# TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

AUTOMATIC COMMERCIAL ICE MAKERS

December 1 , 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 d ocument was prepared for the Department of Energy by staff members of Navigant Consulting, Inc.

and

Pacific Northwest National Laboratory

### CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUM ENT

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

- 1

1.2 SUMMARY OF NATIONAL BENEFITS

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

- 1

1.3 OVERVIEW OF APPLIANC E STANDARDS

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

- 4

1.4 OVERVIEW OF AUTOMATI C COMMERCIAL ICE MAK ERS STANDARDS

....... 1

- 5

1.4.1 Framework and Analysis Methodology

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

- 6

1.5 STRUCTURE OF THE DOC UMENT

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

- 9

REFERENCES

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

- 12

LIST OF TABLES

Table 1.2.1 Annualized Benefits and Costs of Proposed Standards for Automatic

Commercial Ice Makers

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

- 3 Table 1.4.1 ACIM Standards Prescribed by EPCA

– Compliance Required Beginning on January 1, 2010

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

- 5 Table 1.4.2 ACIM Analyses

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

- 7

1

- i

### CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUM ENT

This t echnical s upport 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 automatic

commercial ice makers completed in December 2014. This final rule TSD complements the life

cycle cost (LCC)

, payback period (PBP) , and national impact analysis (NIA) spreadsheets posted

on the U.S. Depa rtment of Energy’s (DOE’s) website at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

.

1.2 SUMMARY OF NATIONAL BENEFITS

DOE’s analyses indicate that the proposed standards would save a significant amount of energy. The lifetime savings for automatic commercial ice

- mak i ng (ACIM) equipment purchased in the 30

- year period that begins in the year of the compliance with amended standards ( 201 8

– 204 7 ) amount to 0

. 179 quadrillion British thermal units (quads) of cumulative

energy

.

The cumulative national net present value (NPV ) of total customer costs and savings of the proposed standards for automatic commercial ice makers in 2013$ ranges from $ 0. 430 billion (at a 7

- percent discount rate) to $ 0.942 billion (at a 3

- percent discount rate). This NPV expresses

the estimated total value of future operating cost savings minus the estimated increased installed costs for equipment purchased in 201 8

– 204 7 , discounted to 2014

.

DOE also studied the potential impact on the ACIM equipment manufacturers. The industry net present value (INPV) is the sum of the discounted cash flows to the industry from the base year ( 201 5 ) through the end of the analysis period ( 204 7 ). Using a real discount rate of

9.2 percent, a 

### DOE estimates that the INPV of ACIM manufacturers is $ 1 21.6 million in the base

case in 201 3 $. Under the proposed standards , DOE expects that manufacturers may lose up to

12.5 percent of their INPV, or approximately $ 15.1 million.

The proposed standards are expected to have significant environmental benefits. Based

on energ y savings from equipment purchased over the period of 2018

– 2047, t he energy savings would result in cumulative greenhouse gas emission reductions of 10.9 million metric tons b 

### of carbon dioxide (CO 2 

### )

, 47.4 thousand tons of methane, 0.1 thousand tons of nitr ous oxide,

9.3 thousand tons of sulfur dioxide (SO 2 

### ), 16.2 thousand tons of nitrogen oxides ( NO

x 

### )

, and

0. 0 3 tons of mercury (Hg)

. c 

### DOE estimates that the net present monetary value of the CO 2 

### emissions reduction would be between $0. 08 and $1. 11 billion , expressed in 2013$ and

discounted to 201 4 . DOE estimates the present monetary value of the NO

x 

### emissions reduction

a This is the rate used to discount future cash flows in the manufacturer impact analysis. A discount rate of 10 percent was calculated based on SEC filings and feedback from manufacturer interviews about the current cost of capital in the industry. For more information, refer to chapter 12 of the NOPR TSD.

b A metric ton is equivalent to 1.1 U.S. short tons. Results for NO

x and Hg are presented in short tons.

c DOE calculated emissions reductions relative to the Annual Energy Outlook 201 4 ( AEO201 4 ) Reference C ase, which generally represents current legislation and environmental regulations for which implementing regulations

were available as of Octo ber 31, 201 3

.

1

- 1

### would be $ 1 2.1 million at a 7

- percent discount rate and $ 2 3. 8 million at a 3

- percent discount

rate. d



### The benefits and costs of the proposed standards, for automatic commercial ice makers purchased between 201 8 and 204 7 , 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 the customer operation of equipment that meets the proposed standards (consisting primarily of operating cost savings from using less energy, minus increases in equipment installed cost, 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. e



### The value of the CO 2 

### reductions is calculated using a range of values per metric ton of CO 2 

### (othe rwise known as the Social Cost of Carbon [ SCC ] ) developed by a recent Federal interagency process. The monetary costs and benefits of cumulative emissions reductions are reported in 2013$ t o permit straightforward comparisons with the other costs and benef its. The derivation of the values of the SCC is discussed in appendices 14A and 14B.

Although combining the values of operating savings and CO 2 

### emission reductions provides a useful perspective, two issues should be considered. First, the national operati ng savings are domestic U.S. customer 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 differe nt methods that use different time frames for analysis. The national operating cost savings are measured over the

lifetimes of automatic commercial ice makers shipped in 201 8

– 204 7 . The SCC values, on the other hand, reflect the present value of some future climate

- related impacts resulting from the

emission of 1 ton of CO 2 

### in each year. These impacts continue well beyond 2100.

Table 1.2

. 1 shows the annualized benefits and costs of the proposed standards. The results of the primary estimate are as follows. Table 1.2

. 1 shows the primary, low net benefits

, and high net benefits scenarios. The primary estimate is the estimate in which the operating cost savings were calculated using the Annual Energy Outlook 201 4 ( AEO2014 ) 1 

### Reference Case

forec ast of future electricity prices. The other two estimates, low net benefits and high net

benefits estimate s , are based on the low and high electricity price scenarios from the AEO2014

forecast. At a 7

- percent discount rate for benefits and costs, the cost in the primary estimate of the standards proposed in this final rule is $ 2 2 million per year in increased equipment costs. The annualized benefits are $ 65 million per year in reduced equipment operating costs, $ 2 0 million in

CO 2 

### reductions ( n ote that DOE used a 3

- percent discount rate

, along with the corresponding

SCC series that uses a 3

- percent discount rate, to calculate the monetized value of CO 2 

### emissions reductions), and $ 1.2 million in reduced NO

x 

### emissions. In this case, the annualized net benefit

d DOE is currently investigating valuation of avoided Hg and SO 2 emissions.

e DOE used a two

- step calculation process to convert the time

- series of costs and benefits into annualized values. First, DOE calculated a present value in 2014 , the year used for discounting the NPV of total consumer costs and savings, for the time

- series of costs and benefits using discount rates of 3 and 7 percent for all costs and benefits except for the value of CO 2 reductions. For the latter, DOE used a range of discount rates , as shown in Table 1.2

. 1

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

- year period ( 2018 through 2047 ) that yields the same present value. The fixed annual payment is the annualized value. Although DOE calculated annualized values, this does not imply that the time

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

1

- 2

### amounts to $ 64 million. At a 3

- percent discount rate for all benefits and costs, the cost in the primary estimate of the amended standards proposed in this final rule is $ 2 3 million per year in increased equipment costs. The benefits are $ 75 m illion per year in reduced operating costs, $ 2 0

million in CO 2 

### reductions, and $ 1.3 million in reduced NO

x 

### emissions. In this case, the net benefit amounts to $ 74 million per year.

Table 1.2

. 1 Annualized Benefits and Costs of Proposed Standards for Automatic

C ommercial I ce M akers

Discount

Rate Primary

Estimate*

million 201 3 $ Low Net

Benefits

Estimate*

million 201 3 $ High Net

Benefits

Estimate*

million 201 3 $

Benefits

Operating Cost Savings 7% 65 62 68

3% 75 71 80

CO 2 at 5% d iscount r ate , average ** 5% 6 6 6

CO 2 at 3% d iscount r ate , average ** 3% 20 20 21

CO 2 at 2.5% d iscount r ate , average ** 2.5% 29 28 30

CO 2 at 3% d iscount r ate , 95th

perc entile ** 3% 62 60 64

NO

x Reduction Monetized Value (at $2,6 84 /Ton)** 7% 1.19 1.16 1.22

3% 1.33 1.29 1.36 Total Benefits (Operating Cost Savings, CO 2 Reduction and NO

x Reduction) † 7% 86 82 90

3% 97 92 102

Costs

Total Incremental Installed Costs 7% 22 23 21

3% 23 24 22

Net Benefits Less Costs

Total Benefits Less Incremental Costs 7% 64 60 69

3% 74 68 80 * The primary, low, and high estimates utilize forecasts of energy prices from the AEO201 4 Reference Case, Low Economic Growth Case, and High Economic Growth Case, respectively. ** These values represent global values (in 201 3 $) of the social cost of CO 2 emissions in 2015 under several
scenarios. The values of $ 12 .0 , $ 40.5 , and $ 62.4 per ton are the averages of SCC d istributions calculated using

5

- percent, 3

- percent, and 2.5

- percent discount rates, respectively. The value of $11 9 .0 per ton represents the 95th percentile of the SCC distribution calculated using a 3

- percent discount rat e. See TSD chapter 8 for details. For NO

x , an average value ($2,6 84 ) of the low ($4 76 ) and high ($4,8 93 ) values was used. † Total monetary benefits for both the 3

- percent and 7

- percent cases utilize the central estimate of social cost of NO

x and CO 2 emissi ons calculated at a 3

- percent discount rate (averaged across three integrated assessment models )

, which is equal to $40. 5 /ton (in 201 3 $).



### DOE also estimated the low net benefits and high net benefits by calculating the operating cost savings and shipment s at the AEO2014 low economic growth case and high economic growth case scenarios, respectively. The low and high benefits for incremental installed costs were derived using the low and high price learning scenarios. The net benefits and

costs for low and high net benefits estimates were calculated in the same manner as the primary estimate by using the corresponding values of operating cost savings and incremental installed

costs.

1

- 3

### 1.3 OVERVIEW OF APPLIANC E 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 Applianc e 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 i ndustrial e quipment

. f 

### (42 U.S.C.

6311

– 6317) EPACT 1992 included amendments to EPCA that expanded Title III to include

additional c ommercial equipment. The Energy Policy and Conservation Act of 2005 (EPACT 2005), Pub. L. 109

- 58, update d several existing standards and test procedures; prescribed definitions, standards, and test procedures for certain new consumer products and

commercia l equipment; and mandated that the Secretary of Energy (the Secretary) commence rulemakings to develop test procedures and standards for certain new consumer products and commercial equipment including ACIM 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. 631 3 ( d )( 4 ) ) 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

f rom the imposition of the standard;

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

4. any lessening of the utility or the performance of the covered products likely to result from the imposition o f 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

f This part w as originally titled Part C. However, it was redesignated Part A

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

- 58.

1

- 4

### 7. other factors the Secretary conside rs relevant.

( See 42 U.S.C. 6 295 (o)(2)(B)(i) ; 631 3 ( d )( 4 ) )

For automatic commercial ice makers , DOE appli ed those factors in a manner consistent with its other energy conservation standards rulemakings to ascertain the maximum improvement in energy efficie ncy that is technologically feasible and economically justified for this equipment.

1.4 OVERVIEW OF AUTOMATI C COMMERCIAL ICE MAK ERS STANDARDS

EPCA prescribed energy conservation standards for automatic commercial ice makers that produce cube type ice with capa cities between 50 and 2,500 lb ice/24 hours. (42 U.S.C. 6313(d)(1)) In a final rule published on October 18, 2005, DOE adopted the energy conservation standards and water conservation standards prescribed by EPCA in 42 U.S.C. 6313(d)(1) for

certain automat ic commercial ice makers manufactured on or after January 1, 2010. 70 FR at 60407, 60415

– 16 . These standards consist of maximum energy use and maximum condenser water use to produce 100 lb of ice for automatic commercial ice makers with harvest rates

betw een 50 and 2,500 lb ice/24 hours. These standards appear at T itle 10 of the Code of Federal Regulations (CFR) part 431, subpart H, Automatic Commercial Ice Makers. Table 1.4

. 1 presents DOE’s current energy conservation standards for automatic commercial ice makers.

Table 1.4

. 1 ACIM Standards Prescribed by EPCA

– Compli ance Required Beginning on January 1, 2010

Equipment Type Type of Cooling Harvest Rate

lb ice/24 hours Maximum Energy

Use

kWh/100 lb ice Maximum Condenser

Water Use * gal/100 lb ice

Ice

- Making Head Water <500 7.8

- 0.0055H ** 200

- 0.022H ** ≥500 and <1,436 5.58

- 0.0011H 200

- 0.022H ≥1,436 4.0 200

- 0.022H

Air <450 10.26

- 0.0086H Not Applicable

≥450 6.89

- 0.0011H Not Applicable Remote Condensing (but not remote compressor) Air <1,000 8.85

- 0.0038H Not Applicable

≥1,000 5.10 Not Applicable

Remote Condensing

and Remote Compressor Air <934 8.85

- 0.0038H Not Applicable

≥934 5.30 Not Applicable

Self

- Contained Water <200 11.4

- 0.019H 191

- 0.0315H
≥200 7.60 191

- 0.0315H

Air <175 18.0

- 0.0469H Not Applicable

≥175 9.80 Not Applicable

Source: 42 U.S.C. 6313(d).

* Water use is for the condenser only and does not include potable water used to make ice.

** H = harvest rate in pounds per 24 hours, indicating the water or energy use for a given harvest rate.



### Furthermore, EPCA requires DOE to review th ese standards and determine, by January 1, 2015, whether amending the applicable standards is technically feasible and economically justified. (42 U.S.C. 6313(d)(3)(A)) If amended standards are technically feasible and economically justified, DOE must issu e a final rule by the same date. (42 U.S.C. 6313(d)(3)(B)) Additionally, EPCA granted DOE the authority to conduct rulemakings to establish new

1

- 5

### standards for automatic commercial ice makers not covered by 42 U.S.C. 6313(d)(1)). (42 U.S.C. 6313(d)(2)(A)) DO E initiated this rulemaking for ACIM equipment to satisfy EPCA’s requirement.

1.4.1 Framework and Analysis Methodology

DOE initiated this rulemaking to fulfill its statutory requirements set forth in 42 U.S.C. 6313( d ) with respect to establishing amended energy conservation standards for automatic

commercial ice makers . As the first step

, o n November 19, 2010, DOE published a Rulemaking Framework for Automatic Commercial Ice Makers describing the procedural and analytical approaches DOE anticipated using to evaluate the establishment of energy conservation

standards for automatic commercial ice makers

. This document is available at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

.

DOE held a public meeting on December 16 , 2010 ( December 2010 framework public meeting) to discuss procedural and analytical approaches to the rulemaking and t o 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 and PBP analyses, the methods propos ed for conducting them, and the relationships among the various analyses.

After the December 2010 framework public meeting, as part of the information gathering and sharing process for the preliminary manufacturer impact analysis ( MIA ) , DOE organized

and held interviews with ACIM manufacturers. DOE selected companies that represented production of all types of automatic commercial ice makers , ranging from small to large

manufacturers. DOE had four objectives for these interviews: ( 1) so licit 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, f or manufacturers to express specific concerns to DOE; and ( 4) foster cooperation between the manufacturers and DOE.

In January 201 2 , DOE published a notice of public meeting and availability of the preliminary TSD ( January 201 2 preliminary analysis) for th e ongoing rulemaking to potentially

amend energy conservation standards for automatic commercial ice makers

. 77 FR 3404 (Jan. 24, 2012).

DOE he ld a public meeting on February 16

, 201 2 ( Fe b ruary 201 2 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 NIA 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 1.4

. 2 for all the analyses discussed at the public meeting and where the analyses are addressed in each of the formal public rulemaking documents.

1

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### Table 1.4

. 2 ACIM Analyses
Preliminary Analysis Notice of Proposed Rulemaking Final Rule* Market and technology assessment R evised preliminary analyses Revised NOPR analyses Screening analysis Customer subgroup analysis Engineering analysis MIA Energy use characterization Emissions analysis Markups to determine equipment price Emissions monetization

LCC and PBP analyses Employment impact analysis Shipments analysis Utility impact analysis

N IA Regulatory impact analysis Preliminary MIA * During the final rule 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))



### DOE developed the spreadsheets for the LCC and PBP analyses and for the NIA to meet the objectives of the July 15, 1996, Procedures for Consideration of New or Revised Energy

Conservation Standards for Consume r Products (the “Process Rule”). 61 FR 36974 (July 15, 1996) . The LCC spreadsheet calculates national distributions of LCC savings at all efficiency

levels above the baseline. The NIA spreadsheet calculates the national energy savings and

national NPVs at all efficiency levels , and includes a mod u l e that forecasts shipments for the various equipment classes of automatic comme rcial ice makers at different efficiency levels.

After the posting of the January 2012 preliminary analysis TSD and the February 2012 preliminary analysis public meeting, DOE revised the analyses pursuant to comments and input received in response to the p reliminary analysis. In addition to revising the various preliminary analyses, DOE also performed a customer subgroup analysis, manufacturer impact analysis, emissions analysis and monetization, utility impact analysis, employment impact analysis, and

regu latory impact analysis for the Notice of Proposed Rulemaking ( NOPR ) . DOE also conducted interviews with ACIM manufacturers as part of the MIA for the NOPR. The results of these interviews were incorporated into the final rule engineering analysis (TSD chap ter 5) and MIA (TSD chapter 12).

On March 17, 2014, DOE published a NOPR in the Federal Register (March 2014 NOPR). 79 FR 14846. In the March 2014 NOPR, DOE addressed, in detail, the comments received in earlier stages of rulemaking, and proposed amended e nergy conservation standards for automatic commercial ice makers. In conjunction with the March 2014 NOPR, DOE also published on its website the complete TSD for the proposed rule, which incorporated the analyses DOE conducted and technical documentation f or each analysis. Also published on DOE’s website were the engineering analysis spreadsheets, the LCC spreadsheet, and the NIA spreadsheet. These materials are available at http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

In the March 2014 NOPR, DOE identified 19 issues for which it was particularly interested in receiving comments and views of interested parties: standards compliance dates

, utilization factors

, baseline efficiency

, screening analysis

, maximum technology feasibility

, markups

, equipment life, installation costs, open

- vs

. closed

- loop installations, ice maker shipments by type of equipment, intermittency of manufacturer research and development ( R&D ) and impact of standards, INPV results and impact of standards, small businesses,

1

- 7

### consumer utility and performance, analysis period, s ocial cost of carbon, remote to rack equipment, design options associated with each TSD, and standard levels for batch type ice makers over 2,500 lb ice/hour

. 79 FR 14846 at 14947

- 49.

After the publication of the March 2014 NOPR, DOE received written com ments on these and other issues. DOE also held a public meeting in Washington, DC, on April 14, 2014, to discuss and receive comments regarding the tools and methods DOE used in the NOPR analysis, as well as the results of the analysis. DOE also invited wr itten comments and announced the availability of a NOPR analysis technical support document (NOPR TSD). The NOPR TSD is

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

- 2010

- BT

- STD

- 0037

- 0061

At the public meeting held on April 14, 2014, DOE presented the methodologies and results of the analyses set for in the NOPR TSD. Interested parties provided comments. Key issues raised by stakeholders includ ed: (1) whether the energy model accurately predicts efficiency improvements; (2) the size restrictions and applications of 22

- inch wide ice makers; (3) the efficiency distributions assumed for shipments of icemakers; and (4) the impact on

manufacturers re lating to design of icemaker models, in light of the proposed compliance date of 3 years after publication of the final rule.

In response to comments regarding the energy model used in the analysis, DOE held a public meeting on June 19, 2014 in order to f acilitate an additional review of the energy model, gather additional feedback and data on the energy model, and to allow for a more thorough explanation of DOE’s use of the model in the engineering analysis. 79 FR 33877 (June 13, 2014). At that meeting, D OE presented the energy model, demonstrated its operations, and described how it was used in the rulemaking’s engineering analysis. DOE indicated in this meeting that it was considering modifications to its NOPR analyses based on the NOPR

comments and addi tional research and information gathering.

On September 11

, 201 4 , DOE published a notice of data availability (NO DA ) in the

F ederal R egist e r ( September 201 4 NO DA ). 79 FR 54215

. The purpose of the September 2014 NODA was to notify industry, manufacturers, customer groups, efficiency advocates, government agencies, and other stakeholders of the publication of the updated rulemaking analysis for new and/or amended energy conservation standards for automatic commercial ice makers. Generally, the revisions to t he NOPR analysis described in the NODA included modifications of inputs for its engineering, LCC, and NIA analyses ; adjustments of its energy

model calculations ; and more thorough considerations of size

- constrained ice maker applications. DOE encouraged stakeholders to provide comments and additional information in response to the September NODA publication, and opened a comment period which was open

until October 1 4 , 2014.

DOE refined the NOPR and NODA analyses based on stakeholder comments for the final

rule TSD.

1

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### 1.5 STRUCTURE OF THE DOC UMENT

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

TSD

.

Chapter 1 Introduction: provides an overview of the appliance standards program and how it applies to the ACIM 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.

C hapter 3 Market and Technology Assessment: provides DOE’s definiti on of automatic

commercial ice makers

, discusses the proposed equipment classes, and names the major industry players. This chapter also provides an overview of ACIM technology, including te chniques employed to improve equipment efficiency.

Chapter 4 Screening Analysis: identifies all the design options that improve ACIM efficiency, and determines 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, including the markups used for converting material costs to manufacturer sales prices.

Chapter 6 Markups for Equipment Price Determination : 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 and Water Use Analysis: DOE used the energy consumption model in the engineering analysis to estimate ACIM energy use. DOE did not conduct a separate energy use analysis for this rulemaking.

Chapter 8 Life

- Cycle Cost and Payback Period Analysi s : presents the methodolog y used

to estimate the impact of potential new or amended standards on customers of

automatic commercial ice makers by calculating LCC and PBP values at all higher efficiency levels

.

Chapter 9 Shipments Analysis : presents the methodology used to estimate the historic al and future shipments of automatic commercial ice makers . 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 national energy savings and NPV at all higher efficiency

levels

.

1

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### Chapter 1 1 Customer Sub g roup Analysis: evaluates impacts on identifiable customer subgroups that may be disproportionately disadvantaged by the proposed new or

amended standards.

Chapter 1 2 Manufacturer Impact Analysis: assesses the impacts on ACIM 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 standar d that may require changes to the manufacturing practices for this equipment.

Chapter 1 3 Emissions Impact Analysis : presents the assess ment of the impacts of proposed

ACIM standard levels on emissions of certain pollutants

.

Chapter 1 4 Monetization of Emiss ion Reductions Benefits : 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

ACIM standard levels 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 expenditu res for purchasing and operating equipment.

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

Appendix 6A Data for Equipment Price Markups: presents detailed data used for m arkups

a nalysis.

Appendix 8A User Instructions for Life

- Cycle Cost Spreadsheet: p resents user instructions for

the LCC spreadsheet.

Appendix 8B Detailed Life

- Cycle Cost and Payback Period Analysis Results: p resents detailed results from the LCC analys is.

Appendix 8C Uncertainty and Variability in Life

- Cycle Cost Analysis: p resents brief discussion on the uncertainty and variability analysis used in the LCC analysis.

Appendix 8D Estimation of Potential Equipment Price Trend for Automatic C ommercial I ce
M akers : p resents experiential learning analysis in the LCC analysis.

Appendix 10A User Instructions for NIA Spreadsheet: p resents user instruction for the NIA

s preadsheet.

Appendix 10B National Net Present Value Using Alternative Sensitivity Analyses : p resents experiential learning sensitivity analysis results , electric price sensitivity analysis results

, and 9

- y ear analysis results

.

1

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### Appendix 10C Trial Standard Levels and Standards Equations: p resents the criteria for t rial

s tandard l evels (TSLs) selecti on and the proposed standards equation at each

TSL.

Appendix 10D Full

- Fuel

- Cycle Multipliers: p resents the development of full

- fuel

- cycle

coefficients

.

Appendix 10E RISC & OIRA Consolidated Information System (ROCIS) Tables: p resents the

ROCIS g 

### tables.

App endix 12A Government Regulatory Impact Model: presents an overview of the model used

in the MIA.

Appendix 1 4 A Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order

12866: p resents the SCC analysis

.

Appendix 14B Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis

- Under Executive Order 12866 : p resents updated SCC analysis

.

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

Combined Information System

.

1

- 11

### REFERENCES

1

. U.S. Department of Energy

– Energy Information Administration. Annual Energy Outlook

201 4 . 201 4 . Washington D.C. DOE/EIA

- 0383(201 4 ).

1

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### CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION

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

- 1

2.2 BACKGROUND

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

- 4

2.3 MARKET A ND TECHNOLOGY ASSESS MENT

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

- 5

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 Analysis Approach

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

- 6

2.5. 2 Baseline Models

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

- 7

2.6 MARKUPS TO DETERMINE EQUIPMENT PRICE

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

- 7

2.7 ENERGY AND WATER USE ANALYSIS

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

- 8

2.8 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

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

- 8

2.9 SHIPMENTS ANALYSIS

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

- 10

2.10 NATIONAL IMPACT ANAL YSIS

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

- 10

2.11 CUSTOMER SUBGROUP AN ALYSIS

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

- 12

2.12 MANUFACTURER IMPACT ANALYSIS

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

- 12

2.13 EMISSIONS ANALYSIS

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

- 13

2.14 MONETIZING CARBON AN D OTHER EMISSIONS RE DUCTIONS BENEFITS

. 2

- 14

2.15 UTILITY IMPACT ANALY SIS

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

- 15

2.16 EMPLOYMENT IMPACT AN ALYSIS

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

- 16

2.17 REGULATORY IMPACT AN ALYSIS

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

- 16

2.18 DEPARTMENT OF JUSTIC E REVIEW

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

- 16

REFERENCES

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

- 17

LIST OF FIGURES

Figure 2.1.1 Analyses for Automatic Commercial Ice Maker Energy Conservation Standards

. 2

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### CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

This chapter describes the analytical framework that the U.S. Department of Energy ( DOE ) used in developing standards and assessing the impact for automatic commercial ice

makers . The chapter describes the methodology, analytical tools, and relationships among the various analyses that are part of this rulemaking. The objective of the rulemaking process is to

determine minimum efficiency standards for automatic commercial ice maker (ACIM) equipment that are technologically feasible and economically justified. In this context, economic justification includes consider ation of the economic impac t on customers and manufacturers , 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 results 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 and information that the analyses require. Some key inputs exist in public databases

, and DOE will also collect inputs from stakeholders or others with special knowledge. Inputs developed by the project team for the

standar ds

- setting process are presented and open for stakeholder review.

The analyses that DOE performed for the final rule include

• a market and technology assessment to characterize the automatic commercial ice

makers’ market and to review techniques and approac hes used to produce more

efficient ACIM equipment;

• a screening analysis to identify design options that improve ACIM equipment efficiency and to determine which should be evaluated and which should be screened

out;

• an engineering analysis to estimate the relationship between the manufacturing cost

of a n ACIM unit and its performance;

• a markup analysis to convert manufacturer selling prices (MSP s ) to customer purchase prices;

• an energy and water use analysis to estimate the energy and water consumption of the equipment ;

• a life

- cycle cost (LCC) and payback period (PBP) analysis to estimate the impact of potential new or amended standards on customers of ACIM equipment by calculating LCC and PBP values at all higher efficiency levels;

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### • a shipments analysis to estimate shipments of ACIM equipment over the time period examined in the analysis;

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

• a customer subgroup analysis to evaluate impacts on identifiable groups of customers who may be disproportionately affected by new or amended standards;

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

amended standard s on ACIM equipment manufacturers and to calculate impacts on competition, employment at the manufacturing plant, and manufacturing capacity;

• an emissions analysis to provide estimates of the effects of amended energy

conservation standards on emissions of carbon dioxide (CO 2 

### ), sulfur dioxide (SO 2 

### ), nitrogen oxides (NO

x 

### ), and mercury (Hg);

• a monetization of reduction of emission benefits from proposed standards;

• a utility impact analysis to estimate the effects of proposed standards on the installed

cap acity and generating base of electric utilities;

• an employment impact analysis to estimate the impacts of amended standards on net jobs eliminated or created in the general economy as a consequence of increased spending on the purchase price of ACIM equipm ent and reduced customer spending on energy; and

• a regulatory impact analysis (RIA) to explore major alternatives to proposed standards that could achieve comparable energy savings.

The analyses and results published by DOE in the final rule reflect commen ts and input received subsequent to the publication of a notice of proposed rulemaking (NOPR) in March 2014 and a notice of data availability (NODA) in September 2014.

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### Figure 2.1

. 1 Analyses for Automatic Commercial Ice Mak er Energy Conservation

Standards

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### 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 f or Consideration of New or Revised Energy Conservation Standards for Consumer Products (the “Process Rule”),

10 CFR part 430 s ubpart C, a ppendix A. The Process Rule outlined the procedural improvements identified by the interested parties and included a re view of the ( 1) economic models; ( 2) analytical tools; ( 3) methodologies; ( 4) non

- regulatory 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. Consist ent with the improved rulemaking framework of the Process Rule, t he following sections provide a general description of the analytical co mponents for this rulemaking

.

DOE developed the analytical framework pertaining to automatic commercial ice makers

in the Rulemaking Framework f or Automatic Commercial Ice Making Equipment (Nov. 4

, 2010). DOE announced the availability of the F ramework document in a notice of public meeting and availability of a F ramework document publis hed in the Federal Register

. 75 FR 70852 (Nov. 19

, 2010 ) A copy of the framework document is available at www1.eere.energy.gov/buildings/appliance_standards /rulemaking.aspx/ruleid/29

DOE presented the analytical approach to interested parties during a public meeting held

o n December 16 , 2010. 1 

### DOE used comments gathered during the F ramework public meeting as

well as additional information during the prelimina ry analysis stage , at the completion of which DOE announced the notice of public meeting and the availability of the preliminary TSD

. 2 

### 77 FR

3404 (Jan. 24, 2012)

. DOE subsequently held a public meeting on February 16, 2012 to present the analysis and to receive comments.

DOE published a NOPR on March 17, 2014 ( 79 FR 14846) and held a public meeting on April 14, 2014 to present the NOPR analyses and to seek public comment. The NOPR analysis

and TSD are available at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

DOE also held a public meeting on June 19, 2014 to provide stakeholders more information regarding the energy modeling u sed as part of its engineering analysis

— this meeting was announced in a notice of proposed meeting (NOPM)

, which was published June 13, 2014.

79 FR 33877.

DOE published a notice of data availability (NODA) on September 11, 2014. 79 FR at 54215. The NODA pr ovided stakeholders with rulemaking analyses conducted after the NOPR

for automatic ice makers.

This TSD contains details of the final rule analyses conducted for automatic commercial

ice makers. The remainder of chapter 2 provide s a brief overview of the analytica l approaches

for the final rule phase analysis of this rulemaking

.

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### 2.3 MARKET AND TECHNOLOG Y ASSESSMENT

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

2.3.1 Market Assessment

DOE reviewed relevant literature and interviewed manufacturers to develop an overall picture of the ACIM industry in the United States. Industry publications and trade journals, government agencies, and trade organizati ons 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, DO E 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. 62 95(q) and 6313(d)(4) )

When initiating a standards rulemaking, DOE develops information on the present and past industry structure and market characteristics of the equipment concerned. This activity consists of both quantitative and qualitative efforts to assess the industry and equipment 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 improve the efficiency of ACIM e quipment, as well as to highlight the developments within those technology categories and their applicability to these equipment classes. The result of technology assessment is a list of technology options to be analyzed in the screening analysis. Chapter 3 of the final rule 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 engineering analysis and which options to screen out

. As stated in section 2.3

, DOE consulted with industry, technical

exp erts, and other interested parties in developing a list of energy

- saving technologies for the technology assessment. DOE then applied screening criteria in the screening analysis to determine which technologies were unsuitable for further consideration in this rulemaking.

These screening criteria examine 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; and (4) have adver se impacts on health and safety. Chapter 4 of the

final rule TSD, the screening analysis, contains details about DOE’s screening criteria.

For t he efficiency

- enhancement technologies that are not eliminate d in the screening analysis , DOE conducts further anal ysis on these technologies in the engineering analysis.

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### 2.5 ENGINEERING ANALYSIS

The purpose of the engineering analysis (chapter 5 of the final rule TSD) is to determine the relationship between manufactur er production cost (MP C ) and energy and/or water consumption for automatic commercial ice makers . In determin ing this relationship, DOE

estimate s the increase in M PC associated with design changes that decrease energy and/or water consumption as compared with baseline

- efficiency m odels. In addition, DOE also identifies the model with the lowest energy consumption that is technologically feasible within each equipment class ( i.e.

, the “max

- tech” model) in the engineering analysis. While this rulemaking

also addresses condenser water consumption, the regulation of condenser water use for ice makers is intended primarily to prevent excess water waste associated with attempts to reduce energy use by increasing condenser water use. Hence, the engineering analysis does not identify

maximu m technology levels for condenser water consumption.

In general, the engineering analysis estimates the efficiency improvement potential of the individual or combinations of design options that passed the four criteria in the screening analysis. DOE, in co nsultation with stakeholders, uses the most appropriate method to determine the manufacturing cost of these design options. DOE recognizes that some of the design options will reduce both energy and water consumption, but others may reduce one and increase the other. To address this issue, DOE developed an approach to consider both energy use and condenser water use in the engineering analysis in a way that maintains the rulemaking’s focus on energy use reduction while appropriately accounting for the cost implications of changing

water use. This MPC

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

DOE develops MPC estimates for the engineering analysis disaggregated into the cost contributions of materials (includin g purchased parts), labor, and ov erhead. MSP combines MPC and a manufacturer markup for overhead and profit. This information is used in DOE’s downstream analyses, including the LCC, MIA , and NIA . DOE create s an industry

- wide analysis based on a variety of data sources, including manufacturer

- supplied data.

2.5.1 Analysis Approach

DOE has develop ed three basic approaches to develop the relationship between cost and performance : design

- option, efficiency

- level, and reverse

- engineering/cost

- assessment.

A design

- op tion approach uses individual design options, or combinations of design options, to identify increases in efficiency. Under this approach, cost estimates are based on manufacturer or component supplier data or engineering computer simulation models. Indivi dual design options, or combinations of design options, are added to the baseline model in ascending

order of cost

- effectiveness. Also, the efficiency levels addressed in the analyses, including the downstream analyses, are based on groups of design option s.

An efficiency

- level approach establishes the relationship between manufacturer cost and increased efficiency at predetermined efficiency levels above the baseline. Under this approach, manufacturers typically provide manufacturer cost data for increment al increases in efficiency, without identifying the technology or design options they would use to achieve such increases.

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### A reverse

- engineering or cost

- assessment approach involves purchasing representative

units of ACIM equipment, disassembling the units, and reverse engineering the manufacturing

costs based on a “bottoms

- up” manufacturing cost assessment. The efficiency levels addressed in the analyses for such an approach are based on the efficiency levels of the rever se

- engineered products.

DOE use d a combination of all three of these approaches to determine the cost

- efficiency relationship for this rulemaking

. DOE obtain s information from a variety of sources, including consultation with outside experts, review of pub licly available cost and performance information, modeling of equipment cost and energy consumption, interviews with manufacturers, product teardowns and reverse engineering, and data submissions provided by the industry.

2.5.2 Baseline Models

In order to anal yze design options for energy efficiency improvements, DOE defines a baseline model unit for each equipment class. As DOE indicated in the F ramework document

, the baseline efficiency levels for equipment currently covered by the DOE standards formed the

ba seline efficiency level for batch type equipment. a 

### For the continuous type ice makers that are not currently covered by DOE’s standards, DOE analyzed available data on continuous type equipment

, including new ly available information on ice hardness data. B ased on the available data, DOE identified the least efficient available for each continuous class. This formed the baseline energy usage for continuous type equipment classes and aided the determination of harvest capacity break points delineating large a nd small equipment class es within each

continuous equipment type.

2.6 MARKUPS TO DETERMINE EQUIPMENT PRICE

DOE uses the markup analysis to determine distri bution channel markups that are used to convert the MSP of the equipment into customer purchase price. DOE uses customer purchase price to analyze the impact of potential energy conservation standard levels from the consumer perspective. To estimate the cost to customers buying ice makers, DOE adds markups to reflect the costs added by the wholesale, distri bution, and contractor communities that distribute, sell, and install equipment.

In this rulemaking, DOE identified three different major channels through which the customers purchase automatic commercial ice makers . DOE then determined the market shares

o f each distribution channel and calculated the overall markup as a weighted average of the three

channels . Sales tax is add ed to the markup associated with distribution channels.

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.

a Batch type equipment includes cube

- type, tube

- type, and others that produce ice in alternative cycles of freezing and harvesting. Continuous type equipment, on the other hand, produces ice continuously. Continuous type ice makers include flake and nugget machines.

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### 2.7 ENERGY AND WATER USE ANALYSIS

In the energy and water use analysis, DOE typically reviews the engineering results to assess their use in the downstream analyses and whether additional analyses are required. For

automatic commercial ice makers , DOE concluded no additional analyses were necessary. Th us, the energy and water usage analysis was limited to converting the engineering estimates of

kilowatt

- hours per 100 lb ice and gallons per 100 lb ice into annual energy and water usage

values.

TSD chapter 7 provides detailed information on the annual ene rgy and water analysis for this rulemaking.

2.8 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

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

individual commercial customers. The effects of amended standards on individual customers usually include a reduction in operating cost and an increase in purchase cost, which together determine the economic impact of standards on individual cust omers. DOE analyzes two metrics to measure this impact:

• Life

- cycle cost . LCC is the total customer cost of an appliance or piece of equipment, generally over the life of the appliance or equipment

— the sum of installed cost and operating costs

. Future opera ting costs are discounted to the time of the purchase and

summed over the lifetime of the equipment.

• Payback period . PBP measures the amount of time it takes customers to recover the assumed higher purchase price of a more energy

- efficient piece of equipme nt through

lower operating costs.

As part of the engineering analysis (chapter 5 of the final rule TSD )

, DOE explored various efficiency levels based on increasing efficiency (decreased energy consumption) and

, typically, increasing MSP values. For the LCC and PBP analysis, DOE treats each higher efficiency level a s a potential new standard level.

The first efficiency level ( l evel 1) in each equipment class is the least efficient and the least expensive equipment in that equipment class. For the equipment classes that are currently covered by DOE’s standards, l evel 1 equipment is equipment that just meets the efficiency standards. For equipment not currently covered by standards, l evel 1 reflects the least efficient equipment currently available in the ACIM market. The higher efficiency levels ( l evel 2 and up) have progressive increase s in efficiency and equipment cost from l evel 1. The highest efficiency level in each equipment class corresponds to the max imum efficiency level obtainable with non

proprietary technology (see final rule TSD chapter 5 for details).

Inputs to the LCC calculation include the installed customer cost of the equipment (customer purchase price plus installation costs and sales tax), and operating expens es (repair,

maintenance

, energy , and water/wastewater costs), future price trends, equipment lifetimes, and

discount rates.

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### The installed cost of equipment to a customer is the sum of the equipment purchase price and installation cost. The purchase price i ncludes the MSP plus markups to the MSP to account
for distribution costs and sales tax (chapter 6 of the final rule TSD ). Installation costs vary by state depending on the prevailing labor rates.

Operating costs for automatic commercial ice makers are th e sum of maintenance costs, repair costs, energy costs , and water costs

. Customers incur t hese costs over the life of the equipment . To facilitate cost comparisons, DOE discount ed operating costs to the discount year (201 8 , the effective date of the standa rds established through this rulemaking). The sum of the installed cost and the operating cost s , discounted to reflect the present value, is termed the life

cycle cost or LCC.

Generally, customers incur higher installed costs when they purchase higher effi ciency equipment, and these c ost increments will be partially or wholly offset 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 equipm ent uses less energy over the lifetime of the equipment. DOE calculated the change in LCC for each efficiency level of each equipment

class.

DOE obtained t he PBP of higher efficiency equipment by dividing the increase in the installed cost by the decrease in annual operating cost. For this calculation, DOE use d the sum of

the first year operating cost changes as the estimate of the annual decrease in operating cost, noting that some of the repair and maintenance costs used herein are annualized estimates of

costs. DOE calculated a PBP for each efficiency level of each equipment class.

The LCC and PBP model are developed using Microsoft Excel spreadsheets combined with Crystal Ball, a commercially available add

- in for modeling uncertainty. DOE estimated

m any inputs for the LCC and PBP analysis from the best available data, and in some cases DOE used inputs that are generally acc epted values within the automatic commercial ice maker industry. In general, there is un certainty associated with many inputs as it is often difficult to

obtain a single representative value for a given input . Therefore, DOE carrie d out the LCC and PBP analysis in the form of Monte Carlo simulations using range s of values and probability

dist ributions for certain inputs that account for the uncertainties. DOE presents t he results of the LCC and PBP analysis 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, DOE carried out 10,000 Monte Carlo simulations

.

DOE calculated LCC savings and PBP by comparing the installed costs and LCC values

of a given standards scenario against those of the base

- case scenario. The b ase

- case scenario is

the scenario in which customers purchase equipment in the absence of the proposed energy

conservation standard. A s tandards

- case scenario is a scenario in which customers purchase equipment after the hypothetical energy conservation standard go es into effect. The number of standards scenarios for an equipment class is equal to one less than the total number of efficiency levels in that equipment class because each efficiency level above l evel 1 represents a potential new standard. Usually, the mar ket will offer equipment at various efficiencies. Therefore, for

b oth the base

- case and the standards

- case scenarios in the LCC and PBP analysis, DOE

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### calculated the market shares of the efficiency levels using a method described in final rule TSD chapter 1 0

.

Different types of buildings and industries face different energy prices and apply different discount rates to purchase decisions. DOE analyzed variability and uncertainty by performing the LCC and PBP calculations for seven types of buildings : health c are, lodging, foodservice, retail, education, food sales, and offices

.

T he typical equipment lifetime is approximately 7 to 10 years with an average of 8.5 years. There was no data to suggest that lifetimes are unique to each equipment class. Therefore,

D OE assume d a distribution of equipment lifetimes that is defined by Weibull survival functions

, with an average value of 8. 5 years

.

Another important factor influencing the LCC and PBP analysis is the location in which

the automatic commercial ice maker is installed. Inputs that vary by location include installation costs, water and energy prices

, distribution chain markups, and sales tax . At the national level,

DOE explicitly modeled variability in water price, electricity price

, and markups using probabil ity distributions based on the relative populations in all states.

F inal rule TSD chapter 8 provides detailed information on DOE’s LCC and PBP analysis for this rulemaking. Results of the LCC and PBP analysis are presented at the end of final rule

TSD chapter 8 and in appendix 8B.

2.9 SHIPMENTS ANALYSIS

The shipments analysis is used to estimate future equipment shipments as well as the

current stock of ACIM equipment . It provides crucial input for estimating the NES, NPV, and

the MIA

. Shipments analysis i s used to estimate future ACIM equipment shipments over the

na tional impact analysis period.

DOE calculated the historic al shipments and stock of ACIM equipment for the year 2010

and earlier based on various shipments data sources. DOE then used the 2014 A nnual Energy

Outlook 3 

### ( AEO2014 ) forecasts of commercial floor space additions, by business type, to calculate the future shipments.

TSD c hapter 9 presents the mathematical formulation of the shipment analysis model and the methodology used to estimate his toric al and future shipments of ACIM equipment.

2.10 NATIONAL IMPACT ANAL YSIS

DOE quantifies national impacts of energy conservation standards using two metrics: (1) NES and (2) NPV of future savings. NES and NPV impacts are the cumulative energy and

economic effects of an amended ACIM equipment energy conservation standard ( TSD chapter 10). The NES values, expressed in quads (quadrillion British thermal units ), are the cumulative energy savings as a result of the new standards for equipment purchased over the 30

- year time frame of DOE’s analysis , 2018

– 2047 . The NPV of future savings is also calculated for equipment purchased from 2018 through 2047. NPV is expr essed in constant 2013 $ discounted to the present year ( 2014 ).

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### DOE analyzed energy savings, energy and water cost savings, equipment costs

, maintenance and repair costs , and NPV of savings (or costs) for efficiency level developed in the engineering analy s is . 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 th at 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 CSL s from the cost

- eff iciency 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. Users can change input quantities within the spreadsheet to test the impact of alternative in put 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 c hapter 10, the national impact a nalysis assesses the NPV of total consumer LCC and NES. DOE conducted an assessment of the aggregate impacts at the national

level for the final rule . Analyzing impacts of Federal energy conservation standards requires a comparison 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 estimated 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.

DOE has historic ally presented NES in terms of primary energy savings. DOE has begun

to also estimate full

- fuel

- cycle (FFC) energy savings. 76 FR 51282 ( Aug

. 18, 2011), as amended at 77 FR 49701 ( Aug

. 17, 2012). The FFC metric includes the energy consumed in extracting,

p rocessing, and transporting primary fuels, and thus presents a more complete picture of the impacts of energy efficiency standards. DOE’s approach is based on the calculation of an FFC multiplier for each of the energy types used by covered equipment.

Whi le DOE stated in that notice that it intended to use the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to conduct the analysis, it also said it would review alternative methods, including the use of the DOE/Energy In formation

Administration ’s (EIA ’s ) National Energy Modeling System (NEMS). b 

### NEMS is a public domain, multi

- sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce the Annual Energy Outlook. After evaluating bo th models and the approaches discussed in the August 18, 2011 notice, DOE has determined NEMS is a more appropriate tool for this application. 77 FR 49701 (Aug. 17, 2012). Therefore, DOE is using the NEMS model to conduct FFC analyses. For the final rule a nalysis, DOE calculated FFC energy

b 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.

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### savings using a methodology described in appendix 10 B . Chapter 10 of this TSD presents both the primary NES and the FFC energy savings for the considered trial standard levels (TSLs)

.

2.11 CUSTOMER SUBGROUP AN ALYSIS

The custom er subgroup 

### analysis in final rule TSD chapter 11 evaluates impacts on identifiable groups of customers of ACIM equipment who may be disproportionately disadvantaged by amended energy conservation standards. The LCC and PBP analysis described in chapter 8 of the final rule TSD is applied to seven major types of businesses

, including health

care, lodging, foodservice, retail, education, food sales, and offices

, that use a majority of the

ACIM equipment. Although the inputs for 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 disadvantaged may be masked by the averaging effect of the LCC and PBP analysis. Therefore,

DOE carried out the customer subgroup analysis by using the LCC and PBP analysis spreadsheet, but applying the inputs that are applicabl e only to the identified subgroups.

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 additi onal capital to purchase equipment that meets 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 high er PBPs.

DOE carried out two LCC subgroup analyses, one each for foodservice and lodging , by using the LCC spreadsheet described in final rule TSD chapter 8 with certain modifications. The input for business type was fixed to the identified subgroup, whic h ensured that the discount rates and electricity 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 discoun t rates faced by small businesses. Apart from these changes, all other inputs for LCC subgroup analysis were the same as those in the LCC analysis described in TSD chapter 8.

2.12 MANUFACTURER IMPACT ANALYSIS

The MIA assesses the impacts of new energy conserva tion 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 impact s 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

th e 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 wer e interviewed, and the impacts of standards were assessed both quantitatively and qualitatively. Industry and subgroup

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### cash flow and NPV were assessed through use of the Government Regulatory Impact Model (GRIM). Then impacts on competition, manufacturing capacity, employment, and cumulative 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

In the emissions analysis, DOE estimated t he reduction in power sector emissions of

CO 2 

### , NO

x 

### , SO 2 

### , and Hg from potential energy conservation standards for ACIM equipment. In addition, DOE estimated emissions impacts in production activities (extracting, processing, and transporting fuels) that pro vide the energy inputs to power plants. These are referred to as “upstream” emissions. Together, these emissions account for the FFC . In accordance with DOE’s FFC Statement of Policy (76 FR 51282 (Aug. 18, 2011)), as amended at 77 FR 49701 ( Aug

. 17, 2012), the FFC analysis includes impacts on emissions of methane (CH 4 

### ) and nitrous oxide (N 2 

### O), both of which are recognized as greenhouse gases (GHGs)

.

DOE conducted the emissions analysis using emissions factors derived from data in

EIA’s AEO2014 , supplemented by data from other sources. DOE developed separate emissions factors for power sector emissions and upstream emissions. The method that DOE used to derive emissions factors is described in chapter 13 of the final rule TSD.

EIA prepares the AEO using the N EMS. Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions. AEO2014 generally represents current legislation and environmental regulations, including recent government actions, for which implementin g regulations were available as of Octob er 31, 201 3

.

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 

### f or affected EGUs in the 48 contiguous States and the District of Columbia (D.C.). SO 2 

### emissions from 28 eastern s tates and D.C. were also limited under the Clean Air Interstate Rule (CAIR), which created an allowance

- based trading program that operates alo ng with the Title IV program in those s tates and D.C. 70 FR 25162 (May 12, 2005). CAIR was remanded to the U.S. Environmental Protection Agency (EPA) by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. Circuit), but it remained in effec t. See North Carolina v.

EPA , 550 F.3d 1176 (D.C. Cir. 2008); North Carolina v. EPA , 531 F.3d 896 (D.C. Cir. 2008). In 2011 EPA issued a replacement for CAIR, the Cross

- State Air Pollution Rule (CSAPR). 76 FR 48208 ( Aug

. 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 emissions factors used for today’s final rule , which

are based on AEO2014

, a ssume that CAIR remains a binding regulation through 2040.

The attainment of emissions caps is typically flexible among 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 adoption of an efficiency standard could be used to permit offsetting increases in SO 2 

### emissions by any regulated EGU. In past rulemakings, DOE recognized that there was uncert ainty about the effects of efficiency standards on SO 2 

### emissions covered by the existing cap

- and

- trade

2

- 13

### system, but it concluded that no reductions in power sector emissions would occur for SO 2 

### as a result of standards.

Beginning in 201 6 , however, SO 2 

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

SO 2 

### (a non

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

same controls are used to reduce HAP and non

- HAP acid gas; thus, SO 2 

### emissions will be

reduc ed as a result of the control technologies installed on coal

- fired power plants to comply with the MATS requirements for acid gas. AEO2014 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent in jection systems installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce

SO 2 

### emissions. Under the MATS, NEMS shows a reduction in SO 2 

### emissions when electricity demand decreases ( e.g. , as a result of energy efficiency standards). Emissions will be far below the cap 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.

CAIR established a cap on NO

x 

### emissions in eastern States and the District of Columbia. Energy conservation standards are expected to have little or no ph ysical effect on these emissions in those States covered by CAIR 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 redu ce NO

x 

### emissions in the s tates not affected by caps, so DOE estimated NO

x 

### emissions reductions from the standards considered in the final rule

for these s tates.

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 will estimate mercury emissions reduction using emissions factors based on AEO2014 , which incorporates the MATS

.

2.14 MONETIZING CARBON AN D OTHER EMISSIONS RE DUCTIONS BENEFITS

DOE considered the estimated monetary benefits from the reduced emissions of CO 2 

### and

NO

x 

### that are expected to result from each of the standard levels considered.

To estimate the monetary value of benefits resulting from reduced emissions of CO 2 

### , DOE use d the most current Social Cost of Carbon (SCC) values developed and/or agreed to by an interagency process. The SCC is intended to be a monetary measure of the incremental damage resulting from GHG emissions, including, but not limited to, net agricultural

p roductivity loss, human health effects, property damage from sea level rise, and changes in ecosystem services. Any effort to quantify and to monetize the harms associated with climate change will raise serious questions of science, economics, and ethics. But with full regard for the limits of both quantification and monetization, the SCC can be used to provide estimates of the

social benefits of reductions in GHG emissions.

2

- 14

### The Interagency Working Group on S CC released an update of its previous report in

2013. c 

### The most recent estimates of the SCC in 2015, expressed in 2013$ , are $12, $40. 5 , $62. 4

, and $11 9 per metric ton of CO 2 

### avoided. For emissions reductions that occur in later years, these values grow in real terms over time. Additionally, the interag ency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although DOE gives preference to consideration of the global benefits of reducing CO 2 

### emissions.

DOE multiplies the CO 2 

### emissions reduction estimated for each year by the SCC value for that year in each of the four cases. To calculate a present value of the stream of monetary values, DOE discounts the values in each of the four cases

, using the discount r ates that had been

used to obtain the SCC values in each case.

DOE recognizes that scientific and economic knowledge continues to evolve rapidly as to

the contribution of CO 2 

### and other GHG s to changes in the future global climate and the potential

resultin g damages to the world economy. Thus, these values are subject to change.

DOE also estimates the potential monetary benefit of reduced NO

x 

### emissions resulting

from the standard levels it considers. For NO

x 

### emissions, estimates suggest a very wide range of monetary values, ranging from $ 4 76 to $4, 8 93 per ton in 2013$

. d 

### In accordance with Office of Management and Budget ( OMB ) guidance, e 

### DOE calculates a range of monetary benefits using

each of the economic values for NO

x 

### and real discount rat es of 3 percent and 7 percent.

2.15 UTILITY IMPACT ANALY SIS

DOE analyzed specific effects of its proposed standard levels on the electric utility industry as part of the final rule analyses, using a variant of the DOE EIA ’s NEMS

. The version of NEMS used for appliance standard s analysis is called NEMS

- Building Technologies (NEMS

BT) f 

### and is based on the AEO2014 Reference Case with minor modifications.

The utility impact analysis reports the changes in installed capacity and generation, by fuel type, that result from the adoptio n of new efficiency standards at each TSL, as well as changes i n electricity consumption. See final rule TSD chapter 15 for more information on DOE’s utility impact analysis for this rulemaking.

c 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.

< www.whitehouse.gov /sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria_2013_update.pdf >

d For additional information, refer to U.S. Office of Management and Budget, Office of Information and Regulatory Affairs, 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal Entities , Washington, D

. C.

e OMB, Circular A

- 4: Regulatory Analysis ( Sept. 17, 2003).

f EIA approves use of the name NEMS to describe only an official version of the model without any modification to code or data. Because this analysis entails some minor code modifications and the model is run under various policy scenarios that are variations on EIA assumptions, DOE refers to it by the name NEMS

- BT (BT is DOE’s Building Technologies Pr ogram, under whose aegis this work has been performed). NEMS

- BT was previously called NEMS

BRS.

2

- 15

### 2.16 EMPLOYMENT IMPACT AN ALYSIS

The imposition of standards can a ffect employment both directly and indirectly. Direct employment impacts are changes in the number of employees at plants that produce covered equipment and at the affiliated distribution and service companies. DOE evaluate s direct employment impacts in the MIA . Indirect employment impacts may result from customers shifting expenditures between goods (the substitution effect) and from changes in income and overall expenditure levels (the income effect). DOE used Pacific Northw est National Laboratory’s ( PNNL’s ) Impact of Sector Energy Technologies (ImSET) model to investigate the combined direct and indirect employment impacts. The ImSET model, which was developed for DOE’s Office of Planning, Budget, and Analysis, estimates the employment and income effects

that energy

- saving technologies produce in buildings, industry, and transportation. Compared with simple economic multiplier approaches, ImSET allows for more complete analysis of the economic impacts of energy conservation i nvestments.

2.17 REGULATORY IMPACT AN ALYSIS

In the final rule stage, DOE prepared an 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 Informa tion and Regulatory Affairs at the OMB . 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

ACIM 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 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 (o)(2)(B)(i)(V) ; 6314(d)(4) ) Pursuant to this requirement, DOE solicit ed the views of the Departme nt of Justice (DOJ) on any lessening of competition that is likely to result from the imposition of a proposed standard. The DOJ reviewed the new and amended standards and determined that they are unlikely to have a significant impact on competition.

2

- 16

### REFE RENCES

1

. U.S. Department of Energy

— Office of Energy Efficiency and Renewable Energy Building Technologies Program. Rulemaking Framework for Automatic Commercial Ice

- Makers

. 2010. Washington, D.C.

< www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29 >

2

. U.S. Department of Energy

— Office of En ergy Efficiency and Renewable Energy. Preliminary Technical Support Document: Energy Conservation Program for Certain Commercial and Industrial Equipment: Automatic Commercial Ice Makers . January 20, 2012. Washington, D.C.

< www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29 >

3

. U.S. Department of Energy

— Energy Information Administration. Annual Energy Outlook

201 4

. 201 4 . Washington, D

. C. DOE/EIA

- 0383(201 4 ).

2

- 17

### CHAPTER 3. MARKET AND TECHNOLOG Y ASSESSMENT

TABLE OF CONTENTS

3.1 INTRODUCTION

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

- 1

3.2 MARKET ASSESSMENT

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

- 3

3.2.1 Trade Association

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

- 3

3.2.2 Manufacturers and Market Share

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

- 4

3.2.2.1 Small Businesses

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

- 6

3.2.2.2 Regulatory Programs

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

- 6

3.2.2.3 Natural Resources Canada

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

- 6

3.2.2.4 Australia/New Zealand

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

- 7

3.2.3 Non

- Regulatory Initiatives

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

- 8

3.2.3.1 ENERGY STAR

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

- 8

3.2.3.1 Federal Energy Management Program

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

- 9

3.2.3.2 Consortium for Energy Efficiency

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

- 9

3.2.3.3 Rebate Programs

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

- 11

3.2.4 Equipment Classes

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

- 12

3.2.5 Shipments and Available Equipment

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

- 13

3.2.5.1 Air

- Conditioning, Heating, and Refrigeration Institute Data

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

- 14

3.2.5.2 North American Association of Food Equipment Manufacturers Data

. 3

- 15

3.2.5.3 Census Bureau Data

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

- 16

3.2.5.4 ENERGY STAR Data

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

- 16

3.2.6 Equipment Lifetimes

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

- 17

3.3 TECHNOLOGY ASSESSMEN T

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

- 17

3.3.1 Baseline Equipment Components and Operation

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

- 17

3.3.1.1 Basic Equipment Description and Components

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

- 18

3.3.1.2 Batch Process

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

- 19

3.3.1.3 Continuous Process

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

- 24

3.3.2 Technology Options

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

- 26

3.3.2.1 Compressor

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

- 27

3.3.2.2 Condenser

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

- 29

3.3.2.3 Higher Efficiency Condenser Fans and Fan Motors

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

- 31

3.3.2.4 Improved Auger Motor Efficiency

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

- 31

3.3.2.5 Improved Pump Motor Efficiency

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

- 31

3.3.2.6 Demand Response Controls

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

- 31

3.3.2.7 Evaporator

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

- 32

3.3.2.8 Improved or Thicker Insulation

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

- 33

3.3.2.9 Larger Diameter Suction Line (Remote Compressor Models)

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

- 33

3.3.2.10 Reduced Potable Water Flow

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

- 33

3.3.2.11 Drain Water Thermal Exchange

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

- 34

3.3.3 Energy Use Data

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

- 34

3.3.4 Condenser Water Consumption Data

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

- 46

REFERENCES

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

- 51

3

- i

### LIST OF TABLES

Table 3.1.1: Current Equipment Classes for Automatic Commercial Ice Makers

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

- 3 Table 3.2.1 NRCan Efficiency Standard for Continuous Automatic Commercial Ice Makers

... 3

- 7 Table 3.2.2 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for Air

- Cooled

Batch

- Type Ice Makers

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

- 8 Table 3.2.3 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for Air

- Cooled

Continuous

- Type Ice Makers

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

- 9 Table 3.2.4 Performance Requirement for Federal Purchases of Water

- Cooled Ice Makers

...... 3

- 9 Table 3.2.5 CEE Efficiency Requirements for Air

- Cooled Ice Makers

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

- 11 Table 3.2.6 CEE Efficiency Requirements for Water

- Cooled Ice Makers

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

- 11

Table 3.2.7 Fina l Rule Automatic Commercial Ice

- Making Equipment Classes

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

- 13 Table 3.2.8 AHRI 2010 Shipments of Automatic Commercial Ice Makers, by Equipment

Class

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

- 15 Table 3.2.9 Shipments of Ice Makers

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

- 16 Table 3.2.10 ENERGY STAR Ice Maker Shipments

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

- 17

Table 3.2.11 Estimates for Automatic Commercial Ice Maker Lifetimes

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

- 17 Table 3.3.1 Brief Description of Prima ry Refrigeration System Components

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

- 19 Table 3.3.2 Batch Evaporator Designs

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

- 21 Table 3.3.3 Technology Options for Automatic Commercial Ice Makers

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

- 27 Table 3.3.5 Compressor Rating Conditions ( o 

### F ( o 

### C))

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

- 28 Table 3.3.6: Comparison of Current and Proposed Condenser Water Standards

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

- 50

LIST OF FIGURES

Figure 3.2.1: Domestic Automatic Commercial Ice Maker Market Shares as of 1996

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

- 5 Figure 3.3.1 Typ ical Vapor Compression Refrigeration Cycle for Batch Type Ice Makers

..... 3

- 20 Figure 3.3.2 Cracked Ice Forming on “Cracked Ice” Evapor ators

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

- 22 Figure 3.3.3 Water System Diagram for Batch Type Ice Makers

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

- 23 Figure 3.3.4 Typical Vapor Compression Refrigeration Cycle for Continuous Ice Makers

..... 3

- 25 Figure 3.3.5 Cross

- Section of Continuous Evaporator and Auger

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

- 26 Figure 3.3.6 Energy Consumption vs. Harvest Rate (Air

- Cooled Cube Ice

- Making Heads)

.... 3

- 35 Figu re 3.3.7 Energy Consumption vs. Harvest Rate (Water

- Cooled Batch Ice

- Making Heads)

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

- 36 Figure 3.3.8 Energy Consumption vs. Harv est Rate (Air

- Cooled Batch Remote Condensing Units [Non

- Remote Compressor])

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

- 37 Figure 3.3.9 Energy Consumption vs. Harvest Rate (Air

- Cooled Batch Remote Condensing Units [Remote Compressor])

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

- 38 Figure 3.3.1 0 Energy Consumption vs. Harvest Rate (Air

- Cooled Batch Self

- Contained Units)

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

- 39 Figure 3.3.11 Energy Consumption vs. Harvest R ate (Water

- Cooled Batch Self

- Contained Units)

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

- 40 Figure 3.3.12 Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous Ice

- M aking Heads)

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

- 41 Figure 3.3.13 Energy Consumption vs. Harvest Rate (Water

- Cooled Continuous Ice

- Making Heads)

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

- 42

3

- ii

### Figure 3.3.14 Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous Remote Condensing Units with Non

- Remote Compressors )

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

- 4 3 Figure 3.3.15: Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous Remote Condensing Units with Remote Compressors)

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

- 44 Figure 3.3.16 Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous

Self

- Contained Units)

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

- 45 Figure 3.3.17 Energy Consumption vs. Harvest Rate (Water

- Cooled Continuous

Self

- Contained Units)

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

- 46 Figure 3.3.17: Condenser Water Consumption vs Harvest Rate (Water

- Cooled

Ice

- Making Head Units)

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

- 47 Figure 3.3.18: Condenser Water Consumption vs Harvest Rate (Water

- Cooled Batch

- Type

Self

- Contained Units)

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

- 48 Figure 3.3.19: Condenser Water Consumption vs Harvest Rate (Water

- Cooled Continuous

Ice

- Making Head Units)

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

- 49 Figure 3.3.20: Condenser Water Consumption vs Harvest Rate (Water

- Cooled Continuous

Self

- Contained Units)

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

- 50

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 the energy conservation standards rulemaking for

automatic commercial ice makers. Automatic commercial ice makers produce a range of ice
types

, including cube type ice, flake ice, nugget ice, tube type ice , and cracked or fragmented ice.

The Energy Policy and Conservation Act (42 U.S.C. 6311

– 631 7 ; EPCA) prescribes energy conservation standards for automatic commercial ice makers that produce cube type ice with capacities between 50 and 2,500 pounds of ice per 24

- hour period (lb/24 hours)

. a 

### (4 2 U.S.C. 6313(d)(1)) EPCA required r eview of these standards prior to January 1, 2015. (42 U.S.C. 6313(d)(3)) DOE is also establish ing , under 42 U.S.C. 6313(d)(2), standards for other types of ice makers with capacities up to 4,000 lb/ 24 hours.

This chapt er consists of a market and technology assessment. The purpose of the market assessment is to develop a qualitative and quantitative characterization of the automatic

commercial ice maker industry and market structure based on publicly available informatio n 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 markets 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 automatic commercial ice makers

.

Automatic commercial ice makers are used in several types of comm ercial sectors

, including health care, lodging, foodservice

, retail

, education

, food sales

, and office bu ildings

.

Definitions

EPC A define s “ automatic commercial ice maker ” as a factory

- made assembly (not necessarily shipped in one package) that :

1. consists of a condensing unit and ice

- making section operating as an integrated unit, with means for making and harvesting ice; and

2. may include means for storing ice, dispensing ice, or storing and dispensing ice.

(42 U.S.C. 6311(19))

The Energy Policy Act of 2005 ( EPACT 2005 ) set energy and condenser water usage

standards for cube type icemakers capable of producing between 50 and 2,500 lb/24 hours, and

added the definition of automatic commercial ice make r to EPCA. EPACT 2005 further

established standards for automatic commercial ice makers that produce cube type ice based on:

a Pounds of ice per 24

- hour period is abbreviated herein as lb/24 hours.

3

- 1

### ( 1 ) the configuration of the ice

- ma king and refrigeration systems ; ( 2 ) the type of cooling media

used ; and ( 3 ) the capacity of the unit.

Accordingly, the main categories of equipment cov ered under this rulemaking are as follows.

1. Ice Making Head (IMH) : An ice

- making head is a category in which all of the ice

making machinery, including freezing, harvesting

, and condensing components

, are integrated into one unit , called the ice making head

. These units do not include a means for storing ice

. Ice

- making heads are generally mounted on top of storage bin s (which are sold separately)

. 2. Remote Condensing Ice Maker s (RCU) : For automatic commercial ice makers, t wo remote condensing configurations e xist

. In one configuration, the compressor is contained within the same cabinet as the ice freezing and harvesting equipment, while

the condenser is located in a separate package. This configuration is referred to in this rulemaking as “remote condensing ( but not remote compressor).” In the other configuration, the compressor is located remotely from the cabinet containing the ice freezing and harvesting equipment , typically with the condenser in a remote condensing unit

. This configuration can also be designed for connect ion to an existing remote compressor rack and is referred to as “remote condensing and remote compressor” in this rulemaking. Ice makers equipped with r emote condensing units are typically larger ice makers, with harvest capacities up t o 4,000 lb/24 hours for

commercial ice makers

. For R CU units, the package containing the ice freezing and harvesting equipment may be called the ice making head portion of the unit.

3. Self

- contained automatic commercial ice makers (SCU) : A self

- contained

au tomatic commercial ice maker is a category in which the ice

- making machinery, including freezing, harvesting, and condensing components, and storage
compartment

, are in an integral cabinet. Self

- contained automatic commercial ice

makers tend to have lower harvest capacities than other equipment types, are typically used in office buildings and retail applications , and are generally designed to fit under

counters

.

In addition to the foregoing categories of ice

- making equipment, self

- contained units and

ice

- m aking heads are also categorized by the means used for disposing of the heat extracted from water when making ice.

1. Air Cooled : Ice makers are referred to as air cooled if the waste heat from the

condenser is released directly into the air.

2. Water Cooled : Ice makers are referred to as water cooled if the heat removed from

the condenser is released into a condenser water loop . Ice makers can be attached to closed cooling

- water loops that recirculate and reuse the same cooling water ; these installations requ ire a cooling tower (or similar device) to cool the circulating water. Conversely, ice makers can also be installed with open

- loop ( or single

- pass ) cooling
system s that use water one time for cooling and then release it into the wastewater
system.

3

- 2

### Most r emote condensing ice makers f eature air

- cooled condensers , and the EPCA ice

maker efficiency standards apply specifically to air

- cooled equipment. DOE notes that at least

one remote condensing ice maker features a water

- cooled condenser

.

The third differen tiating factor within the equipment configuration and cooling type is the

harvest capacity of a given ice maker

. Harvest capacity is defined in terms of the amount of ice an ice maker can produce in 24 hours.

DOE current ly defines 13 different equipment classes for automatic comm ercial ice makers in 10 CFR 431 Subpart H (42 U.S.C. 6313(d)(1)) . These are shown in Table 3.1

. 1

below.

Table 3.1

. 1 : Current Equipment Classes for Automatic Commercial Ice Makers

Equipment Type Type of Cooling Harvest Rate

lb ice/24 hours

Ice

- Making Head Water <500 ≥500 and <1,436 ≥1,436

Air <450
≥450 Remote Condensing (but not remote compressor) Air <1,000 ≥1,000 Remote Condensing and Remote Compressor Air <934
≥934

Self

- Contained Water <200
≥200

Air <175
≥175

3.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade association, domestic

manufacturers of automatic commercial ice makers , 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 (A H RI ) ) is the U.S. trade

association for automatic commercial ice maker manufacturers. A merger by the former Air

Conditioning and Refrigeration Institute, or ARI, and the former Gas Appliance Manufacturers Association, or GAMA resulted in the creation of AHRI. A H RI coordinates with member

m anufacturers to establish the Automatic Commercial Ice Makers and Ice Storage Bins division

3

- 3

### within A H RI . This division serves to develop standards and implement a certification program

for automatic commercial ice makers and storage bins

.

Most manufacturer s of automatic commercial ice makers are also members of t he North American Association of Food Equipment Manufacturers (NAFEM). NAFEM is a trade association of foodservice equipment and supplies manufacturers providing products for food preparation, cooking, storage and table service. NAFEM represents the industry in Washington, D.C., supports programs and organizations that move the foodservice industry forward, and offers educational opportunities for foodservice professionals.

3.2.2 Manufacturers and Ma rket Share

C urrent members of AHRI’s Automatic Commercial Ice Makers and Ice Storage Bins

division are listed below ; parent companies are shown in parentheses if applicable

. 1



### • Hoshizaki America, Inc.

• IMI Cornelius, Inc

.

• ITV Ice Makers SA

• KD Industries, Inc. /Kold Draft (Erie Management Group, LLC)

• Manitowoc Ice, Inc. (The Manitowoc Company, Inc.)

• Mile High Equipment LLC /Ice

- O

- Matic (Scotsman Industries)

• Scotsman Ice Systems (Scotsman Industries)

Other automatic commercial ice maker manufacturers are listed below ; parent companies

are shown in parentheses if applicable.

• A&V Refrigeration

• Arctic

- Temp/Holiday Ice, Inc.

• Brema Ice Makers

• Follett Ice Makers (VGI Holdings Corp)

• Howe Corporation

• North Star Ice

• Orien U.S.A.

• Summit Appliance (Felix Storch, Inc.)

• Vogt Ice, LLC (Vogt Power International)

According to a recent stud y by Koeller & Company

, the ice maker market is dominated by four manufacturers of automatic ice makers

, who produce approximately 90 percent of the

automatic commercial ice makers f or sale in the United States

. 2 

### The four major manufacturers with the largest market share are Manitowoc (30 percent market share ), Scotsman (25 percent), Hoshizaki (20 percent)

, and Mile High Equipment L L C/Ice

- O

- Matic (15 percent) . See Figure

3.2

. 1

, where Koeller cited a 1996 report by Arthur D. Little, Inc.

3

- 4Source: Koeller and Company, Potential Best Management Practices

, June 2008

. 

### Figure 3.2

. 1 : Domestic Automatic Commercial Ice Maker Market Shares as of 1996

The landscape of the automatic commercial ice maker market has changed since this data

was publish ed in 1996

. At about th e same time that the data on Figure 3.2

. 1 was compiled , the parent company of Mile High Equipment L L C/Ice

- O

- Matic was acquired by Enodis, PLC.

Enodis then acquired Scotsman industries in 1999. 3 

### Thus, these two companies no longer exist ed

as independent and separate entities

.

In 2008, Manitowoc a greed to purchase Enodis, PLC. At the time, Manitowoc was

believed to control 40 percent of the market for cube

- making machines, and Enodis controlled

30 percent

. T h e Manitowoc purchase of Enodis, PLC would have concentrated a vast majority of

automatic co mmercial ice maker production in the hands of one company . In a settlement agreement with the U.S. Justice Department, Manitowoc agreed to divest the U.S. ice machine

business lines previously held by Enodis , including Ice

- O

- Matic and Scotsman

. Manitowoc s o l d

the ice machine business lines to Warbur g Pincus Private Equity X, L.P. , who announced they

would use the Scotsman name. 4

, 5 

### In November, 1992, Warburg Pincus announced they had agreed to sell Scotsman Industries to the ALI Group. Ice

- O

- Matic and Scots man exist today as companies owned by the ALI Group. 6 

### Manitowoc
30% Scotsman
25% Hoshizaki
20%

Mile High Equipment LLC/Ice

- O

Matic
15%

IMI Cornelius

and Others
10%

3

- 5

### 3.2.2.1 Small Businesses

DOE has consider ed the possibility that energy conservation standards for automatic

commercial ice makers would adversely affect small businesses

. The Small Business Administration (SBA) defines small business manufacturing enterprises for the commercial refrigeration equipment , in general, as those having 750 employees or fewer. 7 

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

- profit concern can be in that industry and still qualify as a small business for Federal Government

programs. These size standa rds are generally expressed in terms of the average annual receipts or the average employment of a firm. For commercial refrigeration equipment

, b 

### the size standard is matched to NAICS code 333415, Air

- Conditioning and Warm Air Heating Equipment and

Commerc ial and Industrial Refrigeration Equipment Manufacturing , and is 750 employees.

DOE has stud ied the potential impacts on these small businesses in detail during the manufacturer impact analysis, which were conducted as a part of the final rule analysis. In these analyses

, DOE identified seven s mall business manufacturers of automatic commercial ice

makers

. More information on these analyses is available in Chapter 11 of the final rule TSD.

3.2.2.2 Regulatory Programs

Outside of the United States, Canada and Australia / New Zealand have efficiency

standards for automatic commercial ice makers

.

Within the United States, s everal states ( Arizona, California, Oregon, and Washington )

have established efficiency regulations for automatic commercial ice maker s

. The sta te standards all regulate cube type ice makers only

. California’s standards w ere adopted in 2004, and w e re based on the Consortium for Energy Efficiency ( CEE ) Tier 1 and Federal g uidelines for purchasing energy efficient equipment. The California standards also match those passed by the U.S. Congress as part of EPACT 2005. All other state standards were based on the California/EPACT 2005 standards, and became effective January 1, 2008. The state standards have since been pre

- empted by the F ederal standards set forth in EPACT 2005 and effective January 1, 2010.

3.2.2.3 Natural Resources Canada

The Natural Resources Canada (NRCan) Office of Energy Efficiency established energy efficiency standards for automatic commercial ice makers on December 31, 1998

. The NRCan

st andard covers all equipment between 23 and 1,000 kilograms per day ( between 50.6 and 2200

b Automatic commercial ice makers is a small component of the equipment manufacturing category, NAICS code 333415. This NAICS category covers a broad segment of he ating, ventilation, and refrigeration equipment manufacturing including commercial refrigeration, commercial ice makers, heat pumps, air conditioners, warm air furnaces, air conditioners, and a number of other types of equipment and components.

3

- 6

### lb per day) ), and covers not just batch ice makers but also continuous ice (flake and nugget)

makers . NRCan’s standard had a compliance date of January 1, 2008.

The NRCan standards are similar to the EPACT 2005 standards in that the covered batch equipment types are broken out into similar categories. However, NRCan standards cover additional equipment not covered by EPACT 2005 standards , including continuous type ice

makers and batch type ice makers that p roduce other than cube type ice. The NRCan efficiency standards also establish minimum storage effectiveness levels for ice storage bins on self

contained ice makers

. As stated in the final rule

, DOE has not proposed standards for ice storage

effectiveness levels

.

For batch type ice makers, the NRCan standards are the same as those set by EPACT 2005, but they are represented using harvest capacity expressed in terms of kilograms rather than pounds per 24 hours. Table 3.2

. 1 lists the NRCan equipment classes for continuous type

a

utomatic c ommercial i ce m akers

, with harvest capacity converted to lb/ 24 hours

.

NRCan does not require adjustments of continuous ice maker usage for ice hardness. Nor does the continuous ice maker efficiency standard differentiate by the type of equipment (i.e.

, ice

- making head, self

- contained, units with remote condensing). The NRCan website indicates the continuous process efficiency standard went into effect January 1, 2000.

Table 3.2

. 1 NRCan Efficiency Standard for Continuous Automatic Commercial Ice Makers

Equipment T ype Type of

C ooling Ice

- M aking Capacity kg/24 hours (lb

/24 hours *) Maximum E nergy U se kJ/kg Maximum E nergy U se kWh/100 lb**,***

All Continuous Machines A ir < 300 (660)

≥ 300 (660) 875.2

– 1.122 x C

538.6 11.03

– 0.0064 x C

6.79

All Continuous Machines W ater < 300 (660)

≥ 300 (660) 740.5

– 0.8976 x C

471.2 9.33

– 0.0051 x C

5.94

Source: NRCan website. * The NRCan standard expressed capacity in terms of kilograms per 24 hours. DOE converted this to lb/24 hours for presentation in the table. ** The NRCan standard expressed energy usage in terms of kJ/kg ( kilojoule per kilogram ). DOE converted this to kWh/100 lb for presentation in the table. *** The NRCan standard expressed C as c apacity (i ce harvest rate ) in kilograms per 24 hours . DOE conver ted this to kWh/100 lb for presentation in the table. http://www.nrcan.gc.ca/energy/regulations

- codes

- standards/products/6893



### 3.2.2.4 Australia / New Zealand

Australia / New Zealand ha ve also established required efficiency standards for automatic

commercial ice makers and storage bins

. 8 

### The minimum energy performance standards are

established in Australian Standard AS/NZS 4865.3:2008 as maximum energy consumption per 100 kg of ice

. The Australian standards categorize equipment by the following criteria:

• m odular type ice makers, air cooled and water cooled (two size categories )

• s elf

- contained ice makers, air cooled and water cooled (two size categories )

• s plit system, a ir

- cool ed, remote condensing but not remote compressor

• s plit system, a ir

- cooled, remote condensing and remote compressor

3

- 7

### • s torage systems

The standards apply to cube, flake

, and nugget machines without distinguishing between

the machines and, apparently, without a djusting continuous ice maker energy usage for ice

hardness

. The Australian standards also establish a high efficiency rating, above their minimum

standard classes.

3.2.3 Non

- Regulatory Initiatives

DOE reviewed several voluntary programs promoting energy efficient automatic

commercial ice makers in the United States, ENERGY STAR ® 

### , CEE, and the Federal Energy Management Program (FEMP) procurement program. DOE also reviewed various rebate programs offered by utilities.

3.2.3.1 ENERGY STAR

Since the publication of the preliminary analysis, the U.S. Environmental Protection Agency (EPA) has issued an updated set of ENERGY STAR standards. The ENERGY STAR

Version 2.0 standards issued by the EPA have been effective since February 1, 20 13 and in clude cube type and flake/nugget type air

- cooled automatic ice machines. ENERGY STAR also sets a potable water use limit for all ice machines considered. These criteria are summarized in Table

3.2

. 2 and Table 3.2

. 3

.

EPA has implemented a single nonlinear formula showing the relationship of energy use with harvest capacity for each equipment type, instead of a piece

- wise linear function with

different formulas defined for different capacity ranges. By means of a more complicated formula, the ENERGY STAR specification creates a continuous curve while still respecting the

asy mptotic relationship between efficiency and harvest capacity.

Table 3.2

. 2 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for Air

Cooled Batch

- Type Ice Makers

Equipment Type Harvest Rate, H

lb/ 24 hour s Energy Use Limit

kWh/100 lb ice Potable Water Use Limit gal/100 lb ice

Air

- Cooled

IMH 200 ≤ H ≤ 1600 ≤ 37.72 * H

- 0.298 ≤ 2 0.0

RCU 400 ≤ H ≤ 1600 ≤ 22.95 * H

- 0.258 + 1.00 ≤ 2 0.0
1600 ≤ H ≤ 4000 ≤

- 0.00011 * H + 4.6 0 ≤ 2 0.0

SCU 50 ≤ H ≤ 450 ≤ 48.66 * H

- 0.326 + 0.08 ≤ 2 5.0

kWh = kilowatt

- hours , gal = gallons

Source: www.energystar.gov

3

- 8

### Table 3.2

. 3 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for Air

Cooled Continuous

- Type Ice Makers

Equipment Type Energy Use Limit

kWh/100 lb ice Potable Water Use Limit gal/100 lb ice

Air

- Cooled

IMH ≤ 9.18 * H

- 0.057 ≤15.0

RCU ≤ 6.00 * H

- 0.162 + 3.50 ≤15.0

SCU ≤ 59.45 * H

- 0.3 49 + 0.08 ≤15.0

kWh = kilowatt

- hours , gal = gallons

H = Harvest Rate

Source: www.energystar.gov



### 3.2.3.1 Federal Energy Management Program

Federal Energy Management Program ( FEMP ) is a program administered by DOE that oversees the Federal Government’s energy management and investment initiatives. FEMP has established purchasing specifications for energy efficient equipment, including cube type

automatic commercial ice makers , which Federal agencies must follow when buying new equipment for their facilities. 9 

### Federal purchasers are required by EPACT 2005 to purchase equipment that is either ENERGY STAR qualified or FEMP designated. The FEMP designated equipment consists of equipment that is in the upper 25 percent of energy efficiency in its class.

Table 3.2

. 4 provides the requirements for water

- cooled ice makers. 10 

### For air

- cooled ice makers, FEMP refers users to the ENERGY STAR specifications (see Section 3.2.3.1 ).

Table 3.2

. 4 Performance Requirement for Federal Purchases of Water

- Cooled Ice Makers

Type Ice Harvest Rate

lb/24 hours Energy Use

kWh/100 lb *

Self

- Contained Unit ≤ 100 8.1 or less

Self

- Contained Unit 101

– 150 7.3 or less

Self

- Contained Unit 151

- 200 6.6 or less

Self

- Contained Unit ≥200 6.5 or less Ice Making Head ≤300 5.3 or less Ice Making Head 301

– 400 4.8 or less Ice Making Head 401

– 500 4.3 or less Ice Making Head 501

– 750 4.1 or less Ice Making Head 751

– 1,435 3.5 or less Ice Making Head ≥1,436 3.4 or less

Source: DOE Federal Energy Manageme nt Program Covered Products web site, http://energy.gov/eere/femp/covered

- product

- category

- water

- cooled

- ice

machines

. * Measured i n accordance w i th ARI Standard 810

- 200 7 with Addendum 1: Performance Rating of Automatic Commercial/Ice Makers

.



### 3.2.3.2 Consortium for Energy Efficiency

Between 2006 and July 2011, CEE used a three

- tiered system to set efficiency requirements f or cube type machines . CEE set Tier 1 to be equivalent to F EMP standards , most recently the FEMP standards effective as of January 1, 2010. CEE set Tier 2 to be 10 percent below Tier 1, and Tier 3 to be 15 percent below Tier 1. ENERGY STAR standards correspond ed

3

- 9

### approximately with Tier 2 (for air

- cooled machines)

. CEE standards set limits on both energy and water use. The water use is separated into potable water use and cooling water use for water

cooled machines.

Beginning on July 1, 2011, CEE revised their system

, making the Tier 1 equal to the previous Tier 2 levels, and setting the new Ti er 2 to 10 percent lower than Tier 1. 11 

### The new CEE efficiency requirements for air

- cooled ice makers are shown i n Table 3.2

. 5 , while the new requirements for water

- cooled ice makers are shown i n Table 3.2

. 6

.

3

- 10

### Table 3.2

. 5 CEE Efficiency Requirements for Air

- Cooled Ice Makers

Level Corresponding

Base Specification Equipment Type Ice Harvest Rate (H) lb of ice/day Energy Use

Limit

kWh/100 lb of

ice Potable

Water Use

Limit gal/100 lb of

ice

Tier 1 ENERGY

STAR / Former

CEE Tier 2 (pre

- 7/1/2011) Cube Type Machines Only: Ice

- Making

Head <450 9.23

- 0.0077H ≤25

≥450 6.20

- 0.0010H ≤25

Cube Type Machines Only: Remote

Condensing without
remote compressor <1

, 000 8.05

- 0.0035H ≤25

≥

, 1000 4.64 ≤25

Cube Type Machines Only: Remote

Condensing with
remote compressor <934 8.05

- 0.0035H ≤25

≥934 4.82 ≤25

Cube Type Machines Only: Self

- Contained <175 16.7

- 0.0436H ≤35

≥175 9.11 ≤35

Tier 2 Approximately

10% More

Efficient than Average

Performance Cube and Nugget Type Ice Machines <175 14

– 0.0347H ≤30
≥175 and <450 9.6

– 0.0098H ≤20
≥450 and <1

, 000 5.9

– 0.0016H ≤20

≥1

, 000 4.5

– 0.0002H ≤20 Flake Type Ice

Machines <1

, 000 6.5

– 0.0033H ≤20

≥1

, 000 3.2 ≤20

Source: http://library.cee1.org/content/cee

- high

- efficiency

- specifications

- commercial

- ice

- machines/



### Table 3.2

. 6 CEE Efficiency Requirements for Water

- Cooled Ice Makers

Level Corresponding

Base Specification Equipment Type Ice Harvest Rate (H) lb of ice/day Energy Use

Limit

kWh/100 lb ice Potable

Water Use

Limit gal/100 lb of

ice

Tier 1 Former CEE

Tier 2 (pre

7/1/2011) Cube Type Machines Only: Ice

- Making

Head <500 7.02

– 0.0049H ≤25
≥500 and <1436 5.13

– 0.0010H ≤25

≥1436 3.68 ≤25 Cube Type Machines Only: Self

- Contained <200 10.6

– 0.0177H ≤35

≥200 7.07 ≤35

Tier 2 Approximately

10% More

Efficient than Average

Performance Cube and Nugget Type Ice Machines <175 10.6

- 0.0241H ≤30
≥175 and <450 7.1

- 0.0062H ≤20
≥450 and <1000 4.7

- 0.0011H ≤20

≥1

, 000 3.7

- 0.0002H ≤20 Flake Type Ice

Machines <1

, 000 4.8

- 0.0017H ≤20

≥1

, 000 3.1 ≤20



### 3.2.3.3 Rebate Programs

Numerous organizations and entities throughout the United States offer rebate programs for customers who purchase and install qualified automatic commercial ice makers

. Twenty three

utilities

, governmental agencies, and other entities operate rebate programs specifically to provide incentives to customers who purchase ENERGY STAR compliant ice makers. The rebate programs, which can be found on the ENERGY STAR website by using the “ Commercial

3

- 11

### Food Service Eq uipment Incentive Fin der,” offer rebates ranging from $50 to $600 for qualifying purchases.

The EnergySmart Grocer Program is funded by California utility ratepayers under the auspices of the California Public Utilities Commission. Eligible participants include grocery and

co nvenience 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 automatic commercial ice makers that are equi valent to CEE Tier 3

. 12



### The New York State Energy Research and Development Authority (NYSERDA) offers rebates on pre

- qualified commercial refrigeration equipment, including automatic commercial ice makers, ranging from $75 for smaller equipment meeting ENER GY STAR specifications to $ 175 for large equipment meeting ENERGY STAR specifications. 13



### 3.2.4 Equipment Classes

Automatic commercial ice makers are divided into e quipment classes categorized by physical characteristics that affect commercial application, equipment utility, and equipment efficiency : ( 1 ) the ice

- making process ; ( 2 ) the configuration of the ice

- making and refrigeration
systems ; ( 3 ) the type of cooling media used ; and ( 4 ) the capacity of the unit. The following list shows the key characteristics of automatic commercial ice makers that DOE use s to distinguish equipment classes :

1. i ce

- making process

• c ontinuous

• b atch

2. e quipment configuration

• i ce

- making head

• r emote condensing (but not remote compressor)

• r e mote condensing and remote compressor

• s elf

- contained

3. c ondenser c ooling media

• a ir

- cooled

• w ater

- cooled

4. c apacity r ange

• v arious

Table 3.2

. 7 shows the automatic commercial ice

- making equipment class es DOE is

creat ing with this rulemaking

. Note that for batch ice makers, the capacity ranges for these

classes differ from those of th e current ice maker classes as shown in Table 3.1

. 1 . For the purposes of discussion of the different ice maker classes, the discussion in the TSD will refer to

the current class distinctions for these batch ice maker classes unless otherwise indicated.

3

- 12

### Table 3.2

. 7 Final Rule Automatic Commercial Ice

- Making Equipment Classes

Type of Ice

Maker Equipment Type Type of

Conden ser

Cooling Harvest Capacity

Rate

lb ice/24 hours

Batch Ice

- Making Head Water ≥ 50 and < 300
≥ 300 and < 850 ≥ 850 and < 1,500 ≥ 1,500 and < 2,500 ≥ 2,500 and < 4,000

Air ≥ 50 and < 300
≥ 300 and < 800 ≥ 800 and < 1,500

≥ 1500 and < 4,000 Remote Condensing (but not remote

compressor) Air ≥ 50 and < 1,000

≥ 1,000 and < 4, 0 00

Remote Condensing

and Remote Compressor Air ≥ 50 and < 942

≥ 942 and < 4,000

Self

- Contained Unit Water ≥ 50 and < 200
≥ 200 and < 2,500 ≥ 2,500 and < 4,000

Air ≥ 50 and < 110
≥ 110 and < 200 ≥ 200 and < 4,000

Continuous Ice

- Making Head Water ≥ 50 and < 801 ≥ 801 and < 4,000

Air ≥ 50 and < 310
≥ 310 and < 820 ≥ 820 and < 4,000 Remote Condensing (but not remote

compressor) Air ≥ 50 and < 800

≥ 800 and < 4,000

Remote Condensing

and Remote Compressor Air ≥ 50 and < 800

≥ 800 and < 4,000

Self

- Contained Unit Water ≥ 50 and < 900 ≥ 900 and < 2,500 ≥ 2,500 and < 4,000

Air ≥ 50 and < 200
≥ 200 and < 700
≥ 700 and < 4, 000



### 3.2.5 Shipments and Available Equipment

This section presents the shipments data obtained from AHRI, North American Association of Food Equipment Manufacturers (NAFEM), the U

. S

. Census Bureau , and

ENERGY STAR

.

3

- 13

### 3.2.5.1 Air

- Conditioning, Heating, and Refrigeration Institute Data

As part of its comments on the Framework d ocument, A H RI submitted 2010 shipment data by equipment class for its member compa nies , and historical information on total shipments

. DOE used th is shipm ent s data as part of the basis for this analysis. DOE understands that th is

data do es not include the entire industry . As noted in section 3.2.2, AHRI membership includes

some but not all manufacturers. However, A H RI ’s members represent over 90 percent of the

market , so the AHRI data cover s the vast majority of the automatic commercial ice makers sold.

Table 3.2

. 8 shows 20 1 0 annual shipments reported by AHRI for each category of

automatic commercial ice maker by equipment class. Note that the AHRI data have been aggregated to match the primary equipmen t groupings, meaning that for batch and continuous ice maker types the remote condensing unit ice maker s have been aggregated into one group, distinguished only by harvest rates. AHRI’s data include information to distinguish between

remote condenser units with remote compressors and remote condenser units that do not have
remote compressors.

3

- 14

### Table 3.2

. 8 AHRI 20 1 0 Shipments of Automatic Commercial Ice Makers, by Equipment

Class

Type of Ice Equipment Type Type of Cooling Harvest Capacity Rate

lb/24 hours 2010 Shipments

Batch IMH Water ≥50 and <500 7,364

Batch IMH Water ≥500 and <1,436 4,734

Batch IMH Water ≥1,436 and ≤4,000 748

Batch IMH Air ≥50 and <450 43,913

Batch IMH Air ≥450 and ≤4,000 26,173

Batch RCU Air ≥50 and <1,000 8,804

Batch RCU Air ≥ 1,000 and ≤4,000 9,856

Batch SCU Water ≥50 and <200 1,101

Batch SCU Water ≥200 and ≤4,000 351

Batch SCU Air ≥50 and <175 22,468

Batch SCU Air ≥175 and ≤4,000 10,640

Continuous IMH Water ≥50 and <1000 1,265

Continuous IMH Water ≥1

, 000 and <4,000 123

Continuous IMH Air ≥50 and <1000 7,213

Continuous IMH Air ≥1

, 000 and <4,000 260

Continuous RCU Air ≥50 and <1000 1,972

Continuous RCU Air ≥1

, 000 and <4,000 773

Continuous SCU Water ≥50 and <175

-

Continuous SCU Water ≥175 and <4,000 241

Continuous SCU Air ≥50 and <175 7,478

Continuous SCU Air ≥175 and <4,000 6,713



### 3.2.5.2 North American Association of Food Equipment Manufacturers Data

NAFEM prepares a biennial study of the foodservice equipment and supplies market. The latest available report is titled 20 1 0 Size and Shape of the Industry Study

. 14 

### It contains survey

data from NAFEM members

, including shipments numbers and the value of sales in dollars

. Based on the numbers reported by the respondents to the survey, NAFEM developed a total market estimate of shipments ( units shipped and dollar sales) for the year 200 9

. NAFEM published similar study reports for shipments in 200 8 15 

### and 200 6. 16



### The “refrige ration and ice machine study” is part of the NAFEM reports and includes shipments data for cube, flake

, and nugget ice makers as well as ice dispensers

— both manual fill and integrated systems

. Due to the aggregation of the NAFEM data, it provided additiona l data for putting total shipments into context, but not for identifying the relative shares of shipments by equipment class. Shipment estimates have been withheld from publication because the NAFEM reports are not publicly available.

3

- 15

### 3.2.5.3 Census Bureau Data

The U.S. Census Bureau’s Current Industrial Report (CIR) s eries publishes statistics on the quantity and value of shipments based on a survey of manufacturers. The CIR has been published since 1904, and DOE collected CIR data going back to the 1940s for purposes of evaluating prices and shipments. The Census has not updated it s statistics since the publication of the preliminary analysis , and the CIR has been discontinued.

Table 3.2

. 9 shows CIR shipment data for ice

- making machines

.

Table 3.2

. 9 Shipments of Ice Makers
Product Description Year Quantity Value Companies

Self

- contained ice

- cube makers, automatic, under 200

lb 2010 42,293 43,039 4

2009 61,699 55,522

Self

- contained ice

- cube makers, automatic, 200 lb and

over 2010 ( not reported ) 102,696 5

2009 ( not reported ) 84,524

Self

- contained flake or chip machines, 300 lb and

under 2010 ( not reported ) ( not reported ) 3

2009 546 1,369

Self

- contained flake or chip machines, over 300 lb 2010 21,368 50,728 6

2009 17,958 42,016

Ice

- making machines, not self

- contained 2010 107,723 258,408 7

2009 94,876 217,791

Ice

- making machines, combination ice makers and ice/drink dispensers 2010 74,635 181,612 5

2009 62,367 150,330 Source: U.S. Census Bureau, Current Industrial Reports, 2010. www.census.gov/manufacturing/cir/historical_data/ma333m/index.html



### Automatic commercial ice makers are treated in U.S. Census Bureau data series as part of

NAICS 333415 . In general, automatic commercial ice makers are such a small piece of NAICS 333415 that the U.S. Census Bureau does not report them separately in any table, except for the

CIR tables.

In summary, although the U.S. C ensus 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.

3.2.5.4 ENERGY STAR Data

ENERGY STAR publishes res ults annually showing the number of ENERGY STAR compliant units shipped by its partners, and the estimate percentage of total shipments represented by the compliant shipments. The ENERGY STAR shipments presented on Table

3

. 11 covers only equipment types with ENERGY STAR criteria, which during the period covered by the shipments data included only air

- cooled batch equipment.

3

- 16

### Table 3.2

. 10 ENERGY STAR Ice Maker Shipments

Year ENERGY STAR Compliant Shipments Percent of Total Shipments Total Shipments

2008 55,274 40% 138,185

2009 60,000 42% 143,000

2010 70,000 63% 111,000

2011 75,000 62% 121,000

2012 95,000 66% 144,000

Source: ENERGY STAR, Unit Shipment Data. http://www.energystar.gov/index.cfm?c=partners.unit_shipment_data



### 3.2.6 Equipment Lifetimes

DOE reviewed available literature and consulted with experts on automatic commercial

ice makers to establish typical equipment lifetimes. The literature and individuals consulted

estimated a fairly narrow and consistent range of typical equipment lifetimes , shown in Table

3.2

. 11

. Most references cited i n the table appear to have derived the underlying data for their estimates of equipment lifetime from the 1996 Arthur D

. Little report

. 17



### Individuals with experience in the manufacture or distribution of automatic commercial

ice makers suggested a typical life time of 7 to 1 0 years , with some equipment being retired after as short a period as 5 years in cases of building remodels and as long as 20 years in cases where the equipment owners did not care about the appearance of the equipment.

Some literature suggested lifetimes of up to 20 years or more for tube type automatic

commercial ice makers (tube type ice makers are described in Section 3.3.1.2 )

.

Table 3.2

. 11 Estimates for Automatic Commercial Ice Maker Lifetimes

Life Reference

7 to 10 years Arthur D

. Little, 1996 17

8.5 years California Energy Commission, 2004 18

8.5 years Fernstrom, G., 2004 19

8.5 years Koeller J., and H. Hoffman , 2008 2

7 to 10 years Navigant Consulting, Inc. 2009 20



### 3.3 TECHNOLOGY ASSESSMEN T

This section provides a technology assessment for automatic commercial ice makers

. Contained in this technology assessment are details about product operation s and components (section 3.3.1 ), an examination of possible technological improve ments for each product (section 3.3.2 ), and a characterization of the product efficiency levels currently available on the market (section 3.3.3 ).

3.3.1 Baseline Equipment Components and Operation

This section briefly describes the components and operation of automatic commercial ice makers. These descriptions provide a basis for understanding the technologies used to improve product efficiency.

3

- 17

### 3.3.1.1 Basic Equipment Description and Components

A typical ice maker consists of a refrigeration system and a water supply system contained within an insulated case. The ice makers may or may not have an integrated storage

bin. Ice makers that have no integral storage bin generally deliver ice by gravity ( i.e.

, dropping the ice) into a bin or other equipment, such as a beverage/ice dispenser, on which the ice maker is mounted when in use. The refrigeration system may be entirely contained in a single unit, or the condenser and possibly the compressor may be located remotely.

Ice makers are classified into three categories, as discussed in section 3.2.4 : ice

- making

heads ( IMHs ), remote condensing units ( RCUs ), and self

- contained units ( SCUs ) . IMHs have the highest sales levels and are available in the widest range of capacities. They are generally mounted on top of a separate storage bin or other equipment that uses the ice. The indoor

section s of RCUs are similar to IMHs, except that they are mated with remote condensers intended to be located outdoors, rejecting heat directly to the outside air without adding to the

interior air

- conditioning load. SCUs have their own integral ice storage bins. They generally have smaller harvest capacities than IMHs and RCUs, and most of these units are designed for

under

- counter placement.

There are two distinct ice

- making processes that are used by commercial ice makers:

• The batch process involves alternate freezing and harvesting periods, producing batches of solid ice by allowing water to flow over an evaporator and freeze into cubes, tubes, or another shape depending on the evapor ator geometry. Once the ice is fully formed, the refrigeration system switches into harvest mode, in which compressor heat is transferred to the evaporator to release the ice, which is subsequently moved to storage.

• The continuous process makes flake and n ugget

- shaped ice by continuously scraping ice as it freezes on a cylindrical evaporator surface. Most commercial flake and nugget ice makers use a stationary evaporator that freezes incoming water on the enclosed inner surface. These evaporators contain a rotating auger that scrapes the ice

off the evaporator and extrud es it upward. The auger forces the ice through exit orifices past a cutter, whose specific geometry dictates the ice format: in nugget ice makers, the flakes are converted into nuggets using an extrusion head with smaller openings that compresses the flakes to form nuggets. After passing through the entire evaporator, the flakes or nuggets typically drop down into the ice bin.

All commercially available ice makers use vapor compression refrige ration systems to provide the refrigeration needed for ice production. Table 3.3

. 1 provides descriptions for the key refrigeration components. Aside from the evaporat or, ice makers use the same refrigeration components that are used in other medium

- temperature commercial refrigeration applications.

Ice makers use either air

- cooled or water

- cooled condensers. Air

- cooled condensers are used in roughly 90 percent of units shipped

. 21 

### Water

- cooled condensers can increase energy efficiency because the lower inlet water temperature and higher heat transfer possible with water cooling allow condensing temperature to be reduced. However, in traditional installations this results in much higher water consumption because condenser water is most often drained after it is used to

cool the condenser. Water

- cooled ice makers can, however, be used with closed

- water

- loop

3

- 18

### systems in which the condenser heat is remotely rejected in a coolin g tower or other equipment

used to cool the water. Sections 3.3.1.2 ( Batch Process ) and 3.3.1.3 ( Continuous Process )

describe the batch and continuous ice

- making processes in more detail.

Table 3.3

. 1 Brief Description of Primary Refrigeration System Components
Component Description of Typical Unit

Co mpressor Compressor types used:

• Reciprocating compressors for most ice makers

• Scroll compressor for some higher

- capacity ice makers (~1,500 lb/24 hours and higher) Refrigerants used:

• R

- 404A for most icemakers ( > 100 lb/ 24 hours )

• R

- 134a for some low capacity ice makers (< 100 lb/24 hours)

Condenser Air

- Cooled • Copper tube

- aluminum fin condensers, generally with enhanced fins (wavy or slit fin), maximum fin density near 15 fins per inch.

Water

Cooled • Concentric tube heat exchanger with steel outer and copper or cupronickel inner tube, possibly enhanced surface area using spiral grooving. Water flow

controlled by the supply valve to maintain a constant preset condensing
temperature

. Expansion Device • Most ice makers use conventional t hermostat ic expansion valve s. Electronic

valves are rarely used. Smaller

- capacity ice makers may use capillary tube s.

Evaporator • C opper tubing attached to copper or stainless steel ice

- making surfaces (see specific descriptions of batch and continuous evaporators below). Liquid L ine/Suction L ine Heat Exchanger • Liquid and suction lines brazed together.



### 3.3.1.2 Batch Process

This section describes the batch ice

- making process, including the refrigeration cycle, evaporator designs, and water supply systems currently used for batch ice

- making in the ice

maker industry. Cube

- and tube

- shaped ice types are made in batch process ic e machines.

Figure 3.3

. 1 shows key components of a generic refrigeration cycle used for batch ice

making. The system adds to the basic vapor compression cycle a hot

- g as bypass line that enables the evaporator to undergo a harvest or defrost cycle using hot gas directly from the compressor discharge line. The defrost cycle is used to melt the ice layers that hold the ice on the evaporator surface, causing the ice to sli de off the evaporator. The hot gas solenoid valve opens during harvest to let the discharge gas bypass the condenser and thermostatic expansion valve to pass directly into the evaporator.

3

- 19Source: Manitowoc



### Figure 3.3

. 1 Typical Vapor Compression Refrigeration Cycle for Batch T ype Ice Makers

There are five basic evaporator designs used by manufacturers in the ice maker industry today for ice makers with harvest capacit ies in the commercial ran ge ( up to 4,000 lb/24 hours )

. These designs are described in Table 3.3

. 2 . The cube type evaporator consists of copper or

stainless steel ice

- making surfaces brazed to copper serpentine tubing

, enabling the exchange of heat between the water and the refrigerant. The tube and cracked ice evaporators are used for ice

makers with harvest capacit ies of 2,000 lb/24 hours and higher. Figure 3.3

. 2 shows cracked ice during harvest, illustrating the cracks that form due to thermal stresses associated with harvest heat addition just prior to ice release.

3

- 20

### Table 3.3

. 2 Batch Evaporator Designs

Evaporator Design Manufacturers Using Design Ice Shape Description

Vertical Grid Manitowoc,

Scotsman

Industries Rectangular

Cube Nickel

- plated copper grid oriented vertically with copper serpentine tubing brazed onto rear plate; water flows down the face of the grid and freezes in cube

shaped cavities to form ice cubes.

Hoshizaki Style Hoshizaki Crescent Cube Vertically oriented pair of stainless steel surfaces shaped to en sure formation of individual crescent cubes and sandwiching the copper serpentine evaporator tube; water flows down both sides.

Horizontal

Grid Kold

- Draft Rectangular

Cube Nickel

- plated copper grid oriented horizontally with

the ice

- making surface faci ng downward with copper serpentine tubing brazed on the top; water is sprayed

from below onto the ice

- making surface by multiple

nozzles.

Tube Vogt, Others Cylindrical

Tube Vertical cylindrical stainless steel drum housing an array of vertical stainless steel tubes; water flows down and freezes on the inside surface of the tubes, forming long ice tubes that are cut into smaller pieces as they fall through and out the tubes during harvest; refrigerant flows inside the drum outside the tubes.

Cracked Vogt , Arctic Temp, Others Cracked Pieces One or more vertically oriented concentric

- cylindrical stainless steel tube evaporators with refrigerant flowing between the cylinders; water flows down and

ice forms on the inner and outer surfaces. The thermal

stress of harvest starts to crack the cylindrical ice, and it is ground into smaller pieces mechanically.
Image s ources: Vertical Grid

– Manitowoc : www.manitowocice.com Hoshizaki Style : www.ccfse.com/Hoshizaki

- Crescent

- Ice

- Cuber

- p/km

- 650mah.htm

Horizontal Grid

– Kold

- Draft : www.kold

- draft.com/ice

- making

- technology/ice

- sizes.php

Tube Ice : www.prithvinigen.com/tube_ice.jpg

3

- 21Source: Vogt Ice ( www.vogtice.com/downloads/General%20Info

- VT.pdf )



### Figure 3.3

. 2 Cracked Ice Forming on “Cracked Ice” Evaporators

Figure 3.3

. 3 shows th e typical water system for cube type ice makers using vertically oriented evaporators.

3

- 22

### Figure 3.3

. 3 Water System Diagram for Batch Type Ice Makers

Batches of ice are produced through a series of steps, as described below:

1. The s ump fills with water. Some ice maker designs fill the sump completely before beginning the ice

- making process, while others allow the filling process to continue through the ice

- making process. T he continuous fill technique is common in self

contained units.

2. Water is circulated over the evaporator, and ice gradually forms. Water that does not freeze on the evaporator falls to the sump to be recirculated by the pump. Solids and gases dissolved in t he water are carried away by the remaining liquid. This process reduces inclusion of impurities in the ice, thus allowing production of clear ice.

3. When the batch is complete, ice is harvested using hot refrigerant vapor that has been

redirected from the c ompressor discharge to warm the evaporator to free the ice from the surface. The harvested ice falls into a storage bin, which may or may not be part of the ice maker. The ice maker can sense when the ice has reached the proper batch weight by ( 1) measurin g sump water level ; ( 2) waiting a certain amount of time after the compressor suction pressure drops to a preset level ; or ( 3) measuring thickness of ice on the plate.

4. The remaining sump liquid, which contains high concentrations of dissolved solids, is

dr ained. Depending on ice maker design details

, the liquid may or may not be completely drained, and there may or may not be fresh potable water flowing during Water Pump

Sump Water Distributor

Water

Supply

Purge Drain

3

- 23

### this time to help purge the contaminants. Typical potable water consumption ranges

from 18

– 40 gall ons for every 100 lb of ice produced, compared to 12 gallons per

100 lb contained in the ice produced.

5. After completion of draining, the sump is refilled.

Manufacturers of cube ice machines have been able to minimize meltage during harvest using mechanical or pressurized

- air assist. Such methods rely on the formation of an ice bridge

between cubes to transfer the force between cubes and/or ensure that the pressurized air spreads out over the evaporator surface. Hoshizaki ice makers utilize the incoming potable water stream to assist in the harvest process by directing the incoming water behind the two evaporator plates. The water can provide a substantial amount of the heat requir ed for harvest. This approach also

pre

- chills the incoming water for the next batch of ice. Harvest times range from less than

1 minute up to 2 minutes.

Tube and cracked ice machines use a cutter at the base of the evaporator to chop the ice

into small pie ces as it falls.

3.3.1.3 Continuous Process

This section describes the “continuous” ice

- making process, including the refrigeration cycle, the evaporator designs, and the water supply systems currently used for continuous ice

making in the ice maker industry. As previously stated, the continuous process is used to make flake and nugget ice.

Figure 3.3

. 4 shows the typical refrigeration cycle used for continuous ice

- making. The cycle involves the basic vapor compression cycle. In contrast to the batch process, there is no

hot

- gas bypass line, because there is no need for a harvest cycle.

3

- 24Source: Manitowoc



### Figure 3.3

. 4 Typical Vapor Compression Refrigeration Cycle for Continuous Ice Makers

The continuous ice maker evaporator is typically a stainless steel cylinder surrounding an auger that moves ice upward toward an extruding head, as shown in Figure 3.3

. 5 . There are currently two variations of this design used across the industry. The most common design involves wrapping the copper refrigerant tube around the cylinder and brazing the assembly to allow heat to be exchanged between the water and the refrig erant. A variation of this design uses concentric stainless steel cylinders to form a spiral

- shaped space for refrigerant flow, as shown in Figure 3.3

. 5 (B). The auger drives the water and ice upward as the water freezes on the cold inner surface of the evaporator cylinder. The ice is pushed through an extrusion head. The extrusion head for a nugget ice maker has smaller holes than that of a flake ice maker, causing the flakes to be compressed into nuggets as they leave the evaporator. Some designs use a rotating cutter to help shape the ice. At this point the ice is directed to the storage bin, often via gravity drop through a vertical transfer tube. Flake

- and nugget

- t ype ice makers do not have a purging process to remove impurities, and the resulting ice retains the impurities, similar to ice production in residential refrigerators. However, some designs use an occasional flush to remove impurities that may remain in t he evaporator. Compressor Air or Water Condenser

Evaporator Thermostatic Expansion Valve Liquid

- line

Filter Drier

Suction

- Line Heat Exchanger

3

- 25(A) Copper

- tube continuous evaporator and auger motor (Ice

- O

- Matic) (B) Stainless

- steel continuous evaporator (Follett)

Sources: www.kirbysupply .com/Equipment/Ice_Machines/IOM_Flaker_Self_Contained_fp.htm www.follettice.com/images/image_bank/AAI_Evaporator.jpg



### Figure 3.3

. 5 Cross

- S ection of Continuous Evaporator and Auger

An alternative flaker system commercialized by Howe Corporation for capacities of 1,000 to 40,000 lb/ 24 hours uses an ice blade attached to a rotating shaft to score and wedge the

ice off the inside surface of the evaporator drum. In this design, the ice is sub

- cooled so that it is

22 °F when harvested, and it gravity

- drops to a storage bin.

3.3.2 Technology Options

The following sections discuss technology options for energy use reduction in ice

makers. DOE’s pr imary focus in developing its analysis for this rulemaking is the reduction of energy use. Reduction of potable water use and condenser water use are considered in the context of their relationship to energy use. Hence, the technology options do not includ e technology options associated with water use reduction.

Table 3.3

. 3 lists the technology options DOE identified for improving the efficiency of

automatic commercia l ice makers. The technology options are categorized by their associated component or system. Each technology option category and the options available for improving the component or system category are discussed below.

3

- 26

### Table 3.3

. 3 Technology Options for Automatic Commercial Ice Makers

Technology Options Batch

Ice Makers Continuous

Ice Makers Notes

Compressor Improved compressor efficiency 

## √ √

Part

- load operation 

## √ √

Condenser Increased surface area 

## √ √

Enhanced fin surfaces 

## √ √ Air

- cooled only

Increased air flow 

## √ √ Air

- cooled only

Increased water flow 

## √ √ Water

- cooled only

Brazed plate condenser 

## √ √ Water

- cooled only

Microchannel condenser 

## √ √ Air

- cooled only

Fans and Fan

Motor Higher efficiency c ondenser

fan s and fan motors 

## √ √ Air

- cooled only

Other Motors Improved auger motor efficiency 

## √ Continuous only

Improved pump motor efficiency 

## √ Batch only

Controls Smart Technologies 

## √ √

Evaporator Design options which reduce energy loss due to evaporator thermal cycling 

## √ Batch only

Design options which reduce harvest meltage or reduce

harvest time 

## √ Batch only

Increased surface area √ √

Insulation Improved insulating material

and/or thicker insulation around the evaporator compartment 

## √ √

Refrigeration

Line Larger diameter suction line 

## √ √ Primarily remote compressor equipment

Potable Water Reduced potable water flow 

## √ Batch only

Drain w ater t hermal e xchange 

## √ Batch only



### 3.3.2.1 Compressor

The c ompressor is the primary energy consuming component in an ice maker. Most ice makers use hermetic compressors in which the entire motor

- compressor assembly is hermetically

sealed in the welded steel shell. Hermetic r eciprocating compressors are the most co mmonly

used compressors in ice makers, although hermetic scroll compressors and semi

- hermetic reciprocating compressor s are also used for the highest capacity equipment. Semi

- hermetic compressors can be opened for repair of internal components

— they are typ ically very heavy,

because the flanges or sealed plates required for access must be thick and stiff to prevent refrigerant leaks during operation.

Almost all compressors are directly driven by two

- pole

, squirrel

- cage induction motors running at approximat ely 3,000 rpm on 60 Hz power. Four types of single

- phase induction motors have been used in ice maker compressors: capacitor start/capacitor run (CSCR),

3

- 27

### resistance start/induction run ( RSIR )

, capacitor start/induction run ( CSIR ), and resistance start/capac itor run (RSCR). Of the four motor types, the RSIR motor is the least efficient. Single

phase compressors in ice makers run on either 115 or 230 V power. Larger ice makers can also

use three

- phase motors.

Ice maker compressor capacities range from approximately 1,500 British thermal units ( Btu ) per hour to 40,000 Btu/hr. Three organizations have established conditions for rating the performance of compressors: Air

- conditioning, Heating, and Refrigeration In stitute ( AHRI )

, American Society of Heating, Refrigerating and Air

- Conditioning Engineers ( ASHRAE )

, and Comité Européen des Constructeurs de Matériel Frigorifique (CECOMAF). c 

### Table 3.3

. 4 shows the rating conditions of these organizations. The performance ratings are based on the premise that refrigerant tubing connecting system components are adiabatic ( i.e.

, that they undergo no thermal exchange with the environment ) and that they don’t impose refrigerant pressure drop

. Note that the actual operating conditions for compressors in ice makers under DOE energy test conditions can be different than these rating conditions. In fact, batch ice maker compressors
operate over a range of conditions during each ice

- making cycle.

Table 3.3

. 4 Compressor Rating Conditions ( o 

### F ( o 

### C))

Rating Condition ARI 540 MBP Type 1 ARI 540 MBP Type 2 ASHRAE MBP CECOMAF M

Evaporating Temperature * 20 (

- 6.7) 20 (

- 6.7) 20 (

- 6.7) 14 (

- 10)

Condensing Temperature ** 120 (48.9) 120 (48.9) 130 (54.4) 131 (55)

Suction Temperature 65 (18.3) 40 (4.4) 95 (35) 90 (32)

Condenser Outlet Temperature / Liquid Temp † 115 (46.1)

Subcooling † 0 (0) 0 (0) 0 (0)

MBP = m edium b ack p ressure

* Refrigerant dew point temperature corresponding to suction pressure

. ** Refrigerant dew point temperature corresponding to discharge pressure

. † Temperature difference between refrigerant bubble point temperature corresponding to dis charge pressure and temperature at condenser outlet

.



### DOE updated its c ompressor performance data during the NOPR analyses, based on

feedback from stakeholders

. Additional information on the compressor data can be found in chapter 5 of the final rule TSD.

Compressor efficiency is generally expressed as an energy efficiency ratio (EER)

— a ratio of refrigeration capacity to input power with units of Btu/watt

- hour. The EER of a compressor depends on the compressor operating conditions. Also, the peak EERs of compressors vary significantly over the range of compressor capacities used in ice makers, with higher EERs achieved by higher capacity compressors.

c CECOMAF i s a European appliance manufacturer trade association formed in 1958. It merged with EUROVENT in 1996 to become EUROVENT/CECOMAF. This organization is now called EUROVENT.

3

- 28

### Improved Compressor Efficiency

Conversion to high

- efficiency compressors is fairly straightforward for man ufacturers to implement as long as compressors of a suitable capacity are available and they do n o t present additional challenges such as larger physical size or greater noise levels. The potential for efficiency improvement through use of higher efficienc y compressors can be significant , but it depends on the specific circumstances for a given

, existing ice maker design. Chapter 5 discusses in greater depth the potential for improving efficiency through use of higher EER compressors

, including the updated compressor EER levels DOE applied to the final rule analysis

.

Part

- L oad Operation

Part

- load operation may be achieved using variable

- speed, variable

- capacity, or dual

capacity compressors, or by using two compressors. This option would reduce the energy us ed when the ice maker is producing ice at less than its full harvest capacity. During part

- load operation, the temperature differentials between the evaporating refrigerant and the freezing water and also between the condensing refrigerant and the cooling medium can be reduced, thus improving the compressor’s operating EER. Information demonstrating such approaches in ice

makers has not been made public, but variable

- speed compressors are being used in other refrigeration applications, such as residential r efrigerators.

3.3.2.2 Condenser

Increased Surface Area

Increasing the heat transfer surface area of air

- cooled condensers can be achieved by (a) increasing the width of the rows of tubes ; (b) adding more tube rows along the direction of air

flow ; or (c) adding mo re tube r ows across the direction of air flow. These measures can be limited by the geometry of the ice maker. Increasing tube width can also result in an increased refrigerant pressure drop across the condenser, so there is a tradeoff between improving he at transfer to reduce condensing temperature and increasing condenser inlet pressure due to the higher pressure drop. Similarly, adding tube rows can increase the air pressure drop through the condenser, so there is a tradeoff between improving heat transf er and increasing fan work.

The s urface area of water

- cooled ice makers can also be increased through the use of larger coaxial condenser designs.

Increasing condenser size also has implications associated with limits on equipment size. Increasing the size of an ice maker to allow for a condenser size increase may not be acceptable

because many ice makers have standard sizes. Such limitations may be less important for remote condensers, however. There is often greater potential for increase in the size of c ondensers of

water

- cooled ice makers, since water

- cooled and air

- cooled designs are often based on the same platform, and the air

- cooled condensers, condenser fan/motor assemblies, and space for air flow generally take up much more space than water

- cooled condensers.

One issue to consider when implementing condenser size increase is the added refrigerant charge associated with such a design change. The added refrigerant can add cost, but also can increase the challenges associated with refrigerant managemen t throughout both the freeze and

3

- 29

### harvest cycles of batch ice makers. While few IMH

- style batch ice makers have refrigerant receivers, an increase of condenser size can potentially lead to required use of receivers to en sure reliable operation and avoid dam age such as would occur with flooding of the compressor.

Enhanced Fin Surfaces (Air

- C ooled Models)

Enhanced fin surfaces can improve the performance of air

- cooled condensers by reducing

air

- side thermal resistance. Several such surfaces to improve performa nce have become available for use in fluid/air heat exchangers over the years. Among the most common types of fin surface enhancements are wavy fins and slit fins. Most air

- cooled ice makers already take advantage of

such enhanced fin surfaces.

Increased Air Flow (Air

- C ooled Models)

Increasing the air flow rate through the condenser will increase the heat removal rate and reduce condensing temperature. However, increasing air flow generally requires increase d fan input power, so there are limits t o the benefits of this approach. Fan size increase, if necessary to increase air flow rate, can also be limited by the geometry of the ice maker, making it difficult to

increase air flow in some models.

Increased Condenser Water Flow (Water

- C ooled Models)

Increasing the condenser water flow rate for water

- cooled ice makers will improve heat

transfer. However, this measure can significantly increase costs associated with water consumption in installations where condenser water is drained to a building’s wast e water system. Cube ice makers are covered by the current DOE standards , which limit condenser water

use. During the preliminary analysis, DOE provisionally determined that backsliding on condenser water standards to improve energy efficiency is not prohi bited, but that such a change

must consider the cost effectiveness of increased condenser water flow.

Brazed Plate Condenser (Water

- C ooled Models)

Brazed plate water

- cooled condensers can have much larger heat transfer areas for a given volume than the con centric

- tube condensers used in most ice makers.

Microchannel Condenser

Unlike a conventional coil tube with an attached plate fin, microchannel heat exchangers have a rectangular aluminum cross section containing several small channels (typically less th an 1mm across) through which refrigerant passes. Aluminum fins are brazed between the rectangular tubes. Microchannel can increase heat transfer while reducing airside pressure drop as compared with conventional coils. Microchannel heat exchangers generall y weigh less and hold significantly less refrigerant than conventional heat exchangers.

3

- 30

### 3.3.2.3 Higher Efficiency Condenser Fans and Fan Motors

Fans are used to draw or blow air through air

- cooled condensers. Condenser fans used in ice makers almost exclusively are axial design.

One source of inefficiency for axial fans lies in their tendency to throw air outward. The Pax GroupTM has developed a fan (PAX fan) that employs streamlined blades with patented geometrical shapes that reportedly provide better air flow direction and improved efficiency. Though a prototype has not yet been developed for ice makers, Pax is working to optimize the fans for other commercial refrigeration equipment

, including supermarket display cases and vending machines. Tests performed wit h the PAX fan have demonstrated a reduction in fan

motor power of 15 to 20 percent. 22 

### At this point, because the PAX fan is proprietary, the widespread use of the design is highly uncertain.

Most condenser fan motors used in products today have shaded pole induction designs

with efficiencies between 20 and 3 5 percent, or permanent split capacitor (PSC) designs, which reach efficiencies of 40 to 65 percent. Brushless DC motors can have efficiencies up to 80 percent, thus representing an option for further eff iciency improvement.

3.3.2.4 Improved Auger Motor Efficiency

Flake and nugget machines use an auger motor to drive the auger. CSIR, CSCR, and PSC motors are generally used in single

- phase designs. Higher efficiency permanent magnet motors

could also be used for au ger motors.

3.3.2.5 Improved Pump Motor Efficiency

Batch machines use a pump to transfer w ater from the sump to the top of the evaporator. Most ice maker pumps use shaded

- pole motors. However, PSC or brushless

- DC motors could also be used with ice maker pumps.

3.3.2.6 Demand Response Controls

Ice makers can be integrated into communication infrastructures that allow electric utility controllers to broadcast demand response events to customer electric meters, which then

com municate with the target appliance. The ice mak er must be configured with appropriate

hardware and software to enable two

- way communication. According to demand response

studies d 

### , this connectivity can be achieved using an electronic board for communications and an

ultrasonic sensor to read the ice le vel in the ice maker ’s storage bin

. The ice maker would then be able to communicate with a smart meter and limit ice production during a demand response

event. While this capability would alleviate peak loads experienced by electric utilities, it would

li kely not result in energy savings.

d “Demand Response Strategies Using Two

- Way Connectivity For Commercial Ice Machines.” DR 07.07 Report.

March 2009. Southern California Edison.

3

- 31

### 3.3.2.7 Evaporator

Manufacturers generally fabricate a limited number of evaporator sizes, which are used across the product lines matched to the extent possible with the appropriately sized compressors. This manufacturing strate gy contributes to variations of energy efficiency across the product line

because the evaporator/compressor combination cannot be optimized for each ice maker, resulting in some ice makers with undersized evaporators having oversized compressors to

achieve the target production rate. Such products may have correspondingly higher energy consumption. 23 

### This generally holds true for both batch and continuous ice makers.

Batch ice maker evaporator design requires finding a careful balance between the ice growth behavior, water flow rate over the evaporator, localized water distribution, materials selection, and harvest performance ( e.g. , successful ice detachment with little meltage). Evaporator design is a complex process not amenable to analysis, and developing a successful evaporator design requires many hours of laboratory testing. Manufacturers are very reluctant to change the evaporator design once a successful design has been developed.

Increased Evaporator Size

A larger evaporator size would allow for a hi gher evaporating temperature, thus allowing the compressor to operate more efficiently. It is important to note that increasing evaporator size

has implications associated with limits on equipment size. Increasing the size of an ice maker to

allow for an e vaporator size increase may not be acceptable because many ice makers have

standard sizes.

Design Options that Reduce Energy Loss due to Evaporator Thermal Cycling

Energy loss associated with thermal cycling depends on the thermal mass of the portions of the evaporator that must cool down during the freeze cycle and warm up during harvest, and with the temperature extremes of the thermal cycling. T hese losses can be reduced by reducing thermal mass and moderating the temperature extremes. Hence, both thermal mass and thermal resistance between the evaporator and the ice must be considered in alte r native designs to reduce this loss. Numerous patents suggest evaporator designs with reduced thermal mass or otherwise improved performance. However, performance data is publicly available for few of these designs, perhaps just for one: the evaporator design used in most Hoshizaki cube ice machines. DOE request ed information r egarding proprietary status of low

- thermal

- mass evaporator designs during the preliminary analysis phase

. Chapter 4 of the final rule TSD discusses DOE’s findings on the proprietary status of this technology

.

Design Options that Reduce Harvest Meltage or Reduce Harvest Time

Reduction of harvest meltage will improve efficiency by preserving more ice for harvest and by reducing harvest time. The reduction of harvest time improves efficiency by increasing harvest capacity because it reduces total cycle time associated with a given batch weight. H arvest meltage has been reduced in cube ice makers by en suring smooth evaporator surfaces, allowing buildup of ice bridges connecting the cubes, and employing harvest assist. Ensuring that su rfaces in contact with the ice are smooth prevents holdup of the cubes as they slide out of the evaporator cube cells. Ice bridges connecting cubes allow the entire ice sheet to be released at

3

- 32

### once, which can help en sure harvest of individual cubes that mi ght be held up as they release from the evaporator. Harvest assist involves active removal of the ice sheet from the evaporator. This is done in current ice maker designs using a mechanical method employing a rod that pushes on the ice sheet from the evapo rator side 24 

### (this method is used by Scotsman and Ice

- O

Matic ice makers) or by blowing air behind the ice sheet from the rear of the evaporator to create pressure to remove the ice 25 

### (this method is used by Manitowoc ice makers). The speed of

harvest and th e meltage rate are also affected by details of refrigerant compressor design, control of evaporator pressure and refrigerant flow during harvest, whether the ice maker has a remote condenser and/or compressor, and the use of incoming potable water to help in warming the ice. Additional design adjustments may be possible to further improve harvest to reduce harvest meltage or reduce harvest time, but information is not publicly available detailing such additional options and/or their effectiveness.

3.3.2.8 Improved or Thicker Insulation

Insulation can be used around the evaporator compartment and the sump to minimize the thermal load through the walls of these parts of the ice maker. Ice maker manufacturers use

vary ing amounts of insulation. Some manufacturers simply place a piece of batt insulation

between the ice

- making and compressor compartments, while other manufacture r s use polystyrene pieces or blown

- in polyurethane foam in the walls and/or base of the unit. Increased thickness and improved resistivity are both options to reduce the thermal load on the refrigeration system, reducing energy consum ption

.

3.3.2.9 Larger Diameter Suction Line (Remote Compressor Models)

Remote condenser units must be tested with at least 25 feet of interconnecting refrigerant piping between the condens ing unit and the ice

- making unit (A H RI Standard 810

- 200 7 , section 4.1.3). For ice makers with both remote condensers and remote compressors, this significantly increases the length of the suction line, resulting in additional pressure drop betw een the evaporator and the compressor suction inlet. Increasing the size of the suction line would reduce this pressure drop. However, consideration must be given to oil return, which can depend on maintaining relatively high suction velocities. If the suc tion line refrigerant velocity is too low, oil may not return reliably to the compressor, thus increasing the risk of oil loss and compressor

failure.

3.3.2.10 Reduced Potable Water Flow

Potable water use and energy use of ice makers are related. Some or all of the purge water drained from batch ice makers leaves the equipment near 32 ° F. This represents lost refrigeration that could potentially have been used to produce more ice. However, water is purged from batch ice makers to remove dissolved solids that enter w ith the potable water supply. Selecting excessively low potable water levels can lead to increased maintenance cost associated with an increased need for descaling operations, and, after the ice maker has operated for a number of cycles, the scale build

- up can reduce ice production and increase energy use.

3

- 33

### 3.3.2.11 Drain Water Thermal Exchange

Batch ice makers can benefit from drain water thermal exchange that cools potable water supply entering the sump, thereby reducing the energy required to freeze the water. In most batch ice makers, the sump pump circulates water over the evaporator throughout the freeze cycle, and only drains and refills during the harvest. At the end of the freeze cycle, the water in the sump is just above freezing temperature, so this water c ould be drained to a secondary sump or reservoir. The water inlet tube would pass through this secondary sump to facilitate thermal exchange between the water entering the sump at supply water temperature and the recently drained water near freezing temper ature. Efficiency gains are limited by the fact that purge water amounts have

been reduced to about one

- third of the supply water quantity for many ice makers.

During the NOPR phase of the rulemaking , DOE performed testing of a drain water heat exchange r with an ice maker to assess its performance. The results of this testing are described in Chapter 5 of the final rule TSD.

3.3.3 Energy Use Data

DOE gathered data on the energy use of automatic commercial ice makers currently available in the marketplace. DOE cr eated a database of the current models by surveying manufacturers’ websites. The data provide an overview of the energy use of each equipment class covered by this rulemaking according to the current DOE test procedure, which references

A H RI Standard 810

- 2 00 7

. Figure 3.3

. 6 through Figure 3.3

. 15 show energy consumption as a

function of harvest capacity within each equipment class. The current DOE standards and

ENERGY STAR criteria (when applicable), as well as the trial standard levels (TSLs) DOE considered in the rulemaking , are shown in the figures. Where appropriate, the figures show separately the ratings for units with proprietary technology (low

- thermal mass or tube evaporators), and the ratings for units that are 22 inches wide. The “TSL3 rep units” show the TSL3 efficiency levels for the capacities and classes that DOE directly analyzed.

For continuous ice makers, the energy use shown in these plots has been adjusted based

on ice hardness , as required by the DOE test procedure. For ice makers for which ice hardness

data is not available, DOE assumed that ice hardness i s 0.7 for flake ice makers and 0.85 for
nugget ice makers

. The figures showing energy use data for flake and nugget machines indicate

the baseline efficiency levels that DOE adopted for the rulemaking analyses , since there are currently no DOE standards for this equipment.

3

- 34

### Figure 3.3

. 6 Energy Consumption vs. Harvest Rate (Air

- Cooled Cube Ice

- Making Heads) - 2 4 6 8 10 12

0 200 400 600 800 1000 1200 1400 1600 1800Energy Consumption (kWh/100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Current Standard Current Market 22 Inch Wide
TSL3 Rep Units TSL1 TSL4

TSL5 Max Tech TSL3 Max NPV 7% Proprietary Technology Power (ENERGY STAR)

3

- 35

### Figure 3.3

. 7 Energy Consumption vs. Harvest Rate (Water

- Cooled Batch Ice

- Making Heads) - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

0 500 1000 1500 2000 2500 3000Energy Consumption (kWh/100 lbs ice)

Harvest rate (lbs ice / 24 hours) Current Standard Current Market 22 Inch Wide
TSL3 Rep Units TSL1 TSL4

TSL5 Max Tech TSL2 TSL3 Max NPV 7% Proprietary Technology

3

- 36

### Figure 3.3

. 8 Energy Consumption vs. Harvest Rate (Air

- Cooled Batch Remote Condensing

Units [Non

- Remote Compressor] ) - 1 2 3 4 5 6 7 8 9

0 500 1000 1500 2000 2500 3000 3500 4000Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Current Standard Current Market 22 Inch Wide ENERGY STAR

TSL3 Rep Units TSL1 TSL4 TSL5 Max Tech

TSL3 Max NPV 7% Proprietary Technology

3

- 37

### Figure 3.3

. 9 Energy Consumption vs. Harvest Rate (Air

- Cooled Batch Remote Condensing

Units [Remote Compressor] ) - 1 2 3 4 5 6 7 8 9

0 500 1000 1500 2000 2500 3000 3500 4000Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Current Standard Current Market 22 Inch Wide

ENERGY STAR TSL3 Max NPV 7% TSL1

TSL4 TSL5 Max Tech Proprietary Technology

3

- 38

### Figure 3.3

. 10 Energy Consumption vs. Harvest Rate (Air

- Cooled Batch Self

- Contained Units) - 2 4 6 8 10 12 14 16 18

0 50 100 150 200 250 300 350 400 450 500Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Current Standard Current Market ENERGY STAR

TSL3 Rep Units TSL1 TSL4

TSL5 Max Tech TSL3 Max NPV 7% Proprietary Technology

3

- 39

### Figure 3.3

. 11 Energy Consumption vs. Harvest Rate (Water

- Cooled Batch Self

- Contained Units) - 2 4 6 8 10 12

0 50 100 150 200 250 300 350 400 450 500Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Current Standard Current Market TSL3 Rep Unit TSL3 Max NPV 7%

TSL1 TSL4 TSL5 Max Tech

3

- 40

### Figure 3.3

. 12 Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous Ice

- Making Heads)

Note: Energy consumption in this this figure is adjust ed for ice hardness, as specif ied by the

DOE Test Procedure. 0 2 4 6 8 10 12

0 500 1000 1500 2000 2500Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Baseline Current Market 22 Inch Units ENERGY STAR

TSL3 Rep Units TSL1 TSL2 TSL3 Max NPV 7%

TSL4 TSL5 Max Tech

3

- 41

### Figure 3.3

. 13 Energy Consumption vs. Harvest Rate (Water

- Cooled Continuo us Ice

Making Heads)

Note: Energy consumption in this in this figure is adjust ed for ice hardness, as specified by the

DOE Test Procedure. 0 1 2 3 4 5 6 7 8 9

0 500 1000 1500 2000 2500Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Baseline Current Market 22 Inch Wide

TSL3 Rep Units TSL1 TSL2

TSL3 Max NPV 7% TSL4 TSL5 Max Tech

3

- 42

### Figure 3.3

. 14 Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous Remote Condensing Units with Non

- Remote Compressors )

Note: Energy consumption in this in this figure is adjust ed for ice hardness, as specified by the

DOE Test Procedure. - 2 4 6 8 10 12

0 500 1000 1500 2000 2500Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Baseline Current Market 22 Inch Wide ENERGY STAR TSL3 Rep Unit

TSL3 Max NPV 7% TSL1 TSL4 TSL5 Max Tech

3

- 43

### Figure 3.3

. 15 : Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous R emote Condensing Units with Remote Compressors)

Note: Energy consumption in this in this figure is adjust ed for ice hardness, as specified by the

DOE Test Procedure. - 2 4 6 8 10 12

0 500 1000 1500 2000 2500Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Baseline Current Market 22 Inch Wide ENERGY STAR

TSL3 Max NPV 7% TSL1 TSL4 TSL5 Max Tech

3

- 44

### Figure 3.3

. 16 Energy Consumption vs. Harvest Rate (Air

- Cooled Continuous Self

Contained Units)

Note: Energy consumption in this figure is adjust ed for ice hardness, as specified by the DOE

Test Procedure. 0 2 4 6 8 10 12 14 16

0 100 200 300 400 500 600 700 800 900 1000Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Baseline Current Market ENERGY STAR Max NPV 7% Rep Unit TSL3 Max NPV 7% TSL1

TSL4 TSL5 Max Tech TSL2

3

- 45

### Figure 3.3

. 17 Energy Consumption vs. Harvest Rate (Water

- Cooled Continuous Self

Contained Units)

Note: Energy consumption in this figure is adjust ed for ice hardness, as specified by the DOE

Test Procedure.

3.3.4 Condenser Water Consumption Data

DOE’s updated database of products includes information on condenser water consumption for the four equipment classes that are water

- cooled: IMH

- W

- B, SCU

- W

- B, IMH

W

- C, SCU

- W

- C. Figure 3.3

. 17 through Figure 3.3

. 20 contain the condenser water consumption data for these equipment classes.

For the two batch

- type equipment classes, DOE is maintain ing the condenser water consumption standard at the current level through harvest capacity up to 2,500 lb ice/24 hours, and establishing constant standard levels for harvest capacity rates from 2,500 to 4,000 lb ice/24

hours

. - 1 2 3 4 5 6 7 8 9 10

0 100 200 300 400 500 600 700 800 900 1000Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours) Baseline Current Market TSL3 Max NPV 7% TSL1 TSL4 TSL5 Max Tech

3

- 46

### Figure 3.3

. 18 : Condenser Water Consumption vs Harvest Rate (Water

- Cooled Ice

- Making Head Units)

3

- 47

### Figure 3.3

. 19 : Condenser Water C onsumption vs Harvest Rate (Water

- Cooled Batch

- Type

Self

- Contained Units)

For the two continuous

- type equipment classes, there is no existing standard for condenser water consumption. DOE based the condenser water standards for these classes on the respective batch

standards

, but lowered the se standards to reflect the existing market for continuous type units. In the NOPR, DOE proposed setting continuous ice maker condenser water use standards at 20%

below th ose of batch ice makers , citing the reduce d heat rejection required for continuous units, because continuous ice makers do not have a harvest event that adds to heat rejection

. In the final rule, DOE has revised th e standard for IMH

- W continuous ice makers to be 10% below that of

the IMH

- W batch c lass es , as shown in Figure 3.3

. 19

.

3

- 48

### Figure 3.3

. 20 : Condenser Water Consumption vs Harvest Rate (Water

- Cooled Continuo us Ice

- Making Head Units ) 0 50 100 150 200 250

0 500 1000 1500 2000 2500 3000 3500 4000Condenser Water Usage (gallons / 100 lb ice)

Harvest Rate (lb ice / 24 hours) Batch Standard 10% Below Batch Standard Proposed Continuous Standard Market Data

3

- 49

### Figure 3.3

. 21 : Condenser Water Consumption vs Harvest Rate (Water

- Cooled Continuous

Self

- Contained Units)

The current and new condenser water standard equations are described in Table 3.3

. 5

below.

Table 3.3

. 5 : Comparison of Current and Proposed Condenser Water Standards Equipment Class Current Condenser

Water Standard New Condenser Water

Standard

IMH

- W

- B 200

– 0.022 * H 200

– 0.022 * H

SCU

- W

- B 191

– 0.0315 * H 191

– 0.0315 * H

IMH

- W

- C No current standard 1 80

– 0.0 198 * H

SCU

- W

- C No current standard 153

– 0.0252 * H

3

- 50

### REFERENCES

1

. Air

- Conditioning, Heating, and Refrigeration Institute . (Last accessed November 30, 2014.) < http://www.ari.org/site/626/About

- Us/Product

- Sections/Automatic

- Commercial

Ice

- Makers

- Ice

- Storage

- Bins >

2

. Koeller J., and H. Hoffman. A R eport on Potential Best Management Practices . 2008. Prepared by Koeller and Company for the California Urban Water Conservation Council, Sacramento, CA.

3

. Enodis, PLC

. Excerpts from 20

- F SEC Filing, filed by ENODIS PLC on 12/19/2003.

Information found on

- line via a Google search, at the following w eb address (last accessed November 30, 2014) : < http://sec.edgar

- online.com/enodi s

- plc/20

- f

- annual

- and

- transition

report

- foreign

- private

- issuer/2003/12/19/section6.aspx >

4

. Manitow o c Company

. The Manitowoc Company signs definitive agreement to divest

Enodis ice business (press release)

. March 30, 2009. Manitowoc, WI.

< www.manitowocfsusa.com/?xhtml=xhtml/eno/us/en/pressrelease/2009_03_30_manitowo c_company_divest_enodis_ice_bu siness.html&xsl=pressrelease.xsl >

5

. Scotsman Industries

. Warburg Pincus Completes Acquisition of Enodis Global Ice Machine Business from the Manitowoc Company: New Holding Company to be Named Scotsman Industries, Inc. (press release). May 18, 2009. Vernon Hills, IL.

< www.warburgpincus.com/PDF/Enodis%20PRESS%20RELEASE%205

- 15

- 09%20final.pdf >

6

. ALI Group, Ali Group announces the acquisition of Scotsman Industries

, < http://www.aligroup.it/news/ali

- group

- announces

- the

- acquisition

- of

- scotsman

industries/ >

7

. U.S. Small Busines s Administration. Small Business Size Standards Matched to North American Industry Classification System . 2006. (Last accessed June 25, 2007.)

< www.sba.gov /gopher/Financial

- Assistance/Size

- 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.

8

. Standards Australia/Standard s New Zealand. Performance of commercial ice makers and ice storage bins . Part 3: Minimum energy performance standard (MEPS) requirements.

2004. AS/NZS 4865:3:2008.

3

- 51

### 9

. U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. FEMP

Covered Product Category : Air

- Cooled Ice Machines . (Last accessed November 30, 2014 .)

< www1.eere.energy.gov/femp/technologies/eep_ice_makers.html >

10

. U.S. Department of Energy

— Office of Energy Efficiency and Renewable Energy. FEMP

, Covered Product Categories : Water

- Cooled Ice Machines . The ice maker specifications

are dated are dated August 2014 . (Last accessed November 30 , 201 4 .)

< http://energy.gov/eere/femp/covered

- product

- category

- water

- cooled

- ice

- machines >

11

. Consortium for Energy Efficiency. Commercial Kitchens Initiative . (Last accessed

November 30 , 201 4 .) < http://www.cee1.org/content/cee

- program

- resources >

12

. EnergySmart Grocer. EnergySmart Grocer Incentive Worksheet . 201 4 . (Last accessed November 30, 2014 .) < http://www.energysmartgrocer.org/ca/documents/PG&E

- ESG

IncentiveWorksheet.pdf >

13

. New York State Energy Research and Development Agency. Pre

- Qualified Commercial Refrigeration Equipment

. 2014. (Last accessed November 30, 2014 .)

< http://www.nyserda.ny.gov/Energy

- Effic iency

- and

- Renewable

- Programs/Commercial

and

- Industrial/CI

- Programs/Existing

- Facilities

- Program/Pre

- Qualified

- Incentives.aspx >

14

. North American Association of Food Equipment Manufacturers. 2010 Size and Shape of the Industry . 2010. Chicago, IL.

15

. North A merican Association of Food Equipment Manufacturers. 2008 Size and Shape of the Industry . 2008. Chicago, IL.

16

. North American Association of Food Equipment Manufacturers. 2006 Size and Shape of the Industry . 2006. Chicago, IL

17

. Arthur D. Little, Inc. Ene rgy Savings for Commercial Refrigeration . Final Report. June, 1996. Submitted to the U.S. Department of Energy’s Energy Efficiency and Renewable Energy Building Technologies Program. Washington, D.C.

18

. California Energy Commission. Update of Appliance Eff iciency Regulations . 2004. Sacramento, CA.

19

. Fernstrom, G. B. Analysis of Standards Options For Commercial Packaged Refrigerators, Freezers, Refrigerator

- Freezers and Ice Makers: Codes and Standards Enhancement

Initiative For PY2004: Title 20 Standards De velopment . 2004. Prepared by the American Council for an Energy

- Efficient Economy for Pacific Gas & Electric Company, San Francisco, CA.

3

- 52

### 20

. 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.

< http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/commercial_refrig_rep ort_10

- 09.pdf >

21

. AHRI Ice Maker Shipment Data, submitted to DOE by Karim Amrane, May 2011.

22

. Penney, K. Personal communication. PAX Scientific, Inc., San Rafael, CA. Email to Heather Lisle, Navigant Consulting, Inc. July 2011.

23

. Biel, J. Personal communication

. Scotsman Industries, Inc., Vernon Hills, I L. Telephone call with Heather Lisle, Navigant Consulting, Inc. April 2008.

24

. Van Steenburgh, Jr., Leon R. Automatic Ice Cube Making Apparatus . U.S. Patent No. 4,341,087, July 27, 1982.

25

. Shedivy, Scott J. et al. Ice Machine with Assisted Harvest . U.S. Patent No. 6,681,580 B2, January 27, 2004.

3

- 53

### CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION

................................ ................................ ................................ .............. 4

- 1

4.2 SCREENED

- OUT TECHNOLGIES

................................ ................................ .................. 4

- 2

4.2.1 Compressor Part Load Operation

................................ ................................ ............ 4

- 2

4.2.2 Enhanced Fin Surfaces

................................ ................................ ............................ 4

- 2

4.2.3 Brazed Plate Condenser for Water

- Cooled Condensers

................................ ......... 4

- 2

4.2.4 Technology Options to Reduce Evaporator Thermal Cycling

................................ 4

- 3

4.2.5 Technology Options that Reduce Harvest Meltage or Reduc e Harvest Time

........ 4

- 3

4.2.6 Improved or Thicker Insulation

................................ ................................ .............. 4

- 3

4.2.7 Larger Diameter Suction Line (Remote Compressor Equipment)

......................... 4

- 3

4.2.8 Demand Response Controls

................................ ................................ .................... 4

- 3

4.3 REMAINING TECHNOLOG IES

................................ ................................ ...................... 4

- 4

LIST OF TABLES

Table 4.2.1 Screening Justification

................................ ................................ .............................. 4

- 4

4

- i

### CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter details the screening analysis that the U.S. Department of Energy (DOE)

conducted in support of the ongoing energy conservation standards rulemaking for automatic

commercial ice

- mak ing (ACIM) equipment

.

In the market and technology assessment

— chapter 3 of the final rule technical support document (TSD)

— DOE present s an initial list of technolog ies that can improve the energy efficiency of automatic commercial ice makers

. The purpose of the screening analysis is to evaluate the technologies that improve equipment efficiency to determine which technologies to

consider further and which to screen out. DOE consulted with a range of parties, including

ACIM industry representatives , technical experts, and others

, to develop a list of technologies for

consideration. DOE evaluated t he technolog ies pursuant to the criteria set out in t he Energy Policy and Conservation Act ( 42 U.S.C

. 6311

– 6317 ; EPCA), as amended

.

EPCA establishes criteria for prescribing new or amended standards designed to achieve

the maximum improvement in energy efficiency

. Further, EPCA directs the Secretar y of Energy to determine whether a standard is technologically feasible and economically justified. (42

U.S.C. 6295(o)(2)(A)

– (B) and 631 3(d)(4) )

A ppendix A to subpart C of 10 CFR p art 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 equipment energy conservation standards. These procedures elaborate on the

statut ory criteria provided in 42 U.S.C

. 6295(o) and

, in part

, eliminate problematic technologies early in the process of prescribing or amending an energy conservation standard

. In particular

, sections 4(b)(4) and 5(b) of the Process Rule g uid e DOE in determin i ng whether to eliminate from consideration any technology that presents unacceptable problems with respect to the following criteria

. To fulfill statutory criteria for prescribing energy standards for automatic

commercial ice makers as established in 42 U.S.C. 6295(o) and 631 3(d)(4), DOE applied the provisions in the Process Rule in its screening analysis for this rulemaking.

Technological feasibility. Technologies incorporated in commercial equipment or in working prototypes will be considered technolog ically 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 a technology is determined

to have a significant advers e impact on the utility of the equipment to significant subgroups of customers, or result in the unavailability of any covered equipment type with performance characteristics (including reliability), features, sizes, capacities, and volumes that are

4

- 1

### substa ntially the same as equipment generally available in the United States at the time, it will

not be considered further.

Adverse impacts on health or safety. If it is determined that a technology will have significant adverse impacts on health or safety, it will not be considered further.

In sum, if DOE determines that a technology, or a combination of technologies, has unacceptable impacts on the policies stated in section 5(b) of the Process Rule, it will be

eliminated from consideration. If a technology f ails to meet one or more of the four criteria, it

will be screened out. Section 4.2 document s the reasons for eliminating the technolog ies that

were screened out

.

4.2 S CREENED

- O UT T ECHNOLG IES

This section describes the techn olog ies that DOE eliminated for failure to meet one of the following four criteria : ( 1) technological feasibility; ( 2) practicability to manufacture, install, and service; ( 3) impacts on equipment u tility or equipment availability ; and ( 4) adverse impacts on

health or safety. The discussion also points out which technologies have not be en analyzed in the engineering and downstream analyses for other reasons , including that they don’t save energy,

the ir energy savings are not captured in the test procedure, or they are proprietary.

4.2.1 Compressor Part Load Operation

DOE has not changed its initial position on this technology option, first stated in the preliminary analysis. While there may be benefit s associated with operation of an ice maker

compress or at part load when ice demand is moderate or low, DOE is not aware of any commercialized product or prototype that has been constructed and tested to illustrate the

benefits and/or reliability of this te chnology option. In addition, the DOE test procedure does not consider the benefits of part load operation. Hence, this technology option must be screened out based on the technical feasibility criterion

.

4.2.2 Enhanced Fin Surfaces

DOE has not changed its initi al position on this technology option, first stated in the preliminary analysis. DOE is not aware of any enhanced fin surfaces available for air

- cooled condensers that would significantly improve heat transfer and efficiency in automatic

commercial ice mak ers that are not already used extensively in the industry for air

- cooled models. Hence, DOE is screening out this option because it is already in use and does not represent an option for improve d efficiency.

4.2.3 Brazed Plate Condenser for Water

- Cooled Condensers

During the NOPR analyses, DOE concluded that use of brazed plate condensers does not represent a design option that would save energy in i ce makers because most ice makers using

water

- cooled condensers have space for condenser surface area grow th rather than requiring use of more expensive condenser technology such as brazed plate

. DOE instead considered increases in condenser surface area based on testing conducted during the NOPR phase; t hese analyses are

4

- 2

### described in c hapter 5 of the NOPR TSD

. DOE has not altered this position during the final rule analysis.

4.2.4 Technology Options to Reduce Evaporator Thermal Cycling

During the NOPR analyses, DOE determined that the one technology used by commercially available ice makers to reduce thermal mass is proprietary. The evaporators used by Hoshizaki America, Inc. contain proprietary elements that would make it difficult for others to replicate the design. Hence, DOE is screening out this option bec ause of its proprietary status.

4.2.5 Technology O ptions that R educe H arvest M eltage or R educe H arvest T ime

DOE has not changed its initial position on this technology option, first stated in the preliminary analysis. M anufacturers have implemented design changes to reduce harvest meltage

and reduce harvest time in b atch ice makers. DOE has been unable to identif y a dditional design changes that are not already in use in the industry t o further improve harvest cycle efficiency

. Hence, this technology option is screened out because it does not save additional energy.

4.2.6 I mproved or Thicker Insulation

DOE has not changed its initial position on this technology op t ion, first stated in the preliminary analysis. Insulation used in ice makers may reduce thermal loss, but, as indicated by stakeholders in comments (see chapter 2 of the preliminary analysis TSD a 

### ), it is primarily used to prevent condensation of water on cold surfaces. A small amount of additional thermal loss could

be prevented through the use of improved or thicker insulation. However, the impact of this

efficienc y improvement is small enough to be nearly immeasurable . Hence, this technology option has not been considered in the engineering and downstream analyses

.

4.2.7 Larger D iameter S uction L ine (Remote Compressor Equipment)

DOE has not changed its initial position on this technology option, first stated in the preliminary analysis. Increasing the suction line diameter could be considered to reduce suction line pressure drop for remote condenser equipment with remote compressors. However, the

reduce d suction vapor ve locity associated with the approach could degrade oil return

effectiveness. Remote ice maker line sets can be installed in the field so that suction line refrigerant runs up, down, or horizontally to the compressor ; hence

, they are conservatively sized

to provide adequate oil return for a wide range of installation conditions. DOE has not

considered increase in suction line size because of reliability concerns associated with potential

oil hold

- up and compressor failure associated with larger

- diameter line sets.

4.2.8 Demand Response Controls

While there may be energy demand benefits associated with use of “smart technologies” in ice makers in that they reduce energy demand ( e.g., shift the refrigeration system operation to a time of utility lower demand), DOE is not aware of any commercialized products or prototypes

a www.regulations.gov/#!documentDetail;D=EERE

- 2010

- BT

- STD

- 0037

- 0025

4

- 3

### that also demonstra te improved energy efficiency i n automatic commercial ice makers. Demand savings alone do not impact energy efficiency as measured in the test procedure , and hence,

DOE cannot consid er this technolog y in the analysis

.

Table 4.2.1 summarizes the technologies that were screened out for the final rule analysis or were not considered in the engineering analysis for other reasons.

Table 4.2

. 1 Screening Justification

Design Option EPCA Criteria for Screening Not Considered in the A nalysis

for O ther R easonsTechnologicalFeasibilityPracticability toManufacture, Install,and ServiceAdverse Impacts onProduct UtilityAdverse Impacts onHealth and SafetyNoEnergySavings orSavings notMeasurableTestProcedureEfficiencyMetricDoesNotCaptureSavingsSole-SourceProprietaryTechnology

Compressor Part Load Operation X X

Enhanced Fin Surfaces X

Brazed Plate Condenser X Technology Options to Reduce Evaporator Thermal Cycling X

Technology Options Which Reduce Harvest Meltage or Reduce Harvest

Time X

Improved or Thicker Insulation X Larger Diameter Suction Line X

Smart Controls X X



### 4.3 R EMAINING TECHNOLOGIES

After eliminating those technologies that are proprietary, have no significant effect

, or do

not reduce energy use as measured by the test procedure, and screening out those technologies that do not meet the requirements of sections 4(a)(4) and 5(b) of the Process Rule, DOE

consider ed the following technologies in the final rule analysis :

• i mproved compressor efficiency

• i ncreased condenser surface area

• i ncreased condenser air flow

• increased condenser water flow

• h igher efficiency condenser fans and fan motors

• i mproved auger motor efficiency

• i mproved pump motor efficiency

• i ncreased evaporator s urface a rea

• reduced portable water flow

• use of drain water thermal exchange

4

- 4

### CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION

................................ ................................ ................................ ........... 5

- 1

5.2 EQUIPMENT CLASSES AN ALYZED

................................ ................................ .......... 5

- 1

5.3 METHODOLOGY OVERVIE W

................................ ................................ .................... 5

- 3

5.4 EFFICIENCY LEVELS

................................ ................................ ................................ ... 5

- 4

5.4.1 Batch Ice Makers

................................ ................................ ................................ . 5

- 4

5.4.1.1 Baseline: Existing DOE Standards

................................ .......................... 5

- 4

5.4.1.2 Maximum Available Efficiency Levels

................................ ................... 5

- 4

5.4.1.3 Incremental Efficiency Levels

................................ ................................ . 5

- 5

5.4.1.4 Maximum Technology Level

................................ ................................ ... 5

- 6

5.4.2 Continuous Ice Makers

................................ ................................ ........................ 5

- 7

5.4.2.1 Energy Use Metric Incorporating Ice Hardness

................................ ....... 5

- 7

5.4.2.2 Baseline Efficiency Levels

................................ ................................ ...... 5

- 8

5.4.2.3 Maximum Available Efficiency Levels

................................ ................... 5

- 9

5.4.2.4 I ncremental Efficiency Levels

................................ ............................... 5

- 10

5.4.2.5 Maximum Technology Level

................................ ................................ . 5

- 10

5.5 DATA COLLECTION

................................ ................................ ................................ .. 5

- 11

5.5.1 Component Vendor Data

................................ ................................ ................... 5

- 11

5.5.2 Reverse Engineering

................................ ................................ .......................... 5

- 11

5.5.2.1 Selection of Products for Reverse Engineering and Inspections

........... 5

- 12

5.5.2.2 Collection of Data to S upport Energy Use Analysis

............................. 5

- 14

5.5.3 Manufacturer Interviews

................................ ................................ .................... 5

- 14

5.5.4 Energy Testing

................................ ................................ ................................ ... 5

- 15

5.6 MANUFACTURING COST M ODELING

................................ ................................ ... 5

- 15

5.6.1 Generation of Bills of Materials

................................ ................................ ........ 5

- 15

5.6.2 Cost Structure of the Spreadsheet Models

................................ ......................... 5

- 16

5.6.3 Cost Model and Definitions

................................ ................................ ............... 5

- 16

5.6.3.1 Cost Definiti ons

................................ ................................ ..................... 5

- 17

5.6.4 Cost Model Assumptions Overview

................................ ................................ .. 5

- 17

5.6.4.1 Fabrication Estimates

................................ ................................ ............. 5

- 18

5.6.4.2 Factory Parameters Assumptions

................................ ........................... 5

- 18

5.6.4.3 Materia l Cost Assumptions

................................ ................................ .... 5

- 19

5.6.5 Manufacturing Production Cost

................................ ................................ ......... 5

- 19

5.6.6 Incremental Cost Estimates

................................ ................................ ................ 5

- 20

5.6.6.1 G&A and Profit

................................ ................................ ...................... 5

- 21

5.7 MANUFACTURER MARKUP

................................ ................................ .................... 5

- 21

5.8 ENERGY USE CALCULATI ONS

................................ ................................ ............... 5

- 21

5.8.1 Energy Use Calculation Overview

................................ ................................ ..... 5

- 21

5.8.2 Application of Design Options and Calibrations

................................ ............... 5

- 22

5.9 DESIGN OPTIONS

................................ ................................ ................................ ....... 5

- 23

5.9.1 Design Options That Were Not Considered for Further Analysis

..................... 5

- 24

5.9.1.1 Increased Condenser Water Flow

................................ .......................... 5

- 24

5.9.2 Improved Compressor Efficiency

................................ ................................ ...... 5

- 26

5

- i

### 5.9.2.1 Compressor Cost Data

................................ ................................ ........... 5

- 26

5.9.3 Increased Air

- Coole d Condenser Surface Area

................................ ................. 5

- 28

5.9.4 Increased Water

- Cooled Condenser Surface Area

................................ ............ 5

- 29

5.9.5 Evaporator Size Increase

................................ ................................ .................... 5

- 29

5.9.6 Design Options that Require Chassis Size Increases

................................ ......... 5

- 30

5.9.7 Condenser Fan Motors

................................ ................................ ....................... 5

- 30

5.9.8 Auger Motors

................................ ................................ ................................ ..... 5

- 31

5.9.9 Pump Motors

................................ ................................ ................................ ...... 5

- 32

5.9.10 Drain Water Heat Exchanger

................................ ................................ ............. 5

- 32

5.9.11 Batch Water Fill and Reduced Potable Water Flow for Batch

- Type Ice Makers

.. 5

- 33

5.10 ENGINEERING ANALYSIS RESULTS

................................ ................................ ..... 5

- 33

5.10.1 Overview

................................ ................................ ................................ ............ 5

- 33

5.10.2 Design Option Analyses

................................ ................................ .................... 5

- 36

5.10.3 Design Option Calibrations

................................ ................................ ................ 5

- 42

REFERENCES

................................ ................................ ................................ .......................... 5

- 59

LIST OF TABLES

Table 5.2.1 Batch Equipment Classes Analyzed in the Engineering Analysis

............................ 5

- 2 Table 5.2.2 Continuous Equipment Classes Analyzed in the Engineering Analysis

................... 5

- 2

Table 5.4.1 Existing DOE Standards for Batch Ice Makers

................................ ........................ 5

- 4

Table 5.4.2 Batch Ice Maker Maximum Available Levels

................................ .......................... 5

- 5 Table 5.4.3 Incremental Efficiency Levels for Batch Ice Maker Equipment Classes

................. 5

- 6 Table 5.4.4 Maximum Technology Levels for Batch Ice Maker Equipment Classes

................. 5

- 7 Table 5.4.5 Baseline Efficiency Levels for Continuous Ice Maker Equipment Classes

............. 5

- 9 Table 5.4.6 Continuous Ice Maker Maximum Available Efficiency Levels

............................... 5

- 9

Table 5.4.7 Incremental Effic iency Levels for Continuous Ice Maker Equipment Classes

...... 5

- 10 Table 5.4.8 Maximum Technology Levels for Directly

- Analyzed Continuous Ice Maker Equipment Classes

................................ ................................ ............................. 5

- 10 Table 5.5.1 Component Vendors Contacted by DOE during Engineering Analysis

................. 5

- 11 Table 5.5.2 Selected Units for Reverse Engineering and Energy Testing during Preliminary Analysis

................................ ................................ ................................ .............. 5

- 12 Table 5.5.3: Selected Units for Reverse Engineering and Energy Testing during NOPR Analysis

................................ ................................ ................................ ............................ 5

- 13 Table 5.5.4: Selected Units for Reverse Engineering and Energy Testing during Final Rule Analysis

................................ ................................ ................................ .............. 5

- 14

Table 5.6.1 Cost Model In

- House Manufacturing Operation Assumptions

.............................. 5

- 18 Table 5.6.2 ACIM Factory Parameter Assumptions

................................ ................................ .. 5

- 19

Table 5 .8.1 IMH

- A

- Small

- B Design Options

................................ ................................ ............ 5

- 22 Table 5.9.1 Design Options by Equipment Class

................................ ................................ ...... 5

- 24

Table 5.9.2 Condenser Water Flow Test Data for a Water

- Cooled IMH Batch Unit

................ 5

- 25

Table 5.9.3 Condenser Water Flow Test Data for a Water

- Cooled SCU Continuous Unit

...... 5

- 25

Table 5.9.4 Condenser Water Flow Test Data for a Water

- Cooled IMH Continuous Unit

...... 5

- 25

Table 5.9.5 AHR I 540 Medium Temperature Compressor Rating Test Conditions

................. 5

- 26

5

- ii

### Table 5.9.6 Batch Ice Maker Evaluation of Harvest Cycle Dependence on Condensing Temperature

................................ ................................ ................................ ....... 5

- 28 Table 5.9.7 Description of Ice Making Head Size Increases in the Engineering Analysis

....... 5

- 30

Table 5.9.8 Motor Cost Information Use for Final Rule Engineering Analysis

........................ 5

- 31

Table 5.9.9 Energy Consumption Tests with a Drain Water Heat Exchanger

.......................... 5

- 32

Table 5.10.1 Batch Ice Maker Baseline Characteristics

................................ ............................ 5

- 33 Table 5.10.2 Incremental Manufacturer Selling Price Results for Batch Ice makers

................ 5

- 34

Table 5.10.3 Continuous Ice Maker Baseline Characteristics

................................ ................... 5

- 35 Table 5.10.4 Incremental Manufacturer Selling Price Results for Continuous Ice Makers

...... 5

- 35 Table 5.10.5 Directly Analyzed Equi pment Classes Used to Develop Standards for Secondary

Classes

................................ ................................ ................................ ................ 5

- 35 Table 5.10.6 Manufacturing Cost and Energy Consumption Data for A ir

- Cooled IMH Batch Ice Machines with Capacities Less Than 450 lb/24 hours

................................ ....... 5

- 36

Table 5.10.7 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes)

........ 5

- 36 Table 5.10.8 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Larger Sizes)

.......... 5

- 37 Table 5.10.9 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Less Than 500 lb/24 hours

................................ . 5

- 37 Table 5.10.10 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities between 500 and 1,436 lb/24 hours

................... 5

- 37 Table 5.10.11 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Greater Than 1,436 lb/24 hours (Smaller Sizes) 5

- 38 Table 5.10.12: Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Greater T han 1,436 lb/24 hours (Larger Sizes)

. 5

- 38 Table 5.10.13 Manufacturing Cost and Energy Consumption Data for Air

- Cooled RCU Batch Ice Machines with Capacities Greater Than 1000 lb/24 hours (Smaller Sizes)

...... 5

- 38 Table 5.10.14 Manufacturing Cost and Energy Consumption Data for Air

- Cooled RCU Batch Ice Machines with Capacities Greater Than 1000 lb/24 hours (Larger Sizes)

........ 5

- 39

Table 5.10.15 Manufac turing Cost and Energy Consumption Data for Air

- Cooled SCU Batch Ice Machines with Capacities Less Than 175 lb/24 hours

................................ ....... 5

- 39

Table 5.10.16 Manufacturing Cost and Energy Consumption Data for Air

- Cooled SCU Batch Ice Machines with Capacities Greater Than 175 lb/24 hours

................................ .. 5

- 39 Table 5.10.17 Manufacturing Cost and Energy Consumption Data for Water

- Cooled SCU Batch Ice Machines with Capacities Greater Than 200 lb/24 hours

............................ 5

- 40 Table 5.10.18 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

............. 5

- 40 Table 5.10.19: Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Continuous Ice Machines with Capacities Greater Than 700 lb/24 hours (Smaller Sizes)

................................ ................................ ................................ .................. 5

- 40 Table 5.10.20 Manufacturing Cost and Energy Consumption Data for Air

- Cooled SCU Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

............. 5

- 41 Table 5.10.21 Manufacturing Cost and Energy Consumption Data for Air

- Cooled RCU Continuous Ice Machines with Capacities Less Than 8 50 lb/24 hours

............. 5

- 41 Table 5.10.22 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacit ies Less Than 450 lb/24 hours (22 inch Wide)

............. 5

- 41

5

- iii

### Table 5.10.23 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH B atch Ice Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes, 22 inch Wide)

................................ ................................ ................................ .................. 5

- 42 Table 5.10.24 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Less Than 500 lb/24 hours (22 inch Wide)

....... 5

- 42 Table 5.10.25 Maximum Available Calibration Summary and Cost Differentials

................... 5

- 44 Table 5.10.26 Comparison of Analyzed and Max Avail able units for the IMH

- W

- Small

- B Equipment Class

................................ ................................ ................................ 5

- 45 Table 5.10.27 Comparison of Analyzed and Max Available units for the IMH

- W

- S mall

- B Equipment Class (22 inch wide)

................................ ................................ ........ 5

- 45 Table 5.10.28 Comparison of Analyzed and Max Available units for the IMH

- W

- Med

- B Equipme nt Class

................................ ................................ ................................ 5

- 46 Table 5.10.29 Comparison of Analyzed and Max Available units for the IMH

- W

- Large

- B

- 2 Equipment Class

................................ ................................ ................................ 5

- 46 Table 5.10.30 Comparison of Analyzed and Max Available units for the IMH

- A

- Small

- B Equipment Class

................................ ................................ ................................ 5

- 47 Table 5.10.31 Comparison of Analyzed and Max Available units for the IMH

- A

- Small

- B Equipment Class (22 inch wide)

................................ ................................ ........ 5

- 48 Table 5.10.32 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- B

- 1 Equipment Class

................................ ................................ ................................ 5

- 49 Table 5.10.33 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- B

- 1 Equipment Class (22 inch wide)

................................ ................................ ........ 5

- 50 Table 5.10.34 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- B

- 2 Equipment Class

................................ ................................ ................................ 5

- 50 Table 5.10.35 Comparison of Analyzed and Max Available units for the RCU

- NRC

- Large

- B

- 1 Equipment Class

................................ ................................ ................................ 5

- 51 Table 5.10.36 Comparison of Analyzed and Max Available units for the RCU

- NRC

- Large

- B

- 2 Equipment Class

................................ ................................ ................................ 5

- 52 Table 5.10.37 Comparison of Analyzed and Max Available units for the SCU

- A

- Small

- B Equipment Class

................................ ................................ ................................ 5

- 53 Table 5.10.38 Comparison of Analyzed and Max Available units for the SCU

- A

- Large

- B Equipment Class

................................ ................................ ................................ 5

- 54 Table 5.10.39 Comparison of Analyzed and Max Available units for the SCU

- W

- Large

- B Equipment Class

................................ ................................ ................................ 5

- 55 Table 5.10.40 Comparison of Analyzed and Max Available units for the IMH

- A

- Small

- C Equipment Class

................................ ................................ ................................ 5

- 55 Table 5.10.41 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- C Equipment Class

................................ ................................ ................................ 5

- 56

Table 5.10.42 Comparison of Analyzed and Max Available units for the SCU

- A

- Small

- C Equipment Class

................................ ................................ ................................ 5

- 57 Table 5.10.43 Comparison of Analyzed and Max Available units for the RCU

- A

- Small

- C Equipment Class

................................ ................................ ................................ 5

- 58

LIST OF FIGURES

Figure 5.6.1 Full Production Costs

................................ ................................ ............................ 5

- 20

5

- iv

### Figure 5.9.1 Final Rule Cost Curves for Medium

- Temperature Compressors Used in Commercial

Ice Makers

................................ ................................ ................................ .......... 5

- 27

5

- v

### CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

The engineering analysis establishes the relationship between manufacturer production cost ( MPC ) and energy consumption for the automatic commercial ice makers covered in this rulemaking. The “cost

- efficiency” relationshi p serves as the basis for cost

- benefit calculations in

terms of individual cus tomers, manufacturers, and the n ation, which determines the most economically justified, technologically feasible standard level.

The inputs into the engineering analysis include baseline characteristics for each equipment class addressed in the market and technology assessment

— chapter 3 of the final rule technical support document (TSD)

— the design options from the screening analysis (chapter 4), as well as cost and energy use da ta collected from manufacturers, component vendors, reverse engineering, and energy testing. The output of the engineering analysis is the cost

- efficiency relationship for each equipment class , which was used in the life

- cycle cost (LCC) and payback period analyses (chapter 8 of the final rule TSD ) as well as the national impacts analyses (chapter

10 of the final rule TSD)

. This chapter covers the equipment class es that the U.S. Department of Energy ( DOE ) analyzed ; the methodology used by DOE to develop man ufacturing costs and energy consumption estimates for the final rule analysis phase of the rulemaking ; a nd the engineering analysis results.

5.2 EQUIPMENT CLASS ES ANALYZED

The final rule engineering analysis is based on the study of 2 5 equipment classes deriv ed from the existing DOE batch commercial ice maker standards, effective as of January 1, 2010 (42 U.S.C. 6313(d)(1)), and investigation of the existing continuous ice maker market. DOE analyzed 14 of these equipment classes directly through representative units with given characteristics, including type of ice making process, configuration, condenser cooling, and

harvest rate

. Each analysis involved energy testing and reverse engineering, as well as analysis of energy use and manufacturing cost to develop a cost

- efficiency relationship for potential design options to reduce energy use. DOE selected the representative unit s to reflect the general design, and performance of ice makers within their equipment class es

. Table 5.2

. 1 and Table 5.2

. 2 list

the batch and continuous ice maker equipment classe s that DOE directly analyzed in the final

rule engineering an a lysis

, respectively

. a 

### The equipment classes DOE selected for direct analysis represent close to 100 percent of all shipments of automat ic commercial ice maker s

.

a See chapter 3 of the final rule TSD for description of batch and continuous ice maker categories.

5

- 1

### Table 5.2

. 1 Batch Equipment Class es Analyzed in the Engineering Analysis

Equipment Type Condenser Cooling

Type Harvest Capacity Rate

lb/24 hours Abbreviation* Reverse Engineering Unit, Directly Analyzed Equipment

Class

Ice

- Making Head Water Small (<500) IMH

- W

- S mall

- B 
Medium (≥500 and <1,436) IMH

- W

- M ed

- B 
Large (≥1,436) IMH

- W

- L arge

- B 

Air Small (<450) IMH

- A

- S mall

- B 
Large (≥450) IMH

- A

- L arge

- B 
Remote Condensing (but not remote compressor) Air Small (<1,000) RCU

- NRC

- S mall

- B

Large (≥1,000) RCU

- NRC

- L arge

- B 

Remote Condensing

and Remote Compressor Air Small (<1,000) RCU

- RC

- S mall

- B

Large (≥1,000) RCU

- RC

- L arge

- B 

Self

- Contained Unit Water Small (<200) SCU

- W

- S mall

- B Large (≥200) SCU

- W

- L ar g e

- B 

Air Small (<175) SCU

- A

- S mall

- B 
Large (≥175) SCU

- A

- L arge

- B 

*Abbreviation n otation: s ystem t ype ( b atch (B) or c ontinuous (C))

. Equipment t ype ( i ce

- m aking h ead (IMH), r emote c ondenser u nit (RCU), s elf

- c ontained u nit (SCU))

. Condenser c ooling ( a ir (A) or w ater (W)) or c ompressor l ocation for RCUs ( r emote c ompressor (RC), n on

- remote c ompressor (NRC))

.



### Table 5.2

. 2 Continuous Equipment Class es Analyzed in the Engineering Analysis

Equipment Type Condenser Cooling

Type Harvest Capacity Rate

lb/24 hours Equipment Class

Abbreviation Reverse Engineering Unit, Directly Analyzed Equipment Class

Ice

- Making Head Water Small (< 900 ) IMH

- W

- S mall

- C Large (≥ 900 ) IMH

- W

- L arge

- C

Air Small (< 700 ) IMH

- A

- S mall

- C 
Large (≥ 700 ) IMH

- A

- L arge

- C 
Remote Condensing (but not remote compressor) Air Small (< 850 ) RCU

- NRC

- S mall

- C 

Large (≥ 850 ) RCU

- NRC

- L arge

- C

Remote Condensing ( with remote compressor) Air Small ( <850) RCU

- RC

- S mall

- C

Large ( ≥ 850) RCU

- RC

- L arge

- C

Self

- Contained Unit Water Small (< 900 ) SCU

- W

- S mall

- C Large (≥ 900 ) SCU

- W

- L arge

- C *

Air Small (< 700 ) SCU

- A

- S mall

- C 
Large (≥ 700 ) SCU

- A

- L arge

- C *
*DOE was not able to identify any existing products of this equipment class in ice maker databases. Hence, this equipment cla ss was not analyzed, directly or by extrapolation.



### In order t o perform the analysis as efficiently as possible

, DOE did not directly analyze

all covered equipment class es. DOE did not conduct full separate analyses for remote condensing unit ice makers with and without remote compressors. DOE considered analyses for the remote condensing without remote compressor equipment to be representative of equipment classes both

with and without remote compressors. This simplification is discussed in greater detail in section

5

- 2

### 5.4.1.4

. For those classes not directly analyzed (the secondary equipment classes), DOE represented the cost/efficiency relationship usin g the results for direct ly analy z e d equipment

classes with similar design characteristics

. The representation by directly

- analyzed classes of the secondary classes is discussed in section 5.10.1

In some cases, DOE conducted multiple analyses within a given equipment class in order to obtain a more accurate representation of units within that class. For the equipment classes

RCU

- NRC/RC

- Large

- B

, IMH

- W

- Large

- B, and IMH

- A

- Large

- B, DOE considered two different sizes of equipment to more accurately represent the large range of harvest rates within these

classes . Additionally, DOE developed additional cost

- efficiency curves to represent space constrained, 22 inch wide units within the equipment classes IMH

- A

- Small

- B, IMH

- W

- Small

- B,

and IMH

- A

- Large

- B ( for that last of these classes at a 800 lb/24 hours harvest capacity ).

5.3 METHODOLOGY OVERVIEW

This section describes the analytical methodology DOE used in the engineering analysis. In this rulemaking, DOE has adopted a combined efficiency level/design option/reverse engineering approach to developing cost

- efficiency curves. DOE established efficie ncy levels defined as percent energy use lower than that of baseline efficiency products. DOE’s analysis is based on the efficiency improvements associated with groups of design options. Concurrently

, DOE developed manufacturing cost models based heavily o n reverse engineering of products to develop a baseline MPC and to support calculat ion of the incremental costs associated with improvement of efficiency.

The engineering analysis process began with data collection and ended with the

incremental cost curve results. To develop the se cost

- efficiency curves, DOE collected information from various sources on the manufacturing cost and energy use reduction characteristics of each of the design options. DOE reviewed product literature, conducted reverse engineeri ng of current products, and interviewed component vendors of compressors and fan

motors. DOE also conducted interviews with manufacturers throughout the rulemaking, including during the final rule analyses to follow up on the information collected during t he preliminary and NOPR analys es

. In particular, DOE actively sought additional feedback in terms of compo nent costs to calibrate the outputs of the manufacturing cost models.

Cost information from the vendor interviews and engineering questionnaires durin g the preliminary and NOPR phases provided initial input to the manufacturing cost model. DOE

determined i ncremental costs associated with specific design options using vendor information

and the cost model. DOE made adjustments to the cost assumptions bas ed on available sources, including manufacturer interviews conducted throughout the rulemaking. DOE calculated e nergy

use characteristics for different design configurations representing the directly

- analyzed equipment classes. Input for the energy use ana lysis included data from experimental testin g; publicly available information such as ratings databases, and interviews with manufacturer and

vendor s. The incremental cost estimates and the energy modeling results together constitute the energy efficiency curves presented in this chapter.

5

- 3

### 5.4 EFFICIENCY L EVELS

5.4.1 Batch Ice Makers

This section discusses the baseline, incremental, and maximum technology efficiency

levels that DOE used in its final rule analysis for automatic commercial ice makers of the batch equipment classes.

5.4.1.1 Baseline : Existing DOE Standards

The Energy Policy and Conservation Act ( EPCA ) , as amended by the Energy Policy Act of 2005 ( EPACT 2005 ) , prescribed the following standards for batch ice maker s, shown in

Table 5.4

. 1 , effective January 1, 2010. ( 42 U.S.C. 6313(d)(1) ) These efficiency levels represent the baseline efficiency levels for batch ice makers. DOE also adopt ed these baseline efficiency

levels for ice makers with ice harvest capacities of up to 4,000 pounds of ice per 24 hours, b


### except that a more complicated approach may be adopted for large air

- cooled ice

- making head ice makers with harvest capacity rate larger than 1,600 lb/24 hours

.

Table 5.4

. 1 Existing DOE Standards for Batch Ice Makers

Equipment Type Type of Cooling Harvest Capacity Rate

lb/24 hours Maximum Energy

Use

kWh/100 lb ice * Maximum Condenser

Water Use * , **
gal/100 lb ice

Ice

- Making Head Water Small (<500) 7.80

– 0.0055H 200

– 0.022H Medium (≥500 and <1,436) 5.58

– 0.0011H 200

– 0.022H Large (≥1,436) 4.0 N/A

Air Small (<450) 10.26

– 0.0086H N/A Large (≥450) 6.89

– 0.0011H N/A Remote Condensing (but not Remote

C ompressor) Air Small (<1,000) 8.85

– 0.0038H N/A

Large (≥1,000) 5.1 N/A

Remote Condensing

and Remote Compressor Air Small (<934) 8.85

– 0.0038H N/A

Large (≥934) 5.3 N/A

Self

- Contained Unit Water Small (<200) 11. 40

– 0.019H 191

– 0.0315H Large (≥200) 7.6 191

– 0.0315H

Air Small (<175) 18.0

– 0.0469H N/A Large (≥175) 9.8 N/A

* H = Harvest rate in pounds per 24 hours.

* * Water use is for the condenser only and does not include potable water used to make ice.



### 5.4.1.2 Maximum Available Efficiency Levels

Maximum available efficiency levels for the analyzed equipment class es are tabulated in

Table 5.4

. 2

. DOE removed from consideration as maximum available those batch ic e makers that use proprietary technology or those that are known to make half

- cube ice. The energy use

focused on full

- cube ice makers because half

- cube ice makers may have an efficiency advantage.

b For brevity, pounds of ice per 24 hours will be referred to herein as lb ice/24 hours.

5

- 4

### This information is based on a survey of product databases and manufacturer websites (also see data in chapter 3 of the final rule TSD)

. The maximum available efficiency levels are represented by percentage energy use less than the energy use of baseline

- efficiency equipment , which has been selected as equal to t he current DOE energy standard for batch ice makers. DOE used the maximum available efficiency levels to guide selection of incremental efficiency levels.

Table 5.4

. 2 Batch Ice Maker Maximum Available Levels

Equipment Type Type of Cooling Harvest Capacity Rate

lb/24 hours Max Available Efficiency

Level* Harvest Capacity

lb/24 hours Brand & Model Number

Ice

- Making

Head Water Small (<500) 19. 2 % 360 Manitowoc IY

- 0455W Small (<500) ( 22 inch wide) 16.9% 280 Scotsman C0322MW

Medium (≥500 and <1,436) 14.3 % 832 Scotsman C0830MW

Large (≥1,436) 5 .0 % 1628 Cornelius CCM1848WF2

Air Small (<450) 19.3% 300 Manitowoc KD

- 0350A Small (<450) (22 inch wide) 1 6.6 % 370 Ice

- O

- Matic ICE0520FA

Large (≥450 and < 875) 16.1% 844 Scotsman C1030MA

- 32B Large (≥450 and < 875 ) (22 inch wide) 5.5% 590 Scotsman C0722MA

- 32#

Extended ( ≥ 875) 6.0 % 1527 Scotsman C1848MA

Remote Condensing (but not

Remote

C ompressor) Air Small (<1,000) 2 5 .8 % 5 2 0 Ice

- O

- Matic ICE0606FR

Large ( ≥ 1,000) 15.7 % 1645 Scotsman C1848MR /

ERC611

- 32A

Large ( ≥ 1,000) 14.9% 2256 Scotsman C2648MR /

PRC241

- 32A

Remote Condensing

and Remote Compressor Air Small (<934) 20.4 % 696 Manitowoc ID0976C

- 161

Large (≥934) 15 .7 % 1, 132 Manitowoc ID1472C

- 161

Self

Contained

Unit Water Small (<200) 2 6.2 % 141 Manitowoc UD0240W

- 261 Large (≥200) 2 7.6 % 285 Scotsman CU3030MW, SW

- 1#

Air Small (<175) 30.7 % 130 Ice

- O

- Matic ICEU150FA Large (≥175) 26.4 % 247 ITV Spika NG285

- A1 * Percent energy use lower than the baseline efficiency level ( i.e. , the current DOE standard)



### 5.4.1.3 Incremental Efficiency Levels

DOE established a series of incremental efficiency levels for each batch ice maker equipment class and developed corresponding incremental cost data that quantifie s a c ost

efficiency relationship

. DOE established the highest incremental efficiency levels based on a review of the maximum efficiency levels of available products , as discussed in section 5.4.1.2

. Table 5.4

. 3 shows the selected incremental efficiency levels.

5

- 5

### Table 5.4

. 3 Incremental Efficiency Levels for Batch Ice Maker Equipment Class es

Equipment Type* Representative Harvest Capacity Rate

lb/24 hours EL 2** EL 3

EL 3A

*** EL 4

EL 4A

*** EL 5 EL 6 EL7

IMH

- W

- Small

- B 300 10% 15% 20%

21.5% 24%

IMH

- W

- M ed

- B 850 10% 15% 18%

IMH

- W

- Large

- B

- 1 1,500 8%

IMH

- W

- Large

- B

- 2 2,600 7%

IMH

- A

- Small

- B 300 10% 15%

18% 20% 25% 26%

IMH

- A

- Large

- B

- 1 800 10% 15%

16% 20%

IMH

- A

- Large

- B

- 2 1,500 10% 12%

RCU

- NRC/RC

Small

- B Not Directly Analyzed

RCU

- NRC/RC

Large

- B 1,500 10 % 15% 17%
2,400 10 % 14%

SCU

- W

- Small

- B Not Directly Analyzed

SCU

- W

- Large

- B 300 10 % 15% 20% 25% 30%

SCU

- A

- Small

- B 110 10 % 15% 20% 25% 30% 33%

SCU

- A

- Large

- B 200 10 % 15% 20% 25% 28 %

* See Table 5.2.1 for a description of these abbreviations. ** EL = efficiency level; EL1 is the baseline efficiency level, while EL2 through EL7 represent increased efficiency levels.

***DOE considered i ntermediate efficiency levels 3A and 4A for some equipment classes.



### For those classes not directly analyzed (the secondary equipment classes), DOE represented the cost/efficiency relationship using the results for directly analyzed equipment classes with similar design characteristics. The representation by directly

- analyz ed classes of the secondary classes is discussed in section 5.10.1

5.4.1.4 Maximum Technology Level

DOE defines maximum technology level s for each equipment class to repres ent the maximum possible efficiency if all design options are incorporated. The maximum technology

level is not necessarily the same as the maximum available level, which is the highest efficiency unit currently available on the market. In some cases , the maximum technology level is not commercially available because it is not economically feasible. In other cases, the maximum technology level does not incorporate all design options used in maximum available efficiency products because sole

- source prop rietary technologies are not considered in the analysis

.

DOE determined maximum technology levels based on its energy use analysis. Namely, the maximum technology levels were calculated based on incorporating all design options applicable for each equipmen t class . The maximum technology efficiency levels for the directly analyzed batch ice maker equipment class es are presented in Table 5.4

. 4

. The design options

conside red in the analysis are listed for each equipment class in section 5.10.2 . For some equipment classes, the table shows different maximum technology levels for the multiple representative capacities included in the analysis for these classes.

5

- 6

### Table 5.4

. 4 does not show maximum technology levels separately for remote condenser units with and without remote compressors. During the preliminary analysis, DOE considered whether the efficiency improvements associated with the design options used in the engineering analysis would be significantly different for remote compressor ice makers as compared with

non

- remote compressor models. DOE concluded that there would be little differences in the magnitudes or costs of these improvements as the design options are applied to these two types

of ice makers. In the final rule analysis, DOE did not consider remote compressor and non

re mote compressor units separately. This is discussed in more detail in chapter 5 of the preliminary analysis TSD.

Table 5.4

. 4 Maximum Technology Levels for Batch Ice Maker Equipment Classes

Equipment Class Ener gy Use Lower than Baseline

IMH

- W

- Small

- B 23.9 %

IMH

- W

- Small

- B (22 inch wide) 21.5%

IMH

- W

- M ed

- B 18 .1 %

IMH

- W

- Large

- B 8 .3 % (at 1,500 lb/24 hours )

7 .4 % (at 2,600 lb/24 hours )

IMH

- A

- Small

- B 2 5.5 %

IMH

- A

- Small

- B (22 inch wide) 18.1%

IMH

- A

- Large

- B

- 1

IMH

- A

- Large

- B

- 2 23 .4 % (at 800 lb/24 hours )

11.8 % (at 1,500 lb/24 hours )

IMH

- A

- Large

- B

- 1 (22 inch wide) 15.8%

RCU

- S mall

- B Not directly analyzed

– similar to RCU

- Large

- B

RCU

- L arge

- B 17.3 % (at 1,500 lb/24 hours )

13.9 % (at 2,400 lb/24 hours )

SCU

- W

- Small

- B Not directly analyzed

- similar to SCU

- W

- Large

- B

SCU

- W

- Large

- B 29.8 %

SCU

- A

- Small

- B 3 2.7 %

SCU

- A

- Large

- B 29.1 %



### 5.4.2 Continuous Ice Makers

5.4.2.1 Energy Use Metric Incorporating Ice Hardness

Continuous

- type ice makers typically produce ice that is not completely frozen, leaving some liquid water in the total mass of ice produced. The i ce hardness ( for a 32 ° F ice product, t he percentage of frozen ice ) is directly related to the measured energy consumption of these

machines. To account for this impact on energy use, t he t est p rocedure final rule for automatic

commercial ice makers requires that the measured energy use of continuous machines be adjusted based on the ice hardness. 7 7 FR 1591 ( Jan

. 11 , 201 2 ).

The final test procedure calls for use of a calorimeter to determine ice hardness, and calculation of an ice quality adjustment factor based on the calorimeter test results as follows:

5

- 7

### Ice Hardness Adjustment Factor = � 144 Btu lb � + 38 Btu lb � � 144 Btu lb � × IIIIII HHHHHHHHHH HHHH FF FFFF
100 � + 38 Btu l b � �

Eq. 5

. 1

The measured energy consumption per 100 lb of ice and the measured condenser water consumption, as determined using American Society of Heating, Refrigerating and Air

Conditioning Engineers ( ASHRAE ) Standard 29

- 2009, w ould be multiplied by the ice hardnes s adjustment factor to yield the adjusted energy and condenser water consumption values, respectively. DOE’s consideration of continuous ice maker efficiency levels and its rulemaking analysis in gen eral are based on use of the adjusted energy and condenser use values.

5.4.2.2 Baseline Efficiency Levels

There currently are no DOE energy standards for continuous ice makers. For the preliminary analysis, DOE chose to develop efficiency levels by fitting baselin e efficiency level

curves to collected existing ice maker data. DOE has updated the baseline efficiency levels in
response to the availability of new data during the course of the rulemaking, first for the NOPR,

and also for the final rule

.

DOE developed baseline efficiency levels using energy use data available from several sources, as discussed in chapter 3 of the final rule TSD. DOE chose baseline efficiency levels that would be met by nearly all ice makers represented in the databases. DOE primarily

co nsidered those units for which ice hardness data was available

. F or units that did not have reported ice hardness data , DOE used assumptions for the ice hardness factor of 7 0 and 85 for

flake and nugget ice makers , respectively . The baseline efficiency levels are tabulated in Table

5.4

. 5 , and they are plotted with the ice maker energy use data in chapter 3 of the final rule TSD.

5

- 8

### Table 5.4

. 5 Baseline Efficiency Levels for Continuous Ice Maker Equipment Classes

Equipment Type Type of Cooling Harvest Capacity Rate

lb/24 hours Energy Use

kWh/100 lb ice *

Ice

- Making Head Water Small (< 900 ) 8.1

– 0.00 33 3H Large (≥ 900 ) 5.1

Air Small (< 700 ) 1 1

. 0

– 0.00 629 H Large (≥ 700 ) 6. 6

Remote Condensing (but

not Remote C ompressor) Air Small (< 850 ) 10. 0

– 0.004 59 H

Large (≥ 850 ) 6.1 Remote Condensing and Remote Compressor Air Small (< 850 ) 10.2

– 0.004 59 H

Large (≥ 850 ) 6.3

Self

- Contained Unit Water Small (< 900 ) 9. 5

– 0.0 03 78 H Large (≥ 900 ) 6. 1

Air Small (< 700 ) 1 6.3

– 0.0 3H
Large (≥ 200 and < 700 ) 11.84

– 0.0078H

Extended (≥ 700 ) 6.38

* H = Harvest capacity in lb/24 hours

.



### 5.4.2.3 Max imum Available Efficiency Levels

Maximum available efficiency levels for the analyzed equipment class es are tabulated in

Table 5.4

. 6

. This information is based on a survey of product databases and manufacturer

websites (also see data in chapter 3 of the final rule TSD)

. These levels are based on AHRI product directory data that includes ice hardness data. The maximum available efficiency levels

are represe nted by percentage energy use less than that of baseline

- efficiency equipment . DOE used the maximum available efficiency levels to select appropriate ranges of incremental efficiency levels.

Table 5.4

. 6 Contin uous Ice Maker Maximum Available Efficiency Levels

Equipment Type Type of Cooling Harvest Capacity Range Max Available Efficiency level Harvest Capacity

lb/24 hours Brand & Model Number

Ice

- Making

Head Water Small 17 % 532 Hoshizaki RF0650W

- 161
Large 12 % 1, 122 Manitowoc RFS1200W

- 261

Air Small 28 % 531 Hoshizaki F

- 801MAH
Large 36 % 845 Ice

- O

- Matic MFI1256

Remote Condensing (but not remote compressor) Air Small 18% 835 Scotsman N0922R

- 32#

Large 19 % 1, 050 Scotsman F1222R

- 32#

Remote Condensing

and Remote Compressor Air Small 8% 713 Manitowoc RN1078C

- 161

Air Large 1 4 % 962 Manitowoc RN1278C

- 161

Self

- Contained

Unit Water Small 1 9 % 310 Scotsman AFE400W

- 1#
Large No products in this equipment class*

Air Small 29 % 325 Follet 12CI400A
Large No products in this equipment class*

* DOE’s market analysis showed that there were no products in this equipment class

.

5

- 9

### 5.4.2.4 Incremental Efficiency Levels

DOE established a series of incremental efficiency levels for each analyzed continuous ice maker equipment class and developed corresponding incremental cost data that quantifie s a

c ost

- efficiency relationship

. DOE established the highest incremental efficiency levels based on a review of the maximum efficiency levels of available equipment , as discussed in section

5.4.2.3

. Table 5.4

. 7 shows the selected incremental efficiency levels. The efficiency levels are defined by th e percent energy use less than the trial baseline energy use.

Table 5.4

. 7 Incremental Efficiency Levels for Continuous Ice Maker Equipment Class es

Equipment Type* Harvest Capacity

lb/24 hours EL 2** EL 3 EL 4 EL 5 EL6

IMH

- W

- S

- C 800 Not Directly Analyzed

IMH

- W

- L

- C 1000 Not Directly Analyzed

1800 Not Directly Analyzed

IMH

- A

- S

- C 310 10% 15% 20% 25% 26%

IMH

- A

- L

- C 820 10% 15% 20% 23%

1800 Not Directly Analyzed

RCU

- S

- C 800 10% 15% 20% 25% 27%

RCU

- L

- C 1500 Not Directly Analyzed

SCU

- W

- S

- C 300 Not Directly Analyzed

SCU

- W

- L

- C NA No products in this equipment class ‡

SCU

- A

- S

- C 220 7% 15% 20% 25% 2 7 %

SCU

- A

- L

- C NA No products in this equipment class ‡

* See Table 5.2.2 for a description of these abbreviations. ** EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL5 represent increased efficiency levels.
‡ DOE’s market analysis showed that there were no products in this equipment class.



### 5.4.2.5 Max imum Technology Level

The maximum technology efficiency levels for the directly

- analyzed continuous ice

maker equipment class es are presented in Table 5.4

. 8

. The design options considered in the analysis are listed for each equipment class in section 5.10.2

. For two equipment class es , SCU

W

- Large

- C and SCU

- A

- Large

- C

, DOE was not able to find i ce maker s in publicly available

databases and/or manufacturer websites meeting the harvest capacity criteria for these classes

.

Table 5. 4

. 8 Maximum Technology Levels for Directly

- Analyzed Continuous Ice Maker Equipment Classes
Equipment Class Energy Use Lower than Baseline

IMH

- A

- Small

- C 2 5.7 %

IMH

- A

- Large

- C 23 .3 %

RCU

- NRC

- Small

- C 26.6 %

SCU

- A

- Small

- C 2 6

. 6 %

5

- 10

### 5.5 DATA C OLLECTION

5.5.1 Component Vendor Data

DOE contacted major suppliers of key commercial ice maker components to obtain performance and cost data to support its design option analysis. This effort consisted of phone

interviews and email correspondence. Table 5.5.1 lists the vendors contacted. DOE also obtained from the compressor vendors or their websites ’ complete performance data for compressors use d in the energy analyses, including analyses for baseline and improved

- efficiency configurations.

Table 5.5

. 1 Component Vendors Con tacted by DOE during Engineering Analysis
Component Type Vendors

Compressors Bristol Copeland

Danfoss

Embraco

Tecumseh

Condenser Fan Motors A.O. Smith

Bohn EBM Papst Emerson Climate Technologies

Fasco

Marathon Electric

Morrill Motors Regal Beloit Electric Motors Zhongshan Broad Ocean Motors

Auger Motors Brother International

Emerson Industrial Automation

Bison Gear Motors

Pump Motors Fasco

Hartell

Morrill Motors

Sanso Electric

Heat Exchangers Heatcraft

Modine

AlCoil



### 5.5.2 Reverse Engineering

DOE purchased a number of automat ic commercial ice maker s , chosen to represent the designs of various equipment classes, in order to examine design and fabrication details. This

reverse engineering included detailed measurement of dimensions, system and component

- level power measurements, measurement of air flows for products with forced convection heat exchangers, and physical teardowns. The data collected in the reverse engineering process were

used in both manufacturing cost modeling and energy use modeling. This section describes the selection of products for reverse engineering as we ll as some of the measurements made to support subsequent modeling. Section 5.6 more thorough ly descri bes the physical teardown process used to sup port manufacturing cost modeling.

5

- 11

### 5.5.2.1 Selection of Products for Reverse Engineering and Inspections

Table 5.5.2 describes the 19 products selected for re verse engineering during the preliminary analysis and identifies the products for which DOE conducted energy tests (see

s ection 5.5.4 for more on energy testing). DOE aimed to select a range of models that would cover a broad spectrum of equipment class es and capacities based on the California Energy

Commission ( CEC ) , ENERGY STAR, and AHRI databases, as well as manufacturer website s

.

Ta ble 5.5

. 2 Selected Units for Reverse Engineering and Energy Testing during Preliminary Analysis

Type Equipment Class Harvest Capacity

lb/24 hours Energy

Use

kWh/

100 lb Potable

Water Use gal/100 lb Energy

Test Reverse Engineering

Batch IMH

- A

- Small

- B 270 7.70 20.0  

IMH

- A

- Small

- B 324 5.80 22.6  

IMH

- A

- Large

- B 780 5.70 20.5 

IMH

- A

- Large

- B 844 5.00 18.4 

IMH

- A

- Large

- B 1,460 4.95 20.0 

IMH

- A

- Large

- B 1,560 4.10 18.8  *

IMH

- W

- L arge

- B 851 4.40 19.7 

IMH

- W

- Large

- B 2,820 3.60 19.7  

RCU

- A

- Large

- B 1,510 4.59 20.0 

RCU

- A

- Large

- B 1,694 3.80 18.1  *

RCU

- A

- Large

- B 2,350 4.64 19.7 

SCU

- A

- Small

- B 121 8.40 17.8  

SCU

- A

- Small

- B 112 11.80 34.0 

SCU

- W

- Large

- B 285 5.50 18.0  

Continuous IMH

- A

- Small

- C 310 6.31 12.0 

IMH

- A

- Small

- C 822 5.51 13.6 

IMH

- A

- Small

- C 845 3.40 12.0  

RCU

- A

- Large

- C 1780 3.40 12.0  *

SCU

- A

- Large

- C 280 6.00 19.0  



### During the NOPR analyses, DOE performed energy testing and reverse engineering of additional ice maker products. DOE aimed to select a range of models that would complement the existing set of models and provide a set of baseline and high

- efficiency models in several key equipment classes. For these models, reverse engineering consisted of product inspections that captured the vital components and characteristics of each product. DOE also performed energy modeling of three products to further the cost

- effic iency analysis. Table 5.5

. 3 describes the 20 additional products analyzed during the NOPR phase.

5

- 12

### Table 5.5

. 3 : Selected Units for Reverse Engineering and Energy Testing during NOPR Analysis

Type Equipment Class Harvest Capacity

lb/24

hours Energy

Use

kWh/

100 lb Potable

Water Use gal/100 lb Energy

Test Reverse Engineering

Batch IMH

- A

- Small

- B 335 6.0 20.0  

IMH

- A

- Large

- B 528 6.0 19.1  

IMH

- W

- Small

- B 360 4.7 20.0  

IMH

- W

- Small

- B 270 6.19 23.9  

IMH

- W

- Small

- B 275 6.1 39.0  

IMH

- W

- Medium

- B 650 4.1 18.6  

IMH

- W

- Medium

- B 832 4.0 18.9  

RCU

- NRC

- Small

- B 780 4.7 18.4  

RCU

- NRC

- Small

- B 843 5.6 22.8  

RCU

- NRC

- Large

- B 2256 4.34 17.0  

SCU

- A

- Small

- B 143 8.48 32.7  

SCU

- A

- Large

- B 204 9.5 32.4  

SCU

- A

- Large

- B 228 8.6 32.9  

Continuous IMH

- A

- Large

- C

- 921 6.6 * 12.0  

IMH

- A

- Large

- C 1100 5.5 * 12.0  

IMH

- W

- Small

- C 560 5.9 * 12.0  

RCU

- NRC

- Small

- C 610 6.9 * 12.0  

RCU

- NRC

- Small

- C 835 5.0 * 12.0  

SCU

- A

- Small

- C 250 9.1 * 12.0  

SCU

- W

- Small

- C 455 5.0 * 12.0  

* Adjusted energy use calculated using the reported ice quality of the unit



### During the final rule analyses, DOE performed energy testing and reverse engineering of additional ice maker products. DOE select ed models to obtain information about both baseline and high efficiency units within directly analyzed equipment classes for wh ich DOE previously

did not have this information

. R everse engineering of these models involved inspections of the key components and characteristics of each product. Table 5.5

. 4 describes the 1 3 additional products analyzed during the final rule phase.

5

- 13

### Table 5.5

. 4 : Selected Units for Reverse Engineering and Energy Testin g during Final Rule Analysis

Type Equipment Class Harvest Capacity

lb/24

hours Energy

Use

kWh/

100 lb Potable

Water Use gal/100 lb Energy

Test Reverse Engineering

Batch IMH

- A

- Large

- B 714 6.1 20  

IMH

- A

- Large

- B 590 5.9 18  

IMH

- A

- Large

- B 1242 4.6 18.5  

IMH

- A

- Large

- B 760 5.2 12  

IMH

- A

- Small

- B 300 6.2 22  

IMH

- A

- Small

- B 380 6.7 18.5  

IMH

- A

- Small

- B 370 5.9 23.1  

RCU

- NRC

- Large

- B 1645 4.3 17.9  

SCU

- A

- Small

- B 110 11.5 18  

SCU

- A

- Small

- B 130 8.25 25  

SCU

- W

- Large

- B 296 6.5 35.6  

Continuous IMH

- A

- Small

- C 531 5.5 12  

RCU

- NRC

- Small

- C 713 6.4 12  



### 5.5.2.2 Collection of Data to Support Energy Use Analysis

DOE examined each unit prior to teardown to record details that are key parameters in subsequent energy use analysis

. The key measurements are described in this section. The rated capacity and energy use for each product were based on the AHRI database or on product

literature

. DOE contracted with Intertek Testing Services, Inc. (Intertek) and United Laboratories (UL) for testing , which was conducted according to the DOE test procedure

.

DOE measured c omponent

- level power input for fans, water pumps, and other components (e.g., air pumps used for harvest assist, solenoid valves, etc.)

. For some of the

continuous ice mak ers, DOE separately measured augur motor power input while the ice maker was making ice.

DOE made a ir flow measurements for air

- cooled condensers using a hot wire

anemometer. The location of these measurements varied

, depending on the heat exchanger type and configuration. The determination of air flows based on these measurements is not very reliable, so this measurement was used as an indication of air flow trends more than exact indication of air flow for the various products.

Details of the cabinet siz e , heat exchanger dimensions, insulation thickness , etc. were based on direct physical measurements ma de during the teardown process. DOE noted c ondens er details

, such as type, configuration, numbers of tubes and fins, and dimensions. The details of

evapor ators and suction line heat exchangers were similarly determined during teardown. DOE

recorded c omponent manufacturer and model data for key components such as c ompressors, fans, and controls.

5.5.3 Manufacturer Interviews

The manufacturer interviews conducted d uring the preliminary analysis are discussed in

c hapter 5 of the preliminary analysis TSD. This also includes an engineering questionnaire

5

- 14

### shown in appendix 12A of the preliminary analysis TSD. Likewise, interviews conducted during the NOPR analysis are di scussed in chapter 5 of the NOPR TSD.

During preparation of the NODA analsis

, DOE’s contractor conducted further interviews

to obtain additional information to support both cost analysis and energy use analysis. All of

these interviews were conducted under non

- disclosure agreements with the manufacturers. Hence, none of the individual responses can be reported. However, adjustments to values for many of the parameters and costs used in the engineering analysis wer e based on input from

these discussions.

5.5.4 Energy T esting

DOE conducted energy testing to verify energy use of several of the ice makers obtained for reverse engineering, and to provide information about refrigeration system operating conditions during different phases of operation to support energy use modeling. A total of 41 ice makers were tested in the preliminary, NOPR, and final rule phases, as listed in Table 5.5.2

, Table 5.5

. 3 , and Table 5.5

. 4

. The tests were conducted using the DOE test procedure applicable during the time of test. For continuous models tested before applicability of the DOE test procedure (i.e. during the preliminary analysis), the tests were conducted according to AHRI

Standar d 810

- 2007 with Addendum 1. During many of the tests, additional data recorded

included r efrigeration c ircuit temperatures

, r efrigerant p ressures , p otable water circuit
temperature s , additional a ir temperatures (the evaporator compartment, compressor/conde nser compartment, c ondenser fan air outlet for air

- cooled ice makers), and c ooling w ater o utlet
temperature

.

5.6 MANUFACTURING COST M ODELING

5.6.1 Generation of Bills of Materials

E ach reverse

- engineering teardown resulted in a structured bill of materials (BOM).

S tructured BOMs describe each product part and its relationship to the other parts in the

estimated order in which manufacturers assembled them. The BOMs describe each fabrication and assembly operation in detail, including the type of equipment needed ( e.g

. , presses, drills), the process cycle times, and the labor associated with each manufacturing step. The result is a thorough and explicit model of the production process, which includes space, conveyor, and equipment requirements by planned production lev el.

The BOMs incorporate all materials, components, and fasteners classified as either raw materials or purchased parts and assemblies. The classifications into raw materials or purchased parts were based on DOE’s previous industry experience, recent infor mation in trade publications, and discussions with high

- and low

- volume original equipment manufacturers (OEMs). DOE also visited manufacturing plants to reinforce its understanding of the industry’s current manufacturing practices.

For purchased parts, t he purchase price is estimated based on volume

- variable price quotations and detailed discussions with manufacturers and component suppliers. For fabricated parts, the prices of “raw” metals ( e.g. , tube, sheet metal) are estimated based on 5

- year averages (see s ection 5.6.4.3 )

, while all other materials and purchased parts reflect current market costs.

5

- 15

### The cost of transforming the in termediate materials into finished parts is estimated based on current industry pricing.

5.6.2 Cost Structure of the Spreadsheet Models

The manufacturing cost assessment methodology used is a detailed, component

- focused technique for calculating the manufacturi ng cost of a product (direct materials, direct labor, and the overhead costs associated with production). The first step in the manufacturing cost assessment was the creation of a complete and structured BOM from the disassembly of the units

selected for t eardown. The units were dismantled, and each part was characterized according to weight, manufacturing processes used, dimensions, material, and quantity. The BOM incorporate d all materials, components, and fast en ers with estimates of raw material costs an d purchased part costs. Assumptions about the sourcing of parts and in

- house fabrication were based on industry experience, information in trade publications, and discussions with manufacturers. Interview and plant visits were conducted with manufacturers to add industry experience on the methodology and pricing.

The last step was to convert this information into dollar values. To perform this task, DOE collected information on labor rates, tooling costs, raw material prices, and other factors.

DOE assumed values for these parameters using internal expertise and confidential information available to DOE contractors. Although most of the assumptions are manufacturer specific and cannot be revealed, s ection 5.6.4.2 discusses the values used for each assumption.

In summary, DOE assigned costs of labor, materials, and overhead to each part

, whether purchased or produced in

- house. DOE then aggregated single

- part costs into major assemblies ( e.g. , heat exchanger assembly, potable water system, packaging, controls, fan/motor assembly, refrigerant circuit, wiring harnesses) and summarized these costs in a worksheet.

5.6.3 Cost Model and Definitions

Once DOE disassemb led selected units, gathered information from manufacturer catalogs on additional products, and identified technologies, DOE create d an appropriate manufacturing cost model that could translate physical information into M PCs. The cost model is based on

pro duction activities and divides factory costs into the following categories:

• Materials : p urchased parts (compress or, fan motors, control boards , etc.), raw materials (cold rolled steel, copper tube, etc.), and indirect materials that are used for processing and fabrication.

• Labor : f abrication, assembly, indirect, and supervisor labor. Fabrication and assembly

labor cost s are burdened with benefits and supervisory costs.

• Overhead : e quipment, tooling, and building depreciation, as well as utilities, equipment and tooling maintenance, insurance, and property taxes.

5

- 16

### 5.6.3.1 Cost Definitions

Because there are many different accounting systems and methods to monitor costs, DOE

defined the factory cost terms as follows:

• Direct material : p urchased parts (out

- sourced) plus manufactured parts (made in

house from raw materials) ;

• Indirect material : m aterial used during manufacturing ( e.g. , welding rods, adhesives) ;

• Fabrication labor : l abor associated with in

- house piece manufacturing ;

• Assembly labor : l abor associated with final assembly ;

• Indirect labor : l abor costs that scaled with fabrication and assembly labor

— includ ing the cost of technicians, manufacturing engineering support, stocking, etc. that were assigned on a span basis ;

• Equipment and plant depreciation : m oney allocate d to pay for initial equipment installation and replacement as the production equipment wears out ;

• Tooling depreciation : c ost for initial tooling (including nonrecurring engineering and debugging of the tools) and tooling replacement as tools wear out ;

• Bui lding depreciation : m oney allocated to pay for the building space and the conveyors that feed and/or make up the assembly line ;

• Utilities : e lectricity, gas, telephones, etc. ;

• Maintenance : m oney spent annually on maintaining tooling and equipment ;

• Insurance : a ppropriated as a function of unit cost ; and

• Property t ax: a ppropriated as a function of unit cost.

5.6.4 Cost Model Assumptions Overview

As discussed in the previous section, assumptions about manufacturer practices and cost

structure were important to the final product cost estimate

.

In converting physical information about the product into cost information, DOE reconstructed manufacturing processes for each component using internal expertise and knowledge of the methods used by the industry. DOE used assumptions regarding the manufacturing process parameters ( e.g. , equipment use, labor rates, tooling depreciation, and cost of purchased raw materials) to determine the value of each component. DOE then summed the values of the components into assembly c osts and, finally, the total product cost. The product cost included the material, labor, and overhead costs associated with the manufacturing facility.

The material costs included both direct and indirect materials. The labor costs included

5

- 17

### assembly, fabr ication, supervisor, and indirect labor. Overhead costs included equipment depreciation, tooling depreciation, building depreciation, utilities, equipment, tooling maintenance, insurance, property, and taxes.

DOE used the information gathered from manufact urer interviews and factory visits to help in development of the cost model.

The following sections discuss specific assumptions about outsourcing, factory parameters, production volumes, and material prices. M anufacturer

- specific assumptions are presented as industry averages to prevent disclosure of confidential information.

5.6.4.1 Fabrication Estimates

DOE characterized parts based on whether manufacturers purchased them from outside suppliers or fabricated them in

- house. For purchased parts, DOE estimated the purchase price. For fabricated parts, DOE estimated the price of raw materials ( e.g. , tube, sheet metal) and the cost of transforming them into finished parts. Whenever possible, DOE obtained price quotes directly from the manufacturers’ suppliers.

DOE bas ed the manufacturing operations assumptions on internal expertise, interviews with manufacturers, and manufacturing facilities site visits. The major manufacturer processes identified and developed for the spreadsheet model are listed in Table 5.6.1 . Fabrication process cycle times were estimated and entered into the BOM.

Table 5.6

. 1 Cost Model In

- House Man ufacturing Operation Assumptions

Fabrication Finishing Assembly/Joining Quality Control Fixturing Stamping/Pressing

Turret Punch Tube Forming

Machine Cutting & Shearing

T

- Drill Tube/Wire Bending Brazing Vacuum Forming Blow Molding Washing Painting

Powder Coating

De

- burring Polishing Refrigerant Charging Adhesive Bonding Spot Welding Seam Welding Packaging Inspecting & Testing



### 5.6.4.2 Factory Parameters Assumptions

DOE used information gathered from publicly available literature, manufacturer interviews, and an alysis of common industry practices to formulate factory parameters for each type of manufacturer. DOE first made assumptions about a set of preliminary factory parameters before the manufacturer interviews. DOE then revised the assumptions using comments and information gathered during the interviews. Table 5.6.2 lists DOE’s assumptions for ice maker

manufacturers

.

During manufacturer interviews in the NOPR analyses, DOE found that some larger batch equipment classes were produced in much smaller volumes than other batch equipment

5

- 18

### classes. To more accurately capture the costs of these products, DOE created a cost model based on low production volumes f or batch products.

Table 5.6

. 2 ACIM Factory Parameter Assumptions

Parameter Batch (high

- volume) Batch (low

- volume) Continuous

Plant Capacity (units/y ea r) 25,000 5,000 6,000

Actual Annual Production Volume (units/y ea r) 12,000 2,500 3,000 Fabrication Labor Wages ($/h ou r) 16.00 16.00 16.00 Fringe Benefits Ratio 50% 50% 50%



### 5.6.4.3 Material Cost Assumptions

DOE determined the cost of raw materials using publicly available information such as

the American Metals Market

, 1 

### interviews with manufacturers, and direct discussions with material suppliers. Common metals used in the fabrication of ice maker s include cold rolled steel (CRS), s tainless s teel, copper tubing, and aluminum. There have been large fluctuations in metal prices over the last few years. To account for these fluctuations, DOE used a 5

- year average of metal prices from the Bureau of Labor Statistics Producer Price Indices (PPIs) spanning from

200 8 to 201 3 with an adjustment to 2013 dollars ( 201 3 $ )

. 2 

### DOE used the PPIs for copper rolling, drawing, and extruding and steel mill products, and made the adjustments to 201 3 $ using the gross domestic product implicit price deflator. 3 

### For resins us ed in the fabrication of these refrigeration products, DOE used current resin prices gathered from industry research, publications such as Plastics News

, 4 

### and interviews with manufacturers.

5.6.5 Manufacturing Production Cost

After finalizing the cost estimate f or each teardown unit, DOE prepared a detailed summary for relevant components, subassemblies, and processes. The BOM thus details all aspects of product costs. DOE totaled the cost of materials, labor, and direct overhead used to manufacture a product in order to calculate the manufacturing product ion cost. c 

### Figure 5.6.1 shows the general breakdown of costs associated with manufacturing a product.

The full cost of the product is equal to the manufacturer selling price (MSP) minus profits. The full cost i ncludes the MPC, and the non

- production cost.

c When viewed from the companywide perspective, the sum of all material, labor, and overhead costs equals the company’s sales cost, also referred to as the cost of goods sold (COGS).

5

- 19

### Figure 5.6

. 1 Full Production Costs

5.6.6 Incremental Cost Estimates

Incremental costs were determined for design options and are applied to the estimated

cost of the baseline

- efficiency ice makers. The approach for estimating the incremental costs

varied

, depending on the design option. Details in this calculati on that are specific to individual design options are discussed in s ection 5.10.2 . Aspects of the incremental cost calculation that were generally applied to multi ple design options are discussed in this section.

Many of the design options involve replacement of a current component with a higher efficiency component. For these design options, the increased price paid by the OEM for the new component represents the m anufacturing cost increase

— other elements of product cost such as overhead and capital expenditures would be insignificantly affected by these design changes. The appropriate price increases are discussed in s ection 5.10.2 by design option.

For some design changes, calculating the cost impact of the design change required direct use of the manufacturing cost model to determine changes to a number of parts. The baseline manufacturing cost was subtracted from the manufacturing cost of the modified design to determine the incremental cost of the design option. This approach was used in particular for

condenser size increases. Manufacturer Selling Price

Full Cost of

Product

Manufacturer

Production Cost

Direct

Labor Direct

Material Overhead

Indirect Labor

Indirect Material

Maintenance

Depreciation

Taxes

Insurance related

to assets Non

- Production

Cost

Selling

Market

Research

Advertising

POS Promotion

Salesperson Comp & Travel

Logistics (Warehousing, Delivery, Record

Keeping, Order

Processing) General &

Admin.

Costs of

Service &
Staff Units

General Corporate

Costs (Comp, etc) R & D

Costs associated with finding new

and improved

products or

processes

Code Compliance

Modernization Interest

Cost of borrowing

funds Profit

5

- 20

### 5.6.6.1 G&A and Profit

DOE estimated the furthe r addition to the MSP associated with general and

administrative costs ( G&A ) and profit for the appliance industry as 25 percent of MPC . This adder was applied to all of the MPC estimates in order to determine MSP numbers. This markup

is described in more detail in s ection 5.7

.

5.7 MANUFACTURER MARKUP

Manufacturers and dealers apply a markup to cover their operating costs and profit margins in manufacturing and distribu tion. In the engineering analysis, DOE determined a manufacturer markup and applied it to the MPC to arrive at the MSP. DOE is maintaining the estimated 1.25 manufacturer markup factor in the final rule, as utilized in both the preliminary

and NOPR analys e s.

5.8 ENER GY USE CALCULATIONS

5.8.1 Energy Use Calculation Overview

DOE calculated the energy use of ice makers representative of the analyzed equipment

class es, both as built and using alternative designs, to estimate the energy savings potential of design options. R everse engineering of these products, as described in S ection 5.5.2

, provided

the basis for th ese calculations

. Using the estimates of energy use and man ufacturing cost for these designs

, DOE developed incremental cost estimates for multiple efficiency levels for all of

the analyzed classes

.

In response to both public and confidential comments and discussions, DOE adjusted its

energy use analysis. DOE obta ined additional data through equipment testing, as well as

discussions conducted by DOE’s contractor with manufacturers under non

- disclosure
agreements. Therefore

, the current energy analysis is a sy nthesis of data from several sources, including reverse e ngineering

, energy testing (S ection 5.5.4 ) , manufacturer interviews, and simulation using the FREEZE software. FREEZE simulates the ice making process for both

batch and continuous units and is described in more detail in A ppendix 5A as well as chapter 5 of the preliminary analysis TSD

.

During the NOPR a nalysis, DOE made several changes to its analysis of ice maker

energy use , including , for bat ch ice makers :

• adjust ing the compressor performance calculations

— this adjustment involved assuming the compressor delivers less refrigerant flow than projected by compressor vendor performance data, with no reduction in power input ;

• using data from tests o f ice makers to model the increase of harvest energy as condensing temperature decreases;

• considering reduction in potable water use down to 20 gallons per 100 lb ice for ice maker designs using more than this amount of potable water ; and

• incorporating ene rgy use reduction for drain water heat exchangers used in batch equipment.

5

- 21

### The NOPR analysis also considered use of larger water

- cooled condensers, which had not been considered during the preliminary analysis.

For the final rule, DOE made additional changes to the energy use anlaysis , based on public comments and confidential data received in discussions with manufacturers . These adjustments are further discussed in section 5.10.2 and included :

• review of maximum

- available ice makers used for calibration of energy use

calculations for maximum

- available design combinations, leading to adjustment of

some of the maximum available effi ciency levels;

• a data

- based approach for calculating the energy use savings associated with increase in compressor EER ;

• considering sizes for water

- cooled condensers that represent standard sizes available from vendors;

• adjusting the efficiency assumptions for ECM motors;

• elimination of some design options that increase package size for specific classes

.

5.8.2 Application of Design Options and Calibrations

To guide the application of design options in creating cost

- efficiency curves

, DOE calibrated its analysis using data gathered from purchased commercial products. For a given equipment class, DOE compared the calculated efficiency of a given design with energy use ratings and/or energy test data for high efficiency ice makers within the same equipment class.

DO E used such comparison s to calibrate the energy use calculations associated with the maximum available technology level that currently exist s in units available on the market.

For example, consider the IMH

- A

- Small

- B class, where DOE inspected ice makers a t
both the baseline and max available efficiencies , namely 0.1% and 19.3% below the current standards, respectively

. These inspections showed the following differences in the max imum available efficiency unit as compared with the baseline unit :

• i ncreases i n both the evaporator face area and condenser volume, and an increase in the chassis size to accommodate these growths

• a n increase in condenser fan size and a change from a shaded pole motor ( SPM )

motor to a permanent split capacitor ( PSC ) motor

• a n increase in compressor energy efficiency ratio ( EER )

T hrough this process, the energy use analysis projected energy use for ice maker design s that comprise all applied design options. Subsequently, t o create a cost

- efficiency curve, these design options are then ordered sequentially according to their respective impact on energy use reduction on a per unit cost basis. For the IMH

- A

- Small

- B equipment class, DOE selected the following design options to increase efficiency from baseline to 2 5 .5 percent below baseline, as

shown in Table 5.8

. 1

.

Table 5.8

. 1 IMH

- A

- Small

- B Design Options

% Below Baseline Design Option

0.0 Baseline

5

- 221.0 Increase Condenser Width by 1.3"

4. 8 Increase Compressor EER from 4.86 to 5.20

6.5 Switch to PSC Condenser Fan Motor

8.5 Switch to ECM Condenser Fan Motor

21. 3 Increase Evaporator Area by 51% (with chassis growth)

22. 5 Switch to ECM Pump Motor

25.5 Drain Water Heat Exchanger



### The maximum level of efficiency reached may not be equivalent to that of the maximum

available unit. In some cases, DOE inspected multiple high efficiency units in order to gain additional insight into appropriate design options found in the most ef ficient units. Furthermore,

DOE c onsidered design options that were different from the max available unit, based on other available data. For example, when more efficient compressor options were available, DOE may

have se lected a higher compressor EER design option than the maximum available unit inspected. Additionally, DOE may have selected more efficient motors than those present in the maximum available units, based on data supporting its engineering feasibility. DOE d id not consider chassis sizes larger than those available on the market.

In selecting its design options, DOE calibrated each cost

- efficiency curve against the maximum available efficiency. Specifically, DOE aimed to select a set of design options,

exclus ive of ECMs and DWHXs, in order to reach the energy efficiency level of the maximum

available unit , and to represent designs that are consistent with the maximum available units

. Both ECMs and DWHXs were excluded from the calibration, as DOE determined tha t these technologies are not commonly used in current ice makers. In the current example, DOE chose all design options for the IMH

- A

- Small

- B class, except for those involving ECMs and DWHXs,

such that the analysis representing design options used in maximum

- available units predicts

energy use equal to the maximum available efficiency level, i.e. 19.3% below baseline

. DOE adopted this methodology to ensure selection of design options for energy use analysis that reflect designs that currently exist in the market. After the NOPR phase, DOE obtained additional information through rulemaking comments, additional testing, and confidential information exchange between DOE’s contractor and manufacturers. These add itional data

allowed more precise calibration in the final rule analysis than DOE was able to achieve in the NOPR analysis.

5.9 D E SIGN OPTIONS

After conducting the screening analysis described in chapter 4, DOE considered the remaining technologies in the des ign option analysis. Table 5.9

. 1 lists the design options DOE

considered for each equipment class . Some design options are applicable only to certain types of equipme nt. Following the table is a description of how DOE applied each of the design options during the engineering analysis. See chapter 3 for background descriptions of the technologies.

5

- 23

### Table 5.9

. 1 Design Options by Equipment Class

Ice Maker Type Equipment

ClassHigher-EERCompressorHigher-EfficiencyCondenser FanMotorHigher-EfficiencyPump MotorHigher-EfficiencyAuger MotorLargerAir-CooledCondenserLarger WaterCooled CondenserBatch Fill

Batch IMH

- W

- B   

* IMH

- A

- B    

RCU

- B    

SCU

- W

- B   

SCU

- A

- B    

Continuous IMH

- W

- C  

IMH

- A

- C    

RCU

- C    

SCU

- W

- C  

SCU

- A

- C    
*Used in a few limited cases where this design option was applicable. This design option also included reduction of potable water use to 20 gal/100 lb ice, if the potable water use for the analyzed ice maker was greater than this level.



### 5.9.1 Design Options That Were Not Considered for Further A nalysis

5.9.1.1 Increased Condenser Water Flow

In the preliminary analysis, DOE proposed an approach for balancing energy and condenser water use in the engineering analysis that considers the cost implications of changing

condenser water use , while maintain ing the rulemaking’ s focus on energy use reduction . DOE proposed using appropriate representative values for water and energy costs, product lifetime, and discount rates to calculate a representative LCC for baseline and modified design configurations as part of the engineerin g analysis. In this way, the engineering analysis would develop a relationship between energy efficiency and manufacturing cost as is customary in engineering analyses ( i.e. , the cost

- efficiency curves), but the ordering of different design configurations in this curve would be based on minimizing the representative LCC calculated for the candidate design configurations at each successive efficiency level. This approach would have provided a way to simultaneously consider the costs of both electricity and w ater in development of the engineering analysis.

To obtain data for developing a relationship between increased condenser water flow rate and energy consumption, DOE tested three water

- cooled ice makers , testing each multiple times with varying condensing water flow to evaluate the potential for energy savings and the cost

effectiveness of increasing condenser water use . The results of this evaluation are shown in

Table 5.9

. 2 through Table 5.9

. 4 . The analysis assumed that in the field

, half of the ice makers would be used in open systems and half in closed

- loop systems . In open syst ems, the water is drained after passing through the ice maker, hence system water consumption is equal to the flow of water through the ice maker. In closed

- loop systems the water that flows through the ice maker is cooled in a cooling tower or fluid coole r and recirculates back to the ice maker

. DOE’s

5

- 24

### analysis assumed use of a cooling tower and calculated water evaporation based on a heat removal rate of 1,000 Btu/lb water.

DOE contracted with an independent third

- party test lab to conduct the tests . The test lab varied the water flow levels by adjusting the valve s controlling condenser water flow

.

Table 5.9

. 2 Condenser Water Flow Test Data for a Water

- Cooled IMH Batch Unit

Test 1 Test 2 Test 3 Average Condensing Temperature

 ̊F 97 107 111

Harvest Capacity

lb/24 hours 375 361 355

Energy Consumption

kWh/100 lb 4.67 5.13 5.28

LCC Operating Cost
$/100 lb $1.75 $1.38 $1.32

Condenser Water Use

g al/100 l b 165.4 106.5 94.1



### Table 5.9

. 3 Condenser Water Flow Test Data for a Water

- Cooled SCU Continuous Unit

Test 1 Test 2 Test 3 Condensing Temperature

 ̊F 103 107 114

Harvest Capacity

lb/24 hours 515 514 491

Energy Consumption

kWh/100 lb 3.81 3.89 4.17

LCC Operating Cost
$/100 lb $1.21 $1.05 $1.00

Condenser Water Use

g al/100 lb 106.3 81.7 71.1



### Table 5.9

. 4 Condenser Water Flow Test Data for a Water

- Cooled IMH Continuous Unit

Test 1 Test 2 Test 3 Condensing Temperature

 ̊F 97 108 118

Harvest Capacity

lb/24 hours 654 618 521

Energy Consumption

kWh/100 lb 3.65 4.03 4.51

LCC Operating Cost
$/100 lb $1.10 $0.95 $0.91

Condenser Water Use

g al/100 lb 92.3 64.9 52.2



### The analysis shows that increasing condenser water flow would increase cost for all three

units , even for the first step in water use increase/energy use decrease . DOE concludes that this is

not a cost

- effe ctive way to reduce energy use. As a result, DOE d id not comprehensively

evaluate this design option for water

- cooled equipment classes

.

5

- 25

### 5.9.2 Improved Compressor Efficiency

DOE considered the substitution of higher efficiency compressors for all equipment

class es. DOE acquired compressor performa nce data from compressor manufacturers for use in the energy analysis, including capacity and power input for scroll and reciprocating compressors with capacities of 1

, 000 to 36

, 000 British t hermal u nits per hour ( Btu/h ) rated at A H RI 540

m edium t emperature testing cond itions. Table 5.9

. 5 lists the A H RI 540 m edium t emperature operating test conditions. DOE selected compressors for substitution in its analysis with similar capacities to those found in baseline units so as to isolate the impact of compressor efficiency.

Table 5.9

. 5 A H RI 540 Medium Temperature Compressor Rating Test Conditions
Condensing Temperature 120 o F Evaporating Temperature 20 o F Suction Temperature 40 o F Subcooling 0 o F



### During the NOPR phase, DOE updated the compressor performance database with new compressors and solicited further feedback from stakeholders on the acceptability of certain compressor lines. DO E did not consider in its analysis compressors that manufacturers indicated

are unsuitable for use in ice makers. Issues associated with compressors include the following:

• Some compressor families used a direct suction design that has a large impact on

com pressor reliability in batch

- type ice maker applications, due to the requirements of the harvest period

.

• Some compressor families have had reliability issues for both continuous and batch

machines and thus present utility issues for their use

.

• Some compres sor families have unacceptable levels of noise and/or vibrations for this application , again presenting utility issues

.

In its analysis of ice maker energy use in the final rule , DOE adopted a data

- based approach to estimate the impact on energy usage of increases in compressor efficiency. Based on

confidential test data collected from manufacturers under non

- disclosure agreements and test data provided in comments , DOE calculated an average percentage ice maker energy use reduction equal to 57% of the percentage compressor energy use reduction. Hence, the energy

use reduction for the ice maker was calculated as follows.

PP IIIIII MM MM EE EEEE UU RR RR RR = 57% × ( 1

− EEEEEE bbbbbbbbbbbbbbbb

EEEEEE iiiiiiiiii ii 

### )

5.9.2.1 Compressor Cost Data

During the preliminary analysis, DOE developed a cost correlation for medium

temperature compressors as part of the c ommercial r efrigeration e quipment (CRE) rulemaking

, relating the compressor cost to its capacity

. DOE assigned the cos t derived using the cost

correlation to medi an

- EER compressors of a given capacity. Th e cost curve applicable for

medi an

- EER compressors is shown in Figure 5.9

. 1

. The cost curve is based on capacity at

5

- 26

### modified operating conditions, at 15 ° F evaporating temperature and 95 ° F condensing temperature. On average, the capacity for these conditions is 1.29 times the capacity at the AHRI 540 rating conditions. For the NOPR an alysis, b ased on feedback from the manufacturers, DOE applied an additional markup of 1.76 to the purchased cost of the compressors to arrive at the

final cost to the manufacturer

. This markup was maintained in the final rule analysis.

Figure 5.9

. 1 Final Rule Cost Curves for Medium

- Temperature Compressors U sed in

Commercial Ice M aker s

DOE utilized a modified approach for determining the cost of increasing EER by substituting a higher efficiency c ompressor, based on the preliminary analysis and feedback

received in the NOPR phase

. DOE used two approaches to compute the cost for compressor efficiency increase, using the higher of the two in the analysis. The two approaches are explained

below.

The f irst approach

, cost A, applies a 5

- percent cost increase for each 10

- percent increase in EER, consistent with the approach used in the NOPR analysis

. This cost relationship is represented by the following exponential equation :

CC AA = BB BB CC × 1

. 05 � llllll 1

. 1 � NNNNNN EEEEEE

OO OO EEEEEE � �



### Eq. 5

. 2

The alternate approach, cost B

, is based on a pplying a 5% increase in compressor cost for

a future 2% increase in effic iency , i.e., that this 2% increase would exceed the maximum

efficienc y available in currently available compressors

. However, rather than projecting 2% EER improvement over the best

- efficiency compressors currently available, DOE applied the cost to

the la st 2% EER improvement that increases EER to its current maximum level. The calculation

5

- 27

### also assumes that there is otherwise no cost difference among existing compressors of a given capacity. The exponential relationship used for this calculation is as foll ows:

CC BB = BB BB CC × 1

. 05 � llllll 1

. 02 � mmmmmm � NNNNNN EEEEEE

OO OO EEEEEE , 1

. 02 � � �



### Eq. 5

. 3

5.9.3 Increased Air

- Cooled Condenser Surface Area

The condenser transfers heat from the refrigerant to the a mbient air. A l arger surface area allows the heat transfer to occur more efficiently , allowing the refrigerant circuit to operate with a lower condensing temperature . DOE considered an increase in condenser surface area for all

air

- cooled equipment types

. In the final rule analysis, DOE consider ed condenser size increases that would result in an increase in chassis size, after review ing the space available in reverse engineered units. This reverse engineering data determined how much condenser growth c ould be possible before increasing overall equipment size . In some cases, DOE determined that no size increase was possible. For RCU ice makers, DOE considered condenser size increases that required an increase in remote condenser size.

DOE calculated the energ y use reduction associated with c ondenser size increases using a hybrid approach based in part on use of the FREEZE model. DOE adjusted the input parameters describing the condenser geometry ( e.g. the number or length of tube rows

, the fin details, etc.),

the air flow rate , the fan motor power input, and the duration and energy use associated with the harvest cycle

.

The changes in harvest cycle parameters were based on testing conducted on a water

cooled batch ice maker as summarized in Table 5.9

. 6

. Based on these measurements, DOE developed linear relationships between percentages of increases in harvest cycle energy use and duration as functions of condensing tempera ture, which DOE used to adjust FREEZE model inputs for these parameters. DOE calibrated the approach with test data received during the NOPR phase under NDA by DOE’s contractor, for which the condenser of an air

- cooled ice maker was replaced with a larger condenser. DOE’s analysis predicted the reduced energy use

associated with this test to within 0.5%.

Table 5.9

. 6 Batch Ice Maker Evaluation of Harvest Cycle Dependence on Condensing Temperature

Test 1 Test 2 Test 3 Average Condensing Temperature

 ̊F 97 107 111

Harvest Cycle Energy Use

Watt

- Hours 21.2 17.9 17.0

Harvest Cycle Duration

seconds 104 81 73



### To calculate the adjusted fan motor power input, DOE assumed that for the initial design,

three

- quarters of the total condenser air flow system pressure drop is associated with pressure drop through the condenser itself. To calculate the pressure drop asso ciated with a modified design, DOE assumed that pressure drop through the condenser is proportional to condenser face

5

- 28

### velocity. DOE calculated fan motor power input assuming that power input is proportional to air flow times pressure drop.

I n the final rul e analysis, DOE adjusted its condenser coil cost estimate s based on

feedback from stakeholders. DOE calculated t he cost associated with condenser size increase s using the manufacturing cost model. The calculated condenser cost s increase roughly linearly

wi th condenser core volume (finned region face area times fin depth)

.

5.9.4 Increased Water

- Cooled Condenser Surface Area

DOE considered increased size of water

- cooled condensers in its analysis. Water

- cooled

condensers are coaxial

- tube heat exchangers in which w ater flows inside an inner tube and refrigerant condenses in the annular space between the inner and outer tubes. The tube assembly is generally wrapped in a circular or oval pattern to minimize the required dimensions of the ice maker package. The amount of condenser heat transfer surface area can be characterized by the length of the unwrapped tube and its diameter. In the final rule analysis, DOE considered

condenser sizes corresponding to the standard sizes of fered by suppliers.

DOE used the FREEZE mod el with adjustment of condensing temperature and harvest cycle input parameters to calculate the energy use reduction s associated with increase in water

cooled condenser size

. Section 5.9.3 discusses the adjustment of the harvest cycle parameters. The model uses condensing temperature as an input, based on the typical practice of controlling condenser water flow with a regulating valve designed to maintain a specifi c condensing

pressure. DOE related condensing temperature to condenser size using a simplified calculation in which the transferred load is set equal to the UA

- value (overall heat transfer coefficient times surface area) time the log

- mean temperature diffe rence calculated assuming that the refrigerant temperature is equal to the condensing temperature throughout the condenser length. This approach underestimates the required surface area because it doesn’t take into consideration the increased temperature d ifference between the refrigerant and the water in the desuperheating region of the condenser.

5.9.5 Evaporator Size Increase

The evaporator transfer s heat from the freezing liquid water to the refrigerant. Larger surface area allows the heat transfer to occur more efficiently , allowing the refrigeration system to transfer the same amount of heat with a higher evaporating temperature

. In the final rule analyses, DOE considered an increase in evaporator surface area for the IMH

- A

- Small

- B, IMH

W

- Small

- B, and SCU

- W

- Large

- B equipme nt classes. In many batch classes, DOE concluded that space for increased evaporator size would not be available within the ice maker package

size. DOE did not consi der evaporator size increase for continuous classes. DOE calculated energy use reductions associated with this design option using the FREEZE model by increasing the input UA value (heat transfer coefficient times effective evaporator surface area, with ov erall

units of Btu/h

- ° F) consistent with the surface area increase. DOE also increased the input parameters for harvest cycle energy use and harvest cycle time proportional to the surface area increase, to account for the larger size of evaporator required to be warmed during the harvest cycle. In the final rule analysis, DOE doubled its NOPR

- phase cost estimate s associated with increased evaporator size, based on confidential discussions with manufacturers.

5

- 29

### W ith application of evaporator size increase tha t necessitates increase in package size,

DOE also added additional shipping cost associated with the larger packages.

5.9.6 Design Options that Require Chassis Size Increases

DOE has including package

- size

- increasing design options in its final rule engineeri ng analysis for a limited number of equipment classes, specifically IMH

- A

- Small

- B, IMH

- W

Small

- B, and IMH

- A

- Large

- B

. For the last of these classes, DOE considered package size increase only for the analysis at 800 lb/24 hours representative capacity. In re sponse to comments received in the NOPR phase, DOE developed separate analyses for 22 inch wide units

of the IMH

- A

- Small

- B, IMH

- W

- Small

- B, and IMH

- A

- Large

- B classes, for DOE did not consider package size increase. DOE also considered size increase for the remote condensers of

RCU classes. DOE only considered package size increase where the representative baseline unit had space to grow relative to the largest units on the market with similar harvest capacity within the given class

— DOE limited the growth so that final equipment sizes would be consistent with these largest market sizes . The equipment growth allowed for consideration of larger heat exchangers to increase equipment efficiency.

Table 5.9

. 7 shows the maximum size s DOE considered in the analysis. As mentioned, the

new chassis sizes are consistent with sizes of current units of the given classes that are commercially available

.

Table 5.9

. 7 Description of Ice Making Head Size Increase s in the Engineering Analysis
Equipment class Size Descriptor Height

inches Width

inches Depth

inches Volume cubic feet

IMH

- A

- Small

- B Baseline 17 30 25 7.0

Growth 22 30 25 9.1

IMH

- A

- Large

- B ( 800 lb/24 hours representative harvest capacity ) Baseline 26 30 24 10.8

Growth 29 30 24 12. 1

IMH

- W

- Small

- B Baseline 20 30 24 8.3

Growth 22 30 25 9. 1



### DOE initially calculated costs associated with chassis size growths in the NOPR phase using the upda ted cost model and added the se costs to the incremental costs associated with heat exchanger growths in these equipment classes

. For the IMH

- A

- Small

- B and IMH

- W

- Small

- B classes, the size increases were associated with evaporator size increases. As described in section

5.9.5 , DOE doubled the overall cost increases for this design option (including the cost associated with the chassis size increase). For the IMH

- A

- Large

- B class, the package size

increase was assoc iated with an increase in height of the condenser. For the final rule analysis, DOE adjusted costs associated with the chassis and the condenser based on discussions with

manufacturers.

5.9.7 Condenser Fan Motors

A condenser fan in a commercial ice maker draws in ambient air and circulates it through

the condenser coils and over the compressor shell, thereby cooling the condensing refrigerant

and the compressor. The motors used to run these fans are typically s haded p ole (SP) or

p ermanent s plit c apacitor (PSC) motors with shaft power outputs ranging from 3 to 750 W

,

5

- 30

### depending on the ice maker’s harvest capacity . DOE considered use of higher

- efficiency motors, specifically upgrade from SP to PSC or to electronically commutated (ECM) motors

.

In the final rule ana lyses, DOE adjusted its assumptions for motor efficiencies as compared with the NOPR and preliminary analyses . DOE also updated costs associated with

these motors based on stakeholder feedback. In the final engineering analysis, DOE assumed that

the efficiencie s of the three motor types are 3 5 % for SP, 45 % for PSC, and 66 % for ECM s . DOE

also assumed that the relative efficiencies of these motor types are constant through range of shaft power considered . For example, at a lower shaft power level

, a sha ded power motor may have an efficiency of 25 %

. T he efficiency of a replacement standard

- efficiency PSC motor

would have been calculated as 25 % ÷ 35% × 45 % = 32 %

.

DOE ’s final rule cost assumptions for the different motor types as a function of shaft
output power are summarized in Table 5.9

. 8

.

Table 5.9

. 8 Motor Cost Information Use for Final Rule Engineering Analysis

Shaft Power

W Horsepower Costs by Motor Type
$

Shaded Pole Permanent Split Capacitor Electronically

Commutated

6 0.008 $12 $20 $50

9 0.012 $14 $24 $48

12 0.016 $16 $27 $47

25 0.034 $21 $35 $47

37 0.050 $23 $39 $49

60 0.080 $26 $45 $57

100 0.134 $30 $50 $76

200 0.268 $34 $58 $130

373 0.500 $38 $65 $229

560 0.750 $41 $82 $326

746 1.000 $43 $102 $420



### 5.9.8 A uger Motors

Augers are integrated into the evaporators of continuous commercial ice maker s , as discussed in chapter 3 of the final rule TSD. Auger assemblie s incorporate a motor and a gear drive to reduce the motor speed to th at of the auger . The motors typical ly used for augers in ice makers using single

- phase power are PSC or c apacitor s tart i nduction r un (CSIR) motors with shaft power output between 2 0 0 and 400 W

.

For the final rule engineering analysis, DOE considered conversion to a n ECM motor to improve efficiency. d 

### DOE’s analyses involved auger motors with shaft power of 1⁄4 and 1⁄2 horsepow er with corresponding baseline costs of $315 and $400, respectively. DOE further assumed that the motor cost would double when switching to a ECM motor.

d Throughout much of the TSD and Final Rule, DOE discusses these motors as electronically commutated motoros (ECMs). However, the term ECM implies that the motor uses direct current (DC) power. DOE has used the term ECM to refer more broadly to PMDC motors that may be driven using DC or three phase power.

5

- 31

### 5.9.9 P ump Motors

Water pumps are used in batch commercial ice maker s to assist in draining water from

the w ater sump during the harvest cycle and to circulate sump water back to the evaporator during the freeze cycle. The motors used for these pumps are generally shaded pole motors with shaft power outputs between 6 and 2 5 W

. In the final rule analyses, DOE used the updated information collected for condenser fan motors to evaluate pump motor design options, assuming that this information is also representative of the potential for performance improvement and cost increase for pump motors (se e s ection 5.9.7 for details)

.

5.9.10 Drain W ater H eat E xchanger

In the NOPR analysis, DOE reconsidered its preliminary suggestion that external drain water heat exchangers cannot be considered part of an ice maker simply because they are not specifically mentioned in the EPCA definition, and concluded that they can be considered as a design option and to be part of a basic model ice maker, assuming that the drain water heat exchanger is sold and shipped with the unit and that the installation and operating instructions clearly reinforce this inclusion by detailing the installation requiremen ts for the heat exchanger.

As a result , DOE accounted for the use of a drain water heat exchanger as a design option in

batch ice makers. DOE’s final rule analysis also includes consideration of drain water heat exchangers for batch ice makers.

DOE conduct ed testing of a commercially available drain water heat exchanger with a

batch water

- cooled IMH unit to confirm the efficiency improvement possible with this design option

. The test results are shown in Table 5.9.9

.

Table 5.9

. 9 Energy Consumption Tests with a Drain Water Heat Exchanger

Baseline Test With Drain Water Heat Exchanger Average Condensing Temperature

 ̊F 97 96

Harvest Capacity

lb /24 h ou r s 375 384

Energy Consumption

kWh/100 lb 4.67 4.49

Percent Reduction in Energy Consumption from Baseline 0.0% 3.9%



### When calculating the energy use reduction associated with this design option, DOE

reduced the energy use of the previous design configuration by 3.9 percent , resulting in savings

that are less than 3.9 percent of the baseline energy use

.

DOE conducted a teardown on the purchased drain water heat exchanger and als o interviewed several component manufacturers. DOE noted that some parts of the drain water heat exchangers are considered proprietary and protected by patent. Because of this, DOE

evaluated the drain water heat exchanger as a purchased part

— not as a compo nent assembled from parts fabricated with in the factory. As a result, the estimated cost for this design option is relatively high, being based on vendor pricing information. DOE used a cost of $270 for a larger

9

- inch

- diameter version of the heat exchange r (for products with harvest capacity greater than

5

- 32

### 1

, 000 lb/24 hours ) and a cost of $210 for a smaller

, 7

- inch

- diameter version of the heat exchanger (for products with harvest capacity less than 1

, 000 lb/24 hours ). DOE also considered

that two drain water heat exchangers could be attached in series for ice makers with harvest capacity greater than 2,000 lb/24 hours

.

5.9.11 Batch Water Fill and Reduced Potable Water Flow for Batch

- Type Ice Makers

In the final rule analysis, DOE considered use of batch water fill a nd reductions in potable water flow for batch ice makers . With batch water fill, all of the potable water needed for the freeze cycle is added to the ice maker sump upon the initiation of a freeze cycle. This allows for cooling of all of the water at the start of the freeze cycle when the evaporator is relatively warm and the evaporating temperature is higher. Provision of cooling by the refrigeration system during this part of the cycle is more efficient, due to the higher evaporating temperature. General ly, adopting this approach requires use of a solenoid valve for filling the sump, more sophisticated controls, and greater sump volume to contain all of the potable water required for a freeze cycle

. The greater control system sophistication allows more pr ecise control of total potable water use, which DOE considered as part of conversion to batch fill. DOE

calculated costs for adoption of this design option based on these design changes and assuming

that electronic controls would be required. Based on fee dback from stakeholders, DOE limited the decrease in potable water flow to 20 gallons per 100 lb of ice, to ensure proper drainage of particulates from the sump. DOE consider ed this design option only for equipment classes for

which the baseline unit selected for the analysis did not have a batch

- fill system.

5.10 ENGINEERING ANALYSIS RESULTS

This section presents the engineering analysis results, including the incremental cost

curves. DOE develop ed cost

- efficiency curves for ice maker equipment class es, based on combinations of individual design options. DOE subsequently created cost

- efficiency curves based on s elected efficiency levels , as previously defined for each equipment class (see section

5.4 ) for simplified downstream analysis.

5.10.1 Overview

Table 5.10

. 1 presents MPC, MS P , and water use characteristics for baseline bat ch ice

maker configuration s that DOE directly analyzed

. Table 5.10

. 2 presents incremental MSP for the evaluated efficiency levels for batch ice makers. The representative harvest capacities indicated in the tables are those of the products that were the bases of the analyses

.

Table 5.10

. 1 Batch Ice Maker Baseline Characteristics

Equipment Class Repre se ntative

Harvest Capacity

lb/24

hours MPC MS P Potable

Water Use gal/

100 lb Condenser

Water Use gal/

100 lb

IMH

- W

Small 300 $898 $1,122 2 4.0 15 3 Small (22 inch wide) 300 $898 $1,122 24.0 153

Medium 850 $1,545 $1,931 19.7 1 53 Large 1,500 $1,70 9 $2,136 2 2.3 162

5

- 332,600 $3,74 8 $4,684 19.7 174

IMH

- A

Small 300 $91 6 $1,144 2 5.6

NA Small (22 inch wide) 300 $91 6 $1,144 18.5

Large 800 $1,49 4 $1,867 2 2.7
1,500 $1,652 $2,06 5 20.0 Large (22 inch wide) 800 $1,49 4 $1,867 18.0

RCU

Large 1,500 $1,970 $2,463 20 .0 NA 2,400 $3,20 3 $4,003 19.7

SCU

- W Large 300 $1,224 $1,530 30.0 1 32

SCU

- A

Small 110 $1,051 $1,314 3 4.0 NA Large 200 $1,121 $1,40 2 30.0



### Table 5.10

. 2 Incremental Manufacturer Selling Price Results for Batch Ice maker s

Equipment

Class Max

Available Repre sentative

Harvest Capacity

lb/24 hours EL 2 * EL 3 EL 4 EL 5 EL 6 Max Tech

Level Cost

IMH

- W

Small 19.1 % 300 $44 $79 $145 2 3. 9 % $ 373

Small (22 in ch wide ) 16.9% 300 $49 $87 $345 21.5% $363

Medium 14.3 % 850 $67 $204 18 .1 % $410

Large 5 % 1,500 8 .3 % $ 419

2.5 % 2,600 7 .4 % $ 526

IMH

- A

Small 19.3 % 300 $60 $100 $139 $386 25.5% 390

Small (22 in ch wid e ) 16.6% 300 $57 $301 18.1% $324

Large 16.1 % 800 $45 $92 $158 23.4% $381

6.0 % 1,500 $427 11.8% $474
Large (22 inch wide ) 5.5% 800 $75 $316 15.8% $321

RCU

Large 15 .7 % 1,500 $59 $254 17.3% $468

1 4.9 % 2,400 $285 13.9% $796

SCU

- W Large 27.6 % 300 $45 $69 $93 $121 29.8% $361

SCU

- A

Small 31.2 % 110 $24 $38 $53 $97 $148 32.7% $367
Large 27.9 % 200 $29 $45 $72 $107 29.1% $349

* EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL6 represent increased efficiency levels.



### Table 5.10

. 3 and Table 5.10

. 4 present the engineering analysis results for continuous ice

makers. The table presents baseline ice maker condenser water use with adjustment for ice quality as required by the test procedure final rule

.

5

- 34

### Table 5.10

. 3 Continuous Ice Maker Baseline Characteristics

Equipment

Class Representative

Harvest Capacity

lb/24 hours MPC MS P Condenser

Water Use gal/100 lb Potable Water

Use gal/100 lb

IMH

- A

Small 310 $2,706 $3,381 NA 12.0

820 $2,090 $2,613 NA 13.6

RCU

Small 8 00 $2,090 $2,613 NA 12.0

SCU

- A

Small 22 0 $1,411 $1,764 NA 12.0



### Table 5.10

. 4 Incremental Manufacturer Selling Price Results for Continuous Ice Maker s

Equip ment

Class Max

Available Representative

Harvest Capacity

lb/24 hours EL 2 * EL 3 EL 4 EL 5 EL 6 Max Tech

Level Cost

IMH

- A

Small 25.4% 310 $33 $54 $76 $390 25.7% $393

Large 17% 820 $30 $48 $488 23.3% $502

RCU

Small 18.4 % 8 00 $30 $72 $102 $523 26.6% $540

SCU

- A

Small 24.4 % 220 $14 $29 $53 $377 26.6% $382

* EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL6 represent increased efficiency levels.



### For equipment classes that DOE did not d irectly analyze (the secondary equipment classes), DOE used results of a directly analyzed equipment class to represent the cost

- efficiency

characteristics

. Table 5.10

. 5 shows the associations between directly analyzed and secondary equipment class es. These correlations are based on the availability of similar design options for

similar equipment classes and observation that the range of efficiency levels attained by commercially available units are similar

.

Table 5.10

. 5 Directly Analyzed Equipment Classes Used to Develop Standards for Secondary Classes

Seco ndary
Equipment Class Directly Analyzed Equipment Class
Associated with Secondary Equipment Class

RCU

- NRC

- Small

- B RCU

- NRC

- Large

- B

RCU

- RC

- Small

- B RCU

- NRC

- Large

- B

RCU

- RC

- Large

- B RCU

- NRC

- Large

- B

SCU

- W

- Small

- B SCU

- W

- Large

- B

IMH

- W

- Small

- C IMH

- A

- Small

- C

IMH

- W

- Large

- C IMH

- A

- Large

- C

RCU

- NRC

- Large

- C RCU

- NRC

- Small

- C

RCU

- RC

- Small

- C RCU

- NRC

- Small

- C

RCU

- RC

- Large

- C RCU

- NRC

- Small

- C

5

- 35SCU

- W

- Small

- C SCU

- A

- Small

- C

SCU

- W

- Large

- C SCU

- A

- Small

- C

SCU

- A

- Large

- C SCU

- A

- Small

- C



### 5.10.2 Design Option Analyses

Table 5.10

. 6 through Table 5.10

. 24 summarize the engineering analysis at the design option level for the directly

- analyzed equipment classes.

Table 5.10

. 6 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Less Than 450 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

7.68 0.0% $91 6 $1,144 Baseline

7.61 1.0% $91 9 $1,14 9 Increase Condenser Width by 1.3"

7.31 4.8% $936 $1,169 Increase Compressor EER from 4.86 to 5.20

7.18 6.5% $94 8 $1,184 Switch to PSC Condenser Fan Motor

7.03 8.4% $963 $1,20 4 Switch to ECM Condenser Fan Motor

6.05 21.3% $1,065 $1,33 1 Increase Evaporator Area by 51% (with chassis growth)

5.95 22.5% $1,086 $1,358 Switch to ECM Pump Motor

5.72 25.5% $1,305 $1,631 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Representative Harvest Capacity: 30 0 lb/24 hours



### Table 5.10

. 7 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes)

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

6.01 0.0% $1,49 4 $1,867 Baseline

5.47 9.0% $1,53 2 $1,91 5 Increase Compressor EER from 5.66 to 6.7

5.31 11.7% $1,550 $1,93 8 Switch to PSC Condenser Fan Motor

5.13 14.7% $1,563 $1,954 Switch to ECM Condenser Fan Motor

5.01 16.7% $1,59 4 $1,992 Change to Batch Water Fill

4.93 17.9% $1,61 4 $2,017 Switch to ECM Pump Motor

4.85 19.3% $1,6 40 $2,049 Increase Condenser Height by 3" (with chassis growth)

4.79 20.3% $1,65 7 $2,07 1 Increase Condenser Width by 3.25"

4.60 23.4% $1,875 $2,344 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Representative Harvest Capacity: 80 0 lb/24 hours

5

- 36

### Table 5.10

. 8 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Larger Sizes)

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

5.24 0.0% $1,652 $2,06 5 Baseline

5.15 1.6% $1,67 4 $2,092 Switch to PSC Condenser Fan Motor

5.06 3.4% $1,710 $2,13 8 Switch to ECM Condenser Fan Motor

4.99 4.7% $1,73 7 $2,17 1 Increase Compressor EER from 7 to 7.16

4.83 7.8% $1,81 9 $2,273 Increase Condenser Width by 6", Add One

Additional Condenser Row

4.81 8.2% $1,839 $2,299 Switch to ECM Pump Motor

5.24 11.8% $2,126 $2,65 8 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 150 0 lb/24 hours



### Table 5.10

. 9 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Less Than 500 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

6.14 0.0% $898 $1,122 Baseline

5.72 6.8% $91 9 $1,148 Increase compressor EER from 4.59 to 5.2

5.22 15.0% $977 $1,221 Increase Condenser Length by 59%

5.01 18.3% $1,007 $1,259 Change to Batch Water Fill

4.96 19.2% $1,017 $1,271 Increase Evaporator Area by 4.7% (with chassis growth)

4.86 20.8% $1,052 $1,315 Switch to ECM Pump Motor

4.68 23.9% $1,27 1 $1,588 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 30 0 lb/24 hours



### Table 5.10

. 10 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH

Batch Ice Machines with Capacities between 500 and 1

, 436 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

4.65 0.0% $1,545 $1,931 Baseline

4.40 5.2% $1,566 $1,958 Increase Compressor EER from 5.63 to 6.2

4.27 8.1% $1,584 $1,980 Increase Compressor EER from 6.2 to 6.7

4.19 9.7% $1,604 $2,005 Switch to ECM Pump Motor

3.96 14.8% $1,73 6 $2,170 Increase Condenser Length by 72%

3.80 18.1% $1,95 5 $2,443 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 85 0 lb/24 hours

5

- 37

### Table 5.10

. 11 Manufacturing Cost and Energy Consumption Data for Wate r

- Cooled IMH Batch Ice Machines with Capacities Greater Than 1,436 lb/24 hours ( Smalle r Sizes)

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

4.00 0.0% $1,709 $2,136 Baseline

3.95 1.3% $1,733 $2,167 Increase Compressor EER from 7 to 7.16

3.82 4.4% $1,821 $2,276 Increase Condenser Size

3.81 4.7% $1,842 $2,302 Switch to ECM Pump Motor

3.67 8.3% $2,128 $2,660 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 150 0 lb/24 hours



### Table 5.10

. 12 : Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Greater Than 1

, 436 lb/24 hours (Larger Sizes)

Energy Consumption

kWh/100 lb Percent Energy

Use

Re duction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

4.00 0.0% $3,748 $4,684 Baseline

3.90 2.5% $3,787 $4,734 Increase compressor EER from 6.05 to 6.32

3.85 3.7% $3,826 $4,783 Switch to ECM Pump Motors (2)

3.70 7.4% $4,274 $5,342 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Representative Harvest Capacity: 260 0 lb/24 hours



### Table 5.10

. 13 Manufacturing Cost and Energy Consumption Data for Air

- Cooled RCU Batch Ice Machines with Capacities Greater Than 1000 lb/24 hours (Smaller Sizes)

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

5.10 0.0% $1,970 $2,463 Baseline

4.93 3.4% $1,984 $2,480 Increase Condenser Height by 3" (with condenser growth)

4.65 8.9% $2,010 $2,512 Increase Compressor EER from 6.47 to 7.16

4.56 10.5% $2,039 $2,549 Increase Condenser Width by 5" (with unit growth)

4.46 12.5% $2,087 $2,609 Switch to ECM Fan Motor

4.42 13.3% $2,107 $2,634 Switch to ECM Pump Motor

4.38 14.1% $2,142 $2,678 Increase Condenser Width by 6" (with unit growth)

4.22 17.3% $2,439 $3,048 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 150 0 lb/24 hours

5

- 38

### Table 5.10

. 14 Manufacturing Cost and Energy Consumption Data for Air

- Cooled RCU Batch Ice Machines with Capacities Greater Than 1000 lb/24 hours (Larger Sizes)

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

5.10 0.0% $3,203 $4,003 Baseline

4.81 5.7% $3,246 $4,058 Increase compressor EER from 5.68 to 6.32

4.75 6.9% $3,286 $4,107 Switch to ECM Pump Motors (2)

4.65 8.9% $3,375 $4,219 Increase Condenser Width by 18" (with unit growth)

4.55 10.7% $3,561 $4,451 Switch to ECM Condenser Fan Motors (2)

4.39 13.9% $3,998 $4,998 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 240 0 lb/24 hours



### Table 5.10

. 15 Manufacturing Cost and Energy Consumption Data for Air

- Cooled SCU Batch Ice Machines with Capacities Less Than 175 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy Use

Reduction Manufacturer Manufacturer Design Options Successively Added Production

Cost Selling Price

12.84 0.0% $1,051 $1,314 Baseline

12.12 5.6% $1,064 $1,329 Increase condenser width by 2.5”, Add One

Additional Condenser Row

10.02 22.0% $1,109 $1,387 Increase compressor EER from 3.3 to 4.6 (includes smaller compressor)

9.83 23.4% $1,117 $1,396 Switch to PSC Condenser Fan Motor

9.42 26.6% $1,147 $1,434 Change to Batch Water Fill

9.13 28.9% $1,169 $1,461 Switch to ECM Pump Motor

8.96 30.2% $1,200 $1,499 Switch to ECM Condenser Fan Motor

8.64 32.7% $1,418 $1,773 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 11 0 lb/24 hours



### Table 5.10

. 16 Manufacturing Cost and Energy Consumption Data for Air

- Cooled SCU Batch Ice Machines with Capacities Greater Than 175 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy Use

Reduction Manufacturer Manufacturer Design Opti ons Successively Added Production

Cost Selling Price

9.80 0.0% $1,121 $1,402 Baseline

9.26 5.5% $1,136 $1,420 Increase condenser width by 9"

8.20 16.3% $1,171 $1,463 Increase compressor EER from 4.23 to 5.2

7.62 22.2% $1,201 $1,501 Change to Batch Water Fill

7.50 23.5% $1,213 $1,516 Switch to PSC Condenser Fan Motor

7.35 25.0% $1,228 $1,535 Switch to ECM Condenser Fan Motor

7.23 26.3% $1,251 $1,564 Switch to ECM Pump Motor

6.94 29.1% $1,470 $1,838 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 20 0 lb/24 hours

5

- 39

### Table 5.10

. 17 Manufacturing Cost and Energy Consumption Data for Water

- Cooled SCU Batch Ice Machines with Capacities Greater Than 200 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

7.60 0.0% $1,224 $1,530 Baseline

7.07 7.0% $1,254 $1,568 Change to Batch Water Fill

6.12 19.5% $1,315 $1,644 Increase Evaporator Area by 50%

5.70 25.0% $1,345 $1,681 Increase Condenser Length by 50%

5.54 27.1% $1,366 $1,708 Switch to ECM Pump Motor

5.34 29.8% $1,585 $1,981 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 30 0 lb/24 hours



### Table 5.10

. 18 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

9.05 0.0% $2,706 $3,382 Baseline

8.24 9.0% $2,734 $3,417 Increased compressor EER from 4.68 to 5.4

8.03 11.3% $2,744 $3,430 Increased condenser width by 3.5"

7.73 14.6% $2,762 $3,452 Switched to PSC Fan Motor

7.41 18.2% $2,773 $3,466 Switched to ECM Fan Motor

7.2 20.4% $2,784 $3,480 Increase condenser height by 4" (no chassis increase)

6.72 25.7% $3,099 $3,873 Switched to ECM Auger Motor

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 31 0 lb/24 hours



### Table 5.10

. 19 : Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Continuous Ice Machines with Capacities Greater Than 700 lb/24 hours (Smaller Sizes)

Energy Consumption

kWh/100 lb Percent Energy Use

Reduction Manufacturer Manufacturer Design Options Successively Added Production

Cost Selling Price

6.60 0.0% $2,090 $2,613 Baseline

5.79 12.3% $2,127 $2,659 Increase Compressor EER from 5.02 to 6.0

5.57 15.7% $2,141 $2,677 Increase Condenser Width by 1.5", Increase Tube Rows by 1

5.48 17.0% $2,162 $2,703 Switched to PSC Fan Motor

5.38 18.5% $2,189 $2,736 Switched to ECM Fan Motor

5.06 23.3% $2,592 $3,240 Switched to ECM Auger Motor

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 82 0 lb/24 hours

5

- 40

### Table 5.10

. 20 Manufacturing Cost and Energy Consumption Data for Air

- Cooled SCU Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy Use

Reduction Manufacturer Manufacturer Design Options Successively Added Production

Cost Selling Price

10.12 0.0% $1,411 $1,764 Baseline

9.18 9.3% $1,423 $1,779 Increased condenser rows to 4 from 3, larger motor

8.27 18.3% $1,450 $1,812 Changed compressor EER from 4.7 to 5.5

8.11 19.9% $1,463 $1,829 Switched to PSC Fan Motor

7.93 21.6% $1,476 $1,845 Switched to ECM Fan Motor

7.43 26.6% $1,793 $2,242 Switched to ECM Auger Motor

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 11 0 lb/24 hours



### Table 5.10

. 21 Manufacturing Cost and Energy C onsumption Data for Air

- Cooled R CU Continuous Ice Machines with Capacities Less Than 850 lb/24 hours

Energy Consumption

kWh/100 lb Percent Energy Use

Reduction Manufacturer Manufacturer Design Options Successively Added Production

Cost Selling Price

6.33 0.0% $2,090 $2,613 Baseline

5.82 8.1% $2,111 $2,639 Increase Compressor EER from 4.75 to 5.4

5.52 12.7% $2,133 $2,666 Switched to PSC Fan Motor

5.19 17.9% $2,170 $2,712 Switched to ECM Fan Motor

4.87 23.1% $2,226 $2,783 Increase Condenser Width by 23.25 inches

4.65 26.6% $2,630 $3,287 Switched to ECM Auger Motor

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 11 0 lb/24 hours



### Table 5.10

. 22 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Less Than 450 lb/24 hours (22 inch Wide )

Energy Consumption

kWh/100 lb Percent Energy Use

Reduction Manufacturer Manufacturer Design Options Successively Added Production

Cost Selling Price

7.68 0.0% $916 $1,144 Baseline

7.57 1.5% $921 $1,151 Increase Condenser Width by 2"

7.27 5.3% $937 $1,172 Increase Compressor EER from 4.86 to 5.20

7.14 7.0% $949 $1,187 Switch to PSC Condenser Fan Motor

6.99 9.0% $965 $1,206 Switch to ECM Condenser Fan Motor

6.64 13.5% $999 $1,249 Increase Evaporator Area by 18.0% (no chassis growth)

6.55 14.7% $1,021 $1,276 Switch to ECM Pump Motor

6.29 18.1% $1,240 $1,549 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated motor Representative Harvest Capacity: 30 0 lb/24 hours

5

- 41

### Table 5.10

. 23 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes

, 22 inch

Wide )

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

6.01 0.0% $1,494 $1,867 Baseline

5.80 3.5% $1,514 $1,893 Increase Compressor EER from 5.42 to 5.78

5.64 6.2% $1,532 $1,915 Switch to PSC Condenser Fan Motor

5.46 9.2% $1,546 $1,932 Switch to ECM Condenser Fan Motor

5.34 11.2% $1,576 $1,970 Change to Batch Water Fill

5.26 12.4% $1,596 $1,995 Switch to ECM Pump Motor

5.06 15.8% $1,815 $2,268 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Representative Harvest Capacity: 80 0 lb/24 hours



### Table 5.10

. 24 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Less Than 500 lb/24 hours (22 inch Wide)

Energy Consumption

kWh/100 lb Percent Energy

Use

Reduction Manufacturer Manufacturer

Design Options Successively Added Production

Cost Selling Price

6.14 0.0% $898 $1,122 Baseline

5.72 6.8% $919 $1,148 Increase compressor EER from 4.59 to 5.2

5.31 13.5% $977 $1,221 Increase Condenser Length by 59%

5.10 16.9% $1,007 $1,259 Change to Batch Water Fill

5.01 18.5% $1,042 $1,303 Switch to ECM Pump Motor

4.82 21.5% $1,261 $1,576 Drain Water Heat Exchanger

kWh = kilowatt

- hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated

motor Rep resentative Harvest Capacity: 30 0 lb/24 hours



### 5.10.3 Design Option Calibrations

DOE calibrated the analysis of each ice maker configuration by comparing the energy use predictions associated with given sets of design options, as shown in section 5.10.2 , with energy usage and design data collected from existing ice maker models. These comparisons are shown

in Table 5 .10

. 26 through Table 5.10

. 43

. Each table represents the results of the analysis of a given ice mak er configuration and shows major characteristics of the ice maker configuration at

the maximum efficiency level in the analysis (excluding ECM and DWHX design options)

, in comparison with one or more units representing the maximum available efficiency leve l of equipment currently available on the market.

In some cases, the set of design options chosen to represent the maximum efficiency level matched the designs of the maximum available efficiency level equipment. In other cases, the designs did not match exactly, and the design of the DOE analysis may have had more improvement in one component, while the maximum available ice maker had more improvement in another component. In order to ensure that DOE was not underestimating the costs associated

with the overall design improvements required to attain these highest available efficiency levels

,

5

- 42

### DOE estimated the cost differential between the calculated and actual designs by evaluating the

cost of changi ng the major components of the analyzed design configur ation to match those of the maximum available equipment. Major components considered in this estimate were the compressor, evaporator, condenser, and condenser fan. In cases where the analyzed maximum available design configuration and the maximum availabl e unit had different harvest capacity rates, DOE calculated the differential cost for the evaporator and condenser on the basis of surface area per capacity, i.e. for a unit that has the same capacity as the analyzed unit with the same surface area per cap acity as the actual maximum available unit.

Table 5.10

. 25 summarizes th e calibration s for all of the directly analyzed equipment

classes , while Table 5.10

. 26 through Table 5.10

. 43 show details of the the calculations for each

class

. Some components, namely pump motors for batch units and evaporators for continuous units, were not considered in the differential calculations, as the analysis for the maximum available efficiency level did not include any design options involv ing these components. A negative cost differential indicates that the DOE analysis predicted a higher cost at th e maximum

available efficiency level compared with the maximum available unit. The computed cost differentials are zero or negative in all cases but one , showing that the DOE analysis does not underestimate the cost of reaching these higher efficiency levels. For the 22

- inch IMH

- A

- Small

B ice maker, this differential cost is +$4, but the efficiency level of the max available unit si 5% higher tha n the level projected by the DOE analysis. In cases where design details were provided confidentially and not available from reverse engineering , the design details for the maximum

available units are not provided.

5

- 43

### Table 5.10

. 25 Maximum Available Calibration Summary and Cost Differentials

Equipment Class Representative Capacity

lb ice/24h DOE Analysis

Maximum

Avai l able Efficiency Level

% Below B aseline Maximum

Available Efficiency Level

% Below Baseline Cost Differential Moving from Analyzed to

Maximum

Available
$

IMH

- W

- Small

- B 300 19.2 19.2

- 29

IMH

- W

- Small

- B

( 22 inch wide) 300 16.9 16.9

- 34

IMH

- W

- Med

- B 850 10.4 14.3

- 2 40

IMH

- W

- Large

- B

- 2 2600 2.5 2.5 0

IMH

- A

- Small

- B 300 19.3 19.3

- 2 7

IMH

- A

- Small

- B

( 22 inch wide) 300 11.6 1 6.6 +4

IMH

- A

- Large

- B

- 1 800 16.1 16.1

- 74

IMH

- A

- Large

- B

- 1

( 22 inch wide) 590 5.5 5.5

- 13

IMH

- A

- Large

- B

- 2 1500 6.2 6.0

- 1 30

RCU

- NRC

- Large

- B

- 1 1500 15.7 15.7

- 62

RCU

- NRC

- Large

- B

- 2 2400 14.9 14.9

- 329

SCU

- A

- Small

- B 110 26.6 24.9

- 61

SCU

- A

- Large

- B 200 23.5 26.4

- 28

SCU

- W

- Large

- B 300 27.6 27.6 0

IMH

- A

- Small

- C 310 19.8 28.0

- 3 0

IMH

- A

- Large

- C 820 17.0 35.7

- 11

SCU

- A

- Small

- C 220 21.8 30.1

- 62

RCU

- NRC

- Small

- C 610 17.9 18.4

- 4 0

5

- 44

### Table 5.10

. 26 Comparison of Analyzed and Max Available units for the IMH

- W

- Small

- B Equipment Class

Unit Analyzed Configuration at Max

Available Efficiency Max Available Cost

Differential
$

Harvest Rate 300 360

Difference from

Standard (%) - 19.2

- 19.2

Compressor Capacity (Btu/h) 3

, 746 4

, 250

EER (Btu/Wh) 5.2 5.1

- 3

Evaporator Total Plate Area (in 2 ) 12 3 17 6 +38

Plate Area per Capacity (in 2

- 24

hours/lb ice) 0.41 0.49

Condenser Pipe Length (in) 124 7 5

- 64 Pipe Outside Diameter (in) 0.95 0.72

Len g th per Capacity (in

- 24

hours/lb ice) 0.41 0.21

Pump Motor Type SP SP

Output Power (W) 6 6

Total Cost Differential

- 29



### Table 5.10

. 27 Comparison of Analyzed and Max Available units for the IMH

- W

- Small

- B Equipment Class (22 inch wi de )

Unit Analyzed Configuration at

Max Available

Efficiency Cost

Differential *
$

Harvest Rate 300

Difference from

Standard (%) - 16.9

Compressor Capacity (Btu/h) 3

, 746

EER (Btu/Wh) 5.2

- 17

Evaporator Total Plate Area (in 2 ) 117 + 10

Condenser Pipe Length (in) 116

- 27 Pipe Outside Diameter (in) 0.95

Pump Motor Type SP Output Power (W) 6

Total Cost Differential

- 34

* Maximum Available unit data not provided because it was provided under non

- disclosure
agreement.

5

- 45

### Table 5.10

. 28 Comparison of Analyzed and Max Available units for the IMH

- W

- Med

- B Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost

Differential
$

Harvest Rate 850 832

Difference from

Standard (%) - 10.4

- 14.3

Compressor Capacity (Btu/h) 12

, 000 10

, 100 EER (Btu/Wh) 6.2 6.1

- 4

Evaporator Total Plate Area (in 2 ) 30 4 30 4 +10

Plate Area per Capacity (in 2

- 24

hours/lb ice) 0.36 0.37

Condenser Pipe Length (in) 217 79

- 246 Pipe Outside Diameter (in) 1.31 0.9 2

Length per Capacity (in

- 24

hours/lb ice) 0.255 0.095

Pump Motor Type SP SP Output Power (W) 1 5 1 5

Total Cost Differential

- 240



### Table 5.10

. 29 Comparison of Analyzed and Max Available units for the IMH

- W

- Large

- B

2 Equipment Class

Unit Analyzed Configuration

at Max

Available Efficiency Max Available Cost Differential
$

Harvest Rate 2600 2620

Difference from

Standard (%) - 2.5

- 2.5

Compressor Capacity (Btu/h) 34473 34473

EER (Btu/Wh) 6.32 6.32 0

Evaporator Total Plate Area (in 2 ) 1156 1156 0

Condenser Pipe Length (in) Same Same

0 Pipe Outside Diameter (in) Same Same

Pump Motor Type Same Same

0 Output Power (W) Same Same

Total Cost Differential 0

5

- 46

### Table 5.10

. 30 Comparison of Analyzed and Max Available units for the IMH

- A

- Small

- B Equipment Class

Unit Analyzed Configuration

at Max

Available Efficiency Max

Available * Cost

Differential *
$ Max Available 2

Harvest Rate 300 300 316

Difference

from Standard (%) - 19.3

- 19.3

- 15.7

Compressor Capacity (Btu/h) 3

, 944 * * 4

, 250

EER (Btu/Wh) 5.2 * * 5.1

Evaporator Total Plate Area (in 2 ) 1 80 164

- 25 1 80

Condenser Total Face Area (in 2 ) 186 192

- 9 192

Tube Rows 3 2 3 Length per Capacity (in

24 hours/lb ice) 1.86 1.28 1.82

Fan Motor Type PSC PSC

+7 PSC Output Power (W) 19 3 6 50

Pump Motor Type SP SP SP Output Power (W) 6 6 6

Cabinet W x D x H (in) 30 x 24.5 x

21.5 30 x 24.5 x

21.5 30 x 23.5 x 21.5

Total Cost Differential

- 27

* Calibration and cost differential based on first Max A vailable unit.

* * Not a R

- 404A compressor. DOE does not know the performance characteristics of this comparison at the AHRI 540 medium temperature rating conditions used for evaluating other compressors for the analysis. For the purposes of the calibration, DOE assumed that there would be no cost difference as compared with the compressor used in the analysis.

5

- 47

### Table 5.10

. 31 Comparison of Analyzed and Max Available units for the IMH

- A

- Small

- B Equipment Class (22 inch wide)

Unit Analyzed Configuration

at Max

Available Efficiency Max Available Cost

Differential
$

Harvest Rate 300 370

Difference from

Standard (%) - 11.6

- 1 6.6

Compressor Capacity (Btu/h) 3

, 944 3

, 750

EER (Btu/Wh) 5.2 4.69

- 18

Evaporator Total Plate Area (in 2 ) 140 209 +47

Plate Area per Capacity (in 2

- 24

hours/lb ice) 0.47 0.56

Condenser Total Face Area (in 2 ) 182 22 6

- 10

Tube Rows 3 2

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 1.82 1.22

Fan Motor Type PSC SP

- 15 Output Power (W) 1 9 16

Pump Motor Type SP SP Output Power (W) 6 6

Total Cost Differential +4

*

5

- 48

### Table 5.10

. 32 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- B

- 1 Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost

Differential
$

Harvest Rate 800 844

Difference from

Standard (%) - 16.1

- 16.1

Compressor Capacity (Btu/h) 12

, 000 12

, 000

EER (Btu/Wh) 6.7 6.2

- 17

Evaporator Total Plate Area (in 2 ) 30 4 30 4

- 25

Plate Area per Capacity (in 2

- 24

hours/lb ice) 0.38 0.36

Condenser Total Face Area (in 2 ) 702 414

- 12

Tube Rows 2 3

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 1.76 1.47

Fan Motor Type PSC SP

- 20 Output Power (W) 77.1 70

Pump Motor Type SP SP Output Power (W) 15 1 5

Total Cost Differential

- 74

5

- 49

### Table 5.10

. 33 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- B

- 1 Equipment Class (22 inch wide )

Unit Analyzed Configuration

at Max

Available Efficiency Max Available Cost Differential
$

Harvest Rate 590 590

Difference from

Standard (%) - 5.5

- 5.5

Compressor Capacity (Btu/h) 8

, 850 8

, 850

EER (Btu/Wh) 6 5.78

- 13

Evaporator Total Plate Area (in 2 ) 30 2 30 2 0

Condenser Total Face Area (in 2 ) 189 189 0

Tube Rows 2 2

Fan Motor Type SP SP

0 Output Power (W) 32 32

Pump Motor Type SP SP Output Power (W) 1 5 1 5

Total Cost Differential

- 13



### Table 5.10

. 34 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- B

- 2 Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Cost Differential *
$

Harvest Rate 1500

Difference from

Standard (%) - 6.2

Compressor Capacity (Btu/h) 23

, 476

EER (Btu/Wh) 7.16

- 29

Evaporator Total Plate Area (in2) 61 7

- 31

Condenser Total Face Area (in 2 ) 775

- 45

Tube Rows 3

Fan Motor Type PSC

- 25 Output Power (W) 156

Pump Motor Type SP Output Power (W) 6

Total Cost Differential

- 130

* Maximum Available unit data not provided because it was provided under non

- disclosure
agreement.

5

- 50

### Table 5.10

. 35 Comparison of Analyzed and Max Available units for the RCU

- NRC

- Large

B

- 1 Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost

Differential
$

Harvest Rate 1

, 500 1

, 645

Difference from

Standard (%) - 15.7

- 15.7

Compressor Capacity (Btu/h) 23

, 476 20

, 000

EER (Btu/Wh) 7.16 6.4

- 28

Evaporator Total Plate Area (in 2 ) 61 7 603

- 107

Plate Area per Capacity (in 2

- 24

hours/lb ice) 0.41 0.37

Condenser Total Face Area (in 2 ) 924 1

, 536 +77

Tube Rows 3 3

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 1.85 2.80

Fan Motor Type PSC PSC

- 4 Output Power (W) 23 8 186

Pump Motor Type SP SP Output Power (W) 6 25

Total Cost Differential

- 62

5

- 51

### Table 5.10

. 36 Comparison of Analyzed and Max Available units for the RCU

- NRC

- Large

B

- 2 Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost

Differential
$

Harvest Rate 2

, 400 2

, 256

Difference from

Standard (%) - 14.9

- 14.9

Compressor Capacity (Btu/h) 34

, 473 26

, 800

EER (Btu/Wh) 6.32 6.2

- 8

Evaporator Total Plate Area (in 2 ) 1156 918

- 287

Plate Area per Capacity (in 2

- 24

hours/lb ice) 0.48 0.41

Condenser Total Face Area (in 2 ) 1

, 034 85 6
*

Tube Rows 2 1

Fan Motor Type PSC PSC

- 4 Output Power (W) 292 186

Pump Motor Type SP SP Output Power (W) 12 50

Total Cost Differential

- 329

* The max imum available unit used for calibration utilized a different condenser type than the designed unit. DOE has not assigned a cost differential for this component, but expects the cost differential to be negative.

5

- 52

### Table 5.10

. 37 Comparison of Analyzed and Max Available units for the SCU

- A

- Small

- B Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost

Differential
$

Harvest Rate 110 1 43

Difference from

Standard (%) - 26.6

- 24.9

Compressor Capacity (Btu/h) 1

, 530 1,530

EER (Btu/Wh) 4.6 4.3

- 7

Evaporator Total Plate Area (in 2 ) 11 2 11 2

- 41

Plate Area per Capacity (in 2

- 24

hours/lb ice) 1.02 0. 78

Condenser Total Face Area (in 2 ) 116 11 6

- 9

Tube Rows 3 2

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 3.16 1.62

Fan Motor Type PSC SP

- 4 Output Power (W) 6 1 6

Pump Motor Type SP SP Output Power (W) 6 6

Total Cost Differential

- 61

5

- 53

### Table 5.10

. 38 Comparison of Analyzed and Max Available units for the SCU

- A

- Large

- B Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max

Available Adjusted

Max

Available* Cost

Differential
$

Harvest Rate ( lb

ice/24 h) 200 24 7 247

Difference from

Standard (%) - 23.5

- 26.4

- 23.5

Compressor Capacity (Btu/h) 3

, 746 3

, 656 3,656 EER (Btu/Wh) 5.2 5. 03 5.03

- 5

Evaporator Total Plate Area (in 2 ) 111 * 1 50 137 0

Plate Area per Capacity (in 2

- 24

hours/lb ice) 0.55 0.61 0.55

Condenser Total Area (in 2 ) 192 1 70 170

- 9 Tube Rows 3 3 3

Area x Tube Rows per Capacity (in 2

-

24 hours/lb ice) 2.88 2.06 2.06

Fan Motor Type PSC SP (two fans) SP (two fans)

- 14 Output Power (W) 19 5 each ** 5 each **

Pump Motor Type SP PMSM PMSM Output Power (W) 6 21 21

W x D x H 30 x 30 x 32.875 30 x 29.75 x

32.75 30 x 29.75 x

32.75

Total Cost Differential

- 28

PMSM: Permanent Magnet Synchronous Motor *Adjusted for energy use equivalent to the maximum available efficiency level of the analysis. Reduction in e vaporator s urface

area 8.7% based on the IMH

- A

- Small

- B analysis, for which energy use reduction was 12.8% for a 51% increase in evaporator

surface area. **Differential cost calculated as if the max available used one fan with an SP motor with 10W shaft power. *Cost differential was based on a 12% larger evaporator to account for the 2.9% in efficiency between the an alyzed and actual max available

units.

5

- 54

### Table 5.10

. 39 Comparison of Analyzed and Max Available units for the SCU

- W

- Large

- B Equipment Class

Unit Analyzed Configuration

at Max

Available Efficiency Max Available Cost Differential
$

Harvest Rate 300 285

Difference from

Standard (%) - 27.6

- 27.6

Compressor Capacity (Btu/h) 3

, 746 3

, 746

EER (Btu/Wh) 5.2 4.68 0

Evaporator Total Plate Area (in 2 ) 11 4 11 4 0

Condenser Pipe Length (in) 79 79

0 Pipe Outside Diameter (in) 0.915 0.915

Pump Motor Type SP SP Output Power (W) 7 7

Total Cost Differential 0



### Table 5.10

. 40 Comparison of Analyzed and Max Available units for the IMH

- A

- Small

- C Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost Differential
$

Harvest Rate 310 531

Ice Hardness

Factor 73.2 80.8

Difference from

Standard (%) - 19.8

- 28

Compressor Capacity (Btu/h) 4

, 250 4

, 250

EER (Btu/Wh) 5.4 5.1

- 10

Evaporator Outside Diameter (in) 1. 75 2.625

Height (in) 6 .5 14.875

Condenser Total Face Area (in 2 ) 168 281

- 10

Tube Rows 4 3

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 2.17 1.59

Fan Motor Type PSC PSC

- 10 Output Power (W) 70 50

Auger Motor Type PSC PSC Output Power (W) 124 200

Total Cost Differential

- 30

5

- 55

### Table 5.10

. 41 Comparison of Analyzed and Max Available units for the IMH

- A

- Large

- C Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max

Available Max

Av a ilable

2 Max

Available

3 Cost

Differential

*
$

Harvest Rate 820 874 1100 845

Ice Hardness

Factor 86 70 58 72

Difference from

Standard (%) - 17.0

- 13.2

- 16

- 35.7

Compressor Capacity (Btu/h) 9

, 350 5

, 442 14

, 100 6

, 950

EER (Btu/Wh) 6 3.58 6.2 5.4

- 21

Evaporator Outside Diameter (in) 4. 25 3.5 3 3.25

Height (in) 10.5 19.75 1 5 12

Condenser Total Face Area (in 2 ) 20 8 168 341 420 + 21

Tube Rows 4 5 3 3

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 1.01 0.96 0.93 1.49

Fan Motor Type PSC PSC SP SP

- 11 Output Power (W) 34 45 60 9

Auger

Motor Type PSC PSC PSC PSC Output Power (W) 373 186 186 186

Total Cost Differential

- 11

* Calibration and c ost differential based on Max Available 3

5

- 56

### Table 5.10

. 42 Comparison of Analyzed and Max Available units for the SCU

- A

- Small

- C Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost

Differential
$

Harvest Rate 220 280

Ice Hardness

Factor 57 85

Difference from

Standard (%) - 21.8

- 30.1

Compressor Capacity (Btu/h) 2

, 053 3

, 746

EER (Btu/Wh) 5.5 4.68

- 27

Evaporator Outside Diameter (in) 1.75 3

Height (in) 10.125 7. 5

Condenser Total Face Area (in 2 ) 157 156

- 14

Tube Rows 4 3

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 2.85 1.67

Fan Motor Type PSC SP

- 20 Output Power (W) 20 9

Auger Motor Type PSC PSC Output Power (W) 7 5 124

Total Cost Differential

- 62

5

- 57

### Table 5.10

. 43 Comparison of Analyzed and Max Available units for the RCU

- A

- Small

- C Equipment Class

Unit Analyzed Configuration at

Max Available

Efficiency Max Available Cost Differential
$

Harvest Rate 800 835

Ice Hardness

Factor 57 74

Difference from

Standard (%) - 17.9

- 18.4

Compressor Capacity (Btu/h) 5

, 200 8

, 850

EER (Btu/Wh) 5.4 5.78 + 12

Evaporator Outside Diameter (in) 3 3

Height (in) 12 15

Condenser Total Face Area (in 2 ) 1081 10 70

- 16

Tube Rows 1 1

Area x Tube

Rows per Capacity (in 2

- 24 hours/lb ice) 1.35 1.28

Fan Motor Type PSC PSC

- 36 Output Power (W) 24 9 124

Auger Motor Type PSC PSC Output Power (W) 186 186

Total Cost Differential

- 40

5

- 58

### REFERENCES

1

. American Metals Market. (Last accessed November 2008.) < www.amm.com >

2

. U.S. Department of Labor, Bureau of Labor Statistics. Producer Price Indices . (Last accessed November 2008.) < www.bls.gov/ppi >

3

. U.S. Department of Commerce, Bureau Economic Analysis. Gross Domestic Product Implicit Price Deflator . (Last accessed November 2008.)

< https://bea.gov/bea/dn/nipaweb/TableView.asp#Mid >

4

. Plastic News . Resin Pricing. (Last accessed March 21, 2008.)

< http://www.plasticsnews.com/subscribe r/headlines.phtml >

5

- 59

### 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 Overall Markups

................................ ................................ ................................ .. 6

- 4

6.3 BASIC ASSUMPTIONS US ED TO ESTIMATE WHOLE SALER AND

MECHANICAL CONTRACTO R MARKUPS

................................ ................................ . 6

- 5

6.4 ESTIMATION OF WHOLES ALER MARKUPS

................................ ............................. 6

- 6

6.5 ESTIMATION OF MECHAN ICAL CONTRACTOR MARK UPS

................................ .. 6

- 7

6.6 ESTIMATION OF NATION AL ACCOUNT MARKUPS

................................ ................ 6

- 9

6.7 SALES TAX

................................ ................................ ................................ ..................... 6

- 10

6.8 OVERALL MARKUPS

................................ ................................ ................................ .... 6

- 11

REFERE NCES

................................ ................................ ................................ .......................... 6

- 12

LIST OF TABLES

Table 6.2.1 Distribution Channel Market Shares for Baseline and Higher Efficiency

Level Automatic Commercial Ice Makers

................................ ........................... 6

- 4 Table 6.4.1 Typical Wholesaler Expenses and Markups

................................ ............................. 6

- 7

Table 6.5.1 Typical Mechanical Contractor Expenses and Markups

................................ .......... 6

- 8

Table 6.7.1 Combined State and Local Sales Tax Rates

................................ ........................... 6

- 10 Table 6.8.1 Baseline Markups by Distribution Channel and Weighted Overall Markup

.......... 6

- 11 Table 6.8.2 Incremental Markups by Distribution Channel and Weighted Ove rall Markup

.... 6

- 11

LIST OF FIGURES

Figure 6.5.1 Cumulative Distribution of Mechanical Contractor Markup s for Automatic

Commercial Ice

- Making Equipment

................................ ................................ .... 6

- 9

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 is the projected installed cost of automatic commercial

ice

- making (ACIM) equipment . Installed cost include s equipment purchase price and installation

costs. The U.S. Department of Energy ( DOE ) projects the equipment purchase price by using multipliers called “ markups ” that are applied to the manufacturer production cost ( MPC ) to
estimate the customer purchase p rice of the equipment. The manufacturer markup

, projected as part of the engineering analysis (chapter 5 of the final rule t echnical s upport d ocument (TSD) )

, converts the MPC into manufacturer selling price ( MSP )

. D istribution channel markups are also applied to the MSP based on the distribution channel s through which customers currently purchase the equipment.

DOE determined that customers purchase automatic commercial ice mak ers through three major distribution channels:

Manufacturer  Customer ( Nat ional Account Channel )

Manufacturer  Wholesaler  Customer ( Wholesaler Channel )

Manufacturer  Wholesaler  Lo cal Contractor  Customer ( Contractor Channel )

The three distribution channels can also be viewed as a single

- step (national accounts) channel, a two

- step (wholesaler) channel , and a three

- or

- more

- step (contractor) channel

.

DOE calculated an aggregate average markup value for each distribution channel

. DOE then weight

- averaged t he three distribution channel markup values using the market share of

each distribution channel to obtain weighted

- average distribution channel markup s (see section

6.2.3 for market share estima tes)

. Based on suggestions from stakeholders at the Framework and

p reliminary a nalysis p ublic m eeting s , DOE us ed the same weighted

- average distribution channel markup values for all ACIM equipment classes.

DOE calculated a weighted

- average sales tax using the population of each state as the corresponding weight. DOE then multiplied the weighted

- average sales tax by the weighted

average distribution channel markups to estimate overall distribution channel markup values for

automatic c ommercial ice makers

.

DOE notes that Framework and p reliminary a nalysis public meeting stakeholders indicated mechanical contractors, specifically, are seldom involved in the distribution channel ; however, local contractors or installers are generally invo lved. DOE uses mechanical contractor cost data in the estimation of markups since it i s the best data available for quantifying the cost

of a local contractor.

This chapter presents DOE’ s methodology for calculat ing the three distribution channel markups

, weighted

- average sales tax

, and overall distribution channel markups.

6

- 1

### 6.2 BASELINE, INCREMENTA L

, AND OVERALL MARKUPS

Baseline markups are markups that convert the MSP of equipment at the baseline efficiency level ( e fficiency l evel 1 ) to customer purchase price. Incremental markups are markups that convert the projected increase in MSP of equipment at e fficiency l evel 2 or higher

to a projected increase in customer purchase price compared to the price of equipment at

e fficiency l evel 1 . Overall markups include the weighted

- average values of markups based on the

estimated market share for each distribution channel and the sales tax.

6.2.1 Baseline Markups

If the baseline equipment is sold t hrough the N ational A ccount C hannel , the following equation defines the estimated equipment price:

EEEEEE NNNNNNNN AAAA BB BBBB 

### = MMMMMM BB BBBB 

### × MMMM NNNNNNNN AAAA BB BBBB 

### × ( 1 + SSSS ) Eq. 6

. 1

W here:

EQP NATL ACCNT BASE 

### = n ational a ccount price to the customer of baseline equipment ($ /unit )

, MFG BASE 

### = MSP of baseline equipment ($ /unit ),

MU NATL ACCT BASE 

### = national account markup on baseline equipment , and

ST = weighted

- average sales tax rate.

If the baseline equipment is sold through the W holesaler C hannel :

EEEEEE WWWWWW BB BBBB 

### = MMMMMM BB BBBB 

### × MMMM WWWWWW BB BBBB 

### × ( 1 + SSSS ) Eq. 6

. 2

W here:

EQP WHOLE BASE 

### = wholesaler price of baseline equipment ($ /unit ), and

MU WHOLE BASE 

### = wholesaler markup on baseline equipment

.

If the baseline equipment is sold to a customer through the C ontractor C hannel :

EEEEEE BB BBBB 

### = MMMMMM BB BBBB 

### × MMMM WWWWWW BB BBBB 

### × MMMM MM BB BBBB 

### × ( 1 + SSSS ) Eq. 6

. 3

W here:

EQP CONT BASE 

### = contractor or installer price of baseline equipment ($ /unit ), 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 customer purchase price. DOE projects t he increment in MSP of equipment at higher efficiency levels by subtracting the MSP of equipment at the baseline efficiency level ( MFG BASE 

### ) from the projected MSP of equipment at higher efficiency level s :

MMMMMM II II 

### = MMMMMM HH EEEE LL 

### − MMMMMM BB BBBB 

### Eq. 6

. 4

W here:

MFG INCR 

### = projected increment in MSP of higher efficiency equipment ($ /unit ), and

MFG HIGHER EFF LEVEL 

### = projected MSP of equipment at higher efficiency level ($ /unit )

.

If the equipment is sold through the N ational A ccount C hannel

, DOE projects the

increase in equipment pric e using the following equation :

EEEEEE NNNNNNNN AAAA II II 

### = MMMMMM II II 

### × MMMM NNNNNNNN AAAA II II 

### × ( 1 + SSSS ) Eq. 6

. 5

W here:

EQP NATL ACCNT INCR 

### = projected increment in customer purchase price to the n ational a ccount

customer ($ /unit ), and

MU NATL ACCT INCR 

### = national account incremental markup

.

If the customer acquires the higher efficiency equipment through the W holesaler or C ontractor C hannels :

EEEEEE WWWWWW II II 

### = MMMMMM II II 

### × MMMM WWWWWW II II 

### × ( 1 + SSSS ) Eq. 6

. 6

W here:

EQP WHOLE INCR 

### = projected incremental wholesaler price ($ /unit ), and

MU WHOLE INCR 

### = incremental wholesaler markup

.

EEEEEE II II 

### = MMMMMM II II 

### × MMMM WWWWWW II II 

### × MMMM MM II II 

### × ( 1 + SSSS ) Eq. 6

. 7

W here:

EQP CONT INCR 

### = projected incremental contractor or installer price ($ /unit )

, and

MU MECH CONT INCR 

### = incremental mechanical c ontractor markup

.

6

- 3

### 6.2.3 Distribution Channel Market Shares

Table 6.2

. 1 provides the estimated market shares for the three distribution channels for

automatic commercial ice makers used for the final rule analysis of this rulemaking

. DOE used

these distribution c hannel market shares to estimate overall markups. DOE obtained t he market shares from U.S. Census Bureau data for the commercial refrigeration equipment sector, of

which automatic commercial ice makers comprise one of the component product lines

. The U.S. Census Bureau data allocated a small portion of the market to the National Account Channel. At

the p reliminary a nalysis public meeting, stakeholders indicated that the National Account Channel does not truly exist in the ACIM market because customers rely on third parties to

install and service machines. For the final rule analysis , the market share previously allocated to

National Account Channel was combined with the Wholesaler Channel.

Based on s takeholder input at the Framework and p reliminary a nalysis p ublic m eeting s , DOE modeled the majority of automatic commercial ice makers a s purchased through a three

step distribution channel

. DOE’s estimates are listed in Table 6.2

. 1

.

Table 6.2

. 1 Distribution Channel Market Shares for Baseline and Higher Efficiency Level

Automatic Commercial Ice Makers

National Account Channel Wholesaler Channel Contractor Channel

Automatic Commercial Ice Makers, All 0 % 38 % 62 %

Source: U.S. Census Bureau, Sector 42: EC0742SXSB01: Wholesale Trade: Subject Series

- Misc Subjects: Sales by Class of Customer for the United States: 2007, data for NAICS code 423740, Refrigeration equipment and supplies merchant wholesalers

. Downloaded March 4, 2011

.



### 6.2.4 Overall Markups

DOE estimated o verall markup values separately for both baseline and incremental markups by combining the weig ht ed

- average sales tax with the weighted

- average distribution channel markups. DOE estimated o verall baseline markup value s using this equation :

EEEEEE CC BB BBBB 

### = MMMMMM BB BBBB



### × ( WW NNNNNNNN AAAA BB BBBB 

### × MMMM NNNNNNNN AAAA BB BBBB 

### + WW WWWWWW BB BBBB 

### × MMMM WWWWWW BB BBBB 

### + WW BB BBBB 

### × MMMM WWWWWW BB BBBB 

### × MMMM MM BB BBBB 

### )

× ( 1 + SSSS )

= MMMMMM BB 

### × MMMM BB BBBB 

### Eq. 6

. 8

W here:

EQP CUST BASE 

### = customer purchase price for baseline equipment ($ /unit ),

WT NATL ACCNT BASE 

### = estimated market share of baseline equipment sold through the N ational

A ccount C hannel (%),

6

- 4

### WT WHOLE BASE 

### = estimated market share of baseline equipment sold through the W holesaler

C hannel (%),

WT CONT BASE 

### = estimated market share of baseline equipment sold through the C ontractor C hannel (%), and

MU OVERALL BASE 

### = estim ated overall baseline markup

.

DOE projected o verall incremental markups using this equation :

EEEEEE CC II II 

### = MMMMMM II II



### × ( WW NNNNNNNN AAAA II II 

### × MMMM NNNNNNNN AAAA II II 

### + WW WWWWWW II II 

### × MMMM WWWWWW II II 

### + WW II II 

### × MMMM WWWWWW II II 

### × MMMM MM II II 

### ) × ( 1 + SSSS )

= MMMMMM II II 

### × MMMM II II 

### Eq. 6

. 9

W here:

EQP CUST INCR 

### = projected increment in customer purchase price of equipment at a higher efficiency level compared to baseline equipment ($ /unit ),

WT NATL ACCNT INCR 

### = projected market share of higher efficiency level equipment sales through the

N ational A ccount C hannel (%),

WT WHOLE INCR 

### = projected market share of higher efficiency level equipment sales through the

W holesaler C hannel (%),

WT CONT INCR 

### = projected market share of higher efficiency level equipment sales through the

C ontractor C hannel (%) , and

MU OVERALL INC R 

### = overall incremental markup

.

EEEEEE CC 

### = EEEEEE CC BB BBBB 

### + EEEEEE CC II II 

### Eq. 6

. 10

DOE used the overall baseline and overall incremental markups to estimate the baseline

customer price ( EQP CUST BASE 

### ) and to project the incremental customer price ( EQP CUST INCR 

### ). For a particular piece of equipment at a higher efficiency level, the projected total price of that equipment to the customer ( EQP CUST 

### ) is the sum of the baseline customer price ( EQP CUST BASE 

### ) and

the projected incremental customer price ( EQP CUST INCR 

### ).

6.3 BASIC ASSUMPTIONS US ED TO ESTIMATE WHOLE SALER AND

MECHANICAL CONTRACTO R 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

. 1 

### DOE obtained the wholesale industry balance sh eet data from Heating, Air

- c onditioning & Refrigeration Distributors International (HARDI), the trade

association representing wholesalers of r efrigeration and heating, ventilating, and air

conditioning ( HVAC R ) equipment

. 2 

### D OE compiled the U.S. Census Bureau PHAC data following the same form at as used for the balance sheet data for wholesalers. These balance

6

- 5

### sheets itemize the costs incurred by firms that sup ply and install PHAC equipment

. DOE

estimated the wholesaler and mechanical contractor markups based on three key assumptions

about ACIM costs :
1) The balance shee ts accurately represent the various average costs incurred by firms distributing and installing automatic commercial ice mak ers

. 2) The wholesaler and contractor costs can be divided between (a) costs that vary in proportion to the MSP ( variable costs) and (b) costs that do not vary with MSP (fixed costs).

3) W holesaler and contractor prices vary in proportion to the wholesaler and contractor

costs included in the balance sheets.

The basis for the first assumption is that t he industry bal ance sheets itemize costs incurred by a firm by 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 tend t o handle multiple equipment lines ( including refrigerators

, f reezers , b everage dispensers , and other related equipment )

, the data provide the most credible

available information for ACIM distribution costs

.

The basis for the second assumption is th a t firms must incur some costs regardless of the cost of the goods being sold

— costs such as sales, accounting and management staff, office expenses, depreciation, and many other operating costs do not vary with the MSP

. For purposes of this analysis, DOE refers to these costs as fixed. Other costs, such as uncollectable accounts, bad debts, and profit margins, vary as a function of the total cost of sales, including the cost of goods sold. For purposes of this analysis, DOE refers to these costs as variable.

The ba sis for the third assumption is that the HVAC R wholesaler and contractor industry is competitive , while customer demand for ACIM equipment is assumed to be inelastic ( i.e. , the demand is not expected to decrease significantly 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, the 2007 census lists m ore than 700 HVACR manufacturers, 3


### 1,300 wholesalers of refrigeration equipment, 4 

### and 91,000 HVAC contractors

. 5 

### Following standard economic theory, competitive firms facing inelastic demand either set prices in line with costs or quickly go out of business. 6



### 6.4 ESTIMATION OF WHOLES ALER MARKUPS

Annually, HARDI conducts a confidential survey of its mem ber firms in which

w holesalers report median data

. For what they describe as a typical distributor, HARDI disaggregates revenues into categories, including direct cost of goods sold (equipment ( MSP )

, materials, fuels, and contracted services ), labor costs , occupancy costs , other operating costs , and profit. Appendix 6A presents the data for a typical HARDI distributor in terms of specific types of expense s within these categories

. Table 6.4

. 1 summarizes these expenses in units of expenses per dollar of 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. 73 9 per dollar of sales revenue

. I n other words, for every $1 .00 the typical wholesaler take s in as sales revenue, it pays $0. 7 3 9 in direct costs. Labor expenses account for $0. 1 5 1 per dollar of sales revenue, occupancy expenses

6

- 6

### account for $0. 03 5 , other operating expenses account for $0.0 5 2 , and profit accounts for $0.0 2 3 per dollar sales revenue.

Table 6.4

. 1 Typical 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. 73 9 $1.0 0 0 Labor Expenses: Salaries and benefits $0. 1 5 1 $0.2 0 4 Occupancy Expense s : Rent, maintenance, and utilities $0. 03 5 $0.0 4 7 Other Operating Expenses: Depreciation, advertising, and insurance $0. 05 2 $0.0 7 0

Profit $0. 0 2 3 $0.0 31 Baseline Markup ( MU WHOLE BASE ): Revenue per dollar cost of goods sold * 1. 3 5 3 Incremental Markup ( MU WHOLE INCR ): Increased revenue per dollar increase cost of goods sold * 1. 10 1 Source: Heating, Air

- conditioning & Refrigeration Distributors International. 201 3

. 201 3 Profit Report (201 2 Data)

. * Numbers are rounded 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 dividin g each cost category in the first data column of Table 6.4

. 1 by $0. 73 9 ( 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. 2 0 4 to cover labor costs, $0. 0 4 7 to cover occupancy expenses, $0.0 7 0 for other operating expenses, and $0. 0 31 in profits. This totals to $1. 3 5 3 in sales revenue earned for every $1.00 spent on equipment costs. Therefore, the wholesale r baseline markup ( MU WHOLE BASE 

### ) is 1. 3 5 3 ($1.3 5 3 ÷ $1.00).

DOE also used the data in the last column of Table 6.4

. 1 to estimate incremental markups. The total 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 MSP ( i.e. , all costs are variable), the incre ase in wholesaler price will be $1. 3 5 3 , implying that the incremental markup is 1

. 3 5 3 , or the same as the baseline markup. However , if none of the other costs is variable, then a $1.00 increase in the MSP will lead to a $1.00 increase in the wholesaler pri ce, for an incremental markup of 1.0. DOE assume d that the labor and occupancy costs will be fixed and that the other operating costs and profit will

scale with the MSP ( i.e. , be variable). In this case, for a $1.00 increase in the MSP , the

wholesaler price will increase by $1. 1 0 1 . Therefore, the wholesale r incremental markup ( MU WHOLE INCR 

### ) is 1. 10 1 ($1. 10 1 ÷ $1.00). See a ppendix 6A for additional details and data used

for markup calculations.

6.5 ESTIMATION OF MECHAN ICAL CONTRACTOR MARK UPS

DOE derived markups for mechanica l contractors from U.S. Census Bureau data for

PHAC c ontractors

. This sector includes establishments primarily engaged in installing and servicing PHAC equipment, which may include installations , additions, alterations, mainte nance, 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 expenses for the

PH AC industry as a whole in total dollars

, rather than in typical values for the average or

6

- 7

### representative business. Because of this, DOE assumed that the total dollar values that the

U .S. Census Bureau reported, once converted to percentages, represent revenues and expenses

for the average or typical contracting business. Table 6.5

. 1 summarizes the typi cal expenses for

mechanical contractors

. The first data column of Table 6.5

. 1 lists expenses per dollar sales

revenue ( appendix 6 A contains the full set of data). T he direct cost of sales represents about $0. 6 78 per dollar sales revenue to the mechanical contractor. Labor expenses account for $0. 17 5 per dollar sales revenue, occupancy expenses account for $0.02 2 per dollar sales revenue, other

op erating expenses account for $0.03 9 , and profit makes up $0. 086 per dollar sales revenue.

Table 6.5

. 1 Typical Mechanical Contractor Expenses and Markups

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 $0.678 $1.000 Labor Expenses: Salaries (indirect) and benefits $0.175 $0.258 Occupancy Expense: Rent, maintenance, and utilities $0.022 $0.032

Other Operating Expenses: Depreciation, advertising, and insurance $0.039 $0.058

Net Profit Before Taxes $0.086 $0.127 Baseline Markup ( MU MECH CONT BASE ): Revenue per dollar cost of goods sold * 1.47 4 Incremental Markup ( MU MECH CONT INCR ): Increased revenue per dollar increase cost of goods sold 1. 18 4

Sources: U.S. Census Bureau. 2007. Plumbing, Heating, and Air

- Conditioning Contractors. Sector 23: 238220

. Construction: Geographic Area Series: Detailed Statistics for Establishments: 2007; DOE estimates.

* Numbers are rounded and presented so the totals match. The actual markup s in the LCC analysis are 1.474 and 1.184

.



### 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 $0.6 7 8

. 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 typical mechanical contractor e arns $1.00 in sales revenue to cover the equipment cost, $0. 2 58 to cover labor costs, $0.03 2 to cover occupancy expenses, $0. 0 58 for other operating expenses, and $0. 1 27 in profits. This totals $1. 47 4 in sales revenue earned for every $1.00 spent on equipment

costs. This results in a mechanical contractor baseline markup ( MU MECH CONT BASE 

### ) of 1. 47 4 ($1. 47 4 ÷ $1.00).

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 incre ase in mechanical contractor price will be $1. 47 4

, an incremental markup of 1. 47 4 , or the same as the baseline markup. However , if none of the other costs are variable, then a $1.00 increase in the equipment price will lead to a $1.00 increase in the mechanical contractor price, for an incremental markup

of 1.0. DOE assume d the labor and occupancy costs are fixed and the other operating costs and profit scale with the equipment price ( i.e.

, are variable). In this case, for a $1.00 increase in the equip ment price, the typical mechanical contrac tor price will increase by $1. 18 4 , giving a

mechanical contractor incremental markup ( MU MECH CONT INCR 

### ) of 1. 18 4 ($1. 18 4 ÷ $1.00)

.

Mechanical contractor costs differ in various regions of the country depending on availability of labor, cost of living, and union versus non

- union workforce. Because many mechanical contractor costs differ systematically by s tate, DOE characterize d the markups developed from U

. S

. Census Bureau data with a probability distribution base d on a s tate

- by

- s tate

6

- 8

### 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 fo r a particular

state. DOE used the state

- level markup data in combination with state

- level electricity and water prices in the LCC and PBP analys i s to provide additional granularity in the analysis of whether

customers are better or worse off at the effici ency levels analyzed in this rulemaking.

Figure 6.5

. 1 Cumulative Distribution of Mechanical Contractor Markups for Automatic

Commercial Ice

- Making Equipment

6.6 E STIMATION OF NATIONAL ACCOUNT MARKUPS

National accounts are generally large accounts

— large enough to circumvent the

W holesaler C hannel and negotiate directly with, and purchase directly from, manufacturers to

achieve significantly lower equipment prices. The manu facturer s , in turn

, must cover additional expenses related to the distribution of the equipment , but gain in terms of negotiating agreed

upon sales volumes with each national account

.

To estimate the price savings realized from equipment purchased through the national

account distributi on chain

, DOE assum ed that the resulting e quipment price increase is one half

of that realized from distribution through the W holesaler C hannel

. In other words, if the price markup resulting from the wholesale r markups is $100, the national account markup is $50.

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

. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9Cumulative Percentages (%)

Markup

Mechanical Contractor Markup by State

6

- 9

### For example, u sing a base line MSP of $1,000 for a n automatic commercial ice mak er delivered to a restaurant

, and a baseline wholesaler m arkup of 1. 3 5 3 , the resulting baseline

customer equipment price for sales through a wholesaler ( without sales taxes ) is $ 1

, 3 53 ($ 1,000

× 1. 3 53 ). The dollar value increase due to the above distribution c hannel markups is $ 3 53 ($ 1

, 3 53

− $ 1,000 ). Under the assumption that national account customer s realize equipment price increases equal to one

- half of that through the wholesale r distribution channel, the dollar

value of the estimated equipment price increase under the national account is $ 1 76 .50

. The resulting equipment price is $ 1

, 176 .50 ($ 1 76 .5 0 + $ 1,000 ), which results in a national account

baseline markup of 1. 1 7 7 ($ 1, 1 7 7 ÷ $ 1,000 ). U sing a value of 1. 101 for the wholesaler incremental markup and a similar calculation results in a total national account incremental markup of 1. 0 51 ($1, 0 50 .50 ÷ $1,000)

.

6.7 SALES TAX

Sales tax includes state and local sales taxes that are applied to the customer price of

automatic commercial ice makers

. The sales t ax is a multiplicative factor that increases the customer equipment price by an amount equal to MSP plus markups multipli ed by the sales tax

rate . DOE derived sales tax rates from the Sales Tax Clearinghouse

. 7 

### Table 6.7

. 1 lists tax rates by state

. DOE applied t he state

- level combined tax rate to the estimated value of state

- level

ACIM shipments to obtain the total purchase cost to a customer located in a given state.

The distribution of s ales tax rates range from zero to 9.4 5 percent

, with a mean value of

6. 2 5 percent. DOE applied sales taxe s to the customer equipment price irrespective of the

distribution channel or the state in which the customer is located

. For the national impact analysis and other national

- level analyses, DOE calculated a weighted

- average, national

- level

sales tax rate by multiplying the relative population of each state by the tax rates listed in

Table 6.7

. 1

. The weighted

- average, national

- level sales tax rate is 7. 16 percent.

Table 6.7

. 1 Combined State and Local Sales Tax Rates

State Sales Tax State Sales Tax State Sales Tax

Alabama 8. 55 % Kentucky 6.00% North Dakota 5. 9 5%

Alaska 1. 30 % Louisiana 8.75% Ohio 7

. 1 0 %

Arizona 7. 15 % Maine 5. 5 0 % Oklahoma 8. 35 %

Arkansas 8. 90 % Maryland 6.00% Oregon 0.00%

California 8.45 % 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. 15 %

Delaware 0.00% Mississippi 7. 0 5 % South Dakota 5. 40 %

Dist. of Col. 5

. 75 % Missouri 7. 50 % Tennessee 9.45%

Florida 6.65% Montana 0.00% Texas 7.95 % Georgia 7.10 % Nebraska 6.00% Utah 6.70%

Hawaii 4.40 % Nevada 7.85% Vermont 6.05%

Idaho 6.05% New Hampshire 0.00% Virginia 5. 6 0%

Illinois 8. 05 % New Jersey 6.95% Washington 8. 90 %

Indiana 7.00% New Mexico 6. 60 % West Virginia 6. 10 %

Iowa 6.85% New York 8.4 0 % Wisconsin 5.45%

Kansas 7.85 % North Carolina 6.90 % Wyoming 5. 50 % Source: The Sale Tax Clearinghouse, www.thestc.com/STrates.stm

. Last accessed June 17

, 2014

6

- 10

### 6.8 OVERALL MARKUPS

Table 6.8

. 1 presents the baseline markup values for each distribution channel and the overall baseline markup values used for automatic commercial ice makers

. Table 6.8.2 present s the incremental markup values for each distribution channel and the overall incremental markup

values

. Note that the markups shown for wholesaler / c ontractor represent the product of the wholesaler chain and contractor chain markups ( i.e ., they are the product of two terms shown separately in the equations in section 6.2 ).

Table 6.8

. 1 Baseline Markups by Distribution Channel and Weighted Overall Markup

Wholesaler Wholesaler /

Contractor National Account (Mfg Direct) Overall Markup

Channel Mult 1. 35 3 1.995 1. 17 7 1. 7 51

Sales Tax 1. 07 2 1. 07 2 1. 07 2 1. 07 2 Overall Multiplier * 1. 45 0 2. 1 38 1. 26 1 1. 876 *Overall multiplier taken from the LCC model and not subject to rounding of inputs.



### Table 6.8

. 2 Incremental Markups by Distribution Channel and Weighted Overall Markup

Wholesaler Wholesaler /

Contractor National Account (Mfg Direct) Overall Markup

Channel Mult 1. 10 1 1. 30 5 1. 05 1 1. 22 7

Sales Tax 1. 07 2 1. 07 2 1. 07 2 1. 07 2 Overall Multiplier * 1. 18 0 1. 398 1. 1 26 1. 3 15 *Overall multiplier taken from the LCC model and not subject to rounding of inputs.



### DOE used the overall markups to estimate the customer price of baseline and higher efficiency equipment

. For example, if the MSP of a baseline self

- contained u nit is $1

, 000, DOE

obtained the customer purchase price for this baseline equipment by multiplying the MSP by the overall markup value of 1. 876 , or $1

, 876.00

. If the increment al MSP of the equipment at a higher efficiency level is $100, the n DOE obtained the incremental customer purchase price of this equipment by multiplying the MSP by the overall incremental markup of 1. 315 to obtain a n incremental customer purchase price of $ 131.50 . The customer purchase price of this higher efficiency eq uipment is the sum of the baseline price and the increment, or $ 2

, 007.50 ($ 1, 876 + $ 131.50 ).

6

- 11

### REFERENCES

1

. 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 October 29, 2013 .)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_ 2007_US_23A1&prodType=table >

2

. Heating, Air

- conditioning & Refrigeration Distributors International. 201 3 Profit Report (20 12 Data) . 201 3 . Columbus, OH.

3

. 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 Air Heating Manufacturing. (Last accessed November 12, 2014 .)

< http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t#none >

4

. 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. merchant wholesalers. (Last accessed October 29,

2011 .)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_ EC0742I1_FTP&prodType=document >

5

. 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, heating, and air

- conditioning contractors. (Last accessed October 29, 20 13 .)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_ 2007_US_23I1&prodType=table >

6

. P indyck, R. and D. Rubinfeld. Microeconomics. s.l. 5 th ed. 2000. Prentice Hall : Upper Saddle River, NJ

.

7

. The Sale Tax Clearinghouse

. State sales tax rates along with average combined city and

county rates . (Last accessed June 17, 2014.) < http://www.thestc.com/STrates.stm >

6

- 12

### 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 WHOLES ALER AND MECHANICAL CONTRACTOR

MARKUP STANDARD DEVI ATIONS

................................ ................................ ..... 6A

- 3

LIST OF TABLES

Table 6A.2.1 Disaggregated Cos ts and Expenses for Wholesalers

................................ .......... 6A

- 1 Table 6A.3.1 Mechanical Contractor Expenses and Markups Used to Scale the Incremental Markups

................................ ................................ ................................ ............ 6A

- 2 Table 6A.4.1 Mechanical Contractor Baseline Markups by State, 2007

................................ .. 6A

- 3

6A

- i

### APPENDIX 6 A

. 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 D ETA IL ED W HOLESALER COST DATA

Chapter 6 presents w holesaler revenues and costs i n aggregated form

, based on the Heating

, Air

- conditioning & Refrigeration Distributors International (HARDI) 201 3 Profit Report ( 20 12 Data)

. Tab le 6A.2

. 1 provides the complete breakdown of costs and expenses from

the 201 3 Profit Report ( 20 12 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 baselin e and incremental costs are m arked up when there is an incremental change in equipment costs.

Table 6A.2

. 1 Disaggregated Costs and Expenses for Wholesalers*

Item Percent of Revenue Scaling

Cost of Goods Sold 73. 9 Gross Margin 26. 1 Payroll Expenses 15.1

Baseline

Executive Salaries & Bonuses 1. 6 Branch Manager Salaries and Bonuses 1. 3

Sales Executive Salaries & Commissions 0.5

Outside Sales Salaries & Commissions 2. 3

Inside/Counter Sales/Wages 2. 6 Purchasing Salaries/Wages 0. 5 Credit Salaries/Wages 0.2 IT Salaries/Wages 0. 2 Warehouse Salaries/Wages 1.4 Accounting Salaries/Wages 0.5 Delivery Salaries/Wages 0. 8 All Other Salaries/Wages & Bonuses 0.8 Payroll Taxes 1.0 Group Insurance 1. 0

Benefit Plans 0. 4 Occupancy Expenses 3. 5

Baseline

Utilities: Heat, Light, Power, Water 0.4 Telephone 0.3 Building Repairs & Maintenance 0. 3 Rent or Ownership in Real Estate 2. 5 Other Operating Expenses 5. 2

Baseline & Incremental Sales Expense (incl. advertising and promotion) 0.9 Insurance (business liability & casualty) 0.2 Depreciation 0.4 Vehicle Expenses 1. 2 Personal Property Taxes/Licenses 0.1 Collection Exp ense (collection, credit card fees) 0.3

Bad Debt Losses 0.2

6A

- 1

### Table 6A.2.1 (cont)

Item Percent of Revenue Scaling Data Processing 0.3 Employee Training 0.1 All Other Operating Expenses 1.5 Total Operating Expenses 2 3.8 Operating Profit 2. 3

Baseline & Incremental

Other Income ( 0.4 ) Interest Expense 0. 4

Other Non

- operating Expenses 0.0

Profit Before Taxes 2. 3 Source: Heating, Air

- conditioning & Refrigeration Distributors International. 2013 Profit Report ( 2012 Data)

. 2013 . Columbus,

OH. *Wholesaler 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 for m , based

on U.S. Census Bureau data. Table 6A.3

. 1 shows the complete breakdown of cos ts and expenses provided by the U.S. Census Bureau. 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 incremental markups. As described in chapter 6, only those expenses that scale

with baseline and incremental costs are marked up when there is an incremental change in equipment costs.

Table 6A.3

. 1 Mechanical Contractor Expenses and Markups Us ed to Scale the Incremental 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 October 29, 2013 .)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I 1&prodType=table > *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 sales revenue values shown in chapter 6.

6A

- 2

### 6A.4 ESTIMATION OF WHOLES ALER AND MECHANICAL CONTRACTO R

MARKUP STANDARD DEVI ATIONS

The U.S

. Department of Energy (DOE) used the U.S. Census Bureau’s Economic Census data to estimate mechanical contractor markup distributions. For mechanical contractors, the

2007 census data include d 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 i n Table 6A.4

. 1

.

DOE used the s t 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 ) studies

. 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 widely. 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 factors that vary by state to create one of the key sets of cos t components varied during the analysis

.

Table 6A.4

. 1 Mechanical Contractor Baseline Markups by State, 200 7 *

State Value of

Construction
$000 Cost of

Subcontract

Work
$000 Cost of

Materials
$000 Constructi on Payroll

$000 Baseline Markup

Alabama 2,010,305 113,782 876,341 411,100 1.435

Alaska 583,171 52,245 171,575 120,909 1.692

Arizona 3,522,116 179,103 1,508,903 638,469 1.514

Arkansas 1,065,754 68,417 461,924 213,054 1.434

California 16,726,969 1,070,065 6,330,469 3,464,667 1.539

Colorado 3,056,988 261,734 1,195,057 627,663 1.467

Connecticut 1,704,668 145,740 628,720 361,411 1.501

Delaware 481,900 D D 163,343 1.421

District of Columbia 34,600 D D 50,439 1.458

Florida 9,061,426 783,859 3,736,811 1,733,721 1.449
Georgia 4,700,799 365,010 2,224,110 740,722 1.412

Hawaii 800,221 46,753 270,723 137,646 1.758

Idaho 900,698 62,252 387,181 167,732 1.459

Illinois 7,641,642 602,251 2,833,489 1,622,307 1.511

Indiana 4,002,323 431,558 1,319,523 854,157 1.536

Iowa 1,868,483 144,869 801,239 359,775 1.431

Kansas 1,395,359 106,307 580,764 279,636 1.443
Kentucky 1,747,925 128,902 674,500 353,958 1.510

Louisiana 1,997,044 162,063 776,784 378,582 1.516

Maine 580,816 46,692 234,993 113,162 1.471
Maryland 5,329,135 698,381 2,009,957 1,031,222 1.425

Massachusetts 4,099,301 488,098 1,475,525 817,754 1.474
Michigan 4,420,638 604,850 1,569,113 841,985 1.466

Minnesota 3,402,921 386,669 1,230,126 698,535 1.470

6A

- 3

### Table 6A.4

. 1 (cont)

State Value of

Construction
$000 Cost of

Subcontract

Work $000 Cost of

Materials
$000 Construction Payroll

$000 Baseline Markup

Mississippi 1,025,452 76,709 449,851 189,011 1.433

Missouri 3,335,124 345,285 1,319,142 689,171 1.417

Montana 483,578 42,988 216,792 85,678 1.400

Nebraska 1,004,296 94,170 455,264 205,904 1.330

Nevada 2,327,842 121,091 988,605 490,859 1.454
New Hampshire 620,761 39,784 D 128,512 1.472
New Jersey 5,062,336 496,174 1,825,407 1,015,432 1.517

New Mexico 891,914 67,987 356,961 170,711 1.497

New York 10,364,779 1,219,468 3,568,182 1,972,687 1.533

North Carolina 5,111,396 341,129 2,288,841 1,001,832 1.407

North Dakota 360,683 36,037 148,617 70,403 1.414

Ohio 5,618,591 568,837 2,115,568 1,125,401 1.475

Oklahoma 1,352,943 94,153 581,079 249,032 1.464
Oregon 1,893,678 124,070 701,468 412,418 1.530
Pennsylvania 6,487,476 579,901 2,628,602 1,370,864 1.417

Rhode Island 631,202 56,234 229,692 124,727 1.537

South Carolina 1,991,303 126,123 847,690 352,877 1.501

South Dakota 386,186 11,037 158,375 69,605 1.616

Tennessee 2,595,613 189,293 1,159,952 484,997 1.415

Texas 10,810,308 823,920 4,605,624 2,102,520 1.435

Utah 1,746,398 146,052 769,140 319,812 1.414

Vermont 294,806 21,021 D 63,015 1.472
Virginia 4,623,151 347,055 1,791,740 960,534 1.492
Washington 4,111,543 370,741 1,546,819 816,533 1.504
West Virginia 655,100 D D 90,660 1.464

Wisconsin 2,926,545 234,962 1,125,779 662,893 1.446
Wyoming 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 October 29

, 201 3 .)

< http://factfinder2.ce nsus.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I1&prodTyp

e=table > *The Census Bureau withheld data for some states 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 to an average of neighboring states’ baseline markup.

6A

- 4

### CHAPTER 7. ENERGY AND WATER USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION

................................ ................................ ................................ .............. 7

- 1

7.2 ANNUAL ENERGY CONSUM PTION

................................ ................................ ............. 7

- 1

7.2.1 Equipment Class Energy Use and Harvest Rate Capacity

................................ ...... 7

- 1

7.2.2 Utilization Factor

................................ ................................ ................................ .... 7

- 2

7.3 ANNUAL WATER CONSUMP TION

................................ ................................ ............... 7

- 3

7.4 WEIGHTING FACTORS FO R LARGE BATCH EQUIPM ENT CLASSES

................... 7

- 4

REFERENCES

................................ ................................ ................................ ............................ 7

- 6

LIST OF TABLES

Table 7.2.1 Distribution Used for LCC Crystal Ball for Utilization Factor

................................ 7

- 2 Table 7.2.2 Total Annual Energy Usage

................................ ................................ ...................... 7

- 3 Table 7.3.1 Total Annual Water Consumption

................................ ................................ ............ 7

- 4 Table 7.4.1 Large Batch Equipment Class Weighting Factors

................................ .................... 7

- 5

7

- i

### CHAPTER 7. ENERGY AND WATER USE ANALYSIS

7.1 IN TRODUCTION

This chapter presents the U.S. Department of Energy’s ( DOE’s ) analysis of annual

energy and water usage at various efficiency levels of automatic commercial ice makers . These estimated values of annual energy consumption (AEC) in kilowatt

- hours per year ( kWh/yr ) and annual water consumption (AWC) in gallons of water per year are key inputs to the life

- cycle cost and payback period analys i s ( c hapter 8 of the final rule technical support document (TSD))

and national impacts analys i s ( c hapter 10 of the TSD).

T his chapter discuss es the methodology used to estimate the annual energy and water consumption for representative

- sized units for each automatic commercial ice maker equipment

class.

7.2 ANNUAL ENERGY CONSUM PTION

For the final rule analysis, DOE estimate d the AEC for every combination of equip ment
class and efficiency level considered in the analysis. To estimate the AEC, e stimated energy use per 100 lb of ice developed in the engineering analysis (see TSD chapter 5) , assumed equipment

utilization factor s , and maximum daily harvest rates for each combinat ion are used to calculate a daily energy consumption value for the ice

- making function. In addition to the ice

- making related energy consumption, estimated daily standby energy usa g e is also added to yield a total energy consumption value for a single 24

- h our period. Multiplying the sum of the daily ice

making related energy and standby energy use by 365 results in the total AEC for each specific equipment class and efficiency level combination.

For automatic commercial ice makers , the equation for AEC is :

AEC i, j 

### = ( E i,j 

### ÷ 100 × H i 

### × U i 

### + SE i 

### × (1

– U i 

### ) ) × 365 Eq. 7

. 1

Where:

AEC i,j 

### = calculated annual energy consumption for equipment class i , at efficiency level j (kWh/year)

, E i

, j 

### = estimated energy use for equipment class i , at efficiency level j (kWh/100 lb ice)

, H

i 

### = harvest rate for equipment class i (pounds of ice produced per 24 hours)

, U

i 

### = assumed utilization for equipment class i ( percent of time producing and harvesting ice) , and

SE i 

### = estimated “standby” energy use for equipment class i ( kWh/ 24 hours )

.

7.2.1 Equipment Class Energy Use and Harvest Rate Capacity

For each equipment class

, a baseline energy use value is estimated, noted as efficiency

level 1

. Efficiency l evel 1 is the initial val ue against which energy saving percentages for higher efficiency levels are estimat ed. A more complete breakdown of how the incremental percentage savings values are derived is described in TSD c hapter 5. Harvest rate capacity, which provides a representative value for the pounds of ice that can be produced for each ice

- making equipment

7

- 1

### class , does not vary by efficiency level within an equipment class. The values used as estimat es for energy use ( E i,j 

### ) an d harvest rate ( H

i 

### ) are taken from the engineering a nalysis presented in

c hapter 5 and are designed to be representative of a given equipmen t class.

7.2.2 Utilization Factor

Ice

- making equipment is not assumed to be making ice 24 hours out of every day. Instead, a utilization factor ( U i 

### ) is used as the estimate for the percent of time the ice maker is actively producing ice. In the final rule analysis, this value is estimated at 42 percent across all equipment classes and efficiency levels

. The utilization factor was based on data provided by

manufacturers and data obtained from a report found via an I nternet search

. 1 

### The total data set of metered utilization factors included 80 observations. The average utilization factor for the 88

observations was 42 percent. D OE used this factor for the estimation of electricity and water usage values.

In the life

- cycle cost ( LCC ) model, DOE used Crystal Ball to allow the utilization factor to vary. The 88 observations were divided into bins based on 0 to 10 percent, 10 to 20 p ercent, etc. For each bin, the utilization factor was the average for the observations within the bin. The

distribution is shown on Table 7.2

. 1

.

Table 7 .2

. 1 Distribution Used for LCC Crystal Ball for Utilization Factor

Bin Upper

Level

% Weight Assigned to

Bin

% * Average

Utilization

Factor for

Observations

in Bin

%

10 0 0

20 2.3 16

30 31.8 26

40 26.1 35

50 12.5 45

60 10.2 54

7 0 6.8 63

80 5.7 76

90 3.4 86

100 1.1 91 *Numbers might not add to 100 percent due to rounding.



### The complement to the amount of time the ice

- making equipment is actively producing is

the time that the ice

- making equipment is not activ ely producing or harvesting ice . Energy used during non

- producing periods is referred to as standby use energy. T est data indicates that standby use represents an extremely small level of usage. As a conservative assumption, DOE

used a v alue of 5 w atts (W) for all equipment classes but believes the average value may actually

be lower. The standby loss assumption translates to energy consumption of 0. 07 kWh per day

.

Calculated annual energy values for each equipment class and available efficiency levels

are prov ided on Table 7.2

. 2

. For each equipment class listed, AEC values for the maximum

7

- 2

### available energy efficiency levels are included in the column with the highest numbere d efficiency level for that equipment class.

Table 7.2

. 2 Total Annual Energy Usage

Equipment Class * Annual Electricity Usage **

kWh

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 2,832 2,551 2,411 2,270 2,227 2,162

IMH

- W

- Med

- B 6,040 5,439 5,138 4,951

IMH

- W

- Large

- B † 10,750 9,891

IMH

- W

- Large

- B1 9,166 8,405

IMH

- W

- Large

- B2 15,868 14,693

IMH

- A

- Small

- B 3,535 3,184 3,009 2,901 2,833 2,658 2,640

IMH

- A

- Large

- B † 8,078 7,272 6,931 6,881 6,622 6,411

IMH

- A

- Large

- B1 7,350 6,617 6,251 6,192 5,885 5,636

IMH

- A

- Large

- B2 11,999 10,802 10,591

RCU

- Large

- B † 12,118 10,908 10,316 10,066

RCU

- Large

- B1 11,679 10,514 9,931 9,664

RCU

- Large

- B2 18,671 16,807 16,077

SCU

- W

- Large

- B 3,499 3,151 2,978 2,804 2,630 2,464

SCU

- A

- Small

- B 2,177 1,962 1,854 1,747 1,639 1,532 1,473

SCU

- A

- Large

- B 3,011 2,713 2,563 2,414 2,265 2,141

IMH

- A

- Small

- C 4,299 3,872 3,658 3,445 3,231 3,201

IMH

- A

- Large

- C 8,270 7,445 7,033 6,621 6,348

RCU

- Small

- C 7,737 6,966 6,580 6,195 5,809 5,688

SCU

- A

- Small

- C 3,416 3,077 2,907 2,738 2,568 2,515

* See c hapter 5 for a description of these abbreviations. ** A blank cell indicates the efficiency level is not defined.

† IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B values are weighted averages of the two typical units. Weights are

shown on Table 7.4

. 1

.



### 7.3 ANNUAL WATER CONSUMP TION

DOE estimated the AWC for ice

- making equipment similar ly to the way it estimated the

AEC . To calculate the AWC , first the e stimated potable water use and condenser w ater use per
100 lb of ice are summed to provide a total water use per 100 lb of ice. Factoring in the total water use per 100 lb of ice produced with the assum ed equipment utilization factor and representative max imum daily harvest rates yields a total daily water use figure for each equipment class. Multiplying the daily use values by 365 days in a year provides the total AWC

.

For automatic commercial ice makers, the equation for AEC is:

AWC i j 

### = ( PW i j 

### + CW i j 

### ) ÷ 100 × H i 

### × U i 

### × 365 Eq. 7

. 2

Where:

AWC i j 

### = calculated total annual water co nsumption for equipment class i

, at efficiency level j (g al /yr)

, PW i j 

### = estimated potable w ater use for equipment class i

, at efficiency level (gal/100 lb ice)

,

7

- 3

### CW i j 

### = estimated condenser water use for equipment class i

, at efficiency level (gal/100 lb ice)

, U

i 

### = assumed utilization for equipment class i ( percent of time producing and harvesting ice)

,

and

H

i 

### = harvest rate for eq uipment class i (pounds of ice produced per 24 hours)

.

Estimates of the annual combined potable water and condenser water us ag e are summarize d in Table 7.3

. 1

. Note that the increase in usage at level 4 for IMH

- A

- Small

- B and

IMH

- A

- Large

- B1 occur s because of the merging of 22

- inch and non

- 22

- inch cost

- efficiency

curves for those tw o equipment class es (see TSD chapters 5 and 8) , and not because design options were studied that increase d potable water usage.

Table 7.3

. 1 Total A nnual Water Consumption

Equipment Class * Annual Water Usage ** 1,000 gal/yr

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 80.7 79.7 78.9 78.9 78.9 78.9

IMH

- W

- Med

- B 223.6 223.6 223.6 223.6

IMH

- W

- Large

- B † 502.9 502.9

IMH

- W

- Large

- B1 421.1 421.1

IMH

- W

- Large

- B2 767.2 767.2

IMH

- A

- Small

- B 11.3 11.3 11.3 11.3 11.7 11.7 11.7

IMH

- A

- Large

- B † 30.2 30.2 27.6 27.6 27.7 27.7

IMH

- A

- Large

- B1 27.4 27.4 24.3 24.3 24.4 24.4

IMH

- A

- Large

- B2 45.7 45.7 45.7

RCU

- Large

- B † 47.4 47.4 47.4 47.4

RCU

- Large

- B1 45.7 45.7 45.7 45.7

RCU

- Large

- B2 72.0 72.0 72.0

SCU

- W

- Large

- B 74.0 69.4 69.4 69.4 69.4 69.4

SCU

- A

- Small

- B 5.7 5.7 5.7 5.7 3.4 3.4 3.4

SCU

- A

- Large

- B 9.1 9.1 9.1 6.1 6.1 6.1

IMH

- A

- Small

- C 5.7 5.7 5.7 5.7 5.7 5.7

IMH

- A

- Large

- C 17.0 17.0 17.0 17.0 17.0

RCU

- Small

- C 14.6 14.6 14.6 14.6 14.6 14.6

SCU

- A

- Small

- C 4.0 4.0 4.0 4.0 4.0 4.0

* See c hapter 5 for a description of these abbreviations. ** A blank cell indicates the efficiency level is not defined.

† IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B values are weighted averages of the two typical units. Weights are

shown on Table 7.4

. 1

.



### 7.4 WEIGHTING FACTORS FO R LARGE BATCH EQUIPM ENT CLASSES

Because the harvest rate categories are so wide for large equipment classes

, in three cases ( IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B ) the analyses in this final rule focused on two typical units for large batch equipment classes. For illustrative purposes, on input and summary results tables a class level value was created to give an idea of the impact at the class

level. The class level value is strictly illustrative as all life

- cycle cost and national impact analyses took place at the typical unit level. For some tables , such as the national energy savings or net present value results, the typical units were added together to create the class values . In

other tables

, a weighted average was created. The weighted averages were based on weighting

7

- 4

### factors calculated from the relative shipments of automatic commercial ice makers in each size category. The weig hts are shown on Table 7.4

. 1

.

Table 7.4

. 1 Large Batch Equipment Class Weighting Factors
Equipment Class and Typical Uni t Weight

%

IMH

- W

- Large

- B

IMH

- W

- Large

- B1 76.4

IMH

- W

- Large

- B2 23.6

IMH

- A

- Large

- B

IMH

- A

- Large

- B1 84.3

IMH

- A

- Large

- B2 15.7

RCU

- Large

- B

RCU

- Large

- B1 93.7

RCU

- Large

- B2 6.3

7

- 5

### REFERENCES

1

. Karas, A. and D. Fisher. A Field Study to Characterize Water and Energy Use of

Commercial Ice

- Cube Machines and Quantify Saving Potential . 2007. Fisher

- Nickel, Inc.: San Ramon, CA.

7

- 6

### CHAPTER 8. LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

TABLE OF CONTENTS

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

- 3

8.2 LIFE

- CYCLE COST INPUTS

................................ ................................ ........................... 8

- 7

8.2.1 Definition

................................ ................................ ................................ ................ 8

- 7

8.2.2 Total Installed

- Cost Inputs

................................ ................................ ...................... 8

- 8

8.2.2.1 Baseline Energy Consumption and Manufacturer Selling Price

.............. 8

- 9

8.2.2.2 Candidate Standard Level Energy Consumption and Manufacturer Selling

Price Increases

................................ ................................ ................................ ...... 8

- 11

8.2.2.3 Overall Markup

................................ ................................ ...................... 8

- 13

8.2.2.4 Installation Cost

................................ ................................ ..................... 8

- 13

8.2.2.5 Equipment Price Trends

................................ ................................ ......... 8

- 16

8.2.2.6 Weighted Average Total Installed Cost

................................ ................. 8

- 17

8.2.3 Operating

- Cost Inputs

................................ ................................ ........................... 8

- 20

8.2.3.1 Electricity Price Analysis

................................ ................................ ....... 8

- 20

8.2.3.2 Electricity Price Trend

................................ ................................ ........... 8

- 22

8.2.3.3 Water Cost

................................ ................................ ............................. 8

- 23

8.2.3.4 Water Price Trend

................................ ................................ .................. 8

- 24

8.2.3.5 Repair Cost

................................ ................................ ............................. 8

- 24

8.2.3. 6 Maintenance Cost

................................ ................................ ................... 8

- 26

8.2.3.7 Lifetime

................................ ................................ ................................ .. 8

- 27

8.2.3.8 Discount Rate

................................ ................................ ......................... 8

- 27

8.2. 3.9 Compliance Date of Standard

................................ ................................ 8

- 30

8.3 PAYBACK PERIOD INPUT S

................................ ................................ ......................... 8

- 31

8.3.1 Definition

................................ ................................ ................................ .............. 8

- 31

8.3.2 Inpu ts

................................ ................................ ................................ ..................... 8

- 31

8.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS

................................ ......... 8

- 31

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

- 37

8.5 DETAILED RESULTS

................................ ................................ ................................ .... 8

- 38

REFERENCES

................................ ................................ ................................ .......................... 8

- 40

L IST OF TABLES

Table 8.1.1 Summary Information of Inputs for the Determination of Life

- Cycle Cost and Payback Period

................................ ................................ ................................ ....... 8

- 7 Table 8.2.1 Inputs for Total Installed Costs

................................ ................................ ................. 8

- 8 Table 8.2.2 Equipment Classes Evaluated for the Automatic Commercial Ice

- Making Equipment

Standard Life

- Cycle Cost and Payback Period Analysis

................................ ...... 8

- 10

8

- i

### Table 8.2.3 Baseline Energy Consumption Levels and Est imated MSP Values for the Primary Equipment Classes

................................ ................................ ................................ 8

- 11 Table 8.2.4 Baseline and Incremental Manufacturer Selling Prices for the Primary Equipment

Classes*

................................ ................................ ................................ ................ 8

- 12 Table 8.2.5 Energy Consumption Values for the Primary Equipment Classes, by Efficiency

Level*

................................ ................................ ................................ ................... 8

- 12 Table 8.2.6 Installation Cost Assumptions for 22

- Inch Units

................................ ................... 8

- 14 Table 8.2.7 Installation Cost Assumptions for Non

- 22

- Inch Units with Size Growth

.............. 8

- 15 Table 8.2.8 Installation Cost by Equipment Class, U.S. Average

................................ ............. 8

- 15 Table 8.2.9 Installation Cost Indices (National Value = 100.0)

................................ ................ 8

- 16

Table 8.2.10 Costs and Mar kups for Determination of Weighted Average Total Installed Costs (IMH

- A

- Small

- B)

................................ ................................ ................................ . 8

- 17 Table 8.2.11 Weighted Average Equipment Pric e, Installation Cost, and Total Installed Costs for

IMH

- A

- Small

- B at U.S. Average Conditions (2013$)

................................ ......... 8

- 19 Table 8.2.12 Inputs Used to Determine Operating Costs

................................ .......................... 8

- 20 Table 8.2.13 Commercial Electricity Prices by State (2013 cents/kWh)

................................ .. 8

- 21 Table 8.2.14 Derived National Average Commercial Electricity Prices and Ratios by Business Type

................................ ................................ ................................ ...................... 8

- 22 Table 8.2.15 Commercial Water Prices by State (2013 $/1,000 gallons )

................................ ... 8

- 24 Table 8.2.16 Repair Cost Assumptions

................................ ................................ ...................... 8

- 25 Table 8.2.17 Repair Cost by Equipment Class

................................ ................................ .......... 8

- 25 Table 8.2.18 Annual Maintenance Costs by Equipment Class and Efficiency Level

............... 8

- 26 Table 8.2.19 Probability of Equipment Failure by Year

................................ ............................ 8

- 27 Table 8.2.20 Derivation of Typical Discount Rates by Building Type

................................ ..... 8

- 30 Table 8.4.1 LCC Savings Distribution Resul ts for Equipment Class IMH

- A

- Small

- B

............ 8

- 33 Table 8.4.2 Mean LCC Savings for All Equipment Classes and Efficiency Levels

................. 8

- 34 Table 8.4.3 Median LCC Savings for All Equipment Classes and Efficiency Levels

.............. 8

- 35 Table 8.4.4 Payback Period Distribution Results for IMH

- A

- Small

- B

................................ ..... 8

- 36 Table 8.4.5 Median Payback Period for All Equipment Classes and Efficiency Levels

........... 8

- 37 Table 8.4.6 Rebuttable Presumption Payback Periods by Efficiency Level and Equipment Class

................................ ................................ ................................ .............................. 8

- 38 Table 8.5.1 Summary of Results of LCC and PBP Analysis for IMH

- A

- Small

- B Equipment

Class

................................ ................................ ................................ ...................... 8

- 39

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of Life

- Cycle Cost and Payback

Period

................................ ................................ ................................ ...................... 8

- 6 Figure 8.2.1 National Electricity Price Trends for Commercial Rates to 2047

......................... 8

- 23 Figure 8.4.1 LCC and Installed Cost Variation over Efficiency Levels for IMH

- A

- Small

- B Equipment Class

................................ ................................ ................................ ... 8

- 32 Figure 8.4.2 LCC Savings Ranges by Efficiency Level for Equipment Class IMH

- A

- Small

- B

... 8

- 33 Figure 8.4.3 Payback Period for All Efficiency Levels for the Equipment Class IMH

- A

- Small

- B

................................ ................................ ................................ .............................. 8

- 36

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 possible energy conservation standards developed for

automatic commercial ice mak ers on individual commercial customers, henceforth referred to as

customers

. The effect of standards on customers includes change s in operating cost s (usually decreased) and change s in purchase cost s (usually increased). This chapter describes two metrics

used to determine the effect of standards on customers :

• 

### Life

- cycle cost (LCC)

. The total customer cost o ver the life of the equipment is the

sum of installed cost (purchase and installation cost) and operating costs (maintenance, repair, water

, a 

### 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 t he estimated amount of time it takes customers to recover the assumed higher purchase price of more energy efficient equipment through lower (undiscounted) operating costs.

An efficiency improvement in automatic commercial ice makers that is financially

attractive to a customer will typically have a low PBP and a low LCC associated with it

.

The remainder of this section outlines the general approach and provides an overview of the inputs to the LCC and PBP analysis of automatic commercial ice mak ers

. I nputs to the LCC and PBP analysis are discussed in detail in s ections 8.2 and 8.3

. Results for the LCC and PBP analysis are presented in s ections 8.4 and 8.5

.

DOE performed t he calculations discussed in this chapter using a Microsoft Excel spreadsheet

, which is available at www1.eere.energy.gov/buildings/appliance_standards/ commercial/automatic_ice_making_equipment .html

. A ppendix 8A includes i nstructions for using the spreadsheet. A ppendix 8B presents the d etailed results. Appendix 8C discusses uncertainty in the LCC modeling process.

8.1.1 General Approach for L ife

- C ycle C ost and P ayback Period Analysis

This section summarizes DOE’s approach to the LCC and PBP analysis for automatic

commercial ice mak ers

.

As part of the engineering analysis ( c hapter 5 of the final rule technical support document (TSD) )

, DOE explored various efficiency levels based on increasing efficiency (decreased energy consumption) and , typically, increas ing manufacturer selling price ( MSP ) values. For the LCC and PBP analysis, DOE chose a maximum of seven levels

, r eferred to herein as efficiency

levels

. DOE treats each higher eff iciency level a s a potential new standard level.

a Water costs, as used in this chapter, are the total of water and wastewater costs. Wastewater utilities tend to not meter customer wastewater flows, and they base billings on water commodity billings. For this reason, water usage

is used as the basis for both water and wastewater costs, and the two are aggregated in the LCC and PBP analysis.

8

- 1

### The first efficiency level ( l evel 1) in each equipment class is the least efficient and the least expensive equipment in that equipment class

. For many batch

- type equipment classes, l evel 1 equipment is equ ipment that just meets the efficiency standards for cube

- type equipment set by the Energy Policy Act of 2005 (EPACT 2005). For equipment not currently covered by standards, l evel 1 reflects the least efficient equipment currently available. T he higher effi ciency levels ( l evel 2 and up ) have progressive increase s in efficiency and equipment cost from l evel 1

. The highe s t efficiency level in each equipment class corresponds to the max imum efficiency

level obtainable with non

- proprietary technology and without significantly increasing the footprint of the equipment (see final rule TSD chapter 5 for details)

.

The installed cost of equipment to a customer is the sum of the equipment purchase price and installation cost. The purchase price includes manufacturer p roduction cost (MPC), to which a manufacturer markup is applied to obtain the MSP

. DOE calculated t his value as part of the engineering analysis ( c hapter 5 of the final rule TSD ). DOE then applie d additional markups to

the equipment to account for the costs associated with the distribution channels for this type of equipment (chapter 6 of the final rule TSD )

. I nstallation costs vary by state depending on the prevailing labor rates.

Operating costs for automatic commercial ice makers are the sum of main tenance costs, repair costs

, energy costs , and water costs

. Customers incur t hese costs over the life of the equipment

. To facilitate cost comparisons, DOE discount ed operating costs to the discount year ( 201 8

, the effective date of the standards establish ed as part of this rulemaking ) . The sum of the installed cost and the operating cost s , discounted to reflect the present value, is the LCC.

Generally, customers incur higher installed costs when they purchase higher efficiency equipment

, and these c ost increments will be partially or wholly offset 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

. In some equipment classes the operating cost savings also include savings in potable water usage because on e energy

- saving design option also reduces the amount of water

used in batch

- type machines. T he LCC of higher efficiency equipment can be lower compared to

lower efficiency equipment. D OE calculated the change in LCC for each efficiency level of each equipment class.

DOE obtained t he PBP of higher efficiency equipment by dividing the increase in the installed cost by the dec rease in annual operating cost. For this calculation, DOE use d the sum of

the first year operating cost changes as the estimate of the annual decrease in operating cost, noting that some of the repair and mainten an ce costs used herein are annualized estimates of

costs. DOE calculated a PBP for each efficiency level of each equipment class

.

Apart from MSP , installation costs, and maintenance and repair costs, other important inputs for the LCC and PBP analysis are dist ribution chain markups and sales tax, equipment

energy and water consumption, latest available electricity and water prices and future price trends, equipment lifetime, and discount rates.

DOE estimated m any inputs for the LCC and PBP analysis from the bes t available data

, and in some cases DOE used inputs that are generally accepted values within the automatic

8

- 2

### commercial ice

- making (ACIM) industry . In general, there is uncertainty associated with many inputs

, so DOE carrie d out the LCC and PBP analys i s in the form of Monte Carlo simulations using range s of values and probability distributions for certain inputs that account for the

uncertainties. DOE presents t he results of the LCC and PBP analys i s 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, DOE carried out 10,000 Monte Carlo simulations using Microsoft Excel and Crystal Ball , a commercially available Excel add

- in for performing Mon te Carlo simulations

.

DOE calculated LCC savings and PBP by comparing the installed costs and LCC values

of a given standards scenario against those of the base

- case scenario

. The b ase

- case scenario is

the scenario in which customers purchase equipment in the absence of the proposed energy

conservation standard. A s tandards

- case scenario is a scenario in which customers purchase equipment after the hypothetical energy conservation standard go es into effect. The number of

standards scenarios for an equipment class is equal to one less than the total number of efficiency levels in that equipment class because each efficiency level above e fficiency l evel 1 represents a potential new standard

. Usually, the market will offer equipment at various effi ciencies. Therefore, for b oth the base

- case and the standards

- case scenarios in the LCC and PBP analysis,

DOE calculate d the market shares of the efficiency levels using a method described in final rule

TSD chapter 10

.

Different types of buildings and indu stries face different energy prices , and apply

different discount rates to purchase decisions. DOE analyzed variability and uncertainty by performing the LCC and PBP calculations for seven types of buildings : health care

, lodging

, foodservice

, retail

, education

, food sales

, and offices

.

T he typical equipment lifetime is approximately 7 to 10 years with an average of 8.5 years. There were no data to suggest that lifetimes are unique to each equipment class. Therefore,

DOE used a distribution of equipmen t lifetimes that is defined by Weibull survival functions

, with an average value of 8. 5 years ( s ee section 8.2.3.7 )

.

Another important factor influencing the LCC and PBP analys i s is the geographic

location in which the automatic commercial ice mak er is installed

. Inputs that vary by location

include installation costs, water and energy prices

, distribution chain markups , and sales tax

. At the national level, DOE ex plicitly modeled variability in water price

, electricity price

, and markups using probability distributions based on the relative population s in all states.

Results of the LCC and PBP analysis are presented at the end of this chapter and in appendix 8B.

8.1.2 Overview of L ife

- C ycle C ost and P ayback Period Inputs

DOE categorized i nputs to the LCC and PBP analys i s as: (1) inputs for establishing the

total installed cost ; and (2) inputs for calculating operating cost s

.

8

- 3

### The primary inputs for establishing the total installed cost are:

• 

### Baseline manufacturer selling price : T he price charged by the manufacturer (when selling to either a wholesaler or customer ) for equipment meeting the baseline efficiency level

. The MSP includes a manufacturer’s markup , which convert s the

MPC to MSP

.

• 

### Price trends ( l earning ) : A method of adjusting the MSP over time to account for

increas ing cost efficiency in the production of ACIM equipment. DOE assumed that

, with time and experience, the real cost of producing equipment will decrease marginally. The development of price trends is discussed in a ppendix 8D.

• 

### Efficiency l evel manufacturer selling price increase : T he incremental change in MSP associated with producing equipment at a given higher efficiency level.

• 

### Markups and sales tax : T he distribution chain markups and sales tax used to convert
the MSP to a customer purchase price. Final rule TSD chapter 6 presents t he methodology used to determine markups and sales taxes

.

• 

### Installation c ost : T he cost to the customer of installing the equipment , not including the equipment cost

. DOE assumed t he cost of installation as a one

- time cost

, and it is intended to represent the cost of labor, overhead, and other miscellaneous materials and parts.

The primary inputs for calculating the operating cost s are:

• 

### Equipment energy consumption : T he energy consum ed by an automatic commercial

ice mak er per year , expressed in kilowatt

- hours (kWh)

. DOE calculated the kWh/100 lb ice per day as part of the engineering analysis , and the engineering results were used to calculate annual energy usage in the energy and water usage analysis ( final

rule TSD chapter 7).

• 

### Equipment water consumption : T he amount of condenser water ( used to cool refrigerant in the open

- loop condenser syst em) and potable water ( used to form ice) used per year , expressed in gallons. DOE calculated the water usage per 100 lb ice per day as part of the engineering analysis , and the engineering results were used to

calculate annual water consumption in the ener gy and water usage analysis ( final rule TSD chapter 7).

• 

### Electricity price : T he price per kilowatt

- hour

, in cents or dollars

, paid by each customer for electricity. DOE used average commercial electricity prices in each

s tate, as determined from the Energy Information Administration (EIA) form 8 2 6 data

for 2013

. DOE then adjusted the average commercial prices to reflect the fact that the

seven types of businesses analyzed pay electricity prices that are different from average commercial prices. S ectio n 8.2.3.1 d etails the development of electricity prices and the data sources used

.

• 

### Electricity price trends : DOE used t he EIA’s Annual Energy Outlook 201 4 ( AEO201 4 ) 1 

### to forecast electricity prices. For the results presented in this chapter, DOE used the regional prices from the AEO201 4 reference case to forecast future electricity price

8

- 4

### • 

### Water and wastewater price s : DOE combined the prices for w a ter and w astewater in

dollars per 1,000 gallons. DOE used price data from the 2012 American Water Works Association (AWWA) Water and Wastewater Survey , supplemented with data from earlier AWWA surveys

. 2 

### No data existed that disaggregate d water prices for individual building types, so DOE varied prices by state only and not by building type

within a state. S ection 8.2.3.1 d etails th e development of prices and the data sources

used.

• 

### Water and w astewater price trends : DOE used the Consumer Price Index (CPI) data for water related consumption ( 1974

– 2013 ) in developing a real growth rate for water and wastewater price forecasts.

• 

### Mainten ance costs : DOE calculated m aintenance costs as an annual expense representing the estimated labor and materials costs associated with maintaining the operation of the equipment

.

• 

### Repair costs : DOE calculated t he cost for repairs as an annual expense, represent ing

the estimated labor and materials costs associated with repairing or replacing components that have failed.

• 

### Equipment l ifetime : The typical age at which the ACIM 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 relationships between the installed cost and operating cost inputs

for the calculation of the LCC and PBP and how those costs vary with increasing efficiency

. Table 8.1.1 summarizes the characteristics of the inputs to the LCC and PBP analysis

, and lists

the corresponding reference chapter in the final rule TSD for details on th e calculation of the inputs

.

8

- 5

### Figure 8.1

. 1 Flow Diagram of Inputs for the Determination of Life

- Cycle Cost and Payback

Period Baseline

Manufacturer
Selling Price

Std

- Level

Manufacturer Selling Price

Installation

Cost From Engineering Analysis (Price is a

Function of Efficiency)

From Markups for Equipment

Price

Determination Analysis

Repair Cost

Maintenance

Cost Lifetime

Discount Rate s

Electricity and

Water Price

Trend s From Energy Use

Analysis Energy and

Water
Consumption

Electricity and Distributor

Markup

Sales Tax Mechanical

Contractor

Markup Customer

Price

Data Inputs

Intermediate Analysis

Output Results Total Installed

Cost

Annual
Operating
Expense Lifeti me Operating
Expense Payback

Period Life

- Cycle

Cost

Water Prices Annual Energy

and Water Cost

8

- 6

### 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 Efficiency level MSP

increases Var y with equipment class and efficiency 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. Chapte r 6

Installation price Var ies with equipment class and state

. Chapter 8 Operating Cost Primary Inputs Equipment electricity

and water consumption Varies with equipment class and efficiency level within

a n equipment class

. Chapter s 5 , 7

Electricity prices Vary with location, building type

. Chapter 8

Water/wastewater prices Vary with location

. Chapter 8

Electricity and

water /wastewater price

trends Vary with location and price scenario

. Chapter 8

Maintenance costs Vary with equipment class

. Chapter 8 Repair costs Var y with equipment class and efficiency level within an equipment class

. Chapter 8

Lifetime Mean a ssumed to be 8.5 years for all 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 i n

s ections 8.2 and 8.3

.

8.2 LIFE

- CYCLE COST INPUTS

8.2.1 Definition

Life

- cycle cost is the total customer co st over the life of a unit of equipment, including equipment cost , installation cost, and operating costs (energy and water costs, maintenance costs, and repair costs). DOE discounted f uture operating costs to the time of purchase and summed

costs over the lifetime of the equipment. Life

- cycle cost is defined by the following equation:



# ∑

= 

## + + = N

t t

t 

## r OC IC LCC

1 

## ) 1 /(

Eq. 8

. 1

8

- 7

### W here :

LCC = life

- cycle cost ($)

, IC = total installed cost ($)

, N = lifetime of equipment (years)

, b



### OC t 

### = operating cost ($) of the equipment in year t

,

r = discount rate

, and

t = year for which operating cost is being determined.

Because DOE gathered most of its data for the LCC analysis in 201 3

, DOE expresse d all

costs in 20 1 3 $. Total installed cost, operating cost, lifetime, and discount rate are discussed in the following sections. In the LCC analysis, DOE assumed that the first year of equipment purchase is 201 8 , the c ompliance date for standards set in this rulemaking

.

8.2.2 Total Install ed

- Cost Inputs

The following equation defines t he total installed cost:

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 ($).

DOE based t he equipment price on the distribution channel through which the c ustomer purchases the equipment, a s discussed in final rule TSD chapter 6

.

The remainder of this section provides information about the variables DOE used to

calculate the total installed cost for ACIM equipment

. Table 8.2.1 s hows the i nputs used to

determine total instal led cost

.

Table 8.2

. 1 Inputs for Total Installed Costs
Baseline manufacturer selling price ($)

Price trend factor Efficiency l evel manufacturer selling price increases ($) Wholesaler markup

C ontractor markup National account markup Sales tax ($) Installation cost ($)

b Though the average equipment life is 8.5 years, the model uses a range of years to calculate the equipment lifetime.

8

- 8

### 8.2.2.1 Baseline Energy Consumption and Manufacturer Selling Price

The baseline MSP is the price manufacturers charge for equipment just meeting the existing minimum efficiency (or baseline) standards or base

- case efficiency levels (for equipment classes with no standards)

. The MSP includes a markup that is applied to convert

MPC to a n MSP

. DOE developed MSP values for the primary equipment class es (see final rule

TSD chapter 5 for a discussion of primary versus secondary classes )

. Table 8.2.2 lists t he primary equipment class es that DOE evaluat ed during the final rule analysis of the current rulemaking

.

Ten o f the equipment classes in Table 8.2.2 are subject to standards set by the Energy Policy and Conservation Act (EPCA) as amended by EPACT 2005, and the other four equipment classes have not yet been subject to a standard set by leg islation or by DOE. In the cases of three large batch equipment classes

— IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B

— two representative units were modeled in the LCC model. One unit represents the low end of the harvest capacity range covered by the equip ment class while the second represents a unit higher in the harvest capacity and at a capacity representative of the larger machines currently available. To provide illustrative class

- level inputs and results, weighted averages were created from the typica l units’ values using historical shipments data. Note that the weighted averages are illustrative only, and all analyses were conducted using the typical units. The weighting factors are provided in TSD chapter 7.

8

- 9

### Table 8.2

. 2 Equipment Classes Evaluated for the Automatic Commercial Ice

- M aking Equipment Standard Life

- Cycle Cost and Payback Period Analys i s

Abbreviation Equipment Type Condenser Type Harvest Rate

lb ice/ 24 hours Ice Type

IMH

- W

- Small

- B Ice

- Making Head Water <500 Batch

IMH

- W

- Med

- B Ice

- Making Head Water ≥500 and <1,436 Batch

IMH

- W

- Large

- B* Ice

- Making Head Water ≥1,436 and <4,000 Batch

IMH

- A

- Small

- B Ice

- Making Head Air <450 Batch

IMH

- A

- Large

- B* , ** (also IMH

- A

- Large

- B1 ) Ice

- Making Head Air ≥450 and < 8 00 † Batch

IMH

- A

- Extended

- B* , ** (also IMH

- A

- Large

- B2 ) Ice

- Making Head Air ≥ 8 00 ± and <4,000 Batch

RCU

- Large

- B* , 

### ‡ Remote Condensing,

not Remote Compressor Air ≥1,000 and <4,000 Batch

SCU

- W

- Large

- B Self

- Contained Unit Water ≥200 and <4,000 Batch

SCU

- A

- Small

- B Self

- Contained Unit Air <175 Batch

SCU

- A

- Large

- B Self

- Contained Unit Air ≥175 and <4,000 Batch

IMH

- A

- Small

- C Ice

- Making Head Air <700 Continuous

IMH

- A

- Large

- C Ice

- Making Head Air ≥700 and <4,000 Continuous

RCU

- Small

- C Remote Condensing,

not Remote Compressor Air <850 Continuous

SCU

- A

- Small

- C Self

- Contained Unit Air <700 Continuous

*IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- NRC

- Large

- B were modeled as two different typical sizes , one at the lower end of the harvest range and one near the high er end of the harvest range where a significant number of units are available. In the LCC model, the low and high harvest rate models were denoted simply as B1 and

B2 . Where appropriate, the analyses add or perform weighted averages of the two typical sizes to present class

level results. The weights used are presented in TSD c hapter 7.

**IMH

- A

- Large

- B was established by EPACT 2005 as a class between 450 and 2,500 lb ice/24 hours. For analysis, DOE divid ed this into two typical sizes, “Large” and “Very Large” or “Medium” and “Large.” In the

LCC and national impact analysis modeling, the units were denoted as B1 and B2

. † Upper limit corresponding with the proposed standard level. 

### ‡ RCU

- Large

- B equipment w as combined into one class. The classes were disaggregated to develop standards by inclusion of an energy allowance related to lines carrying refrigerants.



### Table 8.2.3 presents the baseline energy consumption values and the baseline MSP s used

in the LCC and PBP analysis for the representative sizes for each of the primary equipment

class es and the typical units for the three large batch classes

. Final rule TSD c hapter 5 explains

how energy use and MSP var y by harvest capacity. Table 8.2.3 also identifies whether DOE

obtained the baseline from the engineering analysis , for equipment with no existing standards, or

set it at the standards baseline, for equipment covered under existing standards . As discussed in chapter 5, f or new equipment on the market today that is not covered by existing U.S. s tandards

, DOE set the baseline at levels reflect ing the least

- efficient equipment that could potentially be

available. Baseline energy consumption values presented in this sectio n provide energy use per

100 pounds of ice produced

. C hapter 7 discusses the methodology to calculate annual energy consumption using these values.

8

- 10

### Table 8.2

. 3 Baseline E nergy C onsumption L evels and Estimated MSP V alues for the

P rimary E quipment C lasses

Equipmen t Class Baseline Energy Consumption

kWh/100 lb ice Manufacturer Selling Price Baseline Type

IMH

- W

- Small

- B 6.14 $1,122 Standards Baseline

IMH

- W

- Med

- B 4.65 $1,931 Standards Baseline

IMH

- W

- Large

- B 4.00 $2,738 Standards Baseline

IMH

- W

- Large

- B1 4.00 $2,136 Standards Baseline

IMH

- W

- Large

- B2 4.00 $4,684 Standards Baseline*

IMH

- A

- Small

- B 7.68 $1,144 Standards Baseline

IMH

- A

- Large

- B 5.89 $1,898 Standards Baseline

IMH

- A

- Large

- B1 6.01 $1,867 Standards Baseline

IMH

- A

- Large

- B2 5.24 $2,065 Standards Baseline

RCU

- Large

- B 5.10 $2,560 Standards Baseline

RCU

- Large

- B1 5.10 $2,463 Standards Baseline

RCU

- Large

- B2 5.10 $4,003 Standards Baseline

SCU

- W

- Large

- B 7.60 $1,530 Standards Baseline

SCU

- A

- Small

- B 12.84 $1,314 Standards Baseline

SCU

- A

- Large

- B 9.80 $1,402 Standards Baseline

IMH

- A

- Small

- C 9.05 $3,382 Engineering Baseline

IMH

- A

- Large

- C 6.60 $2,613 Engineering Baseline

RCU

- Small

- C 6.33 $2,613 Engineering Baseline

SCU

- A

- Small

- C 10.12 $1,764 Engineering Baseline

* The existing standard of 4.00 kWh/100 lb ice applies to equipment up to 2,500 lb/day, while this typical unit is a 2,600 lb/day machine.



### 8.2.2.2 Efficiency Level Energy Consumption and Manufacturer Selling Price

Increases

The efficiency level MSP increase is the change in MSP associated with producing equipment at higher efficiency levels. DOE determined i ncreases in MSP as a function of equipment efficiency for each of the primary equipment class es

. The e ngineering analysis

established a series of MSP increases for each standard level. Table 8.2

. 4 presents the increase in

MSP corresponding to each efficiency level for each primary equipment class.

Table 8.2

. 5 presents the energy consumption of the representative units belonging to each of the primary equipment classes and the typical units for the three large ba tch classes that DOE selected for the engineering analysis

. As discussed in c hapter 7, ice makers are assumed to
operate in ice

- making mode 42 percent of the time and in standby mode 5 8 percent of time. In standby mode, all equipment was assumed to use 5 w atts continuously, or roughly 0.0 7 kWh per day

.

8

- 11

### Table 8.2

. 4 Baseline and Incremental Manufacturer Selling Prices for the Primary Equipment Classes *

Equipment Class Base

line

Level

1 Increase in MSP by Efficiency Level 201 3 $

Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 1,122 57.54 103.78 301.18 446.89 465.92

IMH

- W

- Med

- B 1,931 83.55 255.17 511.99

IMH

- W

- Large

- B** 2,738 555.64

IMH

- W

- Large

- B1 2,136 524.01

IMH

- W

- Large

- B2 4,684 657.84

IMH

- A

- Small

- B 1,144 74.36 157.23 187.52 174.32 481.94 487.05

IMH

- A

- Large

- B** 1,898 132.42 202.01

IMH

- A

- Large

- B1 1,867 57.82 129.52 133.81 197.17 476.82

IMH

- A

- Large

- B2 2,065 534.28 592.54

RCU

- Large

- B** 2,560 91.99 360.33 610.85

RCU

- Large

- B1 2,463 74.30 317.89 585.17

RCU

- Large

- B2 4,003 356.30 994.72

SCU

- W

- Large

- B 1,530 55.89 86.17 116.81 150.76 450.95

SCU

- A

- Small

- B 1,314 30.59 48.12 65.64 121.56 184.47 458.55

SCU

- A

- Large

- B 1,402 36.39 56.16 90.33 133.58 435.89

IMH

- A

- Small

- C 3,382 40.89 67.55 95.18 487.18 491.52

IMH

- A

- Large

- C 2,613 37.52 60.31 609.71 627.14

RCU

- Small

- C 2,613 37.18 89.55 127.34 652.44 673.91

SCU

- A

- Small

- C 1,764 17.65 32.43 47.68 467.06 477.69

* Blank cells imply there are no associated efficiency levels.

** Values for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two sub

- equipment class level typical units shown in the table and using weights provided in TSD chapter 7.



### Table 8.2

. 5 Energy C onsumption V alues for the P rimary E quipment C lasses , by E fficiency

L evel *

Product Class Total Energy Usage ( kWh/100 lb ice )

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 6.14 5.53 5.22 4.91 4.82 4.68

IMH

- W

- Med

- B 4.65 4.18 3.95 3.80

IMH

- W

- Large

- B

IMH

- W

- Large

- B1 4.00 3.67

IMH

- W

- Large

- B2 4.00 3.70

IMH

- A

- Small

- B 7.68 6.91 6.53 6.29 6.14 5.76 5.72

IMH

- A

- Large

- B

IMH

- A

- Large

- B1 6.01 5.41 5.11 5.06 4.81 4.60

IMH

- A

- Large

- B2 5.24 4.72 4.62

RCU

- Large

- B

RCU

- Large

- B1 5.10 4.59 4.34 4.22

RCU

- Large

- B2 5.10 4.59 4.39

SCU

- W

- Large

- B 7.60 6.84 6.46 6.08 5.70 5.34

SCU

- A

- Small

- B 12.84 11.56 10.91 10.27 9.63 8.99 8.64

SCU

- A

- Large

- B 9.80 8.82 8.33 7.84 7.35 6.94

IMH

- A

- Small

- C 9.05 8.15 7.69 7.24 6.79 6.72

IMH

- A

- Large

- C 6.60 5.94 5.61 5.28 5.06

RCU

- Small

- C 6.33 5.70 5.38 5.06 4.75 4.65

SCU

- A

- Small

- C 10.12 9.10 8.60 8.09 7.59 7. 43

* Blank cells imply there are no associated efficiency levels.

8

- 12

### 8.2.2.3 Overall Markup

As discussed in the final rule TSD chapter 6, Markups for Equipment Price Determination, DOE calculated overall markup and applied it to the equipment MSP to calculate the equipment purchase price for customers. DOE calculated baseline markups to convert baseline MSP to baseline customer purchase price and incremental markups to convert the increments in MSP to increments in customer purchase price. DOE use d these markup values in

the LCC and PBP analysis for calculation of baseline and higher efficiency equipment price to

customers.

8.2.2.4 Installation Cost

Most automatic commercial ice makers are installed in fairly standard configurations, which help s in estima ting the cost of installation. DOE defines installation cost for equipment going into a non

- space

- constrained application a s the sum of the base installation cost (materials and labor), and as

- required, additional installation costs addressing two efficien cy increments (represented by specific design options) as discussed below.

The base installation cost for each equipment class was arrived at based on conversations with ice maker installing contractors and RS Means data

. 3 

### The first efficiency increment wa s the inclusion of drain water heat exchangers ( DWHX s)

— an additional installation cost was added at the efficiency level at which a DWHX is added to the engineering curve, and for all higher efficiency levels. The DWHX installation cost was calculated as t he product of hourly labor cost for an installer and the additional hours required to install the DWHX . Based on a review of

DWHX literature, DOE estimated the additional installation time for a DWHX in conjunction

with the ice maker installation to be 0.75 hours. Based on RS Means data

, 3 

### the labor rate was estimated to be $75.65 per hour. For the national impact analysis ( NIA ) , the additional DWHX installation cost was applied to all installations including a DWHX throughout the 30

- year analysis period.

For the two larger remote condensing equipment classes (RCU

- Large

- B2 and RCU

Large

- B1), DOE included add itional installation costs for efficiency levels incorporating

increased condenser size. Based on review of RS Means data

, 3 

### for the RCU

- Large

- B1 equipment

clas s, DOE estimated an additional 0.5 hours of labor to install the larger and heavier condenser

but with no additional cost for materials. For the RCU

- Large

- B2 equipment class, DOE

estimated an additional 1.5 hours of labor to install the heavier condenser a nd an average of $40 in materials for modifications to the base mounting. The labor rate is $79.50 per hour. For the

RCU

- Large

- B2 equipment class, the additional material costs were assumed to apply to replacement of existing equipment, one time, while the labor costs for both classes were assumed to apply to all installations with increased condenser size throughout the 30

- year analysis period.

For equipment classes where increased cabinet size was considered in the engineering curves and for which a porti on of customers will face space constraints, additional installation costs were estimated. For the three IMH equipment classes with 22

- inch, reduced

- footprint equipment, for customers to “go beyond” the end of the 22

- inch space constrained engineering

curv e to the higher efficiency levels available in the non

- 22

- inch space constrained engineering curve, a certain percentage of customers would likely need to retrofit the existing space to make

8

- 13

### possible the installation of larger units. For the non

- 22

- inch pr oducts, in equipment classes in which the engineering curve identified a necessary size increase, a small percentage of customers would also need to retrofit the existing space to install the larger units. The installation costs for equipment classes with space constraints are the weighted average of the installation costs of the

non

- space

- constrained and the space

- constrained units for the equipment class under

consideration.

Calculation of the construction cost expense reflects the following assumptions and data:

• Additional construction costs to address space constraints are applicable only to replacement shipments

• The relative shipment fraction of 22

- inch units in each of the three classes

• The percentage of customers buying 22

- inch units who have space c onstraints

• The percentage of customers buying non

- 22

- inch units who have space constraints

• The average construction cost for installation of the units where space constraints exist ( e.g. , to raise a small area of a ceiling)

.

Some customers buying 22

- inch units would be able to purchase taller units, while others would incur construction costs to do so. To merge the 22

- inch and non

- 22

- inch curves, the

construction cost is introduced at the end of the 22

- inch engineering curve t o “enable” space

constrained customers to buy bigger units. For customers of non

- 22

- inch units, space constraints occur when the design option is added that increases the cabinet size. All inputs to the

calculation for 22

- inch units are shown in Table 8.2

. 6 while the inputs for the calculation of non

22

- inch units are shown in Table 8.2

. 7 . The construction cost adders are one

- time adders, meaning that once a building is renovated one time the adder is no longer needed. Thus, for use in the NIA, the installation cost adders are eliminated after the base year stock is retired for the first time. For the LCC/PBP analysis, the total installation costs for all equipment classes are

shown in Table 8.2

. 8

.

Table 8.2

. 6 Installation Cost Assumptions for 22

- Inch Units

IMH

- A

Small

- B IMH

- A

Large

- B1 IMH

- W

Small

- B Replacement shipment fraction 85.8% 85.8% 85.8%

Estimated construction cost for customers with

size constraints $1,804.0 $1,804.0 $1,804.0

Total 22

- inch

- wide shipments 13.0% 5.0% 48.0% Estimated space

- constrained 22

- inch

- wide shipments 30.0% 35.0% 30.0%

Efficiency level above which construction cost is

added to merge curves 18.1% 15.6% 21.5%

8

- 14

### Table 8.2

. 7 Installation Cost Assumptions for Non

- 22

- Inch Units with Size Growth

IMH

- A

Small

- B IMH

- A

Large

- B1 IMH

- W

Small

- B

Replacement shipment fraction 85.8% 85.8% 85.8%

Estimated construction cost for customers with

size constraints $1,804.0 $1,804.0 $1,804.0

Total non

- 22

- inch

- wide size shipments 87.0% 95.0% 52.0%
Estimated space

- constrained non

- 22

- inch wide shipments 10.0% 15.0% 10.0%

Efficiency level above which construction cost is added to merge curves 18.1% 19.2% 19.2%



### Note that on all of the tables showing assumptions by efficiency levels, levels 3A and 4A

, were added to denote the end of the 22

- inch engineering curves. Thus, for the IMH

- Air Batch product classes, a level 3A was added a t the end of the space

- constrained 22

- inch curve at an 18 percent efficiency improvement above the baseline for the small un its (IMH

- A

- Small

- B) and 16 percent above the base for the large units (IMH

- A

- Large

- B1 ). Similarly, a level 4A was added to

reflect th e end of the space constrained IMH

- W

- Small

- B curves. Above these levels the equipment class curves are merged, but with the additional construction costs added to the installation costs, as necessary, to remove space constraints. These 3A and 4A efficiency levels do not apply to the other classes. While not identified as having significant 22

- inch shipments in

the NOPR comment s, it is noted that a revised engineering curve for IMH

- A

- Small

- C also reflected increased size growth and was treated similarly to the non

- 22 inch IMH

- A

- Small

- B equipment.

Table 8.2

. 8 provides a list of the estimated U.S. average installation cost for each primary equipment class.

Table 8.2

. 8 Installation Cost by Equipment Class, U.S. Average

Equipment Class Installation Cost (2013$)

Level

1 Level

2 Level

3 Level

3A Level

4 Leve

l 4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 305 305 305 412 442 665

IMH

- W

- Med

- B 609 609 666 666

IMH

- W

- Large

- B * 1,015 1,072

IMH

- W

- Large

- B1 1,015 1,072

IMH

- W

- Large

- B2 1,015 1,072

IMH

- A

- Small

- B 305 305 312 312 500 556 556

IMH

- A

- Large

- B * 609 618 620 620 827 875

IMH

- A

- Large

- B1 609 609 612 612 857 913

IMH

- A

- Large

- B2 609 666 666

RCU

- Large

- B * 1,523 1,523 1,627 1,627

RCU

- Large

- B1 1,523 1,523 1,619 1,619

RCU

- Large

- B2 1,523 1,523 1,738

SCU

- W

- Large

- B 609 609 609 609 609 666

SCU

- A

- Small

- B 305 305 305 305 305 305 361

SCU

- A

- Large

- B 609 609 609 609 609 666

* Values for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two sub

- equipment

c lass level typical units shown in the table and using weights provided in TSD chapter 7.

8

- 15

### Table 8.2

. 8 Installation Cost by Equipment Class, U.S. Average

Equipment Class Installation Cost (2013$)

Level

1 Level

2 Level

3 Level

3A Level

4 Leve

l 4A Level

5 Level

6 Level

7

IMH

- A

- Small

- C 305 305 305 305 305 305

IMH

- A

- Large

- C 609 609 609 609 609

RCU

- Small

- C 761 761 761 761 761 761

SCU

- A

- Small

- C 305 305 305 305 305 305

* Values for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two sub

- equipment

c lass level typical units shown in the table and using weights provided in TSD chapter 7.



### Table 8.2

. 9 shows installation cost indices for installation cost s in each of the 50 states
and the District of Columbia , and the weighted average for the entire United States. These are used to adjust t he national installation cost t o reflect differences by s tate

. The state

- level data are based on data published by RS Means for major U.S. cities

. 4 

### To arrive at an average index for each state, DOE first weighted the city indices up to state

- level values u sing the relative city

- to

total population values, using the 201 3 population in each city

. 5 

### DOE used state

- level p opulation weights for 20 1 3 from the U.S. Census Bureau 6 

### to calculate a weighted

- average index for each

state from the RS Means census data.

Table 8.2

. 9 Installation Cost Indices (National Value = 100.0)

State Index State Index State Index

Alabama 82.5 Kentucky 95.2 North Dakota 87.4

Alaska 104.6 Louisiana 86.9 Ohio 99.3

Arizona 94.3 Maine 92.2 Oklahoma 84.6

Arkansas 83.8 Maryland 95.2 Oregon 103.7

California 112.9 Massachusetts 110.1 Pennsylvania 110.8

Colorado 93.3 Michigan 102.3 Rhode Island 106.1

Connecticut 109.2 Minnesota 107.6 South Carolina 83.8

Delaware 109.2 Mississippi 84.7 South Dakota 81.1

Dist. of Columbia 99.8 Missouri 102.2 Tennessee 92.4

Florida 88.5 Montana 91.4 Texas 85.2 Georgia 89.3 Nebraska 93.6 Utah 89.9

Hawaii 106.6 Nevada 101.7 Vermont 91.7

Idaho 91.9 New Hampshire 97.0 Virginia 91.2

Illinois 115.6 New Jersey 112.0 Washington 104.9

Indiana 94.2 New Mexico 90.7 West Virginia 99.0

Iowa 94.8 New York 126.4 Wisconsin 101.7

Kansas 91.1 North Carolina 83.9 Wyoming 88.4



### 8.2.2.5 Equipment Price Trends

In prior energy conservation standards rulemakings, DOE estimated the total installed costs per unit for equipment, and then assumed that costs remain constant throughout the analysis period. This assumption is conservative because installed costs tend to decrease over

ti me. In 2011, DOE issued a n otice of d ata a vailability (NODA) titled Equipment Price Forecasting in Energy Conservation Standards Analysis

. 76 FR 9696 (Feb. 22, 2011)

. In the NODA, DOE proposed a methodology for determining whether equipment prices have tre nded

downward in real terms. The methodology examines so

- called price or experiential learning,

8

- 16

### wherein, with ever

- increasing experience with the production of a product, manufacturers are able to reduce their production costs through innovations in technology and process.

To account for increased efficiency in the automatic commercial ice

- making equipment manufacturing process over time, DOE used a price forecast methodology based on experiential learning (see appendix 8D for more information on expe riential learning). For the LCC model, the impact of experiential learning was a decrease in the MSP to account for changes from the time prices were developed ( 2013 ) until the start of the LCC analysis (201 8 ). The price trend

factor used in the LCC and PB P a nalysis was 0. 9952 , which means MSPs shown on Table 8.2

. 4 were reduced by 0. 4 8 percent in the development of total installed costs.

8.2.2.6 Weighted Average Total Installe d Cost

As presented in Eq. 8

. 2 (see section 8.2.2 ) , the total installed c ost 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 adding to it the product of the incremental MSP and the increm ental markup. Because MSP s, markups, and the sales tax all can take on a variety of values, depending on location, the resulting total installed cost for a particular standard level will not be a single

- point value, but ra ther a distribution of

values.

T he weighted average costs for the IMH

- A

- Small

- B equipment class are presented below

for the baseline level at national average markup rates and national average installation costs for illustration purposes

. DOE used t he baseline MSP and the standard

- level MS P increases as the starting points for determining the total installed cost (values are taken directly from Table 8.2

. 3

and Table 8.2

. 4 ). DOE used t he baseline and incremental markups, the sales tax, and installation

cost to convert the MSP s into total installed costs for case s where the incremental installation

cost is held flat. As an example, Table 8.2.10 summarizes the weighted average or mean costs and markups necessary for determining the weighted average baseline and standard

- level total

installed costs

.

Table 8.2

. 10 Costs and Markups for De termination of Weighted Average Total Installed Costs ( IMH

- A

- Small

- B )

Variable Weighted Average or Mean Value

Baseline MSP $1,144.41

Standard

- Level MSP Increase (Efficiency Level 4) $ 174.32

Baseline Markup Factor with Sales Tax 1.8 764

Incremental Markup Factor with Sales Tax 1.3 153

Installation Cost

– Baseline * $304.53

Installation Cost

– Efficiency Level 4 $ 499.56 Installation Cost Factor, for U.S. Average 1.000

Price Trend Factor ** 0.9952

* Installation cost appl ied to the baseline unit, with no incremental installation cost.

** Price trend ( learning ) factor for 2018. See a ppendix 8D.

8

- 17

### Eq. 8

. 3 for the baseline ( l evel 1) and for a higher efficiency level ( l evel 4) IMH

- A

- Small

- B equipment class illustrate s how DOE deriv ed the weighted average total installed cost based on

the data shown in Table 8.2.10

. For the baseline product, DOE calculat ed the total installed cost at national average conditions as follows : c



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. 41 × 0

. 99 52 × 1. 8 764 + $ 30 4.53 × 1.00

= $ 2, 137.15 + $ 30 4.53

= $ 2, 441.68 Eq. 8

. 3

W here :

IC BASE IMH

- A

- Small

- B 

### = total installed cost of IMH

- A

- Small

- B equipment at baseline efficiency level ($), EQP BASE IMH

- A

- Small

- B 

### = equipment purchase price of IMH

- A

- Small

- B equipment at baseline efficiency level ($),

INST BASE IMH

- A

- Small

- B 

### = installation cost of IMH

- A

- Small

- B equipment at baseline efficiency level ($),

MFG BASE IMH

- A

- Small

- B 

### = MSP of IMH

- A

- Small

- B equipment at baseline efficiency level ($),

Price Trend Factor Analysis Year 

### = Price trend factor for the 2013

– 2018 period, or 0.99 52

, MU BASE IMH

- A

- Small

- B 

### = overall baseline markup for equipment class IMH

- A

- Small

- B

, 1.8764, including the national average state tax factor, and

ISTINDEX = location dependent multiplier on installation cost ; equals 1.0 at the national

average.

DOE calculat ed the total installed cost for the higher efficiency level ( e fficiency l evel 4 )

us ing a n incremental MSP

. DOE applied an incremental markup factor to the increment al

increases in MSP. The l evel 4 price is equal to the baseline price calculated above, plus t he MSP

increment for a higher efficiency level

, multiplied by the incremental markup.

c Note that the numbers shown in Eq. 8

. 3 have been rounded and do not exactly match the numbers in the analysis.

8

- 18

### As an example, DOE calculated the national average , l evel 4

, total installed cost ( IC IMH

- A

Small

- B LEVEL4 

### ) as follows: d



### IIII IIIIII

− − SSSSSSSS

− BB LL LL LL 4



### = EEEEEE IIIIII

− − SSSSSSSS

− BB LL LL LL 4 

### + IIIIIIII IIIIII

− − SSSSSSSS

− BB LL LL LL 4 

### × IIIIIIIIIIII II

= MMMMMM BBBBBBBB IIIIII

− − SSSSSSSS

− BB 

### × PPPPPPPP TTTT FF FFFF AA AAAAAAAA YYYY YY 

### ×

MMMM BBBBBBBB IIIIII

− − SSSSSSSS

− BB 

### +
∆ MMMMMM IIIIII

− − SSSSSSSS

− BB LL LL LL 4 

### × PPPPPPPP FF FFFF AA AAAAAAAA YYYY YY 

### ×

MMMM IIIIII

− − SSSSSSSS

− BB LL LL LL 4 

### + IIIIIIII IIIIII

− − SSSSSSSS

− BB LL LL LL 4 

### × IIIIIIIIIIII II

= $ 1, 144

. 41 × 0.99 5 2 × 1. 8 764 + $ 174.32 × 0. 99 5 2 × 1. 3 153 +

$ 499.56 × 1.000

= $ 2, 864.8 1 Eq. 8

. 4

W here :

IC IMH

- A

- Small

- B LEVEL4 

### = total installed cost of IMH

- A

- Small

- B equipment at e fficiency l evel 4 ($)

, EQP IMH

- A

- Small

- B LEVEL4 

### = equipment price of IMH

- A

- Small

- B equipment at e fficiency l evel 4 ($)

, INST IMH

- A

- Small

- B LEVEL4 

### = installation cost of IMH

- A

- Small

- B equipment at e fficiency l evel 4 ($)

, Δ MFG IMH

- A

- Small

- B LEVEL4 

### = i ncrement al increase in MSP of IMH

- A

- Small

- B equipment at

e fficiency l evel 4 compared to equipment at baseline efficiency level ($)

, and

MU IMH

- A

- Small

- B LEVEL4 

### = i ncremental markup for equipment class IMH

- A

- Small

- B , including state

tax factor

.

Table 8.2.11 presents the weight ed average equipment price, installation cost, and total

installed costs for the IMH

- A

- Small

- B equipment class es at the baseline level and each higher efficiency level examined

.

Table 8.2

. 11 Weighted Average Equipment Price, Installation Cost, and Total Installed

Costs for IMH

- A

- Small

- B at U.S. Average C onditions ( 201 3 $)

Efficiency Level Equipment Price (MSP) Installation Cost Total Installed Cost *

1 (Baseline) $1,144.41 $304.53 $2,441.68

2 $1,218.77 $304.53 $2,539.01

3 $1,301.64 $311.91 $2,654.87

4 $1,331.93 $311.91 $2,694.52

5 $1,318.74 $499.56 $2,864.89

6 $1,626.35 $556.30 $3,324.29

7 $1,631.47 $556.30 $3,330.99

* Numbers provided in this table are taken directly from the LCC analysis, and thus may differ from those provided above in the text due to rounding.

d The numbers shown in Eq. 8

. 4 have been rounded and do not exactly match the numbers used in the analysis.

8

- 19

### 8.2.3 Operating

- Cost Inputs

DOE define s the operating cost as the sum of energy cost, water cost, repair cost, an d maintenance cost, or :

OC = EC+ WC+ RC+ MC Eq. 8

. 5

W here :

OC = operating cost ($)

, EC = energy cost ($)

, WC= water c ost ($),

RC = repair cost ($)

, and

MC = maintenance cost ($)

.

The remainder of this section describes the variables that DOE used to calculate operating cost s for automatic commercial ice makers

. Table 8.2.12 shows the inputs used to

determin e operating costs.

Table 8.2

. 12 Inputs U sed to D etermine Operating Costs
Electricity price ( cents /kWh ) Water/wastewater price ($/1

, 000 gallons) Electricity and water price trend s Repair cost ($) Maintenance cost ($)

Lifetime (y ears )

Discount rate (%)

Effective date of standard Baseline electricity consumption (kWh/ 100 lb ice )

Baseline water consum p tion (gallons of water/100 lb ice) Standard case electricity consumption (kWh/ 100 lb ice )

Standards water consum p tion (gallons of water/100 lb ice)



### 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 (price multiplied by electricity consumption) for automatic

commercial ice makers used in various commercial building types

.

Subdivision of the Country

. Because of the wide variation in electricity consumption patterns, wholesale costs, and retail rates across the country, DOE consider ed regional differences in electricity prices. DOE divided the United States into the 50 states and the Di strict

of Columbia. DOE used average effective commercial electricity prices at the state level from

the EIA web page titled Sales (Consumption) , Revenue, Price s and Customers

. 7 

### At the time DOE developed the LCC/PBP model, t he latest available data was for calendar year 20 1 3 Table

8.2.13 provides the 201 3 electricity prices.

8

- 20

### Table 8.2

. 13 Commercial Electricity Prices by State ( 201 3 cents/k Wh )

State Commercial Electricity Price

ce nts/kWh State Commercial Electricity Price

cents/kWh State Commercial Electricity Price

cents/kWh

Alabama 10.50 Kentucky 8.50 North Dakota 8.31

Alaska 15.62 Louisiana 8.94 Ohio 9.38

Arizona 9.87 Maine 11.72 Oklahoma 7.71

Arkansas 7.98 Maryland 10.70 Oregon 8.39

California 14.57 Massachusetts 14.51 Pennsylvania 9.26

Colorado 9.87 Michigan 11.07 Rhode Island 13.08

Connecticut 14.64 Minnesota 9.53 South Carolina 9.82

Delaware 10.26 Mississippi 10.21 South Dakota 8.44

Dist. of Col. 11.93 Missouri 8.72 Tennessee 10.01

Florida 9.49 Montana 9.52 Texas 8.03 Georgia 9.84 Nebraska 8.62 Utah 8.37

Hawaii 34.06 Nevada 9.02 Vermont 14.64

Idaho 7.40 New Hampshire 13.52 Virginia 8.05

Illinois 7.88 New Jersey 12.80 Washington 7.76

Indiana 9.48 New Mexico 9.78 West Virginia 8.16

Iowa 8.47 New York 15.23 Wisconsin 10.84

Kansas 9.54 North Carolina 8.73 Wyoming 8.60



### DOE recognized that different kinds of businesses typically use electricity in different amounts at different times of the day, week, and year, and therefore experience different effective prices. To make this adjustment, DOE used the 2003 Commercial Building s Energy Consumption Survey ( CBECS ) 8 

### 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.

Because it isn’t explicitly recognized as a CBECS buildin g type, DOE identified multi

- line retail by identifying retail stores for which the data suggest the presence of walk

- in refrigeration and other commercial refrigeration equipment

. e 

### Eq. 8

. 6 shows how DOE calculated t he prices paid by the seven types of businesses in each state or district :

EEEEEE EE CCCC BB BBBBBB BBBB 2013 

### = EEEEEE EE CCCC 2013 

### × � PP BBBBBBBBBBBBBBBB UUUU 2003

CCCCCC UUUU 2003 

### �

Eq. 8

. 6

W here :

EPRICE COM BLD G TYPE STATE 20 1 3 

### = average commercial sector electricity price in a specific building type ( such as health care

, food sales , and foodservice ) in a specific state in 201 3

, EPRICE COM STATE 201 3 

### = average commercial sector electricity price in a specific state in 201 3

, 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 8.2.14 shows the resulting commercial building electricity price ratios.

e Automatic commercial ice makers are not explicitly captured by the CBECS database, so commercial refrigeration is used as a proxy for buildings of interest to this analysis.

8

- 21

### Table 8.2

. 14 Derived National Average Commercial Electricity Price s and Ratios by

Business Type

Business Type Electricity Price

dollars/ kWh Ratio of Elec tricity Price to Average Price for all Commercial Buildings

HealthCare $0.067 85% Lodging $0.077 97%

Food s ervice $0.082 104%

Retail $0.083 105%

Education $0.075 95%

Food Sales $0.076 96%

Office $0.081 103% All commercial buildings $0.079 100%

Source: CBECS 2003.



### The derived ratio equates commercial electricity prices by building type to the overall average commercial building price . DOE then combined the derived ratio with state

- by

- state commercial rates to derive a series of prices for each state and for each building type. DOE

forecast f uture prices as described in section 8.2.3.2

. To obtain a weighted average national price for the business types being explicitly modeled

, DOE weighted the price ratios for each business
type by the percentage of automatic commercial ic e maker shipments each represents

. Shipments by business type are discussed in c hapter 9 of the TSD.

8.2.3.2 Electricity Price Trend

The electricity price trend provides the relative change in electricity prices through the

year 204 7

. Estimating future electricity prices is difficult, especially considering that there are efforts in many states throughout the country to restructure the electricity supply industry.

DOE applie d a projected trend in regional average electricity prices to each customer’s energy prices based on the AEO2014 price scenarios

. f 

### The discussion in this chapter refers to the reference price scenario. In the LCC analysis, DOE can analyze the following four scenarios:

1. Constant (real) energy prices at 201 3 values ( i.e. , a c onstant index of 1.0)

2. AEO201 4 , High Economic Growth (“ AEO201 4 High Growth ” in Figure 8.2

. 1 )

3. AEO201 4 , Reference Case (“ AEO201 4 Reference ” in Figure 8.2

. 1 )

4. AEO201 4 , Low Economic Growth (“ AEO201 4 Low Growth ” in Figure 8.2

. 1 )

Figure 8.2

. 1 shows the trends for the AEO201 4 reference case, high growth, and low growth price projections at the national level

. DOE extrapolated the values for the later years,

after 20 40

— the last year of the AEO201 4 forecast. DOE used the price trend from 20 31 through

20 40 of each forecast scenario to establish prices for the years 20 41 through 204 7

.

f The Annual Energy Outlook develops forecasts based on the nine Census Divisions. Each state is mapped to the appropriate census division and the corresponding growth rates are applied to the base year state commercial price.

8

- 22

### Figure 8.2

. 1 National Electricity Price Trends for Commercial Rates to 204 7

The default electricity price trend scenario used in the LCC analysis is the trend from the

AEO 20 1 4 r eference c ase, shown in Figure 8.2

. 1

. T he LCC model spreadsheets have the capability to analyze the AEO201 4 h igh g rowth trends

, AEO201 4 l ow g rowth price trends

, and constant energy prices for additional sensitivity analysis

.

8.2.3.3 Water Cost

Water costs were developed using AWWA rate survey data. Prices were extracted for each utility in the AWWA database for commercial sector customers using between 50,000 and 374,000 gallons per year. Water and wastewater prices were extracted and combined f or each observation. The number of observations by state contained in the database varies from 0 to 38. To make the results as representative as possible, data from two prior surveys were compiled, adjusted for prices escalation, and compared to data from the 2012 survey for states with few or

no observations. The 201 3 water prices are shown in Table 8.2

. 15

. 0.08900 0.09400 0.09900 0.10400 0.10900 0.11400 0.11900 0.12400 0.12900 0.13400

2010 2015 2020 2025 2030 2035 2040 2045US Weighted Average Real Cost/kWh(2013$)



# Year 

# Electricity Price Projections

AEO 2014 Low Growth AEO 2014 Reference AEO 2014 High Growth

8

- 23

### Table 8.2

. 15 Commercial Water Prices by State ( 201 3 $/1

, 000 gallons )

State Commercial

Water Price $/1,000 gallons State Commercial

Water Price $/1,000 gallons State Commercial

Water Price $/1,000 gallons

Alabama 7.21 Kentucky 7.29 North Dakota 5.52

Alaska 10.32 Louisiana 8.11 Ohio 7.91

Arizona 6.39 Maine 4.15 Oklahoma 7.13

Arkansas 4.89 Maryland 10.13 Oregon 9.24

California 6.51 Massachusetts 11.82 Pennsylvania 6.98

Colorado 5.54 Michigan 4.22 Rhode Island 8.51

Connecticut 6.23 Minnesota 7.71 South Carolina 7.05

Delaware 11.75 Mississippi 6.60 South Dakota 8.57

Dist. of Col. 9.77 Missouri 6.44 Tennessee 8.79

Florida 8.23 Montana 6.72 Texas 6.87 Georgia 8.21 Nebraska 3.72 Utah 3.13

Hawaii 7.73 Nevada 5.98 Vermont 3.61

Idaho 3.01 New Hampshire 6.90 Virginia 9.32

Illinois 6.96 New Jersey 7.86 Washington 8.80

Indiana 7.52 New Mexico 5.79 West Virginia 7.03

Iowa 4.92 New York 6.81 Wisconsin 4.85

Kansas 6.60 North Carolina 9.27 Wyoming 7.55



### 8.2.3.4 Water Price Trend

To identify price trends for water, DOE examined the CPI for w ater and sewerage

maintenance . Consistent with other portions of the analysis like the discount rate analysis, DOE

used a 40

- year historical series. In nominal terms, the annual average growth r ate in this price

index was 6.1 4 percent. The overall CPI for the same period averaged 4. 06 percent. For the analysis period, a real water price escalation of 1. 99 percent was used.

To establish high and low price scenarios, DOE calculated 20

- year rolling averages of the annual growth rates. DOE then identified the 20

- year period with the highest average escalation

rate and the 20

- year period with the lowest average escalation rate. The highest 20

- year period was the period between 199 4 and 201 3 , and the average real escalation rate during the period

was 2.1 9 percent. To create a high water price forecast, water prices were escalated at a constant

2.1 9 percent. The lowest 20

- year period was between 197 9 and 199 8 , and the average real escalation rate during that period was 1. 41 percent. To create a low water price forecast, water prices were escalated at a constant 1. 41 percent. Reference, high and low water prices are shown in TSD appendix 10B.

8.2.3.5 Repair Cost

Total repair cost for each efficiency level is calc ulated as the sum of the replacement component cost and the replacement labor cost for those components that would be repaired in the event of component failure . For automatic commercial ice makers, DOE identified the components prone to failure as compres sors, fan motors, and auger motors. The replacement component cost calculation uses MPC values from the engineering analysis. A replacement product cost markup of 250 percent (2.5 times the MPC) and a probability of replacement of 25 percent for each compo nent are used to estimate the lifetime repair cost at each efficiency level.

8

- 24

### The replacement component cost calculation methodology has remained the same between the

NOPR and the final rule analys e s.

The repair labor cost was calculated as the product of hourly labor cost for a refrigeration technician and the number of hours required to replace the failed component. Based on conversations with ice maker repair contract ors, the repair labor hours for a compressor failure only, a condenser fan motor failure only, and an auger motor failure only were estimated to be 4 hours, 2 hours, and 2 hours, respectively. Labor cost was estimated at $125 per hour based on discussions with companies repairing ACIM equipment and consistent with NOPR comment s

. The repair co st assumptions are summarized in Table 8.2

. 16

.

Table 8.2

. 16 Repair Cost Assumptions

Assumptions Compressors Fan Motors Auger Motors Repair Chain Markup 250% 250% 250% Probability of Replacement 25% 25% 25% Labor Required 4 hours 2 hours 2 hours Labor Rate ($/hr) $125 $125 $125



### Annualized repair costs for each efficiency level were then calculated by dividing the total repair cost at the considered level by the average lifetime of 8.5 years for batch and continuous type ice makers. Total repair costs by equipment class and effici ency level are

summarized in Table 8.2

. 17

All equipment classes other than those with 22

- inch variants have a single annualized repair cost curve. The equipment classe s with 22

- inch variants have two repair cost curves, one

each for the 22

- inch and the non

- 22

- inch units. Shipment data provided by AHRI were used to distribute the equipment under each of these classes and to merge the two annualized repair cost

curves and produce one single annualized repair cost curve for the equipment class

. Repair costs

are shown in Table 8.2

. 17

.

Table 8.2

. 17 Repair Cost by Equipment C lass

Equipment Class Repair Cost ( 2013$/year )

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 45.01 46.53 46.53 46.53 46.53 46.53

IMH

- W

- Med

- B 52.92 55.80 55.80 55.80

IMH

- W

- Large

- B* 62.98 65.04

IMH

- W

- Large

- B1 57.86 59.66

IMH

- W

- Large

- B2 79.53 82.42

IMH

- A

- Small

- B 47.32 50.55 50.55 50.55 50.55 50.55 50.55

IMH

- A

- Large

- B* 55.96 59.73 61.18 61.18 61.24 61.24

IMH

- A

- Large

- B1 54.70 58.02 59.74 59.74 59.80 59.80

IMH

- A

- Large

- B2 62.76 68.98 68.98

RCU

- Large

- B* 62.99 65.46 69.11 69.11

RCU

- Large

- B1 61.39 63.26 66.79 66.79

RCU

- Large

- B2 86.85 98.34 103.73

* Values for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two sub

- equipment class level typical units shown in the table using the weights provided in TSD chapter 7.

8

- 25

### Table 8.2

. 17 (cont)

Equipment Class Repair Cost ( 2013$/year )

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

SCU

- W

- Large

- B 22.06 22.06 22.06 22.06 22.06 22.06

SCU

- A

- Small

- B 37.17 38.07 39.10 40.14 41.10 43.04 43.38

SCU

- A

- Large

- B 45.79 46.84 48.00 48.30 50.34 50.33

IMH

- A

- Small

- C 78.20 80.27 81.65 82.37 102.43 105.53

IMH

- A

- Large

- C 89.34 91.55 92.06 104.69 125.18

RCU

- Small

- C 85.48 87.67 89.47 90.58 106.75 120.24

SCU

- A

- Small

- C 77.27 77.43 78.52 80.27 97.03 104.48



### 8.2.3.6 M aintenance Cost

The maintenance cost is the cost to the customer of ensuring proper equipment operation even if there are no specific equipment failures ( e.g. , checking and maintaining refrigerant levels, replacing filters

, checking water distribution lines for leaks, cleaning , sanitizing, and descaling ).

The maintenance cost does not include the cost s associated with the replacement or repair of components that have failed ( as discussed previously ).

In the f inal rule analyses, DOE estimated m aterial and labor costs for preventative

maintenance based on RS Means cost estimation data Error! Bookmark not defined. 

### and on telephone

conservation s with contractors. DOE a ssumed maintenance cost would remain constant for all efficiency levels within an equipment class. Table 8.2

. 18 shows the annualized maintenance costs by equipment class for each efficiency level.

Table 8.2

. 18 Annual Maintenance Costs by Equipment Class and Efficiency Level

Equipment Class Maintenance Cost (2013$/yea r)

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 401 401 401 401 401 401

IMH

- W

- Med

- B 609 609 609 609

IMH

- W

- Large

- B* 812 812

IMH

- W

- Large

- B1 812 812

IMH

- W

- Large

- B2 812 812

IMH

- A

- Small

- B 751 751 751 751 751 751 751

IMH

- A

- Large

- B* 1,050 1,050 1,050 1,050 1,050 1,050

IMH

- A

- Large

- B1 1,050 1,050 1,050 1,050 1,050 1,050

IMH

- A

- Large

- B2 1,050 1,050 1,050

RCU

- Large

- B* 508 508 508 508

RCU

- Large

- B1 508 508 508 508

RCU

- Large

- B2 508 508 508

SCU

- W

- Large

- B 609 609 609 609 609 609

SCU

- A

- Small

- B 761 761 761 761 761 761 761

SCU

- A

- Large

- B 1,066 1,066 1,066 1,066 1,066 1,066

* Values for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two sub

- equipment class level typical units shown in the table using the weights provided in TSD chapter 7.

8

- 26

### Table 8.2

. 18 (cont)

Equipment Class Maintenance Cost (2013$/year)

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- A

- Small

- C 761 761 761 761 761 761

IMH

- A

- Large

- C 1,066 1,066 1,066 1,066 1,066

RCU

- Small

- C 305 305 305 305 305 305

SCU

- A

- Small

- C 761 761 761 761 761 761



### 8.2.3.7 Lifetime

As discussed in s ection 8.1.2

, DOE defines lifetime as the age at which a typical

automatic commercial ice maker is retired from service. DOE estimated equipment lifetime

based on discussions with industry experts, and co nclude d that a typical lifetime of 8.5 years is appropriate for most automatic commercial ice mak ers

. Because some equipment has remaining

useful life , there is a market for equipment that has been removed from service

. DOE

understands

, however, that the salvage value to the original purchaser is generally very low

, and

thus has not taken this into account in the LCC.

In the LCC model, lifetime was modeled as variable. A Weibull distribution developed for the shipment and national impac t analysis model results in a set of probability of equipment failure by year. While the full distribution covered the entire period of year 0 through year 16, the low and high ends of the distribution exhibited probabilities so low that when multiplied by 10,000 (the number of model runs) the expected times the lifetime would be selected was zero

. The LCC model used the failure year

- probability of failure combinations shown in Table 8.2

. 19

.

Table 8.2

. 19 Probability of Equipment Failure by Year

Years Probability

3 0.0%

4 0.2%

5 1.0%

6 4.1%

7 12.7%

8 27.7%

9 35.0%

10 17.4%

11 1.8%

12 0.0%



### 8.2.3.8 Discount Rate

The discount rate is the rate at which future expenditures are discounted to establish their present value. DOE derived discount rates for the automatic commercial ice mak er analysis by estimating the cost of capital for the types of companies that purchase automatic commercial ice

makers

. The cost of capital is commonly used to estimate the present value of cash flows to be

derived from for a project or investment. Most companies use both debt and equity capital to fund investments, so their overall cost of capital is the weighted average of the cost to the company of equity and debt financing.

8

- 27

### DOE estimated the cost of equity financing by using the C apital A sset P ricing M odel (CAPM)

. 9 

### The CAPM, among the most widely used of models that estimate the cost of equity financing, assumes that the cost of equity is proportional to the amount of systematic risk

associated with a particular company. The cost of equity financing tends to be high when a company faces a large degree of systematic 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 average 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 pric e 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 sto ck market risk.

The following equation gives the cost of equity financing for a particular company:

k

e 

### = R f 

### + (β x ERP) Eq. 8

. 7

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. DOE calculated t he risk

- free rate using Federal Reserve data for the period

19 74 through 20 13

, 10 

### with a resulting rate of 6. 24 percent. DOE used a 5.81

- percent estimate for

the ERP based on the difference between a 40

- year geometric average yield on the Standard &

Poor ’ s 500 Index calculated using data downloaded from the Damodaran Online 11 

### site and the

risk

- free rate

.

The cost of debt financing ( k d



### ) is the interest rate paid on money a company borrow s

. DOE estimated t he cost of debt by adding a risk adjustment factor ( R

a 

### ) to the risk

- free rate.

a f d 

## R R k + =
Eq. 8

. 8

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 (%)

.

8

- 28

### The risk adjustment factor depends on the variability of stock returns represented by standard deviations in stock prices

. DOE calculated stock prices using values from Damodaran

Online individual company cost of capital worksheets. 11



### The weighted average cost of capital (WACC) for 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

. 9

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 financing

.

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 ded ucts expected inflation ( r ) from the nominal rate. DOE calculated expected inflation ( 3. 42 percent) over the

same 1974 through 2013 historical period used for the other financial data calculations

.

To estimate the WACC of automatic commercial ice mak er purchasers, DOE used a

dataset of companies involved in each of the building types being analyzed, 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 th e publicly traded companies in the U nited

S tates.

DOE divided the companies into categories according to their type of activity ( e

. g. , foodservice or food sales). DOE used financial information for firms in the Damodaran database that would be likely to oc cupy one of seven building types that DOE assumed likeliest to use automatic commercial ice maker s

. DOE used all observations from the Damodaran data set for

which sufficiently complete data were available

.

Table 8.2.20 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 types

, there is a

mixture of large 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

— and/or state and local government facilities

. The cost of capital for small, independent grocers ; convenience store franchisees ; gasoline station owner

- operators ; small

hotel owner

- operators ; and others with more limited access to capital is more difficult to determine than for publicly traded companies. Individu al credit worthiness varies considerably, and some franchisees have access to the financial resources of the franchising corporation. The

Small Business Administration has collected data from which DOE was able to estimate the premium small businesses pay on loans relative to AAA corporate bonds . For small business

8

- 29

### loans under $100,000, the premium was approximately 1.9 percent for fixed rate loans

. 12 

### This assumption was applied in the LCC modeling.

Table 8.2

. 20 Derivation of Typical Discount Rates by Building Type

Building

Type Chain/ Publicly

Traded Local/Non

T raded State & Local

Government US Government Weighted Average,

Real No.

o f

Obs. WACC % of

Mkt Rate

With

Adder 7 % of

Mkt Municip.

Bond

Rate % of

Mkt Treasury

Rate % of

Mkt

Health Care

1,2 6.64% 19.6% 0% 0% 2.57% 76.7% 2.73% 3.7% 3.37% 18

Lodging 3 7.08% 60% 1.90% 40% 0.00% 0.0% 0.0% 0.0% 7.90% 28

Food

Service 4 6.30% 26% 1.90% 61% 2.57% 12.5% 0.0% 0.0% 7.07% 52

Retail 5.80% 100% 0.0% 0% 0.0% 0.0% 0.0% 0.0% 5.80% 8

Education 5 7.79% 26% 0.0% 0% 2.57% 73.5% 0.0% 0.0% 3.95% 16

Food Sales 5.88% 50% 1.90% 50% 0.0% 0.0% 0.0% 0.0% 6.88% 24

Office 6 6.74% 86% 0.0% 0% 2.57% 10.0% 2.73% 4.0% 6.16% 1318

1 Percentage of market from American Hospital Association, Fast Facts on Hospitals, Chicago, IL, January 3, 2012. www.aha.org/

. Percentages based on number of community hospitals.

2 Municipal bond rate from Fed eral Reserve Bank of St. Louis, and Federal Reserve H15 data (for 2012 and 2013 only).

Value shown in the table is 40

- year geometric average

, m ixed quality bonds , based on a nnual ave rage data. Non

- seasonally adjusted. Data for 1973

– 2011 from the Federal Reserve Bank o f St. Louis, and 2012

– 2013 from the H15 data.

3 Press reports indicate the percentage of “ branded ” hotels ranges from 60 to 70 percent of total hotel rooms. A rticles d id not
indicate t he percentage of branded hotel rooms that are franchisees essentially in distinguishable from local , small businesses. The low end of the range was used for the percentage of the market.

4 Percentages of market developed represented by state & local government developed from U.S. Census Bureau data, Table 1283. Commercial and Noncommercial Groups

- Food and Drink

. Establishments and Sales: 1990 to 2011, U.S. Census Bureau, Statistical Abstract of the United States: 2012. Publicly traded and small businesses subject to the small business

discoun t adder estimated using a National Restaurant Association comment that 7 of 10 restaurants are single

establishments.

5 Source for the percentage of education that is public versus private: http://nces.ed.gov/programs/digest/d10/tables/dt10_090.asp

. 6 Source for the fraction of offices owned by state and local government, and by federal government, was the 2003 CBECS.

7 Source: Small Business Administration data on loans between $10,000 and $99,000 compared to AAA Corporate Rates. www.sba.gov/advocacy/7540/6282

. Last accessed June 10, 2013.



### For buildings with an educ ation application

, the Damodaran database covers some privately operated schools, but n o t public schools. For the education sector, DOE obtained a 4 0

- year series of municipal bond rate s f ro m the Federal Reserve

. 13 

### As shown in the discount rate calculation, DOE assumed 75 percent of schools are public schools facing the average municipal

bond rates calculated from the Federal Reserve data. The same municipal bond rate was used in

the calculation of office and food service facilities owned by state and local go vernments. The long

- term, real Treasury bond rates were used for the cost of capital of U.S. government

- owned

offices.

8.2.3.9 Compliance Date of Standard

The compliance date is the future date when a new standard will become operative ( i.e.

, the date on which man ufacturers must be compliant with the new DOE standard )

. Under

42 U.S.C. 6313( d )( 2 )( B ), the compliance date of any new energy conservation standard for

automatic commercial ice mak ers must be no less than 3 years after the final rule is published

, and may be up to 5 years after publication. DOE calculated the LCC for all customers as if they

8

- 30

### each would purchase a new automatic commercial ice maker in the year the standard takes effect.

DOE has set the new standards t o take effect in 2018

. For the LCC analysis, the year of equipment purchase is 2018. H owever, all dollar values are expressed in 201 3 $

.

8.3 PAYBACK PERIOD INPUT S

8.3.1 Definition

As stated in s ection 8.1 , the PBP is the amount of time it takes the c ustomer to recover the higher purchase price of more energy efficient equipment through lower operating costs. Numerically, the PBP is the ratio o f 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

. 10

W here :

PB P = p ayback period in years

, ∆ IC = difference in the total installed cost between the more efficient standard level and the baseline equipment

, and



## ∆ OC = difference in annual (first year) operating costs.

PBPs greater than the life of the product mean that the increased total installed cost of the

more energy efficient equipment is not recovered in reduced operating costs over the life of the equipment , even when no discount rate is applied

.

8.3.2 Inputs

The data inputs to PBP are the total installed cost of the equipment to the customer for

each efficiency level and the annual (first year) operating costs for each efficiency level

. The inputs to the total installed cost are the customer’s equipment price and the installation cost. The inputs to the operating costs are the annual energy and water cost s , the annual rep air cost, and the

annual maintenance cost. The PBP calculation uses the same inputs as the LCC analysis

described in section 8.2 , except that electr icity and water price trends and discount rates are not required because t he PBP is a “simple” (undiscounted) payback and the required electricity and

water price s are only for the year in which a new efficien cy standard is to take effect

— in this case, the year 201 8

. The electricity and water price s used in the PBP calculation w ere the price s projected for 201 8 , expressed in 201 3 $

. Discount rates are not used in the PBP calculation.

8.4 LIFE

- CYCLE COST AND PAYBA CK PE RIOD RESULTS

The results of the LCC and PBP analysis are presented in this section. Mean values of LCC savings and PBP are presented along with a summary of the distribution of these values.

8

- 31

### 8.4.1 Life

- Cycle Cost Results

Fig ure 8.4

. 1 shows the change in LCC over the seven efficiency levels for the IMH

- A

Small

- B equipment class. The LCC values on this chart are mean values obtained from the LCC analysis. This curve is pres ented here as an example to illustrate the typical relationship between

installation cost and LCC values over all the efficiency level s in a n equipment class

. The installed costs increase steadily from the baseline to the highest possible efficiency level and the

LCC s decrease from l evel 1 to t he highest possible efficiency level

.

Figure 8.4

. 1 LCC and I nstalled C ost V ariation over E fficiency L evels for IMH

- A

- Small

- B

E quipment C lass

As stated previously

, DOE calculated the LCC savings for a range of building types,

discount rates , and geographic locations

. Figure 8.4

. 2 illustrates an example of the ranges of LCC savings for the IMH

- A

- Small

- B equipment class

. A ppendix 8B includes s imilar plots of LCC savings for all equipment class es analyzed. Table 8.4.1 presents the numerical values

associated with Figure 8.4

. 2

. Figure 8.4

. 2 illustrates the mean and median values on the plot using red and blue markers , respectively

. The elongated

, large rectangular box repre sent s 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 LCC savings of $ 39 or less if

the standard were set at l evel 2

, ( $ 24) or less in LCC saving s if the standard were set at l evel 3,

and so on. The m edian value of LCC savings is equal to the 50 th 

### percentile. 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).

8

- 32

### Figure 8.4

. 2 LCC S avings Ranges by E fficiency L evel for E quipment C lass IMH

- A

Small

- B

Table 8.4

. 1 LCC Savings D istribution R esults for E quipment C lass IMH

- A

- Small

- BLCC Savings2013$ Efficiency

Level Level 1 Level 2 Level 3 Level 3A Level 4 Level 5 Level 6

Mean $136 $72 $77

- $68

- $400

- $393 Median (50th Percentile) $99 $21 $34

- $112

- $457

- $452

5th Percentile

- $2

- $58

- $49

- $207

- $607

- $607

25th Percentile $39

- $25 $5

- $149

- $525

- $523

75th Percentile $196 $109 $95

- $42

- $335

- $324

95th Percentile $401 $396 $363 $226

- $7 $11



### Table 8.4

. 2 and Table 8.4

. 3 summarize the m ean and median LCC savings , respectively, for all equipment classes an alyzed. -$800 -$600 -$400 -$200 $0 $200 $400 $600

2 3 3a 4 5 6Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8

- 33

### Table 8.4

. 2 Mean LCC Savings for All Equipment Classes and Efficiency Levels

Equipment Class Mean Life

- Cycle Cost Savings
2013$ *

Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 175 214 (148) (333) (534)

IMH

- W

- Med

- B 308 165 (63)

IMH

- W

- Large

- B** (172)

IMH

- W

- Large

- B1 (200)

IMH

- W

- Large

- B2 (80)

IMH

- A

- Small

- B 136 72 77 (68) (400) (393)

IMH

- A

- Large

- B** 382 507 361 265 55

IMH

- A

- Large

- B1 439 580 407 294 45

IMH

- A

- Large

- B2 76 110

RCU

- Large

- B** 748 360 144

RCU

- Large

- B1 743 391 161

RCU

- Large

- B2 820 (109)

SCU

- W

- Large

- B 444 527 613 550 192

SCU

- A

- Small

- B 110 134 161 281 230 (145)

SCU

- A

- Large

- B 163 206 400 439 71

IMH

- A

- Small

- C 245 292 313 (207) (165)

IMH

- A

- Large

- C 539 626 (10) 28

RCU

- Small

- C 498 448 505 (41) (73)

SCU

- A

- Small

- C 224 278 290 (291) (268)
* Values on this table represent LCC savings for customers affected by the standard. Blank cells mean no LCC savings were calculated for this efficiency level because design options were unavailable to constitute an additional efficiency level. Values in parenthesis indicate negative numbers. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** Results for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two sub

- equipment class level typical units shown in the table, using the weights provided in TSD chapter 7.

8

- 34

### Table 8.4

. 3 Median LCC Savings for All Equipment Classes and Efficiency Levels

Equipment Class Median Life

- Cycle Cost Savings
2013$ *

Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 140 155 (221) (405) (603)

IMH

- W

- Med

- B 248 73 (173)

IMH

- W

- Large

- B** (259)

IMH

- W

- Large

- B1 (277)

IMH

- W

- Large

- B2 (200)

IMH

- A

- Small

- B 99 21 34 (112) (457) (452)

IMH

- A

- Large

- B** 297 387 238 115 (105)

IMH

- A

- Large

- B1 361 449 272 127 (134)

IMH

- A

- Large

- B2 (50) 52

RCU

- Large

- B** 624 166 (87)

RCU

- Large

- B1 624 204 (66)

RCU

- Large

- B2 628 (400)

SCU

- W

- Large

- B 377 444 511 446 59

SCU

- A

- Small

- B 89 101 117 216 152 (229)

SCU

- A

- Large

- B 133 162 322 347 (30)

IMH

- A

- Small

- C 200 222 216 (317) (208)

IMH

- A

- Large

- C 454 495 (172) (83)

RCU

- Small

- C 418 321 356 (205) (247)

SCU

- A

- Small

- C 189 229 220 (381) (343)
* Values on this table represent LCC savings for customers affected by the standard. Blank cells mean no LCC savings were calculated for this efficiency level because design options were unavailable to constitute an additional efficiency level. Values in parenthesis indicate negative numbers. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** Results for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of t he two sub

- equipment class level typical units shown in the table, using the weights provided in TSD chapter 7

.



### 8.4.2 Payback Period Results

Figure 8.4

. 3 presents the distribution of the PBP results for e fficiency l evels above the

baseline for the equipment class IMH

- A

- Small

- B

. Table 8.4.4 presents t he numerical values associated with this plot. The red marker represents the mean and the blue marker represents the

median PBP for each efficiency level. The lower edge of the elon gated rectangular box represents the 25 th 

### percentile , which means that 25 percent of the customers would experience a

PBP of 2.3 years o r less if the energy conservation standard were set at l evel 2

, 3.2 years or less

if the energy conservation standard were set at l evel 3, and so on. 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

. 4 and Table 8.4.5 summarize the

mean and median PBPs , respectively, for all efficiency levels for all the analyzed equipment

class es.

8

- 35

### Figure 8.4

. 3 P ayback P eriod for A ll E fficiency L evels for the E quipment C lass IMH

- A

Small

- B

Table 8.4

. 4 Payback P eriod D istribution R esults for IMH

- A

- Small

- BPayback Periodyears Efficiency Level Level

1 Level

2 Level

3 Level

3A Level

4 Level

5 Level

6

Mean

- 3.6 5.0 4.9 7.7 12.5 12.3

Median (50th Percentile) - 3.4 4.8 4.7 7.3 12.1 11.9

5th Percentile

- 1.4 2.0 1.9 3.1 5.0 5.0

25th Percentile

- 2.3 3.2 3.1 5.0 8.2 8.1

75th Percentile

- 4.7 6.6 6.4 10.0 16.3 16.0

95th Percentile

- 6.6 9.0 8.6 13.8 22.2 21.8 0.00 5.00 10.00 15.00 20.00 25.00

2 3 3a 4 5 6Years

Efficiency Level Payback Period

Median Mean

8

- 36

### Table 8.4

. 5 Median Payback Period for All Equipment Classes and Efficiency Levels

Equipment Class Median payback period

years * ,**

Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 2.5 2.7 8.1 10.9 13.4

IMH

- W

- Med

- B 2.1 5.0 7.6

IMH

- W

- Large

- B** 10.6

IMH

- W

- Large

- B1 11.1

IMH

- W

- Large

- B2 8.9

IMH

- A

- Small

- B 3.4 4.8 4.7 7.3 12.1 11.9

IMH

- A

- Large

- B** 2.2 2.3 2.3 3.9 5.6

IMH

- A

- Large

- B1 1.2 1.5 1.5 3.4 5.4

IMH

- A

- Large

- B2 7.4 6.9

RCU

- Large

- B** 1.1 3.6 5.0

RCU

- Large

- B1 0.9 3.4 4.9

RCU

- Large

- B2 3.0 7.0

SCU

- W

- Large

- B 1.1 1.4 1.6 1.8 5.1

SCU

- A

- Small

- B 2.2 2.3 2.4 2.6 3.5 8.9

SCU

- A

- Large

- B 1.8 1.9 1.6 2.1 6.5

IMH

- A

- Small

- C 1.5 1.6 1.7 8.7 8.8

IMH

- A

- Large

- C 0.7 0.7 5.9 5.9

RCU

- Small

- C 0.7 1.2 1.2 5.5 5.8

SCU

- A

- Small

- C 0.8 1.1 1.5 10.7 11.4 * Blank cells mean design options were unavailable to constitute an additional efficiency level. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** Results for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two sub

equipment class level typical units shown in the table, using the weights provided in TSD chapter 7.



### 8.4.3 Rebuttable Presumption Payback Period

EPCA establish es a rebuttable presumption for automatic commercial ice

- making equipment. (42 U.S.C. 6295(o)(2)(B)(iii) and 631 3(d)(4)) The rebuttable presumption states that a standard is economically justified if the Secretary of Energy finds that the additional cost to the

customer of purchasing a product complying with an energy conservation standard level will be less than three times the value of the energy savings d uring the first year that the customer 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 p resumption, DOE determined 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 standard s

- compliant equipment includes the cost of installing the equipment for use by the purchaser. DOE calculated the rebuttable presumption payback period (RPBP), or the ratio of ( a) the increase in installed cost above the baseline effic iency level to ( b)

the first year 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

satisfied. T his payback period calculation does not include other components of the annual operating cost of the equipment ( i.e.

, water costs, maintenance costs

, and repair costs). The

RPBPs calculated can thus be different from the PBPs calculated in section 8.4.2

.

8

- 37

### DOE calculated the RPBPs for the range of installed costs and energy prices discussed in

section s 8.4.1 and 8.4.2 , which are representative of the same seven building types and all 50

s tates plus the District of Columbia

. DOE calculated t he RPBP for each higher efficiency level

within each equipment class

.

Table 8.4.6 shows the RPBP s calcu lated for all equipment class es and efficiency levels.

Table 8.4

. 6 Rebuttable Presumption Payback Periods by Efficiency Level and Equipment

Class

Equipment Class Rebuttable Payback Period

years * ,**

Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 2.9 3.4 9.5 12.7 15.4

IMH

- W

- Med

- B 1.9 4.6 7.1

IMH

- W

- Large

- B ** 9.9

IMH

- W

- Large

- B1 10.4

IMH

- W

- Large

- B2 8.3

IMH

- A

- Small

- B 3.0 4.3 4.2 6.4 10.7 10.6

IMH

- A

- Large

- B ** 2.0 2.4 2.4 4.1 5.8

IMH

- A

- Large

- B1 1.1 1.7 1.6 3.7 5.8

IMH

- A

- Large

- B2 6.7 6.3

RCU

- Large

- B ** 1.0 3.3 4.7

RCU

- Large

- B1 0.9 3.1 4.6

RCU

- Large

- B2 2.7 6.2

SCU

- W

- Large

- B 2.2 2.3 2.3 2.4 6.7

SCU

- A

- Small

- B 2.0 2.1 2.1 3.1 4.0 9.9

SCU

- A

- Large

- B 1.7 1.7 2.1 2.5 7.7

IMH

- A

- Small

- C 1.3 1.5 1.6 6.4 6.2

IMH

- A

- Large

- C 0.6 0.7 5.2 4.5

RCU

- Small

- C 0.7 1.1 1.2 4.7 4.6

SCU

- A

- Small

- C 0.7 1.0 1.4 7.8 7.4

* Blank cells mean design options were unavailable to constitute an additional efficiency level

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** Results for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B are weighted averages of the two

sub

- equipment class level typical units shown in the table

, using the weights provided in TSD Chapter 7

.



### 8.5 DETAILED RESULTS

A ppendix 8B presents detailed results f rom the LCC analysis. Plots similar to Figure 8.4

. 2 and Figure 8.4

. 3 are presented in the appendix for all equipment class es. In addition, summary tables with all the necessary data in one table for each equipment class are presented.

Table 8.5.1 is the summary table for IMH

- A

- Small

- B equipment class

. This table presents the mean values of installed costs, annual operating costs, LCC, LCC savings, and median PBP values for all the efficiency levels. It also presents the percentage of customers who experience net cost, no i mpact, and net benefit. The average LCC savings and the percentage of customers experiencing a net benefit or cost are based on a range of efficiency choices. In the base case,

DOE does not assume that all customers buy equipment at the baseline efficiency ( l evel 1). As

discussed in c hapter 10, DOE assumes that s ome are buying at higher efficiency levels. The LCC savings is an average of the savings achieved by customers who, in the base case, were buying

less

- efficient equipment than the efficiency level e xamined. DOE assumed that c ustomers with

8

- 38

### no impact in the base case are already buying more

- efficient equipment, so the efficiency level in question would not affect those customers

.

Table 8.5

. 1 Summary of R esults of LCC and PBP Analysis for IMH

- A

- Small

- B E quipment

C lass

Efficiency

Level

Number Efficiency

Level kWh/yr Life

- Cycle Cost, All Customers Life

- Cycle Cost Savings Payback Period,

Median

years Installed

Cost

201 3 $ Total

Discounted Operating

Cost

201 3 $ LCC,

All

Customers

201 3 $ Affected

Customers ’

Average Savings

201 3 $ % of Customers that Experience

Net

Cost

% No Impact

% Net

Benefit

%

1 3,535 2,442 8,654 11,096 0 NA NA NA NA

2 3,184 2,539 8,420 10,959 136 1 76 22 3. 4

3 3,009 2,655 8,293 10,948 72 21 47 32 4.8

3A 2,901 2,695 8,214 10,909 77 21 0 79 4. 7

4 2,833 2,865 8,189 11,054

- 68 82 0 18 7.3

5 2,658 3,324 8,062 11,386

- 400 95 0 5 12. 1

6 2,640 3,331 8,048 11,379

- 393 95 0 5 11. 9

8

- 39

### REFERENCES

1

. U. S. Department of Energy

— Energy Information Administration. Annual Energy Outlook 2014. 2014. Washington D.C. DOE/EIA

- 0383(2014). (Last accessed November 19, 2014.)

< http://www.eia.gov/oiaf /aeo/tablebrowser/ >

2

. American Water Works Association. 2012 Water and Wastewater Rate Survey. Rep. no. 54008. 2013. See also the 2010 Water and Wastewater Rate Survey and 2008 Water and Wastewater Rate Survey, published in 2011 and 2009, respectively.

3

. RS Means Company, Inc. 2014 RS Means Construction Cost Data. 2014. Kingston, MA.

4

. RS Means 2014. City Cost Indexes, RJ1030

- 010 Building Systems. RS Means Construction Publishers & Consultants, Kingston, MA.

5

. U.S. Census Bureau, Population Divisio n. Annual Estimates of the Resident Population of Incorporated Places: April 1, 2010 to July 1, 2013. May 2014. (Last accessed June 24, 2014.) < www.census.gov/popest/data/citie s/totals/2013/index.html >

6

. U.S. Census Bureau. Annual Estimates of the Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2013. (NST

- EST2013

- 01). December 2013. (Last accessed May 13, 2014.)

< www.census.gov/popest/data/state/totals/2013/index.html >

7

. U.S. Department of Energy

– Energy Information Administration. Form EIA

- 826 data from the EIA web page Sales (Consump tion), Revenue, Prices and Customers. (Last accessed May 19, 2014.) < http://www.eia.gov/electricity/data.cfm#sales >

8

. U.S. Department of Energy

– Energy Information Administration. 2003 CBECS Survey Data. (Last accessed October 28, 2013.)

< www.eia.gov/consumption/commercial/data/2003/ >

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 May 21, 2014.)

< www.federalres erve.gov/releases/h15/data.htm >

11

. Damodoran Online. The Data Page. 2014. (Last accessed June 24, 2014.)

< http://pages.stern.nyu.edu/~adamodar/New_Home_Page/data.html >

12

. U.S. S mall Business Administration, Office of Advocacy. The Small Business Economy 2012. 2012. (Last accessed June 4, 2013.) < http://www.sba.gov/advocacy/7540/6282 >. See

also: U.S. Small Business Administrati on, Office of Advocacy. The Small Business Economy 2009. 2009. < www.sba.gov/advocacy/7540/6282 >

8

- 40

### 13

. Federal Reserve Bank of St. Louis ( http:// research.stlouisfed.org/fred2/ ) and Federal Reserve H15 data ( www.federalreserve.gov/releases/h15/data.htm ).

8

- 41

### APPENDIX 8A. USER INSTRUCTIONS FOR LIFE

- CYCLE COST SPREADSHEET

TABLE OF CONTENTS

8A.1 INTRODUCTION

................................ ................................ ................................ ........ 8A

- 1

8A.2 USER INSTRUCTIONS FO R LIFE

- CYCLE COST SPREADSHE ET

...................... 8A

- 1

REFERENCES

................................ ................................ ................................ ......................... 8A

- 4

LIST OF TABLES

Table 8A.2.1 Description of Worksheets in LCC Spreadsheet

................................ ................ 8A

- 1

8A

- i

### APPENDIX 8A

. USER INSTRUCTIONS FOR LIFE

- CYCLE COST SPREADSHEET

8A.1 INTRODUCTION

D etailed results of the life

- cycle cost ( LCC ) and payback period analyses for automatic

commercial ice

- making (ACIM) equipment can be examined using a Microsoft Excel spreadsheet available on the U.S. Department of Energy’s (DOE’s) Building Technologies (BT )

website at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

.

8A.2 USER INSTRUCTIONS FO R LIFE

- CYCLE COST SPREADSHE ET

The spreadsheet allows the user to perform LCC analyses of any of the primary equipment class es of ACIM equipment. To fully execute the spreadsheet requires Microsoft

Excel and Oracle’s Crystal Ball software, both of which are commercially available.

The spreadsheet pos ted on the DOE website represents the latest version of the applicable

model and has been developed and tested with Microsoft Excel 20 1 0 and with Oracle Crystal Ball, Fusion Edition, Release 11.1.2. 1

. 0 00

. Table 8A.2

. 1 describes the worksheets in the LCC spreadsheet

.

Table 8A.2

. 1 Description of Worksheets in LCC Spreadsheet

Worksheet Description LCC Summary Contains the input selections and a summary table of energy and water use, operating costs,

LCC

, and p ayback.

Labels This is used as a go

- between for user inputs and the rest of the worksheets . Modifying this worksheet can change and possibly impair opera tion of the model, so DOE recommends caution when editing this worksheet. Crystal Ball Contains outputs for each equipment class from the Crystal Ball/Monte Carlo analysis. NIA Output Contains a full output of all data that is uploaded to the National Impacts Analysis (NIA)

Model.

Econ Trends Summarizes e lectricity and w ater price forecasts for use in the NIA model . Also contains experiential (p rice ) l earning data inputs used in the LCC and NIA model

. Markups Contains data used to derive industry baseline and incremental markups. Engineering Contains the manufacturer price data for units at each efficiency level

. Also contains data on harvest rates, utilization rates, and energy and water consumption, and summarizes other engineering derived data.

Discounted Costs Contains annual operating costs in cash flow tables.

Discount Rate Contains all data used to calculate discounts rates for each building type. Electricity Prices Contains electricity prices by state, Annual Energy Outlook 201 4 ( AEO201 4 ) 1 electricity price projections, and electricity price ratios derived for the various bu i lding types using 2003 Commercial Building Energy Consumption (CBECS) data.

Water Prices Contains population

- weighted water/wastewater prices by state, and wa ter/wastewater price projections.

Installation Costs Contains adjustment factors to vary installation cost s by state. Derived from data published by

RS Means Publishers and Consultants and U.S. Census Bureau population estimates

. Sales Tax Contains data on sales tax rates and population data by state. Population Contains 201 3 census data on population by state.

8A

- 1

### Basic instructions for operating the LCC spreadsheets are as follows:

1) Once you have downloaded the LCC file from the DOE BT website , open the file using

Excel.

a) S elect Yes when asked if you want to enable macros.

b) At the bottom of the Excel window , click the tab for the Summary work sheet . Note that if you plan to run the Monte Carlo routine

, you will need to have Crystal Ball

loaded as an a dd

- in and activated . The model is structured to run in the Monte Carlo

mode.

2) Use Excel’s View/Zoom commands at the top menu bar to size the display to your

monitor.

3) You can interact with the spreadsheet by clicking choices or entering data using the

grap hical interface on the LCC Summary worksheet . Select choices from the various drop

- down input boxes throughout the LCC Summary worksheet

.

4) From the user

- defined input options , select from the buttons and boxes for the following: (1) assumed baseline level

, (2) equipment class

, (3) a nalysis year (year of purchase), (4) economic growth case

, (5) year to discount costs to

, (6) standard efficiency level

, (7) building type

, (8) state or region to analyze

, (9) conservation program to model

, (10) installation cost escalation

, (11) repair cost escalation

, (12) analysis type

, (13) price learning toggle, and (14 ) discounting toggle

.

a) You can start a complete Crystal Ball simulation by clicking the “ Monte Carlo ”

button.

b) All inputs, with the exception of some conservation program inputs, are either drop

down option boxes or toggle boxes (checked boxes make the statement true).

c) Equipment classes are defined as particular combinations of equipment families, cooling methods

, harvest rate bins, and ice

- making proc esses . Because there are three potential equipment families, two potential cooling methods ( air and water ), two or three possible harvest rate bins

, and two different ice

- making processes, there are 21 potential equipment classes that could be analyzed. a 

### H owever, during the final rule analyses, 1 3 primary equipment classes were studied, including 3 classes that were modeled at the level of two typical unit size s. If you select a n equipment class that has not been analyzed by DOE, the model will display zero s and/or blank cells in the inputs and results summary on the LCC_Summary tab

.

a Remote condensing units can either have remote compressors or not. Thus, there are 8 possible RCU equipment classes, and these have been condensed dow n to 4 classes.

8A

- 2

### d) There is room for up to eight possible efficiency levels , including the baseline efficiency level, though no equipment classes have more than seven representative

levels

.

5) This spreadsheet gives the user t wo types of calculation methods:

a) If base and sensitivity analysis is chosen , then all calculations are performed for a set of single input values. The new results are shown on the Summary tab as soon as the input values are change d through a drop

- down option box or toggle box

. Note that the model was set up t o run in Monte Carlo mode with a distribution of equipment lifetimes that are integer values. I n base and sensitivity mode the model also run s equipme nt lifeti mes that are integers.

b) When the Monte Carlo option is chosen and the “Monte Carlo” button is clicked, the spreadsheet generates a set of results from calculations for each equipment class with populated engineering data sets

.

• For a number of inputs, the Crystal Ball software has custom distributions that it uses to set the level of the input. The model runs each of the studied classes in turn, performing 10,000 model runs for each. At the end of each equipment class run, the model records output to the Cr ystal Ball tab for further analysis

.

• The “ Monte Carlo ” button executes a macro written in Visual Basic for Applications to loop through all equipment classes and selected efficiency levels.

The Monte Car lo analysis usually takes 2 to 3 hours to complete for all equipment

classes , depending on computer speed and memory

.

8A

- 3

### REFERENCES

1

. U.S. Department of Energy

– Energy Information Administration. Annual Energy Outlook

201 4 . 201 4 . Washington D.C. DOE/EIA

- 0383(201 4 ).

8A

- 4

### APPENDIX 8B. DETAILED LIFE

- CYCLE COST AND PAYBACK PERIOD

ANALYSIS RESULTS

TABLE OF CONTENTS

8B.1 INTRODUCTION

................................ ................................ ................................ ......... 8B

- 1

8B.2 LIFE CYCLE COST RESULTS

................................ ................................ ................... 8B

- 2

8B.2.1 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Water

Cooled, Small, Batch (IMH

- W

- Small

- B)

................................ .......................... 8B

- 2

8B.2.2 L ife

- Cycle Cost and Payback Period Results for Ice

- Making Head, Water

Cooled, Medium, Batch (IMH

- W

- Med

- B)

................................ ........................ 8B

- 4

8B.2.3 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Water

Cooled, Large, Batch (smaller, 1,500 lb unit) (IMH

- W

- Large

- B1 )

.................. 8B

- 6

8B.2.4 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Water

Cooled, Large, Batch (larger, 2,600 lb unit) (IMH

- W

- Large

- B2 )

..................... 8B

- 8

8B.2.5 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Air

Cooled, Small, Batch (IMH

- A

- Small

- B)

................................ ......................... 8B

- 10

8B.2.6 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Air

Cooled, Large, Batch (smaller, 800 lb unit) (IMH

- A

- Large

- B1 )

.................... 8B

- 12

8B.2.7 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Air

Cooled, Large, Batch (larger, 1,500 lb unit) (IMH

- A

- Large

- B2 )

.................... 8B

- 14

8B.2.8 Life

- Cycle Cost and Payback Period Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb unit) (RCU

- Large

- B1 )

................................ ... 8B

- 16

8B.2.9 Life

- Cycle Cost and Payback Period Results for Remote Condensing Unit, Large, Batch (larger , 2,400 lb unit) (RCU

- Large

- B2 )

................................ ..... 8B

- 18

8B.2.10 Life

- Cycle Cost and Payback Period Results for Self

- Contained Unit, Water

Cooled, La rge, Batch (SCU

- W

- Large

- B)

................................ ........................ 8B

- 20

8B.2.11 Life

- Cycle Cost and Payback Period Results for Self

- Contained Unit, Air

Cooled, Small, B atch (SCU

- A

- Small

- B)

................................ ......................... 8B

- 22

8B.2.12 Life

- Cycle Cost and Payback Period Results for Self

- Contained Unit, Air

Cooled, Large, Batch (SCU

- A

- Large

- B)

................................ ......................... 8B

- 24

8B.2.13 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Air

Cooled, Small, Continuous (IMH

- A

- Small

- C)

................................ ................ 8B

- 26

8B.2.14 Life

- Cycle Cost and Payback Period Results for Ice

- Making Head, Air

Cooled, Large, Continuous (Smallest, 8 20 lb unit) (IMH

- A

- Large

- C)

........... 8B

- 28

8B.2.15 Life

- Cycle Cost and Payback Period Results for Remote Condensing Unit, Small, Continuous (RCU

- Small

- C)

................................ ................................ . 8B

- 30

8B.2.16 Life

- Cycle Cost and Payback Period Results for Self

- Contained Unit, Air

Cooled, Small, Continuous (S CU

- A

- Small

- C)

................................ ................ 8B

- 32

LIST OF TABLES

Table 8B.2.1 LCC and PBP Results for Ice

- Making Head, Water

- Cooled, Small, Batch (IMH

- W

- Small

- B)

................................ ................................ ............................. 8B

- 2

8B

- i

### Table 8B.2.2 LCC and PBP Results for Ice

- Making Head, Water

- Cooled, Medium, Batch (IMH

- W

- Med

- B)

................................ ................................ ............................... 8B

- 4

Table 8B.2.3 LCC and PBP Results for Ice

- Making Head, Water

- Cooled, Large, Batch (smaller, 1,500 lb unit) (IMH

- W

- Large

- B1 )

................................ ...................... 8B

- 6

Table 8B.2.4 LCC and PBP Results for Ice

- Making Head, Water

- Cooled, Large, Batch (larger, 2,600 lb unit) (IMH

- W

- Large

- B2 )

................................ ........................ 8B

- 8

Table 8B.2.5 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Small, Batch (IMH

A

- Small

- B)

................................ ................................ ................................ ....... 8B

- 10

Table 8B.2.6 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (smaller, 800 lb unit) (IMH

- A

- Large

- B1 )

................................ ................................ ....... 8B

- 12

Table 8B.2.7 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (larger, 1,500 lb unit) (IMH

- A

- Large

- B2 )

................................ ................................ .... 8B

- 14 Table 8B.2.8 LCC and PBP Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb unit) (RCU

- Large

- B1 )

................................ ................................ ....... 8B

- 16 Table 8B.2.9 LCC and PBP Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb unit) (RCU

- Large

- B2 )

................................ ................................ ................. 8B

- 18

Table 8B.2.10 LCC and PBP Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

- Large

- B)

................................ ................................ ........................... 8B

- 20

Table 8B.2.11 LCC and PBP Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

- Small

- B)

................................ ................................ ............................ 8B

- 22

Table 8B.2.12 LCC and PBP Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

- Large

- B)

................................ ................................ ............................ 8B

- 24

Table 8B.2.13 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Small, Continuous (IMH

- A

- Small

- C)

................................ ................................ ............................ 8B

- 26

Table 8B.2.14 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Large, Continuous (Smallest, 820 lb unit) (IMH

- A

- Large

- C)

................................ ....................... 8B

- 28 Table 8B.2.15 LCC and PBP Results for Remote Condensing Unit, Small, Continuous (RCU

- Small

- C)

................................ ................................ ................................ 8B

- 30

Table 8B.2.16 LCC and PBP Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

- A

- Small

- C)

................................ ................................ ......... 8B

- 32

LIST OF FIGURES

Figure 8B.2.1 LCC Results for Ice

- Making Head, Water

- Cooled, Small, Batch (IMH

- W

Small

- B)

................................ ................................ ................................ ............. 8B

- 2 Figure 8 B.2.2 PBP Results for Ice

- Making Head, Water

- Cooled, Small, Batch (IMH

- W

Small

- B)

................................ ................................ ................................ ............. 8B

- 3 Figure 8B.2.3 LCC Results for Ice

- Making Head, Water

- Cooled, Medium, Batch (IMH

- W

Med

- B)

................................ ................................ ................................ ............... 8B

- 4 Figure 8B.2.4 PBP Results for Ice

- Making Head, Water

- Cooled, Medium, Batch ( IMH

- W

Med

- B)

................................ ................................ ................................ ............... 8B

- 5 Figure 8B.2.5 LCC Results for Ice

- Making Head, Water

- Cooled, Large, Batch (smaller, 1,500 lb unit) (IMH

- W

- Large

- B1 )

................................ ................................ ..... 8B

- 6 Figure 8B.2.6 PBP Results for Ice

- Making Head, Water

- Cooled, Large, Batch (smaller, 1,500 lb unit) (IMH

- W

- Large

- B1 )

................................ ................................ ..... 8B

- 7

8B

- ii

### Figure 8B.2.7 LCC Results for Ice

- Making Head, Water

- Cooled, Large, Batch (larger, 2,600 lb unit) (IMH

- W

- Large

- B2 )

................................ ................................ ............... 8B

- 8 Figure 8B.2.8 PBP Results for Ice

- Making Head, Water

- Cooled, Large, Batch (larger, 2,600 lb unit) (IMH

- W

- Large

- B2 )

................................ ................................ ............... 8B

- 9 Figure 8B.2.9 LCC Results for Ice

- Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

B)

................................ ................................ ................................ ...................... 8B

- 10 Figure 8B.2.10 PBP Results for Ice

- Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

B)

................................ ................................ ................................ ...................... 8B

- 11 Figure 8B.2.11 LCC Results for Ice

- Making Head, Air

- Cooled, Large, Batch (smaller, 800 lb unit) (IMH

- A

- Large

- B1 )

................................ ................................ .................. 8B

- 12 Figure 8B.2.12 PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (smaller, 800 lb unit) (IMH

- A

- Large

- B1 )

................................ ................................ .................. 8B

- 13 Figure 8B.2.13 LCC Results for Ice

- Making Head, Air

- Cooled, Large, Batch (larger, 1,500 lb unit) (IMH

- A

- Large

- B2 )

................................ ................................ .............. 8B

- 14 Figure 8B.2.14 PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (larger, 1,500 lb unit) (IMH

- A

- Large

- B2 )

................................ ................................ .................. 8B

- 15 Figure 8B.2.15 LCC Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb unit) (RCU

- Large

- B1 )

................................ ................................ ..................... 8B

- 16 Figure 8B.2.16 PBP Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb unit) (RCU

- Large

- B1 )

................................ ................................ ..................... 8B

- 17 Figure 8B.2.17 LCC Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb unit) (RCU

- Large

- B2 )

................................ ................................ ..................... 8B

- 18 Figure 8B.2.18 PBP Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb unit) (RCU

- Large

- B2 )

................................ ................................ ..................... 8B

- 19 Figure 8B.2.19 LCC Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

Large

- B)

................................ ................................ ................................ ........... 8B

- 20 Figure 8B.2.20 PBP Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

Large

- B)

................................ ................................ ................................ ........... 8B

- 21 Figure 8B.2.21 LCC Res ults for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

Small

- B)

................................ ................................ ................................ ........... 8B

- 22 Figure 8B.2.22 PBP Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

Small

- B)

................................ ................................ ................................ ........... 8B

- 23 Figure 8B.2.23 LCC Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

Large

- B)

................................ ................................ ................................ ........... 8B

- 24 Figure 8B.2.24 PBP Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

Large

- B)

................................ ................................ ................................ ........... 8B

- 25 Figure 8B.2.25 LCC Results for Ice

- Making Head, Air

- Cooled, Small, Continuous (IMH

- A

Small

- C)

................................ ................................ ................................ ........... 8B

- 26 Figure 8B.2.26 PBP Results for Ice

- Making Head, Air

- Cooled, Small, Continuous (IMH

- A

Small

- C)

................................ ................................ ................................ ........... 8B

- 27 Figure 8B.2.27 LCC Results for Ice

- Making Head, Air

- Cooled, Large, Continuous (Smallest, 820 lb unit) (IMH

- A

- Large

- C)

................................ ................................ ......... 8B

- 28 Figure 8B.2.28 PBP Results for Ice

- Making Head, Air

- Cooled, Large, Continuous (Smallest, 820 lb unit) (IMH

- A

- Large

- C)

................................ ................................ ......... 8B

- 29 Figure 8B.2.29 LCC Results for Remote Condensing Unit, Small, Continuous (RCU

- Small

C)

................................ ................................ ................................ ...................... 8B

- 30

8B

- iii

### Figure 8B.2.30 PBP Results for Remote Condensing Unit, Small, Continuous (RCU

- Small

C)

................................ ................................ ................................ ...................... 8B

- 31 Figure 8B.2.31 LCC Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

A

- Small

- C)

................................ ................................ ................................ ....... 8B

- 32 Figure 8B.2.32 PBP Results for Self

- Cont ained Unit, Air

- Cooled, Small, Continuous (SCU

A

- Small

- C)

................................ ................................ ................................ ....... 8B

- 33

8B

- iv

### APPENDIX 8B

. DETAILED LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

RESULTS

8B.1 INTRODUCTION

This appendix contains detailed output from the l ife

- c ycle c ost (LCC) and payback period (PBP) a nalysis for automatic c ommercial ice mak ers, as developed using the LCC spreadsheet.

LCC and PBPs are presented for each of the primary equipment classes for each efficiency level.

See c hapter 8 of the technical support document ( TSD ) for an explanation of the LCC spreadsheet.

LCC savings output obt ained 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 LCC, product installation costs and operating costs; 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 th e tables in th is appendix.

Also included in this appendix are two graphical presentations for each of the LCC and PBP results. One plots LCC savings distribution for all equipment classes analyzed. The figures illustrate the mean and median values on the p lot 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 edge 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 the 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).

T o simplify the presentation, all efficiency levels are presented as numbered levels.

Therefore , the efficiency level referr ed to as the b ase l ine in other chapters of this TSD is presented as l evel 1. All higher efficiency levels are numbered sequentially, from l evel 1 to l evel

7

.

The remainder of this appendix presents the tabular and graphical results for the equipment classes analyzed for the automatic commercial ice maker rulemaking.

8B

- 1

### 8B.2 LIFE CYCLE COST RESU LTS

8B.2.1 LIFE

- CYCLE C OST AND PAYBACK PERI OD RESULTS FOR ICE

- MAKING HEAD, WATER

- COOLED, SMALL, BATCH (IMH

- W

- SMALL

- B)

Table 8B.2

. 1 LCC and PBP Results for Ice

- Making Head, Water

- Cooled, Small, Batch (IMH

- W

- Small

- B)

Figure 8B.2

. 1 LCC Results for Ice

- Making Head, Water

- Cooled, Small, Batch (IMH

- W

Small

- B) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 2,832 2,401 9,782 12,183 0 NA NA NA NA

2 2,551 2,476 9,533 12,009 175 0 63 37 2.5

3 2,411 2,537 9,381 11,918 214 1 47 52 2.7

4 2,270 2,903 9,279 12,182 -148 82 3 16 8.1

4a 2,227 3,123 9,248 12,370 -333 92 0 8 10.9

5 2,162 3,371 9,200 12,571 -534 96 0 4 13.4

6 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$1,000

-$800 -$600 -$400 -$200 $0 $200 $400 $600 $800

2 3 4 4a 5 6Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 2

### Figure 8B.2

. 2 PBP Results for Ice

- Making Head, Water

- Cooled, Small, Batch (IMH

- W

Small

- B) 0.00 5.00 10.00 15.00 20.00 25.00

2 3 4 4a 5 6Years

Efficiency Level Payback Period

Median Mean

8B

- 3

### 8B.2.2 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R ICE

- MAKING HEAD, WATER

- COOLED, MEDIUM, BATC H (IMH

- W

- MED

- B)

Table 8B.2

. 2 LCC and PBP Results for Ice

- Making Head, Water

- Cooled, Medium, Batch (IMH

- W

- Med

- B)

Figure 8B.2

. 3 LCC Results for Ice

- Making Head, Water

- Cooled, Medium, Batch (IMH

- W

Med

- B) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 6,040 4,216 21,888 26,104 0 NA NA NA NA

2 5,439 4,325 21,470 25,795 308 0 44 56 2.1

3 5,138 4,607 21,251 25,857 165 28 24 47 5.0

4 4,951 4,943 21,115 26,058 -63 65 9 26 7.6

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$600 -$400 -$200 $0 $200 $400 $600 $800 $1,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 4

### Figure 8B.2

. 4 PBP Results for Ice

- Making Head, Water

- Cooled, Medium, Batch (IMH

- W

Med

- B) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 5

### 8B.2.3 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R ICE

- MAKING HEAD, WATER

- COOLED, LARGE, BATCH (SMALLER, 1,500 LB U NIT) (IMH

- W

- LARGE

- B1 )

Table 8B.2

. 3 LCC and PBP Results for Ice

- Making Head, Water

- Cooled, Large, Batch (smaller, 1,500 lb unit) (IMH

- W

- Large

- B1 )

Figure 8B.2

. 5 LCC Results for Ice

- Making Head, Water

- Cooled, Large, Batch (smaller, 1,500 lb unit) (IMH

- W

- Large

- B1 ) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 9,166 5,004 37,051 42,055 0 NA NA NA NA

2 8,405 5,747 36,509 42,256 -200 70 13 17 11.1

3 NA NA NA NA NA NA NA NA NA

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$600 -$500 -$400 -$300 -$200 -$100 $0 $100 $200 $300 $400

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 6

### Figure 8B.2

. 6 PBP Results for Ice

- Making Head, Water

- Cooled, Large, Batch (smaller, 1,500 lb unit) (IM H

- W

- Large

- B1 ) 0.00 5.00 10.00 15.00 20.00 25.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 7

### 8B.2.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R ICE

- MAKING HEAD, WATER

- COOLED, LARGE, BATCH (LARGER, 2,600 LB UN IT) (IMH

- W

- LARGE

- B2 )

Table 8B.2

. 4 LCC and PBP Results for Ice

- Making Head, Water

- C ooled, Large, Batch (larger, 2,600 lb unit) (IMH

- W

- Large

- B2 )

Figure 8B.2

. 7 LCC Results for Ice

- Making Head, Water

- Cooled, Large, Batch (larger, 2,600 lb unit) (IMH

- W

- Large

- B2 ) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 15,868 9,763 62,182 71,945 0 NA NA NA NA

2 14,693 10,681 61,346 72,027 -80 59 13 29 8.9

3 NA NA NA NA NA NA NA NA NA

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$800 -$600 -$400 -$200 $0 $200 $400 $600 $800 $1,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 8

### Figure 8B.2

. 8 PBP Results for Ice

- Making Head, Water

- Cooled, Large, Batch (larger, 2,600 lb unit) (IMH

- W

- Large

- B2 ) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 9

### 8B.2.5 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R ICE

- MAKING HEAD, AIR

- COOLED, SMALL, BATCH (IMH

- A

- SMALL

- B)

Table 8B.2

. 5 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Small, Batch (IMH

A

- Small

- B)

Figure 8B.2

. 9 LCC Results for Ice

- Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

B) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,535 2,442 8,654 11,096 0 NA NA NA NA

2 3,184 2,539 8,420 10,959 136 1 76 22 3.4

3 3,009 2,655 8,293 10,948 72 21 47 32 4.8

3a 2,901 2,695 8,214 10,909 77 21 0 79 4.7

4 2,833 2,865 8,189 11,054 -68 82 0 18 7.3

5 2,658 3,324 8,062 11,386 -400 95 0 5 12.1

6 2,640 3,331 8,048 11,379 -393 95 0 5 11.9 Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$800 -$600 -$400 -$200 $0 $200 $400 $600

2 3 3a 4 5 6Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 10

### Figure 8B.2

. 10 PBP Results for Ice

- Making Head, Air

- Cooled, Small, Batch (IMH

- A

Small

- B) 0.00 5.00 10.00 15.00 20.00 25.00

2 3 3a 4 5 6Years

Efficiency Level Payback Period

Median Mean

8B

- 11

### 8B.2.6 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESU LTS FOR ICE

- MAKING HEAD, AIR

- COOLED, LARGE, BATCH (SMALLER, 800 LB UNI T) (IMH

- A

LARGE

- B1 )

Table 8B.2

. 6 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (smaller, 800 lb unit) (IMH

- A

- Large

- B1 )

Figure 8B.2

. 11 LCC Results for Ice

- Making Head, Air

- Cooled, Large, Batch (smaller, 800 lb unit) (IMH

- A

- Large

- B1 ) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 7,350 4,097 14,454 18,550 0 NA NA NA NA

2 6,617 4,172 13,943 18,115 439 0 66 34 1.2

3 6,251 4,269 13,506 17,775 580 0 38 62 1.5

3a 6,192 4,275 13,464 17,738 407 0 3 97 1.5

4 5,885 4,602 13,247 17,850 294 35 3 63 3.4

5 5,636 5,025 13,066 18,091 45 61 0 39 5.4

6 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$1,000

-$500 $0 $500 $1,000 $1,500 $2,000

2 3 3a 4 5 6Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 12

### Figure 8B.2

. 12 PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (smaller, 800 lb unit) (IMH

- A

- Large

- B1 ) 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

2 3 3a 4 5 6Years

Efficiency Level Payback Period

Median Mean

8B

- 13

### 8B.2.7 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R ICE

- MAKING HEAD, AIR

- COOLED, LARGE, BATCH (LARGER, 1,500 LB UN IT) (IMH

- A

LARGE

- B2 )

Table 8 B.2

. 7 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (larger, 1,500 lb unit) (IMH

- A

- Large

- B2 )

Figure 8B.2

. 13 LCC Results for Ice

- Making Head, Air

- Cooled, Large, Batch (larger, 1,500 lb unit) (IMH

- A

- Large

- B2 ) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 11,999 4,465 18,958 23,424 0 NA NA NA NA

2 10,802 5,222 18,129 23,350 76 9 83 8 7.4

3 10,591 5,298 17,975 23,273 110 10 61 29 6.9

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$600 -$400 -$200 $0 $200 $400 $600 $800 $1,000 $1,200

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 14

### Figure 8B.2

. 14 PBP Results for Ice

- Making Head, Air

- Cooled, Large, Batch (larger, 1,500 lb unit) (IMH

- A

- L arge

- B2 ) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 15

### 8B.2.8 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R REMOTE CONDENSING UNIT, LAR GE, BATCH (SMALLER, 1,500 LB UNIT) (RCU

LARGE

- B1 )

Table 8B.2

. 8 LCC and PBP Results for Remote Condensing Unit, Large, Bat ch (smaller, 1,500 lb unit) (RCU

- Large

- B1 )

Figure 8B.2

. 15 LCC Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb unit) (RCU

- Large

- B1 ) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 11,679 6,122 15,026 21,148 0 NA NA NA NA

2 10,514 6,220 14,190 20,410 743 0 56 44 0.9

3 9,931 6,635 13,790 20,425 391 25 20 55 3.4

4 9,664 6,985 13,595 20,580 161 55 1 44 4.9

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$1,000

-$500 $0 $500 $1,000 $1,500 $2,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 16

### Figure 8B.2

. 16 PBP Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb unit) (RCU

- Large

- B1 ) 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 17

### 8B.2.9 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R REMOTE CONDENSING UNIT, LAR GE, BATCH (LARGER, 2 ,400 LB UNIT) (RCU

LARGE

- B2 )

Table 8B.2

. 9 LCC and PBP Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb unit) (RCU

- Large

- B2 )

Figure 8B.2

. 17 LCC Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb unit) (RCU

- Large

- B2 ) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 18,671 8,999 21,819 30,818 0 NA NA NA NA

2 16,807 9,465 20,540 30,005 820 1 56 43 3.0

3 16,077 10,516 20,046 30,562 -109 57 20 23 7.0

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$1,000

-$500 $0 $500 $1,000 $1,500 $2,000 $2,500

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 18

### Figure 8B.2

. 18 PBP Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb unit) (RCU

- Large

- B2 ) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 1 9

### 8B.2.10 LIF E

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R SELF

CONTAINED UNIT, WATE R

- COOLED, LARGE, BATCH (SCU

- W

- LARGE

- B)

Table 8B.2

. 10 LCC and PBP Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

- Large

- B)

Figure 8B.2

. 19 LCC Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

W

- Large

- B) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,499 3,467 11,136 14,603 0 NA NA NA NA

2 3,151 3,540 10,617 14,158 444 0 28 72 1.1

3 2,978 3,580 10,491 14,071 527 0 28 72 1.4

4 2,804 3,620 10,364 13,984 613 0 28 72 1.6

5 2,630 3,664 10,238 13,902 550 0 5 94 1.8

6 2,464 4,114 10,117 14,231 192 44 0 56 5.1

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$500 $0 $500 $1,000 $1,500 $2,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 20

### Figure 8B.2

. 20 PBP Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

Large

- B) 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 21

### 8B.2.11 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R SELF

CONTAINED UNIT, AIR

- COOLED, SMALL, BATCH (SCU

- A

- SMALL

- B)

Table 8B.2

. 11 LCC and PBP Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

- Small

- B)

Figure 8B.2

. 21 LCC Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

Small

- B) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 2,177 2,759 7,344 10,103 0 NA NA NA NA

2 1,962 2,799 7,193 9,992 110 0 48 52 2.2

3 1,854 2,822 7,122 9,944 134 1 33 66 2.3

4 1,747 2,845 7,051 9,896 161 1 20 79 2.4

5 1,639 2,918 6,843 9,761 281 1 12 87 2.6

6 1,532 3,000 6,778 9,778 230 16 0 84 3.5

7 1,473 3,416 6,737 10,153 -145 77 0 23 8.9 Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$600 -$400 -$200 $0 $200 $400 $600 $800 $1,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 22

### Figure 8B.2

. 22 PBP Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

Small

- B) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 23

### 8B.2.12 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R SELF

CONTAINED UNIT, AIR

- COOLED, LARGE, BATCH (SCU

- A

- LARGE

- B)

Table 8B.2

. 12 LCC and PBP Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

- Large

- B)

Figure 8B.2

. 23 LCC Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

Large

- B) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,011 3,227 10,280 13,507 0 NA NA NA NA

2 2,713 3,275 10,070 13,344 163 0 37 63 1.8

3 2,563 3,301 9,969 13,269 206 0 23 77 1.9

4 2,414 3,345 9,685 13,030 400 0 1 99 1.6

5 2,265 3,402 9,590 12,992 439 0 1 99 2.1

6 2,141 3,854 9,500 13,355 71 54 0 46 6.5

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$400 -$200 $0 $200 $400 $600 $800 $1,000 $1,200

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 24

### Figure 8B.2

. 24 PBP Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

Large

- B) 0.00 2.00 4.00 6.00 8.00 10.00 12.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 25

### 8B.2.13 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R ICE

- MAKING HEAD, AIR

- COOLED, SMALL, CONTI NUOUS (IMH

- A

- SMALL

- C)

Table 8B.2

. 13 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Small, Continuous (IMH

- A

- Small

- C)

Figure 8B.2

. 25 LCC Results for Ice

- Maki ng Head, Air

- Cooled, Small, Continuous (IMH

- A

Small

- C) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 4,299 6,620 9,166 15,787 0 NA NA NA NA

2 3,872 6,674 8,869 15,543 245 0 69 31 1.5

3 3,658 6,709 8,723 15,432 292 0 58 42 1.6

4 3,445 6,745 8,572 15,317 313 0 39 61 1.7

5 3,231 7,258 8,553 15,811 -207 54 33 13 8.7

6 3,201 7,264 8,552 15,816 -165 68 14 18 8.8

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$800 -$600 -$400 -$200 $0 $200 $400 $600 $800 $1,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 26

### Figure 8B.2

. 26 PBP Results for Ice

- Making Head, Air

- Cooled, Small, Continuous (IMH

- A

Small

- C) 0.00 5.00 10.00 15.00 20.00 25.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 27

### 8B.2.14 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R ICE

- MAK ING HEAD, AIR

- COOLED, LARGE, CONTI NUOUS (SMALLEST, 820 LB UNIT) (IMH

- A

- LARGE

- C)

Table 8B.2

. 14 LCC and PBP Results for Ice

- Making Head, Air

- Cooled, Large, Continuous (Smallest, 820 lb unit) (IMH

- A

- Large

- C)

Figure 8B.2

. 27 LCC Results for Ice

- Making Head, Air

- Cooled, Large, Continuous (Smallest, 820 lb unit) (IMH

- A

- Large

- C) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 8,270 5,489 14,861 20,350 0 NA NA NA NA

2 7,445 5,538 14,275 19,813 539 0 57 43 0.7

3 7,033 5,568 13,979 19,547 626 0 35 65 0.7

4 6,621 6,287 13,765 20,052 -10 52 17 31 5.9

5 6,348 6,310 13,705 20,015 28 54 9 37 5.9

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$1,000

-$500 $0 $500 $1,000 $1,500 $2,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 28

### Figure 8B.2

. 28 PBP Results for Ice

- Making Head, Air

- Cooled, Large, Continuous (Smallest, 820 lb unit) (IMH

- A

- Large

- C) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 29

### 8B.2.15 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R REMOTE CONDENSING UNIT, SMA LL, CONTINUOUS (RCU

- SMALL

- C)

Table 8B.2

. 15 LCC and PBP Results for Remote Condensing Unit, Small, Continuous (RCU

- Small

- C)

Figure 8B.2

. 29 LCC Results for Remote Condensing Unit, Small, Continuous (RCU

- Small

C) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 7,737 5,641 9,134 14,776 0 NA NA NA NA

2 6,966 5,690 8,588 14,278 498 0 72 28 0.7

3 6,580 5,758 8,319 14,078 448 0 44 55 1.2

4 6,195 5,808 8,046 13,854 505 0 11 89 1.2

5 5,809 6,495 7,875 14,370 -41 63 6 32 5.5

6 5,688 6,523 7,878 14,402 -73 64 6 31 5.8

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$1,000

-$500 $0 $500 $1,000 $1,500 $2,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 30

### Fig ure 8B.2

. 30 PBP Results for Remote Condensing Unit, Small, Continuous (RCU

- Small

C) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 31

### 8B.2.16 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R SELF

CONTAINED UNIT, AIR

- COOLED, SMALL, CONTI NUOUS (SCU

- A

- SMALL

C)

Table 8B.2

. 16 LCC and PBP Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

- A

- Small

- C)

Figure 8B.2

. 31 LCC Results for Self

- Contained Unit, Air

- Co oled, Small, Continuous (SCU

A

- Small

- C) Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,416 3,598 8,421 12,020 0 NA NA NA NA

2 3,077 3,622 8,175 11,797 224 0 56 44 0.8

3 2,907 3,646 8,059 11,705 278 0 47 53 1.1

4 2,738 3,685 7,948 11,633 290 1 32 67 1.5

5 2,568 4,216 7,938 12,154 -291 73 15 12 10.7

6 2,515 4,224 7,950 12,174 -268 86 0 14 11.4

7 NA NA NA NA NA NA NA NA NA Payback
Period,

Median Efficiency

Level

Number Efficiency

Level Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost Total

Discounted
Operating

Cost LCC, All

Customers Affected

Customers'

Average
Savings

-$800 -$600 -$400 -$200 $0 $200 $400 $600 $800 $1,000

2 3 4 5 6 7Cost Savings (2013$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median

8B

- 32

### Figure 8B.2

. 32 PBP Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

A

- Small

- C) 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean

8B

- 33

### APPENDIX 8C. UNCERTAINTY AND VARIABILITY IN LIFE

- CYCLE COST

ANALYSIS

TABLE OF CONTENTS

8C.1 INTRODUCTION

................................ ................................ ................................ ......... 8C

- 1

8C.2 UNCERTAINTY

................................ ................................ ................................ ........... 8C

- 1

8C.3 VARIA BILITY

................................ ................................ ................................ .............. 8C

- 1

8C.4 APPROACHES TO UNCERTAINTY AND VARIABILITY

................................ ..... 8C

- 1

8C.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL

......................... 8C

- 2

8C

- i

### APPENDIX 8C

. UNCERTAINTY AND VARIABILITY IN LIFE

- CYCLE COST

ANALYSIS

8C.1 INTRODUCTION

The U.S. Department of Energy’s ( DOE’s ) approach to a nalysis of an energy

- efficiency

standard also includes analyzing the range of potential impacts of higher efficiency equipment on

c usto mers. DOE uses the customer l ife

- cycle cos t (LCC) 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. T o 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 quan tity 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 numerical

value s 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 calculat ion, 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 automatic commer cial ice

- making (ACIM) equipment

.

8C.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 n ice maker used in a restaurant is not available at the time of purchase , but rather estimated based upon available

historic al information. When estimating numerical values expected for quantities at some future date, the exact outcome is rarely known in advance.

8C.3 VA RIABILITY

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 faced by a customer is dependent on the state where the customer’s business is located and the type of

business

.

8C.4 APPROACHES TO UNCERT AINTY AND VARIABILIT Y

This section describes two approaches to uncertainty and variability: scenario analysis

and probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then changes one (or more) of the numerical values and repeats the calculation. A number of calculations are done, which provide some ind ication of the extent to which the result depends on

8C

- 1

### 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 point is

the ene rgy price rate at which the c usto mer 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.

Probabil ity analysis considers the probabilities within a range of values. For quantities with variability ( e.g., electricity price s paid by different users ), surveys can be used to generate a frequency distribution of numerical values ( e.g., the number of commerc ial buildings with electricity prices 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 improve energy effi ciency to some level may be estimated to be $10 0 ± $ 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 ad vantage 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 analyse s 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.

8C.5 PROBABILITY ANALYSIS AND THE USE OF CRYST AL BALL

To quantify the uncertainty and variabil ity 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 analys i s is carried out in th e 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 particu lar 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 t he 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 an equipment lifetime of 8.5 years. Crystal Ball chooses the eq uipment 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 respective 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.

8C

- 2

### APPENDIX 8D. ESTIMATION OF POTENTIAL EQUIPMENT PRICE TRENDS FOR

AUTOMATIC COMMERCIAL ICE MAKERS

TABLE OF CONTENTS

8D.1 INTRODUCTION

................................ ................................ ................................ ........... 8D

- 1

8D.2 DATA EVALUATION AND ANALYSIS

................................ ................................ ..... 8D

- 2

LIST OF FIGURES

Figure 8D.1.1 PPI Data for Automatic Commercial Ice Makers

................................ .............. 8D

- 2 Figure 8D. 2.1 Projected Annual Shipments for Automatic Commercial Ice Makers

.............. 8D

- 3 Figure 8D.2.2 Model Fit for Automatic Commercial Ice Maker s

................................ ............ 8D

- 4

8D

- i

### APPENDIX 8D

. ESTIMATION OF POTENTIAL EQUIPMENT PRICE TRENDS FOR

AUTOMATIC COMMERCIAL ICE MAKERS

8D.1 INTRODUCTION

In developing the proposed standards, the U.S. Department of Energy (DOE) assumes that the manufacturer costs and retail prices of products meeting various eff iciency levels remain fixed, in real terms, after 201 3 (the year for which the engineering analysis estimated costs) and throughout the period of the analys is. In its notice of data availability (NODA) published on February 22, 2011, DOE stated that it may consider improv ing regulatory analysis by addressing equipment price trends

. 76 FR at 9696

. Consistent with the NODA, DOE examined historical producer price indices for automatic commercial ice makers

.

DOE stated in the NODA that examination of histo rical price data for certain appliances and equipment that have been subject to energy conservation standards indicates that the assumption of constant real prices and costs may, in many cases, over estimate long

- term appliance and equipment price trends. 7 6 FR 9696 (Feb. 22, 2011). E conomic literature and historical data suggest that the real costs of these products may in fact 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 Experience Curve Approach for Appliance Price Forecasting

, available

at www1.eere.energy.gov/buildings/appliance_standards/pdfs/experience_curve_appliance _price_forecasting_3

- 16

- 11.pdf summarizes the data and literature currently available to DOE

that is relevan t 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 produ ction is related to the cumulative production or “experience” with a manufactured product. To explain the empirical relationship

, DOE would 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



### Eq

. 8D

. 1

Where:

a = an initial price (or cost),

b = a positive constant known as the learning rate parameter,

X = cumulative production, and

Y = the price as a function of cumulative production.

Thus, as experience (production) accumulates, the cost of produci ng 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:

8D

- 1

### LR = 1

– 2 - b



### Eq

. 8D

. 2

In typical learning curve formulations, the learning rate parameter is derived using two historical data series: cumulative production and price (or cost).

DOE examined h istorical price s using the Bureau of Labor Statistics’ (BLS) Producer

Price Index (P PI) and gross domestic product (GDP) deflator, available from the Bureau of Economic Analysis (BEA). The PPI data for commercial refrigerators and related equipment are available for 1978

– 201 3 and are used to represent aggregate automatic commercial ice

- ma ker prices. Figure 8D.1

. 1 shows the PPI data series used.

Figure 8D.1

. 1 PPI D ata for Automatic Commercial Ice Makers

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 automatic commercial ice makers are shipped to commercial customers

, and use of the GDP deflator w as consistent with energy price forecast assumptions by the Energy Information Administration (EIA).

8D.2 DATA EVALUATION AND ANALYSIS

Figure 8D.1

. 1 shows an apparent price trend in automatic commercial ice makers that is trending slightly upward from 1978 to 201 3 , but shows a decrease in the real PPI during two significant periods of tim e : 1982 to 1988 and199 2 to 2004. Based on these price trends, DOE expects that the PPI is likely to resume a downward trend in the future

.

8D

- 2

### T o perform an experience curve fit, DOE assembled a time

- series of a nnual s hipments for

1978 to 2010 for automatic c ommercial ice makers (for calculating cumulative production) based on shipments data from Current Industries Report

.

Projected shipments after 201 0 were obtained from the base

- case projections made for the national impact analysis (see chapter 10 of this technical support document ). Projected annual shipments are depicted in Figure 8D.2

. 1

.

Figure 8D.2

. 1 Projected Annual Shipments for Automatic Commercial Ice Makers

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 equatio n is :

P(X) = P

o 

### X

- b



### Eq

. 8D

. 3

Where:

b = the learning rate parameter

, and

P

o 

### = the price or cost o f the first unit of production.

Both b and P

o 

### are obtained by fitting the model to the data.

8D

- 3

### 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 Eq. 8D.3

, DOE estimated the learning rate (defined as the fractional reduction in price expected from each doubling of cumulative production) as 1. 9
percent

.

With cumulative shipments through 204 7 projected to reach 437,015 units (compared

with 213,728 units in 2010), the modeled trend predicts a drop of 2.4 percent in real price compared to the 201 3 prices in the economy as a whole. Figure 8D.2.2 shows the model fit for the projected values for the period after 2010

.

Figure 8D.2

. 2 Model Fit for Automatic Commercial Ice Makers

8D

- 4

### REFERENCES

1

. Weiss, M., Junginger, H.M., Patel, M.K., Blok, K. A Review of Experience Curve Analyses for Energy Demand Technologies. Technological Forecasting & Social Change

. 2010. 77:411

- 428.

8D

- 5

### CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

................................ ................................ ................................ .............. 9

- 1

9.2 SHIPMENTS MODEL

................................ ................................ ................................ ....... 9

- 2

9.2.1 Shipments Model Equations

................................ ................................ ................... 9

- 2

9.2.2 Stock Events

................................ ................................ ................................ ............ 9

- 3

9.2.2.1 Replacing Equipment

................................ ................................ ............... 9

- 3

9.2.2.2 New Equipment

................................ ................................ ....................... 9

- 4

9.3 DATA INPUTS

................................ ................................ ................................ .................. 9

- 5

9.3.1 Historical Shipments

................................ ................................ ............................... 9

- 5

9.3.2 Historical Shipments and Projected Building Stock Additions

.............................. 9

- 6

9.3.3 Building Space and Market Saturation

................................ ................................ ... 9

- 8

9.3.3.1 Floor Space: New Construction

................................ ............................... 9

- 8

9.3.3.2 Market Shipments

................................ ................................ .................... 9

- 8

9.4 RESULTS

................................ ................................ ................................ ........................... 9

- 9

REFERENCES

................................ ................................ ................................ .......................... 9

- 11

LIST OF TABLES

Table 9.2.1 Probability of Equipment Failure by Year

................................ ................................ 9

- 4

Table 9.3.1 Distribution of Ice Maker Stoc k by Building Type

................................ .................. 9

- 6

Table 9.3.2 AEO2014 Forecast of Commercial Floor Space Additions by Building Type

......... 9

- 7 Table 9.3.3 ACIM Equipment Shipments, Percent by Equipment Class

................................ .... 9

- 7 Table 9.4.1 Forecast Shipments of New and Replacement Automatic Commercial Ice Makers by Building Type, 2018

− 2047 (Base Case)

................................ ...... 9

- 9 Table 9.4.2 Forecast Shipments of Automatic Commercial Ice Makers, 2018

− 2047, by Equipment Type (Base Case)*

................................ ................................ ........... 9

- 10

LIST OF FIGURES

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model

................................ ............. 9

- 1

9

- i

### CHAPTER 9 . SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Automatic c ommercial ice

- making (ACIM) equipment shipment s are key input s to the national energy savings

, net present value

, and manufacturer impact analys e s. This chapter

describes the U.S. Department of Energy ’s (DOE ’s ) methodology for estimating ACIM equipment shipments and annual equipment stocks for 20 18 through 2 04 7

.

DOE’s s hipments m odel predicts ACIM equipment stock flows as a function of year and

age. H istorical shipment data for 13 primary and 12 secondary ACIM equipment classes are key inputs to the shipments model

. Figure 9.1

. 1 outlines the structure of the s hipments m odel.

Figure 9.1

. 1 Flow Chart Showing Inputs to the Shipments Model

The model assumes that, in each year, the existing stock of automatic commercial ice

maker s either ages by 1 year or fails, and that failed equipment is replaced. It also assumes that new equipment can be installed in new commercial floor space and old equipment can be removed through demolitions (not shown in the diagram).

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 energy conservation

standard

- level scenario and discusses the developmen t of higher standard scenarios. Initial Shipments to
New Construction Initial Stock Projections

Initial Rep lacement Shipments Shipments to
Construction NEMS Commercial
Construction Forecast

Initial Stock Total Shipments by Equipment Class

Replacement

Shipments

Estimated Lifetime of

ACIM Equipment

Stock by Equipment
C lass

9

- 1

### 9.2 SHIPMENTS MODEL

DOE examined all 25 equipment classes using the shipments model

. DOE assumed no interdependence between the shipments of the various equipment classes ; thus

, the equations are applicable to each equipment class independently

.

As described in sections 9.3.1 and 9.3.2 , DOE collected historical ACIM equipment shipment data from 1994 through 2010. Using the historical data aggregated over 8 .5

- year periods, DOE developed a multi

- year series of estimates of ACIM equipment stocks. DOE use d

th e estimate s of existing stock and the expected life of the equipment as a basis for calculating the portion of historical shipments that replace d existing stock

. With estimates of historical

stock , total shipments, and replacement shipments, DOE developed a stock accounting model to track equipment stocks through time.

To estimate historic al shipments for new installations

, DOE subtract ed the estimated replacement sales from historical total sales

. DOE then combined the estimated historical sales for new installations with indexes of historical and forecasted construction activity in seven building types to estimate future shipments for new installations

. DOE forecast ed new installations as a function of new construction of floor space in seven building types: health care

, lodging

, foodservice

, retail

, education

, food sales

, and office

.

DOE then appli ed th is stock accounting model across the 2018

– 2047 forecast period by adding new shipmen ts and replacement shipments, and accounting for depreciated stock that was replaced or demolished ( i.e.

, removed permanently from the stock). In doing so, DOE projected

s ales of new and replacement equipment by the year sold for the forecast period

. In th e final step,

DOE allocated s ales in each year to the 1 3 primary and 1 2 secondary equipment classes in proportion to their relative historical sales

. a



### 9.2.1 Shipments Model Equations

DOE formulated the s hipments m odel equations to update the stock from year t to year
t+1 as a function of age a

.

As discussed in c hapter 8 of th is final rule technical support document (TSD)

, DOE

estimates that the average life time of a unit is 8 .5 years

. DOE assume s that all units receive normal repairs that do not affect the lifetime of the equipment. The stock of age a in a given year

t is represented by :

U (t

,a) Eq. 9

. 1

a Primary equipment classes were modeled explicitly in all final rule analyses. Shipments were estimated for all 25 equipment classes previously proposed for energy conservation standards.

9

- 2

### W here:

U

( t

,a ) = stock of age a in a given year t

,

a = age of stock (year)

, and

t = year.

The shipments of new stock in a given year is Uship(t )

. B y definition, the age of the equipment is zero in the year that it is shipped, so that Uship(t) = U(t ,0)

.

9.2.2 Stock Events

In the transition from year t to year t +1

, t hree things could happen to the stock of

automatic commercial ice makers :

1. existing equipment could fail, or be removed during a building renovation and be replaced

, 2. existing equipment could be retired permanently, generally in a building demolition

or renovation during which the business type(s) situated in the building changes, or 3. the stock could simply age by 1 year.

In the shipments model, DOE did not consider early replacements ( i.e. , existing equipment that is replaced before it fails ) and assumed all failed equipment wa s replaced. The following sections present the equations used to represent each possible event.

9.2.2.1 Replacing Equipment

DOE determine d the probability that ACIM equipment of age a from stock U 0 

### will fail or will be replaced using a Weibull survival distribution function . Th e Weibull function describes a series of probabilities of equipment failure in given year s. The function was developed for year 0 through year 16. Equipment failures started exhibiting non

- zero probabilities in year 3. The failure probability decline s to zero in year 13

. The distribution produces an average time of equipment failure of roughly 8 .5 years, and result s in 93 percent of stock being replaced in the 7

- to 10

- year lifetime range, and nearly 99 percent replaced between 6 and 11 years

. The Weibull function resulted in the set of failure probabilities by year shown on Table 9.2

. 1 . To develop the probabilities, DOE used a Weibu ll function with a shape parameter set to 9.0 and a scale parameter set to 8.98. The distribution was estimated at 0.1

- year increments with each calendar year set equal to an average of the values over a range of the calendar year ( e.g. , 9.0) plus or

minus 0.5 years.

9

- 3

### Table 9.2

. 1 Probability of Equipment Failure by Year

Years Probability

3 0.0%

4 0.2%

5 1.0%

6 4.1%

7 12.7%

8 27.7%

9 35.0%

10 17.4%

11 1.8%

12 0.0%



### D OE define d the quantities of replaced equipment as follows:

UB (t ,a) = PB 0 

### (a) × U 0 

### (

t ,a

) Eq. 9

. 2

W here:

UB ( t ,a ) = quantity of units of age a in year t replaced

, PB 0 

### ( a ) = probability that stock of existing units of age a will fail or will be replaced

, U

0 

### ( t ,a ) = original stock of units of age a in year t ( i.e., the probability of failure is applied against the original new stock installed in the historical year

, yielding an age a in year t , not against the remaining stock)

,

a = age of stock (years) , and

t = year.

9.2.2.2 New Equipment

Available information suggests that the purchase of new equipment for use in new buildings is driven by the rate of new construction in proportion to new floor space.

By definition , for each type of building :

EFS (t +1) = EFS ( t ) + NFS ( t )

- DFS (t ) Eq. 9

. 3

W here:

EFS(t ) = existing floor space in year t ( ft 2 

### )

, NFS(t ) = new floor space added in year t ( ft 2 

### )

, and

DFS (t ) = floor space demolished in year t ( ft 2 

### )

.

9

- 4

### The number of units installed in new buildings is as follows :

UN( t ) = UN (t

- 1 ) × NFS (t )/ NFS( t

- 1 ) Eq. 9

. 4

W here:

UN(t ) = the number of units installed in new buildings in year t

.

DOE ha s little information on the variation in the market saturation of automatic

commercial ice mak ers by building type or over time. Therefore, in the model, DOE assumed

that the purchase of new equipment is driven by the construction of new floor space , and that

faile d or removed equipment is replaced on a one

- for

- one basis.

9.3 DATA INPUTS

9.3.1 Historical Shipments

Historical shipments data came from two main sources. T he Air

- Conditioning, Heating, and Refrigeration Institute (AHRI) provided ACIM shipments data for 2010 . The AHRI data provided a distribution of shipments by equipment class . AHRI also provided a 10

- year history of total shipments. Additionally

, DOE collected the U.S. Census Bureau’s C urrent I ndustrial

R eport (CIR) data for the 1973 to 2009 period, including total i ce maker shipment s

. DOE reviewed the total shipments in the AHRI and CIR data and noted that the CIR

- reported shipments were consistently higher than the AHRI

- reported shipments

. DOE considered the possibility that these discrepancies we re associat ed with net exports

. DOE identified one source with identifiable export information, the North American Association of Food Equipment Manufacturers ( NAFEM ) . NAFEM data for two relatively recent calendar years (2007 and 2008) showed approxima tely 20 percent of total ice maker shipments associated with food service equipment as exports. Applying a 20

- percent export factor to the CIR shipments data brought the CIR data into approximate agreement with the AHRI data. Given the similarity between the data

after a djusting the CIR data and given that the AHRI data are better understood than the CIR data, f or the final rule analysis, DOE relied on t he AHRI shipment value s

.

DOE also collected historical data from ENERGY STAR for 2008 through 2012.

ENERGY STAR ® 

### reports the number of units shipped that meet the minimum efficiency levels to qualify for certification and the percentage of total shipments that are represented by the ENERGY STAR qualifying shipments. DOE compared the total shipments implied by the

EN ERGY STAR data to the total AHRI shipments for the equipment classes covered by

ENERGY STAR criteria . On average, the AHRI shipments were 90.7 percent of the ENERGY STAR shipments over the 2008

– 2012 period. DOE believes the difference between the two data sources to be caused by a larger number of manufacturers included in the ENERGY STAR accounting than in the AHRI accounting. Thus, DOE decided to use the ENERGY STAR data to adjust the shipments forecast.

Using CIR data

, DOE create d a rolling estimate of t otal existing stock by aggregating historical shipments across 8 .5

- year historical period s. U sing shipments, stock

, and the estimated

9

- 5

### 8.5

- year life of the equipment, DOE estimates that

, on average, 14 percent of shipments were for

new construction and the remainder for replacement of existing stock.

Next, DOE used t he split between shipments to new buildings and shipments for stock replacement to derive new shipments using the historical AHRI shipments with the adjustment based on ENERGY STAR shipments as a starting point

. Starting with an estimated 20 1 0 stock

value created by aggregating the last 8 .5 years of adjusted AHRI shipments data

, DOE began adding shipments

, subtracting estimated stock retirements, a nd developing a forecast of surviving stock for 201 1 through 204 7

.

T o identify appropriate building construction estimates for future growth in the forecast
step, DOE distributed the estimated total 20 1 0 existing ice maker stock across building types using distributions from a 2009 commercial refrigeration equipment report from Navigant Consulting, Inc

. 1 

### Thus divided, the projections of new construction activity for each building type derived from National Energy Modeling System (NEMS) input files were used to project

sh ipments for each building type

. b 

### The distribution by building type is shown on Table 9.3

. 1

. Ice maker shipment and stock projections were then aggregated across building types by year, and

the totals ultimately were distributed across all 2 5 equipment classes using distributions derived

from the AHRI 2010 2 

### data.

Table 9.3

. 1 Distribution of Ice Maker Stock by Building Type

Building Type Share of Stock

Health Care 9. 1 % Lodging 33. 2 %

Foodservice 22.3%

Retail 7.7%

Education 7.3%

Food Sales 16. 2 %

Office 4. 2 %



### 9.3.2 Historical Shipments and Projected Building Stock Additions

DOE converted the historical shipment data in each equipment class to percent ages of

total shipped units of automatic commercial ice mak ers for use in the s hipments m odel

. This

establish ed the ACIM equipment market share attributed to each equipment class for a base, historical data year.

DOE developed t he projections of building construction from the 201 4 Annual Energy

Outlook ( AEO 2014 ). 3 

### Table 9.3

. 2 shows the forecast square footage of new construction used to

scale annual new ACIM equipment shipments. As the data i n Table 9.3

. 2 show, forecast s quare

footage additions to the building stock s vary from year to year. Th erefore, th e forecast ACIM

b For some types of consumer products and commercial equipment, spe cific classes of equipment are often linked to specific types of buildings. Hence, the step of dividing the equipment stock among building types captures crucial information for the forecast of specific equipment classes. In the case of ACIM equipment, DOE currently has no specific data on such specific linkages between types of buildings and equipment classes. However, because building stocks for each of the seven building types are projected to grow at varying rates, projecting shipments by building type enables DOE to capture as much information as possible from the NEMS commercial building construction projections.

9

- 6

### equipment shipments show some variability as well, tracking the forecast floor space additions.

The AEO201 4 projections covered the period up to 2040. DOE used t he growth rates over the

last 10 years of the AEO 2014 forecast ( 20 31

– 20 40 ) to extrapolate the AEO forecast through the year 204 7 to develop the full 30

- year forecast needed for the n ational i mpacts a nalysis (see chapter 10)

.

Table 9.3

. 2 AEO201 4 F orecast of Commercial Floor Space Additions by Building Type

Year New Construction of Commercial Floor Space million ft 2

Health

C are Lodging Foodservice Retail Education Food Sales Office

2013 66 147 31 279 247 21 174

2018 67 164 51 428 209 36 411

2020 65 176 47 404 197 33 451

2025 63 181 48 444 169 34 392

2030 71 150 55 515 190 39 276

2035 72 207 57 527 228 40 415

2040 76 188 56 565 252 40 403

Annual Growth Factor, 2031

- 2040 2.4% 2.5% 2.4% 2.5% 1.7% 2.3% 2.1%

Source: U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 201 4 , data file kdbout.



### DOE used the A H RI shipment s data to derive the percentage of units shipped for each of

the 13 primary equipment classes and 12 secondary equipment classes

. Table 9.3

. 3 shows the

fraction of the units shipped for each of the 2 5 equipment classes.

Table 9.3

. 3 ACIM Equipment Shipments, Percent by Equipment Class

E quipment Class * Percentage of Shipments Equipment Class Percentage of Shipments

IMH

- W

- Small

- B 4.54% SCU

- W

- Large

- B 0.22%

IMH

- W

- Med

- B 2.90% SCU

- A

- Small

- B 13.85%

IMH

- W

- Large

- B* * 0.00% SCU

- A

- Large

- B 6.56%

IMH

- W

- Large

- B1 0.37% IMH

- W

- Small

- C 0.68%

IMH

- W

- Large

- B2 0.11% IMH

- W

- Large

- C 0.17%

IMH

- A

- Small

- B 27.08% IMH

- A

- Small

- C 3.53%

IMH

- A

- Large

- B* * 0.00% IMH

- A

- Large

- C 1.07%

IMH

- A

- Large

- B

- 1 13.61% RCU

- Small

- C** 0.83%

IMH

- A

- Large

- B

- 2 2.53% RCU

- Large

- C** 0.87%

RCU

- Small

- B † 5.43% SCU

- W

- Small

- C 0.15%

RCU

- Large

- B* * , † 0.00% SCU

- W

- Large

- C 0.00%

RCU

- Large

- B

- 1 5.80% SCU

- A

- Small

- C 8.75%

RCU

- Large

- B

- 2 0.27% SCU

- A

- Large

- C 0.00%

SCU

- W

- Small

- B 0.68% Total 100.00% Source: AHRI shipment data for 2010 submitted to DOE as part of this rulemaking.

* IMH : ice

- making head; R CU: remote condenser unit; SCU: self

- contained unit; A: Air

- cooled; W: Water

cooled; B: Batch; C: Continuous

* * IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B were modeled as two separate typical units rather than one equipment class. † Remote compressing classes for remote condensing and without remote condensing were modeled as combined classes within the small/large and batch type/continuous type groupings. Thus, eight classes are shown

9

- 7on this table as four classes.



### 9.3.3 Building Space and Market Saturation

The amount of commercial floor space is the main driver for ACIM equipment shipments and is one of the basic inputs to the s hipments m odel. T he model divides commercial floor space i nto new construction floor space ( NFS ) and existing floor space ( EFS ).

For this analysis, “ commercial square footage with ACIM equipment ” refers to both the

new and the existing stock of buildings.

9.3.3.1 Floor Space : New Construction

DOE used the projec ted floor space construction after the year 2009 from the NEMS projection underlying AEO2014

. DOE extracted annual estimates of new floor space additions

from a n AEO2014 data file (kdbout) for the 200 4 through 20 40 period. As stated in section 9.3.2

, DOE used the last 10 years of the AEO forecast to develop growth rates used to extend the

forecast to 204 7

.

DOE projecte d t he shipments of ice makers for ne w buildings by multiplying the estimated shipments to new buildings in 2010 by future ind ices of new construction fo r each building type. DOE developed t he index for each building type by dividing floor space projections in each future year by t he prior year’s floor space projection

.

F uture shipments for new buildings were factored into the stock accounting model. Future shipments to replace existing stock were developed using the Weibull function described

in section 9.2.2.1

.

9.3.3.2 Market Sh ipments

DOE combined the shipments for new buildings and replacement of existing stock to yield total shipments in year t

. DOE used total shipments t o estimate shipments for each ACIM equipment class y , MKTSHIP(t,y)

, as shown with Eq. 9

. 5

. Because DOE assumed that the percent of shipments in each class wa s constant over time and the lifetime of each class was the

same , the ratio of shipments of a specific equipment class indicate s the market share for that

class

, MKT(y)

. DOE assume d the MKT(y) for a particular equipment class to be a constant value equal to the percent historical shipments to that class. Table 9.3

. 3 reports t hese percentages

.

MMMMMMMMMMMMMM ( tt

, yy ) = MM ( yy ) × ( UUUU ( tt ) + UUUU ( tt ) ) Eq. 9

. 5

W here:

MKTSHIP (t,y) = total market shipments of equipment class y in year t

, MKT(y) = percentage of total market shipments in equipment class y (%)

, UN(t) = total shipments for new construction in year t

, and

UB (t) = total replacement shipments in year t

.

9

- 8

### 9.4 RESULTS

Table 9.4

. 1 shows DOE’s shipments forecast for the ACIM equipment by type of commercial building.

Table 9.4

. 2 shows DOE’s shipments forecast for th e ACIM equipment class es at the

base

- case energy consumption level (or eff iciency l evel 1 ) by equipment type.

Table 9.4

. 1 Forecast Shipments of New and Replacement Automatic Commercial Ice

Makers by Building Type , 201 8

− 204 7 (Base Case)

Building

Sector Number of Units (for selected years, and cumulative)

2018 2020 2025 2030 2035 2040 2045 2047 Cumulative ( 2018 to 2047 )

Health Care

– New 2,049 1,968 1,905 2,158 2,204 2,314 2,490 2,549 65,419

– Replacement* 15,696 16,093 18,984 18,412 20,431 20,711 22,250 22,098 585,066 Lodging

– New 7,918 8,472 8,741 7,224 9,989 9,082 10,458 10,710 272,594

– Replacement* 57,258 58,403 68,986 68,168 75,772 76,327 84,044 82,960 2,160,796
Food s ervice

– New 9,140 8,532 8,633 9,855 10,175 10,157 11,314 11,566 295,638

– Replacement* 38,597 39,742 49,186 49,174 56,864 59,381 66,186 66,912 1,604,502
Retail

– New 2,304 2,175 2,387 2,772 2,837 3,039 3,285 3,361 83,441

– Replacement* 13,352 13,692 16,368 16,182 18,362 19,053 20,953 21,133 523,651
Education

– New 1,099 1,036 889 1,003 1,201 1,327 1,275 1,290 33,469

– Replacement* 12,693 12,959 14,889 14,244 15,387 15,357 16,272 16,031 445,968
Food Sales

– New 6,719 6,261 6,343 7,266 7,487 7,459 8,285 8,459 217,209

– Replacement* 27,982 28,832 35,738 35,778 41,430 43,332 48,344 48,909 1,168,632
Office

– New 1,970 2,162 1,879 1,322 1,988 1,931 2,063 2,106 56,937

– Replacement* 7,240 7,416 9,208 9,736 10,891 11,179 12,723 12,809 305,877
Total

– New 31,200 30,605 30,778 31,599 35,880 35,310 39,170 40,041 1,024,708

– Replacement * 172,818 177,136 213,359 211,695 239,136 245,341 270,772 270,851 6,794,493

– Total 204,018 207,741 244,137 243,295 275,015 280,650 309,943 310,892 7,819,200 * Replacement includes equipment replaced from original stock (installed before standards) and the standards

- level equipment replaced in subsequent years.

9

- 9

### E quipment purchase price increases associated with lower energy consumption levels

could cause shipments to drop relative to the base case. DOE acknowledges that the shipment projections under the standards cases could be lower than those in the base case because the higher installed costs could cause some customers to forego or delay discretionary equipment purchases. DOE calculated an average impact factor of

- 0.625,

- 6.25 percent for a 10

- percent price increase, based on an average of input received from seven people interviewed during

manufacturer interviews. The difference between the shipments

- weighted average total installed cost of equipment in the baseline condition and TSL 3 is 2.7 percent. Based on the price impact

factor of

- 6.25, the impact of TSL 3 price changes is under 2 percent. DOE performed a sensitivity analysis on the impact of price on shipments and the national impact analysis. The results are reported in appendix 10B. DOE performed low or high range shipments projections as

additi onal sensitivity analyses reported in appendix 10B of this final rule TSD

.

Table 9.4

. 2 Forecast Shipments o f Automatic Commercial Ice Makers

, 2018

− 204 7

, by Equipment Type (Base Case) *

E quipment Class Number of Units by Equipment Class (for selected years and cumulative)

201 8 2020 2025 2030 2035 2040 2045 Cumulative ( 2018

– 204 7 )

IMH

- W

- Small

- B 9,263 9,432 11,085 11,046 12,487 12,743 14,072 355,019

IMH

- W

- Med

- B 5,760 6,024 7,079 7,055 7,975 8,138 8,987 226,732

IMH

- W

- Large

- B 1,074 998 1,173 1,169 1,321 1,348 1,489 37,556

IMH

- W

- Large

- B1 813 762 895 892 1,009 1,029 1,137 28,678

IMH

- W

- Large

- B2 261 236 277 276 312 319 352 8,878

IMH

- A

- Small

- B 63,704 56,246 66,100 65,872 74,460 75,986 83,917 2,117,051

IMH

- A

- Large

- B 39,385 33,524 39,397 39,261 44,380 45,289 50,016 1,261,806

IMH

- A

- Large

- B1 33,229 28,275 33,229 33,114 37,431 38,198 42,185 1,064,241

IMH

- A

- Large

- B2 6,157 5,249 6,169 6,147 6,949 7,091 7,831 197,565

RCU

- Large

- B ** 14,282 12,624 14,836 14,785 16,712 17,055 18,835 475,159

RCU

- Large

- B1 13,637 12,056 14,168 14,119 15,960 16,287 17,987 453,781

RCU

- Large

- B2 645 568 667 665 752 767 847 21,378

SCU

- W

- Large

- B 527 450 528 527 595 607 671 16,922

SCU

- A

- Small

- B 35,116 28,778 33,820 33,703 38,098 38,878 42,936 1,083,185

SCU

- A

- Large

- B 17,176 13,628 16,016 15,961 18,042 18,411 20,333 512,956

IMH

- A

- Small

- C 9,469 7,341 8,628 8,598 9,719 9,918 10,953 276,324

IMH

- A

- Large

- C 2,923 2,230 2,621 2,612 2,953 3,013 3,328 83,950

RCU

- Small

- C** 2,276 1,719 2,020 2,013 2,276 2,322 2,565 64,698

SCU

- A

- Small

- C 24,081 18,177 21,361 21,287 24,063 24,556 27,119 684,150
Total 225,035 191,171 224,663 223,888 253,079 258,264 285,220 7,195,506

* Excludes projections for secondary equipment classes

. ** Remote compressing classes for remote condensing and without remote condensing were modeled as combined classes within the small/large and batch type/continuous type groupings.

9

- 10

### REFERENCES

1

. Navigant Consulting, Inc. Energy Savings Potential and R&D Opportunities for Commercial Refrigeration. September 23, 2009 . Prepared for the U.S. Department of Energy, Washington, D.C.

2

. Air

- Conditioning, Heating

, and Refrigerat ion Institute. 2010 shipments data submittal. February 2011. Arlington, VA.

3

. U. S. Department of Energy

— Energy Information Administration. 201 4 Annual Energy

Outlook

. 201 4 . Washington D.C. DOE/EIA

- 0383(201 4 )

. (Last accessed July 18, 2014 .)

< www.eia.gov/oiaf/aeo/tablebrowser/ >

.

9

- 11

### CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION

................................ ................................ ................................ ............ 10

- 1

10.2 NATIONAL ENERGY SAVI NGS

................................ ................................ .................. 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

- 5

10.2.2.3 Equipment Stock

................................ ................................ .................... 10

- 5

10.2.2.4 National Annual Energy Consumption

................................ .................. 10

- 6

10.2.2.5 Site

- to

- Source Conversion Factor

................................ .......................... 10

- 6

10.2.2.6 Full

- Fuel

- Cycle Energy

................................ ................................ ......... 10

- 7

10.2.3 Water Consumption Inputs

................................ ................................ ................... 10

- 8

10.3 NET PRESENT VAL UE

................................ ................................ ................................ .. 10

- 9

10.3.1 Net Present Value Definition

................................ ................................ ................ 10

- 9

10.3.2 Net Present Value Inputs

................................ ................................ .................... 10

- 10

10.3.2.1 Total Annual Installed Cost

................................ ................................ . 10

- 10

10.3.2.2 Equipment Price Trends

................................ ................................ ....... 10

- 12

10.3.2.3 Total Annual Operating Cost Savings

................................ ................. 10

- 14

10.3.2.4 Discount Factor

................................ ................................ .................... 10

- 17

10.3.2.5 Present Value of Costs

................................ ................................ ......... 10

- 18

10.3.2.6 Present Value of Savings

................................ ................................ ..... 10

- 18

10.4 NATIONAL ENERGY SAVI NGS AND NET PRESENT VALUE RESULTS

........... 10

- 18

10.4.1 National Energy Savings and Net Present Value Inp ut Summary

...................... 10

- 18

10.4.2 National Energy Savings Results

................................ ................................ ........ 10

- 19

10.4.3 Water Impacts

................................ ................................ ................................ ..... 10

- 23

10.4.4 Annual Costs and Savings

................................ ................................ .................. 10

- 25

10.4.5 Net Present Value Results

................................ ................................ ................... 10

- 26

10.5 ANNUALIZED NATIONAL COSTS AND BENEFITS

................................ .............. 10

- 30

10.5.1 Calculation Method

................................ ................................ ............................. 10

- 31

10.5.2 Results for the Adopted Standards

................................ ................................ ...... 10

- 32

REFERENCES

................................ ................................ ................................ ........................ 10

- 34

LIST OF TABLES

Table 10.2.1 National Energy Saving Inputs

................................ ................................ ............. 10

- 2 Table 10.2.2 Market Shares by Efficiency Level, Base Case

................................ .................... 10

- 3 Table 10.2.3 Unit Energy Consumption by Efficiency Level

................................ ................... 10

- 4

Ta ble 10.2.4 Shipment

- Weighted Average Unit Energy Consumption by Efficiency Level

.... 10

- 5 Table 10.2.5 Projected Site

- to

- Source Conver sion Factors

................................ ....................... 10

- 7

Table 10.2.6 Full

- Fuel

- Cycle Energy Multipliers

................................ ................................ ...... 10

- 7 Table 10.2.7 Unit Water Consumption by Efficiency Level

................................ ..................... 10

- 8 Table 10.2.8 Shipment

- Weighted Unit Water Consumption by Efficiency Level

.................... 10

- 9

Table 10.3.1 Net Pre sent Value Inputs

................................ ................................ .................... 10

- 10

10

- i

### Table 10.3.2 Average Total Installed Cost per Unit by Efficiency Level

............................... 10

- 11 Table 10.3.3 Shipment

- Weighted Average Total Installed Cost per Unit, by Efficiency Level

................................ ................................ ............................... 10

- 12

Table 10.3.4 Price Trend Scenarios

................................ ................................ ......................... 10

- 13 Table 10.3.5 Average Annual Repair Cost per Unit

................................ ................................ 10

- 15 Table 10.3.6 Shipment

- Weighted Annual Repair Cost per Unit

................................ ............. 10

- 16 Table 10.3.7 Average Annual Maintenance Cost per Unit

................................ ...................... 10

- 17 Table 10.4.1 NES and NPV Inputs

................................ ................................ .......................... 10

- 19 Table 10.4.2 Cumulative Primary National Energy Savings, by EL, for Automatic

Commercial Ice Makers Purchased in 2018

- 2047

................................ .......... 10

- 20 Table 10.4.3 Primary National Energy Savings, by Trial Standard Level, for Automatic

Commercial Ice Makers Purchased in 2018

- 2047

................................ .......... 10

- 21 Table 10.4.4 Primary Energy TSL National Energy Savings as Percent of Base Energy Usage of Equipment Purchased in 2018

- 2047

................................ ............................... 10

- 21

Table 10.4.5 Cumulative Full

- Fuel

- Cycle National Energy Savings, by EL, for

Automatic Commercial Ice Makers Purchased in 2018

- 2047

........................ 10

- 22

Table 10.4.6 Full

- Fuel

- Cycle National Energy Savings, by Trial Standard Level, for

Automatic Commercial Ice Makers Purchased in 2018

- 2047

........................ 10

- 23 Table 10.4.7 Cumulative Water Savings, by EL, for Automatic Commercial Ice Makers

Purchased in 2018

- 2047

................................ ................................ .................. 10

- 24 Table 10.4.8 Cumulative Water Savings, by TSL, for Automatic Commercial Ice Makers

Purchased in 2018

- 2047

................................ ................................ .................. 10

- 25 Table 10.4.9 Cumulative NPV Results, by EL, Based on a 7

- Percent Discount Rate

............ 10

- 27 Table 10.4.10 Cumulative NPV Results, by TSL, Based on a 7

- Percent Discount Rate

........ 10

- 28 Table 10.4.11 Cumulative NPV Results, by EL, Based on a 3

- Percent Discount Rate

.......... 10

- 29

Table 10.4.12 Cumulative NPV Res ults, by TSL, Based on a 3

- Percent Discount Rate

........ 10

- 30

Table 10.5.1 Annualized Benefits and Costs of New and Amended Standards for Automatic Commercial Ice Makers Shipped in 2018

– 2047*

.......................... 10

- 32

LIST OF FIGURES

Figure 10.4.1 National Annual Costs and Savings for Efficiency Level 4

.............................. 10

- 26

10

- ii

### CHAPTER 10

. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method used t o estimat e the magnitude and net present value (NPV) of future national energy savings (NES) from candidate efficiency levels for automatic

commercial ice

- making (ACIM) equipment energy conservation standard s

. Results of the national impact analysis (NIA) described in this chapter include national en ergy consumption and savings, monetary value of energy savings to the n ation as a res ult of standard s, increase d

total installed costs to the n ation as a result of standards, and the NPV of energy savings ( the

difference between the present monetary value of energy savings and increased total installed costs).

The U.S. Department of Energy (DOE) determined both the NPV and NES for each potential standard level considered for 1 3 primary equipment classes of automatic commercial

ice makers examined in this final rule . Sections 10.2 and 10.3 present the definition s and the inputs to NES and NPV, respectively. Section 10.4 present s the result of the NIA. DOE performed all calculations using a Microsoft Excel spreadsheet, which i s available at www.eere.energy.gov/buildings/appliance_standards/ commercial_products.html

. Details and instructions for using the spreadsheet are discussed in a ppendix 10A of this technical support

docu ment ( TSD )

.

10.2 NATIONAL ENERGY SAVI NGS

10.2.1 National Energy Savings Definition

DOE calculates annual NES for a given year as the difference between two scenarios : a base case without new energy conservation standards and a standards case with new energy conservation standards as shown in Eq. 10

. 1

. Positive values of NES correspond to net energy savings ( i.e.

, national annual energy consumption (AEC) with stand ards is less than AEC in the

base case )

.

NNNNNN tt 

### = AA AA bbbbbbbb

, tt 

### − AA AA ssss ssss

, tt 

### Eq. 10

. 1

Where:

NES t 

### = n ational e nergy s avings in the year t ( quadrillion British thermal units ( quads) )

, AEC base ,t 

### = a nnual national energy consumption in the year t in the base

- case scenario (quads ),

and

AEC standards,t 

### = a nnual national energy consumption in the year t in a standards

- case scenario (quads)

.

DOE calculates cumulative NES as the sum of a nnual NES from each year, starting with the year in which the standards that are the subject of this rulemaking become effective ( 201 8 )

, and through the end of the study period ( 2 04 7 ). As discussed below, the NES also includes

10

- 1

### energy savings for equipment purchased in the final study year , and through the year i n which the final year purchases are retired

. Cumulative NES is explained by Eq. 10

. 2

.

NNNNNN
cccccc 

### = ∑ NNNNNN tt tt 

### Eq. 10

. 2

DOE calculated the AEC for base

- case and standards

- case scenarios by multiplying the

number or stock of automatic commercial ice makers (by vintage) by the unit energy consumption (UEC) (also by vintage)

, as shown by Eq. 10

. 3 :

AA AA tt 

### = ∑ ( SSSS SS VV 

### × UU VV 

### ) × VV 

### ffffff

_ cccccc tt 

### Eq. 10

. 3

Where:

AEC t 

### = national annual energy consumpti on in the year t (quads), summed over vintages of

ACIM stock

, STOCK V 

### = s tock of automatic commercial ice mak ers (millions of units) of vintage V surviving in the year for which DOE calculated AEC (vintages range from 1 to approximately

14 years)

, U

EC V 

### = a nnual unit energy consumption in kilowatt

- hours (kWh) for equipment of vintage V

, ffc _conv t 

### = t ime

- dependent c onversion factor s to convert site energy to full

- fuel

- cycle (FFC) energy by converting site energy to source energy (Btu/kWh) and add ing the energy consumed in extracting, processing, and transporting or distributing primary fuels ( calculated

as a percent of site energy)

t = y ear of forecast.

The ACIM stock depend s on annual shipments and equipment lifetime. DOE acknowledges that the shipment projections under the standards cases could be lower than those

in the base case because the higher installed costs c ould cause some customers to forego or delay discretionary equipment purchases , but , as discussed in Chapter 7, DOE believes that the changes should be modest due to the modest increase in shipment

- weighted average installed costs (under 3 percent for the levels chosen for the new and amended standards).

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 u nit e nergy c onsumption ( UEC ) Shipments Equipment s tock ( STOCKV )

Site

- to

- s ource c onversion f actor ( src_conv )

10

- 2

### 10.2.2.1 Annual Unit Energy Consumption

The annual UEC is the site energy consumed by a n automatic commercial ice mak er in a given year. As described in the following paragraphs

, DOE forecasted annual shipment

- weighted average equipment efficiencies that, in turn, enabled the determination of shipment

- weighted

AEC values.

Table 10.2

. 2 provides estimated shipment

- weighted market shares, by efficiency level, for each of the equipment classes analyzed in this rulemaking. The market shares by efficiency

level for bat ch

- type equipment classes w ere based on data provided by Air

- Conditioning, Heating, and Refrigeration Institute (AHRI) in response to the notice of proposed rulemaking

. The market shares for continuous

- type equipment class es were developed from information about available equipment obtained from published sources (see TSD chapter 3 , Market and Technology Assessment )

.

Table 10.2

. 2 Market Shares by Efficiency Level, Base Case

Equipment Class Market Share by Efficiency Level

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 37.1% 15.6% 44.8% 2.5% 0.0% 0.0%

IMH

- W

- Med

- B 55.8% 20.0% 15.3% 8.9%

IMH

- W

- Large

- B*

IMH

- W

- Large

- B1 87.2% 12.8%

IMH

- W

- Large

- B2 87.2% 12.8%

IMH

- A

- Small

- B 23.7% 29.5% 46.8% 0.0% 0.0% 0.0% 0.0%

IMH

- A

- Large

- B*

IMH

- A

- Large

- B1 34.1% 27.8% 35.1% 0.5% 2.5% 0.0%

IMH

- A

- Large

- B2 16.8% 22.5% 60.8%

RCU

- Large

- B*

RCU

- Large

- B1 43.9% 36.4% 18.8% 1.0%

RCU

- Large

- B2 43.9% 36.4% 19.8%

SCU

- W

- Large

- B 71.6% 0.6% 0.0% 22.5% 5.4% 0.0%

SCU

- A

- Small

- B 51.8% 15.3% 12.9% 8.0% 12.0% 0.0% 0.0%

SCU

- A

- Large

- B 62.6% 14.8% 21.5% 0.0% 1.1% 0.0%

IMH

- A

- Small

- C 30.6% 11.1% 19.4% 5.6% 19.4% 13.9%

IMH

- A

- Large

- C 43.5% 21.7% 17.4% 8.7% 8.7%

RCU

- Small

- C 27.8% 27.8% 33.3% 5.6% 0.0% 5.6%

SCU

- A

- Small

- C 44.1% 8.8% 14.7% 17.6% 14.7% 0.0%

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than model ing these

as one class.



### For continuous

- type equipment, DOE estimate d the market share s based on the number

of available ice maker models for each equipment class with the energy use consistent with the defined efficiency levels ( i.e., the model has energy use no greater than its assigned efficiency level, but not as low as the next higher efficiency level )

. Using the computed breakout of currently available models along with AHRI shipment data, DOE create d a distribution of units by eq uipment class and efficiency level.

10

- 3

### T he market share for each equipment class by efficie ncy level is defined as the efficiency

level share

. Because DOE had no information regarding future changes in market shares among equipment classes, DOE assumed the market share for a particular equipment class to be

constant over time

.

Table 10.2

. 3 provides the annual UEC values for each efficiency level for all 1 3 primary equipment classes. The annual UEC is the amount of energy used by an individual ice maker at each of the specified efficiency levels, in kWh/unit/year.

Table 10.2

. 3 Unit Energy Consumption by Efficiency Level

Equipment Class Unit Energy Consumption by Efficiency Level
kWh/year * , **

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 2,832 2,551 2,411 2,270 2,227 2,162

IMH

- W

- Med

- B 6,040 5,439 5,138 4,951

IMH

- W

- Large

- B 10,750 9,891

IMH

- W

- Large

- B1 9,166 8,405

IMH

- W

- Large

- B2 15,868 14,693

IMH

- A

- Small

- B 3,535 3,184 3,009 2,901 2,833 2,658 2,640

IMH

- A

- Large

- B 8,078 7,272 6,931 6,881 6,622 6,411

IMH

- A

- Large

- B1 7,350 6,617 6,251 6,192 5,885 5,636

IMH

- A

- Large

- B2 11,999 10,802 10,591

RCU

- Large

- B 12,118 10,908 10,316 10,066

RCU

- Large

- B1 11,679 10,514 9,931 9,664

RCU

- Large

- B2 18,671 16,807 16,077

SCU

- W

- Large

- B 3,499 3,151 2,978 2,804 2,630 2,464

SCU

- A

- Small

- B 2,177 1,962 1,854 1,747 1,639 1,532 1,473

SCU

- A

- Large

- B 3,011 2,713 2,563 2,414 2,265 2,141

IMH

- A

- Small

- C 4,299 3,872 3,658 3,445 3,231 3,201

IMH

- A

- Large

- C 8,270 7,445 7,033 6,621 6,348

RCU

- Small

- C 7,737 6,966 6,580 6,195 5,809 5,688

SCU

- A

- Small

- C 3,416 3,077 2,907 2,738 2,568 2,515

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these

as one class. The UECs for these three classe s are weighted averages of the B1 and B2 typical units, using weights provided in TSD chapter 7.



### To estimate NES

, DOE examines the total equipment stock

–

which is distributed across efficiency levels

. In other words, the base

- case electricity consumption is not specifically set to

l evel 1, but is rather, a shipment

- weighted average U EC value for the base

- case efficiency

distribution . The same holds true for standards

- case distributions. To develop standards

- case distributions, DOE used the so

- called “ r oll

- up” method of aggregating shipments. In the roll

- up method , when a standard is contemplated that eliminates equipment below a specific efficiency

level from the market

, all shipments below the standard level roll up into the lowest efficiency

level available under the standard. For example , as Table 10.2

. 2 show s , in the base case, 37 percent of IMH

- W

- Small

- B machines were shipped at l evel 1 efficiency. If l evel 1 is eliminated, those shipments would roll up into efficiency l evel 2. For each equipment class, DOE used the

10

- 4

### roll

- up method to calculate shipment

- weighted average U EC f o r the efficiency distribution at each efficiency level. Table 10.2

. 4 shows the resulting shipment

- weighted average A EC values

.

Table 10.2

. 4 Shipment

- Weighted Average Unit Energy Consumption by Efficiency Level

Equipment Class Shipment

- Weighted Average Unit Energy Consumption kWh/year *

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 2,585 2,481 2,407 2,270 2,227 2,162

IMH

- W

- Med

- B 5,685 5,349 5,121 4,951

IMH

- W

- Large

- B ** 10,640 9,891

IMH

- W

- Large

- B1 9,068 8,405

IMH

- W

- Large

- B2 15,718 14,693

IMH

- A

- Small

- B 3,185 3,102 3,009 2,901 2,833 2,658 2,640

IMH

- A

- Large

- B ** 7,369 7,127 6,923 6,875 6,622 6,411

IMH

- A

- Large

- B1 6,718 6,468 6,242 6,185 5,885 5,636

IMH

- A

- Large

- B2 10,874 10,674 10,591

RCU

- Large

- B ** 11,320 10,789 10,314 10,066

RCU

- Large

- B1 10,907 10,396 9,928 9,664

RCU

- Large

- B2 17,481 16,663 16,077

SCU

- W

- Large

- B 3,294 3,045 2,920 2,795 2,630 2,464

SCU

- A

- Small

- B 2,004 1,892 1,820 1,734 1,639 1,532 1,473

SCU

- A

- Large

- B 2,863 2,676 2,560 2,412 2,265 2,141

IMH

- A

- Small

- C 3,719 3,589 3,500 3,369 3,227 3,201

IMH

- A

- Large

- C 7,565 7,207 6,938 6,597 6,348

RCU

- Small

- C 6,938 6,724 6,509 6,167 5,803 5,688

SCU

- A

- Small

- C 3,067 2,917 2,827 2,713 2,568 2,515

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous * Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these

as one class. The UECs for these three classe s are weighted averages of the B1 and B2 typical units, using weights provided in TSD chapter 7.



### 10.2.2.2 Shipments

DOE forecasted shipments for all years in the analysis period

. These results are presented

in TSD chapter 9

, S hipments A nalysis

.

10.2.2.3 Equipment Stock

The ACIM stock in a given year is the total number of units that are in use in that year. The NES spreadsheet model keeps track of the total number of automatic commercial ice mak ers shipped each year. For the NES and NPV calculations

, DOE estimated that a n average of

11.8 percent of the existing automatic commercial ice makers are retired in each year (based on

an 8

- 1⁄2

- year average lifetime) although the actual retirements are determined in the shipments

model. F or units shipped in 204 7

, DOE assumes that a ny units still remaining at the end of 205 5

will be replaced.

10

- 5

### 10.2.2.4 National Annual Energy Consumption

The national AEC is the product of the annual UEC and the stocks of automatic

commercial ice makers of each vintage, as shown in Eq. 10

. 3

.

DOE initially calculated the AEC at the site ( i.e. , electricity in kilowatt

- hours consumed by th e automatic commercial ice makers ). DOE then calculated primary energy consumption by

a

pplying a conversion factor to account for losses associated with the generation, transmission, and distribution of electricity.

Historically, DOE reported NES i n terms of primary energy expressed in quadrillion

British thermal units , or quads. DOE has beg un to also estimate FFC energy savings. 76 FR 51282 (Aug

. 18, 2011), as amended at 77 FR 49701 (Aug

. 17, 2012). The FFC metric includes the energy consumed in extracting, processing, and transporting primary fuels, and thus presents a more complete picture of the impacts of energy efficiency standards. DOE’s approach is based

on the calculation of an FFC multiplier for each of the energy types used by covered equipment.

While DOE stated in the notice of proposed rulemaking that it intended to use the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to

conduct the ana lysis, DOE also said it would review alternative methods, including the use of the DOE/Energy Information Administration (EIA)’s National Energy Modeling System (NEMS). a 

### NEMS is a public domain, multi

- sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce the Annual Energy Outlook ( AEO )

. After evaluating both models and the approaches discussed in the August 18, 2011 notice, DOE has determined NEMS is a more appropriate tool for this application. 77 FR 49701 ( Aug. 17, 2012). Therefore, DOE is using the NEMS model to conduct FFC analyses. For the final rule analysis, DOE calculated FFC energy savings using a methodology described in appendix 10D.

10.2.2.5 Site

- to

- Source Conversion Factor

DOE calculates primary energy sa vings (power plant consumption) by applying a factor to account for losses associated with the generation, transmission, and distribution of electricity. DOE derived annual average site

- to

- power plant factors based on the version of the N EMS that

correspon ds to EIA’s Annual Energy Outlook 201 4 ( AEO201 4 ). 1 

### The factors change over time in
response to forecasted changes in the types of power plants projected to provide electricity to the

nation. Table 10.2

. 5 shows the site

- to

- power plant factors from 20 18 to the end of the projection period. For years after 20 40 (the last year in the AEO ) , DOE held the site

- to

- source factor

constant at the 20 40 v alue.

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

- 0581(2003), March 2003.

10

- 6

### Table 10.2

. 5 Projected Site

- to

- Source Conversion Factors

Year Site

- to

- Source

Conversion Factor

Btu/kWh Year Site

- to

- Source

Conversion Factor

Btu/kWh

2018 10,727 2038 8,805

2019 10,727 2039 8,821

2020 10,486 2040 8,852

2021 10,388 2041 8,852

2022 10,184 2042 8,852

2023 9,884 2043 8,852

2024 9,714 2044 8,852

2025 9,713 2045 8,852

2026 9,541 2046 8,852

2027 9,331 2047 8,852

2028 9,203 2048 8,852

2029 9,149 2049 8,852

2030 9,073 2050 8,852

2031 9,024 2051 8,852

2032 8,882 2052 8,852

2033 8,814 2053 8,852

2034 8,790 2054 8,852

2035 8,761 2055 8,852

2036 8,767 2056 8,852

2037 8,782 2057 8,852



### 10.2.2.6 Full

- Fuel

- Cycle Energy

The FFC measure includes point

- of

- use (site) energy, the energy losses associated with generation, transmission, and distribution of electricity, and the energy consumed in extracting, processing, and transporting or distributing primary fuels. DOE’s traditional approach encompasses site energy and the energy losses associated with generation, transmission, and distribution of electricity. To complete the full

- fuel

- cycle by encompassing the energy consumed in extracting, processing, and transporting or distributin g primary fuels, which DOE refer s to as “upstream” activities, DOE developed FFC multipliers using the data and projections generated by NEMS and published in AEO201 4 . While the AEO does not provide direct calculations of

FFC metrics, it does provide exten sive information about the energy system, including projections of future oil, natural gas

, and coal supply, energy use for oil and gas field and refinery operations, and fuel consumption and emissions related to electric power production.

This information can be used to define a set of parameters representing the energy intensity of energy production.

Table 10.2

. 6 shows the FFC energy multipliers used for automatic commercial ice

makers for selected years. The method used to calculate a time series of FFC energy multipliers is described in appendix 10D

.

Table 10.2

. 6 Full

- Fuel

- Cycle Energy Multipliers

Fuel 2017 2020 2025 2030 2035 2040 to 2060 Electricity (power plant energy use) 1.043 1.044 1.045 1.046 1.047 1.047

10

- 7

### 10.2.3 Water Consumption Inputs

Automatic commercial ice makers use water both for the ice and, in some equipment types, in the condenser for eliminating heat. In the engineering analysis, one design option was identified that saved electricity by reducing the amount of water used in cer tain types of equipment. Table 10.2

. 7 shows the water base

- case water usage assumed by efficiency level. Note that DOE did not set potable water usage limits in this f inal rule. The change in water usage results from the identification of an electricity

- conserving design option that also reduces water consumption.

Table 10.2

. 8 sho ws the shipment

- weighted water usage by efficiency level. Note that the shipment

- weighted water usage only differs from the unit water consumption in equipment classes for which a design option was specified that impacts water usage.

Table 1 0.2

. 7 Unit Water Consumption by Efficiency Level

Equipment Class Efficiency Level 1,000 gallons/year *

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 81 80 79 79 79 79

IMH

- W

- Med

- B 224 224 224 224

IMH

- W

- Large

- B ** 503 503

IMH

- W

- Large

- B1 421 421

IMH

- W

- Large

- B2 767 767

IMH

- A

- Small

- B 11 11 11 11 12 12 12

IMH

- A

- Large

- B ** 30 30 28 28 28 28 0

IMH

- A

- Large

- B1 27 27 24 24 24 24

IMH

- A

- Large

- B2 46 46 46

RCU

- Large

- B ** 47 47 47 47

RCU

- Large

- B1 46 46 46 46

RCU

- Large

- B2 72 72 72

SCU

- W

- Large

- B 74 69 69 69 69 69

SCU

- A

- Small

- B 6 6 6 6 3 3 3

SCU

- A

- Large

- B 9 9 9 6 6 6

IMH

- A

- Small

- C 6 6 6 6 6 6

IMH

- A

- Large

- C 17 17 17 17 17

RCU

- Small

- C 15 15 15 15 15 15

SCU

- A

- Small

- C 4 4 4 4 4 4

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these

as one class. The values for these three clas ses are weighted averages of the B1 and B2 typical units, using weights provided in TSD chapter 7.

10

- 8

### Table 10.2

. 8 Shipment

- Weighted Unit Water Consumption by Efficiency Level

Equipment Class Efficiency Level 1,000 gallons/year *

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 80 79 79 79 79 79

IMH

- W

- Med

- B 224 224 224 224

IMH

- W

- Large

- B ** 503 503

IMH

- W

- Large

- B1 421 421

IMH

- W

- Large

- B2 767 767

IMH

- A

- Small

- B 11 11 11 11 12 12 12

IMH

- A

- Large

- B ** 29 29 28 28 28 28

IMH

- A

- Large

- B1 26 26 24 24 24 24

IMH

- A

- Large

- B2 46 46 46

RCU

- Large

- B ** 47 47 47 47

RCU

- Large

- B1 46 46 46 46

RCU

- Large

- B2 72 72 72

SCU

- W

- Large

- B 73 69 69 69 69 69

SCU

- A

- Small

- B 5 5 5 5 3 3 3

SCU

- A

- Large

- B 9 9 9 6 6 6

IMH

- A

- Small

- C 6 6 6 6 6 6

IMH

- A

- Large

- C 17 17 17 17 17

RCU

- Small

- C 15 15 15 15 15 15

SCU

- A

- Small

- C 4 4 4 4 4 4

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that water usage was not calculated for th at efficiency level because insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves. ** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using weights provided in TSD chapter 7.



### F or equipment classes with a design option, weighted

- average water usage will remain

constant as the NIA roll

- up proceeds until the efficiency level with the water

- saving design option is reached. At that point, all units roll into that ef ficiency level

, and the water usage drops

to the new level.

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

NPV = PVS

– PVC Eq. 10

. 4

10

- 9

### W here :

PVS = present value of operating cost savings (electricity, water b



### , repair, and maintenance costs),

and

PVC = present value of increased total installed costs (equipment purchase price and installation

cost ).

DOE determined t he PVS and PVC according to the following expressions:

PPPP = ∑ tt 

### × DDDD tt tt 

### Eq. 10

. 5

PPPP = ∑ TT tt 

### × DDDD tt tt 

### 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 ove r 201 8

− 205 5

, and PVC is summed over 201 8

− 204 7 )

.

DOE determined the contribution to PVC for each year, from the effective date of the

standard ( 201 8 ) to the year 204 7 , discounted to the year 201 4

. DOE determined t he contribution

to PVS for each year, from the effective date of the standard to the year when units purchased in

204 7 would be retire d

. DOE calculated costs and savings as the difference between a standards

case and a base case. DOE calculated a discount factor from the discount rate and the number of years between the “present” ( i.e. , year to whic h 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

Table 10.3

. 1 summarizes the inputs to the NPV calculation.

Table 10.3

. 1 Net Present Value Inputs

Input

Total a nnual i nstalled c ost ( TICt)

Total a nnual o perating c ost s avings ( OCSt)

Discount f actor

Present v alue of c osts ( PVC )

Present v alue of s avings ( PVS )



### 10.3.2.1 Total Annual Installed Cost

The increase in the total annual installed cost is equal to the annual change in the per

- unit installed cost (difference between base case and standards case) multiplied by the shipments

b DOE combined water and wastewater prices into a single value.

10

- 10

### forecasted in the standards case. The total installed cost includes the manufacturer selling price

, markups for wholesale and distribution chains, and installation cost s. ( S ee TSD chapter 8 for a discussion of the development of installed cost.) Table 10.3.2 shows the average total installed
cost per unit for each of the equipment classes of automatic commercial ice mak ers by e fficiency

level

.

Table 10.3

. 2 Average Total Installed Cost per Unit by Efficiency Level

Equipment Class Average Total Installed Cost per Unit
2013$*

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 2,401 2,476 2,537 2,903 3,123 3,371

IMH

- W

- Med

- B 4,216 4,325 4,607 4,943

IMH

- W

- Large

- B** 6,129 6,913

IMH

- W

- Large

- B1 5,004 5,747

IMH

- W

- Large

- B2 9,763 10,681

IMH

- A

- Small

- B 2,442 2,539 2,655 2,695 2,865 3,324 3,331

IMH

- A

- Large

- B** 4,154 4,336 4,430 4,435 4,711 5,068

IMH

- A

- Large

- B1 4,097 4,172 4,269 4,275 4,602 5,025

IMH

- A

- Large

- B2 4,465 5,221 5,298

RCU

- Large

- B** 6,303 6,423 6,879 7,206

RCU

- Large

- B1 6,122 6,220 6,635 6,985

RCU

- Large

- B2 8,998 9,465 10,516

SCU

- W

- Large

- B 3,467 3,540 3,580 3,620 3,664 4,114

SCU

- A

- Small

- B 2,759 2,799 2,822 2,845 2,918 3,000 3,416

SCU

- A

- Large

- B 3,227 3,275 3,300 3,345 3,402 3,854

IMH

- A

- Small

- C 6,620 6,674 6,709 6,745 7,258 7,264

IMH

- A

- Large

- C 5,489 5,538 5,568 6,287 6,310

RCU

- Small

- C 5,641 5,690 5,758 5,808 6,495 6,523

SCU

- A

- Small

- C 3,598 3,621 3,646 3,685 4,216 4,224

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using weights provided in TSD chapter 7.



### DOE developed base

- case and standards

- case energy efficiency scenarios as discussed in

section 10.2.2.1

. Using the efficiency level shipments distribution ( Table 10.2

. 2 ) and the data in

Table 10.2.4 , DO E estimated the shipment

- weighted installed cost. Table 10.3.3 shows the

results.

10

- 11

### Table 10.3

. 3 Shipmen t

- Weighted Average Total Installed Cost per Unit

, by Efficiency Level

Equipment Class Shipment

- Weighted Ave rage Total Installed Cost per Unit
2013$ *

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 2,486 2,514 2,546 2,903 3,123 3,371

IMH

- W

- Med

- B 4,362 4,423 4,637 4,943

IMH

- W

- Large

- B** 6,229 6,913

IMH

- W

- Large

- B1 5,099 5,747

IMH

- W

- Large

- B2 9,880 10,681

IMH

- A

- Small

- B 2,570 2,593 2,655 2,695 2,865 3,324 3,331

IMH

- A

- Large

- B** 4,340 4,382 4,437 4,442 4,711 5,068

IMH

- A

- Large

- B1 4,192 4,218 4,277 4,283 4,602 5,025

IMH

- A

- Large

- B2 5,141 5,268 5,298

RCU

- Large

- B** 6,463 6,516 6,882 7,206

RCU

- Large

- B1 6,262 6,305 6,638 6,985

RCU

- Large

- B2 9,468 9,673 10,516

SCU

- W

- Large

- B 3,512 3,565 3,593 3,622 3,664 4,114

SCU

- A

- Small

- B 2,799 2,820 2,835 2,854 2,918 3,000 3,416

SCU

- A

- Large

- B 3,252 3,282 3,302 3,346 3,402 3,854

IMH

- A

- Small

- C 6,864 6,880 6,894 6,917 7,259 7,264

IMH

- A

- Large

- C 5,654 5,675 5,695 6,289 6,310

RCU

- Small

- C 5,752 5,765 5,803 5,847 6,497 6,523

SCU

- A

- Small

- C 3,713 3,724 3,736 3,763 4,216 4,224

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using the weights provided in TSD chapter 7.



### The results i n Table 10.3.3 depend on the forecasted annual shipments by efficiency

level. If a standard were set at l evel 1, customers would sel ect equipment from all of the

available levels . If the standard were set at l evel 2, customers would select from equipment at

l evel 2 or higher . As the standard levels increase, the range of available efficiency levels is

reduced. T he average shipment

- weig hted average cost depends on the equipment costs shown in

Table 10.3

. 2 and the estimated shipments at each level.

10.3.2.2 Equipment Price Trends

I n prior energy conservation standards rulemakings, DOE estimated the total installed costs per unit for equipment, and then assumed that costs remain constant throughout the analysis period. This assumption is conservative because installed costs tend to decrease over

time . In 2011, DOE issued a n otice of d ata a vailability (NODA) titled Equipment Price Forecasting in Energy Conservation Standards Analysis

. 76 FR 9696 ( Feb. 22, 2011)

. In the NODA, DOE proposed a methodology for analyzing whether equipment prices have trended

downward in real terms. The methodology examines so

- called experiential learning, wherein

, with ever

- increasing experience with the production of a product

, manufa cturers are able to reduce their production costs through innovations in technology and process.

10

- 12

### To account for increased efficiency in the ACIM manufacturing process over time, DOE

use d a price forecast methodology based on experiential learning ( s ee a ppe ndix 8D )

. To project

the manufacturer selling price of a unit

, DOE will multiply the selling price for the first year of the analysis period by a coefficient specific to the year of purchase relative to the year in which manufacturer selling price was orig inally estimated

. The coefficient will account for the effects of experiential learning.

Table 10.3

. 4 summarizes four learning scenarios that DOE developed . The first scenario, constant prices, is consistent with the analyses DOE historically performed. In this scenario, prices are held const ant, so the learning multiplier is 1.00. DOE developed t he remaining three

scenarios

— the high , reference

, and low learning scenarios

— for this rulemaking from historical ice maker shipments and Producer Price Index data

. Appendix 8D of this TSD documents th e development of the price learning scenarios. For the final rule

, DOE used the historically derived

reference scenario. The total cost values reported i n Table 10.3

. 2 include the effects of experiential learning for the period between 2013 and 2018

. The other scenarios were used in sensitivity analys e s , included in a ppendix 10B.

Table 10.3

. 4 Price Trend Scenarios

Year Constant Prices Historically Derived Price Trends High Reference Low

2013 100.0% 100.00% 100.00% 100.00%

2014 100.0% 100.02% 99.90% 99.78%

2015 100.0% 100.05% 99.80% 99.56%

2016 100.0% 100.07% 99.71% 99.34%

2017 100.0% 100.09% 99.61% 99.14%

2018 100.0% 100.11% 99.52% 98.94%

2019 100.0% 100.13% 99.44% 98.76%

2020 100.0% 100.14% 99.37% 98.61%

2021 100.0% 100.16% 99.30% 98.45%

2022 100.0% 100.18% 99.22% 98.28%

2023 100.0% 100.20% 99.14% 98.10%

2024 100.0% 100.21% 99.06% 97.92%

2025 100.0% 100.23% 98.98% 97.75%

2026 100.0% 100.25% 98.90% 97.57%

2027 100.0% 100.27% 98.83% 97.41%

2028 100.0% 100.29% 98.75% 97.24%

2029 100.0% 100.30% 98.68% 97.09%

2030 100.0% 100.32% 98.62% 96.95%

2031 100.0% 100.33% 98.56% 96.81%

2032 100.0% 100.35% 98.49% 96.67%

2033 100.0% 100.36% 98.43% 96.53%

2034 100.0% 100.38% 98.36% 96.38%

2035 100.0% 100.39% 98.29% 96.23%

2036 100.0% 100.41% 98.22% 96.09%

2037 100.0% 100.42% 98.16% 95.94%

2038 100.0% 100.44% 98.09% 95.80%

2039 100.0% 100.45% 98.03% 95.67%

2040 100.0% 100.47% 97.97% 95.54%

2041 100.0% 100.48% 97.92% 95.42%

2042 100.0% 100.49% 97.86% 95.30%

10

- 13

### Table 10.3

. 4 (cont)

Year Constant Prices Historically Derived Price Trends High Reference Low

2043 100.0% 100.51% 97.80% 95.17%

2044 100.0% 100.52% 97.74% 95.05%

2045 100.0% 100.53% 97.69% 94.92%

2046 100.0% 100.55% 97.63% 94.79%

2047 100.0% 100.56% 97.57% 94.67%



### 10.3.2.3 Total Annual Operating Cost Savings

The national average annual operating cost savings equal the change in the annual operating costs (difference between base case and standards case) per unit multiplied by the

stock forecasted in the standards case. The annual operating cost includes electricity , water

, repair, and maintenance costs.

Annual Electricity Cost Savings. As d escribed in TSD chapter 8, L ife

- C ycle C ost and

P ayback P eriod 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 each efficiency level

. To calculate annual energy costs , DOE multipl ied the weighte d

- average energy consumption at each efficiency level from Table 10.3.3 by the number of units in the equipment class in each year, and then by the sales

- weighted national average electricity prices for the seven building types i n chapter 8. To determine energy savings, DOE then subtracted the nation al energy costs at each efficiency level ( l evel 2 and higher ) from the national energy costs at the baseline level ( l evel 1).

Annual Water Cost Savings. DOE used a verage state

- level commercial water/wastewater prices to calculate water/wastewater cost savings. To calculate annual water/wastewater cost savings for a particular equipment class in a given year, DOE first calculated the annual water/wastewater costs in each forecast year at each efficiency level by multiplying the weighted

- average consumption at each efficiency level by the number of units in the equipment class in each year. To determine cost savings, DOE subtracted the national water costs at each efficiency level ( l evel 2 and higher ) from the national water costs at the baseline level ( l evel 1). Chapter 8 discusses in more detail t he process of defining water/wastewater prices for each product class and year.

Annual Repair Costs. Table 10.3

. 5 shows the average repair costs per unit for the equipment classes analyzed and Table 10.3

. 6 shows the shipment

- weighted annual repair costs per unit

. The development of annual repair costs i s discussed in c hapter 8.

10

- 14

### Table 10.3

. 5 Average Annual Repair Cost per Unit

Equipment Class Repair Costs by Efficiency Level

201 3 $*

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 45.01 46.53 46.53 46.53 46.53 46.53

IMH

- W

- Med

- B 52.92 55.80 55.80 55.80

IMH

- W

- Large

- B** 62.98 65.04

IMH

- W

- Large

- B1 57.86 59.66

IMH

- W

- Large

- B2 79.53 82.42

IMH

- A

- Small

- B 47.32 50.55 50.55 50.55 50.55 50.55 50.55

IMH

- A

- Large

- B** 55.96 59.73 61.18 61.18 61.24 61.24

IMH

- A

- Large

- B1 54.70 58.02 59.74 59.74 59.80 59.80

IMH

- A

- Large

- B2 62.76 68.98 68.98

RCU

- Large

- B** 62.99 65.46 69.11 69.11

RCU

- Large

- B1 61.39 63.26 66.79 66.79

RCU

- Large

- B2 86.85 98.34 103.73

SCU

- W

- Large

- B 22.06 22.06 22.06 22.06 22.06 22.06

SCU

- A

- Small

- B 37.17 38.07 39.10 40.14 41.10 43.04 43.38

SCU

- A

- Large

- B 45.79 46.84 48.00 48.30 50.34 50.33

IMH

- A

- Small

- C 78.20 80.27 81.65 82.37 102.43 105.53

IMH

- A

- Large

- C 89.34 91.55 92.06 104.69 125.18

RCU

- Small

- C 85.48 87.67 89.47 90.58 106.75 120.24

SCU

- A

- Small

- C 77.27 77.43 78.52 80.27 97.03 104.48

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using the weights provided in TSD chapter 7.

10

- 15

### Table 10.3

. 6 Shipment

- Weighted Annual Repair Cost per Unit

Equipment Class Repair Costs by Efficiency Level

201 3 $*

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 45.97 46.53 46.53 46.53 46.53 46.53

IMH

- W

- Med

- B 54.19 55.80 55.80 55.80

IMH

- W

- Large

- B** 63.25 65.04

IMH

- W

- Large

- B1 58.09 59.66

IMH

- W

- Large

- B2 79.90 82.42

IMH

- A

- Small

- B 49.78 50.55 50.55 50.55 50.55 50.55 50.55

IMH

- A

- Large

- B** 59.17 60.29 61.18 61.18 61.24 61.24

IMH

- A

- Large

- B1 57.54 58.67 59.74 59.74 59.80 59.80

IMH

- A

- Large

- B2 67.94 68.98 68.98

RCU

- Large

- B** 65.10 66.18 69.11 69.11

RCU

- Large

- B1 63.14 63.96 66.79 66.79

RCU

- Large

- B2 94.36 99.41 103.73

SCU

- W

- Large

- B 22.06 22.06 22.06 22.06 22.06 22.06

SCU

- A

- Small

- B 38.26 38.74 39.43 40.25 41.10 43.04 43.38

SCU

- A

- Large

- B 46.47 47.13 48.03 48.32 50.34 50.33

IMH

- A

- Small

- C 87.84 88.47 89.05 89.49 102.86 105.53

IMH

- A

- Large

- C 94.75 95.70 96.04 106.48 125.18

RCU

- Small

- C 89.63 90.24 91.24 92.22 107.50 120.24

SCU

- A

- Small

- C 80.90 80.97 81.55 82.74 97.03 104.48

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling

these as one class. The values for these three clas ses are weighted averages of the B1 and B2 typical units, using the weights provided in TSD chapter 7.



### Annual Maintenance Costs. The estimation of maintenance costs is described in c hapter

8

. DOE assumed that maintenance costs do not increase with efficie ncy level. Table 10.3

. 7 shows the resulting annual maintenance costs per unit by e fficiency level

. Shipment

- weighted

maintenance costs would be identical to the values on Table 10.3

. 7 , and are not shown separately.

10

- 16

### Table 10.3

. 7 Average Annual Maintenance Cost per Unit

Equipment Class Maintenance Costs by Efficiency Level

201 3 $*

Level

1 Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 401 401 401 401 401 401

IMH

- W

- Med

- B 609 609 609 609

IMH

- W

- Large

- B** 812 812

IMH

- W

- Large

- B1 812 812

IMH

- W

- Large

- B2 812 812

IMH

- A

- Small

- B 751 751 751 751 751 751 751

IMH

- A

- Large

- B** 1,050 1,050 1,050 1,050 1,050 1,050

IMH

- A

- Large

- B1 1,050 1,050 1,050 1,050 1,050 1,050

IMH

- A

- Large

- B2 1,050 1,050 1,050

RCU

- Large

- B** 508 508 508 508

RCU

- Large

- B1 508 508 508 508

RCU

- Large

- B2 508 508 508

SCU

- W

- Large

- B 609 609 609 609 609 609

SCU

- A

- Small

- B 761 761 761 761 761 761 761

SCU

- A

- Large

- B 1,066 1,066 1,066 1,066 1,066 1,066

IMH

- A

- Small

- C 761 761 761 761 761 761

IMH

- A

- Large

- C 1,066 1,066 1,066 1,066 1,066

RCU

- Small

- C 305 305 305 305 305 305

SCU

- A

- Small

- C 761 761 761 761 761 761

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are weighted averages of the B1 and B2 typical units.



### 10.3.2.4 Discount Factor

DOE multiplied monetary values in future years by a discount factor to determine the present value of costs and savings

. The discount factor ( DF ) is:

DDDD = 1

( 1 + ) � tt

− tt pp � 

### Eq. 10

. 7

W here :

r = discount rate,

t = year in which the cost or savings are realized

, and

t p 

### = year in which the present value is being determined.

DOE estimated national impacts using two scenarios

— a 3

- percent and a 7

- percent real

discount rate

— for the average real rate of return on private investment in the U.S. economy.

DOE used t hese discount rates 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 Measurin g Benefits and Costs,” therein.

DOE define d the present year as 201 4 for the final rule

.

10

- 17

### 10.3.2.5 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 2014 and

summed over the 2018

- 2047 period over which DOE is considering the installation of ACIM equipment

.

The increase in total installed cost refers to both equipment cost and installation cost associated with the higher energy efficiency of automatic commercial ice makers 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.

10.3.2.6 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 2014 , and summ ed over the period from the effective date of the standard , 201 8 , to the time when the last unit

installed in 204 7 is retired from service. Savings result from net decrease s in total operating costs (including electricity , water

, repair, and maintenance) a ssociated with the automatic commercial

ice makers 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 units of each vintage surviving in a particular year. Equipment consumes energy and water over its entire lifetime

. For example

, for units purchased in 204 5 , the present value of savings includes energy and water consumed

after 2045 until the unit is retired from service at the end of its useful life.

10.4 NATIONA L ENERGY SAVINGS AND NET PRESENT VALUE RE SULTS

DOE used the NES spreadsheet model to estimate the NES and NPV associated with

various efficiency levels. Sections 10.2.2 and 10.3.2 discuss t he inputs to the NES spreadsheet.

T he NES spreadsheet model is available at www1.eere.energy.gov/buildings/appliance_standards/commercial/automatic_ice_making_equip

ment.html

. Appendix 10A provides d etails and instructions for using th e model

.

For this final rule analysis, DOE selected five trial standard level (TSL) groupings based

on the NPV values calculated for each efficiency level ( EL ) . Appendix 10C discusses the criteria used for selection of TSLs. The NIA results in this section are presented for ELs and TSLs. Appendix 10B presents results of alternative NIA model scenarios.

10.4.1 N ational E nergy S avings and N et P resent V alue Input Summary

Table 10.4.1 summarize s the inputs to the NES spreadsheet model. For each input

, the table provides a brief description of the data source.

10

- 18

### Table 10.4

. 1 NES and NPV Inputs

Input Data Description

Shipments Annual shipments from shipments model (see TSD chapter 9,

“ Shipments Analysis ” ).

Effective d ate of s tandard 201 8

Base

- c ase e fficiencies Distribution of base

- case shipments by efficiency level.

Standards

- c ase e fficiencies Distribution of shipments by ef ficiency 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 e nergy and w ater

c onsumption per u nit Annual weighted

- average values are a function of efficiency level (established in the engineering analysis, TSD chapter 5).

Total i nstalled c ost per u nit Annual weighted

- average values of installed cost are a function of efficiency level (see TSD chapter 8).

Repair c ost per u nit Annual weighted

- average values of repair costs vary with efficiency level (see TSD chapter 8).

Maintenance c ost per l inear f oot Annual weighted

- average values of maintenance costs are constant with efficiency level (see TSD chapter 8).

Escalation of e lectricity p rices AEO201 4 forecasts (to 20 40 ) and extrapolation for 20 41 and beyond (see TSD chapter 8).

Escalation of w ater p rices Historic Consumer Price Index for water

- related expenditures extrapolated out to 205 7

. Electricity s ite

- to

- s ource

c onversion Conversion varies yearly and is generated by DOE’s version of the EIA NEMS program (a time series conversion factor; includes electric generation, transmission, and distribution losses).

Discount r ate 3 and 7 percent real.

Present y ear Future costs are discounted to year 201 4

.



### 10.4.2 National Energy Savings Results

The following section provides NES results for each energy consumption level

considered for the 1 3 primary equipment classes of automatic commercial ice mak ers that DOE

has analyzed , with 3 classes represented by 2 units of sizes typical of the high and the low ends of the harvest capacities covered by the class

. Results are cumulative for equipment purchased

from 2018 through 2047

, and results are shown as primary energy savings in quads and as full

fuel

- cycle savings in quads

. DOE based i nputs to the NES spreadsheet model on weighted

average values, yielding results that are discrete point values, rather than a distribution of values

as in the life

- cycle cost analysis (see discussio n of uncertainty in TSD chapter 8)

.

Table 10.4

. 2 shows the primary NES results for the efficiency levels analyzed for each

ACIM equipment class. The primary NES include energy savings at the site level, plus energy losses in the generation, transmission, and distribution systems. Table 10.4

. 3 presents primary energy savings by TSL.

10

- 19

### Table 10.4

. 2 C umulative Primary National Energy Savings , by EL

, for Automatic

Commercial Ice Makers Purchased in 2018

**-** 2047

Equipment Class National Energy Savings by EL primary energy quads*

Level 2 Level 3 Level

3A Level 4 Level

4A Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.002 0.004 0.007 0.008 0.009

IMH

- W

- Med

- B 0.005 0.008 0.010

IMH

- W

- Large

- B** 0.002

IMH

- W

- Large

- B1 0.001

IMH

- W

- Large

- B2 0.001

IMH

- A

- Small

- B 0.011 0.023 0.037 0.046 0.069 0.071

IMH

- A

- Large

- B** 0.019 0.035 0.039 0.058 0.075

IMH

- A

- Large

- B1 0.016 0.031 0.035 0.055 0.071

IMH

- A

- Large

- B2 0.002 0.003

RCU

- Large

- B** 0.015 0.029 0.037

RCU

- Large

- B1 0.014 0.027 0.035

RCU

- Large

- B2 0.001 0.002

SCU

- W

- Large

- B 0.000 0.000 0.001 0.001 0.001

SCU

- A

- Small

- B 0.007 0.012 0.018 0.024 0.032 0.036

SCU

- A

- Large

- B 0.006 0.010 0.014 0.019 0.023

IMH

- A

- Small

- C 0.002 0.004 0.006 0.008 0.009

IMH

- A

- Large

- C 0.002 0.003 0.005 0.006

RCU

- Small

- C 0.001 0.002 0.003 0.005 0.005

SCU

- A

- Small

- C 0.006 0.010 0.015 0.021 0.023

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C:

Continuous

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000. Blank cells indicate that insufficie nt design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these

as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10

- 20

### Table 10.4

. 3 Primary National Energy Savings , by Trial Standard Level, for Automatic

Commercial Ice Makers Purchased in 2018

**-** 2047

Equipment Class National Energy Savings by TSL primary energy quads*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.002 0.002 0.004 0.004 0.009

IMH

- W

- Med

- B 0.005 0.005 0.005 0.008 0.010

IMH

- W

- Large

- B** 0.000 0.000 0.000 0.000 0.002

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 0.001

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 0.001

IMH

- A

- Small

- B 0.011 0.023 0.037 0.037 0.071

IMH

- A

- Large

- B** 0.019 0.034 0.039 0.058 0.075

IMH

- A

- Large

- B1 0.016 0.031 0.035 0.055 0.071

IMH

- A

- Large

- B2 0.002 0.002 0.003 0.003 0.003

RCU

- Large

- B** 0.015 0.015 0.015 0.029 0.037

RCU

- Large

- B1 0.014 0.014 0.014 0.027 0.035

RCU

- Large

- B2 0.001 0.001 0.001 0.001 0.002

SCU

- W

- Large

- B 0.000 0.001 0.001 0.001 0.001

SCU

- A

- Small

- B 0.007 0.018 0.024 0.032 0.036

SCU

- A

- Large

- B 0.006 0.014 0.019 0.023 0.023

IMH

- A

- Small

- C 0.002 0.004 0.006 0.006 0.009

IMH

- A

- Large

- C 0.002 0.002 0.003 0.003 0.006

RCU

- Small

- C 0.001 0.002 0.003 0.003 0.005

SCU

- A

- Small

- C 0.006 0.010 0.015 0.015 0.023

Total 0.077 0.130 0.171 0.219 0.307

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

Large

- B, rather than modeling these as one class. The values for these three classes are the totals of

the B1 and B2 typical units.



### Table 10.4

. 4 presents primary energy savings by TSL as a percentage of base

- case energy

usage.

Table 10.4

. 4 Primary Energy TSL National Energy Savings as Percent of Base Energy Usage of Equipment Purchased in 2018

**-** 2047

Equipment Class Base Energy Usage quads TSL Savings as Percent o f Total 2018



### - 2057 Usage

TSL1 TSL2 TSL3 TSL4 TSL5

IMH

- W

- Small

- B 0.064 4% 4% 6% 6% 15%

IMH

- W

- Med

- B 0.089 5% 5% 5% 9% 12%

IMH

- W

- Large

- B** 0.028 0% 0% 0% 0% 6%

IMH

- W

- Large

- B1 0.018 0% 0% 0% 0% 7%

IMH

- W

- Large

- B2 0.010 0% 0% 0% 0% 6%

IMH

- A

- Small

- B 0.467 2% 5% 8% 8% 15%

IMH

- A

- Large

- B** 0.644 3% 5% 6% 9% 12%

IMH

- A

- Large

- B1 0.495 3% 6% 7% 11% 14%

IMH

- A

- Large

- B2 0.149 2% 2% 2% 2% 2%

RCU

- Large

- B** 0.368 4% 4% 4% 8% 10%

RCU

- Large

- B1 0.343 4% 4% 4% 8% 10%

RCU

- Large

- B2 0.026 4% 4% 4% 4% 7%

10

- 21

### Table 10.4

. 4 (cont)

Equipment Class Base Energy Usage quads TSL Savings as Percent o f Total 2018



### - 2057 Usage

TSL1 TSL2 TSL3 TSL4 TSL5

SCU

- W

- Large

- B 0.004 7% 14% 18% 23% 23%

SCU

- A

- Small

- B 0.150 5% 12% 16% 21% 24%

SCU

- A

- Large

- B 0.102 6% 14% 19% 23% 23%

IMH

- A

- Small

- C 0.071 3% 5% 8% 8% 12%

IMH

- A

- Large

- C 0.044 4% 4% 7% 7% 14%

RCU

- Small

- C 0.031 3% 6% 10% 10% 16%

SCU

- A

- Small

- C 0.145 4% 7% 10% 10% 16%

Total 2.206 3% 6% 8% 10% 14%



### Table 10.4

. 5 reports N ES results for the efficiency levels and for each equipment class, including the FFC adjustment

. FFC energy savings are the standard savings rep orted by DOE.

Table 10.4

. 6 presents FFC NES results by TSL.

Table 10.4

. 5 Cumulative Full

- Fuel

- Cycle National Energy Savings , by EL

, for Automatic

Commercial Ice Makers Purchased in 2018

**-** 2047

Equipment Class National Energy Savings by EL FFC energy quads*

Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 0.002 0.004 0.007 0.008 0.010

IMH

- W

- Med

- B 0.005 0.008 0.011

IMH

- W

- Large

- B** 0.002

IMH

- W

- Large

- B1 0.001

IMH

- W

- Large

- B2 0.001

IMH

- A

- Small

- B 0.011 0.024 0.039 0.048 0.072 0.075

IMH

- A

- Large

- B** 0.020 0.036 0.040 0.061 0.078

IMH

- A

- Large

- B1 0.017 0.033 0.037 0.057 0.075

IMH

- A

- Large

- B2 0.003 0.004

RCU

- Large

- B** 0.016 0.031 0.038

RCU

- Large

- B1 0.015 0.029 0.037

RCU

- Large

- B2 0.001 0.002

SCU

- W

- Large

- B 0.000 0.000 0.001 0.001 0.001

SCU

- A

- Small

- B 0.008 0.013 0.019 0.026 0.033 0.037

SCU

- A

- Large

- B 0.006 0.010 0.015 0.020 0.024

IMH

- A

- Small

- C 0.002 0.004 0.006 0.009 0.009

IMH

- A

- Large

- C 0.002 0.003 0.005 0.007

RCU

- Small

- C 0.001 0.002 0.003 0.005 0.005

SCU

- A

- Small

- C 0.007 0.011 0.016 0.022 0.024

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch;

C: Continuous

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000. Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to

classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes fo r IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling

these as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10

- 22

### Table 10.4

. 6 Full

- Fuel

- Cycle Nationa l Energy Savings , by Trial Standard Level, for

Automatic Commercial Ice Makers Purchased in 2018

**-** 2047

Equipment Class Standard Level*

TSL1 TSL2 TSL3 TSL4 TSL5

IMH

- W

- Small

- B 0.002 0.002 0.004 0.004 0.010

IMH

- W

- Med

- B 0.005 0.005 0.005 0.008 0.011

IMH

- W

- Large

- B ** 0.000 0.000 0.000 0.000 0.002

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 0.001

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 0.001

IMH

- A

- Small

- B 0.011 0.024 0.039 0.039 0.075

IMH

- A

- Large

- B ** 0.020 0.035 0.040 0.061 0.078

IMH

- A

- Large

- B1 0.017 0.033 0.037 0.057 0.075

IMH

- A

- Large

- B2 0.003 0.003 0.004 0.004 0.004

RCU

- Large

- B ** 0.016 0.016 0.016 0.030 0.038

RCU

- Large

- B1 0.015 0.015 0.015 0.029 0.037

RCU

- Large

- B2 0.001 0.001 0.001 0.001 0.002

SCU

- W

- Large

- B 0.000 0.001 0.001 0.001 0.001

SCU

- A

- Small

- B 0.008 0.019 0.026 0.033 0.037

SCU

- A

- Large

- B 0.006 0.015 0.020 0.024 0.024

IMH

- A

- Small

- C 0.002 0.004 0.006 0.006 0.009

IMH

- A

- Large

- C 0.002 0.002 0.003 0.003 0.007

RCU

- Small

- C 0.001 0.002 0.003 0.003 0.005

SCU

- A

- Small

- C 0.007 0.011 0.016 0.016 0.024

Total 0.081 0.136 0.179 0.229 0.321

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes ar e the totals of the B1 and

B2 typical units.



### 10.4.3 Water Impacts

For the final rule

, DOE analyzed o n e design option that resulted in changes in potable
water usage. Table 10.4

. 8 present s national water impacts resulting from the analysis of this design option , by EL

. T he estimate of water savings does not imply that DOE has set a potable water usage standard. Rather, it reflects the impact of using a design option that is effective in reducing energy usage and water usage. ACIM manufacturers may elect to use other design options to achieve the energy consumption limits establish ed through this rulemaking. Table

10.4

. 7 presents water savings by efficiency level while Table 10.4

. 8 presents water savings by

TSL.

Negative water savings shown for the IMH

- A

- Small

- B category result from the pairing of

the 22

- inch units and non

- 22 inch units. The 22

- inch units were estimated to use less water than

10

- 23

### the non

- 22 inc h units. When the curves were merged, after the max tech level of the 22

- inch unit is reached, customers were “ shifted ” to the non

- 22 inch units

, causing the apparent negative water savings. The negative savings do not imply that DOE modeled a design optio n that saved energy but increased water usage.

Table 10.4

. 7 Cumulative Water Savings , by EL

, for Automatic Commercial Ice Makers

Purchased in 2018

**-** 2047

Equipment Class Water Savings by EL
million gallons* , **

Level

2 Level

3 Level

3A Level

4 Level

4A Level

5 Level

6 Level

7

IMH

- W

- Small

- B 761 1,733 1,733 1,733 1,733

IMH

- W

- Med

- B 0 0 0

IMH

- W

- Large

- B** 0

IMH

- W

- Large

- B1 0

IMH

- W

- Large

- B2 0

IMH

- A

- Small

- B 0 0 0 (5,424) (5,424) (5,424)

IMH

- A

- Large

- B** 0 12,501 12,501 11,733 11,733

IMH

- A

- Large

- B1 0 12,501 12,501 11,733 11,733

IMH

- A

- Large

- B2 0 0

RCU

- Large

- B** 0 0 0

RCU

- Large

- B1 0 0 0

RCU

- Large

- B2 0 0

SCU

- W

- Large

- B 336 336 336 336 336

SCU

- A

- Small

- B 0 0 0 13,580 13,580 13,580

SCU

- A

- Large

- B 0 0 9,388 9,388 9,388

IMH

- A

- Small

- C 0 0 0 0 0

IMH

- A

- Large

- C 0 0 0 0

RCU

- Small

- C 0 0 0 0 0

SCU

- A

- Small

- C 0 0 0 0 0

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C:

Continuous

* A value in parentheses is a negative number. A value of 0 indicates no design options were specified that impact water usage.

Blank cells indicate that insufficient design options were identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these

as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10

- 24

### Table 10.4

. 8 Cumulative Water Savings , by TSL, for Automatic Commercial Ice Makers

Purchased in 2018

**-** 2047

Equipment Class Water Savings by TSL million gallons*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 761 761 1,733 1,733 1,733

IMH

- W

- Med

- B 0 0 0 0 0

IMH

- W

- Large

- B** 0 0 0 0 0

IMH

- W

- Large

- B1 0 0 0 0 0

IMH

- W

- Large

- B2 0 0 0 0 0

IMH

- A

- Small

- B 0 0 0 0 ( 5,424 )

IMH

- A

- Large

- B** 0 12,501 12,501 11,733 11,733

IMH

- A

- Large

- B1 0 12,501 12,501 11,733 11,733

IMH

- A

- Large

- B2 0 0 0 0 0

RCU

- Large

- B** 0 0 0 0 0

RCU

- Large

- B1 0 0 0 0 0

RCU

- Large

- B2 0 0 0 0 0

SCU

- W

- Large

- B 336 336 336 336 336

SCU

- A

- Small

- B 0 0 13,580 13,580 13,580

SCU

- A

- Large

- B 0 9,388 9,388 9,388 9,388

IMH

- A

- Small

- C 0 0 0 0 0

IMH

- A

- Large

- C 0 0 0 0 0

RCU

- Small

- C 0 0 0 0 0

SCU

- A

- Small

- C 0 0 0 0 0

Total 1,097 22,987 37,539 36,771 31,347

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* A value in parentheses is a negative number. A value of 0 indicates no design options were specified that impact water usage.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B1 and B2 typical

units.



### 10.4.4 Annual Costs and Savings

As a prelude to provid ing the NPVs for each standard level in each equipment class, this section presents the annual equipment cost (or total installed 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 l evel 4 for an example equipment class ( the small air

- cooled ice

- making h ead, batch ( IMH

- A

- Small

- B ) equipment class )

. The total net annual impact is the discounted value of the difference between annual installed costs and

an nual operating costs at a 7

- percent discount rate. The figure also shows 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 between base and standards cases for equipment purchased each year over the period 201 8

– 204 7

. The annual operating savings are the savings in operating costs for equipment purchased but not retired for each year over the time period

201 8

− 205 5

. DOE determined the annual costs and savings based on the AEO201 4 r eference c ase electricity prices

. The NPV is the difference between the cumulative annual discounted savings

10

- 25

### and the cumulative annual discounted costs. The NIA can produce comparable figures for all other efficiency levels and other equipment classes

.

Figure 10.4

. 1 National Annual Costs and Savings for Efficiency Level 4

10.4.5 Net Present Value Results

The following section provides NPV results for the efficiency levels considered for the equipment classes of automatic commercial ice makers

. Results are cumulative and are shown as the discounted value of these savings. DOE based t he inputs to the NES spreadsheet model on weighted

- average values yielding results that are discrete point values, rather than a distribution

of values as in the life

- cycle cost analysis (see TSD chapter 8 )

. The present value of increased

total installed costs is the total installed cost increase discounted to 201 4 and summed over the

2018

– 2047 period

. The installed costs embodied in the results below are based on refe rence case price trends.

Savings result from decrease s in operating costs associated with the higher energy and

water efficiency of automatic commercial ice maker units purchased 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. The operating cost includes energy and water consumed and maintenance and repair costs incurred until all unit s purchased during the analysis period ( 2018

– 204 7 ) are retired from service. DOE based all results on the reference electricity price trend from

AEO201 4

.

Table 10.4

. 9 shows the NPV results for the standard levels considered for automa tic

commercial ice makers based on a 7

- percent discount rate. Table 10.4

. 10 presents cumulative NPV results by TSL based on a 7

- percent discount rate. Product Class: IMH-A-Small-B Efficiency Level 4 at a 7% Discount Rate

-15.000 -10.000

-5.000 0.000 5.000 10.000 15.000 20.000Millions 2013$

Year 

### Net Savings

Undiscounted Annual Equipment Costs Savings Undiscounted Total O&M Cost Savings Discounted Total Net Annual Impact

10

- 26

### Table 10.4

. 9 Cumulative NPV Results , by EL

, Based on a 7

- Percent Disc ount Rate

Equipment Class Cumulative NPV by EL at 7%

b illion 2013$ * , **

Level 2 Level 3 Level

3A Level 4 Level

4A Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.006 0.011 (0.015) (0.036) (0.049)

IMH

- W

- Med

- B 0.010 0.006 (0.008)

IMH

- W

- Large

- B** (0.002)

IMH

- W

- Large

- B1 (0.002)

IMH

- W

- Large

- B2 (0.000)

IMH

- A

- Small

- B 0.017 0.017 0.036 (0.012) (0.241) (0.238)

IMH

- A

- Large

- B** 0.043 0.110 0.120 0.109 0.021

IMH

- A

- Large

- B1 0.043 0.109 0.119 0.107 0.020

IMH

- A

- Large

- B2 (0.000) 0.001

RCU

- Large

- B** 0.042 0.032 0.007

RCU

- Large

- B1 0.040 0.033 0.008

RCU

- Large

- B2 0.002 (0.001)

SCU

- W

- Large

- B 0.002 0.002 0.002 0.003 0.001

SCU

- A

- Small

- B 0.016 0.026 0.037 0.076 0.068 (0.060)

SCU

- A

- Large

- B 0.014 0.022 0.059 0.064 0.004

IMH

- A

- Small

- C 0.006 0.009 0.014 (0.014) (0.014)

IMH

- A

- Large

- C 0.005 0.009 (0.002) (0.001)

RCU

- Small

- C 0.002 0.004 0.008 (0.002) (0.003)

SCU

- A

- Small

- C 0.018 0.027 0.036 (0.058) (0.062)

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 201 3 $ and thus

rounded to 0.000. Blank cells indicate there were not enough design options identified to specify additional efficiency level s

. Levels 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these

as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10

- 27

### Table 10.4

. 10 Cumulative NPV Results, by TSL, Based on a 7

- Percent Discount Rate

Equipment Class Cumulative NPV by TSL at 7%

billions 2013 $*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.006 0.011 0.011 (0.049)

IMH

- W

- Med

- B 0.010 0.010 0.010 0.006 (0.008)

IMH

- W

- Large

- B** 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.017 0.017 0.036 0.036 (0.238)

IMH

- A

- Large

- B** 0.043 0.109 0.120 0.109 0.021

IMH

- A

- Large

- B1 0.043 0.109 0.119 0.107 0.020

IMH

- A

- Large

- B2 (0.000) (0.000) 0.001 0.001 0.001

RCU

- Large

- B** 0.042 0.042 0.042 0.035 0.007

RCU

- Large

- B1 0.040 0.040 0.040 0.033 0.008

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.001)

SCU

- W

- Large

- B 0.002 0.002 0.003 0.001 0.001

SCU

- A

- Small

- B 0.016 0.037 0.076 0.068 (0.060)

SCU

- A

- Large

- B 0.014 0.059 0.064 0.004 0.004

IMH

- A

- Small

- C 0.006 0.009 0.014 0.014 (0.014)

IMH

- A

- Large

- C 0.005 0.005 0.009 0.009 (0.001)

RCU

- Small

- C 0.002 0.004 0.008 0.008 (0.003)

SCU

- A

- Small

- C 0.018 0.027 0.036 0.036 (0.062)

Total 0.183 0.328 0.430 0.337 (0.406)

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled; W: Water cooled; B: Batch; C: Continuous

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion

2013 $ and thus rounded to 0.000.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, a nd RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B1 and B2 typical units.



### Table 10.4

. 11 provides the NPV results based on the 3

- percent discount rate and electricity price forecasts from the AEO201 4 r eference c ase. Table 10.4

. 12 presents the NPV results, by TSL, based on the 3

- percent discount rate.

10

- 28

### Table 10.4

. 11 Cumulative NPV Results , by EL

, Based on a 3

- P ercent Discount Rate

Equipment Class Cumulative NPV by EL at 3%

b illion 2013$ *

Level 2 Level 3 Level

3A Level 4 Level

4A Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.014 0.025 (0.019) (0.058) (0.074)

IMH

- W

- Med

- B 0.022 0.016 (0.008)

IMH

- W

- Large

- B** (0.003)

IMH

- W

- Large

- B1 (0.003)

IMH

- W

- Large

- B2 (0.000)

IMH

- A

- Small

- B 0.039 0.046 0.092 0.037 (0.368) (0.360)

IMH

- A

- Large

- B** 0.091 0.237 0.259 0.271 0.122

IMH

- A

- Large

- B1 0.090 0.233 0.254 0.266 0.117

IMH

- A

- Large

- B2 0.001 0.005

RCU

- Large

- B** 0.088 0.079 0.039

RCU

- Large

- B1 0.084 0.080 0.039

RCU

- Large

- B2 0.004 (0.001)

SCU

- W

- Large

- B 0.003 0.004 0.005 0.005 0.002

SCU

- A

- Small

- B 0.035 0.055 0.079 0.169 0.159 (0.075)

SCU

- A

- Large

- B 0.030 0.047 0.127 0.138 0.031

IMH

- A

- Small

- C 0.012 0.019 0.030 (0.021) (0.022)

IMH

- A

- Large

- C 0.011 0.019 (0.001) 0.001

RCU

- Small

- C 0.005 0.009 0.017 (0.001) (0.002)

SCU

- A

- Small

- C 0.038 0.057 0.076 (0.096) (0.103)

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 201 3 $ and

thus rounded to 0.000. Blank cells indicate there were not enough design options identified to specify additional efficiency

level s

. Level s 3A and 4A are only applicable to classes with 22

- inch and non

- 22

- inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10

- 29

### Table 10.4

. 12 Cumulative NPV Results, by TSL, Based on a 3

- Percent Discount Rate

Equipment Class Cumulative NPV by TSL at 7%
billions 2013$*

TSL1 TSL2 TSL3 TSL4 TSL5

IMH

- W

- Small

- B 0.014 0.014 0.025 0.025 (0.074)

IMH

- W

- Med

- B 0.022 0.022 0.022 0.016 (0.008)

IMH

- W

- Large

- B ** 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.039 0.046 0.092 0.092 (0.360)

IMH

- A

- Large

- B ** 0.091 0.234 0.259 0.271 0.122

IMH

- A

- Large

- B1 0.090 0.233 0.254 0.266 0.117

IMH

- A

- Large

- B2 0.001 0.001 0.005 0.005 0.005

RCU

- Large

- B ** 0.088 0.088 0.088 0.084 0.039

RCU

- Large

- B1 0.084 0.084 0.084 0.080 0.039

RCU

- Large

- B2 0.004 0.004 0.004 0.004 (0.001)

SCU

- W

- Large

- B 0.003 0.005 0.005 0.002 0.002

SCU

- A

- Small

- B 0.035 0.079 0.169 0.159 (0.075)

SCU

- A

- Large

- B 0.030 0.127 0.138 0.031 0.031

IMH

- A

- Small

- C 0.012 0.019 0.030 0.030 (0.022)

IMH

- A

- Large

- C 0.011 0.011 0.019 0.019 0.001

RCU

- Small

- C 0.005 0.009 0.017 0.017 (0.002)

SCU

- A

- Small

- C 0.038 0.057 0.076 0.076 (0.103)

Total 0.389 0.712 0.942 0.822 (0.453)

IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 2013$ and thus rounded to 0.000.

** The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling thes e as one class. The values for these three classes are the totals of the B1 and

B2 typical units.



### 10.5 ANNUALIZED NATIONAL COSTS AND BENEFITS

The benefits and costs of the standards, for equipment sold in 2018

– 2047, 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 customer operation of equipment that meet the proposed standards (consisting primarily of operating cost savings from using less

energy, m inus 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 carbon dioxide (CO 2 

### ) emission reductions. The derivation of the monetary value of the benefits of emission reductions is described in chapter 14 of this TSD. The value of

the CO 2 

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

### and devel oped by a recent interagency process. The derivation of the time series of SCC values is discussed in appendix 14A of this TSD.

10

- 30

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

### reductions provides a useful perspective, two issues should be consid ered. First, the national operating cost savings are domestic U.S. customer 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 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 ben efits into annualized values. First, DOE calculates a present value in the “present” year used in discounting the NPV of total customer costs and savings. c 

### For this calculation, DOE uses discount rates of 3 and 7 percent for all costs and benefits except f or the value of CO 2 

### reductions. For the latter, DOE uses the discount rate appropriate for each SCC time series (see TSD chapter 14 for discussion).

PPPP xx 

### = � ( xx ( tt )

∙ ( 1 + rr xx 

### ) yy NNNNNN − tt 

### )

tt = yy 1 , yy TT 

### Eq. 10

. 1
Where:

xx ( tt )

= time series under evaluation,

PPPP xx 

### = present value of the time series xx
, yy 1 

### = first year in the analysis period,

yy T 

### = last year in the analysis period,

yy NPV 

### = year to which the NPV of customers’ costs and savings are discounted, and

rr xx 

### = discount rat e used to discount the annual values of time series xx to year yy 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 projecting the present values in the “present” year ahead to the compliance year. The fixed annual payments are the annualized values.

AA
xx

, 

### = PPPP xx 

### ∙ ff yy 1 − yy NNNNNN , 

### ∙ aa 30

, 

### = PPPP xx 

### ∙ ( 1 + rr ) yy 1 − yy NNNNNN



### ∙ rr ∙ ( 1 + rr ) 30



### ( 1 + rr ) 30



### − 1 Eq. 10

. 2
Where:

AA
xx

, 

### = annualized value of the time series xx
,

c 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

- 31

### ff

, 

### = factor to project a value years ahead d 

### with rr discount rate, and

aa 30

, 

### = factor to annualize present values over a 30

- year period with rr 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 determined would be a ste ady 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 automatic commercial ice makers that correspond to TSL 3 . Estimates of annualized values for the proposed standards are shown in Table 10.5

. 1

.

Table 10.5

. 1 Annu alized Benefits and Costs of New and Amended Standards for Automatic

Commercial Ice Makers Shipped in 2018

– 2047*

Discount Rate Primary

Estimate Low

Estimate High

Estimate

Monetized

million 201 3 $/year

Benefits

Operating Cost Savings 7% 65 62 68

3% 75 71 80

CO 2 Reduction at 5% dr, average ** 5% 6 6 6

CO 2 Reduction at 3% dr, average ** 3% 20 20 21

CO 2 Reduction at 2.5% dr, average ** 2.5% 29 28 30

CO 2 Reduction at 3% dr, 95th perc ** 3% 62 60 64

NO X Reduction at $2

, 6 84 /t on ** 7% 1.19 1.16 1.22

3% 1.33 1.29 1.36

Total (Operating Cost Savings, CO 2 Reduction and NO X Reduction)† 7% plus CO 2
range 72 to 128 69 to 123 76 to 133

7% 86 82 90 3% plus CO 2
range 83 to 139 79 to 133 88 to 146

3% 97 92 102

Costs

Incremental Equipment Costs 7% 22 23 21

3% 23 24 22

Net Benefits/Costs

Total (Operating Cost Savings, CO 2 Reduction and NO X Reduction, Minus Incremental Equipment Costs)† 7% plus CO 2
range 50 to 106 46 to 100 54 to 112

7% 64 60 69 3% plus CO 2
range 60 to 116 55 to 109 66 to 123

3% 74 68 80 * This table presents the annualized costs and benefits associated with automatic commercial ice makers shipped in 2018

– 2047. These results include benefits to customers that accrue after 2047 from the automatic commercial ice makers purchased from 2018 through 2047. Costs incurred by manufacturers, some of which may be incurred before 2018 in preparation for the rule, ar e not directly included but are indirectly included as part of incremental equipment costs . 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 equipment price trends in the Low Benefits Estimate, and a high decline rate for projected equipment price trends in the High

d n is the number of years between the “present” year and the compliance year.

10

- 32Benefits Estimate. The methods used to derive projected price trends are explained in a ppendix 10B. Operating costs reflect the

AEO reference case electric prices in the primary estimate and the AEO low and high electric prices in the low and high

estimate s, respectively. The primary estimate reflects the a shipments projection based on AEO reference case commercial floor space growth, while the high and low cases represent cases are based on the AEO High and Low Economics cases. The three cases also include reference, high

, and low water prices. Water price projections are described in TSD chapter 8 while the complete set of projections is contained in TSD appendix 10B.

** The CO 2 values represent gl obal monetized values (in 201 3 $) of the social cost of CO 2 emissions in 2015 under severa l scenarios. The values of $ 12 , $ 40.5 , and $ 62.4 per metric ton (t) are the averages of SCC distributions calculated using 5

- percent,
3

- percent, and 2.5

- percent discou nt rates, respectively. The value of $ 119 /t represents the 95 th percentile of the SCC distribution calculated using a 3

- percent discount rate. The value for NO

x (in 201 3 $) is $2, 6 84 per ton

. † Total benefits for both the 3

- percent and 7

- percent cases are d erived using the SCC value calculated at a 3

- perc ent discount rate, which is $ 40.5 /t in 2015 (in 201 3 $). 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.



### Appendix 10E of this TSD presents detailed Regulatory Information Service Center (RISC) & Office of Information and Regulatory Affairs, OMB (OIRA) Consolidated Information System (ROCIS) tables with annualized benefits and costs by equipment class grouping for all

TSLs considered in this final rule

.

10

- 33

### REFERENCES

1

. 

### U. S. Department of Energy

— Energy Information Administration. 201 4 Annual Energy

Outlook

. 201 4 . Washington D.C. DOE/EIA

- 0383(201 4 )

. (Last accessed May 14, 201 4 .)

< www.eia.gov/oiaf/aeo/tablebrowser/ >

10

- 34

### APPENDIX 10A. USER INSTRUCTIONS FOR NIA SPREADSHEET

TABLE OF CONTENTS

10A.1 INTRODUCTION

................................ ................................ ................................ ...... 10A

- 1

10A.2 USER INSTRUCTIONS FOR NIA SPREADSHEET

................................ ............... 10A

- 1

REFERENCES

................................ ................................ ................................ ....................... 10A

- 6

LIST OF TABLES

Table 10A.2.1 Description of Tabs in the NIA Spreadsheet

................................ .................. 10A

- 1

10A

- i

### APPENDIX 10A

. USER INSTRUCTIONS FOR NIA SPREADSHEET

10A.1 INTRODUCTION

The results obtained in this analysis can be examined and reproduced using a Microsoft Excel spreadsheet available on the U.S. Department of Energy (DOE) Building Technologies

website a t www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

.

The spreadsheet enables the user to perform the DOE’s national impact analysis (NIA)

for 12 equipment classes of automatic commercial ice makers

. The spreadsheet was desi gned and tested using Excel 2010

.

10A.2 USER INSTRUCTIONS FO R NIA SPREADSHEET

The NIA spreadsheet performs calculations to forecast the change in national energy use due to energy efficiency standard s for automatic c ommercial ice

- making ( ACIM) e quipment, and

the net present value of the change in energy usage

. a 

### The energy us ag e and associated costs for a given standard are determined first by calculating the shipments and then by calculating the

energy and resulting costs for ACIM equipmen t shipped under that standard. The differences between the standards and base cases can then be compared and the overall energy savings and present values determined.

The NIA spreadsheet or workbook contains the worksheet s listed in Table 10A.2.1

. Some

of the worksheet s are hidden t o make it easier fo r users to find and examin e final results quickly

. Hidden worksheets can be retrie ved in Excel 2010 by right

- clicking on any worksheet tab

, selecting the “Unhide” option

, and the n choosing a specific worksheet to unhide from the list of

hidden worksheet s

.

Table 10A.2

. 1 Description of Tabs in the NIA Spreadsheet

Worksheet Description

Instructions Provides a description of the NIA spreadsheet.

Labels U sed as a go

- between for user inputs and the rest of the worksheets. Modifying this worksheet will change and may impair the overall operation of the model, so DOE recommends caution when modifying this worksheet. Shipments Summary Contains the input selections and a summary table of shipments and stock across the entire analysis period for the selected equipment class.

Shares Contains market shares of units by equipment class, efficiency level, and building type.

Stock Contains the accounting for equipment stock s for the complete analysis period.

Econ Trends Contains e lectricity and w ater price forecasts and experiential ( p rice ) l earning data inputs. LCC Input Contains data derived in the life

- cycle cost (LCC) analysis spreadshee t . Some data are replicated in the NIA model to allow variations defined explicitly in the NIA. NIA Compiled

Data Contains data from several other worksheet s that ha ve been organized in a way that allows for discounting and output table creation.

TSD Tables Contains output tab les to present in the technical support document (TSD)

. for NIAplus Data compiled from a macro into the format needed for the NIA plus model. NIAplusInputs Compiles energy savings, operating costs, and equipment costs for the NIA plus emissions analysis.

a Note that the NIA model also forecast s impacts arising from changing water usage.

10A

- 1

### Table 10A.2

. 1 (cont)

Worksheet Description

Installed Costs Contains data summarizing the full range of aggregate costs associated with installation. Uses per

unit in s tallation costs from the LCC Inputs worksheet along with shipment data.

Annual Operating Costs Contains data summed from the A nnual R epair & M ain t

, A nnual W ater C ost, and A nnual E lec

C ost worksheet s. Annual Repair &

Maint Contains maintenance and repair cost data summed from the mainten an ce costs worksheet and either the 3% or 7% repair cost worksheet s, depending on the user

- defined discount rate. Repair 3% and 7% contain the same levelized costs. The existence of the two different input streams is a legacy that has been since been superseded. Sele cting one or the other will not change the results. Repair Costs 3% Contains repair cost data using stock data and data imported from the LCC on a per

- unit basis and

used for the 3% discount rate cases. Values are the same as used in the 7% discount case. Repair Costs 7% Contains repair cost data using stock data and data imported from the LCC on a per

- unit basis and

used for the 7 % discount rate cases. Values are the same as used in the 3% discount case.

Maintenance

Costs Contains maintenance cost data using stock data and data imported from the LCC on a per

- unit

basis.

Annual Water

Cost Contains aggregated water cost data using inputs from the LCC Inputs worksheet and stock data.

Annual Elec Cost Contains aggregated electricity cost data using inputs from the LCC Inputs worksheet and stock

data. Electricity Use Contains aggregated water usage data using inputs from the LCC Input worksheet and stock data.

Water Use Contains aggregated electricity usage data using inputs from the LCC Input worksheet an d stock

data. Full Shipments Output Contains the full set of shipments data for all years, efficiency levels, and equipment classes.

Full Affected Stock Output Contains the full set of stock data for all years, efficiency levels, and equipment classes.

MIA Shipments Contains shipment data in the format needed to input into the m anufacture r i mpact a nalysis

. Historical Shipments Contains past shipments data by year and equipment class.

Lifetime

Distribution Contains a Weibull lifetime distribution used in determining probabilities of survival and breakdown by year.

Total Stock and Floor Space Contains the summation of data from the seven building floor space worksheet s.

Healthcare Contains historic al and projected floor space data from th e Annual Energ y Outlook 201 4 1
( AEO2014 ) to account for growth rates in healthcare buildings. Lodging Contains historic al and projected floor space data from th e AEO2014 to account for growth rates in lodging buildings.

Food Service Contains historic al and projected floor space data from th e AEO2014 to account for growth rates in foodservice buildings.

Retail Contains historic al and projected floor space data from th e AEO2014 to account for growth rates in retail buildings.

Education Contains historic al and projected floor space data from th e AEO2014 to account for growth rates in education buildings.

Food Sales Contains historic al and projected floor space data from th e AEO2014 to account for growth rates

in foo d sales buildings.

Office Contains historic al and projected floor space data from th e AEO2014 to account for growth rates in office buildings. Shipment

Weighted Data Contains output tab les to present in the technical support document.

Appendix Charts Contains output charts of detailed res ults

. ROCIS Contains a set of tables with cumulative and annualized values for the Office of Management and Budget (OMB)

10A

- 2

### Basic instructions for using the NIA spreadsheet are as follows:

1) Once the NIA spreadsheet file has been downloaded from the DOE Building Technologies website , open the file using Excel. If you receive a dialog box that asks

whether you want to enable macros, select “ Yes

. ” At the bottom of the Excel

window , click on the worksheet tab for the Shipments Summary worksheet

.

2) Use Excel’s View/Zoom commands at the top menu bar to size the display to your

monitor.

3) C hange the user

- definable model parameters in the Shipments Parameters box by first clicking on a parameter and then choosing a different parameter value from the resulting drop

- down menu

. The parameters are as follows :

a) Discount Year: Selects a year to which all monetary values are discount ed

.

b) Start Yr: 1 st 

### Yr of Stnd: This is the year in which standards are assumed to go into effect. The final rule modeling was based on 2018 , but users can select other start years. Note, if a year other than 2018 is chosen, the user must import inputs from

the LCC model with a matching start year.

c) Equipment C lass : Selects the desired equipment class.

d) Efficiency Level: Selects the desired effi ciency level

. The selected efficiency

level must be less than or equal to the maximum achievable technology ( max

tech ) level. If a higher standard level than max tech is selected , no results will be

available for the levels above max tech

.

e) Market Scenario : Not an option for the notice of proposed rulemaking phase

. The

d efault is “Rollup

. ”

f) Electricity Price Scenario : Selects the desired growth level (Constant, Reference, Low, or High). The scenarios refer to the Energy Information Administration’s (EIA’s) AEO2014 scenarios.

g) Water Price Scenario: Selects the desired growth l evel (reference, low, or high).

h) Discount Rate: Selects the desired discount rate (0, 3, or 7 percent )

.

i) Price Learning Scenario: Selects the desired growth level (no real price change,

NEM S Price Deflator, low, reference, or high).

j) Base Year Stock Scenario : Selects the desired base stock scenario , as follows:

i) 4

- Yr Ave Current Industrial Review ii) AHRI Shipments

- 8.5 year life all eq classes iii) AHRI Shipments

- Continuous 7

- yr life iv) AHRI Shipment s

- Batch Eq Only @ 8.5 yr life

10A

- 3

### k) Full

- Fuel Cycle: Selects the national energy savings (full

- fuel

- cycle savings or primary savings without FFC)

.

l) ENERGY STAR shipments adjustment: Yes or No, will either adjust shipments upward by approximately 9 percent, or not.

m) Price Impact on Shipments: Options include No Impact, an impact referred to as

Short

- Term Impact which incorporates a 6.25 percent decrease per 10 percent increase in average TSL 3 shipment

- weighted installed cost, or, an impact referred to as a Long

- Term Impact which attempts to temper the price impact due to an assumption that such impacts diminish with each year that passes since the price change.

n) Analysis Period

- Number of Yrs: the standard analysis period is 30 year, but options were set up for t he 9

- year analysis period analyzed for the Office of Management and Budget, and for a 28

- year period.

4) Once the user

- defined parameters have been set

, a macro must be run to compile all of the stock and shipments data for each equipment class, efficiency le vel, and year

. To run the m acro , click the “Build Shipment/Stock Tables” button. Please n ote that
output values are not correctly updated until the Build Shipment/Stock Tables macro ha s been run

. All compiled shipments data will be output to tables in the Full Shipments Output worksheet . All compiled stock data will be output to tables in the Full Affected Stock Output worksheet

.

a) If the “ AHRI Shipments

- Continuous 7

- yr life ” is chosen, use for the Base Year Stock Scenario, the Batch Equipment at 8.5 and Co ntinuous Equipment at 7 Year

Lifetime macro button.

b) All other Base Year Stock Scenarios assume an 8.5 year lifetime for all equipment classes, and require use of the All Equipment, 8.5 Year Lifetime macro button.

5) If a new set of LCC results is to be loaded , they must be load ed into the LCC Input

worksheet

. To do this :

a) O pen the LCC workbook and execute as desired (see a ppendix 8A)

.

b) Once results are compile d in the NIA Output worksheet of the LCC workbook,

click the “ Copy All ” button at the top of the page

.

This will copy the exact range that is compatible with the NIA model.

c) Once the range has been copied, switch back to the NIA workbook, in the LCC Input worksheet , and click the “ Paste LCC Input ” button to paste the inputs into

the correct location

.

Th e formulas automatically update throughout the model and present results by equipment class and year in the NIA Compiled Data worksheet

.

10A

- 4

### 6) Results are compiled further, into tab les, in the Shipment

- Weighted Data worksheet

. Although many of the tables are formula

- based, and will automatically update with a change in the user

- defined parameters, some are reliant on a macro to update table

values. To update the table values:

a) Click the “Build Tables” button located at the top right portion of the Shipment

Weig hted Data worksheet.

10A

- 5

### REFERENCES

1

. U.S. Department of Energy

– Energy Information Administration. Annual Energy Outlook

201 4

. 201 4

. Washington D.C. DOE/EIA

- 0383( 201 4 ).

10A

- 6

### APPENDIX 10B. NATIONAL NET PRESENT VALUE USING ALTERNATIVE

SENSITIVITY ANALYSES

TABLE OF CONTENTS

10B.1 ALTERNATIVE NET PRES ENT VALUES

................................ ............................. 10B

- 1

10B.1.1 Price Learning Sensitivity Analyses

................................ ............................ 10B

- 1

10B.1.2 Electric and Water Price Sensitivity Analyses

................................ ............. 10B

- 9

10B.1.3 Nine

- Year Sensitivity Analysis

................................ ................................ .. 10B

- 16

10B.1.4 Shipment Price Sensitivity

................................ ................................ ......... 10B

- 18

LIST OF TABLES

Table 10B.1.1 Price Factor Indices Used in Default Case and Sensitivity Cases

................... 10B

- 3 Table 10B.1.2 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7

- Percent Discount Rate

– High Price Scenario

................................ ....... 10B

- 4 Table 10B.1.3 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3

- Percent Discount Rate

– High Price Scenario

................................ ....... 10B

- 5 Table 10B.1.4 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7

- Percent Discount Rate

– Low Price Sce nario

................................ ....... 10B

- 6 Table 10B.1.5 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3

- Percent Discount Rate

– Low Price Scenario

................................ ....... 10B

- 7 Table 10B.1.6 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7

- Percent Dis count Rate

– Constant Real Price Scenario

........................ 10B

- 8 Table 10B.1.7 Automatic Commercial Ice Makers: Net Present Value in Billions (2013 $)

at a 3

- Percent Discount Rate

– Constant Real Price Scenario

........................ 10B

- 9 Table 10B.1.8 Commercial Water Price Projections

................................ ............................. 10B

- 10 Table 10B.1.9 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7

- Percent Discount Rate

– High Electric and Water Price Scenario

...... 10B

- 11 Table 10B.1.10 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3

- Percent Discount Rate

– High Electric and Water Price Scenario

...... 10B

- 12

Table 10B.1.11 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7

- Percent Discount Rate

– Low Electric and Water Price Scenario

...... 10B

- 13

Table 10B.1.12 Automatic C ommercial Ice Makers: Net Present Value in Billions (2013$)

at a 3

- Percent Discount Rate

– Low Electric and Water Price Scenario

...... 10B

- 14 Table 10B.1.13 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7

- Percent Discount Rate

– Constant Electric and Water Price Scenario 10B

- 15 Table 10B.1.14 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at

a 3

- Percent Discount Rate

– Constant Electric and Water Price Scenario

.... 10B

- 16

Table 10B.1.15 Automatic Commercial Ice Makers: Net Present Value at a 7

- percent
Discount Rate for 9

- Year Analysis Period for Equipment Purchased in

2018

– 2026

................................ ................................ ................................ ...... 10B

- 17

Table 10B.1.16 Automatic Commercial Ice Makers: Net Present Value at a 3

- percent
Discount Rate for 9

- Year Analysis Period for Equipment Purchased in

2018

– 2026

................................ ................................ ................................ ...... 10B

- 18

10B

- i

### Table 10B.1.17 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7

- Percent Discount Rate

– Equipment Price Elasticity Scenario

........... 10B

- 19 Table 10B.1.18 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3

- Percent Discount Rate

– Equipment Price Elasticity Sce nario

........... 10B

- 20 Table 10B.1.19 Automatic Commercial Ice Makers: Cumulative Energy Savings

– Equipment Price Elasticity Scenario

................................ .............................. 10B

- 21

LIST OF FIGURES

Figure 10B.1.1 Automatic Commercial Ice Makers Price Factor Indices for Default Case and Sensitivity Cases

................................ ................................ ....................... 10B

- 2

10B

- ii

### APPENDIX 10 B

. NATIONAL NET PRESENT VALUE USING

ALTERNATIVE SENSITIVITY ANALYSES

10B.1 ALTERNATIVE NET PRES ENT VALUES

10B.1.1 Price Learning Sensitivity Analyses

The n et 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 ) that most closely reflects

automa tic commercial ice

- making (ACIM) equipment . The average annual rate of price decline

in the default case for the 201 8

– 204 7 analysis period is 0. 0 7 percent and is based on historical

PPI data for refrigeration equipment in the 1 978

– 201 3 period as discussed in a ppendix 8D of this

TSD

. For the national impact analysis ( NIA )

, the U.S. Department of Energy (DOE) analyzed

two additional sensitivity cases that also use a price trend based on an exponential

- in

- time extrapolation of refrigeration equipm ent 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 on the upper end of the 95 percent confidence interval for an exponential fit to the inflation

- adjusted PPI series of

19 78 through 20 1 3

. The low price forecast case is based on the lower end of the 95 percent confidence interval for an exponential fit t o the nominal PPI series of 1978 through 20 1 3

. The

average annual rate of price decline over the analys is period is 0. 1 5 percent in the low price

forecast case . In the high price forecast

, the average annual rate of change is an increase of 0.0 2
percent. Because of the nature of the exponential experience curve, greater annual price declines are found in earlier years in th e 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 aut omatic

commercial ice makers

.

For the NPV sensitivity, DOE considered three product price forecast sensitivity cases: ( 1) a high price case based on the PPI trend of 19 78

– 20 1 3 ; ( 2) a low price case based on the PPI

trend in 1978

– 20 1 3 ; and ( 3) a constant real price case

. Each price scen ario is expressed in terms of a price factor index time series

, which is applied to the 201 3 price estimate to forecast pe r unit prices over the 201 8

– 204 7 analysis period

. Figure 10B.1

. 1 shows the equipment price factor

indices for the default case and the three additional price decline forecast s considered in this sensitivity analysis

. T able 10B.1.1 shows the price factor indices tabulated.

Table 10B.1.2 and Table 10B.1.3 provide NPV results for automatic commercial ice

makers at each TSL based on the high price forecast case for 7

- percent and 3

- percent discount rates, respectively. Table 10B.1.4 a nd Table 10B.1.5 provide NPV results for automatic

commercial ice makers based on the low price forecast case at 7

- percent and 3

- percent discount rates, respectively. Table 10B.1

. 6 and Table 10B.1

. 7 provide NPV results for ice makers based on a constant real price case at 7

- percent and 3

- percent discount rates, respectively. These results can be directly compared with the ACIM NPV result s using the default price decline scenario shown in chapter 10 of this TSD.

10B

- 1

### Figure 10B.1

. 1 Automatic Commercial Ice Makers Price Factor Indices for Default Case and Sensitivity Cases 

# 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 1.000 1.010 1.020

2013 2017 2021 2025 2029 2033 2037 2041 2045Price Factor Indices



# Year Low Price Decline
High Price Decline

Reference PPI Price Index

Constant Price

10B

- 2

### Table 10B.1

. 1 Price Factor Indices Used in Default Case and Sensitivity Cases

Year Price Factor Index

201 3 $

Default High Price

Forecast Low Price

Forecast Constant Real

2013 1.000 1.000 1.000 1.0000

2014 0.999 1.000 0.998 1.0000

2015 0.998 1.000 0.996 1.0000

2016 0.997 1.001 0.993 1.0000

2017 0.996 1.001 0.991 1.0000

2018 0.995 1.001 0.989 1.0000

2019 0.994 1.001 0.988 1.0000

2020 0.994 1.001 0.986 1.0000

2021 0.993 1.002 0.984 1.0000

2022 0.992 1.002 0.983 1.0000

2023 0.991 1.002 0.981 1.0000

2024 0.991 1.002 0.979 1.0000

2025 0.990 1.002 0.977 1.0000

2026 0.989 1.003 0.976 1.0000

2027 0.988 1.003 0.974 1.0000

2028 0.988 1.003 0.972 1.0000

2029 0.987 1.003 0.971 1.0000

2030 0.986 1.003 0.970 1.0000

2031 0.986 1.003 0.968 1.0000

2032 0.985 1.003 0.967 1.0000

2033 0.984 1.004 0.965 1.0000

2034 0.984 1.004 0.964 1.0000

2035 0.983 1.004 0.962 1.0000

2036 0.982 1.004 0.961 1.0000

2037 0.982 1.004 0.959 1.0000

2038 0.981 1.004 0.958 1.0000

2039 0.980 1.005 0.957 1.0000

2040 0.980 1.005 0.955 1.0000

2041 0.979 1.005 0.954 1.0000

2042 0.979 1.005 0.953 1.0000

2043 0.978 1.005 0.952 1.0000

2044 0.977 1.005 0.950 1.0000

2045 0.977 1.005 0.949 1.0000

2046 0.976 1.005 0.948 1.0000

2047 0.976 1.006 0.947 1.0000

10B

- 3

### Table 10B.1

. 2 Automatic Commercial Ice Makers : Net Present Value in B illions ( 2013 $) at

a 7

- Percent Discount Rate

– High Price Scenario

Equipment Class * Cumulative NPV by TSL at 7%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.006 0.011 0.011 (0.050)

IMH

- W

- Med

- B 0.010 0.010 0.010 0.005 (0.009)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.017 0.016 0.035 0.035 (0.243)

IMH

- A

- Large

- B † 0.043 0.108 0.120 0.107 0.018

IMH

- A

- Large

- B1 0.043 0.108 0.119 0.106 0.017

IMH

- A

- Large

- B2 (0.000) (0.000) 0.001 0.001 0.001

RCU

- Large

- B † 0.042 0.042 0.042 0.035 0.006

RCU

- Large

- B1 0.040 0.040 0.040 0.033 0.007

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.001)

SCU

- W

- Large

- B 0.002 0.002 0.003 0.001 0.001

SCU

- A

- Small

- B 0.016 0.036 0.076 0.067 (0.063)

SCU

- A

- Large

- B 0.014 0.059 0.063 0.002 0.002

IMH

- A

- Small

- C 0.006 0.009 0.014 0.014 (0.015)

IMH

- A

- Large

- C 0.005 0.005 0.009 0.009 (0.001)

RCU

- Small

- C 0.002 0.004 0.008 0.008 (0.003)

SCU

- A

- Small

- C 0.018 0.027 0.036 0.036 (0.064)

Total 0.182 0.326 0.427 0.331 (0.424)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000

. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 4

### Table 10B.1

. 3 Automatic Commercial Ice Makers : Net Present Value in B illions ( 2013 $) at

a 3

- Percent Discount Rate

– High Price Scenario

Equipment Class * Cumulative NPV by TSL at 3%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.014 0.014 0.025 0.025 (0.076)

IMH

- W

- Med

- B 0.022 0.022 0.022 0.015 (0.009)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.004)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.039 0.044 0.090 0.090 (0.372)

IMH

- A

- Large

- B † 0.091 0.233 0.258 0.268 0.116

IMH

- A

- Large

- B1 0.090 0.232 0.253 0.264 0.111

IMH

- A

- Large

- B2 0.001 0.001 0.004 0.004 0.004

RCU

- Large

- B † 0.088 0.088 0.088 0.083 0.035

RCU

- Large

- B1 0.084 0.084 0.084 0.079 0.036

RCU

- Large

- B2 0.004 0.004 0.004 0.004 (0.001)

SCU

- W

- Large

- B 0.003 0.005 0.005 0.002 0.002

SCU

- A

- Small

- B 0.035 0.079 0.168 0.156 (0.081)

SCU

- A

- Large

- B 0.030 0.127 0.138 0.028 0.028

IMH

- A

- Small

- C 0.012 0.019 0.030 0.030 (0.023)

IMH

- A

- Large

- C 0.011 0.011 0.019 0.019 0.001

RCU

- Small

- C 0.005 0.009 0.016 0.016 (0.003)

SCU

- A

- Small

- C 0.038 0.057 0.076 0.076 (0.106)

Total 0.387 0.708 0.935 0.810 (0.491)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 5

### Table 10B.1

. 4 Automatic Commercial Ice Makers : Net Present Value in B illions ( 2013 $) at

a 7

- Percent Discount Rate

– Low Price Scenario

Equipment Class * Cumulative NPV by TSL at 7%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.006 0.011 0.011 (0.048)

IMH

- W

- Med

- B 0.010 0.010 0.010 0.006 (0.008)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.018 0.017 0.038 0.038 (0.233)

IMH

- A

- Large

- B † 0.043 0.109 0.121 0.110 0.024

IMH

- A

- Large

- B1 0.043 0.109 0.120 0.108 0.023

IMH

- A

- Large

- B2 (0.000) (0.000) 0.001 0.001 0.001

RCU

- Large

- B † 0.042 0.042 0.042 0.036 0.009

RCU

- Large

- B1 0.040 0.040 0.040 0.034 0.010

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.001)

SCU

- W

- Large

- B 0.002 0.002 0.003 0.001 0.001

SCU

- A

- Small

- B 0.017 0.037 0.077 0.069 (0.057)

SCU

- A

- Large

- B 0.014 0.059 0.064 0.005 0.005

IMH

- A

- Small

- C 0.006 0.009 0.014 0.014 (0.014)

IMH

- A

- Large

- C 0.005 0.005 0.009 0.009 (0.001)

R CU

- Small

- C 0.002 0.004 0.008 0.008 (0.003)

SCU

- A

- Small

- C 0.018 0.027 0.036 0.036 (0.060)

Total 0.184 0.330 0.434 0.343 (0.388)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 6

### Table 10B.1

. 5 Automatic Commercial Ice Makers : Net Present Va lue in B illions ( 2013 $) at

a 3

- Percent Discount Rate

– Low Price Scenario

Equipment Class * Cumulative NPV by TSL at 3%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.014 0.014 0.025 0.025 (0.072)

IMH

- W

- Med

- B 0.022 0.022 0.022 0.016 (0.007)

IMH

- W

- Lar ge

- B † 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.040 0.048 0.095 0.095 (0.350)

IMH

- A

- Large

- B † 0.092 0.235 0.260 0.273 0.128

IMH

- A

- Large

- B1 0.090 0.234 0.255 0.268 0.123

IMH

- A

- Large

- B2 0.002 0.002 0.005 0.005 0.005

RCU

- Large

- B † 0.088 0.088 0.088 0.086 0.042

RCU

- Large

- B1 0.084 0.084 0.084 0.081 0.042

RCU

- Large

- B2 0.004 0.004 0.004 0.004 (0.001)

SCU

- W

- Large

- B 0.003 0.005 0.005 0.002 0.002

SCU

- A

- Small

- B 0.036 0.080 0.170 0.161 (0.069)

SCU

- A

- Large

- B 0.030 0.128 0.139 0.034 0.034

IMH

- A

- Small

- C 0.012 0.019 0.030 0.030 (0.021)

IMH

- A

- Large

- C 0.011 0.011 0.020 0.020 0.002

RCU

- Small

- C 0.005 0.009 0.017 0.017 (0.002)

SCU

- A

- Small

- C 0.038 0.057 0.077 0.077 (0.099)

Total 0.391 0.717 0.949 0.835 (0.414)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 7

### Table 10B.1

. 6 Automatic Commercial Ice Makers : Net Present Value in B illions ( 2013 $) at

a 7

- Percent Discount Rate

– Constant Real Price Scenario

Equipment Class * Cumulative NPV by TSL at 7%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.006 0.011 0.011 (0.050)

IMH

- W

- Med

- B 0.010 0.010 0.010 0.005 (0.009)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.017 0.016 0.035 0.035 (0.242)

IMH

- A

- Large

- B † 0.043 0.108 0.120 0.108 0.019

IMH

- A

- Large

- B1 0.043 0.108 0.119 0.107 0.018

IMH

- A

- Large

- B2 (0.000) (0.000) 0.001 0.001 0.001

RCU

- Large

- B † 0.042 0.042 0.042 0.035 0.006

RCU

- Large

- B1 0.040 0.040 0.040 0.033 0.007

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.001)

SCU

- W

- Large

- B 0.002 0.002 0.003 0.001 0.001

SCU

- A

- Small

- B 0.016 0.036 0.076 0.067 (0.063)

SCU

- A

- Large

- B 0.014 0.059 0.063 0.003 0.003

IMH

- A

- Small

- C 0.006 0.009 0.014 0.014 (0.015)

IMH

- A

- Large

- C 0.005 0.005 0.009 0.009 (0.001)

RCU

- Small

- C 0.002 0.004 0.008 0.008 (0.003)

SCU

- A

- Small

- C 0.018 0.027 0.036 0.036 (0.063)

Total 0.182 0.326 0.428 0.332 (0.421)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 8

### Table 10B.1

. 7 Automatic Commercial Ice Makers : Net Present Value in B illions ( 2013 $) at

a 3

- Percent Discount Rate

– Constant Real Price Scenario

Equipment Class * Cumulative NPV by TSL at 3%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.014 0.014 0.025 0.025 (0.076)

IMH

- W

- Med

- B 0.022 0.022 0.022 0.015 (0.009)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.004)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.039 0.045 0.090 0.090 (0.370)

IMH

- A

- Large

- B † 0.091 0.233 0.258 0.269 0.117

IMH

- A

- Large

- B1 0.090 0.232 0.254 0.264 0.113

IMH

- A

- Large

- B2 0.001 0.001 0.004 0.004 0.004

RCU

- Large

- B † 0.088 0.088 0.088 0.083 0.036

RCU

- Large

- B1 0.084 0.084 0.084 0.079 0.037

RCU

- Large

- B2 0.004 0.004 0.004 0.004 (0.001)

SCU

- W

- Large

- B 0.003 0.005 0.005 0.002 0.002

SCU

- A

- Small

- B 0.035 0.079 0.168 0.157 (0.080)

SCU

- A

- Large

- B 0.030 0.127 0.138 0.029 0.029

IMH

- A

- Small

- C 0.012 0.019 0.030 0.030 (0.023)

IMH

- A

- Large

- C 0.011 0.011 0.019 0.019 0.001

RCU

- Small

- C 0.005 0.009 0.016 0.016 (0.003)

SCU

- A

- Small

- C 0.038 0.057 0.076 0.076 (0.106)

Total 0.388 0.708 0.936 0.812 (0.484)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.



### 10B.1.2 Electric and Water Price Sensitivity Analyses

For the NPV sensitivity, DOE considered three electric and water price forecast sensitivity cases: ( 1) a high electric and water price s case , ( 2) a low electric and water price s

case , and ( 3) a constant electric and water price s case

. Table 10B.1

. 8 displays the high, low, and reference water price scenarios used in the sensitivity cases. Table 10B.1

. 9 and Table 10B.1

. 10 provide NPV results for automatic commercial ice makers at each TSL based on the high electric

and water price forecast case for 7

- and 3

- percent discount rates, respectively. Table 10B.1

. 11

and Table 10B.1

. 12 provide NPV r esults for automatic commercial ice makers based on the low

electric and water price forecast case at 7

- and 3

- percent discount rates, respectively. Table

10B.1

. 13 and Table 10B.1

. 14 provide NPV results for ice makers based on a constant electric

and water price forecast case at 7

- and 3

- percent discount rates, respectively. These results can be directly compared with the ACIM NPV results using the default electric price scenario shown in chapter 10 of this TSD.

10B

- 9

### Table 10B.1

. 8 Commercial Water Price Projections

Year Reference Case

2013 $/1000 gal High Case

2013 $/1000 gal Low Case

2013 $/1000 gal Constant Price Case

2013 $/1000 gal

2011 $6.76 $6.92 $7.03 $6.76

2012 $6.99 $7.07 $7.13 $6.99

2013 $7.23 $7.23 $7.23 $7.23

2014 $7.37 $7.39 $7.33 $7.23

2015 $7.52 $7.55 $7.43 $7.23

2016 $7.67 $7.71 $7.54 $7.23

2017 $7.82 $7.88 $7.64 $7.23

2018 $7.98 $8.06 $7.75 $7.23

2019 $8.14 $8.23 $7.86 $7.23

2020 $8.30 $8.41 $7.97 $7.23

2021 $8.46 $8.60 $8.09 $7.23

2022 $8.63 $8.79 $8.20 $7.23

2023 $8.80 $8.98 $8.31 $7.23

2024 $8.98 $9.18 $8.43 $7.23

2025 $9.16 $9.38 $8.55 $7.23

2026 $9.34 $9.58 $8.67 $7.23

2027 $9.53 $9.79 $8.79 $7.23

2028 $9.72 $10.01 $8.92 $7.23

2029 $9.91 $10.23 $9.04 $7.23

2030 $10.11 $10.45 $9.17 $7.23

2031 $10.31 $10.68 $9.30 $7.23

2032 $10.51 $10.92 $9.43 $7.23

2033 $10.72 $11.16 $9.56 $7.23

2034 $10.94 $11.40 $9.70 $7.23

2035 $11.16 $11.65 $9.84 $7.23

2036 $11.38 $11.91 $9.97 $7.23

2037 $11.60 $12.17 $10.12 $7.23

2038 $11.84 $12.43 $10.26 $7.23

2039 $12.07 $12.71 $10.40 $7.23

2040 $12.31 $12.98 $10.55 $7.23

2041 $12.56 $13.27 $10.70 $7.23

2042 $12.81 $13.56 $10.85 $7.23

2043 $13.06 $13.86 $11.00 $7.23

2044 $13.32 $14.16 $11.16 $7.23

2045 $13.59 $14.47 $11.31 $7.23

2046 $13.86 $14.79 $11.47 $7.23

2047 $14.13 $15.11 $11.64 $7.23

2048 $14.42 $15.45 $11.80 $7.23

2049 $14.70 $15.78 $11.97 $7.23

2050 $15.00 $16.13 $12.14 $7.23

2051 $15.30 $16.48 $12.31 $7.23

2052 $15.60 $16.85 $12.48 $7.23

2053 $15.91 $17.22 $12.66 $7.23

2054 $16.23 $17.59 $12.83 $7.23

2055 $16.55 $17.98 $13.02 $7.23

2056 $16.88 $18.37 $13.20 $7.23

2057 $17.22 $18.78 $13.39 $7.23

2058 $17.56 $19.19 $13.57 $7.23

2059 $17.91 $19.61 $13.77 $7.23

2060 $18.27 $20.04 $13.96 $7.23

10B

- 10

### Table 10B.1

. 9 Automatic Commercial Ice Makers: Net Present Value in B illions ( 2013 $) at

a 7

- Percent Discount Rate

– High Electric and Water Price Scenario

Equipment Class * Cumulative NPV by TSL at 7%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.007 0.007 0.012 0.012 (0.048)

IMH

- W

- Med

- B 0.011 0.011 0.011 0.007 (0.007)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.019 0.019 0.040 0.040 (0.231)

IMH

- A

- Large

- B † 0.045 0.114 0.126 0.116 0.031

IMH

- A

- Large

- B1 0.045 0.114 0.124 0.115 0.029

IMH

- A

- Large

- B2 0.000 0.000 0.002 0.002 0.002

RCU

- Large

- B † 0.044 0.044 0.044 0.038 0.011

RCU

- Large

- B1 0.042 0.042 0.042 0.036 0.012

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.001)

SCU

- W

- Large

- B 0.002 0.002 0.003 0.001 0.001

SCU

- A

- Small

- B 0.017 0.039 0.081 0.073 (0.055)

SCU

- A

- Large

- B 0.015 0.062 0.067 0.007 0.007

IMH

- A

- Small

- C 0.006 0.009 0.015 0.015 (0.013)

IMH

- A

- Large

- C 0.006 0.006 0.010 0.010 (0.000)

RCU

- Small

- C 0.003 0.005 0.008 0.008 (0.002)

SCU

- A

- Small

- C 0.019 0.028 0.038 0.038 (0.059)

Total 0.191 0.345 0.453 0.365 (0.369)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 11

### Table 10B.1

. 10 Automatic Commercial Ice Makers : Net Present Value in Billions ( 2013 $)

at a 3

- Percent Discount Rate

– High Electric and Water Price Scenario

Equipment Class * Cumulative NPV by TSL at 3%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.015 0.015 0.026 0.026 (0.071)

IMH

- W

- Med

- B 0.024 0.024 0.024 0.018 (0.005)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.042 0.052 0.102 0.102 (0.343)

IMH

- A

- Large

- B † 0.096 0.247 0.273 0.290 0.146

IMH

- A

- Large

- B1 0.094 0.245 0.268 0.284 0.140

IMH

- A

- Large

- B2 0.002 0.002 0.006 0.006 0.006

RCU

- Large

- B † 0.092 0.092 0.092 0.092 0.048

RCU

- Large

- B1 0.088 0.088 0.088 0.087 0.049

RCU

- Large

- B2 0.005 0.005 0.005 0.005 (0.000)

SCU

- W

- Large

- B 0.004 0.005 0.006 0.003 0.003

SCU

- A

- Small

- B 0.037 0.084 0.180 0.171 (0.061)

SCU

- A

- Large

- B 0.031 0.134 0.146 0.040 0.040

IMH

- A

- Small

- C 0.012 0.020 0.032 0.032 (0.020)

IMH

- A

- Large

- C 0.012 0.012 0.020 0.020 0.003

RCU

- Small

- C 0.005 0.010 0.017 0.017 (0.001)

SCU

- A

- Small

- C 0.040 0.060 0.080 0.080 (0.097)

Total 0.411 0.754 0.999 0.892 (0.361)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 12

### Table 10B.1

. 11 Automatic Commercial Ice Makers : Net Present Value in Billions ( 2013 $)

at a 7

- Percent Discount Rate

– Low Electric and Water Price Scenario

Equipment Class * Cumulative NPV by TSL at 7%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.006 0.010 0.010 (0.051)

IMH

- W

- Med

- B 0.010 0.010 0.010 0.005 (0.009)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.017 0.015 0.034 0.034 (0.242)

IMH

- A

- Large

- B † 0.041 0.102 0.113 0.100 0.012

IMH

- A

- Large

- B1 0.042 0.102 0.112 0.099 0.011

IMH

- A

- Large

- B2 (0.000) (0.000) 0.001 0.001 0.001

RCU

- Large

- B † 0.041 0.041 0.041 0.033 0.005

RCU

- Large

- B1 0.039 0.039 0.039 0.031 0.006

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.001)

SCU

- W

- Large

- B 0.001 0.002 0.002 0.001 0.001

SCU

- A

- Small

- B 0.016 0.035 0.070 0.061 (0.067)

SCU

- A

- Large

- B 0.014 0.055 0.059 (0.001) (0.001)

IMH

- A

- Small

- C 0.005 0.009 0.014 0.014 (0.015)

IMH

- A

- Large

- C 0.005 0.005 0.009 0.009 (0.002)

RCU

- Small

- C 0.002 0.004 0.008 0.008 (0.003)

SCU

- A

- Small

- C 0.018 0.027 0.035 0.035 (0.064)

Total 0.177 0.311 0.406 0.309 (0.439)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 13

### Table 10B.1

. 12 Automatic Commercial Ice Makers : Net Present Value in Billions ( 2013 $)

at a 3

- Percent Discount Rate

– Low Electric and Water Price Scenario

Equipment Class * Cumulative NPV by TSL at 3%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.013 0.013 0.023 0.023 (0.077)

IMH

- W

- Med

- B 0.021 0.021 0.021 0.014 (0.010)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.004)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.037 0.042 0.085 0.085 (0.370)

I MH

- A

- Large

- B † 0.088 0.218 0.242 0.250 0.099

IMH

- A

- Large

- B1 0.087 0.217 0.238 0.246 0.095

IMH

- A

- Large

- B2 0.001 0.001 0.004 0.004 0.004

RCU

- Large

- B † 0.085 0.085 0.085 0.079 0.032

RCU

- Large

- B1 0.081 0.081 0.081 0.075 0.033

RCU

- Large

- B2 0.004 0.004 0.004 0.004 (0.001)

SCU

- W

- Large

- B 0.003 0.004 0.005 0.002 0.002

SCU

- A

- Small

- B 0.034 0.076 0.154 0.142 (0.092)

SCU

- A

- Large

- B 0.029 0.117 0.127 0.019 0.019

IMH

- A

- Small

- C 0.011 0.018 0.029 0.029 (0.024)

IMH

- A

- Large

- C 0.011 0.011 0.019 0.019 0.000

RCU

- Small

- C 0.005 0.009 0.016 0.016 (0.003)

SCU

- A

- Small

- C 0.037 0.055 0.074 0.074 (0.107)

Total 0.374 0.670 0.880 0.752 (0.535)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 14

### Table 10B.1

. 13 Automatic Commercial Ice Makers : Net Present Value in Billions ( 2013 $)

at a 7

- Percent Discount Rate

– Constant Electric and Water Price Scenario

Equipment Class * Cumulative NPV by TSL at 7%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.005 0.005 0.009 0.009 (0.053)

IMH

- W

- Med

- B 0.009 0.009 0.009 0.004 (0.010)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.001)

IMH

- A

- Small

- B 0.015 0.011 0.028 0.028 (0.249)

IMH

- A

- Large

- B † 0.039 0.089 0.100 0.084 (0.007)

IMH

- A

- Large

- B1 0.039 0.090 0.099 0.084 (0.007)

IMH

- A

- Large

- B2 (0.001) (0.001) 0.000 0.000 0.000

RCU

- Large

- B † 0.039 0.039 0.039 0.029 (0.001)

RCU

- Large

- B1 0.037 0.037 0.037 0.027 0.001

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.002)

SCU

- W

- Large

- B 0.001 0.002 0.002 0.000 0.000

SCU

- A

- Small

- B 0.015 0.033 0.058 0.048 (0.081)

SCU

- A

- Large

- B 0.013 0.047 0.050 (0.010) (0.010)

IMH

- A

- Small

- C 0.005 0.008 0.013 0.013 (0.016)

IMH

- A

- Large

- C 0.005 0.005 0.009 0.009 (0.003)

RCU

- Small

- C 0.002 0.004 0.007 0.007 (0.004)

SCU

- A

- Small

- C 0.017 0.025 0.033 0.033 (0.067)

Total 0.165 0.277 0.356 0.253 (0.504)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 15

### Table 10B.1

. 14 Automatic Commercial Ice Makers : Net Present Value in Billions (2013 $)

at a 3

- Percent Discount Rate

– Constant Electric and Water Price Scenario

Equipment Class * Cumulative NPV by TSL at 3%

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.011 0.011 0.019 0.019 (0.083)

IMH

- W

- Med

- B 0.020 0.020 0.020 0.012 (0.014)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.004)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.004)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.001)

IMH

- A

- Small

- B 0.033 0.034 0.072 0.072 (0.386)

IMH

- A

- Large

- B † 0.081 0.187 0.209 0.212 0.055

IMH

- A

- Large

- B1 0.081 0.187 0.207 0.209 0.052

IMH

- A

- Large

- B2 (0.000) (0.000) 0.003 0.003 0.003

RCU

- Large

- B † 0.080 0.080 0.080 0.069 0.019

RCU

- Large

- B1 0.076 0.076 0.076 0.065 0.021

RCU

- Large

- B2 0.004 0.004 0.004 0.004 (0.002)

SCU

- W

- Large

- B 0.003 0.004 0.004 0.001 0.001

SCU

- A

- Small

- B 0.031 0.070 0.125 0.110 (0.126)

SCU

- A

- Large

- B 0.027 0.098 0.106 (0.003) (0.003)

IMH

- A

- Small

- C 0.011 0.017 0.027 0.027 (0.027)

IMH

- A

- Large

- C 0.010 0.010 0.018 0.018 (0.002)

RCU

- Small

- C 0.005 0.008 0.015 0.015 (0.005)

SCU

- A

- Small

- C 0.035 0.052 0.068 0.068 (0.115)

Total 0.346 0.589 0.763 0.620 (0.690)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 201 3 $ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three clas ses are the totals of the B

- 1

and B

- 2 typical units.



### 10B.1.3 Nine

- Year Sensitivity Analysis

For this rulemaking, DOE undertook a sensitivity analysis using 9 years rather than 30 years of product shipments. The choice of a 9

- year period is a proxy for the timeline in Energy Policy and Conservation Act of 1975 (EPCA) for the review of certain energy conservation standards and potential revision of and compliance with such revised standards. a 

### This timeframe may not be statistically relevant with regard to the product lifetime, product manufacturing cycles

, or other factors specific to automatic commercial ice makers. Thus, this information is

a For automatic commercial ice makers, DOE is required to review standards at least every 5 years after the effective date of any amended standards. (42 U.S.C. 6313(d)(3)(B)) If new standards are promulgated, EPCA requires DOE to provide manufacturers a minimum of 3 years and a maximum of 5 years to comply with the standards. (42 U.S.C. 6313(d)( 3)(C)) In addition, for certain other types of commercial equipment that are not specified in 42 U.S.C. 6311(1)(B)

- (G), EPCA requires DOE to review its standards at least once every 6 years (42 U.S.C. 6295(m)(1) and 6316(a) ) , and either a 3

- year or a 5

- yea r period after any new standard is promulgated before compliance is required. (42 U.S.C. 6295(m)(4) and 6316(a)) As a result, DOE’s standards for automatic commercial ice makers can be expected to be in effect for 8 to 10 years between compliance dates, an d its standards governing certain other commercial equipment, the period is 9 to 11 years. A 9

- year analysis was selected as representative of the time

between standard revisions.

10B

- 16

### presented for informational purposes only and is no t indicative of any change in DOE’s analytical methodology. The N PV results based on a 9

- year analysis period are presented in

Table 10B.1.15 and Table 10B.1.16 at 7

- perc ent and 3

- percent discount rates, respectively

. The impacts are counted over the lifetime of equipment purchased in 2018

– 2026

.

Table 10B.1

. 15 Automatic Commercial Ice Makers : Net Present Value at a 7

- percent

Discount Rate for 9

- Year Analysis Period for Equipment Purchased in 2018

– 2026

Equipment Class * Cumulative NPV by TSL at 7 %

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.003 0.003 0.005 0.005 (0.030)

IMH

- W

- Med

- B 0.005 0.005 0.005 0.003 (0.004)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.001)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.001)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.009 0.009 0.018 0.018 (0.137)

IMH

- A

- Large

- B † 0.021 0.051 0.057 0.036 (0.005)

IMH

- A

- Large

- B1 0.021 0.052 0.057 0.036 (0.006)

IMH

- A

- Large

- B2 (0.000) (0.000) 0.001 0.001 0.001

RCU

- Large

- B † 0.021 0.021 0.021 0.018 0.004

RCU

- Large

- B1 0.020 0.020 0.020 0.017 0.005

RCU

- Large

- B2 0.001 0.001 0.001 0.001 (0.001)

SCU

- W

- Large

- B 0.001 0.001 0.001 0.000 0.000

SCU

- A

- Small

- B 0.008 0.018 0.036 0.032 (0.030)

SCU

- A

- Large

- B 0.007 0.028 0.030 0.001 0.001

IMH

- A

- Small

- C 0.003 0.004 0.007 0.007 (0.007)

IMH

- A

- Large

- C 0.003 0.003 0.005 0.005 (0.000)

RCU

- Small

- C 0.001 0.002 0.004 0.004 (0.001)

SCU

- A

- Small

- C 0.009 0.013 0.018 0.018 (0.030)

Total 0.090 0.158 0.207 0.147 (0.241)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000

. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 17

### Table 10B.1

. 16 Automatic Commercial Ice Makers : Net Present Value at a 3

- percent

Discount Rate for 9

- Year Analysis Period for Equipment Purchased in 2018

– 2026

Equipment Class * Cumulative NPV by TSL at 3 %

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.005 0.005 0.009 0.009 (0.038)

IMH

- W

- Med

- B 0.008 0.008 0.008 0.006 (0.002)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.001)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.001)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.014 0.017 0.035 0.035 (0.168)

IMH

- A

- Large

- B † 0.033 0.081 0.090 0.067 0.016

IMH

- A

- Large

- B1 0.033 0.081 0.089 0.065 0.014

IMH

- A

- Large

- B2 0.001 0.001 0.002 0.002 0.002

RCU

- Large

- B † 0.032 0.032 0.032 0.031 0.015

RCU

- Large

- B1 0.030 0.030 0.030 0.030 0.016

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.000)

SCU

- W

- Large

- B 0.001 0.002 0.002 0.001 0.001

SCU

- A

- Small

- B 0.013 0.029 0.057 0.054 (0.029)

SCU

- A

- Large

- B 0.011 0.043 0.047 0.010 0.010

IMH

- A

- Small

- C 0.004 0.007 0.011 0.011 (0.008)

IMH

- A

- Large

- C 0.004 0.004 0.007 0.007 0.001

RCU

- Small

- C 0.002 0.003 0.006 0.006 (0.001)

SCU

- A

- Small

- C 0.014 0.021 0.028 0.028 (0.037)

Total 0.142 0.253 0.332 0.264 (0.241)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than

0.0005 billion 2013 $ and thus rounded to 0.000

. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.



### 10B.1.4 Shipment Price Sensitivity

For the reference case in this rulemaking

, DOE did not include the impa ct on shipments due to price elasticity of demand. As reported in final rule TSD chapter 10, as well as in the notice of proposed rulemaking and preliminary analysis TSDs, during the preliminary analysis DOE asked manufacturers questions about price sensit ivity as part of the preliminary manufacturer impact analysis (MIA) . The average of the responses indicated that a 10

- percent increase in price would lead to a 6. 25

- percent decrease in shipments. DOE conducted extensive secondary research and was unable to find estimates of price elasticity

. DOE also lacks sufficient data with which to develop an estimate. Thus, the results from the preliminary MIA provide the best information about possible price impacts.

W hen the analysis takes into account both the basel ine distribution of equipment purchases by efficiency level and the incremental cost incurred on average to raise all customers’ efficiency level to TSL 3, if the analysis then weight s the resulting cost increase using 2018

10B

- 18

### shipments by equipment class, b 

### t he average TSL 3 increase is 2.7 percent. Thus, the estimated price impact is small. The NPV results for customer savings with a 7

- percent discount rate, by TSL and equipment class, are shown on Table 10B.1

. 17

. (Note that the maximum NPV efficiency levels [ i.e. , TSL 3 ] in the price s ensitivity case match those of the reference case for all equipment classes, as does the efficiency level mapping for all other TSLs.) NPV results with

a 3

- percent discount rate are shown on Table 10B.1

. 18

. Table 10B.1

. 19 shows cumulative energy savings by TSL.

Table 10B.1

. 17 Automatic Commercial Ice Makers : Net Present Value in Billions (2013$)

at a 7

- Percent Discount Rate

– Equipment Price Elasticity Scenario

Equipment Class* Cumulative NPV by TSL at 7%
billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.006 0.011 0.011 (0.048)

IMH

- W

- Med

- B 0.010 0.010 0.010 0.005 (0.008)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.002)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.017 0.016 0.035 0.035 (0.233)

IMH

- A

- Large

- B † 0.041 0.105 0.116 0.104 0.019

IMH

- A

- Large

- B1 0.042 0.105 0.115 0.103 0.018

IMH

- A

- Large

- B2 (0.000) (0.000) 0.001 0.001 0.001

RCU

- Large

- B † 0.041 0.041 0.041 0.034 0.006

RCU

- Large

- B1 0.039 0.039 0.039 0.032 0.008

RCU

- Large

- B2 0.002 0.002 0.002 0.002 (0.001)

SCU

- W

- Large

- B 0.002 0.002 0.002 0.001 0.001

SCU

- A

- Small

- B 0.016 0.035 0.074 0.066 (0.060)

SCU

- A

- Large

- B 0.014 0.057 0.061 0.003 0.003

IMH

- A

- Small

- C 0.005 0.009 0.014 0.014 (0.014)

IMH

- A

- Large

- C 0.005 0.005 0.009 0.009 (0.001)

RCU

- Small

- C 0.002 0.004 0.008 0.008 (0.003)

SCU

- A

- Small

- C 0.018 0.026 0.035 0.035 (0.061)

Total 0.177 0.317 0.415 0.324 (0.401)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 2013$ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 ty pical units.

b The distribution of shipments by equipment class is held constant througho ut the analysis period.

10B

- 19

### Table 10B.1

. 18 Automatic Commercial Ice Makers : Net Present Value in Billions (2013$)

at a 3

- Percent Discount Rate

– Equipment Price Elasticity Scenario

Equipment Class * Cumulative NPV by TSL at 3 %

billions 2013 $* *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.013 0.013 0.024 0.024 (0.073)

IMH

- W

- Med

- B 0.021 0.021 0.021 0.015 (0.008)

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 (0.003)

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 (0.000)

IMH

- A

- Small

- B 0.037 0.044 0.088 0.088 (0.353)

IMH

- A

- Large

- B † 0.088 0.225 0.249 0.258 0.114

IMH

- A

- Large

- B1 0.087 0.224 0.245 0.254 0.109

IMH

- A

- Large

- B2 0.001 0.001 0.004 0.004 0.004

RCU

- Large

- B † 0.085 0.085 0.085 0.081 0.036

RCU

- Large

- B1 0.081 0.081 0.081 0.076 0.037

RCU

- Large

- B2 0.004 0.004 0.004 0.004 (0.001)

SCU

- W

- Large

- B 0.003 0.004 0.005 0.002 0.002

SCU

- A

- Small

- B 0.034 0.076 0.162 0.152 (0.075)

SCU

- A

- Large

- B 0.029 0.122 0.133 0.029 0.029

IMH

- A

- Small

- C 0.011 0.018 0.029 0.029 (0.022)

IMH

- A

- Large

- C 0.011 0.011 0.019 0.019 0.001

RCU

- Small

- C 0.005 0.009 0.016 0.016 (0.002)

SCU

- A

- Small

- C 0.037 0.055 0.073 0.073 (0.100)

Total 0.374 0.684 0.904 0.785 (0.455)

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 2013$ and thus rounded to 0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the totals of the B

- 1

and B

- 2 typical units.

10B

- 20

### Table 10B.1

. 19 Automatic Commercial Ice Makers : Cumulative Energy Savings

– Equipment Price Elasticity Scenario

Equipment Cla ss * National Energy Savings by EL FFC energy quads**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.002 0.002 0.004 0.004 0.009

IMH

- W

- Med

- B 0.005 0.005 0.005 0.008 0.010

IMH

- W

- Large

- B † 0.000 0.000 0.000 0.000 0.002

IMH

- W

- Large

- B1 0.000 0.000 0.000 0.000 0.001

IMH

- W

- Large

- B2 0.000 0.000 0.000 0.000 0.001

IMH

- A

- Small

- B 0.011 0.023 0.037 0.037 0.072

IMH

- A

- Large

- B † 0.019 0.034 0.039 0.059 0.075

IMH

- A

- Large

- B1 0.017 0.031 0.035 0.055 0.072

IMH

- A

- Large

- B2 0.002 0.002 0.003 0.003 0.003

RCU

- Large

- B † 0.016 0.016 0.016 0.029 0.037

RCU

- Large

- B1 0.014 0.014 0.014 0.028 0.035

RCU

- Large

- B2 0.001 0.001 0.001 0.001 0.002

SCU

- W

- Large

- B 0.000 0.001 0.001 0.001 0.001

SCU

- A

- Small

- B 0.008 0.018 0.025 0.032 0.036

SCU

- A

- Large

- B 0.006 0.014 0.019 0.023 0.023

IMH

- A

- Small

- C 0.002 0.004 0.006 0.006 0.009

IMH

- A

- Large

- C 0.002 0.002 0.003 0.003 0.006

RCU

- Small

- C 0.001 0.002 0.003 0.003 0.005

SCU

- A

- Small

- C 0.006 0.010 0.015 0.015 0.023

Total 0.078 0.130 0.172 0.220 0.308

* IMH: ice

- making head; RCU: remote condenser unit; SCU: self

- contained unit; A: Air cooled;

W: Water cooled; B: Batch; C: Continuous

* * A value of 0.000 indicates energy savings rounded to less than 0.0005 quads and thus rounded to

0.000. † The NIA modeled the two typical sizes for IMH

- W

- Large

- B, IMH

- A

- Large

- B, and RCU

- Large

- B, rather than modeling these as one class. The values for these three classes are the tota ls of the B

- 1

and B

- 2 typical units.

10B

- 21

### APPENDIX 10C. TRIAL STANDARD LEVELS AND STANDARDS EQUATIONS

TABLE OF CONTENTS

10C.1 INTRODUCTION

................................ ................................ ................................ ....... 10C

- 1

10C.2 TRIAL STANDARD LEVEL SELECTION CRITERIA

................................ ........... 10C

- 1

10C.3 TRIAL STANDARD LEVEL EQUATIONS

................................ ............................. 10C

- 6

10C.3.1 General M ethodology

................................ ................................ ................ 10C

- 6

10C.3.2 Exceptions

................................ ................................ ................................ .. 10C

- 7

10C.3.3 Summary of Results

................................ ................................ ................... 10C

- 7

LIST OF TABLES

Table 10C.2.1 Mapping B etween TSLs and Efficiency Levels*

................................ ............. 10C

- 2 Table 10C.2.2 Percentage Efficiency Improvement from Baseline by TSL*

......................... 10C

- 3 Table 10C.2.3 Design Options for Analyzed Products Classes at Each TSL

.......................... 10C

- 4 Table 10C.2.4 Directly Analyzed Equipment Classes Used to Develop Standards for Secondary Classes

................................ ................................ ............................ 10C

- 5 Table 10C.3.1 Energy Consumption by TSL for Representative Units Analyzed in the Engineering Analysis for Each Primary Equipment Class

.............................. 10C

- 8 Table 10C.3.2 Equations Representing the TSLs for Batch Equipment Classes (Maximum Energy Use in kWh/100 lb ice)

................................ ................................ ........ 10C

- 9 Table 10C.3.3 Equations Representing the TSLs for Continuous Equipment Classes (Maximum Energy Use in kWh/100 lb ice)

................................ ................... 10C

- 10

Table 10C.3.4 Equations Representing the Standards for Condenser Water

........................ 10C

- 11

10C

- i

### APPENDIX 10C. TRIAL STANDARD LEVELS AND STANDARDS EQUATIONS

10C.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

automatic commercial ice makers . The se efficiency levels generally define pre

- set efficiency improvements o ver base efficiencies and are generally consistent across equipment classes. For all equipment classes, the highest efficiency level listed corresponds to the maximum savings

level. Th e actual value of this efficiency level differs widely. In addition , the number of efficiency levels specified for each equipment class varies widely

, depending on the efficiency of the base equipment and the number of available technologies that can be analyzed for improving efficiency.

For three equipment classes, DOE analyz ed 22

- inch and non

- 22

- inch variants. These 3 equipment classes have “ intermediate ” efficiency levels

— 3A and 4A

— at the percent efficiency improvements above the baseline where the 22

- inch variants reach their maximum technology efficiency levels. The 3A and 4A efficiency levels apply only to these three classes.

DOE develops 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 evaluates the nationa l impacts ( e.g. , national energy savings ( NES )

, net present value ( NPV )

, water savings, employment impacts, impacts on manufacturers, and environmental impacts ) at each TSL. Based on the results, DOE proposes to set the standard at a particular TSL.

This appendix describes DOE’s method for selecting TSL s for automatic commercial ice

makers

. The following sections describe the criteria used for TSL selection and standard level equations associated with each TSL

.

10C.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 a s an intermediate step between TSL 5 ( the max

- tech level ) and TSL 3 ( the maximum c ustomer NPV level ) , subject to the requirement that the TSL 4 NPV must be positive. Where a sufficient number of efficiency levels allow it, TSL 4

is set at least one level below TSL 5 and one level above TSL 3. In one case, the TSL

4 efficien cy level equals the TSL 3 level because the next level had a negative NPV. In cases where the maximum NPV efficiency level is the penultimate efficiency level

and the max

- tech level showed a positive NPV

, the TSL 4 efficiency level is also the

max

- tech lev el.

10C

- 1

### 3) TSL 3 was chosen to represent the efficiency level within each equipment class with the highest NPV at a 7

- percent discount rate

.

4) TSL 2 was selected to provide intermediate efficiency levels that fill the gap between

the TSLs 1 and 3. Note that with th e number of efficiency levels available for each equipment class, there is often overlap between TSL levels. Thus, TSL 2 includes levels that overlap with both TSLs 1 and 3. TSL 2 provide s an intermediate level that compromises between TSLs 1 and 3

.

5) TSL 1 was set equal to a 10

- percent efficiency improvement over the base

Table 10C.2

. 1 presents the efficiency levels within each equipment class that belong to the five TSL groupings. Table 10C.2

. 2 presents the percentage efficiency improvements for each primary equipment class relative to the baseline energy

- usage levels.

Table 10C.2

. 1 Mapping Between TSL s and Efficiency Levels *

Equipment Cl ass Baseline Intermediate

Level Max NPV Fill In Max

Tech

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Level 1 Level 2 Level 2 Level 3 Level 3 Level 5

IMH

- W

- Med

- B Level 1 Level 2 Level 2 Level 2 Level 3 Level 4

IMH

- W

- Large

- B †

IMH

- W

- Large

- B1 Level 1 Level 1 Level 1 Level 1 Level 1 Level 2

IMH

- W

- Large

- B2 Level 1 Level 1 Level 1 Level 1 Level 1 Level 2

IMH

- A

- Small

- B Level 1 Level 2 Level 3 Level 3A Level 3A Level 6

IMH

- A

- Large

- B †

IMH

- A

- Large

- B1 Level 1 Level 2 Level 3 Level 3A Level 4 Level 5

IMH

- A

- Large

- B2 Level 1 Level 2 Level 2 Level 3 Level 3 Level 3

RCU

- Large

- B †

RCU

- Large

- B1 Level 1 Level 2 Level 2 Level 2 Level 3 Level 4

RCU

- Large

- B2 Level 1 Level 2 Level 2 Level 2 Level 2 Level 3

SCU

- W

- Large

- B Level 1 Level 2 Level 4 Level 5 Level 6 Level 6

SCU

- A

- Small

- B Level 1 Level 2 Level 4 Level 5 Level 6 Level 7

SCU

- A

- Large

- B Level 1 Level 2 Level 4 Level 5 Level 6 Level 6

IMH

- A

- Small

- C Level 1 Level 2 Level 3 Level 4 Level 4 Level 6

IMH

- A

- Large

- C Level 1 Level 2 Level 2 Level 3 Level 3 Level 5

RCU

- Small

- C Level 1 Level 2 Level 3 Level 4 Level 4 Level 6

SCU

- A

- Small

- C Level 1 Level 2 Level 3 Level 4 Level 4 Level 6

“Level” stands for “ e fficiency l evel

. ” *For three large equipment classes

— IMH

- W

- Large

- B, IMH

- A

- Large

- B and RCU

- Large

- B

— because the harvest capacity range is so wide

, DOE analyzed two typical models to m odel the low and the high portions of the applicable

range with greater accuracy

. . The smaller of the two is noted as B1 and the larger as B2

. †DOE analyzed impacts for the B1 and B2 typical units and aggregated impacts to the equipment class level.

10C

- 2

### Table 10C.2

. 2 Percentage Efficiency Improvement from Baseline by TSL*
Equipment Class TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 10.0% 10.0% 15.0% 15.0% 23.9%

IMH

- W

- Med

- B 10.0% 10.0% 10.0% 15.0% 18.1%

IMH

- W

- Large

- B 0.0% 0.0% 0.0% 0.0% 8.1%

IMH

- W

- Large

- B1 0.0% 0.0% 0.0% 0.0% 8.3%

IMH

- W

- Large

- B2 0.0% 0.0% 0.0% 0.0% 7.4%

IMH

- A

- Small

- B 10.0% 15.0% 18.1% 18.1% 25.5%

IMH

- A

- Large

- B 10.0% 14.2% 15.2% 18.7% 21.6%

IMH

- A

- Large

- B1 10.0% 15.0% 15.8% 20.0% 23.4%

IMH

- A

- Large

- B2 10.0% 10.0% 11.8% 11.8% 11.8%

RCU

- Large

- B 10.0% 10.0% 10.0% 14.7% 17.1%

RCU

- Large

- B1 10.0% 10.0% 10.0% 15.0% 17.3%

RCU

- Large

- B2 10.0% 10.0% 10.0% 10.0% 13.9%

SCU

- W

- Large

- B 10.0% 20.0% 25.0% 29.8% 29.8%

SCU

- A

- Small

- B 10.0% 20.0% 25.0% 30.0% 32.7%

SCU

- A

- Large

- B 10.0% 20.0% 25.0% 29.1% 29.1%

IMH

- A

- Small

- C 10.0% 15.0% 20.0% 20.0% 25.7%

IMH

- A

- Large

- C 10.0% 10.0% 15.0% 15.0% 23.3%

RCU

- Small

- C 10.0% 15.0% 20.0% 20.0% 26.6%

SCU

- A

- Small

- C 10.0% 15.0% 20.0% 20.0% 26.6% *Percentage improvements for IMH

- W

- Large

- B, IMH

- A

- Large

- B and RCU

- Large

- B are a weighted average of the B1 and B2 units.



### Table 10C.2

. 3 shows the designs options that were used during the engineering analysis to achieve the percentage efficiency improvement of each TSL within each equipme nt class.

10C

- 3

### Table 10C.2

. 3 Design Options for Analyzed Products Classes at Each TSL
Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 Design Options for Each TSL (options are cumulative

– TSL 5 includes all preceding options)

IMH

- W

- Small

- B No BW Fill

SPM PM + Comp EER

+ Cond Same EL as

TSL 1 + Cond Same EL as

TSL 3 BW Fill + Evap

ECM PM

DWHX

IMH

- W

- Small

- B (22 inch wid e ) No BW Fill

SPM PM + Comp EER

+ Cond Same EL as

TSL 1 + Cond

BW Fill Same EL as

TSL 3 N/A for 22

- inch

IMH

- W

- Med

- B BW Fill

SPM PM + Comp EER

ECM PM Same EL as

TSL 1 Same EL as

TSL 1 + Cond DWHX

IMH

- W

- Large

- B1 BW Fill

SPM PM Same EL as

Baseline Same EL as

Baseline Same EL as

Baseline Same EL as

Baseline + Comp EER

+ Cond

ECM PM

DWHX

IMH

- W

- Large

- B2 BW Fill

SPM PM Same EL as

Baseline Same EL as

Baseline Same EL as

Baseline Same EL as

Baseline + Comp EER

+ Cond

ECM PM

DWHX

IMH

- A

- Small

- B BW Fill

SPM PM

SPM FM + Comp EER

+ Cond + Evap

ECM FM + Evap + Evap Same EL as

TSL 3 + Evap

ECM PM

DWHX

IMH

- A

- Small

- B (22 inch wid e ) BW Fill

SPM PM

SPM FM + Comp EER

+ Cond + Evap

ECM FM + Evap ECM PM

DWHX Same EL as

TSL 3 N/A for 22

- inch

IMH

- A

- Large

- B1 No BW Fill

SPM PM

SPM FM + Comp EER

PSC FM ECM FM

BW Fill BW Fill BW Fill

ECM PM

+ Cond DWHX

IMH

- A

- Large

- B1 (22 inch wide) No BW Fill

SPM PM

SPM FM + Comp EER

ECM FM

BW Fill BW Fill

ECM PM

DWHX DWHX N/A for 22

- inch N/A for 22

- inch

IMH

- A

- Large

- B2 BW Fill

SPM PM

SPM FM + Comp EER

ECM FM

ECM PM

+ Cond

DWHX Same EL as

TSL 1 DWHX Same EL as

TSL 3 Same EL as

TSL 3

RCU

- Large

- B1 BW Fill

SPM PM

PSC FM + Cond

+ Comp EER Same EL as

TSL 1 Same EL as

TSL 1 ECM FM

ECM PM

+ Cond

DWHX DWHX

RCU

- Large

- B2 BW Fill

SPM PM

PSC FM + Comp EER

ECM FM

+ Cond

ECM PM Same EL as

TSL 1 Same EL as

TSL 1 Same EL as

TSL 1 DWHX

EL = Efficiency Level

SPM = Shaded Pole Motor PSC = Permanent Split Capacitor Motor ECM = Electronically Commutated Motor FM = Fan Motor (Air

- Cooled Units) AM = Auger Motor (Continuous Units) BW Fill = Batch Water Fill Option Included

+ Cond = Increase in Condenser Size + Evap = Increase in Evaporator Size + Comp EER = Increase in Compressor EER

DWHX = Addition of Drain W ater Heat Exchanger

10C

- 4

### Table 10C.2

. 3 (cont)
Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 Design Options for Each TSL (options are cumulative

– TSL 5 includes all preceding options)

SCU

- W

- Large

- B No BW Fill

SPM PM BW Fill + Evap +Evap

+ Cond + Cond + Cond DWHX

SCU

- A

- Small

- B No BW Fill

SPM PM

SPM FM + Cond + Comp EER + Comp EER PSC FM

BW Fill BW Fill

ECM PM

ECM FM ECM FM

DWHX

SCU

- A

- Large

- B No BW Fill

SPM PM

SPM FM +Cond + Comp EER + Comp EER

BW Fill BW Fill

ECM FM ECM PM

DWHX Same EL as

TSL 4

RCU

- Small

- C PSC AM

SPM FM + Comp EER

PSC FM ECM FM ECM FM

+ Cond Same EL as

TSL3 + Cond

ECM AM

IMH

- A

- Small

- C PSC AM

SPM FM + Comp EER

+ Cond + Cond

ECM FM ECM FM

+ Cond Same EL as

TSL 3 ECM AM

IMH

- A

- Large

- C PSC AM

SPM FM + Comp EER Same EL as

TSL 1 + Comp EER

+ Cond Same EL as

TSL 3 + Cond

ECM FM

ECM AM

SCU

- A

- Small

- C PSC AM

SPM FM + Cond + Comp EER + Comp EER + Comp EER

ECM FM Same EL as

TSL 3 ECM FM

ECM AM EL = Efficiency Level

SPM = Shaded Pole Motor PSC = Permanent Split Capacitor Motor ECM = Electronically Commutated Motor FM = Fan Motor (Air

- Cooled Units) AM = Auger Motor (Continuous Units) BW Fill = Batch Water Fill Option Included

+ Cond = Increase in Condenser Size + Evap = Increase in Evaporator Size + Comp EER = Increase in Compressor EER

DWHX = Addition of Drain W ater Heat Exchanger



### In the final rule analyses, DOE directly studied 1 3 equipment classes. T he final rule proposes energy efficiency standards for the remaining equipment classes by extending results from primary equipment classes to secondary classes that are similar from an engineering perspective. Table 10C.2

. 3 maps the secondary equipment classes to those similar, directly analyzed equipment classes.

Table 10C.2

. 4 Directly Analyzed Equipment Classes Used to Develop Standards for Secondary Classes

Secondary
Equipment Class Directly Analyzed Product Class
Associated with Efficiency Level

for Secondary Product Class

RCU

- NRC

- Small

- B RCU

- NRC

- Large

- B

RCU

- RC

- Small

- B RCU

- NRC

- Large

- B

RCU

- RC

- Large

- B RCU

- NRC

- Large

- B

SCU

- W

- Small

- B SCU

- W

- Large

- B

IMH

- W

- Small

- C IMH

- A

- Large

- C

IMH

- W

- Large

- C IMH

- A

- Large

- C

RCU

- NRC

- Large

- C RCU

- NRC

- Small

- C

RCU

- RC

- Small

- C RCU

- NRC

- Small

- C

RCU

- RC

- Large

- C RCU

- NRC

- Small

- C

SCU

- W

- Small

- C SCU

- A

- Small

- C

SCU

- W

- Large

- C SCU

- A

- Small

- C

SCU

- A

- Large

- C SCU

- A

- Small

- C

10C

- 5

### DOE briefly considered two additional criteria for TSL groupings. One criterion was to group the efficiency levels that exclude max

- tech design options

. However, because this criterion

was not significantly different from the max NPV level , it was discarded. Another c riterion

considered set ting standards t o a 20

- percent across

- the

- board efficiency improvement. This

criterion was discarded because not all equipment classes exhibit desirable customer results at 20
percent.

10C.3 TRIAL STANDARD LEVEL EQUATIONS

Current energy conservation standards for automatic commercial ice makers specify maximum energy consumption levels per 100 lb of ice per 24

- hour period. ( 42 U.S.C. 6313(d)(1)) Similarly, for automatic commercial ice makers using water for cooling in the

condens er, water limits are established in terms of gallons of water per 100 lb of ice per 24

- hour period. In this notice, DOE has retained these metrics for specifying energy and water

conservation standards.

10C.3.1 General Methodology

Developing trial standard level e quations required several steps. In developing TSLs, DOE analyzed each equipment class separately and attributed a percentage reduction with each portion of the equipment standard curve (small/medium/large). To ensure that the standard curve

was continuous ( i.e.

, no gaps at the breakpoints between size categories ), DOE developed a method for expressing the consumption standards

. The method relied on two techniques :

1) adjusting the high

- capacity equipment classes (whose equations were constant with
respect to capacity) by a factor equal to the expected percentage reduction

2) pivoting the low

- capacity equipment classes (whose equations were a function of capacity) about a representative point while staying connected to the high

- capacity equation

.

These steps are described in further detail below. DOE was able to use the same methodology for most equipment classes, with exceptions for the IMH

- W

- B atch , IMH

- A

- B atch

, and RCU

- RC equipment classes.

In drawing a relationship between the harvest capacity (lb ice/24 hours) and the maximum allowed energy usage (kilowatt

- hours per 100 lb of ice), DOE first took the large

capacity equipment class (which is set at a constant value for all equipment types except IMH

- A

Large

- B ) and applied the allocated percentag e reduction ( i.e., the percentage reduction associated with the TSL for that equipment class). For example, for IMH

- W

- Large

- B, the baseline level is

set at 4.0. If the TSL allocated a 10

- percent reduction for IMH

- W

- Large

- B, then the next level

was set at 4 .0 × (1

- 10 percent) = 3.6 kWh/100 lb of ice.

Next , for the small equipment classes, DOE applied the allocated percentage reduction at a designated median capacity in that harvest rate range. The medium capacity was selected based on shipment levels and where the median fell within the shipments data. For example, if the median capacity for the small equipment class was at 300 lb ice/24 hours, DOE would calculate the baseline energy usage and then apply the allocated percentage reduction to obtain a point at

10C

- 6

### 300 lb ice/24 hours. DOE would then draw a line between the start of the large equipment class and this median capacity point to obtain the equation for the small equipment class, ensuring that there were no gaps between small and large

- capacity equipment class standards. This method generally produced trial standard level equations that were consistent with the expected percentage reduction for the small equipment classes.

10C.3.2 Exceptions

For the IMH

- W

- B atch equipment classes, this equipment type has small, medium, and large equipment classes. In this case, for the small equipment class, DOE applied the allocated percentage reduction to the whole equation. So if the percentage reduction was 10 percent, the new equation for the small equipment class would be (1

- 10 percent) × (7.80

– 0.0055H) = 7.02

– 0.00495H

. The method for treating the large equipment class would remain the same. DOE would then draw a line between the end of the small equipment class and the start of the large equipment class to obtain the equation for the medium equipment class.

For the IM H

- A

- B atch equipment classes, DOE sought to obtain a constant efficiency level for the largest equipment classes. In order to achieve this, f or the small equipment class, DOE applied the allocated percentage reduction to the whole equation. DOE then analy zed two different sizes of equipment in the IMH

- A

- Large

- B equipment clas s. For the largest size of equipment, DOE chose a pivot point at 1500 lb/day and applied the allocated percentage reduction for this size; the trial standard level for the largest siz es was set at a constant efficiency level equal to this value. For the smaller size of the IMH

- A

- Large

- B equipment, DOE simply

drew a line between the IMH

- A

- S mall

- B trial standard level and the trial standard level for the largest IMH

- A

- Large

- B equipment. Further details are available in s ection 5.10.3 of c hapter 5 of

this technical support document

.

For the RCU

- RC

- B and RCU

- RC

- C equipment classes, DOE simply took the standard levels calculated for the large RCU

- NRC

- B and RCU

- NRC

- C equipment classes, resp ectively,

and subtracted the 0.2 kWh/100 lb of ice differential discussed in chapter 5 (for suction in the lines connecting the ice

- making portion of the machine to the remote condenser) to arrive at the standard levels. For small RCU classes, remote compr essor standards were developed such that no gap exists at the harvest rate breakpoints.

10C.3.3 Summary of Results

Table 10C.3

. 1 provides an example of the resulting energy c onsumption for representative units analyzed as part of the final rule analyses. Table 10C.3

. 2 and Table 10C.3

. 3 show standard equations for batch and continuous classes, respectively, at each TSL. In all of the standards equations, harvest rate or H is harvest rate in pounds per 24 hours.

10C

- 7

### Table 10C.3

. 1 Energy Consumption by TSL for Representative Units A nalyzed in the Engineering Analysis for Each Primary Equipment C lass

Equipment Class Representative

Harvest Rate

lb ice/24 hours Energy Consumption of the Representative Automatic Commercial

Ice Maker Unit

kWh/100 lb

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 300 5.5 4 5. 54 5.23 5.23 4. 67

IMH

- W

- Med

- B 850 4. 18 4. 18 4.18 3.95 3. 76

IMH

- W

- Large

- B1 1500 4.00 4.00 4.00 4.00 3. 68

IMH

- W

- Large

- B2 2600 4.00 4.00 4.00 4.00 3. 72

IMH

- A

- Small

- B 300 6.9 1 6.5 3 6.30 6.30 5. 68

IMH

- A

- Large

- B1 800 5.4 1 5.1 1 5.05 4. 81 4. 63

IMH

- A

- Large

- B2 1500 4.72 4.7 2 4. 61 4. 61 4. 61

RCU

- Large

- B1 1500 4. 59 4. 59 4. 59 4. 34 4. 23

RCU

- Large

- B2 2400 4. 59 4. 59 4. 59 4. 59 4. 39

SCU

- W

- Large

- B 300 6.84 6. 08 5.7 0 5.3 2 5. 17

SCU

- A

- Small

- B 110 11. 56 10. 27 9. 63 8.99 8.60

SCU

- A

- Large

- B 200 8.82 7.8 4 7.35 6. 96 6. 96

IMH

- A

- Small

- C 310 8.1 5 7. 69 7. 24 7.24 6. 43

IMH

- A

- Large

- C 820 5.9 4 5. 94 5. 61 5.61 5.08

RCU

- Small

- C 800 5.70 5.38 5.06 5.06 4.62

SCU

- A

- Small

- C 220 9.11 8.60 8.10 8.10 7.29

10C

- 8

### Table 10C.3

. 2 Equations Representing the TSL s for Batch Equipment Classes (Maximum Energy Use in kWh/100 lb ice)

Batch Equipment

Class Capacity Range lb/day TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B <300 7.19

– 0.0055H 7.19

– 0.0055H 6.88

– 0.0055H 6.88

– 0.0055H 6.32

– 0.0055H

IMH

- W

- Med

- B ≥300 and <850 6.28

– 0.00247H 6.28

– 0.00247H 5.8

– 0.00191H 5.9

– 0.00224H 5.17

– 0.00165H

IMH

- W

- Large

- B ≥850 and <1500 4.42

– 0.00028H 4.42

– 0.00028H

4.0 4.0 3.86

– 0.00012H

IMH

- W

- Extended

- B ≥1

, 500 and <2

, 600 4.0 4.0 3.62 + 0.00004H
≥2

, 600 3.72

IMH

- A

- Small

- B <300 10.09

– 0.0106H 10.05

– 0.01173H 10

– 0.01233H 10

– 0.01233H 9.38

– 0.01233H

IMH

- A

- Medium

- B ≥300 and <800 7.81

– 0.003H 7.38

– 0.00284H 7.05

– 0.0025H 7.19

– 0.00298H 6.31

– 0.0021H

IMH

- A

- Large

- B ≥800 and <1

, 500 6.21

– 0.00099H 5.56

– 0.00056H 5.55

– 0.00063H 5.04

– 0.00029H 4.65

– 0.00003H

IMH

- A

- Extended

- B >1

, 500 4.73 4.72 4.61 4.61 4.61

RCU

- NRC

- Small

- B <1

, 000 7.97

– 0.00342H 7.97

– 0.00342H 7.97

– 0.00342H 7.52

– 0.00323H 7.35

– 0.00316H

RCU

- NRC

- Large

- B ≥1

, 000 and <1

, 500

4.55 4.55 4.55 4.29 4.19

RCU

- NRC

- Extended

- B ≥1

, 500 and < 2

, 400 3.93 + 0.00017H
≥2

, 400 4.34

RCU

- RC

- Small

- B <94 2 7.97

– 0.00342H 7.97

– 0.00342H 7.97

– 0.00342H 7.52

– 0.00323H 7.35

– 0.00316H

RCU

- RC

- Large

- B ≥942 and < 1,500

4.75 4.75 4.75 4.49 4.39

RCU

- RC

- Extended

- B ≥1

, 500 and < 2

, 400 4.13 + 0.00017H
≥2

, 400 4.54

SCU

- W

- Small

- B <200 10.64

– 0.019H 9.88

– 0.019H 9.5

– 0.019H 9.14

– 0.019H 9.14

– 0.019H

SCU

- W

- Large

- B ≥200 6.84 6.08 5.7 5.34 5.34

SCU

- A

- Small

- B <110 16.72

– 0.0469H 15.43

– 0.0469H 14.79

– 0.0469H 14.15

– 0.0469H 13.76

– 0.0469H

SCU

- A

- Large

- B ≥110 and <200 14.91

– 0.03044H 13.24

– 0.027H 12.42

– 0.02533H 11.47

– 0.02256H 10.6

– 0.02

SCU

- A

- Extended

- B ≥200 8.82 7.84 7.35 6.96 6.96

10C

- 9

### Table 10C.3

. 3 Equations Representing the TSL s for Continuous Equipment Classes (Maximum Energy Use in kWh/100 lb ice)
Continuous Equipment

Class Capacity Range lb/day TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- C <801 7.29

– 0.003H 6.89

– 0.00283H 6.48

– 0.00267H 6.48

– 0.00267H 5.75

– 0.00237H

IMH

- W

- Large

- C ≥801 4.59 4.59 4.34 4.34 3.93

IMH

- A

- Small

- C <310 10.1

– 0.00629H 9.64

– 0.00629H 9.19

– 0.00629H 9.19

- 0.00629H 8.38

– 0.00629H

IMH

- A

- Large

- C ≥310 and <820 9.49

– 0.00433H 8.75

– 0.00343H 8.23

– 0.0032H 8.23

– 0.0032H 7.25

– 0.00265H

IMH

- A

- Extended

- C ≥820 5.94 5.94 5.61 5.61 5.08

RCU

- NRC

- Small

- C <800 9.85

– 0.00519H 9.78

– 0.0055H 9.7

– 0.0058H 9.7

– 0.0058H 9.26

– 0.0058H

RCU

- NRC

- Large

- C ≥800 5.7 5.38 5.06 5.06 4.62

RCU

- RC

- Small

- C <800 10.05

– 0.00519H 9.98

– 0.0055H 9.9

– 0.0058H 9.9

– 0.0058H 9.46

– 0.0058H

RCU

- RC

- Large

- C ≥800 5.9 5.58 5.26 5.26 4.82

SCU

- W

- Small

- C <900 8.55

– 0.0034H 8.08 0.0032H 7.6

– 0.00302H 7.6

– 0.00302H 6.84

– 0.00272H

SCU

- W

- Large

- C ≥900 5.49 5.19 4.88 4.88 4.39

SCU

- A

- Small

- C < 200 15.26

– 0.03 14.73

– 0.03H 14.22

- 0.03H 14.22

– 0.03H 13.4

– 0.03H

SCU

- A

- Large

- C ≥200 and 700 10.66

– 0.00702H 10.06

– 0.00663H 9.47

– 0.00624H 9.47

– 0.00624H 8.52

– 0.00562H

SCU

- A

- Extended

- C ≥700 5.75 5.42 5.1 5.1 4.59

10C

- 10

### In the notice of proposed rulemaking ( NOPR ) analyses, DOE concluded that condenser

water

- changing design options were not cost

- effective, and DOE did not examine these design options . Thus, DOE did not propos e to revise the condenser water usage limits for equipm ent currently subject to efficiency standards. This conclusion has been maint a ined for the final rule. Existing condenser water standards will apply to batch type equipment not previously covered by standards. For equipment with harvest rates greater than or equal to 2,500 lb/24 hours and less than 4,000 lb/24 hours, DOE set a constant water limit for each equipment class, calculated using the existing equation for a machine with a harvest rate of 2,500 lb/24 hour. Based on equipment testing performed by DO E for this rulemaking

, DOE set condenser water limits for continuous type machines at 80 percent of the limits set for batch type machines.

Table 10C.3

. 4 lists the condenser water limits by equipment class. The proposed

condenser water limits are the same for all five TSLs. The limits shown on Table 10C.3

. 4 are for condenser water only, not potable water.

Table 10C.3

. 4 Equations R epresenting the S tandards for Condenser Water

Equipment Type Type of Cooling Harvest Rate

lb ice / 24 hours Maximum

Condenser

Water Use gal/100 lb ice Batch Type Equipment

Ice

- Making Head Water < 300 200

– 0.022H

≥ 300 and < 850 200

– 0.022H

≥ 850 and < 1 ,500 200

– 0.022H

≥ 1 ,500 and < 2,5 00 200

– 0.022H
≥2, 5 00 and <4, 0 00 145

Self

- Contained Water <200 191

– 0.0315H ≥200 and <2,500 191

– 0.0315H ≥2,500 and <4,000 112 Continuous Type Equipment

Ice

- Making Head Water < 801 180

– 0.0198 H

≥ 801 and <2,500 180

– 0.0198 H ≥2,500 and < 4,000 13 0.5

Self

- Contained Water <900 153

– 0.0252H ≥900 and <2,500 153

– 0.0252H ≥2,500 and <4,000 90

10C

- 11

### APPENDIX 10D. FULL

- FUEL

- CYCLE MULTIPLIERS

T ABLE OF CONTENTS

10D.1 INTRODUCTION

................................ ................................ ................................ ...... 10D

- 1

10D.2 METHODOLOGY

................................ ................................ ................................ ..... 10D

- 1

10D.3 FU LL

- FUEL

- CYCLE ENERGY MULTIPLIERS

................................ .................... 10D

- 3

REFERENCES

................................ ................................ ................................ ....................... 10D

- 4

LIST OF TABLES

Table 10D.2.1 Dependence of FFC Parameters on AEO Inputs

................................ ............. 10D

- 3

Table 10D.3.1 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO2014 )

............................ 10D

- 3

10D

- i

### APPENDIX 1 0 D

. FULL

- FUEL

- CYCLE MULTIPLIERS

10D.1 INTRODUCTION

This appendix summarizes the methods used to calculate full

- fuel

- cycle (FFC) energy savings expected to result from potential standards. The FFC measure includes point

- of

- use (site)

e nergy; the energy losses associated with generation, transmission, and d istribution of electricity; and the energy consumed in extracting, processing, and transporting or distributing primary

fuels. The U.S. Department of Energy’s ( DOE’s ) traditional approach encompassed only site energy and the energy losses associated with g eneration, 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. 76 FR 5128 1 (Aug. 18, 2 011) , as amended at 77 FR 49701 (Aug. 17, 2012)

. This appendix summarizes the methods used to incorporate the FFC impacts into the analysis.

This analysis uses several different terms to reference energy use. The physical sources of energy are the primary fuels such as coal, natural gas, liquid fuels

, etc

. Primary energy is equal to the heat content ( British thermal units ) 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 bui lding 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 ene rgy is measured in kilowatt

- hours . In this case

, the primary energy is equal to the quadrillion British thermal units ( quads ) of primary energy required to generate and deliver the site electricity. This primary energy is calculated by multiplying the site kilowatt

- hours by the site

- to

- power plant energy use factor

, given in chapter 1 0 . For the FFC analysis, the upstream 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 u pstream fuel 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

.

10D.2 METHODOLOGY

The mathematical approach is discussed in the paper A Mathematical Analysis o f Full Fuel Cycle Energy Use

, 1 

### and details on the fuel production chain analysis are presented in the

paper Projections of Full Fuel Cycle Energy and Emissions Metrics

. 2 

### The following discussion provides a brief summary of the methods used to calculate FFC energy.

When all 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 r epresenting the energy intensity and material losses at each production stage. These parameters depend only on physical data, so

10D

- 1

### the calculations do not require any assumptions about prices or other economic data. While in general these parameter values ma y 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 f luxes. The fuel cycle parameters are the following :

• ax is the quantity of fuel x burned per unit of electricity output, on average, for grid electricity. The calculation of ax includes a factor to account for transmission and distribution system losses.

• by is the amount of grid electricity used in production of fuel y, in megawatt

- hours per physical unit of fuel y.

• cxy is the amount of fuel x consumed in producing one unit of fuel y.

• qx is the heat content of fuel x ( million British thermal units /physi cal unit)

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x)

.

The parameters are calculated as a function of time with an annual time step; hence, a time series of annual values is used to estimate the FFC energy and emissions savings in each year of the analysis period. Fossil fuel quantities are converted to energy units using the heat content factors q x . To convert electricity in kilowatt

- hours to primary energy units, on

- site electricity consumption is multiplied by the site

- to

- power plant energy use factor. The site

- to

power plant energy use factor is defined as the ratio of the total primary energy consumption by the electric power sector (in quads ) divided by the total electricity generation in each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel used on site. A multiplier is also calculated for electricity reflecting the fuel mix used in its

gene ration. The multipliers are dimensionless numbers that are applied to primary energy savings to obtain the FFC energy savings. The upstream component of the energy savings is proportional to (μ

- 1). The fuel type is denoted by a subscript on the multiplier μ.

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 10D.2

. 1 provides a summary of the AEO data used as inputs to the different parameter calculations. The AEO does not provide all the information needed to estimate total energy use in the fuel product ion chain. Reference 2 describes the additional data sources used to complete the analysis. However, the time dependence in the FFC multipliers

ar ises exclusively from variables taken from the AEO . The FFC analysis for automatic

commercial ice

- makers used data from Annual Energy Outlook 2014 ( AEO201 4 )

. 3 

### AEO201 4 provides projections to 2040.

10D

- 2

### Table 10D.2

. 1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables

qx all Conversion Factors MMBtu per physical unit

ax all Electricity Supply, Disposition, Prices, and

Emissions Generation by fuel type

Energy Consumption by Sector and Source Electric power sector energy consumption

bc, cnc, cpc coal Coal Production by Region and Type Production by coal type

and sulfur content

bp, cnp, cpp 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

cnn natural

gas Oil and Gas Supply U

. S

. dry gas production

Natural Gas Supply, Disposition and Prices Pipeline, lease and plant

fuel

zx all Electricity Supply, Disposition, Prices and Emissions Power sector emissions



### 10D.3 FULL

- FUEL

- CYCLE ENERGY MULTIPL IERS

FFC energy multipliers are presented in Table 10D.3

. 1 for selected years. To extend the analysis period beyond 20 40 , the last year in the AEO201 4 projection , the multi pliers are

assumed constant t hrough 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 10D.3

. 1 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO201 4 )

2015 2020 2025 2030 2035 2040 Electricity (power plant primary energy use) 1.04 3 1.04 4 1.04 5 1.046 1.04 7 1.04 7
Natural Gas (site) 1.10 8 1.10 9 1.1 11 1. 113 1. 114 1. 1 14
Petroleum Fuels (site) 1.1 76 1.1 76 1.1 76 1.1 74 1.1 72 1.1 70

10D

- 3

### REFERENCES

1

. Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use . Energy , 2012. 37(1): pp. 698

- 708. (Last accessed July 8, 2013.)

< www.sciencedirect.com/science/article/pii/S0360544211006803 >.

2

. Coughlin, K. 2013. Projections of Full

- Fuel

- Cycle Energy and Emissions Metrics

. La wrence Berkeley National Laboratory, Berkeley, CA. Report N o. LBNL

- 6025E

.

3

. U.S. Department of Energy

- Energy Information Administration. Annual Energy Outlook

201 4

. April 201 4 . Washington, D.C. < www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf >

.

10D

- 4

### APPENDIX 10E. RISC & OIRA CONSOLIDATED INFORMATION SYSTEM (ROCIS) TABLES

TABLE OF CONTENTS

10E.1 INTRODUCTION

................................ ................................ ................................ ....... 10E

- 1

LIST OF TABLES

Table 10E.1.1 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 1, 3 Percent Discount Rate)

................................ ................................ ................................ ................. 10E

- 2

Table 10E.1.2 Cumulative and Annuali zed Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 2, 3 Percent Discount Rate)

................................ ................................ ................................ ................. 10E

- 4

Table 10E.1.3 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 3, 3 Percent Discount Rate)

................................ ................................ ................................ ................. 10E

- 6

Table 10E.1.4 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 4, 3 Percent Discount Rate)

................................ ................................ ................................ ................. 10E

- 8

Table 10E.1.5 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 5, 3 Percent Discount Rate)

................................ ................................ ................................ ............... 10E

- 10

Table 10E.1.6 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Ship ped in the Period 2018

– 2047 (TSL 1, 7 Percent Discount Rate)

................................ ................................ ................................ ............... 10E

- 12

Table 10E.1.7 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 2, 7 Percent Discount Rate)

................................ ................................ ................................ ............... 10E

- 14

Table 10E.1.8 Cu mulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 3, 7 Percent Discount Rate)

................................ ................................ ................................ ............... 10E

- 16

Table 10E.1.9 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 4, 7 Percent Discount Rate)

................................ ................................ ................................ ............... 10E

- 18

Table 10E.1.10 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the Period 2018

– 2047 (TSL 5, 7 Percent Discount Rate)

................................ ................................ ............................... 10E

- 20

10E

- i

### APPENDIX 10E. RISC & OIRA CONSOLIDATED INFORMATION SYSTEM (ROCIS)

TABLES

10E.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 final rule for automatic commercia l ice

- making (ACIM ) equipment

. In Table 10E.1

. 1 through Table 10E.1

. 10

, the top half of the table present s 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 

### redu ctions 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

, while the

value of CO 2 

### reductions is based on a global v alue. 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

- y ear 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 the considered standard levels, for products sold in 201 8 through 204 7 , also can be expressed in terms of annualized values. The annualized monetary

values shown in Table 10E.1

. 1 through Table 10E.1

. 10 present the sum of ( 1) the annualized national economic value, expressed in 201 3 dollars ( 201 3 $ ) , of the benefits from customer

ope ration 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

.

10E

- 1

### Table 10E.1

. 1 Cumulative and Annualized Benefits and Costs of Con sidered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 ( TSL 1, 3 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.002 0.005 0.000 0.011 0.016 0.020 0.000 0.008 0.006 0.002 0.002 0.001 0.007 0.081 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.006 0.008 0.000 0.028 0.014 0.030 0.001 0.013 0.009 0.003 0.001 0.001 0.004 0.116 Operating Cost Savings billion 2013 $ 0.020 0.030 0.000 0.067 0.102 0.121 0.004 0.048 0.039 0.014 0.012 0.006 0.042 0.505

NPV billion 2013 $ 0.014 0.022 0.000 0.039 0.088 0.091 0.003 0.035 0.030 0.012 0.011 0.005 0.038 0.389 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.146 0.301 0.000 0.697 0.987 1.207 0.379 0.143 0.119 0.055 0.405 1.050 1.074 6.562

NOx kilo

- ton 0.216 0.445 0.000 1.030 1.460 1.786 0.561 0.211 0.176 0.081 0.599 1.554 1.589 9.709 Hg Ton 0.000 0.001 0.000 0.002 0.003 0.003 0.001 0.000 0.000 0.000 0.001 0.003 0.003 0.017

SO 2 kilo

- ton 0.124 0.255 0.000 0.590 0.836 1.022 0.321 0.121 0.101 0.046 0.343 0.889 0.910 5.558 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.076 2.212 0.000 5.121 7.255 8.876 2.789 1.049 0.875 0.403 2.975 7.722 7.899 48.251 3% dr, average million 2013 $ 4.820 9.913 0.000 22.951 32.510 39.774 12.498 4.701 3.920 1.805 13.331 34.606 35.399 216.229 2.5% dr, average million 2013 $ 7.618 15.667 0.000 36.273 51.381 62.862 19.753 7.430 6.196 2.854 21.069 54.695 55.948 341.745 3% dr, 95th perc million 2013 $ 14.878 30.599 0.000 70.843 100.350 122.772 38.578 14.510 12.100 5.573 41.148 106.820 109.268 667.440 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.056 0.116 0.000 0.269 0.381 0.466 0.146 0.055 0.046 0.021 0.156 0.405 0.414 2.531 At 2,684 201 3 $/ton million 2013 $ 0.318 0.655 0.000 1.515 2.147 2.626 0.825 0.310 0.259 0.119 0.880 2.285 2.337 14.278
At 4,893 2013 $/ton million 2013 $ 0.580 1.193 0.000 2.762 3.913 4.787 1.504 0.566 0.472 0.217 1.604 4.165 4.260 26.024

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.015 0.025 0.000 0.046 0.097 0.103 0.033 0.013 0.012 0.006 0.042 0.100 0.095 0.588

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.019 0.033 0.000 0.064 0.123 0.134 0.043 0.017 0.015 0.007 0.052 0.127 0.123 0.756

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.022 0.039 0.000 0.077 0.142 0.157 0.050 0.019 0.018 0.008 0.060 0.147 0.143 0.882

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.029 0.054 0.000 0.111 0.191 0.217 0.069 0.027 0.024 0.011 0.080 0.199 0.197 1.207

10E

- 2

### Table 10E.1

. 1 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.000 0.000 0.000 0.002 0.001 0.002 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.006 Operating Cost Savings billion 2013 $ 0.001 0.002 0.000 0.004 0.006 0.007 0.000 0.003 0.002 0.001 0.001 0.000 0.002 0.028

NPV billion 2013 $ 0.001 0.001 0.000 0.002 0.005 0.005 0.000 0.002 0.002 0.001 0.001 0.000 0.002 0.022

Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.081 0.167 0.000 0.386 0.546 0.668 0.210 0.079 0.066 0.030 0.224 0.582 0.595 3.634

3% dr, average million 2013 $ 0.269 0.553 0.000 1.280 1.812 2.217 0.697 0.262 0.219 0.101 0.743 1.929 1.974 12.055 2.5% dr, average million 2013 $ 0.392 0.806 0.000 1.866 2.644 3.234 1.016 0.382 0.319 0.147 1.084 2.814 2.879 17.583 3% dr, 95th perc million 2013 $ 0.829 1.706 0.000 3.949 5.595 6.845 2.151 0.809 0.675 0.311 2.294 5.955 6.092 37.210 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.003 0.006 0.000 0.015 0.021 0.026 0.008 0.003 0.003 0.001 0.009 0.023 0.023 0.141

At 2,684 201 3 $/ton million 2013 $ 0.018 0.036 0.000 0.084 0.120 0.146 0.046 0.017 0.014 0.007 0.049 0.127 0.130 0.796
At 4,893 2013 $/ton million 2013 $ 0.032 0.067 0.000 0.154 0.218 0.267 0.084 0.032 0.026 0.012 0.089 0.232 0.238 1.451

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $ 0.001 0.001 0.000 0.003 0.006 0.006 0.000 0.002 0.002 0.001 0.001 0.001 0.003 0.026

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.004 0.007 0.007 0.001 0.002 0.002 0.001 0.001 0.002 0.004 0.035

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.004 0.008 0.008 0.001 0.002 0.002 0.001 0.002 0.003 0.005 0.040

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.002 0.003 0.000 0.006 0.011 0.012 0.002 0.003 0.002 0.001 0.003 0.006 0.008 0.060

10E

- 3

### Table 10E.1

. 2 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 2, 3 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.002 0.005 0.000 0.024 0.016 0.035 0.001 0.019 0.015 0.004 0.002 0.002 0.011 0.136 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.006 0.008 0.000 0.103 0.014 0.067 0.001 0.034 0.028 0.005 0.001 0.002 0.009 0.276 Operating Cost Savings billion 2013 $ 0.020 0.030 0.000 0.149 0.102 0.301 0.006 0.113 0.155 0.024 0.012 0.011 0.066 0.989

NPV billion 2013 $ 0.014 0.022 0.000 0.046 0.088 0.234 0.005 0.079 0.127 0.019 0.011 0.009 0.057 0.712 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.146 0.301 0.000 1.478 0.987 2.160 0.913 0.240 0.119 0.110 0.647 2.004 1.074 10.179

NOx kilo

- ton 0.216 0.445 0.000 2.187 1.460 3.196 1.351 0.355 0.176 0.162 0.958 2.964 1.589 15.060 Hg Ton 0.000 0.001 0.000 0.004 0.003 0.006 0.002 0.001 0.000 0.000 0.002 0.005 0.003 0.027

SO 2 kilo

- ton 0.124 0.255 0.000 1.252 0.836 1.830 0.773 0.203 0.101 0.093 0.548 1.697 0.910 8.621 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.076 2.212 0.000 10.868 7.255 15.885 6.714 1.764 0.875 0.806 4.760 14.732 7.899 74.845 3% dr, average million 2013 $ 4.820 9.913 0.000 48.704 32.510 71.187 30.088 7.906 3.920 3.611 21.329 66.019 35.399 335.407 2.5% dr, average million 2013 $ 7.618 15.667 0.000 76.975 51.381 112.510 47.553 12.495 6.196 5.707 33.710 104.342 55.948 530.102 3% dr, 95th perc million 2013 $ 14.878 30.599 0.000 150.335 100.350 219.735 92.873 24.404 12.100 11.146 65.838 203.783 109.268 1035.308 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.056 0.116 0.000 0.570 0.381 0.833 0.352 0.093 0.046 0.042 0.250 0.773 0.414 3.927 At 2,684 201 3 $/ton million 2013 $ 0.318 0.655 0.000 3.216 2.147 4.701 1.987 0.522 0.259 0.238 1.408 4.359 2.337 22.147
At 4,893 2013 $/ton million 2013 $ 0.580 1.193 0.000 5.862 3.913 8.568 3.621 0.952 0.472 0.435 2.567 7.946 4.260 40.367

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.015 0.025 0.000 0.060 0.097 0.255 0.136 0.021 0.012 0.010 0.063 0.252 0.095 1.043

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.019 0.033 0.000 0.098 0.123 0.310 0.159 0.027 0.015 0.013 0.080 0.303 0.123 1.304

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.022 0.039 0.000 0.126 0.142 0.351 0.177 0.032 0.018 0.015 0.092 0.341 0.143 1.498

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.029 0.054 0.000 0.200 0.191 0.458 0.222 0.044 0.024 0.021 0.124 0.441 0.197 2.003

10E

- 4

### Table 10E.1.2 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.000 0.000 0.000 0.006 0.001 0.004 0.000 0.002 0.002 0.000 0.000 0.000 0.001 0.015 Operating Cost Savings billion 2013 $ 0.001 0.002 0.000 0.008 0.006 0.017 0.000 0.006 0.009 0.001 0.001 0.001 0.004 0.055

NPV billion 2013 $ 0.001 0.001 0.000 0.003 0.005 0.013 0.000 0.004 0.007 0.001 0.001 0.001 0.003 0.040 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.081 0.167 0.000 0.818 0.546 1.196 0.506 0.133 0.066 0.061 0.358 1.109 0.595 5.636 3% dr, average million 2013 $ 0.269 0.553 0.000 2.715 1.812 3.969 1.677 0.441 0.219 0.201 1.189 3.681 1.974 18.699 2.5% dr, average million 2013 $ 0.392 0.806 0.000 3.960 2.644 5.789 2.447 0.643 0.319 0.294 1.734 5.369 2.879 27.274 3% dr, 95th perc million 2013 $ 0.829 1.706 0.000 8.381 5.595 12.250 5.178 1.361 0.675 0.621 3.670 11.361 6.092 57.719 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.003 0.006 0.000 0.032 0.021 0.046 0.020 0.005 0.003 0.002 0.014 0.043 0.023 0.219
At 2,684 201 3 $/ton million 2013 $ 0.018 0.036 0.000 0.179 0.120 0.262 0.111 0.029 0.014 0.013 0.079 0.243 0.130 1.235
At 4,893 2013 $/ton million 2013 $ 0.032 0.067 0.000 0.327 0.218 0.478 0.202 0.053 0.026 0.024 0.143 0.443 0.238 2.250

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $ 0.001 0.001 0.000 0.004 0.006 0.015 0.001 0.005 0.007 0.001 0.001 0.002 0.004 0.047

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.005 0.007 0.017 0.002 0.005 0.007 0.001 0.002 0.004 0.005 0.060

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.007 0.008 0.019 0.003 0.005 0.007 0.001 0.002 0.006 0.006 0.068

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.002 0.003 0.000 0.011 0.011 0.026 0.006 0.006 0.008 0.002 0.004 0.012 0.009 0.099

10E

- 5

### Table 10E.1

. 3 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 3, 3 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.004 0.005 0.000 0.039 0.016 0.040 0.001 0.026 0.020 0.006 0.003 0.003 0.016 0.179 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.012 0.008 0.000 0.151 0.014 0.073 0.001 0.074 0.044 0.008 0.002 0.004 0.019 0.411 Operating Cost Savings billion 2013 $ 0.037 0.030 0.000 0.243 0.102 0.333 0.007 0.243 0.183 0.038 0.021 0.020 0.096 1.353

NPV billion 2013 $ 0.025 0.022 0.000 0.092 0.088 0.259 0.005 0.169 0.138 0.030 0.019 0.017 0.076 0.942 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.250 0.301 0.000 2.377 0.987 2.464 1.213 0.383 0.208 0.197 0.957 2.243 1.139 12.719

NOx kilo

- ton 0.370 0.445 0.000 3.517 1.460 3.646 1.794 0.566 0.308 0.292 1.417 3.318 1.685 18.817 Hg Ton 0.001 0.001 0.000 0.006 0.003 0.006 0.003 0.001 0.001 0.001 0.002 0.006 0.003 0.033

SO 2 kilo

- ton 0.212 0.255 0.000 2.013 0.836 2.087 1.027 0.324 0.176 0.167 0.811 1.899 0.965 10.772 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.839 2.212 0.000 17.480 7.255 18.118 8.916 2.813 1.531 1.450 7.040 16.490 8.374 93.519 3% dr, average million 2013 $ 8.239 9.913 0.000 78.335 32.510 81.195 39.955 12.607 6.860 6.500 31.550 73.898 37.528 419.091 2.5% dr, average million 2013 $ 13.022 15.667 0.000 123.807 51.381 128.327 63.148 19.925 10.842 10.273 49.863 116.794 59.312 662.362 3% dr, 95th perc million 2013 $ 25.433 30.599 0.000 241.800 100.350 250.626 123.330 38.914 21.176 20.063 97.385 228.104 115.839 1293.617 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.096 0.116 0.000 0.917 0.381 0.951 0.468 0.148 0.080 0.076 0.369 0.865 0.439 4.906 At 2,684 201 3 $/ton million 2013 $ 0.544 0.655 0.000 5.173 2.147 5.361 2.638 0.832 0.453 0.429 2.083 4.880 2.478 27.673
At 4,893 2013 $/ton million 2013 $ 0.992 1.193 0.000 9.428 3.913 9.772 4.809 1.517 0.826 0.782 3.797 8.894 4.517 50.439

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.027 0.025 0.000 0.115 0.097 0.283 0.150 0.034 0.021 0.018 0.086 0.276 0.099 1.231

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.034 0.033 0.000 0.176 0.123 0.346 0.181 0.043 0.027 0.023 0.110 0.333 0.128 1.557

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.038 0.039 0.000 0.221 0.142 0.393 0.204 0.051 0.031 0.027 0.128 0.376 0.150 1.800

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.051 0.054 0.000 0.339 0.191 0.515 0.264 0.070 0.041 0.037 0.176 0.487 0.207 2.432

10E

- 6

### Table 10E.1.3 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ Operating Cost Savings billion 2013 $ 0.001 0.000 0.000 0.008 0.001 0.004 0.000 0.004 0.002 0.000 0.000 0.000 0.001 0.023

NPV billion 2013 $ 0.002 0.002 0.000 0.014 0.006 0.019 0.000 0.014 0.010 0.002 0.001 0.001 0.005 0.075 Emissions Savings (monetized) 0.001 0.001 0.000 0.005 0.005 0.014 0.000 0.009 0.008 0.002 0.001 0.001 0.004 0.053

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 3% dr, average million 2013 $ 0.138 0.167 0.000 1.316 0.546 1.364 0.671 0.212 0.115 0.109 0.530 1.242 0.631 7.042

2.5% dr, average million 2013 $ 0.459 0.553 0.000 4.367 1.812 4.527 2.228 0.703 0.382 0.362 1.759 4.120 2.092 23.364 3% dr, 95th perc million 2013 $ 0.670 0.806 0.000 6.370 2.644 6.603 3.249 1.025 0.558 0.529 2.566 6.009 3.052 34.079 NOx (3% dr) 1.418 1.706 0.000 13.480 5.595 13.972 6.876 2.169 1.181 1.119 5.429 12.717 6.458 72.119

At 476 2013 $/ton million 2013 $
At 2,684 201 3 $/ton million 2013 $ 0.005 0.006 0.000 0.051 0.021 0.053 0.026 0.008 0.004 0.004 0.021 0.048 0.024 0.274
At 4,893 2013 $/ton million 2013 $ 0.030 0.036 0.000 0.288 0.120 0.299 0.147 0.046 0.025 0.024 0.116 0.272 0.138 1.543

NPV 0.055 0.067 0.000 0.526 0.218 0.545 0.268 0.085 0.046 0.044 0.212 0.496 0.252 2.812

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.002 0.001 0.000 0.007 0.006 0.016 0.001 0.010 0.008 0.002 0.002 0.002 0.005 0.061

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.002 0.002 0.000 0.010 0.007 0.019 0.003 0.010 0.008 0.002 0.003 0.005 0.006 0.077

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.002 0.002 0.000 0.012 0.008 0.021 0.004 0.011 0.008 0.002 0.004 0.007 0.007 0.088

10E

- 7

### Table 10E.1

. 4 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 4, 3 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.004 0.008 0.000 0.039 0.030 0.061 0.001 0.033 0.024 0.006 0.003 0.003 0.016 0.229 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.012 0.036 0.000 0.151 0.101 0.189 0.006 0.125 0.178 0.008 0.002 0.004 0.019 0.831 Operating Cost Savings billion 2013 $ 0.037 0.052 0.000 0.243 0.185 0.459 0.008 0.284 0.209 0.038 0.021 0.020 0.096 1.653

NPV billion 2013 $ 0.025 0.016 0.000 0.092 0.084 0.271 0.002 0.159 0.031 0.030 0.019 0.017 0.076 0.822 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.250 0.505 0.000 2.377 1.825 3.726 1.463 0.383 0.208 0.197 0.957 3.504 1.978 17.374

NOx kilo

- ton 0.370 0.747 0.000 3.517 2.701 5.512 2.164 0.566 0.308 0.292 1.417 5.185 2.926 25.705 Hg Ton 0.001 0.001 0.000 0.006 0.005 0.010 0.004 0.001 0.001 0.001 0.002 0.009 0.005 0.045

SO 2 kilo

- ton 0.212 0.428 0.000 2.013 1.546 3.155 1.239 0.324 0.176 0.167 0.811 2.968 1.675 14.714 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.839 3.714 0.000 17.480 13.422 27.395 10.757 2.813 1.531 1.450 7.040 25.767 14.542 127.750 3% dr, average million 2013 $ 8.239 16.643 0.000 78.335 60.149 122.765 48.204 12.607 6.860 6.500 31.550 115.469 65.167 572.489 2.5% dr, average million 2013 $ 13.022 26.304 0.000 123.807 95.064 194.027 76.186 19.925 10.842 10.273 49.863 182.495 102.994 904.804 3% dr, 95th perc million 2013 $ 25.433 51.373 0.000 241.800 185.663 378.943 148.794 38.914 21.176 20.063 97.385 356.420 201.152 1767.114 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.096 0.195 0.000 0.917 0.704 1.437 0.564 0.148 0.080 0.076 0.369 1.352 0.763 6.702 At 2,684 201 3 $/ton million 2013 $ 0.544 1.099 0.000 5.173 3.972 8.106 3.183 0.832 0.453 0.429 2.083 7.624 4.303 37.802
At 4,893 2013 $/ton million 2013 $ 0.992 2.003 0.000 9.428 7.239 14.775 5.802 1.517 0.826 0.782 3.797 13.897 7.843 68.901

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.027 0.021 0.000 0.115 0.102 0.306 0.045 0.034 0.021 0.018 0.086 0.299 0.103 1.178

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.034 0.034 0.000 0.176 0.148 0.401 0.083 0.043 0.027 0.023 0.110 0.389 0.154 1.622

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.038 0.043 0.000 0.221 0.183 0.473 0.111 0.051 0.031 0.027 0.128 0.456 0.192 1.955

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.051 0.068 0.000 0.339 0.274 0.658 0.183 0.070 0.041 0.037 0.176 0.630 0.290 2.817

10E

- 8

### Table 10E.1.4 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.001 0.002 0.000 0.008 0.006 0.011 0.000 0.007 0.010 0.000 0.000 0.000 0.001 0.046 Operating Cost Savings billion 2013 $ 0.002 0.003 0.000 0.014 0.010 0.026 0.000 0.016 0.012 0.002 0.001 0.001 0.005 0.092

NPV billion 2013 $ 0.001 0.001 0.000 0.005 0.005 0.015 0.000 0.009 0.002 0.002 0.001 0.001 0.004 0.046 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.138 0.280 0.000 1.316 1.011 2.063 0.810 0.212 0.115 0.109 0.530 1.940 1.095 9.620 3% dr, average million 2013 $ 0.459 0.928 0.000 4.367 3.353 6.844 2.687 0.703 0.382 0.362 1.759 6.437 3.633 31.916 2.5% dr, average million 2013 $ 0.670 1.353 0.000 6.370 4.891 9.983 3.920 1.025 0.558 0.529 2.566 9.390 5.299 46.553 3% dr, 95th perc million 2013 $ 1.418 2.864 0.000 13.480 10.351 21.126 8.295 2.169 1.181 1.119 5.429 19.870 11.214 98.517 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.005 0.011 0.000 0.051 0.039 0.080 0.031 0.008 0.004 0.004 0.021 0.075 0.043 0.374
At 2,684 201 3 $/ton million 2013 $ 0.030 0.061 0.000 0.288 0.221 0.452 0.177 0.046 0.025 0.024 0.116 0.425 0.240 2.107
At 4,893 2013 $/ton million 2013 $ 0.055 0.112 0.000 0.526 0.404 0.824 0.323 0.085 0.046 0.044 0.212 0.775 0.437 3.841

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $ 0.002 0.001 0.000 0.007 0.006 0.018 0.001 0.009 0.002 0.002 0.002 0.003 0.006 0.058

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.002 0.002 0.000 0.010 0.008 0.022 0.003 0.010 0.002 0.002 0.003 0.008 0.008 0.080

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.002 0.002 0.000 0.012 0.010 0.026 0.004 0.010 0.002 0.002 0.004 0.011 0.010 0.095

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.003 0.004 0.000 0.019 0.015 0.037 0.009 0.011 0.003 0.003 0.007 0.021 0.016 0.146

10E

- 9

### Table 10E.1

. 5 Cumulative and Annualized Benefits and Costs of Considered S tandard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 5, 3 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.010 0.011 0.002 0.075 0.038 0.078 0.001 0.037 0.024 0.009 0.007 0.005 0.024 0.321 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.147 0.076 0.015 0.797 0.201 0.448 0.006 0.384 0.178 0.063 0.032 0.029 0.200 2.575 Operating Cost Savings billion 2013 $ 0.073 0.068 0.011 0.437 0.240 0.570 0.008 0.309 0.209 0.042 0.033 0.026 0.098 2.122

NPV billion 2013 $

- 0.074

- 0.008

- 0.003

- 0.360 0.039 0.122 0.002

- 0.075 0.031

- 0.022 0.001

- 0.002

- 0.103

- 0.453 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.594 0.657 0.111 4.567 2.350 4.775 1.463 0.567 0.404 0.320 1.493 4.553 2.453 24.307

NOx kilo

- ton 0.879 0.972 0.165 6.757 3.476 7.064 2.164 0.839 0.598 0.473 2.209 6.737 3.628 35.962 Hg Ton 0.002 0.002 0.000 0.012 0.006 0.012 0.004 0.001 0.001 0.001 0.004 0.012 0.006 0.063

SO 2 kilo

- ton 0.503 0.557 0.094 3.868 1.990 4.044 1.239 0.480 0.342 0.271 1.264 3.856 2.077 20.585 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 4.367 4.833 0.818 33.581 17.277 35.109 10.757 4.167 2.971 2.351 10.978 33.481 18.033 178.725 3% dr, average million 2013 $ 19.572 21.658 3.665 150.488 77.424 157.336 48.204 18.676 13.314 10.537 49.198 150.039 80.812 800.924 2.5% dr, average million 2013 $ 30.932 34.230 5.792 237.843 122.366 248.666 76.186 29.516 21.043 16.654 77.755 237.133 127.721 1265.839 3% dr, 95th perc million 2013 $ 60.412 66.853 11.313 464.516 238.985 485.653 148.794 57.647 41.098 32.525 151.859 463.130 249.445 2472.229 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.229 0.254 0.043 1.762 0.906 1.842 0.564 0.219 0.156 0.123 0.576 1.757 0.946 9.377 At 2,684 201 3 $/ton million 2013 $ 1.292 1.430 0.242 9.937 5.112 10.389 3.183 1.233 0.879 0.696 3.249 9.907 5.336 52.885
At 4,893 2013 $/ton million 2013 $ 2.356 2.607 0.441 18.112 9.318 18.936 5.802 2.248 1.602 1.268 5.921 18.058 9.726 96.394

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $

- 0.068

- 0.002

- 0.002

- 0.317 0.061 0.167 0.045

- 0.016 0.005 0.001

- 0.089 0.161 0.068 0.013

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $

- 0.053 0.015 0.001

- 0.200 0.121 0.290 0.083

- 0.002 0.016 0.009

- 0.050 0.277 0.130 0.635

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $

- 0.042 0.028 0.003

- 0.113 0.166 0.381 0.111 0.009 0.023 0.015

- 0.022 0.364 0.177 1.100

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $

- 0.012 0.060 0.008 0.114 0.283 0.618 0.183 0.037 0.043 0.031 0.052 0.590 0.299 2.307

10E

- 10

### Table 10E.1.5 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.008 0.004 0.001 0.044 0.011 0.025 0.000 0.021 0.010 0.004 0.002 0.002 0.011 0.144 Operating Cost Savings billion 2013 $ 0.004 0.004 0.001 0.024 0.013 0.032 0.000 0.017 0.012 0.002 0.002 0.001 0.005 0.118

NPV billion 2013 $

- 0.004 0.000 0.000

- 0.020 0.002 0.007 0.000

- 0.004 0.002

- 0.001 0.000 0.000

- 0.006

- 0.025 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.329 0.364 0.062 2.529 1.301 2.644 0.810 0.314 0.224 0.177 0.827 2.521 1.358 13.459 3% dr, average million 2013 $ 1.091 1.207 0.204 8.390 4.316 8.772 2.687 1.041 0.742 0.587 2.743 8.365 4.505 44.652 2.5% dr, average million 2013 $ 1.592 1.761 0.298 12.237 6.296 12.794 3.920 1.519 1.083 0.857 4.001 12.201 6.571 65.129 3% dr, 95th perc million 2013 $ 3.368 3.727 0.631 25.897 13.323 27.075 8.295 3.214 2.291 1.813 8.466 25.820 13.907 137.827 NOx (3% dr)

At 476 2013 $/ton million 2013 $ 0.013 0.014 0.002 0.098 0.051 0.103 0.031 0.012 0.009 0.007 0.032 0.098 0.053 0.523
At 2,684 201 3 $/ton million 2013 $ 0.072 0.080 0.013 0.554 0.285 0.579 0.177 0.069 0.049 0.039 0.181 0.552 0.297 2.948
At 4,893 2013 $/ton million 2013 $ 0.131 0.145 0.025 1.010 0.519 1.056 0.323 0.125 0.089 0.071 0.330 1.007 0.542 5.374

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $

- 0.004 0.000 0.000

- 0.017 0.004 0.010 0.001

- 0.004 0.002

- 0.001 0.001 0.003

- 0.004

- 0.009

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $

- 0.003 0.001 0.000

- 0.011 0.007 0.016 0.003

- 0.003 0.003

- 0.001 0.003 0.009

- 0.001 0.022

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $

- 0.002 0.001 0.000

- 0.007 0.009 0.020 0.004

- 0.003 0.003 0.000 0.004 0.013 0.001 0.043

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $

- 0.001 0.003 0.000 0.006 0.016 0.034 0.009

- 0.001 0.004 0.001 0.009 0.026 0.008 0.116

10E

- 11

### Table 10E.1

. 6 Cumulative and Annualized Benefits and Costs of Con sidered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 1, 7 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.002 0.005 0.000 0.011 0.016 0.020 0.000 0.008 0.006 0.002 0.002 0.001 0.007 0.081 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.003 0.004 0.000 0.015 0.007 0.016 0.000 0.007 0.005 0.001 0.001 0.000 0.002 0.063 Operating Cost Savings billion 2013 $ 0.009 0.015 0.000 0.033 0.050 0.059 0.002 0.023 0.019 0.007 0.006 0.003 0.021 0.246

NPV billion 2013 $ 0.006 0.010 0.000 0.017 0.042 0.043 0.002 0.016 0.014 0.006 0.005 0.002 0.018 0.183 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.146 0.301 0.000 0.697 0.987 1.207 0.379 0.143 0.119 0.055 0.405 1.050 1.074 6.562

NOx kilo

- ton 0.216 0.445 0.000 1.030 1.460 1.786 0.561 0.211 0.176 0.081 0.599 1.554 1.589 9.709 Hg Ton 0.000 0.001 0.000 0.002 0.003 0.003 0.001 0.000 0.000 0.000 0.001 0.003 0.003 0.017

SO 2 kilo

- ton 0.124 0.255 0.000 0.590 0.836 1.022 0.321 0.121 0.101 0.046 0.343 0.889 0.910 5.558 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.076 2.212 0.000 5.121 7.255 8.876 2.789 1.049 0.875 0.403 2.975 7.722 7.899 48.251 3% dr, average million 2013 $ 4.820 9.913 0.000 22.951 32.510 39.774 12.498 4.701 3.920 1.805 13.331 34.606 35.399 216.229 2.5% dr, average million 2013 $ 7.618 15.667 0.000 36.273 51.381 62.862 19.753 7.430 6.196 2.854 21.069 54.695 55.948 341.745 3% dr, 95th perc million 2013 $ 14.878 30.599 0.000 70.843 100.350 122.772 38.578 14.510 12.100 5.573 41.148 106.820 109.268 667.440 NOx ( 7 % dr)

At 476 2013 $/ton million 2013 $ 0.029 0.059 0.000 0.136 0.193 0.236 0.074 0.028 0.023 0.011 0.079 0.206 0.210 1.284 At 2,684 201 3 $/ton million 2013 $ 0.161 0.332 0.000 0.769 1.089 1.332 0.419 0.157 0.131 0.060 0.447 1.159 1.186 7.244
At 4,893 2013 $/ton million 2013 $ 0.294 0.605 0.000 1.401 1.985 2.429 0.763 0.287 0.239 0.110 0.814 2.113 2.161 13.203

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.008 0.013 0.000 0.023 0.051 0.053 0.017 0.007 0.006 0.003 0.022 0.052 0.049 0.303

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.011 0.021 0.000 0.041 0.076 0.084 0.027 0.010 0.009 0.004 0.032 0.079 0.077 0.471

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.014 0.026 0.000 0.054 0.095 0.107 0.034 0.013 0.012 0.005 0.040 0.099 0.097 0.597

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.021 0.041 0.000 0.089 0.144 0.167 0.053 0.020 0.018 0.008 0.060 0.151 0.150 0.923

10E

- 12

### Table 10E.1.6 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.000 0.000 0.000 0.002 0.001 0.002 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.006 Operating Cost Savings billion 2013 $ 0.001 0.001 0.000 0.003 0.005 0.006 0.000 0.002 0.002 0.001 0.001 0.000 0.002 0.024

NPV billion 2013 $ 0.001 0.001 0.000 0.002 0.004 0.004 0.000 0.002 0.001 0.001 0.001 0.000 0.002 0.018 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.081 0.167 0.000 0.386 0.546 0.668 0.210 0.079 0.066 0.030 0.224 0.582 0.595 3.634 3% dr, average million 2013 $ 0.269 0.553 0.000 1.280 1.812 2.217 0.697 0.262 0.219 0.101 0.743 1.929 1.974 12.055 2.5% dr, average million 2013 $ 0.392 0.806 0.000 1.866 2.644 3.234 1.016 0.382 0.319 0.147 1.084 2.814 2.879 17.583 3% dr, 95th perc million 2013 $ 0.829 1.706 0.000 3.949 5.595 6.845 2.151 0.809 0.675 0.311 2.294 5.955 6.092 37.210 NOx (7 % dr)

At 476 2013 $/ton million 2013 $ 0.003 0.006 0.000 0.013 0.019 0.023 0.007 0.003 0.002 0.001 0.008 0.020 0.021 0.127
At 2,684 201 3 $/ton million 2013 $ 0.016 0.033 0.000 0.076 0.108 0.132 0.041 0.016 0.013 0.006 0.044 0.114 0.117 0.715 At 4,893 2013 $/ton million 2013 $ 0.029 0.060 0.000 0.138 0.196 0.240 0.075 0.028 0.024 0.011 0.080 0.209 0.213 1.303

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $ 0.001 0.001 0.000 0.002 0.005 0.005 0.000 0.002 0.001 0.001 0.001 0.001 0.003 0.022

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.003 0.006 0.007 0.001 0.002 0.002 0.001 0.001 0.002 0.004 0.031

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.004 0.007 0.008 0.001 0.002 0.002 0.001 0.002 0.003 0.005 0.036

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.001 0.003 0.000 0.006 0.010 0.011 0.002 0.002 0.002 0.001 0.003 0.006 0.008 0.056

10E

- 13

### Table 10E.1

. 7 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 2, 7 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.002 0.005 0.000 0.024 0.016 0.035 0.001 0.019 0.015 0.004 0.002 0.002 0.011 0.136 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.003 0.004 0.000 0.056 0.007 0.036 0.001 0.018 0.015 0.003 0.001 0.001 0.005 0.151 Operating Cost Savings billion 2013 $ 0.009 0.015 0.000 0.073 0.050 0.145 0.003 0.055 0.074 0.012 0.006 0.005 0.032 0.479

NPV billion 2013 $ 0.006 0.010 0.000 0.017 0.042 0.109 0.002 0.037 0.059 0.009 0.005 0.004 0.027 0.328 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.146 0.301 0.000 1.478 0.987 2.160 0.913 0.240 0.119 0.110 0.647 2.004 1.074 10.179

NOx kilo

- ton 0.216 0.445 0.000 2.187 1.460 3.196 1.351 0.355 0.176 0.162 0.958 2.964 1.589 15.060 Hg Ton 0.000 0.001 0.000 0.004 0.003 0.006 0.002 0.001 0.000 0.000 0.002 0.005 0.003 0.027

SO 2 kilo

- ton 0.124 0.255 0.000 1.252 0.836 1.830 0.773 0.203 0.101 0.093 0.548 1.697 0.910 8.621 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.076 2.212 0.000 10.868 7.255 15.885 6.714 1.764 0.875 0.806 4.760 14.732 7.899 74.845 3% dr, average million 2013 $ 4.820 9.913 0.000 48.704 32.510 71.187 30.088 7.906 3.920 3.611 21.329 66.019 35.399 335.407 2.5% dr, average million 2013 $ 7.618 15.667 0.000 76.975 51.381 112.510 47.553 12.495 6.196 5.707 33.710 104.342 55.948 530.102 3% dr, 95th perc million 2013 $ 14.878 30.599 0.000 150.335 100.350 219.735 92.873 24.404 12.100 11.146 65.838 203.783 109.268 1035.308 NOx ( 7 % dr)

At 476 2013 $/ton million 2013 $ 0.029 0.059 0.000 0.289 0.193 0.423 0.179 0.047 0.023 0.021 0.127 0.392 0.210 1.992
At 2,684 201 3 $/ton million 2013 $ 0.161 0.332 0.000 1.632 1.089 2.385 1.008 0.265 0.131 0.121 0.715 2.212 1.186 11.236
At 4,893 2013 $/ton million 2013 $ 0.294 0.605 0.000 2.974 1.985 4.347 1.837 0.483 0.239 0.220 1.302 4.031 2.161 20.480

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.008 0.013 0.000 0.029 0.051 0.127 0.067 0.011 0.006 0.005 0.033 0.126 0.049 0.524

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.011 0.021 0.000 0.067 0.076 0.182 0.090 0.017 0.009 0.008 0.049 0.177 0.077 0.785

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.014 0.026 0.000 0.095 0.095 0.223 0.108 0.022 0.012 0.010 0.062 0.215 0.097 0.979

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.021 0.041 0.000 0.169 0.144 0.331 0.153 0.034 0.018 0.016 0.094 0.315 0.150 1.485

10E

- 14

### Table 10E.1.7 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.000 0.000 0.000 0.006 0.001 0.004 0.000 0.002 0.001 0.000 0.000 0.000 0.000 0.015 Operating Cost Savings billion 2013 $ 0.001 0.001 0.000 0.007 0.005 0.014 0.000 0.005 0.007 0.001 0.001 0.001 0.003 0.047

NPV billion 2013 $ 0.001 0.001 0.000 0.002 0.004 0.011 0.000 0.004 0.006 0.001 0.001 0.000 0.003 0.032 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.081 0.167 0.000 0.818 0.546 1.196 0.506 0.133 0.066 0.061 0.358 1.109 0.595 5.636 3% dr, average million 2013 $ 0.269 0.553 0.000 2.715 1.812 3.969 1.677 0.441 0.219 0.201 1.189 3.681 1.974 18.699 2.5% dr, average million 2013 $ 0.392 0.806 0.000 3.960 2.644 5.789 2.447 0.643 0.319 0.294 1.734 5.369 2.879 27.274 3% dr, 95th perc million 2013 $ 0.829 1.706 0.000 8.381 5.595 12.250 5.178 1.361 0.675 0.621 3.670 11.361 6.092 57.719 NOx (7 % dr)

At 476 2013 $/ton million 2013 $ 0.003 0.006 0.000 0.029 0.019 0.042 0.018 0.005 0.002 0.002 0.013 0.039 0.021 0.197
At 2,684 201 3 $/ton million 2013 $ 0.016 0.033 0.000 0.161 0.108 0.235 0.100 0.026 0.013 0.012 0.071 0.218 0.117 1.109 At 4,893 2013 $/ton million 2013 $ 0.029 0.060 0.000 0.294 0.196 0.429 0.181 0.048 0.024 0.022 0.129 0.398 0.213 2.022

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $ 0.001 0.001 0.000 0.003 0.005 0.012 0.001 0.004 0.006 0.001 0.001 0.002 0.003 0.039

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.005 0.006 0.015 0.002 0.004 0.006 0.001 0.002 0.004 0.005 0.052

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.001 0.002 0.000 0.006 0.007 0.017 0.003 0.004 0.006 0.001 0.002 0.006 0.006 0.061

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.001 0.003 0.000 0.010 0.010 0.023 0.005 0.005 0.007 0.002 0.004 0.012 0.009 0.091

10E

- 15

### Table 10E.1

. 8 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 3, 7 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

- A

Small

- C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.004 0.005 0.000 0.039 0.016 0.040 0.001 0.026 0.020 0.006 0.003 0.003 0.016 0.179 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.007 0.004 0.000 0.082 0.007 0.040 0.001 0.040 0.024 0.005 0.001 0.002 0.011 0.224 Operating Cost Savings billion 2013 $ 0.018 0.015 0.000 0.119 0.050 0.160 0.003 0.117 0.088 0.019 0.010 0.010 0.047 0.654

NPV billion 2013 $ 0.011 0.010 0.000 0.036 0.042 0.120 0.003 0.076 0.064 0.014 0.009 0.008 0.036 0.430 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.250 0.301 0.000 2.377 0.987 2.464 1.213 0.383 0.208 0.197 0.957 2.243 1.139 12.719

NOx kilo

- ton 0.370 0.445 0.000 3.517 1.460 3.646 1.794 0.566 0.308 0.292 1.417 3.318 1.685 18.817 Hg Ton 0.001 0.001 0.000 0.006 0.003 0.006 0.003 0.001 0.001 0.001 0.002 0.006 0.003 0.033

SO 2 kilo

- ton 0.212 0.255 0.000 2.013 0.836 2.087 1.027 0.324 0.176 0.167 0.811 1.899 0.965 10.772 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.839 2.212 0.000 17.480 7.255 18.118 8.916 2.813 1.531 1.450 7.040 16.490 8.374 93.519 3% dr, average million 2013 $ 8.239 9.913 0.000 78.335 32.510 81.195 39.955 12.607 6.860 6.500 31.550 73.898 37.528 419.091 2.5% dr, average million 2013 $ 13.022 15.667 0.000 123.807 51.381 128.327 63.148 19.925 10.842 10.273 49.863 116.794 59.312 662.362 3% dr, 95th perc million 2013 $ 25.433 30.599 0.000 241.800 100.350 250.626 123.330 38.914 21.176 20.063 97.385 228.104 115.839 1293.617 NOx ( 7 % dr)

At 476 2013 $/ton million 2013 $ 0.049 0.059 0.000 0.465 0.193 0.482 0.237 0.075 0.041 0.039 0.187 0.439 0.223 2.489
At 2,684 201 3 $/ton million 2013 $ 0.276 0.332 0.000 2.624 1.089 2.720 1.338 0.422 0.230 0.218 1.057 2.476 1.257 14.039
At 4,893 2013 $/ton million 2013 $ 0.503 0.605 0.000 4.783 1.985 4.958 2.440 0.770 0.419 0.397 1.926 4.512 2.291 25.590

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.013 0.013 0.000 0.056 0.051 0.141 0.074 0.017 0.011 0.010 0.044 0.138 0.051 0.620

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.020 0.021 0.000 0.117 0.076 0.204 0.105 0.027 0.016 0.015 0.069 0.196 0.080 0.945

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.024 0.026 0.000 0.163 0.095 0.251 0.128 0.035 0.020 0.018 0.087 0.238 0.102 1.189

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.037 0.041 0.000 0.281 0.144 0.374 0.188 0.053 0.031 0.028 0.135 0.350 0.159 1.820

10E

- 16

### Table 10E.1.8 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.001 0.000 0.000 0.008 0.001 0.004 0.000 0.004 0.002 0.000 0.000 0.000 0.001 0.022 Operating Cost Savings billion 2013 $ 0.002 0.001 0.000 0.012 0.005 0.016 0.000 0.012 0.009 0.002 0.001 0.001 0.005 0.065

NPV billion 2013 $ 0.001 0.001 0.000 0.004 0.004 0.012 0.000 0.008 0.006 0.001 0.001 0.001 0.004 0.042 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.138 0.167 0.000 1.316 0.546 1.364 0.671 0.212 0.115 0.109 0.530 1.242 0.631 7.042 3% dr, average million 2013 $ 0.459 0.553 0.000 4.367 1.812 4.527 2.228 0.703 0.382 0.362 1.759 4.120 2.092 23.364 2.5% dr, average million 2013 $ 0.670 0.806 0.000 6.370 2.644 6.603 3.249 1.025 0.558 0.529 2.566 6.009 3.052 34.079 3% dr, 95th perc million 2013 $ 1.418 1.706 0.000 13.480 5.595 13.972 6.876 2.169 1.181 1.119 5.429 12.717 6.458 72.119 NOx ( 7 % dr)

At 476 2013 $/ton million 2013 $ 0.005 0.006 0.000 0.046 0.019 0.048 0.023 0.007 0.004 0.004 0.018 0.043 0.022 0.246
At 2,684 201 3 $/ton million 2013 $ 0.027 0.033 0.000 0.259 0.108 0.269 0.132 0.042 0.023 0.021 0.104 0.244 0.124 1.386 At 4,893 2013 $/ton million 2013 $ 0.050 0.060 0.000 0.472 0.196 0.489 0.241 0.076 0.041 0.039 0.190 0.445 0.226 2.526

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $ 0.001 0.001 0.000 0.005 0.005 0.014 0.001 0.008 0.006 0.002 0.002 0.002 0.004 0.051

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.002 0.002 0.000 0.008 0.006 0.017 0.003 0.008 0.007 0.002 0.003 0.005 0.006 0.067

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.002 0.002 0.000 0.010 0.007 0.019 0.004 0.009 0.007 0.002 0.004 0.007 0.007 0.078

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.003 0.003 0.000 0.017 0.010 0.026 0.007 0.010 0.007 0.003 0.006 0.014 0.010 0.116

10E

- 17

### Table 10E.1

. 9 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 4, 7 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

- A

Small

- C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.004 0.008 0.000 0.039 0.030 0.061 0.001 0.033 0.024 0.006 0.003 0.003 0.016 0.229 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.007 0.019 0.000 0.082 0.055 0.114 0.003 0.068 0.097 0.005 0.001 0.002 0.011 0.463 Operating Cost Savings billion 2013 $ 0.018 0.025 0.000 0.119 0.090 0.222 0.004 0.136 0.101 0.019 0.010 0.010 0.047 0.801

NPV billion 2013 $ 0.011 0.006 0.000 0.036 0.035 0.109 0.001 0.068 0.004 0.014 0.009 0.008 0.036 0.337 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.250 0.505 0.000 2.377 1.825 3.726 1.463 0.383 0.208 0.197 0.957 3.504 1.978 17.374

NOx kilo

- ton 0.370 0.747 0.000 3.517 2.701 5.512 2.164 0.566 0.308 0.292 1.417 5.185 2.926 25.705 Hg Ton 0.001 0.001 0.000 0.006 0.005 0.010 0.004 0.001 0.001 0.001 0.002 0.009 0.005 0.045

SO 2 kilo

- ton 0.212 0.428 0.000 2.013 1.546 3.155 1.239 0.324 0.176 0.167 0.811 2.968 1.675 14.714 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 1.839 3.714 0.000 17.480 13.422 27.395 10.757 2.813 1.531 1.450 7.040 25.767 14.542 127.750 3% dr, average million 2013 $ 8.239 16.643 0.000 78.335 60.149 122.765 48.204 12.607 6.860 6.500 31.550 115.469 65.167 572.489 2.5% dr, average million 2013 $ 13.022 26.304 0.000 123.807 95.064 194.027 76.186 19.925 10.842 10.273 49.863 182.495 102.994 904.804 3% dr, 95th perc million 2013 $ 25.433 51.373 0.000 241.800 185.663 378.943 148.794 38.914 21.176 20.063 97.385 356.420 201.152 1767.114 NOx ( 7 % dr)

At 476 2013 $/ton million 2013 $ 0.049 0.099 0.000 0.465 0.357 0.729 0.286 0.075 0.041 0.039 0.187 0.686 0.387 3.400
At 2,684 201 3 $/ton million 2013 $ 0.276 0.558 0.000 2.624 2.015 4.113 1.615 0.422 0.230 0.218 1.057 3.868 2.183 19.178
At 4,893 2013 $/ton million 2013 $ 0.503 1.016 0.000 4.783 3.673 7.496 2.943 0.770 0.419 0.397 1.926 7.051 3.979 34.956

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $ 0.013 0.010 0.000 0.056 0.051 0.140 0.016 0.017 0.011 0.010 0.044 0.137 0.051 0.557

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.020 0.023 0.000 0.117 0.097 0.235 0.054 0.027 0.016 0.015 0.069 0.227 0.102 1.002

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.024 0.033 0.000 0.163 0.132 0.307 0.082 0.035 0.020 0.018 0.087 0.294 0.140 1.334

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.037 0.058 0.000 0.281 0.223 0.492 0.154 0.053 0.031 0.028 0.135 0.468 0.238 2.197

10E

- 18

### Table 10E.1.9 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.001 0.002 0.000 0.008 0.005 0.011 0.000 0.007 0.010 0.000 0.000 0.000 0.001 0.046 Operating Cost Savings billion 2013 $ 0.002 0.002 0.000 0.012 0.009 0.022 0.000 0.013 0.010 0.002 0.001 0.001 0.005 0.079

NPV billion 2013 $ 0.001 0.001 0.000 0.004 0.003 0.011 0.000 0.007 0.000 0.001 0.001 0.001 0.004 0.033 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.138 0.280 0.000 1.316 1.011 2.063 0.810 0.212 0.115 0.109 0.530 1.940 1.095 9.620 3% dr, average million 2013 $ 0.459 0.928 0.000 4.367 3.353 6.844 2.687 0.703 0.382 0.362 1.759 6.437 3.633 31.916 2.5% dr, average million 2013 $ 0.670 1.353 0.000 6.370 4.891 9.983 3.920 1.025 0.558 0.529 2.566 9.390 5.299 46.553 3% dr, 95th perc million 2013 $ 1.418 2.864 0.000 13.480 10.351 21.126 8.295 2.169 1.181 1.119 5.429 19.870 11.214 98.517 NOx ( 7 % dr)

At 476 2013 $/ton million 2013 $ 0.005 0.010 0.000 0.046 0.035 0.072 0.028 0.007 0.004 0.004 0.018 0.068 0.038 0.336
At 2,684 201 3 $/ton million 2013 $ 0.027 0.055 0.000 0.259 0.199 0.406 0.159 0.042 0.023 0.021 0.104 0.382 0.216 1.893 At 4,893 2013 $/ton million 2013 $ 0.050 0.100 0.000 0.472 0.363 0.740 0.291 0.076 0.041 0.039 0.190 0.696 0.393 3.451

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $ 0.001 0.001 0.000 0.005 0.005 0.013 0.001 0.007 0.001 0.002 0.002 0.003 0.005 0.045

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $ 0.002 0.002 0.000 0.008 0.007 0.018 0.003 0.007 0.001 0.002 0.003 0.008 0.007 0.067

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $ 0.002 0.002 0.000 0.010 0.009 0.021 0.004 0.008 0.001 0.002 0.004 0.011 0.009 0.082

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.003 0.003 0.000 0.017 0.014 0.032 0.009 0.009 0.002 0.003 0.006 0.021 0.015 0.134

10E

- 19

### Table 10E.1

. 10 Cumulative and Annualized Benefits and Costs of Considered S tandard Levels for ACIM Units Shipped in the

Period 201 8

– 204 7 (TSL 5, 7 Percent Discount Rate)

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.010 0.011 0.002 0.075 0.038 0.078 0.001 0.037 0.024 0.009 0.007 0.005 0.024 0.321 Economic Impacts

Incremental Equipment Cost billion 2013 $ 0.085 0.041 0.008 0.452 0.110 0.255 0.003 0.209 0.097 0.035 0.017 0.016 0.109 1.435 Operating Cost Savings billion 2013 $ 0.035 0.033 0.006 0.214 0.117 0.276 0.004 0.149 0.101 0.020 0.016 0.013 0.047 1.030

NPV billion 2013 $

- 0.049

- 0.008

- 0.002

- 0.238 0.007 0.021 0.001

- 0.060 0.004

- 0.014

- 0.001

- 0.003

- 0.062

- 0.406 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.594 0.657 0.111 4.567 2.350 4.775 1.463 0.567 0.404 0.320 1.493 4.553 2.453 24.307

NOx kilo

- ton 0.879 0.972 0.165 6.757 3.476 7.064 2.164 0.839 0.598 0.473 2.209 6.737 3.628 35.962 Hg Ton 0.002 0.002 0.000 0.012 0.006 0.012 0.004 0.001 0.001 0.001 0.004 0.012 0.006 0.063

SO 2 kilo

- ton 0.503 0.557 0.094 3.868 1.990 4.044 1.239 0.480 0.342 0.271 1.264 3.856 2.077 20.585 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2013 $ 4.367 4.833 0.818 33.581 17.277 35.109 10.757 4.167 2.971 2.351 10.978 33.481 18.033 178.725 3% dr, average million 2013 $ 19.572 21.658 3.665 150.488 77.424 157.336 48.204 18.676 13.314 10.537 49.198 150.039 80.812 800.924 2.5% dr, average million 2013 $ 30.932 34.230 5.792 237.843 122.366 248.666 76.186 29.516 21.043 16.654 77.755 237.133 127.721 1265.839 3% dr, 95th perc million 2013 $ 60.412 66.853 11.313 464.516 238.985 485.653 148.794 57.647 41.098 32.525 151.859 463.130 249.445 2472.229

NOx (7 % dr)

At 476 2013 $/ton million 2013 $ 0.116 0.129 0.022 0.894 0.460 0.935 0.286 0.111 0.079 0.063 0.292 0.891 0.480 4.757
At 2,684 201 3 $/ton million 2013 $ 0.656 0.726 0.123 5.041 2.594 5.271 1.615 0.626 0.446 0.353 1.648 5.026 2.707 26.831
At 4,893 2013 $/ton million 2013 $ 1.195 1.322 0.224 9.189 4.727 9.607 2.943 1.140 0.813 0.643 3.004 9.161 4.934 48.904

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 2013 $

- 0.044

- 0.003

- 0.002

- 0.199 0.027 0.062 0.016

- 0.010 0.002 0.000

- 0.049 0.059 0.030

- 0.111

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $

- 0.029 0.014 0.001

- 0.083 0.087 0.184 0.054 0.005 0.013 0.008

- 0.011 0.175 0.093 0.512

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $

- 0.018 0.027 0.003 0.005 0.132 0.275 0.082 0.016 0.020 0.014 0.017 0.262 0.140 0.977

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $ 0.012 0.059 0.009 0.231 0.249 0.512 0.154 0.044 0.040 0.030 0.092 0.488 0.262 2.183

10E

- 20

### Table 10E.1.10 ( cont. )

Units IMH

W

Small

B IMH

W

Med

B IMH

W

Large

B IMH

A

Small

B RCU

-

***

- Large

B IMH

A

Large

B SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C RCU

-

***

- Small

C SCU

A

Small

C Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 201 3 $ 0.008 0.004 0.001 0.045 0.011 0.025 0.000 0.021 0.010 0.003 0.002 0.002 0.011 0.142 Operating Cost Savings billion 2013 $ 0.003 0.003 0.001 0.021 0.012 0.027 0.000 0.015 0.010 0.002 0.002 0.001 0.005 0.102

NPV billion 2013 $

- 0.005

- 0.001 0.000

- 0.024 0.001 0.002 0.000

- 0.006 0.000

- 0.001 0.000 0.000

- 0.006

- 0.040 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 201 3 $ 0.329 0.364 0.062 2.529 1.301 2.644 0.810 0.314 0.224 0.177 0.827 2.521 1.358 13.459 3% dr, average million 2013 $ 1.091 1.207 0.204 8.390 4.316 8.772 2.687 1.041 0.742 0.587 2.743 8.365 4.505 44.652 2.5% dr, average million 2013 $ 1.592 1.761 0.298 12.237 6.296 12.794 3.920 1.519 1.083 0.857 4.001 12.201 6.571 65.129 3% dr, 95th perc million 2013 $ 3.368 3.727 0.631 25.897 13.323 27.075 8.295 3.214 2.291 1.813 8.466 25.820 13.907 137.827 NOx (7 % dr)

At 476 2013 $/ton million 2013 $ 0.011 0.013 0.002 0.088 0.045 0.092 0.028 0.011 0.008 0.006 0.029 0.088 0.047 0.470
At 2,684 201 3 $/ton million 2013 $ 0.065 0.072 0.012 0.498 0.256 0.520 0.159 0.062 0.044 0.035 0.163 0.496 0.267 2.649 At 4,893 2013 $/ton million 2013 $ 0.118 0.131 0.022 0.907 0.467 0.948 0.291 0.113 0.080 0.064 0.297 0.904 0.487 4.828

NPV

Consumer & Emissions Value

Consumers + CO 2 (1s t) + NOx (Med ) billion 201 3 $

- 0.004 0.000 0.000

- 0.020 0.002 0.005 0.001

- 0.006 0.001

- 0.001 0.001 0.003

- 0.004

- 0.024

Consumers + CO 2 (2nd) + NOx (Med) billion 2013 $

- 0.004 0.000 0.000

- 0.015 0.005 0.011 0.003

- 0.005 0.001

- 0.001 0.003 0.009

- 0.001 0.007

Consumers + CO 2 (3rd) + NOx (Med) billion 2013 $

- 0.003 0.001 0.000

- 0.011 0.007 0.015 0.004

- 0.004 0.002

- 0.001 0.004 0.012 0.001 0.028

Consumers + CO 2 (4th) + NOx (Med ) billion 2013 $

- 0.001 0.003 0.000 0.003 0.014 0.030 0.009

- 0.003 0.003 0.000 0.009 0.026 0.008 0.100

10E

- 21

### CHAPTER 11. CUSTOMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION

................................ ................................ ................................ ............ 11

- 1

11.2 IDENTIFYING THE CUST OMER SUBGROUPS

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

- 1

11.3 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS FO R SMALL BUSINESS

SUBGROUPS

................................ ................................ ................................ ................... 11

- 7

REFERENCES

................................ ................................ ................................ ........................ 11

- 14

LIST OF TABLES

Table 11.2.1 Business Types Purchasing Automatic Commercial Ice Makers

......................... 11

- 1

Table 11.2.2 SBA Size Standards for Businesses that Use Automatic Commercial ice

Makers

................................ ................................ ................................ ................ 11

- 2 Table 11.2.3 Single

- Unit and Multiunit Firms Census Data f or Supermarket and Other Grocery (Except Convenience) Stores

................................ ............................... 11

- 3

Table 11.2.4 Single

- Unit and Multiunit Firms Census Data for Con venience Stores

............... 11

- 4 Table 11.2.5 Single

- Unit and Multiunit Firms Census Data for Gasoline Stations with

Convenience Stores

................................ ................................ ............................ 11

- 4 Table 11.2.6 Single

- Unit and Multiunit Firms Census Data for Full

- Service Restaurants

....... 11

- 5 Table 11.2.7 Single

- Unit and Multiunit Firms Census Data for Limited

- Service

Restaurants

................................ ................................ ................................ ......... 11

- 5 Table 11.2.8 Single

- Unit and Multiunit Firms Census Data for Lodging*

............................... 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 11.3.2 Percentage Change in Mean LCC Savings for the Small Business Subgroup in the Foodservice Sector Compared to National Average Values*

.................. 11

- 9 Table 11.3.3 Comparison of Median Payback Periods for the Small Business Subgroup in

the Foodservice Sector with National Median Values

................................ ..... 11

- 10 Table 11.3.4 Comparison of LCC Savings for the Small Business Subgroup in the Lodging Sector with the National Average Values

................................ ......... 11

- 11 Table 11.3.5 Percentage Change in the Mean LCC Savings for the Small Business Subgroup in the Lodging Sector Compared to the National Average

Values

................................ ................................ ................................ .............. 11

- 12 Table 11.3.6 Comparison of Median Payback Periods for the Small Business Subgroup in the Lodging Sector with the National Me dian Values

................................ ..... 11

- 13

11

- i

### CHAPTER 1 1

. CUSTOMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

The life

- cycle cost (LCC) customer sub group analysis evaluates impacts on identifiable groups of customers of automatic commercial ice

- making (ACIM) equipment who may be disproportionately affected by amended energy conservation standards. The LCC and payback period ( PBP ) analysis described in c hapter 8 of the technical support document (TSD) is applied

to seven major business or building types that use a majority of the ACIM equipment

— health care, lodging, foodservice, retail, food sales, e ducation, and office

. Although the inputs for different types of businesses are different in the LCC and PBP analysis, the final results may not

reflec t the results experienced by certain customer subgroups. In other words, some of the adverse impacts on b usinesses that are disproportionately disadvantaged 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

a

pplying the inputs that are applicable only to the identified subgroups. The LCC spreadshee t
model is accessible at www1.eere.energy.gov/buildings/appliance _standards/rulemaking.aspx/

ruleid/29

.

11.2 IDENTIFYING THE CUST OMER SUBGROUPS

DOE identified small businesses as a subgroup that could potentially be affected disproportionately by the amended energy conservation standard for automatic commercial ice

makers

. D OE was concerned that increases in the purchase price of ACIM equipment could have negative impacts on small businesses ( i.e. , those with low annual revenues). Table 11.2

. 1 lists the business types that typically purchase a utomatic commercial ice makers.

Table 11.2

. 1 Business Types Purchasing Automatic Commercial Ic e Makers

Business Type Business Type as Percent

of ACIM Stock

Health Care 9% Lodging 33%

Foodservice 22%

Retail 8%

Education 7%

Food Sales 16%

Office 4%

Total 100%



### One category, office, is not a specific category that can be associated with specific small business definitions insofar as it includes a wide range of industries. In the case of education, based on available information, DOE estimates that 75 percent of e ducational establishments are owned by state and local governmental entities. 1 

### Similarly, DOE estimates that almost 80 percent of hospitals are owned by governmental entities. 2 

### While it is clearly possible for a school or hospital district to be disadvanta ged, given the ability to levy taxes, special

- purpose districts

have resources not available to small business like family

- owned grocery stores. A fourth category, retail, includes the so

- called “big box” retailers that sell everything from beds, to

11

- 1

### automo tive supplies, to medicines and optical services, to food, and beyond. Retail also includes

low

- end retailers and retailers specializing in a wide range of goods obtained as close

- out or discontinued products and sold inexpensively. While it is possible fo r single

- establishment firms to be in these business lines, DOE views this business category as one dominated by regional or larger chains, and modeled it thusly in the calculation of discount rates. (See chapter 8 of the

TSD for the development of discoun t rates.) For th e s e reason s , DOE confined the research for identifying small businesses to the remaining three business types

— lodging, foodservice, and

food sales.

To identify small businesses, DOE used size standards from the Small Business

Administration (SBA) to define which business entities 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). 3 

### Table 11.2

. 2 presents the size standards established by SBA for various businesses that use ACIM equipment.

Table 11.2

. 2 SBA Size Standards for Businesses that Use Automatic C ommercial ice

M akers

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 All Other Specialty Food Stores $7.0 Beer, Wine and Liquor Stores $7.0

Gasoline Stations with Convenience Stores $27.0 Warehouse Clubs and Superstores $27.0 Hotels (except Casino Hotels) and Motels $30.0

Casino Hotels $30.0

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 Small Business Concerns (13 CFR 121.201);

also available at www.sba.gov/content/table

- small

- business

- si ze

- standards

. (Last

accessed on April 17, 2013

. )



### In examining the businesses that purchase and use automatic commercial ice makers

, DOE analyzed detailed statistical data from the 200 7 economic census

. 4 

### Table 11.2

. 3

, Table 11.2

. 4

, and Table 11.2

. 5 present the census data for single

- unit and multiunit firms for

three different types of businesses that constitute a dominant share of the ACIM equipment

market.

Table 11.2

. 3 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 SBA definition

11

- 2

### of small business concerns and represent 66 percent of the total number of establishments,

23 percent of employment, and 18 percent of sales.

Table 11.2

. 3 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(except convenience) 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

. 4 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 represent 90 percent of th e total number of establishments, 77 percent of employment, and 76 percent of

sales.

11

- 3

### Table 11.2

. 4 Single

- Unit and Multiunit Firms Census Data for Convenience 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

. 5 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 rep resent 60 percent of the total number of establishments, 52 percent of employment, and 43 percent of sales.

Table 11.2

. 5 Single

- Unit and Multiunit Firms Census Data for Gasoline Stations with

Convenience Store s

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.

11

- 4

### Table 11.2

. 6 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 represent 87 percent of the total number of establishments, 64 percent of employment, and 65 percent of sales. 5



### Table 11.2

. 6 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.



### Table 11.2

. 7 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 represent 73 percen t of the total number of establishments, 58 percent of employment, and

58 percent of sales.

Table 11.2

. 7 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.

11

- 5

### Table 11.2

. 8 presents the data for lodging. The data show that over 99 percent of the firms in this category fall under the SBA definition of small business concerns and represent 85 percent of the total number of establishments, 56 percent of employment, and 50 percent of sales.

Table 11.2

. 8 Single

- Unit and Multiunit Firms Census Data for Lodging*

Description Number

of Firms Total Employment Number of

Establishments Sales per

Firm
1000$ Is Average

Firm a

Small

Business?Limited-service Restaurants* All firms 52,001 1,970,661 62,740 3,469 Yes Single

- unit firms 49,340 813,879 49,340 1,277 Yes

Multiunit firms 2,661 1,156,782 13,400 44,104 No

Firms with 1 establishment 1,142 106,804 1,142 8,588 Yes

Firms with 2 establishments 770 102,044 1,540 12,778 Yes

Firms with 3 or 4 establishments 385 86,904 1,275 19,890 Yes

Firms with 5 to 9 establishments 194 121,124 1,259 64,389 No

Firms with 10 establishments or

more 170 739,906 8,184 456,255 No

Fraction of firms classed as small business 0.99 3 Fraction of establishments that belong to firms

classified as small businesses 0.849

Fraction of employment in small businesses 0.563

Fraction of sales in small businesses 0.501

* Lodging is referred to as Accommodations and food service in this particular database. Small business concerns in this category are firms with annual sales less than $ 3 0 million.

**Fraction of sales based on total sales.



### The census data show that nearly 99 percent of all the firms in the food sales,

foodservice , and lodging business sectors fall under the SBA definition of small businesses. Therefore, it can be concluded that 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 below

- average electricity prices are more disadvantaged than others. Higher cost of capital imposes burden on the businesses because they have to borrow more money to purchase equipment that meet s new or amended standards

, compared to the case where there are no new or amended standards. Lower electricity price rates result in lower savings in energy costs and, conse quently, 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. Of the three business types, lodging has the highest

estimated discount rate at 6.76 percent (compared to 5. 8 5 percent for foodservice and 5.1 4 percent for food sales). Based on Commercial Buildings Energy Consumption Survey (CBECS) data, 6 

### foodservice faces the highest electricity prices at 104 percent of the average price for all

com mercial buildings, while lodging is at 97 percent and food sales is the lowest at 96 percent.

Lodging faces a cost of capital that is considerably higher than the other two categories

, and was selected for a subgroup analysis . Foods ervice faces a higher co st of capital than food

s al e s and the highe st electricity price s

. F oodservice was also selected for subgroup analysis . The

11

- 6

### higher cost of capital implies first cost is more important to this customer group than food sales customers, and also that they woul d value future savings less relative to the first cost.

DOE carried out two LCC subgroup analys es 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). In the

calculation of discount rates

, it was assumed that 100 pe rcent of customers face the small business interest rate premium when calculating discount rates. Apart from these changes, all other inputs for LCC subgroup analysis are the same as those in the LCC analysis described in TSD chapter 8.

11.3 LIFE

- CYCLE COST AN D PAYBACK PERIOD RES ULTS FOR SMALL

BUSINESS SUBGROUPS

Table 11.3

. 1 presents the comparison of mean LCC savings for the small business subgroup in the foodservice sect or with the national average values (LCC savings results from chapter 8 of the final rule TSD). For all trial standard levels ( TSLs )

, the LCC savings for the small business subgroup show mixed results, with some higher and some lower than the national

aver age values. Table 11.3

. 2 presents the percentage change in LCC savings compared to national average values. For TSLs 1

– 4 the percentage differences range from

- 3 to 3 percent for most equipment classes. The exceptions include IMH

- A

- Large

- B2 where the TSLs 1 and 2 results are 7 percent above the average, SCU

- W

- Large

- B where the foodservice subgroup shows

- 4 percent savings compared to the average, and SCU

- A

- Large

- B wher e the difference reaches

- 9 percent relative to the average. DOE modeled all equipment classes in this analysis, although DOE believes it is likely that the very large equipment classes are not commonly used in

foodservice establishments.

11

- 7

### Table 11.3

. 1 Comparison of Mean LCC Savings for the Small Business Subgroup in the

Foodservice Sector with the National Average Values

Equipment Class Category Mean LCC Savings
2013$ *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Small Business 174 174 212 212 (535) All Business Types 175 175 214 214 (534)

IMH

- W

- Med

- B Small Business 312 312 312 168 (60) All Business Types 308 308 308 165 (63)

IMH

- W

- Large

- B Small Business NA NA NA NA (169)

All Business Types NA NA NA NA (172)

IMH

- W

- Large

- B1 Small Business NA NA NA NA (198) All Business Types NA NA NA NA (200)

IMH

- W

- Large

- B2 Small Business NA NA NA NA (77) All Business Types NA NA NA NA (80)

IMH

- A

- Small

- B Small Business 139 75 78 78 (390) All Business Types 136 72 77 77 (393)

IMH

- A

- Large

- B Small Business 387 498 359 264 54 All Business Types 382 501 361 265 55

IMH

- A

- Large

- B1 Small Business 444 575 404 292 43 All Business Types 439 580 407 294 45

IMH

- A

- Large

- B2 Small Business 81 81 114 114 114 All Business Types 76 76 110 110 110

RCU

- Large

- B Small Business 754 754 754 424 150 All Business Types 748 748 748 418 144

RCU

- Large

- B1 Small Business 749 749 749 397 166 All Business Types 743 743 743 391 161

RCU

- Large

- B2 Small Business 832 832 832 832 (99) All Business Types 820 820 820 820 (109)

SCU

- W

- Large

- B Small Business 431 601 541 184 184 All Business Types 444 613 550 192 192

SCU

- A

- Small

- B Small Business 112 162 276 226 (148) All Business Types 110 161 281 230 (145)

SCU

- A

- Large

- B Small Business 164 392 432 65 65 All Business Types 163 400 439 71 71

IMH

- A

- Small

- C Small Business 248 296 317 317 (155) All Business Types 245 292 313 313 (165)

IMH

- A

- Large

- C Small Business 544 544 630 630 44 All Business Types 539 539 626 626 28

RCU

- Small

- C Small Business 503 453 509 509 (57) All Business Types 498 448 505 505 (73)

SCU

- A

- Small

- C Small Business 225 281 293 293 (257) All Business Types 224 278 290 290 (268)

* Values in parentheses are negative numbers

.

11

- 8

### Table 11.3

. 2 Percentage Change in Mean LCC Savings for the Small Business Subgroup in

the Foodservice Sector Compared to National Average Values *
Equipment Class TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B (1%) (1%) (1%) (1%) 0%

IMH

- W

- Med

- B 1% 1% 1% 2% 5%

IMH

- W

- Large

- B NA NA NA NA 1%

IMH

- W

- Large

- B1 NA NA NA NA 1%

IMH

- W

- Large

- B2 NA NA NA NA 4%

IMH

- A

- Small

- B 2% 3% 2% 2% 1%

IMH

- A

- Large

- B 1% (1%) (1%) (1%) (2%)

IMH

- A

- Large

- B1 1% (1%) (1%) (1%) (4%)

IMH

- A

- Large

- B2 7% 7% 3% 3% 3%

RCU

- Large

- B 1% 1% 1% 1% 4%

RCU

- Large

- B1 1% 1% 1% 1% 3%

RCU

- Large

- B2 1% 1% 1% 1% 9%

SCU

- W

- Large

- B (3%) (2%) (2%) (4%) (4%)

SCU

- A

- Small

- B 1% 1% (2%) (2%) (2%)

SCU

- A

- Large

- B 1% (2%) (2%) (9%) (9%)

IMH

- A

- Small

- C 1% 1% 1% 1% 6%

IMH

- A

- Large

- C 1% 1% 1% 1% 57%

RCU

- Small

- C 1% 1% 1% 1% 22%

SCU

- A

- Small

- C 1% 1% 1% 1% 4%
* Values in parenthesis are negative numbers. Negative percentage values imply a decrease in LCC savings

, and positive percentage values imply an increase in LCC savings.



### Table 11.3

. 3 presents the comparison of median PBPs for the small business subgroup in

the foodservice sector with national median values (median PBPs from chapter 8 of the final rule TSD). The PBP values are shorter for the small business subgroup in all cases. This arises

because the first

- year operating cost savings

— which are used for payback period

— are higher

, leading to a shorter payback

. ( G iven their higher discount rates, the foodservice customers value future savings less than the current capital expenditure , leading to the result of some cases with lower LCC savings. ) First

- year savings are higher because the foodservice electricity prices are higher than the average of all classes.

Table 11.3

. 4 presents the comparison of mean LCC savings for the small business subgroup in lodging sector (hotels and casinos) with the national average values (LCC savi ngs results from chapter 8 of the final rule TSD). Table 11.3

. 5 presents the percentage change in LCC savings of the lodging sector customer subgroup to national avera ge values. For lodging sector small business, LCC savings are lower across the board. For 11 equipment classes, TSL 1 through 3 savings range from 9 to 1 3 percent lower than the average

. (For the IMH

- W

- Large

- B class, TSLs 1

– 4 are not defined because the on ly efficiency level studies, the max tech level, was not cost effective to customers.) The IMH

- A

- Small

- B class is the exception with the difference exceeding 20 percent lower than the average at TSLs 2 and 3. The differences are even greater at

TSLs 4 and 5. The LCC savings are lower for lodging customers because the energy price for lodging is slightly lower than the average of all commercial business types (97 percent of the average for all commercial business types) while the lodging customers have highe r discount

rates than the average . Th e lower electricity price reduces the nominal value of future operating

and maintenance benefits while the higher discount rate reduces the present value of the benefits, thus decreasing the LCC savings.

11

- 9

### Table 11.3

. 3 Comparison of Median Payback Periods for the Small Business Subgroup in

the Foodservice Sector with National Median Values

Equipment Class Category Median Payback Period

years TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Small Business 2.3 2.3 2.7 2.7 12.7 All Business Types 2.5 2.5 2.7 2.7 13.4

IMH

- W

- Med

- B Small Business 2.0 2.0 2.0 4.8 7.2 All Business Types 2.1 2.1 2.1 5.0 7.6

IMH

- W

- Large

- B Small Business NA NA NA NA 10.0

All Business Types NA NA NA NA 10.6

IMH

- W

- Large

- B1 Small Business NA NA NA NA 10.5 All Business Types NA NA NA NA 11.1

IMH

- W

- Large

- B2 Small Business NA NA NA NA 8.4 All Business Types NA NA NA NA 8.9

IMH

- A

- Small

- B Small Business 3.2 4.5 4.4 4.4 11.4 All Business Types 3.4 4.8 4.7 4.7 11.9

IMH

- A

- Large

- B Small Business 2.1 2.3 2.2 3.7 5.3 All Business Types 2.2 2.4 2.3 3.9 5.6

IMH

- A

- Large

- B1 Small Business 1.1 1.4 1.4 3.2 5.1 All Business Types 1.2 1.5 1.5 3.4 5.4

IMH

- A

- Large

- B2 Small Business 7.0 7.0 6.5 6.5 6.5 All Business Types 7.4 7.4 6.9 6.9 6.9

RCU

- Large

- B Small Business 1.0 1.0 1.0 3.2 4.8 All Business Types 1.1 1.1 1.1 3.3 5.0

RCU

- Large

- B1 Small Business 0.9 0.9 0.9 3.2 4.7 All Business Types 0.9 0.9 0.9 3.4 4.9

RCU

- Large

- B2 Small Business 2.8 2.8 2.8 2.8 6.7 All Business Types 3.0 3.0 3.0 3.0 7.0

SCU

- W

- Large

- B Small Business 1.1 1.5 1.7 4.9 4.9 All Business Types 1.1 1.6 1.8 5.1 5.1

SCU

- A

- Small

- B Small Business 2.0 2.2 2.5 3.3 8.4 All Business Types 2.2 2.4 2.6 3.5 8.9

SCU

- A

- Large

- B Small Business 1.7 1.6 2.0 6.2 6.2 All Business Types 1.8 1.6 2.1 6.5 6.5

IMH

- A

- Small

- C Small Business 1.4 1.5 1.6 1.6 8.3 All Business Types 1.5 1.6 1.7 1.7 8.8

IMH

- A

- Large

- C Small Business 0.6 0.6 0.7 0.7 5.5 All Business Types 0.7 0.7 0.7 0.7 5.9

RCU

- Small

- C Small Business 0.7 1.1 1.2 1.2 5.5 All Business Types 0.7 1.2 1.2 1.2 5.8

SCU

- A

- Small

- C Small Business 0.7 1.0 1.4 1.4 10.6 All Business Types 0.8 1.1 1.5 1.5 11.4

11

- 10

### Table 11.3

. 4 Comparison of LCC Savings for the Small Business Subgroup in the Lodging

Sector with the National Average Values

Equipment Class Category Mean LCC Savings
2013$ *

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Small Business 155 155 189 189 (561) All Business Types 175 175 214 214 (534)

IMH

- W

- Med

- B Small Business 275 275 275 123 (109) All Business Types 308 308 308 165 (63)

IMH

- W

- Large

- B Small Business NA NA NA NA (221) All Business Types NA NA NA NA (172)

IMH

- W

- Large

- B1 Small Business NA NA NA NA (244) All Business Types NA NA NA NA (200)

IMH

- W

- Large

- B2 Small Business NA NA NA NA (148) All Business Types NA NA NA NA (80)

IMH

- A

- Small

- B Small Business 118 54 61 61 (423) All Business Types 136 72 77 77 (393)

IMH

- A

- Large

- B Small Business 337 443 321 211 (10)

All Business Types 382 501 361 265 55

IMH

- A

- Large

- B1 Small Business 398 523 368 237 (25) All Business Types 439 580 407 294 45

IMH

- A

- Large

- B2 Small Business 9 9 70 70 70 All Business Types 76 76 110 110 110

RCU

- Large

- B Small Business 679 679 679 347 71 All Business Types 748 748 748 418 144

RCU

- Large

- B1 Small Business 676 676 676 322 90 All Business Types 743 743 743 391 161

RCU

- Large

- B2 Small Business 718 718 718 718 (205)

All Business Types 820 820 820 820 (109)

SCU

- W

- Large

- B Small Business 404 553 494 129 129 All Business Types 444 613 550 192 192

SCU

- A

- Small

- B Small Business 98 142 248 196 (182) All Business Types 110 161 281 230 (145)

SCU

- A

- Large

- B Small Business 146 361 392 18 18 All Business Types 163 400 439 71 71

IMH

- A

- Small

- C Small Business 222 263 282 282 (189) All Business Types 245 292 313 313 (165)

IMH

- A

- Large

- C Small Business 493 493 571 571 (33) All Business Types 539 539 626 626 28

RCU

- Small

- C Small Business 456 406 456 456 (133) All Business Types 498 448 505 505 (73)

SCU

- A

- Small

- C Small Business 204 253 261 261 (288) All Business Types 224 278 290 290 (268)
* Values in parentheses are negative numbers.

11

- 11

### Table 11.3

. 5 Percentage Change in the Mean LCC Savings for the Small Business

Sub g roup in the Lodging Sector Compared to the National Average Values

Equipment Class TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B (11%) (11%) (12%) (12%) (5%)

IMH

- W

- Med

- B (11%) (11%) (11%) (26%) (72%)

IMH

- W

- Large

- B NA NA NA NA (29%)

IMH

- W

- Large

- B1 NA NA NA NA (22%)

IMH

- W

- Large

- B2 NA NA NA NA (84%)

IMH

- A

- Small

- B (13%) (25%) (21%) (21%) (7%)

IMH

- A

- Large

- B (12%) (12%) (11%) (20%) (118%)

IMH

- A

- Large

- B1 (9%) (10%) (10%) (19%) (155%)

IMH

- A

- Large

- B2 (88%) (88%) (37%) (37%) (37%)

RCU

- Large

- B (9%) (9%) (9%) (17%) (50%)

RCU

- Large

- B1 (9%) (9%) (9%) (18%) (44%)

RCU

- Large

- B2 (12%) (12%) (12%) (12%) (88%)

SCU

- W

- Large

- B (9%) (10%) (10%) (33%) (33%)

SCU

- A

- Small

- B (11%) (11%) (12%) (15%) (26%)

SCU

- A

- Large

- B (10%) (10%) (11%) (75%) (75%)

IMH

- A

- Small

- C (9%) (10%) (10%) (10%) (15%)

IMH

- A

- Large

- C (9%) (9%) (9%) (9%) (215%)

RCU

- Small

- C (8%) (9%) (10%) (10%) (83%)

SCU

- A

- Small

- C (9%) (9%) (10%) (10%) (7%)

* Values in parentheses are negative numbers. Negative percentage values imply

a decrease in LCC savings

, and positive percentage values imply an increase in LCC savings.



### Table 11.3

. 6 presents the comparison of median PBPs for small business subgroup in the lodging sector with national median values (median PBPs from chapter 8 of the final rule TSD).

The PBP values are sli ghtly high er in the small business subgroup in all instances. As noted above, the energy savings should be lower in nominal terms than a national average.

11

- 12

### Table 11.3

. 6 Comparison of Median Payback Periods for the Small Business Subgroup in

the Lodging Sector with the National Median Values

Equipment Class Category Median Payback Period

years TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Small Business 2.5 2.5 2.8 2.8 13.5 All Business Types 2.5 2.5 2.7 2.7 13.4

IMH

- W

- Med

- B Small Business 2.1 2.1 2.1 5.1 7.7 All Business Types 2.1 2.1 2.1 5.0 7.6

IMH

- W

- Large

- B Small Business NA NA NA NA 10.7 All Business Types NA NA NA NA 10.6

IMH

- W

- Large

- B1 Small Business NA NA NA NA 11.2 All Business Types NA NA NA NA 11.1

IMH

- W

- Large

- B2 Small Business NA NA NA NA 9.0 All Business Types NA NA NA NA 8.9

IMH

- A

- Small

- B Small Business 3.4 4.8 4.7 4.7 12.3 All Business Types 3.4 4.8 4.7 4.7 11.9

IMH

- A

- Large

- B Small Business 2.2 2.4 2.3 3.9 5.7 All Business Types 2.2 2.4 2.3 3.9 5.6

IMH

- A

- Large

- B1 Small Business 1.2 1.5 1.5 3.4 5.4 All Business Types 1.2 1.5 1.5 3.4 5.4

IMH

- A

- Large

- B2 Small Business 7.5 7.5 6.9 6.9 6.9

All Business Types 7.4 7.4 6.9 6.9 6.9

RCU

- Large

- B Small Business 1.1 1.1 1.1 3.4 5.1 All Business Types 1.1 1.1 1.1 3.3 5.0

RCU

- Large

- B1 Small Business 0.9 0.9 0.9 3.5 5.0 All Business Types 0.9 0.9 0.9 3.4 4.9

RCU

- Large

- B2 Small Business 3.0 3.0 3.0 3.0 7.1 All Business Types 3.0 3.0 3.0 3.0 7.0

SCU

- W

- Large

- B Small Business 1.1 1.6 1.8 5.2 5.2 All Business Types 1.1 1.6 1.8 5.1 5.1

SCU

- A

- Small

- B Small Business 2.2 2.4 2.6 3.5 8.9 All Business Types 2.2 2.4 2.6 3.5 8.9

SCU

- A

- Large

- B Small Business 1.8 1.6 2.1 6.6 6.6 All Business Types 1.8 1.6 2.1 6.5 6.5

IMH

- A

- Small

- C Small Business 1.5 1.6 1.7 1.7 9.0 All Business Types 1.5 1.6 1.7 1.7 8.8

IMH

- A

- Large

- C Small Business 0.7 0.7 0.7 0.7 6.0 All Business Types 0.7 0.7 0.7 0.7 5.9

RCU

- Small

- C Small Business 0.7 1.2 1.2 1.2 5.9 All Business Types 0.7 1.2 1.2 1.2 5.8

SCU

- A

- Small

- C Small Business 0.8 1.1 1.5 1.5 11.7 All Business Types 0.8 1.1 1.5 1.5 11.4



### The LCC subgroup analysis results show that the identified foodservice subgroup has mixed results with some equipment classes showing higher than average LCC savings while others are lower, but for TSLs 1

– 4 the differences are mostly small. The lodging sector

customers would all r e ceive lower LCC saving s due to their higher cost of c apital and lower electricity prices . Foodservice has a l ower PBP than average because of higher

- than

- average electricity prices. Payback periods for l odging business sector customers are higher than the

average.

11

- 13

### REFERENCES

1

. U.S. Department of Education, National Center for Education Statistics. Table 90. Number of public school districts and public and private elementary and secondary schools: Selected years, 1869

- 70 through 2008

- 09 . (Last accessed January 3, 2012.)

< http://nces.ed.gov/programs/digest/d10/tables/dt10_090.asp >

2

. American Hospital Association. Fast Facts on Hospitals . 2012. Chicago, IL, January 3, 2012. (Last accessed January 3, 2012.) < www.aha.org/ >

3

. 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.

4

. U.S. Cen sus Bureau. 2007 Economic Census: Retail Trade: Subject Series

– Estab & Firm Size: Summary Statistics for Single Unit and Multiunit Firms for the United States: 2007

. (Last accessed October 3 0, 201 3 .)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_ 2007_US_44SSSZ3&prodType=table >

5 U.S. Census Bureau. 2007 Economic Census : Accommodation and Food Services: Subject

Series

- Estab and Firm Size: Summary Statistics by Sales Size of Firms for the United

States: 200

. (Last accessed October 30, 2013.)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_ 2007_US_72SSSZ4&prodType=table >

6

. U.S. Department of Energy

— Energy Information Agency, Commercial Buildings Energy Consumption S urvey (CBECS). CBECS Table C

- 14A Electricity Consumption and Expenditure Intensities for All Buildings (Including Malls) . 2003

. ( Last accessed March 8, 2011.) < www.eia.gov/consumption/com mercial/index.cfm >

11

- 14

### CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION

................................ ................................ ................................ ......... 12

- 1

12.2 METHODOLOGY

................................ ................................ ................................ ........ 12

- 1

12.2.1 Phase I: Industry Profile

................................ ................................ .................. 12

- 2

12.2.2 Phase II: Industry Cash

- Flow Model and Interview Guide

............................. 12

- 2

12.2.2. 1 Industry Cash

- Flow Analysis

................................ ............................ 12

- 2

12.2.2.2 Interview Guide

................................ ................................ ................. 12

- 3

12.2.3 Phase III: Subgroup Analysis

................................ ................................ .......... 12

- 3 12.2.3.1 Manufacturing 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 Small

- Business Manufacturers

................................ .......................... 12

- 4 12.2.3.5 Manufacturing Capacity Impact

................................ ........................ 12

- 5 12.2.3.6 Employment Impact

................................ ................................ .......... 12

- 5 12.2.3.7 Cumulative Regulatory Burden

................................ ......................... 12

- 5

12.3 GRIM INPUTS AND ASSU MPTIONS

................................ ................................ ........ 12

- 5

12.3.1 Overview of the GRIM

................................ ................................ .................... 12

- 5

12.3.2 Sources for GRIM Inputs

................................ ................................ ................ 12

- 6 12.3.2.1 Corporate Annual Reports

................................ ................................ . 12

- 6 12.3.2.2 Standard and Poor Credit Ratings

................................ ..................... 12

- 7 12.3.2.3 Shipment Model

................................ ................................ ................ 12

- 7 12.3.2.4 Engineering Analysis

................................ ................................ ......... 12

- 7

12.3.2.5 Manufacturer Interviews

................................ ................................ ... 12

- 7

12.3.3 Financial Parameters

................................ ................................ ....................... 12

- 8

12.3.4 Corporate Discount Rate

................................ ................................ ................. 12

- 8

12.3.5 Trial Standard Levels

................................ ................................ .................... 12

- 10

12.3.6 NIA Shipment Forecast

................................ ................................ ................. 12

- 12

12.3.7 Production Costs

................................ ................................ ............................ 12

- 12

12.3.8 Conversion Costs

................................ ................................ ........................... 12

- 21 12.3.8.1 Automatic Commercial Ice Maker Product and Capital Conversion

Costs

................................ ................................ ................................ .............. 12

- 21

12.3.9 Markup Scenarios

................................ ................................ .......................... 12

- 22 12.3.9.1 Preservation of Gross Margin Markup Scenario

............................. 12

- 22 12.3.9.2 Preservation of EBIT Markup Scenario

................................ .......... 12

- 22

12.4 INDUSTRY FINANCIAL I MPACTS

................................ ................................ ........ 12

- 32

12.4.1 Impacts on Industry Net Present Value

................................ ......................... 12

- 33

12.4.2 Impacts on Annual Cash Flow

................................ ................................ ...... 12

- 34

12.5 IMPACTS ON SMALL AUT OMATIC COMMERCIAL IC E MAKER

MANUFACTURERS

................................ ................................ ................................ .. 12

- 35

12.5.1 Description and Estimated Number of Small Entities Regulated

................. 12

- 36

12.5.2 Description and Estimate of Compliance Requirements

............................... 12

- 37

12.6 OTHER IMPACTS

................................ ................................ ................................ ...... 12

- 38

12

- i

### 12.6.1 Employment

................................ ................................ ................................ .. 12

- 38 12.6.1.1 Impacts on Direct Employment

................................ ....................... 12

- 38

12.6.2 Production Capacity

................................ ................................ ...................... 12

- 40

12.6.3 Cumulative Regulatory Burden

................................ ................................ ..... 12

- 41

12.6.3.1 Other Regulations That Could Impact Automatic Commercial Ice

Maker Manufacturers

................................ ................................ .................... 12

- 41

12.7 CONCLUSION

................................ ................................ ................................ ............ 12

- 42

12.7.1 Automatic Com mercial Ice Makers

................................ ............................... 12

- 42

REFERENCES

................................ ................................ ................................ ........................ 12

- 45

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by This Rulemaking

................................ ................................ ................................ .................... 12

- 4

Table 12.3.1 GRIM Financial Parameters based on 2005

– 2011 Weighted Company Financial

Data

................................ ................................ ................................ ................................ 12

- 8 Table 12.3.2 Cost of Equity Calculation

................................ ................................ .................... 12

- 9

Table 12.3.3 Cost of Debt Calculation

................................ ................................ ..................... 12

- 10

Table 12.3 .4 Crosswalk between Equipment Class, Capacity Group, and Capacity Group

Number

................................ ................................ ................................ ........................ 12

- 11 Table 12.3.5 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Small

- B

........ 12

- 13 Table 12.3.6 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Medium

- B

.. 12

- 13 Table 12.3.7 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Large

- B

........ 12

- 13 Table 12.3.8 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Ext1

- B

.......... 12

- 13

Table 12.3.9 Manufacture r Production Cost Breakdown (2013$) for IMH

- W

- Ext2

- B

.......... 12

- 13 Table 12.3.10 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Small

- B

....... 12

- 13 Table 12.3.11 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Medium

- B

... 12

- 14 Table 12.3.12 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Large

- B

....... 12

- 14 Table 12.3.13 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Ext

- B

........... 12

- 14

Table 12.3.14 Manufacturer Production Cost Breakdo wn (2013$) for RCU

- NRC

- Small

- B

.. 12

- 14 Table 12.3.15 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Large

- B

.. 12

- 14

Tabl e 12.3.16 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Ext1

- B

... 12

- 15 Table 12.3.17 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Ext1

- B2

. 12

- 15 Table 12.3.18 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Small

- B

.... 12

- 15 Table 12.3.19 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Large

- B

.... 12

- 15 Table 12.3.20 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Ext1

- B

...... 12

- 15

Table 12.3.21 Manufac turer Production Cost Breakdown (2013$) for RCU

- RC

- Ext2

- B

..... 12

- 15 Table 12.3.22 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Small

- B

...... 12

- 16 Table 12.3.23 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Large

- B

...... 12

- 16 Table 12.3.24 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Small

- B

....... 12

- 16 Table 12.3.25 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Large

- B

....... 12

- 16

Table 12.3.26 Manufacturer Production Cost B reakdown (2013$) for SCU

- A

- Ext

- B

........... 12

- 17 Table 12.3.27 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Small

- C

...... 12

- 17

Tabl e 12.3.28 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Large

- C

...... 12

- 17 Table 12.3.29 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Small

- C

....... 12

- 17 Table 12.3.30 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Large

- C

....... 12

- 18

12

- ii

### Table 12.3.31 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Ext

- C

........... 12

- 18 Table 12.3.32 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Small

- C

.. 12

- 18

Table 12.3.33 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Large

- C

.. 12

- 19 Table 12.3.34 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Small

- C

.... 12

- 19 Table 12.3.35 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Large

- C

.... 12

- 19 Table 12.3.36 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Small

- C

...... 12

- 19 Table 12.3.37 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Large

- C

...... 12

- 20

Table 12.3.38 Manufacturer Production Cost Bre akdown (2013$) for SCU

- A

- Small

- C

....... 12

- 20 Table 12.3.39 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Large

- C

....... 12

- 20

Tabl e 12.3.40 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Ext

- C

........... 12

- 20 Table 12.3.41 Product and Capital Conversion Costs for Standard Ice Makers by TSL

........ 12

- 21 Table 12.3.42 Preservation of Gross Margin Markups for Automatic Commercial Ice Makers, Representative of all Equipment Classes

................................ ................................ ..... 12

- 22

Table 12.3.43 Prese rvation of EBIT Markups for IMH

- W

- Small

- B

................................ ....... 12

- 23 Table 12.3.44 Preservation of EBIT Markups for IMH

- W

- Medium

- B

................................ .. 12

- 23

Table 12.3. 45 Preservation of EBIT for IMH

- W

- Large

- B

................................ ...................... 12

- 23 Table 12.3.46 Preservation of EBIT Markups for IMH

- W

- Ext1

- B

................................ ......... 12

- 23

Table 12.3.47 Preservation of EBIT Markups for IMH

- W

- Ext2

- B

................................ ......... 12

- 23 Table 12.3.48 Preservation of EBIT Markups for IMH

- A

- Small

- B

................................ ........ 12

- 24

Table 12. 3.49 Preservation of EBIT Markups for IMH

- A

- Medium

- B

................................ ... 12

- 24 Table 12.3.50 Preservation of EBIT Markups for IMH

- A

- Large

- B

................................ ........ 12

- 24

Ta ble 12.3.51 Preservation of EBIT Markups for IMH

- A

- Ext

- B

................................ ........... 12

- 24 Table 12.3.52 Preservation of EBIT Markups for RCU

- NRC

- Small

- B

................................ .. 12

- 25 Table 12.3.53 Preservation of EBIT Markups for RCU

- NRC

- Large

- B

................................ .. 12

- 25 Table 12.3.54 Preservation of EBIT Markups for RCU

- NRC

- Ext1

- B

................................ .... 12

- 25 Table 12.3.55 Preservation of EBIT Markups for RCU

- NRC

- Ext1

- B2

................................ .. 12

- 25 Table 12.3.56 Preservation of EBIT Markups for RCU

- RC

- Small

- B

................................ ..... 12

- 25 Table 12.3.57 Preservation of EBIT Markups for RCU

- RC

- Large

- B

................................ ..... 12

- 26 Table 12.3.58 Preservation of EBIT Markups for RCU

- RC

- Ext1

- B

................................ ...... 12

- 26 Table 12.3.59 Preservation of EBIT Markups for RCU

- RC

- Ext2

- B

................................ ...... 12

- 26 Table 12.3.60 Preservation of EBIT Markups for SCU

- W

- Small

- B

................................ ....... 12

- 26 Table 12.3.61 Preservation of EBIT Markups for SCU

- W

- Large

- B

................................ ....... 12

- 27 Table 12.3.62 Preservation of EBIT Markups for SCU

- A

- Small

- B

................................ ........ 12

- 27 Table 12.3.63 Preservation of EBIT Markups for SCU

- A

- Large

- B

................................ ........ 12

- 27 Table 12.3.64 Preservation of EBIT Markups for SCU

- A

- Ext

- B

................................ ........... 12

- 28 Table 12.3.65 Preservation of EBIT Markups for IMH

- W

- Small

- C

................................ ....... 12

- 28 Table 12.3.66 Preservation of EBIT Markups for IMH

- W

- Large

- C

................................ ....... 12

- 28 Table 12.3.67 Preservation of EBIT Markups for IMH

- A

- Small

- C

................................ ........ 12

- 29 Table 12.3.68 Preservation of EBIT Markups for IMH

- A

- Large

- C

................................ ........ 12

- 29 Table 12.3.69 Preservation of EBIT Markups for IMH

- A

- Ext

- C

................................ ........... 12

- 29 Table 12.3.70 Preservation of EBIT Markups for RCU

- NRC

- Small

- C

................................ .. 12

- 30 Table 12.3.71 Preservation of EBIT Markups for RCU

- NRC

- Large

- C

................................ .. 12

- 30 Table 12.3.72 Preservation of EBIT Markups for RCU

- RC

- Small

- C

................................ ..... 1 2

- 30 Table 12.3.73 Preservation of EBIT Markups for RCU

- RC

- Large

- C

................................ ..... 12

- 31 Table 12.3.74 Preservation of EBIT Markups for SCU

- W

- Small

- C

................................ ....... 12

- 31 Table 12.3.75 Preservation of EBIT Markups for SCU

- W

- Large

- C

................................ ....... 12

- 31

12

- iii

### Table 12.3.76 Preservation of EBIT Markups for SCU

- A

- Small

- C

................................ ........ 12

- 32 Table 12.3.77 Preservation of EBIT Markups for SCU

- A

- Large

- C

................................ ........ 12

- 32

Table 12.3.78 Preservation of EBIT Marku ps for SCU

- A

- Ext

- C

................................ ........... 12

- 32 Table 12.4.1 Manufacturer Impact Analysis for Automatic Commercial Ice Makers

– Preservation of Gross Margin Markup Scenario

................................ ......................... 12

- 33 Table 12.4.2 Manufacturer Impact Analysis for Automatic Commercial Ice Makers

– Preservation of EBIT Markup Scenario

................................ ................................ ....... 12

- 34 Table 12.5.1 Comparison of Small Business Manufacturers of Automatic Commercial Ice Maker INPV to that of the Industry at Large by TSL under the Preservation of Gross Margin Markup Scenario*

................................ ................................ ................................ ........ 12

- 38 Table 12.5.2 Comparison of Small Bus iness Manufacturers of Automatic Commercial Ice Maker INPV to that of the Industry at Large by TSL under the Preservation of EBIT Markup

Scenario

................................ ................................ ................................ ........................ 12

- 38 Table 12.6.1 Potential Changes in the To tal Number of Domestic Automatic Commercial Ice

Maker Production Workers in 2018*

................................ ................................ ........... 12

- 40 Table 12.6.2 Compliance Dates and Expected Conversion Expenses of Federal Energy

Conservation Standards Affecting Automatic Commercial Ice Maker Manufacturers

... 12

- 41

LIST OF FIGURES

Figure 12.3.1 Using the GRIM to Calculate Cash Flow

................................ ............................ 12

- 6 Figure 12.4.1 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers (Preservation of Gross Margin Markup Scenario)

................................ ....................... 12

- 35 Figure 12.4.2 Annual Ind ustry Net Cash Flows for Automatic Commercial Ice Makers (Preservation of EBIT Markup Scenario)

................................ ................................ .... 12

- 35

12

- iv

### 12.0 MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. De partment of Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers and on the consumers of the products subject to such a standard.” (42 U.S.C. 6295(o)(2)(B)(i) )

The statute also calls for an assessment of the impact of any lessening of competition as determined in writing by the Attorney General. Id . DOE conducted a manufacturer impact analysis (MIA) to estimate the financial impact of more stringent energy conservation standards

on manufacturers of a utomatic c ommerc ial i ce m aker s, 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 industry cash

- flo w model adapted for each equipment class 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) , which is the sum of discounted industry cash

- flow over the analysis period . The model estimates the financial impact of more stringent energy conservation standards by comparing changes in INPV bet ween a base case and at each trial standard levels (TSLs) in the standards case. The qualitative part of the MIA addresses product characteristics, manufacturer characteristics, market and product trends, as well as the impact of standards on subgroups of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phase s. Phase I, “Industry Profile,” consisted of preliminary research directed at characterizing the a utomatic c ommercial i ce m aker manufacturing industry. This research involved collecting data on market share, sales volumes and trends, pricing, employment, a nd the industry financial structure.

In Phase II, “Industry Cash Flow,” DOE created a framework discounted cash flow

model to analyze the economic impact of new and amended energy conservation standards on

the a utomatic c ommercial i ce m aker manufacturing industry as a whole. DO E presented the MIA

results for automatic commercial ice makers based on a set of considered TSLs. These TSLs are

described in section 12.3.5

.

In Phase III, “Subgroup Impact Analysis,” DOE interviewed man ufacturers representing

over 95 percent of the a utomatic c ommercial i ce m aker market. Interviewees included manufacturers with various market shares and product focus, providing a representative cross

section of the industry. During interviews, DOE discuss ed financial topics specific to each manufacturer and obtained each manufacturer’s view of the industry. The interviews provided DOE with valuable information for evaluating the impacts of new and amended energy conservation standards on manufacturer cash flows, investments, and employment.

12

- 1

### 12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the a utomatic c ommercial i ce m aker industry that built upon the market and technology assessment prepared for this rulemaking. (See chapter 3 o f the T echnical Support Document (TSD) . Before initiating the detailed impact studies, DOE collected information on the present and past market structure and characteristics of the industry, tracking trends in market share, product attributes, product ship ments, manufacturer markups, and the cost structure for various manufacturers.

The profile also included a top

- down analysis of manufacturers in the industry using Security and Exchange Commission (SEC) 10

– K filings

, a 

### Standard & Poor (S&P) stock reports, b


### and corporate annual reports released by both public and privately held companies. DOE used this and other publicly available information to derive preliminary financial inputs for the GRIM ( e.g. revenues; cost of goods sold; depreciation; selling, genera l and administrative expenses (SG&A); and research and development (R&D) expenses).

12.2.2 Phase II: Industry Cash

- Flow Model and Interview Guide

Phase II focused on the financial impacts of new and amended energy conservation

standards on the a utomatic c ommerci al i ce m aker manufacturing industry as a whole. New and

amended energy conservation standards can affect manufacturer cash flows in three distinct ways: (1) by creating a need for increased investment, (2) by raising production costs per unit, and (3) by a ltering revenue due to higher per

- unit prices and/or possible changes in sales

volumes. DOE created a framework discounted cash

- flow model to analyze the economic impact

of new and amended energy conservation standards on the automatic commercial ice maker manufacturing industry as a whole

. In p reparing this model , DOE used the financial values derived during Phase I and the shipment assumptions from the NIA. Additionally , DOE prepared

a written guide for manufacturer interviews to collect additional data c ritical to develop ing other inputs for the cash

- flow analysis

.

12.2.2.1 Industry Cash

- Flow Analysis

The model uses several factors to determine a series of annual cash flows from the announcement year of new and amended energy conservation standards until several y ears after the standards’ compliance date. INPV is the sum of these annual cash flows discounted by the industry weighted average cost of capital. Inputs to the model include the manufacturing costs, markups, and shipment forecasts developed in other analy ses as well as the industry weighted average financial parameters developed in Phase I

. DOE derived the manufacturing costs from the engineering analysis as presented in chapter 5 of the TSD , information provided by the industry, publicly available financi al reports, and interviews with manufacturers. To examine the range of possible impacts, DOE developed alternative markup scenarios based on discussions with manufacturers. DOE’s shipments analysis, presented in c hapter 9 of the TSD, provided the

basis for the shipment projections. DOE derived the financial parameters using publicly available reports and revised them using information submitted confidentially during manufacturer

interviews. DOE used the GRIM to compare INPV in the base case with INPV at var ious TSLs

a A vailable online at www.sec.gov

. b A vailable online at www2.standardandpoors.com

.

12

- 2

### (the standards cases). The difference in INPV between the base and standards cases represents the financial impact of the new and amended standard on manufacturers.

12.2.2.2 Interview Guide

During Phase III of the MIA, DOE interviewed manufacturers of a u tomatic c ommercial

i ce m aker s to gather information on the effects of new and amended energy conservation on revenues and finances, direct employment, capital assets, and industry competitiveness. Before the interviews, DOE distributed an interview guide t o representatives of each participating manufacturer. The interview guide provided a starting point to help identify relevant issues and understand the impacts of new and amended energy conservation standards on individual manufacturers or subgroups of man ufacturers. Most of the information DOE received from these meetings is protected by non

- disclosure agreements and resides with DOE’s contractors. The topics covered as part of these interviews include (1) key issues to this rulemaking; (2) engineering and life cycle cost follow

- up; (3) manufacturer markups and profitability; (4) financial parameters; and (5) conversion costs.

12.2.3 Phase III: Subgroup Analysis

While conducting the MIA, DOE interviewed a representative cross

- section of a utomatic

c ommercial i ce m aker manufacturers. The MIA interviews broadened the discussion to include

business

- related topics. DOE sought to obtain feedback from industry on the approaches used in

the cash flow model and to isolate key issues and concerns. During interviews, DOE ide ntif ied

one manufacturer subgroup , small businesses, that would warrant a subgroup analysis.

12.2.3.1 Manufacturing Interviews

The information gathered in Phase I and the cash

- flow analysis performed in Phase II are supplemented with information gathered from manuf acturer interviews in Phase III. The interview process provides 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 cash flow model to reflect the unique financial

characteristics of the a utomatic c ommercial i ce m aker manufacturing industry. Companies with various market shares and product focus were interviewed to provide a representation of the 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, which help clarify responses and identify additional issues. The resulting information provides valuable inputs to the cash

- flow model

.

12.2.3.2 Revised Industry Cash

- Flow Analysis

In Phase II of the MIA, DOE provided manufacturers with preliminary financial figures

for review and evaluation. During the interviews, DOE requested comments on the values it selected for the parameters. DOE revised its industry cash

- flow model based on this feedback.

Section 12.3.3 provides more information o n how DOE calculated the parameters.

12

- 3

### 12.2.3.3 Manufacturer Subgroup Analysis

Using average cost assumptions to develop an industry cash flow estimate may not adequately assess differential impacts of new and amended energy conservation standards on

manufacturer su bgroups. For example, small businesses, manufacturers of niche products, or companies exhibiting a cost structure that differs significantly from the industry average could be more negatively affected. DOE used the results of the industry characterization to group manufacturers exhibiting 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 c haracteristics are the most appropriate to analyze. Through the course of the rulemaking, DOE identified small

business manufacturers as a subgroup which requires separate analysis.

12.2.3.4 Small

- Business Manufacturer s

DOE used the Small Business Administration (S BA) small business size standards as new and amended by the Office of Management and Budget on January 1, 2012, and effective January 7, 2013 , and the North American Industry Classification System (NAICS) code, presented in Table 1 2.2

. 1 , to determine whether any small entities would be affected by the rulemaking. c 

### For the equipment classes under review, the SBA bases its small business definition on the total number of employees for a business including the total employee count of a parent company and its subsidiaries. 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 Pot entially 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 33 3415



### DOE used the Air

- Conditionin g, Heating, and Refrigeration Institute ( AH RI ) 1 

### certified product directory as well as public certification databases California Energy Commission (CEC), 2 

### and ENERGY STAR 3 

### to identify manufacturers of a utomatic c ommercial i ce m aker s. DOE also consulted pub licly available data from the Small Business Association (SBA) to determine the presence of any additional small businesses in the industry. Further, DOE asked interested parties and industry representatives if they were aware of other small business

manuf acturers and checked any companies identified against the small business criteria

. During its research, DOE identified seven manufacturer s of a utomatic c ommercial i ce m aker s that qualify as small business es as specified by the SBA employee limits

. DOE cal culated separate

INPV and cash flow numbers for small businesses in the automatic commercial ice makers GRIM. These figures can be found in the “Summary_SB” tab of the GRIM.

c The size standards are available on the SBA’s website at www.sba.gov/content/table

- small

- business

- size

- standards

12

- 4

### 12.2.3.5 Manufacturing Capacity Impact

One significant outcome of new and amended energy conservation standards could be the obsolescence of existing manufacturing assets, including tooling and production equipment. The manufacturer interview guide contains a series of questions to help identify impacts of new and

amended standards on manufac turing capacity, specifically capacity utilization and plant location decisions in the United States and North America, with and without new and amended standards; the ability of manufacturers to upgrade or remodel existing facilities to accommodate the ne w requirements; the nature and value of any stranded assets; and estimates for any one

- time changes to existing plant, property, and equipment (PPE). DOE’s estimates of the one

- time capital changes and stranded assets affect the annual cash flow projection s in the model . These

estimates can be found in section 12.3.8 ; DOE’s discussion of the capacity impact can be found

in section 12.6.2

.

12.2.3.6 Employment Impact

The impact of new and 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 Automatic

Commercial Ice Maker 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 asso ciated with manufacturers at each production facility, expected future employment levels with and without

new and amended energy conservation standards, and differences in workforce skills and issues related to the retraining of employees. The employment i mpacts are reported in section 12.6.1

.

12.2.3.7 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to new and amended energy conservation standards and other regulatory actio ns 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

Automatic Commercial Ice Maker m anufacturers, such as other Federal regulations that impact other products made by the same manufacturers. Discussion of the cumulative regulatory burden

can be found in section 12.6.3

.

12.3 GRIM INPUTS AND ASSUMPTIO NS

The GRIM ser ves as the main tool for assessing the impacts on industry due to new and amended energy conservation standards analyzed in this rulemaking . DOE relies on several sources to obtain inputs for the GRIM. Data and assumptions from these sources are then fed i nto an accounting model that calculates the industry cash flow both with and without new and amended energy conservation standards.

12.3.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.3

. 1 , is an annual cash flow analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial

12

- 5

### information as inputs, and accepts a set of regulatory conditions such as changes in costs, investments, and associated margins. The GRIM spreadsheet u ses a number of inputs to arrive at a series of annual cash flows, beginning with the base year of the analysis, 2015 , and continuing

to 204 7 . The model calculates the INPV by summing the stream of annual discounted cash flows during this period.

Figure 12.3

. 1 Using the GRIM to Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares changes in INPV between the base

- case and the standard

- case scenario induced by new and

amended energy conservation standards. The difference in INPV between the base case and the standard case(s) represents the estimated financial impact of the new and amended energy conservation standards on manufacturers. Appendix 12

- A provides more te chnical details and

user information for the GRIM.

12.3.2 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, U.S. Census data, credit ratings, the shipments model, the engineering analysis, and the manufacturer interviews.

12.3.2.1 Corporate Annual Reports

Corporate annual reports for publicly held companies are freely available to the general public through the SEC as filings of F orm 10

- K. Addi tionally, some privately held companies publish annual financial reports on their corporate websites. DOE developed initial financial inputs to the GRIM by examining the publicly available annual reports of companies primarily engaged in the manufacture of home appliances whose combined product range includes

Automatic Commercial Ice Maker s. As these companies do not provide detailed information about their individual product lines, DOE used the aggregate financial information at the corporate level in deve loping its initial estimates of the financial parameters to be used in the

12

- 6

### GRIM. In doing so, DOE assumes that the industry

- average figures calculated for these companies were representative of manufacturing for Automatic Commercial Ice Maker s. These figur es were later revised using feedback from interviews to be representative of manufacturing for each product. DOE used corporate annual reports to derive the following initial inputs to the

GRIM:

• Tax rate ;

• Working capital ;

• SG&A ;

• R&D ;

• Depreciation ;

• Capital expenditures ; and

• Net property, plant, and equipment

.

12.3.2.2 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 weighted

average cost of capital.

12.3.2.3 Shipment Model

The GRIM us ed shipment projections from DOE’s shipments model in the national impact analysis (NIA). The model relied on historical shipments data for Automatic Commercial

Ice Maker s. Chapter 9 of the T SD describes the methodology and analytical model DOE used to forecast shipments.

12.3.2.4 Engineering Analysis

DOE conducted the engineering analyses for this rulemaking using the efficiency

- level approach, combined with the cost

- assessment approach, to develop a co st for each efficiency

level in each equipment class and capacity grouping of Automatic Commercial Ice Maker s. During this analysis, DOE used a manufacturing cost model to develop manufacturer production cost (MPC) estimates for Automatic Commercial Ice Maker s. The analysis yielded the labor, materials, overhead, and total production costs for products at each efficiency level. The engineering analysis also estimated a manufacturer markup to determine the manufacturer selling price (MSP) for each product at every efficiency level. Chapter 5 of the TSD describes the engineering analysis in detail.

12.3.2.5 Manufacturer Interviews

As part of the MIA, DOE conducted interviews with a representative cross

- section of manufacturers. Through these discussions, DOE obtaine d information to determine and verify GRIM input assumptions. Key topics discussed during the interviews and reflected in the GRIM

include:

• Capital conversion co sts (one

- time investments in plant, property and equipment );

12

- 7

### • Product conversion costs (on e

- time investments in research, product development, testing, and marketing);

• Product ion cost structure, or the portion of the MPCs related to materials, labor, overhead, and depreciation costs;

• Projected total shipment and shipment distribution mix; and

• MPCs estimated in the engineering analysis.

12.3.3 Financial Parameters

In the manufacturer interviews, DOE used the financial parameters from 2003 to 2010 for four appliance manufacturers with a combined market share of over 90 percent as a starting point for determining the Automatic Commercial Ice Maker industry financial parameters. The industry financial parameters were determined by weighting each manufacturer’s individual financial parameters by their respective market share, and correcting for the f raction of the market that was not represented. Table 12.3

. 1 below shows the data used to determine the initial financial parameter estimates.

Table 12.3

. 1 GRIM Fi nancial Parameters b ased on 2005

– 2011 Weighted Company

Financial Data

Parameter Industry

Weighted Average Manufacturer

A B C D

Tax Rate (% of Taxable Income) 33.6 42.6 25.4 14.0 30.7 Working Capital (% of Revenue) 9.3 11.9 20.7 3.8 3.9

SG&A (% of Revenue) 15.4 17.8 24.3 13.1 10.4

R&D (% of Revenues) 1.5 1.8 2.8 2.8 2.4 Depreciation (% of Revenues) 2.1 2.8 3.4 2.2 3.1 Capital Expenditures (% of Revenues) 1.5 3.1 4.2 2.6 3.2 Net Property, Plant, and Equipment (% of Revenues) 16.4 14.4 16.3 20.9 17.6



### During interviews , manufacturers were asked to provide their own figures for the parameters listed in Table 12.3

. 1 . DOE adjusted the tax rate, depreciation and capital expenditures according to the manufacturers’ feedback.

12.3.4 Corporate Discount Rate

A company’s assets are financed by a combination of debt and equity

, and the weighted

average cost of capital (WACC) represents the minimum rate of return necessary to cover the debt and equity obligations manufacturers use to finance operations. The WACC is the total cost of debt and equity weighted by their respective proportions in the capital structure of the

company.

DOE estimated the WACC for Automatic Commercial Ice Maker industry based on several representative companies, using the following formul a:

WACC = After

- Tax Cost of Debt x (Debt Ratio) + Cost of Equity x (Equity Ratio)

12

- 8

### The cost of equity is the rate of return that equity investors (including, potentially, the company) expect to earn on a company’s stock. These expectations are reflected in the market price of the company’s stock. The capital asset pricing model (CAPM) provides one widely used means to estimate the cost of equity. According to the CAPM, the cost of equity (expected return) is:

Cost of Equity = Risk

- free Rate of Return + β x Risk Premium

where:

Risk

- free rate of return is the rate of return on a “safe” benchmark investment, typically

considered the short

- term Treasury Bill (T

- Bill) yield. In practice, investors use a variety of different maturity T

- Bills to estimate the risk

- free rate. DOE used the 10

- year T

- Bill 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

- Bill

return betwe en 19 28 and 2010.

Risk premium is the difference between the expected return on stocks and the risk

- free

rate of return

. DOE used the average annual return on the S&P 500 between 1928 and 2010 as the expected return on stocks to arrive at an estimated mark et risk premium of 6.1 percent.

Beta (β) is the correlation between the movement in the price of the stock and that of the broader market. In this case, Beta equals one if the stock is perfectly correlated with the S&P 500

market index. A Beta lower than one means the stock is less volatile than the market index. Values for Beta are only available for publicly traded companies.

DOE used the capital asset pricing model to calculate the cost of equity for three publicly

traded Ice Maker manufacturers whose c ombined market share is over 90 percent. DOE determined that the industry

- average cost of equity for the Automatic Commercial Ice Maker industry is 16. 7 percent ( see Table 12.3

. 2 ).

Table 12.3

. 2 Cost of Equity Calculation

Parameter Industry

Weighted Average

% Manufacturer

A B C D

( 1) Average Beta 1. 9 1.5 n/a 1.7 2.0 (2) Yield on 10

- Year

T

- Bill (19 28

- 2010) 5. 2

- - - -
(3) Market Risk Premium (192 8

- 2010 ) 6.1

- - - - Cost of Equ ity (2)+[(1)*(3)] 16. 7 14. 4 n/a 15. 5 17. 5 Equity/Total Capital 68.6 71.0 86.5 92.7 65.8



### 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 compan y’s cost of debt is to treat it as a spread (usually expressed in basis points) over the risk

- free rate. DOE used this

12

- 9

### method to calculate the cost of debt for three manufacturers by using S&P ratings and adding the relevant spread to the risk

- free rate.

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. DOE

determined that the after

- tax industry

- average cost of debt for the Automatic Commercial Ice

Maker industry is 4.5 percent

. Table 12.3

. 3 presents the derivation of the cost of debt and the capital structure of the industry ( i.e. the debt ratio (debt/total capital)).

Table 12.3

. 3 Cost of Debt Calculation

Parameter Industry

Weighted Average

% Manufacturer

A B C D

S&P Bond Rating

-- BBB A AA BBB

(1) Yield on 10

- Year T

- Bill (19 28

- 2010) 5. 2

- - - -
(2) Gross Cost of Debt 6.8 6.8 6.2 5.9 6.8

(3) Tax Rate 33.3 42.6 25.4 14.0 30.7

Net Cost of Debt [ (2) x ((1)

- (3)) ] 4. 5

- - - -
Debt/Total Capital 31.4 29.0 13.5 7.3 34.2



### Correcting for an inflation rate of 3.1 percent over the analysis period, DOE’s calculated

value for the Automatic Commercial Ice Maker indus try’s inflation

- adjusted WACC and the initial estimate of the discount rate is 8.1 percent. DOE adjusted this figure to 8.5 percent for the

GRIM based on feedback received during manufacturer interviews

.

12.3.5 Trial Standard Levels

DOE developed TSLs to analyze the impact on manufacturers of new and amended energy efficiency s tandards on the capacity groups of Automatic Commercial Ice Maker s

. To

assess the impact of each TSL on INPV, DOE established 36 “capacity groups” for use in the GRIM. The capacity groups a lign with the capacity ranges used to define the piece

- wise linear minimum energy consumption equations for each TSL. The capacity groups are more granular

than the equipm ent classes defined in chapter 5 and are consistent with the groupings used in the

en gineering analysis

.

Table 12.3.4 provides a crosswalk between equipment classes and cap acity groups and also provides the minimum energy consumption equations for each TS L, as first defined in chapter 3

.

12

- 10

### Table 12.3

. 4 Crosswalk between Equipment Class, Capacity Group, and Capacity Group

Number

Equipment

Class Capacity Group Capacit

y Group

Numbe

r Capacity Range lb/day TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

B IMH

- W

- Small

- B 01 <300 7.19

– 0.0055H 7.19

– 0.0055H 6.88

– 0.0055H 6.88

– 0.0055H 6.32

– 0.0055H

IMH

- W

- Med

- B IMH

- W

- Medium

- B 02 ≥300 and <850 6.28

– 0.00247H 6.28

– 0.00247H 5.8

– 0.00191H 5.9

– 0.00224H 5.17

– 0.00165H

IMH

- W

- Large

B IMH

- W

- Large

- B 03 ≥850 and <1500 4.42

– 0.00028H 4.42

– 0.00028H

4.0 4.0 3.86

– 0.00012H

IMH

- W

Extended

- B IMH

- W

- Ext1

- B 04 ≥1,500 and <2,600 4.0 4.0 3.62 +

0.00004H

IMH

- W

- Ext2

- B 05 ≥2,600 3.72

IMH

- A

- Small

- B IMH

- A

- Small

- B 06 <300 10.09

– 0.0106H 10.05

– 0.01173H 10

– 0.0123 3H 10

– 0.01233H 9.38

– 0.01233H

IMH

- A

Medium

- B IMH

- A

- Medium

- B 07 ≥300 and <800 7.81

– 0.003H 7.38

– 0.00284H 7.05

– 0.0025H 7.19

– 0.00298H 6.31

– 0.0021H

IMH

- A

- Large

- B IMH

- A

- Large

- B 08 ≥800 and <1,500 6.21

– 0.00099H 5.56

– 0.00056H 5.55

– 0.00063H 5.0 4

– 0.00029H 4.65

– 0.00003H

IMH

- A

Extended

- B IMH

- A

- Ext

- B 09 >1,500 4.73 4.72 4.61 4.61 4.61

RCU

- NRC

Small

- B RCU

- NRC

- Small

B 10 <1,000 7.97

– 0.00342H 7.97

– 0.00342H 7.97

– 0.00342H 7.52

– 0.00323H 7.35

– 0.00316H

RCU

- NRC

Large

- B RCU

- NRC

- Large

B 11 ≥ 1,000 and <1,500

4.55 4.55 4.55 4.29 4.19

RCU

- NRC

Extended

- B RCU

- NRC

- Ext1

- B 12 ≥1,500 and < 2,400 3.93 +

0.00017H

RCU

- NRC

- Ext2

- B 13 ≥2,400 4.34

RCU

- RC

- Small

B RCU

- RC

- Small

- B 14 <942 7.97

– 0.00342H 7.97

– 0.00342H 7.97

– 0.00342H 7.52

– 0.0 0323H 7.35

– 0.00316H

RCU

- RC

- Large

B RCU

- RC

- Large

- B 15 ≥942 and < 1,500

4.75 4.75 4.75 4.49 4.39

RCU

- RC

Extended

- B RCU

- RC

- Ext1

- B 16 ≥1,500 and < 2,400 4.13 +

0.00017H

RCU

- RC

- Ext2

- B 17 ≥2,400 4.54

SCU

- W

- Small

B SCU

- W

- Small

- B 18 <200 10.64

– 0.019H 9.88

– 0.019H 9.5

– 0.019H 9.14

– 0.019H 9.14

– 0.019H

SCU

- W

- Large

B SCU

- W

- Large

- B 19 ≥200 6.84 6.08 5.7 5.34 5.34

SCU

- A

- Small

- B SCU

- A

- Small

- B 20 <110 16.72

– 0.0469H 15.43

– 0.0469H 14.79

– 0.0469H 14.15

– 0.0469H 13.76

– 0.0469H

SCU

- A

- Large

- B SCU

- A

- Large

- B 21 ≥110 and <200 14.91

– 0.03044H 13.24

– 0.027H 12.42

– 0.02533H 11.47

– 0.02256H 10.6

– 0.02

SCU

- A

Extended

- B SCU

- A

- Ext

- B 22 ≥200 8.82 7.84 7.35 6.96 6.96

IMH

- W

- Small

C IMH

- W

- Small

- C 23 <801 7.29

– 0.003H 6.89

– 0.00283H 6.48

– 0.00267H 6.48

– 0.00267H 5.75

– 0.00237H

IMH

- W

- Large

C IMH

- W

- Large

- C 24 ≥801 4.59 4.59 4.34 4.34 3.93

IMH

- A

- Small

- C IMH

- A

- Small

- C 25 <310 10.1

– 0.00629H 9.64

– 0.00629H 9.19

– 0.00629H 9.19

- 0.00629H 8.38

– 0.00629H

IMH

- A

- Large

- C IMH

- A

- Large

- C 26 ≥310 and <820 9.49

– 0.00433H 8.75

– 0.00343H 8.23

– 0.0032H 8.23

– 0.0032H 7.25

– 0.00265H

IMH

- A

Extended

- C IMH

- A

- Ext

- C 27 ≥820 5.94 5.94 5.61 5.61 5.08

RCU

- NRC

Small

- C RCU

- NRC

- Small

C 28 <800 9.85

– 0.00519H 9.78

– 0.0055H 9.7

– 0.0058H 9.7

– 0.0058H 9.26

– 0.0058H

RCU

- NRC

Large

- C RCU

- NRC

- Large

C 29 ≥800 5.7 5.38 5.06 5.06 4.62

RCU

- RC

- Small

C RCU

- RC

- Small

- C 30 <800 10.05

– 0.00519H 9.98

– 0.0055H 9.9

– 0.0058H 9.9

– 0.0058H 9.46

– 0.0058H

RCU

- RC

- Large

C RCU

- RC

- Large

- C 31 ≥800 5.9 5.58 5.26 5.26 4.82

SCU

- W

- Small

C SCU

- W

- Small

- C 32 <900 8.55

– 0.0034H 8.08 0.0032H 7.6

– 0.00302H 7.6

– 0.00302H 6.84

– 0.00272H

SCU

- W

- Large

C SCU

- W

- Large

- C 33 ≥900 5.49 5.19 4.88 4.88 4.39

SCU

- A

- Small

- C SCU

- A

- Small

- C 34 <200 15.26

– 0.03 14.73

– 0.03H 14.22

- 0.03H 14.22

– 0.03H 13.4

– 0.03H

SCU

- A

- Large

- C SCU

- A

- Large

- C 35 ≥200 and 700 10.66

– 0.00702H 10.06

– 0.00663H 9.47

– 0.00624H 9.47

– 0.00624H 8.52

– 0.00562H

SCU

- A

Extended

- C SCU

- A

- Ext

- C 36 ≥700 5.75 5.42 5.1 5.1 4.59

12

- 11

### 12.3.6 NIA Shipment Forecast

The GRIM estimates manufacturer reve nues based on total unit

- shipment forecasts and the distribution of these values by effic iency level. Changes in the efficiency mix at each standard level are a key driver of manufacturer finances. For this analysis, the GRIM used the NIA’s annual shipment forecasts from 201 5 t hrough 2047. To estimate efficiency trends in the standards cases, DOE uses a “roll

- up” scenario in its standards rulemakings. Under the roll

- up scenario, DOE assumes that equipment efficiencies in the base case that do not meet the standard level under consideration would “roll up” to the efficiency le vel that just meets the level set by

the standard

, and equipment already being purchased at efficiencies at or above the sta ndard

level would be unaffected. The assumptions and methodol ogy that drive this anal ysis are described in chapter 9 of the TSD.

12.3.7 Production Costs

Changes in the MPCs of Automatic Commercial Ice Maker s can affect revenues, gross margins, and cash fl ow of the industry, making product ion cost data key GRIM inputs for DOE’s analysis. In the engineering analysis, DOE created cost curves for each equipment class using data from its reverse engineering analysis to develop both the baseline MPCs and the incremental

costs that correspond to the design options. Generally, man ufacturing higher efficiency products is more costly than manufacturing baseline products due to the use of more complex components and higher

- cost raw materials.

The cost model disaggregated the MPCs at each efficiency level into material, labor,

overhea d, and depreciation. For materials, DOE used the incremental component and raw

material c osts that correspond to the design options at each EL. For labor, DOE estimated the

labor contribution at each EL by examining how the design options may influence man ufacturing and assembly practices. For depreciation, DOE used a depreciation value that is consistent with

historical information in SEC 10

- Ks. The remainder of total overhead was allocated to factory

overhead.

Later, manufacturers validated these estima tes and assumptions during interviews. DOE used the resulting MPCs and cost breakdowns as described in section 12.3.2.4 above, and further

detailed in cha pter 5 of the TSD, for each efficiency level analyzed in the GRIM analysi s. An additional efficiency level, 3A, was necessary for several equipment classes. For analytical consistency, DOE included this level in all of the capacity groups . Efficiency level 3A was derived by averaging the values in efficiency levels three and f our. TSD Chapter 5 contains

detailed information on the efficien cy levels used in this analysis.

The MSP is comprised of production costs (the direct manufacturing costs or MPCs), non

production costs (indirect costs like SG&A), and profit. DOE calculated the MSPs for Automatic

Commercial Ice Maker s by multiplying the MPCs by the appropriate manufacturer markup for that product. Table 12.3

. 5 through Table 12.3

. 40 show the production cost estimates used in the GRIM for the repres entative equipment classes for Automatic Commercial Ice Maker s.

12

- 12

### Table 12.3

. 5 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Small

- B

EL Materials Labor Depreciation Overh ead MPC Markup MSP

EL 1 $764.09 $49.75 $23.35 $60.79 $897.98 1.25 $1,122.48

EL 1 $807.70 $49.72 $24.48 $59.70 $941.59 1.25 $1,176.99

EL 3 $843.25 $49.74 $25.41 $58.72 $977.12 1.25 $1,221.40

EL 3A $875.95 $49.90 $26.26 $57.98 $1,010.10 1.25 $1,262.62

E L 4 $908.93 $49.96 $27.12 $57.06 $1,043.07 1.25 $1,303.84

EL 5 $1,129.92 $56.55 $33.04 $51.21 $1,270.71 1.25 $1,588.39



### Table 12.3

. 6 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Medium

- B

EL Ma terials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,180.60 $80.04 $40.17 $244.28 $1,545.08 1.25 $1,931.35

EL 1 $1,247.46 $80.11 $41.91 $242.43 $1,611.91 1.25 $2,014.89

EL 3 $1,384.50 $80.29 $45.48 $238.94 $1,749.21 1.25 $2,186.51

EL 3A $1,484.24 $82.41 $48.15 $237.14 $1,851.94 1.25 $2,314.93

EL 4 $1,586.02 $84.25 $50.82 $233.58 $1,954.67 1.25 $2,443.34



### Table 12.3

. 7 Manufacturer Production Cost Breakdown (2013$) f or IMH

- W

- Large

- B

EL Materials Lab or Depreciation Overhead MPC Markup MSP

EL 1 $1,305.72 $88.52 $44.43 $270.17 $1,708.83 1.25 $2,136.04

EL 2 $1,719.46 $94.06 $55.33 $259.20 $2,128.04 1.25 $2,660.05



### Table 12.3

. 8 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Ext1

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,861.98 $257.08 $97.44 $531.02 $3,747.52 1.25 $4,684.40

EL 2 $3,379.71 $265.40 $111.12 $517.56 $4,273.79 1.25 $5,342.24



### Table 12.3

. 9 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Ext2

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,861.98 $257.08 $97.44 $531.02 $3,747.52 1.25 $4,684.40

EL 2 $3,379.71 $265.40 $111.12 $517.56 $4,2 73.79 1.25 $5,342.24



### Table 12.3

. 10 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Small

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $778.84 $48.71 $23.80 $64.18 $915.53 1.25 $1, 144.41

EL 2 $835.30 $52.08 $25.36 $62.62 $975.36 1.25 $1,219.20

EL 3 $864.01 $63.14 $26.39 $61.62 $1,015.17 1.25 $1,268.96

EL 3A $881.64 $69.86 $27.03 $61.02 $1,039.55 1.25 $1,299.44

EL 4 $892.84 $74.17 $27.43 $60.56 $1,054.99 1.25 $1,318.74

EL 5 $1,1 33.11 $80.02 $33.83 $54.12 $1,301.08 1.25 $1,626.35

EL 6 $1,136.02 $81.05 $33.93 $54.16 $1,305.17 1.25 $1,631.46

12

- 13

### Table 12.3

. 11 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Medium

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $778.84 $48.71 $23.80 $64.18 $915.53 1.25 $1,144.41

EL 2 $835.30 $52.08 $25.36 $62.62 $975.36 1.25 $1,219.20

EL 3 $864.01 $63.14 $26.39 $61.62 $1,015.17 1.25 $1,268.96

EL 3A $881.64 $69.86 $27.03 $61.02 $ 1,039.55 1.25 $1,299.44

EL 4 $892.84 $74.17 $27.43 $60.56 $1,054.99 1.25 $1,318.74

EL 5 $1,133.11 $80.02 $33.83 $54.12 $1,301.08 1.25 $1,626.35

EL 6 $1,136.02 $81.05 $33.93 $54.16 $1,305.17 1.25 $1,631.46



### Table 12.3

. 12 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Large

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,099.28 $115.63 $38.84 $240.23 $1,493.99 1.25 $1,867.49

EL 2 $1,144.05 $115.56 $40.01 $239.12 $1,538.73 1.25 $1 ,923.41

EL 3 $1,191.72 $115.65 $41.25 $237.80 $1,586.42 1.25 $1,983.03

EL 3A $1,195.05 $115.58 $41.33 $237.83 $1,589.80 1.25 $1,987.25

EL 4 $1,256.80 $115.79 $42.94 $236.20 $1,651.73 1.25 $2,064.66

EL 5 $1,476.35 $119.84 $48.76 $230.49 $1,875.44 1.25 $ 2,344.30



### Table 12.3

. 13 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Ext

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,371.80 $80.29 $42.95 $156.94 $1,651.97 1.25 $2,064.96

E L 2 $1,793.68 $86.09 $54.06 $145.56 $2,079.39 1.25 $2,599.24

EL 3 $1,839.84 $86.32 $55.28 $144.57 $2,126.00 1.25 $2,657.50



### Table 12.3

. 14 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Small

- B

E L Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1 ,929.33 $115.88 $60.62 $225.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21



### Table 12.3

. 15 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Large

- B

EL Ma terials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1,929 .33 $115.88 $60.62 $225.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21

12

- 14

### Table 12.3

. 16 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Ext1

- B

EL Materia ls Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.38 $90.68 $229.83 $3,487.55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86



### Table 12.3

. 17 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Ext1

- B2

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.3 8 $90.68 $229.83 $3,487.55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86



### Table 12.3

. 18 Manufacturer Production Cost Breakdown (2013$) for R CU

- RC

- Small

- B

EL Materials Labor D epreciation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1,929.33 $115.88 $60 .62 $225.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21



### Table 12.3

. 19 Manufacturer Production Cost Breakdown (2013$) for R CU

- RC

- Large

- B

EL Materials Labor Depreci ation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1,929.33 $115.88 $60.62 $2 25.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21



### Table 12.3

. 20 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Ext1

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.38 $90.68 $229.83 $3,487.55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86



### Table 12.3

. 21 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Ext2

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.38 $90.68 $229.83 $3,487. 55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86

12

- 15

### Table 12.3

. 22 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Small

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $953.13 $63.17 $31.83 $176.18 $1,224.31 1.25 $1,530.39

EL 2 $993.77 $67.26 $32.99 $175.00 $1,269.02 1.25 $1,586.28

EL 3 $1,011.31 $73.97 $33.62 $174.33 $1,293.24 1.25 $1,616.55

EL 3A $1,020.51 $76.96 $33.94 $174.09 $1,305.50 1.25 $1,63 1.88

EL 4 $1,029.70 $79.99 $34.26 $173.81 $1,317.76 1.25 $1,647.20

EL 5 $1,056.97 $80.02 $34.97 $172.96 $1,344.92 1.25 $1,681.15

EL 6 $1,292.94 $84.17 $41.21 $166.75 $1,585.07 1.25 $1,981.34



### Table 12.3

. 23 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Large

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $953.13 $63.17 $31.83 $176.18 $1,224.31 1.25 $1,530.39

EL 2 $993.77 $67.26 $32.99 $175.00 $1,269.02 1.25 $1,586.28

EL 3 $1,011 .31 $73.97 $33.62 $174.33 $1,293.24 1.25 $1,616.55

EL 3A $1,020.51 $76.96 $33.94 $174.09 $1,305.50 1.25 $1,631.88

EL 4 $1,029.70 $79.99 $34.26 $173.81 $1,317.76 1.25 $1,647.20

EL 5 $1,056.97 $80.02 $34.97 $172.96 $1,344.92 1.25 $1,681.15

EL 6 $1,292.94 $84.17 $41.21 $166.75 $1,585.07 1.25 $1,981.34



### Table 12.3

. 24 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Small

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $729.69 $80.12 $27 .34 $214.28 $1,051.42 1.25 $1,314.28

EL 2 $754.09 $80.15 $27.97 $213.67 $1,075.89 1.25 $1,344.86

EL 3 $768.17 $80.11 $28.34 $213.30 $1,089.91 1.25 $1,362.39

EL 3A $775.14 $80.13 $28.52 $213.13 $1,096.93 1.25 $1,371.16

EL 4 $782.14 $80.15 $28.70 $212.95 $1,103.94 1.25 $1,379.93

EL 5 $826.93 $80.18 $29.87 $211.70 $1,148.67 1.25 $1,435.84

EL 6 $877.19 $80.09 $31.17 $210.54 $1,199.00 1.25 $1,498.75

EL 7 $1,092.34 $84.24 $36.87 $204.80 $1,418.26 1.25 $1,772.83



### Table 12.3

. 25 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Large

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $763.17 $90.39 $29.16 $238.76 $1,121.48 1.25 $1,401.85

EL 2 $792.30 $90.44 $29.92 $237.94 $1,150.59 1.25 $1,43 8.24

EL 3 $808.09 $90.40 $30.33 $237.60 $1,166.41 1.25 $1,458.01

EL 3A $821.69 $90.39 $30.68 $237.31 $1,180.08 1.25 $1,475.09

EL 4 $835.38 $90.37 $31.04 $236.96 $1,193.74 1.25 $1,492.18

EL 5 $870.04 $90.41 $31.94 $235.97 $1,228.35 1.25 $1,535.44

EL 6 $1,107.64 $94.53 $38.22 $229.79 $1,470.19 1.25 $1,837.74

12

- 16

### Table 12.3

. 26 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Ext

- B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $763.17 $90 .39 $29.16 $238.76 $1,121.48 1.25 $1,401.85

EL 2 $792.30 $90.44 $29.92 $237.94 $1,150.59 1.25 $1,438.24

EL 3 $808.09 $90.40 $30.33 $237.60 $1,166.41 1.25 $1,458.01

EL 3A $821.69 $90.39 $30.68 $237.31 $1,180.08 1.25 $1,475.09

EL 4 $835.38 $90.37 $31.04 $236.96 $1,193.74 1.25 $1,492.18

EL 5 $870.04 $90.41 $31.94 $235.97 $1,228.35 1.25 $1,535.44

EL 6 $1,107.64 $94.53 $38.22 $229.79 $1,470.19 1.25 $1,837.74



### Table 12.3

. 27 Manufacturer Production Cost Breakdo wn (2013$) for IMH

- W

- Small

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,991.28 $123.64 $70.34 $520.28 $2,705.55 1.25 $3,381.94

EL 2 $2,023.84 $123.50 $71.19 $519.72 $2,738.25 1.25 $3,422.81

EL 3 $2,045.13 $123.63 $71.75 $519.08 $2,7 59.59 1.25 $3,449.49

EL 3A $2,056.23 $123.57 $72.04 $518.80 $2,770.64 1.25 $3,463.30

EL 4 $2,067.35 $123.51 $72.32 $518.51 $2,781.69 1.25 $3,477.11

EL 5 $2,380.90 $123.50 $80.48 $510.41 $3,095.29 1.25 $3,869.11

EL 6 $2,384.50 $123.64 $80.57 $510.06 $3, 098.76 1.25 $3,873.45



### Table 12.3

. 28 Manufacturer Production Cost Breakdown (2013$) for IMH

- W

- Large

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1 .25 $2,613.01

EL 2 $1,595.83 $136.77 $55.13 $332.69 $2,120.42 1.25 $2,650.53

EL 3 $1,614.26 $136.66 $55.61 $332.13 $2,138.66 1.25 $2,673.33

EL 3A $1,829.43 $137.85 $61.32 $329.83 $2,358.42 1.25 $2,948.03

EL 4 $2,053.78 $136.64 $67.03 $320.73 $2,578.18 1.25 $3,222.73

EL 5 $2,067.73 $136.86 $67.40 $320.13 $2,592.12 1.25 $3,240.15



### Table 12.3

. 29 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Small

- C

EL Materials Labor Depreciation Overhead MPC Mark up MSP

EL 1 $1,991.28 $123.64 $70.34 $520.28 $2,705.55 1.25 $3,381.94

EL 2 $2,023.84 $123.50 $71.19 $519.72 $2,738.25 1.25 $3,422.81

EL 3 $2,045.13 $123.63 $71.75 $519.08 $2,759.59 1.25 $3,449.49

EL 3A $2,056.23 $123.57 $72.04 $518.80 $2,770.64 1.25 $3 ,463.30

EL 4 $2,067.35 $123.51 $72.32 $518.51 $2,781.69 1.25 $3,477.11

EL 5 $2,380.90 $123.50 $80.48 $510.41 $3,095.29 1.25 $3,869.11

EL 6 $2,384.50 $123.64 $80.57 $510.06 $3,098.76 1.25 $3,873.45

12

- 17

### Table 12.3

. 30 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Large

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,991.28 $123.64 $70.34 $520.28 $2,705.55 1.25 $3,381.94

EL 2 $2,023.84 $123.50 $71.19 $519.72 $2,738.25 1.25 $3,422.81

EL 3 $2,045.13 $123.63 $71.75 $519.08 $2,759.59 1.25 $3,449.49

EL 3A $2,056.23 $123.57 $72.04 $518.80 $2,770.64 1.25 $3,463.30

EL 4 $2,067.35 $123.51 $72.32 $518.51 $2,781.69 1.25 $3,477.11

EL 5 $2,380.90 $123.50 $80.48 $510.41 $3,095.29 1.25 $3,869.11

EL 6 $2,384.50 $123.64 $80.57 $510.06 $3,098.76 1.25 $3,873.45



### Table 12.3

. 31 Manufacturer Production Cost Breakdown (2013$) for IMH

- A

- Ext

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1, 565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.83 $136.77 $55.13 $332.69 $2,120.42 1.25 $2,650.53

EL 3 $1,614.26 $136.66 $55.61 $332.13 $2,138.66 1.25 $2,673.33

EL 3A $1,829.43 $137.85 $61.32 $329.83 $2,358.42 1.25 $2,948.03

EL 4 $2 ,053.78 $136.64 $67.03 $320.73 $2,578.18 1.25 $3,222.73

EL 5 $2,067.73 $136.86 $67.40 $320.13 $2,592.12 1.25 $3,240.15



### Table 12.3

. 32 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Small

- C

EL Ma terials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $136.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652 .69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $136.63 $67.92 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93

12

- 18

### Table 12.3

. 33 Manufacturer Production Cost Breakdown (2013$) for RCU

- NRC

- Large

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $1 36.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652.69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $ 136.63 $67.92 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93



### Table 12.3

. 34 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Small

- C

EL Materials Lab or Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $136.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652.69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $136.63 $67.92 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93



### Table 12.3

. 35 Manufacturer Production Cost Breakdown (2013$) for RCU

- RC

- Large

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $136.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652.69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $136.63 $67.9 2 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93



### Table 12.3

. 36 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Small

- C

EL Materials Labor Depreciat ion Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50

12

- 19

### Table 12.3

. 37 Manufacturer Production Cost Breakdown (2013$) for SCU

- W

- Large

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.2 5 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $ 2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50



### Table 12.3

. 38 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Small

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50



### Table 12.3

. 39 Manufac turer Production Cost Breakdown (2013$) for SCU

- A

- Large

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93 .07 $46.62 $167.84 $1,793.20 1.25 $2,241.50



### Table 12.3

. 40 Manufacturer Production Cost Breakdown (2013$) for SCU

- A

- Ext

- C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $1 76.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50

12

- 20

### 12.3.8 Conversion Costs

New and amended energy conservation standards typically cause manufacturers to i ncur

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 costs into two major groups: capital conversion costs and product conversion costs. Capital conversion costs are investments in property, plant, and equipment to adapt or change existing production facilities so that new product designs can be fabricated and assembled. Product conversion costs are investments in research, developm ent, testing, marketing, and other non

- capitalized costs focused on making product designs comply with the new and amended energy conservation standard. The following sections describe the inputs DOE used in the GRIM in greater detail.

12.3.8.1 Automatic Commercia l Ice Maker Product and Capital Conversion Costs

DOE used confidential information received during manufacturer interviews to develop

estimates of industry conversion cost s

. Specifically, DOE estimated the industry

- wide conversion costs required to meet e ach TSL, for each capacity group. DOE developed these estimates using a three step process:

1. DOE reviewed public information in the CEC, ENERGY STAR, CEE product

databases

, as well as manufacturer websites

, to determine the total number of products that wo uld require a redesign for each capacity g roup , at each TSL

.

2. DOE used confidential manufacturer

- supplied information to estimate the average cost to redesign a single non

- compliant model for each capacity g roup at each TSL.

3. For each capacity group, DOE multiplied the single

- model redesign costs at each TSL by the number of models requiring redesign at each TSL.

The total industry product and capital conversion costs at each TSL are the sum s of the capital and product conversion costs determined for eac h capacity group

. Table 12.3

. 41 shows

DOE’s estimates of the industry wide product and capital conv ersion costs necessary for

Automatic Commercial Ice Maker equipment classes at each TSL.

Table 12.3

. 41 Product and Capital Conversion Costs for Standard Ice Maker s by TSL

Conversion Costs by TSL (2013$M)

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Product Conversion $12.3 $18.1 $23.8 $28.1 $40.3
Capital Conversion Costs $0.2 $0.6 $1.3 $2.0 $3.9

Total Conversion Costs $12.6 $18.7 $25.1 $30.0 $44.1

12

- 21

### 12.3.9 Markup Scenarios

DOE modeled two standards

- case markup scenarios to represent the uncertainty regarding the potential impacts on prices and profitability for manufacturers following the implementati on of new and amended energy conservation standards: (1) a preservation of gross margin percentage markup scenario; and (2) a preservation of earnings before interest and taxes (EBIT) markup scenario. These scenarios lead to different markups values that, when applied to the MPCs, result in varying revenue and cash flow impacts.

12.3.9.1 Preservation of Gross Margin Markup Scenario

The preservation of gross margin markup scenario assess ed the lower bound of potential impacts (higher profitability)

. DOE modeled a preservation of gross margin percentage markup scenario, in which a uniform “gross margin percentage” markup is applied across all efficiency levels. In this scenario, DOE assumed that a manufacturer’s absolute dollar markup would increase as production co sts increase in the amended energy conservation standards case. Manufacturers have indicated that it is optimistic to assume that they would be able to maintain the same gross margin percentage markup as their production costs increase in response to a new or amended energy conservation standard, particularly at higher TSLs.

12.3.9.2 Preservation of EBIT Markup Scenario

T he preservation of EBIT markup scenario assess ed the upper bound of potential impacts (lower profitability)

. DOE modeled the preservation of the E BIT markup scenario, which assumes that manufacturers would not be able to preserve the same overall gross margin

, but

instead would lower their markup for marginally compliant products to maintain a cost

competitive product offering and keep the same over all level of EBIT as in the base case. Table

12.3.42 through Table 12.3

. 78 list s the products DOE analyzed with the corresponding markups

at each TSL for Automatic Commercial Ice Maker s.

Table 12.3

. 42 Preservation of Gross Margin Markups for Automatic Commercial Ice

Maker s , Representative of all Equipment Classes

EL (EAEU) Markups by EL

E L 1 E L 2 E L 3 E L 3 A E L 4 EL 5 EL 6 EL 7

EL 1 1.25 0

EL 2 1.25 0 1.25 0

EL 3 1.25 0 1.25 0 1.25 0

EL 3A 1.25 0 1.25 0 1.25 0 1.25 0

EL 4 1.25 0 1.25 0 1.25 0 1.25 0 1.25 0

EL 5 1.25 0 1.25 0 1.25 0 1.25 0 1.25 0 1.25 0

EL 6 1.25 0 1.25 0 1.25 0 1.25 0 1.25 0 1.25 0 1.25 0

EL 7 1.25 0 1.25 0 1.25 0 1.2 5 0 1.25 0 1.25 0 1.25 0 1.25 0

12

- 22

### Table 12.3

. 43 Preservation of EBIT Markups for IMH

- W

- Small

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1 .248

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.250 1.250 1.250 1.250 1.250 1.238



### Table 12.3

. 44 Preservation of EBIT Markups for IMH

- W

- Medium

- B

EL (EAEU) Markups by E L

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.247

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243



### Table 12.3

. 45 Preservation of EBIT f or IM H

- W

- Large

- B

EL (EAEU) Markups by EL

EL 1 EL 2

EL 1 1.250

EL 2 1.250 1.245



### Table 12.3

. 46 Preservation of EBIT Markups for IMH

- W

- Ext1

- B

EL (EAEU) Markups by EL

EL 1 EL 2

EL 1 1.250

EL 2 1.250 1 .245



### Table 12.3

. 47 Preservation of EBIT Markups for IMH

- W

- Ext2

- B

EL (EAEU) Markups by EL

EL 1 EL 2

EL 1 1.250

EL 2 1.250 1.245

12

- 23

### Table 12.3

. 48 Preservat ion of EBIT Markups for IMH

- A

- Small

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 El 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.248

EL 3A 1.250 1.250 1.250 1.247

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.2 50 1.250 1.250 1.250 1.250 0.000

El 6 1.250 1.250 1.250 1.250 1.250 1.250 1.238



### Table 12.3

. 49 P reservation of EBIT Markups for IMH

- A

- Medium

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.248

EL 3A 1.250 1.250 1.250 1.247

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.238



### Table 12.3

. 50 Preservation of EBIT Markups for IMH

- A

- Large

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 1.249

E L 4 1.250 1.250 1.250 1.250 1.247

EL 5 1.250 1.250 1.250 1.250 1.250 1.242



### Table 12.3

. 51 Preservation of EBIT Markups for IMH

- A

- Ext

- B
EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1 .2448

EL 3 1.250 1.250 1.2465

12

- 24

### Table 12.3

. 52 Preservation of EBIT Markups for RCU

- NRC

- Small

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.246

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243



### Table 12.3

. 53 Preservation of EBIT Markups for RCU

- NRC

- Large

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.247

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243



### Table 12.3

. 54 Preservation of EBIT Markups for RCU

- NRC

- Ext1

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 1.244



### Table 12.3

. 55 Preservation of EBIT Markups for RCU

- NRC

- Ext1

- B2

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.25 0 1.248

EL 3 1.250 1.250 1.244



### Table 12.3

. 56 Preservation of EBIT Markups for R CU

- RC

- Small

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1 .246

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243

12

- 2 5

### Table 12.3

. 57 Preservation of EBIT Markups for R CU

- RC

- Large

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.246

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.242



### Table 12.3

. 58 Preservation of EBIT Markups for RCU

- RC

- Ext1

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 1.244



### Table 12.3

. 59 Preservation of EBIT Markups for RCU

- RC

- Ext2

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250



### Table 12.3

. 60 Preservation of EBIT Markups for SCU

- W

- Small

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.247

EL 5 1.250 1.250 1.250 1.250 1.250 1.246

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.240

12

- 26

### Table 12.3

. 61 Preservation of EBIT Markups for SCU

- W

- Large

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 1.250

EL 4 1.250 1.250 1.250 1.250 1.248

EL 5 1.250 1.2 50 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.240



### Table 12.3

. 62 Preservation of EBIT Markups for SCU

- A

- Small

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6 EL 7

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.245

EL 7 1.250 1.25 0 1.250 1.250 1.250 1.250 1.250 1.239



### Table 12.3

. 63 Preservation of EBIT Markups for SCU

- A

- Large

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.248

EL 5 1.250 1.250 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.240

12

- 27

### Table 12.3

. 64 P reservation of EBIT Markups for SCU

- A

- Ext

- B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.248

EL 5 1.250 1.250 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.239



### Table 12.3

. 65 Preservation of EBIT Markups for IMH

- W

- Small

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

El 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250



### Table 12.3

. 66 Preservation of EBIT Markups for IMH

- W

- Large

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 1.250

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 1.250

12

- 28

### Table 12.3

. 67 Preservation of EBIT Markups for IMH

- A

- Small

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1. 250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.245



### Table 12.3

. 68 Preservation of EBIT Markups for IMH

- A

- Large

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1 .250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

El 6 1.250 1.250 1.250 1.250 1.250 1.250 1.245



### Table 12.3

. 69 Preservation of EBIT Markups for IMH

- A

- Ext

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.250 1.250 1.250 1.250 1.250 1.242

12

- 29

### Table 12.3

. 70 Preservation of EBIT Markups for RCU

- NRC

- Small

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.242



### Table 12.3

. 71 Preservation of EBIT Markups for RCU

- NRC

- Large

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1 .250 1.242



### Table 12.3

. 72 Preservation of EBIT Markups for RCU

- RC

- Small

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.242

12

- 30

### Table 12.3

. 73 Preservation of EBIT Markup s for RCU

- RC

- Large

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.25 0 1.250 0.000

El 5 1.250 1.250 1.250 1.250 1.250 1.250 1.250



### Table 12.3

. 74 Preservation of EBIT Markups for SCU

- W

- Small

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250



### Table 12.3

. 75 Preservation of EBIT Markups for SCU

- W

- Large

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6 EL 7

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.2 50 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250

EL 7 1.250 1.250 1.250 1.250 1.250 1.250 1.250 0.000

12

- 31

### Table 12.3

. 76 Preservation of EBIT Markups for SCU

- A

- Small

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.241



### Table 12.3

. 77 Preservation of EBIT Markups for SCU

- A

- Large

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.2 50

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.241



### Table 12.3

. 78 Preservation of EBIT Markups for SCU

- A

- Ext

- C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.2 50 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250



### 12.4 INDUSTRY FINANCIAL I MPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated

the financial impa cts on the Automatic Commercial Ice Maker industr y . The following sections detail additional inputs and assumptions for Automatic Commercial Ice Makers . The main

12

- 32

### results of the MIA are also reported in this section. The MIA consists of two key financial

me tri cs: INPV and annual cash flows.

12.4.1 I mpacts on Industry Net Present Value

The INPV measures the industry value and is used in the MIA to compare the economic impacts of different TSLs in the standards case. The INPV is different from DOE’s net present

valu e, 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 Automatic Commercial Ice

Maker GRIM estimate s cash flows from 2015 through 2047. This timeframe models bo th the

short

- term impacts on the industries from the announcement of the standard until the compliance date (2015 until an estimated compliance date of 2018) and a long

- term assessment over the 30

- year analysis period used in the NIA (2018

– 2047).

In the MIA, DOE compares the INPV of the base case (no new and amended energy conservation standards) to that of each TSL in the standards case. The difference between the

base

- case and a standards

- case INPV is an estimate of the economic impacts that implementi ng that particular TSL would have on the industry. For the Automatic Commercial Ice Maker industry , DOE examined the t wo markup scenarios described above : the preservation of gross margin markup scenario and the preservation of EBIT markup scenario.

Table 12.4.1 and Table 12.4

. 2 provide the INPV estimates for the Automatic Commercial

Ice Maker s.

Table 12.4

. 1 Manufacturer Impact Analysis for Auto matic Commercial Ice Maker s

– Preservation of Gross Margin Markup Scenario

Units Base

Case Trial Standard Level

1 2 3 4 5

INPV 2013$ millions 121.6 115.0 112.3 109.5 109.3 109.8

Change in INPV 2013$ millions

- ( 6.6 ) ( 9.3 ) ( 12.1 ) ( 12.3 ) ( 11.8 )

%

- ( 5.4) ( 7.7 ) ( 10.0 ) ( 10.1 ) ( 9.7 )

Product

Conversion Costs 2013$ millions

- 12.3 18.1 23.8 28.1 40.3

Capital

Conversion Costs 2013$ millions

- 0.2 0.6 1.3 2.0 3.9

Total Conversion

Costs 2013$ millions

- 12.6 18.7 25.1 30.0 44.1

* Values in parentheses are negative numbers.
*For tables in section 12.5 , values in parenthesis indicate negative numbers

12

- 33

### Table 12.4

. 2 Manufacturer Impact Analysis for Automatic Commercial Ice Maker s

– Preser vation of EBIT Markup Scenario

Units Base

Case Trial Standard Level

1 2 3 4 5

INPV 2013$ millions 121.6 114.1 110.4 106.5 103.0 91.6

Change in INPV 2013$ millions

- ( 7.5 ) ( 11.2 ) ( 15.1 ) ( 18.6 ) ( 30.0 )

%

- ( 6.2 ) ( 9.2) ( 12.5) ( 15.3) ( 24.6)

Product

Co nversion Costs 2013$ millions

- 12.3 18.1 23.8 28.1 40.3

Capital

Conversion Costs 2013$ millions

- 0.2 0.6 1.3 2.0 3.9

Total Conversion

Costs 2013$ millions

- 12.6 18.7 25.1 30.0 44.1

* Values in parentheses are negative numbers.
*For ta bles in section 12.5 , values in parenthesis indicate negative numbers



### 12.4.2 Impacts on Annual Cash Flow

Annual cash flows are d iscounted to the base year, 2015. Between 2015 and the 201 8 compliance date for , cash flows are driven by the level of conversion cos ts and the proportion of these investments spent every year. After the standard 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 new and ame nded energy conservation standard. The more stringent

the new and amended energy conservation standard, the greater the impact on industry cash flows in the years leading up to the compliance date, as product conversion costs lower cash inflows from operat ions and capital conversion costs increase cash outf lows for capital expenditures. Figure 12.4

. 1 and Figure 12.4

. 2 present the annual free cash flows for the Automatic Commercial Ice Maker Industry.

12

- 34

### Figure 12.4

. 1 Annual Industry Net Cash Flows for Automatic Commercial Ice Maker s ( Preservation of Gross Margin Markup Scenario)

Figure 12.4

. 2 Annual Indust ry Net Cash Flows for Automatic Commercial Ice Maker s ( Preservation of EBIT Markup Scenario)

12.5 IMPACTS ON S MALL AUTOMATIC COMMERCIAL ICE MAKER

MANUFACTURERS

For manufacturers of automatic commercial ice makers , the Small Business Administration (SBA) has set a size threshold, which defines those entities classified as “small businesses” for the purposes of the statute. DOE used the SBA’s small business size standards to ($10.0) ($5.0) $0.0 $5.0 $10.0 $15.0 $20.0Millions $ Net Free Cash Flow Totals

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

($10.0) ($5.0) $0.0 $5.0 $10.0 $15.0Millions $ Net Free Cash Flow Totals

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

12

- 35

### determine whether any small entities would be subject to the requirements of the rule. 65 FR 30836, 30848 (May 15, 2000), as new and amended at 65 FR 53533, 53544 (Sept ember 5, 2000) and codified at 13 CFR part 121. The size standards are listed by North American Industry Classification System (NAICS) code and industry description and are ava ilable at http://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf

. Commercial refrigeration equipment manufacturing is classified under NAICS 333415, “Air

- C onditioning and Warm Air Heating Equipment and Commercial and Industrial Refrig eration Equipment Manufacturing ”

which includes ice

- making machinery manufacturing. The SBA sets a threshold of 750 employees or less for an entity to be considered as a small b usiness for this category.

12.5.1 Description and Estimated Number of Small Entities Regulated

During its market survey, DOE used available public information to identify potential small manufacturers. DOE’s research involved industry trade association membersh ip directories (including AHRI), public databases ( e.g. , AHRI Directory, d 

### the SBA Database e 

### ), individual company websites, and market research tools ( e.g. , Hoovers reports f 

### ) to create a list of companies that manufacture or sell products covered by this ru lemaking. DOE also asked stakeholders and industry representatives if they were aware of any other small manufacturers during manufacturer interviews and at DOE public meetings. DOE reviewed publicly available data and contacted select companies on its lis t, as necessary, to determine whether they met the

SBA’s definition of a small business manufacturer of covered automatic commercial ice makers

. DOE screened out companies that do not offer products covered by this rulemaking, do not meet

the definition of a “small business,” or are foreign owned.

DOE identified 16 manufacturers of automatic commercial ice makers . S even of those

are small businesses manufacturers operating in the United States. DOE contacted each of these companies, but only one accepted the invitation to participate in a confidential manufacturer impact analysis interview with DOE contractors.

Based on a 2008 study by Koeller & Company g



### , DOE understands that the

ACIM market is dominated by four manufacturers who produce approximately 90 percent of the automatic commercial ice makers for sale in the United States. The four major manufacturers with the largest market share are Manitowoc

, Scotsman, Hoshizaki, and Ice

- O

- Matic. The remaining 12 large and small manufacturers account for ten percent of domestic sales.

d "AHRI Certification Directory. " AHR I Certification Directory. AHRI. (Available at: https://www.ahridirectory.org/ahridirectory/pages/home.aspx ) (Last accessed October 10, 2011) . See www.ahridirectory.org/ahriDirectory/pages/home.aspx

.

e “D ynamic Small Business Search.” SBA . (Available at: See http://dsbs.sba.gov/dsbs/search/dsp_dsbs.cfm ) (Last

accessed Oct ober 12, 2011 )..

f “ Hoovers | Company Information | Industry Information | Lists .” D&B ( 2013 ) (Available at: See http:// www.hoovers.com/ ) (Last accessed December 12, 2012)

. . g Koeller, John, P.E., and Herman Hoffman, P.E. A Report on Potential Best Management Practices. Rep. The

Califo rnia Urban Water Conservation Council, n.d. Web. 19 May 2014.

12

- 36

### 12.5.2 Description and Estimate of Compliance Requirements

For the purposes of analysis, DOE assumes that the seven small domestic manufacturers of automatic commercial ice makers identified account for approximately 5 percent of in dustry shipments. While small business manufacturers of automatic commercial ice makers have small overall market share, some hold substantial market share in specific equipment classes. Several of these smaller firms specialize in producing industrial ice machines and the covered equipment they manufacture are extensions of industrial product lines that fall within the range of capacity covered by this rule. Others serve niche markets. Most have substantial portions of their business derived from equipment outside the scope of this rulemaking, but are still considered small businesses based on the SBA limits for number of employees.

At the new and amended level, small business manufacturers of automatic commercial ice makers are expected to face ne gative impacts on INPV that are approximately four times as severe as those felt by the industry at large: a loss of 49.8 percent of INPV for small businesses alone as compared to a loss of 12.5 percent for the industry at large. Where conversion costs are

driven by the number of platforms requiring redesign at a particular standard level, small business manufacturers may be disproportionately affected. Product conversion costs including the investments made to redesign existing equipment to meet new or new and am ended standards or to develop entirely new compliant equipment, as well as industry certification costs, do not scale with sales volume. As small manufacturers’ investments are spread over a much lower volume of shipments, recovering the cost of upfront in vestments is proportionately more

difficult. Additionally, smaller manufacturers typically do not have the same technical resources and testing capacity as larger competitors.

The product conversion investments required to comply are estimated to be over 10 times larger than the typical R&D expenditures for small businesses, where as the industry as a whole is estimated to incur 4 times larger than typical R&D expenditures . Where the covered equipment from several small manufacturers are adaptations of larg er platforms with capacities above the 4,000 lb ice/24 hour threshold, it may not prove economical for them to invest in redesigning such a small portion of their product offering to meet standards.

In confidential interviews, manufacturers indicated tha t many design options evaluated in the engineering analysis ( e.g. , higher efficiency motors and compressors) would require them to purchase more expensive components. In many industries, small manufacturers typically pay higher prices for components due to smaller purchasing volumes while their large competitors receive volume discounts. However, this effect is diminished for the automatic commercial ice maker manufacturing industry for two distinct reasons. One reason relates to the fact that the

automatic commercial ice maker industry as a whole is a low volume industry. In confidential interviews, manufacturers indicated that they have little influence over their suppliers, suggesting the volume of their component orders is similarly insufficient to recei ve substantial discounts. The second reason relates to the fact that, for most small businesses, the equipment covered by this rulemaking represents only a fraction of overall business. Where small businesses are ordering similar components for non

- covered equipment, their purchase volumes may not be as low as is indicated by the total unit shipments for small businesses. For these reasons, it is

12

- 37

### expected that any volume discount for components enjoyed by large manufacturers would not be substantially diffe rent from the prices paid by small business manufacturers.

To estimate how small manufacturers would be potentially impacted, DOE developed specific small business inputs and scaling factors for the GRIM. These inputs were scaled from

those used in the w hole industry GRIM using information about the product portfolios of small businesses and the estimated market share of these businesses in each equipment class. DOE used this information in the GRIM to estimate the annual revenue, EBIT, R&D expense, and

c apital expenditures for a typical small manufacturer and to model the impact on INPV. DOE then compared these impacts to those modeled for the industry a t large. The results are shown i n Table 12.5

. 1 and T able 12.5

. 2

.

Table 12.5

. 1 Comparison of Small Business Manufacturers of Automatic Commercial Ice Maker INPV to that of the Industry at Large by TSL under the Preservation of Gross Margin Markup Scenario *

T SL 1 TSL 2 TSL 3 TSL 4 TSL 5 Industry at Large

– Impact on INPV (%) ( 6.2) ( 9.2) ( 12.5) ( 15.3) ( 24.6)

Small Businesses

– Impact on INPV (%) ( 18.3) ( 34.2) ( 48.8) ( 51.5) ( 57.2)

*Values in parentheses are negative numbers.



### Table 12.5

. 2 Comparison of Small Business Manufacturers of Automatic Commercial Ice Maker INPV to that of the Industry at Large by TSL under the Preservation of EBIT Markup Scenario

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 Industry at Large

– Impact on INPV (%) (5.4) (7.7) (10.0) (10.1) (9.7)

Small Businesses

– Impact on INPV (%) (19.1) (35.1) (49.8) (52.6) (68.4)

*Values in parentheses are negative numbers.



### 12.6 OTHER IMPACTS

12.6.1 Employment

12.6.1.1 Impacts on Direct Employment

DOE used the GRIM to estimate the do mestic labor expenditures and number of domestic production workers in the base case and at each TSL from 201 5 t hrough 2047. DOE

used statistical data from the most recent U.S Census Bureau’s “ 2011 Annual Survey of Manufacturers (ASM) ” the results of the e ngineering analysis, and interviews with manufacturers to determine the inputs necessary to calculate industry

- wide labor expenditures and domestic employmen t levels. Labor expenditures related the manufacture of a product are a

function of the labor inten sity of the product, the sales volume, and an assumption that wages in

real terms remain constant.

12

- 38

### In the GRIM, DOE used the labor content of each product and the manufacturing production costs from the engineering analysis to estimate the annual labor ex penditures in the automatic commercial ice maker industry. 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 (producti on worker hours multiplied by the labor

rate found i n the U.S. Census Bureau's ASM).

The estimates of production worker s in this section cover workers , including line

supervisor s, who are directly involved in fabricating and assembling automatic commerci al ice makers within an original equipment manufacturer (OEM) facility. Workers performing services that are closely associated with production operations, such as material handling with a forklift, are also included as production labor.

The employment i mpacts shown in Table 12.6

. 1 represent the potential production employment changes that could result following the compliance date of new and amended energy conservation standards. The upper end of the employment results in Table 12.6

. 1

estimates the maximum increase in the number of production workers after implementation of

new or amended energy conservation standards and it assume s that manufacturers continue to produce the same scope of covered product s in the U.S

. T he lower end of employment results in

Table 12.6

. 1 represent the maximum decrease to the total number of U.S. production workers in the industry due to manufacturers moving production outside of the U

. S. While the results present a range of employment impacts following the compliance date of the new and amended energy conservation standards, the following discussion also includes a qualitative discussion of the likelihood of negative employment impacts at the variou s TSLs. Finally, the employment impacts shown are independent of the employment impacts from the broader U.S. economy, which are documented in chapter 13 of the final rule TSD.

DOE estimates that in the absence of new and amended energy conservation stand ards,

there would be 389 domestic production workers involved in manufacturing automatic commercial ice makers in 2018. Using 20 11 Census Bureau data and interviews with manufacturers, DOE estimates that approximately 84 percent of automatic commercial ice makers sold in the United States are manufactured domestically. Table 12.6

. 1 shows the range of the impacts of potential new and amended energy conservation standards on U.S. production

workers in the automatic commercial ice mak er industry.

12

- 39

### Table 12.6

. 1 Potential Changes i n the Total Number of Domestic Automatic Commercial

Ice Maker Production Workers in 201 8 *

Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Total Number of

Domestic Pro duction

Workers in 2018 (without changes in production locations) 389 391 402 414 418 444

Potential Changes in

Domestic Production

Workers in 2018* - (389) to 2 (389) to 13 (389) to 25 (389) to 29 (389) to 55

* DOE presents a range of potential employment impacts. Values in parentheses are negative numbers.



### At all TSLs, most of the design options analyzed by DOE do not greatly alter the labor content of the final product. For example, the use of higher efficiency compressors or fan motors

inv olve one

- time changes to the final product but do not significantly change the amount of production hours required for the final assembly. One manufacturer suggested that their domestic production employment levels would only change if market demand contra cted following higher overall prices. However, more than one manufacturer suggested that where they already have overseas manufacturing capabilities, they would consider moving additional manufacturing to those facilities if they felt the need to offset a significant rise in materials costs. Provided the changes in materials costs do not support the relocation of manufacturing facilities, DOE expect s only modest changes to domestic manufacturing employment balancing additional requirements for assembly labo r with the effects of price elasticity.

12.6.2 Production Capacity

According to the majority of automatic commercial ice maker manufacturers interviewed,

new and amended energy conservation standards that require modest changes to product efficiency will not si gnificantly affect manufacturers’ production capacities. Any redesign of automatic commercial ice makers would not change the fundamental assembly of the equipment, but manufacturers do anticipate some potential for additional lead time immediately followi ng standards associated with changes in sourcing of higher efficiency components, which may be supply constrained.

One manufacturer cited the possibility of a 3

- to 6

- month shutdown in the event that new and amended standards were set high enough to requ ire retooling of their entire product line. Most of the design options being evaluated are already available on the market as product options. Thus, DOE believes that

, short of widespread retooling, manufacturers would be able to maintain manufacturing cap acity levels and continue to meet market demand under new and

amended energy conservation standards.

12

- 40

### 12.6.3 Cumulative Regulatory Burden

While any one regulation may not impose a significant burden on manufacturers, the combined effects of recent or impending re gulations 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. In addition to energy conservation standards, other regulations can significantly affect manufacturers’ financial operations. Multiple regulations affecting the same manufacturer can strain profits and lead companies to abandon product lines or markets with lower expected future returns than competing produ cts. For these reasons, DOE conducts an analysis of cumulative regulatory burden as part of its rulemakings pertaining to equipment efficiency.

For the cumulative regulatory burden analysis, DOE looks at other regulations that could

affect ACIM manufactu rers that will take effect approximately 3 years before or after the 201 8 compliance date of new and amended energy conservation standards for these products. In

written comments , manufacturers cited Federal regulations on equipment other than automatic

co mmercial ice makers that contribute to their cumulative regulatory burden. The compliance years and expected industry conversion costs of relevant new and amended energy conservation

standards are indicated in Table 12.6

. 2

.

Table 12.6

. 2 Compliance Dates and Expected Conversion Expenses of Federal Energy Conservation Standards Affecting Automatic Commercial Ice Maker Manufacturers
Federal Energy Conservation

Standards Approximate Compl iance

Date Estimated Total Industry Conversion Expense Commercial Refrigeration Equipment 79 FR 17725 (March 28, 2014) 

### 2017 $ 184.0 M (20 12 $)

Walk

- in Coolers and Freezers

79 FR 32049 (June 3, 2014) 

### 2017 $ 33.6 .0M (2012$)

Miscellaneous Refrigeration Equipme nt* TBD TBD

The final rule for this energy conservation standard has not been published. The compliance date and analysis of

conversion costs have not been finalized at this time

.



### 12.6.3.1 Other Regulations That Could Impact Automatic Commercial Ice Maker

Manufa cturers

While the cumulative regulatory burden focuses on the impacts on manufacturers of other Federal requirements, in this section DOE has described a number of other regulations below that could also impact the Automatic Commercial Ice Maker s covered b y this rulemaking

.

12

- 41

### State Energy Conservation Standards

Manufacturers indicated that California has several programs that are either already in place or are currently in development that affect manufacturers of Automatic Commercial Ice

Maker s . Various bui lding, electrical, mechanical, and plumbing codes in California affect Ice

Maker s , and products are also subject to California’s laws on the Restriction on the use of certain Hazardous Substances (RoHS). California’s RoHS law took effect January 1, 2007 an d was

modeled after the European Union’s ( EU’s ) directive (described below), which bans certain

hazardous substances from elect rical and electronic equipment.

International Energy Conservation Standards

Automatic Commercial Ice Maker manufacturers that sel l products outside of the United States are subject to several international energy conservation standards. In the EU, products are also subject to RoHS. This regulation bans the sale of new equipment in the EU that contains quantities in excess of agreed upon levels for lead, cadmium, mercury, hexavalent chromium, polybrominated biphenyl (PBB) and PBDE flame retardants. Waste Electrical and Electronic Equipment (WEEE) and the Registration, Evaluation, Authorization, and restriction of Chemicals (REACH) are additional regulations that create compliance costs for manufacturers that compete in Europe. REACH deals with chemicals and their safe use and has provisions that will be phased

- in over 11 years, beginning June 1, 2007. The EU also sets limits for the am ount of energy consumed by equipment when it is in standby mode and off mode. Additionally, HFCs are banned in refrigerants in several countries, such as Austria, Denmark, and Switzerland.

Canada and several other foreign countries have regulations or have initiated regulations affecting Ice Maker manufacturers.

12.7 CONCLUSION

The following sections summarize the different impacts for the scenarios DOE believes are most likely to capture the range of impacts on Automatic Commercial Ice Maker

manufacturers at ea ch TSL in the standards case . While these scenarios bound the range of the most plausible impacts on manufacturers, some circumstances could cause manufacturers to experience impacts outside this range.

12.7.1 Automatic Commercial Ice Maker s

At TSL 1, DOE estimat es impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $7.5 million to

- $6.6 million, or a change in INPV of

- 6.2 percent to

- 5.4 percent. At this TSL, industry free cash flow is estimated to decrease to $6.7 million, or a d rop of 35.7 percent, compared to the base

- case value of $10.4 million in the year before the compliance date (2017).

DOE estimates that approximately 27 percent of all batch commercial ice makers and 29 percent of all continuous commercial ice makers on t he market will require redesign to meet standards at TSL 1. At this TSL DOE expects capital and product conversion costs of $0.2 million and $12.3 million, respectively. Combined, the total conversion cost is $12.5 million.

12

- 42

### At TSL 2, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $11.2 million to

- $9.3 million, or a change in INPV of

- 9.2 percent to

- 7.7 percent. At this TSL, industry free cash flow is estimated to decrease to $4.8 million, or a drop of 53 .5 percent, compared to the base

- case value of $10.4 million in the year before the compliance date (2017).

DOE estimates that approximately 39 percent of all batch commercial ice makers and 41 percent of all continuous commercial ice makers on the marke t will require redesign to meet

standards at TSL 2. At this TSL

, DOE expects industry capital and product conversion costs of $0.6 million and of $18.1 million, respectively. Combined, the total conversion cost is $18.7 million, 48 percent higher than thos e incurred by industry at TSL 1

.

At TSL 3, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $15.1 million to

- $12.1 million, or a change in INPV of

- 12.5 percent

to

- 10.0 percent. At this TSL, industry free cas h flow is estimated to decrease to $2.9 million , or a drop of 72.4 percent, compared to the base

- case value of $10.4 million in the year before the compliance date (2017).

DOE estimates that approximately 51 percent of all batch commercial ice makers and 55 percent of all continuous commercial ice makers on the market will require redesign to meet

standards at TSL 3

. At this TSL, DOE expects industry capital and product conversion costs of $23.8 million and of $ 1.3 million , respectively. Combined, the total conversion cost is $25.1 million, 34 percent higher than those incurred by industry at TSL 2.

At TSL 4, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $ 18.6 million to

- $ 12.3 million, or a change in INPV of

- 15.3 percent

to

- 10.1 percent. At this TSL, industry free cash flow is estimated to decrease to $9.50.9 million, or a drop of 91.1 percent, compared to the base

- case value of $10.4 million in the year before the compliance date (2017).

DOE estimates that approximately 66 percent of all batch commercial ice makers and 55 percent of all continuous commercial ice makers on the market will require redesign to meet standards at TSL 4. Additionally, for four equipment classes, t here is only one manufacturer with products that currently meet the standard. At this TSL

, DOE expects industry capital and product conversion costs of $2.0 million and of $28.1 million, respectively. Combined, the total conversion cost is $30.0 million, 2 0 percent higher than those incurred by industry at TSL 3.

At TSL 5, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $30.0 million to

- $11.8 million, or a change in INPV of

- 24.6 percent

to

- 9.7 percent. At this TSL, industry free cas h flow is estimated to decrease to

- $5.3 million, or a drop of 151.1 percent, compared to the base

- case value of $10.4 million in the year before the compliance date (2017).

DOE estimates that approximately 84 percent of all batch commercial ice makers and 78 percent of all continuous commercial ice makers on the market will require redesign to meet

standards at TSL 5

. Additionally, for five equipment classes, there is only one manufacturer with

12

- 43

### products that currently meet th e standard. At this TSL, DOE expects industry capital and product

conversion costs of $3.9 million and of $ 40.3 million , respectively. Combined, the total conversion cost is $44.1 million, 47 percent higher than those incurred by industry at TSL 4.

12

- 44

### REFER ENCES

1 Association of Home Appliance Manufacturers. Current Members . Last a ccessed October 2,

20 11

. Available online at: www.aham.org/ht/d/Organizations/divisions/Y/pid/2344

.

2 California Energy Commission. Appliance Efficiency Database . Last accessed October 2011.

Available online at: www.appliances.energy.ca.gov/QuickSearch.aspx

12

- 45

### APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL (GRIM)

OVERVIEW

TABLE OF CONTENTS

12A.1 INTRODUCTION AND PUR POSE

................................ ................................ .......... 12A

- 1

12A.2 MODEL DESCRIPTION

................................ ................................ ........................... 12A

- 1

LIST OF TABLES

Table 12A.2.1 Detailed Cash Flow Example

................................ ................................ .......... 12A

- 4

12A

- i

### APPENDIX 1 2 A

. GOVERNMENT REGULATORY 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

mo de 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 ti me, 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 year 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 met rics, 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 sh ipments 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 oper ating 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) Reven ues: Annual revenues

– computed by multiplying products’ unit prices at each efficiency level by the appropriate manufacturer markup.

(2) Unit Sales: Total annual shipments for the industry were obtained from the National Impact Analysis Spreadsheet

.

(3) Material s : The portion of COGS that includes materials.

(4) Labor: The portion 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

.

(5) 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 separat e line item

.

12A

- 1

### (6) 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

.

(7) Standard SG&A: Sell ing, general, and administrative costs are computed as a percentage of Revenues (1 )

.

(8) R&D: GRIM separately accounts for ordinary research and development (R&D) as a percentage of Revenues (1 )

.

(9) Product Conversion Costs : Product conversion costs are one

- tim e 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 complian ce dates

.

(10) Stranded Assets: In the year the st andard becomes effective, a one

- time write

- off of

stranded assets is accounted for

.

(11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions for interest paid and taxes

.

(12) EBIT as a Percentag e 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 (12 ) are calculated by multiplying the tax rate contained in Major Assumptions by EBIT (12 )

.

(14) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods Sold ((3) to (6))

, SG&A ( 7 )

, R&D ( 8

)

, Product Conversion Costs (9 ) , and Taxes (13) from Revenues (1 )

.

(15) NOPAT repeated : NOPAT is repeated in the Statement of Cash Flows

.

(16) D epreciation 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 necessa ry 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 s ubtracting 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 (1 )

.

12A

- 2

### (20) Capital Conversion Costs : Cap ital conversion costs are one

- time investments in property, plant, and equipment to adapt or change existing production facilities so that new product designs can be fabricated and ass embled 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 ca sh 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) Discounted 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 (2 5 )

.

12A

- 3

### Table 12A.2

. 1 Detailed Cash Flow Example

12A

- 4

### CHAPTER 13. EMISSIONS IMPACT ANA LYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION

................................ ................................ ................................ ............ 13

- 1

13.2 AIR QUALITY REGULATI ONS AND EMISSIONS IM PACTS

................................ .. 13

- 2

13 .3 POWER SECTOR EMISSIO NS FACTORS

................................ ................................ ... 13

- 3

13.4 UPSTREAM FACTORS

................................ ................................ ................................ .. 13

- 4

13.5 EMISSIONS IMPACT RES ULTS

................................ ................................ ................... 13

- 5

REFERENCES

................................ ................................ ................................ .......................... 13

- 9

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors

................................ ................................ ............. 13

- 4 Table 13.4.1 Electricity Upstream Emissions Factors

................................ ............................... 13

- 5

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Automatic

Commercial Ice Makers

................................ ................................ ..................... 13

- 5

LIST OF FIGURES

Figure 13.5.1 Automatic Commercial Ice Makers: CO 2 

### Total Emissions Reduction

............... 13

- 6 Figure 13.5.2 Automatic Commercial Ice Makers: SO 2 

### Total Emissions Reduct ion

................ 13

- 6 Figure 13.5.3 Automatic Commercial Ice Makers: NO

x 

### Total Emissions Reduction

............... 13

- 7 Figure 13.5.4 Automatic Commercial Ice Makers: Hg Total Emissions Reduction

................. 13

- 7 Figure 13.5.5 Automatic Commercial Ice Makers: N 2 

### O Total Emissions Reduction

............... 13

- 8 Figure 13.5.6 Automatic Commercial Ice Makers: 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 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 a potential standard on emissions of tw o 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 f uel 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. T ogether, these emissions account for the full

- fuel

- cycle (FF C)

, i n accordance with the U.S. Department of Energy’s ( DOE’s ) FFC Statement of Policy

. 76 FR 51282 (Aug. 18, 2011 )

.

The 

### analysis of most power sector emissions uses marginal emissions intensity factors

derived from runs of DOE’s National Energy Modeling System

– Building Technologies ( NEMS

- BT ) model, described in Chapter 15. DOE used the version of NEMS based on the Annual Energy Outlook 201 4 (AEO201 4 )

. 1 

### Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions. AEO201 4 generally represents

current Federal and s tate legislation and final implementation regulations in place as of the end

of Octo ber 201 3

. Where applicable, s ite emissions of CO 2 

### and NO

x 

### are estimated using emissions intensity factors from a publication of the Environmental Protection Agency (EPA)

. 2



### Combustion emissions of CH 4 

### and N 2 

### O (from power plants and from upstream combustion) 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 methodolo gy 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 megawatt

hours ( MWh ) or million B ritish thermal units ( MMBtu ) of site energy savings. Total emissions reductions are estimated using the energy savings calculated in the national impact analysis (chapter 10).

For CH 4 

### and N 2 

### O , DOE also presents results in terms of units of carbon dioxide

e quivalent (CO 2 

### e). Gases are converted to CO 2 

### e by multiplying the physical units by the gas' global warming potential (GWP) over a 100

- year time horizon. Based on the Fifth Assessment

a http://www.epa.gov/climateleadership/inventory/ghg

- emissions.html

13

- 1

### Report of the Intergover nmental Panel on Climate Change, 4 

### DOE used GWP values of 2 8 for

CH 4 

### and 2 65 for N 2 

### O

. b



### 13.2 AIR QUALITY REGULATI ONS AND EMISSIONS IMPACTS

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 limited under the Clean Air Interstate Rule (CAIR ) , which created an all owance

- based trading program that operates along

with the Title IV program in those states and D.C. 70 FR 25162 (May 12, 2005). CAIR was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. Circuit) but parts of it remain ed in effect. In 2011 EPA issued a replacement for CAIR, the

Cross

- State Air Pollution Rule (CSAPR). 76 FR 48208 ( Aug

. 8, 2011). On August 21, 2012, the

D.C. Circuit issued a decision to vacate CSAPR. c 

### The court ordered EPA to continue administering CAIR. The emissions factors used for this analysis , which are based on AEO201 4

,

assume that CAIR remains a binding regulation through 2040. d



### The attainment of emissions caps is typically flexible among affected 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 

### emissio ns by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 

### emissions covered by the existing

cap

- and

- trade system, but it concluded that no reductions in power sector emissions w ould occur

for SO 2 

### as a result of standards.

Beginning in 201 6 , 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). e 

### In the final MATS rule, EPA established a standard f or hydrogen chloride as a surrogate for acid gas hazardous air

b The values are without inclusion of climate

- carbon feedbacks in response to emissions of the indicated non

- CO 2
gases.

c See EME Homer City Generation, LP v. EPA

, 696 F.3d 7, 38 (D.C. Cir. 2012).

d On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit. The case will now return to the D.C. Circuit for further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in part that EPA's methodology for quantifying emissions that must be eliminated in certain states due to their impacts

in other downwind states was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision that provides statutory authorit y for CSAPR. See EPA v. EME Homer City Generation , No 12

- 1182, s lip

o p. at 32 ( U.S. April 29, 2014). B ecause DOE is using emissions factors based on AEO2014

, it assumes that CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not relevant for the purpose of DOE's analysis of SO 2 emissions.

e On July 20, 2012, EPA announced a partial stay, for a limited duration, of the effectiveness of national new source emission standards for hazardous air pollutants from coal

- and oil

- fi red electric utility steam generating units. www.reginfo.gov/public/do/eAgendaViewRule?pubId=201110&RIN=2060

- AP52

13

- 2

### 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

no n

- 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.

AEO201 4 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 201 6 . 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 d ecreases ( e.g. , as a result of energy efficiency 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 offse tting increases in SO 2 

### emissions by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce SO 2 

### emissions in 201 6 and beyond.

CAIR established a cap on NO

x 

### emissions in 28 eastern states and the District of Columbia. Energy conservation standards are expected to have little effect on NO

x 

### emissions in

those states covered by CSAPR because excess NO

x 

### emissions allowances resulting from the lower electricity demand could be used to permit of fsetting increases in NO

x 

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

x 

### emissions in the states not affected by

CAIR

, so DOE estimate d NOx emissions reductions from potential standards for th ose states

.

The MATS limit mercury emissions fro m 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

AEO201 4 , which incorporates the MATS.

13.3 POWER SECTOR EMISSIO NS 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 Annua l Energy Outlook

201 4 (AEO201 4 )

. 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 multiplied by a transmission and distribution ( T&D ) loss factor to estimate the reduction in generation for each trial standard level ( TSL )

. Details on the approach use d may be found in Coughlin (2013 ). 3



### Table 13.3.1 presents the average power plant emissions factors for selected years. The se power plant emissions factors are derived fr om the emissions factors of the plant types used to supply electricity to buildings . DOE used the commercial refrigeration end use load shape. The

average factors for each year take into account the projected shares of each of the source s in total electricity generation

.

13

- 3

### 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 8 2020 2025 2030 2035 2040

CO 2 kg/MWh 734 650 586 536 489 734

SO 2 g/MWh 735 574 482 405 362 735

NO

x g/MWh 585 488 426 375 340 585 Hg g/MWh 0.002 0.002 0.001 0.001 0.001 0.002

N 2 O g/MWh 7.2 7.1 6.9 6.6 6.4 7.2

CH 4 g/MWh 50 49 48 46 45 50



### 13.4 U PSTREAM FACTORS

The upstream emissions accounting uses the same approach as the upstream energy accounting described in appendix 10 D

. See also Coughlin (201 3 ). 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 e nergy use 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 fugitiv e emissions associated with the fuel used

on site.

Fugitive emissions of CO 2 

### occur during oil and gas production, but are small relative to combustion emissions. They comprise about 2.5 percent of total CO 2 

### emissions for natural gas

and 1.7 percent for pe troleum 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 9 9 percent of total methane emissions for natural gas, about 95 percent for coal, and 93 percent for petroleum fuels.

U pstream emissions factors account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primary fuels. Fugitive emissions factors for

methane fr om coal mining and natural gas production were estimated based on a review of recent studies compiled by Burnham (2011 ). 5 

### This review includes estimates of the difference between fugitive emissions fa ctors for conventional production of natural vs. unconventional (shale or tight gas). These estimates rely in turn on data gathered by EPA under new greenhouse gas ( GHG ) reporting requirements for the petroleum and natural gas industries. 6

, 7 

### As more data a re made available, DOE will continue to update these estimated emissions factors.

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 13.4

. 1 presents the electricity upstream emissions factors for selected years . The caps that apply to power sector NO

x 

### emissions do not apply to upstream combustion sources.

13

- 4

### Table 13.4

. 1 Electricity Upstream Emissions Factors

Unit 2018 2020 2025 2030 2035 2040

CO 2 kg/MWh 29.1 29.4 29.7 29.9 29.8 29.1

SO 2 g/MWh 5.0 5.1 4.9 4.7 4.6 5.0

NO

x g/MWh 368 375 382 387 387 368 Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

N 2 O g/MWh 0.252 0.247 0.241 0.234 0.228 0.252

CH 4 g/MWh 2,149 2,195 2,216 2,248 2,255 2,149



### 13.5 EMISSIONS IMPACT RES ULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of equipment sold in 201 8

- 204 7 for each TSL.

Table 13.5

. 1 Cumulative Emissions Reduction for Potential Standards for Automatic

Commercial Ice Makers

Trial Standard Level

1 2 3 4 5

Power Sector Emissions

CO 2 ( million metric tons ) 4.68 7.87 10.38 13.25 18.62

NO

x ( thousand tons ) 3.71 6.23 8.22 10.50 14.75 Hg ( tons ) 0.01 0.02 0.03 0.04 0.05

N 2 O ( thousand tons ) 0.06 0.11 0.14 0.18 0.25

CH 4 ( thousand tons ) 0.44 0.73 0.97 1.24 1.74

SO 2 ( thousand tons ) 4.13 6.95 9.17 11.70 16.45 Upstream Emissions

CO 2 ( million metric tons ) 0.25 0.42 0.56 0.72 1.00

NO

x ( thousand tons ) 3.59 6.03 7.96 10.17 14.29 Hg ( tons ) 0.000 0.000 0.000 0.000 0.000

N 2 O ( thousand tons ) 0.00 0.00 0.00 0.01 0.01

CH 4 ( thousand tons ) 20.91 35.15 46.40 59.23 83.24

SO 2 ( thousand tons ) 0.04 0.08 0.10 0.13 0.18

Total Emissions

CO 2 ( million metric tons ) 4.93 8.29 10.94 13.97 19.63

NO

x ( thousand tons ) 7.30 12.26 16.19 20.67 29.04 Hg ( tons ) 0.01 0.02 0.03 0.04 0.05

N 2 O ( thousand tons ) 0.06 0.11 0.14 0.18 0.26

CH 4 ( thousand tons ) 21.35 35.89 47.37 60.47 84.97

SO 2 ( thousand tons ) 4.18 7.02 9.27 11.83 16.62



### Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for each type of emission from each TSL

. The reductions reflect the lifetime impacts of equipment

sold in 201 8

- 204 7

.

13

- 5

### Figure 13.5

. 1 Automatic Commercial Ice Makers : CO 2 

### Total Emissions Reduction

Figure 13.5

. 2 Automatic Commercial Ice Makers : SO 2 

### Total Emissions Reduction 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0201820232028203320382043204820532058kt 

### CO 2 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0201820232028203320382043204820532058ton 

### SO 2 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13

- 6

### Figure 13.5

. 3 Automatic Commercial Ice Makers : NO

x 

### Total Emissions Reduction

Figure 13.5

. 4 Automatic Commercial Ice Makers : Hg Total Emissions Reduction 0.0 200.0 400.0 600.0 800.0 1000.0 1200.0201820232028203320382043204820532058ton 

### NO

x 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0000 0.0003 0.0006 0.0009 0.0012 0.0015 0.0018 0.0021 0.0024201820232028203320382043204820532058ton 

### Hg Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13

- 7

### Figure 13.5

. 5 Automatic Commercial Ice Makers : N 2 

### O Total Emissions Reduction

Figure 13. 5

. 6 Automatic Commercial Ice Makers : CH 4 

### Total Emissions Reduction 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0201820232028203320382043204820532058ton 

### N 2 

### O Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0201820232028203320382043204820532058ton 

### CH 4 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13

- 8

### REFERENCES

1

. U.S. Department of Energy

— Energy Informati on 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/in dex.html >

3

. Coughlin, K. Projections of Full

- Fuel

- Cycle Energy and Emissions Metrics . 2013. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL

- 6025E.

4

. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.

- K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Chapter 8.

5

. Burnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, I. Palou

- Rivera

. Life

- Cycle Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum

. Environmental Science & Technology

. 2011. 46(2): pp. 619

– 

### 627

.

6

. U.S. Environmental Protection Agency

. Fugitive Emissions Reporting from the Petroleum and Natural Gas Industry

. 2009. U.S. Environmental Protection Agency. Washington, DC.

7

. U.S. Environmental Protection Agency

. Oil and Natural Gas Sector : Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution, Background Supplemental Technical Support Document for the Final New Source Performance Standards

. 2012. U.S. Environmental Protection Agency. Washington, DC.

13

- 9

### CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

TABLE OF CONTENTS

14.1 INTRODUCTION

................................ ................................ ................................ ............ 14

- 1

14.2 MONETIZING CARBON DI OXIDE EMISSIONS

................................ ........................ 14

- 1

14.2.1 Social Cost of Carbon

................................ ................................ ........................... 14

- 1

14.2.2 Monetizing Carbon Dioxide Emissions

................................ ................................ 14

- 1

14.2.3 Current Approach and Key Assumptions

................................ ............................. 14

- 2

14.3 VALUATION OF OTHER E MISSIONS REDUCTIONS

................................ .............. 14

- 5

14.4 RESULTS

................................ ................................ ................................ ......................... 14

- 5

REFERENCES

................................ ................................ ................................ .......................... 14

- 8

LIST OF TABLES

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010

– 2050 (in 2007$ per metric ton CO 2 

### )

................................ ................................ ............................ 14

- 4

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010

– 2050 (in 2007$ per metric ton CO 2 

### )

................................ ................................ ............................ 14

- 4

Table 14.4.1 Estimates of Global Pres ent Value of CO 2 

### Emissions Reduction under

Automatic Commercial Ice Makers Trial Standard Levels

............................... 14

- 6

Table 14.4.2 Estimates of Dom estic Present Value of CO 2 

### Emissions Reduction under

Automatic Commercial Ice Makers Trial Standard Levels

............................... 14

- 6

Table 14.4.3 Estima tes of Present Value of NO

x 

### Emissions Reduction under Automatic

Commercial Ice Makers Trial Standard Levels

................................ ................. 14

- 7

14

- i

### CHAPTER 1 4

. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

14.1 INTRODUCTION

As part of its assessment of energy conservation standards for automatic commercial ice

makers

, the U.S. Department of Energy ( 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 trial standard levels ( 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 DI OXIDE 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 em issions 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 resulting from a unit change in carbon dioxide emissions, while a global SCC value is meant to reflect the value of damages worldwide.

Und er section 1(b) of Executive Order 12866, 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 regulati on 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 allow agencies to incorporate the monetized social benefits of reducing CO 2 

### emissions into cost

- ben efit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a clear understanding that they should be updated ov er 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 literat ure 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 econ omic literatures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

14.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic i mpacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A report from the National Research

Counci l 1 

### points out that any assessment will suffer from uncertainty, speculation, and lack of

14

- 1

### 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 th e 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, economic s, and ethics and should be viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. Most Federal regulatory actions can be expected to have marginal impacts on global emissions. For such policies, the agency can estimate the benefits from reduced (or costs from increased) emissions in any future year by multiplying the change in emissions in that year by the SCC value

appro priate 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 in creased 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.

In 2009, an interag ency 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 agencies, the Administration sought to develop a transparen t 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 exi sting 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 prop osed and final rules.

14.2.3 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular basis to generate improved SCC estimates, which were considered for this final rule. Specifically, the group c onsidered public comments and further explored the technical literature in relevant fields. The interagency group relied on three integrated assessment models (IAMs)

comm only used to estimate the SCC: the FUND, DICE, and PAGE models. a 

### These models are freq uently cited in the peer

- reviewed literature and were used in the last assessment of the Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC values that were developed.

a The models are described in appendix 14A of this technical support document (TSD).

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 app roaches 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: (1) climate sensitivity

, (2) socio

- econ omic and emissions trajectories, an d (3) 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.

The interagency group selected four SCC values f or use in regulatory analyses. b 

### (The 2010 report is reproduced in appendix 14A .) 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 estimate across all three models at a 3

- percent discount rate, is included to repr esent higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution. The values grow in real terms over time, as depicted in

Table 14.2

. 1 . Additionally, the interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, c 

### although preference is given to consideration of the global benefits of reducing CO 2 

### emissions

.

The SCC values used for th is final rule 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)

. d



### Table 14.2

. 2 shows the updated sets of SCC est imates in 5

- year increments from 2010 to 2050. Appendix 14B provides the full set of SCC estimates, as well as the 2013 report from the interagency group. The central value that emerges is the ave rage SCC across models at the 3

- percent discount rate. Howev er, 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.

b 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

c It is recognized that this calculation for domestic values is 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.

d Technical Update of the Soc ial 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 14.2

. 1 Annual SCC Values from 2010 Interagency Report, 2010

– 2050 (in 2007 $ per metric ton CO 2 

### )

Year Discount Rate

%

5 3 2.5 3 Average Average Average 95 th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2



### Table 14.2

. 2 Annual SCC Values from 2013 Interagency Update, 2010

– 2050 (in 2007 $ per metric ton 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 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



### 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 gr oup also recognizes that the existing models are imperfect and incomplete. The National Research Council report mentioned above points out that there is tension between the goal of producing 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 concerns and problems that should be addressed by the research community, including research programs housed in many of the agencies participating in the interag ency process to estimate the SCC. The interagency group intends to 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 improvemen ts in modeling.

In summary, in considering the potential global benefits resulting from reduced CO 2 

### emissions, DOE used the values from the 2013 interagency report, whi ch is reprinted in appendix

14B of this TSD, escalated to 201 3 $ using the gross domestic product ( GDP ) price deflator. For each of the four cases specified, the values used for emissions in 2015 were $12. 0 , $40. 5 , $62. 4

,

14

- 4

### and $11 9 per metric ton avoided. DOE derived values after 2050 using the relevant growth rates

for the 2040

– 2050 period in the interagency update.

DOE multiplied the CO 2 

### emissions reduction estimated for each year by the SCC value for that year in each of the four cases. To calculate a present value of the stream of monetary values, DOE discounted the values in each 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 E MISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NO

x 

### emissions from the TSLs

it considered. As noted in c hapter 1 3 , new or amended energy conservation standards would

reduce NO

x 

### emissions in those 22 s tates 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 environme ntal damage estimates found in the relevant scientific literature. Available estimates suggest a very wide range of monetary values, ranging from $ 4 7 6 to $4,89 3 per ton in

201 3 $)

. 4 

### In accordance with OMB guidance, DOE calculated a range of monetary benefit s using

each of the economic values for NO X 

### and real discount rates of 3 percent and 7 percent

. 5



### 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 final rule

.

14.4 R E SULTS

Table 14.4

. 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 14.4

. 2

.

14

- 5

### Table 14.4

. 1 Estimates of Global Present Value of CO 2 

### Emissions Reduction under

Automatic Commercial Ice Makers 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 201 3 $ Primary Energy Emissions

1 34.5 154.3 243.8 476.2

2 57.9 259.4 409.9 800.5

3 76.4 342.3 541.0 1,056.6

4 97.6 437.0 690.6 1,348.9

5 137.1 614.1 970.5 1,895.5 Upstream Emissions

1 1.8 8.2 13.0 25.4

2 3.0 13.8 21.9 42.7

3 4.0 18.2 28.8 56.3

4 5.1 23.3 36.8 71.9

5 7.2 32.7 51.8 101.0

Total Emissions

1 36.3 162.5 256.8 501.6

2 61.0 273.2 431.7 843.1

3 80.5 360.6 569.8 1,112.9

4 102.7 460.3 727.5 1,420.8

5 144.3 646.8 1,022.3 1,996.5 * For each of the four cases, the corresponding SCC value for emissions in 2015 is $ 12.0 , $ 40.5

, $6 2.4 , and $11 9 .0 per metric ton (201 3 $).



### Table 14.4

. 2 Estimates of Domestic Present Value of CO 2 

### Emissions Reduction under

Automatic Commercial Ice Makers 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 201 3 $ Primary Energy Emissions

1 2.4 to 7.9 10.8 to 35.5 17.1 to 56.1 33.3 to 109.5

2 4.1 to 13.3 18.2 to 59.7 28.7 to 94.3 56.0 to 184.1

3 5.4 to 17.6 24.0 to 78.7 37.9 to 124.4 74.0 to 243.0

4 6.8 to 22.4 30.6 to 100.5 48.3 to 158.8 94.4 to 310.2

5 9.6 to 31.5 43.0 to 141.3 67.9 to 223.2 132.7 to 436.0 Upstream Emissions

1 0.1 to 0.4 0.6 to 1.9 0.9 to 3.0 1.8 to 5.8

2 0.2 to 0.7 1.0 to 3.2 1.5 to 5.0 3.0 to 9.8

3 0.3 to 0.9 1.3 to 4.2 2.0 to 6.6 3.9 to 13.0

4 0.4 to 1.2 1.6 to 5.3 2.6 to 8.5 5.0 to 16.5

5 0.5 to 1.7 2.3 to 7.5 3.6 to 11.9 7.1 to 23.2

Total Emissions

1 2.5 to 8.3 11.4 to 37.4 18.0 to 59.1 35.1 to 115.4

2 4.3 to 14.0 19.1 to 62.8 30.2 to 99.3 59.0 to 193.9

3 5.6 to 18.5 25.2 to 82.9 39.9 to 131.1 77.9 to 256.0

4 7.2 to 23.6 32.2 to 105.9 50.9 to 167.3 99.5 to 326.8

5 10.1 to 33.2 45.3 to 148.8 71.6 to 235.1 139.8 to 459.2 * For each of the four cases, the corresponding SCC value for emissions in 2015 is $ 12.0 , $ 40.5

, $6 2.4 , and $11 9 .0 per metric ton (201 3 $).

14

- 6

### Table 14.4

. 3 presents the present value of cumulative NO

x 

### emissions reductions for each TSL, calculated using the average dollar

- per

- ton values and 3

- percent a nd 7

- percent discount

rates.

Table 14.4

. 3 Estimates of Present Value of NO

x 

### Emissions Reduction under Automatic

Commercial Ice Makers Trial Standard Levels

TSL 3% discount rate 7% discount rate

million 201 3 $

Power Sector Emissions

1 5.6 2.9

2 9.4 4.9

3 12.4 6.5

4 15.8 8.2

5 22.2 11.6 Upstream Emissions

1 5.2 2.5

2 8.7 4.3

3 11.4 5.6

4 14.6 7.2

5 20.5 10.1

Total Emissions

1 10.7 5.4

2 18.0 9.2

3 23.8 12.1

4 30.4 15.4

5 42.7 21.7

14

- 7

### REFERENCES

1

. National Research C ouncil

. Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Use

. 2009. National Academies Press. Washington, D

. C.

< www.nap.edu/catalog.php?record_id=12794 >

2

. Interagency Wor king Group on Social Cost of Carbon

. Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866

. February 2010. United States

Government. < www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

Cost

- of

- Carbon

- for

- RIA.pdf >

3

. Interagency Working Group on Social Cost of Carbon

. Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. November 2013.

United States Government.

< http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical

- update

- social

cost

- of

- carbon

- for

- regulator

- impact

- analysis.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, D

. C.

< www.w hitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_final_r eport.pdf >

5

. U.S. Office of Management and Budget (OMB)

. Circular A

- 4: Regulatory Analysis

. 2003. (Posted September 17, 2003) (Last accessed April 2013.)

< http://www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a

- 4.pdf >

14

- 8

### 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 REFERENCES

................................ ................................ ................................ ......... 14A

- 36

14A.9.1 ANNEX

................................ ................................ ................................ ..... 14A

- 42

14A.9.2 Other (Non

- CO 2 

### ) Gases

................................ ................................ ............ 14A

- 43

14A.9.3 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 CO 2 

### 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 t he 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

Table 14A.9.4 2010 Global SCC Estimates at 5

- Percent Discount Rate (2007$/ton CO 2 

### )

.. 14A

- 51

14A

- i

### 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 E stimates 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 zer o in 2300)

................................ .......... 14A

- 47 Figure 14A.9.4 Global Fossil and Industrial CO 2 

### Emissions, 2000

- 2300 (post

- 2100 extrapolations assume growth rat e of CO 2 

### intensity (CO 2 

### /GDP) over 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 Figure 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 justif y 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 small, 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 a nd 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 he alth, 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 agencies met on a r egular 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. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

14A

- 1

### The interagency group selected four SCC val ues 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 distribut ion.

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 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 incre ased 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 fac es 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 e ffects 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 an d 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 estimates can be useful in estimating the social benefits of reducing CO 2 

### emissions. Under 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

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).

14A

- 2

### intended regulation justify its costs.” The purpo se of the SCC estimates presented here is to make it possible for agencies to incorporate the social benefits from reducing CO 2 

### emissions into cost

benefit analyses of regulatory actions that have small or “marginal” impacts on cumulative 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 the change in emissions in t hat 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 th at 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

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 interag ency 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 the rulemaking process.

The in teragency 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 higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for th e 95 th 

### percentile at a 3

- percent
discount rate. The central value is the average SCC across models at the 3

- percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range. These SCC estimates also grow over time. For instance, the central value increases to $24 per ton of CO 2 

### in 2015 and $26 per ton of

CO 2 

### in 2020. See 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 revisiti ng the SCC

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

14A

- 3

### raised in this document and consider public comment s 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 reflect 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

refl ect 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,

the 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 of fer 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 desig ned 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 u ntil 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. 

### The $ 33 and $5 values represented model

- weighted means of the published estimates produced from the most recently available versions of three integrated assessment models

— DICE, PAGE, and FUND

— at approximately 3

- and 5

- percent discount rates. The $55 and $10

val ues were derived by adjusting the published estimates for uncertainty in the discount rate (using factors developed by Newell and Pizer (2003)) at 3

- and 5

- percent discount rates, respectively. The $19 value was chosen as a central value between the $5 and $33 per 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 eff ort 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 and were offered for public comment in

14A

- 4

### 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 release 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 s ection details the several choices and assumptions that underlie the resulting estimates of the SCC.

It is important to recognize that a number of key uncertainties remain, and that current SCC estimates should be treated as provisional and revisable sinc e 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 re search community, including research programs housed in many of the agencies participating in the interagency process to estimate the SCC

.

The U.S. Government will periodically review and reconsider estimates of the SCC used

for cost

- benefit analyses to r eflect 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 interagency group offers the new SCC values with all due humility about the uncertainties embedded in them and with a sincere promise to continue work to improve them.

14A.4.1 Integrated Assessment Models

We rely on three integrated assessment models (IAMs) comm only used 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 i n 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 th is advantage at the expense of a more detailed representation of the underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reduced

form approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the possible a dvantages of this approach). Other IAMs may better reflect the complexity of the

b The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of energy models and was first presented in 1990 (Nordha us 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).

14A

- 5

### 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 damages, which makes this exercise even more difficult. Underlying the three IAMs selected for this exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ best attempts to synthesize the available scientific and eco nomic 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 emiss ions projections used in the models are based on specified socio

- economic ( gross domestic product (GDP) gross domestic product (GDP) and population) pathways. These emissions are translated into concentrations using the carbon cycle built into each model, a nd 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 requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result in changes in economic damages. In PAGE, for example, the consumption

- e quivalent 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 affects both consumption and investment. We describe each model in greater detail here. In a later section, we discuss key gaps in how the models account for various scientific and economic processes ( e.g

. , the probability of catastrophe, and the ability to adapt to climate change and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key objective of the interagency process was to enable a consisten t 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: climat e 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 addition, the interagency group used a range of scenarios for the socio

- eco nomic 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 cons tants; in PAGE, most parameters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically.

14A

- 6

### The sensitivity of the results to other aspects of the models ( e.g. , the carbon cycle or damage fu nction) is also important to explore in the context of future revisions to the SCC but has not been incorporated into these estimates. Areas for future research are highlighted at the

end of this document.

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 emissio ns

— implying reduced consumption

— harmful effects of climate 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 e xpected to occur under more extreme climate change, and is calibrated to include the effects of warming on the production of market and nonmarket goods and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vuln erable 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 imp acts is then added to the other market and non

market impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is included implicitly through the choice of studies used to calibrate the aggregate damage function. For example, its agricultural impact estimates assume that farmers can adjust land use decisions in response to changing climate conditions, and its health impact estimates assume improvements in healthcare over time. In addition, the small impa cts 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 dama ge 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. ”

14A

- 7

### Note that the damage function in DICE has a somewhat different meaning from the damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because damages in a given year reduce investment in that year, damages propagate forward in time and reduce GDP in future years. In contrast, GDP is exogenous in FU ND 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 separate ly for eight geographic regions. Damages in each region are expressed as a fraction of output, where the fraction lost depends on the temperature change in each region. Damages are expressed as power functions of temperature change. The exponents of the da mage function are the same in all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 2 as in DICE)

.

PAGE2002 includes the consequences of catastrophic events in a separate damage sub

function. Unlike DICE, PAG E2002 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 u ltimately 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 no n

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 (bas ed 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 secto r has a different functional form, and is calculated separately for sixteen geographic regions. In some

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

. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing representative agent in the model

. We made two modifications to DICE to make it consistent with EMF GDP trajectories (see next sectio n): we assumed a fixed rate of savings of 20%, and we re

- calibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly

matched the EMF scenarios.

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- 8

### 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

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 several omitted impacts as “big unknowns : ” for instance, extreme clima te scenarios, biodiversity loss, and effects on economic development and political violence. With regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of changes or what effects they would have are not well

- understood, although the chance of any one of them happening seems low. But they do have the potential to happen relatively quickly, and if they did, the costs could be substantial. Only a few studies of climate change have examined

these issues.”

Adaptation is inclu ded 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 to the level of temperature change (damages can be positive or negative depending on region and temperature); and (3) those from CO 2 

### fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate change happens more slowly. The combined effect of CO 2 

### fertilization in the agricultural sector, positive impacts to some regions from higher temperatures, and sufficiently slow increases in temperature across these sectors can result in negative economic damages from climate change.

14A.4.1.4 Damage Funct ions

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

- average surface temperature) on the consumption

- equivalent value of both market and non

- market goods (represented as a fraction of global GDP). We recognize that these representations are incomplete and highly uncertain. G iven the paucity of data linking the physical impacts to economic damages, we were not able to identify a better way to translate changes in climate into net economic damages, short of launching our own research program.

The damage functions for the three IAMs are presented in Figure 14A.4

. 1 and Figure

14A.4

. 2 , using the modeler’s default scenarios and mean input assumptions. There are significant

differences between the three models both at lower ( Figure 14A.4

. 1 ) and higher ( Figure 14A.4

. 2 )

increases in global

- average te mperature.

d In the deterministic versio n 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 . 2006).

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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 Models 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 emis sions parameters will produce a different realization of damages for each IAM. The damage functions represented in Figure 14A.4

. 1 and Figure 14A. 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.

14A

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### The lack of agreement among the models at lower temperature increases is underscored by the fact that the damages from FUND are well below the 5 th 

### percentile estimated by PAGE, while the damages estimated by DICE are roughly equal to the 95 th 

### percentile estimated by PAGE. This is significant because at higher discount rates we expect that a greater proportion of the SCC value is due to damages in yea rs with lower temperature increases. For example, when the discount 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 approximatel y 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

dam ages. Gaps in the literature make modifying these aspects of the models challenging, which highlights the need for additional research. As knowledge improves, the Federal government is 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 significan t 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 externali ty: 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 emiss ions. 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 countrie s would also need to take action to reduce emissions if significant changes in the global climate are to be avoided. Emphasizing the need for a global solution to a global problem, the United States has been actively involved in seeking international agree ments to reduce emissions and in encouraging other nations, including emerging major economies, to take significant steps to reduce emissions. When these

f It is tr ue 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 foreign sovereigns. But use of a global measure for the SCC does not give

extraterritorial effect to fe deral law and hence does not intrude on such interests.

14A

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### considerations are taken as a whole, the interagency group concluded that a global measure of

the bene fits 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 weigh ting 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 interagenc y group concluded that this approach would not be appropriate 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 approp riate approach.

14A.4.2.2 Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the relatively few region

- or country

- specific estimates of the SCC in the literature. One potential

source of estimates comes from the FUND mo del. 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 benef it, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP lost due to climate change is assumed to be similar across countries, the domestic benefit would be proportional to the U.S. share of global GDP, 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 recogn ized 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 co uld 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 prevalent 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 nation than on a wealthy one. Even if equity weighting

— fo r 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 th is time

. h Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report

.

14A

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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) ov er a particular timeframe relative to CO 2 

### . However, because these gases differ in both radiative forcing and atmospheric lifetimes, their relative damages are not constant over time. For example, because methane has a short lifetime, its impacts occur prim arily in the near term and thus are not discounted as heavily as those caused by longer

- lived gases. Impacts other than temperature change also vary across gases in ways that are not captured by GWP. For instance, CO 2 

### emissions, unlike methane and other gr eenhouse gases, contribute to ocean acidification. Likewise, damages from methane emissions are not offset by the positive effect of

CO 2 

### fertilization. Thus, transforming gases into CO 2 

### - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in 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 economi c 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 g reenhouse 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)). Uncer tainties in this important parameter have received substantial 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 Clima te 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 w ell as data limitations, values substantially higher 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 t erm (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 thousands of years ( e.g. , Hansen et al . 2007).

j This is in ac cord 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.

14A

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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)

Afte r consulting with several lead authors of this chapter of the IPCC report, the interagency workgroup selected four candidate probability distributions and calibrated them to be consistent with the above statement: Roe and Baker (2007), log

- normal, gamma, a nd 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

- Normal Gamma Weibull Pr(ECS < 1.5 °C) 0.013 0.050 0.070 0.102 Pr(2 °C < ECS < 4.5 °C) 0.667 0.667 0.667 0.667

5 th 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 selected 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 greenhouse gas concentrations (Roe and Baker 2007 ; Roe 2008). In contrast, the other three distributions are mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general shape. The Roe and Baker distribution results from three assumptions about climate response: (1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback factors are proportional to the change in surface temperature; and (3) uncerta inties in feedback factors are normally distributed. There is widespread agreement on the first point and the second and third points are common assumptions.

k Strictly speak ing, “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 percentile that is more consistent with IPCC judgments and the litera ture. For example, setting the mean and mode equal to 3 °C produced 95 th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the range in the literature. Finally, the median is closer to 3 °C than is the mode for the trunca ted 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 median set equal to 3 °C.

14A

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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 o f 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 cal ibrated distributions (5.2

– 6.0 °C).

Finally, 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 sensitivi ty being greater than 1.5 °C is almost 99 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 Baker 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 betw een 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 Function for Equilibrium Climate Sensitivity

l The estimates based on instrumental da ta 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 socio

economic and emissions parameters for use i n PAGE, DICE, and FUND. Socio

- economic pathways are closely tied to climate damages because, all else equal, more and wealthier people tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid climate disruptions. For this reason, we consider how to model several input parameters in tandem: GDP, population, CO 2 

### emissions, and non

- CO 2 

### 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 rec ent 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, and emis sion 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 extreme outliers in one or more variables. Although the EMF

- 22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are recent, peer

reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic poli cies 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 14A.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 lower

than

- BAU trajectory. m 

### Out of the 10 models included in the EMF

- 22 exercise, we selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories from each of

these fou r models. For the 550 ppm CO 2 

### e scenario, we averaged the GDP, population, and emission trajectories implied by these same four models.

m Such an emissions path would be consistent with widespread action by countries to mitigate GHG e missions, 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 R eference

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 informat ion on the likelihood of a full range of

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, rath er than the wider range of possible outcomes. Nevertheless, these views of the

n While the EMF

- 22 m odels 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 accurately describes relative standards of living acr oss 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 cou ntries. 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 un realistically 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 that differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts ( i.e. , using cross

- sectional PPP measures for relative incomes and outputs and national accounts price and qu antity 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 uncertainties about population, technological change, o r the many geophysical uncertainties.

14A

- 17

### most likely outcome span a wide range, from the more optimistic ( e.g. , abundant low

- cost, low

carbon energy) to more pessimistic ( e.g. , constraints on the availability of nuclea r 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 o utcome. 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 socio

economic trajectories that achieve even lower GHG concent rations at this time, given the difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 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 fos sil and industrial CO 2 

### emissions, 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 ra diative 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 non

market sectors from an additional unit of CO 2 

### emitted in a particular year in terms of reduced

c onsumption (or consumption equivalents) 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 rate, 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 along a BAU path CO 2 concentrations in 2100 would drop from 794 ppmv in its reference case to 762 ppmv.

14A

- 18

### 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 r ates of 3 and 7 percent.” For the specific purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive approach reflects a positive (non

- normative) perspective based on observations of people’s

actual choices

— e.g. , savings versus consumption decisions over time, and allocations of savings among more and less risky investm ents. Advocates of this approach 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 us e” (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 cos ts, because it is the market 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 qu alification; 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 d ecision

- maker wants explicitly 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 that it is “ethically indefensible” to apply a positive pure rate of time preference to discount values across generations, and many agree with this view.

Other concerns also motivate making adjustments to descriptive discount rates. In particular, it has been noted that the preferences of future generations with regard to consumption versus environmental amenities may not be the same as those today, making the current market rate on consumption an inappropriate metric by which to discou nt future climate

related 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 future generations for damage (a potentially controversial assumption, as noted above ; Arrow et al . 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that they tend to obscure important heterogeneity in the population. The utility function that underlies

14A

- 19

### 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 smo oth 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 an alysis 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

ap propriate 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 ra tes, 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 Rates

In a market with no distortions, the return to savings would equal the private return on investment, and the market rate of interest would be the appropriate choice for the social discount rate. In the real world risk, taxes, and other market imperfections drive a wedge

between the risk

- free rate of return on capital and the consumption rate of interest. Thus, the literature recognizes two conceptual discount concepts

— the consumption rate of interest and the opportunity cost of capital.

According to OMB’s C ircular 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 expe cted 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 examin ed 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.

Individuals use a variety of savings instruments that vary with risk level, time horizon, and tax characteristics. The standard analytic framework used to develop intuition about the

14A

- 20

### discount rate typically assumes a representative agent with perfect foresight and no credit

constraints. The risk

- free rate is appropria te 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

certa in amount that we would exchange for the uncertain amount. However, the calculation of the certainty

- equivalent requires first estimating the correlation between the benefits of the policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certainty

equivalent values), then the appropriate discount rate generally does not equal the risk

- free rate. If the benefits of the policy tend to be high in those states of the world in which consumption is low, then the ce rtainty

- 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 willingnes s 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 prop er 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.

This discussion suggests that both th e post

- tax riskless and risky rates can be used to capture individuals’ consumption

- equivalent interest rate. As a measure of the post

- tax riskless rate, we calculate the average real return from Treasury notes over the longest time period available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal rate from tax years 2003 through 2006 is around 27 percent). 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 obtained 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 values for the key parameters of the Ramsey equation: η ( coefficient of relative risk aversion or

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 it is roughly 3

- 4 percent. OMB cites evidence of a 3.1 percent pre

- tax rate for

10

- year Treasury notes in the A

- 4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and

4 percent for 30

- year Treasury securities.

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 real return from stocks for 1900

- 1995 was about 7 percent. The

annual real rate of return for the S &P 500 from 1950

– 2008 was about 6.8 percent. In the absence of a better way to population

- weight the tax rates, we use the middle of the 20

– 40 percent range to derive a post

- tax interest rate (Kotlikoff and Rapson 2006).

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- 21

### 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

articulat e 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 inter

- generational setting, it may make sense to use a small positive pure rate of time preference becaus e of the small probability of unforeseen cataclysmic events (Stern 2006).

• g. A commonly accepted approximation is around 2 percent per year. For the socio

economic scenarios used for this exercise, the EMF models assume that g is about

1.5

– 2 percent to 21 00.

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 lower weight on utility in the future. The
parameter η captures diminishing marginal utility : consu mption 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 income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in income is less valuable

to wealthier individuals

.

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 ben chmark 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), Weitzman (2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a tight upper bound on the curvature of utility over wealth ( CRRA < 2) with the mean implied value of 0.71 and

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting established facts about labor supply

. Recent work has jointly estimated the components of the Ramsey equation. Evans and Sezer (2005) estimate η = 1.49 for 2 2 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.

14A

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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 tr eatment to one generation over another

. The choice of η has also been posed as an ethical choice linked to the value of an additional dollar in poorer countries compared to wealthier ones. Stern (2006) applies this perspective through his choice of ρ = 0.1 percent per year

, η = 1 and g = 1.3 percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent

of their income. w



### R ecently, 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 of 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, just as we do for climate sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Piz er (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 these 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 uncertainty remains an active area of research. Newell and Pizer (2003) employ a model of how long

- term inter est 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

lev el of persistence over time.

w Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied savings rate and that η at or above 2 are inconsistent with observed behavior with regar d 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 Es timating 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

- equival ent 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 cent ral 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 positi vely correlated with market returns. Additionally, this discount rate may be justified by the high interest rates that many consumers use to smooth consumption across periods.

The low value ( 2.5 percent ) is included to incorporate the concern that interest rates are highly uncertain over time. It represents the average certainty

- equivalent rate using the mean

reverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate of 3 percent. Using this approach, the certainty equival ent is about 2.2 percent using the random walk model and 2.8 percent using the mean reverting approach. z 

### Without giving preference to a particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless rate would be justifie d 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

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 perc ent 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 uncertain ty 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 r ate 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,

utilit y, and consumption. Summers 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 simulation program from Newell and Pizer (2003).

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### or normative approach and to ethical objections that have been raised about rates o f 3 percent or higher.

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 distributio n for the climate sensitivity parameter bounded between 0 and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of two

thirds.

• Five sets of GDP, population and carbon emissions trajectories based on EMF

- 22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and FUND incorporate uncertainty in other model parameters, the final output from each

model run is a distribution over the SCC in yea r 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 forec ast, depending on the temperature in that period relative to the pre

- industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of

tem perature change in that period.

c. In DICE, temperature affects bot h 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 r ecalculate 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 expecte d 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 va ry.)

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### 6. Discount the resulting path of marginal damages back to the year of emissions using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed in step 6, divided by the unit of carbon em issions 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 mod el 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 o f 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 fraction of damages under certain assumptions about the growth of marginal damages and discounting, so each model is run here through 2300. This step required a small adjustment in the PAGE model only. This step also required assumptions about GDP, population, and greenhouse gas emission trajectories after 2100, the last year for which these

data are available from the EMF

- 22 models. (A more detailed d iscussion 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 rang e of plausible estimates that still reflects the uncertainty in the estimation exercise, the distributions from each of the models and scenarios are equally weighed and combined to produce three separate probability distributions for SCC in a given year, o ne 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 sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context, we present SCCs based on the average values across models

and socioeconomic scena rios for each discount rate

.

The interagency 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 scenarios

at the 2.5

- , 3

- , and 5

- percent discount rates. T he fourth value is included to represent the higher

than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th 

### percentile at a 3

- percent discount rate. ( The full set of 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. Fo r purposes

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### of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high temperature outcomes, which in turn lead to 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 d amages 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 discount rates consistently result in lower SCC values, while lower discount rates result in higher SCC values for each socioeconomic trajecto ry. 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 CO 2 

### 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 estimat es at or
near zero for a 5

- percent discount rate and around $9 per ton for a 3

- percent discount rate. There are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using similar adjustments to generate 2010 estimates, we calcu late 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

14A

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### comparisons are only approximate since the l iterature generally relies on Ramsey discounting,

while we have assumed constant discount rates. aa



### The SCC estimates from FUND are sensitive 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 DICE and PAGE, the fraction of economic output lost due to climate damages increases with the level of temperature alone, whe reas 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 structu ral differences among the models make FUND more sensitive to the path of emissions and less sensitive to GDP compared to DICE and PAGE

.

Figure 14A.5

. 1 s hows 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 Across 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 s uch 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 pe r

- capita consumption growth, 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 o ut 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

14A

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### Table 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 esti mate the growth rate of the SCC directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are internally consistent with other mod eling 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 b etween 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 in regulatory analysis. Damages from future emissions should be discounted at the same rate as that used to calculate

the S CC estimates themselves to ensure internal consistency

— i.e. , future damages from climate change, whether they result from emissions today or emissions in a later year, should be discounted using the same rate. For example, climate damages in 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 discount rate. bb



### 14A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to furt her refinement (and possibly significant change) in accordance with evolving scientific, economic, and ethical understandings. During the course of our modeling, it became apparent that there are

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

.

14A

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### several areas in particular need of additional exploration a nd research. These caveats and additional observations in the following section are necessary to consider when interpreting and applying the SCC estimates.

Incomplete treatment of non

- catastrophic damages

. The impacts of climate change are expected to be w idespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain because of the inherent complexity of climate processes, the economic behavior of current and future populations, and our inability to accurately forecast technological change and adaptation. Current IAMs do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature (some of which are discussed above) because of lack of p recise 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. I t 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 wildli fe loss is another example that is exceedingly difficult to monetize.)

Incomplete treatment of potential catastrophic damages

. There has been considerable recent discussion of the risk of catastrophic impacts and how best to account for extreme scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. Weitzman (2009) suggests that catastrophic damages are extremely large

— so lar ge, 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) c oncluded 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 prem iums 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

- impa ct
low

- probability risks, using a right

- skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficient, but in most cases found only a modest risk premium. Given this difference in opinion, further research in this area is nee ded before its practical significance can be fully understood and a reasonable approach developed to account for such risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures

. The damage functions in these IAMs are typically calibrated by estimating damages at moderate temperature increases ( e.g. , DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming that damages increase as some power of the temperature change. Hence, estimated damages are far more uncertain under more extreme climate change scenarios

.

14A

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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 adaptation in FUND, including widespread reliance on air conditioning

, so much so that the largest si ngle 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 c limate 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 othe r hand, the calibrations do not account for increases in climate variability, pests, or diseases, which could make adaptation more difficult than assumed by the IAMs for a given temperature change. Hence, models do not adequately account for potential adap tation or technical change that might alter the emissions pathway and resulting damages. In this respect, it is difficult to determine whether the incomplete treatment of adaptation and technological change in these IAMs under or overstate the likely damag es.

Risk aversion

. A key question unanswered during this interagency process is what to assume about relative risk aversion with regard to high

- impact outcomes. These calculations do not take into account the possibility that individuals may have a higher willingness to pay to

reduce the likelihood of low

- probability, high

- 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 fina l 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 fact should be taken into account for regulatory policy. Even if individuals are not

risk

- averse for suc h 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 analy sis are usually based on the average or the expected value and that “emphasis on these expected values is appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] analysis.”

Nordhaus (2008) points to the need to explore the relationship between risk and income in the context of climate change across models and to explore the role of uncertainty regarding various para meters 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).

14A

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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 DISCUSS ION 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 u nderestimates of the SCC (Mastrandrea 2009). In particular, the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping point” behavior in Earth systems ; (2) inter

- sectoral and inter

- regional interactions, including glob al 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 a nd 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 Climate Damages to High Levels of Warming

T he 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 th e Indian Summer Monsoon, dieback of the 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 Oscilla tion, 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 elicitat ion in 2005

– 2006 by Kriegler et al. (2009); results from this study are highlighted in Table 14A.7

. 1 . Ranges of probability are averaged across core experts on each topic.

14A

- 32

### Table 14A.7

. 1 Probabilities of Various Tipping Points from Expert Elicita tion
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 14A.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 in stance, 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 in

2010. We cannot evaluate ho w well the three models account for catastrophic or non

catastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the distribution to the assumptions made about catastrophic impacts.

PAGE treats the possibility 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 to a

catast rophic 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 probability 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 will not neces sarily 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 dramatically reduced ic e 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 incorporate either inter

- sectoral or inter

- regional interactions. For instance, while damages to the agricultural sector are incorporated, the effects of changes in food supply on human health are not fully captured and depend on the modeler’s

choice of studies used t o calibrate the IAM. Likewise, the effects of climate damages in one

14A

- 33

### region of the world on another region are not included in some of the models (FUND includes the effects of migration from sea level rise). These inter

- regional interactions, though diffic ult to quantify, are the basis for climate

- induced national and economic security concerns ( e.g. , Campbell et al. 2007; U.S. Department of Defense 2010) and are particularly worrisome at higher levels of warming. High

- end warming scenarios, for instance, p roject 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 E nvironmental 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 lan d mammals (Gingerich 2006) and soil fauna (Smith et al. 2009).

The three IAMs used here assume that it is possible to compensate for the econoic consequences of damages to natural systems through increased consumption of non

- climate goods, a common assumpt ion 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 (Levy

et al. 2005). Fo r 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 ameni ties 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

201 0, 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 i ncluded to represent the higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th 

### percentile at a 3

- percent discount rate. The central value is the average SC C 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 non

- catastrophic im pacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of

14A

- 34

### damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impacts to economic damages makes this modeling exercise even more difficult. It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue t o evolve with improvements in modeling.

14A

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### 14A.9 REFERENCES

Andronova, N. and M. Schlesinger. 2001. “Objective E stimation of the P robability D ensity

F unction for C limate S ensitivity.” J ournal of Geophys ical Re search 106 (D19) : 22605

– 22611.

Annan, J. D., J.C. Hargreaves, R. Ohgaito, A. Abe

- Ouchi, and S. Emori. 2005. “Efficiently

C onstraining C limate S ensitivity with P aleoclimate S imulations.” Scientific Online Letters on the Atmosphere 1 : 181

– 184.

Anthoff

, D

., C. Hepburn, and R. Tol. 2009a. “Equity Wei ghting and the Marginal Damage Costs of Climate Change.” Ecological Economics 68:836

- 849.

Anthoff, D., R. Tol, and G. Yohe. 2009b. “Risk A version, T ime P reference, and the S ocial C ost
of C arbon.” Environmental Research Letters 4:024002.

Arrow, K. 2007. “ Global C limate C hange: A C hallenge to P olicy.” Economist’s Voice 4(3):Article 2.

Arrow, K. 2000. “A Comment on Cooper.” The World Bank Research Observer 15 ( 2 )

.

Arrow, K. J.

, M.L. Cropper, G.C. Eads, R.W. Hahn, L.B. Lave, R.G. Noll, P.R. Portney,

R. Schma lensee, V.K. Smith, R.N. Stavins. 1996 a

. Benefit

- Cost Analysis in Environmental, Health, and Safety Regulation: A Statement of Principles . Washington, D.C., AEI Press. pp. 13

- 14.

Arrow, K.J., W.R. Cline, K.G. Maler, M. Munasinghe, R. Squitieri, and J.E. Stinglitz. 1996 b

.

“ Intertemporal E quity, D iscounting and E conomic E fficiency

. ” I n Climate Change 1995: Economic and Social Dimensions of Climate Change, Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on Cli mate Chang e

.

Campbell, J., P. Diamond, and J. Shoven. 2001. “Estimating the Real Rate of Return on Stocks Over the Long Term.” Presented to the Social Security Advisory Board. August.

Campbell, K. , J. Gulledge, J.R. McNeill, J. Podesta, P. Ogden, L. Fuer th, R.J. Woolsey,

A.TJ. Lennon, J. Smith, R. Weitz, and D. Mix. 2007. The A ge of C onsequences: The F oreign

P olicy and N ational S ecurity I mplications of G lobal C limate C hange . Center for Strategic & International Studies, 119 pp.

Castles, I. and D. Henders on. 2003.“The IPCC Emission Scenarios: An Economic

- Statistical Critique.” Energy and Environment 14(2

- 3): 159

- 185.

Chetty, R. 2006. “A New Method of Estimating Risk Aversion.” American Economic Review 96(5): 1821

– 1834.

14A

- 36

### Dasgupta

, P. 2006. “Comments on the Stern Review’s E conomics of C limate C hange.” University of Cambridge working paper.

Dasgupta P. 2008. “Discounting C limate C hange.” Journal of Risk and Uncertainty 37:141

- 169.

Easterling, W., P. Aggarwal, P. Batima, K. Brander, L. Erda, M. Howden, A. K irilenko,

J. Morton, J.F. Soussana, S. Schmidhuber, and F. Tubiello. 2007. “Food, Fibre and Forest products.” In Climate Change 2007: Impacts, Adaptation, and Vulnerability . Intergovernmental Panel on Climate Change. Eds. M.L. Parry, O.F. Canziani, J.P. Pa lutikof, P.J. can der Linden, and C.E. Hanson. Cambridge University Press. pp. 273

– 313.

Evans D. and H. Sezer. 2005. “Social D iscount R ates for M ember C ountries of the European

Union.” Journal of Economic Studies 32 : 47

– 59.

Forest, C., P.H. Stone, A.P. Sokolov, M.R. Allen, and M.D. Webster . 2002. “Quantifying

U ncertainties in C limate S ystem P roperties with the U se of R ecent O bservations.” Science

295 : 113.

Forest, D., P. Stone, and A. Sokolov. 2006. “Estimated PDFs of C limate S ystem P roperties including N atural and A nthropogenic F orcings.” Geophy sical Res earch Lett ers 33 : L01705.

Forster, P. and J. Gregory. 2006. “The C limate S ensitivity and its C omponents D iagnosed from

Earth R adiation B udget D ata.” J ournal of Clim ate 19 : 39

– 52.

Frame, D., B.B.B. Boot h, J.A. Kettleborough, D.A. Stainforth, J.M. Gregory, M. Collins, and

M.R. Allen . 2005. “Constraining C limate F orecasts: The R ole of P rior A ssumptions.” Geophys ical Res earch Lett ers 32 (2005): L09702.

Gingerich, P. 2006. “Environment and E volution T hrough the Paleocene

- Eocene T hermal

M aximum.” Trends Ecol ogy & Evolution 21:246

– 253.

Gollier, C. 2008. “Discounting with F at

- T ailed E conomic G rowth.” Journal of Risk and Uncertainty 37:171

– 186.

Gollier , C. and M. Weitzman. 2009. “ How Should the Distant Future be Discounted When

Discount Rates are Uncertain? ” Harvard University, mimeo

. Nov ember

.

Gregory, J. M.

, R.J. Stouffer, S.C.B. Raper, P.A. Scott, and N.A. Rayner . 2002. “An

O bservationally B ased E stimate of the C limate S ensitivity.” J ournal of Clim ate 15 (22) : 3117

– 3121.

Hall

, R

. and C. Jones. 2007. “The V alue of L ife and the R ise in H ealth S pending.” Quarterly Journal of Economics 122(1):39

– 72.

Hansen, J.,M. Sato, P. Kharecha, G. Russell, D. W. Lea and M. Siddall. 2007. “Climate C hange

and T race G ase s.” Phil osophical Trans actions of the Roy al Soc iety A 365:1925

– 1954.

14A

- 37

### Hegerl G .C., F.W. Zwiers, P. Branconnot, N.P. Gillett, Y. Luo, J.A. Marengo Orsini.,

N. Nicholls, J.E. Penner, and P.A. Scott . 2007. “Understanding and A ttributing C limate

C hange.” I n Cl imate Change 2007: T he P hysical S cience B asis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

. Eds.

S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and

H.L. Mill er. Cambridge, UK and New York, NY : Cambridge University Press.

Hegerl, G. C. , T. J. Crowley, W. T. Hyde, and D. J. Frame. 2006. “Constraints on C limate

S ensitivity from T emperature R econstructions of the P ast S even C enturies.” Nature 440 (April 2006):1030

– 1032

.

Holtsmark, B. and K. Alfsen. 2005. “PPP Correction of the IPCC Emission Scenarios

– Does it

Matter?” Climatic Change 68(1

– 2):11

– 19.

Hope

, C. 2008. “Optimal C arbon E missions and the S ocial C ost of C arbon U nder U ncertainty.” The Integrated Assessmen t Journal 8(1):107

– 122.

Hope

, C. 2006. “The M arginal I mpact of CO 2 

### from PAGE2002: A n I ntegrated A ssessment

M odel I ncorporating the IPCC’s F ive R easons for C oncern.” The Integrated Assessment Journal 6(1):19

– 56.

Intergovernmental Panel on Climate Change

. 2007. “Summary for Policymakers.” 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 . Cambridge University

Press.

Just, R., D. Hueth , and A. Schmitz. 2004. The Welfare Economics of Public Policy . Glos UK: Edward Elgar Publishing Limited.

Knutti, R., T. Stocker, F. Joos, and G. Plattner. 2002. “Constraints on R adiative F orcing and

F uture C limate C hange from O bservations and C limate M o del E nsembles.” Nature 416 : 719

– 723.

Kriegler, E. , J.W. Hall, H. Held, R. Dawson, and H.J. Schellnhuber . 2009. “Imprecise

P robability A ssessment of T ipping P oints in the C limate S ystem.” Proceedings of the Nat ional Academy of Sciences 106:5041

– 5046.

Kotl ikoff, L. and D. Rapson. 2006. “Does It Pay, at the Margin, to Work and Save?

– Measuring Effective Marginal Taxes on Americans’ Labor Supply and Saving.” National Bureau of

Economic Research , Working Paper 12533.

14A

- 38

### Le Treut

, H. , R. Somerville

, U. Cubasch , Y. Ding, C. Mauritzen, A. Mokssit, T. Peterson, and

M. Prather . 2007. “Historical Overview of Climate Change.” In Climate Change 2007: T he

P hysical S cience B asis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Pa nel on Climate Change

. Eds. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller. Cambridge, UK and New York, NY : Cambridge University Press.

Lenton, T. M.

, H. Held, E. Kriegler, J.W. Hall, W. Lucht, S. Rahmstorf , and H.J. Schellnhuber

. 2008. “Tipping E lements in the Earth’s C limate S ystem.” Proc eedings of the National Academy of Sciences 105:1786

– 1793.

Levy, M., S. Babu, and K. Hamilton . 2005. “Ecosystem conditions and human well

- being.” In Ecosystems and Human Well

- being: Current State and Trends, Volume 1 . Eds. R. Hassan,

R. Scholes, and N. Ash

. Washington: Island Press. pp. 123

– 1 64.

Lind, R. 1990. “Reassessing the Government’s Discount Rate Policy in Light of New Theory and Data in a World Economy with a High Degree of Capital Mobility.” Journal of Environmental Economics and Management 18 : S

- 8

– S

- 28.

Mastrandre, M. 2009. “Calculating the B enefits of C limate P olicy: Examining the A ssumptions of Integrated Assessment Models.” Pew Center on Global Climate Change Working Paper

. 60 pp.

Meehl, G .A.

, T.F. Stocker, W.D. Collins, A.T. Friedlingstein, A.T. Gaye, J.M. Gregory,

A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J. Weaver, and

Z. Zhao . 2007. “Global Climate Projections.” In Climate Change 2007: T he P hysical S cience

B asis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

. Eds. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller. Ca mbridge, UK and New York, NY : Cambridge University Press. pp. 747

– 845.

National Research Council

. 2009. Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Use . National Academies Press.

Newbold

, S . and A. Daigneault. 2009. “Climate R esponse U ncertainty and the B enefits of

G reenhouse G as E missions R eductions.” Environmental and Resource Economics 44:351

– 377.

Newell, R. and W. Pizer. 2003. Discounting the D istant F uture: H ow M uch D o U ncertain R ates

I ncrease V aluations? Journal of Envir onmental Economics and Management 46:52

– 71.

Nordhaus, W. 1994. “Expert Opinion on Climate Change.” American Scientist 82:45

– 51.

Nordhaus, W. 2007a. Accompanying N otes and D ocumentation on D evelopment of DICE

- 2007

M odel: N otes on DICE

- 2007.delta.v8 as of September 21, 2007.

14A

- 39

### Nordhaus, W. 2007b. “Alternative M easures of O utput in G lobal E conomic

- E nvironmental

M odels: Purchasing P ower P arity or M arket E xchange R ates?” Energy Economics 29:349

– 372.

Nordhaus

, W. 2008. A Question of Balance: Weighing the Optio ns on Global Warming Policies

. New Haven, CT: Yale University Press.

Nordhaus, W. 2009. “An Analysis of the Dismal Theorem. Cowles Foundation Discussion Paper. No. 1686. January.

Nordhaus

, W. and J. Boyer. 2000. Warming the World: Economic Models of Global Warming

. Cambridge, MA: MIT Press.

Panipoulou, E., B. Groom, P. Koundouri, and T. Pantelidis. 200 4 . “An Econometric Approach to Estimating Long

- Run Discount Rates.” Royal Economic Society Annual Conference 2004

No. 70.

Pindyck, R. 2009. “Uncertai n Outcomes and Climate Change Policy.” NBER Working Paper No. 15259. August.

Ramsey, F. 1928. “A Mathematical Theory of Saving.” The Economic Journal 38(152):543

– 559.

Roe, G. 2008. “Feedbacks, T imescales, and S eeing R ed.” Annual Review of Earth and Plane tary
Sciences 37:5.1

– 5.23.

Roe, G., and M. Baker. 2007. “Why is C limate S ensitivity S o U npredictable?” Science 318:629

– 632.

Schneider von Deimling, T., H. Held, A. Ganopolski, and S. Rahmstorf. 2006. “Climate sensitivity estimated from ensemble simulati ons of glacial climate.” Clim ate Dy namics 27 : 149

– 163.

Smith, J. , S.T. Hasiotis, M.J. Kraus, and D.T. Woody . 2009. “Transient D warfism of S oil F auna
D uring the Paleocene

- Eocene Thermal Maximum.” Proc eedings of the Nat ional Acad emy of

Sci ences 106:17665

– 17660.

Stern, N. 2006

. Stern Review: The Economics of Climate Change . London: HM Treasury

.

Stern

, N. 2008. “The E conomics of C limate C hange.” American Economic Review 98(2):1

– 37.

Sterner, T., and U. Persson. 2008. “ An E ven Sterner R eview: Intr oducing R elative P rices into the

D iscounting D ebate. ” Rev iew of Env ironmental Econ omics and Pol icy 2:61

– 76.

Summers, L., and R. Zeckhauser. 2008. “Policymaking for Prosperity.” Journal of Risk and Uncertainty 37: 115

– 140.

14A

- 40

### Szpiro, G. 1986. “Measuring Risk Aversion: An Alternative Approach.” The Review of

Economics and Statistics 68(1):156

– 15 9.

Tol, R. 2002a. “Estimates of the D amage C osts of C limate C hange. Part I: B enchmark

E stimates.” Environmental and Resource Economics 21:47

– 73.

Tol , R. 2002b. “Estimates of the D amage C osts of C limate C hange. Part II: D ynamic E stimates.”

Environmental and Resource Economics 21:135

– 160.

Tol, R. 2006. “Exchange Rates and Climate Change: An Application of FUND.” Climatic Change 75(1

– 2):59

– 80.

Tol, R. 2009. “An A nalysis of M itigation as a R esponse to C limate C hange.” Copenhagen Consensus on Climate. Discussion Paper.

U.S. Department of Defense. 2010. Quadrennial Defense Review Report . February.

Warren, R., C. Hope, M. Mastrandrea, R.S.J. Tol, W.N. Adg er, and I. Lorenzoni. 2006. “Spotlight ing the Impact Functions in Integrated Assessment : Research Report Prepared for the Stern Review on the Economics of Climate Change .” Tyndall Center for Climate Change Research, Working Paper 91.

Weitzman, M. 2009. “O n M odeling and I nterpreting the E conomics of C atastrophic C limate

C hange.” Review of Economics and Statistics 91:1

– 19.

Weitzman, M. 2007. “A R eview of t he Stern Review of the Economics of Climate Change .” Journal of Economic Literature 45:703

– 724.

Weitzm an, M. 1999. “Just Keep Discounting, But . . .” In Discounting and Intergenerational Equity . Eds. P.R. Portney and J.P. Weyant

. Washington, D.C. : Resources for the Future

.

Weitzman, M. 1998. “Why the Far

- Distant Future Should Be Discounted at Its Lowest Possible

Rate.” Journal of Environmental Economics and Management 36 (3):201

– 208.

Wing, S. L., G.J. Harrington, F.A. Smith, J.I. Bloch, D.M. Boyer, and K.H. Freeman

. 2005.

“T ransient floral change and rapid global warming at the Paleocene

- Eocene boundary.” Science

310: 993

– 996.

14A

- 41

### 14A.9.1 A NNEX

This A nnex provides additional technical information about the non

- CO 2 

### emission projections used in the modeling and the method for extrapolatin g 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

14A

- 42

### 14A.9.2 Other (N on

- 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 only, we calculated the RF

associated with t he 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 trea ts

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

scena rio 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 in cludes the RF for everything else. To include the EMF values, we

removed the default CH 4 

### and 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 we ll as the model default values for aerosols and other factors. Net land use CO 2 

### emissions were added to the fossil and industrial CO 2 

### emissions pathway.

DICE : DICE presents the greatest challenge because all forcing due to factors other than

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 in creases 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 concentrati ons 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 concentr ations 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

em issions from the other 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.

14A

- 43

### For s ubsequent years, we calculated the DICE default RF from aerosols and other non

CO 2 

### gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR and then stays 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 ae rosols 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 sc enarios 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

consist ent with the AR4 discussion of the post

- SRES literature on aerosols:

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor emissions, including sulphur dioxide, black carbon and organic carbon, has improved in the

pos t

- SRES 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:

method s 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 pol lution, 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

– 183 7.

14A

- 44

### 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 possi ble, 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 r educes 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 sligh tly 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.9.3 Extrapolating Emissions Project ions 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 which these projections are available from the EMF

- 22 models. These inputs we re 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.

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.

14A

- 45

### 5. Non

- CO 2 

### radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic sim plifying 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 assim ilating 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 decli ne 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 p opulation 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 ( Table 14A.9

. 2 ) also encompass the UN medium scenario forecasts through 2300

– global population of 8.5 billion by 2200, and

9 billion by 2300

.

Maintaining the decline in the 2090

– 2100 carbon intensity gro wth 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 same 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 f or assuming a long 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 ( p o st

- 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

- ex ceed scenarios considere d 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 scenario, 2000

– 2100 GDP is equal to the average of the GDP under the 550 ppm CO 2 e, full

participation, not

- to

- exceed scenarios considered by each of the four models

. 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 Industrial CO 2 

### Emissions, 2000

- 2300 ( p ost

- 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 ( p ost

- 2100 extrapolations

assume emissions decline l inearly, 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 b iosphere 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 ( p ost

- 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 t he emissions under the 550 ppm CO2e, full

- participation, not

- to

- exceed scenarios considered by each of the four models

.



### Figure 14A.9

. 7 Global CO 2 

### Intensity (fossil & industrial CO 2 

### emissions/GDP), 2000

– 230 0 ( p ost

- 2100 extrapolations assume decline in CO 2 

### /GDP growth rate over 2090

– 2100 is maintained through 2300)

Note: In the fifth scenario, 2000

– 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four models

. 0.00 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

Rate 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 14B. TECHNICAL UPDATE OF THE SOCIAL COST OF CARBON FOR

REGULATORY IMPACT ANALYSIS ­ UNDER EXECUTIVE ORDER 12866

TABLE OF CO NTENTS

14B.1 EXECUTIVE SUMMARY

................................ ................................ ....................... 14B

- 1

14B. 2 PURPOSE

................................ ................................ ................................ ................. 14B

- 2

14B.3 SUMMARY OF MODEL UPD ATES

................................ ................................ ...... 14B

- 3

14B.3.1 DICE

................................ ................................ ................................ .............. 14B

- 4

14B.3.2 FUND

................................ ................................ ................................ ............ 14B

- 6

14B.3.3 PAGE

................................ ................................ ................................ ............ 14B

- 8

14B.4 REVISED SCC ESTIMATE S

................................ ................................ ................ 14B

- 11

14B.5 OTHER MODEL LIMITATI ONS AND RESEARCH GAP S

............................... 14B

- 13

REFERENCES

................................ ................................ ................................ ...................... 14B

- 15

APPENDIX A

................................ ................................ ................................ ........................ 14B

- 17

APPENDIX B

................................ ................................ ................................ ........................ 14B

- 21

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO2, 2010

– 2050 (in 2007 dollars per metric ton of

CO 2 

### )

................................ ................................ ................................ ................. 14B

- 2

Table 14B.4.2 Summary of Key Model Revisions Relevant to the Interagency SCC

............ 14B

- 4 Table 14B.4.1 Revised Social Cost of CO2, 2010

– 2050 (in 2007 dollars per metric ton of

CO 2 

### )

................................ ................................ ................................ ............... 14B

- 12 Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050

.. 14B

- 13

LIST OF FIGURE S

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per metric ton CO2)

..... 14B

- 12

14B

- i

### APPENDIX 14B. 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

With participation by

Council of Economic Advisers Council on Environmental Quality Department of Agriculture Department of Commerce

Department of Energy Department of Transportation Domestic Policy Council Environmental Protection Agency

National Economic Council Office of Management and Budget Office of Science and Technology Policy Department of the Treasury

May 2013

Revised November 2013 See Appendix B for Details on Revision

14B.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the

social bene fits of reducing carbon dioxide (CO 2 

### ) emissions into cost

- benefit analyses of regulatory actions that impact cumulative global emissions. The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is 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.

The interagency process that developed t he original U.S. government’s SCC estimates is described in the 2010 interagency technical support document (TSD) (Interagency Working Group on Social Cost of Carbon 2010). Through that process the interagency group selected four SCC values for use in regu latory analyses. Three values are based on the average SCC from three integrated assessment models (IAMs), at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate across all three models at a 3

- percen t discount rate, is included to represent higher

- than

- expected impacts from temperature change

further out in the tails of the SCC distribution.

14B

- 1

### While acknowledging the continued limitations of the approach taken by the interagency group in 2010, this docu ment provides an update of the SCC estimates based on new versions of each IAM (DICE, PAGE, and FUND). It does not revisit other interagency modeling decisions (e.g., with regard to the discount rate, reference case socioeconomic and emission scenarios, or equilibrium climate sensitivity). Improvements in the way damages are modeled are confined to those that have been incorporated into the latest versions of the models by the developers themselves in the peer

- reviewed literature.

The SCC estimates using t he updated versions of the models are higher than those reported in the 2010 TSD. By way of comparison, the four 2020 SCC estimates reported in the 2010 TSD were $7, $26, $42 and $81 (2007$). The corresponding four updated SCC estimates for 2020 are $12, $ 43, $64, and $128 (2007$). The model updates that are relevant to the SCC estimates include: an explicit representation of sea level rise damages in the DICE and PAGE models; updated adaptation assumptions, revisions to ensure damages are constrained by GD P, updated regional scaling of damages, and a revised treatment of potentially abrupt shifts in climate damages in the PAGE model; an updated carbon cycle in the DICE model; and updated damage functions for sea level rise impacts, the agricultural sector, and reduced space heating requirements, as well as changes to the transient response of temperature to the buildup of GHG

concentrations and the inclusion of indirect effects of methane emissions in the FUND model. The SCC estimates vary by year, and the f ollowing table summarizes the revised SCC estimates from 2010 through 2050.

Table 14B.4

. 1 R evised Social Cost of CO2, 2010

– 2050 (in 2007 dollars per metric ton of

CO 2 

### )

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2015 12 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220



### 14B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) estimates from the 2010 interagency technical support document (TSD) ( Interagency Working Group on Social Cost of Carbon 2010 )

. a 

### E.O. 13563 commits the Administration to regulatory

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 carbon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12 = 3.67).

14B

- 2

### decision making “based on the best available sc ience.” b 

### Additionally, t he 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

. c 

### New

versions of the three inte grated assessment models used by the U.S. government to estimate the

SCC ( DICE, FUND, and PAGE ) , are now available and have been published in the peer

reviewed literature

. While acknowledging the continued limitations of the approach taken by the interagency group in 2010 (documented in the original 2010 TSD) , this document provides an update of the SCC estimates based on the latest peer

- reviewed version of the models , replacing model versions that were developed up to ten years ago in a rapidly ev olving 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 models by the developers themselves in the peer

- reviewed literature. The agencies participating in the interagency working group continue to investigate potential improvements to the way in which economic damage s associated with changes in CO 2 

### emissions are quantified.

Section 14B.3 summarizes the major updates relevant to SCC estimation that are

contained in the new ver sions of the integrated assessment models released since the 2010 interagency report

. 14B.4 presents the updated schedule of SCC estimates for 201 0

– 2050 based

on t hese versions of the models. Section 14B.5 provides a discus sion of other model limitations and research gaps

.

14B.3 SUMMARY OF MODEL UPDATES

This section briefly summarizes changes to 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 carbon , as summarized in Table 14B.4

. 2

. 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 damage s are constrained by GDP, updated regional scaling of damages

, and a revised treatment of potentially abrupt shifts in climate damages. The

DICE model’s simple carbon cycle has been update d to be more consistent with a more complex

climate model. The FUND model includes updated damage functions for sea level rise impacts,

the agricultur al sector , and reduced space heating requirements , as well as changes to the

transient response of tempera ture to the buildup of GHG concentrations and the inclusion of

indirect effects of methane emissions

. Changes made to parts of the models that are superseded by the interagency working group’s modeling assumptions

– regarding equilibrium climate

sensitivit y, discounting, and socioeconomic variables

– are not discussed here but can be found in the references provided in each section below.

b http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

c See p. 1, 3, 4, 29, and 33 ( Interagency Working Group on Social Co st of Carbon 2010)

.

14B

- 3

### Table 14B.4

. 1 Summary of Key Model Revisions Relevant to the Interagency SCC

IAM Version used in 2010 Interagency Analysis New

Version Key changes relevant to interagency SCC

DICE 2007 2010 Updated calibration of the carbon cycle model and explicit representation of sea level rise (SLR) and associated damages.

FUND 3.5 (2009) 3.8 (2012) Updated damage functions for space heating, SLR, agricultural impacts, changes to transient response of temperature to buildup of GHG concentrations, and

inclusion of indirect climate effects of methane.

PAGE 2002 2009 Explicit rep resentation of SLR damages, revisions to damage function to ensure damages do not exceed 100% of GDP, change in regional scaling of damages, revised treatment of potential abrupt damages, and updated adaptation assumptions.



### D ICE 14B.3.1

DICE 2010 includes a number of changes over the previous 2007 version used in the 2010 interagency report. The model changes that are relevant for the SCC estimates developed by the interagency working group include: 1) updated parameter values for the carbon cycle model, 2) a n explicit representation of sea level dynamics, and 3) a re

- calibrated damage function that includes an explicit representation of economic damages from sea level rise. Changes were also made to other parts of the DICE model

— including the equilibrium clim ate sensitivity parameter, the rate of change of total factor productivity, and the elasticity of the marginal utility of consumption

— but these components of DICE are superseded by the interagency working group’s assumptions and so will not be discussed he re. More details on DICE2007 can be found in Nordhaus (2008) and on DICE2010 in Nordhaus (2010). The DICE2010 model and documentation is also available for download from the homepage of

William Nordhaus.

Carbon Cycle Parameters

DICE uses a three

- box model of carbon stocks and flows 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

Assessmen t of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44). d


### Carbon cycle transfer coefficient values in DICE2010 are based on re

- calibration of the model to match the newer 2009 version of MAGICC (Nordhaus 2010 p 2). For example, in DICE201 0, 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, 1 8.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each

d MAGICC is a simple climate model initially developed by the U.S. National Center for Atmospheric Research that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from more sophisticate d state of the art earth system simulation models ( Randall et al. 2007).

14B

- 4

### decade

, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 percent remains in the shallo w 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 pa th 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.

Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global average sea level anomaly to be used in the updated damage function (discussed below). This section contains a brief description of the sea level rise (SLR) module; a more detailed description can be found on the model developer’s we bsite. e 

### 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 (AR4). f 

### The rise in sea level from thermal expansion in each time period (decade) is 2 percent of the difference between the sea level in the previous period and the long run equilibrium sea level, which is 0.5 meters per degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the melting of glaciers and small ic e caps occurs at a rate of 0.008 meters per decade per °C above the average global temperature in 1900.

The contribution to sea level rise from melting of the Greenland ice sheet is more complex. The equilibrium contribution to SLR is 0 meters for tempera ture anomalies less than

1 o 

### C and increases linearly from 0 meters to a maximum of 7.3 meters for temperature anomalies

between 1 o 

### C and 3.5 °C. The contribution to SLR in each period is proportional to the difference between the previous period’s sea leve l anomaly and the equilibrium sea level anomaly, where the constant of proportionality increases with the temperature anomaly in the current period. The contribution to SLR from the melting of the Antarctic ice sheet is

- 0.001 meters per decade

when the te mperature anomaly is below 3 °C and increases linearly between 3 °C and 6 °C to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

Re

- calibrated Damage Function

Economic damages from climate change in the DICE model are represente d by a fractional loss of gross economic output in each period. A portion of the remaining economic output in each period (net of climate change damages) is consumed and the remainder is invested in the physical capital stock to support future economic pro duction, so each period’s climate damages will reduce consumption in that period and in all future periods due to the lost

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: http://no rdhaus.econ.yale.edu/documents/SLR_021910.pdf

. f For a review of post

- IPCC AR4 research on sea level rise, see Nicholls et al. (2011) and NAS (2011).

14B

- 5

### investment. The fraction of output in each period that is lost due to climate change impacts is represented as one minus a fraction, which is one divided by a quadratic function of the temperature anomaly, producing a sigmoid (“S”

- shaped) function. g 

### 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), who notes that “...damages in the uncontrolled (baseline) [i.e., reference] case ... in 2095 are $12 trillion, or 2.8 percent of global output, for a global temperature increase of 3.4 o 

### C above 1900 levels.” This compares to a loss of 3.2 percent of global output at 3.4 o 

### C in DICE2007. However, in DICE2010, annual damages are lower in most of the early periods of the modeling horizon but higher in later periods than would be calculated using the DICE2007 damage function. Specifically, the percent difference between damages in the base run of DICE2010 and those that would be calculated using the DICE2007 damage function starts at +7 percent in 2005, decreases

to a low of

- 14 percent in 2065, then continuously increases to +20 percent by 2300 (the end of the interagen cy 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 th at 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 given that relative increases in damage s in later periods are discounted more heavily, all else equal.

F UND 14B.3.2

FUND version 3.8 includes a number of changes over the previous version 3.5 (Narita et al. 2010) used in the 2010 interagency report. Documentation supporting FUND and the model’s

source code for all versions of the model is available from the model authors. h 

### Notable changes, due to their impact on the SCC estimates, are adjustments to the space heating, agriculture, and sea level rise damage functio ns 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.

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

g The model and documentation, including formulas, are available on the author’s webpage at http://www.econ.yale.edu/~nordhaus/homepage/RICEmodels.htm

. h http://www.fund

- model.org/

. This report uses version 3.8 of the FUND model, which represe nts a modest update to the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013). For the purpose of computing the SCC, the relevant changes (between 3.7 to 3.8) are associated with improving consistency

with IP CC AR4 by adjusting the atmospheric lifetimes of CH 4 and N 2 O and incorporating the indirect forcing

effects of CH 4 , 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 significantly updated.

14B

- 6

### based on the forecasted temperature anomaly’s deviation from the one degree benchmark and adjusted for changes in vulnerability due to economic and energy ef ficiency growth. In FUND 3.5, the function that scales the base year damages adjusted for vulnerability allows for the possibility that in some simulations the benefits associated with reduced heating needs may be an unbounded convex function of the temper ature anomaly. In FUND 3.8, the form of the scaling has been modified to ensure that the function is everywhere concave and that there will exist an upper bound on the benefits a region may receive from reduced space heating needs. The new formulation appr oaches a value of two in the limit of large temperature anomalies, or in other words, assuming no decrease in vulnerability, the reduced expenditures on space heating at any level of warming will not exceed two times the reductions experienced at one degre e 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 SCC. This update accounts for a significant portion of the difference in the expected SCC estimates reported by the two versions of the model when run probabilistically.

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 potential land lost in a given year due to sea level rise is linear in

the rate of sea le vel 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 inland. In FUND 3.8 the function defining the potential land lost has been changed to be a

convex function of sea level rise, thereby assuming that the slope of the shore line increases moving inland. The effect of this change is to typically reduce the vulnerability of some regions to sea level rise based land loss, thereby lowering the ex pected SCC estimate. j



### Agriculture

In FUND, the damages associated with the agricultural sector are measured as proportional to the sector’s value. The fraction is bounded from above by one and is made up of

three additive c omponents that represent the e ffects 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 functio n with an intercept of zero. In FUND 3.5, the coefficients of this loss function are modeled as the ratio of two random normal variables. This specification had the potential for unintended extreme behavior as draws from the parameter in the denominator ap proached zero or went negative. In FUND 3.8, the coefficients are drawn directly from truncated normal distributions so that they remain in the range [0, ) ∞ 

### and ( ,0]

− ∞ 

### , respectively, ensuring the correct sign and eli minating the potential for divide by zero errors. 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 decrease the range of the distribution while

j For stability purposes this report also uses an update to the model which assumes that regional coastal protection measures will be built to protect the most valuable land first, such that the marginal benefits of coastal protection is decreasing in the l evel of protection following Fankhauser (1995).

14B

- 7

### spreading out the distributions’ mass over the remaining range relative to the previous version. The net effect of this change on the SCC estimates is difficult to predict.

Transient Temperature Response

The temperature response model translates changes in global levels of radiative forcing into the current expected temperature anomaly. In FUND, a given year’s increase in the temperature anomaly is based on a mean reverting function where the mean equals the equilibrium temperatur e 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 defined 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 tem perature response has been updated to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity of the rate of temperature response to the level of the equilibrium climate sensitivity, a relationship first noted by Ha nsen et al. (1985) based on the heat uptake of the deep ocean. Therefore in FUND 3.8, the temperature response will typically be faster than in the previous version. The overall effect of this change is likely to increase estimates of the SCC as higher

tem peratures are reached during the timeframe analyzed and as the same damages experienced in the previous version of the model are now experienced earlier and therefore discounted less.

Methane

The IPCC AR4 notes a series of indirect effects of methane emis sions, and has developed methods for proxying such effects when computing the global warming potential of methane (Forster et al. 2007). FUND 3.8 now includes the same methods for incorporating the indirect effects of methane emissions. Specifically, the a verage atmospheric lifetime of methane has been set to 12 years to account for the feedback of methane emissions on its own lifetime. The radiative forcing associated with atmospheric methane has also been increased by 40% to account for its net impact on ozone production and stratospheric water vapor. All else equal, the effect of this increased radiative forcing will be to increase the estimated SCC values, due to greater projected temperature anomaly.

PAGE 14B.3.3

PAGE09 (Hope 2013) includes a number of changes from PAGE2002, the version used in the 2010 SCC interagency report. The changes that most directly affect the SCC estimates include: explicitly modeling the impacts from sea level rise, revisions to the damag e 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 revision s to the carbon cycle feedback and

14B

- 8

### the calculation of regional temperatures. k 

### More details on PAGE09 can be found in Hope (2011a, 2011b, 2011c). A description of PAGE2002 can be found in Hope (2006).

Sea Level Rise

While PAGE2002 aggregates all damages i nto two categories

– ec onomic and non

economic impacts

– , PAGE09 adds a third explicit category: damages from sea level rise. In the previous version of the model, damages from sea level rise were subsumed by the other damage categories. In PAGE09 sea leve l damages increase less than linearly with sea level under the assumption that land, people, and GDP are more concentrated in low

- lying shoreline areas. Damages from the economic and non

- economic sector were adjusted to account for the

introduction of this new category.

Revised Damage Function to Account for Saturation

In PAGE09, small initial economic and non

- economic benefits (negative damages) are modeled for small temperature increases, but all regions eventually experience economic damages from climate change, where 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 certain proportion of remaining Gross Domestic Product (GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, which allowed Eastern Europe to potentially experience large benefits from temperature increases, and which also did not bound the possible damages that coul d be experienced.

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 proportional based on a given scaling factor. The scali ng factor in PAGE09 is based on the length of a region’s coastline relative to the EU (Hope 2011b). Because of the long coastline in the EU, other regions are, on average, less vulnerable than the EU for the same sea level and temperature increase, but

all regions have a positive scaling factor. PAGE2002 based its scaling factors on four studies reported in the IPCC’s third assessment report, and allowed for benefits from temperature increase in Eastern Europe, smaller impacts in developed countries, and hi gher damages in developing countries.

Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” (nonlinear extreme event) were modeled as an expected value. Specifically, a stochastic probability of a discontinuity was

mul tiplied by the damages associated with a discontinuity to obtain an expected value, and this

was added to the economic and non

- economic impacts. That is, additional damages from an extreme event, such as extreme melting of the Greenland ice sheet, were mul tiplied by the

k Because several changes in the PAGE model are structural (e.g., the addition of sea level rise and treatment of discontinuity), it is not possible to assess the direct impact of each change on the SCC in is olation as done for the

other two models above

.

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- 9

### 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 expected value. A large

‐ scale discontinuity becomes possible when the temperature rises beyond

some threshold value between 2 and 4°C. The probability that a discontinuity will occur beyond

this threshold then in creases 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 ot her damages, and other regions lose an amount determined 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 a nomaly and the damages that result from a discontinuity 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.

Adaptati on

As in PAGE2002, adaptation is available to help mitigate any climate change impacts that occur. In PAGE this adaptation is the same regardless of the temperature change or sea level rise and is therefore akin to what is more commonly considered a reduct ion in vulnerability. It is modeled 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 2°C, it will reduce damages by 15

- 30 percent (depending on t he region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation was assumed to reduce economic sector damages up to 2°C by 50

- 90 percent after 20 years. Beyond 2°C, no adaptation is assumed to be available to mitigate the impacts of climate change. For the non

- economic sector, in PAGE09 adaptation is available to reduce 15 percent of the damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to fully implement, instead of 25 percent of th e 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 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c) estimates that the less o ptimistic assumptions regarding the ability to offset impacts of temperature and sea level rise via adaptation increase the SCC by approximately 30 percent.

Other Noteworthy Changes

Two other changes in the model are worth noting. The re is a change in the way the model

accounts for decreased CO 2 

### absorption on land and in the ocean as temperature rises. PAGE09 introduces a linear feedback from global mean temperature to the percentage gain in the excess

concentration of CO 2 

### , capped at a maximum level. In PA GE2002, 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 determin e annual average regional temperature anomalies to be used in the regional damage functions. In PAGE2002, the scaling was determined solely based on

14B

- 10

### regional difference in emissions of sulfate aerosols. In PAGE09, this regional temperature anomaly is furth er 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, to capture relatively greater changes in temperature forecast to be experienced at higher latitudes.

14B.4 REVISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same methodology detailed in the 2010 TSD ( Interagency Working Group on Social Cost of Carbon

2010 ). 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 sens itivity distribution calibrated to the IPCC AR4, and three constant discount rates of 2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five scenarios produces 45 separate distributions for the global SCC . The approach laid out in the

2010 TSD applied equal weight to each model and socioeconomic scenario in order to reduce the dimensionality down to three separate 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 socio

- economic

- emissions

scenarios at the 2.5

- , 3

- , and 5

- percent discount rates, respectively. The fourth value was chosen
to repr esent the higher

- than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3

- percent discount rate was chosen. (A detailed set of percentiles by mode l and scenario

combination and additional summary statistics for the 2020 values is available in the Appendix.)

As noted in the 2010 TSD, “the 3 percent discount rate is the central value, and so the central value that emerges is the average SCC across mod els at the 3 percent discount rate” ( Interagency Working Group on Social Cost of Carbon 2010 , p. 25). However, for purposes of capturing the uncertainties involved in regulatory impact analysis, the interagency group emphasizes the importance and value of including all four SCC values.

Table 14B.4

. 2 shows the four selected SCC estimates in five year increments from 2010

to 2050

. Values for 2010, 2020, 2030, 2040, and 2 050 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 linear interpolation

. The full set of revised

annual SCC estima tes between 2010 and 2050 is reported in the Appendix

.

14B

- 11

### Table 14B.4

. 1 R evised Social Cost of CO2, 2010

– 2050 (in 2007 dollars per metric ton of

CO 2 

### )

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2015 12 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220



### The SCC estimates using the updated versions of the models are higher than those reported in the 2010 TSD due to the changes to the models outlined in the previous section. By way of comparison, the 2020 SCC estimates reported in the original TSD were $7, $26, $42 and $81 (2007$) (Interagency Working Group on Social Cost of Carbon 2010). Figure 14B.4

. 1

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 th e lower the discount rate, the longer the right tail of the distribution.

Figure 14B.4

. 1 Distribution of SCC Estimates for 2020 (in 2007$ per metric ton CO2)

As was the case in the 2010 TSD, t he 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 compute the cost of a marginal ton emitted in the future by running the models for a

14B

- 12

### set of perturbation years out to 2050. Table 14B.4

. 2 illustrates how the growth rate for these fou r SCC estimates varies over time

.

Table 14B.4

. 2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050

Average Annual Growth 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 2010 TSD, damages from future emissions should be discounted at the same rate as that used to

calculate the SCC esti mates 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.

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 fu ll (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

sub stantial climate change. Other countries would also need to take action to reduce emissions if significant changes in the global climate are to be avoided. Emphasizing the need for a global solution to a global problem, the United States has been actively involved in seeking international agreements to reduce emissions and in encouraging other nations, including emerging major economies, to take significant steps to reduce emissions. When these considerations are taken as a whole, the interagency group conc luded that a global measure of the benefits from reducing U.S. emissions is preferable. For additional discussion, see the 2010

TSD.

14B.5 OTHER MODEL LIMITATIONS AND RESEARCH GAPS

The 2010 interagency SCC TSD discusse s a number of important limitations for whic h

additional research is needed

. In particular, the document highlight s the need to improve the quantification of both non

- catastrophic and catastrophic damages, the treatment of adaptation and technological change, and the way in which inter

- regional and inter

- sectoral linkages are

modeled . While the new version of the models discussed above offer some improvements in these areas, further work remains warranted. The 2010 TSD also discusse s the need to more carefully assess the implications of risk aversion for SCC estimation as well as the inability to

14B

- 13

### 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 resea rch on modeling and valuation of climate impacts that can potentially improve

SCC estimation in the future

.

14B

- 14

### REFERENCES

Anthoff, D. and Tol, R.S.J. 2013a. The uncertainty about the social cost of carbon: a decomposition analysis using FUND. Climatic Change 117: 515

– 530

.

Anthoff, D. and Tol, R.S.J. 2013b. Erratum to: The uncertainty about the social cost of carbon: A decomposition analysis using FUND. Climatic Change . Advance online publication. doi:

10.1007/s10584

- 013

- 0959

- 1.

Fankhauser, S. 1995. Valu ing climate change: The economics of the greenhouse. London, England: Earthscan.

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. 2007. 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 [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Hope, Chris. 2006. “The Marginal Impact of CO 2 

### from PAGE2002: An Integrated Assessment Model Incorp orating the IPCC’s Five Reasons for Concern.” The Integrated Assessment Journal

. 6(1): 19

– 56.

Hope, Chris. 2011a “The PAGE09 Integrated Assessment Model: A Technical Description” Cambridge Judge Business School Working Paper No. 4/2011 (April). Accessed No vember 23,

2011: http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf

.

Hope, Chris. 2011b “The Social Cost of CO 2 

### from the PAGE09 Model” Cambridge Judge Business School Working Paper No. 5/2011 (June). Accessed November 23, 2011: http://www.jbs.cam.ac.uk/research/working_papers/2011/wp11 05.pdf

.

Hope, Chris. 2011c “New Insights from the PAGE09 Model: The Social Cost of CO 2 

### ” Cambridge Judge Business School Working Paper No. 8/2011 (July). Accessed November 23,

2011: http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf

.

Hope, C. 2013. Critical issues for the calculation of the social cost of CO 2 

### : why the estimates from PAGE09 are higher than those from PAGE2002. Climatic Change 117: 531

– 543.

Interagen cy Working Group on Social Cost of Carbon. 2010. Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866. February. United States Government. http://www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

- Cost

- of

- Carbon

for

- RIA.pdf

.

Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J. Weaver and Z.

- C. Zhao. 2007. Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental

14B

- 15

### Panel on Cli mate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Narita, D., R. S. J. Tol and D. Anthoff. 2010. Economic costs of extratropical storms under climate change: an application of FUND. Journal of Environmental Planning and Management 53(3): 371

- 384.

National Academy of Sciences. 2011. Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to M illennia. Washington, DC: National Academies Press, Inc.

Nicholls, R.J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. Hinkel and

R.S.J. Tol. 2011. Sea

- level rise and its possible impacts given a ‘beyond 4°C world’ in the
twenty

- first ce ntury. Phil. Trans. R. Soc. A 369(1934): 161

- 181.

Nordhaus, W. 2010. Economic aspects of global warming in a post

- Copenhagen environment. Proceedings of the National Academy of Sciences 107(26): 11721

- 11726.

Nordhaus, W. 2008. A Question of Balance: Weighi ng the Options on Global Warming Policies. New Haven, CT: Yale University Press.

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. 2007. Climate Mode ls 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 [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Avery t, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY,

USA.

14B

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### APPENDIX A

Table A1 Annual SCC Values: 2010

- 2050 (2007$/metric 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

14B

- 17

### Table A2 20 2 0 Global SCC Estimates at 2.5

- Percent Discount Rate (2007$/ metric ton CO 2 

### )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95 th 99th

Scenario l PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991 MERGE Optimistic 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 Optimistic 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 Optimistic

- 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$/metric ton CO 2 

### )

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 Optimistic 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 Optimistic 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 Optimistic

- 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

l See 2010 TSD for a description of these scenarios.

14B

- 18

### Table A4 20 2 0 Global SCC Estimates at 5

- Percent Discount Rate (2007$/ metric ton CO 2 

### )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244 MERGE Optimistic 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 Optimistic 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 Optimistic

- 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

14B

- 19

### Table A5 Additional Summary Statistics of 2020 Global SCC Estimates

Discount rate: 5.0% 3.0% 2.5%

Statistic: Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis

DICE 12 26 2 15 38 409 3 24 57 1097 3 30

PAGE 22 1616 5 32 71 14953 4 22 101 29312 4 23

FUND 3 41 5 179 19 1452

- 42 8727 33 6154

- 73 14931

14B

- 20

### APPENDIX 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 mis specified in the model’s c omputer code. This correction is covered in an erratum to Anthoff and Tol (2013) published in the same journal ( Climatic Change ) in October 2013 (Anthoff and Tol (2013b)). 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 va riance and faster decay of the upper tail, as compared to the intended specification based on the Roe and Baker distribution. The difference between the original estimates reported in the May 2013 version of this technical support document and this

revisio n are all one dollar or less.

14B

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### CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION

................................ ................................ ................................ ......... 15

- 1

15.2 METHODOLOGY

................................ ................................ ................................ ........ 15

- 1

15.3 UTILITY IMPACT RESUL TS

................................ ................................ ..................... 15

- 2

15.3.1 Installed Capacity

................................ ................................ ............................... 15

- 2

15.3.2 Electricity Generation

................................ ................................ ........................ 15

- 6

15.3.3 Results Summary

................................ ................................ ............................... 15

- 9

REFERENCES

................................ ................................ ................................ ........................ 15

- 10

LIST OF F IGURES

Figure 15.3.1 Automatic Commercial Ice Makers: Total Capacity Reduction

......................... 15

- 3 Figure 15.3.2 Automatic Commercial Ice Makers: Coal Capacity Reduction

.......................... 15

- 3 Figure 15.3.3 Automatic Commercial Ice Makers: Nuclear Capacity Reduction

..................... 15

- 4 Figure 15.3.4 Automatic Commercial Ice Makers: Gas Combined Cycle Capacity Reduction 15

- 4 Figure 15.3.5 Automatic Commercial Ice Makers: Peaking Capacity Reduction

..................... 15

- 5 Figure 15.3.6 Automatic Commercial Ice Makers: Renewables Capacity Reduction

.............. 15

- 5 Figure 15.3.7 Automatic Commercial Ice Makers: Total Generation Reduction

...................... 15

- 6 Figure 15.3.8 Automatic Commercial Ice Makers: Coal Generation Reduction

....................... 15

- 6 Figure 15.3.9 Automatic Commercial Ice Makers: Nuclear Generation Reduction

.................. 15

- 7 Figure 15.3.10 Automatic Commercial Ice Makers: Gas Combined Cycle Generation Reduction

................................ ................................ ................................ ............................ 15

- 7 Figure 15.3.11 Automatic Commercial Ice Makers: Oil Generation Reduction

....................... 15

- 8 Figure 15.3.12 Automatic Commercial Ice Makers: Renewables Generation Reduction

......... 15

- 8

LIST OF TABLES

Table 15.3.1 Automatic Commercial Ice Makers: Summary of Utility Impact Results

............ 15

- 9

15

- i

### CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis

, U.S. Department of Energy ( DOE ) analyze s the changes in electric installed capacity and generation that result for each trial sta ndard level (TSL).

The utility impact analysis uses a variant of the DOE Energy Information

Administration ’s (EIA ’s ) National Energy Modeling 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 use s a variant of this model, referred to as NEMS

- BT, b 

### to account for selected utility impacts of 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 decremented to reflect the impact of standards

. For t he analysis of standards

on automatic commercial ice makers , DOE used the version of NEMS based on AEO201 4

. 1



### NEMS

- BT has a number of advantages that have led to its use in the analysis of energy

conserva tion 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 impac ts 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 marginal and average values m ay 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. In general, when electricity demand deviates from the AEO R eference C ase, three inter

- related

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 2003 , DOE/EIA

- 0581(2003),

March 2003.

b DOE/EIA approves use of the name NEMS to describe only an official version of the model without any modification to code or data. Because this analysis entails some minor code modifications and the model is run under various policy scenarios that are 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

### effects can occur : the annual generation (terawatt

- hours (TWh) ) from the stock of electric generating capacity cha nges; the total generation capacity itself (gigawatts (GW) ) may change, and the mix of capacity by fuel type may change. Each of these effects can var y 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. Regiona l 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 ( i.e.

, ratio of the change in fuel consumption in quadrilli on British thermal units ( quads ) to the change in generation in TWh) and the capacity reduction by fuel type for a given reduction in total generation. DOE uses the site

- energy savings multiplied by a transmission and distribution loss

factor to estimate t he 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.

Det ails on this approach use d may be found in Coughlin (2013 ). 2



### NEMS

- BT provides output for the following capacity types: coal, nuclear, combined cycle (natural gas), renewable sources, oil and 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 ( i.e., coal, natural gas and diesel) and renewables. Pumped storage and nuclear power are very insensitive to small changes in demand, while fuel cells and distributed generation ma ke up a very small fraction (less than 1 percent) of the generation capacity base.

15.3 UTILITY IMPACT RESUL TS

This section presents results of the analysis for all of the capacity types except “ other

, ” for which the impacts are very small.

15.3.1 Installed Capacity

Figure 15.3

. 1 through Figure 15.3

. 6 show the changes in U.S. electric ity inst alled capacity that result for each TSL by major plant type for selected years.

c These reductions are defined relative to the AEO Reference C ase.

15

- 2

### Figure 15.3

. 1 Automatic Commercial Ice Makers : Total Capacity Reduction

Figure 15.3

. 2 Automatic Commercial Ice Makers : Coal Capacity Reduction 0 50 100 150 200 250 300 350 400 450201820232028203320382043204820532058MW 

### Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 10 20 30 40 50 60 70 80 90 100201820232028203320382043204820532058MW 

### Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 3

### Figure 15.3

. 3 Automatic Commercial Ice Makers : Nuclear Capacity Reduction

Figure 15.3

. 4 Automatic Commercial Ice Makers : Gas Combined Cycle Capacity

Reduction 0 1 2 3 4 5 6 7 8201820232028203320382043204820532058MW 

### Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 20 40 60 80 100 120 140 160201820232028203320382043204820532058MW 

### NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 4

### Figure 15.3

. 5 Automatic Commercial Ice Makers : Peaking Capacity Reduction

Figure 15.3

. 6 Automatic Commercial Ice Makers : Renewables Capacity Reduction 0 20 40 60 80 100 120201820232028203320382043204820532058MW 

### Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 10 20 30 40 50 60 70201820232028203320382043204820532058MW 

### Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 5

### 15.3.2 Electricity Generation

Figure 15.3

. 7 through F igure 15.3

. 12 show the annual change s in electric ity generation

that result for each TSL by plant type. Gas combined cycle power plants account for most of the generation reduction.

Figure 15.3

. 7 Automatic Commercial Ice Makers : Total Generation Reduction

Figure 15.3

. 8 Automatic Commercial Ice Makers : Coal Generation Reduction 0 200 400 600 800 1000 1200 1400 1600201820232028203320382043204820532058GWh 

### Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 100 200 300 400 500 600201820232028203320382043204820532058GWh 

### Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 6

### Figure 15.3

. 9 Automatic Commercial Ice Makers : Nuclear Generation Reduction

Figure 15.3

. 10 Automatic Commercial Ice Makers : Gas Combined Cycle Generation

Reduction 0 10 20 30 40 50 60201820232028203320382043204820532058GWh 

### Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 100 200 300 400 500 600 700 800201820232028203320382043204820532058GWh 

### NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 7

### Fi g ure 15.3

. 11 Automatic Commercial Ice Makers : Oil Generation Reduction

Figure 15.3

. 12 Automatic Commercial Ice Makers : Renewables Generation Reduction 0 1 1 2 2 3 3201820232028203320382043204820532058GWh 

### Oil

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 50 100 150 200 250201820232028203320382043204820532058GWh 

### Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 8

### 15.3.3 Resu lts Summary

Table 15.3

. 1 presents a summary of the utility impact results for automatic commercial

ice makers

.

Table 15.3

. 1 Automatic Commercial Ice Makers : Summary of Utility Impact Results

TSL

1 2 3 4 5 Installed Capacity Reduction

M W

2020 22.82 38.36 50.63 64.64 90.83

2025 66.87 112.40 148.36 189.40 266.16

2030 74.31 124.91 164.87 210.48 295.78

2035 83.09 139.67 184.36 235.35 330.73

2040 92.59 155.64 205.43 262.26 368.54 Electricity Generation Reduction

G Wh

2020 104.56 175.75 231.98 296.15 416.16

2025 286.46 481.49 635.55 811.35 1,140.15

2030 295.27 496.31 655.11 836.33 1,175.25

2035 305.81 514.02 678.48 866.17 1,217.17

2040 317.98 534.48 705.49 900.65 1,265.64

15

- 9

### REFERENCES

1

. U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook

201 4

. 201 4 . Washington, DC. (Last accessed May 1, 201 4 .)

< http://www.eia.gov/forecasts/aeo/ >.

2

. Coughlin, K. 2013. Proje ctions of Full

- Fuel

- Cycle Energy and Emissions Metrics. Lawrence Berkeley National Laboratory, Report Number LBNL

- 6025E.

15

- 10

### CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION

................................ ................................ ................................ ............ 16

- 1

16.2 ASSUMPTIONS

................................ ................................ ................................ ............... 16

- 1

16.3 METHO DOLOGY

................................ ................................ ................................ ........... 16

- 1

16.4 SHORT

- TERM RESULTS

................................ ................................ ............................... 16

- 2

16 .5 LONG

- TERM RESULTS

................................ ................................ ................................ . 16

- 3

REFERENCES

................................ ................................ ................................ .......................... 16

- 5

LIST OF TABLES

Table 16.4.1 Net Short

- Term Change in Employment (Number of Employees)

...................... 16

- 3

16

- i

### CHAPTER 16. EMPLOYMENT IMPACT AN ALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy ’s ( DOE ’s ) emplo yment impact analysis is designed to
estimate indirect national job creation or elimination resulting from proposed standards due to

reall ocation of associated expenditures for purchasing and operating automatic commercial ice

making (ACIM) equipment

. Job increases or decreases reported in this chapter are se parate from

the direct ACIM sector employment impacts reported in c hapter 12 and reflect the employment impact of efficiency standards on all other sectors of the economy. DOE conducted this analysis as part of the final rule

.

16.2 ASSUMPTIONS

DOE expects amen ded energy conservation standards to decrease energy use and

, therefore

, reduce energy expenditures. Because at least one design option would also reduce water usage, the electricity usage standards could also have the effect of reducing water expenditures as well. The savings in energy and water expenditures may be spent on new

investment s or not spent a t all ( i.e. , they may remain “saved”). Amended standards may increase the purchase price of automatic commercial ice makers , including the retail price plus sales tax,

and could

, in some cases

, increase installation costs

.

Using an input/output econometri c model of the U.S. economy, this analysis estimated

the short

- term effect of these expenditure impacts on net economic output and employment. DOE intends this analysis to quantify the indirect employment impacts of these expenditure changes. DOE evaluated direct employment impacts at manufacturers’ facilities in the manufacturer impact analysis (see chapter 12).

DOE notes that the Impact of Sector Energy Technologies ( ImSET ) model is not a general equilibrium forecasting model and understands the uncertain ties 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 overestimate the magnitude of actual job impacts over the long run for this rule. Because input/output models do not allow prices to bring markets into equilibrium, they are best used for short

- run analysis. Therefore, DOE include d a qualitative discussion of how labor markets are likely to respond in the longer term. In future rulemakings, DOE may consider the use of other modelin g approaches for examining long

- run employment impacts

.

16.3 METHODOLOGY

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 net impact of standards on jobs. Pacific Northwest National Laboratory (PNNL) d eveloped the model using

ImSET 3.1.1 2 

### as a successor to Impact of Building Energy Efficiency Programs ( ImBuild )

, 3 

### a special purpose version of the Impact Analysis for Planning ( IMPLAN ) 4 

### national input/output

model. Im SET esti mates the employment and income effects of building energy technologies.

16

- 1

### C ompared with simple economic multiplier approaches, Im SET allows for more complete and automated analysis of the economic impacts of energy

- efficiency investments in buildings.

In an input/output model, the level of employment in an economy is determined by the relationship 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. , d ue to the effects of an efficiency standard) in one sector of the economy will affect flows in other sectors, which affects the overall level of employment.

ImSET uses a 187

- sector model of the national economy to predict the economic effects

of residentia l 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 calc ulates 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

mai ntain energy

- eff icient equipment

. The increased cost of equipment leads to higher employment 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, utility sector investment funds are released for use in

other sector s of the economy. When customers use less energy or water , electric and/or water utilities experience relative reductions in demand

, which leads to re ductions in utility sector investment and employment.

DOE notes that the employment impacts estimated with ImSET for the entire economy differ from the employment impacts in the ACIM manufacturing sector estimated in c hapter 12 using the Government Regulat ory 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 ACIM standards relative to the base case.

DOE dis aggregated the impact o f standards on employment into three component effects : increased capital investment c osts, decreased energy and water costs, and changes in operations

and maintenance costs

.

Conceptually, one can consider the impact of the rule in its first year on three aggregate

sectors : the ACIM production sector, the energy generation sector, and the general consumer good s sector (as mentioned previously, ImSET’s calculations are made at a much more disaggregate level). By raising energy efficiency, the rule increases the purchase price of

automatic commercial ice makers ; this increase in expenditures causes an increase in employment in this sector. At the same time, the improvements in en ergy efficiency reduce expenditures on electricity. The reduction in electricity demand causes a reduction in

16

- 2

### employment in that sector. Finally, based on the net impact of increased expenditures on

automatic commercial ice makers and reduce d expenditures on electricity, expenditures on everything else are either positively or negative ly 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 ( e.g., as more workers are hired

, they consume more goods, which generates more employment ; the converse is true for workers laid off).

Table 16.4

. 1 presents the modeled net employment impact from the rule in 201 8 and

202 1

. As mentioned in c hapter 12, 84 percent of automatic commercial ice makers are produced domestically and 16 percent are imported. The net employment impact estimate is sensitive to assumptions regarding the return to the U.S. economy of mo ney spent on imported automatic

commercial ice makers

. The two scenarios bounding the ranges presented in Table 16.4

. 1 represent situations in which none of the money spent on imported automatic com mercial ice

makers returns to the U.S. economy and all of the money spent on imported automatic

commercial ice makers returns to the U.S. economy (low and high bounds, respectively). The U.S. trade deficit in recent years suggests that between 50 and 75 pe rcent of the money spent on imported automatic commercial ice makers is likely to return, with employment impacts falling within the ranges presented below.

Table 16.4

. 1 Net Short

- T erm Change in Employment (Nu mber of Employees)

Trial Standard Level 201 8 * 2022

1 18 to 21 104 to 107

2 31 to 38 196 to 204

3 41 to 52 263 to 276

4 41 to 63 315 to 340

5 4 to 82 376 to 464 * Compliance date of standard levels is 201 8

.



### 16.5 LONG

- TERM RESULTS

Due to the short payback period of energy

- efficiency improvements mandated by this rule, over the long term DOE expect s the energy savings to customers to increasingly d ominate the increase in equipment costs, resulting in increas ed aggregate savings to customers. As a

res ult, DOE expect s demand for electricity to decrease over time and demand for other goods to

increase. Because the electricity generation sector is relatively capital intensive compared to the consumer goods sector, the net effect will be an increase in labor demand. In equilibrium, this should lead to upward pressure on wages and a shift in employment away from el ectricity generation and toward consumer goods. Note that in long

- run equilibrium there is no net effect on total employment because wages adjust to bring the labor market into equilibrium. For

context, the Office of Management and Budget ( OMB ) currently a ssumes that the official unemployment rate may decline to an annual average of 6.3 percent in 2014 and drop further to

5. 4 percent in 201 7

. 5 

### The unemployment rate in 201 7 is generally considered to be close to “full employment.” When an economy is at full employment

, any effects on net employment are likely to be transitory as workers change jobs ( e.g. , some workers may need to be retrained or require time to search for new jobs) rather than enter or exit longer

- term unemployment. Nonetheless, even to the e xtent th at markets are slow to adjust, DOE anticipate s that net labor market impacts will be negligible over time due to the small magnitude of the short

- term effects

16

- 3

### presented in Table 16.4

. 1

. The ImSET model projections, assuming no price or wage effects until

2 02 2 , are included in Table 16.4

. 1

.

16

- 4

### REFERENCES

1

. Scott, M., J

. M

. Roop, R

. W

. Schultz, D

. M

. Anderson, and K

. A

. 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. Scott M.J., O.V. Livingston, P.J. Balducci, J.M. Roop, and R.W. Schultz. ImSET: Impact of Sector Energy Technologies Model Description and User’s Guide . 2009. Pacific Northwest National Laboratory: Richland, WA. Report No. PNNL

- 18412.

3

. Scott, M.J., D.J. Hostick, and D.B. Belzer

. ImBuild: Impact of Building Energy Efficiency Programs

. 1998. Pacific Northwest National Laboratory

, Richland, WA. Report No. PNNL

11884.

4

. Minnesota IMPLAN Group Inc. IMPLAN Professional: User ’ s Guide, Analysis Guide,

Data Guide

. 1997. Stillwater, MN.

5

. U.S. Office of Management and Budget. Fiscal Year 201 5 Mid

- Session Review

. 2014

. Washington, D.C.

< http://www.whitehouse.gov/sites/default/files/omb/budget/fy2015/assets/15msr.pdf >

16

- 5

### CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION

................................ ................................ ................................ ......... 17

- 1

17.2 METHODOLOGY

................................ ................................ ................................ ........ 17

- 1

17.3 NON

- REGULATORY POLICIES

................................ ................................ ................ 17

- 2

17.3.1 No New Regulatory Action

................................ ................................ ............... 17

- 2

17.3.2 Customer Rebates

................................ ................................ .............................. 17

- 2

17.3.3 Consumer Tax Credits

................................ ................................ ....................... 17

- 4

17.3.4 Voluntary Energy E fficiency Programs

................................ ............................. 17

- 5

17.3.5 Early Replacement

................................ ................................ ............................. 17

- 5

17.4 SUMMARY OF RESULTS F OR NON

- REGULATORY ALTERNATI VES

............ 17

- 5

REFERENCES

................................ ................................ ................................ .......................... 17

- 7

LIST OF TABLES

Table 17.1.1 Non

- Regulatory Alternatives to Standards

................................ ........................... 17

- 1 Table 17.3.1 Customer Rebate NES and NPV Comparison to TSL 3

................................ ....... 17

- 4 Table 17.3.2 Tax Credit NES and NPV Comparison to TSL 3

................................ ................. 17

- 4

Table 17.4.1 Cumulative NES of Non

- Regulatory Alternatives Compared to the Final Energy

Conservation Standard

................................ ................................ ....................... 17

- 5

Table 17.4.2 Cumulative NPV of Non

- Regulatory Alternatives Compared to the Final Energy

Conservation Standard

................................ ................................ ....................... 17

- 6

17

- i

### CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

Under the Process Rule ( Procedures for Consideration of New or Revised Energy Conservation Standards for Consumer Products , 61 FR 36974 (July 15, 1996), the U.S. Department of Energy (DOE) is committed to continually explore non

- regulatory alternatives to

standards. DOE will prepare a draft reg ulatory impact analysis pursuant to E.O. 12866, Regulatory Planning and Review , which will be subject to review under the Executive Order by the Office of Management and Budget’s Office of Information and Regulatory Affairs.

58 FR 51735 ( Sept. 30, 1993)

.

DOE has identified five major alternatives to standards that represent feasible policy options to reduce automatic commercial ice

- making (ACIM) equipment energy consumption

. DOE evaluate d each alternative ’s ability to achieve significant energy savings at a reasonable cost, and compare d the effectiveness of each one to the effectiveness of the standards in the final

rule.

The non

- regulatory means of achieving energy savings that DOE ana lyze d are listed in

Table 17.1

. 1

. I n support of DOE’s final rule , DOE include s a quantitative analysis of each alternative, the methodology for which is discussed briefly in this technical support document (TSD)

.

Table 17.1

. 1 Non

- Regulatory Alternatives to Standards
No new regulatory ac tion

Consumer tax credits

Customer rebates
Voluntary energy efficiency targets
Early replacement



### 17.2 METHODOLOGY

DOE uses the n ational i mpact a nalysis (NIA) s preadsheet m odels to calculate the national energy savings (NES) and the net present value (NPV) corresponding to each non

- regulatory

alternative. The NIA m odel is discussed in c hapter 10 of this TSD . To compare each alternative quantitatively to the energy conservation standards, DOE quantif ie s the effect of e ach alternative on the purchase and use of energy

- efficient ACIM equipment

. After quantifying each alternative,

DOE makes the appropriate revisions to the inputs in the NIA m odels to estimate energy savings compared to the base

- case scenario . Key inputs th at DOE typically revise s in these models include the following :

• e nergy prices and escalation factors

,

• i mplicit market discount rates for trading off purchase price against operating expense when choosing product efficiency

,

• b usiness purchase price s and operating cost s ,

• p urchase price

- versus

- efficiency relationships

, and

17

- 1

### • p roduct stock data

.

The key measures of the impact of each alternative include the following :

• Energy use reflects the c umulative energy use of the product from the effective date

of the new standar d t hrough the year 2047

.

• NES represents the c umulative national energy use from the base

- case projection

minus the alt ernative policy case projection, given in quadrillion British thermal units (quads)

.

• NPV r epres ents the value in 201 3 $ (discounted to 201 4 ) of net monetary savings from products bought during the period from the effective date of the policy ( 2018 ) through the end of the analysis period ( 2047 ).

17.3 NON

- REGULATORY POLICIES

17.3.1 No New Regulatory Action

The base

- case scenario is the one in which no new regulatory action is taken with regard to the energy efficiency of ACIM equipment , as described in the NIA ( chapter 10 of this TSD)

. The base

- case scenario provides the basis of comparison for all other non

- reg ulatory alternatives

. By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 Customer R ebates

Customer rebates cover a portion of the difference in incremental product price between products meeting baseline effic ien cy level s and those meeting higher effic ien cy levels, resulting in a higher percentage of consumers purc hasing more efficacious models and decreased aggregated energy use compared to the base

- case scenario

.

DOE surveyed the various rebate programs available in the United States in 2013

. Typically, local utility companies offer rebates to commercial customers ( i.e. , business customers taking service through a commercial rate code) that replace their existing c ommercial

ice maker with an energy

- efficien t machine

. Machines qualifying as energy efficient are frequently defined as ENERGY STAR ® 

### compliant or meeting the Consortium for Energy Efficiency’s (CEE ’s ) Tier 2 or 3. As an example, Eugene Water and Electric B oard offers rebates starting at $1 5 per machine for an air

- cooled ice making head under 200 lb/day that meets ENERGY STAR criteria and increasing up to $271 for an air

- cooled remote condensing unit greater than or equal to 1,800 lb/day that meets CEE Tier 3

. 1 

### The Pacific Gas and Electric Company offers a series of potential rebates depending on the type and harvest capacity of the ice maker, ranging from $100 for a 100

– 300 lb/day machine to $500 for a 1,500 lb/day or larger

machine. 2 

### The Bonneville Power Ad ministration and 60 participating utilities

, with the Portland Energy Conservation, Inc. (PECI), operate an EnergySmart Grocer program offering a series of rebates ranging from $40 to $300

. Grant County Public Utility District also works with PECI t o offer similar rebates for ACIM equipment. 3



### In some cases, energy conservation and water conservation are rewarded with rebates. The Saving Water Partnership in the Seattle, Washington

, metropolitan area combines electricity

17

- 2

### conservation rebates ranging fr om $100 to $600 with water conservation rebates of $100 or $300 (depending on harvest capacity). 4



### DOE used the amounts o f current rebates and compared them to the installed cost

increments at the levels corresponding to trial standard level (TSL) 3. ( See c hapter 10 of this

TSD for more information on installed costs and TSL 3. ) A $100 rebate equals or exceeds 40 percent of the incremental installed cost for all small equipment classes based on the difference between an efficiency level 1 installed cost and a level corresponding to TSL 3 , with percentages ranging from 40 percent of an IMH

- A

- Small

- B unit to 116 percent of a SCU

- A

- Small

- C unit

cost dif ferential

. For large machines, a $200 rebate is equivalent to 24 percent of the incremental

cost of a n IMH

- Large

- B2 unit and at the high end is over 2 00 percent of the incremental cost of

a n RC U

- Large

- B 1 or IMH

- A

- Large

- C unit

. For the IMH

- W

- Med

- B category, a $100 rebate is over 90 percent of the incremental cost and a $200 rebate is over 180 percent of the incremental

cost.

DOE chose to model a scenario where customers are offered, as rebates, 60 percent of the

incremental inst alled cost of the ACIM equipment

.

To estimate the market shares of efficiency levels (see chapter 10 of this TSD ) that would

result from such a rebate

, DOE examined available information on efficiency choices in equipment classes covered by both ENERGY STA R and rebate programs

. I nformation obtained by DOE indicates that, on average, 60 percent of total shipments in those equipment classes meet or exceed the ENERGY STAR level. For determining the potential impact of rebate programs targeting TSL 3, D OE developed a shift scenario. In the shift scenario, market share wa s shifted upward such that the total market share of shipments at or above TSL 3 was 60 percent. To do this, DOE calculated the existing market shares of shipments above TSL 3, if any, and the remainder of 60 percent minus existing market shares was assigned to the TSL 3 efficiency level. Shipments below TSL 3 were distributed across efficiency levels in proportion to base

- case efficiency distributions. In cases (if any) where the existi ng cumulative base

- case market share at or above TSL 3 exceeded 60 percent, the distribution was left at the base

- case distribution.

Although the rebate program reduces the total installed cost to the customer, it is financed by tax revenues or by utilit y revenues . Therefore, from a societal perspective, the installed cost at any efficiency level does not change with the rebate program; rather, part of the cost is

transferred from the customer to taxpayers /ratepayers as a whole. Consequently, DOE assumed that equipment costs in the rebates scenario were identical to the NIA base

- case scenario

.

DOE assumed that rebates would remain in effect for the duration of the analysis period.

Table 17.3

. 1 presents the NES and NPV values for the 60 percent rebate scenario and compares them against the NES and NPV values at TSL 3

. NES and NPV are calculated for equipment purchased in the 2018

– 20 47 analysis period and include energy savings

, operation and

maintenance costs

, and savings extending for the life of equipment purchased in 2047.

17

- 3

### Table 17.3

. 1 C ustomer Rebate NES and NPV Comparison to TSL 3

Policy A lternatives Cumulative Primary Energy Savings *
quads Net Present Value**

billion 2013 $

7% Discount Rate 3% Discount Rate No New Regulatory Action 0 0 0

Consumer Rebates (60% of price increments) 0.09 0.229 0.501

TSL 3 0.1 8 0.430 0.942 * Energy savings are in primary energy quads. ** Net present value is the value in the present of a time series of costs and savings.



### 17.3.3 Consumer T ax C redits

Consumer tax credits are considered a viable non

- regulatory market transformation program, as shown by the inclusion of Federal consumer tax credits in the Energy Policy Act of 2005 ( EPA CT 2005 ; Pub L. 109

- 58, 119 Stat 1026 (2005) ) for various residential appliances. From a consumer perspec tive, the most important difference between rebate and tax credit programs is that a rebate can be obtained relatively quickly, whereas receipt of tax credits is delayed until income taxes are filed or a tax refund is provided by the Internal Revenue Servi ce.

As with consumer rebates, DOE assumed that consumer tax credits paid 60 percent of the incremental product price , but estimated a different response rate. The delay in reimbursement

makes tax credits less attractive than rebates

. C onsequently, DOE esti mated a response rate that is 80 percent of that for rebate programs (6 0 percent ) and

, therefore

, a corresponding shift of 48 percent in market shares to or above TSL 3 with no change in total shipments. In cases (if any) where the existing cumulative base

- case market share at or above TSL 3 exceeded 48 percent,

the distribution was left at the base

- case distribution. DOE estimated NPV and NES values under these assumptions

, and the results are presented in 

### Table 17.3

. 2

From a societal perspective, tax credits (like rebates) do not change the installed cost of the equipment, but rather transfer a portion of the cost from the consumer to taxpayers as a

whole. DOE, therefore, assumed that equipment costs in the consumer tax credits scenario were

identical to the NIA base case.

DOE assumed that tax credits would remain in effect for the duration of the analysis period. Table 17.3

. 2 presents the NES and NPV values for the tax credit scenario and compares them against the NES and NPV values at TSL.

Table 17.3

. 2 Tax Credit NES and NPV Comparison to TSL 3

Policy A lternatives Cumulative Primary Energy Savings *
quads Net Present Value**

billion 2013 $

7% Discount Rate 3% Discount Rate No New Regulatory Action 0 0 0

Customer Tax Credits 0.07 0.172 0.378 Today's Standards at TSL 3 0.1 8 0.430 0.942

* Energy savings are in primary energy quads.

** Net present value is the value in the present of a time series of costs and savings.

17

- 4

### 17.3.4 Voluntary Energy Efficiency Programs

While voluntary programs for equipment c ould be effective, DOE lacks a quantitative

basis to determine their effectiveness . As previously noted, broader economic and social considerations are in play than simple economic return to the equipment purchaser. DOE lacks the data necessary to quantita tively project the degree to which such voluntary programs for more expensive, higher efficiency equipment like ACIM equipment would modify the market.

17.3.5 Early R eplacement

Early replacement refers to the replacement of equipment before the end of its useful life

. The purpose of this policy is to retrofit or replace old, inefficient equipment with high

- efficiency

units. DOE studied the feasibility of a Federal program to promote early replacement of appliances and equipment under EPACT 1992. In this study, DOE identified Federal policy options for early replacement that include a direct national program, replacement of Federally owned equipment, promotion through equipment manufacturers, customer incentives, incentives to utilities, market behavior research, an d building regulations.

While cost

- effective opportunities to install more

- efficient units exist, DOE determined that a Federal early replacement program is not economically justified because the market for

automatic commercial ice makers is relatively small, especially for Federally owned equipment,

and distributed across a broad set of customers ; thus

, the savings would not be likely to be significant.

17.4 SUMMARY OF RESULTS FOR NON

- REGULATORY ALTERNATI VES

Table 17.4

. 1 and Table 17.4

. 2 show the NES and NPV for the non

- regulatory alternatives analyzed. The case in whic h no regulatory action is taken constitutes the base

- case scenario.

Because this is the base

- case scenario

, NES and NPV are zero by defini tion. For comparison, the

tables include the results of the NES and NPV at TSL 3 associated with the energy conservation

standard.

As shown in Table 17.4

. 1 and Table 17.4

. 2 , none of th e policy alternatives DOE

examined would achieve the amount of energy or monetary savings that could be realized under

the final rule. In addition, implementing either tax credits or customer rebates would incur initial

and/or administrative costs not considered in this analysis.

Table 17.4

. 1 Cumulative NES of Non

- Regulatory Alternatives Compared to the Final Energy Conservation Standard

Policy Alternatives Cumulative NES quads No new regulat ory action 0

Consumer tax credits 0. 07

Customer rebates 0. 09 Voluntary energy efficiency targets 0 Early replacement 0

Final s tandards (TSL 3 ) 0. 18

17

- 5

### Table 17.4

. 2 Cumulative NPV of Non

- Regulatory Alternatives Compared to the Final Energy Conservation Standard

Policy Alternatives Cumulative Net Present Value

billion 2013 $

7% Discount 3% Discount No new regulatory action 0 0

Consumer tax credits 0. 172 0.378

Customer rebates 0. 229 0.501 Voluntary energy efficiency targets 0 0 Early replacement 0 0

Final standards (TSL 3) 0. 430 0.942

17

- 6

### REFERENCES

1

. Eugene Water and Electric Board

. Commercial Food Service Equipment Catalog

. 201 3

. Eugene, OR. < http://eweb.org/public/documents/energy/FoodServicesCatalog.pdf >

2

. Pacific Gas and Electric Company

. Food Service Rebate Catalog. 2013

. (Last accessed February 12, 2013 .)

< www.pge.com/en/mybusiness/energysavingsrebates/rebatesincentives/index.page >

3

. Portland Energy Conservation, Inc

. Energy Smart . (Last accessed February 12 , 201 3 .)

< www.energysmartonline.org/rebates/index.html >

4

. Saving Water Partnership. Rebates. Sea ttle, WA. (Last accessed February 13, 2013.)

< www.savingwater.org/rebates.htm >

17

- 7