. 64(9): pp. 65-66.

6 U.S. Department of Energy (DOE)–Energy Information Administration (EIA). Annual

Ene rgy Outlook 2012 with Projections to 2035. June 25, 2012. Washington, D.C. www.eia.gov/forecasts/aeo/

. (Accessed May 2013.)

7 AHAM. Average Useful Life of Major Home Appliances. September 1996. AHAM: Washington, D.C. Prepared by NFO Research, Inc. www.aham.org

. (Accessed May 2013.)

8 U.S. Department of Energy (DOE)–Energy Information Administration (EIA).

Re sidential Energy Consumption Survey: 1993 Public Use Data Files

. www.eia.doe.gov/emeu/recs/93publicuse.html

. (Accessed May 2013.)

9 DOE -EIA. Residential Energy Consumption Survey: 1997 RECS Public Use Data. EIA: Washington, D.C www.eia.doe.gov/emeu/recs/public.html . (Accessed May 2013.)

10 DOE -EIA. Residential Energy Consumption Survey: 2001 RECS Public Use Microdata

Files. 2004. EIA: Washington, D.C. www.eia.doe.gov/emeu/recs/recs2001/publicuse2001.html

. (Accessed May 2013.)

11 DOE -EIA. Residential Energy Consumption Survey: 2005 RECS Public Use Microdata

Files . February 2009. EIA: Washington, D.C. www.eia.doe.gov/emeu/recs/recspubuse05/pubuse05.html . (Access May 2013.)

12 DOE -EIA. Residential Energy Consumption Survey: 2009 RECS Survey Data—Public

Use Microdata File . October 2011. EIA: Washington, D.C. www.eia.gov/consumption/residential/data/2009/#microdata . (Access May 2013.)

13 Hymans, S. Consumer Durable Spending: Explanation and Prediction

. Brookings Papers on Economic Activity . 1971. 1971(1): pp. 234–239

.

14 Dale, L., and K.S. Fujita. An Analysis of the Price Elasticity of Demand of Household Appliances

. 2008. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No.

LB NL 326E. www.osti.gov/bridge/servlets/purl/929429-Vm1S9X/929429.pdf

. (Access May 2013.)10

- i CHAPTER 10

. NATIONAL IMPACT ANALYSIS

TABLE OF CO NTENTS

10.1 INTRODUCTION

................................ ................................ ................................ ......... 10

- 1

10.2 TRIAL STANDARD LEVEL S

................................ ................................ ..................... 10

- 1

10.3 FORECASTED EFFICIENC Y LEVELS

................................ ................................ ...... 10

- 2

10.4 NATIONAL ENERGY SAVI NGS

................................ ................................ ............... 10

- 4

10.4.1 Definition

................................ ................................ ................................ ....................... 10

- 4

10.4.2 Inputs

................................ ................................ ................................ .............................. 10

- 5

10.4.2.1 Shipments and Product Stock

................................ ................................ ...... 10

- 5

10.4.2.2 Annual Energy Consumption per Unit

................................ ........................ 10

- 6

10.4.2.3 National Annual Energy Consumption

................................ ........................ 10

- 7

10.4.2.4 Energy Site

- to

- Source Conversion Factors

................................ .................. 10

- 7

10.5 NET PRESENT VALUE

................................ ................................ ............................... 10

- 8

10.5.1 Definition

................................ ................................ ................................ ....................... 10

- 8

10.5.2 Inputs

................................ ................................ ................................ ............................ 10

- 10

10.5.2.1 Total Installed Cost per Unit

................................ ................................ ...... 10

- 10

10.5.2.2 Annual Ope rating Cost Savings per Unit

................................ ................... 10

- 15

10.5.2.3 Total Increases in Product Cost

................................ ................................ . 10

- 15

10.5.2.4 Total Savings in Annual Operating Cos t

................................ ................... 10

- 15

10.5.2.5 Discount Factors

................................ ................................ ........................ 10

- 15

10.5.2.6 Present Value of Costs

................................ ................................ ............... 10

- 16

10.5.2.7 Present Value of Savings

................................ ................................ ........... 10

- 16

10.6 NATIONAL ENERGY SAVI NGS AND NET PRESENT VALUE RESULTS

........ 10

- 16

10.6.2 Re sults for National Energy Savings

................................ ................................ ........... 10

- 17

10.6.3 Results for Net Present Value

................................ ................................ ...................... 10

- 18

LIST OF TABLES

Table 10.2.1 Trial Standard Levels for Microwave Oven Standby Mode and Off

- Mode Energy Use

................................ ................................ ................................ ......... 10

- 2

Table 10.3.1 Microwave

- Only Ovens and Countertop Convection Microwave Ovens:

Distri butions of Standby Energy Consumption in 2016 for Base and

Standards Cases

................................ ................................ ................................ . 10

- 3

Table 10.3.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: Distributions of Standby Energy Consumption in 2016 for Base and Standards Cases

.......... 10

- 4

Table 10.4.1 Microwave

- Only and Countertop Convection Microwave Ovens: Shipments

- Weighted Average per

- Unit Annual Energy Consumption in

2016

................................ ................................ ................................ .................... 10

- 6

Table 10.4.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: Shipments

Weighted Average per

- Unit Annual Energy Consumption in 2016

.................. 10

- 6

Table 10.5.1 Shipments

- Weighted Average Product Costs in 2016 for Base and

Standards Cases

................................ ................................ ............................... 10

- 1110

- ii Table 10.6.1 Inputs to National Energy Savings and Net Present Value Calculations

......... 10

- 17

Table 10.6.2 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Cumulative National Energy Savings for 2016

- 2045

................................ ...... 10

- 18

Table 10.6.3 Built

- In and Over

- the

- Range Convection Microwave Ovens: Cumulative National Energy Savings for 2016

- 2045

................................ .......................... 10

- 18

Table 10.6.4 Microwave

- Only Ovens and Countertop Convectio n Microwave Ovens:

Cumulative Net Present Value of Consumer Benefit

................................ ...... 10

- 19

Table 10.6.5 Built

- In and Over the Range Convection Microwave Ovens: Cumulative

Net Present Value of Consumer Benefit

................................ .......................... 10

- 20

LIST OF FIGURE S

Figure 10.4.1 Marginal Site

- to

- Source Conversion Factors for Electricity

............................. 10

- 8 Figure 10.5.1 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Average Product Costs over the Forecast Period with the default price

factor index

................................ ................................ ................................ ...... 10

- 12 Figure 1 0.5.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: Average Product Costs over the Forecast Period with the default price factor index

.... 10

- 12 Figure 10.5.3 Equipment Price Factor Ind ex es for Default and Sensitivity Cases

................ 10

- 13 Figure 10.5.4 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Average Product Costs over the Forecast Period with and without the default price index and constant prices

................................ ............................ 10

- 14 Figure 10.5.5 Built

- In and Over

- the

- Range Convection Microwave Ovens: Average Product Costs over the Forecast Period with the default price facto r index and constant prices

................................ ................................ ........................... 10

- 14 Figure 10.6.1 Annual Costs and Savings for TSL 3

................................ ............................... 10

- 1910-1 CHAPTER 10. NATIONAL IMPACT ANAL YSIS

10.1 INTRODUCTION

This chapter describes the method the U.S. Department of Energy (DOE) used to

estimate national impacts of new energy conservation standards for microwave oven standby

mode and off

- mode power

. To estimate tho se impacts, DOE calculated the quantity and net valu e to consumers of future national energy savings (NES) and the net present value (NPV) of

consumer benefits resulting from the various trial standard levels (TSLs). Results described here include (1) national energy consumption and savings, (2) monetary va lue of national energy savings associated with each TSL, ( 3) increased total installed costs nationwide due to TSLs, and (4) the NPV of consumer benefits (difference between value of energy savings and increased total installed costs).

DOE determined bo th the NES and the NPV for all the TSL s considered for micr owave oven standby mode and off

- mode power

. A ll calculations were performed using a Microsoft Excel spreadsheet model

. The spreadsheet model, referred to as the national impact analysis (NIA) model , incorporates the calculations of NES and NPV, along with input from the shipment s model described in chapter 9. Details and instructions for using the NIA model are provided in appendix 10 A.

Chapter 9 provides a detailed des cription of the shipments m odel DOE used to forecast future purchase s of microwave ovens. Chapter 9 describes consumers’ sensitivities (otherwise known as elasticities) related to total installed cost, operating costs, and household income, and how DOE captured them within the model

.

10.2 TRIAL STANDARD LEVEL S

DOE analyzed the benefits and burdens of several trial standard levels ( TSLs ) for

micr owave oven standby mode and off

- mode energy use. As shown in Table 10.2.1 , DOE

considered four TSLs

. TSL 1 , which corresponds to the first candidate standard level (CSL) from each product class

, represents the standby power level that involves the least significant design change. TSL 4 corresponds to the max

- tech CSL (the current maximum microwave oven standby technology, or the lowest available standby power) . TSLs 2 and 3 are intermediate levels

between TSL s 1 and 4.10-2 Table 10.2.1 Trial Standard Levels for Microwave Oven Standby Mode and Off

- Mode Energy Use

TSL Standby Power W

Product Class 1: Microwave

Only and Countertop

Convection Microwave Ove ns Product Class 2: Built

- In and

Over

- the

- Range Convection

Microwave Ovens

1 2.00 3.70

2 1.50 2.70

3 1.00 2.20

4 0.02 0.04

10.3 FORECASTED EFFICIENCY LEVELS

A key component of DOE’s estimates of NES and NPV is the standb y power consumption forecasted over time for the base case (without new standards) and each potential standards case (with new standards). The forecasted values represent the annual shipment s

weighted standby power consumption of microwave ovens throughout the forecast period ( i.e.

, from the assumed compliance date of a new standard to 30 years after that date , or 201 6

– 2045 ). For calculating NES, per

- unit annual energy consumption is a direct function of product standby power consumption

. For calculating NP V, two inputs that depend on the level of standby power consumption are needed: per

- unit total installed cost and per

- u nit annual operating cost. P er

- unit total installed cost is a direct function of standby power consumption

, because units that have

lower standby energy consumption tend to cost more . The per

- unit annual operating cost is also a function of standby power consumption. The inputs to the calculation of NES and NPV are

discussed in detail in sections 10.4 and 10.5 , respectively

.

DOE used base

- case standby energy levels from the previous microwave oven rulemaking to predict standby mode and off

- mode energy consumption for microwave ovens in the base case for all years in the forecast. T h e standby energy consumption values for microwave

ovens in the base case are described further in section 8.2.5 of chapter 8. In forecasting base

- case energy consumption for microwave ovens, DOE assumed there would be no increase in the shipment s

- weighted efficiency and no change in the distribution of product efficiencies until the end of the forecast period ( 2045 ). Although DOE recognizes that product efficiencies may change in the future, there are no historical data for microwave ovens to indicate how

e fficienc ies have changed in the past or may change in the future.

To forecast standby energy consumption levels for the standards cases, DOE assumed a

“roll

- up” scenario that incorporates the shipment s

- weighted standby energy levels for the compliance y ear ( 2016 ). DOE assumed that standby energy consumption levels that did not meet the standard under consideration would “roll up” to meet the new standard. Also, DOE assumed that all products having standby energy consumption levels that exceed the standar d under consideration would not be affected. DOE made the same assumptions regarding increases in efficiency under the standards cases as under the base case ; namely that forecasted efficiencies

remain at the 2016 efficiency level s until the end of the for ecast period. Although frozen trends10-3 may not exactly predict efficiencies in the future, DOE considers that maintaining a frozen efficiency for the base and standards cases allows for a reasonable estimate of the effect of standards on product standby ener gy consumption.

Table 10.3.1 and Table 10.3.2 show the distributions of product annual energy consumption levels and standby power in 2016 in the base and s tandards cases that DOE

examined for micr owave ovens. DOE maintained the shipments

- weighted standby power and annual energy use shown in the following tables thro ughout the forecast period, 2016

– 2045

.

Table 10.3.1 Microwave

- Only Ovens and Countertop Convection Microwave Ovens:

Distributions of Standby Energ y Consumption in 2016 for Base and

Standards Cases

TSL Standby

Power W Annual Energy

Use kWh/yr Market Share %

Base

Case Trial Standard Level

1 2 3 4

Baseline 4.00 34. 76 46

1 2.00 17. 38 35 81

2 1.50 13.0 3 19 19 100

3 1.00 8.69 0 0 0 1 00

4 0.02 0.17 0 0 0 0 100

Shipments

- weighted standby power 2.83 1.90 1.50 1.00 0.02

Shipments

- weighted annual energy use 24.6 16.5 13.0 8.7 0.1710-4 Table 10.3.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: Distributions of Standby Energy Consumption in 2 016 for Base and Standards Cases

TSL Standby

Power W Annual Energy

Use kWh/yr Market Share %

Base

Case Trial Standard Level

1 2 3 4

Baseline 4.50 39.1 100

1 3.70 32.1 0 100

2 2.70 23.5 0 0 100

3 2.20 19.1 0 0 0 100

4 0.04 0.35 0 0 0 0 100

Shipments

- weighted standby power 4.50 3.70 2.70 2.20 0.04

Shipments

- weighted annual energy use 39.1 32.1 23.5 19.1 0.35

10.4 NATIONAL ENERGY SAVI NGS

DOE calculate s annual national energy savings ( NES ) and cumulative NES throughout

the forecast period , which extends from 2016 to 2045

. Positive values of NES represent energy savings, meaning national energy consumption under standards is lower than in the base case.

10.4.1 Definition

As the following equation indicates, DOE calculates annual NES as the differ ence between two projections: a base case (without new standards) and a standards case (with new standards).

STD BASE y AEC AEC NES

_ **=**

Cumulative NES is the sum of annual energy savings throughout the forecast period,

which extends from the assumed complia nce date of a new standard, 2016 , to 30 years after the

effective date ( 2045 ). This calculation is represented by the following equation.



# ∑

**=** y cum 

### NES NES

DOE calculated national annual energy consumption by multiplying the number, or stock, of mi crowave ovens (by vintage) by the associated unit energy consumption (also by vintage). National annual energy consumption is calculated by the following equation.



# ∑ **× =**  V V 

### UEC STOCK AEC10-5 For the above expressions, DOE defined th e various quantities as f ollows.

AEC = national annual energy consumption each year summed over vintages of the product stock, STOCK V (quads);

NES = annual national energy savings (quads);

STOCK V = stock of product (millions of units) of vintage V surviving in the year for

which DOE is calculating annual energy consumption;

UEC V = annual energy consumption per product in kilowatt

- hours (kWh);

V = year in which the product was purchased as a new unit; and

y = year in the forecast ( 2016

– 2045 ).

The stock of a product depends o n annual shipments and the lifetime of the product. As described in chapter 9 , DOE projected shipments under the base and each standards case. DOE determined that shipments under the standards cases would be slightly lower than under the base case, because more efficient products have slightly higher purchase prices. In other words, DOE believes that higher purchase prices cause some consumers to forego purchasing new products, resulting in dropped, or displaced, shipments.

To avoid including savings att ributable to shipments displaced because of standards, DOE used the projected standards

- case shipments and, in turn, the standards

- case stock, to calculate the annual energy consumption for the base case.

10.4.2 Inputs

The inputs to the calculation of national e nergy savings (NES) are:

**•** s hipments;

**•** product stock ( STOCK V );

**•** annual energy consumption per unit ( UE C);

**•** national annual energy consumption ( AEC ); and

**•** a site

- to

- source conversion factor ( src_conv ).

10.4.2.1 Shipments and Product Stock

DOE forec asted shipments of each product class under the base case and each standards

case . Several factors affect forecasted shipments, including purchase cost

, operating cost, and household income. As noted earlier, the increased cost of more efficient products c auses some consumers to forego buying the products. Consequently, shipments forecasted under the

standards cases are lower than under the base case. The method DOE used to calculate and generate the shipments forecasts for each considered product class is described in detail in

C hapter 9, Shipments Analysis.

The produc t stock in a given year is the number of products shipped from earlier years

that survive in th at year. The NIA shipments model track s the number of units shipped each year

. DOE assumes that products have an increasing probability of retiring as they age. The probability of survival as a function of years since purchase is the survival function. Chapter 910-6 provides additional details on the survival fu nction that DOE used for both product clas ses of

microwave ovens

.

10.4.2.2 Annual Energy Consumption per Unit DOE developed annual per

- unit energy consumption as a function of product energy efficiency for each product class ( see C hapter 7, Energy Use Analysis , and C hapter 8, Life

- Cycle Cost and Payback P eriod Analys i s). Because annual per

- unit energy consumption depend s directly on energy efficiency, DOE used the shipments

- weighted energy efficiencies annual

energy use for the base and standards cases (presented in section 10.3 ), along with the annual energy use data presented in chapter 8, to estimate the shipments

- weighted average annual per

unit energy consumption under the base and standards cases.

As noted in section 10. 3 , DOE assumed that forecasted energ y efficiencies in the base

and standards cases remain constant at 2016 levels. Because annual per

- unit energy consumption is a function of energy efficiency, DOE held the values shown in Table 10.4.1 and Table 10.4.2

constant throughout the forecast period.

Table 10.4.1 Microwave

- Only and Countertop Convection Microwave Ovens : Shipments

Weighted Average per

- Unit Annual Energy Consumption in 2016

TSL Standby

Power W Annual Energy

Use kWh/yr

Baseline 4.00 34.8

1 2.00 17.4

2 1.50 13.0

3 1.00 8. 69

4 0.02 0. 17

Table 10.4.2 Built

- In and Over

- the

- Range Convection Microwave Ovens : Shipments

Weighted Average per

- Unit Annual Energy Consumption in 2016

TSL Standby

Power W Annual Energy

Use kWh/yr

Baseline 4.50 39 .1

1 3.70 32.1

2 2.70 23.5

3 2.20 19.1

4 0.04 0.3510-7 10.4.2.3 National Annual Energy Consumption

N ational annual energy consumption is the product of the annual energy consumption per unit and the number of units of each vintage. This calculation accounts for differences in unit energy consumption from year to year. The equation for determining annual energy consumption, shown in section 10 .3, is repeated here.



# ∑ **× =**  V V 

### UEC STOCK AEC

In determining national annual energy consumption, DOE initially calculat e s annual energy consumption at the site and then applie s a conversion factor to calculate primary energy consumption (see section 10.4.2.4 ).

10.4.2.4 Energy Site

- to

- Source Conversion Factors In determining national annual energy consumption , DO E initially calculate s the annual energy consumption at the site (for electricity, the energy in kilowatt

- hours ( kWh ) consumed at the household or establishment). To account for losses associated with the generation, transmission, and distribution of e lectrici ty , DOE then applie s a conversion factor to the site energy consumption to calculate primary (source) energy consumption

. Th e site

- to

- source conversion factor is a multiplicative factor used to convert site energy consumption into primary

or source energy consumption, expressed in quadrillion British thermal units ( quads ). DOE used

annual site

- to

- source conversion factors based on the version of the National Energy Modeling System (NEMS) a that corresponds to the Energy Information Administration ’s ( EIA ) Annual Energy Outlook 201 2 ( AEO201 2 ). 1

Figure 10.4.1 The factors are marginal values, which represent the response of the system to an incremental d ecrease in consumption. For electricity, the conversion factors change over time in response to projected changes in generation sources ( i.e., the types of power plants projected to provide electricity to the Nation ). shows the site

- to

source conversion factors DOE applied from 2005 to the end of the forecast period. DOE

assumed that conversion factors remain at 2030 values throughout the rest of the forecas t period

.

a For m ore information on NEMS, refer to the U.S. Department of Energy

– Energy Information Admi nistration

documentation. A useful summary is Na t ional Energy Modeling System: An Overview 2000 , D OE/EIA

0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model without any modification to code or data

. Because this analysis entails some minor code modi fications and the model is run under various policy scenarios that are variations on EIA assumptions, DOE refers to the model by the

name NEMS

- BT (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed).

NEMS

- BT previousl y was called NEMS

- BRS.10-8 0 2000 4000 6000 8000 10000 12000 14000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

YearMarginal Site-to-Source Conversion Factor forElectricty (Btu/kWh)

Figure 10.4.1 Marginal Site

- to

- Source Conversion Factors for Electricity

10.5 NET PRESENT VALUE

DOE calculated the net present value (NPV) of the increased product cost and reduced operating cost associated with the difference between the base case and each potential standards

case for both microwave oven product classes examined for standby mode and off

- mode energy

use

.

10.5.1 Definition

The NPV is the value in the present of a time

- seri es of costs and savings. The NPV is described by the following equation.

PVC PVS NPV

_ =

Where:

PVS = present value of savings in operating cost, and

PVC = present value of increased total product cost to consumers.10-9 DOE determined the PVS and PVC according to the following expressions.



# ∑

y y 

### DF OCS PVS **× =** 



# ∑

y y 

### DF TIC PVC **× =** 

Where:

OCS = total annual savings in operating cost each year summed over vintages of the product stock, STOCK V ; TIC = total annual increases in product cos t each year summed over years of the product shipments, SHIP y ;
DF = discount factor in each year; and

y = year in the forecast.

DOE calculated the total annual consumer savings in operating cost by multiplying the number or stock of a given product c lass (by vintage) by its per

- unit operating cost savings (also by vintage). DOE calculated the total annual increases in consumer product cost by multiplying

the number or shipments of the given product class (by v intage) by its per

- unit increase in

consum er produc t cost (also by vintage). The calculation of total annual operating cost savings and total annual produc t cost increases is represented by the following equations.



# ∑

V V y 

### UOCS STOCK OCS **× =** 



# ∑

y y y 

### UTIC SHIP TIC **× =** 

Where:

STOCK V = stock of produc t s of vintage V that survive in the year for which DOE calculated annual energy consumption ; UOCS V = annual per

- unit savings in operating cost ;
V = year in which the product was purchased as a new unit ; SHIP y = shipments of products in year y ; and

UTIC y = annual per

- unit increase in installed product cost in year y

.

DOE determined the total inc reased product cost for each year from the compliance date

of the new standard ( 2016 ) to 2045

. DOE determined the present value of operating cost savings

f or each year from the compliance date of the standard to the year when all units purchased by

2045 have been retired. DOE calculated costs and savings as the difference between a standards

case and a base case without new standards.10-10 DOE develop ed a disc ount factor from the national discount rate and the number of years between the present ( i.e. , year to which the sum is being discounted) and the year in which the costs and savings occur. The NPV is the sum over time of the discounted net savings.

10.5.2 Inputs

The inputs to calculation of the net present value (NPV) are:

**•** a verage annual product cost;

**•** average annual savings in operating cost;

**•** total annual increases in product cost;

**•** total annual savings in operating cost;

**•** discount factor;

**•** present value of costs; and

**•** present value of savings.

The increase in total annual product cost is equal to the annual change in the average annual product cost (difference between base case and standards case) multiplied by the shipments forecasted in t he standards case. As with the calculation of the NES, DOE did not calculate total annual product costs using base

- case shipments. To avoid including savings due to displaced shipments ( when consumers decid e not to buy higher

- cost products), DOE used the

s tandards

- case projection of shipments and, in turn, the standards

- case stock, to calculate product

costs.

The total annual savings in operating cost s are equal to the change in annual operating cost (difference between base case a nd standards case) per unit multiplied by the shipments

forecasted for the standards case.

10.5.2.1 Total Installed Cost per Unit The per

- unit total installed cost of each product class is described in chapter 8 as a function of product energy efficiency. Because the per

- un it total annual installed cost depend s directly on energy efficiency, DOE used the shipments

- weighted energy efficiencies of the base

and standards cases described in section 10.3 , in combination with the total installed costs developed in chapte r 8, section 8.2.1.7, to estimate the shipments

- weighted average annual per

unit total installed cost under the base and standards cases.

DOE based average consumer costs for microwave oven on average manufacturer costs plus average overall markup value s. Using the relationship between microwave oven standby

mode and off

- mode energy use a nd total installed cost presented in chapter 8, section 8.2.1.7, DOE derived the per

- unit total installed cost based on the shipments

- weighted energy

efficiencies that c orrespond to the base case and each standards case for the two product classes

.

For the Final Rule

, DOE applied the price forecasting method described in chapter 8

, section 8.2.1.2, to forecast the prices of microwave ovens sold in each year of the fore cast period10-11 ( 2016

– 2045 ). The price in each year is a function of the cumulative production of microwave

ovens forecast for that year.

DOE derived a price factor index to forecast prices in each future year. The default index value in a given year is a f unction of the cumulative production forecast through that year, which is based on the shipments forecast described in chapter 9. DOE applied the same value to forecast prices for each microwave oven product class at each considered efficiency level.

For microwave ovens , the estimated fractional reduction in price expected from each

do ubling of cumulative production i s 10.1 percent. Table 10.5.1 shows the costs in 2016

.

Table 10.5.1 Shipment s

- Weighted Average Product Costs in 2016 for Bas e and Standard s Case s

Base

Case Trial Standards Level

1 2 3 4

Microwave

- Only Ovens and Countertop Convection

Microwave Ovens
Shipment s

- w eighted annual energy use kWh 3 4. 9 1 7

. 4 13. 1 8. 72 0.17

Av era g e product cost 2011 $ 234 234 234 238 243

Built

- In and Over

- the

- Range Convection Microwave

Ovens
Shipment s

- w eighted annual energy use kWh 39. 2 32. 3 23.5 19. 2 0.35
Average product cost 2011 $ 505 505 50 7 513 514

Figures 10.5.1 and 10.5.2 show how the average product costs for the base case and each TSL decline over the forecast period with the default price factor index. Figure 10.5.1 pertains to product class 1, microwave

- only ovens and countertop convection microwave ovens, while Figure 10.5.2 pertains to product class 2, built

- in and over

- th e

- range convection microwave

ovens.10-12 Figure 10.5.1 Mi crowave

- Only Ovens and Countertop Convection Microwave Ovens: Average Product Costs over the Forecast Period with the default price factor index

Figure 10.5.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: Average Pr oduct Costs over the Forecast Period with the default price factor index10-13 DOE recognizes that there is uncertainty in its estimates of produc t price forecasts . To provide a n indication of potential long

- term changes in the forecast , DOE performed price

- tr end fits to two periods in the historical data, equal to the first and second half of the total period. For electric cooking equipment in general , the selected periods were 1969

– 1989 and 1990

– 2010. Appendix 8C shows the estimated learning parameters for th o se periods. DOE also performed a sensitivity calculation using data specific to microwave ovens.

In addition to using learning parameters, DOE examined the case involving a constant real price sensitivity. DOE also used a n index forecasted for AEO2012

, one based on the “chained price index

— other consumer durable goods except ophthalmic

. ” Th at index is the most disaggregated category that includes appliances. To develop an inflation

- adjusted index, DOE

normalized the above index with the “ chained price i ndex

— gross domestic product ” forecasted

for AEO201 2

.

Figure 10.5.3 shows the default price factor index and the ind ex es that corresponding to

each of the sensitivities for microwave ovens. All ind ex es are adjusted for inflati on.

Figure 10.5.3 Equipment Price Factor Ind ex es for Default and Sensitivity Cases

Figures 10.5.4 and 10.5.5 show the average product costs for the base case and each TSL over the forecast period with the default price factor index and one of the sensitivities, co nstant prices. Comparing the average product costs with and without a price factor index demonstrates the impact of using price factor index on forecasted product costs. Figure 10.5.4 pertains to product class 1, microwave

- only ovens and countertop convec tion microwave ovens, while Figure 10.5.4 pertains to product class 2, built

- in and over

- the

- range convection microwave

ovens.10-14 Figure 10.5.4 Microwave

- Only Ovens and Countertop Convection Microwave Ovens: Average Product Costs over the Forecast Period with and

witho ut the de fault price index and constant prices

Figure 10.5.5 Built

- In and Over

- the

- Range Convection Microwave Ovens: Average Product Costs over the Forecast Period with the default price factor index and constant prices10-15 The NIA results reported in this chapter are based on the default LR. With cumulative shipments through 2045 projected to reach 1, 280 million (compared with 522 million in 2010 ), the p rice is projected to drop to 0.64 times the 2010 value. Appendix 10B presents annualized NPV results using the other price factor indexes.

10.5.2.2 Annual Operating Cost Savings per Unit

Per

- unit annual operating cost represents the annual cost for energy for each unit . DOE determined the savings in per

- unit annual energy cost for each product class by multiplying the savings in per

- unit annual energy consumption by the appropriate energy price.

Estimates of per

- unit annual energy consumption for the base case and each standards case were presented in section 10.4.2.2

. DOE forecasted energy prices based on EIA’s AEO20 1 2

. E nergy prices and price trends are described in chapter 8.

10.5.2.3 Total Increases in Product Cost

The total annual increase in product cost for any given standards case is the product of the average cost increase per unit due to the stan dard and the number of units of each vintage shippe d. The equation for determining the total annual increase in product cost for a given standards case, shown in section 10.5.1 , is repeated here.



# ∑

y y y 

### UTIC SHIP TIC **× =** 

10.5.2.4 Tota l Savings in Annual Operating Cost The total annual savings in operating cost for any given standards case is the product of the annual savings in operating cost per unit attributable to the standard and the number of units of each vintage. This method ac counts for differences in annual operating cost savings from year to year. The equation for determining the total annual savings in operating cost for a given standards case, which was presented in section 10.5.1 , is repeated here.



# ∑

V V 

### UOCS STOCK OCS **× =** 

10.5.2.5 Discount Factors DOE multiplies monetary values for future years by a discount factor to determine their present value. The discount factor (DF) is described by the equation:

) ( 

### ) 1 ( 1

yp y 

### r DF

**−** 

### + **=**

Where:

r = discount rate,

y = year of the monetary value, and

y

P = year in which the present value is being determined.10-16 A lthough DOE used consumer discount rates to determine the life

- cycle cost (LCC) of

microwave ovens , it used national discount rates to ca lculate national NPV. DOE estimated NPV using both a 3

- percent and a 7

- percent real discount rate, in accordance with the Office of Management and Budget’s guidance to Federal agencies on the development of regulatory analysis, particularly section E there in

. 2

10.5.2.6 Present Value of Costs DOE defined the present year as 2013

.

The present value of increased installed costs is the annual increase in installed cost for each year ( i.e ., the difference between the standards case and base case), discounted to the present and summed over the period for which DOE is considering the installed products (from

the compliance date of energy conservation standards through 30 years thereafter ).

The increase in total installed cost refers to both product and installation cost s associated

with the more energy efficien t products purchased in the standards case compared to the base

case. DOE calculated annual increases in installed cost as the difference in total cost of new products installed each year, mul tiplied by the shipments in the standards case.

10.5.2.7 Present Value of Savings The present value of savings in operating cost is the annual savings in operating cost ( i.e.

, the difference between the base case and standards case), discount ed to the present and summed over the period that begins with the compliance date of standards and ends when the last installed

unit is retired from service.

Savings represent decreases in operating cost s (including costs for energy, repair, and

maintena nce) associated with the more energy efficien t products purchased in a standards case compared to the base case. Total annual savings in operating cost are the savings per unit multiplied by the number of units of each vintage that survive in a particular year. Because a product consumes energy throughout its lifetime, the energy con sumption for units installed in

past year s includes energy consumed until the unit is retired from service.

10.6 N ATIONAL ENERGY SAVIN GS AND NET PRESENT V ALUE RESULTS

The NIA model provides estimates of the NES and NPV that would result from standards

at various efficiency levels. The inputs to the NIA model were discussed in sections 10.4.2 ( inputs to NES) and 10.5.2 ( inputs to NPV). DOE generated the NES and NPV results using a Microsoft Excel spreadsheet

. Details and instructions for using the spreadsheet are provided in

A ppendix 10 A, User Instructions for the National Impact Analysis Spreadsheet.

Table 10.6.1 summarizes inputs to the NIA model. The data source for each input also is

noted.10-17 Table 10.6.1 Inputs to National Energy Savings and Net Present Value Calculations
Input Data Description

Shipments Annual shi p ments from shipments model ( chapter 9).
Compliance date of standard 2016

Energy efficiency in base case Shipments

- weighted unit energy consumption (UEC) determined for each year.

Energy efficiency in standards

cases “Roll

- up” scenario assumed for d etermining shipments

weighted UEC for each standards case

. Annual energy consumption per

unit Annual weighted

- average values are a function of shipments

- weighted UEC.
Learning rates See chapter 8 and appendix 8 C

. Total installed cost per unit Annual wei ghted

- average values are a function of efficiency level.

Energy cost per unit Annual weighted

- average values are a function of the annual UEC and energy prices (see chapter 8 for energy prices).

Escalation of energy prices Based on EIA AEO201 2 forecasts (to 2035) and on extrapolation after 2035 (see chapter 8).

Energy site

- to

- source conversion

factor Conversion, which differs yearly, is generated by DOE/EIA’s NEMS program (a time

- series conversion factor that includes electric generation, transmission, and distribution losses).

Discount rat e 3 percent and 7 percent real.

Present year 2013

.

10.6.2 Results for National Energy Savings This section provides results of NES calculation s for standards at each efficiency level analyzed for the two product classe s of microwave ovens . NES results, which are cumulative through 2045 , are shown as primary energy savings. Because DOE based the inputs to the NIA model on weighted

- average values, results are discrete point values, rather than a distribution of values as p roduced by the LCC and payback period analys i s. DOE reports both undiscounted and discounted values of energy savings. Discounted energy savings represent a policy perspective in

which energy savings in the future are less significant than energy savings i n the present.10-18 Table 10.6.2 Microwave

- Only Ovens and Countertop Convection Microwave Ovens : Cumulative National Energy Savings for 2016

- 2045

Trial Standard

Level Standby

Power W National Energy Savings quads

1 2.00 0.24

2 1.50 0.35

3 1.00 0.4 7

4 0.02 0.7 2

Table 10.6.3 Built

- In and Over

- the

- Range Convection Microwave Ovens: Cumulative National Energy Savings for 2016

- 2045

Trial Standard

Level Standby

Power W National Energy Savings quads

1 3.70 0.00

2 2.70 0.00

3 2.20 0.01

4 0.04 0.01

10.6.3 Results for Net Present Va lue This section provides the results of calculating the NPV of consumer benefit s for

standards at each considered efficiency level for the two product classe s of microwave ovens

. Results, which are cumulative, are shown as the dis counted value of the net savings in dollar

terms.

The present value of increased total installed cos t is the total annual increase in installed cost (the difference between a standards case and the base case), discounted to the present an d summed throug hout the period for which DOE evaluate d the effe ct s of standards.

Savings are decreases in operating cost associated with the more energy efficien t products purchased in the standards case compared to the base case. Total savings in operating

cost are th e savings per unit multiplied by the number of units of each vintage ( i.e., year of manufacture) that s urvive in a particular year. T he operating cost includes expenditures until the last unit purchased during the forecast period is retired from service.

As an example, Figure 10.6.1 illustrates the net costs and benefits for both product

classes under TSL 3. DOE can use analysis data to produce f igures similar to the one presented

below for each TSL

. The annual product cost is the sum of the increase in the total installed cost for products purchased each year during the forecast period. The annual savings in operating cost is the savings for products operating in each year. The NPV is the difference between the

cumulative annu al discounted savings and the cumulative annual discounted costs.10-19 Figure 10.6.1 Annual Costs and Savings for TSL 3

Table 10.6.4 and Table 10.6.5 present the cumulative NPV of consumer benefits for both prod uct classes of microwave ovens. Benefits are provided for each TSL at both a 3

- percent and

a 7

- percent discount rate.

Table 10.6.4 Microwave

- Only Ovens and Countertop Convection Microwave Ovens:

Cumulative Net Present Value of Consumer Benefit

Trial

Standard

Level Sta ndby

Power W Net Present Value

7% Discount Rate

billion 2011 $ 3% Discount Rate

billion 2011 $

1 2.00 1.13 2.32

2 1.50 1. 61 3. 31

3 1.00 1. 51 3. 34

4 0.02 2.00 4.5610-20 Table 10.6.5 Built

- In and Over the Range Convection Microwave Ovens: Cumulative Net

Present Value o f Consumer Benefit

Trial

Standard

Level Standby

Power W Net Present Value

7% Discount Rate

billion 201 1 $ 3% Discount Rate

billion 2011 $

1 3.70 0.01 0.02

2 2.70 0.02 0.0 5

3 2.20 0.02 0.04

4 0.04 0.04 0.0 9

The NPV values in this chapter were calcul ating using the default price forecast. DOE

consid ered three price forecasting sensitivities in addition to the default rate of 10.1 percent: (1) a constant price forecast ; (2) a high decline forecast ; and (4) an AEO2012 chained

- price index

forecast

. Appen dix 10 C presents NPV results for each sensitivity case

.10-21 REFERENCES

1 U.S. Department of Energy

– Energy Information Administration ( EIA )

. Annual Energy Outlook 2012 with Projections to 2035. June 25, 2012. EIA: Washington, D.C. www.eia.gov/forecasts/aeo/ . (Accessed May 2013.)

2 The White House Office of Management and Budget. Circular A

- 4: Regulatory Analysis : Section E: identifying and measuring benefits and costs

. September 17, 2003. Office of Manageme nt and Budget: Washington, D.C. www.whitehouse.gov/omb/circulars_a004_a

- 4/ . (Accessed May 2013.)11

- i CHAPTER 11

. CONSUMER SUB

- GROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION

................................ ................................ ................................ ......... 11

- 1

11.2 DEFINITIONS OF SUB

- GR OUPS

................................ ................................ ............... 11

- 1

11.2.1 Senior

- Only Households

................................ ................................ ................................ 11

- 1

11.2.2 Low

- Income Households

................................ ................................ ............................... 11

- 1

11.3 INPUTS TO LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS F OR

SUB

- GROUPS

................................ ................................ ................................ ............... 11

- 2

11.4 RESULTS

................................ ................................ ................................ ...................... 11

- 3

11.4.1 Senior

- On ly Households

................................ ................................ ................................ 11

- 3

11.4.2 Low

- Income Households

................................ ................................ ............................... 11

- 4

LIST OF TABLES

Table 11.2.1 RECS 2009 Definitions of Low

- Income Households by Yearly Income

.......... 11

- 2

Table 11.3.1 Household Population

................................ ................................ ........................ 11

- 3

Table 11.3.2 Weighted

- Av erage Annual Energy Use for Microwave

- Only and Countertop Convection Microwave Ovens (kWh)

................................ ............. 11

- 3

Table 11.3.3 Weighted

- Average Annual Energy Use for Built

- In and Over

- the

- Range

Convection Micro wave Ovens (kWh)

................................ ................................ 11

- 3

Table 11.4.1 Microwave

- Only and Countertop Convection Microwave Ovens: LCC and

PBP Results for Senior

- Only Households

................................ ......................... 11

- 4

Table 11.4.2 Built

- In and Over

- the

- Range Convection Microwave Ovens: LCC and

PBP Results for Senior

- Only Households

................................ ......................... 11

- 4

Table 11.4.3 Microwave

- Only and Countertop Convection Microwave Ovens: LCC and

PBP Results for Low

- Income Households

................................ ........................ 11

- 5

Table 11.4.4 Built

- In and Over

- the

- Range Convection Microwave Ovens: LCC and

PBP Results for Low

- Income Households

................................ ........................ 11

- 511

- 1 CHAPTER 11. CONSUMER SUB

- GROUP ANALYSIS

11.1 INTRODUCTION

Chapter 8 describes the life

- cycle c ost (LCC) and payback p eriod (PBP) analysi s , which

examine s effe cts of energy conservation standards on the U.S. population. In analyzing the potential effects of new or amended standards on residential consumers, the U.S. Department of Energy ( DOE ) further evaluates the impact on identifiable groups of consumers ( i.e. , sub

- groups) that may be disproportionately affected by a natio nal standard level. The consumer sub

- group analysis evaluates impacts by analyzing the LCC and PBPs for sub

- groups of residential

consumers.

For new standards related to microwave oven standby mode and off mode , DOE

identified two consumer sub

- groups that warranted further study: (a) households composed of people 65 years of age or older (senior

- only) ; and (b) households at or below the poverty line (low

- income)

.

DOE determined the effe ct on consumer sub

- groups for two product classes: (1)

microwave

- only ovens and countertop convection microwave ovens and (2) b uilt

- in and over

the

- range convection microwave ovens . To perform the consumer sub

- group analysis, DOE used

the LCC spreadsheet model , which allows for the examination of particular consumer sub

grou ps. Chapter 8 explains in detail the model inputs u sed in determining LCC and PBP

.

This chapter identifies the two consumer sub

- group s in further detail and gives the results of the LCC and PBP analys i s for the considered sub

- group s

.

11.2 DEFINITION S OF SUB

- G ROUPS

In the case of microwave ovens, DOE analyzed two sub

- groups: (1) low

- income households and (2) households occupied by senior citizens. The two sub

- groups are defined

below

.

11.2.1 Senior

- Only Households

The occupants of s enior

- only households are all at l east 65 years of age. Based on the

DOE Energy Information Administration ’s (EIA’s ) 2009 Residential Energy Consumption Survey ( RECS 2009 ) , senior

- only households comprise 17 percent of the country’s households

. 1

11.2.2 Low

- Income Households

As defined in the RECS survey, low

- income households are considered to be those at or below the poverty line. The poverty line varies with household size, age of head of househo ld

, and family income. Table 11.2.1 summarizes the income level baseline s for selecting low

-11

- 2 income households from the RECS 2009 sample. The RECS survey classifies 15 percent of the country’s households as low

- income.

Table 11.2.1 RECS 200 9 Definitions of Low

- Income Households by Yearly Income

Household

Size Average Income 20 09 $

48 Con tiguous

States and D.C. Alaska Hawaii

1 10,830 13,530 12,460

2 14,570 18,210 16,760

3 18,310 22,890 21,060

4 22,050 27,570 25,360

5 25,790 32,250 29,660

6 29,530 36,930 33,960

7 33,270 41,610 38,260

8 37,010 46,290 42,560

For households in the 4 8 contiguous states and D.C., add 3,740 for each additional person. For households in Alaska, add 4,680 for each additional person. For households in Hawaii, add 4,300 for each additional person.

11.3 INPUTS TO LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS FOR

SU B

- GROUP S

Table 11.3.1 summarize s the household population for microwave

- only and convection

ovens

. Table 11.3.2 and Table 11.3.3 summarize the weighted

- average annual en ergy use for the

households examine d in the cons umer sub

- group analys i s

. Tho se values are compared against the weighted

- average values for the national sample

.11

- 3 Table 11.3.1 Household Population

All Microwave Ovens

Population Count Weight

National 11,616 108,995,6 09

Senior

- o nly 1756 17,784,266

Senior

- o nly % 15.1 16.3

Low

- i ncome 1554 15,574,932

Low

- i ncome % 13.4 14.3

Table 11.3.2 Weighted

- Average Annual Energy Use for Microwave

- Only and Countertop

Convection Microwave Ovens ( kWh )

Trial Standard

Level All

Households Senior

Only Low

Income

Baseline 34.76 34. 76 34.69

1 17.38 17.3 8 17.35

2 13.03 13.0 3 13.01

3 8.69 8.69 8.67

4 0.17 0.17 0.17

Table 11.3.3 Weighted

- Average Annual Energy Use for Built

- In and Over

- the

- Range

Convection Microwave Ovens ( kWh )

Trial Standard

Level All

Hous eholds Senior

Only Low

Income

Baseline 39.10 39. 10 39.03

1 32.15 32. 15 32.09

2 23.46 23. 46 23.42

3 19.1 2 19. 12 19.08

4 0.35 0.35 0.35

11.4 RESULTS

Table 11.4.1 t hrough Table 11.4.4 summarize results of DOE’s LCC and PBP analysis performed for the two identified sub

- groups, senior

- only and low

- income households. The tables provide the average installed price ; average lifetime operating cost (discounted) ; average life

cycle cost ; average life

- cycle cost savings ; the percentage of consumers who are burdened with net costs, realize net savings, or are no t affe ct ed ; and the median payback period.

11.4.1 Senior

- Only Households

Table 11.4.1 and Table 11.4.2 summarize the analytical results for standby and off

- mode efficiency levels for both product classes of microwave ovens for senior

- only households

.11

- 4 Table 11.4.1 Microwave

- Only and Countertop Convection Microwave Ovens : LCC and

PBP R esults for Senior

- Only Households

Standby

Power

W Life

- Cycle Cost 2011$ Life

- Cycle Cost Savings Median Payback

Period

years Average

Installed

Price Average Operating

Cost Average

LCC Average Savings

2011$ % Households Having

Net

Cost No Impact* N et

Benefit

4.00 234 3 5 26 8

2.00 234 1 7 2 51 8 0 54 46 0.2

1.50 23 4 1 3 24 7 1 1 0 19 81 0.3

1.00 23 9 9 24 7 1 1 13 0 87 3.5

0.02 24 3 0 24 3 1 5 5 0 95 3.5

* “No impact” means that the base

- case product assigned t o the household has an efficiency greater than the level

indicated, so the household is not affected.

Table 11.4.2 Built

- In and Over

- the

- Range Convection Microwave Ovens : LCC and PBP

Results for Senior

- Only Households

Standby

Power

W Life

- Cycle Cost 2011$ Life

- Cycle C ost Savings Median Payback

Period

years Average

Installed

Price Average Operating

Cost Average

LCC Average Savings

2011$ % Households Having

Net

Cost No Impact* Net

Benefit

4.50 50 5 3 9 544 NA 0 100 0 NA

3.70 505 32 53 8 7 0 0 100 0. 1

2.70 50 5 2 3 52 9 1 6 0 0 100 0 .1

2.20 513 1 9 53 2 12 0 0 100 3. 3

0.04 51 4 0 51 5 30 0 0 100 2. 0

* “No impact” means that the base

- case product assigned to the household has an efficiency greater than the level

indicated, so the household is not affected.

11.4.2 Low

- Income Households

Table 11.4.3 and Table 11.4.4 summarize the analytical results for standby and off

- mode efficiency levels for both product classes of microwave ovens for low

- income households.11

- 5 Table 11.4.3 Microwa ve

- Only and Countertop Convection Microwave Ovens: LCC and

PBP Results for Low

- Income Households

Standby

Power

W Life

- Cycle Cost 2011$ Life

- Cycle Cost Savings Median Payback

Period

years Average

Installed

Price Average Operating

Cost Average

LCC Average Savings

2011$ % Households Having

Net

Cost No Impact* Net

Benefit

4.00 234 3 5 26 9 NA 0 100 0 NA

2.00 234 1 7 25 2 8 0 54 46 0.2

1.50 235 1 3 24 8 1 1 0 19 81 0.3

1.00 23 9 9 24 8 1 1 13 0 87 3.5

0.02 244 0 244 1 5 5 0 95 3.5

* “No impact” means that t he base

- case product assigned to the household has an efficiency greater than the level

indicated, so the household is not affected.

Table 11.4.4 Built

- In and Over

- the

- Range Convection Microwave Ovens: LCC and PBP

Results for Low

- Income Households

Standby

Power

W Life

- Cycle Cost 2011$ Life

- Cycle Cost Savings Median Payback

Period

years Average

Installed

Price Average Operating

Cost Average

LCC Average Savings

2011$ % Households Having

Net

Cost No Impact* Net

Benefit

4.50 50 6 3 9 54 5 NA 0 100 0 NA

3.70 506 3 2 53 8 7 0 0 100 0.7

2.70 50 6 2 3 5 30 1 5 0 0 100 1.2

2.20 514 1 9 53 3 12 0 0 100 3. 4

0.04 515 0 51 6 2 9 0 0 100 2. 0

* “No impact” means that the base

- case product assigned to the household has an efficiency greater than the level

indicated, so the househol d is not affected.11

- 6 R EFERENCE

1 U.S. Department of Energy

– Energy Information Administration (EIA). Residential Energy Consumption Survey: 2009 RECS Survey Data

— Public Use Microdata File. October 2011. EIA: Washington, D.C. www.eia.gov/consumption/residential/data/2009/#microdata . (Accessed May 2013.)12

- i CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION

................................ ................................ ................................ ......... 12

- 1

12.2 METHODOLOGY

................................ ................................ ................................ ........ 12

- 1

12.2.1 Phase I: Industry Profile

................................ ................................ ..................... 12

- 2

12.2.2 Phase II: Analysis Framework and Interview Guides

................................ ........ 12

- 2 12.2.2.1 Analysis Framework

................................ ................................ ............ 12

- 2

12.2.2.2 Interview Guide

................................ ................................ ................... 12

- 3

12.2.3 Phase III: Manufacturer Interviews, Revised Industry Cash Flow Analysis, and Additional Analyses

................................ ................................ ................................ ....... 12

- 3

12.2.3.1 Manufacturer Interviews

................................ ................................ ...... 12

- 3 12.2.3.2 Revised Industry Cash Flow Analysis

................................ ................. 12

- 4 12.2.3.3 Manufacturer Subgroup Analysis

................................ ........................ 12

- 4 12.2.3.4 Small Business Manufacturer Subgroup

................................ .............. 12

- 5 12.2.3.5 Manufacturing Capacity Impact

................................ .......................... 12

- 5 12.2.3.6 Employment Impact

................................ ................................ ............. 12

- 5 12.2.3.7 Cumulative Regulatory Burden

................................ ........................... 12

- 6

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

................................ .......... 12

- 6

12.3.1 Consumer Utility

................................ ................................ ................................ 12

- 6

12.3.2 Profitability

................................ ................................ ................................ ........ 12

- 6

12.4 GRIM INPUTS AND ASSU MPTIONS

................................ ................................ ........ 12

- 7

12.4.1 Overview of the GRIM

................................ ................................ ...................... 12

- 7

12.4.2 Sources of GRIM Inputs

................................ ................................ .................... 12

- 7 12.4.2.1 Corporate Annual Reports

................................ ................................ ... 1 2

- 8 12.4.2.2 Standard and Poor Credit Ratings

................................ ........................ 12

- 8 12.4.2.3 Dun and Bradstreet Reports

................................ ................................ . 12

- 8 12.4.2.4 Shipments Model

................................ ................................ ................. 12

- 8

12.4.2 .5 Engineering Analysis

................................ ................................ ........... 12

- 9

12.4.2.6 Manufacturer Interviews

................................ ................................ ...... 12

- 9

12.4.3 Financial Parameters

................................ ................................ .......................... 12

- 9

12.4.4 Corporate Discount Rate

................................ ................................ .................. 12

- 10

12.4.5 Trial Standard Levels

................................ ................................ ....................... 12

- 12

12.4.6 NIA

- Shipments Forecast

................................ ................................ .................. 12

- 13

12.4.6.1 Base

- Case Shipments Forecast

................................ .......................... 12

- 13

12.4.6.2 Standards

- Case Shipments Forecast

................................ .................. 12

- 14 12.4.6.3 Price Elasticity of Demand Shipments Scenario

............................... 12

- 15 12.4.6.4 Economic Growth Shipments Scenario

................................ ............. 12

- 15

12.4.7 Production Costs

................................ ................................ .............................. 12

- 16

12.4.8 Conversion Costs

................................ ................................ ............................. 12

- 17

12.4.8 .1 Capital Conversion Costs

................................ ................................ ... 12

- 17

12.4.8.2 Product Conversion Costs

................................ ................................ .. 12

- 18

12.4.9 Markups

................................ ................................ ................................ ........... 12

- 20 12.4.9.1 Preservation of Gross Margin Percentage

................................ .......... 12

- 2012

- ii 12.4.9.2 Preservation of Gross Margin in Absolute Dollars

............................ 12

- 20

12.5 MICROWAVE OVEN INDUS TRY FINANCIAL IMPACT S

................................ .. 12

- 21

12.5.1 Impacts on Industry Net Present Value

................................ ........................... 12

- 22

12.5.2 Impacts on Annual Cash Flow

................................ ................................ ......... 12

- 24

12.6 OTHER IMPACTS

................................ ................................ ................................ ...... 12

- 28

12.6.1 Employment

................................ ................................ ................................ ..... 12

- 28 12.6.1.1 Methodology

................................ ................................ ...................... 12

- 28

12.6.2 Production Capacity

................................ ................................ ......................... 12

- 30

12 .6.3 Cumulative Regulatory Burden

................................ ................................ ....... 12

- 31 12.6.3.1 Standby Power Requirements

................................ ............................ 12

- 31 12.6.3.2 Additional Federal Energy Conservation Standards

.......................... 12

- 32

12.6.3.3 Restriction of Hazardous Substance Directive

................................ .. 12

- 35 12.6.3.4 International Energy Conservation Standards

................................ ... 12

- 36

12.7 CONCLUSIONS

................................ ................................ ................................ .......... 12

- 37

LIST OF TABLES

Table 12.4.1 GRIM Financial Parameters Based on 2005

– 2011 Weighted Company Financial

Data

................................ ................................ ................................ ..................... 12

- 10 Table 12.4.2 Cost of Equity Calculation

................................ ................................ .................. 12

- 11

Table 12.4.3 Cost of Debt Calculation

................................ ................................ ..................... 12

- 12 Table 12.4.4 Product Class 1 Standby Power Efficiency Levels and TSLs

............................ 12

- 12 Table 12.4.5 Product Class 2 Standby Power Efficiency Levels and TSLs

............................ 12

- 13

Table 12.4.6 Tot al NIA

- Shipments Forecast in 2016

................................ .............................. 12

- 13

Table 12.4.7 Base

- Case Standby Power Distribution of Efficiencies for Product Class 1 in 2016

................................ ................................ ................................ ............................. 12

- 13

Table 12.4.8 Base

- Case Standby Power Distribution of Efficiencies for Product Class 2 in 2016

................................ ................................ ................................ ............................. 12

- 14 Table 12.4.9 Distribution of Standby Power Product Class 1 S hipments in the Standards Case in

2016

– Rollup Scenario

................................ ................................ ....................... 12

- 14 Table 12.4.10 Distribution of Standby Power Product Class 2 Shipments in the Standards Case in

2016

– Rollup Scenario

................................ ................................ ....................... 12

- 15

Table 12.4.11 Base

- Case Standby Power Production Costs (2011$) used in the GRIM for

Product Class 1

................................ ................................ ................................ ... 12

- 16

Table 12.4.12 Base

- Case Standby Power Production Costs (2011$) used in the GRIM for

Product Class 2

................................ ................................ ................................ ... 12

- 16 Table 12.4.13 Standby Power Capital Conversion Costs for Product Class 1 by TSL

........... 12

- 18 Table 12.4.14 Standby Power Capital Conversion Costs for Product Class 2 by TSL

........... 12

- 18 Table 12.4.15 Per Platform Assumptions for Cap ital Conversion Costs (Product Class 1 and Product Class 2)

................................ ................................ ................................ .. 12

- 18 Table 12.4.16 Standby Power Product Conversion Costs for Product C lass 1 by TSL

.......... 12

- 19 Table 12.4.17 Standby Power Product Conversion Costs for Product Class 2 by TSL

.......... 12

- 19

Table 12.4.18 Per

- Platform Assumptions for Product Conversion Costs (Product Class 1)

... 12

- 1912

- iii Table 12.4.19 Per Platform Assumptions for Product Conversion Costs (Product Class 2)

... 12

- 2 0 Table 12.4.20 Product Class 1 Preservation of Gross Margin in Absolute Dollars Manufacturer Markups

................................ ................................ ................................ .............. 12

- 21

Table 12.4.21 Product Class 2 Preservation of Gross Margin in Absolute Dollars Manufacturer Markups

................................ ................................ ................................ .............. 12

- 21

Table 12.5.1 Product Class 1 Manufacturer Impact Analysis Under the Preservation of Gross Margin Percentage Markup Scenario

................................ ................................ .. 12

- 22

Table 12.5.2 Product Class 1 M anufacturer Impact Analysis Under the Preservation of Gross Margin in Absolute Dollars Markup Scenario

................................ .................... 12

- 22

Table 12.5.3 Product C lass 2 Manufacturer Impact Analysis Under the Preservation of Gross Margin Percentage Markup Scenario

................................ ................................ .. 12

- 23

Table 12.5.4 Product Class 2 Manufacturer Impact Analysis Under the Preservation of Gross Margin in Absolute Dollars Markup Scenario

................................ .................... 12

- 23

Table 12.5.5 M anufacturer Impact Analysis Under the Preservation of Gross Margin Percentage Markup Scenario

................................ ................................ ................................ . 12

- 23 Table 12.5.6 Manufacturer Impact Analysis Under the Preservation of Gross Margin in Absolute Dollars Markup Scenario for Product Class 1 and 2 Combined

......................... 12

- 24

Table 12.6.1 Industry Estimated Employment Impacts in 2016 for Product Classes 1 and 2

Combined

................................ ................................ ................................ ............ 12

- 29

Table 12.6.2 Other DOE and Federal Actions Affecting Microwave Oven Manufacturers

... 12

- 33

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow

................................ ............................ 12

- 7 Figure 12.5.1 Annual Net Cash Flows for Product Class 1 (Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34)

................................ .............. 12

- 24 Figure 12.5.2 Annual Net Cash Flows Product Class 1 (Preservation of Gross Margin in

Absolute Do llars Markup Scenario, RP Elasticity 0.34)

................................ .... 12

- 25 Figure 12.5.3 Annual Net Cash Flows Product Class 2 (Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34)

................................ ................................ . 12

- 25 Figure 12.5.4 Annual Net Cash Flows Product Class 2 (Preservation of Gross Mar gin in Absolute Dollars Markup Scenario. RP Elasticity 0.34)

................................ .... 12

- 26 Figure 12.5.5 Annual Net Cash Flows for Product Class 1 and Prod uct Class 2 Combined (Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34) 12

- 26 Figure 12.5.6 Annual Net Cash Flows for Product Class 1 and Product Class 2 Combined (Preservation of Gross Margin in Absolute Dollars Markup Scenario, RP Elasticity 0.34)

................................ ................................ ................................ .................... 12

- 27 Figure 12.6.1 Total Microwave Ovens Industry Domestic Employment by Year for Product

Classes 1 and 2 Combined

................................ ................................ .................. 12

- 3012

- 1 12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers and on the consumers of the products subject to such a standard.” (42 U.S.C. 6295(o)(2)(B)(i) )

The statute also calls for an assessment of the impact of any lessening of competition as determined in writing by the Attorney General. Id

. DOE conducted a manufacturer impact analysis (MIA) to estimate the financial impact of energy con servation standards for standby

mode and off mode power on manufacturers of microwave ovens and to assess the impact of such standards on employment and manufacturing capacity.

The MIA has both quantitative and qualitative aspects. The quantitative part of the MIA primarily relies on the Government Regulatory Impact Model (GRIM), a discounted cash

- flow

model that is adapted to reflect the industry affected by this rulemaking. The GRIM inputs include information on industry cost structure, shipments, and p ricing strategies. The GRIM’s key output is the industry net present value (INPV) , which is the sum of industry cash flows over the analysis period, discounted using the industry weighted average cost of capital . The model estimates the financial impact of energy conservation standards on each industry by comparing changes in INPV between a base case and the various trial standard levels (TSLs) in the standards case. The qualitative part of the MIA addresses product characteristics ; manufacturer

characteris tics ; market and product trends ; as well as the impact of standards on subgroups of

manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I , “Industry Profile,” consisted of characterizing the microwave oven indust ry by collecting data on manufacturer market share,

sales volumes

, pricing, employment

, and financial structure. In Phase II , “ Framework for Analysis and Interview Guides ,” DOE developed a framework GRIM for analysis of i ndustry

cash flow s . Using publicly available informa tion developed in Phase I, DOE adapted the GRIM’s generic structure to facilitate analysis of new standby power requirements for

microwave oven manufacturers. For the purposes of this analysis

, t wo product classes of

microwave oven s were identified:

**•** Produ ct Class 1: Microwave

- Only Ovens and Countertop Combination Microwave

Ovens

**•** Product Class 2: Built

- In and Over

- the

- Range Combination Microwave Ovens

.

In Phase III , “ Manufacturer Interviews and Revised Industry Cash Flow Analysis,” DOE

interviewed three microwave oven manufacturers with varying product focus and market shares

, providing a cros s

- section of t he industr y

. During these interviews, DOE discussed financial topics specific to each manufacturer and obtained each manufacturer’s view of the industr y as a whole. The interviews provided valuable information that DOE used to evaluate the impacts of

new standby mode and off mode standard s on manufacturers’ cash flows, investment12

- 2 requirements , and employment levels. The following subsections describe in detail the steps DOE took in developing the information on which it based the MIA

.

12.2.1 Phase I : Industry Profile

In Phase I of the MIA, DOE prepared a profile of the microwave oven industr y that built upon the market and technology assessment prepared for this rulemaking (see c hapter 3 of this

T echnical Support Document (TSD) )

. Before initiating the detailed impact studies, DOE

collect ed quantitative and qualitative financial information , as well as current and historical

market data

. This included estimated market share s , corporate operating ratios, wages, employment, and financial ratios for several home appliance manufacturers

. The industry profile included a top

- down cost analysis of microwave oven manufacturers that DOE used to derive cost and preliminary financial inputs for the GRIM ( e.g. , revenues; material; labor; overhead; depreciation; selling, general and administrative expenses (SG&A); and research and development (R&D) expenses).

DOE also used public information to further calibrate its initial characterization of the

microwave oven industr y , including Securities and Exchange Commission (SEC) 10

– K reports, Standard & Poor’s (S&P) credit ratings

, corporate annual reports

, and U.S. Cens us Bureau’s

2010 Annual Survey of Manufactures ( 2010 ASM )

. In addition, DOE relied on information from

its engineering analysis, life

- cycle cost analysis, and analysis of markups to determine product price s to characterize the industry

. DOE also interviewed manufacturers of microwave ovens

. These preliminary interviews were designed to solicit key concerns, insights into industry structure, and other relevant information to consider in subsequent analysis.

12.2.2 Phase II : Analysis Framework and In terview Guide s

In Phase II

, DOE developed a framework GRIM to analyze industry cash

- flow s and

prepare d written guid es to facilitate manufacturer interview s

. Phase II focused on the financial impacts of new standb y mode and off mode standards on the microwave oven industr y

.

12.2.2.1 Analysis Framework

T he GRIM uses several factors to determine a series of annual cash flows starting with the announcement of the standard and extending over a 30 year period following the effective

date of the standard. These fa ctors include annual e xpected revenues; costs of sales ; SG&A and R&D expenses; taxes; and capital expenditures

. New standards can affect manufacturer cash

flows in three distinct ways . They may : (1) require additional investment, (2) raise production costs, and (3) impact revenues through higher prices and possibly lower shipments. DOE developed a framework GRIM to analyze industry cash flows and assess the financial impacts of energy conservati on standards on manufacturers of microwave ovens. The GRIM’s primary output is the INPV, which is the sum of industry annual cash flows, discounted at the industry weighted average cost of capital. The impact of new standby mode and off mode standards is the

difference between the INPV in the base case and at each TSL represented in the standards cases.

Inputs to the GRIM include manufacturing costs, shipments forecasts, and price trends developed in other analyses as well as markups, conversion costs, an d industry financial12

- 3 parameters developed specifically for the MIA

. For preliminary GRIM inputs, DOE estimated industry financial parameters and manufacturer markups from publicly available financial

reports. DOE derived the manufacturing costs from the en gineering analysis (see c hapter 5 of this TSD) , shipments from the shipments analysis (see c hapter 9 of this TSD) , and price trends

from the Life

- Cycle Cost analysis ( c hapter 8 of this TSD)

. Later, DOE revised these inputs based on feedback received during manufacturer interviews.

12.2.2.2 Interview Guide

To develop other key GRIM inputs including product and capital conversion costs, and to gather additional information on the anticipated effects of energy conservation standards on revenues, direct employment, cap ital assets, and industry competitiveness

, DOE conduct ed

interviews with manufacturer s of microwave ovens. Prior to the interviews, DOE distribute d a standard interview guide which included questions about c urrent o rganizational c haracteristics,

i ndustry i nfrastructure, manufacturer c ash

- flow s , a s well as a nticipated impacts to competition,

domestic manufacturing employment , and manufacturing capacity

. The interview guide also served to help identify sub

- groups of manufacturers a nd key issues faced by the i ndustry as a result of new energy conservation standards. The i nterview guides used to conduct the

manufacturer interviews are found in appendix 1 2 A of this TSD.

12.2.3 Phase III : Manufacturer Interviews

, Revised Industry Cash Flow Analysis, and

Additional Analyses

In Phase III, DOE interviewed manufacturers representing approximately twenty percent
of microwave oven sales

. Many of these same companies also participated in interviews for the engineering analysis

. However, the MIA interviews broadened the dis cussion from primarily technology

- related issues to include busines s

- related topics. The se interviews served to gather feedback from industry on the approaches used in the GRIM

, to isolate key issues and concerns

, and to inform additional analyses pertaini ng to manufacturer impacts

. DOE revised the industry cash flow analysis based on information gained through these interviews. The following sections

summarize the methodology and findings of this assessment.

12.2.3.1 Manufacturer Interviews

The information gathere d in Phase I and Phase II is supplemented with insights ga ined through interviews with manufacturers during Phase III

. The interview process play s a key role in the manufacturer impact analys i s, since it provides an opportunity for interested parties to12

- 4 express their views privately on important issues, allowing confidential or sensitive information to be considered in the rulemaking process

.

DOE prepared two d iffe rent interview guides

— one for microwave oven manufacturers dealing with energy factor standa rds and one for microwave oven manufacturers regarding standby power requirements

. a

12.2.3.2 Revised Industry Cash Flow Analysis DOE used information gained through t hese interview s to develop additional inputs for the GRIM

. DOE contacted companies from its database of manufacturers,

and conduct ed detailed interviews with all manufacturers that agreed to participate. In the end,

DOE interviewed three microwave oven manufacturers with varying product focus and market

shares. Interviews were scheduled well in advance in order to provide every opportu nity for key

individuals to be available for comment. Although a written respo nse to the questionnaire was acceptable, DOE preferred an interactive in terview process because it helped clarify responses and provide the opportunity to identify additional iss ues.

During interviews

, DOE provide d manufacturers with preliminary industry financial parameters and requested comment and suggestions regarding the proposed values

. Information gained during manufacturer interviews also served as the basis for DOE’s estimates of both product and capital conversion costs. Upon completion of the interviews, DO E revise d its industry cash flow model s based on the feedback provided. More i nformation on how DOE calculated the parameters for the GRIM is found in section 12.4.3 of this chapter

.

12.2.3.3 Manufacturer Sub g roup Analysis

Certain s ubgroups of manufacturers must be analyzed apart from the rest of the industry

. M anufacturers exhibiting a cost structure that differs significantly from the industry average could be more negatively affected if average cost assumptions are used to develop an industry

cash flow estimate

. Ideally, DOE would consider the impact on every manufacturer individually; however, it typically uses the results of the industry characterization to group manufacturers exhibiting similar characteristics. During the interview process, DOE discuss ed the potential

sub g roups and sub g roup members that have been identified for the analysis. DOE look ed to the

manufacturers and other interested parties to suggest what su bg roups or characteristics are the

most appr opriate for the analysis. F or this rulemaking, DOE did not identify any subgroups

which required a separate analysis of the impacts on INP V.

a DOE published its final rule setting energy factor microwave oven standards on April 8, 2009.

Please see : www.regulations.gov/#!documentDetail;D=EERE

- 2006

- STD

- 0127

- 0004

.12

- 5 12.2.3.4 Small Business Manufacturer Subgroup

DOE used the small business size standards published on November 5, 2010

, as amended, by the Small Business Administration (SBA) to determine whether any small entities would be required to comply with the rule (c odified at 13 CFR P art 121 )

. The size standards are listed by North American Industry Classification System (NAICS) code and industry description.

DOE used the SBA limits for NAICS code 335221 ( Household Cooking Appliance Manufacturing ) to determine if any small business manufacturers would be affected by this rulemaking. T he SBA bases its small business definition on t he total number of employees for a business, its subsidiaries, and its parent companies. Whenever an aggregated business entity has less than the listed number of employees, it is considered a small business. For NAICS code 335221, the SBA size limit is a total of 750 employees.

DOE surveyed the Association of Home Appliance Manufacturers ( AHAM ) member’s directory to identify manufacturers of microwave oven products

. DOE also asked stakeholders

and AHAM representatives within the industry if they were awar e of any small business

manufacturers. DOE then consulted publicly available data , purchased company reports from

vendors such as D un and B radstreet

, and contacted manufacturers, as needed, to determine if they meet the SBA’s definition of a small business manufacturing facility and have their manufacturing facilities located within the United States

. Based on this analysis, DOE did not identify any small business manufacturers of microwave ovens.

12.2.3.5 Manufacturing Capacity Impact

One of the significant outco mes of energy conservation standard s could be the consequential obsolescence of existing manufacturing assets, including tooling and investment.

The manufa cturer interview guide has a series of questions to help identify impacts on manufacturing capacity, specifically capacity utilization and plant location decisions in the

U nited S tates and North America with and without a standar d; the ability of manufacturers to upgrade or remodel existing facilities to accommodate the new requirements due to energy

cons ervation stand ards ; the nature and value of stranded assets, if any; and estimates for any one

time restructuring and other charges, where applicable.

12.2.3.6 Employment Impact

The impact o f energy conservation standards on employment is an important

consideratio n in the rulemaking process. T o assess how domestic employment patterns might be affected, the interview s explore d current employment trends in the microwave oven manufacturing industr y

. In addition, the interview s solicit ed manufacture r views on changes in employment patterns that may result from more stringent standard levels. The employment impacts sect ion of the interview guide focus ed on current employment levels associated with manufacturers at each of their production facilities, e xpected future employment levels with and

without energy conservation standard s for standby mode and off mode , a nd differences in workforce skills and issues related to the retraining of employees.12

- 6 12.2.3.7 Cumulative Regulatory Burden

DOE recognizes and seeks to mitigate the overlapping effects on manufacturers of energy conservation standards and other Federal regulatory actions affectin g the same products

. DOE analyze d and consider ed the impact on ma nufacturers of multiple, product

- specific regulatory

actions. Based on its own research and discussions with manufacturers, DOE identified se veral regulations relevant to microwave oven manufacturers including Federal energy conservation

standards for other products ma nufactured by these manufacturers and other envir onmental regulations

.

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA starts by asking : “What are the key issues for your company regarding this energy conservation standard s rulemaking?” This question opens up dialogue with manufacturers, enabling them to identify the issues that they feel DOE should explore and

discuss further during the interview. The following section describes key issues mentioned for all product classes under review. Manufacturers indicated that, for the most part, the risks associated

with these issues increase with more stringent TSL s

.

12.3.1 Consumer Utility

All manufacturers of microwave oven s expressed concerns that a new standby power requirement could reduce consumer utility. For example, one manufacturer expressed the position that because all cooking sensors require standby power , depending on the level chosen by DOE, a new standby power requirement could lead to the elimination of cooking sensor s from its products. However, m ultiple manufacturers stated that they already use or are in the process of adopting cooking sensors that require no standby power

. A nother potential impact on consumer utility related to cooking sensors mentioned by manufacturers was wait ing for sensor s to activate or a hard off switch causing microwave ovens to have a delayed start

.

On a related note, manufacturers stated that low standby power requirements could impact display and control choices

, which also impact consumer utility. M anufacturers indicated

that some other features that provide consumer utility ( i.e. , a clock or nightlight) could also be impacted by standby mode and off m ode standards since these features consume standby power.

12.3.2 Profitability

M anufacturers of microwave ovens stated that standby mode and off mode standards have the potential to hurt profitability. Manufacturers typically earn a premium on higher

- end

models

, while low

- end models are commodity products

. The premium on high

- end products would be squeezed if standards eliminated product differentiators ( such as larger, premium displays) or forced high

- end features to lower end models ( such as cooking sensors)

. Manufacturers indicated that standards have the potential to eliminate remaining U.S. production

due to already tight margins, especially if standards impact consumer utility.12

- 7 12.4 GRIM INPUTS AND ASSU MPTIONS

The GRIM serves as the main tool for assessing the impact on indus try due standby mode

and off mode energy conservatio n standards

. DOE relie s on several sources to obtain inputs for

the GRIM

. D ata and assumptions from these sources are then fed into an accounting model that

details the industry cash flow both with and without new energy conservation standards

.

12.4.1 Overview of the GRIM

The basic structu re of the GRIM, illustrated in Figure 12.4

. 1 , is a n annual cash flow analysis that uses manufacturing costs, shipments, and industry financial information as inputs, and accepts a set of regulatory conditions such as changes in costs, investments, and associated margins. The GRIM spreadsheet uses a number of inputs to arrive at a series of annual cash flows, beginning with the base year of the analysis, 2013

, and continuing explicitly through

204 5

. The model calculates the INPV by summing the stream of annual discounted cash flows during this period and adding a terminal value

. 1

Figure 12.4

. 1 Using the GRIM to Calculate Cash Flow

The GRIM project s cash flows using standard accounting principles and compare s changes in INPV between the base

- case scenario and each of the standard s

- case scenario s , evaluating changes induced by the energy conservat ion standards

. The difference in INPV

between the base case and the standard s case s represents the estimated financial impact of the

energy co nservation standard on manufacturers

. Appendix 1 2 B provides more technical detail s and user information for the GRIM.

12.4.2 Sources of GRIM Inputs

The GRIM use s several different sources for data inputs in determining the cash flows for the industry, including corporate annual reports, company profiles, Census data, credit ratings, the shipments model, the engineering analysis, and manufa cturer interviews.12

- 8 12.4.2.1 Corporate Annual Reports

Corporate annual reports to the SEC ( 10

- Ks) provided many of t he financial inputs to the

GRIM. 2 T hese reports exist for publicly held companies and are freely available to the general publi c. DOE developed average financial inputs to the GRIM by examining the annual SEC 10

- K reports filed by publicly

- traded manufacturers primarily engaged in appliance manufacturing and whose combined product range includes microwave ovens

. Because these companies are typica lly diversified, producing a ra nge of different appliances, an industry average was assumed by DOE to be representative for the manufacture of each type of appliance. In deter mining financial parameters for the industry

, DOE weighted corporate financial in f ormation contained in

the SEC 10

- Ks by each company’s estimated market share to arrive at industry

- weighted

averages

. DOE used corporate annual reports to derive the following GRIM inputs:

• Tax rate,

• Working capital,

• SG&A

,

• R&D expenses,

• Depreciation,

• Capital expenditures, and

• Net property, plant, and equipment (PPE)

.

DOE also used information from co mpany SEC 10

- K reports to calibrate the GRIM’s operating profit margin against the industry weighted average.

12.4.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. S&P reports are available for a nominal fee. DOE relied on S&P reports and similar sources to determine the industry’s average cost of debt for the cost of capital calcu lation.

12.4.2.3 Dun and Bradstreet Reports

D un and Bradstreet (D &B ) provides independent research regarding company cash flows, revenues, employees, and credit

- worthiness. Besides conducting manufacturer interviews, DOE used D&B reports to profile several manufac turers which had the potential to be small

businesses.

12.4.2.4 Shipment s Model

The GRIM used shipment projections derived from DOE’s shipments model i n the national impact analysis

. The model relied on historical shipment data for microwave oven s from

AHAM

. Chap ter 9 of th is TSD describes the methodology and analytical model DOE used to forecast shipments.12

- 9 12.4.2.5 Engineering Analysis

During the engineering analysis, DOE used data submitted from AHAM, manufacturer

interviews

, as well as product testing and teardowns to develop manufacturing cost estimates for

Product Class 1 ( Microwave

- Only Ovens and Countertop Combination Microwave Ovens ) and

Product Class 2 ( Built

- In and Over

- the

- Range Combination Microwave Ovens )

.

12.4.2.6 Manufacturer Interviews

During the course of the M IA , DOE conducted interviews with manufacturers of

microwave oven s

. Through these discussions, DOE obtained information

, which it used to determine and verify GRIM input assumptions for each industry

. Key topics discussed during the

interviews and reflecte d in the GRIM include:

**•** Capital c onversion costs (one

- time investments in property, plant, and equipment) ;

**•** Product conversion costs ( o ne

- time investments in research, product development, testing, and marketing) ;

**•** Stranded assets (w rite downs of un

- depreciated manufacturing capital rendered obsolete by the rulemaking ) ;

**•** Manufacturer production costs (as estimated in the engineering analysis );

**•** Product cost structure (t he allocation of manufacturer production costs related to materials, labor, overhead, and depreciation ; and

**•** Projected total shipment s and the distribution of shipment s across the various efficiency levels

.

12.4.3 Financial Parameters

Table 12.4 .1 provides financial parameters for six public companies engaged in manufacturing and selling microwave ovens

. The values listed are averages over a seven

- year period ( 2005

– 2011 ).12

- 10 Table 12.4

. 1 GRIM Financial Parameters Based on 2005

– 2011 Weighted Company

Financial Data

Parameter Industry

Weighted Average Manufacturer

A B C D E F

Tax Rate

(% of Taxable Income) 28.2 41.1 35.2 16.4 20.5 45.1 24.2

Working Capital

(% of Revenue) 6.9 9.8 11.7 13.7 1.9 7.0 3.4

SG&A

(% of Revenue) 15.4 18.6 8.1 19.4 17.5 10.8 7.5

R&D

(% of Revenues) 1.2 1.6 1.1 1.5 0.9 1.6 0.6

Depreciation

(% of Revenues) 4.7 3.4 2.2 7.8 5.1 3.6 3.1

Capital Expenditures

(% of Revenues) 4.8 4.8 1.6 12.2 2.8 4.2 2.2

Net Property, Plant, and Equipment

(% of Revenues) 27.3 20.4 23.8 35.6 24.4 34.2 17.0

These companies constitute approximately 77 percent of the microwave oven industr y

. For other companies in the industry, public data was not available and hence not used to calculate the parameters

. The values used in the GRIM

, therefore

, represent the industry’s weighted average estimate s using financial data from six publicly traded manufacturers. During manufacturer interviews, microwave ovens manufacturers were asked to review the parameters
listed in Table 12.4 .1 and comment on how accurately they reflect the microwave oven manufacturing industry

. Where applicable, t he parameters were then adjusted for the GRIM.

12.4.4 Corporate Discount Rate

DOE used the weighted

- average cost of capital (WACC) for the industry as the discount

rate to calculate the INPV. A company’s assets are financed by a combination of debt and equity.

T he WACC is the cost of debt and cost of equity weighted by their respective proportions in the capital structure of the industry. DOE estimated the WACC for the microwave oven industr y based on several representative companies, using the following formula:

WACC = (After

- Tax Cost of Debt x Debt Ratio) + (Cost of Equity x Equity Ratio)

The cost of equity is the rate of return that equity investors (including, potentially, the company) expect to earn on a company’s stock. These expectations are reflected in the market price of the company’s stock. The capital asset pricing model (CAPM) provides one widely used means to estimate the cost of equity. According to the CAPM, the cost of equity (expected return) is:

Cost of Equity = Risk Free Rate of Return + (β x Risk Premium)12

- 11 where:

Risk Free R ate of R eturn is the rate of return on a “safe” benchmark investment, typically

considered the short

- term Treasury bill (T

- Bill) yield.

Risk P remium is the difference between the expected return on stocks and the riskless rate.

Beta ( β ) is the correlation between the movement in the price of the stock and that of the broader

market. In this case, Beta equals one if the stock is perfectly correlated with the S&P 500 market

index. A Beta lower than one means the sto ck is less volatile than the market index.

DOE determined that the industry average cost of equity for the microwave oven industry is 13.6 percent as calculated in Table 12.4 .2

.

Table 12.4

. 2 Cost of Equity Calculation

Parameter Industry

Weighted Average Manufacturer

A B C D E F

(1) Average Beta 1. 39 0.89 1.26 * 2.63 * 1.03 1.32 1.67

(2) Yield on 10 Year T

Bill ( 192 8

– 2011) (%) 5.20

(3) Market Risk Premium (192 8

– 2011) (%) 6.01

Cost of Equity (2) + [(1)*(3)] (%) 13.55

Equity/Total Capital (%) 71.86 71.71 28.96 96.51 75.54 65.19 70.77

* E stimated Beta

Bond ratings are a tool to measure default risk and arrive at a cost of debt. Each bond rating is associated with a particular spread. One way of estimating a company’s cost of debt is to treat it as a spread (usually expressed in basis poin ts) over the risk

- free rate. DOE used this

method to calculate the cost of debt for all six manufacture s. DOE used bond ratings from S&P or a similar source to estimate the manufacturers’ cost of debt by adding the relevant spread to

the risk

- free rate

. In practice, investors use a variety of different maturity Treasury bonds to

estimate the risk

- free rate. DOE used a long

- term Treasury bond return (10

- year bond return) because it captures long

- term inflation expectations and is less volatile than short

- ter m rates. The

risk

- free rate is estimated to be 5.2 0 percent, which is the average 10

- year Treasury bond return over the period of 1927 to 2011.12

- 12 In order to calculate the c ost of debt for the industry

, DOE added the industry

- weighted average spread to the average T

- Bill yield over the same period. S ince proceeds from debt issuance are tax deductible, DOE adjusted the gross cost of debt by the industry average tax rate

to determine the net cost of debt for the industry. Table 12.4

. 3 presents the derivation of the cost

of debt. Also shown is the capital structure of the industry, i.e. the debt ratio (debt/total capital).

Table 12.4

. 3 Cost of Debt Calculation

Parameter Industry

Weighted Average Manufacture r

A B C D E F

S&P Bond Rating

-- A

- BBB A BBB BB+ BBB

(1) Yield on 10 year T

Bill (1927

– 2011) (%) 5.20%

(2) Gross Cost of Debt (%) 6.88% 6.30% 6.80% 6.20% 6.80% 8.20% 6.80%

(3) Tax Rate (%) 28.24% 41.06

% 35.22

% 16.40

% 20.54

% 45.10

% 24.22

% Net Cost of Debt (2)x(1

- (3)) (%) 4.94%

Debt/Total Capital (%) 28.14% 28.29

% 71.04

% 3.49% 24.46

% 34.81

% 29.23

%

DOE estimated the microwave oven industr y weighted average WACC to be approximately 11 .1 percent. S ubtracting an inflation rate of 3

. 1 percent between 1927 and 2011

, the inflation adjusted WACC, and the corporate discount rate used in the GRIM, is 8.0 percent.

12.4.5 Trial Standard Levels

DOE developed TSLs for microwave oven standby mode and off mode energy

conservation standards

. The efficiency levels used in the GRIM and other analyses are presented

in Table 12.4

. 4 and Table 12.4

. 5

.

Table 12.4

. 4 Product Class 1 Standby Power Efficiency Levels and TSLs

Product Class 1 Watts Description

Baseline 4.00 Baseline

TSL 1 2.00 FEMP Procurement Efficiency

Recommendation

TSL 2 1.50 Gap Fill

TSL 3 1.00 IEA 1

- Watt Program

TSL 4 0.02 Max Tech12

- 13 Table 12.4

. 5 Product Class 2 Standby Power Efficiency Levels and TSLs

Product Class 2 Watts Description

Baseline 4.50 Baseline

TSL 1 3.70 FEMP Procurement Efficiency

Recommendation

TSL 2 2.70 Gap Fill

TSL 3 2.20 IEA 1

- Watt Program

TSL 4 0.04 Max Tech

12.4.6 NIA

- Shipments Forecast

The GRIM estimates manufacturer revenues based on total

- unit

- shipment forecasts and

the distribution of these shipments by efficiency level

. Changes in the efficiency mix at each standard level are a key driver of manufacturer finances

. For this analysis, the GR IM used the national impact analysis (NIA ) shipments forecasts for microwave ovens from 2013 to 204 5

. Only the shipments in 2013 and beyond have an impact on INPV because 2013 is the base year

to which future cash flows are discounted

. Further explan ation of approaches and calculations of total shipments can be found in the shipment analysis section of this TSD ( chapter 9 ). Total shipments forecasted in the shipment analysis for all efficiency levels in 2016 , the year new standards for microwave oven standby and off mode power consumption would become

effective are shown in Table 12.4

. 6 and are further detailed below.

Table 12.4

. 6 Total N IA

- Shipments Forecast in 201 6

Product Class Total Industry Shipments *

Product Class 1 14

, 267

, 710

Product Class 2 144

, 118

* Estimates are rounded to the nearest whole number

12.4.6.1 Base

- Case Shipments Forecast

As part of the shipment analysis, DOE estimated the shipment distribution by efficiency level

for Product Class 1 and Product Class 2

. Table 12.4

. 7 and Table 12.4

. 8 show the base

- case distr ibutions of shipments by TSL estimated in the NIA for various product classes for 201 6

.

Table 12.4

. 7 Base

- Case Standby Power Distribution of Efficiencies for Product Class 1 in

2016

Efficiency Level Baselin

e EL 1 EL 2 EL 3 EL 4

Standby Power ( W) 4

. 0 0 2.0 0 1.5 0 1.0 0 0.02

% of Shipments 46.2 % 34.6 % 19 .2 % 0 .0% 0 .0%12

- 14 Table 12.4

. 8 Base

- Case Standby Power Distribution of Efficiencies for Product Class 2 in

201 6

Efficiency Level Baselin

e EL 1 EL 2 EL 3 EL 4

Standby Power (W) 4. 50 3.70 2

. 70 2

. 20 0.0 4

% of Shipments 100

. 0 % 0.0 % 0.0 % 0 .0% 0 .0%

12.4.6.2 Standards

- Case Shipments Forecast

To exam ine the e ffects of energy conservation standards on shipments, which affect the INPV, DOE used the base

- case shipments described in the previous section

. For the standards

case, DOE assumed shipments at lower efficiencies would roll up to the minimally compliant efficiency levels in response to more stringent energy conservation standard s

. This scenario assumes that demand for high efficiency equipment is a function of its price witho ut regards for

the standard level. Table 12.4

. 9 and Table 12.4

. 10 show the distributions of efficiencies for the various equipment classes in 201 6 un der the roll

- up scenario.

Table 12.4

. 9 Distribution of Standby Power Product Class 1 Shipmen ts in the Standards

Case in 20 16

– Rollup Scenario Efficiency

Level Baseline EL 1 EL 2 EL 3 EL 4

Standby Power (W) 4.0 0 2.0 0 1.5 0 1.0 0 0.02

Baseline 46.2 % 34.6 % 19.2 % 0 .0% 0 .0%

TSL 1 80.8% 19.2% 0 .0% 0 .0%

TSL 2 100 .0 % 0 .0% 0 .0%

TSL 3 100 .0 % 0 .0%

TSL 4 100 .0 %12

- 15 Table 12.4

. 10 Distribution of Standby Power Product Class 2 Shipmen ts in the Standards

Case in 201 6

– Rollup Scenario Efficiency

Level Baseline EL 1 EL 2 EL 3 EL 4

Standby Power (W) 4. 50 3.70 2

. 70 2

. 20 0.0 4

Baseline 100.0% 0 .0% 0 .0% 0 .0% 0 .0%

TSL 1 100.0% 0 .0% 0 .0% 0 .0%

TSL 2 100.0% 0 .0% 0 .0%

TSL 3 100.0% 0 .0%

TSL 4 100.0%

12.4.6.3 Price Elasticity of Demand Shipments Scenario

In the GRIM, DOE modeled a shipment scenario that considers the impacts of changes in

relative prices on consumer demand for each product. As described in c hapter 9 of this TSD , this shipment scenario reflects how the combined effects of increases in purchase price and decreases in operating costs due to energy conservation standards may affect shipments. DOE calculated the relative price elasticity of demand over time to determine how shipments would likely change following standard s

. U nder the “price elasticity scenario

, ” the effects from the increase in product purchase prices offset the effects from dec reased operating costs, resulting in a net decrease in shipments. For the INPV impacts described in this chapter, DOE uses the shipments scenario that includes the effects of price elasticity, though results using the shipments scenario

without the relativ e price elasticity can also be generated in the GRIM. See c hapte r 9 of this TSD

f or more details on the different shipment scenarios considered

.

12.4.6.4 Economic Growth Shipments Scenario

In the GRIM, DOE modeled three shipment scenarios that consider the impacts of changes in the overall U.S. economy. These shipment scenarios are based on the Energy

Information Administration’s ( EIA) 201 2 Annual Energy Outlook Report ( AEO201 2 ). 3

AEO201 2 provides three scenarios: the Reference case, the High Economic Growth case, and the

Low Economic Growth case. DOE used the Reference case for economic growth wh e n generating the results (impact on INPV ) that are described in this chapter

. However, the user still

has the option to examine other economic growth scenarios and their impact on INPV in the

GRIM. Further, i t is also possible for the user to combine the scenarios with and without price elasticity with each economic scenario in the GRIM

. See c hapter 9 of this TSD for more details

on the economic growth shipment scenarios.12

- 16 12.4.7 Production Costs

Changes in production costs impact revenues and gross profits. As shown in the engineering a nalysis ( c hapter 5 of this TSD ), products that are more efficient typicall y cost more to pr oduce than baseline products

. For the MIA, DOE used the manufacturing production costs (MPCs) derived in the engineering analysis, with appropriate production volume estimates

. For instance, more efficient products sold under existing ener gy conservation standards are manufactured at lower production volumes t han products at the baseline for standby mode power consumption

. E nact ing more stringent energy conservation standards will increase production

volumes for more efficien t units

.

As described in c hapter 5 of this TSD , the costs for Product Class 1 and Product Class 2

were calculated using data supplied by AHAM and product testing and teardowns

. For this final

rule , DOE updated the MPC s it estimated for the supplemental notice of pr oposed rulemaking ( S NOPR) published on February 1 4

, 20 12 ( 7 7 FR 8526 ) (hereinafter referred to as the February

20 12 S NOPR)

, to 2011 $ using the Producer Price Index ( PPI )

. For the GRIM, DOE estimated the proportion of costs associated with materials, labor, overhead, and depreciation using information provided by AHAM 4

, the U.S. Census Bureau ’s Current Industry Reports ( CIR ) 5

Table 12.4

. 11 and the 2010 ASM

. and Table 12.4

. 12 provide the MPC result s used in the GRIM

for microwave ovens

.

Table 12.4

. 11 Base

- Case Stan dby Power Production Costs ( 2011 $ ) used in the GRIM for

Product Class 1

TSL (W) Labor Material Overhead Depreciatio

n MP C

Baseline (4.00) $ 10.63 $ 101.28 $ 1.08 $ 7.36 $ 120.35

TSL 1 (2.00) $ 10.63 $ 101. 54 $ 1.08 $ 7.36 $ 120. 61

TSL 2 (1.50) $ 10.63 $ 101. 66 $ 1.08 $ 7.36 $ 120. 73

TSL 3 (1.00) $ 10.63 $ 10 4.56 $ 1.08 $ 7.36 $ 123. 63

TSL 4 (0.02) $ 10.63 $ 107. 5 1 $ 1.08 $ 7.36 $ 126.58

Table 12.4

. 12 Base

- Case Standby Power Production Costs ( 201 1 $ ) used in the GRIM for

Product Class 2

TSL (W) Labor Material Overhead Depreciation MPC

Baseline (4.50) $ 23.00 $ 21 9.15 $ 2.33 $ 15.9 3 $ 2 60.41

TSL 1 (3.70) $ 23.00 $ 219. 21 $ 2.33 $ 15.9 3 $ 260. 47
TSL 2 (2.70) $ 23.00 $ 219. 23 $ 2.33 $ 15.9 3 $ 26 0.49
TSL 3 (2.20) $ 23.00 $ 22 4.16 $ 2.33 $ 15.9 3 $ 265. 42
TSL 4 (0.04) $ 23.00 $ 225. 0 2 $ 2.33 $ 15.9 3 $ 266. 2712

- 17 12.4.8 Conversion Costs

E nergy conservation standards typically cause manufacturers to incur one

- time conversion costs to bring their p roduction facilities and product designs in to compliance with new regulation s

. For the purpose of the MIA, DOE classified these one

- time conversion costs into two major groups : (1) capital conversion costs

, and (2) product conversion costs

. C apital

conversion costs are one

- time investments in propert y, plant, and equipment to adapt or replace existing production facilities so that new product designs that meet the new standard can be

fabricated and assembled

. Product conversion costs are one

- time investments in research, development, testing, and mar keting focused on making product designs comply with new energy conservation standards. The following sections describe in greater detail the inputs DOE

used in the GRIM

.

12.4.8.1 Capital Conversion Costs

DOE evaluated the level of capital investment needed to comply with the energy conservation standards for standby power consumption at each TSL

. DOE estimate d the portion

of products on the market that will have to be redesigned to meet each TSL

. The portion of products on the market requiring redesign increases with each higher TSL

.

DOE used a platform approach to estimate the capital conversion costs

. For standby

mode and off mode standards, DOE used information submitted during manufacturer interv iews

, conversations with industry experts, and product catalogs to estimate the percentage of platforms at each TSL that would require minor tooling changes to accommodate more efficient

components , as well as the cost of the tooling

. DOE scaled the per pl atform conversion costs presented by individual manufacturers to account for all necessary conversion costs across the industry.

Because the design options for Product Class 1 and Product Class 2 are similar at each TSL, DOE used the same per

- platform c osts for both product classes. DOE also used the same per platform cost as in the February 2012 S NOPR, but converted each cost to 201 1 $ using the PPI. To segment the total capital conversion costs between Product Class 1 and Product Class 2, DOE used the same split as the shipments in the NIA.

Substituting c omponent s to reduce standby power do es not require major changes to

microwave en closure s or cavit ies because the se design options all have standard mounting

features and locations. Table 12.4

. 13 and Table 12.4

. 14 s ummarize the breakdown of capital

conversion costs by TSL for energy conservation standards for standby mode and off mode

. Table 12.4

. 15 shows the per

- platform capital cost assumptions DOE made for the tooling changes required for each platform that did not meet the require d efficiency.12

- 18 Table 12.4

. 13 Standby Power Capital Conversion Costs for Product Class 1 by TSL

TSL (W) Total Industry Capital Conversion Costs (Millions 2011$) Baseline (4.00) 0.0
TSL 1 (2.00) 3.9
TSL 2 (1.50) 4.3
TSL 3 (1.00) 4.7
TSL 4 (0.02) 7.8

Table 12.4

. 14 Standby Power Capital Conversion Costs for Product Class 2 by TSL

TSL (W) Total Industry Capital Conversion Costs (Millions 2011$) Baseline (4.50) 0.0
TSL 1 (3.70) 0.0*
TSL 2 (2.70) 0.0*

TSL 3 (2.20) 0.0*
TSL 4 (0.04) 0.1

* Conversion Costs are less than $50,000.

Table 12.4

. 15 Per Platform Assumptions for Capital Conversion Costs (Product Class 1 and Product Class 2)

TSL Per

- Platform Tooling Capital Cost Assumption ( 2011$)

TSL 1 $158,058

TSL 2 $173,864

TSL 3 $189,669

TSL 4 $316,116

12.4.8.2 Product Conversion Costs

DOE conducted interviews with manufacturers to better understand the many steps that manufacturers have to take before they can release new or improved products

. Multiple

manufacturers estimate d that the time and resources spent on research and development , testing,

and certification of products would be significant depending on the design path chosen to achieve higher efficiency. The magnitude of the required investments varies by manufacturer and

the TSL analyzed.

For Product Class 1 and Product Class 2

, DOE estimated the engineering, reliability testing, and product development expenses per platform necessary at each TSL. DOE used the

same per

- platform cost as in the February 2012 SNOPR, but converted these product and capital
conversion costs to 2011$ u sing the PPI . To segment the total product conversion costs between Product Class 1 and Product Class 2, DOE used the same split as the shipments in the NIA. DOE multiplied the estimate of these development costs per platform by its approximation of the

nu mber of platforms available in the United States that would require changes to meet the efficiency standards at each TSL

.12

- 19 For standby power, conversion costs become more expensive at higher TSLs as the complexity of further lowering standby power increas es, substantially driving up engineering and product testing time

. T here is a large jump in conversion expenses at TSL 4 because the design options included at this efficiency level impact other function s of every platform

. Table 12.4

. 16

and Table 12.4

. 17 summarize the breakdown of product conversion costs by TSL for PC 1 and

PC 2 for standby mode and off mode standards

. Table 12.4

. 18 and Table 12.4

. 19 sho w the per

platform product conversion cost assumptions DOE made for each platform that did not meet the required efficiency.

Table 12.4

. 16 Standby Power Product Conversion Costs for Product Class 1 by TSL

TSL (W) Total Industry Product Conversion Costs (Millions 2011$) Baseline (4.00) $0.0
TSL 1 (2.00) $16.7
TSL 2 (1.50) $30.0
TSL 3 (1.00) $38.0
TSL 4 (0.02) $73.4

Table 12.4

. 17 Standby Power Product Conversion Costs for Product Class 2 by TSL

TSL (W) Total Industry Product Conversion Costs (Millions 2011$) Baseline (4.50) $0.0
TSL 1 (3.70) $0.2
TSL 2 (2.70) $0.3
TSL 3 (2.20) $0.4
TSL 4 (0.04) $0.7

Table 12.4

. 18 Per

- Platform Assumptions for Product Conversion Costs (Product Class 1)

TSL (W) Per

- Platform Engineering

Cost Assumption (2011$) Per

- Platform Reliability Cost Assumption (2011$) Per

- Platform

Product Development

Expenses Assumption (2011$) Extraordinary Marketing Expenses per

Platform

(2011$)

TSL 1 (2.00) $52,686 $52,686 $26,343 $0 TSL 2 (1.50) $105,372 $105,372 $26,343 $0 TSL 3 (1.00) $158,058 $105,372 $26,343 $9,879 TSL 4 (0.02) $210,744 $210,744 $52,686 $105,37212

- 20 Table 12.4

. 19 Per Platform Assumptions for Product Conversion Costs (Product Class 2)

TSL (W) Per

- Platform Engineering

Cost Assumption (2011$) Per

- Platform Reliability Cost Assumption (2011$) Per

- Platform

P roduct Development

Expenses Assumption (2011$) Extraordinary Marketing Expenses per

Platform

(2011$)

TSL 1 (3.70) $52,686 $52,686 $26,343 $0 TSL 2 (2.70) $105,372 $105,372 $26,343 $0 TSL 3 (2.20) $158,058 $105,372 $26,343 $0 TSL 4 (0.04) $210,744 $210,744 $52,686 $105,372

12.4.9 Markups

T o understand how baseline and more efficient products are differentiated, DOE reviewed manufacturer catalogs and utilized information gathered from manufacturers

. To

estimate the price s at which manufacturers sell their products , DOE applied markups to the production costs it had developed earlier

. DOE used markup s cenarios to set the range of expected product prices following energy conservation standards. For each product class , DOE used the markup scenarios that best characterized the prevailing markups and described the range of market responses as a result of energy conservation standards for standby mode and off mode

. DOE analyzed two distinct markup scenarios : (1) p reservation of gross margin percentage

, and (2) preservation of gross margin in absolute dollars

.

12.4.9.1 Preservation of Gross Margin Percentage

The gross margin is defined as revenue less the cost of good s sold. Under the preservation of gross margin percentage scenario in the GRIM , DOE applied a single unif orm “gross margin percentage” markup to MPCs across all efficiency levels. As production cost s increase by efficiency level , this scenario implies that the absolute dollar markup will also

increase. DOE used a manufacturer markup of 1. 298

, which covers SG& A expenses

, R&D

expenses , interest

, and profit. This markup is consistent with the manufacturer markup DOE

assumed in the base case for the GRIM. Most m anufacturers stated it is optimistic to assume that they could maintain their gross margin percentages d espite production cost increase s due to new standby mode and off mode standard s

. Given this feedback, DOE assumes this scenario represents a high bound to industry profitability under a n energy conservation standard.

12.4.9.2 Preservation of Gross Margin in Absolut e Dollars

The implicit assumption behind the “ preservation of gross margin in absolute dollars” markup scenario is that in response to the standards the industry will lower its selling price of minimally compliant products to maintain a price competitive product offering. This lower MSP

relative to the MPC translates into a lower markup as the gross margin percentage will be lower. The industry would do so by passing through part of the increase in production costs while

increased R&D a nd SG&A reduce profit s

. DOE implemented this scenario in the GRIM by calibrating the markups for each TSL to yield the same gross margin in dollars in the year12

- 21 standard s become effective in both the base case and standards cases

. Table 12.4

. 20 and Table

12.4

. 21 show the manufacturer markups for Produ ct Class 1 and Product Class 2 under the Preservation of Gross Margin in Absolute Dollars markup scenario.

Table 12.4

. 20 Product Class 1 Preservation of Gross Margin in Absolute Dollars Manufacturer Markups

TSL (W) Baseline (4.00 W) EL 1 (2.00 W) EL 2 (1.50 W) EL 3 (1.00 W) EL 4 (0.02 W)

Baseline (4.00) 1.2980 1.2980 1.2980 1.2980 1.2980

TSL 1 (2.00) - 1. 297 7 1.2980 1.2980 1.2980

TSL 2 (1.50) - - 1. 297 6 1.2980 1.2980

TSL 3 (1.00) - - - 1. 29 26 1.2980

TSL 4 (0.02) - - - - 1.2878

Table 12.4

. 21 Product Class 2 Preservation of Gross Margin in Absolute Dollars Manufacturer Markups

TSL (W) Baseline (4.50 W) EL 1 (3.70 W) EL 2 (2.70 W) EL 3 (2.20 W) EL 4 (0.04 W)

Baseline (4.50) 1.298 0 1.298 0 1.298 0 1.298 0 1.298 0

TSL 1 (3.70) - 1. 29 80 1.298 0 1.298 0 1.298 0

TSL 2 (2.70) - - 1. 29 7 9 1.298 0 1.298 0

TSL 3 (2.20) - - - 1. 29 40 1.298 0

TSL 4 (0.04) - - - - 1. 293 3

12.5 MICROWAVE OVEN INDUSTRY FINANCIAL I MPACTS

Using the inputs and assumptions described in the previous sections , the GRIM modeled

the financial impacts of new standby and off mode power consumption standards on the

microwav e oven industry. The following sections detail additional inputs and assumpti ons for

microwave ovens . The main results of the MIA are also reported in this section. The MIA results consist of two key financial metrics: INPV and annual cash flows

.12

- 22 12.5.1 Impacts on Industry Net Present Value

The INPV measures the industry value and is used in the MIA to compare the economic impacts of different TSLs. For the microwave oven industry, t he GRIM estimated cash flows

between 2013 and 2045 , consistent with the forecast period used in the national impact analysis ( c hapter 10 of this TSD ).

In the MIA, DOE compares the INPV of the base case (no new standby mode and off mode standards) to that at each TSL. The difference between the base

- case INPV and a standards case INPV is an estimate of the economic impacts that implementing that particula r TSL would have on the entire industry. For the microwave oven industry, DOE examined the two markup scenarios described in the manufacturer markup section above. Table 12.5.1 through Table

12.5.6 provide the INPV estimates for the microwave oven industry under the different

scenarios.

Table 12.5

. 1 Product Class 1 Manufacturer Impact Analysis Under the Preservation of Gross Margin Percentage Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,356.8

- -
TSL 1 1,341.9

- 14.9

- 1.1

TSL 2 1,332.5

- 24.3

- 1.8

TSL 3 1,317.3

- 39.5

- 2.9

TSL 4 1,281.4

- 75.4

- 5.6
Parentheses indicate negative (

- ) values.

Table 12.5

. 2 Product Class 1 Manufacturer Impact Analysis Under the Preservation of Gross Margin in Absolute Dollars Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,356.8

- -
TSL 1 1,339.7

- 17.1

- 1.3

TSL 2 1,328.6

- 28.2

- 2.1

TSL 3 1,261.6

- 95.2

- 7 .0

TSL 4 1,174.0

- 182.8

- 13.5
Parentheses indicate negative (

- ) values.12

- 23 Table 12.5

. 3 Product Class 2 Manufacturer Impact Analysis Under the Preservation of Gross Margin Percentage Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 29.7

- -
TSL 1 29.5

- 0.1

- 0.5

TSL 2 29.4

- 0.2

- 0.8

TSL 3 29.2

- 0.5

- 1.5

TSL 4 28.9

- 0.8

- 2 .5
Parentheses indicate negative (

- ) values.

Table 12.5

. 4 Product Class 2 Manufacturer Impact Analysis Under the Preservation of Gross Margin in Absolute Dollars Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 29.7

- -
TSL 1 29.5

- 0.2

- 0.5

TSL 2 29.4

- 0.3

- 0.9

TSL 3 28.3

- 1.4

- 4.6

TSL 4 27.8

- 1.8

- 6.1
Parentheses indicate negative (

- ) values.

Table 12.5

. 5 Manufacturer Impact Analysis Under the Preservation of Gross Margin Percentage Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,386.5

- -
TSL 1 1,371.4

- 15.1

- 1.1

TSL 2 1,361.9

- 24.6

- 1.8

TSL 3 1,346.5

- 40 .0

- 2.9

TSL 4 1,310.3

- 76.1

- 5.5
Parentheses indicate negative (

- ) values.12

- 24 Table 12.5

. 6 Manufacturer Impact Analysis Under the Preservation of Gross Margin in

Absolute Dollars Markup Scenario for Product Class 1 and 2 Combined

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,386.5

- -
TSL 1 1,369.2

- 17.3

- 1.2

TSL 2 1,358.0

- 28.5

- 2.1

TSL 3 1,289.9

- 96.6

- 7 .0

TSL 4 1,201.9

- 184.6

- 13.3

Parentheses indicate negative (

- ) values.

12.5.2 Impacts on Annual Cash Flow

DOE reports the annual free cash flows from 20 1 3 through 2045 for each TSL. Figure

12.5

. 1 through Figure 12.5

. 6 present the annual net cash flows for the base case and each of the

four standb y mode and off mode TSLs for the microwave oven industry for a period of fifteen

years starting from the announcement of the standard

, assuming the different markup scenarios

.

Figure 12.5

. 1 Annual Net Cash Flows for Product Class 1 (Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34)12

- 25 Figure 12.5

. 2 Annual Net Cash Flows Product Class 1 (Preservation of Gross Margin in

Absolute Do llars Markup Scenario, RP Elasticity 0.34)

Figure 12.5

. 3 Annual Net Cash Flows Product Class 2 (Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34)12

- 26 Figure 12.5

. 4 Annual Net Cash Flows Product Class 2 (Preservation of Gross Margin in Absolute Dollars Markup Scenario. RP Elasticity 0.34)

Figure 12.5

. 5 Annual Net Cash Flows for Pro duct Class 1 and Product Class 2 Combined (Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34)12

- 27 Figure 12.5

. 6 Annual Net Cash Flows for Product Class 1 and Product Class 2 Combined (Preservation of Gross Margin in Absolute Dollars Markup Scenario, RP Elasticity 0.34)

Prior to the compliance date of the energy conservation standard, c ash flows are driven by the level of capital and product conversion costs and the portion of these investments spent

each year. After the standard announcement date

, industry free cash flows decline as companies use their financial resources to prepare for the new standard. T he more stringent the energy conservation standard, th e greater the negative impact on industry cash flows in the years prior to

the compliance date

. Since the capital and conversion costs are the same regardless of the markup scenario, the impacts in the years prior to the compliance date of the standards ar e the

same in both markup scenarios. Since DOE assumed that standards compliant microwave ovens would be based on existing products, all existing manufacturing capital is left in place and there

is no one

- time return of cash in the compliance year resultin g from a write

- down of stranded

assets

.

The markup scenario has a large impact on industry cash flows beginning in the compliance year of the standard. The preservation of gross margin percentage markup scenario restores the original (baseline) cash flow within approximately two years of the compliance date

. Higher average selling prices increase operating profit

, thereby increasing free cash flow, while

lower unit sales ha ve the opposite effect. For the preservation of gross margin in absolute dollar s markup scenario, cash flow s increase for all TSLs in 2016

, as they did in the previous scenario

. However, after 2016 , h igher TSLs have a lower cash flow relative to the base case because

manufacturers only recover a portion of their increased costs throu gh increase d selling prices. The magnitude of the negative cash flow impact under the preservation of gross margin in absolute dollars scenario depends on the incremental cost of standards

- compliant products. The higher the relative cost, the larger the im pacts on operating revenue and cash flow in the years following the compliance date of the standard.12

- 28 12.6 OTHER IMPACTS

12.6.1 Employment

12.6.1.1 Methodology

To quantitatively assess the impacts of energy conservation standards on microwave oven manufacturing employment, DOE used the GRIM to estimate the domestic labor expenditures and number of employees in the base case and at each TSL

. DOE used statistical data from the

Census Bureau’s 20 10 ASM and 2010 CIR

, the results of the engineering anal ysis, and interviews with manufacturers to estimate the inputs necessary to calculate industry

- wide labor expenditures

and domestic employment levels.

To calculate the annual domestic labor expenditures, DOE used the 2010 CIR to estimate the percentage o f domestic microwave oven sales that are produced in the United States

. DOE

estimated that 1.3 percent of microwave ovens that are sold in the United States are manufactured domestically as well

. DOE multiplied the total annual labor expenditures by the

ra te of domestic production to calculate domestic labor expenditures at each TSL

. The domestic annual labor expenditures at each TSL were converted to domestic production employment levels by dividing production labor expenditures by the annual payment per production worker (production worker hours times the labor rate found in the 20 10 ASM ) b a nd correcting for the

cost of benefits.

The impacts on domestic employment due to standards can be assessed by comparing the employment results in the base case to t he results at each TSL. In the GRIM, the employment results represent domestic production workers that are impacted by this rulemaking. W orkers involved in manufacturing or supporting products for exports would not be impacted and are not included as part of the labor impacts.

T he domestic industry labor expenditure for microwave oven production workers in 2016 is approximately $ 2.0 million. Using the $ 15

. 64 wage rate and 1,891 production hours per year per employee found in the 2010 ASM , the GRIM estimates there are approximately 6 8 production employees involved in manufacturing microwave ovens covered by this rulemaking

b 20 10 ASM labor rates and production hours per year per employee are very similar to figures reported in the engineering analysis. DOE used 20 10 ASM figures to ensure a consistent set of publicly available data is used for the manufacturing employment analysis.12

- 29 in the United States

. In addition , DOE estimates that 9 indirect employees in the United States support microwave oven production. The annual domestic employment impacts due to energy

conservation standards are shown in further detail on the employment analysis spreads heet of the

microwave oven GRIM.

Table 12.6 .1 illustrates the impact of new standby mode and off mode standards on employment levels at each TSL for the microwave oven industry calculated by the GRIM.

Table 12.6

. 1 Industry Estimated Employment Impacts in 2016 for Product Classes 1 and 2

Combined

Base

Case TSL 1 TSL 2 TSL 3 TSL 4

Total Number of Domestic

Microwave Oven Production Employees in 2016 6 8 6 8 6 8 6 8 6 7

Total Number of Domestic

Microwave Oven Non

- Production Employees in 2016 9 9 9 9 9

Total Number of Domestic Microwave Oven Employees in

2016 7 7 7 7 7 7 7 7 7 6

Change in Total Number of

Domestic Microwave Oven Production Employees in 2016 Due

to Standards - 0. 0 % 0. 0 % 0.0 %

- 1

. 3 %

Figure 12.6

. 1 shows annual domestic employment levels for each TSL calculated in the GRIM for standby mode and off mode standards.12

- 30 Figure 12.6

. 1 Total Microwave Ovens Industry Domestic Employment by Year for Product

Classes 1 and 2 Combined

Nearly 99 percent of microwave ovens sold in the United States are imported. T he GRIM finds that TSL1 and TSL2 maintain the same domestic employment as in the baseline in 2016

. However, the GRIM calculates a small decrease in domestic labor relative to the baseline for

TSL3 and TSL4

. Because per unit labor costs are assumed to be the same regardless of efficiency level, the total labor expenditure only decreases at higher TSLs because of the price elasticity of demand , which decreases overall shipments

. However, because so few products are manufactured in the United States, the overall impact on domestic manufacturing employment is

small.

Th is analysis of the impact to domestic production employment ignore s the possible relocation of domestic jobs to lower

- labor

- cost countries, which may occur independently of new standards or may be influenced by the level of investments required by new standar ds. Since most microwave ovens are already imported and the employment impacts in the GRIM are small, the actual impacts on domestic employment would be mostly impacted if any manufacturer decided to shift remaining domestic production to lower

- cost

- countr ies.

12.6.2 Production Capacity

Based on manufacturer feedback provided during interviews, energy conservation standards will not significantly affect production capacity. As with conventional cooking products, any necessary microwave oven redesigns involve comp onent switches that will not change the fundamental assembly of the equipment. However, manufacturers anticipate minor changes to tooling at all TSLs for more efficient standby power. For all efficiency levels, the most significant conversion costs are res earch and development, testing, and certification

, which12

- 31 do not affect production line capacity. Thus, DOE believes manufacturers will be able to maintain manufacturing capacity levels and continue to meet market demand through the implementation of standb y and off mode energy conservation standards for microwave ovens

.

12.6.3 Cumulative Regulatory Burden

While any one regulation may not impose a significant burden on manufacturers, the combined effects of several impending regulations may have serious consequence s for some manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a single regulation may overlook this cumulative regulatory burden. For the cumulative regulatory burden analysis, DOE describes other significant product

- spe cific regulations that could affect

microwave oven manufacture r s that will take effect 3 years before or 3 years after the compliance date of the amended energy conservation standards for these products c

.

Companies which produce a wide range of regulated products may be faced with more capital and product development expenditures than competitors with a narrower scope of products

. Regulatory burdens can prompt companies to exit the market or reduce their product offerings, potentially reducing competition. In addition to the energy conservation standards for microwave oven standby mode and off mode

, several other Federal regulations and pending regulations apply to these products and other equipment produced by the same manufacturers. DOE recogniz es that each regulation can significantly impact manufacturers’ financial operations. The following sections provide a qualitative discussion of some of these regulations

and standards.

12.6.3.1 Standby Power Requirements

Section 310 of the Energy Independence and S ecurity Act of 200 7 ( EISA 2007 ) amends

Section 325 of the Energy Policy and Conservation Act ( EPCA ) to require DOE to regulate standby mode and off mode energy consumption as part of an energy conservation standard for all covered products for which a fina l rule is adopted after July 10, 2010. For this rulemaking,

EISA 2007 specifies that the test procedure for residential ranges and ovens and microwave

ovens be amended to include measurement of standby mode and off mode energy consumption, taking into cons ideration the most current version of International Electrotechnical

c The compliance date for energy conservation standards for microwave oven standby mode and

off m ode is 3 years from the date of publication of the final rule.12

- 32 Commission’s (IEC) Standard 62301

, “ Household electrical appliances

– Measurement of standby power ” (IEC Standard 62301).

Besides today’s standard which sets a minimum standby mode and off mode requirement,

manufacturers are concerned that the EISA 2007 requirement that all covered products measure standby power and off mode will impose a heavy burden on their testing facilities going forward. In addition, because the test procedure cha nge will affect all covered products, manufacturers expressed a concern that this had the potential to create many overlapping regulatory compliance

costs in the future.

12.6.3.2 Additional Federal Energy Conservation Standards

Besides the energy conservation regulations on microwave ovens

, several other Federal regulations and pending regulations apply to other products and equipment produced by the same

manufacturers. Table 12.6

. 2 list s the Federal regulations that could also affect manufacturers of

the microwave oven industr y in the 3 years leading up to and the 3 years preceding the compliance date of the amended energy conservation standards for the se products . It must be noted that while the list of products is long, the amount of cumulative burden on any particular firm is extremely variable since the product scope of each company is different.12

- 33 Table 12.6

. 2 Other DOE and Federal Actions Affecting Microwave Oven Manufacturers

Regulation Approximate Compliance

Date* Number of Impacted Companies

from the

Market and Technology

Assessment (MTA) (See

C hapter 3) Estimated Total Industry Conversion

Costs

Commercial Clothes Washers 2013& 2018* 2 $20.4 million (2008$) d

Battery Chargers and External Power Supplies 2013* 5 N/A ††

Residential Dishwashers 2013 5 93.9 million (2010$) e

Residential Furnaces &

Residential Central Air Conditioners and Heat Pumps 2013 (Furnaces); 2015 (Central

AC & Heat

Pumps) 4 46.0 million (2009$) f

Room Air Conditioners 2014 4 $171 million (2009$) g

d Estimated industry conversion expenses relate to the 2013 Compliance Date and were

published in the TSD for the January 2010 commercial clothes washers final rule. 75 FR 1122

. The TSD for t he 2010 commercial clothes washers final rule can be found at : www.regulations.gov/#!documentDetail;D=EERE

- 2006

- STD

- 0127

- 0118

.
e The TSD for Residential Dishwashers Di rect Final Rule can be found at: www.regulations.gov/#!documentDetail;D=EERE

- 2011

- BT

- STD

- 0060

- 0007

. f Forty

- six million includes $43.2 million conversion costs for active mode standard (TSL 4) and $2.8 million conversion costs for standby and off modes standard (TSL 2). The TSD and GRIM for Residential Furnaces and Central Air Conditioners and Heat Pumps Direct Final Rule from

2011 can be found at: www.regulations.gov/#!documentDetail;D=EERE

- 2011

- BT

- STD

- 0011

- 0012 and www. regulations.gov/#!documentDetail;D=EERE

- 2011

- BT

- STD

- 0011

- 0010

. g The TSD for Residential Clothes Dryers and Room Air Conditioners Direct Final Rule from

2011 can be found at: www.regulations.gov/#!documentDetail;D=EERE

- 2007

- BT

- STD

- 0010

- 0053

.12

- 34 Residential Refrigerators and

Freezers 2014 8 $1,243 million (2009$) h

Regulation Approximate Compliance

Date* Number of Impacted Companies

from the

Market and Technology

Assessment (MTA) (See

C hapter 3) Estimated Total Industry Conversion

Costs

Fluorescent Lamp Ballasts 2014 1 $ 74 million (2010$) i

Residential Clothes Dryers 2015 4 $95 million (2009$) j

Residential Clothes Washers 2015 & 2018 4 $418.5 million (2010$) Commercial Refrigeration Equipment 2016* 1 N/A ††

Automatic Ice Makers 2017* 1 N/A ††

Residential Furnace Fans 2018* 4 N/A ††

Wine Chillers & Misc. Refrigeration 2018* 4 N/A ††

*The dates listed are an approximation. The exact dates are pending final DOE action. † Energy conservation standards and compliance dates for residential boilers can be found at 10 CFR 430.32(e)(2)(ii)

- (iv). †† For energy conservation standards for rulemakings awai ting DOE final action, DOE does not have a finalized estimated total industry conversion cost. ††† For minimum performance requirements prescribed by the Energy Independence and Security Act of 2007 (EISA 2007), DOE did not estimate total industry convers ion costs because an MIA was not completed as part of a rulemaking. Pub. L. 110

- 140. EISA 2007 made numerous

h The TSD for Refrigerator, Refrigerator

- Freezer and Freezers Final Rule can be found at: www.regulations.gov/#!documentDetail;D=EERE

- 2008

- BT

- STD

- 0012

- 0128

. i The TSD for Fluorescent Lamp Ballasts can be found at: www.regulations.gov/#!documentDetail ;D=EERE

- 2007

- BT

- STD

- 0016

- 0067

. j The TSD for Residential Clothes Dryers and Room Air Conditioners Direct Final Rule from

2011 can be found at: www.regulations.gov/#!documentDetail;D=EERE

- 2007

- BT

- STD

- 0010

- 0053

.12

- 35 amendments to t he Energy Policy and Conservation Act (EPCA) of 1975, Pub. L. 94

- 163, (42

U.S.C. 6291

– 6309) , which established an energy conservatio n program for major household appliances and industrial and commercial equipment.

Additional investments necessary to meet these potential standards could have significant impacts on manufacturers of the covered products. However, DOE has limited data on the importance of these other regulated products for manufacturers of microwave ovens. Differences in market shares and manufacturing processes of other regulated products for each manufacture r could cause varying degrees of burdens on these manufacture r s. Therefore

, DOE only estimated the cost of compliance to meet other energy conservation standards for regulated products if DOE had published a final rule

.

12.6.3.3 Restriction of Hazardous Substance Directive

According to manufacturers, the Restriction of Haza rdous Substance Directive k (RoHS) will have some global impact on manufacturing of electrical and electronic equipment. Under the directive, all manufacturers are banned from placing on the European Union (EU) market new electrical and electronic equipment containing more than agreed

- upon levels of lead, cadmium, mercury, hexavalent chromium, polybrominated biphenyl and polybrominated diphenyl ether

flame retardants. Although there is no Federal regulation on RoHS, California has passed SB 20:

Electronic Wa ste Recycling Act of 2003. Under this law, California limits the amount of

hazard ous substances included in the RoHS directive that can be sold in California.

In addition, s everal northeastern states have placed a ban on any product that contains

more th an 1 ounce of mercury. This primarily affects mercury

- based gas valves found in

commercial and residential conventional cooking products. Manufacturers of conventional cooking products have said that these bans and RoHS will have some impacts in the way the electronic components in the cooktops and ovens are incorporated into the design

. Currently, these manufacturers have electronic components in the cooktop and oven designs that contain substances banned by the RoHS an d mercury limit passed by northeastern states. However, manufacturers do not anticipate these restrictions will significantly impact the cooking products industry as a whole.

k Further information about RoHS can be found at : http://eur

- lex.europa.eu/LexUriServ/si te/en/oj/2003/l_037/l_03720030213en00190023.pdf

. (Accessed May 2013.)12

- 36 A significant worry to manufacturers is the uncertainty about the future of Ro HS

. One manufacturer stated that California’s resumption of the RoHS initiative demonstrates the ease with which any state could selectively choose to ban its products. O ne manufacturer stated that there are number of exemptions found in RoHS which must be periodically renewed by the EU.

M anufacturers use a number of these exempted substances in their products and are not sure if the exemptions will be renewed. Lastly, confirming RoHS

- compliance will place additional testing burdens on manufacturers.

Mo st manufacturers interviewed for this rulemaking are already compliant with the

RoHS directive. The most significant cumulative regulatory burden for gas cooking appliance

manufacturers is a state

- by

- state restriction on mercury l

12.6.3.4 International Energy Conservation Standard s , which affects the gas val ves used in their appliances. Most gas cooking appliance manufacturers have already eliminated mercury switches or already have plans in place to do so. However, all appliance manufacturers are concerned about potential restrictions of other hazardous subs tances in the future, such as fire protection materials, which could be costly to remove from existing products.

Canada does set energy conservation standards for cooking products. However, these

standards are l ess stringent than those of the United States. M anufacturers stated their concern that Canada will begin to regulate products at the provincial level. Unlike the United States,

Canada does not have F ederal preemption clauses for efficiency standards so province

- by

province standards c ould create a patchwork of standards and result in even great er testing costs.

DOE surveyed foreign regulatory and non

- regulatory programs, identifying Korea’s e

standby program, Australia’s standby program, and Japan’s To p Runner Program

. All of them aim to meet or exceed the goals of the International Energy Agency (IEA)’s One

- Watt program to lower standby power consumption below 1 watt ( W ) for microwave ovens

. m

l For example, the Interstate Mercury Education & Reduction Clearinghouse (IMERC) is a coalition of Northeast states coordinating the banning of products containing mercury . Please

see : Korea’s e

standby program currently has a voluntary labelin g program for microwave ovens with less than

www.newmoa.org/prevention/mercury/imerc.cfm

. (Accessed May 2013).

m IEA Energy Information Centre, Standby Power Use and the IEA “1

- Watt Plan” . Please see: http://greenshorenstein.info/pdf/Standby%20Power%20Fact%20Sheet%20

- %20IEA%20

- %20April%202007.pdf

. (Accessed May 2013.)1 2

- 37 1 W of standby power. The program is currently transitioning to a mandatory 1 W maximum standby limit by the year 2010. Australia has plans to implement a mandatory 1 W standard by the year 2012. In accordance w ith Japan’s Top Runner Program, Japanese appliance manufacturers made a voluntary declaration to reduce standby power consumption of microwave

ovens without a timer to as close to 0 watts as possible and that of microwave ovens with a timer

to 1 W or less

.

12.7 CONCLUSIONS

The following sections summarize the different impacts for the scenarios DOE believes are most likely to capture the range of impacts on microwave oven manufacturers as a result of energy conservation standards for standby mode and off mode

. D OE also notes that while these scenarios bound the range of most plausible impacts on manufacturers, potentially there could be

circumstances that lead manufacturers to experience impacts outside of this range.

To assess the lower end of the range of potential impacts for t he microwave oven industry, DOE considered the scenario reflecting the preservation of gross margin percentage. As production cost increases with efficiency, this scenario implies manufacture r s will be able to maintain gross margins as a percentage of revenues

. To assess the higher end of the range of potential impacts for the microwave oven industry, DOE considered the scenario reflecting preservation of gross margin in absolute dollars. Under this scenario, DOE assumed that the

indu stry can maintain its gross margins in absolute dollars after the standard compliance date. The industry would do so by passing through its increased costs to customers without increasing its gross margin in absolute dollars

.

TSL 1 represents an improvem ent in standby power from the baseline level of 4.0 W to 2.0 W for Product Class 1 and an improvement in standby power from the baseline level of 4.5 W to 3.7 W for Product Class 2. At TSL 1, the impact on INPV and cash flow varies depending

on the manufac turers’ ability to pass on increases in MPCs to their customers. DOE estimated the impacts in INPV at TSL 1 to range

- $ 15. 1 million to

- $ 17. 3 million, or a change in INPV of

- 1.1 percent to

- 1.2 percent. At this level, the industry cash flow decreases by a pproximately 6 .0 percent, to $ 99.7 million, compared to the base

- case value of $ 106.1 million in the year leading up to the standards.

TSL 2 represents an improvement in standby power from the baseline level of 4.0 W to

1.5 W for Product Class 1 and an im provement in standby power from the baseline level of 4.5 W to 2.7 W for Product Class 2. At TSL 2, the impact on INPV and cash flow would be similar to TSL 1 and depend on whether manufacturers can fully recover the increases in MPCs from

their customers. DOE estimated the impacts in INPV at TSL 2 to range from

- $ 24. 6 million to

- $ 28.5 million, or a change in INPV of

- 1.8 percent to

- 2. 1 percent. At this level, the industry cash flow decreases by approximately 9.7 percent, to $ 95.8 million, compared to the base

- case value
of $ 106.1 million in the year leading up to the standards.

TSL 3 represents an improvement in standby power from the baseline level of 4.0 W to 1.0 W for Product Class 1 and an improvement in standby power from the baseline level of 4.512

- 38 W to 2.2 W for Product Class 2. At TSL 3, the impact on INPV and cash flow continues to vary depending on the manufacturers and their ability to pass on increases in MPCs to their customers. DOE estimated the impacts in INPV at TSL 3 to range from approxima tely

- $ 40.0

million to

- $ 96.6 million, or a change in INPV of

- 2

. 9 percent to

- 7.0 percent. At this level, the industry cash flow decreases by approximately 12.0 percent, to $ 93.4 million, compared to the

base

- case value of $ 106.1 million in the year leadi ng up to the standards.

TSL 4 represents an improvement in standby power from the baseline level of 4.0 W to 0.02 W for Product Class 1 and an improvement in standby power from the baseline level of 4.5 W to 0.04 W for Product Class 2. At TSL 4, DOE estim ated the impacts in INPV to range from approximately

- $ 76. 1 million to

- $ 184.6 million, or a change in INPV of

- 5.5 percent to

- 1 3.3 percent. At this level, the industry cash flow decreases by approximately 22. 7 percent, to $ 82. 0 million, compared to the b ase

- case value of $ 106.1 million in the year leading up to the standards. At higher TSLs, manufacturers have a harder time fully passing on larger increases in MPCs to their customers. At TSL 4, the conversion costs are higher than the other TSLs because

t he design of all microwave platforms must be more significantly altered.

For new standby mode and off mode energy conservation standards, conversion costs increase at higher TSLs as the complexity of further lowering standby power increases, substantially driving up engineering time and also increasing the testing and product development time. If the increased production costs are fully passed on to consumers (the preservation of gross margin percentage scenario), the operating revenue from higher prices i s still not enough to overcome the negative impacts from the substantial conversion costs. The incremental costs are small for each TSL, meaning the positive impact on cash flows is small compared to the conversion costs required to achieve these efficienc ies. As a result of the small incremental costs and large conversion expenses, INPV is negative for all TSLs under the preservation of gross margin percentage scenario. If the incremental costs are not fully passed along to customers (the preservation of g ross margin in absolute dollars scenario), the negative impacts on INPV are amplified at each TSL.12

- 39 REFERENCES

1 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

2 Securities and Exchange Commission, Annual 10

- K Reports , Various Years, Washington D.C., www.sec.gov

.

3 U.S. Department of Energy

– Energy Information Administration. Annual Energy Outlook 2010 with Projections to 2035 . April 2010. U.S. DOE

– EIA: Washington, D.C. DOE/EIA

- 0383(2010).

4 Association of Home Appliance Manufacturers. AHAM 2005 Fact Book , 2005. Washington,

DC.

5 U.S. Census Bureau, July 2010,“Table 2

– Quantity and Value of Shipments of Refrigeration,

Air

- Conditioning, and Warm Air Heating Equipment: 2010 and 200 9 .” 20 06

– 20 10 Current Industrial Reports ( CIR ).13

- i CHAPTER 13 . EM PLOYMENT IMPACT ANALYSIS

TABLE OF CO NTENTS

13.1 INTRODUCTION

................................ ................................ ................................ ......... 13

- 1

13.2 ASSUMPTIONS

................................ ................................ ................................ ............ 13

- 1

13.3 METHODOLOGY

................................ ................................ ................................ ........ 13

- 1

13.4 SHORT

- TER M RESULTS

................................ ................................ ............................ 13

- 2

13.5 LONG

- TERM IMPACTS

................................ ................................ .............................. 13

- 3

LIST OF TABLES

Table 13.4.1 Net National Short

- term Change in Employment (number of jobs)

.................. 13

- 313

- 1 CHAPTER 13. EMPLOYMENT IMPACT AN ALYSIS

13.1 INTRODUCTION

DOE’s employment impact analysis is designed to estimate indirect national job creation or elimination resulting from possible standards, due to reallocation of the associated expenditures for purchasing and operating microwave ovens. Job increases or dec reases reported in this chapter are separate from the direct microwave sector employment impacts reported in Chapter 12, and reflect the employment impact of efficiency standards on all other sectors of the

economy. DOE examine d the combined employment im pacts for Microwave

- only Ovens and Countertop Combination Ovens (Product Class 1) and Over

- the

- range Combination Ovens (Product Class 2).

13.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption, and therefore to reduce energy expenditures. The savings in energy expenditures may be spent on new investment or not at all ( i.e. , they may remain “saved”). The standards may increase the purchase price of appliances, including the retail price plus sales tax, and increase installati on

costs.

Using an input/output econometric model of the U.S. economy, this analysis estimated

the short

- term effect of these expenditure impacts on net economic output and employment. DOE intends this analysis to quantify the indirect employment impa cts of these expenditure changes. It evaluated direct employment impacts at manufacturers’ facilities in the manufacturer impact analysis (see c hapter 1 2 ).

DOE notes that ImSET is not a general equilibrium forecasting model, and understands

the uncertainties involved in projecting employment impacts, especially changes in the later years of the analysis. 1 Because ImSET does not incorporate price changes, the employment effects predicted by ImSET would over

- estimate the magnitude of actual job imp acts over the long run for this rule. Since input/output models do not allow prices to bring markets into equilibrium, they are best used for short

- run analysis. DOE therefore include a qualitative

discussion of how labor markets are likely to respond in the longer term. In future rulemakings, DOE may consider the use of other modelin g approaches for examining long run employment impacts

.

13.3 METHODOLOGY

The Department based its analysis on an input/output model of the U.S. economy that

estimates the effects of standards on major sectors of the economy related to buildings and the net impact of standards on jobs. The Pacific Northwest National Laboratory developed the model, ImSE T 3.1. 1 2 ( Im pact of S ector E nergy T echnologies) as a successor to ImB uild 3 , a special

- purpose version of the IMPLAN 4 national input/output model. Im SET esti mates the emplo yment and income effects of building energy technologies. In comparison with simple13

- 2 economic multiplier approaches, Im SET allows for more complete and automated analysis of the economic impacts of energy

- efficiency investments in buildings.

In an input/o utput model, the level of employment in an economy is determined by the relationship of different sectors of the economy and the spending flows among them. Different sectors have different levels of labor intensity and so changes in the level of spending ( e.g. , due to the effects of an efficiency standard) in one sector of the economy will affect flows in other sectors, which affects the overall level of employment.

ImSET uses a 187

- sector model of the national economy to predict the economic effects

of r esidential and commercial buildings technologies. ImSET collects estimates of initial investments, energy savings, and economic activity associated with spending the savings resulting from standards ( e.g. , changes in final demand in personal consumption, b usiness investment and spending, and government spending). It provides overall estimates of the change in national output for each input

- output sector. The model applies estimates of employment and wage income per dollar of economic output for each sector and calculates impacts on national employment and wage income.

Energy

- efficiency technology primarily affects the U.S. economy along three spending pathways. First, general investment funds are diverted to sectors that manufacture, install, and

maintain energy

- efficient appliances. The increased cost of appliances leads to higher employment in the appliance manufacturing sectors and lower employment in other economic sectors. Second, commercial firm and residential spending are redirected from utilities t oward firms that supply production inputs. Third, electric utility sector investment funds are released for use in other sectors of the economy. When consumers use less energy, electric utilities experience relative reductions in demand which leads to redu ctions in utility sector investment and employment.

DOE also notes that the employment impacts estimated with ImSET for the entire economy differ from the employment impacts in the microwave manufacturing sector estimated in Chapter 12 using the Governme nt Regulatory Impact Model (GRIM). The methodologies used and the sectors analyzed in the ImSET and GRIM models are different.

13.4 SHORT

- TERM RESULTS

The results in this section refer to impacts of microwave oven standards relative to the

base case. DOE dis aggregated the impact of standards on employment into three component

effects : increased capital investment costs, decreased energy costs, and changes in operations and

maintenance costs

. DOE ant icipates no change in operations and maintenance costs for microwave ovens. DOE presents the summary impact.

Conceptually, one can consider the impact of the rule in its first year on three aggregate sectors, the microwave production sector, the energy generation sector, and the general consumer good sector (as mentioned above ImSET’s calculations are made at a much more disaggregate13

- 3 level) . By r aising energy efficiency, t he rule increas es the purchase price of microwaves , this increase in expenditures causes an increase in employment in this sector. At the same time, the improvements in energy efficiency reduce consumer expenditures on electricity. The reduction in electricity demand causes a reduction in employment in that sector. Finally, based on the net impact of increased expenditures on microwaves and reduced expenditures on electricity, consumer expenditures on everything else are either positively or negatively affected, increasing or reducing jobs in that sector accordingly. The model also captu res any indirect jobs created or lost by changes in consumption due to changes in employment (as more workers are hired they consume more goods, which generates more employment , the converse is true for workers laid

off ).

Table 13.4.1 presents the model ed net employment impact from the rule in 2015. As mentioned in Chapter 12, 1 % of microwaves are produced domestically and 9 9 % are imported. The net employment impact estimate is sensitive to assumptions regarding the return to the U.S. economy of money spent on imported microwaves. The two scenarios bounding the ranges presented in Table 13.4.1 represent situations in which none of the money spent on imported

microwaves returns to the U.S. economy and all of the money spent on imported microwaves

return s to the U.S. economy ( low and high bounds, respectively ). The U.S. trade deficit in recent years suggests that between 50% and 75% of the money spent on imported microwaves is likely to return, with employment impacts falling with in the ranges presented below.

Table 13.4.1 Net National Short

- term Change in Employment (number of jobs)

Trial Standard Level 201 6 2020

1 65 to 88 400 to 422

2 43 to 112 527 to 593

3

- 551 to 17 153 to 697

4

- 1426 to

- 102

- 339 to 935

For context, OMB currently project s that the office unemployment rate may decline to 6.9% in

2014 an d drop further to 5.3% in 2017. 5 The unemployment rate in 2017 is projected to be close to “full employment.” When an economy is at full employment any effects on net employment are likely to be tr ans itory as workers change jobs, rather t han enter or exit longer

- term employment.

13.5 LONG

- TERM IMPACTS

Due to the short payback period of energy efficiency improvements mandated by this

rule , over the long term DOE expect s the energy savings to consumers to increasingly dominate the increase in appliance costs, resulting in increased aggregate savings to consumers. As a result, DOE expect s demand for electricity to decline over time and demand for other goods to

increase. S ince the electricity generation sector is relatively capital intensive compared to the consumer good s sector, the net effect will be an increase in labor demand. In equilibrium, this13

- 4 should lead to upward pressure on wages and a shift in employment away from electricity generation towards consumer goods. Note that in long

- run equilibrium there is no net e ffect on total employment since wages adjust to bring the labor market into equilibrium. Nonetheless, even to the extent that markets are slow to adjust, DOE anticipate s that net labor market impacts will be negligible over time due to the small magnitude of the short

- term effects presented in

Table 13.4.1. The ImSET model projections, assuming no price or wage effects until 2020, are

included in the second column of Table 13.4.1.13

- 5 REFERENCES

1. Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Building Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment

. Energy Economics , 2008. 30(5): pp. 2283

- 2301

2

. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy

Te chnologies , 2005. Pacific Northwest National Laboratory. Richland, WA. Report No.

PNNL

- 15273.

3

. Scott, M. J., D. J. Hostick, and D. B. Belzer, ImBuild: Impact of Building Energy Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. R ichland, WA. Report No. PNNL

- 11884. Prepared for the U.S. Department of Energy under Contract

DE

- AC06

- 76RLO 1830.

4

. Minnesota IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide,

Data Guide , 1997. Stillwater, MN.

5. Office of Manageme nt and Budget, Fiscal Year 2012 Mid

- Session Review: Budget of the

U.S. Government

, Washington, DC. www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf . (Accessed May 2013)14

- i CHAPTER 14 . UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

14.1 INTRODUCTION

................................ ................................ ................................ ......... 14

- 1

14.2 METHODOLOGY

................................ ................................ ................................ ........ 14

- 1

14.3 RESULTS

................................ ................................ ................................ ...................... 14

- 2

LIST OF TABLES

Table 14.3.1 AEO 2012 Reference Case Forecast

................................ ................................ .. 14

- 3

Table 14.3.2 Residential Microwave Oven Standby P ower and Off Modes Trial

Standard Level 1 Forecast

................................ ................................ .................. 14

- 4

Table 14.3.3 Residential Microwave Oven Standby Power and Off Modes Trial

S tandard Level 2 Forecast

................................ ................................ .................. 14

- 4

Table 14.3.4 Residential Microwave Oven Standby Power and Off Modes Trial

Standard Level 3 Forecast

................................ ................................ .................. 14

- 5

Table 14.3.5 Residential Microwave Oven Standby Power and Off Modes Trial

Standard Level 4 Forecast

................................ ................................ .................. 14

- 5 Table 14.3.6 Reduction in Electric Generating Capacity in 2045 Under Microwave Oven Standby Power and Off Modes Trial Standard Levels

............................. 14

- 614

- 1 CHAPTER 14. UTILITY IMPACT ANALY SIS

14.1 INTRODUCTION

DOE analyzed the effects of its amended standard levels on the electric utility industry using a variant of the DOE/Energy Information Administration (EIA)’s National Energy Modeling System (NEMS). a NEMS is a public domain, multi

- sectored, partial equilibrium model of the U.S. energy

sector. E ach year, DOE/EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO) . The AEO for 201 2 (AEO2012 ) forecasts energy sup ply and demand through 2040

. 1 DOE used a variant of this model, referred to as NEMS

- BT, b

The utility impact analysis reports the changes in electric installed capacity and generation that result for each trial standard level (TSL) by plant type, as well as change s in res ident ial electricity consumption

. to account for the impacts of standby po wer energy conservation standards for microwave ovens . DOE’s utility impact analysis consists of a comparison between model results for the AEO2012 Reference Case and for cases in which standards are in place, and applies the same basic set of assumptions as the AEO201 2

. The AEO2012 reference case corresponds to medium economic growth.

NEMS

- BT has several advantages that have led to its adoption as the forecasting tool in the analysis of energy conservation standards. NEMS

- BT uses a set of assumptions that are well known and fairly transparent, due to the exposure and scrutiny each AEO r eceives. In addition, the comprehensiveness of NEMS

- BT permits the modeling of interactions among the various energy supply and demand sectors, producing a complete picture of the effects of energy conservation standards. Perhaps most importantly, NEMS

- BT can be used to estimate marginal effects, which yield a better estimate of the actual impact of energy conservation standards than considering only average effects.

14.2 METHODOLOGY

NEMS provides reference case load shapes for several end uses. The model uses predicted growth in demand for each end use to build up a projection of the total electric system load growth for each region, which it uses in turn to predict the necessary additions to capacity. DOE uses

NEMS

- BT to account for the implementation of energ y conservation standards by decrementing the appropriate reference case load shape. For microwave oven standby power the refrigeration end use

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration documentation. A useful summary is National Energy Modeling System: An Overvie w 2003 , DOE/EIA

- 0581(2003), March, 2003.

b DOE/EIA approves use of the name NEMS to describe only an official version of the model without any modification to code or data. Because this analysis entails some minor code modifications and the model is run under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the name NEMS

BT (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed). NEMS

- BT was previously called NEMS

- BRS.14

- 2 was decremented

. The refrigeration end use is the best representation of a baseline energy load

which is characteristic of the standby power in microwave ovens

. The energy conservation

decrement is also divided amongst the nine U.S. Census divisions based upon the share of energy end use consumption in each division, as given in NEMS.

DOE used the site energy savings developed in the national impact analysis (chapter 10) for each TSL as input to NEMS

- BT. The magnitude of the energy decrement that would be required for

NEMS

- BT to produce stable results out of the range of numerical noise is large r than the highest efficiency standard under consideration. Therefore, DOE estimated results corresponding to each TSL using interpolation. DOE ran higher energy use reduction levels in NEMS

- BT, representing multipliers of each TSL, and used these outputs to linearly interpolate the results to estimate actual changes in generation and capacity due to the standard.

Although the current time horizon of NEMS

- BT is 2040 , other parts of the energy

conservation standards analysis extend through the year 204 5

. It is not feasible to extend the forecast period of NEMS

- BT for the purposes of this analysis, nor does DOE/EIA have an approved method for extrapola tion of many outputs beyond 2040 . While it might seem reasonable to make simple linear extrapolations of re sults, in practice this is not advisable because outputs could be contradictory. An analysis of various trends sufficiently detailed to guarantee consistency is beyond the scope of this work, and, in any case, would involve a great deal of uncertainty. The refore , all extrapolations beyond 2040 are simple replications of year 2040 results. To emphasize the extrapolated results wherever they appear, they are shaded in gray to distinguish them from actual

NEMS

- BT results.

14.3 RESULTS

This utility impact analysis reports NE MS

- BT forecasts for residential sector electricity consumption

, total electricity generation by fuel type, and installed electricity generation capacity by fuel type. Results are pres ented in five

- year increments through 2040. Beyond 2040 , an ext rapol ation through 2045 for each TSL represents a simple replication of the 2040 results.

The results from the AEO2012 Refe rence Case are shown in Table 14 .3.1. The results for microwave oven standby power TSLs are presented in Tables 1 4 .3.2 through 1 4 .3. 5. Each table shows forecasts using interpolated results, as described in section 1 4 .2, for total U.S. electricity generation and installed capacity.

The considered TSLs reduce electricity consumption compared to the AEO2012 Reference

Case. The electr icity savings predicted by the NIA Model range from 0.0 71 to 0.216 percent of total residential electricity consumption in the year 2040

.14

- 3 Table 14.3

. 1 AEO 2012 Reference Case Forecast



### NEMS-BT Results: AEO2012 Reference Case

2010 2015 2020 2025 2030 2035 2040

Residential Sector Energy Consumption 1

Electricity Sales (TWh) 2 1,451 1,392 1,454 1,533 1,626 1,718 1,831
Natural Gas (EJ) 5.34 5.25 5.22 5.15 5.10 5.02 4.98
Other (EJ) 1.74 1.60 1.53 1.47 1.43 1.39 1.36
Natural Gas (Quads) 5.06 4.97 4.95 4.88 4.84 4.76 4.72

Other (Quads) 3 1.65 1.52 1.45 1.39 1.35 1.31 1.29

Total U.S. Electric Generation 4

Coal (TWh) 1,851 1,581 1,671 1,786 1,841 1,897 1,924
Gas (TWh) 982 1,129 1,112 1,138 1,267 1,394 1,473
Petroleum (TWh) 37 28 28 29 29 30 30
Nuclear (TWh) 807 830 887 917 914 887 964
Renewables (TWh) 429 562 614 662 702 760 842

Total (TWh) 5 4,106 4,131 4,312 4,533 4,754 4,968 5,233

Installed Generating Capacity 6

Coal (GW) 318 286 281 282 284 285 288

Other Fossil (GW) 7 461 467 475 495 525 568 598
Nuclear (GW) 101 104 111 115 114 111 121
Renewables (GW) 134 163 168 176 184 201 225
Total (GW) 8 1,013 1,019 1,036 1,068 1,107 1,165 1,232

1 Comparable to Table A2 of AEO2012: Energy Consumption, Residential

2 Comparable to Table A8 of AEO2012: Electricity Sales by Sector

3 Includes distillate fuel, kerosene, LPG, coal, and renewable energy

4 Comparable to Table A8 of AEO2012: Electric Generators and Cogenerators

5 Excludes "Other Gaseous Fuels" cogenerators and "Other" cogenerators

6 Comparable to Table A9 of AEO2012: Electric Generators and Cogenerators Capability

7 Includes "Other Gaseous Fuels" cogenerators

8 Excludes Pumped Storage and Fuel Cells14

- 4 Table 14.3

. 2 Residential Microwave Oven Standby Power and Off Modes Trial Standard Level

1 Forecast

Table 14.3

. 3 Residential Microwave Oven Standby Power and Off Modes Trial Standard Level

2 Forecast NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045
Residential Sector Energy Consumption Residential Sector Energy Consumption
Electricity Sales (TWh) 1,392 1,453 1,531 1,625 1,716 1,829 Electricity Sales (TWh) 0.00 -0.55 -1.08 -1.19 -1.24 -1.29 -1.30 -1.35

Total U.S. Electric Generation Total U.S. Electric Generation
Coal (TWh) 1,581 1,671 1,786 1,841 1,896 1,924 Coal (TWh) -0.05 -0.31 -0.44 -0.42 -0.47 -0.44 -0.44 -0.44
Gas (TWh) 1,129 1,111 1,137 1,266 1,393 1,472 Gas (TWh) -0.03 -0.19 -0.58 -0.66 -0.64 -0.59 -0.59 -0.59
Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.04 -0.05 -0.05 -0.08 -0.24 -0.24 -0.24
Renewables (TWh) 562 614 662 702 759 842 Renewables (TWh) -0.02 -0.08 -0.12 -0.15 -0.14 -0.18 -0.18 -0.18
Total (TWh) 4,131 4,311 4,532 4,752 4,966 5,231 Total (TWh) -0.09 -0.62 -1.19 -1.29 -1.33 -1.45 -1.45 -1.45

Installed Generating Capacity Installed Generating Capacity
Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.02 -0.04 -0.06 -0.06 -0.07 -0.07 -0.07 -0.07
Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) 0.00 -0.01 -0.05 -0.06 -0.10 -0.09 -0.09 -0.09
Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 0.00 -0.01 -0.01 -0.01 -0.03 -0.03 -0.03
Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.01 -0.01 -0.02 -0.03 -0.04 -0.05 -0.05 -0.05
Total (GW) 1,019 1,036 1,067 1,107 1,165 1,232 Total (GW) -0.03 -0.07 -0.14 -0.16 -0.21 -0.24 -0.24 -0.24 Extrapolation

NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045
Residential Sector Energy Consumption Residential Sector Energy Consumption
Electricity Sales (TWh) 1,392 1,453 1,531 1,624 1,716 1,829 Electricity Sales (TWh) 0.00 -0.80 -1.56 -1.72 -1.79 -1.87 -1.88 -1.95

Total U.S. Electric Generation Total U.S. Electric Generation
Coal (TWh) 1,581 1,671 1,786 1,841 1,896 1,923 Coal (TWh) -0.07 -0.45 -0.63 -0.61 -0.67 -0.64 -0.64 -0.64
Gas (TWh) 1,129 1,111 1,137 1,266 1,393 1,472 Gas (TWh) -0.04 -0.28 -0.83 -0.96 -0.92 -0.85 -0.85 -0.85
Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 0.00 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.05 -0.07 -0.08 -0.12 -0.35 -0.35 -0.35
Renewables (TWh) 562 614 662 702 759 842 Renewables (TWh) -0.02 -0.11 -0.17 -0.21 -0.20 -0.26 -0.26 -0.26
Total (TWh) 4,131 4,311 4,531 4,752 4,966 5,231 Total (TWh) -0.13 -0.90 -1.71 -1.87 -1.92 -2.10 -2.10 -2.10

Installed Generating Capacity Installed Generating Capacity
Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.03 -0.06 -0.09 -0.09 -0.09 -0.10 -0.10 -0.10
Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) -0.01 -0.02 -0.08 -0.09 -0.14 -0.13 -0.13 -0.13
Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 -0.01 -0.01 -0.01 -0.02 -0.04 -0.04 -0.04
Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.01 -0.02 -0.03 -0.04 -0.06 -0.08 -0.08 -0.08
Total (GW) 1,019 1,036 1,067 1,107 1,165 1,232 Total (GW) -0.04 -0.11 -0.21 -0.23 -0.31 -0.35 -0.35 -0.35 Extrapolation14

- 5 Table 14.3

. 4 Resid ential Microwave Oven Standby Power and Off Modes Trial Standard Level

3 Forecast

Table 14.3

. 5 Residential Microwave Oven Standby Power and Off Modes Trial Standard Level

4 Forecast NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045
Residential Sector Energy Consumption Residential Sector Energy Consumption
Electricity Sales (TWh) 1,392 1,453 1,530 1,624 1,715 1,828 Electricity Sales (TWh) 0.00 -1.10 -2.15 -2.37 -2.46 -2.57 -2.60 -2.69

Total U.S. Electric Generation Total U.S. Electric Generation
Coal (TWh) 1,581 1,671 1,785 1,840 1,896 1,923 Coal (TWh) -0.09 -0.62 -0.87 -0.84 -0.93 -0.88 -0.88 -0.88
Gas (TWh) 1,129 1,111 1,137 1,265 1,393 1,472 Gas (TWh) -0.06 -0.38 -1.15 -1.32 -1.27 -1.17 -1.17 -1.17
Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.07 -0.10 -0.11 -0.16 -0.48 -0.48 -0.48
Renewables (TWh) 562 614 662 702 759 842 Renewables (TWh) -0.03 -0.15 -0.23 -0.29 -0.28 -0.36 -0.36 -0.36
Total (TWh) 4,131 4,311 4,530 4,751 4,965 5,230 Total (TWh) -0.18 -1.23 -2.36 -2.57 -2.65 -2.90 -2.90 -2.90

Installed Generating Capacity Installed Generating Capacity
Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.04 -0.09 -0.12 -0.13 -0.13 -0.13 -0.13 -0.13
Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) -0.01 -0.02 -0.11 -0.12 -0.19 -0.18 -0.18 -0.18
Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 -0.01 -0.01 -0.01 -0.02 -0.06 -0.06 -0.06
Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.01 -0.03 -0.04 -0.05 -0.08 -0.11 -0.11 -0.11
Total (GW) 1,019 1,036 1,067 1,107 1,165 1,232 Total (GW) -0.06 -0.15 -0.28 -0.32 -0.43 -0.48 -0.48 -0.48 Extrapolation

NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045
Residential Sector Energy Consumption Residential Sector Energy Consumption
Electricity Sales (TWh) 1,392 1,452 1,529 1,622 1,714 1,827 Electricity Sales (TWh) 0.00 -1.69 -3.31 -3.64 -3.79 -3.95 -3.99 -4.13

Total U.S. Electric Generation Total U.S. Electric Generation
Coal (TWh) 1,581 1,671 1,785 1,840 1,895 1,923 Coal (TWh) -0.14 -0.96 -1.34 -1.30 -1.43 -1.36 -1.36 -1.36
Gas (TWh) 1,129 1,111 1,136 1,265 1,392 1,471 Gas (TWh) -0.08 -0.59 -1.77 -2.03 -1.96 -1.79 -1.79 -1.79
Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.11 -0.15 -0.17 -0.24 -0.74 -0.74 -0.74
Renewables (TWh) 562 613 662 702 759 842 Renewables (TWh) -0.05 -0.23 -0.36 -0.44 -0.43 -0.55 -0.55 -0.55
Total (TWh) 4,131 4,310 4,529 4,750 4,963 5,228 Total (TWh) -0.28 -1.90 -3.63 -3.96 -4.07 -4.45 -4.45 -4.45

Installed Generating Capacity Installed Generating Capacity
Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.07 -0.14 -0.18 -0.19 -0.20 -0.20 -0.20 -0.20
Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) -0.01 -0.04 -0.16 -0.18 -0.30 -0.27 -0.27 -0.27
Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 -0.01 -0.02 -0.02 -0.03 -0.10 -0.10 -0.10
Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.02 -0.04 -0.07 -0.08 -0.13 -0.17 -0.17 -0.17
Total (GW) 1,019 1,035 1,067 1,107 1,164 1,232 Total (GW) -0.10 -0.23 -0.43 -0.48 -0.66 -0.74 -0.74 -0.74 Extrapolation14

- 6 Table 14.3.6 presents the estimated reduction in electri city generating capacity in 204 5 for the TSLs that DOE considered in this rulemaking.

Table 14.3

. 6 Reduction in Elec tric Generating Capacity in 2045 Under Microwave Oven Standby Power and Off Modes Trial Standard Levels

TSL 1 TSL 2 TSL 3 TSL 4

MWO Standby Power Gigawatts

0. 24 1 0. 3 48 0. 48 0 0. 7 3714

- 7 REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2012 Reference Case Service Report , 2012 . Washington, DC. Report No. DOE/EIA

- 0383(2012 ). www.eia.gov/forecasts/aeo/ . (Accessed May 2013)15

- i CHAPTER 1 5 . EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION

................................ ................................ ................................ ......... 15

- 1

15.2 AIR QUALITY REGULATI ONS AND EMISSIONS IM PACTS

............................... 15

- 1

15.3 METHODOLOGY

................................ ................................ ................................ ........ 15

- 2

15.4 EMISSIONS IMPACT RES ULTS

................................ ................................ ................ 15

- 3

LIST OF TABLES

Table 15.4.1 Power Sector Emissions Forecast from AEO2012 Reference Case

................... 15

- 3

Table 15.4.2 Power Sector Emissions Impacts for Microwave Oven Standby Power

TSLs

................................ ................................ ................................ ................... 15

- 415

- 1 CHAPTER 15. EMISSIONS IMPACT ANALYSIS

15.1 INTRODUCTION

In the emissions analysis, DOE estimates the reduction in power sector emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 )

, and mercury (Hg) from potential energy conservation standards for the considered p roducts.

DOE conducted the emissions impact analysis using the NEMS

- BT model based on EIA’s

Annua l Energy Outlook 2012 (AEO 2012 )

. 1 EIA prepares the Annual Energy Outlook using the National Energy Modeling System (NEMS).

Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions. AEO 2012 generally represents current Federal and State legislation and final implementation regulations available as of the end of December 2011.

15.2 AIR QUALITY REGULATI ONS AND EMISSIONS IMPACT S

SO 2 emissions from affected electric generating units (EGUs) are subject to nationwide and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual emissions cap on SO 2 for affected EGUs in the 48 con tiguous states and the District of Columbia (D.C.). SO 2 emissions from 28 eastern states and D.C. were also limited under the Clean Air Interstate Rule (CAIR ) , which created an allowance

- based trading program that that operates along with the Title IV prog ram. 70 FR 25162 (May 12, 2005). CAIR was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. Circuit) but it remained in effect. On July 6, 2011 EPA issued a replacement for CAIR, the Cross

- State Air Pollution Rule (CSA PR). 76 FR 48208 (August 8, 2011). The AEO 2012 NEMS used for this analysis assumes the implementation of CSAPR. a

The attainment of emissions caps is typically flexible among affected Electric Generating Units (EGUs) and is enforced through the use of emi ssions allowances and tradable permits. Under existing EPA regulations, any excess SO 2 emissions allowances resulting from the lower electricity demand caused by the imposition of an efficiency standard could be used to permit offsetting increases in SO 2 e missions by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions

a On December 30, 2011, the D.C. Circuit stayed the new rules while a panel of judges reviews them, and told EPA

to continue enforcing CAIR

. S ee EME Homer City Generation, LP v. EPA

, Order, No. 11

- 1302 , Slip Op. at *2 (D.C. Cir. Dec. 30

, 2011). On August 21, 2012, the D.C. Circuit vacated CSAPR and related Federal Implementation Plans that would have superseded the State Implem entation Plans that EPA typically approves for compliance with Clean Air Act stationary source regulations. See EME Homer City Generation, LP v. EPA

, No.

11

- 1302

, 2012 WL 3570721 at *24 (D.C. Cir. Aug. 21, 2012). The court required EPA to continue adminis tering

CAIR. See id

. The AEO 2012, however, had been finalized prior to both these decisions. DOE understands, however, that CAIR and CSAPR are similar with respect to their effect on emissions impacts of energy efficiency

standards.15

- 2 covered by the existing cap

- and

- trade system, but it concluded that no reductions in power sector

emiss ions would occur for SO 2 as a result of standards.

Beginning in 2015, however, SO 2 emissions will fall as a result of the Mercury and Air Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 2011. 77 FR 9304 (Feb. 16, 2012 ). b In the final MATS rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air pollutants (HAP), and also

established a standard for SO 2 (a non

- HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. The same controls are used to reduce HAP and non

- HAP acid gas; thus, SO 2 emissions will be reduced as a result of the control technologies installed on coal

- fired

powe r plants to comply with the MATS requirements for acid gas. AEO 2012 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 2015. Both technologies, which are used t o reduce acid gas emissions, also reduce SO 2 emissions. Under the MATS, NEMS shows a reduction in SO 2 emissions when electricity demand decreases ( e.g. , as a result of energy efficiency standards). Emissions will be far below the cap that would be establis hed by CSAPR, so it is unlikely that

excess SO 2 emissions allowances resulting from the lower electricity demand would be needed or used to permit offsetting increases in SO 2 emissions by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce SO 2 emissions in 2015 and beyond.

CSAPR established a cap on NO

x emissions in 28 eastern States and the District of Columbia. Energy conservation standards are expected to have little effect on NO

x emissions in those States covered by CSAPR because excess NO

x emissions allowances resulting from the lower electricity demand could be used to permit offsetting increases in NO

x emissions. However, standards would be expected to reduce NO

x emissions in t he States not affected by

CSAPR . Therefore, DOE estimate d NOx emissions reductions from potential standards in th ose States

.

The MATS limit mercury emissions from power plants, but they do not include emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. For this rulemaking, DOE estimated mercury emissions reductions using the NEMS

BT based on AEO 2012 , which incorporates the MATS.

15.3 METHODOLOGY

The impact of the energy conservation standard on power sector air emissions is determined by calculating the difference between emissions projected in the NEMS

- BT reference case run and emissions projected in the NEMS

- BT policy run. The policy run was

dec remented with site energy savings resulting from the standard which were developed in the national impact analysis (chap ter 10)

. The impact of the standard on power sector emissions was determined for each of the four TSLs (see chapter 14 for a description of the NEMS

- BT model).

b On July 20, 2012, EPA announced a partial stay, for a limited duration, of the effectiveness of national new source emission standards for hazardous air pollutants from coal

- and oil

- fired electric utility steam generating units. http://www.epa.gov/airqua lity/powerplanttoxics/pdfs/20120727staynotice.pdf15

- 3 All extrapolations beyond 2040 are simple replications of year 2040 results. To emphasize the extrapolated results wherever they appear, they are shaded in gray to distinguish them from actual

NEMS

- BT results.

15.4 EMISSIONS IMPACT RESULTS

Table 15.4 .1 shows AEO2012 Reference Case power plant emissions in selected years. The Reference Case emissions are the emissions shown by the NEMS

- BT model to result if none of the TSLs are promulgated (the base case).

Table 15. 4

. 1 Power Sector Emissions Forecast from AEO2012 Reference Case

NEMS

- BT Results 2015 2020 2025 2030 2035 2040

CO 2 (million metric tons) 2,011 2,067 2,17 9 2,25 8 2,3 30 2,35 8

NO X (million tons) 1.79 1.87 1.92 1.94 1.96 1.97

SO 2 (million tons) 1.26 1.31 1.55 1.6 2 1.71 1.74

Hg (tons) 6.4 6.7 7.2 7.5 7.9 8.0

Table 15. 4 .2 shows the estimated changes in power plant emissions of CO 2 , NO X , SO 2 , and Hg in selected years for each of the TSLs considered in t his rulemaking. As in Table 15.4 .1,

values for CO 2 are given in metric tons, while values for NO X , SO 2 , and Hg are given in short

tons. Over the time period 2016 through 2045, c umulative CO 2 , NO X , SO 2 , and Hg emissions are reduced compared to the Reference case for all TSLs. For comparison, the cumulative power

sector emissions in the Reference case, over the same thirty year period , are 67,037 Mt for CO 2 , 57,696 kt for NOx, 47,079 kt for SO 2 , and 223 tons for Hg.15

- 4 Table 15.4

. 2 Power Sector Emissions Impacts for Microwave Oven Standby Power TSLs

NEMS-BT Results* Difference from AEO2012 Reference Case

Total

2015 2020 2025 2030 2035 2040 2041 2045 2016-2045

Standard Level 1

CO 2 (Mt/yr) -0.06 -0.37 -0.75 -0.79 -0.78 -0.71 -0.71 -0.71 -19.2

NOx (kt/yr) -0.05 -0.34 -0.67 -0.68 -0.66 -0.59 -0.59 -0.59 -16.5

SO 2 (1,000 tons/yr) -0.03 -0.24 -0.54 -0.56 -0.57 -0.52 -0.52 -0.52 -13.7

Hg (ton/yr) 0.000 -0.001 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.048

Standard Level 2

CO 2 (Mt/yr) -0.08 -0.54 -1.09 -1.14 -1.13 -1.03 -1.03 -1.03 -27.8

NOx (1,000 tons/yr) -0.07 -0.49 -0.96 -0.98 -0.95 -0.86 -0.86 -0.86 -23.8

SO 2 (1,000 tons/yr) -0.05 -0.34 -0.77 -0.81 -0.83 -0.76 -0.76 -0.76 -19.8

Hg (ton/yr) 0.000 -0.002 -0.003 -0.003 -0.002 -0.003 -0.003 -0.003 -0.069

Sandard Level 3

CO 2 (Mt/yr) -0.11 -0.75 -1.50 -1.57 -1.55 -1.42 -1.42 -1.42 -38.3

NOx (1,000 tons/yr) -0.10 -0.67 -1.32 -1.35 -1.31 -1.18 -1.18 -1.18 -32.8

SO 2 (1,000 tons/yr) -0.07 -0.47 -1.06 -1.12 -1.14 -1.04 -1.04 -1.04 -27.3

Hg (ton/yr) 0.000 -0.003 -0.004 -0.004 -0.003 -0.004 -0.004 -0.004 -0.095

Standard Level 4

CO 2 (Mt/yr) -0.17 -1.15 -2.31 -2.41 -2.38 -2.18 -2.18 -2.18 -58.8

NOx (1,000 tons/yr) -0.15 -1.04 -2.04 -2.07 -2.01 -1.82 -1.82 -1.82 -50.4

SO 2 (1,000 tons/yr) -0.11 -0.73 -1.64 -1.72 -1.75 -1.60 -1.60 -1.60 -41.9

Hg (ton/yr) 0.000 -0.004 -0.006 -0.005 -0.005 -0.006 -0.006 -0.006 -0.146

*CO 2 results are in metric tons, NO X , SO 2 , and Hg results are in short tons. Extrapolation15

- 5 REFERENCES

1. U.S. Department of Energy

– Energy Information Administration. 201 2. Annual Energy

Outlook 2012

. Washington, DC. www.eia.gov/forecasts/aeo/ . (Accessed May 2013.)16

- i CHAPTER 16. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

TABLE OF CONTENTS

16.1 INTRODUCTION

................................ ................................ ................................ ......... 16

- 1

16.2 MONETIZING CARBON DIOXIDE EMISSIONS

................................ ..................... 16

- 1

16.2.1 Social Cost of Carbon

................................ ................................ ................................ .... 16

- 1

16 .2.1.1 Monetizing Carbon Dioxide Emissions

................................ ..................... 16

- 2

16.2.2 Current Approach and Key Assumptions

................................ ................................ ...... 16

- 2

16.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

................................ ........... 16

- 5

16.4 RESULTS

................................ ................................ ................................ ...................... 16

- 5

LIST OF TABLES

Table 16.2

- 1 Annual SCC Values from 2010 Interagency Report, 2010

– 2050 (in 2007 dollars per metric ton CO 2 )

................................ ................................ ................ 16

- 4

Table 16.2

- 2 Annual SCC Values from 2013 Interagency Update, 2010

– 2050 (in 2007 dollars per metric ton CO 2 )

................................ ................................ ................ 16

- 4

Table 16.4.1 Microwave Ovens: Estimates of Global Present Value of CO 2 Emissions

Reduction Under Trial Standard Levels

................................ ............................ 16

- 6

Table 16.4.2 Microwave Ovens: Estimates of Domestic Present Value of CO 2 Emissions Reduction Under Trial Standard Levels

................................ ........... 16

- 6

Table 16.4.3 Microwave Ovens: Estimates of Present Value of NO X Emissions

Reduction Under Trial Standard Levels

................................ ............................ 16

- 716

- 1 CHAPTER 16. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

16.1 INTRODUCTION

As part of its assessment of energy conservation standards , DOE considered the estimated monetary benefits likely to result from the reduced emissions of carbon dioxide ( CO 2 ) and nitrogen oxides ( NO X ) that are expected to result from each of the TSLs considered. This chapter

summarizes the basis for the estimated monetary values used for each of these emissions and presents the benefits estimates considered.

16.2 MONETIZING CARBON DIOXIDE EMISSIONS

16.2.1 Social Cost of Carbon

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services. Es timates of the SCC are provided in dollars per metric ton of carbon dioxide. A domestic SCC value is meant to reflect the value of damages in the United States resulting from a unit change in carbon dioxide emissions, while a global SCC

value is meant to r eflect the value of damages worldwide.

Under section 1(b)(6) of Executive Order 12866, “Regulatory Planning and Review,” 58 FR 51735 (Oct. 4, 1993), agencies must, to the extent permitted by law, assess both the costs and the benefits of the intended reg ulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs. The purpose of the SCC estimates presented her e is to allow agencies to incorporate the monetized social benefits of reducing CO 2 emissions into cost

benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an ackno wledgement of the many uncertainties involved and with a clear understanding that they should be updated over time to reflect increasing knowledge of the science and economics of climate impacts.

As part of the interagency process that developed the SCC estimates, technical experts from numerous agencies met on a regular basis to consider public comments, explore the technical literature in relevant fields, and discuss key model inputs and assumptions. The main objective of this process was to develop a r ange of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic literatures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC

estimates used in the r ulemaking process.16

- 2 16.2.1.1 Monetizing Carbon Dioxide Emissions
When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Research Council points out that any assessment will suffer from uncertainty, speculation, and lack of information about: (1) future emissions of greenhouse gases; (2) the effects of past and future emissions on the climate system; (3) the impact of changes in climate on the physica l and biological environment; and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and et hics and should be viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. Most Federal regulatory actions can be expec ted to have marginal impacts on global emissions. For such policies, the agency can estimate the benefits from reduced emissions in any future year by multiplying the change in emissions in that year by the SCC value appropriate for that year. The

net pres ent value of the benefits can then be calculated by multiplying the future benefits by an appropriate discount factor and summing across all affected years. This approach assumes that the marginal damages from increased emissions are constant for small dep artures from the baseline emissions path, an approximation that is reasonable for policies that have effects on emissions that are small relative to cumulative global carbon dioxide emissions.

In 2009, an interagency process was initiated to offer a pre liminary assessment of how best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in how benefits are evaluated across agencies, the Administration sought to develop a transparent and defensible method, specifically des igned for the rulemaking process, to quantify avoided climate change damages from reduced CO 2 emissions. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the existing literature to use as

interim values until a more comprehensive analysis could be conducted. The outcome of the preliminary assessment by the interagency group was a set of five interim values: global SCC estimates for 2007 (in 2006$) of $55, $33, $19, $10, and $5 per metric ton of CO 2 . Thes e interim values represented the first sustained interagency effort within the U.S. government to develop an SCC for use in regulatory analysis. The results of this preliminary effort were presented in several proposed and final rules.

16.2.2 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular basis to generate improved SCC estimates , which were considered for this proposed rule. Specifically, the group considered public comments and further explored the technical literature

in relevant fields. The interagency group relied on three integrated assessment models commonly used to estimate the SCC: the FUND, DICE, and PAGE models. These models are frequently cited in the peer

- reviewed literature and were used in the last assessment of the Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC values that were developed.16

- 3 Each model takes a slightly different approach to model how changes in emissions result

in ch anges in economic damages. A key objective of the interagency process was to enable a consistent exploration of the three models while respecting the different approaches to quantifying damages taken by the key modelers in the field. An extensive review of the literature was conducted to select three sets of input parameters for these models: climate sensitivity,

socio

- economic and emissions trajectories, and discount rates. A probability distribution for climate sensitivity was specified as an input into a ll three models. In addition, the interagency group used a range of scenarios for the socio

- economic parameters and a range of values for the discount rate. All other model features were left unchanged, relying on the model developers’ best estimates and j udgments.

The interagency group selected four sets of SCC values for use in regulatory analyses.

Three sets of values are based on the average SCC from three integrated assessment models, at discount rates of 2.5 percent, 3 percent, and 5 percent. The fo urth set , which represents the 95th

percentile SCC estimate across all three models at a 3

- percent discount rate, is included to represent higher

- than

- expected impacts from climate change further out in the tails of the SCC

distribution. T he values grow in real terms over time

. Additionally, the interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although preference is given to consideration of the global benefits of reducing CO 2 emissions. Table 16.2

- 1 presents the values in the 2010 interagency

group report , which is presented in appendix 16

- A

. a

The SCC values used in this TSD were generated using the most recent versions of the three integrated assessment models that have been published in the peer

- reviewed literature

. b

Table 16.2

- 2 shows the updated sets of SCC estimates in five year increments from 2010 to

2050. The full set of annual SCC estimates between 2010 and 2050 is reported in appendix 16

- B

. The central value that emerges is the average SCC across models at the 3 percen t discount rate. However, for purposes of capturing the uncertainties involved in regulatory impact analysis, the interagency group emphasizes the importance of including all four sets of SCC values.

a Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 . Interagency Working Group on Social Cost of Carbon, United States Government , 2010.

b Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Ord er 12866

. Interagency Working Group on Social Cost of Carbon, United States Government , April 2013.16

- 4 Table 16.2

- 1 Annual SCC Values from 2010 Interagency Report, 2010

– 2050 (in 2007 dollars per metric ton CO 2 )

Year Discount Rate %

5 3 2.5 3

Average Average Average 95 th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

Table 16.2

- 2 Annual SCC Values from 2013 Interagency Update, 2010

– 2050 (in 2007 dollars per metric ton CO 2 )

Year Discount Rate %

5 3 2.5 3

Average Average Average 95 th Percentile

2010 11 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

It is important to recognize that a number of key uncertainties remain, and that current SCC estimates should be treated as provisional and revisable since they will evolve with improved scientific and economic understanding. The interagency group also recognizes that the existing models are imperfect and incomplete. The National Research Council report mentioned above points out that there is tension between the goal of producing quantifi ed estimates of the economic damages from an incremental ton of carbon and the limits of existing efforts to model these effects. There are a number of concerns and problems that should be addressed by the research community, including research programs ho used in many of the agencies participating in the interagency process to estimate the SCC. The interagency group intends to periodically16

- 5 review and reconsider estimates of the SCC used for cost

- benefit analyses to reflect increasing knowledge of the scienc e and economics of climate impacts, as well as improvements in modeling.

In summary, in considering the potential global benefits resulting from reduced CO 2 emissions, DOE used the values from the 2013 interagency draft report

, which is reprinted in appendix 1 6

- B of this TSD, adjusted to 20 1 1 $ using the Gross Domestic Product price deflator

. For each of the four cases specified, the values used for emissions in 2016 were $ 12.6 , $ 41 .1, $ 63.2 , and $ 119 per metric ton avoided (values expressed in 2011$). DOE derived values after 2050 using the growth rate for the 2040

- 2050 period in the interagency update.

DOE multiplied the CO 2 emissions reduction estimated for each year by the SCC value for that year in each of the four cases. To calculate a pre sent value of the stream of monetary values, DOE discounted the values in each of the four cases using the discount rates that had

been used to obtain the SCC values in each case.

16.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetar y benefit of reduced NO X emissions from the TSLs it considered. As noted in chapter 1 5 , new or amended energy conservation standards would

reduce NO X emissions in those 22 States that are not affected by caps . DOE estimated the

monetized value of NO X emiss ions reductions resulting from each of the TSLs considered based on environmental damage estimates found in the relevant scientific literature. Available estimates suggest a very wide range of monetary values, ranging from $468 to $4,809 per ton in

2012 $). c In accordance with OMB guidance, DOE calculated a range of monetary benefits using

each of the economic values for NO X and real discount rates of 3 percent and 7 percent.

. d

DOE is still evaluating appropriate values to use to monetize avoided SO 2 and Hg

emissions

. I t did not monetize these emissions for the MWO final rule

.

16.4 RESULTS

Table 16.4.1 pr esent s the global values of CO 2 emissions reductions at each TSL. DOE

calculated d omestic values as a range from 7 percent to 23 percent of the global values, and th o se results are presented in Table 16.4.2

.

c F or additional information , refer to U.S. Office of Management and Budget , Office of Information and Regulatory Affairs, ‘‘2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tr ibal Entities,’’ Washington, DC

. d OMB, Circular A

- 4: Regulatory Analysis ( Sept. 17, 2003).16

- 6 Table 16.4.1 Microwave Ovens : Estimates of Global Present Value of CO 2 Emissions

Reduction U nder Trial Standard Levels

TSL Million 201 1 $

5% Discount Rate, Average * 3% Discount Rate, Average * 2.5% Discount Rate, Average * 3% Discount Rate, 95 th

Percentile *

1 $ 128 $ 592 $ 942 $1, 815

2 $ 185 $ 855 $ 1,360 $ 2,621

3 $ 255 $ 1,179 $1, 876 $ 3,615

4 $ 392 $1, 812 $ 2,882 $ 5,554

* Columns are labeled by the discount rate used to calculate the SCC and whether it is an average value or drawn from a different part of the distribution. Values presented in the table are based on escalating 2007$ to 20 1 1 $ for consistency with other values presented in this notice, and incorporate the escalation of the SCC over time.

Table 16.4.2 Microwave Oven s : Estimates of Domestic Present Value of CO 2 Emissions Reduction Under Trial Standard Levels

TSL 5% Discount Rate, Average* Million 201 1 $

3% Discount Rate, Average * 2.5% Discount Rate, Average * 3% Discount Rate, 95 th

Percentile *

1 9 to 29 41 to 136 66 to 217 127 to 417

2 13 to 43 60 to 197 95 to 313 183 to 603

3 18 to 59 83 to 271 131 to 431 253 to 831

4 27 to 90 127 to 417 202 to 663 389 to 1277

* Domestic values are presented as a range between 7% and 23 % of the global values.

** Columns are labeled by the discount rate used to calculate the SCC and whether it is an average value or drawn from a different part of the distribution. Values presented in the table are based on escalating 2007$ to 20 1 1 $ for consistency with other values presented in this notice, and incorporate the escalation of the SCC over time.

Table 16.4.3 present s the cumulative monetary value of the economic benefits associated

with NO X emissions reductions for each TSL

, calculated using seven

- percent and three

- percent
discount rates.16

- 7 Table 16.4.3 Microwave Oven s : Estimates of Present Value of NO X Emissions

Reduction Under Trial Standard Levels

TSL 3% discount rate 7% discount rate

million 201 1 $

1 million 201 1 $

$22.3 $11.0

2 $32.3 $15.8

3 $44.5 $21.8

4 $68.4 $33.617

- i CHAPTER 17. REGULATORY IMPACT ANALYSIS

T ABLE OF CONTENTS

17.1 INTRODUCTION ......................................................................................................... 17 -1

17.2 NON -REGULATORY POLICIES ................................................................................ 17-2

17.2.1 Methodology .................................................................................................................. 17 -2

17.2.2 Policy Assumptions ....................................................................................................... 17 -3

17.2.3 Policy Interactions ......................................................................................................... 17 -3

17.3 NON -REGULATORY POLICY ASSUMPTIONS ...................................................... 17-4

17.3.1 No New Regulatory Action ........................................................................................... 17-4

17.3.2 Financial Incentives Policies .......................................................................................... 17-4

17.3.2.1 Consumer Rebates ....................................................................................... 17-5

17.3.2.2 Consumer Tax Credits ................................................................................. 17 -8

17.3.2.3 Manufacturer Tax Credits .......................................................................... 17 -11

17.3.3 Voluntary Energy Efficiency Targets .......................................................................... 17 -11

17.3.4 Early Replacement ....................................................................................................... 17 -13

17.3.5 Bulk Government Purchases ........................................................................................ 17-14

17.4 NON -REGULATORY POLICY IMPACTS ............................................................... 17-15

17.5 RESULTS SUMMARY FOR NON -REGULATORY ALTERNATIVES ................. 17 -16

LIST OF TABLES

Table 17.1.1 Policy Alternatives to National Standards ......................................................... 17-1

Table 17.2.1 Trial Standard Level 3 for Microwave Ovens ................................................... 17 -3

Table 17.3.1 Benefit/Cost Ratios for Microwave Oven Proposed Standard and Rebate

Policy Case......................................................................................................... 17 -6

Table 17.3.2 Oregon Tax Credits Claimed for Residential Dishwashers from 1998 to

2006 and their Attribution for 2006 ................................................................. 17 -10

Table 17.3.3 Oregon Tax Annual Increased Market Shares of Microwave Ovens at

Target Efficiency Levels from Consumer Tax Credits .................................... 17 -10

Ta ble 17.3.4 Annual Increased Market Shares of Microwave Ovens at Target

Efficiency Level from Voluntary Efficiency Targets ...................................... 17 -12

Table 17.5.1 Non-Regulatory Alternatives for Product Class 1 with Target Level at TSL

3 ........................................................................................................................ 17-17

Table 17.5.2 Non-Regulatory Alternatives for Product Class 2 with Target Level at TSL

3 ........................................................................................................................ 17-17

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curve for Microwave Ovens (product class 1) .................. 17 -71 7

- ii

Figure 17.3.2 Market Penetration Curve for Microwave Ovens (product class 2) .................. 17 -7 Figure 17.4.1 Penetration Rates of Units Meeting the Target Level for Microwave Ovens.. 17 -1617-1 CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

The Department of Energy (DOE) has determined that energy conservation standards for microwave ovens constitute an “economically significant regulatory action” under Executive Order (E.O.) 12866 “Regulatory Planning and Review.” 58 FR 51735 (October 4, 1993). Therefore, DOE’s proposed energy conservation standards require a regulatory impact analysis (RIA), which involves an evaluation of non-regulatory alternatives to the standards. This

doc ument evaluates several possible alternatives to the proposed standards, and compares the costs and benefits of each to the proposed standards. As described in section 17.2.2 of this report, the pr oposed standards for microwave ovens the proposed standards are those in TSL 3.

Under the Process Rule ( Procedures for Consideration of New or Revised Energy Conservation Standards for Consumer Products , 61 FR 36974 (July 15, 1996); 10 CFR Part 430,

S ubpart C, Appendix A), DOE is committed to continually explore non-regulatory alternatives to standards. This RIA, which DOE has prepared pursuant to E.O. 12866, is subject to review under

the E xecutive Order by the Office of Management and Budget (OMB)’s Office of Information and Regulatory Affairs (OIRA). 58 FR 51735.

DOE identified six major non-regulatory alternatives to standards as representing feasible policy options to achieve consumer product energy efficiency for the appliance products that are the subject of this rulemaking. These are listed in Table 17.1.1. DOE evaluated each alternative

in t erms of its ability to achieve significant energy savings at a reasonable cost, and compared the effectiveness of each one to the effectiveness of the proposed standards.

T able 17.1.1 Policy Alternatives to National Standards
No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits
Voluntary Energy Efficiency Targets
Early Replacement

Bulk Government Purchases17-2

17.2 NON-REGULATORY POLICIES

17.2.1 Methodology

This section describes the approach DOE used to analyze non-regulatory policies for

microwave ovens.

To calculate the national energy savings and the net present value (NPV) corresponding to each policy alternative, DOE used its national impact analysis (NIA) spreadsheet models. (See

C hapter 10 of the technical support document (TSD) for a description of the NIA spreadsheet models.) To c ompare each alternative to the proposed standards, DOE quantified the effect of each alternative on the purchase of microwave ovens meeting the target l evels , which are defined as the efficiency levels in the proposed standards. Once it had made the quantitative assumptions for each alternative policy, DOE made the appropriate revisions to the inputs in the NIA spreadsheet models. The main model inputs that DOE revised were market shares of equipment

a t target efficiencies, shipment-weighted average annual energy consumption, and equipment replacement rates. The shipments for any given year are comprised of a distribution of efficiency

leve ls. DOE assumed that standards would affect 100 percent of the shipments, while the nonregulatory policies would affect a smaller percentage of the shipments. In each policy case, DOE made particular assumptions about the percentage of shipments impacted by the policy under analysis. DOE then calculated the shipment-weighted average energy consumption and costs using these market shares.

A shift in the market share of higher efficiency units may increase the average installed cost of energy-consuming equipment. Operating costs will generally decrease due to a decline in

e nergy consumption. Therefore, DOE calculated an NPV for non-regulatory alternatives in the

sa me way as it did for the proposed standards. In some scenarios, total installed cost increases are partially mitigated by government rebates or tax credits. However, DOE assumed that credits

a nd rebates would be paid for by consumers in another form (such as additional taxes), and therefore did not include them as a consumer benefit for the purposes of calculating the national

NPV . DOE did not consider administrative costs for any of the non-regulatory policies in its analysis. Inclusion of such costs would decrease their NPVs by a small amount.

The key measures of the impact of each alternative are:

• National energy savings in quadrillion Btus (quads): Cumulative national primary energy savings for equipment bought in the period from the effective date of the policy case (2016 ) to the year 2045.

• Net present value: The value of net monetary savings from equipment bought in the period from the effective date of the policy case (2016) to the year 2045. DOE calculated

the N PV as the difference between the present value of equipment and operating expenditures (including energy) in the base case and the present value of expenditures in17-3

each alternative policy case. DOE calculated operating expenses (including energy) for

the life of the equipment.

17.2.2 Policy Assumptions

The impacts of non-regulatory policies are by nature uncertain, since they depend on program implementation and marketing efforts and the subsequent consumer behavior response. The projected impacts depend on the assumptions regarding the consumer participation rate, and are therefore subject to more uncertainty than the impacts of mandatory standards, which DOE assumes will have full compliance. To increase the robustness of the analysis, DOE conducted a

li terature review on each non-regulatory policy and consulted with key experts to gather information on similar incentive programs that have already been implemented in the U.S. By

stud ying field experience with sample programs of each type, DOE sought to make credible

a ssumptions of their potential market impacts. Section 17.3 below reports the conclusions from

thi s research as they apply to the policy modeling assumptions and includes the corresponding

literature citations.

Each of the policy alternatives that DOE considered to the proposed standards would improve the average efficiency of new microwave ovens relative to their base cases (no new regulatory action). The analysis considered that each alternative policy would induce residential

or commercial consumers to purchase units at the same efficiency levels as required by the proposed standards, or the target levels . In contrast to the proposed standards, however, their market penetration rate in the alternative policy cases may not be 100 percent.

The proposed standards for microwave ovens are those in TSL 3, as shown in Table

17.2.1.

T able 17.2.1 Trial Standard Level 3 for Microwave Ovens

No. Product Class Proposed Energy Conservation Standards

1 Microwave oven with or without thermal elements Maximum standby power = 1.0 Watt

2 Over

- the

- range combination ovens Maximum standby power = 2.2 watts

DOE assumed that the non-regulatory policy impacts would last from the effective date for proposed standards for microwave ovens—2016—through the end of the analysis period,

2045

.

17.2.3 Policy Interactions

DOE calculated the impacts of each regulatory policy separately from those of the other policies. In actual practice, certain policies are often most effective when implemented in combination to provide incentives, such as early replacement with consumer rebates, or early replacement with bulk government purchases. DOE attempted to make conservative assumptions

to avoid double -counting policy impacts. Therefore, the policy impacts reported below are not17-4

additive; the combined impact of several or all of the policies may not be inferred from adding the results together.

Section 17.3 presents graphs showing market penetration or shipment-weighted efficiency parameters for each of the non-regulatory policy cases for microwave ovens.

17.3 NON-REGULATORY POLICY ASSUMPTIONS

17.3.1 No New Regulatory Action

The case in which no new regulatory action is taken with regard to microwave oven efficiency constitutes the base case scenario described in Chapter 10 on the national impact

a nalysis. This case defines the basis of comparison for all other scenarios. By definition, no new

re gulatory action yields zero energy savings and an NPV of zero dollars.

17.3.2 Financial Incentives Policies

DOE considered scenarios in which the Federal government would provide two types of financial incentives: rebates and tax credits. The government could provide consumers with a

re bate for purchasing an energy efficient appliance meeting the target level for each product. Tax

c redits could be offered to consumers who purchased target-level microwave ovens. The

g overnment could also provide tax credits to manufacturers to offset costs associated with

pr oducing such equipment.

DOE’s evaluation of consumer rebates used a comprehensive study of the potential for energy efficiency in California performed by Xenergy, Inc., which summarized experience with

va rious utility rebate programs. 1 This analysis method is based on curves that estimate the market penetration of a technology based on its benefit/cost (B/C) ratio. DOE consulted with

e xperts and reviewed several other methods of estimating market penetration of efficient technologies due to consumer rebate programs that were developed since the referenced Xenergy

re port was published. 2

, 3

, 4

, 5 However, these methods were based either on other economic parameters (payback period) or on expert surveys predicting penetration of a new technology over time. Therefore, DOE decided to use the penetration curve method based on B/C ratio,

whic h incorporates lifetime operating cost savings, and was calibrated with utility rebate program participation results.

Xenergy’s information diffusion model estimates market impacts induced by financial

ince ntives for energy efficient appliances. The basic premise of this model is that information diffusion drives technology adoption. The model is formulated to characterize the influences of

both i nternal and external sources of information on consumer behavior by superimposing two

c omponents in the equation, each capturing the effect of one of two different types of information source. The effects of these two types of information diffusion mechanisms are

diff erent. Internal sources of information influence consumers to purchase new products due17-5

mainly to word- of

-mouth from early adopters, while external information sources influence consumers to change their adoption decisions as a result of marketing efforts and information coming from outside the consumer group. (Appendix 17A of the TSD contains further details on modeling these influences.)

Xenergy’s model combined these two information diffusion mechanisms and generated a

se t of measure “implementation curves ” or penetration curves

, which Xenergy calibrated using evaluation data from utility rebate programs. Consumer response to rebate incentives appears to

be a combination of the two information source types. The penetration curves illustrate the increased penetration (i.e., increased market share) of efficient equipment as a result of consumer response to B/C ratio changes induced by a specific rebate program. The penetration curves are used to depict various diffusion patterns based on perceived barriers to consumer purchase of high -efficiency equipment. There are penetration curves for varying levels of market barriers,

fr om “no barriers” to “extremely high barriers.” These curves provide a means to study the impact of changing the B/C ratio, by reducing the initial equipment cost through financial incentives, on the consumer participation rate.

DOE based its estimates of the impacts of consumer tax credits on actual program

e xperience with State tax credits in Oregon. DOE studied State tax credits in Montana as we ll

. DOE also attempted to determine residential consumer participation due to the Federal appliance tax credits, which were mandated by the Energy Policy Act of 2005 (EPACT 2005). For the

manuf acturer tax credits policy, DOE attempted to investigate manufacturer participation due to

the e fficient equipment tax credits from EPACT 2005. Both the Federal consumer and

manuf acturer credits were in effect in 2006 and 2007. Unfortunately, the Internal Revenue

S ervice (IRS) had not yet published data on taxpayer response to either of these tax credits.

DOE also incorporated previous research that had differentiated the impact of tax credits

int o the “direct price effect,” which arises from the incremental equipment cost savings, and the
“announcement effect,” which is independent of the credit amount. 6

, 7 The announcement effect derives from the credibility that a particular technology receives from its inclusion in an incentive program, as well as changes in product marketing strategy, and the resulting modifications in markups and pricing. DOE assumed that the direct price effect and the

a nnouncement effect would apply to the consumer tax credit policy as well as the consumer rebate policy, and that half of the increases in market penetration associated with either policy

would be due to the direct price effect and half to the announcement effect.

17.3.2.1 Consumer Rebates

DOE modeled the impact of the consumer rebate policy by determining the increase in market penetration of target-level equipment relative to its market penetration in the base case.

DOE assumed a re bate value of 4.00 dollars, which would cover 78 percent (for product class 1) and 51 percent (for product class 2) of the incremental installed cost between a microwave

me e ting the baseline efficiency level and a unit meeting the target efficiency

. DO E assumed the rebates would remain in effect until they had transformed the market s so that the shift in market17-6

share of efficient units seen in the first year of the programs would be maintained throughout the

for ecast period (2016– 2045).

DOE first calculated the B/C ratio for the baseline unit without a rebate. It then calculated another B/C ratio for the unit meeting the target level, with a rebate, relative to the baseline unit.

B ecause of the incremental cost reductions due to the rebates, the B/C ratios for the rebate policy

unit were larger. Table 17.3.1 shows the benefits as lifetime operating cost savings, incremental

inst alled costs without rebates and with rebates, and B/C ratios without rebates and with rebates.

T able 17.3.1 Benefit/Cost Ratios for Microwave Oven Proposed Standard and Rebate

Policy Case

PC 1 PC 2
Benefit (Lifetime Operating Cost Savings) $ 31.04 $ 23.80
Incremental Installed Cost (Increased Installed Cost) $ 5.14 $ 7.84

B/C Ratio with No Rebate 6.0 3.0

Rebate Amount $ 4 .00 $ 4 .00
Adjusted Incremental Installed Cost (Increased Installed Cost after Rebate) $ 1.14 $ 3.84
B/C Ratio for Rebate Policy Case 27.2 6.2

DOE then used the B/C ratios with the penetration curves shown in Figure 17.3.1 to estimate the increased percentage of consumers who would purchase the units that meet the policy target levels if given a rebate incentive. For microwave ovens, DOE chose the “no

ba rriers ” curve. The incremental cost between the baseline and the TSL is minimal, and there are

no pr oduct utility issues that would cause barriers to consumer acceptance. Figures 17.3.1 and 17.3.2 show the increase in penetration rates of target-level units as a function of their higher

B /C ratios. Using the method discussed above, DOE estimated that the market share of equipment meeting the policy target due to a rebate policy would increase by 11.7 percent for

pr oduct class 1 and 11.9 percent for product class 2.

To calculate the impacts of this policy, DOE adjusted the market shares of microwave ovens at the target efficiencies in its NIA model to represent the policy case scenarios.17-7

Figure 17.3.1 Market Penetration Curve for Microwave Ovens (product class 1)

F igure 17.3.2 Market Penetration Curve for Microwave Oven s (product class 2)17-8

17.3.2.2 Consumer Tax Credits

To analyze this policy, DOE studied taxpayer participation in tax credit programs for energy efficient appliances, which were available at both the Federal and the State levels. DOE

a nalyzed a consumer tax credit program whose credit amounts would be similar to the percentage of equipment costs covered by existing State tax credits. Because tax credits have not been available for microwave ovens, DOE analyzed Oregon's residential tax credit data for an analogous product—dishwashers.

EPACT 2005 included Federal tax credits for consumers who installed efficient air conditioners or heat pumps, gas or oil furnaces, furnace fans, and gas, oil, or electric heat pump water heaters in new or existing homes. 8 While these credits were available during tax years 2006 and 2007, as mentioned above, the IRS had not published data on the numbers of taxpayers taking these credits. Appendix 17A gives details of the equipment covered and the Federal tax

c redit amounts for residential appliances.

The States of Oregon and Montana have had consumer tax credits for efficient appliances for several years. a DOE based most of its estimates on Oregon's experience with this policy. Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon legislature

e xpanded the RETC program in 1997 to include home appliances and participation in the

pr ogram increased significantly after they became eligible. b In response to changes in the appliance market, the RETC program updates its lists of eligible model numbers monthly, and

make s occasional adjustments to the maximum tax credit dollar amount for each appliance. Oregon offers tax credits on residential refrigerators, clothes washers, and dishwashers. 9 The tax credit is either an amount noted on the list of qualifying appliances c or 25 percent of the purchase price of the appliance, whichever is less.

Montana has had an Energy Conservation Installation Credit for residential energy

c onservation measures since 1998. 10 The tax credit covers a variety of residential energy and water efficiency installations, including ENERGY STAR heating/cooling equipment, water

he aters, low-flow showerheads and faucets, and light fixtures and controls

. The amount of the

credit increased in 2002 from 5 percent of equipment costs (up to $150) to 25 percent (up to $500 ). DOE obtained data from the Montana Department of Revenue (DER) on the numbers of

tax credits claimed from 1998 through 2006. 11 However the DER did not have data disaggregated by appliance, so the number of tax credits claimed for large household appliances could not be distinguished from those for plumbing products. DOE analyzed the available data

a nd noted that the number of tax credits claimed increased steadily from 1998 through 2006, which contributed to its assumptions for this policy scenario.

a The District of Columbia (Washington D.C.) passed legislation entitled the “Residential Energy Conservation Tax

Credit Act Of 2005” for consumer tax credits, but did not appropriate the necessary funding and so the credits did not go into effect.

b The program added fuel cells in 1999 and high-efficiency heat pump systems, furnaces, and boilers in 2002.

c These lists change frequently, and generally require units that exceed ENERGY STAR specifications

-- for example units that meet Consortium for Energy Efficiency (CEE) specifications.17-9

DOE obtained data from the Oregon Department of Energy (ODOE) on the number of efficient appliance tax credits claimed by Oregon residential taxpayers for the years 1998 through 2006, as well as the credits claimed by appliance type for the years 2000 to 2006. 12
DOE also obtained Oregon appliance shipments data for clothes washers and dishwashers for

2006. 13

, 14 The number of credits claimed generally increased each year, although there were some fluctuations that ODOE attributed to changes in qualifying models. DOE assumed that a tax credit program would be designed to provide consistent consumer information and slightly adjusted the ODOE data for the years 2003 and 2004 to provide a steady trend line. DOE

c alculated that the number of clothes washer tax credits claimed comprised 24 percent of thetotal number of Oregon clothes washer shipments in 2006. However, not all of these highe fficiency clothes washers were likely purchased due to the tax credit program alone. Some

tax payers would have been motivated by utility rebate incentives, and others would be "free riders" who would have purchased efficient appliances without a tax credit or rebate incentive but still claimed the tax credit. To better understand this interaction between tax credits, rebates,

a nd free riders/market impacts, DOE reviewed a report prepared by KEMA on efficient clothes washers in the Northwest. KEMA compared the penetration of “ultra high efficiency” (UHE) d
clothes washers in Oregon and Washington, two neighboring States that have similar utility rebate programs for residential clothes washers. 15 The study attributed the difference in market share of these UHE units (50 percent for Oregon versus 15 percent for Washington) to the Oregon tax credits. DOE estimated that 38 percent of the tax credits claimed (9 percent of total shipm ents) were actually due to rebates. To estimate the free riders, DOE used the market

pe netration of the baseline commercial clothes washer units, which was 9.2 percent of total shipments (38 percent of the number of tax credit claims). DOE then estimated that the

re maining 24 percent of the Oregon clothes washer tax credits claimed for the years 1998–2005 (or 5.8 percent of total shipments) were attributable to tax credits alone.

Microwave Ovens
The percentage of total annual dishwasher shipments comprised by tax credit claims declined in 2005 and 2006, which ODOE attributed to changes in the listings of eligible models. Observing that clothes washer tax credits claimed generally rose each year between 1998 and 2006, and also that Montana's tax credit claims increased annually during the same period (see below), DOE assumed that the tax credit program for this policy would be designed for increasing annual participation and adjusted the dishwasher percentages for the years 2004 through 2006 to provide a steady trend line. For 2006, the estimated percentage of total shipments comprised by tax credit claims was 14 percent. DOE did not find data comparing

Or egon and Washington’s efficient dishwasher sales. To estimate the portions of these tax credit claims that were likely due to utility rebates and to market impacts, DOE reviewed a report by Itron on utility rebate programs in California, which compared the percentage of free riders in residential clothes washer and dishwasher rebate programs in 2005. 16 Itron found that the market penetration rate of ENERGY STAR dishwashers was much higher than that of ENERGY STAR

c lothes washers and the percentage of free riders was greater for dishwasher programs than for clothes washer programs. Also, DOE estimated that the incremental cost from the baseline was

d Units meeting specifications of minimum modified energy factor (MEF) of 2.20 and maximum water factor (WF)

of 6.5.17-10

much smaller for dishwashers than for clothes washers. Thus DOE estimated that market impacts (pa rt of which are due to free riders) were responsible for a higher percentage of dishwasher tax credit claims than for residential clothes washer credit claims. DOE estimated that 49 percent of

Or egon's dishwasher tax credits claimed, or 6.9 percent of total dishwasher shipments, were due to market impacts. It estimated that utility rebates were responsible for 32 percent of tax credits claimed, or 4.5 percent of total shipments. DOE attributed the remaining 20 percent of tax credits

c laimed, or 2.8 percent of total shipments, to tax credits. DOE then took 20 percent of the

number of Oregon dishwasher tax credits claimed for the years 1998–2005 to estimate the impact

a ttributable to tax credits for those years.

Table 17.3.2 sh ows the n umber of Oregon residential tax credits claimed for dishwashers

a s percentages of total annual shipments these claims represent for the years 1998–2006 . It also shows the percentages of total shipments attributed to the tax credits themselves, to utility rebates, and to other market influences/free riders for 2006, and the percentage attributed to tax

credits for 1998– 200 5

.

Table 17.3.2 Oregon Tax Credits Claimed for Residential Dishwashers from 1998 to 2006

an d their Attribution for 2006

1998 1999 2000 2001 2002 2003 2004 2005 2006

Tax Credits Claimed 4% 7% 9% 10% 12% 14% 12% 7.5% 7.6%
Tax Credits Claimed, Adjusted 4% 7% 9% 10% 12% 14% 14% 14% 14%

Due to Tax Credits 0.7% 1.4% 1.8% 2.0% 2.4% 2.8% 2.8% 2.8% 2.8%

Due to Rebates NA NA NA NA NA NA NA NA 4.5%

Due to Market NA NA NA NA NA NA NA NA 6.9%

DOE estimated that, due to consumer tax credits, the market share of target-efficiency

mi crowave ovens would increase by the same percentages shown for dishwashers for the first nine years and remain constant at 2.8 percent thereafter. Table 17.3.3 shows the resulting market

shar e increases.

Table 17.3.3 Oregon Tax Annual Increased Market Shares of Microwave Ovens at Target

Efficiency Levels from Consumer Tax Credits
Year of Program Annual Market Share Increase

201 6 0.7%

201 7 1.4%

201 8 1.8%

201 9 2.0%

20 20 2.4%

20 2 1 2.8%

202 2 2.8%

202 3 2.8%

202 4 2.8%

20 2 5

– 204 5 2.8%17-11

17.3.2.3 Manufacturer Tax Credits

EPACT 2005 provides Energy Efficient Appliance Credits to manufacturers for

pr oduction of high-efficiency refrigerators, clothes washers, and dishwashers at the Federal level through the IRS. 17 Manufacturers receive these credits only for the increase in production of qualifying appliances over a three-year rolling baseline. Each manufacturer is limited to a certain

a mount for all credits under this provision. The credits were available for models produced in

2006 a nd 2007. Legislation pending in Congress would extend the manufacturer tax credits in modified form through 2010. 18 Appendix 17A gives details of the equipment covered and the Federal tax credit amounts for appliance manufacturers.

DOE assumed that a manufacturer tax credit program would effectively result in a lower price to the consumer by an amount equivalent to that provided by consumer tax credits as described above. Because these tax credits would go directly to manufacturers, DOE assumed that manufacturers would pass on the reduced costs to consumers, causing the direct price effect. However, DOE assumed that the announcement effect would not occur because the program would not be visible to consumers. Since the direct price effect is approximately equivalent to

the a nnouncement effect, 6 DOE estimated that half of the consumers assumed to take advantage of consumer tax credits would purchase more-efficient products due to a manufacturer tax credit program. DOE thus applied 50 percent to the market penetrations attributed to Oregon’s

c onsumer tax credits in the year 2006 for dishwashers and used the resulting market penetrations to represent the impact of manufacturer tax credits for all years of the analysis period. DOE

a ssumed the impact of this policy would be to permanently transform the market so that the market share increase seen in the first year of the program would be maintained throughout the forecast period.

DOE estimated that this policy could increase the market share of efficient microwave ovens by 1.4 percent annually over the base case at the TSL. Because of the small incremental

c ost between the baseline and TSL for this product, a manufacturer tax credit might be easier to implement than a consumer tax credit for microwave ovens.

17.3.3 Voluntary Energy Efficiency Targets

For microwave ovens DOE assumed that voluntary targets would be achieved through

manuf acturer participation in a gradual phase-out of production of units below their respective

TS Ls. DOE assumed that this would be achieved by a program similar to the ENERGY STAR endorsement labeling program conducted by the Environmental Protection Agency (EPA) and

DOE . The ENERGY STAR program sets minimum energy efficiency specifications for various

pr oducts. ENERGY STAR encourages consumer adoption of these products through marketing to promote consumer label recognition, adoption of the specifications by various efficiency incentive programs, and manufacturer production and promotion of ENERGY STAR-compliant appliances. ENERGY STAR prepares projections of market penetration of compliant appliances,

a nd estimates the percentage of the sales of those compliant appliances that are attributed to the ENERGY STAR program.17-12

Several reports have analyzed the impacts of ENERGY STAR programs for specific

pr oducts, which are generally based on a combination of information dissemination and utility or agency rebates. These studies base their analysis on the ENERGY STAR statewide data on

pe rcentages of each appliance’s shipments that meet the ENERGY STAR specifications. These

a nalyses have generally concluded that the market penetration of ENERGY STAR-qualifying appliances is higher in regions or States where such promotional programs have been

active. 15 , 16 , 19

, 20

, 21

There has been no ENERGY STAR program for microwave ovens. Because of the low

incr emental costs and the small overall operating cost savings opportunity at the TSL, DOE

a ssumed that an ENERGY STAR effort for this product would focus on an information program. DOE assumed that such a voluntary program would induce similar market penetration behavior as that of televisions with cathode ray tube (CRT) screens, for which there was an ENERGY STAR specification, since energy savings for both products are achieved by reducing standby

powe r. DOE used estimates of the increased market penetration due to the ENERGY STAR

pr ogram for CRT televisions during its first six years (1998–2003), applying those percentage

incr eases to microwaves for the first six years of the analysis period, from 2016 through 2021

. Beginning in 2004, televisions with liquid crystal display (LCD) screens dominated the market, so DOE did not use the ENERGY STAR forecast for televisions from 2004 on. Instead, it

c alculated the average market penetration of all consumer electronics products in 2003—58

pe rcent—and assumed this level could eventually be achieved by a voluntary program for

microwave ovens. 22 Thus DOE estimated that, from 2021 through 2025 , the market penetration of microwave ovens meeting the TSL would increase an additional 1.7 percentage points per year, reaching 58 percent by 2025. After 2025 DOE assumed that market penetration would remain at that level through the remainder of the forecast. Table 17.3.4 shows the annual projected market share increases of the targeted units due to the programs resulting from this voluntary efficiency targets policy.

Table 17.3.4 Annual Increased Market Shares of Microwave Ovens at Target Efficiency

L evel from Voluntary Efficiency Targets
Year of Program Microwave Ovens*

201 6 15%

201 7 37%

201 8 44%

201 9 43%

20 20 42%

20 2 1 47%

20 2 2 49%

20 2 3 52%

202 4 55%

202 5

– 204 5 58%

* Percentages in each column refer to shares of the eligible market.17-13

17.3.4 Early Replacement

Early replacement refers to the replacement of microwave ovens before the ends of their

useful lives. The purpose of this policy is to replace old, inefficient equipment with highere fficiency units. The economic feasibility of early replacement depends on the vintage of the unit

be ing replaced, the cost for the new equipment, and the energy cost savings.

There has been limited experience with early replacement programs for appliances in the

U.S. However, DOE studied several reports to inform its analysis of this policy. One report

de tailed the Connecticut Appliance Retirement Program (ARP) conducted in 2004. 23 Another was an electric energy efficiency potentials study performed for the State of Vermont in 2006. 24
DOE had also performed an earlier study on Federal potential for early retirement of appliances

under EPACT 1992.

The Connecticut ARP was conducted from June through December 2004 by Nexus Market Research, Inc. and RLW Analytics, Inc. for Northeast Utilities–Connecticut Light and

P ower and the United Illuminating Company’s State programs. The ARP intended to assist

uti lity customers in Connecticut to overcome barriers to recycling room air conditioners (RACs), secondary refrigerators, and freezers. The program picked up used appliances at customers’

homes or at turn-in events, paid participants to retire their units, and educated customers about the costs of running older appliances. In addition, the program paid incentives to consumers to replace their RACs with ENERGY STAR-qualified units. DOE considered the RAC program to

most closely resemble the early replacement policy scenario, since these consumers were replacing primary units rather than retiring second units. Nexus’ report on ARP results estimated

the numbe r of RACs retired by program participants, the percentage of those replaced with an ENERGY STAR model, and the number of RACs replaced by non-participants during the

pr ogram duration, using program data and surveys. According to their analysis, about seven

pe rcent of all RACs retired during the program were retired through the ARP, and 63 percent of those were replaced with an ENERGY STAR model. This meant that the program directly

re sulted in early replacement with a more efficient unit of about four percent of the total eligible RAC population. DOE assumed that this type of program most likely eliminates second

owne rship of the RAC, thus shortening its useful service life.

In 2006, GDS Associates, Inc. conducted an electricity and peak demand potentials study

for energy efficiency and fuel conversion measures for the State of Vermont. GDS developed a special “early retirement” scenario with all residential appliances replaced during the four-year period from 2006 to 2009, and analyzed similar early replacements in the commercial sector.

The y estimated achievable market penetrations assuming that consumers would receive a financial incentive equal to 50 percent of the incremental cost of the measure in most programs. GDS assumed an 80 percent penetration limit for the program. It estimated a maximum achievable annual program participant level of two percent of applicable single-family or multifamily homes in 2005.

DOE also reviewed a study it conducted in the 1990s, under EPACT 1992, which analyzed the feasibility of a Federal program to promote early replacement of appliances. 25 This17-14

study identified policy options for early replacement that included a direct national early replacement program, replacement of Federally-owned appliances, promotion through equipment

manuf acturers, consumer incentives, incentives to utilities, and building regulations. e

For this analysis, DOE considered a program that targets the units in the stock that have efficiency levels lower than the policy target efficiency level and encourages their early replacement with products at the target level. The program affects a portion of units in the stock

in 2016 that would be replaced at the end of their useful or service life (at the “natural” replacement rate) without the effects of this policy, under which they would be replaced sooner. Shipments in 2016 and beyond are not affected by this program and remain at base case

e fficiency levels.

Based on their average service lives, 11 percent of microwaves are replaced annually; these are their natural replacement rates. For this policy study, DOE assumed that an additional percentage of microwave ovens in the existing stock in 2016, the first year of the analysis period,

would be replaced by models meeting the target levels. For each product, DOE modeled this poli cy by replacing four percent per year of the units in the stock in addition to those being replaced at the end of their service lives (using the estimated percentage from the Connecticut study since it was based on actual program experience). DOE assumed that these early

re placement programs would last as long as it took to completely replace all of the eligible microwave ovens that were in the stock in the year that the program began (2016). After the

2016 stock had been completely replaced, there would be no additional impacts from this policy.

17.3.5 Bulk Government Purchases

DOE assumed that a bulk government purchase policy would encourage Federal, State, and local governments to purchase equipment meeting the target efficiency levels. Aggregating publi c sector demand could provide a market signal to manufacturers and vendors that some of their largest customers sought suppliers with products that met an efficiency target at favorable prices. This program also could induce “market pull” impacts through manufacturers and

ve ndors achieving economies of scale for high-efficiency products.

While there have been several bulk government purchasing initiatives at the Federal,

S tate, and municipal levels, most of these programs have not tracked data on number of purchases or degree of compliance with procurement specifications. In many cases, procurement programs are decentralized, being part of larger State or regional initiatives. At the Federal level,

e The analysis concluded that, while cost-effective opportunities for early replacement exist, a widespread Federal early replacement program was not economically justified. Because premature retirement means that a unit may be replaced by an appliance less efficient than the eventual replacement would probably have been, energy savings would be smaller than anticipated. Early replacement programs could increase sales volatility in the long run by encouraging a temporary increase in production followed by a lull in demand. Early replacement could be economical in localities with high energy cost conditions or environmental constraints, when replacement appliances are much more efficient than existing stock, or when a major technology breakthrough has recently occurred, creating the need for a ready market.17-15

the Federal Energy Management Program (FEMP) has performed studies of savings potential for its procurement specifications for appliances and other equipment on which DOE based its assumptions for this policy. Ye t FEMP does not track purchasing data, due to the complexity of the purchasing systems, number of vendors, etc. There is evidence of increasing interest and activity in “green purchasing" on the State, county, and municipal levels. While many of these

prog rams target office equipment, the existence of a growing infrastructure for efficient purchasing specifications indicates that such impacts that DOE attributed to bulk government
purchasing programs are feasible. 26

, 27

For microwave ovens, DOE assumed that government agencies, such as the Department of Housing and Urban Development (HUD), would administer such a program. At the Federal

leve l, this program could be incorporated into the existing FEMP program. FEMP has procurement guidelines for Federal government equipment purchasing and Federal construction requirements include these guidelines for installing or replacing equipment. Error! Bookmark

not defined. There are currently no FEMP procurement guidelines for microwave ovens.

DOE assumed that this policy would impact a subset of housing units for which government agencies purchase or influence the purchase of microwave ovens. This subset would consist mainly of public housing and housing on military bases. To represent this subset, DOE

c onsidered publicly owned housing identified in the American Housing Survey (AHS) for 2005, which was 2.0 million households, or about 1.6 percent of all U.S. households. 28 (Based on the AHS, there 124.4 million U.S. households. 29 ) According to the 2001 Residential Energy Consumption Survey (RECS 2001), 76 percent of publicly owned households used microwave

ovens. 30 DOE assumed that the same percentage of publicly owned housing units would operate microwave ovens. Hence, DOE estimated that 1.2 percent of US households are publicly owned

house holds using microwave ovens, and thus constitute the eligible population to which this poli cy would apply. Of government-owned residential buildings, DOE’s life-cycle cost and

pa yback period analysis estimated that, by the start year of 2016, none had microwave ovens at efficiency levels meeting the amended energy conservation standards.

Based on the above percentages, at the end of the first year of the policy case (2016),

DO E estimated that eight percent of shipments of government-purchased microwave ovens would be equipment meeting the target levels due to existing bulk government purchase programs. By 2026 , DOE estimated that bulk government purchase programs would increase their market share to 80 percent. Th us, DOE modeled the enhanced bulk government purchase program assuming that an annual eight-percent market share increase would be maintained over 10 years starting in 2016

. S ection 17.4 below presents the resulting efficiency trends for the bulk government purchase policy case for microwave ovens.

17.4 NON-REGULATORY POLICY IMPACTS

Figure 17.4.1 shows the market share impacts (increased penetration rates) for

mi crowave ovens for each policy case. In the base case (i.e., the case without standards or non-17-16

regulatory programs), no products are forecasted to meet the target level. All policy cases

incr ease the market share of products meeting the target level, with voluntary energy efficiency targets being the most effective. As a reminder, the proposed standards (not shown in Figure 17.4.2) would result in a 100 percent penetration of products meeting the target level.

Figure 17.4.1 Penetration Rates of Units Meeting the Target Level for Microwave

Ovens

17.5 RESULTS SUMMARY FOR NON-REGULATORY ALTERNATIVES

Tables 17.5.1 and 17.5.2 show the national energy savings and NPV resulting from the various non-regulatory alternative policy cases, when the efficiency target levels are equal to the proposed standard levels

. The case in which no regulatory action is taken with regard to microwave ovens constitutes the base case (or "No Action") scenarios, in which energy savings

a nd NPV are zero by definition. For co mparison, the tables include the impacts of the proposed energy conservation standards. The NPV amounts shown in Tables 17.5.1 and 17.5.2 are based

on two dis count rates (seven percent and three percent real).17-17

Table 17.5.1 Non-Regulatory Alternatives for Product Class 1 with Target Level at TSL 3

Policy Alternatives Primary Energy Savings (quads) Net Present Value*

(billion 20 11 $)

7% discount rate 3% discount rate

No New Regulatory Action 0.0 0.0 0.0

Consumer Rebates 0.056 0.178 0.393

Consumer Tax Credits 0.020 0.073 0.162

Manufacturer Tax Credits 0.011 0.041 0.088

Voluntary Energy Efficiency Targets 0.370 1.305 2.884

Early Replacement 0.016 0.086 0.134

Bulk Government Purchases 0.006 0.021 0.050

Proposed Standards At TSL 3 0.475 1.522 3.358

* Net present value (NPV) is the value of a time series of costs and savings

. DOE determined the NPV from 2016

to 2045 in billions of 20 11 $.

Table 17.5.2 Non-Regulatory Alternatives for Product Class 2 with Target Level at TSL 3

Policy Alternatives Primary Energy Savings (quads) Net Present Value*

(billion 20 11 $)

7% discount rate 3% discount rate

No New Regulatory Action 0.0 0.0 0.0

Consumer Rebates 0.001 0.002 0.005

Consumer Tax Credits 0.000 0.000 0.001

Manufacturer Tax Credits 0.000 0.000 0.001

Voluntary Energy Efficiency Targets 0.003 0.008 0.020

Early Replacement 0.000 0.001 0.001

Bulk Government Purchases 0.000 0.000 0.000

Proposed Standards At TSL 3 0.006 0.017 0.038

* Net present value (NPV) is the value of a time series of costs and savings

. DOE determined the NPV from 2016

to 2045 in billions of 20 11 $.17-18

REFERENCES

1 Rufo, M. and F. Coito. California's Secret Energy Surplus: The Potential for Energy Efficiency . Prepared for The Energy Foundation and The Hewlett Foundation. 2002.

XEN ERGY Inc.: Oakland, CA.

2 Rufo, M. Personal communication. Itron, Inc., Oakland, CA. Telephone conversation with Barbara Atkinson, LBNL. January 2008.

3 ICF International. Arizona Public Service Energy Efficient Market Potential Study

. 2007.

ICF International: Fairfax, VA.

4 Lee, A. Personal communication

. Quantec, LLC, Portland, OR. Telephone conversation with Barbara Atkinson, LBNL. January 2008.

5 Mosenthal, P. Personal communication

. Optimal Energy, Bristol, VT. Telephone conversation with Barbara Atkinson, LBNL. January 2008.

6 Train, K. Customer Decision Study: Analysis of Residential Customer Equipment Purchase

Decisions . Prepared for Southern California Edison by Cambridge Systematics, Pacific Consulting Services, The Technology Applications Group, and California Survey Research

Services. 1994.

7 LBNL. Analysis of Tax Credits for Efficient Equipment

. Lawrence Berkeley National Laboratory, End-Use Forecasting Group: Berkeley, CA. Last accessed December 10, 2010.)

< http://enduse.lbl.gov/Projects/TaxCredits.html >

8 Tax Incentives Assistance Project, Consumer Tax Incentives: Hom e Heating & Cooling Equipment, (Last accessed December 10, 2010. )

< http://www.energytaxincentives.org/consumers/heating-cooling.php >

9 Oregon Department of Energy–Conservation Division, Re sidential Energy Tax Credits, (Last accessed December 10, 2010.)

< http://www.oregon.gov/ENERGY/CONS/RES/RETC.shtml >

10 Montana Department of Environmental Quality, Tax Incentives, (Last accessed December 10, 2010.) < http://deq.mt.gov/Energy/warmhomes/taxincentives.mcpx >

11 Dodds, D. Personal communication . Montana Department of Revenue, Helena, MT. Email to Barbara Atkinson, LBNL. December 2007– Ap ril 2008.

12 Barker, D. Personal communication . Oregon Department of Energy, Salem, OR. Email to Barbara Atkinson, LBNL. December 2007–April 2008.

13 Association of Home Appliance Manufacturers (AHAM). Estimated Distribution Sales by17-19

State for Room Air Conditioners - 2006. Washington, D.C. (Item ID: BDSTATE)

< htt p://www.aham.org/ht/d/ProductDetails/sku/BDSTATE/from/5256/pid/ > (Last accessed May 1, 2008.

14 ENERGY STAR. National, State, and Regional Appliance Sales Data. (Last accessed December 10, 2010. )

< http://www.energystar.gov/index.cfm?c=manuf_res.pt_appliances >

15 KEMA. Consumer Product Market Progress Evaluation Report 3 . 2007. Prepared for Northwest Energy Efficiency Alliance. KEMA, Inc.: Portland, OR. Report #07-174.

16 Itron and KEMA. 2004/2005 Statewide Residential Retrofit Single-Family Energy Efficiency

Rebate Evaluation . 2007. Prepared for the California Public Utilities Commission, Pacific Gas and Electric Company, San Diego Gas and Electric Company, Southern California Edison, and Southern California Gas Company. Itron, Inc. and KEMA, Inc.: Oakland, CA.

C PUC-ID#: 1115-04.

17 U.S. Department of the Treasury–Internal Revenue Service, Form 8909, (Last accessed December 10, 2010.)

< http://www.irs.gov/pub/irs-pdf/f8909.pdf >

18 Tax Incentives Assistance Project, Legislative Language and Pending Update, (Last accessed December 10, 2010.)

< http://www.energytaxincentives.org/general/legislative.php >

19 Feldman,S., L. Hoegfen, L. Wilson-Wright, and A. Li. Modelling the effects of U.S.

ENERGY STAR® appliance programs. In Energy Savings: What Works and Who Delivers? 2005 ECEEE Summer Study Proceedings. 2005. Mandelieu La Napoule, France, May 30- June 4. European Council for an Energy Efficient Economy. Pp

. 855 -866.

20 Rosenberg, M. The Impact of Regional Programs on the Market Share of ENERGY STAR® Applianc es. In 2003 International Energy Program Evaluation Conference Proceedings

. 2003. Seattle, WA, August 20-22. International Energy Program Evaluation Conference.

P p. 1069-1074.

21 Titus, E. and S. Feldman. Tracking the Effectiveness of Energy-Efficient Appliance Programs in the U.S. In 2003 International Energy Program Evaluation Conference Proceedings . 2003. Seattle, WA, August 20-22. International Energy Program Evaluation Conference. Pp. 1087- 10 98

.

22 Sanchez, M., C. Webber, R. Brown, and G. Homan. 2007 Status Report - Savings Estimates for the ENERGY STAR® Voluntary Labeling Program

. 2007. Lawrence Berkeley National Laboratory: Berkeley CA. LBNL-56380.17-20

23 Nexus and RLW Analytics

. Impact, Process, and Market Study of the Connecticut Appliance

Re tirement Program: Overall Report, Final. 2005

. Submitted to Northeast Utilities– Connecticut Light and Power and the United Illuminating Company. Nexus Market Research, Inc. and RLW Analytics, Inc.: Cambridge, MA.

24 GDS Associates, Inc. Vermont Electric Energy Efficiency Potential Study: Final Report.

2007 . Prepared for the Vermont Department of Public Service. GDS Associates, Inc.: Marietta, GA.

25 McMahon, J. E. and J. P. Harris. Early Replacement of Appliances

. 1994. Unpublished

re port for US Department of Energy. Lawrence Berkeley National Laboratory: Berkeley,

C A.

26 Smith , N. Personal communication. National Association of State Procurement Officials (NAPSO), Lexington, KY. Telephone conversation with Barbara Atkinson, LBNL. April,

2008.

27 Responsible Purchasing Network. (Last accessed December 10, 2010.)

< http://www.responsiblepurchasing.org/ >

28 HUD. A Picture of Subsidized Households – 2000. US Department of Housing and Urban Development. (Last accessed December 10, 2010.)

< http://www.huduser.org/picture2000/ >

29 U.S. Census Bureau, Housing and Household Economic Statistics Division. Th e American Housing Survey, 2005. Washington, DC. (Last accessed December 10, 2010.)

< http://www.census.gov/hhes/www/housing/ahs/ahs.html >

30 U.S. Department of Energy

— Energy Information Administration. Residential Energy Consumption Survey: Household Energy Consumption and Expenditures 2001

. 2001. Washington, DC. (Last accessed December 10, 2010.)

< http://www.eia.doe.gov/emeu/recs/public.html >5 A

- i APPENDIX 5A. AHAM DATA SUBMITTAL

TABLE OF CO NTENTS

5A.1 INTRODUCTION

................................ ................................ ................................ .. 5A

- 1

5A.2 AHAM MICROWAVE OVEN DATA SUBMITTAL

................................ .......... 5A

- 1

LIST OF FIGURE S

Figure 5A.2.1 AHAM Standby Power Test Data

................................ ................................ . 5A

- 65A

- 1 APPENDIX 5A. AHAM DATA SUBMITTAL

5A.1 INTRODUCTION

This appendix presents the shipments and incremental cost data submitted to the U.S. Department of En ergy (DOE) by the Association of Home Appliance Manufacturers (AHAM) in support of the engineering analysis for this rulemaking. See c hapter 5 of the technical support document (TSD) for details of the eng ineering analysis for which these data were used.

5A.2 AHAM MICROWAVE OVEN DATA SUBMITTAL

AHAM submitted the following microwave efficiency and standby tes t data and report to

DOE on October 23, 2006.

October 23 , 2006

Mr. Michael Rivest

Managing Director

Navigant Consulting, Inc.

1801 K Street, NW

, Suite 500

Washington, D.C. 20006

- 1301

Subject: AHAM Test Data on Microwave Ovens

Summary:

The Association of Home Appliance Manufacturers is the trade association representing the

manufacturers of over 95% of the microwave ovens sold in the U.S. To assist the Department of Energy in its rulemaking for Microwave Ovens (MWOs), AHAM commissioned the independent testing at

Inte r tek/ETL of 22 randomly selected MWOs (provided by 7 different manufacturers). The ovens represented a broad spectrum of un its presently in the market place, and varied by size, power rating, components, and features.5A

- 2 The tests were conducted using the DOE test procedures prescribed in 10 CFR Part 430 Appendix I,

which references IEC Standard 705

- 1988 with Amendment 2

- 1993. The results of the tests are shown in the file embedded below. Each unit was tested 3 times. The average Energy Factor of the 3 runs for each unit is highlighted in yellow in the embedded file.

[File reproduced at the end of this letter]

The test resul ts show that there is a very narrow range in energy efficiency between the least and most efficient unit ( 54.8% minimum to 61.8% maximum

- less than a 13% spread)

. Using the annual useful cooking energy output of 79.8kWh/yr, as determined by DOE in its Se ptember 8, 1998 rulemaking, this range in efficiencies results in a difference of only about 16 kWh/yr (which is about $1.60/yr) between the

least and most efficient unit

.

The September 8, 1998 Final Rule on cooking products states that, “DOE has determin ed that there would be no significant conservation of energy for ... microwave ovens, and standards would not be economically justified.” a W e believe the test data and information provided herein confirms that an efficiency standard for MWOs is still not warranted.

DOE considered many design options for microwave ovens in its 1998 rule , and after extensive analysis, it was determined that no design options were technology feasibl e or economically justifiable. There have been no technologica l or economic breakthroughs since the previous determination which would change the previous conclusion.

Factors and Features Impacting Efficiency:

There are many factors tha t impact the efficiency of MWOs, and many different features that are d riven

b y the demands of the marketplace. These features d etermine the need for components that consume electrical energy or absorb micr owave energy during operation. Common components that affect the energy efficiency are shown in the table below:

Turntable m otor Waveguide & cavity Magnetron tube

Stirrer motor Glass turntable High Voltage Transformer Cavity lamp Turntable roller High Voltage Diode

Fan motor Turntable hub High Voltage Capacitor Display, relays, & electronics Stirrer blade

Many of the components listed above may impact the energy consumption differently, depending upon the specific oven features and application. For example, an Over

- The

- Range (OTR) oven may require a larger cooling fan motor than a counter

- top oven. This is because the ambient air at the OTR air inlet is at an elevated temperature due to the range beneath being in operation.

W attage of the cavity lamp is another example. The consumer desires brightly illuminated oven cavities. Increasing the wattage of the lamp by on ly 10 watts c ould lower the efficiency of an oven by about 0. 5 %. Some manufacturers may choose higher wattage lamps for product differentiation.

a 63 Fed. Reg. 48,038 (Tuesday, September 8, 1998).5A

- 3 Manufacturers do not optimize the efficiency of their designs around the DOE test procedure, which calls

for a simplified method of heating one liter of water in the center of the oven. Rather, ovens are designed to heat various sizes, shapes, and types of food most efficiently and evenly. Again, consumer needs are the key drivers on MWO design.

For example, d esigners often use a stirrer to improve cooking performance as determined by Consumer Union testing in categories such as thermal uniformity and defrosting. The stirrer is a rotating device used to vary how the electromagnetic field is applied to the food in the oven cavity. As the stirrer sweeps through its rotation, the magnetron’s frequency and efficiency also change

. The net effect is to improve cooking performance for the consumer, in exchange for a small decrease in oven efficiency.

Design Options :

In DOE’s March 15, 2006 Framework Document, several design options are listed for microwave ovens as possible considerations to improve energy efficiency. We offer the following comments on those design options:

**•** Add insulation

– This is not a practica l option, as it would not improve the Energy Factor. The DOE test is only about 45 seconds in duration and the temperature of the oven walls does not increase during use, thus no significant energy would be saved by adding insulation. It is likely

that t he net result would not even be measurable.

**•** Reflective surfaces

– Manufacturers are already using surface finishes to optimize efficiency. If DOE is referring to the possibility of adding metallic plates inside the oven cavity to adjust cavity impedance to the highest oscillation impedance of the magnetron, this is best accomplished by better oven design without such metallic plates.

**•** More efficient fan

– A typical fan only draws about 30 watts, resulting in less than 2% of the total energy consumed durin g the DOE test. Therefore, improving the fan efficiency by going to more expensive motors would at best improve the energy factor by less than 0.5%. Thus more efficient fans motors are not economically justified.

**•** Improve efficiency of magnetron

– The ma gnetron tube is the heart of the MWO. As such, considerable engineering effort has already gone into optimizing their efficiency, and there is very little difference in available technologies. Virtually all magnetrons are the same. A review of magnetron manufacturers’ specifications shows that the typical efficiency is about 73%, with only a plus or minus 2% variance. Thus, there is no available technology for improvement.

**•** Improve efficiency of the power supply

– There are two types of high voltage pow er supplies that are used in MWOs. The predominant type is the inductive capacitance transformer, known as the LC power supply, which has an efficiency of about 82%. The other type is the inverter type, which is more expensive, and has an efficiency of a bout 84%. (General purpose transformers have higher efficiencies, but the need for stable output power in MWOs requires the use of special transformers which have inherently lower efficiencies.)

Three of the units tested utilized an inverter type power supply, whereas all others used the more typical LC transformer

. Even though inverter power supplies are slightly more efficient than LC
types, t he Energy Factors of the 3 units with an inverter were right a round the median of all the

units tested . They ranged around 56% to 59%, with the average of the 3 units being 58%

.5A

- 4 Therefore, the use of an inverter as a design option does not necessarily improve efficiency. We see no cost effective opportunity for improving the efficiency of the power supply.

**•** Eliminate or improve ceramic stirrer cover

– Virtually no residential solo

- function microwave ovens still use a ceramic stirrer. Almost all MWOs use a polypropylene or mica film, which are low energy loss materials resulting in less losses than the cerami c type. Thus this design option has already been optimized.

**•** Modify wave guide

– We are not aware of any improvement to the wave guide that would result in any significant increase in energy efficiency. Losses associated with the wave guide are typically less than 0.5% of the overall energy consumption.

**•** Dual magnetrons

– This would not be an effective way to improve energy efficiency since magnetrons require energy to heat the magnetron heater. This energy is not converted to microwave energy, and thus is wasted. Thus, the more magnetrons used, the more wasted energy is consumed, not to mention the additional cost of extra magnetrons.

Standby Power:

Standby power was measured on all the ovens tested, in accordance with IEC Standard 62301

- 2005,

Househol d electrical appliances

– Measurement of standby power

. The measurements ranged between 1.5 and 5.8 watts, with an average of 2.9 watts. Each watt of standby power is approximately 8.7 kWh/yr, which amounts to about 85 cents/yr. Even if DOE limited the standby power of MWOs to 2 watts, it would only result in less than a dollar per year savings on average per unit.

There are three major types of displays used in microwave ovens today. They are LED (light emitting diodes), VFD (vacuum fluorescent display) and LCD (liquid crystal display). Typically, LCD without

back

- lighting uses the least amount of energy while VFD uses the most. Energy consumed by LCD would depend on whether back

- lighting is present or not. And if present, the number of LEDs u sed for

back

- lighting is a major factor in energy consumption.

For LEDs, the number of segments, color and operating temperature has an impact on its energy consumption. The size of the display (number of digits and icons) also affects the energy consume d by the display. For VFDs, the size of display is a major factor in its energy consumption.

The type of display used, its size, and type of back

- lighting, etc., all have a direct impact on the product’s appeal to the consumer. Thus, while an LCD with out back

- lighting may be most energy efficient, it may not have as much consumer appeal as a VFD, which is typically the best from a legibility perspective.

Manufacturers (driven by consumer/market desires) want the flexibility to produce microwave oven s using the various displays described above in order to provide products with market differentiation , in turn meeting the diverse needs of consumers . This is the reason for the variance in the test results for measured standby power. Therefore, AHAM reco mmends that standby power not be considered as a separate prescriptive requirement.

Conclusion:

All the ovens we tested were within a relatively narrow efficiency range without any current energy standards in effect. The tests indicated a range in eff iciencies that results in a difference of only about 165A

- 5 kWh/yr (which is about $1.60/yr) between the least and most efficient unit . T his narrow range of

efficiencies is a result of optimization of current cost effective design options and market competiti on to produce high quality microwave ovens at low prices for the consumer , while still being able to offer ample product features and differentiation.

Efficiency standards for microwave ovens are not justified, as very little energy savings would result.

T he cost and risk of modifying today’s well

- performing products with questionable design options should

not be underestimated. Microwave ovens are much more efficient than conventional cooking products, and their use should be encouraged, not discourage d by adding cost to the products for very little overall energy savings benefit.

Therefore, we recommend that a “No Standard, Standard” be re

- established for these products and that significant resources not be expended to conduct unnecessary analyses. S ufficient data and precedent exists to support and affirm DOE’s previous conclusion that standards for MWOs are not justifie d. DOE and industry resources w ould be better spent on expediting other rulemakings.

Thank you for the opportunity to provide this information. Please feel free to contact me with any questions that you might have.

Sincerely,

Lawrence R. Wethje, P.E.

Vice President

, Technical Services5A

- 6 Figure 5 A.2

. 1 AHAM Standby Power Test Data

Client: AHAM
Project No.: 3100653
Microwave Oven Re-Testing: 09/20-22/06
Tested per IEC 705-1988 and Amendment 2-1993

Unit

No. Rated

Volume

(ft3) Humidity

Sensor
(Y/N) Rated

Watts Mw ,
(g) Mc,
(g) T0,
(oC) T1,
(oC) T2,
(oC) t,
(Sec.) Win Watts @

20 Sec. P,
(Watts) EF,
(%) SP,**
(Watts)

1000 515 20.8 10.0 19.5 52 18.9 1292 753.6 58.3

1000 515 19.6 10.4 19.7 52 18.8 1286 749.7 58.3

1000 515 19.9 10.0 19.1 52 18.8 1292 725.8 56.2

**57.6**

1005 515 19.5 10.4 19.9 35 18.4 1875 1147.3 61.2

1000 515 21.9 11.7 21.3 35 18.4 1876 1140.7 60.8

995 515 20.8 10.5 20.2 35 18.3 1872 1146.8 61.3

**61.1**

1000 515 18.9 9.8 20.0 42 20.0 1730 1028.7 59.5

1000 515 19.5 10.1 20.3 42 20.1 1735 1025.5 59.1

1000 515 19.9 10.4 20.3 42 19.7 1705 991.3 58.1

**58.9**

1000 515 19.5 10.1 19.9 42 19.6 1696 981.3 57.9

1000 515 20.7 10.5 20.2 42 19.7 1704 961.6 56.4

995 515 20.5 10.5 20.3 42 19.4 1678 969.9 57.8

**57.4**

1000 515 20.7 10.8 20.4 42 19.1 1604 953.8 59.5

1000 515 19.3 10.1 19.8 42 19.1 1602 972.4 60.7

1000 515 19.2 9.9 19.8 42 19.1 1607 993.4 61.8

**60.7**

1000 515 19.1 10.1 20.1 35 19.6 1946 1209.2 62.1

1005 515 19.2 10.1 20.1 35 19.6 1951 1213.9 62.2

1000 515 21.1 10.8 20.8 35 19.6 1950 1192.4 61.1

**61.8**

1000 515 19.2 9.8 18.8 32 17.6 1988 1171.9 59.0

995 515 20.4 10.2 19.4 32 17.6 1986 1183.6 59.6

1000 515 20.8 10.8 19.8 32 17.5 1984 1163.4 58.6

**59.1**

1000 515 21.3 11.0 20.4 34 18.7 2010 1145.6 57.0

1000 515 19.6 10.6 19.9 34 18.8 2010 1149.3 57.2

1000 515 20.0 10.7 19.9 34 18.8 2010 1131.6 56.3

**56.8**

1005 515 18.7 9.3 18.6 34 18.8 2008 1149.7 57.3

995 515 19.3 9.8 19.3 34 18.7 2006 1164.0 58.0

1000 515 19.2 9.7 19.3 34 19.1 2009 1183.5 58.9

**58.1** 2.2 Y **Average:** Y 1300 2.0 1.2 **Average:** Y 1200 3.7 1.6 **Average:**

1.6 N

9 8 **Average:** 1.5

3.0

3.9

4.2 **Average:**

**Average:**

**Average:**

**Average:**

1.2 N 1.8

1250 2.2

1250 2.9 **Average:** N 1200

1.6*

1000 1.6* 1.5

Y 1000 N 800

N 1000 0.8 1

3

4

5 2

6

75A

- 7 1000 515 19.6 10.0 19.8 60 20.2 1216 685.4 56.4

1000 515 21.8 11.8 21.3 60 20.3 1218 659.2 54.1

1000 515 21.7 11.8 21.4 60 20.0 1210 667.7 55.2

**55.2**

1000 515 19.7 10.1 20.3 60 20.0 1192 716.3 60.1

1000 515 21.6 11.4 21.4 60 19.6 1180 696.3 59.0

1000 515 21.7 11.1 21.0 60 19.6 1176 685.6 58.3

**59.1**

1000 515 21.8 11.3 21.0 42 19.8 1690 958.4 56.7

1000 515 19.2 10.1 19.9 42 20.1 1712 984.5 57.5

1000 515 19.6 10.0 19.6 42 19.6 1668 957.0 57.4

**57.2**

1000 515 18.5 9.1 19.2 38 20.9 1934 1121.2 58.0

1000 515 19.1 9.9 20.1 38 20.9 1959 1135.8 58.0

1000 515 19.1 9.9 19.8 38 20.6 1909 1099.2 57.6

**57.8**

1000 515 19.8 8.9 19.0 42 19.9 1670 998.2 59.8

1000 515 21.7 11.3 21.1 42 20.3 1686 970.5 57.6

1000 515 21.0 10.4 20.4 42 20.2 1683 990.4 58.8

**58.7**

1000 515 18.9 9.7 20.0 42 19.7 1672 1038.7 62.1

1000 515 19.1 10.1 20.3 42 19.7 1675 1029.8 61.5

1000 515 20.4 10.2 20.4 42 19.8 1676 1016.8 60.7

**61.4**

1000 515 19.5 10.0 20.0 52 21.0 1421 809.6 57.0

1000 515 21.7 11.2 21.1 52 20.6 1402 791.9 56.5

1000 515 21.6 11.0 20.8 52 20.6 1402 782.1 55.8

**56.4**

995 515 19.6 10.5 20.3 35 18.7 1878 1175.6 62.6

1000 515 21.1 11.1 20.7 35 18.6 1874 1143.3 61.0

1000 515 21.0 10.6 20.2 35 18.6 1875 1138.1 60.7

**61.4**

1000 515 21.3 10.6 20.7 42 20.8 1740 1000.4 57.5

1000 515 19.4 9.7 19.8 42 20.8 1745 1011.2 57.9

1005 515 20.0 10.5 20.3 42 20.8 1745 985.1 56.5

**57.3**

1000 515 19.4 9.2 18.8 42 19.9 1668 950.6 57.0

1000 515 20.7 10.3 19.7 42 19.8 1669 926.3 55.5

1000 515 21.3 11.6 20.8 42 19.8 1668 911.8 54.7

**55.7**

1000 515 20.0 10.7 20.2 38 21.1 1936 1049.1 54.2

1005 515 20.0 9.6 19.4 38 21.1 1941 1078.0 55.5

1000 515 20.1 10.0 19.6 38 20.8 1922 1051.8 54.7

**54.8**

1000 515 20.2 10.6 20.3 44 20.5 1630 924.1 56.7

995 515 21.1 11.1 20.6 44 20.5 1628 894.3 54.9

1000 515 19.9 10.4 19.9 44 20.2 1623 904.0 55.7

**55.8**

Legend:

**P - is the microwave power output, in watts**
**M w - is the mass of the water, in grams**
**M c - is the mass of the container, in grams**
**T0 - is the ambient temperature, in degrees Celsius**
**T1 - is the initial temperature of the water, in degrees Celsius**
**T2 - is the final temperature of the water, in degrees Celsius**
**t - is the heating time, in seconds, excluding the magnetron filament heating-up time Average:**  **Average:**

Y 1000 1000 N 1000

**Average: Average:** 

**Average:**

**Average:** N 0.7

Y 1100 1000 **Average:**

**Average:**

**Average:** N

DAMAGED UNIT!!!!!

5.8

22 1.4* N 950 2.6 1.5* Y 1100 **Average:**

**Average:** 21 4.4

20 1.0 N 1000 1.7 1.5 19 2.0

18 1.5 N

- 1.2 N 1200 **Average:**

**Average:** 17 1.8

16 0.8* Y 800 5.7 1.1 15 3.6

14 0.9* Not

Listed 1000 2.0 2.0 13 700 2.0 0.7 2.1

1.9 700

1.6 N 12 10

115A

- 8 **Win - is the energy input, in watt-hours**
**EF - is the Energy Factor, or efficiency, in percent**
**SP - is the standby power, in watts**

**All tests were performed at 120 Vac, 60 Hz**
*** - Physical measurement performed by ETL**

  - M easured with the microwave clock set to 12:00 and with door closed.** 5 B

- i APPENDIX 5B. MICROWAVE OVEN STANDBY POWER TESTING

REGRESSION METHODOLOGY

TABLE OF CO NTENTS

5B.1 INTRODUCTION

................................ ................................ ................................ ... 5B

- 1

5B.2 METHODOLOGY

................................ ................................ ................................ .. 5B

- 1

LIST OF TABLES

Table 5B.2.1 Active Element Lookup Table

................................ ................................ ..... 5B

- 1

Table 5B.2.2 Sample Regression Table

................................ ................................ ............. 5B

- 2

Table 5B.2.3 Sample Regression Output

................................ ................................ ........... 5B

- 2

Table 5B.2.4 Sample Integration Table

................................ ................................ ............. 5B

- 35B

- 1 APPENDIX 5B. MICROWAVE OVEN STAND BY POWER TESTING

REGRESSION METHODOLO GY

5B.1 INTRODUCTION

The U.S. Department of Energy ( DOE ) considered alternate testing methods to determine standby power over a shorter period than 12 hours that were highly representative in order to reduce the test burden on manufacturers. Thus, DOE developed an algorithm by which the standby power consumpti on and line voltage are measured as the clock is cycled through all the possible digit combinations (in terms of active elements) and then a regression analysis is performed to quantify the impact of the number of lit elements (by digit) and voltage on pow er consumption. The results are then integrated across the number of minutes that each active element combination is “on” through the course of the 12 hours.

5B.2 METHODOLOGY

The test method would require that the tester enter the following 18 clock combinati ons at 15 second intervals into the microwave oven clock display: 1:11, 1:17, 1:14, 1:15, 1:16, 1:18, 1:41, 1:51, 1:01, 1:55, 7:55, 4:55, 5:55, 6:55, 8:55, 11:55, 12:55, and 10:55. For each programmed time, the average line voltage and standby power consu mption over a 15

- second period are measured. The tester also determines, for each clock time displayed, the total number of active elements. For the purpose of calculating the active number of elements for each digit displayed on the microwave oven displ ay, DOE developed the following simplified lookup table.

Table 5B.2.1 Active Element Lookup Table

Digit Displayed Number of Active Elements

Blank / None 0

0 6

1 2

2 5

3 5

4 4

5 5

6 6

7 3

8 7

9 6

The results are then used as inputs to the regression analysis, as shown for illustrative purposes with example data in Table 5B.2.2 below. The regression uses five input factors: the number of active elements for each digit (AE1 through AE4, assuming a four

- digit display) and5B

- 2 the average line voltage in volts alternating

- current (VAC), while the standby power consumption in watts (W) is the output.

Table 5B.2.2 Sample Regressio n Table

Clock Time Average Voltage (VAC) Number of Active Elements by Digit
Average Standby Power (W) 1 st 2 nd 3 rd 4 th

1:11 119.718 0 2 2 2 1.16308

1:17 119.8 0 2 2 4 1.22024

1:14 119.8128 0 2 2 4 1.22018

1:15 119.5997 0 2 2 5 1.23941

1:16 119.7364 0 2 2 6 1.2659

1:18 119.7421 0 2 2 7 1.28572

1:41 119.7179 0 2 4 2 1.21773

1:51 119.9457 0 2 5 2 1.24857

1:01 119.9266 0 2 6 2 1.2708

1:55 119.8723 0 2 5 5 1.32908

7:55 119.8025 0 4 5 5 1.38373

4:55 119.7811 0 4 5 5 1.3812

5:55 119.6811 0 5 5 5 1.40734

6:55 119.7248 0 6 5 5 1.43133

8:55 119.758 0 7 5 5 1.45239

11:55 119.7374 2 2 5 5 1.38162

12:55 119.6646 2 5 5 5 1.46521

10:55 119.6489 2 6 5 5 1.48858

The regression results consist of six coefficients: AE1

- AE4, voltage, and the intercept. A sample Microsoft Excel regression result is shown below in Table 5B.2.3

.

Table 5B.2.3 Sample Regression Output

SUMMARY OUTPUT

Regression Statistics Multiple R 0.998071 R Square 0.996146 Adjusted R 0.99454

Standard E 0.009197

Observatio 18

ANOVA

df SS MS F ignificance F Regression 5 0.26235 0.05247 620.3353 4.78E-14

Residual 12 0.001015 8.46E-05

Total 17 0.263365

Coefficients tandard Erro t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -2.989038 0.434515 -6.879024 1.7E-05 -3.935764 -2.042311 -3.935764 -2.042311

Volts 0.035709 0.00358 9.975633 3.68E-07 0.02791 0.043508 0.02791 0.043508

AE1 0.047066 0.003238 14.5357 5.57E-09 0.040011 0.054121 0.040011 0.054121

AE2 0.030723 0.001635 18.79208 2.88E-10 0.027161 0.034286 0.027161 0.034286

AE3 0.030894 0.001898 16.28024 1.52E-09 0.02676 0.035029 0.02676 0.035029

AE4 0.032807 0.00169 19.41726 1.97E-10 0.029126 0.036488 0.029126 0.0364885B

- 3 The five input factors are input into a small integration table which accounts for how many minutes each digit is displaying a given number of active elements per day. A sample integration table appears as Table 5B.2.4 below.

Table 5B.2.4 Sample Integration Table

Active

Elements Minutes the Number of Active Elements are on For a Given Digit per Day Standby Energy Consumption AE1 AE2 AE3 AE4

2 360 240 240 144 0.913 Wh

3 0 0 0 0 0.000 Wh

4 0 240 240 288 1.367 Wh

5 0 480 720 432 3.661 Wh

6 0 360 240 432 2.743 Wh

7 0 120 0 144 0.808 Wh Voltage 28.292 Wh
Intercept

- 4.207 Wh
Average Daily Total 33.577 Wh

1.399 W Yearly 12.2557 kWh

The integration table then predicts the average standby power in W, the daily total watt

hours (Wh) consumed, and the yearly standby energy consumption in kilowatt

- hours (kWh), assuming stable 120 VAC input power.5 C

- i APPENDIX 5C. MICROWAVE OVEN DISPL AY CONFIGURATIONS

TABLE OF CO NTENTS

5C.1 INTRODUCTION

................................ ................................ ................................ ... 5C

- 1

5C.2 DISPLAY CONFIGURATIO NS

................................ ................................ ............ 5C

- 1

LIST OF FIGURE S

Figure 5C.2.1 Numeric Display

................................ ................................ .......................... 5C

- 1 Figure 5C.2.2 Alphanumeric Display

................................ ................................ ................. 5C

- 1 Figure 5C.2.3 Dot

- Matrix Array Display

................................ ................................ ............ 5C

- 25C

- 1 APPENDIX 5C. MICROWAVE OVEN DISPL AY CONFIGURATIONS

5C.1 INTRODUCTION

The U.S. Department of Energy ( DOE ) reviewed various configurations of microwave oven displays in order to gain a better understanding of how the number of active elements, and hence display power consumption, can vary as a function of the digits or char acters being displayed. Such an analysis helped DOE to determine the optimal method to measure a representative average standby power, given the possible variation in standby power during testing.

5C.2 DISPLAY CONFIGURATIO NS

The simplest displays are the nume ric variety, which can only show the time as a series of digits. A simplified representation can be found below in Figure 5C.2.1 , which shows four individual digits . Each digit may have up to seven active elements (for the purpose of this discussion, an element is considered “active” whenever the consumer can view it in contrast to the background.) Thus, each digit has a changing number of active elements as the cl ock time advances or a cooking timer counts down.

Figure 5C.2.1 Numeric Display

More sophisticated displays can represent alphanumeric characters via additional elements. A simplified version of such a display appears below in Figure 5C.2.2. Such displays usually have more than four digits, allowing an original equipment manufacturer (OEM) to scroll messages to a microwave oven user.

Figure 5C.2.2 Alphanumeric Display Digit Individual Element5C

- 2 The most flexible displays use dot matrix arrays. Such higher

- resolution displays allow a microwave oven OEM to represent most Western fonts and some Asian fonts with acceptable clarity, allowing the most effective communication with a user. Changeable mi crowave oven controller daughter

- cards allow an OEM to readily customize the microwave oven language to suit local markets. The array shown below in Figure 5C2.3 is a simplified example.

Figure 5C.2.3 Dot

- Matrix Array Display5 D

- i APPENDIX 5D. MICROWAVE OVEN DESIGN PATHWAYS

TABLE OF CO NTENTS

5D.1 INTRODUCTION

................................ ................................ ................................ ..... 5

- 1

5D.2 PRODUCT TYPES

................................ ................................ ................................ .... 5

- 1

5D.3 DESIGN PATHWAYS

................................ ................................ .............................. 5

- 2

5D.4 MODEL

- BASED WEIGHTING

................................ ................................ ............... 5

- 2

5D.5 RESULTS

................................ ................................ ................................ .................. 5

- 2

LIST OF TABLES

Table 5.1 Product Class 1 Design Options and Model

- Based Share by Product Type

............... 5

- 3 Table 5.2 Product Class 2 Design Options and Model

- Based Share by Product Type

............... 5

- 55

- 1 APPENDIX 5D. MICROWAVE OVEN DISPL AY CONFIGURATIONS

5D.1 INTRODUCTION

The U.S. Department of Energy ( DOE ) reviewed various configurations of microwave

oven s to determine likely design approaches which manufacturers would implement in order to achieve different standby power levels. After characterizing the sequence of design options that

would be incorporated ( i.e. , “design pathways”), DOE weighted the estim ated costs of the design options by the model

- based sh are of each product type in Product Class 1 and Product Class 2 in

order to estimate the market

- average incremental cost for each standby power level

.

5D.2 PRODUCT TYPES

DOE identified five microwave oven f eatures that affect standby power, and thus the choice of design options necessary for achieving a specific standby power level. The features

include:

1. Installation Configuration

– whether the product is designed for countertop or over

- the

range use. DOE considered a product designed for built

- in applications to be equivalent

to over

- the

- range units based on the requirements for thermal management.

2. Heating Technology

– whether the product heats or cooks food by means of microwave energy only, or by microwa ve plus convection (and possibly other) means of cooking.

3. Display Type

– the display, which typically includes a clock, may be a liquid crystal display (LCD) with or without backlighting, a light emitting diode (LED) display, or a vacuum fluorescent displa y (VFD).

4. Sensor Cooking Capability

– whether or not the product incorporates a cooking sensor to automatically detect the end of a cooking process

5. Magnetron Power Supply

– certain products incorporate an inverter

- based high

- voltage power supply for the mag netron, which adds standby power compared to a typical inductive capacitance inverter.

DOE reviewed the features for 540 stock

- keeping units (SKUs) which DOE determined

were on the market in the United States as of October 2012, of which 459 were classifi ed as belonging to Product Class 1 and 81 as belonging to Product Class 2. DOE identified 25 unique combinations of the five design features among the units classified in Product Class 1. Each combination was designated as a product type. In addition, DOE identified six product types in

Product Class 2.5

- 2 5D.3 DESIGN PATHWAYS

In the market and technology assessment and screening analysis, DOE identified multiple design options as means to reduce standby power. (See chapters 3 and 4 of the final rule technical support Document (TSD) for further information.) These design options included :

1. Lower

- power display technologies

2. Zero or low

- standby power cookin g sensors

3. Improved power supply and control board options

4. Automatic power

- down

For the development of the design pathways, DOE considered improved power supply and control board options to consist of the use of a switch

- mode power supply in place of a

con ventional linear power supply and/or the use of high

- efficiency electromechanical or solid

state relays in place of low

- efficiency electromechanical ones.

DOE then considered different ways in which the design options could be sequentially implemented to achieve successively lower standby power consumption. The standby power consumption and incremental manufacturing cost associated with each design option in the pathway were summed to develop cost

- efficiency relationships for each approach. In total, DOE

i dentified 14 pathways that could be utilized by the manufacturer. For each product type, DOE

selected the lowest

- cost pathway that meets the required standby power levels as the one most likely to be implemented by manufacturers.

5D.4 MODEL

- BASED WEIGHTING

For each product class, DOE calculated the percentage of the total number of models that were represented by each product type. These percentages were used as a proxy for the market share, and were assigned as weighting factors to the costs for each product type at the various standby power levels. The result is that product types with many available models will most heavily influence the incremental costs, and those design pathways can be considered as the most typical that manufacturers will use.

5D.5 RESULTS

The following tables present the product types that DOE observed to be on the market for each of the two product classes, along with the most likely design pathway to achieve each standby power level and the product type’s model

- based share.5

- 3 Table 5

. 1 Product Class 1 Design Options and Model

- Based Share by Product Type

Type Microwave Oven Features Design Option s Implemented SKU

Share (%) * Installation Convection? Display Type Cooking

Sensor? Magnetron Supply SL 1 SL 2 SL 3 SL 4

1 Countertop No LED No Conventional Same as

Baseline Same as

Baseline Switch

- Mode Power Supply Auto Power

Down 21.4%

2 Countertop No LED Yes Conventional Zero

- Standby

Sensor Same as SL 1 SL 2 + Switch

Mode Power Supply Auto Power

Down 7.2%

3 Countertop No LED Yes Inverter Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 SL 2 + High Efficiency

Relays,

Inverter Shutoff Relay Auto Power

Down 0.7%

4 Countertop Yes LED No Conventional Same as

Baseline Switch

- Mode Power Supply Same as SL 2 Auto Power

Down 1.1%

5 Countertop Yes LED Yes Conventional Zero

- Standby

Sensor SL 1 + Switch

Mode Power Supply Same as SL 2 Auto Power

Down 0.7%

6 Over

- the

Range/Built

- In No LED No Conventional Same as Baseline Switch

- Mode Power Supply Same as SL 2 Auto Power

Down 8.3%

7 Over

- the

Range/Built

- In No LED Yes Conventional Zero

- Standby

Sensor SL 1 + Switch

Mode Power Supply Same as SL 2 Auto Power

Down 2.2%

9 Countertop No LCD No Conventional Same as

Baseline Same as

Baseline Switch

- Mode Power Supply Auto Power

Down 0.2%

10 Countertop No LCD Yes Conventional Zero

- Standby

Sensor Same as SL 1 SL 2 + Switch

Mode Power Supply Auto Power

Down 0.7%

11 Countertop Yes LCD Yes Conventional Zero

- Standby

Sensor SL 1 + Switch

Mode Power Supply Same as SL 2 Auto Power

Down 0.4%

12 Over

- the Range/Built

- In No LCD No Conventional Same as

Baseline Switch

- Mode Power Supply Same as SL 2 Auto Power

Down 0.9%

13 Countertop No Backlit LCD No Conventional Same as

Baseline Switch

- Mode Power Supply Same as SL 2 Auto Power

Down 0.7%5

- 4 Type Microwave Oven Features Design Option s Implemented SKU

Share (%) * Installation Convection? Display Type Cooking

Sensor? Magnetron Supply SL 1 SL 2 SL 3 SL 4

14 Countertop No Backlit LCD Yes Conventional Zero

- Standby

Sensor SL 1 + Switch

Mode Power Supply Same as SL 2 Auto Power

Down 3.5%

15 Countertop No Backlit LCD Yes Inverter Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 SL 2 + High Efficiency

Relays,

Inverter Shutoff Relay Auto Power

Down 5.2%

16 Countertop Yes Backlit LCD No Conventional Same as

Baseline Switch

- Mode Power Supply Same as SL 2 Auto Power

Down 0.9%

17 Countertop Yes Backlit LCD Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 0.9%

18 Countertop Yes Backlit LCD Yes Inverter Zero

- Standby

Sensor &

Switch

- Mode Power Supply SL 1 + High Efficiency

Relays,

Inverter Shutoff Relay Auto Power

Down Same as SL 3 0.9%

19 Over

- the

Range/Built

- In No Backlit LCD No Conventional Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 1.1%

20 Over

- the

Range/Built

- In No Backlit LCD Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 6.5%

21 Countertop No VFD Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 7.6%

22 Countertop Yes VFD No Conventional Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 0.4%

23 Countertop Yes VFD Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 2.8%5

- 5 Type Microwave Oven Features Design Option s Implemented SKU

Share (%) * Installation Convection? Display Type Cooking

Sensor? Magnetron Supply SL 1 SL 2 SL 3 SL 4

24 Over

- the

Range/Built

- In No VFD No Conventional Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Auto Power

Down 3.1%

25 Over

- the

Range/Built

- In No VFD Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same a s SL 1 Auto Power

Down Auto Power

Down 21.8%

* Numbers do not add to 100% due to rounding

Table 5

. 2 Product Class 2 Design Options and Model

- Based Share by Product Type

Type Microwave Oven Features Design Option s Implemented SKU

Share

(%) Installation Convection? Display Type Cooking

Sensor? Magnetron Supply SL 1 SL 2 SL 3 SL 4

1 Over

- the

Range/Built

- In Yes LED No Conventional Switch

- Mode Power Supply Same as SL 1 Same as SL 2 Auto Power

Down 6.2%

2 Over

- the

Range/Built

- In Yes LED Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 Same as SL 2 Auto Power

Down 4.9%

3 Over

- the

Range/Built

- In Yes LC D Yes Conventional Zero

- Standby

Sensor Switch

- Mode Power Supply Same as SL 2 Auto Power

Down 3.7%

4 Over

- the

Range/Built

- In Yes Backlit LCD Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 16.1%

5 Over

- the

Range/Built

- In Yes VFD No Conventional Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 1.2%

6 Over

- the

Range/Built

- In Yes VFD Yes Conventional Zero

- Standby

Sensor &

Switch

- Mode Power Supply Same as SL 1 Auto Power

Down Same as SL 3 67.9%7A

- i APPENDIX 7A. VARIABLES AND VALUES FROM RESIDENTIAL ENERGY

CONSUMPTION SURVEY

TABLE OF CO NTENTS

7A.1 INTRODUCTION

................................ ................................ ................................ .. 7A

- 1

7A.2 SAMPLE DETERMINATION

................................ ................................ ............... 7A

- 1

7A.3 VARIABLE RESPONSE CO DES IN 2009 DATABASE

................................ ..... 7A

- 2

LIST OF TABLES

Table 7A.2.1 Selection of RECS 2009 Records

................................ ................................ 7A

- 1

Table 7A.2.2 Variables Tracked in RECS 2009

................................ ............................... 7A

- 2

Table 7A.3.1 Variable Response Codes in RECS 2009

................................ .................... 7A

- 37A

- 1 APPENDIX 7A. VARIABLES AND VALUES FROM R ESIDENTIAL ENERGY

CONSUMPTION SURVEY

7A.1 INTRODUCTION

The U.S. Department of Energy ( DOE) created a database containing a subset of the records and variables from DOE’s Energy Information Administration 's (EIA ' s ) 2009 Residential Energy Consumption Survey ( RECS 2009 )

. 1 DOE used this RECS subset in its life

- cycle cost analysis for both product classes of microwave ovens : (1) microwave

- only ovens and countertop

convection microwave ovens

, and (2) built

- in and over

- the

- range convection microwave ovens

. This appendix explains the abbreviations for the variable name s and defin es the variable values.

7A.2 SAMPLE DETERMINATION

RECS 2009 does not distinguish between the two product classes of micro wave ovens. Therefore, t he subset of RECS 2009 households used to represent both product class 1 ( microwave

- only ovens and countertop convection microwave ovens ) and product class 2 ( built

in and over

- the

- range convection microwave ovens ) met the following criterion.

**•** The household had a microwave.

The RECS 200 9 variables used to match the selection criteria are listed in Table 7A.2.1

.

Table 7A.2.1 Selection of RECS 200 9 Records

Product

Class Selection

Criterion No. of

Records No. of U.S.

Households Represented

million No. Households with

Microwave Oven

million Percent of

Households with

Microwave

Oven

1 and 2 MICRO = 1 11

, 616 113.6 109 .0 96%

Source: EIA. 2009 RECS Survey Data: Table HC 3.1 Appliances in U.S. homes, b y housing unit type , 2009

.

Table 7A.2.2 lists all the variables associated with a household microwave oven that

RECS 2009 tracked . Table 7A.2.2 shows the variables used in the life

- cycle cost analysis.7A

- 2 Table 7A.2.2 Variables Tracked in RECS 200 9

Variable Description

Location Variables

DIVISION Census division

DIVISION + LRG STATE* Combined large state (LRGSTATE) and census division (DIVISION)

REPORTABLE_DOMAIN Reportable states and groups of states

Variables for Household Characteristics

AMTMICRO Amount of food cooked in the microwave

DOEID DOE 4

- digit identification number

NUMMEAL Hot meal preparation frequency

NWEIGHT Final weight

YEARMADE Residence build year

* Not among RECS 200 9 variables.

7A.3 VARIABLE RESPONSE CODES IN 2009 DATABASE

Table 7A.3.1 provides the response codes for all RECS 200 9 variables used in the household sample.7A

- 3 Table 7A.3.1 Variable Response Codes in RECS 2009

Variable Definition

AMTMICRO Amount of food cooked in the microwave

1 = Most or all

2 = About half

3 = Some or very little 4 = Used only for snacks, defrosting, or reheating food

DIVISION Census division 1 = New England

2 = Middle Atlantic

3 = East North Central

4 = West North Central

5 = South Atlantic

6 = East South Central

7 = West South Central

8 = Mountain

9 = Pacific

DIVISION + LRG STATE* Combined large state (LRGSTATE) and census division (DIVISION) 1 = New England 2 = Middle Atlantic (not including New York)

3 = East North Central

4 = West North Central 5 = South Atlantic (not including Florida)

6 = East South Central 7 = West South Central (not including Texas)

8 = Mountain 9 = Pacific (not including Califor nia)

10 = New York

11 = California

13 = Texas

14 = Florida7A

- 4 Variable Definition REPORTABLE_DOMAIN 1 = Connecticut, Maine, New Hampshire, Rhode Island, Vermon t

2 = Massachusetts

3 = New York

4 = New Jersey

5 = Pennsylvania

6 = Illinois

7 = Indiana, Ohio

8 = Michigan

9 = Wisconsin

10 = Iowa, Minnesota, North Dakota, South Dakota

11 = Kansas, Nebraska

12 = Missouri

13 = Virginia

14 = Delaware, District of Columbia, Maryland, West Virginia; 15 = Georgia 16 = North Carolina, South Carolina

17 = Florida

18 = Alabama, Kentucky, Mississippi

19 = Tennessee 20 = Arkansas, Louisiana, Oklahoma

21 = Texas

22 = Colorado 23 = Idaho, Montana, Utah, Wyoming

24 = Arizona 25 = Nevada, New Mexico

26 = California 27 = Alaska, Hawaii, Oregon, Washington

DOEID DOE 4

- digit identification number

NHSLDMEM Number that live in household

NUMMEAL Hot meal preparation frequency 1 = Three or more times a day 2 = Two times a day 3 = Once a day

4 = A few times a week

5 = About once a week

6 = Less than once a week

0 = Doesn’t cook/never cooks

NWEIGHT Final weight

* Not among RECS 200 9 variables.7A

- 5 REFERENCES

1. U.S. Department of Energy

– Energy Information Administration (EIA). Residential Energy Consumption Survey: 2009 RECS Survey Data

. October 2011. EIA: Washington,

D.C. Available online at www.eia.gov/consumption/residential/data/2009/ ; www.eia.gov/consumption/residential/data/2009/#microdata

. ( A ccessed September 2012.)8A

- i APPENDIX 8A. USER INSTRUCTIONS FO R THE LIFE

- CYCLE COST

ANALYSIS SPREADSHEET

TABLE OF CO NTENTS

8A.1 USER INSTRUCTIONS

................................ ................................ ........................ 8A

- 1

8A.2 STARTUP

................................ ................................ ................................ ............... 8A

- 1

8A.3 DESCRIPTION OF LIFE

- CYCLE COST WORKSHEET S

................................ . 8A

- 1

8A.4 BASIC INSTRUCTIONS F OR OPERATING THE LIF E

- CYCLE COST

SPREADSHEET

................................ ................................ ................................ ..... 8A

- 28A

- 1 APPENDIX 8A. USER INSTRUCTIONS FO R THE LIFE

- CYCLE COST ANALYSIS

SPREADSHEET

8A.1 USER INSTRUCTIONS

The results obtained through the U.S. Department of Energy's (DOE's) life

- cycle cost analysis for microwave ovens can be examined and reproduced using the Microsoft Excel spreadsheets available on DOE's Home Appliances Rulemaking website at www.eere.energy.gov/buildings/appliance_standards /

. S preadsh eets are available on the Building Technologies Program

– Cooking Products website at www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_pr oducts.html

.

8A.2 STARTUP

DOE’s spreadsheet enables users to perform life

- cycle cost (LCC) and payback period (PBP) analyses for both product classes

.

To examine the spreadsheet

, DOE assumes that the user has access to a personal

computer having a hardware configuration capable of running Windows NT/2000/XP. All LCC spreadsheets require Microsoft Excel 2000 or later installed under the Windows operating

system. Because certain variables inside the work s heets are defined as distributions

, the user ’s computer requires a c opy of Crystal Ball (a commercially available add

- on program)

.

8A.3 DESCRIPTION OF LIFE

- CYCLE COST WORKSHEET S

Each work sheet represents a conceptual component within the LCC calculation. To facilitate navigability and identify how work sheets are related, each work sheet contains an area

on the extreme left that show s the variables imported to and exported from the current

work sheet. Each LCC spreadsheet con tains the following worksheets.

Summary The Summary work sheet contains results of LCC and PBP simulation

run s for eac h design option and product class

.

Statistics The Statistics work sheet contains statistics for each design option and product class

.

LCC & PB Calcs The LCC&PB Calcs worksheet shows the results of LCC calculation s for all efficiency levels based on data from the 2009 Residential Energy Consumption Survey ( RECS 2009 )

. 1

Rebuttable PBP The Rebuttable PBP work sheet c ontains the calculations of total and

incremental manufac turer costs, retail prices, installation costs, repair

and maintenance costs , energy use calculations, and simple payback8A

- 2 period for each efficiency level.

Equipment Cost This work sheet calculate s the retail price values used as inputs to the

calculation of LCC in the Summary work sheet.

RECS Households The RECS Households worksheet contains the RECS 200 9 household data for each product class.

Base Case Eff Dist The Base Case E ff Dist worksheet determines the efficiency of the base

case unit.

Energy Prices The Energy Prices worksheet calculates energy prices

.

Energy Price Trends The Energy Price Trends worksheet shows future energy price trends.

Discount Rate The Discount Rate worksheet contains the distributions of discount rates for replacement and new units.

Lifetime The Lifetime worksheet contains the distribution of lifetimes for products in each product class.

8A.4 BASIC INSTRUCTIONS F OR OPERATING THE LIF E

- CYCLE COST

SPREADSHEET

Basic instructions for operating the LCC spreadsheet are given below.

1. After downloading the LCC spreadsheet from the Internet , open the file using

Microsoft Excel

. Click Enable Macro

2. Use Excel's when prompted

, and then click on the tab for

the Summary worksheet.

View/Zoom

3. You can change the parameters listed under USER INPUT on the Summary

worksheet

. There are four drop

- down boxes and one command button. The default

parameters and associated options are given below

. commands at the top menu bar to change the size of the display to fit your monitor.

a. Energ y Price Trend

b. : Defaults to “ AEO 201 2

– r eference

. ” To change the input,

use the drop

- down menu and select the desired trend ( reference, low

, or high)

. Start Year

c. : Defaults to “ 2014

. ” To change the value

, use the drop

- down menu and select the desired year.

# of Trials

d. : Defaults to “ 10,000. ” To change the value

, use the drop

- down

menu and select the desired number of trials ( 100 ; 1,000 ; 2,000 ; 5,000 ; 10,000 ; or 50,000 ). Analysis Group : Defaults to “National.” To analyze a subgroup, use the drop

down menu and select the desired subgroup.8A

- 3 4. To run the Crystal Ball simulation, click the run button (you must re

- run after changing any parameters). The spreads heet will then be minimized. You can monitor the progress of the simulation by wat ching the count of iterations in the bottom

- left

corner of the screen

. Wh en the simulation is finished, the spreadsheet will re

- open

.

Additional information can be found in the Statistics and Summary worksheet s

.8A

- 4 REFERENCES

1. U.S. Department of Energy

– Energy Information Administration (EIA). Residential Energy Consumption Survey: 2009 RECS Survey Data . October 2011. EIA: Washington,

D.C. Available online at www.eia.gov/consumption/residential/data/2009/

. ( A ccessed September 2012.)8B

- i APPENDIX 8B. UNCERTAINTY AND VARI ABILITY

TABLE OF CO NTENTS

8B.1 INTRODUCTION

................................ ................................ ................................ ... 8B

- 1

8B.2 UNCERTAINITY

................................ ................................ ................................ .... 8B

- 1

8B.3 VARIABILITY

................................ ................................ ................................ ........ 8B

- 1

8B.4 APPROACHES TO UNCERT A INTY AND VARIABILITY

............................... 8B

- 2

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYST AL BALL

................... 8B

- 2

FIGURE

Figure 8B.5.1 Normal, Trian gular, Uniform, Weibull, and Custom Probability

Distributions

................................ ................................ ................................ . 8B

- 38B

- 1 APPENDIX 8B. UNCERTAINTY AND VARI ABILITY

8B.1 INTRODUCTION

Analysis of energy conservation standard s involves calculating impacts ; for example, the impact of a standard on consumer life

- cycle cost (LCC). In order to perform the calculation, the analyst must first: ( 1) specify the equation or model that will be used; ( 2) define the quantities in the equation; and ( 3) provide numerical values for each quantity. In the simples t case, the equation is unambiguous (contains all relevant quantities and no others), each quantity has a single numerical value, and the calculation results in a single value. However, complete clarity and precision are rarely the case. In almost all case s, the model and/or the numerical values for each quantity in the model are not known completely ( i.e. , there is uncertainty) or the model and/or the numerical values for each quantity in the model depend upon other conditions ( i.e.

, there is variability).

Thorough analysis involves accounting for uncertainty and variability. Although the simplest analysis involves a single numerical value for each quantity in a calculation, arguments can arise about what the appropriate value is for each quantity. Explic it analysis of uncertainty

and va riability is intended to more complete ly inform the decision

- making process.

8B.2 UNCERTAINITY

When making observations of past events or speculating about the future, imperfect knowledge is the rule rather than the exception. For example, the energy actually consumed by a particular appliance type (such as the average U.S. microwave oven ) is not recorded directly, but

rather estimated based on available information. Even direct laboratory measurements have some margin of error. When estimating numerical values expected for quantities at some future date, the exact outcome is rarely known in advance.

8B.3 VARIABILITY

Variability means that when calculating a quantity, different applications or situations produce different numerical v alues. Specifying an exact value for a quantity may be difficult if the value depends on something else. For example, the number of hours a household operates a microwave oven depends on the specific circumstances and behaviors of the occupants ( e.g. , numb er of persons, personal habits about food preparation ). Variability makes specifying an appropriate population value difficult because any one value is unlikely to be representative of the entire population. Surveys can be helpful in understanding variabil ity , and analysis of surveys can relate the variable of interest ( e.g. , hours of use) to other variables that are better known or easier to forecast ( e.g. , persons per household).8B

- 2 8B.4 APPROACHES TO UNCERT AINTY AND VARIABILIT Y

This section describes two approa ches to uncertainty and variability:

**•** scenario analysis, and

**•** probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then changes one (or more) of the numerical values and repeats the calculation. Numer ous calculations are performed , which provide some indication of the exten t to which the result depends on the assumptions. For example, the LCC of an appliance could be calculated for energy rates of 2, 8, and 14 cents per kilowatt

- hour

.

The advantages of scenario analysis are that each calculation is simple; a range of

estimates is used ; and crossover points can be identified. An example of a crossover point is the energy rate above which the LCC is reduced, holding all other inputs constant. That is, the crossover point is the energy rate at which the consumer achieves savings in operating cost that more than compensate for the increased purchase price . The disadvantage of scenario analysis is

that there is no information about the likelihood of each s cenario.

Probability analysis considers the probabilities within a range of values. For quantities

that contain variability ( e.g. , electricity rates among households), survey data can be used to generate a frequency distribution of numerical values ( e.g. , the number of households that experience electricity rates at particular levels) to estimate the probability of each value. For quantities that contain uncertainty, statistical or subjective measures can be used to provide probabilities ( e.g. , manufactur ing cost to improve energy efficiency to some level may be estimated to be $10 ± $3).

The major disadvantage of the probability approach is that it requires more information than required for the scenario approach , namely information about the shapes an d magnitudes of the variability and uncertainty in each quantity. The advantage of the probability approach is that it provides greater information about the outcome of the calculations ; that is, it provides the probability that the outcome will fall withi n a particular range.

Scenario and probability analyse s provide some indication of the robustness of a policy given the uncertainties and variability. A policy is robust when the impacts are acceptable over a wide range of possible conditions.

8B.5 PROBABILI TY ANALYSIS AND THE USE OF CRYSTAL BALL

To quantify the uncertainty and variability in inputs to the engineerin g, LCC, and payback period analyses, DOE used Microsoft Excel spreadsheets combined with Crystal Ball, a commercially available add

- in software product , to conduct probability analyses. The probability analyses used Monte Carlo simulation and probability distributions.8B

- 3 NORMAL UNIFORM TRIANGULAR

WEIBULL CUSTOM Simulation refers to any analytical method meant to imitate a real

- life system, especially when other analyses are too mathemati cally complex or difficult to reproduce. Without the aid of simulation, a spreadsheet model will reveal only a single outcome, generally the most likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and simulation to

automati cally analyze the effect of varying inputs on outputs of the modeled system. One type of spreadsheet simulation is Monte Carlo simulation, which performs a calculation repeatedly based on randomly generating values for uncertain variables again and again. Monte Carlo simulation was named for Monte Carlo, Monaco, where the primary attractions are casinos containing games of chance. Games of chance

, such as roulette wheels, dice, and slot machines, exhibit

random behavior. The random behavior in games of chan ce is similar to how Monte Carlo

simulation randomly selects variable values to simulate a model. When you roll a die, you know

that a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular roll. T he

same condition applies to variab les that have a known range of values but an uncertain value for any particular time or event ( e.g. , product lifetime, discount rate, and installation cost).

For each uncertain variable (one that has a range of possible values), possible values can be d efined using a probability distribution. The type of distribution selected is based on the conditions surrounding that variable. Probability distribution types include those shown in Figure

8B.5.1

.

Figure 8B.5.1 Normal, Triangular, Uniform , Weibull, and Custom Probability Distributions

During a simulation, multiple scenarios of a model are calculated by repeatedly sampling values from the probability distributions for the uncertain variables. Crystal Ball simulations can

consist o f as many trials (or scenarios) as desired

— hundreds or even thousands. During a single8B

- 4 trial, Crystal Ball randomly selects a value from the defined possibilities (the range and shape of the probability distribution) for each uncertain variable and then re calculates the spreadsheet.8D

- i APPENDIX 8D. DISTRIBUTIONS USED F OR DISCOUNT RATES

TABLE OF CO NTENTS

8D.1 INTRODUCTION

................................ ................................ ................................ .. 8D

- 1

8D.2 DISTRIBUTION OF MORT GAGE INTEREST RATES

................................ ..... 8D

- 1

8D.3 DISTRIBUTION OF RATE S FOR DEBT CLASSES CONSIDERED FOR

REPLACEMENT PRODUCTS

................................ ................................ ............. 8D

- 2

8D.4 DISTRIBUTION OF RATE S FOR EQUITY CLASSES CONSIDERED FOR

REPLACEMENT PRODUCTS

................................ ................................ ............. 8D

- 5

LIST OF FIGURE S

Figure 8D.2.1 Distribution of Interest Rates on New Home Mortgages

............................ 8D

- 1 Figure 8D.3.1 Distribution of Interest Rates for Home Equity Loans

............................... 8D

- 2 Figure 8D.3.2 Distribution of Interest Rates for Credit Cards

................................ ........... 8D

- 3 Figure 8D.3.3 Distribution o f Interest Rates for Installment Loans

................................ ... 8D

- 3 Figure 8D.3.4 Distribution of Interest Rates for Other Residence Loans

.......................... 8D

- 4 Figure 8D.3.5 Distribution of Interest Rates for Other Lines of Credit Loans

.................. 8D

- 4 Figure 8D.4.1 Distribution of Annual Rates of Return on CDs

................................ ......... 8D

- 5 Figure 8D.4.2 Distribution of Annual Rates of Return on Savings Bonds

........................ 8D

- 6 Figure 8D.4.3 Distribution of Annual Rates of Return on Corporate AAA Bonds

........... 8D

- 6 Figure 8D.4.4 Distribution of Annual Rates on Savings Accounts

................................ ... 8D

- 7 Figure 8D.4.5 Distribution of Annual Rates of Return on S&P 500

................................ . 8D

- 7 Figure 8D.4.6 Distribution of Annual Rates of Return on Mutual Funds

.......................... 8D

- 88D

- 1 APPENDIX 8D. DISTRIBUTIONS USED F OR DISCOUNT RATES

8D.1 INTRODUCTION

The U.S. Department of Energy ( DOE ) derived discount rates for the life

- cycle cost analysis (described in chapter 8) using data on interest or return rates for various types of

consumer debt and equity. To account for variation in rates for each type of debt or equity , DOE sampled a rate for each household in the sample from a distribution of rates for each debt and equity type. This appendix describes the distributions of rates DOE used.

8D.2 DISTRIBUTION OF MORT GAGE INTEREST RATES

Figure 8D.2.1 shows the distribution of real interest rates for new home mortgages. The data source DOE used for mortgage interest rates is the Federal Reserve Board’s Survey of Consumer Finances (SCF) in 1989, 1992, 1995, 1998, 2001, 2004, an d 2007. 1 Using the appropriate SCF data for each year, DOE adjusted the nominal mortgage interest rate for each relevant household for mortgag e tax deduction and inflation. In cases whe re the effective interest

rate wa s equal to or less than the inflation rate (resulting in a negative real interest rate), DOE set

the real effective interest rate to zero.

0% 5% 10% 15% 20% 25% 30%0.5%1.6%2.5%3.5%4.4%5.4%6.4%7.4%8.5%9.5%10.5%11.6%12.4%13.5%14.4%16.7%22.9%Probability

Real Interest Rate
Figure 8D.2.1 Distribution of Interest Rates on New Home Mortgage s8D

- 2 8D.3 DISTRIBUTION OF RATE S FOR DEBT CLASSES C ONSIDERED FOR

REPLACEMENT PRODUCTS

Figure 8D.3.1 through Fi gure 8D.3.5 show the distribution s of real interest rates for

various types of debt used to finance replacement microwave and combination ovens. The data source for the interest rates for home equity loa ns, credit cards, installment loans, other residence

lo ans, and other lines of credit wa s the Federal Reserve Board’s SCF in 1989, 1992, 1995, 1998,
2001, 2004, and 2007

. 1 DOE us ed the annual inflation rate in each year to adjust the nominal

rates to real rates . For home equity loans, DOE calculated effective interest rates in a similar manner as for mortgage rates, becaus e interest on both type s of loans is tax deductible.

0% 5% 10% 15% 20% 25% 30%0.4%1.5%2.6%3.5%4.5%5.4%6.4%7.5%8.5%9.5%10.6%11.8%12.7%13.7%14.7%16.7%17.5%42.5%Probability

Real Interest Rate
Figure 8D.3.1 Distribution of Interest Rates for Home Equity

Loan s8D

- 3 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%0.0%2.5%4.5%6.7%8.7%10.6%12.6%14.7%16.4%18.5%20.5%22.5%24.6%26.3%28.4%36.9%Probability

Real Interest Rate
Figure 8D.3.2 Distribution of Interest Rates for Credit Card s

0% 5% 10% 15% 20% 25% 30% 35% 40%0.0%2.4%4.5%6.7%8.6%10.6%12.5%14.7%16.5%18.5%20.5%22.3%24.6%26.6%28.5%34.1%43.6%47.2%Probability

Real Interest Rate
Figure 8D.3.3 Distribution of Interest Rates for Installment Loan s8D

- 4 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%0.2%1.5%2.5%3.5%4.5%5.5%6.5%7.5%8.6%9.4%10.6%11.6%12.6%14.7%15.7%16.6%17.9%Probability

Real Interest Rate
Figure 8D.3.4 Distribution of Interest Rates for Other Residence

Loan s

0% 2% 4% 6% 8% 10% 12% 14%0.1%2.5%4.5%6.7%8.7%10.5%12.5%14.7%16.5%18.5%20.2%22.1%25.4%Probability

Real Interest Rate
Figure 8D.3.5 Distribution of Interest Rates for Other Lines of

Credit Loan s8D

- 5 8D.4 DISTRIBUTION OF RATE S FOR EQUITY CLASSES CONSIDERED FOR

REPLACEMENT PRODUCTS

Figure 8D.4.1 through Figure 8D.4.6 show the distribution s of real interest rates for

various types of equity used to finance replacement microwave and combination ovens. Data for equity classes are not available from the Federal Reserve Board’s SCF , so DOE derived data for the equity classes from national

- level historical data. The interest rates associated with certificates of deposit (CDs), 2 savings bonds, 3 and AAA corpora te bonds 4 were derived from

Federal Reserve Board time

- series data covering 1977

– 2009. DOE assumed rates on checking

accounts to be zero. Rates on savi ngs and money market accounts were derived from Cost of Savings Index data covering 1984

– 2009. 5 The rates for stocks are the annual returns on the Standard and Poor’s (S&P) 500 from 1977 to 2009. 6 The mutual fund rates are a weighted average of the stock rates (two

- thirds weight) and the bond rates (one

- third weight) in each year

from 1977 to 2009. DOE used the annual inflation rate in each year to adjust the nominal rates to

real rates.

- 2% 0% 2% 4% 6% 8%197719791981198319851987198919911993199519971999200120032005200720092011Annual Rate of Return

Year

Figure 8D.4.1 Distribution of Annual Rate s of Return on CDs8D

- 6 - 2% 0% 2% 4% 6% 8%197719791981198319851987198919911993199519971999200120032005200720092011Annual Rate of Return

Year

Figure 8D.4.2 Distribution of Annual Rate s of Return on Savings

Bonds

- 2% 0% 2% 4% 6% 8%197719791981198319851987198919911993199519971999200120032005200720092011Annual Rate of Return

Year

Figure 8D.4.3 Distribution of Annual Rate s of Return on Corporate AAA Bonds8D

- 7 - 2% 0% 2% 4% 6% 8%19841986198819901992199419961998200020022004200620082010Annual Rate of Return

Year
Figure 8D.4.4 Distribution of Annual Rate s on Savings Accounts

- 40% - 30% - 20% - 10%

0% 10% 20% 30% 40%197719791981198319851987198919911993199519971999200120032005200720092011Annual Rate of Return

Year
Figure 8D.4.5 Distribution of Annual Rate s of Return on S&P 5008D

- 8 - 30% - 20% - 10%

0% 10% 20% 30%197719791981198319851987198919911993199519971999200120032005200720092011Annual Rate of Return

Year
Figure 8D.4.6 Distribution of Annual Rate s of Return on Mutual

Funds8D

- 9 REFERENCES

1. Board of Governors of the Fe deral Reserve System. Research Resources: Survey of

Consumer Finances

. 1989, 1992, 1995, 1998, 2001, 2004, and 2007

. Av ailable online at www.federalreserve.gov/pubs/oss/oss2/scfindex.html

. (Accessed September 2012.)

2. Board of Governors of the Federal Reserve System. Federal Reserve Statistical Relea se, Selected Interest Rates

, Historical Data : Instrument: CDs (secondary market) ; Maturity:

6

- month ; Frequency: Annual ; Description: Average rate on 6

- month negotiable certificates of deposit (secondary market), quoted on an investment basis

. 2010.

A vailable online at www.federalreserve.gov/releases/H15/data.htm

. (Accessed September 2012.)

3. Board of Governors of the Federal Reserve System. Federal Reserve Statistical Release,
Selected Interest Rates, Historical Data : Instrument: State and local bonds ; Maturity:

20

- year ; Frequency: Monthly ; Description: Bond buyer go 20

- bond municipal bond

index. 2010. A vailable online at www.federalreserve.gov/releases/H15 /data.htm

. (Accessed September 2012.)

4. Board of Governors of the Federal Reserve System. Federal Reserve Statistical Release,
Selected Interest Rates, Historical Data : Instrument: Corporate bonds/Moody's Seasoned

AAA ; Frequency: Annual ; Description: Moody's yield on seasoned corporate bonds

— all
industries, AAA

. 2010. Available online at www.federalreserve.gov/releases/H15/data.htm

. (Accessed September 2012.)

5. Mortgage

- X

– Mortgage Information Service

. Cost of Sav ings Index (COSI) Index History.

2010. Available online at

mortgage

- x.com/general/indexes/default.asp

. (Accessed September 2012.)

6. Damodaran Online

. Historical Returns on Stocks, Bonds and Bills

– United States

. Use

link for Updated Data: Data Sets. 2012

. A. Damodaran , New York University . Available

online at pages.stern.nyu.edu/~adamodar/ . (Accessed September 2012.)9 A

- i APPENDIX 9A. RELATIVE PRICE ELAST ICITY OF DEMAND FOR

APPLIANCE S

TABLE OF CO NTENTS

9A.1 INTRODUCTION

................................ ................................ ................................ .. 9A

- 1

9A.2 LITERATURE REVIEW

................................ ................................ ....................... 9A

- 1

9A.2.1 Price

................................ ................................ ................................ ........................ 9A

- 1

9A.2.2 Income

................................ ................................ ................................ ..................... 9A

- 1

9A.2.3 Appliance Efficiency and Discount Rates

................................ .............................. 9A

- 2

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRI GERATORS, CLOTHES WASHERS, AND DISHWASHERS

................................ .................. 9A

- 2

9A.3.4 Physical Household/Appliance Variables

................................ ............................... 9A

- 3

9A.3.5 Economic variables

................................ ................................ ................................ . 9A

- 3

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTI NG APPLIANCE

SHIPMENTS

................................ ................................ ................................ .......... 9A

- 4

9A.4.1 Broad Trends

................................ ................................ ................................ ........... 9A

- 5

9A.4.2 Specification of Model

................................ ................................ ............................ 9A

- 7

9A.4.3 Discussion of Model

................................ ................................ ............................... 9A

- 8

9A.4.4 Analysis Results

................................ ................................ ................................ ...... 9A

- 8

9A.4.4.1 Individual Appliance Model

................................ ................................ . 9A

- 8

9A.4.4.2 Combined Appliance Mod el

................................ ................................ . 9A

- 9

9A.4.5 Additional Regression Specifications and Results

................................ ............... 9A

- 10

9A.4.5.1 Lower Consumer Discount Rate

................................ ......................... 9A

- 10

9A.4.5.2 Disaggregated Variables

................................ ................................ ..... 9A

- 11

9A.5 LONG RUN IMPACTS

................................ ................................ ........................ 9A

- 11

9A.6 SUMMARY

................................ ................................ ................................ .......... 9A

- 12

9A.7 DATA USED IN THE ANA LYSIS

................................ ................................ ..... 9A

- 13

LIST OF TABLES

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile and Appliance Sales

................................ ................................ 9A

- 2

Table 9A.3.1 Physical Household/Appliance Variables

................................ ................... 9A

- 3

Table 9A.3.2 Economic Variables

................................ ................................ .................... 9A

- 4

Table 9A.4.1 Estimate of Total P rice Elasticity of Demand

................................ ............. 9A

- 6

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand

......... 9A

- 7

Table 9A.4.3 Individual Appliance Model Results

................................ .......................... 9A

- 9

Table 9A.4.4 Combined Appliance Model Result

................................ ............................ 9A

- 9

Table 9A.4.5 Combined and Indi vidual Results, 20 percent discount rate

..................... 9A

- 10

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate

.................... 9A

- 11

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a Purchase Price Change

................................ ................................ .............. 9A

- 129 A

- ii Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a Purchase Price Change

................................ ................................ .............. 9A

- 12

LIST OF FIGURE

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements

............ 9A

- 4 Figure 9A.4.1 Residual Shipments and Appliance Price

................................ ................... 9A

- 69A

- 1 APPENDIX 9A. RELATIVE PRICE ELAST ICITY OF DEMAND FOR APPLIANCES

9A.1 INTRODUCTION

This appendix summarizes DOE’s study of the price elastic ity of demand for home appliances, including refrigerators, clothes washers and dishwashers. DOE chose t his particular

set of appliances because of the availability of data to determine a price elasticity. Section 9 A.2

review s the existing economics litera ture describing the impact of economic variables on the sa le of durable goods

. S ection 9A .3 describes the market for home appliances and the changes that

have occurred over the past 20 years . In s ection 9A .4

, DOE summarize s the results of its regression analysis and present s estimates of the price elasticity of demand for the three appliances. In section 9A.5, DOE presents development of an ‘effective’ purchase price elasticity. DOE’s interpretation of its re sults is presented in s ection 9A .6

. Finally, section 9A.7 describes the data used in DOE’s analysis.

9A.2 LITERATURE REVIEW

R elatively few studies measur e the impact of price, income and efficiency on the sale of household appliances. T his section briefly review s the literature that describes t he likely importance of these variables on the purchase of household appliances

.

9A.2.1 Price

DOE reviewed many studies that sought to measure the impact of price on sales in a dynamic market. One study of the automobile market prior to 1970 finds the price elas ticity of demand to decline over time. The author explains this as the result of buyers delaying purchases after a price increase but eventua lly making the purchase (Table 9A.2.1 ). 1 A contrasting study of household white goods also prior to 1970, finds the elasticity of demand to increase over time as more price

- conscious buyers enter the market. 2 A n analysis of refrigerator market survey data finds that consumer purchase probability decreases with survey asking price. 3 Estimates of the price elasticity of demand for different brands of the same product tend to vary. A review of 41 studies of the impact of price on market share found the average price elasticity to be

- 1.75. 4

9A.2.2 Income The

aver age estimate of price elasticity of demand reported in these studies is

- 0 .33 in the appliance

market and

- 0 .47 in the combined automobile and appliance markets.

Higher income households are more likely to own household appliances. 5 The impact of income on appliance shipments is explored in two econometric studies of the automobile and appliance markets. 1

, 2 The average income elasticity of demand is 0 .50 in the appliance study cited in the literature review, much larger in the automobile study (Tab le 9.A.2.1 ).9A

- 2 9A.2.3 Appliance Efficiency and Discount Rates

Many studies estimate the impact of appliance efficiency on consumer s’ choice of appliance. Typically, this impact is summarized by the implicit discount rate ; that is , the rate

consumers use to compare future savings in appliance operating costs against a higher initial purchase price of a n appliance. One early and much cited study co ncludes that consumers use a 20 percent implicit discount rate when purchasin g room air conditioners (Table 9A.2.1 ). 6 A

su rvey of several studies of different appliances suggests that the consumer implicit discount rate has a broad range and averages about 37 percent

. 7

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile and Appliance Sales

Durable Good Price Elasticity Income Elasticity Brand

Price Elasticity Implicit

Discount

Rate Model Data

Years Time

Period

Automobiles 1

- 1.07 3.08

- - Linear Regression, stock adjustment - Short run

Automobiles 1

- 0.36 1.02

- - Linear Regression, stock adjustment - Long run

Clothes Dryers 2

- 0.14 0.26

- - Cobb

- Douglas, diffusion 1947

- 1961 Mixed

Room Air

Conditioners 2 - 0.37 8 0.45

- - Cobb

- Douglas, diffusion 1946

- 1962 Mixed

Dishwashers 2

- 0.42 0.79

- - Cobb

- Douglas, diffusion 1947

- 1968 Mixed

Refrigerators 3

- 0.37

- - 39% Logit probability, survey

data 1997 Short run

Various 4

- - - 1.76 9

- Multiplicative regression

- Mixed

Room Air

Conditioners 5 - - - 1.72

- Non

- linear diffusion 1949

- 1961 Short run

Clothes Dryers 5

- - - 1.32

- Non

- linear diffusion 1963

- 1970 Short run

Room Air

Conditioners 6 - - - 20% Qualitative choice, survey

data - -

Household Appliances 7 - - - 37% 1 0 Assorted

- -

Sources: 1 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997;

4 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985.

Notes: 8 Logit probability results are not directly comparable to other elasticity estimates in this table.

9 Average brand price elasticity across 41 studies.

10 Averaged across several household appliance studies referenced in this work.

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRI GERATORS,

CLOTHES WASHERS, AND DISHWASHERS

In this section DOE evaluate s variables that appear to account for refrigerator, clothes washer and dishwasher shipments, including physical household/appliance variables and

economic variables.9A

- 3 9A.3.4 Physical Household/Appliance Variables

Several variables influence the sale of refrigerat ors, clothes washers and dishwashers. The most important for explaining appliance sales trends are the annual number of new households formed (housing starts) and the number of appliances reaching the end of their operating life (replacements). Housing sta rts influence sales because new homes are often provided with, or soon receive, new appliances, including dishwashers and refrigerators. Replacements are correlated with sales because new appliances are typically purchased when old

ones wear out. In princi ple, if households maintain a fixed number of appliances, shipments should equal housing starts plus appliance replacements.

9A.3.5 Economic variables

Appliance price, appliance operating cost and household income are important economic variables affecting ship ments. Low prices and costs encourage household appliance purchases and a rise in income increases householder ability to purchase appliances. In principle, changes in economic variables should explain changes in the number of appliances per household.

During a 1980

– 2002 study p eriod, annual shipments grew 69 percent for clothes washers, 81 percent for refrigerators a nd 105 percent for dishwashers (Table 9A.3.1 ). This rising shipments trend is explained in part by housing starts, which increased 6 perce nt and by appliance repl acements, which rose between 49 percent and 90 percent, depending on the appliance, over the period (Table 9A.3.1 ). a For mature markets such as these, replacements exceed appliance sales associated with new housing construction.

Table 9A.3.1 Physical Household/Appliance Variables

Shipments 1 (millions) Housing Starts 2 (millions) Replacements 3 (millions)
Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change
Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49%

Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50%

Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90%

1 Shipments: Number of units sold. Sources: AHAM Fact Book and Appliance Magazine.

2 Housing Starts: Annual number of new homes constructed. Source: U.S. Census.

3 Replacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, ± 5 years.

S hipments increased somewhat more rapidly than housing starts and replacements. This is shown by comparing the beginning and end points of lines that represent “starts plus replacements” (uppermost solid line in Figure 9A.3.1 ) and “shipment s” (diamond linked line in Figure 9A.3.1). In 1980 the “shipment” line begins below the “starts plus replacements” line. In 2002, the “shipments” line ends above the “starts plus replacements” line. This more rapid

a Appliance replacements are determined from the expected operating life of refrigerators (19 years), clothes washers (14 years), and dishwashers (12 years) and from past shipments. Replacements are further discussed in

section 9A.3. The dishwasher lifetime used in this analysis does not match the dishwasher used in the primary analysis.9A

- 4 increase in shipments, compared to housing starts plus replacements, suggests that the appliance per household ratio increased over the s tudy period.

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements

Economic variables, including price, cost and income, may explain this increase in appliances per household. Over the period , appliance prices decreased 40 percent to 50 perce nt

, operating costs fell between 33 percent and 72 percent, and median household income rose 16

percent (Table 9A.3.2 ).

Table 9A.3.2 Economic Variables

Price 1 (1999$) Operating Cost 2 (1999$) Household Income 3 (1999$)
Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change
Refrigerators 1208 726

- 40% 333 94

- 72% 37,447 43,381 16 %

Clothes Washers 779 392

- 50% 262 175

- 33% 37,447 43,381 16 %

Dishwashers 713 369

- 48% 183 95

- 48% 37,447 43,381 16 %

1 Price: Shipment weighted retail sales price. Sources: AHAM Fact Book and Appliance Magazine.

2 Operating Cost: Annual electricity price times electricity consumption. Source: AHAM Fact Book.

3 Income: Mean Household income. Source: U.S. Census.

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTI NG APPLIANCE

SHIPMENTS

Few data are available to estimat e the impact of economic variables on the demand for appliances. Industry operating cost data is incomplete

— appliance energy use data are available for only 12 years of the 1980

- 2002 study period. Industry price data are also incomplete

— available for only 8 years of the study period for each of the appliances.

The lack of data suggests that regression analysis can at best evaluate broad data trends, utilizing relatively few explanatory variables. This section begin s by describing broad trends apparent in the economic and physical household data sets and then specifies a simple regression Refrigerator Shipments

0 1 2 3 4 5 6 7 8 9 10

1980 1984 1988 1992 1996 2000Millions New Housing

Retirements

Shipments Clothes Washer Shipments

0 1 2 3 4 5 6 7 8

1980 1984 1988 1992 1996 2000Millions Dishwasher Shipments

0 1 2 3 4 5 6

1980 1984 1988 1992 1996 2000Millions9A

- 5 model to measure these trends, making assumptions to minimize the number of explanatory

variables. Finally, results of the regression a nalysis are presented along with an estimate of the price elasticity of demand for appliances. In section 9A. 4.5

, DOE present s the results of regression analysis performed with more complex models, which are used to test assumptions underlying the simple m odel. These results support the specification of the simple model and the price elasticity of appliance demand estimated with that model.

9A.4.1 Broad Trends

In this section DOE review s trends in the physical household and economic data sets and posit s a simple approach for estimating the price elasticity of appliance demand. As noted above, the physical household variables ( housing starts and appliance replacements) explain most of the variability in appliance shipments during the study period (1980

- 2002)

. b DOE assume s the rest of the variability in shipments ( referred to as “ residual shipments ” ) is explained by economic

variables . Below, DOE present s a tabular method for measuring price elasticities.

To illustrate this tabular approach, DOE define s two new va riables

— residual shipments and total price. Residual shipments are defined as the difference between shipments and physical household demand (starts plus replacements). Total price , represented by the following equation, is defined as appliance price plus the present value of lifetime appliance operating cost : c

PVOC PP TP + =

where:

TP = Total price,

PP = Appliance purchase price, and

PVOC = Present value of operating cost.

Over the study period, residual shipments increase d in proportion to total shipments by 30 percent for refrigerators, 19 percent for clothes washers

, a nd 23 percent for dishwashers. At

the sam e time, total prices decline d 47 percent, 45 percent and 48 percent for refrigerators,

clothes washers

, and dishwas hers, respectively. Assuming that total price explains the entire change in per household appliance usage, a rough estimate is calculated of the total price elasticity of demand equal to

- 0 .48 for refrigerators,

- 0 .32 for clothes washers a nd

- 0 .37 for

dish washers (Table 9A.4.1 ).

b A log regression of the form: Shipments = a + b • Housing Starts + c • Retirements, indicates that these two variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer shipments, and 97 percent of the variation in dishwasher shipments.

c Present value operating cost is calculated assuming a 19

- year operating life for refrigerators, 14

- year operating life

for cloth es washers, and a 12

- year operating life for dishwashers. A 37 percent discount rate is used to sum annual operating costs into a present value operating cost.9A

- 6 Table 9A.4.1 Estimate of Total Price Elasticity of Demand

Residual Shipments (millions) Total Price (199$)
Elasticity Appliance 1980 2002 Difference Change 1980 2002 Change
Refrigerators

- 0.5 1.6 2.1 30% 1541 820

- 61%

- 0.48

Clothes Washers

- 1.0 0.2 1.1 19% 1042 567

- 59%

- 0.32

Dishwashers

- 1.0

- 0.01 1.0 23% 896 464

- 64%

- 0.37

The negative correlation between total price and residual shipments suggested by these negative price elasticities is illustrated in a graph of residual shipments on the y

- axis and to tal price on the x

- axis (Figure 9A.4.1 ).

Yellow points are observed price data; red points are interpolated price data. Figure 9A.4.1 Residual Shipments and Appliance Price

Household income rose during the study period, making it easier for households to purchase appliances. Assuming that a rise in income has a similar impact on shipments as a decline in price, the impact of income is incorporated by defining a third variable, termed

relative pric e, which is calculated as total price divided by household income and represented by the following equation

. d

Income TP RP =

where:

R P = Relative price,

T

P = Total price, and

Income = Household income.

d Recall that the income elasticity of demand cited in the literature review is 0.50 and the pri ce elasticity of demand cited in the review averages

- 0.35. This suggests that combining the effects of income and price will yield an elasticity less negative than price elasticity alone. Refrigerators

400 800 1200 1600 2000

-2.0 -1.0 0.0 1.0 2.0

Total PriceResidual Shipments Clothes Washers

400 600 800 1000 1200

-1.5 -1.0 -0.5 0.0 0.5

Total PriceResidual Shipments Dishwashers

200 400 600 800 1000

-2.0 -1.5 -1.0 -0.5 0.0 0.5

Total PriceResidual Shipments9A

- 7 The percent decline in relative price fo r the three appliances divided by the percent decline in residual shipments suggests a rough estimate of relative price elasticity equal to

- 0 .40 for refrigerators,

- 0 .26 for clothes washers a nd

- 0 .30 for dishwashers (Table 9A.4.2 ).

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand

Residual Shipments (millions) Relative Price (1999$)
Elasticity Appliance 1980 2002 Change 1980 2002 Change
Refrigerators

- 0.532 1.597 30% 0.041 0.019

- 74%

- 0.40

Clothes Washers

- 0.953 0.174 19% 0.028 0.013

- 72%

- 0.26

Dishwashers

- 0.974

- 0.005 23% 0.024 0.011

- 76%

- 0.30

9A.4.2 Specification of Model

The limited price data suggest it is appropriate to us e a simple regression model to estimate the impact of economic variables on shipments, using few explanatory variables. The following equation

, chosen for this analysis

, includes one physical household variable ( housing starts plus replacements) and one relative price variable (the sum of purchase price plus

ope rating cost, divided by income).



## [ ] Rplc Starts c RP b a Ship + × + × + = Eq. 9A.1

where:

Ship = Quantity of appliance sold,

R P = Relative price,

Starts = Number of new homes, and Rplc = Number of appliances at the end of their operating life.

The natural logs are taken of all variables so that the estimated coefficients for each variable in the model may be interpreted as the percent change in shipments associated with the percent change in the variable. Thus, the coefficient b in this model is interpreted as the relative price elasticity of demand for the three appliances.

DOE used the following combined regression equation to estimate an average price elasticity of demand across the three appliances, using pool ed data in a single regression. A combined regression specificat ion is justified, given the limited data availab le and the similarity in price and shipment behavior across appliances ( see Figure 9A.4.1 ). Thus, the model represented by the combined regression equation is considered the basic model in DOE’s analysis of a ppliance shipments.



## [ ] DW e CW d Rplc Starts c RP b a Ship × + × + + × + × + = Eq. 9A.2

where:

CW = Quantity of clothes washers sold, and9A

- 8 DW = Quantify of dishwashers sold.

9A.4.3 Discussion of Model

The most important assumption used to specify this model is that changes in economic variables over the study period

— income, price

, and operating cost

— are responsible for all observed growth in residual appliance shipments. In other words, DOE assume s no impact from other possible factors , such as changing consumer preferences or increases in the quality of appliances. This assumption seems unlikely

, but without additional data, the impact of this assumption on the price elasticity of demand cannot be measured. DOE effectively assume s that changes in consumer preferences and ap pliance characteristics, while affecting which models are purchased, have relatively little impact on the total number of appliances purchased in a year.

Three additional assumptions used to specify this model deserve comment. The relative price variable is specified in the model, assuming that (1) the correct implicit discount rate is used to combine appliance price and operating cost and that (2) rising income has the same impact on shipments as falling total price. The “ starts + replacements ” variable is specified, assuming (3) that starts and replacements have similar impacts on shipments.

To investigate the first assumption about discount rates, DOE calculated “present v alue operating cost” using a 20 percent implicit discount rate and performed a second regression analysis based on the models described in equations 9A.1 and 9A.2. The results of this analysis, presented in section 9A. 4. 5 , indicate that the elasticity of relative price is fairly insensitive to changes in the discount rate.

To investigate the second and third assumptions, DOE specified a regression model separating income from total price and replacements from starts, th ereby adding two additional explanatory variables to the basic mo del as shown in the following equation:

DW g CW f Rplc e Start d Incone c TP b a Ship **× +**  **× +**  **× +**  **× +**  **× +**  **× +**  **=** Eq. 9A.3

The results of the regression analysis of this model are presented in section 9A. 4. 5

. These results suggest that the elasticity of total price (coefficient b ) is relatively insensitive to changes

i n the treatment of income and “starts + r eplacements ” in the model.

9A.4.4 Analysis Results

The following sections describe results of analyses using both the individual and combined models for appliances and the effects of a lower consumer discount rate and disaggregated variables.

9A.4.4.1 Individual Appliance Model

The individual appliance regression eq uations are specified in the following equation.9A

- 9 

## [ ] Rplc Starts c RP b a Ship + × + × + =

In regression analysis of this model, the elasticity of relati ve price ( b ) is estimated to be

- 0 .4 0 for refrigerators,

- 0 .31 for clothes washers and

- 0 .32 for dishwashers (T able 9A.4.3 ), averaging

- 0 .35. These elasticities are similar to those reported in the literatur e survey for appliances (Table 9A.2.1 ). They are remarkably similar to the price elasticity calculated using a

tabular a pproach (Table 9A.4.2 ).

The estimated coefficient associated with the “s tarts + r eplacements ” variable is close to

one. A coefficient equal to one for this variable would imply that , holding economic variables

constant

, shipments increase in direct proportion to an increase in “s tarts + r eplacements. ” The high R

- squared values (above 95) and t

- statistics (above 5) in the results provide a measure of confidence in this analysis, despite the very small data set.

Table 9A.4.3 Individua l Appliance Model Results

Refrigerator Clothes Washer Dishwasher

Variable Coefficient t

- stat Coefficient t

- stat Coefficient t

- stat
Intercept

- 1.51

- 7.26

- 1.47

- 8.23

- 2.08

- 16.78

Relative Price

- 0.40

- 6.60

- 0.31

- 5.69

- 0.32

- 7.03
Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46

R 2 0.954 0.954 0.975

Observations 23 23 23

9A.4.4.2 Combined Appliance Model

The combined appliance regression equation is specified in the following equation.



## [ ] DW e CW d Rplc Starts c RP b a Ship × + × + + × + × + =

This regression analysis indicates that the model fits the existing shipments data well (high R

- squared) and that the variables included in the model are st atistically significant (Table

9A.4.4 ). Estimated with this model, t he elasticity of relative price is

- 0 .34, close to the average value estimated in the individual appliance models (

- 0 .35). It is also similar to elasticity estimates reported in the literature survey and calculated using the tabular approach in Table

9A.4.2

.

Table 9A.4.4 Combined Appliance Model Result

Variable Coefficient t

- stat istic
Intercept

- 1.60

- 15.54

Relative Price

- 0.34

- 10.74
Starts + Replacements 1.21 13.95

CW

- 0.20

- 9.04

DW

- 0.32

- 6.58

R 2 0.983

Observations 699A

- 10 9A.4.5 Additional Regression Specifications and Results

As described in section 9A.4.3, DOE used three assumptions to specify its appliance

models. The first , made to aggregate appliance price and operating cost

, is that the implicit price variable in the basic regressio n model is specified using a 37 percent i mplicit discount rate. The

second states that the implicit price variable is defined assuming that rising income has the same impact on shipments as falling total price. The third states that the “s tarts + r eplacements ” variable is defined assuming that ho using starts have a similar impact on shipments as appliance replacements.

9A.4.5.1 Lower Consumer Discount Rate

To investigate the first assumption about discount rates, DOE calculated “present value operating cost” using a 20 percent implicit discount rate and performed a second regression analysis based on the models described in equations 9A.1 and 9A.2

. The estimated coefficient

associated with the relative price variable in these regressions is almost identical to the

coefficients estimated for the same variable based on a 37 percent implicit discount rate. The elasticity of relative price calculated using a 20 percent discount rate is

- 0 .33 in the c ombined regression and averages

- 0 .35 f or the three appliances (Table 9A.4.5 ). The elasticity o f price calculated using a 37 percent discount rate is

- 0 .34 in the combined regression and averages

- 0.35 for the three appliances. DOE conclude s from this analysis that the elasticity of relative price is fairly insensitive to changes in the discount rate.

Table 9A.4.5 Co mb ined and Individual Results, 20 percent discount rate

Three Appliances

Variable Coefficient t-Stat
Intercept -1.53 -14.61

Total Price / Income -0.33 -10.69

Starts + Retirements 1.20 13.65

CW -0.18 -8.69

DW -0.32 -6.57

R 2 0.982

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat
Intercept -1.36 -6.26 -1.41 -7.49 -2.04 -17.23

Total Price / Income -0.38 -6.50 -0.32 -5.29 -0.33 -7.30

Starts + Retirements 1.04 5.73 1.06 5.83 1.34 11.64

R 2 0.953 0.950 0.977

Observations 23 23 239A

- 11 9A.4.5.2 Disaggregated Variables

To investigate the second and third assumptions, DOE constructed a regression model

that separat es income from total price and replacements from starts, thus adding two addi tional explanatory variables to the basic model ( as shown earlier as Eq. 9A.3 and shown below ).

DW g CW f Rplc e Start d Income c TP b a Ship **× +**  **× +**  **× +**  **× +**  **× +**  **× +**  **=**

The estimated coefficient associated with the total price variable in these regressions is

almost identical to the coefficients estimated for the relative price variable reported above. The elasticity of total price in the above equation is

- 0 .36 in the co mbined appliance regression and

averages

- 0 .35 f or the three appliances (Table 9A.4.6 ). The elasticity of relative price based on the model described in equation 9A.2 is

- 0 .34 in the combined regression (Table 9A.4.4) and

averages

- 0 .35 across t he individu al appliances (Table 9A.4.3 ). DOE conclude s that the price elasticity calculated in this analysis is relatively insensitive to the specifi cation of household

income and “s tarts + r eplacements ” variables in the model.

Table 9A.4.6 Disag gregated Regression Results, 37 percent discount rate

9A.5 LONG RUN IMPACTS

As noted above in Table 9A.2.1, the literature review provides price elasticities over short and long time periods, also referred to as short run and long run price elasticities. As noted in the first two rows of Table 9A.2.1, one source ( i.e. , Hymans) shows that the price elasticity of demand is significantly different over the short run and long run for automobiles

. 1 Because DOE’s forecasts of shipments and national impacts due t o standards is over a 30

- year time period, consideration must be given to how the relative price elasticity is affected once a new

standard takes effect. Three Appliances

Variable Coefficient t-Stat
Intercept -2.92 -1.26

Income 0.58 2.92

Total Price -0.36 -7.06 Housing Starts 0.44 10.02

Retirements 0.62 8.12

CW -0.24 -9.25

DW -0.46 -7.68

R 2 0.985

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23

Income 0.89 3.80 0.87 2.31 0.20 0.52

Total Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18 Housing Starts 0.41 7.38 0.25 3.29 0.62 8.24

Retirements 0.56 6.06 0.56 2.09 0.65 5.86

R 2 0.984 0.958 0.979

Observations 23 23 239A

- 12 DOE considers the relative price elasticities determined above in section 9A.4 to be short

run elas tcities. DOE was unable to identify sources specific to household durable goods, such as appliances, to indicate how short run and long run price elasticities differ. Therefore, to estimate

how the relative price elasticity changes over time, DOE relied on the Hymans study pertaining

to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the automobile price elasticity of demand changes in the years following a purchase price change. With increasing years after the price change, the price elastic ity becomes more inelastic until it reaches a terminal value around the tenth year after the price change.

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a Purchase Price Change
Years Following Price Change

1 2 3 5 10 20

Price Elasticity of Demand

- 1.20

- 0.93

- 0.75

- 0.55

- 0.42

- 0.40
Relative Change in Elasticity to 1 st year 1.00 0.78 0.63 0.46 0.35 0.33

Source: Hymans, 1971.

Based on the relative change in the automobile price elasticity of demand shown in Table 9A.5.1, DOE developed a time series of relative price elasticities for home appliances

. Table 9A.5.2 presents the time series.

Table 9A.5.2 Change in Relative Price Elasticity fo r Home Appliances following a Purchase Price Change
Years Following Price Change

1 2 3 5 10 20
Relative Change in Elasticity to 1 st year 1.00 0.78 0.63 0.46 0.35 0.33

Relative Price Elasticity

- 0.34

- 0.26

- 0.21

- 0.16

- 0.12

- 0.11

9A.6 SUMMARY

This appendix describes the results of a literature search, tabular analysis

, and regression analys e s of the impact of price and other variables on appliance shipments. In the literature, DOE

f ound only a few studies of appliance markets that are relevant to this analysis and no studies

after 1980 using time series price and shipments data. The information that can be summarized from the literature suggests that the demand for appliances is price inelastic. Other information in the literature suggests that ap pliances are a normal good, such that rising incomes increase the demand for appliances. Finally, the literature suggests that consumers use relatively high implicit discount rates, when comparing appliance prices and appliance operating costs.

There ar e too few price and operating cost data available to perform complex analysis of dynamic changes in the appliance market. In this analysis, DOE use d data available for9A

- 13 refrigerators, clothes washers

, and dishwashers to evaluate broad market trends and perf orm simple regression analysis.

These data indicate an increase in appliance shipments and a decline in appliance price and operating cost over the study period 1980

- 2002

. Household income has also risen during this time. To simplify the analysis, DOE c ombined the available economic information into one variable, termed relative price, and used th at variable in a tabular analysis of market trends and a regression analysis.

DOE’s tabular analysis of trends in the number of appliances per household sugge sts that the price elasticity of demand for the three appliances is inelastic. Our regression analysis of these same variables suggests that the relative price elasticity of demand is

- 0 .34

. The price elasticity is consistent with estimates in the literat ure. Nevertheless, DOE stress es that the measure is based on a small data set, using very simple statistical analysis. More important, the measure is based on an assumption that economic variables, including price, income and operating costs, explain most of the trend in appliances per household in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but they are not accounted for in this analysis.

9A.7 DATA USED IN THE ANA LYSIS

• Appliance Ship ments are defined as the annual number of units shipped in millions. These

data were collected from the Association of Home Appliance Manufacturers ( AHAM ) 8 and Appliance Magazine 9 as annual values for each year, 1980

– 2002

. AHAM was used for the period 1989

– 2002 while Appliance Magazine was used for the period 1980

– 1988.

• Appliance Price is defined as the shipments weighted retail sales price of the unit in 1999 dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998 , and 2002 were collected

from AHAM Fact B ook s

. 10 Price values for other years were interpolated from these eight years of data.

• Housing Starts data were collected from the U.S. Census construction statistics (C25 reports) as annual values for each year, 1 980

– 2002. 11

• Replacements , driven by equipment retirements, are estimated with the assumption that some fraction of sales arise from consumers replacing equipment at the end of its useful life. Since each appliance has a different expected lifespan (19 year s for refrigerators 12 , 14 years

for clothes washers 13 , 12 years for dishwashers 14 ), replacements are calculated differently for each appliance type. Replacements are estimated as the average of shipments 14

– 24 years previous for refrigerators, 9

– 19 years prev ious for clothes washers, and 7

– 17 years previous

for dishwashers. Historical shipments data were collected from AHAM and Appliance Magazine.9A

- 14 • Annual Electricity Consumption (UEC) is defined as the energy consumption of the unit in

kilowatt

- hours. Electric ity consumption depend s on appliance capacity and efficiency. These data were provided by AHAM for 1980, 1990

– 1997 and 1999

– 2002. 15 Data were interpolated in the years for which data were not available.

• Operating Cost is the present value of the electricity consumption of an appliance over its expected lifespan. The lifespans of refrigerators, clothes washers and dishwashers are assumed to be 19, 14, and 12 years resp ectively. Discount rates of 20 percent 6 and 37

perce nt 16 were used, producing similar estimates of price elasticity. A study by Hausman

recommend ed a discount rate of “about 20 percent ” in its introduction and pres ented results ranging from 24.1 percent to 29 percent based on his calculations for room air co nditioners. A study by Train suggests a range of impli cit discount rates averaging 35 percent for appliances.

• Income : Median annual household income in 2003 dollars. Th ese data w ere collected for each year, 1980

– 2002, from Table H

- 6 of the U.S. Census

. 179A

- 15 REFERENCES

1 S. Hymans. Consumer Durable Spending: Explanation and Prediction, Brookings Papers on Economic Activity , 1971. Vol. 1971, No. 1, pp. 234

- 239.

2 P. Golder and G. Tellis. Beyond Diffusion: An Affordability Model of the Growth of New Consumer Durables, Journal of Forecasting , 1998.

3 D. Revel t and K. Train. Mixed Logit with Repeated Choices: Household's Choice of Appliance Efficiency Level, Review of Economics and Statistics , 1997.

4 G. Tellis. The Price Elasticity of Selective Demand: A Meta

- Analysis of Econometric Models of Sales, Journal of Marketing Research , 1988.

5 U.S. Department of Energy

- Energy Information Agency. The Effect of Income on Appliances
in U.S. Households, 2002. Washington, DC. Available online at www.eia.doe.gov/emeu/recs/appliances/appliances.html

.

6 J. Hausman. Individual discount rates and the purchase and utilization of energy using durables, The Bell Journal of Economic s , 1979.

7 K. Train. Discount rates in consumer' energy related decisions: a review of the literature, Energy , 1985.

8 Association of Home Appliance Manufacturers. AHAM Fact Books , 2000 and 2003. Washington, DC.

9 Statistical Review. Appliance Magaz ine , April, 1990.

10 Association of Home Appliance Manufacturers. AHAM Fact Books , 1987, 1988, 1993, 1995, 2000, and 2003. Washington, DC.

11 U.S. Census Bureau. Current Construction Reports, C25

– New One

- Family Houses Sold. Washington, DC. Available o nline at www.census.gov/prod/www/decennial.html

.

12 Duemling, Reubin. Product Life of Refrigerators , 1999. University of California, Berkeley, Energy and Resources Group masters thesis.

13 U.S. Department of Energy

- Office of Building Research and Standards. Final Rule Technical Support Document (TSD): Energy Efficiency Standards for Consumer Products: Clothes
Washers, December, 2000. Washington, DC. Chapter 7.9A

- 16 14 U.S. Department of Energ y

- Office of Codes and Standards. Technical Support Document: Energy Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, and Clothes Dryers, December, 1990. Washington, DC. DOE/CE

- 02999P. Chapter 4.

15 Association of Home Appliance Manufacturers. AHAM 2003 Fact Book , 2003. Washington,

DC.

16 K.E. Train, and Atherton, T. Rebates, loans, and customers’ choice of appliance efficiency level: combining stated

- and revealed

- preference data, The Energy Journal , 1995.

17 U.S. Census Burea u. Income, Historical Income Tables

– Households, Table H

- 6. Washington, DC. Available online at www.census.gov/hhes/www/income/data/historical/index.html

.10A

- i APPENDIX 10A. USER INSTRUCTIONS FO R THE NATIONAL IMPACTS

ANALYSIS SPREADSHEET

TABLE OF CO NTENTS

10A.1 USER INSTRUCTIONS

................................ ................................ ...................... 10A

- 1

10A.2 STARTUP

................................ ................................ ................................ ............. 10A

- 1

10A.3 DESCRIPTION OF NATIO NAL IMPACT A NALYSIS WORKSHEETS

........ 10A

- 1

10A.4 BASIC INSTRUCTIONS F OR OPERATING THE NAT IONAL IMPACT

ANALYSIS SPREADSHEET

................................ ................................ .............. 10A

- 210A

- 1 APPENDIX 10A. U SER INSTRUCTIONS FOR SHIPMENTS AND NATION AL

IMPACT ANALYSIS SPREADSHEET MODEL

10A.1 USER INSTRUCTIONS

The results of DOE's analysis of product shipments and national impacts can be examined and rep licated using the Microsoft Excel spreadsheets accessible on the Internet from

DOE's Building Technologies Program webpage for Appliance Stan dards : Cooking Products. www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products.html

10A.2 STARTUP

The spreadsheet enable s users to perform the national impact analy sis (NIA) for energy efficiency standards for microwave oven s tandby and off

- mode power

. To examine the spreadsheets, the user needs access to a personal computer that has a hardware configuration capable of running Win dows NT/2000/XP. The sprea dsheets require Microsoft Excel 2000 or

later

, installed under the Windows operating system.

10A.3 DESCRIPTION OF NATION A L IMPACT ANALYSIS WORKSHEETS

The NIA spreadsheets perform calculations to forecast the change in national energy u se and net present value of financial impacts attributable to a revised energy conservation standard. The energy use and associated costs for a given standard are determined by first calculating the shipments and then by calculating the energy use and cost s for all microwave ovens shipped

under that standard. The differences between national energy use and net present value under the standards and base case can be compared and the overall energy savings and net present values

determined. The NIA spreadsheet model consist s of the following workshee ts.

Input and Summary The Input and Summary sheet contains the user 's input selections and a summary table for each product class

.

Charts The Charts sheet contains various charts and other information.

Summ ary (All TSLs) The Summary (All TSLs) worksheet contains results for all trial standard levels ( TSLs ) under various cost scenarios.

ROCIS The ROCIS worksheet contains the ROCIS results table.

Base Case The Base Case sheet contains a tabular summary and chart o f the annual shipments forecast for each product class if no new standards are enacted

, along with calculations of energy and operating cost s

.

Standards Case The Standards Case sheet contains a tabular summary and chart o f the10A

- 2 annual shipme nts forecast for each product class at each TSL , along with calculations of energy and operating costs

.

Learning Rates The Learning Rates worksheet contains the default learning rate along

with other learning rate sensitivities.

Housing Projections The Housing Projections sheet contains housing forecasts based on the Energy Information Administration's Annual Energy Outlook for 2012 ( AEO2012 )

.

Historical Shipment The Historical Shipment sheet contains data on historical shipments.

Lifetime The Lifetime sheet calculates the distribution of lifetime expectancies

.

Elec Prices The Elec Prices sheet contains electricity price projections based on

AEO2012

.

10A.4 BASIC INSTRUCTIONS F OR OPERATING THE NATIONAL IMPACT

ANALYSIS SPREADSHEET

Basic instru ctions fo r operating the NIA spreadsheet are given below.

1. After downloading the NIA spreadsheet file from the Internet , open the file using

Excel. Click Enable Macro

2. Use Excel's when prompted and then click on the tab for the National Impacts Summary Analysis workshe et

. View/Zoom

3. You can change the parameters listed under USER INPUTS on the Summary

worksheet

. The default parameters and associated options are given b elow

. commands in the top menu bar to change the size of the display to make it fit your monitor.

a. TSL Level

b. (cell I3): Defaults to " 3. " To change the TSL analyzed, change the value of this cell. Acceptable values are 1 through 4.

Discount Rate

c. : Defaults to " 7 percent

. " To change

, click on cell C5 and choose 3
percent

. Year to Discount t o

d. : Default s to " 2011

. " To change value, click on cell C6 and set

t he desired year.

Economic Growth

e. : Defaults to " reference case

. " To change the value, use the pull

- down menu and select the desired trend ( reference , low, or high ). Market Impact

f. : Defaults to "

- 0.34. " To change the price elasticity, use the pull

down menu and select the desired level (RP elasticity =

- 0.34 or No Impact). Learning Case : Defaults to "d efault. " To change the learn ing rate utilized, use the pull

- down menu to select the desired rate.

The re sults are updated automatically and reported in the national impact summary table for each product class

. Annualized values are presented as well.10B

- i APPENDIX 10B. NATIONAL ENERGY SAVI NGS AND NET PRESENT VALUE

USING ALTERNATIVE ECONOMIC GROWTH SCENARIOS

TABLE OF CO NTENTS

10B.1 INTRODUCTION

................................ ................................ ................................ . 10B

- 1

10B.2 RESULTS USING ALTERN ATIVE GROWTH SCENARI OS

.......................... 10B

- 2

LIST OF TABLES

Table 10B.2.1 Cumulative National Energy Savings and Net Present Value of Consumer Benefits: High Economic Growth Case

................................ ... 10B

- 2

Table 10B.2.2 Cumulative National Energy Savings and Net Present Value of

Consumer Benefits: Low Economic Growth Case

................................ .... 10B

- 3

LIST OF FIGURE S

Figure 10B.1.1 Forecast Housing Starts Under Three AEO2012 Economic Growth

Scenarios

................................ ................................ ................................ .... 10B

- 1 Figure 10B.1.2 Forecast Average Residential Electricity Price Under Three AEO2012

Economic Growth Scenarios

................................ ................................ ...... 10B

- 210B

- 1 APPENDIX 10B. NATIONAL ENERGY SAVI NGS AND NET PRESENT VALUE

USING ALTERNATIVE ECONOMIC GROWTH SCENARIOS

10B.1 INTRODUCTION

This appendix presents results for national energy savings (NES) and ne t present value (NPV) using inputs from alternative economic growth scenarios . The scenarios use the energy price and housing starts forecasts in the high and the low economic growth

case s from The Energy Information Administration's ( EIA’s ) Annual Energy Outlook

201 2 ( AEO 201 2 ). 1

Figure 10B.1.1 show s the forecasts for housing starts under each of the three AEO

ec onomic growth scenarios. Figure 10B.1.2 shows the three forecasts for residential electricity prices

. AEO201 2 provides forecast s to 2035. To estimate the trend after 2035,

DOE follo wed guidelines that the EIA provided to the Federal Energy Management Program, call ing for using the average rate of ch ange for el ectricity prices during 2020

– 2035.

Figure 10B.1.1 Fo recast Housing Starts Under Three

AEO201 2 Economic Growth Scenarios10B

- 2 Figure 10B.1.2 Fo recast Average Residential Electricity

Price U nder Three AEO201 2 Economic

Growth Scenarios

10B.2 RESULTS USING ALTERN ATIVE GROWTH SCENARI OS

This sec tion presents tables that show NIA results under high and low AEO economic growth cases. NIA results for the reference case are presented in chapter 10.

Table 10B.2.1 shows the cumulative NES and the NPV of consumer benefits un der AEO's high economic growth case. Table 10B.2.2 shows those results under the low

growth scenario.

Table 10B.2.1 Cumulative National Energy Savings and Net Present Value of

Consumer Benefits : High Economic Growth Case

Trial

Standard

Le vel NES

q uads NPV billion 2011 $

7% Discount

Rate 3% Discount

Rate

1 0.22 1.13 2.22

2 0.32 1.58 3.11

3 0.44 2.03 4.04

4 0.67 2.54 5.2310B

- 3 Table 10B.2.2 Cumulative National Energy Savings and Net Present Value of

Consumer Benefits : Low Economic Growth Case

Trial

Sta ndard

Level NES

q uads NPV billion 201 1 $

7% Discount

Rate 3% Discount

Rate

1 0.20 0.92 1.76

2 0.28 1.27 2.46

3 0.39 1.62 3.17

4 0.60 1.98 4.0010B

- 4 REFERENCE

1 U.S. Depart ment of Energy

– Energy Information Administration ( EIA )

. Annual Energy Outlook 2012 with Projections to 2035. June 25, 2012. EIA: Washington,

D.C. Available online at http://www.eia.gov/forecasts /aeo/ . (A ccessed September 2012.)10C

- i APPENDIX 10C. NATIONAL NET PRESENT VALUE AND EMISSIONS

REDUCTIONS USING ALTERNATIVE PRODUCT PRICE SCENARIOS

TABLE OF CO NTENTS

10C.1 INTRODUCTION

................................ ................................ ................................ . 10C

- 1

10C.2 COMBINED RESULTS USI NG ALTERNATIVE LEARN ING RATES

........... 10C

- 3

LIST OF TABLES

Table 10C.1.1 Factors Affecting Microwave Oven Prices

................................ ................ 10C

- 2

Table 10C.2. 1 Microwave Oven Standby Power: Annualized Present Value of Consumer Impacts (3

- Percent Discount Rate) and Annualized Present

Value of Monetized Benefits from CO 2 and NO X Emissions Reductions (3

- Percent Discount Rate) Under Alternative Price

Scenarios

................................ ................................ ................................ .... 10C

- 4

Table 10C.2.2 Microwave Oven Standby Power: Annualized Present Value of Consumer Impacts (7

- Percent Discount Rate) and Annualized Present

Value of Monetized Benefits from CO 2 and NO X Emissi ons Reductions (Discount Rates of 3 percent for CO 2 and 7 percent for

NO X ) Under Alternative Price Scenarios

................................ ................... 10C

- 510C

- 1 APPENDIX 10C. NATIONAL NET PRESENT VALUE AND EMISSIONS

REDUCTIONS USING ALTERNATIVE PRODUCT PRICE SCENARIOS

10C.1 INTRODUCT ION

The U.S. Department of Energy ( DOE ) investigated the effe ct s of experience curves and

other factors on the combined net present value ( NPV ) of consumer benefits for each trial standard level (TSL) c onsidered for microwave oven standby power con s umptio n. The NPV

results for product prices that are presented in chapter 10 are based on a learning rate of 28.9
percent ( the default learning rate ) for the manufactur e of electric cooking products

. The experience curves are functions relating the cost of produ ction to quantity produced (typically cumulative production). The learning rate (LR) derived from the experience curve functions represents the cost reduction factor associated with economies of scale ,capital investment and automation in the particular in dustry

. Detailed experience curve analysis is documented in appendix 8C. DOE considered five sensitivities: (1) a low learning rate; (2) a high learning rate; (3) a learning rate based on data for microwave ovens only

, (4) an AEO2011 c hained price index

fo recast , and (5 ) no learning rate , which assumes constant re al prices throughout the forecast period. Refer to appendix 8C for details regarding the development of learning rates.

DOE derived a price factor index, with 2011 equal to 1, to forecast prices in each future

year of the analysis period. The default index value in a given year is a function of the LR and the cumulative production forecast through that year, which is based on the shipments forecast described in chapter 9. Table 10C.1.1 presents the factors DOE used to forecast product prices

for microwave ovens (MWOs) under each considered sensitivity

.10C

- 2 Table 10C.1.1 Factors Affecting Microwave Oven Price s

Year Default LR ( 28.9% ) Low LR ( 19.2% ) High LR ( 37.0% ) LR with MWO

Data Only

( 39.6% ) AEO2011

Chained Price

Index Forecast No LR (constant real prices)

2010 1.000 1.000 1.000 1.000 1.000 1.000

2011 0.981 0.986 0.979 0.968 0.989 1.000

2012 0.963 0.971 0.958 0.938 0.978 1.000

2013 0.946 0.958 0.939 0.909 0.964 1.000

2014 0.930 0. 945 0.920 0.883 0.949 1.000

2015 0.914 0.933 0.903 0.859 0.932 1.000

2016 0.900 0.921 0.886 0.837 0.916 1.000

2017 0.886 0.910 0.871 0.817 0.899 1.000

2018 0.873 0.900 0.857 0.799 0.883 1.000

2019 0.861 0.890 0.842 0.781 0.866 1.000

2020 0.848 0.880 0.828 0.763 0.848 1.000

2021 0.836 0.870 0.814 0.745 0.829 1.000

2022 0.823 0.860 0.801 0.728 0.810 1.000

2023 0.812 0.850 0.788 0.711 0.791 1.000

2024 0.800 0.841 0.775 0.696 0.772 1.000

2025 0.790 0.832 0.763 0.681 0.754 1.000

2026 0.779 0.824 0.75 2 0.667 0.736 1.000

2027 0.770 0.816 0.741 0.654 0.719 1.000

2028 0.760 0.808 0.731 0.642 0.701 1.000

2029 0.751 0.800 0.721 0.630 0.684 1.000

2030 0.742 0.793 0.711 0.618 0.667 1.000

2031 0.733 0.785 0.702 0.607 0.651 1.000

2032 0.725 0.778 0.692 0. 596 0.634 1.000

2033 0.716 0.771 0.683 0.585 0.619 1.000

2034 0.708 0.764 0.674 0.575 0.604 1.000

2035 0.700 0.757 0.665 0.565 0.589 1.000

2036 0.692 0.751 0.657 0.555 0.575 1.000

2037 0.685 0.745 0.649 0.546 0.561 1.000

2038 0.678 0.739 0.641 0.537 0.547 1.000

2039 0.670 0.733 0.633 0.529 0.534 1.000

2040 0.663 0.727 0.626 0.521 0.521 1.000

2041 0.657 0.721 0.618 0.513 0.508 1.000

2042 0.650 0.715 0.611 0.505 0.496 1.000

2043 0.643 0.709 0.604 0.497 0.484 1.000

The results presen ted here comb ine the NPV of consumer savings

, calculated for each

TSL using both 3

- percent and 7

- percent discount rates, with the present value of the potential economic benefits resulting from reduced CO 2 and NO X emissions

. T he economic benefits from

reduced CO 2 emiss ions were calculated using a social cost of carbon ( SCC ) value of $ 41.1 /metric

ton in 2016 (in 20 1 1 $) for CO 2 , increasing at 3 percent per year, and a discount rate of 3 percent

. The economic benefits from reduced NO X emissions were calculated using a valu e of $ 2,567 /ton (in 20 1 1 $), which is the average of the low and high values used in DOE’s analysis , and either a

3

- percent or 7

- percent discount rate

. C hapter 16 provides information regarding the derivation of10C

- 3 th e values assigned to emissions reductions

. All results refer to lifetime impacts of products shipped during 2014

– 2043.

The results presented here are annualized values. DOE used a two

- step process to convert
the time

- series of costs and benefits into annualized values. First, DOE calculated a pre sent value

for the time

- series of costs and benefits in 2011, the year used for discounting the NPV of total consumer costs and savings . In that calculation DOE used discount rates of 3 percent and 7 percent for all costs and benefits except for the valu e of CO 2 reductions. For the latter, DOE used the discount rate appropriate for each SCC time

- series

. From that present value, DOE calculated the fixed annual payment during a 30

- year period, starting in 2011, that yields the same present

value. The fixed annual payment is the annualized value. A nnualized values do not imply that

the time

- series of cost and benefits from which th os e annualized values were determined would actually be a steady stream of payments.

10C.2 COMBINED RESULTS USING ALTERNATIVE LEARNI N G R ATES

Table 10C.2.1 and Table 10C.2.2 present annualized present values of consumer impacts

and monetized benefits from reductions in CO 2 and NO X for microwave oven s tandby power at each TSL considered in this rulemaking. The values are based on alternative forecasts of product prices as determined primarily by learning rates (LRs)

. Table 10C.2.1 presents results for

consume r impacts and emissions reductions at 3

- percent discount rates. Table 10C.2.2 provides results for consumer impacts at a 7

- percent discount rate and for emissions reductions at a 3

- percent rate for CO 2 and a 7

- p ercent discount rate for NO X

.10C

- 4 Table 10C.2.1 Microwave Oven Standby Power : Annualized Present Value of Consumer Impacts (3

- Percent Discount Rate) and Annualized Present Value of Monetized Benefits from CO 2 and NO X Emissions Reductions (3

- Percent Discount Rate) Un der Alternative Price Scenarios

Trial

Standard

Level Parameter Billion 2011$

Default LR

(28.9%) Low LR (19.2%) High LR (37.0%) LR with MWO Data Only

(39.6%) AEO2011

Chained Price

Index Forecast No Learning (constant real prices)

1 Incr emental installe d cost 0.002 0.002 0.002 0.002 0.002 0.003 Operating cost savings 0.103 0.103 0.103 0.103 0.103 0.103

Value of emissions

reduction s 0.030 0.030 0.030 0.030 0.030 0.030

Net present value 0.131 0.131 0.131 0.131 0.131 0.131

2 Incr emental installed cos t 0.007 0.008 0.007 0.006 0.007 0.009 Operating cost savings 0.149 0.149 0.149 0.149 0.149 0.149

Value of emissions

reduction s 0.044 0.044 0.044 0.044 0.044 0.044

Net present value 0.186 0.186 0.186 0.187 0.187 0.184

3 Incr emental installed cost 0.0 22 0.023 0.021 0.019 0.020 0.027 Operating cost savings 0.205 0.205 0.205 0.205 0.205 0.205

Value of emissions

reduction s 0.061 0.061 0.061 0.061 0.061 0.061

Net present value 0.244 0.243 0.245 0.247 0.245 0.238

4 Incr emental installed cost 0.079 0. 083 0.076 0.068 0.074 0.099 Operating cost savings 0.314 0.314 0.314 0.314 0.314 0.314

Value of emissions

reduction s 0.093 0.093 0.093 0.093 0.093 0.093

Net present value 0.328 0.323 0.340 0.373 0.298 0.308

Note: Parentheses indicate negative (

- ) va lues.10C

- 5 Table 10C.2.2 Microwave Oven Standby Power : Annualized Present Value of Consumer Impacts (7

- Percent Discount Rate) and Annualized Present Value of Monetized Benefits from CO 2 and NO X Emissions Reductions (Discount Rate s of 3 percent for CO 2 and 7 percent for NO X ) Under Alternative Price Scenarios

Trial

Standard

Level Parameter Billion 2011$

Default LR

(28.9%) Low LR (19.2%) High LR (37.0%) LR with MWO Data Only

(39.6%) AEO2011

Chained Price

Index Forecast No Learning (constant real prices)

1 Incremental install ed cost 0.002 0.002 0.002 0.002 0.002 0.003 Operating cost savings 0.084 0.084 0.084 0.084 0.084 0.084

Value of emissions

reduction s 0.030 0.030 0.030 0.030 0.030 0.030

Net present value 0.112 0.112 0.112 0.112 0.112 0.111

2 Incremental installed co st 0.007 0.007 0.007 0.006 0.007 0.009 Operating cost savings 0.121 0.121 0.121 0.122 0.121 0.121

Value of emissions

reduction s 0.044 0.044 0.044 0.044 0.044 0.044

Net present value 0.158 0.158 0.159 0.159 0.159 0.157

3 Incremental installed cost 0. 020 0.021 0.020 0.018 0.019 0.025 Operating cost savings 0.167 0.167 0.167 0.167 0.167 0.167

Value of emissions

reduction s 0.060 0.060 0.060 0.060 0.060 0.060

Net present value 0.207 0.206 0.207 0.209 0.208 0.202

4 Incremental installed cost 0.075 0 .078 0.072 0.066 0.071 0.090 Operating cost savings 0.256 0.256 0.256 0.256 0.256 0.256

Value of emissions

reduction s 0.092 0.092 0.092 0.092 0.092 0.092

Net present value 0.273 0.270 0.276 0.282 0.277 0.258

Note: Parentheses indicate negative (

- ) v alues.12 A

- i APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW G UIDES

TABLE OF CO NTENTS

12A.1 MICROWAVE OVENS (ENE RGY FACTOR STANDARDS )

......................... 12A

- 1

12A.2 MICROWAVE OVENS (STA NDBY POWER STANDARDS )

......................... 12A

- 512A

- 1 APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW G UIDES

12A.1 MICROWAVE OVENS (ENE RGY FACTOR STANDARDS )



## Cooking Products Rulemaking Manufacturer Impact Analysis Interview Guide: Microwave Ovens

March 3

, 200812A

- 2 1 KEY ISSUES

The U.S. Department of Energy (DOE) is currently reviewing a set of efficiency levels for Microwave Ovens (MWO s ). The efficiency levels are presented below as they apply to the single product class. In responding to this questionnaire, please refer to the energy factor (EF)

values described below.

Table 1.1 Efficiency Levels (EF %)

Product

Class Baseline Efficiency

Level 1 Efficiency

Level 2 Efficiency

Level 3 Efficiency

Level 4

Microwave

Ovens 55.70 58.60 58.80 59.70 60.20

1.1 What are the key issue s for your company regarding MWO s related to energy conservation

standards and this rulemaking?

1.2 For the issues identified, h ow significant are they for each efficiency level ?

1.3 How can we most effectively incorporate these issues in the Manufacturer Impact Analysis (MIA) ?

2 SHIPMENT PROJECTION AND MARKET SHARE

2.1 Would you expect your market share to change once standar ds become effective? Does your outlook change with higher efficiency levels?

2.2 How would you expect shipments to change for the industry as a whole as a function of standards and why?

3 CONVERSION COSTS

An increase in energy conservation standards may cause your company to incur one

- time capital and product conversion costs. DOE calls these conversion costs because they are the costs of changing your business to comply with new energy conservation standards.

3.1 What level of conversion costs do you anticipate incurring under the different efficiency levels? Refer to the tables below to provide your company’s estimates. Please provide additional qualitative information to help us understand the nature of your investments.

Table 3.1 MWO Conversion Costs

Stand ard Level Conversion Capital Expenditures Product Conversion Expenses Stranded Assets

Baseline (55.70)

Level 1 (58.60)

Level 2 (58.80)

Level 3 (59.70)

Level 4 (60.20)12A

- 3 4 PROFITABILITY AND MANUFACTURING CAPACITY One of the primary objectives of the MIA is to assess the impact of efficiency levels on industry profitability and changes in manufacturing capacity.

4.1 Does your company have any MWO manufacturing facilities located in the United States? How would you expect shipments to cha nge for the industry as a whole as a function of standards and why?

4.2 Do you expect any changes in your estimated profitability following the new energy conservation standard? If so, please explain why.

4.3 How would the imposition of new energy conservation s tandards affect your manufacturing capacity at your production facilities? Would any facilities be closed, downsized, added, or upgraded?

5 CUMULATIVE REGULATORY BURDEN

Cumulative regulatory burden refers to the burden that industry faces from overlapping effects of new or revised DOE standards and/or other regulatory actions affecting the same products or industry.

5.1 Are there other recent or impending regulations that MWO manufacturers face (from DOE or otherwise)? What level of investment are you expecting to incur as a result of these regulations?

5.2 Under what circumstances would you be able to coordinate any expenditures related to other regulations thereby lessening the cumulative burden?

6 DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of new ene rgy conservation standards on employment is an important consideration in the rulemaking process. This section of the interview guide seeks to explore current trends in MWO production employment and solicit manufacturer views on how domestic employment

pa tterns might be affected by new energy conservation standards.

6.1 Would your domestic employment levels change under new energy conservation standards? If so, please explain how they would change.

6.2 Would the workforce skills necessary under new energy conser vation standards require extensive retraining or replacement of employees?

7 CONSOLIDATION

New energy conservation standards can alter the competitive dynamics of the market. This can include prompting companies to enter or exi t the market, or to merge. DOE and the Justice Department are both interested in any potential reduction in competition that wo uld result from a new energy conservation standard.12A

- 4 7.1 In the absence of new standards, do you expect any industry consolidation? Please describe your expect ations.

7.2 How would new standards affect your ability to compete?

7.3 Could new standards disproportionately advance or harm the competitive positions of some firms ( e.g. , small businesses)?12A

- 5 12A.2 MICROWAVE OVENS (STA NDBY POWER STANDARDS )



## Cooking Products Rulemaking Standby Power Interview Guide: Microwave Ovens

INTRODUCTION:

The U.S. Department of Energy (DOE) is currently reviewing energy efficiency standards for cooking products. Of high interest are the standby power requirements of microwave ovens. As a result, the Department invites manufacturers to participate in this questionna ire to help the Department better understand the issues regarding standby power, as well as any potential impacts on consumers that standby power legislation could have.

The Department tested 32 microwave ovens for sale in the United States within the p ast year. The sample included a wide range of input powers and cavity sizes to investigate cooking efficiency and standby power requirements. DOE subsequently published its findings at the December 17, 2007 Advance Notice of Proposed Rulemaking (ANOPR) mee ting in Washington DC. Due to comments submitted to DOE during and after this meeting, the Department would

like to solicit additional feedback from manufacturers.

During testing, DOE noted that microwave ovens with cooking sensors tend to consume more standby power than those without cooking sensors. DOE also noted how display size and technology and the associated controller could influence standby power requirements. At the end of the testing cycle, every microwave oven was completely disassembled. DO E noted a wide variety of display types and cooking sensors, even if most other aspects of microwave construction have been largely standardized. This questionnaire will thus focus on the controllers, displays, and power supplies.

Cooking Sensors

DOE wou ld like to understand why manufacturers incorporate cooking sensors into their microwave ovens, and factors influencing the choice of a specific sensor type from among the range of available technologies.

1.1 On a unit shipment

- basis, what percentage of micro wave ovens that you sell into the

United States market feature cooking sensors?

1.2 Do you have any data on the percentage of consumers that use the sensor function? Is there any data available quantifying the energy savings associated with the use of a

cook ing sensor?12A

- 6 1.3 What sensor technologies do you use for microwave ovens sold in the United States ? What are the standby requirements of these sensors? Are there sensor technologies available that require less power in standby that can be applied in microwav e ovens ?

Displays

DOE also noted a wide range of standby power consumption based on the display technology. For example, displays based on vacuum

- fluorescent displays tended to consume more power than comparable

- sized displays based on light emitting dio des. DOE noted three display types in its market sample, consisting of light

- emitting diodes (LEDs), liquid crystal displays (with or without LED backlighting), and vacuum

- fluorescent displays.

1.4 What is the consumer utility associated with each type of dis play? Do consumers demand displays with specific technologies?

1.5 What are the power requirements for each type of display assuming a comparable size and general capability (complexity of the characters that a display can show)? What are the

relative costs associated with each type of display?

Controllers and Power Supplies

While cooking sensors and displays consumed the most standby power, DOE also noted that controllers and the control boards also consume standby power. The Department recognizes that

th ere may be tradeoffs between standby power consumption and the ability of control systems to tackle complex tasks.

1.6 What consumer utility is associated with microwave oven controllers? Could controller attributes or functionalities be impacted by reducing microwave oven standby requirements? If so, how?

1.7 Are lower

- power versions of these controllers available? What control board design options are available for reducing standby power? At what cost?

1.8 What is the range of efficiencies for controller power supplies? Can higher efficiency power supplies be implemented? At what cost?

Potential Standby Power Standard Levels

DOE surveyed foreign regulatory and non

- regulatory programs, identifying Korea’s e

- standby program, Australia’s standby program, and Japan’s Top Runner Program as pursuing reductions in standby power consumption through support for the International Energy Agency’s One

- Watt12A

- 7 program to lower standby power consumption below 1 watt (W) for microwave ovens a , among others. DOE is also aware of zero

- watt

- standby microwave ovens in Japan that feature cooking

sensors.

1.9 What design changes from current US products w ould be required if the following standby

standard level were established?

**•** 4 W/h

**•** 2 W/h

**•** 1.5 W/h

**•** 1 W/h

**•** 0 W/h

**•** What would be the manufacturing cost impacts of these changes?

**•** Are there any impacts on consumer utility as standby power levels are lowered? If so, what are they, and at what standby power levels do they occur?

Standby Power Standards Manufacturer Impacts

DOE understands that by establishing standby power standards, manufacturer could potentially

incur one

- time capital and product conversion costs

. b DOE calls these conversion costs because they are the costs of changing your business to comply with new standby power standards. In addition, DOE would like to understand possible effects of new standby power standards on industry shipments, pro fitability, and manufacturing capacity.

What level of conversion costs do you anticipate incurring under different standby power standard level? Refer to the tables below to provide your company’s estimates. Please provide additional qualitative informat ion to help us understand the nature of your investments.

a IEA Energy Information Centre, Standby Power Use and the IEA “1

- Watt Plan ” http://www.iea.org/textbase/subjectqueries/standby.asp

b Capital and product conversion costs include capital conversion expenditures, product conversion expenditures and stranded assets. Capital Conversion Expenditures

– One time investments in plant, property, and equipment (PPE) necessitated by an energy con servation standard. These may be completely incremental to existing PPE or they could involve replacement of existing PPE. Product Conversion Expenditures

– One

- time expenses in research, product development, testing, and marketing necessitated by an appl iance energy conservation standard. Stranded

A ssets

– Investments such as equipment or tooling that become obsolete as a result of new regulation.12A

- 8 Table 1.1 MWO Conversion Costs

Standard Level Conversion Capital Expenditures Product Conversion Expenses Stranded Assets

4 W/h

2 W/h

1.5 W/h

1 W/h

0 W/h

1.10 How would you expect shipments to change for the industry as a whole as a function of standby power standards and why?

1.11 Do you expect any changes in your estimated profitability following new standby power standard? If so, please explain why.

1.12 How would the imposition of new standby power standards affect your manufacturing capacity at your production facilities? Would any facilities be closed, downsized, added, or upgraded?

Standby Test Procedure

As part of the analysis, DOE reviewed the existing DOE cooking efficie ncy test procedure and possible standby test procedures that could be incorporated. For standby testing, DOE is considering adopting by reference the IEC 62301 standby test procedure c

.

1.13 Do you currently conduct microwave oven standby power testing? If s o, which test procedure do you use?

1.14 How do you feel about the incorporation of IEC 62301 to measure standby with the provision that a 12

- hour (or possibly 24

- hour) test be performed with the clock set to a specific starting time?

c See http://iec.org for further details12 B

- i APPENDIX 12B. GOVERNMENT REGULATOR Y IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12B.1 INTRODUCTION AND PUR POSE

................................ ................................ .... 12B

- 1

12B.2 MODEL DESCRIPTION

................................ ................................ ..................... 12B

- 1

12B.3 DETAILED CASH FLOW E XAMPLE

................................ ............................... 12B

- 412 B

- 1 APPENDIX 12 B

. GOVERNMENT REGULATOR Y IMPACT MODEL

12B.1 INTRODUCTION AND PURPOSE

The p urpose of the Government Regulatory Impact Model (GRIM) is to help quantify the impacts of energy conservation standards and other regulations on manufacturers. The basic mode of analysis is to estimate the change in value of the industry or manufacturers( s) following a regulation or a series of regulations. The model structure also allows a n analysis of multiple products with regulations taking effect over a period of time, and of multiple r egulations on the same products

.

Industry net present value is defined, for the purpose of this analysis, as the discounted sum of industry free cash flows plus a discounted terminal value. The model calculates the actual cash flows by year and then determines the present value of those cash flows both without an

amen ded energy conservation standard ( i.e ., the base case) and under different trial standard levels (TSLs).

Output from the model consists of summary financial metrics, graphs of major variables, and, when appropriate, access to the complete cash flow calculation.

12B.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters as inputs and accepts a set of regulatory cond itions as changes in costs and investments. The cash flow analysis is separated into two major blocks: income and cash flow. The income calculation determines net operating profit after taxes. The cash flow calculation converts net operating profit after t axes into an annual cash flow by including investment and non

- cash items. Below are definitions of listed items on the printout of the output sheet (see s ection 1 2. B .3).

Unit Sales: Total annual shipments for the industry were obtained from the National Im pact Analysis Spreadsheet;

Revenues: Annual revenues

- c omputed by multiplying unit price s at each efficiency level by the app ropriate manufacturer markup;

Labor: The portion of cost of goods sold (COGS) that includes direct labor, commissions, dismissal p ay, bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor up

- time;

Material: The portion of COGS that includes materials;12 B

- 2 Overhead: The portion of COGS that includes indirect labor, indirect material, energy use,

mainten ance, depreciation, property taxes, and insurance related to assets. While included in overhead, the depreciation is shown as a separate line item;

Depreciation: Annual depreciation computed as a percentage of Revenues

. While included in overhead, the depr eciation is shown as a separate line item;

Stranded Assets: In the compliance year of the standard, a one

- time write

- off of net property, plant, and equipment ( PPE ) assets to account for the book value of these assets that would have enjoyed a longer life if not for the standard ;

Standard SG&A: Selling, general, and administrative costs are computed as a percentage of

Revenues ;

R&D: the GRIM separately accounts for ordinary research and development (R&D) as a percentage of Revenues ;

Product Conversion Costs : Product conversion costs are one

- time investments in research, development, testing, mar ke ting , and other costs focused on making equipment design s comply with the new energy conservation standard. The GRIM allocates these costs over the period between the standard’s announcement and the effective date ;

Earnings Before Interest and Taxes (EBIT): Includes profit before deductions for interest paid and taxes;

EBIT as a Percentage of Sales (EBIT/Revenues) : the GRIM calculates EBIT as a percentage of sales to compare with the industry’s average reported in financial statements;

Taxes: Taxes on EBIT are calculated by multiplying the tax rate contained in Major

Assum ptions by EBIT ;

Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods Sold

, SG&A

, R&D

, Product Conversion Costs , and Taxes from Revenues ;

NOPAT repeated : NOPAT is repeated in the Statement of Cash Flows;

Depreciation repeated : Depreciation and Stranded Assets are added back in the Statement of

Cash Flows because they are non

- cash expense s ;

Change in Working Capital : Change in cash tied up in accounts receivable, inventory, and other cash investments necessary to support operatio ns is calculated by multiplying working capital (as a percentage of revenues) by the change in annual revenues ;

Cash Flow From Operations : Calculated by taking NOPAT , adding back non

- cash items

such as a Depreciation , and subtracting out Change in Working Capital ;12 B

- 3 Ordinary Capital Expenditures: Ordinary investments in property, plant, and equipment to maintain and replace existing production assets, computed as a percentage of Revenues ;

Capital Conversion Costs : Capital c onversion costs are one

- time invest ments in property, plant, and equipment to adapt or change existing production facilities so that new product designs can be fabricated and assembled under the new regulation;

Capital Investment: Total investments in property, plant, and equipment are comp uted by adding Ordinary Capital Expenditures and Capital Conversion Costs ;

Free Cash Flow: Annual cash flow from operations and investments; computed by subtracting Capital Investment from Cash Flow from Operations ;

Terminal Value: Estimate of the continuing value of the industry after 2045

. Computed by growing the Free Cash Flow in year 2045 at a constant rate in perpetuity;

Present Value Factor: Factor used to calculate an estimate of the present value of an amount

to be received i n the future;

Discounted Cash Flow: Free Cash Flows multiplied by the Present Value Factor

. For 204 5

the discounted cash flow includes the discounted Terminal Value ; and

Industry Value thru 2045 : The sum of Discounted Cash Flows

.12 B

- 4 12B.3 DETAILED CASH FLOW E XAMPLE

Ancmt Yr Std Yr Industry Income Statement 2012 2013 2014 2015 2016 2017 2018 2019 2020

Revenues 2,370.1 $ 2,300.1 $ 2,362.5 $ 2,319.4 $ 2,270.8 $ 2,160.0 $ 2,083.1 $ 2,099.0 $ 2,184.1 $

- Materials 1,537.0 $ 1,491.7 $ 1,532.1 $ 1,504.2 $ 1,497.5 $ 1,424.4 $ 1,373.7 $ 1,384.2 $ 1,440.3 $

- Labor 161.0 $ 156.3 $ 160.5 $ 157.6 $ 148.1 $ 140.9 $ 135.9 $ 136.9 $ 142.5 $
- Depreciation 111.5 $ 108.3 $ 111.2 $ 109.2 $ 102.6 $ 97.6 $ 94.1 $ 94.8 $ 98.7 $

- Overhead 16.3 $ 15.8 $ 16.3 $ 16.0 $ 15.0 $ 14.3 $ 13.8 $ 13.9 $ 14.4 $

- Standard SG&A 365.5 $ 354.7 $ 364.3 $ 357.6 $ 350.2 $ 333.1 $ 321.2 $ 323.7 $ 336.8 $

- R&D 28.4 $ 27.6 $ 28.4 $ 27.8 $ 27.2 $ 25.9 $ 25.0 $ 25.2 $ 26.2 $

- Product Conversion Costs

- $ 18.2 $ 25.4 $ 29.1 $ 1.5 $

- $

- $

- $

- $

- Stranded Assets

- $

- $

- $

- $

- $

- $

- $

- $

- $ Earnings Before Interest and Taxes (EBIT) 150.2 $ 127.6 $ 124.3 $ 117.9 $ 128.7 $ 123.8 $ 119.4 $ 120.3 $ 125.2 $

EBIT/Revenues 6.3% 5.5% 5.3% 5.1% 5.7% 5.7% 5.7% 5.7% 5.7%

- Taxes 42.4 $ 36.0 $ 35.1 $ 33.3 $ 36.3 $ 35.0 $ 33.7 $ 34.0 $ 35.4 $ Net Operating Profit after Taxes (NOPAT) 107.8 $ 91.6 $ 89.2 $ 84.6 $ 92.3 $ 88.9 $ 85.7 $ 86.3 $ 89.8 $

Cash Flow Statement

NOPAT 107.8 $ 91.6 $ 89.2 $ 84.6 $ 92.3 $ 88.9 $ 85.7 $ 86.3 $ 89.8 $
+ Depreciation 111.5 $ 108.3 $ 111.2 $ 109.2 $ 102.6 $ 97.6 $ 94.1 $ 94.8 $ 98.7 $
+ Loss on Disposal of Stranded Assets

- $

- $

- $

- $

- $

- $

- $

- $

- $ - Change in Working Capital

- $ (4.8) $ 4.3 $ (3.0) $ (3.3) $ (7.6) $ (5.3) $ 1.1 $ 5.9 $
Cash Flows from Operations 219.3 $ 204.6 $ 196.1 $ 196.8 $ 198.3 $ 194.1 $ 185.1 $ 180.1 $ 182.7 $ - Ordinary Capital Expenditures 114.0 $ 110.6 $ 113.6 $ 111.6 $ 109.2 $ 103.9 $ 100.2 $ 101.0 $ 105.1 $
- Capital Conversion Costs

- $ 2.0 $ 2.8 $ 3.2 $

- $

- $

- $

- $

- $

Free Cash Flow 105.3 $ 92.0 $ 79.7 $ 82.0 $ 89.1 $ 90.2 $ 84.9 $ 79.1 $ 77.6 $

Discounted Cash Flow

Free Cash Flow 105.3 $ 92.0 $ 79.7 $ 82.0 $ 89.1 $ 90.2 $ 84.9 $ 79.1 $ 77.6 $

Terminal Value

- $

- $

- $

- $

- $

- $

- $

- $

- $

Present Value Factor 0.000 1.000 0.926 0.857 0.793 0.734 0.680 0.629 0.582

Discounted Cash Flow

- $ 92.0 $ 73.8 $ 70.3 $ 70.7 $ 66.2 $ 57.7 $ 49.8 $ 45.2 $

INPV at TSL 4 1,201.9 $16A

- i APPENDIX 16A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866

TABLE OF CO NTENTS

16A.1 EXECUTIVE SUMMARY

................................ ................................ .................. 16A

- 1

16A.2 MONETIZING CARBON DIOXIDE EMISS IONS

................................ ............ 16A

- 2

16A.3 SOCIAL COST OF CARBO N VALUES USED IN PAS T REGULATORY

ANALYSES

................................ ................................ ................................ .......... 16A

- 4

16A.4 APPROACH AND KEY ASSUMPTIONS

................................ ......................... 16A

- 5

16A.4.1 Integrated Assessment Models

................................ ................................ ............. 16A

- 5

16A.4.2 G lobal versus Domestic Measures of SCC

................................ ......................... 16A

- 11

16A.4.3 Valuing Non

- CO 2 Emissions

................................ ................................ .............. 16A

- 13

16A.4.4 Equilibrium Climate Sensitivity

................................ ................................ ......... 16A

- 13

16A.4.5 Socioeconomic and Emissions Trajectories

................................ ........................ 16A

- 16

16A.4.6 Discount Rate

................................ ................................ ................................ ...... 16A

- 19

16A.5 REVISED SCC ESTIMATE S

................................ ................................ ............ 16A

- 26

16A.6 LIMITATIONS OF THE A NALYSIS

................................ ............................... 16A

- 32

16A.7 A FURTHER DISCU SSION OF CATASTROPHI C IMPACTS AND

DAMAGE FUNCTIONS

................................ ................................ ................... 16A

- 34

16A.8 CONCLUSION

................................ ................................ ................................ ... 16A

- 37

16A.9 ANNEX

................................ ................................ ................................ ............... 16A

- 43

16A.9.1 Other (non

- CO2) gases

................................ ................................ ....................... 16A

- 44

16A.9.2 Extrapolating Emissions Projections to 2300

................................ ..................... 16A

- 46

LIST OF TABLES

Table 16A.1.1 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ .. 16A

- 2

Table 16A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity

Distributions

................................ ................................ ............................ 16A

- 14

Table 16 A.4.2 Socioeconomic and Emissions Projections from Select EMF

- 22

Reference Scenarios

................................ ................................ ................ 16A

- 18

Table 16A.5.1 Disaggregated Social Cost of CO 2 Values by Model, Socioeconomic Trajectory, and Discount Rate for 2010 (in 2007 dollars)

...................... 16A

- 29

Table 16A.5.2 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ 16A

- 31

Table 16A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates

between 2010 and 2050

................................ ................................ .......... 16A

- 31

Table 16A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

............ 16A

- 35

Table 16A.9.1 Annual SCC Values: 2010

– 2050 (in 2007 dollars)

................................ 16A

- 43

Table 16A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 16A

- 53

Table 16A. 9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 16A

- 5416A

- ii Table 16A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 16A

- 55

Table 16A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates

............. 16A

- 56

LIST OF FIGURE S

Figure 16A.4.1 Annual Consumption Loss as a Fraction of Global G DP in 2100 Due to an Increase in Annual Global Temperature in the DICE, FUND, and

PAGE models

................................ ................................ .......................... 16A

- 10 Figure 16A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE

................................ ................................ .................. 16A

- 11 Figure 16A.4.3 Estimates of the Probability Density Function for Equilibrium Climate Sensitivity (°C)

................................ ................................ ....................... 16A

- 16 Figure 16A.5.1 Level of Global GDP across EMF Scenarios

................................ ......... 16A

- 30 Figure 16A.9.2 Sulfur Diox ide Emission Scenarios

................................ ........................ 16A

- 46 Figure 16A.9.3 Global Population, 2000

- 2300 (Post

- 2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.)

................................ ................................ ................................ ...... 16A

- 48 Figure 16A.9.4 World GDP, 2000

- 2300 (Post

- 2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in the year 2300)

........... 16A

- 49 Figure 16A.9.5 Global Fossil and Industrial CO 2 Emissions, 2000

- 2300 (Post

- 2100 extrapolations assume growth rate of CO 2 intensity (CO 2 /GDP) over

2090

- 2100 is maintained through 2300)

................................ ................. 16A

- 50 Figure 16A.9.6 Global Net Land Use CO 2 Emissions, 2000

- 2300 (Post

- 2100 extrapolations assume emissions decline linearly, reaching zero in the year 2200)

................................ ................................ ............................... 16A

- 50 Figure 16A .9.7 Global Non

- CO 2 Radiative Forcing, 2000

- 2300 (Post

- 2100 extrapolations assume constant non

- CO 2 radiative forcing after 2100)

. 16A

- 51 Figure 16A.9.8 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000

- 2300 (Post

- 2100 extrapolations assume decline in CO 2 /GDP growth

rate over 2090

- 2100 is maintained through 2300)

................................ .. 16A

- 52 Figure 16A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by

discount rate

................................ ................................ ............................ 16A

- 5516A

- 1 APPENDIX 16A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866 a

16A.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the social benefits of reducing carbon dioxi de (CO 2 ) emissions into cost

- benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a

clear understandi ng that they should be updated over time to reflect increasing knowledge of the science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagen cy process that developed these SCC estimates. Technical experts from numerous agencies met on a regular basis to consider public comments, explore the technical literature in relevant fields, and discuss key model inputs and assumptions. The main objecti ve of this process was to develop a range of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic literatures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

a Prepared by Interagency Working Group on Social Cost of Carbon, United States Government. With participation by:

Council of Economic Advisers Council on Environmental Quality Department of Agriculture Department of Commerce Department of Energy Department of Transportation Environmental Protection Agency

National Economic Council Office of Energy and Climate Change Office of Management and Budget Office of Science and Technology Policy Department of th e Treasury16A

- 2 The interagency group selected four SCC values for use in regulatory analyses. Three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95 th percentile SCC estimate across all three models at a 3 percent discount rate, is included to represent higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribu tion.

Table 16A.1.1 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

Discount Rate

5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

16A.2 MONETIZING CARBON DIOXIDE EMISS IONS

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. We report estimates of the SCC in dollars per metric ton of carbon dioxide throughout this document. b

When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC 2009) points out that any assessment will suffer from unc ertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.

b In this document, we present all values of the SCC as the cost per metric ton of CO 2 emissions. Alternatively, one could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of

CO 2 and the mass of carbon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12 = 3.67).16A

- 3 Despite the serious limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive Order 12866, agencies are required, to the extent permitted by law , “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation j ustify its costs.” The purpose of the SCC estimates presented here is to make it possible for agencies to incorporate the social benefits from reducing carbon dioxide

emissions into cost

- benefit analyses of regulatory actions that have small, or “marginal ,” impacts on cumulative global emissions. Most federal regulatory actions can be expected to have marginal impacts on global emissions.

For such policies, the benefits from reduced (or costs from increased) emissions in any future year can be estimat ed by multiplying the change in emissions in that year by the SCC value appropriate for that year. The net present value of the benefits can then be calculated by multiplying each of these future benefits by an appropriate discount factor and summing acro ss all affected years. This approach assumes that the marginal damages from increased emissions are constant for small departures from the baseline emissions path, an approximation that is reasonable for policies that have effects on emissions that are sm all relative to cumulative global carbon dioxide emissions. For policies that have a large (non

- marginal) impact on global cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore the technical literature in relevant fields, and discuss key inputs and assumptions in order to generate SCC estimates. Agencies that actively participated in the interagency process include the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, Transportation, and Treasury. This process was convened by the Council of Econom ic Advisers and the Office of Management and Budget, with active participation and regular input from the Council on Environmental Quality, National Economic Council, Office of Energy and Climate Change, and Office of Science and Technology Policy. The ma in objective of this process was to develop a range of SCC values using a defensible set of input assumptions that are ground ed in the existing literature. In this way, key uncertainties and model differences can more transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are

based on th e average SCC across models and socioeconomic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, respectively. The fourth value is included to represent the higher

than

- expected impacts from temperature change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. The central value is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range. These SCC estimates also grow over time. For instance, the central value increases to $24 per ton of CO 2 in 2015 and $26 per ton of CO 2 in 2020. See

section 16

- A .5 for the full range of annual SCC estimates from 2010 to 2050.16A

- 4 It is important to emphasize that the interagency process is committed to updating these estimates as the science and economic understanding of climate change and its impacts on society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC values within two years or at such time as substantially updated models become available, and to continue to support research in this area. In the meantime, we will c ontinue to explore the issues raised in this document and consider public comments as part of the ongoing interagency

process.

16A.3 SOCIAL COST OF CARBO N VALUES USED IN PAS T REGULATORY

ANALYSES

To date, economic analyses for Federal regulations have used a wi de range of values to estimate the benefits associated with reducing carbon dioxide emissions. In the final model year 2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of $2 per ton of CO 2 and a “global” SCC value of $33 per ton of CO 2 for 2007 emission reductions (in 2007 dollars), increasing both v alues at 2.4 percent per year. It also included a sensitivity analysis at $80 per ton of CO 2 . A domestic SCC value is meant to reflect the val ue of damages in the United States resulting from a unit change in carbon dioxide emissions, while a global SCC value is meant to reflect the value of damages worldwide.

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO 2 ( in 2006 dollars) for 2011 emission reductions (with a range of $0

- $14 for sensitivity analysis), also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a domestic SCC range of $0 to $20 per ton CO 2 for 2007 emission reductions (in 2007 dollars). In addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean values were $68 and $40 per ton CO 2 f or discount rates of approximately 2 percent and 3 percent, respectively (in 2006 dollars for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing carbon dioxi de emissions. To ensure consistency in how benefits are evaluated across agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemaking process, to quantify avoided climate change damages from reduced CO 2 emissions. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the existing literature to use as interim values until a more comprehensive analysis could be conducted.

The outcome of the p reliminary assessment by the interagency group was a set of five interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per

ton of CO 2. The $33 and $5 values represented model

- weighted means of the published

estimate s produced from the most recently available versions of three integrated assessment

models

— DICE, PAGE, and FUND

— at approximately 3 and 5 percent discount rates. The $55 and $10 values were derived by adjusting the published estimates for uncertainty in the discount rate (using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, respectively. The $19 value was chosen as a central value between the $5 and $33 per ton16A

- 5 estimates. All of these values were assumed to increase at 3 per cent annually to represent growth in incremental damages over time as the magnitude of climate change increases.

These interim values represent the first sustained interagency effort within the U.S. government to develop an SCC for use in regulatory anal ysis. The results of this preliminary effort were presented in several proposed and final rules and were offered for public comment in connection with proposed rules, including the joint EPA

- DOT fuel economy and CO 2 tailpipe emission proposed rules.

16A.4 APPRO ACH AND KEY ASSUMPTI ONS

Since the release of the interim values, the interagency group has reconvened on a regular basis to generate improved SCC estimates. Specifically, the group has considered public comments and further explored the technical literat ure in relevant fields. This section details the several choices and assumptions that underlie the resulting estimates of the SCC.

It is important to recognize that a number of key uncertainties remain, and that current

SCC estimates should be treated as provisional and revisable

, since they will evolve with improved scientific and economic understanding. The interagency group also recognizes that the existing models are imperfect and incomplete. The National Academy of Science (2009) points

out that t here is tension between the goal of producing quantified estimates of the economic damages from an incremental ton of carbon and the limits of existing efforts to model these effects. Throughout this document, we highlight a number of concerns and problem s that should be addressed by the research community, including research programs housed in many of the agencies participating in the interagency process to estimate the SCC

.

The U.S. Government will periodically review and reconsider estimates of the SCC used

for cost

- benefit analyses to reflect increasing knowledge of the science and economics of climate impacts, as well as improvements in modeling. In this context, statements recognizing the limitations of the analysis and calling for further resea rch take on exceptional significance. The interagency group offers the new SCC values with all due humility about the uncertainties embedded in them and with a sincere promise to continue work to improve them.

16A.4.1 Integrated Assessment Models

We rely on thr ee integrated assessment models (IAMs) commonly used to estimate the SCC: the FUND, DICE, and PAGE models. c

c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decision

makers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). T he FUND (Climate Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g.,

Tol 200 2a, Tol 2002b, Anthoff et al. 2009, Tol 2009). These models are frequently cited in the peer

-16A

- 6 reviewed literature and used in the IPCC assessment. Each model is given equal weight in the

SCC va lues developed through this process, bearing in mind their different limitations (discussed below).

These models are useful because they combine climate processes, economic growth, and feedbacks between the climate and the global economy into a single modeling framework. At the same time, they gain this advantage at the expense of a more detailed representation of the underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reduced

form approaches (see NRC 2009 for a more deta iled discussion; see Nordhaus 2008 on the possible advantages of this approach). Other IAMs may better reflect the complexity of the science in their modeling frameworks but do not link physical impacts to economic damages. There is currently a limited a mount of research linking climate impacts to economic damages, which makes this exercise even more difficult. Underlying the three IAMs selected for this exercise are a number of simplifying assumptions and judgments reflecting the various modelers’

best attempts to synthesize the available scientific and economic research characterizing these relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse concentrations, atmospheric concentrations into changes in temperature, an d changes in temperature into economic damages. The emissions projections used in the models are based on specified socioeconomic (GDP and population) pathways. These emissions are translated into concentrations using the carbon cycle built into each model, and concentrations are translated into warming based on each model’s simplified representation of the climate and a key

paramet er, climate sensitivity. Each model uses a different approach to translate warming into damages. Finally, transforming the stream of economic damages over time into a single value requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result in changes in economic damages. In PAGE, for example, the consumption

- equivalent damages in each period are calculated as a fraction of GDP, depending on the temperature in that period

relative t o the pre

- industrial average temperature in each region. In FUND, damages in each period also depend on the rate of temperature change from the prior period. In DICE, temperature affects both consumption and investment. We describe each model in greater detail here. In a later section, we discuss key gaps in how the models account for various scientific and economic processes (e.g. the probability of catastrophe, and the ability to adapt to climate change and the physical changes it causes).

The param eters and assumptions embedded in the three models vary widely. A key objective of the interagency process was to enable a consistent exploration of the three models while respecting the different approaches to quantifying damages taken by the key modeler s in the field. An extensive review of the literature was conducted to select three sets of input parameters for these models: climate sensitivity, socioeconomic and emissions trajectories, and discount rates. A probability distribution for climate sensi tivity was specified as an input into all three models. In addition, the interagency group used a range of scenarios for the socioeconomic parameters and a range of values for the discount rate. All other model features were left unchanged, relying on th e model developers’ best estimates and judgments. In DICE, these16A

- 7 parameters are handled deterministically and represented by fixed constants; in PAGE, most parameters are represented by probability distributions. FUND was also run in a mode in which

para meters were treated probabilistically.

The sensitivity of the results to other aspects of the models (e.g. the carbon cycle or damage function) is also important to explore in the context of future revisions to the SCC but has not been incorporated into these estimates. Areas for future research are highlighted at the

end of this document.

The DICE Model

The DICE model is an optimal growth model based on a global production function with an extra stock variable (atmospheric carbon dioxide concentratio ns). Emission reductions are treated as analogous to investment in “natural capital.” By investing in natural capital today through reductions in emissions

— implying reduced consumption

— harmful effects of climate change can be avoided and future consumpti on thereby increased.

For purposes of estimating the SCC, carbon dioxide emissions are a function of global GDP and the carbon intensity of economic output, with the latter declining over time due to technological progress. The DICE damage function li nks global average temperature to the overall impact on the world economy. It varies quadratically with temperature change to capture the more rapid increase in damages expected to occur under more extreme climate change, and is

calibrated to include the effects of warming on the production of market and nonmarket goods and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vulnerable market sectors” (based primarily on changes in energy use), human health (bas ed on

climate

- related diseases, such as malaria and dengue fever, and pollution), non

- market amenities (based on outdoor recreation), and human settlements and ecosystems. The DICE damage function also includes the expected value of damages associated wi th low probability, high impact “catastrophic” climate change. This last component is calibrated based on a survey of experts (Nordhaus 1994). The expected value of these impacts is then added to the other market

and non

- market impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is included implicitly through the choice of studies used to calibrate the aggregate damage function. For example, its agricultural impact estimates assume that farmer s can adjust land use decisions in response to changing climate conditions, and its health impact estimates assume improvements in healthcare over time. In addition, the small impacts on forestry, water systems, construction, fisheries, and outdoor recreat ion imply optimistic and costless adaptation in these sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level rise are incorporated into damage estimates, but their magnitude is not clearly reported.

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective adaptation, and largely ignores adaptation costs."

Note that the damage function in DICE has a somewhat different meaning from the damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because damages in a given year reduce investment in that year, damages propagate forward in time and16A

- 8 r educe GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in any given year do not propagate forward. d

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into economic, non

- economic, and catastrophic categories and calculates these impacts separately for eight geographic regions. Damages in each region are expressed as a fraction of output, where the fraction lost depends on the temperature change in each region. Damages are expressed as power functions of temperature change. The exponents of the damage function are the same in all regions but are treated as uncertain, with valu es ranging from 1 to 3 (instead of being fixed at 2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage sub

function. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a

“discontin uity” (i.e., a catastrophic event) is assumed to increase with temperature above a specified threshold. The threshold temperature, the rate at which the probability of experiencing a discontinuity increases above the threshold, and the magnitude of the re sulting catastrophe are all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature increases above some tolerable level (2°C for developed countries and 0°C for developing

countries for economi c impacts, and 0°C for all regions for non

- economic impacts), but adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond

the to lerable 2°C increase and that developing countries can eventually eliminate 50 percent of their economic impacts. All regions are assumed to be able to mitigate 25 percent of the non

economic impacts through adaptation (Hope 2006).

The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, energy (based on heating and cooling demand), sea level rise (based on the value of land lost and the cost of protection), ecosystems, human health (diarrhea, vector

- borne diseases, and cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a different functional form, and is calculated separately for s ixteen geographic regions. In some impact sectors, the fraction of output lost or gained due to climate change depends not only on

d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous

. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it consistent with EMF GDP trajectories (see next se ction): we assumed a fixed rate of savings of 20%, and we re

- calibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly matched the EMF scenarios.16A

- 9 the absolute temperature change but also on the rate of temperature change and level of regional

income. e In the forestry a nd agricultural sectors, economic damages also depend on CO 2 concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a relatively small effect on damage estimates (both positive and negative). However, he

c haracterizes several omitted impacts as “big unknowns”: for instance, extreme climate scenarios, biodiversity loss, and effects on economic development and political violence. With regard to potentially catastrophic events, he notes, “Exactly what would c ause these sorts of changes or what effects they would have are not well

- understood, although the chance of any one of them happening seems low. But they do have the potential to happen relatively quickly, and if they did, the costs could be substantial. Only a few studies of climate change have examined

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as energy and human health, where wealthier populations are assumed to be less vulnerable to climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those due to the rate of temperature change (damages are always positiv e); (2) those due to the level of temperature change (damages can be positive or negative depending on region and temperature); and (3) those from CO 2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate change happens more slowly. The combined effect of CO 2 fertilization in the agricultural sector, positive impacts to some regions from higher temperatures, and sufficiently slow increases in

temperatu re across these sectors can result in negative economic damages from climate change.

Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of how to represent the effects of climate change (represented by t he increase in global

- average surface temperature) on the consumption

- equivalent value of both market and non

- market goods (represented as a fraction of global GDP). We recognize that these representations are incomplete and highly uncertain. But given t he paucity of data linking the physical impacts to economic damages, we were not able to identify a better way to translate changes in climate into net economic damages, short of launching our own research program.

The damage functions for the three IAMs are presented in Figures 1 6 A.4.1 and 1 6 A.4.2

, using the modeler’s default scenarios and mean input assumptions. There are significant differences between the three models both at lower (figure 16 A.4.2 ) and higher (figure 16 A.4.1 ) increases in global

- average temperature.

e In the deterministic ver sion of FUND, the majority of damages are attributable to increased air conditioning demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in those regions at low to moderate levels of warming (War ren et al., 2006).16A

- 10 Figure 16A.4.1 Annual Consumption Loss as a Fraction of Global

GDP in 2100 Due to an Increase in Annual Global Temperature in the DICE, FUND, and PAGE

models f

The lack of agreement among the models at lower temperature increases is underscored by the fact that the damages from FUND are well below the 5 th percentile estimated by PAGE, while the damages estimated by DICE are roughly equal to the 95 th percentile estimated by PAGE. This is significant because at higher discount rates we expect that a greater proportion of the SCC value is due to damages in years with lower temperature increases. For example, when the discount rate is 2.5 percent, abou t 45 percent of the 2010 SCC value in DICE is due to damages that occur in years when the temperature is less than or equal to 3 °C. This increases to approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These diffe rences underscore the need for a thorough review of damage functions

— in particular, how the models incorporate adaptation, technological change, and catastrophic damages. Gaps in the literature make modifying these aspects of the models challenging, which highlights the need for additional research. As knowledge improves, the Federal government is

f The x

- axis represents increases in annual, rather than equilibrium, temperature, while the y

- axis represents the annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity,

socioeconomic , and emissions parameters will produce a different realization of damages for each IAM. The damage functions represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under alternate assumptions, the damages from FUND may cross fr om negative to positive at less than or greater than 3

°C.16A

- 11 committed to exploring how these (and other) models can be modified to incorporate more accurate estimates of damages.

Figure 16A.4.2 Annual Consumption Loss for Lower Te mperature Changes in DICE, FUND, and PAGE

16A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current attention on a global measure of SCC. This approach is the same as that taken for the interim values, but it otherwise represents a departure from past practices, which tended to put greater emphasis on a domestic measure of SCC (limited to impacts of climate change experienced within U.S. borders). As a matter of law, consideration of bot h global and domestic values is generally permissible; the relevant statutory provisions are usually ambiguous and allow

selection of either measure. g

Global SCC

Under current OMB guidance contained in Circular A

- 4, analysis of economically significant proposed and final regulations from the domestic perspective is required, while analysis from the international perspective is optional. However, the climate change problem is highly unusual in at least two respects. First, it involves a global external ity: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in the

g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the United States respect the interests of foreign sovereigns. But use of a g lobal measure for the SCC does not give

extraterritorial effect to federal law and hence does not intrude on such interests.16A

- 12 United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages caused by GHG emi ssions. Second, climate change presents a problem that the United States alone cannot solve. Even if the United States were to reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid substantial climate change. Other co untries would also need to take action to reduce emissions if significant changes in the global climate are to be avoided. Emphasizing the need for a global solution to a global problem, the United States has been actively involved in seeking

internationa l agreements to reduce emissions and in encouraging other nations, including emerging major economies, to take significant steps to reduce emissions. When these considerations are taken as a whole, the interagency group concluded that a global measure of

t he benefits from reducing U.S. emissions is preferable.

When quantifying the damages associated with a change in emissions, a number of analysts (e.g., Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in consumption across regions. This weighting takes into account the relative reductions in wealth in different regions of the world. A per

- capita loss of $500 in GDP, for instance, is weighted more heavily in a country with a per

- capita GDP of $2,000 than in one with a per

- capita GDP of $40,000. The main argument for this approach is that a loss of $500 in a poor country causes a greater reduction in utility or welfare than does the same loss in a wealthy nation. Notwithstanding the theoretical claims on behalf of equity weighting, t he interagency group concluded that this approach would not be appropriate for estimating a SCC value used in domestic regulatory analysis. h For this reason, the group concluded that using the global (rather than domestic) value, without equity weighting, is the appropriate approach.

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the relatively few region

- or country

- specific estimates of the SCC in the literature. One potential

source of estimates come s from the FUND model. The resulting estimates suggest that the ratio of domestic to global benefits of emission reductions varies with key parameter assumptions. For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7

- 10 percent of the global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP lost due to climate change is assumed to be similar across countries, the domestic benefit would be proportional to the U.S. share of global GDP, whic h is currently about 23 percent. i

On the basis of this evidence, the interagency workgroup determined that a range of values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic effects. Reported domestic values should use this range. It is recognized that th ese values are

h It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account

would have to conside r that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor nation than on a wealthy one. Even if equity weighting

— for both the costs and benefits of emissions reductions

— is appropriate when considering the utility or we lfare effects of international action, the interagency group concluded that it should not be used in developing an SCC for use in regulatory policy at this time.

i Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Repo rt

.16A

- 13 approximate, provisional, and highly speculative. There is no a priori reason why domestic benefits should be a constant fraction of net global damages over time. Further, FUND does not account for how damages in other regions could affect the United States (e.g., global migration, economic and political destabilization). If more accurate methods for calculating the domestic SCC become available, the Federal government will examine these to determine whether to update its approach.

16A.4.3 Valuing Non

- CO 2 Emissions

While CO 2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these gases is commonly discussed in terms of their 100

- year global warming potential (GWP). GWP measures the ability of different gases to trap heat in the atmosphere (i.e., radiative forcing per unit of mass) over a particular timeframe relative to C O 2 . However, because these gases differ in both radiative forcing and atmospheric lifetimes, their relative damages are not constant over time. For example, because methane has a short lifetime, its impacts occur primarily in the near

term and thus are n ot discounted as heavily as those caused by longer

- lived gases. Impacts other than temperature change also vary across gases in ways that are not captured by GWP. For instance, CO 2 emissions, unlike methane and other greenhouse gases, contribute to ocean acidification. Likewise, damages from methane emissions are not offset by the positive effect of

CO 2 fertilization. Thus, transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accu rate estimates of the

social costs of non

- CO 2 gases.

In light of these limitations, and the significant contributions of non

- CO 2 emissions to climate change, further research is required to link non

- CO 2 emissions to economic impacts.

Such work would feed into efforts to develop a monetized value of reductions in non

- CO 2 greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the interagency group hopes to develop methods to value these other greenhouse gases. The goal is to develop these estimates by the time we issue revised SCC estimates for carbon dioxide

emissions.

16A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and

FUND models. j It is defined a s the long

- term increase in the annual global

- average surface temperature from a doubling of atmospheric CO 2 concentration relative to pre

- industrial levels (or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties

i n this important parameter have received substantial attention in the peer

- reviewed literature.

j The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas concentrations over the short to medium term (up to 100

- 200 years), but it does not include long

- term feedback effects due to possible large

- s cale changes in ice sheets or the biosphere, which occur on a time scale of many hundreds to thousands of years (e.g. Hansen et al. 2007).16A

- 14 The most authoritative statement about equilibrium climate sensitivity appears in the Fourth Assessment Report of the Intergovernmental Panel on Climate Chang e (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including observed climate change and the strength of known feedbacks simulated in [global climate models], we conclude that the global mean equilibrium warming for doubling CO 2 , or ‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C, with a most likely value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. k

For fundamental physical reasons as well as da ta limitations, values substantially higher than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is generally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et al., 2007, p 799)

After cons ulting with several lead authors of this chapter of the IPCC report, the interagency workgroup selected four candidate probability distributions and calibrated them to be consistent with the above statement: Roe and Baker (2007), log

- normal, gamma, and Wei bull.

Table 1 6 A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 16A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Roe & Baker Log

- normal Gamma Weibull Pr(ECS < 1.5°C) 0.013 0.050 0.070 0.102 Pr(2°C < ECS < 4.5°C) 0.667 0.667 0.667 0.667

5 th percentile 1.72 1.49 1.37 1.13

10 th percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90 Median (50 th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90 th percentile 5.86 5.14 4.93 4.69

95 th percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by applying three constraints from the IPCC:

k This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of “likely” as greater than 66 percent probability (Le Treut et al.2007). “Very likely” indicates a greater than 90 percent probability.16A

- 15 (1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”; l
(2) two

- thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C;

and (3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 721).

We selected the calibrated Roe and Baker distribution from the four candidates for two reasons. First, the Roe a nd Baker distribution is the only one of the four that is based on a theoretical understanding of the response of the climate system to increased greenhouse gas concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general shape. The Roe and Baker distribution results from three assumptions about climate response: (1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback factors are proportional to the change in surface temperature; and (3) uncertainties in feedback factors are normally distributed. There is widespread agreement on the first point and the second and third points are common assumptions.

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that “values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no quantitative judgment, the 95 th percentile of the calibrat ed Roe & Baker distribution (7.1 °C) is much closer to the mean and the median (7.2 °C) of the 95 th percentiles of 21 previous studies summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and median (7.9 °C) of the nine trun cated distributions examined by the IPCC (Hegerl, et al., 2006)

than are the 95 th percentiles of the three other calibrated distributions (5.2

- 6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very likely larger tha n 1.5°C.” Although the calibrated Roe & Baker distribution, for which the probability of equilibrium climate sensitivity being greater than 1.5°C is almost 99 percent, is not inconsistent with the IPCC definition of “very likely” as “greater than 90 percen t probability,” it reflects a greater degree of certainty about very low values of ECS than was expressed by the

IPCC.

l Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC report is not specific on this point. For the distributions we considered, the median was between the mode and the

mean. For the Roe and Baker distr ibution, setting the median equal to 3°C, rather than the mode or mean, gave a 95 th

percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode equal to 3°C produced 95 th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the ave rage mode is 2.3 °C, which is most consistent with a Roe and Baker distribution with the median set equal to 3 °C.16A

- 16 Figure 16A.4.3 Estimates of the Probability Density Function for Equilibrium Climate Sensitivity (°C)

To show how the calibrated Roe and Baker di stribution compares to different estimates of the probability distribution function of equilibrium climate sensitivity in the empirical literature, Figure 16 A.4.3 (above ) overlays it on Figure 9.20 from the IPCC Fourth Assessment Report. These functions a re scaled to integrate to unity between 0 °C and 10 °C. The horizontal bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate. m

16A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic pathways are closely tied to climate damages because, all else equal, more and wealthier people tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid climate disruptions. For this reason, we consider how to model several input parameters in tandem: GDP, population, CO 2 emissions, and non

- CO 2 radiati ve forcing. A wide variety of scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed to select scenarios that span most o f the plausible ranges of outcomes for these variables.

m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed line, anthropogenic forcing s only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. (2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on multiple palaeoclimatic reconstructions of n orth hemisphere mean temperatures over the last 700 years. Also shown

are the 5

- 95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.16A

- 17 To accomplish this task in a transparent way, we decided to rely on the recent Stanford Energy Modeling Forum exercise, EMF

- 22. EMF

- 22 uses ten well

- recognized models to evaluate substantial, coor dinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, and emission trajectories are internally consistent for each model and scenario evaluated. The EMF

- 22 modeling effort also is

p referable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the

EMF

- 22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are

recent, peer

- reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect on global cumulative emissions, we use socioeconomic and emission trajectories t hat span a range of plausible scenarios. Five trajectories were s e lected from EMF

- 22 (see Table 16 A.4.2 below). Four of these represent potential business

- as

- usual (BAU) growth in population, wealth, and emissions and are associated with CO 2 (only) conc entrations ranging from 612 to 889 ppm in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm

CO 2 e (i.e., CO 2 - only concentrations of 425

– 484 ppm or a radiative forcing of 3.7 W/m 2 ) in 2100, a lower

- than

- BAU trajectory. n Ou t of the 10 models included in the EMF

- 22 exercise, we selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories

from each of these four models. For the 550 ppm CO 2 e scenario, we averaged the GDP, population, and emission trajectories implied by these same four models.

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, though it could also result from technological advances. It was chosen because it represents the most string ent case analyzed by the EMF

- 22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.16A

- 18 Table 16A.4.2 Socioeconomic and Emissions Projections from Select EMF

- 22 Reference

Scenarios

Reference Fossil and Industrial CO 2 Emissions (GtCO 2 /yr)

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1 MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7
MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5 550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP (using market exchange rates in trillion 2005$) o

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6 MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9
MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5 550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9

Global Population (billions)

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1 MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4
MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7 550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to various assumptions about how the future will evolve without prejudging what is likely to occur. The interagency group considered formally assigning probability weights to different states of the world, but this prov ed challenging to do in an analytically rigorous way given the dearth of information on the likelihood of a full range of

future socioeconomic pathways.

o While the EMF

- 22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use purchasing power parity (PP P). PPP takes into account the different price levels across countries, so it more accurately describes relative standards of living across countries. MERs tend to make low

- income countries appear poorer than they actually are. Because many models assume convergence in per capita income over time, use of

MER

- adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that convergence of the emi ssions

- intensity gap across countries at least partially offset the overstated income gap so that differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross

- sectional PPP measures for relative incomes and outputs and national accounts price and quantity indexes for time

- series extrapolations). However, he notes that it important to keep this debate in perspect ive; it is by no means clear that exchange

- rate

- conversion issues are as important as uncertainties about population, technological change, or the many geophysical uncertainties.16A

- 19 T here are a number of caveats. First, EMF BAU scenarios represent the modelers’ judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the most likely outcome span a wide range, from the more optimistic (e.g. abundant low

- cost, low

carbon energy) to more pessimistic (e.g. constraints on the availability of nuclear and renewables). p Second, the socioeconomic trajectories associated with a 550 ppm CO 2 e concentration scenario are not derived from an assessment of what policy is optimal from a

benefit

- cost standpoint. Rather, it is indicative of one possible future outcome. The emission trajectories underlying some BAU scenarios (e.g. MESSAGE’s 612 ppm) also are consistent with some modest policy action to address climate change

. q We chose not to include

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 A nnual Energy Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, respectively. These projections are consistent with one or more EMF

- 22 scenarios. Likewise, the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people in 2000 to 9.1 billion people i n 2050, which is close to the population trajectories for the IMAGE, MiniCAM, and MERGE models.

In addition to fossil and industrial CO 2 emissions, each EMF scenario provides projections of methane, nitrous oxide, fluorinated greenhouse gases, and net la nd use CO 2 emissions out to 2100. These assumptions also are used in the three models while retaining the default radiative forcings due to other factors (e.g. aerosols and other gases). See the Annex for greater detail.

16A.4.6 Discount Rate

The choice of a di scount rate, especially over long periods of time, raises highly contested and exceedingly difficult questions of science, economics, philosophy, and law. Although it is well understood that the discount rate has a large influence on the current value of future damages, there is no consensus about what rates to use in this context. Because carbon dioxide emissions are long

- lived, subsequent damages occur over many years. In calculating the SCC, we first estimate the future damages to agriculture, human h ealth, and other market and

non

- market sectors from an additional unit of carbon dioxide emitted in a particular year in terms of reduced consumption (or consumption equivalents) due to the impacts of elevated

p For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and

non

- biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100.

q For example, MiniCAM projects if all non

- US OECD countries reduce CO 2 emissions to 83 percent below 2005 levels by 2050 (per the G

- 8 agreement) but all other countries continue alo ng a BAU path CO 2 concentrations in 2100 would drop from 794 ppmv in its reference case to 762 ppmv.16A

- 20 temperatures, as represented in each of the th ree IAMs. Then we discount the stream of future damages to its present value in the year when the additional unit of emissions was released using the selected discount rate, which is intended to reflect society's marginal rate of substitution

between cons umption in different time periods.

For rules with both intra

- and intergenerational effects, agencies traditionally employ constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A

- 4.

As Circular A

- 4 acknowledges, how ever, the choice of discount rate for intergenerational problems raises distinctive problems and presents considerable challenges. After reviewing those challenges, Circular A

- 4 states, “ If your rule will have important intergenerational benefits

or costs you might consider a further sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive approach reflects a positive (non

- normative) perspective based on ob servations of people’s

actual choices

— e.g., savings versus consumption decisions over time, and allocations of savings among more and less risky investments. Advocates of this approach generally call for inferring

the discount rate from market rates of re turn “because of a lack of justification for choosing a social welfare function that is any different than what decision makers [individuals] actually use” (Arrow et al. 1996).

One theoretical foundation for the cost

- benefit analyses in which the socia l cost of carbon

will be used

— the Kaldor

- Hicks potential

- compensation test

— also suggests that market rates should be used to discount future benefits and costs, because it is the market interest rate that would govern the returns potentially set aside toda y to compensate future individuals for climate damages that they bear (e.g., Just et al. 2004)

. As some have noted, the word “potentially” is an important qualification; there is no assurance that such returns will actually be set aside to provide compens ation, and the very idea of compensation is difficult to define in the intergenerational context. On the other hand, societies provide compensation to future generations through investments in human capital and the resulting increase in knowledge, as

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the normative judgments that the decision

- maker wants explicitly to incorporate into the policy

evaluation

— e.g., how inter

- person al comparisons of utility should be made, and how the welfare of future generations should be weighed against that of the present generation. Ramsey (1928), for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time preference to discount values across generations, and many agree with this view.

Other concerns also motivate making adjustments to descriptive discount rates. In particular, it has been noted that the preferences of future generations with regard to consumption versus environmental amenities may not be the same as those today, making the current market rate on consumption an inappropriate metric by which to discount future climate

related damages. Others argue that the discount rate should be below m arket rates to correct for16A

- 21 market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth,

which in the Kaldor

- Hicks logic are presumed to compensate future generations for damage (a potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that they tend to obscure important heterogeneity in the population. The utility function that underlies the prescripti ve approach assumes a representative agent with perfect foresight and no credit constraints. This is an artificial rendering of the real world that misses many of the frictions that characterize individuals’ lives and indeed the available descriptive evide nce supports this. For instance, many individuals smooth consumption by borrowing with credit cards that have relatively high rates. Some are unable to access traditional credit markets and rely on payday lending operations or other high

- cost forms of smo othing consumption. Whether one puts greater weight on the prescriptive or descriptive approach, the high interest rates that

credit

- constrained individuals accept suggest that some account should be given to the discount rates revealed by their behavior.

We draw on both approaches but rely primarily on the descriptive approach to inform the choice of discount rate. With recognition of its limitations, we find this approach to be the most defensible and transparent given its consistency with the standa rd contemporary theoretical

foundations of benefit

- cost analysis and with the approach required by OMB’s existing guidance. The logic of this framework also suggests that market rates should be used for discounting future consumption

- equivalent damages. Regardless of the theoretical approach used to derive the appropriate discount rate(s), we note the inherent conceptual and practical difficulties of adequately capturing consumption trade

- offs over many decades or even centuries. While relying primarily on the descriptive approach in selecting specific discount rates, the interagency group has been keenly aware of the deeply normative dimensions of both the debate over discounting in the intergenerational context and the consequences of selecting one disc ount rate
over another.

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on investment, and the market rate of interest would be the appropriate choice for the social

discount r ate. In the real world risk, taxes, and other market imperfections drive a wedge

between the risk

- free rate of return on capital and the consumption rate of interest. Thus, the literature recognizes two conceptual discount concepts

— the consumption rate o f interest and the opportunity cost of capital.

According to OMB’s Circular A

- 4, it is appropriate to use the rate of return on capital when a regulation is expected to displace or alter the use of capital in the private sector. In this case, OMB recommends Agencies use a discount rate of 7 percent. Wh en regulation is expected to primarily affect private consumption

— for instance, via higher prices for goods and services

— a lower discount rate of 3 percent is appropriate to reflect how private individuals trade

- off current and future consumption.16A

- 22 The i nteragency group examined the economics literature and concluded that the consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2 000). The consumption rate of interest also is appropriate when the impacts of a regulation are measured in consumption (

- equivalent) units, as is done in the three integrated assessment models used for estimating the SCC.

Individuals use a variety of savings instruments that vary with risk level, time horizon, and tax characteristics. The standard analytic framework used to develop intuition about the discount rate typically assumes a representative agent with perfect foresight and no credit

constrai nts. The risk

- free rate is appropriate for discounting certain future benefits or costs, but the benefits calculated by IAMs are uncertain. To use the risk

- free rate to discount uncertain benefits, these benefits first must be transformed into "certainty equivalents," that is the maximum certain amount that we would exchange for the uncertain amount. However, the calculation of the certainty

- equivalent requires first estimating the correlation between the benefits of the policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certainty

equivalent values), then the appropriate discount rate generally does not equal the risk

- free rate. If the benefits of the policy tend to be high in those states of the world in which consumption is low, then the certainty

- equivalent benefits will be higher than the expected benefits (and vice versa). Since many (though not necessarily all) of the important impacts of climate change will flow through market sectors such as agriculture and energy, and since willingness to pay for environmental protections typically increases with income, we might expect a positive (though not necessarily perfect) correlation between the net benefits from climate policies and market

returns . This line of reasoning suggests that the proper discount rate would exceed the riskless rate. Alternatively, a negative correlation between the returns to climate policies and market returns would imply that a discount rate below the riskless rate is a ppropriate.

This discussion suggests that both the post

- tax riskless and risky rates can be used to capture individuals’ consumption

- equivalent interest rate. As a measure of the post

- tax riskless rate, we calculate the average real return from Treasury notes over the longest time period available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal rate from tax years 2003 through 2006 is around 27 percent). r This calculation produces a real interest rate of about 2.7 p ercent, which is roughly consistent with Circular A

- 4’s recommendation to use 3 percent to represent the consumption rate of interest. s

r The literature argues for a risk

- free rate on government bonds as an appropriate measure of the consumption rate of interest. Arrow (2000) suggests that it is roughly 3

- 4 percent. OMB cites evidence of a 3.1 percent pre

- tax rate for

10

- year Treasury notes in the A

- 4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 percent for 30

- year Treasury securities. A measure of the post

- tax risky rate for investments whose returns are positively correlated with overall equity

s The positive appro ach reflects how individuals make allocation choices across time, but it is important to keep in mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.16A

- 23 marke t returns can be obtained by adjusting pre

- tax rates of household returns to risky investments (approximately 7 percent) for taxes , which yields a real rate of roughly 5 percent. t

The Ramsey Equation

Ramsey discounting also provides a useful framewor k to inform the choice of a discount rate. Under this approach, the analyst applies either positive or normative judgments in selecting values for the key parameters of the Ramsey equation: η ( coefficient of relative risk aversion or elasticity of the mar ginal utility of consumption) and ρ ( pure rate of time preference). u These are

then combined with g (growth rate of per

- capita consumption) to equal the interest rate at which future monetized damages are discounted: ρ + η ∙g. v In the simplest version of the Ramsey model, with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey discount rate,” ρ + η ∙g, will be equal to the rate of return to capital, i.e., the market

interest rate.

A review of the literature provides some guidance on reasonable parameter values for the Ramsey discounting equation , based on both prescriptive and descriptive approaches.

**•** η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 (Weitzman cites plausibl e values as those ranging from 1 to 4), although not all authors articulate whether their choice is based on prescriptive or descriptive reasoning. w

t Cambell et al (2001) estimates that the annua l real return from stocks for 1900

- 1995 was about 7 percent. The

annual real rate of return for the S&P 500 from 1950

– 2008 was about 6.8 percent. In the absence of a better way to population

- weight the tax rates, we use the middle of the 20

– 40 percen t range to derive a post

- tax interest rate (Kotlikoff and Rapson 2006). Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η equal to 1 suggests savings rates that do not conform to observed behavior.

u The parameter ρ  measures the pure rate of time preference : people’s behavior reveals a preference for an increase in utility today versus the future. Consequently, it is standard t o place a lower weight on utility in the future. The parameter η captures diminishing marginal utility : consumption in the future is likely to be higher than consumption today, so diminishing marginal utility of consumption implies that the same monetary d amage will cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent

increa se in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in

income is less valuable to wealthier individuals.

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative a ssumptions about the rate of consumption growth.

w Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (20 06, 2008), Weitzman (2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a tight upper bou nd on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting established facts about labor supply. Recen t work has jointly estimated the components of the Ramsey equation. Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 1.07.16A

- 24 **•** ρ. With respect to the pure rate of time preference, most papers in the climate change literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates tend to follow from moral judgments involving intergenerational neutrality. Some have argued that to use any value other than ρ = 0 would unjustly discriminate against future generations (e.g., Arrow et al. 1996, Stern et al. 2006). However, even in an inter

generational setting, it may make sense to use a small positive pure rate of time preference because of the small probability of unforeseen cataclysmic events (Stern et al. 2006).

**•** g. A commonly accepted approximation is around 2 percent per year. F or the

socioeconomic scenarios used for this exercise, the EMF models assume that g is about

1.5

- 2 percent to 2100.

Some economists and non

- economists have argued for constant discount rates below 2 percent based on the prescriptive approach. When grounded in the Ramsey framework, proponents of this approach have argued that a ρ of zero avoids giving preferential treat ment to one generation over another.  The choice of η has also been posed as an ethical choice linked to the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. (2006) applies this perspective through his choice of ρ = 0.1 percent per year

, η = 1 and g = 1.3 percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent of their inc ome. x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is a case to be made for raising η due to the amount of weight lower values place on damages far in the future (over 90 percent of expected damages occur af ter 2200 with η = 1). Using Stern’s assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, yields a discount rate of greater than 2 percent.

We conclude that arguments made under the prescriptive approach can be u sed to justify discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most appropriate value for η , we find it difficult to justify rates at the lower end of this range under the Ramsey framework.

Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate sensitivity. Weitzman (1998, 2001) showe d theoretically and Newell and Pizer (2003) and Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on net present values. A main result from these studies is that if there is a persistent element to the

x Stern (2008) argues that building in a positive rate of exogenous technical change over t ime reduces the implied savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity . (At the same time, adding exogenous technical change

— all else equal

— would increase g as well.)16A

- 25 uncertai nty in the discount rate (e.g., the rate follows a random walk), then it will result in an effective (or certainty

- equivalent) discount rate that declines over time. Consequently, lower discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and Gollier and Weitzman 2009).

The proper way to model discount rate uncertainty remains an active area of research. Newell and Pizer (2003) empl oy a model of how long

- term interest rates change over time to forecast future discount rates. Their model incorporates some of the basic features of how interest rates move over time, and its parameters are estimated based on historical observations

of l ong

- term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility of interest rates depends on whether rates are currently low o r high and the variation in the level of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model uncertainty in the discount rate, others argue for a declining scale of discount rates applied over time (e.g. , Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach uses a higher discount rate initially, but applies a graduated scale of lower discount rates further

out in time. y A key question that has emerged with regard to both of t hese approaches is the

trade

- off between potential time inconsistency and giving greater weight to far future outcomes (see the EPA Science Advisory Board’s recent comments on this topic as part of its review of

their Guidelines for Economic Analysis ). z

T

he Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in this context and uncertainty about how interest rates may change over time, we use three discount rates to span a p lausible range of certainty

- equivalent constant discount rates: 2.5, 3, and 5 percent per year. Based on the review in the previous sections, the interagency workgroup determined that these three rates reflect reasonable judgments under both descriptive a nd prescriptive approaches.

The central value, 3 percent, is consistent with estimates provided in the economics

literature and OMB’s Circular A

- 4 guidance for the consumption rate of interest. As previously

y For instance, the UK applies a dis count rate of 3.5 percent to the first 30 years; 3 percent for years 31

- 75; 2.5 percent for years 76

- 125; 2 percent for years 126

- 200; 1.5 percent for years 201

- 300; and 1 percent after 300 years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time.

z Uncertainty in future damages is distinct from uncertainty in the discount rate . Weitzman (2008) argues that Stern’s choice of a low discount rate was “right for the wrong reasons.” He demons trates how the damages from a low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value calculation and result in an infinite willingness

- to

- pay for mitigation today. Newbold and Daigneault, (2009) an d Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in the other direction by incr easing the benefits of waiting to learn the appropriate level of mitigation required.16A

- 26 mentioned, the consumption rate of interest i s the correct discounting concept to use when future damages from elevated temperatures are estimated in consumption

- equivalent units. Further, 3 percent roughly corresponds to the after

- tax riskless interest rate. T he upper value of 5 percent is

include d to represent the possibility that climate damages are positively correlated with market returns. Additionally, this discount rate may be justified by the high interest rates that many consumers use to smooth consumption across periods.

The low value, 2.5 percent, is included to incorporate the concern that interest rates are highly uncertain over time. It represents the average certainty

- equivalent rate using the mean

reverting and random walk approaches from Newell and Pizer (2003) starting at a disc ount rate of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random walk model and 2.8 percent using the mean reverting approach. aa

16A.5 REVISED SCC ESTIMATE S Without giving preference to a particular model, the average of the two rates is 2. 5 percent. Further, a rate below the riskless rate would be justified if climate investments are negatively correlated with the overall market rate of return. Use of this lower value also responds to certain judgments using the prescriptive

or normative approach and to ethical objections that have been raised about rates of 3 percent or higher.

Our general approach to estimating SCC values is to run the three integrated assessment models (FUND, DICE, and PAGE) using the following in puts agreed upon by the interagency

group:

**•** A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of two

thirds.

**•** Five sets of GDP, population

, and carbon emissions trajectories based on EMF

- 22.

**•** Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and FUND incorporate uncertainty in other model parameter s, the final output from each model run is a distribution over the SCC in year t

.

For each of the IAM s , the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected E MF

- 22 scenarios, and the extrapolations based on these scenarios for post

- 2100 years.

2. Calculate the temperature effects and (consumption

- equivalent) damages in each year resulting from the baseline path of emissions.

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).16A

- 27 a. In PAGE, the consumption

- equivalent damages in each period are calculated as a fraction of the EMF GDP forecast, depending on the temperature in that period relative to the pre

- industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of temperature change in that period.

c. In DICE, temperature affects both consumption and investment, so we first adjust the EMF GDP paths as follows: Using the Cobb

- Douglas production function with the DICE2007 parameters, we extract the path of

exoge nous technical change implied by the EMF GDP and population paths, then we recalculate the baseline GDP path taking into account climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t. (The exac t unit varies by model.)

4. Recalculate the temperature effects and damages expected in all years beyond t resulting from this adjusted path of emissions, as in step 2.

5. Subtract the damages computed in step 2 from those i n step 4 in each year. (DICE is run in 10

- year time steps, FUND in annual time steps, while the time steps in PAGE vary.)

6. Discount the resulting path of marginal damages back to the year of emissions using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed in step 6, divided by the unit of carbon emissions used to shock the models in step 3.

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of

CO 2 (2007 dollars) in DICE and FUND. (Al l calculations are done in tons of CO 2 in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.

This i s an issue for the multi

- model approach because differences in SCC estimates may arise simply due to the model time horizon. Many consider 2200 too short a time horizon because it could miss a significant fraction of damages under certain assumptions about the growth of marginal damages and discounting, so each model is run here through 2300. This step required a small adjustment in the PAGE model only. This step also required assumptions about GDP,16A

- 28 population, and greenhouse gas emission trajectories after 2100, the last year for which these

data are available from the EMF

- 22 models. (A more detailed discussion of these assumptions is

included in the Annex .)

This exercise pro duces 45 separate distributions of the SCC for a given year, the product of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate distributions for consideration in a regulatory impact analysis.

To produce a range of plausible estimates that still reflects the uncertainty in the estimation exercise, the distributions from each of the models and scenarios are equally weighed and combined to produce three separate probability distributions for SCC in a given year, one for each assumed discount rate. These distributions are then used to define a range of point estimates for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than another. Because the literature shows that the SCC is q uite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context, we present SCCs based on the average values across models and

socioeconomic scenarios for each discount r ate.

The interagency group selected four SCC values for use in regulatory analyses. Three values are based on the average SCC across models and socioeconomic and emissions scenarios at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higher

than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. ( The full set of distributio ns by model and scenario combination is included in the Annex .) As noted above, the 3 percent discount rate is the central value, and so the central value that emerges is the average SCC across models at the 3 percent discount rate. For purposes of

captu ring the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the SCC values through explicit consider ation of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high

- temperature outcomes, which in turn lead to hig her projections of damages. Although FUND does not include catastrophic damages (in contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity parameter will directly affect the non

- catastrophic damages that are a function of the rate of temperature change.

In Table 1 6 A.5.1 , we begin by presenting SCC estimates for 2010 by model, scenario, and discount rate to illustrate the variability in the SCC across each of these input parameters. As expected, higher discount rates consistently result in lower SCC values, while lower discou nt rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there are differences in the SCC estimated across the three main models. For these estimates, FUND produces the lowest estimates, while PAGE generally produce s the highest estimates.16A

- 29 Table 16A.5.1 Disaggregated Social Cost of CO 2 Values by Model, Socioeconomic Trajectory, and Discount Rate for 2010 (in 2007 dollars)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95thDICE IMAGE 10.8 35.8 54.2 70.8

MERGE 7.5 22.0 31.6 42.1

Message 9.8 29.8 43.5 58.6

MiniCAM 8.6 28.8 44.4 57.9

550 Average 8.2 24.9 37.4 50.8PAGE IMAGE 8.3 39.5 65.5 142.4

MERGE 5.2 22.3 34.6 82.4

Message 7.2 30.3 49.2 115.6

MiniCAM 6.4 31.8 54.7 115.4

550 Average 5.5 25.4 42.9 104.7FUND IMAGE

- 1.3 8.2 19.3 39.7

MERGE

- 0.3 8.0 14.8 41.3

Message

- 1.9 3.6 8.8 32.1

MiniCAM

- 0.6 10.2 22.2 42.6

550 Average

- 2.7

- 0.2 3.0 19.4

These results are not surprising when compared to the estimates in the literature for the

latest versions of each model. For example, adjusting the values from the literature that were used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using similar adjustments to generate 2010 estimat es, we calculate a SCC from DICE (based on Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE (based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these comparisons are only approximate since the literature generally relies on Ramsey discounting,

while we have assumed constant discount rates. bb

bb Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g. Anthoff et al. 2009). The path of per

- capita consumption growth, g, varies over time but is treated deter ministically in two of the three models. In DICE, g is16A

- 30 The SCC estimates from FUND are sensitive to differences in emissions paths but relatively insensitive to differences in GDP paths across scenari os, while the reverse is true for DICE and PAGE. This likely occurs because of several structural differences among the models. Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages increases with the level of tempera ture alone, whereas in FUND the fractional loss also increases with the rate of temperature change. Furthermore, in FUND increases in income over time decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in

DICE and PAGE. These structural differences among the models make FUND more sensitive to the path of emissions and less sensitive to GDP compared to DICE and PAGE.

Figure 1 6 A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE Optimistic has the lowe st. The ordering of global GDP levels in 2100 directly corresponds to the rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result that is to be expected given its less direct relationship between its damage function an d GDP.

Figure 16A.5.1 Level of Global GDP across EMF Scenarios

Table 1 6 A.5.2 shows the four selected SCC values in five

- year increments from 2010 to 2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per mode l run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using a simple linear interpolation.

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate change that occur far out in the future are discounted at a lower rate than impacts that oc cur in the nearer term. 0 50 100 150 200 250 300 350 400 450

2000 2010 2020 2030 2050 2100 Global

GDP (trillions

of U.S.
dollars) IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing16A

- 31 Table 16A.5.2 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC

di rectly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are internally consistent with other modeling assumption s. Table 1 6 A.5.3 i llustrates how the growth

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 2010 and 2050 is reported in the Annex

.

Table 16A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between

2010 and 2050

Average Annual Growth Rate (%) 5% 3% 2.5% 3.0%
Avg Avg Avg 95th

2010

- 2020 3.6% 2.1% 1.7% 2.2%

2020

- 2030 3.7% 2.2% 1.8% 2.2%

2030

- 2040 2.7% 1.8% 1.6% 1.8%

2040

- 2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions reductions in each year (the SCC in year t multiplied by the change in emissions in year t ) must be discounted to the present to determine its total net present value for use in regulatory analysis. Damages from future emissions should be discounted at the same rate as that used to calculate the SCC estimates themselves to ensure internal consistency

— i.e., future damages from climate change, whether they result from emissions today or emissions in a later year, should be discounted using the same rate. For example, climate damages in the year 2020 that are16A

- 32 calculated using a SCC based on a 5 percent discount rate also should be discounted back to the analysis year using a 5 per cent discount rate. cc

16A.6 LIMITATIONS OF THE A NALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further refinement (and possibly significant change) in accordance with evolving scientific, economic,

and ethical understandin gs. During the course of our modeling, it became apparent that there are several areas in particular need of additional exploration and research. These caveats, and additional observations in the following section, are necessary to consider when interpret ing and applying the SCC estimates.

Incomplete treatment of non

- catastrophic damages. The impacts of climate change are expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain because of the inherent complexity of climate processes, the economic behavior of current and future populations, and our inability to accurately forecast technological change and adaptation. Current IAMs do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature (some of which are discussed above) because of lack of precise information on the nature of damages and because the science incorporated into these models understandably la gs behind the most recent research. Our ability to quantify and monetize impacts will undoubtedly improve with time. But it is also likely that even in future applications, a number of potentially significant damage categories will remain non

- monetized. ( Ocean acidification is one example of a potentially large damage from CO 2 emissions not quantified by any of the three models. Species and wildlife loss is another example that is exceedingly difficult to monetize.)

Incomplete treatment of potential catastrophic damages. There has been considerable recent discussion of the risk of catastrophic impacts and how best to account for extreme scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. Weitzman (2009) suggests that catastrophic damages are extremely large

— so large, in fact, that the damages from a low probability, catastrophic even t far in the future dominate the effect of the discount rate in a present value calculation and result in an infinite willingness

- to

- pay for mitigation today. However, Nordhaus (2009) concluded that the conditions under which

Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain

scenarios."

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for large catastrophe risk premiums but also showed that the aggregate benefit estimates can be hig hly sensitive to the shapes of both the climate sensitivity distribution and the damage function at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high

-

cc However, it is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from those used to develop the SCC estimates.16A

- 33 impact

, low

- probability risks, using a right

- skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficient, but in most cases found only a modest risk premium. Given this difference in opinion, further research in this area is needed before its practical significance can be fully understood and a reasonable approach developed to account for such risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in these IAMs are typically calibrated by estimating damages at moderate temperature increases (e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming that damages increase as some power of the temperature ch ange. Hence, estimated damages are far more uncertain under more extreme climate change scenarios.

Incomplete treatment of adaptation and technological change: Each of the three integrated assessment models used here assumes a certain degree of low

- o r no

- cost adaptation. For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on air conditioning; so much so, that the largest single benefit category in FUND is the reduced electricity costs from not having to run ai r conditioning as intensively (NRC 2009).

Climate change also will increase returns on investment to develop technologies that allow individuals to cope with adverse climate conditions, and IAMs to do not adequately account for this directed technologi cal change. dd For example, scientists may develop crops that are better able to withstand higher and more variable temperatures. Although DICE and FUND have both calibrated their agricultural sectors under the assumption that farmers will change land

use practices in response to climate change (Mastrandrea, 2009), they do not take into account technological changes that lower the cost of this adaptation over time. On the other hand, the calibrations do not account for increases in climate variability, pes ts, or diseases, which could make adaptation more difficult than assumed by the IAMs for a given temperature change. Hence, models do not adequately account for potential adaptation or technical change that might alter the emissions pathway and resulting damages. In this respect, it is difficult to determine whether the incomplete treatment of adaptation and technological change in these IAMs

under state or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency pr ocess is what to assume about relative risk aversion with regard to high

- impact outcomes. These calculations do not take into account the possibility that individuals may have a higher willingness to pay to

reduce the likelihood of low

- probability, high

- i mpact damages than they do to reduce the likelihood of higher

- probability

, but lower

- impact

, damages with the same expected cost. (The

inclusion of the 95 th percentile estimate in the final set of SCC values was largely motivated by this concern.) If ind ividuals do show such a higher willingness to pay, a further question is whether that fact should be taken into account for regulatory policy. Even if individuals are not

risk

- averse for such scenarios, it is possible that regulatory policy should include a degree of

risk

- aversion.

dd However these research dollars will be diverted from whatever their next best use would have been in the absence of climate change (so productivity/GDP would have been still higher).16A

- 34 Assuming a risk

- neutral representative agent is consistent with OMB’s Circular A

- 4, which advises that the estimates of benefits and costs used in regulatory analysis are usually based on the average or the expected value and that “emphasis on these expected values is appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] analysis.”

Nordhaus (2008) points to the need to explore the relationship between risk and income in the context of climate change across models and to explore the role of uncertainty regarding various parameters in the results. Using FUND, Anthoff et al (2009) expl ored the sensitivity of the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude that “the assumed rate of risk aversion is at least as important as the assumed rate of time preference in determining the social cost of car bon.” Since Circular A

- 4 allows for a different assumption on risk preference in regulatory analysis if it is adequately justified, we plan to continue investigating this issue.

16A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPA CTS AND

DAMAGE FUNCTIONS

As noted ab ove, the damage functions underlying the three IAMs used to estimate the SCC may not capture the economic effects of all possible adverse consequences of climate change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping point” behavior in Earth systems, (2) inter

- sectoral and inter

- regional interactions, including global security impacts of high

- end warming, and (3) limited ne ar

- term substitutability between damage to natural systems and increased consumption.

It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis b y the Federal government will continue to evolve with improvements in modeling. In the meantime, we

discuss some of the available evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at m oderate levels of warming and should therefore be viewed cautiously when extrapolated to the high temperatures found in the upper end of the distribution. Recent science suggests that there are a number of potential climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with potentially severe social and economic consequences (e.g., Lenton et al, 2008, Kriegler et al., 2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of

the Amazon Rai nforest and boreal forests, collapse of the Greenland Ice Sheet and the West Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, strengthening of El Niño

- Southern Oscillation, and the release of methane from melting16A

- 35 perm afrost. Many of these tipping points are estimated to have thresholds between about 3 °C and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed through expert elicitation in 2005

– 2006 by Kriegler et al. (2009); res ults from this study are highlighted in Table 1 6 A.7.1

. Ranges of probability are averaged across core experts on each topic.

As previously mentioned, FUND does not include potentially catastrophic effects. DICE assumes a small probability of catastrophic damages that increases with increased warming, but the damages from these risks are incorporated as expected values (i.e., ig noring potential risk aversion). PAGE models catastrophic impacts in a probabilistic framework (see Figure 1 6 A.4.1 ), so the high

- end output from PAGE potentially offers the best insight into the SCC if the world were to experience catastrophic climate chan ge. For instance, at the 95 th percentile and a 3 percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission trajectories of $113 per ton of CO 2 is almost double the value estimate d by DICE, $58 per ton in

2010. We cannot e valuate how well the three models account for catastrophic or non

catastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the distribution to the assumptions made about catastrophic impacts.

Table 16A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points Duration before effect is fully realized (in years) Additional Warming by 2100

0.5

- 1.5 C 1.5

- 3.0 C 3

- 5 C

Reorganization of Atlantic Meridional Overturning Circulation about 100 0

- 18% 6

- 39% 18

- 67%

Greenland Ice Sheet collapse at least 300 8

- 39% 33

- 73% 67

- 96%

West Antarctic Ice Sheet collapse at least 300 5

- 41% 10

- 63% 33

- 88%

Dieback of Amazon rainforest about 50 2

- 46% 14

- 84% 41

- 94%

Strengthening of El Niño

- Southern Oscillation about 100 1

- 13% 6

- 32% 19

- 49%

Dieback of boreal forests about 50 13

- 43% 20

- 81% 34

- 91%

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it deterministically (that is, by adding the expected value of the damage from a catastrophe to the aggregate damage function). In part, this results in different pro babilities being assigned to a catastrophic event across the two models. For instance, PAGE places a probability near zero on a catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 2.5 °C. By comparison, Kriegler e t al. (2009) estimate a probability of at least 16

- 36 percent of16A

- 36 crossing at least one of their primary climatic tipping points in a scenario with temperatures

about 2

- 4 °C warmer than pre

- Industrial levels in 2100.

It is important to note that crossin g a climatic tipping point will not necessarily lead to an economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across which some aspect of the Earth system starts to shifts into a qualitatively different state (for

i nstance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a catastrophe is a low

- probability environmental change with high economic impact.

Failure to incorporate inter

- sectoral and inter

- regional interactions

The da mage functions do not fully incorporate either inter

- sectoral or inter

- regional interactions. For instance, while damages to the agricultural sector are incorporated, the effects of changes in food supply on human health are not fully captured and depend on the modeler’s choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one region of the world on another region are not included in some of the models (FUND includes the effects of migration from sea level rise). These inter

- regional interactions, though difficult to quantify, are the basis for climate

- induced national and economic security concerns (e.g., Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at higher levels of warming. High

- end warming scenarios, for instance, project water scarcity affecting 4.3

- 6.9 billion people by 2050, food scarcity affecting about 120 million additional people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; Camp bell et al., 2007).

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming may have severe consequences for natural systems. For instance, during the Paleocene

- Eocene Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically rapid release of carbon associated with an approximately 5 °C increase in global mean temperatures, the effects included shifts of about 400

- 900 miles in the range of p lants (Wing et al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 2009).

The three IAMs used here assume that it is possible to compensate for the economic consequences of damages to natural systems through increased consumption of non

- climate goods, a common assumption in many economic models. In the context of climate change, however, it is possible that the damages to natural systems could become so great that no increase in consumption of non

- climate good s would provide complete compensation (Levy et al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile

and less bio

- diverse, the services they provide may become increasingly more costly to replace.

Uncalibrated attem pts to incorporate the imperfect substitutability of such amenities into IAMs (Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be considerably greater than is commonly recognized.16A

- 37 16A.8 CONCLUSION

The interagency group se lected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3,

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higher

than

- expected impacts from temperature change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. The central value is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range. These SCC estimates also grow over time. For instance, the central value increases to $24 per ton of CO 2 in 2015 and $26 per ton of CO 2 in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which

the int egrated assessment models capture catastrophic and non

- catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impacts to economic damages makes this modeling exercise even more difficult. It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimate s used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling.16A

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Tol, R. 2006. “Exchange Rates and Climate Change: An Application of FUND.” Climatic Change 75(1

- 2): 59

- 80.

Tol, R. 2009. “An analysis of mitigation as a response to climate change.” Copenhagen Consensus on Climate. Discussion Paper.

U.S. Department of Defense. 2010. Quadrennial Defense Review Report. February.

Warren, R., et al. 2006. “Spotlight Impact Functions in Integrated Assessment.” Tyndall Center

for Clim ate Change Research, Working Paper 91.

Weitzman, M. 2009. “On modeling and interpreting the economics of catastrophic climate change.” Review of Economics and Statistics 91:1

- 19.

Weitzman, M. 2007. “A review of The Stern Review of the Economics of Clima te Change .” Journal of Economic Literature 45:703

- 724.

Weitzman, M. 1999. In Portney P.R. and Weyant J.P. (eds.), Discounting and Intergenerational Equity , Resources for the Future, Washington, D.C.

Weitzman, M. 1998. “Why the Far

- Distant Future Should Be Discounted at Its Lowest Possible

Rate.” Journal of Environmental Economics and Management 36 (3): 201

- 208.

Wing, S. et al. 2005. “Transient floral change and rapid global warming at the Paleocene

- Eocene boundary.” Science 310: 993

- 996.16A

- 43 16A.9 A NNEX

Table 16A.9.1 Annual S CC Values: 2010

– 2050 (in 2007 dollars)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non

- CO 2 emission projections used in the modeling and the method for extrapolating emissions forecasts through 2300 and shows the full distribution of 2010 SCC estimates by model and scenario combination.16A

- 44 16A.9.1 Other (non

- CO2) gases

In addition to fossil and industr ial CO 2 emissions, each EMF scenario provides projections of methane (CH 4 ), nitrous oxide (N 2 O), fluorinated gases, and net land use CO 2 emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s default radiative forcing s (RF) due to other factors (e.g., aerosols and other gases). Specifically,

to obtain the RF associated with the non

- CO 2 EMF emissions only, we calculated the RF associated with the EMF atmospheric CO 2 concentrations and subtracted them from the EMF

total RF. ee This approach respects the EMF scenarios as much as possible and at the same time takes account of those components not included in the EMF projections. Since each model treats

non

- CO 2 gases differently (e.g., DICE lumps all other gases into one composite exogenous input), this approach was applied slightly differently in each of the models.

FUND : Rather than relying on RF for these gases, the actual emissions from each

scenario were used in FUND. The model default trajectories for CH 4 , N 2 0, SF 6 , and the CO 2 emissions from land were replaced with the EMF values.

PAGE : PAGE models CO 2 , CH 4 , sulfur hexafluoride (SF 6 ), and aerosols and contains an "excess forcing" vector that includes the RF for everything else. To include the EMF values, we

removed the default CH 4 and SF 6 factors ff , decomposed the excess forcing vector, and constructed a new excess forcing vector that includes the EMF RF for CH 4 , N 2 0, and fluorinated

gases, as well as the model default values for aerosols and other factors. Net land use CO 2 emissions were added to the fossil and industrial CO 2 emissions pathway.

DICE : DICE presents the greatest challenge because all forcing due to factors other than

industr ial CO 2 emissions is embedded in an exogenous non

- CO 2 RF vector. To decompose this exogenous forcing path into EMF non

- CO 2 gases and other gases, we relied on the references in DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Asse ssment Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report (TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing from all non

- CO 2 sources is

- 0.06 W/m 2 in 2005, as reported in AR4 , and increases linearly to 0.3 W/m 2 in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH 4 , N 2 0, and halocarbons (approximately

similar to the F

- gases in the EMF

- 22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m 2 and RF

from total aerosols was

- 1.2 W/m 2 . Thus, the

- .06 W/m 2 non

- CO 2 forcing in DICE can be

ee Note EMF did not provide CO 2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed the fossil, industrial

, and land CO 2 emissions into MAGICC (considered a "neutral arbiter" model, which is tuned to emulate the major global climate models) and the resulting CO 2 concentrations were used. Note also that MERGE assumes a neutral biosph ere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use emissions from the other three models and divide by 4).

ff Both th e model default CH 4 emissions and the initial atmospheric CH 4 is set to zero to avoid double counting the effect of past CH 4 emissions.16A

- 45 decomposed into: 0.98 W/m 2 due to the EMF non

- CO 2 gases,

- 1.2 W/m 2 due to aerosols, and the remainder, 0.16 W/m 2 , due to other residual fo rcing.

For subsequent years, we calculated the DICE default RF from aerosols and other non

CO 2 gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR and then stays cons tant thereafter ; and (2) With respect to RF from non

- CO 2 gases not included in the EMF

- 22 scenarios, the share

of non

- aerosol RF matches the share implicit in the AR4 summary statistics cited above and

remains constant over time.

Assumption (1) means t hat the RF from aerosols in 2100 equals 66 percent of that in 2000, which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since the SRES marker scenarios were not updated for the AR4, the TAR provides the most recent IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides one of the lower aerosol forecasts among the SRES marker scenarios and is more consistent with the AR4 discussion of the post

- SRES literature on aerosols:

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the

post

- SRES scenarios. Generally, these emissions are projected to be lower than reported in SRES. {WGIII 3.2, TS.3, SPM}. gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent

with the recent literature on these emissions. For example, the f igure below shows that the sulfur dioxide emissions peak over the short term of some SRES scenarios above the upper bound

estimates of the more recent scenarios. hh Recent scenarios project sulfur emissions to peak earlie r and at lower levels compared to the SRES in part because of new information about present and planned sulfur legislation in some developing countries, such as India and China. ii The lower

bound projections of the recent literature have also shifted downw ard slightly compared to the SRES scenario (IPCC 2007).

gg AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment

- report/ar4/syr/ar4_syr.pdf

hh See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850

- 2000: methods and results. Joint Global Research Institute, College Park, 14 pp.

ii See C armichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental Science and Technology, 36(22):4707

- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.

- Q. Zhang, D. Xu, M. Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835

- 1837.16A

- 46 With these assumptions, the DICE aerosol forcing changes from

- 1.2 in 2005 to

- 0.792 in

2105 W/m 2 ; forcing due to other non

- CO 2 gases not included in the EMF scenarios declines from

0.160 to 0.153 W/m 2

.

. Figure 16A.9.2 Sulfur Dioxide Emission Scenarios

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines show the median, 5 th

, and 95 th percentile of the frequency distribution for the full ensemble of 40 SRES scenarios. The blue area (and the thin dashed lines in blue) illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines indicate

the minimum and maximum of SO 2 emissions scenarios developed pre

- SRES. Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3

- ens3

- 2

- 2

- 4.html

.

Although other approaches to decomposing the DICE exogenous forcing vector are possible, initial sensitivity analysis suggests that the differences among reasonable alternative approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values (for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 2100 increases average 2010 SCC values by 6

- 7 percent (or $0.50

- $3)

– depending on the discount rate. These differences increase slightly for SCC values in later years but are still well within 10 percent of each other as far out as 2050.

Finally, as in PAGE, the EMF net land use CO 2 emissions are added to the fos sil and

industrial CO 2 emissions pathway.

16A.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which16A

- 47 these projections are available from the EMF

- 22 models. These inputs were extrapolated from

2100 to 2300 as follows:

1. Population growth rate declines linearly, reaching zero in the year 2200. 2. GDP/per capita growth rate declines linearly, rea ching zero in the year 2300. 3. The decline in the fossil and industrial carbon intensity (CO 2 /GDP) growth rate over

2090

- 2100 is maintained from 2100 through 2300.

4. Net land use CO 2 emissions decline linearly, reaching zero in the year 2200.

5. Non

- C O 2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying assumption than a linear or exponential extrapolation of the pre

- 2100 economic growth rate of

each EMF scenario. This i s based on the idea that increasing scarcity of natural resources and the degradation of environmental sinks available for assimilating pollution from economic production activities may eventually overtake the rate of technological progress. Thus, the

ove rall rate of economic growth may slow over the very long run. The interagency group also considered allowing an exponential decline in the growth rate of GDP per capita. However, since this would require an additional assumption about how close to zero t he growth rate would get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by

2300.

The population growth rate is also assumed to decline linearly, reaching zero by 2200. This assumption is reasonably consistent with the United Nations long run population forecast, which estimates global population to be fairly stable after 2150 in the medium scenario (UN 2004). jj The resulting range o f EMF population trajectories (f igure below ) also encompass the

UN m edium scenario forecasts through 2300

— global population of 8.5 billion by 2200, and 9 billion by 2300.

Maintaining the decline in the 2090

- 2100 carbon intensity growth rate (i.e., CO 2 per dollar of GDP) through 2300 assumes that technological improveme nts and innovations in the areas of energy efficiency and other carbon reducing technologies (possibly including currently unavailable methods) will continue to proceed at roughly the same pace that is projected to occur towards the end of the forecast per iod for each EMF scenario. This assumption implies that total cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range of the total potential global carbon stock estimated in the literature.

Net land use CO 2 emissio ns are expected to stabilize in the long run, so in the absence of any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori reasons for assuming a long run increase or decline in non

- CO 2 radiative forcing, it is ass umed to remain at the 2100 levels for each EMF scenario through 2300.

jj United Nations. 2004. World Population to 2300

. http://www.un.org/esa/po pulation/publications/longrange2/worldpop2300final.pdf16A

- 48 Figures below show the paths of global population, GDP, fossil and industrial CO 2 emissions, net land CO 2 emissions, non

- CO 2 radiative forcing, and CO 2 intensity (fossil and

industri al CO 2 emissions/GDP) resulting from these assumptions.

Figure 16A.9.3 Global Population, 2000

- 2300 (Post

- 2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.)

Note: In the fifth scenario, 2000

- 2100 population is equal to the average of the population under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 5.5 6.5 7.5 8.5 9.5 10.5 11.5

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsGlobal Population [billion]

Year16A

- 49 Figure 16A.9.4 World GDP, 2000

- 2300 (Post

- 2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in the year 2300)

Note: In the fifth scenario, 2000

- 2100 GDP is equal to the average of the GDP under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four models. 0 500 1000 1500 2000

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

YearWorld GDP [$trillion]

0 50 100 150 200

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil&Industrial CO2 Emissions [GtCO2/decade]

Year16A

- 50 Figure 16A.9.5 Global Fossil and Industrial CO 2 Emissions, 2000

- 2300 (Post

- 2100 extrapolations assume growth rate

of CO 2 intensity (CO 2 /GDP) over 2090

- 2100 is maintained through 2300 )

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, fu ll

- participation, not

- to

- exceed scenarios considered by each of the four

models.

Figure 16A.9.6 Global Net Land Use CO 2 Emissions, 2000

- 2300 (Post

- 2100 extrapolations assume emissions decline linearly, reaching zero in the year 2200 ) kk

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models.

kk MERGE assumes a neutral biosphere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use emissions from the other three models and divide by 4). -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

ExtrapolationsNet land CO2 emissions [GtCO2/yr]

Year z16A

- 51 Figure 16A.9.7 Global Non

- CO 2 Radiative Forcing, 2000

- 2300 (Post

- 2100 extrapolations assume const ant non

- CO 2 radiative forcing after 2100 )

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 0.00 0.75 1.50 2.25 3.00 3.75

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsnonCO2 RF [W/m^2]

Year16A

- 52 Figure 16A.9.8 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000

- 2300 (P ost

- 2100 extrapolations assume decline in CO 2 /GDP growth

rate over 2090

- 2100 is maintained through 2300 )

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 0 0 0 0 0 1 1 1 1

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil & Industrial CO2 intensity [GtCO2/trillion $]

Year16A

- 53 Table 16A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8 MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3 Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0 MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3 Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE

- 33.1

- 18.9

- 13.3

- 5.5 4.1 19.3 18.7 43.5 67.1 150.7 MERGE optimistic

- 33.1

- 14.8

- 10

- 3 5.9 14.8 20.4 43.9 65.4 132.9 Message

- 32.5

- 19.8

- 14.6

- 7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base

- 31.0

- 15.9

- 10.7

- 3.4 6 22.2 21 46.4 70.4 152.9

5th scenario

- 32.2

- 21.6

- 16.7

- 9.7

- 2.3 3 6.7 20.5 34.2 96.816A

- 54 Table 16A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4 MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0 Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1 MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8 Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE

- 25.2

- 15.3

- 11.2

- 5.6 0.9 8.2 10.4 25.4 39.7 90.3 MERGE optimistic

- 24.0

- 12.4

- 8.7

- 3.6 2.6 8 12.2 27 41.3 85.3 Message

- 25.3

- 16.2

- 12.2

- 6.8

- 0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base

- 23.1

- 12.9

- 9.3

- 4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario

- 24.1

- 16.6

- 13.2

- 8.3

- 3

- 0.2 2.9 11.2 19.4 53.616A

- 55 Table 16A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2 MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4 Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1 MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4 Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE

- 11.7

- 8.4

- 6.9

- 4.6

- 2.2

- 1.3 0.7 4.1 7.4 17.4 MERGE optimistic

- 10.6

- 7.1

- 5.6

- 3.6

- 1.3

- 0.3 1.6 5.4 9.1 19.0 Message

- 12.2

- 8.9

- 7.3

- 4.9

- 2.5

- 1.9 0.3 3.5 6.5 15.6

MiniCAM base

- 10.4

- 7.2

- 5.8

- 3.8

- 1.5

- 0.6 1.3 4.8 8.2 18.0

5th scenario

- 10.9

- 8.3

- 7

- 5

- 2.9

- 2.7

- 0.8 1.4 3.2 9.2

Figure 16A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by discount rate

* The distribution of SCC values ranges from

- $5,192 to $66,116

, but the X

- axis has been truncated at approximately the 1 st and 99 th percentiles to better show the data. 0 2000 4000 6000 8000 10000 12000 14000 16000-25-12114274053667992105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%16A

- 56 Table 16A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates

Discount

Rate Scenario

DICE PAGE FUND

5% Mean 9 6.5

- 1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3% Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50% Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30i APPENDIX 16B

TECHNICAL UPDATE OF THE SOCIAL COST OF CARBO N FOR

REGULATORY IMPACT AN ALYSIS UNDER EXECUTI VE ORDER 12866

TABLE OF CO NTENTS

16B.1 PREFACE

................................ ................................ ................................ ...................... 1

16B.2 EXECUTIVE SUMMARY

................................ ................................ ........................... 1

16B.3 PURPOSE

................................ ................................ ................................ ...................... 2

16B.4 SUMMARY OF MODEL UPD ATES

................................ ................................ ........... 3

16B.4.1 DICE

................................ ................................ ................................ .............................. 4

16

- B.4.1.1 Carbon Cycle Parameters

................................ ................................ ............ 4

16

- B.4.1.2 Sea Level Dynam ics

................................ ................................ ................... 5

16

- B.4.1.3 Re

- calibrated Damage Function

................................ ................................ . 6

16B.4.2 FUND

................................ ................................ ................................ ............................. 6

16

- B.4.2.1 Space Heating

................................ ................................ ............................. 7

16

- B.4.2.2 Sea Level Rise and Land Loss

................................ ................................ .... 7

16

- B.4.2.3 Agriculture

................................ ................................ ................................ .. 7

16

- B.4.2.4 Transient Temperature Response

................................ ................................ 8

16

- B.4.2.5 Methane

................................ ................................ ................................ ....... 8

16B.4.3 PAGE

................................ ................................ ................................ ............................. 9

16

- B .4.3.1 Sea Level Rise

................................ ................................ ............................. 9

16

- B.4.3.2 Revised Damage Function to Account for Saturation

................................ 9

16

- B.4.3.3 Regional Scaling Factors

................................ ................................ ............ 9

16

- B.4.3.4 Probability of a Discontinuity

................................ ................................ ... 10

16

- B.4.3.5 Adaptation

................................ ................................ ................................ . 10

16

- B.4.3.6 Other Noteworthy Changes

................................ ................................ ....... 11

16B.5 REVISED SCC ESTIMATE S

................................ ................................ ..................... 11

16B.6 OTHER MODEL LIMITATI ONS AND RESE ARCH GAPS

................................ ... 14

ANNEX A ADDITIONAL TABLES

LIST OF TABLES

Table 16B.5.1 Revised Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars per ton of

CO 2 )

................................ ................................ ................................ ................. 12

Table 16B.5.2 Average Annual Growth Rates of SCC Estimates between 2010 and

2050

................................ ................................ ................................ .................. 13

Table 16B.6.1 Annual SCC Values: 2010

- 2050 (2007$/m etric ton CO 2 )

............................... 17

Table 16B.6.2 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/metric ton CO 2 )

................................ ................................ .................... 18

Table 16B.6.3 2 020 Global SCC Estimates at 3 Percent Discount Rate (2007$/metric ton CO2)

................................ ................................ ................................ ........... 18

Table 16B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/metric

ton CO 2 )

................................ ................................ ................................ ........... 19ii LIST OF FIGURE S

Figure 16B.5.2 Distribution of SCC Estimates for 2020 (in 2007$ per metric ton CO 2 )

......... 131 APPENDIX 16B. TECHNICAL UPDATE OF THE SOCIAL COST OF

CARBON FOR REGULATOR Y IMPACT ANALYSIS UNDER

EXECUTIVE ORDER 1286 6

16B.1 PREFACE

The following text is reproduced from the April 2013 report of the Interagency Working Group on Social Cost of Carbon

, United States Government , titled “ Technical Update of the

Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 .” a

16B.2 EXECUTIVE SUMMARY M inor changes were made to the working group's report format and reference style to make it more

consisten t with the rest of this technical support document.

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the social benefits of reducing car bon dioxide (CO 2 ) emissions into cost

- benefit analyses of regulatory actions that impact cumulative global emissions. The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year.

It is intende d to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services due to climate change.

The interagency process that developed the original U.S. g overnment’s SCC estimates is described in the 2010 interagency technical support document (TSD) ( Interagency Working Group on Social Cost of Carbon 2010 )

. Through that process the interagency group selected four SCC values for use in regulatory analyses. Three values are based on the average SCC from three integrated assessment models (IAMs), at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate across all three models at a 3 percent discount rate, is included to represent higher

- than

- expected impacts from temperature change

further out in the tails of the SCC distribution.

While acknowledging the continued limitations of the approach taken by the interagency group in 2010, this document provides a n update of the SCC estimates based on new versions of each IAM (DICE, PAGE, and FUND). It does not revisit other interagency modeling decisions ( e.g. , with regard to the discount rate, reference case socioeconomic and emission scenarios, or

a With participation by Council of Economic Advisers, Council on Environmental Quality, Departmen t of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, Department of the Treasury2 equilibrium cl imate sensitivity ). Improvements in the way damages are modeled are confined to

those that have been incorporated into the latest versions of the models by the developers themselves in the peer

- reviewed literature.

The SCC estimates using the updated v ersions of the models are higher than those reported in the 2010 TSD. By way of comparison, the four 2020 SCC estimates reported in the

2010 TSD were $7, $26, $42 and $81 (2007$). The corresponding four updated SCC estimates for 2020 are $12, $43, $65, an d $129 (2007$). The model updates that are relevant to the SCC estimates include: an explicit representation of sea level rise damages in the DICE and PAGE models; updated adaptation assumptions, revisions to ensure damage s are constrained by GDP, update d regional scaling of damages

, and a revised treatment of potentially abrupt shifts in climate damages in the PAGE model; an updated carbon cycle in the DICE model; and updated damage functions for sea level rise impacts, the agricultur al sector , and reduc ed space heating requirements , as well as changes to the transient response of temperature to the buildup of GHG

concentrations and the inclusion of indirect effects of methane emissions in the FUND model

. The SCC estimates vary by year, and the following table summarizes the revised SCC estimates from 2010 through 2050.

Table 16B .2.1 Revised Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars per metric ton CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

16B.3 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) b

1 estimates from the 2010 intera gency technical support document (TSD) (Interagency Working Group on Social Cost of Carbon 2010). The 2010 TSD is reproduced in appendix 16

- A

.

E.O. 13563 commits the Administration to regulatory decision making “based on the best

available science.” c

b In this document, we present all values of the SCC as the cost per metric ton of CO 2 emissions. Alternatively, one could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of

CO 2 and the mass of car bon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12 = 3.67)

. Additionally , the interagency group recommended in 2010 that the SCC

c http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf3 estimates be revisited on a regular basis or as model updates that reflect the growing body of scientific and economic knowledge become available. d New versions of the three integrated

assessment mo dels used by the U.S. government to estimate the SCC (DICE, FUND, and PAGE), are now available and have been published in the peer reviewed literature. While acknowledging the continued limitations of the approach taken by the interagency group in 2010 (do cumented in the original 2010 TSD), this document provides an update of the SCC estimates based on the latest peer

- reviewed version of the models, replacing model versions that were developed up to ten years ago in a rapidly evolving field. It does not rev isit other assumptions with regard to the discount rate, reference case socioeconomic and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are modeled are confined to those that have been incorporated into the latest versions of the models by the developers themselves in the peer

- reviewed literature. The agencies participating in the interagency working

group continue to investigate potential improvements to the way in which economic damages associated with changes in CO 2 emissions are quantified.

Section 16 b .4 summarizes the major updates relevant to SCC estimation that are contained in the new versions of the integrated assessment models released since the 2010 interagency report. Section 16 b .5 presents the updated schedule of SCC estimates for 2010

– 2050 based on these versions of the models. Section 16 b .6 provides a discussion of other model limitations and research gaps

.

16B.4 SUMMARY OF MODEL UPD ATES

This section briefly summarizes changes to the most recent version s of the three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on describing those model updates that are relevant to estimating the social cost of carbon

, as summarized in Table 16 B .4.1 . For example, both the DICE and P AGE models now include an explicit representation of sea level rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to ensure damages are constrained by GDP, updated regional scaling of damages, and a revised treatment o f potentially abrupt shifts in climate damages. The

DICE model’s simple carbon cycle has been updated to be more consistent with a more complex climate model. The FUND model includes updated damage functions for sea level rise impacts, the agricultural se ctor, and reduced space heating requirements, as well as changes to the

transient response of temperature to the buildup of GHG concentrations and the inclusion of indirect effects of methane emissions. Changes made to parts of the models that are supersed ed by the interagency working group’s modeling assumptions

– regarding equilibrium climate sensitivity, discounting, and socioeconomi c variables

– are not discussed here but can be found in the references provided in each section below.

Table 16B .4.1 : Sum mary of Key Model Revisions Relevant to the Interagency SCC

IAM Version used

in 2010 Interagency New

Version Key changes relevant to interagency SCC

d See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social C ost of Carbon 2010). 14 Analysis

DICE 2007 2010 Updated calibration of the carbon cycle model and explicit representation o f sea level rise (SLR) and associated damages.

FUND 3.5 (2009) 3.8 (2012) Updated damage functions for space heating, SLR, agricultural impacts, changes to transient response of temperature to buildup of GHG concentrations, and inclusion of

indirect climate effects of methane.

PAGE 2002 2009 Explicit representation of SLR damages, revisions to damage function to ensure damages do not exceed 100% of GDP, change in regional scaling of damages, revised treatment of potential abrupt damages, and upda ted adaptation assumptions.

16B.4.1 DICE

DICE 2010 includes a number of changes over the previous 2007 version used in the 2010 interagency report. The model changes that are relevant for the SCC estimates developed by the interagency working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit representation of sea level dynamics, and 3) a re

- calibrated damage function that includes an explicit representation of economic damages from sea level rise. Changes were also made to ot her parts of the DICE model

— including the equilibrium climate sensitivity parameter, the rate of change of total factor productivity, and the elasticity of the marginal utility of consumption

— but these components of DICE are superseded by the interagency w orking group’s assumptions and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008) 2 and on DICE2010 in Nordhaus (2010)

. 3 The DICE2010 model and documentation is also available for download from the homepage of

William Nordhaus

.

16

- B.4.1.1 Carbon Cycle Parameters

DICE uses a three

- box model of carbon stocks and flows to represent the accumulation

and tr ansfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44). 2 e

3 Carbon cycle transfer coefficient values in DICE2010 are based on re

- calibration of the model to

match the 2009 version of MAGICC (Nordhaus 2010 p 2). For example, in DICE2010

, in each decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7

e MAGICC is a simple climate model initially developed by the U.S. National Center for Atmospheric Research that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from more sophisticated state of the art earth system simulation models (Randall et al. 2007). 45 percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each decade, 9.7 percent of the carbon in the shallow ocean is t ransferred to the atmosphere, 85.3 percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as a carbon sink and therefore a higher concentr ation of carbon in the atmosphere than in DICE2007, for a given path of emissions. All else equal, these changes will generally increase the level of warming and therefore the SCC estimates in DICE2010 relative to those from

DICE2007.

16

- B.4.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global average sea level anomaly to be used in the updated damage function (discussed below). This section contains a brief description of the sea level rise (SLR) module; a mor e detailed description can be found on the model developer’s website. f The average global sea level anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal expansion of the oceans, 2) melting of glaciers and small ice cap s, 3) melting of the Greenland ice sheet, and 4) melting of the Antarctic ice sheet.

The parameters of the four components of the SLR module are calibrated to match consensus results from the IPCC’s Fourth Assessment Report (AR4)

. 4 g The rise in sea level from thermal expansion in each time period (decade) is 2 percent of the difference between the sea level in the previous period and the long run equilibrium sea level, which is 0.5 meters per degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above the average global temperature in 1900.

The contribution to sea level rise from melting of the Greenland ice sheet is more complex. The equilibrium contribution to SLR is 0 me ters for temperature anomalies less than 1

o C and increases linearly from 0 meters to a maximum of 7.3 meters for temperature anomalies

between 1 o C and 3.5 °C . The contribution to SLR in each period is proportional to the difference between the previous p eriod’s sea level anomaly and the equilibrium sea level anomaly, where the constant of proportionality increases with the temperature anomaly in the current period.

The contribution to SLR from the melting of the Antarctic ice sheet is

- 0.001 meters per decade when the temperature anomaly is below 3 °C and increases linearly between 3 °C and 6

°C to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

f Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: http://nordhaus.econ.yale.edu/documents/SLR_021910.pdf.

g For a review of post

- IPCC AR4 research on sea level rise, see Nicholls et al. (2011) 5 and NAS (2011). 66 16

- B.4.1.3 Re

- calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a fractional loss of gross economic output in each period. A portion of the remaining economic output in each period (net of climate change damages) is consumed and the remainder is invested in the physical capital stock to support future economic production, so each period’s climate damages will reduce consumption in that period and in all future periods due to the lost investment. The fraction of output in each period that is lost due to climate change impacts is represented as on e minus a fraction, which is one divided by a quadratic function of the temperature anomaly, producing a sigmoid (“S”

- shaped) function. h The loss function in DICE2010 has been expanded by adding a quadratic function of SLR to the quadratic function of

temp erature. In DICE2010 the temperature anomaly coefficients have been recalibrated to avoid

double

- counting damages from sea level rise that were implicitly included in these parameters in

DICE2007.

The aggregate damages in DICE2010 are illustrated by Nor dhaus (2010 p 3), 3 who notes that “...damages in the uncontrolled (baseline) [ i.e. , reference] case ... in 2095 are $12 trillion,

or 2.8 percent of global output, for a global temperature increase of 3.4 o C above 1900 levels.” This compares to a loss of 3.2 percent of global output at 3.4 o C in DICE2007. However, in DICE2010 annual damages are lower in most of the early periods of the modeling horizon but higher in later periods than would be calculated using the DICE2007 damage function. Specifically, the percent difference between damages in the base run of DICE2010 and those that would be calculated using the DICE2007 damage function starts at +7 percent in 2005, decreases

to a low of

- 14 percent in 2065, then continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), and to +160 percent by the end of the model time horizon in 2595. The large inc reases in the far future years of the time horizon are due to the permanence associated with damages from sea level rise, along with the assumption that the sea level is projected to continue to rise long after the global average temperature begins to decr ease. The changes to the loss function generally decrease the interagency working group SCC estimates slightly given that relative increases in damages in later periods are discounted more heavily , all else equal.

16B.4.2 F UND

FUND version 3.8 includes a number of changes over the previous version 3.5 (Narita et al. 2010) used in the 2010 interagency report. Documentation supporting FUND and the model’s

source code for all versions of the model is available from the model authors. i

h The model and documentation, including formulas, are available on the author’s Notable changes,

due to their i mpact on the SCC estimates , are adjustments to the space heating, agriculture, and

webpage at http://www.econ.yale.edu/~nordhaus/homepage/RICEmodels.htm

.

i http://www.fund

- model.org/ . This report uses version 3.8 of the FUND model, which represents a modest update

to the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013). 7 For the purpose of computing the SCC, the relevant changes (between 3.7 to 3.8) are associated with improving consistency with IPCC AR4 by adjusting the atmospheric lifetimes of CH4 and N2O and incorporati ng the indirect forcing effects of CH4, along with making minor stability improvements in the sea wall construction algorithm.7 sea level rise damage functions in addition to changes to the temperature response function and

the inclusion of indirect effects from methane emissions. j

16

- B.4.2.1 Space Heating We discuss each of these in turn.

In FUND, the damages associated with the change in energy needs for space heating are based on the estimated impact due to one degree of warming. These baseline damages are scaled based on the forecasted temperature anomaly’ s deviation from the one degree benchmark and adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 3.5, the function that scales the base year damages adjusted for vulnerability allows for the possibility that in some simulations the benefits associated with reduced heating needs may be an unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling has been modified to ensure that the function is everywhere concave and that there will exi st an upper bound on the benefits a region may receive from reduced space heating needs. The new formulation approaches a value of two in the limit of large temperature anomal ies , or in other words, assuming no decrease in vulnerability, the reduced expend itures on space heating at any level of warming will not exceed two times the reductions experienced at one degree of warming. Since the reduced need for space heating represents a benefit of climate change in the model, or a negative damage, this change w ill increase the estimated SCC. This update accounts for a significant portion of the difference in the expected SCC estimates reported by the two versions of the model when run probabilistically.

16

- B.4.2.2 Sea Level Rise and Land Loss

The FUND model explicitly in cludes damages associated with the inundation of dry land due to sea level rise. The amount of land lost within a region is dependent upon the proportion of the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND

3.5 t he function defining the potential land lost in a given year due to sea level rise is linear in the rate of sea level rise for that year. This assumption implicitly assumes that all regions are well represented by a homogeneous coastline in length and a co nstant uniform slope moving inland. In FUND 3.8 the function defining the potential land lost has been changed to be a

convex function of sea level rise, thereby assuming that the slope of the shore line increases moving inland. The effect of this change i s to typically reduce the vulnerability of some regions to sea level rise based land loss, thereby lowering the expected SCC estimate

. k

16

- B.4.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as proportional to the sector’s value. The fraction is bounded from above by one and is made up of three additive components that represent the effects from carbon fertilization, the rate of

j The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and extreme weather) were not significant ly update d

. k For stability purposes this report also uses an update to the model which assumes that regional coastal protection measures will be built to protect the most valuable land first, such that the marginal benefits of coastal protection is decreasing in the level of protection following Fankhauser (1995).8 temperature change, and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8,

the fraction of the sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function with an intercept of zero. In FUND 3.5, the coefficients of this loss function

are modeled as the ratio of two random normal variables

. T his specification had the potential for

unintended extreme behavior as draws from the parameter in the denominator approached zero or went negative . In FUND 3.8, the coefficients are drawn directly from truncated normal distributions so that they remain in the range [0, ) **∞** and ( ,0]

**− ∞**  , respectively, ensuring the correct sign and eliminating the potential for divide by zero errors. T he means for the new distributions are set equal to the ratio of the means from the normal d istributions used in the previous version. In general the impact of this change has been to decrease the range of the distribution while spreading out the distributions’ mass over the remaining range relative to the previous version

. The net effect of this change on the SCC estimates is difficult to predict.

16

- B.4.2.4 T ransient T emperature Response

The temperature response model translates changes in global levels of radiative forcing into the current expected temperature anomaly. In FUND, a given year’s increase in the temperature anomaly is based on a mean reverting function where the mean equals the equilibrium temperature anomaly that would eventually be reached if that year’s level of radiative forcing were sustained. The rate of mean reversion defines the ra te at which the transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact that the progressive heat uptake of the deep o cean causes the rate to slow at higher values of the equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity

of the rate of temperature response to the level of the equilibrium climate sensitivity , a relationship first noted by Hansen et al. (1985) based on the heat uptake of the deep ocean

. Therefore in FUND 3.8, the temperature response will typically be faster than i n the previous version. The overall effect of this change is likely to increase estimates of the SCC as higher temperatures are reached during the timeframe analyzed and as the same damages experienced in the previous version of the model are now experienc ed earlier and therefore discounted less.

16

- B.4.2.5 Methane

The IPCC AR4 notes a series of indirect effects of methane emissions, and has developed methods for proxying such effects when computing the global warming potential of methane (Forster et al. 2007). 8 FUND 3.8 now includes the same methods for incorporating the indirect effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been set to 12 years to account for the feedback of methane emissions on its own lifetime. The

radiative fo rcing associated with atmospheric methane has also been increase d by 40% to account for its net impact on ozone production and stratospheric water vapor. All else equal, the effect of this increased radiative forcing will be to increase the estimated SCC v alues, due to greater projected temperature anomaly

.9 16B.4.3 PAGE

PAGE09 (Hope 201 3 ) 9 includes a number of changes from PAGE2002, the version used

in the 20 10 SCC interagency report. Th e changes that most directly affect the SCC estimates include: explicitly modeling the impacts from sea level rise, revisions to the damage function to ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised

treatment for the probability of a discontinuity within the damage function, and revised assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and the calculation of regional temperatures. l

10 More details on PAGE09 can be found in H ope ( 2011a, 2011b, 2011c). , 11

, 12 A description of PA GE2002 can be found in Hope (2006). 13

16

- B.4.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories

– eco no mic and non

economic impacts

- PAGE09 adds a third explicit category: damages from sea level rise. In the previous version of the model, damages from sea level rise were subsumed by the other damage categories. In PAGE09

, sea level damages increas e less than linearly with sea level under the assumption that land, people, and GDP are more concentrated in low

- lying shoreline areas. Damages from the economic and non

- economic sector were adjusted to account for the introduction of this new category.

16

- B.4.3.2 Revised Damage Function to Account for Saturation

In PAGE09, small initial economic and non

- economic benefits (negative damages) are modeled for small temperature increases, but all regions eventually experience economic damages from climate change, where damage s are the sum of additively separable polynomial functions of temperature and sea level rise. Damages transition from this polynomial function to a logistic path once they exceed a certain proportion of remaining Gross Domestic Product (GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, which allowed Eastern Europe to potentially experience large benefits from temperature increases, and which also did not bound the possible damages that could be experienced.

16

- B.4.3.3 Regional S caling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the European Union (EU) and then, assumes that damages for other regions are proportional based on a given scaling factor. The scaling factor in PAGE09 is base d on the length of a region’s coastline relative to the EU (Hope 2011b). 11 Because of the long coastline in the EU, other regions are, on average, less vulnerable than the EU for the same sea level and temperature increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on four studies reported in the IPCC’s third assessment report, a nd allowed for benefits from temperature increase in Eastern Europe, smaller impacts in develop ed countries, and higher damages in developing countries.

l Because several changes in the PAGE model are structural ( e.g. , the addition of sea level rise and treatment of discontinuity), it is not possible to assess the direct impac t of each change on the SCC in isolation as done for the

other two models above

.10 16

- B.4.3.4 Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” (nonlinea r extreme event) were modeled as an expected value. Specifically, a stochastic probability of a discontinuity was multiplied by the damages associated with a discontinuity to obtain an expected value, and this

was added to the economic and non

- economic imp acts. That is, additional damages from an extreme event, such as extreme melting of the Greenland ice sheet, were multiplied by the probability of the event occurring and added to the damage estimate. In PAGE09, the probability of “discontinuity” is trea ted as a discrete event for each year in the model. The damages for each

model run are estimated either with or without a discontinuity occurring, rather than as an expected value. A large

‐ scale discontinuity becomes possible when the temperature rises beyond some threshold value between 2 and 4°C. The probability that a discontinuity will occur beyond this threshold then increases by between 10 and 30 percent for every 1°C rise in temperat ure beyond the threshold. If a discontinuity occurs, the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a mean of 15 percent) in addition to other damages, and other regions lose an amount determined by the reg ional scaling factor. The threshold value for a possible discontinuity is lower than in PAGE2002, while the rate at which the probability of a discontinuity increases with the temperature anomaly and the damages that result from a discontinuity are both hi gher than in PAGE2002. The model assumes that only one discontinuity can occur and that the impact is phased in over a period of time, but once it occurs, its effect is permanent.

16

- B.4.3.5 Adaptation

As in PAGE2002, adaptation is available to help mitigate any cl imate change impacts that occur. In PAGE this adaptation is the same regardless of the temperature change or sea level rise and is therefore akin to what is more commonly considered a reduction in vulnerability. It is modeled by reducing the damages by som e percentage. PAGE09 assumes a smaller decrease in vulnerability than the previous version of the model and assumes that it will take longer for this change in vulnerability to be realized. In the aggregated economic sector, at the time of full implementat ion, this adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature anomalies between 1°C and 2 °C, it will reduce damages by 15

- 30 percent (depending on the region). However, it takes 20 years to fully implement this ad aptation. In PAGE2002, adaptation was assumed to reduce economic sector damages up to 2 °C by 50

- 90 percent after 20 years. Beyond 2 °C, no adaptation is assumed to be available to mitigate the impacts of climate change. For the non

- economic sector, in PAGE0 9 adaptation is available to reduce 15 percent of the damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. Similarly, adaptatio n is assumed to alleviate 25

- 50 percent of the damages from the first 0.20 to 0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c) 12 estimates that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea level rise via adaptation increase the SCC by approximately 30 percent.11 16

- B.4.3.6 Other Noteworthy Changes

Two other changes in the model are worth noting. There is a change in the way the model

accounts for decreased CO 2 absorption on land and in the ocean as temperature rises. PAGE09 introduces a linear feedback from global mean temperatu re to the percentage gain in the excess

concentration of CO 2 , capped at a maximum level. In PAGE2002 , an additional amount was

added to the CO 2 emissions each period to account for a decrease in ocean absorption and a loss of soil carbon. Also updated is t he method by which the average global and annual temperature anomaly is downscaled to determine annual average regional temperature anomalies to be used in the regional damage functions. In PAGE2002 , the scaling was determined solely based on regional diff erence in emissions of sulfate aerosols. In PAGE09, this regional temperature anomaly is further adjusted using an additive factor that is based on the average absolute latitude of a region relative to the area weighted average absolute latitude of the Ear th’s landmass

, to capture relatively greater changes in temperature forecast to be experienced at higher latitudes

.

16B.5 REVISED SCC ESTIMATE S

The updated versions of the three integrated assessment models were run using the same methodology detailed in the 20 10 TSD (Interagency Working Group on Social Cost of Carbon 2010)

. 1 The approach along with the inputs for the socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount rate remains the same. This includes the

five reference scenar ios based on the EMF

- 22 modeling exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the IPCC AR4 , and three constant discount rates of 2.5, 3, and 5 percent.

As was previously the case, the use of three models, three di scount rates, and five scenarios produces 45 separate distributions for the global SCC. The approach laid out in the

2010 TSD applied equal weight to each model and socioeconomic scenario in order to reduce the dimensionality down to three separate distrib utions representative of the three discount rates. The interagency group selected four values from these distributions for use in regulatory analysis. Three values are based on the average SCC across models and socio

- economic

- emissions

scenarios at the 2.5 , 3, and 5 percent discount rates, respectively. The fourth value was chosen to represent the higher

- than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, the 95th percentile of the SCC estim ates at a 3 percent discount rate was chosen. (A detailed set of percentiles by model and scenario combination and additional summary statistics for the 2020 values is available in the Annex.) As noted in the

2010 TSD, “the 3 percent discount rate is the central value, and so the central value that emerges is the average SCC across models at the 3 percent discount rate” ( Interagency Working Group on Social Cost of Carbon 2010, p. , 25). However, for purposes of capturing the uncertainties involved in regula tory impact analysis, the interagency group emphasizes the importance and value of including all four SCC values.

Table 16B.5.1 shows the four selected SCC estimates in five year increments from 2010 to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate.12 Values for the years in between are calculated using linear interpolation. The full set of re vised annual SCC estimates between 2010 and 2050 is reported in Annex A

.

Table 16B.5.1 Revised Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars per ton of CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

The SCC estimates using the updated versions of the models are higher than those reported in the 2010 TSD due to the changes to the mo dels outlined in the previous section. By way of comparison, the 2020 SCC estimates reported in the original TSD were $7, $26, $42 and $81 (2007$) (Interagency Working Group on Social Cost of Carbon 2010). Figure 16B.5.2

illus trates where the four SCC values for 2020 fall within the full distribution for each discount rate based on the combined set of runs for each model and scenario (150,000 estimates in total for each discount rate). In general, the distributions are skewed t o the right and have long tails. The Figure also shows that the lower the discount rate, the longer the right tail of the distribution.13 Figure 16B.5.2 Distribution of SCC Estimates for 2020 (in 2007$ per metric

ton CO 2 )

As was the case in the 2010 TSD, the SCC incr eases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. The approach taken by the interagency group is to compute the cost of a marginal ton emitted in the future by running the models for a set of perturbation years out to 2050

. Table 16B.5.2 illustrates how the growth rate for these four

SCC estimates varies over time.

Table 16B.5.2 Average Annual Growth Rates o f SCC Estimates between 2010 and 2050 Average Annual 5.0% 3.0% 2.5% 3.0% Rate (%) Avg Avg Avg 95th

2010

- 2020 1.2% 3.2% 2.4% 4.3%

2020

- 2030 3.4% 2.1% 1.7% 2.4%

2030

- 2040 3.0% 1.8% 1.5% 2.0%

2040

- 2050 2.6% 1.6% 1.3% 1.5%

The future monetized v alue of emission reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must be discounted to the present to determine its total net present value for use in regulatory analysis. As previously discussed in the 2010 TSD, damages from future emissions should be discounted at the same rate as that used to calculate the SCC estimates themselves to ensure internal consistency

– i.e. , future damages from climate change, whether they result from emissions today or emissions in a later year, should be discounted using the same rate.14 Under current OMB guidance contained in Circular A

- 4, analysis of economically significant proposed and final regulations from the domestic perspective is required, while analysis from the internati onal perspective is optional. However, the climate change problem is highly unusual in at least two respects. First, it involves a global externality: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in t he United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages caused by GHG emissions. Second, climate change presents a problem that the United States alone cannot solve. Even if the United States were to reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid substantial climate change. Other countries would also need to take action to reduce emissions if significant changes in the global climate are to be a voided. Emphasizing the need for a global solution to a global problem, the United States has been actively involved in seeking international agreements to reduce emissions and in encouraging other nations, including emerging major economies, to take signi ficant steps to reduce emissions. When these considerations are taken as a whole, the interagency group concluded that a global measure of the benefits from reducing U.S. emissions is preferable. For additional discussion, see the 2010

TSD.

16B.6 OTHER MODEL L IMITATIONS AND RESEARCH GAPS

The 2010 interagency SCC TSD discusses a number of important limitations for which additional research is needed. In particular, the document highlights the need to improve the quantification of both non

- catastrophic and catas trophic damages, the treatment of adaptation and technological change, and the way in which inter

- regional and inter

- sectoral linkages are

modeled. While the new version of the models discussed above offer some improvements in these areas, further work rem ains warranted. The 2010 TSD also discusses the need to more carefully assess the implications of risk aversion for SCC estimation as well as the inability to perfectly substitute between climate and non

- climate goods at higher temperature increases, both of which have implications for the discount rate used. EPA, DOE, and other agencies continue to engage in research on modeling and valuation of climate impacts that can potentially improve

SCC estimation in the future.15 REFERENCES

1

. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States

Government. Available online at http://www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

- Cost

- of

Carbon

- for

- RIA.pdf

.

2. Nordhaus, W., A Question of Balance . 2008. Yale University Press: New Have n

.

3. No rdhaus, W., Economic aspects of global warming in a post

- Copenhagen environment

. Proceedings of the National Academy of Sciences , 2010. 107(26): pp. 11721

- 11726

4. Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A.

Pitman , J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models

and Their Evaluation . In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on C limate Change , S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA

5. Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. V ellinga, D. de Gusmão, J. Hinkel and R.S.J. Tol, Sea

- level rise and its possible impacts given a ‘beyond 4°C world’ in the

twenty

- first century

. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161

- 181

6. National Academy of Sciences, Climate Stabilization Ta rgets: Emissions, Concentrations, and Impacts over Decades to Millennia . 2011. National Academies Press, Inc: Washington, DC.

7. Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a decomposition analysis using FUND

. Climatic Change 117: 515

- 530

8. Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland, Changes in Atmospheric Constituents and in Radiative Forc ing . In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L.

Mi ller Editor. 2007. Cambridge University Press: Cambridge, Unit ed Kingdom and New York, NY

9. Hope, C., Critical issues for the calculation of the social cost of CO 2 : why the estimates from PAGE09 are higher than those from PAGE2002

. Climatic Change 117: 5 31

- 543

10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description . 2011, Cambridge Judge Business School Working Paper No. 4/2011 . Available online at16 http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf

.

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model . 2011, Cambridge Judge

Business School Working Paper No. 5/2011 . Avaiable online at http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf

.

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2 . 2011, Cambridge Judge Business School Working Paper No. 8/2011 . Avaiable online at http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf

.

13. Hope, C., The Marginal Impact of CO 2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern

. The Integrated Assessment

Journal , 2006. 6(1): pp. 19

- 5617 ANNEX A:

ADDITIONAL TABLES

Table 16B.6.1 Annual SCC Values: 2010

- 2050 (2007$/ metric ton CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95t h

2010 11 33 52 90
2011 11 34 54 94

2012 11 35 55 98

2013 11 36 56 102

2014 11 37 57 106

2015 12 38 58 109

2016 12 39 60 113

2017 12 40 61 117

2018 12 41 62 121

2019 12 42 63 125

2020 12 43 65 129

2021 13 44 66 132

2022 13 45 67 135

2023 13 4 6 68 138

2024 14 47 69 141

2025 14 48 70 144

2026 15 49 71 147

2027 15 49 72 150

2028 15 50 73 153

2029 16 51 74 156

2030 16 52 76 159

2031 17 53 77 163

2032 17 54 78 166

2033 18 55 79 169

2034 18 56 80 172

2035 19 57 81 176

2036 19 58 82 179

2037 20 59 84 182

2038 20 60 85 185

2039 21 61 86 188

2040 21 62 87 192

2041 22 63 88 195

2042 22 64 89 198

2043 23 65 90 200

2044 23 65 91 203

2045 24 66 92 206

2046 24 67 94 209

2047 25 68 95 212

2048 25 69 96 215

2049 26 70 97 218

2050 27 71 98 22118 Table 16B.6.2 202 0 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ metric

ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95 th 99th
Scenario m PAGE
IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

ME SSAGE 4 8 11 20 42 108 107 278 483 918
MiniCAM Base 5 9 12 22 47 107 113 266 431 872
5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE
IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 2 0 24 28 37 51 58 71 95 109 221
MiniCAM Base 20 25 29 38 53 61 76 102 117 135
5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND
IMAGE

- 17

- 1 5 17 34 44 59 90 113 176

MERGE

- 7 2 7 16 30 35 49 72 91 146

MESSAGE

- 19

- 4 2 12 27 32 46 70 87 135
MiniCAM Base

- 9 1 8 18 35 45 59 87 108 172
5th Scenario

- 30

- 12

- 5 6 19 24 35 57 72 108

Table 16B.6.3 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ metric ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th
Scenari o PAGE
IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641
MiniCAM Base 3 5 8 14 30 73 75 184 300 623
5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE
IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83
MiniCAM Base 13 17 20 26 35 39 49 65 73 85
5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND
IMAGE

- 14

- 3 1 9 20 25 35 54 69 111

MERGE

- 8

- 1 3 9 18 22 31 47 60 97

MESSAGE

- 16

- 5

- 1 6 16 18 28 43 55 88
MiniCAM Base

- 9

- 1 3 10 21 27 35 53 67 107

m See 2010 TSD for a description of these scenarios.19 5th Scenario

- 22

- 10

- 5 2 10 13 20 33 42 63

Table 16B.6.4 20 2 0 Global SCC Estimates at 5 Percent Discount Rate (2007$/ metric ton

CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th
Scenario PAGE
IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216
MiniCAM Base 1 1 2 3 8 21 21 54 94 190
5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE
IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25
MiniCAM Base 5 6 7 8 11 12 14 18 20 22
5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND
IMAGE

- 9

- 5

- 3

- 1 2 3 6 11 15 25

MERGE

- 6

- 3

- 2 0 3 4 7 12 16 27

MESSAGE

- 10

- 6

- 4

- 1 2 2 5 9 13 23
MiniCAM Base

- 7

- 3

- 2 0 3 4 7 11 15 26
5th Scenario

- 11

- 7

- 5

- 2 0 0 3 6 8 14

Table 16A.5.5: Additional Summary Statistics of 2020 Global SCC Estimates

Discount 5.0% 3.0% 2.5% Statistic: Mea Varian Skewne Kurtos Mea Varian Skewne Kurtos Mea Varian Skewne Kurtos

DICE 12 26 2 15 38 409 3 24 57 1097 3 30

PAGE 22 1616 5 32 71 14953 4 22 101 29312 4 2 3

FUND 3 560

- 170 35222 21 22487

- 85 18842 36 68055

- 46 1310520 ANNEX B

SOCIAL COST OF CARBON FOR REGULATORY IMPACT ANALYSIS UNDER

EXECUTIVE ORDER 12866 n

n17

- A

- i APPENDIX 17

- A. REGULATORY IMPACT AN ALYSIS SUPPORTING

MATERIAL

TABLE OF CO NTENTS

17

- A.1 MARKET PENETRATION C URVES DEVELOPED BY X ENERGY

............ 17

- A

- 1

17

- A.2 IMPACTS OF CONSUMER TAX CREDITS

................................ .................... 17

- A

- 2

17

- A.3 EPACT 2005 FEDERAL T AX CREDITS FOR CONSU MERS AND

MANUFACTURERS OF RES IDENTIAL APPLIANCES

................................ 17

- A

- 3

17

- A.3.1 Tax Credits for Consumers of Residential Appliances

................................ ........ 17

- A

- 3

17

- A.3.2 Tax Credits for Manufacturers of Energy Efficient Appliances

.......................... 17

- A

- 4

LIST OF FIGURE S

Figure 17

- A.1.1 Comparison of Exponential and Logistic Curves Showing External and

Internal Influences on Consumers

................................ ........................... 17

- A

- 217

- A

- 1 APPENDIX 17

- A. SUPPORTING MATERIAL: REGULATORY IMPACT AN ALYSIS

17

- A.1 MARKET PENETRATION CURVES D EVELOPED BY XENERGY

Xenergy , Inc., developed a re

- parameterized, mixed

- source model of information diffusion to estimate market impacts induced by financial incentives for energy efficient appliances

. The basic premise of this mixed

- s ource model is that information diffusion drives technology adoption. There is extensive economic literature discussing the diffusion process of new products as technologies evolve. Some of the literature focuses primarily on the development of analytical models of diffusion patterns of new products for individual consumers or for technologies from competing firms. 1

, 2

, 3 One study records researchers’ attempts to investigate underlying factors that drive diffusion processes. 4 Because of the distinct

charac teristics of diverse new products, few studies have conclusively developed a universally applicable model. Some key findings, however, seem to have gained wide recognition in academia and industry.

First, all potential users may adopt new technologies, regardless of their economic benefits and technological merits. Many products face a ceiling on their adoption rate. Second, not all adopters purchase new products at the same time. Some act quickly after the introduction of a new product, while others res pond slowly, waiting for products to become more mature. Third, diffusion processes can be approximately characterized by asymmetric S

- curves, depicting three stages of diffusion: starting, accelerating, and decreasing as the adoption ceiling is

reached.

One important diffusion model, the epidemic model , is used widely in marketing and social studies on diffusion. It assumes that (1) all consumers value the benefits of a new product identically and (2) the cost of a new product is constant or declines mo notonically over time. What induces a consumer to purchase the new product is information about the availability and benefits of the product. In other words, it is information diffusion that drives individual consumers’ adoption of new product. 3 By superimposing a logistic function on an exponential function, the model incorporates information diffusion from both internal sources (news spread by word of mouth from early adopters) and external sources (the “announcement effect” by government, other institutions, or commercial advertising). 1

, 4

The relative degree of influence by internal and external sources determines the general shape of the diffusion curve for the specific product. 1

, 4 For instance, if the adoption of a product is influenced more by external sources of information diffusion (announcement effect) than by internal sources (word of mou th among early adopters to prospective adopters), the rate of diffusion at the beginning of the diffusion process is much higher. This effect reflects the immediate information exposure to a significant number of prospective adopters brought about by exter nal sources, in contrast to the more gradual exposure of early adopters, a small proportion of the population, to other prospective adopters. Graphically speaking, a relatively dominant external source of information diffusion gives an immediate jump

- start to the adoption17

- A

- 2 of a new product in the first years, forming a concave curve with respect to the Y axis (the exponential curve in Figure 17

- A.1.1). Adoption of a new product influenced more strongly by internal sources of information diffusion (such as a social network formed by prospective adopters) may start with a few early adopters and increase gradually as the number of adopters increases. This process forms a convex curve (the logistic curve in Figure 17

- A.1.1).

Figure 17

- A.1.1 Comparison of Exponential and Logisti c Curves Showing External and

Internal Influences on Consumers

17

- A.2 IMPACTS OF CONSUMER TAX CREDITS

Because there have been no consumer tax credits for microwave ovens, DOE analyzed data from Oregon's Residential Energy Tax Credit (RETC) for dishwashers as an analogous kitchen product. DOE calculated that for 2006 Oregon taxpayers claimed credits for 14 percent of dishwasher shipments to the State. DOE found no data comparing sales of efficient dishwashers in Oregon and Washington. To estimate how many of the tax credit claims likely were due to utility rebates and to market impacts, DOE reviewed a report by Itron on utility rebate programs in California. That report compared the percentage of residential clothes washer and dishwasher rebate programs in 20 05. 5 Free riders are consumers who would have purchased efficient appliances without a tax credit or rebate incentive but claimed the credit. Itron found that the penetration rate of ENERGY STAR dishwashers (85 percent) was much higher than that17

- A

- 3 of ENERGY STAR clothes washers (40 percent). Itron also found that the percentage of free riders was greater for dishwasher programs than for clothes washer programs. DOE’s previous estimates showed that the incremental cost compared to a baseline unit was much smal ler for high efficiency dishwashers than for high efficiency clothes washers, 6

, 7 which could explain the higher penetration rate for efficient dishwashers. An incentive program for a product having a higher market share would tend to have more free riders. Based on these various factors, DOE estimated that market impacts (partly due t o free riders) were responsible for a higher percentage

of tax credit claims for dishwashers than for residential clothes washers.

Itron’s report contained two estimates of free riders for ENERGY STAR dishwashers in 2005. DOE took the simple average of the supplier estimate of 93 percent and the self

- reported consumer estimate of 59 percent, yielding 76 percent. DOE also developed a simple average of estimated free riders for residential clothes washers, averaging 77 percent from suppliers and 43

percen t from consumers to yield 60 percent. DOE assumed that the difference in numbers of free riders between the two products would be on the order of the difference between the two estimates (16 percent).

DOE interpreted the higher percentage of consumers pu rchasing dishwashers as free riders, and the lower percentage of taxpayers influenced primarily by rebates, to estimate that 49 percent of Oregon's 2006 dishwasher tax credits claimed (6.9 percent of total dishwasher shipments to the State) were due to mar ket impacts. It estimated that utility rebates were responsible for 32 percent of tax credits claimed (4.5 percent of total shipments). DOE attributed the remaining 20 percent of tax credits claimed (2.8 percent of total shipments) to the ability of

the co nsumer to claim a tax credit.

17

- A.3 EPACT 2005 FEDERAL T AX CREDITS FOR CONSUMERS AND

MANUFACTURERS OF RES IDENTIAL APPLIANCES

This section describes the products that qualified for Federal tax credits for consumers and manufacturers of energy efficient resid ential appliances under EPACT 2005

.

17

- A.3.1 Tax Credits for Consumers of Residential Appliances

EPACT 2005 included Federal tax credits for consumers who installed efficient air conditioners or heat pumps; gas or oil furnaces; furnace fans; and/or gas, oil, or el ectric heat

pump water heaters in new or existing homes. 8

, 9

, 10 The se tax credits were in eff ect in 2006 and 2007, expired in 2008, and were reinstated for 2009

– 2010 by the American Recovery and Reinvestment Act ( ARRA). There was a $1,500 cap on the credit p er home, including the amount received for insulation, windows, and air and duct sealing. Congress extended this provision for 2011 with some modifications to eligibility requirements, and reductions in the cap to $500 per home

.

Residential appliances mus t meet the following specifications, as applicable, to qualify

for Federal tax credits.17

- A

- 4 **•** Furnaces and Boilers:

o Until December 31, 2010: Natural gas & propane furnaces must meet an Annual Fuel Use Efficiency (AFUE) 95 or higher, oil furnaces and gas, oil and propane

boilers must meet an AFUE of 90 or better.

o From January 1 through December 31, 2011: Natural gas & propane furnaces

must meet an Annual Fu el Use Efficiency (AFUE) 95 or higher, oil furnaces and gas, oil and propane boilers must meet an AFUE of 95 or better. In addition, the credit only covers costs up to $150

.

**•** Central Air Conditioning Units:

o Until December 31, 2010: Central air conditioning units and air

- source heat pumps must meet the highest tier standards set by the Consortium for Energy Efficiency (CEE) as of February 17, 2009, which in most cases requires a Seasonal Energy Efficiency Ratio (SEER) of 16. SEER measures performance through out the cooling season.

o Until December 31, 2011: same specifications, the credit only covers costs up to
$300

.

**•** Fans for heating and cooling systems:

o Until December 31, 2010: fan uses no more than 2% of total heating system energy use, as defined by DOE t est procedure.

o From January 1 through December 31, 2011: $50 for any advanced main air circulating fan

**•** Water Heaters:

o Until December 31, 2010:

 Gas or propane water heaters

— Energy Factor of at least 0.82, or a ther mal efficiency of at least 90%

 Heat pump water heaters

— Energy Factor of at least 2.0

o From January 1 through December 31, 2011: same specifications, the credit only covers costs up to $300

.

17

- A.3.2 Tax Credits for Manufacturers of Energy Efficient Appliances

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that produced high

- efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007. 11
The Emergency Economic Stabilization Act of 2008 12 amended the credits and extended them through 2010. The credits were exten ded again to 2011 with modifications in the eligibility requirements. Manufacturers who produce these appliances receive the credits for increasing their production of qualifying appliances. The credits were available for models produced in 2008, 2009, and 2010 and 2011.

The credit amounts applied to each unit manufactured. The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers is capped at $75 million for the period

of 2 008

- 2010. However, the most efficient refrigerator (30%) and clothes washer (2.2 MEF/4.5 wcf) models are not subject to the cap. The credit to manufacturers is capped at $25 million for17

- A

- 5 2011. The most efficient refrigerators (35%) and clothes washers (2.8 MEF/3.5 WCF) are exempted from this cap. 1317

- A

- 6 REFERENC ES

1 Geroski, P.A. Models of Technology Diffusion. Research Policy . 2000. 29: pp.603

– 625.

2 Hall, B. H. and B. Khan, Adoption of New Technology

. Working Paper No. E03

- 330 , 2003.

Department of Economics, University of California, Berkeley, CA.

3 Lekvall. P and C. Wahlbin, "A Study of Some Assumptions Underlying Innovation Diffusion

Functions"

. Swedish Journal of Economics , 1973

4 Van den Bulte, C., "Want to Know How Diffusion Speed Varies Across Countries and

Products? Try a Bass Model"

. Product Development & Management Association , 2002. XXVI (4)

.

5 Itron and KEMA . 2004/2005 Statewide Residential Retrofit Single

- Family Energy Efficiency

Rebate Evaluation. 2007. Prepared for t he California Public Utilities Commission, Pacific Gas and Electric Company, San Diego Gas and Electric Company, Southern California Edison, and Southern California Gas Company. Itron, Inc., and KEMA, Inc.: Oakland, CA. CPUC

- Id#:

1115

- 04.

6 U.S. Departmen t of Energy. Technical Support Document: Residential Dishwashers,

Dehumidifiers, and Cooking Products and Commercial Clothes Washers . November 2007. Washington, D.C. Chapter 8. Available online at www.eere.energy.gov/buildings/appliance_standards/residential/home_appl_tsd.html

. (Accessed October 2012.)

7 U.S. Department of Energy. Clothes Washer Tech nical Support Document. December 2000.

Washington, D.C. Chapt er 7. Available online at http://www.eere.energy.gov/buildings/appliance_standards/residential /clwash_0900_r.html. (Accessed October 2012 .)

8 Tax Incentives Assistance Project, Consumer Tax Incentives: H ome Heating & Cooling Equipment, 2011. Available online at www.energytaxincentives.org/consumers/heating

- cooling.php

. (Accessed October 2012.)

9 Energy Policy Act of 2005. 2005. Public Law 109

– 58

–Aug. 8, 2005. Available online at

www.gpo.gov/fdsys/pkg/PLAW

- 109publ58/pdf/PLAW

- 109publ58.pdf

. (A ccessed October 2012.)

10 Consortium for Energy Efficiency, CEE High

- Efficiency Specification: Central Air

Conditioners and Air Source Heat Pumps

, January 1, 2009. Available online at www.cee1.org/resid/rs

- ac/res

- ac_specs.pdf . (Accessed October 2012 .)17

- A

- 7 11 U.S. Department of the Treasury

— Internal Revenue Service . Energy Efficient Appliance

Credit Form 8909. Available online at www.irs.gov/pub/irs

- pdf/f8909.pdf

. (A ccessed October 2012 .)

12 Emergency Economic Stabilization Act of 2008, Available online at www.gpo.gov/fdsys/pkg/PLAW

- 110publ343/pdf/PLAW

- 110publ343.pdf

. (Accessed October 2012.)

13 Tax Incentives Assistance Project, Manufacturer s Incentives, Available online at

e nergytaxincentives.org/builders/appliances.php

. (A ccessed October 2012 .)