# TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT

COMMERCIAL REFRIGERATION EQUIPMENT

February 2014

U.S. Department of Energy Assistant Secretary Office of Energy Efficiency and Renewable Energy Building Technologies Program Appliances and Commercial Equipment Standards Washington, DC 20585

### This Document was prepared for the Department of Energy by staff members of

Ernest Orlando Lawrence Berkeley National Laboratory

and Navigant Consulting, Inc.

### CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUMENT ................................................................................. 1-1

1.2 SUMMARY OF NATIONAL BENEFITS ..................................................................... 1-1

1.3 OVERVIEW OF APPLIANCE STANDARDS .............................................................. 1-3

1.4 OVERVIEW OF COMMERCIAL REFRIGERATION EQUIPMENT STANDARDS ......................................................................................... 1-4

1.4.1 Definitions ............................................................................................................ 1-5

1.4.2 Ru lemaking History ............................................................................................. 1-6

1.4.2.1 Standards Prescribed by Statute ............................................................... 1-6

1.4.2.2 Standards Established by Rulemaking ..................................................... 1-6

1.4.3 Current Energy Conservation Standards .............................................................. 1-9

1.4.4 Framework and Analysis Methodology ............................................................. 1-10

1.5 STRUCTURE OF THE DOCUMENT .......................................................................... 1-12

LIST OF TABLES

Table 1.2.1 Annualized Benefits and Costs of Proposed Standards for Walk-in Coolers and

Walk

- in Freezers .................................................................................................. 1-2 Table 1.3.1 Commercial Refrigeration Equipment Standards Prescribed by EPCA – Effective January 1, 2010 .................................................................................................... 1-9 Table 1.3.2 Commercial Refrigeration Equipment Standards Established in the 2009 Final Rule – Effective January 1, 2012 .................................................................................. 1-10 Table 1.3.3 Commercial Refrigeration Equipment Analyses .................................................... 1-11

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### CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUM ENT

This t echnical support d ocument (TSD) is a “stand -alone” report that provides the technical analyses and results in support of the information presented in the final rule for commercial refrigeration equipment

. XX FR XXXXX, [MONTH] [DAY], [YEAR] . This TSD also complements the engineering analysis, life -cycle cost (LCC) and payback period (PBP), and national impact analysis (NIA) spreadsheets that are posted on the U.S. Depa rtment of Energy’s (DOE’s) website at: www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/27

.

1.2 SUMMARY OF NATIONAL BENEFITS

DOE’s analyses indicate that today’s standards would save a significant amount of energy. The lifetime savings for comme rcial refrigeration equipment purchased in the 30- year period that begins in the year of compliance with amended standards (2017–2046) amount to 2.89 quadrillion British thermal units (quads).

The cumulative net present value (NPV) of total consumer costs and savings in 2012$ of today’s standards for commercial refrigeration equipment ranges from $4.93 billion (at a 7

- percent discount rate) to $11.74 billion (at a 3 -percent discount rate). a 

### This NPV expresses the

estimated total value of future operat ing cost savings minus the estimated increased product costs for products purchased in 2016–2047.

In addition, today’s standards are expected to have significant environmental benefits. The energy savings would result in cumulative emission reductions of approximately 142 million metric tons (Mt) b 

### of carbon dioxide (CO 2 

### ), 762 thousand tons of methane, 207 thousand tons of

sulfur dioxide (SO 2 

### ), 94 tons of nitrogen oxides (NO X 

### ) and 0.25 tons of mercury (Hg). c 

### Through 2030, the estimated energy savings would result in cumulative emissions reductions of 48 Mt of

CO 2.



### The benefits and costs of today’s proposed standards, for equipment sold in 2017-2046, can also be expressed in terms of annualized values. The annualized monetary values are the sum of (1) the annualized national economic value of the benefits from consumer operation of equipment that meets the proposed standards (consisting primarily of operating cost savings from using less energy, minus increases in equipment purchase and installation costs, and (2) the annualized monetary value of the benefits of emission reductions, including CO 2 

### emission

reductions. The value of the CO 2 

### redu ctions, otherwise known as the Social Cost of Carbon (SCC), is calculated using a range of values per metric ton of CO 2 

### developed by a recent interagency process. The derivation of the SCC values is discussed in chapter 16 of the TSD.

a All present value results reflect discounted to beginning of 201 4.

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

c DOE calculated emissions reductions relative to the AEO 2013 R eference case, which generally represents current legislation and environmental regulations for which implementing regulations were available as of December 31,

2012.

1-1

### Although combining the values of operating savings and CO 2 

### emission reductions provides a useful perspective, two issues should be considered. First, the national operating savings are domestic U.S. consumer monetary savings that occur as a result of market

transactions whil e the value of CO 2 

### reductions is based on a global value. Second, the assessments of operating cost savings and CO 2 

### savings are performed with different methods that use different time frames for analysis. The national operating cost savings is measured fo r the lifetime of walk -ins shipped from 2017–2046. The SCC values, on the other hand, reflect the present value of some future climate -related impacts resulting from the emission of one ton of carbon dioxide in each year. These impacts continue well beyond 2100.

Table 1.2.1 shows the annualized values for today’s proposed standards. (All monetary values below are expressed in 2012$.) The results under t he primary estimate are as follows. Using a 7 -percent discount rate for benefits and costs other than CO 2 

### reduction, for which DOE

used a 3

- percent discount rate along with the average SCC series that uses a 3 -percent discount rate, the cost of the amended standards in today’s rule is $256 million per year in increased equipment costs, while the benefits are $710 million per year in reduced equipment operating costs, $246 million in CO 2 

### reductions, and $3.01 million in reduced NO X 

### emissions. In this case,
the net benefit amounts to $704 million per year. Using a 3

- percent discount rate for all benefits and costs and the average SCC series, the cost of the standards in today’s rule is $264 million per year in increased equipment cos ts, while the benefits are $ 900 million per year in reduced operating costs, $246 million in CO 2 

### reductions, and $5.64 million in reduced NO X 

### emissions. In this case, the net benefit amounts to $888 million per year.

Table 1.2.1 Annualized Benefits and Costs of Proposed Standards for Walk -in Coolers and

Walk- in Freezers

Discount

Rate Primary

Estimate* Low Net

Benefits

Estimate* High Net

Benefits

Estimate*

million 2013$/year

Benefits

Operating Cost Savings 7% 710 688 744

3% 900 865 947

CO 2 Reduction at ($11.8/t case)** 5% 73 73 73

CO 2 Reduction at ($39.7/t case)** 3% 246 246 246

CO 2 Reduction at ($61.2/t case)** 2.5% 361 361 361

CO 2 Reduction at ($117.0/t case)** 3% 760 760 760

NO X Reduction at ($2,591/ton)** 7% 3.01 3.01 3.01

3% 5.64 5.64 5.64

Total Benefits† 7% plus

CO 2 range 786 to 1,474 764 to 1,451 820 to 1,508

7% 960 937 994

3% plus 978 to 1,666 943 to 1,631 1,026 to 1,713

1-2CO 2 range

3% 1,152 1,117 1,200

Costs

Incremental Product Costs 7% 256 250 261

3% 264 258 271

Net Benefits

Total† 7% plus

CO 2 range 530 to 1,218 513 to 1,201 559 to 1,246

7% 704 687 733

3% plus

CO 2 range 714 to 1,402 685 to 1,373 755 to 1,442

3% 888 859 929

* This table presents the annualized costs and benefits associated with commercial refrigeration equipment shipped

in 2017

- 2046 . These results include benefits to customers which accrue after 2046 from the products purchased in

2017

- 2046

. The results a ccount for the incremental variable and fixed costs incurred by manufacturers due to the

amended standard , some of which may be incurred in preparation for the final rule. The primary, low, and high estimates utilize projections of energy prices from the A EO 2013 Reference case, Low Estimate, and High Estimate, respectively. In addition, incremental equipment costs reflect a medium decline rate for projected product price trends in the Primary Estimate, a low decline rate for projected product price trends in the Low Benefits Estimate, and a high decline rate for projected product price trends in the High Benefits Estimate. The method used to derive projected price trends are explained in chapter 10.

** The CO 2 values represent global monetized values of the SCC, in 2012 $, in 201 5 under several scenarios of the updated SCC values . The first three cases use the averages of SCC distributions calculated using 5%, 3%, and 2.5% discount rates, respectively. The fourth case represents the 95 th percentile of the SCC distribution calculated using a 3% discount rate. The SCC time series used by DOE incorporate an escalation factor. The value for NO X is the average of the low and high values used in DOE’s analysis.



### 1.3 OVERVIEW OF APPLIANCE STANDARDS

Part B of Title III of the Energy Policy and Conservation Act of 1975 (EPCA or the Act)

, Pub. L. 94-163, as amended by the National Energy Conservation Policy Act of 1978 (NECPA),

Pub. L. 95-619; the National Appliance Energy Conservation Act of 1987 (NAECA), Pub. L. 100-12; the National Appliance Energy Conservation Amendments of 1988 (NAECA 1988),

Pub. L. 100-357; and the Energy Policy Act of 1992 (EPACT 1992), Pub. L. 102-486, established the Energy Conservation Program for Consumer Products other than Automobiles. (42 U.S.C. 6291–6309) Part 3 of Title IV of NECPA amended EPCA to add Part A-1 of Title III, which established an energy conservation program for c ertain industrial equipment. d 

### (42 U.S.C. 6311–6317) EPACT 1992 included amendments to EPCA that expanded Title III to include additional commercial equipment. The Energy Policy and Conservation Act of 2005 (EPACT 2005), Pub. L. 109-58, updated several existing standards and test procedures; prescribes definitions, standards, and test procedures for certain new consumer products and commercial equipment; and mandates that the Secretary of Energy (the Secretary) commence

d This part was originally titled Part C. However, it was redesignated Part A

- 1 after Part B of Title III of E PCA was repealed by Pub. L. 109

- 58.

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### rulemakings to develop test procedures and standards for certain new consumer products and

commercial equipment.

DOE is required to design each standard for this equipment to (1) achieve the maximum improvement in energy efficiency that is technologically feasible and economically justified ; and (2) result in significant conservation of energy. (42 U.S.C. 6295(o)(2)(A) and (o)(3) (B) , 42 U.S.C. 6316(e)(1)(A)) To determine whether a proposed standard is economically justified, DOE will, after receiving comments on the proposed standard, determine whether the benefits of the

standard exceed its burdens to the greatest extent practicable, considering the following seven

factors:

1. The economic impact of the standard on the manufacturers and on the consumers of the products subject to such standard;

2. The savings in operating costs throughout the estimated average life of the covered product in the type (or class) compared to any increase in the price of, or in the initial charges for maintenance expenses of, the covered products that are likely to result from the imposition of the standard;

3. The total projected am ount of energy savings likely to result directly from the imposition of the standard;

4. Any lessening of the utility or the performance of the covered products 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 considers relevant.

( See 42 U.S.C. 6295 (o)(2)(B)(i) ; 6316(e)(1)(A))

For commercial refrigeration equipment, DOE is applying those factors in a manner consistent with its other energy conservation standards rulemakings to ascertain the maximum improvement in energy efficiency that is technologically feasible and economically justified for this equipment.

1.4 OVERVIEW OF COMMERCIAL REFRIGERATION EQU IPMENT

STANDARDS

EPACT 2005 included amendments to EPCA that updated several existing standards and test procedures; prescribed definitions, standards, and test procedures for certain new consumer products and commercial equipment; and mandated that DOE commence rulemakings to develop test procedures and standards for certain new consumer products and commercial equipment. With respect to the standards for commercial refrigeration eq uipment, EPCA, as ame nded by EPACT 2005, also states that:

(A) Not later than January 1, 2013, the Secretary shall issue a final rule to

determine whether the standards established under this subsection should be

amended.

1-4

### (B) Not later than 3 years after the effective date of any amended standards under subparagraph (A) or the publication of a final rule determining that the standards should not be amended, the Secretary shall issue a final rule to determine whether the standards established under this subsection or the amended standards, as applicable, should be amended. (C) If the Secretary issues a final rule under subparagraph (A) or (B) establishing amended standards, the final rule shall provide that the amended standards apply to products manufactur ed on or after the date that is

– (i) 3 years after the date on which the final amended standard is published; or (ii) if the Secretary determines, by rule, that 3 years is inadequate, not later than 5 years after the date on which the final rule is published.

42 U.S.C. 6313(c)(6)

1.4.1 Definitions

Section 136(a)(3) of EPACT 2005 amended section 340 of EPCA by striking paragraph 9

and inserting definitions for the following terms that describe commercial refrigeration equipment:

(9)(A) The term “ commercial refrigerator, freezer, and refrigerator

- freezer ” means refrigeration equipment that

— (i) is not a consumer product (as defined in section 321 of EPCA); (ii) is not designed and marketed exclusively for medical, scientific, or research

purposes; (iii) operates at a chilled, frozen, combination chilled and frozen, or variable

temperature; (iv) displays or stores merchandise and other perishable materials horizontally,

semi

- vertically, or vertically; (v) has transparent or solid doors, sliding or hinged doors, a combination of hinged, sliding, transparent, or solid doors, or no doors; (vi) is designed for pull-down temperature applications or holding temperature applications; and (vii) is connected to a self -contained condensing unit or to a remote condensing

u nit. (B) The term “ holding temperature application ” means a use of commercial refrigeration equipment other than a pull-down temperature application, except a

blast chiller or freezer.

* * * (D) The term “pull- down temperature application ” means a c ommercial refrigerator with doors that, when fully loaded with 12 ounce beverage cans at 90 degrees F, can cool those beverages to an average stable temperature of 38 degrees F in 12 hours or less. (E) The term “remote condensing unit” means a factory

- made assembly of refrigerating components designed to compress and liquefy a specific refrigerant that is remotely located from the refrigerated equipment and consists of one or

1-5

### more refrigerant compressors, refrigerant condensers, condenser fans and motors, and factory supplied accessories. (F) The term ” self -contained condensing unit” means a factory

- made assembly of refrigerating components designed to compress and liquefy a specific refrigerant that is an integral part of the refrigerated equipment and cons ists of one or more refrigerant compressors, refrigerant condensers, condenser fans and motors, and factory supplied accessories.

42 U.S.C. 6311(9)

1.4.2 Rulemaking History

The current standards for commercial refrigeration equipment are a result of two legislative actions and one rulemaking: standards prescribed by EPACT 2005 for certain equipment, standards for other equipment established by DOE through issuance of a final rule,

and a standard prescribed by American Energy Manufacturing Technical Correc tions Act (AEMTCA) , Pub. L. 112-210 (2012).

1.4.2.1 Standards Prescribed by Statute

Section 136(c) of EPACT 2005 amended EPCA to prescribe energy conservation standards for self-contained equipment consisting of commercial refrigerators with solid doors,

commercia l refrigerators with transparent doors, commercial freezers with solid doors,

commercial freezers with transparent doors, commercial refrigerator/freezers with solid doors designed for holding temperature applications , and commercial refrigerators with tra nsparent doors designed for pull-down temperature applications. (42 U.S.C. 6313(c)(1– 3)) These standards became effective on January 1, 2010. See Table 1.3.1 in section 1.3.3

.

Section 4 of AEMTCA established a new standard for self-contained service over counter commercial refrigerators for medium tempe rature applications (SOC.SC.M) by amending section 342(c) of EPCA. (42 U.S.C. 6313(c)(4)) SOC.SC.M equipment had previously been inadvertently classified by EPACT 2005 under the category self

- contained commercial refrigerators with transparent doors. (42 U.S.C. 6313(c)(2)) Section 4 of AEMTCA was aimed at addressing this discrepancy.

1.4.2.2 Standards Established by Rulemaking

Section 136(c) of EPACT 2005 also amended EPCA to mandate that DOE set standards

for the following additional categories of equipment: ice

- cream freezers; self

- contained commercial refrigerators, freezers, and refrigerator -freezers without doors; and remote condensing commercial refrigerators, freezers, and refrigerator -freezers. (42 U.S.C. 6313(c)(5)(A)) DOE undertook a rulemaking process beginning in April 2006, when it published

the Rulemaking Framework for Commercial Refrigeration Equipment Including Ice- Cream Freezers; Self -Contained Commercial Refrigerators, Freezers, and Refrigerator- Freezerswithout doors; and Remote Condensing Commercial Refrigerators, Freezers, and RefrigeratorFreezers (April 2006 framework document). The April 2006 framework document described the procedural and analytical approaches DOE anticipated using to evaluate the establishment of energy conservation standards for these types of commercial refrigeration equipment . This

1-6

### document is available at www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/comml_refrig_framewor k.pdf

DOE held a public meeting on May 16, 2006 to discuss procedural and analytical approaches t o the rulemaking, and to inform and facilitate the involvement of interested parties in the rulemaking process. The analytical framework presented at the public meeting described different analyses, such as the engineering analysis and the LCC and PBP anal yses, the methods proposed for conducting them, and the relationships among the various analyses.

After the public meeting associated with the April 2006 framework document, as part of the information gathering and sharing process for the preliminary manufacturer impact analysis (MIA) , DOE conducted interviews with commercial refrigeration equipment manufacturers. DOE selected companies that represented production of all types of equipment covered by the rulemaking , ranging from small to large manufact urers, and included both Air-Conditioning and Refrigeration Institute (ARI) e 

### member companies and non- ARI member companies. DOE had four objectives for these interviews: (1) solicit feedback on the draft engineering analysis (including methodology, production costs, manufacturing processes, and findings); (2) solicit feedback on topics related to the preliminary MIA ; (3) provide an opportunity, early in the rulemaking process, for these manufacturers to express specific concerns to DOE; and (4) foster cooperation between the manufacturers and DOE.

DOE developed a preliminary engineering analysis to estimate the cost of manufacturing equipment at efficiencies above the baseline level s. DOE also developed spreadsheets to conduct the LCC, PBP, and NIA . The LCC spreadsheet calculates national distributions of LCC savings at various energy efficiency levels above the baseline. It can also provide LCC savings based on typical input values for several business types that use commercial refrigeration equipment . The

NIA spreadsheet calculates the national energy savings ( NES ) and national net present values (NPVs) at various energy efficiency levels. It also includes a model that forecasts shipments for the various equipment classes of commercial refrigeration equipment at different efficiency

levels.

In July 2007, DOE published an a dvance n otice of proposed rulemaking (July 2007 ANOPR) for c ommercial r efrigeration equipment including i ce -c ream f reezers; s elf -contained

c ommercial r efrigerators, f reezers, and r efrigerator -freezers without doors; and r emote condensing c ommercial r efrigerators, f reezers, and r efrigerator -f reezers . 72 FR 41162 (July 26, 2007). In the July 2007 ANOPR analysis , DOE considered establishing energy conservation

standards for these types of commercial refrigeration equipment and announced a public meeting to receive comments on a variety of issues. This document is available at www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/comml_refrig_anopr_07 2607.pdf

.

DOE held a public meeting on August 23, 2007 (August 2007 ANOPR public meeting) to provide interested parties the opportunity to comment on the equipment classes proposed by

e On January 1, 2008, t he Air

- Conditioning and Refrigeration Institute (ARI) and the Gas Appliance Manufacturers Association (GAMA) merged to become the Air

- Conditioning, Heating, and Refrigeration Institute ( AHRI ), to

r epresent the interests of cooling, heating, and commercial refrigeration equipment manufacturers.

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### DOE in the July 2007 ANOPR” for clarification; the analytical framework, models, and tools ( e.g. , LCC and NES spreadsheets) that DOE had developed to perform analyses of the impacts of potential energy conservation standar ds; the results of the preliminary analyses; and the candidate energy conservation standard levels.

After the publication of the July 2007 ANOPR and the presentation of the ANOPR to interested parties at the August 2007 ANOPR public meeting, DOE conducted additional interviews with commercial refrigeration equipment manufacturers as part of its development of

the MIA for the NOPR. There were 13 general topics discussed during each of the interviews: (1) general key issues; (2) company overview and organiza tional characteristics ; (3) company financial parameters ; (4) production cost breakdown; (5) shipment projections and market

shares ; (6) equipment mixes; (7) conversion costs; (8) markups and profitability; (9) cumulative regulatory burden; (10) exports, foreign competition, and outsourcing; (11) direct employment impact assessment ; (12) market consolidation; and (13) baseline products and different design options.

Based on findings from the preliminary engineering, LCC and NIA, and public comments provided in response to the July 2007 ANOPR, DOE update d these analyses. In updating these analyses , DOE reviewed the recommendations made on April 21, 1998 by the Advisory Committee on Appliance Energy Efficiency Standards. (Advisory Committee, No. 96) f 

### DOE’s analysis implemented recommendations related to (1) defining a range of energy price futures for each fuel used in the economic analyses ; and (2) defining a range of primary energy conversion factors and associated emission reductions based on the generation of energy and emissions that would be displaced by energy efficiency standards for each rulemaking. In addition, DOE performed additional analyses assessing impacts on national employment, consumer subgroups, utilities, and the environment. DOE also developed analysis of alternatives to efficiency standard regulations.

On August 25, 2008, DOE published a NOPR (August 2008 NOPR) for c ommercial

r efrigeration equipment including i ce -c ream f reezers; s elf -contained c ommercial r efrigerators,

f reezers, and r efrigerator -freezers without doors; and r emote condensing c ommercial

r efrigerators, f reezers, and r efrige rator -f reezers , to propose energy conservation standards for these types of commercial refrigeration equipment , and to announce a public meeting to receive comments on a variety of issues. 73 FR at 50072. This document is available at www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cre_nopr_fr_final.pdf

.

DOE held a public meeting on September 23, 2008 (September 2008 public meeti ng) to provide interested parties the opportunity to comment on the proposed standards, results of the analyses and the trial standard levels (TSL s ).

After the publication of the August 2008 NOPR and the September 2008 NOPR public meeting , DOE received mo re than 100 comments from a diverse set of interested parties,

f Advisory Committee, No. 96 refers to the recommendations of the Advisory Committee on Appliance Energy Efficiency Standards and is available for inspection at the U.S. Department of Energy, 950 L’Enfant Plaza SW., Suite 600, Washington, DC, 20024 (Resource Room) in the file under “Energy Conservation Program for Consumer Products: Procedures for Consideration of New or Revised Energy Conservation Standards for Consumer Products,” RIN [1904

– AA83], as document number 96.

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### including manufacturers and their representatives, trade associations, wholesalers and distributors, energy conservation advocates, and electric utilities. Comments addressed DOE methodology, the information DOE used in its analyses, results of and inferences drawn from the analyses, impacts of standards, the merits of the different TSLs, standards options DOE

considered , and other issues affecting adoption of standards for commercial refrigeration equipment.

DOE considered these comments in developing a final rule for commercial refrigeration equipment, published on January 9, 2009. 74 FR at 1092 (herein referred to as th e “January 2009

final rule

, ” respectively ). Th e January 2009 final rule established standards for ice- cream freezers; self

- contained commercial refrigerators, freezers, and refrigerator

- freezers without doors; and remote condensing commercial refrigerators , freezers, and refrigerator

- freezers,

which became effective on January 1, 2012.

1.4.3 Current Energy Conservation Standards

Table 1.3.1 and Table 1 .3 .2 show the current standards for the two subsets of commercial refrigeration equipment.

In addition to the standards in Table 1.3.1 and Table 1.3.2 , the standard for equipment class SOC.SC.M, established by AEMTCA, is given by the expression 0.6 × TDA + 1.0, and has an effective date of January 1, 2012. (42 U.S.C. 6313(c)(4))

Table 1.3.1 Commercial Refrigeration Equipment Standards Prescribed by EPCA

– Effective January 1, 2010

Category Maximum Daily Energy Consumption

kWh/day *

Refri gerators with solid doors 0.10 V + 2.04 Refrigerators with transparent doors 0.12 V + 3.34

Freezers with solid doors 0.40 V + 1.38 Freezers with transparent door s 0.75 V + 4.10 Refrigerators/freezers with solid doors the greater of 0.27 AV

- 0.71 or 0.70

Self

- contained refrigerators with transparent doors designed for pull

- down temperature applications 0.126V + 3.51

* kilowatt

- hours per day

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### Table 1.3.2 Commercial Refrigeration Equipment Standards Established in the 2009 Final

Rule – Effective January 1, 2012

Equipment

Class * Standard Level * *

, †

kWh/day Equipment

Class Standard Level **, †

kWh/day

VOP.RC.M 0.82 × TDA + 4.07 VCT.RC.I 0.66 × TDA + 3.05

SVO.RC.M 0.83 × TDA + 3.18 HCT.RC.M 0.16 × TDA + 0.13

HZO.RC.M 0.35 × TDA + 2.88 HCT.RC.L 0.34 × TDA + 0.26

VOP.RC.L 2.27 × TDA + 6.85 HCT.RC.I 0.4 × TDA + 0.31

HZO.RC.L 0.57 × TDA + 6.88 VCS.RC.M 0.11 × V + 0.26

VCT.RC.M 0.22 × TDA + 1.95 VCS.RC.L 0.23 × V + 0.54

VCT.RC.L 0.56 × TDA + 2.61 VCS.RC.I 0.27 × V + 0.63

SOC.RC.M 0.51 × TDA + 0.11 HCS.RC.M 0.11 × V + 0.26

VOP.SC.M 1.74 × TDA + 4.71 HCS.RC.L 0.23 × V + 0.54

SVO.SC.M 1.73 × TDA + 4.59 HCS.RC.I 0.27 × V + 0.63

HZO.SC.M 0.77 × TDA + 5.55 SOC.RC.L 1.08 × TDA + 0.22

HZO.SC.L 1.92 × TDA + 7.08 SOC.RC.I 1.26 × TDA + 0.26

VCT.SC.I 0.67 × TDA + 3.29 VOP.SC.L 4.37 × TDA + 11.82

VCS.SC.I 0.38 × V + 0.88 VOP.SC.I 5.55 × TDA + 15.02

HCT.SC.I 0.56 × TDA + 0.43 SVO.SC.L 4.34 × TDA + 11.51

SVO.RC.L 2.27 × TDA + 6.85 SVO.SC.I 5.52 × TDA + 14.63

VOP.RC.I 2.89 × TDA + 8.7 HZO.SC.I 2.44 × TDA + 9.

SVO.RC.I 2.89 × TDA + 8.7 SOC.SC.I 1.76 × TDA + 0.36

HZO.RC.I 0.72 × TDA + 8.74 HCS.SC.I 0.38 × V + 0.88

* For this rulemaking, equipment class designations consist of a combination (in sequential order separated by periods) of (1) an equipment family code (VOP=vertical open, SVO=semivertical open, HZO=horizontal open, VCT=vertical closed with transparent doors , VCS=vertical closed

with solid doors, HCT=horizontal closed with transparent doors, HCS=horizontal closed with solid doors, or SOC=service over counter); (2) an operating mode code (RC=remote condensing

or SC=self

- contained); and ( 3) a rating temperature code (M=medium temperature (38 °F) ), L=low temperature (0 °F), or I=ice

- cream temperature (

- 15 °F)). For example, “VOP.RC.M” refers to the “vertical open, remote condensing, medium temperature” equipment class.

** TDA is the total display area of the cas e, as measured in ARI Standard 1200

- 2006, appendix D.

† V is the volume of the case, as measured in ARI Standard 1200

- 2006, appendix C.



### 1.4.4 Framework and Analysis Methodology

DOE initiated this rulemaking to fulfill its statutory requirements set forth in 42 U.S.C. 6313(c) with respect to establishing amended energy conservation standards for commercial refrigeration equipment. As the first step in April 2010, DOE published a Rulemaking Framework for Commercial Refrigeration Equipment (April 2010 framework document) describing the procedural and analytical approaches DOE anticipated using to evaluate the establishment of energy conservation standards for commercial refrigeration equipment

. This

document is available at www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cre_framework_04-30- 10.pdf

DOE held a public meeting on May 18, 2010 (May 2010 framework public meeting) to discuss procedural and analytical approaches to the rulemaking, and to inform interested parties and facilitate their involvement in the rulemaking process. The analytical framework presented at the public meeting described different analyses, such as the engineering analysis and the LCC

1- 10

### and PBP analyses, the methods proposed for conducting them, and the relationships among the various analyses.

After the analytical framework public meeting, as part of the information gathering and sharing process for t he preliminary MIA , DOE organized and held interviews with commercial refrigeration equipment manufacturers. DOE selected companies that represented production of all types of commercial refrigeration equipment, ranging from small to large manufacturers. DOE had four objectives for these interviews: (1) solicit feedback on the draft engineering analysis (including methodology, production costs, manufacturing processes, and findings); (2) solicit feedback on topics related to the preliminary MIA ; (3) provide an opportunity, early in the rulemaking process, to express specific concerns to DOE; and (4) foster cooperation between the

manufacturers and DOE.

In March 2011, DOE published a notice of public meeting and availability of the preliminary TSD (March 2011 preliminary analysis) for the ongoing rulemaking to potentially

amend energy conservation standards for commercial refrigeration equipment , This document is

available at www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/fr_nopm_publication_20 11_03_30.pdf

DOE he ld a public meeting on April 19 , 2011 (April 2011 preliminary analysis public meeting) to give stakeholders the opportunity to comment on the proposed equip ment classes DOE is considering; t he analytical framework, models, an d tools ( e.g., LCC and NES spreadsheets) that DOE has been using to perform analyses of the impacts of energy

conservation standards ; the results of the preliminary analyses; and the candidate energy

conservation standard levels. See Table 1.3.3 for all the analyses discussed at the public meeting to be undertaken in each of the formal public rulemaking documents.

Table 1.3.3 Commercial Refrigeration Equipment Analyses

Preliminary Analysis NOPR Final Rule* Market and technology assessment Revised preliminary analyses Revised NOPR analyses Screening analysis Life

- cycle cost subgroup analysis Engineering analysis Manufacturer impact analysis Energy use characterization Utility impact analysis Markups to determine equipment price Employment impact analysis

Life

- cycle cost and payback period analyses Environmental assessment Shipments analysis Regulatory impact analysis National impact analysis Preliminary manufacturer impact analysis
* During the f inal r ule phase, DOE considers the comments submitted by the U.S. Department of Justice in the NOPR phase concerning the impact of any lessening of competition that is likely to result from the imposition of the standard. (42 U.S.C. 6295(o)(2)(B)(v))



### After the posting of the March 2011 preliminary analysis TSD and the Apri l 2011 preliminary analysis public meeting, DOE conducted interviews with commercial refrigeration equipment manufacturers as part of the MIA for the NOPR. A number of general topics were discussed during each interview: (1) general key issues; (2) company overview and organizational characteristics ; (3) company financial parameters ; (4) production cost breakdown; (5) shipment projections and market shares; (6) equipment mixes; (7) conversion costs; (8)

1- 11

### markups and profitability; (9) cumulative regulatory burden; (10) exports, foreign competition, and outsourcing; (11) direct employme nt impact assessment ; (12) market consolidation; and (13) baseline products and different design options.

DOE developed spreadsheets for the LCC and PBP analyses and for the NIA in an effort to meet the objectives of the Process Rule. The LCC spreadsheet calculates national distributions of life cycle cost savings at all efficiency levels above the baseline. DOE also developed a n NIA spreadsheet that calculates the NES and national NPVs at all efficiency levels. This spreadsheet includes a model that forecasts shipments for the various equipment classes of commercial refrigeration equipment at different efficiency levels.

DOE reviewed the recommendations made on April 21, 1998, by the Advisory Committee on Appliance Energy Efficiency Standards. (Advisory Committee, No. 96) These recommendations related to: (1) using the full range of consumer marginal energy rates (CMERs) in the LCC analysis (replacing the use of national average energy prices) ; (2) defining a range of energy price futures for each fuel used in the economic analyses ; and (3) defining a range of primary energy conversion factors and associated emission reductions based on the generation of energy and emissions that would be displaced by energy efficiency standards for each rulemaking. DOE’s a nalysis implemented (2) and (3) above; however

, as discussed previously, DOE conducted the LCC analysis using regional average electricity prices for affected business types and did not develop CMERs in the LCC analysis.

In September 2013, DOE published a Notice of Proposed Rulemaking to propose energy conservation standards for commercial refrigeration equipment, and to announce a public meeting to receive comments on a variety of issues. This document is available at http://www.regulations.gov/#!documen tDetail;D=EERE -2010- BT

- STD -0003-0052

DOE held a public meeting on October 3, 2013, to provide interested stakeholders the opportunity to comment on the proposed standards, results of the analyses and the trial standard levels (TSL).

After the publication of the NOPR and the presentation of the NOPR to stakeholders at the public meeting, DOE received comments from a diverse set of parties, including manufacturers and their representatives, trade associations, wholesalers and distributors, energy conservation advocates, and electric utilities. Comments addressed DOE methodology, the information DOE used in its analyses, results of and inferences drawn from the analyses, impacts of standards, the merits of the different trial standard levels (TSLs), standards options DOE considered and other issues affecting adoption of amended standards for commercial refrigeration equipment.

1.5 STRUCTURE OF THE DOCUMENT

Listed below are the 17 TSD chapters and related appendices that collectively form the

TSD

.

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### Chapter 1 Introduction: provides an overview of the appliance standards program and how it applies to the commercial refrigeration equipment rulemaking

, provides a history of DOE’s actions to date, and outlines the structure of

this document.

Chapter 2 Analytical Framework: describes the rulemaking process.

Chapter 3 Market and Technology Assessment: provides DOE’s definition of commercial refrigeration equipment

, discusses the proposed equipment classes, and names the major industry players. This chapter also provides

an overview of commercial refrigeration technology, including techniques employed to improve equipment efficiency.

Chapter 4 Screening Analysis: identifies all the design options that improve commercial refrigeration equipment efficiency, and determ ines which of

these will be evaluated and which will be screened out.

Chapter 5 Engineering Analysis: presents detailed cost and efficiency information for the units of analysis. This chapter describes DOE’s approach for determining manufacturer costs, in cluding the markups used for converting material costs to manufacturer sales prices.

Chapter 6 Markups Analysis : presents the methodology used to determine the distribution channel markups that are used to convert manufacturer selling prices into customer purchase price of the equipment.

Chapter 7 Energy Use Analysis: DOE used the energy consumption model in the engineering analysis to estimate commercial refrigeration equipment energy use. DOE did not conduct a separate energy use analysis for this

rulem aking.

Chapter 8 Life

- Cycle Cost Analysis: presents the methodology used to estimate the impact of potential new or amended standards on customers of commercial refrigeration equipment by calculating LCC and PBP values at all higher efficiency levels

.

C hapter 9 Shipments Analysis: presents the methodology used to estimate the historic and future shipments of commercial refrigeration equipment. The estimated shipments numbers are used as inputs to NIA and other downstream analyses.

Chapter 1 0 National Impact Analysis: presents the methodology used to estimate national impacts by calculating NES and NPV at all higher efficiency

levels

.

1- 13

### Chapter 1 1 Customer Subgroup Analysis: evaluates impacts on identifiable customer subgroups that may disproportionately disadvantaged by the proposed new

or amended standards.

Chapter 1 2 Manufacturer Impact Analysis: assesses the impacts on commercial refrigeration equipment manufacturers of any new or amended standards. In addition to financial impacts , a wide range of quantitative and qualitative effects may occur following adoption of a standard that may require changes to the manufacturing practices for this equipment.

Chapter 1 3 Environmental Assessment: presents the assess ment of the impacts of proposed commercial refrigeration equipment standard levels on certain

environmental indicators.

Chapter 1 4 Monetization of Emissions Reductions: presents the methodology to estimate monetary benefits likely to result from the reduced emissions of certain pollutants

.

Chapter 1 5 Utility Impact Analysis: analyzes the effects of proposed new or amended commercial refrigeration equipment standard leve ls on the electric utility industry.

Chapter 1 6 Employment Impact Analysis: estimates national job creation or elimination (indirect effects) resulting from possible amended standards due to reallocation of the associated commercial expenditures for purchasing and operating equipment.

Chapter 1 7 Regulatory Impact Analysis Report: evaluates potential major alternatives to standards to achieve customer product energy efficiency.

Appendix 5A Engineering Data: contains f ull engineering specifications for al l equipment classes directly analyzed

.

Appendix 6A Data for Equipment Price Markups: presents detailed data used for markups a nalysis.

Appendix 8A Presents detailed results from the LCC analysis.

Appendix 8B Presents brief discussion on the uncertainty and variability analysis used in the LCC analysis.

Appendix 10A Presents detailed results from the NIA

.

Appendix 10B Presents the criteria for TSL selection and the proposed standards equation

at each TSL.

1- 14

### Appendix 10C Presents price trend sensitivity analysis results.

Appendix 10D Presents the ROCIS g 

### tables.

Appendix 10E Presents the Full

- Fuel

- Cycle Multipliers

Appendix 12A Presents overview of the model used in the MIA

.

Appendix 14A Presents the social cost of carbon for regulatory im pact analysis under

Executive Order 12866.

Appendix 14B Presents the technical update of social cost of carbon for regulatory analysis under Executive Order 12866

Appendix 17A Presents the Regulatory Impact Analysis: Supporting Materials.

g Regulatory Information Service Center (RISC) and Office of Information and Regulatory Affairs (OIRA) Combined Information System

.

1- 15

### CHAPTER 2: ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION ........................................................................................................... 2-1

2.2 BACKGROUND ............................................................................................................. 2-4

2.3 MARKET AND TECHNOLOGY ASSESSMENT ........................................................ 2-4

2.3.1 Market Assessment .............................................................................................. 2-5

2.3.2 Technology Assessment ....................................................................................... 2-5

2.4 SCREENING ANALYSIS .............................................................................................. 2-5

2.5 ENGINEERING ANALYSIS .......................................................................................... 2-6

2.5.1 Baseline Models ................................................................................................... 2-7

2.5.2 Manufacturing Cost Analysis .............................................................................. 2-7

2.6 ENERGY USE ANALYSIS ............................................................................................ 2-7

2.7 MARKUPS FOR EQUIPMENT PRICE DETERMINATION ....................................... 2-7

2.8 LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS ..................................... 2-8

2.9 SHIPMENTS ANALYSIS ............................................................................................ 2-10

2.10 NATIONAL IMPACT ANALYSIS .............................................................................. 2-11

2.11 LIFE

- CYCLE COST SUB

- GROUP ANALYSIS ......................................................... 2-11

2.12 MANUFACTURER IMPACT ANALYSIS .................................................................. 2-12

2.13 EMISSIONS ANALYSIS .............................................................................................. 2-12

2.14 MONETIZATION OF EMISSIONS REDUCTIONS BENEFITS ............................... 2-13

2.15 UTILITY IMPACT ANALYSIS ................................................................................... 2-13

2.16 EMPLOYMENT IMPACT ANALYSIS ....................................................................... 2-13

2.17 REGULATORY IMPACT ANALYSIS ....................................................................... 2-14

2.18 DEPARTMENT OF JUSTICE REVIEW ..................................................................... 2-14

LIST OF FIGURES

Figure 2.1.1 Analyses for Commercial Refrigeration Equipment Energy

Conservation Standards .......................................................................................... 2-3

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### CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

This chapter provides a description of the general analytical framework used by the U.S. Department of Energy (DOE) in developing standards and assessing the ir impacts for commercial refrigeration equipment

. The description addresses the methodology, the analytical tools, and the relationship between the various analyses conducted in the rulemaking. The objective of the rulemaking process is to determine minimum efficiency standar ds for commercial refrigeration equipment that are technologically feasible and economically justified. In this context, economic justification includes consideration of the economic impact on manufacturers and consumers, the national benefits, the impacts on utilities, and the impacts from any lessening of competition.

Figure 2.1.1 summarizes the analytical components of the standard s- setting process. The focus of this figure is the third column, identified as “Analysis.” The columns labeled “ Key Inputs” and “Key Outputs” indicate how the analyses fit into the rulemaking process, and how the analyses relate to each other. Key outputs are analytical resu lts that feed directly into the

standard s- setting process. Dotted lines connecting analyses indicate types of information that

feed from one analysis to another. Key inputs are the types of data an d information that the analyses require. Some key inputs exist in public databases, and DO E also collects inputs from stakeholders or others with special knowledge. Inputs developed by the projec t team for the standards-setting process are presented and open for stakeholder review.

The analyses that DOE performed in developing this final rule include:

• a market and technology assessment to characterize the commercial refrigeration equipment market and to re view techniques and approaches used to produce more efficient commercial refrigeration equipment ;

• a screening analysis to identify design options that improve commercial refrigeration equipment efficiency and to determine which should be evaluated and which should be screened out;

• a n engineering analysis to estimate the relationship between the manufacturing cost

of a commercial refrigeration unit and its performance ;

• a markup analysis to convert manufacturer selling prices to customer purchase prices;

• an energy use analysis to estimate the energy consumption of the equipment (f or this

final rule , DOE used the energy consumption model from the engineering analysis and did not conduct a separate energy use analysis );

• a life

- cycle cost (L CC) and payback period (PBP) analysis to estimate the impact of potential amended standards on customers of commercial refrigeration equipment by calculating LCC and PBP values at all efficiency levels above the baseline ;

• a shipments ana lysis to estimate s hipments of commercial refrigeration equipment

over the time period examined in the analysis;

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### • a national impacts analysis to assess the aggregate impacts at the national level of net present value (NPV) of total customer savings and national energy savings (NES) ;

• an customer subgroup analysis to evaluate impacts on identifiable groups of

customers who may be disproportionately affected by a mended standards;

• a manufacturer impact analysis (MIA) to estimate the financial impact of potential

amended standards on commercial refrigeration equipment manufacturers and to

ca lculate impacts on competition, employment at the manufacturing plant, and manufacturing capacity;

• a utility impact analysis to estimate the effects of amended standards on the ins talle d capacity and generating base of electric utilities;

• a n employment impact analysis to estimate the i mpacts of amended standards on net jobs eliminated or created in the general economy as a consequence of increased spending on the purchase price of commercial refrigeration equipment and reduced customer spending on energy;

• a n environmental assessment to assess the impacts o f amended standards on certain environmental indicators; and

• a regulatory impact analysis (RIA) to explore major alternatives t o amende d

standards that could achieve comparable energy savings.

2-2

### Figure 2.1.1 Analyses for Commercial Refrigeration Equipment Energy Conservation

Standards

2-3

### 2.2 BACKGROUND

As described in chapter 1 of this technical support document ( TSD ), DOE announced a formal effort to consider further improvements to the process used to develop appliance efficiency standards. DOE called on energy efficiency groups, manufacturers, trade associations, state agencies

, utilities, and other interested parties to provide input to this effort. As a result of this combined effort, DOE published Procedures, Interpretations and Policies for Consideration of New or Revised Energy Conservation Standards for Consumer Products (the “Process Rule”),

10 CFR part 430, subpart C, appendix A. The Process Rule outlined the procedural improvements identified by the interested parties, and included a review of the (1) economic models; (2) analytical tools ; (3) methodologies; (4) non- reg ulatory approaches ; and ( 5) prioritization of future rules. The Process Rule recommended that DOE take into account uncertainty and variability by carrying out scenario or probability analysis. The following sections provide a general description of the analytical components of the improved rulemaking

framework.

DOE developed the analytical framework pertaining to commercial refrigeration equipment in the Rulemaking Framework for Commercial Refrigeration Equipment (April 30, 2010). DOE announced the availab ility of the F ramework document in a n otice of public m eeting and a vailability of a F ramework document published in the Federal Register on May 6, 2010. 75 FR at 24824.

DOE presented the analytical approach to interested parties during a public meeting held on May 18, 2010. 1 

### DOE used comments gathered during the F ramework public meeting as well as additional information for the preliminary analysis stage. DOE announced the notice of public meeting and the availability of the preliminary TSD 2 

### on March 30, 2011. 76 FR at 17573.

After publishing the preliminary analysis holding the preliminary analysis public meeting, DOE conducted analyses culminating in the publication of a Notice of Proposed Rulemaking for commercial refrigeration equipment 3 

### on September 11, 2013. 78 FR at 55890. DOE also held an associated public meeting for the NOPR stage in Washington, D.C. on October, 3, 2013. After gathering stakeholder comments through the public meeting and open comment period, DOE revised and updated its analysis for today’s final rule.

The following sections provide a brief overview of the different analytical approaches of this rulemaking analysis plan. DOE has used the most reliable data available at the time of each analysis in this rulemaking. DOE has also considered the submissions of additional data from interested parties during the rulemaking process.

2.3 MARKET AND TECHNOLOG Y ASSESSMENT

The market and technology assessment characterizes the commercial refrigeration equipment market and existing technology options for making a unit of commercial refrigeration equipment more efficient.

2-4

### 2.3.1 Market Assessment

DOE reviewed relevant literature and interviewed manufacturers to develop an overall picture of the commercial refrigeration equipment industry in the United States. Industry publications and trade journals, government agencies, and trade organizations provided the bulk of the information, including (1) manufacturers and their market shares; (2) shipments by product type and capacity; (3) product information; and (4) industry trends.

When evaluating and establishing energy conservation standards, DOE generally divides covered equipment into equipment classes by the type of energy used, capacity, or other performance

- related features that affect efficiency. Different energy conservation standards may apply to different equipment classes. (42 U.S.C. 6295(q))

When initiating a standards rulemaking, DOE develops information on the present and past industry structure and market characteristics of the product(s) concerned. This activity consists of both quantitative and qualitative efforts to assess the industry and products based on publicly available information.

2.3.2 Technology Assessment

The function of the technology assessment is to develop a preliminary list of technologies that could potentially be used to reduce the energy consumption of commercial refrigeration equipment, as well as to highlight the developments within those technology categories and their applicability to these equipment c lasses. The result i s a list of technology options to be analyzed in the screening analysis. Chapter 3 of the TSD includes a detailed list of all technology options DOE identified for this rulemaking.

2.4 SCREENING ANALYSIS

The purpose of the screening analysis is to evaluate the technologies identified in the technology assessment to determine which options to consider further in the analysis and which options to screen out. DOE consulted with industry, technical experts, and other interested parties in developing a list of energy-saving technologies for the technology assessment. DOE then applied the screening criteria to determine which technologies were unsuitable for further consideration in this rulemaking. Chapter 4 of the TSD, the screening analysis, contains details about DOE’s screening criteria.

The screening analysis examines whether various technologies (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 consultation with interested parties, DOE reviewed the list of commercial refrigeration equipment technologies according to these criteria. In the engineering a nalysis, DOE further considers the efficiency

- enhancement technologies that it did not eliminate in the screening analysis.

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### 2.5 ENGINEERING ANALYSIS

The engineering analysis ( chapter 5 of the TSD) develops cost- efficiency relationships

for commercial refrigera tion equipment , estimating manufacturer costs of achieving increased efficiency levels. Manufacturing costs are used as the means of determining retail prices in the

LCC analysis, and are needed for the MIA. The engineering analysis also determines the maximum technologically feasible energy efficiency level.

In general, the engineering analysis estimates the efficiency improvement potential of individual design options or combinations of design options that passed the four criteria in the screening analysi s. DOE, in consultation with stakeholders, uses the most appropriate method to determine the manufacturing cost

- energy efficiency relationship. This cost- efficiency relationship developed in the engineering analysis is used in the LCC analysis.

As describe d in TSD chapter 1, DOE will consider those commercial refrigeration equipment units that are designed to achieve the maximum improvement in energy efficiency that the Secretary of Energy determines are technologically feasible and economically justified. (42 U.S.C 6295(o)(2)(A)) Therefore, an important role of the engineering analysis is to identify the maximum technologically feasible level. The maximum technologically feasible level is one

that is reached through the addition of efficiency improvements and/or design options, both commercially available and in prototypes, to the baseline units. DOE believes that the design options comprising the maximum technologically feasible level must have been physically

demons trated in at least a prototype form to be considered technologically feasible.

DOE typically structures its engineering analysis around one of three methodologies: (1) the design -option approach, which calculates the incremental costs of adding specific de sign options to a baseline model; (2) the efficiency- level approach, which calculates the relative costs of achieving increases in energy efficiency levels without regard to design options used to achieve such increases; and/or (3) the reverse

- engineering or cost -assessment approach, which

involves a “bottom- up” manufacturing cost assessment based on a detailed bill of materials derived from tear-downs of the equipment being analyzed.

In the Framework document, preliminary analysis, and notice of proposed rulemaking, DOE stated its intention to use a design -option approach for the engineering analysis, as it was the methodology employed in the 2009 final rule analyses and was found to be the approach most appropriate for the equipment and technologies on the market because this equipment is designed and marketed based on the inclusion of specific features and components, rather than being sold as possessing a certain, standardized efficiency rating. In a design-option approach, analysis is performed in terms of incremental increases in efficiency due to the implementation of selected design options. For each equipment class, the engineering analysis estimates manufacturer production costs for each successive design option. Stakeholder comments did not

refute t his choice of a design-option approach, and thus this approach was retained in the final

rule engineering analysis. DOE also augmented this approach with reverse

- engineering analysis to develop base manufacturing costs for portions of the equipment analyze d.

2-6

### 2.5.1 Baseline Models

In order to analyze design options for energy efficiency improvements, DOE defined a baseline model unit for each equipment class. DOE defined baseline models as units with the

most basic features that may currently be available on the market . It should be noted that this engineering baseline may, for some equipment classes, be comprised of less- efficient equipment than mandated by past standards, specifically the EPACT 2005 and 2009 DOE final rule

standards. This is due to the fact that these standards did not mandate the use of specific features,meaning that DOE could not assume that a given set of features were being used in currentlycompliant equipment. In these instances, DOE started at a baseline level of performance below that req uired by the current standard and then considered higher- efficiency technologies in order of ascending payback period. In the process of developing equipment baselines, DOE considered technical descriptions of the covered equipment, definitions of the equi pment classes as described in the previous rulemaking documents, results of the market assessment, and suggestions from stakeholders.

2.5.2 Manufacturing Cost Analysis

There are several ways to develop the relationship between cost and performance. DOE

chose to use a design option approach in this rulemaking. This approach identifies potential technological paths manufacturers could use to achieve increased equipment ene rgy efficiency. To develop a base cost for the core case of the commercial refrigeration units, DOE purchased

units available on the market for specific equipment classes and dismantled them component- by

component to develop a bill of materials and cost mo del for the core of the refrigerated case

. DOE then parameterized and expanded this information to apply to all equipment classes being modeled. Then, in the engineering cost model, DOE added these core costs to the costs of the

energy

- consuming components , developed using independent costing methods in conjunction

with manufacturer data. The result was a cost for an entire production unit at each of the efficiency levels analyzed.

2.6 ENERGY USE ANALYSIS

Based on the energy use analysis conducted as part of the 2009 final rule analysis , DOE concluded that the energy consumption model, which is part of the engineering analysis, was sufficiently accurate to calculate the energy use of commercial refrigeration equipment. Therefore, DOE did not conduct a separate energy use analysis as part of this rulemaking.

2.7 MARKUPS FOR EQUI P MENT PRICE DETERMINA TION

DOE used the markup analysis to determine distribution channel markups that were used

to convert the manufacturer selling price ( MSP ) of the equipment into customer purchase price.

DOE identified three different major channels through which customers purchase commercial refrigeration equipment. DOE then determined the market shares of each distribution channel. The markup values associated with each distribution channel w ere calculated from the industry profit data. Sales tax is an additional markup in addition to the markups associated with distribution channels. DOE calculated a weighted- average sales tax for the entire nation. Finally,

2-7

### the overall markups were calculated by weighted

- averaging the dist ribution channel markups and adding the weighted- average sales tax. DOE calculated baseline markups that were applied to baseline MSPs and incremental markups that were applied to MSP increments at higher efficiency levels. See TSD chapter 6 for details on the markups analysis.

2.8 LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

DOE carried out the LCC and PBP analysis to evaluate the economic impacts of possible amended energy conservation standards developed for commercial refrigeration equipment on

individual commercial customers. The effect of standards on customers includes a change in operating cost (usually decreased) and a change in purchase cost (usually increased). Two

metrics were used to determine the effect of standards on customers:

• Life

- cycle cost

. LCC is t he total customer cost over the life of the equipment– the sum of installed cost (purchase and installation costs) and operating costs (maintenance, repair, and energy costs). Future operating costs are discounted to the time of purchase and summed over the lifetime of equipment.

• Payback period . P BP is the estimated amount of time it would take customers to

recover the assume d higher purchase price of more-efficient equipment through lower operating costs.

An efficiency improvement to commercial re frigeration equipment that is financially attractive to a customer will typically have a low incremental PBP and a low incremental change

in LCC associated with it.

As part of the engineering analysis (TSD chapter 5), design option levels we re ordered on the basis of increasing efficiency (decreased energy consumption) and increasing MPC values. The order was determined based on the cost-effectiveness of each design option; that is, the ratio of incremental cost increase to incremental energy savings. For the LCC and PBP analysis, DOE

chose a maximum of seven levels, henceforth referred to as efficiency levels (EL) , from the list of engineering design option levels.

The first efficiency level or baseline efficiency level ( EL 0 ) in each equipment class

re presents the least efficient and the least expensive equipment in that equipment class which complies with current standards . The higher efficiency levels ( EL 1 and up) have a progressive increase in efficiency and cost from EL 0 . The highest efficiency level in each equipment class corresponds to the max imum technologically feasible (max

- tech) level (see TSD chapter 5 for

details ))

The installed cost of equipment to a customer is the sum of the equipment purchase price and installation costs. The purchase price includes manufacturer production cost, to which a manufacturer markup and outbound freight costs are applied to obtain the MSP. This value is calculated as part of the engineering analysis ( TSD chapter 5). DOE then applies additional markups to the equipment to account for the markups associated with the distribution channels

2-8

### for this type of equipment ( TSD chapter 6). Installation costs vary by state depending on the prevailing labor rates.

Operating costs for commercial refrigeration equipment are a sum of maintenance costs, repair costs, and energy costs. These costs are incurred over the life of the equipment and

therefore are discounted to the base year (2017, which is the compliance date of the amended standards that will be established as part of this rulemaking). The sum of the installed cost and the operating cost, discounted to reflect the present value, is termed the life- cycle cost or LCC.

Generally, customers incur higher installed costs when they purchase higher efficiency equipment, and these cost increments will be offset partially or wholly by savings in the operating costs over the lifetime of the equipment. Usually, the savings in operating costs are due to savings in energy costs because higher efficiency equipment uses less energy o ver the lifetime of the equipment. LCC savings are calculated for each EL of each equipment class.

The PBP of a EL is obtained by dividing the increase in the installed cost (from the baseline efficiency level) by the decrease in annual operating cost (fro m the baseline efficiency level). For this calculation, DOE uses the first year operating cost changes as the estimate of the decrease in operating cost, noting that some of the repair and replacement costs used herein are

annualized estimates of costs. PB P is calculated for each EL of each equipment class.

Apart from MSP, installation costs, and maintenance and repair costs, other important inputs for the LCC analysis are markups and sales tax, equipment energy consumption, electricity prices and future pr ice trends, equipment lifetime, and discount rates.

Many inputs for the LCC analysis are estimated from the best available data in the market, and in some cases the inputs are generally accepted representative values within the commercial refrigeration equ ipment industry. However, in most cases each input has a range of values. For example, even though the average (and representative) lifetime of commercial refrigeration units in certain equipment classes may be 10 years, in general, equipment lifetimes

of a typical refrigerator belonging to that equipment class may vary from 5 years to 15 years. While calculations based on the representative values yield average or representative values for the outputs (such as LCC or PBP), such values do not give an estima te of the ranges of values that these outputs could lie in. Therefore, DOE performed the LCC analys is in the form of Monte Carlo simulations in which certain inputs are provided a range of values and probability distributions. The results of the LCC analys is are presented in the form of mean and median LCC savings; percentages of customers experiencing net savings, net cost, and no impact in LCC; and median PBP. For each equipment class, 10,000 Monte Carlo simulations were carried out. The

simulations were conducted using Microsoft Excel and Crystal Ball, a commercially available

Excel add

- in for carrying out Monte Carlo simulations

.

Usually, the equipment available in the market will have a distribution of efficiencies

, that is, each EL within an equipment class will have a corresponding market share associated with it. Usually, within an equipment class, the market share of the baseline efficiency level is the highest and the market share values decrease with an increase in EL . LCC savings and PBP

are calcu lated by comparing the installed costs and LCC values of the standards

- case scenario s against those of the base

- case scenario. The base

- case scenario is the scenario in which

2-9

### equipment is assumed to be purchased by customers in the absence of amended energy

conservation standards. Standards- case scenarios are scenarios in which equipment is assumed to be purchased by customers after the amended energy conservation standards go into effect. The

number of standards- case scenarios for an equipment class i s equal to one less than the total number of efficiency levels in that equipment class because each EL above the baseline efficiency level represents a potential new standard. For the standards- case scenario at a particular EL , the market share of the effi ciency levels were obtained using a roll-up scenario, in which market shares of the efficiency levels (in the base

- case scenario) below the corresponding

EL were rolled -up into the EL . For the base

- case scenario in the LCC analysis, DOE estimated the market shares of the efficiency levels using a method described in TSD chapter 8.

Recognizing that each commercial building that uses the commercial refrigeration equipment is unique, DOE analyzed the LCC and PBP calculations for seven types of businesses: (1) supermarkets; (2) wholesaler/retailer multi -line stores, such as “big-box stores,” “warehouses,” and “supercenters” ; (3) convenience and small specialty stores, such as meat markets, wine, beer, and liquor stores; (4) convenience stores associated with gas oline stations; (5) full service restaurants; (6) limited service restaurants; and (7) other foodservice businesses, such as caterers and cafeterias. Different types of businesses face different energy prices and also exhibit differing discount rates that they apply to purchase decisions.

Equipment lifetime is another input that does not justify usage of a single value for each equipment class. Therefore, DOE assumes a distribution of equipment lifetimes that are defined by Weibull survival functions.

Anoth er important factor influencing the LCC analysis is the state (location) in which the commercial refrigeration equipment is installed. Inputs that vary based on this factor include energy prices , installation costs, contractor markups, and sales tax. At the national level, the spreadsheets explicitly modeled variability in the model inputs for electricity price and markups using probability distributions based on the relative shipments of units to different states and business types.

2.9 SHIPMENTS ANALYSIS

Co mmercial refrigeration equipment shipment numbers are key inputs to the NES analysis, NPV calculations, and the MIA . Shipments analysis is used to estimate future commercial refrigeration equipment shipments over the national impact analysis period.

DOE ca lculated the historic al shipments of commercial refrigeration based on various shipments data sources. DOE then used the Annual Energy Outlook 4 

### ( AEO ) forecasts of commercial floor space additions, equipment lifetimes

, and estimates of existing equipment stock to calculate the future shipments.

TSD c hapter 9 presents the mathematical formulation of the shipment analysis model and the methodology used to estimate historic al and future shipments of commercial refrigerati on equipment.

2-10

### 2.10 NATIONAL IMPACT ANALYSIS

N ES and NPV impacts are the cumulative energy and economic effects of an amended commercial refrigeration equipment energy conservation standard ( TSD c hapter 10). DOE projected the impacts from the year the standard w ould take effect through a selected number of years in the future. DOE analyzed energy savings, energy cost savings, equipment costs, and

NPV of savings (or costs) for each candidate standard level. The national energy and cost savings (or increases) that would result from amended energy conservation standards depend on the projected energy savings per unit and the anticipated amount of equipment sold. DOE created

base -case shipment projections that include units at various efficiency levels. It based the projections on historical information plus forecasts of market influences, national economic growth, and electricity consumption. DOE then derived energy savings for various candidate

standard levels from the cost- efficiency schedules.

To make the analysis more accessible and transparent to all stakeholders, DOE used an Excel spreadsheet model to calculate the NES and the NPV ( i.e.

, national economic costs and savings from amended standards). Users can change input quantities within the spreadsheet to test the impact of alternative input assumptions. Unlike the LCC analysis, the NES spreadsheet does not use distributions for inputs or outputs. Users can demonstrate sensitivities by running different scenarios using the spreadsheet.

As discussed in TSD chapte r 10, the national impact analysis assesses the NPV of total

consumer LCC and NES . DOE conducted an asse ssment of the aggregate impacts at the national

level for the final rule . Analyzing impacts of Federal energy conservation standards requires a comparis on of projected U.S. energy consumption with and without amended standards. The base case, which is the projected U.S. energy consumption without standard, includes the mix of efficiencies being sold at the time the standard becomes effective.

DOE estimate d national energy consumption for each year beginning with the expected

effective date of the standard. DOE calculated national annual energy savings as the difference between two projections: a base case and a standards case. Analysis included estimated e nergy savings by fuel type used for generating electricity. DOE estimated energy consumption and savings based on site energy ( kilowatt -hours of electricity), and then converted the electricity consumption and savings to primary energy consumption.

2.11 LIFE

- CYCLE COST SUB

- GROUP ANALYSIS

The LCC subgroup analysis evaluates impacts on identifiable groups of customers of commercial refrigeration equipment who may be disproportionately disadvantaged by amended energy conservation standards. The LCC and PBP analysis described in chapter 8 of this TSD is applied to seven major types of businesses belonging to the food- retail and foodservice sectors that use a majority of the commercial refrigeration equipment in the market . Although the inputs for different typ es of businesses are different in the LCC and PBP analysis, the final results may not reflect the results experienced by certain customer subgroups. In other words, some of the adverse impacts on businesses that are disproportionately disadvantaged may be masked by the averaging effect of the LCC and PBP analysis. Therefore, DOE carried out the LCC subgroup

2-11

### analysis by using the LCC and PBP analysis spreadsheet, but applying the inputs that are applicable only to the identified subgroups.

In general, the s ubgroups that face higher cost of capital and lower electricity price rates are more disadvantaged than others. Higher cost of capital imposes burden on the businesses because they have to borrow additional capital to purchase equipment that meets amended standards, compared to the case of where there are no amended standards. Lower electricity price rates result in lower savings in energy costs and, consequently, lower LCC savings and higher

PBPs.

DOE carried out two LCC subgroup analyses, one each for fu ll

- service restaurants and gasoline stations with convenience stores, by using the LCC spreadsheet described in TSD chapter 8, but with certain modifications. The input for business type was fixed to the identified subgroup, which ensured that the discount rates and electricity price rates associated with only that subgroup were selected in the Monte Carlo simulations (see TSD chapter 8). Additionally, a small business premium was added to the discount rate to reflect the higher discount rates faced by smal l businesses. Another major change from the LCC analysis was an added assumption that the subgroups do not have access to national accounts, which results in higher distribution channel markups for the subgroups, leading to higher equipment purchase prices . Apart from these changes, all other inputs for LCC subgroup analysis are same as those in the LCC analysis described in TSD chapter 8.

2.12 MANUFACTURER IMPACT ANALYSIS

The MIA assesses the impacts of amended energy conservation standards on

manufacturers of the considered equipment. Potential impacts include financial effects, both quantitative and qualitative, that might lead to changes in the manufacturing practices for this equipment. DOE identified these potential impacts through interviews with manufacturers and other interested parties.

DOE conducted the MIA in three phases, and further tailored the analytical framework based on interested parties’ comments. In Phase I, an industry profile was created to characterize the industry, and a preliminary MIA was conducted to identify important issues that required consideration. In Phase II, an industry cash flow model and an interview questionnaire were prepared to guide subsequent discussions. In Phase III, manufacturers were interviewed, and the impacts of standards were assessed both quantitatively and qualitatively. Industry and subgroup cash flow and NPV were assessed through use of the Government Regulatory Impact Model (GRIM). Then impacts on competition, manufacturing capacity, employment, and cumulat ive regulatory burden were assessed based on manufacturer interview feedback and discussions. DOE discusses its findings from the MIA in chapter 12 of the TSD.

2.13 EMISSIONS ANALYSIS

The primary environmental effects of energy conservation standards for the co mmercial refrigeration equipment covered under this rulemaking will be decreased emissions resulting from reduced electrical energy consumption. The environmental assessment tracks the impact of

2-12

### possible standards on carbon dioxide (CO 2 

### ), sulfur dioxide (SO 2 

### ), nitrogen oxides (NO

x 

### ), and mercury (Hg)

. The impac ts on air emissions are largely driven by changes in power plant types and quantities of electricity generated under each of the considered TSLs. DOE bases these calculations on the NEMS-BT modeling work proposed for the utility impact analysis. This approach has the advantage of examining the marginal impact of potential higher efficiency standards for the covered equipment on the utility generation mix and the subsequent

environmental emissions.

2.14 MON ETIZATION OF EMISSIONS REDUCTIONS BENEFITS

As part of its assessment of energy conservation standards for commercial refrigeration equipment, DOE estimated the monetary benefits likely to result from the reduced emissions of

CO 2 

### and NO

x 

### . To make this calcu lation similar to the calculation of the NPV of customer benefit, DOE considered the reduced emissions expected to result over the lifetime of equipment shipped in the forecast period.

2.15 UTILITY IMPACT ANALY SIS

DOE analyzed specific effects of amended standa rd levels on the electric utility industry as part of the final rule analyses, using a variant of the DOE Energy Information Administration’s (EIA) National Energy Modeling System (NEMS). a 

### NEMS is a large, public domain, multi- sectoral, partial equilibrium model of the U.S. energy sector. Each year, EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy Outlook

. The version of NEMS used for appliance standards analysis is called NEMS -Building Technologies (NEMS

- BT) b 

### and is based on the AEO2012 Reference Case with minor modifications.

The utility impact analysis reports the changes in installed capacity and generation, by fuel type, that result from the adoption of amended efficiency standards at each trial standard level (TSL), as well as changes i n electricity consumption.

2.16 EMPLOYMENT IMPACT ANALYSIS

The DOE employment impact analysis is designed to estimate indirect national job

creation or elimination resulting from amended standards due to reall ocation of associated

exp enditures for purchasing and operating commercial refrigeration equipment

. Job increases or decreases reported in this chapter are separate from the direct commercial refrigeration equipment sector employment impacts reported in TSD chapter 12, and reflect the employment impact of efficiency standards on all other sectors of the economy.

a For more information on NEMS, refer to DOE EIA documentation. A useful summary is National Energy Modeling System: An Overview 2003 , DOE/EIA

- 0581(2003), March 2003.

b EIA app roves 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 EI A 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.



### 2-13

### DOE 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 ne t impact of standards on jobs. Pacific Northwest National Laboratory (PNNL) developed the model using

ImSET (Impact of Sector Energy Technologies). 5 

### Im SET estimates the employment and income effects of building energy technologies. Compared with simple economic multiplier approaches,

Im SET allows for more complete and automated analysis of the economic impacts of energyefficiency investments in buildings.

In an input/output model, the level of employment in an economy is determined by the

r elationship s of different sectors of the economy and spending flows among them . D ifferent 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 eco nomy 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 residential 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, business 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

- efficien cy 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 equipment. The increased cost of equipment leads to higher empl oyment in the equipment manufacturing sectors and lower employment in other economic sectors. Second, commercial firm and residential spending are redirected from utilities toward firms that supply production inputs. Third, electric utility sector investme nt funds are released for use in other sectors of the economy. When customers use less energy, electric utilities experience relative reductions in demand, which leads to reductions in utility sector investment and employment.

2.17 REGULATORY IMPACT ANALYSIS

DO E prepared a n RIA pursuant to Executive Order 12866, Regulatory Planning and

Review , 58 FR 51735 (Oct. 4, 1993), which is subject to review under the Executive Order by the Office of Information and Regulatory Affairs at the Office of Management and Budget

. The RIA addressed the potential for non-regulatory approaches to supplant or augment energy conservation standards to improve the energy efficiency or reduce the energy consumption of the commercial refrigeration equipment covered under this rulemaking.

2.18 DEPARTMENT OF JUSTIC E REVIEW

Section 325(o)(2)(B)(i)(V) of the Energy Policy and Conservation Act states that before the Secretary of Energy may prescribe a new or amended energy conservation standard, the

2-14

### Secretary shall ask the Attorney General to make a determination of “the impact of any lessening of competition...that is likely to result from the imposition of the standard.” (42 U.S.C. 6295) Pursuant to this requirement, DOE solicit ed the views of the D epartment of Justice (DOJ) on any lessening of compe tition that is likely to result from the imposition of a n amended standard and will give the views provided full consideration in assessing the economic justification of an

amended standard. In a letter to DOE on Nov. 25, 2013, DOJ, based on its review of the proposed standards contained the NOPR, 78 FR 176 (Sept., 11, 2013), public meeting transcript held on Oct. 3, 2013, and supplementary information submitted to the Attorney General by DOE, concluded the amended energy conservation standards for CRE are unlikely to have a significant adverse impact on competition.

2-15

### REFERENCES

1. U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy Building Technologies Program. Rulemaking Framework for Commercial Refrigeration Equipment. 2010. Washington, D.C. <https://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/27>

2. U.S. Department of Energy– Office of Energy Efficiency and Renewable Energy. Preliminary Technical Support Document: Energy Conservation Program for Certain Commercial and Industrial Equipment: Commercial Refrigeration Equipment. March 2011. Washington, D.C. <https://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/27 >

3. U.S. Department of Energy– Office of Energy Efficiency and Renewable Energy. Notice of Proposed Rulemaking: Energy Conservation Program for Certain Commercial and Industrial Equipment: Commerci al Refrigeration Equipment. September 2013. Washington, D.C. <https://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/27>

4. U.S. Department of Energy- Energy Information Administration. Annual Energy Outlook

2012 , 2012. Washington, DC. DOE/EIA-0383(2012 ).

5. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy Technologies

, 2005. Pacific Northwest National Laboratory. Richland, WA. Report No. PNNL- 15273.

2-16

### CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

3.1 INTRODUCTION ........................................................................................................... 3-1

3.2 MARKET ASSESSMENT .............................................................................................. 3-4

3.2.1 Trade Association ................................................................................................ 3-4

3.2.2 Manufacturers and Market Share ......................................................................... 3-4

3.2.2.1 Small Businesses ................................................................................. 3-7

3.2.3 Regulatory Programs ........................................................................................... 3-8

3.2.3.1 Natural Resources Canada ................................................................... 3-8

3.2.3.2 Canadian Standards Association ......................................................... 3-9

3.2.3.3 Australia ............................................................................................ 3-10

3.2.4 Non

- Re gulatory Initiatives ................................................................................. 3-10

3.2.4.1 ENERGY STAR ................................................................................ 3-10

3.2.4.2 Consortium for Energy Efficiency .................................................... 3-12

3.2.4.3 Federal Energy Management Program .............................................. 3-12

3.2.4.4 Rebate Programs ................................................................................ 3-12

3.2.5 Equipment Classes ............................................................................................. 3-13

3.2.6 Shipments ........................................................................................................... 3-18

3.2.6.1 ARI Data ............................................................................................ 3-18

3.2.6.2 NAFEM Data .................................................................................... 3-20

3.2.6.3 Freedonia Market Reports ................................................................. 3-20

3.2.6.4 Appliance Magazine Data ................................................................. 3-21

3.2.6.5 Census Bureau Data .......................................................................... 3-22

3.2.7 Equipment Lifetimes .......................................................................................... 3-24

3.2.8 Market Performance Data .................................................................................. 3-25

3.2.8.1 Remote Condensing Equipment ........................................................ 3-25

3.2.8.2 Self -Contained Equipment ................................................................ 3-26

3.3 TECHNOLOGY ASSESSMEN T .................................................................................. 3-30

3.3.1 Technologies and Designs Relevant to All Equipment Classes ........................ 3-30

3.3.1.1 Higher Efficiency Lighting ............................................................... 3-30

3.3.1.2 Higher Efficiency Lighting Ballasts .................................................. 3-30

3.3.1.3 Remote Lighting Ballast Location .................................................... 3-31

3.3.1.4 Higher Efficiency Expansion Valves ................................................ 3-31

3.3.1.5 Higher Efficiency Evaporator Fan Motors ........................................ 3-32

3.3.1.6 Variable -Speed Evaporator Fan Motors and Evaporator Fan Motor

Controllers .......................................................................................................... 3-33

3.3.1.7 Higher Efficiency Evaporator Fan Blades ......................................... 3-33

3.3.1.8 Improved Evaporator Coil Design .................................................... 3-33

3.3.1.9 Low

- Pressure Differential Evaporators ............................................. 3-34

3.3.1.10 Case Insulation Increases or Improvements ...................................... 3-34

3.3.1.11 Defrost Mechanisms .......................................................................... 3-34

3.3.1.12 Defrost Cycle Control ....................................................................... 3-35

3.3.1.13 Vacuum Insulated Panels .................................................................. 3-36

3.3.1.14 Occupancy Sensors for Lighting Controls ........................................ 3-36

3-i

### 3.3.2 Designs Relevant Only to Equipment with Doors ............................................. 3-36

3.3.2.1 Improved Transparent Doors ............................................................. 3-36

3.3.2.2 Anti -Fog Films on Transparent Doors .............................................. 3-37

3.3.2.3 Anti

- Sweat Heater Controllers .......................................................... 3-37

3.3.3 Designs Relevant Only to Equipment Without Doors ....................................... 3-37

3.3.3.1 Air Curtain Design ............................................................................ 3-37

3.3.3.2 Night Curtains ................................................................................... 3-38

3.3.4 Te chnologies and Designs Relevant Only to Self-Contained Equipment ......... 3-38

3.3.4.1 Higher Efficiency Compressors ........................................................ 3-38

3.3.4.2 Liquid Suction Heat Exchangers ....................................................... 3-39

3.3.4.3 Improved Condenser Coil Design ..................................................... 3-40

3.3.4.4 Higher Efficiency Condenser Fan Motors ......................................... 3-40

3.3.4.5 Variable -Speed Condenser Fan Motors and Condenser Fan Motor

Controllers .......................................................................................................... 3-40

3.3.4.6 Higher Efficiency Condenser Fan Blades ......................................... 3-40

LIST OF TABLES

Table 3.2.1 Canadian Standards Association Equipment Classes and Efficiency Ratings ......... 3-9 Table 3.2.2 ENERGY STAR Commercial Refrigerator and Freezer Key Criteria ................... 3-11 Table 3.2.3 Commercial Refrigeration Equipment Families ..................................................... 3-15 Table 3.2.4 Commercial Refrigeration Equipment Classes ....................................................... 3-17 Table 3.2.5 ARI 2005 Shipments of Commercial R efrigerators and Freezers by Equipment Class ................................................................................................ 3-19 Table 3.2.6 NAFEM Shipments Categories and Corresponding DOE Equipment Classes ...... 3-20 Table 3.2.7 Freedonia Shipments Categories and Corresponding DOE Equipment Classes .... 3-21 Table 3.2.8 Historical Shipments of Refrigerated Display Cases .............................................. 3-21 Table 3.2.9 Statistical Forecasts of Refrigerated Display Case Shipments ............................... 3-21 Table 3.2.10 Historical Shipments of Commercial Refrigerators .............................................. 3-22 Table 3.2.11 Statistical Forecasts of Commercial Refrigerator Shipments ............................... 3-22 Table 3.2.12 Shipments of Open Refrigerated Display Cases (Remote Condensing and

Self -Contained) .................................................................................................. 3-23 Table 3.2.13 Shipments of Closed Refrigerated Display Cases (Remote Condensing and

Self -Contained) .................................................................................................. 3-23 Table 3.2.14 Shipments of Commercial Reach -In Cabinets (Remote Condensing and

Self -Contained) .................................................................................................. 3-24 Table 3.2.15 Estimates for Commercial Refrigeration Equipm ent Lifetimes ........................... 3-25

3- ii

### LIST OF FIGURES

Figure 3.2.1 Domestic Refrigerated Display Case Market Shares as of 2005 ............................. 3-6 Figure 3.2.2 Domestic Commercial Refrigerator Market Share as of 2008 ................................ 3-7 Figure 3.2.3 Market Performance Data for the VOP.SC.M Equipment Class

... Error! Bookmark

not defined. Figure 3.2.4 Market Performance Data for the VCT.SC.M Equipment Class .......................... 3-27 Figure 3.2.5 Market Performance Data for the VCT.SC.L Equipment Class ........................... 3-27 Figure 3.2.6 Market Performance Data for the VCS.SC.M Equipment Class ........................... 3-28 Figure 3.2.7 Market Performance Data for the VCS.SC.L Equipment Class ............................ 3-28 Figure 3.2.8 Market Performance Data for the HCT.SC.M Equipment Class

... Error! Bookmark

not defined. Figure 3.2.9 Market Performance Data for the HCT.SC.L Equipment Class ........................... 3-29 Figure 3.2.10 Market Performance Data for the HCS.SC.M Equipment Class ......................... 3-29

3- iii

### C HAPTER 3. MARKET AND TECHNOLOG Y ASSESSMENT

3.1 INTRODUCTION

This chapter details the market and technology assessment that the U.S. Department of Energy (DOE) has conducted in support of its energy conservation standards rulemaking for commercial refrigeration equipment, including self

- contained and remote condensing commercial refrigerators, freezers, refrigerator

- freezers , and ice- cream freezers; and self

contained commercial refrigerators with transparent doors designed for pull-down temperature applications.

The purpose of the market assessment is to develop a qualitative and quantitative

characterization of the commercial refrigeration equipment industry and market structure based on publicly available information and information submitted by manufacturers and other

stakeholders. M anufacturer characteristics and market shares, existing regulatory and non

regulatory efficiency improvement initiatives, equipment classes, and trends in market and equipment characteristics are addressed

. The purpose of the technology assessment is to develop a preliminary list of technologies that could improve the efficiency of commercial refrigeration equipment.

Definitions

Sectio n 136(a)(3) of the Energy Po licy Act of 2005 ( EPACT 2005) amended section 340

of the Energy Policy and Conservation Act ( EPCA ), in part by adding subsection 340(9) (42 U.S.C 6311(9)) with definitions for the following terms that describe commercial refrigeration equipment:

(9)(A) The term “ commercial refrigerator, freezer, and refrigerator

- freezer ” means refrigeration equipment that

— (i) is not a consumer product (as defined in section 321); (ii) is not designed and marketed exclusively for medical, scientific, or research

purposes; (iii) operates at a chilled, frozen, combination chilled and frozen, or variable

temperature; (iv) displays or stores merchandise and other perishable materials horizontally, semivertically, or vertically; (v) has transparent or solid doors, sliding or hinged doors, a combination of hinged, sliding, transparent, or solid doors, or no doors; (vi) is designed for pull-down temperature applications or holding temperature applications; and (vii) is connected to a self- contained condensing unit or to a remote condensing

unit. (B) The term “ holding temperature application ” means a use of commercial refrigeration equipment other than a pull-down temperature application, except a

blast chiller or freezer.

* * *

3-1

### (D) The term “pull-down tem perature application ” means a commercial refrigerator with doors that, when fully loaded with 12 ounce beverage cans at 90 degrees F, can cool those beverages to an average stable temperature of 38 degrees F in 12 hours or less. (E) The term “ remote conde nsing unit” means a factory

- made assembly of refrigerating components designed to compress and liquefy a specific refrigerant that is remotely located from the refrigerated equipment and consists of one or more refrigerant compressors, refrigerant condensers, condenser fans and motors, and factory supplied accessories. (F) The term “ self -contained condensing unit” means a factory

- made assembly of refrigerating components designed to compress and liquefy a specific refrigerant that is an integral part of th e refrigerated equipment and consists of one or more refrigerant compressors, refrigerant condensers, condenser fans and motors, and factory supplied accessories.

(42 U.S.C. 6311(9))

EPACT 2005 does not explicitly define the terms “self

- contained commerc ial refrigerator, freezer, or refrigerator

- freezer” or “remote condensing commercial refrigerator, freezer, or refrigerator

- freezer .” DOE interpreted these two terms to mean “commercial refrigerator, freezer, or refrigerator

- freezer that is connected to a self -contained condensing unit” and “commercial refrigerator, freezer, or refrigerator

- freezer that is connected to a remote condensing unit,” respectively.

Accordingly, the four categories of equipment covered under this rulemaking are as follows.

1. Self -c ontained r efrigerators, f reezers, and r efrigerator -f reezers : EPCA defines a

“self -contained condensing unit,” in part, as “an integral part of the refrigerated equipment.” (42 U.S.C. 6311(9)(F)) Under the definitions quoted above , a self

contained commerci al refrigerator, freezer, or refrigerator

- freezer is a category of commercial refrigeration equipment in which the refrigerated cabinet and the condensing unit are integrated into one unit . Self

- contained commercial refrigeration equipment is primarily use d for storing, displaying, and/or merchandising food products in small to medium

- size d grocery and other food retail stores, restaurants and hotels, and in cafeteria-style foodservice venues. EPACT 2005 prescribed energy

conservation standards f or self

- contained commercial refrigerators, freezers, and refrigerator -freezers with doors. For self- contained commercial refrigerators, freezers, and refrigerator -freezers without doors, DOE established energy conservation

standards in the 2009 commercial refrigerat ion equipment final rule. 74 FR 1092 (Jan. 9, 2009).

2. Remote c ondensing c ommercial r efrigerators, f reezers, and r efrigerator

f

reezers : Under the definitions presented above, a remote condensing refrigerator, freezer, or refrigerator

- freezer is a type of co mmercial refrigeration equipment that is connected to a remote condensing unit. Remote condensing commercial refrigeration

3-2

### equipment is generally used to display and merchandise food products in large retail

installations like supermarkets and grocery stor es . EPCA defines a “remote condensing unit,” in part, as being “remotely- located from the refrigerated equipment.” (42 U.S.C. 6311(9)(F)) In the 2009 final rule DOE adopted energy

conservation standards for remote condensing commercial refrigerators, freezers, and refrigerator -freezers that apply to the refrigerated equipment, but not the remote condensing units.

3. Self

- contained commercial refrigerators designed for pull

- down temperature applications : EPCA defines “pull-down temperature application” to mean “a commercial refrigerator with doors that, when fully loaded with 12 ounce beverage cans at 90 degrees F, can cool those beverages to an average stable temperature of 38 degrees F in 12 hours or less.” (42 U.S.C. 6311(9)(D)) Units fitting this description are typically known as beverage merchandisers or beverage coolers because of their use in displaying individually packaged beverages for sale. Even though this equipment has a similar configuration to self -contained refrigerators with transparent

doo rs , EPCA prescribed separate standards for pull-down refrigerators with transparent doors. (42 U.S.C. 6313(c)(3)) Correspondingly, DOE intends to keep this equipment as a separate class in this rulemaking. Additionally, DOE notes that EPCA does not currently contain a standard for self- contained commercial refrigerators for pull-down temperature applications with solid doors or for equipment operating at other temperatures

.

4. Commercial i ce -c ream f reezers : On December 8, 2006, DOE published a final rule in which it adopted test procedures for commercial refrigeration equipment. In this final rule, DOE adopted the following definition for “ ice

- cream freezer ” : “a commercial freezer that is designed to operate at or below -5 ° F ( -21 °C) and that the

manufacture r designs, markets, or intends for the storing, displaying, or dispensing of ice cream.” 10 CFR 431.62, 71 FR at 71340, 71369. In addition, this final rule prescribed a rating temperature of -15°F for ice-cream freezers. 10 CFR 431.64, 71

FR at 71370 (Dec. 8, 2006). Under this definition, unless equipment is designed, marketed, or intended specifically for the storage, display, or dispensing of ice cream,

it would not be considered an “ice- cream freezer.” Multi

- purpose commercial freezers, manufactured for storage and display, for example, of frozen foods as well as ice cream would not meet this definition, and DOE has not treated them as

commercial ice

- cream freezers in this rulemaking. This is in accordance with the comments DOE received during the 2009 rulemaking that indicated that DOE should not classify such freezers as ice

- cream freezers. 74 FR at 1103 (Jan. 9, 2009). On t he other hand, any commercial freezer that is specifically manufactured for storing, displaying , or dispensing ice cream, and that is designed so that in normal operation it can operate at or below -5 °F (-21 °C), would meet the definition. This includes

e quipment that some interested parties referred to as true ice

- cream cabinets

— freezers that are designed to operate considerably below -5 °F ( sometimes referred to

as “ hardening ” cabinets ) and are specifically designed for ice cream storage, for example

— as well as those ice

- cream dipping cabinets that are designed to operate

below -5 °F.

3-3

### 3.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade association, domestic manufacturers of commercial refrigeration equipment, manufacturer market share, regulatory programs, and non- regulatory initiatives; defines equipment classes; provides historical shipment data, shipment projections, and equipment lifetime estimates; and summarizes market performance data.

3.2.1 Trade Association

The Air -Conditioning , Heating, and Refrigeration Institute (AH RI , formerly the Air

Conditioning and Refrigeration I nstitute, or ARI, before merging with the Gas Appliance Manufacturers Association, or GAMA, to form AHRI ) is the most prominent trade association

for commercial refrigeration equipment manufacturers. On January 12, 2005, ARI developed an agreement with member manufacturers to establish the C ommercial R efrigerator M anufacturers

D ivision (CRMD) within ARI and to develop and implement a certification program for commercial refrigerators, commercial freezers, and commercial refrigerator

- freezers.

The CRMD wa s originally a separate trade organization founded in 1933. I t serves supermarkets, food stores, convenience stores, restaurants, hotels, motels, food processing establishments, and hospitals. The technical activities of CRMD include :

• harmonization of international equipment standards;

• d evelopment of industry performance standards for commercial refrigeration equipment;

• updating of industry guidelines for retail store fixture installation, design, energy conservation, electronic case controls, and specific ations for equipment installation;

• c ommunicating with refrigerant suppliers and government agencies about environmentally acceptable refrigerants ; and

• providing input to government agencies concerning regulations affecting the industry.

3.2.2 Manufacturers and Market Share

C urrent AHRI CRMD members are listed below ; parent companies are shown in parentheses if applicable

. 1

3-4

### • Continental Refrigerator • Hussmann (Ingersoll Rand) • Southern Store

Fixtures

• CSC Worldwide (formerly Columbus Showcase) • Killion Industries • Structural Concepts Corp.

• Hill Phoenix (Dover Corp.) • Kysor/Warren (Enodis) • Zero Zone

• Hoshizaki America,

Inc. • Master

- Bilt Products (Standex)

Other commercial refrigeration equipment manufacturers are listed below ; parent companies are shown in parenthes es if applicable.

• Amtekco • Howard/McCray • Tor Rey Refrigeration

• Arctic Air • Kelvinator (Electrolux) • Traulsen

• Arctic Star • McCall Refrigeration (Manitowoc Food Service)* • True Manufacturing

• Arneg USA • Northland Refrigeration • Turbo Air

• Beverage

- Air* • Regal

- Pinnacle • Victory Refrigeration

• Custom Deli • Royal Store Fixtures (Parisi) • Vogel

• Custom Fabricators • Spartan Showcase

• Delfield (Manitowoc Food Service)* • Silver King

*Current AHRI member



### According to Appliance Magazine , which most recently published market share data for refrigerated display cases in 2005, four companies represent ed approximately 85 percent of the U.S. refrigerated display case market

, with about 185,000 units shipped in 2004. 2 

### However, Appliance Magazine provides no precise definition of a refrigerated display case and it is therefore unclear what specific types of equipment the data covers

— whether it is equipment that

is self -contained or remote condensing

, or equipment with doors or without doors.

As of 2004, Hussmann Corporation, a division of Ingersoll Rand, was the largest

domestic manufacturer of refrigerated display cases according to the Appliance Magazine data

, holding approximately 48 percent of the U.S. market ( Figure 3.2.1 ).

3-5Source: Appliance Magazine

, “ 28 th Annual Portrait of the U.S. Appliance Industry, ” September 2005.



### Figure 3.2.1 Domestic Refrigerated Display Case Market Shares as of 2005

Manufacturers in the “Other” category (in Figure 3.2.1 ) are listed below; parent companies are listed in parenthesis if applicable.

• Arneg USA • Master

- Bilt Products (Standex) • Structural Concepts Corp.

• CSC Worldwide • Regal

- Pinnacle • True Manufacturing

• Federal Industries (Standex) • Royal Store Fixture (Parisi) • Turbo Air

• Howard/McCray • Southern Store Fixtures • Zero Zone

• Killion Industries • Spartan Showcase

The landscape of the commercial refrigeration equipment market has changed since this

data was collected in 2005 . For example, Tyler Refrigeration no longer exists independently, and consolidation has occurred with large- market share companies having made acquisitions in the

time since this data was collected. DOE has additional information regarding the commercial refrigeration equipment market share by company, but since that data was obtaine d from purchased reports that are not publicly available, DOE is not presenting that information in this technical support document ( TSD ).

Appliance Magazine also publishes data regarding market share for “commercial refrigerators

. ” As is the case with refrigerated display cases, Appliance Magazine does not provide a precise definition of what a commercial refrigerator is . It is therefore unclear what specific types of equipment that data covers— whether it is equipment that is self -contained or

remote cond ensing, or equipment with doors or without doors.

According to Appliance Magazine , which most recently published new data on the market share of commercial refrigerators in September 2009, seven companies comprised 78 Tyler Refrigeration

16% Hill Phoenix

15% Kysor/Warren

6% Other

15%

Hussmann

48%

3-6

### percent of the U.S. commercial refrig erator market as of 2008 . Of these, True Manufacturing represented the largest market share, with 41 percent . The additional six named companies comprised 37 percent of the market, and other manufacturers comprised the remaining 22 percent of the market ( Figure 3.2.2 ).

Source: Appliance Magazine , “ 32 nd Annual Portrait of the U.S. Appliance Industry,” September 2009

.



### Figure 3.2.2 Domestic Commercial Refrigerator Market Share as of 2008

Manufacturers in the “ O ther” category (in Figure 3.2.2 ) are listed below; parent companies are listed in parentheses if applicable.

• Arctic Air • Hoshizaki America, Inc. • Silver King

• Arctic Star • Hussmann (Ingersoll Rand) • Structural Concepts Corp.

• CSC Worldwide • Kelvinator • Tor Rey Refrigeration

• Custom Deli • Kysor/Warren (Enodis) • Turbo Air

• Custom Fabricators • Master

- Bilt Products (Standex) • Victory Refrigeration

• Hill Phoenix (Dover Corp.) • McCall Refrigeration • Zero Zone

3.2.2.1 Small Businesses

DOE is considering the possibility that energy conservation standards for commercial refrigeration equipment would adversely affect small businesses

. The Small Business Administration (SBA) defines small business manufacturing enterprises for commercial refrigera tion equipment as those having 750 employees or fewer. 3 

### SBA lists small business size standards for industries as they are described in the North American Industry Classification System (NAICS). The size standard for an industry is the largest size that a for

- profit company can have in that industry and still qualify as a small business for Federal Government programs. These size standards are generally expressed in terms of the average annual receipts or the True
41%

Traulsen
9% Victory

10% Beverage

Air

6% Delfield

7% Continental
4% Northland
1% Other
22%

3-7

### average employment of a firm. For commerc ial refrigeration equipment, the size standard is matched to NAICS code 333415, Air -Conditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing

.

DOE studied the potential impacts on these small businesses in detail during the manufacturer impact analysis, which was conducted as a part of the notice of proposed rulemaking (NOPR) analysis. DOE identified a number of commercial refrigeration equipment

manufacturers that qualify as small businesses

, which are listed below ; parent companies are listed in parentheses if applicable.

• Admiral Craft • Killion Industries

• Amtekco • MaxxCold

• Arctic Air • Northland Refrigeration

• Arctic Star • Regal

- Pinnacle

• Ascend Mfg. • Royal Store Fixture (Parisi)

• Beverage Air • Saturn Equipment

• Blue Air Commercial Refrigeration • Silver King

• ColdTech USA • Southern Store Fixtures

• Continental Refrigerator • Spartan Showcase

• CSC Worldwide • Structural Concepts Corp.

• Everest Refrigeration • Summit Commercial

• Fagor Refrigeration • Tor Rey Refrigeration

• Fogel USA • Utility Refrigerator

• Global Refrigeration • Victory Refrigeration

• Howard/McCray • Zero Zone

3.2.3 Regulatory Programs

Outside of the United States, Canada and Australia have efficiency standards for

commercial refrigeration equipment. Add itionally, t he following states have established appliance efficiency regulations in the past: Arizona, California, Connecticut, Maryland, Massachusetts, Minnesota, New Jersey, New York, Oregon, Rhode Island, Vermont, and Washington. Of these, California is the only state that explicitly regulated all types of

commercial refrigerators, freezers, and refrigerator

- freezers with past state standards . Arizona, Oregon, Rhode Island, Connecticut, Maryland, New Jersey, New York, and Washington regulate d certain types of commercial refrigeration equipment. Many of these standards have since been pre

- empted by the F ederal standards for self -contained equipment with doors set forth in EPACT 2005 and effective January 1, 2010. The documents that set forth individual state appliance efficiency standards can be accessed via the Database of State Incentives for Renewables & Efficiency (DSIRE), a DOE -funded online database. 4



### 3.2.3.1 Natural Resources Canada

The Natural Resources Canada (NRCan) Office of Energy Efficiency has energy efficiency standards for commercial refrigerators, freezers, and refrigerator

- freezers

. A May 2010 NRCan bulletin proposed standards for self-contained equipment without doors, equipment

3-8

### with remote condensing units, and commercial ice cream freezers

. These changes also serve to align the standards with those in the United States

. 5 

### The test method for determining compliance

is AHRI Standard 1200-2008, Performance Rating of Commercial Refrigerated Display Merchandisers and Storage Cabinets, with the following specifications for the integrated average temperature ( IAT ):

• R efrigerator c ompartment: 38 ° F ± 2 ° F (3.3oC ± 1.1oC)

• Freezer c ompartment: 0 ° F ± 2 ° F ( -17.8oC ± 1.1oC)

• Wine c hiller: 45 ° F ± 2 ° F (7.2oC ± 1.1oC)

• Ice -c ream c abinet: -5 ° F ± 2 ° F ( -20.6oC ± 1.1oC)

3.2.3.2 Canadian Standards Association

The Canadian Standards Association ( CSA ) is an independent standards- setting agency that establishes test procedures and efficiency standards that the Canadian government typically adopts. The Canadian standard CAN/CSA C657-04, Energy Performance Standard for Refrigerated Display Cabinets (Merchandisers) , applies to remote condensing commercial equipment with and without doors, and self -contained commercial equipment with and without doors, except as covered by CSA C827-10. Commercial refrigerators and commercial freezers with doors (including commercial ice

- cream freezers) are covered in a separate test procedure and standard, CSA C827-10, Energy Performance Standard for Food Service Refrigerators and

Freezers . This standard is a revision of the original CSA standard published in 1995. Among the changes are redefined equipment categories and new minimum energy performance standards (MEPS)

. a



### The CSA C657-04 standard divides commercial refrigeration equipment into seven

classes and several sub

- classes

. Table 3.2.1 summarizes classes and MEPS levels from CSA C657-04. For equipment with a remote condensing unit, t he MEPS use a pre

- determined remote condensing unit efficiency to calculate daily energy use

.

Table 3.2.1 Canadian Standards Association Equipment Classes and Efficiency Ratings

Cl ass IAT*

°F Temperature Open/Closed Deck Number of

Air

Curtains Angle of Air

Curtain

from

Vertical MEPS

2004 kWh/ft per day

1 41.0 Medium Open Single/Multi 1 0

- 30° 4.0

2 41.0 Medium Open Single/Multi 1 30

- 60° 2.9

3 41.0 Medium Open Single/Multi 1 60

- 90° 1.6

4 0.0 Low Open Multi 2 o r 3 0

- 30° 9.4

5 0.0 Low Open Single 1 60

- 90° 4.6

6a 41.0 Low/Medium Closed Multi Single Vent with Glass 2.3

6b 0.0 Same as 6a 6.1

7a 41.0 Medium Closed Single/Multi Glass N/A 2.6

7b 41.0 Same as 7a, except with only a gravity coil (no fan coil) 1.0

Source: CSA C657

- 04, E nergy Performance Standard for Refrigerated Display Cabinets (Merchandisers)

. * IAT = the average temperature of all test packages recorded during testing.

a Changes to the standard make it extremely difficult to compare levels between the 1995 and updated standards.

3-9

### 3.2.3.3 Australia

Australia has required efficiency standards for commercial refrigeration equipment since October 1, 2004. 6 

### The standards apply to both remote condensing and self -contained commercial equipment used to store chilled and frozen food. The MEPS are established in Australian Standard (AS) 1731.14-2003 as total energy consumption per total display area (TEC/TDA) in

kilowatt -hours per day per square meter for various equipment types. The Australian standards categorize equipment by the following criteria:

• r emote or self -contained condensing unit

• lit shelves or unlit shelves

• number of shelves

• solid door or glass door

• fan coil or gravity coil

• high, medium, or low temperature

The standards also specify minimum energy performance by M

- package temperatures (temperature of a load package) for self- contained cabinets. AS 1731-2003 specifies the test procedures used to measure energy consumption.

3.2.4 Non -Regulatory Initiatives

DOE reviewed several voluntary programs promoting energy efficient commercial refrigeration equipment in the United States, including the ENERGY STAR ® 

### program for

commercial refrigerators and commercial freezers, the Consortium for Energy Efficiency (CEE) initiative for commercial refrigeration equipment , and the Federal Energy Management Program (FEMP) p rocurement program for energy efficient commercial refrigerators and commercial

freezers. DOE also reviewed various rebate programs offered by utilities.

3.2.4.1 ENERGY STAR

The ENERGY STAR labeling program currently has a specification, in version 2.0 as of April 2009, for self -contained solid and glass door commercial refrigerators and commercial

freezers

. 7 

### This program does not apply to remote condensing commercial equipment or self

contained commercial equipment without doors. The ENERGY STAR version 2.0 specif ication

criteria for commercial equipment list the maximum energy consumption as follows.

3- 10

### Table 3.2.2 ENERGY STAR Specification Version 2.0 Commercial Refrigerator and Freezer Key Criteria

Product Volume ( i n c ubic f eet) * Refrigerator Freezer Vertical Configuration

Solid Door Cabinets

0 < V < 15 <= 0.089V + 1.411 <= 0.250V + 1.250

15 <= V < 30 <= 0.037V + 2.200 <= 0.400V

- 1.000

30 <= V < 50 <= 0.056V + 1.635 <= 0.163V + 6.125

50 <= V <= 0.060V + 1.416 <= 0.158V + 6.333

Glass Door Cabinets

0 < V < 15 <= 0.118V + 1.382 <= 0.607V + 0.893

15 <= V < 30 <= 0.140V + 1.050 <= 0.733V

- 1.000

30 <= V < 50 <= 0.088V + 2.625 <= 0.250V + 13.500

50 <= V <= 0.110V + 1.500 <= 0.450V + 3.500 Chest Configuration

Solid or Glass Door Cabinets <= 0.125V + 0.475 <=0.270V + 0.130

*V = Association of Home Manufacturers ( AHAM ) volume in cubic feet



### In addition to the scope of coverage, there are several notable differences between the

ENERGY STAR Version 2.0 criteria and those currently used by DOE. For one, ENERGY

STAR uses the American National Standards Institute/American Society of Heating, Refrigerating and Air -Conditioning Engineers ( ANSI/ASHRAE ) Standard 72-2005, Method of Testing C ommercial Refrigerators and Freezers , as the sole method of test . As ASHRAE

Standard 72-2005 incorporates the same rating temperatures as DOE for refrigerators and

freezers and is the same test method referenced in AHRI Standard 1200 for self -contained refrigerators and freezers

, this difference is nominal and does not impact the test result . The

ENERGY STAR criteria also specify ANSI/AHAM HRF -1-2004 for measur ing internal volume.

The ENERGY STAR Version 2.0 criteria also differ from DOE’s existing standards in the

treatment of e nergy management devices and in the definition of door type (solid or transparent) based on percentage of transparent area.

On November 22, 2013, the ENERGY STAR program released a final draft of a Version 3.0 Product Specification for Commercial Refrigerators and Freezers. This specification establishes new, more stringent performance levels which various types of commercial refrigeration equipment must meet in order to receive ENERGY STAR certification. These standard levels become effective October 1, 2014. In addition to updating the standard levels required for qualification with ENERGY STAR, the new specification also updated the method of test used for demonstration of product performance to align with the DOE test procedure, referencing 10 CFR Part 4 31 Subpart C, 10 CFR Part 431.64 and 10 CFR Part 431.66(d) as the sources for the ENERGY STAR method of test. The new specification also updated the product nomenclature used by ENERGY STAR so as to align with that used by DOE in describing equipment classes. The ENERGY STAR version 3.0 specification criteria for commercial equipment list the maximum energy consumption as follows.

3- 11

### Table 3.2.3 ENERGY STAR Specification Version 3.0 Commercial Refrigerator and Freezer Key Criteria

Product Volume (in cubic feet)* Refrigerator Freezer

Vertical Closed

Solid Door Cabinets VCS.SC.M* VCS.SC.L

0 < V < 15 0.02V+1.60 0.25V+1.55

15 ≤ V < 30 0.09V+0.55 0.20V+2.30

30 ≤ V < 50 0.01V+2.95 0.25V+0.80

50 ≤ V 0.06V+0.45 0.14V+6.30 Transparent Door Cabinets VCT.SC.M VCT.SC.L

0 < V < 15 0.10V+1.07 0.56V+1.61

15 ≤ V < 30 0.15V+0.32 0.30V+5.50

30 ≤ V < 50 0.06V+3.02 0.55V

- 2.00

50 ≤ V 0.08V+2.02 0.32V+9.49

Horizontal Closed Solid or Transparent Door

Cabinets HCT.SC.M, HCS.SC.M HCT.SC.L, HCS.SC.L

All volumes 0.06V+0.60 0.10V+0.20

* DOE Equipment Class designations relevant to ENERGY STAR eligible product scope: (1) Equipment family code (HCS= horizontal closed with solid doors, HCT=horizontal closed with transparent doors, VCS= vertic al closed with solid doors, VCT=vertical closed with transparent doors), (2) Operating mode (SC=self

- contained), and (3) Rating Temperature (M=medium temperature (38 °F), L=low temperature (0 °F)).



### 3.2.4.2 Consortium for Energy Efficiency

The CEE has a Commercial Kitchen Initiative , which encourages equipment purchasers to buy more efficien t commercial refrigeration equipment and directs them toward other rebate programs for which these purchases may qualify them. The CEE program applies to solid door and glass -door reach- in commercial refrigerators and commercial freezers. 8 

### This program is

based on ENERGY STAR version 2.0 standard levels, as listed in Table 3.2.2 , and applies to

self -contained commercial equipment with do ors. The program specifies standard levels in terms of maximum daily energy use in kilowatt -hours per day for various door types for both vertical and horizontal equipment.

3.2.4.3 Federal Energy Management Program

FEMP is a program administered by DOE that overse es the Federal Government’s energy management and investment initiatives. FEMP has established purchasing specifications for

energy efficient equipment, including commercial refrigeration equipment , which F ederal agencies must follow when buying new equipm ent for their facilities

. 9 

### Federal purchasers are required by EPACT 2005 to purchase equipment that is either ENERGY STAR qualified or

FEMP designated. Th e FEMP designated equipment consists of equipment that is in the upper 25 percent of energy efficiency in its class.

3.2.4.4 Rebate Programs

Numerous organizations and entities throughout the United States offer rebate programs for customers who purchase and install qualified commercial refrigeration equipment. Most of

3- 12

### the se incentive programs are accessible through the DSIRE online database. DSIRE lists all available incentives, including those offered by states, municipalities, utility companies, and the

F ederal government . This includes numerous incentives for purchasers of commercial refrigeration equipment units that meet specific criteria. While many entities offer rebates for commercial refrigeration equipment , most of which can be found on the DSIRE website, some are listed below as examples.

Efficiency Vermont, an organization offering technical assistance and financial incentives to encourage energy efficiency in Vermont, offers rebates for the installation of commercial refrigeration equipment that exhibits certain performance or design characteristics. These rebates include $100 for zero-energy transparent doors, $150 for light

- emitting diode ( LED ) refrigerated display case light fixtures, and $6 per linear foot of display case strip curtains and continuous

covers. 10



### The EnergySmart Grocer Program is funded by California utility ratepayers under the auspices of the California Public Utilities Commission. Eligible participants include grocery and convenience stores, food processors, and refrigerated warehouses operating in the Pacific Gas and Electric Company ( PG&E ) electric service territory

. The program offers rebates for us e of specific technologies on commercial equipment such as night covers on equipment without doors, upgrades to more efficient c ase lighting, anti

- sweat heater controls, improved reach -in door gaskets, and electronically commutated permanent magnet ( ECM ) motors . Additionally, the program provides rebates to end users who replac e older equipment with new high- efficiency equipment. 11



### NV Energy offers incentives promoting the installation of energy efficient commercial refrigeration equipment through the Nevada Sure Bet Program. These rebates are available to the utility’s commercial, industrial, and institutional customers and are base d on qualifying equipment from the ENERGY STAR program. Additionally, the program offers rebates for the installation of specific technologies, such as $4 per linear foot for the installation of night covers

on commercial refrigeration equipment without doors and $30 per motor for the installation of ECM motors in refrigerated cases

. 12



### Otter Tail Power Company offers rebates to Minnesota commercial customers under the Conservation Improvement Program. Under this program, customers are eligible to receive

b etween $ 20 and $40 per linear foot of, or upgrade to, high

- efficiency commercial refrigeration equipment , among other rebate offers

. 13



### 3.2.5 Equipment Classes

Commercial refrigeration equipment can be divided into various equipment classes categorized by physical characteristics that affect equipment efficiency. Most of these

characteristics affect the kind of merchandise that the equipment can be used to display and

determine how the customer can access that merchandise. Key physical characteristics are the

opera ting temperature, the presence or absence of doors (closed cases or open cases), the type of doors used (transparent or solid), the angle of the door or air curtain (horizontal, semivertical, or

ver tical) , and the type of condensing unit (remote condensing or self -contained). The following list shows the key characteristics of commercial refrigeration equipment that DOE developed as

3- 13

### part of the 2009 f inal rule and identified in the Framework document, preliminary analysis , and

NOPR for this rulemaking:

1. Operating t emperature

• Medium temperature ( 38 °F rating temperature

, refrigerators)

• L ow temperature ( 0 °F rating temperature

, freezers)

• Ice

- cream temperature (-15 °F rating temperature

, ice

- cream freezers)

2. Door t ype

• Equipment with transparent doors

• Equipmen t with solid doors

• Equipment without doors

3. Orientation (air -curtain or door angle)

• Horizontal

• Semivertical

• Vertical

4. Type of condensing unit

• Remote condensing

• Self

- contained

Additionally, because this rulemaking covers equipment for which standards were set in the EPACT 2005 legislation, DOE has included a separate class for commercial refrigerators with transparent doors for pull-down temperature applications. The inclusion of this equipment

as a separate class reflects the distinction that was made in the EPACT 2005 standards, which set specific standards for this type of equipment separate from the standards for other commercial refrigerators with transparent doors. DOE expressed its plans to include this equipment in the form of a separate family with a single class, and this was included in the May 2010 F ramework document, March 2011 preliminary analysis, and September 2013 NOPR. In response to stakeholder feedback, DOE has retained this designation in the final rule

.

Table 3.2.4 shows the nine commercial refrigeration equipment families DOE has

considered within the scope of this rulemaki ng

.

3- 14

### Table 3.2.4 Commercial Refrigeration Equipment Families

Equipment Family Designation Description

Vertical Open VOP E quipment without doors and an air

- curtain angle ≥ 0° and < 10°

Semivertical Open SVO E quipment without doors and an air

- curtain angle ≥ 10° and

< 80°

Horizontal Open HZO E quipment without doors and an air

- curtain angle ≥ 80°

Vertical Closed Transparent VCT E quipment with hinged or sliding transparent doors and a door angle < 45°

Vertical Closed Solid VCS E quipment with hinged or sliding solid (opaque) doors and a door angle ≥ 45°

Horizontal Closed Transparent HCT E quipment with hinged or sliding transparent doors and a door angle < 45°

Horizontal Closed Solid HCS E quipment with hinged or sliding solid (opaque) doors and a door angle ≥ 45°

Service Over Counter SOC E quipment with sliding or hinged doors intended for use by sales personnel and fixed or hinged glass for displaying merchandise

Pull

- Down * PD * Commercial r efrigerators with t ransparent d oors for p ull

- d own

t emperature a pplications

*This equipment family is only applicable to PD.SC.M



### T he first eight equipment families contain equipment that can have one of two condensing unit types and one of three operating temperature s. The ninth family, pull-down equipment, contains only a single class as defined by EPACT 2005. The condensing unit type

has a significant impact on utility and energy use, and can be either remote condensing (RC) or

self

- contained (SC ). Remote condensing equipment is typically more efficient on a normalized

basis than self

- contained equipment because of economies of scale incurred in the use of large, multiplex compressor rack systems that feed multiple units. Remote condensing equipment cannot be easily relocated , due to the refrigerant piping attachments, whereas self -contained equipment is more mobile, generally requiring only an electrical outlet for operation.

The operating temperature of the equipment also has a significant impact on utility and

energy use

. DOE has specified operating temperature classes ofmedium (M), low (L) or icecream (I), representing rating temperature s (or IAT) of 38 ° F, 0 ° F, or -15 ° F, respectively (± 2

° F). Because different types of merchandise require different temperatures ( e.g., chilled versus frozen), operating temperature is a necessary distinction. Also, t he larger temperature differences and thermodynamic behavior of refrigerants means that equipment with lower operating

temper atures runs less efficiently than equipment with higher operating temperatures.

For open cases , “ vertical ” represents an air

- curtain angle of < 10 ° from the vertical,

“ semivertical ” represents an air

- curtain angle of ≥ 10 ° and < 80 ° from the vertical, and

“horizontal” represents an air

- curtain angle of ≥ 80 ° from the vertical. DOE developed a

definition for air

- curtain angle as part of the January 2009 final rule analysis :

(1) For equipment without doors and without a discharge air grille or discharge air honeycomb, the angle between a vertical line extended down from the highest point on the manufacturer’s recommended load limit line and the load limit line

3- 15

### itself, when the equipment is viewed in cross- section; and (2) For all other equipment without doors, the angle formed between a vertical line and the straight line drawn by connecting the point at the inside edge of the discharge air opening with the point at the inside edge of the return air opening, when the equipment is

viewed in cross- section

.

Using all combinations of equipment famil y, operating mode, and temperature for the first eight families , plus the self-contained medium- temperature pull -down class, 49 equipm ent classes are possible, as illustrated in Table 3.2.5

. DOE developed a lettering system in the 2009 rulemaking to simplify discussion of equipment classes , and has retained that system in this rulemaking. The lettering designation for a particular equipment class consists of the abbreviations for the equipment family, operating mode, and temperature, separated by periods.

Table 3.2.5 shows a complete list of equipment classes with lettering designations organized by family, operating mode, and temperature.

3- 16

### Table 3.2.5 Commercial Refrigeration Equipment Classes

Equipment Family Equipment Family Designation Sample Equipment Family Image Operating

Mode Designation Temperature Designation Equipment Class Designation

Vertical Open VOP RC M (38 °F) VOP.RC.M L (0 °F) VOP.RC.L I (

- 15 °F) VOP.RC.I

SC M (38 °F) VOP.SC.M L (0 °F) VOP.SC.L I (

- 15 °F) VOP.SC.I

Semivertical Open SVO RC M (38 °F) SVO.RC.M L (0 °F) SVO.RC.L I (

- 15 °F) SVO.RC.I

SC M (38 °F) SVO.SC.M L (0 °F) SVO.SC.L I (

- 15 °F) SVO.SC.I

Horizontal Open HZO RC M (38 °F) HZO.RC.M L (0 °F) HZO.RC.L I (

- 15 °F) HZO.RC.I

SC M (38 °F) HZO.SC.M L (0 °F) HZO.SC.L I (

- 15 °F) HZO.SC.I

Vertical Closed

Transparent VCT RC M (38 °F) VCT.RC.M L (0 °F) VCT.RC.L I (

- 15 °F) VCT.RC.I

SC M (38 °F) VCT.SC.M L (0 °F) VCT.SC.L I (

- 15 °F) VCT.SC.I

Vertical Closed Solid VCS RC M (38 °F) VCS.RC.M L (0 °F) VCS.RC.L I (

- 15 °F) VCS.RC.I

SC M (38 °F) VCS.SC.M L (0 °F) VCS.SC.L I (

- 15 °F) VCS.SC.I

Horizontal Closed Transparent HCT RC M (38 °F) HCT.RC.M L (0 °F) HCT.RC.L I (

- 15 °F) HCT.RC.I

SC M (38 °F) HCT.SC.M L (0 °F) HCT.SC.L I (

- 15 °F) HCT.SC.I

Horizontal Closed

Solid HCS RC M (38 °F) HCS.RC.M L (0 °F) HCS.RC.L I (

- 15 °F) HCS.RC.I

SC M (38 °F) HCS.SC.M L (0 °F) HCS.SC.L I (

- 15 °F) HCS.SC.I

Service Over Counter SOC RC M (38 °F) SOC.RC.M L (0 °F) SOC.RC.L I (

- 15 °F) SOC.RC.I

SC M (38 °F) SOC.SC.M L (0 °F) SOC.SC.L I (

- 15 °F) SOC.SC.I

Pull

- Down PD SC M (38 °F) PD.SC.M

3- 17

### 3.2.6 Shipments

This section presents the shipments data DOE obtained to conduct the final rule analyses

. DOE gathered data from ARI (submitted as part of the 2009 DOE rulemaking process), Freedonia Group, the North American Association of Food Equipment Manufacturers (NA FEM), Appliance Magazine , and the U.S. Census Bureau.

3.2.6.1 ARI Data

As part of its comments on the Framework d ocument for the 2009 rulemaking ( Docket

No. EE -2006- STD -0126, ARI, No. 7, Exhibit B at p. 1), ARI submitted annual shipment data by equipment class for its member compa nies for the year 2005. DOE used th is shipments data as part of the basis for this analysis , as newer shipments data were not available. DOE understands

that this data do es not include the entire industry because ARI did not represent all

manufacturers at that time

. b 

### However, because th is data covered a significant portion of the commercial refrigeration equipment sold, and because no other detailed data w as available at that

time , the 2005 ARI shipments data w as used as the total commercial refrigeration equipment shipments for the 2009 final rule, and has been incorporated as an input into the analyses for the current rulemaking

.

Table 3.2.6 shows 2005 annual shipments for each category of commercial refrigeration equipment by equipment class. The ARI data included shipments for equipment that operates at an “application” temperature aside from the specific operating temperature ranges considered by

DOE ( e.g. , wine chillers that operate at 45 °F and freezers that operate at

- 30 ° F). H owever, DOE only considered in its analyses shipments of equipment at the three temperatures used in this rulemaking (38 °F, 0 °F, and -15 °F ; see section 3.2.5 ). The shipments of equipment that operate

at one of these three temperatures constitute approximately 98 percent of the shipmen ts that ARI reported

.

b Most notably, True Manufacturing, which DOE understands to have a large market share of self

- contained equipment.

3- 18

### Table 3.2.6 ARI 2005 Shipments of Commercial Refrigerators and Freezers by Equipment

Class

Equipment

Family Equipment Family Designation Operating

Mode Designation Temperature Designation Equipment Class Designation ARI Shipments*

Vertical Open VOP RC M (38 °F) VOP.RC.M 38

, 743 L (0 °F) VOP.RC.L 0 I (

- 15 °F) VOP.RC.I 0

SC M (38 °F) VOP.SC.M 6

, 512 L (0 °F) VOP.SC.L 0 I (

- 15 °F) VOP.SC.I 0

Semivertical Open SVO RC M (38 °F) SVO.RC.M 29

, 552 L (0 °F) SVO.RC.L 0 I (

- 15 °F) SVO.RC.I 0

SC M (38 °F) SVO.SC.M 9

, 750 L (0 °F) SVO.SC.L 0 I (

- 15 °F) SVO.SC.I 0

Horizontal Open HZO RC M (38 °F) HZO.RC.M 4

, 541 L (0 °F) HZO.RC.L 14

, 278 I (

- 15 °F) HZO.RC.I 0

SC M (38 °F) HZO.SC.M 838 L (0 °F) HZO.SC.L 1

, 738 I (

- 15 °F) HZO.SC.I 0

Vertical Closed

Transparent VCT RC M (38 °F) VCT.RC.M 2

, 767 L (0 °F) VCT.RC.L 38

, 483 I (

- 15 °F) VCT.RC.I 0

SC M (38 °F) VCT.SC.M 43

, 374 L (0 °F) VCT.SC.L 2

, 472 I (

- 15 °F) VCT.SC.I 1

, 898

Vertical Closed

Solid VCS RC M (38 °F) VCS.RC.M 49 L (0 °F) VCS.RC.L 2 I (

- 15 °F) VCS.RC.I 43

SC M (38 °F) VCS.SC.M 4 L (0 °F) VCS.SC.L 4

, 202 I (

- 15 °F) VCS.SC.I 470

Horizontal Closed Transparent HCT RC M (38 °F) HCT.RC.M 0 L (0 °F) HCT.RC.L 15 I (

- 15 °F) HCT.RC.I 0

SC M (38 °F) HCT.SC.M 724 L (0 °F) HCT.SC.L 0 I (

- 15 °F) HCT.SC.I 9

, 056

Horizontal Closed

Solid HCS RC M (38 °F) HCS.RC.M 37 L (0 °F) HCS.RC.L 0 I (

- 15 °F) HCS.RC.I 0

SC M (38 °F) HCS.SC.M 39

, 761 L (0 °F) HCS.SC.L 4

, 109 I (

- 15 °F) HCS.SC.I 0

Service Over

Counter SOC RC M (38 °F) SOC.RC.M 9

, 312 L (0 °F) SOC.RC.L 9

I (

- 15 °F) SOC.RC.I 0

SC M (38 °F) SOC.SC.M 1

, 108 L (0 °F) SOC.SC.L 0 I (

- 15 °F) SOC.SC.I 0

Pull

- Down PD SC M (38 °F) PD.SC.M N.A.**

* S ource: Docket No. EE

- 2006

- STD

- 0126

, ARI, No. 7

, Exhibit B at p. 1.

** Self

- contained pull

- down refrigerators with transparent doors were not explicitly addressed in the 2005 shipments data from ARI.

3- 19

### 3.2.6.2 NAFEM Data

NAFEM publishes a biennial study of the foodservice equipment and supplies market. The latest available report is titled 2008 Size and Shape of the Industry Study

. 14 

### It contains survey data from NAFEM members (not all members provided data for the report) including shipments numbers and the value of sales in dollars. Based on the numbers reported by the respondents to the survey, NAFEM develop ed a total market estimate of shipments ( units shipped and dollar sales) for the year 2007. NAFEM also estimated the projected shipments for 2008. NAFEM published similar study reports for shipments in 2006, 15 

### 2004, 16 

### and 2002. 17



### The “refrigeration and ice m achine study” is part of the NAFEM reports and includes shipments data for under- counter, reach -in, pass- through, roll

- in/roll -through refrigerators and

freezers ; air curtain refrigerators ; milk coolers ; and ice

- cream cabinets, freezers , and dispensers (gelato equipment, dipping freezer/cabinets). Table 3.2.7 shows the DOE equipment classes to which the NAFEM equipment was assigned. Sh ipments numbers have been withheld from publication because the NAFEM reports are not publicly available documents.

Table 3.2.7 NAFEM Shipments Categories and Corresponding DOE Equipment Classes

NAFEM Catego ry Assigned DOE Equipment Class

Air c urtains or a ir c urtain r efrigerators VOP.SC.M

Milk c oolers VCS .SC.M

Under

- counter refrigerators VCS.SC.M

Under

- counter freezers VCS.SC.L

Reach

- in/ p ass

- through refrigerators VCS.SC.M,VCT.SC.M

Reach

- in/ p ass

- through freezers VCS.SC.L,VCT.SC.L

Roll

- in/ r oll

- through refrigerators VCS.SC.M

Roll

- in/ r oll

- through freezers VCS.SC.L

Ice

- c ream c abinets, f reezers & d ispensers ( g elato e quipment, d ipping f reezers/ c abinets) HCT.SC.I, HCT.SC.L

, HCS.SC.I



### 3.2.6.3 Freedonia Market Reports

The Freedonia Group published a study on the United States commercial refrigeration

ind ustry titled Commercial Refrigeration Equipment to 2014

. 18 

### This report contains dollar values of commercial refrigeration equipment sales and in some case s shipments data in number of units. Shipments data are reported by market type (food and beverage retail, foodservice), equipment type (open and closed display case, reach-ins), and operating temperature (normal, low, and ice

- cream temperatures). T he report also presents the year -on- year percentage changes in total commercial refrigeration market from 1999 to 2009, which includes commercial refrigeration equipment used in food production and food distribution market sectors, and other equipment categories like beverage vending machines, walk -ins, liquid chillers, transportation systems, and refrigeration parts. Table 3.2.8 shows the classificatio n of the Freedonia equipment categories into DOE product classes. Shipments numbers have been withheld from publication because Freedonia reports are not publicly available.

3- 20

### Table 3.2.8 Freedonia Shipments Categories and Corresponding DOE Equipment Classes

Freedonia Equipment Type Designation Freedonia Operating Temperature Designation DOE Equipment Class(es)

Open self

- service display

cases Normal VOP.RC.M,HZO.RC.M,SVO.RC.M VOP.SC.M,SVO.SC.M,HZO.SC.M Closed display cases Normal VCT.RC.M,VCT.SC.M Open display cases Low VOP.RC.L,HZO.RC.L, SVO.RC.L VOP.SC.L,HZO.SC.L ,SVO.SC.L Closed display cases Low VCT.RC.L,VCT.SC.L, SOC.RC.L,SOC.SC.L

Ice

- cream freezer & other Ice

- cream VCT.RC.I,VCT.SC.I ,HCT.SC.I,

HCT.SC.L

Reach

- in refrigerators Normal VCS.SC.M

Reach

- ins freezers Low VCS.SC.L



### 3.2.6.4 Appliance Magazine Data

Appliance Magazine publishes historical and forecasted shipments of refrigerated display cases, shown in Table 3.2.9 and Table 3.2.10

. c



### Table 3.2.9 Histor ical Shipments of Refrigerated Display Cases

Year Unit Shipments

1999 340,453

2000 347,262

2001 175,000

2002 183,300

2003 191,549

2004 185,000

2005 177,000

2006 180,540

2007 184,000

2008 172,129

Source: Appliance Magazine , “56 th Annual Statistical Review,” May 2009.



### Table 3.2.10 Statistical Forecasts of Refrigerated Display Case Shipments

Year Unit Shipments

2009 152,000

2010 156,000

2011 162,000

2012 166,000

Source: Appliance Magazine , “58 th Annual Appliance Industry Forecasts,” February 2010.



### It is unclear what is responsible for the sharp decline in display case shipments between 2000 and 2001 reported by A ppliance Magazine

. A similar decline is repo rted in other market

literature

. 19 

### DOE believe s that some of the observed decline in shipments is the result of a general slowdown in the U.S. economy in the second and third quarters of 2001. Financial

c As mentioned earlier, Appliance Magazine describes the data as representing refrigerated display cases but does not provide a precise defin ition of the equipment. It is unclear what types of equipment this term covers

— whether it is equipment that is self

- contained or remote condensing , or equipment with doors or without doors.

3- 21

### reports by commer cial refrigeration equipment manufacturers discuss depressed investment and uncertainty in the retail food industry between 2000 and 2005 as contributing to low sales of commercial refrigeration equipment. There may be other explanations, including possible changes in what equipment Appliance Magazine included in its categorization of “refrigerated display cases .”

Appliance Magazine also published historical and forecasted shipments of commercial refrigerators, shown in Table 3.2.11 and Table 3.2.12

. d



### Table 3.2.11 Historical Shipments of Commercial Refrigerators

Year Unit Shipments

1999 233,750

2000 245,437

2001 260,000

2002 263,000

2003 268,000

2004 275,000

2005 289,000

2006 307,000

2007 309,375

Source: Appliance Magazine , “54 th Annual Statistical Review,” May 2007, “31 st Annual Portrait of the U.S. Appliance Industry

, ” September 2008.



### Table 3.2.12 Statistical Forecasts of Commercial Refrigerator Shipments

Year Unit Shipments

2009 270,455

2010 275,323

2011 280,830

Source: Appliance Magazine , “57 th Annual Appliance Industry Forecasts,” January 2009.



### Appliance Magazine appears to have stopped offering historical and forecast information on shipments of commercial refrigerators and display cases after 2010. Thus the information presented here reflects the latest available data from that source.

3.2.6.5 Census Bureau Data

The U.S. Census Bureau’s Economic Census of Manufacturing Industry Series publishes statistics on the quantity and value of shipments for those companies with shipments over $100,000 in value. 20

, 21

, 22 

### There are eight categories for display cabinets and display cases

, including reach-ins. These are sub -divided into reach-in, closed display, and open display.

Table 3.2.13 shows shipment data for open refrigerated display cases, which include both

self -contained units without doors and remote condensing units without doors. It is not possible

d Similar to its treatment of refrigerated display cases

, Applianc e Magazine describes the data as representing commercial refrigerators, but does not provide a precise definition of the equipment. It is unclear what types of equipment this term covers

— whether it is equipment that is self

- contained or remote condensing

, or equipment with

doors or without doors.

3- 22

### to determine from Table 3.2.13 which shipments are for self-contained units and which are for remote condensing units since the census data reports these together

.

Table 3.2.13 Shipments of Open Refrigerated Display Cases ( Remote C ondensing and Self

Contained)

Type Open or

Closed Temperature Survey Year Number of Companies with

Sales O ver $100,000 Shipment Quantity

1, 000s Shipment

Value
$1

, 000

Display Case

One Level Open Normal 2007 15 (q)47.7 237,791

2002 6

- 89,622

1997 13

- 140,259

Display Case

Multi Level Open Normal 2007 10 S 170,628

2002 9

- 341,472

1997 11 58.6 258,310

Display Case Open Frozen Food 2007 7 S 29,366

2002 6

- 11,538

1997 7 20.4 90,213

Source: U.S. Census, Economic Census, Manufacturing Industry Series 2007, http://factfinder.census.gov/servlet/DatasetMainPageServlet? _program=ECN&_tabId=ECN1&_submenuId=datasets_4&_lang=en ; U.S. Census, Economic Census, Manufacturing Industry Series 1997, EC97M

- 3334D, www.census.gov/epcd/www/97EC31.HTM ; U.S. Census, Economic Cen sus, Manufacturing Industry Series 2002, EC02

- 31I

- 333415 (RV), www.census.gov/econ/census02

. * S

- Withheld because estimate did not meet publication standards **(q)

- 20

– 29 percent estimated



### Table 3.2.14 shows shipment data for closed refrigerated display cases, which include both self-contained units with doors and remote condensing units with doors. It is not possible to

determine from Table 3.2.14 which shipments are for remote condensing units and which are for

self -contained units.

Table 3.2.14 Shipments of Closed Refrigerated Display Cases ( Remote C ondensing and

Self

- Contained)

Type Open or

Closed Temperature Survey Year Number of Companies with

Sales Over $100,000 Shipment Qua ntity

1, 000s Shipment

Value
$1

, 000

Display Case Closed Normal 2007 8 S 108,627

2002 11

- 50,601

1997 15

- 91,892

Cabinet Closed Frozen Food 2007 7 S 25,100

2002 8

- 20,618

1997 9

- 112,873

Other Display

Cases Not stated Low Temp. 2007 6 S D

2002 7

- 58,549

1997 6

- 43,150

Source: U.S. Census, Economic Census, Manufacturing Industry Series 2007, http://factfinder.census.gov/servlet/DatasetMainPageServlet?_program=ECN&_tabId=ECN1&_submenuId=datasets_4&_lang=en ; U.S. Census, Economic Census, Manufacturing Industry Series 1997, EC97M

- 3334D, htt p://www.census.gov/epcd/www/97EC31.HTM ; U.S. Census, Economic Census, Manufacturing Industry Series 2002, EC02

- 31I

- 333415 (RV), http://www.census.gov/econ/census02

. * S

- Withheld because estimate did not meet publication standards

** D

- Withheld to avoid disclosing data for individual companies; data are included in higher level totals

3- 23

### Table 3.2.15 shows shipment data for commercial reach -in (vertical) cabinets

. These cabinets can have either solid or glazed doors. It is not possible to determine from

Table 3.2.15 which shipments are for remote condensing units and which shipments are for selfcontained units.

Table 3.2.15 Shipments of Commercial Reach

- In Cabinets ( Remote C ondensing and Self

Contained)

Type Open or

Closed Temperature Survey Year Number of Companies with

Sales Over $100,000 Shipment Quantity

1, 000s Shipment

Value
$1

, 000

Display

Cabinets

-

Not for

Frozen

Foods Not Specified Normal 2007 21 S 655,800

2002 21 381.7 465,553

1997 29

- 439,081

Display

Cabinet Not Specified Low Temperature 2007 11 S 341,136

2002 16

- 259,105

1997 24

- 307,605
Source: U.S. Census, Economic Census, Manufacturing Industry Series 2007, http://factfinder.census.gov/servlet/DatasetMainPage Servlet?_program=ECN&_tabId=ECN1&_submenuId=datasets_4&_lang=en ; U.S. Census, Economic Census, Manufacturing Industry Series 1997, EC97M

- 3334D, http://www.census.gov/epcd/www/97EC31.HTM ; U.S. Census, Economic Census, Manufacturing Industry Series 2002, EC02

- 31I

- 333415 (RV), http://www.census.gov/econ/census02

. *S

- Withheld because estimate did not meet publication standards



### In summary, although the U.S. Census Bureau d ata contain some limited shipments data that would be useful for conducting technical analyses, not enough detail is available to provide specific assessments for shipments within each of the primary categories covered in this rulemaking.

Additional shipments sources/discussion?

3.2.7 Equipment Lifetimes

DOE reviewed available literature and consulted with experts on commercial refrigeration equipment to establish typical equipment lifetimes. The literature and individuals consulted estimated a wide range of typical equipment lifetimes, shown in Table 3.2.16

.

Individuals with experience in the manufactur ing or dist ribution of commercial refrigeration equipment suggested a typical case life of 5 to 15 years. Experts in the field suggested that while the equipment is typically robust in design, much of the equipment is replaced for cosmetic reasons during store remode ling (typically every 10 years or so). O ne distributor suggested that U.S. tax depreciation schedules, which allow depreciation over a 5- year period for retail fixtures, including commercial refrigerators and commercial freezer s, 23 

### are one driver for regular replacement of commercial refrigeration equipment in the United States.

Some literature suggested lifetimes of up to 20 years or more for commercial refrigeration equipment. Many of the studies cited here are related to examinat ion of environmental impacts of refrigerant emissions and therefore may not always clearly distinguish

3- 24

### between the lifetime of the case and the lifetime of compressor racks. 24 

### However, available

literature and consultation with experts in the field suggested that smaller, independently owned grocery stores were more likely to keep equipment longer than larger chain stores.

Several industry experts and stakeholders suggested during interviews and in public meetings that there is a significant used and refurbi shed equipment market. However, DOE did

not determine the size of the used market relative to the new market. Those consulted generally agreed that the salvage value of used equipment was very low compared to the initial purchase price. This is due to both cosmetic concerns and the custom nature of much of the equipment. The difficulty in collecting used equipment of the same “look” for planned display case line-ups

in retail stores was cited as another reason for the low price of used equipment. A survey in the Pacific Northwest reported that for small, independent grocery stores (< 20,000 square feet) and independently owned convenience stores, the fraction of owners who would consider purchase of refurbished equipment was 25 and 16 percent, respectively. For larger regional chains, this fraction was approximately 11 percent. None of the large grocery chains surveyed had plans to purchase refurbished equipment. See TSD chapter 8 and chapter 9 for the equipment lifetime values used in the LCC and shipments a nalyses.

Table 3.2.16 Estimates for Commercial Refrigeration Equipment Lifetimes

Life Reference 15 years Heshong Mahone Group 2004 25

5

- 7 years ( l arge c hains) Verisae 2006 26

15 years ( s maller chains and independent grocers

m ay go up to 20 years) Verisae 2006 27

7

- 15 years Mark Ellis & Associates 2003 28

15 year s Foster

- Miller 2001 29

15

- 20 years U.S. Environmental Protection Agency 2001 30

15 years Arthur D. Little 2002 31

9

- 10 years (9 years with doors, 10 years without doors) CEC 2004 32

5

- 15 years ( t ypical ly 10 years) Hansen 2006 33

10 years PG&E 2004 3

7

- 10 years ( remote condensing )

8

- 12 years (self

- contained) Intergovernmental Panel on Climate Change 2001 34

7 years Fisher 1991 35

10

- 15 years Navigant Consulting, Inc. 2009 36



### 3.2.8 Market Performance Data

3.2.8.1 Remote Condensing Equipment

During the 2009 rulemaking, DOE conduct ed a survey of existing remote condensing refrigerated equipment from major manufacturers and compil ed a performance database based on available information. Information such as total refrigeration load, evaporator temperature, lighting power draw, defrost power draw, and motor power draw were used to analytically

determine calculated daily energy consumption (CDEC) according to the methodology of AHRI Standard 1200, Performance Rating of Commercial Refrigerated Display Merchandisers and Storage Cabinets.

3- 25

### This data was used to develop figures included in the market and technology assessment chapter of the 2009 final rule TSD sh owing the relationship between CDEC and TDA for the VOP.RC.M, SVO.RC.M, and HZO.RC.M classes, as well as a plot showing the relationship between air curtain angle and performance. Because the market baseline did not change between the 2009 final rule and the publication of the preliminary analysis TSD for the current rulemaking, DOE retained those figures in the preliminary analysis TSD chapter 3.

Since the publication of the preliminary analysis for the current rulemaking, the compliance date of the 2009 standards (January 1, 2012) has passed, altering the market baseline. DOE does not have sufficiently detailed information for specific equipment models to allow it to show similar plots representative of the current market . Based on discussions with manufacturers, DOE believes that many remote condensing equipment lines offer models at or near the baseline, with options to add higher-efficiency features upon customer request.

3.2.8.2 Self

- Contained Equipment

For self -contained equipment, market perfor mance plots were developed for selected equipment classes based on rated test data for this equipment from publicly accessible sources.

These included the California Energy Commission (CEC) , and ENERGY STAR directories. The rated energy consumption values, capacities, and equipment types were used to produce market performance plots for self -contained equipment classes. However, because these directories are produced by different parties independent from DOE and are in some cases voluntary, the data

contain ed within them does not always align exactly with the DOE equipment classes, nor does it necessarily reflect the entire range of efficiencies on the market within a given equipment class

. For example, for some classes currently covered under a TDA

- based st andard, only volume

information was available in these directories

. Figure 3.2.3 through Figure 3.2.8 show energy use

as a function of size for select self

- contained equipment classes.

3- 26

### Figure 3.2.3 Market Performance Data for the VCT.SC.M E quipment Class

Figure 3.2.4 Market Performance Data for the VCT.SC.L Equipment Class

3- 27

### Figure 3.2.5 Market Performance Data for the VCS.SC.M Equipment Class

Figure 3.2.6 Market Performance Data for the VCS.SC.L Equipment Class

3- 28

### Figure 3.2.7 Market Performance Data for the HCT.SC. L Equipment Class

Figure 3.2.8 Market Performance Data for the HCS.SC.M Equipment Class

3- 29

### 3.3 TECHNOLOGY ASSESSMEN T

The purpose of the technology assessment is to develop a preliminary list of technologies that could potentially be used to improve the efficiency of commercial refrigeration equipment. The following assessment provides descriptions of technologies and designs that apply to all equipment classes , designs that apply to equipment without doors only, and technologies and designs that apply to self-contained equipment only.

3.3.1 Technologies and Designs Relevant to All Equipment Classes

The following technologies and designs are relevant to all equipment classes: higher efficiency lighting, higher efficiency lighting ballasts, remote bal last location, higher efficiency expansion valves, higher efficiency evaporator fan motors, variable -speed evaporator fan motors and evaporator fan motor controllers, higher efficiency evaporator fan blades, improved evaporator coil design , low- pressure differential evaporators, insulation increases or improvements, defrost mechanisms, defrost cycle control, vacuum insulated panels (VIPs) , and occupancy sensors for lighting controls.

3.3.1.1 Higher Efficiency Lighting

Commercial refrigeration equipment often includes lighting to illuminate the contents. Some commercial refrigeration equipment also includes an illuminated sign on the exterior of the

unit. Higher efficiency lighting leads to energy savings in two ways: less energy is used directly for lighting, and less heat energy is dissipated into the refrigerated case by the lamp. Efficiency in lighting is commonly measured as efficacy (lumens/watt), or the quantity of light output (lumens) per electrical energy input (watts, W).

During the time frame of the 2009 rulemaking, DOE found that the commercial refrigeration industry had beg un using T8 fluorescent lighting in most cases. T8 lighting is substantially more efficacious than T12 lighting and is predominantly used with electronic ballasts, which are more eff icient than the magnetic ballasts commonly used in T12 lighting.

A significant trend in recent years has been the use of LED technology. Although LEDs currently on the market consume more energy per lumen of light output than fluorescents , they provide better directionality than linear fluorescent bulbs, allowing for greater control of the light

output. The result is comparable product illumination with less total wattage. There have been recent advancements in LED efficacy and numerous large retailers have adopt ed LED technology. Every major display door manufacturer offers LED lighting as design option, and some are moving to make LEDs a standard feature on their doors.

3.3.1.2 Higher Efficiency Lighting Ballasts

Higher efficiency lighting ballasts reduce ene rgy consumption by requiring less electrical power to operate and by reducing waste heat generation, which contributes to case heat load.

3- 30

### Some illuminated display cases currently installed in supermarkets use fluorescent lighting with magnetic ballasts, wh ich use inductance to modulate power flow to fluorescent lamps. Magnetic ballasts have significant electrical resistance losses. More recently, the market

has moved toward the use of e lectronic ballasts , which use solid state electronics to modulate the power provided to fluorescent lamps. Electronic ballasts, which convert power at high frequency, have lower electrical resistance losses compared to magnetic ballasts

, which operate at line frequency. F luorescent lamps also operate more efficiently at the hi gher frequency provided by

electronic ballasts. In addition to the direct reductions in electrical power consumption, heat generated by the lighting and the lighting ballast contribute to the case heat load , as both the lighting and ballasts are often loca ted inside the refrigerated space . I ncreasing ballast efficiency

also reduces the case heat load, and thus the compressor load. DOE notes that LED lighting does not require independent lighting ballasts, as the LED emitters generally are powered by onboard

drivers built into each fixture.

3.3.1.3 Remote Lighting Ballast Location

Because ballasts may be located apart from the fluorescent lamps they power, manufacturers could choose to place them within the display case cabinet but outside of the refrigerated space, reducing heat load and case energy consumption.

3.3.1.4 Higher Efficiency Expansion Valves

Expansion valves are refrigerant metering devices that control the amount of refrigerant flowing to the evaporator coil. In doing so, they simultaneously decrease th e temperature and pressure of the refrigerant, creating a cold liquid -vapor mixture. The low temperature of the refrigerant leaving the expansion valve creates the driving force to move heat out of the refrigerated space and into the evaporator.

The most basic type of expansion device is a capillary tube, which may be found in selfcontained commercial refrigeration equipment. The capillary tube is a long, thin pipe that creates a pressure drop in the refrigerant through frictional losses along the interio r wall of the tube. Capillary tubes must be sized to the particular application and cannot adjust for variations in load or ambient operating conditions. They are often oversized for worst-case conditions, and therefore may operate at reduced efficiency du ring normal operation.

The thermostatic expansion valve (TXV) is common in remote condensing commercial refrigeration equipment. This device uses an orifice to reduce the pressure of the entering refrigerant and a sensing bulb to monitor and maintain the t emperature of the superheated vapor leaving the evaporator. Because the TXV allows for some degree of adjustment of refrigerant expansion, it may be somewhat more efficient than the capillary tube device under varying

conditions.

The electronic expansion valve (EEV) is similar to the TXV, but uses an electronic control system to optimize refrigeration -system performance under all operating conditions. Because it does this with greater flexibility than a TXV allows, an EEV theoretically allows for

increases in energy efficiency under varying conditions. However, as with the TXV, an EEV will

3- 31

### likely not provide significant energy savings over a properly sized capillary tube or a TXV under

fixed ambient conditions such as those used in the DOE test procedure.

3.3.1.5 H igher Efficiency Evaporator Fan Motors

Evaporator fan motors are fractional horsepower in size (generally on the order of 6 -12 watts) , are responsible for moving air across the evaporator coil, and typically run at one speed.

The manufacturer will match t he motor size and blade design to the evaporator coil to meet the expected load on the case under most conditions. Higher efficiency evaporator fan motors reduce energy consumption by requiring less electrical power to generate motor shaft output power.

El ectric motors operate based on the interaction between a field magnet and a magnetic

rotor. In a brushed motor, the field magnets are permanent magnets and the rotor is an electromagnet; the situation is reversed in a brushless motor. The electromagnetic i nteractions between these two magnets cause the rotor to rotate.

Single -phase motors, t he simplest type of electric motor

, suffer from a serious shortcoming. Single -phase motors only produce a rotating magnetic field when the rotor is rotating, and simply powering the electromagnet is therefore not sufficient to start such a motor. One of the most significant differences between different types of single -phase motors is the way in which they handle this start-up problem.

Nearly all inexpensive fan motors are either shaded -pole or permanent split capacitor (PSC), and the same is true for baseline commercial refrigeration equipment fan motors (fan motors not marketed as “high efficiency”). In both cases, the electromagnet co nsists of windings of electrical wire through which current is driven. In a shaded -pole motor, a portion of these windings is “shaded” by a copper loop. The interactions between the magnetic field generated by the shaded portion and that generated by the unshaded portion induce rotation when the motor is powered. Because the imbalance between the shaded and un -shaded portions of the magnet remains throughout operation, however, shaded- pole motors are inefficient, with typical motor

efficiencies near 20 per cent. 37 

### Shaded

- pole motors are, however, electrically simple and inexpensive.

In a PSC motor, a smaller, start -up winding is present in addition to the main winding.

The start

- up winding is electrically connected in parallel with the main winding and in ser ies with a capacitor. At start up, the interactions between the magnetic field generated by the start -up winding and that generated by the main winding induce rotation. Because of the capacitor, however, the current to the start-up winding is cut off as th e motor reaches steady state. Due to

this design , PSC motors are more energy efficient than their shaded -pole counterparts, with motor efficiencies ranging from 25 to 40 percent , according to DOE’s research, depending on motor size, design, and manufacturer. The energy efficiency of commercial refrigeration equipment fans that use shaded -pole motors can be substantially improved by replacing them

with PSC motors.

A third type of electric motor, the ECM motor (also known as the brushless permanent
magnet mot or), is more energy efficient than both shaded -pole and PSC motors. ECM motors, which are sold in high volumes, are three- phase electric motors with efficiencies even greater

3- 32

### than PSC motors. These motors are actually DC motors, often with a built -in inver ter allowing them to run off of AC current. They also feature a permanent magnet rotor. An electronic controller pulses power to the motor, and these signals drive different groups of windings inside the motor. The controller modulates these pulses of powe r in order to maintain a desired speed and torque output. The result is a motor that is capable of maintaining constant torque and of varying its speed as needed, resulting in high operating efficiencies, on the order of 60–70

percent at the sizes used in commercial refrigeration equipment . However, ECM motors can weigh about twice as much and are more expensive than equivalent PSC motors.

3.3.1.6 Variable-Speed Evaporator Fan Motors and Evaporator Fan Motor

Controllers

Evaporator fan motor controllers allow fan motors to run at variable speed to match changing conditions in the case. During periods of frost build-up, fan motor power requirements increase; during the post- defrost period, power requirements decrease. Evaporator fan speeds could be modulated to account for increased or decreased refrigeration load due to product loading, ambient conditions, or other factors. Evaporator fan motor controllers could also allow the case to run more efficiently at different ambient humidity and temperature levels.

3.3.1.7 Higher Efficiency Evaporator Fan Blades

High

- efficiency evaporator fan blades, or tangential evaporator fans, move air more efficiently, yielding energy consumption savings by reducing the required fan shaft power. The evaporator fans typically used in commercial refrigeration equipment have sheet metal blades. Usually, the fan blade manufacturer supplies the blades and the equipment manufacturer mounts

them . These fan blades are not optimized for commercial refrigeration equipment. Evaporator fans may have lower efficiencies due to the higher required pressure drops, for which sheet metal

fans are not well suited. Required fan shaft power could be reduced if the fan blades were optimized for each application.

Tangential fans are one technology that provide an opportunity to decrease equipment energy consumption. 38 

### Tangential fans u s e a vortex builder to generate a uniform airflow over a large surface. This makes them better suited to evaporator and condenser fans, which need to distribute airflow over a large coil s urface area. A single long tangential fan can meet the airflow requirements for an entire refrigerated cabinet, and would require only one high

- efficiency fan

motor.

3.3.1.8 Improved Evaporator Coil Design

The evaporator is a refrigerant

- to

- air heat exchanger comp osed of metals with high thermal conductivity such as aluminum and copper. It is responsible for evaporating and superheating the entering refrigerant liquid- vapor mixture while extracting heat from the air in the refrigerated space. The internal heat

- exchanging surfaces in contact with refrigerant are commonly referred to as “refrigerant -side, ” while the external heat

- exchanging surfaces in

contact with the air are referred to as “air -side.”

3- 33

### Depending on the requirements of the equipment, the evaporator can be designed to have a discharge air temperature (DAT) that can be roughly 5-15 ° F colder than the desired temperature of the refrigerated space

. Because a temperature difference is necessary to drive heat into the refrigerant from the air passing over the coil, the saturated evaporator temperature (SET) must be considerably colder than the DAT. The magnitude of this driving force is directly related to the total refrigeration load and the thermal characteristics of the evaporator.

Increasing the effective heat transfer surface area of the coil or improving other thermal

characteristics of the coil will decrease the necessary temperature differential between the SET and DAT, allowing for an increase in the SET. This results in an increased compressor ener gy efficiency ratio (EER) and lower compressor energy consumption.

Enhancements to the refrigerant

- side surface area of the evaporator typically include rifled or diamond-pattern tubing and an increase in the number of tube passes. Enhancements to

the air

- side surface area can include increased fin pitch (decreased fin spacing), fin patterns (wavy or zig -zag), and increased numbers of tube passes.

Increasing the overall size of the coil in one or more dimensions without changing other aspects of the coil is another way to increase the area. However, many applications place limits

on the feasible increase in the size of the coil

.

3.3.1.9 Low- Pressure Differential Evaporators

Decreasing the air -side pressure drop of an evaporator coil allows for the use of lower

pow er evaporator fan motors. This can be enabled through utilization of designs that reduce

restrictions to the flow of air across the coil

, such as reducing the fin pitch and decreasing the number of tube passes. Large spaces between fins and tubes also reduce “bridging” due to frost buildup, which can completely block airflow through portions of the coil.

3.3.1.10 Case Insulation Increases or Improvements

Either increasing the insulation thickness or reducing insulation conductivity will reduce the energy consumption of commercial refrigeration equipment. Typical baseline insulation thickness for refrigerated display cases ranges from 1 .5 inches for medium- temperature units to

2.5 inches for ice

- cream temperature units . This thickness also varies by manufacturer and

model. Foamed- in

- place polyurethane foam is used for most cases. Improved polyurethane foam

insulation can reduce conductivity, and thus case heat load. The improvement arises mainly due

to the formation of smaller air cells within the foam insulation stru cture and better cell

- size consistency. Additionally, a variety of blowing agents are available for use on the market, with different blowing agents resulting in different thermal properties for the foam produced. The impact of an increase in insulation th ickness or insulation quality is generally limited for open

cases because a large portion of the cooling load is due to infiltration. Cases with doors stand to gain more significant energy savings due to increased insulation thickness and/or quality.

3.3.1.11 Defrost Mechanism s

As the air in the refrigerated space is cooled, water vapor condenses on the surface of the evaporator coil. In refrigerators and freezers, where the evaporator coil is below 32 °F, this water

3- 34

### freezes as it collects, forming a growing layer of frost. The frost reduces cooling performance by increasing the thermal resistance to heat transfer from the coil to the air, and by obstructing

airflow. Both the method in which defrost is performed and control of the defrost cycle can lead to increased energy savings.

There are several methods available for defrosting the evaporator coil: off -cycle defrost, electric defrost, and hot

- gas defrost. Off -cycle defrost involves shutting off refrigerant flow to the coil while leaving the evaporator fan runnin g. This method is used where the refrigerated case air is above the freezing point of water and can be used to melt the frost. Electric defrost is

used when the case interior air temperature is not high enough to defrost the coil, and when defrost must occur quickly to prevent any significant rise in product temperature. Electric defrost involves melting frost by briefly turning on an electric resistance heater that is in contact with or near the evaporator coil.

Hot -gas defrost involves the use of the hot compressor discharge gas to warm the evaporator from the refrigerant side. Electricity usage is reduced in comparison to the electric defrost method because available heat, which would otherwise be rejected in the condenser, is used. The hot gas defrost sy stem requires more complicated piping and control than electric

defrost. An additional drawback is the thermal stress inflicted upon the refrigerant piping by the alternating flow of hot gas and cold refrigerant.

3.3.1.12 Defrost Cycle Control

Management of frost buildup on coils is essential in ensuring continued efficient operation of the unit. Traditionally, defrosting systems have been run on regular intervals utilizing a simple timer. However, such system s have two possible negative consequences in that the defroster may be run too often, resulting in wasted energy, or not often enough, resulting in decreased system performance. Some systems have been developed that allow for control of the

termination of defrost ing based on temperature; when the coils reach a specified temperature, the defroster is turned off. However, initiation of the cycle still occurs on a periodic basis using a

timer.

Control of the defrost cycle requires the use of sensors to determine whe ther a defrost cycle is needed. The data collected can consist of either the temperature drop across the coil or detection of the physical thickness of frost buildup using photocells. The first of these two methods is based on the idea that decreased airflow across the coil is a result of frost buildup, meaning that the temperature differential across the coil will increase. However, there are issues in that external factors aside from frost buildup on the coil may be the reason for decreased airflow. The second method is more accurate but requires more sophisticated sensors. There has

been significant research performed regarding the topic of defrost cycle control, and several

manufacturers have introduced controllers that are now commercially available . Ho wever, due largely to concerns about reliability and accuracy, these technologies have not achieved large

scale acceptance in the commercial refrigeration equipment market.

3- 35

### 3.3.1.13 Vacuum Insulated Panels

The importance of the amount of refrigerated volume that a unit offers and the desire to reduce unit energy consumption suggest that technologies that would allow an increase in insulation thermal resistivity (R

- value) while maintaining insulation thickness would be of

interest. VIPs could provide such performan ce. VIPs consist of an outer airtight membrane surrounding a core material to form a cavity , which is then evacuated to remove the air from the panel. The result is a product that performs in a manner similar to a traditional vacuum flask, resulting in gre atly reduced thermal conduction per unit of thickness as compared to traditional foam insulations. VIP technology has exist ed for many years, but has only recently begun to see widespread application, most notably in residential refrigerators, due to decreasing production

costs.

3.3.1.14 Occupancy Sensors for Lighting Controls

Lighting is one of the major sources of energy consumption in commercial refrigeration equipment. While higher efficiency lighting reduces the total lighting energy consumption, it

could be decreased further by employing occupancy sensors to switch off or dim the display case lighting when not necessary, such as during periods of low customer traffic and when the store is

closed. Occupancy sensors turn off or dim the case lighting when no motion is sensed near the case for a preset period of time.

Occupancy sensors are not used with fluorescent lighting due to restarting issues at low temperatures. However, LED lighting does not have such problems. Therefore, occupancy sensors can be reliably used only in conjunction with LED lighting.

3.3.2 Designs Relevant Only to Equipment with Doors

The following technologies and designs are relevant only to equipment classes with doors: improved transparent doors, anti -fog films on transparent doors, and anti

- sweat heater

controllers.

3.3.2.1 Improved Transparent Doors

Transparent doors allow refrigerated products to be displayed to consumers while keeping cold air inside the display case. Transparent doors a lso generally have a lower thermal

resistance than solid insulated walls or doors, thus allowing heat transfer into the refrigerated space at a higher rate. In cases with transparent doors, these surfaces are responsible for a significant portion of the case heat load. On freezers and some refrigerators, glass doors must

also be heated to prevent frost or condensation from forming on the outside . These “anti

- sweat” heaters often run continuously and consume significant amounts of energy, and also contribute to

the case heat load . T otal case energy consumption can be reduced both by improving the overall insulation value (U-factor) of the door and by reducing the required anti- sweat heater power. Improvements to heat transfer performance could include the use of additional panes of glass and expanded use of inert gas filled panes using argon, krypton, or xenon. The treatment of the glass with advanced low emissivity coatings and increasing the number of coated surfaces could also

reduce losses due to radiation heat transfer. Additionally, improvements to door frame insulation and gasketing would reduce conduction and infiltration losses. Reductions in the anti- sweat

3- 36

### heater power needed can often be achieved as a function of improved conductive performance of

th e door, as well as in improvements to the gasketing and other door features. This is because the

exterior surface of a better -insulated door will have a higher temperature than one with poor insulating properties, thus making condensation on the exterior of the door less likely

.

3.3.2.2 Anti -Fog Films on Transparent Doors

Most commercial refrigeration equipment with transparent doors for merchandising and product access currently utilize s anti -sweat heaters to prevent fogging and condensation buildup. However, the re are anti- fog treatments composed of advanced hydrophobic materials that prevent condensate from attaching or lingering on a glass surface and therefore prevent the formation of water droplets that may obscure a customer’s view of the product. Such anti- fog films could potentially be used on transparent doors as a passive means of preventing fogging, eliminating the need for active energy -consuming systems.

3.3.2.3 Anti

- Sweat Heater Controllers

Anti

- sweat heater controllers match the run time of anti

- sweat heaters to the anti

- sweat heating requirements imposed by the ambient humidity, reducing energy consumption when ambient humidity is low. Anti

- sweat heaters are used to prevent moisture condensation on surfaces of display cases, the temperat ures of which can be below the ambient dew point.

Freezer

- door gaskets are a typical example of such a surface. Anti

- sweat heaters on

freezer -door gaskets are typically always on, but the installation of controls that sense ambient

conditions can turn off the heater s when anti

- sweat heating is not required, reducing electricity consumption. Such control requires measuring the local dew point or relative humidity, and measuring external surface temperatures. A particular heater can be turned on when the temperature of a particular surface falls below the dew point, or the heaters can be cycled with

on- times increasing with dew point. Reducing anti

- sweat heater on

- time will also yield additional energy savings in display- case heat load reductions, since anti

- sweat heaters

contribute to case heat load.

It is possible that electric anti -sweat heaters could be replaced by a hot gas line running

around the door frame. Although manufacturers of commercial refrigeration equipment have

claimed that this is a difficul t technology to implement, it has seen widespread and successful

use in residential freezers.

3.3.3 Designs Relevant Only to Equipment Without Doors

The following technologies and designs are relevant only to equipment classes without

doors: air curtain design a nd night curtains.

3.3.3.1 Air Curtain Design

Refrigerated display cases without doors allow consumers easy access to products while maintaining temperatures that ensure food safety. In the absence of doors, a circulated air curtain

is used to prevent infiltratio n and keep cold air inside the case. The refrigeration load for these cases is dominated by infiltration, or the entrainment of warm and moist air into the curtain. This

3- 37

### infiltration adds both sensible and latent heat to the case, and deposits additional moisture on the evaporator coil, which must be removed through defrosting.

Improved air curtain design is aimed at lessening the impact of infiltration by reducing

the entrainment of warm ambient air. Making the air curtain flow as laminar as p ossible reduces

entrainment. This involves configuring the plenum before the air curtain discharge grill to shape the velocity profile using a honeycomb grill to align the airstreams and encourage laminar flow. Improvements to the velocity profile and disc harge air grill may enhance the performance of the

air curtain and reduce the infiltration load.

3.3.3.2 Night Curtains

Open display cases have a high rate of infiltration of warm external air into the refrigerated case interior . During store operating hours, when the refrigerated products are being taken out by customers and restocked by staff, such infiltration losses are inevitable. However, infiltration could be substantially reduced during the hours when the store is closed by using

infiltration reduction mech anisms called night curtains. Night curtains typically take the form of a flexible barrier, often composed of plastic or metalized fabric, which can be pulled down over the open case and fastened to provide a temporary cover over the opening. These devices reduce

the heat and moisture entry into the refrigerated space through various heat transfer mechanisms. By fully or partially covering the case opening, night curtains reduce the convective heat transfer into the case through reduced air infiltration. Ad ditionally, they provide a measure of insulation, reducing conduction into the case, and also decrease radiation into the case by blocking radiated heat from entering the refrigerated space.

3.3.4 Technologies and Designs Relevant Only to Self- Contained Equipment

The following technologies and designs are relevant only to self- contained equipment: higher efficiency compressors, liquid suction heat exchangers, improved condenser coil design, higher efficiency condenser fan motors, variable -speed condenser fan motors and condenser fan motor controllers, and higher efficiency condenser fan blades.

3.3.4.1 Higher Efficiency Compressors

Several technologies exist to increase the efficiency of commercial refrigeration equipment compressors. High

- efficiency reciprocating and scroll compressors, sometimes incorporating variable-speed motors, all have the potential to reduce energy consumption compared to the traditional reciprocating compressors commonly used in commercial refrigeration equipment.

Scroll compressors compress gas between two spirals, one fixed and one rotating. This

method is fundamentally different from that of traditional compressors. High

- efficiency reciprocating compressors are as efficient, or more efficient, than scroll compres sors. However, when compared to scroll compressors, some drawbacks exist to the use of high- efficiency reciprocating compressors , including noise, cost, and reliability.

3- 38

### Variable -speed compressors are implemented through an electronic control on the compressor motor, which allows the motor to operate at different speeds. Variable

- speed compressors reduce energy consumption in three ways: e



### 1. When refrigerant flow is reduced during part-load operation, the condenser and evaporator (designed for full flow conditions) are more effective and thus more

efficient. Temperature drops decrease, resulting in reduced pressure rise across the compressor, which also improves efficiency.

2. Close matching of load eliminates the cycling that occurs with single

- stage

compr essors. Maintaining a constant pressure is more efficient because losses at higher pressure rise are greater than gains at lower pressure rise.

3. During the off cycle, the pressure in the system equilibrates. At intermediate pressure, refrigerant vapor will condense in the cold evaporator rather than the condenser. Essentially, some of the heat rejection load is rejected to the evaporator during this time, reducing overall system performance. Variable -speed operation would eliminate or significantly reduce c ompressor off

- time and the related inefficiencies.

However , scroll and variable-speed compressor technologies have not become present in the domestic commercial refrigeration equipment market on a significant scale, and DOE’s research

has indicated that a significant amount of research and development work would be required before these technologies could reach the market. Instead, commercial refrigeration equipment manufacturers often rely on compressor manufacturers to continuously update their designs f or traditional reciprocating compressors. As updated models of reciprocating compressors, usually featuring higher efficiencies than the models they replace, appear on the market, these models are generally incorporated into the equipment offerings of the commercial refrigeration unit

manufacturers.

3.3.4.2 Liquid Suction Heat Exchangers

The goal of a liquid suction heat exchanger is to further cool the flow of liquid refrigerant entering the expansion valve using the flow of gaseous refrigerant leaving the evaporator. The exchanger provid es sub-cooling for the entering liquid by super- heating the exiting suction vapor. Hotter suction vapor is less susceptible to heat gains in the return piping to the compressor

rack. The compressor work is increased, however, because the suction vapor has greater enthalpy. In addition, the possibility of compressor overheating problems brought on by the combination of increased compressor work and hotter vapor limits the use of this method in

some situations. The possibility for these problems and the potential efficiency gains f rom liquid suction heat exchangers depend on several factors, including evaporator temperature, type of refrigerant used, and system pressures. Additionally, some parties have expressed concern regardi ng the reliability of liquid suction heat exchangers.

e Variable

- speed compressors typically increase efficiency over a broad operating range but do not inherently increase maximum efficiency at the compressor ratin g point or any other single set of operating conditi ons.

3- 39

### 3.3.4.3 Improved Condenser Coil Design

Like the evaporator, the condenser is a refrigerant

- to

- air heat exchanger composed of metals with high thermal conductivity , such as aluminum and copper. It is responsibl e for condensing and sub- cooling the entering refrigerant vapor while rejecting heat from the refrigerant into the ambient air.

The condenser ’s saturated condenser temperature (SCT) is markedly warmer than the

ambient air , with the exact temperature differ ential being a function of the equipment design and operating conditions. As with evaporator coils, increasing the area of the condenser coil or otherwise improving its heat transfer capability will decrease the necessary temperature

differential across the coil and therefore allow for a decrease in the SCT, resulting in increased compressor efficiency ( and thus increased EER).

Enhancements to the refrigerant

- side surface area of the condenser typically include rifled or diamond-pattern tubing and an increase in the number of tube passes. Enhancements to

the air

- side surface area can include increased fin pitch (decreased fin spacing), fin patterns (wavy or zig -zag), and increased numbers of tube passes.

Increasing the overall size of the coil in one or mor e dimensions without changing other aspects of the coil is another way to increase the area. However, many applications place limits

on the feasible increase in the size of the coil.

3.3.4.4 Higher Efficiency Condenser Fan Motors

Condenser fan motors are responsible for moving air across the condenser coil and typically run at one speed. The manufacturer matches the motor size and blade to the condenser coil to meet the expected load on the case under most conditions. Higher efficiency condenser

fan motors reduce energy consumption by requiring less electrical power to generate motor shaft

output power. Condenser fan motors are generally of the same size and type as evaporator fan

motors. See section 3.3.1.5 for a discussion of higher efficiency fan motor technology.

3.3.4.5 Variable- Speed Condenser Fan Motors and Condenser Fan Motor

Controllers

Condenser fan motor controllers could allow fan motors to run at variab le speed to match changing conditions in the case. Matching the fan speed to varying conditions and heat loads could then improve system performance

, allowing the refrigeration system to run more efficiently at different ambient humidity and temperature le vels. However, under constant

ambient conditions, the energy savings benefit with condenser fan motor controllers would likely

be small

.

3.3.4.6 Higher Efficiency Condenser Fan Blades

Conventional fans have sheet metal blades mounted to a central hub, and are generally not optimized for the specific application in which they will be used. Instead, they are designed for mass production and scalability in order to minimize production cost and waste. Optimization of fan design for specific applications could significantly reduce input energy needed in order to

3- 40

### perform the necessary work. Higher efficiency fan blades could be capable of moving more air at a given rotational speed when compared to traditional fan blades. This means that a smaller motor could be used, or the existing motor could be run at a lower speed, resulting in direct energy savings.

3- 41

### REFERENCES

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Standards /sizetablestandards.txt > The June 25, 2007 material from this website is

available in Docket EE -2006- STD -0126. For more information, contact Brenda Edwards-Jones at (202) 586-2945.

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5 Natural Resources Canada

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freezers

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Australian Greenhouse Office. Requirements for Commercial Refrigeration . 2004. (Last accessed June 27, 2007.)

< http://www.energyrating.gov.au/commrefrig1.html > The June 27, 2007 material

from this website is available in Docket EE -2006- STD -0126. For more information,

contact Brenda Edwards -Jones at (202) 586-2945.

7. U.S. Environmental Protection Agency, ENERGY ST AR. Commercial Refrigerator and Freezers Key Product Criteria . 2010. (Last accessed March 9, 2011.) <https://www.energystar.gov/index.cfm?c=archives.comm_refrigerators_freezers>

8. Consortium for Energy Efficiency, Commercial Kitchens Initiative. High Eff iciency Specifications for Commercial Refrigerators and Freezers . 2010. (Last accessed August 30, 2010.) < http://www.cee1.org/com/comkit/files/RefrigerationSpecification.pd f >

9. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy, Federal Energy Management Program. Procuring Energy- Efficient Products . 2010. (Last accessed August 30, 2010.)

< http://www1.eere.energy.gov/femp/technologies/procuring_eeproducts.html >

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### 10. Efficiency Vermont. Refrigeration and Controls Rebates . 2011. (Last accessed March 9, 2011.) < http://www.efficiencyvermont.com/for_my_business/ways- to

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< http://www.energysmartgrocer.org/pdfs/PGE/2010_12%20EnergySmartGrocer%2 0IncentiveWorksheet_v2.pdf >

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< http://www.otpco.com/SaveEnergyMoney/Rebates.asp >

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16. North American Association of Food Equipment Manufacturers. 2004 Size and Shape of the Industry . 2004. Chicago, IL

17. North American Association of Food Equipment Manufacturers. 2002 Size and Shape of the Industry . 2002. Chicago, IL

18. Freedonia Group, Incorporated. Commercial Refrigeration Equipment to 2014

. 2010. Cleveland, OH. Study 2661.

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. Commercial Refrigeration Equipment to 2008

. 2004. Cleveland, OH. Study 1849.

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. 2007 Economic Census, Manufacturing, Industry Series, AirConditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing: 2007. October 2009. EC07- 31I1 -333415. Washington, D.C.

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. 2002 Economic Census, Manufacturing, Industry Series, AirConditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing: 2002 . December 2004. EC02-31I-333415 (RV). Washington, D.C.

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. 1997 Economic Census, Manufacturing, Industry Series, AirConditioning and Warm Air Heating Equipment and Commercial and Industrial

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### Refrigeration Equipment Manufacturing: 1997 . September 1999. EC97M-3334D. Washington, D.C.

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< http://www.irs.gov/businesses/article/0,,id=151273,00.html > The June 25, 2007

material from this website is available in Docket EE -2006- STD -0126. For more information, contact Brenda Edwards-Jones at (202) 586-2945.

24. Sand, J.R., S.K. Fisher, and V.D. Baxter. Energy and Global Warming Impacts of HFC Refrigerants and Emerging Technologies . 1997. Oak Ridge National Laboratory: Oak Ridge, TN. (Last accessed June 25, 2007.)

< http://www.afeas.org/tewi/tewi -iii.pdf > The June 25, 2007 material from this

website is available in Docket EE -2006- STD -0126. For more information, contact Brenda Edwards-Jones at (202) 586-2945.

25. Heshong Mahone Group, Inc. New York Energy Smart Program Cost- Effectiveness

Assessment December 2004

. Prepared for the New York State Energy Research and Development Authority . 2004. (Last accessed June 26, 2007.)

< http://www.nyserda.org/Energy_Information/SBC/cost_ef fectiveness.pdf > The June 26, 2007 material from this website is available in Docket EE-2006- STD

0126. For more information, contact Brenda Edwards-Jones at (202) 586-2945.

26. Glasch, V. Personal communication . Verisae. Communication with Keith Glasch.

2006.

27. Hepperla, P. Personal communication . Verisae. Communications with Paul Hepperla. 2006.

28. Mark Ellis & Associates. New Zealand Minimum Energy Performance Standards for Commercial Refrigeration Cabinets, Prepared for the Energy Efficiency and Conservation Authority . 2003. (Last accessed June 26, 2007.)

< http://www.eeca.govt.nz/eeca- library/products/standards/report/meps -forcommercial

- refridgeration

- cabinets -03.pdf > The June 26, 2007, material from this

website is available in Docket EE -2006- STD -0126. For more information, contact Brenda Edwards-Jones at (202) 586-2945.

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- Miller. Development and Demonstration of an Advanced Supermarket Refrigeration/HVAC System . 2001. Oak Ridge National Laboratory: Oak Ridge,

TN. Report No. ORL-970163.

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− Office of Air and Radiation. U.S. High

GWP Gas Emissions 1990–2010: Inventories, Project ions, and Opportunities for Reductions, June 2001 . 2001. (Last accessed June 26, 2007.)

< http://www.epa.gov/highgwp/projections.html > The June 26, 2007, material from

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### this website is available in Do cket EE -2006- STD -0126. For more information,

contact Brenda Edwards -Jones at (202) 586-2945.

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< http://www.arap.org/adlittle/toc.html > The June 26, 2007 material from this

website is available in Docket EE -2006- STD -0126. For more information, contact Brenda Edwards-Jones at (202) 586-2945.

32. California Energy C ommission. Update of Appliance Efficiency Regulations: California Energy Commission Staff Report, November 2004 . 2004. California Energy Commission: Sacramento, CA. Report No. 400-04-007F.

33. Hansen, M. Personal communication . Key Mechanical, Kent, WA . Communication with Mary Hansen. 2006.

34. Intergovernmental Panel on Climate Change, World Meteorological Organization/United Nations Environment Programme . Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

. Chapter 3

, Good Practices. 2001. (Last accessed June 26, 2007.) < http://www.ipcc

nggip.iges.or.jp/public/gp/english/3_Industry.pdf > The June 26, 2007 material from

this website is avail able in Docket EE -2006- STD -0126. For more information,

contact Brenda Edwards -Jones at (202) 586-2945.

35. Fischer, S., et al

. Energy and Global Warming Impacts of CFC Alternative Technologies . 1991. Prepared by Oak Ridge National Laboratory and Arthur D. Little, Inc. Sponsored by Alternative Fluorocarbons Environmental Acceptability Study (AFEAS) and the U.S. Department of Energy.

36. Navigant Consulting, Inc. Energy Savings Potential and R&D Opportunities for Commercial Refrigeration. Final Report. 2009. Submitted to the U.S. Department of Energy’s Energy Efficiency and Renewable Energy Building Technologies Program. Washington, D.C.

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37. Leeson. Single-phase Electric Motors . 2006. (Last accessed on June 26, 2007.)

< http://www.iprocessmart.com/leeson/leeson_singlephase_article.htm > The June 26, 2007 material from this website is available in Docket EE-2006- STD -0126. For more information, contact Brenda Edwards-Jones at (202) 586-2945.

38. Walker, D.H. Invest igation of Energy- Efficient Supermarket Display Cases . 2004. Oak Ridge National Laboratory: Oak Ridge, TN. Report No. ORNL/TM-2004/292.

3- 45

### CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION .............................................................................................................. 4-1

4.2 TECHNOLOGIES THAT DO NOT CONSISTENTLY AFF ECT CALCULATED

DAILY ENERGY CONSUMP TION ................................................................................. 4-2

4.2.1 Higher Efficiency Expansion Valves ...................................................................... 4-2

4.2.2 Variable -Speed Condenser Fans and Condenser Fan Motor Controllers ............... 4-3

4.2.3 Anti

- Sweat Heater Controllers ................................................................................ 4-3

4.2.4 Liquid-Suction Heat Exchangers ............................................................................ 4-3

4.3 SCREENED

- OUT TECHNOLOGY

– AIR CURTAIN DESIGN ..................................... 4-4

4.4 REMAINING TECHNOLOGI ES ...................................................................................... 4-4

4-i

### CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter describes the screening analysis that the U.S. Department of Energy (DOE)

has performed in support of the energy conservation standards rulemaking for commercial refrigeration equipment.

In the market and technology assessment (c hapter 3 of t his technical support document (TSD) ), DOE presented an initial list of technologies that have the potential to reduce the energy consumption of commercial refrigeration equipment. The goal of the screening analysis is to screen out technologies that will not be considered further in the rulemaking analyses. DOE

evaluated t he list of remaining technologies using the screening criteria set forth in the Energy Policy and Conservation Act (EPCA). (42 U.S.C. 6311–6317)

Section 325(o) of EPCA establishes criteria for prescribing new or amended standards that are designed to achieve the maximum improvement in energy efficiency. Further, 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), as directed by 42 U.S.C. 6316(a)(1)

− (3) ) In addition, EPCA establishes guidelines for determining whether a standard is economically justified. (42 U.S.C. 6295(o)(2)(B)) In view of the EPCA requirements for determining whether

a stand ard is technologically feasible and economically justified, Appendix A to subpart C of 10

CFR part 430, “Procedures, Interpretations, and Policies for Consideration of New or Revised Energy Conservation Standards for Consumer Products” (the Process Rule), sets forth procedures to guide DOE in its consideration and promulgation of new or revised efficiency standards. These procedures elaborate on the statutory criteria provided in 42 U.S.C. 6295(o) and, in part, eliminate inapplicable technologies early in t he process of prescribing or amending an energy efficiency standard. In particular, sections 4(b)(4) and 5(b) of the Process Rule provide guidance to DOE in determining whether to eliminate from consideration any technology that presents unacceptable probl ems with respect to the following criteria

.

Technological feasibility

. Technologies incorporated in commercial equipment or in working prototypes will be considered technologically feasible.

Practicability to manufacture, install, and service . If mass production of a technology in commercial equipment and reliable installation and servicing of the technology could be achieved on the scale necessary to serve the relevant market at the time of the effective date of the standard, then that technology will be considered practicable to manufacture, install, and

service.

Impacts on equipment utility or equipment availability

. If DOE determines that a technology will have significant adverse impact on the utility of the equipment to significant subgroups of consumers, or result in the unavailability of any covered equipment type with performance characteristics (including reliability), features, sizes, capacities, and volumes that are substantially the same as equipment generally available in the United States at th e time, DOE

will not consider it further.

4-1

### Adverse impacts on health or safety

. If DOE determines that a technology will have significant adverse impacts on health or safety, DOE will not consider it further.

In sum mary, if DOE determines that a technology, or a combination of technologies, has unacceptable effects on the policies stated in section 5(b) of the Process Rule, it will be eliminated from consideration. If a particular technology fails to meet one or more of the four criteria, it will be screened out. The reasons for eliminating any technology are documented in

section 4.3

.

4.2 TECHNOLOGIES THAT DO NOT CONSISTENTLY AFFECT CALCULATED

DAILY ENERGY CONSUMP TION

DOE understands that some of the technologies presented in c hapter 3 of this TSD may potentially reduce annual energy consumption for specific pieces of equipment under certain field operating conditions, but may not consistently reduce calculated daily en ergy consumption (CDEC), as measured by the DOE test procedure, utilizing the American National Standards Institute/American Society of Heating, Refrigerating and Air

- Conditioning Engineers (ANSI/ASHRAE) Standard 72

, Method of Testing Commercial Refrigerat ors and Freezers

, for all units of a given class

. In s o me cases, while a technology may benefit a specific individual model or unit produced by a given manufacturer, it may not necessarily be considered to have

similar effects across all units of that same equipment class , depend ing on the design of each unit. Moreover, certain technologies may provide benefits within specific operating conditions that they may encounter in some field applications, but would not produce benefits within the scope of the ASHRAE method of test. Therefore, DOE removed from consideration those technologies that cannot be considered to consistently a ffect or reduce CDEC during the tests across the range of equipment analyzed . These technologies include higher efficiency expansion valves, variable -speed condenser fans and condenser fan motor controllers, anti

- sweat heater controllers, and liquid

- suction heat exchangers (LSHXs)

.

4.2.1 Higher Efficiency Expansion Valves

Higher efficiency expansion valves can reduce the annual energy consumption in some commercial refrigeration units by controlling refrigerant flow to adapt to varying loads and ambient conditions. However, this is largely a function of the design of a spec ific unit. While some models may benefit from the use of a higher efficiency expansion valve, others may not see the same efficiency increase due to a refrigeration system being configured differently. Moreover, while there are small thermodynamic fluctuat ions during the ASHRAE 72 test, the

test as a whole is conducted under ambient and internal conditions that are held as close to constant as possible. Much of the advantage in the field performance of improved expansion valves arises from their ability to adapt to extreme variations in conditions, which are not likely to be experienced during the ASHRAE 72 test. Therefore, since this technology could not be found to have a consistent efficiency improvement effect across entire equipment classes

, DOE did not consider higher efficiency expansion valves in the engineering analysis.

4-2

### 4.2.2 Variable- Speed Condenser Fans and Condenser Fan Motor Controllers

Variable -speed condenser fan motors and controllers driving the condenser fan motors can adapt condenser operation t o changing ambient temperatures (effectively by creating floating head pressure), and thereby could reduce the energy consumption of self- contained commercial refrigeration equipment operating in areas with varying ambient conditions. However, because

test ing under the ANSI/ASHRAE Standard 72 test procedure is conducted at a constant ambient temperature, there is little opportunity to account for the adaptive technology of varying condenser fan motor speed to reduce CDEC. Moreover, DOE understands that cond enser fan

motor controllers would function best when paired with a variable-speed modulating compressor, a technology that DOE understands to be only in the early stages of implementation in this industry. Therefore, DOE did not consider variable

- speed con denser fan motors or condenser fan motor controllers in the engineering analysis.

4.2.3 Anti

- Sweat Heater Controllers

A commercial refrigeration equipment manufacturer typically sizes anti

- sweat heaters according to the ambient temperature and humidity of a particular operating environment. T he

end

- user must maintain that environment to prevent condensation ( i.e., fog) from forming on surfaces such as display case glass. Anti -sweat heater controllers modulate the operation of antisweat heaters by reducing anti -sweat heater power when humidity is low. Anti

- sweat heater controllers operate most effectively when a constant ambient dew point cannot be maintained. However, in the context of the test procedure, anti- sweat heater controllers will solely serve to keep the power to the anti- sweat heaters at the levels necessary for the test conditions. These

fixed conditions of 75 ° F and 55 percent relative humidity are the conditions that ASHRAE has determined to be generally representative of commercial refrigerati on equipment operating environments and which DOE has adopted in its test procedure. While anti

- sweat heater

controllers could modulate the anti- sweat power to a further extent in the field so as to account for more or less extreme ambient conditions, a sy stem equipped with anti- sweat heater controllers will not likely exhibit significantly different performance at test procedure conditions

than a unit with anti- sweat heaters tuned for constant 75/55 conditions. Therefore, DOE did not

consider anti- sweat heater controllers in the engineering analysis.

4.2.4 Liquid

- Suction Heat Exchangers

An LSHX is an indirect liquid

- to

- vapor heat transfer device that evaporate s any residual liquid refrigerant that remain s in the evaporator discharge line, and thereby minimizes th e risk of liquid refrigerant carrying over in to the compressor. G eneral ly , LSHXs are installed in refrigeration systems to ensure proper system operation and increase system performance. However, the performance of an LSHX is dependent on the specific desi gn used in a given piece of equipment, as well as other properties of the system and the operating conditions. A combination of refrigerant type, operating temperature, ambient conditions, and other factors

determines whether an LSHX will increase or decrease the CDEC as measured by the

ANSI/ASHRAE Standard 72 test procedure. In some cases, an LSHX can produce enough of a pressure differential across the device, which requires additional compressor energy to overcome, that the result is a net increase in en ergy consumption. Manufacturers have stated that, while an LSHX can reduce energy consumption in a lower efficiency or baseline system, these

4-3

### devices often produce negative energy impacts in more advanced equipment designs. DOE has

also heard from stakehol ders that LSHXs may have issues with unreliability, resulting in refrigerant leakage and increased system energy consumption. Because LSHX s do not consistently reduce CDEC in the equipment classes analyzed , DOE did not consider this technology in the engineering analysis.

4.3 SCREENED

- OUT TECHNOLOGY

– AIR CURTAIN DESIGN

An air curtain is a fan

- powered device that creates a moving wall (curtain) of air, which separates two spaces of different temperatures. Air curtains are used in commercial refrigeration equipment to minimize the infiltration of warm er external air into the refrigerated space. In its market and technology assessment ( TSD chapter 3), DOE noted that its research had presented the possibility of advanced air curtain designs with levels of performance beyond the traditional air curtains generally employed in open display cases being used in the commercial refrigeration equipment industry. However, DOE has determined that advanced air curtain designs are only in the research stage and

, therefore, it would be im practicable to manufacture, install, and service this technology on the scale necessary to serve the relevant market at the time the standard

becomes effective

. Sections 4(a) and 5(b) of the Process Rule specifically set “practicability to manufacture, install, and service” as a criterion that should be satisfied for technology to be considered as a design option. Therefore, DOE screened out improved air curtains as a design option for improving the energy efficiency of commercial refrigeration equipment.

4.4 REMAINING TECHNOLOGIES

After eliminating those technologies that do not reduce CDEC and screening out technologies that do not meet the requirements of sections 4(a)(4) and 5(b) of the Process Rule, DOE is considering the following technologi es :

• higher efficiency lighting

• higher efficiency lighting ballasts

• remote lighting ballast location

• higher efficiency evaporator fan motors

• variable -speed evaporator fan motors and evaporator fan motor controllers

• improved evaporator coil design

• higher efficiency evaporator fan blades

• low- pressure differential evaporators

• case insulation increases or improvements

• defrost mechanisms

• defrost cycle controls

• vacuum insulated panels

• occupancy sensors for lighting controls

• improved transparent doors (equipment with doors only)

• anti -fog films on transparent doors (equipment with doors only)

• night curtains (equipment without doors only)

• higher efficiency compressors (self -contained equipment only)

• improved condenser coil design (self -contained equipment only)

4-4

### • higher efficiency condenser fan motors (self -contained equipment only)

• higher efficiency condenser fan blades (self -contained equipment only)

4-5

### CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 METHODOLOGY OVERVIEW .................................................................................... 5-1

5.3 EQ UIPMENT CLASSES ANAL YZED .......................................................................... 5-3

5.3.1 Classes Chosen for Analysis ................................................................................ 5-3

5.3.2 Baseline Equipment ............................................................................................. 5-4

5.3.3 Service over Counter Equipment ......................................................................... 5-5

5.4 COST MODEL ................................................................................................................ 5-6

5.4.1 Development of Core Case Costs ........................................................................ 5-6

5.4.1.1 Selection of Units for Teardown Analysis ............................................... 5-7

5.4.1.2 Manufacturer Production Cost Estimates and Assumptions .................... 5-7

5.4.1.3 Structure of the Cost Model ..................................................................... 5-8

5.4.1.4 Material Prices ......................................................................................... 5-9

5.4.1.5 Results .................................................................................................... 5-10

5.4.2 Design Option Costs .......................................................................................... 5-10

5.4.2.1 Light

- Emitting Diode Price Forecasting ................................................ 5-11

5.4.3 Representative Unit Manufacturer Production Cost Values .............................. 5-11

5.5 MANUFACTURER SELLING PRICE ........................................................................ 5-12

5.5.1 Manufacturer Markup ........................................................................................ 5-12

5.5.2 Shipping Costs ................................................................................................... 5-13

5.6 ENERGY CONSUMPTION MODEL .......................................................................... 5-13

5.6.1 Representative Unit Design Specifications ........................................................ 5-13

5.6.1.1 Anti

- Sweat Heat for Solid -Door Models ............................................... 5-15

5.6.1.2 Compressor Oversize Multipliers .......................................................... 5-15

5.6.1.3 Condensate Pan Heaters ......................................................................... 5-15

5.6.1.4 Electric Defrost Performance ................................................................. 5-16

5.6.1.5 Fan Motor Specifications ....................................................................... 5-16

5.6.1.6 Infiltrated Air Mass Values .................................................................... 5-16

5.6.1.7 Evaporator Temperature Modeling ........................................................ 5-16

5.6.2 Non

- Numerical Assumptions ............................................................................. 5-17

5.6.3 Numerical Constants and Assumptions ............................................................. 5-18

5.6.4 Screened -In Technologies .................................................................................. 5-18

5.6.5 Screened -In Technologies Not Considered in the Engineering Analysis .......... 5-19

5.6.5.1 Remote Lighting Ballast Location ......................................................... 5-19

5.6.5.2 Variable -Speed Evaporator Fan Motors and Evaporator Fan

Motor Controllers............................................................................................... 5-20

5.6.5.3 Higher Efficiency Evaporator and Condenser F an Blades .................... 5-20

5.6.5.4 Low

- Pressure Differential Evaporators ................................................. 5-20

5.6.5.5 Defrost Cycle Control ............................................................................ 5-21

5.6.5.6 Defrost Mechanisms .............................................................................. 5-21

5.6.5.7 Anti -Fog Films on Transparent Doors ................................................... 5-21

5.6.6 Design Options ................................................................................................... 5-22

5.6.6.1 Higher Efficiency Lighting and Occupancy Sensors ............................. 5-22

5-i

### 5.6.6.2 Higher Efficiency Evaporator Fan Motors ............................................ 5-25

5.6.6.3 Improved Evaporator Coil Design ......................................................... 5-26

5.6.6.4 Improved Insulation and Va cuum Insulated Panels ............................... 5-27

5.6.6.5 Improved Transparent Doors ................................................................. 5-29

5.6.6.6 Higher Efficiency Condenser Fan Motors ............................................. 5-32

5.6.6.7 Improved Condenser Coil Design .......................................................... 5-32

5.6.6.8 Higher Efficiency Compressors ............................................................. 5-33

5.6.6.9 Night Curtains ........................................................................................ 5-35

5.6.7 Mo del Components ............................................................................................ 5-35

5.6.7.1 Component Energy Consumption .......................................................... 5-36

5.6.7.2 Compressor Energy Consumption ......................................................... 5-37

5.6.7.3 Component Load Model ........................................................................ 5-38

5.6.7.4 Radiation Load Model ........................................................................... 5-39

5.6.7.5 Conduction Load Model ........................................................................ 5-39

5.6.7.6 Infiltration Load Model.......................................................................... 5-40

5.6.8 Validation of Energy Consumption Model ........................................................ 5-40

5.7 COST

- EFFICIENCY CURVES .................................................................................... 5-41

5.8 OFFSET FACTORS ...................................................................................................... 5-67

5.9 EXTENSIO N OF ANALYSIS TO SECONDARY EQUIPMENT CLA SSES ............ 5-69

5.9.1 Development of Extension Multiplier Approach in 2009 Rulemaking ............. 5-70

5.9.2 Current Use of Extension Multiplier Methodology ........................................... 5-72

REFERENCES .......................................................................................................................... 5-74

LIST OF TABLES

Table 5.3.1 Equipment Classes Analyzed in the Engineering Analysis ...................................... 5-4 Table 5.4.1 Cost Model Output Classifications ........................................................................... 5-7 Table 5.4.2 Examples of Major Manufacturing Processes .......................................................... 5-8 Table 5.6.1 Representative Unit Specifications ......................................................................... 5-14 Table 5.6.1 Energy Consumption Model Numerical Constants and Assumptions .................... 5-18 Table 5.6.2 Details for Lighting for VOP, SVO, HZO, and SOC Equipment Families Design Option .................................................................................................... 5-25 Table 5.6.3 Details for Lighting for VCT and PD Equipment Families Design Option ........... 5-25 Table 5.6.4 Details for Evaporator Fan Motor Design Option .................................................. 5-26 Table 5.6.5 Details for Evaporator Coil Design Option ............................................................ 5-26 Table 5.6.6 Properties of Standard and Enhanced Evaporator Coil Designs ............................. 5-27 Table 5.6.7 Assumptions in Cost Calculation Methodology for Insulation Thickness Increase5-28 Table 5.6.8 Assumptions in Cost Calculation Methodology for Vacuum Insulated Panels ...... 5-28 Table 5.6.9 Details for Insulation Design Options .................................................................... 5-29 Table 5.6.10 Details for Doors for VCT Equipment Family, Low and Ice- Cream Temperature Design Option .................................................................................................... 5-30 Table 5.6.11 Details for Doors for VCT and PD Equipment Families, Medium Temperature Design Option .................................................................................................... 5-30 Table 5.6.12 Details for HCT Equipment Family, Medium Temperature Design Option ........ 5-31 Table 5.6.13 Details for Doors for HCT Equip ment Family, Low, and Ice

- Cream Temperature Design Option .................................................................................................... 5-31

5- ii

### Table 5.6.14 Details for Doors for SOC Equipment Family, Medium Temp erature Design Option

............................................................................................................................ 5-32 Table 5.6.15 Details for “Increased Condenser Surface Area” Design Option ......................... 5-32 Table 5.6.16 Properties of Standard and Enhanced Condenser Coil ......................................... 5-33 Table 5.6.17 Details for “Night Curtains” Design Option ......................................................... 5-35 Table 5.7.1 Figure, Table, and Page Numbers for Cost- Efficiency Results .............................. 5-42

Table 5.7.2 Cost- Efficiency Data for the VOP.RC.M Equipment Class ................................... 5-43

Table 5.7.3 Cost- Efficiency Data for the VOP.RC.L Equipment Class .................................... 5-44

Table 5.7.4 Cost- Efficiency Data for the VOP.SC.M Equipment Class ................................... 5-45

Table 5.7.5 Cost- Efficiency Data for the SVO.RC.M Equipment Class ................................... 5-46

Table 5.7.6 Cost- Efficie ncy Data for the SVO.SC.M Equipment Class ................................... 5-47

Table 5.7.7 Cost- Efficiency Data for the HZO.RC.M Equipment Class ................................... 5-48

Table 5.7.8 Cost- Efficiency Data for the HZO.RC.L Equipment Class .................................... 5-49

Table 5.7.9 Cost- Efficiency Data for the HZO.SC.M Equipment Class ................................... 5-50

Table 5.7.10 Cost- Effici ency Data for the HZO.SC.L Equipment Class .................................. 5-51 Table 5.7.11 Cost-Efficiency Data for the VCT.RC.M Equipment Class ................................. 5-52

Table 5.7.12 Cost- Efficiency Data for the VCT.RC.L Equipment Class .................................. 5-53 Table 5.7.13 Cost-Efficiency Data for the VCT.SC.M Equipment Class ................................. 5-54

Table 5.7.14 Cost- Efficiency Data for the VCT.SC.L Equipment Class .................................. 5-55

Table 5.7.15 Cost- Effici ency Data for the VCT.SC.I Equipment Class .................................... 5-56 Table 5.7.16 Cost-Efficiency Data for the VCS.SC.M Equipment Class .................................. 5-57

Table 5.7.17 Cost- Efficiency Data for the VCS.SC.L Equipment Class ................................... 5-58

Table 5.7.18 Cost- Efficiency Data for the VCS.SC.I Equipment Class .................................... 5-59 Table 5.7.19 Cost-Efficiency Data for the HCT.SC.M Equipment Class ................................. 5-60

Table 5.7.20 Cost- Effici ency Data for the HCT.SC.L Equipment Class .................................. 5-61

Table 5.7.21 Cost- Efficiency Data for the HCT.SC.I Equipment Class .................................... 5-62 Table 5.7.22 Cost-Efficiency Data for the HCS.SC.M Equipment Class .................................. 5-63

Table 5.7.23 Cost- Efficiency Data for the HCS.SC.L Equipment Class ................................... 5-64 Table 5.7.24 Cost-Efficiency Data for the SOC.RC.M Equipment Class ................................. 5-65 Table 5.7.25 Cost-Efficiency Data for the SOC.SC.M Equipment Class .................................. 5-66

Table 5.7.26 Cost- Effici ency Data for the PD.SC.M Equipment Class .................................... 5-67 Table 5.9.1 Extension Multipliers for Equipment Without Doors........................................... 5-71 Table 5.9.2 Extension Multipliers .............................................................................................. 5-73 Table 5.9.2 Extension Multipliers by Equipment Class ............................................................ 5-73

LIST OF FIGURES

Figure 5.4.1 Copper and Stainless Steel Price Trends ............................................................... 5-10 Figure 5.5.1 Components of Manufacturer Selling Price .......................................................... 5-12 Figure 5.6.1 Composition of the Energy Consumption Model .................................................. 5-36 Figure 5.7.1 Cost-Efficiency Curve for the VOP.RC.M Equipment Class ............................... 5-43 Figure 5.7.2 Cost-Efficiency Curve for the VOP.RC.L Equipment Class ................................ 5-44 Figure 5.7.3 Cost-Efficiency Curve for the VOP.SC.M Equi pment Class ................................ 5-45 Figure 5.7.4 Cost-Efficiency Curve for the SVO.RC.M Equipment Class ............................... 5-46 Figure 5.7.5 Cost-Efficiency Curve for the SVO.SC.M Equipment Class ................................ 5-47 Figure 5.7.6 Cost-Efficiency Curve for the HZO.RC.M Equipment Class ............................... 5-48

5- iii

### Figure 5.7.7 Cost- Ef ficiency Curve for the HZO.RC.L Equipment Class ................................ 5-49 Figure 5.7.8 Cost-Efficiency Curve for the HZO.SC.M Equipment Class................................ 5-50 Figure 5.7.9 Cost-Efficiency Curve for the HZO.SC.L Equipment Class ................................. 5-51 Figure 5.7.10 Cost-Efficiency Curve for the VCT.RC.M Equipment Class ............................. 5-52 Figure 5.7.11 Cost-Efficiency Curve for the VCT.RC.L Equipment Class .............................. 5-53 Figure 5.7.12 Cost-Efficiency Curve for the VCT.SC.M Equipment Class .............................. 5-54 Figure 5.7.13 Cost-Efficiency Curve for the VCT.SC.L Equipment Class ............................... 5-55 Figure 5.7.14 Cost-Efficiency Curve for the VCT.SC.I Equipment Class ................................ 5-56 Figure 5.7.15 Cost-Efficiency Curve for the VCS.SC.M Equipment Class .............................. 5-57 Figure 5.7.16 Cost-Efficiency Curve for the VCS.SC.L Equipment Class ............................... 5-58 Figure 5.7.17 Cost-Efficiency Curve for the VCS.SC.I Equipment Class ................................ 5-59 Figure 5.7.18 Cost-Efficiency Curve for the HCT.SC.M Equipment Class .............................. 5-60 Figure 5.7.19 Cost-Efficiency Curve for the HCT.SC.L Equipment Class ............................... 5-61 Figure 5.7.20 Cost-Efficiency Curve for the HCT.SC.I Equipment Class ................................ 5-62 Figure 5.7.21 Cost-Efficiency Curve for the HCS.SC.M Equipment Class .............................. 5-63 Figure 5.7.22 Cost-Efficiency Curve for the HCS.SC.L Equipment Class ............................... 5-64 Figure 5.7.23 Cost-Efficiency Curve for the SOC.RC.M Equipment Class.............................. 5-65 Figure 5.7.24 Cost-Efficiency Curve for the SOC.SC.M Equipment Class .............................. 5-66 Figure 5.7.25 Cost-Efficiency Curve for the PD.SC.M Equipment Class ................................. 5-67 Figure 5.8.1 Illustr ation of Offset Factor using TDA as the Normalization Metric .................. 5-68

5- iv

### CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

The engineering analysis establishes the relationship between manufacturer selling price (M SP ) and energy consumption for the commercial refrigeration equipment directly examined in this rulemaking. This equipment includes self- contai ned and remote condensing commercial refrigerators, freezers, refrigerator

- freezers , and ice cream freezers

, as well as self -contained commercial refrigerators with transparent doors designed for pull-down temperature applications. The “cost

- efficiency” re lationship, which depicts a manufacturer’s cost of achieving increased equipment efficiency for a given equipment class, serves as the basis for

downstream cost

- benefit calculations with respect to individual customers, manufacturers, and

the nation.

5.2 METHODOLOGY OVERVIEW

This section describes the analytical methodology that the U.S. Department of Energy ( DOE ) used for the engineering analysis. In this rulemaking, DOE adopted a design option approach to produce analytically derived curves depicting the cost

- efficiency relationship for each equipment class analyzed

. In a design option approach, the goal of the analysis is to
calculate the incremental cost and energy consumption impacts of implementing specific energy

saving technologies, known as “design options, ” in a representative baseline design for a given equipment type.

To implement the design option analysis in this rulemaking, DOE first selected specific classes of covered equipment for which to quantitatively calculate the manufacturing cost and daily energy consumption directly (section 5.3.1 ). DOE analytically developed cost

- efficiency relationships only for equipment classes with high shipment volume s, henceforth referred to as “primary” equipment classes. Some equipment classes were not included in the direct engineering analysis because they had low shipments volumes. These are referred to as “secondary” equipment classes. For each primary equipment class , a “representative” unit,

intended to physically approximate a typical high -shipment-volume design currently on the market, was defined using sets of baseline parameters and specifications gathered from market research, industry publications, and manufacturer interviews (section 5.3.2 ). This unit served as the point of analysis for the modeling of the equipment class that it represented. Baseline parameters were selected to represent typical geometries, construction characteristics, and feature

sets of lowest -efficiency equipment designs. These parameters included specific physical

attributes such as case volume, number of fans, and standard wall thickness.

In order to explore the efficiency improvements available for use with the equipment analyzed, DOE conducted research into energy -saving technologies applicable for implementation into commercial refrigeration equipment. This research, performed using sources including manufacturer interviews and reviews of trade literature, is summarized in chapter 3 of

this technical support document ( TSD ), Market and Technology Assessment. The results of th at analysis, a set of technology options, were compared agains t a set of screening criteria as

described in TSD chapter 4, S creening Analysis. The outputs of the s creening a nalysis were

5-1

### considered in the engineering analysis, and it was from these technologies that design options

were selected for quantitative modeling (sections 5.6.4

– 5.6.6 ).

For each equipment class analyzed, DOE developed estimates of the cost to manufacture the representative unit at the baseline and higher efficiencies. In order to facilitate this, DOE developed estimates of the manufacturer production cost (MPC) of the unit at each design option

level. DOE estimated two separate sets of costs in the analysis. The first of these was the cost to manufacture the refrigerated case of each representative unit. This “core case” consisted of components, such as structural members, shelving, wiring, air curtain grilles, and trim, that did not change at higher design option levels. Core case costs were developed through p hysical

teardown s and cost modeling of equipment purchased on the market during the analysis period (section 5.4.1 ). The second set of cost data estimated the costs to manufacture and install the

components that make up each design option. These were elements of the equipment, including

heat exc hangers, fans, glass doors, and lighting, that directly affected daily energy consumption

and thus were manipulated in the engineering analysis. The costs for the design options were developed from a number of sources and corroborated with feedback from industry ( section 5.4.2 ). Core case and design option costs were coupled to yield system MPCs for each representative unit at each level of efficiency modeled (section 5.4.3 ).

In order to calculate estimates of daily energy consumption for each representative unit at each design option level examined, DOE developed an analytical model to simulate the performance of each u nit, as configured, when tested under the DOE test procedure. This model

u sed the representative unit specifications, design option data (section 5.6.6 ), test proc edure provisions, and a set of assumptions (sections 5.6.2 and 5.6.3 ) to produce estimates of daily energy consumption for each equipment configuration studied. To produce these estimates, the model calculated the heat load placed on the given piece of equipment by the ambient conditions

and heat -producing internal components. The model selected an appropriately

- sized remote condensing or self-contained compressor and then calculated the electrical input energy into the compressor required to remove the heat load during the simulated test period. The additional energy consumption during the test period of energy-consuming components such as fans, defrost heaters, and lighting was also calculated, summed, and added to the compressor energy consumption as applicable to yield estimates of daily electrical energy consumption for eac h equipment configuration modeled (section 5.6.7 ).

DOE organized the results of the energy consumption and cost models in the form of

cost

- efficiency curves for each equipment class analyzed, depicting MPC versus daily energy consumption. To form the curves, DOE ordered the design options and their associated cost and energy consumption data based on cost effectiveness , ranging from the baseline to the maximum technologically feasible (“max -tech”) equipment configuration for each class. DOE then applied

manufacturer markups and added outbound freight costs to the MPC estimates to express the relationship between MPC and MSP (section 5.5 ). The final result was a set of cost

- efficiency curves comparing MSP and daily energy consumption at all modeled design option levels for each primary equipment class (section 5.7 ).

Energy conservation standards for the covered equipment classes take the form of linear equations expressed as a function of refrigerated volume or total display area and defined by a slope and y-intercept. These equations were developed using the outputs of the engineering

5-2

### analysis , with specific analysis points corresponding to calculated daily energy consumption (CDEC) values for each class . T he engineering analysis contained an ancillary calculation necessary in developing standard-level equations for the covered equipment. Specifically, this calculation developed the y- intercept values, referred to in the January 2009 final rule analysis as “offset factors

. ” These offse t factors serve to represent energy consumption end effects inherent in equipment operation regardless of the size of the equipment, and originated out of stakeholder concerns during the 2009 rulemaking that standards based on a single analysis point could be insufficient to account for the energy consumption of smaller pieces of equipment. The offset factors prevent the allowable maximum energy use from going to zero at small volume or total display area ( TDA ) values. Section 5.8 further explains the offset factor methodology that DOE used in developing its standard

- level equations

.

Another auxiliary analysis sought to develop standards for the secondary equipment

c lasses that were not directly analyzed in the cost and energy consumption models. In the January 2009 final rule analysis , DOE developed standards for the se equipment classes using

energy -consumption conversion factors called “extension multipliers

. ” These factors were developed using analytical correlations between energy -consumption values for sets of equipment classes with similar features. The extension multipliers were then applied to the

standard -level equations de veloped for the primary classes to obtain standard- level equation s for secondary classes. DOE adopted these same extension multipliers in developing standard- level equations for the notice of proposed rulemaking ( NOPR ) and final rule stage s of this rulemaking ; this methodology is explain ed in detail in s ection 5.9

.

5.3 EQUIPMENT CLASSES ANALYZED

5.3.1 Classes Chosen for Analysis

In the Framework document, DOE provided a list of nomenclature consisting of 49 equipment class designations. This list includes the 38 equipment classes for which standards

were set in th e 2009 rulemaking, as well as 11 class designations for equipment covered by

standards set in the Energy Policy Act o f 2005 (EPACT 2005). In its analyses for the 2009 DOE

final rule on commercial refrigeration equipment , D OE only analyzed 15 high -ship ment -volume equipment classes (“primary” classes) and then extended the standards developed from those analyses to cover the remaining 23 equipment classes with smaller number of shipments (“secondary” classes) . 74 FR 1092, 1121 (Jan. 9, 2009). Because DOE did not receive any comments or data indicating that there had been major changes in shipment patterns since the 2009 final rule or other reasons that would necessitate a change in the primary classes, DOE elected to retain the primary equipment classe s from the 2009 rulemaking. DOE also directly analyzed 10 of the 11 classes consisting of equipment previously covered by the EPACT 2005 standards, with the exception of the SOC.SC.L class (a low -shipment- volume class that was treated as a secondary class) . Combined with the 15 primary equipment classes from the 2009 final rule, the result is a total of 25 primary equipment classes that were directly analyzed in this engineering analysis and a total of 49 equipment classes for which DOE is setting standards.

Table 5.3.1 shows the equipment classes for which amended standards were considered in this rulemaking, organized by equipment family, condensing unit type, and rati ng temperatu re
,

5-3

### and highlights the 25 primary equipment classes that DOE directly analyzed in the engineering analysis

.

Table 5.3.1 Equipment Classes Analyzed in the Engineering Analysis

Equipment

Family Remote Condensing Self

- Contained

Medium Low Ice

Cream Medium Low Ice

Cream

Without

Doors VOP   x  x x
SVO  x x  x x
HZO   x   x

With

Doors VCT   x   

VCS x x x   

HCT x x x   

HCS x x x   x
SOC  x x  x x
PD * * *  * *

 Primary equipment class

. x Secondary equipment class

* Class not covered in the rulemaking.

HCS = Horizontal Closed Solid.

HCT = Horizontal Closed Transparent.

HZO = Horizontal Open.

PD = Pull

- Down.

SOC = Service Over Counter. SVO = Semi

- Vertical Open.

VCS = Vertical Closed Solid.

VCT = Vertical Closed Transparent.

VOP = Vertical Open.



### The engineering analysis specifically considered refrigerators (medium temperature), freezers (low temperature), and ice

- cream freezers (ice

- cream temperature ) individually, but it

did not explicitly consider refrigerator

- freezers (combinations of compartments at medium and

low temperatures) directly . Instead, DOE plans to maintain the approach used in the 2009 final

rule

, in which DOE develop ed a method to combine the standards for refrigerators, freezers, and

ice

- cream freezers to create standards for refrigerator

- freezers

. Similarly, while the engineering analysis did not explicitly consider hybrid equipment, consisting of multiple compartments from

differ ent equipment families contained in a single unit, the 2009 final rule presented a methodology for combining standards to create standards for hybrid units. DOE intends to

preserve that methodology for use with all of the equipment covered this rulemaking. The 2009

final rule Federal Register notice describes this methodology in detail . 74 FR 1092, 1122 ( Jan. 9, 2009).

5.3.2 Baseline Equipment

Fo r the engineering analysis, DOE modeled each primary class starting at a baseline design option level , increasing unit efficiency as design options were upgraded to higher technology levels. Baseline efficiencies were established by reviewing available manufacturer data regarding the lowest -efficiency features and designs which could be incorporated into the applicable classes of commercial refrigeration equipment. For classes covered by the 2009 final rule, DOE retained the baseline specifications and associated technologies used in that final rule engineering analysis

. Additionally, DOE expanded its sets of representative equipment specifications to include the equipment classes covered under standards established by the Energy Policy and Conservation Act ( EPCA ). DOE notes that the baseline designs in many cases represent levels of performance below those required by the current DOE 2009 final rule and EPACT 2005 standard levels. This is due to the fact that DOE did not want to assume any one

5-4

### single means of reaching the current standard levels. Instead, DOE started at a baseline level below current standards and added increasingly efficient design options until higher levels of equipment performance were reached.

5.3.3 S ervice o ver Counter Equipment

In the preliminary analysis for this rulemaking , DOE chose not to include the SOC.SC.M equipment class in the engineering analysi s because of ongoing issues with the standards set for that class by EPACT 2005. Standards prescribed in EPCA, as amended by EPACT 2005, for

self -contained refrigerators and freezers with doors we re based on the California Energy

Commission Appliance Efficiency Regulations published in April 2005. (CEC-400-2005-012, section 1605.3) The C alifornia E nergy C ommission regulations set standards for “reach -in cabinets, pass -through cabinets, and roll-in or roll-through cabinets.” However, EPCA does not expli citly outline such equipment subsets beyond defining the terms “commercial refrigerator, freezer, and refrigerator

- freezer” and “self -contained condensing unit,” among other definitions related to this equipment. These EPCA definitions resulted in the application of these standards

to all self

- contained refrigerators, freezers, and refrigerator -freezers, including SOC.SC.M.

In December 2009, DOE’s Office of Hearings and Appeals (OHA) responded to an application for exception relief from a manufacturer of se rvice over counter equipment. This manufacturer argued that it was entitled to relief because its service over counter units could not meet the EPACT 2005 standards for self-contained equipment with doors. OHA responded that DOE did not have jurisdiction to consider such exceptions for equipment covered by the statutorily mandated standards. (Case no. TEE-0066, Dec. 29, 2009)

In response to the concerns manufacturers expressed in comments at the F ramework public meeting and in written comments, as part of i ts preliminary analysis for this rulemaking DOE compared the standards set for a specific type of service over counter units in the 2009

final rule , namely remote condensing medium- temperature units, with the EPACT 2005

standards for self- contained commerc ial refrigerators with transparent doors, which include selfcontained service over counter units. For a full description of the analysis and results, please see section 5.3.3 of the preliminary analysis TSD chapter 5. This analysis showed that SOC.SC.M equipment was not capable, even at the max

- tech level, of meeting the required standard level set by EPACT 2005 for self-contained refrigerators with transparent doors. For that reason, and

because DOE did not have the authority to lessen the stringency of t he legislatively prescribed EPACT 2005 standards, DOE excluded SOC.SC.M equipment from its analysis

.

In December 2012, while the NOPR analysis for the current rulemaking was in progress, the American Energy Manufacturing Technical Corrections Act (AEMTCA), Pub. L. 112-210 (2012), amended EPCA to establish standards for self-contained service over counter refrigerators that belong to the equipment class SOC.SC.M. Paragraph (3) of section 4 of

AEMTCA states as follows: “Each SOC

- SC

- M manufactured on or after January 1, 2012, shall have a total daily energy consumption (in kilowatt hours per day) of not more than 0.6 × TDA + 1.0.” AEMTCA also directed DOE to determine, within three years of January 1, 2012, whether the standard established for equipment class SOC.SC.M should be amended, and if DOE determines that the standard should be amended, DOE should issue a final rule establishing an amended standard within 3 years of January 1, 2012.

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### The inclusion of this language in EPCA by way of AEMTCA allowed DOE to f ully analyze the SOC.SC.M equipment class along with all other primary equipment classes in this rulemaking, and to propose a standard level to satisfy the requirement that DOE make a determination of amendment of standards within 3 years of January 1, 2012. DOE included a proposed standard level for SOC.SC.M equipment in its September 2013 NOPR, and has included an updated standard level in its final rule.

5.4 COST MODEL

One major output of the engineering analysis is the development of costs for the

repre sentative units analyzed at the baseline and at each higher design option level. These values were developed using a cost model that was divided into two parts for this rulemaking. The first

of these was as standalone cost model, based on physical teardowns , that was used for developing the core case costs for the cabinets of each of the 2 5 directly analyzed classes. These core case costs consisted of the costs to manufacture the refrigerated case itself, consisting of structural members, insulation, shelving, wiring, etc., without the components that could directly affect energy consumption. The second part of the cost model was a component of the engineering analytical model , and operated in unison with the energy consumption model. This model received inputs in the form of the core case costs and the prices for design optionsimplemented at and above the baseline, such as baseline and improved glass doors, higherefficiency compressors, and higher- efficiency lighting . These two sets of da ta (core case costs and design option costs) were used to build up total system costs for each representative unit at

each design option level modeled.

5.4.1 Development of Core Case Costs

The development of the case costs was based on physical teardowns of uni ts available on the market at the time of the analysis. The first step in the assessment was the creation of a complete and structured bill of materials (BOM) from the disassembly of commercial refrigeration equipment from selected equipment classes. DOE dismantled each unit and characterized each part of the units according to weight, manufacturing processes, d imensions, material, and quantity. The result was a set of BOMs that included the costs for materials, components, and fasteners, and contained estimates for the cost of raw materials and purchased

parts , and other costs such as labor, depreciation, and ov erhead costs . DOE based assumptions about the sourcing of parts and in-house fabrication on industry experience, information from trade publications, and discussions with manufacturers. DOE conducted interviews and plant visits with manufacturers to ensure accuracy in methodology and pricing.

The BOMs from the teardowns were fed into a factory model to produce estimates of

MPCs for each of the units analyzed. T hose estimates were then expanded to produce case costs for the equipment classes not directly exa mined via teardown. The cost model was based on production activities and divided factory cost s into the categories shown in Table 5.4.1

.

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### Table 5.4.1 Cost Model Output Classifications

Major Category Sub

- Category Description

Material Costs Direct Raw materials ( e.g

. , coils of sheet metal) and purchased parts ( e.g., fasteners, wiring )

Indirect Supplies ( e.g.
, w elding rods, die oil, release media )

Labor Assembly Parts / unit assembly on manufacturing line

Fabrication Conversion of raw material into parts ready for assembly

Indirect Fraction of overall labor not associated directly with equipment

fabrication or assembly ( e.g. , forklift drivers, quality control

, purchasing of raw material and tools )

Supervisory Fraction of assembly, fabrication, and indirect labor is paid a higher

wage

Depreciation Equipment, Conveyor, Building Straight

- line depreciation over expected life.

Tooling Cost is allocated on a per

- use basis or obsolescence, whichever is

shorter

Other

Overhead Utilities A fixed fraction of all material costs meant to cover electricity and other utility costs Facility and Equipment

Maintenance Based on installed equipment and tooling investment

Property Tax and

Insurance A fixed fraction based on total unit costs



### The cost model analysis created cost estimates for each of the commercial refrigeration equipment units analyzed. The cost model used certain assumptions to provide cost estimates; the following sections describe those assumptions.

5.4.1.1 Selection of Units for Teardown Analysis

The selection of units for physical teardown analysis was performed in a manner so as to allow for the greatest coverage of the range of equipment modeled in this analysis and to provide sufficient data to allow for the expansion of the modeling to classes for which teardowns were not performed. This equipment covered all of the equipment families except for the HZO family, and spanned all three of the DOE rating temperatures. All of the equipment selected for teardown

was self -contained equipment so that DOE could gain additional data for its modeling of other components ( e.g., coils and compressors) by analyzing th e self

- contained refrigeration systems. The equipment was chosen from the product lines of major manufacturers and at sizes similar to

what DOE had determined to be an appropriate representative unit size for the given equipment

class.

5.4.1.2 Manufacturer Prod uction Cost Estimates and Assumptions

MPC includes the sum of direct labor, direct material, and overhead, including investment depreciation. The cost of specific models―or costs to individual manufacturers―will vary, depending on the equipment’s precise c haracteristics, the actual manufacturing processes, the equipment mix in the factory, and other elements. There are also considerable differences in the levels of vertical integration (companies with a large market share

and/or revenue base tend to be more vertically integrated than their smaller competitors) that

5-7

### affect cost structure and thus the cost of equipment. Yet, DOE assumed that all manufacturers buy at least some of their parts and/or subsystems from outside vendors.

The commercial refrigeration equipment market includes manufacturers that build a wide range of equipment— from mass -produced equipment to tailored, one-of-a-kind units. Most equipment listed in catalogs consists of general -purpose models that can then be custo mized to meet the particular needs of customers. Depending on the manufacturer and the degree of customization, engineering costs can thus represent a significant portion of the MPC for some producers of this equipment.

DOE built a parametric model that al lowed the scaling of most input factors. The assumptions behind the model are based on published data by manufacturers, general industry practice (based on site visits), manufacturer interviews, and previously published DOE reports. DOE compared the model results to published unit data and list prices. For example, DOE compared listed shipping weights with the calculated weights for cabinets of representative units

as a method of checking its results

.

The lack of detailed teardowns for every equipment class and the varying degrees of vertical integration in the industry ma d e calculating representative investment requirements

difficult. Not only does the market share vary for each manufacturer across every equipment class, the scale of operations also varies greatly. It is also quite likely that high -volume manufacturers derive a cost advantage based on their purchasing volume for common raw materials and purchased parts alike. Lacking detailed data, DOE did not try to account for lowversus high-volume purchasing power in the development of the cost model, instead using industry-averaged aggregate data to represent all equipment offerings modeled.

5.4.1.3 Structure of the Cost Model

DOE used a detailed, component-focused manufacturing cost assessment methodology to estimate the MPC of each equipment class analyzed in the cost model, taking into account, for example, direct materials, direct labor, and factory overhead costs.

Following the development of detailed BOM s for each piece of equipment physically examined, DOE identified the major manufacturing processes and developed the spreadsheet

model. Table 5.4.2 lists these examples of these processes.

Table 5.4.2 Examples of Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Fixturing Washing Adhesive b onding Stamping/ p ressing Powder c oating Spot w elding

Brake f orming De

- burring Seam w elding

Cut ting and s hearing Polishing Inspecting and t esting Insulating

— —



### DOE estimated fabrication process cycle times and entered them into the BOM. For this analysis, DOE estimated an average fully burdened hourly cost of labor based on the typical annual wages and benefits of industry employees. In the final step of the cost assessment, DOE estimated assembly times and associated direct labor costs.

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### Once the cost estimate for each unit was finalized, DOE prepared a detailed summary of relevant components, subassemblies, and processes. Because the intent of this cost modeling

sub- analysis was solely to yield costs for the refrigerated case structures and not the design option components, assemblies accounted for by the design options ( e.g., glass doors, hea t exchangers ) were stripped away from the BOMs for each unit. The result was a set of costs corresponding to the MPCs for the core cases of each of the unit types analyzed.

The final step in the cost modeling process was to expand the results to apply to t he remainder of the primary equipment classes for which teardowns were not performed. To achieve this, DOE added parametric scaling features to the model, which a llowed the teardown units to be virtually scaled by size within the model. It also allowed material types, numbers of components, etc. to be modulated in order to best simulate the construction of representative units from other equipment classes. DOE incorporated features into these additional BOM s to reflect the necessary changes between equipment classes. For example, receiver valves were

added to the models for remote condensing cases, and additional insulation thickness was taken into account for ice cream equipment. DOE utilized manufacturer data sheets and information gathered from interviews to aid in the modeling of these cases. The end result was a full set of 25 core case costs, which served as the starting point for the development of whole- system costs at the baseline and improved design option levels in the engineering model.

5.4.1.4 Mater ial Prices

The cost model uses multiple proprietary databases to determine raw material costs and purchased part prices. Most prices are based on the most up- to

- date data that the DOE has been able to obtain. The sole exceptions are metals prices, which are averaged over a 5 -year period to reduce price volatility. Metals prices can have a large impact on the overall raw material costs and picking any particular point in time to select a raw material cost may hence lead to a

distorted MPC.

As a general exam ple, most refrigeration appliances contain significant amounts of copper in their heat exchangers, tubing, etc. Additionally, commercial refrigeration equipment is frequently externally clad in stainless steel for wash -down purposes. Figure 5.4.1 depicts copper and 304- series stainless steel price trends in the U nited States from 2002 to mid -2012. Note the extreme dip for both stainless steel and copper raw material co sts in 2008. The price of copper more than halved from its previous high in 2007, a level that copper has since surpassed. In the example, the 5

- year average price for copper is currently nearly a $1/lb lower than the current price while the two prices are about equivalent for 304- series stainless steel.

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### Figure 5.4.1 Copper and Stainless Steel Price Trends

By averaging metals raw materials costs over a 5

- year period, the analysis is less affected by metals price volatility. The swings for other inputs ( e.g., plastics, purchased parts) are not as pronounced and hence current prices are used for them. Past quotes for materials and purchased parts are inflated using Bureau of Labor S tatistics data and other sou rces, such as American

Metal Market

. Purchased part prices and raw material costs are reviewed with manufacturers at the appropriate purchasing volumes for accuracy.

5.4.1.5 Results

The result of the development of the core case cost model, generation of BOM s,

pa rameterization , and extension of the cost model to classes for which physical teardowns were not performed was a set of 25 core case cost values. These values comprise the cost, for each representative unit, of manufacturing the housing, structural members, shelving, solid doors, wiring, and other components of the refrigerated case that do not vary by design option level. These core costs were entered into the engineering model and serve d as the starting points from which the costs of the units at various design option levels we re developed.

5.4.2 Design Option Costs

Design option costs were developed independently of costs for the core case and were procured through a combination of manufacturer estimates, wholesalers’ prices, list prices, and

other sources. These data included the pricing information for components, including glass doors, compressors, lighting, heat exchangers, night curtains, and other componentry considered

as design options. For a listing of all components considered as design options, ple ase see

5- 10

### section 5.6.5 . Data provided by industry through interviews were aggregated across manufacturers and, where relevant, combined with cost data obtained from other sources to provide a general estimate of the prices paid by industry for baseline and higher efficiency components for each design option. For further details regarding the specific design options, please see section 5.6.5

.

5.4.2.1 Light- Emitting Diode Price Forecasting

After release of the preliminary analysis documents for this rulemaking, DOE received comments from stakeholders stating that forecasts of the light

- emitting diode ( LED ) lighting industry, including those performed by the Department, suggest that LED light ing is an emerging technology that will continue to experience significant price decreases in coming years. For this reason, in an effort to capture the anticipated cost reduction in LED fixtures in the analyses for this rulemaking, DOE incorporated price projections from its Solid State Lighting program into its MPC values for the primary equipment classes. The price projections for LED case lighting were developed from projections developed for the DOE’s Solid State Lighting Program’s 2012

report, Energy Savings Potential of Solid-State Lighting in General Illumination Applications

2010 to 2030 (“the energy savings report”). In the appendix of this report, price projections from 2010 to 2030 were provided in $/klm for LED lamps and LED luminaires. DOE anal yzed the models used in the Solid State Lighting program work and determined that the LED luminaire projection would serve as an appropriate proxy for a cost projection to apply to refrigerated case

LEDs.

The price projections presented in the Solid State Lighting program’s energy savings report are based on the DOE’s 2011 Solid State Lighting R&D Multi- Year Program Plan (MYPP). The MYPP is developed based on input from manufacturers, researchers, and other industry experts. This input is collected by the DOE at annual roundtable meetings and conferences. The projections are based on expectations that depend on the continued investment

into solid

- state lighting by the DOE.

DOE incorporated the price projection trends from the energy savings report into it s engineering analysis in the NOPR stage of the rulemaking by using the data to develop a curve of decreasing LED prices normalized to a base year. That base year corresponded to the year when LED price data was collected from catalogs, manufacturer interv iews, and other sources for the NOPR analyses . DOE started with this LED cost data specific to commercial refrigeration equipment and then applied the anticipated trend from the energy savings report to forecast the projected cost of LED fixtures for this equipment at the time of required compliance with the proposed rule (2017). These 2017 cost figures for LED fixtures, including the emitters, drivers, wiring, and housing, were incorporated into the engineering analysis as comprising the LED cost portions of the MPCs for the primary equipment classes. DOE retained and refined this methodology in its final rule analysis.

5.4.3 Representative Unit M anufacturer P roduction Cost Values

For each representative unit analyzed in the engineering analysis, the analytical m odel

calculated a cost at the baseline

, as well as a cost at each design option level above the baseline up to the max- tech level

. This was achieved by starting with the core case cost , developed as

5- 11

### discussed in section 5.4.1 , and adding to it the costs of the design options needed to represent all the components in a complete un it of the given equipment class. For e xample, a VCT self

contained refrigerator would require the core case cost plus the cost of its evaporator and condenser coils and fans, compressor, glass doors, and lighting to yield a baseline MPC. For units above the baseline, costs for improved design option levels were substituted by the analytical model in the order in which those design options were implemented.

5.5 MANUFACTURER SELLING PRICE

The MSP is the price of the equipment when it is sold by the manufacturer to the first party in the distribution chain. It includes all direct and indirect production costs, manufacturer markup, and the cost of shipping the units from the manufacturer to the first party in the distribution chain. The components of MSP are shown in greater detail in Figure 5.5.1

.

Figure 5.5.1 Components of Manufacturer Selling Price

The MSP is expressed as the product of the MPC and the manufacturer markup, added to the outbound shipping cost, as shown in Eq. 5.1 :

shipping markup MPC MSP +×= 

### Eq. 5.1

The markup and shipping costs are described in the following subsections.

5.5.1 Manufacturer Markup

In it s engineering analysis, DOE included manufacturer markup in the estimates of MSP. This markup consists of a value applied to the MPC estimates that accounts fornon-productioncost elements of the MSP, including selling, general and administrative costs, research and development, interest, and profit. The manufacturer markup was calculated as a market share weighted average value applied to the entire industry. DOE developed this manufacturer markup by examining several major commercial refrigeration equipment manufacturers’ gross margin information from annual reports and Securities and Exchange Commission (SEC) 10- K reports. MSP

Manufacturer Production Cost

Direct

Labo r Direct

Material Overhead Depreciation Non Production Costs Profit

EBIT Research & Development

Costs Other

Costs Selling,
General, and

Administrative

Costs Shipping Manufacturer Markup

5- 12

### Most of these companies are subsidiar ies of more diversified parent compan ies that manufacture equipment other than commercial refrigeration equipment. Because the 10 -K reports do not provide gross margin information at the subsidiary level, the estimated markups represent the average markups that the parent company applies over its entire range of equipment offerings and does not necessarily represent the manufacturer markup of the subsidiary. In its preliminary analysis, DO E estimate d the average manufacturer markup to be 1.39. Based on further analysis and discussion with manufacturers during the NOPR stage of this rulemaking, DOE adjusted this average manufacturer markup value to 1.42. DOE did not receive any further comme nt on this figure after publication of the NOPR, and thus retained this value for the final rule.

5.5.2 Shipping C osts

The third constituent component of the MSP, in addition to the MPC and the manufacturer markup, is the cost to ship the unit from the manufac turing facility to the first point

on the distribution chain. Manufacturers stated that the specific party (manufacturer or buyer) that incurs that cost for a given shipment may vary based on the terms of the sale, the type of account, the manufacturer’s o wn business practices, and other factors. However, for consistency, DOE includes shipping costs as a component of MSP. In calculating the shipping costs, DOE first gathered estimates of the costs to ship a full trailer of manufactured equipment an average distance in the United States. DOE then used the representative unit sizes to calculate a volume for each unit. Along with the dimensions of a shipping trailer and a loading factor to account for inefficiencies in packing, DOE used this cost and volume information to develop an average shipping cost for each equipment class directly analyzed.

5.6 ENERGY CONSUMPTION MODEL

The energy consumption model is the second key analytical model used in constructing

cost

- efficiency curves and is implemented in the engine ering analysis spreadsheet . This model

estimates the CDEC of commercial refr igeration equipment in kilowatt-hours (kWh) at various

pe rformance levels using a design option approach. The model is specific to the types of equipment covered under this rulemaking (described in c hapter 3 of th e TSD ), but is sufficiently generalized to model the energy consumption of all covered equipment classes. DOE developed the energy consumption model, coupled with the system cost model, as a Microsoft Excel spreadsheet.

For a given equipment class, the model estimates the daily energy consumption for the baseline and the energy consumption of subsequent levels of performance above the baseline.

The mod el calculates the energy consumption of each design option level separately.

5.6.1 Representative Unit Design Specifications

For each of the primary equipment classes analyzed in the engineering analysis, DOE developed a representative unit for which the cost and energy consumption would be modeled at

each design option level.

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### DOE defined each representative unit quantitatively in the form of a set of “design specifications” at the baseline . These specifications included case dimensions, numbers of components, nominal power ratings, and other features that we re necessary to calculate the energy consumption of a given unit. Table 5.5.1 shows the specifications that DOE defin ed for each representative unit

. Not all specifications shown are applicable to every equipment class modeled ( e.g. , specifications relating to doors would be inapplicable to open equipment).

Table 5.6.1 Representative Unit Specifications

Specification Units

Case l ength ft

Case g ross r efrigerated v olume ft 3

Case t otal d isplay a rea ft 2

Number of d oors # Single d oor a rea ft 2

Non

- d oor g lass a rea ft 2

Non

- d oor a nti

- s weat p ower W

Wall a rea (ft 2 ) ft 2

Insulation t hickness In.

Case i nterior s urface a rea ft 2

Air c urtain a ngle from v ertical °

Infiltrated a ir m ass f low lb/hr

Number of b ulbs in c onditioned s pace #

Number of b ulbs n ot in c onditioned s pace #

Number of b allasts in c onditioned s pace #

Number of b allasts n ot in c onditioned s pace # Evaporator f an n ominal r ated w attage W

Number of e vaporator f ans per c ase #

Condenser f an n ominal r ated w attage W

Number of c ondenser f ans per c ase # Discharge a ir t emperature (DAT) °F

Baseline e vaporator t emperature (SET) °F

Baseline saturated c ondenser t emperature (SCT) °F Compressor o versize m ultiplier #

Defrost m echanism n/a

Defrost t ime per d ay hr

Defrost and d rain h eater p ower W

Condensate p an h eater p ower W



### In conjunction with the lowest technological level of each design option (section 5.6.5 ),

the se specifications were used in the engineering model to define the energy consumption and

cost of baseline equipment. The specifications that did not vary as a function of any design option ( e.g., case volume) were held constant from the baseline through to the max- tech configuration. Others ( e.g., discharge air temperature) were modified due to the implementation of higher technological levels of various design options. At these higher design option levels, the updated specifications were used to produce cost- efficiency data for more efficient equi pment.

DOE established the baseline design specifications by re viewing available manufacturer

data for equipment models offered across the range of available units within a given class.

DOE focused this review on units exhibiting sizes and design character istics that DOE had found through its market research to be most representative of the highest shipment volume offerings at the baseline for each equipment class analyzed. The aggregated data from this analysis were used

5- 14

### to develop a representative unit fo r each equipment class with typical characteristics for physical parameters ( e.g., volume, TDA ) and design features ( e.g., number of fans, number of light fixtures)

.

After publication of the NOPR, DOE received a number of comments pertaining to the design specifications used as the basis for modeling each representative unit. DOE reviewed these comments and implemented the specific suggestions provided, where appropriate. Additionally, during the final rule stage of the rulemaking, DOE conducted additional teardowns

of self -contained equipment models available on the market. DOE used the data from these teardowns to update its design specifications for the relevant self -contained equipment classes. The following subsections provide additional information on the updates made in the final rule

stage.

5.6.1.1 Anti -Sweat Heat for Solid- Door Models

In comments after publication of the NOPR, some stakeholders suggested that DOE

should add anti- sweat heat to solid

- door vertical refrigerators and freezers, stating that thi s is necessary to prevent the buildup and/or freezing of condensate around the doors and gaskets. In response to these comments, and after additional investigation through teardowns, DOE added

anti -sweat heat to its modeling of these equipment types. Anti- sweat heater wattages were calculated based on the modeled door perimeter lengths and values for power per foot of heater wire suggested in stakeholder comments. The anti- sweat power levels modeled are presented in Appendix 5A.

5.6.1.2 Compressor Oversize Multipliers

In written comments after the NOPR, some stakeholders stated that DOE’s modeled compressor oversize multipliers for self -contained vertical equipment were too low, as implementation of the compressor sizing modeled would result in units having duty c ycle values higher than those allowed by NSF food safety performance tests. Stakeholders provided specific suggested values for update of these oversize multipliers. DOE implemented these values in its modeling of compressor selection and performance for the final rule. The compressor oversize multiplier values used are included by class in Appendix 5A.

5.6.1.3 Condensate Pan Heaters

Some stakeholders suggested that the DOE’s modeling of some types of mediumtemperature self -contained equipment with doors as lacking condensate pan heaters was inconsistent with industry standard practice. They stated that these units are usually not piped to floor drains and that they produce sufficient levels of condensate to necessitate the use of active means of evaporating the water. Additionally, stakeholders suggested that DOE’s modeling of condensate pan energy use for types where it was included was too conservative, and that it did not include an allowance for the energy needed to get the pan itself up to temperature. In the final rule stage, DOE implemented modeling of condensate pan heaters for vertical self

contained equipment with doors. Additionally, DOE added to its modeling of condensate pan

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### heaters in all classes where relevant a factor of 10% additional energy use to account for the energy needed to raise the pan itself to the appropriate temperature.

5.6.1.4 Electric Defrost Performance

DOE received comment after publication of the NOPR regarding the defrost power and duration values DOE had used in modeling the perfo rmance of units featuring electric defrost. DOE reviewed and, where appropriate, revised its modeled electric defrost power and duration figures in the final rule stage, taking into account data provided from stakeholder comments, laboratory tests, and teardowns conducted by DOE. The defrost heater wattages and defrost durations modeled are listed in Appendix 5A.

5.6.1.5 Fan Motor Specifications

DOE conducted additional teardowns of currently-available equipment during the final rule stage of the rulemaking. Based on the information gathered through these teardowns, DOE updated its modeling of the number and power of fan motors used on several self- contained equipment types. In one specific instance, DOE observed that manufacturers were generally using single 16-watt condenser fan motors on vertical closed-door equipment, as opposed to the dual condenser fan motors previously modeled by DOE. DOE updated its analysis to reflect this and other information gained during teardowns. Specifications on the number and rated power of fan motors modeled for each equipment class are available in Appendix 5A.

5.6.1.6 Infiltrated Air Mass Values

Stakeholders provided comment after publication of the NOPR regarding DOE’s modeling of infiltrated air mass values. Generally, stakeholde rs expressed concern that the values used for self-contained equipment with doors were low and did not fully represent trends present across different equipment types, such as an increase in infiltration seen in vertical-door units when moving from medium to low operating temperatures. Based on the specific feedback provided by stakeholders, DOE reviewed and revised its infiltrated air mass values for certain equipment classes. These updated values are included in Appendix 5A.

5.6.1.7 Evaporator Temperature Mod eling

Some stakeholder expressed concern that the temperature differentials modeled between case design temperature and saturated evaporator temperature as modeled in the NOPR were too

low for some classes of self -contained equipment with doors. These stakeholders suggested that the baseline temperature differential between saturated evaporator temperature and case temperature should be closer to 15 degrees Fahrenheit. After reviewing the stakeholder comments and other sources, DOE updated its baseline sat urated evaporator temperature values for applicable classes of equipment. The revised values are presented in Appendix 5A.

5- 16

### 5.6.2 Non

- Numerical Assumptions

In developing the energy consumption model, DOE made certain general non- numerical assumptions about the analysis, as well as specific assumptions about load components and design options.

DOE based its analysis on the modeling of new equipment tested in a controlled

environment chamber subjected to the provisions of the DOE commercial refrigeration equipment test procedure. 77 FR 10292 (February 21, 2012). This test procedure incorporates

Air -Conditioning, Heating, and Refrigeration Institute (AHRI) 1200-2010, which references the

American National Standards Institute (ANSI)/American Society of Heating, Refrigerating and

Air -Conditioning Engineers (ASHRAE) Standard 72-2005 (ASHRAE 72), Method of Testing Commercial Refrigerators and Freezers . Lighting occupancy sensors and scheduled controls, as

well as night curtains, were modeled in the energy consumption model as specified in the DOE test procedure. Manufacturers that certify their equipment to comply with Federal standards will be required to test new units to this test method, which specifies ambient temperature, humidity, light level, and other requirements.

In performing the energy consumption calculations, DOE used normalized hourly refrigeration load values for calculating compressor power. When options such as lighting controls and occupancy sensors or night curtains were implemented over a portion of the 24-hour period, the energy savings during these periods were distributed across all 24 hours. Then, the average heat load per hour was used in combination with the calculated compressor energy efficiency ratio ( EER ) to determine daily compressor energy consumption. However, the model selected the appropriate compressor size based upon the maximum load ( i.e ., the load without night curtains or occupancy sensors) to ensure sufficient heat- removal capacity. Normalization of heat loads was performed for the purposes of simplifying calculations and does not impact the

final results.

DOE did not include a pull -down load associated with re-shelving products because the test procedure does not address product re-shelving. Product re-shelving is the act of loading new products into refrigerated display cases as existing products are sold. Typically, commercial refrigeration equipment is not designed to pull down the temperature of warm products, but on ly to display products that were already chilled or frozen in a refrigerated storage unit. An exception to this is in the case of beverage merchandisers, which are represented by the pull

down class included in this rulemaking analysis; however, this equip ment is still tested at steady state, so pull-down load effects will not be quantified by testing.

While DOE did account for the heat load introduced into a case by defrost heaters, as well as the energy consumption of those heaters, DOE assumed that ther e are no pull-down loads associated with post-defrost periods. During defrost periods, the compressor (or the flow of refrigerant for remote condensing cases) stops and the coil warms to a temperature above freezing (aided by electric resistance heating in the case of electric defrosts). After the evaporator coil has been cleared of frost, the merchandise in the case will typically have warmed several degrees. The merchandise must be returned to normal operating temperature when the refrigeration cycle resu mes, adding an additional load to the condensing unit. Between equipment families and even within equipment classes there is a large variation in defrost

5- 17

### mechanism, defrost cycle time, temperature recovery time, and product mass. Because of the uncertainty of these factors, DOE was unable to calculate the defrost pull -down load with sufficient certainty and did not include it in the model, but understands the impact of this phenomenon on daily energy consumption to be very small.

5.6.3 Numerical Constants and Assumptions

In developing the energy consumption model, DOE identified constants and made assumptions concerning numerical values used in the analysis. These include ambient conditions, financial assumptions, and parameters necessary to calculate the compone nt and non- electric

loads. Table 5.6.1 shows details of these assumptions.

Table 5.6.2 Energy Consumption Model Numeric al Constants and Assumptions

Numerical Constants and Assumptions Value Source

Test c hamber t emperature (°F) 75 ASHRAE 72 1

Test c hamber r elative h umidity (%) 55 ASHRAE 72 1

Test c hamber p ressure (pounds per square in (psi), absolute) 14.7 Assumed

Manufacturer m arkup ($/$) 1.42 Publicly available corporate financial data

Fraction of a nti

- s weat h eater p ower into c ase (W/W) 0.7 DOE estimate based on discussions with

manufacturers

Fraction of l ighting p ower into c ase for

l ighting o utside of a ir c urtain (W/W) 0.5 Electric Power Research Institute (EPRI), Supermarket Simulation Tool v3.0 2

Lighting o perating t ime per d ay (hr) 24 Assumed

– for cases without occupancy sensors

Convective f ilm c oefficient i nside c ase w alls (Btu/hr

- ft

2

- °F) 4.00 Communication with the Southern California Edison Refrigeration & Thermal Test Center (RTTC)

Convective f ilm c oefficient o utside c ase

w alls (Btu/hr

- ft

2

- °F) 1.46 Communication with RTTC

Overall U

- factor of s ingle

- p ane g lass (Btu/hr

- ft

2

- ° F) 1.059 Lawrence Berkeley National Laboratory WINDOW

Software Emissivity of t est c hamber w alls (

- ) 0.9 Communication with RTTC Emissivity of c ase i nner w alls (

- ) 0.9 Communication with RTTC

Area of t est c hamber w alls (ft 2 ) 1

, 000 Communication with RTTC

Case i nterior r elative h umidity (%) 65 R. Faramarzi, Efficient Display Case Refrigeration

, ASHRAE Journal , November 1999 3

Evaporator c oil o verall b ypass f actor (%) 17 DOE estimate High

- e fficiency c ompressor c ost p remium 5% Communication with manufacturers , public comment High

- e fficiency c ompressor p ower r eduction 2 % Communication with manufacturers , public comment



### 5.6.4 Screened

- In Technologies

The technology options that were analyzed ( i.e.

, were not screened out as part of the screening analysis, chapter 4) are as follows:

• higher efficiency lighting

• higher efficiency lighting ballasts

• remote lighting ballast location

• higher efficiency evaporator fan motors

• variable -speed evaporator fan motors and evaporator fan motor controllers

• improved evaporator coil design

5- 18

### • higher efficiency evaporator fan blades

• low- pressure differential evaporators

• case insulation increases or improvements

• improved transparent doors

• defrost mechanism s

• defrost cycle controls

• vacuum insulated panels

• occupancy sensors for lighting controls

• anti -fog films on transparent doors

• night curtains (equipment without doors only)

• higher efficiency compressors (self -contained equipment only)

• improved condenser coil design (self -contained equipment only)

• higher efficiency condenser fan motors (self -contained equipment only)

• higher efficiency condenser fan blades (self -contained equipment only)

5.6.5 Screened

- In Technologies Not Considered in the Engineering Analysis

In the market and technology assessment (c hapter 3 of the TSD), DOE defined an initial list of technologies that can reduce the energy consumption of commercial refrigeration equipment. In the screening analysis (chapter 4) , DOE first shortened th is list by eliminating from consideration those technologies that could potentially reduce field energy consumption of commercial refrigeration equipment but do not reduce energy consumption as measured by the

DOE test procedure, because the test procedure conditions and requirements do not allow for these technologies to have a significant impact on the energy consumption values. These include

remote location of lighting ballasts, variable speed evaporator motors and evaporator fan motor controllers, higher efficiency evaporator and condenser fan blades, low- pressure differential evaporators, defrost cycle controls, defrost mechanisms, and anti-fog films on transparent doors.

5.6.5.1 Remote Lighting Ballast Location

Relocation of fluorescent lamp ballasts outside t he refrigerated space can reduce energy consumption by lessening the refrigeration load on the compressor. However, for the majority of commercial refrigeration equipment currently manufactured, ballasts are already located in electrical trays outside the refrigerated space, in either the base or top of the equipment. The notable exceptions are the equipment classes in the VCT equipment family, where ballasts are

most often located on the interior of each door mullion.

Most commercial refrigeration equipme nt manufacturers purchase doors for VCT units that are preassembled with the entire lighting system in place rather than configured for separate

ballasts. DOE believes that most commercial refrigeration equipment manufacturers choose these kinds of doors because it would be labor intensive and time consuming to relocate these ballasts at the factory and wire separate ballasts. This also aligns with the manufacturing practices of the door manufacturers, who often produce similar door designs, with integrated lighting, for many equipment types. Also, the potential energy savings are small because modern electronic ballasts are very efficient and do not significantly impact the refrigeration load. Therefore, DOE did not consider remote relocation of ballasts as a design option.

5- 19

### 5.6.5.2 Variable-Speed Evaporator Fan Motors and Evaporator Fan Motor

Controllers

Variable -speed evaporator fan motors, as well as evaporator fan motor controllers, allow fan motors to run at variable speed to match changing conditions inside the case. I n practice

, there is some opportunity for energy savings because the pressure drop of air moving across the evaporator coil varies significantly depending on the level of frost buildup on the coil. Theoretically, less fan power is required when the coil is free of frost , and variable-speed motors or controllers could adapt motor operation to these conditions. Moreover, such a system would

also allow the coil to operate at a more stable temperature during frost buildup by varying fan speed proportion ally

.

However, there are also negative attributes to the implementation of such technologies. For example , the effectiveness of the air curtain in equipment without doors is very sensitive to changes in airflow, and fan motor controllers could disrupt the air curtain. The potential of disturbance to the air curtain, which could lead to higher infiltration loads, does not warrant the use of evaporator fan motor controllers in equipment without doors, even if there were some reduction in fan energy use. With respect to equipment with doors, DOE, in its discussions withmanufacturers, found that there are concerns in industry about the implementation of variablespeed fan technology due to the need to meet food safety and maximum temperature requirements. Vary ing the fan speed would reduce the movement of air within the case, potentially leading to the development of “hot spots” in some areas of the case, where temperatures could exceed the desired value. Some industry representatives also stated that the

use o f such controllers could have unintended consequences, in which fans would be inadvertently run at full power to attempt to overcome a frosted or dirty coil, resulting in wasted energy. Due to the uncertainties that exist with these technologies , DOE did n ot consider

variable -speed evaporator fan motors or evaporator fan motor controllers as a design option.

5.6.5.3 Higher Efficiency Evaporator and Condenser Fan Blades

Higher efficiency evaporator and condenser fan blades reduce motor shaft power requirements by moving air more efficiently. Current technology used in commercial refrigeration equipment is stamped sheet metal or plastic axial fan blades. These fan blades are lightweight and inexpensive. DOE was not able to identify any axial fan blade technology that is significantly more efficient than what is currently used, but did identify one alternative fan blade technology that might improve efficiency: tangential fan blades. They can produce a wide, even airflow, and have the potential to allow for increased sa turated evaporator temperature (SET) through improved air distribution across the evaporator coil, which would reduce compressor

power. However, tangential fan blades in small sizes are themselves less efficient at moving air, and thus require greater moto r shaft power. Because of these competing effects, DOE did not consider tangential fan blades as a design option.

5.6.5.4 Low- Pressure Differential Evaporators

Low -pressure differential evaporators reduce energy consumption by reducing the power of evaporator fan motors , often by increasing the air gap between fins . However, in spaceconstrained equipment such as commercial refrigeration equipment, this reduction usually comes

5- 20

### from a decrease in evaporator coil surface area, which generally requires a lower SET to achieve the same discharge air temperature and cooling potential. This, in turn, results in a reduction in compressor efficiency. Because of these competing effects, DOE did not consider low- pressure differential evaporators as a design option.

5.6.5.5 Defrost Cyc le Control

Defrost cycle control can reduce energy consumption by reducing the frequency and duration of defrost periods. The majority of equipment currently manufactured already uses partial defrost cycle control in the form of cycle temperature

- terminati on control. However, defrost cycle initiation is still scheduled at regular intervals. Full defrost cycle control would involve a method of detecting frost buildup and initiating defrost. As described in the market and technology assessment ( chapter 3), this could be accomplished through an optical sensor or sensing the temperature differential across the evaporator coil. However, DOE understands that

both of these methods are currently unreliable due to fouling of the coil with dust and other

surface conta minants , which becomes more of an issue as cases age

. Because of these issues, DOE did not consider defrost cycle control as a design option.

5.6.5.6 Defrost Mechanisms

Defrosting for medium

- temperature equipment is typically accomplished with off

- cycle

defrost. B ecause off -cycle defrost uses no energy (and decreases compressor on- time), there is no defrost design option capable of reducing defrost energy in cases that use off- cycle defrost.

A ll low

- temperature and ice

- cream temperature cases use supplemental heat for defrost. Electric resistance heating (electric defrost) is commonly used in these cases. An alternative to electric defrost in those cases that require supplemental defrost heat is hot- gas defrost. This defrost

mechanism involves using the hot compressor discharge gas to warm the evaporator from the refrigerant side. Manufacturers told DOE during interviews that the use of hot- gas defrost is a subject of division within the industry, with some manufacturers employing it on many of their models and others using it very rarely, if at all. These manufacturers mentioned various positive and negative attributes of the technology, depending on their stance on the issue. However, independent of the technical factors related to implementation of hot- gas defrost, t he test procedure for commercial refrigeration equipment is not capable of quantifying the energy expenditure of the compressor during a hot-gas defrost cycle for remote condensing equipmen t. Therefore, DOE did not consider hot- gas defrost as a design op tion.

5.6.5.7 Anti -Fog Films on Transparent Doors

Anti -fog films are offered as an option by some manufacturers on their transparent display doors and consist of advanced hydrophobic materials that are applied to the glass surface on the inside of the door. Without such coatings, condensation can attach to the glass surface and

form beads, resulting in visible fog that can obscure views of the product. These materials cause the water to instead simply slide off the surface of the door, maintaining a clear app earance.However, DOE understands that these films alone do not necessarily eliminate the need for antisweat heaters in many cases, including conditions of high ambient humidity, as these films do not prevent the formation of condensation on the outside o f the case, which can present a major problem for consumers. Moreover, DOE understands that delamination of anti

- fog films presents

5- 21

### a major issue, making them unreliable in the long term. Discussions with manufacturers have led

DOE to believe that other im provements in door construction provide the capacity to reduce antisweat heat without the drawbacks previously discussed. Because of these issues, DOE did not consider anti-fog films on transparent doors as a design option.

5.6.6 Design Options

After conducting the screening analysis and removing from consideration those technologies described above, DOE implemented the remaining technologies as design options in the energy consumption model:

• higher efficiency lighting and occupancy sensors for VOP, SVO, and SOC equipment families (horizontal fixtures)

• higher efficiency lighting and occupancy sensors for VCT and PD equipment famil ies (vertical fixtures)

• improved evaporator coil design

• higher efficiency evaporator fan motors

• improved case insulation

• improved doors for VCT equipment family, low and ice- cream temperature

• improved doors for VCT and PD equipment famil ies , medium temperature

• improved doors for HCT equipment family, low and ice

- cream temperature

• improved doors for HCT equipment family, medium temperat ure

• improved doors for SOC equipment family, medium temperature

• improved condenser coil design (for self -contained equipment only)

• higher efficiency condenser fan motors (for self-contained equipment only)

• higher efficiency compressors (for self -contained equipment only)

• night curtains (equipment without doors only)

Each design option has at least two technology levels , ranging from the minimum ( lowest performing) to the maximum (best performing) technology. The design options and the

techno logy levels for each design option are described in the following sections.

5.6.6.1 Higher Efficiency Lighting and Occupancy Sensors

Lighting is an important characteristic of commercial refrigeration equipment because it makes the product visible to the consumer. Lighting systems typically operate continuously and provide an opportunity for significant energy savings. As lighting system efficiency increases, reductions in total case energy consumption can be achieved through a direct reduction in electricity consu mption by the lighting system, and a reduction of heat inside the case, thereby reducing compressor work.

It is important that product illumination not degrade with higher design option levels

because this would decrease the utility of the equipment. DOE t ried to maintain approximately constant system illumination across all design option levels . This approach meant that DOE had to consider lighting as a system, rather than distinguishing lamps and ballasts as separate design

5- 22

### options. This approach becomes more important when considering L ED lighting systems, which

do no t use ballasts, as fluorescent lighting systems do.

Although LED systems generally have lower absolute efficacy in lumens per watt than fluorescent systems, the fixtures produce light that is much more directional in nature. And,

w hile the total lumen output of LED systems is lower than comparable fluorescent systems, the amount of light incident on the product (illuminance) is roughly equivalent. Consultations with commercial refrigeration equipment manufacturers, lighting manufacturers, and other technical experts indicate that current LED technology provides product lighting that is adequate and in most cases comparable to fluorescent lighting. In recent years, acc ording to discussions with manufacturers, the trend within the market has been toward a much greater use of LED lighting in this equipment.

To account for the variation in design between equipment families, DOE used two lighting design options in the ener gy consumption model. DOE used the “higher efficiency lighting and ballasts for VOP, SVO, and SOC equipment families” design option for lighting in a horizontal configuration, and the “higher efficiency lighting and ballasts for VCT and PD equipment famil i es ” design option for the lighting in a vertical configuration. The VCS and HCS equipment families do not require lighting because they are not designed to display food, while

the HCT and HZO equipment famil ies typ ically do not have lighting because they r ely on store ambient lighting for product illumination . Therefore, DOE did not consider lighting design options for these four equipment families.

DOE also considered occupancy sensors, which allow for case lighting to be reduced or shut off during periods of inactivity around the case, as part of the design options for the VCT/PD and VOP/SVO/SOC groupings. These equipment families generally are used for display purposes and include lighting for product illumination, making occupancy sensor implementation a n option for this equipment. Because fluorescent lamps require a start -up period after being powered on and fluorescent lamp lifetime is greatly reduced by frequent cycling on and off, DOE only considered occupancy sensors to be an option compatible with L ED lighting.

In the preliminary engineering analysis, DOE based its modeling of occupancy sensors on past empirical studies and discussions with commercial refrigeration equipment

manufacturers . For that analysis, DOE determined that a net 30

- percent redu ction in lighting run time due to the implementation of occupancy sensors would be an appropriate figure for modeling the performance of these sensors. 4

, 5 

### This value was directly implemented into the energy consumption model, such that a unit with occupancy sensors would see a lighting time

reduction of 30 percent from the standard 24-hour daily run time. However, in the time between the standards pre-analysis and NOPR publications, DOE published a final rule amending the commercial refrigeration eq uipment test procedure. 77 FR 10292 (February 21, 2012). T hat rule includes provisions for the testing of occupancy sensors and scheduled controls, allowing for 8 to

10.8 hours of lighting off or dimmed , depending on whether lighting occupancy sensors,

sch eduled lighting controls, or both are installed on a case and whether the respective lighting technologies dim or turn off the lights. For this analysis, DOE assumed 2.8 hours of dimmed lighting from sensors and an 8-hour lighting run time reduction due to scheduled controls per 24- hour test period. DOE believes this is lighting configuration is representative of those found in

the field

, and DOE incorporated the se specifications from the updated DOE test procedure into

5- 23

### the standards NOPR engineering analysis. The reduction in lighting run time, as input into the model, affect s both the calculated energy consumption of the lighting and the lighting contribution to the case heat load. Additionally, in some cases, where incremental implementation of occu pancy sensors proved in the analysis to be more cost effective than the implementation of LEDs alone, DOE implemented the two together, going directly from T8 or Super T8 lighting to LEDs with occupancy sensors, without an intermediate option of LEDs

alone.

Because of the horizontal configuration of shelving and the linear nature of display case lineups in the VOP, SVO, and SOC equipment families, lighting for these cases is typically

installed in the horizontal plane. Details for the “higher efficiency li ghting and ballasts for VOP, SVO, and SOC equipment families” design option are shown in Table 5.6.2 . Remote condensing versions of these display cases are most often sold in 8-foot and 12-foot sections, using multiples

of 4 -foot fixtures (either fluorescent bulbs or LED strips) to continuously light the entire width of

the case. Self -contained versions are commonly sold in 4-foot lengths, so that a sin gle 4 -foot light fixture will light the full width of the case. Therefore, 4 -foot fixtures were specified for all lighting systems in the horizontal configuration. Th is lighting was also required to have a color temperature of 3,500 k elvin (K), which is typical for this type of equipment.

Because of the vertical configuration of the doors in the VCT and PD equipment families, fluorescent lamps typically are installed vertically behind the mullions between doors. Such lighting systems typic ally consist of a single 5- foot lamp and single ballast per mullion as well as a lamp installed at each end of the case. All lighting systems in the vertical configuration were specified to have 5 -foot lamps and a color temperature of 4,100 K, which DOE fo und to be typical for this equipment family. Table 5.6.3 shows details for the “lighting for VCT and PD equipment families” design option. After publication of the NOPR, DOE received comment from manufacturers expressing concern over the modeled light output in lumens of the LED fixtures included in the engineering analysis. Additionally, other stakeholders presented comments in the

form of data on LED efficacy. Specifically, stakeholders referenced the DesignLights

Consortium database 6 

### entries for commercial refrigerated display case lighting, indicating that LED fixtures currently available on the market exhibit higher efficacies than those modeled in DOE’s NOPR analysis. DOE reviewed the data provided and updated its modeling of LED fixtures to reflect a roughly 20% increase in light output, consistent with the database entries for currently

- available products. Table 5.6.2 and Table 5.6.3 include these updated values for system efficacy and light output.

5- 24

### Table 5.6.3 Details for Lighting for VOP, SVO, HZO, and SOC Equipment Families Design Option

Level Description Lamp Type Lamp

Rated

Power

W Lamp Rated Light Output

l umen s System Efficacy

l umens/W System Light Output

l umens

T8 N 4 ft, T8 Elec. F32T8 32.0 2

, 850.0 85.0 2

, 679.0

T8S 4 ft, Super T8 Elec. F32T8/HL 32.0 3

, 100.0 94.6 2

, 697.0

LED 4 ft, LED LED 4 ft 15.0 1085 .0 72.3 1085.0

OCC 4 ft, LED with Occupancy Sensors LED 4 ft 15.0 1085.0 72.3 1085.0



### Table 5.6.4 Details for Lighting for VCT and PD Equipment Famil ies Design Option

Level Description Lamp Type Lamp

Rated

Power

W Lamp Rated Light Output

lu mens System Efficacy

l umens/W System Light Output

l umens

T8 N 5 ft, T8 Elec. F58T8/835 58.0 5

, 400.0 93.1 5

, 400.0

LED 5 ft, LED LED 5 ft 29.0 1912.0 65.9 1912.0

OCC 5 ft, LED with Occupancy Sensors LED 5 ft 29.0 1912.0 65.9 1912.0



### 5.6.6.2 Higher Efficiency Evaporator Fan Motors

In conjunction with fan blades, fan motors are necessary for transferring heat from the display case to the refrigerant and, in the case of self

- contained equipment, rejecting heat from the refrigerant into the ambient air. Fan motors are also responsible for maintaining product temperatures and air curtains on ope n cases. They must operate virtually continuously, and therefore use a significant amount of energy. As motor efficiency increases, reductions in total case energy consumption are achieved through a direct reduction both in electricity consumption and waste heat inside the case, reducing compressor load

.

Table 5.6.4 shows details for the evaporator fan motor design option. DOE considers

shaded -pole motors (SPM) as the baseline (or lowest

- efficiency ) technology, permanent split capacitor (PSC) motors as the mid -level technology, and brushless direct current or electronically commutated motors (ECMs) as the maximum technology level. DOE adapted motor efficiency levels

, listed in Table 5.6.4 , from the 2009 final rule . DOE verified these efficiency estimates through discussions with equipment manufacturers. During its discussions with manufacturers, some manufacturer representatives pointed out that there can be significant variations in efficiency between motors of the same type but different models. According to

these manufac turers, this variation was largely a function of equipment supplier. Some manufacturers stated that, from higher quality suppliers, some specific models of PSC motors, for example, could reach efficiencies as high as 40 percent . However, manufacturers gene rally agreed that the values listed in Table 5.6.4 are fairly representative of the efficiencies of motors available for use in commercial refrigeration equipment. Therefore, DOE retained these values for use in its NOPR engineering analysis. DOE received additional comments confirming its modeling of fan per formance after the NOPR, and retained these figures in the final rule.

5- 25

### Table 5.6.5 Details for Evaporator Fan Motor Design Option

Rated

Power

W Shaded

- Pole Motor Permanent Split Capacitor

Motor Brushless DC Motor (ECM)

Actual

Power

W Efficiency

% Actual

Power

W Efficiency

% Actual

Power

W Efficiency

%

12 .0 60.0 20 41.4 29 18.2 66

9.0 45.0 20 31.0 29 13.6 66

6.0 30.0 20 20.7 29 9.1 66



### 5.6.6.3 Improved Evaporator Coil Design

Evaporator coils are another component necessary for transferring heat from the display case to the refrigerant. Table 5.6.5 shows performance factors for the evaporator coil design options used in the engineering analysis . In view of available information, DOE considered a

baseline and a maximum technology level for this design option. For each level, DOE specified

an overall UA -value a 

### and a coil cost. The UA -value is normalized to the standard coil value , and the coil cost is normalized to the heat removal capacity of the coil. This allowed DOE to apply these details of coil design across all equipment classes and at different capacities

. In consultation with outside experts, DOE determined that applying the same coil design improvements to different sized coils would result in similar increases in coil performance

.

Table 5.6.6 Details for Evaporator Coil Design Option

Level Description Normalized UA (

- )

EVAP1 Standard Coil 1

EVAP2 High

- Performance Coil 1.438



### DOE based the details of coil construction ( Table 5.6.6 ) on baseline and prototype highperformance coil specifications developed by DOE contractors. These coil designs were developed by first performing teardown analyses of existing coils on the market and then using the data as inputs into a numerical simulation model to develop performance values for those baseline designs. The same modeling tools were then used to calculate new performance estimates for improved coil designs based on manipulation of the physical coil parameters. Finally, the baseline and improved coil designs were input into the cost model in the same

manner as the core cases discussed in section 5.4.1 to yield costs to manufacture each design.

After publication of the NOPR, DOE received a number of comments regarding evaporator coil design, which it used to update and refine its modeling of enhanced evaporator coils. Stakeholders expressed concern that increase fin pitches could produce icing issues, that tube rifling was not effective in commercial refrigeration equipment applications, that smaller

diameter tubing was not practical, and that interior volume constrains precluded significant increases in evaporator coil size. DOE also conducted additional equipment teardowns after the NOPR which it used to improve its evaporator coil modeling. Taking into account the

stakeholder comments and teardown data, DOE revised its modeling of baseline and improved evaporator coils at the final rule stage.

a The overall UA

- value is the product of the overall heat transfer coefficient (Btu/h

- ft 2

- °F) and the total surface area (ft 2 ) of the coil. This value can be derived from the total heat transfer rate of the coil (Btu/h) divided by the average temperature di fference between the discharge air and the saturated evaporator temperature (ΔT).

5- 26

### The high -performance coil uses a combination of enhancements to the heat transfer

surfaces to increase its overall UA

- value. These enhancements inc lude higher fin thickness, wavy fins , and the addition of an extra row of tubes to the coil , while maintaining the same

overall size of the coil . In sum, these improvements allow the prototype coil to run at a SET that

is warmer than the baseline coil whil e maintaining the same discharge air temperature and heat removal capacity.

Table 5.6.7 Properties of Standard and Enhanced Evaporator Coil Designs

Property Standard Coil High

- Performance Coil

Overall w idth ( in. ) 11.0 11.0

Overall h eight ( in. ) 7.375 7.375

Overall d epth ( in. ) 4.0 4.0

Tube r ows t ransverse to a irflow 7 8

Tube r ows p arallel to a irflow 4 4 Tubing m aterial Copper Copper Tubing o uter d iameter ( in. ) 0.375 0.375 Tubing w all t hickness ( in.) 0.015 0.015 Tubing i nner s urface Smooth Smooth

Fin m aterial Aluminum Aluminum

Fin s urface Flat Wavy Fin thickness ( in. ) 0.005 0.008

Fin p itch ( fins per inch ) 7 7



### Because compressor performance is directly related to SET, reductions in total case

energy consumption are realized through an improved EER at the condensing unit. In consultation with outside experts, DOE determined that applying the same coil improvements to different sized coils and at different temperatures would result in similar SET improvements. Consequently, the coil design was scaled as appropriate to model the coil in the representative unit for each equipment class analyzed

.

5.6.6.4 Improved Insulation and Vacuum Insulated Panels

Several improvements to case insulation were implemented i n the engineering model. DOE included increased foam insulation thickness as a design option, and modeled a half-inch

increase in insulation thickness for all equipment classes based on discussions with manufacturers regarding case design and foam insulati on fixturing. DOE added this increase in

thickness to the baseline value of insulation thickness and recalculated the conduction load in the energy consumption model (section 5.6.6.5 ). The cost of increasing the insulation thickness includes a sunk cost per unit, considering foam fixture engineering and tooling costs, production line lifetime, and number of fixtures and units produced. In the 2009 fin al rule and in its preliminary engineering analysis for this rulemaking, DOE assumed the cost increase due to

additional foam material to be insignificant compared to the cost of upgrading foam fixtures. However, in response to stakeholder comments after the preliminary analysis, DOE included the differential cost of additional foam insulation in the engineering analysis for the NOPR, and retained this figure for the final rule . DOE calculated the volume of additional insulation resulting from the added half inch of thickness and multiplied this by a cost per cubic foot for the foam and blowing agent. Additionally, DOE received further comment after publication of the NOPR stating that its estimated costs to update foam fixtures were too low. Therefore, DOE updated these costs for the final rule, consistent with values provided in stakeholder comments.

5- 27

### Table 5.6.7 provides details of the assumptions used to calculate the additional cost of insulation

thickness increases.

Table 5.6.8 Assumptions in Cost Cal culation Methodology for Insulation Thickness

Increase

Item Value Notes

Cost to u pgrade s ingle

i nsulation f ixture $150 ,000

Number of f ixtures per product

line* 25 Based on a survey of the number of products offered by each

manufacturer

Engineering c osts $ 416,667 Assumes $100,000 per year cost of labor and one month to complete redesign per machine plus one month for testing

Interest r ate 7.0%

Product l ine l ifetime (years) 7.0 Units per y ear 25 ,000

Sunk f ixturing c ost p er u nit $ 30.93

*Product line includes all models built off of the same cabinet, which can be foamed using the same fixture. Different cabine ts will require

different sets of fixtures.



### DOE also considered vacuum insulated panels (VIPs) as an option for improving the

insu lation performance of the insulated walls of commercial refrigeration equipment cases. Data regarding VIP performance was gathered from discussions with VIP manufacturers in conjunction with past and ongoing rulemakings on residential refrigerators and walk-in coolers and freezers. These discussions yielded a representative material cost for VIPs with an R- value of 30 per inch of thickness. This cost was then multiplied in the engineering cost model by the

insulated wall area and thickness to produce a diff erential cost of upgrading from traditional

foam insulation to VIPs. This model simulated the entire modeled case as being composed of VIP material, and assumed that the VIP material would be the dominant component of the thermal performance of the case. Additionally, as with the increased foam thickness design option, a sunk cost per unit of upgrading to VIPs was calculated and applied to the unit cost. This

consist ed of an estimate for the additional costs of product engineering and redesign, new production equipment, and new tooling, amortized over the typical equipment production lifetime and divided by the number of units per year. The result was a levelized cost per unit produced. The assumptions used for this design option are shown in Table 5.6.8

.

Table 5.6.9 Assumptions in C ost Calculation Methodology for Vacuum Insulated Panels

Item Value

Additio nal tooling/engineering/product redesign costs * $500,000 New equipment costs $300,000

Interest r ate 7.0%

Product line l ifetime (years) 7 Units per y ear 2 5,000

Sunk cost p er u nit $ 5.94

*Product line includes all models built off of the same cabinet. Different cabinets will incur separate redesign costs.



### For each equipment class analyzed, DOE considered both the increase in conventional

insulation thickness and the switch to VIP s as desi gn options. While these insulation improvement design options benefit some equipment types more than others, DOE modeled

5- 28

### them for each of the directly analyzed equipment classes, as conduction through the case is present in all equipment classes. Table 5.6.9 summarizes these design options.

Table 5.6.10 Details for Insulation Design Options

Level Description Additional

Thickness Above

Baseline

inches Insulation

R- Value Per

Inch Thickness

IN1 Baseline insulation 0 8

IN2 Extra 1/2

- inch insulation

thickness 0.5 8

VIP Vacuum insulated panels 0 30



### 5.6.6.5 Improved Transparent Doors

Transparent doors allow refrigerated products to be displayed to consumers while keeping cold a ir inside t he display case. Transparent doors also allow heat to radiate into the display case and generally have a lower insulation value than solid insulated walls. In cases with transparent doors, these surfaces are responsible for a significant portion of the case heat load. On freezers and some refrigerators, glass doors must be heated to prevent frost or condensation from forming. These “anti

- sweat” heaters often run continuously and consume significant amounts of energy. Reductions in total case energy consumption can be achieved both byimproving the overall insulation value (U-factor) of the door and by reducing the required antisweat heater power. Improvements to heat transfer performance could include the use of additional panes of glass and expanded use of inert gas fil l between panes of glass . The treatment of the window glass with advanced low-emissivity coatings and increasing the number of coated

surface s could also reduce losses resulting from radiation heat transfer. Reductions in the antisweat heater power needed can often be achieved as a function of improved conductive performance of the door, as well as in improvements to the gasketing and other do or features.

A wide variety of door types are used on the equipment covered in this rulemaking. Door construction and performance can vary by equipment family as well as operating temperature of

the case. To account for this variation, DOE developed s eparate design option data for each of the different door types represented in the primary equipment classes that DOE analyzed. For

selected door design s , DOE estimated the thermal performance of the door (expressed as an

overall U -factor) using information about door construction obtained from manufacturers and the

WINDOW modeling software

, available from Lawrence Berkley National Laboratory. Cost estimates for transparent doors for commercial refrigeration equipment applications were

obtained from manufacturer interviews and publicly available sales sheet data. DOE then extended this cost and performance data to apply to various geometries of horizontal and vertical display and service doors.

After publication of the NOPR notice, TSD, and engineering model, D OE received a number of comments on modeling of baseline and improved transparent doors. DOE reviewed these comments, revised the inputs to its analyses, and updated its modeling of transparent doors using the LBL Window software. DOE also updated its valu es for anti

- sweat power for some door types based on stakeholder input.

5- 29

### Doors for the VCT equipment family operating at low and ice

- cream temperature are hinged and are a representative size of 30 inches wide and 67 inches tall with three panes of glass. Table 5.6.10 shows details of thermal performance and anti

- sweat heater requirements for this door type.

DOE considered two technology levels for this design option: the high- performance door

that uses a combination of low- emissivity coating, an improved frame material, and inert fill

- gas to reduce the overall U -factor; and a standard door.

Table 5.6.11 Details for Doors for VCT Equipment Family, Low and Ice

- Cream Temperature Design Option

Level Description Overall U

Factor

Btu/hr

- ft

2

- °F Anti

- Sweat Heater

Power

W/door

DR1 Standard d oor 0.39 180

DR2 High

- p erformance

d oor 0.22 90



### Doors for the VCT and PD equipment famil ies operating at medium temperature are hinged and have a representative size of 30 inches wide and 67 inches tall with two panes of glass at the baseline

. Table 5.6.11 shows details of thermal performance and anti

- sweat heater requirements for this door type.

DOE considered two technology levels for this design option. The high-performance door

uses a combination of low- emissivit y coating, improved frame material, and inert fill -gas to
reduce the overall U -factor compared to the standard door. At the NOPR stage of the rulemaking, DOE modeled improved medium-temperature doors of this type as having three panes of glass. However, commenters suggested that the use of triple-paned glass doors on all VCT and PD equipment operating at medium temperature was not feasible. In response, DOE revised its modeled high-performance doors to only include two panes of glass for the final rule.

Based on interviews conducted with commercial refrigeration equipment manufacturers,DOE understands that the implementation of high-performance glass doors in mediumtemperature equipment can allow for significant reduction in the level of anti- sweat powe r needed . At the NOPR stage, DOE modeled these doors as being completely without anti- sweat heat, consistent with some inputs received. However, comments after the publication of the NOPR suggested that complete elimination of anti

- sweat power was unrealis tic. As a result, the high -performance door design option for the VCT and PD famil ies at medium temperature was

revised to include an anti- sweat heater power of 20 watts per door, a reduction from the modeled baseline power of 80 watts.

Table 5.6.12 Details for Doors for VCT and PD Equipment Famil ies, Medium Temperature Design Option

Level Description Overall U

Factor

Btu/hr

- ft

2

- °F Anti

- Sweat Heater

Power

W/door

DR1 Standard d oor 0.47 80

DR2 High

- p erformance

d oor 0.27 20

5- 30

### Doors for the HCT equipment family operating at medium temperature are sliding and are a representative size of 18 inches wide and 20.5 inches tall with one pane of glass at the

baseline. Table 5.6.12 shows details of thermal performance and anti- sweat heater requirements for this door type.

DOE considered two technology levels for this design option. The high-performance door uses a combination of an additional pane of glass, low- emissivity coating, an improved frame material, and inert fill

- gas to reduce the overall U -factor compared to the standard door. Typically, these door types do not require anti- sweat power.

Table 5.6.13 Details for HCT Equipment Family, Medium Temperature Design Option

Level Description Overall U

Factor

Btu/hr

- ft

2

- °F Anti

- Sweat Heater

Power

W/door

DR1 Standard d oor 1.05 0

DR2 High

- p erformance

d oor 0.32 0



### Doors for the HCT equipment family operating at low and ice

- cream temperature s are sliding and are a representative size of 18 inches wide and 20.5 inches tall with one pane of glass

at the baseline

. Table 5.6.13 shows details of thermal performance and anti

- sweat heater requirements for this door type.

DOE considered two technology levels for this design option: a high

- performance door

that uses a c ombination of low- emissivity coating, an improved frame material, and an extra

pane of glass with inert fill

- gas to reduce the overall U -factor; and a standard door. Typically, these door types do not require anti- sweat heater power.

Table 5.6.14 Details for Doors for HCT Equipment Family, Low, and Ice

- Cream Temperature Design Option

Level Description Overall U

Factor

Btu/hr

- ft

2

- °F Anti

- Sweat Heater

Power

W/door

DR1 Standard d oor 1.05 0

DR2 High

- p erformance

d oor 0.33 0



### Doors for the SOC equipment family operating at medium temperature are of the sliding type and have a representative size of 24 inches wide and 20 inches tall with two panes of glass.

Table 5.6.14 shows details of door thermal performance and anti- sweat heater requirements for this door type. DOE considered two technology levels for this design option: a high

- performance

door that uses a combination of low- emissivity coating, an improved frame material, and inert

fill

- gas to achieve a reduced overall U -factor; and a standard door. Typically, SOC doors do not require anti

- sweat heater power.

5- 31

### Table 5.6.15 Details for Doors for SOC Equipment Family, Medium Temperature Design Option

Level Description Overall U

Factor

Btu/hr

- ft

2

- °F Anti

- Sweat Heater

Power

W/door

DR1 Standard d oor 0.55 0

DR2 High

- p erformance

d oor 0.32 0



### 5.6.6.6 Higher Efficiency Condenser Fan Motors

The condenser fan motor design option applies only to those equipment classes that are

self -contained. Details for the condenser fan motor design option are identical to those shown in

Table 5.6.4 . As with evaporator fan motors, the SPM is the baseline technology, the PSC motor

is the mid

- level technology, and the ECM is the maximum technology level. Because condenser

fan motors are outside the refrigerated space, efficiency improvements only affect the direct electrical consumption of the motors and not the total case heat load.

5.6.6.7 Improved Condenser Coil Design

For the NOPR stage of this rulemaking, DOE performed additional analysis of condensercoils to develop a more thorough set of inputs to the energy consumption model for selfcontained equipment. Table 5.6.15 shows performance factors for this design option. Details of

coil construction are based on data from teardowns of equipment available on the market, as well as analytical modeling performed by DOE and its contractors. The methods used for developing

the conden ser coil model were the same as those used to model evaporator coils and are

described in section 5.6.5.3 . Based on this information, DOE considered both baseline and high

performance technology levels for this design option. For each level, DOE specified an overall

UA -value and a coil cost. The UA -value is normalized to the standard coil, and the coil cost is normalized to the heat removal capacity of the modeled coil in British thermal units per hour. This approach allowed DOE to apply the details of coil design across all self-contained equipment classes. In consultation with outside experts, DOE determined that applying the same coil improvements to different sized coils would result in similar performance improvements.

Table 5.6.16 Details for “Increased Condenser Surface Area” Design Option

Level Description Normalized

UA (

- )

CD1 Standard c oil 1.00

CD2 High

- p erformance c oil 1.60



### After publication of the NOPR, DOE received a number of comments regarding condenser coil design, which it used to update and refine its modeling of enhanced evaporator coils. Stakeholders expressed concern that increased fin pitch could produce clogging issues, that tube rifling was not effective in commercial refrigeration equipment applications, that smaller

diameter tubing was not practical, and that volume constraints precluded significant increases in coil size. DOE also conducted additional equipment teardowns after the NOPR which it used to improve its condenser coil modeling. Taking into account the stakeholder comments and

5- 32

### teardown data, DOE revised its modeling of baseline and improved condenser coils at the final rule stage.

Table 5.6.16 shows details of coil construction. The high- performance coil uses a

combination of enhancements to the heat transfer surfaces that increased its overall UA

- value.

These enhancements include increased fin thickness and the use of wavy fins. Additionally, DOE modeled an addition of one row of tube passes to the depth of the coil. This increased the overall size of the improved coil design as compared to the baseline, but DOE believes that this size increase is not sufficient to incur the concerns expressed by manufacturers regarding coil size constraints. The modeled size of the improved coil is also consistent with the size of condenser coils see in teardowns of high-performance units currently on the market. These improvements allow the prototype coil to run at a saturated condenser temperature (SCT) that is cooler than the baseline coil while maintaining the same heat rejection rate. B ecause compressor performance is directly related to SCT, reductions in total case energy consumption are achieved through an improved EER at the condensing unit.

Table 5.6.17 Properties of Standard and Enhanc ed Condenser Coil

Property Standard Coil High

- Performance Coil

Overall w idth ( in

. ) 12.5 12.5

Overall h eight ( in

. ) 10.875 10.875

Overall d epth ( in

. ) 5.625 6.875

Tube r ows t ransverse to a irflow 10 10

Tube r ows p arallel to a irflow 4 5 Tubing m aterial Copper Copper Tubing o uter d iameter ( in

. ) 0.375 0.375 Tubing w all t hickness ( in

. ) 0.015 0.015 Tubing i nner s urface Smooth Smooth

Fin m aterial Aluminum Aluminum

Fin s urface Flat Wavy Fin thickness ( in. ) 0.006 0.01

Fin p itch (fins per inch) 6 6



### 5.6.6.8 Higher Efficiency Compressors

In consultation with manufacturers and external technical experts, DOE determined that two levels of technology were applicable for the compressor design option. The baseline technology level is a standard single- speed hermeti c compressor, and the maximum technology level is a high

- efficiency single

- speed hermetic compressor. Reductions in total case energy consumption are achieved through a reduction in compressor power consumption.

Several manufacturers provide publicly- available performance data for standard single

speed hermetic compressors over a range of capacities applicable to the covered equipment. DOE used this data to find appropriately sized compressors when developing each design option

curve. (See section 5.6.6.2 for information on the calculation of compressor energy consumption.) During the NOPR analyses, DOE updated its selection of compressor models

within the engineering analytical spreadsheet to better account for the wide variations in capacity between the representative unit sizes analyzed for the various equipment classes.

5- 33

### Although several compressor manufacturers produce high- efficiency compressors, l ittle

data are currently available on their performance. Often, compressor manufacturers do not explicitly brand their compressors as “high

- efficiency ,” but instead maintain only one line of products for a given application. Therefore, in the NOPR, DOE app roximated a set of high

efficiency compressors by adjusting the power consumption of the standard compressors using a constant multiplier. This multiplier assumed a 10

- percent reduction in energy consumption with

an associated 5

- percent cost premium. DOE developed this multiplier through its own research, consultation with outside experts, and discussion through discussion with commercial refrigeration equipment manufacturers. However, after publication of the NOPR, DOE received

numerous stakeholder commen ts stating that the performance increases assumed to be possible within single -speed reciprocating compressors were too aggressive. After reviewing the input which it received at this stage of the rulemaking, DOE adjusted its modeling of high- efficiency compressor performance for the final rule to reflect an efficiency increase of 2% at an added cost

of 5% over the baseline.

Scroll Compressors

Some manufacture rs mentioned in interviews that scroll compressors were available from certain producers for commercial refrigeration equipment applications. However, these manufacturers also stated that the scroll compressors, while presenting a slight performance improvement over reciprocating compressors, had an extremely high associated cost premium.

These manufacturers stated that this cost premium made scroll compressors a viable option only in specific design scenarios, such as when certain geometric configurations were required or in instances where noise reduction is very important. They also mentioned th at scroll compressors were only available over a certain range of capacities , which would not cover the entire commercial refrigeration equipment market. As a result, DOE did not consider scroll compressors in its analysis.

Variable-Speed Compressors

At t he preliminary analysis public meeting and in written comments submitted during the preliminary analysis comment period, several stakeholders raised the subject of variable-speed compressors as applicable to commercial refrigeration equipment. DOE research ed this subject and raised it during interviews with commercial refrigeration equipment manufacturers

. According to these sources, only one compressor manufacturer currently sell s a variable

- speed compressor for commercial refrigeration applications in the United States, and that product is sized only for equipment with heat loads less than 3,000 Btu per hour. Such a compressor size would be applicable only to some smaller open cases and medium- sized equipment in classes with doors. Additionally, manufactur ers raised concerns regarding the state of the technology at this time, saying that not enough research and development had been performed to evaluate the efficacy and reliability of variable

- speed compressors for commercial refrigeration application. These compressors would also require complex controls and sensor- driven interfaces to be developed. As a result, DOE has elected not to consider variable- speed compressors explicitly in its analysis because its curren t understanding is that this technology has not yet been implemented for widespread use within the commercial refrigeration equipment industry.

5- 34

### 5.6.6.9 Night Curtains

In response to stakeholder input and discussions with commercial refrigeration equipment manufacturers, DOE included night curtains as a design option for the VOP and SVO equipment families in the engineering analysis . DOE based its modeling of this design option on specifications obtained from manufacturer interviews, publicly available data from night curtain manufacturers, and past studies of night curtain performance. For cases with night curtains implemented, a curtain down time of 6 hours per day was used in the energy consumption model, in accordance with the time specified in the commercial r efrigeration equipment test procedure

final rule . 77 FR 10292 ( February 21, 2012 ). The performance of the curtains in the model was based on a survey of field studies of night curtain effectiveness, which resulted in an average 39-

percent reduction in case heat load during periods when the night curtains were deployed. 7

, 8


### When the night curtain design option was implemented in the energy consumption model, the

39- percent reduction in case heat load was applied over a period of 6 hours to the load calculated

for the same configuration unit without night curtains, and the new heat load was normalized over 24 hours to give a standard hourly load. Table 5.6.17 shows the specifications for the night curtain design option.

Table 5.6.18 Details for “Night Curtains” Design Option

Level Description Curtain

Down Time

hr Case Heat Load Multiplier

OFF No n ight c urtains 0 1.00

NC1 Night c urtains 6 0.61



### 5.6.7 Model Components

Figure 5.6.1 presents a schematic showing the components in the energy consumption model. The model calculates energy consumption in two major subsections (expressed as kWh/day): compressor energy consumption and component energy consumption. Component energy consumption is the sum of electrical energy directly consumed by each fan motor, lamp, defrost and drain heater, anti- sweat heater, and pan heater.

Compressor energy consumption is calculated from the total heat load (expressed as

Btu/h r) and one of two compressor models: one version for remote condensing equipment, and

one for self- contained equipment. The total heat load is a sum of the component load and the

non- electric load. The component load is a sum of the heat emitted by evaporator fan motors, lighting, defrost heaters, drain heaters, and anti- sweat heaters inside and adjacent to the refrigerated space (condenser fan motors and pan heaters are outside the refrigerated space and do not contribute to the component heat load). The non- electric load is the sum of the heat contributed by radiation through glass and openings, heat conducted through walls and doors, and warm air infiltration through openings.

5- 35

### Figure 5.6.1 Composition of the Energy Consumption Model

5.6.7.1 Component Energy Consumption

Component energy consumption consists of calculated energy consumption of each of the

system components that directly consume s energy.

Fan motor energy consumption is calculated by summing the power draw of each evaporator and condenser fan motor and multiplying the total motor power by the total running

time over a 24 -hour period.

Lighting energy consumption is calculated by summing the power draw of each lamp and ballast and multiplying the total by the total operating time of the lighting system over a 24 -hour period. (This c alculation assumes that lighting operates continuously at the baseline.) For cases where lighting controls and occupancy sensors have been implemented, a run- time reduction is used, based on the specifications for lighting controls and occupancy sensors in the commercial refrigeration equipment test procedure.

Daily d efrost and drain heater energy consumption are calculated by summing the power

draw of each defrost heater and drain heater and multiplying by the total time the case is in defrost operation over a 24-hour period.

Anti

- sweat energy consumption is calculated by summing the power draw of each anti

sweat heater and multiplying by the total operating time of the heaters over a 24 -hour period. Total Heat

Load [Btu/h]

Component

Load
[Btu/h] Non

- Electric

Load
[Btu/h]

Radiation

Load [Btu/h] Conduction

Load [Btu/h] Infiltration

Load [Btu/h] Daily Energy Consumption [kWh/day]

Compressor

Energy Consumption

[kWh/day]

Compressor Model (ARI 540) Component
Energy Consumption

[kWh/day]

5- 36

### (This calculation assumes that anti

- sweat heaters run c ontinuously, a position supported by

manufacturer literature and interviews .)

Pan heater energy consumption is calculated by multiplying the power draw of each pan heater by the total operating time of the heater over a 24 -hour period. The total operating time is calculated as the time it would take to evaporate all of the defrost melt water.

5.6.7.2 Compressor Energy Consumption

Compressor energy consumption (CEC) is calculated from the total heat load and one oftwo compressor models: one version for remote condensing equipment and one for selfcontained equipment. CEC for remote condensing equipment is calculated using default efficiency values from A HRI 1200. Table 1 in AHRI 1200 lists remote condensing compressor

EER in Btu/W -h as a function of adjusted dew point temperature.

Adjusted dew point (ADP) temperature (°F) is calculated as:

ADP = SET – 2 °F (for medium temperature) Eq. 5.2

ADP = SET – 3 °F (for low/ice -cream temperature) Eq. 5.3

W here :

SET = the saturated evaporator temperature (°F).

Once ADP is calculated, Table 1 in A HRI 1200 is used to find the corresponding EER

value. The CEC (kWh/day) is then calculated as:

CEC = Q tot 

### x (24 - t defrost 

### ) / (EER × 1000) Eq. 5.4

W here :

Q tot 

### = the total heat load (Btu/hr), and

t defrost 

### = the total defrost time in a 24 -hour period (hr).

The CEC for self -contained equipment is calculated by using the compressor model described in section 6.4 of AHRI Standard 540-2004 (AHRI 540), Performance Rating of Positive Displacement Refrigerant Compressors and Compressor Units . This model is based on

a 10 -coefficient polynomial derived from empirical compressor p erformance data for power, mass flow, current, and efficiency. The coefficients are derived for each parameter as a function

of SET and SCT. Compressor coefficients are available from compressor manufacturers. Similar to the method used for remote condensing equipment, the EER of any compressor can be determined given the SET and SCT values along with the 10 coefficients for the specific compressor model. Using Eq. 5.4 and the EER for the unit at the specific conditions being

modeled , the CEC can be determi ned.

5- 37

### In the engineering analysis, DOE uses adjusted capacities for compressors modeled, rather than capacities at standard ASHRAE rating evaporator and condenser rating conditions.

This is b ecause the actual ASHRAE 72 test conditions for most commercial re frigeration equipment are not the same as standard compressor rating conditions, causing capacities at the

modeled test conditions to differ from listed capacities. The compressor model then uses a lookup function to select the most appropriate compressor based on the total refrigeration load in the case and the compressor oversize factor from the baseline design specifications.

5.6.7.3 Component Load Model

The component load is the sum of the heat emitted by evaporator fan motors , lamp s, defrost and drain heater s, and anti- sweat heater inside to the refrigerated space. Each component creates waste heat that is rejected to the refrigerated space and must be removed by the

compressor.

DOE assumed that all electrical energy consumed by the evaporator fan motors ends up as heat inside the refrigerated space. In electric motor s, friction present with in the motor windings, bearings, and other mechanical components converts much of the input electrical energy into heat. The rest of the energy is used in moving air inside the case. This moving air is slowed by friction

, and the kinetic energy of the air is converted into heat.

For lighting inside the refrigerated space, DOE assumed that all electrical energy consumed by the light fixtures ends up as heat inside the s pace. For lighting adjacent to the air curtain, DOE assumed that 50 percent of the electrical energy consumed ends up as heat inside the space. For fluorescent ballasts inside the refrigerated space, DOE assumed that all electrical

energy that the ballasts consume ends up as heat inside the space. Ballasts outside the refrigerated space do not contribute any heat to the space. In cases in which occupancy sensors have been implemented as a design option, the heat inside the case resulting from lighting as

ca lculated as above, but the run- time multiplier for the lighting is used to scale that heat load proportionately.

The phase change that occurs when defrost heaters melt the frost from evaporator coils

consumes most of the electrical energy that supplied during a defrost period. Over a 24 -hour period, the total heat of melting is determined by :



### Q m H

melt frost f water 

### =

, 

### Eq. 5.5

W here :

Q melt 

### = the heat of melting (Btu/hr),

m

frost 

### = the frost mass in a 24-hour period (lb/hr), and

H

f,water 

### = the heat of fusion of water (Btu/lb).

DOE assumed that all the electrical energy that defrost heaters consume, other than what is used to melt the frost, ends up as heat inside the refrigerated s pace. For drain heaters, DOE assumed that all electrical energy ends up as heat inside the refrigerated space.

5- 38

### DOE assumed that

, on average, 70 percent of the electrical energy consumed by anti

sweat heaters adjacent to the refrigerated space (frame, rail, glass, sill

, and air grille heaters) ends up as heat inside the space.

5.6.7.4 Radiation Load Model

The radiation heat load model accounts for the gray-body radiation between the warm surfaces of the surrounding environment (the test chamber) and the cold inner s urfaces of the refrigerated space. For cases without doors, the net radiation is determined as : 

# ( )
Q T T

A A F A rad room case

room

room room room case room case

case case 

### = −

− + + −

− 

### σ

ε

ε ε

ε 4 4



### 1 1 1

Eq. 5.6

W here :

Q rad 

### = the net radiation load (Btu/hr ),

σ = the Stefan

- Boltzmann constant (Btu/hr- ft 2 

### -°F 4 

### ),

T

room 

### = the temperature of the room walls (°F),

T

case 

### = the temperature of the case inner walls (°F),

ε room 

### = the emissivity of the room walls (dimensionless),

ε case 

### = the emissivity of the case inner walls (dimensionless),

A

room 

### = the area of the room walls (ft 2 

### ),

A

case 

### = the area of the interior of the case (ft 2 

### ), and

F

case

- room 

### = the view factor from the case interior to the room walls (dimensionless).

DOE assumed that the wall temperatures of the case were in thermal equilibrium with the air temperature in the case, and that the wall temperatures of the room were in thermal equilibrium with the air temperature in the room. See Eq. 5.6 for numerical constants pertaining

to the radiation model.

For glass doors and other glass, the net radiation is incorporated into the overall U- factor of the door ( Table 5.6.10 through Table 5.6.14 , and Eq. 5.7 ). See section 5.6.6.5 for a discussion of the calculation of the combined radiation and conduction loads for glass doors and other glass.

5.6.7.5 Conduction Load Model

The conduction load model accounts for the heat conducted through walls and doors. For solid walls and doors, the conduction is given by:

Q A T T

h d

k h cond walls room case

o ins

ins i 

### = −

+ + 1 1



### Eq. 5.7

5- 39

### W here :

A walls 

### = the area of the exterior of the case (ft 2 

### ),

h

o 

### = the convective film coefficient on the outside of case walls (Btu/h- ft 2



### - °F),

h i 

### = the convective film coefficient on the inside of case walls (Btu/h

- ft 2



### - °F),

d ins 

### = the insulation thickness (in .), and

k ins 

### = the insulation thermal conductivity (Btu -in/hr- ft 2



### - °F).

Because of its high thermal conductivity, the sheet metal that encloses the insulation has negligible effect on the conduction load, and therefore was not included in the calculation of

conduction load. See Table 5.6.1 for numerical constants pertaining to the conduction model.

For glass doors and other glass, the overal l U -factor of the door assembly or glass is based on data obtained thought WINDOW 5 calculations ( Table 5.6.1 and Table 5.6.10 through

Table 5.6.14 ). The combined radiation and conduction load for glass doors and other glass is

calculated as:


# ( ) Q U A T T

glass overall glass room case 

### =

− 

### Eq. 5.8

W here :

U

overall 

### = the overall U -factor, including convection and radiation (Btu/h- ft 2



### - °F), and

A glass 

### = the area of the glass (ft 2 

### ).

5.6.7.6 Infiltration Load Model

In the engineering analysis, DOE used values for infiltrated air (in lb/hr) for all equipment classes. DOE estimated infiltrated air by using manufacturers’ detailed specification sheets, recognizing that infiltration load is the only load component that cannot be directly

calculated. These estimates were directly calculated for some equipment classes, and then

extended for use in the remaining primary classes. DOE then used the infiltrated air mass data to

calculate a sensible heat load and latent heat load due to infiltration, based on the thermodynamic properties of the water in the air as well as the assumed ambient and operating conditions. The sum of these two values constituted the portion of the case heat load attributed to ambient air

infiltration.

5.6.8 Validation of Energy Consumption Model

In comments received during the NOPR public meeting and in written comments received after publication of the NOPR, a number of stakeholders suggested that DOE undertake activities to validate the outputs of its engineering energy consumption model, specifically with respect to the performance of self

- contained equipmen t classes. In response to these comments, at the final rule stage, DOE procured several units available on the market from a variety of self

contained equipment classes, including VCT.SC.M, VCS.SC.M, VCS.SC.L, and HCT.SC.L. Energy performance data in the f orm of daily energy consumption as measured using the DOE test procedure were gathered either from the ENERGY STAR directory or through independent

5- 40

### third -party testing conducted by DOE on these units. DOE then conducted physical examination and teardown of the units which it procured, gathering detailed data on the geometries, feature sets, and configurations of these units as well as on operating parameters such as heat exchanger temperatures. The data points measured align ed with the necessary inputs to t he engineering model for simulation of a given unit (namely, the design specifications discussed in section

5.6.1 ). After the specifications for a given unit were collected, these data points were input into the engineering simulation to model the performance of the specific unit under examination. The

result s of the energy consumption model were compared against the performance data gathered through testing or certification, and the two showed sound agreement, with the energy consumption model generally being slightly conservative (modeling the units as using slightly more energy than they consumed as tested). DOE believes that this exercise verifies the validity

of its engineering model in simulating equipment performance.

5.7 COST

- EFFICIENCY CURVES

The result of the engineering analysis is a set of cost

- efficiency curves. DOE developed

25 curves representing the directly analyzed primary equipment classes, using t he baseline specifications and design options described above. (See appendix 5A for details .) The methodology for developing curves started with determining the baseline energy consumption

and MPC using the methodology discussed in this chapter. To develop engineering efficiency

levels a bove the baseline, DOE implemented design options in order from highest to lowest return on cost. Only one design option was implemented at each design option level , except for LED lighting and lighting occupancy sensors , wh ich, in some classes, were implemented simultaneously due to synergistic effects within the energy consumption model (See

section 5.6.5.1 for details .) Design options were implemented until all equipment classes reached

the max -tech level based upon the available design options.

The 25 cost

- efficiency curves are shown in Figure 5.7.1 through Figure 5.7.25 in the form

of daily energy consumption versus MSP. Supporting data for each primary class, including

CDEC , MPC, MSP, and the design option ordering used in DOE’s analysis, is shown in

Table 5.7.2 through Table 5.7.26

. Table 5.7.1 shows a list of the 25 analyzed equipment classes and their corresponding figure, table, and page numbers.

5- 41

### Table 5.7.1 Figure, Table, and Page Numbers for Cost

- Efficiency Results

Equipment Class Figure Table Page Number

VOP.RC.M Figure 5.7.1 Table 5.7.2 5

- 43

VOP.RC.L Figure 5.7.2 Table 5.7.3 5

- 44

VOP.SC.M Figure 5.7.3 Table 5.7.4 5

- 45

SVO.RC.M Figure 5.7.4 Table 5.7.5 5

- 46

SVO.SC.M Figure 5.7.5 Table 5.7.6 5

- 47

HZO.RC.M Figure 5.7.6 Table 5.7.7 5

- 48

HZO.RC.L Figure 5.7.7 Table 5.7.8 5

- 49

HZO.SC.M Figure 5.7.8 Table 5.7.9 5

- 50

HZO.SC.L Figure 5.7.9 Table 5.7.10 5

- 51

VCT.RC.M Figure 5.7.10 Table 5.7.11 5

- 52

VCT.RC.L Figure 5.7.11 Table 5.7.12 5

- 53

VCT.SC.M Figure 5.7.12 Table 5.7.13 5

- 54

VCT.SC.L Figure 5.7.13 Table 5.7.14 5

- 55

VCT.SC.I Figure 5.7.14 Table 5.7.15 5

- 56

VCS.SC.M Figure 5.7.15 Table 5.7.16 5

- 57

VCS.SC.L Figure 5.7.16 Table 5.7.17 5

- 58

VCS.SC.I Figure 5.7.17 Table 5.7.18 5

- 59

HCT.SC.M Figure 5.7.18 Table 5.7.19 5

- 60

HCT.SC.L Figure 5.7.19 Table 5.7.20 5

- 61

HCT.SC.I Figure 5.7.20 Table 5.7.21 5

- 62

HCS.SC.M Figure 5.7.21 Table 5.7.22 5

- 63

HCS.SC.L Figure 5.7.22 Table 5.7.23 5

- 64

SOC.RC.M Figure 5.7.23 Table 5.7.24 5

- 65

SOC.SC.M Figure 5.7

. 24 Table 5.7

. 25 5

- 66

PD.SC.M Figure 5.7.25 Table 5.7.26 5

- 67



### As stated above, DOE used the cost- efficiency curves from the engineering analysis as an inp ut to the life

- cycle cost analysis to determine the overall cost to the customer of purchasing, installing, maintaining, and using a given piece of commercial refrigeration equipment over the

duration of its lifetime (c hapter 8 of the TSD ).

5- 42

### Figure 5.7.1 Cost

- Efficiency Curve for the VOP.RC.M Equipment Class

Table 5.7.2 Cost

- Efficiency Data for the VOP.RC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production Cost $ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 57.90 3,650.17 5,435.54 Baseline

AD2 55.28 3,674.99 5,470.79 AD1 + Permanent Split Cap. Evap. Fan Motor

AD3 51.99 3,727.73 5,545.67 AD2 + Brushless DC Evap.

Fan Motor

AD4 50.52 3,756.68 5,586.79 AD3 + Super T8 Lighting

AD5 46.84 3,880.77 5,762.99 AD4 + Night Curtains

AD6 38.02 4,754.26 7,003.35 AD5 + LED Lighting with Occupancy Sensors

AD7 37.76 4,808.98 7,081.05 AD6 + Additional 1/2"

Insulation

AD8 36.10 5,231.22 7,680.63 AD7 + Enhanced

- UA Evaporator Coil

AD9 35.65 7,092.80 10,324.07 AD8 + Vacuum Insulated

Panels

5- 43

### Figure 5.7.2 Cost

- Efficiency Curve for the VOP.RC.L Equipment Class

Table 5.7.3 Cost

- Efficiency Data for the VOP.RC.L Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production Cost $ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 133.60 3,851.21 5,721.02 Baseline

AD2 126.90 3,909.12 5,803.25 AD1 + Permanent Split Cap. Evap. Fan Motor

AD3 118.44 4,032.18 5,977.99 AD2 + Brushless DC Evap.

Fan Motor

AD4 111.58 4,156.27 6,154.19 AD3 + Night Curtains

AD5 110.93 4,168.67 6,171.81 AD4 + Super T8 Lighting

AD6 105.60 4,459.73 6,585.11 AD5 + LED Lighting with Occupancy Sensors

AD7 104.94 4,519.37 6,669.80 AD6 + Additional 1/2"

Insulation

AD8 101.69 5,016.48 7,375.70 AD7 + Enhanced

- UA Evaporator Coil

AD9 100.01 8,061.07 11,699.01 AD8 + Vacuum Insulated

Panels

5- 44

### Figure 5.7.3 Cost

- Efficiency Curve for the VOP.SC.M Equipment Class

Table 5.7.4 Cost

- Efficiency Data for the VOP.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production Cost $ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 41.04 1,928.25 2,807.02 Baseline

AD2 40.38 1,937.56 2,820.24 AD1 + Permanent Split Cap. Evap. Fan Motor

AD3 39.55 1,952.55 2,841.53 AD2 + Brushless DC Evap.

Fan Motor

AD4 38.98 1,963.41 2,856.94 AD3 + Super T8 Lighting

AD5 36.99 2,004.77 2,915.68 AD4 + Night Curtains

AD6 36.54 2,017.18 2,933.30 AD5 + Permanent Split Cap. Cond. Fan Motor

AD7 36.25 2,030.11 2,951.66 AD6 + High

- Eff. Reciprocating Compressor

AD8 35.69 2,056.48 2,989.10 AD7 + Brushless DC

Cond. Fan Motor

AD9 33.90 2,177.42 3,160.83 AD8 + Enhanced

- UA

Condenser Coil

AD10 30.82 2,524.59 3,653.82 AD9 + LED Lighting with Occupancy Sensors

AD11 30.01 2,667.00 3,856.04 AD10 + Enhanced

- UA Evaporator Coil

AD12 29.91 2,706.86 3,912.64 AD11 + Additional 1/2"

Insulation

AD13 29.71 3,335.46 4,805.25 AD12 + Vacuum Insulated

Panels

5- 45

### Figure 5.7.4 Cost

- Efficiency Curve for the SVO.RC.M Equipment Class

Table 5.7.5 Cost

- Efficiency Data for the SVO.RC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production Cost $ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 43.56 3,107.43 4,552.37 Baseline

AD2 41.78 3,123.97 4,575.86 AD1 + Permanent Split Cap. Evap. Fan Motor

AD3 39.58 3,159.13 4,625.79 AD2 + Brushless DC Evap.

Fan Motor

AD4 38.48 3,179.81 4,655.16 AD3 + Super T8 Lighting

AD5 35.74 3,303.90 4,831.36 AD4 + Night Curtains

AD6 29.45 3,964.92 5,770.02 AD5 + LED Lighting with Occupancy Sensors

AD7 28.15 4,259.28 6,188.01 AD6 + Enhanced

- UA Evaporator Coil

AD8 28.02 4,305.92 6,254.24 AD7 + Additional 1/2"

Insulation

AD9 27.70 5,496.70 7,945.14 AD8 + Vacuum Insulated

Panels

5- 46

### Figure 5.7.5 Cost

- Efficiency Curve for the SVO.SC.M Equipment Class

Table 5.7.6 Cost

- Efficiency Data for the SVO.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 34.54 1,536.60 2,224.53 Baseline

AD2 34.18 1,541.25 2,231.14 AD1 + Permanent Split Cap. Evap. Fan Motor

AD3 33.68 1,549.53 2,242.88 AD2 + Permanent Split Cap.

Cond. Fan Motor

AD4 33.24 1,557.02 2,253.53 AD3 + Brushless DC Evap. Fan

Motor

AD5 32.82 1,564.52 2,264.18 AD4 + Super T8 Lighting

AD6 31.05 1,605.88 2,322.91 AD5 + Night Curtains

AD7 30.51 1,623.46 2,347.87 AD6 + Brushless DC Cond. Fan

Motor

AD8 30.23 1,633.03 2,361.46 AD7 + High

- Eff. Reciprocating Compressor

AD9 28.61 1,722.97 2,489.18 AD8 + Enhanced

- UA Condenser

Coil

AD10 26.32 2,001.48 2,884.66 AD9 + LED Lighting with Occupancy Sensors

AD11 25.65 2,107.39 3,035.05 AD10 + Enhanced

- UA Evaporator Coil

AD12 25.57 2,144.57 3,087.84 AD11 + Additional 1/2"

Insulation

AD13 25.40 2,550.01 3,663.58 AD12 + Vacuum Insulated

Panels

5- 47

### Figure 5.7.6 Cost

- Efficiency Curve for the HZO.RC.M Equipment Class

Table 5.7.7 Cost

- Efficiency Data for the HZO.RC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 19.34 2,646.71 3,871.82 Baseline

AD2 17.60 2,663.26 3,895.31 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 15.44 2,698.42 3,945.23 AD2 + Brushless DC Evap. Fan Motor

AD4 14.63 2,830.02 4,132.11 AD3 + Enhanced

- UA Evaporator Coil

AD5 14.47 2,874.05 4,194.64 AD4 + Additional 1/2" Insulation

AD6 14.15 3,848.92 5,578.94 AD5 + Vacuum Insulated Panels

5- 48

### Figure 5.7.7 Cost

- Efficiency Curve for the HZO.RC.L Equipment Class

Table 5.7.8 Cost

- Efficiency Data for the HZO.RC.L Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 38.83 2,713.98 3,967.34 Baseline

AD2 36.78 2,730.53 3,990.83 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 34.23 2,765.69 4,040.76 AD2 + Brushless DC Evap. Fan Motor

AD4 33.72 2,815.76 4,111.86 AD3 + Additional 1/2" Insulation

AD5 32.36 2,979.94 4,345.00 AD4 + Enhanced

- UA Evaporator Coil

AD6 31.08 4,962.79 7,160.64 AD5 + Vacuum Insulated Panels

5- 49

### Figure 5.7.8 Cost

- Efficiency Curve for the HZO.SC.M Equipment Class

Table 5.7.9 Cost

- Efficiency Data for the HZO.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 16.86 766.63 1

, 108.88 Baseline

AD2 16.50 771.28 1

, 115.48 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 16.06 778.78 1

, 126.13 AD2 + Brushless DC Evap. Fan Motor

AD4 15.90 783.43 1

, 132.74 AD3 + Permanent Split Cap. Cond. Fan

Motor

AD5 15.72 790.93 1

, 143.38 AD4 + Brushless DC Cond. Fan Motor

AD6 15.54 798.17 1

, 153.66 AD5 + High

- Eff. Reciprocating Compressor

AD7 14.66 844.90 1

, 220.03 AD6 + Enhanced

- UA Condenser Coil

AD8 14.16 899.93 1

, 298.17 AD7 + Enhanced

- UA Evaporator Coil

AD9 14.02 938.89 1

, 353.49 AD8 + Additional 1/2" Insulation

AD10 13.75 1

, 492.39 2

, 139.47 AD9 + Vacuum Insulated Panels

5- 50

### Figure 5.7.9 Cost

- Efficiency Curve for the HZO.SC.L Equipment Class

Table 5.7.10 Cost

- Efficiency Data for the HZO.SC.L E quipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 36.32 1

, 052.74 1

, 515.16 Baseline

AD2 35.74 1

, 056.88 1

, 521.03 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 35.02 1

, 065.67 1

, 533.51 AD2 + Brushless DC Evap. Fan Motor

AD4 34.87 1

, 070.32 1

, 540.12 AD3 + Permanent Split Cap. Cond. Fan

Motor

AD5 34.44 1

, 085.11 1

, 561.12 AD4 + High

- Eff. Reciprocating Compressor

AD6 32.16 1

, 167.61 1

, 678.27 AD5 + Enhanced

- UA Condenser Coil

AD7 32.00 1

, 175.11 1

, 688.91 AD6 + Brushless DC Cond. Fan Motor

AD8 30.69 1

, 272.25 1

, 826.86 AD7 + Enhanced

- UA Evaporator Coil

AD9 30.49 1

, 310.95 1

, 881.82 AD8 + Additional 1/2" Insulation

AD10 29.92 2

, 031.2 0 2

, 904.56 AD9 + Vacuum Insulated Panels

5- 51

### Figure 5.7.10 Cost

- Efficiency Curve for the VCT.RC.M Equipment Class

Table 5.7.11 Cost

- Efficiency Data for the VCT.RC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 32.39 4

, 691.3 0 6

, 896.71 Baseline

AD2 30.98 4

, 714.56 6

, 929.75 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 24.84 4

, 835.67 7

, 101.72 AD2 + LED Lighting

AD4 23.08 4

, 873.15 7

, 154.95 AD3 + Brushless DC Evap. Fan Motor

AD5 13.65 5

, 089.66 7

, 462.39 AD4 + High

- Performance Door

AD6 11.80 5

, 180.14 7

, 590.87 AD5 + LED Lighting with Occupancy

Sensors

AD7 11.55 5

, 239.14 7

, 674.64 AD6 + Additional 1/2" Insulation

AD8 11.49 5

, 348.99 7

, 830.64 AD7 + Enhanced

- UA Evaporator Coil

AD9 10.99 7

, 565.41 10

, 977.96 AD8 + Vacuum Insulated Panels

5- 52

### Figure 5.7.11 Cost

- Efficiency Curve for the VCT.RC.L Equipment Class

Table 5.7.12 Cost

- Efficiency Data for the VCT.RC.L Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 67.26 5

, 261.85 7

, 706.89 Baseline

AD2 64.88 5

, 282.53 7

, 736.26 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 61.90 5

, 326.48 7

, 798.67 AD2 + Brushless DC Evap. Fan Motor

AD4 44.57 5

, 615.16 8

, 208.59 AD3 + High

- Performance Door

AD5 37.49 5

, 736.26 8

, 380.56 AD4 + LED Lighting

AD6 35.34 5

, 826.74 8

, 509.04 AD5 + LED Lighting with Occupancy

Sensors

AD7 34.78 5

, 884.57 8

, 591.16 AD6 + Additional 1/2" Insulation

AD8 34.50 6

, 064.6 0 8

, 846.8 0 AD7 + Enhanced

- UA Evaporator Coil

AD9 33.04 8

, 908.32 12

, 884.88 AD8 + Vacuum Insulated Panels

5- 53

### Figure 5.7.12 Cost

- Efficiency Curve for the VCT.SC.M Equipment Class

Table 5.7.13 Cost

- Efficiency Data for the VCT.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 17.94 1

, 923.07 2

, 792.56 Baseline

AD2 16.92 1

, 931.34 2

, 804.31 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 12.74 1

, 973.27 2

, 863.84 AD2 + LED Lighting

AD4 11.51 1

, 990.84 2

, 888.81 AD3 + Brushless DC Evap. Fan Motor

AD5 6.99 2

, 077.45 3

, 011.78 AD4 + High

- Performance Door

AD6 6.83 2

, 091.41 3

, 031.61 AD5 + Brushless DC Cond. Fan Motor

AD7 5.99 2

, 181.89 3

, 160.09 AD6 + LED Lighting with Occupancy

Sensors

AD8 5.64 2

, 221.23 3

, 215.95 AD7 + Enhanced

- UA Condenser Coil

AD9 5.59 2

, 230.79 3

, 229.53 AD8 + High

- Eff. Reciprocating Compressor

AD10 5.45 2

, 272.21 3

, 288.34 AD9 + Additional 1/2" Insulation

AD11 5.41 2

, 318.53 3

, 354.12 AD10 + Enhanced

- UA Evaporator Coil

AD12 5.15 3

, 075.87 4

, 429.55 AD11 + Vacuum Insulated Panels

5- 54

### Figure 5.7.13 Cost

- Efficiency Curve for the VCT.SC.L Equipment Class

Table 5.7.14 Cost

- Efficiency Data for the VCT.SC.L Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 34.68 2

, 681.43 3

, 869.44 Baseline

AD2 33.59 2

, 689.71 3

, 881.19 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 28.79 2

, 731.63 3

, 940.72 AD2 + LED Lighting

AD4 27.46 2

, 749.21 3

, 965.69 AD3 + Brushless DC Evap. Fan Motor

AD5 19.41 2

, 864.68 4

, 129.66 AD4 + High

- Performance Door

AD6 18.43 2

, 929.32 4

, 221.45 AD5 + Enhanced

- UA Condenser Coil

AD7 18.23 2

, 943.28 4

, 241.27 AD6 + Brushless DC Cond. Fan Motor

AD8 17.31 3

, 033.76 4

, 369.75 AD7 + LED Lighting with Occupancy

Sensors

AD9 17.16 3

, 051.55 4

, 395.01 AD8 + High

- Eff. Reciprocating Compressor

AD10 16.92 3

, 092.97 4

, 453.82 AD9 + Additional 1/2" Insulation

AD11 16.66 3

, 169.08 4

, 561.9 0 AD10 + Enhanced

- UA Evaporator Coil

AD12 16.05 4

, 190.63 6

, 012.5 0 AD11 + Vacuum Insulated Panels

5- 55

### Figure 5.7.14 Cost

- Efficiency Curve for the VCT.SC.I Equipment Class

Table 5.7.15 Cost

- Efficiency Data for the VCT.SC.I Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer

Se lling Price $ Design Option Added

Above the Baseline

AD1 39.63 2

, 761.41 3

, 986.05 Baseline

AD2 38.39 2

, 769.68 3

, 997.79 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 33.10 2

, 811.61 4

, 057.33 AD2 + LED Lighting

AD4 31.60 2

, 829.19 4

, 082.29 AD3 + Brushless DC Evap. Fan Motor

AD5 22.21 2

, 944.66 4

, 246.26 AD4 + High

- Performance Door

AD6 22.08 2

, 947.76 4

, 250.67 AD5 + Permanent Split Cap. Cond. Fan

Motor

AD7 20.88 3

, 015.88 4

, 347.39 AD6 + Enhanced

- UA Condenser Coil

AD8 20.73 3

, 026.74 4

, 362.81 AD7 + Brushless DC Cond. Fan Motor

AD9 19.72 3

, 117.22 4

, 491.29 AD8 + LED Lighting with Occupancy

Sensors

AD10 19.52 3

, 137.28 4

, 519.78 AD9 + High

- Eff. Reciprocating Compressor

AD11 19.26 3

, 179.21 4

, 579.33 AD10 + Additional 1/2" Insulation

AD12 18.95 3

, 259.42 4

, 693.22 AD11 + Enhanced

- UA Evaporator Coil

AD13 18.11 4

, 617.04 6

, 621.04 AD12 + Vacuum Insulated Panels

5- 56

### Figure 5.7.15 Cost

- Efficiency Curve for the VCS.SC.M Equipment Class

Table 5.7.16 Cost

- Efficiency Data for the VCS.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 6.31 1

, 248.48 1

, 833.64 Baseline

AD2 5.29 1

, 256.75 1

, 845.38 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 4.03 1

, 274.33 1

, 870.35 AD2 + Brushless DC Evap. Fan Motor

AD4 3.84 1

, 288.29 1

, 890.17 AD3 + Brushless DC Cond. Fan Motor

AD5 3.69 1

, 304.03 1

, 912.51 AD4 + Enhanced

- UA Condenser Coil

AD6 3.48 1

, 345.57 1

, 971.51 AD5 + Additional 1/2" Insulation

AD7 3.45 1

, 352.81 1

, 981.78 AD6 + High

- Eff. Reciprocating Compressor

AD8 3.43 1

, 371.34 2

, 008.09 AD7 + Enhanced

- UA Evaporator Coil

AD9 3.03 2

, 139.41 3

, 098.75 AD8 + Vacuum Insulated Panels

5- 57

### Figure 5.7.16 Cost

- Efficiency Curve for the VCS.SC.L Equipment Class

Table 5.7.17 Cost

- Efficiency Data for the VCS.SC.L Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 16.47 1

, 443.12 2

, 110.03 Baseline

AD2 15.34 1

, 451.4 0 2

, 121.78 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 13.94 1

, 468.98 2

, 146.74 AD2 + Brushless DC Evap. Fan Motor

AD4 13.20 1

, 500.27 2

, 191.18 AD3 + Enhanced

- UA Condenser Coil

AD5 12.94 1

, 514.23 2

, 211 .00 AD4 + Brushless DC Cond. Fan Motor

AD6 12.53 1

, 555.78 2

, 270 .00 AD5 + Additional 1/2" Insulation

AD7 12.19 1

, 592.63 2

, 322.32 AD6 + Enhanced

- UA Evaporator Coil

AD8 12.08 1

, 605.91 2

, 341.19 AD7 + High

- Eff. Reciprocating Compressor

AD9 11.13 2

, 641.81 3

, 812.17 AD8 + Vacuum Insulated Panels

5- 58

### Figure 5.7.17 Cost

- Efficiency Curve for the VCS.SC.I Equipment Class

Table 5.7.18 Cost

- Efficiency Data for the VCS.SC.I Equipment Class

Design Option

Level Calculated

D aily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 23.57 1

, 551.4 0 2

, 267.84 Baseline

AD2 22.18 1

, 559.67 2

, 279.58 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 20.47 1

, 577.25 2

, 304.55 AD2 + Brushless DC Evap. Fan Motor

AD4 20.24 1

, 580.36 2

, 308.95 AD3 + Permanent Split Cap. Cond. Fan

Motor

AD5 18.94 1

, 616.36 2

, 360.08 AD4 + Enhanced

- UA Condenser Coil

AD6 18.70 1

, 627.22 2

, 375.5 0 AD5 + Brushless DC Cond. Fan Motor

AD7 18.01 1

, 669.61 2

, 435.7 0 AD6 + Enhanced

- UA Evaporator Coil

AD8 17.83 1

, 683.68 2

, 455.67 AD7 + High

- Eff. Reciprocating Compressor

AD9 17.43 1

, 725.74 2

, 515.39 AD8 + Additional 1/2" Insulation

AD10 16.04 3

, 101.33 4

, 468.73 AD9 + Vacuum Insulated Panels

5- 59

### Figure 5.7.18 Cost

- Efficiency Curve for the HCT.SC.M Equipment Class

Table 5.7.19 Cost

- Efficiency Data for the HCT.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 1.87 586.87 853.62 Baseline

AD2 1.75 591.01 859.5 0 AD1 + Permanent Split Cap. Cond. Fan

Motor

AD3 1.62 597.64 868.91 AD2 + Enhanced

- UA Condenser Coil

AD4 0.99 655.94 951.69 AD3 + High

- Performance Door

AD5 0.90 664.72 964.17 AD4 + Brushless DC Cond. Fan Motor

AD6 0.88 668.14 969.02 AD5 + High

- Eff. Reciprocating Compressor

AD7 0.79 704.23 1

, 020.27 AD6 + Additional 1/2" Insulation

AD8 0.61 1

, 019.88 1

, 468.49 AD7 + Vacuum Insulated Panels

5- 60

### Figure 5.7.19 Cost

- Efficiency Curve for the HCT.SC.L Equipment Class

Table 5.7.20 Cost

- Efficiency Data for the HCT.SC.L Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 4.09 641.08 930.6 0 Baseline

AD2 2.25 699.37 1

, 013.38 AD1 + High

- Performance Door

AD3 2.15 703.51 1

, 019.25 AD2 + Permanent Split Cap. Cond. Fan

Motor

AD4 2.03 712.3 0 1

, 031.73 AD3 + Brushless DC Cond. Fan Motor

AD5 1.92 723.28 1

, 047.32 AD4 + Enhanced

- UA Condenser Coil

AD6 1.88 728.29 1

, 054.44 AD5 + High

- Eff. Reciprocating Compressor

AD7 1.73 764.38 1

, 105.69 AD6 + Additional 1/2" Insulation

AD8 1.32 1

, 195.23 1

, 717.5 0 AD7 + Vacuum Insulated Panels

5- 61

### Figure 5.7.20 Cost

- Efficiency Curve for the HCT.SC.I Equipment Class

Table 5.7.21 Cost

- Efficiency Data for the HCT.SC.I Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 6.95 674.76 976.4 0 Baseline

AD2 3.68 733.06 1

, 059.18 AD1 + High

- Performance Door

AD3 3.57 737.2 0 1

, 065.06 AD2 + Permanent Split Cap. Cond. Fan

Motor

AD4 3.42 745.99 1

, 077.54 AD3 + Brushless DC Cond. Fan Motor

AD5 3.35 752.81 1

, 087.23 AD4 + High

- Eff. Reciprocating Compressor

AD6 3.13 789.54 1

, 139.39 AD5 + Additional 1/2" Insulation

AD7 2.33 1

, 424.35 2

, 040.82 AD6 + Vacuum Insulated Panels

5- 62

### Figure 5.7.21 Cost

- Efficiency Curve for the HCS.SC.M Equipment Class

Table 5.7.22 Cost

- Efficiency Data for the HCS.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 1.49 518.33 759.33 Baseline

AD2 1.42 521.92 764.43 AD1 + Enhanced

- UA Condenser Coil

AD3 1.36 526.06 770.3 0 AD2 + Permanent Split Cap. Cond. Fan

Motor

AD4 1.28 534.85 782.79 AD3 + Brushless DC Cond. Fan Motor

AD5 1.26 538.26 787.63 AD4 + High

- Eff. Reciprocating Compressor

AD6 1.17 573.51 837.69 AD5 + Additional 1/2" Insulation

AD7 0.98 819.1 0 1

, 186.43 AD6 + Vacuum Insulated Panels

5- 63

### Figure 5.7.22 Cost

- Efficiency Curve for the HCS.SC.L Equipment Class

Table 5.7.23 Cost

- Efficiency Data for the HCS.SC.L Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 1.93 527.88 772.9 0 Baseline

AD2 1.78 531.54 778.1 0 AD1 + Enhanced

- UA Condenser Coil

AD3 1.67 535.68 783.97 AD2 + Permanent Split Cap. Cond. Fan

Motor

AD4 1.53 544.47 796.45 AD3 + Brushless DC Cond. Fan Motor

AD5 1.50 547.78 801.15 AD4 + High

- Eff. Reciprocating Compressor

AD6 1.29 583.03 851.2 0 AD5 + Additional 1/2" Insulation

AD7 0.71 920.19 1

, 329.97 AD6 + Vacuum Insulated Panels

5- 64

### Figure 5.7.23 Cost

- Efficiency Curve for the SOC.RC.M Equipment Class

Table 5.7.24 Cost

- Efficiency Data for the SOC.RC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 31.07 5

, 160.49 7

, 493.05 Baseline

AD2 29.96 5

, 179.1 0 7

, 519.48 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 28.80 5

, 199.78 7

, 548.85 AD2 + Super T8 Lighting

AD4 27.44 5

, 229.77 7

, 591.43 AD3 + Brushless DC Evap. Fan Motor

AD5 22.74 5

, 642.81 8

, 177.95 AD4 + LED Lighting

AD6 22.31 5

, 746.22 8

, 324.79 AD5 + High

- Performance Door

AD7 21.92 5

, 858.42 8

, 484.12 AD6 + Enhanced

- UA Evaporator Coil

AD8 21.80 5

, 901.34 8

, 545.06 AD7 + Additional 1/2" Insulation

AD9 21.56 6

, 783.13 9

, 797.2 0 AD8 + Vacuum Insulated Panels

5- 65

### Figure 5.7.24 Cos t- Efficiency Curve for the SOC.S C.M Equipment Class

Table 5.7.25 Cost

- Efficiency Data for the SOC.S C.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 41.73 5

, 729.27 8

, 300.73 Baseline

AD2 39.79 5

, 745.82 8

, 324.22 AD1 + Permanent Split Cap. Evap. Fan

Motor

AD3 37.37 5

, 780.98 8

, 374.15 AD2 + Brushless DC Evap. Fan Motor

AD4 36.04 5

, 801.66 8

, 403.51 AD3 + Super T8 Lighting

AD5 34.68 5

, 829.58 8

, 443.16 AD4 + Brushless DC Cond. Fan Motor

AD6 34.40 5

, 840.23 8

, 458.28 AD5 + High

- Eff. Reciprocating Compressor

AD7 32.82 5

, 938.84 8

, 598.32 AD6 + Enhanced

- UA Condenser Coil

AD8 27.72 6

, 351.89 9

, 184.84 AD7 + LED Lighting

AD9 27.17 6

, 455.29 9

, 331.67 AD8 + High

- Performance Door

AD10 26.61 6

, 571.41 9

, 496.56 AD9 + Enhanced

- UA Evaporator Coil

AD11 26.45 6

, 614.33 9

, 557.5 0 AD10 + Additional 1/2" Insulation

AD12 26.12 7

, 496.12 10

, 809.65 AD11 + Vacuum Insulated Panels

5- 66

### Figure 5.7.25 Cost

- Efficiency Curve for the PD.SC.M Equipment Class

Table 5.7.26 Cost

- Efficiency Data for the PD.SC.M Equipment Class

Design Option

Level Calculated Daily Energy Consumption

kWh/day Manufacturer

Production

Cost

$ Manufacturer Selling Price $ Design Option Added

Above the Baseline

AD1 13.04 1

, 154.56 1

, 676.97 Baseline

AD2 12.35 1

, 158.18 1

, 682.11 AD1 + Permanent Split Cap. Evap.

Fan Motor

AD3 8.13 1

, 200.11 1

, 741.64 AD2 + LED Lighting

AD4 7.30 1

, 209.93 1

, 755.59 AD3 + Brushless DC Evap. Fan Motor

AD5 5.01 1

, 253.23 1

, 817.08 AD4 + High

- Performance Door

AD6 4.73 1

, 280.27 1

, 855.48 AD5 + Enhanced

- UA Condenser Coil

AD7 3.90 1

, 370.76 1

, 983.96 AD6 + LED Lighting with Occupancy

Sensors

AD8 3.78 1

, 384.72 2

, 003.79 AD7 + Brushless DC Cond. Fan Motor

AD9 3.75 1

, 393.2 0 2

, 015.83 AD8 + High

- Eff. Reciprocating Compressor

AD10 3.64 1

, 432.67 2

, 071.88 AD9 + Additional 1/2" Insulation

AD11 3.62 1

, 464.51 2

, 117.1 0 AD10 + Enhanced

- UA Evaporator

Coil

AD12 3.41 2

, 060.93 2

, 964.01 AD11 + Vacuum Insulated Panels



### 5.8 O FFSET FACTORS

Equipment energy use scales with equipment size, but smaller equipment tends to use more energy per unit of TDA or volume than larger equipment in the same equipment class. This extra energy is attributed to components of case load that do not scale with equipment size. These load components therefore have a disproportionate effect on the energy consumption of small equipment. In its 2009 final rule, DOE developed offset factors to account for these load

5- 67

### components, commonly referred to as “end effects,” as a way to adjust standards to allow more energy use for smaller equipment.

During the early stages of the 2009 final rule analysis , stakeholders raised concerns that standards developed for large sizes of equipment would be unfair when applied to smaller equipment in the same class, because of the end effects that disproportionately affect smaller equipment. In its engineering analysis, DOE developed cost

- efficiency curves for a single size within each equipme nt class. The representative size selected for each class was toward the larger end of the equipment available within that class. DOE intended for standards to be based

on this sin gle analysis point (the point defined by TDA or volume and CDEC). Accordingl y, i n the 2009 rulemaking advance notice of proposed rulemaking , DOE expressed intent to implement standards by requiring equipment to meet an energy consumption limit defined by a line drawn between the origin and the point determined by the engineering cost- efficiency curve. (See the left pane of Figure 5.8.1 .) However, the stakeholder concerns previously mentioned led

DOE to account for end effects i n later stages of the rulemaking and in the final rule. As the right pane of Figure 5.8.1 shows, the offset factor fixes the left end of the standard equation at a fixed offset on the CDEC axis, effectively providing a higher limit on energy consumption for smaller equipment.

Figure 5.8.1 Illustration of Offset Factor using TDA as the Normalization M etric

Using this methodology, a standard equation consists of two components: a sizedependent multiplier (slope) and a constant (offset factor). DOE calculated the slope s of the standard equations for its 2009 final rule standard levels as the difference between the CDEC at the analysis point corresponding to the selected standard level and the offset factor, divided by

the value of the normalization metric at the analysis point

.

In determining offset factors in the 2009 final rule, DOE considered different end effects for each equipment family. For all open cases (VOP, SVO, and HZO), DOE considered the effects of heat conduction through the case ends, as well as the infiltration load end effects. For

VCT cases, the conduction through the ends was taken into account, as well as the heat load and

electrical load of one light fixture . In the remaining equipment families (SOC, HCT, VCS), DOE only considered the conduction effects through the case ends. DOE then summed these heat

loads for each class and calculated t he daily energy use of the compressor for that class. For all equipment classes except for those in the VCT equipment family, this became the offset factor.

5- 68

### For VCT equipment, DOE also considered the amount of electrical energy consumed by lighting.

These values then become the offset factors, representing energy use end effects for each equipment class in the 2009 final rule standard-level equations. Similarly, the EPACT 2005

standard -level equations also contained analogous values that would provide a non- zero energy allowance at zero volume.

When developing standard-level equations in this rulemaking for each primary equipment class at the five trial standard levels ( TSLs ) considered in the NOPR, DOE revised the offset

factors in the existing DOE 2009 and EPACT 2005 standard levels commensurately to reflect decreasing energy use by a given unit at higher TSLs. DOE scaled the existing offset factors using the engineering analysis results for each equipment class at each TSL. DOE first calculated the allowable energy consumption under the existing standard for each class at the representative size analyzed, and then compared those standards values to the energy consumption values at each TSL produced by the engineering analysis. The ratio of these two values was then used as a multiplier on the existing offset factor for each equipment class at each TSL, and the resulting

values were used as the offset factors for the new standard -level equations.

In written comments submitted after publication of the NOPR, and during discussions at the NOPR public meeting, some stakeholders expressed concerns that the methodology DOE employed in the NOPR, in which allowable energy consumption was scaled by a consta nt factor across all equipment sizes within a given class, would lead to unfairly stringent standards for smaller equipment. Stakeholders stated the opinion that reducing the offset factors by a fixed proportion failed to preserve the necessary allowance for equipment end effects.

Taking into consideration stakeholder comments, DOE reviewed its offset factor methodology and agreed with the stakeholders who expressed concerns over proper representation of end effects . In response, DOE applied a revised methodology in developing its standard level equations at the final rule stage. For classes for which DOE calculated offset factors in the 2009 final rule, DOE retained those offset factors, as it believes that those figures continue to represent the end effects inherent in the operation of those equipment types. DOE likewise retained the offset factor of 1 kWh/day set for SOC.SC.M equipment in the 2012 AEMTCA standard level. For the EPACT 2005 equipment classes, DOE examined the offset factors in comparison to trends in market data from publicly available sources such as the ENERGY STAR directory. In cases where the market data showed an adjustment to the offset factor to be appropriate, DOE included such an adjustment in its final rule standard level equations . In cases where the existing offset factor appeared to agree with market trends, DOE retained that factor for the standard levels included in today’s final rule.

5.9 EXTENSION OF ANALYSIS TO SECONDARY EQUIPME NT CLASSES

In its 2009 final rule, DOE did not fu lly analyze all covered equipment classes in its engineering and downstream analyses , but focused its analysis on 15 high-shipment equipment

classes. In that rule, DOE developed an extension approach to apply the standards developed for these 15 “primary” classes to the remaining 23 “secondary” classes. This approach involved extension multipliers developed with the 15 primary results and a set of focused matched- pair analyses. The matched

- pair analyses compared calculated energy consumption levels for pieces of equipment with similar designs but one major construction or operational difference

5- 69

### corresponding to a change in the equipment family, condenser configuration, or operating temperature. For example, vertical open remote condensing cases operating at medium and low

temperatures were compared in one portion of the analysis . The relationships between these sets

of units were then used to determine the isolated effect of the given design or operational difference on applicable equipment. These extension m ultipliers represent the relationship in terms of energy consumption between different equipment classes , such as remote condensing

and self -contained equipment with a given operating temperature and door configuration. In addition, DOE determined that sta ndards for certain primary equipment classes could be directly applied to other similar secondary equipment classes , implying that the extension multiplier is equal to 1.0. Section 5.9.1 describes in detail the methodology that DOE used in the 2009 final rule analyses to develop extension multipliers.

After examining the performance and characteristics of the equipment classes analyzed in the current rulemaking, DOE preserved the 2009 extension multiplier approach for application in this rulemaking. DOE’s use of this methodology in the current rulemaking is discussed in detail

in section 5.9.2

.

5.9.1 Development of Extension Multiplier Approach in 2009 Rulemaking

During the 2009 rulemaking, DOE examined extending standards using the 15 primary equipment classes developed in its engineering analysis. Using size -normalized baseli ne energy consumption values, several trends became apparent for certain sub-groups of equipment. DOE examined several related pairs of equipment classes to develop extension multipliers.

First, DOE examined the energy consumption /TDA relationship between remote and

self -contained medium-temperature equipment without doors ( Table 5.9.1 ). There wa s reasonable agreement across equipment families among medium

- temperatur e open equipment, with an average multiplier of 2.51 between remote and self-contained equipment. The

differences between remote and self -contained equipment were similar for VOP, SVO, and HZO equipment. In addition to the inclusion of a self-contained condensing unit, which is less efficient than remote condensing systems, self -contained open equipment must have provisions for dealing with defrost meltwater. Because self -contained equipment is designed to be mobile,

defrost meltwater must be collected i n a drain pan and evaporated using electric

- resistance heating. This feature adds considerable energy use to open self- contained equipment , helping to explain the larger energy consumption of self-contained equipment. The 2.51 multiplier wa s also applica ble to low- and ice- cream -temperature equipment without doors. The differences in design we re found to be similar for low

- temperature and ice

- cream

- temperature equipment, consisting of a self-contained condensing unit and the addition of a defrost meltwat er evaporation system. Thus, the 2.51 multiplier wa s appli ed to the following standard extensions

in the 2009 final rule:

• VOP.RC.L to VOP.SC.L

• SVO.RC.L to SVO.SC.L

• VOP.RC.I to VOP.SC.I

• SVO.RC.I to SVO.SC.I

5- 70

### • HZO.RC.I to HZO.SC.I

Table 5.9.1 Extension Multipliers for Equipment Without Doors

Relationship CDEC/TDA Multiplier

VOP.RC.M to VOP.SC.M 2.431

SVO.RC.M to SVO.SC.M 2.376

HZO.RC.M to HZO.SC.M 2.712



### DOE next examined the CDEC/TDA relationship between low

- temperature and ice

cream

- temperature remote equipment using a focused matched

- pair analysis. The focused

matched -pair analysis methodology established a correlation between rating temperature levels and CDEC/TDA, using data collected from man ufacturer specification sheets. DOE quantified the differences in energy consumption for matched pairs of equipment classes that we re very similar in features and dimensions, but had different operating temperatures. Pairs of units at

low- and ice

- cream

- temperatures were selected from several different manufacturers for comparison. DOE selected identical units from a given manufacturer designed to operate at multiple temperatures to isolate operating temperature as the only variable.

The matched

- pair r esults show ed that VCT.RC.I units (at -15°F) had on average

CDEC/TDA values that we re 1.17 times higher than comparable VCT.RC.L units. The 1.17 multiplier wa s also applicable to other similar equipment types with doors. Differences in design

consist ed of a lower operating temperature (resulting in a lower compressor EER) and higher defrost and anti-sweat heater power. Thus, the 1.17 multiplier wa s appli ed to the following

standard extensions:

• SOC.RC.L to SOC.RC.I

• HCT.RC.L to HCT.RC.I

The matched

- pair results further show ed that HZO.RC.I units (at -15°F) had on average

CDEC/TDA v alues that we re 1.27 times higher than comparable HZO.RC.L units. The 1.27 multiplier wa s also applicable to other similar equipment types without doors. Differences in design consist ed of a lower operating temperature (resulting in a lower compressor EER) and higher defrost and anti- sweat heater pow er. Thus, the 1.27 multiplier was appli ed to the following standard extensions:

• VOP.RC.L to VOP.RC.I

• SVO.RC.L to SVO.RC.I

• VOP.SC.L to VOP.SC.I

• SVO.SC.L to SVO.SC.I

• HZO.SC.L to HZO.SC.I

DOE next examined the CDEC/TDA relationship between remote and self

- contained ice

cream -temperature equipment with transparent doors. To make this comparison, DOE used the

matched

- pair results to estimate the baseline CDEC/TDA values for VCT.RC.I equipment, and

5- 71

### compared the results to VCT.SC.I equipment. The comparison showed that CDEC/TDA values for VCT.SC.I equipment were 1.40 times higher than VCT.RC.I equipment. DOE understood

that this d ifference in energy use wa s due to the differences in efficiency of the self

- contained condensing unit, as well as the addition of defrost meltwater evaporation systems. However, in contrast to open equipment, equipment with transparent doors is subject to a much lower infiltration load, and thus a lower melt water evaporation requirement. T his le d to a lower multiplier than was seen for open equipment. The 1.40 multiplier was be applied to the following standard extensions:

• SOC.RC.I to SOC.SC.I

• HCT.RC. I to HCT.SC.I

• VCS.RC.I to VCS.SC.I

• HCS.RC.I to HCS.SC.I

Finally, DOE examined the relationship between medium- and low- temperature remote equipment with transparent doors. Both the VCT.RC.M and VCT.RC.L equipment classes were directly examined in the engineering analysis. Using the engineering results, the comparison showed that CDEC/TDA values for VCT.RC.L equipment we re 2.10 times higher than VCT.RC.M equipment. This difference wa s due to the higher defrost heater power and antisweat heater power req uirements of low-temperature equipment. The 2.10 multiplier was applied to the standard extension from HCT.RC.M to HCT.RC.L in the 2009 final rule.

5.9.2 Current Use of Extension Multiplier Methodology

In the current rulemaking, DOE preserved the extension multiplier approach and values used in the 2009 final rule analysis (and described in section 5.9.1 ) for the classes covered under that rulemaking . D ue to the fact that these multipliers largely reflect the existence of basic differences in design features and thermal performance between classes, which will always exist regardless of efficiency improvement s with the respective classes, DOE believes that these multipl iers remain applicable to the equipment designs covered in the current rulemaking.

DOE used these extension multipliers by applying them, alone or in combination, to the

standard

- level equations that it had developed for the primary equipment classes at each of the

five TSLs. The result was a set of standard-level equations for all of the secondary equipment

classes at the TSLs examined . The values of the extension multipliers used and the equipment type relationships to which they correspond are listed in Table 5.9.2

.

5- 72

### Table 5.9.2 Extension Multipliers

Extension Multiplier Value Extension Relationship

2.51 Medium temperature to low temperature ; equipment without doors ; Example,

VOP.SC.M to VOP.SC.L

1.17 Low temperature to ice

- cream temperature for equipment with transparent doors ; Example, VCT.RC.L to VCT.RC.I

1.27 Low temperature to ice

- cream temperature for equ ipment without doors ; Example

VOP.RC.L to VOP.RC.I

1.40 Remote condensing to self

- contained for equipment with doors ; Example

VCS.RC.I to VCS.SC.I

2.10 Medium temperature to low temperature for equipment with doors ; Example

SOC.RC.M to SOC.RC.L



### These five extension multipliers were sufficient to extend standard -level equations from the primary equipment classes to all 24 secondary equipment classes. The specific extension multiplier combinations used to yield standard -level equations at each TSL for th e secondary classes are depicted in Table 5.9.2

.

Table 5.9.3 Extension Multipliers by Equipment Class

RC SC

I L M I L MOpen VOP 1.27a a * b 1.27(2.51c) 2.51c c
SVO 1.27a a ** d 1.27(2.51e) 2.51e e
HZO 1.27f f g 1.27h h iClosed SOC 1.17(2.10j) 2.10j j 1.40(1.17(2.10j)) 2.10p p
VCT 1.17k k l m 

### EPACT 2005 Primary Classes † HCT n/1.40 (n/1.40)/1.17 ((n/1.40)/1.17)/2.10 n
VCS o/1.40 (o/1.40)/1.17 ((o/1.40)/1.17)/2.10 o
HCS o/1.40 (o/1.40)/1.17 ((o/1.40)/1.17)/2.10 o**
*Classes represented by bold letters are primary classes examined directly in the engineering analysis **The SVO.RC.L and HCS.SC.I classes were determined to have the exact same normalized baseline energy consumption levels as the VOP.RC.L and VCS.SC.I classes, respectively. † Equipment classes for which standards were set in EPACT 2005 are all primary classes except for SOC.SC.L

. Thus no extension multipliers were needed or relevant in developing proposed standards for these classes of EPACT 2005 equipment.

5- 73

### REFERENCES

1. ANSI/ASHRAE. Standard 72-2005, Method of Testing Commercial Refrigerators and

Freezers . 2005. American Society of Heating, Refrigerating, and Air -Conditioning Engineers, Inc., Atlanta, GA

.

2. Electric Power Research Institute. Supermarket Simulation Tool v3.0 . 2007. Palo Alto,

CA.

3. Faramarzi , R. Efficient Display Case Refrigeration. ASHRAE Journal . November 1999.

4. Watt Stopper/Legrand. Case Study – Watt Stopper/Legrand Helps Wal- Mart Achieve

Environmental Goals . 2007. (Last accessed March 17, 2011.)

< www.wattstopper.com/getdoc/2240/Casestudy_WalMart0707.pdf >

5. U. S. DOE Solid State Lighting Technology Demonstration GATEWAY Program. Demonstration Assessment of Light- Emitting Diode (LED) Freezer Case Lighting . 2009. U.S. Department of Energy, Washington, D.C.

6 

### DesignLights Consortium Qualified Products List. (Last accessed December 22, 2013) <http://www.designlights.org/>

7. Southern California Edison Refrigeration Technology and Test Center. Effects of the Low Emissivity Shields on Performance and Power Use of a Refrigerated Display Case . 1997. Southern California Edison, Rancho Cucamonga, CA.

8. Technischer Uberwachungs-Verein Rheinland E.V. Laboratory test results for energy savings on refrigerated dairy case, conducted for Econofrost.

5- 74

### APPENDIX 5A. ENGINEERING DATA

TABLE OF CONTENTS

5A.1 INTRODUCTION ........................................................................................................ 5A-1

5A.2 BASELINE SPECIFICATIONS .................................................................................. 5A-1

5A.3 LIGHTING CONFIGURATIONS ............................................................................... 5A-8

LIST OF TABLES

Table 5A.2.1 Baseline Design Options ..................................................................................... 5A-2 Table 5A.2.2 Baseline Specifications ....................................................................................... 5A-5

LIST OF FIGURES

Figure 5A.3.1 Lighting Configurations for VCT.RC.M ......................................................... 5A-10 Figure 5A.3.2 Lighting Configurations for VCT.RC.L .......................................................... 5A-11 Figure 5A.3.3 Lighting Configurations for VCT.SC.M ......................................................... 5A-12 Figure 5A.3.4 Lighting Configurations for VCT.SC.L .......................................................... 5A-12 Figure 5A.3.5 Lighting Configurations for VCT.SC.I ........................................................... 5A-13 Figure 5A.3.6 Lighting Configurations for PD.SC.M ............................................................ 5A-13 Figure 5A.3.7 Lighting Configurations for VOP.RC.M ......................................................... 5A-14 Figure 5A.3.8 Lighting Configurations for VOP.SC.M ......................................................... 5A-15 Figure 5A.3.9 Lighting Configurations for VOP.RC.L .......................................................... 5A-16 Figure 5A.3.10 Lighting Configurations for SVO.RC.M ....................................................... 5A-17 Figure 5A.3.11 Lighting Configurations for SVO.SC.M ....................................................... 5A-18 Figure 5A.3.12 Lighting Configurations for SOC.RC.M ....................................................... 5A-18

5A -i

### APPENDIX 5A. ENGINEERING DATA

5A.1 INTRODUCTION

This appendix presents baseline specifications and other details for each of the commercial refrigeration equipment classes directly analyzed in the engineering analysis (c hapter 5 of the technical support document (TSD)).

5A.2 BASELINE SPECIFICATIONS

Table 5A.2.1 shows baseline design options for each of the commercial refrigeration equipment classes analyzed in the engineering analysis. A ll changes to cost and efficiency are measured relative to this level in the engineering analysis. Refer to chapter 5 of the TSD for

details about each baseline technology.

Table 5A.2.2 shows baselin e specifications (or case design specifications) for each of the commercial refrigeration equipment classes analyzed in the engineering analysis. These specifications include dimensions, numbers of components, temperatures, nominal power ratings, and other case features that are necessary to calculate the energy consumption of each equipment class. In conjunction with baseline design option levels , the baseline specifications define the energy consumption and cost of the lowest

- efficiency equipment which DO E analyzed in developing the final rule.

5A -1

### Table 5A.2.1 Baseline Design Options a

VOP.RC.M VOP.RC.L VOP.SC.M SVO.RC.M SVO.SC.M HZO.RC.M HZO.RC.L HZO.SC.M Lighting for VOP, SVO,

and SOC T8

Electronic T8

Electronic T8

Electronic T8

Electronic T8 Electronic

- - -

Lighting for VCT and PD

- - - - - - - -

Evaporator Coil Standard

Coil Standard

Coil Standard

Coil Standard

Coil Standard Coil Standard

Coil Standard

Coil Standard

Coil

Evaporator Fan Motors Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole

Case Insulation Standard Standard Standard Standard Standard Standard Standard Standard

Doors for VCT.XX.L/I

- - - - - - - -

Doors for VCT /PD .XX.M

- - - - - - - -

Doors for HCT.XX.L/I

- - - - - - - -

Doors for HCT.XX.M

- - - - - - - -

Doors for SOC.XX.L/I

- - - - - - - -

Doors for SOC.XX.M

- - - - - - - -
Condenser Coil Area (SC Only) - - Standard

- Standard

- - Standard

Condenser Fan Motors (SC only) - - Shaded Pole

- Shaded Pole

- - Shaded Pole

Compressor (SC only)

- - Single

Speed

Hermetic - Single

- Speed

Hermetic - - Single

Speed

Hermetic

Night Curtains None None None None None
- - -

a Equipment class designations consist of a combination

— in sequential order separated by a period

— of a n equipment family code (VOP

- vertical open, SVO

- semivertical open, HZO

- horizontal open, VCT

- vertical closed with transparent doors , VCS

- vertical closed with solid doors , HCT

- horizontal closed with

transparent doors , HCS

- horizontal closed with solid doors , SOC

- service over counter , or PD

– pull

- down equipment ), an operating mode code (RC

- remote
condens ing or SC

- self

- contained), and a rating temperature code (M

- medium temperature, L

- low temperature, or I

- ice

- cream temperature). See c hapter 3,

M arket and T echnology A ssessment, for a more detailed explanation of the equipment class terminology.

5A -2

### Table 5A.2.1 (cont)

HZO.SC.L VCT.RC.M VCT.RC.L VCT.SC.M VCT.SC.L VCT.SC.I VCS.SC.M VCS.SC.L VCS.SC.I Lighting for VOP, SVO, and SOC - - - - - - - - -

Lighting for VCT and

PD - T8

Electronic T8

Electronic T8

Electronic T8

Electronic T8

Electronic - - -

Evaporator Coil Standard Standard Standard Standard Standard Standard Standard Standard Standard

Evaporator Fan

Motors Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded

Pole

Case Insulation Standard Standard Standard Standard Standard Standard Standard Standard Standard

Doors for

VCT.XX.L/I - - Standard

- Standard Standard

- - -

Doors for

VCT/PD.XX.M - Standard

- Standard

- - - - -

Doors for

HCT.XX.L/I - - - - - - - - -

Doors for

HCT.XX.M - - - - - - - - -

Doors for

SOC.XX.L/I - - - - - - - - -

Doors for SOC.XX.M

- - - - - - - - -

Condenser Coil Area (SC Only) Standard

- - Standard Standard Standard Standard Standard Standard

Condenser Fan

Motors (SC only) Shaded Pole

- - Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded

Pole

Compressor (SC only) Single

- Speed

Hermetic - - Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic

Night Curtains

- - - - - - - - -

5A -3

### Table 5A.2.1 (cont)

HCT.SC.M HCT.SC.L HCT.SC.I HCS.SC.M HCS.SC.L SOC.SC.M SOC.RC.M PD.SC.M Lighting for VOP, SVO, and SOC - - - - - T8

Electronic T8

Electronic -

Lighting for VCT and

PD - - - - - - - T8

Electronic

Evaporator Coil

- - - - - Standard Standard Standard

Evaporator Fan

Motors - - - - - Shaded Pole Shaded Pole Shaded Pole

Case Insulation Standard Standard Standard Standard Standard Standard Standard Standard

Doors for

VCT.XX.L/I - - - - - - - -

Doors for

VCT/PD.XX.M - - - - - - - Standard

Doors for

HCT.XX.L/I - Standard Standard

- - - - -

Doors for HCT.XX.M Standard

- - - - - - -

Doors for

SOC.XX.L/I - - - - - - - -

Doors for SOC.XX.M

- - - - - Standard Standard

-

Condenser Coil Area (SC Only) Standard Standard Standard Standard Standard Standard

- Standard

Condenser Fan

Motors (SC only) Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole Shaded Pole

- Shaded Pole

Compressor (SC only) Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic Single

Speed

Hermetic - Single

Speed

Hermetic

Night Curtains

- - - - - - - -

5A -4

### Table 5A.2.2 Baseline Specifications

VOP.RC.M VOP.RC.L VOP.SC.M SVO.RC.M SVO.SC.M HZO.RC.M HZO.RC.L Case Length (ft) 12 12 4 12 4 12 12 Case Gross Refrigerated Volume (ft 3 ) 130.2 109.83 32 46.55 9.4 33 55 Case Total Display Area (ft 2 ) 53.3 44.66 14.93 40 12.8 33 46 Number of Doors (#) 0 0 0 0 0 0 0 Single Door Area (ft 2 ) 0 0 0 0 0 0 0

Non

- Door Glass Area (ft 2 ) 0 0 0 0 0 0 0

Non

- Door Anti

- Sweat Power (W) 0 600 0 50 40 40 150 Wall Area (ft 2 ) 175.925 214 61 113.4 40.2 93.275 140 Insulation Thickness (in

. ) 1.5 2 1.5 1.5 1.5 1.5 2 Case Interior Surface Area (ft 2 ) 130.225 118.5 47.5 72.5 21.3 48.35 82 Air Curtain Angle from Vertical (°) 8.5 7.28 6.05 47 57 82 90 Infiltrated Air Mass Flow (lb/hr) 860 530 300 590 220 250 140 Number of Bulbs in Conditioned Space (#) 12 0 4 9 3 0 0 Number of Bulbs Not in Conditioned Space (#) 9 9 3 6 2 0 0 Number of Ballasts in Conditioned Space (#) 0 0 0 0 0 0 0 Number of Ballasts Not in Conditioned Space (#) 7 3 3 5 2 0 0 Evaporator Fan Nominal Rated Wattage (W/fan) 9 9 6 9 6 9 9 Number of Evaporator Fans per Case (#) 6 14 2 4 1 4 4

Condenser Fan Nominal Rated Wattage (W/fan) 0 0 9 0 9 0 0 Number of Condenser Fans per Case (#) 0 0 3 0 2 0 0 Discharge Air Temperature (DAT) (F) 25

- 10 25 25 25 25

- 10 Baseline Evaporator Temperature (SET) (F) 15

- 20 15 15 15 15

- 20

Baseline Condenser Temperature (SCT) (F) 0 0 95 0 95 0 0 Compressor Oversize Multiplier 0 0 1.3 0 1.3 0 0 Defrost Mechanism (OFF, ELE, MAN) OFF ELE OFF OFF OFF ELE ELE Defrost Time per Day (hr) 4.5 2 2.8 3 2.8 1 1 Defrost and Drain Heater Power (W) 0 8700 0 0 0 1000 3000 Condensate Pan Heater Power (W) 0 0 1500 0 1100 0 0

5A -5

### Table 5A.2.2 (cont)

HZO.SC.M HZO.SC.L VCT.RC.M VCT.RC.L VCT.SC.M VCT.SC.L VCT.SC.I Case Length (ft) 4 4 12.725 12.74 5 5 5 Case Gross Refrigerated Volume (ft 3 ) 7.5 7.4 142 133.5 49 49 48

Case Total Display Area (ft 2 ) 12 12 65 65 26 26 26 Number of Doors (#) 0 0 5 5 2 2 2 Single Door Area (ft 2 ) 0 0 13.96 13.96 13.96 13.96 13.96

Non

- Door Glass Area (ft 2 ) 4 4 0 0 0 0 0

Non

- Door Anti

- Sweat Power (W) 40 200 75 0 0 0 0 Wall Area (ft 2 ) 54 52 209 200 73 73 77 Insulation Thickness (in

. ) 1.5 2 1.5 2 1.5 2 2.5 Case Interior Surface Area (ft 2 ) 19.8 19.5 N.A. N.A. N.A. N.A. N.A. Air Curtain Angle from Vertical (°) 85 85

- - - - - Infiltrated Air Mass Flow (lb/hr) 100 100 34.25 37 13.7 14.8 15 Number of Bulbs in Conditioned Space (#) 0 0 6 6 3 3 3 Number of Bulbs Not in Conditioned Space (#) 0 0 0 0 0 0 0 Number of Ballasts in Conditioned Space (#) 0 0 6 6 3 3 3 Number of Ballasts Not in Conditioned Space (#) 0 0 0 0 0 0 0
Evaporator Fan Nominal Rated Wattage (W/fan) 6 9 6 9 9 9 9 Number of Evaporator Fans per Case (#) 1 1 5 5 2 2 2 Condenser Fan Nominal Rated Wattage (W/fan) 6 6 0 0 16 16 16 Number of Condenser Fans per Case (#) 1 1 0 0 1 1 1 Discharge Air Temperature (DAT) (F) 25

- 10 32

- 5 32

- 5

- 20 Baseline Evaporator Temperature (SET) (F) 15

- 20 27

- 11 23

- 15

- 30 Baseline Condenser Temperature (SCT) (F) 95 95 0 0 95 95 95 Compressor Oversize Multiplier 1.3 1.3 0 0 2.6 2.4 2.2 Defrost Mechanism (OFF, ELE, MAN) ELE ELE OFF ELE OFF ELE ELE Defrost Time per Day (hr) 0.75 1.5 1 1 1.5 1.5 1 Defrost and Drain Heater Power (W) 400 900 0 5000 0 1900.00 2580 Condensate Pan Heater Power (W) 500 500 0 0 200 200 200

5A -6

### Table 5A.2.2 (cont)

VCS.SC.M VCS.SC.L VCS.SC.I HCT.SC.M HCT.SC.L HCT.SC.I HCS.SC.M Case Length (ft) 5 5 5 3.42 3.42 3.42 4.16 Case Gross Refrigerated Volume (ft 3 ) 49 49 48 8.83 8.83 10.2 7.03 Case Total Display Area (ft 2 ) 0 0 0 4.78 4.78 4.78 0 Number of Doors (#) 2 2 2 2 2 2 1 Single Door Area (ft 2 ) 13.96 13.96 13.96 2.56 2.56 2.56 8.32

Non

- Door Glass Area (ft 2 ) 0 0 0 0 0 0 0

Non

- Door Anti

- Sweat Power (W) 20 100 200 0 0 0 20 Wall Area (ft 2 ) 74 74 78 31.83 31.83 36.77 25.3 Insulation Thickness (in

. ) 1.5 2 2.5 1.5 2 2.5 1.5 Case Interior Surface Area (ft 2 ) N.A. N.A. N.A. N.A. N.A. N.A. N.A. Air Curtain Angle from Vertical (°)

- - - - - - - Infiltrated Air Mass Flow (lb/hr) 13.7 14.8 15 4 2.5 2 3.2 Number of Bulbs in Conditioned Space (#) 0 0 0 0 0 0 0 Number of Bulbs Not in Conditioned Space (#) 0 0 0 0 0 0 0 Number of Ballasts in Conditioned Space (#) 0 0 0 0 0 0 0 Number of Ballasts Not in Conditioned Space (#) 0 0 0 0 0 0 0 Evaporator Fan Nominal Rated Wattage (W/fan) 9 9 9 0 0 0 0 Number of Evaporator Fans per Case (#) 2 2 2 0 0 0 0

Condenser Fan Nominal Rated Wattage (W/fan) 16 16 16 9 9 9 9 Number of Condenser Fans per Case (#) 1 1 1 1 1 1 1 Discharge Air Temperature (DAT) (F) 32

- 5

- 20 32

- 5

- 20 32 Baseline Evaporator Temperature (SET) (F) 23

- 15

- 30 27

- 10

- 30 27

Baseline Condenser Temperature (SCT) (F) 95 95 95 95 95 95 95 Compressor Oversize Multiplier 2.6 2.4 2.2 1.3 1.3 1.3 1.3 Defrost Mechanism (OFF, ELE, MAN) OFF ELE ELE OFF MAN MAN OFF Defrost Time per Day (hr) 1.5 1.5 1 1 0 0 1 Defrost and Drain Heater Power (W) 0 1900.00 2580 0 0 0 0 Condensate Pan Heater Power (W) 200 200 200 0 0 0 0

5A -7

### Table 5A.2.2 (cont)

HCS.SC.L SOC.SC.M SOC.RC.M PD.SC.M Case Length (ft) 4.16 12 12 2.5 Case Gross Refrigerated Volume (ft 3 ) 7.03 66 66 27 Case Total Display Area (ft 2 ) 0 51 51 13 Number of Doors (#) 1 6 6 1 Single Door Area (ft 2 ) 8.32 3.3 3.3 13.96

Non

- Door Glass Area (ft 2 ) 0 35.5 35.5 0

Non

- Door Anti

- Sweat Power (W) 0 200 200 0 Wall Area (ft 2 ) 25.3 84.6 84.6 58 Insulation Thickness (in

. ) 2 1.5 1.5 1.5 Case Interior Surface Area (ft 2 ) N.A. N.A. N.A. N.A. Air Curtain Angle from Vertical (°)

- - - - Infiltrated Air Mass Flow (lb/hr) 2 30 30 7.5 Number of Bulbs in Conditioned Space (#) 0 15 15 3 Number of Bulbs Not in Conditioned Space (#) 0 0 0 0 Number of Ballasts in Conditioned Space (#) 0 0 0 2 Number of Ballasts Not in Conditioned Space (#) 0 5 5 1 Evaporator Fan Nominal Rated Wattage (W/fan) 0 9 6 12 Number of Evaporator Fans per Case (#) 0 4 4 1 Condenser Fan Nominal Rated Wattage (W/fan) 9 16 0 16 Number of Condenser Fans per Case (#) 1 2 0 1 Discharge Air Temperature (DAT) (F)

- 5 30 30 32 Baseline Evaporator Temperature (SET) (F)

- 10 20 20 23 Baseline Condenser Temperature (SCT) (F) 95 95 0 95 Compressor Oversize Multiplier 1.3 1.3 0 3 Defrost Mechanism (OFF, ELE, MAN) MAN ELE ELE OFF Defrost Time per Day (hr) 0 1.5 1.5 1.5 Defrost and Drain Heater Power (W) 0 1600 1600 0 Condensate Pan Heater Power (W) 0 300 0 200



### 5A.3 LIGHTING CONFIGURATIONS

Lighting for use in cases with transparent doors functions differently than lighting for use in cases without doors. Cases with transparent doors typically display boxed merchandise, and only products on the front of the shelves are visible to the consumer. Therefore, the only portion of the display case that requires illumination is the area on the front surface of the case at the front of the shelves. Since this is only a limited ar ea that requires lighting, light

- emitting diodes ( LEDs ) offer an advantage over fluorescent lighting in vertical refrigerated cases with transparent doors because of the directional nature of LED lighting.

As part of the engineering analysis for the 2009 final rule (74 FR 1092 (Jan. 9, 2009)), the U.S. Department of Energy ( DOE ), with input from manufacturers and other stakeholders, developed lighting configurations for cases with transparent doors. DOE has retained these configurations as part of this analysis, and has further developed additional configurations for equipment classes not covered in the 2009 rulemaking. For the VCT equipment family, DOE

assumed one fluorescent fixture on each mullion and a single fluorescent bulb on each end of the

case. Th ere are two different types of LED lighting used in cases with transparent doors. A center mullion lighting fixture is used between doors and is designed to have half of the LED

emitters directed toward one door and half of the LED chips directed toward the other door. An

5A -8

### end mullion lighting fixture has half the light output, cost, and power consumption of a center mullion lighting fixture. The LED emitters in an end mullion lighting fixture are all directed toward the one door next to which they are located. Therefore, two end mullion lighting fixtures are the approximately equivalent to a single center mullion lighting fixture with regard to cost, light output, and power consumption. DOE modeled the LED lighting for the VCT equipment family using a center mullion lighting fixture and assumed one center mullion lighting fixture per door. For pull-down equipment, DOE assumed the presence of one additional lighting fixture due to the fact that pull-down units often feature an illuminated sign panel. Illustrat ive front views of the lighting configurations for both fluorescent and LED lighting for the VCT and PD equipment families are shown in Figure 5A.3.1 through Figure 5A.3 .6 . The red strips represent a fluorescent bulb inside the refrigerated volume, the blue strips represent an LED center mullion lighting fixture inside the refriger ated volume, and the green strips represent an LED end mullion lighting fixture inside the refrigerated volume.

5A -9

### Figure 5A.3 .1 Lighting Configuration s for VCT.RC.M Bulb In =

LED In = Center Mullion

1/2 LED In = End Mullion (one on either end of case, resulting in the equivalent of a single center mullion)

VCT.RC.M Lighting Type: Fluorescent Case Length [ft]: 12.7 Bulb Length [ft]: 5

Bulbs In: 6

Bulbs Out: 0

VCT.RC.M Lighting Type: LED Case Length [ft]: 12.7 Bulb Length [ft]: 5

LEDs In: 5

LEDs Out: 0

5A

- 10

### Figure 5A.3 .2 Lighting Configuration s for VCT.RC.L VCT.RC.L Lighting Type: Fluorescent Case Length [ft]: 12.7 Bulb Length [ft]: 5

Bulbs In: 6

Bulbs Out: 0

VCT.RC.L Lighting Type: LED Case Length [ft]: 12.7 Bulb Length [ft]: 5

LEDs In: 5

LEDs Out: 0

5A

- 11

### Figure 5A.3 .3 Lighting Configuration s for VCT.SC.M

Figure 5A.3 .4 Lighting Configuration s for VCT.SC.L VCT.SC.L Lighting Type: LED Case Length [ft]: 5 Bulb Length [ft]: 5

LEDs In: 2

LEDs Out: 0 VCT.SC.L Lighting Type: Fluorescent Case Length [ft]: 5 Bulb Length [ft]: 5

Bulbs In: 3

Bulbs Out: 0 VCT.SC.M Lighting Type: LED Case Length [ft]: 5 Bulb Length [ft]: 5

LEDs In: 2

LEDs Out: 0 VCT.SC.M Lighting Type: Fluorescent Case Length [ft]: 5 Bulb Length [ft]: 5

Bulbs In: 3

Bulbs Out: 0

5A

- 12

### Figure 5A.3 .5 Lighting Configuration s for VCT.SC.I

Figure 5A.3 .6 Lighting Configuration s for PD.SC.M

For equipment classes without doors (i.e. , VOP, SVO, and HZO equipment families), as well as service over counter equipment, merchandise throughout the entire refrigerated volume is

visible to the consumer. Therefore, the entire refrigerated volume must be illuminated. b 

### For this application, the directionality characteristic of LED ligh ting tends to be less effective than fluorescent lighting, which outputs light in all directions surrounding the bulb. In the 2009 final

b D OE assumes that the HZO equipment family does not contain any lighting because the ambient light of the store provides adequate illumination of the displayed merchandise. This is consistent with current manufacturer practice. PD.SC.M Lighting Type: LED Case Length [ft]: 2.5 Bulb Length [ft]: 5

LEDs In: 2

LEDs Out: 0 PD.SC.M Lighting Type: Fluorescent Case Length [ft]: 2.5 Bulb Length [ft]: 5

Bulbs In: 3

Bulbs Out: 0 VCT.SC.I Lighting Type: LED Case Length [ft]: 5 Bulb Length [ft]: 5

LEDs In: 2

LEDs Out: 0 VCT.SC.I Lighting Type: Fluorescent Case Length [ft]: 5

Bulb Length [ft]: 5

Bulbs In: 3

Bulbs Out: 0

5A

- 13

### rule, DOE developed case lighting configurations for these classes as well. Based on discussions with LED refrigerated display case lighting manufacturers and comments from commercial refrigeration equipment manufacturers, DOE determined that there a re two different types of

LED luminaries used in this equipment. A shelf light is used to illuminate merchandise close to

it. Due to the directionality of the light output from an LED luminary, DOE assumes that two shelf lights are used per shelf to provide the desired illumination throughout an entire shelf: one on the front of the shelf and one midway under the shelf. A canopy light is typically located on the canopy of a display case. A canopy light has effectively twice the light output, cost, and power consumption of a shelf light and is typically is used to provide additional illumination of the product in the bottom well o f the display case. DOE modeled the LED lighting for the VOP, SVO, and SOC equipment families, in the engineering analysis, using a shelf light. DOE also assumed that the number of LED lighting fixtures per shelf would have to be doubled from what

was assu med for fluorescent lighting to provide adequate illumination for the merchandise displayed on each shelf. Illustrative cross -sections of lighting configurations for both fluorescent and LED lighting for example classes in the VOP, SVO, and SOC equipment f amilies are shown

in Figure 5A.3.7 through Figure 5A.3.12

. The green circles represent a bulb or LED inside the refrigerated volume and the red circles represent a bulb or LED outside the refrigerated volume.

Figure 5A.3 .7 Lighting Configurations for VOP.RC.M Bulb/LED In =

Bulb/LED Out =

VOP.RC.M VOP.RC.M Lighting Type: Fluorescent Lighting Type: LED Case Length [ft]: 12 Case Length [ft]: 12 Bulb Length [ft]: 4 LED Length [ft]: 4

Bulbs In: 12 LEDs In: 24

Bulbs Out: 9 LEDs Out: 9

5A

- 14

### Figure 5A.3 .8 Lighting Configurations for VOP.SC.M VOP.SC.M VOP.SC.M Lighting Type: Fluorescent Lighting Type: LED Case Length [ft]: 4 Case Length [ft]: 4 Bulb Length [ft]: 4 LED Length [ft]: 4

Bulbs In: 4 LEDs In: 8

Bulbs Out: 3 LEDs Out: 3

5A

- 15

### Figure 5A.3 .9 Lighting Configurations for VOP.RC.L VOP.RC.L VOP.RC.L Lighting Type: Fluorescent Lighting Type: LED Case Length [ft]: 12 Case Length [ft]: 12 Bulb Length [ft]: 4 LED Length [ft]: 4

Bulbs In: 0 LEDs In: 0

Bulbs Out: 9 LEDs Out: 9

5A

- 16

### Figure 5A.3 .10 Lighting Configurations for SVO.RC.M SVO.RC.M SVO.RC.M Lighting Type: Fluorescent Lighting Type: LED Case Length [ft]: 12 Case Length [ft]: 12 Bulb Length [ft]: 4 LED Length [ft]: 4

Bulbs In: 9 LEDs In: 18

Bulbs Out: 6 LEDs Out: 6

5A

- 17

### Figure 5A.3 .11 Lighting Configurations for SVO.SC.M

Figure 5A.3 .12 Lighting Configurations for SOC.RC.M SVO.SC.M SVO.SC.M Lighting Type: Fluorescent Lighting Type: LED Case Length [ft]: 4 Case Length [ft]: 4 Bulb Length [ft]: 4 LED Length [ft]: 4

Bulbs In: 3 LEDs In: 6

Bulbs Out: 2 LEDs Out: 2

SOC.RC.M SOC.RC.M Lighting Type: Fluorescent Lighting Type: LED Case Length [ft]: 12 Case Length [ft]: 12 Bulb Length [ft]: 4 LED Length [ft]: 4

Bulbs In: 15 LEDs In: 18

Bulbs Out: 0 LEDs Out: 0

5A

- 18

### CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

TABLE OF CONTENTS

6.1 INTRODUCTION .............................................................................................................. 6-1

6.2 BASELINE, INCREMENTA L, AND OVERALL MARKU PS ........................................ 6-2

6.2.1 Baseline Markups ................................................................................................. 6-2

6.2.2 Incremental Markups ........................................................................................... 6-3

6.2.3 Distribution Channel Market Shares .................................................................... 6-4

6.2.4 Ov erall Markups .................................................................................................. 6-4

6.3 BASIC ASSUMPTIONS US ED TO ESTIMATE WHOLE SALER AND

MECHANICAL CONTRACTOR MARKUPS ................................................................. 6-6

6.4 ESTIMATION OF WHOLESALER MARKUPS ............................................................. 6-7

6.5 ESTIMATION OF MECHAN ICAL CONTRACTOR MARK UPS .................................. 6-8

6.6 ESTIMATION OF NATIONAL ACCOUNT MARKUPS .............................................. 6-10

6.7 SALES TAX ..................................................................................................................... 6-11

6.8 MARKUPS RESU LTS ..................................................................................................... 6-12

LIST OF TABLES

Table 6.2.1 Distribution Channels Market Shares for Commercial Refrigeration Equipment ............................................................................................................ 6-4 Table 6.4.1 Wholesaler Expenses and Markups* ........................................................................ 6-7 Table 6.5.1 Mechanical Contractor Expenses and Markups* ...................................................... 6-9

Table 6.7.1 State Sales Tax Rates .............................................................................................. 6-11 Table 6.8.1 Baseline Markups by Distribution Channel and Overall Weighted Average Markup ............................................................................................................... 6-12 Table 6.8.2 Incremental Markups by Distribution Channel and Overall Weighted Average Markup ............................................................................................................... 6-12

LIST OF FIGURES

Figure 6.5.1 Cumulative Distribution of Mechanical Contractor Markups for Commercial Refrigeration Equipment .................................................................................... 6-10

6-i

### CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

6.1 INTRODUCTION

One of the most important inputs to the life

- cycle cost (LCC) and payback period (PBP) analysis and the national impact analysis (NIA) is the installed cost of the commercial refrigeration equipment. Installed cost includes equipment purchase price and installation costs. The U.S. Department of Energy ( DOE ) determines the equipment pu rchase price by using multipliers called “markups” that are applied to the manufacturer production cost ( MPC ) to obtain the customer purchase price of the equipment. The manufacturer markup , calculated as part of the engineering analysis (see chapter 5 of the t echnical support document (TSD)),

converts the MPC into manufacturer selling price ( MSP ). Additional markups, called distribution channel markups, are applied to the MSP based on the distribution channels through which customers purchase the equipment to obtain the customer purchase price of the equipment. This chapter describes the methodology used by DOE to calculate the distribution channel markups and also the methodology by which the markups values are used to obtain the customer purchase price of the commercial refrigeration equipment.

DOE determined that commercial refrigeration equipment is purchased by the customers through three major distribution channels:

Manufacturer  Customer (National Account Channel)

Manufacturer  Wholesaler  Customer (Wholesaler Channel)

Manufacturer  Wholesaler  Mechanical Contractor  Customer (Contractor Channel)

In addition to the manufacturer markup and distribution channel markups, sales tax is applied to obtain the customer purchase price of the equipment . Sales tax varies by the state in which the equipment is installed.

DOE first calculated national-average markup values for each distribution channel. DOE carried out the LCC analysis in the form of Monte Carlo simulations and one of the inputs to each analysis simulation was the state (location) in which the equipment was installed (see TSD chapter 8 for detailed description). For each analysis simulation in the LCC analysis, t he values used for the national account channel markup and wholesaler channel markup were the national average values. However, the contractor channel markup was varied by state (explained later in this chapter)

. The weighted average was calculated for three distribution channel markup values using the market share of each distribution channel to obtain weight ed -average distribution channel markups for each analysis simulation in the LCC analysis . In addition to the distribution channel markups, a sales tax was applied to obtain the customer purchase price of the equipment.

6-1

### 6.2 BASELINE, INCREMENTA L, AND OVERALL MARKUPS

Baseline markups are the markups that convert the MSP of equipment of the baseline efficiency level to customer purchase price. Incremental markups are markups that convert the increase in MSP of equipment at higher efficiency levels to an increase in customer purchase price compared to the price of equipment at the baseline efficiency level . Overall markups include the weighted -average distribution channel markups, based on the distribution channel market share, and sales tax.

6.2.1 Baseline Markups

If the baseline equipment is sold to a customer by the manufacturer through the national

account channel , the following equation defines the equipment price:

EQP NATL ACCT BASE 

### = MFG BASE 

### × MU NATL ACCT BASE 

### × ( 1 + ST )

Eq. 6.1

W here:

EQP NATL ACCNT BASE 

### = national account price to the customer of baseline equipment ($),

MFG BASE 

### = MSP of baseline equipment ($),

MU NATL ACCT BASE 

### = national account markup on baseline equipment, and

ST = sales tax rate.

If the baseline equipment is sold to a customer through the wholesaler channel:

EQP WHOLE BASE 

### = MFG BASE 

### × MU WHOLE BASE 

### × ( 1 + ST ) Eq. 6.2

W here:

EQP WHOLE BASE 

### = wholesaler price of baseline equipment ($), and

MU WHOLE BASE 

### = wholesaler markup on baseline equipment.

If the baseline equipment is sold to a customer through the contractor channel:

EQP MECH CONT BASE 

### = MFG BASE 

### × MU WHOLE BASE 

### × MU MECH CONT BASE 

### × ( 1 + ST ) Eq. 6.3

W here:

EQP MECH CONT BASE 

### = mechanical contractor price of baseline equipment ($), and

MU MECH CONT BASE 

### = mechanical contractor markup on baseline equipment.

6-2

### 6.2.2 Incremental Markups

Incremental markups are cost multipliers that relate increment s in the MSP of equipment at higher efficiency levels to the corresponding increments in the customer purchase price. Similar to the baseline markups, DOE calculated one incremental markup value for each

distribution channel. The increment in MSP of equipment at higher efficiency levels is obtained by subtracting the MSP of equipment at the baseline efficiency level ( MFG BASE 

### ) from the MSP of equipment at higher efficiency level s.

MFG INCR 

### = MFG HIGHER EFF LEVEL 

### − MFG BASE 

### Eq. 6.4

W here:

MFG INCR 

### = increment in MSP of higher efficiency equipment ($), and

MFG HIGHER EFF LEVEL 

### = MSP of equipment at higher efficiency level ($)

.

If the equipment is sold to a customer by the manufacturer through a national account, the following equation defines the increase in equipment price:

EQP NATL ACCT INCR 

### = MFG INCR 

### × MU NATL ACCT INCR 

### × ( 1 + ST ) Eq. 6.5

W here:

EQP NATL ACCNT INCR 

### = increment in customer purchase price to the n ational account customer ($),

and

MU NATL ACCT INCR 

### = national account channel incremental markup.

If the customer acquires the higher efficiency equipment through the wholesaler or

contractor channels :

EQP WHOLE INCR 

### = MFG INCR 

### × MU WHOLE INCR 

### × ( 1 + ST ) Eq. 6.6

W here:

EQP WHOLE INCR 

### = incremental wholesaler price ($), and

MU WHOLE INCR 

### = wholesaler channel incremental markup.

EQP MECH CONT INCR 

### = MFG INCR 

### × MU WHOLE INCR 

### × MU MECH CONT INCR 

### × ( 1 + ST ) Eq. 6.7

W here:

EQP MECH CONT INCR 

### = incremental mechanical contractor price ($)

, and

6-3

### MU MECH CONT INCR 

### = mechanical contractor channel incremental markup.

6.2.3 Distribution Channel Market Shares

For the 2009 final rule analysis, market shares of the three distribution channels were based on estimates provided by Carrier Corporation to DOE during the public review of the

advanced n otice of proposed rule making . 73 FR 50094 (Aug. 25, 2008). Also, during the preliminary analysis , DOE obtained additional data from articles on the Foodservice Equipment & Supplies (FE&S) m agazine website, 1 

### which provided market shares for distribution channels for all foodservice equipment sales. Refrigeration equipment used in the foodservice industry is primarily composed of self- contained equipment. Refrigeration equipment constitutes only 8 percent of the total equipment sales to the foodservice industry 1 

### and the distribution channel

shares for refrigeration equipment may be different from the rest of the foodservice equipment. However, due to lack of availability of additional data, DOE used the market share values of the overall foodservice equipment sales for all self

- contained commercial refrigeration equipment.

During the preliminary analysis public meeting, many stakeholders commented that national accounts compose a larger share of the glass -door cases and that DOE’s market share

values for self

- contained equipment were applicable only to solid-door cases. Some manufacturers implied that their market share distribution fo r glass

- doored cases was closer to

that of the remote condensing cases as they sold a major share of their glass -doored cases through national account channels. DOE, further, recognized that the data from FE&S magazine

website 1 

### was applicable more to solid -doored cases, which form a majority of equipment used in the foodservice industry. Therefore, DOE agreed with these comments from the stakehold ers and consequently altered the market share of the distribution channels by grouping equipment families into two groups: (1) display cases (VOP, SVO, HZO, VCT, HCT, SOC and PD), and (2)

solid -door equipment (VCS and HCS). Table 6.2.1 provides the market shares of the three distribution channels for display cases and solid -door equipment used for the notice of proposed rulemaking. DOE appli ed the market shares for remote condensing equipment from the January 2009 final rule to display cases and used the FE&S magazine website data for the solid-door equipment.

Table 6.2.1 Distribution Channels Market Shares for Commercial Refrigeration Equipment

Equipment Type National Account

Channel Wholesaler

Channel Contractor

Channel Display Cases (VOP, SVO, HZO, VCT, HCT, SOC, and PD) 70 % 15% 15%

Solid

- Door Equipment (VCS and HCS) 30 % 60% 10 %



### 6.2.4 Overall Markups

O verall markup values were separately obtained for both baseline and incremental markups by combining the state sales tax with the weighted -average distribution channel markups. Overall baseline markup value s were calculated using the following:

6-4

### EQP CUST BASE 

### = MFG B ASE



### × ( WT NATL ACCT 

### × MU NATL ACCT BASE 

### + WT WHOLESALER 

### × MU WHOLE BASE 

### + WT MECH CONT 

### × MU WHOLE BASE 

### × MU MECH CONT BASE 

### )

× ( 1 + ST )

= MFG BASE 

### × MU OVERALL BASE 

### Eq. 6.8

W here:

EQP CUST BASE 

### = customer purchase price for baseline equipment ($),

WT NATL ACCT 

### = market share of baseline equipment sales through national account cha nnel (%),

WT WHOLE SALER 

### = market share of baseline equipment sales through wholesaler channel (%),

WT MECH CONT 

### = market share of baseline equipment sales through contractor channel (%), and

MU OVERALL BASE 

### = overall baseline markup

.

Overall incremental markups were calculated using the following equation:

EQP CUST INCR 

### = MFG INCR



### × ( WT NATL ACCT 

### × MU NATL ACCT INCR 

### + WT WHOLESALER 

### × MU WHOLE INCR 

### + WT MECH CONT 

### × MU WHOLE INCR 

### × MU MECH CONT INCR 

### ) × ( 1 + ST )

= MFG INCR 

### × MU OVERALL INCR 

### Eq. 6.9

W here:

EQP CUST INCR 

### = increment in customer purchase price of equipment at a higher efficiency level compared to baseline equipment ($),

MU OVERALL INCR 

### = overall incremental markup

.

For a particular piece of equipment at a higher efficiency level, the total price of that equipment to the customer (

EQP CUST 

### ) is the sum of the baseline customer price ( EQP CUST BASE 

### )

and the incremental customer price ( EQP CUST INCR 

### ).

EQP CUST 

### = EQP CUST BASE 

### + EQP CUST INCR 

### Eq. 6.10

6-5

### 6.3 BASIC ASSUMPTIONS US ED TO ESTIMATE WHOLE SALER AND

MECHANICAL CONTRACTOR MARKUPS

DOE based the wholesaler markups on industry balance

- sheet data and based the

mechanical contractor markups on U.S. Census Bureau data for the plumbing, heating, and air

conditioning ( PH AC) industry. 2 

### DOE obtained the industry balance

- sh eet data from the Heating,

Air conditioning & Refrigeration Distributors International (HARDI), the trade association representing wholesalers of heating, ventilation, air-conditioning and r efrigeration ( HVAC R) equipment. 3 

### D OE compiled the U.S. Census Bureau PHAC data following the same format as the balance

- sheet data for wholesalers. These balance sheets break out the components of all costs incurred by firms that supply and install PHAC equipment. DOE derived the wholesaler and mechanical contractor markups from three key assumptions about commercial refrigeration equipment costs :

1. The firm balance sheets accurately represent the various average costs incurred by firms distributing and installing commercial refrigeration equip ment

.

2. The wholesaler and contractor costs can be divided into two categories: (1) costs that vary in proportion to the MSP of commercial refrigeration equipment ( variable costs); and (2) costs that do not vary with the MSP of commercial refrigeration equipment (fixed costs).

3. Wholesaler and contractor prices vary in proportion to wholesaler and contractor

costs included in the balance sheets.

The basis for the first assumption is that t he industry balance sheets itemize firm costs

into a number of cost categories, including direct costs to purchase or install the equipment,

o perating labor and occupancy costs, and other operating costs and profit. Although wholesalers

and contractors ten d to handle multiple commodity lines (including air conditioners, furnaces, and boilers), the data provides the most accurate available data for commercial refrigeration equipment costs

.

Information obtained from trade literature and from selected HVAC R wholesalers, contractors, and consultants supports the second assumption that wholesale and contractor markups vary according to the quantity of labor and materials used to distribute and install

HVAC R equipment. In the following discussion, DOE assumes a division of costs between those

that do not scale with the MSP (labor and occupancy expenses) and those that do vary with MSP (operating expenses and profit). This division of costs led to the estimate of wholesale and contractor markups described below.

The basis for the third assumption is that the HVAC R wholesaler and contractor industry is competitive and consumer demand for commercial refrigeration, heating , and air-conditioning equipment is inelastic ( i.e. , the demand is not expected to decrease sig nificantly with an increase in price of equipment). The large number of HVACR firms listed in the 2007 economic census indicates the competitive nature of the market. For example, there are more than 700 HVACR

manufacturers

, 4 

### 1,300 wholesalers of refrigera tion equipment, 5 

### 36,000 c ommercial and institutional building contractors, 6 

### and 91,000 HVAC contractors 7 

### listed in the 2007 economic census. Following standard economic theory, competitive firms facing inelastic demand either set prices in line with costs or quickly go out of business. 8

6-6

### 6.4 ESTIMATION OF WHOLES ALER MARKUPS

Annually, HARDI conducts a confidential survey of its member firms in which

w holesalers report data . In the survey, HARDI itemizes revenues and costs into cost categories, including direct equipment expenses (cost of goods sold), labor expenses, occupancy expenses, other operating expenses, and profit. DOE presents the data for a typical HARDI distributor in terms of specific types of expense s within these categories in appendix 6A. Table 6.4.1

summarizes these expenses in units of expenses per dollar sales revenue and revenue per dollar of goods sold. As shown in the first column of Table 6.4.1 , the direct cost of equipment sold

represent s $0.737 per dollar of sales revenue . In other words, for every $1.00 wholesalers take in as sales revenue, they use $0.737 to pay for the direct equipment costs. Labor expenses account for $0.151 per dollar of sales revenue, occupancy expenses account for $0.0 36, other operating expenses account for $0.055, and profit accounts for $0.021 per dollar sales revenue.

Table 6.4.1 Wholesaler Expenses and Markups*

Description Wholesale Firm Expenses or

Revenue

Per Dollar

Sales Revenue Per Dollar

Cost of Goods

Sold Direct Cost of Equipment Sales: Cost of goods sold $0. 7 37 $1.0 0 0 Labor Expenses: Salaries and benefits $0. 1 51 $0. 205 Occupancy Expense: Rent, maintenance, and utilities $0. 0 36 $0.0 49 Other Operating Expenses: Depreciation, advertising, and

insurance $0.0 55 $0.0 75

Profit $0.0 21 $0.0 29 Baseline Markup ( MUWHOLE BASE ): Revenue per dollar cost of goods sold * 1. 357 Incremental Markup ( MUWHOLE INCR ): Increased revenue per dollar increase cost of goods sold ** 1. 103

* Source: Heating, Air c onditioning & Refrigeration Distributors International. 20 12. 20 12 Profit Report (20 11 Data)

. Based on median Control and Refrigeration Firm. ** Numbers include rounding errors and may not add up to the totals.



### The last column of Table 6.4.1 shows the data converted from costs per dollar revenue into revenue per dollar cost of goods sold. DOE performed this conversion by dividing each cost category in the first data column of Table 6.4.1 by $0.737 ( i.e. , equipment expenditure per dollar revenue). The data in th is column show that, for every $1.00 the wholesaler spends on equipment costs, the wholesaler earns $1.00 in sales revenue to cover the equipment cost, $0.205 to cover labor costs, $0.049 to cover occupancy expenses, $0.075 for other operating expenses, and $0.029 in profits. This totals to $1.357 in sales revenue earned for every $1.00 spent on equipment costs. Therefor e, the wholesale r baseline markup ( MU WHOLE BASE 

### ) is 1.357 ($1.357 ÷ $1.00).

6-7

### DOE also used the data in the last column of Table 6.4.1 to estimate the incremental markups. The incremental markup depends on which of the costs in Table 6.4.1 are variable and

which are fixed. For example, for a $1.00 increase in the manufacturer equipment price, if all of

the other costs scale with the manufacturer selling price ( i.e. , all costs are variable), the increase

in wholesaler price will be $1.357 , implying that the incremental markup is 1 .357 , or the same as

the baseline markup. However , if none of the other costs is variable, then a $1.00 increase in the manufacturer selling price will lead to a $1.00 increase in the wholesaler pri ce, for an

incremental markup of 1.0. DOE assumes that the labor and occupancy costs will be fixed and

that the other operating costs and profit will scale with the manufacturer selling price ( i.e. , be variable). In this case, for a $1.00 increase in the m anufacturer selling price , the wholesaler price will increase by $0.103, which is the sum of other operating expenses and profit in the last

column of Table 6.4.1

. Therefore, the wholesale r incremental markup ( MU WHOLE INCR 

### ) is 1.103 ($1.103 ÷ $1.00). See appendix 6A for additional details and data used for markup calculations.

6.5 ESTIMATION OF MECHANICAL CONTRACTOR MARK UPS

DOE derived markups for mechanica l contractors from U.S. Census Bureau data for

PHAC contractors. This sector includes establishments primarily engaged in installing and servicing PHAC equipment, which may include new work, additions, alterations, maintenance, and repairs. The U.S. Census Bureau data for the PH AC sector include detailed statistics for establishments with payrolls, similar to the data reported by HARDI for wholesalers. The prim ary difference is that the U.S. Census Bureau reports itemized revenues and expense s for the

PH AC industry as a whole in total dollars rather than in typical values for an average or representative business. Because of this, DOE assumed that the total dollar values that the U.S. Census Bureau reported, once converted to a percentage basis, represented revenues and expenses for an average or typical contracting business. As with the data for wholesalers, Table

6.5.1 summarizes the expe nses for mechanical contractors

. The expenses per dollar sales revenue are given in the first data column of Table 6.5.1 (appendix 6A contains the full set of data). T he direct cost of sales represents about $0.680 per dollar sales revenue to the mechanical contractor. Labor expenses account for $0.170 per doll ar sales revenue, occupancy expenses account for $0.020 per dollar sales revenue, other op erating expenses account for $0.0 40 , and profit makes up $0.090 per dollar sales revenue. DOE converted these expenses per dollar sales into revenue per dollar cost of goods sold, by dividing each figure in the first data column by $0.678. The data in the last column of Table

6.5.1 show that, for every $1.00 the mechanical contractor spends on equipment costs, the mechanical contractor earns $1.00 in sales revenue to cover the equipment cost, $0.258 to cover labor costs, $0.032 to cover occupancy expenses, $0.058 for other operating expenses, and $0.127 in profits. This totals $1.475 in sales revenue earned for every $1.00 spent on equipment

costs. Th erefore, the mechanical contractor baseline markup ( MU MECH CONT BASE 

### ) is 1.475 ($1.471 ÷ $1.00).

6-8

### Table 6.5.1 Mechanical C ontractor Expenses and Markup s*

Description Mechanical Contractor Firm Expenses or Revenue

Per Dollar

Sales Revenue Per Dollar Cost

of Goods Sold Direct Cost of Equipment Sales: Cost of goods sold $0. 6 78 $1.00 0 Labor Expenses: Salaries (indirect) and benefits $0. 17 5 $0. 25 8 Occupancy Expense: Rent, maintenance, and utilities $0. 0 22 $0. 0 3 2 Other Operating Expenses: Depreciation, advertising, and insurance $0. 0 39 $0. 05 8

Net Profit Before Taxes $0. 0 86 $0. 1 27 Baseline Markup ( MU MECH CONT BASE ): Revenue per dollar cost of goods sold ** 1. 47 5 Incremental Markup ( MU MECH CONT INCR ): Increased revenue per dollar increase cost of goods sold ** 1. 1 84

* Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air

- Conditioning Contractors. Sector 23: 238220. Construction: Geographic Area

Series: Detailed Statistics for Establishments: 2007. ** Numbers include rounding errors and may not add up to the totals.



### DOE was also able to use the data in the last column in Table 6.5.1 to estimate the incremental markups by separat ing the fixed and variable costs. For example , if all of the other costs scale with the equipment price ( i.e. , all costs are variable), the increase in mechanical contractor price will be $1. 475 , implying tha t the incremental markup is 1.475 , or the same as the baseline markup. However , if none of the other costs is variable, then a $1.00 increase in the equipmen t price will lead to a $1.00 increase in the mechanical contractor price, for an incremental markup of 1.0. DOE assumes the labor and occupancy costs to be fixed and the other operating costs and profit to scale with the equipment price ( i.e. , be variable). In this case, for a $1.00 increase in the equipment price, the mechanical contrac tor price will increase by $ 0.184, which is the sum of other operating expenses and profit in the last column of Table 6.5.1 , giving

a mechanical contractor incremental markup ( MU MECH CONT INCR 

### ) of 1. 184 ($1. 184 ÷ $1.00).

Mechanical contractor costs differ in various regions of the country for reasons including availability of labor, cost of living, and union versus non-union workforce. Because many mechanical contractor costs differ systematically by s tate, DOE characteriz ed the markups developed from U.S. Census Bureau data with a probability distribution based on a s tate

- by -s tate analysis of U.S. Census Bureau data for PHAC contractors. The s tate

- by -s tate analysis provided

a distribution on the relative markups of m echanical contractors in the United States

.

Figure 6.5.1 shows the cumulative probability distribution of the state markup index that

DOE used to characterize the mechanical contractor baseline and incremental markups. As

mentioned in section 6.1 , the contractor channel markup index was varied by state. The baseline and incremental markups in Table 6.5.1 are the national average markup values for the contractor channel. In the LCC analysis (TSD chapter 8) , these national average values were multiplied by the state markup index to obtain the contractor channel markups for a particular state.

6-9

### Fig ure 6.5.1 Cumulative D istribution of M echanical C ontractor M arkups for

C ommercial Refrigeration Equipment

6.6 ESTIMATION O F NATIONAL ACCOUNT M ARKUPS

Large customer s of commercial refrigeration equipment use national accounts to

circumvent the wholesaler channel , thereby allowing them to negotiate significantly lower equipment prices directly with the manufacturer. The manufacturer , in turn, must cover additional expenses related to the distribution of the equipment, but gains in terms of negotiating agreed -upon sales volumes with the customer

.

To capture the price savings realized from equipment purchased through national

accounts, DOE derived a national account markup, assuming that the resulting equipment price

increase was one

- half of that realized from distribution through the wholesaler channel . In other words, if the price markup resulting from the wholesaler markups is $100, the national account markup is $50. DOE based the use of a national account markup that is one- half of that realized

from the wholesaler distribution channel on the assumption that the resulting national account equipment price must fall somewhere between the manufacturer selling price and the customer price under the wholesaler distribution channel

. Because DOE d oes not have data suggesting typical values for the actual national account markup , it chose a value halfway between the MSP and the wholesaler markup.

For example, using a base line MSP of $1,000 for a unit of commercial refrigeration equipment delivered to a supermarket

, and a baseline wholesaler markups of 1.357, the resulting

baseline customer equipment price for sales through a wholesaler ( without sales taxes ) is $ 1,357 ($ 1,000 × 1.357 ). The dollar value increase due to the above distribution channel markups is
$357 ($ 1,357 - $1,000 ). Under the assumption that national account customers realize equipment price increases equal to one

- half of that through the wholesale distribution chan nel, the dollar value of the equipment price increase under the national account is $ 178. The resulting equipment price is $1,178 ($ 178 + $1,000), which results in national account baseline markup of approximately 1.178 ($ 1,196 ÷ $ 1,000 ). A similar calculation using a value of 1.103 for the 0% 20% 40% 60% 80% 100%

1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9Cumulative Percentages (%)

Markup

Mechanical Contractor Markup by

...

6- 10

### wholesaler incremental markup, results in a national account incremental markup of 1.052 ($1,052 ÷ $1,000).

6.7 SALES TAX

Sales tax represents state and local sales taxes that are applied to the customer price of commercial refrigeration equipment. The sales tax is a multiplicative factor that increases the customer equipment price. DOE derived sales taxes representative of the combined s tate and local sales tax rates from the Sales Tax Clearinghouse, shown in Table 6.7.1

. The state level combined tax rates can be applied to the estimated value of state level commercial refrigeration equipment shipments to obtain the total purchase cost to the customer located in any state.

The distribution of sales tax rates ranges from a minimum of zero percent to a maximum

of 9.5 percent with a mean value of 6.2 percent. DOE calculated the weighted -average national level sales tax rate by multiplying the relative population of each state by the tax rates in Table

6.7.1

. The weighted national average sales tax rate was found to be 7.1 percent. The sales tax was applied in the LCC analysis (TSD chapter 8), according to the state in which the equipment

was installed.

Table 6.7.1 State Sales Tax Rates

State Combined

State and

Local Tax

Rate State Combined

State and

Local Tax

Rate State Combined

State and

Local Tax

Rate

Alabama 8.50% Kentucky 6.00% North Dakota 5.85%

Alaska 1.35% Louisiana 8.75% Ohio 6.85%

Arizona 8.15% Maine 5.00% Oklahoma 8.30%

Arkansas 8.35% Maryland 6.00% Oregon 0.00%

California 8.20% Massachusetts 6.25% Pennsylvania 6.40%

Colorado 6.10% Michigan 6.00% Rhode Island 7.00%

Connecticut 6.35% Minnesota 7.20% South Carolina 7.10%

Delaware 0.00% Mississippi 7.00% South Dakota 5.35%

Dist. of Columbia 6.00% Missouri 6.55% Tennessee 9.45%

Florida 6.65% Montana 0.00% Texas 7.95% Georgia 6.95% Nebraska 6.00% Utah 6.70%

Hawaii 4.40% Nevada 7.85% Vermont 6.05%

Idaho 6.05% New Hampshire 0.00% Virginia 5.00%

Illinois 8.15% New Jersey 6.95% Washington 8.90%

Indiana 7.00% New Mexico 6.60% West Virginia 6.05%

Iowa 6.85% New York 8.40% Wisconsin 5.45%

Kansas 8.00% North Carolina 6.85% Wyoming 5.15%

Source: The Sale Tax Clearinghouse

. http://www.thestc.com/STrates.stm

. Last accessed 11/7/2012

.

6- 11

### 6.8 M ARKUPS R ESULTS

Table 6.8.1 presents the baseline markup values for each distribution channel and the weighted

- average baseline markup values for display cases and for solid door cases. Table 6.8.2
present s the incremental markup values for each distribution channel and the weighted

- average incremental markup values for display cases and for solid door cases. The mec hanical contractor channel markup values presented in both the tables are the national average values.

Table 6.8.1 Baseline Markups by Distribution Channel and Overall Weighted Average Markup

Wholesaler

Channel Contractor Channel (includes wholesaler markup) * National

Account

Channel Weighted

- Average Markup * Display

Cases Solid

- Door Equipment Markup 1.3 57 2.002 1.1 78 1.3 29 1.3 68

*National average value.



### Table 6.8.2 Incremental Markups by Distribution Channel and Overall Weighted Average Markup

Wholesaler

Channel Contractor Channel (includes wholesaler markup) * National

Account

Channel Weighted

- Average Markup * Display

Cases Solid

- Door Equipment Markup 1.1 03 1. 306 1.05 2 1. 0 98 1.1 0 8

*National average value.



### DOE used the weighted

- average markups to estimate the customer price, before sales tax,

of baseline and higher efficiency equipment. For example, if the MSP of a baseline solid door

u nit is $1 ,000, the customer purchase price before sales tax for this baseline equipment is obtained by multiplying the MSP by the weighted

- average markup value of 1.358 to obtain the baseline customer purchase price, before sales tax, of $1,358. If the increment in the MSP of the equipment at a higher efficiency level is $100, then the customer purchase price increment, before sales tax, of this equipment can be obtained by multiplying by the weighted

- average incremental markup of 1.103 to obtai n a customer purchase price increment, before sales tax, of
$110.30 . The customer purchase price of this higher efficiency equipment before sales tax is the sum of the baseline price and the increment, which is equal to $1,468.30 ($ 1,358 + $110.30 ). Even though the example calculation has been shown with the national average values for the contractor channel markups included in the weighted-average markup , the calculation s in the LCC analysis (TSD chapter 8) were performed by using the state

- wide con tractor channel markup values. Finally, the sales tax is applied to customer purchase price before sales tax according to the state in which the equipment is installed, to obtain the final customer purchase price.

6- 12

### REFERENCES

1. Carbonara, J. M. 2010 Foodservice Industry Forecast: Achieving Stability. Foodservice Equipment & Supplies . 2009 . (Last accessed December 10, 2010 .)

< http://www.fesmag.com/index.php/r esearch/industry

- forecast >

2. U.S. Census Bureau. 2007 Economic Census, Release Date: 11/24/2009. Sector 23: EC0723A1: Construction: Geographic Area Series: Detailed Statistics for Establishments : 2007

. (Last accessed May 28, 2010.)

< http://factfinder.census.gov/servlet/IBQTable?_bm=y&-ds_name=EC0723A1&-

NAICS2007=238220&- _lang=en. >

3. Heating, Air conditioning & Refrigeration Distributors International. 2012 Profit Report (20 11 Data) . 2012. Columbus, OH.

4. U.S. Census Bureau. 2007 Economic Census, Release Date: 10/30/2009, Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United

States: 2007

. Air -Conditioning, Refrigeration, and Forced A ir H eating Manufacturing. (Last accessed March 2, 2011.) < http://factfinder.census.gov/servlet/IBQTable?_bm=y&- fds_name=EC0700A1&- geo_id=& -_skip=300&-ds_name=EC0731I1&- _lang=en >

5. U.S. Census Bureau. 2007 Economic Census, Release Date: 11/24/2009, Sector 42: EC0742I1: Wholesale Trade: Industry Series: Preliminary Summary Statistics for the United

States: 2007 . HVAC equip. m erchant w holesaler s

. (Last accessed March 2, 2011.)

< http://factfinder.census.gov/servl et/IBQTable?_bm=y& -geo_id=&-ds_name=EC0742I1&- _lang=en >

6. U.S. Census Bureau. 2007 Economic Census, Release Date: 8/14/2009, Construction: Industry Series: Preliminary Detailed Statistics for Establishments: 2007

. Total of New

s ingle -f amily g eneral contractors, N ew m ultifamily housing construction (e xcept o perative builders), N ew housing o perative builders residential, and R esidential r emodelers; Commercial and institutional building construction . (Last accessed March 2, 2011.)

< http://factfinder.census.gov/servlet/IBQTable?_bm=y&-geo_id=&- fds_name=EC0700A1&-ds_name=EC0723I1&- _lang=en .>

7. U.S. Census Bureau. 2007 Economic Census, Release Date: 8/14/2009, Sector 23: EC0723I1: Construction: Industry Series, Preliminary Detailed Statistics for Establishments:

2007 . Plumbing, h eating, and a ir -conditioning c ontractors. (Last accessed March 2, 2011.)

< http://factfinder.census.gov/servlet/IBQTable?_bm=y&-geo_id=&-ds_name=EC0723I1&- _lang=en >

8. Pindyck, R. and Rubinfeld, D. Microeconomics. s.l. : Prentice Hall, 2000.

6- 13

### APPENDIX 6A. DATA FOR EQUIPMENT PRICE MARKUPS

TABLE OF CONTENTS

6A.1 INTRODUCTION ........................................................................................................ 6A-1

6A.2 DETAILED WHOLESALER COST DATA ............................................................... 6A-1

6A.3 DETAILED MECHANICAL CONTRACTOR DATA ............................................... 6A-2

6A.4 ESTIMATION OF WHOLESALER AND MECHANICAL CONTRACTOR

MARKUP STANDARD DEVIATIONS ..................................................................... 6A-3

LIST OF TABLES

Table 6A.2.1 Disaggregated Costs and Expenses for Wholesalers* ........................................ 6A-2 Table 6A.3.1 Mechanical Contractor Expenses and Markups * ............................................... 6A-3

Table 6A.4.1 Mechanical Contractor Baseline Markups by State, 2007* ................................ 6A-4

6A -i

### APPENDIX 6A. DATA FOR EQUIPMENT PRICE MARKUPS

6A.1 INTRODUCTION

This appendix provides further details on information presented in chapter 6, Markups for Equipment Price Determination.

6A.2 DETAILED WHOLESALER COST DATA

Chapter 6 presents w holesaler revenues and costs i n aggregated form , based on the Heating , Airconditioning & Refrigeration Distributors International (HARDI) 2012 Profit Report (20 11 Data)

. Table 6A.2 .1 provides the complete breakdown of costs and expenses from the 2012 Profit Report (2011 Data)

. The column labeled “Scaling” indicates which expenses were assumed to scale with only the baseline markup and which were assumed to scale with both the

baseline and the incremental markups. As described in chapter 6, only those expenses that scale

with incremental costs are m arked up when there is an incremental change in equipment costs.

6A -1

### Table 6A.2.1 Disaggregated Cost s and Expenses for Wholesalers*

Item Percent of Revenue Scaling

Cost of Goods Sold 73.7% Not applicable Gross Margin 26.3% Payroll Expenses 15.1% Baseline

Executive Salaries & Bonuses 1.7% Branch Manager Salaries and Commissions 1.5%

Sales Executive Salaries & Commissions 0.5%

Outside Sales Salaries & Commissions 2.1% Inside/Counter Sales/Wages 2.8% Purchasing Salaries/Wages 0.4% Credit Salaries/Wages 0.2% IT Salaries/Wages 0.1% Warehouse Salaries/Wages 1.4% Accounting 0.5% Delivery Salaries/Wages 0.7% All Other Salaries/Wages & Bonuses 0.8% Payroll Taxes 1% Group Insurance 1.1%

Benefit Plans 0.3% Occupancy Expenses 3.6% Baseline

Utilities: Heat, Light, Power, Water 0.4% Telephone 0.3% Building Repairs & Maintenance 0.2% Rent or Ownership in Real Estate 2.7% Other Operating Expenses 5.5% Baseline & Incremental Sales Expenses (incl. Advertising & Promotion ) 0.9% Insurance (business liability & casualty) 0.2% Depreciation 0.4% Vehicle Expenses 1.4% Personal Property Taxes/Licenses 0.1% Collection Exp (collection, credit card fees) 0.3%

Bad Debt Losses 0.2% Data processing 0.3% All Other Operating Expenses 1.7% Total Operating Expenses 24.2%

- Operating Profit 2.1%

Baseline & Incremental

Other Income 0.4% Interest Expense 0.5%

Other Non

- operating Expenses 0.0%

Profit Before Taxes 2.0%

- Source: Heating, Airconditioning & Refrigeration Distributors International. 201 2 Profit Report (20 11 Data) . 201 2 . Columbus, OH.

*W holesaler costs and expenses are percentage values as opposed to the per dollar of sales revenue values shown in chapter 6

.



### 6A.3 DETAILED MECHANICAL CONTRACTOR DATA

Chapter 6 presents mechanical contractor revenues and costs in aggregated form, based

on U.S. Census Bureau data. Table 6A. 3.1 shows the complete breakdown of costs and expenses provided by the U.S. Census Bureau. The column labeled “Scaling” i ndicates which expenses were assumed to scale with only the baseline markup and which were assumed to scale with both the baseline and incremental markups. As described in chapter 6, only those expenses that scale

with incremental costs are marked up when there is an incremental change in equipment costs.

6A -2

### Table 6A. 3.1 Mechanical Contractor Expenses and Markups *

Item Dollar Value Percentage Scaling Total Cost of Equipment Sales $107,144,428 67.80%

Baseline Cost of materials, components, and supplies $59,023,964 37.35% Payroll, construction workers $31,373,558 19.85%

Cost of construction work subcontracted out to others 13,646,192 8.64% Cost of selected power, fuels, and lubricants $3,100,714 1.96% Gross Margin $50,895,129 32.20%

- Payroll Expenses $27,626,376 17.48%

Baseline Fringe benefits, all employees $13,585,040 8.60% Payroll, other employees $14,041,336 8.89% Occupancy Expenses Baseline Rental cost for machinery, equipment, and buildings; Cost of repairs to machinery and equipment;

Purchased communication services $3,436,208 2.17% Baseline

Other Operating Expenses $6,165,776 3.90%

Baseline & Incremental Depreciation charges during year $2,297,550 1.50% Computers; Insurance and other business services; Advertising and promotions; Taxes and license fees $3,868,226 2.40%

Net Profit Before Income Taxes $13,666,769 8.60% Baseline & Incremental

Source: U.S. Census Bureau. 2007 Economic Census, Release Date: 8/14/2009, Sector 23: EC0723I1: Construction: Industry Series: Preliminary Detailed Statistics for Establishments: 2007 . (Last accessed, March 8, 2011.)

< http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- geo_id=&

- ds_name=EC0723I1&

- _lang=en >
*Mechanical contractor costs and expenses are first presented as total dollar values and then converted to percentage values. This is in contrast to per dollar of sa les revenue values shown in chapter 6.



### 6A.4 ESTIMATION OF WHOLES ALER AND MECHANICAL CONTRACTOR

MARKUP STANDARD DEVIATIONS

The U.S Department of Energy (DOE) used the U.S. Census Bureau’s Economic Census data to estimate commercial refrigeration equipment whole saler and mechanical contractor markup distributions. In the case of wholesalers, 2007 Economic Census data were available only at the national level

. In the case of mechanical contractors, the 2007 census data included

state -level plumbing, heating, and air conditioning data for total value of work, number of firms, cost of goods sold, cost of subcontract work, cost of materials, and construction payroll, as

shown in Table 6A.4.2 . The most recent census was performed in 2007.

DOE used the st ate

- by

- state variation in heating, ventilating, and air conditioning ( HVAC ) contractor markups as a component of state- by

- state variation in the life

- cycle cost ( LCC ) analysis . In the case of the contractor markups, the variation in contractor markup by state is captured explicitly in the subsequent L CC analysis

. By “selecting” a state during the Monte Carlo analysis, DOE varied the installation costs. Looking at contractor markups on a relative basis, the lowest state is 90 percent of the average markup while the highest state is 119 percent of the average. Using population as a weighting factor, HVAC was combined with other factor s that vary by state to create one of the key sets of cost components varied during the analysis.

6A -3

### Table 6A.4.1 Mechanical Contractor Baseline Markups by State, 200 7*

State Number of

Firms Value of

Constructio

n ($000) Cost of

Subcontrac

t Work ($000) Cost of

Materials

($000) Constructio n Payroll

($000) Baseline Markup

Alabama 1,425 2,010,305 113,782 876,341 411,100 1.435

Alaska 229 583,171 52,245 171,575 120,909 1.692

Arizona 1,510 3,522,116 179,103 1,508,903 638,469 1.514

Arkansas 1,045 1,065,754 68,417 461,924 213,054 1.434

California 7,272 16,726,969 1,070,065 6,330,469 3,464,667 1.539

Colorado 2,015 3,056,988 261,734 1,195,057 627,663 1.467

Connecticut 1,321 1,704,668 145,740 628,720 361,411 1.501

Delaware 306 481,900 D D 163,343 1.421

District of Columbia 22 34,600 D D 50,439 1.458

Florida 5,069 9,061,426 783,859 3,736,811 1,733,721 1.449
Georgia 2,534 4,700,799 365,010 2,224,110 740,722 1.412

Hawaii 280 800,221 46,753 270,723 137,646 1.758

Idaho 594 900,698 62,252 387,181 167,732 1.459

Illinois 3,848 7,641,642 602,251 2,833,489 1,622,307 1.511

Indiana 1,867 4,002,323 431,558 1,319,523 854,157 1.536

Iowa 1,066 1,868,483 144,869 801,239 359,775 1.431

Kansas 966 1,395,359 106,307 580,764 279,636 1.443
Kentucky 1,219 1,747,925 128,902 674,500 353,958 1.510

Louisiana 1,469 1,997,044 162,063 776,784 378,582 1.516

Maine 458 580,816 46,692 234,993 113,162 1.471
Maryland 2,024 5,329,135 698,381 2,009,957 1,031,222 1.425

Massachusetts 2,520 4,099,301 488,098 1,475,525 817,754 1.474
Michigan 3,051 4,420,638 604,850 1,569,113 841,985 1.466

Minnesota 1,635 3,402,921 386,669 1,230,126 698,535 1.470
Mississippi 655 1,025,452 76,709 449,851 189,011 1.433

Missouri 1,816 3,335,124 345,285 1,319,142 689,171 1.417

Montana 432 483,578 42,988 216,792 85,678 1.400

Nebraska 683 1,004,296 94,170 455,264 205,904 1.330

Nevada 498 2,327,842 121,091 988,605 490,859 1.454
New Hampshire 531 620,761 39,784 D 128,512 1.472

New Jersey 3,551 5,062,336 496,174 1,825,407 1,015,432 1.517

New Mexico 599 891,914 67,987 356,961 170,711 1.497

New York 5,750 10,364,779 1,219,468 3,568,182 1,972,687 1.533

North Carolina 2,978 5,111,396 341,129 2,288,841 1,001,832 1.407

North Dakota 272 360,683 36,037 148,617 70,403 1.414

Ohio 3,514 5,618,591 568,837 2,115,568 1,125,401 1.475

Oklahoma 1,158 1,352,943 94,153 581,079 249,032 1.464

Oregon 1,031 1,893,678 124,070 701,468 412,418 1.530
Pennsylvania 3,653 6,487,476 579,901 2,628,602 1,370,864 1.417

Rhode Island 397 631,202 56,234 229,692 124,727 1.537

South Carolina 1,472 1,991,303 126,123 847,690 352,877 1.501

6A -4South Dakota 337 386,186 11,037 158,375 69,605 1.616

Tennessee 1,370 2,595,613 189,293 1,159,952 484,997 1.415

Texas 5,653 10,810,308 823,920 4,605,624 2,102,520 1.435

Utah 892 1,746,398 146,052 769,140 319,812 1.414

Vermont 282 294,806 21,021 D 63,015 1.472
Virginia 2,547 4,623,151 347,055 1,791,740 960,534 1.492
Washington 1,602 4,111,543 370,741 1,546,819 816,533 1.504
West Virginia 416 655,100 D D 90,660 1.464

Wisconsin 1,839 2,926,545 234,962 1,125,779 662,893 1.446
Wyoming 262 289,391 14,391 128,922 54,792 1.461
Average Baseline Markup 1.477

Standard Deviation 0.070

Relative Standard

Deviation 0.048

Source: U.S. Census Bureau. 2007 Economic Census, Release Date: 11/24/2009, Sector 23: EC0723A1: Construction: Geographic Area Series: Detailed Statistics for Establishments: 2007

. (Last accessed May 28, 2010.)

< http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- ds_name=EC0723A1&

- NAICS2007=238220&

- _lang=en >

* The Census Bureau withheld data for some state s due to sample sizes and the size of errors relative to means. For states where a D appears under the headings for Subcontractor Costs, Materials & Supplies, or Construction Payroll, data was withheld. States missing one or more variables were set equal t o an average of neighboring states' baseline markup.

6A -5

### CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION .............................................................................................................. 7-1

7-i

### CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

An energy use analysis is generally carried out for appliance standards rulemakings to calculate the energy consumption of the equipment in question. For commercial refrigeration equipment, the U.S. Department of Energy (DOE) calculated the energy consumption of the equipment as part of the engineering analysis (see technical support document (TSD) chapter 5) using an energy consumption model. During the analysis for the 2009 final rule for commercial refrigeration equipment (74 FR 1092 (Jan. 9 2009)), DOE conducted an energy use analysis for certain remote condensing equipment and concluded that the results agreed reasonably well with those calculated by the energy consumption model used in the engineering analysis. Even though

self -contained and remote condensing equipment differ with respect to their compressor and condenser configurations, the equipment load calculations, which include conduction, radiation and infiltration loads, and loads from the electrical components, are similar for both types of equipment. Therefore, for the current rulemaking, DOE retained the 2009 final rule analysis conclusions and used the engineering analysis energy consumption model calculations of equipment energy consumption values for life- cycle cost and payback period analysis (TSD chapter 8) and national impact analysis (TSD chapter 10). DOE did not carry out a separate

energy u se analysis for this rulemaking.

7-1

### CHAPTER 8. LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

CHAPTER 8. LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS ........................... 8-i

8.1 INTRODUCTION ........................................................................................................... 8-1

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis ................ 8-1

8.1.2 Overview of Life -Cycle Cost and Payback Period Inputs ................................... 8-4

8.1.3 Effect of Current Standards ................................................................................. 8-6

8.2 LIFE

- CYCLE COST INPUTS ........................................................................................ 8-8

8.2.1 Definition ............................................................................................................. 8-8

8.2.2 Total Installed Cost Inputs ................................................................................... 8-9

8.2.2.1 Baseline Manufacturer Selling Price ..................................................... 8-10

8.2.2.2 Efficiency Level Energy Consumption and Manufacturer Selling Price

Increases ............................................................................................................. 8-11

8.2.2.3 Markups ................................................................................................. 8-13

8.2.2.4 Installation Cost ..................................................................................... 8-13

8.2.2.5 Weighted

- Average Total Installed Cost ................................................. 8-15

8.2.3 Operating Cost Inputs ........................................................................................ 8-17

8.2.3.1 Electricity Price Analysis ....................................................................... 8-18

8.2.3.2 Electricity Price Trend ........................................................................... 8-20

8.2.3.3 Repair Cost ............................................................................................. 8-21

8.2.3.4 Maintenance Costs ................................................................................. 8-22

8.2.3.5 Lifetime .................................................................................................. 8-27

8.2.3.6 Discount Rate ......................................................................................... 8-27

8.2.3.7 Compliance Date of Standard ................................................................ 8-31

8.3 PAYBACK PERIOD INPUT S ...................................................................................... 8-31

8.3.1 Definition ........................................................................................................... 8-31

8.3.2 Inputs .................................................................................................................. 8-32

8.4 LIFE

- CYCLE COST AND PAYBACK PERIOD RESULTS ...................................... 8-32

8.4.1 Life

- Cycle Cost Results ..................................................................................... 8-32

8.4.2 Payback Period Results ...................................................................................... 8-35

8.4.3 Rebuttable Presumption Payback Period ........................................................... 8-36

8.5 DETAILED RESULTS ................................................................................................. 8-38

REFERENCES .......................................................................................................................... 8-39

8-i

### L IST OF TABLES

Table 8.1.1 Summary Information of Inputs for the Determination of Life- Cycle Cost and Payback Period ..................................................................................................... 8-6 Table 8.1.2 Design Option Levels for VOP.SC.M Obtained from Engineering Analysis .......... 8-8 Table 8.1.3 Efficiency Levels for VOP.SC.M ............................................................................. 8-8 Table 8.2.1 Inputs for Total Installed Costs ................................................................................. 8-9 Table 8.2.2 Equipment Classes Evaluated for LCC Analysis ................................................... 8-10 Table 8.2.3 Baseline Energy Consumption Levels and MSP Values for the Representative Commercial Refrigeration Equipment Units of All 24 Primary Equipment Classes

............................................................................................................................ 8-11 Table 8.2.4 MSP Increase by Efficiency Level (Price Increases Relative to the Price of Baseline Efficiency Level) ............................................................................................... 8-12 Table 8.2.5 Energy Consumption Values for Representative Commercial Refrigeration Equipment Units of the 25 Primary Equipment Classes at All Efficiency Levels 8-

12 Table 8.2.6 Installation Cost Indices (National Value = 100.0) ................................................ 8-15

Table 8.2.7 Costs and Markups for Determination of Weighted-Average Total Installed Costs for Convenience Stores (VCT.SC.L)* ..................................................................... 8-16 Table 8.2.9 Inputs for Operating Costs ...................................................................................... 8-18 Table 8.2.10 Commercial Electricity Prices by State (2012 cents/kWh) .................................. 8-18 Table 8.2.11 Derived Average Commercial Electricity Price by Business Type ...................... 8-19 Table 8.2.12 Estimated Replacement Rate of Components During Equipment Service Life (Values Retained from January 2009 Final Rule Analysis) ............................... 8-22 Table 8.2.12 Lighting Technologies by Equipment Class for Each EL for the Representative Units Analyzed in the Engineering Analysis ..................................................... 8-25 Table 8.2.13 Annualized Lighting Maintenance Costs by Equipment Class for Each EL for the Representative Units Analyzed in the Engineering A nalysis ............................ 8-26 Table 8.2.14 Derivation of Real Discount Rates by Building Type .......................................... 8-30 Table 8.4.2 Mean LCC Savings for All Equipment Classes and ELs ....................................... 8-34 Table 8.4.5 Medean Payback Period for All Equipment Classes and ELs ................................ 8-36 Table 8.4.7 Rebuttable Presumption Payback Periods by EL and Equipment Class ................ 8-37 Table 8.5.1 Summary of Results of LCC and PBP Analysis for VCT.SC.L Equipment Class 8-38

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of Life- Cycle Cost and Payback

Period ................................................................................................................... 8-5 Figure 8.2.1 Cumulative Probability Distribution Showing the Electricity Prices Paid by Convenience/Small Market Sector in 2012 (2013$) .......................................... 8-20 Figure 8.2.2 Electricity Price Trends for Commercial Rates to 2046 ........................................ 8-21 Figure 8.4.1 LCC and Installed Cost Variation over ELs for the VCT.SC.L Equipment Class 8-33 Figure 8.4.2 LCC Savings Distribution for All the ELs for Equipment Class VCT.SC.L ........ 8-34 Figure 8.4.3 Mean Payback Period for All ELs for the Equipment Class VCT.SC.L ............... 8-35

8- ii

### CHAPTER 8. LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

This chapter describes the analysis the U.S. Department of Energy (DOE) has carried out
to evaluate the economic impacts of amended energy conservation standards developed for commercial refrigeration equipment on individual commercial customers, henceforth referred to

as customers . The effect of standards on customers includes a change i n operating cost (usually decreased) and a change in purchase cost (usually increased). This chapter describes two metrics

used to determine the effect of standards on customers :

• Life

- cycle cost (LCC) . The total customer cost over the life of the equipment is the sum

of installed cost (purchase and installation costs) and operating costs (maintenance, repair, and energy costs). Future operating costs are discounted to the time of purchase and summed over the lifetime of equipment.

• Payback period (PBP) . Payback period is the estimated amount of time it would take customers to recover the higher purchase price of more efficient equipment through lower operating costs.

An efficiency improvement to commercial refrigeration equipment that is financially

attra ctive to a customer will typically have a low PBP and a low LCC associated with it

.

This chapter is organized as follows. The remainder of this section outlines the general approach and provides an overview of the inputs to the LCC and PBP analysis of com mercial refrigeration equipment. Inputs to the LCC and PBP analysis are discussed in detail in s ections 0

and 8.3

. Results for the LCC and PBP analysi s are presented in sections 8.4 and 8.5

.

The calculations discussed in this chapter were performed with a series of Microsoft

Excel spreadsheets , w hich are available at www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/52

. Detailed

results are presented in appendix 8A.

8.1.1 General Approach for L ife -C ycle Cost and P ayback Period Analysis

This section summarizes DOE’s approach to the LCC and PBP analysis for commercial refrigeration equipment.

As part of the engineering analysis (TSD chapter 5), design option levels were ordered on th e basis of increasing efficiency (decreased energy consumption) and increas ing manufacturer selling price ( MSP ) values. The order was determined based on the cost

- effectiveness of each design option; that is, the ratio of incremental cost increase to incre mental energy savings. For the LCC and PBP analysis, DOE chose a maximum of seven levels

, henceforth referred to as efficiency levels (ELs) , from the list of engineering design option levels. For those equipment

classes for which fewer than seven design option levels were defined in the engineering analysis,

each design option level was assigned a corresponding efficiency level . However, for equipment

8-1

### classes where more than seven design option levels were defined, DOE selected specific levels to

a nalyze based on three criteria:

1. The lowest and highest energy consumption levels provided in the engineering analysis were preserved

.

2. If the difference in reported energy consumptions and reported manufacturer price between sequential levels was small, onl y the design option level with the lower amount of energy consumption was selected to be an efficiency level

.

3. If the energy consumption savings benefit relative to the increased cost was similar across multiple

, sequential design option levels, intermediate design option level s were removed.

The first efficiency level or baseline efficiency level (Level 0 ) in each equipment class represents the least efficient and the least expensive equipment in that equipment class . T he higher efficienc y levels (Level 1 and up ) have a progressive increase in efficiency and cost from

Level 0. The highes t efficiency level in each equipment class corresponds to the max- tech level (see TSD chapter 5 for details)

.

The installed cost of equipment to a customer is the sum of the equipment purchase price and installation costs. The purchase price includes manufacturer production cost, to which a manufacturer markup and outbound freight costs are applied to obtain the M SP . This value is calculated as part of the engineering analysis ( TSD chapter 5). DOE then applies additional markups to the equipment to account for the markups associated with the distribution channels for this type of equipment ( TSD chapter 6) . I nstalla tion costs vary by state depending on the prevailing labor rates.

Operating costs for commercial refrigeration equipment are a sum of maintenance costs, repair costs , and energy costs. These costs are incurred over the life of the equipment and

therefore are discounted to the base year (2017, which is the compliance date of the amended

standards that will be established as part of this rulemaking ). The sum of the installed cost and the operating cost , discounted to reflect the present value, is termed the life

- cycle cost or LCC.

Generally, customers incur higher installed costs when they purchase higher efficiency equipment, and these cost increments will be offset partially or wholly by savings in the operating costs over the lifetime of the equipment. Usually, the savings in operating costs are due to savings in energy costs because higher efficiency equipment uses l ess energy over the lifetime of the equipment . LCC savings are calcu lated for each EL of each equipment class.

The PBP of a EL is obtained by dividing the increase in the installed cost (from the baseline efficiency level) by the decrease in annual operating cost (from the baseline efficiency level)

. For this calculation, DOE uses the first year operating cost changes as the estimate of the decrease in operating cost, noting that some of the repair and replacement costs used herein are

annualized estimates of costs. PBP is calculated for each EL of each equipment class

.

Apa rt from MSP , installation costs, and maintenance and repair costs, other important inputs for the LCC analysis are markups and sales tax, equipment energy consumption, electricity prices and future price trends, equipment lifetime, and discount rates.

8-2

### Many inputs for the LCC analysis are estimated from the best available data in the

market

, and in some cases the inputs are generally accepted representative values within the commercial refrigeration equipment industry . However, in most cases each input has a range of values. For example, even though the average (and representative) lifetime of commercial refrigeration units in certain equipment classes may be 10 years, in general, equipment lifetimes of a typical refrigerator belonging to that equipment class may vary from 5 years to 15 years. While calculations based on the representative values yield average or representative values for the outputs (such as LCC or PBP), such values do not give an estimate of the ranges of values that these outputs could lie in. Therefore, DOE performed the LCC analys is in the form of Monte Carlo simulations in which certain inputs are provided a range of values and probability distributions. The results of the LCC analysis are presented in the form of mean and median LCC savings ; percentages of customers experiencing net savings, net cost, and no impact in LCC ; and

median PBP . For each equipment class, 10,000 Monte Carlo simulations were carried out. The

simulations were conducted using Microsoft Excel and Crystal Ball , a commercially available

Excel add

- in for carrying out Monte Carlo simulations

.

Usually, the equipment available in the market will have a distribution of efficiencies

, that is, each EL within an equipment class will have a corresponding market share associated with it. Usually, within an equipment class, the market share of the baseline efficiency level is the highest and the market share values decrease with an increase in EL . LCC savings and PBP are calculated by comparing the installed costs and LCC value s of the standards- case scenario s against those of the base

- case scenario

. The base

- case scenario is the scenario in which equipment is assumed to be purchased by customers in the absence of the amended energy conservation standards. Standards- case scenarios are scenarios in which equipment is assumed to be purchased by customers after the amended energy conservation standards go into effect. The number of standards-case scenarios for an equipment class is equal to one less than the total

number of efficiency levels in that equipment class because each EL above the baseline efficiency level represents a potential new standard . For the standards- case scenario at a particular EL , the market share of the efficiency levels were obtained using a roll -up scenario, in which market shares of the efficiency levels (in the base

- case scenario) below the corresponding

EL were rolled -up into the EL . For the base- case scenario in the LCC analysis, DOE calculated the market shares of the efficiency levels using a method described in TSD chapter 10.

Recognizing that each commercial building that uses the commercial refrigeration equipment is unique, DOE analyzed the LCC and PBP calculations for seven types of

businesses: (1) supermarkets; (2) wholesaler/retailer multi -line stores, such as “big-box stores,”

“warehouse s,” and “supercenters;” (3) convenience and small specialty stores, such as meat markets, wine, beer, and liquor stores; (4) convenience stores associated with gasoline stations; (5) full service restau rants; (6) limited service restaurants; and (7) other foodservice businesses,

such as caterers and cafeterias

. Different types of businesses face different energy prices and also exhibit differing discount rates that they apply to purchase decisions.

Equip ment lifetime is another input that does not justify usage of a single value for each equipment class. Therefore, DOE assumes a distribution of equipment lifetimes that are defined by Weibull survival functions.

8-3

### Another important factor influencing the LCC analysis is the state (location) in which the commercial refrigeration equipment is installed . Inputs that vary based on this factor include energy prices , installation costs, contractor markups, and sales tax. At the national level, the spreadsheets expl icitly modeled variability in the model inputs for electricity price and markups using probability distributions based on the relative shipments of units to different states and business types.

Appendix 8C presents additional discussion about the uncertainty and variability in inputs and the advantages of Monte Carlo simulations.

8.1.2 Overview of L ife -C ycle Cost and P ayback Period Inputs

Inputs to the LCC analysis are categorized as follows: (1) inputs for establishing the total

installed cost ; and (2) inputs for calculating the operating cost.

The primary inputs for establishing the total installed cost are as follows:

• Baseline MSP is t he MSP of equipment meeting the baseline efficiency level

.

• EL MSP increase is t he difference in MSP of a EL and the baseline MSP.

• Markups and sales tax are t he markups and sales tax associated with converting the MSP to a customer purchase price (see TSD chapter 6)

.

• Installation cost is t he cost to the customer of installing the equipment. It includes cost of labor, overhead, and miscellaneous materials and parts.

The primary inputs for calculating the operating cost s are as follows:

• Equipment energy consumption : Consumption is the total daily energy consumption of the commercial refrigeration equipment. This value is calculated as part of the engineering analysis (TSD chapter 5) for each design option level in each equipment

class

.

• Electricity prices : Electricity prices used in the analysis are the price per kilowatt -hour paid by each customer for electricity. Electricity prices are determined using average commercial electricity prices in each s tate, as determined from the Energy Information Administration (EIA) data for 2012. The 2012 average commercial prices d erived were

modified to reflect the fact that the seven types of businesses analyzed pay electricity prices that are different from the average commercial prices.

• Electricity price trends : The EIA’s Annual Energy Outlo ok 2013 1 

### (AEO2013) is used to

forecast future electricity prices. For the results presented in this chapter, DOE used the regional prices from the AEO2013 R eference C ase to forecast future electricity prices

.

• Maintenance costs: T he labor and materials costs associated with maintaining the operation of the equipment.

• Repair costs : The labor and materials costs associated with repairing or replacing components that have failed.

8-4

### • Equipment l ifetime : The age at which the commercial refrigeration equipment is retired

from service.

• Discount rate : The rate at which future costs are discounted to establish their present

value.

Figure 8.1.1 depicts the relat ionships between the installed cost and operating cost inputs

for the calculation of the LCC and PBP. Table 8.1.1 summarizes the characteristics of the inputs

to the LCC and PBP analysis and lists the corresponding reference chapter in the TSD for details on the calculation of the inputs.

Figure 8.1.1 Flow Diagram of Inputs for the Determination of Life -Cycle Cost and Payback

Period

8-5

### Table 8.1.1 Summary Information of Inputs for the Determination of Life -Cycle Cost and Payback Period

Input Description TSD Chapter Reference Total Installed Cost Primary Inputs

Baseline MSP Varies with equipment class

. Chapter 5

Candidate s tandard level

MSP increases Var y with equipment class and candidate standard level

within a n equipment class

. Chapter 5

Markups and sales tax Markups v ar y with distribution channel

, and sales tax

varies with location (state) where equipment is installed. Chapter 6

Installation price Varies with location (state) where equipment is installed

. Chapter 8 Operating Cost Primary Inputs Equipment energy consumption Varies with equipment class and candidate standard level

within a n equipment class

. Chapter 5

Electricity prices Vary with location, building type

. Chapter 8 Electricity price trends Vary with location (regional) and price scenario

. Chapter 8

Maintenance costs Vary with equipment class and candidate standard level

within equipment class

. Chapter 8

Repair costs Var y with equipment class and candidate standard level

within equipment class

. Chapter 8

Lifetime Weibull survival functions. Average values a ssumed to 15 years for small business, and 10 years for all other equipment.

. Chapter s 3, 8

Discount rate Varies with type of business

. Chapter 8



### All of the inputs depicted in Figure 8.1.1 and summarized in Table 8.1.1 are discussed in

s ections 0 and 8.3

.

8.1.3 Effect of Current Standards

Standards set by this rulemaking are likely to go into effect in 2017, and the standards set by the 2009 DOE final rule on commercial refrigeration equipment (th e January 2009 final rule), 74 FR 1092 (Jan. 9, 2009), went into effect on January 1, 2012. The compliance date for the January 2009 DOE final rule standards has since been changed. DOE does not have sufficient

data concerning the commercial refrigeration equipment market at the time of today’s final rule. However, DOE assume d that the equipment manufactured before 2017 will be compliant with the January 2009 final rule standards. The general practice in DOE appliance standards rulemakings is to assume that the current technology level of the market will continue to remain

at

, or very near , the current level until new DOE standards are brought into effect at a future

date. The design option levels for each equipment class (TSD chapter 5) were chosen based on technolog y levels in the commercial refrigeration equipment market at the time of the NOPR analysis (TSD chapter 3). However, the composition of th is market will change as a result of the January 2009 final rule standards, which go into effect before 2017, at which time the standards established by the current rulemaking would go into effect. While it is difficult to predict the state of the market in the year 201 7, DOE devised a method to estimate the efficiency level of

the market baseline in 2017 based on certain assumptions.

8-6

### DOE assumed that the standards established by the January 2009 final rule will form the

lowest efficiency level prior to the amended standards

, established as part of this rulemaking—

which will be in effect in 201 7. This is a reaso nable assumption considering that the 2009 standards are appreciably more stringent than the current market baseline and that notable technology improvements are necessary to reach the efficiency levels prescribed by the 2009 final rule. DOE is not in a position to speculate on the other potential improvement s of equipment efficiency in the market from the time the 2009 final rule standards go into effect and 2017, aside from assuming compliance with the 2009 final rule standards. As a result, even though t he market could potentially continue to improve the efficiency of commercial refrigeration equipment beyond that required by the standards established by the 2009 final rule,

DOE assumed that the market will remain in a similar state from the compliance date of the 2009 final rule standards until 2017. To approximate this assumed market efficiency level in 2017 in the current analysis, DOE introduced a new baseline efficiency level

— henceforth referred to as

the standards baseline level

— that is set at the same level as the 2009 final rule standard. Any design option level s from the engineering analysis that were less efficient than the corresponding

standards b aseline l evel were disregarded for the downstream analyses (LCC analysis and

national i mpact analysis ( NIA )); that is, they were not included as efficiency levels (candidate standard levels) . Design option levels from the engineering results that were more efficient than

the standards b aseline l evel were considered for higher efficiency levels (Level 2, Level 3, and so on). It should be noted that, in general, there is no design option level from the engineering analysis that corresponds specifically to this assumed market baseline ( standards b aseline ). The process of e stimating the 201 7 market baseline level is explained with the aid of an example

below.

Table 8.1.2 shows the 13 design option levels for equipment cla ss VOP.SC.M (self

contained vertical open refrigerator), obtained from the engineering analysis. This table represents the current (201 2 ) technology levels modeled for VOP.SC.M equipment on the market. The energy conservation standard for this class prescribed by the January 2009 final rule is given by the expression 1.74× TDA +4.71 kilowatt -hours per day (kWh/day), where TDA (in

ft 2 

### ) represents the total display area of the equipment. The TDA value for th is representative

VOP.SC.M unit , which was modeled in the engineering analysis , is 14.93 ft 2 

### . When substituted into the expression, it yields a maximum allowable total daily energy consumption value of 30.69 kWh/day. This value, when compared against the list of design option levels ( Table 8.1.2 ),

is between the design option levels 10 and 11 (AD 10 and AD11). As explained above, DOE

assumed that when the January 2009 final rule standards go int o effect , 30.69 kWh/day would represent the minimum efficiency level of the market for this unit and that it would remain so until 2017. To approximate this state of market technology, DOE assumed the efficiency levels

shown in Table 8.1.3 for VOP.SC.M equipment. The first column of Table 8.1.3 represents the efficiency levels for the LCC analysis, and the second column represents the corresponding design option levels. As stated above, there is no design option level in the current rulemaking that corresponds directly to t he first efficiency level. Instead, this design option level is designated as “ SB ,” which stands for “standards b aseline. ” The total daily energy consumption

for Efficiency Level 1 of this unit is equal to the next design option meeting that meets the

curr ent standard, i.e. AD 11.

8-7

### Table 8.1.2 Design O ption L evels for VOP.SC.M O btained from E ngineering Analysis

Design Option

Level Total Daily Energy Consumption kWh/day Manufacturer Selling Price

2012 $ Design O ption A dded

AD1 41.04 $2,807 Baseline

AD2 40.38 $2,820 High

- Eff. Reciprocating Compressor

AD3 39.55 $2,842 Enhanced

- UA Condenser Coil

AD4 38.98 $2,857 Permanent Split Cap. Evap. Fan Motor

AD5 36.99 $2,916 Enhanced

- UA Evaporator Coil

AD6 36.54 $2,933 Brushless DC Evap. Fan Motor

AD7 36.25 $2,952 Super T8 Lighting

AD8 35.69 $2,989 Night Curtains

AD9 33.90 $3,161 Permanent Split Cap. Cond. Fan Motor

AD10 30.82 $3,654 Brushless DC Cond. Fan Motor

AD11 30.01 $3,856 LED Lighting with Occupancy Sensors

AD12 29.91 $3,913 Additional 1/2” Insulation

AD13 29.71 $4,805 Vacuum Insulated Panels



### Table 8.1.3 Efficiency L evels for VOP.SC.M

Efficiency Level

for LCC Analysis Corresponding Design Option

Level Total Daily Energy Consumption kWh/day Manufacturer Selling Price

2012 $

Level 0 SB (AD11) 30.01 $3,856

Level 1 AD12 29.91 $3,913

Level 2 AD13 29.71 $4,805



### 8.2 LIFE

- CYCLE COST INPUTS

8.2.1 Definition

Life

- cycle cost is the total customer cost over the life of a piece of equipment, including purchase cost and operating costs (energy costs, maintenance costs, and repair costs). Future operating costs are discounted to the time of purchase and summed over the lifetime of the equipment. Life -cycle cost is defined by the following equation:

8-8

# ∑

= 

### + += N

t t

t 

### r OC ICLCC

1 

### )1 /( 

### Eq. 8.1

W here :

LCC = life

- cycle cost ($)

, IC = total installed cost ($)

, N = lifetime of equipment (years)

, OC t 

### = operating cost ($) of the equipment in year t

,

r

= discount rate, and

t = year for which operating cost is being determined.

DOE expresse d all costs in 2013$. Total installed cost, operating cost, lifetime, and discount rate are discussed in the following sections. In the LCC analysis, the first year of equipment purchase is assumed to be 2017.

8.2.2 Total Installed Cost Inputs

The total installed cost to the customer is defined by the following equation:

INST EQP IC +=



### Eq. 8.2

W here :

EQP = customer purchase price for the equipment ($), and

INST = installation cost or the customer price to install equipment ($).

The remainder of this section provides information about the inputs DOE used to calculate the total installed cost for commercial refrigeration equipment

. Table 8.2.1 shows inputs for the determination of total instal led cost

.

Table 8.2.1 Inputs for Total Installed Costs

Baseline manufacturer selling price ($)

Candidate s tandard l evel manufacturer selling price increases ($) Wholesaler markup Mechanical contractor markup National account markup Sales tax ($)

8-9Installation cost ($)



### 8.2.2.1 Baseline Manufacturer Selling Price

The baseline MSP is the price charged by manufacturers for equipment meeting existing minimum efficiency (or baseline) standards. DOE developed MSP values for the 24 primary equipment classes (TSD chapter 5)

. Table 8.2.2 shows t he set of 24 primary equipment classes

that DOE evaluated during the preliminary analysis of the current rulemaking.

Table 8.2.2 Equipm ent Classes Evaluated for LCC Analysis

Description (Equipment Family. Operating Mode. Temperature) Abbreviation Current Standards Set By Vertical Open.Remote Condensing.Medium VOP.RC.M January 2009 final rule Vertical Open.Remote Condensing.Low VOP.RC.L January 2009 final rule Vertical Open.Self

- Contained.Medium VOP.SC.M January 2009 final rule Vertical Closed Transparent.Remote Condensing.Medium VCT.RC.M January 2009 final rule Vertical Closed Transparent.Remote Condensing.Low VCT.RC.L January 2009 final rule Vertical Closed Transparent.Self

- Contained.Medium VCT.SC.M EPCA Vertical Closed Transparent.Self

- Contained.Low VCT.SC.L EPCA Vertical Closed Transparent.Self

- Contained.Ice Cream VCT.SC.I January 2009 final rule

Vertical Closed Solid.Self

- Contained.Medium VCS.SC.M EPCA

Vertical Closed Solid.Self

- Contained.Low VCS.SC.L EPCA

Vertical Closed Solid.Self

- Contained.Ice Cream VCS.SC.I January 2009 final rule

Semi

- Vertical Open.Remote Condensing.Medium SVO.RC.M January 2009 final rule

Semi

- Vertical Open.Self

- Contained.Medium SVO.SC.M January 2009 final rule Service Over Counter.Remote Condensing.Medium SOC.RC.M January 2009 final rule Horizontal Open.Remote Condensing.Medium HZO.RC.M January 2009 final rule Horizontal Open.Remote Condensing.Low HZO.RC.L January 2009 final rule Horizontal Open.Self

- Contained.Medium HZO.SC.M January 2009 final rule Horizontal Open.Self

- Contained.Low HZO.SC.L January 2009 final rule Horizontal Closed Transparent.Self

- Contained.Medium HCT.SC.M EPCA Horizontal Closed Transparent.Self

- Contained.Low HCT.SC.L EPCA Horizontal Closed Transparent.Self

- Contained.Ice Cream HCT.SC.I January 2009 final rule

Horizontal Closed Solid.Self

- Contained.Medium HCS.SC.M EPCA

Horizontal Closed Solid.Self

- Contained.Lo w HCS.SC.L EPCA

Pull

- Down.Self

- Contained.Medium PD.SC.M EPCA

Service Over Counter. Self

- Contained. Medium SOC.SC.M AEMTCA



### Nine primary equipment classes in Table 8.2.2 are subject to standards set by the Energy Policy and Conservation Act ( EPCA ) as amended by the Energy Policy Act of 2005 (42 U.S.C. 6313(c)(2)- (3)); 15 primary equipment classes are subject to standards set by DOE in the January 2009 final rule (74 FR 1092 (Jan. 9, 2009)), and one primary equipment class is subject to standards set by the American Energy Manufacturing Technical Corrections Act (AEMTCA) (42 U.S.C. 6313(c)(4)). Table 8.2.3 presents the baseline energy consumption values and the baseline

MSPs used in the LCC analysis for the representative sizes for each of the 25 primary equipment

classes ( TSD chapter 5)

. Table 8.2.3 also identifies whether the baseline was obtained from the engineering analysis or was set at the standards baseline, as explained in s ection 8.1.3 . For some equipment classes, the January 2009 final rule standards, EPCA standards , or AEMTCA

8- 10

### standard form the baseline efficiency level, and for the remaining equipment class es, the market baseline (from the engineering analysis) forms the baseli n e efficiency level because the market baseline for these equipment classes was found to be more efficient than the current standard

level

.

Table 8.2.3 Baseline E nergy C onsumption L evels and MSP V alues for the Representative Commercial Refrigeration Equipment Units of All 24 P rimary E quipment C lasses

Equipment Class Baseline

energy consumption

kWh/day Manufacturer Selling Price
2012$

VOP.RC.M 46.84 $5,763

VOP.RC.L 105.60 $6,585

VOP.SC.M 30.01 $3,856

VCT.RC.M 13.65 $7,462

VCT.RC.L 37.49 $8,381

VCT.SC.M 6.99 $3,012

VCT.SC.L 34.68 $3,869

VCT.SC.I 19.72 $4,491

VCS.SC.M 6.31 $1,834

VCS.SC.L 16.47 $2,110

VCS.SC.I 18.94 $2,360

SVO.RC.M 35.74 $4,831

SVO.SC.M 26.32 $2,885

SOC.RC.M 22.74 $8,178

SOC.SC.M 27.72 $9,185

HZO.RC.M 14.47 $4,195

HZO.RC.L 32.36 $4,345

HZO.SC.M 14.66 $1,220

HZO.SC.L 29.92 $2,905

HCT.SC.M 1.87 $854

HCT.SC.L 4.09 $931

HCT.SC.I 3.13 $1,139

HCS.SC.M 1.49 $759

HCS.SC.L 1.93 $773

PD.SC.M 5.01 $1,817



### 8.2.2.2 Efficiency Level Energy Consumption and Manufacturer Selling Price

Increases

The EL MSP increase is the change in MSP associated with producing equipment at higher efficiency levels above the baseline. Increases in MSP as a function of equipment efficiency were developed for each of the 25 primary equipment classes (TSD chapter 5)

. The

e ngineering analysis (TSD chapter 5) established a series of MSP increases for each EL

. Table

8.2.4 presents the increase in MSP values corresponding to all efficiency levels for each equipment class.

8- 11

### Table 8.2.5 presents the daily energy consumption of the representative units belonging

to each of the 25 primary equ ipment classes that were selected for the engineering analysis (TSD chapter 5)

.

Table 8.2.4 MSP Increase by Efficiency Level (Price Increases Relative to the Price of

B aseline E fficiency L evel )

Equipment

Class Increase in MSP by Efficiency Level* ( 2013$ )

Base EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M $5,763 $1,240 $1,318 $1,918 $4,561

VOP.RC.L $6,585 $85 $791 $5,114

VOP.SC.M $3,856 $57 $949

VCT.RC.M $7,462 $128 $212 $368 $3,516

VCT.RC.L $8,381 $128 $211 $466 $4,504

VCT.SC.M $3,012 $20 $148 $204 $218 $277 $342 $1,418

VCT.SC.L $3,869 $96 $352 $372 $500 $526 $584 $2,143

VCT.SC.I $4,491 $28 $88 $202 $2,130

VCS.SC.M $1,834 $12 $37 $57 $79 $138 $148 $1,265

VCS.SC.L $2,110 $37 $81 $101 $160 $212 $231 $1,702

VCS.SC.I $2,360 $15 $76 $96 $155 $2,109

SVO.RC.M $4,831 $939 $1,357 $1,423 $3,114

SVO.SC.M $2,885 $150 $203 $779

SOC.RC.M $8,178 $147 $306 $367 $1,619

SOC.SC.M $9,185 $147 $312 $373 $1,625

HZO.RC.M $4,195 $1,384

HZO.RC.L $4,345 $2,816

HZO.SC.M $1,220 $78 $133 $919

HZO.SC.L $2,905

HCT.SC.M $854 $6 $15 $98 $111 $115 $167 $615

HCT.SC.L $931 $83 $89 $101 $117 $124 $175 $787

HCT.SC.I $1,139 $901

HCS.SC.M $759 $5 $11 $23 $28 $78 $427

HCS.SC.L $773 $5 $11 $24 $28 $78 $557

PD.SC.M $1,817 $38 $167 $187 $199 $255 $300 $1,147 * ‘NA’ implies no design options associated with the efficiency level



### Table 8.2.5 Energy C onsumption V alues for R epresentative C ommercial R efrigeration

E quipment U nits of the 25 P rimary E quipment C lasses a t A ll E fficiency L evels
Equipment

Class Total Annual Energy Usage * kWh/day

Base EL 1 EL 2 El 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M 46.8 38.0 37.8 36.1 35.7

VOP.RC.L 105.6 104.9 101.7 100.0

VOP.SC.M 30.0 29.9 29.7

VCT.RC.M 13.6 11.8 11.6 11.5 11.0

VCT.RC.L 37.5 35.3 34.8 34.5 33.0

VCT.SC.M 7.0 6.8 6.0 5.6 5.6 5.5 5.4 5.1

VCT.SC.L 34.7 27.5 18.4 18.2 17.3 17.2 16.9 16.0

VCT.SC.I 19.7 19.5 19.3 18.9 18.1

VCS.SC.M 6.3 5.3 4.0 3.8 3.7 3.5 3.4 3.0

VCS.SC.L 16.5 13.9 13.2 12.9 12.5 12.2 12.1 11.1

8- 12VCS.SC.I 18.9 18.7 18.0 17.8 17.4 16.0

SVO.RC.M 35.7 29.4 28.2 28.0 27.7

SVO.SC.M 26.3 25.6 25.6 25.4

SOC.RC.M 22.7 22.3 21.9 21.8 21.6

SOC.SC.M 27.7 27.2 26.6 26.4 26.1

HZO.RC.M 14.5 14.1

HZO.RC.L 32.4 31.1

HZO.SC.M 14.7 14.2 14.0 13.7

HZO.SC.L 29.9

HCT.SC.M 1.9 1.7 1.6 1.0 0.9 0.9 0.8 0.6

HCT.SC.L 4.1 2.2 2.2 2.0 1.9 1.9 1.7 1.3

HCT.SC.I 3.1 2.3

HCS.SC.M 1.5 1.4 1.4 1.3 1.3 1.2 1.0

HCS.SC.L 1.9 1.8 1.7 1.5 1.5 1.3 0.7

PD.SC.M 5.0 4.7 3.9 3.8 3.8 3.6 3.6 3.4

* ‘NA’ implies no design options associated with the efficiency level



### 8.2.2.3 Markup s

As discussed in TSD chapter 6, DOE calculated distribution channel markups to

determine the equipment purchase price to customers from the equipment MSP. DOE calculated baseline markups to convert baseline MSP to baseline customer purchase price and incremental markups to convert the increments in MSP into increments in customer purchase pric e. DOE used these markup values in the LCC analysis for calculation of baseline and higher efficiency equipment price to customers.

8.2.2.4 Installation Cost

Most refrigerated display cases are installed in fairly standard configurations, which helps

in creating standardized estimates for the cost of installation across all equipment classes

. For example, supermarkets commonly configure display cases as part of a “lineup” of similar cases. Horizontal open cases are commonly installed as single units placed in appropriate store

locations. Self -contained display cases are used for portable or temporary product displays, or

sometimes used as end

- caps of aisles.

For remote condensing equipment, typical steps in the installation of display cases in a lineup

are as f ollows:

• move new case to lineup position in store;

• position case in lineup, providing shims for vertical spacing as needed;

• c aulk or seal adjacent cases together ;

• b olt adjacent cases together ;

• trim cases together for good visual look (installing bumpers an d covering seams) ;

• b raze refrigerant lines as necessary to system piping (already in place) ;

8- 13

### • i f hot gas defrost is used, braze hot gas defrost refrigerant lines as necessary to defrost piping (already in place) ;

• provide electrical conduit and tie-in electrical connections to case ;

• i nstall display lamps ; and

• set refrigerant and defrost control settings.

Note that final operational testing of cases is undertaken after initial installation of all cases in a lineup and refrigerant is piped to each case.

For self -contained refrigeration equipment, typical installation steps are as follows:

• move new equipment to the target position in store;

• w here applicable, make sure that the condensate pan is installed properly;

• p lug cord into electrical outlet and turn on main power;

• m ake sure the evaporator and condenser fans are functioning properly;

• where applicable, turn on lights; and

• a djust temperature settings to desired levels.

The installation steps listed above for both self-contained and remote condensing equipment are not influenced by any of the engineering design options accounted for in this rulemaking. DOE assumes that the night curtains (for open display cases) are provided by the manufacturer in pre-installed condition and hence that no additional costs are incurred for installation of night curtains. From conversations with consultants with experience in retrofitting display cases with light

- emitting diode ( LED ) lighting and occupancy sensors, DOE determined

that the additional costs incurred in installa tion of occupancy sensors are minimal, and it is unlikely that installers would bill additional charges to the customers. Therefore, DOE assume s that the installation costs do not vary with efficiency levels in any equipment class.

The installation costs may vary from one equipment class to another, but they do not vary with efficiency levels within an equipment class . Costs that do not vary with efficiency levels do not impact the LCC, PBP, or NIA results. DOE retained the nationally representative installation

cost values from the January 2009 final rule analysis for all remote condensing equipment a s $2,000 and fo r all self -contained equipment as $750, and simply escalated the values from 2007$ to 2013$, resulting in 2013 installation costs of $2,301 and $863, respectively

. DOE designed the LCC spreadsheet such that installation costs can be varied by EL , but has modeled installation

cost as constant across EL s for the NOPR analysis.

Table 8.2.6 shows installation cost indices for installations in each of the 50 s tates , plus

the District of Columbia , which we re used to adjust the nationally representative installation

costs for each s tate . To arrive at an average index for each state, DOE first weighted the city indices in each state by their population within the state

. DOE used city - and state

- level population weights for 2011 from the U.S. Census Bureau to calculate a weighted -average index

for each state

.

8- 14

### 8.2.2.5 Weighted -Average Total Installed Cost

As presented in Eq. 8.2 , the total installed cost is the sum of the equipment price and the

installation cost. DOE derived the customer equipment price for any given standard level by multiplying the baseline MSP by the baseline markup and sales tax and adding to it the product

of the incremental MSP and the incremental markup and sales tax

. Because MSP s, markups, and the sales tax all can take on a variety of values depending on location (state) , the resulting total installed cost for a particular EL will not be a single -point value, but rather a distribution of

values.

Table 8.2.6 Installation Cost Indices (National Value = 100.0)

State Index State Index State Index

Alabama 57.9 Kentucky 87.0 North Dakota 62.6

Alaska 113.4 Louisiana 64.9 Ohio 100.5

Arizona 86.2 Maine 67.0 Oklahoma 57.5

Arkansas 60.8 Maryland 90.7 Oregon 107.8

California 131.3 Massachusetts 129.0 Pennsylvania 128.2

Colorado 85.1 Michigan 109.8 Rhode Island 117.2

Connecticut 124.1 Minnesota 125.2 South Carolina 39.4

Delaware 122.7 Mississippi 59.8 South Dakota 45.3

Dist. of Columbia 101.9 Missouri 105.1 Tennessee 77.0

Florida 73.8 Montana 76.2 Texas 62.8 Georgia 71.2 Nebraska 84.3 Utah 77.1

Hawaii 118.7 Nevada 108.9 Vermont 67.2

Idaho 73.7 New Hampshire 91.0 Virginia 73.2

Illinois 140.7 New Jersey 136.0 Washington 113.8

Indiana 88.5 New Mexico 73.8 West Virginia 91.9

Iowa 86.8 New York 172.3 Wisconsin 105.1

Kansas 75.7 North Carolina 39.5 Wyoming 59.1



### T he weighted

- average costs for the VCT.SC.L equipment class are presented below for the baseline level at national average markup rates and national average installation costs for illustration purposes. D erivation of the total installed cost is straightforward. The baseline MSP

and the standard- level MSP increases are the starting points for determining the total installed cost (values are taken directly from Table 8.2.4 and

Table 8.2.5 ). DOE used t he baseline and increme ntal markups, the sales tax, and

installation costs to convert the MSP s into total installed costs for a case where the incremental

installation costs are held flat. Table 8.2.7 summarizes the weighted average or mean costs and markups necessary for determining the weighted- average baseline and standard

- level total

installed costs for c onvenience s tores as an example

.

8- 15

### Table 8.2.7 Costs and Markups for Determination of Weighted-Average Total Installed

Costs for Convenience Stores ( VCT.SC.L )*

Variable Weighted Average or Mean Value

Baseline MSP $3, 869.44

Standard

- Level MSP Increase ( EL 3 ) $ 371.83 Overall Markup Factor

– Baseline 1.395468 Overall Markup Factor

– Incremental 1.156518

Installation Cost

– Baseline $863 Installation Cost Factor, for U.S. Average 1

* Installation costs apply to the baseline unit, with no incremental installation costs.



### To illustrate the derivation of the weighted

- average total installed cost based on the data

shown in Table 8.2.7

, DOE presents the calculation below for the baseline ( EL 0 ) and for a higher efficiency level ( EL 3 ) VCT.SC.L equipment class. For the baseline product, the calculation of the total installed cost at national average conditions is as follows : a



### IC BASE VCT

. SC

. L 

### = EQP BASE VCT

. SC

. L 

### + INST BASE VCT

. SC

. L 

### × ISTINDEX

= MFG BASE VCT

. SC

. L 

### × MU BASE VCT

. SC

. L 

### + INST BASE VCT

. SC

. L 

### × ISTINDEX

= $ 3,869.44 × (1.396 ) + $ 863 x (1.00)

= $ 5399.68 + $ 863

= $ 6262.68

Eq. 8.3

W here :

IC BASE VCT.SC.L 

### = total installed cost of VCT.SC.L equipment at baseline efficiency level ($), EQP BASE VCT.SC.L 

### = equipment purchase price of VCT.SC.L equipment at baseline efficiency level ($),

IN ST BASE VCT.SC.L 

### = installation cost of VCT.SC.L equipment at baseline efficiency level ($),

MFG BASE VCT.SC.L 

### = MSP of VCT.SC.L equipment at baseline efficiency level ($),

MU BASE VCT.SC.L 

### = overall baseline markup for equipment class VCT.SC.L, and

ISTINDEX = location dependent multiplier on installation costs ; approximately 1.0 at a national

average.

The calculation of the higher EL 3 total insta lled cost includes the use of an MSP

increment

. DOE uses an incremental markup factor that applies to inc remental increases in MSP.

The EL 3 price is equal to the baseline price calculated above, plus the MSP increment for a higher efficiency level multiplied by the incremental markup.

a Note that the numbers shown in Eq. 8.3 have been rounded and do not exactly match the numbers in the analysis.

8- 16

### As an example, DOE calculated the national average Level 3 total installed cost ( IC VCT,SC,L

LEVEL4 

### ) as follows: b



### IC VCT

. SC

. L LEVEL 4 

### = EQP VCT

. SC

. L LEVEL 4 

### + INST VCT

. SC

. L LEVEL 4 

### × ISTINDEX

= MFG BASE VCT

. SC

. L 

### × MU BASE VCT

. SC

. L 

### + ∆ MFG VCT

. SC

. L LEVEL 4 

### × MU VCT

. SC

. L LEVEL 4 

### +

INSTVCT

. SC

. L LEVEL 4 × ISTINDEX

= $3,869.44× (1.396 ) + $ 371.83 × (1.157 ) + $ 863 × (1.000)
= $ 6692.89

Eq. 8.4

W here :

IC VCT.SC.L LEVEL4 

### = total installed cost of VCT.SC.L equipment at EL 3 ($)

, EQP VCT.SC.L LEVEL4 

### = equipment price of VCT.SC.L equipment at EL 3 ($)

, INST VCT.SC.L LEVEL4 

### = installation cost of VCT.SC.L equipment at EL 3 ($)

, Δ MFG VCT.SC.L LEVEL4 

### = i ncremental increase in MSP of VCT.SC.L equipment at EL 3 compared to equipment at baseline efficiency level ($)

, and

MU VCT.SC.L LEVEL4 

### = i ncremental markup for equipment class VCT.SC. L.

8.2.3 Operating Cost Inputs

DOE defines the operating cost as the sum of e nergy cost, repair cost, and maintenance cost, as shown in the following equation:

OC = EC+ RC+ MC

Eq. 8.5

W here :

OC = operating cost ($)

, EC = energy cost ($)

, RC = repair cost ($)

, and

MC = maintenance cost ($).

The remainder of this section provides information about the variables that DOE used to calculate the operating cost for commercial refrigeration equipment

. Table 8.2.8 shows the inputs for the determination of operating costs.

b Note that the numbers shown in Eq. 8.4 h ave been rounded and do not exactly match the numbers in the analysis.

8- 17

### Table 8.2.8 Inputs for Operating Costs
Electricity price ( cents /kWh ) Electricity price trend Repair cost ($) Maintenance cost ($)

Lifetime (y ears )

Discount rate (%)

Effective date of standard Baseline electricity consumption (kWh/day) Standard case electricity consumption (kWh/day)



### 8.2.3.1 Electricity Price Analysis

This section describes the electricity price analysis used to develop the energy portion of the annual operating costs for commercial refrigeration equipment used in different commercial building types.

Subdivision of the Country

. Because of the wide vari ation in electricity consumption patterns, wholesale costs, and retail rates across the country, it is important to consider regional differences in electricity prices. For this reason, DOE divided the United States into the 50 states
and the District of Columbia. DOE used reported average effective commercial electricity prices

at the state level from the EIA publication Form EIA -826 Database Monthly Electric Utility

Sales and Revenue Data

. 2 

### The prices used from this source are for the calendar year 2009. These were adjusted to represent 2013$ prices using the gross domestic product ( GDP ) price deflator

from AEO2013

. 3 

### Table 8.2.9 provides data on the adjusted electricity prices.

Table 8.2.9 Commercial Electricity Prices by State ( 2012 cents/k Wh)

State Commercial Electricity

Price

cents/kWh State Commercial Electricity

Price

cents/kWh State Commercial Electricity

Price

cents/kWh

Alabama 10.83 Kentucky 8.73 North Dakota 8.13

Alaska 14.82 Louisiana 7.91 Ohio 9.56

Arizona 9.62 Maine 11.76 Oklahoma 7.52

Arkansas 7.89 Maryland 10.61 Oregon 8.49

California 13.56 Massachusetts 14.36 Pennsylvania 9.52

Colorado 9.46 Michigan 11.12 Rhode Island 12.40

Connecticut 14.78 Minnesota 8.77 South Carolina 9.78

Delaware 9.96 Mississippi 9.40 South Dakota 8.22

Dist. of Col. 12.12 Missouri 8.34 Tennessee 10.27

Florida 10.04 Montana 9.22 Texas 8.46 Georgia 9.67 Nebraska 8.54 Utah 8.30

Hawaii 37.59 Nevada 8.92 Vermont 14.64

Idaho 6.66 New Hampshire 13.57 Virginia 8.52

Illinois 8.40 New Jersey 12.91 Washington 7.85

Indiana 9.29 New Mexico 9.48 West Virginia 8.68

Iowa 8.05 New York 15.23 Wisconsin 10.71

Kansas 9.26 North Carolina 8.97 Wyoming 8.31

8- 18

### DOE recognized that different kinds of businesses typically use electricity in different amounts at different times of the day, week, and year, and therefore face different effective prices. To make this adjustment, DOE used the 2003 Commercial Buildings Energy Consumption Survey ( CBECS ) data set to identify the average prices paid by the seven kinds of

businesses in this analysis compared with the average prices paid by all commercial customers.

Since multi

- line retail is not explicitly recognized as a CBECS building type, it was identified by identifying retail stores with data indicating the presence of walk -in refrigeration and other commercial refrigeration on the premises. Eq . 8.6 shows t he ratio s of prices paid by the seven types of businesses that were used to increase or decrease the average commercial prices.

EPRICE COM BLDGTYPE STATE 2012 

### = EPRICE COM STATE 2012 

### × � EPRICE BLDGTYPE US 2003

EPRICE COM US 2003 

### �

Eq. 8.6

W here :

EPRICE COM BLD G TYPE STATE 2012 

### = average commercial sector electricity price in a specific building type ( such as supermarkets , convenience stores, and restaurants ) in a specific state in 20 12

EPRICE COM STATE 2012 

### = average commercial sector electricity price in a specific state in 20 12,

EPRICE BLDG TYPE US 2003 

### = national average commercial sector electricity price in a specific building
type in 2003 CBECS, and

EPRICE COM US 2003 

### = national average commercial sector electricity price in 2003 CBECS

.

Table 8.2.10 shows the derivation of the EPRICE ratios from CBECS.

Table 8.2.10 Derived Average Commercial Electricity Price by Business Type

Business Type Electricity Price

cents/kWh Ratio of Electricity Price to Average Price

for all Commercial Buildings Grocery store/food market 0.07222 0.910

Convenience store * 0.08583 1.082 Convenience store with gas station 0.07722 0.973

Multi

- line retail ** 0.07262 0.915

Limited service restaurant 0.07962 1.003

Full service restaurant 0.08467 1.067

Other food service 0.07664 0.966 All commercial buildings 0.07936 1.000

Source: CBECS 2003 * This group is assumed to include convenience stores without gas stations, specialty stores (such as meat markets), and beer, wine, and liquor stores. **This group is assumed to include mainly large multi

- line retailers and supercenters that sell both grocery and non

grocery items.



### The derived ratio of commercial electricity prices by building type to the overall average commercial building price was then combined with state

- by

- state commercial rates to derive a

8- 19

### series of prices for each state and for each building type. Future price s were forecasted as

described in section 8.2.3.2 . To obtain a weighted- average national price, DOE weighted the prices paid by each business in each state by the 2009 population in each state

. 4



### For evaluation purposes, the resulting electricity prices and the calculated market weight ing factors can be depicted as a cumulative probability distribution. The effective prices range from approximately 6.56 cents per kilowatt -hour to approximately 37.05 cents per
kilowatt -hour. Figure 8.2.1 illustrates the results for the convenience and small food re tail market

sector.

Figure 8.2.1 Cumulative Probability Distribution Showing the Electricity Prices Paid by Convenienc e/ Small Market Sector in 2012 (2013$)

8.2.3.2 Electricity Price Trend

The electricity price trend provides the relative change in electricity prices for future years out to 2046. Estimating future electricity prices is difficult, especially considering that there are efforts in many states throughout the country to restructure the electricity sup ply industry.

DOE applied a projected trend in national average electricity prices to each customer’s energy prices based on the AEO2013 price scenarios. The discussion in this chapter refers to the

2012 reference price scenario. In the LCC analysis, the following four scenarios can be analyzed:

1. Constant (real) energy prices at 20 12 values ( i.e. , a c onstant index of 1.0 in Figure 8.2.2 )

2. AEO2013 , High Economic Growth (“AEO2013 High Growth ” in Figure 8.2.2 )

3. AEO2013 , Reference Case (“ AEO2013 Reference” in Figure 8.2.2 )

4. AEO2013 , Low Economic Growth (“ AEO2013 Low Growth ” in Figure 8.2.2 ) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

5 10 15 20 25 30 35 40 45Cumulative Percentage of Electricity Prices

Cents/kWh Electricity Prices Faced By CREs Sold To Convenience

Stores

Elec Price

8- 20

### Figure 8.2.2 shows the trends for the three AEO2013 price projections where prices are assumed to change. DOE extrapolated the values in later years ( i.e. , after 2035— the last year of

the AEO20 13 forecast ). To arrive at values for these later years, DOE used the price trend from

2026 to 2035 of each forecast scenario to establish prices for the years 203 6 to 2046.

Figure 8.2.2 Electricity Price Trends for Commercial Rates to 2046

The default electricity price trend scenario used in the LCC analysis is the trend from the

AEO2013 Reference Case, shown in Figure 8.2.2

. Spreadsheets used in calculating the LCC have the capability to analyze the other electricity price trend scenarios, namely, the AEO2013 High

Growth and the AEO2013 Low Growth price trends and constant energy prices.

8.2.3.3 Repair Cost

The repair cost is the cost to the consumer for replacing or repairing failed components in commercial refrigeration equipment . For the January 2009 final rule analysis, DOE obtained estimated rates of component failures as shown in Table 8.2.11

. DOE based the annualized repair

cost on the foll owing expression:

RC = k × EQP OEM 

### × MU REPLACE 

### / LIFE

Eq. 8.7 0.800 0.850 0.900 0.950 1.000 1.050 1.100 1.150 1.200 1.250 1.300 1.350 1.400

2010 2015 2020 2025 2030 2035 2040 2045Index (2012=1.00)

Year Electricity Price Index Projections

AEO 2012 High Growth AEO 2012 Reference AEO 2012 Low Growth

8- 21

### W here :

RC = repair cost ($)

, k = fraction of the components likely to be replaced during the equipment lifetime, EQP OEM 

### = original equipment manufacturer cost of the component ($),

MU REPLACE 

### = markup applied to original equipment manufacturer cost of the component to the cost to replace the component (i ncludes the labor cost to replace/repair ) , and

LIFE = lifetime of the equipment in years.

As the components used for higher efficiency commercial refrigeration equipment have a higher original equipment manufacturer (OEM) cost, t he above equation yields an increasing repair cost s scenario for higher efficiency equipment.

There are other refrigeration parts that typically require repair, such as door handles, hinges, shelves, drain pans, and condensate pan heater s. However, th ese parts are the same for all efficiency levels, so the repair costs for these parts remain constant for all efficiency levels. Therefore, these additional repair costs were not taken into consideration for the analysis.

Table 8.2.11 Estimated R eplacement Rate of C omponents D uring E quipment S ervice L ife (V alues R etained from January 2009 F inal R ule Analysis)

Component Estimated Replacement Rate (over 10 year period) Evaporator f ans 50%

Condenser f ans 25% Compressors 25%

Coils * 5%

Doors 5 %

*Applied only to remote

- condensing equipment



### 8.2.3.4 Maintenance Cost s

M aintenance cost s are the cost s to the customer of maintaining installed equipment.

M aintenance cost s are not the costs associated with the replacement or repair of components that have failed (as discussed above). Rather, they are the cost s asso ciated with general maintenance

.

DOE obtained annualized maintenance costs for commercial refrigeration equipment

from data in RS Means Facilities Maintenance & Repair Cost Data. 5 

### RS Means provides estimates on the person -hours, labor rates, and materials required t o maintain commercial refrigeration equipment. RS Means specifies preventative maintenance activities for commercial display cases expected to occur on a semi -annual basis as including the following actions: cleaning evaporator coils, drain pans, fans, and intake screens ; lubricating motors; inspecting door gaskets and seals, and lubricating hinges; clean ing condenser coils; checking refrigerant pressures and compressor oil as necessary ; checking starter panels and controls; and checking defrost system operation. From the RS Means data DOE obtained costs of $ 224 per year (2013$ ) for preventative maintenance activities for all remote condensing equipment classes and $35 per

year (2013$ ) for self -contained equipment classes . B ecause data were not available to indicate

8- 22

### how maintenance costs vary with EL

, DOE decided to use preventative maintenance costs that remain constant as equipment efficiency is increased . It should be noted that since the preventative maintenance cost is assumed to be constant over all EL s within an equipment class,

it does not affect the LCC analysis or NIA results because only costs that vary with EL s (incremental costs) lead to changes in these results.

DOE considered l amp replacements and other lighting maintenance activities as required

maintenance for commercial refrigeration equipment , and apart from preventative maintenance. Thus, DOE did not itemize them in the preventative maintenance activities described by RS

Means

. Different commercial refrigeration equipment classes hav e different numbers of lamps (and ballasts) , and many of the technologies DOE considered in the engineering analysis involved changes to the lighting configuration (lamp, ballast, or use of LED lighting systems). Because the lighting configurations c an var y by EL

, DOE estimate d the relative maintenance costs for lighting for each equipment class at each EL

. DOE’ s methodology was to estimate the frequency of failure and replacement of individual lighting components, to estimate the cost of replacement in the field, and to develop an annualized maintenance cost based on the sum of the total lighting maintenance costs (in 2013$) over the estimated life of the equipment divided by the estimated life of the equipment.

Life time estimates for particular lighting components were as follows:

• Fluorescent lamps would be replaced every 24 months in a preventative fashion.

• Fluorescent lamp ballasts would be replaced once over the estimated 10

- year life of the equipment based on a typical ballast life of 80,000 hours.

• L ED lamps would be replaced once every 5.7 years based on a typical fixture life of 50,000 hours. 6



### • LED lamps with occupational sensors (OCC) would be replaced once every 10.0 years based on the typical LED life run at a rate of 13.7 hours a day c



### .

The appro ach taken was to estimate the costs of field replacement using labor cost hours

from RS Means Electrical Cost Data 7 

### for typical lamp or ballast replacement as baseline values. The costs for replacement of lamps and ballasts can be split into cost of material and cost of

labor. The cost of labor was determined fr om the RS Means database as the cost to replace one fluorescent bulb and the cost to replace one fluorescent lamp ballast. The cost of equipment was determined by using the OEM costs o f the lamps and ballasts and applying material cost markups to reflect retail pricing as explained below.

DOE estimated the material cost markup to be 250 percent by comparing the OEM costs of fluorescent lamps and ballasts with typical retail prices. How ever, typically, when large food retailers replace lamps or ballasts in their display cases, they tend to replace the lamps and ballasts in all display cases at the same time. L arge food retailers house a large number of remote condensing display cases, an d therefore tend to purchase lamps and ballasts in bulk . DOE estimated that through such bulk purchases large food retailers are able to negotiate a large

volume discount. Therefore, for remote condensing equipment, the material cost markup was assumed to be a 150-percent multiplier on OEM costs for lamps and ballasts. Small businesses,

c See chapter 5 for a description of daily usage for LEDs with occupational sensors.

8- 23

### such as restaurants, that typically house only self-contained equipment do not get such a volume

discount because they do not purchase a large number of lamps and ballasts

. Therefore, for self

contained equipment, the material cost markup was assumed to be a 250- percent multiplier on OEM costs for lamps and ballasts to reflect retail pricing. DOE is aware that many large food

retail stores also house self

- contained equipment, and some small businesses may use remote condensing equipment. However, DOE does not have the data to reliably determine the fraction

of self -contained equipment in large food retail stores and the remote condensing equipment in

small business establishme nts. DOE believes the above assumptions reflect a reasonable compromise in the calculation of the lighting maintenance costs.

Based on the cost values from RS Mean database, the typical markup value on labor costs (labor cost markup) was calculated to be approximately 157 percent. However, this labor cost markup was applied to the RS Means labor cost values for self-contained equipment only. No labor cost markup was applied to the RS Means labor cost values for remote condensing equipment. Once again, the assumption was that the remote condensing equipment that is housed mainly in large food retail stores has the advantage of volume discount, whereas self-contained equipment, which is typically housed in small business establishments, does not have the

advan tage of volume discount. In effect, no markup for remote condensing equipment amounts to the assumption that large food retailers use in-house labor for replacement of lamps and ballasts. DOE believes these assumption s provide reasonably accurate estimates for lighting

maintenance costs.

T he lamp and ballast replacement costs obtained from the RS Means database are applicable for replacement of lamps and ballasts in overhead lamp fixtures. The labor effort and time involved in replacing lamps and ballasts i n display cases is much lower compared th ose associated with overhead lamp fixtures. DOE assumed that the labor costs for replacing lamps and ballasts in display cases would be about half of the costs for overhead lamp fixtures.

Fluorescent lamp and balla st technology is mature. Available information suggests that there would be no change in inflation-adjusted costs for these components. However, because of rapid technological improvement, costs for LED lamps are declining. As discussed in TSD chapter 5, DOE estimated the annual reduction in the prices of LED lamps from 2013 through

2030. DOE used different price reductions to estimate the cost of LED lamps , and OCC lamps. For LED lamps DOE estimate the price reduction in 2022, 6 years into the compliance date (2017) of this rule when LED lamps would be first replaced , for OCC DOE estimate the price reduction in 2027, 10 years into the compliance date of today’s rule when OCC lamps would be first replaced.

DOE determined that the effort required to replace a fluorescent lamp ballast is similar to the effort required to replace the power supply (driver) of an LED lamp. However, replacement of LED lamps involves additional effort to replace the LED lighting fixture. DOE estimate d that the total labor cost to replace an LED lamp (lighting fixture and power supply) would be approximately 25 percent higher than the cost to replace a fluorescent lamp ballast. The assumption of material cost markup (of 150-percent for remote condensing equipment and 250- percent for self -contained equipment) was retained for LED lamps replacement cost estimation

.

8- 24

### The total costs for lamp, ballast , or LED fixture replacement were annualized by dividing the total estimated replacement costs over the lifetime of commercial refrigeration equipment as shown in Eq. 8.8

.

Annualized Lig h ting Maintenance Cost = 1

Life 

### ∑ � OEM

_ Cost Lamp / Ballast 

### × Life



### Marku p Retail 

### + �N Bulb 

### × Labor Bulb 

### + N Ballast 

### × Labo r Ballast 

### + N LED

_ Lamps 

### ×

Labo r LED

_ Lamps 

### ) × Marku p Labor 

### �

Eq. 8.8

W here :

Annualized Lighting Maintenance Cost = annualized l ighting m aintenance cost ($) for an efficiency level

, Life = commercial refrigeration equipment l ife time (y ea rs)

, OEM_Cost Lamp/Ballast 

### = OEM c osts for all the fluorescent bulbs , LED lamps

, and fluorescent lamp

ballast s in each unit of equipment; obtained from e ngineering a nalysis (TSD chapter 5) ($)

, Markup Retail 

### = 250 percent for self-contained equipment, and 150 percent for remote condensing equipment,

N

Bulb 

### = number of fluorescent bulbs in the particular efficiency level of the equipment class analyzed

, Labor Bulb 

### = labor cost to replace one fluorescent bulb ($),

N

Ballast 

### = number of fluorescent lamp ballasts in the particular efficiency level of equipment class analyzed

, Labor Ballast 

### = labor cost to replace one fluorescent ballast ($)

, N

LED_ L amps 

### = n umber of LED lamps in the particular efficiency level of equipment class analyzed,

Labor LED_Lamps 

### = labor cost to replace one LED lamp, including the power supply ($). This value

is 1.25 times Labor Ballast 

### , and Markup Labor 

### =157 percent for self-contained equipment, and 100 percent for remote condensing equipment.

quip ment Class for Each EL for the Representative Units Analyzed in the Engineering Analysis

Table 8.2.13 shows the annualized lighting maintenance costs for each efficiency level for the rep resentative units defined in the engineering analysis. Total annualized maintenance costs are the sum of the preventative maintenance and the lighting maintenance costs.

Table 8.2.12 Lighting Technologies by Equipment Class for Each EL for the Representative Units Analyzed in the Engineering Analysis
Equipment Class Base EL1 EL2 EL3 EL4 EL5 EL6 EL7

VOP.RC.M T8S OCC OCC OCC OCC

8- 25VOP.RC.L OCC OCC OCC OCC

VOP.SC.M OCC OCC OCC

VCT.RC.M LED OCC OCC OCC OCC

VCT.RC.L LED OCC OCC OCC OCC

VCT.SC.M LED LED OCC OCC OCC OCC OCC OCC

VCT.SC.L T8N LED LED LED OCC OCC OCC OCC

VCT.SC.I OCC OCC OCC OCC OCC

VCS.SC.M

- - - - - - - - VCS.SC.L

- - - - - - - - VCS.SC.I

- - - - - - SVO.RC.M T8S OCC OCC OCC OCC

SVO.SC.M OCC OCC OCC OCC

SOC.RC.M LED LED LED LED LED

SOC.SC.M LED LED LED LED LED

HZO.RC.M

- - HZO.RC.L

- - HZO.SC.M

- - - - HZO.SC.L

- HCT.SC.M

- - - - - - - - HCT.SC.L

- - - - - - - - HCT.SC.I

- - HCS.SC.M

- - - - - - - HCS.SC.L

- - - - - - - PD.SC.M LED LED OCC OCC OCC OCC OCC OCC



### Table 8.2.13 A nnualized Lighting M aintenance C osts by Equipment Clas s for E ach EL for the Representative Units Analyzed in the Engineering Analysis
Equipment Class Base EL1 EL2 EL3 EL4 EL5 EL6 EL7

VOP.RC.M $96 $59 $59 $59 $59

VOP.RC.L $18 $18 $18 $18

VOP.SC.M $32 $32 $32

VCT.RC.M $39 $16 $16 $16 $16

VCT.RC.L $39 $16 $16 $16 $16

VCT.SC.M $29 $29 $12 $12 $12 $12 $12 $12

VCT.SC.L $43 $29 $29 $29 $12 $12 $12 $12

VCT.SC.I $12 $12 $12 $12 $12

VCS.SC.M $0 $0 $0 $0 $0 $0 $0 $0

VCS.SC.L $0 $0 $0 $0 $0 $0 $0 $0

VCS.SC.I $0 $0 $0 $0 $0 $0

SVO.RC.M $68 $46 $46 $46 $46

SVO.SC.M $25 $25 $25 $25

SOC.RC.M $86 $86 $86 $86 $86

SOC.SC.M $139 $139 $139 $139 $139

HZO.RC.M $0 $0

HZO.RC.L $0 $0

HZO.SC.M $0 $0 $0 $0

8- 26HZO.SC.L $0

HCT.SC.M $0 $0 $0 $0 $0 $0 $0 $0

HCT.SC.L $0 $0 $0 $0 $0 $0 $0 $0

HCT.SC.I $0 $0

HCS.SC.M $0 $0 $0 $0 $0 $0 $0

HCS.SC.L $0 $0 $0 $0 $0 $0 $0

PD.SC.M $17 $17 $7 $7 $7 $7 $7 $7



### 8.2.3.5 Lifetime

DOE defines lifetime as the age at which a commercial refrigeration equipment unit is

retired from service. DOE based its estimates of equipment lifetime on discussions with industry experts and concluded that a typical lifetime of 10 years is appropriate for most commercial refrigeration equipment in large grocery/multi

- line stores and restaurants

. TSD c hapter 3, Market and Technology Assessment, discusses equipment life and tabulates estimates from various

sources used in assessing equipment life. Remote condensing commercial refrigeration equipment units typically are replaced when stores are renovated

, which is before the commercial refrigeration equipment units would have physically worn out. Typically, the gap between store renovations is around 8 to 9 years for major store chains. 8 

### DOE assumed that an average grocery store or supermarket renovates every 10 years. Because some equipment thus has remaining useful life , there is a market for commercial refrigeration equipment that has been

removed from service. DOE understands, however, that the salvage value to the original

pur chaser is very low , and thus has not taken this into account in the LCC. Operators of small food retail stores and other small businesses, on the other hand, tend to CRE for a longer

duration. DOE used 15 years as the average equipment lifetime for CRE us ed in small

businesses.

To account for uncertainty and variability, DOE determine d the probability that a unit of commercial refrigeration equipment of age a will break or will be replaced using a Weibull

survival distribution function W(8,10.62) . Th is Weibull survival function yield ed an average lifetime of 10 years. The minimum lifetime was truncated at 7 years, and the maximum lifetime

was truncated at 13 years to remove the unrealistic probabilities associated with the lifetimes outside this range, w hich were mostly an artifact of the assumption of the Weibull function. For CRE used in small businesses, the equipment lifetimes were represented by the Weibull function W(8,16) , which yield ed an average lifetime of 15 years. The Weibull function was trun cated above and below the range of 9 to 20 years to remove the unrealistic probabilities associated with the lifetimes outside this range, which were mostly an artifact of the assumption of the Weibull

function.

8.2.3.6 Discount Rate

The discount rate is the rate at which future expenditures are discounted to establish their present value. DOE derived the discount rates for the commercial refrigeration equipment analysis by estimating the cost of capital for companies that purchase commercial refrigeration equipment. The cost of capital is commonly used to estimate the present value of cash flows to

8- 27

### be derived from a typical company project or investment. Most companies use both debt and equity capital to fund investments, so their cost of capital is the wei ghted average of the cost to the company of equity and debt financing.

DOE estimated the cost of equity financing by using the C apital A sset P ricing Model (CAPM)

. 9 

### The CAPM, among the most widely used models to estimate the cost of equity financing, assumes that the cost of equity is proportional to the amount of systemic risk associated with a company. The cost of equity financing tends to be high when a company faces a large degree of systemic risk and it tends to be low when the company faces a small degree of systematic risk.

DOE determined the cost of equity financing by using several variables, including the risk coefficient of a company

, 

### β ( beta) ; the expected return on “risk free” assets ( R

f 

### ); and the additional return expected on assets facing aver age market risk, also known as the equity risk premium or ERP

. The risk coefficient of a company

, 

### β

, indicates the degree of risk associated with a given firm relative to the level of risk (or price variability) in the overall stock market. Risk coefficients usually vary between 0.5 and 2.0. A company with a risk coefficient of

0.5 faces half the risk of other stocks in the market; a company with a risk coefficient of

2.0 faces twice the overall stock market risk.

The following equation gives the cost of equity financing for a particular company:

k

e 

### = R f 

### + (β x ERP)

Eq. 8.9

W here :

k

e 

### = the cost of equity for a company (%),

R

f 

### = the expected return of the risk free asset (%), 

### β = the risk coefficient , and

ERP = the expected equity risk premium (%)

.

DOE defined the risk

- free rate as the 40- year geometric average yield on long- term government bonds. The risk free rate was calculated using Federal Reserve data for the period 1972 to 2011, 10 

### wit h a resulting rate of 6. 61 percent. DOE used a 2.94 percent estimate for the

ERP based on data from the Damodaran Online 11 

### site.

The cost of debt financing ( k d 

### ) is the interest rate paid on money borrowed by a company. The cost of debt is estimated by adding a risk adjustment factor ( R

a 

### ) to the risk

- free

rate.

afd 

### RRk +=

,

Eq. 8.10

8- 28

### Where:

k d 

### = the cost of debt financing for each firm (%),

R

f



### = the expected return on risk- free assets (%), and

a 

### R = the risk adjustment factor to risk

- free rate for each firm (%)

.

The risk adjustment factor depends on the variability of stock returns represented by standard deviations in stock prices and was taken from Damodaran Online individ ual company cost of capital worksheets. 12 

### The weighted

- average cost of capital (WACC) of a company is the weighted

- average cost of debt and equity financing:

k =k

e 

### x w e 

### + k d 

### x w d



### Eq. 8.11

W here :

k = the (nominal) cost of capital (%),

k

e 

### = the expected rate of return on equity (%),

k

d 

### = the expected rate of return on debt (%),

w e 

### = the proportion of equity financing in total annual financing, and

w d 

### = the proportion of debt financing in total annual financ ing

.

The cost of capital is a nominal rate, because it includes anticipated future inflation in the expected returns from stocks and bonds. The real discount rate or WACC deducts expected inflation ( r ) from the nominal rate. DOE calculated inflation ( 3.78 percent) as the 40

- year

average GD P deflator derived from U.S. Bureau of Labor Statistics data covering the 1972–2011 period. 13



### To estimate the WACC of commercial refrigeration equipment purchasers, DOE used a sample of companies involved in grocery and m ulti

- line retailing and restaurants drawn from a database of U.S. companies given on the Damodaran Online individual company worksheet cited

earlier

. Th e Damodaran database includes most of the publicly traded companies in the United

S tates.

DOE divided th e companies into categories according to their type of activity ( e.g. , Small Grocery & Convenience

, which covers convenience stores with and without gasoline stations).

DOE used financial information for all of the firms in the Damodaran database engaged in each

of the seven classes of business

. Two classes —O ther Food S ervice and Gas Station with

Convenience Store—were not identifiable and therefore were calculated differently.

Table 8.2.14 outlines the building type and ownership categories as well as the number of companies used for determining discount rates. For five of the seven building categories there is

a mixture of lar ge companies with stock traded on major U.S. stock exchanges and smaller companies that are not publicly traded

— e.g. , single- store or small, local chains of convenience

stores or restaurants. The cost of capital for small, independent grocers, convenience store franchisees, gasoline station owner-operators, and others with more limited access to capital is

8- 29

### more difficult to determine than for publicly traded companies. Individual credit -worthiness varies considerably, and some franchisees have access to the financial resources of the franchising corporation. During research leading up to the January 2009 final rule , DOE

contacted a sample of commercial bankers regarding cost for debt financing and the contacts yielded an estimate for the small operator weigh ted cost of capital of about 200 to 300 basis points (2 to 3 percent) above the rates for large grocery chains

. The average premium of 2.5

percent (real) was retained for small operators.

Table 8.2.14 Derivation of Real Discount Rates by Building Type

Building Type

Description Major Chain Local or Non

Chain Governmental

No.

Obs. * WACC Percent

of

Stock Small

Firm

Premium Percent

of

Stock Muni

Bond

Rate Percent

of

Stock Discount

Rate

Large Grocery 4.16% 100% 0.0% 0% 0% 0% 4.16% 18 Small Grocery &

Convenience 4.20% 50% 3.2% 50% 0% 0% 5.86% 5

Gas Station With

Convenience Store 4.20% 50% 3.2% 50% 0% 0% 5.86% NA

Multi

- Line Retail 4.33% 100% 0.0% 0% 0% 0% 4.33% 6

Restaurant

- Limited

Service 5.29% 50% 3.2% 50% 0% 0% 6.97% 21

Restaurant

- Full Service 5.61% 50% 3.2% 50% 0% 0% 7.30% 24

Restaurant

- Other Food

Service 5.61% 25% 3.2% 25% 2.34% 50% 4.82% NA

Source: Pacific Northwest National Laboratory (PNNL) WACC calculations applied to firms sampled from the Damodaran Online web site. Assumptions for weighting factors for convenience and food service reflect lack of reliable data sources.

*N umber of Damodaran observations available.



### For two building types, Gas Station with Convenience Store and Restaurant – Other Food Service, no representative data was identifiable in the Damodaran database. Gas Station with Convenience Store was set equal to the discount rate for Small Grocery and Convenience. Other

Food Service was based on the WACC derived for Full Service Restaurants. The main difference between the resulting discount rate is that a significant portion of this building category consists

of cafeterias, a large percentage of which a re located in schools, universities, and governmental buildings. No data exist on the exact percentage, so it was weighted as 50 percent schools and 50 percent privately owned. The discount rate for schools, universities, and governmental buildings

was set equal to a 40-year geometric average of municipal bond rates, mixed quality, obtained

from the Federal Reserve Bank of St. Louis. 14



### DOE’s research identified multiple data sources indicating the percentages of building stock represented by major chains and by local or non- chain establishments. All of the data

sources exhibited ambiguities and in many cases contradicted other data sources. For the NOPR

, the percentages were set at levels reflecting the uncertainty of the data, as shown in Table 8.2.14

. Both Large Grocery and Multi -Line Retail were assumed to be dominated by major chains, and the percentage divisions were set accordingly

.

8- 30

### 8.2.3.7 Compliance Date of Standard

The compliance date is the future date after which all manufacturers selling equipment in the United States should comply by the standards. Under 42 U.S.C. 6313(a)(6)(c), the compliance date of any new energy conservation sta ndard for commercial refrigeration equipment will be 3 years after the final rule is published. DOE calculated the LCC for all customers as if they each would purchase a new commercial refrigeration equipment unit in the

year starting on the compliance date . Consistent with its published regulatory agenda, DOE

assumed that the final rule will be issued in 2014 and therefore the compliance date for the new

standards will be in 2017. For the LCC analysis, the year of equipment purchase wa s assumed to

be 2017. A ll dollar values are expressed in 2013$.

8.3 PAYBACK PERIOD INPUT S

8.3.1 Definition

Payback period is the amount of time it takes the customer to recover the higher purchase cost of more energy efficient equipment as a result of lower operating costs. Numerically, the PBP is the ratio of the increase in purchase cost to the decrease in annual operating expenditures. This type of calculation is known as a “simple” payback period because i t does not take into account changes in operating cost 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:

PBP =∆ IC/ ∆O C

Eq. 8.12

W here :

PB P

= payback period in years,

∆ IC = difference in the total installed cost between the EL and the baseline level equipment, and



### ∆ OC = difference in the first year annual operating costs between the EL and the baseline level equipment.

PBPs are expressed in years. PBPs greater than the life of the product mean that the

increased total installed cost of the EL is not likely to be recovered in reduced operating costs over the life of the equipment.

8- 31

### 8.3.2 Inputs

The data inputs to PBP are the total installed cost of the equipment to the customer for

each EL and the annual (first year) operating costs for each EL

. The inputs to the total installed cost are the equipment price and the installation cost. The inputs to the operating costs are the annual energy cost, the annual repair cost, and the annual maintenance cost. The PBP calculation uses the same inputs as the LCC analysis described in section 0 , except that electricity price

trends and dis count rates are not required because t he PBP is a “simple” (undiscounted) payback

and the required electricity price is only for the year in which a new efficien cy standard is to take

effect

— in this case, the year 201 7. The electricity price used in the PBP calculation of electricity cost was the price projected for 2017 , expressed in 2013$. Discount rates are not used in the PBP

calculation.

8.4 LIFE

- CYCLE COST AND PAYBACK PERIOD RESULTS

This section presents the results of the LCC and PBP analysis, including the mean and

median values of LCC savings and PBP. Appendix 8B presents a dditional details along with distribution of impacts on customers.

8.4.1 Life

- Cycle Cost Results

Figure 8.4.1 shows the change in LCC over the eight efficiency levels for an example equipment class ( VCT.SC.L ) . The LCC values on this chart are mean values obtained from the LCC analysis. This curve is presented here as an example to illustrate the typical relationship

between installation cost and LCC over all the efficiency level s for an equipment class

. The installed costs increase steadily from the baseline to the highest possible efficiency level (L evel 8) and the LCC s decrease from Level 1 to Level 7. The increase in installed cost from Level 7 to

Level 8 is not offset by the decrease in operating cost because of the large increase in installed cost with relatively small gains in energy savings. Therefore, for this equipment class there is a n increase i n LCC at Level 8 when compared to Level 7.

8- 32

### Figure 8.4.1 LCC and I nstalled C ost Variation over EL s for

the VCT.SC.L E quipment Class

Since the LCC analysis was carried out in the form of Monte Carlo simulations, the LCC savings outputs obtained from the LCC analysis are in the form of distributions. LCC savings

distributions are illustrated here with the example of the VCT.SC.L equipment class as shown in Figure 8.4.2 . Similar plots of LCC savings distribution are presented in appendix 8B for all equipment classes analyzed.

. Figure 8.4.2 illustrates the mean and median values on the plot with the help of red and black markers, respectively

. The elongated large rectangular box is used to

repre sent the 25 th 

### and 75 th 

### percentile values. The lower edge of the elongated rectangle represents

25 th 

### percentile , which means that 25 percent of the customers would experience

. The upper edge of the elongated rectangle represents the 75 th 

### percentile. The two ends of the vertical black line for each efficiency level represent the 5 th 

### percentile (lower end) and 95 th 

### percentile (upper end).

Mean LCC savings for all equipment classes analyzed are summarized in Table 8.4.1

. 0 5000 10000 15000 20000 25000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Costs (2012$)

Life-Cycle Cost Total Installed Cost

8- 33

### Figure 8.4.2 LCC Savings D istribution for A ll the EL s for E quipment Class VCT.SC.L

Table 8.4.1 Mean LCC S avings for A ll E quipment Class es and EL s

Equipment

Class Mean LCC Savings * , **
2013$

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M 922 482

- 5

- 4,203

VOP.RC.L 53

- 148

- 6,701

VOP.SC.M

- 54

- 1,384

VCT.RC.M 542 323 41

- 4,937

VCT.RC.L 647 526 93

- 6,036

VCT.SC.M

- 10 214 226 209 163 74

- 1,541

VCT.SC.L 2,503 5,200 4,709 4,996 5,001 4,979 2,812

VCT.SC.I 18 4

- 68

- 2,834

VCS.SC.M 223 518 365 363 313 305

- 1,428

VCS.SC.L 588 513 550 565 507 495

- 1,640

VCS.SC.I 41 114 111 113

- 2,710

SVO.RC.M 564 48

- 19

- 2,691

SVO.SC.M 6

- 51

- 917

SOC.RC.M

- 128

- 223

- 287

- 2,268

SOC.SC.M

- 144

- 209

- 274

- 2,204

HZO.RC.M

- 2,180

HZO.RC.L

- 4,249

HZO.SC.M 55

- 4

- 1,154

HZO.SC.L

HCT.SC.M 36 66 165 101 91 43

- 599

HCT.SC.L 415 428 435 293 286 248

- 613

HCT.SC.I

- 1,240

HCS.SC.M 12 17 15 5

- 52

- 568

HCS.SC.L 31 50 64 58 33

- 590

PD.SC.M 8 163 165 150 97 32

- 1,252

* NA implies there are no associated EL s. -6,000 -4,000 -2,000 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Life-Cycle Cost Savings (2013$)

Median

Average Savings 2012$

8- 34

### 8.4.2 Payback Period Results

Figure 8.4.3 presents the distribution of the PBP results for EL s 1 to 7 of an example equipment class ( VCT.SC.L ). The red marker represents the PBP for each EL . The lower edge of the elongated rectangula r box represents the 25 th 

### percentile , t he upper edge of the rectangular

box represents the 75 th 

### percentile. The two ends of the vertical line represent the 5 th 

### percentile (lower end) and 95 th 

### percentile (upper end). Table 8.4.2 summarize the median PBPs

, respectively, for all EL s of all the analyzed equipment classes. Results similar to Figure 8.4.3 are presented in appendix 8A for all equipment classes.

Figure 8.4.3 Mean Payback P eriod for A ll EL s for the E quipment Class VCT.SC.L 0 1 10 100

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Payback Period (log. years)

8- 35

### Table 8.4.2 Medean Payback P eriod for A ll E quipment Class es and EL s

Equipment C lass Mean Payback Period*

years
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M 5.7 7.2 9.9 34.1

VOP.RC.L 6.1 11.3 310.0

VOP.SC.M 63.1 593.2

VCT.RC.M 2.1 3.5 6.6 364.7

VCT.RC.L 1.8 2.7 6.3 194.7

VCT.SC.M 23.4 4.8 5.3 5.7 7.0 9.0 96.2

VCT.SC.L 0.5 0.8 0.8 1.0 1.1 1.2 4.7

VCT.SC.I 7.2 10.7 16.2 663.6

VCS.SC.M 0.5 0.6 1.0 1.4 2.4 2.6 48.0

VCS.SC.L 0.6 1.1 1.3 1.9 2.5 2.7 31.8

VCS.SC.I 2.6 3.6 4.0 5.0 183.7

SVO.RC.M 6.2 9.9 10.4 29.9

SVO.SC.M 10.9 17.5 151.6

SOC.RC.M 38.0 41.9 47.0 114.1

SOC.SC.M 47.5 28.7 34.5 25.3

HZO.RC.M

--- HZO.RC.L 288.9

HZO.SC.M 6.9 11.8 194.7

HZO.SC.L

HCT.SC.M 1.8 2.5 4.7 5.8 6.5 9.2 46.6

HCT.SC.L 1.8 1.8 2.0 2.5 2.7 3.6 19.5

HCT.SC.I 23.8

HCS.SC.M 2.9 3.7 5.5 7.5 19.9 680.6

HCS.SC.L 1.4 1.7 2.5 3.1 6.2 68.9

PD.SC.M 9.3 5.3 5.6 6.0 7.6 9.4 102.2
* NA implies there are no associated EL s.



### 8.4.3 Rebuttable Presumption Payback Period

Sections 325(o)(2)(B)(iii) and 345(e)(1)(A) of EPCA (42 U.S.C. 6295(o)(2)(B)(iii) and 42 U.S.C. 6316(e)(1)(A)) establish a rebuttable presumption for commercial refrigeration equipment. The rebuttable presumption states that a standard is economically justified if the Secretary finds that “the additional cost to the consumer of purchasing a product complying with an energy conservation standard level will be less than three times the value of the energy savings during the first year that the consumer will receive as a result of the standard, as calculated under the applicable test procedure .” This rebuttable- presumption test is an alternative path to establishing economic justification.

To evaluate the rebuttable presumption, DOE estimated the additional cost of purchasing more efficient, standard s-compliant equipment, and compared this cost to the value of the energy saved during the first year of operation of the equipment. DOE interprets that the increased cost of purchasing standards-compliant equipment includes the cost of installing the equipment for use by the purchaser. DOE calculated the rebuttable presumption payback period (RPBP), or the

8- 36

### ratio of the value of the increased installed price above the baseline efficiency level to the first year’s energy cost savings. When RPBP is less than 3 years, the rebuttable presumption is satisfied; when RPBP is equal to or more than 3 years, the rebuttable presumption is not

satisfie d. Note that this R PBP calculation does not include other components to the annual operating cost of the equipment ( i.e.

, maintenance costs and repair costs). The RPBPs calculated

can thus be different from the PBPs calculated in section 8.4.2

.

DOE calculated the RPBPs for the distribution of installed costs and energy prices

discussed in section s 8.4.1 and 8.4.2 , which are representative of the same seven types of

businesses and all 50 s tates. The RPBP was calculated for each EL within each equipment class

.

Table 8.4.3 shows the nationally averaged RPBP s calculated for all equipment classes

and EL s.

Table 8.4.3 Rebuttable Presumption Payback Periods by EL and Equipment Class

Equipment

C lass Median R ebuttable P resumption P ayback P eriod *

years
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M 5.1 6.2 7.6 17.3

VOP.RC.L 4.6 7.3 36.2

VOP.SC.M 21.2 127.9

VCT.RC.M 2.5 3.7 6.8 56.3

VCT.RC.L 2.2 3.0 6.6 43.0

VCT.SC.M 4.4 5.4 5.5 5.6 6.5 7.9 28.1

VCT.SC.L 0.5 0.8 0.8 1.0 1.1 1.2 4.2

VCT.SC.I 5.0 6.9 9.5 48.7

VCS.SC.M 0.4 0.6 0.9 1.2 2.0 2.1 16.5

VCS.SC.L 0.5 1.0 1.2 1.6 2.1 2.3 13.6

VCS.SC.I 2.3 3.0 3.2 3.8 28.7

SVO.RC.M 5.4 7.6 7.8 16.5

SVO.SC.M 8.1 11.4 35.9

SOC.RC.M 12.4 13.6 14.3 54.3

SOC.SC.M 9.6 10.2 10.6 39.8

HZO.RC.M 156.3

HZO.RC.L 79.5

HZO.SC.M 5.6 8.1 42.9

HZO.SC.L

HCT.SC.M 1.7 2.2 4.0 4.4 4.6 6.6 20.9

HCT.SC.L 1.6 1.7 1.8 2.2 2.3 3.0 11.4

HCT.SC.I 40.8

HCS.SC.M 2.5 2.9 4.0 4.5 8.9 30.5

HCS.SC.L 1.3 1.6 2.2 2.4 4.5 16.7

PD.SC.M 4.9 5.4 5.5 5.7 6.8 7.8 26.7
* NA implies there are no associated EL s.

8- 37

### 8.5 DETAILED RESULTS

DOE presents detailed results f rom the LCC analysis in appendix 8B. Plots similar to Figure 8.4.2 and Figure 8.4.3 are presented in the appendix for all equipment classes. In addition, summary tables with all the necessary data in one table for each equipment class are presented in appendix 8B. Table 8.5.1 is a reproduction of the summary table for an example equipment class

, VCT.SC.L. This table presents the mean values of i nstalled costs, annual operating costs, LCC, LCC savings, and median values of PBP for all the EL s. It also presents the distribution of customer impacts in the form of percentage s of customers who experience net cost, no impact,

and net benefit as compare d to the base- case scenario. The average LCC savings and the percentage of customers experiencing a net benefit or cost are based on the market shares of the efficiency levels . Some are assumed to be buying at higher efficiency levels. The LCC savings is

a n average of the savings achieved by customers who, in the base case, were buying less efficient equipment than the efficiency level examined. Customers with no impact were assumed in the base case to be already buying more efficient equipment, so the effi ciency level in question would not affect them. Summary tables for each of the equipment classes are provided in appendix 8A.

Table 8.5.1 Summary of R esults of LCC and PBP Analysis for VCT.SC.L E quipment Class

Efficiency

Level Energy Consumption kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average Savings
2012$ Customers that Experience ( % )

Net

Cost No Impact Net

Benefit

0 12,659 6,384 1,531 21,642

--- 0 100 0

--- 1 10,022 6,498 1,270 19,135 2,503 0 10 90 0.5

2 6,727 6,799 970 16,433 5,200 0 10 90 0.8

3 6,654 6,822 964 16,397 4,709 0 0 100 0.8

4 6,318 6,974 921 16,110 4,996 0 0 100 1.0

5 6,262 7,003 917 16,105 5,001 0 0 100 1.1

6 6,174 7,073 913 16,127 4,979 0 0 100 1.2

8- 38

### REFERENCES

1. U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook

2012 . 2012. Washington D.C. DOE/EIA -0383(2012 ).

2. U.S. Department of Energy–Energy Information Administration. Form EIA -826 Database Monthly Electric Utility Sales and Revenue Data ( EIA -826 Sales and Revenue Spreadsheets ). (Last accessed October 30, 2012.)

< www.eia.doe.gov/cneaf/electricity/page/eia826.html >

3. U. S. Department of Energy—Energy Information Administration. 2012 Annual Energy

Outlook . Table 20. Macroeconomic Indicators. 2010. (Last accessed October 30, 2012.)

< www.eia.doe.gov/oiaf/archive/aeo10/aeoref_tab.html >.

4. U.S. Census Bureau. Annual Population Estimates 2000 to 2009

. Table 1 . Annual

Es timates of the Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2011. < http://www.census.gov/popest/states/NST

- ann

- est.html > Also: Annual Estimates of Incorporated Places: April 1, 2010 to July 1, 2011.

< http://www.census.gov/popest/data/cities/totals/2011/index.html > Last accessed July 20,

2012.

5. RS Means Compa ny, Inc. Means Costworks 2013: Facility Maintenance & Repair Cost

Data . 2013. Kingston, MA

.

6. General Electric Company (GE Lighting)

. Efficiency, Pure and Simple: LED lighting solutions for interiors and outdoors

. 2010. East Cleveland, OH.

< www.gelighting.com/eu/resources/literature_library/catalogs/lamp_catalog/downloads/G E_LED_catalogue2011.pdf >

7. RS Means Company, Inc. 2013 RS Means Electrical Cost Data . 2013. Kingston, MA

.

8. FMI Research

. The Food Retailing Industry Speak 2011 . 2011. Food Marketing Institute, Arlington, VA.

9. Harris, R .S. Applying the Capital Asset Pricing Model . UVA -F-1456. Available at SSRN: http://ssrn.com/abstract=909893

10. U.S. Federal Reserve

. H.15: Selected Interest Rates . (Last accessed November 08, 2012.)

< www.federalreserve.go v/releases/h15/data.htm .>

11. Damodaran Online. The Data Page . 2012. S&P 500 data from link entitled Annual Returns on Stock, T.Bonds and T.Bills: 1928 - Current . (Last accessed November 26, 2012.)

< http://pages.stern.nyu.edu/~adamodar/New_Home_Page/data.html >

12. Damodaran Online. The Data Page

. Individual Company Information, 2012 . (Last accessed November 26, 2012 .) < http://pages.stern.nyu.edu/~adamodar/New_Home_Page/data.html > The data used herein was the individual company data for the U.S. for January 2012.

8- 39

### 13. U.S. Bureau of Economic Analysis. All NIPA Tables, Section 1 – Domestic Product and

Income , Table 1.1.9, Implicit Price Deflators for Gross Domestic Product. (Last accessed November 26, 2012.) < www.bea.gov/national/nipaweb/Selec tTable.asp >

14. Federal Reserve Bank of St. Louis. Data retrieved using the ‘FRED’ data tool. (Last accessed November 27, 2012.)

< http://research.stlouisfed.org/fred2/series/MSLB20/downloaddata?cid=32995 >

8- 40

### APPENDIX 8A. DETAILED LIFE

- CYCLE COST AND PAYBACK PERIOD

ANALYSIS RESULTS

TABLE OF CONTENTS

8A.1 INTRODUCTION ........................................................................................................ 8A-1

8A.2 LCC RESULTS ............................................................................................................ 8A-2

8A.2.1 Life -Cycle Cost Results for Vertical Open, Remote Condensing, Medium Temperature (VOP.RC.M) ................................................................................ 8A-2

8A.2.2 L ife -Cycle Cost Results for Vertical Open, Remote Condensing, Low Temperature (VOP.RC.L) ................................................................................. 8A-3

8A.2.3 Life

- Cycle Cost Results for Ver tical Open, Self -Contained, Medium Temperature (VOP.SC.M) ................................................................................ 8A-5

8A.2.4 Life

- Cycle Cost Results for Vertical Closed Transparent, Remote Condensing, Medium Temperature (VCT.RC.M) ................................................................. 8A-6

8A.2.5 Life -Cycle Cost Results for Vertical Closed Transparent, Remote Condensing, Low Temperature (VCT.RC.L) ........................................................................ 8A-8

8A.2.6 Life

- Cycle Cost Results for Vertical Closed Transparent, Self -Contained, Medium Tempera ture (VCT.SC.M) ................................................................. 8A-9

8A.2.7 Life

- Cycle Cost Results for Vertical Closed Transparent, Self

- Contained, Low Temperature (VCT.SC.L) ............................................................................... 8A-11

8A.2.8 Life

- Cycle Cost Results for Vertical Closed Transparent, Self

- Contained, Ice

Cream Temperature (VCT.SC.I) ..................................................................... 8A-12

8A.2.9 Life

- Cycle Cost Results for Vertical Closed Solid, Self -Contained, Medium Temperature (VCS.SC.M) .............................................................................. 8A-14

8A.2.10 Life

- Cycle Cost Results for Vertical Closed Solid, Self

- Contained, Low Temperature (VCS.SC.L) ............................................................................... 8A-15

8A.2.11 Life

- Cycle Cost Results for Vertical Closed Solid, Self

- Contained, Ice

Cream Temperature (VCS.SC.I) ..................................................................... 8A-17

8A.2.12 Life

- Cycle Cost Results for Semi -Vertical Open, Remote Condensing, Medium Temperature (SVO.RC.M) ............................................................... 8A-18

8A.2.13 Life

- Cycle Cost Results for Semi

- Vertical Open, Self -Contained, Medium Temperature (SVO.SC.M) .............................................................................. 8A-20

8A.2.14 Life -Cycle Cost Results for Service Over Counter, Remote Condensing, Medium Temperature (SOC.RC.M) ............................................................... 8A-21

8A.2.15 Life

- Cycle Cost Results for Service Over Counter, Self -Contained, Medium Temperature (SOC.SC.M) .............................................................................. 8A-23

8A.2.16 Life -Cycle Cost Results for Horizontal Open, Remote Condensing, Medium Temperature (HZO.RC.M) ............................................................................. 8A-24

8A.2.17 Life

- Cycle Cost Results for Horizontal Open, Remote Condensing, Low Temperature (HZO.RC.L) .............................................................................. 8A-26

8A.2.18 Life -Cycle Cost Results for Horizontal Open, Self-Contained, Medium Temperature (HZO.SC.M) .............................................................................. 8A-27

8A.2.19 Life -Cycle Cost Results for Horizontal Open, Self- Contained, Low Temperature (HZO.SC.L) ............................................................................... 8A-29

8A.2.20 Life -Cycle Cost Results for Horizontal Closed Transparent, Self-Contained, Medium Temperature (HCT.SC.M) ............................................................... 8A-30

8A-i

### 8A.2.21 Life -Cycle Cost Results for Horizontal Closed Transparent, Self-Contained, Low Temperature (HCT.SC.L) ....................................................................... 8A-32

8A.2.22 Life -Cycle Cost Results for Horizontal Closed Transparent, Self-Contained,

Ice

- Cream Temperature (HCT.SC.I) .............................................................. 8A-33

8A.2.23 Life -Cycle Cost Results for Horizontal Closed Solid, Self-Contained, Medium Temperature (HCS.SC.M) ................................................................ 8A-35

8A.2.24 Life -Cycle Cost Results for Horizontal Closed Solid, Self- Contained, Low Temperature (HCS.SC.L) ............................................................................... 8A-36

8A.2.25 Life

- Cycle Cost Results for Pull -Down, Self-Contained, Medium Temperature (PD.SC.M) ................................................................................. 8A-38

LIST OF TABLES

Table 8B.2.1 LCC Results for Vertical Open, Remote Condensing, Medium Temperature (VOP.RC.M) ..................................................................................................... 8A-2 Table 8B.2.2 LCC Results for Vertical Open, Remote Condensing, Low Temperature (VOP.RC.L) ...................................................................................................... 8A-3 Table 8B.2.3 LCC Results for Vertical Open, Self-Contained, Medium Temperature (VOP.SC.M) ..................................................................................................... 8A-5 Table 8B.2.4 LCC Results for Vertical Closed Transparent, Remote Condensing, Medium Temperature (VCT.RC.M) ................................................................................ 8A-6 Table 8B.2.5 LCC Results for Vertical Closed Transparent, Remote Condensing, Low Temperature (VCT.RC.L) ................................................................................. 8A-8 Table 8B.2.6 LCC Results for Vertical Closed Transparent, Self-Contained, Medium Temperature (VCT.SC.M) ................................................................................ 8A-9

Ta ble 8B.2.7 LCC Results for Vertical Closed Transparent, Self- Contained, Low Temperature (VCT.SC.L) ............................................................................... 8A-11 Table 8B.2.8 LCC Results for Vertical Closed Transparent, Self- Contained, Ice

- Cream Temperature (VCT.SC.I) ................................................................................ 8A-12 Table 8B.2.9 LCC Results for Vertical Closed Solid, Self-Contained, Medium Temperature (VCS.SC.M) .................................................................................................... 8A-14 Table 8B.2.10 LCC Results for Vertical Closed Solid, Self- Contained, Low Temperature (VCS.SC.L) ..................................................................................................... 8A-15 Table 8B.2.11 LCC Results for Vertical Closed Solid, Self- Contained, Ice

- Cream Temperature (VCS.SC.I) ................................................................................ 8A-17 Table 8B.2.12 LCC Results for Semi-Vertical Open, Remote Condensing, Medium Temperature (SVO.RC.M) .............................................................................. 8A-18

Table 8B.2.13 LCC Results for Semi- Vertical Open, Self -Contained, Medium Temperature (SVO.SC.M) ................................................................................................... 8A-20 Table 8B.2.14 LCC Results for Service Over Counter, Remote Condensing, Medium Temperature (SOC.RC.M) .............................................................................. 8A-21 Table 8B.2.15 LCC Results for Service Over Counter, Self-Contained, Medium Temperature (SOC.SC.M) .................................................................................................... 8A-23 Table 8B.2.16 LCC Results for Horizontal Open, Remote Condensing, Medium Temperature (HZO.RC.M) ................................................................................................... 8A-24

8A- ii

### Table 8B.2.17 LCC Results for Horizontal Open, Remote Condensing, Low Temperature (HZO.RC.L) .................................................................................................... 8A-26 Table 8B.2.18 LCC Results for Horizontal Open, Self-Contained, Medium Temperature (HZO.SC.M) ................................................................................................... 8A-27 Table 8B.2.19 LCC Results for Horizontal Open, Self-Contained, Low Temperature (HZO.SC.L) .................................................................................................... 8A-29 Table 8B.2.20 LCC Results for Horizontal Closed Transparent, S elf -Contained, Medium Temperature (HCT.SC.M) .............................................................................. 8A-30 Table 8B.2.21 LCC Results for Horizontal Closed Transparent, Self- Contained, Low Temperature (HCT.SC.L) ............................................................................... 8A-32 Table 8B.2.22 LCC Results for Horizontal Closed Transparent, Self- Contained, Ice

- Cream Temperature (HCT.SC.I) ................................................................................ 8A-33 Table 8B.2.23 LCC Results for Horizontal Closed Solid, Self-Contained, Medium Temperature (HCS.SC.M) .............................................................................. 8A-35 Table 8B.2.24 LCC Results for Horizontal Closed Solid, Self-Contained, Low Temperature (HCS.SC.L) ..................................................................................................... 8A-36 Table 8B.2.25 LCC Results for Pull-Down, Self-Contained, Medium Temperature (PD.SC.M) ...................................................................................................... 8A-38

LIST OF FIGURES

Figure 8B.2.1 LCC Savings Statistical Results for Vertical Open, Remote Condensing, Medium Temperature (VOP.RC.M) ................................................................. 8A-2 Figure 8B.2.2 PBP Statistical Results for Vertical Open, Remote Condensing, Medium Temperature (VOP.RC.M) ................................................................................ 8A-3 Figure 8B.2.3 LCC Savings Statistical Results for Vertical Open, Remote Condensing, Low Temperature (VOP.RC.L) ................................................................................. 8A-4 Figure 8B.2.4 PBP Statistical Results for Vertical Open, Remote Condensing, Low Temperature (VOP.RC.L) ................................................................................. 8A-4 Figure 8B.2.5 LCC Savings Statistical Results for Vertical Open, Self -Contained, Medium Temperature (VOP.SC.M) ................................................................................ 8A-5 Figure 8B.2.6 PBP Statistical Results for Vertical Open, Self -Contained, Medium Temperature (VOP.SC.M) ................................................................................ 8A-6 Figure 8B.2.7 LCC Savings Statistical Results for Vert ical Closed Transparent, Remote Condensing, Medium Temperature (VCT.RC.M) ............................................ 8A-7 Figure 8B.2.8 PBP Statistical Results for Vertical C losed Transparent, Remote Condensing, Medium Temperature (VCT.RC.M) ................................................................. 8A-7 Figure 8B.2.9 LCC Savings Statistical Results for Vertical Closed Transparent, Remote Condensing, Low Temperature (VCT.RC.L) ................................................... 8A-8 Figure 8B.2.10 PBP Statistical Results for Vertical Closed T ransparent, Remote Condensing, Low Temperature (VCT.RC.L) ........................................................................ 8A-9 Figure 8B.2.11 LCC Savings Statistical Results for Vertical Closed T ransparent, Self

Contained, Medium Temperature (VCT.SC.M) ............................................. 8A-10 Figure 8B.2.12 PBP Statistical Results for Vertical Closed Transparent, Self -Contained, Medium Temperature (VCT.SC.M) ............................................................... 8A-10

8A- iii

### Figure 8B.2.13 LCC Savings Statistical Results for Vertical Closed Transparent, Self

Contained, Low Temperature (VCT.SC.L) .................................................... 8A-11 Figure 8B.2.14 PBP Statistical Results for Vertical Closed Transpare nt, Self- Contained, Low Temperature (VCT.SC.L) ............................................................................... 8A-12 Figure 8B.2.15 LCC Savings Statistical Results for Vertical Closed Transparent , Self

Contained, Ice

- Cream Temperature (VCT.SC.I) ............................................ 8A-13 Figure 8B.2.16 PBP Statistical Results for Vertical Closed Transparent, Se lf

- Contained, Ice

Cream Temperature (VCT.SC.I) ..................................................................... 8A-13 Figure 8B.2.17 LCC Savings Statistical Results for Vertical Closed Solid, Self -Contained, Medium Temperature (VCS.SC.M) ................................................................ 8A-14 Figure 8B.2.18 PBP Statistical Results for Vertical Closed Solid, Self -Contained, Medium Temperature (VCS.SC.M) .............................................................................. 8A-15 Figure 8B.2.19 LCC Savings Statistical Results for Vertical Closed Solid, Self -Contained,

Low Te mperature (VCS.SC.L) ....................................................................... 8A-16 Figure 8B.2.20 PBP Statistical Results for Vertical Closed Solid, Self

- Contained, Low Temperature (VCS.S C.L) ............................................................................... 8A-16 Figure 8B.2.21 LCC Savings Statistical Results for Vertical Closed Solid, Self -Contained,

Ice

- Cream Temperature (VCS.SC. I) ............................................................... 8A-17 Figure 8B.2.22 PBP Statistical Results for Vertical Closed Solid, Self

- Contained, Ice

- Cream Temperature (VCS.SC.I) ................................................................................ 8A-18 Figure 8B.2.23 LCC Savings Statistical Results for Semi

- Vertical Open, Remote Condensing, Medium Temperature (SVO.RC.M) .......................................... 8A-19 Figure 8B.2.24 PBP Statistical Results for Semi -Vertical Open, Remote Condensing, Medium Temperature (SVO.RC.M) ............................................................... 8A-19 Figure 8B.2.25 LCC Savings Statistical Results for Semi

- Vertical Open, Self -Contained, Medium Temperature (SVO.SC.M) ............................................................... 8A-20 Figure 8B.2.26 PBP Statistical Results for Semi

- Vertical Open, Self -Contained, Medium Temperature (SVO.SC.M) .............................................................................. 8A-21 Figure 8B.2.27 LCC Savings Statistical Results for Service Over Counter, Remote Condensing, Medium Temperature (SOC.RC.M) .......................................... 8A-22 Figure 8B.2.28 PBP Statistical Results for Service Over Counter, Remote Condensing, Medium Temperature (SOC.RC.M) ............................................................... 8A-22 Figure 8B.2.29 LCC Savings Statistical Results for Service Over Counter, Self-Contained, Medium Temperature (SOC.SC.M) ................................................................ 8A-23 Figure 8B.2.30 PBP Statistical Results for Service Over Counter, Self-Contained, Medium Temperature (SOC.SC.M) .............................................................................. 8A-24 Figure 8B.2.31 LCC Savings Statistical Results for Horizontal Open, Remote Condensing, Medium Temperature (HZO.RC.M) ............................................................... 8A-25 Figure 8B.2.32 PBP Statistical Results for Horizontal Open, Remote Condensing, Medium Temperature (HZO.RC.M) ............................................................................. 8A-25 Figure 8B.2.33 LCC Savings Statistical Results for Horizontal Open, Remote Condensing, Low Temperature (HZO.RC.L) ...................................................................... 8A-26 Figure 8B.2.34 PBP Statistical Results for Horizontal Open, Remote Condensing, Low Temperature (HZO.RC.L) .............................................................................. 8A-27 Figure 8B.2.35 LCC Savings Statistical Results for Horizontal Open, Self-Contained, Medium Temperature (HZO.SC.M) ............................................................... 8A-28

8A- iv

### Figure 8B.2.36 PBP Statistical Results for Horizontal Open, Self-Contained, Medium Temperature (HZO.SC.M) .............................................................................. 8A-28 Figure 8B.2.37 LCC Savings Statistical Results for Horizontal Open, Self- Contained, Low Temperature (HZO.SC.L) ............................................................................... 8A-29 Figu re 8B.2.38 PBP Statistical Results for Horizontal Open, Self- Contained, Low Temperature (HZO.SC.L) ............................................................................... 8A-30 Figure 8B.2.39 LCC Savings Statistical Results for Horizontal Closed Transparent, Self

Contained, Medium Temperature (HCT.SC.M) ............................................. 8A-31 Figure 8B.2.40 PBP Statistical Results for Horizontal Closed Transparent, Self-Contained, Medium Temperature (HCT.SC.M) ............................................................... 8A-31 Figure 8B.2.41 LCC Savings Sta tistical Results for Horizontal Closed Transparent, Self

Contained, Low Temperature (HCT.SC.L) .................................................... 8A-32 Figure 8B.2.42 PBP Statistical Results for Horizontal Closed Transparent, Self-Contained, Low Temperature (HCT.SC.L) ....................................................................... 8A-33Figure 8B.2.43 LCC Savings Statistical Results for Horizontal Closed Transparent, SelfContained, Ice

- Cream Temperature (HCT.SC.I) ............................................ 8A-34 Figure 8B.2.44 PBP Statistical R esults for Horizontal Closed Transparent, Self-Contained,

Ice

- Cream Temperature (HCT.SC.I) .............................................................. 8A-34 Figure 8B.2.45 LCC Savings Statistical Results for Horizontal Closed Solid, Self-Contained, Medium Temperature (HCS.SC.M) ................................................................ 8A-35 Figure 8B.2.46 PBP Statistical Results for Horizontal Closed Solid, Self-Contained, Medium Temperature (HCS.SC.M) .............................................................................. 8A-36 Figure 8B.2.47 LCC Savings Statistical Results for Horizontal Closed Solid, Self-Contained, Low Temperature (HCS.SC.L) ....................................................................... 8A-37 Figure 8B.2.48 PBP Statistical Results for Horizontal Closed Solid, Self- Contained, Low Temperature (HCS.SC.L) ............................................................................... 8A-37 Figure 8B.2.49 LCC Savings Statistical Results for Pull -Down, Self-Contained, Medium Temperature (PD.SC.M) ................................................................................. 8A-38 Figure 8B.2.50 PBP Statistical Results for Pull -Down, Self-Contained, Medium Temperatu re (PD.SC.M) ...................................................................................................... 8A-39

8A-v

### APPENDIX 8 A . DETAILED LIFE

- CYCLE COST AND PAYBACK PERIOD

ANALYSIS RESULTS

8A.1 INTRODUCTION

This appendix contains detailed output from the life- cycle cost (LCC) analysis for commercial refrigeration equipment using the LCC spreadsheet. LCC and payback periods (PBPs) are presented for each of the 24 product classes at each efficiency level. See chapter 8 of

the technical support document for an explanation of the LCC spreadsheet.

LCC savings output obtained from the LCC analysis are in the form of distributions. LCC savings distributions were generated by performing 10,000 separate simulations using the LCC spreadsheet, and allowing the spreadsheet to select values for numerous inputs given probability distributions for each input. Given the results of the LCC model simulations for each equipment type, the U.S. Department of Energy (DOE) calculated summary statistics

— average values for

the LCC, product installation costs and operati ng costs for a distribution of customers who: (1) receive benefits in the form of lower LCC results; (2) are not affected by the standards; or (3) experience a cost in the form of increased LCC from equipment ownership. Results of the LCC and accompanying PBP analyses are shown in the tables in this appendix.

Also included in this appendix are two graphical presentations for each of the LCC and PBP results. One of these presentations plots the LCC savings distribution for all equipment classes analyzed. The figures illustrate the mean and median values on the plot with the help of red and blue markers, respectively. Additionally, the mean and median values are surrounded by rectangular boxes used to represent the 25 th 

### and 75 th 

### percentile values. The lower ed ge of the elongated rectangle represents 25th percentile, which means that 25 percent of the customers will experience LCC savings below the value represented by the bottom of the rectangle if the standard were set at level in question. The upper edge of t he elongated rectangle represents the 75th percentile. Finally, each of the rectangles includes two “tails,” or two vertical black lines, extending downward to represent the 5th percentile (lower end) and upward to represent the 95th percentile (upper end)

.

The remainder of this appendix presents the tabular and graphical results for each of the 24 equipment classes analyzed for the commercial refrigeration equipment rulemaking.

8A-1

### 8A.2 LCC RESULTS

8A.2.1 LIFE

- CYCLE COST RESULTS FOR VERTICAL OPEN, RE MOTE CONDENSING, MEDIUM TEMPERATURE (VOP.RC.M)

Table 8B.2.1 LCC Results for Vertical Open, Remote Condensing, Medium Temperature (VOP.RC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 13,877 11,988 2,099 29,826 922 3.5 41.1 55.4 5.7

2 13,781 12,080 2,096 29,887 482 45.8 0.0 54.2 7.2

3 13,177 12,786 2,071 30,374

- 4.8 63.9 0.0 36.1 9.9

4 13,013 15,901 2,202 34,572

- 4,203 99.6 0.0 0.4 34.1



### Figure 8B.2.1 LCC Savings Statistical Results for Vertical Open, Remote Condensing, Medium Temperature (VOP.RC.M) -6000 -5000 -4000 -3000 -2000 -1000 0 1000 2000 3000

EL 1 EL 2 EL 3 EL 4Average LCC Savings ($)

Efficiency Level 

### VOP.RC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-2

### Figure 8B.2.2 PBP Statistical Results for Vertical Open, Remote Condensing, Medium Temperature (VOP.RC.M)

8A.2.2 LIFE

- CYCLE COST RESULTS FOR VERTICAL OPEN, RE MOTE CONDENSING, LOW TEMPERATURE (VOP.RC.L)

Table 8B.2.2 LCC Results for Vertical Open, Remote Condensing, Low Temperature (VOP.RC.L)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 38,301 11,799 4,427 49,521 53.5 7.3 40.1 52.6 6.1

2 37,117 12,631 4,353 49,707

- 148 58.8 20.0 21.2 11.3

3 36,502 17,725 4,534 56,289

- 6,701 100 0.0 0.0 310 1 10 100 1000

EL 1 EL 2 EL 3 EL 4Average Payback Period (years)

Efficiency Level 

### VOP.RC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-3

### Figure 8B.2.3 LCC Savings Statistical Results for Vertical Open, Remote Condensing, Low Temperature (VOP.RC.L)

Figure 8B.2.4 PBP Statistical Results for Vertical Open, Remote Condensing, Low Temperature (VOP.RC.L) -8000 -7000 -6000 -5000 -4000 -3000 -2000 -1000 0 1000

EL 1 EL 2 EL 3Average LCC Savings ($)

Efficiency Level 

### VOP.RC.L: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000 10000 100000

EL 1 EL 2 EL 3Average Payback Period (years)

Efficiency Level 

### VOP.RC.L: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-4

### 8A.2.3 LIFE

- CYCLE COST RESULTS FOR VERTICAL OPEN, SELF

- CONTAINED, MEDIUM TEMPERATURE ( VOP.SC.M)

Table 8B.2.3 LCC Results for Vertical Open, Self

- Contained, Medium Temperature (VOP.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

M edian Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 10,917 6,432 1,339 20,391

- 54.2 59.9 39.8 0.3 63.1

2 10,846 7,483 1,368 21,742

- 1,384 100 0.0 0.0 593



### Figure 8B.2.5 LCC Savings Statistical Results for Vertical Open, Self- Contained, Medium Temperature (VOP.SC.M) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0

EL 1 EL 2Average LCC Savings ($)

Efficiency Level 

### VOP.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-5

### Figure 8B.2.6 PBP Statistical Results for Vertical Open, Self

- Contained, Medium Temperature (VOP.SC.M)

8A.2.4 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED TRANSPARENT, REMOTE CONDENSING, M EDIUM TEMPERATURE (V CT.RC.M)

Table 8B.2.4 LCC Results for Vertical Closed Transparent, Remote Condensing, Medium Temperature (VCT.RC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 4,307 13,102 1,185 23,454 542 0.0 40.2 59.8 2.1

2 4,216 13,201 1,187 23,569 323 14.2 26.0 59.8 3.5

3 4,192 13,384 1,193 23,803 40.9 36.1 12.9 51.1 6.6

4 4,011 17,093 1,341 28,775

- 4,937 100 0.0 0.0 365 1 10 100 1000 10000 100000

EL 1 EL 2Average Payback Period (years)

Efficiency Level 

### VOP.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-6

### Figure 8B.2.7 LCC Savings Statistical Results for Vertical Closed Transparent, Remote Condensing, Medium Temperature (VCT.RC.M)

Figure 8B.2.8 PBP Statistical Results for Vertical Closed Transparent, Remote Condensing, Medium Temperature (VCT.RC.M) -6000 -5000 -4000 -3000 -2000 -1000 0 1000 2000

EL 1 EL 2 EL 3 EL 4Average LCC Savings ($)

Efficiency Level 

### VCT.RC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000 10000

EL 1 EL 2 EL 3 EL 4Average Payback Period (years)

Efficiency Level 

### VCT.RC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-7

### 8A.2.5 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED TRANSPARENT,

REMO TE CONDENSING, LOW TEMPERATURE (VCT.RC.L )

Table 8B.2.5 LCC Results for Vertical Closed Transparent, Remote Condensing, Low Temperature (VCT.RC.L)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 12,898 14,411 2,081 32,705 647 0.0 39.6 60.4 1.8

2 12,694 14,508 2,066 32,665 526 4.1 19.7 76.3 2.7

3 12,593 14,809 2,070 32,996 92.6 43.1 0.0 56.9 6.3

4 12,061 19,567 2,232 39,125

- 6,036 100 0.0 0.0 195



### Figure 8B.2.9 LCC Savings Statistical Results for Vertical Closed Transparent, Remote Condensing, Low Temperature (VCT.RC.L) -8000 -7000 -6000 -5000 -4000 -3000 -2000 -1000 0 1000 2000

EL 1 EL 2 EL 3 EL 4Average LCC Savings ($)

Efficiency Level 

### VCT.RC.L: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-8

### Figure 8B.2.10 PBP Statistical Results for Vertical Closed Transparent, Remote Condensing, Low Temperature (VCT.RC.L)

8A.2.6 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED TRANSPARENT,

SELF

- CONTAINED, MEDIUM TEMPERATURE (VCT.SC.M)

Table 8B.2.6 LCC Results for Vertical Closed Transparent, Self

- Contained, Medium Temperature (VCT.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 2,491 5,184 490 10,025

- 10.3 71.5 10.1 18.5 23.4

2 2,184 5,336 452 9,800 214 0.8 10.1 89.1 4.8

3 2,057 5,401 442 9,767 226 3.3 0.0 96.7 5.3

4 2,039 5,417 443 9,784 209 6.8 0.0 93.2 5.7

5 1,991 5,487 440 9,830 163 17.5 0.0 82.5 7.0

6 1,973 5,564 442 9,919 74.1 35.8 0.0 64.2 9.0

7 1,879 6,831 478 11,534

- 1,541 99.8 0.0 0.2 96.2 1 10 100 1000 10000

EL 1 EL 2 EL 3 EL 4Average Payback Period (years)

Efficiency Level 

### VCT.RC.L: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-9

### Figure 8B.2.11 LCC Savings Statistical Results for Vertical Closed Transparent, Self

Contained, Medium Temperature (VCT.SC.M)

Figure 8B.2.12 PBP Statistical Results for Vertical Closed Transparent, Self -Contained, Medium Temperature (VCT.SC.M) -2000 -1500 -1000

-500 0 500 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average LCC Savings ($)

Efficiency Level 

### VCT.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average Payback Period (years)

Efficiency Level 

### VCT.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-10

### 8A.2.7 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED TRANSPARENT,

SELF

- CONTAINED, LOW TEMPERATURE (VCT.SC.L)

Table 8B.2.7 LCC Results for Vertical Closed Transparent, Self -Contained, Low Temperature (VCT.SC.L)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 10,022 6,498 1,270 19,135 2,503 0.0 10.1 89.9 0.5

2 6,727 6,799 970 16,433 5,200 0.0 10.1 89.9 0.8

3 6,654 6,822 964 16,397 4,709 0.0 0.0 100 0.8

4 6,318 6,974 921 16,110 4,996 0.0 0.0 100 1.0

5 6,262 7,003 917 16,105 5,001 0.0 0.0 100 1.1

6 6,174 7,073 913 16,127 4,979 0.1 0.0 99.9 1.2

7 5,857 8,909 948 18,294 2,812 10.8 0.0 89.2 4.7



### Figure 8B.2.13 LCC Savings Statistical Results for Vertical Closed Transparent, Self

Contained, Low Temperature (VCT.SC.L) -4000 -2000 0 2000 4000 6000 8000 10000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average LCC Savings ($)

Efficiency Level 

### VCT.SC.L: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-11

### Figure 8B.2.14 PBP Statistical Results for Vertical Closed Transparent, Self -Contained, Low Temperature (VCT.SC.L)

8A.2.8 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED TRANSPARENT,

SELF

- CONTAINED, ICE

- CREAM TEMPERATURE (VCT.SC.I)

Table 8B.2.8 LCC Results for Vertical Closed Transparent, Self -Contained, Ice- Cream Temperature (VCT.SC.I)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 7,124 7,305 1,015 17,384 18.0 9.6 40.2 50.2 7.2

2 7,030 7,375 1,009 17,396 4.2 36.7 32.4 31.0 10.7

3 6,916 7,509 1,003 17,468

- 67.6 64.6 24.3 11.1 16.2

4 6,609 9,780 1,057 20,242

- 2,834 83.8 16.2 0.0 664 0 1 10 100

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average Payback Period (years)

Efficiency Level 

### VCT.SC.L: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-12

### Figure 8B.2.15 LCC Savings Statistical Results for Vertical Closed Transparent, Self

Contained, Ice- Cream Temperature (VCT.SC.I)

Figure 8B.2.16 PBP Statistical Results for Vertical Closed Transparent, Self -Contained,

Ice

- Cream Temperature (VCT.SC.I) -3500 -3000 -2500 -2000 -1500 -1000

-500 0 500

EL 1 EL 2 EL 3 EL 4Average LCC Savings ($)

Efficiency Level 

### VCT.SC.I: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000 10000 100000

EL 1 EL 2 EL 3 EL 4Average Payback Period (years)

Efficiency Level 

### VCT.SC.I: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-13

### 8A.2.9 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED S OLID, SELF

CONTAINED, MEDIUM TEMPERATURE (VCS.SC .M)

Table 8B.2.9 LCC Results for Vertical Closed Solid, Self

- Contained, Medium Temperature (VCS.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 1,929 3,572 368 6,378 223 0.0 39.6 60.4 0.5

2 1,469 3,601 326 6,083 518 0.0 39.6 60.4 0.6

3 1,401 3,625 321 6,065 365 1.1 9.9 89.0 1.0

4 1,346 3,651 318 6,067 363 7.5 9.9 82.7 1.4

5 1,269 3,722 315 6,117 313 24.4 9.9 65.7 2.4

6 1,258 3,734 314 6,125 305 25.2 9.9 64.9 2.6

7 1,105 5,062 365 7,828

- 1,428 99.8 0.0 0.2 48.0



### Figure 8B.2.17 LCC Savings Statistical Results for Vertical Closed Solid, Self -Contained, Medium Temperature (VCS.SC.M) -2000 -1500 -1000

-500 0 500 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average LCC Savings ($)

Efficiency Level 

### VCS.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-14

### Figure 8B.2.18 PBP Statistical Results for Vertical Closed Solid, Self

- Contained, Medium Temperature (VCS.SC.M)

8A.2.10 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED S OLID, SELF

CONTAINED, LOW TEMPERATURE (VCS.SC.L)

Table 8B.2.10 LCC Results for Vertical Closed Solid, Self

- Contained, Low Temperature (VCS.SC.L)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 5,088 4,007 702 9,374 588 0.0 40.2 59.8 0.6

2 4,817 4,060 679 9,253 513 0.0 10.3 89.7 1.1

3 4,722 4,083 672 9,215 550 0.0 10.3 89.7 1.3

4 4,573 4,154 661 9,201 565 0.2 10.3 89.5 1.9

5 4,448 4,216 653 9,201 507 7.2 0.0 92.8 2.5

6 4,410 4,238 651 9,213 495 9.1 0.0 90.9 2.7

7 4,062 5,988 703 11,349

- 1,640 99.6 0.0 0.4 31.8 0 1 10 100 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average Payback Period (years)

Efficiency Level 

### VCS.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-15

### Figure 8B.2.19 LCC Savings Statistical Results for Vertical Closed Solid, Self -Contained, Low Temperature (VCS.SC.L)

Figure 8B.2.20 PBP Statistical Results for Vertical Closed Solid, Self -Contained, Low Temperature (VCS.SC.L) -2500 -2000 -1500 -1000

-500 0 500 1000 1500

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average LCC Savings ($)

Efficiency Level 

### VCS.SC.L: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

0 1 10 100 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average Payback Period (years)

Efficiency Level 

### VCS.SC.L: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-16

### 8A.2.11 LIFE

- CYCLE COST RESULTS FOR VERTICAL CLOSED SOLID, SELF

CONTAINED, ICE

- CREAM TEMPERATURE (V CS.SC.I)

Table 8B.2.11 LCC Results for Vertical Closed Solid, Self -Contained, Ice- Cream Temperature (VCS.SC.I)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 6,824 4,349 895 11,195 41.4 0.0 40.3 59.7 2.6

2 6,574 4,420 876 11,117 114 0.4 32.1 67.5 3.6

3 6,509 4,444 871 11,109 111 2.8 24.1 73.2 4.0

4 6,361 4,515 861 11,096 113 8.5 16.7 74.8 5.0

5 5,855 6,839 927 13,909

- 2,710 91.8 8.2 0.0 184



### Figure 8B.2.21 LCC Savings Statistical Results for Vertical Closed Solid, Self -Contained,

Ice

- Cream Temperature (VCS.SC.I) -3500 -3000 -2500 -2000 -1500 -1000

-500 0 500

EL 1 EL 2 EL 3 EL 4 EL 5Average LCC Savings ($)

Efficiency Level 

### VCS.SC.I: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-17

### Figure 8B.2.22 PBP Statistical Results for Vertical Closed Solid , Self -Contained, Ice- Cream Temperature (VCS.SC.I)

8A.2.12 LIFE

- CYCLE COST RESULTS F OR SEMI

- VERTICAL OPEN, REMOT E CONDENSING, MEDIUM TEMPERATURE (SVO.RC.M )

Table 8B.2.12 LCC Results for Semi

- Vertical Open, Remote Condensing, Medium Temperature (SVO.RC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 10,748 10,304 1,694 24,841 564 7.0 39.5 53.5 6.2

2 10,276 10,797 1,672 25,134 48.1 63.1 0.0 36.9 9.9

3 10,226 10,875 1,670 25,201

- 18.5 66.6 0.0 33.5 10.4

4 10,111 12,867 1,752 27,873

- 2,691 99.6 0.0 0.4 29.9 1 10 100 1000 10000

EL 1 EL 2 EL 3 EL 4 EL 5Average Payback Period (years)

Efficiency Level 

### VCS.SC.I: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-18

### Figure 8B.2.23 LCC Savings Statistical Results for Semi- Vertical Open, Remote Condensing, Medium Temperature (SVO.RC.M)

Figure 8B.2.24 PBP Statistical Results for Semi -Vertical Open, Remote Condensing, Medium Temperature (SVO.RC.M) -4000 -3000 -2000 -1000 0 1000 2000

EL 1 EL 2 EL 3 EL 4Average LCC Savings ($)

Efficiency Level 

### SVO.RC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100

EL 1 EL 2 EL 3 EL 4Average Payback Period (years)

Efficiency Level 

### SVO.RC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-19

### 8A.2.13 LIFE

- CYCLE COST RESULTS F OR SEMI

- VERTICAL OPEN, SELF

CONTAINED, MEDIUM TEMPERATURE (SVO.SC.M)

Table 8B.2.13 LCC Res ults for Semi

- Vertical Open, Self

- Contained, Medium Temperature (SVO.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 9,361 5,157 1,132 16,728 5.5 32.4 40.1 27.5 10.9

2 9,331 5,219 1,132 16,782

- 50.9 87.1 0.0 12.9 17.5

3 9,271 5,897 1,151 17,648

- 917 99.9 0.0 0.1 152



### Figure 8B.2.25 LCC Savings Statistical Results for Semi- Vertical Open, Self -Contained, Medium Temperature (SVO.SC.M) -1200 -1000

-800 -600 -400 -200 0 200

EL 1 EL 2 EL 3Average LCC Savings ($)

Efficiency Level 

### SVO.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-20

### Figure 8B.2.26 PBP Statistical Results for Semi

- Vertical Open, Self

- Contained, Medium Temperature (SVO.SC.M)

8A.2.14 LIFE

- CYCLE COST RESULTS FOR SERVICE OVER COUN TER, REMOTE CONDENSING, MEDIUM TEMPERATURE (SOC.RC.M )

Table 8B.2.14 LCC Results for Service Over Counter, Remote Condensing, Medium Temperature (SOC.RC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 8,144 14,144 1,674 28,301

- 128 59.5 40.1 0.4 38.0

2 8,000 14,332 1,669 28,447

- 223 99.5 0.0 0.5 41.9

3 7,957 14,404 1,668 28,511

- 287 99.5 0.0 0.5 47.0

4 7,869 15,879 1,729 30,492

- 2,268 100 0.0 0.0 114 1 10 100 1000 10000

EL 1 EL 2 EL 3Average Payback Period (years)

Efficiency Level 

### SVO.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-21

### Figure 8B.2.27 LCC Savings Statistical Results for Service Over Counter, Remote Condensing, Medium Temperature (SOC.RC.M)

Figure 8B.2.28 PBP Statistical Results for Service Over Counter, Remote Condensing, Medium Temperature (SOC.RC.M) -3000 -2500 -2000 -1500 -1000

-500 0

EL 1 EL 2 EL 3 EL 4Average LCC Savings ($)

Efficiency Level 

### SOC.RC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000 10000

EL 1 EL 2 EL 3 EL 4Average Payback Period (years)

Efficiency Level 

### SOC.RC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-22

### 8A.2.15 LIFE

- CYCLE COST RESULTS FOR SERVICE OVER COUN TER, SELF

CONTAINED, MEDIUM TEMPERATURE (SOC.SC.M)

Table 8B.2.15 LC C Results for Service Over Counter, Self

- Contained, Medium Temperature (SOC.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 9,917 14,138 1,817 28,006

- 144 59.7 40.0 0.4 47.5

2 9,711 14,332 1,808 28,128

- 209 99.5 0.0 0.5 28.7

3 9,652 14,404 1,807 28,193

- 274 99.5 0.0 0.5 34.5

4 9,533 15,880 1,868 30,123

- 2,204 100 0.0 0.0 25.3



### Figure 8B.2.29 LCC Savings Statistical Results for Service Over Counter, Self -Contained, Medium Temperature (SOC.SC.M) -3000 -2500 -2000 -1500 -1000

-500 0

EL 1 EL 2 EL 3 EL 4Average LCC Savings ($)

Efficiency Level 

### SOC.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-23

### Figure 8B.2.30 PBP Statistical Results for Service Over Counter, Self

- Contained, Medium Temperature (SOC.SC.M)

8A.2.16 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL OPEN, REMOTE CONDENSING, MEDIUM TEMPERATURE (HZO.RC.M )

Table 8B.2.16 LCC Results for Horizontal Open, Remote Condensing, Medium Temperature (HZO.RC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 5,165 9,921 1,103 19,137

- 2,180 59.6 40.4 0.0

--- 1 10 100 1000

EL 1 EL 2 EL 3 EL 4Average Payback Period (years)

Efficiency Level 

### SOC.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-24

### Figure 8B.2.31 LCC Savings Statistical Results for Horizontal Open, Remote Condensing, Medium Temperature (HZO.RC.M)

Figure 8B.2.32 PBP Statistical Results for Horizontal Open, Remote Condensing, Medium Temperature (HZO.RC.M) -2300 -2250 -2200 -2150 -2100 -2050 -2000 -1950

EL 1Average LCC Savings ($)

Efficiency Level 

### HZO.RC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10

EL 1Average Payback Period (years)

Efficiency Level 

### HZO.RC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-25

### 8A.2.17 LIFE

- CYCLE COST RESULTS FOR HORIZONT AL OPEN, REMOTE CONDENSING, LOW TEMPERATURE (HZO.RC.L)

Table 8B.2.17 LCC Results for Horizontal Open, Remote Condensing, Low Temperature (HZO.RC.L)

Level Annual Energy

Use kWh/yr Mean Valu es of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 11,344 11,822 1,787 26,795

- 4,249 60.3 39.7 0.0 289



### Figure 8B.2.33 LCC Savings Statistical Results for Horizontal Open, Remote Condensing, Low Temperature (HZO.RC.L) -4600 -4500 -4400 -4300 -4200 -4100 -4000 -3900 -3800 -3700

EL 1Average LCC Savings ($)

Efficiency Level 

### HZO.RC.L: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-26

### Figure 8B.2.34 PBP Statistical Results for Horizontal Open, Remote Condensing, Low Temperature (HZO.RC.L)

8A.2.18 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL OPEN, SELF

CONTAINED, MEDIUM TEMPERATURE (HZO.SC.M)

Table 8B.2.18 LCC Results for Horizontal Open, Self

- Contained, Medium Temperature (HZO.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 5,168 2,698 615 8,967 55.5 5.4 40.2 54.4 6.9

2 5,118 2,763 613 9,013

- 4.3 50.1 20.5 29.4 11.8

3 5,018 3,689 636 10,163

- 1,154 100 0.0 0.0 195 1 10 100 1000

EL 1Average Payback Period (years)

Efficiency Level 

### HZO.RC.L: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-27

### Figure 8B.2.35 LCC Savings Statistical Results for Horizontal Open, Self -Contained, Medium Temperature (HZO.SC.M)

Figure 8B.2.36 PBP Statistical Results for Horizontal Open, Self

- Contained, Medium Temperature (HZO.SC.M) -1400 -1200 -1000

-800 -600 -400 -200 0 200 400

EL 1 EL 2 EL 3Average LCC Savings ($)

Efficiency Level 

### HZO.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000 10000

EL 1 EL 2 EL 3Average Payback Period (years)

Efficiency Level 

### HZO.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-28

### 8A.2.19 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL OPEN, SELF

CONTAINED, LOW TEMPERATURE (HZO.SC.L)

Table 8B.2.19 LCC Results for Horizontal Open, Self

- Contained, Low Tempera ture (HZO.SC.L)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1

2

3

4

5

6

7



### Figure 8B.2.37 LCC Savings Statistical Results for Horizontal Open, Self -Contained, Low Temperature (HZO.SC.L) -520 -500 -480 -460 -440 -420 -400

1 2 3 4 5 6 7 8Life-Cycle Cost Savings ($)

Efficiency Level Median Mean `



### 8A-29

### Figure 8B.2.38 PBP Statistical Results for Horizontal Open, Self -Contained, Low Temperature (HZO.SC.L)

8A.2.20 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF

- CONTAINED, MEDIUM TE MPERA TURE (HCT.SC.M)

Table 8B.2.20 LCC Results for Horizontal Closed Transparent, Self

- Contained, Medium Temperature (HCT.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 637 2,090 144 3,607 35.9 0.0 40.2 59.8 1.8

2 590 2,101 140 3,577 66.0 0.0 40.2 59.8 2.5

3 360 2,198 122 3,478 165 0.0 40.2 59.8 4.7

4 327 2,213 120 3,476 101 19.8 0.0 80.2 5.8

5 321 2,219 121 3,485 91.2 32.0 0.0 68.0 6.5

6 289 2,279 120 3,534 42.7 45.2 0.0 54.9 9.2

7 224 2,807 131 4,175

- 599 99.7 0.0 0.3 46.6 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

1 2 3 4 5 6 7 8Payback Period (Years)

Efficiency Level Median Mean `



### 8A-30

### Figure 8B.2.39 LCC Savings Statistical Results for Horizontal Closed Transparent, SelfContained, Medium Temperature (HCT.SC.M)

Figure 8B.2.40 PBP Statistical Results for Horizontal Closed Transparent, Self -Contained, Medium Temperature (HCT.SC.M) -800 -600 -400 -200 0 200 400

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average LCC Savings ($)

Efficiency Level 

### HCT.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average Payback Period (years)

Efficiency Level 

### HCT.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-31

### 8A.2.21 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF

- CONTAINED, LOW TEMPERATURE (HCT.SC.L)

Table 8B.2.21 LCC Results for Horizontal Closed Transparent, Self -Contained, Low Temperature (HCT.SC.L)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 820 2,290 192 3,895 415 0.0 40.5 59.5 1.8

2 785 2,297 190 3,882 428 0.0 40.5 59.5 1.8

3 742 2,312 187 3,876 435 0.0 40.5 59.5 2.0

4 701 2,330 185 3,870 293 10.0 10.0 80.1 2.5

5 687 2,339 184 3,877 286 19.5 10.0 70.5 2.7

6 632 2,399 182 3,915 248 29.1 10.0 61.0 3.6

7 480 3,120 200 4,775

- 613 87.5 10.0 2.6 19.5



### Figure 8B.2.41 LCC Savings Statistical Results for Horizontal Closed Transparent, Self

Contained, Low Temperature (HCT.SC.L) -1200 -1000

-800 -600 -400 -200 0 200 400 600 800 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average LCC Savings ($)

Efficiency Level 

### HCT.SC.L: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-32

### Figure 8B.2.42 PBP Statistical Results for Horizontal Closed Transparent, Self -Contained, Low Temperature (HCT.SC.L)

8A.2.22 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF

- CONTAINED, ICE

- CREAM TEMPERATURE (HCT.SC.I)

Table 8B.2.22 LCC Results for Horizontal Closed Transparent, Self -Contained, Ice- Cream Temperature (HCT.SC.I)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 849 3,553 264 5,587

- 1,240 61.0 39.0 0.0 23.8 1 10 100

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average Payback Period (years)

Efficiency Level 

### HCT.SC.L: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-33

### Figure 8B.2.43 LCC Savings Statistical Results for Horizontal Closed Transparent, SelfContained, Ice- Cream Temperature (HCT.SC.I)

Figure 8B.2.44 PBP Statistical Results for Horizontal Closed Transparent, Self -Contained,

Ice

- Cream Temperature (HCT.SC.I) -1400 -1350 -1300 -1250 -1200 -1150 -1100 -1050 -1000

EL 1Average LCC Savings ($)

Efficiency Level 

### HCT.SC.I: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

1 10 100 1000

EL 1Average Payback Period (years)

Efficiency Level 

### HCT.SC.I: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-34

### 8A.2.23 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL CLOSED SOLID, SELF

CONTAINED, MEDIUM TEMPERATURE (HC S.SC.M)

Table 8B.2.23 LCC Results for Horizontal Closed Solid, Self

- Contained, Medium Temperature (HCS.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 518 1,986 146 3,100 11.5 0.1 9.5 90.5 2.9

2 495 1,993 145 3,095 16.6 0.5 9.5 90.1 3.7

3 466 2,008 143 3,097 14.6 10.5 9.5 80.1 5.5

4 461 2,014 144 3,107 4.8 42.4 9.5 48.2 7.5

5 427 2,073 143 3,163

- 51.7 89.6 9.5 1.0 19.9

6 358 2,488 157 3,679

- 568 90.6 9.5 0.0 681



### Figure 8B.2.45 LCC Savings Statistical Results for Horizontal Closed Solid, Self

Contained, Medium Temperature (HCS.SC.M) -700 -600 -500 -400 -300 -200 -100 0 100

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6Average LCC Savings ($)

Efficiency Level 

### HCS.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-35

### Figure 8B.2.46 PBP Statistical Results for Horizontal Closed Solid, Self -Contained, Medium Temperature (HCS.SC.M)

8A.2.24 LIFE

- CYCLE COST RESULTS FOR HORIZONTAL CLOSED SOLID, SELF

CONTAINED, LOW TEMPERATURE (HCS.SC.L)

Table 8B.2.24 LCC Results for Horizontal Closed Solid, Self

- Contained, Low Temperature (HCS.SC.L)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 650 2,006 160 3,224 31.4 0.0 9.9 90.1 1.4

2 609 2,013 156 3,205 49.7 0.0 9.9 90.1 1.7

3 558 2,028 153 3,191 64.2 0.0 9.9 90.1 2.5

4 548 2,034 153 3,197 58.3 0.1 9.9 90.0 3.1

5 472 2,093 148 3,222 33.0 19.7 9.9 70.4 6.2

6 260 2,663 156 3,845

- 590 89.7 9.9 0.4 68.9 1 10 100 1000 10000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6Average Payback Period (years)

Efficiency Level 

### HCS.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-36

### Figure 8B.2.47 LCC Savings Statistical Results for Horizontal Closed Solid, Self

Contained, Low Temperature (HCS.SC.L)

Figure 8B.2.48 PBP Statistical Results for Horizontal Closed Solid, Self -Contained, Low Temperature (HCS.SC.L) -800 -700 -600 -500 -400 -300 -200 -100 0 100 200

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6Average LCC Savings ($)

Efficiency Level 

### HCS.SC.L: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average

0 1 10 100 1000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6Average Payback Period (years)

Efficiency Level 

### HCS.SC.L: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-37

### 8A.2.25 LIFE

- CYCLE COST RESULTS FOR PULL -DOWN, SELF- CONTAINED, MEDIUM TEMPERATURE ( PD.SC.M)

Table 8B.2.25 LCC Results for Pull -Down, Self- Contained, Medium Temperature (PD.SC.M)

Level Annual Energy

Use kWh/yr Mean Values of Life

- Cycle Cost Savings

Median Payback

Period

years Installed

Cost
2012$ Annual Operating

Cost
2012$ LCC
2012$ Average

LCC Savings
2012$ Customers that Experience ( %)

Net

Cost No Impact Net

Benefit

1 1,726 3,502 342 6,732 7.5 27.5 39.3 33.2 9.3

2 1,422 3,654 310 6,574 163 3.3 0.0 96.7 5.3

3 1,381 3,677 308 6,572 165 4.8 0.0 95.2 5.6

4 1,369 3,691 308 6,587 150 7.6 0.0 92.4 6.0

5 1,329 3,757 307 6,640 96.9 24.3 0.0 75.7 7.6

6 1,320 3,811 308 6,705 32.4 45.2 0.0 54.8 9.4

7 1,243 4,808 340 7,989

- 1,252 99.8 0.0 0.2 102



### Figure 8B.2.49 LCC Savings Statistical Results for Pull -Down, Self

- Contained, Medium Temperature (PD.SC.M) -2000 -1500 -1000

-500 0 500

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average LCC Savings ($)

Efficiency Level 

### PD.SC.M: Average LCC Savings Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-38

### Figure 8B.2.50 PBP Statistical Results for Pull -Down, Self- Contained, Medium Temperature (PD.SC.M) 1 10 100 1000 10000

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7Average Payback Period (years)

Efficiency Level 

### PD.SC.M: Average Payback Period Average; Median; Box 25%

- 75%; Whisker 5%

- 95% median

average



### 8A-39

### APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN LIFE

- CYCLE COST

ANALYSIS

8B.1 INTRODUCTION .........................................................................................................8B-1

8B.2 UNCERTAINTY ...........................................................................................................8B-1

8B.3 VARIABILI TY ..............................................................................................................8B-1

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY .....................................8B-1

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL ® 

### ......................8B-2

8B -i

### APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN LCC ANALYSIS

8B.1 INTRODUCTION

The U.S. Department of Energy’s ( DOE’s ) approach to analysis of an energy- efficiency

standard also includes analyzing the range of potential impacts of higher efficiency equipment on

consumers. DOE uses the consumer life- cycle cost (L CC) as the key metric for this analysis and

examine s the variability of the LCC of higher efficiency equipment by varying a wide range of input assumptions. To perform the calculation, analyst s 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 simplest 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, in most cases, the model and/or the numerical values for each quantity in the model are not completely known ( i.e., there is uncertainty) , and/or the nume rical values for each quantity in the model depend upon other conditions ( i.e., there is variability).

Thorough analysis involves accounting for uncertainty and variability. While 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. Explicit analysis of uncertainty and variability is intended to provide more complete information to the decision- making process for establishing standards for comme rcial refrigeration equipment

.

8B.2 UNCERTAINTY

When making observations of past events or speculating about the future, imperfect knowledge is the rule rather than the exception. For example, the lifetime of a refrigerator used

in a restaurant is not available at the time of purchase , but rather estimated based upon available historic information. 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 different applications or situations produce different numerical values when calculating a quantity. Specifying an exact value for a quantity may be difficult because the value depends on another parameter . For example, the electricity price rate faced by a customer is dependent on the state where the customer’s business is located and the type of

business.

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILIT Y

This section describes two approaches to uncertainty and variability: scenario analysis

and probability analy sis.

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. A number of calculations are done, which provides some indication of the extent to which the result depends on the assumptions. 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

8B -1

### point i s the energy price rate at which the consumer achieves savings in operating expense that more than compensate for the increased purchase expense.) The disadvantage of scenario analysis is that there is no information about the likelihood of each scenario.

Probability analysis considers the probabilities within a range of values. For quantities with variability ( e.g., electricity price rates paid by different users), surveys can be used to
generate a frequency distribution of numerical values ( e.g., the num ber of commercial buildings with electricity rates at particular levels) to estimate the probability of each value. For quantities with uncertainty, statistical or subjective measures can be used to provide probabilities ( e.g., manufacturing cost to improv e energy efficiency to some level may be estimated to be
$100 ± $20 ). The major disadvantage of the probability approach is that it requires more information, namely information about the shapes and magnitudes of the variability and uncertainty of each quantity. The advantage of the probability approach is that it provides greater information about the outcome of the calculations ( i.e., it provides the probability that the

outcome will be in a particular range ). Scenario and probability analysis provide some indication of the robustness of the policy given the uncertainties and variability. A policy is robust when the impacts are acceptable over a wide range of possible conditions.

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYST AL BALL ®



### To quantify the uncertainty and variability that exist in inputs to the engineering, LCC, and payback period analyses, DOE used Microsoft Excel spreadsheets combined with Crystal Ball, a commercially available add -in, to conduct probability analyses. The probability analysi s is carried out in the form of Monte Carlo simulation s. Simulation refers to any analytical method

meant to imitate a real

- life system, especially when other analyses are mathematically complex or difficult to reproduce. Without the aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most likely or average scenario.

Monte Carlo simulations are carried out by performing the LCC analysis multiple times (10,000 times for each equipment class for this rulemaking). Each simulation has one particular set of inputs. The inputs are chosen from their respective probability distributions. For example, there are 50 states in the United States and the probability of choosing one state for a simulation is based on the population of the state. The higher the population of a state , the higher the probability of choosing that state for a particular simulation. Similarly, the equipment lifetime is specified by Weibull distribution curves with mean equipment lifetime of 10 or 15 years. C rystal Ball chooses the equipment lifetime for any particular simulation based on the Weibull probability distribution curve. Thus, each Monte Carlo simulation is a random combination of input values, with each input value chosen according to its respectiv e probability distribution. That is, d uring a single trial, Crystal Ball selects a value from the defined p ossibilities (the range and shape of the probability distribution) for each uncertain variable and then recalculates the spreadsheet.

8B -2

### CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION .............................................................................................................. 9-1

9.2 SHIPMENTS MODEL EQUA TIONS ............................................................................... 9-1

9.2.1 Shipments Model .................................................................................................... 9-2

9.2.2 Stock Events............................................................................................................ 9-2

9.2.2.1 Replacement Equipment .......................................................................... 9-3

9.2.2.2 New Equipment ....................................................................................... 9-3

9.3 DATA INPUTS .................................................................................................................. 9-4

9.3.1 Historical Shipments ............................................................................................... 9-4

9.3.2 Building Stock Additions ........................................................................................ 9-6

9.3.3 Market Saturation .................................................................................................... 9-8

9.4 RESULTS ........................................................................................................................... 9-9

LIST OF TABLES

Table 9.2 Building types used in the NOPR vs. Final Rule shipments model..................... 9-2

Table 9.3 AEO2012 Forecast of new food sales and food service square footage .............. 9-6

Table 9.4 Equipment linear dimensions assumed for shipments analysis ........................... 9-7

Table 9.5 Percent of shipped linear feet of commercial refrigeration dquipment ............... 9-7

Table 9.6 Market saturations and other data used in the shipments model ......................... 9-8

Table 9.7 Projected shipments for Commercial Refrigeration Equipment, 2017

− 2046, by equipment type) ................................................................................................. 9-10

LIST OF FIGURES

Figure 9.1 Projected shipments for Commercial Refrigeration Equipment, 2017-2046 ....... 9-9

9-i

### CHAPTER 9 . SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Commercial refrigeration equipment shipment numbers are key inputs to the national energy savings analysis

, net present value calculations , and the manufacturer impacts analysis. This chapter describes the U.S. Department of Energy ’s (DOE’s ) methodology for estimating commercial refrigeration equipment shipments and annual equipment stocks for 2009 through

2046.

The Shipments Model results are driven primarily by the combination of equipment saturations, expressed as the number of units per building, and floorspace projections by building type. The shipment model is validated using available historical shipment s data for the

25 primary classes of commercial refrigeration equipment under consideration.

The model assumes that, in each year, a given unit that is part of the existing commercial refrigeration equipment stock either ages by one year or breaks. Broken equipment is replaced. In addition, new equipment can be installed into new commercial floor space, and old equipment can be removed through demolitions.

Section 9.2 presents the mathematical formulation of the model

, section 9.3 describes the data input to the model, and section 9.4 presents the results for the base

- case scenario , and discusses the development of higher standard scenarios.

9.2 SHIPMENTS MODEL EQUA TIONS

The Shipments Model is a description of commercial refrigeration equipment stock flows as a function of year and age. The stock model uses the equipment categories defined in the

CBECS survey data for the years 1999 and 2003, which consist of open and closed refrigeration

cases. S hipments of these two types of equipment are then disaggregated across the 25 classes of covered equipment using fixed market shares.

The Shipments Model equations are used to calculate the functioning equipment stock as a function of the analysis year y and the stock age a , or equivalently vintage v . The vintage is the year in which the equipment is purchased, with a = y -v . Shipments forecasts are calculated for

two market segments consisting of (i) new installations in new buildings, and (ii) replacement

installation in existing buildings. The demand for replacements is calculated using a retirement

function based on the lifetime distribution us ed in the LCC analysis

. DOE typically refers to new shipments intended to replace retired units as replacement shipments. Such shipments are usually the largest part of total shipments. Equipment saturations are assumed to remain constant throughout the analysis period. The model is initialized in 1992, using an estimate of the standing stock of equipment based on the CBECS 1992 survey data. The model is then run for the years 1992 to 2017, to produce an estimate of the number of units in the stock by building type and vintage in the start year of the standards analysis. Finally, the model is run through the full analysis period to forecast the total stock by vintage and total shipments in each year.

9-1

### 9.2.1 Shipments Model

In the NOPR analysis, DOE divided the building stock into a number of building types covering different types of food sales and food service businesses. DOE received comment from

stakeholders indicating that equipment lifetimes are substantially longer in smaller businesses. To account for this effect, DOE revised the stock accounting in the Final Rule to separate all business types into small and large, with small buildings defined as having floor space of 5,000 square feet or less . The correspondence between the NOPR building or business categories and those used in the final rule is presented in Table 1

.

Table 9.1 Building types used in the NOPR vs. Final Rule shipments model

NOPR Building Type Final Rule Building Type

Large Grocery / Multi

- Line Retail Large Food Sales

Small Grocery / Convenience Small Food Sales

Limited Service Restaurants Small Food Service

Full Service Restaurants Large Food Service

Other Small Other

Other Large Other



### The model equations are used to calculate t he stock U(b,s,y,v) , where

• U is the stock of equipment of a given type

• b is the building type (food sales, food service or other)

• s is the building size (small, defined as less than or equal to 5000 square feet, and large)

• y is the year

• v is the vintage, the year of equipment purchase

9.2.2 Stock Events

In the transition from year y to year y +1

, three things could happen to the stock of

commercial refrigeration equipment: (1) existing equipment could fail and be replaced ; or (2) the stock could simply age by 1 year , or (3) stock may be removed when the building that contains

it is demolished. In the model, early replacements ( i.e. , existing equipment that is replaced before

it is broken) are not considered, and all broken equipment is assumed to be replaced.

9-2

### 9.2.2.1 Replac ement Equipment

DOE determines the probability that commercial refrigeration equipment of age a from

stock U will break or will be replaced using a Weibull survival distribution function P( a) = W( τ,β) where τ is the average lifetime and β controls the width of the distr ibution ( τ = 15 years

, β = 16 ;

τ = 10 years , β = 10.62). These probabilities do not depend on the model year y . D OE defines the number of broken units to be replaced as

UB (

b,s,y ) = ∑
v 

### P(y-v) U( b,s,y,v ) Eq. 9

. 1

W here:

• UB ( b,s,v ) = quantity of replacement units

• P ( a ) = probability that stock of existing units of age a will break

9.2.2.2 New Equipment

The model assumes that new commercial refrigeration equipment is purchased to replace

the existing units at the end of their lifetime, and for service in new buildings. T he purchase of new equipment for use in new buildings is driven by the rate of construction as projected by the AEO and equipment saturations.

Floorspace growth is determined by

EFS(b,s,y)=EFS(b,s,y−1)+NFS(b,s,y)−DFS(b,s,y) Eq. 9

. 2

W here:

• EFS( b,s,y) = the square footage of existing floor space in year

• NFS(b,s,y ) = the square footage of new floor space

• DFS (b,s,y ) = the square footage of demolished floorspace

AEO provides projection of new and existing floorspace by building type. These are further disaggregated into small and large based on CBECS 2003 floorspace data. Equipment saturations, n(b,s) , defined as the average number of units of a given type per unit of floorspace are used to convert new floorspace estimates to estimates of shipments to new buildings. The stock of new equipment of vintage v=y is simply the sum of shipments to new buildings plus replacements:

U(b,s,y,v=y) = n(b,s)EFS(b, s,y) + UB(b,s,y) Eq. 9

. 3

9-3

### W here:

• n(b,s) = the average number of units per square foot

The functioning stock for v<y is updated through the equation:

U(b,s,y,v) = U(v,s,y

- 1,v) - UB(b,s,y

- 1,v) , v<y Eq. 9.4

The model is initialized in the year 1992 assuming that the distribution of stock across vintages is flat. The model is then run from 1992 to the beginning of the analysis period to provide the estimate of initial stock by vintage. Comparison between model predictions and historical shipments data are used to refine the estimate of market saturations.

9.3 DATA INPUTS

9.3.1 Historical Shipments

Shipments data for commercial refrigeration equipment could not be obtained in a complete form from any one source ; therefore, DOE used data from multiple sources to estimate shipments and to compare and cross-verify the shipments data from one source to another. The

data sources include

• 2005 shipments data provided by the Air -Conditioning, Heating, and Refrigeration Institute ( AHRI ) as part of its comments to the 2009 rulemaking F ramework document (Docket No. EERE-2006- STD -0126, ARI, No. 7, Exhibit B at p. 1)

• Commercial Refrigeration Equipment to 2014 by Freedonia Group , Inc. ( the Freedonia 2010 report) 1



### • 2008 Size and Shape of Industry by the North American Association of Food Equipment

Manufacturers and the updated 2013 report ( NAFEM report s) 2



### • 2012 Size and Shape of Industry by the North American Association of Food Equipment

Manufacturers and the updated 2013 report ( NAFEM report s) 3



### • Energy Savings Potential and R&D Opportunities for Commercial Refrigeration prepared by Navigant Consulting, Inc ., for DOE (NCI 2009 report). 4



### • CBECS 1999 and 2003 survey micro

- data

.

• Energy Star Unit Equipment and Market Penetration Reports , 2006-2013.

The AHRI 2005 shipments data was a compilation of shipments numbers provided by

AHRI members , and as such did not represent the entire commercial refrigeration equipment

market. However, the AHRI 2005 shipments data contained shipments explicitly broken down

9-4

### by DOE equipment class. The Freedonia 2010 report provided shipments data in terms of total

dollar sales and number of units shipped. Shipments we re split by market type ( e.g. , foodservice industry, food and beverage retail industry) and by certain equipment types ( e.g., open display

cases
, reach -ins). The two NAFEM report s provided shipment numbers and doll ar sales values for refrigeration and ice machines used in the foodservice industry. Reach- in refrigerators and freezers, chest freezer s, milk coolers, and ice-cream storage and dipping cabinets make up the majority of the commercial refrigeration equipmen t used in the foodservice industry. The Energy

Star reports provide estimates of total shipments of CRE products covered by existing Energy Star specifications, and the percentage of those shipments that are Energy Star compliant. The CBECS data provide an estimate of number of units per building, which can be converted to a saturation of number of units per square foot of floor space, by building type.

Because c omplete shipments data could not be obtained from a single source, DOE compiled data from all of the aforementioned sources and developed estimates of parameters

needed for the shipments model using simple assumptions. The Freedonia 2010 report contains

historical data used to derive a year -to- year percentage change in total commercial refrigeration equipment sales through 2009 . DOE assumed that subsequent years’ shipments of the equipment reported in the 2005 AHRI summary grew at the year -to- year growth rates reported by Freedonia, and used the percentage changes from the Fre edonia 2010 report to estimate the shipments by equipment class for 2009. These numbers were used to define an initial estimate of market share by equipment class.

The 2009 shipments numbers derived from the 2005 AHRI shipments data were modified

for certa in equipment classes using shipments data obtained f rom the other sources. Shipments for equipment classes VCS.SC.M and VCS.SC.L were obtained from estimates from the NAFEM 2008 report. (Chapter 3 of th is TSD defines the acronyms used for the equipment

cla sses in this section.) The Freedonia 2010 report provided shipments for open display cases that operate at normal (medium) and low temperatures, and closed display cases that operate at

normal (medium) , low, and ice- cream temperatures. T hese aggregate numbers compared reasonably well with the corresponding aggregate shipments numbers based on the 2005 AHRI shipments data. While the aggregate shipments numbers for ice- cream display cases compared well for both the 2005 AHRI shipments data and Freedonia 2010 report, the 2005 AHRI shipments number of 9,056 units for HCT.SC.I seemed too high, especially considering the fact that the shipments number for HCT.SC.L is zero. HCT.SC.L equipment is predominantly used in

ice

- cream stores in the form of ice

- cream dipping cabinets. While this equipment is advertised as

ice

- cream dipping cabinets, the operating temperature range is typically -10 ° F to 5 ° F, which is

above the ice- cream rating temperature of -15 ° F. DOE assumed that the AHRI shipments numbers for HCT.SC.L may have been subsumed into the HCT.SC.I numbers. DOE allocated half of the shipments from HCT.SC.I to HCT.SC.L. These numbers compared reasonably well to

the ice

- cream cabinets, freezer , and dispensers numbers from the NAFEM 2008 report. Shipments for equipment class PD.SC.M were obtained based on estimates from the NCI 2009

report.

.

In addition to market share by equipment class, the shipments model requires an allocation of equipment classes to business types. T hese allocations were est imated based on information in the NAFEM and Freedonia reports. Shipments numbers and assumptions have

9-5

### been withheld because these report s are not public documents and are available only for purchase.

9.3.2 Building Stock Additions

As described above, projection s of commercial floor space by building type are used to drive the shipments model. These projections are taken from the EIA’s Annual Energy Outlook ( AEO ) 2013. Table 9.3.2 s ummarizes the projected d square footage of new construction by building type. As the data in Table 9.3.2 show, these projections vary year to year , with the first few years exhibiting lower levels of growth due to the lingering impacts of the U.S. economic

recession

. The projected commercial refrigeration equipment shipments show similar variability

. The AEO2013 forecast period ends in 2040, while the shipments model analysis period extends through 2078. The annual growth rate in new and surviving f loorspace, averaged over 2035- 2040, was used to provide projections through the analysis period.

Table 9.2 AEO2012 Forecast of n ew food s ales and food s ervice s quare footage

Year New Construction

m illion ft 2

Food s ervice Food Sales

2009 34 48

2012 22 31

2017 34 49

2020 33 48

2025 33 48

2030 38 54

2035 39 56

2040 39 56

Annual Growth Factor, 2031

- 2040 2.41 % 2.28%

Source: U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2012.



### While most of the data describing the CRE market refer to equipment units, the measure of capacity used in the analysis is linear feet. The shipments model provides an estimate of the total number of typical units. DOE determined the linear feet shipped fo r any given year by multiplying each typical unit shipped by its associated length, and then summing all the linear footage values. Table 9.3.3 presents the representative equipment class lengths used for the conversion of per- unit shipments to linear footage within each equipment class.

9-6

### Table 9.3 Equipment l inear dimensions assumed for shipments a nalysis

Equipment

Class Assumed Length ft Basis

VOP.RC.M 10 Average of 8 ft and 12 ft, m anufacturer i nterviews

VOP.RC.L 10 Average of 8 ft and 12 ft, m anufacturer i nterviews

VOP.SC.M 4 Baseline e quipment used for engineering a nalysis

SVO.RC.M 10 Average of 8 ft and 12 ft, m anufacturer i nterviews

SVO.SC.M 4 Baseline e quipment used for engineering a nalysis

HZO.RC.M 10 Average of 8 ft and 12 ft, m anufacturer i nterviews

HZO.RC.L 10 Average of 8 ft and 12 ft, manufacturer interviews

HZO.SC.M 4 Baseline equipment used for engineering analysis

HZO.SC.L 4 Baseline equipment used for engineering analysis

VCT.RC.M 10 Average of 3

- door and 5

- door (30 in. per door), manufacturer interviews

VCT.RC.L 10 Average of 3

- door and 5

– door (3 in. per door), manufacturer interviews

VCT.SC.M 4 Engineering estimate *

VCT.SC.L 3.5 Average of 1

- door and 2

- door freezer

VCT.SC.I 5 Baseline equipment used for engineering analysis

VCS.SC.M 4 Engineering estimate

VCS.SC.L 3.5 Average of 1

- door and 2

- door freezer

VCS.SC.I 5 Baseline equipment used for engineering analysis

HCT.SC.M 3.0 Engineering estimate *

HCT.SC.L 3.0 Engineering estimate *

HCT.SC.I 3.4 Baseline equipment used for engineering analysis

HCS.SC.M 4.0 Engineering estimate *

HCS.SC.L 5.0 Engineering estimate *

SOC.RC.M 8 Average of 4 ft, 8ft, 12 ft, all common equipment lengths

PD.SC.M 2.5 Baseline equipment used for engineering analysis

SOC.SC.M 5 Engineering estimate*
*For equipment classes that exhibit a wide range of equipment lengths in the market, DOE assumed a value for equipment length based on the best engineering judgment.



### Allocation of linear feet shipped to equipment class is provided in Table 9.3.3

.

Table 9.4 Percent of s hipped linear feet of commercial refrigeratio n dquipment

Equipment

Class* Percentage of Linear Feet Shipped

* Equipment Class Percentage of Linear Feet Shipped *

VOP.RC.M 10.3% SOC.RC.M 2.1%

VOP.RC.L 0.5% SOC.SC.M 0.2%

VOP.SC.M 1.3% HZO.RC.M 1.3%

VCT.RC.M 0.8% HZO.RC.L 4.0%

VCT.RC.L 10.7% HZO.SC.M 0.1%

VCT.SC.M 4.8% HZO.SC.L 0.2%

VCT.SC.L 0.2% HCT.SC.M 0.1%

VCT.SC.I 0.3% HCT.SC.L 0.4%

VCS.SC.M 25.4% HCT.SC.I 0.4%

VCS.SC.L 15.0% HCS.SC.M 4.4%

9-7VCS.SC.I 0.1% HCS.SC.L 0.6%

SVO.RC.M 8.2% PD.SC.M 7.6%

SVO.SC.M 1.1%

* Numbers do not add to 100 % because secondary equipment classes (chapter 5) are excluded from the table

.



### 9.3.3 Market Saturation

Market saturations provide the link between the floorspace projections and the projected number of equipment units shipped . DOE initially estimated market sat urations using the CBECS 1999 and 2003 survey data. Stakeholders commented on the model results derived usingthese saturations, noting that the estimated shipments in linear feet were severely underestimated, possibly by as much as a factor of four for some product classes. The model output also produced shipments estimates that were approximately 50% of the unit sales estimates quoted in the Energy Star 2012 and 2013 reports. The CBECS and other data sources provide unit counts, while the industry typica lly measures shipments in linear feet. It is not known whether the typical unit sizes of Table 9.3.3 are consistent with CBECS survey practices, so this may be the source of the discrepancy. In response to comments and taking into account, DOE adjusted the market saturations used in the model to the values show in Table 5 . Model results with the new saturations agree well with the most recent Energy Star data. Table 9.6 presents the average floorspace per building and the percentage of building floorspace assigned to the small category by building type.

Table 9.5 Market saturations and other data used in the shipments model

Small Buildings Large Buildings

Food

Sales Food

S ervice O ther Food

S ales Food

Service Other

Average Building Size (sq

- ft) 2343 2851 2768 16410 11391 28465

Percent of floorspace that is

small 46% 59% 9%

Closed cases per building 1.78 0.94 0.06 13.37 1.69 0.41

Open cases per building 5.54 5.09 0.22 11.51 6.06 1.04



### Market saturations are held constant throughout the analysis period, and are assumed to be the same for all standard levels. In general, when the data are available DOE incorporates a purchase price elasticity into the shipments model. This allows for the possibility that total shipments will fall under a standard, due to a rise in the first cost of the equipment. For commercial refrigeration equipment, DOE did not have access to any data that would allow the

9-8

### estimation of purchase price elasticities. There fore the total shipments in the standards case scenarios are the same as the total shipments in the base case scenario. As most users of this equipment are subject to health codes and other regulations, it is not very likely that a business owner would forego the purchase of needed equipment even under a price increase. Price sensitivity is more likely to occur in the form of increased equipment lifetimes. However, equipment lifetimes for food sales and service are driven primarily by the remodeling cycle, and so are unlikely to be affected on the average by a standard.

9.4 RESULTS

Figure 1 shows the shipments forecast for the commercial refrigeration equipment classes

at the base

- case energy consumption level divided into new and replacement units for large and

small food sales and foodservice outlets

. Table 9.4.2 shows total shipments by equipment type.

Figure 9.1 Projected shipments for Commercial Refrigeration Equipment, 2017 -2046 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000201720182019202020212022202320242025202620272028202920302031203220332034203520362037203820392040204120422043204420452046 Closed Cases New Closed Cases Replacement

Open Cases New Open Cases Replacement

9-9

### Table 9.6 Projected s hipments for Commercial Refrigeration Equipment, 2017

− 204 6, by e quipment type )

Equipment

Class Thousands of Linear Feet Shipped by Year and Equipment Class *

2017 2020 2025 2030 2035 2040 2045 2046 Cumulative

VOP.RC.M 692 782 785 881 977 1,015 1,125 1,150 28,550

VOP.RC.L 34 38 38 43 47 49 55 56 1,386

VOP.SC.M 87 99 99 111 123 128 142 145 3,603

VCT.RC.M 54 61 61 68 76 79 87 89 2,217

VCT.RC.L 719 813 816 915 1,015 1,055 1,169 1,194 29,659

VCT.SC.M 323 365 366 411 455 473 524 536 13,305

VCT.SC.L 13 15 15 17 19 20 22 22 554

VCT.SC.I 20 23 23 26 28 30 33 33 832

VCS.SC.M 1,707 1,929 1,936 2,172 2,409 2,503 2,775 2,835 70,405

VCS.SC.L 1,008 1,139 1,144 1,283 1,422 1,478 1,639 1,674 41,578

VCS.SC.I 7 8 8 9 9 10 11 11 277

SVO.RC.M 551 623 625 701 778 808 896 915 22,729

SVO.SC.M 74 84 84 94 104 108 120 123 3,049

SOC.RC.M 141 159 160 180 199 207 229 234 5,821

HZO.RC.M 87 99 99 111 123 128 142 145 3,603

HZO.RC.L 269 304 305 342 379 394 437 447 11,087

HZO.SC.M 7 8 8 9 9 10 11 11 277

HZO.SC.L 13 15 15 17 19 20 22 22 554

HCT.SC.M 7 8 8 9 9 10 11 11 277

HCT.SC.L 27 30 30 34 38 39 44 45 1,109

HCT.SC.I 27 30 30 34 38 39 44 45 1,109

HCS.SC.M 296 334 335 376 417 434 481 491 12,196

HCS.SC.L 40 46 46 51 57 59 66 67 1,663

PD.SC.M 511 577 579 650 721 749 830 848 21,066

SOC.SC.M 13 15 15 17 19 20 22 22 554

Total 6,728 7,602 7,631 8,561 9,492 9,866 10,937 11,174 277,462

*V alues include only equipment classes modeled in the e ngineering, l ife -c ycle c ost, and n ational i mpact a nalyses.

9- 10

### REFERENCES

1. Freedonia Group, Inc

. Commercial Refrigeration Equipment to 2014 . 2010. Cleveland, Ohio. Study 2261. < http://www.freedoniagroup.com/Commercial- Refrigeration -Equipment.html >

2. North American Association of Food Equipment Manufacturers. 2008 Size and Shape of Industry . 2008. Chicago, IL.

3. North American Association of Food Equipment Manufacturers. 2012 Size and Shape of Industry . 2012. Chicago, IL.

4. Navigant Consulting, Inc. Energy Savings Potential and R&D Opportunities for Commercial Refrigeration

. 2009. P repared by Nav igant Consulting, Inc. for the U.S. Department of Energy, Washington, DC.

9- 11

### CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ............................................................................................................ 10-1

10.2 NATIONAL ENERGY SAVINGS .................................................................................. 10-1

10.2.1 National Energy Savings Definition ..................................................................... 10-1

10.2.2 National Energy Savings Inputs ............................................................................ 10-2

10.2.2.1 Annual Unit Energy Consumption ......................................................... 10-3

10.2.2.2 Shipments ............................................................................................... 10-6

10.2.2.3 Equipment Stock .................................................................................... 10-6

10.2.2.4 National Annual Energy Consumption .................................................. 10-7

10.2.2.5 Site

- to -Source Conversion Factor .......................................................... 10-7

10.3 NET PRESENT VALUE .................................................................................................. 10-8

10.3.1 Net Present Value Definition ................................................................................ 10-8

10.3.2 Net Present Value Inputs ...................................................................................... 10-9

10.3.2.1 Total Annual Installed Cost ................................................................... 10-9

10.3.2.2 Total Annual Operating Cost Savings ................................................. 10-10

10.3.2.3 Discount Factor .................................................................................... 10-11

10.3.2.4 Present Value of Costs ......................................................................... 10-12

10.3.2.5 Present Value of Savings ..................................................................... 10-12

10.4 NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE RESULTS

........... 10-12

10.4.1 National Energy Savings and Net Present Value Input Summary ...................... 10-13

10.4.2 National Energy Savings Results ........................................................................ 10-13

10.4.3 Annual Costs and Savings .................................................................................. 10-15

10.4.4 Net Present Value Results ................................................................................... 10-16

10.5 ANNUALIZED NATIONAL COSTS AND BENEFITS .............................................. 10-18

10.5.1 Calculation Method ............................................................................................. 10-19

10.5.2 Results for the Adopted Standards ...................................................................... 10-20

REFERENCES ........................................................................................................................ 10-23

LIST OF TABLES

T able 10.2.1 National Energy Saving Inputs ............................................................................. 10-2 Table 10.2.2 Risk Premiums by Risk Class ( j ) in the NEMS Commercial Model .................... 10-4 Table 10.2.3 Market Shares of Efficiency Level, Base Case ..................................................... 10-5 Table 10.2.4 Average Annual Unit Energy Consumption per Linear Foot by Efficiency Level ................................................................................................. 10-6 Table 10.2.5 Shipment-Weighted Average Annual Energy Consumption per Linear Foot by Efficiency Level

..................................................... Error! Bookmark not defined.

Table 10.2.6 Site- to -Source Conversion Factors* ..................................................................... 10-8 Table 10.3.2 Average Installed Cost per Linear Foot by Efficiency Level

. Error! Bookmark not

defined. Table 10.3.3 Shipment- Weighted Average Total Installed Cost per Linear Foot by Efficiency

Level

...................................................................... Error! Bookmark not defined.

10 -i

### Table 10.3.4 Average Annual Repair Cost per Linear Foot by Efficiency Level ................... 10-11 Table 10.3.5 Shipment-Weighted Average Annual Repair Cost per Linear Foot by Efficiency

Level

...................................................................... Error! Bookmark not defined. Table 10.3.6 Average Annual Maintenance Cost per Linear Foot by Efficiency Level

....... Error!

Bookmark not defined. Table 10.3.7 Shipment-Weighted Average Annual Maintenance Cost per Linear Foot by Efficiency Level

..................................................... Error! Bookmark not defined. Table 10.4.1 NES and NPV Inputs .......................................................................................... 10-13

Table 10.4.2 Cumulative National Energy Savings for Commercial Refrigeration Equipment by TSL for equipment purchased in 2017-2046 ................................................... 10-14 Table 10.4.4 Cumulative Energy Savings by Percent for equipment purchased in 2017-2046

................................................................................ Error! Bookmark not defined. Table 10.4.7 Cumulative NPV Results by TSL Based on a 7

- Percent Discount Rate (billion 2013$) ................................................................................................. 10-17 Table 10.4.9 Cumulative NPV Results by EL Based on a 3- Percent Discount Rate (billio n 2012$) ................................................................................................. 10-18

Table 10.5.1 Annualized Benefits and Costs of New and Amended Standards for Commercial Refrigeration Equipment Sold in 2017–2046 .................................................. 10-21

LIST OF FIGURES

Figure 10.4.1 National Annual Costs and Savings for Efficiency Level 4 for Equipment Class

VOP.RC.M. ...................................................................................................... 10-16

10

- ii

### CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method for estimating the magnitude and net present value (NPV) of future national energy savings (NES) from possible standards levels for commercial refrigeration equipment. Results of the national impact analysis (NIA) described in this chapter

include NES

, monetary value of energy savings to the n ation as a result of standard s, increase d

total installed costs to the nation as a result of standards, and the NPV of future savings ( the difference between the present monetary value s of energy savings and increased total installed costs).

The U.S. Department of Energy (DOE) determined both the NPV and NES for each efficiency level (E L) it considered for each of the 25 primary equipment classes of commercial refrigerati on equipment analyzed in this rulemaking . Sections 10.2 and 0 present the definition s and the inputs to NES and NPV, respectively. Section 10.4 p resent s the result s of the NIA. DOE performed all calculations using a Microsoft Excel spreadsheet, which i s available at http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/52

. Detailed

results are available in appendix 10A.

10.2 NATIONAL ENERGY SAVI NGS

10.2.1 National Energy Savings Definition

DOE calculates annual NES for a given year (denoted as t , below) as the difference

between two scenarios : a base

- case scenario without new energy conservation standards and

standards- case scenarios with new energy conservation standards. Positive values of NES correspond to net energy savings ( i.e. , national annual energy consumption (AEC) with stan dards

is less than national AEC in the base case ).

NES t 

### = AEC base

, t 

### − AEC standards

, t



### Eq. 10.1

Where:

NES t 

### = n ational e nergy savings in the year t ( quadrillion British thermal units ( quads))

, AEC base ,t 

### = annual national energy consumption in the year t in the base- case scenario (quads ),

and

AEC standards,t 

### = annual national energy consumption in the year t in a standards- case scenario (quads).

10 -1

### Annual NES from each year, starting with th e year when the standards that are the subject of this rulemaking become effective (201 7 ), can be summed to calculate the c umulative NES ( NES

cum 

### ).

NES
cum 

### = ∑ NES tt



### Eq. 10.2

For each equipment class, DOE calculated the AEC for base

- case and standards

- case scenarios by multiplying the stock of commercial refrigeration equipment (by vintage) by the unit energy consumption (UEC) (also by vintage) as shown by the following equation:

AEC t 

### = ∑ ( STOCK V 

### × UEC V 

### ) × V 

### src

_ conv t



### Eq. 10.3

Where:

AEC t 

### = national annual energy consumption in the year t (quads), summed over vintages of commercial refrigeration equipment stock

, STOCK V 

### = stock of commercial refrigeration equipment (millions of units) of vintage V surviving in the year for which DOE calculated annual energy consumption; vintages range from 1 to approximately 1 0 years (or 15 years for small businesses , see chapter 8 of the technical support document ( TSD )), a function of an assumed 10- year (or 15

- year) lifetime of the equipment,

U

EC V 

### = annual unit energy consumption in kilowatt -hours (kWh) for equipment of vintage V

, src_conv t 

### = t ime -dependent conversion factor to convert from site energy to source energy ( British thermal units per kilowatt -hour (Btu/kWh) ), and

t = y ear of forecast.

The stock of commercial refrigeration equipment is dependent on annual shipments and the lifetime of the equipment. DOE acknowledges that the shipment projections under the

standards- case scenarios could be lower than those in the base- case scenario, because the higher

installed costs could cause some customers to forego or delay discretionary equipment purchases that are not occasioned by the need to replace failed equipment or to furnish a new building.
However, DOE has no information that would allow a calculation of this effect, so shipments

were assumed to be the same in both base

- case and standards

- case scenarios

.

10.2.2 National Energy Savings Inputs

Table 10.2.1 lists the inputs for the determination of NES.

Table 10.2.1 National Energy Saving Inputs

Input

Annual Unit Energy Consumption ( UEC ) Shipments

10 -2Equipment Stock ( STOCK V )

Site

- to

- Source Conversion Factor ( src_conv )



### 10.2.2.1 Annual Unit Energy Consumption

The annual UEC is the site energy consumed by a commercial refrigeration unit in a given year. Because the equipment classes analyzed represent equipment sold across a range of sizes, DOE’s “unit” in the NES is actually expressed as a linear foot of equipment in an equipment class and not an individual unit of commercial refrigeration equipment of a specific

size . As described below, DOE determined annual forecasted shipment

- weighted average equipment efficiencies that, in turn, enabled determination of shipment- weighted AEC values.

DOE did not have data on the market shares by effic iency level within each of the equipment classes and for the NOPR used an econometric model to estimate these market shares, with no supporting data. The methodology is described in the NOPR TSD . DOE received

comment from stakeholders on problems with this methodology , an d in the public meeting a 

### of DOE committed to performing a thorough review of the approach. The methodology used in the NOPR was an econometric approach based loosely on methods used in national input-output models such as the Energy Information Administration (EIA’s) National Energy Modeling System 1 

### (NEMS) and in the Canadian Integrated Modeling System (CIMS) b

, 2 

### . T he market share

for each equipment class by efficiency level is defined as EFF_Level_Share(i,y) , for each equipment clas s, y , at each e fficiency level

, i

. DOE assumed the market share for a particular equipment class to be constant over time

. DOE calculated the EFF_Level_Share(i,y) for each efficiency level i using a formula, based on the relative annualized cost of each efficiency level

.

The formula uses the installed cost, equipment lifetime and annual operating costs from the LCC analysis, annualized using an implied discount rate (or hurdle rate)., The implied discount rates are set equal to the real risk

- free rate of return based on Treasury bonds, plus an adder. The adder is equal to the time- preference premiums used in the EIA’s NEMS Commercial Demand Module. These premiums are specific to investments in commercial refrigeration equipment c 

### . NEMS defines seven classes of risk preference, with populations weights and time preference premiums for AEO 2013 as itemized in Table 10.2.2 .DOE’s review of this approach

ident ified three signif icant problems. First, the analysis presented in the NOPR contains an error in the definition of the cost index used to define the consumer utility associated with a given product efficiency. The definition used the incremental TIC divide d by the lifetime, instead of the incremental TIC itself. Given the sensitivity of this functional form to changes in the cost index, this resulted in a significant over- estimate of the market share of higher efficiency products. Second, the methodology did not properly account for the availability of higher efficiency products in real markets. This resulted in assignment of significant market share to

a http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/27#

b The CIMS Model was originally known as the Canadian Integrated Modeling System, but as the model is now being applied to other countries, the acronym is now used as its proper name.

c Energy Information Agency, Commercial Demand Module of the National Energy Modeling System : Model Documentation 2013. EIA Report No. m066(2013), November, 2013. http://www.eia.gov/forecasts/aeo/nems/documentation/commercial/pdf/m066(2 013).pdf

10 -3

### higher efficiency products that, according to stakeholders, are not actually manufactured and sold. Third, the use of an econometric approach pre- supposes the existence of data that can be used to calibrate the model. For the model used in the NOPR, the time preference premiums and heterogeneity factor determine the degree to which consumer purchase decisions are sensitive to first cost. The NOPR analysis took these parameters from two different models (NEMS and the CIMS). Even though each of these models has been validated individually, it is incorrect to assume that a model based on a combination of parameters is valid; this can only be established by comparison with real data. The necessary data have not been made available to DOE. Hence, DOE has decided to replace the NOPR model with an approach that relies solely on the parameters and assumptions used in NE MS , specifically for AEO2013.

NEMS model output includes database files that provide a breakdown of commercial refrigeration equipment market shares by technology types. Technology types are characterized by first cost and efficiency. The NEMS model also includes a schedule for the introduction of new technologies, which constrains the available choices in any given year. The consumer choice is based on minimum life-cycle costs, with operating costs discounted using the time preference premiums listed in Table 10.2.2 . DOE translated AEO technology types to equivalent efficiency levels based on the cost increase relative to first cost. The AEO2013 market shares do not include the impact of the recently published Energy Star 3.0. DOE accounted for the new Energy Star specification by allocating 5% of market share to Energy Star compliant products in the first year of the new specification, and an additional 5% each following year up to a cap of 40%. The cap of 40% is based on Energy Star market reports that show that the market share of compliant products in the category commercial refrigerators and freezers to top out at this level. The penetration rate of 5% per year is somewhat slower than the rate shown in historical data for Energy Star 2.0, because the Energy Star 3.0 specification represents a significantly higher relative increase in both cost and efficiency. This approach provides a base- case effici ency distribution for the full analysis period.

Table 10.2.2 Risk Premiums by Risk Class ( j

) in the NEMS Commercial Model

Percentage of Users in

Class Time Preference

Premium

0.4% 0.0%

0.6% 6.5%

9.7% 15%

17% 25%

221.3% 40%

24.8% 100%

26.2% 1,000.0%

100%



### Table 10.2.3 provides estimated base- case market shares of efficiency levels for each of the 25 equipment classes analyzed in this rulemaking.

DOE used a “roll -up” method to obtain the market share of th e efficiency levels in the

standards- case scenarios. In the roll -up method, the market shares of all efficiency levels below

10 -4

### the efficiency level corresponding to the standards- case scenario ar e reassigned to the efficiency level corresponding to the standards- case scenario

. For example, if a Level 4 standard were imposed in 2017 on the VOP.RC.M equipment class (vertical open refrigerator connected to a remote condensing unit), then the market shares for Levels 1, 2, and 3 would be assigned to

Level 4

. The market shares for higher levels would not be affected.

Table 10.2.3 Market Shares of E fficiency Level , Base Case
Equipment

Class Shipment

- Weighted Market Shares by Efficiency Level* , **

Base EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M 60% 40% 0% 0% 0% 0% 0% 0%

VOP.RC.L 60% 20% 20% 0% 0% 0% 0% 0%

VOP.SC.M 60% 40% 0% 0% 0% 0% 0% 0%

VCT.RC.M 60% 14% 13% 13% 0% 0% 0% 0%

VCT.RC.L 60% 20% 20% 0% 0% 0% 0% 0%

VCT.SC.M 90% 0% 10% 0% 0% 0% 0% 0%

VCT.SC.L 90% 0% 10% 0% 0% 0% 0% 0%

VCT.SC.I 60% 8% 8% 8% 8% 8% 0% 0%

VCS.SC.M 60% 0% 30% 0% 0% 0% 10% 0%

VCS.SC.L 60% 30% 0% 0% 10% 0% 0% 0%

VCS.SC.I 60% 8% 8% 8% 8% 8% 0% 0%

SVO.RC.M 60% 40% 0% 0% 0% 0% 0% 0%

SVO.SC.M 60% 40% 0% 0% 0% 0% 0% 0%

SOC.RC.M 60% 40% 0% 0% 0% 0% 0% 0%

SOC.SC.M 60% 40% 0% 0% 0% 0% 0% 0%

HZO.RC.M 60% 40% 0% 0% 0% 0% 0% 0%

HZO.RC.L 60% 20% 20% 0% 0% 0% 0% 0%

HZO.SC.M 60% 20% 20% 0% 0% 0% 0% 0%

HZO.SC.L 100% 0% 0% 0% 0% 0% 0% 0%

HCT.SC.M 60% 0% 0% 40% 0% 0% 0% 0%

HCT.SC.L 60% 0% 0% 30% 0% 0% 0% 10%

HCT.SC.I 60% 40% 0% 0% 0% 0% 0% 0%

HCS.SC.M 90% 0% 0% 0% 0% 0% 10% 0%

HCS.SC.L 90% 0% 0% 0% 0% 0% 10% 0%

PD.SC.M 60% 40% 0% 0% 0% 0% 0% 0% * Shares may not add to 100% exactly due to rounding.

** A value of “ NA ” indicates that there are no associated efficiency levels at this level for this equipment

class.



### Table 10.2.4 provides the annual UEC values for each efficiency level for all 25 primary equipment classes obtained from the LCC analysis (chapter 8 of the TSD) . Since the equipment

is available in various sizes within each equipment class, DOE used a linear foot of equipment as the unit measure for commercial refrigeration equipment. Therefore, the UEC values are expressed in kilowatt-hour per linear foot per year (kWh/linear foot/year). The UEC value multiplied by the length of a piece of equipment gives the average AEC of that piece of

10 -5

### equipment. DOE combined the UEC values in Table 10.2.4 with the market shares of efficiency

levels (base

- case scenario) in Table 10.2.3 to obtain shipment- weighted UEC values for each efficiency le vel in the base- case scenario in Table 10.2.5

.

Table 10.2.4 Average Annual Unit Energy Consumption per Linear Foot by Efficiency

Level

Equipment

Class Average Annual Unit Energy Consumption by Efficiency Level * kWh/linear foot/year

Base EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M 1425 1156 1148 1098 1084 1084 1084 1084

VOP.RC.L 3212 3192 3093 3042 3042 3042 3042 3042

VOP.SC.M 2738 2729 2711 2711 2711 2711 2711 2711

VCT.RC.M 391 338 331 329 315 315 315 315

VCT.RC.L 1074 1012 996 988 947 947 947 947

VCT.SC.M 567 554 485 457 453 442 438 418

VCT.SC.L 2813 2227 1495 1479 1404 1392 1372 1302

VCT.SC.I 1674 1657 1635 1608 1537 1537 1537 1537

VCS.SC.M 512 429 327 311 299 282 279 246

VCS.SC.L 1336 1131 1070 1049 1016 988 980 903

VCS.SC.I 1608 1587 1529 1514 1479 1362 1362 1362

SVO.RC.M 1087 896 856 852 843 843 843 843

SVO.SC.M 2402 2340 2333 2318 2318 2318 2318 2318

SOC.RC.M 692 679 667 663 656 656 656 656

SOC.SC.M 843 826 809 804 794 794 794 794

HZO.RC.M 440 430 430 430 430 430 430 430

HZO.RC.L 984 945 945 945 945 945 945 945

HZO.SC.M 1338 1292 1280 1255 1255 1255 1255 1255

HZO.SC.L 2730 2730 2730 2730 2730 2730 2730 2730

HCT.SC.M 179 168 155 95 86 85 76 59

HCT.SC.L 392 216 207 195 184 181 166 126

HCT.SC.I 334 248 248 248 248 248 248 248

HCS.SC.M 130 123 118 111 110 102 85 85

HCS.SC.L 168 155 145 133 130 112 62 62

PD.SC.M 732 690 569 552 548 532 528 497



### 10.2.2.2 Shipments

DOE forecasted shipments for the base case and all standards cases

. These results are presented in TSD chapter 9, Shipments A nalysis

.

10.2.2.3 Equipment Stock

The commercial refrigeration equipment stock in a given year is the total linear footage

of commercial refrigeration equipment shipped from earlier years that is still in service during

that year. The NES spreadsheet model keeps track of the total linear footage of commercial refrigeration uni ts shipped each year. For equipment with an average lifetime of 10 years, the replacement rate per year is 10 percent, and for equipment with 15- year average lifetime, the

10 -6

### replacement rate is 6.67 percent. F or units shipped in 2046 , any units still remaini ng at the end of

2060 were assumed to be replaced.

10.2.2.4 National Annual Energy Consumption

The national AEC is the product of the annual UEC and the stocks of commercial refrigeration equipment units of each vintage for each equipment cl ass , as shown in Eq. 10.3

.

DOE initially calculated the AEC at the site ( i.e. , electricity in kilowatt -hours consumed by th e commercial refrigeration equipment units)

, then calculated primary energy consumption from site energy consumption by applying a site

- to

- source conversion factor.

10.2.2.5 Site

- to

- Source Conversion Factor

The site

- to -source conversion factor is the multiplicative factor used for converting site

energy consumption, expressed in kilowatt -hours, into primary or source energy consumption, expressed in British thermal units

, or quads. The site

- to -source conversion factor accounts for

losses in electricity generation, transmission, and distribution.

As part of the utility impact analysis, described in TSD chapter 14 , DOE use s the energy savings estimated in the NIA and models the savings us in g NEMS. DOE then analyzes the impact on the generation equipment needed to meet projected national demand and the marginal

site

- to -source conversion factors arising from the generation portfolio. Because each type of residential appliance or commercial equipment has a different energy usage profile, each type will cause the electric utility grid to provide energy at t imes and in quantities that are unique. Thus, at the NOPR and f inal r ule stages, the energy savings are explicitly modeled to identify this electric system

- level impact. One output of this analysis is a calculated set of site

- to -source conversion factors s pecific to the energy usage profile of the appliance/equipment.

For th e preliminary analysis of this rulemaking, DOE used annual site- to

- source conversion factors specific to the commercial refrigeration usage profile, calculated from the

results of detail ed modeling using NEMS to study savings from the January 2009 final rule. 74 FR 1092 (Jan. 9, 2009). For this final rule analysis, DOE updated the NEMS model and analyzed

the site

- to -source conversion factors. DOE derived new site

- to

- source conversion factors specific to each year of this analysis using the DOE Building Technologies Program version of the National Energy Modeling System (NEMS -BT) that corresponds to the 2013 version of the EIA Annual Energy Outlook (

AEO201 3 ). 3 

### Table 10.2.6 shows the average conversion factors that

were used in the analysis.

10 -7

### Table 10.2.5 Site

- to

- Source Conversion Factors *

Year Site

- to

- Source

Conversion

Factor

Mn Btu/M Wh Year Site

- to

- Source

Conversion Factor

Mn Btu/MWh

2017 8.503 2039 8.725

2018 8.503 2040 8.725

2019 8.503 2041 8.725

2020 8.503 2042 8.725

2021 8.266 2043 8.725

2022 8.266 2044 8.725

2023 8.266 2045 8.725

2024 8.266 2046 8.725

2025 8.266 2047 8.725

2026 7.909 2048 8.725

2027 7.909 2049 8.725

2028 7.909 2050 8.725

2029 7.909 2051 8.725

2030 7.909 2052 8.725

2031 8.346 2053 8.725

2032 8.346 2054 8.725

2033 8.346 2055 8.725

2034 8.346 2056 8.725

2035 8.346 2057 8.725

2036 8.725 2058 8.725

2037 8.725 2059 8.725

2038 8.725 2060 8.725

10.3 NET PRESENT VALUE

10.3.1 Net Present Value Definition

The NPV is the value in the present of a time series of costs and savings. The NPV is given by the equation:

NPV = PVS

– PVC

Eq. 10.4

W here :

PVS = present value of operating cost savings (energy, repair, and maintenance costs), and

PVC = present value of increased total installed costs (equipment purchase price and installation

cost ).

10 -8

### The PVS and PVC are determined according to the following expressions:

PVS = ∑ OCS t 

### × DF t t



### Eq. 10.5

PVC = ∑ TIC t 

### × DF t t



### Eq. 10.6

W here :

OCS t 

### = total annual operating cost savings in the year t ($)

, TIC t 

### = total annual installed cost increases in the year t ($)

, DF t 

### = discount factor for the year t

, and

t = year ( PVS is summed over 2017

− 2060, and PVC is summed over 2017

− 2046).

DOE determined the contribution to PVC for each year, from the compliance date of the standard (2017 ) to 2046, discounted to 2013. Likewise, the contribution to PVS was determined for each year, from the compliance date of the standard (2017 ) to the year when units purchased

in 2017

- 2046 would be retire d. DOE calculated costs and savings as the difference between a

standards case and a base case. DOE calculated a disc ount factor from the 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. DOE calculated the NPV as the sum over time of the discounted net savings.

10.3.2 Net Present Value Inputs

The following list summarizes the inputs to the NPV calculation.

• Total Annual Installed Cost ( TIC t 

### )

• Total Annual Operating Cost Savings ( OCS t 

### )

• Discount Factor ( DF t 

### )

• Present Value of Costs ( PVC )

• Present Value of Savings ( PVS )

10.3.2.1 Total Annual Installed Cost

The increase in the total annual installed cost for each standards case is equal to the annual change in the per-unit installed cost (difference between base case and standards case) multiplied by the shipments forecasted in th e standards case. The total installed cost includes the manufacturer selling price , distribution channel markups, and installation cost s. (S ee chapter 8 of

the TSD for a discussion of the development of installed cost.) Table 10.3.1 shows the average

10 -9

### total installed costs per linear foot for each of the equipment classes of commercial refrigeration units by e fficiency l evel obtained from the LCC analysis (chapter 8 of TSD).

As d iscussed in section 10.2.2 , DOE first developed the market shares of efficiency levels

for the base-case scenario and then used them in combination with the roll-up method to estimate

the market share s of efficiency levels in the standards- case scenarios

. DOE used these base

- case and standards- case market shares of efficiency levels in combi nation with the installation costs to calculate a weighted

- average installed costs for each standards

- case scenario, which are also referred to as the shipment-weighted average installation costs.

10.3.2.2 Total Annual Operating Cost Savings

The annual operating cost savings to the n ation are equal to the change in the annual operating costs (difference between base case and standards case) per unit multiplied by the shipments forecasted in the standards case.

The annual operating cost includes energy , repair, and maintenance costs.

Annual Electricity Cost Savings. As described in TSD chapter 8, L ife -C ycle Cost and Payback Period A nalysis, DOE calculated annual electricity costs based on average s tate

- level commercial electricity prices. To calculate annual energy cost savings for a particular equipment class in a given year, DOE first calculated the annual energy costs in each forecast year at the

base

- case scenario and each standards- case scenario by multiplying the weighte d- average ener gy consumption at each efficiency level from Table 10.2.5 by the linear feet of equipment stock in

the equipment class in each year, and then by the sales

- weighted national average electricity prices for the seven building types in TSD chapter 8. To determine energy savings at each

standards- case scenario , the nation al energy costs at each standards

- case scenario were then

subtracted from the natio nal energy costs at the base- case scenario . Because projections of the stock of commercial refrigeration equipment (expressed in linear feet) are the primary driver used to estimate future commercial refrigeration equipment shipments, DOE calculated the

na tional energy cost by expressing the AEC on a linear -footage basis.

Annual Repair Costs. DOE based average annual repair costs on the value of the components of the equipment at each efficiency level (see TSD chapter 8 ). Table 10.3.3 shows

the combined average repair and maintenance costs per linear foot for each efficiency level for the equipment classes analyzed

.

Annual Maintenance Costs. DOE determined average annual maintenance costs in two parts. The first was a preventative maintenance cost of $35/year for self -contained units and $220/year for remote condensing units (see TSD chapter 8). The second was a light ing maintenance cost for equipment classes that feature lights within the equipment. The lighting cost varied with efficiency level because the costs were directly proportional to the original equipment manufacturer costs of the lighting (see TSD chapter 8 Table 10.3.3 shows the combined average repair and maintenance costs per linear foot for each efficiency level for the equipment classes analyzed.

10

- 10

### Table 10.3.1 Average Annual Repair and Maintenance Cost s per Linear Foot by Efficiency

Level

Equipment

Class Average Annual Repair Cost per Linear Foot *

201 2 $ /

l inear f oot

Base EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

VOP.RC.M 61.31 59.06 59.55 62.32 74.55 62.21 62.67 65.94

VOP.RC.L 60.72 61.18 64.44 84.46 24.08 24.27 27.30 65.39

VOP.SC.M 64.27 64.83 73.91 196.16 192.84 195.52 197.58 239.06

VCT.RC.M 58.61 59.35 60.19 60.84 73.88 67.38 66.29 66.63

VCT.RC.L 64.26 64.94 65.28 66.34 83.09 19.32 19.67 19.05

VCT.SC.M 48.34 49.33 51.28 51.81 52.23 52.78 53.42 63.55

VCT.SC.L 70.33 62.09 65.85 66.08 67.29 67.71 68.56 83.23

VCT.SC.I 73.83 74.33 75.10 76.22 95.50 41.46 42.62 43.04

VCS.SC.M 39.61 40.72 41.13 41.39 41.86 42.91 43.05 57.45

VCS.SC.L 46.02 47.53 48.28 48.55 49.31 50.17 50.70 69.67

VCS.SC.I 55.64 55.86 56.96 57.41 58.22 84.88 147.28 154.08

SVO.RC.M 52.78 51.37 53.28 53.58 61.31 18.99 19.50 19.68

SVO.SC.M 50.77 52.29 52.83 59.02 220.52 223.03 225.33 226.17

SOC.RC.M 66.34 67.17 67.94 68.22 74.02 70.82 72.15 73.04

SOC.SC.M 59.25 60.58 61.47 61.87 67.86 44.06 50.59 44.97

HZO.RC.M 44.06 50.59 44.97 58.23 9.66 9.94 10.19 12.90

HZO.RC.L 44.97 58.23 9.66 9.94 10.19 12.90 18.06 6.91

HZO.SC.M 28.99 29.82 30.56 38.70 54.17 20.73 20.80 20.96

HZO.SC.L 54.17 20.73 20.80 20.96 21.85 22.22 22.46 22.96

HCT.SC.M 21.82 21.89 22.07 23.00 23.39 23.65 24.17 28.71

HCT.SC.L 28.72 29.89 30.14 30.58 30.89 31.19 31.91 40.44

HCT.SC.I 38.14 53.30 28.14 28.23 28.42 28.73 29.04 29.89

HCS.SC.M 22.91 22.99 23.14 23.40 23.65 24.34 29.16 23.11

HCS.SC.L 23.11 23.19 23.34 23.60 23.84 24.54 31.15 41.05

PD.SC.M 62.02 63.82 66.69 67.22 67.71 68.74 69.61 85.25

* A value of “ NA ” indicates that there are no associated efficiency levels at this level for this equipment

class.



### 10.3.2.3 Discount Factor

DOE multiplied monetary values in future years by the discount factor in order to determine the present value of costs and savings. The discount factor ( DF ) is described by the equation:

DF = 1

( 1+r ) �t−t p �



### Eq. 10.7

W here :

r = discount rate,

t = year of the monetary value, and

t p 

### = year in which the present value is being determined.

10

- 11

### DOE estimated national impacts with both a 3- percent and a 7- percent real discount rate as the average real rate of return on private investment in the U.S. economy. These discount rates are used in accordance with the Office of Management and Budget (OMB) guidance to Federal agencies on the development of regulatory analysis (OMB Circular A -4, September 17, 2003), and section E, “Identifying and Measuring Benefits and Costs,” therein. DOE defined the present

year as 2013 for the final rule

.

10.3.2.4 Present Value of Costs

The present value of increased installed costs is the annual installed cost increase in each year ( i.e. , the d ifference between the standards case and base case) discounted to the present year and summed for the time period over which DOE is considering the installation of commercial refrigeration equipment ( i.e. , from the compliance date of standards, 2017, to the year 2046 ).

The increase in total installed cost refers to both equipment cost and installation cost associated with the higher energy efficiency of commercial refrigeration units purchased in the standards case compared to the base case. DOE calculated annual installed costs as the difference in total installed cost for new equipment purchased each year, multiplied by the shipments in the

standards case.

10.3.2.5 Present Value of Savings

The present value of the operating cost savings is the annual operating cost savings ( i.e.

, the difference between the base case and standards case) discounted to the present year, and summed over the period from the compliance date of the standard, 2017, to the time when the

last unit installed in 2017

- 2046 is retired from service. Savings are decreases in operating costs (including electricity, repair, and maintenance) associated with the higher energy efficiency of commercial refrigeration u nits purchased in the standards case compared to the base case. Total annual operating cost savings are the savings per unit multiplied by the number of uni ts of each vintage surviving in a particular year. Equipment consumes energy over its entire lifetime, and for units purchased in 2046, the present value of savings includes energy consumed until the unit

is retired from service at the end of 2060.

10.4 N ATIONAL E NERGY S AVINGS AND N ET P RESENT V ALUE RESULTS

The NES spreadsheet model provides estimates of the NES and NPV due to various efficiency levels. The inputs to the NES spreadsheet are discussed in sections 10.2.2 and Table

10.4.1

. DOE generated the NES and NPV results using a Microsoft Excel spreadsheet, which is

available at http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/52

.

10

- 12

### For this final rule analysis, DOE selected five trial standard level ( TSL ) groupings based

on the NPV values calculated for each EL. Appendix 10D discusses the criteria used for selection of TSLs. The NIA results in this section are presented by the selected TSLs.

10.4.1 National E nergy Savings and N et P resent Value Input Summary

Table 10.4.1 summarizes the inputs to the NES spreadsheet model. For each input, a brief description of the data source is given.

10.4.2 National Energy Savings Results

The following section provides NES results for each energy consumption lev el considered for the 25 equipment classes of commercial refrigeration equipment DOE directly analyzed. Results are cumulative to 2060 and are shown as primary energy savings in quads. Inputs to the NES spreadsheet model are based on weighted- average values, yielding results that are discrete point values, rather than a distribution of values as in the LCC analysis.

Table 10.4.2 shows the cumulative national energy savings results for all TSLs for all equipment classes

. Table 10.4.3 shows the cumulative national energy savings results including

full fuel

- cycle impacts for all TSLs for all equipment classes. Table 10.4.3 and Table 10.4.4 present NES results for the efficiency levels and for each equipment class, wit h the quantities discounted using 7- percent and 3- percent discount rates, respectively.

Table 10.4.1 NES and NPV Inputs

Input Data Description Shipments Annual shipments from shipments model (see TSD chapter 9

, S hipments

A na lysis ).

Effective Date of Standard 201 7

Base

- Case Efficiencies Distribution of base

- case shipments by efficiency level.

Standard s

- Case Efficiencies Distribution of shipments by efficiency level for each standards case.

Standards

- case annual market shares by efficiency level remain constant

over time for the base case and each standards case. Annual Energy Consumption per

Linear Foot Annual weighted

- average values are a function of e fficiency level (e stablished in the e ngineering a nalysis, TSD chapter 5) c onverted to a per

linear foot basis. Total Installed Cost per Linear Foot Annual weighted

- average values of installed cost are a function of

e fficiency level ( see TSD chapter 8 ) , expressed on a per linear foot basis. Repair Cost per Linear Foot Annual weighted

- average values of repair costs are constant with e fficiency

level ( see TSD chapter 8 ). Converted to a per linear foot basis. Maintenance Cost per Linear Foot Annual weighted

- average value equals $ 35 for self

- contained and $220 for remote condensing units ( see TSD chapter 8 ), plus lighting maintenance

cost . Converted to a per linear foot basis. Escalation of Electricity Prices EIA AEO201 3 forecasts (to 2040 ) and extrapolation for 20 41 and beyond ( see TSD chapter 8 ). Electricity Site

- to

- Source Conversion Conversion varies yearly and is generated by DOE ’s version of the EIA

NEMS 4 program (a time series conversion factor; includes electric generation, transmission, and distribution losses).

Discount Rate 3

- and 7

- percen t real

Present Year Future costs are discounted to year 201 3

.

10

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### Table 10.4.2 Cumulative National Energy Savings for Commercial Refrigeration Equipment by TSL for equipment purchased in 201 7-2046

Equipment

Class National Energy Savings by TSL * quads

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M 0.000 0.000 0.403 0.550 0.584

VOP.RC.L 0.000 0.000 0.001 0.011 0.017

VOP.SC.M 0.000 0.000 0.000 0.002 0.007

VCT.RC.M 0.000 0.000 0.006 0.008 0.010

VCT.RC.L 0.096 0.096 0.130 0.150 0.259

VCT.SC.M 0.010 0.060 0.093 0.110 0. 139

VCT.SC.L 0.018 0.041 0.045 0.050 0.05 0

VCT.SC.I 0.001 0.001 0.001 0.003 0.008

VCS.SC.M 0.309 0.687 0.794 0.870 1.080

VCS.SC.L 0.450 0.631 0.808 0.839 1.121

VCS.SC.I 0.000 0.001 0.002 0.002 0.005

SVO.RC.M 0.229 0.229 0.229 0.316 0.335

SVO.SC.M 0.000 0.000 0.000 0.010 0.016

SOC.RC.M 0.000 0.000 0.000 0.004 0.016

SOC.SC.M 0.000 0.000 0.000 0.001 0.002

HZO.RC.M 0.000 0.000 0.000 0.000 0.002

HZO.RC.L 0.000 0.000 0.000 0.000 0.023

HZO.SC.M 0.000 0.001 0.001 0.001 0.002

HZO.SC.L 0.000 0.000 0.000 0.000 0.000

HCT.SC.M 0.000 0.001 0.001 0.002 0.00 2

HCT.SC.L 0.011 0.012 0.012 0.013 0. 016

HCT.SC.I 0.000 0.000 0.000 0.000 0.005

HCS.SC.M 0.004 0.008 0.013 0.013 0. 030

HCS.SC.L 0.001 0.002 0.003 0.005 0.010

PD.SC.M 0.046 0.271 0.301 0.310 0.403

Total 1.176 2.041 2.844 3.270 4.140 *A value of 0.000 means NES values are less than 0.0005 quads.

10

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### Table 10.4.3 Cumulative Full Fuel -Cycle National Energy Savings for Commercial Refrigeration Equipment by TSL for equipment purchased in 2017 -2046

Equipment

Class National Energy Savings by TSL* quads

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M 0.000 0.000 0.410 0.559 0.593

VOP.RC.L 0.000 0.000 0.001 0.011 0.018

VOP.SC.M 0.000 0.000 0.000 0.002 0.007

VCT.RC.M 0.000 0.000 0.006 0.008 0.010

VCT.RC.L 0.098 0.098 0.132 0.153 0.263

VCT.SC.M 0.010 0.061 0.094 0.112 0.1 4 1

VCT.SC.L 0.018 0.042 0.046 0.050 0.05 0

VCT.SC.I 0.001 0.001 0.001 0.003 0.008

VCS.SC.M 0.314 0.699 0.807 0.884 1.097

VCS.SC.L 0.458 0.641 0.821 0.852 1.139

VCS.SC.I 0.000 0.001 0.002 0.002 0.005

SVO.RC.M 0.233 0.233 0.233 0.321 0.340

SVO.SC.M 0.000 0.000 0.000 0.010 0.016

SOC.RC.M 0.000 0.000 0.000 0.004 0.016

SOC.SC.M 0.000 0.000 0.000 0.001 0.002

HZO.RC.M 0.000 0.000 0.000 0.000 0.002

HZO.RC.L 0.000 0.000 0.000 0.000 0.023

HZO.SC.M 0.000 0.001 0.001 0.001 0.002

HZO.SC.L 0.000 0.000 0.000 0.000 0.000

HCT.SC.M 0.000 0.001 0.001 0.002 0.00 2

HCT.SC.L 0.011 0.012 0.012 0.013 0. 016

HCT.SC.I 0.000 0.000 0.000 0.000 0.005

HCS.SC.M 0.004 0.008 0.013 0.014 0. 030

HCS.SC.L 0.001 0.002 0.003 0.005 0.010

PD.SC.M 0.047 0.275 0.306 0.315 0.410

Total 1.195 2.074 2.889 3.323 4.207

*A value of 0.000 means NES values are less than 0.0005 quads.



### 10.4.3 Annual Costs and Savings

This section presents the annual equipment cost increases and annual operating cost savings at the national level. Figure 10.4.1 shows the changes over time of the non-discounted annual equipment price increases and the non-discounted operating cost savings at TSL 4 for the

VOP.RC.M equipment class. The total net annual impact is the discounted value of the difference between annual equipment purchases and annual operating costs at a 7- percent discount rate. Appendix 10B shows similar figures for all efficiency levels for all equipment

classes. Each figure of appendix 10B also show s the net annual impact, which is the difference between the savings and costs for each year. The annual equipment price change is the increase in equipment price for equipment purchased each year over the period 2017–2046. The annual operating savings is the savings in operating costs for equipment purchased, and which has not been retired, for each year over the time period 2017

− 2060. DOE determined the annual costs and savings presented in each figure based on the AEO20 13 Reference Case electricity prices

. 3

10

- 15

### The NPV is the difference between the cumulative annual discounted savings and the cumulative

annual discounted costs.

Figure 10.4.1 National Annual Costs and Savings for Efficiency Level 4 for Equipment

Class VOP.RC.M.

Figure 10.4.1 initially shows smaller annual operating cost savings compared to the increased equipment price costs (shown on the figure as operating savings). O perating cost savings increase with time, as more and more equipment meeting the efficiency level comprises the commercial refrigeration equipment stock.

10.4.4 Net Present Value Results

The following section provides NPV results for the EL s considered for the selected equipment classes of commercial ref rigeration units. Results are cumulative and are shown as the discounted values of these savings in dollar terms. The inputs to the NES spreadsheet model are based on weighted- average values yielding results that are discrete point values, rather than a distribution of values as in the LCC analysis. The present value of increased total installed costs is the total installed cost increase ( i.e.

, the difference between the standards case and base case) discounted to 2013$ and summed over the time period in which DOE evaluates the impact of

standards.

Savings are decreases in operating costs (including electricity, repair, and maintenance) associated with the higher energy efficiency of commercial refrigeration units purcha sed in the standards case compared to the base case. Total operating cost savings are the savings per unit multiplied by the number of units of each vintage ( i.e. , the year of manufacture) that remain in operation in a particular year. Commercial refrigera tion equipment consumes energy and must be - 0.1 - 0.05 0 0.05 0.1 0.15 0.2 0.25

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ Product Class: VOP.RC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10

- 16

### maintained over its entire lifetime. The operating cost includes energy consumed and maintenance and repair costs incurred until all units purchased in the analysis period 2017-2046

are retired from service (2060).

Table 10.4.4 shows the NPV results for the TSLs considered for commercial refrigerati on equipment based on a 7- percent discount rate. DOE based all results on electricity price forecasts

from the AEO20 13 Reference Case. Appendix 10A provides detailed results showing the breakdown of the NPV into national equipment costs and national o perating savings

.

Table 10.4.4 Cumulative NPV Results by TSL Based on a 7- Percent Discount Rate (b illion 2013$)

Equipment

Class billion 201 2 $ * , **

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M 0.000 0.000 0.570 0.171

- 2.941

VOP.RC.L 0.000 0.000 0.001

- 0.004

- 0.240

VOP.SC.M 0.000 0.000 0.000

- 0.009

- 0.374

VCT.RC.M 0.000 0.000 0.013

- 0.003

- 0.271

VCT.RC.L 0.212 0.212 0.234

- 0.005

- 4.423

VCT.SC.M

- 0.006 0.039 0.058

- 0.003

- 1.531

VCT.SC.L 0.059 0.118 0.123 0.040 0.040

VCT.SC.I 0.000 0.000 0.000

- 0.004

- 0.141

VCS.SC.M 0.756 1.748 1.829 1.659

- 6.820

VCS.SC.L 1.164 1.502 1.579 1.550

- 4.692

VCS.SC.I 0.001 0.002 0.003 0.003

- 0.050

SVO.RC.M 0.291 0.291 0.291 0.081

- 1.493

SVO.SC.M 0.000 0.000 0.000

- 0.003

- 0.215

SOC.RC.M 0.000 0.000 0.000

- 0.011

- 0.342

SOC.SC.M 0.000 0.000 0.000

- 0.003

- 0.032

HZO.RC.M 0.000 0.000 0.000 0.000

- 0.123

HZO.RC.L 0.000 0.000 0.000 0.000

- 0.734

HZO.SC.M 0.000 0.000 0.000 0.000

- 0.025

HZO.SC.L 0.000 0.000 0.000 0.000 0.000

HCT.SC.M 0.001 0.002 0.002 0.000

- 0.014

HCT.SC.L 0.024 0.024 0.025 0.022

- 0.030

HCT.SC.I 0.000 0.000 0.000 0.000

- 0.076

HCS.SC.M 0.008 0.012 0.012 0.007

- 0.342

HCS.SC.L 0.003 0.005 0.006 0.004

- 0.047

PD.SC.M 0.007 0.183 0.183 0.146

- 3.475

Total 2.519 4.139 4.928 3.637

- 28.390
* A value of “NA” is used to indicate that there are no associated efficiency levels at this level for this equipment class. ** 0.000 indicates savings are less than $0.0005 billion



### Table 10.4.5 provides the NPV results for each TSL based on the 3- percent discount rate and electricity price forecasts from the AEO20 13 Reference Case. As with the NPV results based

on a 7- percent discount rate, detailed results showing the breakdown of the NPV into national

10

- 17

### equipment costs and national operating costs based on a 3- percent discount rate are provided in appendix 10A.

Table 10.4.5 Cumulative NPV Results by EL Based on a 3-P ercent Discount Rate ( b illion 2012$)

Equipment

Class billion 201 2 $ * , **

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M 0.000 0.000 1.500 0.882

- 4.894

VOP.RC.L 0.000 0.000 0.004 0.003

- 0.433

VOP.SC.M 0.000 0.000 0.000

- 0.016

- 0.683

VCT.RC.M 0.000 0.000 0.029 0.001

- 0.496

VCT.RC.L 0.481 0.481 0.551 0.125

- 8.007

VCT.SC.M

- 0.006 0.119 0.185 0.086

- 2.712

VCT.SC.L 0.124 0.252 0.265 0.116 0.116

VCT.SC.I 0.001 0.001 0.001

- 0.005

- 0.254

VCS.SC.M 1.656 3.838 4.074 3.825

- 11.832

VCS.SC.L 2.551 3.333 3.626 3.592

- 7.824

VCS.SC.I 0.001 0.005 0.007 0.007

- 0.090

SVO.RC.M 0.790 0.790 0.790 0.476

- 2.443

SVO.SC.M 0.000 0.000 0.000 0.003

- 0.383

SOC.RC.M 0.000 0.000 0.000

- 0.018

- 0.625

SOC.SC.M 0.000 0.000 0.000

- 0.004

- 0.058

HZO.RC.M 0.000 0.000 0.000 0.000

- 0.227

HZO.RC.L 0.000 0.000 0.000 0.000

- 1.350

HZO.SC.M 0.000 0.001 0.001 0.000

- 0.044

HZO.SC.L 0.000 0.000 0.000 0.000 0.000

HCT.SC.M 0.002 0.004 0.004 0.002

- 0.024

HCT.SC.L 0.054 0.056 0.057 0.053

- 0.039

HCT.SC.I 0.000 0.000 0.000 0.000

- 0.137

HCS.SC.M 0.019 0.029 0.033 0.022

- 0.594

HCS.SC.L 0.006 0.010 0.014 0.012

- 0.076

PD.SC.M 0.046 0.577 0.602 0.537

- 6.090

Total 5.727 9.497 11.742 9.698

- 49.199
* A value of “NA” is used to indicate that there are no associated efficiency levels at this level for this equipment class. ** 0.000 indicates savings are less than $0.0005 billion



### 10.5 ANNUALIZED NATIONAL COSTS AND BENEFITS

The benefits and costs of today’s proposed standards, for equipment sold in 2017–2060, can be expressed in terms of annualized values. The annualized monetary values are the sum of (1) the annualized national economic value of the benefits from consumer operation of equipment that meet the proposed standards (consisting primarily of operating cost savings from using less energy, minus increases in equipment purchase and installation costs, which is another way of representing consumer NPV); and (2) the annualized monetary value of t he benefits of emission reductions, including carbon dioxide (CO 2 

### ) emission reductions. The derivation of the monetary value of the benefits of emission reductions is described in chapter 16 of this final rule

TSD. The value of the CO 2 

### reductions, otherwise known as the Social Cost of Carbon (SCC), is

10

- 18

### calculated using a range of values per metric ton of CO 2 

### developed by a recent interagency process. The derivation of the time series of SCC values is discussed in appendix 16A of this

final rule TSD.

Although combining the values of operating savings and CO 2 

### reductions provides a useful perspective, two issues should be considered. First, the national operating cost savings are domestic U.S. consumer monetary savings that occur as a result of market transactio ns, while the

value of CO 2 

### reductions is based on a global value. Second, the assessments of operating cost savings and CO 2 

### savings are performed with different methods that use quite different time frames for analysis. The national operating cost savings is measured for the lifetime of equipment shipped in the 30-year analysis period. The SCC values, on the other hand, reflect the present value of future climate

- related impacts resulting from the emission of 1 ton of CO 2 

### in each year. These impacts go well beyond 2100.

10.5.1 Calculation Method

DOE uses a two

- step calculation process to convert each time

- series of costs and benefits into annualized values. First, DOE calculates a present value in the “present” year used in discounting the NPV of total consumer costs and savings. d 

### For this calculation, DOE uses discount rates of 3 and 7 percent for all costs and benefits except for the value of CO 2 

### reductions. For the latter, DOE uses the discount rate appropriate for each SCC time- series (see TSD chapter 16 for discussion).

PV
x 

### = � ( x ( t )

∙ ( 1 + r x 

### ) y NPV −t 

### )

t=y 1 , y T



### Eq. 10.8

Where:

x ( t ) = time

- series under ev aluation,

PV
x 

### = present value of the time

- series x
, y 1 

### = first year in the analysis period,

y T 

### = last year in the analysis period,

y NPV 

### = year to which the NPV of consumers’ costs and savings are being discounted, and

r x 

### = discount rate used to discount the annual values of time- series x to year y NPV



### .

In the second step, DOE calculates, from the present values, the fixed annual payments over a 30-year period, starting in the first year of the analysis period ( i.e. , the compliance year), which yields the same present values with discount rates of 3 and 7 percent. This requires

d For the value of emissions reductions, DOE uses a time

- series that corresponds to the time period used in calculating the operating cost savings ( i.e. , through the final year in which equipment shipped are still operating).

10

- 19

### projecting the present values in the “present” year ahead to the compliance year. The fixed annual payments are the annualized values.

Ann
x

, r 

### = PV
x 

### ∙ f y 1 −y NPV , r 

### ∙ a 30

, r 

### = PV
x 

### ∙ ( 1 + r ) y 1 −y NPV



### ∙ r ∙ ( 1 + r ) 30



### ( 1 + r ) 30



### − 1

Eq. 10.9

Where:

Ann
x

, r 

### = annualized value of the time- series x
, f

n

, r 

### = factor to project a value n years ahead e 

### with r discount rate, and

a 30

, r 

### = factor to annualize present values over a 30 -year period with r discount rate.

Although DOE calculates annualized values, this does not imply that the time- series of

cost and benefits from which the annualized values were det ermined would be a steady stream of
payments.

10.5.2 Results for the Adopted Standards

The final rule associated with this TSD states that DOE is adopting amended energy conservation standards for commercial refrigeration equipment that correspond to TSL 3.

Est imates of annualized values for the proposed standards are shown in Table 10.5.1

.

e n is the number of years between the “present” year and the compliance year.

10

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### Table 10.5.1 Annualized Benefits and Costs of New and Amended Standards for Commercial Refrigeration Equipment Sold in 201 7–2046

Discount

Rate Primary

Estimate* Low Net

Benefits

Estimate* High Net

Benefits

Estimate*

million 2012$/year

Benefits

Operating Cost Savings 7% 710 688 744

3% 900 865 947

CO 2 Reduction at ($11.8/t case)** 5% 73 73 73

CO 2 Reduction at ($39.7/t case)** 3% 246 246 246

CO 2 Reduction at ($61.2/t case)** 2.5% 361 361 361

CO 2 Reduction at ($117.0/t case)** 3% 760 760 760

NO X Reduction at ($2,591/ton)** 7% 3.01 3.01 3.01

3% 5.64 5.64 5.64

Total Benefits† 7% plus

CO 2 range 786 to 1,474 764 to 1,451 820 to 1,508

7% 960 937 994

3% plus

CO 2 range 978 to 1,666 943 to 1,631 1,026 to 1,713

3% 1,152 1,117 1,200

Costs

Incremental Product Costs 7% 256 250 261

3% 264 258 271

Net Benefits

Total† 7% plus

CO 2 range 530 to 1,218 513 to 1,201 559 to 1,246

7% 704 687 733

3% plus

CO 2 range 714 to 1,402 685 to 1,373 755 to 1,442

3% 888 859 929

* This table presents the annualized costs and benefits associated with commercial refrigeration equipment shipped in 2017

- 2046. These results include benefits to consumers that accrue after 2046 from the commercial refrigeration equipment purchased from 2 017 to 2046. Costs incurred by manufacturers, some of which may be incurred prior to 2017 in preparation for the rule, are not directly included, but are indirectly included as part of incremental equipment costs.

** The CO 2 values represent global moneti zed values (in 2012$) of the social cost of CO 2 emissions in 2015 under several scenarios. The values of $6.2, $25.9, and $41.7 per metric ton are the averages of SCC distributions calculated using 5

- percent,

3- percent, and 2.5

- percent discount rates, respectively. The value of $79.1/t represents the 95 th percentile of the SCC distribution calculated using a 3

- percent discount rate. The value for NO

x (in 2012$) is $2,639.

† Total benefits for both the 3

- percent and 7

- percent cases are derived using the SCC value calculated at a 3

- percent discount rate, which is $25.9/t in 2015 (in 2012$). In the rows labeled as “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.

10

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### Appendix 10D of this final rule TSD presents detailed RISC & OIRA Consolidated Information System (ROCIS) tables with annualized benefits and costs by equipment class grouping for all TSLs.

10

- 22

### REFERENCES

1. U.S. Department of Energy

− Energy Information Administration. Commercial Demand Module of the National Energy Modeling System: Model Documentation 2012 DOE/EIA

M066. 2012. Washington, D.C.

2. Energy Research Group / M.K. Jaccard & Associates. Integration of GHG Emission Reduction Options using CIMS . 2000. Vancouver, B.C.

< http://www.emrg.sfu.ca/media/publications/Reports%20for%20Natural%20Resources%20 Canada/Rollup.pdf >

3. U.S. Department of Energy– Energy Information Administration. Annual Energy Outlook

2012 . Washington, D.C. DOE/EIA-0383(2012 ).

4. U.S. Department of En ergy

– Office of Energy Efficiency and Renewable Energy . Final Rule Technical Support Document (TSD). Energy Efficiency Standards f or Commercial and Industrial Equipment: Commercial Ice

- Cream Freezers; Self

- Contained Commercial Refrigerators, Freezers, and Refrigerator -Freezers without Doors; and Remote Condensing Commercial Refrigerators, Freezers, and Refrigerator

- Freezers. Chapter 11: National Impact Analysis, and Appendix H. December 31, 2008. Washington, D.C.

< http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/52 >

10

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### APPENDIX 10A. DETAILED NATIONAL ENERGY SAVINGS AND NATIONAL NET

PRESENT VALUE RESULTS

TABLE OF CONTENTS

10A.1 INTRODUCTION ................................................................................................ 10A-1

10A.2 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL OPEN, REMOTE CONDENSING, MEDIUM (VOP.RC.M) .......................................... 10A-1

10A.3 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL OPEN, REMOTE CONDENSING, LOW TEMPERATURE (VOP.RC.L) .................... 10A-5

10A.4 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL OPEN, SELF CONTAINED, MEDIUM TEMPERATURE (VOP.SC.M) ................................ 10A-8

10A.5 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, REMOTE CONDENSING, MEDIUM TEMPERATURE (VCT.RC.M) ....................................................................................................... 10A-11

10A.6 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, REMOTE CONDENSING, LOW TEMPERATURE (VCT.RC.L) ........................................................................................................ 10A-15

10A.7 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, SELF CONTAINED, MEDIUM TEMPERATURE (VCT.SC.M) ....................................................................................................... 10A-19

10A.8 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, SELF CONTAINED, LOW TEMPERATURE (VCT.SC.L) 10A-22

10A.9 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, SELF CONTAINED, ICE CREAM TEMPERATURE (VCT.SC.I) .......................................................................................................... 10A-25

10A.10 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED SOLID, SELF CONTAINED, MEDIUM TEMPERATURE (VCS.SC.M) ...... 10A-29

10A.11 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED SOLID, SELF CONTAINED, LOW TEMPERATURE (VCS.SC.L) ............... 10A-32

10A.12 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED SOLID, SELF CONTAINED, ICE CREAM TEMPERATURE (VCS.SC.I) .... 10A-36

10A.13 NATIONAL IMPACT ANALYSIS RESULTS FOR SEMI

- VERTICAL OPEN, REMOTE CONDENSING, MEDIUM TEMPERATURE (SVO.RC.M) .......... 10A-39

10A.14 NATIONAL IMPACT ANALYSIS RESULTS FOR SEMI

- VERTICAL OPEN, SELF CONTAINED, MEDIUM TEMPERATURE (SVO.SC.M) .................... 10A-43

10A.15 NATIONAL IMPACT ANALYSIS RESULTS FOR SERVICE OVER COUNTER, REMOTE CONDENSING, MEDIUM TEMPERATURE (SOC.RC.M) ....................................................................................................... 10A-46

10A.16 NATIONAL IMPACT ANALYSIS RESULTS FOR SERVICE OVER COUNTER, SELF CONTAINED, MEDIUM TEMPERATURE (SOC.SC.M) 10A-50

10A.17 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, REMOTE CONDENSING, MEDIUM TEMPERATURE (HZO.RC.M) .......... 10A-53

10A.18 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, REMOTE CONDENSING, LOW TEMPERATURE (HZO.RC.L) .................. 10A-57

10A -i

### 10A.19 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, SELF CONTAINED, MEDIUM TEMPERATURE (HZO.SC.M) .................... 10A-60

10A.20 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, SELF CONTAINED, LOW TEMPERATURE (HZO.SC.L) ............................ 10A-64

10A.21 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF CONTAINED, MEDIUM TEMPERATURE (HCT.SC.M) ....................................................................................................... 10A-67

10A.22 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF CONTAINED, LOW TEMPERATURE (HCT.SC.L) 10A-71

10A.23 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF CONTAINED, ICE CREAM TEMPERATURE (HCT.SC.I) .......................................................................................................... 10A-74

10A.24 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED SOLID, SELF CONTAINED, MEDIUM TEMPERATURE(HCS.SC.M) ...... 10A-78

10A.25 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED SOLID, SELF CONTAINED, LOW TEMPERATURE (HCS.SC.L) ............... 10A-81

10A.26 NATIONAL IMPACT ANALYSIS RESULTS FOR PULL

- DOWN, SELF CONTAINED, MEDIUM TEMPERATURE (PD.SC.M) ................................. 10A-84

LIST OF FIGURES

Figure 10A.2.1 Cumulative NPV and NES Results for VOP.RC.M ...................................... 10A-1 Figure 10A.2.2 National Annual Costs and Savings for VOP.RC.M, TSL 1 ......................... 10A-2 Figure 10A.2.3 National Annual Costs and Savings for VOP.RC.M, TSL 2 ......................... 10A-2 Figure 10A.2.4 National Annual Costs and Savings for VOP.RC.M, TSL 3 ......................... 10A-3 Figure 10A.2.5 National Annual Costs and Savings for VOP.RC.M, TSL 4 ......................... 10A-3 Figure 10A.2.6 National Annual Costs and Savings for VOP.RC.M, TSL 5 ......................... 10A-4 Figure 10A.3.1 Cumulative NPV and NES Results for VOP.RC.L ....................................... 10A-5 Figure 10A.3.2 National Annual Costs and Savings for VOP.RC.L, TSL 1 .......................... 10A-5 Figure 10A.3.3 National Annual Costs and Savings for VOP.RC.L, TSL 2 .......................... 10A-6 Figure 10A.3.4 National Annual Costs and Savings for VOP.RC.L, TSL 3 .......................... 10A-6 Figure 10A.3.5 National Annual Costs and Savings for VOP.RC.L, TSL 4 .......................... 10A-7 Figure 10A.3.6 National Annual Costs and Savings for VOP.RC.L, TSL 5 .......................... 10A-7 Figure 10A.4.1 Cumulative NPV and NES Results for VOP.SC.M ...................................... 10A-8 Figure 10A.4.2 National Annual Costs and Savings for VOP.SC.M, TSL 1 ......................... 10A-8 Figure 10A.4.3 National Annual Costs and Savings for VOP.SC.M, TSL 2 ......................... 10A-9 Figure 10A.4.4 National Annual Costs and Savings for VOP.SC.M, TSL 3 ......................... 10A-9 Figure 10A.4.5 National Annual Costs and Savings for VOP.SC.M, TSL 4 ....................... 10A-10 Figure 10A.4.6 National Annual Costs and Savings for VOP.SC.M, TSL 5 ....................... 10A-10 Figure 10A.5.1 Cumulative NPV and NES Results for VCT.RC.M .................................... 10A-11 Figure 10A.5.2 National Annual Costs and Savings for VCT.RC.M, TSL 1 ....................... 10A-12 Figure 10A.5.3 National Annual Costs and Savings for VCT.RC.M, TSL 2 ....................... 10A-12 Figure 10A.5.4 National Annual Costs and Savings for VCT.RC.M, TSL 3 ....................... 10A-13 Figure 10A.5.5 National Annual Costs and Savings for VCT.RC.M, TSL 4 ....................... 10A-13 Figure 10A.5.6 National Annual Costs and Savings for VCT.RC.M, TSL 5 ....................... 10A-14 Figure 10A.6.1 Cumulative NPV and NES Results for VCT.RC.L ..................................... 10A-15

10A

- ii

### Figure 10A.6.2 National Annual Costs and Savings for VCT.RC.L, TSL 1 ........................ 10A-16 Figure 10A.6.3 National Annual Costs and Savings for VCT.RC.L, TSL 2 ........................ 10A-16 Figure 10A.6.4 National Annual Costs and Savings for VCT.RC.L, TSL 3 ........................ 10A-17 Figure 10A.6.5 National An nual Costs and Savings for VCT.RC.L, TSL 4 ........................ 10A-17 Figure 10A.6.6 National Annual Costs and Savings for VCT.RC.L, TSL 5 ........................ 10A-18 Figure 10A.7.1 Cumulative NPV and NES Results for VCT.SC.M .................................... 10A-19 Figure 10A.7.2 National Annual Costs and Savings for VCT.SC.M, TSL 1 ....................... 10A-19 Figu re 10A.7.3 National Annual Costs and Savings for VCT.SC.M, TSL 2 ....................... 10A-20 Figure 10A.7.4 National Annual Costs and Savings for VCT.SC.M, TSL 3 ....................... 10A-20 Figure 10A.7.5 National Annual Costs and Savings for VCT.SC.M, TSL 4 ....................... 10A-21 Figure 10A.7.6 National Annual Costs and Savings for VCT.SC.M, TSL 5 ....................... 10A-21 Figure 10A.8.1 Cumulative NPV and NES Results for VCT.SC.L ..................................... 10A-22 Figure 10A.8.2 National Annual Costs and Savings for VCT.SC.L, TSL 1 ........................ 10A-22 Figure 10A.8.3 National Annual Costs and Savings for VCT.SC.L, TSL 2 ........................ 10A-23 Figure 10A.8.4 National Annual Costs and Savings for VCT.SC.L, TSL 3 ........................ 10A-23 Figure 10A.8.5 National Annual Costs and Savings for VCT.SC.L, TSL 4 ........................ 10A-24 Figure 10A.8.6 National Annual Costs and Savings for VCT.SC.L, TSL 5 ........................ 10A-24 Figure 10A.9.1 Cumulative NPV and NES Results for VCT.SC.I ....................................... 10A-25 Figure 10A.9.2 National Annual Costs and Savings for VCT.SC.I, TSL 1 ......................... 10A-26 Figure 10A.9.3 National Annual Costs and Savings for VCT.SC.I, TSL 2 ......................... 10A-26 Figure 10A.9.4 National Annual Costs and Savings for VCT.SC.I, TSL 3 ......................... 10A-27 Figure 10A.9.5 National Annual Costs and Savings for VCT.SC.I, TSL 4 ......................... 10A-27 Figure 10A.9.6 National Annual Costs and Savings for VCT.SC.I, TSL 5 ......................... 10A-28 Figure 10A.10.1 Cumulative NPV and NES Results for VCS.SC.M ................................... 10A-29 Figure 10A.10.2 National Annual Costs and Savings for VCS.SC.M, TSL 1 ..................... 10A-29 Figure 10A.10.3 National Annual Costs and Savings for VCS.SC.M, TSL 2 ..................... 10A-30 Figure 10A.10.4 National Annual Costs and Savings for VCS.SC.M, TSL 3 ..................... 10A-30 Figure 10A.10.5 National Annual Costs and Savings for VCS.SC.M, TSL 4 ..................... 10A-31 Figure 10A.10.6 National Annual Costs and Savings for VCS.SC.M, TSL 5 ..................... 10A-31 Figure 10A.11.1 Cumulative NPV and NES Results for VCS.SC.L .................................... 10A-32 Figure 10A.11.2 National Annual Costs and Savings for VCS.SC.L, TSL 1 ...................... 10A-33 Figure 10A.11.3 National Annual Costs and Savings for VCS.SC.L, TSL 2 ...................... 10A-33 Figure 10A.11.4 National Annual Costs and Savings for VCS.SC.L, TSL 3 ...................... 10A-34 Figure 10A.11.5 National Annual Costs and Savings for VCS.SC.L, TSL 4 ...................... 10A-34 Figure 10A.11.6 National Annual Costs and Savings for VCS.SC.L, TSL 5 ...................... 10A-35 Figure 10A.12.1 Cumulative NPV and NES Results for VCS.SC.I ..................................... 10A-36 Figure 10A.12.2 National Annual Costs and Savings for VCS.SC.I, TSL 1 ....................... 10A-36 Figure 10A.12.3 National Annual Costs and Savings for VCS.SC.I, TSL 2 ....................... 10A-37 Figure 10A.12.4 National Annual Costs and Savings for VCS.SC.I, TSL 3 ....................... 10A-37 Figure 10A.12.5 National Annual Costs and Savings for VCS.SC.I, TSL 4 ....................... 10A-38 Figure 10A.12.6 National Annual Costs and Savings for VCS.SC.I, TSL 5 ....................... 10A-38 Figure 10A.13.1 Cumulative NPV and NES Results for SVO.RC.M .................................. 10A-39 Figure 10A.13.2 National Annual Costs and Savings for SVO.RC.M, TSL 1 ..................... 10A-40 Figure 10A.13.3 National Annual Costs and Savings for SVO.RC.M, TSL 2 ..................... 10A-40 Figure 10A.13.4 National Annual Costs and Savings for SVO.RC.M, TSL 3 ..................... 10A-41 Figure 10A.13.5 National Annual Costs and Savings for SVO.RC.M, TSL 4 ..................... 10A-41

10A

- iii

### Figure 10A.13.6 National Annual Costs and Savings for SVO.RC.M, TSL 5 ..................... 10A-42 Figure 10A.14.1 Cumulative NPV and NES Results for SVO.SC.M .................................. 10A-43 Figure 10A.14.2 National Annual Costs and Savings for SVO.SC.M, TSL 1 ..................... 10A-43 Figure 10A.14.3 National Annual Costs and Savings for SVO.SC.M, TSL 2 ..................... 10A-44 Figure 10A.14.4 National Annual Costs and Savings for SVO.SC.M, TSL 3 ..................... 10A-44 Figure 10A.14.5 National Annual Costs and Savings for SVO.SC.M, TSL 4 ..................... 10A-45 Figure 10A.14.6 National Annual Costs and Savings for SVO.SC.M, TSL 5 ..................... 10A-45 Figure 10A.15.1 Cumulative NPV and NES Results for SOC.RC.M .................................. 10A-46 Figure 10A.15.2 National Annual Costs and Savings for SOC.RC.M, TSL 1 ..................... 10A-47 Figure 10A.15.3 National Annual Costs and Savings for SOC.RC.M, TSL 2 ..................... 10A-47 Figure 10A.15.4 National Annual Costs and Savings for SOC.RC.M, TSL 3 ..................... 10A-48 Figure 10A.15.5 National Annual Costs and Savings for SOC.RC.M, TSL 4 ..................... 10A-48 Figure 10A.15.6 National Annual Costs and Savings for SOC.RC.M, TSL 5 ..................... 10A-49 Figure 10A.16.1Cumulative NPV and NES Results for SOC.SC.M .................................... 10A-50 Figure 10A.16.2 National Annual Costs and Savings for SOC.SC.M, TSL 1 ..................... 10A-50 Figure 10A.16.3 National Annual Costs and Savings for SOC.SC.M, TSL 2 ..................... 10A-51 Figure 10A.16.4 National Annual Costs and Savings for SOC.SC.M, TSL 3 ..................... 10A-51 Figure 10A.16.5 National Annual Costs and Savings for SOC.SC.M, TSL 4 ..................... 10A-52 Figure 10A.16.6 National Annual Costs and Savings for SOC.SC.M, TSL 5 ..................... 10A-52 Figure 10A.17.1 Cumulative NPV and NES Results for HZO.RC.M .................................. 10A-53 Figure 10A.17.2 National Annual Costs and Savings for HZO.RC.M, TSL 1 .................... 10A-54 Figure 10A.17.3 National Annual Costs and Savings for HZO.RC.M, TSL 2 .................... 10A-54 Figure 10A.17.4 National Annual Costs and Savings for HZO.RC.M, TSL 3 .................... 10A-55 Figure 10A.17.5 National Annual Costs and Savings for HZO.RC.M, TSL 4 .................... 10A-55 Figure 10A.17.6 National Annual Costs and Savings for HZO.RC.M, TSL 5 .................... 10A-56 Figure 10A.18.1 Cumulative NPV and NES Results for HZO.RC.L ................................... 10A-57 Figure 10A.18.2 National Annual Costs and Savings for HZO.RC.L, TSL 1 ...................... 10A-57 Figure 10A.18.3 National Annual Costs and Savings for HZO.RC.L, TSL 2 ...................... 10A-58 Figure 10A.18.4 National Annual Costs and Savings for HZO.RC.L, TSL 3 ...................... 10A-58 Figure 10A.18.5 National Annual Costs and Savings for HZO.RC.L, TSL 4 ...................... 10A-59 Figure 10A.18.6 National Annual Costs and Savings for HZO.RC.L, TSL 5 ...................... 10A-59 Figure 10A.19.1 Cumulative NPV and NES Results for HZO.SC.M .................................. 10A-60 Figure 10A.19.2 National Annual Costs and Savings for HZO.SC.M, TSL 1 ..................... 10A-61 Figure 10A.19.3 National Annual Costs and Savings for HZO.SC.M, TSL 2 ..................... 10A-61 Figure 10A.19.4 National Annual Costs and Savings for HZO.SC.M, TSL 3 ..................... 10A-62 Figure 10A.19.5 National Annual Costs and Savings for HZO.SC.M, TSL 4 ..................... 10A-62 Figure 10A.19.6 National Annual Costs and Savings for HZO.SC.M, TSL 5 ..................... 10A-63 Figure 10A.20.1 Cumulative NPV and NES Results for HZO.SC.L ................................... 10A-64 Figure 10A.20.2 National Annual Costs and Savings for HZO.SC.L, TSL 1 ...................... 10A-64 Figure 10A.20.3 National Annual Costs and Savings for HZO.SC.L, TSL 2 ...................... 10A-65 Figure 10A.20.4 National Annual Costs and Savings for HZO.SC.L, TSL 3 ...................... 10A-65 Figure 10A.20.5 National Annual Costs and Savings for HZO.SC.L, TSL 4 ...................... 10A-66 Figure 10A.20.6 National Annual Costs and Savings for HZO.SC.L, TSL 5 ...................... 10A-66 Figure 10A.21.1 Cumulative NPV and NES Results for HCT.SC.M .................................. 10A-67 Figure 10A.21.2 National Annual Costs and Savings for HCT.SC.M, TSL 1 ..................... 10A-68 Figure 10A.21.3 National Annual Costs and Savings for HCT.SC.M, TSL 2 ..................... 10A-68

10A

- iv

### Figure 10A.21.4 National Annual Costs and Savings for HCT.SC.M, TSL 3 ..................... 10A-69 Figure 10A.21.5 National Annual Costs and Savings for HCT.SC.M, TSL 4 ..................... 10A-69 Figure 10A.21.6 National Annual Costs and Savings for HCT.SC.M, TSL 5 ..................... 10A-70 Figure 10A.22.1 Cumulative NPV and NES Results for HCT.SC.L ................................... 10A-71 Figure 10A.22.2 National Annual Costs and Savings for HCT.SC.L, TSL 1 ...................... 10A-71 Figure 10A.22.3 National Annual Costs and Savings for HCT.SC.L, TSL 2 ...................... 10A-72 Figure 10A.22.4 National Annual Costs and Savings for HCT.SC.L, TSL 3 ...................... 10A-72 Figure 10A.22.5 National Annual Costs and Savings for HCT.SC.L, TSL 4 ...................... 10A-73 Figure 10A.22.6 National Annual Costs and Savings for HCT.SC.L, TSL 5 ...................... 10A-73 Figure 10A.23.1 Cumulative NPV and NES Results for HCT.SC.I ..................................... 10A-74 Figure 10A.23.2 National Annual Costs and Savings for HCT.SC.I, TSL 1 ....................... 10A-75 Figure 10A.23.3 National Annual Costs and Savings for HCT.SC.I, TSL 2 ....................... 10A-75 Figure 10A.23.4 National Annual Costs and Savings for HCT.SC.I, TSL 3 ....................... 10A-76 Figure 10A.23.5 National Annual Costs and Savings for HCT.SC.I, TSL 4 ....................... 10A-76 Figure 10A.23.6 National Annual Costs and Savings for HCT.SC.I, TSL 5 ....................... 10A-77 Figure 10A.24.1 Cumulative NPV and NES Results for HCS.SC.M ................................... 10A-78 Figure 10A.24.2 National Annual Costs and Savings for HCS.SC.M, TSL 1 ..................... 10A-78 Figure 10A.24.3 National Annual Costs and Savings for HCS.SC.M, TSL 2 ..................... 10A-79 Figure 10A.24.4 National Annual Costs and Savings for HCS.SC.M, TSL 3 ..................... 10A-79 Figure 10A.24.5 National Annual Costs and Savings for HCS.SC.M, TSL 4 ..................... 10A-80 Figure 10A.24.6 National Annual Costs and Savings for HCS.SC.M, TSL 5 ..................... 10A-80 Figure 10A.25.1 Cumulative NPV and NES Results for HCS.SC.L .................................... 10A-81 Figure 10A.25.2 National Annual Costs and Savings for HCS.SC.L, TSL 1 ...................... 10A-81 Figure 10A.25.3 National Annual Costs and Savings for HCS.SC.L, TSL 2 ...................... 10A-82 Figure 10A.25.4 National Annual Costs and Savings for HCS.SC.L, TSL 3 ...................... 10A-82 Figure 10A.25.5 National Annual Costs and Savings for HCS.SC.L, TSL 4 ...................... 10A-83 Figure 10A.25.6 National Annual Costs and Savings for HCS.SC.L, TSL 5 ...................... 10A-83 Figure 10A.26.1 Cumulative NPV and NES Results for PD.SC.M ..................................... 10A-84 Figure 10A.26.2 National Annual Costs and Savings for PD.SC.M, TSL 1 ........................ 10A-85 Figure 10A.26.3 National Annual Costs and Savings for PD.SC.M, TSL 2 ........................ 10A-85 Figure 10A.26.4 National Annual Costs and Savings for PD.SC.M, TSL 3 ........................ 10A-86 Figure 10A.26.5 National Annual Costs and Savings for PD.SC.M, TSL 4 ........................ 10A-86 Figure 10A.26.6 National Annual Costs and Savings for PD.SC.M, TSL 5 ........................ 10A-87

10A -v

### APPENDIX 10 A . DETAILED NATIONAL ENERGY SAVINGS AND NATIONAL NET

PRESENT VALUE RESULTS

10A.1 INTRODUCTION

This appendix contains detailed output results from the National Energy Savings/National

Economi c Impact spreadsheet model. Results are presented for each e quipment class in terms of

c umulative energy savings (primary ) and cumulative net present value at each candidate standard level and at a national level. See chapter 10 for an explanation of the figures in this appendix.

10A.2 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL OPEN, REMOTE CONDENSING, MEDIUM (VOP.RC.M)

Figure 10A.2.1 Cumulative NPV and NES Results for VOP.RC.M -6 -5 -4 -3 -2 -1 0 1 2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VOP.RC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A -1

### Figure 10A.2.2 National Annual Costs and Savings for VOP.RC.M , TSL 1

Figure 10A.2.3 National Annual Costs and Savings for VOP.RC.M , TSL 2 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -2

### Figure 10A.2.4 National Annual Costs and Savings for VOP.RC.M , TSL 3

Figure 10A.2.5 National Annual Costs and Savings for VOP.RC.M , TSL 4 -0.1 -0.05 0 0.05

0.1 0.15

0.2 0.25

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.2 -0.15

-0.1 -0.05 0 0.05

0.1 0.15

0.2 0.25

0.3

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -3

### Figure 10A.2.6 National Annual Costs and Savings for VOP.RC.M , TSL 5 -0.5 -0.45

-0.4 -0.35

-0.3 -0.25

-0.2 -0.15

-0.1 -0.05 0 0.05

0.1 0.15

0.2

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -4

### 10A.3 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL OPEN, REMOTE CONDENSING, LOW TEMPERATURE (VOP.RC.L)

Figure 10A.3.1 Cumulative NPV and NES Results for VOP.RC.L

Figure 10A.3.2 National Annual Costs and Savings for VOP.RC.L , TSL 1 -0.5 -0.45 -0.4 -0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05

0 0.002 0.004 0.006 0.008

0.01 0.012 0.014 0.016 0.018

0.02

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VOP.RC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -5

### Figure 10A.3.3 National Annual Costs and Savings for VOP.RC.L , TSL 2

Figure 10A.3.4 National Annual Costs and Savings for VOP.RC.L , TSL 3 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -6

### Figure 10A.3.5 National Annual Costs and Savings for VOP.RC.L , TSL 4

Figure 10A.3.6 National Annual Costs and Savings for VOP.RC.L, TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.RC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -7

### 10A.4 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL OPEN, SELF CONTAINED, MEDIUM TEMPERATURE (VOP.SC.M)

Figure 10A.4.1 Cumulati ve NPV and NES Results for VOP.SC.M

Figure 10A.4.2 National Annual Costs and Savings for VOP.SC.M , TSL 1 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VOP.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -8

### Figure 10A.4.3 National Annual Costs and Savings fo r VOP.SC.M , TSL 2

Figure 10A.4.4 National Annual Costs and Savings for VOP.SC.M , TSL 3 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A -9

### Figure 10A.4.5 National Annual Costs and Savings for VOP.SC.M , TSL 4

Figure 10A.4.6 National Annual Costs and Savings for VOP.SC.M , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VOP.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 10

### 10A.5 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, REMOTE CONDENSING, MEDIUM TEMPERATURE (VCT.RC.M)

Figure 10A.5.1 Cumulative NPV and NES Results for VCT.RC.M -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1

0 0.002 0.004 0.006 0.008

0.01 0.012

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCT.RC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

- 11

### Figure 10A.5.2 National Annual Costs and Savings for VCT.RC.M , TSL 1

Figure 10A.5.3 National Annual Costs and Savings for VCT.RC.M , TSL 2 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 12

### Figure 10A.5.4 National Annual Costs and Savings for VCT.RC.M , TSL 3

Figure 10A.5.5 National Annual Costs and Savings for VCT.RC.M , TSL 4 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 13

### Figure 10A.5.6 National Annual Costs and Savings for VCT.RC.M , TSL 5 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 14

### 10A.6 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, REMOTE CONDENSING, LOW TEMPERATURE (VCT.RC.L)

Figure 10A.6.1 Cumulative NPV and NES Results for VCT.RC.L -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1

0 0.05

0.1 0.15

0.2 0.25

0.3

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCT.RC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

- 15

### Figure 10A.6.2 National Annual Costs and Savings for VCT.RC.L , TSL 1

Figure 10A.6.3 National Annual Costs and Savings for VCT.RC.L , TSL 2 -0.05 0 0.05

0.1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

0.1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 16

### Figure 10A.6.4 National Annual Costs and Savings for VCT.RC.L , TSL 3

Figure 10A.6.5 National Annual Costs and Savings for VCT.RC.L , TSL 4 -0.05 0 0.05

0.1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

0.1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 17

### Figure 10A.6.6 National Annual Costs and Savings for VCT.RC.L , TSL 5 -0.5 -0.45

-0.4 -0.35

-0.3 -0.25

-0.2 -0.15

-0.1 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.RC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 18

### 10A.7 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, SELF CONTAINED, MEDIUM TEMPERATURE (VCT.SC.M)

Figure 10A.7.1 Cumulative NPV and NES Results for VCT.SC.M

Figure 10A.7.2 National Annual Costs and Savings for VCT.SC.M , TSL 1 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5

0 0.02 0.04 0.06 0.08

0.1 0.12 0.14 0.16

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCT.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 19

### Figure 10A.7.3 National Annual Costs and Savings for VCT.SC.M , TSL 2

Figure 10A.7.4 National Annual Costs and Savings for VCT.SC.M , TSL 3 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 20

### Figure 10A.7.5 National Annual Costs and Savings for VCT.SC.M , TSL 4

Figure 10A.7.6 National Annual Costs and Savings for VCT.SC.M , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.2 -0.15

-0.1 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 21

### 10A.8 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, SELF CONTAINED, LOW TEMPERATURE (VCT.SC.L)

Figure 10A.8.1 Cumulative NPV and NES Results for VCT.SC.L

Figure 10A.8.2 National Annual Costs and Savings for VCT.SC.L , TSL 1 0 0.05 0.1 0.15 0.2 0.25 0.3

0 0.01 0.02 0.03 0.04 0.05 0.06

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCT.SC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 22

### Figure 10A.8.3 National Annual Costs and Savings for VCT.SC.L , TSL 2

Figure 10A.8.4 National Annual Costs and Savings for VCT.SC.L , TSL 3 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 23

### Figure 10A.8.5 National Annual Costs and Savings for VCT.SC.L , TSL 4

Figure 10A.8.6 National Annual Costs and Savings for VCT.SC.L , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 24

### 10A.9 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED TRANSPARENT, SELF CONTAINED, ICE CREAM TEMPERATURE (VCT.SC.I)

Figure 10A.9.1 Cumulative NPV and NES Results for VCT.SC.I -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCT.SC.I Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

- 25

### Figure 10A.9.2 National Annual Costs and Savings for VCT.SC.I, TSL 1

Figure 10A.9.3 National Annual Costs and Savings for VCT.SC.I , TSL 2 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.I
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.I
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 26

### Figure 10A.9.4 National Annual Costs and Savings for VCT.SC.I , TSL 3

Figure 10A.9.5 National Annual Costs and Savings for VCT.SC.I , TSL 4 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.I
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.I
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 27

### Figure 10A.9.6 National Annual Costs and Savings for VCT.SC.I , TSL 5 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCT.SC.I
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 28

### 10A.10 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED SOLID, SELF CONTAINED, MEDIUM TEMPERATURE (VCS.SC.M)

Figure 10A.10.1 Cumulative NPV and NES Results for VCS.SC.M

Figure 10A.10.2 National Annual Costs and Savings for VCS.SC.M , TSL 1 -14 -12 -10 -8 -6 -4 -2 0 2 4 6

0 0.2 0.4 0.6 0.8

1 1.2

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCS.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

-0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 29

### Figure 10A.10.3 National Annual Costs and Savings for VCS.SC.M , TSL 2

Figure 10A.10.4 National Annual Costs and Savings for VCS.SC.M , TSL 3 -0.05 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.1 -0.05 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 30

### Figure 10A.10.5 National Annual Costs and Savings for VCS.SC.M , TSL 4

Figure 10A.10.6 National Annua l Costs and Savings for VCS.SC.M , TSL 5 -0.1 -0.05 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.95

-0.9 -0.85

-0.8 -0.75

-0.7 -0.65

-0.6 -0.55

-0.5 -0.45

-0.4 -0.35

-0.3 -0.25

-0.2 -0.15

-0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 31

### 10A.11 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED SOLID, SELF CONTAINED, LOW TEMPERATURE (VCS.SC.L)

Figure 10A.11.1 Cumulative NPV and NES Results for VCS.SC.L -10 -8 -6 -4 -2 0 2 4 6

0 0.2 0.4 0.6 0.8

1 1.2

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCS.SC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

- 32

### Figure 10A.11.2 National Annual Costs and Savings for VCS.SC.L , TSL 1

Figure 10A.11.3 National Annual Costs and Savings for VCS.SC.L , TSL 2 -0.05 0 0.05

0.1 0.15

0.2 0.25

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 33

### Figure 10A.11.4 National Annual Costs and Savings for VCS.SC.L , TSL 3

Figure 10A.11.5 National Annual Costs and Savings for VCS.SC.L , TSL 4 -0.1 -0.05 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.1 -0.05 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 34

### Figure 10A.11.6 National Annual Costs and Savings for VCS.SC.L , TSL 5 -0.75

-0.7 -0.65

-0.6 -0.55

-0.5 -0.45

-0.4 -0.35

-0.3 -0.25

-0.2 -0.15

-0.1 -0.05 0 0.05

0.1 0.15

0.2 0.25

0.3

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 35

### 10A.12 NATIONAL IMPACT ANALYSIS RESULTS FOR VERTICAL CLOSED SOLID, SELF CONTAINED, ICE CREAM TEMPERATURE (VCS.SC.I)

Figure 10A.12.1 Cumulative NPV and NES Results for VCS.SC.I

Figure 10A.12.2 National Annual Costs and Savings for VCS.SC.I , TSL 1 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02

0 0.001 0.002 0.003 0.004 0.005 0.006

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: VCS.SC.I Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.I
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 36

### Figure 10A.12.3 National Annual Costs and Savings for VCS.SC.I , TSL 2

Figure 10A.12.4 National Annual Costs and Savings for VCS.SC.I , TSL 3 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.I
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.I
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 37

### Figure 10A.12.5 National Annual Costs and Savings for VCS.SC.I , TSL 4

Figure 10A.12.6 National Annual Costs and Savings for VCS.SC.I , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.I
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: VCS.SC.I
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 38

### 10A.13 NATIONAL IMPACT ANALYSIS RESULTS FOR SEMI

- VERTICAL OPEN, REMOTE CONDENSING, MEDIUM TEMPERATURE (SVO.RC.M)

Figure 10A.13.1 Cumulative NPV and NES Results for SVO.RC.M -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: SVO.RC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

- 39

### Figure 10A.13.2 National Annual Costs and Savings for SVO.RC.M , TSL 1

Figure 10A.13.3 National Annual Costs and Savings for SVO.RC.M , TSL 2 -0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.RC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.RC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 40

### Figure 10A.13.4 National Annual Costs and Savings for SVO.RC.M , TSL 3

Figure 10A.13.5 National Annual Costs and Savings for SVO.RC.M , TSL 4 -0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.RC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.15

-0.1 -0.05 0 0.05

0.1 0.15

0.2

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.RC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 41

### Figure 10A.13.6 National Annual Costs and Savings for SVO.RC.M , TSL 5 -0.3 -0.25

-0.2 -0.15

-0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.RC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 42

### 10A.14 NATIONAL IMPACT ANALYSI S RESULTS FOR SEMI

- VERTICAL OPEN, SELF CONTAINED, MEDIUM TEMPERATURE (SVO.SC.M)

Figure 10A.14.1 Cumulative NPV and NES Results for SVO.SC.M

Figure 10A.14.2 National Annual Costs and Savings for SVO.SC.M , TSL 1 -0.45 -0.4 -0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05

0 0.002 0.004 0.006 0.008

0.01 0.012 0.014 0.016 0.018

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: SVO.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 43

### Figure 10A.14.3 National Annual Costs and Savings for SVO.SC.M , TSL 2

Figure 10A.14.4 National Annual Costs and Savings for SVO.SC.M , TSL 3 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 44

### Figure 10A.14.5 National Annual Costs and Savings for SVO.SC.M , TSL 4

Figure 10A.14.6 National Annua l Costs and Savings for SVO.SC.M , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SVO.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 45

### 10A.15 NATIONAL IMPACT ANALYSIS RESULTS FOR SERVICE OVER COUNTER, REMOTE CONDENSING, MEDIUM TEMPERATURE (SOC.RC.M)

Figure 10A.15.1 Cumulative NPV and NES Results for SOC.RC.M -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0

0 0.002 0.004 0.006 0.008

0.01 0.012 0.014 0.016 0.018

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: SOC.RC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

- 46

### Figure 10A.15.2 National Annual Costs and Savings for SOC.RC.M , TSL 1

Figure 10A.15.3 National Annual Costs and Savings for SOC.RC.M , TSL 2 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.RC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.RC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 47

### Figure 10A.15.4 National Annual Costs and Savings for SOC.RC.M , TSL 3

Figure 10A.15.5 National Annua l Costs and Savings for SOC.RC.M , TSL 4 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.RC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.RC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.15.6 National Annual Costs and Savings for SOC.RC.M , TSL 5 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.RC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.16 NATIONAL IMPACT ANALYSIS RESULTS FOR SERVICE OVER COUNTER, SELF CONTAINED, MEDIUM TEMPERATURE (SOC.SC.M)

Figure 10A.16.1 Cumulative NPV and NES Results for SOC.SC.M

Figure 10A.16.2 National Annual Costs and Savings for SOC.SC.M , TSL 1 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0

0 0.0005

0.001 0.0015

0.002 0.0025

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: SOC.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 50

### Figure 10A.16.3 National Annual Costs and Savings for SOC.SC.M , TSL 2

Figure 10A.16.4 National Annual Costs and Savings for SOC.SC.M , TSL 3 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.16.5 National Annual Costs and Savings for SOC.SC.M , TSL 4

Figure 10A.16.6 National Annual Costs and Savings for SOC.SC.M , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: SOC.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.17 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, REMOTE CONDENSING, MEDIUM TEMPERATURE (HZO.RC.M)

Figure 10A.17.1 Cumulative NPV and NES Results for HZO.RC.M -0.25 -0.2 -0.15 -0.1 -0.05 0

0 0.0002 0.0004 0.0006 0.0008

0.001 0.0012 0.0014 0.0016 0.0018

0.002

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HZO.RC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

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### Figure 10A.17.2 National Annual Costs and Savings for HZO.RC.M , TSL 1

Figure 10A.17.3 National Annual Costs and Savings for HZO.RC.M, TSL 2 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 54

### Figure 10A.17.4 National Annual Costs and Savings for HZO.RC.M, TSL 3

Figure 10A.17.5 National Annual Costs and Savings for HZO.RC.M, TSL 4 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.17.6 National Annual Costs and Savings for HZO.RC.M, TSL 5 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.18 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, REMOTE CONDENSING, LOW TEMPERATURE (HZO.RC.L)

Figure 10A.18.1 Cumulative NPV and NES Results for HZO.RC.L

Figure 10A.18.2 National Annual Costs and Savings for HZO.RC.L , TSL 1 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0

0 0.005

0.01 0.015

0.02 0.025

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HZO.RC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 57

### Figure 10A.18.3 National Annual Costs and Savings for HZO.RC.L, TSL 2

Figure 10A.18.4 National Annual Costs and Savings for HZO.RC.L, TSL 3 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.18.5 National Annual Costs and Savings for HZO.RC.L, TSL 4

Figure 10A.18.6 National Annual Costs and Savings for HZO.RC.L, TSL 5 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.1 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.RC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.19 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, SELF CONTAINED, MEDIUM TEMPERATURE (HZO.SC.M)

Figure 10A.19.1 Cumulative NPV and NES Results for HZO.SC.M -0.05 -0.045 -0.04 -0.035 -0.03 -0.025 -0.02 -0.015 -0.01 -0.005 0 0.005

0 0.0002 0.0004 0.0006 0.0008

0.001 0.0012 0.0014 0.0016

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HZO.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

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### Figure 10A.19.2 National Annual Costs and Savings for HZO.SC.M , TSL 1

Figure 10A.19.3 National Annual Costs and Savings for HZO.SC.M , TSL 2 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.19.4 National Annual Costs and Savings for HZO.SC.M , TSL 3

Figure 10A.19.5 National Annual Costs and Savings for HZO.SC.M , TSL 4 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.19.6 National Annual Costs and Savings for HZO.SC.M, TSL 5 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.20 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL OPEN, SELF CONTAINED, LOW TEMPERATURE (HZO.SC.L)

Figure 10A.20.1 Cumulative NPV and NES Results for HZO.SC.L

Figure 10A.20.2 National Annual Costs and Savings for HZO.SC.L , TSL 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HZO.SC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 64

### F igure 10A.20.3 National Annual Costs and Savings for HZO.SC.L, TSL 2

Figure 10A.20.4 National Annual Costs and Savings for HZO.SC.L, TSL 3 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.20.5 National Annual Costs and Savings for HZO.SC.L, TSL 4

Figure 10A.20.6 National Annual Costs and Savings for HZO.SC.L, TSL 5 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HZO.SC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.21 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF CONTAINED, MEDIUM TEMPERATURE (HCT.SC.M)

Figure 10A.21.1 Cumulative NPV and NES Results for HCT.SC.M -0.025 -0.02 -0.015 -0.01 -0.005 0 0.005 0.01

0 0.0005

0.001 0.0015

0.002 0.0025

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HCT.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

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### Figure 10A.21.2 National Annual Costs and Savings for HCT.SC.M , TSL 1

Figure 10A.21.3 National Annual Costs and Savings for HCT.SC.M , TSL 2 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.21.4 National Annual Costs and Savings for HCT.SC.M , TSL 3

Figure 10A.21.5 National Annual Costs and Savings for HCT.SC.M , TSL 4 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.21.6 National Annua l Costs and Savings for HCT.SC.M , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.22 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF CONTAINED, LOW TEMPERATURE (HCT.SC.L)

Figure 10A.22.1 Cumulative NPV and NES Results for HC T.SC.L

Figure 10A.22.2 National Annual Costs and Savings for HCT.SC.L , TSL 1 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08

0 0.002 0.004 0.006 0.008

0.01 0.012 0.014 0.016 0.018

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HCT.SC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.22.3 National Annual Costs and Savings for HCT.SC.L , TSL 2

Figure 10A.22.4 National Annual Costs and Savings for HCT.SC.L , TSL 3 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.22.5 National Annual Costs and Savings for HCT.SC.L , TSL 4

Figure 10A.22.6 National Annual Costs and Savings for HCT.SC.L , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.23 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED TRANSPARENT, SELF CONTAINED, ICE CREAM TEMPERATURE (HCT.SC.I)

Figure 10A. 23.1 Cumulative NPV and NES Results for HCT.SC.I -0.16 -0.14 -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0

0 0.001 0.002 0.003 0.004 0.005 0.006

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HCT.SC.I Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

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### Figure 10A.23.2 National Annual Costs and Savings for HCT.SC.I , TSL 1

Figure 10A.23.3 National Annual Costs and Savings for HCT.SC.I , TSL 2 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.I
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.I
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.23.4 National Annual Costs and Savings for HCT.SC.I , TSL 3

Figure 10A.23.5 National Annual Costs and Savings for HCT.SC.I , TSL 4 0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.I
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0.9 0.95 1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.I
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.23.6 National Annual Costs and Savings for HCT.SC.I, TSL 5 -0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCT.SC.I
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### 10A.24 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED SOLID, SELF CONTAINED, MEDIUM TEMPERATURE(HCS.SC.M)

Figure 10A.24.1 Cumulative NPV and NES Results for HCS.SC.M

Figure 10A.24.2 National Annual Costs and Savings for HCS.SC.M , TSL 1 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1

0 0.005

0.01 0.015

0.02 0.025

0.03 0.035

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HCS.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

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### Figure 10A.24.3 National Annual Costs and Savings for HCS.SC.M , TSL 2

Figure 10A.24.4 National Annual Costs and Savings for HCS.SC.M , TSL 3 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 79

### Figure 10A.24.5 National Annual Costs and Savings for HCS.SC.M , TSL 4

Figure 10A.24.6 National Annual Costs and Savings for HCS.SC.M , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 80

### 10A.25 NATIONAL IMPACT ANALYSIS RESULTS FOR HORIZONTAL CLOSED SOLID, SELF CONTAINED, LOW TEMPERATURE (HCS.SC.L)

Figure 10A.25.1 Cumulative NPV and NES Results for HCS.SC.L

Figure 10A.25.2 National Annual Costs and Savings for HCS.SC.L , TSL 1 -0.09 -0.08 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02

0 0.002 0.004 0.006 0.008

0.01 0.012

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: HCS.SC.L Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.L
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 81

### Figure 10A.25.3 National Annual Costs and Savings for HCS.SC.L , TSL 2

Figure 10A.25.4 National Annual Costs and Savings for HCS.SC.L , TSL 3 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.L
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.L
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 82

### Figure 10A.25.5 National Annual Costs and Savings for HCS.SC.L , TSL 4

Figure 10A.25.6 National Annua l Costs and Savings for HCS.SC.L , TSL 5 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.L
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: HCS.SC.L
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 83

### 10A.26 NATIONAL IMPACT ANALYSIS RESULTS FOR PULL

- DOWN, SELF CONTAINED, MEDIUM TEMPERATURE (PD.SC.M)

Figure 10A.26.1 Cumulative NPV and NES Results for PD.SC.M -7 -6 -5 -4 -3 -2 -1 0 1

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

1 2 3 4 5NPV (billion 2012$)Primary Energy Savings (quads)

TSL 

### Product Class: PD.SC.M Cumulative Primary Energy Savings and Net Present Value (NPV)

Primary NES NPV (3%) NPV (7%)

10A

- 84

### Figure 10A.26.2 National Annual Costs and Savings for PD.SC.M , TSL 1

Figure 10A.26.3 National Annual Costs and Savings for PD.SC.M , TSL 2 -0.05 0 0.05

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: PD.SC.M
Net Savings, TSL 1

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: PD.SC.M
Net Savings, TSL 2

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 85

### Figure 10A.26.4 National Annual Costs and Savings for PD.SC.M , TSL 3

Figure 10A.26.5 National Annual Costs and Savings for PD.SC.M , TSL 4 -0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: PD.SC.M
Net Savings, TSL 3

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

-0.1 -0.05 0 0.05

0.1 0.15

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: PD.SC.M
Net Savings, TSL 4

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 86

### Figure 10A.26.6 National Annual Costs and Savings for PD.SC.M , TSL 5 -0.5 -0.45

-0.4 -0.35

-0.3 -0.25

-0.2 -0.15

-0.1 -0.05 0 0.05

0.1

2017 2022 2027 2032 2037 2042 2047 2052 2057Billion 2012$ 

### Product Class: PD.SC.M
Net Savings, TSL 5

Undiscounted Operating Cost Savings Undiscounted Equipment Purchase Savings NPV (3%) NPV (7%)

10A

- 87

### APPENDIX 10B. TRIAL STANDARD LEVELS AND STANDARDS EQUATIONS

TABLE OF CONTENTS

10B.1 INTRODUCTION .......................................................................................................10B-1

10B.2 TRIAL STANDARD LEVEL SELECTION CRITERIA ...........................................10B-1

10B.3 TRIAL STANDARD LEVEL EQUATIONS .............................................................10B-4

LIST OF TABLES

Table 10B.2.1 TSL and Efficiency Levels Mapping ...............................................................10B-2 Table 10B.2.2 TSL and Design Option Levels Mapping ........................................................10B-2 Table 10B.2.3 Efficiency Levels and Analytical Design Option Levels Mapping .................10B-3 Table 10B.3.1 CDEC Values by TSL for Representative Units Analyzed in the Engineering Analysis for Each Primary Equipment Class ...................................................10B-5 Table 10B.3.2 Equations Representing the Standards at Each TSL for All Primary Equipment

Classes ..............................................................................................................10B-6 Table 10B.3.3 Equations Representing the Standards at Each TSL for All Secondary Equipment Classes ...........................................................................................10B-8

10B -i

### APPENDIX 10 B. TRIAL STANDARD LEVEL S AND STANDARDS EQUATIONS

10B.1 INTRODUCTION

The U.S. Department of Energy ( DOE ) carried out the life

- cycle cost ( LCC ) analysis and national impact analysis ( NIA ) by defining efficiency levels within each equipment class of commercial refrigeration equipment. These efficiency levels were chosen from the design option levels defined as part of the engineering analysis. The se design option levels were defined based on the available energy efficiency technologies in the current market and are, generally, different for different equipment classes. Therefore, in general, the efficiency levels belonging to different equipment classes are independent of one another. For example , Efficiency Level 4 of one equipment class is not related to , or is dependent of the Efficiency Level 4 belonging to another equipment cl ass (see chapter 8 of the technical support document (TSD) for criteria used in the selection of efficiency levels from the available design option levels)

.

DOE develop ed trial standard levels (TSLs) by combining selected efficiency levels from

each equipment class into TSL groupings based on a set of specific criteria. This process enables the grouping of efficiency levels across all equipment classes such that the efficiency levels belonging to a TSL grouping all have a certain uniform characteristic or criterion associated with

them

. DOE evaluate d the national impacts, such as national energy savings ( NES ), net present value ( NPV ) , employment impacts, impacts on manufacturers, and environmental impacts, at each TSL. Based on the results, DOE sets the sta ndard at a particular TSL.

This appendix describes DOE’s method for selecting TSL s for commercial refrigeration equipment. The following sections describe the criteria used for TSL selection and standard level equations associated with each TSL

.

10B.2 TRIAL STANDARD LEVEL SELECTION CRITERIA

DOE selected five TSLs for this rulemaking based on the following criteria:

1. TSL 5 was set at the maximum technologically feasible ( max

- tech ) level for each equipment class

.

2. TSL 4 was chosen so as to include the highest efficiency level, within each equipment class, with a near positive NPV at a 7 -percent discount rate.

3. . TSL 3 was chosen to represent the group of efficiency levels with the highest

customer NPV at a 7 -percent discount rate. TSL 2 and TSL 1 were chosen to provide intermediate efficiency levels that fill the gap between the baseline efficiency levels

and TSL 3.

Table 10B.2.1 presents the efficiency levels within each equipment class that bel ong to the five TSL groupings. For e quipment classes HZO.RC. M, HZO.RC.L , and HCT.SC.I there is only one efficiency level above baseline. For the HZO.SC.L equipment class, there are no efficiency

l evels above the baseline, because there was only one engineering efficiency level analyzed

which was compliant with the 2009 final rule standard level . While TSL 5 was associated with

the max

- tech level for HZO.RC. M, HZO.RC.L, and HCT.SC.I equipment classe s, TSLs 1

10B -1

### through 4 did not have corresponding efficiency levels that satisfied the TSL formulation criteria. Therefore, the baseline efficiency level was assigned to TSL 1 through TSL 4 for each of these equipment classes.

Table 10B.2.2 presents the mapping between the TSLs and design option levels. Table

10B.2.3 present the mapping between efficiency levels and design option levels.

Table 10B.2.1 TSL and Efficiency Levels Mapping

Equipment

Class Intermediate

Level Intermediat

e Level Max NPV* Max NES

NPV * > 0

- † Max

- tech

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M Baseline Baseline EL 1 EL 3 EL 4

VOP.RC.L Baseline Baseline EL 1 EL 2 EL 3

VOP.SC.M Baseline Baseline Baseline EL 1 EL 2

VCT.RC.M Baseline Baseline EL 1 EL 3 EL 4

VCT.RC.L EL 1 EL 1 EL 2 EL 3 EL 4

VCT.SC.M EL 1 EL 2 EL 3 EL 5 EL 7

VCT.SC.L EL 1 EL 3 EL 5 EL 7 EL 7

VCT.SC.I EL 1 EL 1 EL 1 EL 3 EL 4

VCS.SC.M EL 1 EL 2 EL 4 EL 6 EL 7

VCS.SC.L EL 1 EL 3 EL 5 EL 6 EL 7

VCS.SC.I EL 1 EL 2 EL 4 EL 4 EL 5

SVO.RC.M EL 1 EL 1 EL 1 EL 3 EL 4

SVO.SC.M Baseline Baseline Baseline EL 1 EL 3

SOC.RC.M Baseline Baseline Baseline EL 1 EL 4

SOC.SC.M Baseline Baseline Baseline EL 2 EL 4

HZO.RC.M Baseline Baseline Baseline Baseline EL 1

HZO.RC.L Baseline Baseline Baseline Baseline EL 1

HZO.SC.M Baseline EL 1 EL 1 EL 2 EL 3

HZO.SC.L Baseline Baseline Baseline Baseline Baseline

HCT.SC.M EL 2 EL 3 EL 4 EL 6 EL 7

HCT.SC.L EL 2 EL 3 EL 4 EL 6 EL 7

HCT.SC.I Baseline Baseline Baseline Baseline EL 1

HCS.SC.M EL 1 EL 2 EL 3 EL 4 EL 6

HCS.SC.L EL 1 EL 2 EL 3 EL 5 EL 6

PD.SC.M EL 1 EL 2 EL 3 EL 4 EL 7

*NPV is estimated at a 7 percent discount rate



### Table 10B.2.2 TSL to Analytical Design Option Levels Mapping

Equipment

Class TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M 

### 5 5 6 8 9

VOP.RC.L 

### 6 6 7 8 9

VOP.SC.M 

### 11 11 11 12 13

10B -2VCT.RC.M 

### 5 5 6 8 9

VCT.RC.L 

### 6 6 7 8 9

VCT.SC.M 

### 6 7 8 10 12

VCT.SC.L 

### 4 7 9 12 12

VCT.SC.I 

### 10 10 10 12 13

VCS.SC.M 

### 2 3 5 7 9

VCS.SC.L 

### 3 5 7 8 9

VCS.SC.I 

### 6 7 9 9 10

SVO.RC.M 

### 6 6 6 8 9

SVO.SC.M 

### 10 10 10 11 13

SOC.RC.M 

### 5 5 5 6 9

SOC.SC.M 

### 8 8 8 10 12

HZO.RC.M 

### 5 5 5 5 6

HZO.RC.L 

### 5 5 5 5 6

HZO.SC.M 

### 7 8 8 9 10

HZO.SC.L 

### 10 10 10 10 10

HCT.SC.M 

### 3 4 5 7 8

HCT.SC.L 

### 3 4 5 7 8

HCT.SC.I 

### 6 6 6 6 7

HCS.SC.M 

### 2 3 4 5 7

HCS.SC.L 

### 2 3 4 6 7

PD.SC.M 

### 6 7 8 9 12

“AD” represents “Design Option Level.” For example, AD12 represents Design Option Level 12. For details on the design options associated with each design option level within each equipment class, see TSD chapter 5.



### Table 10B.2.3 Efficiency Levels and Analytical Design Option Levels Mapping
Equip Class Base EL1 EL2 EL3 EL4 EL5 EL6 EL7

VOP.RC.M AD 5 AD 6 AD 7 AD 8 AD 9

VOP.RC.L AD 6 AD 7 AD 8 AD 9

VOP.SC.M AD 11 AD 12 AD 13

VCT.RC.M AD 5 AD 6 AD 7 AD 8 AD 9

VCT.RC.L AD 5 AD 6 AD 7 AD 8 AD 9

VCT.SC.M AD 5 AD 6 AD 7 AD 8 AD 9 AD 10 AD 11 AD 12

VCT.SC.L AD 1 AD 4 AD 6 AD 7 AD 8 AD 9 AD 10 AD 12

VCT.SC.I AD 9 AD 10 AD 11 AD 12 AD 13

VCS.SC.M AD 1 AD 2 AD 3 AD 4 AD 5 AD 6 AD 7 AD 9

VCS.SC.L AD 1 AD 3 AD 4 AD 5 AD 6 AD 7 AD 8 AD 9

VCS.SC.I AD 5 AD 6 AD 7 AD 8 AD 9 AD 10

SVO.RC.M AD 5 AD 6 AD 7 AD 8 AD 9

SVO.SC.M AD 10 AD 11 AD 12 AD 13

SOC.RC.M AD 5 AD 6 AD 7 AD 8 AD 9

SOC.SC.M AD 8 AD 9 AD 10 AD 11 AD 12

HZO.RC.M AD 5 AD 6

10B -3HZO.RC.L AD 5 AD 6

HZO.SC.M AD 7 AD 8 AD 9 AD 10

HZO.SC.L AD 10

HCT.SC.M AD 1 AD 2 AD 3 AD 4 AD 5 AD 6 AD 7 AD 8

HCT.SC.L AD 1 AD 2 AD 3 AD 4 AD 5 AD 6 AD 7 AD 8

HCT.SC.I AD 6 AD 7

HCS.SC.M AD 1 AD 2 AD 3 AD 4 AD 5 AD 6 AD 7

HCS.SC.L AD 1 AD 2 AD 3 AD 4 AD 5 AD 6 AD 7

PD.SC.M AD 5 AD 6 AD 7 AD 8 AD 9 AD 10 AD 11 AD 12



### 10B.3 T RIAL STANDARD LEVEL EQUATIONS

Because of the equipment size variation within each equipment class and the use of daily energy consumption as the efficiency metric, DOE developed a methodology to express efficiency standards in terms of a normalizing metric. DOE utilized one of two norm alization

metrics for each equipment class: (1) volume (V) or (2) total display area ( TDA ). The use of

these two normalization metrics allow ed for the development of the energy conservation standard in the form of a linear equation that could be used to re present the entire range of equipment sizes within a given equipment class. DOE retained the respective normalization metric (TDA or volume) previously used in the Energy Policy Act of 2005 (EPACT 2005), the January 2009 final rule standards for each cover ed equipment class, or the American Energy Manufacturing Technical Corrections Act (AEMTCA) . (42 U.S.C. 6313(c)(2)– (3)); 74 FR 1092, 1093 (Jan. 9, 2009); (42 U.S.C. 6313(c)(4)).

In its January 2009 final rule, DOE developed offset factors as a way to adju st the energy efficiency requirements for smaller equipment in each equipment class analyzed. These offset factors, which form the y- intercept on a plot of each standard level equation (representing a fictitious case of zero volume or zero TDA), accounted for certain components of the refrigeration load (such as conduction end effects) that remain constant even when equipment sizes vary. These constant loads affect smaller cases disproportionately. The offset factors were intended to approximate these constant loads and provide a fixed end point in an equation that describes the relationship between energy consumption and the corresponding normalization metric. 74 FR at 1118–19 (Jan. 9, 2009).The standard levels equations prescribed by EPACT

2005 also contai ned similar fixed parts not multiplied by the volume metric and which correspond to these offset factors. In this notice of proposed rulemaking, Where appropriate, DOE modified the January 2009 final rule (74 FR at 1118–19 (Jan. 9, 2009)) and EPACT 2005

of fset factors at each TSL to reflect changes in energy consumption for each equipment class, as modeled in the engineering analysis. See chapter 5 of the TSD for further details and discussion

of offset factors.

For the equipment classes covered under this rulemaking, the standards equation at each

TSL is proposed in the form of maximum daily energy consumption ( MDEC ) ( in kilowatt -hours (kWh) per day) normalized by a volume (V) or TDA metric. These equations take the form:

MDEC = A × TDA + B (for equipment using TDA as a normalizing metric)

or

10B -4

### MDEC = A × V + B (for equipment using volume as a normalizing metric)

For equipment classes directly analyzed in the engineering analysis, the offset factor

, B

,

was developed for each class (see chapter 5 of the TSD fo r discussion of offset factors). The slope, A

, was derived based on the offset factor, B , and the calculated daily energy consumption ( CDEC ) of the representative unit modeled in the engineering analysis for that equipment class (presented in Table 10B.3 .1 ). The standards equations (presented in Table 10B.3.2 at each TSL)

are used to prescribe the MDEC for equipment of different sizes within a given equipment class.

Table 10B.3.1 CDEC Values by TSL fo r Representative Units A nalyzed in the Engineering Analysis for Each Primary Equipment Class

Equipment Class CDEC Values by TSL kWh/day

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M 

### 46.84 46.84 38.02 36.10 35.65

VOP.RC.L 

### 105.60 105.60 104.94 101.69 100.01

VOP.SC.M 

### 30.01 30.01 30.01 29.91 29.71

VCT.RC.M 

### 13.65 13.65 11.80 11.48 10.99

VCT.RC.L 

### 35.34 35.34 34.78 34.50 33.04

VCT.SC.M 

### 6.83 5.98 5.64 5.45 5.15

VCT.SC.L 

### 27.46 18.23 17.16 16.05 16.05

VCT.SC.I 

### 19.52 19.52 19.52 18.95 18.11

VCS.SC.M 

### 5.28 4.03 3.69 3.45 3.03

VCS.SC.L 

### 13.94 12.94 12.19 12.08 11.13

VCS.SC.I 

### 18.70 18.01 17.43 17.43 16.04

SVO.RC.M 

### 29.45 29.45 29.45 28.02 27.70

SVO.SC.M 

### 26.32 26.32 26.32 25.65 25.40

SOC.RC.M 

### 22.74 22.74 22.74 22.31 21.56

HZO.RC.M 

### 27.72 27.72 27.72 26.61 26.12

HZO.RC.L 

### 14.47 14.47 14.47 14.47 14.15

HZO.SC.M 

### 32.36 32.36 32.36 32.36 31.08

HZO.SC.L 

### 14.66 14.16 14.16 14.02 13.75

HCT.SC.M 

### 29.92 29.92 29.92 29.92 29.92

HCT.SC.L 

### 1.62 0.99 0.90 0.79 0.61

HCT.SC.I 

### 2.15 2.03 1.92 1.73 1.32

HCS.SC.M 

### 3.13 3.13 3.13 3.13 2.33

HCS.SC.L 

### 1.42 1.35 1.28 1.26 0.98

PD.SC.M 

### 1.78 1.67 1.53 1.29 0.71

SOC.SC.M 

### 4.73 3.90 3.78 3.75 3.40

10B -5

### Table 10B.3.2 Equations Representing the Standards at Each TSL for All Primary Equipment Classes

Equipment

Class Trial Standard Levels for Primary Equipment Classes Analyzed

Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
VCT.RC.L 0.56 x TDA + 2.61 0.5 x TDA + 2.61 0.49 x TDA + 2.61 0.49 x TDA + 2.61 0.49 x TDA + 2.61 0.47 x TDA + 2.61

VOP.RC.M 0.82 x TDA + 4.07 0. 8 x TDA + 4.07 0.64 x TDA + 4.07 0. 63 x TDA + 4.07 0. 6 x TDA + 4.07 0.59 x TDA + 4.07

SVO.RC.M 0.83 x TDA + 3.18 0.66 x TDA + 3.18 0. 62 x TDA + 3.18 0. 62 x TDA + 3.18 0. 62 x TDA + 3.18 0.61 x TDA + 3.18

HZO.RC.L 0.57 x TDA + 6.88 0.55 x TDA + 6.88 0.55 x TDA + 6.88 0.55 x TDA + 6.88 0.55 x TDA + 6.88 0.53 x TDA + 6.88

HZO.RC.M 0.35 x TDA + 2.88 0.35 x TDA + 2.88 0.35 x TDA + 2.88 0.35 x TDA + 2.88 0.35 x TDA + 2.88 0.34 x TDA + 2.88

VCT.RC.M 0.22 x TDA + 1.95 0. 18 x TDA + 1.95 0.15 x TDA + 1.95 0.15 x TDA + 1.95 0.15 x TDA + 1.95 0.14 x TDA + 1.95

VOP.RC.L 2.27 x TDA + 6.85 2. 21 x TDA + 6.85 2.2 x TDA + 6.85 2. 2 x TDA + 6.85 2.12 x TDA + 6.85 2.09 x TDA + 6.85

SOC.RC.M 0.51 x TDA + 0.11 0.44 x TDA + 0.11 0.44 x TDA + 0.11 0.44 x TDA + 0.11 0. 44 x TDA + 0.11 0.42 x TDA + 0.11

VOP.SC.M 1.74 x TDA + 4.71 1.69 x TDA + 4.71 1.69 x TDA + 4.71 1.69 x TDA + 4.71 1.69 x TDA + 4.71 1.67 x TDA + 4.71

SVO.SC.M 1.73 x TDA + 4.59 1. 7 x TDA + 4.59 1. 7 x TDA + 4.59 1. 7 x TDA + 4.59 1. 65 x TDA + 4.59 1.63 x TDA + 4.59

HZO.SC.L 1.92 x TDA + 7.08 1.9 x TDA + 7.08 1.9 x TDA + 7.08 1.9 x TDA + 7.08 1.9 x TDA + 7.08 1.9 x TDA + 7.08

HZO.SC.M 0.77 x TDA + 5.55 0. 76 x TDA + 5.55 0. 72 x TDA + 5.55 0. 72 x TDA + 5.55 0. 71 x TDA + 5.55 0.68 x TDA + 5.55

HCT.SC.I 0.56 x TDA + 0.43 0.56 x TDA + 0.43 0.56 x TDA + 0.43 0.56 x TDA + 0.43 0.56 x TDA + 0.43 0.4 x TDA + 0.43

VCT.SC.I 0.67 x TDA + 3.29 0.62 x TDA + 3.29 0.62 x TDA + 3.29 0. 62 x TDA + 3.29 0.6 x TDA + 3.29 0.57 x TDA + 3.29

VCS.SC.I 0.38 x V + 0.88 0. 37 x V + 0.88 0. 35 x V + 0.88 0. 35 x V + 0.88 0.34 x V + 0.88 0.32 x V + 0.88

VCT.SC.M 0.12 x V +3.34 0.1 x V + 1.21 0.1 x V + 0.86 0.1 x V + 0.81 0.1 x V + 0.68 0.1 x V + 0.38

VCT.SC.L 0.75 x V + 4.1 0. 48 x V + 4.1 0.29 x V + 4. 1 0.29 x V + 2.95 0.29 x V + 1.84 0.29 x V + 1.84

VCS.SC.M 0.1 x V + 2.04 0. 07 x V + 2.04 0.05 x V + 1.5 0.05 x V + 1. 5 0.05 x V + 1. 36 0.05 x V + 0.7

VCS.SC.L 0.4 x V + 1.38 0. 26 x V + 1.38 0.24 x V + 1.38 0. 22 x V + 1.38 0.22 x V + 1.38 0.2 x V + 1.38

HCT.SC.M 0.12 x V + 3.34 0.06 x V + 1. 22 0.06 x V + 0. 35 0.06 x V + 0. 35 0.06 x V + 0. 27 0.06 x V + 0.09

HCT.SC.L 0.75 x V + 4.1 0.08 x V + 1. 47 0.08 x V + 1.23 0.08 x V + 1.2 0.08 x V + 1. 05 0.08 x V + 0.63

HCS.SC.M 0.1 x V + 2.04 0.05 x V + 1.05 0.05 x V + 0. 91 0.05 x V + 0. 91 0.05 x V + 0.89 0.02 x V + 0.81

HCS.SC.L 0.4 x V + 1.38 0.06 x V + 1. 38 0.06 x V + 1.12 0.06 x V + 1. 12 0.06 x V + 0.89 0.06 x V + 0.31

PD.SC.M 0.126 x V + 3.51 0.11 x V + 1.76 0.11 x V + 0.81 0.11 x V + 0.81 0.11 x V + 0.78 0.11 x V + 0.44

SOC.SC.M 0.6 x TDA + 1 0. 52 x TDA + 1 0. 52 x TDA + 1 0. 52 x TDA + 1 0.5 x TDA + 1 0.49 x TDA + 1

10B -6

### In addition to the 25 primary equipment classes analyzed, DOE examined the standards

for 24 secondary classes of commercial refrigeration equipment covered in this rulemaking that were not directly analyzed in the engineering analysis. DOE’s approach involves extension multipliers developed using the primary equipment classes analyzed and a set of matched

- pair analyses performed during the January 2009 final rule analysis. In addition, DOE believes that standards for certain primary equipment classes can be directly applied to similar secondary equipment classes. Chapter 5 of the TSD discusses the development of the extension multipliers.

Using the extension multiplier approach, DOE developed an additional set of TSLs and associated equations for the secondary equipment classes , as shown in Table 10B.3.3

. The TSLs

shown in Table 10B.3.3 do not necessarily satisfy the criteria spelled out in section 10B.2

because the analyses required to evaluate the criteria were not performed for those classes . DOE is presenting the standards equations developed for each TSL for all 49 equipment classes to allow interested parties to better review the ramifications of each TSL across the range of

equ ipment sizes on the market.

10B -7

### Table 10B.3.3 Equations Representing the Standards at Each TSL for All Secondary Equipment Classes

Equipment

Class Trial Standard Levels for Secondary Equipment Classes Analyzed

Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.I 2.89 x TDA + 8.7 2.81 x TDA + 8.7 2.79 x TDA + 8.7 2.79 x TDA + 8.7 2.7 x TDA + 8.7 2.65 x TDA + 8.7

SVO.RC.L 2.27 x TDA + 6.85 2.21 x TDA + 6.85 2.2 x TDA + 6.85 2.2 x TDA + 6.85 2.12 x TDA + 6.85 2.09 x TDA + 6.85

SVO.RC.I 2.89 x TDA + 8.7 2.81 x TDA + 8.7 2.79 x TDA + 8.7 2.79 x TDA + 8.7 2.7 x TDA + 8.7 2.65 x TDA + 8.7

HZO.RC.I 0.72 x TDA + 8.74 0.7 x TDA + 8.74 0.7 x TDA + 8.74 0.7 x TDA + 8.74 0.7 x TDA + 8.74 0.67 x TDA + 8.74

VOP.SC.L 4.37 x TDA + 11.82 4.25 x TDA + 11.82 4.25 x TDA + 11.82 4.25 x TDA + 11.82 4.24 x TDA + 11.82 4.2 x TDA + 11.82

VOP.SC.I 5.55 x TDA + 15.02 5.4 x TDA + 15.02 5.4 x TDA + 15.02 5.4 x TDA + 15.02 5.38 x TDA + 15.02 5.34 x TDA + 15.02

SVO.SC.L 4.34 x TDA + 11.52 4.26 x TDA + 11.52 4.26 x TDA + 11.52 4.26 x TDA + 11.52 4.13 x TDA + 11.52 4.08 x TDA + 11.52

SVO.SC.I 5.52 x TDA + 14.63 5.41 x TDA + 14.63 5.41 x TDA + 14.63 5.41 x TDA + 14.63 5.24 x TDA + 14.63 5.18 x TDA + 14.63

HZO.SC.I 2.44 x TDA + 9 2.42 x TDA + 9 2.42 x TDA + 9 2.42 x TDA + 9 2.42 x TDA + 9 2.42 x TDA + 9

SOC.RC.L 1.08 x TDA + 0.22 0.93 x TDA + 0.22 0.93 x TDA + 0.22 0.93 x TDA + 0.22 0.91 x TDA + 0.22 0.88 x TDA + 0.22

SOC.RC.I 1.26 x TDA + 0.26 1.09 x TDA + 0.26 1.09 x TDA + 0.26 1.09 x TDA + 0.26 1.07 x TDA + 0.26 1.03 x TDA + 0.26

SOC.SC.I 1.76 x TDA + 0.36 1.53 x TDA + 0.36 1.53 x TDA + 0.36 1.53 x TDA + 0.36 1.5 x TDA + 0.36 1.45 x TDA + 0.36

VCT.RC.I 0.66 x TDA + 3.05 0.59 x TDA + 3.05 0.58 x TDA + 3.05 0.57 x TDA + 3.05 0.57 x TDA + 3.05 0.55 x TDA + 3.05

HCT.RC.M 0.16 x TDA + 0.13 0.16 x TDA + 0.13 0.16 x TDA + 0.13 0.16 x TDA + 0.13 0.16 x TDA + 0.13 0.12 x TDA + 0.13

HCT.RC.L 0.34 x TDA + 0.26 0.34 x TDA + 0.26 0.34 x TDA + 0.26 0.34 x TDA + 0.26 0.34 x TDA + 0.26 0.24 x TDA + 0.26

HCT.RC.I 0.4 x TDA + 0.31 0.4 x TDA + 0.31 0.4 x TDA + 0.31 0.4 x TDA + 0.31 0.4 x TDA + 0.31 0.28 x TDA + 0.31

VCS.RC.M 0.11 x V + 0.26 0.11 x V + 0.26 0.1 x V + 0.26 0.1 x V + 0.26 0.1 x V + 0.26 0.09 x V + 0.26

VCS.RC.L 0.23 x V + 0.54 0.23 x V + 0.54 0.22 x V + 0.54 0.22 x V + 0.54 0.21 x V + 0.54 0.19 x V + 0.54

VCS.RC.I 0.27 x V + 0.63 0.27 x V + 0.63 0.25 x V + 0.63 0.25 x V + 0.63 0.25 x V + 0.63 0.23 x V + 0.63

HCS.SC.I 0.38 x V + 0.88 0.37 x V + 0.88 0.35 x V + 0.88 0.35 x V + 0.88 0.34 x V + 0.88 0.32 x V + 0.88

HCS.RC.M 0.11 x V + 0.26 0.11 x V + 0.26 0.1 x V + 0.26 0.1 x V + 0.26 0.1 x V + 0.26 0.09 x V + 0.26

HCS.RC.L 0.23 x V + 0.54 0.23 x V + 0.54 0.22 x V + 0.54 0.22 x V + 0.54 0.21 x V + 0.54 0.19 x V + 0.54

HCS.RC.I 0.27 x V + 0.63 0.27 x V + 0.63 0.25 x V + 0.63 0.25 x V + 0.63 0.25 x V + 0.63 0.23 x V + 0.63

SOC.SC.L* 0.75 x V + 4.10 1.1 x TDA + 2.1 1.1 x TDA + 2.1 1.1 x TDA + 2.1 1.05 x TDA + 2.1 1.03 x TDA + 2.1

* Equipment class SOC.SC.L was inadvertently grouped under the category self

- contained commercial freezers with transparent doors in the standards prescribed by EPCA, as amended by EPACT 2005. (42 U.S.C. 6313(c)(2)) The baseline expression is thus given by the expression 0.75 × V + 4.10, which is the current standard for SOC.SC.L equipment. A similar anomaly (of inadvertent classification under a different equipment category) for SOC.SC.M equipment was corrected by the standard established by AE MTCA. However, no such corrective action has been prescribed for standards for SOC.SC.L equipment. In establishing a new standard for SOC.SC.M equipment, AEMTCA also changed the normalizatio n metric from volume (V) to total display area (TDA). Accordingly, DOE is promulgating amended standards for SOC.SC.M equipment with TDA as the normalization metric, DOE derives the standard for secondary equipment classes based on the standard of a primary equipment that has similar characteristics as the secondary equipment class under consideration (see chapter 5 of the final rule TSD for details). For the equipment class SOC.SC.L, the standard was derived from the standard level selected for equipment class SOC.SC.M. Since the standard for SOC.SC.M is in terms of TDA, the standard for SOC.SC.L equipment has also been specified in terms of TDA. Therefore, while the baseline expression has been shown with V as the normalization metric, the ex pressions for TSLs 1 through 5 have been shown in terms of TDA. This change of normal ization metric for equipment class SOC.SC.L is consistent with the legislative intent, evident in AEMTCA, for equipment class SOC.SC.M.

10B -8

### APPENDIX 10C. NATIONAL NET PRESENT VALUE USING

ALTERNATIVE PRICE FORECASTS

TABLE OF CONTENTS

10C.1 INTRODUCTION ...................................................................................................... 10C-1

10C.2 DATA EVALUATION AND ANALYSIS ................................................................ 10C-2

10C.3 ALTERNATIVE NET PRESENT VALUES ............................................................. 10C-4

LIST OF TABLES

Table 10C.3.1 Price Factor Indicies Used in Default Case and Sensitivity Cases ..................10C-6 Table 10C.3.2 Commercial Refrigeration Equipment: Net Present Value in Billions (2012$ )

at a 7

- percent Discount Rate

– High Price Decline Scenario ..........................10C-7 Table 10C.3.3 Commercial Refrigeration Equipment: Net Present Value in Billions (2012$)

at a 3

- percent Discount Rate

– High Price Decline Scenario ..........................10C-8 Table 10C.3.4 Commercial Refrigeration Equipment: Net Present Value in Billions (2012$)

at a 7

- percent Discount Rate

– Low Price Decline Scenario ...........................10C-9 Table 10C.3.5 Commercial Refrigeration Equipment: Net Present Value in Billions (2012$)

at a 3

- percent Discount Rate

– Low Price Decline Sce nario .........................10C-10 Table 10C.3.6 Commercial Refrigeration Equipment: Net Present Value in Billions (2012$)

at a 7

- percent Discount Rate

– Constant Real Price Scenario .......................10C-11 Table 10C.3.7 Commercial Refrigeration Equipment: Net Present Value in Billions (2012$)

at a 3

- percent Discount Rate

– Constant Real Price Scenario .......................10C-12

LIST OF FIGURES

Figure 10C.1.1 PPI Data for Commercial Refrigeration Equipment .......................................10C-2 Figure 10C.2.1 Projected Annual Shipments for Commercial Refrigeration Equipment .......10C-3 Figure 10C.2.2 Model Fit for Commercial Refrigeration Equipment .....................................10C-4 Figure 10C.3.1 Commercial Refrigeration Equipment Price Factor Indices for Default Case and Sensitivity Cases .......................................................................................10C-5

10C -i

### APPENDIX 10C. NATIONAL NET PRESENT VALUE USING ALTERNATIVE

PRICE FORECASTS

10C.1 INTRODUCTION

In developing standards, the U.S. Department of Energy (DOE) assumes that the manufacturer costs and retail prices of products meeting various efficiency levels remain fixed, in real terms, after 2011 (the year for which the engineering analysis estimated costs) and throughout the period of the analysis. In its notice of data availability (NODA) published on February 22, 2011 (76 FR 9696), DOE stated that it may consider improving regulatory analysis by addressing equipment price trends. Consistent with the NODA, DOE ex amined historical producer price indices for commercial refrigeration equipment.

DOE stated in the NODA that examination of historical price data for certain appliances and equipment that have been subject to energy conservation standards

indicates that t he assumption of constant real prices and costs may, in many cases, overestimate long -term appliance and equipment price trends. 76 FR at 9696 (Feb. 22, 2011). Economic literature and historical data suggest that the real costs of these products may

in fa ct trend downward over time according to “learning” or “experience” curves, or alternatively that the price trends for certain sectors of the U.S. economy may be different than the price trends for the economy as a whole. A draft paper, “Using the Experien ce Curve Approach for Appliance Price Forecasting,” available at http://www1.eere.energy.gov/buildings/appliance_standards/supplemental_info_equipme nt_price_forecasting.html , summarizes the data and literature currently available to DOE that is relevant to price forecasts for selected appliances and equipment.

The extensive literature on the “learning” or “experience” curve phenomenon is typically based on observations in the manufacturing sector. 1 

### In the experience curve method, the real cost of production is related to the cumulative production or “experience” with a manufactured product. To explain the empirical relationship, DOE

wou ld use the theory of technology learning to substantiate a decline in the cost of producing a given product as firms accumulate experience with the technology. A common functional relationship used to model the evolution of production costs in this

case is :

Y = aX - b



### where a is an initial price (or cost), b is a positive constant known as the learning rate

parameter, X is cumulative production, and Y is the 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), given by:

LR = 1 – 2 - b

10C -1

### In typical learning curve formulations, the learning rate parameter is derived using two historical data series: cumulative production and price (or cost).

DOE examined historical prices using the Bureau of Labor Statistics’ (BLS) Producer Price Index (PPI) and gross domestic product (GDP) deflator, available from the Bureau of Economic Analysis (BEA). The PPI data for air conditioning, refrigeration, and forced air heating is available for 1978 –2012 and is used to represent aggregate prices refrigeration system prices. Figure 10C.1.1 shows the PPI data series used.

Figure 10C.1.1 PPI Data for Commercial Refrigeration Equipment

Inflation

- adjusted price indices were calculated by dividing the fitted PPI series by the GDP deflator for the same years. The GDP deflator was used as opposed to the Consumer Price Index because nearly all commercial refrigeration equipment is shipped to commercial customers and use of the GDP deflator was consistent with energy price forecast assumptions by Energy Information Administration (EIA).

10C.2 DATA EVALUATION AND ANALYSIS

Figure 10C.3.1 shows an apparent price trend in commercial refrigeration equipment that is trending slightly upward from 1978 to 2012, but shows a decrease in the real PPI during two significant periods of time: 1982 to 1988 and1992 to 2004. Based on these price trends shown above, DOE expects that, in the future, the PPI is likely to

resume a downward trend. 0.6 0.7 0.8 0.9 1.0 1.1 1.2

1978 1983 1988 1993 1998 2003 2008PPI



### Year PPI End

- point Interpolation

PPI

- Deflated, Normalized to 2012

Linear Regression to PPI

10C -2

### In order to perform an experience curve fit, DOE assembled a time

- serie s of annual shipments for 1950 to 2009 for commercial refrigeration equipment (for calculating cumulative production) based on shipments data for 2009.

Projected shipments after 2009 were obtained from the base- case projections made for the national impac t analysis (see chapter 10 of this TSD). Projected annual shipments are depicted in Figure 10C.2.1

.

Figure 10C.2.1 Projected Annual Shipments for Commercial Refrigeration Equipment

To estimate potential product price trends, DOE performed a least

- squares power

law fit on the commercial refrigeration equipment price index versus cumulative shipments. The form of the fitting equation is:

P ( X

) = P

o 

### X - b



### ,

where b (the learning rate parameter) and P

o 

### (the price or cost of the first unit of prod uction) are obtained by fitting the model to the data. DOE notes that the cumulative shipments on the right-hand side of the equation can depend on price, so there is an issue with simultaneity where the independent variable is not truly independent. DOE’s use of a simple least squares fit is equivalent to an assumption of no significant first price elasticity effects in the cumulative shipments variable.

After modeling the data to the above fitting equation DOE estimated the learning rate (defined as the fractional reduction in price expected from each doubling of cumulative production) as 3 percent. 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 4,500,000 5,000,000

2011 2016 2021 2026 2031 2036 2041Shipments (linear feet)



### Year

10C -3

### With cumulative shipments through 2046 projected to reach 215.6 million linear feet (compared with 92.4 million linear feet in 2010), the modeled trend predicts a drop

of 3.3 percent in real price compared to the 2012 prices in the economy as a whole. Figure 10C.2.2 shows the model fit for the projected values for the period after 2009.

Figure 10C.2.2 Model Fit for Commercial Refrigeration Equipment

10C.3 ALTERNATIVE NET PRESENT VALUES

The net present value ( NPV ) results presented in chapter 10 of this technical support document (TSD) reflect a price trend based on an experience curve derived using historical data on shipments and refrigeration equipment producer price index ( PPI ).

The average annual rate of price decline in the default case for the 2017–2046 analysis period is 0.11 percent and is based on historical PPI data for refrigeration equipment

between 1978–2012. For the national impact analysis ( NIA ) , DOE analyzed two

additional sensitivity cases that also use a price trend based on an exponential- in

- time extrapolation of refrigeration equipment PPI data. DOE developed a high price decline case and a low price decline case in this analysis. The high price decline case is based onthe upper end of the 95 percent confidence interval for an exponential fit to the inflationadjusted PPI series of 1978 to 2012 . The low price decline case is based on the lower end of the 95 percent confidence interval for an exponential fit to the nomina l PPI series of

1978 to 2012. The average annual rate of price decline over the analys is period 0.03 percent in the high price decline case and 0.25 percent in the low price decline case. Because of the nature of the exponential experience curve, greater annual price declines

10C -4

### are found in earlier years in the analysis period and lesser annual price declines are found in the latter years in the analysis period. DOE investigated the impact of these different product price forecasts on the consumer net NPV for the considered trial standard levels ( TSLs ) for commercial refrigeration equipment.

For the NPV sensitivity, DOE considered three product price forecast sensitivity cases: (1) a high price decline case based on the PPI trend of 1978–2012; (2) a low price

decline case based on the PPI trend in 1978–2012 ; ( 3) a constant real price case. Each price decline scenario is expressed in terms of a price factor index time series , which is applied to the 2012 price estimate to forecast per unit prices over the 2017–2046 analysis period. Figure 10C.3.1 shows the equipment price factor indices for the default case and

the three additional price decline forecast s considered in this sensitivity analysis

. Table

10C.3.1 shows the price factor indices tabulated.

Figure 10C.3.1 Commercial Refrigeration Equipment Price Factor Indices for Default Case and Sensitivity Cases

10C -5

### Table 10C.3.1 Price Factor Indicies Used in Default Case and Sensitivity Cases

Year Price Factor Index

201 2 $

Default Low Price Decline High Price

Decline Constant Real

2012 1.0000 1.0000 1.0000 1.0000

2013 0.9989 0.9975 1.0003 1.0000

2014 0.9978 0.9950 1.0006 1.0000

2015 0.9967 0.9925 1.0010 1.0000

2016 0.9956 0.9900 1.0013 1.0000

2017 0.9945 0.9875 1.0016 1.0000

2018 0.9934 0.9850 1.0019 1.0000

2019 0.9923 0.9824 1.0023 1.0000

2020 0.9912 0.9800 1.0026 1.0000

2021 0.9902 0.9777 1.0029 1.0000

2022 0.9892 0.9754 1.0032 1.0000

2023 0.9882 0.9732 1.0035 1.0000

2024 0.9872 0.9710 1.0038 1.0000

2025 0.9862 0.9687 1.0041 1.0000

2026 0.9852 0.9665 1.0044 1.0000

2027 0.9843 0.9643 1.0047 1.0000

2028 0.9833 0.9620 1.0050 1.0000

2029 0.9823 0.9598 1.0053 1.0000

2030 0.9813 0.9576 1.0055 1.0000

2031 0.9803 0.9555 1.0058 1.0000

2032 0.9794 0.9534 1.0061 1.0000

2033 0.9785 0.9513 1.0064 1.0000

2034 0.9776 0.9493 1.0067 1.0000

2035 0.9766 0.9473 1.0069 1.0000

2036 0.9757 0.9452 1.0072 1.0000

2037 0.9748 0.9432 1.0075 1.0000

2038 0.9739 0.9411 1.0078 1.0000

2039 0.9730 0.9391 1.0080 1.0000

2040 0.9721 0.9371 1.0083 1.0000

2041 0.9712 0.9352 1.0086 1.0000

2042 0.9703 0.9332 1.0089 1.0000

2043 0.9694 0.9313 1.0091 1.0000

2044 0.9686 0.9294 1.0094 1.0000

2045 0.9677 0.9275 1.0097 1.0000

2046 0.9668 0.9256 1.0099 1.0000



### Table 10C.3.2 and Table 10C.3.3 provide NPV results for commercial refrigeration equipment at each TSL level based on the high price decline case for 7 - and 3-percent discount rates , respectively

. Table 10C.3.4 and Table 10C.3.5 provide NPV

results for commercial refrigeration equipment based on the low price decline case at 7
- and 3-percent discount rates , respectively

. Table 10C.3.6 and Table 10C.3.7 provide NPV results for refrigeration systems based on a constant real price case at 7
- and 3

- percent discount rates, respectively

. These results can be directly compared with the commercial refrige ration equipment NPV results using the default price decline scenario shown in chapter 10 of this TSD

.

10C -6

### Table 10C.3.2 Commercial Refrigeration Equipment : Net Present Value in B illions (2012$) at a 7- percent Discount Rate

– High Price Decline Scenario

billion 201 2 $

TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M 0.00 0.00 0.52 0.10

- 3.07

VOP.RC.L 0.00 0.00 0.00

- 0.01

- 0.25

VOP.SC.M 0.00 0.00 0.00

- 0.01

- 0.38

VCT.RC.M 0.00 0.00 0.01 0.00

- 0.28

VCT.RC.L 0.20 0.20 0.22

- 0.03

- 4.53

VCT.SC.M

- 0.01 0.03 0.05

- 0.02

- 1.58

VCT.SC.L 0.06 0.11 0.12 0.03 0.03

VCT.SC.I 0.00 0.00 0.00 0.00

- 0.14

VCS.SC.M 0.73 1.69 1.75 1.57

- 7.06

VCS.SC.L 1.12 1.44 1.50 1.47

- 4.91

VCS.SC.I 0.00 0.00 0.00 0.00

- 0.05

SVO.RC.M 0.26 0.26 0.26 0.04

- 1.56

SVO.SC.M 0.00 0.00 0.00 0.00

- 0.22

SOC.RC.M 0.00 0.00 0.00

- 0.01

- 0.35

SOC.SC.M 0.00 0.00 0.00 0.00

- 0.03

HZO.RC.M 0.00 0.00 0.00 0.00

- 0.12

HZO.RC.L 0.00 0.00 0.00 0.00

- 0.75

HZO.SC.M 0.00 0.00 0.00 0.00

- 0.03

HZO.SC.L 0.00 0.00 0.00 0.00 0.00

HCT.SC.M 0.00 0.00 0.00 0.00

- 0.01

HCT.SC.L 0.02 0.02 0.02 0.02

- 0.03

HCT.SC.I 0.00 0.00 0.00 0.00

- 0.08

HCS.SC.M 0.01 0.01 0.01 0.01

- 0.35

HCS.SC.L 0.00 0.00 0.01 0.00

- 0.05

PD.SC.M 0.00 0.15 0.15 0.11

- 3.59

Total 2.40 3.93 4.63 3.26

- 29.39

10C -7

### Table 10C.3.3 Commercial Refrigeration Equipment: Net Present Value in B illions (2012$) at a 3- percent Discount Rate

– High Price Decline Scenario

billion 201 2 $

TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M 0.00 0.00 1.38 0.70

- 5.19

VOP.RC.L 0.00 0.00 0.00 0.00

- 0.45

VOP.SC.M 0.00 0.00 0.00

- 0.02

- 0.70

VCT.RC.M 0.00 0.00 0.03 0.00

- 0.51

VCT.RC.L 0.46 0.46 0.52 0.08

- 8.24

VCT.SC.M

- 0.01 0.10 0.16 0.05

- 2.81

VCT.SC.L 0.12 0.24 0.25 0.10 0.10

VCT.SC.I 0.00 0.00 0.00

- 0.01

- 0.26

VCS.SC.M 1.58 3.68 3.88 3.60

- 12.39

VCS.SC.L 2.45 3.18 3.42 3.37

- 8.33

VCS.SC.I 0.00 0.00 0.01 0.01

- 0.09

SVO.RC.M 0.72 0.72 0.72 0.37

- 2.61

SVO.SC.M 0.00 0.00 0.00 0.00

- 0.40

SOC.RC.M 0.00 0.00 0.00

- 0.02

- 0.64

SOC.SC.M 0.00 0.00 0.00 0.00

- 0.06

HZO.RC.M 0.00 0.00 0.00 0.00

- 0.23

HZO.RC.L 0.00 0.00 0.00 0.00

- 1.38

HZO.SC.M 0.00 0.00 0.00 0.00

- 0.05

HZO.SC.L 0.00 0.00 0.00 0.00 0.00

HCT.SC.M 0.00 0.00 0.00 0.00

- 0.02

HCT.SC.L 0.05 0.05 0.05 0.05

- 0.04

HCT.SC.I 0.00 0.00 0.00 0.00

- 0.14

HCS.SC.M 0.02 0.03 0.03 0.02

- 0.61

HCS.SC.L 0.01 0.01 0.01 0.01

- 0.08

PD.SC.M 0.03 0.49 0.51 0.44

- 6.34

Total 5.43 8.97 10.97 8.75

- 51.46

10C -8

### Table 10C.3.4 Commercial Refrigeration Equipment: Net Present Value in B illions (2012$) at a 7- percent Discount Rate – Low Price Decline Scenario

billion 201 2 $

TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M 0.00 0.00 0.58 0.20

- 2.87

VOP.RC.L 0.00 0.00 0.00 0.00

- 0.24

VOP.SC.M 0.00 0.00 0.00

- 0.01

- 0.37

VCT.RC.M 0.00 0.00 0.01 0.00

- 0.27

VCT.RC.L 0.21 0.21 0.24 0.00

- 4.34

VCT.SC.M

- 0.01 0.04 0.06 0.00

- 1.50

VCT.SC.L 0.06 0.12 0.12 0.04 0.04

VCT.SC.I 0.00 0.00 0.00 0.00

- 0.14

VCS.SC.M 0.76 1.75 1.83 1.67

- 6.67

VCS.SC.L 1.17 1.51 1.59 1.56

- 4.57

VCS.SC.I 0.00 0.00 0.00 0.00

- 0.05

SVO.RC.M 0.30 0.30 0.30 0.10

- 1.45

SVO.SC.M 0.00 0.00 0.00 0.00

- 0.21

SOC.RC.M 0.00 0.00 0.00

- 0.01

- 0.34

SOC.SC.M 0.00 0.00 0.00 0.00

- 0.03

HZO.RC.M 0.00 0.00 0.00 0.00

- 0.12

HZO.RC.L 0.00 0.00 0.00 0.00

- 0.72

HZO.SC.M 0.00 0.00 0.00 0.00

- 0.02

HZO.SC.L 0.00 0.00 0.00 0.00 0.00

HCT.SC.M 0.00 0.00 0.00 0.00

- 0.01

HCT.SC.L 0.02 0.02 0.02 0.02

- 0.03

HCT.SC.I 0.00 0.00 0.00 0.00

- 0.07

HCS.SC.M 0.01 0.01 0.01 0.01

- 0.34

HCS.SC.L 0.00 0.00 0.01 0.00

- 0.05

PD.SC.M 0.01 0.19 0.19 0.16

- 3.40

Total 2.53 4.17 4.99 3.73

- 27.76

10C -9

### Table 10C.3.5 Commercial Refrigeration Equipment: Net Present Va lue in B illions (2012$) at a 3- percent Discount Rate – Low Price Decline Scenario

billion 201 2 $

TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M 0.00 0.00 1.53 0.94

- 4.74

VOP.RC.L 0.00 0.00 0.00 0.00

- 0.42

VOP.SC.M 0.00 0.00 0.00

- 0.02

- 0.67

VCT.RC.M 0.00 0.00 0.03 0.00

- 0.49

VCT.RC.L 0.48 0.48 0.56 0.14

- 7.84

VCT.SC.M

- 0.01 0.12 0.19 0.10

- 2.65

VCT.SC.L 0.12 0.25 0.27 0.12 0.12

VCT.SC.I 0.00 0.00 0.00 0.00

- 0.25

VCS.SC.M 1.66 3.84 4.09 3.85

- 11.53

VCS.SC.L 2.55 3.34 3.65 3.62

- 7.58

VCS.SC.I 0.00 0.00 0.01 0.01

- 0.09

SVO.RC.M 0.81 0.81 0.81 0.51

- 2.36

SVO.SC.M 0.00 0.00 0.00 0.00

- 0.37

SOC.RC.M 0.00 0.00 0.00

- 0.02

- 0.61

SOC.SC.M 0.00 0.00 0.00 0.00

- 0.06

HZO.RC.M 0.00 0.00 0.00 0.00

- 0.22

HZO.RC.L 0.00 0.00 0.00 0.00

- 1.33

HZO.SC.M 0.00 0.00 0.00 0.00

- 0.04

HZO.SC.L 0.00 0.00 0.00 0.00 0.00

HCT.SC.M 0.00 0.00 0.00 0.00

- 0.02

HCT.SC.L 0.05 0.06 0.06 0.05

- 0.04

HCT.SC.I 0.00 0.00 0.00 0.00

- 0.13

HCS.SC.M 0.02 0.03 0.03 0.02

- 0.58

HCS.SC.L 0.01 0.01 0.01 0.01

- 0.07

PD.SC.M 0.05 0.60 0.62 0.56

- 5.94

Total 5.76 9.56 11.86 9.89

- 47.92

10C

- 10

### Table 10C.3.6 Commercial Refrigeration Equipment: Net Present Value in B illions (2012$) at a 7- percent Discount Rate

– Constant Real Price Scenario

billion 201 2 $

TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M 0.00 0.00 0.56 0.15

- 3.00

VOP.RC.L 0.00 0.00 0.00 0.00

- 0.24

VOP.SC.M 0.00 0.00 0.00

- 0.01

- 0.38

VCT.RC.M 0.00 0.00 0.01 0.00

- 0.27

VCT.RC.L 0.21 0.21 0.23

- 0.01

- 4.49

VCT.SC.M

- 0.01 0.04 0.05

- 0.01

- 1.56

VCT.SC.L 0.06 0.12 0.12 0.04 0.04

VCT.SC.I 0.00 0.00 0.00 0.00

- 0.14

VCS.SC.M 0.76 1.75 1.82 1.65

- 6.94

VCS.SC.L 1.16 1.50 1.57 1.54

- 4.79

VCS.SC.I 0.00 0.00 0.00 0.00

- 0.05

SVO.RC.M 0.28 0.28 0.28 0.07

- 1.53

SVO.SC.M 0.00 0.00 0.00 0.00

- 0.22

SOC.RC.M 0.00 0.00 0.00

- 0.01

- 0.35

SOC.SC.M 0.00 0.00 0.00 0.00

- 0.03

HZO.RC.M 0.00 0.00 0.00 0.00

- 0.12

HZO.RC.L 0.00 0.00 0.00 0.00

- 0.74

HZO.SC.M 0.00 0.00 0.00 0.00

- 0.02

HZO.SC.L 0.00 0.00 0.00 0.00 0.00

HCT.SC.M 0.00 0.00 0.00 0.00

- 0.01

HCT.SC.L 0.02 0.02 0.02 0.02

- 0.03

HCT.SC.I 0.00 0.00 0.00 0.00

- 0.08

HCS.SC.M 0.01 0.01 0.01 0.01

- 0.35

HCS.SC.L 0.00 0.00 0.01 0.00

- 0.05

PD.SC.M 0.01 0.18 0.17 0.14

- 3.53

Total 2.51 4.12 4.88 3.56

- 28.88

10C

- 11

### Table 10C.3.7 Commercial Refrigeration Equipment: Net Present Value in B illions (2012$) at a 3- percent Discount Rate

– Constant Real Price Scenario

billion 201 2 $

TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M 0.00 0.00 1.48 0.84

- 5.02

VOP.RC.L 0.00 0.00 0.00 0.00

- 0.44

VOP.SC.M 0.00 0.00 0.00

- 0.02

- 0.69

VCT.RC.M 0.00 0.00 0.03 0.00

- 0.50

VCT.RC.L 0.48 0.48 0.55 0.11

- 8.14

VCT.SC.M

- 0.01 0.12 0.18 0.08

- 2.76

VCT.SC.L 0.12 0.25 0.26 0.11 0.11

VCT.SC.I 0.00 0.00 0.00 0.00

- 0.26

VCS.SC.M 1.65 3.83 4.06 3.81

- 12.07

VCS.SC.L 2.55 3.33 3.61 3.57

- 8.02

VCS.SC.I 0.00 0.00 0.01 0.01

- 0.09

SVO.RC.M 0.78 0.78 0.78 0.45

- 2.51

SVO.SC.M 0.00 0.00 0.00 0.00

- 0.39

SOC.RC.M 0.00 0.00 0.00

- 0.02

- 0.63

SOC.SC.M 0.00 0.00 0.00 0.00

- 0.06

HZO.RC.M 0.00 0.00 0.00 0.00

- 0.23

HZO.RC.L 0.00 0.00 0.00 0.00

- 1.37

HZO.SC.M 0.00 0.00 0.00 0.00

- 0.04

HZO.SC.L 0.00 0.00 0.00 0.00 0.00

HCT.SC.M 0.00 0.00 0.00 0.00

- 0.02

HCT.SC.L 0.05 0.06 0.06 0.05

- 0.04

HCT.SC.I 0.00 0.00 0.00 0.00

- 0.14

HCS.SC.M 0.02 0.03 0.03 0.02

- 0.60

HCS.SC.L 0.01 0.01 0.01 0.01

- 0.08

PD.SC.M 0.04 0.56 0.58 0.52

- 6.21

Total 5.70 9.45 11.65 9.54

- 50.22

10C

- 12

### REFERENCES

1. Weiss, M., Junginger, H.M., Patel, M.K., Blok, K., (2010a). A Review of Experience Curve Analyses for Energy Demand Technologies. Technological Forecasting & Social Change . 77:411-428.

10C

- 13

### APPENDIX 10.D. RISC & OIRA CONSOLIDATED INFORMATION SYSTEM (ROCIS) TABLE S

TABLE OF CO NTENTS

10.D.1 INTRODUCTION

............................................................................................... 10.D-1

10.D.2 ROCIS TABLES FOR TSL 1 .............................................................................. 10.D-2

10.D.3 ROCIS TABLES FOR TSL 2 .............................................................................. 10.D-9

10.D.4 ROCIS TABLES FOR TSL 3 ............................................................................ 10.D-16

10.D.5 ROCIS TABLES FOR TSL 4 ............................................................................ 10.D-23

10.D.6 ROCIS TABLES FOR TSL 5 ............................................................................ 10.D-30

LIST OF TABLES

Table 10.D.1.1 Equipment class groupings ...................................................................... 10.D-1

Table 10.D.2.1 TSL 1, VOP.RC Equipment ..................................................................... 10.D-3

Table 10.D.2.2 TSL 1, SVO.RC and HZO.RC Equipment .............................................. 10.D-4

Table 10.D.2.3 TSL 1, Open Self-Contained Equipment ................................................. 10.D-5

Table 10.D.2.4 TSL 1, EPCA Refrigerators and Pull-Down Equipment ......................... 10.D-6

Table 10.D.2.5 TSL 1, EPCA Freezers ............................................................................. 10.D-7

Table 10.D.2.6 TSL 1, VCT.RC and SOC.RC Equipment ............................................... 10.D-8

Table 10.D.2.7 TSL 1, Ice- cream Freezers ....................................................................... 10.D-9

Table 10.D.3.1 TSL 2, VOP.RC Equipment ................................................................... 10.D-10

Table 10.D.3.2 TSL 2, SVO.RC and HZO.RC Equipment ............................................ 10.D-11

Table 10.D.3.3 TSL 2, Open Self-Contained Equipment ............................................... 10.D-12

Table 10.D.3.4 TSL 2, EPCA Refrigerators and Pull-Down Equipment ....................... 10.D-13

Table 10.D.3.5 TSL 2, EPCA Freezers ........................................................................... 10.D-14

Table 10.D.3.6 TSL 2, VCT.RC and SOC.RC Equipment ............................................. 10.D-15

Table 10.D.3.7 TSL 2, Ice- cream Freezers ..................................................................... 10.D-16

Table 10.D.4.1 TSL 3, VOP.RC Equipment ................................................................... 10.D-17

Table 10.D.4.2 TSL 3, SVO.RC and HZO.RC Equipment ............................................ 10.D-18

Table 10.D.4.3 TSL 3, Open Self-Contained Equipment ............................................... 10.D-19

Table 10.D.4.4 TSL 3, EPCA Refrigerators and Pull-Down Equipment ....................... 10.D-20

Table 10.D.4.5 TSL 3, EPCA Freezers ........................................................................... 10.D-21

Table 10.D.4.6 TSL 3, VCT.RC and SOC.RC Equipment ............................................. 10.D-22

Table 10.D.4.7 TSL 3, Ice- cream Freezers ..................................................................... 10.D-23

Table 10.D.5.1 TSL 4, VOP.RC Equipment ................................................................... 10.D-24

Table 10.D.5.2 TSL 4, SVO.RC and HZO.RC Equipment ............................................ 10.D-25

Table 10.D.5.3 TSL 4, Open Self-Contained Equipment ............................................... 10.D-26

Table 10.D.5.4 TSL 4, EPCA Refrigerators and Pull-Down Equipment ....................... 10.D-27

Table 10.D.5.5 TSL 4, EPCA Freezers ........................................................................... 10.D-28

Table 10.D.5.6 TSL 4, VCT.RC and SOC.RC Equipment ............................................. 10.D-29

Table 10.D.5.7 TSL 4, Ice- cream Freezers ..................................................................... 10.D-30

Table 10.D.6.1 TSL 5, VOP.RC Equipment ................................................................... 10.D-31

10.D -i

### Table 10.D.6.2 TSL 5, SVO.RC and HZO.RC Equipment ............................................ 10.D-32

Table 10.D.6.3 TSL 5, Open Self-Contained Equipment ............................................... 10.D-33

Table 10.D.6.4 TSL 5, EPCA Refrigerators and Pull-Down Equipment ....................... 10.D-34

Table 10.D.6.5 TSL 5, EPCA Fre ezers ........................................................................... 10.D-35

Table 10.D.6.6 TSL 5, VCT.RC and SOC.RC Equipment ............................................. 10.D-36

Table 10.D.6.7 TSL 5, Ice- cream Freezers ..................................................................... 10.D-37

10.D

- ii

### APPENDIX 10.D. RISC & OIRA CONSOLIDATED INFORMATION

SYSTEM (ROCIS) TABLE S

10.D.1 INTRODUCTION

The net present value (NPV) of the monetized benefits associated with emissions reductions can be viewed as a complement to the NPV of the customer savings calculated for each trial standard level (TSL) considered in this notice of public rulemaking for co mmercial refrigeration equipment. In Error! Reference source not found. through Error! Reference source not

found. , the top half of the table presents the NPV values that would result if the U.S. Department of Energy (DOE) were to add the estimates of the potential economic benefits resulting from reduced carbon dioxide (CO 2 

### ) and nitrogen oxide (NO

x 

### ) emissions to the NPV of customer savings calculated for each TSL considered in this final rule, at both a 7- percent and 3

- percent
discount rate.

Although combining the values of operating savings and CO 2 

### reductions provides a useful

per spective, two issues should be considered. First, the national operating savings are domestic U.S. consumer monetary savings that occur as a result of market transactions while the value of

CO 2 

### reductions is based on a global value. Second, the assessments of operating cost savings and

CO 2 

### savings are performed with different methods that use quite different time frames for analysis. The national operating cost savings is measured for the lifetime of products shipped in the 30-year analysis period. The social cost of carbon values, on the other hand, reflect the present value of future climate -related impacts resulting from the emission of one ton of CO 2 

### in each year. These impacts go well beyond 2100.

The benefits and costs of today’s considered standard levels, for products sold in 2017 through 2046, also can be expressed in terms of annualized values. The annualized monetary values

shown in Error! Reference source not found. through Error! Reference source not found. present the sum of (1) the annualized national economic value, expressed in 2012 doll ars (2012$), of the benefits from customer operation of products that meet the considered standard levels (consisting primarily of operating cost savings from using less energy, minus increases in equipment purchase and installation costs, which is another way of representing customer NPV); and (2) the annualized monetary value of the benefits of emission reductions, including CO 2 

### emission reductions. These results tables address all TSLs and equipment class groups. For the sake of brevity, the 25 equipment classes have been grouped into 7 groups as shown in Error!

Reference source not found.

.

Table 10.D.1.1 Equipment class groupings
Group Name Equipment Classes Belonging to the Group VOP.RC Equipment VOP.RC.M and VOP.RC.L SVO.RC and HZO.RC Equipment SVO.RC.M, HZO.RC.M, and HZO.RC.L Open Self

- Contained Equipment VOP.SC.M, SVO.SC.M, HZO.SC.M, and HZO.SC.L EPCA Refrigerators and Pull

- Down Equipment VCS.SC.M, VCT.SC.M, HCS.SC.M, HCT.S C.M, PD.SC.M, and SOC.SC.M

EPCA Freezers VCS.SC.L, VCT.SC.L, HCS.SC.L, and HCT.SC.L

10.D -1VCT.RC and SOC.RC Equipment VCT.RC.M, VCT.RC.L, and SOC.RC.M

Ice

- cream Freezers VCT.SC.I, VCS.SC.I, and HCT.SC.I



### 10.D.2 ROCIS TABLES FOR TSL 1

10.D -2

### Table 10.D.2.1 TSL 1, VOP.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 0 0 0

3% 0 0 0

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 0 0 0

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.00 0.00 0.00

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

10.D -3

### Table 10.D.2.2 TSL 1, SVO.RC and HZO.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 63 62 66

3% 81 78 85

CO2 at 5% dr, average 5% 6 6 6

CO2 at 3% dr, average 3% 20 20 20

CO2 at 2.5% dr, average 2.5% 29 29 29

CO2 at 3% dr, 95th perc 3% 61 61 61

NOx at 2,639 2012$/ton 7% 0.25 0.25 0.25

3% 0.46 0.46 0.46

Total 7% plus CO2

range 70 to 125 68 to 123 72 to

127

7% 83 82 86
3% plus CO2

range 87 to 142 84 to 140 91 to

146

3% 101 98 105

Costs

Incremental Product

Costs 7% 37 36 37

3% 38 37 39

Net Benefits/Costs

Total 7% plus CO2

range 33 to 88 32 to 87 35 to 90

7% 47 46 49
3% plus CO2

range 49 to 104 47 to 102 52 to

107

3% 63 61 66

10.D -4

### Table 10.D.2.3 TSL 1, Open Self

- Contained Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 0 0 0

3% 0 0 0

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 0 0 0

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.00 0.00 0.00

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

10.D -5

### Table 10.D.2.4 TSL 1, EPCA Refrigerators and Pull

- Down Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 83 80 87

3% 106 101 112

CO2 at 5% dr, average 5% 9 9 9

CO2 at 3% dr, average 3% 32 32 32

CO2 at 2.5% dr, average 2.5% 47 47 47

CO2 at 3% dr, 95th perc 3% 98 98 98

NOx at 2,639 2012$/ton 7% 0.40 0.40 0.40

3% 0.74 0.74 0.74

Total 7% plus CO2

range 93 to 182 90 to 179 97 to

186

7% 115 112 120
3% plus CO2

range 116 to

205 111 to 200 122 to

211

3% 138 134 144

Costs

Incremental Product

Costs 7% 12 12 12

3% 13 12 13

Net Benefits/Costs

Total 7% plus CO2

range 80 to 169 78 to 167 85 to

174

7% 103 100 107
3% plus CO2

range 103 to

192 99 to 188 109 to

198

3% 126 121 131

10.D -6

### Table 10.D.2.5 TSL 1, EPCA Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 123 119 129

3% 156 150 164

CO2 at 5% dr, average 5% 12 12 12

CO2 at 3% dr, average 3% 42 42 42

CO2 at 2.5% dr, average 2.5% 61 61 61

CO2 at 3% dr, 95th perc 3% 128 128 128

NOx at 2,639 2012$/ton 7% 0.51 0.51 0.51

3% 0.96 0.96 0.96

Total 7% plus CO2

range 136 to

252 132 to 248 142 to

258

7% 165 161 171
3% plus CO2

range 169 to

285 164 to 279 178 to

293

3% 199 193 207

Costs

Incremental Product

Costs 7% 8 8 8

3% 8 8 8

Net Benefits/Costs

Total 7% plus CO2

range 128 to

244 124 to 240 134 to

249

7% 157 154 163
3% plus CO2

range 161 to

277 156 to 271 169 to

285

3% 191 185 198

10.D -7

### Table 10.D.2.6 TSL 1, VCT.RC and SOC.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 138 47 49

3% 98 44 47

CO2 at 5% dr, average 5% 2 2 2

CO2 at 3% dr, average 3% 8 8 8

CO2 at 2.5% dr, average 2.5% 12 12 12

CO2 at 3% dr, 95th perc 3% 26 26 26

NOx at 2,639 2012$/ton 7% 0.10 0.10 0.10

3% 0.19 0.19 0.19

Total 7% plus CO2

range 140 to

163 50 to 73 52 to 75

7% 146 56 58
3% plus CO2

range 101 to

124 47 to 70 50 to 73

3% 107 53 56

Costs

Incremental Product

Costs 7% 77 20 20

3% 48 15 15

Net Benefits/Costs

Total 7% plus CO2

range 63 to 87 30 to 53 31 to 55

7% 69 36 37
3% plus CO2

range 53 to 76 32 to 56 35 to 58

3% 59 38 41

10.D -8

### Table 10.D.2.7 TSL 1, Ice- cream Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 0 0 0

3% 0 0 0

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 0 0 0

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.00 0.00 0.00

Total 7% plus CO2

range 0 to 1 0 to 1 0 to 1

7% 0 0 0
3% plus CO2

range 0 to 1 0 to 1 0 to 1

3% 0 0 0

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0



### 10.D.3 ROCIS TABLES FOR TSL 2

10.D -9

### Table 10.D.3.1 TSL 2, VOP.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 0 0 0

3% 0 0 0

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 0 0 0

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.00 0.00 0.00

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

10.D

- 10

### Table 10.D.3.2 TSL 2, SVO.RC and HZO.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 63 62 66

3% 81 78 85

CO2 at 5% dr, average 5% 6 6 6

CO2 at 3% dr, average 3% 20 20 20

CO2 at 2.5% dr, average 2.5% 29 29 29

CO2 at 3% dr, 95th perc 3% 61 61 61

NOx at 2,639 2012$/ton 7% 0.25 0.25 0.25

3% 0.46 0.46 0.46

Total 7% plus CO2

range 70 to 125 68 to 123 72 to

127

7% 83 82 86
3% plus CO2

range 87 to 142 84 to 140 91 to

146

3% 101 98 105

Costs

Incremental Product

Costs 7% 37 36 37

3% 38 37 39

Net Benefits/Costs

Total 7% plus CO2

range 33 to 88 32 to 87 35 to 90

7% 47 46 49
3% plus CO2

range 49 to 104 47 to 102 52 to

107

3% 63 61 66

10.D

- 11

### Table 10.D.3.3 TSL 2, Open Self

- Contained Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 0 0 0

3% 0 0 0

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 0 0 0

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.00 0.00 0.00

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

10.D

- 12

### Table 10.D.3.4 TSL 2, EPCA Refrigerators and Pull

- Down Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 250 242 262

3% 316 303 333

CO2 at 5% dr, average 5% 26 26 26

CO2 at 3% dr, average 3% 89 89 89

CO2 at 2.5% dr, average 2.5% 130 130 130

CO2 at 3% dr, 95th perc 3% 274 274 274

NOx at 2,639 2012$/ton 7% 1.10 1.10 1.10

3% 2.05 2.05 2.05

Total 7% plus CO2

range 277 to

525 269 to 517 289 to

537

7% 340 332 352
3% plus CO2

range 344 to

592 332 to 580 362 to

610

3% 407 394 424

Costs

Incremental Product

Costs 7% 67 65 68

3% 69 67 71

Net Benefits/Costs

Total 7% plus CO2

range 210 to

458 204 to 452 221 to

469

7% 273 266 284
3% plus CO2

range 275 to

524 264 to 512 291 to

539

3% 338 327 354

10.D

- 13

### Table 10.D.3.5 TSL 2, EPCA Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 174 168 182

3% 220 211 231

CO2 at 5% dr, average 5% 18 18 18

CO2 at 3% dr, average 3% 59 59 59

CO2 at 2.5% dr , average 2.5% 87 87 87

CO2 at 3% dr, 95th perc 3% 183 183 183

NOx at 2,639 2012$/ton 7% 0.72 0.72 0.72

3% 1.36 1.36 1.36

Total 7% plus CO2

range 192 to

358 187 to 352 200 to

366

7% 234 228 242
3% plus CO2

range 239 to

405 230 to 396 250 to

416

3% 281 272 292

Costs

Incremental Product

Costs 7% 22 21 22

3% 22 22 23

Net Benefits/Costs

Total 7% plus CO2

range 170 to

336 165 to 331 178 to

344

7% 212 207 220
3% plus CO2

range 216 to

382 209 to 374 227 to

393

3% 258 250 269

10.D

- 14

### Table 10.D.3.6 TSL 2, VCT.RC and SOC.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 186 47 49

3% 126 44 47

CO2 at 5% dr, average 5% 2 2 2

CO2 at 3% dr, average 3% 8 8 8

CO2 at 2.5% dr, average 2.5% 12 12 12

CO2 at 3% dr, 95th perc 3% 26 26 26

NOx at 2,639 2012$/ton 7% 0.10 0.10 0.10

3% 0.19 0.19 0.19

Total 7% plus CO2

range 188 to

212 50 to 73 52 to 75

7% 194 56 58
3% plus CO2

range 129 to

152 47 to 70 50 to 73

3% 135 53 56

Costs

Incremental Product

Costs 7% 107 20 20

3% 66 15 15

Net Benefits/Costs

Total 7% plus CO2

range 81 to 104 30 to 53 31 to 55

7% 87 36 37
3% plus CO2

range 63 to 86 32 to 56 35 to 58

3% 69 38 41

10.D

- 15

### Table 10.D.3.7 TSL 2, Ice

- cream Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 0 0 0

3% 1 1 1

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 1 1 1

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.00 0.00 0.00

Total 7% plus CO2

range 0 to 1 0 to 1 1 to 1

7% 1 1 1
3% plus CO2

range 1 to 1 1 to 1 1 to 1

3% 1 1 1

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 1 0 to 1 0 to 1

7% 0 0 0
3% plus CO2

range 0 to 1 0 to 1 0 to 1

3% 1 0 1



### 10.D.4 ROCIS TABLES FOR TSL 3

10.D

- 16

### Table 10.D.4.1 TSL 3, VOP.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 114 110 118

3% 145 140 152

CO2 at 5% dr, average 5% 10 10 10

CO2 at 3% dr, average 3% 35 35 35

CO2 at 2.5% dr, average 2.5% 51 51 51

CO2 at 3% dr , 95th perc 3% 108 108 108

NOx at 2,639 2012$/ton 7% 0.44 0.44 0.44

3% 0.81 0.81 0.81

Total 7% plus CO2

range 124 to

222 121 to 219 129 to

227

7% 149 146 154
3% plus CO2

range 156 to

253 151 to 248 163 to

260

3% 181 176 187

Costs

Incremental Product

Costs 7% 61 60 62

3% 63 62 65

Net Benefits/Costs

Total 7% plus CO2

range 63 to 161 61 to 159 67 to

165

7% 88 86 92
3% plus CO2

range 92 to 190 89 to 187 98 to

195

3% 117 114 122

10.D

- 17

### Table 10.D.4.2 TSL 3, SVO.RC and HZO.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 63 62 66

3% 81 78 85

CO2 at 5% dr, average 5% 6 6 6

CO2 at 3% dr, average 3% 20 20 20

CO2 at 2.5% dr, average 2.5% 29 29 29

CO2 at 3% dr, 95th perc 3% 61 61 61

NOx at 2,639 2012$/ton 7% 0.25 0.25 0.25

3% 0.46 0.46 0.46

Total 7% plus CO2

range 70 to 125 68 to 123 72 to

127

7% 83 82 86
3% plus CO2

range 87 to 142 84 to 140 91 to

146

3% 101 98 105

Costs

Incremental Product

Costs 7% 37 36 37

3% 38 37 39

Net Benefits/Costs

Total 7% plus CO2

range 33 to 88 32 to 87 35 to 90

7% 47 46 49
3% plus CO2

range 49 to 104 47 to 102 52 to

107

3% 63 61 66

10.D

- 18

### Table 10.D.4.3 TSL 3, Open Self

- Contained Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 0 0 0

3% 0 0 0

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 0 0 0

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.00 0.00 0.00

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 0 0 to 0 0 to 0

7% 0 0 0
3% plus CO2

range 0 to 0 0 to 0 0 to 0

3% 0 0 0

10.D

- 19

### Table 10.D.4.4 TSL 3, EPCA Refrigerators and Pull

- Down Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 286 277 301

3% 362 347 382

CO2 at 5% dr, average 5% 31 31 31

CO2 at 3% dr, average 3% 104 104 104

CO2 at 2.5% dr, average 2.5% 153 153 153

CO2 at 3% dr, 95th perc 3% 322 322 322

NOx at 2,639 2012$/ton 7% 1.27 1.27 1.27

3% 2.38 2.38 2.38

Total 7% plus CO2

range 318 to

609 309 to 600 333 to

623

7% 392 382 406
3% plus CO2

range 395 to

686 380 to 671 415 to

706

3% 469 454 489

Costs

Incremental Product

Costs 7% 94 92 96

3% 97 95 99

Net Benefits/Costs

Total 7% plus CO2

range 224 to

515 217 to 507 237 to

527

7% 298 290 310
3% plus CO2

range 298 to

589 286 to 576 316 to

607

3% 372 359 389

10.D

- 20

### Table 10.D.4.5 TSL 3, EPCA Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 212 205 222

3% 268 257 282

CO2 at 5% dr, average 5% 22 22 22

CO2 at 3% dr, average 3% 75 75 75

CO2 at 2.5% dr, average 2.5% 111 111 111

CO2 at 3% dr, 95th perc 3% 233 233 233

NOx at 2,639 2012$/ton 7% 0.91 0.91 0.91

3% 1.71 1.71 1.71

Total 7% plus CO2

range 235 to

445 228 to 438 245 to

455

7% 288 281 298
3% plus CO2

range 292 to

502 281 to 491 306 to

516

3% 345 334 359

Costs

Incremental Product

Costs 7% 52 51 53

3% 53 52 54

Net Benefits/Costs

Total 7% plus CO2

range 183 to

393 177 to 388 192 to

403

7% 236 230 245
3% plus CO2

range 238 to

449 229 to 439 252 to

462

3% 292 282 305

10.D

- 21

### Table 10.D.4.6 TSL 3, VCT.RC and SOC.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 236 64 66

3% 161 59 62

CO2 at 5% dr, average 5% 3 3 3

CO2 at 3% dr, average 3% 12 12 12

CO2 at 2.5% dr, average 2.5% 17 17 17

CO2 at 3% dr, 95th perc 3% 36 36 36

NOx at 2,639 2012$/ton 7% 0.15 0.15 0.15

3% 0.27 0.27 0.27

Total 7% plus CO2

range 240 to

273 67 to 100 70 to

103

7% 248 76 78
3% plus CO2

range 165 to

197 62 to 95 66 to 99

3% 173 71 74

Costs

Incremental Product

Costs 7% 140 31 32

3% 87 23 24

Net Benefits/Costs

Total 7% plus CO2

range 100 to

133 36 to 69 38 to 71

7% 108 44 46
3% plus CO2

range 77 to 110 39 to 72 42 to 75

3% 86 47 51

10.D

- 22

### Table 10.D.4.7 TSL 3, Ice- cream Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 1 1 1

3% 1 1 1

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 0 0 0

CO2 at 3% dr, 95th perc 3% 1 1 1

NOx at 2,639 2012$/ton 7% 0.00 0.00 0.00

3% 0.01 0.01 0.01

Total 7% plus CO2

range 1 to 1 1 to 1 1 to 1

7% 1 1 1
3% plus CO2

range 1 to 2 1 to 2 1 to 2

3% 1 1 1

Costs

Incremental Product

Costs 7% 0 0 0

3% 0 0 0

Net Benefits/Costs

Total 7% plus CO2

range 0 to 1 0 to 1 0 to 1

7% 1 1 1
3% plus CO2

range 1 to 1 0 to 1 1 to 1

3% 1 1 1



### 10.D.5 ROCIS TABLES FOR TSL 4

10.D

- 23

### Table 10.D.5.1 TSL 4, VOP.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 134 130 141

3% 171 164 180

CO2 at 5% dr, average 5% 14 14 14

CO2 at 3% dr, average 3% 48 48 48

CO2 at 2.5% dr, average 2.5% 71 71 71

CO2 at 3% dr, 95th perc 3% 149 149 149

NOx at 2,639 2012$/ton 7% 0.61 0.61 0.61

3% 1.12 1.12 1.12

Total 7% plus CO2

range 149 to

284 144 to 280 155 to

291

7% 183 179 190
3% plus CO2

range 187 to

322 180 to 315 196 to

331

3% 221 214 230

Costs

Incremental Product

Costs 7% 119 116 121

3% 123 120 126

Net Benefits/Costs

Total 7% plus CO2

range 30 to 165 28 to 164 34 to

170

7% 64 62 69
3% plus CO2

range 63 to 198 59 to 194 70 to

205

3% 97 93 104

10.D

- 24

### Table 10.D.5.2 TSL 4, SVO.RC and HZO.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 76 73 79

3% 97 93 102

CO2 at 5% dr, average 5% 8 8 8

CO2 at 3% dr, average 3% 27 27 27

CO2 at 2.5% dr, average 2.5% 40 40 40

CO2 at 3% dr , 95th perc 3% 84 84 84

NOx at 2,639 2012$/ton 7% 0.34 0.34 0.34

3% 0.63 0.63 0.63

Total 7% plus CO2

range 84 to 160 81 to 158 88 to

164

7% 103 101 107
3% plus CO2

range 105 to

181 101 to 177 111 to

187

3% 124 121 130

Costs

Incremental Product

Costs 7% 68 67 70

3% 71 69 73

Net Benefits/Costs

Total 7% plus CO2

range 16 to 92 15 to 91 18 to 94

7% 35 34 37
3% plus CO2

range 34 to 111 32 to 108 38 to

114

3% 54 51 57

10.D

- 25

### Table 10.D.5.3 TSL 4, Open Self

- Contained Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 2 2 3

3% 3 3 3

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 1 1 1

CO2 at 2.5% dr, average 2.5% 2 2 2

CO2 at 3% dr, 95th perc 3% 3 3 3

NOx at 2,639 2012$/ton 7% 0.01 0.01 0.01

3% 0.03 0.03 0.03

Total 7% plus CO2

range 3 to 6 3 to 6 3 to 6

7% 4 3 4
3% plus CO2

range 3 to 6 3 to 6 4 to 7

3% 4 4 4

Costs

Incremental Product

Costs 7% 4 4 4

3% 4 4 4

Net Benefits/Costs

Total 7% plus CO2

range -1 to 2 -1 to 2 -1 to 2

7% 0 0 0
3% plus CO2

range 0 to 3 0 to 3 0 to 3

3% 0 0 1

10.D

- 26

### Table 10.D.5.4 TSL 4, EPCA Refrigerators and Pull

- Down Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 297 287 313

3% 376 360 398

CO2 at 5% dr, average 5% 33 33 33

CO2 at 3% dr, average 3% 113 113 113

CO2 at 2.5% dr, average 2.5% 166 166 166

CO2 at 3% dr, 95th perc 3% 350 350 350

NOx at 2,639 2012$/ton 7% 1.37 1.37 1.37

3% 2.57 2.57 2.57

Total 7% plus CO2

range 332 to

649 322 to 638 348 to

664

7% 412 402 428
3% plus CO2

range 412 to

729 396 to 712 434 to

750

3% 492 476 514

Costs

Incremental Product

Costs 7% 131 128 133

3% 134 131 137

Net Benefits/Costs

Total 7% plus CO2

range 201 to

518 194 to 510 214 to

531

7% 281 274 294
3% plus CO2

range 278 to

594 265 to 582 296 to

613

3% 358 345 376

10.D

- 27

### Table 10.D.5.5 TSL 4, EPCA Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 215 208 225

3% 272 261 287

CO2 at 5% dr, average 5% 23 23 23

CO2 at 3% dr, average 3% 79 79 79

CO2 at 2.5% dr, average 2.5% 115 115 115

CO2 at 3% dr, 95th perc 3% 243 243 243

NOx at 2,639 2012$/ton 7% 0.95 0.95 0.95

3% 1.79 1.79 1.79

Total 7% plus CO2

range 239 to

458 232 to 451 250 to

469

7% 294 287 305
3% plus CO2

range 297 to

516 285 to 505 312 to

531

3% 352 341 367

Costs

Incremental Product

Costs 7% 65 64 67

3% 67 66 69

Net Benefits/Costs

Total 7% plus CO2

range 173 to

393 168 to 387 183 to

402

7% 229 223 238
3% plus CO2

range 229 to

449 220 to 439 243 to

462

3% 285 275 298

10.D

- 28

### Table 10.D.5.6 TSL 4, VCT.RC and SOC.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 244 68 71

3% 165 61 65

CO2 at 5% dr, average 5% 4 4 4

CO2 at 3% dr, average 3% 14 14 14

CO2 at 2.5% dr, average 2.5% 21 21 21

CO2 at 3% dr, 95th perc 3% 43 43 43

NOx at 2,639 2012$/ton 7% 0.17 0.17 0.17

3% 0.32 0.32 0.32

Total 7% plus CO2

range 248 to

287 73 to 112 76 to

115

7% 258 82 86
3% plus CO2

range 169 to

208 65 to 104 70 to

109

3% 179 75 79

Costs

Incremental Product

Costs 7% 167 56 58

3% 113 48 49

Net Benefits/Costs

Total 7% plus CO2

range 81 to 120 16 to 55 18 to 57

7% 91 26 28
3% plus CO2

range 56 to 95 17 to 56 20 to 59

3% 66 27 30

10.D

- 29

### Table 10.D.5.7 TSL 4, Ice- cream Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 1 1 1

3% 1 1 1

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 0 0 0

CO2 at 2.5% dr, average 2.5% 1 1 1

CO2 at 3% dr, 95th perc 3% 1 1 1

NOx at 2,639 2012$/ton 7% 0.01 0.01 0.01

3% 0.01 0.01 0.01

Total 7% plus CO2

range 1 to 3 1 to 3 1 to 3

7% 2 2 2
3% plus CO2

range 2 to 3 1 to 3 2 to 3

3% 2 2 2

Costs

Incremental Product

Costs 7% 1 1 1

3% 1 1 1

Net Benefits/Costs

Total 7% plus CO2

range 0 to 1 0 to 1 0 to 1

7% 0 0 0
3% plus CO2

range 0 to 2 0 to 2 0 to 2

3% 1 1 1



### 10.D.6 ROCIS TABLES FOR TSL 5

10.D

- 30

### Table 10.D.6.1 TSL 5, VOP.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 58 53 65

3% 77 70 87

CO2 at 5% dr, average 5% 15 15 15

CO2 at 3% dr, average 3% 52 52 52

CO2 at 2.5% dr, average 2.5% 76 76 76

CO2 at 3% dr, 95th perc 3% 160 160 160

NOx at 2,639 2012$/ton 7% 0.65 0.65 0.65

3% 1.20 1.20 1.20

Total 7% plus CO2

range 74 to 219 69 to 214 81 to 226

7% 111 106 118
3% plus CO2

range 94 to 239 86 to 231 104 to

249

3% 130 123 140

Costs

Incremental Product

Costs 7% 352 344 359

3% 366 357 375

Net Benefits/Costs

Total 7% plus CO2

range - 278 to

- 133 - 275 to

- 130 - 278 to

- 133

7% -241 -238 -241
3% plus CO2

range - 272 to

- 127 - 270 to

- 126 - 271 to

- 126

3% -235 -234 -234

10.D

- 31

### Table 10.D.6.2 TSL 5, SVO.RC and HZO.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 23 21 28

3% 32 28 38

CO2 at 5% dr, average 5% 9 9 9

CO2 at 3% dr, average 3% 31 31 31

CO2 at 2.5% dr, average 2.5% 46 46 46

CO2 at 3% dr, 95th perc 3% 96 96 96

NOx at 2,639 2012$/ton 7% 0.39 0.39 0.39

3% 0.72 0.72 0.72

Total 7% plus CO2

range 33 to 120 30 to 117 37 to 124

7% 55 52 59
3% plus CO2

range 42 to 129 38 to 124 48 to 135

3% 64 59 70

Costs

Incremental Product

Costs 7% 240 235 245

3% 250 244 256

Net Benefits/Costs

Total 7% plus CO2

range - 207 to

- 121 - 205 to

- 118 - 208 to

- 121

7% -185 -183 -186
3% plus CO2

range - 208 to

- 121 - 206 to

- 119 - 208 to

- 121

3% -186 -184 -186

10.D

- 32

### Table 10.D.6.3 TSL 5, Open Self

- Contained Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% -7 -7 -7

3% -9 -9 -8

CO2 at 5% dr, average 5% 1 1 1

CO2 at 3% dr, average 3% 2 2 2

CO2 at 2.5% dr, average 2.5% 3 3 3

CO2 at 3% dr, 95th perc 3% 7 7 7

NOx at 2,639 2012$/ton 7% 0.03 0.03 0.03

3% 0.05 0.05 0.05

Total 7% plus CO2

range -6 to 0 -7 to -1 -6 to 0

7% -5 -5 -5
3% plus CO2

range -8 to -2 -8 to -2 -7 to -1

3% -6 -7 -6

Costs

Incremental Product

Costs 7% 50 49 51

3% 52 50 53

Net Benefits/Costs

Total 7% plus CO2

range -56 to -50 -55 to -49 - 57 to

- 51

7% -54 -54 -55
3% plus CO2

range -59 to -53 -58 to -52 - 60 to

- 54

3% -58 -57 -59

10.D

- 33

### Table 10.D.6.4 TSL 5, EPCA Refrigerators and Pull

- Down Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 29 16 49

3% 46 26 74

CO2 at 5% dr, average 5% 42 42 42

CO2 at 3% dr, average 3% 144 144 144

CO2 at 2.5% dr, average 2.5% 211 211 211

CO2 at 3% dr, 95th perc 3% 443 443 443

NOx at 2,639 2012$/ton 7% 1.74 1.74 1.74

3% 3.27 3.27 3.27

Total 7% plus CO2

range 73 to 474 60 to 461 93 to 494

7% 175 162 194
3% plus CO2

range 92 to 493 71 to 472 119 to 520

3% 193 173 221

Costs

Incremental Product

Costs 7% 1156 1132 1181

3% 1200 1171 1229

Net Benefits/Costs

Total 7% plus CO2

range - 1,083 to

- 682 - 1,072 to

- 671 - 1,088 to

- 687

7% -982 -971 -986
3% plus CO2

range - 1,108 to

- 707 - 1,099 to

- 698 - 1,110 to

- 709

3% -1,006 -998 -1,009

10.D

- 34

### Table 10.D.6.5 TSL 5, EPCA Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 109 99 123

3% 142 128 162

CO2 at 5% dr, average 5% 31 31 31

CO2 at 3% dr, average 3% 104 104 104

CO2 at 2.5% dr, average 2.5% 152 152 152

CO2 at 3% dr, 95th perc 3% 320 320 320

NOx at 2,639 2012$/ton 7% 1.26 1.26 1.26

3% 2.36 2.36 2.36

Total 7% plus CO2

range 141 to

430 131 to 420 155 to

444

7% 214 204 228
3% plus CO2

range 175 to

465 160 to 450 195 to

485

3% 248 234 268

Costs

Incremental Product

Costs 7% 545 534 557

3% 566 552 580

Net Benefits/Costs

Total 7% plus CO2

range - 404 to

- 115 - 403 to

- 113 - 402 to

- 112

7% -331 -329 -329
3% plus CO2

range - 390 to

- 101 - 392 to

- 102 - 385 to

- 95

3% -317 -318 -311

10.D

- 35

### Table 10.D.6.6 TSL 5, VCT.RC and SOC.RC Equipment

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% 220 5 11

3% 92 -36 -28

CO2 at 5% dr, average 5% 7 7 7

CO2 at 3% dr, average 3% 25 25 25

CO2 at 2.5% dr, average 2.5% 36 36 36

CO2 at 3% dr, 95th perc 3% 76 76 76

NOx at 2,639 2012$/ton 7% 0.31 0.31 0.31

3% 0.57 0.57 0.57

Total 7% plus CO2

range 228 to

297 13 to 81 18 to 87

7% 245 30 36
3% plus CO2

range 100 to

169 -28 to 41 -20 to 49

3% 118 -11 -3

Costs

Incremental Product

Costs 7% 557 415 431

3% 499 411 430

Net Benefits/Costs

Total 7% plus CO2

range - 329 to

- 261 - 402 to

- 333 - 413 to

- 344

7% -312 -385 -395
3% plus CO2

range - 399 to

- 330 - 439 to

- 370 - 450 to

- 382

3% -382 -422 -433

10.D

- 36

### Table 10.D.6.7 TSL 5, Ice- cream Freezers

Discount Rate Reference Low

Benefits High

Benefits

Annualized (million 2012$/year)

Benefits

Operating Cost Savings 7% -3 -3 -2

3% -3 -3 -3

CO2 at 5% dr, average 5% 0 0 0

CO2 at 3% dr, average 3% 2 2 2

CO2 at 2.5% dr, average 2.5% 2 2 2

CO2 at 3% dr, 95th perc 3% 5 5 5

NOx at 2,639 2012$/ton 7% 0.02 0.02 0.02

3% 0.04 0.04 0.04

Total 7% plus CO2

range -2 to 2 -2 to 2 -2 to 2

7% -1 -1 -1
3% plus CO2

range -3 to 2 -3 to 1 -2 to 2

3% -2 -2 -1

Costs

Incremental Product

Costs 7% 22 22 22

3% 23 22 23

Net Benefits/Costs

Total 7% plus CO2

range -24 to -20 -24 to -20 - 24 to

- 20

7% -23 -23 -23
3% plus CO2

range -26 to -21 -25 to -21 - 26 to

- 22

3% -24 -24 -25

10.D

- 37

### APPENDIX 10.E. FULL

- FUEL

- CYCLE MULTIPLIERS

TABLE OF CO NTENTS

10.E.1 INTRODUCTION ...................................................................................................... 10.E-1

10.E.2 METHODOLOGY ..................................................................................................... 10.E-1

10.E.3 FULL

- FUEL

- CYCLE ENERGY MULTIPL IERS .................................................... 10.E-3

LIST OF TABLE S

Table 10.E.2.1 Dependence of FFC Parameters on AEO Inputs ...................................... 10.E-3

Table 10.E.3.1 Upstream Energy Multipliers (Based on AEO 2013) ............................... 10.E-3

10.E-i

### APPENDIX 10.E. FULL

- FUEL

- CYCLE MULTIPLIERS

10.E.1 INTRODUCTION

This appendix summarizes the methods used to calculate full- fuel

- cycle (FFC) energy savings expected to result from potential standard s. The FFC measure includes point-of- use (site) energy, the energy losses associated with generation, transmission, and distribution of elect ricity, and the energy consumed in extracting, processing, and transporting or distributing primary

fuels. DOE’s traditional approach encompassed only site energy and the energy losses associated with generation, transmission, and distribution of electricity . Per DOE’s 2011 Statement of Policy for Adopting Full Fuel Cycle Analyses , DOE now uses FFC measures of energy use and emissions in its energy conservation standards analyses. 1 

### This appendix summarizes the methods

used to incorporate the full

- fuel

- cycle impacts into the analysis.

This analysis uses several different terms to reference energy use. The phys ical sources of energy are the p rimary f uels such as coal, natural gas, liquid fuels, etc . Primary energy is equal to the heat content ( Btu ) of the primary fuels used to provide an end- use service

. Site energy use is defined as the energy consumed at the point-of-use in a building or industrial process . Where natural gas and petroleum fuels are consumed at the site (for example in a furnace), site energy is identical to primary energy, with both equal to the heat content of the primary fuel consumed. For electricity, site energy is measured in kWh . In this case the primary energy is equal to the quads of primary energy required to generate and deliver the site electricity. This p rimary energy

is calculated by multiplyi ng the site kWh times the site

- to -power-plant conversion factor , given

in c hapter 1 2. For the FFC analysis, the u pstream energy use is defined as the energy consumed in extracting, processing, and transporting or distributing primary fuels. FFC energy use is the sum of primary plus upstream energy use.

Both primary fuels and electricity are used in upstream activities. The treatment of electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels and uranium, and electricity generated from renewable fluxes (wind, solar and hydro). For the former, the upstream fu el cycle impacts are derived from the amount of fuel consumed at the power plant. For the latter, no fuel per se is used, so there is no upstream component.

10.E.2 METHODOLOGY

The mathematical approach is discussed in the paper A Mathematical Analysis of Full Fuel Cycle Energy Use

, 2 

### and details on the fuel production chain analysis are presented in the

paper Projections of Full Fuel Cycle Energy and Emissions Metrics 3 

### The text below provides a brief summary of the methods used to calculate FFC energy

.

When a ll energy quantities are normalized to the same units, the FFC energy use can be represented as the product of the primary energy use and an FFC multiplier

. The FFC multiplier is defined mathematically as a function of a set of parameters representing the energy intensity and material losses at each production stage. The se parameters depend only on physical data, so

10.E-1

### the calculations do not require any assumptions about prices or other economic data. While in general these parameter values may vary by geographic region , for this analysis national averages are used.

In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, x=g for natural gas, x=p for petroleum fuels, x=u for uranium and x=r for renewable fluxes. The fuel cycle parameters are:

• a x 

### is the quantity of fuel x burned per unit of electricity output, on average, for grid electricity. The calculation of a

x 

### includes a factor to account for transmission and distribution system losses.

• b y 

### is the amount of grid electricity used in production of fuel y, in MWh per physical unit of fuel y.

• c xy 

### is the amount of fuel x consumed in producing one unit of fuel y.

• q x 

### is the heat content of fuel x (MBtu/physical unit) a



### • z x 

### (s) is the emissions intensity for fuel x (mass o f pollutant s per physical unit of x)

The parameters are calculated as a function of time with an annual time step ; h ence, a time series of annual values is used to estimate the FFC energy and emissions savings in each year of the analysis period. Fossi l fuel quantities are converted to energy units using the heat

content factors q x 

### . To convert electricity in kWh to primary energy units, on- site electricity consumption is multiplied by the s ite

- to

- power -plant conversion factor indicated in chapter 1 0.

The s ite

- to -power-plant conversion factor is defined as the ratio of the total primary energy consumption by the electric power sector (in quadrillion Btu) divided by the total electricity

consumed in each year.

The FFC multiplier is a dimensionless num ber denoted μ (mu). The upstream component of the energy savings is proportional to (μ- 1). A separate multiplier is calculated for each fuel used on site. The fuel type is denoted by a subscript on the multiplier μ. A multiplier is also

calculated for elec tricity reflecting the fuel mix used in its generation.

For DOE’s appliance standards energy savings estimates, the fuel cycle analysis methodology is designed to make use of data and projections published in the Annual Energy

Outlook (

AEO ). Table 10- E.2 .1 provides a summary of the AEO data used as inputs to the

different parameter calculation s. The AEO does not provide all the information needed to

estimate total energy use in the fuel production chain. Reference 3 describes the additional data sources used to complete the analysis. However, the time dependence in the FFC multipliers arises exclusively from variables taken from the AEO

. The FFC analysis f or this preliminary analysis used data from AEO 2013

. 4

a Where MBtu = 1000 Btu.



### 10.E-2

### Table 10.E.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables

q x 

### all Conversion Factors MBtu per physical unit

a x 

### all Electricity Supply, Disposition, Prices, and Emissions Generation by fuel type Energy Consumption by Sector and

Source Electric power sector energy consumption

b

c 

### , c nc 

### , c pc 

### coal Coal Production by Region and Type Production by coal type and

sulfur content

b p 

### , c np 

### , c pp 

### petroleum Refining Industry Energy Consumption Refining only energy use
Liquid Fuels Supply and Disposition Crude supply by source International Liquids Supply and Disposition Crude oil imports
Oil and Gas Supply Crude oil domestic production

c nn 

### natural

gas Oil and Gas Supply US dry gas production Natural Gas Supply, Disposition and

Prices Pipeline, lease and plant fuel

z x 

### all Electricity Supply

, Disposition

, Prices and Emissions Power sector emissions

10.E.3 FULL

- FUEL

- CYCLE ENERGY MULTIPL IERS

FFC upstream energy m ultipliers are presented in Table 10- E. 3.1 for selected years. To extend the analysis period beyond 2035, the last year in the AEO 2013 projection, the years 2020 to 2035 are used to define a linear trend to 2040, which is then extrapolated to the final year of the analysis period. The multiplier for electricity reflects the shares of various primary fuels in total electricity generation over the forecast period.

Table 10.E.3.1 Upstream Energy M ultipliers (Based on AEO 2013)

2015 2020 2025 2030 2035 2040 Electricity 0 .042 0 .041 0 .040 0 .040 0 .041 0 .041

Natural Gas 0 .102 0 .103 0 .100 0 .099 0 .098 0 .097

Petroleum Fuels 0 .142 0 .146 0 .153 0 .163 0 .172 0 .181

10.E-3

### REFERENCES

1 

### United States. Office of the Federal Re gister

. Federal Register, Volume 76, Number 160, August 18, 2011, Pages 51281-51868 . Washington D.C.. UNT Digital Library.

2 

### Coughlin, K., A Mathematical Analysis of Full Fuel Cycle Energy Use

. Energy , 2012. 37(1): pp. 698-708 http://www.sciencedirect.com/science/article/pii/S0360544211006803

3 

### Coughlin, K. 2013. Projections of Full- Fuel -Cycle Energy and Emissions Metrics. Lawrence Berkeley National Laboratory, Report Number LBNL-6025E.

4 

### U.S. Department of Energy- Energy Information Administration. Annual Energy Outlook

2013 with Projections to 2040. June 2013. Washington, D.C. http://www.eia.gov/forecasts/aeo/

10.E-4

### CHAPTER 11. CUSTOMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ............................................................................................................ 11-1

11.2 IDENTIFYING THE CUSTOMER SUBGROUPS ......................................................... 11-1

11.3 LIFE

- CYCLE COST AND PAYBACK PERIOD RESULTS FO R SMALL BUSINESS

SUB

- GROUPS .................................................................................................................. 11-7

LIST OF TABLES

Table 11.2.1 SBA Size Standards for Businesses that Use Commercial Refrigeration Equipment .......................................................................................................... 11-2

Table 11.2.2 Single Unit and Multiunit Firms Census Data for Supermarket and Other Grocery (Except Convenience) Stores ............................................................... 11-3 Table 11.2.3 Single Unit and Multiunit Firms Census Data for Convenience Stores ............... 11-4 Table 11.2.4 Single Unit and Multiunit Firms Census Data for Gasoline Stations with

Convenience Stores ............................................................................................ 11-5 Table 11.2.5 Single Unit and Multiunit Firms Census Data for Full- Service Restaurant s ........ 11-5 Table 11.2.6 Single Unit and Multiunit Firms Census Data for Limited- Service

Restaurants ......................................................................................................... 11-6 Table 11.3.1 Comparison of Mean LCC Savings for the Small Business Subgroup in the Foodservice Sector with the National Average Values ..................................... 11-8 Table 11.3.3 Comparison of Median Payback Periods for the Small Business Subgroup in

the Foodservice Sector with National Median Values ..................................... 11-10Table 11.3.4 Comparison of LCC Savings for the Small Business Subgroup in the FoodRetail Sector with the National Average Values ............................................. 11-11 Table 11.3.6 Comparison of Median Payback Periods for the Small Business Subgroup in

the Food- Retail Sector with the National Median Value s ............................... 11-13

11 -i

### CHAPTER 11 . CUSTOMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

The life

- cycle cost (LCC) sub group analysis evaluates impacts on identifiable groups of

customers of commercial refrigeration equipment who may be disproportionately affected by amended energy conservation standards. The LCC and payback period ( PBP ) analysis described in chapter 8 of the technical support document (TSD) is applied to seven major types of businesses belonging to the food-retail and foodservice sectors that use a majority of the commercial refrigeration equipment . Although the inputs f or different types of businesses are different in the LCC and PBP analysis, the final results may not reflect the results experienced by certain customer subgroups. In other words, some of the adverse impacts on businesses that are disproportionately disad vantaged may be masked by the averaging effect of the LCC and PBP analysis. Therefore, the U.S. Department of Energy (DOE) carried out the LCC subgroup analysis by using the LCC and PBP analysis spreadsheet, but applying the inputs that are applicable only to the identified subgroups. The LCC spreadshee t model is accessible at http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/52

.

11.2 IDENTIFYING THE CUSTOMER SUBGROUPS

DOE identified small businesses as a subgroup that could potentially be affected disproportionately by the amended energy conservation standard for commercial refrigeration equipment. DOE was concerned that increases in the purchase price of commercial refrigeration equipment could have negative impacts on small businesses ( i.e. , those with low annual revenues). To identify small businesses, DOE used size standards from the Small Business Administration (SBA) to define which business entiti es are considered to be small. The SBA established size standards for types of economic activity, or industry, under the North American Industry Classification System (NAICS). 1 

### Table 11.2.1 presents the size standards established by SBA for various businesses that use commercial refrigeration equipment.

11 -1

### Table 11.2.1 SBA Size Standards fo r Businesses that Use Commercial Refrigeration Equipment

Business Type SBA Size Standard

Annual Sales

millions Supermarkets and Other Grocery (except Convenience) Stores $30.0

Convenience Stores $27.0

Meat Markets $7.0

Fish and Seafood Markets $7.0 Fruit and Vegetable Markets $7.0

Baked Goods Stores $7.0 Confectionery and Nut Stores $7.0 All Other Specialty Food Stores $7.0 Beer, Wine and Liquor Stores $7.0

Gasoline Stations with

Convenience Stores $27.0

Full

- Service Restaurants $7.0

Limited

- Service Restaurants $10.0
Cafeterias, Grill Buffets, and

Buffets $25.5

Snack and Nonalcoholic Beverage

Bars $7.0

Food Service Contractors $35.5

Caterers $7.0

Mobile Food Services $7.0 Drinking Places (Alcoholic Beverages) $7.0

Source: Size Standards Used To Define S mall Business Concerns (13 CFR 121.201); a lso available at www.sba.gov/content/table

small

- business

- size

- standards (Last accessed on October 4,

2011 ).



### In examining the businesses that purchase and use commercial refrigeration equipment

, DOE analyzed detailed statistical data from the 200 7 economic census data. 2 

### Table 11.2.2

, Table

11.2.3

, Table 11.2.4

, Table 11.2.5 and Table 11.2 .6 present the census data for single unit and

multiunit firms for five different types of businesses that constitute a dominant share of the commercial refrigeration equipment market.

Table 11.2.2 presents the data for supermarkets and other grocery (except convenience) stores. The data show that 98.9 percent of the firms in this category fall under the SB A definition of small business concerns, and comprise of 66 percent of the total number of establishments, 23 percent of employment, and 18 percent of sales.

11 -2

### Table 11.2.2 Single Unit and Multiunit Firms Census Data for Supermarket and Other Grocery (Except Convenience) Stores

Description Number

of Firms Total Employment Number of

Establishments Sales per

Firm
1000$ Is Average

Firm a

Small

Business?Supermarkets & other grocery (exceptconvenience) stores* All firms 41,885 2,432,425 64,881 11,131 Yes Single unit firms 39,878 438,104 39,878 1,676 Yes

Multiunit firms 2,007 1,994,321 25,003 198,998 No

Firms with 1 establishment 577 18,882 577 4,481 Yes

Firms with 2 establishments 597 49,967 1,194 11,824 Yes

Firms with 3 or 4 establishments 374 62,813 1,233 25,250 Yes

Firms with 5 to 9 establishments 235 87,447 1,514 64,030 No

Firms with 10 to 24 establishments 128 125,436 1,797 178,695 No

Firms with 25 to 49 establishments 38 88,107 1,262 422,285 No

Firms with 50 to 99 establishments 22 133,964 1,533 1,130,223 No

Firms with 100 establishments or

more 36 1,427,705 15,893 8,374,159 No

Fraction of firms classed as small business 0.989 Fraction of establishments that belong to firms

classified as small businesses 0.661

Fraction of employment in small businesses 0.234

Fraction of sales in small businesses 0.184

* Small business concerns in this category are firms with annual sales less than $30 million.



### Table 11.2.3 presents the data for convenience stores. The data show that 99.9 percent of the firms in this category fall under the SBA definition of small business concerns, and comprise of 90 percent of the total number of establishments, 77 percent of employment, and 76 percent of

sales.

11 -3

### Table 11.2.3 Single Unit and Multiunit Firms Census Data for Conven ience Stores

Description Number

of Firms Total Employment Number of

Establishments Sales per

Firm
1000$ Is Average

Firm a

Small

Business?Convenience Stores* All firms 22,168 118,787 25,510 942 Yes Single unit firms 21,529 79,302 21,529 661 Yes

Multiunit firms 639 39,485 3,981 10,408 Yes

Firms with 1 establishment 300 2,458 300 1,087 Yes

Firms with 2 establishments 183 2,804 366 2,318 Yes

Firms with 3 or 4 establishments 86 2,251 281 4,111 Yes

Firms with 5 to 9 establishments 25 1,014 151 5,843 Yes

Firms with 10 to 24 establishments 19 3,369 302 23,634 Yes

Firms with 25 to 49 establishments 12 3,320 443 45,925 No

Firms with 50 to 99 establishments 10 6,365 694 116,707 No

Firms with 100 establishments or

more 4 17,904 1,444 808,383 No

Fraction of firms classed as small business 0.999 Fraction of establishments that belong to firms

classified as small businesses 0.899

Fraction of employment in small businesses 0.768

Fraction of sales in small businesses 0.763

*Small business concerns in this category are firms with annual sales less than $27 million.



### Table 11.2.4 presents the data for gasoline stations with convenience stores. The data show that 98.8 percent of the firms in this category fall under the SBA definition of small business concerns, and comprise of 60 percent of the total number of establishments, 52 percent of employment, and 43 percent of sales.

11 -4

### Table 11.2.4 Single Unit and Multiunit Firms Census Data for Gasoline Stations with

Convenience Stores

Description Number

of Firms Total Employment Number of

Establishments Sales per

Firm
1000$ Is Average

Firm a

Small

Business?Gasoline Stations with Convenience Stores* All firms 53,375 719,108 97,508 6,300 Yes Single unit firms 49,010 287,220 49,010 2,249 Yes

Multiunit firms 4,365 431,888 48,498 51,788 No

Firms with 1 establishment 1,160 11,836 1,160 3,363 Yes

Firms with 2 establishments 1,168 21,386 2,336 6,622 Yes

Firms with 3 or 4 establishments 802 24,023 2,697 11,713 Yes

Firms with 5 to 9 establishments 573 30,488 3,709 22,945 Yes

Firms with 10 to 24 establishments 402 47,037 5,978 51,678 No

Firms with 25 to 49 establishments 150 45,235 5,020 128,070 No

Firms with 50 to 99 establishments 58 31,187 3,865 272,955 No

Firms with 100 establishments or

more 52 220,696 23,733 2,616,567 No

Fraction of firms classed as small business 0.988 Fraction of establishments that belong to firms

classified as small businesses 0.604

Fraction of employment in small businesses 0.521

Fraction of sales in small businesses 0.429 *Small business concerns in this category are firms with annual sales less than $27 million.



### Table 11.2.5 presents the data for full

- service restaurants . The data show that 99.5 percent of the firms in this category fall under the SBA definition of small business concerns, and comprise of 87 percent of the total number of establishments, 64 percent of employment, and

65 percent of sales.

Table 11.2.5 Single Unit and Multiunit Firms Census Data for Full

- Service Restaurants

Description Number

of Firms Total Employment Number of

Establishments Sales per

Firm
1000$ Is Average

Firm a

Small

Business?Full-service Restaurants* All firms 188,758 4,603,747 220,089 1,019 Yes Single unit firms 183,759 2,633,075 183,759 614 Yes

Multiunit firms 4,999 1,970,672 36,330 15,880 No

Firms with 1 establishment 1,477 57,098 1,477 1,696 Yes

Firms with 2 establishments 1,762 119,803 3,524 2,797 Yes

Firms with 3 or 4 establishments 833 115,801 2,796 5,638 Yes

Firms with 5 to 9 establishments 455 142,324 2,966 12,156 No

Firms with 10 establishments or

more 472 1,535,646 25,567 130,770 No

Fraction of firms classed as small business 0.995 Fraction of establishments that belong to

firms classified as small businesses 0.870

Fraction of employment in small businesses 0.636

Fraction of sales in small businesses 0.650 *Small business concerns in this category are firms with annual sales less than $7 million.

11 -5

### Table 11.2.6 presents the data for limited

- service restaurants. The data show that 99 percent of the firms in this category fall under the SBA definition of small business concerns, and comprise of 73 percent of the total number of establishments, 58 percent of employment, and 58 percent of sales.

Table 11.2.6 Single Unit and Multiunit Firms Census Data for Limited

- Service Restaurants

Description Number

of Firms Total Employment Number of

Establishments Sales per

Firm
1000$ Is Average

Firm a

Small

Business?Limited-service Restaurants* All firms 136,505 3,384,517 211,313 1,109 Yes Single unit firms 126,341 1,263,854 126,341 447 Yes

Multiunit firms 10,164 2,120,663 84,972 9,331 Yes

Firms with 1 establishment 1,271 27,674 1,271 981 Yes

Firms with 2 establishments 2,821 143,768 5,642 2,151 Yes

Firms with 3 or 4 establishments 2,717 221,095 9,174 3,460 Yes

Firms with 5 to 9 establishments 1,934 319,625 12,391 7,154 Yes

Firms with 10 establishments or

more 1,421 1,408,501 56,494 45,239 No

Fraction of firms classed as small business 0.990 Fraction of establishments that belong to

firms classified as small businesses 0.733

Fraction of employment in small businesses 0.584

Fraction of sales in small businesses 0.575 *Small business concerns in this category are firms with annual sales less than $ 10 million.



### The census data clearly show that nearly 99 percent of all the firms in both the food- retail and foodservice sectors fall under the SBA definition of small businesses. Therefore, it can be concluded that in all business types, there are a substantial number of firms that may be disproportionately disadvantaged by the new or amended standards.

In general, the subgroups that face higher cost of capital and lower electricity price rates are more disadvantaged than others. Higher cost of capital imposes burden on the businesses because they have to borrow additional capital to purchas e equipment that meet s new or amended standards, compared to the case of where there are no new or amended standards. Lower electricity price rates result in lower savings in energy costs and, consequently, lower LCC savings and higher PBPs

. Discount rates and average electricity price rates associated with different types of businesses are presented in TSD chapter 8.

In the food- retail sector, small grocery and convenience stores and convenience stores with gas stations face similar discount rates, which are higher than the discount rates for large grocery stores (supermarkets) and multi- line retail stores (supercenters). However, convenience stores with gasoline station face a lower electricity price rate than small grocery and convenience stores. Even though large grocery stores and multi- line retail stores face lower electricity price rates, the far higher discount rates faced by convenience stores and small grocery stores make them likely to be disproportionally disadvantaged.

In the foodservice sector , full

- service restaurant face higher discount rates but lower electricity price rates compared to limited

- service restaurants. In this case, it is a toss -up between

full

- service and limited

- service restaurants. Full

- service restaurants were chosen over li mited

11 -6

### service restaurants because a greater share of firms in full

- service restaurant category can be classified under the SBA definition of small business concerns (compare data in Table 11.2.5 and

Table 11.2.6 ).

When the full-service restaurants and gasoline stations with conven ience stores are compared to one another, full- service restaurants face a far higher discount rate, even though they face slightly lower electricity price rates. However, full

- service restaurants use only limited types of commercial refrigeration equipment and some of the large types of equipment such as VOP.RC.M and SOC.RC.M are almost never seen in restaurants. Therefore, DOE identified one subgroup each in the food- retail and foodservice sectors for LCC subgroup analysis . In the foodretail sector, gasol ine stations with convenience stores were selected , and in the foodservice

sector, full

- service restaurant were selected for LCC subgroup analysis.

DOE carried out two LCC subgroup analyses, one each for full- service restaurants and gasoline stations with convenience stores, by using the LCC spreadsheet described in TSD chapter 8, but with certain modifications. The input for business type was fixed to the identified subgroup, which ensured that the discount rates and electricity price rates associated with only that subgroup were selected in the Monte Carlo simulations (see TSD chapter 8). The discount rates for these small businesses were increased by adding the small firm premium to the weighted average cost of capital (see TSD chapter 8 for details). Ano ther major change from the LCC analysis was an added assumption that the subgroups do not have access to national accounts, which results in higher distribution channel markups for the subgroups, leading to higher equipment purchase prices. Apart from these changes, all other inputs for LCC subgroup analysis are same as those in the LCC analysis described in TSD chapter 8.

11.3 LIFE

- CYCLE COST AND PAYBACK PERIOD RESULTS FOR SMALL

BUSINESS SUB

- GROUP S

Table 11.3.1 presents the comparison of mean LCC savings for the small business subgroup in foodservice sector (full- service restaurants) with the national average values (LCC savings results from TSD chapter 8). The results are presented only for self -contained equipment

classes because full

- service restaurants that are small businesses generally do not use remote condensing equipment. In almost all cases for TSL 1 through TSL 4, the small business subgroup shows greater LCC savings than the national group.

It is uncommon to find display type equipment in small full- service restaurants. An overwhelming majority of commercial refrigeration equipment in small restaurants is comprised of solid door refrigerators and freezers that are used for food storage in the kitchen.. Table 11.3.3 presents the comparison of median PBPs for the small business subgroup in foods ervice sector with national median values (median PBPs from TSD chapter 8). The PBP values are higher for the small business subgroup in all cases, which is consistent with the decrease in LCC savings.

11 -7

### Table 11.3.1 Comparison of Mean LCC Savings for the Full- service Restaurants Subgroup with the Savings for All CRE Customers

Equipment

Class Category Mean LCC Savings

2013$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.SC.M Small Business

--- --- --- (57) (1,508)

All Business Types --- --- --- (54) (1,384)

VCT.SC.M Small Business 0 299 330 280 (1,391)

All Business Types (10) 214 226 163 (1,541)

VCT.SC.L Small Business 3,073 5,868 6,254 4,163 4,163

All Business Types 2,503 4,709 5,001 2,812 2,812

VCT.SC.I Small Business 34 34 34 (12) (2,706)

All Business Types 18 18 18 (68) (2,834)

VCS.SC.M Small Business 375 870 652 632 (1,031)

All Business Types 223 518 363 305 (1,428)

VCS.SC.L Small Business 979 971 999 1,000 (936)

All Business Types 588 550 507 495 (1,640)

VCS.SC.I Small Business 81 257 321 321 (2,241)

All Business Types 41 114 113 113 (2,710)

SOC.SC.M Small Business

--- --- --- (74) (1,952)

All Business Types --- --- --- (209) (2,204)

SVO.SC.M Small Business

--- --- --- 53 (871)

All Business Types --- --- --- 6 (917)

HZO.SC.M Small Business

--- 92 92 33 (1,097)

All Business Types --- 55 55 (4) (1,154)

HZO.SC.L Small Business

--- --- --- --- --- All Business Types --- --- --- --- ---

HCT.SC.M Small Business 81 216 137 85 (546)

All Business Types 66 165 101 43 (599)

HCT.SC.L Small Business 687 707 487 468 (319)

All Business Types 428 435 293 248 (613)

HCT.SC.I Small Business

--- --- --- --- (1,081)

All Business Types --- --- --- --- (1,240)

HCS.SC.M Small Business 23 38 48 38 (477)

11 -8All Business Types 12 17 15 5 (568)

HCS.SC.L Small Business 55 91 127 133 (381)

All Business Types 31 50 64 33 (590)

PD.SC.M Small Business 32 267 280 268 (1,081)

All Business Types 8 163 165 150 (1,252)

* Only self

- contained equipment have been shown for this subgroup analysis because the remote condensing equipment is not generally used by small full

- service restaurants. ** Values in parentheses are negative values. Negative percentage values imply decrea se in LCC savings and positive percentage values imply increase in LCC savings.

†TSLs 1 through 4 for this equipment class are associated with the baseline efficiency level. Hence, the LCC savings are shown as zero.

11 -9

### Table 11.3.2 Comparison of Median Payback Periods for the Small Business Subgroup in

the Foodservice Sector with National Median Values

Equipment

Class* Category Mean Payback Period

years
TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.SC.M Small Business

--- --- --- 54.1 541.3 All Business Types

--- --- --- 63.1 593.2

VCT.SC.M Small Business 12.9 4.1 4.5 5.9 64.8 All Business Types 23.4 4.8 5.3 7.0 96.2

VCT.SC.L Small Business 0.4 0.7 0.9 4.0 4.0 All Business Types 0.5 0.8 1.1 4.7 4.7

VCT.SC.I Small Business 5.8 5.8 5.8 12.4 310.0 All Business Types 7.2 7.2 7.2 16.2 663.6

VCS.SC.M Small Business 0.4 0.5 1.2 2.1 22.4 All Business Types 0.5 0.6 1.4 2.6 48.0

VCS.SC.L Small Business 0.5 1.1 2.0 2.2 19.2 All Business Types 0.6 1.3 2.5 2.7 31.8

VCS.SC.I Small Business 2.1 2.9 3.9 3.9 91.7 All Business Types 2.6 3.6 5.0 5.0 183.7

SOC.SC.M Small Business

--- --- --- 15.5 221.7 All Business Types

--- --- --- 28.7 25.3

SVO.SC.M Small Business

--- --- --- 8.9 124.3

All Business Types

--- --- --- 10.9 151.6

HZO.SC.M Small Business

--- 5.7 5.7 9.5 166.7 All Business Types

--- 6.9 6.9 11.8 194.7

HZO.SC.L Small Business

--- --- --- --- --- All Business Types

--- --- --- --- ---

HCT.SC.M Small Business 2.1 4.0 4.7 7.5 33.9 All Business Types 2.5 4.7 5.8 9.2 46.6

HCT.SC.L Small Business 1.5 1.6 2.0 2.9 14.0 All Business Types 1.8 2.0 2.5 3.6 19.5

HCT.SC.I Small Business

--- --- --- --- 176.3 All Business Types

--- --- --- --- 23.8

HCS.SC.M Small Business 2.3 2.9 4.2 5.4 136.0 All Business Types 2.9 3.7 5.5 7.5 680.6

HCS.SC.L Small Business 1.1 1.4 2.1 4.7 27.9 All Business Types 1.4 1.7 2.5 6.2 68.9

PD.SC.M Small Business 6.9 4.5 4.7 5.0 63.3 All Business Types 9.3 5.3 5.6 6.0 102.2



### Table 11.3.4 presents the comparison of mean LCC savings for the small business subgroup in food- retail sector (convenience stores with gasoline stati ons) with the national average values (LCC savings results from TSD chapter 8). This comparison shows mixed results with higher LCC savings for the subgroup in some instances and lower in others. The higher LCC savings for the subgroup are exhibited in the case of large remote condensing display cases such as VOP.RC.M, VOP.RC.L, VCT.RC.M, VCT.RC.L, SVO.RC.M, and SOC.RC.M. This equipment was assumed to have an average lifetime to be 15 years (see TSD chapter 8 for discussion on equipment lifetime assumptions). In general, longer the equipment lifetime, the higher the LCC savings because of a l onger available timeframe to offset the initial cost

increases by savings in energy costs.

11

- 10

### Table 11.3.6 presents the comparison of median PBPs for small business subgroup in foodservice sector with national median values (median PBPs from TSD chapte r 8). The PBP values are higher in the small business subgroup in all instances , including instances in which the LCC savings for the subgroup are higher compared to national average values. This is an expected outcome because the PBP values are obtained b y dividing the increase in equipment installed cost by the first year savings in operating costs and are not affected by the higher average lifetime of the equipment in the convenience stores with gas stations.

Table 11.3.3 Comparison of LCC Savings for the Small Business Subgroup in the Food

Retail Sector with the National Average Values

Equipment

Class Category Mean LCC Savings*

2012$

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

VOP.RC.M Small Business

--- --- 1,334 299 (4,003)

All Business Types

--- --- 922 (5) (4,203)

VOP.RC.L Small Business

--- --- 82 2 (6,703)

All Business Types

--- --- 53 (148) (6,701)

VOP.SC.M Small Business

--- --- --- (62) (1,485)

All Business Types

--- --- --- (54) (1,384)

VCT.RC.M Small Business

--- --- 636 135 (4,544)

All Business Types

--- --- 542 41 (4,937)

VCT.RC.L Small Business 751 751 634 213 (5,486)

All Business Types 647 647 526 93 (6,036)

VCT.SC.M Small Business (8) 214 229 169 (1,479)

All Business Types (10) 214 226 163 (1,541)

VCT.SC.L Small Business 2,489 4,699 4,988 2,878 2,878

All Business Types 2,503 4,709 5,001 2,812 2,812

VCT.SC.I Small Business 19 19 19 (59) (2,732)

All Business Types 18 18 18 (68) (2,834)

VCS.SC.M Small Business 299 696 511 476 (1,157)

All Business Types 223 518 363 305 (1,428)

VCS.SC.L Small Business 785 765 763 758 (1,190)

All Business Types 588 550 507 495 (1,640)

VCS.SC.I Small Business 62 189 224 224 (2,354)

All Business Types 41 114 113 113 (2,710)

SVO.RC.M Small Business 966 966 966 340 (2,148)

All Business Types 564 564 564 (19) (2,691)

SVO.SC.M Small Business

--- --- --- 5 (891)

All Business Types

--- --- --- 6 (917)

SOC.RC.M Small Business

--- --- --- (93) (2,058)

11

- 11All Business Types

--- --- --- (128) (2,268)

HZO.RC.M** Small Business

--- --- --- --- (2,015)

All Business Types

--- --- --- --- (2,180)

HZO.RC.L** Small Business

--- --- --- --- (3,880)

All Business Types

--- --- --- --- (4,249)

HZO.SC.M Small Business

--- 55 55 (3) (1,114)

All Business Types

--- 55 55 (4) (1,154)

HZO.SC.L** Small Business

--- --- --- --- ---
All Business Types

--- --- --- --- ---

HCT.SC.M Small Business 62 151 92 35 (591)

All Business Types 66 165 101 43 (599)

HCT.SC.L Small Business 535 548 374 343 (451)

All Business Types 428 435 293 248 (613)

HCT.SC.I Small Business

--- --- --- --- (1,106)

All Business Types

--- --- --- --- (1,240)

HCS.SC.M Small Business 18 28 32 23 (498)

All Business Types 12 17 15 5 (568)

HCS.SC.L Small Business 44 71 97 87 (453)

All Business Types 31 50 64 33 (590)

PD.SC.M Small Business 14 186 190 177 (1,159)

All Business Types 8 163 165 150 (1,252)
* Values in parentheses are negative values. ** TSLs 1 through 4 for equipment classes HZO.RC.M, HZO.RC.L and HZO.SC.L are associated with the baseline efficiency level. Hence, the LCC savings are shown as ‘NA.’

11

- 12

### Table 11.3.4 Comparison of Median Payback Periods for the Small Business Subgroup in

the Food- Retail Sector with the National Median Values

Equipment

Class Category Median Payback Period

years
TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M Small Business

--- --- 5.5 9.0 25.1 All Business Types

--- --- 5.7 9.9 34.1

VOP.RC.L Small Business

--- --- 5.8 10.2 195.3 All Business Types

--- --- 6.1 11.3 310.0

VOP.SC.M Small Business

--- --- --- 69.5 513.9

All Business Types

--- --- --- 63.1 593.2

VCT.RC.M Small Business

--- --- 1.9 5.8 308.8 All Business Types

--- --- 2.1 6.6 364.7

VCT.RC.L Small Business 1.7 1.7 2.5 5.7 171.0 All Business Types 1.8 1.8 2.7 6.3 194.7

VCT.SC.M Small Business 18.2 4.5 5.0 6.5 82.7 All Business Types 23.4 4.8 5.3 7.0 96.2

VCT.SC.L Small Business 0.4 0.8 1.0 4.4 4.4 All Business Types 0.5 0.8 1.1 4.7 4.7

VCT.SC.I Small Business 6.6 6.6 6.6 14.3 531.1 All Business Types 7.2 7.2 7.2 16.2 663.6

VCS.SC.M Small Business 0.5 0.6 1.3 2.3 26.4 All Business Types 0.5 0.6 1.4 2.6 48.0

VCS.SC.L Small Business 0.5 1.2 2.2 2.4 22.2 All Business Types 0.6 1.3 2.5 2.7 31.8

VCS.SC.I Small Business 2.3 3.2 4.3 4.3 118.4 All Business Types 2.6 3.6 5.0 5.0 183.7

SVO.RC.M Small Business 5.4 5.4 5.4 8.4 20.7 All Business Types 6.2 6.2 6.2 10.4 29.9

SVO.SC.M Small Business

--- --- --- 10.0 150.5 All Business Types

--- --- --- 10.9 151.6

SOC.RC.M Small Business

--- --- --- 23.2 656.6 All Business Types

--- --- --- 38.0 114.1

SOC.SC.M Small Business

--- --- --- 18.2 265.4 All Business Types

--- --- --- 28.7 25.3

HZO.RC.M Small Business

--- --- --- --- --- All Business Types

--- --- --- --- ---

HZO.RC.L Small Business

--- --- --- --- 59.8 All Business Types

--- --- --- --- 288.9

HZO.SC.M Small Business

--- 6.4 6.4 10.8 174.0 All Business Types

--- 6.9 6.9 11.8 194.7

HZO.SC.L Small Business

--- --- --- --- --- All Business Types

--- --- --- --- ---

HCT.SC.M Small Business 2.3 4.4 5.4 8.5 40.5 All Business Types 2.5 4.7 5.8 9.2 46.6

HCT.SC.L Small Business 1.7 1.8 2.3 3.3 15.6 All Business Types 1.8 2.0 2.5 3.6 19.5

HCT.SC.I Small Business

--- --- --- --- 208.9 All Business Types

--- --- --- --- 23.8

HCS.SC.M Small Business 2.6 3.3 4.7 6.2 151.6

All Business Types 2.9 3.7 5.5 7.5 680.6

11

- 13HCS.SC.L Small Business 1.3 2.3 2.3 5.3 33.7 All Business Types 1.4 2.5 2.5 6.2 68.9

PD.SC.M Small Business 8.0 5.2 5.2 5.6 78.9 All Business Types 9.3 5.6 5.6 6.0 102.2



### The LCC subgroup analysis results show that the identified subgroups in foodservice and food-retail sector face slightly lower LCC savings and higher PBP values. Comparison of LCC savings values at TSL 4 shows the decrease in LCC savings is less than 15 percent for a majority of the equipment used in small businesses. In all cases at TSL 4 , and for both the subgroups, the LCC savings are positive, as are the national average values, indicating that although LCC savings are lower for the customer subgroups , customers still save money over the lifetimes of the equipment. Therefore, DOE concludes that the identified subgroups do not face a substantial disadvantage when compared with an average customer of commercial refrigeration equipment.

11

- 14

### REFERENCES

1. Title 13, Code of Federal Regulations, Chapter I

– Small Business Administration, Part 121

– Small Business Administration, Subpart A– Size Eligibility Provisions and Standards

. May 4, 2005.

2. U.S. Census Bureau. 2007 Economic Census, Sector 44: Retail Trade: Subject Series–Estab & Firm Size: Summary Statistics for Single Unit and Multiunit Firms for the United States: 2007

. (Last a ccessed Oct ober 20, 2011. ) < http://factfinder.ce nsus.gov/servlet/IBQTable?_bm=y&- geo_id=&-ds_name=EC0744SSSZ3&-_lang=en >

11

- 15

### 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........................................................................................ 12-1

12.2.2 Phase II: Industry Cash -Flow Analysis and Interview Guide ............................... 12-2

12.2.2.1 Industry Cash- Flow Analysis ................................................................. 12-2

12.2.2.2 Interview Guides .................................................................................... 12-3

12.2.3 Phase III: Subgroup Analysis................................................................................ 12-3

12.2.3.1 Manufacturer Interviews ........................................................................ 12-3

12.2.3.2 Revised Industry Cash

- Flow Analysis ................................................... 12-3

12.2.3.3 Manufacturer Subgroup Analysis .......................................................... 12-4

12.2.3.4 Manufacturing Capacity Impact ............................................................ 12-5

12.2.3.5 Employment Impact ............................................................................... 12-5

12.2.3.6 Cumulative Regulatory Burden ............................................................. 12-5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES ............................................. 12-6

12.3.1 Enforcement .......................................................................................................... 12-6

12.3.2 Certification, Compliance, and Enforcement Costs .............................................. 12-6

12.3.3 Disproportionate Impact on Small Businesses ..................................................... 12-6

12.3.4 Potential Loss of Product Utility and Decrease in Food Safety ............................ 12-7

12.4 GRIM INPUTS AND ASSUMPTIONS ........................................................................... 12-7

12.4.1 Overview of the GRIM ......................................................................................... 12-7

12.4.2 Sources for GRIM Inputs ...................................................................................... 12-8

12.4.2.1 Corporate Annual Reports ..................................................................... 12-8

12.4.2.2 Standard and Poor Credit Ratings .......................................................... 12-9

12.4.2.3 Shipment Model ..................................................................................... 12-9

12.4.2.4 Engineering Analysis ............................................................................. 12-9

12.4.2.5 Manufacturer Interviews ........................................................................ 12-9

12.4.3 Financial Parameters ........................................................................................... 12-10

12.4.4 Corporate Discount Rate ..................................................................................... 12-10

12.4.5 Trial Standard Levels .......................................................................................... 12-12

12.4.6 NIA Shipments.................................................................................................... 12-13

12.4.7 Manufacturer Production Costs .......................................................................... 12-14

12.4.8 Conversion Costs ................................................................................................ 12-19

12.4.8.1 Capital Conversion Costs ..................................................................... 12-19

12.4.8.2 Product Conversion Costs .................................................................... 12-20

12.4.9 Markup Scenarios ............................................................................................... 12-21

12.4.9.1 Preservation of Gross Margin Percentage Scenario ............................. 12-21

12.4.9.2 Preservation of Operating Profit Scenario ........................................... 12-21

12.5 INDUSTRY FINANCIAL IMPACTS ........................................................................... 12-22

12.5.1 Introduction ......................................................................................................... 12-22

12.5.2 Commercial Refrigeration Industry Financial Impacts ....................................... 12-23

12.6 IMPACTS ON SMALL BUSINESS MANUFAC TURERS .......................................... 12-25

12 -i

### 12.7 OTHER IMPACTS ......................................................................................................... 12-26

12.7.1 Employment ........................................................................................................ 12-26

12.7.2 Production Capacity ............................................................................................ 12-28

12.7.3 Cumulative Regulatory Burden .......................................................................... 12-28

12.7.3.1 DOE Regulations for Other Products Produced by Commercial Refrigeration Equipment Manufacturers ............................................................ 12-28

12.7.3.2 Other DOE and Federal Actions Affecting the Commercial Refrigeration Equipment Industry ............................................................................................. 12-29

12.7.3.3 Other Regulations That Could Impact Commercial Refrigeration Equipment Manufacturers ................................................................................... 12-30

12.8 CONCLUSION ............................................................................................................... 12-31

REFERENCES ........................................................................................................................ 12-33

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by This Rulemaking ........................................................................................................ 12-4

Table 12.4.1 GRIM Financial Parameters Based on 2004– 2010 Weighted Company Financial

Data .................................................................................................................. 12-10 Table 12.4.2 Cost of Equity Calc ulation .................................................................................. 12-11

Table 12.4.3 Cost of Debt Calculation..................................................................................... 12-12 Table 12.4.4 Trial Standard Levels for Energy Efficiency Analysis of Commercial Refrigeration Equipment ........................................................................................................ 12-13 Table 12.4.5 Manufacturer Production Cost Breakdown (2012$) for VOP.RC.M ................. 12-14 Table 12.4.6 Manufacturer Production Cost Breakdown (2012$) for VOP.RC.L .................. 12-14

Table 12 .4.7 Manufacturer Production Cost Breakdown (2012$) for VOP.SC.M.................. 12-14 Table 12.4.8 Manufacturer Production Cost Breakdown (2012$) for VCT.RC.M ................. 12-15 Table 12.4.9 Manufacturer Production Cost Breakdown (2012$) for VCT.RC.L .................. 12-15 Table 12.4.10 Manufacturer Production Cost Breakdown (2012$) for VCT.SC.M ................ 12-15 Table 12.4.11 Manufacturer Production Cost Breakdown (2012$) for VCT.SC.L ................. 12-15

Table 12 .4.12 Manufacturer Production Cost Breakdown (2012$) for VCT.SC.I .................. 12-15 Table 12.4.13 Manufacturer Production Cost Breakdown (2012$) for VCS.SC.M ................ 12-16 Table 12.4.14 Manufacturer Production Cost Breakdown (2012$) for VCS.SC.L ................. 12-16 Table 12.4.15 Manufacturer Production Cost Breakdown (2012$) for VCS.SC.I .................. 12-16 Table 12.4.16 Manufacturer Production Cost Breakdown (2012$) for SVO.RC.M ............... 12-16

Table 12.4.17 Manufacturer Production Cost Breakdown (2012$) for SVO.SC.M................ 12-16 Table 12.4.18 Manufacturer Production Cost Breakdown (2012 $) for SOC.RC.M ............... 12-17 Table 12.4.19 Manufacturer Production Cost Breakdown (2012$) for HZO.RC.M ............... 12-17 Table 12.4.20 Manufacturer Production Cost Breakdown (2012$) for HZO.RC.L ................ 12-17 Table 12.4.21 Manufacturer Production Cost Breakdown (2012$) for HZO.SC.M ............... 12-17 Table 12.4.22 Manufacturer Production Cost Breakdown (2012$) for HZO.SC.L ................ 12-17 Table 12.4.23 Manufacturer Production Cost Breakdown (2012$) for HCT.SC.M ................ 12-17

Table 12 .4.24 Manufacturer Production Cost Breakdown (2012$) for HCT.SC.L ................. 12-18 Table 12.4.25 Manufacturer Production Cost Breakdown (2012$) for HCT.SC.I .................. 12-18 Table 12.4.26 Manufacturer Production Cost Breakdown (2012$) for HCS.SC.M ................ 12-18 Table 12.4.27 Manufacturer Production Cost Breakdown (2012$) for HCS.SC.L ................. 12-18

12

- ii

### Table 12.4.28 Manufacturer Production Cost Breakdown (2012$) for PD.SC.M .................. 12-18

Table 12.4.29 Manufacturer Production Cost Breakdown (2012$) for SOC.SC.M ................ 12-19 Table 12.4.30 Industry Cumulative Capital Conversion Cost ................................................. 12-20 Table 12.4.31 Industry Cumulative Product Conversion Cost ................................................ 12-20 Table 12.5.1 Preservation of Gross Margin Percentage Scenario Changes in INPV for Commercial Refrigeration Equipment ............................................................. 12-23 Table 12.5.2 Preservation of Operating Profit Scenario Changes in INPV for Commercial Refrigeration Equipment .................................................................................. 12-23 Table 12.7.1 Potential Changes in the Number of Commercial Refrigeration Equipment

Production Workers in 2017 ............................................................................ 12-27 Table 12.8.1 TSLs for the Commercial Refrigeration Equipment Rulemaking . Error! B ookmark

not defined.

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow ............................................................ 12-8 Figu re 12.5.1 Annual Industry Net Cash Flows for Commercial Refrigeration Equipment (Preservation of Gross Margin Percentage Markup Scenario) ........................ 12-24 Figure 12.5.2 Annual Industry Net Cash Flows for Commercial Refrigeration Equipment (Preservation of Operating Profit Markup Scenario) ....................................... 12-25

12

- iii

### CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of Energy (DOE or the Department ) 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. 6313(a)(6)(B)(i)) The law also calls for an assessment of the impact of any les sening of competition as determined in writing by the Attorney General. Id . DOE conducted a manufacturer impact analysis (MIA) to estimate the financial impact of amended energy conservation standards on manufacturers of commercial refrigeration equipment, and assessed the impact of such standards on direct 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), an indus try cash

- flow model adapted for each product in this rulemaking. The GRIM inputs include information on industry cost structure, shipments, and pricing strategies. The GRIM’s key output is the industry net present value (INPV). The model estimates the fina ncial impact of more -stringent energy conservation standards for each product 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 characteristics, and 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 preparing an industry charact erization for the commercial refrigeration industry, including data on sales volumes, pricing, employment, and financial structure. In Phase II, “Industry Cash Flow,” DOE used the GRIM to assess the potential impacts of amended energy conservation

standard s on manufacturers. DOE also developed interview guides to gather information on the potential impacts on these manufacturers. In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers representing a broad cross

- section of the commercial refr igeration industry. Additionally, DOE refined its analysis in the GRIM, develo ped additional analyses for subgroups that required special consideration, and incorporated qualitative data from interviews into its analysis.

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the commercial refrigeration industry

that built upon the Market and Technology A ssessment prepared for this rulemaking (refer to chapter 3 of the technical support document ( TSD) ). Before initiating the detailed impact studies, DOE collected information on the present and past structure and market characteristics of the commercial refrigeration industry. This information included shipments, manufacturer markups,

and the cost structu re s of various manufacturers. The industry profile included : (1) further detail on the overall market and product characteristics; (2) estimated manufacturer market shares; (3) financial parameters such as net plant, property, and equipment (PPE) ; selling, general and

12 -1

### administrative (SG&A) expenses; cost of goods sold, etc.; and (4) trends in the number of firms,in the market, and in product characteristics. The industry profile additionally included a topdown cost analysis of commercial refrigeration equ ipment manufacturers

, which DOE used to derive the preliminary financial inputs for the GRIM ( e.g. , revenues, depreciation, SG&A, and research and development (R&D) expenses).

DOE also used public information to further calibrate its initial characterizat ion of the industry, including Securities and Exchange Commission (SEC) 10- K reports, 1 

### Standard & Poor’s (S&P) stock reports, 2 

### market research tools ( i.e. , Hoovers 3 

### ), corporate annual reports, and the U.S. Census Bureau’s 2011 Annual Survey of Manufacturers. 4 

### DOE also characterized these industries using information from its engineering analysis and the life- cycle cost analysis.

12.2.2 Phase II: Industry Cash -Flow Analysis and Interview Guide

Phase II focused on the financial impacts of potential amended energy conservation standards on manufacturers of commercial refrigeration equipment. More- stringent energy conservation standards can affect manufacturer cash flows in three distinct ways: (1) create a

need for increased investment ; (2) raise productio n costs per unit; and (3) alter revenue due to higher per -unit prices and/or possible changes in sales volumes. To quantify these impacts, DOE used the GRIM to perform a cash -flow analysis for the commercial refrigeration industry. In performing these anal yses, DOE used the financial values derived during Phase I and the shipment scenarios used in the national impact analysis (NIA). In Phase II, DOE performed these preliminary industry cash -flow analyses and prepared written guides for manufacturer

intervie ws.

12.2.2.1 Industry Cash -Flow Analysis

The GRIM uses several factors to determine a series of annual cash flows from the announcement year of amended energy conservation standards until 30 years after the standards’ compliance date. These factors include annual e xpected revenues, costs of goods sold, SG&A, taxes, and capital expenditures related to the amended standards. Inputs to the GRIM include

manufacturer production costs (MPCs) , markup assumptions, and shipments forecasts developed in other analyses. DOE der ived the manufacturing costs from the engineering analysis and information provided by the industry. It estimated typical manufacturer markups from public financial reports and interviews with manufacturers. DOE developed alternative markup

scenarios for t he GRIM based on discussions with manufacturers. DOE’s shipments analysis, presented in chapter 9 of the TSD, provided the basis for the shipment projections in the GRIM. The financial parameters were developed using publicly available manufacturer data and were revised with information submitted confidentially during manufacturer interviews. The GR IM

results are then compared to base-case projections for the industry. The financial impact of amended energy conservation standards is the difference between t he discounted annual cash

flows in the base case and standards case at each TSL.

12 -2

### 12.2.2.2 Interview Guides

During Phase II of the MIA, DOE distributed an interview guide to interviewees. The interview guide provided a starting point for identifying relevant issues and impacts of amended energy conservation standards on individual manufacturers or subgroups of manufacturers. Information received from these meetings is protected by non- disclosure agreements and resides with DOE’s contractors. The MIA interview topics included: (1) key i ssues to this rulemaking; (2) company overview and organizational characteristics; (3) engineering analysis follow- up; (4) manufacturer markups and profitability; (5) shipment projections and market shares; (6) distribution channels; (7) financial parameters; ( 8) conversion costs; (9 ) cumulative regulatory burden; (10 ) direct employment impact assessment; (1 1) exports, foreign competition, and outsourcing; (12) consolidation; and (13 ) impacts on small businesses.

12.2.3 Phase III: Subgroup Analysis

Phase III focused on refining the GRIM based on industry interviews, finalizing cashflow results, analyzing sub-groups, and considering other key MIA factors, such as production capacity, employment, and cumulative regulation burden impacts. While conducting the MIA, DOE interviewed a representative cross -section of commercial refrigeration equipment

manufacturers. The MIA interviews broadened the discussion to include business- related topics. DOE sought feedback from industry on the approaches used in the GRIM and as well as key issues and concerns. During interviews, DOE defined one manufacturer subgroup, small manufacturers, that could be disproportionately impacted by amended energy conservation

standards.

12.2.3.1 Manufactur er Int erviews

The information gathered in Phase I and the cash- flow analysis performed in Phase II are supplemented with information gathered from manufacturer interviews in Phase III. The interview process provided an opportunity for interested parties to express their views on important issues privately, allowing confidential or sensitive information to be considered in the rulemaking process.

DOE used these interviews to tailor the GRIM to reflect financial characteristics unique

to the commercial refrigeration equipment industry . Interviews were scheduled well in advance to provide every opportunity for key individuals to be available for comment. Although a written response to the questionnaire was acceptable, DOE sought interactive interviews, wh ich helped clarify responses and identify additional issues. The resulting information provided valuable inputs to the GRIM developed for the equipment classes.

12.2.3.2 Revised Industry Cash -Flow Analysis

In Phase II of the MIA, DOE provided manufacturers with pre liminary GRIM input financial figures for review and evaluation. During the Phase III interviews, DOE requested comments on the values it selected for the parameters. DOE revised its industry cash

- flow model

based on this feedback. Section 12.4.3 provides more information on how DOE calculated the

parameters.

12 -3

### 12.2.3.3 Manufacturer Subgroup Analysis

Using average cost ass umptions to develop an industry cash

- fl ow estimate may not adequately assess differential impacts of amended energy conservation standards among manufacturer subgroups. For example, small manufacturers, niche players, or manufacturers exhibiting a cost structure that largely differs from the in dustry average could be more negatively affected. To address this possible impact, DOE used the results of the industry characterization analysis in Phase I to group manufacturers that exhibit similar characteristics.

During the interviews, DOE discussed the potential subgroups and subgroup members it identified for the analysis. DOE asked manufacturers and other interested parties to suggest what subgroups or characteristics are the most appropriate to analyze. As described in section 12.2.3

, DOE presents the industry impacts on commercial refrigeration equipment manufacturers as a

whole because most of the equipment class es repre sent the same market served by the same manufacturers. However, as discussed below, DOE identified one manufacturer subgroup that warranted a separate impact analysis: small manufacturers.

Small

- Business Manufacturer Subgroup

DOE investigated whether small business manufacturers should be analyzed as a manufacturer subgroup. DOE used the Small Business Administration (SBA) small business size standards effective on November 5, 2010, as amended, and the North American Industry Classification System (NAICS) c ode, presented in Table 12.2.1 , to determine whether any small entities would be affected by the rulemaking. 5 

### For the equipment classes under review, the SBA bases its small business definition on the total number of employees for a business, its subsidiaries, and its parent companies. An aggregated business entity with fewer employees than

the listed limit is considered a small business.

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS

Air

- conditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing N/A 750 333415



### DOE used publicly available and proprietary information to identify potential small manufacturers. DOE’s research involved industry trade association membership directories (including American Heating and Refrigeration Institute ( AHRI ) and North American Association of Food Equipment Manufacturers ( NAFEM )), product databases ( e.g. , AHRI Directory, NSF International listings, the SBA Database), individual company websites, and market research tools ( e.g. , Hoovers.com) to create a list of companies that manufacture or sell equipment covered by this rulemaking. DOE also asked stakeholders and industry representatives if they were aware of any other small manufacturers during manufacturer

in terviews and at previous DOE public meetings. DOE screened out companies that did not offer equipment covered by this rulemaking, did not meet the definition of a “small business,” or are foreign owned and operated.

12 -4

### Based on this analysis

, DOE identified 32 commercial refrigeration equipment

manufacturers that are small businesses. DOE made an effort to contact small businesses to solicit feedback on the potential impacts of energy conservation standards. The businesses replied with varying amounts of information in written responses and/or interviews. In addition to posing a subset of modified MIA interview questions, DOE solicited data on differential impacts these companies might experience from amended energy conservation standards. Based on these interviews and industry research, DOE reports the potential impacts of this rulemaking

on small manufacturers in section 12.6

.

12.2.3.4 Manufacturing Capacity Impact

One significant outcome of amended energy conservation standards could be the obsolescence of existing manufacturing assets, including tooling and investment. The manufacturer interview guides have a series of questions to help identify impacts of amended standards on manufacturing capacity, specifically, capacity utilization and plant location

dec isions in the United States and North America, with and without amended standards; the ability of manufacturers to upgrade or remodel existing facilities to accommodate the new requirements; the nature and value of any stranded assets; and estimates for an y one- time changes to existing PPE. DOE’s estimates of the one

- time capital changes and stranded assets

affect the cash flow estimates in the GRIM. These estimates can be found in section 12.4.8

. DOE’s discussion of the capacity impact can be found in section 12.7.2

.

12.2.3.5 Employment Impact

The impac t of amended energy conservation standards on employment is an important consideration in the rulemaking process. To assess how domestic direct employment patterns might be affected, the interviews explored current employment trends in the commercial

refri geration industry. The interviews also solicited manufacturer views on changes in employment patterns that may result from more

- stringent standards. The employment impacts section of the interview guide focused on current employment levels associated with manufacturers at each production facility, expected future employment levels with and without amended energy conservation standards, and differences in workforce skills and issues related to the retraining of employees. The employment impacts are reported in section 12.7.1

.

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to amended energy conservation standards and other regulatory actions affecting the same products. DOE analyzed the impact on manufacturers of multiple, product-specific regulatory actions. Based on its own research and discussions with manufacturers, DOE identified regulations relevant to commercial refrigeration equipment, such as State regulations and other Federal regulations that impact other products made by the same manufacturers. Discussion of the cumulative regulatory burden can

be found in section 12.7.3

.

12 -5

### 12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company regarding the energy conservation standard rulemaking?” This question prompts manufacturers to identify the issues they feel DOE should explore and discuss further during the interview. The following sections describe the most significant issues identified by manufacturers. These summaries are prov ided in aggregate to protect manufacturer confidentiality.

12.3.1 Enforcement

Interviewed manufacturers expressed concern about the enforcement of an energy efficiency standard for commercial refrigeration equipment. Manufacturers believe that

insufficient enfor cement will lead to market distortions, as companies that make the necessary investments to meet standards and compliance requirements would be at a distinct pricing disadvantage to unscrupulous competitors that do not fully comply. The manufacturers reque sted that DOE take the enforcement action necessary to maintain a level playing field and to eliminate non-compliant products from the market.

12.3.2 Certification

, Compliance , and Enforcement Costs

Nearly all manufacturers expressed concern over certification, compliance, and enforcement ( CC&E ) costs. In particular, confusion over the definition of “basic model” and the implementation of alternative efficiency determination methods ( AEDMs ) has made it difficult for some manufacturers to anticipate their total te sting needs and total testing costs.

Manufacturers were concerned that CC&E requirements for commercial refrigeration equipment do not take into account the customized nature of the commercial refrigeration equipment industry. Manufacturers stated that their industry has a high level of end- user specification and low production volumes compared to other industries, such as residential refrigeration. As a result, the strictest interpretations of the CC&E requirements could lead to hundreds of thousands of tests per company. Additional clarification of how basic models and AEDMs apply to the commercial refrigeration equipment industry would help manufacturers understand the testing investments that will be necessary. DOE is aware of the confusion and issued a rulemaking for AEDM s on December 31 , 2012 to address these concerns. (78 FR 79579)

12.3.3 Disproportionate Impact on Small Businesses

Manufacturers noted that small businesses will be disproportionately impacted when compared to larger businesses. One manufacturer indicated that small and large manufacturers of the same equipment tend to have similar numbers of basic models, but large manufacturers offer a broader suite of products based on those basic models and have higher sales. Therefore, small

manufacturers will be at a disadvantage because they will need to spread both industry certification and conversion costs over a smaller number of shipments.

Also, small manufacturers indicated they have fewer resources to manage CC&E requirements. As a result, they will be forced to focus on compliance rather than innovation.

12 -6

### Small manufacturers believe that their large competitors will have greater resources to continue innovating while meeting amended energy conservation standards requirements.

12.3.4 Potential Loss of Prod uct Utility and Decrease in Food Safety

A majority of manufacturers expressed concern about the potential impact of energy conservation standards on product performance. Specifically, manufacturers serving the foodservice industry were concerned about the impacts on food safety, while manufacturers serving the food retail industry were concerned about the impacts on merchandising design.

One manufacturer of commercial refrigeration equipment for the foodservice industry summarized the challenge of amended e nergy conservation standards as “the design trade- off between product price, energy efficiency, and food safety.” In the foodservice industry, refrigeration equipment must maintain safe food temperatures despite frequent door openings in challenging enviro nments, such as kitchens with high temperatures and high humidity. The infiltration of warm, moist air places additional burden on the refrigeration equipment and increases energy usage. Manufacturers were concerned that more -efficient equipment would have trouble maintaining food safety in extreme, but not uncommon, conditions.

Manufacturers in the food retail market design their equipment to optimally present merchandise. Some manufacturers were concerned that energy conservation standards would limit their ability to tailor their commercial refrigeration equipment for specific merchandise. Specifically, manufacturers noted that the highly directional light from light- emitting diode ( LED ) bulbs could potentially provide lower quality lighting in certain display case applications where the product is presented in multiple layers, such as prepared food display cases. Additionally, manufacturers were concerned that higher efficiency designs could result in less desirable presentation for meats and increased icing on products. In general, more- efficient standards limit manufacturer options for optimizing the presentation features of equipment. Food retail customers such as supermarkets make purchasing decisions based on the various presentation features of com mercial refrigeration equipment offered by different manufacturers.

12.4 GRIM INPUTS AND ASSU MPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to amended energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM. Data and assumptions from these sources are then fed into an accounting model that calculates the industry cash flow both with and without amended energy conservation standards.

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4.1 , is an annual cash flow analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial

inform ation 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

to 2046 . The model calculates the INPV by summing the stream of annual discounted cash flows during this period and adding a discounted terminal value. 6

12 -7

### Figure 12.4.1 Using the GRIM t o Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares changes in INPV between the base

- case scenario and the standards- case scenario induced by amended energy conservation standards. The diff erence in INPV between the base case and the

standard s case(s) represents the estimated financial impact of the amended energy conservation

standard on manufacturers. Appendix 12A provides more technical details and user information

for the GRIM.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash flow. These sources include corporate annual reports, company profiles, census data, credit ratings, the shipments model, the engineering analysis, and the manufacturer in terviews.

12.4.2.1 Corporate Annual Reports

Corporate annual reports to the SEC (SEC 10-Ks) provided many of the initial financial inputs to the GRIM. These reports exist for publicly held companies and are freely available to the general public. DOE developed init ial financial inputs to the GRIM by examining the annual

SEC 10 -K reports filed by publicly traded manufacturers of commercial refrigeration equipment

. Since these companies do not provide detailed information about their individual product lines,

DOE used the financial information for the entire companies as its initial estimates of the financial parameters in the GRIM analysis. These figures were later revised using feedback from interviews to be representative of manufacturing for each product grouping. DOE used corporate annual reports to derive the following initial inputs to the GRIM:

• t ax rate

• w orking capital

• SG&A

• R&D

• d epreciation

• c apital expenditures

• n et PPE

12 -8

### 12.4.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. DOE relied on S&P reports to determine the industry’s average cost of debt when calculating the cost of capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the NIA. Chapter 9 of the TSD describes the methodology and analytical model DOE used to forecast shipments.

12.4.2.4 Engineering Analysis

The engineering analysis establishes the relationship between manufacturer selling price (MSP) and energy efficiency for the equipment covered in this rulemaking. DOE adopted a design option approach to develop cost-efficiency curves in its engineering analysis. DOE began its analysis by conducting industry research to select equipment classes in order to directly analyze, develop baseline unit specifications, and to select representative commercial refrigeration equipment for further analysis. Next, DOE determined efficiency levels based on the design options applicable to the specific equipment studied and the maximum technologically feasible efficiency level for each equipment class modeled. To develop cost estimates, DOE conducted a price analysis, based upon physical teardowns of selected units, cost estimates from publicly available sources, and price quotes from manufacturers. DOE then developed a cost

model to determine manufacturer production costs ( MPCs ). By applying derived manufacturer markups to the MPC, DOE calculated the MSP and constructed industry cost- efficiency curves.

See chapter 5 of the TSD for a complete discussion of the engineering analysis.

12.4.2.5 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative crosssection of manufacturers. DOE also interviewed manufacturers representing a significant portion of sales in every equipment class. During these discussions, DOE obtained information to determine and verify GRIM input assumptions in each industry. Key topics discussed during the

interviews and reflected in the GRIM include:

• capital conversion costs (one- time investments in PPE);

• product conversion costs (one-time investments in research, product development, testing, and marketing);

• product cost structure, or the portion of the MPCs related to materials, labor, overhead, and depreciation costs;

• MPCs estimated in the engineering analysis; and

• p ossib le profitability impacts.

12 -9

### 12.4.3 Financial Parameters

Table 12.4.1 provides financial parameters for seven large companies engaged in manufacturing and selling commercial refrigeration equipment . The val ues listed are averages

over a 7 -year period (2004 to 2010 ).

Table 12.4.1 GRIM Financial Parameters Based on 2004–2010 Weighted Company

Financial Data

Parameter Industry

W eighted Average Manufacturers

A B C D E F G

Tax Rate (% of Taxable Income) 25% 27.3% 21.4% 34.3% 16.2% 31.5% 30.3% 25.8%

Working Capital (% of Revenue) 9.2% 8.1% 22.4% 10.1%

- 7.9% 16.6% 10% 16.2%

SG&A (% of Revenue) 13.1% 11.5% 11.1% 19.3% 15.2% 23.1% 15.3% 5.1%

R&D (% of Revenues) 1.6% 3.1% 1.1% 1.3% 1.6% 0.5% 1.9% 0.6%

Depreciation (% of Revenues) 2% 2.3% 2.1% 1.4% 2.5% 2.3% 2.9% 0.6%

Capital Expenditures (% of Revenues) 1.7% 1.9% 1.9% 1.8% 1.6% 1.5% 2.8% 0.5%



### While most of these companies also manufacture products not covered by this rulemaking, DOE used these parameters as initial estimates. During interviews, manufacturers were asked to provide their own figures for the parameters listed in Table 12.4.1 . Where applicable, DOE adjusted the parameters in the GRIM using manufacturer feedback and market

share information.

12.4.4 Corporate Discount Rate

DOE used the weighted

- average cost of capital (WACC) as the discount rate to calculate the INPV. A company’s assets are financed by a combination of debt and equity. The WACC is the total cost of debt and equity weighted by their respective proportions in the capital s tructure of the industry. DOE estimated the WACC for the commercial refrigeration equipment industry based on several representative companies, using the following formula:

WACC = After -Tax Cost of Debt × ( Debt Ratio ) + Cost of Equity × ( 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 C apital A sset P ricing M odel (CAPM) pr ovides one widely used means to estimate the cost of equity. According to the CAPM, the cost of equity (expected return) is:

Cost of Equity = Riskless Rate of Return + β × Risk Premium

12

- 10

### W here:

Riskless Rate of R eturn = the rate of return on a “safe” benchmark investment, typically

considered the short- te rm Treasury Bill (T

- Bill) yield,

Risk P remium = the difference between the expected return on stocks and the riskless rate, and Beta (β) = the correlation between the mo vement 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 stock is less volatile than the market index

, while a Beta hi gher than one indicates that a stock is more volatile than the market index

.

DOE calculated that the industry average cost of equity for the commercial refrigeration industry is 14.7 percent ( Table 12.4.2 ).

Table 12.4.2 Cost of Equity Calculation

Parameter Industry Weighted Average A B C D E F G

(1) Average Beta 1.55 1.30 2.70 0.76 1.77 0.91 1.29 1.29 (2) Yield on 10

- Year (1928

- 2010) 5.23%

(3) Market Risk

Premium 6.09 %

Cost of Equity (2)+[(1)*(3)] 14.7%

Equity/Total Capital 61.9% 69.0% 19.8% 65.0% 73.4% 67.3% 71.0% 71.7%



### 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 points) over the ri sk

- free rate. DOE used this

method to calculate the cost of debt for seven public manufacturers by using S&P ratings and adding the relevant spread to the risk- free rate.

In practice, investors use a variety of different maturity Treasury bonds to estimat e the

risk

- free rate. DOE used the 10 -year Treasury bond return because it captures long- term inflation expectations and is less volatile than short- term rates. The risk free rate is estimated to be approximately 5.2 percent, which is the average 10- year T reasury bond return between 1928 and

2010.

For the cost of debt, S&P’s Credit Services provided the average spread of corporate

bonds for the seven manufacturers between 2004 and 2010. DOE added the industry- weighted average spread to the average T

- Bill yi eld over the same period. Since 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 and the capital structure of the industry ( i.e.

, the debt ratio (debt/total capital)).

12

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### Table 12.4.3 Cost of Debt Calculation

Parameter Industry Weighted Average A B C D E F G

S&P Bond Rating AAA CC AAA A AAA AAA AAA
( 1 ) Yield on 10

- Year (1928

- 2010) 5.23 %

(2) Gross Cost of Debt 6.9% 6.23% 8.98% 6.83% 6.83% 5.73% 6.83% 6.23%
(3) Tax Rate 25% 27.3% 21.4% 34.3% 16.2% 31.5% 30.3% 25.8%

Net Cost of Debt (2) x [1

- (3)] 5.2%

Debt/Total Capital 38.1% 31.0% 80.2% 35.0% 26.6% 32.7% 29.0% 28.3%



### Using public information for these seven companies, the initial estimate for the industry’s

WACC was approximately 11.1 percent. Subtracting an inflation rate of 3.0 9 percent over the analysis period used in the initial estimate, the inflation

- adjusted WACC and the initial estimate

of the discount rate used in the straw- man GRIM is 8 percent. DOE also ask ed for feedback on the discount rate during manufacturer interviews. Based on the estimated WACC and the

manufacturer feedback, DOE used a discount rate of 10 percent for the commercial refrigeration industry.

12.4.5 Trial Standard Levels

DOE developed a number of efficiency levels for each type of equipment class. TSLs were then developed by selecting likely groupings of efficiency levels for all equipment types.

Table 12.4.4 presents the TSLs used for energy e fficiency analysis in the GRIM.

12

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### Table 12.4.4 Trial Standard Levels for Energy Efficiency Analysis of Commercial Refrigeration Equipment

Equipment

Class Baseline TSL1 TSL2 TSL3 TSL4 TSL5

VOP.RC.M EL 0 EL 0 EL 0 EL 1 EL 3 EL 4

VOP.RC.L EL 0 EL 0 EL 0 EL 1 EL 2 EL 3

VOP.SC.M EL 0 EL 0 EL 0 EL 0 EL 1 EL 2

VCT.RC.M EL 0 EL 0 EL 0 EL 1 EL 3 EL 4

VCT.RC.L EL 0 EL 1 EL 1 EL 2 EL 3 EL 4

VCT.SC.M EL 0 EL 1 EL 2 EL 3 EL 5 EL 7

VCT.SC.L EL 0 EL 1 EL 3 EL 5 EL 7 EL 7

VCT.SC.I EL 0 EL 1 EL 1 EL 1 EL 3 EL 4

VCS.SC.M EL 0 EL 1 EL 2 EL 4 EL 6 EL 7

VCS.SC.L EL 0 EL 1 EL 3 EL 5 EL 6 EL 7

VCS.SC.I EL 0 EL 1 EL 2 EL 4 EL 4 EL 5

SVO.RC.M EL 0 EL 1 EL 1 EL 1 EL 3 EL 4

SVO.SC.M EL 0 EL 0 EL 0 EL 0 EL 1 EL 3

SOC.RC.M EL 0 EL 0 EL 0 EL 0 EL 1 EL 4

SOC.SC.M EL 0 EL 0 EL 0 EL 0 EL 2 EL 4

HZO.RC.M EL 0 EL 0 EL 0 EL 0 EL 0 EL 1

HZO.RC.L EL 0 EL 0 EL 0 EL 0 EL 0 EL 1

HZO.SC.M EL 0 EL 0 EL 1 EL 1 EL 2 EL 3

HZO.SC.L EL 0 EL 0 EL 0 EL 0 EL 0 EL 0

HCT.SC.M EL 0 EL 2 EL 3 EL 4 EL 6 EL 7

HCT.SC.L EL 0 EL 2 EL 3 EL 4 EL 6 EL 7

HCT.SC.I EL 0 EL 0 EL 0 EL 0 EL 0 EL 1

HCS.SC.M EL 0 EL 1 EL 2 EL 3 EL 4 EL 6

HCS.SC.L EL 0 EL 1 EL 2 EL 3 EL 5 EL 6

PD.SC.M EL 0 EL 1 EL 2 EL 3 EL 4 EL 7



### 12.4.6 NIA Shipments

The GRIM estimates manufacturer revenues based on total -unit-shipment forecasts and the distribution of these values by efficiency level. Changes in the efficiency mix and the total industry shipments for a given standards case are a key driver of manufacturer finances. For this analysis, the GRIM applied the NIA shipments forecasts.

As part of the shipments analysis, DOE estimated the base- case shipment distribution by efficiency level for each equipment class. In the standards case, DOE estimated new efficiency distributions for 2017 and beyond. DOE assumed that all shipments in the base case that did not meet the standard under consideration would meet the new standard in 2017 under a roll-up

scenario . Consumers in the base case who purchase units above the standard level are not affected as they are assumed to continue to purchase the same base

- case unit in the standards

case.

See chapter 9 of the TSD for more information on the commercial refrigeration equipment standards- case shipments.

12

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### 12.4.7 Manufacturer Production Costs

Changes in production costs affect revenues and gross profits. Products that are more efficient typically cost more to produce than baseline products (as shown in chapter 5 of the TSD). For the MIA, DOE used the MPCs derived in the engineering analysis.

Manufacturing a higher efficiency product is typically more expensive than manufacturing a baseline product. MPCs increase at higher efficiency levels due to the use of

more complex components, which are more costly than baseline components. The se changes in

MPC s can affect the revenues, gross margins, and cash flow of the industry, making these product cost data key GRIM inputs for DOE’s analysis.

To calculate baseli ne MSP, DOE followed a three-step process. First, DOE derived

MPC s from the engineering and teardown analyses. Second, DOE applied a manufacturer markup, which varies with the markup scenario (discussed in detail in section 12.4.9 ), to the

MPCs. Third, shipping costs from the engineering analysis were added to the marked-up MPCs.

Table 12.4.5 through Table 12.4.29 show the production cost estimates used in the GRIM for each analyzed equipment class.

Table 12.4.5 Manufacturer Production Cost Breakdown ( 2012$ ) for VOP.RC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $3,015.29 $587.33 $118.05 $160.10 $3,880.77 $252.30 1.42 $5,762.99

EL 1 $3,888.78 $587.33 $118.05 $160.10 $4,754.26 $252.30 1.42 $7,003.35

EL 2 $3,943.50 $587.33 $118.05 $160.10 $4,808.98 $252.30 1.42 $7,081.05

EL 3 $4,365.74 $587.33 $118.05 $160.10 $5,231.22 $252.30 1.42 $7,680.63

EL 4 $6,192.14 $622.51 $118.05 $160.10 $7,092.80 $252.30 1.42 $10,324.08



### Table 12.4.6 Manufacturer Production Cost Breakdown ( 2012$ ) for VOP.RC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $3,543.98 $620.98 $124.82 $169.95 $4,459.73 $252.30 1.42 $6,585.12

EL 1 $3,603.62 $620.98 $124.82 $169.95 $4,519.37 $252.30 1.42 $6,669.81

EL 2 $4,100.73 $620.98 $124.82 $169.95 $5,016.48 $252.30 1.42 $7,375.70

EL 3 $7,102.52 $663.78 $124.82 $169.95 $8,061.07 $252.30 1.42 $11,699.02



### Table 12.4.7 Manufacturer Production Cost Breakdown ( 2012$) for VOP.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $2,197.10 $289.65 $83.84 $96.40 $2,667.00 $68.90 1.42 $3,856.04

EL 1 $2,236.96 $289.65 $83.84 $96.40 $2,706.86 $68.90 1.42 $3,912.64

EL 2 $2,853.36 $301.85 $83.84 $96.40 $3,335.46 $68.90 1.42 $4,805.25

12

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### Table 12.4.8 Manufacturer Production Cost Breakdown ( 2012$ ) for VCT.RC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $4,177.47 $640.82 $121.31 $150.06 $5,089.66 $235.07 1.42 $7,462.39

EL 1 $4,267.95 $640.82 $121.31 $150.06 $5,180.14 $235.07 1.42 $7,590.87

EL 2 $4,326.95 $640.82 $121.31 $150.06 $5,239.14 $235.07 1.42 $7,674.65

EL 3 $4,436.80 $640.82 $121.31 $150.06 $5,348.99 $235.07 1.42 $7,830.64

EL 4 $6,612.42 $681.62 $121.31 $150.06 $7,565.41 $235.07 1.42 $10,977.95

EL 5 $4,177.47 $640.82 $121.31 $150.06 $5,089.66 $235.07 1.42 $7,462.39



### Table 12.4.9 Manufacturer Production Cost Breakdown ( 2012$ ) for VCT.RC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $4,836.25 $630.17 $119.27 $150.57 $5,736.26 $235.07 1.42 $8,380.56

EL 1 $4,926.73 $630.17 $119.27 $150.57 $5,826.74 $235.07 1.42 $8,509.04

EL 2 $4,984.56 $630.17 $119.27 $150.57 $5,884.57 $235.07 1.42 $8,591.16

EL 3 $5,164.59 $630.17 $119.27 $150.57 $6,064.60 $235.07 1.42 $8,846.80

EL 4 $7,968.31 $670.17 $119.27 $150.57 $8,908.32 $235.07 1.42 $12,884.88

EL 5 $4,836.25 $630.17 $119.27 $150.57 $5,736.26 $235.07 1.42 $8,380.56



### Table 12.4.10 Manufacturer Production Cost Breakdown ( 2012$ ) for VCT.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $1,651.07 $221.07 $120.85 $84.46 $2,077.45 $61.81 1.42 $3,011.79

EL 1 $1,665.03 $221.07 $120.85 $84.46 $2,091.41 $61.81 1.42 $3,031.61

EL 2 $1,755.51 $221.07 $120.85 $84.46 $2,181.89 $61.81 1.42 $3,160.09

EL 3 $1,794.85 $221.07 $120.85 $84.46 $2,221.23 $61.81 1.42 $3,215.96

EL 4 $1,804.41 $221.07 $120.85 $84.46 $2,230.79 $61.81 1.42 $3,229.53

EL 5 $1,845.83 $221.07 $120.85 $84.46 $2,272.21 $61.81 1.42 $3,288.35

EL 6 $1,892.15 $221.07 $120.85 $84.46 $2,318.53 $61.81 1.42 $3,354.12

EL 7 $2,634.89 $235.67 $120.85 $84.46 $3,075.87 $61.81 1.42 $4,429.55



### Table 12.4.11 Manufacturer Production Cost Breakdown ( 2012$ ) for VCT.SC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $2,284.48 $241.56 $77.35 $78.04 $2,681.43 $61.81 1.42 $3,869.44

EL 1 $2,352.26 $241.56 $77.35 $78.04 $2,749.21 $61.81 1.42 $3,965.69

EL 2 $2,532.37 $241.56 $77.35 $78.04 $2,929.32 $61.81 1.42 $4,221.44

EL 3 $2,546.33 $241.56 $77.35 $78.04 $2,943.28 $61.81 1.42 $4,241.27

EL 4 $2,636.81 $241.56 $77.35 $78.04 $3,033.76 $61.81 1.42 $4,369.75

EL 5 $2,654.60 $241.56 $77.35 $78.04 $3,051.55 $61.81 1.42 $4,395.01

EL 6 $2,696.02 $241.56 $77.35 $78.04 $3,092.97 $61.81 1.42 $4,453.83

EL 7 $3,779.08 $256.16 $77.35 $78.04 $4,190.63 $61.81 1.42 $6,012.50



### Table 12.4.12 Manufacturer Production Cost Breakdown ( 2012$ ) for VCT.SC.I

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $2,698.63 $254.02 $81.43 $83.13 $3,117.22 $64.85 1.42 $4,491.30

EL 1 $2,718.69 $254.02 $81.43 $83.13 $3,137.28 $64.85 1.42 $4,519.79

EL 2 $2,760.62 $254.02 $81.43 $83.13 $3,179.21 $64.85 1.42 $4,579.33

EL 3 $2,840.83 $254.02 $81.43 $83.13 $3,259.42 $64.85 1.42 $4,693.23

EL 4 $4,183.05 $269.42 $81.43 $83.13 $4,617.04 $64.85 1.42 $6,621.05

12

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### Table 12.4.13 Manufacturer Production Cost Breakdown ( 2012$ ) for VCS.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $791.54 $235.19 $131.60 $90.15 $1,248.48 $60.79 1.42 $1,833.63

EL 1 $799.81 $235.19 $131.60 $90.15 $1,256.75 $60.79 1.42 $1,845.38

EL 2 $817.39 $235.19 $131.60 $90.15 $1,274.33 $60.79 1.42 $1,870.34

EL 3 $831.35 $235.19 $131.60 $90.15 $1,288.29 $60.79 1.42 $1,890.16

EL 4 $847.09 $235.19 $131.60 $90.15 $1,304.03 $60.79 1.42 $1,912.51

EL 5 $888.63 $235.19 $131.60 $90.15 $1,345.57 $60.79 1.42 $1,971.50

EL 6 $895.87 $235.19 $131.60 $90.15 $1,352.81 $60.79 1.42 $1,981.78

EL 7 $1,667.87 $249.79 $131.60 $90.15 $2,139.41 $60.79 1.42 $3,098.75



### Table 12.4.14 Manufacturer Production Cost Breakdown ( 2012$ ) for VCS.SC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $968.20 $244.09 $136.58 $94.26 $1,443.12 $60.79 1.42 $2,110.02

EL 1 $994.06 $244.09 $136.58 $94.26 $1,468.98 $60.79 1.42 $2,146.74

EL 2 $1,025.35 $244.09 $136.58 $94.26 $1,500.27 $60.79 1.42 $2,191.17

EL 3 $1,039.31 $244.09 $136.58 $94.26 $1,514.23 $60.79 1.42 $2,211.00

EL 4 $1,080.86 $244.09 $136.58 $94.26 $1,555.78 $60.79 1.42 $2,270.00

EL 5 $1,117.71 $244.09 $136.58 $94.26 $1,592.63 $60.79 1.42 $2,322.32

EL 6 $1,130.99 $244.09 $136.58 $94.26 $1,605.91 $60.79 1.42 $2,341.18

EL 7 $2,152.29 $258.69 $136.58 $94.26 $2,641.81 $60.79 1.42 $3,812.16



### Table 12.4.15 Manufacturer Production Cost Breakdown ( 2012$ ) for VCS.SC.I

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $1,167.76 $229.16 $128.08 $91.35 $1,616.36 $64.85 1.42 $2,360.08

EL 1 $1,178.62 $229.16 $128.08 $91.35 $1,627.22 $64.85 1.42 $2,375.50

EL 2 $1,221.01 $229.16 $128.08 $91.35 $1,669.61 $64.85 1.42 $2,435.70

EL 3 $1,235.08 $229.16 $128.08 $91.35 $1,683.68 $64.85 1.42 $2,455.68

EL 4 $1,277.14 $229.16 $128.08 $91.35 $1,725.74 $64.85 1.42 $2,515.40

EL 5 $2,637.33 $244.56 $128.08 $91.35 $3,101.33 $64.85 1.42 $4,468.74

EL 6 $1,167.76 $229.16 $128.08 $91.35 $1,616.36 $64.85 1.42 $2,360.08



### Table 12.4.16 Manufacturer Production Cost Breakdown ( 2012$ ) for SVO.RC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $2,523.73 $542.86 $100.11 $137.20 $3,303.90 $139.83 1.42 $4,831.37

EL 1 $3,184.75 $542.86 $100.11 $137.20 $3,964.92 $139.83 1.42 $5,770.02

EL 2 $3,479.11 $542.86 $100.11 $137.20 $4,259.28 $139.83 1.42 $6,188.01

EL 3 $3,525.75 $542.86 $100.11 $137.20 $4,305.92 $139.83 1.42 $6,254.24

EL 4 $4,693.85 $565.54 $100.11 $137.20 $5,496.70 $139.83 1.42 $7,945.14



### Table 12.4.17 Manufacturer Production Cost Breakdown ( 2012$ ) for SVO.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $1,624.48 $218.16 $77.07 $81.76 $2,001.48 $42.56 1.42 $2,884.66

EL 1 $1,730.39 $218.16 $77.07 $81.76 $2,107.39 $42.56 1.42 $3,035.05

EL 2 $1,767.57 $218.16 $77.07 $81.76 $2,144.57 $42.56 1.42 $3,087.85

EL 3 $2,084.97 $306.20 $77.07 $81.76 $2,550.01 $42.56 1.42 $3,663.57

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### Table 12.4.18 Manufacturer Production Cost Breakdown ( 2012$ ) for SOC.RC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $4,653.10 $667.40 $107.32 $215.00 $5,642.81 $165.16 1.42 $8,177.95

EL 1 $4,756.51 $667.40 $107.32 $215.00 $5,746.22 $165.16 1.42 $8,324.79

EL 2 $4,868.71 $667.40 $107.32 $215.00 $5,858.42 $165.16 1.42 $8,484.12

EL 3 $4,911.63 $667.40 $107.32 $215.00 $5,901.34 $165.16 1.42 $8,545.06

EL 4 $5,776.50 $684.32 $107.32 $215.00 $6,783.13 $165.16 1.42 $9,797.20



### Table 12.4.19 Manufacturer Production Cost Breakdown ( 2012$ ) for HZO.RC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $2,294.35 $384.10 $77.94 $117.66 $2,874.05 $113.48 1.42 $4,194.63

EL 1 $3,250.56 $402.76 $77.94 $117.66 $3,848.92 $113.48 1.42 $5,578.95



### Table 12.4.20 Manufacturer Production Cost Breakdown ( 2012$ ) for HZO.RC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $2,457.12 $346.24 $70.27 $106.31 $2,979.94 $113.48 1.42 $4,344.99

EL 1 $4,411.97 $374.24 $70.27 $106.31 $4,962.79 $113.48 1.42 $7,160.64



### Table 12.4.21 Manufacturer Production Cost Breakdown ( 2012$ ) for HZO.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $689.17 $89.17 $36.04 $30.52 $844.90 $20.26 1.42 $1,220.02

EL 1 $744.20 $89.17 $36.04 $30.52 $899.93 $20.26 1.42 $1,298.16

EL 2 $783.16 $89.17 $36.04 $30.52 $938.89 $20.26 1.42 $1,353.48

EL 3 $1,325.86 $99.97 $36.04 $30.52 $1,492.39 $20.26 1.42 $2,139.45



### Table 12.4.22 Manufacturer Production Cost Breakdown ( 2012$ ) for HZO.SC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $1,846.51 $101.63 $44.89 $38.17 $2,031.20 $20.26 1.42 $2,904.56



### Table 12.4.23 Manufacturer Production Cost Breakdown ( 2012$ ) for HCT.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $418.85 $102.73 $38.17 $27.12 $586.87 $20.26 1.42 $853.62

EL 1 $422.99 $102.73 $38.17 $27.12 $591.01 $20.26 1.42 $859.49

EL 2 $429.62 $102.73 $38.17 $27.12 $597.64 $20.26 1.42 $868.91

EL 3 $487.92 $102.73 $38.17 $27.12 $655.94 $20.26 1.42 $951.69

EL 4 $496.70 $102.73 $38.17 $27.12 $664.72 $20.26 1.42 $964.16

EL 5 $500.12 $102.73 $38.17 $27.12 $668.14 $20.26 1.42 $969.02

EL 6 $536.21 $102.73 $38.17 $27.12 $704.23 $20.26 1.42 $1,020.27

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### Table 12.4.24 Manufacturer Production Cost Breakdown ( 2012$ ) for HCT.SC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $481.32 $97.54 $36.24 $25.98 $641.08 $20.26 1.42 $930.59

EL 1 $539.61 $97.54 $36.24 $25.98 $699.37 $20.26 1.42 $1,013.37

EL 2 $543.75 $97.54 $36.24 $25.98 $703.51 $20.26 1.42 $1,019.24

EL 3 $552.54 $97.54 $36.24 $25.98 $712.30 $20.26 1.42 $1,031.73

EL 4 $563.52 $97.54 $36.24 $25.98 $723.28 $20.26 1.42 $1,047.32

EL 5 $568.53 $97.54 $36.24 $25.98 $728.29 $20.26 1.42 $1,054.43

EL 6 $604.62 $97.54 $36.24 $25.98 $764.38 $20.26 1.42 $1,105.68

EL 7 $1,028.51 $104.50 $36.24 $25.98 $1,195.23 $20.26 1.42 $1,717.49



### Table 12.4.25 Manufacturer Production Cost Breakdown ( 2012$ ) for HCT.SC.I

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $608.94 $110.27 $41.12 $29.20 $789.54 $18.24 1.42 $1,139.39

EL 1 $1,236.39 $117.63 $41.12 $29.20 $1,424.35 $18.24 1.42 $2,040.82



### Table 12.4.26 Manufacturer Production Cost Breakdown ( 2012$ ) for HCS.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $367.53 $88.66 $34.86 $27.28 $518.33 $23.30 1.42 $759.33

EL 1 $371.12 $88.66 $34.86 $27.28 $521.92 $23.30 1.42 $764.43

EL 2 $375.26 $88.66 $34.86 $27.28 $526.06 $23.30 1.42 $770.31

EL 3 $384.05 $88.66 $34.86 $27.28 $534.85 $23.30 1.42 $782.79

EL 4 $387.46 $88.66 $34.86 $27.28 $538.26 $23.30 1.42 $787.63

EL 5 $422.71 $88.66 $34.86 $27.28 $573.51 $23.30 1.42 $837.68

EL 6 $662.80 $94.16 $34.86 $27.28 $819.10 $23.30 1.42 $1,186.42



### Table 12.4.27 Manufacturer Production Cost Breakdown ( 2012$ ) for HCS.SC.L

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $369.70 $95.90 $34.80 $27.48 $527.88 $23.30 1.42 $772.89

EL 1 $373.36 $95.90 $34.80 $27.48 $531.54 $23.30 1.42 $778.09

EL 2 $377.50 $95.90 $34.80 $27.48 $535.68 $23.30 1.42 $783.97

EL 3 $386.29 $95.90 $34.80 $27.48 $544.47 $23.30 1.42 $796.45

EL 4 $389.60 $95.90 $34.80 $27.48 $547.78 $23.30 1.42 $801.15

EL 5 $424.85 $95.90 $34.80 $27.48 $583.03 $23.30 1.42 $851.20

EL 6 $755.05 $102.86 $34.80 $27.48 $920.19 $23.30 1.42 $1,329.97



### Table 12.4.28 Manufacturer Production Cost Breakdown ( 2012$ ) for PD.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $957.64 $185.72 $63.88 $45.99 $1,253.23 $37.49 1.42 $1,817.08

EL 1 $984.68 $185.72 $63.88 $45.99 $1,280.27 $37.49 1.42 $1,855.47

EL 2 $1,075.17 $185.72 $63.88 $45.99 $1,370.76 $37.49 1.42 $1,983.97

EL 3 $1,089.13 $185.72 $63.88 $45.99 $1,384.72 $37.49 1.42 $2,003.79

EL 4 $1,097.61 $185.72 $63.88 $45.99 $1,393.20 $37.49 1.42 $2,015.83

EL 5 $1,137.08 $185.72 $63.88 $45.99 $1,432.67 $37.49 1.42 $2,071.88

EL 6 $1,168.92 $185.72 $63.88 $45.99 $1,464.51 $37.49 1.42 $2,117.09

EL 7 $1,753.82 $197.24 $63.88 $45.99 $2,060.93 $37.49 1.42 $2,964.01

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### Table 12.4.29 Manufacturer Production Cost Breakdown (2012$) for SOC.SC.M

Efficiency

Level Material Labor Depre

ciation Overhead MPC Shipping Markup MSP

EL 0 $5,289.74 $703.46 $121.89 $236.80 $6,351.89 $165.16 1.42 $9,184.84

EL 1 $5,393.14 $703.46 $121.89 $236.80 $6,455.29 $165.16 1.42 $9,331.67

EL 2 $5,509.26 $703.46 $121.89 $236.80 $6,571.41 $165.16 1.42 $9,496.56

EL 3 $5,552.18 $703.46 $121.89 $236.80 $6,614.33 $165.16 1.42 $9,557.51

EL 4 $6,417.05 $720.38 $121.89 $236.80 $7,496.12 $165.16 1.42 $10,809.65



### For commercial refrigeration equipment, DOE also appli ed a price curve to LED costs in order to reflect a projected decrease in the price of this component over time. As a result, total material costs for each equipment class will also decrease over time. The material costs in the tables above apply to the base year of the analysis period. The price projections for LEDs in each year of the analysis period can be found in the GRIM.

12.4.8 Conversion Costs

Amended energy conservation standards typically cause manufacturers to incur one

- time conversion costs to bring their production facilities and product designs into compliance with new regulations. For the MIA, DOE classified these one-time conversion cost s into two majorgroups: capital conversion costs and product conversion costs. Capital conversion costs are onetime investments in PPE to adapt or change existing production facilities in order to fabricate and assemble new product designs that comply with amended energy conservation standards. Product conversion costs are one-time investments in research, development, industry certification testing ( i.e.

, Underwriters Laboratories (UL) certifications and NSF International certifications)

, marketing , and other costs to make product designs comply with amended energy conservation standards. DOE based its estimates of the conversion costs for each efficiency level on information obtained from manufacturer interviews and the design pathways analyzed in the engineering analysis.

12.4.8.1 Capital Conversion Costs

To evaluate the level of capital conversion expenditures manufacturers would likely incur to comply with energy conservation standards, DOE used the manufacturer interviews and

written comments from the NOPR to gather data on the level of capital investment required at each efficiency level. DOE validated manufacturer comments through estimates of capital expenditure requirements derived from the product teardown analysis and engineering model

described in c hapter 5 of the TSD.

In interviews, manufacturers noted that most of the design options being considered are offered as options today. As a result, many of the design options do not incur capital expenditures for new tooling or equipment. Overall, c apital conv ersion costs are primarily driven by changes in equipment insulation. An increase in insulation thickness by one- half inch was determined to require either new foaming fixtures or modification of existing foaming fixtures

in most facilities. Additionally, manufacturers noted that changes in foam thickness could lead to

foam reformulations. A move to vacuum insulated panels (VIPs) could require significant changes in production processes and, based on manufacturer input, was estimated to require

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### capital exp enditures that are double those of an increase in foam insulation thickness. Expected capital conversion costs for each TSL are listed in Table 12.4.30

.

Table 12.4.30 Industry Cumulative Capital Conversion Cost

TSL Capital Conversion Cost $millions

TSL 1 3.5

TSL 2 3.6

TSL 3 58.1

TSL 4 160.7

TSL 5 499.7



### At TSL 1 and TSL 2 , DOE expects limited capital conversion costs, which are related to changes in tooling to accommodate high-performance doors. All design options considered at this level are currently offered by manufacturers today.

At TSL 3 and TSL 4, the capital conversion costs for the industry steadily increase as more equipment classes will likely require thicker insulation to meet the standard. At TSL 3, capital conversion costs reach $ 58.1 million as a total of five of the twenty- five analyzed equipment classes are expected to use thicker or improved insulation. At TSL 4, capital conversion costs reach $160.7 million as a total of sixteen of the twenty- five analyzed equipment classes are expected to use thicker or improved insulat ion.

At TSL 5, DOE models a large increase in capital conversion costs, which jump to $499.7 million. This increase is associated with the incorporation of vacuum insulated panel (VIP) technology into production units. Since the industry does not use VIPs today, significant new investments are required to produce structural members that integrate VIP technology. This conversion costs number assumes that all manufacturers remain in the industry and is an upper

bound.

12.4.8.2 Product Conversion Costs

DOE assessed th e product conversion costs at each level by integrating data from quantitative and qualitative sources. DOE considered feedback regarding the potential costs of each efficiency level from multiple manufacturers to determine conversion costs such as R&D expenditures and certification costs. Manufacturer numbers were aggregated to better reflect the industry as a whole and to protect confidential information.

Based on both manufacturer feedback and the engineering analysis, many design options

were considered to be component swaps, which required substitutions of new components but did not require product redes i gns . Lighting changes, fan motor substitutions, compressor upgrades, and night curtain retrofits were understood to not require significant product

dev elopment investments. However, changes in evaporator coil, condenser coil, and insulation were modeled to incur development expenses. Additionally, DOE included the cost of industry certifications in the product conversion costs. Expected product conversion costs for each TSL

are listed in Table 12.4. 31

.

Table 12.4.31 Industry Cumulative Product Conversion Cost

TSL Product Conversion Cost

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- 20$millions

TSL 1 20.6

TSL 2 32.1

TSL 3 125.9

TSL 4 194.2

TSL 5 282.1



### At TSL 1, product conversion costs may be incurred by the industry in order to incorporate the new components in existing designs. This product conversion cost is associated with sourcing, component testing, and writing the specifications for the new components. At TSL 2, product conversion costs ramp up as manufacturers make more significant changes that require engineering investments and could necessitate new industry certification, such as UL testing and NSF testing. These testing costs and redesign costs continue to ramp up in TSL 3 and

TSL 4 as more equipment requires redesign . At TSL 5, conversion costs increase significantly as the industry must develop VIP technology for use in commercial refrigeration equipment. All design and engineering resources would go into VIP panel development.

12.4.9 Markup Scenarios

DOE used multiple standards- case markup scenarios to represent the uncertainty about the impacts of energy conservation standards on prices and profitability. In the base case, DOE used the same markups applied in the engineering analysis. In the standards case, DOE modeled two markup scenarios to represent the uncertainty about the potential impacts on prices and profitability following the implementation of amended ener gy conservation standards: (1) a preservation of gross margin percentage scenario and (2) a preservation of earnings before interest and tax (EBIT) scenario. These scenarios lead to different markups values that , when applied to the inputted MPCs, result in varying revenue and cash flow impacts.

12.4.9.1 Preservation of Gross Margin Percentage Scenario

Under the preservation

- of

- gross

- margin -percentage scenario, DOE applied a single uniform “gross margin percentage” markup across all efficiency levels. As production costs increase with efficiency, this scenario implies that the absolute dollar markup will increase as well. DOE assumed the non-production cost markup—which includes SG&A expenses, R&D expenses, interest, and profit— to be 1.42 for the commercial refriger ation industry . Th is markup

is equal to the one DOE assumed in the engineering analysis. Manufacturers indicated that it

would be optimistic to assume that, as their MPC s increase in response to an energy conservation standard, they would be able to maintain the same gross margin percentage markup. Therefore, DOE assumes that this scenario represents a n upper bound to industry profitability under an energy conservation standard.

12.4.9.2 Preservation of Operating Profit Scenario

During interviews, multiple manufacturers expressed concern that the higher production costs could harm profitability. Because of market characteristics, several manufacturers suggested that the additional costs of higher minimum efficiency products could not be fully passed through to customers. Incorporating this feedback, DOE modeled the preservation of operating profit scenario.

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### In the preservation of operating profit scenario, manufacturer markups are set so that operating profit one year after the compliance date of the new ener gy conservation standards is

the same as in the base case. Under this scenario, as the cost of production and the cost of sales

increase , manufacturers are generally required to reduce their markups to a level that maintains

base -case operating profit. The implicit assumption behind this markup scenario is that the industry can only maintain its operating profit in absolute dollars after the standard. Operating margin in percentage terms is sque ezed (reduced) between the base case and standards case.

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated indicators of financial impacts on the commercial refrigeration industry. The following sections detail additional inputs and assumptions for commerc ial refrigeration equipment. The main results of the MIA are also reported in this section. The MIA consists of two key financial

metrics: INPV and annual cash flows.

12.5.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the economic impacts of different TSLs in the standards case. The INPV is different from DOE’s net present value, which is applied to the U.S. economy. The INPV is the sum of all net cash flows discounted at the industry’s cost of capital or discount rate. The GRIM for this rulemaking

estimates cash flows from 2013 to 2046 , the same an alysis period used in the NIA (chapter 10 of the TSD). This timeframe models both the short- term impacts on the industry from the base year of the analysis until the compliance dat e ( 2013 until 2017) and a long- term assessment over the 30-year analysis period used in the NIA (2017 – 2046 ).

In the MIA, DO E compares the INPV of the base case (no amended energy conservation standards) to that of each TSL. The difference between the bas e case and a standards case INPV is an estimate of the economic impacts the TSL would have on the industry. The markup scenarios are described in greater detail in section 12.4.9

.

While INPV is useful for evaluating the long

- term effects of amended energy conservation standards, short-term changes in cash flow are also important indicators of the industry’s financial situation. For example, a large investment over 1 or 2 years could strain the industry’s access to capital. Consequently, the sharp drop in financial performance could cause investors to flee, even though recovery may be possible. Thus, a short- term disturbance can have long- term effects t hat the INPV cannot capture. To provide an idea of the behavior of short- term annual net cash flows, Figure 12.5.1 and Figure 12.5.2 present the annual net cash flows through

2027.

Annual cash flows are discounted to the base year, 2013 . After the standards announcement date ( i.e. , the publication date of the final rule), industry cash flows begin to decline as companies use their financial resources to prepare for the amended energy conservation standard. Cash flows between the announcement date and the compliance date are driven by the level of conversion costs and the proportion of these investments spent every year. The more stringent the amended energy conservation standard, the greater the impact on industry

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### cash flows in the years leading up to the compliance date, as product conversion costs lower cash inflows from operations and capital conversion costs increase cash outflows for capital expenditures.

Free cash flow in the year the amended energy conservation standards take effect is driven by two competing factors. In addition to capital and product conversion costs, amended energy conservation standards could create stranded assets, i.e. , tooling and equipment that could have been used longer if the energy conservation standard had not made them obsolete. In this year, manufacturers write down the remaining book value of existing tooling and equipment whose value is affected by the amended energy conservation standard. This one- time write -down acts as a tax shield that alleviates decreases in cash flow from operations in the year of the write

down. In this year, there is also an increase in working capital that reduces cash flow from operations. A large increase in working capital is needed due to more costly production components and materials, higher inventory carrying to sell more expensive products, and higher accounts receivable for more expensive products. Depending on these two competing factors,

cash flow can be either positively or nega tively affected in the year the standard takes effect.

12.5.2 Commercial Refrigeration Industry Financial Impacts

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for commercial refrigeration equipment for the two markup scenarios. Figure 12.5.1 and Figure 12.5.2 present the net annual

cash flows for the two scenarios.

Table 12.5.1 Preservation of Gross Margin Percentage Scenario Changes in INPV for Commercial Refrigeration Equipment

Units Base

Case Trial Standard Level

1 2 3 4 5

INPV 2012$ M 2,660.0 2,650.1 2,651.3 2,566.1 2,470.6 2,475.6 Change in

INPV 2012$ M

- (9.9) (8.7) (93.9) (189.4) (184.4)
(%)

- (0.37) (0.33) (3.53) (7.12) (6.93) * Numbers in parentheses indicate negative numbers.



### Table 12.5.2 Preservation of Operating Profit Scenario Changes in INPV for Commercial Refrigeration Equipment

Units Base

Case Trial Standard Level

1 2 3 4 5

INPV 2012$ M 2,660.0 2,636.1 2,617.1 2,495.0 2,339.1 1,515.2 Change in

INPV 2012$ M

- (23.9) (42.9) (165.0) (320.9) (1,144.8)
(%)

- (0.90) (1.61) (6.20) (12.07) (43.04) * Numbers in parentheses indicate negative numbers.

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### Figure 12.5.1 Annual Industry Net Cash Flows for Commercial Refrigeration Equipment (Preservation of Gross Margin Percentage Markup Scenario) -$150.00 -$100.00 -$50.00

$0.00 $50.00 $100.00 $150.00 $200.00 $250.00 $300.00 $350.002012 $Millions

Year

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

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### Figure 12.5.2 Annual Industry Net Cash Flows for Commercial Refrigeration Equipment (Preservation of Operating Profit Markup Scenario)

12.6 IMPACTS ON SMALL BUSINESS MANUFACTURERS

DOE conducted a more focused inquiry of the companies that could be small business manufacturers of products covered by this rulemaking. For the category “Air -Conditioning and Warm Air Heating Equipment and Commercia l and Industrial Refrigeration Equipment Manufacturing,” the SBA has set a size threshold of 750 employees or less for an entity to be considered as a small business. During its market survey, DOE used all available public information to identify potential small manufacturers. DOE’s research involved industry trade association membership directories (including AHRI

, NAFEM , and NSF International), product databases ( e.g. , Federal Trade Commission ( FTC ), The Thomas Register, California Energy Commission ( CEC ) , and ENERGY STAR ® 

### databases), individual company websites, and market research tools ( e.g. , Hoovers reports) to create a comprehensive list of companies that manufacture or sell products covered by this rulemaking. DOE also asked stakeholders and industry representatives if they were aware of any other small manufacturers during manufacturer interviews and at previous DOE public meetings. DOE reviewed publicly available data and contacted select companies on its list, as necessary, to determine whether the y met the

SBA’s definition of a small business manufacturer of covered commercial refrigeration equipment. DOE screened out companies that did not offer products covered by this rulemaking, did not meet the definition of a “small business,” or are foreign owned and operated. -$150.00 -$100.00 -$50.00

$0.00 $50.00 $100.00 $150.00 $200.00 $250.00 $300.002012 $Millions

Year

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

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### DOE identified 45 manufacturers in the commercial refrigeration industry, and 32 of the

manufacturers identified are believed to be small businesses. As part of the MIA, the Department

interviewed eight commercial refrigeration equipment manufacturers, including four small business operations. Based on the large number of small commercial refrigeration equipment manufacturers and the potential scope of the impact, DOE could not certify that the final rule would not have a significant impact on a significant number of small businesses with respect to the commercial refrigeration industry.

DOE recognizes that amended energy conservation standards can potentially have disproportionate impact s on small businesses. Larger manufacturers could have a competitive advantage due to their size and ability to access capital that may not be available to small businesses. Larger businesses also have larger production volumes over which to spread costs. DOE provides additional analysis in section VI.B, “ Review under the Regulatory Flexibility
Act, ” of the Final Rule notice

.

12.7 OTHER IMPACTS

12.7.1 Employment

To quantitatively assess the impacts of amended energy conservation standards on employment, DOE used the GRIM to estimate t he domestic labor expenditures and number of employees in the base case and at each TSL from 2013 through 2046. DOE used statistical data from the U.S. Census Bureau’s 2011 Annual Survey of Manufacturers (ASM), the results of the engineering analysis, the commercial refrigeration equipment shipments forecast, and interviews with manufacturers to determine the inputs necessary to calculate industry-wide labor expenditures and domestic employment levels. Labor expenditures related to manufacturing of the product are a function of the labor intensity of the product, the sales volume, and an assumption that wages remain fixed in real terms over time. The total labor expenditures in each year are calculated by multiplying the MPCs by the labor percentage of MPCs.

The total labor expenditures in the GRIM were then 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 U.S. Census Bureau’s 2011 ASM). The estimates of production workers in this section cover workers, including line supervisors who are directly involved in fabricating and assembling a product within the OEM facility. Workers performing services that are closely associated with production operations, such as materials handling tasks using forklifts, are also included as production labor. DOE’s estimates only account for production workers who manufacture the specific products covered by this rulemaking.

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### Table 12.7.1 Potential Changes in the Number of Commercial Refrigeration Equipment

Production Workers in 2017

Trial Standard Level

Base

Case 1 2 3 4 5

Total Number of

Domestic Production

Workers in 2017 7,7 79 7,7 79 7,7 79 7,7 79 7,780 8,2 20

Range of Potential Changes in Domestic

Production Workers in

2017 * - ( 7,740 ) to 0 ( 7,740 ) to 0 ( 7,740 ) to 0 ( 7,740 ) to 1 ( 7,740 ) to

441

* DOE presents a range of potential employment impacts, where the lower range represents the scenario in which all domestic manufacturers move production to other countries.



### The employment impacts shown in Table 12.7.1 represent the potential production employment changes that could result following the compliance date of an amended energy conservation standard. The upper end of the results in the table estimates the maximum increase in the number of production workers after the implementation of new energy conservation standards and it assumes that manufacturers would continue to produce the same scope of covered products within the United States. The lower end of the range indicates the total number of U.S. production workers in the industry who could lose their jobs if all existing production were moved outside of the United States. Though manufacturers stated in interviews that shifts in production to foreign countries is unlikely, the industry did not provide enough information

for DOE fu lly quantify what percentage of the industry would move production at each evaluated

standard level.

The majority of design options analyzed in the engineering analysis require manufacturers to purchase more -efficient components from suppliers. These components do not require significant additional labor to assemble. A key component of a commercial refrigeration equipment unit that requires fabrication labor by the commercial refrigeration equipment manufacturer is the shell of the unit, which needs to be formed and foamed in. Although this activity may require new production equipment if thicker insulation is needed to meet higher efficiency levels, the process of building the panels would essentially remain the same, and therefore require no additional labor costs. As a result, labor needs are not expected to increase as the amended energy conservation standard increases from baseline to TSL 4.

At TSL 5, the introduction of hybrid vacuum insulation panels may lead to higher labor requirements. In general, the production and handling of hybrid VIPs will require more labor than the production of standard panels. This is due to the delicate nature of VIPs and the additional labor necessary to embed them into a hybrid panel. The additional labor and handling

as sociated with hybrid panels account for the increase in labor at the max- tech trial standard

level.

DOE notes that the employment impacts discussed here are independent of the employment impacts from the broader U.S. economy, which are documented in chapter 15 of the

TSD.

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### 12.7.2 Production Capacity

According to the majority of commercial refrigeration equipment manufacturers in

interviews , amended energy conservation standards will not significantly affect manufacturers’ production capacities. Any necessary redesign of commercial refrigeration equipment will not change the fundamental assembly of the equipment, but manufacturers do anticipate the potential

for changes in tooling. The most significant of these would come as a result of any redesigns performed to accommodate additional foam insulation thickness. Additionally, most of the design options being evaluated are available on the market as product options today. Thus, DOE believe manufacturers will be able to maintain manufacturing capacity levels and c ontinue to

meet market demand under amended energy conservation standards.

12.7.3 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 consequences 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 looks at other significant product- specific regulations that could affect commercial refrigeration equipment manufacturers. In addition to the amended energy conservation regulations on commercial refrigeration equipment, several other federal and state regulations apply to these products and other equipment produced by the same manufacturers.

Companies that 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. Smaller companies in particular can be disproportionately affected by regulatory costs since these companies have lower sales volumes over which they can amortize the costs of meeting new regulations. A standard is not economically justified if it contributes to an unacceptable level of cumulative regulatory burden.

12.7.3.1 DOE Regulations for Other Products Produced by Commercial Refrigeration Equipment Manufacturers

In addition to the amended energy conservation standards on commercial refrigeration equipment, other Federal regulations and pending regulations may apply to other products produced by the same manufacturers. The on-going walk -in cooler and freezer energy conservation standard rulemaking could also affect manufacturers of commercial refrigeration in the years leading up to and after the compliance date of amended energy conservation standards for these products.

Walk- in Cooler and Freezer Energy Conservation Standard Rulemaking

Another DOE rulemaking that may have a significant impact on commercial refrigeration equipment manufacturers is the walk

- in cooler and freezer rulemaking, which will have a compliance date of 2017

. Nine commercial r efrigeration equipment manufacturers also produce

walk

- ins, and therefore they must comply with two rulemakings that follow similar timelines. These manufacturers will incur conversion costs for both products at around the same time,

12

- 28

### which could be a significant strain on resources. In the 2013 NOPR for walk-ins, the proposed standard was estimated to require conversion costs of $71 million (in 2012$) to be incurred by the industry ahead of the 2017 compliance date. (78 FR55781). However, the analysis is no t final and these figure are subject to change in the Final Rule for walk

- in coolers and freezers.

12.7.3.2 Other DOE and Federal Actions Affecting the Commercial Refrigeration Equipment Industry

Certification, Compliance, and Enforcement Rule

Many manufacturers ha ve expressed concerns about the CC&E March 2011 final rule, which allows DOE to enforce the energy and water conservation standards for covered products and equipment, and provides for more accurate, comprehensive information about the energy and

water use characteristics of products sold in the United States. The rule revises former certification regulations so that the Department has the information it needs to ensure that regulated products sold in the United States comply with the law. According to the rule, manufacturers of covered consumer products and commercial and industrial equipment must certify on an annual basis, by means of a compliance statement and a certification report, that each of their basic models meets its applicable energy conservatio n, water conservation, and/or design standard before it is distributed within the United States. For purposes of certification testing, the determination that a basic model complies with the applicable conservation standard must be based on sampling procedures, which currently require that a minimum of two units of abasic model must be tested in order to certify that the model is compliant (unless the productspecific regulations specify otherwise). 76 FR 12422 (March 7, 2011).

However, DOE recognizes that sampling requirements can create burden for certain commercial refrigeration equipment manufacturers who build one-of-a kind customized units and have a large number fo basic models. Therefore, DOE conducted a rulemaking to expand

a lternative e fficiency d etermination method ( AEDM ) coverage and issued a final rule on December 31, 2013. (78 FR 79579) An AEDM is a computer modeling or mathematical tool that predicts the performance of non- tested basic models. In the final rule, DOE is allowing CRE

manufacture rs to rate their basic models using AEDMs, reducing the need for sample units and reducing burden on manufacturers. More information can be found at: http://www1.eere.energy.gov/buildings/appliance_standards/implement_cert_and_enforce.html

.

EPA and ENERGY STAR

Some stakeholders have also expressed concern regarding potential conflicts with other certification programs, in particular U.S. Environmental Protection Agency ( EPA ) ENERGY STAR requirements.

DOE realizes that the cumulative effect of several regulations on an industry may significantly increase the burden faced by manufacturers who need to comply with multiple regulations and certification programs from different organizations and levels of government. However, DOE notes that certain standards, such as ENERGY STAR, are optional for

manufacturers.

12

- 29

### 12.7.3.3 Other Regulations That Could Impact Commercial Refrigeration Equipment Manufacturers

State Regulati ons

California Code of Regulations, Title 24

According to the latest California Code of Regulations, title 24, p art 6

, 7 

### any appliance for which there is a California standard established in the Appliance Efficiency Regulations may be installed only if the manufacturer has certified to the Commission, as specified in those regulations, that the appliance complies with the applicable standard for that appliance. The Commission’s appliance efficiency regulations require that the maximum daily energy consumption (in kilowatt -hours) for commercial refrigerators manufactured on or after January 1, 2010 does not exceed the following:

• refrigerators with solid doors: 0.10V + 2.04

• refrigerators with transparent doors: 0.12V + 3.34

• freezers with solid doors: 0.40V + 1.38

• freezers with transparent doors: 0.75V + 4.10

• refrigerator/freezers with solid doors: the greater of 0.27AV–0.71 or 0.70

• r efrigerators with self -condensing unit designed for pull-down temperature applications: 0.126V + 3.51

Since these standards are i dentical to the ones prescribed in the Energy Policy Act of 2005 (EPACT 2005) and the efficiency levels set by the current rulemaking will either exceed or be equivalent to the EPACT 2005 levels, DOE does not expect the Title 24 regulations to create a cumulative regulatory burden on manufacturers. California has started a rulemaking proceeding to adopt changes to the building energy efficiency standards contained in the California Code of Regulations, title 24, part 6. The proposed amended standards from this rulemaking will be adopted in 2014. More information can be found at: http://www.energy.ca.gov/title24/2013standards/rulemaking/index.html

.

Refrigerant Management Program

The California Air Resources Board ( CARB ) is currently limiting the in

- sta te use of high global warming potential ( GWP ) refrigerants in non

- residential refrigeration systems through its Refrigerant Management Program, effective January 1, 2011. According to this new regulation, facilities with refrigeration systems that have a r efrigerant capacity exceeding 50 pounds must repair leaks within 14 days of detection, maintain on- site records of all leak repairs, and keep receipts of all refrigerant purchases. The regulation applies to any person or company that installs, services, or disposes of appliances with high- GWP refrigerants. Refrigeration systems with refrigerant capacity exceeding 50 pounds typically belong to food retail operations with remote condensing racks that store refrigerant serving multiple commercial refrigeration equipment units within a business. However, commercial refrigeration equipment units in food retail are usually installed and serviced by refrigeration contractors, not manufacturers. As a result, although these CARB regulations do apply to refrigeration technicians and owners of

12

- 30

### facilities with refrigeration systems, they are unlikely to be a regulatory burden for commercial refrigeration manufacturers.

12.8 CONCLUSION

The following section summarizes the impacts for the scenarios DOE believes are most likely to capture the range of impacts on commercial refrigeration equipment manufacturers as a result of amended energy conservation standards. DOE also notes that while these scenarios bound the range of most plausible impacts on manufacturers, there potentially could be

circumstances that cause manufacturers to experience impacts outside of this range.

At TSL 1, DOE estimates impacts on INPV for commercial refrigeration equipment manufacturers to range from -$23.9 million to -$9.9 million, or a change in INPV of -0.9 percent to -0.37 percent. At this potential standard level, industry free cash flow is estimated to decrease by approximately 4.16 percent to $192.1 million, compared to the base-case value of $200.4 million in the year before the compliance date ( 2016).

The INPV impacts at TSL 1 are relatively minor because DOE manufacturer production costs do not increase significant ly . The average unit price for the industry (calculated by dividing industry revenue by industry unit shipments) increases 0.8% from $2,892.72 to $2,916.55 in the standards year. Few capital conversion costs are expected because DOE anticipates that manufacturers would be able to make simple component swaps to meet the efficiency levels for each equipment class at this TSL. However, pr oduct conversion costs are required for industry certifications to incorporate the new components into existing designs. Industry conversion costs total $24.1 million.

Under the preservation of gross margin percentage markup scenario, impacts on

manufactur ers are marginally negative . While manufacturers maintain their gross margin percentages, they also incur conversion costs that offset the higher profits that they gain from increasing their selling prices to accommodate higher production costs. T he effects of these conversion costs are more apparent in the preservation of operating profit markup scenario because manufacturers earn the same operating profit at TSL 1 as they do in the base case but must make capital and product conversion investments . In gen eral, manufacturers stated that the preservation of operating profit scenario is a more likely representation of the industry than the preservation of operating profit scenario, especially as MPCs increase.

At TSL 2, DOE estimates impacts on INPV for commercial refrigeration equipment manufacturers to range from -$42.9 million to -$8.7 million, or a change in INPV of -1.61

percent to -0.33 percent. At this potential standard level, industry free cash flow is estimated to decrease by approximately 6.04 percent to $188.3 million, compared to the base- case value of $200.4 million in the year before the compliance date (2016).

Although DOE continues to expect mild INPV impacts on the industry at TSL 2, product conversion costs do increase. Up to 20% of product o fferings in the industry would require some level of component redesign, such as changes in evaporator coil, condenser coil, or compressor selection, that would necessitate UL or NSF certification changes. These industry certification investments push total industry conversion costs to $35.4 million.

12

- 31

### At TSL 3, DOE estimates impacts on INPV for commercial refrigeration equipment manufacturers to range from -$165.0 million to -$93.9 million, or a change in INPV of -6.20

percent to -3.53 percent. At this pote ntial standard level, industry free cash flow is estimated to decrease by approximately 33.64 percent to $133.0 million, compared to the base- case value of $200.4 million in the year before the compliance date (2016).

At TSL 3, the expected design options do not dramatically alter manufacturer per unit production costs. Average unit costs increase by 4.1% to $3,011.93 while industry shipments remain steady. However, DOE expects higher conversion costs at TSL 3 due to the possible need for improved insulation for high-volume products, such as those in equipment class VCS.SC.L, which account for approximately 18.3 percent of total shipments, and those in equipment class VCT.RC.L, which account for approximately 4.1 percent. In total, DOE expects 5 of the 24 equipment classes to require improved insulation due to higher standards. The need for improved insulation necessitates redesign efforts for the cabinet as well as interior components. Furthermore, thicker insulation requires investment in new production tooling. Total industry conversion costs reach $184.0 million.

At TSL 4, DOE estimates impacts on INPV for commercial refrigeration equipment manufacturers range from -$320.9 million to -$189.4 million, or a change in INPV of -12.7

percent to -7.12 percent. At this potential standard level, industry free cash flow is estimated to decrease by approximately 67.84 percent to $64.4 million, compared to the base- case value of $200.4 million in the year before the compliance date (2016).

The drop in INPV at TSL 4 is driven by conversion costs. Industry average unit price increases 7.6% and industry shipments are modeled to remain steady. However, the need for new tooling to accommodate additional foam insulation in 16 of the 25 analyzed equipment classes pushes up industry conversion costs. The redesign effort, coupled with industry certification costs, push product conversion costs up to $194.2 million. Total industry conversion costs are expected to reach $354.9 million.

At TSL 5, DOE estimates impacts on INPV for commercial refrigeration equipment manufacturers to range from -$1,144.85 million to -$184.4 million, or a change in INPV of - 43.04 percent to -6.93 percent. At this potential standard level, industry free cash flow is estimated to decrease by approxim ately 158.32 percent to -$116.9 million, compared to the basecase value of $200.4 million in the year before the compliance date (2016).

A substantial increase in conversion costs are expected at TSL 5 due to the possible need for VIP technology. VIPs are not currently used by any commercial refrigeration equipment manufacturers and the production of VIPs would require processes different from those used to produce standard foam panels. High R&D investments would be necessary to integrate the technology into CRE cases. Based on industry feedback, DOE estimated the R&D investment to

be 1 -2 times the industry’s typical annual R&D expenditure and the capital conversion cost to be more than double the cost of all current fixtures in use. Total industry conversion costs total $781.8 million.

12

- 32

### REFERENCES

1. U.S. Securities and Exchange Commission. Annual 10-K Reports. Various Years . (Last accessed October 22, 2011.) < www.sec.gov >

2. Standard and Poor’s Financial Services LLC . Company Credit Ratings, Various Companies. (Last accessed October 22, 2011.) < www2.standardandpoors.com >

3. Hoovers Inc. Company Profiles. Various Companies. (Last accessed October 22, 2011.)

< www.hoovers.com/ >

4. U.S. Census Bureau. 2011 Annual Survey of Manufacturers . 2012. (Last accessed December 20, 2012.) < http://factfinder2.census.gov/faces/tableservices/jsf/pages/ productview.xhtml?pid=ASM_2011_31GS101&prodType=table >

5. U.S. Small Business Association. Table of Small Business Size Standards . 2007. (Last accessed November 22, 2011.) < www.sba.gov/content/table- small -business- size

standards >

6. McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies

, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

7. California Energy Commission

. California’s Energy Efficiency Standards for Residential and Nonresidential Buildings: 2008 . 2011. (Last accessed October 21, 2011.)

< www.energy.ca.gov/title24/>

12

- 33

### APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL (GRIM)

OVERVIEW

TABLE OF CONTENTS

12A.1 Introduction and Purpose ............................................................................................ 12A-1

12A.2 Model Description ...................................................................................................... 12A-1

LIST OF TABLES

Table 12A.2.1 Detailed Cash Flow Example .......................................................................... 12A-4

12A -i

### APPENDIX 12A. GOVERNMENT REGULATOR Y IMPACT MODEL (GRIM )

OVERVIEW

12A.1 INTRODUCTION AND PURPOSE

The purpose 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 the value of the industry or manufacturers(s) following a regulation or a series of regulations. The model structure also allows an analysis of multiple products with regulations taking effect over a period of time , and of multiple regulations 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 y ear and then determines the present value of those cash flows both without an energy conservation standard ( i.e. , the base case) and under different trial standard levels ( i.e.

, the standards case).

Output from the model consists of summary financial metri cs, graphs of major variables, and, when appropriate, access to the complete cash flow calculation.

12A.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses manufacturer selling prices, manufacturing costs, a ship ments forecast, and financial parameters as inputs and accepts a set of regulatory conditions 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 taxes 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 of the GRIM

.

(1) Unit Sal es: Total annual shipments for the industry were obtained from the National Impact Analysis Spreadsheet

.

(2) Revenues: Annual revenues – computed by multiplying products’ unit prices at each efficiency level by the appropriate manufacturer markup

.

(3) Labor: The p ortion of cost of goods sold (COGS) that includes direct labor, commissions, dismissal pay, bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor up

- time

.

(4) Material: The portion of COGS that includes materials.

(5) Overhead: The portion of COGS that includes indirect labor, indirect material, energy use, maintenance, depreciation, property taxes, and insurance related to assets. While included in overhead, the depreciation is shown as a separate line item

.

12A -1

### (6) Depreciation: The portion of overhead that includes an allowance for the total amount of fixed assets used to produce that one unit. Annual depreciation is computed as a percentage

of COGS. While included in overhead, the depreciation is shown as a separate line item.

(7) Strande d Assets: In the year the standard becomes effective, a one

- time write

- off of

stranded assets is accounted for

.

(8) Standard SG&A: Selling, general, and administrative costs are computed as a percentage of Revenues (2)

.

(9) R&D: GRIM separately accounts for ordinary research and development (R&D) as a percentage of Revenues (2)

.

(10) Product Conversion Costs: Product conversion costs are one- time investments in research, development, testing, marketing, and other costs focused on making products designs comply with the new energy conservation standard. The GRIM allocates these costs over the period between the standard’s announcement and compliance dates.

(11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions fo r interest paid and taxes

.

(12) EBIT as a Percentage of Sales (EBIT/Revenues) : GRIM calculates EBIT as a percentage of sales to compare with the industry’s average reported in financial statements.

(13) Taxes: Taxes on EBIT (11) are calculated by multiplying the ta x rate contained in Major Assumptions by EBIT (11)

.

(14) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods Sold ((3) to (6))

, SG&A ( 8)

, R&D ( 9)

, Product Conversion Costs (10) , and Taxes (13)

from Revenues (2)

.

(15) NOPAT repeated : NOPA T is repeated in the Statement of Cash Flows

.

(16) Depreciation repeated : Depreciation and Stranded Assets are added back in the Statement of Cash Flows because they are non- cash expenses

.

(17) Change in Working Capital : Change in cash tied up in accounts receivable, inventory, and other cash investments necessary to support operations is calculated by multiplying working capital (as a percentage of revenues) by the change in annual revenues.

(18) Cash Flow From Operations : Calculated by taking NOPAT (15) , adding back non- cash

items such as a Depreciation (16) , and subtracting the Change in Working Capital (17)

.

(19) Ordinary Capital Expenditures: Ordinary investments in property, plant, and equipment to maintain and replace existing production assets, computed as a percentage of Revenues (2)

.

(20) Capital Conversion Costs: Capital conversion costs are one-time investments in property, plant, and equipment to adapt or change existing production facilities so that new product

12A -2

### designs can be fabricated and assembled under the new regulation. The GRIM allocates these costs over the period between the standard’s announcement and compliance dates

.

(21) Capital Investment: Total investments in property, plant, and equipment are computed by adding Ordinary Capital Expenditures (19) and Capital Conversion Costs (20)

.

(22) Free Cash Flow: Annual cash flow from operations and investments; computed by subtracting Capital Investment (2 1) from Cash Flow from Operations (18)

.

(23) Terminal Value: Estimate of the continuing value of the industry after the analysis period. Computed by growing the Free Cash Flow at the beginning of 2047 at a constant rate in perpetuity.

(24) Present Value Factor: Factor used to calculate an estimate of the present value of an

amount to be received in the future.

(25) Discount ed Cash Flow: Free Cash Flows (2 2) multiplied by the Present Value Factor (2 4) . For the end of 2046, the discounted cash flow includes the discounted Terminal Value (2 3)

.

(26) Industry Value thru the end of 2046: The sum of Discounted Cash Flows (25)

.

12A -3

### Table 12A.2.1 Detailed Cash Flow Example

12A -4

### CHAPTER 1 3 . EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS ............................... 13-1

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS ............................................. 13-3

13.4 UPSTREAM FACTORS ............................................................................................... 13-3

13.5 EMI SSIONS IMPACT RESULT S ................................................................................ 13-4

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors .......................................................................... 13-3

Table 13.4.1 Electricity Upstream Emissions Factors ............................................................ 13-4

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Commercial Refrigeration Equipment .................................................................................... 13-5

LIST OF FIGURES

Figure 13.5.1 Commercial Refrigeration Equipment: CO 2 

### Total Emissions Reduction ......... 13-6 Figure 13.5.2 Commercial Refrigeration Equipment: SO 2 

### Total Emissions Reduction .......... 13-6 Figure 13.5.3 Commercial Refrigeration Equipment: NO

x 

### Total Emissions Reduction ......... 13-7 Figure 13.5.4 Commercial Refrigeration Equipment: Hg Total Emissions Reduction ............ 13-7 Figure 13.5.5 Commercial Refrigeration Equipment: N 2 

### O Total Emissions Reduction ......... 13-8 Figure 13.5.6 Commercial Refrigeration Equipment: CH 4 

### Total Emissions Reduction ......... 13-8

13 -i

### CHAPTER 13. EMISSIONS IMPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the effect of potential energy conservation standards on power sector and site combustion emissions of carbon dioxide (CO 2 

### ), nitrogen oxides (NO X 

### ), sulfur dioxide (SO 2 

### ) and merc ury (Hg). The second component estimates the impacts of potential standard s on emissions of two additional greenhouse gases, methane (CH 4 

### ) and nitrous oxide (N 2 

### O), as well as the reductions to

emissions of all species due to “upstream” activities in the fuel production chain. These upstream activities comprise extraction, processing, and transporting fuels to the site of combustion. T he

associated emissions are 

### referred to as upstream emissions. Together, these emissions account for

the full- fuel

- cycle (FFC) , i n accordance with DOE’s FFC Statement of Policy . 76 FR 51282 (Aug. 18, 2011)

.

The 

### analysis of power sector emissions uses marg inal emissions intensity factors derived

from runs of DOE’s NEMS -BT model, described in Chapter 14. DOE used the version of NEMS

based on the Annual Energy Outlook 2013 (AEO 2013). 1 

### Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions. AEO 2013 general ly represents current Federal and State legislation and final implementation regulations in place as of the end of December 2012.

Combustion emissions of CH 4 

### and N 2 

### O are estimated using emissions intensity factors published by the EPA , GHG Emissions Factors Hub. a 

### The FFC upstream emissions a re estimated based on the methodology developed by Coughlin (2013). 3 

### The upstream emissions include both emissions from fuel combustion during extraction, processing and transportation of fuel, and “fugitive” emissions (direct leakage to the atmosphere) of CH 4 

### and CO 2



### .

The 

### emissions intensity factors are expressed in terms of physical units per MWh or MMBtu of site energy savings. Tota l emissions reductions are estimated using the energy savings calculated in the national impact analysis (chapter 10).

13.2 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 contiguous states and the District of Columbia (D.C.). SO 2 

### emissions from 28 eastern states and D.C. were also limit ed under the Clean Air Interstate Rule (CAIR ) , which created an allowance -based trading program that 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 fo r the District of Columbia Circuit (D.C. Circuit) but parts of it remained in effect. On July 6, 2011 EPA issued a replacement for CAIR,

a http://www.epa.gov/ climateleadership/guidance/ghg

- emissions.html

13 -1

### the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation,

LP v. EPA , 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering

CAIR. The AEO 2013 emissions factors used for this analysis assume that CAIR remains a binding regulation through 2040.

The attainment of emissions caps is typically flexible among affected Electric Generating Units (EGUs) and is enforced through the use of emissions 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 

### emissions by any regulated EGU. In past rulemak ings, 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 standa rds.

Beginning in 2015, however, SO 2 

### emissions will fall as a result of the Mercury and Air Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). 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 power plants to comply with the MATS requirements for acid gas. AEO

2013 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 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

ef

ficiency standards). Emissions will be far below the cap established by CAIR, 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.

CA I R established a cap on NO

x 

### emissions in 28 eastern States and the District of Columbia. Energy conservation standards are expected to have li ttle 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 the States not affected by

CA IR, so DOE estimate d NOx emissions reductions from potential standards for 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. DOE estimated mercury emissions reductions using the NEMS -BT based on AEO

2013 , which incorporates the MATS.

13 -2

### 13.3 POWER SECTOR AND SITE EMISSIONS FACTORS

The 

### analysis of power sector emissions uses marginal emissions intensity factors derived

from runs of DOE’s NEMS -BT model, using the version updated to the Annual Energy Outlook

2013 (AE O 2013). To model the impact of a standard, DOE inputs a reduction to annual energy demand for the corresponding end use in the appropriate start year. The NEMS-BT model is run with the decremented energy demand to determine the modified build-out of capacity, fuel use and power sector emissions. A marginal emissions intensity factor is defined by dividing the reduction in the total emissions of a given pollutant by the reduction in total generation (in billion kWh). DOE uses the site energy savings mu ltiplied by a T&D loss factor to estimate the reduction in generation for each TSL. Details on the approach used may be found in Coughlin (2013 ). 3



### Table 15.3.1 presents the average power plant emissions factors for selected years. The se power plant emissions factors are derived from the emissions factors of the plant ty pes used to supply electricity for commercial refrigeration. The average factors for each year take into

account the projected shares of each of the sources in total electricity generation

.

The power plant emissions factor for NOx is an average for the entire U.S. The marginal

calculation based on the NEMS

- BT model accounts for the fact that NOx emissions are capped

in some States.

Table 13.3.1 Power Plant Emissions Factors

Unit* 201 7 2020 2025 2030 2035 2040

Commercial Sector

CO 2 

### kg/MWh 598 598 559 524 497 309

SO 2 

### g/MWh 570 570 691 732 454 524

NOx g/MWh

- 902

- 902

- 418

- 218

- 69.7 43.2 Hg g/MWh 0.0015 0.0015 0.0004 0.0009 0.0007 0.0006

N 2 

### O g/MWh 7.0 7.2 7.2 7.1 7.1 6.9

CH 4 

### g/MWh 49.0 50.2 50.1 49.6 49.2 47.8
* Refers to site electricity savings.



### 13.4 UPSTREAM FACTORS

The upstream emissions accounting uses the same approach as the upstream energy accounting described in appendix 10-E. See also Coughlin (2013 ). 3 

### When demand for a particular fuel is reduced, there is a corresponding reduction in the emissions from combustion of that fuel at either the building site or the power plant. The associated reduction in energy u se for upstream activities leads to further reductions in emissions. These upstream emissions are defined to include the combustion emissions from the fuel used upstream, the fugitive emissions associated with the fuel used upstream, and the fugitive emiss ions associated with the fuel used

on site.

13 -3

### Fugitive emissions of CO 2 

### occur during oil and gas production, but are small relative to combustion emissions. They comprise about 2.5% of total CO 2 

### emissions for natural gas and 1.7% for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal production. Combustion emissions of CH 4 

### are very small, while fugitive emissions (particularly for gas production) may be relatively large. Hence, fugitive emissions make up over 99% of total methane emissions for natural gas, about 95% for coal, and 93% for petroleum fuels.

U pstream emissions factors account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primary fuels. For ease of application in its analysis, DOE developed all of the emissions factors using site (point of use) energy savings in

the denominator. Table 15 .4.1 present s upstream emissions factors for selected years . The caps that apply to power sector NO

x 

### emissions do not apply to upstream combustion sources.

Table 13.4.1 Electricity Upstream Emissions Factors

Unit* 201 7 2020 2025 2030 2035 2040

CO 2 

### kg/MWh 27. 9 27.3 26.9 26.8 26.9 26.3

SO 2 

### g/MWh 5. 0 5.3 5.3 5.2 5.2 5.1

NOx g/MWh 3 53 340 334 333 336 329 Hg g/MWh 0.000013 0.000014 0.000014 0.000013 0.000013 0.000013

N 2 

### O g/MWh 0.2 49 0.254 0.252 0.249 0.244 0.238

CH 4 

### g/MWh 20 84 2025 2008 2025 2057 1999
* Refers to site electricity savings.



### 13.5 EMISSIONS IMPACT RES ULTS

Table 15 .5.1 presents the estimated cumulative emissions reductions for the lifetime of products sold in 2017-2046 for each TSL. Negative values indicate that emissions increase.

13 -4

### Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Commercial Refrigeration Equipment

TSL

1 2 3 4 5

Power Sector and Site Emissions*



### CO 2 

### (million metric tons) 54.9 95.4 133 153 194



### SO 2 

### (thousand tons) 84.9 147 205 236 299



### NO X 

### (thousand tons)

- 11.4

- 19.9

- 28.1

- 32.3

- 40.7 

### Hg (tons) 0.100 0.174 0.243 0.279 0.353



### N 2 

### O (thousand tons) 1.33 2.32 3.23 3.71 4.70



### CH 4 

### (thousand tons) 7.69 13.3 18.6 21.4 27.1 Upstream Emissions



### CO 2 

### (million metric tons) 3.68 6.38 8.90 10.2 13.0



### SO 2 

### (thousand tons) 0.788 1.37 1.91 2.19 2.78



### NO X 

### (thousand tons) 50.6 87.8 122 141 178 

### Hg (tons) 0.002 0.003 0.005 0.005 0.007



### N 2 

### O (thousand tons) 0.037 0.064 0.090 0.103 0.130



### CH 4 

### (thousand tons) 307 533 743 855 1,082

Total Emissions



### CO 2 

### (million metric tons) 58.6 102 142 163 207



### SO 2 

### (thousand tons) 85.7 149 207 238 302



### NO X 

### (thousand tons) 39.2 67.9 94.3 108 137 

### Hg (tons) 0.102 0.178 0.248 0.285 0.360



### N 2 

### O (thousand tons) 1.37 2.38 3.32 3.81 4.83



### CH 4 

### (thousand tons) 315 547 762 876 1,109 

### CH4 (million tons of CO 2 

### eq) 7. 87 13. 6 7 19. 04 21.90 27. 7 3

N 2 

### O (thousand tons of

CO 2 

### eq) 409 709 988 1,136 1,439

Figure 15.5.1 through Figure 15.5.6 show the annual reductions in total emissions for each type of emission from each TSL

. The reductions reflect the lifetime impacts of products

sold in 2017-2046.

13 -5

### Figure 13.5.1 Commercial Refrigeration Equipment : CO 2 

### Total

Emissions Reduction

Figure 13.5.2 Commercial Refrigeration Equipment : SO 2 

### Total

Emissions Reduction 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,00020172022202720322037204220472052205720622067kt 

### CO 2 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 2,000 4,000 6,000 8,000 10,000 12,000 14,00020172022202720322037204220472052205720622067ton 

### SO 2 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13 -6

### Figure 13.5.3 Commercial Refrigeration Equipment : NO

x 

### Total

Emissions Reduction

Figure 13.5.4 Commercial Refrigeration Equipment : Hg Total

Emissions Reduction - 4,000 - 2,000 0 2,000 4,000 6,000 8,000 10,00020172022202720322037204220472052205720622067ton 

### NO

x 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.01620172022202720322037204220472052205720622067ton 

### Hg Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13 -7

### Figure 13.5.5 Commercial Refrigeration Equipment : N 2 

### O Total

Emissions Reduction

Figure 13.5.6 Commercial Refrigeration Equipment : CH 4 

### Total

Emissions Reduction 0 50 100 150 200 250 30020172022202720322037204220472052205720622067ton 

### N 2 

### O Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,00020172022202720322037204220472052205720622067ton 

### CH 4 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13 -8

### REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2013 with Projections to

2040 , 2013. Washington, DC. < http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf >

2. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors,

AP -42, Fifth Edition, Volume I: Stationary Point and Area Sources . 1998. http://www.epa.gov/ttn/chief/ap42/index.html

3. Coughlin, K., Projections of Full

- Fuel -Cycle Energy and Emissions Metrics , 2013. Lawrence Berkeley National Laboratory. Report No. LBNL -6025E.

13 -9

### CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

TABLE OF CONTENTS

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon .................................................................................................... 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ......................................................................... 14-2

14.2.3 Development of Social Cost of Carbon Values ............................................................. 14-2

14.2.4 Current Approach and Key Assumptions ...................................................................... 14-2

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.4 RESULTS ...................................................................................................................... 14-5

LIST OF TABLES

Table 16.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007 dollars per metric ton of CO 2 

### ) ........................................................................... 14-4 Table 16.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007 dollars per metric ton of CO 2 

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

Table 16.4.1 Estimates of Global Present Value of CO 2 

### Emissions Reduction for Commercial Refrigeration Equipment Trial Standard Levels ........................... 14-6

Table 16.4.2 Estimates of Domestic Present Value of CO 2 

### Emissions Reduction for Commercial Refrigeration Equipment Trial Stand ard Levels ........................... 14-7

Table 16.4.3 Estimates of Present Value of NO X 

### Emissions Reduction for Commercial Refrigeration Equipment Trial Stan dard Levels ................................................ 14-8

14 -i

### CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

14.1 INTRODUCTION

As part of its assessment of energy conservation standards, DOE estimated the 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 monetary values used for each of these emissions and presents the benefits

estimates considered.

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon

The social cost of carbon (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. Estimates 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 resul ting from a unit change in carbon dioxide emissions, while a global SCC value is meant to reflect 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 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 o f the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to allow agencies to incorporate the monetized social benefits of reducing CO 2 

### emissions

into cost- benefit analyses of regulatory actions. The estimates are presented with an acknowledgement 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 these SCC estimates, technical experts from numerous agencies met on a regular basis to explore the technical literature in relevant fields, discuss key model inputs and assumptions, and consider public comments. The main objective 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.

14 -1

### 14.2.2 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 report from the National Research

Council 1 

### 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 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.

Despite the limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing CO 2 

### emissions. The agency can estimate the benefits from reduced (or costs from increased) em issions in any future year by multiplying the change in emissions in that year by the SCC values 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 across 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 small relative to cumulative global carbon dioxide emissions.

14.2.3 Development of Social Cost of Carbon Values

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in

how benefits are evaluated across Federal 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 preliminary 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



### . 2 

### These 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.

14.2.4 Cu rrent Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular basis to generate improved SCC estimates. Specially, the group considered public comments and further explored the technical literatu re 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 assess ment of the Intergovernmental Panel on Climate Change (IPCC). Each model was given equal weight in the SCC values that were developed.

14 -2

### Each model takes a slightly different approach to model how changes in emissions result in changes 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 conduc ted 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 all three models. In addit ion, 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 judgments.

The interagen cy 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 95th percentile SCC est imate 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. The values grow in real terms over time . Additionally, the interagen cy 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, a 

### which is reproduced in appendix 16- A of the TSD.

The SCC values used for this analysis were generated using the most recent versions of the three integrated assessment models that have been published in the peer- reviewed literature

, as described in the 2013 update 3 

### from the interagency working group (revised November 2013). 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-A

of the TSD. The central value that emerges is the average SCC across models at the 3 percent 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. For the years after 2050, DOE applied the average annual growth rate of the SCC

estimates in 2040−2050 associated with each of the four sets of 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, February 2010. http://www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

- Cost

- of

- Carbon

- for

- RIA.pdf

. b Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group on Social Cost of Carbon, United States Government . May 2013; revised November

2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical

- update

- social

- cost

- of

- carbon

- for

regulator

- impact

- analysis.pdf

.

14 -3

### Table 16.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007 dollars per metric ton of 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 of CO 2 

### )

Year Discount Rate %

5 3 2.5 3

Average Average Average 95 th Percentile

2010 11 3 2 5 1 8 9

2015 11 37 57 109

2020 12 43 6 4 12 8

2025 14 47 69 143

2030 16 52 75 159

2035 19 5 6 8 0 17 5

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220



### 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 2009 National Research Council report mentioned above points out that there 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. There are a number of analytic challenges that are being addressed by the research community, i ncluding research programs housed in many of the Federal agencies participating in the interagency process to estimate the SCC. The interagency group intends to periodically review and reconsider those estimates to reflect increasing knowledge of the science and economics of climate impacts, as well as improvements in modeling.

14 -4

### In summary, in considering the potential global benefits resulting from reduced CO2 emissions, DOE used the values from the 2013 interagency report adjusted to 2012$ using the

GDP price deflator. For each of the four sets of SCC values, the values for emissions in 2015 were $11.8, $39.7, $61.2, and $117 per metric ton avoided (values expressed in 2012$). DOE derived values after 2050 using the relevant growth rates for the 2040-2050 period in the interagency update.

.

DOE multiplied the CO2 emissions reduction estimated for each year by the SCC value for that year in each of the four cases. To calculate a present value of the stream of monetary values, DOE discounted the values in e ach of the four cases using the specific discount rate that

had been used to obtain the SCC values in each case.

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NO X 

### emissions from the TSLs it considered. As noted in chapter 13, new or amended energy conservation standards would

reduce NO X 

### emissions in those States that are not affected by caps. DOE estimated the

monetized value of NO X 

### emissions reductions resulting from each of the TSLs considered based

on en vironmental damage estimates found in the relevant scientific literature. E stimates of monetary value for reducing NOx from stationary sources range from $ 476 to $4,889 per ton in

2013$. 4 

### DOE calculated monetary benefits using a medium value for NO X 

### emissions of $2,683 per short ton (in 2013 $), and real discount rates of 3 percent and 7 percent.

DOE is still evaluating appropriate values to use to monetize avoided SO 2 

### and Hg

emissions. I t did not monetize these emissions for this analysis

.

14.4 RESULTS

Table 16.4.1 p resent s the global values of CO 2 

### emissions reductions for each considered TSL. DOE calculated domestic values as a range from 7 percent to 23 percent of the global values, and these results are presented in Table 16.4.2

.

Table 16.4.3 presents the present value of cumulative NO X 

### emissions reductions for each TSL, calculated using the average dollar- per

- ton values and seven -percent and three- percent
discount rates.

14 -5

### Table 16.4.1 Estimates of Global Present Value of CO 2 

### Emissions Reduction for Commercial Refrigeration Equipment Trial Standard Levels

TSL SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate, 95 th
percentile*

Million 2013$

Site and Power Sector Energy Emissions

1 392 1,762 2,787 5,438

2 682 3,063 4,844 9,452

3 952 4,274 6,758 13,187

4 1,095 4,916 7,773 15,167

5 1,385 6,220 9,836 19,192

Upstream Emissions

1 25.0 115 183 356

2 43.5 200 317 617

3 60.6 278 442 861

4 69.7 320 508 990

5 88.2 405 643 1,253

Total Emissions

1 417 1,877 2,970 5,794

2 725 3,263 5,161 10,070

3 1,012 4,552 7,200 14,047

4 1,164 5,236 8,281 16,157

5 1,473 6,625 10,479 20,444

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.0,
$40. 4 , $62. 2 , and $119 per metric ton ( 2013 $).

14 -6

### Table 16.4.2 Estimates of Domestic Present Value of CO 2 

### Emissions Reduction for Commercial Refrigeration Equipment Trial Standard Levels

TSL SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate,

95 th percentile*

Million 2013$

Site and Power Sector Energy Emissions

1 27.4 to 90.2 123.4 to 405.3 195.1 to 641.1 380.7 to 1250.8

2 47.7 to 156.8 214.4 to 704.6 339.1 to 1114.2 661.7 to 2174.0

3 66.6 to 218.9 299.2 to 983.0 473.1 to 1554.4 923.1 to 3032.9

4 76.6 to 251.8 344.1 to 1130.6 544.1 to 1787.8 1061.7 to 3488.4

5 96.9 to 318.5 435.4 to 1430.6 688.5 to 2262.2 1343.4 to 4414.0

Upstream Emissions

1 1.8 to 5.8 8.0 to 26.4 12.8 to 42.0 24.9 to 81.8

2 3.0 to 10.0 14.0 to 45.9 22.2 to 72.9 43.2 to 142.0

3 4.2 to 13.9 19.5 to 64.0 30.9 to 101.6 60.2 to 197.9

4 4.9 to 16.0 22.4 to 73.6 35.6 to 116.9 69.3 to 227.6

5 6.2 to 20.3 28.4 to 93.2 45.0 to 147.9 87.7 to 288.1

Total Emissions

1 29.2 to 95.9 131.4 to 431.8 207.9 to 683.0 405.6 to 1332.6

2 50.8 to 166.8 228.4 to 750.5 361.3 to 1187.1 704.9 to 2316.0

3 70.9 to 232.8 318.6 to 1047.0 504.0 to 1656.0 983.3 to 3230.9

4 81.5 to 267.8 366.5 to 1204.2 579.7 to 1904.7 1131.0 to 3716.0

5 103.1 to 338.8 463.7 to 1523.7 733.5 to 2410.2 1431.1 to 4702.2

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.0, $40.4, $62.2, and $119 per metric ton (2013$).

14 -7

### Table 16.4.3 Estimates of Present Value of NO X 

### Emissions Reduction for Commercial Refrigeration Equipment Trial Standard Levels

TSL 3% discount rate 7% discount rate

Million 2013$

Site and Power Sector Emissions

1

- 25.3

- 18.9

2

- 44.4

- 33.2

3

- 62.4

- 46.6

4

- 71.9

- 53.7

5

- 90.6

- 67.7

Upstream Emissions

1 68.7 32.6

2 119 56.7

3 167 79.3

4 192 91.2

5 242 115

Total Emissions

1 43.4 13.7

2 75.0 23.6

3 104 32.6

4 120 37.4

5 152 47.6

14 -8

### REFERENCES

1. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Use , 2009. National Academies Press. Washington, DC.

< http://www.nap.edu/catalog.php?record_id=12794 >

2. 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. < http://www.whitehouse.gov/sites/default/files/omb/inforeg/for

agencies/Social -Cost-of-Carbon- for -RIA.pdf >

3. I 

### nteragency Working Group on Social Cost of Carbon, Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis under E xecutive Order 12866

, May 2013.

United States Government. http://www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria _2013_update.pdf

4. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal

Entities , 2006. Washington, DC.

< http://www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_c b_final_report.pdf >

14 -9

### APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14A.1 EXECUTIVE SUMMARY ........................................................................................ 14A-1

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS .................................................. 14A-2

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST

REGULATORY ANALYSES ................................................................................... 14A-4

14A.4 APPROACH AND KEY ASSUMPTIONS ............................................................... 14A-5

14A.4.1 Integrated Assessment Models ................................................................... 14A-5

14A.4.2 Global versus Domestic Measures of SCC ............................................... 14A-11

14A.4.3 Valuing Non- CO 2 

### Emissions .................................................................... 14A-12

14A.4.4 Equilibrium Climate Sensitivity ............................................................... 14A-13

14A.4.5 Socio -Economic and Emissions Trajectories ............................................ 14A-16

14A.4.6 Discount Rate ............................................................................................ 14A-18

14A.5 REVISED SCC ESTIMATES .................................................................................. 14A-25

14A.6 LIMITATIONS OF THE ANALYSIS ..................................................................... 14A-29

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND

DAMAGE FUNCTIONS ......................................................................................... 14A-32

14A.7.1 Extrapolation of Climate Damages to High Levels of Warming .............. 14A-32

14A.7.2 Failure to Incorporate Inter

- Sectoral and Inter

- Regional Interactions ...... 14A-33

14A.7.3 Imperfect Substitutability of Environmental Amenities ........................... 14A-34

14A.8 CONCLUSION ......................................................................................................... 14A-34

14A.9 ANNEX ..................................................................................................................... 14A-42

14A.9.1 Other (non- CO 2 

### ) Gases ............................................................................. 14A-43

14A.9.2 Extrapolating Emissions Projections to 2300 ........................................... 14A-45

LIST OF TABLES

Table 14A.1.1 Social Cost of CO 2 

### , 2010–2050 (2007$) ........................................................ 14A-2 Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions ................................................................................ 14A-14 Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF- 22 Reference

Scenarios ....................................................................................................... 14A-17 Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic Trajectory, and Discount Rate for 2010 (2007$) .......................................... 14A-27

Table 14A.5.2 Social Cost of CO 2 

### , 2010–2050 (2007$) ...................................................... 14A-29

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between

2010 and 2050 ............................................................................................... 14A-29

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation .................. 14A-33 Table 14A.9.1 Annual SCC Values: 2010–2050 (2007$) .................................................... 14A-42 Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 

### )14A-50 Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO 2 

### ) .. 14A-50

14A -i

### Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 

### ) .. 14A-51

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates ................... 14A-52

LIST OF FIGURES

Figure 14A.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 ........................................................................................................... 14A-10 Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND,

and PAGE ..................................................................................................... 14A-10 Figure 14A.4.3 Estimat es of the Probability Density Function for Equilibrium Climate Sensitivity ..................................................................................................... 14A-15 Figure 14A.5.1 Level of Global GDP Across EMF Scenarios ............................................. 14A-28 Figure 14A.9.1 Sulphur Dioxide Emission Scenarios .......................................................... 14A-45 Figure 14A.9.2 Global Population, 2000–2300 (post-2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200) 14A-47 Figure 14A.9.3 World GDP, 2000-2300 (post-2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in 2300) ............................... 14A-47 Figure 14A.9.4 Global Fossil and Industrial CO 2 

### Emissions, 2000-2300 (post-2100 extrapolations assume growth rate of CO 2 

### intensity (CO 2 

### /GDP) ove r 2090– 2100 is maintained through 2300) ................................................................ 14A-48 Figure 14A.9.5 Global Net Land Use CO 2 

### Emissions, 2000–2300 (post-2100 extrapolations assume emissions decline linearly, reaching zero in 2200) .......................... 14A-48 Figure 14A.9.6 Global Non- CO 2 

### Radiative Forcing, 2000–2300 (post-2100 extrapolations

assume constant non- CO 2 

### radiative forcing after 2100) ............................... 14A-49 Figu re 14A.9.7 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) ...................................................... 14A-49 Figure 14A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 

### ), by Discount

Rate* ............................................................................................................. 14A-51

14A

- ii

### APPENDIX 14 A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXECUTIVE ORDER 12866

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 the Treasury

14A.1 EXECUTIVE SUMMARY

Under Executive Order (E.O.) 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 dioxide (CO 2 

### ) emissions into cost- benefit analyses of regulatory actions that have s mall, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement 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.

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 interagency process that developed these SCC estimates. Technical experts from numerous agenci es 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 range of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic literatures.

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### In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC values for use in regulatory analyses ( Table

14A.1.1

. 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 distribution.

Table 14A.1.1 Social Cost of CO 2 

### , 2010–2050 (2007$)

Year Discount Rate

%

5 3 2.5 3 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



### 14A.2 MONETIZING CARBON DIOXIDE EMISSIONS

The “social cost of carbon” (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. We report estimates of the social cost of carbon in dollars per metric ton of CO 2 

### throughout this document. a



### When attempting to assess the incremental economic impacts of CO 2 

### 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 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 physical and biological environment; and (4) the translation of these environmental impacts into economic damages. As a resul t, 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.

Despite the serious limits of both quantification and monetization, SCC es timates can be useful in estimating the social benefits of reducing CO 2 

### emissions. Under E.O. 12866, agencies

a 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 carb on is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12 = 3.67).

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### 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 SCC estimates presented here is to make it possible for agencies to incorporate the social benefits from reducing CO 2 

### emissions into costbenefit analyses of regulatory actions that have small or “marginal” impacts on cumula tive global emissions. Most F ederal 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 estimated by multiplying th e 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 across 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 small relative to cumula tive global

CO 2 

### 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 Economic 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 main objective of this process was to develop a range of SCC values using a defensible set of input assumptions that are grounded 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 th e rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$ ). The first three estimates are based on the average SCC across models and socio- eco nomic and emissions scenarios at the 5, 3,and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan -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 t he 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 Appendix A for the full range of annual SCC estimates from 2010 to 2050.

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

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### values within 2 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 continue to explore the issues raised in this document and consider public comments as part of the ongoing interagency

process.

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY

ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to estimate the benefits associated with reducing CO 2 

### 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 (2007$), increasing both values 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 r eflect the value of damages in the United States resulting from a unit change in CO 2 

### 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 (2007$). 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 

### for discount rates of approximately 2 percent and 3 percent, respectively ( 2006$ for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing CO 2 

### 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 preliminary assessment by the intera gency 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 estimates produced from the most recently av ailable 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 develo ped 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 ton estimates. All of these values were assumed to increase at 3 percent 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 analysis. The results of this preliminar y

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### 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.

14A.4 APPROACH AND KEY ASSUMPTIONS

Since the rel ease of the interim values, 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 literature in relevant fields. This section detail s 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 there 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 problems that should be addressed by the research comm unity, 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 research take on exceptional significance. The

intera gency 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.

14A.4.1 Integrated Assessment Models

We rely on three integrated assessment models (IAMs) commonly us ed to estimate the SCC: the FUND, DICE, and PAGE models. b 

### These models are frequently cited in the peer

reviewed literature and used in the IPCC assessment. Each model is given equal weight in the SCC values 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 theunderlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedb 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). The FUND (Clim ate 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 2002a, Tol 2002b , Anthoff et al. 2009, Tol 2009).

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### form approaches (see NRC 2009 for a more detailed 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 amount of research linking climate impacts to economic dam ages, 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, and changes in temperature into economic damages. The emissi ons projections used in the models are based on specified socio -economic (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 parameter, 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

requ ires 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 to 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 affec ts 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 ada pt to climate change and the physical changes it causes).

The parameters 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 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 trajectorie s, and discount rates. A probability distribution for climate sensitivity was specified as an input into all 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 judgments. In DICE, these parameters are handled deterministically and represented by fixed constants; in PAGE, most parameters are represented by prob ability distributions. FUND was also run in a mode in which parameters 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 o f 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.

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### 14A.4.1.1 The DICE Model

The DICE model is an optimal growth model based on a global production function with

an extra stock variable (atmospheric CO 2 

### concentrations). 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 c limate change can be avoided and future consumption thereby increased.

For purposes of estimating the SCC, CO 2 

### 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 links 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 service s. 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 (based 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 with 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 nonmarket 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 farmers can adjust land use decisions in response to changing climate conditions, and its h ealth impact estimates assume improvements in healthcare over time. In addition, the small impacts on forestry, water systems, construction, fisheries, and outdoor recreation 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, dam ages propagate forward in time and

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### reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in any given year do not propagate forward. c



### 14A.4.1.2 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 o f 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 values r anging from 1 to 3 (instead of being fixed at 2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a “discontinuity” ( 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 resulting 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 economic 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 tolerable 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).

14A.4.1.3 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 sixteen geog raphic regions. In some impact sectors, the fraction of output lost or gained due to climate change depends not only on the absolute temperature change but also on the rate of temperature change and level of regional

c 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. Specifical ly, 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 section): we as sumed 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.

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### income. d 

### In the forestry and 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

characterizes s everal 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 cause these sort s 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 studi es 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 positive); (2) those due t o 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 d amages 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 temperature across these sectors can result in negative economic damages from climate change.

14A.4.1.4 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 the increase in global

- avera ge 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. Given the paucity of data linking the ph ysical 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 Figure 14A.4.1 and Figure

14A.4.2 , using the modeler’s default scenarios and mean input assumptions. There are signif icant differences between the three models both at lower ( Figure 14A.4.1 ) and higher ( Figure 14A.4.2 )

increases in global- average temperature.

d In the deterministic version 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 (Warren et al . 2 006).

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### Figure 14A.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 M odels e



### Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE

e 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, socio

economic, and emissions parameters will produce a different realization of damages for each IAM. The damage functions represented in Figure 17A.4 .1 and Figure 17A.4 .2 are the outcome of default assumptions. For instance, under alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater

than 3 °C.

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- 10

### 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 DIC E 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 disco unt rate is 2.5 percent, about 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 differences 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 t he models challenging, which highlights the need for additional research. As knowledge improves, the Federal government is committed to exploring how these (and other) models can be modified to incorporate more accurate estimates of damages.

14A.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 both global and domestic values is generally permissible; the relevant statutory provisions are usually ambiguous and allow

selection of either measure. f



### 14A.4.2.1 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 externality: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in the 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 signif icant 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 international agreements to reduce emissions and in encouraging other nations, including emerging major economies, to take significant steps to reduce emissions. When these

f It is true that F ederal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the United States respect the interests of f oreign sovereigns. But use of a global measure for the SCC does not give

extraterritorial effect to federal law and hence does not intrude on such interests.

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- 11

### considerations are taken as a whole, the interagency group concluded that a global measure of the 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, the interagency group concluded that this approach would not be appro priate for estimating a SCC value used in domestic regulatory analysis. g 

### For this reason, the group concluded that using the global (rather than domestic) value, without equity weighting, is the appropriate approach.

14A.4.2.2 Domestic SCC

As an empirical matter, th e 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 comes 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. Alternati vely, 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, which is currently about 23 percent. h



### 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 these values are 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.

14A.4.3 Valuing Non- CO 2 

### Emissions

While CO 2 

### is the most prev alent greenhouse gas emitted into the atmosphere, the U.S. included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide,

g 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 consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor

na tion 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 welfare 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.

h Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report

.

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### 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 CO 2 

### . However, becaus e 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 not discounted as heav ily 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. Likewis e, 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 accurate 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 CO 2 

### emissions.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and

FUND models. i 

### It is defined as 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 in this important parameter have received subst antial attention in the peer

- reviewed literature.

The most authoritative statement about equilibrium climate sensitivity appears in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (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. j



### For fundamental physical reasons as well as data limitations, values substantially high er than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is generally

i 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

- scale changes in ice sheets or the biosphere, which occur on a time scale of many

hundreds to thous ands of years ( e.g. , Hansen et al . 2007).

j 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.

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### worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et al . 2007, p. 799)

After consulting with several lead authors of this c hapter 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 Weibull.

Table 14A.4.1 gives summary statistics for the four calibrated distributions.

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Rank Roe & Baker Log

- N ormal 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 P ercentile 1.72 1.49 1.37 1.13

10 th P ercentile 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 P ercentile 5.86 5.14 4.93 4.69

95 th P ercentile 7.14 5.97 5.59 5.17



### Each distribution was calibrated by applying three constraints from the IPCC:

(1) a median equal to 3 °C, to reflect the judgment of “a most likely value of about 3 °C;” k


### (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 (Hegerl et al. 2006, p. 721).

We selecte d the calibrated Roe and Baker distribution from the four candidates for two reasons. First, the Roe and 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 greenhou se 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 dist ributed. There is widespread agreement on the first point and the second and third points are common assumptions.

k 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 distribution, setting the median equal to 3 °C, rather than the mode or mean, gave a

95 th perc entile 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. Fina lly, 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 average mode is 2.3 °C, which is

most consistent with a Roe and Baker distribution with the m edian set equal to 3 °C.

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### 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 calibrated 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 truncated 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).

Fina lly, we note the IPCC judgment that the equilibrium climate sensitivity “is very likely larger than 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 9 9 percent, is not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it reflects a greater degree of certainty about very low values of ECS than was expressed by the IPCC.

To show how the calibrated Roe and B aker distribution compares to different estimates of the probability distribution function of equilibrium climate sensitivity in the empirical

literature

, Figure 14A.4.3 overlays it on Figure 14A.9.2 from the IPCC Fourth Assessment Report. These functions are 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. l



### Figure 14A.4.3 Estimates of the Probability Density Fu nction for Equilibrium Climate Sensitivity

l The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed line, anthropogenic forcings only), Forest et al . (2006; solid line, anthropogenic and natural forcings), Gregory et al

. (2002), Knutti et al . (2002), Frame et al . (2005), and Forster and Gregory (2006). Hegerl et al . (2006) are based on multiple palaeoclimatic reconstructions of north 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.

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### 14A.4.5 Socio

- Economic 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. Socio

- economic

p athways 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 

### radiative 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 of the plausible ranges of outcomes for these variables.

To accomplish this task in a transparent way, we decided to rely on the recent Stanford Energy Modeling Forum exercise, EMF -22, which uses ten well

- recognized models to evaluate substantial, coordinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, a nd emission trajectories are internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is preferable 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 e xtreme 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 socio- economic and emission trajectories that span a range of plausible scenarios. Five trajectories were selected from EMF

- 22 ( Table 1 4A.4.2 ). Four of these represent potential business- as -usual (BAU) growth in population, wealth, and

emissions and are associated with CO 2 

### (only) concentrations 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 lowerthan

- BAU trajectory. m 

### Out of the 10 models included in the EMF- 22 e xercise, 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 av eraged the GDP, population, and emission trajectories implied by these same four models.

m Such an emissions path would be consistent with wid espread action by countries to mitigate GHG emissions, though it could also result from technological advances. It was chosen because it represents the most stringent case analyzed by the EMF

- 22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

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### Table 14A.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 market exchange rates in trillion 2005$ n

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 proved challenging to do in an analytically rigorous way given the dearth of information on the likelihood of a full range o f future socio-economic pathways.

There 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

n While the EMF

- 22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use purchasing power parity (PPP) , which takes into account the different price levels across countries, so it more

accurate ly 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 emissions

- intensity gap across countries at least partially offset the overstated income gap so tha t 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 a nd outputs and national accounts price and quantity indexes for time

- series extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that exchange

- rate

- conversion issues are as important as uncertaint ies about population, technological change, or the many geophysical uncertainties.

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### most likely outcome span a wide range, from the more optimistic ( e.g. , abundant low-cost, lowcarbon energy) to more pessimistic ( e.g. , constraints on the availability of nuclear and renewables). o 

### Second, the socio- economic 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. p 

### We chose not to include socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the

diff iculty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy

Outlook projected that global CO 2 

### 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 $93.9 trillion (2005$ 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 in 2050, which is close to the population trajectories for the IMAGE, MiniCAM, and

MERGE models.

In addition to fossil and industrial CO 2 

### emissio ns, each EMF scenario provides projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land 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 Appendix for greater detail.

14A.4.6 Discount Rate

The choice of a discount 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 CO 2 

### emissions are long-lived, subsequent damages occur over many years. In calculating the SCC,we first estimate the future damages to agriculture, human health, and other market and nonmarket sectors from an additional unit of CO 2 

### emitted in a particular year in terms of reduced consumption (or consumption equi valents) due to the impacts of elevated temperatures, as represented in each of the three 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 ra te, which is intended to reflect society ’ s marginal rate of substitution between consumption in different time periods.

o 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.

p 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 alon g a BAU path CO 2 concentrations in 2100 would drop from 794 ppmv in its reference case to 762 ppmv.

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### 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, however, 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.” Fo r 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 de scriptive approach reflects a positive (non-normative) perspective based on observations 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 ap proach generally call for inferring the discount rate from market rates of return “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 social 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 ma rket interest rate that would govern the returns potentially set aside today 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 compensation, 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 ex plicitly to incorporate into the policy

evaluation

— e.g. , how inter-personal 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 th at 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 thecurrent market rate on consumption an inappropriate metric by which to discount future climatere lated damages. Others argue that the discount rate should be below market rates to correct for market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, which in the Kaldor-Hicks logic are presumed to compensate futur e generations for damage (a potentially controversial assumption, as noted above ; Arrow et al . 1996, Weitzman 1999).

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### 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 prescriptive approach assumes a representative agent with perfe ct 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 evidence 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 smoothing 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 standard 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 discount rate over another.

14A.4.6.1 Historically Observed Interest Rate s

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 rate. In the real world risk, taxes, and other market imperfectio ns 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 of 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. When 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.

The interagency 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 (Lind 1990, Arrow et al ., 1996, Arrow 2000). 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.

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### Individuals use a variety of sav ings 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

constraints. 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, ” i.e.

, 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 I AM 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 appropriate.

Th is 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 l ongest 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). q 

### This calculation produces a real interest rate of about 2.7 percent, which is roughly consistent with Circular A- 4’s recommendation to use 3 percent to represent the consumption rate of interest. r 

### A measure of the

post- tax risky rate for investments whose returns are positively correlated with overall equity

market returns can be obt ained by adjusting pre-tax rates of household returns to risky investments (approximately 7 percent) for taxes yields a real rate of roughly 5 percent. s



### 14A.4.6.2 The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount rate. Under this approach, the analyst applies either positive or normative judgments in selecting

q 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 i t 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.

r The positive approach 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.

s Cambell et al

. (2001) estimates that the annual r eal 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 percent range t o derive a post

- tax interest rate (Kotlikoff and Rapson 2006).

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### values for the key parameters of the Ramsey equation: η ( coefficient of relative risk aversion or elasticity of the marginal utility of consumption) and ρ ( pure rate of time preference). t 

### 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

. u 

### 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 plausible values as those ranging from 1 to 4), although not all authors articulate whether their choice is based on prescriptive or descriptive reasoning. v 

### 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.

• 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 th at to use any value other than ρ = 0 would unjustly discriminate against future generations ( e.g. , Arrow et al . 1996, Stern 2006). However, even in an int er

- 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 2006).

• g. A commonly accepted approximation is around 2 percent per year. For the socioeconomic scenarios used for this exercise, the EMF models assume that g is about 1.5–2 percent to 2100.

t 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 to place a l ower 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 damage 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 increase in incom e 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.

u In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the rate of consumption growth.

v 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 (2006, 2008), W eitzman (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 bound on the curv ature 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. Recent work has joi ntly 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.

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### 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 treatment to one generatio n 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 (2006) applies this perspective through his choice of ρ = 0.1 percent per year

, η = 1 and g = 1.3 per cent 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 income. w



### Recently, Stern (2008) revisited the values used in Stern (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 after 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 greater 2 percent.

We conclude that arguments made under the prescriptive approach can be used 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 o f this range under the Ramsey framework.

14A.4.6.3 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, j ust as we do for climate sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and Panipoulou et al. (2004) confirm empirically that discount rate uncertainty can have a large

effect on net present values. A main result from t hese studies is that if there is a persistent element to the uncertainty 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 (Weitzman 1998, 1999, 2001; Newell and Pizer 2003; Panipoulou et al. (2004); Gollier 2008; Summers and Zeckhauser 2008; and Gollier and Weitzman 2009).

The proper way to model discount rate uncertai nty remains an active area of research. Newell and Pizer (2003) employ 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 long-term rates. Subsequent work on this topic, most notably Panipoulou et al. (2004), 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 or high and variation in the level of persistence over time.

w Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the i mplied 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.)

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### While Newell and Pizer (2003) and Panipoulou et al. (2004) 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. x 

### A key question that has emerged with regard to both of these 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 ). y



### 14A.4.6.4 The 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 plausible 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 and 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 mentioned, the consumption rate of interest is 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 included 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 meanreverting and ra ndom walk approaches from Newell and Pizer (2003) starting at a discount 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. z 

### Without giving pr eference 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.

x For instance, the UK applies a discount 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.

y 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 demonstrates 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) and Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, utility, and consumption. Sum mers and Zeckhauser (2008) argue that uncertainty in future damages can also work in the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required.

z Calculations done by Pizer et al . using the original si mulation program from Newell and Pizer (2003).

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### 14A.5 REVISED SCC ESTIMATES

Our general approach to estimating SCC values is to run the three integrated assessment models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds.

• 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 parameters, the final output from each model run is a distribution over the SCC in year t

.

For each of the IAMS, 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 EMF-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.

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, d amages in each period depend on both the level and the rate of

tem perature 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 exogenous 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 exact 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 in 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.)

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### 6. Discount the resulting path of marginal damages back to the year of emissions using the agreed upon fixed discoun t 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. (All 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 is 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 fracti on 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, population, a nd 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 Appendix.)

This exercise produces 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 r eflects 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 integrated assessment model or socioeconomic scenario is given greater weight than another. Because the literature shows that the SCC is quite sensitiv e 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 rate.

The i nteragency group selected four SCC values for use in regulatory analyses. Three values are based on the average SCC across models and socio- economic and emissions scenariosat the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan

- 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 distributions by model and scenario combination is included in the Appendix.) 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

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### of capturing the uncertain ties 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 consideration of their eff ects 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 higher 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 14A.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 discou nt rates consistently result in lower SCC values, while lower discount 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 produces the highest estimates.

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio- Economic Trajectory, and Discount Rate for 2010 (2007$)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th

DICE 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 50.8

PAGE 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 42.9 104.7

FUND 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 us ing the latest versions of DICE and PAGE in the literature: Using similar adjustments to generate 2010 estimates, 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

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### comparisons are only approximate since the literature generally relies on Ramsey discounting,

while we have assumed constant discount rates. aa



### The SCC estimates from FUND are sen sitive to differences in emissions paths but relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for DICE and PAGE. This likely occurs because of several structural differences among the models. Specifically in DI CE and PAGE, the fraction of economic output lost due to climate damages increases with the level of temperature alone, whereas in FUND the fractional loss also increases with the rate of temperature change. Further, 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 PAG E.

Figure 14A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE Optimistic has the lowest. 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 and GDP.

Figure 14A.5.1 Level of Global GDP A cross EMF Scenarios

Table 14A.5.2 shows the four selected SCC values in 5-year increments from 2010 to 2050. Values for 2010, 2020, 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. Values for the years in between are calculated using a simple linear interpolation.

aa 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 gr owth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 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 occur 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 forcing

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### Ta ble 14A.5.2 Social Cost of CO 2 

### , 2010–2050 (2007$)

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



### 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 directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as

was done for th e interim estimates (using 3 percent). This helps to ensure that the estimates are internally consistent with other modeling assumptions. Table 14A.5.3 illustrates 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 Appendix.

Table 14A.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%

Year Range 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 i n 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 to day or emissions in a later year, should be discounted using the same rate. For example, climate damages in 2020 that are calculated using a SCC based on a 5 percent discount rate also should be discounted back to the analysis year using a 5 percent discou nt rate. bb



### 14A.6 LIMITATIONS OF THE ANALYSIS

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,

bb 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.

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### and ethical understandings. 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 interpreting and applyi ng 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 complexi ty 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 impa cts 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 lags behind the most recent research. Our ability to quantify and monetize impacts will undoubtedly improve

with time. 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

. Ther e 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 metha ne 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 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. 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 highly 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- impact low-probability risks, using a right- skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficie nt, 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 dama ges 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 change. Hence, estimated damages are far more uncertain under more extreme climate change scenarios.

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### Incomplete treatment of adaptation and technological change . Each of the three integrated assessment models used here assumes a certain degree of low

- or no -cost adaptation. For instance, Tol assumes a great deal of adap tation 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 air 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 technological change. cc 

### 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, pests, or diseases, which could make adaptation more difficult than assumed b y 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 incomple te treatment of adaptation and technological change in these IAMs under or overstate the likely damages.

Risk aversion . A key question unanswered during this interagency process is what to assume about relative risk aversion with regard to high- impact ou tcomes. 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-impact 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 individuals do show such a higher willingness to pay, a further question is

whether that f act 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.

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 neutr al’ 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

i n 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) explored 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 carbon.” Since Circular A -4 allows for a different

cc 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).

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### assumption on risk preference in regulatory analysis if it is adequately justified, we plan to continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE

FUNCTIONS

As noted above, 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 p articular, 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 near

- 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 by the Federal government will continue to evolve with improvements in modeling. In the meantime, we

discuss some of the available evidence.

14A.7.1 Extrapolation of C limate D amages to High Le vels of W arming

The damage functions in the models are calibrated at moderate 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

th e Amazon Rainforest 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 melting permafrost. 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. (2 009); results from this study are highlighted in Table 14A.7.1 . Ranges of probability are averaged across core experts on each topic.

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### Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points Duration before effect is fully realized

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



### 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. , ignoring potential risk aversion). PAGE models catastrophic impacts in a probabilistic framework (Figure 16-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 change . For instance, at the 95 th 

### percentile and a 3 percent discount rate, the SCC estimated by PAGE across the five socio -economic and emission trajectories of $113 per ton of CO 2 

### is almost double the value estimated by DICE, $58 per ton in2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the distribution to the assumptions made about catastrophic impacts.

PAGE treats the possibi lity of a catastrophic event probabilistically, while DICE treats it deterministically ( i.e. , by adding the expected value of the damage from a catastrophe to the aggregate damage function). In part, this results in different probabilities being assigned t o 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 et al. (2009) estimate a prob ability of at least 16

– 

### 36 percent of 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 crossing a climatic tipping point wi ll 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 instance, one with dramaticall y reduced ice sheet volumes and higher sea levels). In the IAMs, a catastrophe is a low -probability environmental change with high economic impact.

14A.7.2 Failure t o Incorporate Inter

- Sectoral a nd Inter -Regional Interactions

The damage functions do not fully inco rporate 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 studie s used to calibrate the IAM. Likewise, the effects of climate damages in one

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### 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, thoug h 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 ins tance, 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; Campbell et al . 2007).

14A.7.3 Imperfect S ubstitutability of Environmental A menities

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 plants (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 goods would provide complete compensation (Lev y 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 attempts 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.

14A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$ ). The first three estimates are based on the average SCC across models and socio-economic and emissions scenarios at the 5, 3,and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan -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 i s 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 integrated assessment models capture catastrophic and n on- 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

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### 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 estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling.

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### 14A.9 A NNEX

This A nnex 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. Annual SCC values for the next 40 years are provided in Table 14A.9.1

.

Table 14A.9.1 Annual SCC Values: 2010– 2050 ( 2007$)

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

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### 14A.9.1 Other (non

- CO 2 

### ) G ases

In addition to fossil and industrial 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 forcings (RF) due to other factors ( e.g. , aerosols and other gases). Specifically,

to obtain the RF associated with the non- CO 2 

### EMF emissions on ly, we calculated the RF associated with the EMF atmospheric CO 2 

### concentrations and subtracted them from the EMF

total RF. dd 

### 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 

### O, 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 

### an d SF 6 

### factors

, ee 

### decomposed the excess forcing vector, and constructed a new excess forcing vector that includes the EMF RF for CH 4 

### , N 2 

### O, and fluorinated gases, as well as the model default values for aerosols and other factors. Net land use CO 2 

### emissions w ere added to the fossil and industrial CO 2 

### emissions pathway.

DICE : DICE presents the greatest challenge because all forcing due to factors other than

industrial 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 Assessment 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 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 forcing.

dd 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 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 othe r three models and divide by 4).

ee Both the 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.

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### For subsequent years, we calculated the DICE default RF from aerosols and other nonCO 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 constant 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 that the RF from aerosol s 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 scenari os 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 w ith 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 sulphur dioxide, black carbon and organic carbon, has improved in the

post- SRE S scenarios. Generally, these emissions are projected to be lower than reported in SRES. {WGIII 3.2, TS.3, SPM}. ff



### Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent with the recent literature on these emissions. For example, Figure 14A.9.1 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. gg 

### Recent scenarios project sulfur emissions to peak earlier and at lower levels compared to the SRES in part bec ause of new information about present and planned sulfur legislation in some developing countries, such as India and China. hh 

### The lower bound projections of the recent literature have also shifted downward slightly compared to the SRES scenario (IPCC 2007).

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



### .

ff AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment

- report/ar4/syr/ar4_syr.pdf

gg 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.

hh See Carmichael, 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.

14A

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### .

Figure 14A.9.1 Sulphur 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 fossil and

industrial CO 2 

### emissions pathway.

14A.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 whic h 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 2200. 2. GDP/per capita growth rate declines linearly, reaching zero in 2300.

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### 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 2200.

5. Non

- CO 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 is 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

overall 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 the 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). ii 

### The resulting range of EMF population trajectories (Figure A2) also encompass the UN

medium scenario fore casts 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 improvements 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 sam e pace that is projected to occur towards the end of the forecast period 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 

### emissions 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 l ong run increase or decline in non- CO 2 

### radiative forcing, it is assumed to remain at the 2100 levels for each EMF scenario through 2300.

Figure 14A.9.2 through Figure 14A.9.8 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 industrial CO 2 

### emissions/GDP) resulting from these assumptions.

ii United Nations. 2004. World Population to 2300

. http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf

.

14A

- 46

### Figure 14A.9.2 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.



### Figure 14A.9.3 World GDP, 2000- 2300 ( post-2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in 2300)

Note: In the fifth sce nario, 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. 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]

Year

0 500 1000 1500 2000

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

YearWorld GDP [$trillion]

14A

- 47

### Figure 14A.9.4 Global Fossil and Indust rial 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 CO2e, full

- participation, not

- to

- exceed scenarios considered by each of the four models.



### Figure 14A.9.5 Global Net Land Use CO 2 

### Emissions, 2000–2300 (post-2100 extrapolations

assume emissions decline linearly, reaching zero in 2200 ) jj

Note: In the fifth scenario, 2000

– 2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, full

- participation, not

- to

- exceed scenarios considered by each of the four models.

jj 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). 0 50 100 150 200

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil&Industrial CO2 Emissions [GtCO2/decade]

Year

-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 z

14A

- 48

### Figure 14A.9.6 Global Non

- CO 2 

### Radiative Forcing, 2000–2300 (post-2100 extrapolations

assume constant 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 CO2e, full

- participation, not

- to

- exceed scenarios considered by each of the four models.



### Figure 14A.9.7 Gl obal 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)

Note: In the fifth scenario, 2000

– 2100 emissions are equal to the average of th e 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]

Year

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 $]

Year

14A

- 49

### Table 14A.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 96.8



### Table 14A.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 53.6

14A

- 50

### Table 14A.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 14A.9.8 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%

14A

- 51

### Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates

Discount

R ate 5% 3% 2.5%

Scenario Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis

DICE 9.0 13.1 0.8 0.2 28.3 209.8 1.1 0.9 42.2 534.9 1.2 1.1

PAGE 6.5 136.0 6.3 72.4 29.8 3,383.7 8.6 151.0 49.3 9,546.0 8.7 143.8

FUND

- 1.3 70.1 28.2 1,479.0 6.0 16,382.5 128.0 18,976.5 13.6 150,732.6 149.0 23,558.3

14A

- 52

### APPENDIX 14 -B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14- B.1 PREFACE

............................................................................................................ 14-B-1

14- B.2 PURPOSE

............................................................................................................ 14-B-1

14- B.3 SUMMARY OF MODEL UPDATES

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

14-B.3.1 DICE

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

14-B.3.1.1 Carbon Cycle Parameters

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

14-B.3.1.2 Sea Level Dynamics

...................................................................... 14-B-3

14-B.3.1.3 Re

- calibrated Damage Function

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

14-B.3.2 FUND

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

14-B.3.2.1 Space Heating

................................................................................ 14-B-5

14-B.3.2.2 Sea Level Rise and Land Loss

...................................................... 14-B-5

14-B.3.2.3 Agriculture ..................................................................................... 14-B-6

14-B.3.2.4 Temperature Response Model ....................................................... 14-B-6

14-B.3.2.5 Methane

......................................................................................... 14-B-6

14-B.3.3 PAGE

................................................................................................................... 14-B-7

14-B.3.3.1 Sea Level Rise

............................................................................... 14-B-7

14-B.3.3.2 Revised Damage Function to Account for Saturation ................... 14-B-7

14-B.3.3.3 Regional Scaling Factors

............................................................... 14-B-7

14-B.3.3.4 Probability of a Discontinuity ....................................................... 14-B-8

14-B.3.3.5 Adaptation ..................................................................................... 14-B-8

14-B.3.3.6 Other Noteworthy Changes

........................................................... 14-B-9

14- B.4 REVISED SCC ESTIMATE S ............................................................................. 14-B-9

14- B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS

............................ 14-B-12

LIST OF TABLES

Table 14 -B.4.1 Revised Social Cost of CO 2 

### , 2010 – 2050 (in 2007 dollars per ton of

CO 2 

### ) ....................................................................................................... 14-B-11

Table 14 -B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and

2050 ........................................................................................................ 14-B-12 Table A1. Annual SCC Values: 2010-2050 (2007$/ton CO 2 

### ) ............................................ 14-B-15 Table A2. 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 

### ) ..... 14-B-16 Table A3. 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) ....... 14-B-16 Table A4. 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 

### ) ........ 14-B-17

LIST OF FIGURES

Figure 14 -B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 

### ) .......... 14-B-11

14 -B-i

### APPENDIX 14 -B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

14

- B.1 PREFACE

The following text is reproduced almost verbatim from the May 2013 report (revised November 2013) of the Interagency Working Group on the Social Cost of Carbon of the United

States Government

. a 

### Minor changes were made to the working group's report to make it more consistent with the rest of this technical support document. Annex B describes the revisions that were made to the May 2013 report.

14

- B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) b



### estimates f rom the 2010 interagency technical support document (TSD) (Interagency Working Group on Social Cost of Carbon 2010). 1 

### E.O. 13563 commits the Administration to regulatory decision making “based on the best available science.” c 

### Additionally, the interagency group recommended in 2010 that the SCC 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 models used by the U.S. go vernment 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 (documented in the original 2010 TSD), this document provides an update of the SCC estimates based solely 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 revisit 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 m odels by the developers themselves in the peer

- reviewed literature. The Environmental Protection Agency (EPA), in collaboration with other Federal agencies such as the Department of Energy (DOE), continues to investigate potential improvements to the way in which economic damages associated with changes in CO 2 

### emissions are quantified.

a Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group on Social Cost of Carbon, United States Government. May 2013; revised November

2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical

- update

- social

- cost

- of

- carbon

- for

regulator

- impact

- analysis.pdf

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 transl ating between mass of

CO 2 and the mass of carbon is 3.67

.

c http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

d See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010). 1

14 -B-1

### Section 3 summarizes the major updates relevant to SCC estimation that are contained in the new versions of the integrated assessment models released since the 2010 intera gency report.
Section 4 presents the updated schedule of SCC estimates for 2010 – 2050 based on these versions of the models. Section 5 provides a discussion of recent workshops to support improvements in SCC estimation.

14

- B.3 SUMMARY OF MODEL UPD ATES

This sect ion briefly summarizes changes integrated into the most recent versions 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 ca rbon. For example, both the DICE and PAGE 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 GDP, updated regional scaling of damages, and a revised treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, the model’s simple carbon cycle has been updated to be more consistent with a relatively more complex climate model. The FUND model incl udes updated damage functions for sea level rise impacts, the agricultural sector, and reduced space heating requirements, as well as changes to the response of temperature to the buildup of GHG concentrations and the inclusion of indirect

effects of metha ne emissions. Changes made to parts of the models that are superseded by the interagency working group’s modeling assumptions

– regarding climate sensitivity, discounting,

and socioeconomic variables – are not discussed.

14-B.3.1 DICE

Changes in the DICE model rel evant 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 re presentation of economic damages from sea level rise. Changes were also made to other 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 utili ty of consumption

— but these components of DICE are superseded by the interagency working 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 

### and the associated on

- line appendix containing supplemental information.

14-B.3.1.1 Carbon Cycle Parameters

DICE uses a three

- box model of carbon stocks and flow s to represent the accumulation and transfer 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

14 -B-2

### Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44). 2 e 

### Carbon cycle transfer coefficient values in DICE2010 are based on re

- calibration of the model to match the newer version of MAGICC (Nordhaus 2010 p 2). 3 

### For example, in DICE2010 in each decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 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 transferred 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 concentration 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.

14-B.3.1.2 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 l evel rise (SLR) module; a more 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 caps, 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. 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 glo bal 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 Green land ice sheet is more complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1

o 

### C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in each period is proportional to the differen ce between the previous period’s sea level anomaly and

e MAGICC is a simple climate model initially developed within 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 much more sophisticated state of the art earth system simulation mo dels (Randall et al. 2007). 4

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). 6

14 -B-3

### 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 to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

14-B.3.1.3 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 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 one

minu s a fraction, which is one divided by a quadratic function of the temperature anomaly, producing a sigmoid (“S”

- shaped) function. The loss function in DICE2010 has been expanded by adding a quadratic function of SLR to the quadratic function of temperature. 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 Nordhaus (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 (as downloaded from the homepage of William Nordhaus), annual damages ar e lower in most of the early periods but higher in later periods of the time horizon 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 increases 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 decrease. The changes to the loss function generally decrease the interagency working group SCC estimates slightly, all else equal.

14-B.3.2 F UND

FUND version 3.8 includes a number of changes over the previous version 3.5 used in the interagency report. Documentation supporting FUND and the model’s source code for all

versions of the model is available from the model authors. h 

### Notable changes, due to their impact

h 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 are associated with improving consistency with IPCC AR4 by

14 -B-4

### on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level rise damage functions in addition to changes to the temperature response function and the

inclusion of indirect effects from methane emissions. i 

### We discuss each of these in turn.

14-B.3.2.1 Space Heating

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 ch anges 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 nee ds 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, meaning that for every simulation there will exist 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 as the temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the reduced expenditures on space heating at an y 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 will

increase the estimated SC C. 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.

14-B.3.2.2 Sea Level Rise and Land Loss

The FUND model explicitly includes 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 the function defining the poten tial 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 constant uniform slope moving

in land. In FUND 3.8 the function defining the potential land lost has been changed to be a non

linear function of sea level rise, thereby assuming that the slope of the shore line is not constant moving inland, with a positive first derivative. The effect of this change is to typically reduce the vulnerability of some regions to sea level rise based land loss, therefore having an effect of lowering the expected SCC estimate. The model has also been updated to assume that the value of dry land at risk of inundation is not uniform across a region but will be a decreasing function of protection measure, thereby implicitly assuming that the most valuable land will be protected

first.

adjusting the atmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along with making minor stability improvements in the sea wall construction algorithm.

i The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and extreme weather) were not the subject of significant updates.

14 -B-5

### 14-B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are me asured as proportional to the sector’s value. The fraction is made up of three additively separable components that represent the effects from carbon fertilization, the rate of 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 linear and quadratic coefficients are modeled as the

ratio of two normal distri butions. Within this specification, as draws from the distribution in the denominator approached zero the share of the sector’s value “lost” approaches (+/

- ) infinity independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic

coef ficients are drawn directly from truncated normal distributions so that they remain in the

range [0, ) ∞ 

### and ( ,0] −∞ 

### , respectively, where the means for the new distributions are set equal to

the ratio of the means from the normal distributions used in the previous version. In general the impact of this change has been to increase the likelihood that increases in the temperature level will have either larger positive or negative effects on the agricultural sector relative to the previous version (through eliminating simulations in which the “lost” value approached (+/

- ) infinity). The net effect of this change on the SCC estimates is difficult to predict.

14-B.3.2.4 Temperature Response Model

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 cumulative 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 rate at which the transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response

is d efined as a decreasing linear function of equilibrium climate sensitivity to capture the fact that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the equilibrium climate sensitivity. In FUND 3.8, the rate of tempe rature 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. Therefore in FUND 3.8, the temperature response will typically be faster than in 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 pr evious version of the model are now experienced earlier and therefore discounted less.

14-B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emissions, and has developed methods for proxying such effects when computing the global warming potentia l 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 CH4 emissions on its own lifetime. The radiative forcing associated with atmospheric methane has also been increase by 40% to account for its net

14 -B-6

### impact on ozone production and increase in stratospheric water vapor. The general effect of this increased radiative forcing will be to increase the estimated SCC values, where the degree to which this occurs will be dependent upon the relative curvature of the damage functions with respect to the temperature anomaly.

14-B.3.3 PAGE

PAGE09 (Hope 2012) 9 

### includes a number of changes from PAGE2002, the version used in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates include: explicitly modeling the impacts from sea level rise, r evisions 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. More details on PAGE2009 can be found in three working papers (Hope 2011a, 2011b, 2011c). 10

, 11

, 12 

### A description of PAGE2002 can be found in Hope (2006). 13



### 14-B.3.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories

– economic and non

economic impacts - PAGE2009 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. PAGE09 models damages from sea level rise as increasing less than linearly with sea level based on the assumption that low

- lying shoreline areas will be associated with higher damages than current inland areas. Damages from the economic and non

- economic sector were adjusted to account for the introduction of this new category.

14-B.3.3.2 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 positive economic damages from climate change, where damages 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 cer tain 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.

14-B.3.3.3 Regional Scaling 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 proportiona l based on a given scaling factor. The scaling factor in PAGE09 is based 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

14 -B-7

### 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, and allowed for benefits from temperature increase in Eastern Europe, smaller impacts in developing countries, and higher damages in developing countries.

14-B.3.3.4 Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” were modeled as an expected value. 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 treated 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 expec ted 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 temperature 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 det ermined by the regional 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 discont inuity are both higher 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.

14-B.3.3.5 Adaptation

As in PAGE2002, adaptation is available to inc rease the tolerable level of temperature change and can help mitigate any climate change impacts that still 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 modifying the temperature change and sea level rise used in the damage function or by reducing the damages by some 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 implementation, this adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature anomalies between 1°C and 3°C, it will reduce damages by 15

- 30 percent (depending on the region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation was assumed to reduce economic sector damages up to 3° C by 50

- 90 percent after 20 years. Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change.

For the non

- economic sector, in PAGE09 adaptation is available to reduce 15 percent of the damages due to a temperature incre ase 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, adaptation is assumed to alleviate 25

- 50 percent of the damages from the first 0.20 to

0.25 meter s of sea level rise but is assumed to be ineffective thereafter. Hope (2011c) 12 

### estimates

14 -B-8

### 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.

14-B.3.3.6 Other Noteworthy Changes

Two other changes in the model are worth noting. A revised carbon cycle f eedback is

introduced to simulate decreased CO 2 

### absorption by the terrestrial biosphere and ocean as the temperature rises. This feedback is linear in the average global and annual temperature anomaly but is capped at a maximum value. In the previous version of PAGE, 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 the 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 the previous version of PAGE, the scaling was determined solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional

temp erature 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 Earth’s

landmass.

14

- B.4 REVISED SCC ESTIMATE S

The updated versions of the three integrated assessment models were run using the same methodology detailed in the 2010 TSD. 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 scenarios based on the EMF

- 22 modeling exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD applied equal weight to each model and socioeconomic scenario in order to reduce the dimensionality down to three separate distributions 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 so cio -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 pu rpose, the 95th percentile of the SCC estimates at a 3 percent discount rate was chosen. (A detailed set of percentiles by model and scenario combination is available in the Annex.) As noted in the original 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” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in regulatory impact analysis, the interagency group emphasizes the importance and value of including all four SCC values.

Table 14 -B.4.1 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

14 -B-9

### outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using basic linear interpolation. The ful l set of annual SCC estimates between 2010 and 2050 is reported in the Annex.

14 -B- 10

### Table 14-B.4.1 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 3 2 5 1 89

2015 1 1 3 7 5 7 109

2020 12 43 6 4 12 8

2025 14 4 7 69 14 3

2030 16 52 7 5 159

2035 19 5 6 8 0 17 5

2040 21 6 1 8 6 19 1

2045 24 66 92 206

2050 2 6 71 9 7 22 0

The SCC estimates using the updated versions of the models are higher than those reported in the TSD due to the changes to the models outlined in the previous section. Figure 14-

B.4.2 illustrates 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 to the right and have long tails. The Figure also shows that the lower the discount rate, the longer the right tail of the

distribution.

Figure 14 -B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 

### )

As was the case in the original TSD, 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. The approach taken by the interagency group is to allow the growth rate to be determined endogenously by the models

14 -B- 11

### through running them for a set of perturbation years out to 2050. Table 14 -B.4.2 illustrates how the growth rate for these four SCC estimates varies over time.

Table 14-B.4.2 Average Annual Growth Rates of 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. 3 % 2.4% 4. 4 %

2020

- 2030 3.4% 2.1% 1.7% 2.4%

2030

- 2040 3.0% 1. 9 % 1.5% 2. 1 %

2040

- 2050 2.6% 1.6% 1.3% 1.5%

The future monetized value 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 original 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

- B.5 OTHER MODEL LIMITATI ONS OR RESEARCH GAPS

The 2010 interagency SCC technical support report 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 catastrophic damages, the treatment of adap tation and technological change, and the way in which inter-regional and intersectoral linkages are modeled. It 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 long- term research work on modeling and valuation of climate impac ts that we expect will inform improvements in SCC estimation in the future.

14 -B- 12

### 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. < 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 Haver.

3. Nordhaus, 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 Climate 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: Cambri dge, United Kingdom and New York, NY, USA

5. Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, 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 Targets: 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 , 2013 (Forthcoming)

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 Forcing . 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. Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA

9. Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates from PAGE09 are higher than those from PAGE2002

. Climatic Change

, 2012(Forthcoming)

14 -B- 13

### 10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description . 2011, Cambridge Judge Business School 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 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 http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf

13. Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern

. The Integrated Assessment

Journal , 2006. 6(1): pp. 19-56

14. Anthoff D. and Tol, R. S. J., Erratum to: The uncertainty about the social cost of carbon: A decomposition analysis using FUND

. Climatic Change , 2013. Advance online publication. doi: 10.1007/s10584-013-0959-1

14 -B- 14

### ANNEX A

Table A1. Annual SCC Values: 2010-2050 (2007$/ton CO 2 

### )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 32 51 89
2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 220

14 -B- 15

### Table A2. 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 

### )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95 th 

### 99th
Scenario PAGE
IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 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 20 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

- 14

- 2 4 15 31 39 55 86 107 157

MERGE

- 6 1 6 14 27 35 46 70 87 141

MESSAGE

- 16

- 5 1 11 24 31 43 67 83 126
MiniCAM Base

- 7 2 7 16 32 39 55 83 103 158
5th Scenario

- 29

- 13

- 6 4 16 21 32 53 69 103

Table A3. 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th
Scenario 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

- 13

- 4 0 8 18 23 33 51 65 99

MERGE

- 7

- 1 2 8 17 21 29 45 57 95

MESSAGE

- 14

- 6

- 2 5 14 18 26 41 52 82
MiniCAM Base

- 7

- 1 3 9 19 23 33 50 63 101
5th Scenario

- 22

- 11

- 6 1 8 11 18 31 40 62

14 -B- 16

### Table A4. 2020 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 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

- 4

- 1 2 3 6 10 14 24

MERGE

- 6

- 4

- 2 0 3 4 6 11 15 26

MESSAGE

- 10

- 6

- 4

- 1 1 2 5 9 12 21
MiniCAM Base

- 7

- 4

- 2 0 3 4 6 11 14 25
5th Scenario

- 11

- 7

- 5

- 3 0 0 3 5 7 13

14 -B- 17

### ANNEX B

The November 2013 revision of this technical support document is based on two
corrections to the runs based on the FUND model. First, the potential dry land loss in the
algorithm that estimates regional coastal protections was misspecified in the model’s computer
code. This correction is covered in an erratum to Anthoff and Tol (2013) published in the same journal ( Climatic Change ) in October 2013

. 14 

### Second, the equilibrium climate sensitivity
distribution was inadvertently specified as a truncated Gamma distribution (the default in FUND)
as opposed to the truncated Roe and Baker distribution as was intended. The truncated Gamma
distribution used in the FUND runs had approximately the same mean and upper truncation
point, but lower variance and faster decay of the upper tail, as compared to the intended
specification based on the Roe and Baker distribution. The difference s between the original
estimates reported in the May 2013 version of this technical support document and this revision

are all one dollar or less.

14 -B- 18

### CHAPTER 1 5. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-3

15.3.1 Installed Capacity ........................................................................................................... 15-3

15.3.2 Electricity Generation .................................................................................................... 15-7

15.3.3 Results Summary ......................................................................................................... 15-11

LIST OF TABLES

Table 15.3.1 Summary of Utility Impact Results ................................................................. 15-11

LIST OF FIGURES

Figure 15.3.1 Total Electric Capacity Reduction ..................................................................... 15-3 Figure 15.3.2 Coal Capacity Reduction ................................................................................... 15-4 Figure 15.3.3 Nuclear Capacity Reduction .............................................................................. 15-4 Figure 15.3.4 Gas Combined Cycle Capacity Reduction ......................................................... 15-5 Figure 15.3.5 Peaking Capacity Reduction .............................................................................. 15-5 Figure 15.3.6 Renewables Capacity Reduction ........................................................................ 15-6 Figure 15.3.7 Other Capacity Reduction .................................................................................. 15-6 Figure 15.3.8 Total Generation Reduction ............................................................................... 15-7 Figure 15.3.9 Coal Generation Reduction ................................................................................ 15-8 Figure 15.3.10 Nuclear Generation Reduction ........................................................................... 15-8 Figure 15.3.11 Gas Combined Cycle Generation Reduction ..................................................... 15-9 Figure 15.3.12 Peaking Generation Reduction .......................................................................... 15-9 Figure 15.3.13 Renewables Generation Reduction .................................................................. 15-10 Figure 15.3.14 Other Generation Reduction ............................................................................ 15-10

15 -i

### CHAPTER 15. UTILITY IMPACT ANALY SIS

15.1 INTRODUCTION

In the utility impact analysis

, the U.S. Department of Energy ( DOE ) analyze s the changes in electric installed capacity and power generation that result for each trial standard level (TSL).

In the case of commercial refrigeration equipment, total electricity usage and capacity are

reduced for all TSLs.

The utility impact analysis uses a variant of the DOE Energy Information Administration (EIA)’s National Energy Mo deling System (NEMS). a 

### NEMS is a public domain, multi- sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO) . DOE uses a variant

of th is model, referred to as NEMS

- BT, b 

### to account for selected utility impacts from energy conservation standards. DOE’s analysis consists of a comparison between model results for the

most recent AEO Reference Case and for cases in which energy use is decreme nted to reflect the impact s of standards. For the analysis of standards for commercial refrigeration equipment DOE

used the version of NEMS based on AEO 201 3

. 2



### NEMS -BT has a number of advantages that have led to its use 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 receives.

• NEMS -BT is updated each year, with each edition of the AEO, to reflect changes in energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS- BT permits the modeling of interactions among the various energy supply and demand sectors.

15.2 METHODOLOGY

DOE uses NEMS

- BT to estimate the marginal impacts of re duction in energy demand on

the energy supply sector. In principle, marginal values should provide a better estimate of the actual impact of energy conservation standards . In practice, the numerical differences between

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

. 1

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 vari ations 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).

15 -1

### marginal and average values may turn out to be smaller than the intrinsic uncertainties in the

AEO.

NEMS uses predicted growth in demand for each end use to build up a projection of the total electric system load growth . The system load shapes are converted internally to load duration curves, which are then used to estimate the most cost

- effective additions to capacity. When electricity demand deviates from the AEO reference case, in general there are three inter

related effects: the annual generation ( terawatt hours, TWh) from the stock of el ectric generating capacity changes, the total generation capacity itself ( gigawatts

, GW) may change, and the mix of capacity by fuel type may change. Each of these effects can vary for different types of end use. The change in total generating capacity is sensitive to the degree to which the end- use is peak coincident, while the capacity mix is sensitive to the hourly load shape associated with the

end use.

To model the impact of a standard, DOE inputs a reduction to annual energy demand for the corresponding end use in the appropriate start year. The NEMS -BT model is run with the decremented energy demand to determine the modified build -out of capacity and total generation. Regional effects of a standard can be accounted for by defining the energy demand

decrement as a function of census division.

The output of the NEMS- BT analysis includes the effective marginal heat rate (ratio of the change in energy consumption in quads to the change in generation in TWh), and the capacity reduction by plant type for a given reduction in total generation. DOE uses the site energy savings multiplied by a transmission

- and

- distribution ( T&D ) loss factor to estimate the reduction in generation for each TSL. The relationship between a reduction c 

### in electricity generation (TWh ) and the reduction in capacity (GW) is estimated based on the output of

NEMS -BT model runs using the end- use specific energy demand decrement. Details on the approach used may be found in Coughlin (2013 ). 3



### NEMS -BT provides output for the following capacity types: coal, nuclear,

combined cycle (natural gas), renewable sources, oil an d natural gas steam, combustion turbine/diesel, pumped storage, fuel cells, and distributed generation (natural gas). DOE grouped oil and natural gas steam and combustion turbine/diesel into a “peaking” category, and grouped pumped storage, fuel cells, and distributed generation (natural gas) into an “other”
category.

In general, energy conservation standards i mpact primarily fossil combustion (coal, natural gas and diesel) and renewables. Pumped storage and nuclear power are very insensitive to small chang es in demand, while fuel cells and distributed generation make up a very small fraction (less than 1 percent) of the generation capacity base.

For the analysis of commercial refrigeration equipment, DOE derived factors based on the plant types used to supp ly energy for commercial refrigeration

.

c These reductions are defined relative to the AEO Reference case.

15 -2

### 15.3 UTILITY IMPACT RESUL TS

This section presents results of the analysis for all of the capacity types

. Negative values indicate that capacity or generation increase.

15.3.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that result for each TSL by major plant type for selected years. The changes have been calculated based on factors ( megawatts ( MW ) of capacity reduction per gigawatt hours ( GWh ) of generation reduction) estimated from a NEMS

- BT model run that simulated a decrement in energy demand for the load shape corresponding to commercial refrigeration

.

Figure 15.3.1 Total Electric Capacity Reduction 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,00020172022202720322037204220472052205720622067MW 

### Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15 -3

### Figure 15.3.2 Coal Capacity Reduction

Figure 15.3.3 Nuclear Capacity Reduction 0 100 200 300 400 500 60020172022202720322037204220472052205720622067MW 

### Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 10 20 30 40 50 60 70 80 90 10020172022202720322037204220472052205720622067MW 

### Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15 -4

### Figure 15.3.4 Gas Combined Cycle Capacity Reduction

Figure 15.3.5 Peaking Capacity Reduction 0 100 200 300 400 500 600 700 800 90020172022202720322037204220472052205720622067MW 

### NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

-20 -10

0 10 20 30 40 50 60 70 8020172022202720322037204220472052205720622067MW 

### Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15 -5

### Figure 15.3.6 Renewables Capacity Reduction

Figure 15.3.7 Other Capacity Reduction 0 500 1,000 1,500 2,000 2,500 3,000 3,50020172022202720322037204220472052205720622067MW 

### Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

-140 -120 -100

-80 -60 -40 -20

0 2020172022202720322037204220472052205720622067MW



### Other

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15 -6

### 15.3.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for each TSL by plant type. The change by capacity type has been calculated based on shares (percentage of generation reduction for each capacity type over total generation reduction)

estimated from a NEMS

- BT model run that simulated a decrement in energy demand for the load shape corresponding to commercial refrigeration

.

Figure 15.3.8 Total Generation Reduction 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,00020172022202720322037204220472052205720622067GWh 

### Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15 -7

### Figure 15.3.9 Coal Generation Reduction

Figure 15.3.10 Nuclear Generation Reduction 0 500 1,000 1,500 2,000 2,500 3,000 3,50020172022202720322037204220472052205720622067GWh 

### Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 100 200 300 400 500 600 700 80020172022202720322037204220472052205720622067GWh 

### Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15 -8

### Figure 15.3.11 Gas Combined Cycle Generation Reduction

Figure 15.3.12 Peaking Generation Reduction 0 2,000 4,000 6,000 8,000 10,000 12,00020172022202720322037204220472052205720622067GWh 

### NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

-300 -200 -100 0 100 200 300 400 500 60020172022202720322037204220472052205720622067GWh 

### Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15 -9

### Figure 15.3.13 Renewables Generation Reduction

Figure 15.3.14 Other Generation Reduction 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,00020172022202720322037204220472052205720622067GWh 

### Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

-1,000

-900 -800 -700 -600 -500 -400 -300 -200 -100 020172022202720322037204220472052205720622067GWh



### Other

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 10

### 15.3.3 Results Summary

Table 1 4.3.1 presents a summary of the utility impact results estimated for commercial refrigeration equipment.

Table 15.3.1 Summary of Utility Impact Results

TSL

1 2 3 4 5 Installed Capacity Reduction (MW)

2020 50.9 90 127 146 184



### 2025 187 327 458 527 666



### 2030 399 692 965 1,110 1,405



### 2035 632 1,096 1,524 1,752 2,220



### 2040 1,127 1,952 2,715 3,121 3,955 

### Electricity Generation Reduction (GWh)

2020 1,394 2,457 3,469 4,000 5,031



### 2025 3,162 5,523 7,738 8,908 11,248



### 2030 4,032 7,001 9,760 11,224 14,208



### 2035 4,435 7,684 10,687 12,285 15,568



### 2040 4,820 8,347 11,609 13,344 16,911

15

- 11

### REFERENCES

1. U.S. Department of Energy- Energy Information Administration, National Energy Modeling System: An Overview , 2009. Report No. DOE/EIA-0581(2009).

< http://www.eia.gov/oiaf/aeo/overview/ >

2. Energy Information Administration, Annual Energy Outlook 2013 with Projections to

2040 , 2013. Washington, DC. < http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf >

3. Coughlin, K., H. Shen, P. Chan, B. McDevitt, and A. Sturges, Marginal Utility Sector Impacts of Reduced Electricity Demand , 2012.

15

- 12

### CHAPTER 1 6 . EMPLOYMENT IMPACT ANALYSIS

TABLE OF CO NTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 ASSUMPTIONS ............................................................................................................ 16-1

16.3 METHODOLOGY ........................................................................................................ 16-1

16.4 SHORT

- TERM RESULTS ............................................................................................ 16-2

16.5 LONG

- TERM RESULTS .............................................................................................. 16-3

LIST OF TABLES

Table 16.4.1 CRE Net National Short-term Change in Employment (Number of Jobs) ........ 16-3

16-i

### CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 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 CRE

. Job increases or decreases repor ted in this chapter are separate from the direct CRE production sector employment impacts reported in the manufacturer impact analysis (Chapter 12) , and reflect the aggregate employment impact of efficiency standards on all sectors of the economy.

16.2 ASSUMP TIONS

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 m ay increase the purchase price of products , including the retail price plus sales tax, and increase installation

costs.

Using an input/output econometric model of the U.S. economy, this analysis estimated

the short- term effect of these expenditure impa cts on net economic output and employment. DOE intends this analysis to quantify the indirect employment impacts of these expenditure changes. It evaluated direct employment impacts at manufacturers’ facilities in the manufacturer impact analysis (see Chapter 12).

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 impacts 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 modeling approaches for examining long run employment impacts

.

16.3 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 ec onomy 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 estimates the employment and income effects of building energy technologies. In comparison with simple economic multiplier approaches, Im SET allows for more complete and automated analysis of the economic impacts of energy

- efficiency investments in buildings.

16-1

### In an input/output 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 efficien cy 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 residential 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, business 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 product s. The increased cost of products leads to higher employment in

the product manufacturing sectors and lower employment in other economic sectors. Second, commercial firm and residential spending are redirected from utilities toward 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 reductions in utility sector inves tment and employment.

DOE also notes that the employment impacts estimated with ImSET for the entire economy differ from the employment impacts in the CRE manufacturing sector estimated in Chapter 12 using the Government Regulatory Impact Model (GRIM). The methodologies used and the sectors analyzed in the ImSET and GRIM models are different.

16.4 SHORT

- TERM RESULTS

The results in this section refer to impacts of CRE standards relative to the base case.

DOE dis aggregated the impact of standards on employm ent into three component effects : increased capital investment costs, decreased energy costs, and changes in operations and

maintenance costs . DOE anticipates no change in operations and maintenance costs for CRE

. DOE presents the summary impact.

Conc eptually, one can consider the impact of the rule in its first year on three aggregate sectors, the CRE production sector, the energy generation sector, and the general consumer goods sector ( this is for illustrative purposes; as mentioned above ImSET’s ca lculations are made at a much more disaggregate level) . By r aising energy efficiency, t he rule is projected to increase the purchase price of CRE ; this increase in expenditures causes an increase in employment in this sector. At the same time, the improvem ents in energy efficiency reduce consumer expenditures on electricity , freeing up this money to be spend in other sectors. The reduction in electricity demand causes a reduction in employment in that sector. Finally, based on the net

16-2

### impact of increased ex penditures on CRE 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 captures 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 present the modeled net employment impact from the rule in 2017, rounded to the nearest hundred jobs. Approximately 93.8 % of CRE are domestically produced and 6.2 % are imported . The net employment impact estimat e is sensitive to assumptions regarding the return to the U.S. economy of money spent on imported CRE . The two scenarios bounding the ranges presented in Table 13.4.1 represent situations in which none of the money spent on imported CRE returns to the U.S. economy and all of the money spent on imported CRE returns to the U.S. economy. The U.S. trade deficit in recent years suggests that between 50% and 75% of the money spent on imported CRE is likely to return, with employment impacts falling with in the ranges presented below.

Table 16.4.1 CRE Net National Short- term Change in Employment ( Number of J obs)

Trial Standard

Level 2017 2022

TSL 1 200 1,5 00

TSL 2 400 to 5 00 2,600 to 2,7 00

TSL 3 600 to 7 00 3,700 to 3,8 00

TSL 4 800 to 1,000 4,100 to 4,3 00

TSL 5 2,200 to 3,600 2,900 to 4,400

For context, the Office of Management of Budget currently assumes that the unemployment rate may decline to 6.9% in 2014 and 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 ansitory as workers change jobs, rather t han enter or exit longer- term em ployment.

16.5 LONG

- TERM RESULTS

Over the long term DOE expect s the energy savings to consumers to increasingly

dominate the increase in product 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. Since 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 equil ibrium, this 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 effect 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 in general be negligible over time due to the small magnitude of the short- term effects

16-3

### presented in Table 13.4.1 for most product classes and TSLs. The ImSET model projections, assuming no price or wage effects until 2022, are included in the second column of Table 13.4.1

.

16-4

### 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 Technologies , 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. Richland, 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 Management and Budget, Fiscal Year 2012 Mid-Session Review: Budget of the
U.S. Government, Washington, DC. <http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf>

16-5

### CHAPTER 1 7 . REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION

................................................................................................... 17-1

17.2 NON

- REGULATORY POLICIES

.......................................................................... 17-2

17.2.1 Methodology ............................................................................................................ 17-2

17.2.2 Assumptions Regarding Non- Regulatory Policies

.................................................. 17-3

17.2.3 Policy Interactions

................................................................................................... 17-4

17.3 NON

- REGULATORY POLICY AS SUMPTIONS

................................................ 17-4

17.3.1 No New Regulatory Action

..................................................................................... 17-4

17.3.2 Consumer Rebates

................................................................................................... 17-4

17.3.2.1 Methodology .......................................................................................... 17-5

17.3.2.2 Analysis

.................................................................................................. 17-6

17.3.3 Consumer Tax Credits ........................................................................................... 17-12

17.3.4 Manufacturer Tax Credits

...................................................................................... 17-14

17.3.5 Voluntary Energy Efficiency Targets .................................................................... 17-16

17.3.6 Bulk Government Purchases .................................................................................. 17-18

17.4 IMPACTS OF NON

- REGULATORY ALTERNATIVES

................................... 17-20

LIST OF TABLES

Table 17.1.1 Non

- Regulatory Alternatives to National Standards ...................................... 17-1

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Refrigeration Equipment (kWh/yr per foot) ................................................... 17-3

Table 17 .3.1 Benefit/Cost Ratios Without and With Rebates for Commercial Refrigeration Equipment ................................................................................. 17-7

Table 17.3.2 Market Penetrations in 2017 Attributable to Consumer Rebates for Commercial Refrigeration Equipment .......................................................... 17-11

Table 17.3.3 Market Penetrations in 2017 Attributable to Consumer Tax Credits for Commercial Refrigeration Equipment .......................................................... 17-14

Table 17.3.4 Market Penetrations in 2017 Attributable to Manufacturer Tax Credits for Commercial Refrigeration Equipment .................................................... 17-16

Table 17.3.5 Market Barriers Reduction After 10 Years of Vol untary Energy Efficiency Targets for Commercial Refrigeration Equipment ..................... 17-17

Table 17.3.6 Market Penetrations in 2017 Attributable to Voluntary Energy Efficiency Targets for Commercial Refrigeration Equipment ..................... 17-18

Table 17.3.7 Market Penetrations in 2017 Attributable to Bulk Government Purchasing of Commercial Refrigeration Equipment ................................... 17-20

Table 17.4.1 Impacts of Non- Regulatory Alternatives for Vertical Open Remote Condensing Medium (TSL 3)* ..................................................................... 17-24

Table 17.4.2 Impacts of Non- Regulatory Alternatives for Vertical Closed Tr ansparent Remote Condensing Low (TSL 3)* .............................................................. 17-24

17 -i

### Table 17.4.3 Impacts of Non- Regulatory Alternatives for Vertical Closed Solid Self

Contained Medium (TSL 3)* ........................................................................ 17-24

Table 17.4.4 Impacts of Non- Regulatory Alternatives for Vertical Closed Solid Self

Contained L ow (TSL 3)* .............................................................................. 17-25

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Commercial Refrigeration Equipment ........ 17-11 Figure 17.4.1 Market Penetration of Vertical Open Remote Condensing Medium (TSL 3) ................................................................................................................... 17-21 Figure 17.4.2 Market Penetration of Vertical Closed Transparent Remote Condensing Low (TSL 3) ................................................................................................. 17-21 Figure 17.4.3 Market Penetration of Vertical Closed Solid Self -Contained Medium (TSL 3) .......................................................................................................... 17-22 Figure 17.4.4 Market Penetration of Vertical Closed Solid Self

- Contained Low (TSL 3) ................................................................................................................... 17-22

17

- ii

### CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

The U.S. Department of E nergy (DOE) has determined that energy conservation

standards for consumers of commerci al refrigeration equipment constitute an “economically significant regulatory action” under Executive Order (E.O.) 12866, Regulatory Planning and Review. 58 FR 51735 (October 4, 1993). This Regulatory Impact Analysis ( RIA ), which DOE has prepared pursuant to E.O. 12866, evaluates potential non- regulatory alternatives for four selected equipment classes of commercial refrigeration equi pment, a 

### and compares the costs and

benefits from each alternative to those of the proposed standard s. 58 FR 51735, 51741.

For this analysis , DOE used an integrated National Impact Analysis ( NIA )- RIA model

built on the NIA model discussed in C hapter 10 for its analysis.

DOE identifi ed five non- regulatory policy alternatives that possibly could provide

incentives for the same energy efficiency levels as the ones in the trial standard level s (TSL) for

the equipment that are the subject of this rulemak ing. The non- regulatory policy alternatives are

listed in Table 17.1.1

, that also includes the No New Regulatory Action alternative which corresponds to a business- as

- usual scenario. DOE evaluated each alternative in terms of its ability to achieve significant e nergy savings at a reasonable cost, and compared the effectiveness

of each to the effectiveness of the proposed standard.

Table 17.1.1 Non -Regulatory Alternatives to National Standards No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits Voluntary Energy Efficiency Targets

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed in Table 17.1.1 (excluding the alternative of No N ew Regulatory A ction

. Section 17.4 present s the results of the policy alternatives

.

a The four equipment classes covered in this RIA are those with shipment shares greater than 10%. Cumulatively, these four equipment classes correspond to more than 60% of commercial refrigeration equipment shipments. The four selected equipment classes are : Vertical Open Remote Condensing Medium (VOP.RC.M), Vertical Closed Transparent Remote Condensing Low (VCT.RC.L), Vertical Closed Solid Self

- Contained Medium (VCS.SC.M),

and Vertical Closed Solid Self

- Contained Low (VCS.SC.L).

17 -1

### 17.2 NON

- REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost

effectiveness of the non- regulatory policy alternatives for commercial refrigeration equipment

. This section also describes the assumptions underlying the analysis.

17.2.1 Methodology

DOE used its integrated NIA

- RIA spreadsheet model to calculate the national energy savings (NES) and net present value (NPV) associated with each non -regulatory policy

alternative . Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet model. Appendix 17-A discusse s the NIA -RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet the efficiency levels corresponding to the levels set for each TSL . After establishing the quantitative assumptions underlying each alternative, DOE appropriately revised inputs to the NIA- RIA

sprea dsheet model . The primary model inputs revised were market shares of equipment meeting the target efficiency levels set for each TSL . The shipments of equipment for any given year reflect a distribution of efficiency levels. DOE assumed , for each TSL, that new energy efficiency standards would affect 100 percent of the shipments of equipment that did not meet

the TSL target level in the base case, b 

### whereas the non

- regulatory policies would affect a smaller percentage of those shipments. DOE made certain assumptions about the percentage of shipments affected by each alternative policy

. DOE used those percentages to calculate the shipment-weighted average energy consumption and costs of commercial refrigeration equipment attributable to each policy alternative.

Increasing the efficiency of equipment often increases its average installed cost.

However , operating costs generally decrease because energy consumption declines. DOE

therefore calculated an NPV for each non

- regulatory alternative in the same way it did for the proposed standards. In some policy scenarios, increases in total installed cost are mitigated by government rebates or tax credits. Because DOE assumed that consumers w ould re- pay credits and rebates in some way (such as additional taxes) , DOE did not include rebates or tax credits as a consumer benefit when calculating national NPV. DOE’s analysis also excluded any

administrative costs for the non- regulatory policies; i ncluding such costs would decrease the NPVs slightly.

The following are key measures for evaluating the impact of each alternative policy.

• National Energy Savings (NES) , given in quadrillion Btus (quads), describes the cumulative national primary energy saved over the lifetime of equipment purchased

b The base case for the NIA is a market

- weighted average energy efficiency calculated from units at several efficiency levels.

17 -2

### during the 30- year analysis period starting in the effective date of the policy (2017-

2046 ).

• Net Present Value (NPV)

, represents the value of net monetary savings in 2013, expressed in 2012$, from equipment purchased during the 30- year analysis period starting in the effective date of the policy (2017-2046). DOE calculated the NPV as the difference between the present value of installed equip ment cost and operating expenditures in the base case and the present value of th o se costs in each policy case. DOE calculated operating expenses (including energy costs ) for the life of equipment.

17.2.2 Assumptions Regarding Non- Regulatory Policies

The effect s of non-regulatory policies are by nature uncertain because they depend on program implementation

, marketing efforts , and on consumers’ responses to a program. Because the projected effects depend on assumptions regarding the rate of consumer participation, they are subject to more uncertainty than are the impacts of mandatory standards, which DOE assumes will meet with full compliance. To increase the robustness of the analysis, DOE conducted a literature review regarding each non

- regulatory p olicy and consulted with recognized experts to gather information on similar incentive programs that have been implemented in the United States. By studying experiences with the various types of programs, DOE sought to make credible assumptions regarding p otential market impacts . Section 17.3 presents the sources DOE relied on in developing assumptions about each alternative policy and reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each

alternative policy.

Each of the five non- regulatory policies that could provide incentives for more efficient equipment that DOE considered would improve the average efficien cy of new commercial refrigeration equipment relative to their base case efficiency scenario . The analysis considered that each alternative policy would induce consumers to purchase units h aving the same efficiency level as required by the proposed standard s (the target level ) set for each TSL . As opposed to the standards case, however, the policy cases may not lead to 100 percent market penetration of units that meet the target level

.

Table 17.2.1 shows the efficiency level stipulated in the proposed standards for commercial refrigeration equipment

.

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Refrigeration Equipment (kWh/yr per foot)

Equipment Class Trial Standard Levels



### 1 2 3 4 5

VOP.RC.M 1,425 1,425 1,156 1,098 1,084

VCT.RC.L 1,012 1,012 996 988 947

17 -3

### VCS.SC.M 429 327 299 279 246

VCS.SC.L 1,131 1049 988 980 903

VOP.RC.M: V ertical Open Remote Condensing M edium

VCT.RC.L : Vertical Closed Transparent Remote Condensing Lo w VCS.SC.M: Vertical Closed Solid Self

- Contained M edium

VCS.SC.L: Vertical Closed Solid Self

- Contained L ow



### DOE assumed that the effects of non- regulatory policies would last from the effective date of standards—2017—through the end of the analysis period, which is 2046.

17.2.3 Policy Interactions

DOE calculated the effects of each non -regulatory policy separately from those of the other policies. In practice, some policies are most effective when implemented in combinati on, such as voluntary efficiency targets implemented with consumer rebates or tax credits. However, DOE attempted to make conservative assumptions to avoid double-counting policy impacts. The resulting policy impacts are not additive ; the combined effect of several or all policies cannot be inferred from summing their results.

Section 17.4 presents graphs that show the market penetration estimated under each nonregulatory policy for the four equipment classes covered in this RIA.

17.3 NON

- REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the six nonregulatory policy alternatives to proposed standard s for commercial refrigeration equipment. (Because the alternative of No New Regulatory Action has no energy or economic impacts, essentially representing the NIA base case, DOE did not perform any additional analysis for that alternative.) DOE developed estimate s of the market penetration of high

- efficiency equipment

both with and without each of the non- regulatory policy alternatives

.

17.3.1 No New Regulatory Action

The case in which no new regulatory action is taken with regard to the energy efficiency

of commercial refrigerat ion equipment constitutes the base case, as described in C hapter 10, National Impact Analysis. The base case provides the basis of comparison for all other policies. By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 Consum er Rebates

DOE considered th e scenario in which the Federal government would provide financial

incentives in the form of rebate s to consumers for purchasing energy- efficient equipm ent

. This

17 -4

### policy provides a consumer rebate for purchasing commercial refrigeration equipment that

operate at the same efficiency as stipulated in each TSL ( target level ).

17.3.2.1 Methodology

DOE based its evaluation methodology for consumer rebates on a comprehensive study of California’s potential for achieving energy efficiency

. Th e study, performed by XENERGY,
Inc., c 

### summarize d experiences with various utility rebate programs. 1 

### XENE RGY’s analytical

method utilized graphs, or penetration curves , that estimate the market penetration of a technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other methods of estimating the effect of consumer rebate programs on the market penetration of efficient technologies. The other methods, developed after the referenced XENERGY report was published, 2

, 3

, 4

, 5

, 6

, 7 

### used different approaches: other economic parameters (e.g., payback period), expert surveys, or model calibration based on specific utility program data rather than

multi

- utility data . Some models in use by energy efficiency program evaluation experts were so

client

- specific that generic relationships between economic parameters and consumer res ponse
could not be established. 5 

### DOE decided that the most appropriate available method for this RIA analysis was the XENERGY approach of pene tration curves based on B/C ratio, which incorporates lifetime operating cost savings.

XENERGY’s model estimates market impacts induced by financial incentives based on the premise that two types of information diffusion drive the adoption of new techno logies.

Internal sources of information encourage consumers to purchase new equipment primarily through word-of-mouth from early adopters. External sources affect consumer purchase decisions through marketing efforts and information from outside the consumer group. Appendix

17-A contains additional details on internal and external information diffusion.

XENERGY’s model equation accounts for the influences of both internal and external sources of information by superimposing the two components. Combining the two mechanisms for information diffusion, XENERGY’s model generates a set of penetration (or implementation)

curves for a po licy measure. XENERGY calibrated the curves based on participation data from utility rebate programs. The curves illustrate the increased penetration (i.e., increased market share) of efficient equipment driven by consumer response to changes in B/C ratio induced by rebate programs. The penetration curves depict various diffusion patterns based on perceived

market barriers (from no- barriers to extremely -high- barriers ) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetration curves based on expert advice founded on more recent utility program experience. 5

, 8



### DOE modeled the effects of a consumer rebate po licy for commercial refrigeration equipment by determining , for each TSL, the increase in market penetration of equipment meeting the target level relative to their market penetration in the base case. It used the

c XENERGY is now owned by KEMA, Inc. ( www.kema.com )

17 -5

### interpolation method presented in Blum et al (2011) 9 

### to create customized penetrati on curves based on relationships between actual base case market penetrations and actual B/C ratios. To inform its estimate of B/C ratios provided by a rebate program DOE performed a thorough nationwide search for existing rebate programs for commercial re frigeration equipment . It gathered data on utility or agency rebates throughout the nation for this equipment, and used this data to calibrate the customized penetration curves it developed for each of the four analyzed equipment classes so they can best r eflect the market barrier level faced by each equipment class

. Section 17.3.2.2 shows the interpolated curves used in the analysis.

17.3.2.2 Analysis

For the four equipment classes it analyzed , DOE estimated t he effect of increasing its

B/ C ratio via a rebate that would pay – depending on the equipment class and TSL – all or part of

the increased installed cost of a unit that met the target efficiency level , compared to one meeting the baseline efficiency level

. d 

### To inform its estimate of an appropriate rebate amount, DOE performed a thorough nationwide search for existing rebate programs for commercial refrigeration equipment. It gathered data from a sample of utility and agency rebate programs

that includes 41 rebate program s for commercia l refrigeration equipment initiated by 35 utilities or agencies in various States. (Appendix 17-A, identifies the rebate programs .) To represent the rebate level, DOE used the simple average of the rebate amounts in these programs , calculated

for each equipment category as defined by E NERGY STAR

. e 

### DOE assumed th at rebates would

remain in effect at the same levels throughout the forecast period (2017-2046 ).

For each of the four analyzed equipment classes

, DOE first calculated the B/C ratio

without a rebate using the difference in total installed costs (C) and lifetime operating cost savings (B) between the unit meeting the target level and the baseline unit. It then calculated the

B/C ratio given a rebate for the unit meeting the target efficiency level

. Because the rebate reduced the incremental cost, the unit receiving the rebate had a l arger B/C ratio. Table 17.3.1

show s the effect of con sumer rebates on t he B/C ratio for each TSL

.

d The baseline technology for each equipment class is defined in the en gineering a nalysis , Chapter 5, as the technology that represents the basic characteristics of equipment in that class . A baseline unit typically is one that just meets current Federal energy conservation standards and provides basic consumer utility

.

e Th e ENERGY STAR program classifies commercial refrigerators in nine categories. Eight categories refer to vertical refrigerators, and include four volume ranges of solid door and glass door cabinets; one category refers to chest refrigerators. (For details s ee http://www.energystar.gov/index.cfm?c=commer_refrig.pr_crit_commercial_refrigerators .)

17 -6

### Table 17.3.1 Benefit/Cost Ratios Without and W ith Rebates for Commercial Refrigeration Equipment

Trial Standard Levels

VOP.RC.M 1 2 3 4 5

B/C Ratio Without Rebate 1.7 1.4 0.6 Rebate Amount (2012$) 264.48 264.48 264.48

Perc of Incremental Cost * n/a** n/a** 22.0% 14.2% 6.0%

B/C Ratio With Rebate 2.2 1.6 0.6

Market Barriers No

- Low Extra

- High High

- Extra

- High

VCT.RC.L 1 2 3 4 5

B/C Ratio Without Rebate 4.0 4.0 3.1 1.5 0.2 Rebate Amount (2012$) 264.48 264.48 264.48 264.48 264.48

Perc of Incremental Cost 100.0% 100.0% 100.0% 62.1% 6.4%

B/C Ratio With Rebate infinite infinite infinite 4.0 0.2

Market Barriers Moderate

- High Moderate

- High Low

- Moderate Extra

- High Moderate

- High

VCS.SC.M 1 2 3 4 5

B/C Ratio Without Rebate 20.6 14.8 7.9 4.6 0.6 Rebate Amount (2012$) 135.33 135.33 135.33 135.33 135.33

Perc of Incremental Cost 100.0% 100.0% 100.0% 87.5% 10.2%

B/C Ratio With Rebate infinite infinite infinite 36.5 0.6

Market Barriers Extra

- High High

- Extra

- High Extra

- High Moderate

- High High

- Extra

- High

VCS.SC.L 1 2 3 4 5

B/C Ratio Without Rebate 16.4 8.3 4.8 4.5 0.7 Rebate Amount (2012$) 135.33 135.33 135.33 135.33 135.33

Perc of Incremental Cost 100.0% 100.0% 69.8% 64.1% 8.7%

B/C Ratio With Rebate infinite infinite 15.8 12.5 0.8

Market Barriers Extra

- High Extra

- High Extra

- High Extra

- High High

- Extra

- High

VOP.RC.M: Vertical Open Remote Condensing Medium

VCT.RC.L : Vertical Closed Transparent Remote Condensing Low

VCS.SC.M: Vertical Closed Solid Self

- Contained Medium

VCS.SC.L: Vertical Closed Solid Self

- Contained Low


### * Percentage of the incremental total installed cost covered by rebate.

** Rebates are not applicable to this case because t he efficiency set for VOP.RC.M at TSL 1 is the baseli ne efficiency.



### DOE used the se B/C ratios along with the penetration curves shown in Figure 17.3.1 to estimate the percentage of consumers who would purchase commercial refrigeration equipment

that meet the target level both with and without a rebate incentive. DOE calculated the penetration curve s that best represent the market barriers for each of the four analyzed equipment

classes at each TSL . The se market barriers are indicated in Table 17.3.1

.

17 -7Vertical Open Remote Condensing Medium Vertical Closed Tr ansparent Remote Condensing LowTSL 1 

### (no change in market penetration)TSL 2 

### (no change in market penetration)TSL 3

17 -8TSL 4TSL 5

Vertical Closed Solid Self

- Contained Medium Vertical Closed Solid Self

- Contained LowTSL 1

17 -9TSL 2TSL 3TSL 4

17

- 10TSL 5



### Figure 17.3.1 Market Penetration Curves for Commercial Refrigeration Equipment

For each equipment class, DOE next estimated the percent increase s represented by the change in penetration rate shown on the corresponding penetration curve. It then added this percent increase to t he market share of units that meet the target level in the base case to obtain the market share of units that meet the target level in the rebate policy case. Table 17.3.2 summarizes DOE’s assumptions for commercial refrigeration equipment regarding the market penetration of products in 2017 that meet the target efficiency level at each

TSL given the consumer rebate s presented in Table 17.3.1

.

Table 17.3.2 Market Penetrations in 2017 Attributable to Consumer R ebates for Commercial Refrigeration Equipment

Trial Standard Levels

VOP.RC.M 1 2 3 4 5

Base

- Case Market Share 60.0% 60.0% 40.0% 0.0% 0.0% Policy Case Market Share 60.0% 60.0% 45.7% 0.1% 0.0%

Increased Market Share 0.0% 0.0% 5.7% 0.1% 0.0%

VCT.RC.L 1 2 3 4 5

Base

- Case Market Share 20.0% 20.0% 20.0% 0.0% 0.0% Policy Case Market Share 69.8% 69.8% 70.8% 1.1% 0.0%

Increased Market Share 49.8% 49.8% 50.8% 1.1% 0.0%

VCS.SC.M 1 2 3 4 5

Base

- Case Market Share 0.0% 30.0% 0.0% 10.0% 0.0% Policy Case Market Share 50.0% 59.2% 50.0% 52.2% 0.0%

Increased Market Share 50.0% 29.2% 50.0% 42.2% 0.0%

VCS.SC.L 1 2 3 4 5

Base

- Case Market Share 0.0% 0.0% 0.0% 0.0% 0.0% Policy Case Market Share 50.0% 50.0% 12.6% 8.9% 0.0%

Increased Market Share 50.0% 50.0% 12.6% 8.9% 0.0%
VOP.RC.M: Vertical Open Remote Condensing Medium

17

- 11VCT.RC.L : Vertical Closed Transparent Remote Condensing Low

VCS.SC.M: Vertical Closed Solid Self

- Contained Medium

VCS.SC.L: Vertical Closed Solid Self

- Contained Low



### DOE used the resulting annual increase s in market shares as inputs t o represent the rebate policy case scenario in i ts NIA -RIA model. Appendix 17-A shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetration trends for the policy case of consumer rebates for commercial refrigeration equipment

.

17.3.3 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous analysis of consumer participation in tax credits. DOE supported its approach using data from Oregon State’s tax credit program for energy

- efficient appliances. DOE also incorporated previous research that disaggregated the effe ct of rebates and tax credits into a direct price effect

, which deriv es from the savings in purchase price

, and an announcement effect , which is independent of the amount of the incentive. 10

, 11 

### The announcement effect derives from the credibility that a technology receives from being included in an incentive program, as well as changes in product marketing and modifications in markup and pricing. DOE assumed that the rebate and consumer tax credit policies would encompass both dire ct price effect s and

announcement effect s, and that half the in crease in market penetration associated with either policy would be due to the direct price effect and half to the announcement effect.

In estimat ing the effects of a tax credit on purchases of consumer products that meet new efficiency standards, DOE ass umed the amount of the tax credit would be the same as the corresponding rebate amount discussed above.

DOE estimated that fewer consumers would participate in a tax credit program than would take advantage of a rebate. Research has shown that the delay required for a consumer to receive a tax credit, plus the added time and cost in preparing the tax return, make a tax credit

incentive less effective than a rebate received at the time of purchase. Based on previous analyses, DOE assumed that only 60 percent of the consumers who would take advantage of a rebate would take advantage of a tax credit. 12



### In preparing its assumptions to estimate the effects of tax credits on consumer purchases

of commercial refrigeration equipment, DOE also reviewed other tax cr edit programs that have been offered at both the Federal and State levels for energy

- efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005) included Federal tax credits for

consumers who purchase energy

- efficient products. 13 

### Those tax credits were in effect in 2006 and 2007, expired in 2008, were reinstated for 2009–2010 by the American Recovery and Reinvestment Act of 2009 ( ARRA ), extended by Congres s for 2011 with some modifications

, and expired at the end of 2011

. 14

, 15 

### The American Taxpayer Relief Act of 2012 extended, with some modifications, residential tax credits for a ir conditioners, heat pumps, furnaces, and water

17

- 12

### heaters placed in service between January 1, 2012 and December 31, 2013. 16 

### DOE reviewed Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation tax credit program in place in the 1980s. However, DOE did not find data specific enough to commercial refrigeration equipment to warrant adjusting its analysis method for the Consumer Tax Credits policy case. Appendix 17-A contains more information on Federal consumer tax

credits.

DOE also reviewed its previous analysis of Oregon’s tax credits for clothes washers to provide support for its assumptions. 17 

### In that previous analysis, DOE compared the market

shares of ultra -high efficiency (UHE) residential clothes washers in Oregon, which offered both State tax credits and utility rebates, with those in Washington State, which offered only utility rebates during the same pe riod. Based on this analysis, DOE estimated that in Oregon the impact of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equivalent efficiency. This finding supports its original assumption that participation in a tax cre dit program would be about 60 percent of participation in a rebate program. Additional discussion of State tax credits for Oregon and other states is in Appendix 17- A.

DOE applied the assumed 60 percent participation described above to the increase in

p enetration rates estimated for the rebate policy to estimate penetration rates attributable to consumer tax credits. In doing so, DOE incorporated the assumptions for consumer response to financial incentives from the penetration curves selected for commer cial refrigeration equipment.

Table 17.3.3 summarizes DOE’s assumptions for commercial refrigeration equipment regarding the market penetration of units in 2017 t hat meet the efficiency level at each TSL given a consumer tax credit.

17

- 13

### Table 17.3.3 Market Penetrations in 2017 Attributable to Consumer Tax Credits for Commercial Refrigeration Equipment

Trial Standard Levels

VOP.RC.M 1 2 3 4 5

Base

- Case Market Share 60.0% 60.0% 40.0% 0.0% 0.0% Policy Case Market Share 60.0% 60.0% 43.4% 0.1% 0.0%

Increased Market Share 0.0% 0.0% 3.4% 0.1% 0.0%

VCT.RC.L 1 2 3 4 5

Base

- Case Market Share 20.0% 20.0% 20.0% 0.0% 0.0% Policy Case Market Share 49.9% 49.9% 50.5% 0.6% 0.0%

Increased Market Share 29.9% 29.9% 30.5% 0.6% 0.0%

VCS.SC.M 1 2 3 4 5

Base

- Case Market Share 0.0% 30.0% 0.0% 10.0% 0.0% Policy Case Market Share 30.0% 47.5% 30.0% 35.3% 0.0%

Increased Market Share 30.0% 17.5% 30.0% 25.3% 0.0%

VCS.SC.L 1 2 3 4 5

Base

- Case Market Share 0.0% 0.0% 0.0% 0.0% 0.0% Policy Case Market Share 30.0% 30.0% 7.6% 5.4% 0.0%

Increased Market Share 30.0% 30.0% 7.6% 5.4% 0.0%
VOP.RC.M: Vertical Open Remote Condensing Medium

VCT.RC.L : Vertical Closed Transparent Remote Condensing Low

VCS.SC.M: Vertical Closed Solid Self

- Contained Medium

VCS.SC.L: Vertical Closed Solid Self

- Contained Low



### The increased market shares attributable to consumer tax credits shown in Table 17.3.3 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual market share increases due to this policy. Section 17.4 pre sents the resulting market penetratio n trends for the policy case of consumer tax credits for commercial refrigeration equipment that meet s the efficiency level for each TSL

.

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that produce commercial refrigeration equipment that meet s the target efficiency level at each TSL, DOE assumed that a manufacturer tax credit would lower the consumer’s purchase cost by an amount equivalent to that provided by the consumer rebates or tax credits described above. DOE further assumed that manufacturers would pass on some of their reduced costs to consumers, causing a direct price effect. DOE assumed that no announcement effect would occur, because

17

- 14

### the program would not be visible to consumers. f 

### Because the direct price effect is approximately equivalent to the announcement effect, 10 

### DOE estimated that a manufacturer tax credit would induce half the number of consumers assumed to take advantage of a consumer tax credit to purchase more efficient equipment. Thus the assumed participation rate is equal to 30 percent of the number of consumers who would participate in a rebate program.

DOE attempted to investigate manufacturer response to the Ener gy Efficient Appliance Credits for manufacturers mandated by EPACT 2005. 18 

### Those manufacturer tax credits have been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009.

DOE was unable to locate data from the Internal Revenue Service or other sources on manufacturer response to the Federal credits. Appendix 17-A presents details on Federal

manufacturer tax credits.

DOE applied the assumption of 30 percent participation to the penetration rates predic ted for the rebate policy to estimate the effects of a manufacturer tax credit policy. In doing so, the Department incorporated the assumptions for consumer response to financial incentives from the penetration curves calculated for commercial refrigeratio n equipment.

Table 17.3.4 summarize DOE’s assumptions for commercial refrigeration equipment regarding the market penetration of units in 2017 meeting the efficiency level at each TSL given

a manufacturer tax credit.

f Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency programs might promote the models for which manufa cturers increase production due to the tax credits, which in turn might induce some announcement effect. However, DOE found no data on such programs on which to base an estimate of the magnitude of this possible announcement effect on consumer behavior.

17

- 15

### Table 17.3.4 Market Penetrations in 2017 Attributable to Manufacturer Tax Credits

for Commercial Refrigeration Equipment

Trial Standard Levels

VOP.RC.M 1 2 3 4 5

Base

- Case Market Share 60.0% 60.0% 40.0% 0.0% 0.0% Policy Case Market Share 60.0% 60.0% 41.7% 0.0% 0.0%

Increased Market Share 0.0% 0.0% 1.7% 0.0% 0.0%

VCT.RC.L 1 2 3 4 5

Base

- Case Market Share 20.0% 20.0% 20.0% 0.0% 0.0% Policy Case Market Share 34.9% 34.9% 35.2% 0.3% 0.0%

Increased Market Share 14.9% 14.9% 15.2% 0.3% 0.0%

VCS.SC.M 1 2 3 4 5

Base

- Case Market Share 0.0% 30.0% 0.0% 10.0% 0.0% Policy Case Market Share 15.0% 38.8% 15.0% 22.6% 0.0%

Increased Market Share 15.0% 8.8% 15.0% 12.6% 0.0%

VCS.SC.L 1 2 3 4 5

Base

- Case Market Share 0.0% 0.0% 0.0% 0.0% 0.0% Policy Case Market Share 15.0% 15.0% 3.8% 2.7% 0.0%

Increased Market Share 15.0% 15.0% 3.8% 2.7% 0.0%
VOP.RC.M: Vertical Open Remote Condensing Medium

VCT.RC.L : Vertical Closed Transparent Remote Condensing Low

VCS.SC.M: Vertical Closed Solid Self

- Contained Medium

VCS.SC.L: Vertical Closed Solid Self

- Contained Low



### The increased market shares attributable to a manufacturer tax credit shown in Table

17.3.4 were used as inputs in the NIA

- RIA model. Appendix 17-A shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetratio n trends

for the policy case of manufacturer tax credits for commercial refrigeration equipment

.

17.3.5 Voluntary Energy Efficiency Targets

For each equipment class , DOE assumed that voluntary energy efficiency targets would be achieved as manufacturers gradually stopped producing units that operated below the efficiency level set for each TSL . DOE assumed that the impetus for phasing out production of

low- efficiency units would be a program similar to the ENERGY STAR labeling program conducted by the Environmental Protection Agency (EPA) and DOE in conjunction with industry partners. The ENERGY STAR program specifie s the minimum energy efficienc ies that various products must have to receive the ENERGY STAR label . ENERGY STAR encourages consumers to purchase efficient products via marketing that promotes consumer label recognition, various incentive programs that adopt the ENERGY STAR specifications, and manufacturers’ promotion of their qualifying appliances. ENERGY STAR projects market penetration of compliant appliances and estimates the percentage of sales of compliant appliances that are attributable to the ENERGY STAR program. There is currently an E NERGY

17

- 16

### STAR program for commercial refrigerators and freezers. ENERGY STAR is revising the

current program and the new version is expected to take effect on October 1, 2014. 19



### Researchers have analyzed the ENERGY STAR program’s effects on sales of several consumer products. Program efforts generally involve a combination of information dissemination and utility or agency rebates. The analyses have been based on State

- specific data
on percentages of shipments of various appliances that meet ENERGY STAR specifications. The analyses generally have concluded that the market penetration of ENERGY STAR

- qualifying appliances is higher in regions or States where ancillary promotional programs have been

active

. 20

, 21

, 22



### DOE believes that informational incentive programs

– like ENERGY STAR, or any other labeling program sponsored by industry or other organizations – are likely to reduce the market barriers to more efficient equipment o ver time. During the rebate analysis, when assessing the B/C ratio and market penetration in the base case for each equipment class, DOE observed that

the market barriers for the four equipment classes are mostly extra

- high , with few cases where they reach high, moderate , or even low levels depending on equipment class and TSL . DOE

estimates that voluntary energy efficiency targets could reduce these barriers to a lower level over 10 years, and followed the methodology presented by Blum et al (2011) 23 

### to evaluate the effects that such a reduction in market barriers have on the increase in market penetration of each equipment class of commercial refrigeration equipment . The methodology relies on interpolated market penetration curves to calculate

– given a B/C ratio – how the market penetration of more

efficient units increases as the market barrier level to those units decreases. Table 17.3.5 presents
the market barrier levels DOE calculated for the base case (same as the ones reported in Table

17.3.1 ) and the reduced market barrier levels that DOE estimated would be reached after 10 years of a voluntary energy efficiency targets program

.

Table 17.3.5 Market Barriers Reduction After 10 Years of Voluntary Energy Efficiency Targets for Commercial Refrigeration Equipment

Trial Standard Levels

VOP.RC.M 1 2 3 4 5

Base

- Case Market Barriers (no change in barriers) (no change in barriers) Low Extra

- High Extra

- High

Reduced Market Barriers No High High

VCT.RC.L 1 2 3 4 5

Base

- Case Market Barriers Moderate Moderate Low Extra

- High Moderate

Reduced Market Barriers Low Low No High Low

VCS.SC.M 1 2 3 4 5

Base

- Case Market Barriers Extra

- High High Extra

- High High Extra

- High

Reduced Market Barriers High Moderate High Moderate High

VCS.SC.L 1 2 3 4 5

Base

- Case Market Barriers Extra

- High Extra

- High Extra

- High Extra

- High Extra

- High

Reduced Market Barriers High High High High High

17

- 17

### Table 17.3.6 summarizes DOE’s assumptions for commercial refrigeration equipment regarding the market penetration of units in 2017 that meet the efficiency level at each TSL given voluntary energy efficie ncy targets.

Table 17.3.6 Market Penetrations in 2017 Attributable to Voluntary Energy Efficiency

T argets for Commercial Refrigeration Equipment

Trial Standard Levels

VOP.RC.M 1 2 3 4 5

Base

- Case Market Share 60.0% 60.0% 40.0% 0.0% 0.0% Policy Case Market Share 60.0% 60.0% 42.5% 0.1% 0.0%

Increased Market Share 0.0% 0.0% 2.5% 0.1% 0.0%

VCT.RC.L 1 2 3 4 5

Base

- Case Market Share 20.0% 20.0% 20.0% 0.0% 0.0% Policy Case Market Share 26.7% 26.7% 27.9% 0.1% 0.1%

Increased Market Share 6.7% 6.7% 7.9% 0.1% 0.1%

VCS.SC.M 1 2 3 4 5

Base

- Case Market Share 0.0% 30.0% 0.0% 10.0% 0.0% Policy Case Market Share 20.4% 33.3% 5.1% 11.6% 0.0%

Increased Market Share 20.4% 3.3% 5.1% 1.6% 0.0%

VCS.SC.L 1 2 3 4 5

Base

- Case Market Share 0.0% 0.0% 0.0% 0.0% 0.0% Policy Case Market Share 15.8% 5.6% 2.0% 1.7% 0.0%

Increased Market Share 15.8% 5.6% 2.0% 1.7% 0.0%
VOP.RC.M: Vertical Open Remote Condensing Medium

VCT.RC.L : Vertical Closed Transparent Remote Condensing Low

VCS.SC.M: Vertical Closed Solid Self

- Contained Medium

VCS.SC.L: Vertical Closed Solid Self

- Contained Low



### The increased market shares attributable to voluntary energy efficiency targets shown in

Table 17.3.6 were used as inputs in the NIA- RIA model. Appendix 17-A shows the annual market share increases due to this policy

. Section 17.4 presents the resulting market penetratio n trends for the policy case of voluntary energy efficiency targets for commercial refrigeration equipment that meet s the efficiency level for each TSL

.

17.3.6 Bulk Government Purchases

Bulk government purchase program s can lead Federal, State, and local governments to purchas e large quantities of equipment that meet the target efficiency level . Combining the

market demands of mu ltiple public sectors can provide a market signal to manufacturers and vendors that some of their largest customers seek equipment that meet an efficiency target at favorable prices. Such a program also can induce “market pull,” whereby manufacturers and vendors would achieve economies of scale for high efficiency equipment.

17

- 18

### Most of the previous bulk government purchase (procurement) initiatives at the Federal, State, and municipal levels have not tracked data on numbers of purchases or degree of compliance with procurement spe cifications. In many cases, procurement programs are decentralized, being part of larger State or regional initiatives. DOE based its assumptions regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) performed regardi ng the savings potential of its procurement specifications for appliances and other equipment. FEMP, however, does not track purchasing data, because of the complex range of purchasing systems, large number of vendors, and so on. States, counties, and muni cipalities have demonstrated increasing interest and activity in “green purchasing." Although many of the programs target office equipment, the growing infrastructure for developing and applying efficient purchasing specifications indicates that bulk government purchase programs are

feasible

. 24

, 25



### DOE assumed that government agencies would administer bulk purchasing programs for commercial refrigeration equipment . At the federal level, this type of program could modify the current FEMP procurement guidelines for commercial refrigeration equipment , which refer to the ENERGY STAR requirements for commercial refrigeration equipment 26



### . DOE reviewed its own previous research on the potential for market transformation through bulk government purchases. Its major study analyzed several scenarios based on the assumption that 20 percent of Federal equipment purchases in 2000 already incorporated energy efficiency requirements based

on FEMP guidelines. One scenario in the DOE report showed energy efficient purchasing

ram ping up during 10 years from 20 percent to 80 percent of all Federal purchases. 27 

### Based on this study, DOE estimated that a bulk government purchase program instituted within a 10

- year period would result in at least 80 percent of government- purchased commercial refrigeration equipment meeting target efficiency leve ls.

DOE assumed that bulk government purchases would affect commercial refrigeration equipment used in federal government-owned commercial buildings. Th ese buildings consist primarily of buildings used for p ublic order and safety

, and service. DOE used the 2003 Commercial Buildings Energy Consumption Survey (CBECS 2003) 26 

### to calculate the sha re of commercial refrigeration equipment used in commercial buildings owned by the federal government. The calculation resulted in 4.9 percent for open commercial refrigerators and 4.6 percent for closed commercial refrigerators. DOE assumed those percenta ges as the share s of the market to which this policy would apply.

DOE estimated that, starting in 2017, each year of a bulk government purchase policy would result in an increasing percent of shipments of government- purchased units that would

meet the ta rget efficiency levels. DOE estimated that within 10 years (by the end of 2026), bulk government purchasing programs would result in 80 percent of the commercial refrigeration equipment used in federal government- owned commercial buildings meeting the target level. DOE modeled the bulk government purchase program assuming that the market share s achieved in 2026 would be maintained throughout the rest of the analysis period.

17

- 19

### Table 17.3.7 summarizes DOE’s assumptions for commercial refrigeration equipment regarding the market penetration of units in 2017 that meet the efficiency level at each TSL given bulk government purchasing.

Table 17.3.7 Market Penetrations in 2017 Attributable to Bulk Government Purchasing o f Commercial Refrigeration Equipment

Trial Standard Levels

VOP.RC.M 1 2 3 4 5

Base

- Case Market Share 60.0% 60.0% 40.0% 0.0% 0.0% Policy Case Market Share 60.0% 60.0% 40.2% 0.4% 0.4%

Increased Market Share 0.0% 0.0% 0.2% 0.4% 0.4%

VCT.RC.L 1 2 3 4 5

Base

- Case Market Share 20.0% 20.0% 20.0% 0.0% 0.0% Policy Case Market Share 20.3% 20.3% 20.3% 0.4% 0.4%

Increased Market Share 0.3% 0.3% 0.3% 0.4% 0.4%

VCS.SC.M 1 2 3 4 5

Base

- Case Market Share 0.0% 30.0% 0.0% 10.0% 0.0% Policy Case Market Share 0.4% 30.2% 0.4% 10.3% 0.4%

Increased Market Share 0.4% 0.2% 0.4% 0.3% 0.4%

VCS.SC.L 1 2 3 4 5

Base

- Case Market Share 0.0% 0.0% 0.0% 0.0% 0.0% Policy Case Market Share 0.4% 0.4% 0.4% 0.4% 0.4%

Increased Market Share 0.4% 0.4% 0.4% 0.4% 0.4%
VOP.RC.M: Vertical Open Remote Condensing Medium

VCT.RC.L : Vertical Closed Transparent Remote Condensing Low

VCS.SC.M: Vertical Closed Solid Self

- Contained Medium

VCS.SC.L: Vertical Closed Solid Self

- Contained Low



### The increased market shares attributable to bulk government purchasing shown in Table

17.3.7 were used as inputs in the NIA-RIA model. Appendix 17- A shows the annual market share increases due to this policy that DOE used as inputs to the NIA- RIA model. Section 17.4

below presents the resulting market penetration trends for the policy case of bulk government purchase of commercial refrigeration equipment

.

17.4 IMPACT S OF NON

- REGULATORY ALTERNATI VE S

Figure 17.4.1

, Figure 17.4.2

, Figure 17.4.3 , and Figure 17.4.4 show the effects of each non-regulatory policy on market penetration for each of the four analyzed equipment classes. g



### R elative to the base case, the policy cas es increase the market shares that meet the target level.

Recall th e proposed standards (not shown in the figures) would result in a 100- percent market penetration of equipment that meet s the target efficiency level.

g Results from the No New Regulatory Action policy measure correspond to the Base Case scenario.

17

- 20

### Figure 17.4.1 Market Penetration of Vertical Open Remote Condensing

Medium (TSL 3)

Figure 17.4.2 Market Penetration of Vertical Closed Transparent Remote Condensing Low (TSL 3)

17

- 21

### Figure 17.4.3 Market Penetration of Vertical Closed Solid Self

- Contained

Medium (TSL 3)

Figure 17.4.4 Market Penetration of Vertical Closed Solid Self

- Contained

Low (TSL 3)

Tabl e 17.4.1 through Table 17.4.4 show the national energy savings and net present value

for five non- regulatory policies analyzed in detail for each of the four analyzed equipment

classes . T he target level for each policy equals the efficiency level s proposed for standard s in

TSL 3. The case in which no regulatory action is taken with regard to commercial refrigeration

17

- 22

### equipment constitute s the base case (or "No New Regulatory Action" scenario ), in which NES

and NPV are zero by definition. For comparison, the tables include the impacts of the proposed

standard s. Energy savings are given in quadrillion British thermal units (quads). The NPV s shown in Table 17.4.1 through Table 17.4.4 are based on two discount rates, 7 percent and 3
percent

.

The policies with the highest projected cumulative results at TSL 3 var y across equipment classes. For VOP.RC.M the policy alternative the leads to the greatest NES and NPV is voluntary energy efficiency standards. For this equipment class, the remaining alternatives provide little benefits compared to voluntary standards. As for the other three equipment classes

, the policy with greatest benefits is consumer rebates, followed by voluntary energy efficiency
targets in case of VCT.RC.L, and by consumer tax credits in case of VCS.SC.M and VCS.SC.L.

The difference in the policies with greatest benefits across equipment classes stems from the

difference in the market barriers implied for each equipment class and the market penetration increase driven by each alternative policy

.

The market barriers level implied fo r equipment class VOP.RC.M is Low, and it is estimated that voluntary energy efficiency targets would completely remove those barriers over time. On the other hand, the monetary incentive provided by rebates and tax credits would cover only 22% of the total incremental costs and lead to lower increases in the market penetration of the target efficiency level. The market barriers level implied for equipment class VCT.RC.L is

Low-M oderate

, and voluntary energy efficiency targets are also estimated to reduce i t and promote the market penetration of the target efficiency level. In this case, however, the monetary incentive provided by rebates would cover the total incremental costs in full and therefore promote a higher increase in the market penetration of the target level. E quipment classes

VCS.SC.M and VCS.SC.L have zero market shares in the base case scenario for the target efficiency level , and therefore assumed to present E xtra -H igh level of market barriers. T he financial incentive provided by rebates and tax credits would cover in this case a significant share of the total incremental costs (100% for VCS.SC.M and 70% for VCS.SC.L). Hence t he increase in market penetration promoted by rebates and consumer tax credits outweighs the

increase that voluntary energy efficiency targets would provide by reducing the market barriers

level from E xtra -High to High.

17

- 23

### Table 17.4.1 Impacts of Non- Regulatory Alternatives for Vertical Open Remote Condensing Medium (TSL 3)*

Policy Alternative Primary Energy Savings (quads) Net Present Value ( billion 2012 $ )

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.038 0.056 0.145

Consumer Tax Credits 0.023 0.033 0.087

Manufacturer Tax Credits 0.012 0.017 0.044
Voluntary Energy Efficiency Targets 0.120 0.157 0.443

Bulk Government Purchases 0.012 0.015 0.043
Proposed Standards 0.403 0.584 1.528

* For equipment shipped 201 7

– 204 6

Table 17.4.2 Impacts of Non- Regulatory Alternatives for Vertical Closed Transparent Remote Condensing Low (TSL 3)*

Policy Alternative Primary Energy Savings (quads) Net Present Value ( billion 2012$ )

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.082 0.150 0.353

Consumer Tax Credits 0.049 0.090 0.212

Manufacturer Tax Credits 0.025 0.045 0.106
Voluntary Energy Efficiency Targets 0.076 0.118 0.307

Bulk Government Purchases 0.004 0.006 0.016
Proposed Standards 0.130 0.237 0.557

* For equipment shipped 2017

– 204 6

Table 17.4.3 Impacts of Non- Regulatory Alternatives for Vertical Closed Solid Self

Contained Medium (TSL 3)*

Policy Alternative Primary Energy Savings (quads) Net Present Value ( billion 2012$ )

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.638 1.419 3.242

Consumer Tax Credits 0.383 0.851 1.945

Manufacturer Tax Credits 0.191 0.426 0.973
Voluntary Energy Efficiency Targets 0.187 0.382 0.925

17

- 24

### Bulk Government Purchases 0.042 0.082 0.204
Proposed Standards 0.794 1.835 4.086

* For equipment shipped 2017

– 204 6

Table 17.4.4 Impacts of Non- Regulatory Alternatives for Vertical Closed Solid Self

Contained Low (TSL 3)*

Policy Alternative Primary Energy Savings (quads) Net Present Value ( billion 2012$ )

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.102 0.200 0.459

Consumer Tax Credits 0.061 0.120 0.275

Manufacturer Tax Credits 0.030 0.060 0.138
Voluntary Energy Efficiency Targets 0.052 0.092 0.227

Bulk Government Purchases 0.026 0.044 0.111
Proposed Standards 0.808 1.590 3.648

* For equipment shipped 201 7

– 2046

17

- 25

### REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy Efficiency, 2002. XENERGY Inc. (Last accessed November, 2013.)

< http://www.is suelab.org/resource/californias_secret_energy_surplus_the_potential_for_ energy_efficiency >

2. ICF International, Arizona Public Service Energy Efficient Market Potential Study , 2007. Fairfax, VA.

3. Mosenthal, P., Telephone Conversation with Barbara At kinson of LBNL, personal

communication. Optimal Energy

. January, 2008.

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Quantec, LLC

. January, 2008.

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Itron, Inc

. January 2008 and March 2009.

6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last accessed November, 2013.) < http://calmac.org/publications/PGE0264_Final_Report.pdf >

7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market Potential Study, 2010. (Last accessed November, 2013.)

< http://www.ameren.com/sites/aue/Environment/Renewables/Documents/AmerenUEVol ume2MarketResearchReport2.pdf >

8. Coito, F., E- mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc

. June, 2010.

9. Blum, H., B. Atkinson, and A. Lekov, A Framework for Comparative Assessments of Energy Efficiency Policy Measures, May, 2011. Report No. LBNL-4749E.

< http://efficiency.lbl.gov/drupal.files/ees/A%20Framework%20for%20Comparative%20

Assessments% 20of%20Energy%20Efficiency%20Policy%20Measures_LBNL4749E.pdf >

10. Lawrence Berkeley National Laboratory, Energy End -Use Forecasting: Analysis of Tax Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and Assessment Group (Last ac cessed November, 2013.)

< http://enduse.lbl.gov/Projects/TaxCredits.html >

11. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment

Purchase Decisions , 1994. Prepared for Southern California Edison by Cambridge

17

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### Systematics, Pacific Consulting Services, The Technology Applications Group, and California Survey Research Services.

12. U.S. Department of Energy: Office of Codes and Standards, Technical Support

Document: Ener gy Efficiency Standards for Consumer Products: Refrigerators, Refrigerator-Freezers, and Freezers, Including Environmental Assessment and Regulatory Impact Analysis, November, 1995. Washington, DC. Report No. DOE/EE0064.

13. Energy Policy Act of 2005 , in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC
25C note, . 2005. http://www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf

14. American Recovery and Rein vestment Act of 2009, 2009. Government Printing Office. (Last accessed November, 2013.) < http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf >

15. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling Equipment., 2011. (Last accessed November, 2013.)

< www.e nergytaxincentives.org/consumers/heating-cooling.php >

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013. (Last accessed November, 2013.) < http://energytaxincentives.or g/ >

17. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products,

and Commercial Clothes Washers , 2008. Washington, DC.

18. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed November, 2013.) < http://energytaxincentives.org/bu ilders/appliances.php >

19. Energy Star, Commercial Refrigerators & Freezers Specification Version 3.0, (Last accessed December, 2013.)

20. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modeling the Effects of U.S. ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who Delivers?, May 30 -June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La Napoule, France.

< http://www.eceee. org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper

>

17

- 27

### 21. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy Program Evaluation Conference Proceedings.

22. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy

- Efficient Appliance Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program Evaluation Conference Proceedings. Seattle, WA.

23. Blum, H., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of Energy Efficiency Policy Measures, May, 2011.

24. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal

communication. National Association of State Procurem ent Officials

. April, 2008.

25. Responsible Purchasing Network, (Last accessed November, 2013.)

< www.responsiblepurchasing.org/ >

26. Federal Energy Management Program (FEMP), Covered Product Category: Commercial Refrigerators and Freezers, (Last accessed December, 2013.)

27. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase

. ACEEE: Summer Study on Energy Efficiency in Buildings , 2000: pp. 147-166

17

- 28

### APPENDIX 17

- A. REGULATORY IMPACT ANALYSIS: SUPPORTING

MATERIALS

TABLE OF CO NTENTS

17- A.1 INTRODUCTION

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

17- A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

................................ 17-A-3

17- A.3 NIA

- RIA INTEGRATED MODEL

..................................................................... 17-A-7

17- A.4 CONSUMER REBATE POLI CY MARKET PENETRATIO N CURVES

........ 17-A-7

17-A.4.1 Introduction .......................................................................................................... 17-A-7

17-A.4.2 Adjustment of XENERGY Penetration Curves ................................................... 17-A-9

17-A.4.3 Interpolation of Penetration Curves ................................................................... 17-A-10

17- A.5 CONSUMER REBATE PROGRAMS

.............................................................. 17-A-11

17- A.6 FEDERAL AND STATE TAX CREDITS

........................................................ 17-A-15

17-A.6.1 Federal Tax Credits for Consumers ................................................................... 17-A-15

17-A.6.2 Federal Tax Credits for Manufacturers

.............................................................. 17-A-16

17-A.6.3 State Tax Credits

................................................................................................ 17-A-17

LIST OF TABLES

Table 17 -A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Commercial Refrigeration Equipment (Vertical Open Remote Condensing Medium) ................................................................. 17-A-3

Table 17 -A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Commercial Refrigeration Equipment (Vertical Closed Transparent Remote Condensing Low) ................................................... 17-A-4

Table 17 -A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Commercial Refrigeration Equipment (Vertical Closed

Solid Self -Contained Medium) ................................................................ 17-A-5

Table 17 -A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Commercial Refrigeration Equipment (Vertical Closed

Solid Self

- Contained Low) ...................................................................... 17-A-6

Table 17 -A.5.1 Rebate Amount by Equipment Class ..................................................... 17-A-11 VOP.RC.M: Vertical Open Remote Condensing Medium .................................................. 17-A-11

Table 17 -A.5.2 Rebates for Commercial Refrigeration Equipment ................................ 17-A-12

LIST OF FIGURE S

Figure 17 -A.4.1 S-Curves Showing Effects of External and Internal Sources on Adoption of New Technologies ............................................................... 17-A-9

17 -A-i

### APPENDIX 17

- A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

17

- A.1 INTRODUCTION

This appendix contains sections discussing the following topics:

• Projections of annual market share increases for the alternative policies;

• NIA

- RIA Integrated Model;

• XENERGY penetration curves used to analyze consumer rebates, including:

o Background material,

o DOE’s adjustment of these curves for this analysis, and

o The m ethod DOE used for interpolating the curves;

• Detailed table of rebates offered for t he considered products; and

• Background material on Federal and state tax credits for appliances

.

17 -A-2

### 17

- A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17 -A.2.1 through Table 17 -A.2.4 show the annual increases in market shares of the four analyzed equipment classes meeting the target efficiency levels for the proposed TSL (TSL 3). DOE used these market share increases as inputs to the NIA

- RIA spreadsheet model.

Table 17-A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Commercial Refrigeration Equipment ( Vertical Open Remote Condensing Medium )

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary Energy Efficiency

Targets Bulk

Government

Purchases



### 2017 5.7% 3.4% 1.7% 2.5% 0.2%

2018 5.7% 3.4% 1.7% 4.8% 0.4%

2019 5.7% 3.4% 1.7% 7.0% 0.6%

2020 5.7% 3.4% 1.7% 9.2% 0.8%

2021 5.7% 3.4% 1.7% 11.2% 1.0%

2022 5.7% 3.4% 1.7% 13.1% 1.2%

2023 5.7% 3.4% 1.7% 15.0% 1.4%

2024 5.7% 3.4% 1.7% 16.8% 1.6%

2025 5.7% 3.4% 1.7% 18.5% 1.8%

2026 5.7% 3.4% 1.7% 20.1% 2.0%

2027 5.7% 3.4% 1.7% 20.1% 2.0%

2028 5.7% 3.4% 1.7% 20.1% 2.0%

2029 5.7% 3.4% 1.7% 20.1% 2.0%

2030 5.7% 3.4% 1.7% 20.1% 2.0%

2031 5.7% 3.4% 1.7% 20.1% 2.0%

2032 5.7% 3.4% 1.7% 20.1% 2.0%

2033 5.7% 3.4% 1.7% 20.1% 2.0%

2034 5.7% 3.4% 1.7% 20.1% 2.0%

2035 5.7% 3.4% 1.7% 20.1% 2.0%

2036 5.7% 3.4% 1.7% 20.1% 2.0%

2037 5.7% 3.4% 1.7% 20.1% 2.0%

2038 5.7% 3.4% 1.7% 20.1% 2.0%

2039 5.7% 3.4% 1.7% 20.1% 2.0%

2040 5.7% 3.4% 1.7% 20.1% 2.0%

2041 5.7% 3.4% 1.7% 20.1% 2.0%

2042 5.7% 3.4% 1.7% 20.1% 2.0%

2043 5.7% 3.4% 1.7% 20.1% 2.0%

2044 5.7% 3.4% 1.7% 20.1% 2.0%

2045 5.7% 3.4% 1.7% 20.1% 2.0%

2046 5.7% 3.4% 1.7% 20.1% 2.0%

17 -A-3

### Table 17-A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Commercial Refrigeration Equipment ( Vertical Closed Transparent Remote Condensing Low )

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary Energy Efficiency

Targets Bulk

Government

Purchases



### 2017 50.8% 30.5% 15.2% 7.9% 0.3%

2018 50.8% 30.5% 15.2% 13.2% 0.5%

2019 50.8% 30.5% 15.2% 17.3% 0.8%

2020 50.8% 30.5% 15.2% 20.9% 1.1%

2021 50.8% 30.5% 15.2% 24.0% 1.4%

2022 50.8% 30.5% 15.2% 28.2% 1.6%

2023 50.8% 30.5% 15.2% 35.8% 1.9%

2024 50.8% 30.5% 15.2% 42.2% 2.2%

2025 50.8% 30.5% 15.2% 48.0% 2.5%

2026 50.8% 30.5% 15.2% 53.1% 2.7%

2027 50.8% 30.5% 15.2% 53.1% 2.7%

2028 50.8% 30.5% 15.2% 53.1% 2.7%

2029 50.8% 30.5% 15.2% 53.1% 2.7%

2030 50.8% 30.5% 15.2% 53.1% 2.7%

2031 50.8% 30.5% 15.2% 53.1% 2.7%

2032 50.8% 30.5% 15.2% 53.1% 2.7%

2033 50.8% 30.5% 15.2% 53.1% 2.7%

2034 50.8% 30.5% 15.2% 53.1% 2.7%

2035 50.8% 30.5% 15.2% 53.1% 2.7%

2036 50.8% 30.5% 15.2% 53.1% 2.7%

2037 50.8% 30.5% 15.2% 53.1% 2.7%

2038 50.8% 30.5% 15.2% 53.1% 2.7%

2039 50.8% 30.5% 15.2% 53.1% 2.7%

2040 50.8% 30.5% 15.2% 53.1% 2.7%

2041 50.8% 30.5% 15.2% 53.1% 2.7%

2042 50.8% 30.5% 15.2% 53.1% 2.7%

2043 50.8% 30.5% 15.2% 53.1% 2.7%

2044 50.8% 30.5% 15.2% 53.1% 2.7%

2045 50.8% 30.5% 15.2% 53.1% 2.7%

2046 50.8% 30.5% 15.2% 53.1% 2.7%

17 -A-4

### Table 17-A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Commercial Refrigeration Equipment ( Vertical Closed

Solid Self

- Contained Medium )

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary Energy Efficiency

Targets Bulk

Government

Purchases



### 2017 50.0% 30.0% 15.0% 5.1% 0.4%

2018 50.0% 30.0% 15.0% 6.2% 0.7%

2019 50.0% 30.0% 15.0% 7.4% 1.1%

2020 50.0% 30.0% 15.0% 8.5% 1.5%

2021 50.0% 30.0% 15.0% 9.8% 1.8%

2022 50.0% 30.0% 15.0% 11.0% 2.2%

2023 50.0% 30.0% 15.0% 12.2% 2.6%

2024 50.0% 30.0% 15.0% 13.5% 2.9%

2025 50.0% 30.0% 15.0% 14.7% 3.3%

2026 50.0% 30.0% 15.0% 15.9% 3.6%

2027 50.0% 30.0% 15.0% 15.9% 3.6%

2028 50.0% 30.0% 15.0% 15.9% 3.6%

2029 50.0% 30.0% 15.0% 15.9% 3.6%

2030 50.0% 30.0% 15.0% 15.9% 3.6%

2031 50.0% 30.0% 15.0% 15.9% 3.6%

2032 50.0% 30.0% 15.0% 15.9% 3.6%

2033 50.0% 30.0% 15.0% 15.9% 3.6%

2034 50.0% 30.0% 15.0% 15.9% 3.6%

2035 50.0% 30.0% 15.0% 15.9% 3.6%

2036 50.0% 30.0% 15.0% 15.9% 3.6%

2037 50.0% 30.0% 15.0% 15.9% 3.6%

2038 50.0% 30.0% 15.0% 15.9% 3.6%

2039 50.0% 30.0% 15.0% 15.9% 3.6%

2040 50.0% 30.0% 15.0% 15.9% 3.6%

2041 50.0% 30.0% 15.0% 15.9% 3.6%

2042 50.0% 30.0% 15.0% 15.9% 3.6%

2043 50.0% 30.0% 15.0% 15.9% 3.6%

2044 50.0% 30.0% 15.0% 15.9% 3.6%

2045 50.0% 30.0% 15.0% 15.9% 3.6%

2046 50.0% 30.0% 15.0% 15.9% 3.6%

17 -A-5

### Table 17-A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Commercial Refrigeration Equipment ( Vertical Closed

Solid Self

- Contained Low )

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary Energy Efficiency

Targets Bulk

Government

Purchases



### 2017 12.6% 7.6% 3.8% 2.0% 0.4%

2018 12.6% 7.6% 3.8% 2.4% 0.7%

2019 12.6% 7.6% 3.8% 2.9% 1.1%

2020 12.6% 7.6% 3.8% 3.5% 1.5%

2021 12.6% 7.6% 3.8% 4.0% 1.8%

2022 12.6% 7.6% 3.8% 4.6% 2.2%

2023 12.6% 7.6% 3.8% 5.3% 2.6%

2024 12.6% 7.6% 3.8% 5.9% 2.9%

2025 12.6% 7.6% 3.8% 6.6% 3.3%

2026 12.6% 7.6% 3.8% 7.3% 3.6%

2027 12.6% 7.6% 3.8% 7.3% 3.6%

2028 12.6% 7.6% 3.8% 7.3% 3.6%

2029 12.6% 7.6% 3.8% 7.3% 3.6%

2030 12.6% 7.6% 3.8% 7.3% 3.6%

2031 12.6% 7.6% 3.8% 7.3% 3.6%

2032 12.6% 7.6% 3.8% 7.3% 3.6%

2033 12.6% 7.6% 3.8% 7.3% 3.6%

2034 12.6% 7.6% 3.8% 7.3% 3.6%

2035 12.6% 7.6% 3.8% 7.3% 3.6%

2036 12.6% 7.6% 3.8% 7.3% 3.6%

2037 12.6% 7.6% 3.8% 7.3% 3.6%

2038 12.6% 7.6% 3.8% 7.3% 3.6%

2039 12.6% 7.6% 3.8% 7.3% 3.6%

2040 12.6% 7.6% 3.8% 7.3% 3.6%

2041 12.6% 7.6% 3.8% 7.3% 3.6%

2042 12.6% 7.6% 3.8% 7.3% 3.6%

2043 12.6% 7.6% 3.8% 7.3% 3.6%

2044 12.6% 7.6% 3.8% 7.3% 3.6%

2045 12.6% 7.6% 3.8% 7.3% 3.6%

2046 12.6% 7.6% 3.8% 7.3% 3.6%

17 -A-6

### 17

- A.3 NIA

- RIA INTEGRATED MODEL

For this analysis, DOE used its integrated NIA

- RIA a 

### model appro ach that built on the NIA model discussed in Chapter 10 and documented in Appendix 10-A.The resulting integrated

NIA -RIA model featured both the NIA analysis inputs and result s and the RIA inputs and had

the capability to generate results for each of the RIA policies. A separate module produced results summaries for the tables and figures in the RIA document. For the RIA methodology documentation in Chapter 17, the module created summaries of parameters calculated by the model for the consumer rebates policy, generated its penetration curves (discussed in S ection 17-

A.4.3 below), and reported market share impacts for the rebate and tax cred it policies by product class. For the RIA results reported in Chapter 17, the module produced graphs of the market

sh are increases resulting from each of the policies analyzed and created summary tables for the national energy savings (NES) and net present value (NPV) results . This module also generated tables of market share increases for each policy reported in S ection 17- A.2 of this Appendix

.

17

- A.4 CONSUMER REBATE POLI CY MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that DOE used to analyze the Consumer Rebates policy. Next it discusses the adjustments it made to the maximum penetration rates. It then refers to the method it used to develop interpolated penetration curves for each specific product class and efficiency level in the analysis. The resulting curves for each of the four analyzed equipment classes are in Chapter 17.

17-A.4.1 Introduction

XENERGY, Inc. b 

### , developed a re- parameterized, mixed -source information diffusion model to estimate market impacts induced by financial incentives for purchasing energy efficient appliance s. 1 

### The ba sic premise of the mixed -source model is that information diffusion drives the adoption of technology.

Extensive economic literature describes the diffusion of new products as technologies evolve. Some research focuses primarily on developing analytical models of diffusion patterns applicable to individual consumers or to technologies from competing firms. 2

, 3

, 4 

### One study records researchers’ attempts to investigate the factors that drive diffusion processes. 5 

### Because a new product generally has its own distinct characteristics, few studies have been able to conclusively develop a universally applicable model. Some key findings, however, generally are accepted in academia and industry.

One accepted finding is that, regardless of their economic benefits and technological merits, new technologies are unlikely to be adopted by all potential users. For many products, a ceiling must be placed on the adoption rate. A second conclusion is that not all adopters purchase

a NIA = national impact analysis; RIA = regulatory impact analysis

b XENERGY is now owned by KEMA, Inc. ( www.kema.com )

17 -A-7

### new products at the same time: some act quickly after a new product is introduced; others wait for the product to mature. Third, diffusion processes can be characterized approximately by asymmetric S -curves that depict three stages of diffusion: starting, acce lerating, and decreasing (as the adoption ceiling is approached).

A so -called epidemic model of diffusion is used widely in marketing and social studies. The epidemic model assumes that (1) all consumers place identical value on the benefits of a new product, and (2) the cost of a new product is constant or declines monotonically over time. What induces a consumer to purchase a new product is information about the availability and benefits of the product. In other words, information diffusion drives consumers’ adoption of a

new product. 3 

### The model incorporates information diffusion from both internal sources (spread by word of mouth from early adopters to prospective adopters) and external so urces (the “announcement effect” produced by government agencies, institutions, or commercial advertising). The model incorporates both internal and external sources by combining a logistic function with an exponential function. 4

, 5



### The relative degree of influence from the internal and external sources determines the general shape of the diffusion curve for a specific product. 4

, 5 

### If adoption of a product is influenced primarily by external sources of information (the announcement effect), for instance, a high rate of diffusion occurs at the beginning of the process. In this scenari o, external sources provide immediate information exposure to a significant number of prospective adopters. In contrast, internal sources (such as a network of prospective adopters) are relatively small in size and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, information diffusion dominated by external sources is represented by a concave curve (the exponential curve in Figure 17-A.4.1 ). If adoption of a new product is influenced most strongly by internal sources of information, the number of adopters increases gradually, forming a convex curve (the logistic curve in Figure 17 -A.4.1 ).

17 -A-8

### Figure 17 -A.4.1 S- Curves Showing Effects of External and Internal Sources on Adoption of New Technologies

17-A.4.2 Adjustment of XENERGY Penetration Curves

In consultation with the primary authors of the 2002 XENERGY study who later conducted similar California studies, DOE made some adjustments to XENERGY’s original implementation (penetration) curves. 6 

### The experiences with utility programs since the XENERGY study indicate that incentive programs have difficulty achieving pene tration rates as high as 80 percent. Consumer response is limited by barriers created by consumer utility issues and other non-economic factors. DOE therefore adjusted the maximum penetration parameters for some of the curves from 80 percent to the followi ng levels:

Moderate Barriers: 70% High Barriers: 60% Extremely High Barriers: 50%

The low barriers and no barriers curves (the latter used only when a product has a very high base- case -market share) remained, respectively, with 80 percent and 100 percent as their maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set

the no barri ers and extremely high barriers curves as the upper and lower bounds, respectively, for any benefit/cost ratio points higher or lower than the curves. It set another constraint such

17 -A-9

### that the policy case market share cannot be great than 100 percent, as mig ht occur for products with high base case market shares of the target

- level technology.

17-A.4.3 Interpolation of Penetration Curves

As discussed above, the XENERGY penetration (implementation) curves followed a functional form to estimate the market implementation rate caused by energy efficiency

measures such as consumer rebates. c 

### The XENERGY report presents five reference market implementation curves that vary according to the level of market barriers to technology penetration. 1 

### Such curves have been used by DOE in the Regulatory Impact Analyses for rulemakings for appliance energy efficiency standards to estimate market share increases in response to rebate programs. d 

### They provide a framework for evaluating technology penetration, yet require matching the studied market to the curve that best represents it. This approximate matching can introduce some inaccuracy to the analysis.

Blum et al (2011, Appendix A) 7 

### presents an alternative approach to such evaluation: a method to estimate market implementation rates more accurately by performing interpolations of the

reference curves. The ref erred report describes the market implementation rate function and the reference curves, the method to calibrate the function to a given market, and the limitations of

the method.

c The RIA chapter refers to these curves as penetration curves . This section, in references to the original source, uses

the term implementation curve

. d DOE has also used this method to estimate market share increase s resulting from consumer tax credit and manufacturer tax credit programs, since the effects of tax credits on market s can be considered proportional to the rebate impacts

.

17 -A- 10

### 17

- A.5 CONSUMER REBATE PROGRAMS

DOE performed a search for rebate programs that offered incentive s for commercial refrigeration equipment. Some organizations nationwide, comprising elect ric utilities and regional agencies, offer rebat e programs for this equipment. Table 17 -A.5.2 provides the organizations’ names, states , and program websites. If there is more than one entry for an organization, it offers different rebates in different states . When an organization off ers rebates through several utilities, it is represented only once in each table.

Most of the programs DOE surveyed associate rebate values to refrigerator characteristics as specified by ENERGY STAR. DOE calculated the rebate amount for each equipment class in two steps. First DOE calculated the average rebate value offered towards each ENERGY STAR refrigerator category. Then, DOE associated each equipment class covered by this RIA to an ENERGY STAR category, according to its type (vertical or chest) an d size. Table 17 -A.5.1 presents the rebate amounts DOE calculated for each equipment class. Notice that ENERGY STAR covers only vertical closed and chest commercial refrigerators. DOE a ssumed for the

“ Vertical Open

, Remote Condensing , Medium” ( VOP.RC.M ) equipment class the same rebate

amount it attributed to “ Vertical Closed , Transparent, Remote Condensing, Low ” ( VCT.RC.L ) equipment class

.

Table 17-A.5.1 Rebate Amount by Equipment Class

Equipment Class ENERGY STAR Category Rebate Amount (2012$)

VOP.RC.M Assumed as Vertical, Glass Door >50 cf 264.48

VCT.RC.L Vertical, Glass Door >50 cf 264.48

VCS.SC.M Vertical, Solid Door 30

- 50 cf 135.33

VCS.SC.L Vertical, Solid Door 30

- 50 cf 135.33

VOP.RC.M: Vertical Open Remote Condensing Medium

VCT.RC.L : Vertical Closed Transparent Remote Condensing Low

VCS.SC.M: Vertical Closed Solid Self

- Contained Medium

VCS.SC.L: Vertical Closed Solid Self

- Contained Low

17 -A- 11

### Table 17-A.5.2 Rebates for Commercial Refrigeration Equipment
Organization State Website


### AEP Ohio OH https://www.aepohio.com/save/programs/CustomProgram.aspx Alliant Energy IA http://www.alliantenergy.com/SaveEnergyAndMoney/Rebates/BusIA/030566 Alliant Energy MN http://www.alliantenergy.com/SaveEnergyAndMoney/Rebates/BusMN/032414

Ameren Illinois

- ActOnEnergy IL http://www.actonenergy.com/for

- my

- business/explore

- incentives/sp ecialty

- equipment

incentives

Ameren Missouri MO http://www.tradeallynetwork.com/online

- applications/

Avista ID http://www.avistautilities.com/business/rebates/Pages/IDCommercialRebates.aspx

Avista WA http://www.avistautilities.com/business/rebates/washington/Pages/default.aspx BGE Smart Energy Savers Program MD http://www.bgesmartenergy.com/business/energy

- solutions

- business/application

- forms

Central Iowa Power Cooperation - A Touchstone Cooperative (offered by 12 utilities) IA http://www.cipco.net/members.cfm

City of Palo Alto CA http://www.cityofpaloalto.org/gov/depts/utl/business/rebates/cap.asp

Columbia River PUD OR http://www.crpud.net/business/commercial

- kitchen

- food

- service

- equipment

ComEd IL https://www.comed.com/business

- savings/programs

- incentives/Pages/refrigeration.aspx Detroit Public Lighting Department

- ENERGY WISE MI http://www.detroitmi.gov/DepartmentsandAgencies/PublicLighting/EnergyWiseProgram.

aspx DTE Energy MI https://www2.d teenergy.com/wps/portal/dte/business/saveEnergy/details/incentives%20an d%20offers/how%20to%20participate/!ut/p/b1/ Duke Energy NC http://www.duke

- energy.com/north

- carolina

- large

- business/smart

- saver/customer/food

service

- incentives.asp Duke Energy OH http://www.duke

- energy.com/ohio

- business/smart

- saver/trade

- ally/food

- service

incentives.asp Duke Energy SC http://www.duke

- energy.com/south

- carolina

- business/smart

- saver/customer/food

- service

incentives.asp Energizing Indiana (offered by 6 utilities) IN https://energizingindiana.com/commercial

- industrial

- rebates2/

Energy Optimization (offered MI http://www.michigan

- energy.org/thumbbusiness

17 -A- 12Organization State Website 

### by 12 utilities) Energy Smart Program (offered by 18 utilities) MI http://www.mienergysmart.com/pawpaw.html

First Energy (offered by 3 utilities) PA http://www.energysavepa

- business.com/index.php

Idaho Power OR https://www.idahopower.com/EnergyEfficiency/Business/Programs/EasyUpgrades/default

.cfm Illinois Municipal Electric Agency IL http://www.imea.org/EEProj ects.aspx

Lansing Board of Water & Light MI http://www.lbwl.com/EnergySavers/

MidAmerican Energy IL http://www.midamericanenergy.com/ee/il_bus_rebates_kitchen.aspx MidAmerican Energy IL http://www.midamericanenergy.com/ee/il_bus_rebates_kitchen.aspx New Jersey's Clean Energy Program NJ http://www.njcleanenergy.com/commercial

- industrial/programs/nj

- smartstart

buildings/food

- service

- equipment

- incentives

Pacific Power WA https://www.pacificpower.net/bus/se/epi/washington/ilc/fe.html

Pacific Power

- FinAnswer Express CA http://www.pacificpower.net/bus/se/epi/california/ilc/fe2/fse.html

PG&E CA http://www.fishnick.com/saveenergy/rebates/ Platte River Power Authority CO http://www.prpa.org/energy

- efficiency/business

- customers/electric

- efficiency

- progr am/

PPL PA https://www.pplelectric.com/save

- energy

- and

- money/rebates

- and

- discounts/business

- and

nonprofit.aspx Rocky Mountain Power

- FinAnswer Express ID http://www.rockymountainpower.net/content/dam/rocky_mountain_power/doc/Business/S ave_Energy_Money/ID_FinAnswer_Express_Food_Service_Incentives.pdf Rocky Mountain Power

- FinAnswer Express UT http://www.rockymountainpower.net/content/dam/rocky_mountain_power/doc/Business/S ave_Energy_Money/UT_wattsmart_Business_Food_Service_Incentives.pdf Rocky Mountain Power

- FinAnswer Express WY http://www.rockymountainpower.net/content/dam/rocky_mountain_po wer/doc/Business/S ave_Energy_Money/WY_FinAnswer_Express_Food_Service_Retrofits_Incentives.pdf Santee Cooper SC https://www.santeecooper.com/business/reduce

- the

- use

- amp;

- save/rebates

- and

benefits.aspx

SDGE CA http://www.sdge.com/rebates

- finder/earn

- rebates

- your

- improvements

17 -A- 13Organization State Website


### Southern California Edison CA https://www.sce.com/wps/portal/home/business/savings

- incentives/express

- solutions/how

to

- apply/! Southern Minnesota Municipal Power Agency (offered by 15

util ities) MN http://smmpa.org/members/blooming

- prairie

- public

- utilities.aspx

Southern Minnesota Municipal Power Agency (offered by 3 utilities) MN http://smmpa.org/members/austin

- utilities.aspx

Town of Concord

Massachusetts MA http://www.concordma.gov/pages/ConcordMA_LightPlant/Commercial%20Appliance%2

0Rebate

17 -A- 14

### 17

- A.6 FEDERAL AND STATE TA X CREDITS

This section summarizes the Federal and State tax credits available to consumers who purchase energy efficient app liances. This section also describes tax credits available to manufacturers who produce certain energy efficient appliances.

17-A.6.1 Federal Tax Credits for Consumers

EPACT 2005 included Federal tax credits for consumers who installed efficient air conditioners or heat pumps; gas, oil and propane furnaces and boilers ; furnace fans; and/or gas, oil, or electric heat pump water heaters in new or existing homes. 8

, 9 

### These tax credits were in effect in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by the American Recovery and Reinvestment Act (ARRA). 10 

### There was a $1,500 cap on the credit per 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

. The Americ an Taxpayer Relief Act of 2012 extended, with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and

water heaters placed in service between January 1, 2012 and December 31, 2013. 8

, 11



### The tax credit for fur nace fans was $50 in 2011, after which it expired.

The importance of the Federal tax credits has been emphasized in research in the residential heating industry on the impacts of the relatively large credits that were available for HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors conducted by Vermont Energy Investment Corporation, respondents indicated that the ample

credit had had a notable impact on sal es of higher-efficiency heating and cooling equipm ent

. Some distributors combined the Federal tax credits with manufacturer re bates and ut ility

program rebates for a greater consumer incentive. However, when the amount of the Federal tax

credit was reduced , smaller utility rebate incentives had not ind uced the same levels of equipment sales increases . Th e decrease in incentive size from a $1,500 cap in 2009-2010 to a $500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a decline in total sales of residential HVAC prod ucts . Distributors stated that an incentive needed to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence consumer choice. The industry publication “2011 HVAC Review and Outlook” noted a decline in sales of air conditioning units with >14 SEER in 2011 and a return in sales of units with >16 SEER to 2009 levels (after an increase in 2010). The large majority of distributor observed no impacts from the utility programs with their lower rebate amounts available in 2011. Distr ibutors also commented on the advantages of the Federal tax credit being nationwide in contrast to utility rebate programs that target regional markets. 12

, 13



### In an effort to evaluate the potential impact of a Federal appliance tax credit program

, DOE reviewed Internal Revenue Service (IRS) data on the numbers of taxpayers who claimed the tax credits during tax years 2006 and 2007 . It estimated the percentage of taxpayers who filed
Form 5695, Residential Energy Credits. 14 

### It also estimated the percentage of taxpayers with entries under Form 5695’s section 3, Re sidential energy property costs , line 3b, qualified natural gas, propane, or oil furnace or hot water boiler . DOE reasoned that the percentage of taxpayers

17 -A- 15

### with an entry on Line 3b could serve as a rough indication of the potential of taxpayer participatio n in a Federal tax credit program for furnaces during the initial program years. DOE found that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 2007, claimed a credit for a furnace or boiler. DOE further found that t he percentages of those filing Form 5695 for any qualifying energy property expenditure (which also included installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 respectively.

DOE also reviewed data from an earlier Federal energy conservation tax credit program in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located data for only the first three years of the program

. 15

, 16

, 17 

### For those three years - 1979, 1980, and 1981 - the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 percent. Given that the data from this earlier tax credit program were not disaggregated by type of energy property, this data series served only to indicate a possible trend of greater participation in the initial program year, followed by slightly smaller participation in subsequent years. However, DOE did not find detailed analysis of this program to indicate the possible reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 2005 energy tax credit program data for its first two program years.

As discussed in C hapter 17, DOE analyzed the percentage of participation in consumer tax credit programs using its estimates of consumer participation in rebate programs that was based on benefit/cost data specific to each product class. Hence it was difficult to compare these detailed estimates to the more general data analysis described above from the existing Federal tax credit program, or to use the IRS data analysis in its consumer tax credit analysis.

17-A.6.2 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that produced high-efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007. 18



### The Emergency Economic Stabilization Act of 2008 19 

### amended the credits and extended them through 2010. The credits were extended again to 2011 with modifications in the eligibility requirements. Manufacturer tax credits were extended again, by the American Taxpayer Relief Act of 2012, for clothes washers, refrigerators, and dishwashers manufactured between January
1, 2012 and December 31, 2013. 20



### Manufacturers who produce these appliances receive the credits for increasing their production of qualifying appliances. These credits had sev eral efficiency tiers in 2011. For 2012- 2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers

for cloth es washers and dishwashers

. 11 

### The credit amounts applied to each unit manufactured. The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers wa s capped at $75 million for the period of 2008-2010. However, the most efficient refrigerator (30%) and clothes washer (2.2 MEF/4.5 wcf) models was not subject to the cap. The credit to

manufacturers wa s capped at $25 million for 2011, with t he most effici ent refrigerators (35%) and clothes washers (2.8 MEF/3.5 WCF) exempted from this cap. 21

17 -A- 16

### 17-A.6.3 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for efficient appliances for several years, and the States of Kentucky, Michigan and Indiana began offering such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on taxpayer participation in credits for eligible products. (See the discussion in Chapter 17, S ection 17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not disaggregate participation data by appliance, although DOE reviewed Montana's overall participation trends and found them congruent with its analysis of Oregon's clothes washer tax

credits.

Oregon’s Residential Energy Tax Credit (RE TC) was created in 1977. The Oregon legislature expanded the RETC program in 1997 to include residential refrigerators, clothes washers, and dishwashers, which significantly increased participation in the program . The program subsequently added credits for high-efficiency heat pump systems, air conditioners, and water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, and renewable energy equipment. Beginning in 2012 a Tax Credit Ex tension Bill (HB3672) eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters

, and heat pump water heaters. 22

, 23 

### Those technologies recognized by the Oregon Department of Energy as “premium efficiency” are eligi ble for tax credit of $0.60 per kWh saved in the first year (up to $1,500). 22

, 24



### Montana has had an Energy Conservation Tax Credit for residential measures since

1998. 25 

### The tax credit covers various residential energy and water efficient pro ducts, including split system central air conditioning; package system central air conditioning; split system air source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water boilers; advanced main air circulating fans; he at recovery ventilators; gas, oil, or propane water heaters; electric heat pump water heaters; low-flow showerheads and faucets; light fixtures ; and controls. In 2002 the amount of the credit was increased from 5 percent of product costs (up to $150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in new construction or remodeling projects. The tax credit covers only that part of the cost and

materials that exceed established standards of construction.

17 -A- 17

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12. Foster, R., J. Grevatt, N. Lange, and T. Gee, Efficiency Programs and Distributors: Opportunities to Fully Leverage the Supply Chain for Greater Impact, August 12-17, 2012. American Council for an Energy-Efficient Economy. Asilomar, CA.

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### 13. Tusa, C. S., D. Pierson, P. Mammola, and J. P. Vora, JPM/HARDI 2011 HVAC Survey,

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19. Emergency Economic Stabilization Act of 2008, (Last accessed September, 2011.)

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17 -A- 19

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17 -A- 20