# TECHNICAL SUPPORT DOCUMENT:
ENERGY EFFICIENCY PROGRAM
FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

AUTOMATIC COMMERCIAL ICE MAKERS

March 20 1 4

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

### This Document was prepared for the Department of Energy by staff members of Navigant Consulting, Inc.
and
Pacific Northwest National Laboratory1

- i 

### CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUM ENT

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

- 1
1.2 SUMMARY OF NATIONAL BENEFITS

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

- 1
1.3 OVERVIEW OF 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

- 7
REFERENCES

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

- 11

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

- 71

- 1 

### 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 n o tice of p roposed r ulemaking ( NOPR) for automati c commercial ice makers

. XX FR XXXXX, [MONTH] [DAY], [YEAR]

. This NOPR 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 ing (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

. 286 quadrillion B ritish 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 2012$ ranges from $ 0. 791 billion (at a 7

- percent discou nt rate) to $ 1. 751 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 2013

.

DOE also studie d 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 ( 2013 ) 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 $ 101.8 million in 201 2 $. Under the proposed standards , DOE expects that manufacturers may lose up to 23.5 percent of their INPV, or approximately $ 23.9 mill ion.

The proposed standards are expected to have significant environmental benefits. Based on energy savings from equipment purchased over the period of 2018

– 2047, t he energy savings would result in cumulative greenhouse gas emission reductions of 14.6 mi llion metric tons b 

### of carbon dioxide (CO 2 

### )

, 75. 8 thousand tons of methane, 0.3 thousand tons of nitrous oxide,
2 1.0 thousand tons of sulfur dioxide (SO 2 

### ), 8.7 thousand tons of nitrogen oxides ( NO
x 

### )

, and
0. 0 2 tons of mercury (Hg)

. c 

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

### emissions reduction would be between $0.10 2 and $1. 426 billion , expressed in 2012$ and
discounted to 2013 . DOE also estimates the present monetary value of the NO
x 

### emissions

a This is the rate used to discount future cash flows in the m anufacturer i mpact a nalysis. 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 c hapter 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 2013 ( AEO2013 ) Reference C ase, which generally represents current legislation and environmental regulations for which implementing regulations were available as of December 31, 201 2

.1

- 2 

### reduction would be between $ 0.54 and $ 5.53 m illion at a 7

- percent discount rate and between $ 1.71 and $ 17.56 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 reductio ns. e



### The value of the CO 2 

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

### (otherwise 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 2012$ t o permit straightforward comparisons with the other costs and benefits. 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 operating 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 glob al value. Second, the assessments of operating cost savings and CO 2 

### savings are performed with different methods that use different time frames for analysis. The national operating cost savings is measured over the
lifetimes of automatic commercial ice mak ers 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 2013 ( AEO2013 ) 1 

### Reference Case forecast 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 AEO2013
forecast. At a 7

- percent discount ra te for benefits and costs, the cost in the primary estimate of the standards proposed in this NOPR is $ 20 million per year in increased equipment costs. The annualized benefits are $ 104 million per year in reduced equipment operating costs, $ 27 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 $ 0.32 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 in to annualized values. First, DOE calculated a present value in 2013 , 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

exc ept 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 ( 201 8 through 204 7 ) 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 fr om which the annualized values were determined is a steady stream of payments.1

- 3 

### amounts to $ 110 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 NOPR is $ 21 million per year in increased equi pment costs. The benefits are $ 121 million per year in reduced operating costs, $ 2 7 million in CO 2 

### reductions, and $ 0

. 5 5 million in reduced NO
x 

### emissions. In this case, the net benefit amounts to $ 1 28 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 2012$ Low Net Benefits
Estimate* million 2012$ High Net Benefits
Estimate* million 2012$
Benefits Operating Cost Savings 7% 104 98 112
3% 121 113 132
CO 2 Reduction Monetized Value (at $1 1.8 /Metric Ton)** 5% 8 8 8

CO 2 Reduction Monetized Value (at $ 39.7 /Metric Ton)** 3% 27 26 2 7

CO 2 Reduction Monetized Value (at $6 1.2 /Metric Ton)** 2.5% 39 38 40

CO 2 Reduction Monetized Value (at $117.0/Metric Ton)** 3% 82 80 8 4

NO

x Reduction Monetized Value (at $2,639/Ton)** 7% 0.32 0.31 0.33

3% 0.55 0.53 0.58

Total Benefits (Operating Cost Savings, CO 2 Reduction and NO

x Reduction) † 7% 131 124 1 39

3% 149 1 39 160

Costs
Total Incremental
Installed Costs 7% 20 21 20
3% 21 22 20
Net Benefits Less Costs
Total Benefits Less
Incremental Costs 7% 11 0 103 120
3% 128 118 140 * The primary, low, and high estimates utilize forecasts of energy prices from the AEO2013 Reference Case, Low Economic Growth Case, and High Economic Growth Case, respectively.
** The CO 2 values represent global monetized values of the SCC, in 2012$

, in 2015 under several scenarios of the updated SCC values . The values of $1 1.8 , $ 39.7

, and $6 1.2 per ton are the averages of SCC distributions calculated using 5, 3, and 2.5 percent discount rates, respectively. The value of $117.0 per ton represents the 95th percentile of the SCC distribution calculated using a 3

- percent discount rate. See chapter 14 for details. For NO

x , an average value ($2,639) of the low ($468) and high ($4,809) values was used. † Total monetary benefits for both the 3

- and 7

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

x and CO 2 emissions calculated at a 3

- percent discount rate (averaged across three integrated assessment models), which is equal to $ 39.7 /ton (in 2012$).



### DOE also estimated the low net benefits and high net benefits by calculating the operating cost savings and shipments at the AEO2013 low economic growth case and high1

- 4 

### 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 OVERVIEW OF APPLIANCE STANDARDS

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

, Pub. L. 94

- 163, as amended by the National Energy Conservation Policy Act of 1978 (NECPA),
Pub. L. 95

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

- 12; the National A ppliance 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 commercial 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 commercial 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 (o r class) compared to any increase in the price of, or in the initial charges for maintenance expenses of, the covered products that are likely to result from the imposition of the standard;

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

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

f This part was 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

- 5 

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

6. the need for national energy conservation; and

7. other factors the Secretary considers relevant.

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

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

1.4 OVERVIEW OF AUTOMATIC COMMERCIAL ICE MAKERS STANDARDS

EPCA prescribed energy conservation standards for automatic commercial ice makers that produce cube type ice with capacities 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 automatic 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 between 50 and 2,500 l b 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

– Compliance Required Beginning on January 1, 2010

Equipment Type Type o f 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.1

- 6 

### Furthermore, EPCA requires DOE to review these 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 issue 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 standards for automatic commercial ice makers not covered by 42 U.S.C. 6313(d)(1)). (42 U.S.C. 6313(d)(2)(A)) DOE 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 in November 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 to inform
in terested 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 proposed for cond ucting 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 intervi ews 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) solicit feedback on the draft engi neering analysis (including methodology, production costs, manufacturing processes, and findings); ( 2) solicit feedback on topics related to the preliminary MIA ; ( 3) provide an opportunity, early in the rulemaking process, for 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 the ongoing rulema king 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 p reliminary analyses; and the candidate energy1

- 7 

### conservation standard levels. See Table 1.4

. 2 for all the analyses discussed at the public meeting to be undertaken in each of the formal public rulemaking documents.

Table 1.4

. 2 ACIM Analys es Preliminary Analysis NOPR Final Rule* Market and technology assessment Revised 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))



### After the posting of the January 2012 preliminary analysis TSD and the February 201 2 preliminary analysis public meeting, DOE conducted interviews with ACIM manufacturers as part of the MIA for the NOPR. A number of general topics were discussed during each
interview: ( 1) general key issues ; ( 2) company overview and organizational characteristics ; ( 3) company financial parameters ; ( 4) production cost breakdown ; ( 5) shipment projections and
market shares ; ( 6) equipment mixes ; ( 7) conversion costs ; ( 8) markups and profitability ; ( 9) cumulative regulatory burden ; ( 10) exports, foreign competition, and outsourcing ; ( 11) direct employment impact assessment ; ( 12) market consolidation ; and ( 13) baseline products and different design options.

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 n ational 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 commercial ice makers at different efficiency levels.

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 rulema king process.1

- 8 

### Chapter 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 technolog y, including techniques 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 pric es.

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 t he methodology 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 nat ional impacts by calculating national energy savings and NPV at all higher efficiency
levels

.
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 standard that may require changes to the manufacturing practices for this equipment.

Chapter 1 3 Emissions Analysis : presents the assess ment of the impacts of proposed ACIM
standard levels on emissions of certain pollutants

.1

- 9 

### Chapter 1 4 Monetization of Emis sion 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 re sents experiential learning sensitivity analysis results , electric price sensitivity analysis results

, and 9

- y ear analysis results

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

s tandard l evels (TSLs) selection and the proposed standards equation at each
TSL.

Appendix 10D Full

- Fuel

- Cycle Multipliers: p resents the development of full

- fuel

- cycle
coefficients

.1

- 10 

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

### tables.

Appen dix 12A Manufacturer Impact Analysis Interview Guide: presents the exhibits and questions used during the manufacturer interviews for the MIA.

Appendix 12B Government Regulatory Impact Model : p resents an over view 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 Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order
12866: T echnical M odel U pdate : p resents updated SCC analys is

.

g Regulatory Information Servic e 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
2013 . 2013. Washington D.C. DOE/EIA

- 0383(2013).2

- i 

### 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

- 4
2.3.1 Market Assessment

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

- 4
2.3.2 Technology Assessment

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

- 5
2.4 SCREENING ANALYSIS

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

- 5
2.5 ENGINEERING ANALYSIS

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

- 5
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

- 7
2.8 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSES

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

- 8
2.9 SHIPMENTS ANALYSIS

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

- 10
2.10 NATIONAL IMPACT ANAL YSIS

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

- 10
2.11 CUSTOMER SUBGROUP AN ALYSIS

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

- 11
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

- Making Equipment Energy Conservation
Standards

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

- 32

- 1 

### CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

This chapter describes the analytical framework that the U.S. Department of Energy ( DOE ) u sed 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

- making (ACIM) equipment that are technologically feasible and economically justified. In this context, economic justification includes conside ration 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 th e 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
standa rds

- setting process are presented and open for stakeholder review.

The analyses that DOE performed for the notice of proposed rulemaking ( NOPR )
include:

• a market and technology assessment to characterize the automatic commercial ice
makers’ market and revi ew techniques and approaches 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 a nalysis 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;

• a shipments analysis to estimate shipments of ACIM equipment over the time period examined in the analysis;2

- 2 

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

• 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 p otential
amended standards 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 stand ards on the installed capacity 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 purc hase price of ACIM equipment 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.

In response to comments it receives after publish ing the NOPR, DOE may revise some of its analyses before publishing the final rule.2

- 3 

### Figure 2.1

. 1 Analyses for Automatic Commercial Ice

- Making Equipment Energy
Conservation Standards2

- 4 

### 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 a ssociations, state agencies utilities, and other interested parties to provide input to this effort. As a result of this combined effort, DOE published Procedures, Interpretations and Policies for Consideration of New or Revised Energy Conservation Standar ds 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 review of the ( 1) economic models; ( 2) analytical tools; ( 3) m ethodologies; ( 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 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 icemakers
in the Rulemaking Framework f or Automa tic 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 published 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 preliminary analysis stage , at the completion of which,
DOE annou nced the notice of public meeting and the availability of the preliminary TSD

. 2 

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

.
The following sections provide a brief overview of the analytica l approaches for the NOPR phase analysis of this rulemaking . DOE has used the most reliable data available at the time of each analysis in this rulemaking. DOE has also considered the submissions of additional
data fr om interested parties during the NOPR phase of the rulemaking process.

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 a n overall picture of the ACIM industry in the United States. Industry publications and trade journals, government agencies, and trade organizations provided the bulk of the information, including (1) manufacturers and their market shares; (2) shipments by product type and capacity; (3) product information; and (4) industry trends.2

- 5 

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

- related features that affect efficiency. Different energy conservation standards may apply to different equipment classes. (42 U.S.C. 6295(q) ; 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 an d 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 equipment, as well as t o 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 NOPR TSD incl udes 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 furth er in the engineering analysis and which options to screen out

. As stated above, DOE consulted with industry, technical experts, and other interested parties in developing a list of energy

- saving technologies for the technology assessment. DOE then applied 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
NOPR 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 analysis on these technologies in the engineering analysis.

2.5 ENGINEERING ANALYSIS

The purpose of the engineering analysis (chapter 5 of the NOP R 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 t hat decrease energy and/or water consumption as compared with baseline

- efficiency models. 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 by2

- 6 

### incre asing condenser water use. Hence, the engineering analysis does not identify maximum 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 consultation with stakeholders, uses the most appropria te 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 a pproach 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

- efficien cy 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 (including 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

- option approach uses individual design options, or comb inations 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. Individual design options, or combinations of design option s, 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 options.

An efficiency

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

A reverse

- engineering or cost

- assessment approach involves purchasing representative
units of ACIM equipment, disassembling the units, and reverse engineering the manufacturing
costs bas ed 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 reverse

- engineered products.

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

- efficiency relationship for this rulemaking

. DOE obtain s information from a variety of sources, including consultation with outside experts, review of publicly available cost and performance information, modeling of equipment cost and energy consumption, interviews with manufacturers, product
teardowns and reve rse engineering, and data submissions provided by the industry.2

- 7 

### 2.5.2 Baseline Models

In order to analyze 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 baseline efficiency level for batch type equipment. a 

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

, including new ly available information on ice hardness data. Based 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 and small equipment class es within each
continuous equipment type.

2.6 MARKUPS TO DETERMINE EQUIPME NT 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 c ustomers buying ice makers, DOE adds markups to reflect the costs added by the wholesale, distribution, 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
of 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 wit h 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.

2.7 ENERGY AND WATER U SE 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 a nalyses were necessary. Thus, 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 inf ormation on the annual energy and water analysis for this rulemaking.

a Batch type equipment includes cube

- type, tube

- type, and others that produce ice in alternat ive cycles of freezing and harvesting. Continuous type equipment, on the other hand, produces ice continuously. Continuous type ice makers include flake and nugget machines.2

- 8 

### 2.8 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSES

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 sta ndards on individual customers. 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

oper ating costs

. Future operating 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 equipment through
lower operating costs.

As part of the engineering analysis (chapter 5 of the NOPR 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 e quipment 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 and without significantly increasing the footprint of the equipment (see
NOPR TSD chapter 5 for details).

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

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

The installed cost of equipment to a customer is the sum of the equipment purchase price and installation cost. The purchase price includes the MSP plus markups to the MSP to account for distribution costs and sales tax (chapter 6 of the NOPR TSD ). Installation costs vary by state depending on the prevai ling labor rates.

Operating costs for automatic commercial ice makers are the 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 assumed effective date of the standards established through this rulemaking). The sum2

- 9 

### 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 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 i n operating costs are due to savings in energy costs because higher efficiency equipment 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 Crysta l 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 uncertainty associated with most of the 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 simulatio ns 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 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 using Microsoft Excel and Crystal Ball, a commercially available Excel add

- in for p erforming 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 , or a candidate standard level (CSL) . Usually, the market 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 calculated the market shares of the efficiency levels using a method described in NOPR TSD chapter 10

.
Different types o f 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 care, lodging,
foods ervice, retail, education, food sales, and offices

.2

- 10 

### There is a general consensus among industry stakeholders that the 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 u nique to each equipment class. Therefore, DOE 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 probability distributions based on the relative populations in all states.

NOPR TSD chapter 8 provides detailed information on DOE’s LCC and PBP analysis for this rulemaking. Results of the LCC and PBP analysis are p resented at the end of NOPR 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, an d
the MIA

. Shipments analysis is used to estimate future ACIM equipment shipments over the national 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 2013 Annual Energy
Outlook 3 

### ( AEO 2013 ) 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 historic 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 th e 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 between 2018 and 2047. NPV is expressed in constant 2012 $ discounted to the present year (2013).

DOE anal yzed energy savings, water savings, energy and water cost savings, equipment

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

- case shipment projections that include units at various efficiency levels. It based the projections on historical information plus forecasts of market2

- 11 

### influences, national economic growth, and electricity consumption. DOE then derived energy savings for various CSL s from the cost

- effic iency 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 inpu t 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 ana lysis assesses the NPV of total consumer LCC and NES. DOE conducted an assessment of the aggregate impacts at the national level for the NOPR. Analyzing impacts of Federal energy conservation standards requires a comparison of projected U.S. energy consump tion 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 e ach 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 historically 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, 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 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 t he DOE/Energy Information
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. Aft er 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 NOPR analysis, DOE calculated FFC energy savings using a methodology described in appendix 10A. 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 ANALYSIS

The customer subgroup 

### analysis in NOPR 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

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

- 0581(2003), March 2003.2

- 12 

### in chapter 8 of the NOPR 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 av eraging 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 applicable only to the identified subgroups.

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

DOE carried out two LCC subgroup analyses, one each for foodservice and lodging , by using the LCC spreadsheet described in NOPR TSD chapter 8 with certain modifications. The input for business type was fixed to the identified subgroup, which 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 discount rates faced by small businesses. Apart from these changes, all other inputs for LCC subgroup analysis are same as those in the LCC analysis described in TSD chapter 8.

2.12 MANUFACTURER IMPACT ANALYSIS

The MIA assesses the impacts of new energy conservation standards on manufacturers of the considered equipment. Potential impac ts include financial effects, both quantitative and qualitative, that might lead to changes in the manufacturing practices for this equipment. DOE identified these potential impacts through interviews with manufacturers and other interested parties.

DOE c onducted the MIA in three phases, and further tailored the analytical framework based on interested parties’ comments. In Phase I, an industry profile was created to characterize the industry, and a preliminary MIA was conducted to identify important issue s that required consideration. In Phase II, an industry cash flow model and an interview questionnaire were prepared to guide subsequent discussions. In Phase III, manufacturers were interviewed, and the impacts of standards were assessed both quantitative ly and qualitatively. Industry and subgroup cash flow and NPV were assessed through use of the Government Regulatory Impact Model (GRIM). Then impacts on competition, manufacturing capacity, employment, and 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 

### 2.13 EMISSION S ANALYSIS

In the emissions analysis, DOE estimated the reduction in power sector emissions of
CO 2 

### , NO
x 

### , SO 2 

### , and Hg from potent ial energy conservation standards for ACIM equipment. In addition, DOE estimated emissions impacts in production activities (extracting, processing, and transporting fuels) that provide the energy inputs to power plants. These are referred to as “upstream” emissions. Together, these emissions account for the 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 the
latest version of EIA’s AEO2013 , supplemented by data from other sources. DOE developed

se parate 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 NOPR TSD.

EIA prepares the AEO using the NEMS. Each annual version of NEMS incorporates the

p rojected impacts of existing air quality regulations on emissions. AEO2013 generally represents current legislation and environmental regulations, including recent government actions, for which implementing regulations were available as of December 31, 201 2.

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 t he District of Columbia (D.C.). SO 2 

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

- based trading program that operates along with the Title IV program in those States 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 effect. See North Carolina v.
EPA , 550 F.3d 1176 (D.C. Cir. 2008); North Carolina v. EPA , 531 F.3d 896 (D.C. Cir. 2008). On July 6, 2011, EPA issued a replacement for CAIR, the Cross

- State Air Pollution Rule (CSAPR; also known as the Transport Rule). 76 FR 48208 ( Aug

. 8, 2011). On August 21, 2012,
the D.C. Cir cuit 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 AEO2013 emissions factors used for the NOPR assume that CAIR remains a binding

regu lation 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 low er 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 uncertainty about the effects of efficiency standa rds on SO 2 

### emissions covered by the existing cap

- and

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

### as a result of standards.2

- 14 

### Beginning in 2015, however, SO 2 

### emissions will fall as a result of the Mercury an d 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 es tablished a standard for
SO 2 

### (a non

- HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. The
same controls are used to reduce HAP and non

- HAP acid gas; thus, SO 2 

### emissions will be reduced as a result of the control technologies i nstalled on coal

- fired power plants to comply with the MATS requirements for acid gas. AEO2013 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 2015. Both tech nologies, 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 expec ted to have little or no physical 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, standard s would be expected to reduce NO
x 

### emissions in the States not affected by caps, so DOE estimated NO
x 

### emissions reductions from the standards considered in the NOPR
for these States.

The MATS limit mercury emissions from power plants, but they do not includ e 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 AEO2013 , which incorporates the MATS

.

2.14 MONETIZ ING CARBON AND OTHER EMISSIONS REDUCTION S BENEFITS

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

### and NO
x 

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

To estimate the monetary value of benefits resulting fro m 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, b ut not limited to, net agricultural productivity 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 question s 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

- 15 

### 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 2012$, are $12.9, $40.8, $62.2, and $117 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 interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to
calculate domestic effects, although 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 rates 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 resulting damages to the world economy. Thus, these values are subject to change.

DOE also estimates the potential monetary benefit of reduced N O
x 

### emissions resulting
from the standard levels it considers. For NO
x 

### emissions, estimates suggest a very wide range of monetary values, ranging from $ 468 to $4,809 per ton in 2012$

. 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 rates of 3 percent and 7 percent.

DOE is evaluating appropriate valuation of avoided SO 2 

### and Hg emissions. Whether monetization of reduced Hg emissions wi ll occur in this rulemaking is yet to be determined

.

2.15 UTILITY IMPACT ANALY SIS

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

. The version of NEMS used for appliance standards analysis is called NEMS

- Building Technologies (NEMS

BT) f 

### and is based on the AEO2013 Reference Case with minor modifications.

The utility impact analysis reports the changes in installed capacity and generation, by
fuel type, that result from the adoption of new efficiency standards at each TSL, as well as

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 Stat es Government, May 2013. www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria_2013_update.pdf

d For addition al 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, DC

. 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 Program, under whose aegis this work has been performed). NEMS

- BT was previously called NEMS

BRS.2

- 16 

### changes i n electricity consumption. See NOPR TSD chapter 15 for more information on DOE’s utility impact analysis for this rulemaking.

2.16 EMPLOYMENT IMPACT AN ALYSIS

The i mposition of standards can affect 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 dir ect 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 Northwest 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 em ployment and income effects

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 investmen ts.

2.17 REGULATORY IMPACT AN ALYSIS

In the NOPR 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 Information and Regul atory 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 rulemaki ng.

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 ma ke 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 will solicit the views of the Department of Justice (DOJ) on any lessening of competition that is likely to result from the imposition of a proposed standard and will give the views provided full consideration in assessing the economic justification of a proposed standard. DOE may consult with DOJ a t earlier stages in the standards development process to seek to obtain preliminary views on competitive impacts.2

- 17 

### REFERENCES

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 Energy 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/building s/appliance_standards/rulemaking.aspx/ruleid/29 >

3

. U.S. Department of Energy

– Energy Information Administration. Annual Energy Outlook
201 3 , 201 3 . Washington, DC. DOE/EIA

- 0383(201 2 ).3

- i 

### 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 Natural Resources Canada

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

- 6
3.2.2.3 Australia and 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

- 13
3.2.5.2 North American Association of Food Equipment Manufacturers Data

. 3

- 14
3.2.5.3 Census Bureau Data

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

- 15
3.2.6 Equipment Lifetimes

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

- 15
3.3 TECHNOLOGY ASSESSMEN T

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

- 16
3.3.1 Baseline Equipment Components and Operation

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

- 16
3.3.1.1 Basic Equipment Description and Components

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

- 16
3.3.1.2 Batch Process

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

- 18
3.3.1.3 Continuous Process

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

- 22
3.3.2 Technology Options

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

- 24
3.3.2.1 Compressor

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

- 25
3.3.2.2 Condenser

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

- 27
3.3.2.3 Higher Efficiency Condenser Fans and Fan Motors

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

- 29
3.3.2.4 Improved Auger Motor Efficiency

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

- 29
3.3.2.5 Improved Pump Motor Efficiency

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

- 29
3.3.2.6 Demand Response Controls

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

- 29
3.3.2.7 Evaporator

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

- 30
3.3.2.8 Improved or Thicker Insulation

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

- 31
3.3.2.9 Larger Diameter Suction Line (Remote Compressor Models)

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

- 31
3.3.2.10 Reduced Potable Water Flow

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

- 31
3.3.2.11 Drain Water Thermal Exchange

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

- 32
3.3.3 Energy Use Data

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

- 32
3.3.4 Condenser Water Consumption Data

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

- 43
REFERENCES

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

- 13

- ii 

### 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 Proposed NOPR Automatic Commercial Ice

- Making Equipment Classes

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

- 13 Table 3.2.8 AHRI 2010 Shipments of Automatic Commercial Ice Makers, by Equipment Class 3

- 14 Table 3.2.9 Shipm ents of Open Refrigerated Display Cases (Remote Condensing and Self

Contained)

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

- 15
Table 3.2.10 Estimates for Automatic Commercial Ice Maker Lifetimes

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

- 16 Table 3.3.1 Brief Description of Primary Refrigeration System Components

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

- 17 Table 3.3.2 Batch Evaporator Designs

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

- 19 Table 3.3.3 Technology Options for Automatic Commercial Ice Makers

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

- 25 Table 3.3.4 Compressor Rating Conditions ( o 

### F ( o 

### C) )

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

- 26 Table 3.3.5: Comparison of Current and Proposed Condenser Water Standards

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

- 47

LIST OF FIGURES

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

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

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

..... 3

- 18 Figure 3.3.2 Cracked Ice Forming on “Cracked Ice” Evaporators

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

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

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

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

..... 3

- 23 Figure 3.3.5 Cross

- Section of Continuous Evaporator and Auger

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

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

- Cooled Cube Ice

- Making Heads)

.... 3

- 33 Figure 3.3.7 Energy Consumption vs. Harvest Rate (Water

- Cooled Batch Ice

- Making Heads)

.. 3

- 34 Figure 3.3.8 Energy Consumption vs. Har vest Rate (Air

- Cooled Cube Remote Condensing Units [Remote Compressor and Non

- Remote Compressor])

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

- 35 Figure 3.3.9 Energy Consumpti on vs. Harvest Rate (Air

- Cooled Cube Self

- Contained Units) 3

- 36 Figure 3.3.10 Energy Consumption vs. Harvest Rate (Water

- Cooled Cub e Self

- Contained Units)

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

- 37 Figure 3.3.11 Energy Consumption vs. Harvest Rate (Air

- Cooled Flake/Nugget Ice

- Making Heads)

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

- 38 Figure 3.3.12 Energy Consumption vs. Harvest Rate (Water

- Cooled Flake/Nugget Ice

- Making Heads)

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

- 39 Figure 3.3.13 Energy Consumption vs. Harvest Rate (Air

- Cooled Flake/Nugget Remote Condensing Units with Non

- Remote Compressors)

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

- 403

- iii 

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

- Cooled Flake/Nugget Remote Condensing Units with Non

- Remote Compressors)

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

- 41 Figure 3.3.15 Energy Consumption vs. Harvest Rate (Air

- Cooled Flake/Nugget Self

- Contained Units)

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

- 42 Figure 3.3.16 Energy Consumption vs. Harvest Rate (Water

- Cooled Flake/Nugget Self

Contained Units)

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

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

- Cooled Ice

- Making Head Units)

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

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

- Cooled Batch

- Type Self

Contained Units)

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

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

- Cooled Flake/Nugget Ice

Making Head Units)

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

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

- Cooled Flake/Nugget
Self

- Contained Units)

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

- 463

- 1 

### 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 ongoing energy conservation standards rulemaking for automatic commercial ice makers, including self

- contained ice makers with air

or water

- cool ed condensers ; ice

- making heads with air

- or water

- cool ed condensers ; remote condensing and remote compressor ice makers ; and remote condensing (but not remote compressor) ice makers

. Automatic commercial ice makers produce a range of ice ty pes , 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

. a 

### (42 U.S.C. 6313(d)(1)) Review of these standards is required by EPCA prior to January 1, 2015. (42 U.S.C. 6313(d)(3)) DOE is also proposing, under 42 U.S.C. 6313(d)(2), s tandards for other types of ice makers and equipment with capacities up to 4,000 lb/ 24 hours.

This chapter 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

- mak ing (ACIM) equipment industry and market structure based on publicly available information and information submitted by manufacturers and other stakeholders.
M anufacturer characteristics and market shares, existing regulatory and non

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

. The purpose of the tech nology 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 commercial sectors

, including health care, lodging, foodse rvice

, 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

s tandards for cube type icemakers capable of producing between 50 and 2,500 lb/24 hours, and added the definition given above to EPCA. EPACT 2005 further established standards for automatic commercial ice makers that produce cube type ice based on: ( 1 ) the configuration of

a Pounds of ice per 24

- hour period is abbreviated herein as lb/24 hours.3

- 2 

### 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 covered under this rulemaking are as follows.

1. Self

- c ontained automatic commercial ice mak ers (SCU) : A self

- contained automatic commercial ice maker is a category in which the ice

- making machinery, including freezing, harvesting

, and condensing components

, and storage compartment

, are in an in tegra l cabinet

. Self

- contained automatic com mercial ice
makers tend to have lower harvest capacit ies than other equipment types

, are typically
used in office buildings and retail applications , and are generally designed to fit under

counters

. 2. 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

. 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)

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

. 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 contai ning 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 to 4,000 lb/24 hours for
commercial ice makers.

In addition to the foregoing categories of ice

- making equipment, self

- contained units and
ice

- making 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 potable water loop . Ice makers can be attached to closed cooling

- water loop s that recirculate and reuse the same cooling water ; these installations require 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

- 3 

### Most remote condensing ice makers f eature air

- cooled condensers , and the EPCA ACIM 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 differentiating 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 make r can produce in 24 hours.

DOE current defines 13 different product 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 hi storical 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 commerc ial 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
manufacturers to establish the Automatic Commercial Ice Makers and Ice Storage Bins division3

- 4 

### within A H RI . This division serves to develop standards and implement a certification program
for automatic commercial ice makers and storage bins

.

The technical ac tivities of AHRI include b 

### :

• working to h armoniz e international equipment standards;

• d evelop ing industry performance standards for automatic commercial ice makers ;

• u pdating industry guidelines for operation and installation of ACIM equipment ;

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

,

• p roviding input to government agencies concerning regulations affecting the industry.

3.2.2 Manufacturers and Market 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 Managemen t 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 Interna tional)

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 for 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.

b http://www.ahrinet.org/hvacr+_+water+heating+products.aspx3

- 5 Source: 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 exi st ed

as independent and separate entities

.
In 2008, Manitowoc agreed 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 Eno dis, PLC would have concentrated a vast majority of automatic commercial 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



### Thus, the ice

- making companies have been consol id at ed since this data was collected. Based on DOE’s latest NOPR information, t he three largest firms

— Manitowoc, Scotsman/Ice

O

- Matic, and Hoshizaki

— are believed to have increased their market share to approximately 9 5 percent of the market in the United States. Manitowoc 30% Scotsman 25% Hoshizaki 20%

Mile High Equipment LLC/Ice

- O

Matic 15%

IMI Cornelius

and Others 10%3

- 6 

### 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. 6 

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

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

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

, c 

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

- Conditioning and Warm Air Heating Equipment and Commercial 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 notice of proposed rulemaking (NOPR) analysis. As part of the NOPR analyses

, DOE identified seven s mall
business ma nufacturers of automatic commercial ice makers

. More information on these analyses is available in Chapter 11 of the NOPR 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 state standards all regulate cube type ice makers only

. Californi a’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 Natura l 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 standard covers all equipment between 23 and 1,000 kilograms per day ( between 50.6 and 2200 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.

c Automatic commercial ice makers is a small component of the equip ment manufacturing category, NAICS code 333415. This NAICS category covers a broad segment of heating, 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

- 7 

### 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 NOPR, 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 webs ite 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 Ic e
- 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 converted this to kWh/100 lb for presentation in the table. www.oee.nrcan.gc.ca/regulations/product/automatic_ice_makers.cfm?attr=0



### 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

. 7 

### The minimum energy performance standards are
established in Australian Standard AS/NZS 4865.3:2008 as maximum energy consumpti on 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

- cooled, remote condensing but not remote compressor

• s plit system, a ir

- cooled, remote condensing and remote compressor

• s torage systems3

- 8 

### The standards apply to cube, flake

, and nugget machines without distinguishing between
the machines and, appar ently, without adjusting 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 promotin g 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 publicat ion 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 include 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 asymptotic relationship bet ween 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 hours 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.gov3

- 9 

### 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. 8 

### 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. 9 

### For air

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

T able 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 ≤199 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, www1.eere.energy.gov/femp/technologies/eep_purchasingspecs.html

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

- 2003

, 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 percent3

- 10 

### below Tier 1, and Tier 3 to be 15 percent below Tier 1. ENERGY STAR standards correspond ed approximately with Tier 2 (for air

- cooled machines)

. CE E 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 leve ls, and setting the new Tier 2 to 10 percent lower than Tier 1. 10 

### 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

- 11 

### 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: CEE website

, www.cee1.org/com/com

- kit/com

- kit

- equip.php3

.



### 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 progra ms 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 “ Commercial3

- 12 

### Food Service Eq uipment Incentive Finder,” offer rebates ranging fr om $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 convenience stores, food process ors, 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 equivalent to CEE Tier 3 or ENERGY STAR

. 11



### 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 CEE Tier 2 specific ations to $500 for large equipment meeting CEE Tier 3 specifications. 12



### 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 commerc ial ice makers that DOE is proposing to use for this rulemaking 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 emote 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 proposing to create within the scope of this rulemaking

.3

- 13 

### Table 3.2

. 7 Proposed NOPR Automatic Commercial Ice

- Making Equipment Classes

Equipment Type Type of Cooling Harvest Capacity Rate
lb/24 hours Type of Ice

Ice

- Making Head Water ≥50 and <500 Batch ≥500 and <1

, 436 Batch
≥1

, 436 and ≤ 4,000 Batch

Air ≥50 and <450 Batch
≥450 and ≤ 4,000 Batch Remote Condensing (but not remote compressor) Air ≥50 and <1

, 000 Batch
≥1

, 000 and ≤ 4,000 Batch Remote Condensing and Remote Compressor Air ≥50 and <934 Batch
≥934 and ≤ 4,000 Batch

Self

- Contained Unit Water ≥50 and <200 Batch
≥200 and ≤ 4,000 Batch

Air ≥50 and <175 Batch
≥175 and ≤ 4,000 Batch

Ice

- Making Head Water ≥50 and < 1000 Continuous

≥ 1

, 0 00 and < 4,000 Continuous

Air ≥50 and < 1000 Continuous

≥ 1

, 0 00 and < 4,000 Continuous Remote Condensing (but not remote compressor) Air ≥50 and < 1000 Continuous

≥ 1

, 0 00 and < 4,000 Continuous Remote Condensing and Remote Compressor Air ≥50 and < 1000 Continuous

≥ 1

, 0 00 and < 4,000 Continuous

Self

- Contained Unit Water ≥50 and < 175 Continuous

≥ 175 and < 4,000 Continuous

Air ≥50 and < 175 Continuous

≥ 175 and < 4,000 Continuous



### 3.2.5 Shipments and Available Equipment

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

. S

. Census Bureau.

3.2.5.1 Air

- Conditioning, Heating, and Refrigeration Institute Data

As part of its comments on the Framewor k 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 shipment s data as part of the basis for this analysis. DOE understands that th is
data do es not include t he 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

aggr egated to match the primary equipment 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 distin guish 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

. 13 

### 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 14 

### and 200 6. 15



### The “refrigeration 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 provid ed additional 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 B ureau 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 1 940s for purposes of evaluating prices and shipments. The Census has not updated it s statistics since the publication of the preliminary analysis.

Table 3.2

. 9 shows CIR shipment data for ice

- making machines

.

Table 3.2

. 9 Shipments of Open Refrigerated Display Cases ( Remote C ondensing and Self

Containe d) 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 li mited 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.

3.2.6 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

. 16



### 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 re models and as long as 20 years in cases where the equipment owners did not care about the appearance of the equipment.3

- 16 

### 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

. 10 Estimates for Automatic Commercial Ice Maker Lifetimes

Life Refere nce 7 to 10 years Arthur D

. Little, 1996 16
8.5 years California Energy Commission, 2004 17
8.5 years Fernstrom, G., 2004 18
8.5 years Koeller J., and H. Hoffman , 2008 2

7 to 10 years Navigant Consulting, Inc. 2009 19



### 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.

3.3.1.1 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. Ic e 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 co ntained 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 into3

- 17 

### cubes, tubes, or another shape depending on the evaporator geom etry. 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 nugget

- sha ped 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 extrus ion 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 refrigeration sy stems to provide the refrigeration needed for ice production. Table 3.3

. 1 provides descriptions for the key refrigeration components. Aside from the evaporator, ice ma kers 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

. 20 

### 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 hi gher 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 systems in which the condenser heat is remotely rejected in a cooling 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

Compressor 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 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 groovi ng. Water flow
controlled by the supply valve to maintain a constant preset condensing temperature

. Expansion Device • Most ice makers use conventional t hermostatic 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

- 18 

### 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 ba tch process ice 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

- gas 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 slide 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.

Source: Manitowoc


### Figure 3.3

. 1 Typical Vapor Compression Refrigeration Cycle for Batch T ype Ice Makers3

- 19 

### 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 r ange ( 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.

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.jpg3

- 20 Source: 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

- 21 

### 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 Drain3

- 22 

### 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 required for harvest. This approach also

pre

- chills t he 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 pieces as it falls.

3.3.1.3 Continuous Process

This secti on 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 us ed 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 cycl e. In contrast to the batch process, there is no
hot

- gas bypass line, because there is no need for a harvest cycle.3

- 23 Source: 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 Exchanger3

- 24 (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. As discus sed in TSD chapter 2 of the preliminary analyses , DOE’s primary 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 include technology options associated with water use
reduction.

Table 3.3

. 3 lists the technology options DOE iden tified for improving the efficiency of automatic commercial 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

- 25 

### 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 

## √ √

Fans and Fan
Motor Higher efficiency c ondenser
fan s and fan motors 

## √ √ Air

- cooled only

Other Motors Improved auger motor efficiency 

## √

Improved pump motor efficiency 

## √

Controls Smart Technologies 

## √ √

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

## √

Design options which reduce harvest meltage or reduce
harvest time 

## √

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 

## √

Drain w ater t hermal e xchange 

## √

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 commonly
used compressors in ice makers, alt hough 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 typically 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 approximately 3,000 rpm on 60 Hz power. Four types o f single

- phase induction motors have been used in ice maker compressors: capacitor start/capacitor run (CSCR),3

- 26 

### resistance start/induction run ( RSIR )

, capacitor start/induction run ( CSIR ), and resistance start/capacitor 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 ) pe r hour to 40,000 Btu/hr. Three organizations have established conditions for rating the performance of compressors: Air

- conditioning, Heating, and Refrigeration Institute ( AHRI )

, American Society of Heating, Refrigerating and Air

- Conditioning Engineers ( ASHRAE )

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

### Table 3.3

. 4 shows the rating conditions of these organizations. The performance rat ings 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 conditi ons 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

. † Condition just prior to refrigerant expansion

.


### 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 NOPR 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.

d CECOMAF is a European appliance manufacturer trade association formed in 1958. It merged with EUROVENT in 1996 to become EUROVENT/CECOMAF. This organization is now calle d EUROVENT.3

- 27 

### Improved Compressor Efficiency

Conversion to high

- efficiency compressors is fairly straightforward for manufacturers 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 power levels. The potential for efficiency improvement through use of higher efficiency 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 NOPR 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 used 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 varia ble

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

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 more 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 increa sed refrigerant pressure drop across the condenser, so there is a tradeoff between improving heat transfer to reduce condensing temperature and increasing condenser inlet pressure due to the higher pressure drop. Similarly, adding tube rows can increase th e air pressure drop through the condenser, so there is a tradeoff between improving heat transfer 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 con denser 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 condensers 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 fa n/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 refri gerant can add cost, but also can increase the challenges associated with refrigerant management throughout both the freeze and3

- 28 

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

- style batch ice makers have refrigerant receivers, an increase of condenser si ze can potentially lead to required use of receivers to en sure reliable operation and avoid damage such as would occur with flooding of the compressor.

These considerations and DOE’s treatment of condensers in the engineering analysis is discussed in Chap ter 5 of the NOPR TSD.

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 performance 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 m akers 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 gener ally 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 insta llations where condenser water is drained to a building’s waste water system. Cube ice makers covered by the current DOE standards limit condenser water use. During the preliminary analysis, DOE provisionally determined that backsliding on condenser

water standards to improve energy efficiency is not prohibited, 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 coil

- in

- coil condensers, resulting in higher heat transfer efficiency.

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 than 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 generally weigh less and hold significantly less refrigerant than conventional heat exchangers.3

- 29 

### 3.3.2.3 Higher Efficiency Condenser Fans and Fan Motors

Fans ar e 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 co mmercial refrigeration equipment

, including supermarket display cases and vending machines. Tests performed with the PAX fan have demonstrated a reduction in fan

motor power of 15 to 20 percent. 21 

### At this point, because the PAX fan is proprietary, the
wides pread 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 30 percent, or permanent split capacitor (PSC) designs, which reach efficiencies of 40 to 65 p ercent. Brushless DC motors can have efficiencies up to 80 percent, thus representing an option for further efficiency 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 auger motors.

3.3.2.5 Improved Pump Motor Efficiency

Batch machines use a pump motor to pump water from the sump to the top of the evaporator. Most ice ma ker 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 dem and response events to customer electric meters, which then

com municate with the target appliance. The ice maker must be configured with appropriate
hardware and software to enable two

- way communication. According to demand response
studies e 

### , this connectivity can be achieved using an electronic board for communications and an
ultrasonic sensor to read the ice level 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 de mand response

event. While this capability would alleviate peak loads experienced by electric utilities, it would likely not result in energy savings.

e “Demand Response Strategies Using Two

- Way Connectivity For Commercial Ice Machines.” DR 07.07 Report.
March 2009. Southern California Edison.3

- 30 

### 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 strategy 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. 22 

### This generally holds true for both batch an d 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 higher 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 evaporator 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 Ho shizaki cube ice machines. DOE request ed information regarding proprietary status of low

- thermal

- mass evaporator designs during the preliminary analysis phase

. Chapter 4 of the NOPR 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
harves t 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 ass ist. Ensuring that surfaces 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 at3

- 31 

### once, which can help en sure harvest of ind ividual cubes that might 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 evaporator side 23 

### (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 24 

### (this method is used by Manitowoc ice makers). The sp eed of harvest and the 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 p otable 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 eff ectiveness.

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 refri gerant piping between the condens ing unit and the ice

- making unit (ARI Standard 810

- 2003, 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 additiona l pressure drop between 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 ve locities. If the suction 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 with 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

- 32 

### 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 f reeze the water. In most batch ice makers, the sump pump cycles 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 could 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 temperature. 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.

For the NOPR analysis, DOE performed additional testing of the effects of a dra in water thermal exchange on ice maker performance. The results of this testing are described in Chapter
5 of the NOPR 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 created 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
ARI Standard 810

- 2003. Figure 3.3

. 6 through Figure 3.3

. 15 show energy consumption as a
functi on of harvest capacity within each equipment class. The current DOE standards and
ENERGY STAR criteria (when applicable), as well as the proposed DOE standard levels, are shown in the figures.

For continuous ice makers, the energy use shown in these plots has been adjusted consistent with the test procedure NOPR proposal for adjustment of the energy use and
condenser water use metrics based on ice hardness. 76 FR at 18432 ( April 4, 2011 )

. By the time of the NOPR analyses, the AHRI product directory containe d ice hardness data for a number of continuous ice makers and DOE incorporated this information into the database of current
models. For data points that were not obtained from the AHRI product directory (such as manufacturer websites) , DOE assumed for th is adjustment that ice hardness was 0.7 for flake machines and 0.85 for nugget machines , consistent with the preliminary analysis assumptions

.

In the RCU plot ( Figure 3.3

. 8 ), the indicated DOE s tandard is for RCU units without remote compressors

— the standard is 0.2 kWh/100 lb higher for equipment with a harvest capacity greater than roughly 950 lb/day. The figur es showing energy use data for flake and nugget machines indicate the “ proposed DOE baseline” levels for each equipment class, since there are no DOE standards for this equipment. For more information about how DOE established trial baseline levels, see c h apter 5 of the NOPR TSD

.

The remote condensing flake and nugget machines (shown in Figure 3.3

. 13 ) include a number of data points that appear to have extremely low en ergy use ratings (less than 3
kWh/100 lb). DOE expects that these data points represent models that are intended to be3

- 33 

### connected to compressor rack systems, and that these ratings do not include compressor energy

use.

Figure 3.3

. 6 Energy Consumption vs. Harvest Rate (Air

- Cooled Cube Ice

- Making Heads)3

- 34 

### Figure 3.3

. 7 Energy Consumption vs. Harvest Rate (Water

- Cooled Batch Ice

- Making Heads)3

- 35 

### Figure 3.3

. 8 Energy Consumption vs. Harvest Rate (Air

- Cooled Cube Remote Condensing
Units [Remote Compressor and Non

- Remote Compressor] )3

- 36 

### Figure 3.3

. 9 Energy Consump tion vs. Harvest Rate (Air

- Cooled Cube Self

- Contained Units)3

- 37 

### Figure 3.3

. 10 Energy Consumption vs. Harvest Rate (Water

- Cooled Cube Self

- Contained Units)3

- 38 

### Figure 3.3

. 11 Energy Consumption vs. Harvest Rate (Air

- Cooled Flake/Nugget Ice

- Making Heads)

Note: The ice makers in this figure adjust for ice hardness, as specif ied by the DOE Test
Procedure.3

- 39 

### Figure 3.3

. 12 Energy Consumption vs. Harvest Rate (Water

- Cooled Flake/Nugget Ice

Making Heads)

Note: The ice makers in this f igure adjust for ice hardness, as specified by the DOE Test
Procedure.3

- 40 

### Figure 3.3

. 13 Energy Consumption vs. Harvest Rate (Air

- Cooled Flake/Nugget Remote Condensing Units with Non

- Remote Compressors )

Not e: The ice makers in this figure adjust for ice hardness, as specified by the DOE Test
Procedure.3

- 41 

### Figure 3.3

. 14 : Energy Consumption vs. Harvest Rate (Air

- Cooled Flake/Nugget Remote Condensing Units with Non

- Remote Compressors)

Note: The ice makers in this figure adjust for ice hardness, as specified by the DOE Test
Procedure.3

- 42 

### Figure 3.3

. 15 Energy Consumption vs. Harvest Rate (Air

- Cooled Flake/Nugget Self

Contained Units)

Note: The ice makers in this figure adjust for ice hardness, as specified by the DOE Test
Procedure.3

- 43 

### Figure 3.3

. 16 Energy Consumption vs. Harvest Rate (Water

- Cooled Flake/Nugget Sel f

Contained Units)

Note: The ice makers in this figure adjust 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 proposing to maintain the condenser water consumption standard at the existing level. The current standard is downward sloping, and simply extending the slope may result in standards that are not technically feasible. So, for the newly covered reg ion for IMH

- W

- B and SCU

- W

- B between 2,500 lbs/day and 4,000 lbs/day, DOE is proposing to keep the condenser water standard at a constant slope equal to the standard value at 2,500 lbs/day.3

- 44 

### Figure 3.3

. 17 : Condenser Water Consumption vs Harvest Rate (Water

- Cooled Ice

- Making Head Units)3

- 45 

### Figure 3.3

. 18 : Condenser Water Consumption 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 proposed standards on the respective batch standards, but lowered the standards by 20% to match the existing market for continuous type units. Continuous units typically have lower condenser water consumption requirements, due to the lack of a harvest event (which requires higher heat rejection).3

- 46 

### Figure 3.3

. 19 : Condenser Water Consumption vs Harvest Rate (Water

- Cooled Flake/Nugget Ice

- Making Head Units )

Figure 3.3

. 20 : Condenser Water Consumption vs Harvest Rate (Water

- Cooled Flake/Nugget Self

- Contained Units)3

- 47 

### Th e current and proposed 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 Proposed Cond enser 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 160

– 0.0176 * H
SCU

- W

- C No current standard 153

– 0.0252 * H3

- 1 

### REFERENCES

1

. Air

- Conditioning, Heating, and Refrigeration Institute

. AHRI Members

. (Last accessed November 16, 2011.) < www.ahrinet.org/a hri+members.aspx >

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, file d by ENODIS PLC on 12/19/2003.
Information found on

- line via a Google search, at the following w eb address (last accessed November 19, 2011) : < http://sec.edgar

- online.com/enodis

- plc/20

- f

- annual

- and

- transition

report

- foreign

- private

- issuer/2003/12/19/section6.aspx >

4

. Manitowac Company

. The Manitowoc Company signs definitive agreement to divest
Enodis ice business (press re lease)

. March 30, 2009. Manitowoc, WI.

< www.manitowocfsusa.com/?xhtml=xhtml/eno/us/en/press release/2009_03_30_manitowo c_company_divest_enodis_ice_business.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

. U.S. Small Business 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. F or more information, contact Brenda Edwards

- Jones at (202) 586

- 2945.

7

. Standards Australia/Standards New Zealand. Performance of commercial ice makers and ice storage bins . Part 3: Minimum energy performance standard (MEPS) requirements.
2004. AS/NZS 486 5:3:2008.

8

. U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. FEMP Designated Product: Air

- Cooled Ice Machines . 2010. (Last accessed August 30, 2010.)

< www1.eere.energy.gov/femp/technologies/eep_ice_makers.html >

9

. U.S. Department of Energy

— Office of Energy Efficiency and Renewable Energy. Federal Energy Management Program, Covered Product Categories . The air

- cooled ice maker specifications are dat ed September 2009 while the water

- cooled ice maker specifications are dated April, 2011. (Last accessed November 1, 2011.)

< www1.eere.energy.gov/femp/technologies/eep_pu rchasingspecs.html >3

- 2 

### 10

. Consortium for Energy Efficiency. Commercial Kitchens Initiative . (Last accessed November 1, 2011.) < www.cee1.org/com/com

- kit/com

- kit

- equip.php3 >

11

. EnergySmart Grocer. EnergySmart Grocer Incentive Worksheet . 2011. (Last accessed September 19, 2011.)

< www.energysmartgrocer.org/pdfs/PGE/2010_12%20Energ ySmartGrocer%20IncentiveW orksheet_v2.pdf >

12

. New York State Energy Research and Development Agency. Pre

- Qualified Commercial Refrigeration Equipment . (Last accessed September 19, 2011.)

< www.nyserda.org/programs/Existing_facilities/pdfs/EESEFP_CommRefrigeration.pdf >

13

. North American Association of Food Equipment Manufacturers. 2010 Size and Shape of the Industry . 2010. Chicago, IL.

14

. North American Associ ation of Food Equipment Manufacturers. 2008 Size and Shape of the Industry . 2008. Chicago, IL.

15

. North American Association of Food Equipment Manufacturers. 2006 Size and Shape of the Industry . 2006. Chicago, IL

16

. Arthur D. Little, Inc. Energy 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.

17

. California Energy Commission. Update of Appliance Efficiency Regu lations . 2004. Sacramento, CA.

18

. 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 Development . 2 004. Prepared by the American Council for an Energy

- Efficient Economy for Pacific Gas & Electric Company, San Francisco, CA.

19

. Navigant Consulting, Inc. Energy Savings Potential and R&D Opportunities for Commercial Refrigeration . Final Report. 2009. Submi tted to the U.S. Department of Energy’s Energy Efficiency and Renewable Energy Building Technologies Program, Washington, D.C.

< http://app s1.eere.energy.gov/buildings/publications/pdfs/corporate/commercial_refrig_rep ort_10

- 09.pdf >

20

. AHRI Ice Maker Shipment Data, submitted to DOE by Karim Amrane, May 2011.

21

. Penney, K. Personal communication. PAX Scientific, Inc., San Rafael, CA. Email to Heather Lisle, Navigant Consulting, Inc. July 2011.3

- 3 

### 22

. Biel, J. Personal communication

. Scotsman Industries, Inc., Vernon Hills, I L. Telephone call with Heather Lisle, Navigant Consulting, Inc. April 2008.

23

. Van Steenburgh, Jr., Leon R. Automatic Ice Cube Making Apparatus . U.S. Patent No. 4,341,087, July 27, 1982.

24

. Shedivy, Scott J. et al. Ice Machine with Assisted Harvest . U.S. Patent No. 6,681,580 B2, January 27, 2004.4

- i 

### 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 Reduce 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 TECHNOLOGI ES

................................ ................................ ...................... 4

- 4

LIST OF TABLES

Table 4.2.1 Screening Justification

................................ ................................ .............................. 4

- 44

- 1 

### 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 notice of proposed rulemaking ( NOPR ) 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 technol ogies 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 t o 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 di rects the Secretary 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 el aborate on the statutory 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 ing 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 technologically feasible.

Practicability to manufacture, install, and service. If mass production of a technology in commercial equipment and reliable installation and servicing of the technology could be achieved on the scale necessary to serve the relevant market at the time of the effective date of the standard, then that technology will be considered practicable to manufacture, install, and
service.

Impacts on equipment utility or equipment availability

. If a technology is determined
to have a significant adverse 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 vol umes that are4

- 2 

### substantially 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 consideratio n. If a technology fails 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) im pacts on equipment utility 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 do n’t save energy, their 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 position from 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 technology option. 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 position from the preliminary analysis. DOE is not aware of any
enhanced fin surfaces available for air

- cooled condensers tha t would significantly improve heat transfer and efficiency in automatic commercial ice makers 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 rep resent an option for improve d efficiency.

4.2.3 Brazed Plate Condenser for Water

- Cooled Condensers

For the NOPR analyses, DOE has concluded that use of brazed plate condensers does not represent a viable design option for batch ice makers because most ice makers using water

cooled condensers have space for condenser surface area growth 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 d uring the NOPR phase; t hese analyses are
described in c hapter 5 of the NOPR TSD.4

- 3 

### 4.2.4 Technology Options to Reduce Evaporator Thermal Cycling

During the NOPR analyses, DOE determined that the one technology used by ice makers to reduce thermal mass is proprieta ry. 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 ar vest M eltage or R educe H arvest T ime

DOE has not changed its position from the preliminary analysis. M anufacturers have implemented design changes to reduce harvest meltage and reduce harvest time in batch 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 Improved or Thicker Insulation

DOE has not changed its position from 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 efficiency improvement is small enough to be nearly immeasurable . Hence, this technology option has been screened out.

4.2.7 Larger D iameter S uction L ine (Remote Compressor Equipment)

DOE has not changed its position from the preliminary analysis. Increasing the suction
line diameter could be considered to reduce suction line pressure drop for remote condenser equi pment with remote compressors. However, the reduce d suction vapor velocity associated with the approach can degrade oil return effectiveness. Remote ice maker line sets can be
installed in the field so that suction line refrigerant runs up, down, or horizo ntally 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 refrig eration system operation to a time of utility lower demand), DOE is not aware of any commercialized products or prototypes
that also demonstra te improved energy efficiency i n automatic commercial ice makers. Demand savings alone do not impact energy effici ency , and DOE cannot consider technologies that do
not offer energy savings as measured by the test procedure. Since the scope of this rulemaking is to consider energy conservation standards that increase the energy efficiency of automatic

a www .regulations.gov/#!documentDetail;D=EERE

- 2010

- BT

- STD

- 0037

- 00254

- 4 

### commercial ice m akers, not how they operate, for example, in relation to utility demand, thi s technology option has been screened out because it does not save energy as measured by the test procedure.

Table 4.2.1 summarizes the technologies that were screened out for the NOPR analysis or were not considered in the engineering analysis for other reasons.

Table 4.2

. 1 Screen ing 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 andSafetyNoEnergySavings orSavings notMeasurableTestProcedureEfficiencyMetricDoesNotCaptureSavingsProprietaryTechnology

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



### 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 NOPR 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 mpro ved auger motor efficiency

• i mproved pump motor efficiency

• i ncreased evaporator s urface area

• reduced portable water flow

• use of drain water thermal exchange5

- i 

### CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION

................................ ................................ ................................ ........... 5

- 1
5.2 EQUIPMENT CLASSES AN ALYZED

................................ ................................ .......... 5

- 1
5.3 METHODOLOGY OVERVIEW

................................ ................................ .................... 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

- 5
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 Incremental 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

- 12
5.5.2.1 Selection of Products for Reverse Engineering and Inspections

........... 5

- 12
5.5.2.2 Collection of Energy Modeling Data

................................ ..................... 5

- 14
5.5.3 Manufacturer Interviews

................................ ................................ .................... 5

- 15
5.5.4 Energy Testing

................................ ................................ ................................ ... 5

- 15
5.6 MANUFACTURING COST M ODELING

................................ ................................ ... 5

- 16
5.6.1 Generation of Bills of Materials

................................ ................................ ........ 5

- 16
5.6.2 Cost Structure of the Spreadsheet Models

................................ ......................... 5

- 16
5.6.3 Cost Model and Definitions

................................ ................................ ............... 5

- 17
5.6.3.1 Cost Definitions

................................ ................................ ..................... 5

- 17
5. 6.4 Cost Model Assumptions Overview

................................ ................................ .. 5

- 18
5.6.4.1 Fabrication Estimates

................................ ................................ ............. 5

- 19
5.6.4.2 Factory Parameters Assumptions

................................ ........................... 5

- 19
5.6.4.3 Material Cost Assumptions

................................ ................................ .... 5

- 20
5.6.5 Manufacturing Production Cost

................................ ................................ ......... 5

- 20
5.6.6 Incremental Cost Estimates

................................ ................................ ................ 5

- 21
5.6.6.1 G&A and Profit

................................ ................................ ...................... 5

- 22
5.7 MANUFACTURER MARKUP

................................ ................................ .................... 5

- 22
5.8 ENERGY MODELING

................................ ................................ ................................ . 5

- 22
5.8.1 FREEZE Model Overview

................................ ................................ ................. 5

- 22
5.9 DESIGN OPTIONS

................................ ................................ ................................ ....... 5

- 24
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
5.9.2.1 Compressor Cost Data

................................ ................................ ........... 5

- 265

- ii 

### 5.9.3 Increased Air

- Cooled Condenser Surface Area

................................ ................. 5

- 27
5.9.4 Increased Water

- Cooled Condenser Surface Area

................................ ............ 5

- 28
5.9.5 Evaporator Size Increase

................................ ................................ .................... 5

- 28
5.9.6 Design Options that Require Chassis Size Increases

................................ ......... 5

- 29
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 Reduced Potable Water Flow for Batch

- Type Ice Makers

................................ 5

- 33
5.10 ENGINEERING ANALYSIS RESULTS

................................ ................................ ..... 5

- 36
5.10.1 Overview

................................ ................................ ................................ ............ 5

- 36
5.10.2 Design Option Analyses

................................ ................................ .................... 5

- 39
5.10.3 Standard Equations for Extended Harvest Capacity Air

- Cooled Ice

- Making Head
Batch Ice Makers

................................ ................................ ................................ ........... 5

- 46
REFERENCES

................................ ................................ ................................ .......................... 5

- 49

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 Trial 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 Efficiency Levels for Continuous Ice Maker Equipment Classes

...... 5

- 10 Table 5.4.8 Maximum Technology Levels for Continuous Ice Maker Equipment Classes

...... 5

- 10 Table 5.5.1 Component Vendors Contacted by DOE during Engineering Analysis

................. 5

- 12
Table 5.5.2 Selected Units for Rever se Engineering and Energy Testing during Preliminary Analysis

................................ ................................ ................................ .............. 5

- 13 Table 5.5.3: Selected Units for Reverse Engineering and Energy Testing during NOPR Analysis

................................ ................................ ................................ ............................ 5

- 14
Table 5.6.1 Cost Model In

- House Manufacturing Operation Assumptions

.............................. 5

- 19 Table 5.6.2 ACIM Factory Parameter Assumptions

................................ ................................ .. 5

- 19
Table 5.8.1 IMH

- A

- Small

- B Design Options

................................ ................................ ............ 5

- 23 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 AHRI 540 Medium Temperature Operating Test Conditions

................................ 5

- 26 Table 5.9.6 Analyzed Equipment Classes Where DOE Analyzed Size

- Increasing Design Options

................................ ................................ ................................ ............................ 5

- 30 Table 5.9.7 Description of Size Increase Design Options in the Engineering Analysis

............ 5

- 30 Table 5.9.8 Expected Shaft Power Output Range and Efficiencies for Different Types of Motors

................................ ................................ ................................ ............................ 5

- 315

- iii 

### Table 5.9.9 Motor Cost Information Use for NOPR Engineering Analysis

.............................. 5

- 31
Table 5.9.10 Energy Consumption Tests with a Drain Water Heat Exchanger

........................ 5

- 32 Table 5.10.1 Batch Ice Maker Baseline Characteristics and Extrapolation Approach

.............. 5

- 36
Table 5.10.2 Incremental Manufacturer Selling Price Results for Batch Ice makers

................ 5

- 37 Table 5.10.3 Continuous Ice Maker Baseline Characteristics and Extrapolation Appro ach

..... 5

- 38 Table 5.10.4 Incremental Manufacturer Selling Price Results for Continuous Ice Makers

...... 5

- 38 Table 5.10.5 Directly Analyzed Equipment Classes Used to Develop Standards for Secondary
Classes

................................ ................................ ................................ ................ 5

- 39 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

................................ ....... 5

- 40 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

- 40 Table 5.10.8 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Batch Ice Machines with Capacities Greater Th an 450 lb/24 hours (Larger Sizes)

.......... 5

- 41 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

- 41 Table 5.10.10 Manufacturing Cost and Energy Consumption Data for Water

- C ooled IMH Batch Ice Machines with Capacities between 500 and 1436 lb/24 hours

.................... 5

- 42 Table 5.10.11: Manufacturing Cost and Energy C onsumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Greater Than 1436 lb/24 hours (Larger Sizes)

.. 5

- 42
Table 5.10.12 Manufacturing Cost and Energy Consumption Data for Air

- Cooled RCU Batch Ice Machines with Capacities Greater Than 1000 lb/24 hours (Smaller Sizes)

...... 5

- 43 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 (Larger Sizes)

........ 5

- 43 Table 5.10.14 Manufacturing Cost and Energy Consumption Data for Air

- Cooled SCU Batch Ice Machines with Capacities Less Than 175 lb/24 hours

................................ ....... 5

- 44 Table 5.10.15 Manufacturing Cost and Energy Consumption Data for Air

- Cooled SCU Batch Ice Machines with Capacities Greater Than 175 lb/24 hours

................................ .. 5

- 44 Table 5.10.16 Manufacturing Cost and Energy Consumption Data for Water

- Cooled SCU Batch Ice Machines with Capacities Greater Than 200 lb/24 hours

............................ 5

- 45 Table 5.10.17 Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

............. 5

- 45 Table 5.10.18: Manufacturing Cost and Energy Consumption Data for Air

- Cooled IMH
Continuous Ice Machines wit h Capacities Greater Than 700 lb/24 hours (Smaller Sizes)

................................ ................................ ................................ .................. 5

- 46 Table 5.10.19 Manufacturing Cost and Energy Consumption Data for A ir

- Cooled SCU Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

............. 5

- 46

LIST OF FIGURES

Figure 5.3.1 Flow Diagram of Engineering Analysis Methodology

................................ ........... 5

- 3 Figure 5.6.1 Full Production Costs

................................ ................................ ............................ 5

- 21 Figure 5.9.2 NOPR Cost Curves for Medium

- Temperature Compressors used in Commercial Ice
Makers

................................ ................................ ................................ ................ 5

- 27 Figure 5.10.1 Illustration of the Proposed Standard Levels for IMH

- A

- B Equipment Classes 5

- 485

- 1 

### 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” relationship serves as the basis for cost

- benefit calculations in terms of individual customers, manufacturers, and the Nation, from which the most economically justified, technologica lly feasible standard level is ultimately determined.

The inputs into the engineering analysis include baseline characteristics for each equipment class addressed in the market and technology assessment

— chapter 3 of the notice of public rulemaking ( NOPR ) technical support document (TSD)

— the design options from the screening analysis (chapter 4), as well as cost and energy use data 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 NOPR TSD ) as well as the
nationa l impacts analyses (chapter 10 of the NOPR TSD)

.
This chapter covers the equipment class es that the U.S. Department of Energy ( DOE ) analyzed and the methodology used by DOE to develop manufacturing costs and energy consumption estimates for the NOPR analys is phase of the rulemaking , as well as the results of these analyses.

5.2 EQUIPMENT CLASS ES ANALYZED

In the NOPR engineering analysis, DOE directly analyzed 1 2 of the 2 6 proposed equipment classes for batch and continuous ice

- making machines . This mean s that DOE developed representative analyses for specific existing products of these equipment classes, conducting energy testing, reverse engineering, and cost

- efficiency analyses based on energy use and manufacturing cost models based on the products acquired f or reverse engineering

. For the downstream analyses described in subsequent chapters, DOE extended the results of the direct analyses to other equipment classes based on similarity of classes.

Table 5.2

. 1 summarizes the batch ice

- making machine equipment classe s analyzed in the
NOPR engineering an a lysis

, and Table 5.2

. 2 lists the c ontinuous ice

- making machine equipment classes analyzed

. a 

### These equipment classes represent close to 100 percent of the shipments of

automat ic commercial ice maker s

. 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 analys e s for the remote condensing without remote compressor e quipment to be representative of equipment classes both with and without remote compressors. This is discussed in greater detail in section 5.4.1.6

. Also, in two cases

a See chapter 3 of the NOPR TSD for description of batch and continuous ice maker categories.5

- 2 

### (RCU

- NRC/RC

- Large

- B and IMH

- W

- Large

- B), DOE considered two different sizes of equipment within a single product class

Table 5.2

. 1 Batch Equipment Class es Analyzed in the Engineering Analysis

Equipment Type Type of Cooling Harvest Capacity Rate
lb/24 hours Equipment Class
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 and <875 ) IMH

- A

- L arge

- B 
Extended ( ≥ 875) † IMH

- A

- Extended

- 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 

* Includes units designed for connection to a compressor rack system.
**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))

. † Proposed product class new to the NOPR analyses

.



### In Table 5.2

. 2 , DOE has adjusted the harvest capacity rate ranges for the continuous equipment classes to better match the existing market. The description of market units is
available in chapter 3 of the N OPR TSD.

Table 5.2

. 2 Continuous Equipment Class es Analyzed in the Engineering Analysis

Equipment Type Type of Cooling 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

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 class was not analyzed, directly or by extrapolation.5

- 3 

### 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. Also, DOE developed manufacturing cost models based heavily on revers e engineering of products to develop a baseline MPC and to support calculat ion of the incremental costs associated with improvement of efficiency.

Figure 5.3

. 1 presents the steps in the analysis and illustrates how they contributed to developing the cost

- efficiency curves. The process began with data collection and ended with the
incremental cost curve results.

To develop the analytically derived 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 engineering of current products, and interviewed com ponent vendors of compressors and fan motors. DOE also conducted interviews with manufacturers during the NOPR analyses to follow up on the information collected during the preliminary analysis

. In particular, DOE actively sought
additional feedback in ter ms of compo nent costs to calibrate the outputs of the manufacturing
cost models.

Figure 5.3

. 1 Flow Diagram of Engineering Analysis Methodology

Cost information from the vendor interviews and engineering que stionnaires provided input to the manufacturing cost model. DOE determined i ncremental costs associated with specific design options both from vendor information and using the cost model. DOE modeled

e nergy use reduction using the FREEZE program , which was developed in the 1990s

, upgraded5

- 4 

### as part of the preliminary analysis , and then further upgraded during the NOPR analyses . The reverse engineering, vendor interviews, and manufacturer interviews provided input for the energy analysis. The incremental cost estimates and the energy modeling results together constitute the energy efficiency curves presented in this chapter.

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 NOPR 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 effi ciency levels will represent the baseline efficiency levels for batch ice makers. DOE also adopt 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 . This approach is
described in 5.4.1.4

.

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 compressor) 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.

b For brevity, pounds of ice per 24 hours will be referred to herein as lb/24 hours.5

- 5 

### 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 . This information is based on a survey of product databases and man ufacturer
websites (also see data in chapter 3 of the NOPR 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 the current DOE energy standard for batch ice
makers. DOE used the maximum available efficiency levels to guide selection of 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) 25% 382 Hoshizaki KMD

- 410MWH Medium (≥500 and <1,436) 22% 1,323 KM

- 1301SWH

Large (≥1,436) 23% 1

, 850 Hoshizaki KM

- 1900SWH

Air Small (<450) 24% 280 Scotsman C0330MA

- 1#,

- 32# Large (≥450) 21% 1,530 Hoshizaki KM

- 1900SAH 3

Remote Condensing (but not remote compressor) Air Small (<1,000) 2 5 % 55 0 Cornelius CCM0630RF2,H2

Large ( ≥ 1,000) 40 % 1, 804 Vogt

- Ice HES

- 20 / 02KAPHB426AC

Remote Condensing
and Remote Compressor Air Small (<934) 1 9 % 520 Manitowoc ID

- 0686C

- 161

Large (≥934) 15% 1,5 5 0 Manitowoc SD

- 1872C,
Manitowoc SY

- 1874C

Self

- Contained
Unit Water Small (<200) 2 3 % 1 41 Hoshizaki KM

- 201BWH Large (≥200) 28%** 285 Scotsman CU3030MW, SW

- 1#

Air Small (<175) 3 6 % 55 Hoshizaki AM

- 50BAE Large (≥175) 3 3 % 4 3 6 Hoshizaki DKM

- 500BAH

* Percent energy use lower than the baseline efficiency level ( i.e.

, the current DOE standard)

** Max imum efficiency level a vailable for units that are not ice makers + dispensers



### 5.4.1.3 Incremental Efficiency Levels

DOE established for each of the 1 1 analyzed batch ice equipment class es a series of incremental efficiency levels for which it has collected incremental cost data and quantified the

cost

- efficiency relationship. DOE established the highest incremental efficiency levels based on a review of the maximum efficiency levels of avai lable products , as discussed in section 5.4.1.2

. Table 5.4

. 3 shows the sel ected incremental efficiency levels. In some cases, DOE analyzed two
different harvest capacity rate sizes within the same equipment class; each analyzed harvest rate is listed separately in the table below.

The ENERGY STAR  

### program published new standards for automatic commercial ice
makers that became effective on February 1, 2013. These standards are covered in greater detail in chapter 3 of the NOPR TSD. DOE completed its NOPR engineering analyses prior to5

- 6 

### publication of the updated standards, and so th e efficiency levels (ELs) listed for EL2 are based
on the 2012 ENERGY STAR standards for commercial ice makers.

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 ** (2012 ENERGY STAR) EL 3 EL 4 EL 5 EL 6

IMH

- W

- Small

- B 300 10% 15% 20% 25%

IMH

- W

- M ed

- B 850 10% 15% 20%

IMH

- W

- Large

- B 1

, 500 10% 15%

2

, 600 10% 15%

IMH

- A

- Small

- B 300 10% (E

- STAR † ) 15% 20% 25% 30%

IMH

- A

- Large

- B 800 10% (E

- STAR *** ) 15% 20% 25%

1

, 500 10% (E

- STAR † ) 15% 2 0 %

RCU

- NRC/RC

Small

- B 700 Not Directly Analyzed ‡

RCU

- NRC/RC

Large

- B 1

, 500 9% (E

- STAR † ) 15% 20%

2

, 400 9% (E

- STAR *** ) 15% 20%

SCU

- W

- Small

- B 110 Not Directly Analyzed ‡

SCU

- W

- Large

- B 300 7% 15% 20% 25% 30%

SCU

- A

- Small

- B 110 7% (E

- STAR † ) 15% 20% 25% 30%

SCU

- A

- Large

- B 200 7% (E

- STAR † ) 15% 20% 25% 30%

* See Table 5.2.1 for a description of these abbreviations. ** EL = efficiency level; EL1 is the baseline efficiency level

, while EL2 through EL6 represent increased efficiency levels.
† E

- STAR Levels are based on the 2012 ENERGY STAR levels, not on the 2013 ENERGY STAR levels

. ‡ Non

- analyzed equipment classes were assigned representative efficiencies and costs from a similar analyzed equipment
class

. See discussion in the text below



### For the two non

- analyzed equipment classes, DOE applied the costs and efficiency levels of one of the analyzed equipment classes to each non

- analyzed equipment class. The mapping of

non

- analyzed equipment classes to their analyzed counterparts is described in Chapter 8 of the
NOPR TSD

.

5.4.1.4 Maximum Technology Level

DOE defines maximum technology level s for each equipment class to represent 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
tech nology level does not incorporate all design options used in maximum available efficiency products , due to proprietary technologies

.5

- 7 

### DOE determined maximum technology levels by using energy modeling. The energy models for the maximum technology levels were based on use of all design options applicable for the specific equipment class es. The maximum technology efficiency levels for the analyzed
batch ice maker equipment class es are presented in Table 5.4

. 4

. The design options considered in the analysis are listed for each equipment class in section 5.9

.
For some equipment classes, the table shows two maximum technology levels. For these cases, the two levels are representative of analyses conducted for two different harvest capaci ty

rates

.
Also, the table does not show maximum technology levels separatel y 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 r emote 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 NOPR analysis, DOE did not consider remote compressor and non

- remote compressor units separately. This is discussed in more detail in chapter 5 of the preliminary analysis TSD.

Table 5.4

. 4 Maximum Technology Level s for Batch Ice Maker Equipment Classes Equipment Class Energy Use Lower than Baseline

IMH

- W

- Small

- B 30 %

IMH

- W

- M ed

- B 22 %

IMH

- W

- Large

- B 1 7 % (at 1,500 lb/ 24 hours )
16 % (at 2,600 lb/ 24 hours )

IMH

- A

- Small

- B 33 %

IMH

- A

- Large

- B 33 % (at 800 lb/ 24 hours )
21 % (at 1,500 lb/ 24 hours )

RCU

- S mall

- B Not analyzed

– similar to IMH

- A

- Large

- B (1500)

RCU

- L arge

- B 21 % (at 1,500 lb/ 24 hours )
21 % (at 2,400 lb/ 24 hours )

SCU

- W

- Small

- B Not analyzed

- similar to SCU

- A

- Large

- B
SCU

- W

- Large

- B 35 %
SCU

- A

- Small

- B 41 %
SCU

- A

- Large

- B 36 %



### 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 states that the measured ener gy use of continuous machines must be adjusted based on the ice hardness. 7 7 FR 1591 ( Jan

. 11 , 201 2 ).5

- 8 

### 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 calori meter test results as follows:

Ice Quality Adjustment Factor = ⎣ ⎢ ⎢ ⎢ ⎢ ⎡

144 Btu lb � + 38 Btu lb � � 144 Btu lb �

calorimeter constant �

+ 38 Btu lb � ⎦ ⎥ ⎥ ⎥ ⎥ ⎤

Eq. 5

. 1

The calorimeter constant of this expression is inversely proportional to the ice hardness.

The measured energy consumption per 100 lb of ice and the measured condenser water consumption, as determined using American Society of Heating, Refrigerating and A ir

Conditioning Engineers ( ASHRAE ) Standard 29

- 2009, w ould be multiplied by the ice quality 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 general 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 based on ice maker
data and fitting of baseline efficiency level curves to the data. For the NOPR analyses, DOE took the same approach that was adopted during the preliminary an alysis but updated the baseline efficiency levels because of the availability of new data, including new ice hardness data that became available through the Air

- conditioning, Heating, and Refrigeration Institute ( AHRI ) product database.

DOE developed bas eline efficiency levels using energy use data available from several sources, as discussed in chapter 3 of the NOPR TSD. DOE chose baseline efficiency levels that would be met by nearly all ice makers represented in the databases. DOE primarily considered those units for which ice hardness data was available, but for data points that did not have ice
hardness data , DOE used assumptions of 0.7 ice hardness for flake ice makers and 0.85 for nugget ice makers to adjust the energy use data. The baseline efficie ncy levels are tabulated in
Table 5.4

. 5 , and they are plotted with the ice maker energy use data in chapter 3 of the NOPR
TSD.5

- 9 

### Table 5.4

. 5 Baseline Efficiency Levels for Continuous Ic e 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 Air Small (< 850 ) 10.2

– 0.004 59 H

Large (≥ 850 ) 6.1

Self

- Contained Unit Water Small (< 900 ) 9. 1

– 0.0 033 3H Large (≥ 900 ) 6. 1

Air Small (< 700 ) 1 1

. 5

– 0.0 0629 H Large (≥ 700 ) 7

. 1

* 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 NOPR TSD)

. These levels are based on AHRI product directory data that contained ice hardness data. The maximum available efficiency levels are represented by percentage energy use less than the energy use of baseline

- efficiency equipment

. DOE used the maximum available efficiency levels to select appropriate ranges of increm ental efficiency levels.

Table 5.4

. 6 Continuous 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 25 % 568 Hoshizaki F

- 801MAH Large 1 7 % 1,100 Hoshizaki F

- 1500MAH

Remote Condensing (but not remote compressor) Air Small 18% 835 Scotsman N0922R

- 32#

Large 19 % 1, 050 Scotsman F 1222R

- 32#

Remote Condensing
and Remote Compressor Air Small 8 % 71 3 Manitowoc RN1078C

- 161

Air Large 13.5 % 962 Manitowoc RN1278C

- 161

Self

- Contained
Unit Water Small 18.7% ** 310 Scotsman AFE400W

- 1# Large No products in this equipment class*

Air Small 24.4 % 238 Hoshizaki F

- 330BAH Large No products in this equipment class*

* DOE’s market analysis showed that there were no products in this equipment class

. ** DOE could find no products in this equipment class that were certified in the AHRI database at the time of DOE’s investigation.5

- 10 

### 5.4.2.4 Incremental Efficiency Levels

DOE established for each of the 10 analyzed continuous ice maker equipment class es a series of incremental efficiency levels, for which it has developed incremental cost data and quantified the cost

- efficiency relationship. DOE established the highest incremental efficiency levels based on a review of the maximum efficiency levels of available equi pment , as discussed
in section 5.4.2.3

. Table 5.4

. 7 shows the selected incr emental efficiency levels. The efficiency levels are defined by the 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 EL 6

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% 30%

IMH

- A

- L

- C 820 10% 15% 20% 25% 30%

1800 Not Directly Analyzed †

RCU

- S

- C 700 Not Directly Analyzed †

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 110 7% 15% 20% 25%

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 EL6 represent increased efficiency levels.
† Non

- analyzed equipment classes were assigned representative efficiencies and costs from a similar analyzed equipment class ‡ 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 analyzed batch ice maker equipment
class es are presented in Table 5.4

. 8

. The design options considered in the analysi s are listed for each equipment class in section 5.9

.
For one equipment class, the table shows two maximum technology levels. For these cases, the two levels are r epresentative of analyses conducted for two very different harvest capacit y rates

For two equipment class es , SCU

- W

- Large

- C and SCU

- A

- Large

- C , the table shows no maximum technology efficiency level . Ice maker databases indicated that there is no equipment
in those classes . Hence, DOE did not conduct analysis of those equipment class es . DOE expects to set energy standards for those equipment class es at the level s indicated in Table 5.4

. 7 that

ensure continuity with the standards for the SCU

- W

- Large

- C class. Thus, the chosen efficiency level for the small equipment class would also be applied to the large equipment class.

Table 5.4

. 8 Maximum Technology Levels for Continuous Ice Maker Equipment Classes Equipment Class Energy Use Lower than Baseline5

- 11 IMH

- W

- Small

- C Not analyzed

– similar to IMH

- A

- Large

- C (820)

IMH

- W

- Large

- C Not analyzed at 1

, 000 lb/ 24 hours

– similar to IMH

- A

- Large

- C (820) Not analyzed at 1

, 800 lb/ 24 hours

– similar to IMH

- A

- Large

- C (820)

IMH

- A

- Small

- C 25.3 %

IMH

- A

- Large

- C 17 % (at 820 lb/24 hours) Not analyzed at 1

, 800 lb/ 24 hours

– similar to IMH

- A

- Large

- C (820)
RCU

- NRC

- Small

- C Not analyzed

– similar to IMH

- A

- Large

- C (820)
RCU

- NRC

- Large

- C Not analyzed

– similar to IMH

- A

- Large

- C (820)
SCU

- W

- Small

- C Not analyzed

– similar to SCU

- A

- Small

- C
SCU

- W

- Large

- C *
SCU

- A

- Small

- C 24 %
SCU

- A

- Large

- C *

* DOE was not able to identify any products of this equipment class in ice maker databases. Hence, DOE did not conduct analysis to determine maximum technology level for this class.



### 5.5 DATA C OLLECTION

5.5.1 Component Vendor Data

DOE contacted major suppliers of key commercial ice maker components t o 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 used in the energy analyses, including analyses for baseline and improved

efficiency configurations.5

- 12 

### 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 representative automat ic commercial ice maker s as part of the engineering analysis 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 results of the reverse engineering process wer e used as input to the manufacturing cost modeling and the energy use modeling. This section describes the selection of products for reverse engineering as well as some of the measurements made to support subsequent modeling. Section 5.6 more thorough ly descri bes the physical teardown process used to support manufacturing cost modeling.

5.5.2.1 Selection of Products for Reverse Engineering and Inspections

Table 5.5.2 describes the 19 products selected for reverse engineering during the preliminary analysis and identifies the products for which DOE conduc ted 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 equipme nt class es and capacities based on the California Energy
Commission ( CEC ) , ENERGY STAR, and AHRI databases, as well as manufacturer website s

.5

- 13 

### Table 5.5

. 2 Selected Units for Reverse Engineering and Energy Testing during Preliminary Analysis

Type Equipment Class Harvest Cap a city
lb/ 24 hours Energy
Use kWh/
100 lb Potable
Water Use gal/100 lb Energy
Test Reverse Engineering Energy
Use
Model

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   

* Reverse engineering and modeling not completed during preliminary analysis for these units , but completed during the NOPR analyses

.


### 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

- efficiency analysis. Table 5.5

. 3 describes the 20 additional products analyzed during the NOPR phase.5

- 14 

### Table 5.5

. 3 : Selected Units for Reverse Engineering and Energy Testing duri ng NOPR Analysis

Type Equipment Class Harvest Capacity

lb/ 24
hours Energy
Use kWh/
100 lb Potable
Water Use gal/100 lb Energy
Test Reverse Engineeri

ng Energy
Use
Model

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



### 5.5.2.2 Collection of Energy Modeling Data

DOE examined each unit prior to teardown to record details to be used as input for the energy modeling. 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 for testing. For the units tested during the preliminary analysis, t he test lab oratory measured the
harvest capacity and energy use of the tested ice makers in accordance with AHRI Standard 810

- 2007

.

For batch ice makers with multiple purge water settings, Intertek tested the units in both the “standard” purge water setting and the highest purge water setting to evaluate the wate r use and energy use impacts of adjusting the purge water quantity. Only one of the tested ice makers had adjustable purge water control. DOE also had Intertek conduct calorimeter tests for the continuous ice makers that were tested, and bin effectiveness tests in accordance with AHRI
Standard 820

- 2000, Standard for Ice Storage Bins, for the self

- contained units that were tested.5

- 15 

### DOE measured c omponent

- level power input for fans, water pumps, and air pumps for the products that had these components. For som e of the continuous ice makers, DOE measured augur motor power input while the ice maker was making ice. In some cases, DOE also
measured the energy use associated with solenoid valves and the control board s

.

DOE made a ir flow measurements for all air

- cool ed 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 a n indication of air flow trends more than exact indication of air flow for the various products.

Details of the cabinet size , 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 evaporators 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 during the preliminary analysis are discussed in

c hapter 5 of the preliminary analysis TSD. This also includes an engineering questionnaire shown in appendix 12A of the preliminary analysis TSD.

During the NOPR analysis, DOE’s contractor conducted further interviews to calibrate the results of the cost model. The key technical topics included the following :

• i nput on the cost of c ompressors, heat exchangers, motors, and chassis pieces

• i nput on the overall cost of the product and the percentage of total cost allocated to materials, labor, depreciation, and overhead

• i nput on which compressors on the market are considered acceptable f or ice maker

use

All of these interviews were conducted under non

- disclosure agreements with the manufacturers. Hence, none of the individual responses can be reported. However, values for many of the parameters and costs used in the engineering analysis were based on aggregated 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
condition s during different phases of operation to support energy use modeling. Ten ice makers
were tested in the preliminary analysis , as indicated in Table 5.5.2 , and twenty more ice makers were tested in the NOPR analysis, as indicated in Table 5.5

. 3

. During the tests, the following
data were recorded:5

- 16 

### • p otable water use

• r efrigeration c ircuit temperatures

• r efrigerant p ressures

• p otable water circuit temperature s

• a ir temperatures, including the evaporator compartment, compressor/condenser compartment, c ondenser fan air outlet (for air

- cooled and remote air

- cooled machines)

• c ooling w ater inlet and outlet temperatures

• m achine p ower input raw data and accumulated energy use

• Air temperatures requ ired by the test procedure

5.6 MANUFACTURING COST M ODELING

5.6.1 Generation of Bills of Materials

E ach teardown resulted in a structured bill of materials (BOM). Structured BOMs describe each product part and its relationship to the other parts in the estimated orde r 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 level.

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 information in trade publications, and discussions with high

- and low

- volume original equipment manufactur ers (OEMs). DOE also visited manufacturing plants to reinforce its understanding of the industry’s current manufacturing practices.

For purchased parts, the 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. The cost of transforming the intermediate materials into finished parts is estimated based on current industry pricing.

5.6.2 Cost Structure of the Spread sheet Models

The manufacturing cost assessment methodology used is a detailed, component

- focused technique for calculating the manufacturing cost of a product (direct materials, direct labor, and the overhead costs associated with production). The first st ep in the manufacturing cost assessment was the creation of a complete and structured BOM from the disassembly of the units selected for teardown. 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 and purchased part costs. Assumptions about the sourcing of parts and in

- house fabrication were5

- 17 

### based on industry experi ence, 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 v alues. 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. Althou gh 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 summa ry, 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 disassembled selected units, gathered information from manufacturer catalogs on additional products, and identified technologie s, DOE create d an appropriate manufacturing cost model that could translate physical information into M PCs. The cost model is based on production activities and divides factory costs into the following categories:

• Materials : p urchased parts (compress or, fa n 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.

5.6.3.1 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 ;5

- 18 

### • Indirect labor : l abor costs that scaled with fabrication and ass embly 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 allocated to pay for initial equipment installation and replacement as the producti on 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 ;

• Building depreciation : m oney allocated to pay for the building space and the

c onveyors 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. , eq uipment 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 costs 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 assembly, fabrication, supervisor, and indirect labor. Overhead costs included equi pment depreciation, tooling depreciation, building depreciation, utilities, equipment, tooling maintenance, insurance, property, and taxes.

DOE used the information gathered from manufacturer 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 informati on.5

- 19 

### 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 based the manufacturing operations assumptions on internal expertise, in terviews 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 Manufacturing 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 analysis of common industry practices to formulate factory parameters for each type of manufacturer. DOE first made assumptions about a set of preliminary factor y 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
classes. To more accurate ly capture the costs of these products, DOE created a cost model based on low production volumes for 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

- 20 

### 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 Lab or Statistics Producer Price Indices (PPIs) spanning from
200 7 to 201 2 with an adjustment to 201 2 dollars ( 2011$ )

. 2 

### DOE used the PPIs for copper rolling, drawing, and extruding and steel mill products, and made the adjustments to 201 2 $ using the gross dome stic product implicit price deflator. 3 

### For resins used 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 for 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, a nd 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

- 21 

### 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 estimati ng the incremental costs
varied

, depending on the design option. Details in this calculation that are specific to individual design options are discussed in s ection 5.9 . Aspects of the incremental cost calculation that were generally applied to multiple 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 manufacturing cost increase

— other elements of product cost such as overhead and capital expenditures would be insignificantly affected by these design chan ges. The appropriate price increases are discussed in s ection 5.9 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 o ption. 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 Profit5

- 22 

### 5.6.6.1 G&A and Profit

DOE estimated the further addition to the MSP associated with general and
administrative costs ( G&A ) and profit for the appliance industry as 25 percent of MPC . This

a dder 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

Manufact urers and dealers apply a markup to cover their operating costs and profit margins in manufacturing and distribution. In the engineering analysis, DOE determined a manufacturer markup and applied it to the MPC to arrive at the MSP. DOE is maintaining the

e stimated 1.25 manufacturer markup factor from the preliminary analysis in the NOPR analysis.

5.8 ENERGY MODELING

DOE carried out detailed energy modeling of representative ice makers , and on design variations of these products that included one or more of the design options considered for the engineering analysis. This energy modeling work served as the basis for estimates of energy savings potential associated with the design options. The products selected for reverse engineering provided the basis for the ene rgy modeling. Energy model input was determined for these products from the data collected during the reverse engineering work, described in s ection
5.5.2 . Addition al data, used both as input and for calibration of individual product energy models, was provided by energy testing as described in s ection 5.5.4 . Using the energy modeling results and manufacturing cost modeling results for these designs allowed DOE to develop incremental cost estimates for multiple efficiency levels based on each of the baseline products analyzed.

DOE carried out energy modeling during this rulemaking using an improved version of
the FREEZE simulation program . Section 5.8.1 describes th is model briefly. A more detailed description of the program and its recent development is presented in appendix 5 A

.

5.8.1 FREEZE Model Overview

FREEZE is an energy model that simulates the performance of both batch and continuous
ice m akers

. As applied to batch ice makers, FREEZE is a transient model that can calculate changes in operating conditions as a function of time. T his feature is important for batch

- type machines for which refrigerant temperatures and pressures undergo large ch anges during the freeze cycle because of ice buildup on the evaporator. The development of the FREEZE model during the preliminary analysis is detailed in c hapter 5 of the preliminary analysis TSD.

During the NOPR a nalysis, DOE made several changes to the FREEZE energy model used to estimate energy use of different ice maker design configurations. DOE adjusted its energy models based on input received in manufacturers’ public and confidential comments and
discussions DOE’s contractor conducted under non

- dis closure agreements. These changes
included:

• adjust ing the compressor coefficients for batch

- type ice makers;5

- 23 

### • using data from tests of ice makers to model the increase of harvest energy as condensing temperature decreases for batch

- type ice makers;

• developing an approach based on test data to determine the condensing temperature reductions associated with use of larger water

- cooled condensers;

• limiting adjustments to the potable water use of batch products to a minimum of 20 gallons per 100 lb (or th e starting potable water use level, if lower) ; and

• incorporating energy use reduction for drain water heat exchangers used in batch equipment.

These adjustments are discussed in the sections discussing design options, s ection 5.9

.
DOE also performed a series of calibrations against existing data from purchased commercial products. DOE calibrated the efficiency gains calculated by the energy model

aga inst the design options and test results gathered during the empirical analysis investigation. DOE used this comparison to determine the suite of design options that should be found at the appropriate high

- efficiency level, and calibrated the results of th e energy model against the inspected results.

For example, DOE inspected a pair of IMH

- A

- Small

- B automatic commercial ice makers with measured efficiency levels of 2.2 percent below baseline and 17.5 percent below baseline, and noted the following changes between units:

• i ncreases in 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 )

In the energy model, DOE separated out each of the different design options and
considered them separately, ordering them based on cost

- efficiency. For this equipment class,
DOE had the following design options to increase efficiency from baseline to 23.5 percent below baseline, as shown in Table 5.8

. 1

.

Table 5.8

. 1 IMH

- A

- Small

- B Design Options

% Below Baseline Design Option
0.00% Baseline
6.22% Increase compressor EER from 4.86 EER to 5.25 EER
7.71% Increase condenser width (no chassis size increase)
20.52% Increase e vaporator a rea (with chassis size increase)
23.51% Switch to PSC c ondenser f an m otor



### In some instances, DOE considered slightly different design options, especially when DOE’s analysis found that more efficient compressor options were available. For example, the maximum comp ressor EER used in the energy modeling analysis was more efficient than the inspected unit compressor EER. This is the reason this suite of design options reaches higher efficiencies. DOE did not consider chassis sizes larger than those available on the ma rket.5

- 24 

### DOE believes that these changes help ensure that the energy model results accurately reflect technology behavior in the market.

5.9 D E SIGN OPTIONS

After conducting the screening analysis described in chapter 4, DOE considered the remaining technologies in the design 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 equipment. 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.

Table 5.9

. 1 Design Options by Equipment Class

Ice Maker Type Equipment ClassCompressorUpgradeCondenser FanMotorsPump MotorsAuger MotorsAir-CooledCondensersHarvest AssistBatch 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.



### 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 use and condenser water use in the engineering analysis in a way that maintains the rulemaking’s focus on energy use reduction while appropriately considering the cost implications of changing condenser water use. DOE proposed using appropriate representative values for water and energy costs, product lifetime, and discount rates to calculate a representative LCC for baseline
and modifi ed design configurations as part of the engineering 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), bu t 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.5

- 25 

### To obtain data for developing a relationship between increased condenser water flow rate and energy consumption, DOE tested three water

- cooled ice makers with varying condensing water flow to evaluate the potential for energy savings and the cost

- effectiveness of using this approach. The results of this eva luation 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, which significantly reduce water flow . This reflects an averaging of the electricity and water LCC costs for each type of system.

DOE’s testing was con ducted at an independent third

- party test lab. The test lab varied the water flow levels by adjusting the water flow valve on each water

- cooled condenser. The temperature ranges reflect the availa ble range of water flows ; the test labs adjusted the water flow as far as possible to get the resulting temperatures

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 52.25

- 26 

### The analysis shows that increasing condenser water flow is not a cost

- effe ctive way to reduce energy use, in all three units. As a result, DOE did not comprehensively evaluate this approach for all water

- cooled equipment classes in its engineering analysis. Instead, DOE
consider ed growth in condenser size for water

- cooled conden sers and use of ECM pump motors

.

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 analysi s, 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 conditions. Table 5.9

. 2 lists the A H RI 540 m edium t emperature operating test conditions. Not all compressor vendors provided compressor performance data at these operating conditions.

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



### DOE updated the compressor performance database with new compressors and solicited further feedback from stakeholders on the acceptability of certain compressor lines. DOE noted that stakeholders had the following feedback:

• Some compressor families used a direct suction design that has a large impact on

compress or 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 compressor f amilies have unacceptable levels of noise and/or vibrations for this application , again presenting utility issues

.
DOE removed from its analysis those compressors that manufacturers have indicated are
unsuitable for use in ice makers , further limiting from consideration those compressors deemed
unsuitable for use in batch ice makers

. While this removed from consideration some high

efficiency compressors that were considered in the preliminary analysis, DOE also added additional compressor families to its d atabase, and so the overall max

- tech compressor EER in each directly analyzed equipment class may have risen or fallen as compared with the preliminary analysis due to these changes. DOE used the energy model to determine the energy impacts of each compress or design option. The energy model accounted for the impact on ice maker energy use of both the EER improvement and the capacity change in the analysis.

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 cost derived using the cost5

- 27 

### 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

. 2

. The cost curve is based on capacity at 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 analysis, 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

.

Fig ure 5.9

. 1 NOPR Cost Curves for Medium

- Temperature Compressors U sed in
Commercial Ice M aker s

DOE maintained the same approach from the preliminary analysis for determining the cost of increasing EER. DOE used the following exponential relationship to determine the cost increase associated with higher

- EER compressors:

New Cost = Baseline Cost × 1

. 05 � log ( New EER ÷ Old EER ) log ( 1

. 1 ) �



### Eq. 5

. 2

For a 10

- percent increase in EER, this relationship yields a 5

- percent increase in cost, consistent with the approach used for the CRE rulemaking. Further details are available in chapter 5 of the ACIM preliminary analysis TSD.

5.9.3 Increased Air

- Cooled Con denser Surface Area

The condenser transfers heat from the refrigerant to the ambient 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 conside red an increase in condenser surface area for all5

- 28 

### air

- cooled products analyzed. In the NOPR analysis, DOE did consider condenser size increases that would result in an increase in chassis size, but first DOE reviewed the space available in the teardown pro ducts that served as the basis for the energy modeling to determine how much size increase would be possible without requiring increase in the 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.

C ondenser size increases were implemented in the energy analysis through direct adjustment of the input parameters describing the condenser geometry. DOE increased eit her the number or length of tube rows as appropriate for the space available for size increase , increasing air flow to maintain a constant face velocity, or to maintain a constant air flow, depending on which approach was most effective in reducing overall energy use

. DOE accounted for the impact of condenser size increase and air flow changes on condenser fan power input, assuming that for the initial design, three

- quarters of the total condenser air flow system pressure drop is associated with pressure dr op through the condenser itself, that pressure drop is proportional to condenser face velocity, and that fan power input is proportional to air flow times pressure drop.

In the NOPR analysis, DOE adjusted its condenser coil costs based on feedback from

sta keholders. T he cost associated with the condenser size increase was calculated using a cost curve between the volume of the condenser and the overall cost

. The formula is provided as follows :

Condenser Cost = Condenser Volume × 0

. 0744 + 26

. 13 Eq. 5

. 3

5.9.4 Increased Water

- Cooled Condenser Surface Area

The testing conducted for analyzing the option of increasing condenser water flow indicated that reductions in condenser temperature would result in some energy savings (see

s ection 5.9.1 ). As noted in section 5.9.1 , DOE found that increases in condenser water flow are

not cost

- effective from an LCC standpoint. Therefore, ins tead of considering increases in
condenser water flow for water

- cooled products, DOE considered increases in surface area to

water

- cooled condensers to achieve these observed energy savings.

For each analyzed product class, DOE noted whether there was suff icient space to grow the condenser within the chassis and applied the condenser growth design option if available

. DOE noted the current condensing temperature and simply grew the water

- cooled condenser
component in length until the condenser temperature dropped to 101 ° F, a pre

- existing limit that was determined through testing of multiple ice makers and water

- cooled ice maker settings. This limited the growth of the condenser to levels that would preserve the utility of the ice maker.

5.9.5 Evaporator Size Incr ease

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 te mperature

.5

- 29 

### In the NOPR analyses, DOE considered an increase in evaporator surface area for both
batch and continuous ice makers analyzed but only applied it in cases where reverse engineering
work indicated that it was an essential step for increasing efficiency

. Thus, DOE applied this design option to the IMH

- A

- Small

- B, IMH

- W

- Small

- B, and IMH

- A

- Small

- C equipment classes; in these cases, DOE had a clear comparison of a baseline and high

- efficiency unit that showe d evaporator growth was an existing design option to increase efficiency.

DOE used the recalibrated cost model to produce costs for this design option. DOE implemented these changes in the energy model by increasing the model input UA value (heat
transfer coefficient times effective evaporator surface area, with overall units of Btu/h

- ° F)
consistent with the surface area increase.

5.9.6 Design Options that Require Chassis Size Increases

Consideration of design options that increase the size of ice makers is stro ngly related to
consideration of size

- constrained design options. DOE notes that, while stakeholders have pointed out that many automatic ice maker applications are space

- constrained, as described in section IV.B.1.a, DOE does not have access to sufficient ly detailed data that would either indicate what percentage of applications could not allow size increase, or be the basis to set size limits for space

- constrained classes. Thus, DOE has also decided not to create size

- constrained equipment classes.

DOE also notes that there are a wide range of product sizes within most equipment classes, and that DOE must seek out the most

- efficient configurations. DOE noted that the equipment it purchased for reverse engineering inspections reflected a general trend that more

efficient units were often larger, had larger condensers, and in some cases had larger evaporators. Based on DOE’s market study and equipment inspections, larger chassis sizes appeared often to
be a means of achieving higher efficiencies.

Thus, DOE is including these package

- size

- increasing technologies as design options in the NOPR analysis. DOE only applied these design options for those equipment classes where the representative baseline unit had space to grow relative to the largest units on the market. The equipment growth allowed for larger heat exchangers to increase equipment efficiency.

For equipment classes with remote condensers, DOE only applied this design option to the condenser package, and not to the ice

- maki ng head that is place d indoors.

In addition, DOE recognizes that space constraints are more critical for SCU units; hence, DOE did not consider package size growth for SCU equipment classes. Table 5.9

. 6 indicates the analyzed equipment classes for which DOE considered chassis growing design options.5

- 30 

### Table 5.9

. 6 Analyzed Equipment Classes Where DOE Analyzed Size

- Increasing Design Options

Unit Harvest Capacity
lb ice/24 hours Used Design Options that
Increased Size?

IMH

- A

- Small

- B 300 Yes

IMH

- A

- Large

- B (med) 800 Yes

IMH

- A

- Large

- B (large) 1,500 No

IMH

- W

- Small

- B 300 Yes

IMH

- W

- Med

- B 850 No

IMH

- W

- Large

- B 2,600 No

RCU

- ***

- Large

- B (med) 1,500 For the remote condenser, but
not for the ice

- making head

RCU

- ***

- Large

- B (large) 2,400 For the remote condenser, but
not for the ice

- making head
SCU

- A

- Small

- B 110 No
SCU

- A

- Large

- B 200 No
SCU

- W

- Large

- B 300 No

IMH

- A

- Small

- C 310 No

IMH

- A

- Large

- C (med) 820 No
SCU

- A

- Small

- C 110 No



### Table 5.9

. 7 shows the size increases that DOE considered in the analysis. DOE only considered these size increases when a unit existed on the market that was larger than the
baseline unit. DOE based the new chassis sizes on the sizes of current units on the market.

Table 5.9

. 7 Description of Size Increase Design Options in the Engineering Analysis Equipment
class Equipment Type Size Descriptor Height
inches Width
inches Depth inches Volume cubic feet

IMH

- A

- Small

- B IMH Baseline 16.5 30 24.5 7.02
Growth 21.5 30 24.5 9.14

IMH

- A

- Large

- B (Med) IMH Baseline 26 30 24 10.83
Growth 29 30 24 12.08

IMH

- W

- Small

- B IMH Baseline 20 30 24 8.33
Growth 23.5 30 23.5 9.59



### DOE acquired costs for each of these chassis size growths through the upda ted cost model, and applied these costs to the incremental costs associated with heat exchanger growths requiring chassis costs.

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 hade d p ole (SP) or
p ermanent s plit c apacitor (PSC) motors with shaft power outputs ranging from 3 to 750 W

, depending on the ice maker’s harvest capacity . DOE analyzed the efficiencies of these motors through and found that p ermanent m agnet DC (PMDC) fan motors are more efficient than SP or PSC motors currently used in baseline commercial ice maker s.

In the NOPR analyses, DOE kept the same range of motor efficiencies as was used in the preliminary analysis. DOE did update the costs ass ociated with these motors based on5

- 31 

### stakeholder feedback. Table 5.9

. 3 lists the shaft output power range and efficienc y ranges of different types of mo tors suitable for use in condenser fans. This information was obtained through supplier interviews , motor literature , and examination of the motors used in the reverse engineering ice makers

. In the preliminary engineering analysis, DOE assumed that the
ef

ficiencies of the three motor types are 25 percent for SP, 45 percent for standard

- efficiency PSC, 65 percent for highest efficiency PSC, and 75 percent for PMDC. While these efficiencies may apply only for a specific shaft power level, DOE assumed that th e efficiencies at different shaft power output levels scale similar ly . For example, at a lower shaft power level a shaded power motor may have an efficiency of 15 percent . DOE then calculated the efficiency of a replacement standard

- efficiency PSC motor as 15 percent × 45 ÷ 25 = 27 percent.

Table 5.9

. 8 Expected Shaft Power Output Range and Effi ciencies for Different Types of
Motors Motor Type Output Power Range
W Efficiency Range
% Shaded Pole (SP) 3 to 60 14 to 25 Permanent Split Capacitor (PSC) 9 to 750 50 to 70 Permanent Magnet Direct Current (PMDC) 3 to 750 75 to 83



### Based on the information obtained from the sources mentioned above , DOE developed a
series of cost curves for the different motor types as a function of shaft output power. Th e output of these curves , which was used for the NOPR engineering analysis, is summarized in
Table 5.9

. 9

.

Table 5. 9

. 9 Motor Cost Information Use for NOPR Engineering Analysis

Shaft Power
W Horsepower Costs by Motor Type $

Shaded Pole Standard

Efficiency PSC Highest

Efficiency PSC Permanent Magnet DC

6 0.008 $12 $20 $20 $35

9 0.012 $14 $21 $24 $36

12 0.016 $16 $21 $27 $37

25 0.034 $21 $22 $35 $41

37 0.050 $23 $24 $39 $44

60 0.080 $26 $26 $45 $51

100 0.134 $30 $31 $50 $63

200 0.268 $34 $42 $58 $94

373 0.500 $38 $61 $65 $147

560 0.750 $41 $82 $82 $204

746 1.000 $43 $102 $102 $261



### 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 NOPR 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

.5

- 32 

### For the NOPR engineering analysis, DOE assumed that the cost of an efficiency improvemen t would be proportional to the full cost of the motor. As a conservative estimate, DOE assumed that the price of the auger motor would double when moving from a PSC motor to a permanent

- magnet motor. This incremental cost is consistent with market data for motors of
this size. DOE assumed for this analysis that CSIR motor performance and cost are equivalent to the performance and cost of standard

- efficiency PSC motors.

5.9.9 P ump Motors

Water pumps are used in batch commercial ice maker s to transport water from t he water sump to the ice tray where water is frozen. The motors used for these pumps are exclusively SP motors with shaft power outputs between 6 and 15 W

. In the NOPR analyses, DOE used the updated information collected for condenser fan motors to evaluat e pump motor design options, assuming that this information is also representative of the potential for performance improvement and cost increase for pump motors. See s ection 5.9.7 for more information on condenser fan motors

.

5.9.10 Drain W ater H eat E xchanger

In the NOPR analysis, DOE has 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 now conclud es 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 re quirements for the heat exchanger. Because of this, DOE accounted for the use of a drain water heat exchanger as a design option in batch ice makers.

DOE conducted testing of a commercially available drain water heat exchanger with a
batch water

- cooled IMH unit to determine the relative effect. The test results are shown in
Table 5.9.10

.

Table 5.9

. 10 Energy Consumption Tests with a Drain Water Heat Exchanger

Baseline Run 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

LCC Operating Cost $/100 lb $1.75 $1.70

Percent Reduction in Energy Consumption from Baseline 0.0% 3.9%



### When applying this design option to the cost

- efficiency curve, DOE did not consider the effect of the drain water heat exchanger through the energy model but simply applied the e nergy5

- 33 

### savings of 3.9 percent to the energy consumption of the preceding design option. The actual savings from baseline would be smaller than 3.9 percent

.
DOE conducted a teardown on the purchased drain water heat exchanger and also 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 decided to consider the cost of the drain water heat exchanger only as a purchased part, and not as a piece assembled in the factory. This approach resulted in a cost of $270 for a larger 9

- inch

diameter version of the heat exchanger (for products with harvest capacity larger than 1

, 000 lb/day) and a cost of $210 for a sm aller

, 7

- inch

- diameter version of the heat exchanger (for products with harvest capacity smaller than 1

, 000 lb/day). DOE also considered that two drain water heat exchangers could be attached in series to service very large equipment.

5.9.11 Reduced Potable Water Flow for Batch

- Type Ice Makers

In the NOPR analysis, DOE considered reductions in potable water flow for batch ice makers, as a switch from a float

- valve style fill to a controlled fill can have significant impacts on the energy consumption of a batch ice maker. DOE calculated costs for the valving and controls required to make a batch system , as well as the cost to increase the sump to hold more water. Based on feedback 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 only applied this design option when the baseline product used for energy modeling did not already incorporate a batch

- fill system.

5.10 ALTERNATE ENGINEERIN G APPROACHES

As part of the NOPR analyses, DOE explored potential alternatives to the energy modeling and cost

- modeli ng approach used. Section 5.10.1 discusses an analysis based on inspection of retail costs and model ratings to construct a cost

- efficiency relationship. DOE note s that these analyses were chosen due to concerns about the validity of the results. DOE chose the updated energy

- modeling approach as the most appropriate option to conduct the engineering analyses with.

5.10.1 Retail Cost Analysis

As part of the NOPR analyses, DOE compiled a database of ice makers currently available on the market. This database is described in chapter 3 of the NOPR TSD. Using the data from the database, DOE conducted a study of retail prices and whether lower energy consumption correlated w ith higher retail prices.

The two main considerations when conducting an analysis of retail prices versus energy consumption is that:

• retail prices increase as automatic commercial ice makers become larger (increasing harvest rate), and

• retail prices incr ease as the size of automatic commercial ice makers becomes larger (22 inch width versus 30 inch width, etc.)5

- 34 

### • energy consumption naturally decreases as automatic commercial ice makers become larger (increasing harvest rate)

DOE’s approach for the retail co st analysis accounted for these three factors. First, for each product class, DOE established a trend between increasing harvest rate and estimated retail price. DOE assumed that the average retail price would be 50% of the listed price on
manufacturer w ebsites. Where enough data was available, DOE established different trend equations for products at different sizes (22 inch width products versus 30 inch products versus 48 inch products). These data points were taken from a database consisting of all 4 70 icemakers of all equipment classes that were on the market and for which DOE had retail cost data.

Next, for each equipment class, DOE established a relationship between increasing ice market harvest rate and energy consumption rate. Again, this relati onship was based on the same 470 ice maker data points.

Using these two relationships, DOE established an estimated retail price and estimated energy consumption rate for each product based on its harvest rate and equipment class. DOE compared these valu es to the actual values in the database, and aggregated the results as a comparison of the percentage difference of estimated retail price versus the percentage difference of energy consumption rate. The aggregated results of all 470 data points are presen ted in Figure X.X below. These results are presented using two metrics:

• Delta ECR is defined as: (Predicted energy consumption rate

– actual energy consumption rate) / actual energy consumption rate [as a percentage]

• Delta Price is defined as: (Predicted retail price

– estimated retail price from actual) / actual retail price from actual [as a percentage]5

- 35 

### Figure 5.10

. 1 : Aggregated results to compare Delta Price metric versus Delta ECR metric5

- 36 

### Figure 5.10

. 2 : IMH

- A

- Large

- B results to compare Delta Price metric versus Delta ECR
metric

Using Figure 5.10

. 1 , DOE could establis h whether products with lower energy consumption than expected also possessed retail prices that were higher than expected. This would indicate that there is a premium for products with lower energy consumption, controlled
for harvest rate and size effect s. In fact, as shown in Figure 5.10

. 2 , DOE noted that there was a trend of increasing retail prices (versus expected) with increasing energy consumption.

Ultimately, DOE noted that there was no significant trend in the retail data for retail prices against increased energy efficiency. The calculated trend results in a negative cost

efficiency model. In addition, DOE was unable to establish a significant c orrelation between

5.11 ENGINEERING ANALYSIS RESULTS

This section presents the engineering analysis results, including the incremental cost

curves. DOE generated cost

- efficiency curves for ice maker equipment class es, based on combinations of individual design options. DOE normalized the curves by converting to costs at specific efficiency levels , as previously defined for each equipment class (see section 5.4 ) for simplified downstream analysis.

5.11.1 Overview

Table 5.10

. 1 presents MPC, MSC, and water use characteristics for baseline batch ice

m akers. The table also indicates which equipment classes were used for extrapolation of results for equipment classes that were not directly analyzed. Only three of the 14 product sizes were not directly analyzed; in one of these cases (IMH

- W

- Large

- B (1500) ), DOE extrapolated the incremental cost results of another equipment class to obtain incremental costs. DOE did this to allow for proper weighting of the incremental costs for the overall IMH

- W

- Large

- B equipment class, as the costs at 1500 lb/day were co mbined with the costs at 2600 lb/day for the purposes of conducting the downstream analysis.

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 analyzed

— they indica te the variation of baseline equipment characteristics and incremental MSP as a function of harvest capacity within given equipment classes.

Table 5.10

. 1 Batch Ice Maker Baseline Characteristics and Extrapolat ion Approach

Equip ment Class Repre sentative
Harvest Capacity
lb/ 24 hours MPC MSC Potable
Water Use gal/
100 lb Condenser
Water Use gal/
100 lb Extrapolation : Results for this equipment

class based on extrapolation from the

following equipment
class

IMH

- W
Small 300 $893 $1,116 2 4.0 15 3 Direct Analysis
Medium 850 $1,536 $1,920 19.7 1 53 Direct Analysis5

- 37 Large 1,500 * $1,732 $2,165 2 2.3 NA IMH

- A

- Large

- B (1500) 2,600 $3,725 $4,656 19.7 174 Direct Analysis

IMH

- A
Small 300 $910 $1,138 2 5.6
NA Direct Analysis

Large 800 $1,485 $1,856 2 2.7 Direct Analysis 1,500 $1,642 $2,053 20.0 Direct Analysis
RCU
Small 700 Not Directly Analyzed RCU

- Large

- B

Large 1,500 $1,959 $2,448 20 .0 NA Direct Analysis 2,400 $3,183 $3,979 19.7 Direct Analysis
SCU

- W
Small 110 Not Directly Analyzed SCU

- W

- Large

- B Large 300 $1,217 $1,521 30.0 1 32 Direct Analysis
SCU

- A
Small 110 $1,045 $1,306 3 4.0 NA Direct Analysis Large 200 $1,115 $1,393 30.0 Direct Analysis

* For the IMH

- W

- Large

- B (1500) analyzed equipment class, DOE extrapolated the results of the IMH

- A

- Large

- B (1500) equipment class to obtain a set of efficiency level costs



### 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 10% 15% 20% 25%
Small 2 2 % 300 $20 $37 $72 $136 29 % $ 370
Medium 16 % 850 $26 $53 $279 2 1% $ 288

Large 8 % 1,500 * * $46 $359 1 7 % $ 402
8% 2,600 $64 $520 15 % $ 524

IMH

- A 10% 15% 20% 25% 30%
Small 24% 300 $26 $45 $64 $86 $316 31 % $ 324

Large 21% 800 $24 $39 $78 $121 2 9 % $ 35 6
17% 1,500 $35 $118 $429 20 % $ 429
RCU 9% 15% 20%
Small 2 5 % 700 Not Directly Analyzed

Large 15 % 1,500 $28 $76 $386 21 % $ 407
16 % 2,400 $89 $198 $701 21 % $ 719
SCU

- W 7% 15% 20% 25% 30%
Small 2 2 % 110 Not Directly Analyzed Large 28% 300 $ 30 $ 48 $ 62 $ 88 $ 304 3 0 % $ 305
SCU

- A 7% 15% 20% 25% 30%
Small 27 % 110 $1 4 $ 32 $4 4 $56 $90 39 $ 340 Large 26 % 200 $1 9 $ 40 $ 68 $ 79 $118 3 5 % $ 333

* EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL6 represent increased efficiency levels.

** For the IMH

- W

- Large

- B (1500) analyzed equipment class, DOE extrapolated the results of the IMH

- A

- Large

- B (1500) equipment class to obtain a set of efficiency level costs



### 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 adjustme nt for ice quality as required by the test procedure final rule

.5

- 38 

### Table 5.10

. 3 Continuous Ice Maker Baseline Characteristics and Extrapolation Approach

Equip

ment Class Representative
Harvest Capacity
lb/24 hours MPC MSC Condenser
Water Use gal/100 lb Potable Water
Use gal/100 lb Extrapolation: Results for this equipment class based on extrapolation from the following equipment class

IMH

- W

Small 800 Not Directly Analyzed IMH

- A

- Large

- C

Large 1,000 Not Directly Analyzed IMH

- A

- Large

- C 1,800 Not Directly Analyzed IMH

- A

- Large

- C

IMH

- A

Small 310 $ 2,689 $ 3,362 NA 12.0 Direct Analysis

820 $2, 078 $ 2,597 NA 13.6 Direct Analysis 1,800 Not Directly Analyzed IMH

- A

- Small

- C
RCU

Small 700 Not Directly Analyzed IMH

- A

- Large

- C
Large 1,500 Not Directly Analyzed IMH

- A

- Large

- C

SCU

- W
Small 300 Not Directly Analyzed SCU

- A

- Small

- C Large NA No Products to Analyze No Analysis *

SCU

- A

Small 110 $1, 403 $ 1,753 NA 12.0 Direct Analysis

Large NA No Products to Analyze No Analysis *

* No products of this equipment class were identified in product databases.



### 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

- W 10% 15% 20% 25% 30%
Small 17 % 800 Not Directly Analyzed

Large 12 % 1,000 Not Directly Analyzed
9% 1,800 Not Directly Analyzed

IMH

- A 10% 15% 20% 25% 30%
Small 25 % 310 $ 26 $3 9 $ 57 $ 190 $ 495 3 1 % $ 525

Large 17% 820 $ 30 $ 46 $ 61 $ 98 $ 500 30 % $ 501
8% 1,800 Not Directly Analyzed
RCU 7% 17% 22% 26% 30%
Small 18 % 700 Not Directly Analyzed Large 18 % 1,500 Not Directly Analyzed
SCU

- W 3% 10% 16% 20%
Small 23% 300 Not Directly Analyzed Large NA* * NA* * No Products on the Market
SCU

- A 7% 15% 20% 25%
Small 24 % 110 $ 10 $ 28 $45 $362 2 8% $ 372 Large NA* * NA* * No Products on the Market

* EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL6 represent increased efficiency levels.
** No products of this equipment class were identified in product databases.



### For the non

- analyzed equipment classes, DOE associated each class with a directly analyzed equipment class. Table 5.10

. 4 shows the associations for each non

- analyzed product5

- 39 

### class, and they are based on the availability of similar design options between different equipment classes. For example, equipment classes with continuous

- type evaporators are associated with other continuous

- type equipment classes as they have similar energy use splits and similar available design o ptions.

These associations allowed the downstream analyses to the conducted by aggregating the shipments under one set of incremental costs. Further details are available in the subsequent NOPR TSD chapters.

Table 5.10

. 5 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

- Small

- C IMH

- A

- Large

- C

RCU

- NRC

- Large

- C IMH

- A

- Large

- C

RCU

- RC

- Small

- C IMH

- A

- Large

- C

RCU

- RC

- Large

- C IMH

- A

- Large

- 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



### 5.11.2 Design Option Analyses

Table 5.10

. 6 and Table 5.10

. 18 illustrate the design option analyses used to calculate the incremental manufacturer selling prices in the efficiency

- level analysis. The following tables correspond to those equipment class es that were directly analyzed for this rulemaking.5

- 40 

### 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 Consumptio n

kWh/100 lb Percent Energy

Use
Reduction Manufacturer Manufacturer Design Options Successively Added Production
Cost Selling Price

7.68 0.00% $910.07 $1,137.59 Baseline

7.20 6.22% $923.19 $1,153.99 Increase Compressor EER from 4.86 to 5.25

7. 09 7.71% $927.36 $1,159.20 Increase Condenser Width by 2"

6.10 20.52% $975.84 $1,219.80 Increase Evaporator Area by 51% (with chassis growth)

5.87 23.51% $987.92 $1,234.90 Switch to PSC Condenser Fan Motor

5.77 24.88% $995.62 $1,244.53 Switch to ECM Condenser Fan Motor

5.65 26.37% $1,003.21 $1,254.01 Increase Condenser Height by 3"

5.48 28.61% $1,024.52 $1,280.65 Switch to ECM Pump Motor

5.27 31.3% $1,234.52 $1,543.15 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% $1,485.08 $1,856.35 Baseline

5.53 8.0% $1,503.23 $1,879.04 Switch to PSC Condenser Fan Motor

5.21 13.4% $1,518.49 $1,898.11 Increase Compressor EER from 5.78 to 6.2

4.88 18.8% $1,536.02 $1,920.03 Increase Compressor EER from 6.2 to 6.7

4.76 20.8% $1,566.02 $1,957.53 Change to Batch Water Fill

4.59 23.7% $1,585.91 $1,982.39 Switch to ECM Pump Motor

4.56 24.2% $1,592.54 $1,990.68 Switch to ECM Condenser Fan Motor

4.47 25.5% $1,616.05 $2,020.06 Increase Condenser Height by 3" (with chassis growth)

4.41 26.6% $1,630.74 $2,038.43 Increase Condenser Width by 3.25"

4.24 29.4% $1,840.74 $2,300.93 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 hours5

- 41 

### 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 Successivel y Added Production
Cost Selling Price

5.24 0.0% $1,642 $2,052.65 Baseline

5.06 3.5% $1,652.44 $2,065.55 Increase Compressor EER from 5.85 to 6.1

4.75 9.4% $1,672.48 $2,090.60 Increase Compressor EER from 6.1 to 6.6

4.61 12.0% $1,693.98 $2,117.48 Switch to PSC Condenser Fan Motor

4.45 15.1% $1,762.34 $2,202.93 Increase Condenser Width by 6 inches

, Add
One Additional Condenser Row

4.41 15.9% $1,780.62 $2,225.78 Switch to ECM Condenser Fan Motor

4.37 16.7% $1,801.24 $2,251.55 Switch to ECM Pump Motor

4.19 20.0% $2,071.24 $2,589.05 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% $893 $1,116 Baseline

5.52 10.1% $913.21 $1,142 Increase compressor EER from 4.59 to 5.2

5.02 18.3% $939.88 $1,175 Increase Condenser Length by 32%

4.81 21.7% $969.88 $1,212 Change to Batch Water Fill

4.63 24.6% $1,018.36 $1,273 Increase Evaporator Area by 47%

4.51 26.5% $1,053.10 $1,316 Switch to ECM Pump Motor

4.34 29.4% $1,263.10 $1,579 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 hours5

- 42 

### Table 5.10

. 10 Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities between 500 and 1436 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% $1,536 $1,920 Baseline

4.39 5.4% $1,548.91 $1,936 Increase Compressor EER from 5.84 to 6.2

4.11 11.6% $1,566.44 $1,958 Increase Compressor EER from 6.2 to 6.7

3.91 15.7% $1,594.07 $1,993 Increase Condenser Length by 17%

3.80 18.2% $1,613.96 $2,017 Switch to ECM Pump Motor

3.65 21.3% $1,823.96 $2,280 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



### Table 5.10

. 11 : Manufacturing Cost and Energy Consumption Data for Water

- Cooled IMH Batch Ice Machines with Capacities Greater Than 1436 lb/24 hours (Larger Sizes)

Energy Consumption

kWh/100 lb Percent Energy

Use
Reduction Manufacturer Manufacturer Design Options Successively Added Production
Cost Selling Price

4.00 0.0% $3,725.17 $4,656 Baseline

3.90 2.5% $3,735.91 $4,670 Increase compressor EER from 6.66 to 6.84

3.60 10.0% $3,789.59 $4,737 Increase Condenser Length by 18%

3.52 12.0% $3,828.95 $4,786 Switch to ECM Pump Motors (2)

3.38 15.5% $4,248.95 $5,311 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 hours5

- 43 

### Table 5.10

. 12 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 Successiv ely Added Production
Cost Selling Price

5.10 0.0% $1,958.68 $2,448.35 Baseline

4.92 3.6% $1,969.00 $2,461.25 Increase Compressor EER from 5.85 to 6.1

4.61 9.7% $1,989.04 $2,486.30 Increase Compressor EER from 6.1 to 6.6

4.43 13.1% $2,000.09 $2,500.11 Increase Condenser Height by 3" (with unit growth)

4.35 14.7% $2,023.53 $2,529.41 Increase Condenser Width by 5" (with unit growth)

4.29 15.8% $2,047.41 $2,559.26 Switch to ECM Fan Motor

4.25 16.6% $2,075.53 $2,594.41 Increase Condenser Width by 6 inches (with unit growth)

4.21 17.4% $2,095.42 $2,619.28 Switch to ECM Pump Motor

4.05 20.6% $2,365.42 $2,956.78 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

. 13 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% $3,183.41 $3,979.26 Baseline

4.88 4.3% $3,203.82 $4,004.78 Increase compressor EER from 6.5 to 6.84

4.51 11.5% $3,309.39 $4,136.74 Increase Condenser Width by 19 inches , Add
One Additional Condenser Row and One
Extra Fan

4.38 14.1% $3,348.75 $4,185.94 Switch to ECM Pump Motors (2)

4.32 15.2% $3,389.61 $4,237.01 Increase Condenser Width by 10 inches (with unit growth)

4.22 17.2% $3,482.04 $4,352.55 Switch to ECM Condenser Fan Motors (2)

4.05 20.5% $3,902.04 $4,877.55 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 hours5

- 44 

### Table 5.10

. 14 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% $1,045.15 $1,306.44 Baseline

12.45 3.1% $1,052.73 $1,315.91 Switch to PSC Condenser Fan Motor

11.73 8.7% $1,062.55 $1,328.19 Increase condenser width by 2.5 inches , Add
One Additional Condenser Row

9.22 28.2% $1,108.16 $1,385.20 Increase compressor EER from 3.3 to 4.4

8.80 31.4% $1,138.16 $1,422.70 Change to Batch Water Fill

8.35 35.0% $1,159.33 $1,449.16 Switch to ECM Pump Motor

8.10 36.9% $1,174.74 $1,468.43 Switch to ECM Condenser Fan Motor

7.79 39.3% $1,384.74 $1,730.93 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

. 15 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 Options Successively Added Production
Cost Selling Price

9.80 0.0% $1,114.79 $1,393.49 Baseline

8.43 14.0% $1,152.90 $1,441.13 Increase compressor EER from 4.38 to 5.2

7.88 19.5% $1,164.95 $1,456.19 Increase condenser width by 9 inches

7.31 25.4% $1,194.95 $1,493.69 Change to Batch Water Fill

7.08 27.7% $1,207.03 $1,508.79 Switch to PSC Condenser Fan Motor

6.96 29.0% $1,214.73 $1,518.41 Switch to ECM Condenser Fan Motor

6.64 32.2% $1,237.72 $1,547.15 Switch to ECM Pump Motor

6.38 34.9% $1,447.72 $1,809.65 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 hours5

- 45 

### Table 5.10

. 16 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% $1,217 $1,521 Baseline

7.06 7.1% $1,247.01 $1,559 Change to Batch Water Fill

6.61 13.0% $1,259.96 $1,575 Increase compressor EER from 4.86 to 5.25

5.76 24.2% $1,290.47 $1,613 Increase Condenser Length by 39%

5.52 27.4% $1,311.78 $1,640 Switch to ECM Pump Motor

5.30 30.2% $1,521.78 $1,902 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

. 17 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% $2,689 $3,361.76 Baseline

8.45 6.6% $2,706.72 $3,383.40 Switched to PSC Fan Motor

7.49 17.3% $2,734.72 $3,418.40 Increased compressor EER from 4.68 to 5.5

7.28 19.6% $2,742.53 $3,428.16 Increased condenser width by 3.5 inches

7.19 20.5% $2,748.10 $3,435.13 Switched to PM Fan Motor

6.72 25.7% $2,900.76 $3,625.95 Increase evaporator area by 17% and increase condenser height by 4 inchses (with chassis increase)

6.25 31.0% $3,213.95 $4,017.44 Switched to PM 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 hours5

- 46 

### Table 5.10

. 18 : 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% $2,077.94 $2,597.43 Baseline

6.14 7.0% $2,099.03 $2,623.79 Increase Condenser Width by 3 inches

, Increase Tube Rows by 1

5.17 21.6% $2,143.80 $2,679.75 Increase Compressor EER from 5.08 to 6.3

5.01 24.2% $2,164.30 $2,705.38 Switched to PSC Fan Motor

4.92 25.4% $2,177.54 $2,721.93 Switched to PM Fan Motor

4.60 30.2% $2,578.50 $3,223.12 Switched to PM 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



### Table 5.10

. 19 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

10.81 0.0% $1,402.63 $1,753.29 Baseline

9.81 9.3% $1,412.81 $1,766.01 Increased condenser rows to 4 from 3, larger

motor

8.89 17.7% $1,439.28 $1,799.10 Changed compressor EER from 4.7 to 5.5

8.49 21.5% $1,452.39 $1,815.49 Switched to PSC Fan Motor

8.3 23.3% $1,458.86 $1,823.58 Switched to ECM Fan Motor

7.76 28.2% $1,774.35 $2,217.93 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



### 5.11.3 Standard Equations for Extended Harvest Capacity Air

- Cooled Ice

- Making Head
Batch Ice Makers

The current DOE energy conservation standard for large air

- cooled ice

- making head, batch ice makers decreases with harvest capacity well beyond the harvest capacity of existing products. At the current maximum harvest capacity for this equipment class, the standard may not be practical to achieve. When the harvest capacity range for ice makers extends to 4,000 lb/24 hours, this issue would be further exacerbated. Even though no ice makers of this equipment class are currently produced in this capacity range , DOE is considering adopting incremental efficiency levels in the analysis for this equipment class that do not further exacerbate this issue. The incremental efficiency levels would, to the extent possible, level out the standard so that it is independen t of harvest capacity, as is the case for all other high

- harvest

capacity equipment classes. The leveling of the standard is subject to the EPCA anti

- backsliding5

- 47 

### provisions; so for some part of the harvest capacity range, some of the incremental efficiency levels are equal to the current standard.

In the NOPR analysis, DOE followed the approach for the IMH

- A

- Large

- B product class outlined in chapter 5 of the preliminary analysis TSD and the preliminary analysis public meeting. For the engineering analysis , DOE analyzed two different sizes of equipment in the
IMH

- A

- Large

- B equipment class: an 800 lb/ 24 hrs product and a 1500 lb/ 24 hrs product. As
described in Section 5.10 above, DOE conducted a full engineering analysis on both equipment sizes and produced separate efficiency levels for each product. DOE is proposing to consider these two sizes separately as it considers building a standard level for different trial standard levels (TSLs).

DOE had a number of considerations when building proposed standard levels for the
IMH

- A

- B product class, including:

• e nsuring that the new standard levels are shifted appropriately (from the existing standard) at each analyzed equip ment size

• e nsuring the existence of one continuous standard without discontinuities, despite having to lower the standard different

• c reating a constant value s tandard for the largest product sizes

• e nsuring that no backsliding occurs while leveling out the standard

In order to meet these criterion, DOE has taken the following steps to build a proposed standard level, based on the approach proposed during the preliminary analysis:

• For each trial standard level, DOE chose appropriate efficiency levels at each analyzed size (300 lb/day, 800 lb/day, and 1500 lb/day)

.

• For the constant part of the IMH

- A

- B standard, DOE calculated the value of the
current IMH

- A

- B standard at 1500 lb/day reduced by the appropriate efficiency level

at 1500 lb/day

.

• If the constant v alue selected for 1500 lb/day was greater than 4.1 kWh/100 lb of ice, the proposed constant standard would result in backsliding should be extended to 4000 lb/day. In these cases, DOE use d the calculated value as the proposed standard
until it intersecte d with the existing standard, and the proposed standard would follow the existing standard until the limit at 2500 lb/day. In these cases, between 2500 lb/day and 4000 lb/day, DO E set the proposed standard equal to the current standard at 2500 lb/day, whi ch is 4.1 kWh/100 lb of ice

.

• For the capacity range between 50 lb/day and 450 lb/day (designated IMH

- A

- Small

B), DOE set the proposed standard level based on a reduction of the current standard level by the chosen efficiency level for 300 lb/day

.5

- 48 

### • For the capacity range between 450 lb/day and the constant value portio n of the proposed standard, DOE set the slope of the proposed standard by reducing the slope of the existing standard by the chosen efficiency level for the 800 lb/day analyzed
size. DOE then extend ed a line from the proposed IMH

- A

- Small

- B standard to the proposed constant value along a line with the chosen slope. The intersection point of this line with the constant slope define s the boundaries of these two proposed standard equations

.

• To dis tinguish between each portion of the proposed standard, DOE is proposing to add an equipment class labeled IMH

- A

- Extended

- B, which would occupy the proposed constant portion of the graph.

Figure 5.10

. 1 shows the proposed standard levels for the IMH

- A

- B equipment classes.

Figure 5.11

. 1 Illustration of the Proposed Standard Levels for IMH

- A

- B Equipment
Classes5

- 49 

### 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/nipaw eb/TableView.asp#Mid >

4

. Plastic News . Resin Pricing. (Last accessed March 21, 2008.)

< http://www.plasticsnews.com/subscriber/headlines.phtml >6

- i 

### 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 Commercial Refrigeration Equipment

................................ ................................ ...................... 6

- 96

- 1 

### 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 price of the equipme nt. The manufacturer markup

, projected as part of the engineering analysis (chapter 5 of the notice of proposed rulemaking (NOPR)

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 ( National 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 estimates)

. 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 stat e 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 commercial 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 involved. DOE uses mechanical contractor cost data in the estimation of markups as such is the best data available for quantifying the cost of a local contractor. DOE welcomes alternative data for quantifying the 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

- 2 

### 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 project ed 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 M arkups

If the baseline equipment is sold t hrough the N ational A ccount C hannel , the following equation defines the estimated equipment price:

EQP NATL ACCT BASE 

### = MFG BASE 

### × MU NATL ACCT BASE 

### × ( 1 + ST ) Eq. 6

. 1

W here:

MFG BASE 

### = MSP of baseline equipment ($ /unit ), EQP NATL ACCNT BASE 

### = n ational a ccount price to the customer 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 :

EQP WHOLE BASE 

### = MFG BASE 

### × MU WHOLE BASE 

### × ( 1 + ST ) 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 :

EQP CONT BASE 

### = MFG BASE 

### × MU WHOLE BASE 

### × MU MECH CONT BASE 

### × ( 1 + ST ) 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

- 3 

### 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 :

MFG INCR 

### = MFG HIGHER EFF LEVEL 

### − MFG BA SE 

### 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 price using the following equation :

EQP NATL ACCT INCR 

### = MFG INCR 

### × MU NATL ACCT INCR 

### × ( 1 + ST ) 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 :

EQP WHOLE INCR 

### = MFG INCR 

### × MU WHOLE INCR 

### × ( 1 + ST ) Eq. 6

. 6

W here:

EQP WHOLE INCR 

### = projected incremental wholesaler price ($ /unit ), and
MU WHOLE INCR 

### = incremental wholesaler markup

.

EQP CONT INCR 

### = MFG INCR 

### × MU WHOLE INCR 

### × MU MECH CONT INCR 

### × ( 1 + ST ) 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

- 4 

### 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 NOPR phase analysis of this rulemaking

. DOE
used these distribution channel 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 NOPR analyses, the market share previously
allocated to National Account Channel was combined with the Wholesaler Channel.

Stakeholders at the Framework and p reliminary a nalysis p ublic m eeting s in dicat ed that the majority of automatic commercial ice makers are purchased through a three

- step distribution
channel

. DOE’s estimates are listed o n Table 6.2

. 1

.

Table 6.2

. 1 Distribution Channel Market Shares for Baseline and Higher Efficiency Level
Automatic Commercial Ice Makers

National Account Chann el 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 bas eline 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:

EQP CUST BASE 

### = MFG BASE


### × ( WT NATL ACCT BASE 

### × MU NATL ACCT BASE 

### + WT WHOLE BASE 

### × MU WHOLE BASE 

### + WT CONT BASE 

### × MU WHOLE BASE 

### × MU MECH CONT BASE 

### )

× ( 1 + ST )

= MFG BASE 

### × MU OVERALL BASE 

### Eq. 6

. 8

W here:

EQP CUST BASE 

### = customer purchase price for baseline equipment ($ /unit ),
WT NATL ACCNT BASE 

### = estimated market share of baseline equipment sold through the N ational
A ccount C hannel (%),6

- 5 

### 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 

### = estimated over all baseline markup

.
DOE projected o verall incremental markups using:

EQP CUST INCR 

### = MFG INCR


### × ( WT NATL ACCT INCR 

### × MU NATL ACCT INCR 

### + WT WHOLE INCR 

### × MU WHOLE INCR 

### + WT CONT INCR 

### × MU WHO L E INCR 

### × MU MECH CONT INCR 

### ) × ( 1 + ST )

= MFG INCR 

### × MU OVERALL INCR 

### 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 INCR 

### = overall increme ntal markup

.

EQP CUST 

### = EQP CUST BASE 

### + EQP CUST INCR 

### 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 data6

- 6 

### following the same form at as used for the balance sheet data for wholesalers. These balance
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 sheets 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 balance 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 to handle multiple equipment lines ( including refrigerators

, f reez ers , 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 account s, 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 basis for the third assumption is that the HVAC R wholesaler and con tractor 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 eco nomic 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 member 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 7 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 37 in6

- 7 

### direct costs. Labor expenses account for $0. 1 5 1 per dollar of sales revenue, occupancy expenses account for $0. 03 6 , other operating expenses account for $0.0 5 5 , and profit accounts for $0.0 2 1 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 7 $1.0 0 0 Labor Expenses: Salaries and benefits $0. 1 5 1 $0.2 05 Occupancy Expense s : Rent, maintenance, and utilities $0. 03 6 $0.0 49 Other Operating Expenses: Depreciation, advertising, and insurance $0. 05 5 $0.0 7 5
Profit $0. 0 21 $0.0 2 8 Baseline Markup ( MU WHOLE BASE ): Revenue per dollar cost of goods sold * 1. 3 57 Incremental Markup ( MU WHOLE INCR ): Increased revenue per dollar increase cost of goods sold * 1. 103 Source: Heating, Air

- conditioning & Refrigeration Distributors International. 201 2

. 201 2 Profit Report (20 11 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 dividing each cost category in the first data column of Table 6.4

. 1 by $0. 73 7 ( i.e. , equipment expenditure per dollar revenue). The d ata 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 5 to cover labor costs, $0. 0 49 to cover occupancy expenses, $0.0 7 5 for other operating expenses, and $0. 0 28 in profits. This totals to $1. 3 57 in sales revenue earned for every $1.00 spent on equipment costs. Therefore, the wholesale r baseline markup ( MU WHOLE BASE 

### ) is 1. 3 57 ($1.3 57 ÷ $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 increase in wholesaler price will be $1. 3 57 , implying that the in cremental markup is 1

. 3 57 , 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 price, 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 3 . Therefore, the wholesale r increm ental markup ( MU WHOLE INCR 

### ) is 1. 10 3 ($1. 10 3 ÷ $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, maintenance, and repairs. The U.S. Census Bureau data for the PH AC sector include detailed statistics for establishments with payrolls, similar to the data reported by HARDI for wholesalers. The prim ary difference is that the U.S. Census Bureau reports itemized revenues and expenses for the
PH AC industry as a whole in total dollars

, rat her than in typical values for the average or6

- 8 

### 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 typical 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 contra ctor. 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 * 1.475 Incremental Markup ( MU MECH CONT INCR ): Increased revenue per dollar increase cost of goods sold 1. 18 5

Source s : 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 round ed 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 earns $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 5 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. 4 7 5 ($1. 47 5 ÷ $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 increase in mechanical contractor price will be $1. 47 5

, an incremental markup of 1. 47 5 , 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 l ead 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, fo r a $1.00 increase in the equipment price, the typical mechanical contrac tor price will increase by $1. 18 5 , giving a
mechanical contractor incremental markup ( MU MECH CONT INCR 

### ) of 1. 18 5 ($1. 18 5 ÷ $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 ma rkups developed from U

. S

. Census Bureau data with a probability distribution based on a s tate

- by

- s tate6

- 9 

### 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 t he mechanical contractor baseline and incremental markups. As
mentioned in section 6.1 , the Contractor Channel markup index was varied by state. The baseline
and in cremental markups in Table 6.5

. 1 are the national average markup values for the
Contractor Channel . In the LCC analysis (TSD chapter 8), these national average values were multiplied by the state markup index to obtain the Contractor Channel markups for a particular
state. 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 efficiency levels analyzed in this rulemaking.

Figure 6.5

. 1 Cumulative Distribution of Mechanical Contractor Markups for Automatic Commercial Ice Making Equipment

6.6 ESTIMATION OF NATIONAL ACCOUNT MARKUPS

National accounts are generally large accounts

— large enough to circumvent the
W holesaler C hannel and negotiate d irectly 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 distribution chain

, DOE assum ed that the resulting e quipment price increase is one half
of that realized from distri bution 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 State6

- 10 

### 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 57 , the resulting baseline

customer eq uipment price for sales through a wholesaler ( without sales taxes ) is $ 1

, 3 57 ($ 1,000

× 1. 3 57 ). The dollar value increase due to the above distribution channel markups is $ 3 57 ($ 1

, 3 57

− $ 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 78.50

. The resulting equipment price is $ 1

, 1 78.50 ($ 1 78.5 0 + $ 1,000 ) , which results in a national account
baseline markup of 1. 1 78 ($ 1, 1 78 ÷ $ 1,000 ). U sing a value of 1. 103 for the wholesaler incremental markup and a similar calculation results in a total national account incremental markup of 1. 0 52 ($1, 0 51.50 ÷ $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 tax 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. 24 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. 15 percent.

Table 6.7

. 1 Combined State and Local Sales Tax Rates

State Sales Tax Stat e Sales Tax State Sales Tax
Alabama 8. 55 % Kentucky 6.00% North Dakota 5. 9 5%
Alaska 1. 35 % Louisiana 8.75% Ohio 6.80%
Arizona 7.2 % Maine 5.00% Oklahoma 8. 40 %
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. 20 %
Delaware 0.00% Mississippi 7.00% South Dakota 5. 40 %
Dist. of Col. 6.00% Missouri 7. 45 % 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 6.0 5 %
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 July 10, 20136

- 11 

### 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 1.357 2.000 1.178 1. 7 56

Sales Tax 1.071 1.071 1.071 1.071 Overall Multiplier * 1. 45 4 2. 14 3 1. 26 3 1. 881 *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 1.103 1.307 1.052 1. 22 9

Sales Tax 1.071 1.071 1.071 1.071 Overall Multiplier * 1. 18 2 1. 400 1. 12 7 1. 31 7 *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. 881 , or $1

, 881

. If the increment al MSP of the equipment at a higher efficiency level is $100, then DOE obtained the incremental customer purchase price of this equipment by multiplying the MSP by the overall incremental markup of 1. 317 to obtain a n incremental customer purchase price of $ 1 3 1

. 70 . The customer purchase price of this higher efficiency equi pment is the sum of the baseline price and the increment, or $ 2, 012.70 ($ 1, 881 + $ 13 1

. 70 ).6

- 12 

### 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/tableserv ices/jsf/pages/productview.xhtml?pid=ECN_ 2007_US_23A1&prodType=table >

2

. Heating, Airconditioning & Refrigeration Distributors International. 201 2 Profit Report (20 11 Data) . 201 2 . Columbus, OH.

3

. U.S. Census Bureau. 2007 Economic Census, Release Date: 1 0/30/2009, 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 October 29, 2013 .)

< http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- fds_name=EC0700A1&

- geo_id=&

- _skip=300&

- ds_name=EC0731I1&

- _lang=en >

4

. U.S. Census Bureau. 2007 Eco nomic 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, 2013 .)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_ 2007_US_23I1&prodType=table >

6

. Pindyck, R. and Rubinfeld, D. Microeconomics. s.l. : Prentice Hall, 2000.

7

. The Sale Tax Clearinghouse

. State sales tax rates along with average combined city and
county rates

. (Last accessed July 10, 2013.) < http://www.thestc.com/STrates.stm >7

- i 

### 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

LIST OF TABLES

Table 7.2.1 Total Annual Energy Usage

................................ ................................ ...................... 7

- 2 Table 7.3.1 Total Annual Water Consumption

................................ ................................ ............ 7

- 4 Table 7.4.1 Large Batch Equipment Class Weighting Factors

................................ .................... 7

- 57

- 1 

### CHAPTER 7. ENERGY AND WATER USE ANALYSIS

7.1 INTRODUCTION

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 con sumption (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 notice of proposed rule (NOPR) technical support document (TSD)) and national impacts analys i s ( c hapter 10 of the NOPR 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 CONSUMPTION

For the NOPR 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 analys is (see TSD chapter 5) , assumed equipment
utilization factor s , and maximum daily harvest rates for each combination 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

- hour 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 value against which energy saving percentages for higher efficiency levels are estimat ed. A more complete breakdown of how the incremental perce ntage savings values are derived is described in 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
class , does not vary by efficiency level within an equipment c lass. The values used as estimat es7

- 2 

### for energy use ( E i,j 

### ) and 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 equipment 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 NOPR analysis, this value is estimated at 50 percent across all equipmen t classes and efficiency levels

. 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. NOPR test data indicates that standby use represents an extremely small level of usage. As a conservative assumption, DOE used a v alue of 5 W for all equipment classes, but believes the average value may actually be lower. The st andby loss assumption translates to energy consumption of 0. 06 kWh per day

.

Calculated annual energy values for each equipment class and available efficiency levels are provided on Table 7.2

. 1

. For each equipment class listed, AEC values for the maximum available energy efficiency levels are included in the column with the highest numbered efficiency level for that equipment class.

Table 7.2

. 1 Total Annual Energy Usage

Equipment Class * Total Annual Energy Usage ** kWh/year
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 3,389 3,052 2,884 2,716 2,547 2,400

– IMH

- W

- Med

- B 7,227 6,507 6,147 5,786 5,691

– – IMH

- W

- Large

- B † 12,870 11,585 10,943 10,783

– – – IMH

- W

- Large

- B

- 1 10,972 9,877 9,329 9,147

– – – IMH

- W

- Large

- B

- 2 19,002 17,104 16,155 16,067

– – – IMH

- A

- Small

- B † 4,227 3,806 3,596 3,386 3,176 2,965 2,909

IMH

- A

- Large

- B 9,669 8,704 8,222 7,739 7,369 7,041

– IMH

- A

- Large

- B

- 1 8,797 7,919 7,480 7,042 6,603 6,213

– IMH

- A

- Large

- B

- 2 14,366 12,932 12,215 11,498

– – – RCU

- Large

- B † 14,508 13,205 12,335 11,611 11,526

– – RCU

- Large

- B

- 1 13,983 12,727 11,889 11,191 11,108

– – RCU

- Large

- B

- 2 22,360 20,349 19,009 17,892 17,779

– – SCU

- W

- Large

- B 4,183 3,892 3,559 3,351 3,143 2,935 2,925
SCU

- A

- Small

- B 2,600 2,419 2,213 2,084 1,955 1,826 1,585
SCU

- A

- Large

- B 3,599 3,349 3,062 2,884 2,705 2,526 2,351

IMH

- A

- Small

- C 5,142 4,630 4,374 4,118 3,862 3,606 3,555

IMH

- A

- Large

- C 9,899 8,911 8,417 7,923 7,430 6,936 6,912
SCU

- A

- Small

- C 2,192 2,040 1,866 1,758 1,649 1,580

–

* See c hapter 5 for a description of these abbreviations.
** “

– ” 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 

### 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 p otable 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 maximum 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)

, 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

.7

- 4 

### Table 7.3

. 1 Total A nnual Water Consumption

Equipment Class * Annual water use per unity by efficiency level gal/yr **

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 96,661 96,661 96,661 94,471 94,471 94,471

– IMH

- W

- Med

- B 267,901 267,901 267,901 267,901 267,901

– – IMH

- W

- Large

- B † 606,709 606,709 606,709 606,709

– – – IMH

- W

- Large

- B

- 1 509,996 509,996 509,996 509,996

– – – IMH

- W

- Large

- B

- 2 919,107 919,107 919,107 919,107

– – – IMH

- A

- Small

- B 14,016 14,016 14,016 14,016 14,016 14,016 14,016

IMH

- A

- Large

- B † 36,525 36,525 36,525 33,200 33,200 33,200

– IMH

- A

- Large

- B

- 1 33,142 33,142 33,142 29,200 29,200 29,200

– IMH

- A

- Large

- B

- 2 54,750 54,750 54,750 54,750

– – RCU

- Large

- B † 56,728 56,728 56,728 56,728 56,728

– – RCU

- Large

- B

- 1 54,750 54,750 54,750 54,750 54,750

– – RCU

- Large

- B

- 2 86,286 86,286 86,286 86,286 86,286

– – SCU

- W

- Large

- B 88,640 83,165 83,165 83,165 83,165 83,165 83,165

SCU

- A

- Small

- B 6,826 6,826 6,826 6,826 6,826 4,015 4,015

SCU

- A

- Large

- B 10,950 10,950 10,950 7,300 7,300 7,300 7,300

IMH

- A

- Small

- C 6,789 6,789 6,789 6,789 6,789 6,789 6,789

IMH

- A

- Large

- C 20,352 20,352 20,352 20,352 20,352 20,352 20,352

SCU

- A

- Small

- C 2,409 2,409 2,409 2,409 2,409 2,409

–

* See chapter 5 for a description of these abbreviations.
** “

– ” 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 FA CTORS FOR LARGE BATC H EQUIPMENT 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 NOPR 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
factors calculated from the relative shipments of automatic commercial ice makers in each size category. The weights are shown on Table 7.4

. 1

.7

- 5 

### Table 7.4

. 1 Large Batch Equipment Class Weighting Factors Equipment Class and Typical Unit Weight
%

IMH

- W

- Large

- B

IMH

- W

- Large

- B

- 1 76.4

IMH

- W

- Large

- B

- 2 23.6

IMH

- A

- Large

- B

IMH

- A

- Large

- B

- 1 84.3

IMH

- A

- Large

- B

- 2 15.7
RCU

- Large

- B
RCU

- Large

- B

- 1 93.7
RCU

- Large

- B

- 2 6.38

- i 

### 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

- 4
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

- 8
8.2.2.2 Candidate Standard Level Energy Consumption and Manufacturer Selling
Price Increases

................................ ................................ ................................ ...... 8

- 10
8.2.2.3 Overall Markup

................................ ................................ ...................... 8

- 12
8.2.2.4 Installation Cost

................................ ................................ ..................... 8

- 12
8.2.2.5 Equipment Price Trends

................................ ................................ ......... 8

- 13
8.2.2.6 Weighted Average Total Installed Cost

................................ ................. 8

- 14
8.2.3 Operating

- Cost Inputs

................................ ................................ ........................... 8

- 17
8.2.3.1 Electricity Price Analysis

................................ ................................ ....... 8

- 17
8.2.3.2 Electricity Price Trend

................................ ................................ ........... 8

- 19
8.2.3.3 Water Cost

................................ ................................ ............................. 8

- 20
8.2.3.4 Water Price Trend

................................ ................................ .................. 8

- 21
8.2.3.5 Repair Cost

................................ ................................ ............................. 8

- 21
8.2.3. 6 Maintenance Cost

................................ ................................ ................... 8

- 22
8.2.3.7 Lifetime

................................ ................................ ................................ .. 8

- 23
8.2.3.8 Discount Rate

................................ ................................ ......................... 8

- 24
8.2. 3.9 Compliance Date of Standard

................................ ................................ 8

- 27
8.3 PAYBACK PERIOD INPUT S

................................ ................................ ......................... 8

- 28
8.3.1 Definition

................................ ................................ ................................ .............. 8

- 28
8.3.2 Inpu ts

................................ ................................ ................................ ..................... 8

- 28
8.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS

................................ ......... 8

- 28
8.4.1 Life

- Cycle Cost Results

................................ ................................ ........................ 8

- 29
8.4.2 Payback Period Results

................................ ................................ ......................... 8

- 32
8.4.3 Rebuttable Presumption Payback Period

................................ .............................. 8

- 33
8.5 DETAILED RESULTS

................................ ................................ ................................ .... 8

- 34
REFERENCES

................................ ................................ ................................ .......................... 8

- 36

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

- 98

- ii 

### Table 8.2.3 Baseline Energy Consumption Levels and Est imated MSP Values for the 12 Primary Equipment Classes

................................ ................................ ................................ 8

- 10 Table 8.2.4 Baseline and Incremental Manufacturer Selling Prices for t he 12 Primary Equipment
Classes*

................................ ................................ ................................ ................ 8

- 11 Table 8.2.5 Energy Consumption Values for the 12 Primary Equipment Classes, by Efficiency
Le vel*

................................ ................................ ................................ ................... 8

- 11 Table 8.2.6 Installation Cost by Equipment Class, U.S. Average

................................ ............. 8

- 12 Table 8.2.7 Installation Cost Indices (National Value = 100.0)

................................ ................ 8

- 13
Table 8.2.8 Costs and Markups for Determination of Weighted Average Total Installed Costs (IMH

- A

- Small

- B)

................................ ................................ ................................ . 8

- 14 Table 8.2.9 Weighted Average Equipment Price, Installation Cost, and Total Installed Costs for
IMH

- A

- Small

- B at U.S. Average Conditions (2012$)

................................ ......... 8

- 16 Table 8.2.10 Inputs Used to Determine Operating Costs

................................ .......................... 8

- 17 Table 8.2.11 Commercial Electricity Prices by State (2012 cents/kWh)

................................ .. 8

- 18
Table 8.2.12 Derived National Average Commercial Electricity Prices and Ratios by Business Type

................................ ................................ ................................ ...................... 8

- 19 Table 8.2.13 Commercial Water Prices by State (201 2 $/1,000 gallons )

................................ ... 8

- 21 Table 8.2.14 Repair Cost by Equipment Class

................................ ................................ .......... 8

- 22 Table 8.2.15 Annual Maintenance Costs by Equipment Class and Efficiency Level

............... 8

- 23 Table 8.2.16 Probability of Equipment Failure by Year

................................ ............................ 8

- 24 Table 8.2.17 Derivation of Typical Discount Ra tes by Building Type

................................ ..... 8

- 27 Table 8.4.1 LCC Savings Distribution Results for Equipment Class IMH

- A

- Small

- B*

.......... 8

- 30 Table 8.4.2 Mean LCC Savings for All Equipment Classes and Efficiency Levels

................. 8

- 31 Table 8.4.3 Median LCC Savings for All Equipment Classes and Efficiency Levels

.............. 8

- 31 Table 8.4.4 Payback Period Distribution Results for IMH

- A

- Small

- B

................................ ..... 8

- 32 Table 8.4.5 Median Payback Period for All Equipment Classes and Efficiency Levels

........... 8

- 33 Table 8.4.6 Rebuttable Presumption Payback Periods by Efficiency Level and Equipment Class

................................ ................................ ................................ .............................. 8

- 34 Table 8.5.1 Summary of Results of LCC and PBP Analysis for IMH

- A

- Small

- B Equipment
Class

................................ ................................ ................................ ...................... 8

- 35

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

- 20 Figure 8.4.1 LCC and Installed Cost Variation over Efficiency Levels for IMH

- A

- Small

- B Equipment Class

................................ ................................ ................................ ... 8

- 29 Figure 8.4.2 LCC Savings Ranges by Efficiency Level for Equipment Class
IMH

- A

- Small

- B

................................ ................................ ................................ .... 8

- 30 Figure 8.4.3 Payback Period for All Efficiency Levels for the Equipment Class IMH

- A

- Small

- B

................................ ................................ ................................ .............................. 8

- 328

- 1 

### 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 purcha se 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 over 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 est imated 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
attracti ve 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 series of Microsoft
Excel spreadsheets

, which are available at www1.eere.energy.gov/buildings/appliance_standards/ commercial/automatic_ice_making_equipment.html

. A ppendix 8A includes i nstructions for using the spreadsheets. A ppendix 8B presents the d etailed r esults. 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 notice of proposed rulemaking (NOPR) 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 choose s a maximum of seven levels

,

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 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

- 2 

### r eferred to herein as efficiency levels

. 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 many batch

- type equipment classes, l evel 1 equipment is equipment that just meets the efficiency standards for cube

- type equipment set by the Energy Polic y 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 efficiency 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 NOPR 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 production 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 NOPR 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 NOPR TSD )

. I nsta llation costs vary by state depending on the prevailing labor rates.

Operating costs for automatic commercial ice makers are the sum of maintenance costs, repair costs

, energy costs , and water costs

. Customers incur t hese costs over the life of the equipm ent

. To facilitate cost comparisons, DOE discount ed operating costs to the discount year ( 201 8

, which is assumed to be the effective date of the standards that will be established as part of 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 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

. T he LCC of higher efficiency equ ipment 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 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 mainten an ce costs used herein are annualized est imates 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 distribution 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.8

- 3 

### DOE estimated m any inputs for the LCC and PBP analysis from the best available data

, and in some cases D OE used inputs that are generally accepted values within the automatic
commercial ice maker industry . In general, there is uncertainty associated with most of the 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 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 t he 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 co mmercially available Excel add

- in for performing 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 e fficiency l evel 1 represents a potential new standard , or a c andidate s tandard l evel

. Usually, the market 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 calculate d the market shares of the efficiency levels using a method
described in NOPR TSD chapter 10

.
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 care

, lodging

, foodservice

, retail

, education

, food sales

, and offices

.
There is a general consensus among industry stakeholders that the 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, DOE assume d a distribution of equipment 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 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 explicitly 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

- 4 

### 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

.
The primary input s 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 automatic commercial ice

- making 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.

• 

### Candidate s tandard 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. NOPR 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, overhe ad, 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 (NOPR TSD chapter 7).

• 

### Equipment water consumption : T he amount of condenser water ( used to cool refrigerant in the open

- loop condenser system) 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 wer e used to
calculate annual water consumption in the energy and water usage analysis (NOPR 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 2012

. DOE then adjusted the average commercial prices to reflect the fact that the

seven types of businesses analyzed pay electricity prices that are different from8

- 5 

### average commercial prices. S ection 8.2.3.1 d etails the development of electricity prices and the data so urces used

.

• 

### Electricity price trends : DOE used t he EIA’s Annual Energy Outlook 2013 ( AEO201 3 ) 1 

### to forecast electricity prices. For the results presented in this chapter, DOE used the regional prices from the AEO201 3 reference case to forecast future electricity price

• 

### Water and wastewater price s : DOE combined the prices for w ater 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 ( 1973

– 201 2 ) in deve loping a real growth rate for water and wastewater price forecasts.

• 

### Maintenance 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 automatic comm ercial ice

- making 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 NOPR TSD for details on the calculation of the inputs

.8

- 6 

### 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 Cost8

- 7 

### Table 8.1

. 1 Summary Information of Inputs for the De termination of Life

- Cycle Cost and Payback Period
Input Description TSD Chapter Reference Total Installed Cost Primary Inputs
Baseline MSP Varies with equipment class

. Chapter 5
Candidate s tandard level
MSP increases Var y with equipment class and candidate standard level
within a n equipment class

. Chapter 5

Markups and sales tax Markups v ar y with distribution channel and sales tax
varies with location (state) where equipment is installed. Chapter 6

Installation price Var ies with equipment class and state

. Chapter 8 Operating Cost Primary Inputs Equipment electricity and

water consumption Varies with equipment class and candidate standard 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 candidate standard 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 in

s ections 8.2 and 8.3

.

8.2 LIFE

- CYCLE COST INPUTS

8.2.1 Definition

Life

- cycle cost is the total customer cost 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

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.

b Though the average equipment life is 8.5 years, the model uses a range of years to calculate the equipment lifetime.8

- 8 

### Because DOE gathered most of its data for the LCC analysis in 201 2

, DOE expresse d all
costs in 20 1 2 $. 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 presumed compliance date for standards set as a result of this rulemaking

.

8.2.2 Total Installed

- 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 NOPR TSD chapter 6

.
The remainder of this section provides information about the variables DOE used to
calculate the total installed cost for automatic commercial ice

- making equipment

. Table 8.2.1
shows 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
Candidate s tandard l evel manufacturer selling price increases ($) Wholesaler markup
C ontractor markup National account markup Sales tax ($) Installation cost ($)



### 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 s tandards)

. The MSP includes a markup that is applied to convert
MPC to a n MSP

. DOE developed MSP values for the 12 primary equipment class es (see NOPR
TSD chapter 5)

. Table 8.2.2 lists t he 1 2 primary equipment class es that DOE evaluated during
the NOPR analysis of the current rulemaking

.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

- B

- 1) Ice

- Making Head Air ≥450 and <875 † Batch

IMH

- A

- Extended

- B* , ** (also IMH

- A

- Large

- B

- 2) Ice

- Making Head Air ≥875 ± 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
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 units, one at the lower end of the harvest range and one near the high end of the harvest range in which a significant number of units are available. In the LCC model, the low and high harvest rate models were denoted simply as B

- 1 and B

- 2. 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. In the NOPR analyses and the NOPR document, DOE considers the question of dividing this into two classes, which could be
considered either “Large” and “Very Large” or “Medium” and “Large.” In the LCC and national impact analysis modeling, the units were denoted as B

- 1 and B

- 2. † Upper limit corresponding with the proposed standard level. 

### ‡ RCU

- Large

- B equipment w as combined into one cl ass. The classes were disaggregated to develop proposed standards by inclusion of an allowance related to lines carrying refrigerants.



### Nine o f the twelve 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 three primary equipment classes have not yet been subject to a standard se t by legislation 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 equipment 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 typical 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 c hapter 7.

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 12 primary equipment
class es and the typical units for the 3 large batch classes

. NOPR TSD c hapter 5 explains how energy use and MSP var y by harvest capacity. Table 8.2.3 a lso 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. standards

, DOE8

- 10 

### set the baseline at levels approximating the least

- efficient equipment that could potentially be
available. Baseline energy consumption values presented in this sectio n provide energy use per 100 lb of ice produced

. C hapter 7 discusses the methodology to calculate annual energy consumption using these values.

Table 8.2

. 3 Baseline E nergy C onsumption L evels and Estimated MSP V alues for the 12
P rimary E quipment C lasses

Equipment Class Baseline Energy Consumption
kWh/100 lb ice Manufacturer Selling Price Baseline Type

IMH

- W

- Small

- B 6.15 $1,116 Standards Baseline

IMH

- W

- Med

- B 4.65 $1,920 Standards Baseline

IMH

- W

- Large

- B 4.00 $2 ,754 Standards Baseline

IMH

- W

- Large

- B

- 1 4.00 $2,1 65 Standards Baseline

IMH

- W

- Large

- B

- 2 4.00 $4,656 Standards Baseline *

IMH

- A

- Small

- B 7.68 $1,138 Standards Baseline

IMH

- A

- Large

- B 5.8 9 $1, 887 Standards Baseline

IMH

- A

- Large

- B

- 1 6.01 $1,856 Standards Baseline

IMH

- A

- Large

- B

- 2 5.24 $2,053 Standards Baseline
RCU

- Large

- B 5.10 $2, 544 Standards Baseline
RCU

- Large

- B

- 1 5.10 $2,448 Standards Baseline
RCU

- Large

- B

- 2 5.10 $3,979 Standards Baseline
SCU

- W

- Large

- B 7.60 $1,521 Standards Baseline
SCU

- A

- Small

- B 12.84 $1,306 Standards Baseline
SCU

- A

- Large

- B 9.80 $1,393 Standards Baseline

IMH

- A

- Small

- C 9.05 $3,362 Engineering Baseline

IMH

- A

- Large

- C 6.60 $2,597 Engineering Baseline
SCU

- A

- Small

- C 10.81 $1,753 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 Candidate Standard Level Energy Consumption and Manufacturer Selling Price Increases

The candidate standard level MSP increase is the change in MSP associated with producing equipment at higher efficiency levels. DOE estimated 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 3 large batch classes that DOE selected for the engineering analysis

. As discussed in Chapter 7, ice m akers are assumed to operate in ice

- making mode 50 percent of the time and in standby mode 50 percent of time. In standby mode, all equipment was assumed to use 5 watts continuously, or roughly 0.06 kWh per day

.8

- 11 

### Table 8.2

. 4 Baseline and Incremental Manufacturer Selling Prices for the 1 2 Primary Equipment Classes *

Equipment Class Baseline
Level 1 Increase in MSP by Efficiency Level 2012$

Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B $1,116 $25 $46 $90 $169 $463

IMH

- W

- Med

- B $1,920 $33 $67 $349 $360

IMH

- W

- Large

- B** $2, 754 $63 $497 $539

IMH

- W

- Large

- B

- 1 $2, 165 $57 $449 $503

IMH

- W

- Large

- B

- 2 $4,656 $81 $651 $655

IMH

- A

- Small

- B $1,138 $32 $56 $80 $108 $395 $406

IMH

- A

- Large

- B** $1,887 $32 $64 $167 $212 $459

IMH

- A

- Large

- B

- 1 $1,856 $30 $48 $98 $152 $445

IMH

- A

- Large

- B

- 2 $2,053 $44 $148 $536

RCU

- Large

- B** $2,544 $40 $105 $507 $533

RCU

- Large

- B

- 1 $2,448 $35 $95 $482 $508

RCU

- Large

- B

- 2 $3,979 $111 $248 $876 $898

SCU

- W

- Large

- B $1,521 $38 $60 $77 $110 $380 $381

SCU

- A

- Small

- B $1,306 $18 $40 $55 $69 $112 $424

SCU

- A

- Large

- B $1,393 $24 $50 $86 $99 $148 $416

IMH

- A

- Small

- C $3,362 $33 $49 $71 $238 $619 $656

IMH

- A

- Large

- C $2,597 $38 $57 $76 $123 $625 $626

SCU

- A

- Small

- C $1,753 $13 $35 $56 $452 $465

* 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 c hapter 7.



### Table 8.2

. 5 Energy C onsumption V alues for the 1 2 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 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 6.15 5.54 5.23 4.92 4.61 4.34

IMH

- W

- Med

- B 4.65 4.18 3.95 3.72 3.65

IMH

- W

- Large

- B ** 4.00 3.60 3.40 3.34

IMH

- W

- Large

- B

- 1 4.00 3.60 3.40 3.33

IMH

- W

- Large

- B

- 2 4.00 3.60 3.40 3.38

IMH

- A

- Small

- B 7.68 6.91 6.53 6.14 5.76 5.38 5.27

IMH

- A

- Large

- B** 5.89 5.30 5.01 4.71 4.46 4.23

IMH

- A

- Large

- B

- 1 6.01 5.41 5.11 4.81 4.51 4.24

IMH

- A

- Large

- B

- 2 5.24 4.72 4.45 4.19
RCU

- Large

- B ** 5.10 4.64 4.34 4.08 4.05
RCU

- Large

- B

- 1 5.10 4.64 4.34 4.08 4.05
RCU

- Large

- B

- 2 5.10 4.64 4.34 4.08 4.05
SCU

- W

- Large

- B 7.60 7.07 6.46 6.08 5.70 5.32 5.30
SCU

- A

- Small

- B 12.84 11.94 10.91 10.27 9.63 8.99 7.79
SCU

- A

- Large

- B 9.80 9.11 8.33 7.84 7.35 6.86 6.38

IMH

- A

- Small

- C 9.05 8.15 7.69 7.24 6.79 6.34 6.25

IMH

- A

- Large

- C 6.60 5.94 5.61 5.28 4.95 4.62 4.60
SCU

- A

- Small

- C 10.81 10.05 9.19 8.65 8.11 7.76

* 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 c hapter 7.8

- 12 

### 8.2.2.3 Overall Markup

As discussed in NOPR 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 used these markup values in the LCC and PBP analy sis 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 estimating the cost of installation. DOE defines ins tallation cost as a one

- time
fixed cost that incorporate s the labor and materials required to fully install an automatic
commercial ice maker. For the NOPR analysis, DOE assum ed that the engineering design options do not impact the installation cost within an equipment class and

, therefore, within a given equipment class

, the installation cost will not vary with efficiency level

. I nstallation cost may vary from one equipment class to another. If engineering design options change, DOE can vary installation c ost with efficiency level if technologies warrant an adjustment. DOE estimated

m aterial and labor cost to install equipment based on RS Means cost estimation data 3 

### and on telephone conservations with contractors. Estimated installation costs vary by equipment class and by s tate. Table 8.2

. 6 provides a list of the estimated U.S. average installation cost for each primary equipment class.

Table 8. 2

. 6 Installation Cost by Equipment Class, U.S. Average

Equipment Class Installation Cost 2012$

IMH

- W

- Small

- B $300

IMH

- W

- Med

- B $600

IMH

- W

- Large

- B $1,000

IMH

- W

- Large

- B

- 1 $1,000

IMH

- W

- Large

- B

- 2 $1,000

IMH

- A

- Small

- B $300

IMH

- A

- Large

- B $600

IMH

- A

- Large

- B

- 1 $600

IMH

- A

- Large

- B

- 2 $600

RCU

- ***

- Large

- B $1,500

RCU

- ***

- Large

- B

- 1 $1,500

RCU

- ***

- Large

- B

- 2 $1,500

SCU

- W

- Large

- B $600

SCU

- A

- Small

- B $300

SCU

- A

- Large

- B $600

IMH

- A

- Small

- C $300

IMH

- A

- Large

- C $600

S CU

- A

- Small

- C $3008

- 13 

### Table 8.2

. 7 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 the 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 using the relative city

- to

total population values, using the 2012 population in each city

. 5 

### DOE used state

- level p opulation weights for 20 1 2 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

. 7 Installation Cost Indices (National Value = 100.0)

State Index State Index State Index

Alabama 82.9 Kentucky 94.0 North Dakota 85.5
Alaska 104.9 Louisiana 86.0 Ohio 99.7
Arizona 93.7 Maine 92.7 Oklahoma 80.3
Arkansas 84.1 Maryland 95.5 Oregon 102.6
California 113.0 Massachusetts 111.2 Pennsylvania 110.8
Colorado 93.5 Michigan 103.1 Rhode Island 107.4
Connecticut 110.3 Minnesota 108.7 South Carolina 75.9
Delaware 110.1 Mississippi 83.6 South Dakota 78.2
Dist. of Columbia 100.7 Missouri 102.3 Tennessee 90.7
Florida 89.0 Montana 91.6 Texas 85.1 Georgia 88.2 Nebraska 93.4 Utah 90.6
Hawaii 108.3 Nevada 102.4 Vermont 90.8
Idaho 89.8 New Hampshire 98.1 Virginia 91.6
Illinois 115.1 New Jersey 113.9 Washington 106.0
Indiana 94.6 New Mexico 89.9 West Virginia 97.2

Iowa 94.2 New York 127.8 Wisconsin 101.2

Kansas 89.6 North Carolina 83.6 Wyoming 88.7



### 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 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 determining whether equipment prices h ave trended
downward in real terms. The methodology examines so

- called price or experiential learning, 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 exp eriential 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 (2012) until the start of the LCC analysis (201 8 ). The price trend
factor used in the LCC and P BP a nalysis was 0.99 6 2 , which means MSPs shown on Table 8.2

. 4 were reduced by 0. 38 percent in the development of total installed costs.8

- 14 

### 8.2.2.6 Weighted Average Total Installed Cost

As presented in Eq. 8

. 2 , the total installed cost is the sum of the equipment price and the
installation cost. DOE derived the customer equipment price for any given standard level by multiplying the baseline MSP by the baseline markup and adding to it the product of the
incremental MSP and the incremental 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 MSP 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 8.2.8 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

. 8 Costs and Markups for De termination of Weighted Average Total Installed Costs ( IMH

- A

- Small

- B )

Variable Weighted Average or Mean Value
Baseline MSP $1,137.59

Standard

- Level MSP Increase (Efficiency Level 4) $79.74

Baseline Markup Factor with Sales Tax 1.881
Incremental Markup Factor with Sales Tax 1.317
Installation Cost

– Baseline * $300 Installation Cost Factor, for U.S. Average 1.000
Price Trend Factor ** 0.996 2

* Installation cost appl ied to the baseline unit, with no incremental installation cost.
** Price trend ( learning ) factor for 2018. See a ppendix 8D.8

- 15 

### The calculation below for the baseline ( l evel 1) and for a higher efficiency level ( l evel 4) IMH

A

- Small

- B equipment class illustra te s how DOE deriv ed the weighted average total installed cost
based on the data shown in Table 8.2.8

. For the baseline product, DOE calculat ed the total installed cost at national average conditions as follows : c



### IC BASE IMH

− A

− Small

− B 

### = EQP BASE IMH

− A

− Small

− B 

### + I N ST BASE IMH

− A

− Small

− B 

### × ISTINDEX

= MFG BASE IMH

− A

− Small

− B 

### × Price Trend Factor Analysis Year



### × MU BASE IMH

− A

− Small

− B 

### + INST BASE IMH

− A

− Small

− B


### × ISTINDEX

= $ 1,137. 59 × 0

. 9962 × 1.881 3 + $ 300 × 1.00

= $ 2,13 2.02 + $ 300

= $ 2,43 2.02 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 equip ment at baseline efficiency level ($),
Price Trend Factor Analysis Year 

### = Price trend factor for the 2012

– 2018 period, or 0.996 2

, MU BASE IMH

- A

- Small

- B 

### = overall baseline markup for equipment class IMH

- A

- Small

- B

, including the national average 1.072 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

- 16 

### As an example, DOE calculated the national average , l evel 4

, total installed cost ( IC IMH

- A

Small

- B LEVEL4 

### ) as follows: d



### IC IMH

− A

− Small

− B LEVEL 4



### = EQP IMH

− A

− Small

− B LEVEL 4 

### + INST IMH

− A

− Small

− B LEVEL 4 

### × ISTINDEX

= MFG BASE IMH

− A

− Small

− B 

### × Price Trend Factor Analysis Year 

### ×
MU BASE IMH

− A

− Small

− B 

### + ∆ MFG IMH

− A

− Small

− B LEVEL 4 

### × Price Tren d Factor Analysis Year 

### ×
MU IMH

− A

− Small

− B LEVEL 4 

### + INST IMH

− A

− Small

− B LEVEL 4 

### × ISTINDEX

= $ 1,13 7

. 59 × 0.996 2 × 1.881 3 + $ 79. 7 4 × 0.996 2 × 1.317 1 +

$ 3 00 × 1.000

= $ 2,536. 64 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 8.2.9 presents the weighted average equ ipment 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

. 9 Weighted Average Equipment Price, Installation Cost, and Total Installed

Costs for IMH

- A

- Small

- B at U.S. Average C onditions ( 201 2 $)

Efficiency Level Equipment Price (MSP) Installation Cost Total Installed Cost *

1 (Baseline) $1,137.59 $300.00 $2,432.04

2 $1,170.03 $300.00 $2,474.60

3 $1,193.68 $300.00 $2,505.63

4 $1,217.33 $300.00 $2,536.67

5 $1,245.31 $300.00 $2,573.38

6 $1,532.96 $300.00 $2,950.79

7 $1,543.15 $300.00 $2,964.16

* 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

- 17 

### 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 8.2.10 shows the inputs used to
determin e operating costs.

Table 8.2

. 10 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 2 , with monthly data available for the year

. DOE utilized the monthly data to estimate annual average 2012 electricity prices

. Table 8.2.11 provides the 2012 electricity prices.8

- 18 

### Table 8.2

. 11 Commercial Electricity Prices by State ( 201 2 cents/k Wh )

State Commercial Electricity Price
cents/kWh State Commercial Electricity Price
cents/kWh State Commercial Electricity Price
cents/kWh
Alabama 10.58 Kentucky 8.66 North Dakota 7.98
Alaska 14.79 Louisiana 7.79 Ohio 9.47
Arizona 9.54 Maine 11.58 Oklahoma 7.26
Arkansas 7.68 Maryland 10.52 Oregon 8.34
California 13.60 Massachusetts 13.97 Pennsylvania 9.37
Colorado 9.34 Michigan 10.95 Rhode Island 12.04
Connecticut 14.70 Minnesota 8.86 South Carolina 9.57
Delaware 10.11 Mississippi 9.28 South Dakota 8.01
Dist. of Col. 12.00 Missouri 8.16 Tennessee 10.29
Florida 9.76 Montana 9.16 Texas 8.17 Georgia 9.47 Nebraska 8.40 Utah 8.05
Hawaii 34.83 Nevada 8.86 Vermont 14.30
Idaho 6.83 New Hampshire 13.40 Virginia 8.11
Illinois 8.19 New Jersey 12.83 Washington 7.67
Indiana 9.07 New Mexico 9.30 West Virginia 8.42

Iowa 8.00 New York 15.08 Wisconsin 10.54

Kansas 9.13 North Carolina 8.61 Wyoming 8.23



### 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 building 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 :

EPRICE COM BLDGTYPE STATE 2012 

### = EPRICE COM STATE 2012 

### × � EPRICE BLDGTYPE US 2003

EPRICE COM US 2003 

### �

Eq. 8

. 6

W here :

EPRICE COM BLD G TYPE STATE 20 1 2 

### = average commercial sector electricity price in a specific building type ( such as health care

, food sales , and foodservice ) in a specific state in 2012

, EPRICE COM STATE 2012 

### = average commercial sector electricity price in a specific state in 2012

, 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 8.2.12 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 thi s analysis.8

- 19 

### Table 8.2

. 12 Derived National Average Commercial Electricity Price s and Ratios by
Business Type

Business Type Electricity Price
dollars/ kWh Ratio of Electricity 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 ice 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 c onsidering 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 AEO201 3 price scenar ios

. 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 2 values ( i.e. , a c onstant index of 1.0)

2. AEO201 3 , High Economic Growth (“ AEO201 3 High Growth ” in Figure 8.2

. 1 )

3. AEO201 3 , Reference Case (“ AEO201 3 Reference ” in Figure 8.2

. 1 )

4. AEO201 3 , Low Economic Growth (“ AEO201 3 Low Growth ” in Figure 8.2

. 1 )

Figure 8.2

. 1 shows the trends for the AEO201 3 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 3 forecast. DOE used the price trend from 20 31 to 20 40
of each forecast scenario to establish prices for the years 20 41 to 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

- 20 

### 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 3 r eference c ase, shown in Figure 8.2

. 1

. T he LCC model spreadsheets have the capability to analyze the AEO2013 h igh g rowth trends

, AEO2013 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 for 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 2012 water prices are shown on Table 8.2

. 13

. 0.0890 0.0940 0.0990 0.1040 0.1090 0.1140 0.1190 0.1240 0.1290 0.1340

2010 2015 2020 2025 2030 2035 2040 2045US Weighted Average Real Cost/kWh (2012$)



### Year 

## Electricity Price Projections

AEO 2013 Low Growth AEO 2013 Reference AEO 2013 High Growth8

- 21 

### Table 8.2

. 13 Commercial Water Prices by State ( 2012 $/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 7.10 Kentucky 7.19 North Dakota 5.44
Alaska 10.17 Louisiana 8.00 Ohio 7.80
Arizona 6.29 Maine 4.09 Oklahoma 7.02
Arkansas 4.82 Maryland 9.98 Oregon 9.11
California 6.42 Massachusetts 11.65 Pennsylvania 6.88
Colorado 5.46 Michigan 4.16 Rhode Island 8.39
Connecticut 6.14 Minnesota 7.60 South Carolina 6.95
Delaware 11.58 Mississippi 6.51 South Dakota 8.45
Dist. of Col. 9.63 Missouri 6.34 Tennessee 8.66
Florida 8.11 Montana 6.62 Texas 6.78 Georgia 8.09 Nebraska 3.67 Utah 3.09
Hawaii 7.62 Nevada 5.89 Vermont 3.56
Idaho 2.97 New Hampshire 6.80 Virginia 9.18
Illinois 6.86 New Jersey 7.74 Washington 8.68
Indiana 7.41 New Mexico 5.71 West Virginia 6.92

Iowa 4.85 New York 6.71 Wisconsin 4.78

Kansas 6.51 North Carolina 9.14 Wyoming 7.44



### 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 6.11 percent. The overall CPI for the same period averaged 4.42 percent. For the analysis period, a real water price escalation of 1.62 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 1991 and 2010, and the av erage real escalation rate during the period was 2.13 percent. To create a high water price forecast, water prices were escalated at a constant 2.13 percent. The lowest 20

- year period was between 1972 and 1991, and the average real escalation rate during t hat period was 1.22 percent. To create a low water price forecast, water prices were escalated at a constant 1.22 percent.

8.2.3.5 Repair Cost

The repair cost is the average annual cost to the customer for replacing or repairing components in the automatic commerc ial ice maker that ha ve failed. DOE examined the major components of ice makers and identified expected failure rates for each component. For those components for which available information indicates a failure might occur within the expected
8.5

- year equi pment life, DOE estimated repair or replacement costs based on original manufacturer equipment costs and RS Means cost estimation data

. 3 

### Under this methodology, repair and replacement costs are based on the original equipment costs, so the more expensive the components are, the greater the expected repair or replacement cost. For design options modeled in the engineering analysis, DOE estimated repair costs

, and if they were different from8

- 22 

### the baseline cost, the repair costs were either increased or decreased accordingly. (Although theoretically possible, in the case of the ice maker analysis, repair costs did not decrease with efficiency levels for any equipment class.) DOE estimate d different repair and replacement costs for all equipment classes. Repair costs are shown on Table 8.2

. 14

.

Table 8.2

. 14 Repair Cost by Equipment C lass

Equipment Class Annualized Repair Costs ( 2012 $/yr) by Efficiency Level

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B $22.95 $24.45 $24.46 $24.46 $24.46 $24.46

IMH

- W

- Med

- B $30.86 $32.78 $33.11 $33.11 $33.11

IMH

- W

- Large

- B* $40.35 $42.24 $42.24 $42.24

IMH

- W

- Large

- B

- 1 $35.04 $37.28 $37.28 $37.28

IMH

- W

- Large

- B

- 2 $57.47 $58.26 $58.26 $58.26

IMH

- A

- Small

- B $25.26 $26.23 $26.23 $26.23 $27.68 $27.68 $27.68

IMH

- A

- Large

- B* $33.38 $34.37 $35.38 $36.59 $37.34 $37.75

IMH

- A

- Large

- B

- 1 $32.64 $33.34 $34.31 $35.49 $36.39 $36.87

IMH

- A

- Large

- B

- 2 $37.33 $39.93 $41.15 $42.49

RCU

- Large

- B* $40.67 $42.70 $43.34 $44.93 $44.93

RCU

- Large

- B

- 1 $39.06 $41.12 $41.80 $43.05 $43.05

RCU

- Large

- B

- 2 $64.79 $66.29 $66.29 $73.09 $73.09

SCU

- W

- Large

- B $23.63 $23.63 $24.58 $24.58 $24.58 $24.58 $24.58

SCU

- A

- Small

- B $15.11 $15.66 $16.75 $17.61 $18.47 $19.02 $20.15

SCU

- A

- Large

- B $23.73 $25.13 $26.53 $26.53 $26.53 $27.99 $27.99

IMH

- A

- Small

- C $48.79 $50.72 $51.68 $52.30 $52.53 $71.24 $75.56

IMH

- A

- Large

- C $59.93 $60.60 $61.73 $62.86 $65.37 $93.71 $95.18

SCU

- A

- Small

- C $47.86 $47.86 $49.18 $50.39 $59.41 $74.44

* 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 c hapter 7

.



### 8.2.3.6 Maintenance 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 c ost does not include the cost s associated with the replacement or repair of components that have failed (as discussed above).

In the NOPR analyses, DOE estimated m aterial and labor costs for preventative
maintenance based on RS Means cost estimation data 3 

### and on telephone conservations with

contractors. DOE a ssumed maintenance cost would remain constant for all efficiency levels within an equipment class. Table 8.2

. 15 shows the annualized maintenance costs by equipment class for each efficiency level.8

- 23 

### Table 8.2

. 15 Annual Maintenance Costs by Equipment Class and Efficiency Level

Equipment Class Annual Maintenance Costs ( 2012 $/yr) by Efficiency Level

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B $400 $400 $400 $400 $400 $400

IMH

- W

- Med

- B $600 $600 $600 $600 $600

IMH

- W

- Large

- B * $800 $800 $800 $800

IMH

- W

- Large

- B

- 1 $800 $800 $800 $800

IMH

- W

- Large

- B

- 2 $800 $800 $800 $800

IMH

- A

- Small

- B $750 $750 $750 $750 $750 $750 $750

IMH

- A

- Large

- B * $1,050 $1,050 $1,050 $1,050 $1,050 $1,050

IMH

- A

- Large

- B

- 1 $1,050 $1,050 $1,050 $1,050 $1,050 $1,050

IMH

- A

- Large

- B

- 2 $1,050 $1,050 $1,050 $1,050

RCU

- Large

- B * $500 $500 $500 $500 $500

RCU

- Large

- B

- 1 $500 $500 $500 $500 $500

RCU

- Large

- B

- 2 $500 $500 $500 $500 $500

SCU

- W

- Large

- B $600 $600 $600 $600 $600 $600 $600

SCU

- A

- Small

- B $750 $750 $750 $750 $750 $750 $750

SCU

- A

- Large

- B $1,050 $1,050 $1,050 $1,050 $1,050 $1,050 $1,050

IMH

- A

- Small

- C $750 $750 $750 $750 $750 $750 $750

IMH

- A

- Large

- C $1,050 $1,050 $1,050 $1,050 $1,050 $1,050 $1,050

SCU

- A

- Small

- C $750 $750 $750 $750 $750 $750

* 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 c hapter 7

.



### 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 0 . The
LCC model used the failure year

- probability of failure combinations shown on Table 8.2

. 16

.8

- 24 

### Table 8.2

. 16 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 c ommercial 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.

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 ten ds 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
ad ditional 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 price variability) in the overall stock market. Risk coefficients usually vary between 0.5 and 2.0. A company with a risk coefficient of
0.5 faces half the risk of other stocks in the market; a company with a risk coefficient of
2.0 faces twice the overall stock market risk.8

- 25 

### 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 73 to 20 12

, 10 

### with a resulting rate of 6. 4 1 percent. DOE used a 3. 99

- 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 compan y 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 (%)

.

The risk adjustment factor depends on the variability of stock returns represented by standard deviations in stock price s

. DOE calculated stock prices using values from Damodaran
Online individual company cost of capital worksheets. 118

- 26 

### 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 deducts expected inflation ( r ) from the nominal rate. DOE calculat ed expected inflation ( 3. 68 percent) over the

same 1973

– 2012 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 build ing 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 the 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 occupy one of seven building types that DOE assumed lik eliest to use automatic commercial ice maker s

. DOE used all observations from the Damodaran data set for
which sufficiently complete data were available

.

Table 8.2.17 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 faci lities

. 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. Individual 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 s mall businesses pay on loans relative to AAA corporate bonds . For small business loans under $100,000, the premium was approximately 1.9 percent for fixed rate loans

. 12 

### This assumption was applied in the NOPR modeling.8

- 27 

### Table 8.2

. 17 Derivation of Typical Discount Rates by Building Type

Building Type Chain / Publicly
Traded Local / Non

traded State & Local
Government US Government Weighted Average,

Real No.

o f
Obs. WACC % of
Mkt Rate
With
Adder 6 % of
Mkt Municipal
Bond Rate % of
Mkt Treasury
Rate % of
Mkt

Health Care 1,2 4 .35 % 20.3% 0% 0% 2.34% 79.7% 2.63% 0.0% 2. 75 % 14 Lodging 3 5.95 % 60% 7.97 % 40% 0.00% 0.0% 0.0% 0.0% 6.76 % 26
Food Service 4 5. 72 % 40% 7.73 % 40% 2.34% 20.0% 0.0% 0.0% 5. 85 % 45
Retail 4. 55 % 100% 0.0% 0% 0.0% 0.0% 0.0% 0.0% 4. 55 % 8
Education 5 5. 1 0% 25% 0.0% 0% 2.34% 75.0% 0.0% 0.0% 3. 0 3 % 17
Food Sales 4. 16 % 50% 6.13 % 50% 0.0% 0.0% 0.0% 0.0% 5. 14 % 24
Office 5.34 % 75% 0.0% 0% 2.34% 12.5% 2.63% 12.5% 4. 6 3 % 1

, 052

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 only). Value shown on 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 Ban k of St. Louis, and 2012 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 indi stinguishable from local , small businesses. The low end of the range was used for the percentage of the market.

4 Percentages of market developed from the c ommercial r efrigeration energy conservation standards r ulemaking. See: www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/27

. 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: 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 education 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 fr om 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 governments. The long

- term, real Treasury Bond rates were used for the cost of capital of U.S. government

- ow ned
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 manufacturers 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 each would purchase a new automatic commercial ice maker in the year the standard takes effect.
DOE assumed that the new standards would take effect in 2018 for the NOPR analysis

. For the LCC analysis, the y ear of equipment purchase is 2018. H owever, all dollar values are expressed
in 2012$

.8

- 28 

### 8.3 PAYBACK PERIOD INPUT S

8.3.1 Definition

As stated in s ection 8.1 , the PBP is the amo unt 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 of the increase in purchase cost to the decrease in annual operating expenditures. This type of calculation is known as a “simple” payback period because
i t does not take into account changes in operating cost over time or the time value of money, that is, the calculation is done at an effective discount rate of zero percent.

The equatio n 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 , eve n 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 repair cost, and the
annual maintenance cost. The PBP calculation uses the same inputs as the LCC analysis
described in section 8.2 , except that electricity and water price trends and discount rates are not required bec ause 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 calcula tion w ere the price s projected for 201 8 , expressed in 201 2 $

. Discount rates are not used in the PBP calculation.

8.4 LIFE

- CYCLE COST AND PAYBACK PERIOD 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

- 29 

### 8.4.1 Life

- Cycle Cost Results

Figure 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 presented 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 earlier, DOE calculated the LCC savings for a range of building types, WACCs, 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
savi ngs 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 m arkers , 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 $ 201 or less if the
standard were set at l evel 2

, $ 116 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 

### perc entile. 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). Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Life Cycle Cost $11,774.63 $11,520.42 $11,399.67 $11,278.91 $11,174.11 $11,399.74 $11,372.34

Installed Cost $2,432.12 $2,474.68 $2,505.71 $2,536.75 $2,573.46 $2,950.88 $2,964.26 $- $2,000.00 $4,000.00 $6,000.00 $8,000.00 $10,000.00 $12,000.00 $14,000.00 

### IMH

- Air Cooled, Small, Batch: Life Cycle and Installed Costs8

- 30 

### 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

- B *LCC Savings2012$ Efficiency Level 1 2 3 4 5 6 7

Mean $0.00 $254.23 $258.54 $308.37 $396.00 $170.37 $197.77

Median (50th Percentile) $0.00 $235.82 $244.10 $257.75 $348.54 $124.12 $151.58

5th Percentile $0.00 $155.62 $80.68 $88.39 $119.88 ($134.89) ($117.99)

25th Percentile $0.00 $200.55 $116.35 $152.71 $228.50 ($10.01) $12.88

75th Percentile $0.00 $296.31 $357.86 $423.18 $518.04 $296.57 $327.16

95th Percentile $0.00 $391.36 $539.09 $679.73 $826.30 $634.45 $673.67
* Values in parentheses indicate negative numbers.



### Table 8.4

. 2 and Table 8.4

. 3 summarize the m ean and median LCC savings , respectively, for all equipment classes an alyzed. -$200 $0 $200 $400 $600 $800 $1,000

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8

- 31 

### Table 8.4

. 2 Mean LCC Savings for All Equipment Classes and Efficiency Levels

Equipment Class Mean Life

- Cycle Cost Savings 2012$ *
Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 199.08 214.57 348.92 327.95 49.00

IMH

- W

- Med

- B 464.44 587.43 405.20 459.66

IMH

- W

- Large

- B ** 833.12 549.50 581.89

IMH

- W

- Large

- B

- 1 701.11 582.75 607.27

IMH

- W

- Large

- B

- 2 1,259.56 442.10 499.93

IMH

- A

- Small

- B 254.23 258.54 308.37 396.00 170.37 197.77

IMH

- A

- Large

- B ** 647.60 624.74 943.27 1,083.20 993.58

IMH

- A

- Large

- B

- 1 589.66 572.21 1,001.98 1,167.88 1,061.63

IMH

- A

- Large

- B

- 2 959.72 907.73 627.03
RCU

- Large

- B ** 874.95 983.02 870.33 897.41
RCU

- Large

- B

- 1 846.66 963.34 857.00 882.35
RCU

- Large

- B

- 2 1,297.81 1,277.25 1,069.57 1,122.57
SCU

- W

- Large

- B 483.11 687.47 586.60 693.73 142.84 148.95
SCU

- A

- Small

- B 102.81 143.20 198.01 235.66 395.52 106.24
SCU

- A

- Large

- B 140.24 224.40 521.53 580.75 502.27 239.99

IMH

- A

- Small

- C 314.79 313.59 390.93 306.68 (202.09) (237.43)

IMH

- A

- Large

- C 659.83 743.68 908.11 1,025.65 523.61 500.03
SCU

- A

- Small

- C 93.26 139.67 145.63 (384.92) (441.15)

* Values on this table represent LCC savings for customers affected by the standard. Blank cells mean no LCC savings were calcu lated for this efficiency level because design options were unavailable to constitute an additional efficiency level. Values in pa renthesis indicate negative
numbers.
** 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 c hapter 7

.



### Table 8.4

. 3 Median LCC Savings for All Equipment Classes and Efficiency Levels

Equipment Class Me di an Life

- Cycle Cost Savings 2012$ *
Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 184.28 213.22 319.23 305.03 24.71

IMH

- W

- Med

- B 433.77 585.55 410.62 455.06

IMH

- W

- Large

- B ** 778.65 420.77 460.49

IMH

- W

- Large

- B

- 1 654.89 522.41 552.25

IMH

- W

- Large

- B

- 2 1,178.39 92.45 164.06

IMH

- A

- Small

- B 235.82 244.10 257.75 348.54 124.12 151.58

IMH

- A

- Large

- B ** 606.45 520.38 843.14 1,009.32 912.27

IMH

- A

- Large

- B

- 1 552.45 445.95 924.05 1,121.07 1,006.01

IMH

- A

- Large

- B

- 2 897.30 921.33 407.33
RCU

- Large

- B ** 819.44 894.87 789.20 817.15
RCU

- Large

- B

- 1 793.21 888.74 784.69 810.96
RCU

- Large

- B

- 2 1,211.44 986.39 856.50 909.68
SCU

- W

- Large

- B 477.46 670.03 692.54 776.46 (122.76) (116.35)
SCU

- A

- Small

- B 95.14 124.77 182.62 224.18 383.40 98.11
SCU

- A

- Large

- B 128.91 205.80 502.47 587.24 559.16 290.49

IMH

- A

- Small

- C 291.69 257.44 328.91 251.58 (274.61) (305.31)

IMH

- A

- Large

- C 617.09 775.79 880.27 945.80 438.97 384.35
SCU

- A

- Small

- C 86.77 131.11 135.73 (400.80) (469.80)

* Values on this table represent LCC savings for customers affected by the standard. Blank cells mean no LCC savings were calcu lated for this efficiency level because design options were unavailable to constitute an additional efficiency level. Values in pa renthesis indicate negative
numbers.

** 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 c hapter 7

.8

- 32 

### 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 f or each efficiency level. The lower edge of the elongated rectangular box represents the 25 th 

### percentile , which means that 25 percent of the customers would experience a
PBP of 0. 89 years o r less if the energy conservation standard were set at l evel 2

, 1.02 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 (upp er 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.

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 2 3 4 5 6 7
Mean 1.06 1.21 1.28 1.40 4.28 4.19
Median (50th Percentile) 1.07 1.22 1.30 1.42 4.32 4.24
5th Percentile 0.77 0.88 0.93 1.02 3.10 3.04
25th Percentile 0.89 1.02 1.08 1.18 3.59 3.53
75th Percentile 1.19 1.36 1.45 1.59 4.83 4.74
95th Percentile 1.40 1.59 1.69 1.85 5.64 5.53 0.00 1.00 2.00 3.00 4.00 5.00 6.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8

- 33 

### 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 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 1.07 1.26 1.43 2.27 5.42

IMH

- W

- Med

- B 0.63 0.85 3.33 3.22

IMH

- W

- Large

- B ** 0.69 3.59 3.60

IMH

- W

- Large

- B

- 1 0.72 3.75 3.77

IMH

- W

- Large

- B

- 2 0.58 3.10 3.02

IMH

- A

- Small

- B 1.07 1.22 1.30 1.42 4.32 4.24

IMH

- A

- Large

- B ** 0.46 0.57 0.95 1.10 2.16

IMH

- A

- Large

- B

- 1 0.46 0.50 0.65 0.82 2.08

IMH

- A

- Large

- B

- 2 0.42 0.94 2.58
RCU

- Large

- B ** 0.41 0.65 2.39 2.44
RCU

- Large

- B

- 1 0.38 0.62 2.37 2.42
RCU

- Large

- B

- 2 0.75 1.00 2.70 2.70
SCU

- W

- Large

- B 0.67 0.76 0.81 1.00 3.01 3.00
SCU

- A

- Small

- B 1.40 1.47 1.52 1.54 1.56 4.79
SCU

- A

- Large

- B 1.37 1.34 1.17 1.14 1.49 3.72

IMH

- A

- Small

- C 0.90 0.90 0.97 2.59 6.47 6.83

IMH

- A

- Large

- C 0.52 0.53 0.53 0.69 3.25 3.24
SCU

- A

- Small

- C 1.13 1.53 1.85 14.62 19.12

* Blank cells mean design options were unavailable to constitute an additional efficiency level

. ** 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 p roduct complying with an energy conservation standard level will be less than three times the value of the energy savings during the first year that the 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 presumption, DOE estimated the additional cost of purchasing

more

- efficient, standard s

- compliant equipment, and compared this cost to the value of the energy saved during the first year of operation of the equipment

. DOE interprets that the increased cost of purchasing standard s

- compliant equipment includes the cost of installing the equipment for use by the purchaser. DOE calculated th e rebuttable presumption payback period (RPBP), or the
ratio of ( a) the increase in installed cost above the baseline efficiency 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). Th e RPBPs calculated can thus
be different from the PBPs calculated in section 8.4.2

.

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 508

- 34 

### s tates plus the District of Columbia

. DOE cal culated t he RPBP for each higher efficiency level
within each equipment class

.

Table 8.4.6 shows the RPBP s calculated 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 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.95 1.14 1.68 2.53 5.88

IMH

- W

- Med

- B 0.57 0.77 3.04 2.95

IMH

- W

- Large

- B ** 0.63 3.30 3.31

IMH

- W

- Large

- B

- 1 0.65 3.44 3.46

IMH

- W

- Large

- B

- 2 0.53 2.87 2.80

IMH

- A

- Small

- B 0.97 1.12 1.19 1.29 3.94 3.87

IMH

- A

- Large

- B ** 0.42 0.52 0.96 1.10 2.19

IMH

- A

- Large

- B

- 1 0.43 0.46 0.70 0.87 2.16

IMH

- A

- Large

- B

- 2 0.39 0.86 2.35
RCU

- Large

- B ** 0.37 0.59 2.19 2.24
RCU

- Large

- B

- 1 0.35 0.57 2.17 2.22
RCU

- Large

- B

- 2 0.70 0.93 2.46 2.46
SCU

- W

- Large

- B 1.62 1.22 1.17 1.33 3.83 3.81
SCU

- A

- Small

- B 1.26 1.31 1.34 1.35 1.82 5.26
SCU

- A

- Large

- B 1.19 1.18 1.50 1.39 1.73 4.19

IMH

- A

- Small

- C 0.81 0.81 0.87 2.33 5.07 5.19

IMH

- A

- Large

- C 0.48 0.48 0.48 0.63 2.65 2.63
SCU

- A

- Small

- C 1.05 1.36 1.62 10.47 9.54

* Blank cells mean design options were unavailable to constitute an additional efficiency level

. ** 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 media n PBP values for all the efficiency levels. It also presents the percentage of customers who experience net cost, no impact, 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 examined. DOE assumed that c ustomers with no impact in the base case are already buying more

- efficient equipment, so the efficiency level i n question would not affect those customers

.8

- 35 

### 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 2012$ Total
Discounted Operating
Cost 2012$ LCC,
All
Customers

2012$ Affected
Customers ’

Average Savings 2012$ % of Customers that Experience
Net
Cost

% No Impact
% Net
Benefit
%
1 4,227 2,432 9,343 11,775 0 NA NA NA NA
2 3,806 2,475 9,046 11,520 254 0 63 37 1.0 7
3 3,596 2,506 8,894 11,400 259 0 32 68 1.2 2
4 3,386 2,537 8,742 11,279 308 0 6 94 1.3 0
5 3,176 2,573 8,601 11,174 396 0 0 100 1.4 2

6 2,965 2,951 8,449 11,400 170 2 7 0 7 3 4. 3 2

7 2,909 2,964 8,408 11,372 198 2 2 0 78 4. 2 48

- 36 

### REFERENCES

1

. U. S. Department of Energy

— Energy Information Administration. 2013 Annual Energy
Outlook

. 2013. Washington D.C. DOE/EIA

- 0383(2013)

. (Last accessed June 14, 2013.)

< 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 Compan y, Inc. 2013 RS Means Electrical Cost Data . 201 3 . Kingston, MA.

4

. RS Means 201 3

. City Cost Indexes, RJ103 0

- 010 Building Systems . RS Means Construction Publishers & Consultants, Kingston, MA

.

5

. U.S. Census Bureau, Population Division. Annual Estimates of the Resident Population of Incorporated Places : April 1, 2010 to July 1, 2012. May 2013 . (Last accessed July 10, 2013.) < www.census.gov/popest/data/cities/totals/2012/index.ht ml >

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, 2012. (NST

- EST2012

- 01) . December 2012. (Last accessed February 21, 2013.)

< www.census.gov/popest/data/state/totals/2012/index.html >

7

. U.S. Department of Energy

– Energy Information Administration. Form EIA

- 8 2 6 data from the EIA web page Sales (Consumpt ion) , Revenue, Price s and Customers using the link
labeled Sales and Revenue Data Files by State , Monthly Back to 1990 (Form EIA

- 826)

. (Last accessed June 26, 2013.) < 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 February 2 0, 201 3.)

< www.federalreserve.gov/releases/h15/data.htm >

11

. Damodoran O nline. The Data Page . 2013. (Last accessed January 31, 2013.)

< http://pages.stern.nyu.edu/~adamodar/New_Home_Page/data.html >

12

. U.S. Small Business Administration, Office of Advoc acy. The Small Business Economy
2012

. 2012. (Last a ccessed June 4, 2013. ) < http://www.sba.gov/advocacy/7540/6282 >. See also: U.S. Small Business Administration, Office of Advocacy. The Small Business Economy 2009

. 2009. < www.sba.gov/advocacy/7540/6282 >8

- 37 

### 13

. Federal Reserve Bank of St. Louis ( http://research.stlouisfed.org/fred2/ ) and Federal Reserve H15 data ( www.federalreserve.gov/releases/h15/data.htm ).

### 9

- i 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

- 9
9.4 RESULTS

................................ ................................ ................................ ........................... 9

- 9
REFERENCES

................................ ................................ ................................ .......................... 9

- 12

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 AEO2013 Forecast of Commercial Floor Space Additions by Building Type

......... 9

- 7 Table 9.3.3 ACIM Equipment Shipments, Percent by Equipment Class

................................ .... 9

- 8 Table 9.4.1 Forecast Shipments of New and Replacement Automatic Commercial Ice Makers by Building Type, 2018

− 2047 (Base Case)

................................ ............................ 9

- 10 Table 9.4.2 Forecast Shipments of Automatic Commercial Ice Makers, 2018

− 2047, by Equipment Type (Base Case)*

................................ ................................ ........... 9

- 11

LIST OF FIGURES

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model

................................ ............. 9

- 19

- 1 

### 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 12 primary and 13 secondary proposed ACIM equipment
classes are key inputs to the ship ments 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 comm ercial floor space and old equipment can be removed through demolitions (not shown in the diagram).

Section 9.2 presents the mathematical formulati on of the model

, section 9.3 describes the data input to the model

, and section 9.4 presents the results for th e base

- case energy conservation
standard

- level scenario and discusses the development of higher standard scenarios.

Initial Shipments to New Construction Initial Stock Projections

Initial Replacement Shipments Shipments to Construction NEMS Commer cial Construction Forecast

Initial Stock Total Shipments by Equipment Class

Replacement

Shipments

Estimated Lifetime of

ACIM Equipment

Stock by Equipment C lass9

- 2 

### 9.2 SHIPMENTS MODEL

DOE examined all 25 proposed equipment classes using the shipments model

. DOE
assumed no interdependence between the shipmen ts 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 estimates of existing stock and the expected life of the equipment as a basis for calculating the portio n 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
bu ilding 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 to 2047 forecast period by adding new shipments and replacement shipments, and accounting for depreciated stock that was replaced or demolished ( i.e.

, removed permanently from the stock). In doin g so, DOE projected

s ales of new and replacement equipment by the year sold for the forecast period

. In the final step,
DOE allocated s ales in each year to the 12 primary and 13 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 notice of proposed rulemaking ( NOPR ) 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 NOPR analyses. Shipments were estimated for all 25 equipment classes proposed for energy conservation standards in this notice.9

- 3 

### 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 co nsider 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 to 0 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 Weibull 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 ye ar 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

- 4 

### 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

- 5 

### The number of units installed in new buildings is :

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
failed 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 provid ed a 10

- year history of total shipments. Additionally

, DOE collected the U.S. Census Bureau’s C urrent I ndustrial
R eports (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 associated with net exports

. DOE identif ied 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 as sociated 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 adjusting the CIR data and given that the AHRI data is better understood than the CIR data,
f

or the NOPR analysis, DOE relied on t he AHRI shipment value s

.
Using AHRI data

, DOE create d a rolling estimate of total existing stock by aggregating historical shipments across 8 .5

- year historical period s. U sing shipments, stock

, and the estimated
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 b etween shipments to new buildings and shipments for stock replacement to derive new shipments using the 20 1 0 historical shipments as a starting point

. Starting with the 20 1 0 values

, DOE began adding shipments

, subtracting estimated stock retirements, a nd d eveloping a forecast of surviving stock for 201 1 to 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 distrib utions from a 2009 commercial refrigeration equipment report from Navigant Consulting, Inc

. 1 

### Thus divided, the projections of new construction activity for each building9

- 6 

### type derived from National Energy Modeling System (NEMS) input files were used to project shipments 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 ac ross building types by year, and
the totals ultimately were distributed across all proposed 2 5 equipment classes using distributions
derived from the AHRI 2010 2 

### data.

Table 9.3

. 1 Distribution of Ice Maker Stoc k by Building Type
Building Type Share of Stock
Health Care 9. 1 % Lodging 33.1%
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 3 Annual Energy
Outlook ( AEO 201 3 ). 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 addit ions to the building stock s vary from year to year. Th erefore, th e forecast ACIM equipment shipments show some variability as well, tracking the forecast floor space additions.
The AEO2013 projections covered the period up to 2040. DOE used t he growth rates over the
last 10 years of the AEO 201 3 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)

.

b For some types of consumer products and commercial equipment, specific 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 forecas t 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 project ed 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

- 7 

### Table 9.3

. 2 AEO201 3 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 30 276 247 21 173

2018 67 164 50 424 208 35 409

2020 65 178 48 407 197 33 452

2025 63 181 48 442 169 33 392

2030 71 150 54 508 191 38 273

2035 73 207 56 522 228 39 412

2040 76 190 56 562 252 39 405

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 3 , data file kdbout.



### DOE used the A H RI shipment s data to derive the percentage of units shipped for each of
the 12 primary equipment classes and 13 secondary equipment classes

. Table 9.3

. 3 shows the
fraction of the units shipped for each of the 2 5 proposed equipment classes.9

- 8 

### Table 9.3

. 3 ACIM Equipment Shipments, Percent by Equipment Class

Equipment 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%

IMH: ice

- making head; RCU: 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. The last six equipment classes shown on the table indicate the percentage of shipments allocated to the typical units for these 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. Thus, eight classes are shown
on this table as four classes. Source: AHRI shipment data for 2010 submitted to DOE as part of this rulemakin g.



### 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 projected floor space construction after the year 2009 from the NEMS projection underlying AEO201 3

. DOE extracted annual estimates of new floor space additions
from a n AEO201 3 data file (kdbout) for the 2009 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 new buildings by multiplying the

est imated 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 utu re 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

- 9 

### 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 AC IM 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 e ach 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

.

MKTSHIP ( t

, y ) = MKT ( y ) × ( UN ( t ) + UB ( t ) ) 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

.

9.4 RESULTS

Table 9.4

. 1 shows DOE’s shipments forecast for the ACIM equipment by type of commercial building.

Table 9.4

. 2 sho ws DOE’s shipments forecast for th e ACIM equipment class es at the
base

- case energy consumption level (or efficiency l evel 1 ) by equipment type.9

- 10 

### 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 1,695 1,629 1,573 1,785 1,821 1,917 2,060 2,109 54,093

– Replacement* 15,347 13,574 16,791 15,748 17,860 17,746 19,069 19,830 508,081 Lodging

– New 6,537 7,094 7,198 5,966 8,250 7,565 8,660 8,870 225,694

– Replacement* 56,017 49,439 60,893 58,262 66,240 65,037 71,856 74,616 1,872,733
Food s ervice

– New 7,444 7,070 7,056 7,962 8,245 8,287 9,160 9,358 240,534

– Replacement* 37,724 33,412 42,584 41,807 48,047 50,080 54,414 57,495 1,363,419
Retail

– New 1,885 1,806 1,962 2,258 2,317 2,496 2,682 2,743 68,323

– Replacement* 13,054 11,547 14,309 13,830 15,770 16,178 17,540 18,441 449,764
Education

– New 906 857 736 829 993 1,098 1,054 1,066 27,665

– Replacement* 12,416 10,965 13,245 12,271 13,649 13,286 14,207 14,665 391,409
Food Sales

– New 5,471 5,189 5,184 5,870 6,067 6,088 6,708 6,846 176,737

– Replacement* 27,348 24,228 30,919 30,402 34,971 36,520 39,700 41,969 992,274
Office

– New 1,618 1,789 1,549 1,081 1,627 1,600 1,694 1,728 46,856

– Replacement* 7,080 6,259 7,918 8,236 9,248 9,393 10,510 11,064 259,966
Total

– New 25,555 25,433 25,258 25,751 29,320 29,051 32,019 32,721 839,901

– Replacement * 168,986 149,422 186,659 180,557 205,785 208,240 227,296 238,079 5,837,647

– Total 194,541 174,855 211,917 206,307 235,105 237,290 259,315 270,800 6,677,548

* Replacement includes equipment replaced from original stock (installed before standards) and the standards

- level equipment replaced in subsequent years.



### 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. Further, DOE recognizes that shipments could be higher than projected in the base case. If DOE based the shipments model on the CIR data with a 20 percent reduction for exports, total cumulativ e shipments would be approximately 30 percent higher than in the base case. DOE did not model low or high range shipments projections as part of this NOPR analyses.9

- 11 

### 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 8,833 7,939 9,622 9,367 10,675 10,774 11,774 303,184

IMH

- W

- Med

- B 5,641 5,070 6,145 5,982 6,817 6,881 7,519 193,628

IMH

- W

- Large

- B 934 840 1,018 991 1,129 1,140 1,245 32,072

IMH

- W

- Large

- B

- 1 713 641 777 757 862 870 951 24,490

IMH

- W

- Large

- B

- 2 221 199 241 234 267 269 294 7,582

IMH

- A

- Small

- B 52,672 47,342 57,377 55,858 63,655 64,246 70,210 1,807,948

IMH

- A

- Large

- B 31,394 28,217 34,198 33,292 37,939 38,292 41,846 1,077,574

IMH

- A

- Large

- B

- 1 26,478 23,799 28,843 28,080 31,999 32,297 35,294 908,855

IMH

- A

- Large

- B

- 2 4,915 4,418 5,354 5,213 5,940 5,996 6,552 168,719
RCU

- Large

- B 11,822 10,626 12,878 12,537 14,287 14,420 15,758 405,783
RCU

- Large

- B

- 1 11,290 10,148 12,298 11,973 13,644 13,771 15,049 387,526
RCU

- Large

- B

- 2 532 478 579 564 643 649 709 18,257
SCU

- W

- Large

- B 421 378 459 446 509 514 561 14,451
SCU

- A

- Small

- B 26,949 24,223 29,357 28,580 32,569 32,872 35,923 925,033
SCU

- A

- Large

- B 12,762 11,471 13,902 13,534 15,423 15,567 17,012 438,061

IMH

- A

- Small

- C 6,875 6,179 7,489 7,291 8,308 8,386 9,164 235,979

IMH

- A

- Large

- C 2,089 1,877 2,275 2,215 2,524 2,548 2,784 71,693
SCU

- A

- Small

- C 17,022 15,299 18,542 18,051 20,571 20,762 22,689 584,260
Total 177,414 159,461 193,260 188,144 214,406 216,400 236,485 6,089,666

* Excludes projections for secondary equipment classes

.9

- 12 

### REFERENCES

1

. Navigant Consulting, Inc., Energy Savings Potential and R&D Opportun ities 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.

3

. DOE. 201 3

. Annual Energy Outlook 201 3

. U.S. Department of Energy

- Energy Information Administration. Washington, D.C. DOE/EIA

- 0383(2010).10

- i 

### 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

- 2
10.2.2.2 Shipments

................................ ................................ ............................... 10

- 5
10.2.2.3 Equipment Stock

................................ ................................ .................... 10

- 5
10.2.2.4 National Annual Energy Consumption

................................ .................. 10

- 5
10.2.2.5 Site

- to

- Source Conversion Factor

................................ .......................... 10

- 6
10.2.2.6 Full

- Fuel

- Cycle Energy

................................ ................................ ......... 10

- 7
10.2.3 National Water Savings Inputs

................................ ................................ ............. 10

- 8
10.3 NET PRESENT VALUE

................................ ................................ ................................ .. 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 National 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

- 3 4

LIST OF TABLES

Table 10.2.1 National Energy Saving Inputs

................................ ................................ ............. 10

- 2 Table 10.2.2 Shipment

- Weighted Market Shares by Efficiency Level, Base Case

................... 10

- 3 Table 10.2.3 Unit Energy Consumption by Efficiency Level

................................ ................... 10

- 4 Table 10.2.4 Shipment

- Weighted Average Unit Energy Consumption by Efficiency Level

.... 10

- 5 Table 10.2.5 Projected Sit e

- to

- Source Conversion Factors

................................ ....................... 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 Present Value Inputs

................................ ................................ .................... 10

- 10 Table 10.3.2 Average Total Installed C ost per Unit by Efficiency Level

............................... 10

- 1110

- ii 

### 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 Shipments

- Weighted Annual Repair Cost per Unit

................................ ............ 10

- 16 Table 10.3.7 Average Annual Main tenance 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 CSL, for Automatic Commercial
Ice Makers Purchased in 2018

- 2047

................................ ............................... 10

- 20 Table 10.4.4 Primary Energy TSL National Energy Savings as Percent of Base Energy Usage of Equipment purchased between 2018

- 2047

................................ ..................... 10

- 21 Table 10.4.8 Cumulative National Water Savings, by TSL, for Automatic Commercial Ice
Makers Purchased in 2018

- 2047

................................ ................................ ..... 10

- 25 Table 10.4.9 Cumulative NPV Results, by CSL, 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 CSL, Based on a 3

- Percent Discount Rate

........ 10

- 29
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

- 2610

- 1 

### 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 con sumption and savings, monetary value of energy savings to the n ation as a result 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
candidate standard level it considered for each of the 1 2 primary equipment classes of automatic
commercial ice makers examined in this n otice of p roposed r ulemaking (NOPR) . 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 document ( 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 standards is less than AEC in the
base case )

.

NES t 

### = AEC base

, t 

### − AEC standards

, t 

### 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 energy savings for equipment purchased in the final study year , and through the year i n which the final year purchases are retired

. Cumulati ve NES is explained by Eq. 10

. 2

.10

- 2 

### NES cum 

### = ∑ NES t t 

### 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 :

AEC t 

### = ∑ ( STOCK V 

### × UEC V 

### ) × V 

### ffc

_ conv t 

### 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 ( calc ulated

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.

10.2.2 National Energy Savings Inputs

Table 10.2

. 1 l ists 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 )



### 10.2.2.1 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.10

- 3 

### Table 10.2

. 2 provides estimated shipment

- weighted market shares, by efficiency level, for each of the equipment classes analyzed in this rulemaking. To estimate these market shares,
DOE combin ed 20 10 auto matic commercial ice mak er shipments by equipment class developed
from information provided by the industry with information about available equipment obtained
from published sources (see TSD chapter 3)

.

Table 10.2

. 2 Shipment

- Weighted Market Shares by Efficiency Level, Base Case

Equipment Class Market Share by Efficiency Level
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 39.1% 26.1% 23.9% 10.9% 0.0% 0.0%

IMH

- W

- Med

– B 69.0% 16.7% 11.9% 0.0% 2.4%

IMH

- W

- Large

- B*

IMH

- W

- Large

- B

- 1 71.4% 0.0% 4.8% 23.8%

IMH

- W

- Large

- B

- 2 33.3% 50.0% 0.0% 16.7%

IMH

- A

- Small

- B 37.0% 31.5% 25.9% 5.6% 0.0% 0.0% 0.0%

IMH

- A

- Large

- B*

IMH

- A

- Large

- B

- 1 41.5% 43.9% 7.3% 7.3% 0.0% 0.0%

IMH

- A

- Large

- B

- 2 33.3% 26.7% 26.7% 13.3%
RCU

- Large

- B*
RCU

- Large

- B

- 1 42.9% 39.3% 8.9% 0.0% 8.9%
RCU

- Large

- B

- 2 27.3% 45.5% 9.1% 0.0% 18.2%
SCU

- W

- Large

- B 28.6% 0.0% 14.3% 0.0% 42.9% 0.0% 14.3%
SCU

- A

- Small

- B 17.1% 40.0% 5.7% 11.4% 14.3% 11.4% 0.0%
SCU

- A

- Large

- B 28.6% 35.7% 0.0% 7.1% 21.4% 7.1% 0.0%

IMH

- A

- Small

- C 22.9% 22.9% 14.3% 8.6% 17.1% 2.9% 11.4%

IMH

- A

- Large

- C 35.0% 20.0% 15.0% 15.0% 0.0% 5.0% 10.0%
SCU

- A

- Small

- C 26.7% 20.0% 16.7% 13.3% 3.3% 20.0%

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 modeling these as one class. In the NES and NPV tables, results for these three classes are the sum of the B

- 1 and
B

- 2 typical units.



### DOE estimate d the market share s based on the number of available ice maker mo dels of each equipment class with 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
Air

- Conditioning, Heating, and Refrigeration Institute ( AHRI ) shipment data, DOE create d a shipment

- weighted distribution of units by equipment class and efficiency level.

T he market share for each equipment class b y 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 2 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.10

- 4 

### 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 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 3,389 3,052 2,884 2,716 2,547 2,400

IMH

- W

- Med

- B 7,227 6,507 6,147 5,786 5,691

IMH

- W

- Large

- B 12,870 11,585 10,943 10,783

IMH

- W

- Large

- B

- 1 10,972 9,877 9,329 9,147

IMH

- W

- Large

- B

- 2 19,002 17,104 16,155 16,067

IMH

- A

- Small

- B 4,227 3,806 3,596 3,386 3,176 2,965 2,909

IMH

- A

- Large

- B 9,669 8,704 8,222 7,739 7,369 7,041

IMH

- A

- Large

- B

- 1 8,797 7,919 7,480 7,042 6,603 6,213

IMH

- A

- Large

- B

- 2 14,366 12,932 12,215 11,498
RCU

- Large

- B 14,508 13,205 12,335 11,611 11,526
RCU

- Large

- B

- 1 13,983 12,727 11,889 11,191 11,108
RCU

- Large

- B

- 2 22,360 20,349 19,009 17,892 17,779
SCU

- W

- Large

- B 4,183 3,892 3,559 3,351 3,143 2,935 2,925
SCU

- A

- Small

- B 2,600 2,419 2,213 2,084 1,955 1,826 1,585
SCU

- A

- Large

- B 3,599 3,349 3,062 2,884 2,705 2,526 2,351

IMH

- A

- Small

- C 5,142 4,630 4,374 4,118 3,862 3,606 3,555

IMH

- A

- Large

- C 9,899 8,911 8,417 7,923 7,430 6,936 6,912
SCU

- A

- Small

- C 2,192 2,040 1,866 1,758 1,649 1,580

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

. ** 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 UEC s for these three classes are weighted averages of the B

- 1 and B

- 2 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 b ase

- 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 e liminates from the market equipment below a specific efficiency level, all shipments below the candidate standard level roll up into the lowest efficiency level available under the candidate standard. For example , as Table 10.2

. 2 show s , in the base case, 3 9 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 l evel 2. For each equipment clas s, DOE
used the 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 s hipment

- weighted average
A EC values

.10

- 5 

### 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 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 3,107 2,975 2,866 2,716 2,547 2,400

IMH

- W

- Med

- B 6,942 6,445 6,136 5,784 5,691

IMH

- W

- Large

- B ** 12,139 11,392 10,906 10,783

IMH

- W

- Large

- B

- 1 10,459 9,677 9,286 9,147

IMH

- W

- Large

- B

- 2 17,564 16,931 16,140 16,067

IMH

- A

- Small

- B 3,884 3,728 3,584 3,386 3,176 2,965 2,909

IMH

- A

- Large

- B ** 8,945 8,563 8,180 7,739 7,369 7,041

IMH

- A

- Large

- B

- 1 8,187 7,823 7,448 7,042 6,603 6,213

IMH

- A

- Large

- B

- 2 13,028 12,549 12,119 11,498
RCU

- Large

- B ** 13,501 12,962 12,256 11,603 11,526
RCU

- Large

- B

- 1 13,046 12,507 11,819 11,183 11,108
RCU

- Large

- B

- 2 20,309 19,760 18,786 17,872 17,779
SCU

- W

- Large

- B 3,468 3,385 3,290 3,201 3,112 2,933 2,925
SCU

- A

- Small

- B 2,266 2,235 2,117 2,036 1,940 1,826 1,585
SCU

- A

- Large

- B 3,190 3,119 2,935 2,820 2,692 2,526 2,351

IMH

- A

- Small

- C 4,383 4,266 4,149 3,995 3,820 3,600 3,555

IMH

- A

- Large

- C 8,736 8,390 8,119 7,773 7,353 6,933 6,912
SCU

- A

- Small

- C 1,909 1,869 1,788 1,719 1,636 1,580

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

. ** 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 UEC s for these three classes are weighted averages of the B

- 1 and B

- 2 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 approximately
11.8 percent of the existing automatic commercial ice makers are retired in each year (based on

an 8

- 1⁄2

- year average lifetime) and that

, for units shipped in 204 7 , any units still remaining at the
end of 205 5 will be replaced.

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

.10

- 6 

### 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 applyin g 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 begun 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 th e 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 that notice that it intended to use the Greenhouse Gases, Regulated
Emiss ions, 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 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 NOPR 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 savings (power plant consumption) by applying a factor to account for losses associated with the generation, transmission, and distribution of electricity.
DOE derived annual ave rage site

- to

- power plant factors based on the version of the N EMS that corresponds to EIA’s Annual Energy Outlook 201 3 ( AEO201 3 ). 1 

### The factors change over time in response to projected changes in the types of power plants projected to provide electricity t o the
nation. Table 10.2

. 5 shows the site

- to

- power plant factors from 20 17 to the end of the projection period. For years after 20 40 (the last year in the AEO ) , DOE e xtrapolated the trend from 20 31 through 20 40

.

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

- 0581(2003), March 2003.10

- 7 

### 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 8,503 2038 8,725
2019 8,503 2039 8,725
2020 8,503 2040 8,725
2021 8,266 2041 8,725
2022 8,266 2042 8,725
2023 8,266 2043 8,725
2024 8,266 2044 8,725
2025 8,266 2045 8,725
2026 7,909 2046 8,725
2027 7,909 2047 8,725
2028 7,909 2048 8,725
2029 7,909 2049 8,725
2030 7,909 2050 8,725
2031 8,346 2051 8,725
2032 8,346 2052 8,725
2033 8,346 2053 8,725
2034 8,346 2054 8,725
2035 8,346 2055 8,725
2036 8,725 2056 8,725
2037 8,725 2057 8,725



### 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 AEO2013 . 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 (Based on AEO201 3 )

F uel 2017 2020 2025 2030 2035 2040 to 2060 Electricity (power plant energy use) 1.042 1.041 1.040 1.040 1.041 1.04010

- 8 

### 10.2.3 National Water Savings 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.

Table 10.2

. 8 shows the shipment

- weighted water usage by efficiency level. Note that the shipments

- 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 10.2

. 7 Unit Water Consumption by Efficiency Level

Equipment Class Efficiency Level *
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 96,661 96,661 96,661 94,471 94,471 94,471

IMH

- W

- Med

- B 267,901 267,901 267,901 267,901 267,901

IMH

- W

- Large

- B ** 606,709 606,709 606,709 606,709

IMH

- W

- Large

- B

- 1 509,996 509,996 509,996 509,996

IMH

- W

- Large

- B

- 2 919,107 919,107 919,107 919,107

IMH

- A

- Small

- B 14,016 14,016 14,016 14,016 14,016 14,016 14,016

IMH

- A

- Large

- B ** 36,525 36,525 36,525 33,200 33,200 33,200

IMH

- A

- Large

- B

- 1 33,142 33,142 33,142 29,200 29,200 29,200

IMH

- A

- Large

- B

- 2 54,750 54,750 54,750 54,750
RCU

- Large

- B ** 56,728 56,728 56,728 56,728 56,728
RCU

- Large

- B

- 1 54,750 54,750 54,750 54,750 54,750
RCU

- Large

- B

- 2 86,286 86,286 86,286 86,286 86,286
SCU

- W

- Large

- B 88,640 83,165 83,165 83,165 83,165 83,165 83,165
SCU

- A

- Small

- B 6,826 6,826 6,826 6,826 6,826 4,015 4,015
SCU

- A

- Large

- B 10,950 10,950 10,950 7,300 7,300 7,300 7,300

IMH

- A

- Small

- C 6,789 6,789 6,789 6,789 6,789 6,789 6,789

IMH

- A

- Large

- C 20,352 20,352 20,352 20,352 20,352 20,352 20,352
SCU

- A

- Small

- C 2,409 2,409 2,409 2,409 2,409 2,409

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

. ** 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 B

- 1 and B

- 2 typical units , using weights provided in
TSD ch apter 7

.10

- 9 

### Table 10.2

. 8 Shipment

- Weighted Unit Water Consumption by Efficiency Level

Equipment Class Efficiency Level *
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 96,423 96,423 96,423 94,471 94,471 94,471

IMH

- W

- Med

- B 267,901 267,901 267,901 267,901 267,901

IMH

- W

- Large

- B ** 606,709 606,709 606,709 606,709

IMH

- W

- Large

- B

- 1 509,996 509,996 509,996 509,996

IMH

- W

- Large

- B

- 2 919,107 919,107 919,107 919,107

IMH

- A

- Small

- B 14,016 14,016 14,016 14,016 14,016 14,016 14,016

IMH

- A

- Large

- B ** 36,282 36,282 36,282 33,200 33,200 33,200

IMH

- A

- Large

- B

- 1 32,854 32,854 32,854 29,200 29,200 29,200

IMH

- A

- Large

- B

- 2 54,750 54,750 54,750 54,750
RCU

- Large

- B ** 56,728 56,728 56,728 56,728 56,728
RCU

- Large

- B

- 1 54,750 54,750 54,750 54,750 54,750
RCU

- Large

- B

- 2 86,286 86,286 86,286 86,286 86,286
SCU

- W

- Large

- B 84,730 83,165 83,165 83,165 83,165 83,165 83,165
SCU

- A

- Small

- B 6,504 6,504 6,504 6,504 6,504 4,015 4,015
SCU

- A

- Large

- B 9,646 9,646 9,646 7,300 7,300 7,300 7,300

IMH

- A

- Small

- C 6,789 6,789 6,789 6,789 6,789 6,789 6,789

IMH

- A

- Large

- C 20,352 20,352 20,352 20,352 20,352 20,352 20,352
SCU

- A

- Small

- C 2,409 2,409 2,409 2,409 2,409 2,409

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

. ** 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 weighted averages of the B

- 1 and B

- 2 typical units , using weights provided in
TSD chapter 7

.


### Note also that for equipment classes with a design option, weighted

- average water usage will remain constant as the NIA roll

- up proceeds until the efficiency level is reached that
includes the water

- saving design option. At that point, all units roll into that efficiency 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

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 ).

b DOE combined water and wastewater prices into a single value.10

- 10 

### DOE determined t he PVS and PVC according to the following expressions:

PVS = ∑ OCS t 

### × DF t t 

### Eq. 10

. 5

PVC = ∑ TIC t 

### × DF t t 

### Eq. 10

. 6

W here :

OCS t 

### = total annual operating cost savings in the year t ($)

, TIC t 

### = total annual installed cost increases in the year t ($)

, DF t 

### = discount factor for the year t

, and

t = year ( PVS is summed 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 3

. 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
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

.10

- 11 

### Table 10.3

. 2 Average Total Installed Cost per Unit by Efficiency Level

Equipment Class Average Total Installed Cost per Unit 2012$ *
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B $2,391 $2,425 $2,451 $2,509 $2,614 $2,999

IMH

- W

- Med

- B $4,198 $4,241 $4,286 $4,656 $4,671

IMH

- W

- Large

- B** $6,161 $6,243 $6,813 $6,868

IMH

- W

- Large

- B

- 1 $5,057 $5,132 $5,646 $5,717

IMH

- W

- Large

- B

- 2 $9,727 $9,833 $10,581 $10,586

IMH

- A

- Small

- B $2,432 $2,475 $2,506 $2,537 $2,573 $2,951 $2,964

IMH

- A

- Large

- B** $4,137 $4,179 $4,221 $4,355 $4,415 $4,739

IMH

- A

- Large

- B

- 1 $4,079 $4,118 $4,143 $4,208 $4,278 $4,662

IMH

- A

- Large

- B

- 2 $4,447 $4,505 $4,641 $5,151

RCU

- Large

- B** $6,269 $6,321 $6,406 $6,933 $6,968

RCU

- Large

- B

- 1 $6,089 $6,135 $6,214 $6,721 $6,756

RCU

- Large

- B

- 2 $8,958 $9,104 $9,283 $10,107 $10,136

SCU

- W

- Large

- B $3,451 $3,500 $3,530 $3,553 $3,596 $3,950 $3,951

SCU

- A

- Small

- B $2,748 $2,772 $2,801 $2,820 $2,840 $2,896 $3,305

SCU

- A

- Large

- B $3,212 $3,243 $3,278 $3,324 $3,342 $3,405 $3,758

IMH

- A

- Small

- C $6,601 $6,644 $6,665 $6,694 $6,913 $7,413 $7,461

IMH

- A

- Large

- C $5,468 $5,518 $5,543 $5,568 $5,629 $6,288 $6,289

SCU

- A

- Small

- C $3,586 $3,603 $3,632 $3,659 $4,179 $4,196

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

. ** 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 B

- 1 and B

- 2 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 , DOE estimated the shipment

- weighted installed cost. Table 10.3.3 shows the
results.10

- 12 

### Table 10.3

. 3 Shipment

- Weighted Average Total Installed Cost per Unit
, by Efficiency Level

Equipment Class Shipment

- Weighted Ave rage Total Installed Cost per Unit *
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B $2,427 $2,440 $2,457 $2,509 $2,614 $2,999

IMH

- W

- Med

- B $4,227 $4,256 $4,295 $4,656 $4,671

IMH

- W

- Large

- B** $6,349 $6,398 $6,826 $6,868

IMH

- W

- Large

- B

- 1 $5,242 $5,296 $5,663 $5,717

IMH

- W

- Large

- B

- 2 $9,923 $9,958 $10,582 $10,586

IMH

- A

- Small

- B $2,470 $2,486 $2,507 $2,537 $2,573 $2,951 $2,964

IMH

- A

- Large

- B** $4,188 $4,205 $4,235 $4,355 $4,415 $4,739

IMH

- A

- Large

- B

- 1 $4,110 $4,127 $4,147 $4,208 $4,278 $4,662

IMH

- A

- Large

- B

- 2 $4,608 $4,627 $4,709 $5,151

RCU

- Large

- B** $6,371 $6,392 $6,461 $6,937 $6,968

RCU

- Large

- B

- 1 $6,177 $6,197 $6,262 $6,724 $6,756

RCU

- Large

- B

- 2 $9,268 $9,308 $9,438 $10,113 $10,136

SCU

- W

- Large

- B $3,596 $3,610 $3,618 $3,628 $3,647 $3,950 $3,951

SCU

- A

- Small

- B $2,799 $2,803 $2,820 $2,832 $2,846 $2,896 $3,305

SCU

- A

- Large

- B $3,273 $3,281 $3,304 $3,334 $3,346 $3,405 $3,758

IMH

- A

- Small

- C $6,803 $6,812 $6,822 $6,839 $6,990 $7,418 $7,461

IMH

- A

- Large

- C $5,627 $5,645 $5,658 $5,676 $5,728 $6,288 $6,289

SCU

- A

- Small

- C $3,748 $3,753 $3,767 $3,784 $4,182 $4,196

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

. ** 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 B

- 1 and B

- 2 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
l evel. If a standard were set at l evel 1, customers would select 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 avail able efficiency levels is
reduced. T he average shipment

- weighted 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

, manufacturers are able to reduce their producti on costs through innovations in technology and process.10

- 13 

### 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 ppendix 8D )

. To project
the manufacturer sel ling 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 originally 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,

consta nt prices, is consistent with the analyses DOE historically performed. In this scenario, prices are held constant, so the learning multiplier is 1.00. DOE developed t he remaining three
scenarios

— the high , reference

, and low learning scenarios

— for this rule making from historical ice maker shipments and Producer Price Index data

. Appendix 8D of this TSD documents the development of the price learning scenarios. For the NOPR

, 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 2012 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
2012 100.00% 100.0% 100.0% 100.0%
2013 100.00% 100.1% 99.9% 99.8%
2014 100.00% 100.1% 99.9% 99.6%
2015 100.00% 100.2% 99.8% 99.4%
2016 100.00% 100.2% 99.7% 99.2%
2017 100.00% 100.3% 99.7% 99.1%
2018 100.00% 100.4% 99.6% 98.9%
2019 100.00% 100.4% 99.6% 98.7%
2020 100.00% 100.5% 99.5% 98.6%
2021 100.00% 100.5% 99.5% 98.5%
2022 100.00% 100.5% 99.4% 98.3%
2023 100.00% 100.6% 99.4% 98.2%
2024 100.00% 100.6% 99.3% 98.0%
2025 100.00% 100.7% 99.3% 97.9%
2026 100.00% 100.7% 99.2% 97.7%
2027 100.00% 100.8% 99.2% 97.6%
2028 100.00% 100.8% 99.1% 97.4%
2029 100.00% 100.9% 99.1% 97.3%
2030 100.00% 100.9% 99.0% 97.2%
2031 100.00% 101.0% 99.0% 97.1%
2032 100.00% 101.0% 98.9% 96.9%
2033 100.00% 101.0% 98.9% 96.8%
2034 100.00% 101.1% 98.9% 96.7%
2035 100.00% 101.1% 98.8% 96.6%
2036 100.00% 101.2% 98.8% 96.4%
2037 100.00% 101.2% 98.7% 96.3%
2038 100.00% 101.3% 98.7% 96.2%
2039 100.00% 101.3% 98.6% 96.1%
2040 100.00% 101.3% 98.6% 96.0%
2041 100.00% 101.4% 98.6% 95.8%
2042 100.00% 101.4% 98.5% 95.7%10

- 14 

### Table 10.3.4 (cont)

Year Constant Prices Historically Derived Price Trends High Reference Low
2043 100.00% 101.4% 98.5% 95.6%
2044 100.00% 101.5% 98.5% 95.5%
2045 100.00% 101.5% 98.4% 95.4%
2046 100.00% 101.5% 98.4% 95.3%
2047 100.00% 101.6% 98.3% 95.2%



### 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 described 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 ene rgy 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 in chapter 8. To determine energy s avings, 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 multipl ying 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 wa ter 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 shipments

- weighted annual repair costs per unit

. The development of annual repair costs i s discussed in c hapter 8.10

- 15 

### Table 10.3

. 5 Averag e Annual Repair Cost per Unit

Equipment Class Repair Costs by Efficiency Level 2012$*

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 22.95 24.45 24.46 24.46 24.46 24.46

IMH

- W

- Med

- B 30.86 32.78 33.11 33.11 33.11

IMH

- W

- Large

- B** 40.35 42.24 42.24 42.24

IMH

- W

- Large

- B

- 1 35.04 37.28 37.28 37.28

IMH

- W

- Large

- B

- 2 57.47 58.26 58.26 58.26

IMH

- A

- Small

- B 25.26 26.23 26.23 26.23 27.68 27.68 27.68

IMH

- A

- Large

- B** 33.38 34.37 35.38 36.59 37.34 37.75

IMH

- A

- Large

- B

- 1 32.64 33.34 34.31 35.49 36.39 36.87

IMH

- A

- Large

- B

- 2 37.33 39.93 41.15 42.49
RCU

- Large

- B** 40.67 42.70 43.34 44.93 44.93
RCU

- Large

- B

- 1 39.06 41.12 41.80 43.05 43.05
RCU

- Large

- B

- 2 64.79 66.29 66.29 73.09 73.09
SCU

- W

- Large

- B 23.63 23.63 24.58 24.58 24.58 24.58 24.58
SCU

- A

- Small

- B 15.11 15.66 16.75 17.61 18.47 19.02 20.15
SCU

- A

- Large

- B 23.73 25.13 26.53 26.53 26.53 27.99 27.99

IMH

- A

- Small

- C 48.79 50.72 51.68 52.30 52.53 71.24 75.56

IMH

- A

- Large

- C 59.93 60.60 61.73 62.86 65.37 93.71 95.18
SCU

- A

- Small

- C 47.86 47.86 49.18 50.39 59.41 74.44

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

. ** 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 B

- 1 and B

- 2 typical units

, using the weights provided in TSD chapter 7

.10

- 16 

### Table 10.3

. 6 Shipments

- Weighted Annual Repair Cost per Unit

Equipment Class Repair Costs by Efficiency Level 2012$ *

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 23.87 24.45 24.46 24.46 24.46 24.46

IMH

- W

- Med

- B 31.50 32.82 33.11 33.11 33.11

IMH

- W

- Large

- B** 40.96 42.24 42.24 42.24

IMH

- W

- Large

- B

- 1 35.68 37.28 37.28 37.28

IMH

- W

- Large

- B

- 2 58.00 58.26 58.26 58.26

IMH

- A

- Small

- B 25.87 26.23 26.23 26.23 27.68 27.68 27.68

IMH

- A

- Large

- B** 34.29 34.67 35.48 36.59 37.34 37.75

IMH

- A

- Large

- B

- 1 33.28 33.57 34.40 35.49 36.39 36.87

IMH

- A

- Large

- B

- 2 39.73 40.60 41.32 42.49
RCU

- Large

- B** 42.14 43.00 43.52 44.93 44.93
RCU

- Large

- B

- 1 40.47 41.36 41.92 43.05 43.05
RCU

- Large

- B

- 2 67.12 67.53 67.53 73.09 73.09
SCU

- W

- Large

- B 24.31 24.31 24.58 24.58 24.58 24.58 24.58
SCU

- A

- Small

- B 16.64 16.73 17.36 17.90 18.53 19.02 20.15
SCU

- A

- Large

- B 25.34 25.73 26.64 26.64 26.64 27.99 27.99

IMH

- A

- Small

- C 54.29 54.73 55.17 55.54 55.69 71.73 75.56

IMH

- A

- Large

- C 65.99 66.22 66.84 67.63 69.77 93.86 95.18
SCU

- A

- Small

- C 54.12 54.12 54.73 55.50 62.42 74.44

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

. ** 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 B

- 1 and B

- 2 typical units

,
us ing 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 efficiency level. Table 10.3

. 7 shows the resulting annual maintenance costs per unit by e fficiency level

. Shipments

- weighted
maintenance costs would be identical to the values on Table 10.3

. 7 , and are not shown separately.10

- 17 

### Table 10.3

. 7 Average Annual Maintenance Cost per Unit

Equipment Class Maintenance Costs by Efficiency Level 2012$ *

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 400 400 400 400 400 400

IMH

- W

- Med

- B 600 600 600 600 600

IMH

- W

- Large

- B** 800 800 800 800

IMH

- W

- Large

- B

- 1 800 800 800 800

IMH

- W

- Large

- B

- 2 800 800 800 800

IMH

- A

- Small

- B 750 750 750 750 750 750 750

IMH

- A

- Large

- B** 1,050 1,050 1,050 1,050 1,050 1,050

IMH

- A

- Large

- B

- 1 1,050 1,050 1,050 1,050 1,050 1,050

IMH

- A

- Large

- B

- 2 1,050 1,050 1,050 1,050
RCU

- Large

- B** 500 500 500 500 500
RCU

- Large

- B

- 1 500 500 500 500 500
RCU

- Large

- B

- 2 500 500 500 500 500
SCU

- W

- Large

- B 600 600 600 600 600 600 600
SCU

- A

- Small

- B 750 750 750 750 750 750 750
SCU

- A

- Large

- B 1,050 1,050 1,050 1,050 1,050 1,050 1,050

IMH

- A

- Small

- C 750 750 750 750 750 750 750

IMH

- A

- Large

- C 1,050 1,050 1,050 1,050 1,050 1,050 1,050
SCU

- A

- Small

- C 750 750 750 750 750 750

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

. ** 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 B

- 1 and B

- 2 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:

D F = 1
( 1 + r ) � t

− t p � 

### 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 Measuring Benefits and Costs,” t herein.
DOE define d the present year as 20 11 for the NOPR

.10

- 18 

### 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 2013 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 automati c 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 2013 , and summed over the period from the effective date of the sta ndard , 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) associated with the automatic commercial
ice makers pu rchased 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 N ATIONAL E NERGY S AVINGS AND N ET P RESENT V ALUE RESULTS

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 NOPR analysis, DOE selected five trial standard level (TSL) group ings based on
the NPV values calculated for each candidate standard level ( CSL ) . Appendix 10C discusses the criteria used for selection of TSLs. The NIA results in this section are presented for CSLs 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 summarizes the inputs to the NES spreadsheet model. For each input

, the table provides a brief description of the data source.10

- 19 

### 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 efficiency level for each standards case. Standards case annual market shares by efficiency level
remain constant over time for the base case and each standards

case. Annual e nergy and w ater

c onsump tion 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 (s ee 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 201 3 EIA AEO201 3 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 2055. 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 3

.



### 10.4.2 National Energy Savings Results

The following section provides NES results for each energy consumption level
considered for the 1 2 primary equipment classes of automatic commercial ice mak ers that DOE
has analyzed. Results are cumulative for equipment purchased between 2018 and 2047

, and are shown as primary energy 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 discussion 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

- 20 

### Table 10.4

. 2 C umulative Primary National Energy Savings , by CSL, for Automatic
Commercial Ice Makers Purchased in 2018

**-** 2047

Equipment Class National Energy Savings by CSL primary energy quads*

Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.002 0.004 0.007 0.010 0.013

IMH

- W

- Med

- B 0.006 0.009 0.013 0.014

IMH

- W

- Large

- B** 0.001 0.002 0.003

IMH

- W

- Large

- B

- 1 0.001 0.002 0.002

IMH

- W

- Large

- B

- 2 0.000 0.001 0.001

IMH

- A

- Small

- B 0.017 0.032 0.054 0.076 0.099 0.105

IMH

- A

- Large

- B** 0.024 0.049 0.077 0.101 0.122

IMH

- A

- Large

- B

- 1 0.020 0.040 0.062 0.086 0.107

IMH

- A

- Large

- B

- 2 0.005 0.009 0.015
RCU

- Large

- B** 0.013 0.030 0.046 0.047
RCU

- Large

- B

- 1 0.012 0.028 0.043 0.045
RCU

- Large

- B

- 2 0.001 0.002 0.003 0.003
SCU

- W

- Large

- B 0.000 0.000 0.000 0.000 0.000 0.000
SCU

- A

- Small

- B 0.002 0.008 0.013 0.018 0.024 0.037
SCU

- A

- Large

- B 0.002 0.007 0.010 0.013 0.017 0.022

IMH

- A

- Small

- C 0.002 0.003 0.005 0.008 0.011 0.012

IMH

- A

- Large

- C 0.001 0.003 0.004 0.006 0.008 0.008
SCU

- A

- Small

- C 0.001 0.004 0.007 0.010 0.011

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

. ** 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.10

- 21 

### 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 0.004 0.010 0.010 0.013

IMH

- W

- Med

- B 0.006 0.006 0.009 0.013 0.014

IMH

- W

- Large

- B** 0.001 0.001 0.001 0.002 0.003

IMH

- W

- Large

- B

- 1 0.001 0.001 0.001 0.002 0.002

IMH

- W

- Large

- B

- 2 0.000 0.000 0.000 0.001 0.001

IMH

- A

- Small

- B 0.017 0.032 0.076 0.099 0.105

IMH

- A

- Large

- B** 0.024 0.045 0.095 0.122 0.122

IMH

- A

- Large

- B

- 1 0.020 0.040 0.086 0.107 0.107

IMH

- A

- Large

- B

- 2 0.005 0.005 0.009 0.015 0.015
RCU

- Large

- B** 0.013 0.013 0.030 0.046 0.047
RCU

- Large

- B

- 1 0.012 0.012 0.028 0.043 0.045
RCU

- Large

- B

- 2 0.001 0.001 0.002 0.003 0.003
SCU

- W

- Large

- B 0.000 0.000 0.000 0.000 0.000
SCU

- A

- Small

- B 0.002 0.013 0.024 0.037 0.037
SCU

- A

- Large

- B 0.002 0.010 0.017 0.022 0.022

IMH

- A

- Small

- C 0.002 0.003 0.005 0.008 0.012

IMH

- A

- Large

- C 0.001 0.003 0.006 0.008 0.008
SCU

- A

- Small

- C 0.001 0.004 0.007 0.007 0.011
Total 0.072 0.134 0.281 0.374 0.395

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 B

- 1 and B

- 2 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 between 2018

**-** 2047

Equipment Class Base Energy Usage quads TSL Savings As Percent Of Total 2018

- 205 5 Usage

TSL1 TSL2 TSL3 TSL4 TSL5

IMH

- W

- Small

- B 0.062 4% 7% 16% 16% 21%

IMH

- W

- Med

- B 0.089 6% 6% 10% 15% 16%

IMH

- W

- Large

- B** 0.026 6% 6% 6% 9% 10%

IMH

- W

- Large

- B

- 1 0.017 7% 7% 7% 10% 11%

IMH

- W

- Large

- B

- 2 0.009 3% 3% 3% 7% 8%

IMH

- A

- Small

- B 0.463 4% 7% 16% 21% 23%

IMH

- A

- Large

- B** 0.635 4% 7% 15% 19% 19%

IMH

- A

- Large

- B

- 1 0.490 4% 8% 17% 22% 22%

IMH

- A

- Large

- B

- 2 0.145 3% 3% 6% 11% 11%
RCU

- Large

- B** 0.357 4% 4% 8% 13% 13%
RCU

- Large

- B

- 1 0.333 4% 4% 8% 13% 13%
RCU

- Large

- B

- 2 0.024 2% 2% 7% 11% 11%10

- 22 

### Table 10.4.4 (cont)

Equipment Class Base Energy Usage quads TSL Savings As Percent Of Total 2018

- 2055 Usage

TSL1 TSL2 TSL3 TSL4 TSL5
SCU

- W

- Large

- B 0.003 2% 5% 9% 14% 14%
SCU

- A

- Small

- B 0.138 1% 9% 18% 27% 27%
SCU

- A

- Large

- B 0.092 2% 10% 19% 24% 24%

IMH

- A

- Small

- C 0.068 2% 5% 8% 12% 17%

IMH

- A

- Large

- C 0.041 4% 6% 14% 19% 19%
SCU

- A

- Small

- C 0.073 2% 6% 9% 9% 16%
Total 2.047 4% 7% 14% 18% 19%



### 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 reported by DOE.
Ta ble 10.4

. 6 presents FFC NES results by TSL.

Table 10.4

. 5 Cumulative Full

- Fuel

- Cycle National Energy Savings , by CSL, for Automatic
Commercial Ice Makers Purchased in 2018

**-** 2047

Equipment Class National Energy Savings by CSL FFC energy quads*

Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.002 0.004 0.007 0.010 0.013

IMH

- W

- Med

- B 0.006 0.009 0.014 0.015

IMH

- W

- Large

- B** 0.001 0.002 0.003

IMH

- W

- Large

- B

- 1 0.001 0.002 0.002

IMH

- W

- Large

- B

- 2 0.000 0.001 0.001

IMH

- A

- Small

- B 0.017 0.033 0.054 0.077 0.100 0.107

IMH

- A

- Large

- B** 0.025 0.050 0.079 0.103 0.124

IMH

- A

- Large

- B

- 1 0.020 0.041 0.063 0.087 0.108

IMH

- A

- Large

- B

- 2 0.005 0.009 0.016
RCU

- Large

- B** 0.013 0.030 0.046 0.048
RCU

- Large

- B

- 1 0.013 0.029 0.044 0.045
RCU

- Large

- B

- 2 0.001 0.002 0.003 0.003
SCU

- W

- Large

- B 0.000 0.000 0.000 0.000 0.000 0.000
SCU

- A

- Small

- B 0.002 0.008 0.013 0.018 0.025 0.038
SCU

- A

- Large

- B 0.002 0.007 0.010 0.013 0.018 0.022

IMH

- A

- Small

- C 0.002 0.003 0.006 0.008 0.011 0.012

IMH

- A

- Large

- C 0.001 0.003 0.004 0.006 0.008 0.008
SCU

- A

- Small

- C 0.001 0.004 0.007 0.010 0.012

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

. ** 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.10

- 23 

### Table 10.4

. 6 Full

- Fuel

- Cycle National Energy Savings , by Trial Standard Level, for
Automatic Commercial Ice Makers Purchased in 2018

**-** 2047

Equipment Class National Energy Savings by TSL FFC energy quads*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.002 0.004 0.010 0.010 0.013

IMH

- W

- Med

- B 0.006 0.006 0.009 0.014 0.015

IMH

- W

- Large

- B** 0.001 0.001 0.001 0.002 0.003

IMH

- W

- Large

- B

- 1 0.001 0.001 0.001 0.002 0.002

IMH

- W

- Large

- B

- 2 0.000 0.000 0.000 0.001 0.001

IMH

- A

- Small

- B 0.017 0.033 0.077 0.100 0.107

IMH

- A

- Large

- B** 0.025 0.045 0.096 0.124 0.124

IMH

- A

- Large

- B

- 1 0.020 0.041 0.087 0.108 0.108

IMH

- A

- Large

- B

- 2 0.005 0.005 0.009 0.016 0.016
RCU

- Large

- B** 0.013 0.013 0.030 0.046 0.048
RCU

- Large

- B

- 1 0.013 0.013 0.029 0.044 0.045
RCU

- Large

- B

- 2 0.001 0.001 0.002 0.003 0.003
SCU

- W

- Large

- B 0.000 0.000 0.000 0.000 0.000
SCU

- A

- Small

- B 0.002 0.013 0.025 0.038 0.038
SCU

- A

- Large

- B 0.002 0.010 0.018 0.022 0.022

IMH

- A

- Small

- C 0.002 0.003 0.006 0.008 0.012

IMH

- A

- Large

- C 0.001 0.003 0.006 0.008 0.008
SCU

- A

- Small

- C 0.001 0.004 0.007 0.007 0.012
Total 0.073 0.136 0.286 0.380 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 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 B

1 and B

- 2 typical units.



### 10.4.3 National Water Impacts

For the NOPR

, 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 CSL

. Note that the estimate of water savings does not imply DOE is setting 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 that will ultimately be established thro ugh this rulemaking. Table 10.4

. 7 presents national water savings by TSL.10

- 24 

### Table 10.4

. 7 Cumulative National Water Savings , by CSL, for Automatic Commercial Ice
Makers Purchased in 2018

**-** 2047

Equipment Class National Water Savings by CSL million gallons*

Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0 0 3, 6 99 3, 6 99 3, 699

IMH

- W

- Med

- B 0 0 0 0

IMH

- W

- Large

- B** 0 0 0 0 0 0

IMH

- W

- Large

- B

- 1 0 0 0

IMH

- W

- Large

- B

- 2 0 0 0

IMH

- A

- Small

- B 0 0 0 0 0 0

IMH

- A

- Large

- B** 0 0 20, 753 20, 753 20, 753

IMH

- A

- Large

- B

- 1 0 0 20, 753 20, 753 20 753

IMH

- A

- Large

- B

- 2 0 0 0
RCU

- Large

- B** 0 0 0 0 0 0
RCU

- Large

- B

- 1 0 0 0 0
RCU

- Large

- B

- 2 0 0 0 0
SCU

- W

- Large

- B 141 141 141 141 141 141
SCU

- A

- Small

- B 0 0 0 0 14, 391 14, 391
SCU

- A

- Large

- B 0 0 6, 424 6, 424 6, 424 6, 424

IMH

- A

- Small

- C 0 0 0 0 0 0

IMH

- A

- Large

- C 0 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 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

. ** 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.10

- 25 

### Table 10.4

. 8 Cumulative National Water Savings , by TSL, for Automatic Commercial Ice
Makers Purchased in 2018

**-** 2047

Equipment Class National Water Savings by TSL million gallons*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0 0 3, 699 3, 699 3, 699

IMH

- W

- Med

- B 0 0 0 0 0

IMH

- W

- Large

- B** 0 0 0 0 0

IMH

- W

- Large

- B

- 1 0 0 0 0 0

IMH

- W

- Large

- B

- 2 0 0 0 0 0

IMH

- A

- Small

- B 0 0 0 0 0

IMH

- A

- Large

- B** 0 0 20, 753 20, 753 20, 753

IMH

- A

- Large

- B

- 1 0 0 20,753 20,753 20,753

IMH

- A

- Large

- B

- 2 0 0 0 0 0
RCU

- Large

- B** 0 0 0 0 0
RCU

- Large

- B

- 1 0 0 0 0 0
RCU

- Large

- B

- 2 0 0 0 0 0
SCU

- W

- Large

- B 141 141 141 141 141
SCU

- A

- Small

- B 0 0 14, 391 14, 391 14, 391
SCU

- A

- Large

- B 0 6, 424 6, 424 6, 424 6, 424

IMH

- A

- Small

- C 0 0 0 0 0

IMH

- A

- Large

- C 0 0 0 0 0
SCU

- A

- Small

- C 0 0 0 0 0
Total 141 6, 565 45,407 45,407 45,407

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 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

. ** 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 o f the B

- 1
and B

- 2 typical units.



### 10.4.4 Annual Costs and Savings

As a prelude to providing 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 sav ings 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 savi ngs at l evel 4 for an example equipment class ( the small air

- cooled ice

- making head, 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 stand ards 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 an d savings based on the AEO201 3 r eference c ase electricity prices

. The NPV is the difference between the cumulative annual discounted savings
and the cumulative annual discounted costs. The NIA can produce comparable figures for all other efficiency levels and other equipment classes

.10

- 26 

### 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 candidate standards 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 d iscrete 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 2013 and summed
over the 2018

– 2047 pe riod

. The installed costs embodied in the results below are based on reference 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 cos ts 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
AEO2013

.

Table 10.4

. 9 shows the NPV results for the standard levels considered for automatic
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

-10.0

-5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0Millions 2012$

Year 

### Net Savings

Undiscounted Annual Equipment Costs Savings Undiscounted Total O&M Cost Savings Discounted Total Net Annual Impact10

- 27 

### Table 10.4

. 9 Cumulative NPV Results , by CSL, Based on a 7

- Percent Dis count Rate

Equipment Class Cumulative NPV by CSL at 7%
b illion 2012$ *
Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.006 0.011 0.0245 0.0246 (0.002)

IMH

- W

- Med

- B 0.016 0.025 0.017 0.019

IMH

- W

- Large

- B** 0.004 0.003 0.003

IMH

- W

- Large

- B

- 1 0.003 0.002 0.002

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001

IMH

- A

- Small

- B 0.043 0.080 0.131 0.177 0.046 0.058

IMH

- A

- Large

- B** 0.070 0.136 0.241 0.294 0.256

IMH

- A

- Large

- B

- 1 0.057 0.113 0.221 0.274 0.236

IMH

- A

- Large

- B

- 2 0.014 0.023 0.020
RCU

- Large

- B** 0.038 0.082 0.073 0.075
RCU

- Large

- B

- 1 0.036 0.078 0.070 0.072
RCU

- Large

- B

- 2 0.002 0.004 0.004 0.004
SCU

- W

- Large

- B 0.001 0.001 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.019 0.029 0.040 0.085 0.012
SCU

- A

- Large

- B 0.004 0.016 0.039 0.048 0.052 0.021

IMH

- A

- Small

- C 0.004 0.009 0.014 0.011 (0.016) (0.018)

IMH

- A

- Large

- C 0.004 0.007 0.012 0.016 0.007 0.007
SCU

- A

- Small

- C 0.004 0.009 0.013 (0.054) (0.062)

* A value in parentheses is a negative number. A value of 0.000 indicates N PV was less than 0.0005 billion 2012$
and thus rounded to 0.000. Blank cells indicate there were not enough design options identified to specify additional efficiency level s

. ** 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.10

- 28 

### 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 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.011 0.025 0.025 (0.002)

IMH

- W

- Med

- B 0.016 0.016 0.025 0.017 0.019

IMH

- W

- Large

- B** 0.004 0.004 0.004 0.003 0.003

IMH

- W

- Large

- B

- 1 0.003 0.003 0.003 0.002 0.002

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.001 0.001

IMH

- A

- Small

- B 0.043 0.080 0.177 0.046 0.058

IMH

- A

- Large

- B** 0.070 0.127 0.297 0.256 0.256

IMH

- A

- Large

- B

- 1 0.057 0.113 0.274 0.236 0.236

IMH

- A

- Large

- B

- 2 0.014 0.014 0.023 0.020 0.020
RCU

- Large

- B** 0.038 0.038 0.082 0.073 0.075
RCU

- Large

- B

- 1 0.036 0.036 0.078 0.070 0.072
RCU

- Large

- B

- 2 0.002 0.002 0.004 0.004 0.004
SCU

- W

- Large

- B 0.001 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.029 0.085 0.012 0.012
SCU

- A

- Large

- B 0.004 0.039 0.052 0.021 0.021

IMH

- A

- Small

- C 0.004 0.009 0.014 0.011 (0.018)

IMH

- A

- Large

- C 0.004 0.007 0.016 0.007 0.007
SCU

- A

- Small

- C 0.004 0.009 0.013 0.013 (0.062)
Total 0.198 0.368 0.791 0.484 0.370

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 2012$ and thus rounded to 0.000.
** The N IA 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.



### Table 10.4

. 11 provides the NPV results based on the 3

- percent discount rate and electricity price forecasts from the AEO201 3 r eference c ase. Table 10.4

. 12 presents the NPV results, by TSL, based on the 3

- percent discount rate.10

- 29 

### Table 10.4

. 11 Cumulative NPV Results , by CSL, Based on a 3

- P ercent Discount Rate

Equipment Class Cumulative NPV by CSL at 3%
b illion 2012$ *

Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

IMH

- W

- Small

- B 0.013 0.023 0.055 0.057 0.010

IMH

- W

- Med

- B 0.034 0.054 0.042 0.047

IMH

- W

- Large

- B** 0.009 0.007 0.008

IMH

- W

- Large

- B

- 1 0.007 0.006 0.006

IMH

- W

- Large

- B

- 2 0.002 0.001 0.002

IMH

- A

- Small

- B 0.094 0.176 0.290 0.394 0.163 0.190

IMH

- A

- Large

- B** 0.152 0.296 0.532 0.652 0.596

IMH

- A

- Large

- B

- 1 0.123 0.245 0.482 0.602 0.546

IMH

- A

- Large

- B

- 2 0.030 0.051 0.050

RCU

- Large

- B** 0.081 0.178 0.174 0.179

RCU

- Large

- B

- 1 0.078 0.169 0.165 0.170

RCU

- Large

- B

- 2 0.004 0.009 0.009 0.009

SCU

- W

- Large

- B 0.001 0.002 0.002 0.002 0.001 0.001

SCU

- A

- Small

- B 0.009 0.042 0.064 0.089 0.190 0.062

SCU

- A

- Large

- B 0.010 0.035 0.086 0.105 0.118 0.062

IMH

- A

- Small

- C 0.009 0.019 0.031 0.027 (0.023) (0.028)

IMH

- A

- Large

- C 0.009 0.016 0.025 0.034 0.018 0.018

SCU

- A

- Small

- C 0.008 0.021 0.030 (0.096) (0.114)

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 2012$
and thus rounded to 0.000. Blank cells indicate there were not enough design options identified to specify additional efficiency level s

. ** 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.1 0

- 30 

### Table 10.4

. 12 Cumulative NPV Results, by TSL, Based on a 3

- Percent Discount Rate

Equipment Class Cumulative NPV by TSL at 3% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.013 0.023 0.057 0.057 0.010

IMH

- W

- Med

- B 0.034 0.034 0.054 0.042 0.047

IMH

- W

- Large

- B** 0.009 0.009 0.009 0.007 0.008

IMH

- W

- Large

- B

- 1 0.007 0.007 0.007 0.006 0.006

IMH

- W

- Large

- B

- 2 0.002 0.002 0.002 0.001 0.002

IMH

- A

- Small

- B 0.094 0.176 0.394 0.163 0.190

IMH

- A

- Large

- B** 0.152 0.275 0.653 0.596 0.596

IMH

- A

- Large

- B

- 1 0.123 0.245 0.602 0.546 0.546

IMH

- A

- Large

- B

- 2 0.030 0.030 0.051 0.050 0.050
RCU

- Large

- B** 0.081 0.081 0.178 0.174 0.179
RCU

- Large

- B

- 1 0.078 0.078 0.169 0.165 0.170
RCU

- Large

- B

- 2 0.004 0.004 0.009 0.009 0.009
SCU

- W

- Large

- B 0.001 0.002 0.002 0.001 0.001
SCU

- A

- Small

- B 0.009 0.064 0.190 0.062 0.062
SCU

- A

- Large

- B 0.010 0.086 0.118 0.062 0.062

IMH

- A

- Small

- C 0.009 0.019 0.031 0.027 (0.028)

IMH

- A

- Large

- C 0.009 0.016 0.034 0.018 0.018
SCU

- A

- Small

- C 0.008 0.021 0.030 0.030 (0.114)
Total 0.430 0.806 1.751 1.238 1.032

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 2012$
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.



### 10.5 ANNUALIZED NATIONAL COSTS AND BENEFITS

The b enefits and costs of today’s proposed 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 operati on of equipment that meet the proposed standards (consisting primarily of operating cost savings from using less energy, minus increases in equipment purchase and installation costs, which is another way of representing 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 NOPR TSD. The
value of the CO 2 

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

### developed by a recent interagency process. The derivation of the time series of SCC values is discussed in appendix 14A of this NOPR TSD.

Although combining the values of operating savings and CO 2 

### reductions provides a useful perspective, two issues should be considered. First, the national operating cost savings are domestic U.S. customer monetary savings that occur as a result of market tr ansactions, 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 of10

- 31 

### equipment shipped in the 30

- year analysis period. The SCC values, on the other hand, reflect the present value of future climate

- related impacts resulting from the emission of 1 ton of CO 2 

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

10.5.1 Calculation Method

DOE uses a two

- step calculation process to convert each time series of costs and benefits
i nto 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 for 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).

PV x 

### = � ( x ( t )

∙ ( 1 + r x 

### ) y NPV − t 

### )

t = y 1 , y T 

### Eq. 10

. 1
Where:

x ( t ) = time series under evaluation,
PV x 

### = present value of the time series x , y 1 

### = first year in the analysis period,

y T 

### = last year in the analysis period,

y NPV 

### = year to which the NPV of customers’ costs and savings are being discounted, and

r x 

### = discount rate used to discount the annual values of time series x to year y NPV


### .
In the second step, DOE calculates, from the present values, the fixed annual payments
over a 30

- year period, starting in the first year of the analysis period ( i.e. , the compliance year), which yields the same present values with discount rates of 3 and 7 percent. This requires projecting the present values in the “present” year ahead to the compliance year. The fixed annual payments are the annualized values.

Ann x
, r 

### = PV x 

### ∙ f y 1 − y NPV , r 

### ∙ a 30

, r 

### = PV x 

### ∙ ( 1 + r ) y 1 − y NPV



### ∙ r ∙ ( 1 + r ) 30


### ( 1 + r ) 30



### − 1 Eq. 10

. 2
Where:

Ann x
, r 

### = annualized value of the time series x , f

n
, r 

### = factor to project a value n years ahead d 

### with r discount rate, and

a 30

, r 

### = factor to annualize present values over a 30

- year period with r discount rate.

Although DOE calculates annualized values, this does not imply that the time series of
cost and benefits from which the annualized values were det ermined would be a steady stream of payments.

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).

d n is the number of years between the “present” year and the compliance year.10

- 32 

### 10.5.2 Results for the Adopted Standards

The NOPR 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 2012$/year

Benefits

Operating Cost Savings 7% 104 98 112
3% 121 113 132
CO 2 Reduction at $11.8 /t** 5% 8 8 8
CO 2 Reduction at $39.7 /t** 3% 27 26 27
CO 2 Reduction at $61.2 /t** 2.50% 39 38 40
CO 2 Reduction at $117.0/t** 3% 82 80 84

NO X Reduction at $ 2

, 63 9 /t on ** 7% 0.32 0.31 0.33
3% 0.55 0.53 0.58

Total (Operating Cost Savings,
CO 2 Reduction and NO X Reduction)† 7% plus CO 2 range 112 to 186 106 to 178 120 to 19 6
7% 131 124 139
3% 130 to 204 121 to 193 141 to 217 3% plus CO 2 range 149 139 160
Costs

Incremental Equipment Costs 7% 20 21 20
3% 21 22 20
Net Benefits/Costs Total (Operating Cost Savings,
CO 2 Reduction and NO X Reduction, Minus Incremental Equipment Costs)† 7% plus CO 2 range 92 to 166 85 to 157 101 to 177
7% 110 103 120
3% plus CO 2 range 109 to 183 100 to 171 121 to 197
3% 128 118 140 * 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, are not directly included but are indirectly included as part of incremental equipment costs . In addition, incremental equipmen t costs reflect a medium decline rate for projected product price trends in the Primary Estimate, a low decline rate for projected eq uipment price trends in the Low Benefits Estimate, and a high decline rate for projected equipment price trends in the High Benefits 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 estimates, 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 AE O 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 g lobal monetized values (in 2012$) of the social cost of CO 2 emissions in 2015 under severa l scenarios. The values of $11.8 , $ 39.7 , and $6 1. 2 per metric ton are the averages of SCC distributions calculated using 5

- percent, 3

- percent, and 2.5

- percent discoun t rates, respectively. The value of $117.0/t represents the 95 th percentile of the SCC distribution calculated using a 3

- percent discount rate. The value for NO

x (in 2012$) is $2,639 per ton

. † Total benefits for both the 3

- percent and 7

- percent cases are derived using the SCC value calculated at a 3

- perc ent discount rate, which is $39.7 /t in 2015 (in 2012$). In the rows labeled as “7% plus CO 2 range” and “3% plus CO 2 range,” the operating cost and
NO

x benefits are calculated using the labeled discount rate , and those values are added to the full range of CO 2 values.10

- 33 

### Appendix 10E of this NOPR 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 NOPR.10

- 34 

### REFERENCES

1

. 

### U. S. Department of Energy

— Energy Information Administration. 2013 Annual Energy
Outlook

. 2013. Washington D.C. DOE/EIA

- 0383(2013)

. (Last accessed June 14, 2013.)

< www.eia.gov/oiaf/aeo/tablebrowser/ >11

- i 

### CHAPTER 11. CUSTOMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION

................................ ................................ ................................ ............ 11

- 1
11.2 IDENTIFYING THE CUST OMER SUBGROUPS

................................ ......................... 11

- 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 for Supermarket and Other Grocery (Except Convenience) Stores

................................ ............................... 11

- 3
Table 11.2.4 Single

- Unit and Multiunit Firms Census Data for Convenience Stores

............... 11

- 4 Table 11.2.5 Single

- Unit and Multiunit Firms Census Data for Gasoline Stations with
Convenience Stores

................................ ................................ ............................ 11

- 5 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

- 6 Table 11.2.8 Single

- Unit and Multiunit Firms Cen sus 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 Fo odservice Sector Compared to National Average Values*

.................. 11

- 9 Table 11.3.3 Comparison of Median Payback Periods for the Small Busine ss 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 Sav ings 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 Median Values

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

- 1311

- 1 

### 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, education, 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 businesses that are di sproportionately 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 applying the inputs th at 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

. DOE 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 automatic commercial ice makers.

Table 11.2

. 1 Business Types Purchasing Automatic Commercial Ice 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 disadvantaged, 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, incl udes the so

- called “big box” retailers that sell everything from beds, to11

- 2 

### automotive 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 cl ose

- out or discontinued products and sold inexpensively. While it is possible for 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 calcula tion of discount rates. (See chapter 8 of the
TSD for the development of discount 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
In dustry 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.s ba.gov/content/table

- small

- business

- size

- 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 typ es 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 definition11

- 3 

### 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 o f the firms in this category fall under the SBA definition of small business concerns and represent 90 percent of the total number of establishments, 77 percent of employment, and 76 percent of
sales.11

- 4 

### 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 represent 60 percent of the total number of establishments, 52 percent of employment, and 43 percent of sales.11

- 5 

### Table 11.2

. 5 Single

- Unit and Multiunit Firms Census Data for Gasoline Stations with
Convenience St ores

Description Number
of Firms Total Employment Number of
Establishments Sales per
Firm 1000$ Is Average
Firm a
Small
Business?Gasoline Stations with Convenience Stores* All firms 53,375 719,108 97,508 6,300 Yes Single

- unit firms 49,010 287,220 49,010 2,249 Yes
Multiunit firms 4,365 431,888 48,498 51,788 No
Firms with 1 establishment 1,160 11,836 1,160 3,363 Yes
Firms with 2 establishments 1,168 21,386 2,336 6,622 Yes
Firms with 3 or 4 establishments 802 24,023 2,697 11,713 Yes
Firms with 5 to 9 establishments 573 30,488 3,709 22,945 Yes
Firms with 10 to 24 establishments 402 47,037 5,978 51,678 No
Firms with 25 to 49 establishments 150 45,235 5,020 128,070 No
Firms with 50 to 99 establishments 58 31,187 3,865 272,955 No
Firms with 100 establishments or

more 52 220,696 23,733 2,616,567 No
Fraction of firms classed as small business 0.988 Fraction of establishments that belong to firms
classified as small businesses 0.604

Fraction of employment in small businesses 0.521
Fraction of sales in small businesses 0.429 *Small business concerns in this category are firms with annual sales less than $27 million.



### Table 11.2

. 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.11

- 6 

### 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.



### 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.11

- 7 

### 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 cost of capital than food

s al e s and the highe st electricity price s

. F oodservice was also selected for subgroup analysis . The higher cost of capital implies first cost is more important to this customer group than food sales customers, and a lso that they would 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
identifie d 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 percent of custo mers 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 AND PAYBA CK PERI OD RESULTS FOR SMALL
BUSINESS SUB G ROUP S

Table 11.3

. 1 presents the comparison of mean LCC savings for the small business subgroup in the foodservice sector with the na tional average values (LCC savings results from chapter 8 of the notice of proposed rulemaking ( NOPR ) TSD). For most trial standard levels ( TSLs ) in all equipment classes, the LCC savings for the small business subgroup are lower than the national average values. The exception is the TSL 5 result for SCU

- A

- Small

- C. Table 11.3

. 2 presents the percentage change in LCC savings compared to national average values. 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 food service establishments. For all but t hree equipment classes in Table 11.3

. 2 , t he percentage decrease in LCC savings is less than 10 percent for all TSLs. For SCU

- W

- Lar ge

- B, the TSL 4 and 5 differences are

- 1 1 percent ; for11

- 8 

### SCU

- A

- Small

- B

, the TSL 4 and 5 differences are

- 17 percent ; and for IHM

- W

- Small

- B

, the TSL
5 difference is

- 37 percent.

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 2012$ *
TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Small Business 195 210 312 312 31 All Business Types 199 215 328 328 49

IMH

- W

- Med

- B Small Business 455 455 575 390 443 All Business Types 464 464 587 405 460

IMH

- W

- Large

- B Small Business 816 816 816 528 559 All Business Types 833 833 833 550 582

IMH

- W

- Large

- B

- 1 Small Business 687 687 687 561 585 All Business Types 701 701 701 583 607

IMH

- W

- Large

- B

- 2 Small Business 1,233 1,233 1,233 419 476 All Business Types 1,260 1,260 1,260 442 500

IMH

- A

- Small

- B Small Business 249 253 387 159 185 All Business Types 254 259 396 170 198

IMH

- A

- Large

- B Small Business 635 621 1,094 956 956 All Business Types 648 633 1,127 994 994

IMH

- A

- Large

- B

- 1 Small Business 578 561 1,132 1,021 1,021
All Business Types 590 572 1,168 1,062 1,062

IMH

- A

- Large

- B

- 2 Small Business 941 941 888 604 604 All Business Types 960 960 908 627 627

RCU

- ***

- Large

- B Small Business 858 858 963 843 869 All Business Types 875 875 983 870 897

RCU

- ***

- Large

- B

- 1 Small Business 830 830 944 831 855 All Business Types 847 847 963 857 882

RCU

- ***

- Large

- B

- 2 Small Business 1,270 1,270 1,249 1,032 1,084 All Business Types 1,298 1,298 1,277 1,070 1,123

SCU

- W

- Large

- B Small Business 455 655 666 126 132 All Business Types 483 687 694 143 149

SCU

- A

- Small

- B Small Business 100 194 378 88 88 All Business Types 103 198 396 106 106

SCU

- A

- Large

- B Small Business 137 498 483 219 219
All Business Types 140 522 502 240 240

IMH

- A

- Small

- C Small Business 308 307 383 296 (238) All Business Types 315 314 391 307 (237)

IMH

- A

- Large

- C Small Business 647 729 1,006 512 489 All Business Types 660 744 1,026 524 500

SCU

- A

- Small

- C Small Business 91 137 143 143 (434) All Business Types 93 140 146 146 (441)
* Values in parentheses are negative numbers

.11

- 9 

### 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 (2%) (2%) (5%) (5%) (37%)

IMH

- W

- Med

- B (2%) (2%) (2%) (4%) (4%)

IMH

- W

- Large

- B (2%) (2%) (2%) (4%) (4%)

IMH

- W

- Large

- B1 (2%) (2%) (2%) (4%) (4%)

IMH

- W

- Large

- B2 (2%) (2%) (2%) (5%) (5%)

IMH

- A

- Small

- B (2%) (2%) (2%) (7%) (6%)

IMH

- A

- Large

- B (2%) (2%) (3%) (4%) (4%)

IMH

- A

- Large

- B1 (2%) (2%) (3%) (4%) (4%)

IMH

- A

- Large

- B2 (2%) (2%) (2%) (4%) (4%)
RCU

- Large

- B (2%) (2%) (2%) (3%) (3%)
RCU

- Large

- B1 (2%) (2%) (2%) (3%) (3%)
RCU

- Large

- B2 (2%) (2%) (2%) (3%) (3%)
SCU

- W

- Large

- B (6%) (5%) (4%) (11%) (11%)
SCU

- A

- Small

- B (2%) (2%) (5%) (17%) (17%)
SCU

- A

- Large

- B (2%) (4%) (4%) (9%) (9%)

IMH

- A

- Small

- C (2%) (2%) (2%) (3%) 0%

IMH

- A

- Large

- C (2%) (2%) (2%) (2%) (2%)

SCU

- A

- Small

- C (2%) (2%) (2%) (2%) 2% * Values in parenthesis are negative numbers. Negative percentage values imply
decrease in LCC savings and positive percentage values imply 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 NOPR 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 perio d

— are higher

, leading to a shorter payback, but given their higher discount rates, these customers value future savings less, leading to 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 aver age values (LCC savings results from chapter 8 of the NOPR TSD). Table 11.3

. 5 presents the percentage change in LCC savings of the lodging sector customer subgroup to national average values. For lodging sector small business, LCC savings are lower across the board. For TSLs 1 through 3, the differences range from 9 to 1 3 percent lower. The differences are even greater at TSLs 4 and 5. The reason
for this is that the en ergy 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 discount rate is higher . Th e lower electricity price reduces the nominal value of future oper ating and
maintenance benefits while the higher discount rate reduces the present value of the benefits, thus decreasing the LCC savings.11

- 10 

### 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 1.02 1.20 2.16 2.16 5.14 All Business Types 1.07 1.26 2.27 2.27 5.42

IMH

- W

- Med

- B Small Business 0.60 0.60 0.81 3.17 3.06 All Business Types 0.63 0.63 0.85 3.33 3.22

IMH

- W

- Large

- B Small Business 0.65 0.65 0.65 3.42 3.42 All Business Types 0.69 0.69 0.69 3.59 3.60

IMH

- W

- Large

- B1 Small Business 0.68 0.68 0.68 3.57 3.59 All Business Types 0.72 0.72 0.72 3.75 3.77

IMH

- W

- Large

- B2 Small Business 0.55 0.55 0.55 2.95 2.88 All Business Types 0.58 0.58 0.58 3.10 3.02

IMH

- A

- Small

- B Small Business 1.02 1.16 1.35 4.11 4.03 All Business Types 1.07 1.22 1.42 4.32 4.24

IMH

- A

- Large

- B Small Business 0.44 0.47 0.80 2.06 2.06 All Business Types 0.46 0.49 0.84 2.16 2.16

IMH

- A

- Large

- B1 Small Business 0.44 0.48 0.78 1.99 1.99 All Business Types 0.46 0.50 0.82 2.08 2.08

IMH

- A

- Large

- B2 Small Business 0.40 0.40 0.90 2.45 2.45 All Business Types 0.42 0.42 0.94 2.58 2.58

RCU

- Large

- B Small Business 0.39 0.39 0.62 2.27 2.32 All Business Types 0.41 0.41 0.65 2.39 2.44

RCU

- Large

- B1 Small Business 0.37 0.37 0.59 2.25 2.31 All Business Types 0.38 0.38 0.62 2.37 2.42

RCU

- Large

- B2 Small Business 0.72 0.72 0.96 2.57 2.57 All Business Types 0.75 0.75 1.00 2.70 2.70

SCU

- W

- Large

- B Small Business 0.65 0.73 0.96 2.88 2.86 All Business Types 0.67 0.76 1.00 3.01 3.00

SCU

- A

- Small

- B Small Business 1.33 1.44 1.48 4.54 4.54 All Business Types 1.40 1.52 1.56 4.79 4.79

SCU

- A

- Large

- B Small Business 1.29 1.11 1.42 3.54 3.54 All Business Types 1.37 1.17 1.49 3.72 3.72

IMH

- A

- Small

- C Small Business 0.86 0.86 0.92 2.46 6.38 All Business Types 0.90 0.90 0.97 2.59 6.83

IMH

- A

- Large

- C Small Business 0.50 0.50 0.65 3.06 3.05 All Business Types 0.52 0.53 0.69 3.25 3.24

SCU

- A

- Small

- C Small Business 1.08 1.45 1.76 1.76 17.09 All Business Types 1.13 1.53 1.85 1.85 19.1211

- 11 

### 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 2012$*
TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Small Business 179 192 285 285 (3)
All Business Types 199 215 328 328 49

IMH

- W

- Med

- B Small Business 421 421 531 334 382 All Business Types 464 464 587 405 460

IMH

- W

- Large

- B Small Business 756 756 756 449 476 All Business Types 833 833 833 550 582

IMH

- W

- Large

- B1 Small Business 635 635 635 484 503 All Business Types 701 701 701 583 607

IMH

- W

- Large

- B2 Small Business 1,144 1,144 1,144 338 390 All Business Types 1,260 1,260 1,260 442 500

IMH

- A

- Small

- B Small Business 229 232 354 115 139 All Business Types 254 259 396 170 198

IMH

- A

- Large

- B Small Business 589 575 1,018 862 862 All Business Types 648 633 1,127 994 994

IMH

- A

- Large

- B1 Small Business 536 520 1,056 926 926 All Business Types 590 572 1,168 1,062 1,062

IMH

- A

- Large

- B2 Small Business 873 873 816 521 521 All Business Types 960 960 908 627 627

RCU

- Large

- B Small Business 796 796 890 744 766 All Business Types 875 875 983 870 897

RCU

- Large

- B1 Small Business 771 771 873 734 754 All Business Types 847 847 963 857 882

RCU

- Large

- B2 Small Business 1,175 1,175 1,149 891 937 All Business Types 1,298 1,298 1,277 1,070 1,123

SCU

- W

- Large

- B Small Business 440 624 626 96 102 All Business Types 483 687 694 143 149

SCU

- A

- Small

- B Small Business 92 177 353 55 55 All Business Types 103 198 396 106 106

SCU

- A

- Large

- B Small Business 126 470 448 179 179 All Business Types 140 522 502 240 240

IMH

- A

- Small

- C Small Business 284 283 352 257 (281) All Business Types 315 314 391 307 (237)

IMH

- A

- Large

- C Small Business 600 676 929 412 394 All Business Types 660 744 1,026 524 500

SCU

- A

- Small

- C Small Business 84 125 128 128 (452) All Business Types 93 140 146 146 (441) * Values in parentheses are negative numbers.11

- 12 

### 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 (10%) (10%) (13%) (13%) (107%)

IMH

- W

- Med

- B (9%) (9%) (10%) (18%) (17%)

IMH

- W

- Large

- B (9%) (9%) (9%) (18%) (18%)

IMH

- W

- Large

- B1 (9%) (9%) (9%) (17%) (17%)

IMH

- W

- Large

- B2 (9%) (9%) (9%) (24%) (22%)

IMH

- A

- Small

- B (10%) (10%) (11%) (32%) (30%)

IMH

- A

- Large

- B (9%) (9%) (10%) (1 3 %) (1 3 %)

IMH

- A

- Large

- B1 (9%) (9%) (10%) (13%) (13%)

IMH

- A

- Large

- B2 (9%) (9%) (10%) (17%) (17%)
RCU

- Large

- B (9%) (9%) (9%) (15%) (15%)
RCU

- Large

- B1 (9%) (9%) (9%) (14%) (15%)
RCU

- Large

- B2 (9%) (9%) (10%) (17%) (16%)
SCU

- W

- Large

- B (9%) (9%) (10%) (33%) (32%)
SCU

- A

- Small

- B (11%) (11%) (11%) (49%) (49%)
SCU

- A

- Large

- B (10%) (10%) (11%) (25%) (25%)

IMH

- A

- Small

- C (10%) (10%) (10%) (16%) (18%)

IMH

- A

- Large

- C (9%) (9%) (9%) (21%) (21%)
SCU

- A

- Small

- C (10%) (11%) (12%) (12%) (2%)

* Values in parentheses are negative numbers. Negative percentage values imply decrease in LCC savings and positive percentage values imply 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 NOPR TSD). The PBP values are slightly 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

- 13 

### 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 1.09 1.28 2.27 2.27 5.42 All Business Types 1.07 1.26 2.27 2.27 5.42

IMH

- W

- Med

- B Small Business 0.64 0.64 0.86 3.38 3.26 All Business Types 0.63 0.63 0.85 3.33 3.22

IMH

- W

- Large

- B Small Business 0.70 0.70 0.70 3.65 3.65 All Business Types 0.69 0.69 0.69 3. 59 3.60

IMH

- W

- Large

- B1 Small Business 0.73 0.73 0.73 3.80 3.83 All Business Types 0.72 0.72 0.72 3.75 3.77

IMH

- W

- Large

- B2 Small Business 0.58 0.58 0.58 3.14 3.07 All Business Types 0.58 0.58 0.58 3.10 3.02

IMH

- A

- Small

- B Small Business 1.08 1.24 1.44 4.39 4.30 All Business Types 1.07 1.22 1.42 4.32 4.24

IMH

- A

- Large

- B Small Business 0.46 0.50 0.85 2.19 2.19 All Business Types 0.46 0.49 0.84 2.16 2.16

IMH

- A

- Large

- B1 Small Business 0.47 0.51 0.83 2.11 2.11 All Business Types 0.46 0.50 0.82 2.08 2.08

IMH

- A

- Large

- B2 Small Business 0.43 0.43 0.96 2.61 2.61 All Business Types 0.42 0.42 0.94 2.58 2.58

RCU

- Large

- B Small Business 0.41 0.41 0.66 2.42 2.48 All Business Types 0.41 0.41 0.65 2.39 2.44

RCU

- Large

- B1 Small Business 0.39 0.39 0.63 2.40 2.46 All Business Types 0.38 0.38 0.62 2.37 2.42

RCU

- Large

- B2 Small Business 0.77 0.77 1.02 2.74 2.74 All Business Types 0.75 0.75 1.00 2.70 2.70

SCU

- W

- Large

- B Small Business 0.67 0.75 1.01 3.01 3.00 All Business Types 0.67 0.76 1.00 3.01 3.00

SCU

- A

- Small

- B Small Business 1.42 1.54 1.56 4.79 4.79 All Business Types 1.40 1.52 1.56 4.79 4.79

SCU

- A

- Large

- B Small Business 1.38 1.17 1.49 3.72 3.72 All Business Types 1.37 1.17 1.49 3.72 3.72

IMH

- A

- Small

- C Small Business 0.92 0.92 0.99 2.63 6.88 All Business Types 0.90 0.90 0.97 2.59 6.83

IMH

- A

- Large

- C Small Business 0.53 0.53 0.70 3.28 3.28 All Business Types 0.52 0.53 0.69 3.25 3.24

SCU

- A

- Small

- C Small Business 1.15 1.55 1.88 1.88 19.13 All Business Types 1.13 1.53 1.85 1.85 19.12



### The LCC subgroup analysis results show that the identified subgroups in foodservice and lodging sector s would perceive lower LCC savings due to their higher cost of capital

. Foodservice has a l ower PBP than average because of higher

- than

- average electricit y prices. Payback periods for l odging business sector customers are higher than the average, with the difference likely to reflect a narrower results distribution caused by the lower than average electricity price

.11

- 14 

### 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. Census Bureau. 2007 Economic Census, Sector 44: Retail Trade: Subject Series

– Estab & Firm Size: Summary Statistics for Single Unit and Multiunit Firms for the United
States: 2007

. (Last 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 Survey (CBECS). CBECS Table C

- 14A Electricity Consumption and Expenditure Intensities for All Buildings (Including Malls) . 2003

. ( Last accessed March 8, 2011.) < www.eia.gov/c onsumption/commercial/index.cfm >12

- i 

### CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION

................................ ................................ ................................ ......... 12

- 1
12.2 METHODOLOGY

................................ ................................ ................................ ........ 12

- 1
12.2.1 Pha se 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 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

................................ .......... 12

- 5
12.3.1 Price Sensitivity

................................ ................................ .............................. 12

- 6
12.3.2 Enforcement

................................ ................................ ................................ .... 12

- 6
12.3.3 R eliability Impacts

................................ ................................ .......................... 12

- 6
12.3.4 Impact on Innovation

................................ ................................ ...................... 12

- 6
12.4 GRIM INPUTS AND ASSUMPTIONS

................................ ................................ ........ 12

- 7
12.4.1 Overview of the GRIM

................................ ................................ ................... 12

- 7
12.4.2 Sources for GRIM Inputs

................................ ................................ ................ 12

- 7
12.4.2.1 Corporate Annual Reports

................................ .............................. 12

- 7
12.4.2.2 Standard and Poor Credit Ratings

................................ .................. 12

- 8
12.4.2.3 Shipment Model

................................ ................................ ............. 12

- 8
12 .4.2.4 Engineering Analysis

................................ ................................ ..... 12

- 8
12.4.2.5 Manufacturer Interviews

................................ ................................ 12

- 8
12.4.3 Financial Parameters

................................ ................................ ....................... 12

- 9
12.4.4 Corporate Discount Rate

................................ ................................ ................. 12

- 9
12.4.5 Trial Standard Levels

................................ ................................ .................... 12

- 11
12.4.6 NIA Shipment Forecast

................................ ................................ ................. 12

- 13
12.4.6.1 Base

- Case Shipments Forecast

................................ .................... 12

- 14
12.4.6.2 Standards

- Case Shipments Forecast

................................ ............ 12

- 16
12.4.7 Production Costs

................................ ................................ ........................... 12

- 17
12.4.8 Conversion Costs and Stranded Assets

................................ ......................... 12

- 23
12.4.8.1 Automatic Commercial Ice Maker Product and Capital Conversion
Costs 12

- 23
12.4.9 Markup Scenarios

................................ ................................ ......................... 12

- 29
12.4.9.1 Preservation of Gross Margin Percentage Markup Scenario

....... 12

- 30
12.4.9.2 Preservation of EBIT Markup Scenario

................................ ....... 12

- 30
12.5 INDUSTRY FINANCIAL IMPACTS

................................ ................................ ........ 12

- 3512

- ii 

### 12.5.1 Impacts on INPV

................................ ................................ ........................... 12

- 36
12 .5.2 Impacts on Free Cash Flow

................................ ................................ ........... 12

- 36
12.6 IMPACS ON SMALL BUSINESS MANUFACTURERS OF AUTOMATIC
COMMERCIAL ICE MAKERS

................................ ................................ ................. 12

- 39
12.7 OTHER IMPACTS

................................ ................................ ................................ ...... 12

- 41
12.7.1 Employment

................................ ................................ ................................ .. 12

- 41
12.7.2 Production Capacity

................................ ................................ ...................... 12

- 44
12.7.3 Cumulative Regulatory Burden

................................ ................................ .... 12

- 45
12.7.3.1 DOE Regulations for Other Products Produced by Automatic
Commercial Ice Maker Manufacturers

................................ ......................... 12

- 45
12.7.3.2 Other Regulations That Could Impact Automatic Commercial Ice
Maker Manufacturers

................................ ................................ .................... 12

- 46
12.8 CONCLUSION

................................ ................................ ................................ ............ 12

- 47
12.8.1 Automatic Commercial Ice Makers

................................ .............................. 12

- 47
REFERENCES

................................ ................................ ................................ ........................ 12

- 51

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classificatio n of Small Businesses Potentially Affected by This Rulemaking

................................ ................................ ................................ ........ 12

- 4
Table 12.4.1 GRIM Financial Parameters based on 2003

– 2010 Weighted Company Financial
Data

................................ ................................ ................................ .................... 12

- 9 Table 12.4.2 Cost of Equity Calculation

................................ ................................ .................. 12

- 10
Table 12.4.3 Cost of Debt Calculation

................................ ................................ ..................... 12

- 11
Table 12.4.4 Trial Standard Levels for Batch Automatic Commercial Ice Makers

................ 12

- 12
Table 12.4.5 Trial Standard Levels for Continuous Au tomatic Commercial Ice Makers

....... 12

- 13
Table 12.4.6 Total Base

- Case 2018 NIA Shipments

................................ ............................... 12

- 14
Table 12.4.7 Base

- Case Distribution of Shipments for Batch Automatic Commercial Ice Makers
in 2018

................................ ................................ ................................ ............. 12

- 15
Table 12.4.8 Base

- Case Distribution of Shipments for Continuous Automatic Commercial Ice
Makers in 2018

................................ ................................ ................................ 12

- 16 Table 12.4.9 MPC Breakdown for Equipment Class 1: IMH

- W

- Small

- B

.............................. 12

- 18
Table 12.4.10 MPC Breakdown for Equipment Class 2: IMH

- W

- Medium

- B

........................ 12

- 18 Table 12.4.11 MPC Breakdown for Equipment Class 3: IMH

- W

- Large

- B

............................ 12

- 18 Table 12.4.12 MPC Breakdown for Equipment Class 4: IMH

- A

- Small

- B

............................. 12

- 18 Table 12.4.13 MPC Breakdown for Equipment Class 5: IMH

- A

- Large

- B

............................. 12

- 18 Table 12.4.14 MPC Breakdown for Equipment Class 6: RCU

- NRC

- Small

- B

....................... 12

- 19 Table 12.4.15 MPC Breakdown for Equipm ent Class 7: RCU

- NRC

- Large

- B

....................... 12

- 19 Table 12.4.16 MPC Breakdown for Equipment Class 8: RCU

- RC

- Small

- B

.......................... 12

- 19 Table 12.4.17 MPC Breakdown for Equipment Class 9: RCU

- RC

- Large

- B

.......................... 12

- 19 Table 12.4.18 MPC Breakdown for Equipment Class 10: SCU

- W

- Small

- B

.......................... 12

- 19
Table 12.4 .19 MPC Breakdown for Equipment Class 11: SCU

- W

- Large

- B

.......................... 12

- 20 Table 12.4.20 MPC Breakdown for Equipment Class 12: SCU

- A

- Small

- B

........................... 12

- 20 Table 12.4.21 MPC Breakdown for Equipment Class 13: SCU

- A

- Large

- B

........................... 12

- 20 Table 12.4.22 MPC Breakdown for Equipment Class 14: IMH

- W

- Small

- C

.......................... 12

- 20 Table 12.4.23 MPC Breakdown for Equipment Class 15: IMH

- W

- Large

- C

.......................... 12

- 2112

- iii 

### Table 12.4.24 MPC Breakdown for Equipment Class 16: IMH

- A

- Small

- C

........................... 12

- 21 Table 12.4.25 MPC Breakdown for Equipment Class 17: IMH

- A

- Large

- C

........................... 12

- 21 Table 12.4.26 MPC Breakdown for Equipment Class 18: RCU

- NRC

- Small

- C

..................... 12

- 21 Table 12.4.27 MPC Breakdown for Equipment Class 19: RCU

- NRC

- Large

- C

..................... 12

- 22 Table 12.4.28 MPC Breakdown for Equipment Class 20: RCU

- RC

- Small

- C

........................ 12

- 22
Table 12.4.29 MPC Breakdown for Equipment Class 21: RCU

- RC

- Large

- C

........................ 12

- 22 Table 12.4.30 MPC Breakdown for Equipment Class 22: SCU

- W

- Small

- C

.......................... 12

- 22 Table 12.4.31 MPC Breakdown for Equipment Class 23: SCU

- W

- Large

- C

.......................... 12

- 22 Table 12.4.32 MPC Breakdown for Equipment Class 24: SCU

- A

- Small

- C

........................... 12

- 23 Table 12.4.33 MPC Breakdown for Equipment Class 25: SCU

- A

- Large

- C

........................... 1 2

- 23
Table 12.4.34 Prod uct Conversion Costs for Batch Automatic Commercial Ice Makers by EL 12

- 24
Table 12.4.35 Product Conversion Costs for Continuous Automatic Commercial Ice Makers by
EL

................................ ................................ ................................ ..................... 12

- 25 Table 12.4.36 Capital Conversion Costs for Batch Automatic Commercial Ice Makers by EL 12

- 26 Table 12.4.37 Capital Conversion Costs for Continuous Automatic Commercial Ice Makers by
EL

................................ ................................ ................................ ..................... 12

- 27 Table 12.4.38 Stranded Assets for Batch Automatic Commercial Ice Makers by EL

............ 12

- 28 Table 12.4.39 Stranded Assets for Continuous Automatic Commercial Ice Makers by EL

... 12

- 29 Table 12.4.40 Preservation of EBIT Markups for Equipment Class 1: IMH

- W

- Small

- B

...... 12

- 30
Table 12.4.41 Preservation of EBIT Markups for Equipment Class 2: IMH

- W

- Medium

- B

.. 12

- 31 Table 12.4.42 Preservation of EBIT Markups for Equipment Class 3: IMH

- W

- Large

- B

...... 12

- 31 Table 12.4.43 Preservation of EBIT Markups for Equipment Class 4: IMH

- A

- Small

- B

....... 12

- 31 Table 12.4.44 Preservation of EBIT Markups for Equipment Class 5: IMH

- A

- Large

- B

....... 12

- 31 Table 12.4.45 Preservation of EBIT Markups for Equipment Class 6: RCU

- NRC

- Small

- B

. 12

- 31 Table 12.4.46 Preservation of EBIT Markups for Equipment Class 7: RCU

- NRC

- Large

- B

.. 12

- 32 Table 12.4.47 Preservation of EBIT Markups for Equipment Class 8: RCU

- RC

- Small

- B

.... 12

- 32 Table 12.4.48 Preservation of EBIT Markups for Equipment Class 9: RCU

- RC

- Large

- B

.... 12

- 32 Table 12.4.49 Preservation of EBIT Markups for Equipment Class 10: SCU

- W

- Small

- B

.... 12

- 32
Table 12.4.50 Preservation o f EBIT Markups for Equipment Class 11: SCU

- W

- Large

- B

.... 12

- 32 Table 12.4.51 Preservation of EBIT Markups for Equipment Class 12: SCU

- A

- Small

- B

..... 12

- 33 Table 12.4.52 Preservation of EBIT Markups for Equipment Class 13: SCU

- A

- Large

- B

..... 12

- 33 Table 12.4.53 Preservation of EBIT Markups for Equipment Class 14: IMH

- W

- Small

- C

.... 12

- 33 Table 12.4.54 Preservation of EBIT Markups for Equipment Class 15: IMH

- W

- Large

- C

.... 12

- 33 Table 12.4.55 Preservation of EBIT Markups for Equipment Class 16: IMH

- A

- Small

- C

..... 12

- 33
Table 12.4.56 Preservation of EBIT Markups for Equipment Class 17: IMH

- A

- Large

- C

..... 12

- 34 Table 12.4.57 Preservation of EBIT Markups for Equipment Class 18: RCU

- NRC

- Small

- C 12

- 34 Table 12.4.58 Preservation of EBIT Markups for Equipment Class 19: RCU

- NRC

- Large

- C 12

- 34 Table 12.4.59 Preservation of EBIT Markups for Equipment Class 20: RCU

- RC

- Small

- C

.. 12

- 34 Table 12.4.60 Preservation of EBIT Markups for Equipment Class 21: RCU

- RC

- Large

- C

.. 12

- 35 Table 12.4.61 Preservation of EBIT Markups for Equipment Class 22: SCU

- W

- Small

- C

.... 12

- 35 Table 12.4.62 Preservation of EBIT Markups for Equipment Class 23: SCU

- W

- Large

- C

.... 12

- 35 Table 12.4.63 Preservation of EBIT Markups for Equipment Class 24: SCU

- A

- Small

- C

..... 12

- 35 Table 12.4.64 Preservation of EBIT Markups for Equipment Class 25: SCU

- A

- Large

- C

..... 12

- 35
Table 12.5.1 Manufacturer Im pact Analysis for Automatic Commercial Ice Makers

– Preservation of Gross Margin Percentage Markup Scenario

........................... 12

- 3612

- iv 

### Table 12.5.2 Manufacturer Impact Analysis for Automatic Commercial Ice Makers

– Preservation of EBIT Markup Scenario

................................ ........................... 12

- 36 Table 12.6.1 Compari son 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

- 40 Table 12.6.2 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.

................................ ................................ ............................. 12

- 41 Table 12.7.1 Potential Changes in the Total Number of Domestic Automatic Commercial Ice
Maker Production Workers in 2018

................................ ................................ . 12

- 43 Table 12.7.2 Batch Equipment: Percent of Current Shipments and the Number of Manufacturers Currently Producing Compliant Equipment by TSL

................................ ....... 12

- 44 Table 12.7.3 Continuous Equipment: Percent of Current Shipments and the Number of Manufacturers Currently Producing Compliant Equipment by TSL

............... 12

- 44 Table 12.7.4 Other DOE and Federal Actions Affecting the Automatic Commercial Ice Maker Industry

................................ ................................ ................................ ............ 12

- 46 Table 12.7.5 Other Covered Products Produced by Automatic Commercial Ice Maker
Manufacturers

................................ ................................ ................................ .. 12

- 46

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow

................................ ............................ 12

- 7 Figure 12.5.1 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers (Preservation of Gross Margin Percentage Markup Scenario)

........................ 12

- 38 Figure 12.5.2 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers (Preservation of EBIT Markup Scenario)

................................ ........................ 12

- 38 Figure 12.7.1 Automatic Commercial Ice Maker Domestic Production Employment by Year

................................ ................................ ................................ ............. 12

- 4312

- 1 

### CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers and on the consumers of the products subject to such a standard.” (42 U.S.C. 6295(o)(2)(B)(i) )
The statute also calls for an assessment of the impact of any lessening of competition as determined in writing by the Attorney General. Id . DOE conducted a manufacturer impact analysis (MIA) to estimate the financial impact of more

- strin gent energy conservation standards
on manufacturers of a utomatic c ommercial 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

- flow model adapted to analyze the equipment classes covered by this rulemaking. The GRIM inputs include information on industry cost structure, shipments, and p ricing strategies. The GRIM’s key output is the industry net present value (INPV) , which is the sum of discounted industry cash
flow over the analysis period . The model estimat es the financial impact of more

- stringent energy conservation standards by comparing changes in INPV between a base case and the various trial standard levels (TSLs) in the standards case. The qualitative part of the MIA addresses equipment characteristics, manufacturer characteristics, market trends, and the impact of standards on subgroups of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of preliminary research directed at characterizing the a utomatic c ommercial i ce m aker
manuf acturing industry. This research involved collecting data on market share, sales volumes and trends, pricing, employment, and the industry financial structure.

In Phase II, “Industry Cash Flow Model and Interview Guide ,” DOE created a framework
GRIM to an alyze the economic impact of amended energy conservation standards on the

a utomatic c ommercial i ce m aker manufacturing industry as a whole. The DOE also developed a manufacturer interview guide to gather additional information used to assess the potential impacts on manufacturers in Phase III

.
In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers representing
an estimated 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 discussed financial topics specific to each
manufacturer and obtained each manufactu rer’s view of the industry. The interviews provided DOE with valuable information for evaluating the impacts of amended energy conservation standards on manufacturer cash flows, investments, and employment.12

- 2 

### 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 of the n otice of p roposed r ulemaking (NOPR) t echnical s upport d ocument (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, equipmen t attributes, shipments, manufacturer markups, and the cost stru cture 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 amended energy conservation standards on
the a utomatic c ommercial i ce m aker manufacturing industry as a whole. 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 altering

revenu e due to higher per

- unit prices and/or possible changes in sales volumes. DOE created a
framework GRIM to analyze the economic impact of amended energy conservation standards on the automatic commercial ice maker manufacturing industry as a whole

. In p reparing the GRIM

, DOE used the financial values derived during Phase I and the shipment assumptions from the national impact analysis ( NIA )

. Additionally , DOE prepared a written guide for manufacturer
interviews to collect additional data critical to deve lop ing other inputs for the GRIM

.

12.2.2.1 Industry Cash

- Flow Analysis

The GRIM uses several factors to determine a series of annual cash flows from the announcement year of amended energy conservation standards until several years after the standards’ compliance d ate. The INPV is the sum of these annual cash flows discounted by the industry average weighted average cost of capital (WACC)

. Inputs to the GRIM include the manufacturing costs, markups, and shipment forecasts developed in other analyses as well as the
i ndustry average financial parameters developed in Phase I

. DOE derived the manufacturing costs from the engineering analysis as presented in chapter 5 of th is NOPR TSD , information provided by the industry, publicly available financial reports, and intervi ews 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 chapter 10 of the
NOPR TSD, provided the basis for the shipment projections. DOE derived the financial parameters using publicly available reports and revised them using information received during
confidential manufacturer interviews. DOE used the GRIM to compare INPV in the ba se case

a A vailable online at www.sec.gov

. b A vailable online at www2.standardan dpoors.com

.12

- 3 

### with INPV at various TSLs (the standards cases). The difference in INPV between the base and standards cases represents the financial impact of the amended standard on manufacturers.

12.2.2.2 Interview Guide

During Phase III of the MIA, DOE interviewed manu facturers of a utomatic c ommercial
i ce m aker s to gather information on the effects of amended energy conservation standards on revenues and finances, direct employment, capital assets, and industry competitiveness. Before the interviews, DOE distributed an interview guide to representatives of each participating manufacturer. The interview guide provided a starting point to help identify relevant issues and understand the impacts of amended energy conservation standards on individual manufacturers or subgrou ps of manufacturers. 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 an d life cycle cost
follow

- up; (3) manufacturer markups and profitability; (4) financial parameters; and (5) conversion costs. The interview guide is presented in TSD a ppendix 12A

.

12.2.3 Phase III: Subgroup Analysis

While conducting the MIA, DOE interviewed a rep resentative 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 GRIM and to isolate key iss ues and concerns. During interviews, DOE identif ied one manufacturer subgroup , small business manufacturers

, 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 manufacturer 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 informati on to be considered in the rulemaking process.

DOE used these interviews to tailor the GRIM 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 soug ht interactive interviews, which help clarify responses and identify additional issues. The resulting information provides valuable inputs to the GRIM.

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 12.4 .3 provides more information on how DOE calculated the parameters.12

- 4 

### 12.2.3.3 Manufacturer Subgroup Analysis

Using average cost assumptions to develop an estimate of industry cash flow may not adequ ately assess differential impacts of amended energy conservation standards on manufacturer subgroups. For example, small businesses, manufacturers of niche equipment , or companies exhibiting a cost structure that differs significantly from the industry ave rage 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 characteristics are the most appropriate to analyze. Through the course of the rulemaking, DOE identified small
business manufacturers as a subgroup that req uires separate analysis.

12.2.3.4 Small

- Business Manufacturer s

DOE used the Small Business Administration (SBA) small business size standards as amended by the Office of Management and Budget (OMB) on January 1, 2012, and effective January 7, 2013 , and the North Am erican Industry Classification System (NAICS) code, presented in Table 12.2

. 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 Potentially Affected by This Rulemaking Industry Description Revenue Limit Employee Limit NAICS
Air

- Conditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing N/A 750 33 3415



### DOE used the Air

- Conditioning, Heating, and Refrigeration Institute ( AH RI ) 1 

### certified product directory as well as public certification databases from the California Energy Commission (CEC) 2 

### and ENERGY STAR 3 

### to identify manufacturers of a utomatic c ommercial
i ce m aker s. DOE then checked this list of i ce m aker manufacturers against the employee limi t for small businesses using industry reports from vendors such as Dun & Bradstreet. DOE also consulted publicly available data from the 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 manufacturers and checked any companies identified against the small business criteria

. During its research, DOE identified seven manufacturer s of

a utomat ic c ommercial i ce m aker s that qualify as small business es as specified by the SBA employee limits

.

c The size standards are available on the SBA’s website at www.sba.gov/content/table

- small

- business

- size

- standards12

- 5 

### 12.2.3.5 Manufacturing Capacity Impact

O utcome s of amended energy conservation standards may include the obsolescence of existing manufacturing assets and temporary scarcity of critical high

- efficiency components . The manufacturer interview guide contains a series of questions to help identify impacts of amended standards on manufacturing capacity, specifically capacity utilization and plant location
de cisions in the United States and North America, with and without amended standards; the ability of manufacturers to upgrade or remodel existing facilities to accommodate the new requirements; the nature and value of any stranded assets; estimates for any o ne

- time changes to existing plant, property, and equipment (PPE) ; and the availability of high

- efficiency components identified as design options in the engineering analysis . DOE’s estimates of the one

time capital changes and stranded assets affect the pr ojections of annual cash flow in the GRIM.
These estimates can be found in section 12.4.8 ; DOE’s discussion of the capacity impact can be
found in section 12.7.2

.

12.2.3.6 Employment Impact

The impact of amended energy conservation standards on employment is an important consideration in the rulemaking process. To ass ess how domestic direct employment patterns might be affected, the interviews explored current employment trends in the a utomatic

c ommercial i ce m aker industry. The interviews also solicited manufacturer views on changes in employment pat terns that may res ult from more

- stringent standards. The employment impacts section of the interview guide focused on current employment levels associated with manufacturers at each production facility, expected future employment levels with and without amended energy conse rvation standards, and differences in workforce skills and issues related to the retraining of employees. The employment impacts are reported in section 12.7.1

.

12.2.3.7 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to amended energy conservation standards and other regulatory actions affecting the same equipment . DOE analyzed the impact on manufacturers of multiple, equipment

- specific regulatory actions. Based on its own research and discussions with manufacturers, DOE identified regulations relevant to

a utomatic c ommercial i ce m aker manufacturers, such as other Federal regulations that impact other products and equipment made by the same manufacturers. Discussion of the cumulative regulatory burden can be found in section 12.7.3

.

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company regarding the energy conservation standard rulemaking?” This question prompts manufacturers to identify the issu es they feel DOE should explore and discuss further during the interview. The following section describes key issues manufacturers mentioned for all product classes under
review.12

- 6 

### 12.3.1 Price Sensitivity

All manufacturers interviewed characterized the market for automatic commercial ice makers as extremely price sensitive. They hold the position that new and amended standards will result in decreased profit margins as they will be unable to pass through costs relating to standards compliance. They noted that this will be particularly troublesome for lower capacity equipment classes ( small self

- contained units ( SCU ) and small ice

- making heads ( IMH) ) , which are sold primarily to smaller restaurants and food service establishments with limited access to capital. Addi tionally, they noted that distributors tend to be individual proprietors or small franchises with limited opportunities to extend financing to their customers. Manufacturers went on to report that while energy efficiency is important, it is not a feature f or which customers would pay a premium.

One manufacturer also noted that replacement parts represented a large fraction of sales, and while sales of parts had increased since 2009, unit sales had decreased, indicating that customers were holding onto unit s longer. The ability to extend the life of a unit through repairs and refurbishment presents a further economic challenge to manufacturers facing energy efficiency standards.

12.3.2 Enforcement

Manufacturers characterized the automatic commercial ice maker marke t as a niche market with a high degree of competition. The recent entrance of foreign manufacturers has led to a further tightening of price competition due to the lower labor costs of these foreign manufacturers. Several domestic manufacturers expressed c oncern about the enforcement of an amended energy efficiency standard for automatic commercial ice makers produced overseas. Manufacturers believe that insufficient enforcement will lead to market distortions, as companies that make the necessary investmen ts to meet amended standards would be at a distinct pricing disadvantage to unscrupulous competitors that do not fully comply. The manufacturers requested that DOE take the enforcement action necessary to maintain a level playing field and to eliminate

non

- compliant products from the market.

12.3.3 Reliability Impacts

Some manufacturers expressed concerns that future energy conservation standards would have an adverse impact on the reliability of their equipment . One manufacturer stated that any time new components or designs are introduced, that there is an increase in service calls and the mean time between failures drops as they work out the issues. This manufacturer went on to emphasize that reliability is the most important feature o f their equipment

.

12.3.4 Impact on Innovation

Several manufacturers expressed concerns over the imbalance of internal engineering resources brought about by the regular revision and introduction of energy conservation standards. As energy use has become increasingly regulated, manufacturers have had t o shift engineering and support resources away from other initiatives, adversely affecting innovation outside of energy efficiency. One manufacturer reported that a previous round of standards required nearly all of the company’s engineering resources for between 1 and 2 years. Where the12

- 7 

### R&D effort required for compliance is intermittent, innovation is impacted without adding to overall employment.

12.4 GRIM INPUTS AND ASSU MPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to ame nded energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM. Data and assumptions from these sources are then fed into an accounting model that calculates the industry cash flow both with and without amended energy conse rvation standards.

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4

. 1 , is an annual cash flow analysis that uses manufacturer prices, m anufacturing costs, shipments, and industry financial information as inputs, and accepts a set of regulatory conditions such as changes in costs, the
need for additional investments, and changes in associated margins. The GRIM spreadsheet uses
a number of inputs to arrive at a series of annual cash flows, beginning with the base year of the analysis, 201 3 , and continuing to 204 7 , 30 years after the compliance year of the rulemaking

. The model calculates the INPV by summing the stream of annual discounted ca sh flows during this period. 4



### Figure 12.4

. 1 Using the GRIM to Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares INPV
between the base

- case and the standard

- case scenario s

. The difference in INPV between the base
case and each TSL in the standard case represents the estimated financial impact of the amended energy conservation standards on manufacturers. Appendix 12B provides more technical details

an d user information for the GRIM.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash flow. These sources include corporate annual reports, company profiles, U.S. Census data, credit ratings, the ship ments model, the engineering analysis, and the manufacturer interviews.

12.4.2.1 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. Additionally, some pri vately held companies12

- 8 

### publish annual financial reports on their corporate websites. DOE developed initial financial inputs for the GRIM by examining the publicly available annual reports of companies engaged in
the manufacture of a utomatic c ommercial i ce m aker 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 developing its initial estimates of the financial parameters to
be used in the GRIM. In doing so, DOE assumes that the industry

- average figures calculated for these companies were representative of manufacturing for a utomatic c ommercial i ce m aker s. These figures were later revised using feedback from interviews. DOE used corporate annual

r eports to derive the following initial inputs to the GRIM:

• t ax rate

• w orking capital

• SG&A

• R&D

• d epreciation

• c apital expenditures

• n et PPE

12.4.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. DOE relied on S&P reports to determine the industry’s average cost of debt when calculating the weighted

average cost of capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the national impact analysis. The model re lied on historical shipments data for a utomatic

c ommercial i ce m aker s. Chapter 10 of the NOPR TSD describes the methodology and analytical model DOE used to forecast shipments.

12.4.2.4 Engineering Analysis

DOE conducted the engineering analys i s for this rulema king using the efficiency

- level
and cost

- assessment approach, to develop a cost estimate for each efficiency level of each equipment class of a utomatic c ommercial i ce m aker s. During this analysis, DOE used a manufacturing cost model to develop manufacturer production cost (MPC) estimates for

a utomatic c ommercial i ce m aker s. The analysis yielded the materials, labor, overhead, and total production costs for equipment at each efficiency level. The engineering analysis also estimated a manufacturer markup to d etermine the manufacturer selling price (MSP) for each equipment
class at each efficiency level. Chapter 5 of the NOPR TSD describes the engineering analysis in greater detail.

12.4.2.5 Manufacturer Interviews

As part of the MIA, DOE conducted interviews with a representative cross

- section of manufacturers. Through these discussions, DOE obtained information to determine and verify12

- 9 

### GRIM input assumptions. Key topics discussed during the interviews and reflected in the GRIM
include the following :

• c apital conversion costs (one

- time investments in PPE);

• p roduct conversion costs (one

- time investments in research, product development, testing, and marketing);

• e quipment cost structure, or the portion of the MPCs relate d to materials, labor, overhead, and depreciation costs;

• p rojected total shipment and shipment distribution mix; and

• MPCs estimated in the engineering analysis.

12.4.3 Financial Parameters

In the manufacturer interviews, DOE used the financial parameters from 200 5 to 201 1 for

two manufacturers of automatic commercial ice makers who command a combined market share
of over 65 percent as a starting point for determining the a utomatic c ommercial i ce m aker 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 fraction of the market that was not represented. Table 12.4

. 1 shows the data used to determine the initial financial parameter estimates.

Table 12.4

. 1 GRIM Financial Parame ters based on 2003

– 2010 Weighted Company
Financial Data

Parameter Industry Weighted Average Manufacturer

A B

Tax Rate (% of Taxable Income) 33.6 40.5 29.5 Working Capital (% of Revenue) 9.3 16.4 5.2 SG&A (% of Revenue) 15.4 14.9 15.7 R&D (% of Revenues) 1.5 1.2 1.7 Depreciation (% of Revenues) 2.1 2.5 1.9 Capital Expenditures (% of Revenues) 1.5 1.7 1.3 Net Property, Plant, and Equipment (% of Revenues) 16.4 23.1 12.5



### During interviews, manufacturers were asked to provide their own figures for the parameters listed in Table 12.4

. 1

. Where applicable, DOE adjusted the financial parameters according to the manufacturers’ feedback.

12.4.4 Corporate Discount Rate

A company’s assets are financed by a combination of debt and equity

, and the WACC represents the minimum rate of return necessary to cover the d ebt 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 the a utomatic c ommercial i ce m ak er manufacturing industry based on two representative companies, using the following formula:12

- 10 

### WACC = [ After

- Tax Cost of Debt x (Debt Ratio) ] + [ Cost of Equity x (Equity Ratio) ] Eq. 12

. 1

The cost of equity is t he 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 ) Eq. 12

. 2

W here:

Risk

- free rate of return = 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 bec ause it captures long

- term inflation expectations and is less volatile than short

- term rates. The risk

- free rate is estimated to be approximately 5. 1 percent, which is the average 10

- year T

- Bill return between 19 28 and 201 2

. Risk premium = the difference b etween 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 2012 as the expected return on stocks to arrive at an estimated market risk premium of 6. 2 percent. β = the correlati on between the movement in the price of the stock and that of the broader market. In this case, β ( b eta ) equals one if the stock is perfectly correlated with the S&P 500
market index. A β lower than one means the stock is less volatile than the market inde x. Values for β are only available for publicly traded companies.

DOE used the capital asset pricing model to calculate the cost of equity for two publicly
traded manufacturers whose combined market share is over 65 percent. DOE determined that the industry average cost of equity for the a utomatic c ommercial i ce m aker industry is 1 9.4 percent ( see Table 12.4

. 2 ).

Table 12.4

. 2 Cost of Equity Calculation

Parameter Industry Weighted Average
% Manufacturer

A B

( 1) Average Beta ( β )* 2.3 0.7 3.3 (2) Yield on 10

- Year
T

- Bill (19 28

- 2010) 5. 1

- - (3) Market Risk Premium (192 8

- 2010 ) 6. 2

- - Cost of Equity (2)+[(1)*(3)] 19.4 9

. 4 25.4 Equity/Total Capital 50.1 100 .0 20.7

* β is the correlation between the movement in the price of the stock and that of the broader market.



### Bond ratings are a tool to measure default risk and arrive at a cost of debt. Each bond rating is associated with a particular spread. One way of estimating a company’s cost of debt is to treat it as a spread (usually expressed in basis points) over the ri sk

- free rate. DOE used this12

- 11 

### method to calculate the cost of debt for two manufacturers by using S&P and other estimates of corporate credit ratings and adding the relevant spread to the risk

- free rate.

Because proceeds from debt issuance are tax deductibl e, 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 a utomatic c ommercial i ce

m aker industry is 7.8 percent

. Table 12.4

. 3 presents the derivation of the cost of debt and the capital structure of the industry ( i.e. the debt ratio (debt/total capital)).

Table 12 .4

. 3 Cost of Debt Calculation

Parameter Industry Weighted Average
% Manufacturer

A B

Estimated Bond Rating

-- AAA B+ (1) Yield on 10

- Year T

- Bill (19 28

- 2010) 5. 1

- -
(2) Gross Cost of Debt 7.8 5.7 9.0 (3) Tax Rate 33.6 40.5 29.5
Net Cost of Debt [ (2) x ((1)

- (3)) ] 5.2

- - Debt/Total Capital 49.9 0.0 79.3



### Correcting for an inflation rate of 3.1 percent over the analysis period, DOE’s calculated
value for the a utomatic c ommercial i ce m aker industry’s inflation

- adjusted WACC and the initial
estimate of the discount rate is 9.2 percent.

12.4.5 Trial Standard Levels

DOE developed TSLs to analyze the impact on manufacturers of amended energy efficiency standards for 25 equipment classes of a utomatic c ommercial i ce m aker s

. For equipment classes with existing energy conservation standards, e fficiency levels (ELs) are based

on the percent reduction of energy use below existing standards according to the current test procedure . For equipment classes that were not p reviously covered, efficiency levels are based on the percent reduction below equivalent existing standards. Table 12.4.4 and Table 12.4

. 5 present the TSLs and the corresponding equipment class efficiency levels. 10 CFR part 431

, subpart H

TSL 5 was selected at the max

- tech l evel for all equipment classes. TSL 4 was chosen as
an intermediate level between the max

- tech level and the maximum customer NPV level, subject to the requirement that the TSL 4 NPV must be positive. “Customer NPV” is the NPV of future savings obtained from the NIA. It provides a measure of the benefits only to the customers of the
automatic commercial ice m akers

. TSL 3 was chosen to represent the group of efficiency levels with the highest customer NPV at a 7

- percent discount rate. TSL 2 was selected to provide intermediate efficiency levels that fill the gap between the TSLs 1 and 3. Note that with the
numb er 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. The intent of TSL 2 is to provide an intermediate level to preclude big jumps in efficie ncy between TSLs 1 and 3.
EL 2 was selected for all equipment classes in TSL 1

. In the analysis, EL 2 corresponds to the
ENERGY STAR specification for products rated by ENERGY STAR, and an equivalent efficiency improvement for other equipment classes.12

- 12 

### Tabl e 12.4

. 4 Trial Standard Levels for Batch Automatic Commercial Ice Maker s

Equipment Class Base
Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 5 EL 6

Reduction (%) 0.0% 10.0% 15.0% 25.0% 25.0% 29.4%

IMH

- W

- Medium

- B Efficiency Level EL 1 EL 2 EL 2 EL 3 EL 4 EL 5

Reduction (%) 0.0% 10.0% 10.0% 15.0% 20.0% 21.3%

IMH

- W

- Large

- B Efficiency Level EL 1 EL 2 EL 2 EL 2 EL 3 EL 4

Reduction (%) 0.0% 10.0% 10.0% 10.0% 15.0% 15.5%

IMH

- A

- Small

- B Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 7

Reduction (%) 0.0% 10.0% 15.0% 25.0% 30.0% 31.3%

IMH

- A

- Large

- B Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 6

Reduction (%) 0.0% 10.0% 15.0% 23.7% 26.9% 26.9%

RCU

- NRC

- Small

- B Efficiency Level EL 1 EL 2 EL 2 EL 3 EL 6 EL 6

Reduction (%) 0.0% 9.0% 9.0% 15.0% 29.4% 29.4%

RCU

- NRC

- Large

- B Efficiency Level EL 1 EL 2 EL 2 EL 3 EL 4 EL 5

Reduction (%) 0.0% 9.0% 9.0% 15.0% 20.0% 20.6%

RCU

- RC

- Small

- B Efficiency Level EL 1 EL 2 EL 2 EL 3 EL 6 EL 6

Reduction (%) 0.0% 9.0% 9.0% 15.0% 29.4% 29.4%

RCU

- RC

- Large

- B Efficiency Level EL 1 EL 2 EL 2 EL 3 EL 4 EL 5

Reduction (%) 0.0% 9.0% 9.0% 15.0% 20.0% 20.6%

SCU

- W

- Small

- B Efficiency Level EL 1 EL 2 EL 4 EL 6 EL 7 EL 7

Reduction (%) 0.0% 7.0% 20 .0% 3 0 .0% 31.3 % 31.3%

SCU

- W

- Large

- B Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 7

Reduction (%) 0.0% 7.0% 15.0% 25.0% 30.0% 30.2%

SCU

- A

- Small

- B Efficiency Level EL 1 EL 2 EL 4 EL 6 EL 7 EL 7

Reduction (%) 0.0% 7.0% 20.0% 30.0% 39.3% 39.3%

SCU

- A

- Large

- B Efficiency Level EL 1 EL 2 EL 4 EL 6 EL 7 EL 7

Reduction (%) 0.0% 7.0% 20.0% 30.0% 34.9% 34.9%12

- 13 

### Table 12.4

. 5 Trial Standard Levels for Continuous Automatic Commercial Ice Maker s

Equipment Class Base
Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- C Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 6

Reduction (%) 0.0% 10.0% 15.0% 2 5 .0% 26.4% 26.4%

IMH

- W

- Large

- C Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 6

Reduction (%) 0.0% 10.0% 15.0% 25.0% 26.4% 26.4%

IMH

- A

- Small

- C Efficiency Level EL 1 EL 2 EL 3 EL 4 EL 5 EL 7

Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 31.0%

IMH

- A

- Large

- C Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 7

Reduction (%) 0.0% 10.0% 15.0% 25.0% 30.0% 30.2%

RCU

- NRC

- Small

- C Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 7

Reduction (%) 0.0% 7.0% 17.0% 26.0% 30.0% 30.2%

RCU

- NRC

- Large

- C Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 7

Reduction (%) 0.0% 7.0% 17.0% 26.0% 30.0% 30.2%

RCU

- RC

- Small

- C Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 7

Reduction (%) 0.0% 7.0% 17.0% 26.0% 30.0% 30.2%

RCU

- RC

- Large

- C Efficiency Level EL 1 EL 2 EL 3 EL 5 EL 6 EL 7

Reduction (%) 0.0% 7.0% 17.0% 26.0% 30.0% 30.2%

SCU

- W

- Small

- C Efficiency Level EL 1 EL 2 EL 3 EL 4 EL 4 EL 6

Reduction (%) 0.0% 3.0% 10.0% 16.0% 16.0% 22.7%

SCU

- W

- Large

- C Efficiency Level EL 1 EL 2 EL 2 EL 2 EL 2 EL 2

Reduction (%) 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

SCU

- A

- Small

- C Efficiency Level EL 1 EL 2 EL 3 EL 4 EL 4 EL 6

Reduction (%) 0.0% 7.0% 15.0% 20.0% 20.0% 28.2%

SCU

- A

- Large

- C Efficiency Level EL 1 EL 2 EL 2 EL 2 EL 2 EL 2

Reduction (%) 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%



### 12.4.6 NIA Shipment Forecast

The GRIM estimates manufacturer revenues based on total

- unit

- shipment forecasts and the distribution of these values by efficiency level. Changes in the efficiency mix at each standard level are a key driver of manufacturer finances. For this analysis, the GRIM used
automatic commercial ice maker shipment data from the NIA

. Chapter 10 of the NOPR TSD explains DOE’s calculations of total shipments in detail. Table 12.4.6 shows total s hipments
forecast s for a utomatic c ommercial i ce m aker s in 201 8

.12

- 14 

### Table 12.4

. 6 Total Base

- Case 201 8 NIA Shipments

Equipment Class Total Industry Shipments

IMH

- W

- Small

- B 9,004

IMH

- W

- Medium

- B 5,751

IMH

- W

- Large

- B 954

IMH

- A

- Small

- B 53,695

IMH

- A

- Large

- B 32,002
RCU

- NRC

- Small

- B 6,512
RCU

- NRC

- Large

- B 8,647
RCU

- RC

- Small

- B 4,254
RCU

- RC

- Large

- B 3,405
SCU

- W

- Small

- B 1,345
SCU

- W

- Large

- B 429
SCU

- A

- Small

- B 27,474
SCU

- A

- Large

- B 13,009

IMH

- W

- Small

- C 1,356

IMH

- W

- Large

- C 340

IMH

- A

- Small

- C 7,008

IMH

- A

- Large

- C 2,128
RCU

- NRC

- Small

- C 1,331
RCU

- NRC

- Large

- C 1,336
RCU

- RC

- Small

- C 308
RCU

- RC

- Large

- C 378
SCU

- W

- Small

- C 295
SCU

- W

- Large

- C

- SCU

- A

- Small

- C 17,351
SCU

- A

- Large

- C

-



### 12.4.6.1 Base

- Case Shipments Forecast

As part of the shipment analysis, DOE estimated the shipment distribution by efficiency
level for a utomatic c ommercial i ce m aker s. DOE held the base

- case energy efficiency distribution constant throughout the forecast period. Table 12.4

. 7 and Table 12.4

. 8 show the
base

- case distributions of shipments by efficiency level estimated in the NIA for batch and
continuous a utomatic c ommercial i ce m aker equipment classes.12

- 15 

### Table 12.4

. 7 Base

- Case Distribution of Shipments for Batch Automatic Commercial Ice
Maker s in 201 8

Product Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 29.4%

% of the Market at EL 39.1% 26.1% 23.9% 10.9% 0.0% 0.0%

IMH

- W

- Medium

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 21.3%

% of the Market at EL 69.0% 16.7% 11.9% 0.0% 2.4%

IMH

- W

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 15.5%

% of the Market at EL 62.4% 11.8% 3.7% 22.1%

IMH

- A

- Small

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.3%

% of the Market at EL 37.0% 31.5% 25.9% 5.6% 0.0% 0.0% 0.0%

IMH

- A

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 23.7% 26.9%

% of the Market at EL 40.2% 41.2% 10.3% 8.3% 0.0% 0.0%

RCU

- NRC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4%

% of the Market at EL 32.7% 42.3% 17.3% 7.7% 0.0% 0.0%

RCU

- NRC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6%

% of the Market at EL 42.2% 39.6% 8.9% 0.0% 9.3%

RCU

- RC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4%

% of the Market at EL 32.7% 42.3% 17.3% 7.7% 0.0% 0.0%

RCU

- RC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6%

% of the Market at EL 42.2% 39.6% 8.9% 0.0% 9.3%

SCU

- W

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 31.3%

% of the Market at EL 21.4% 42.9% 21.4% 14.3% 0.0% 0.0% 0.0%

SCU

- W

- Large

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 30.2%

% of the Market at EL 28.7% 0.0% 14.2% 0.0% 42.9% 0.0% 14.2%

SCU

- A

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 39.3%

% of the Market at EL 17.1% 40.0% 5.7% 11.4% 14.3% 11.4% 0.0%

SCU

- A

- Large

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 34.9%

% of the Market at EL 28.6% 35.7% 0.0% 7.1% 21.4% 7.1% 0.0%12

- 16 

### Table 12.4

. 8 Base

- Case Distribution of Shipments for Continuous Automatic Commercial
Ice Maker s in 201 8

Product Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 26.4%

% of the Market at EL 23.1% 23.1% 23.1% 3.8% 7.7% 19.2%

IMH

- W

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 26.4%

% of the Market at EL 46.8% 13.2% 20.0% 0.0% 20.0% 0.0%

IMH

- A

- Small

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.0%

% of the Market at EL 22.9% 22.9% 14.3% 8.6% 17.1% 2.9% 11.4%

IMH

- A

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 30.2%

% of the Market at EL 35.0% 20.0% 15.0% 15.0% 0.0% 5.0% 10.0%

RCU

- NRC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%

% of the Market at EL 26.7% 26.7% 46.7% 0.0% 0.0% 0.0% 0.0%

RCU

- NRC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%

% of the Market at EL 13.0% 30.5% 56.5% 0.0% 0.0% 0.0% 0.0%

RCU

- RC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%

% of the Market at EL 26.6% 26.6% 46.8% 0.0% 0.0% 0.0% 0.0%

RCU

- RC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%

% of the Market at EL 13.0% 30.4% 56.6% 0.0% 0.0% 0.0% 0.0%

SCU

- W

- Small

- C Reduction (%) 0.0% 3.0% 10.0% 16.0% 20.0% 22.7%

% of the Market at EL 100.0% 0.0% 0.0% 0.0% 0.0% 0.0%

SCU

- W

- Large

- C Reduction (%) 0.0% 0.0%

% of the Market at EL 0.0% 0.0%

SCU

- A

- Small

- C Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 28.2%

% of the Market at EL 26.7% 20.0% 16.7% 13.3% 3.3% 20.0%

SCU

- A

- Large

- C Reduction (%) 0.0% 0.0%

% of the Market at EL 0.0% 0.0%



### 12.4.6.2 Standards

- Case Shipments Forecast

To examine the impact of amended energy conservation standards on shipments, which in turn affects the INPV, DOE used the base

- case shipments described in the previous section as a point of comparison for shipments forecast in the standards case. For each TSL described in the standards case, DOE used the shipments forecasts developed in the NIA for automatic
commercial ice m aker s. DOE used a roll

- up scenario to determine efficiency distributions for the
standards case. In this scenario, shipments of products that fall below the amended energy
conservation standards are assumed to “roll

- up” to the new standards on the compliance d ate d



### and thereafter

.
As in the shipments analysis, DOE assumed no relative price elasticity in the a utomatic

c ommercial i ce m aker market, meaning that amended energy conservation standards that

d The estimated compliance date for the automatic commercial ice maker energy conservation standard is January
2018.12

- 17 

### increase the first cost of a utomatic c ommercial i ce m aker s would not impact the number of total shipments.

12.4.7 Production Costs

Changes in the MPCs of a utomatic c ommercial i ce m aker s can affect revenues, gross margins, and cash flow of the industry, making equipment production cost data key GRIM inputs
for DOE’s ana lysis. In the engineering analysis, DOE created separate cost curves for 12 equipment classes using data from tear

- downs to develop both the baseline MPCs and the incremental costs that correspond to the proposed design options. For equipment classes in which DOE directly analyzed equipment at either end of the range of applicable harvest capacities, DOE weighted production costs at each efficiency level by relative shipments to combine these cost efficiency curves for analysis in the GRIM

. For the remaining 13 equipment classes, which were not directly analyzed, DOE created scaled cost curves b ased on the aggregate cost of constituent components f rom analogous product classes and the design options identified. Generally, manufactu ring higher efficiency equipment is more costly than manufacturing
baseline equipment due to the use of more

- complex , high

- efficiency components.

The cost model disaggregated the MPCs at each efficiency level into material, labor, overhead, and depreciatio n. For materials, DOE used the incremental component and raw material costs that correspond to the proposed design options at each EL . For labor, DOE
estimated the labor contribution at each EL by examining how the proposed 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 estimat es and assumptions during interviews. DOE used the resulting MPCs and cost breakdowns as described in section 12.4.2.4 , and further detailed in chapter 5 of the NOP R TSD, for each efficiency level analyzed in the GRIM 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

a utomatic c ommercial i ce m aker s by multiplying the MPCs by the appropriate manufacturer markup for that product. Table 12.4.9 through Table 12.4

. 33 show the production cost estimates used in the GRIM for the representative equi pment classes of a utomatic c ommercial i ce m aker s.12

- 18 

### Table 12.4

. 9 MPC Breakdown for Equipment Class 1 : IMH

- W

- Small

- B

EL % Reduction ( below
baseline ) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $759.53 $49.44 $23.43 $60.22 $892.62 1.250 $1,115.78

EL 2 10.0% $779.89 $49.44 $23.97 $59.68 $912.98 1.250 $1,141.23

EL 3 15.0% $796.04 $49.44 $24.39 $59.26 $929.13 1.250 $1,161.42

EL 4 20.0% $831.35 $49.44 $25.32 $58.33 $964.44 1.250 $1,205.55

EL 5 25.0% $893.64 $50.03 $26.99 $57.52 $1,028.19 1.250 $1,285.24

EL 6 29.4% $1,127.02 $50.66 $33.16 $52.26 $1,263.10 1.250 $1,578.88



### Table 12.4

. 10 MPC Breakdown for Equipment Class 2 : IMH

- W

- Medium

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,173.61 $79.61 $40.32 $242.32 $1,535.86 1.250 $1,919.83

EL 2 10.0% $1,199.69 $79.61 $41.00 $241.64 $1,561.94 1.250 $1,952.43

EL 3 15.0% $1,226.92 $79.61 $41.72 $240.92 $1,589.17 1.250 $1,986.46

EL 4 20.0% $1,452.87 $79.61 $47.65 $234.99 $1,815.12 1.250 $2,268.90

EL 5 21.3% $1,461.71 $79.61 $47.88 $234.76 $1,823.96 1.250 $2,279.95



### Table 12.4

. 1 1 MPC Breakdown for Equipment Class 3 : IMH

- W

- Large

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,683.17 $122.90 $57.83 $339.07 $2,202.98 1.250 $2,753.72

EL 2 10.0% $1,733.99 $122.79 $59.14 $337.13 $2,253.05 1.250 $2,816.31

EL 3 15.0% $2,084.97 $122.22 $68.26 $324.82 $2,600.27 1.250 $3,250.34

EL 4 15.5% $2,120.16 $122.03 $69.15 $322.76 $2,634.10 1.250 $3,292.62



### Table 12.4

. 12 MPC Breakdown for Equipment Class 4 : IMH

- A

- Small

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $774.20 $48.38 $23.89 $63.59 $910.07 1.250 $1,137.59

EL 2 10.0% $799.64 $48.59 $24.57 $63.22 $936.02 1.250 $1,170.03

EL 3 15.0% $817.44 $49.03 $25.07 $63.41 $954.94 1.250 $1,193.68

EL 4 20.0% $835.23 $49.47 $25.56 $63.60 $973.86 1.250 $1,217.33

EL 5 25.0% $857.50 $49.51 $26.15 $63.08 $996.25 1.250 $1,245.31

EL 6 30.0% $1,090.50 $49.51 $32.19 $54.16 $1,226.37 1.250 $1,532.96

EL 7 31.3% $1,095.77 $49.51 $32.41 $56.83 $1,234.52 1.250 $1,543.15



### Table 12.4

. 13 MPC Breakdown for Equipment Class 5 : IMH

- A

- Large

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,133.15 $110.03 $39.63 $226.85 $1,509.67 1.250 $1,887.09

EL 2 10.0% $1,158.73 $110.07 $40.30 $226.27 $1,535.37 1.250 $1,919.22

EL 3 15.0% $1,171.42 $110.00 $40.63 $225.78 $1,547.84 1.250 $1,934.80

EL 4 20.0% $1,217.24 $110.17 $41.85 $224.95 $1,594.21 1.250 $1,992.76

EL 5 23.7% $1,253.84 $110.17 $42.80 $223.75 $1,630.57 1.250 $2,038.21

EL 6 26.9% $1,499.16 $110.50 $49.27 $217.90 $1,876.83 1.250 $2,346.0412

- 19 

### Table 12.4

. 14 MPC Breakdown for Equipment Class 6 : RCU

- NRC

- Small

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,196.47 $84.98 $39.34 $178.05 $1,498.84 1.250 $1,873.56

EL 2 9.0% $1,217.57 $84.98 $39.90 $177.49 $1,519.95 1.250 $1,899.93

EL 3 15.0% $1,235.17 $84.98 $40.36 $177.03 $1,537.54 1.250 $1,921.93

EL 4 20.0% $1,274.78 $84.98 $41.40 $175.99 $1,577.15 1.250 $1,971.44

EL 5 25.0% $1,317.40 $85.22 $42.53 $175.12 $1,620.26 1.250 $2,025.33

EL 6 29.4% $1,551.79 $85.37 $48.68 $168.66 $1,854.50 1.250 $2,318.13



### Table 12.4

. 15 MPC Breakdown for Equipment Class 7 : RCU

- NRC

- Large

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,609.47 $116.31 $52.86 $235.15 $2,013.78 1.250 $2,517.23

EL 2 9.0% $1,640.11 $116.32 $53.67 $234.49 $2,044.59 1.250 $2,555.74

EL 3 15.0% $1,690.62 $116.47 $55.01 $233.44 $2,095.54 1.250 $2,619.43

EL 4 20.0% $2,008.53 $116.58 $63.36 $225.15 $2,413.62 1.250 $3,017.02

EL 5 20.6% $2,029.30 $116.70 $63.91 $224.65 $2,434.56 1.250 $3,043.19



### Table 12.4

. 16 MPC Breakdown for Equipment Class 8 : RCU

- RC

- Small

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,196.47 $84.98 $39.34 $178.05 $1,498.84 1.250 $1,873.56

EL 2 9.0% $1,217.57 $84.98 $39.90 $177.49 $1,519.95 1.250 $1,899.93

EL 3 15.0% $1,235.17 $84.98 $40.36 $177.03 $1,537.54 1.250 $1,921.93

EL 4 20.0% $1,274.78 $84.98 $41.40 $175.99 $1,577.15 1.250 $1,971.44

EL 5 25.0% $1,317.40 $85.22 $42.53 $175.12 $1,620.26 1.250 $2,025.33

EL 6 29.4% $1,551.79 $85.37 $48.68 $168.66 $1,854.50 1.250 $2,318.13



### Table 12.4

. 17 MPC Breakdown for Equipment Class 9 : RCU

- RC

- Large

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,609.47 $116.31 $52.86 $235.15 $2,013.78 1.250 $2,517.23

EL 2 9.0% $1,640.11 $116.32 $53.67 $234.49 $2,044.59 1.250 $2,555.74

EL 3 15.0% $1,690.62 $116.47 $55.01 $233.44 $2,095.54 1.250 $2,619.43

EL 4 20.0% $2,008.53 $116.58 $63.36 $225.15 $2,413.62 1.250 $3,017.02

EL 5 20.6% $2,029.30 $116.70 $63.91 $224.65 $2,434.56 1.250 $3,043.19



### Table 12.4

. 18 MPC Breakdown for Equipment Class 10 : SCU

- W

- Small

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $848.61 $56.26 $28.62 $156.64 $1,090.13 1.250 $1,362.66

EL 2 7.0% $864.67 $56.26 $29.04 $156.21 $1,106.18 1.250 $1,382.72

EL 3 15.0% $886.41 $56.26 $29.61 $155.64 $1,127.92 1.250 $1,409.90

EL 4 20.0% $914.14 $56.26 $30.34 $154.92 $1,155.65 1.250 $1,444.57

EL 5 25.0% $927.73 $56.26 $30.69 $154.56 $1,169.24 1.250 $1,461.5512

- 20 

### Table 12.4

. 19 MPC Breakdown for Equipment Class 11 : SCU

- W

- Large

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $947.39 $62.81 $31.95 $174.87 $1,217.01 1.250 $1,521.26

EL 2 7.0% $977.39 $62.81 $32.73 $174.08 $1,247.01 1.250 $1,558.76

EL 3 15.0% $995.66 $62.81 $33.21 $173.60 $1,265.28 1.250 $1,581.61

EL 4 20.0% $1,009.30 $62.81 $33.57 $173.24 $1,278.92 1.250 $1,598.65

EL 5 25.0% $1,035.58 $62.81 $34.26 $172.55 $1,305.20 1.250 $1,631.50

EL 6 30.0% $1,251.51 $62.81 $39.93 $166.88 $1,521.13 1.250 $1,901.42

EL 7 30.2% $1,252.16 $62.81 $39.95 $166.87 $1,521.78 1.250 $1,902.23



### Table 12.4

. 20 MPC Breakdown for Equipment Class 12 : SCU

- A

- Small

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $725.32 $79.65 $27.44 $212.75 $1,045.15 1.250 $1,306.44

EL 2 7.0% $739.81 $79.65 $27.82 $212.37 $1,059.64 1.250 $1,324.56

EL 3 15.0% $757.54 $79.65 $28.28 $211.90 $1,077.37 1.250 $1,346.72

EL 4 20.0% $769.21 $79.65 $28.59 $211.59 $1,089.05 1.250 $1,361.31

EL 5 25.0% $780.89 $79.65 $28.89 $211.29 $1,100.72 1.250 $1,375.90

EL 6 30.0% $814.96 $79.65 $29.79 $210.39 $1,134.79 1.250 $1,418.49

EL 7 39.3% $1,064.91 $79.65 $36.35 $203.83 $1,384.74 1.250 $1,730.93



### Table 12.4

. 21 MPC Breakdown for Equipment Class 13 : SCU

- A

- Large

- B

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $758.60 $89.88 $29.26 $237.05 $1,114.79 1.250 $1,393.49

EL 2 7.0% $777.63 $89.88 $29.76 $236.55 $1,133.81 1.250 $1,417.27

EL 3 15.0% $798.84 $89.88 $30.32 $235.99 $1,155.03 1.250 $1,443.79

EL 4 20.0% $827.02 $89.88 $31.06 $235.25 $1,183.21 1.250 $1,479.02

EL 5 25.0% $837.93 $89.88 $31.35 $234.96 $1,194.11 1.250 $1,492.64

EL 6 30.0% $876.65 $89.88 $32.36 $233.95 $1,232.83 1.250 $1,541.04

EL 7 34.9% $1,091.53 $89.88 $38.00 $228.31 $1,447.72 1.250 $1,809.65



### Table 12.4

. 22 MPC Breakdown for Equipment Class 14 : IMH

- W

- Small

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,566.68 $97.25 $55.88 $408.91 $2,128.72 1.250 $2,660.90

EL 2 10.0% $1,597.43 $97.25 $56.69 $408.11 $2,159.47 1.250 $2,699.34

EL 3 15.0% $1,613.02 $97.25 $57.10 $407.70 $2,175.06 1.250 $2,718.83

EL 4 20.0% $1,630.87 $97.25 $57.56 $407.23 $2,192.91 1.250 $2,741.14

EL 5 25.0% $2,032.54 $97.25 $68.11 $396.68 $2,594.58 1.250 $3,243.22

EL 6 26.4% $2,037.54 $97.25 $68.24 $396.55 $2,599.58 1.250 $3,249.4712

- 21 

### Table 12.4

. 23 MPC Breakdown for Equipment Class 15 : IMH

- W

- Large

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,714.76 $149.75 $60.09 $364.51 $2,289.11 1.250 $2,861.39

EL 2 10.0% $1,752.62 $149.79 $61.09 $363.63 $2,327.13 1.250 $2,908.91

EL 3 15.0% $1,771.81 $149.81 $61.59 $363.19 $2,346.41 1.250 $2,933.01

EL 4 20.0% $1,793.77 $149.84 $62.17 $362.69 $2,368.47 1.250 $2,960.59

EL 5 25.0% $2,287.35 $150.63 $75.21 $351.90 $2,865.10 1.250 $3,581.37

EL 6 26.4% $2,293.49 $150.64 $75.37 $351.77 $2,871.28 1.250 $3,589.09



### Table 12.4

. 24 MPC Breakdown for Equipment Class 16 : IMH

- A

- Small

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,979.33 $122.86 $70.60 $516.62 $2,689.41 1.250 $3,361.76

EL 2 10.0% $2,005.59 $122.86 $71.29 $515.93 $2,715.67 1.250 $3,394.59

EL 3 15.0% $2,018.73 $122.86 $71.63 $515.58 $2,728.80 1.250 $3,411.00

EL 4 20.0% $2,036.17 $122.86 $72.09 $515.13 $2,746.24 1.250 $3,432.80

EL 5 25.0% $2,169.03 $123.11 $75.59 $511.92 $2,879.65 1.250 $3,599.56

EL 6 30.0% $2,474.13 $123.10 $83.60 $503.89 $3,184.72 1.250 $3,980.90

EL 7 31.0% $2,503.25 $123.15 $84.37 $503.19 $3,213.95 1.250 $4,017.44



### Table 12.4

. 25 MPC Breakdown for Equipment Class 17 : IMH

- A

- Large

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,600.37 $139.76 $56.08 $340.19 $2,136.40 1.250 $2,670.50

EL 2 10.0% $1,635.16 $139.79 $57.00 $339.37 $2,171.32 1.250 $2,714.15

EL 3 15.0% $1,652.87 $139.81 $57.46 $338.95 $2,189.10 1.250 $2,736.37

EL 4 20.0% $1,670.58 $139.83 $57.93 $338.54 $2,206.88 1.250 $2,758.60

EL 5 25.0% $1,713.54 $139.88 $59.06 $337.54 $2,250.03 1.250 $2,812.53

EL 6 30.0% $2,175.67 $140.48 $71.25 $327.06 $2,714.47 1.250 $3,393.09

EL 7 30.2% $2,176.52 $140.48 $71.28 $327.05 $2,715.33 1.250 $3,394.16



### Table 12.4

. 26 MPC Breakdown for Equipment Class 18 : RCU

- NRC

- Small

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,055.71 $92.19 $36.99 $224.41 $1,409.31 1.250 $1,761.63

EL 2 7.0% $1,076.80 $92.19 $37.55 $223.86 $1,430.40 1.250 $1,788.00

EL 3 17.0% $1,107.42 $92.19 $38.35 $223.05 $1,461.02 1.250 $1,826.28

EL 4 22.0% $1,124.77 $92.19 $38.81 $222.60 $1,478.37 1.250 $1,847.96

EL 5 26.0% $1,543.23 $92.19 $49.79 $211.61 $1,896.83 1.250 $2,371.03

EL 6 30.0% $1,555.53 $92.19 $50.11 $211.29 $1,909.13 1.250 $2,386.41

EL 7 30.2% $1,556.26 $92.19 $50.13 $211.27 $1,909.86 1.250 $2,387.3312

- 22 

### Table 12.4

. 27 MPC Breakdown for Equipment Class 19 : RCU

- NRC

- Large

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $2,073.39 $181.07 $72.66 $440.74 $2,767.86 1.250 $3,459.82

EL 2 7.0% $2,115.57 $181.07 $73.76 $439.63 $2,810.04 1.250 $3,512.54

EL 3 17.0% $2,176.82 $181.07 $75.37 $438.03 $2,871.29 1.250 $3,589.11

EL 4 22.0% $2,211.52 $181.07 $76.28 $437.12 $2,905.98 1.250 $3,632.48

EL 5 26.0% $3,048.43 $181.07 $98.25 $415.15 $3,742.89 1.250 $4,678.62

EL 6 30.0% $3,073.03 $181.07 $98.90 $414.50 $3,767.49 1.250 $4,709.36

EL 7 30.2% $3,074.50 $181.07 $98.94 $414.46 $3,768.97 1.250 $4,711.21



### Table 12.4

. 28 MPC Breakdown for Equipment Class 20 : RCU

- RC

- Small

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,055.71 $92.19 $36.99 $224.41 $1,409.31 1.250 $1,761.63

EL 2 7.0% $1,076.80 $92.19 $37.55 $223.86 $1,430.40 1.250 $1,788.00

EL 3 17.0% $1,107.42 $92.19 $38.35 $223.05 $1,461.02 1.250 $1,826.28

EL 4 22.0% $1,124.77 $92.19 $38.81 $222.60 $1,478.37 1.250 $1,847.96

EL 5 26.0% $1,543.23 $92.19 $49.79 $211.61 $1,896.83 1.250 $2,371.03

EL 6 30.0% $1,555.53 $92.19 $50.11 $211.29 $1,909.13 1.250 $2,386.41

EL 7 30.2% $1,556.26 $92.19 $50.13 $211.27 $1,909.86 1.250 $2,387.33



### Table 12.4

. 29 MPC Breakdown for Equipment Class 21 : RCU

- RC

- Large

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $2,073.39 $181.07 $72.66 $440.74 $2,767.86 1.250 $3,459.82

EL 2 7.0% $2,115.57 $181.07 $73.76 $439.63 $2,810.04 1.250 $3,512.54

EL 3 17.0% $2,176.82 $181.07 $75.37 $438.03 $2,871.29 1.250 $3,589.11

EL 4 22.0% $2,211.52 $181.07 $76.28 $437.12 $2,905.98 1.250 $3,632.48

EL 5 26.0% $3,048.43 $181.07 $98.25 $415.15 $3,742.89 1.250 $4,678.62

EL 6 30.0% $3,073.03 $181.07 $98.90 $414.50 $3,767.49 1.250 $4,709.36

EL 7 30.2% $3,074.50 $181.07 $98.94 $414.46 $3,768.97 1.250 $4,711.21



### Table 12.4

. 30 MPC Breakdown for Equipment Class 22 : SCU

- W

- Small

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,176.94 $99.30 $39.50 $189.10 $1,504.85 1.250 $1,881.06

EL 2 3.0% $1,178.50 $99.30 $39.54 $189.06 $1,506.41 1.250 $1,883.01

EL 3 10.0% $1,184.05 $99.30 $39.69 $188.92 $1,511.96 1.250 $1,889.95

EL 4 16.0% $1,202.83 $99.30 $40.18 $188.42 $1,530.73 1.250 $1,913.42

EL 5 20.0% $1,515.39 $99.30 $48.39 $180.22 $1,843.30 1.250 $2,304.12

EL 6 22.7% $1,523.71 $99.30 $48.60 $180.00 $1,851.62 1.250 $2,314.52



### Table 12.4

. 31 MPC Breakdown for Equipment Class 23 : SCU

- W

- Large

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $0.00 $0.00 $0.00 $0.00 $0.00 1.250 $0.00

EL 2 0.0% $0.00 $0.00 $0.00 $0.00 $0.00 1.250 $0.0012

- 23 

### Table 12.4

. 32 MPC Breakdown for Equipment Class 24 : SCU

- A

- Small

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $1,097.00 $92.56 $36.82 $176.26 $1,402.63 1.250 $1,753.29

EL 2 7.0% $1,107.18 $92.56 $37.09 $175.99 $1,412.81 1.250 $1,766.01

EL 3 15.0% $1,125.12 $92.56 $37.56 $175.52 $1,430.76 1.250 $1,788.45

EL 4 20.0% $1,141.65 $92.56 $37.99 $175.09 $1,447.29 1.250 $1,809.11

EL 5 25.0% $1,458.71 $92.56 $46.31 $166.76 $1,764.34 1.250 $2,205.43

EL 6 28.2% $1,468.71 $92.56 $46.58 $166.50 $1,774.35 1.250 $2,217.93



### Table 12.4

. 33 MPC Breakdown for Equipment Class 25 : SCU

- A

- Large

- C

EL % Reduction (below baseline) Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL 1 0.0% $0.00 $0.00 $0.00 $0.00 $0.00 1.250 $0.00

EL 2 0.0% $0.00 $0.00 $0.00 $0.00 $0.00 1.250 $0.00



### 12.4.8 Conversion Costs and Stranded Assets

Amended energy conservation standards typically cause manufacturers to incur one

- time conversion costs to bring their production facilities and equipmen t designs into compliance with new regulations. For the MIA, DOE classified these one

- time conversion co sts into two major

groups: product conversion costs and capital conversion costs. Product conversion costs are investments in research, development, testing, marketing, and other non

- capitalized costs focused on making equipment designs that comply with th e amended energy conservation
standard. Capital conversion costs are investments in property, plant, and equipment needed to adapt or change existing production facilities so that new equipment designs can be fabricated
and assembled. In the instance where changes to energy conservation standards result in the obsolescence of manufacturing capital, the un

- depreciated value of any obsolete manufacturing equipment is considered a stranded asset. In addition to product and capital conversion costs,
stranded assets also factor into the GRIM’s calculation of annual cash flows

. The following sections describe the inputs DOE used in the GRIM in greater detail.

12.4.8.1 Automatic Commercial Ice Maker Product and Capital Conversion C osts

DOE based its estimates of the conversion cost s required to meet each TSL on confidential information received during manufacturer interviews. DOE asked manufacturers to estimate the ir investment s in product development and new manufacturing capital a nd their anticipated stranded capital assets at various efficiency levels

. DOE then reviewed public information in the CEC, ENERGY STAR, and AHRI product databases

, as well as manufacturer websites to understand which products manufacturers would need to u pgrade at each efficiency
level

. DOE mapped manufacturers’ estimates of investments and stranded assets to a composite database of products to disaggregate their reporting and allocate the estimated costs across the various equipment classes and efficiency levels that each representative company manufactures

. From this, DOE also developed an average cost estimate to bring non

- compliant designs in each equipment class into compliance at each efficiency level

. DOE applied these average cost estimate s to the r emaining products in the database to develop estimates of the compliance costs required by the remainder of industry

. These figures were then added to the disaggregated12

- 24 

### estimates of individual manufacturer conversion costs to arrive at a total industry estimate of product and capital conversion costs and stranded assets for each efficiency level of each equipment class

.

Table 12.4.34 through Table 12.4

. 39 show DOE’s estimates of the industry total product and capital conversion costs and the un

- depreciated value of stranded assets for each a utomatic

c ommercial i ce m aker equipment class at each efficiency level identified

.

Table 12.4

. 34 Product Conversion Costs for Batch Automatic Commercial Ice Maker s by
E L Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- B Reduction (%) 0 .0% 10.0% 15.0% 20.0% 25.0% 29.4%
Product Conversion Costs ($Millions) $

- $1.387 $2.622 $3.234 $3.566 $3.831

IMH

- W

- Medium

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 21.3%
Product Conversion Costs ($ Millions) $

- $ 2.009 $ 2.730 $ 3.214 $ 3.277

IMH

- W

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 15.5%
Product Conversion Costs ($Millions) $

- $ 1.116 $ 1.362 $ 1.362

IMH

- A

- Small

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.3%
Product Conversion
Costs ($Millions) $

- $ 1.290 $ 2.259 $ 2.923 $ 3.460 $ 3.997 $ 4.037

IMH

- A

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 23.7% 26.9%
Product Conversion Costs ($Millions) $

- $ 1.982 $ 3.758 $ 4.189 $ 4.541 $ 4.843

RCU

- NRC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4%
Product Conversion Costs ($Millions) $

- $ 1.236 $ 1.866 $ 2.908 $ 3.097 $ 3.248

RCU

- NRC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6%
Product Conversion Costs ($Millions) $

- $ 0.792 $ 2.598 $ 3.850 $ 3.908

RCU

- RC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4%
Product Conversion Costs ($Millions) $

- $ 0.884 $ 1.571 $ 1.900 $ 1.949 $ 1.988

RCU

- RC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6%
Product Conversion Costs ($Millions) $

- $ 1.818 $ 2.708 $ 2.801 $ 2.807

SCU

- W

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 31.3%
Product Conversion Costs ($Millions) $

- $ 0.029 $ 0.210 $ 0.664 $ 0.846 $ 1.028 $ 1.043

SCU

- W

- Large

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 30.2%
Product Conversion Costs ($Millions) $

- $ 0.299 $ 0.299 $ 0.449 $ 0.460 $ 0.471 $ 0.471

SCU

- A

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 39.3%
Product Conversion Costs ($Millions) $

- $ 0.049 $ 0.327 $ 0.511 $ 0.777 $ 1.043 $ 2.039

SCU

- A

- Large

- B Reduction (%) 0 .0% 7.0% 15.0% 20.0% 25.0% 30.0% 34.9%
Product Conversion Costs ($ Millions) $

- $ 0.047 $ 0.164 $ 0.255 $ 0.407 $ 0.558 $ 0.69112

- 25 

### Table 12.4

. 35 Product Conversion Costs for Continuous Automatic Commercial Ice
Maker s by E L Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- C Reduction (%) 0 .0% 10.0% 15.0% 20.0% 25.0% 26.4%
Product Conversion Costs ($Millions) $

- $0.658 $0.895 $1.966 $2.006 $2.014

IMH

- W

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 26.4%
Product Conversion Costs ($Millions) $

- $0.718 $0.878 $1.225 $1.235 $1.237

IMH

- A

- Small

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.0%
Product Conversion Costs ($Millions) $

- $1.119 $1.927 $2.609 $2.920 $3.231 $3.239

IMH

- A

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 30.2%
Product Conversion Costs ($Millions) $

- $0.271 $0.326 $1.042 $1.141 $1.240 $1.240

RCU

- NRC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Product Conversion Costs ($Millions) $

- $0.480 $0.757 $0.856 $0.906 $0.956 $0.956

RCU

- NRC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Product Conversion Costs ($Millions) $

- $0.480 $0.926 $1.006 $1.122 $1.238 $1.238

RCU

- RC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Product Conversion Costs ($Millions) $

- $0.175 $0.530 $0.530 $0.530 $0.530 $0.530

RCU

- RC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Product Conversion Costs ($Millions) $

- $

- $0.672 $0.919 $0.990 $1.061 $1.061

SCU

- W

- Small

- C Reduction (%) 0.0% 3.0% 10.0% 16.0% 20.0% 22.7%
Product Conversion Costs ($Millions) $

- $0.012 $0.020 $0.024 $0.037 $0.079

SCU

- W

- Large

- C Reduction (%) 0.0% 0.0%
Product Conversion Costs ($Millions) $

- $

-

SCU

- A

- Small

- C Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 28.2%
Product Conversion Costs ($Millions) $

- $0.160 $0.233 $0.502 $1.276 $1.741

SCU

- A

- Large

- C Reduction (%) 0.0% 0.0%
Product Conversion Costs ($Millions) $

- $

-12

- 26 

### Table 12.4

. 36 Capital Conversion Costs for Batch Automatic Commercial Ice Maker s by
E L Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 29.4% Capital Conversion Costs ($Millions) $0.000 $0.028 $0.088 $0.119 $0.468 $0.748

IMH

- W

- Medium

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 21.3% Capital Conversion Costs ($Millions) $0.000 $0.053 $0.115 $0.213 $0.260

IMH

- W

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 15.5% Capital Conversion Costs ($Millions) $0.000 $0.029 $0.062 $0.062

IMH

- A

- Small

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.3% Capital Conversion Costs ($Millions) $0.000 $0.037 $0.125 $0.156 $0.562 $0.968 $1.059

IMH

- A

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 23.7% 26.9% Capital Conversion Costs ($Millions) $0.000 $0.033 $0.120 $0.247 $0.460 $0.643

RCU

- NRC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4% Capital Conversion Costs ($Millions) $0.000 $0.015 $0.047 $0.065 $0.375 $0.622

RCU

- NRC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6% Capital Conversion Costs ($Millions) $0.000 $0.028 $0.060 $0.081 $0.114

RCU

- RC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4% Capital Conversion Costs ($Millions) $0.000 $0.006 $0.023 $0.043 $0.212 $0.347

RCU

- RC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6% Capital Conversion Costs ($Millions) $0.000 $0.018 $0.040 $0.052 $0.073

SCU

- W

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 31.3% Capital Conversion Costs ($Millions) $0.000 $0.002 $0.019 $0.035 $0.137 $0.238 $0.261

SCU

- W

- Large

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 30.2% Capital Conversion Costs ($Millions) $0.000 $0.001 $0.004 $0.006 $0.053 $0.100 $0.100

SCU

- A

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 39.3% Capital Conversion Costs ($Millions) $0.000 $0.004 $0.029 $0.045 $0.209 $0.372 $0.681

SCU

- A

- Large

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 34.9% Capital Conversion Costs ($Millions) $0.000 $0.001 $0.015 $0.023 $0.081 $0.139 $0.19412

- 27 

### Table 12.4

. 37 Capital Conversion Costs for Continuous Automatic Commercial Ice Maker s by E L Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 26.4% Capital Conversion Costs ($Millions) $0.000 $0.012 $0.057 $0.103 $0.166 $0.179

IMH

- W

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 26.4% Capital Conversion Costs ($Millions) $0.000 $0.014 $0.040 $0.079 $0.124 $0.133

IMH

- A

- Small

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.0% Capital Conversion Costs ($Millions) $0.000 $0.012 $0.053 $0.094 $0.191 $0.289 $0.313

IMH

- A

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 30.2% Capital Conversion Costs ($Millions) $0.000 $0.027 $0.078 $0.125 $0.196 $0.267 $0.267

RCU

- NRC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2% Capital Conversion Costs ($Millions) $0.000 $0.027 $0.092 $0.159 $0.246 $0.334 $0.334

RCU

- NRC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2% Capital Conversion Costs ($Millions) $0.000 $0.027 $0.081 $0.129 $0.204 $0.278 $0.278

RCU

- RC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2% Capital Conversion Costs ($Millions) $0.000 $0.016 $0.057 $0.067 $0.075 $0.083 $0.083

RCU

- RC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2% Capital Conversion Costs ($Millions) $0.000 $0.000 $0.081 $0.104 $0.136 $0.167 $0.167

SCU

- W

- Small

- C Reduction (%) 0.0% 3.0% 10.0% 16.0% 20.0% 22.7% Capital Conversion Costs ($Millions) $0.000 $0.000 $0.000 $0.002 $0.004 $0.028

SCU

- W

- Large

- C Reduction (%) 0.0% 0.0% Capital Conversion Costs ($Millions) $0.000 $0.000

SCU

- A

- Small

- C Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 28.2% Capital Conversion Costs ($Millions) $0.000 $0.003 $0.024 $0.055 $0.252 $0.370

SCU

- A

- Large

- C Reduction (%) 0.0% 0.0% Capital Conversion Costs ($Millions) $0.000 $0.00012

- 28 

### Table 12.4

. 38 Stranded Assets for Batch Automatic Commercial Ice Maker s by E L Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 29.4%
Stranded Assets ($Millions) $0.000 $0.002 $0.013 $0.043 $0.170 $0.248

IMH

- W

- Medium

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 21.3%
Stranded Assets ($Millions) $0.000 $0.010 $0.020 $0.040 $0.063

IMH

- W

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 15.5%
Stranded Assets ($Millions) $0.000 $0.003 $0.006 $0.006

IMH

- A

- Small

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.3%
Stranded Assets ($Millions) $0.000 $0.002 $0.015 $0.043 $0.170 $0.268 $0.270

IMH

- A

- Large

- B Reduction (%) 0.0% 10.0% 15.0% 20.0% 23.7% 26.9%
Stranded Assets ($Millions) $0.000 $0.006 $0.022 $0.050 $0.154 $0.233

RCU

- NRC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4%
Stranded Assets ($Millions) $0.000 $0.004 $0.013 $0.029 $0.121 $0.185

RCU

- NRC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6%
Stranded Assets ($Millions) $0.000 $0.005 $0.013 $0.025 $0.033

RCU

- RC

- Small

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 25.0% 29.4%
Stranded Assets ($Millions) $0.000 $0.003 $0.013 $0.036 $0.142 $0.207

RCU

- RC

- Large

- B Reduction (%) 0.0% 9.0% 15.0% 20.0% 20.6%
Stranded Assets ($Millions) $0.000 $0.005 $0.015 $0.029 $0.037

SCU

- W

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 31.3%
Stranded Assets ($Millions) $0.000 $0.002 $0.019 $0.029 $0.097 $0.192 $0.203

SCU

- W

- Large

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 30.2%
Stranded Assets ($Millions) $0.000 $0.002 $0.009 $0.015 $0.049 $0.096 $0.096

SCU

- A

- Small

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 39.3%
Stranded Assets ($Millions) $0.000 $0.005 $0.028 $0.044 $0.170 $0.384 $0.592

SCU

- A

- Large

- B Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 30.0% 34.9%
Stranded Assets ($Millions) $0.000 $0.002 $0.009 $0.015 $0.049 $0.096 $0.12212

- 29 

### Table 12.4

. 39 Stranded Assets for Continuous Automatic Commercial Ice Maker s by E L Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

IMH

- W

- Small

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 26.4%
Stranded Assets ($Millions) $0.000 $0.013 $0.017 $0.084 $0.214 $0.228

IMH

- W

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 26.4%
Stranded Assets ($Millions) $0.000 $0.013 $0.035 $0.084 $0.214 $0.228

IMH

- A

- Small

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 31.0%
Stranded Assets ($Millions) $0.000 $0.013 $0.035 $0.084 $0.250 $0.379 $0.380

IMH

- A

- Large

- C Reduction (%) 0.0% 10.0% 15.0% 20.0% 25.0% 30.0% 30.2%
Stranded Assets ($Millions) $0.000 $0.052 $0.069 $0.112 $0.286 $0.379 $0.379

RCU

- NRC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Stranded Assets ($Millions) $0.000 $0.000 $0.000 $0.000 $0.000 $0.000 $0.000

RCU

- NRC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Stranded Assets ($Millions) $0.000 $0.000 $0.000 $0.000 $0.000 $0.000 $0.000

RCU

- RC

- Small

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Stranded Assets ($Millions) $0.000 $0.005 $0.022 $0.045 $0.076 $0.095 $0.095

RCU

- RC

- Large

- C Reduction (%) 0.0% 7.0% 17.0% 22.0% 26.0% 30.0% 30.2%
Stranded Assets ($Millions) $0.000 $0.000 $0.022 $0.045 $0.076 $0.095 $0.095

SCU

- W

- Small

- C Reduction (%) 0.0% 3.0% 10.0% 16.0% 20.0% 22.7%
Stranded Assets ($Millions) $0.000 $0.000 $0.000 $0.000 $0.000 $0.000

SCU

- W

- Large

- C Reduction (%) 0.0% 0.0%
Stranded Assets ($Millions) $0.000 $0.000

SCU

- A

- Small

- C Reduction (%) 0.0% 7.0% 15.0% 20.0% 25.0% 28.2%
Stranded Assets ($Millions) $0.000 $0.034 $0.095 $0.174 $0.443 $0.541

SCU

- A

- Large

- C Reduction (%) 0.0% 0.0%
Stranded Assets ($Millions) $0.000 $0.000



### 12.4.9 Markup Scenarios

MSP is equal to MPC multiplied by a manufacturer markup. The MSP includes direct manufacturing production costs ( i.e.

, labor, material, and overhead estimated in DOE’s MPCs)
and all non

- production costs ( i.e.

, SG&A, R&D, and interest), along with profit.

In the base case, DOE used the same baseline markups calculated in the engineering analysis for all equipment classes. In the standards case, DOE modeled two markup scenarios to

repre sent the uncertainty about the potential impacts on prices and profitability following the implementation of amended energy conservation standards: (1) a preservation of gross margin percentage markup scenario ; and (2) a preservation of earnings before int erest and taxes (EBIT) markup scenario . Modifying these markups from the base case to the standards cases yields different sets of impacts on manufacturers by changing industry revenue and cash flow.12

- 30 

### To calculate the baseline manufacturer markup, DOE evalu ated publicly available
financial information for manufacturers whose equipment offerings include automatic
commercial ice maker s . During manufacturer interviews, DOE received feedback supporting the
calculated 1. 2 5 baseline manufacturer markup. For both G RIM markup scenarios, DOE assumed a predominantly flat markup structure

, placing no premium on higher efficiency equipment . This assumption is informed by a regression analysis of commercial ice makers evaluating equipment list prices and rated efficiencie s

.

12.4.9.1 Preservation of Gross Margin Percentage Markup Scenario

The preservation of gross margin percentage markup scenario assumes that the baseline markup of 1. 2 5 is maintained for all equipment in the standards case. As production costs
increase with efficie ncy, this scenario implies that the absolute dollar markup will increase as
well. This scenario represents the upper bound of industry profitability as manufacturers are able to fully mark up and pass through higher production costs to their customers.

12.4.9.2 Preservation of EBIT Markup Scenario

DOE also modeled the preservation of EBIT markup scenario to estimate a lower bound of profitability for the industry. This is similar to the preservation of gross margin percentage markup scenario with the exception th at in the standards case, minimally compliant equipment
lose s a fraction of the baseline markup. The lower markup for minimally compliant equipment is
calibrated such that industry

- wide EBIT in the year standards go into effect matches EBIT of the

same yea r in the base case. This scenario represents a more substantial impact to the commercial
ice maker industry as manufacturers vie to maintain the lowest possible prices for marginally compliant equipment while securing the same level of EBIT they saw before new and amended
standards take effect

.

W hile all compliant equipment receive s the 1.25 markup in the preservation of gross margin percentage markup scenario, Table 12.4

. 40 through Table 12.4

. 64 list the calibrated markups used in the preservation of EBIT markup scenario.

Table 12.4

. 40 Preservation of EBIT Markups for Equipment Class 1 : IMH

- W

- Small

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.249 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.248 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.245 0.000

EL 6 29.4% 1.250 1.250 1.250 1.250 1.250 1.23712

- 31 

### Table 12.4

. 41 Preservation of EBIT Markups for Equipment Class 2 : IMH

- W

- Medium

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.249 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.243 0.000
EL 5 21.3% 1.250 1.250 1.250 1.250 1.243



### Table 12.4

. 42 Preservation of EBIT Markups for Equipment Class 3 : IMH

- W

- Large

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4
EL 1 0.0% 1.250 0.000 0.000 0.000
EL 2 10.0% 1.250 1.249 0.000 0.000
EL 3 15.0% 1.250 1.250 1.243 0.000
EL 4 15.5% 1.250 1.250 1.250 1.244



### Table 12.4

. 43 Preservation of EBIT Markups for Equipment Class 4 : IMH

- A

- Small

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.249 0.000 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000

EL 4 20.0% 1.250 1.250 1.250 1.248 0.000 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.247 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.239 0.000

EL 7 31.3% 1.250 1.250 1.250 1.250 1.250 1.250 1.239



### Table 12.4

. 44 Preservation of EBIT Markups for Equipment Class 5: IMH

- A

- Large

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.250 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.246 0.000 0.000
EL 5 23.7% 1.250 1.250 1.250 1.250 1.247 0.000

EL 6 26.9% 1.250 1.250 1.250 1.250 1.250 1.241



### Table 12.4

. 45 Preservation of EBIT Markups for Equipment Class 6: RCU

- NRC

- Small

- B

EL % Below
St andard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 9.0% 1.250 1.250 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.248 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.247 0.000

EL 6 29.4% 1.250 1.250 1.250 1.250 1.250 1.24212

- 32 

### Table 12.4

. 46 Preservation of EBIT Markups for Equipment Class 7: RCU

- NRC

- Large

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000
EL 2 9.0% 1.250 1.250 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.243 0.000
EL 5 20.6% 1.250 1.250 1.250 1.250 1.243



### Table 12.4

. 47 Preservation of EBIT Markups for Equipment Class 8: RCU

- RC

- Small

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 9.0% 1.250 1.250 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.248 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.247 0.000

EL 6 29.4% 1.250 1.250 1.250 1.250 1.250 1.242



### Table 12.4

. 48 Preservation of EBIT Markups for Equipment Class 9: RCU

- RC

- Large

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000
EL 2 9.0% 1.250 1.250 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.243 0.000
EL 5 20.6% 1.250 1.250 1.250 1.250 1.243



### Table 12.4

. 49 Preservation of EBIT Markups for Equipment Class 10: SCU

- W

- Small

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.248 0.000 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.248 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.240 0.000

EL 7 31.3% 1.250 1.250 1.250 1.250 1.250 1.250 1.240



### Table 12.4

. 50 Preservation of EBIT Markups for Equipment Class 11: SCU

- W

- Large

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.249 0.000 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.248 0.000 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.249 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.242 0.000

EL 7 30.2% 1.250 1.250 1.250 1.250 1.250 1.250 1.24312

- 33 

### Table 12.4

. 51 Preservation of EBIT Markups for Equipment Class 12: SCU

- A

- Small

- B

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.249 0.000 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.249 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.248 0.000

EL 7 39.3% 1.250 1.250 1.250 1.250 1.250 1.250 1.240



### Table 12.4

. 52 Preservation of EBIT Markups for Equipment Class 13: SCU

- A

- Large

- B

EL % Below
Standard Minimall y Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.248 0.000 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.248 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.247 0.000

EL 7 34.9% 1.250 1.250 1.250 1.250 1.250 1.250 1.240



### Table 12.4

. 53 Preservation of EBIT Markups for Equipment Class 14: IMH

- W

- Small

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.250 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.250 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.249 0.000 0.000
EL 5 25. 0% 1.250 1.250 1.250 1.250 1.243 0.000

EL 6 26.4% 1.250 1.250 1.250 1.250 1.250 1.244



### Table 12.4

. 54 Preservation of EBIT Markups for Equipment Cla ss 15: IMH

- W

- Large

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.249 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.249 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.243 0.000

EL 6 26.4% 1.250 1.250 1.250 1.250 1.250 1.243



### Table 12.4

. 55 Preservation of EBIT Markups for Equipment Class 16: IMH

- A

- Small

- C

EL % Below
Standard Minimally Compliant EL

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.250 0.000 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.249 0.000 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.248 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.244 0.000

EL 7 31.0% 1.250 1.250 1.250 1.250 1.250 1.250 1.24412

- 34 

### Table 12.4

. 56 Preservation of EBIT Markups for Equipment Class 17: IMH

- A

- Large

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 10.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.249 0.000 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.248 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.241 0.000

EL 7 30.2% 1.250 1.250 1.250 1.250 1.250 1.250 1.242



### Table 12.4

. 57 Preservation of EBIT Markups for Equipment Class 18: RCU

- NRC

- Small

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 17.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 22.0% 1.250 1.250 1.250 1.249 0.000 0.000 0.000
EL 5 26.0% 1.250 1.250 1.250 1.250 1.239 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.239 0.000

EL 7 30.2% 1.250 1.250 1.250 1.250 1.250 1.250 1.239



### Table 12.4

. 58 Preservation of EBIT Markups for Equipment Class 19: RCU

- NRC

- Large

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 17.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 22.0% 1.250 1.250 1.250 1.249 0.000 0.000 0.000
EL 5 26.0% 1.250 1.250 1.250 1.250 1.239 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.239 0.000

EL 7 30.2% 1.250 1.250 1.250 1.250 1.250 1.250 1.238



### Table 12.4

. 59 Preservation of EBIT Markups for Equipment Class 20: RCU

- RC

- Small

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 17.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 22.0% 1.250 1.250 1.250 1.249 0.000 0.000 0.000
EL 5 26.0% 1.250 1.250 1.250 1.250 1.239 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.239 0.000

EL 7 30.2% 1.250 1.250 1.250 1.250 1.250 1.250 1.23912

- 35 

### Table 12.4

. 60 Preservation of EBIT Markups for Equipment Class 21: RCU

- RC

- Large

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000 0.000
EL 3 17.0% 1.250 1.250 1.249 0.000 0.000 0.000 0.000
EL 4 22.0% 1.250 1.250 1.250 1.249 0.000 0.000 0.000
EL 5 26.0% 1.250 1.250 1.250 1.250 1.239 0.000 0.000

EL 6 30.0% 1.250 1.250 1.250 1.250 1.250 1.239 0.000

EL 7 30.2% 1.250 1.250 1.250 1.250 1.250 1.250 1.238



### Table 12.4

. 61 Preservation of EBIT Markups for Equipment Class 22: SCU

- W

- Small

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 3.0% 1.250 1.250 0.000 0.000 0.000 0.000
EL 3 10.0% 1.250 1.250 1.250 0.000 0.000 0.000
EL 4 16.0% 1.250 1.250 1.250 1.249 0.000 0.000
EL 5 20.0% 1.250 1.250 1.250 1.250 1.241 0.000

EL 6 22.7% 1.250 1.250 1.250 1.250 1.250 1.241



### Table 12.4

. 62 Preservation of EBIT Markups for Equipment Class 23: SCU

- W

- Large

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2
EL 1 0.0% 1.250

EL 2 0.0% 1.250 1.250



### Table 12.4

. 63 Preservation of EBIT Markups for Equipment Class 24: SCU

- A

- Small

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2 EL 3 EL 4 EL 5 EL 6
EL 1 0.0% 1.250 0.000 0.000 0.000 0.000 0.000
EL 2 7.0% 1.250 1.250 0.000 0.000 0.000 0.000

EL 3 15.0% 1.250 1.250 1.249 0.000 0.000 0.000
EL 4 20.0% 1.250 1.250 1.250 1.249 0.000 0.000
EL 5 25.0% 1.250 1.250 1.250 1.250 1.241 0.000

EL 6 28.2% 1.250 1.250 1.250 1.250 1.250 1.243



### Table 12.4

. 64 Preservation of EBIT Markups for Equipment Class 25: SCU

- A

- Large

- C

EL % Below
Standard Minimally Compliant EL
EL 1 EL 2
EL 1 0.0% 1.250

EL 2 0.0% 1.250 1.250



### 12.5 INDUSTRY FINANCIAL I MPACTS

Using the inputs and scenarios described in the previous sections, DOE used the GRIM to estimate the financial impacts on the a utomatic commercial ice maker industry. The MIA uses two key financial metrics: INPV and annual cash flows. The main results of the MIA are reported in this section.12

- 36 

### 12.5.1 Impacts on I NPV

The INPV measures the industry value and is used in the MIA to compare the economic impacts of differen t TSLs in the standards case. The INPV is different from DOE’s NPV, which is applied to the U.S. economy at large . The INPV is specific to the automatic commercial i ce

m aker manufacturing industry, and is the sum of all net cash flows discounted at the ind ustry’s weighted average cost of capital. The GRIM for the a utomatic c ommercial i ce m aker industry
models cash flows from 201 3 to 204 7 . This timeframe models both the short

- term impacts on the industry from the announcement of the standard until the compli ance date in 2018 , and a long

term assessment over the 30

- year analysis period immediately thereafter.

In the MIA, DOE compares the INPV of the base case (no 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 implementing that particular TSL would have on the industry. For the a utomatic c ommercial i ce m aker industry, DOE examined the two markup scenarios described in sec tion 12.4.9 : the preservation of gross margin percentage markup scenario and the preservation of EBIT markup scenario.

Table 12.5

. 1 and Table 12.5

. 2 p rovide the INPV estimates for the automatic commercial ice maker manufacturing indus try

.

Table 12.5

. 1 Manufacturer Impact Analysis for Automatic Commercial Ice Maker s

– Preservation of Gross Margin Percentage M arkup S cenario

Base Case Trial Standard Level
1 2 3 4 5

INPV (201 2 $ millions) $ 101.8 $ 93.4 $ 89.0 $ 80.9 $ 82. 2 $ 81.9

Change
in INPV (201 2 $ millions) - $( 8.4 ) $( 12.8 ) $( 20.9 ) $( 19. 6 ) $( 19.9 )

(%)

- ( 8.2 ) % ( 12.6 ) % ( 20.5 ) % ( 19. 2 ) % ( 19.5 ) %

*For tables in section 12.5 , values in parenthesis indicate negative numbers



### Table 12.5

. 2 Manufacturer Impact Analysis for Automatic Commercial Ice Maker s

– Preservation o f EBIT Markup Scenario

Base Case Trial Standard Level
1 2 3 4 5

INPV (2010$ millions) $ 101.8 $ 93.1 $ 88.2 $ 77.9 $ 71.3 $ 69.2

Change
in INPV (2010$ millions) - $( 8.7 ) $( 13.6 ) $( 23.9 ) $( 30.5 ) $( 32.6 )

(%)

- ( 8.5 ) % ( 13.4 ) % ( 23.5 ) % ( 30.0 ) % ( 32.0 ) %

*For tables in section 12.5 , values in parenthesis indicate negative numbers



### 12.5.2 Impacts on Free Cash Flow

While INPV is useful for evaluating the long

- term effects of amended energy conservation standards, short

- term chan ges in cash flow are also important indicators of the industry’s financial situation. For example, a large investment over one or two years could strain the industry’s access to capital. Consequently, the sharp drop in financial performance could12

- 37 

### cause inv estors to flee, even if recovery is possible. Thus, a short

- term disturbance can have long

- term effects that the INPV cannot capture.

Annual cash flows are discounted to the base year, 201 3

. From 201 3 to 201 8 , cash flows are driven by the level of convers ion costs and the portion of these investments made e ach 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 amended

e nergy conservation standard. The more stringent the 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 operations and capit al conversion costs increase cash outflows for capital expenditures.

Free cash flow in the year the amended energy conservation standards take effect is driven by two competing factors. In addition to capital and product conversion costs, amended

energy co nservation standards could create stranded assets, i.e.

, the residual un

- depreciated value
of tooling and equipment that would have enjoyed longer use if the energy conservation standard
had not rendered it obsolete. In this year, manufacturers write down the remaining book value of existing tooling and equipment affected by the amended energy conservation standard. This one

time write

- down acts as a tax shield that alleviates decreases in cash flow from ope rations in the year of the write

- down. In this year, there is also an increase in working capital that reduces cash flow from operations. A large increase in working capital can be attributed to more costly production components and materials, higher inven tory carrying to sell more expensive equipment , and higher accounts receivable for more expensive equipment . Depending on these two competing factors, cash flow can be either positively or negatively affected in the year the
standard takes effect.

In t he years following the compliance date of the standard, the impact on cash flow depends on the operating revenue. U nder the preservation of gross margin percentage markup
scenario

, m ore

- stringent TSLs typically have a positive impact on cash flows relative to the base
case because manufacturers generate higher revenues by marking up more costly equipment

, which increase cash flow from operations. There is very little impact on cash flow from operations following standards under the preservation of EBIT scen ario because this scenario is
calibrated to have the same earnings before interest and taxes in the standards case at each TSL

as in the base case in the year after the standard s take effect. In this scenario production costs increase, but EBIT remains app roximately equal to the base case, effectively decreasing profit margins as a percentage of revenue. To illustrate the behavior of annual net cash flows, Figure 12.5

. 1 and Figure 12.5

. 2 present the annual net or free cash flows from 201 3 through
20 47 for the base case and each TSL in the standards case.12

- 38 

### Figure 12.5

. 1 Annual Industry Net Cash Flows for Automatic Commercial Ice Maker s ( Preservation of Gross Margin Percentage Markup Scenario)

Figure 12.5

. 2 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers (Preservation of EBIT Markup Scenario)12

- 39 

### 12.6 IMPAC T S ON SMALL BUSINESS MANUFACTURERS OF AUT OMATIC
COMMERCIAL ICE MAKER S

To estimate the number of small busin esses that could be impacted by the amended energy conservation standards, DOE conducted a market survey using available public information to identify potential small business manufacturers. DOE’s research included product databases ( AHRI, CEC, and ENERGY STAR)

, individual company websites , the SBA ’s Dynamic Small Business Search (DSBS) , and Dun & Bradstreet ’s Hoovers to find potential
small business manufacturers of automatic commercial ice makers . DOE also asked interes ted parties and industry representatives if they were aware of any other small business manufacturers during manufacturer interviews and at previous DOE public meetings. DOE reviewed all available data and contacted various companies, as necessary, to dete rmine whether they met the
SBA’s definition of a small business manufacturer of covered a utomatic c ommercial i ce m aker s. DOE screened out companies that did not offer equipment covered by this rulemaking, did not meet the definition of a “small business,” or are foreign owned and operated.

The vast majority of a utomatic c ommercial i ce m aker s are currently manufactured by
three companies that together account for approximately 95 percent of the total market. The remaining 5 percent of the market (approximate ly 10 ,000 shipments annually ) is supplied by a
combination of 11 international and domestic companies, all of which have small market shares.
Wh ile 4 of t hese companies are either foreign owned and operated or exceed the SBA’s employment threshold of 750 e mployees

, the 7 remaining companies qualify as small business
manufacturers of automatic commercial ice makers for consideration as a small business under
NAICS code 333415, “Air

- Conditioning and Warm Air Heating Equipment and Commercial and
Industrial Refrigeration Equipment Manufacturing

, ” which includes i ce making machinery manufacturing

.

DOE estimates that the 7 small domestic manufacturers of automatic commercial ice makers identified by DOE account for less than 5 percent of industry shipments. Whi le small business manufacturers of automatic commercial ice makers have small overall market share, some hold substantial market share in specific equipment class es. Several of these smaller firms specialize in producing industrial ice machines

, and the co vered equipment they manufacture are extensions of existing equipment offerings 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 sco pe of this rulemaking

, but are still considered small businesses based on the SBA limits for number of employees.

I n confidential interviews, manufacturers indicated that many design options evaluated in the engineering analysis ( e.g. , higher efficiency mo tors and compressors) woul d require them to purchase more

- expensive components. In many industries, small manufacturers typically pay higher prices for components because of 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 is that the industry as a whole is a low

- volume industry. In confidential interviews, manufacturers indicated that they h ave little influence over their suppliers, suggesting the volume of their component orders is similarly insufficient to receive substantial discounts. The second reason is that, for most small businesses, the equipment covered by this rulemaking represents only a fraction of12

- 40 

### 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 expe cted that any volume discount for components enjoyed by large manufacturers would not be substantially different from the prices paid by
small business manufacturers.

In contrast to component costs, investments in product development and manufacturing

cap ital may be disproportionately burdensome for small businesses. In instances where small
business manufacturers hold substantial market share within an equipment class

, this may not pose as serious an issue

. However, for equipment classes in which small business manufacturers hold a small market share, their opportunity to recoup upfront investments in product development is diminished

. Similarly, depreciated manufacturing capital can only be spread across a disproportionately lower volume of shipments

, c ontributing to higher per

- unit production costs.

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 whole

- industr y 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 capital expen ditures for a typical small manufacturer and to model the impact on INPV. DOE then compared these impacts to those modeled for the industry at large. The results are shown i n Table 12.6

. 1 and Table 12.6

. 2

.

Table 12.6

. 1 Comparison of Small Business Manufacturers of Automatic Commercial I ce Maker INPV to that of the Industry at Large by TSL under the Preservation of Gross Margin Markup Scenario.

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 Industry at Large

– Impact on INPV ($2012) $( 8.4 ) $( 12.8 ) $( 20.9 ) $( 19. 6 ) $( 19.9 )

Industry at Large

– Impact on INPV (%) (8.2) % (12.6) % (20.5) % (19. 2 ) % (19.5) %

Small Businesses

– Impact on INPV ($2012) $( 1.8 ) $( 2.9 ) $( 3.9 ) $( 4.1 ) $( 4.5 )

Small Businesses

– Impact on INPV (%) ( 35.4 ) % ( 57.0 ) % ( 76.6 ) % ( 80.5 ) % ( 88.4 ) %12

- 41 

### Table 12.6

. 2 Comparison of Small Business Manufacturers of Automatic Commercial Ice Maker INPV to that of the Industry at Large by TSL under the Preserva tion of EBIT Markup Scenario.

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 Industry at Large

– Impact on INPV ($2012) $( 8.7 ) $( 13.6 ) $( 23.9 ) $( 30.5 ) $( 32.6 )

Industry at Large

– Impact on INPV (%) (8.5) % (13.4) % (23.5) % (30.0) % (32.0) %

Small Businesses

– Impact on INPV ($2012) $( 1.8 ) $( 3.0 ) $( 4.0 ) $( 4.6 ) $( 5.1 )

Small Businesses

– Impact on INPV (%) ( 35.4 ) % ( 58.9 ) % ( 78.6 ) % ( 90.3 ) % ( 100.2 ) %



### In summary, DOE recognizes that amended energy conservation standards potentially
have disproportionate impact s on small businesses

. Larger manufacturers may have a competitive advantage due to their size and ability to access capital that may not be available to
small businesses. DOE provides additional analysis in section VI.B, Review Under the Regulatory Flexibility Act , in the NOPR.

12.7 OTHER IMPACTS

12.7.1 Employment

For a utomatic c ommercial i ce m aker s, DOE used the GRIM to estimate the domestic labor expenditures and number of domestic production workers in the base case and at each TSL
from 201 3 to 204 7 . DOE used statistical data from the most recent U.S Census Bureau’s 20 11 Annual Survey of Manufacture r s ( ASM )

, e 

### the results of the engineering analysis, and interviews with manufacturers to determine the inputs necessary to calculate industry

- wide labor expenditures and domestic employment levels. Labor expenditures for the manufacture of equipment are a function o f the labor intensity of the equipment , the sales volume, and an assumption that wages in real terms remain constant.

In the GRIM, DOE used the manufacturing production costs and labor content of each equipment class at each efficiency level from the engin eering analysis to estimate the annual labor expenditures in the a utomatic c ommercial i ce m aker industry. DOE used c ensus data and interviews with manufacturers to estimate the portion of the total labor expenditures that is
attributable to domestic labor.

The production worker estimates in this section only cover workers up to the line

supervisor level who are directly involved in fabricating and assembling c overed automatic
commercial ice

- making equipment within an o riginal e quipment m anufacturer (OEM) fa cility. Workers performing services that are closely associated with production operations, such as material handling with a forklift, are also included as production labor. DOE’s estimates account

e Statistics for industry groups and industries are available on the U.S. Census Bureau’s website at http://www.census.gov /manufacturing/asm/

.12

- 42 

### only for production workers who manufacture the specific e quipment covered by this rulemaking.

The employment impacts shown in Table 12.7.1 represent the potential production employment that could result follo wing amended energy conservation standards. The upper end of the results in this table estimates the total potential increase in the number of production workers after amended energy conservation standards. To calculate the total potential increase,
DOE as sumed that manufacturers continue to produce the same scope of covered equipment in domestic production facilities and that domestic production is not shifted to lower

- labor

- cost countries. Because there is a risk of manufacturers evaluating sourcing decis ions in response to amended energy conservation standards, the lower end of the range of employment results in
Table 12.7.1 includes the estimated total number of U.S. production workers in the industry who could lose their jobs if all existing production were moved outside of the United States.

W hile the results present a range of employment impacts following the compliance date of amended energy conservation standards, the following discussion also includes a qualitative
evaluation of the likelihood of negative domestic production employment impacts at the vario us TSLs

.

DOE forecasts that the domestic labor expendit ure for a utomatic c ommercial i ce m aker production labor in 201 8 will be approximately $ 13.6 million. Using the $ 25.97 hourly wage rate including fringe benefits and 1,955 production hours per yea r per employee found in the 20 11
ASM

, DOE estimates there will be approximately 268 domestic production workers involved in
the manufacturing of a utomatic c ommercial i ce m aker s in 201 8 , the year in which amended standards would go into effect for TSL 1, TS L 3, and TSL 4 . In addition, DOE estimates that 95

non

- production employees in the United States will support a utomatic c ommercial i ce m aker production. f 

### Approximately 84 percent of a utomatic c ommercial i ce m aker s sold in the United
States are manufacture d domestically. The employment tab of the automatic commercial ice
maker GRIM contains more detailed information on the annual domestic employment impacts.

Table 12.7.1 illustrates the range of potential impact s of amended energy conservation
standards on domestic production employment levels at each TSL for the a utomatic c ommercial
i ce m aker market.

f As defined in the 2011 ASM , the production workers number includes “workers (up through the line

supervisor level) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, warehousing, shippi ng (but not delivering), maintenance, repair, janitorial and guard services, product development, auxiliary production for plant's own use ( e.g. , power plant), recordkeeping, and other services closely associated with these production operations at the est ablishment covered by the report. Employees above the working

- supervisor level are excluded from this item.” Non

- production workers are defined as “employees of the manufacturing establishment including those engaged in factory supervision
above the line

- s upervisor level. It includes sales (including driver

- salespersons), sales delivery (highway truck drivers and their helpers), advertising, credit, collection, installation and servicing of own products, clerical and routine office functions, executive, pur chasing, financing, legal, personnel (including cafeteria, medical, etc.), professional, and technical employees. Also included are employees on the payroll of the manufacturing establishment engaged in the construction of major additions or alterations ut ilized as a separate work force.”12

- 43 

### Table 12.7

. 1 Potential Changes i n the Total Number of Domestic Automatic Commercial
Ice Maker Production Workers in 201 8

Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
Total Number of Domestic Production
Workers in 2018 (without changes in production locations) 268 268 268 269 269 269

Potential Changes in Domestic Production
Workers in 2018* - 0

- ( 268 ) 0

- ( 268 ) 1

- ( 268 ) 1

- ( 268 ) 1

- ( 268 )

* DOE presents a range of potential employment impacts. Numbers in parentheses indicate negative numbers.



### Figure 12.7

. 1 shows total annual domestic employment levels for each TSL calculated by
the GRIM.

Figure 12.7

. 1 Automatic Commercial Ice Maker Domestic Production Employment by
Year

All examined TSLs show relatively minor impacts on domestic employment levels relative to total industry employment. At all TSLs, most of the design opt ions analyzed by DOE do not greatly alter the labor content. For example, switching to a higher efficiency motor does

not significantly change the number or complexity of steps required in manufacturing an
automatic commercial ice maker (which would add la bor).

In confidential interviews, several manufacturers indicated that they already source some equipment classes exclusively from overseas manufacturing facilities. While it is unlikely that the labor contribution to production costs would change in light of new or amended standards, the threat of manufacturers moving additional production to countries with lower labor costs to offset the effects of higher cost components is somewhat credible when the benefit of this move outweighs additional shipping costs.12

- 44 

### 12.7.2 Production Capacity

Very few shipments of batch automatic commercial ice makers already comply with the amended energy conservation standards at progressively higher TSLs

. Similarly, very few manufacturers produce batch equipment that currently co mplies with the standards associated with higher TSLs

. In contrast, the continuous equipment portion of the market is much less
concentrated

, and a more substantial number of products currently meet the standards associated with higher TSLs

. Table 12.7

. 2 and Table 12.7

. 3 show the percent of current shipments that meet a given TSL along with the number of manufacturers involved in current production.

Table 12.7

. 2 Batch Equipment: Percent of Current Shipments and the Number of Manufacturers Currently Producing Compliant Equipment by TSL

Baseline TSL 1 TSL 2 TS L 3 TSL 4 TSL 5
2013 Shipments that Already Comply with
Standards at Each TSL (%) 100% 65% 35% 6% 1% 1%

Manufacturers Currently Producing Compliant Equipment (#) 11 7 6 5 2 2



### Table 12.7

. 3 Continuous Equipment: Percent of Current Shipments and the Number of Manufacturers Currently Producing Compliant Equipment by TSL

Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 2013 Shipments that Already Comply with Standards at Each TSL (%) 100% 74% 52% 31% 29% 15%

Manufacturers Currently Producing Compliant Equipment (#) 6 6 6 6 6 3



### The concentration of current production of batch equipment at TSL s 4 and 5 presents two
issues. Only two manufacturers currently produce batch equipment that complies with the efficiency levels specified at TSL 4 and TSL 5. This includes one small business manufacturer whose niche products have among the very largest harvest capacities in their respective equipment classes and are sold in small quantities relative to th e rest of the industry. In contrast to this small business manufacturer, the other manufacturer is Hoshizaki, which produces more
mainstream batch equipment and commands substantial market share. Hoshizaki holds intellectual property covering the design of the evaporator used in their batch equipment, which limits the range of possible alternative paths to achieving the efficiency levels for batch equipment specified at TSL 5. While the engineering analysis identified other means to achieve these high effic iencies, given this limitation on design options, other manufacturers expressed significant doubts regarding their ability to do so.

Further, DOE’s analysis indicates that these efficiency levels require the use of permanent magnet motors and, for batch e quipment, drain water heat exchangers. DOE was able to identify only one supplier of the latter technology, whose design is patented. In addition, there is currently very limited use of permanent magnet motors in commercial ice makers; hence, motor supplie rs would be required to develop and initiate production for a broad range of new motor designs suitable for automatic commercial ice makers. These needs could severely impact batch equipment manufacturers’ ability to procure the required components in suff icient quantities to supply the market

.12

- 45 

### Additionally, where only 1 percent of current batch equipment shipments meet the standards specified at TSL 4 and TSL 5, even if other paths to achieving these efficiency levels prove fruitful, these standard levels would still require that every other manufacturer retool their entire production lines for batch equipment . In confidential interviews, o ne manufacturer cited the possibility of a 3

- month to 6

- month shutdown in the event that amended standards were set
hi gh enough to require retooling of their entire product ion line. Compounding this effect across the industry could severely impact manufacturing capacity in the interim period between the announcement of the standards and the compliance date.

12.7.3 Cumulative Reg ulatory Burden

While any one regulation may not impose a significant burden on manufacturers, the combined effects of several impending regulations may have serious consequences for some manufacturers, groups of manufacturers, or an entire industry. Regul atory burdens can prompt companies to exit the market or reduce their equipment offerings, potentially reducing competition. Smaller companies in particular can be affected by regulatory costs because these companies have lower sales volumes over which they can amortize the costs of meeting new regulations. Assessing the impact of a single regulation may overlook this cumulative regulatory burden. A proposed standard is not economically justified if it contributes to an unacceptable
level of cumulative regulatory burden.

For the cumulative regulatory burden analysis

, DOE looks at other significant equipment

specific regulations that will take effect 3 years before or after the 2018 compliance date of the amended energy conservation standards for a utomatic c ommercial i ce m aker s . In addition to energy conservation standards regulations, several other Federal regulations apply to a utomatic

c ommercial i ce m aker s . While this analysis focuses on the impacts on manufacturers born of other Federal requirements, DOE also has described a number of other non

- Federal regulations
in section 12.7.3.2 because it re cognizes that these regulations also impact the equipment covered by this rulemaking.

12.7.3.1 DOE Regulations for Other Products Produced by Automatic
Commercial Ice Maker Manufacturers

Companies that produce a wide range of regulated products and equipment may face more capital and product development expenditures than competitors with a narrower scope of products and equipment

. A few manufacturers of a utomatic c ommercial i ce m aker s also produce other appliances and commercial equipment

. In addition to the amend ed energy conservation
standards for a utomatic c ommercial i ce m aker s , these manufacturers contend with several other Federal regulations and pending regulations that apply to other products and equipment

. DOE recognizes that each regulation can significant ly affect a manufacturer’s financial operations. Multiple regulations affecting the same manufacturer can quickly strain manufacturers’ profits and possibly cause an exit from the market. Table 12.7.4 lists the other DOE energy conservation
standards that could also affect manufacturers of a utomatic c ommercial i ce m aker s in the 3 years leading up to and after the compliance date of amended energy conservation standards for th is equipment

.12

- 46 

### Table 12.7

. 4 Other DOE and Federal Actions Affecting the Automatic Commercial Ice
Maker Industry

Regulation Approximate Compliance
Date* Number of Impacted Companies from the Market and Technology Assessment (MTA) (See Chapter 3 of the
NOPR TSD) Estimated Total Industry
Conversion Costs

Commercial Refrigeration Equipment 201 7 * 2 N/A **

Walk

- In Coolers and Freezers 201 7 * 1 N/A **
*The dates listed are an approximation. The exact dates are pending final DOE action.

** For energy conservation standards for rulemakings awaiting DOE final action, DOE does not have a finalized estimated total industry conversion cost.



### In Table 12.7

. 5

, DOE identified manufacturers of other products covered by additional efficiency standards as identified in Table 12.7

. 4

.

Table 12.7

. 5 Other Covered Products Produced by Automatic Commercial Ice Maker
Manufacturers

Manufacturer Other Covered Products Manufactured

Hoshizaki Commercial Refrigeration Equipment

Manitowoc Commercial Refrigeration Equipment , Walk

- In Coolers and Freezers



### 12.7.3.2 Other Regulations That Could Impact Automatic Commercial Ice Maker
Manufacturers

While the cumulative regulatory burden focuses on the impacts on manufacturers of other Federal requirements, this section describe s a number of other regulations that could also impact
the a utomatic c ommercial i ce m aker s covered by this rulemaking

.

State Energy Conservation Standards

Manufacturers indicated that California has several programs that either are already in place or are currently in development that affect manufacturers of a utomatic c ommercial i ce

m aker s . Various building, electrical, mechanical

, and plumbing codes in California affect i ce

m aker s , and this equipment is 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 and
was modeled after the European Union’s ( EU’s ) directive (described in the following paragraph ),
which bans certain hazardous substances from elect rical and electronic equipment.

International Energy Conservation Standards

Automatic commercial ice maker manufacturers t hat sell equipment outside of the United States are subject to several international energy conservation standards. In the EU, automatic
commercial ice

- making equipment is also subject to RoHS. This regulation bans the sale in the
EU of new equipment 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,12

- 47 

### 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, beg inning June 1, 2007. The EU also sets limits for the amount of energy consumed by equipment when it is in standby mode and off mode. Additionally, hydrofluorocarbons ( HFCs ) are banned in refrigerants in several countries, such as Austria, Denmark, and Swit zerland. Canada and several other foreign countries have regulations or have initiated regulations affecting manufacturers of automatic
commercial ice makers

.
Food Service Equipment Sanitation Certification

Several manufacturers stated that NSF Internatio nal (formerly the National Sanitation Foundation) is in the process of amending criteria for food service equipment sanitation certification pertain ing to automatic commercial ice makers. New standards would specify low

lead materials to be used in gaskets in the product assemblies and require additional testing in the certification process

.
Potential Climate Change and Greenhouse Gas Legislation

Several manufacturers expressed concern about potential climate change legislation. Manufacturers expect a clim ate change bill that regulates the use and emissions of greenhouse gases (GHGs) to be passed by Congress in the near future

. For example, on May 9 th



### , Congressman Scott Peters introduced the S uper Pollutant Emissions Reduction Act of 2013

. Should this or a similar act pass

, the use of HFCs , the most commonly used refrigerants and
foam

- blowing agents, would become restricted. Eventually, t hese substances would be phased out, or their use capped at a restricted level. This presents a problem for manufacturers because the key alternative insulation foam blowing agent is cyclopentane. Insulation made with this blowing agent has higher conductivity than current state

- of

- the art foam insulation, and conversion to cyclopentane presents factory issues due to its flam mability. Conversion of the currently

- used refrigerant, HFC

- 134a, to the most likely alternative, R

- 600a or isobutane, would require product redesign, and causes issues associated with safety certification compliance under Underwriters Laboratories ( UL ) re gulations. Proposed legislation presents uncertainty for
manufacturers about how hydrocarbon refrigerants and foam

- blowing agents would be regulated for safety within the United States

.

12.8 CONCLUSION

The following section summarize s the different impacts for the scenarios that DOE believes are most likely to capture the range of impacts on a utomatic c ommercial i ce m aker
manufacturers at each TSL in the standards case . While these scenarios bound the range of the most plausible impacts on manufacturers, some c ircumstances could cause manufacturers to experience impacts outside this range.

12.8.1 Automatic Commercial Ice Maker s

At TSL 1, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $ 8.4 million to

- $ 8.7 million, or a change in INPV of

- 8.2 percent

to

- 8.5 percent. At this TSL , industry free cash flow is estimated to decrease by approximately12

- 48 

### 61 percent to $ 3.3 million, compared to the base

- case value of $ 8.4 million in the year before the compliance date (2017).

DOE estimates that approximately 40 percent of all batch commercial ice maker s and
30 percent of all continuous commercial ice makers on the market will require redesign to meet
standards at TSL 1

. Additionally, for both batch and continuous equipment , the number of units requiring redesign at this TSL is commensurate with each manufacturer’s estimated market share. Twelve manufacturers, including three small businesses, produce equipment that complies with the efficiency levels spe cified at TSL 1.

At TSL 1 , the majority of efficiency gains could be made through swapping purchased components for higher efficiency equivalents. I t is expected that very few evaporators and
condensers are affected at TSL 1, le a ding to very low expected i ndustry capital conversion costs totaling only $0. 4 million. However, moderate product conversion costs of $ 17.0 million are expected, as redesigned units will require low levels of engineering design labor, as well as testing for equipment certification.

At TSL 2, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $ 12.8 million to

- $ 13.6 million, or a change in INPV of

- 12.6 percent

to

- 13.4 percent. At this TSL , industry free cash flow is estimated to decrease by approximately
97 percent to $ 0.3 million, compared to the base

- case value of $ 8.4 million in the year before the compliance date (2017).

At TSL 2, total conversion costs increase to $ 26.6 milli on, 53 percent higher than those incurred by industry at TSL 1. DOE estimates that approximately 58 percent of all units on the market will require redesign to meet the standards outlined at TSL 2

. As with TSL 1, for batch
and continuous commercial ice mak ers, the number of units requiring redesign at this TSL is largely commensurate with each manufacturer’s estimated market share. Ten manufacturers, including three small businesses, produce equipment that complies with the efficiency levels specified at TS L 2.

The majority of redesigns still rely on switching to higher efficiency components, but a limited number of units are expected to require more complex system redesigns including the evaporator and condenser. The increased but moderate complexity of th ese redesigns causes product conversion costs to grow at a slightly higher rate than the additional number of units requiring redesign, resulting in industry

- wide product conversion costs totaling $ 25.4 million. Capital conversion costs continue to remain relatively low at $ 1.2 million, as most design options considered at TSL 2 can be integrated into production without changes to manufacturing capital.

At TSL 3, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range fro m

- $ 20.9 million to

- $ 23.9 million, or a change in INPV of

- 20.5 percent

to

- 23.5 percent. At this TSL , industry free cash flow is estimated to decrease by approximately
179 percent to

- $ 6.7 million, compared to the base

- case value o f $ 8.4 million in the year before the compliance date (2017).

At TSL 3, total conversion costs grow significantly to $ 42.2 million, an increase of
59 percent over those incurred by manufacturers at TSL 2. DOE estimates that approximately12

- 49 

### 88 percent of a ll batch equipment and 75 percent of all continuous equipment on the market will require redesign to meet this TSL. Six of the twelve manufacturers of batch equipment currently produce batch commercial ice makers that comply with the efficiency levels spec ified at TSL 3. This includes one small business manufacturer. In contrast, all six identified manufacturers of continuous commercial ice makers produce products that comply with the efficiency levels specified at TSL 3.

T he majority of redesigns necessar y to meet the standards at TSL 3 involve more complex changes to the evaporator and condenser systems. These complex redesigns result in product conversion costs increasing at a rate higher than simply the additional number of units that require redesign. At TSL 3, the resulting industry product conversion costs total $ 38.3
million. Additionally, capital conversion costs jump significantly to $ 3.9 million, as evaporator and condenser redesigns spur investments in tooling for both of these components and the surrounding enclosure.

At TSL 4, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $ 19. 6 mi llion to

- $ 30.5 million, or a change in INPV of

- 19. 2 percent

to

- 30.0 percent. At this TSL, industry free cash flow is estimated to decrease by approximately
227 percent to

- $ 10.7 million, compared to the base

- case value of $ 8.4 mil lion in the year before the compliance date (2017).

At TSL 4, total conversion costs grow to $ 51.2 million. Relative to the change between
TSL s 2 and 3, the increases in conversion costs at TSL 4 are smaller as the percentage of batch
and continuous units requiring redesign grows to 96 percent and 77 percent, respectively. These fractions are up from 88 percent and 75 percent

, respectively, at TSL 3. Only two manufacturers, including one small business manufacturer, currently produce batch commercial ice ma kers that comply with the efficiency levels specified at TSL 4. In contrast, all six identified manufacturers of continuous commercial ice makers produce equipment that complies with the efficiency levels specified at TSL 4.

With very few additional units needing redesigns, costs incurred are mainly incremental, and account for the increasing complexity of condenser and evaporator redesigns. Product conversion costs grow to $ 44 .8 million, 17 percent greater than those at TSL 3 . However, the increasing complexity of redesign does incur greater capital conversion costs, which grow to $ 6.4 million as additional capital investments are required to modify production lines to
manufacture the se more complex designs.

At TSL 5, DOE estimates impacts on INPV for manufacturers of automatic commercial ice makers to range from

- $ 19.9 million to

- $ 32.6 million, or a change in INPV of

- 19.5 percent

to

- 32.0 percent. At this TSL , industry free cash flow is estimated to decre ase by approximately
242 percent to

- $ 12.0 million, compared to the base

- case value of $ 8.4 million in the year before the compliance date (2017).

As with TSL 4, only two manufacturers, including one small business manufacturer, currently produc e batch commercial ice makers that comply with the efficiency levels specified
at TSL 5. Similarly, only four of the six identified manufacturers of continuous commercial ice makers produce equipment that complies with the efficiency levels specified at TS L 5. As12

- 5 0 

### compared to the previous increases in required efficiency between TSLs, the changes between
TSL 4 and TSL 5 are minimal. As a result

, total conversion costs grow only slightly, rising 6 percent to $ 54.2 million. This consists of $ 46.9 million in product conversion costs and $ 7.3 million in capital conversion costs.12

- 51 

### REFERENCES

1

. A ir

- Conditioning, Heating, and Refrigeration Institute

. ( Last a ccessed October 20 12

. )

< www.ahridirectory.org/ahridirectory/pages/home.aspx >

2

. California Energy Commission. Appliance Efficiency Database . (Last accessed October 2012.) < www.appliances.energy.ca.gov/QuickSearch.aspx >

3

. 

### ENERGY STAR. Qualified Automatic Commercial Ice Makers

. (Last accessed October 2012.)

< www.energystar.gov/index.cfm?fuseaction=find_a_product.showProductGroup&pgw_code
=CIM >

4

. Mc Kinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies , 3rd Edition. 2000. Copeland, Koller, Murrin. New York: John Wiley & Sons.13

- i 

### CHAPTER 13. EMISSIONS ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION

................................ ................................ ................................ ............ 13

- 1
13.2 AIR QUALITY REGULATI ONS AND EMISSIONS IM PACTS

................................ .. 13

- 1
13.3 POWER SECTOR EMISSIO NS FACTORS

................................ ................................ ... 13

- 3
13.4 UPSTREAM AND GREENHO USE GAS EMISSIONS FA CTORS

.............................. 13

- 3
13.5 EMISSIONS IMPACT RES ULTS

................................ ................................ ................... 13

- 4
REFERENCES

................................ ................................ ................................ .......................... 13

- 9

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors

................................ ................................ ............. 13

- 3 Table 13.4.1 Electricity Upstream Emissions Factors

................................ ............................... 13

- 4
Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for 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 Reduction

................ 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

- 813

- 1 

### CHAPTER 13

. EMISSIONS ANALYSIS

13.1 I NTRODUCTION

Th e emissions analysis consists of two components. The first component estimates the effect of potential energy conservation standards on power sector and site combustion emissions of carbon dioxide (CO 2 

### ), nitrogen oxides (NO
x 

### ), sulfur dioxide (SO 2 

### )

, and mer cury (Hg). The second component estimates the impacts of a potential standard on emissions of two additional greenhouse gases, methane (CH 4 

### ) and nitrous oxide (N 2 

### O), as well as the reductions to emissions of all species due to “upstream” activities in the fuel production chain. These upstream activities comprise extraction, processing, and transporting fuels to the site of combustion. The associated emissions are referred to as upstream emissions. Together, these emissions account for the full

fuel

- cycle (F FC), in accordance with the U.S. Department of Energy’s ( DOE’s ) FFC Statement of Policy. 76 FR 51282 (Aug. 18, 2011), as amended at 77 FR 49701 (Aug

. 17, 2012).

The 

### analysis of power sector emissions uses marginal emissions intensity factors derived
from runs of DOE’s National Energy Modeling System

– Building Technologies ( NEMS

- B T ) model, described in c hapter 14 . DOE used the version of NEMS based on the Annual Ener gy Outlook 201 3 ( AEO2013 )

. 1 

### Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions. AEO2013 generally represents current Federal and State legislation and final implementation regulations in place as of the end of December 201 2

.

Combustion emissions of CH 4 

### and N 2 

### O are estimated using emissions intensity factors published by the E PA , GHG Emissions Factors Hub. a 

### The FFC upstream emissions are estimated based on the methodology developed by Coughlin ( 201 3 ). 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 ( M Wh ) or million British thermal units ( MMBtu ) of site energy savings. Total emissions reductions are estimated using the energy savings calculated in the national imp act analysis (chapter 10).

13.2 AIR QUALITY REGULATI ONS AND EMISSIONS IM PACTS

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

em issions 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 allowance

- based trading progr am that that operates along with the Title IV program in those states and D.C. 70 FR 25162 (May 12, 2005 ). CAIR was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. Circuit)

, but parts of it remained in effect. See No rth Carolina v. EPA , 550 F.3d 1176 (D.C. Cir. 2008); North Carolina v. EPA , 531 F.3d 896 (D.C. Cir. 2008). On July 6, 2011

, EPA issued a replacement for CAIR, the Cross

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

. 8, 2011). On August 21, 2012, the D .C. Circuit issued a decision to vacate CSAPR. See EME

a http://www.epa.gov/climateleadership/inventory/ghg

- emissions.html13

- 2 

### Homer City Generation, LP v. EPA , 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering CAIR. The AEO2013 emissions factors used for the notice of proposed rulemaking assume that CAIR remains a binding regulation through 2040.

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 

### emi ssions allowances resulting from the lower electricity demand caused by the imposition of an efficiency standard could be used to permit offsetting increases in
SO 2 

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

### emissions covered by the existing

cap

- and

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

### as a result of standards.

Beginning in 2015, however, SO 2 

### emissio ns will fall as a result of the Mercury and Air Toxics Standards (MATS) for power plants , which were announced by EPA on December 21,
2011 . 77 FR 9304 (Feb. 16, 2012). b 

### In the final MATS rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air pollutants (HAP), and also
established a standard for SO 2 

### (a non

- HAP acid gas) as an alternative equivalent surrog ate standard for acid gas HAP. The same controls are used to reduce HAP and non

- HAP acid gas; thus, SO 2 

### emissions will be reduced as a result of the control technologies installed on coal

- fired power plants to comply with the MATS requirements for acid gas. AEO2013 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce SO 2 

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

### emissions when electricity demand decreases ( e.g. , as a result of ener gy efficiency standards). Emissions will be far below the cap that would be established by C AIR , 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.

C AIR established a cap on NO
x 

### emissions in 28 eastern states and the District of
Columbia. E nergy conservation standards are e xpected to have little effect on NO
x 

### 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 reduce NO
x 

### emissions in the states not affected by the caps

, so DOE estimate d NO
x 

### emissions reductions from potential standards for th ose states

.
The MATS limit mercury emissions from power plants, but they do not i nclude emissions

caps and, as such, DOE’s energy conservation standard s wou ld likely reduce Hg emissions. DOE estimated mercury emissions reductions using the NEMS

- BT based on AEO2013 , which incorporates the MATS.

b On July 20, 2012, EPA announced a partial stay, for a limited duration, of the effectiveness of national new source emission standards for hazardous air pollutants from coal

- and oil

- fired electric utility steam generating units. < http://www.reginfo.gov/public/do/eAgendaViewRule?pubId=201110&RIN=2060

- AP52 >13

- 3 

### 13.3 P OWER SECTOR EMISSION S FACTORS

The 

### analy sis of power sector emissions uses marginal emissions intensity factors derived
from runs of DOE’s NEMS

- BT model, using the version updated to t he AEO2013

. 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, fue l 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 kilowatt

- hours ). DOE uses the site energy savings mul tiplied 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 used may be found in Coughlin ( 201 3 ). 3



### Table 13.3

. 1 presents the average power plant emissions factors for selected years. These power plant emissions factors are derived from 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 sources in total electricity generation.

The power plant emissions factor for 

### NO
x 

### is an average for the entire U .S. The marginal
calculation based on the NEMS

- BT model accounts for the fact that NO
x 

### 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 598 598 559 524 497 309
SO 2 g/MWh 570 570 691 732 454 524
NOx g/MWh

- 902

- 902

- 418

- 218

- 70 43 Hg g/MWh 0.0015 0.0015 0.0004 0.0009 0.0007 0.0006

N 2 O g/MWh 7.1 7.2 7.2 7.1 7.1 6.9
CH 4 g/MWh 49 50 50 50 49 48



### 13.4 U PSTREAM AND GREENHOU SE GAS EMISSIONS 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 emis sions from combustion of that fuel at either the building site or the power plant. The associated reduction in energy use for upstream activities leads to further reductions in emissions. These upstream emissions are defined to
include the combustion emiss ions from the fuel used upstream, the fugitive emissions associated with the fuel used upstream, and the fugitive emissions associated with the fuel used on site.

Fugitive emissions of CO 2 

### occur during oil and gas production, but are small relative to

com bustion emissions. They comprise about 2.5 percent of total CO 2 

### emissions for natural gas
and 1.7 percent for petroleum fuels. Fugitive emissions of methane occur during oil, gas

, and coal production. Combustion emissions of CH 4 

### are very small, while fugit ive emissions (particularly for gas production) may be relatively large. Hence, fugitive emissions make up over13

- 4 

### 99 percent of total methane emissions for natural gas, about 95 percent for coal, and 93 percent for petroleum fuels.

Upstream emissions factor s account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primary fuels. For ease of application in its analysis, DOE developed all of the emissions factors using site (point of use) energy savings in
the de nominator. 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.

Table 13.4

. 1 Electricity Upstream Emissions Factors

Unit 201 8 2020 2025 2030 2035 2040
CO 2 kg/MWh 27.6 27.3 26.9 26.8 26.9 26.3
SO 2 g/MWh 5.2 5.3 5.3 5.2 5.2 5.1
NOx g/MWh 347.0 340 334 333 336 329 Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

N 2 O g/MWh 0.25 0.25 0.25 0.25 0.24 0.24
CH 4 g/MWh 2,054 2,025 2,008 2,025 2,057 1,999



### 13.5 EMISSIONS IMPACT RES ULTS

Table 13.5.1 present s the estimated cumulative emissions reductions for the lifetime of equipment sold in 201 8

- 204 7 for each TSL , respectively

.13

- 5 

### 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 ) 3.50 6.52 13.68 18.19 19.19
NO

x ( thousand tons )

- 0.89

- 1.66

- 3.49

- 4.64

- 4.89 Hg ( tons ) 0.01 0.01 0.02 0.03 0.03

N 2 O ( thousand tons ) 0.08 0.15 0.31 0.41 0.43
CH 4 ( thousand tons ) 0.47 0.88 1.84 2.45 2.58
SO 2 ( thousand tons ) 5.31 9.89 20.76 27.60 29.12 Upstream Emissions
CO 2 ( million metric tons ) 0.23 0.42 0.89 1.18 1.24
NO

x ( thousand tons ) 3.11 5.80 12.18 16.19 17.08 Hg ( tons ) 0.000 0.000 0.000 0.001 0.001

N 2 O ( thousand tons ) 0.00 0.00 0.01 0.01 0.01
CH 4 ( thousand tons ) 18.89 35.22 73.93 98.30 103.68
SO 2 ( thousand tons ) 0.05 0.09 0.19 0.25 0.27
Total Emissions
CO 2 ( million metric tons ) 3.72 6.94 14.57 19.37 20.43
NO

x ( thousand tons ) 2.22 4.14 8.69 11.56 12.19 Hg ( tons ) 0.01 0.01 0.02 0.03 0.03

N 2 O ( thousand tons ) 0.08 0.15 0.32 0.42 0.45
CH 4 ( thousand tons ) 19.36 36.09 75.77 100.75 106.27
SO 2 ( thousand tons ) 5.35 9.98 20.95 27.86 29.38



### 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

- 6 

### 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 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0201820232028203320382043204820532058kt 

### CO 2 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0 200.0 400.0 600.0 800.0 1000.0 1200.0 1400.0201820232028203320382043204820532058ton 

### SO 2 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 513

- 7 

### 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 -400.0 -200.0 0.0 200.0 400.0 600.0 800.0201820232028203320382043204820532058ton 

### NO

x 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0201820232028203320382043204820532058ton 

### Hg Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 513

- 8 

### 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 5.0 10.0 15.0 20.0 25.0201820232028203320382043204820532058ton 

### 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 3500.0 4000.0 4500.0201820232028203320382043204820532058ton 

### CH 4 

### Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 513

- 9 

### REFERENCES

1. U.S. Energy Information Administration. Annual Energy Outlook 20 13 with Projections
to 2040

. 2013. < www.eia.gov/forecasts/aeo/ >

3

. Coughlin, K

. Projections of Full

- Fuel

- Cycle Energy and Emissions Metrics

. 2013. Lawrence Berkeley National Laboratory . Berkeley, CA . Report No. LBNL

- 6025E.14

- i 

### CHAPTER 14. MONETIZATION OF EMIS SION REDUCTIONS BENE FITS

TABLE OF CONTENTS

14.1 Introduction

................................ ................................ ................................ .................... 14

- 1
14.2 Monetizing Carbon Dioxide Emissions

................................ ................................ ......... 14

- 1
14.2.1 Social Cost of Carbon

................................ ................................ ........................ 14

- 1
14.2.2 Monetizing Carbon Dioxide Emissions

................................ ............................. 14

- 1
14.2.3 Current Approach and Key Assumptions

................................ .......................... 14

- 2
14.3 Valuation Of Other Emissions 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 Present Value of CO 2 

### Emissions Reduction under
Automatic Commercial Ice Makers Trial Standa rd Levels

............................... 14

- 6
Table 14.4.2 Estimates of Domestic Present Value of CO 2 

### Emissions Reduction under
Automatic Commercial Ice Makers Trial Standard Levels

............................... 14

- 6
Table 14.4.3 Estimates of Present Value of N O
x 

### Emissions Reduction under Automatic
Commercial Ice Makers Trial Standard Levels

................................ ................. 14

- 714

- 1 

### 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 reduc ed 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 emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services. Estimates of the SCC are provided in dollars per metric ton of CO 2



### . A domestic SCC v alue 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 reflect the value of damages worldwide.

Under section 1( b)(6) of Executive Order 12866, “ Regulatory Planning and Review,”
58 FR 51735 (Oct. 4, 1993), agencies must, to the extent permitted by law, “assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits 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

- 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 up dated over time to reflect increasing knowledge of the science and economics of climate impacts.

As part of the interagency process that developed these SCC estimates, technical experts from numerous agencies met on a regular basis to explore the technical literature in relevant fields, discuss key model inputs and assumptions, and consider public comments. The main objective of this process was to develop a range of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic literatures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

14.2.2 Monetizing Carbon Dioxide Emissions

A number of serious challenges arise w hen at tempting to assess the incremental economic impacts of CO 2 

### emissions. A report from the National Research Council 1 

### points out
that such assessments will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases ; (2) the effects of past and future emissions on the14

- 2 

### 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
effor t to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can b e useful in estimating the social benefits of reducing CO 2 

### 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 increase d) emissions in any future year by multiplying the change in emissions in that year by the SCC value appropriate for that year. The net present value of the benefits can then be calculated by multiplying each of these future benefits by an appropriate disc ount factor and summing across all affected years. This approach assumes that the marginal damages from increased emissions are constant for small departures from the baseline emissions path, an approximation that is reasonable for policies that
have effec ts on emissions that are small relative to cumulative global CO 2 

### emissions.

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing CO 2 

### emissions. To ensure consistency in how
benef its are evaluated across agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemaking process, to quantify avoided climate change damages from reduced CO 2 

### emissions. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the existing literature to use as interim values until a more comprehensive analysis could be conducted. The outcome of the preliminary assessment by the interagency group was a s et of five interim values: global SCC estimates for 2007 (in 2006 $ ) 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 an alysis. The results of this preliminary effort were presented in several proposed 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 es timates, which were considered for this proposed rule. Specifically, the group considered public comments and further explored the technical literature in relevant fields. The interagency group relied on three integrated assessment models commonly
used to estimate the SCC: the FUND, DICE, and PAGE models. a 

### These models are frequently cited in the peer

- reviewed literature and were used in the last assessment of the Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC
values that were developed.

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 respe cting 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: (1) climate sensitivity;

a The models are described in appendix 14A of this technical support document (TSD).14

- 3 

### (2) socio

- economic and emi ssions trajectories; and (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 v alues 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 for use in regulatory analyses. Three values are based on the average S CC 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 represent higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution. The values grow in real terms over time

. In addition , the interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although preference is given to consideration of the global benefits of reducing CO 2 

### emissions. Table 14.2

. 1 presents the values in the 2010 interagency group report, b 

### which is reproduced in appendix 14 A of this TSD

.
The SCC values used for this analysis were generated using the most recent versions of the three integrated assessment models that have been published in the peer

- reviewed literature

. c



### Table 14.2

. 2 shows the updated sets of SCC estimates in 5

- year i ncrements from 2010 to 2050. The full set of annual SCC estimates between 2010 and 2050 is reported in appendix 14 B of this
TSD

. The central value that emerges is the average SCC across models at the 3

- percent discount rate. However, for purposes of captur ing the uncertainties involved in regulatory impact analysis, the interagency group emphasizes the importance of including all four sets of SCC values. For the years after 2050, DOE applied the average annual growth rate of the SCC estimates in
2040−2050 a ssociated with each of the four sets of 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. www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

- Cost

- of

- Carbon

- for

- RIA.pdf

.
c Technic al 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

. November 2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg /technical

- update

- social

- cost

- of

- carbon

- for

regulator

- impact

- analysis.pdf14

- 4 

### 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 group also rec ognizes that the existing models are imperfect and incomplete. The National Research Council report previously mentioned 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 interagency pr ocess 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 improvements
in m odeling.

In summary, in considering the potential global benefits resulting from reduced CO 2 

### emissions, DOE used the values from the 2013 interagency report, escalated to 2012$ using the gross domestic product ( GDP ) price deflator. For each of the four cases specified, the values
used for emissions in 2015 are $1 1.8 , $ 39.7 , $6 1 .2, and $117 per metric ton avoided

. 314

- 5 

### DOE multiplied the CO 2 

### emissions reduction estimated for each year by the SCC value for that year in eac h of the four cases. To calculate a present value of the stream of monetary values, the interagency report notes that damages from future emissions should be discounted at
the same rate as that used to calculate the SCC estimates themselves to ensure inter nal consistency . Thus, 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 Emissions Reductions

DOE considered the potential monetary benefit of reduced NO
x 

### emissions from the TSLs it considered. As noted in chapter 13, new or amended energy conservation standards would
reduce NO
x 

### emissions in those States that are not affected by caps. DOE estimated the monetized
value of NO
x 

### emissions reductions resulting from each of the TSLs considered based on environmental damage estimates found in the relevant scientific literature. Available estimates suggest a very wide range of monetary values, ranging from $468 to $4,809 per ton ( in 2012$). 4



### In accordance with Office of Management and Budget ( OMB ) guidance, DOE calculated a range of monetary benefits using each of the economic values for NO
x 

### and real discount rates of 3 and 7 percent. 5



### DOE is still evaluati ng appropriate values to use to monetize avoided SO 2 

### and Hg
emissions. It did not monetize these emissions for this analysis

.

14.4 RESULTS

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 to 23 percent of the global values ; the results are presented in Table 14.4

. 2

.14

- 6 

### 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

m illion 2012$ Primary Energy Emissions
1 24.6 111.2 176.2 342.8
2 45.9 207.3 328.5 639.0
3 96.3 435.2 689.5 1,341.5

4 128.0 578.6 916.8 1,783.6
5 135.1 610.3 967.0 1,881.4 Upstream Emissions
1 1.5 7.0 11.2 21.7
2 2.8 13.1 20.8 40.4
3 6.0 27.5 43.7 84.9
4 7.9 36.5 58.1 112.8
5 8.4 38.5 61.3 119.0
Total Emissions
1 26.1 118.2 187.4 364.5

2 48.7 220.4 349.3 679.5
3 102.3 462.6 733.2 1,426.3
4 136.0 615.1 974.9 1,896.4
5 143.4 648.8 1,028.3 2,000.4 * For each of the four cases, the corresponding SCC value for emissions in 2015 is $1 1.8 , $ 39.7

, $6 1 .2, and $117.0 per metric ton (2012$).



### 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

m illion 2012$ Primary Energy Emissions
1 1.7 to 5.7 7.8 to 25.6 12.3 to 40.5 24.0 to 78.8
2 3.2 to 10.6 14.5 to 47.7 23.0 to 75.5 44.7 to 147.0
3 6.7 to 22.1 30.5 to 100.1 48.3 to 158.6 93.9 to 308.5

4 9.0 to 29.4 40.5 to 133.1 64.2 to 210.9 124.9 to 410.2
5 9.5 to 31.1 42.7 to 140.4 67.7 to 222.4 131.7 to 432.7 Upstream Emissions
1 0.1 to 0.4 0.5 to 1.6 0.8 to 2.6 1.5 to 5.0

2 0.2 to 0.7 0.9 to 3.0 1.5 to 4.8 2.8 to 9.3
3 0.4 to 1.4 1.9 to 6.3 3.1 to 10.0 5.9 to 19.5

4 0.6 to 1.8 2.6 to 8.4 4.1 to 13.4 7.9 to 26.0
5 0.6 to 1.9 2.7 to 8.9 4.3 to 14.1 8.3 to 27.4
Total Emissions
1 1.8 to 6.0 8.3 to 27.2 13.1 to 43.1 25.5 to 83.8

2 3.4 to 11.2 15.4 to 50.7 24.4 to 80.3 47.6 to 156.3
3 7.2 to 23.5 32.4 to 106.4 51.3 to 168.6 99.8 to 328.1
4 9.5 to 31.3 43.1 to 141.5 68.2 to 224.2 132.8 to 436.2
5 10.0 to 33.0 45.4 to 149.2 72.0 to 236.5 140.0 to 460.1 * For each of the four cases, the corresponding SCC value for emissions in 2015 is $1 1.8 , $ 39.7

, $6 1 .2, and $117.0 per metric ton (2012$).14

- 7 

### Table 14.4

. 3 present s the present value of cumulative NO
x 

### emissions reductions for each TSL, calculated using the average dollar

- per

- ton values and 7

- a nd 3

- 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

m illion 2012$

Power Sector Emissions
1

- 1.8

- 1.3
2

- 3.4

- 2.4
3

- 7.2

- 5.0

4

- 9.5

- 6.6
5

- 10.1

- 7.0 Upstream Emissions
1 4.3 2.1
2 8.0 3.8
3 16.8 8.0
4 22.3 10.7
5 23.6 11.3
Total Emissions
1 2.5 0.8
2 4.6 1.4
3 9.6 3.0
4 12.8 4.0
5 13.5 4.314

- 8 

### REFERENCES

1

. National Research Council, 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 Working Group on Social Cost of Carbon, Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States
Governme nt. < 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. Interagency Working Group on Social Cost of Carbon

, November

, 2013. United States Government.

< http://www.whi tehouse.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 Unfunde d Mandates on State, Local, and Tribal
Entities , 2006. Washington, D

. C.

< www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/200 6_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.)

< www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a

- 4.pdf >15

- i 

### 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

- 5
15.3.3 Results Summary

................................ ................................ ............................... 15

- 8
REFE RENCES

................................ ................................ ................................ ........................ 15

- 10

LIST OF FIGURES

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: Peaking 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

- 915

- 1 

### 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 standar ds

on automatic commercial ice makers , DOE used the version of NEMS based on AEO201 3

. 1



### NEMS

- BT has a number of advantages that have led to its use in the analysis of energy
conservation standards :

• NEMS

- BT uses a set of assumptions that are well known and fairly transparent, due to the exposure and scrutiny each AEO receives.

• NEMS

- BT is updated each year, with each edition of the AEO , to reflect changes in energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS

- BT permits the modeling of interactions among the various energy supply and demand sectors.

15.2 METHODOLOGY

DOE uses NEMS

- BT to estimate the marginal impacts of re duction in energy demand on
the energy supply sector. In principle, marginal values should provide a better estimate of the actual impact of energy conservation standards . In practice, the numerical differences between marginal and average values may turn out to be smaller than the intri nsic 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
effects can occur : the annual generation (terawatt

- hours (TWh) ) from the stock of electric

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

- 2 

### 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 vary for different types of end use. The change in total generating capacity is sensitive to the degree to wh ich the
end

- use is peak coincident, while the capacity mix is sensitive to the hourly load shape
associated with the end use.

To model the impact of a standard, DOE inputs a reduction to annual energy demand for the corresponding end use in the appropriate start year. The NEMS

- BT model is run with the decremented energy demand to determine the modified build

- out of capacity and total generation. Regional effects of a standard can be accounted for by defining the energy demand
decrement as a function of cens us division.

The output of the NEMS

- BT analysis includes the effective marginal heat rate ( i.e.

, ratio of the change in fuel consumption in quadrillion British thermal units ( quads ) to the change in generation in TWh) and the capacity reduction by fuel ty pe for a given reduction in total generation. DOE uses the site

- energy savings multiplied by a transmission and distribution loss factor to estimate the reduction in generation for each TSL. The relationship between a
reduction c 

### in electricity generation ( TWh) and the reduction in capacity (GW) is estimated based on the output of NEMS

- BT model runs using the end

- use

- specific energy demand decrement.
Details on this approach use d may be found in Coughlin (2013 ). 2



### NEMS

- BT provides output for the following cap acity 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 tu rbine/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 diese l) and renewables. Pumped storage and nuclear power are very insensitive to small changes in demand, while fuel cells and distributed generation make up a very small fraction (less than 1 percent) of the generation capacity base.

15.3 UTILITY IMPACT RESULTS

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

The figures in this section show the changes in U.S. electric ity installed capacity that
result for each TS L by major plant type for selected years. Note that a negative number means an
increase in capacity under a TSL.

c These reductions are defined relative to the AEO Reference C ase.15

- 3 

### 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 5 10 15 20 25 30 35 40 45 50201820232028203320382043204820532058MW 

### Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 515

- 4 

### 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 8 9201820232028203320382043204820532058MW 

### Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 10 20 30 40 50 60 70 80201820232028203320382043204820532058MW 

### NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 515

- 5 

### Figure 15.3

. 5 Automatic Commercial Ice Makers : Peaking Capacity Reduction

Figure 15.3

. 6 Automatic Commercial Ice Makers : Renewables Capacity Reduction

15.3.2 Electricity Generation

The figures in this section show the annual change in electric ity generation that result for
each TSL by plant type

. Gas combined cycle power plants account for most of the generation reduction. Note that a negative number means an increase in generation under a TSL. -3 -2 -1 0 1 2 3 4 5 6 7 8201820232028203320382043204820532058MW 

### Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 50 100 150 200 250 300201820232028203320382043204820532058MW 

### Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 515

- 6 

### 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 1600 1800201820232028203320382043204820532058GWh 

### Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 50 100 150 200 250 300 350 400 450201820232028203320382043204820532058GWh 

### Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 515

- 7 

### 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 60 70201820232028203320382043204820532058GWh 

### Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 200 400 600 800 1000 1200201820232028203320382043204820532058GWh 

### NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 515

- 8 

### Fi g ure 15.3

. 11 Automatic Commercial Ice Makers : Peaking Generation Reduction

Figure 15.3

. 12 Automatic Commercial Ice Makers : Renewables Generation Reduction

15.3.3 Results Summary

Table 15.3

. 1 presents a summary of the utility impact results for automatic commercial
ice makers

. -30 -20 -10

0 10 20 30 40 50 60201820232028203320382043204820532058GWh 

### Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0 100 200 300 400 500 600 700 800 900201820232028203320382043204820532058GWh 

### Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 515

- 9 

### 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 3.65 6.81 14.29 19.00 20.05
2025 16.01 29.85 62.66 83.32 87.89
2030 27.36 51.01 107.09 142.38 150.19
2035 40.57 75.64 158.78 211.11 222.68
2040 68.72 128.11 268.94 357.58 377.18 Electricity Generation Reduction
G Wh
2020 99.96 186.35 391.19 520.13 548.63
2025 270.60 504.45 1058.96 1407.99 1485.16
2030 276.76 515.93 1083.06 1440.04 1518.96
2035 284.56 530.47 1113.58 1480.62 1561.77
2040 293.87 547.81 1149.99 1529.02 1612.8315

- 10 

### REFERENCES

1

. U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook
201 3

. 201 3 . Washington, DC. (Last accessed May 1, 2013.)

< 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.16

- i 

### CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION

................................ ................................ ................................ ............ 16

- 1
16.2 ASSUMPTIONS

................................ ................................ ................................ ............... 16

- 1
16.3 METHO DOLOGY

................................ ................................ ................................ ........... 1 6

- 1
16.4 SHORT

- TERM RESULTS

................................ ................................ ............................... 16

- 2
16 .5 LONG

- TERM RESULTS

................................ ................................ ................................ . 16

- 3
REFERENCES

................................ ................................ ................................ .......................... 16

- 4

LIST OF TABLES

Table 16.4.1 Net Short

- Term Change in Employment

................................ .............................. 16

- 316

- 1 

### CHAPTER 16. EMPLOYMENT IMPACT AN ALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy ’s ( DOE ) 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 this notice of proposed rulemaking (NOPR)

.

16.2 A SSUMPTIONS

DOE expects amended 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 uncertainties involved in projecting employment impacts, especially changes in the later years of the analysis. 1 

### Because ImSET does not incorporate price changes, the employment effects predicted by ImSET would overestimate the magnitude of actual job impa cts 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 i n 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 standar ds 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. Compared with simple economic multiplier approaches, Im SET allows for more complete and automated analysis of the economic impacts of energy

- efficiency investments in buildings.16

- 2 

### In an input/output model, the level of employment in an economy is determined by the relationship s of different sectors of the economy a nd spending flows among them

. D ifferent sectors have different levels of labor intensity and so changes in the level of spending ( e.g. , due to the effects of an efficiency standard) in one sector of the economy will affect flows in other sectors, which aff ects the overall level of employment.

ImSET uses a 187

- sector model of the national economy to predict the economic effects of residential and commercial buildings ’ technologies. ImSET collects estimates of initial investments, energy savings, and economic activity associated with spending the savings resulting from standards ( e.g. , changes in final demand in personal consumption, business investment and spending, and government spending). It provides overall estimates of the change in national output for e ach input/output sector. The model applies estimates of employment and wage income per dollar of economic output for each sector and calculates impacts on national employment and wage income.

Energy

- efficiency technology primarily affects the U.S. economy along three spending pathways. First, general investment funds are diverted to sectors that manufacture, install, and
mai ntain energy

- efficient 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 econ omy. When customers use less energy or water , electric and/or water utilities experience relative reductions in demand

, which leads to reductions in utility sector investment and employment.

DOE notes that the employment impacts estimated with ImSET for th e entire economy differ from the employment impacts in the automatic commercial ice maker manufacturing
sector estimated in c hapter 12 using the Government Regulatory Impact Model (GRIM). The methodologies used and the sectors analyzed in the ImSET and GRI M 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 of 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 genera l 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 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 negatively affected, increasing or reducing jobs in that16

- 3 

### 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 1 6.4

. 1 presents the modeled net employment impact from the rule in 201 8 and
202 1

. As mentioned in c hapter 12, 85 percent of automatic commercial ice makers are produced domestically and 1 5 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 commercial ice
makers returns to the U.S. economy and all of the money spent on imported automa tic
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 percent of the money spent on imported automatic commercial ice makers is likely to return, wi th employment impacts falling within the ranges presented below.

Table 16.4

. 1 Net Short

- T erm Change in Employment

Trial Standard Level 201 8 * 2022
1 19 to 20 100 to 101
2 36 to 40 192 to 19 6
3 75 to 8 7 431 to 4 42
4 44 to 91 506 to 552
5 34 to 9 0 518 to 5 72 * 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 result, 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 electricity 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 assumes that the official unemployment rate may decline to 6.9 percent in 2014 and drop further to 5.2 percent in
201 8

. 5 

### The unemployment rate in 201 8 is generally considered to be close to “full employment.” When an economy is at full employment

, a ny 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 extent th at ma rkets are slow to adjust, DOE anticipate s that net labor market impacts will be negligible over time due to the small magnitude of the short

- term effects presented in Table 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 2012 Mid

- Session Review

. 2011. Washington, D.C. < www.whitehouse.gov/sites/default/files/omb/budget/fy2012/ assets/12msr.pdf >17

- i 

### 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 Efficiency Programs

................................ ............................. 17

- 4
17.3.5 Early Replacement

................................ ................................ ............................. 17

- 4
17 .4 SUMMARY OF RESULTS F OR NON

- REGULATORY ALTERNATI VES

............ 17

- 5
REFERENCES

................................ ................................ ................................ .......................... 17

- 6

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

- 3 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 Proposed
Standard

................................ ................................ ................................ ............. 17

- 5
Ta ble 17.4.2 Cumulative NPV of Non

- Regulatory Alternatives Compared to the Proposed
Standard

................................ ................................ ................................ ............. 17

- 517

- 1 

### 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 regulatory 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 proposed standards
rule.

The non

- regulatory means of achieving energy savings that DOE proposes to analyze are
listed in Table 17.1

. 1

. I n support of DOE’s notice of proposed rulemaking , DOE i nclude 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 action

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 proposed 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 that 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

• p roduct stock data

.17

- 2 

### 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 to 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 epresents the value in 2012 $ (discounted to 2013 ) 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

Th e 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

- r egulatory 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 levels 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 commercial
ice maker with an energy

- efficien t machine

. Mac hines qualifying as energy efficient are frequently defined as ENERGY STAR ® 

### compliant or meeting the Consortium for Energy Efficiency’s (CEE) Tier 2 or 3. As an example, Eugene Water and Electric Board 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 Administration and 60 participating utilities

, with the Portl and 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 being rewarded with rebates. The Saving Water Partnership in the Seattle, Washington metropolitan area combines electricity conservation rebates ranging from $100 to $600 with water conservation rebates of $100 or $ 300 (depending on harvest capacity). 417

- 3 

### DOE used the amounts o f current rebates and compared them to the installed cost
increments at the levels corresponding to trial standard level 3 (TSL 3). ( See chapter 10 of this
TSD for more information on installed co sts and TSL 3. ) A $100 rebate is equivalent to roughly

one

- half of the incremental installed cost for most small equipment classes based on the difference between an efficiency level 1 installed cost and a level corresponding to TSL 3 , with percentages ran ging from 42 percent of an IMH

- A

- Small

- C unit to 71 percent of an IMH

- A

Small

- B unit

. For large machines, a $200 rebate is equivalent to 42 percent of the incremental
cost of a RCU

- Large

- B

- 2 unit and at the high end is equivalent to 100 percent of the incremental
cost of a SCU

- W

- Large

- B or SCU

- A

- Large

- B unit

.

DOE chose to model a scenario where customers are offered, as rebates, 60 percent of the
incremental installed cost of the ACIM equipment

.

To estimate the market shares of efficiency levels (see c hapter 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 STAR and rebate programs

. I nformation given to DOE indicates that, on average, 60 percent of t otal shipments in those equipment classes meet or exceed the ENERGY STAR level. For determining the potential impact of rebate programs targeting TSL 3, DOE developed a shift scenario. In the shift scenario, market share wa s shifted upward such that the to tal 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 existing 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 utility 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 compar es them against the NES and NPV values at TSL 3

. NES and NPV are calculated for equipment purchased in the 2018

– 2047 analysis period and include energy savings

, operation and
maintenance costs

, and savings extending for the life of equipment purchased in 2 047.

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 201 2 $

7% Discount Rate 3% Discount Rate

No New Regulatory Action 0 0 0

Consumer Rebates (60% of price increments) 0. 1 90 0. 6 78 1

. 319

TSL 3 0. 281 0.7 91 1.7 51 * 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 

### 17.3.3 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 perspective, 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 r efund is provided by the Internal Revenue Service.

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 estimated 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 i n 

### 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 fr om 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 201 2$

7% Discount Rate 3% Discount Rate No New Regulatory Action 0 0 0

Customer Tax Credits 0. 14 5 0. 520 1.011 Today's Standards at TSL 3 0. 281 0. 791 1. 751

* Energy savings are in primary energy quads.

** Net present value is the value in the present of a time series of costs and savings.



### 17.3.4 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 quantitatively 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

- efficiency17

- 5 

### units. DOE studied the feasibility of a Federal program to promote early replacement of appliances and equipm ent 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, and 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 RE S ULTS FOR NON

- REGULATORY ALTERNATI VES

Table 17.4

. 1 and Table 1 7.4

. 2 show the NES and NPV for the non

- regulatory alternatives analyzed. The case in which 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 proposed energy conservation standard.

As shown in Table 17.4

. 1 and Table 17.4

. 2 , none of the policy alternatives DOE
examined would achieve the amount of energy or monetary savings that cou ld be realized under the proposed 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 Proposed
Standard

Policy Alternatives Cumulative NES quads No new regulatory action 0
Consumer tax credits 0. 14 5
Customer rebates 0.1 90 Voluntary energy efficiency targets 0 Early replacement 0 Proposed s tandards (TSL 3 ) 0. 281



### Table 17.4

. 2 Cumulative NPV of Non

- Regulatory Alternatives Compared to the Proposed
Standard

Policy Alternatives Cumulative Net Present Value
billion 201 2 $
7% Discount 3% Discount No new regulatory action 0 0
Consumer tax credits 0. 520 1.011
Customer rebates 0. 6 78 1. 319 Voluntary energy efficiency targets 0 0 Early replacement 0 0 Proposed s tandards (TSL 3 ) 0.7 91 1.7 5117

- 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 >6A

- i 

### APPENDIX 6A. DATA FOR EQUIPMENT PRICE MARKUPS

TABLE OF CONTENTS

6A.1 INTRODUCTION

................................ ................................ ................................ ........ 6A

- 1
6A.2 DETAILED WHOLESALER COST DATA

................................ ............................... 6A

- 1
6A.3 DETAILED MECHANICAL CONTRACTOR DATA

................................ ............... 6A

- 2
6A.4 ESTIMATION OF WHOLESALER AND MECHANICAL CONTRACTOR
MARKUP STANDARD DEVI ATIONS

................................ ................................ ..... 6A

- 3

LIST OF TABLES

Table 6A.2.1 Disaggregated Costs 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

- 36A

- 1 

### 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 DETAILED WHOLESALER COST DATA

Chapter 6 presents w holesaler revenues and costs i n aggregated form

, based on the Heating

, Air

- conditioning & Refrigeration Distributors International (HARDI) 20 1 2 Profit Report (20 11 Data)

. Table 6A.2

. 1 provides the complete breakdown of costs and expenses from
the 20 1 2 Profit Report (20 11 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 73.7 Gross Margin 26.3 Payroll Expenses 15.1

Baseline

Executive Salaries & Bonuses 1.7 Branch Manager Salaries and Bonuses 1.5
Sales Executive Salaries & Commissions 0.5
Outside Sales Salaries & Commissions 2.1
Inside/Counter Sales/Wages 2.8 Purchasing Salaries/Wages 0.4 Credit Salaries/Wages 0.2 IT Salaries/Wages 0.1 Warehouse Salaries/Wages 1.4 Accounting Salaries/Wages 0.5 Delivery Salaries/Wages 0.7 All Other Salaries/Wages & Bonuses 0.8 Payroll Taxes 1.0 Group Insurance 1.1
Benefit Plans 0.3 Occupancy Expenses 3.6

Baseline

Utilities: Heat, Light, Power, Water 0.4 Telephone 0.3 Building Repairs & Maintenance 0.2 Rent or Ownership in Real Estate 2.7 Other Operating Expenses 5.5

Baseline & Incremental Sales Expense (incl. advertising and promotion) 0.9 Insurance (business liability & casualty) 0.2 Depreciation 0.4 Vehicle Expenses 1.4 Personal Property Taxes/Licenses 0.1 Collection Exp ense (collection, credit card fees) 0.3
Bad Debt Losses 0.26A

- 2 

### Table 6A.2.1 (cont)

Item Percent of Revenue Scaling

Data Processing 0.3 All Other Operating Expenses 1.7 Total Operating Expenses 24.2 Operating Profit 2.1

Baseline & Incremental

Other Income 0.4 Interest Expense 0.5
Other Non

- operating Expenses 0.0
Profit Before Taxes 2.0 Source: Heating, Air

- conditioning & Refrigeration Distributors International. 2012 Profit Report (2011 Data) . 2012. 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 costs 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 Used 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, March 8, 2011.)

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I1&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

- 3 

### 6A.4 ESTIMATION OF WHOLES ALER AND MECHANICAL CONTRACTOR
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 t he 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 cost components varied during the analysis

.

Table 6A.4

. 1 Mechanic al Contractor Baseline Markups by State, 200 7 *

State Value of
Construction ($000) Cost of
Subcontract
Work ($000) Cost of
Materials

($000) Construction 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 1.4706A

- 4 

### 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 May 28, 2010.)

< http://factfinder2.census.gov/faces/nav/jsf/pages/searchre sults.xhtml?refresh=t > *The Census Bureau withheld data for some states due to sample sizes and the size of errors relative to means. For states whe re a D appears under the headings for Subcontractor Costs, Materials & Supplies, or Construction Payroll, da ta was withheld. States missing one or more variables were set equal to a n average of neighboring states’ baseline markup.8A

- i 

### APPENDIX 8A. USER INSTRUCTIONS FOR LIFE

- CYCLE COST SPREADSHEET

TABLE OF CONTENTS

8A.1 Introduction

................................ ................................ ................................ ................... 8A

- 1
8A.2 User Instructions for Life

- Cycle Cost Spreadsheet

................................ ...................... 8A

- 1
REFERENCES

................................ ................................ ................................ ......................... 8A

- 4

LIST OF TABLES

Table 8A.2.1 Description of Worksheets in LCC Spreadsheet

................................ ................ 8A

- 18 A

- 1 

### 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 posted 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 operation 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 Contains 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

e ngineering 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 2013 ( AEO201 3 ) 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 water/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

. S ales Tax Contains data on sales tax rates and population data by state. Population Contains 201 2 census data on population by state.8A

- 2 

### 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 add

- in and activated.

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 graphical 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) analysis 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 esc alation ; (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. Ho wever, during
the notice of proposed rulemaking analyses, only 12 primary equipment classes were studied, including 3 classes that were modeled at the level of two typical
units. If you select a n equipment class that has not been analyzed by DOE, the
model will display zeros and/or blank cells in the inputs and results summary on the LCC_Summary tab

.

d. There is room for up to eight possible efficiency levels , including the baseline efficiency level, though no equipment classes have more than seven representa tive levels

.8A

- 3 

### 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

. The model was set up largely from the perspective of running in Monte Carlo mode.

b. Alternatively, if the Monte Carlo option is chosen or if the “Monte Carlo” button is

c licked, the spreadsheet generates a set of results from calculations for each equipment family

, cooling method

, harvest rate bin, and ice

- making process (which together define an equipment class).

• 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 mo del records to the Crystal Ball tab a large number of outputs that you can then work with for further analyses.

• The “ Monte Carlo ” button executes a macro written in Visual Basic for Applications to loop through all equipment classes and selected efficienc y levels.
The Monte Car lo analysis usually takes 2 to 3 hours to complete for all equipment
classes.8A

- 4 

### REFERENCES

1

. U.S. Department of Energy

– Energy Information Administration. Annual Energy Outlook
201 3 . 201 3 . Washington D.C. DOE/EIA

- 0383(201 3 ).8B

- i 

### 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 Results for Ice Making Head, Water

- Cooled, Small, Batch (IMH

- W

- Small

- B)

................................ ................................ ............................. 8B

- 2
8B.2.2 Life

- Cycle Cost Resul ts for Ice Making Head, Water

- Cooled, Medium, Batch (IMH

- W

- Med

- B)

................................ ................................ ............................... 8B

- 4
8B.2.3 Life

- Cycle Cost Results for Ice Making Head, Water

- Cooled, Large, Batch

- 1 (IMH

- W

- Large

- B

- 1)

................................ ................................ .......................... 8B

- 6
8B.2.4 Life

- Cycle Cost Results for Ice Making Head, Water

- Cooled, Large, Batch

- 2 (IMH

- W

- Large

- B

- 2)

................................ ................................ .......................... 8B

- 8
8B.2.5 Life

- Cycle Cost Results for Ice Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

- B)

................................ ................................ ............................ 8B

- 10
8B.2.6 Life

- Cycle Cost Results for Ice Making Head, Air

- Cooled, Large, Batch

- 1 (IMH

- A

- Large

- B

- 1)

................................ ................................ ......................... 8B

- 12
8B.2.7 Lif e

- Cycle Cost Results for Ice Making Head, Air

- Cooled, Large, Batch

- 2 (IMH

- A

- Large

- B

- 2)

................................ ................................ ......................... 8B

- 14
8B.2.8 Life

- Cycle Cost Results for Remote Condensing Unit, Large, Batch

- 1 (RCU

Large

- B

- 1)

................................ ................................ ................................ ........ 8B

- 16
8B.2.9 Life

- Cycle Cost Results for Remote Condensing Unit, Large, Ba tch

- 2 (RCU

Large

- B

- 2)

................................ ................................ ................................ ........ 8B

- 18
8B.2.10 Life

- Cycle Cost Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

- Large

- B)

................................ ................................ ................. 8B

- 20
8B.2.11 Life

- Cycle Cost Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

- Small

- B)

................................ ................................ ............................ 8B

- 22
8B.2.12 Life

- Cycle Cost Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

- Large

- B)

................................ ................................ ............................ 8B

- 24
8B.2.13 Life

- Cycle Cost Results for Ice Making Head, Air

- Cooled, Small, Continuous (IMH

- A

- Small

- C)

................................ ................................ ......... 8B

- 26
8B.2.14 Life

- Cycle Cost Results for Ice Making Head, Air

- Cooled, Large, Continuous (IMH

- A

- Large

- C)

................................ ................................ ......... 8B

- 28
8B.2.15 Life

- Cycle Cost Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

- A

- Small

- C)

................................ ................................ ......... 8B

- 30

LIST OF TABLES

Table 8B.2.1 LCC Results for Ice Making Head, Water

- Cooled, Small, Batch (IMH

- W

Small

- B)

................................ ................................ ................................ ............. 8B

- 2 Table 8B.2.2 LCC Results for Ice Making Head, Water

- Cooled, Medium, Batch (IMH

- W

Med

- B)

................................ ................................ ................................ ............... 8B

- 48B

- ii 

### Table 8B.2.3 LCC Results for Ice Making Head, Water

- Cooled, Large, Batch

- 1 (IMH

- W

Large

- B

- 1)

................................ ................................ ................................ .......... 8B

- 6 Table 8B.2.4 LCC Results for Ice Making Head, Water

- Cooled, Large, Batch

- 2 (IMH

- W

Large

- B

- 2)

................................ ................................ ................................ .......... 8B

- 8 Table 8B.2.5 LCC Results for Ice Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

B)

................................ ................................ ................................ ...................... 8B

- 10
Table 8B.2.6 LCC Results for Ice Mak ing Head, Air

- Cooled, Large, Batch

- 1 (IMH

- A

- Large

B

- 1)

................................ ................................ ................................ .................. 8B

- 12 Table 8B.2.7 LCC Results for Ice Making Head, Air

- Cooled, Large, Batch

- 2 (IMH

- A

- Large

B

- 2)

................................ ................................ ................................ .................. 8B

- 14 Table 8B.2.8 LCC Results for Remote Condensing Unit, Large, Batch

- 1 (RCU

- Large

- B

- 1) 8B

- 16 Table 8B.2.9 LCC Results for Remote Condensing Unit, Large, Batch

- 2 (RCU

- Large

- B

- 2) 8B

- 18
Table 8B.2.10 LCC Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

Large

- B)

................................ ................................ ................................ ........... 8B

- 20
Table 8B.2.11 LCC Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

Small

- B)

................................ ................................ ................................ ........... 8B

- 22
Table 8B.2.12 LCC Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

Large

- B)

................................ ................................ ................................ ........... 8B

- 24
Table 8B.2.13 LC C Results for Ice Making Head, Air

- Cooled, Small, Continuous (IMH

- A

Small

- C)

................................ ................................ ................................ ........... 8B

- 26 Table 8B.2.14 LCC Results for Ice Making Head, Air

- Cooled, Large, Continuous (IMH

- A

Large

- C)

................................ ................................ ................................ ........... 8B

- 28
Table 8B.2.15 LCC Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

A

- Small

- C)

................................ ................................ ................................ ....... 8B

- 30

LIST OF FIGURES

Figure 8B.2.1 LCC Statistical Results for Ice Making Head, Water

- Cooled, Small, Batch (IMH

- W

- Small

- B)

................................ ................................ ............................. 8B

- 2 Figure 8B.2.2 PBP Statistical Results for Ice Making Head, Water

- Cooled, Small, Batch (IMH

- W

- Small

- B)

................................ ................................ ............................. 8B

- 3 Figure 8B.2.3 LCC Statistical Results for Ice Making Head, Water

- Cooled, Medium, Batch (IMH

- W

- Med

- B)

................................ ................................ ............................... 8B

- 4 Figure 8B.2.4 PBP Statistical Results for Ice Making Head, Water

- Cooled, Medium, Batch (IMH

- W

- Med

- B)

................................ ................................ ............................... 8B

- 5 Figure 8B.2.5 LCC Statistical Results for Ice Making Head, Water

- Cooled, Large, Batch

- 1 (IMH

- W

- Large

- B

- 1)

................................ ................................ .......................... 8B

- 6 Figure 8B.2.6 PBP Statistical Results for Ice Making Head, Water

- Cooled, Large, Batch

- 1 (IMH

- W

- Large

- B

- 1)

................................ ................................ .......................... 8B

- 7 Figure 8 B.2.7 LCC Statistical Results for Ice Making Head, Water

- Cooled, Large, Batch

- 2 (IMH

- W

- Large

- B

- 2)

................................ ................................ .......................... 8B

- 8 Figure 8B.2.8 PBP Statistical Results for Ice Making Head, Water

- Cooled, Large, Batch

- 2 (IMH

- W

- Large

- B

- 2)

................................ ................................ .......................... 8B

- 9 Figure 8B.2.9 LCC Statistical Result s for Ice Making Head, Air

- Cooled, Small, Batch (IMH

A

- Small

- B)

................................ ................................ ................................ ....... 8B

- 108B

- iii 

### Figure 8B.2.10 PBP Statistical Results for Ice Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

- B)

................................ ................................ ............................ 8B

- 11 Figure 8B.2.11 LCC Statistical Results for Ice Making Head, Air

- Cooled, Large, Bat ch

- 1 (IMH

- A

- Large

- B

- 1)

................................ ................................ ......................... 8B

- 12 Figure 8B.2.12 PBP Statistical Results for Ice Making Head, Air

- Cooled, Large, Batch

- 1 (IMH

- A

- Large

- B

- 1)

................................ ................................ ......................... 8B

- 13 Figure 8B.2.13 LCC Statistical Results for Ice Making Head, Air

- Cooled, Large, Batch

- 2 (IMH

- A

- Large

- B

- 2)

................................ ................................ ......................... 8B

- 14 Figure 8B.2.14 PBP Statistical Results for Ice Making Head, Air

- Cooled, Large, Batch

- 2 (IMH

- A

- Large

- B

- 2)

................................ ................................ ......................... 8B

- 15 Figure 8B.2.15 LCC Statistical Results for Remote Condensing Unit, Large, Batch

- 1 (RCU

Large

- B

- 1)

................................ ................................ ................................ ........ 8B

- 16 Figure 8B.2.16 PBP Statistical Results for Remote Condensing Unit, Large, Batch

- 1 (RCU

Large

- B

- 1)

................................ ................................ ................................ ........ 8B

- 17 Figure 8B.2.17 LCC Statistical Results for Remote Condensing Unit, Large, Batch

- 2 (RCU

Large

- B

- 2)

................................ ................................ ................................ ........ 8B

- 18 Figure 8B.2.18 PBP Statistical Results for Remote Condensing Unit, Large, Batch

- 2 (RCU

Large

- B

- 2)

................................ ................................ ................................ ........ 8B

- 19
Fi gure 8B.2.19 LCC Statistical Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

- Large

- B)

................................ ................................ ........................... 8B

- 20 Figure 8B.2.20 PBP Statistical Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

- Large

- B)

................................ ................................ ........................... 8B

- 21 Figure 8B.2.21 LCC Statistical Res ults for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

- Small

- B)

................................ ................................ ............................ 8B

- 22 Figure 8B.2.22 PBP Statistical Results for Self

- Cont ained Unit, Air

- Cooled, Small, Batch (SCU

- A

- Small

- B)

................................ ................................ ............................ 8B

- 23 Figure 8B.2.23 LCC Statistical Results for Self

- Contained Unit, Air

- Co oled, Large, Batch (SCU

- A

- Large

- B)

................................ ................................ ............................ 8B

- 24 Figure 8B.2.24 PBP Statistical Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

- Large

- B)

................................ ................................ ............................ 8B

- 25 Figure 8B.2.25 LCC Statistical Results for Ice Making Head, Air

- Cooled, Small, Continuous (IMH

- A

- Small

- C)

................................ ................................ ............................ 8B

- 26 Figure 8B.2.26 PBP Statistical Results for Ice Making Head, Air

- Cooled, Small, Continuous (IMH

- A

- Small

- C)

................................ ................................ ............................ 8B

- 27 Figure 8B.2.27 LCC Statistical Results for Ice Making Head, Air

- Cooled, Large, Continuous (IMH

- A

- Large

- C)

................................ ................................ ............................ 8B

- 28 Figure 8B.2.28 PBP Statistical Results for Ice Making Head, Air

- Cooled, Large, Continuous (IMH

- A

- Large

- C)

................................ ................................ ............................ 8B

- 29 Figure 8B.2.29 LCC Statistical Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

- A

- Small

- C)

................................ ................................ ......... 8B

- 30 Figure 8B.2.30 PBP Statistical Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

- A

- Small

- C)

................................ ................................ ......... 8B

- 318B

- 1 

### 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 ef ficiency level.
See c hapter 8 of the technical support document ( TSD ) for an explanation of the LCC spreadsheet.

LCC savings output obtained from the LCC analysis are in the form of distributions. LCC savings distributions were generated by performing 10,000 separate simulations using the LCC spreadsheet, and allowing the spreadsheet to select values for numerous inputs given probability distributions for each input. Given the results of the LCC model simulations, for each equipment type

, the U.S. Depar tment 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 the 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 plot with the help of red and blue markers, respectively. Additionally, the mean and median values are surrounded by r ectangular 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 th e 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 thi s 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 m aker rulemaking.8B

- 2 

### 8B.2 LIFE CYCLE COST RESU LTS

8B.2.1 LIFE

- CYCLE COST RESULTS F OR ICE MAKING HEAD, WATER

COOLED, SMALL, BATCH (IMH

- W

- SMALL

- B)

Table 8B.2

. 1 LCC Results for Ice Making Head, Water

- Cooled, Small, Batch (IMH

- W

Small

- B)

Figure 8B.2

. 1 LCC Statistical Results for Ice Making Head, Water

- Cooled, Small, Batch (IMH

- W

- Small

- B) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 3,389 2,391 11,094 13,485 0 NA NA NA NA

2 3,052 2,425 10,862 13,286 199 0 61 39 1.07

3 2,884 2,451 10,740 13,191 215 0 35 65 1.26

4 2,716 2,509 10,490 12,999 349 0 11 89 1.43

5 2,547 2,614 10,369 12,982 328 3 0 97 2.27

6 2,400 2,999 10,262 13,261 49 45 0 55 5.42

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

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 3 

### Figure 8 B.2

. 2 PBP Statistical Results for Ice Making Head, Water

- Cooled, Small, Batch (IMH

- W

- Small

- B) 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 4 

### 8B.2.2 LIFE

- CYCLE COST RESULTS F OR ICE MAKING HEAD, WATER

COOLED, MEDIUM, BATC H (IMH

- W

- MED

- B)

Table 8B.2

. 2 LCC Results for Ice Making Head, Water

- Cooled, Medium, Batch (IMH

- W

Med

- B)

Figure 8B.2

. 3 LCC Statistical Results for Ice Making Head, Water

- Cooled, Medium, Batch (IMH

- W

- Med

- B) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 7,227 4,198 25,365 29,563 0 NA NA NA NA

2 6,507 4,241 24,859 29,100 464 0 31 69 0.63

3 6,147 4,286 24,601 28,886 587 0 14 86 0.85

4 5,786 4,656 24,341 28,997 405 15 2 83 3.33

5 5,691 4,671 24,272 28,942 460 11 2 86 3.22

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

-$200 $0 $200 $400 $600 $800 $1,000 $1,200

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 5 

### Figure 8B.2

. 4 PBP Statistical Results for Ice Making Head, Water

- Cooled, Medium, Batch (IMH

- W

- Med

- B) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 6 

### 8B.2.3 LIFE

- CYCLE COST RESULTS F OR ICE MAKING HEAD, WATER

COOLED, LARGE, BATCH

- 1 (IMH

- W

- LARGE

- B

- 1)

Table 8B.2

. 3 LCC Results for Ice Making Head, Water

- Cooled, Large, Batch

- 1 (IMH

- W

Large

- B

- 1)

Figure 8B.2

. 5 LCC Statistical Results for Ice Making Head, Water

- Cooled, Large , Batch

- 1 (IMH

- W

- Large

- B

- 1) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 10,972 5,057 43,694 48,751 0 NA NA NA NA

2 9,877 5,132 42,919 48,051 701 0 29 71 0.72

3 9,329 5,646 42,523 48,170 583 0 29 71 3.75

4 9,147 5,717 42,392 48,109 607 0 24 76 3.77

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

$0 $200 $400 $600 $800 $1,000 $1,200 $1,400

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 7 

### Figure 8B.2

. 6 PBP Statistical Results for Ice Making Head, Water

- Cooled, Large, Batch

- 1 (IMH

- W

- Large

- B

- 1) 0.00 1.00 2.00 3.00 4.00 5.00 6.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 8 

### 8B.2.4 LIFE

- CYCLE COST RESULTS F OR ICE MAKING HEAD, WATER

COOLED, LARGE, BATCH

- 2 (IMH

- W

- LARGE

- B

- 2)

Table 8B.2

. 4 LCC Results for Ice Making Head, Water

- Cooled, Large, Batch

- 2 (IMH

- W

Large

- B

- 2)

Figure 8B.2

. 7 LCC Statistical Results for Ice Making Head, Water

- Cooled, Large, Batch

- 2 (IMH

- W

- Large

- B

- 2) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 19,002 9,727 73,619 83,346 0 NA NA NA NA

2 17,104 9,833 72,254 82,087 1260 0 67 33 0.58

3 16,155 10,581 71,569 82,150 442 35 17 48 3.10

4 16,067 10,586 71,506 82,092 500 29 17 54 3.02

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

-$500 $0 $500 $1,000 $1,500 $2,000 $2,500

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 9 

### Figure 8B.2

. 8 PBP Statistical Results for Ice Making Head, Water

- Cooled, Large, Batch

- 2 (IMH

- W

- Large

- B

- 2) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 10 

### 8B.2.5 LIFE

- CYCLE COST RESULTS F OR ICE MAKI NG HEAD, AIR

- COOLED, SMALL, BATCH (IMH

- A

- SMALL

- B)

Table 8B.2

. 5 LCC Results for Ice Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

- B)

Figure 8B.2

. 9 LCC Statistical Results for Ice Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

- B) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 4,227 2,432 9,343 11,775 0 NA NA NA NA

2 3,806 2,475 9,046 11,520 254 0 63 37 1.07

3 3,596 2,506 8,894 11,400 259 0 32 68 1.22

4 3,386 2,537 8,742 11,279 308 0 6 94 1.30

5 3,176 2,573 8,601 11,174 396 0 0 100 1.42

6 2,965 2,951 8,449 11,400 170 27 0 73 4.32

7 2,909 2,964 8,408 11,372 198 22 0 78 4.24 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

-$200 $0 $200 $400 $600 $800 $1,000

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 11 

### Figure 8B.2

. 10 PBP Statistical Results for Ice Making Head, Air

- Cooled, Small, Batch (IMH

- A

- Small

- B) 0.00 1.00 2.00 3.00 4.00 5.00 6.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 12 

### 8B.2.6 LIFE

- CYCLE COST RESULTS FOR ICE MAKING HEAD, AIR

- COOLED, LARGE, BATCH

- 1 (IMH

- A

- LARGE

- B

- 1)

Table 8B.2

. 6 LCC Results for Ice Making Head, Air

- Cooled, Large, Batch

- 1 (IMH

- A

Large

- B

- 1)

Figure 8B.2

. 11 LCC Statistical Results for Ice Making Head, Air

- Cooled, Large, Batch

- 1 (IMH

- A

- Large

- B

- 1) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 8,797 4,079 15,931 20,010 0 NA NA NA NA

2 7,919 4,118 15,303 19,421 590 0 59 41 0.46

3 7,480 4,143 14,993 19,135 572 0 15 85 0.50

4 7,042 4,208 14,453 18,661 1002 0 7 93 0.65

5 6,603 4,278 14,143 18,421 1168 0 0 100 0.82

6 6,213 4,663 13,865 18,527 1062 1 0 99 2.08

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

$0 $500 $1,000 $1,500 $2,000 $2,500

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 13 

### Figure 8B.2

. 12 PBP Statistical Results for Ice Making Head, Air

- Cooled, Large, Batch

- 1 (IMH

- A

- Large

- B

- 1) 0.00 0.50 1.00 1.50 2.00 2.50 3.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 14 

### 8B.2.7 LIFE

- CYCLE COST RESULTS F OR ICE MAKING HEAD, AIR

- COOLED, LARGE, BATCH

- 2 (IMH

- A

- LARGE

- B

- 2)

Table 8B.2

. 7 LCC Results for Ice Making Head, Air

- Cooled, Large, Batch

- 2 (IMH

- A

Large

- B

- 2)

Figure 8B.2

. 13 LCC Statistical Results for Ice Making Head, Air

- Cooled, Large, Batch

- 2 (IMH

- A

- Large

- B

- 2) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 14,366 4,447 21,252 25,699 0 NA NA NA NA

2 12,932 4,505 20,234 24,739 960 0 67 33 0.42

3 12,215 4,641 19,725 24,366 908 0 40 60 0.94

4 11,498 5,151 19,217 24,368 627 16 13 70 2.58

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

-$500 $0 $500 $1,000 $1,500 $2,000 $2,500

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 15 

### Figure 8B.2

. 14 PBP Statistical Results for Ice Making Head, Air

- Cooled, Large, Batch

- 2 (IMH

- A

- Large

- B

- 2) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 16 

### 8B.2.8 LIFE

- CYCLE COST RESULTS F OR REMOTE CONDENSING UNIT, LARGE,
BATCH

- 1 (RCU

- LARGE

- B

- 1)

Table 8B.2

. 8 LCC Results for Remote Condensing Unit, Large, Batch

- 1 (RCU

- Large

- B

- 1)

Figure 8B.2

. 15 LCC Statistical Results for Remote Condensing Unit, Large, Batch

- 1 (RCU

Large

- B

- 1) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 13,983 6,089 17,107 23,195 0 NA NA NA NA

2 12,727 6,135 16,214 22,349 847 0 57 43 0.38

3 11,889 6,214 15,614 21,828 963 0 18 82 0.62

4 11,191 6,721 15,119 21,840 857 6 9 85 2.37

5 11,108 6,756 15,059 21,815 882 5 9 86 2.42

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

-$500 $0 $500 $1,000 $1,500 $2,000 $2,500

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 17 

### Figure 8B.2

. 16 PBP Statistical Results for Remote Condensing Unit, Large, Batch

- 1 (RCU

Large

- B

- 1) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 18 

### 8B.2.9 LIFE

- CYCLE COST RESULTS F OR REMOTE CONDENSING UNIT, LARGE,
BATCH

- 2 (RCU

- LARGE

- B

- 2)

Table 8B.2

. 9 LCC Results for Remote Condensing Unit, Large, Batch

- 2 (RCU

- Large

- B

- 2)

Figure 8B.2

. 17 LCC Statistical Results for Remote Condensing Unit, Large, Batch

- 2 (RCU

Large

- B

- 2) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 22,360 8,958 25,184 34,142 0 NA NA NA NA

2 20,349 9,104 23,743 32,847 1298 0 73 27 0.75

3 19,009 9,283 22,775 32,058 1277 0 27 73 1.00

4 17,892 10,108 22,017 32,124 1070 7 18 75 2.70

5 17,779 10,137 21,935 32,072 1123 6 18 76 2.70

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

-$500 $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 19 

### Figure 8B.2

. 1 8 PBP Statistical Results for Remote Condensing Unit, Large, Batch

- 2 (RCU

Large

- B

- 2) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 20 

### 8B.2.10 LIFE

- CYCLE COST RESULTS F OR SELF

- CONTAINED UNIT, WATE R

COOLED, LARGE, BATCH (SCU

- W

- LARGE

- B)

Table 8B.2

. 10 LCC Results for Self

- Contained Unit, Water

- Cooled, Lar ge, Batch (SCU

- W

Large

- B)

Figure 8B.2

. 19 LCC Statistical Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

- Large

- B) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 4,183 3,451 12,613 16,065 0 NA NA NA NA

2 3,892 3,500 12,082 15,583 483 0 71 29 0.67

3 3,559 3,530 11,849 15,379 687 0 71 29 0.76

4 3,351 3,553 11,698 15,251 587 0 57 43 0.81

5 3,143 3,596 11,548 15,144 694 0 57 43 1.00

6 2,935 3,950 11,398 15,348 143 49 14 36 3.01

7 2,925 3,951 11,391 15,342 149 49 14 37 3.00 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 $1,400

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 21 

### Figure 8B.2

. 20 PBP Statistical Results for Self

- Contained Unit, Water

- Cooled, Large, Batch (SCU

- W

- Large

- B) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 22 

### 8B.2.11 LIFE

- CYCLE COST RESULTS F OR SELF

- CONTAINED UNIT, AIR

COOLED, SMALL, BATCH (SCU

- A

- SMALL

- B)

Table 8B.2

. 11 LCC Results for S elf

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

Small

- B)

Figure 8B.2

. 21 LCC Statistical Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

- Small

- B) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 2,600 2,749 7,675 10,423 0 NA NA NA NA

2 2,419 2,772 7,548 10,321 103 0 83 17 1.40

3 2,213 2,801 7,407 10,209 143 0 43 57 1.47

4 2,084 2,821 7,320 10,141 198 0 37 63 1.52

5 1,955 2,840 7,233 10,073 236 0 26 74 1.54

6 1,826 2,896 6,979 9,875 396 0 11 89 1.56

7 1,585 3,306 6,813 10,119 106 32 0 68 4.79 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

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 23 

### Figure 8B.2

. 22 PBP Statistical Results for Self

- Contained Unit, Air

- Cooled, Small, Batch (SCU

- A

- Small

- B) 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 24 

### 8B.2.12 LIFE

- CYCLE COST RESULTS F OR SELF

- CONTAINED UNIT, AIR

COOLED, LARGE, BATCH (SCU

- A

- LARGE

- B)

Table 8B.2

. 12 LCC Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

Large

- B)

Figure 8B.2

. 23 LCC Statistical Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

- Large

- B) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 3,599 3,212 10,815 14,027 0 NA NA NA NA

2 3,349 3,243 10,645 13,887 140 0 71 29 1.37

3 3,062 3,278 10,448 13,726 224 0 36 64 1.34

4 2,884 3,324 10,105 13,429 522 0 36 64 1.17

5 2,705 3,342 9,976 13,318 581 0 29 71 1.14

6 2,526 3,405 9,857 13,262 502 0 7 93 1.49

7 2,351 3,758 9,731 13,489 240 34 0 66 3.72 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 (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 25 

### Figure 8B.2

. 24 PBP Statistical Results for Self

- Contained Unit, Air

- Cooled, Large, Batch (SCU

- A

- Large

- B) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 26 

### 8B.2.13 LIFE

- CYCLE COST RESULTS F OR ICE MAKING HEAD, AIR

- COOLED, SMALL, CONTINUOUS (I MH

- A

- SMA LL

- C)

Table 8B.2

. 13 LCC Results for Ice Making Head, Air

- Cooled, Small, Continuous (IMH

- A

Small

- C)

Figure 8B.2

. 25 LCC Statistical Results for Ice Making Hea d, Air

- Cooled, Small, Continuous (IMH

- A

- Small

- C) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 5,142 6,601 9,746 16,347 0 NA NA NA NA

2 4,630 6,644 9,390 16,034 315 0 77 23 0.90

3 4,374 6,665 9,212 15,877 314 0 54 46 0.90

4 4,118 6,694 9,031 15,725 391 0 40 60 0.97

5 3,862 6,913 8,848 15,761 307 8 31 61 2.59

6 3,606 7,413 8,795 16,208 -202 67 14 19 6.47

7 3,555 7,461 8,789 16,250 -237 73 11 16 6.83 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 (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 27 

### Figure 8B.2

. 26 PBP Statistical Results for Ice Making Head, Air

- Cooled, Small, Continuous (IMH

- A

- Small

- C) 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 Mean8B

- 28 

### 8B.2.14 LIFE

- CYCLE COST RESULTS F OR ICE MAKING HEAD, AIR

- COOLED, LARGE, CONTINUOUS (I MH

- A

- LARGE

- C)

Table 8B.2

. 14 LCC Results for Ice Making Head, Air

- Cooled, Large, Continuous (IMH

- A

Large

- C)

Figure 8B.2

. 27 LC C Statistical Results for Ice Making Head, Air

- Cooled, Large, Continuous (IMH

- A

- Large

- C) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 9,899 5,468 16,170 21,638 0 NA NA NA NA

2 8,911 5,518 15,462 20,979 660 0 65 35 0.52

3 8,417 5,543 15,113 20,656 744 0 45 55 0.53

4 7,923 5,568 14,765 20,333 908 0 30 70 0.53

5 7,430 5,629 14,426 20,055 1026 0 15 85 0.69

6 6,936 6,288 14,269 20,557 524 21 15 64 3.25

7 6,912 6,289 14,262 20,552 500 21 10 69 3.24 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 $2,500

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 29 

### Figure 8B.2

. 28 PBP Statistical Results for Ice Making Head, Air

- Cooled, Large, Continuous (IMH

- A

- Large

- C) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

2 3 4 5 6 7Years

Efficiency Level Payback Period

Median Mean8B

- 30 

### 8B.2.15 LIFE

- CYCLE COST RESULTS FOR SEL F

- CONTAINED UNIT, AIR

COOLED, SMALL, CONTI NUOUS (SCU

- A

- SMALL

- C)

Table 8B.2

. 15 LCC Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

A

- Small

- C)

Figure 8B.2

. 29 LCC Statistical Results for Self

- Contained Unit, Air

- Cooled, Small, Continuous (SCU

- A

- Small

- C) Net Cost No Impact Net Benefit

kWh/year 2012$ 2012$ 2012$ 2012$ % % % Years

1 2,192 3,586 7,353 10,939 0 NA NA NA NA

2 2,040 3,603 7,243 10,846 93 0 73 27 1.13

3 1,866 3,632 7,127 10,759 140 0 53 47 1.53

4 1,758 3,659 7,057 10,717 146 0 37 63 1.85

5 1,649 4,179 7,043 11,222 -385 76 23 0 14.62

6 1,580 4,196 7,099 11,294 -441 80 20 0 19.12

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

2 3 4 5 6 7Cost Savings (2012$)

Efficiency Level Life

- Cycle Cost Savings

Mean Median8B

- 31 

### Figure 8B.2

. 30 PBP Statistical 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 Mean8C

- i 

### APPENDIX 8C. UNCERTAINTY AND VARIABILITY IN LIFE

- CYCLE COST
ANALYSIS

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

- 28C

- 1 

### APPENDIX 8C

. UNCERTAINTY AND VARIABILITY IN L CC 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

consumers. DOE uses the consumer life

- cycle cost (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 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 UNCERTAINTY AND VARIABILITY

This section describes two approaches to uncertainty and variability: scenario an alysis
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 s some indication of the extent to which the result depends on the assumptions. The advantages of scenario analysis are that each calculation is simple

, a range of estimates is used

, and crossover points can be identified. (An example of a crossover8C

- 2 

### point i s the energy price rate at which the consumer achieves savings in operating expense that more than compensate for the increased purchase expense.) The disadvantage of scenario analysis is that there is no information about the likelihood of each scenario.

Probability analysis considers the probabilities within a range of values. For quantities with variability ( e.g., electricity price s paid by different users ), surveys can be used to generate a frequency distribution of numerical values ( e.g., the number o f commercial 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 en ergy efficiency 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 quantit y. The advantage of the probability approach is that it provides greater
information about the outcome of the calculations ( i.e., it provides the probability that the
outcome will be in a particular range )

. Scenario and probability analysis provide some in dication 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 CRYSTAL BALL

To quantify the uncertainty and variability that exist in inputs to the engineering, LCC, and payback period analyses, DOE used Microsoft Excel spreadsheets combined with Crystal Ball, a commercially available add

- in, to conduct probability analyses. The probability analys i s is carried out in the form of Monte Carlo simulation s

. Simulation refers to any analytical method
meant to imitate a real

- life system, especially when other analyses are mathematically complex or difficult to reproduce. Without the aid of simulation, a spreadsheet mo del 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 on e particular set of inputs. The inputs are chosen from their respective probability distributions. For example, there are 50 states in the United States and the probability of choosing one state for a simulation is based on the population of the state. The higher the population of a state , the higher the probability of choosing that state for a particular simulation. Similarly, the equipment lifetime is specified by Weibull distribution curves with mean equipment lifetime of 8

- 1/2 years. Crystal
Ball choose s the equipment lifetime for any particular simulation based on the Weibull probability distribution curve. Thus, each Monte Carlo simulation is a random combination of input values, with each input value chosen according to its respective probability dist ribution. 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.8D

- i 

### 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 Makers

................................ ............ 8D

- 48D

- 1 

### 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 efficiency levels remain fixed, in real terms, after 201 2 (the year for which the engineering analysis estimated costs) and throughout the period of the analys is. In its notice of d ata 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 automat ic commercial ice makers

.
DOE stated in the NODA that examination of historical price data for certain appliances and equipment that have been subject to energy conservation standards indicates that the assumption of constant real prices and costs may, in many cases, over

- estimate long

- term appliance and equipment price trends. 76 FR at 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

, avail able

at www1.eere.energy.gov/buildings/appliance_standards/pdfs/experience_curve_appliance _price_forecasting_3

- 16

- 11.pd f , summarizes the data and literature currently available to DOE that is relevant to price forecasts for selected appliances and equipment.

The extensive literature on the “learning” or “experience” curve phenomenon is typically
based on observations in t he manufacturing sector. 1 

### In the experience curve method, the real cost of production is related to the cumulative production or “experience” with a manufactured product. To explain the empirical relationship

, DOE would use the theory of technology learnin g 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 producing the next unit decreases. The percentage reduction in cost that occurs with each doubling of cumulative production is known as the learning rate (LR), given by:8D

- 2 

### 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 Statist ics’ (BLS) Producer
Price Index (PPI) and gross domestic product (GDP) deflator, available from the Bureau of Economic Analysis (BEA). The PPI data for commercial refrigerators and related equipment is
available for 1978

– 2012 and is used to represent aggre gate automatic commercial ice makers ’ 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 was 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 2 , but shows a decrease in the real PPI during two significant periods of time : 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

- 3 

### T o perform an experience curve fit, DOE assembled a time

- series of a nnual s hip ments for
1978 to 2010 for automatic commercial ice makers (for calculating cumulative production) based on shipments data from Current Industries Report

.
Projected shipments after 2011 were obtained from the base

- case projections made for
the national im pact 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 s hipments. The form of the fitting equation 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. 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000

2011 2016 2021 2026 2031 2036 2041 2046Shipments (units)



## Year8D

- 4 

### 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 o f a simple least

- squares fit is equivalent to an assumption of no significant first price elasticity effects in the cumulative shipments variable.

After modeling the data to the Eq. 8D.3

, DOE estimated the learning rate (defined as the
fractional reductio n in price expected from each doubling of cumulative production) as 1. 3 percent

.
With cumulative shipments through 204 7 projected to reach 435,468 units (compared
with 213,728 units in 2010), the modeled trend predicts a drop of 1. 7 percent in real price compared to the 201 2 prices in the economy as a whole. Figure 8D.2.2 shows the model fit for the projected values for the period after 2 010

.

Figure 8D.2

. 2 Model Fit for Automatic Commercial Ice Makers 0.94 0.95 0.95 0.95 0.95 0.95 0.96 0.96 0.96 0.96 0.96 0.97

0 5 10 15 20 25Fitted PPI



## Cumulative Shipments (millions of units)8D

- 5 

### REFERENCES

1

. Weiss, M., Junginger, H.M., Patel, M.K., Blok, K., (2010a). A Review of Experience Curve Analyses for Energy Demand Technologies. Technological Forecasting & Social Change . 77:411

- 428.10A

- i 

### 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

- 5

LIST OF TABLES

Table 10A.2.1 Description of Tabs in the NIA Spreadsheet

................................ .................. 10A

- 110A

- 1 

### APPENDIX 10A

. USER INSTRUCTIONS FOR NIA SPREADSHEET

10A.1 INTRO DUCTION

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 FOR 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 retr ieved 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 is 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.

a Note that the NIA model also forecast s impacts arising from changing water usage.10A

- 2 

### Table 10A.2

. 1 (cont)

Worksheet Description
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.
Installed Costs Contains data summarizing the full range of aggregate costs associated with installation. Uses per

- unit in s tallation costs from the LCC Inp uts 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 bee n superseded. Selecting 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 annualized at 3%. Repair Costs 7% Contains repair cost data using stock data and data imported from the LCC on a per

- unit
basis and annualized at 7%.
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 dat a 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 and
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 i nput 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 Energy Outlook 201 3 1
( AEO201 3 ) to account for growth rates in healthcare buildings. Lodging Contains historic al and projected floor space data from th e AEO201 3 to account for growth rates in lodging buildings.
Food Service Contains historic al and projected floor space data from th e AEO201 3 to account for growth rates in foodservice buildings.
Retail Contains historic al and projected floor space data from th e AEO201 3 to account for growth rates in retail buildings.
Education Contains historic al and projected floor space data from th e AEO201 3 to account for growth rates in education buildings.10A

- 3 

### Table 10A.2

. 1 (cont)

Worksheet Description
Food Sales Contains historic al and projected floor space data from th e AEO201 3 to account for growth
rates in foo d sales buildings.
Office Contains historic al and projected floor space data from th e AEO201 3 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 results

. ROCIS Contains a set of tables with cumulative and annualized values for the Office of Management and Budget (OMB)



### 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 th e 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. You can change 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. Equipment C lass : Selects the desired equipment class.

c

. Efficiency Level: Selects the desired efficiency 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

.

d. Market Scenario : Not an option for the notice of proposed rulemaking phase

. The
d efault is “Rollup

. ”

e. Electricity Price Scenario : Selects the desired growth level (Constant, Reference, Low, or High). The scenarios refer to the Energy Information Administration’s (EIA’s) AEO201 3 scenarios.

f. Water Price Scenario: Selects the desired growth level (reference, low, or high).

g. Discount Rate: Selects the desired dis count rate (0, 3, or 7 percent )

.

h. Price Learning Scenario: Selects the desired growth level (no real price change, NEMS Price Deflator, low, reference, or high).10A

- 4 

### i. Base Year Stock Scenario

– Selects the desired base stock scenario ( AHRI Historical Shipment s , 4

- Yr Ave CIR

– 20% Exports

, or 4

- Yr Ave CIR

– No Exports )

.

j. Full

- Fuel Cycle: Selects the national energy savings (full

- fuel

- cycle savings or primary savings without FFC)

.

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 level, 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

.

5. If a new set of LCC results is to be loaded, they mus t 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

.

The formulas automatically update throughout the model and present results by equipment class and year in the NIA Compiled Data worksheet

.

6. Results are compiled further, into tab les, in the Shipment

- Weighted Data worksheet

. Although many of the tables are fo rmula

- 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:

• 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 3 . 201 3

. Washington D.C. DOE/EIA

- 0383(201 3 ).10B

- i 

### APPENDIX 10B. NATIONAL NET PRESENT VALUE USING ALTERNATIVE
SENSITIVITY ANALYSES

TABLE OF CONTENTS

10B.1 ALTERNATIVE NET PRESENT VALUES

................................ ............................. 10B

- 1
10B.1.1 Price Learning Sensitivity Analyses

................................ ............................ 10B

- 1
10B.1.2 Electric Price Sensitivity Analyses

................................ .............................. 10B

- 9
10B.1.3 9

- Year Sensitivity Analysis

................................ ................................ ....... 10B

- 16

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 (2012$) at a

7

- Percent Discount Rate

– High Pri ce Scenario

................................ .............. 10B

- 4 Table 10B.1.3 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at a
3

- Percent Discount Rat e

– High Price Scenario

................................ .............. 10B

- 5 Table 10B.1.4 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at a

7

- Percent Discount Rate

– Low Price Scenario

................................ ............... 10B

- 6 Table 10B.1.5 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at a 3

- Percent Discount Rate

– Low Price Scenario

................................ ............... 10B

- 7
Table 10B.1.6 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at a

7

- Percent Discount Rate

– Constant Real Price Scenario

................................ 10B

- 8
Table 10B.1.7 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at a
3

- Percent Discount Rate

– Constant Real Price Scenario

................................ 10B

- 9
Table 10B.1.8 Automatic Commer cial Ice Makers: Net Present Value in Billions (2012$) at a

7

- Percent Discount Rate

– High Electric Price Scenario

............................... 10B

- 11
Table 10 B.1.9 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at a
3

- Percent Discount Rate

– High Electric Price Scenario

............................... 10B

- 12 Table 10B.1.10 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at

a 7

- Percent Discount Rate

– Low Electric Price Scenario

............................. 10B

- 13 Table 10B.1.11 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at
a 3

- Percent Discount Rate

– Low Electric Price Scenario

............................. 10B

- 14 Table 10B.1.12 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at

a 7

- Percent Discount Rate

– Constant Electric Price Scenario

...................... 10B

- 15 Table 10B.1.13 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at
a 3

- Percent Discount Rate

– Constant Electric Price Scenario

...................... 10B

- 16
Table 10B.1.14 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.1.15 Automatic Commercial Ice Makers: Net Present Value at a 3

- percent Di scount Rate for 9

- Year Analysis Period for Equipment Purchased in 2018

– 2026

................................ ................................ ................................ ................ 10B

- 1810B

- ii 

### LIST OF FIGURES

Fi gure 10B.1.1 Automatic Commercial Ice Makers Price Factor Indices for Default Case and Sensitivity Cases

................................ ................................ .............................. 10B

- 210B

- 1 

### APPENDIX 10 B

. NATIONAL NET PRESENT VALUE USING ALTERNATIVE PRICE
FORECASTS

10B.1 ALTERNATIVE NET PRESENT VALUES

10B.1.1 Price Learning Sensitivity Analyses

The net present value ( NPV ) results presented in chapter 10 of this technical support document (TSD) reflect a price trend based on an experience curve derived using historical data
on sh ipments and refrigeration equipment producer price index ( PPI ) that most closely reflects
automatic commercial ice

- making equipment . The average annual rate of price decline in the
default case for the 201 8

– 204 7 analysis period is 0. 04 percent and is based on historical PPI data
for refrigeration equipment in the 1 978

– 201 2 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 equipment PPI data. DOE developed a high price decline case and

a low price decline case in this analysis. The high price decline case is based on the upper end of the 95 percent confide nce interval for an exponential fit to the inflation

- adjusted PPI series of
19 78 through 20 1 2

. 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 2

. The

average annual rate of price decline over the analys is period is 0. 12 percent in the low price
forecast case . In the high price forecast

, the average annual rate of change is an increase of 0.04 percent. Because of the nature of the exponential exper ience curve, greater annual price declines are found in earlier years in the analysis period

, and lesser annual price declines are found in the latter years in the analysis period. DOE investigated the impact of these different product price
forecasts on t he consumer net NPV for the considered trial standard levels ( TSLs ) for automatic
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 2 ; ( 2) a low price case based on the PPI
trend in 1978

– 20 1 2 ; 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 2 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 automatic comme rcial ice makers NPV results using the default price decline scenario shown in chapter 10 of this TSD.10 B

- 2 

### Figure 10B.1

. 1 Automatic Commercial Ice Makers Price Factor Indices for Default Case and Sensitivity Ca ses10B

- 3 

### Table 10B.1

. 1 Price Factor Indices Used in Default Case and Sensitivity Cases

Year Price Factor Index
201 2 $

Default High Price
Forecast Low Price
Forecast Constant Real

2012 1.000 1.000 1.000 1.0000
2013 0.999 1.001 0.998 1.0000
2014 0.999 1.001 0.996 1.0000
2015 0.998 1.002 0.994 1.0000
2016 0.997 1.002 0.992 1.0000
2017 0.997 1.003 0.991 1.0000
2018 0.996 1.004 0.989 1.0000
2019 0.996 1.004 0.987 1.0000
2020 0.995 1.005 0.986 1.0000
2021 0.995 1.005 0.985 1.0000
2022 0.994 1.005 0.983 1.0000
2023 0.994 1.006 0.982 1.0000
2024 0.993 1.006 0.980 1.0000
2025 0.993 1.007 0.979 1.0000
2026 0.992 1.007 0.977 1.0000
2027 0.992 1.008 0.976 1.0000
2028 0.991 1.008 0.974 1.0000
2029 0.991 1.009 0.973 1.0000
2030 0.990 1.009 0.972 1.0000
2031 0.990 1.010 0.971 1.0000
2032 0.989 1.010 0.969 1.0000
2033 0.989 1.010 0.968 1.0000
2034 0.989 1.011 0.967 1.0000
2035 0.988 1.011 0.966 1.0000
2036 0.988 1.012 0.964 1.0000
2037 0.987 1.012 0.963 1.0000
2038 0.987 1.013 0.962 1.0000
2039 0.986 1.013 0.961 1.0000
2040 0.986 1.013 0.960 1.0000
2041 0.986 1.014 0.958 1.0000
2042 0.985 1.014 0.957 1.0000
2043 0.985 1.014 0.956 1.0000
2044 0.985 1.015 0.955 1.0000
2045 0.984 1.015 0.954 1.0000
2046 0.984 1.015 0.953 1.0000
2047 0.983 1.016 0.952 1.000010B

- 4 

### Table 10B.1

. 2 Automatic Commercial Ice Makers : Net Present Value in B illions (201 2 $) at

a 7

- Percent Discount Rate

– High Price Scenario

Equipment Class Cumulative NPV by TSL at 7% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.011 0.024 0.024 (0.002)

IMH

- W

- Med

- B 0.016 0.016 0.025 0.016 0.019

IMH

- W

- Large

- B** 0.004 0.004 0.004 0.003 0.003

IMH

- W

- Large

- B

- 1 0.003 0.003 0.003 0.002 0.002

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.001 0.001

IMH

- A

- Small

- B 0.043 0.080 0.177 0.042 0.054

IMH

- A

- Large

- B** 0.070 0.127 0.296 0.253 0.253

IMH

- A

- Large

- B

- 1 0.057 0.113 0.273 0.234 0.234

IMH

- A

- Large

- B

- 2 0.014 0.014 0.023 0.020 0.020
RCU

- Large

- B** 0.038 0.038 0.082 0.072 0.074
RCU

- Large

- B

- 1 0.036 0.036 0.078 0.069 0.071
RCU

- Large

- B

- 2 0.002 0.002 0.004 0.003 0.004
SCU

- W

- Large

- B 0.001 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.028 0.084 0.010 0.010
SCU

- A

- Large

- B 0.004 0.039 0.052 0.020 0.020

IMH

- A

- Small

- C 0.004 0.009 0.014 0.011 (0.019)

IMH

- A

- Large

- C 0.004 0.007 0.016 0.006 0.006
SCU

- A

- Small

- C 0.004 0.009 0.013 0.013 (0.064)
Total 0.197 0.367 0.788 0.471 0.354

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 2012$ 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

. 3 Automatic Commercial Ice Makers : Net Present Value in B illions (201 2 $) at

a 3

- Percent Discount Rate

– High Price Scenario

Equipment Class Cumulative NPV by TSL at 3% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.013 0.023 0.057 0.057 0.008

IMH

- W

- Med

- B 0.034 0.034 0.054 0.041 0.046

IMH

- W

- Large

- B** 0.009 0.009 0.009 0.007 0.008

IMH

- W

- Large

- B

- 1 0.007 0.007 0.007 0.005 0.006

IMH

- W

- Large

- B

- 2 0.002 0.002 0.002 0.001 0.002

IMH

- A

- Small

- B 0.094 0.175 0.392 0.153 0.180

IMH

- A

- Large

- B** 0.152 0.274 0.651 0.589 0.589

IMH

- A

- Large

- B

- 1 0.122 0.245 0.601 0.541 0.541

IMH

- A

- Large

- B

- 2 0.030 0.030 0.050 0.049 0.049
RCU

- Large

- B** 0.081 0.081 0.178 0.171 0.177
RCU

- Large

- B

- 1 0.078 0.078 0.169 0.163 0.168
RCU

- Large

- B

- 2 0.004 0.004 0.009 0.009 0.009
SCU

- W

- Large

- B 0.001 0.002 0.002 0.001 0.001
SCU

- A

- Small

- B 0.009 0.063 0.189 0.057 0.057
SCU

- A

- Large

- B 0.010 0.086 0.117 0.059 0.059

IMH

- A

- Small

- C 0.009 0.019 0.030 0.027 (0.029)

IMH

- A

- Large

- C 0.009 0.016 0.034 0.017 0.017
SCU

- A

- Small

- C 0.008 0.021 0.030 0.030 (0.116)
Total 0.429 0.804 1.744 1.209 0.997

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 2012$ 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 B

- 1
and B

- 2 typical units.10B

- 6 

### Table 10B.1

. 4 Automatic Commercial Ice Makers : Net Present Value in B illions (201 2 $) at
a 7

- Percent Discount Rate

– Low Price Scenario

Equipment Class Cumulative NPV by TSL at 7% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.011 0.025 0.025 (0.001)

IMH

- W

- Med

- B 0.016 0.016 0.025 0.017 0.020

IMH

- W

- Large

- B** 0.004 0.004 0.004 0.003 0.003

IMH

- W

- Large

- B

- 1 0.003 0.003 0.003 0.002 0.003

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.001 0.001

IMH

- A

- Small

- B 0.043 0.080 0.178 0.051 0.063

IMH

- A

- Large

- B** 0.071 0.127 0.298 0.259 0.259

IMH

- A

- Large

- B

- 1 0.057 0.113 0.275 0.239 0.239

IMH

- A

- Large

- B

- 2 0.014 0.014 0.023 0.021 0.021
RCU

- Large

- B** 0.038 0.038 0.082 0.075 0.077
RCU

- Large

- B

- 1 0.036 0.036 0.078 0.071 0.073
RCU

- Large

- B

- 2 0.002 0.002 0.004 0.004 0.004
SCU

- W

- Large

- B 0.001 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.029 0.085 0.015 0.015
SCU

- A

- Large

- B 0.004 0.039 0.053 0.022 0.022

IMH

- A

- Small

- C 0.004 0.009 0.014 0.011 (0.018)

IMH

- A

- Large

- C 0.004 0.007 0.016 0.007 0.007
SCU

- A

- Small

- C 0.004 0.009 0.013 0.013 (0.061)
Total 0.198 0.369 0.794 0.498 0.386

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 2012$ 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 B

- 1
and B

- 2 typical units.10B

- 7 

### Table 10B.1

. 5 Automatic Commercial Ice Makers : Net Present Va lue in B illions (201 2 $) at

a 3

- Percent Discount Rate

– Low Price Scenario

Equipment Class Cumulative NPV by TSL at 3% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.013 0.023 0.058 0.058 0.012

IMH

- W

- Med

- B 0.034 0.034 0.054 0.042 0.048

IMH

- W

- Large

- B** 0.009 0.009 0.009 0.007 0.008

IMH

- W

- Large

- B

- 1 0.007 0.007 0.007 0.006 0.006

IMH

- W

- Large

- B

- 2 0.002 0.002 0.002 0.002 0.002

IMH

- A

- Small

- B 0.094 0.177 0.396 0.172 0.200

IMH

- A

- Large

- B** 0.153 0.275 0.655 0.602 0.602

IMH

- A

- Large

- B

- 1 0.123 0.245 0.604 0.551 0.551

IMH

- A

- Large

- B

- 2 0.030 0.030 0.051 0.051 0.051
RCU

- Large

- B** 0.081 0.081 0.179 0.176 0.182
RCU

- Large

- B

- 1 0.078 0.078 0.169 0.168 0.172
RCU

- Large

- B

- 2 0.004 0.004 0.009 0.009 0.009
SCU

- W

- Large

- B 0.001 0.002 0.002 0.001 0.001
SCU

- A

- Small

- B 0.009 0.064 0.191 0.068 0.068
SCU

- A

- Large

- B 0.010 0.086 0.118 0.064 0.064

IMH

- A

- Small

- C 0.009 0.019 0.031 0.028 (0.026)

IMH

- A

- Large

- C 0.009 0.016 0.035 0.018 0.018
SCU

- A

- Small

- C 0.008 0.021 0.030 0.030 (0.111)
Total 0.431 0.808 1.758 1.267 1.066

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 2012$ 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

. 6 Automatic Commercial Ice Makers : Net Present Value in B illions (201 2 $) at

a 7

- Percent Disc ount Rate

– Constant Real Price Scenario

Equipment Class Cumulative NPV by TSL at 7% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.011 0.024 0.024 (0.002)

IMH

- W

- Med

- B 0.016 0.016 0.025 0.016 0.019

IMH

- W

- Large

- B** 0.004 0.004 0.004 0.003 0.003

IMH

- W

- Large

- B

- 1 0.003 0.003 0.003 0.002 0.002

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.001 0.001

IMH

- A

- Small

- B 0.043 0.080 0.177 0.044 0.056

IMH

- A

- Large

- B** 0.070 0.127 0.297 0.255 0.255

IMH

- A

- Large

- B

- 1 0.057 0.113 0.274 0.235 0.235

IMH

- A

- Large

- B

- 2 0.014 0.014 0.023 0.020 0.020
RCU

- Large

- B** 0.038 0.038 0.082 0.073 0.075
RCU

- Large

- B

- 1 0.036 0.036 0.078 0.069 0.071
RCU

- Large

- B

- 2 0.002 0.002 0.004 0.003 0.004
SCU

- W

- Large

- B 0.001 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.028 0.084 0.011 0.011
SCU

- A

- Large

- B 0.004 0.039 0.052 0.020 0.020

IMH

- A

- Small

- C 0.004 0.009 0.014 0.011 (0.019)

IMH

- A

- Large

- C 0.004 0.007 0.016 0.006 0.007
SCU

- A

- Small

- C 0.004 0.009 0.013 0.013 (0.063)
Total 0.197 0.368 0.789 0.477 0.362

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 2012$ 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 B

- 1
and B

- 2 typical units.10B

- 9 

### Table 10B.1

. 7 Automatic Commercial Ice Makers : Net Present Value in B illions (201 2 $) at

a 3

- Percent Discount Rate

– Constant Real Price Scenario

Equipment Class Cumulative NPV by TSL at 3% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.013 0.023 0.057 0.057 0.009

IMH

- W

- Med

- B 0.034 0.034 0.054 0.041 0.047

IMH

- W

- Large

- B** 0.009 0.009 0.009 0.007 0.008

IMH

- W

- Large

- B

- 1 0.007 0.007 0.007 0.005 0.006

IMH

- W

- Large

- B

- 2 0.002 0.002 0.002 0.001 0.002

IMH

- A

- Small

- B 0.094 0.176 0.393 0.158 0.185

IMH

- A

- Large

- B** 0.152 0.275 0.652 0.593 0.593

IMH

- A

- Large

- B

- 1 0.123 0.245 0.602 0.543 0.543

IMH

- A

- Large

- B

- 2 0.030 0.030 0.051 0.049 0.049
RCU

- Large

- B** 0.081 0.081 0.178 0.173 0.178
RCU

- Large

- B

- 1 0.078 0.078 0.169 0.164 0.169
RCU

- Large

- B

- 2 0.004 0.004 0.009 0.009 0.009
SCU

- W

- Large

- B 0.001 0.002 0.002 0.001 0.001
SCU

- A

- Small

- B 0.009 0.063 0.190 0.060 0.060
SCU

- A

- Large

- B 0.010 0.086 0.117 0.060 0.060

IMH

- A

- Small

- C 0.009 0.019 0.030 0.027 (0.029)

IMH

- A

- Large

- C 0.009 0.016 0.034 0.017 0.018
SCU

- A

- Small

- C 0.008 0.021 0.030 0.030 (0.115)
Total 0.429 0.805 1.747 1.223 1.014

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 2012$ 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 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 XXX displays the high, low, and reference water price scenarios used in the sensitivity cases. Tab le 10B.1

. 8 and Table 10B.1

. 9 provide NPV results for automatic commercial ice makers at each TSL b ased on the high electric
and water price forecast case for 7

- and 3

- percent discount rates, respectively. Table 10B.1

. 10
and Table 10B.1

. 11 provide NPV results for automatic commercial ice makers based on the low
electric a nd water price forecast case at 7

- and 3

- percent discount rates, respectively. Table
10B.1

. 12 and Table 10B.1

. 13 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 automatic commercial ice makers NPV results using the default
electric price scenario shown in chapter 10 of this TSD.

TABLE xxxx Commercial Water Price Projections

Reference High Low Constant Price

Case Case Case Case10B

- 10 

### Year (2012$/1000 gal) (2012$/1000 gal) (2012$/1000 gal) (2012$/1000 gal)

2010 $6.8979 $6.8289 $6.9526 $6.8979

2011 $7.0095 $6.9744 $7.0373 $7.0095

2012 $7.1230 $7.1230 $7.1230 $7.1230

2013 $7.2382 $7.2747 $7.2097 $7.1230

2014 $7.3554 $7.4297 $7.2975 $7.1230

2015 $7.4744 $7.5880 $7.3863 $7.1230

2016 $7.5954 $7.7497 $7.4762 $7.1230

2017 $7.7183 $7.9148 $7.5672 $7.1230

2018 $7.8432 $8.0835 $7.6594 $7.1230

2019 $7.9701 $8.2557 $7.7526 $7.1230

2020 $8.0991 $8.4316 $7.8470 $7.1230

2021 $8.2302 $8.6113 $7.9425 $7.1230

2022 $8.3633 $8.7947 $8.0392 $7.1230

2023 $8.4987 $8.9821 $8.1371 $7.1230

2024 $8.6362 $9.1735 $8.2362 $7.1230

2025 $8.7760 $9.3690 $8.3365 $7.1230

2026 $8.9180 $9.5686 $8.4379 $7.1230

2027 $9.0623 $9.7725 $8.5407 $7.1230

2028 $9.2090 $9.9807 $8.6447 $7.1230

2029 $9.3580 $10.1933 $8.7499 $7.1230

2030 $9.5095 $10.4105 $8.8564 $7.1230

2031 $9.6633 $10.6323 $8.9643 $7.1230

2032 $9.8197 $10.8589 $9.0734 $7.1230

2033 $9.9786 $11.0903 $9.1839 $7.1230

2034 $10.1401 $11.3266 $9.2957 $7.1230

2035 $10.3042 $11.5679 $9.4088 $7.1230

2036 $10.4710 $11.8144 $9.5234 $7.1230

2037 $10.6404 $12.0661 $9.6393 $7.1230

2038 $10.8126 $12.3232 $9.7567 $7.1230

2039 $10.9876 $12.5857 $9.8755 $7.1230

2040 $11.1654 $12.8539 $9.9957 $7.1230

2041 $11.3461 $13.1278 $10.1174 $7.1230

2042 $11.5297 $13.4075 $10.2406 $7.1230

2043 $11.7163 $13.6932 $10.3653 $7.1230

2044 $11.9059 $13.9849 $10.4914 $7.1230

2045 $12.0986 $14.2829 $10.6192 $7.1230

2046 $12.2944 $14.5872 $10.7485 $7.1230

2047 $12.4934 $14.8980 $10.8793 $7.1230

2048 $12.6955 $15.2155 $11.0118 $7.1230

2049 $12.9010 $15.5397 $11.1458 $7.1230

2050 $13.1098 $15.8708 $11.2815 $7.123010B

- 11 

### 2051 $13.3219 $16.2089 $11.4189 $7.1230

2052 $13.5375 $16.5543 $11.5579 $7.1230

2053 $13.7566 $16.9070 $11.6986 $7.1230

2054 $13.9792 $17.2672 $11.8410 $7.1230

2055 $14.2054 $17.6351 $11.9852 $7.1230

2056 $14.4353 $18.0109 $12.1311 $7.1230

2057 $14.6689 $18.3946 $12.2788 $7.1230

2058 $14.9063 $18.7866 $12.4283 $7.1230

2059 $15.1476 $19.1869 $12.5796 $7.1230

2060 $15.3927 $19.5957 $12.7328 $7.1230



### Table 10B.1

. 8 Automatic Commercial Ice Makers: Net Present Value in B illions (201 2$) at

a 7

- Percent Discount Rate

– High Electric and Water Price Scenario

Equipment Class Cumulative NPV by TSL at 7% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.011 0.027 0.027 0.001

IMH

- W

- Med

- B 0.017 0.017 0.026 0.018 0.021

IMH

- W

- Large

- B** 0.004 0.004 0.004 0.003 0.003

IMH

- W

- Large

- B

- 1 0.003 0.003 0.003 0.002 0.003

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.001 0.001

IMH

- A

- Small

- B 0.045 0.084 0.187 0.059 0.072

IMH

- A

- Large

- B** 0.074 0.133 0.316 0.278 0.278

IMH

- A

- Large

- B

- 1 0.059 0.118 0.292 0.256 0.256

IMH

- A

- Large

- B

- 2 0.014 0.014 0.024 0.022 0.022
RCU

- Large

- B** 0.039 0.039 0.086 0.079 0.082
RCU

- Large

- B

- 1 0.038 0.038 0.081 0.075 0.077
RCU

- Large

- B

- 2 0.002 0.002 0.004 0.004 0.004
SCU

- W

- Large

- B 0.001 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.030 0.092 0.021 0.021
SCU

- A

- Large

- B 0.005 0.042 0.057 0.026 0.026

IMH

- A

- Small

- C 0.004 0.009 0.015 0.012 (0.017)

IMH

- A

- Large

- C 0.004 0.008 0.017 0.008 0.008
SCU

- A

- Small

- C 0.004 0.010 0.014 0.014 (0.061)
Total 0.207 0.388 0.841 0.546 0.435

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
bi llion 2012$ 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

. 9 Automatic Commercial Ice Makers : Net Present Value in Billions (2012$) at

a 3

- Percent Discount Rate

– High Electric and Water Price Scenario

Equipment Class Cumulative NPV by TSL at 3%
billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.014 0.025 0.063 0.063 0.016

IMH

- W

- Med

- B 0.036 0.036 0.057 0.046 0.052

IMH

- W

- Large

- B** 0.009 0.009 0.009 0.008 0.009

IMH

- W

- Large

- B

- 1 0.007 0.007 0.007 0.006 0.007

IMH

- W

- Large

- B

- 2 0.002 0.002 0.002 0.002 0.002

IMH

- A

- Small

- B 0.099 0.186 0.418 0.194 0.223

IMH

- A

- Large

- B** 0.160 0.289 0.698 0.649 0.649

IMH

- A

- Large

- B

- 1 0.129 0.257 0.644 0.595 0.595

IMH

- A

- Large

- B

- 2 0.031 0.031 0.053 0.055 0.055
RCU

- Large

- B** 0.085 0.085 0.188 0.188 0.194
RCU

- Large

- B

- 1 0.082 0.082 0.178 0.179 0.184
RCU

- Large

- B

- 2 0.004 0.004 0.010 0.010 0.010
SCU

- W

- Large

- B 0.001 0.002 0.003 0.001 0.001
SCU

- A

- Small

- B 0.009 0.067 0.209 0.085 0.085
SCU

- A

- Large

- B 0.010 0.094 0.128 0.073 0.073

IMH

- A

- Small

- C 0.010 0.020 0.032 0.029 (0.024)

IMH

- A

- Large

- C 0.010 0.017 0.036 0.020 0.020
SCU

- A

- Small

- C 0.008 0.022 0.032 0.032 (0.110)
Total 0.452 0.852 1.872 1.388 1.188

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 2012$ 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

. 10 Automatic Commercial Ice Makers : Net Present Value in Billions (2012$)

at a 7

- Percent Dis count Rate

– Low Electric and Water Price Scenario

Equipment Class Cumulative NPV by TSL at 7% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.010 0.023 0.023 (0.003)

IMH

- W

- Med

- B 0.015 0.015 0.024 0.015 0.018

IMH

- W

- Large

- B** 0.004 0.004 0.004 0.003 0.003

IMH

- W

- Large

- B

- 1 0.003 0.003 0.003 0.002 0.002

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.000 0.001

IMH

- A

- Small

- B 0.041 0.077 0.171 0.038 0.049

IMH

- A

- Large

- B** 0.068 0.123 0.285 0.241 0.241

IMH

- A

- Large

- B

- 1 0.055 0.110 0.262 0.222 0.222

IMH

- A

- Large

- B

- 2 0.013 0.013 0.022 0.019 0.019
RCU

- Large

- B** 0.036 0.036 0.079 0.069 0.071
RCU

- Large

- B

- 1 0.035 0.035 0.075 0.066 0.068
RCU

- Large

- B

- 2 0.002 0.002 0.004 0.003 0.003
SCU

- W

- Large

- B 0.001 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.027 0.080 0.006 0.006
SCU

- A

- Large

- B 0.004 0.037 0.050 0.018 0.018

IMH

- A

- Small

- C 0.004 0.008 0.013 0.010 (0.019)

IMH

- A

- Large

- C 0.004 0.007 0.015 0.006 0.006
SCU

- A

- Small

- C 0.004 0.009 0.013 0.013 (0.063)
Total 0.191 0.355 0.757 0.442 0.326

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 2012$ 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

. 11 Automatic Commercial Ice Makers : Net Present Value in Billions (2012$)

at a 3

- Percent Discount Rate

– Low Electric and Water Price Scenario

Equipment Class Cumulative NPV by TSL at 3% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.013 0.022 0.053 0.053 0.005

IMH

- W

- Med

- B 0.033 0.033 0.052 0.039 0.044

IMH

- W

- Large

- B** 0.008 0.008 0.008 0.006 0.007

IMH

- W

- Large

- B

- 1 0.007 0.007 0.007 0.005 0.006

IMH

- W

- Large

- B

- 2 0.002 0.002 0.002 0.001 0.002

IMH

- A

- Small

- B 0.090 0.169 0.377 0.141 0.166

IMH

- A

- Large

- B** 0.147 0.265 0.621 0.558 0.558

IMH

- A

- Large

- B

- 1 0.118 0.236 0.572 0.511 0.511

IMH

- A

- Large

- B

- 2 0.029 0.029 0.049 0.046 0.046
RCU

- Large

- B** 0.078 0.078 0.172 0.164 0.168
RCU

- Large

- B

- 1 0.075 0.075 0.163 0.156 0.160
RCU

- Large

- B

- 2 0.003 0.003 0.009 0.008 0.009
SCU

- W

- Large

- B 0.001 0.002 0.002 0.001 0.001
SCU

- A

- Small

- B 0.009 0.061 0.177 0.047 0.047
SCU

- A

- Large

- B 0.009 0.081 0.110 0.053 0.053

IMH

- A

- Small

- C 0.009 0.018 0.029 0.025 (0.030)

IMH

- A

- Large

- C 0.009 0.016 0.033 0.016 0.016
SCU

- A

- Small

- C 0.008 0.020 0.028 0.028 (0.116)
Total 0.413 0.772 1.664 1.130 0.919

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 2012$ 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

. 12 Automatic Commercial Ice Makers : Net Present Value in Billions (2012 $)

at a 7

- Percent Discount Rate

– Constant Electric and Water Price Scenario

Equipment Class Cumulative NPV by TSL at 7% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.006 0.010 0.021 0.021 (0.006)

IMH

- W

- Med

- B 0.015 0.015 0.023 0.015 0.017

IMH

- W

- Large

- B** 0.004 0.004 0.004 0.002 0.003

IMH

- W

- Large

- B

- 1 0.003 0.003 0.003 0.002 0.002

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.000 0.001

IMH

- A

- Small

- B 0.041 0.076 0.167 0.033 0.044

IMH

- A

- Large

- B** 0.067 0.121 0.269 0.225 0.225

IMH

- A

- Large

- B

- 1 0.054 0.108 0.248 0.207 0.207

IMH

- A

- Large

- B

- 2 0.013 0.013 0.022 0.018 0.018
RCU

- Large

- B** 0.036 0.036 0.078 0.067 0.069
RCU

- Large

- B

- 1 0.034 0.034 0.074 0.064 0.066
RCU

- Large

- B

- 2 0.002 0.002 0.004 0.003 0.003
SCU

- W

- Large

- B 0.000 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.004 0.027 0.071 (0.003) (0.003)
SCU

- A

- Large

- B 0.004 0.033 0.045 0.013 0.013

IMH

- A

- Small

- C 0.004 0.008 0.013 0.010 (0.020)

IMH

- A

- Large

- C 0.004 0.007 0.015 0.006 0.006
SCU

- A

- Small

- C 0.003 0.009 0.012 0.012 (0.064)
Total 0.188 0.346 0.720 0.401 0.285

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 2012$ 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 B

- 1
and B

- 2 typical units.10B

- 16 

### Table 10B.1

. 13 Automatic Commercial Ice Makers : Net Present Value in Billions (2012$)

at a 3

- Percent Discount Rate

– Constant Electric Price Scenario

Equipment Class Cumulative NPV by TSL at 3% billions 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.012 0.022 0.047 0.047 (0.002)

IMH

- W

- Med

- B 0.032 0.032 0.050 0.036 0.041

IMH

- W

- Large

- B** 0.008 0.008 0.008 0.006 0.007

IMH

- W

- Large

- B

- 1 0.006 0.006 0.006 0.005 0.005

IMH

- W

- Large

- B

- 2 0.002 0.002 0.002 0.001 0.001

IMH

- A

- Small

- B 0.087 0.163 0.363 0.122 0.147

IMH

- A

- Large

- B** 0.142 0.256 0.576 0.508 0.508

IMH

- A

- Large

- B

- 1 0.114 0.228 0.529 0.464 0.464

IMH

- A

- Large

- B

- 2 0.028 0.028 0.047 0.043 0.043
RCU

- Large

- B** 0.076 0.076 0.166 0.155 0.159
RCU

- Large

- B

- 1 0.073 0.073 0.157 0.147 0.152
RCU

- Large

- B

- 2 0.003 0.003 0.009 0.008 0.008
SCU

- W

- Large

- B 0.001 0.001 0.002 0.001 0.001
SCU

- A

- Small

- B 0.008 0.058 0.154 0.021 0.021
SCU

- A

- Large

- B 0.009 0.070 0.099 0.041 0.041

IMH

- A

- Small

- C 0.009 0.017 0.028 0.024 (0.033)

IMH

- A

- Large

- C 0.009 0.015 0.032 0.014 0.015
SCU

- A

- Small

- C 0.007 0.019 0.027 0.027 (0.118)
Total 0.400 0.738 1.551 1.000 0.785

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 2012$ 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.1.3 9

- 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 revise d 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

- 17 

### presented for informational purposes only and is not 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

. 14 and Table 10B.1.15 at 7

- perc ent and 3

- percent discount rates, respectively

. The impacts are counted over the lifetime of equipment purchased in 20 18

– 2026

.

Table 10B.1

. 14 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 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.003 0.005 0.012 0.012 (0.001)

IMH

- W

- Med

- B 0.008 0.008 0.012 0.008 0.009

IMH

- W

- Large

- B** 0.002 0.002 0.002 0.001 0.002

IMH

- W

- Large

- B

- 1 0.002 0.002 0.002 0.001 0.001

IMH

- W

- Large

- B

- 2 0.000 0.000 0.000 0.000 0.000

IMH

- A

- Small

- B 0.021 0.039 0.086 0.023 0.029

IMH

- A

- Large

- B** 0.034 0.062 0.143 0.123 0.123

IMH

- A

- Large

- B

- 1 0.028 0.055 0.132 0.113 0.113

IMH

- A

- Large

- B

- 2 0.007 0.007 0.011 0.010 0.010
RCU

- Large

- B** 0.018 0.018 0.040 0.036 0.037
RCU

- Large

- B

- 1 0.017 0.017 0.038 0.034 0.035
RCU

- Large

- B

- 2 0.001 0.001 0.002 0.002 0.002
SCU

- W

- Large

- B 0.000 0.000 0.001 0.000 0.000
SCU

- A

- Small

- B 0.002 0.014 0.040 0.005 0.005
SCU

- A

- Large

- B 0.002 0.018 0.025 0.010 0.010

IMH

- A

- Small

- C 0.002 0.004 0.007 0.005 (0.009)

IMH

- A

- Large

- C 0.002 0.004 0.008 0.003 0.003
SCU

- A

- Small

- C 0.002 0.005 0.006 0.006 (0.031)
Total 0.096 0.179 0.381 0.233 0.177

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 2012$ 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

- 18 

### Table 10B.1

. 15 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 2012$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 0.005
0.008 0.020 0.020 0.003

IMH

- W

- Med

- B 0.012 0.012 0.019 0.015 0.017

IMH

- W

- Large

- B** 0.003 0.003 0.003 0.003 0.003

IMH

- W

- Large

- B

- 1 0.002 0.002 0.002 0.002 0.002

IMH

- W

- Large

- B

- 2 0.001 0.001 0.001 0.001 0.001

IMH

- A

- Small

- B 0.034 0.063 0.141 0.058 0.068

IMH

- A

- Large

- B** 0.054 0.098 0.230 0.209 0.209

IMH

- A

- Large

- B

- 1 0.044 0.088 0.211 0.191 0.191

IMH

- A

- Large

- B

- 2 0.011 0.011 0.018 0.018 0.018
RCU

- Large

- B** 0.029 0.029 0.064 0.062 0.064
RCU

- Large

- B

- 1 0.028 0.028 0.060 0.059 0.061
RCU

- Large

- B

- 2 0.001 0.001 0.003 0.003 0.003
SCU

- W

- Large

- B 0.000 0.001 0.001 0.000 0.000
SCU

- A

- Small

- B 0.003 0.023 0.065 0.020 0.020
SCU

- A

- Large

- B 0.003 0.030 0.041 0.021 0.021

IMH

- A

- Small

- C 0.003 0.007 0.011 0.010 (0.010)

IMH

- A

- Large

- C 0.003 0.006 0.012 0.006 0.006
SCU

- A

- Small

- C 0.003 0.007 0.011 0.011 (0.042)
Total 0.153 0.287 0.617 0.434 0.359

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 2012$ 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 typica l units.10C

- i 

### 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

- 5

LIST OF TABLES

Table 10C.2.1 TSL and Efficiency Levels Mapping*

................................ ............................. 10C

- 2 Table 10C.2.2 Percentage Efficiency Improvement from Baseline by TSL*

......................... 10C

- 3 Table 10C.2.3 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

- 7 Table 10C.3.2 Equations Representing the Standards at Each TSL for All Primary Equipment
Classes

................................ ................................ ................................ .............. 10C

- 9 Table 10C.3.3 Equations Representing the Standards at Each TSL for All Secondary Equipment Classes

................................ ................................ ........................... 10C

- 9
Table 10C.3.4 Equations representing the standards for Condenser Water

.......................... 10C

- 1010C

- 1 

### APPENDIX 10 C

. TRIAL STANDARD LEVEL S AND STANDARDS EQUATI ONS

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 over b ase efficiencies and are generally consistent across equipment classes. However, t he percentage energy reductions set for efficiency level 1 do differ between product
classes

. For example

, efficiency level 1 f o r five batch type equipment class es is set to a 10 percent energy

- usage reduction from the baseline level while efficiency level 1 for the 3 self

contained batch equipment classes is set to 7 percent. For all equipment classes, the highest efficiency level listed corresponds to the maximum savings le vel. 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.

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 level s 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 national impacts (e.g., national energy savings ( NES )

, net present val ue ( 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 fo llowing 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 requir ement 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 efficiency 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 level.

3. TSL 3 was chosen to r epresent the efficiency level within each equipment class with the highest NPV at a 7

- percent discount rate

.10C

- 2 

### 4. TSL 2 was selected to provide intermediate efficiency levels that fill the gap between the TSLs 1 and 3. Note that with the number of efficiency le vels 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 efficien cy level 2. In the analysis, efficiency level 2 was set equivalent to ENERGY STAR  

### for products covered by ENERGY STAR standards

, and an equivalent efficiency improvement for other equipment classes

.

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 TSL and Efficiency Levels Mapping *

Equipment Class Baseline ENERGY
STAR* 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 3 Level 5 Level 5 Level 6

IMH

- W

- Med

- B Level 1 Level 2 Level 2 Level 3 Level 4 Level 5

IMH

- W

- Large

- B †

IMH

- W

- Large

- B

- 1 Level 1 Level 2 Level 2 Level 2 Level 3 Level 4

IMH

- W

- Large

- B

- 2 Level 1 Level 2 Level 2 Level 2 Level 3 Level 4

IMH

- A

- Small

- B Level 1 Level 2 Level 3 Level 5 Level 6 Level 7

IMH

- A

- Large

- B †

IMH

- A

- Large

- B

- 1 Level 1 Level 2 Level 3 Level 5 Level 6 Level 6

IMH

- A

- Large

- B

- 2 Level 1 Level 2 Level 2 Level 3 Level 4 Level 4
RCU

- Large

- B †

RCU

- Large

- B

- 1 Level 1 Level 2 Level 2 Level 3 Level 4 Level 5
RCU

- Large

- B

- 2 Level 1 Level 2 Level 2 Level 3 Level 4 Level 5
SCU

- W

- Large

- B Level 1 Level 2 Level 3 Level 5 Level 6 Level 7
SCU

- A

- Small

- B Level 1 Level 2 Level 4 Level 6 Level 7 Level 7
SCU

- A

- Large

- B Level 1 Level 2 Level 4 Level 6 Level 7 Level 7

IMH

- A

- Small

- C Level 1 Level 2 Level 3 Level 4 Level 5 Level 7

IMH

- A

- Large

- C Level 1 Level 2 Level 3 Level 5 Level 6 Level 7
SCU

- A

- Small

- C Level 1 Level 2 Level 3 Level 4 Level 4 Level 6

“Level” stands for “ e fficiency l evel” or alternative c andidate s tandard l evel (CSL) *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 ensure models at the low and the higher portions of the applicable range were accurately modeled. The smaller of the two is not ed 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

- 3 

### 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% 15.0% 25.0% 25.0% 29.4%

IMH

- W

- Med

- B 10.0% 10.0% 15.0% 20.0% 21.3%

IMH

- W

- Large

- B 10.0% 10.0% 10.0% 15.0% 16.4%

IMH

- W

- Large

- B1 10.0% 10.0% 10.0% 15.0% 16.7%

IMH

- W

- Large

- B2 10.0% 10.0% 10.0% 15.0% 15.5%

IMH

- A

- Small

- B 10.0% 15.0% 25.0% 30.0% 31.3%

IMH

- A

- Large

- B 10.0% 13.7% 22.3% 26.9% 26.9%

IMH

- A

- Large

- B1 10.0% 15.0% 25.0% 29.4% 29.4%

IMH

- A

- Large

- B2 10.0% 10.0% 15.0% 20.0% 20.0%
RCU

- Large

- B 9.0% 9.0% 15.0% 20.0% 20.6%
RCU

- Large

- B1 9.0% 9.0% 15.0% 20.0% 20.6%
RCU

- Large

- B2 9.0% 9.0% 15.0% 20.0% 20.5%
SCU

- W

- Large

- B 7.0% 15.0% 25.0% 30.0% 30.2%
SCU

- A

- Small

- B 7.0% 20.0% 30.0% 39.3% 39.3%
SCU

- A

- Large

- B 7.0% 20.0% 30.0% 34.9% 34.9%

IMH

- A

- Small

- C 10.0% 15.0% 20.0% 25.0% 31.0%

IMH

- A

- Large

- C 10.0% 15.0% 25.0% 30.0% 30.2%
SCU

- A

- Small

- C 7.0% 15.0% 20.0% 20.0% 28.2% *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 X.X.X shows the designs options that were used during the engineering analysis to achieve the percentage efficiency improvement of each TSL within each equipment class.

Table X.X. Engineering Design Option Mapping by TSL10C

- 4 Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B No BW Fill,
PSC PM Increase Comp EER,
Increase Cond Same as previous Increase Cond, BW
Fill BW Fill, Increase Evap,
ECM PM ECM PM,
DWHX

IMH

- W

- Med

- B BW Fill, PSC
PM Increase Comp EER Same as previous Increase Comp EER, Increase Cond Increase Comp EER, ECM PM,
DWHX DWHX

IMH

- W

- Large

- B

IMH

- W

- Large

B1 BW Fill, PSC
PM Increase Comp EER,
Increase Cond Same as previous Same as previous Increase Cond, ECM PM,

DWHX DWHX

IMH

- W

- Large

B2 BW Fill, PSC
PM Increase Comp EER,
Increase Cond Same as previous Same as previous ECM PM,
DWHX DWHX

IMH

- A

- Small

- B BW Fill, PSC PM, SPM FM Increase Comp EER, Increase Cond, Increase Evap Increase Evap Increase Evap, PSC FM, ECM FM,
Increase Cond Increase Cond, ECM PM,

DWHX DWHX

IMH

- A

- Large

- B

IMH

- A

- Large

- B1 BW Fill, PSC PM, SPM FM PSC FM, Comp EER Increase Comp EER Increase Comp EER, BW Fill, ECM PM, ECM FM, Increase Cond Increase Cond,
DWHX DWHX

IMH

- A

- Large

- B2 BW Fill, PSC PM, SPM FM Increase Comp EER,
PSC FM Same as previous PSC FM, Increase
Cond ECM FM, ECM PM, DWHX ECM FM,
ECM PM,
DWHX RCU

- Large

- B

RCU

- Large

- B1 BW Fill, PSC PM, PSC FM Increase Comp EER Same as previous Increase Comp EER, Increase Cond, ECM FM ECM FM, Increase Cond, ECM PM,

DWHX DWHX

RCU

- Large

- B2 BW Fill, PSC PM, PSC FM Increase Comp EER,
Increase Cond Same as previous ECM PM
Increase Cond Increase Cond, ECM FM,

DWHX DWHX

SCU

- W

- Large

- B No BW Fill,
PSC PM BW Fill BW Fill,
Increase Comp EER,
Increase Cond Increase Cond,
ECM PM ECM PM,
DWHX DWHX

SCU

- A

- Small

- B No BW Fill, PSC PM, SPM
FM PSC FM, Increase
Cond Increase Cond,
Increase Comp EER Increase Comp EER, BW Fill BW Fill, ECM PM, ECM FM,
DWHX Same as previous

SCU

- A

- Large

- B No BW Fill, PSC PM, SPM
FM Increase Comp EER Increase Comp EER,
Increase Cond, BW Fill BW Fill, PSC FM, ECM FM, ECM
PM ECM PM,
DWHX Same as previous

IMH

- A

- Small

- C PSC AM, SPM
FM PSC FM, Increase Comp EER PSC FM,
Increase Comp EER Increase Comp EER, Increase Cond, ECM FM ECM FM, ECM
AM ECM
AM

IMH

- A

- Large

- C PSC AM, SPM
FM Increase Cond, Increase Comp EER Increase Comp EER Increase Comp EER, PSC FM,
ECM FM ECM FM, ECM
AM ECM
AM

SCU

- A

- Small

- C PSC AM, SPM
FM Increase Cond Increase Cond,
Increase Comp EER Increase Comp EER, PSC FM Same as previous ECM FM,
ECM

AM
SPM = Shaded Pole Motor PSC = Permanent Split Capacitor Motor ECM = Electronically Commutated Motor FM = Fan Motor (Air

- Cooled Units) PM = Pump Motor (Batch Units) AM = Auger Motor (Continuous Units) BW Fill = Batch Water Fill Option Included
Increase Cond = Increase in Condenser Size Increase Evap = Increase in Evaporator Size Increase Comp EER = Increase in Compressor EER DWHX = Addition of Drainwater Heat Exchanger10C

- 5 

### In the notice of public rulemaking ( NOPR ) analyses, DOE explicitly studied 12 equipment classes. However, the NOPR proposes energy efficiency standards for 25 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

. 3 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

- Small

- C IMH

- A

- Large

- C

RCU

- NRC

- Large

- C IMH

- A

- Large

- C

RCU

- RC

- Small

- C IMH

- A

- Large

- C

RCU

- RC

- Large

- C IMH

- A

- Large

- 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



### 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 criterion
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 T RIAL 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 condenser, water limits are established in terms of gallons of water per 100/lb ice per 24

- hour period. In this notice, DOE ha s retained these metrics for specifying energy and water
conservation standards.

General Methodology10C

- 6 

### Developing trial standard level equations required several steps. In developing TSLs, DOE analyzed each equipment class separately, and attributed a perce ntage 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 stand ards

. 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, and

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 percentage reduction ( i.e., the percentage reduction associated with the TSL for that equi pment 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 classe s, 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 300 lb ice/24 hours. DOE would then draw a line between the start of the large e quipment 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.

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 l arge 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.10C

- 7 

### For the IMH

- A

- B atch equipment classes, DO E 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 analyzed two different sizes of equi pment in the IMH

- A

- Large

- B equipment clas s. For the largest size of equipment, DOE chose a pivot point at 1500 lbs/day and applied the allocated percentage reduction for this size; the trial standard level for the largest sizes was set at a constant effic iency 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 availabl e in Section 5.10.3 of Chapter 5
of this NOPR TSD.

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, respectively,
and subtracted the 0.2 kWh/100 lb of i ce 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 compressor standards were developed such that

no gap exists at the harvest rate breakpoints.

Summary of Results

Table 10C.3

. 1 provides an example of the resulting energy consumption for representative units analyzed as part of the NOPR analyses. In all of the standards equations, harvest rate or H is harvest rate in pounds per 24 hours.

Using the typical unit size for directly analyzed equipment classes, the potential standards

are shown on Table 10C.3

. 2

.

In addition to the 12 primary equipment classes analyzed, DOE intends to amend
standards for 13 secondary classes of ACIM equipment covered in this rulemaking that were not
directl y analyzed in the engineering analysis ( Table 10C.3

. 3 )

.

Table 10C.3

. 1 Energy Consumption by TSL for Representative Unit s 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 5.2 4.6 4.6 4.3

IMH

- W

- Med

- B 850 4.2 4.0 3.7 3.6 3.5

IMH

- W

- Large

- B

- 1 1500 3.6 3.6 3.6 3.4 3.3

IMH

- W

- Large

- B

- 2 2600 3.6 3.6 3.6 3.4 3.3

IMH

- A

- Small

- B 300 6.9 6.5 5.8 5.4 5.3

IMH

- A

- Large

- B

- 1 800 5.4 5.1 4.5 4.2 4.1

IMH

- A

- Large

- B

- 2 1500 3.7 4.7 4.5 4.2 4.2
RCU

- Large

- B

- 1 1500 4.6 4.6 4.3 4.1 4.1
RCU

- Large

- B

- 2 2400 4.6 4.6 4.3 4.1 4.1
SCU

- W

- Large

- B 300 7.1 6.5 5.7 5.3 5.310C

- 8 SCU

- A

- Small

- B 110 11.9 10.3 9.0 7.8 7.8
SCU

- A

- Large

- B 200 9.1 7.8 6.9 6.4 6.4

IMH

- A

- Small

- C 310 8.1 7.7 7.3 6.9 6.2

IMH

- A

- Large

- C 820 5.9 5.6 5.0 4.6 4.6
SCU

- A

- Small

- C 110 10.1 9.2 8.7 8.7 7.810C

- 9 

### Table 10C.3

. 2 Equations Representing the Standards at Each TSL for All Primary Equipment Classes

Equipment Class ENERGY STAR Fill In Max NPV Max > 0 NPV Max

- Tech
TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH

- W

- Small

- B 7.01

– 0.0050H 6.62

– 0.0047H 5.84

– 0.0041H 5.84

– 0.0041H 5.49

– 0.0039H

IMH

- W

- Med

- B 5.04

– 0.0010H 4.65

– 0.0007H 3.88

– 0.0002H 3.98

– 0.0004H 3.63

– 0.0002H

IMH

- W

- Large

- B 3.6 3.6 3.6 3.4 3.3

IMH

- A

- Small

- B 9.23

– 0.0077H 8.74

– 0.0073H 7.70

– 0.0065H 7.18

– 0.0060H 7.05

– 0.0059H

IMH

- A

- Large

- B 6.20

– 0.0010H 5.86

– 0.0009H 5.17

– 0.0008H 4.82

– 0.0008H 4.74

– 0.0008H

IMH

- A

- Extended

- B (>= 2,500 and <4,000)

3.7; (>=1,240 and <1,975) 4.7; (>=1,975 and <2,500)
6.89

– 0.0011H; (>= 2,500)
4.1 (>=875 and <2,210) 4.5; (>=2,210 and <2,500)
6.89

– 0.0011H; (>= 2,500)
4.1 (>=815 and <2,455) 4.2; (>=2,455 and <2,500)
6.89

– 0.0011H; (>= 2,500)
4.1 (>=710 and <2,455) 4.2; (>=2,455 and <2,500)
6.89

– 0.0011H; (>= 2,500)
4.1
RCU

- NRC

- Large

- B 4.6 4.6 4.3 4.1 4.1
SCU

- W

- Large

- B 7.1 6.5 5.7 5.3 5.3
SCU

- A

- Small

- B 16.74

– 0.0436H 14.40

– 0.0375H 12.6

– 0.0328H 10.34

– 0.0227H 10.34

– 0.0227H
SCU

- A

- Large

- B 9.1 7.8 6.9 6.4 6.4

IMH

- A

- Small

- C 9.90

– 0.0057H 9.35

– 0.0053H 9.24

– 0.0061H 8.69

– 0.0058H 7.55

– 0.0042H

IMH

- A

- Large

- C 5.9 5.6 5.0 4.6 4.6
SCU

- A

- Small

- C 10.70

– 0.0058H 9.75

– 0.0053H 9.20

– 0.0050H 9.20

– 0.0050H 8.26

– 0.0045H



### Table 10C.3

. 3 Equations Representing the Standards at Each TSL for All Secondary Equipment Classes

Equipment Class ENERGY STAR Fill In Max NPV Max > 0 NPV Max

- Tech
TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
RCU

- NRC

- Small

- B 8.04

– 0.0034H 8.04

– 0.0034H 7.52

– 0.0032H 7.08

– 0.0030H 7.05

– 0.0030H
RCU

- RC

- Small

- B 8.02

– 0.0034H 8.02

– 0.0034H 7.52

– 0.0032H 7.08

– 0.0030H 7.06

– 0.0030H
RCU

- RC

- Large

- B 4.8 4.8 4.5 4.3 4.3
SCU

- W

- Small

- B 10.60

– 0.0177H 9.69

– 0.0162H 8.55

– 0.0143H 7.98

– 0.0133H 7.96

– 0.0133H

IMH

- W

- Small

- C 7.29

– 0.0030H 6.86

– 0.0028H 6.08

– 0.0025H 5.67

– 0.0023H 5.65

– 0.0023H

IMH

- W

- Large

- C 4.6 4.3 3.8 3.6 3.6
RCU

- NRC

- Small

- C 9.00

– 0.0041H 8.50

– 0.0039H 7.5

– 0.0034H 7.00

– 0.0032H 6.98

– 0.0032H
RCU

- NRC

- Large

- C 5.5 5.2 4.6 4.3 4.3
RCU

- RC

- Small

- C 9.18

– 0.0041H 8.67

– 0.0039H 7.65

– 0.0034H 7.14

– 0.0031H 7.12

– 0.0031H
RCU

- RC

- Large

- C 5.7 5.4 4.8 4.5 4.5
SCU

- W

- Small

- C 8.46

– 0.0031H 7.74

– 0.0028H 7.28

– 0.0027H 7.28

– 0.0027H 6.53

– 0.0024H
SCU

- W

- Large

- C 5.7 5.2 4.9 4.9 4.4
SCU

- A

- Large

- C 6.6 6.0 5.7 5.7 5.110C

- 10 

### In the NOPR analyses, DOE concluded that condenser water

- changing design options

were not cost

- effective, and did not examine these design options . Thus, DOE is not proposing to revise the condenser water usage limits for equipment currently subject to efficiency standards. For batch type equipment not covered by standards, DOE proposes to use the existing condenser water standards. For equipment wi th harvest rates greater than or equal to 2,500 lb/24 hours and less than 4,000 lb/24 hours, DOE proposes to take the existing condenser water standards calculated using the existing equation calculated for a 2,500 lb/24

- hour machine and set that as
the co ndenser water allowance for that capacity range

.
Based on equipment testing performed by DOE for this rulemaking

, DOE proposes setting condenser water limits for continuous type machines at 80 percent of the limits set for batch type machines.

Table 10C.3

. 4 lists the proposed 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 representing the standards 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 <500 200

- 0.022H ≥ 500 and < 2,500 200

- 0.022H

≥ 2,500 and <4,000 145

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 <900 160

- 0.0176H ≥900 and <2,500 160

- 0.0176H ≥ 2,500 and 4,000 116

Self

- Contained Water <900 153

- 0.0252H ≥900 and < 2,500 153

- 0.0252H ≥2,500 and <4,000 9010D

- i 

### APPENDIX 10D. FULL

- FUEL

- CYCLE MULTIPLIERS

TABLE 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 AEO2013 )

............................ 10D

- 310D

- 1 

### 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) energy, 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, 2011)

, 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 building or industrial process. Where natural gas and petroleum fuels are consumed at the site (for example

, in a furnace), site energy is identical to primary energy, with both equal to the heat content of the primary fuel consumed. For electrici ty, site energy 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 multiply ing 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 en ergy 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, an d electricity generated from renewable fluxes (wind, solar

, and hydro). For the former, the upstream 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 of 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 Emissi ons 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 representing the energy intensity and material losses at each production stage. These parameters depend only on physical data, so the calculations do not req uire any assumptions about prices or other economic data. While in10D

- 2 

### general these parameter values may vary by geographic region, for this analysis national averages are used.

In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, x=g for natural gas, x=p for petroleum fuels, x=u for uranium

, and x=r for renewable fluxes. The fuel cycle parameters are the following :

• a x 

### is the quantity of fuel x burned per unit of electricity output, on average, for grid electricity. The calculation of a x 

### includes a factor to account for transmission and distribution system losses.

• b y 

### is the amount of grid electricity used in production of fuel y, in megawatt

- hours per physical unit of fuel y.

• c xy 

### is the amount of fuel x consumed in producing one unit of fuel y.

• q x 

### is the heat content of fuel x ( million British thermal units /physical unit)

• z x 

### (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 wit h 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 el ectricity 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
th e 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 t he fuel mix used in its generation. 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 arises exclusively from variables taken from the AEO . The FFC analysis for commercial refrigeration equipment used data from Annual Energy Outlook 201 3 

### ( AEO201 3 )

. 3 

### AEO2013 provides projections to 2040.10D

- 3 

### 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 US 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 3 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 3 )

2015 2020 2025 2030 2035 2040 Electricity (power plant primary energy use) 1.042 1.041 1.040 1.040 1.041 1.04 0
Natural Gas (site) 1.103 1.10 1 1.10 0 1.09 8 1.09 9 1. 100
Petroleum Fuels (site) 1.1 32 1.14 0 1.1 48 1.1 58 1.1 66 1.1 6810D

- 4 

### 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

. Lawrence Berkeley National Laboratory, Berkeley, CA. Report N o. LBNL

- 6025E

.

3

. U.S. Department of Energy

- Energy Information Administration. Annual Energy Outlook
201 3

. April 201 3 . Washington, D.C. < www.eia.gov/forecasts/aeo/pdf/0383%282012%29.pdf >

.10E

- i 

### 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

- 2010E

- 1 

### 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 notice of public rulemaking (NOPR) for
automatic commercial 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 NOPR , at both a 7

- percent and 3

- percent discount rate

.
Although combining the values of operating savings and CO 2 

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

, while the
value of CO 2 

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

### savings are performed with different methods that use quite different time frames for analysis. The national operating cost savings is measured for the lifetime of products shipped in the 30

- year an alysis 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 con sidered 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 2 dollars ( 201 2 $ ) , of the benefits from customer operation of products that meet the considered standard levels (consisting primarily of operating cost savings from using less energy, minus increases in equipment purchase and installation costs, which is a nother way of representing customer NPV) and ( 2) the annualized monetary value of the benefits of emission reductions, including CO 2 

### emission reductions

.10E

- 2 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.002 0.006 0.001 0.017 0.013 0.000 0.002 0.002 0.002 0.001 0.001 0.025 0.073 Economic Impacts

Incremental Equipment Cost billion 2012$ 0.002 0.003 0.001 0.016 0.005 0.000 0.002 0.002 0.001 0.001 0.001 0.010 0.045 Operating Cost Savings billion 2012$ 0.015 0.037 0.009 0.110 0.086 0.001 0.011 0.012 0.011 0.010 0.009 0.163 0.475

NPV billion 2012$ 0.013 0.034 0.009 0.094 0.081 0.001 0.009 0.010 0.009 0.009 0.008 0.152 0.430 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.123 0.297 0.074 0.867 0.673 0.004 0.088 0.097 0.085 0.076 0.073 1.267 3.723

NOx kilo

- ton 0.073 0.177 0.044 0.517 0.402 0.002 0.053 0.058 0.051 0.046 0.043 0.756 2.221 Hg ton 0.000 0.001 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.006
SO 2 kilo

- ton 0.177 0.427 0.106 1.247 0.968 0.005 0.127 0.139 0.122 0.110 0.105 1.821 5.354 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.863 2.082 0.518 6.085 4.727 0.026 0.618 0.677 0.597 0.536 0.512 8.890 26.130 3% dr, average million 2012$ 3.906 9.419 2.342 27.530 21.383 0.118 2.798 3.064 2.700 2.423 2.314 40.219 118.217 2.5% dr, average million 2012$ 6.190 14.929 3.712 43.632 33.890 0.186 4.434 4.856 4.280 3.841 3.668 63.743 187.361 3% dr, 95th perc million 2012$ 12.042 29.041 7.221 84.879 65.928 0.363 8.626 9.447 8.325 7.471 7.135 124.002 364.481 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.014 0.035 0.009 0.102 0.079 0.000 0.010 0.011 0.010 0.009 0.009 0.149 0.437 At 2,639 2012$/ton million 2012$ 0.081 0.196 0.049 0.574 0.445 0.002 0.058 0.064 0.056 0.050 0.048 0.838 2.463 At 4,809 2012$/ton million 2012$ 0.148 0.358 0.089 1.045 0.812 0.004 0.106 0.116 0.103 0.092 0.088 1.527 4.488
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.014 0.037 0.009 0.101 0.086 0.001 0.010 0.010 0.010 0.010 0.009 0.162 0.458

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.017 0.044 0.011 0.122 0.103 0.001 0.012 0.013 0.012 0.012 0.010 0.193 0.550

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.019 0.049 0.012 0.138 0.116 0.001 0.013 0.015 0.014 0.013 0.012 0.217 0.620

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.025 0.064 0.016 0.179 0.148 0.002 0.018 0.019 0.018 0.017 0.015 0.277 0.79710E

- 3 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.003 Operating Cost Savings billion 2012$ 0.001 0.002 0.001 0.006 0.005 0.000 0.001 0.001 0.001 0.001 0.001 0.009 0.027

NPV billion 2012$ 0.001 0.002 0.000 0.005 0.005 0.000 0.001 0.001 0.001 0.001 0.000 0.009 0.025 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.068 0.165 0.041 0.481 0.374 0.002 0.049 0.054 0.047 0.042 0.040 0.703 2.066 3% dr, average million 2012$ 0.224 0.541 0.134 1.581 1.228 0.007 0.161 0.176 0.155 0.139 0.133 2.310 6.788 2.5% dr, average million 2012$ 0.326 0.787 0.196 2.301 1.787 0.010 0.234 0.256 0.226 0.203 0.193 3.362 9.881 3% dr, 95th perc million 2012$ 0.692 1.668 0.415 4.874 3.786 0.021 0.495 0.542 0.478 0.429 0.410 7.121 20.929 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.001 0.002 0.000 0.006 0.005 0.000 0.001 0.001 0.001 0.001 0.000 0.009 0.025 At 2,639 2012$/ton million 2012$ 0.005 0.011 0.003 0.033 0.026 0.000 0.003 0.004 0.003 0.003 0.003 0.048 0.141 At 4,809 2012$/ton million 2012$ 0.009 0.021 0.005 0.060 0.047 0.000 0.006 0.007 0.006 0.005 0.005 0.088 0.258
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.001 0.002 0.001 0.006 0.005 0.000 0.001 0.001 0.001 0.001 0.001 0.010 0.027

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.001 0.003 0.001 0.007 0.006 0.000 0.001 0.001 0.001 0.001 0.001 0.011 0.032

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.001 0.003 0.001 0.008 0.006 0.000 0.001 0.001 0.001 0.001 0.001 0.012 0.035

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.001 0.004 0.001 0.010 0.008 0.000 0.001 0.001 0.001 0.001 0.001 0.016 0.04610E

- 4 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.004 0.006 0.001 0.033 0.013 0.000 0.013 0.010 0.003 0.003 0.004 0.045 0.136 Economic Impacts

Incremental Equipment Cost billion 2012$ 0.005 0.003 0.001 0.038 0.005 0.000 0.017 0.015 0.003 0.001 0.006 0.021 0.114 Operating Cost Savings billion 2012$ 0.029 0.037 0.009 0.214 0.086 0.002 0.081 0.101 0.021 0.017 0.027 0.296 0.920

NPV billion 2012$ 0.023 0.034 0.009 0.176 0.081 0.002 0.064 0.086 0.019 0.016 0.021 0.275 0.806 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.226 0.297 0.074 1.669 0.673 0.008 0.655 0.500 0.170 0.136 0.219 2.315 6.940

NOx kilo

- ton 0.135 0.177 0.044 0.996 0.402 0.005 0.390 0.298 0.101 0.081 0.130 1.381 4.141 Hg ton 0.000 0.001 0.000 0.003 0.001 0.000 0.001 0.001 0.000 0.000 0.000 0.004 0.012
SO 2 kilo

- ton 0.324 0.427 0.106 2.400 0.968 0.011 0.941 0.719 0.245 0.196 0.314 3.329 9.980 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 1.583 2.082 0.518 11.714 4.727 0.056 4.594 3.507 1.194 0.956 1.535 16.247 48.711 3% dr, average million 2012$ 7.161 9.419 2.342 52.995 21.383 0.252 20.783 15.868 5.401 4.327 6.942 73.502 220.376 2.5% dr, average million 2012$ 11.349 14.929 3.712 83.991 33.890 0.399 32.938 25.150 8.559 6.858 11.003 116.492 349.271 3% dr, 95th perc million 2012$ 22.078 29.041 7.221 163.392 65.928 0.777 64.076 48.924 16.651 13.342 21.405 226.617 679.451 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.026 0.035 0.009 0.196 0.079 0.001 0.077 0.059 0.020 0.016 0.026 0.272 0.814 At 2,639 2012$/ton million 2012$ 0.149 0.196 0.049 1.104 0.445 0.005 0.433 0.331 0.113 0.090 0.145 1.531 4.591 At 4,809 2012$/ton million 2012$ 0.272 0.358 0.089 2.012 0.812 0.010 0.789 0.602 0.205 0.164 0.264 2.790 8.366
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.025 0.037 0.009 0.189 0.086 0.002 0.069 0.090 0.020 0.017 0.023 0.293 0.859

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.031 0.044 0.011 0.230 0.103 0.002 0.085 0.102 0.024 0.021 0.028 0.350 1.031

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.035 0.049 0.012 0.261 0.116 0.002 0.097 0.112 0.027 0.023 0.032 0.393 1.160

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.046 0.064 0.016 0.341 0.148 0.002 0.128 0.135 0.035 0.030 0.043 0.503 1.49010E

- 5 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.000 0.000 0.000 0.002 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.001 0.007 Operating Cost Savings billion 2012$ 0.002 0.002 0.001 0.012 0.005 0.000 0.005 0.006 0.001 0.001 0.002 0.017 0.053

NPV billion 2012$ 0.001 0.002 0.000 0.010 0.005 0.000 0.004 0.005 0.001 0.001 0.001 0.016 0.046 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.125 0.165 0.041 0.926 0.374 0.004 0.363 0.277 0.094 0.076 0.121 1.285 3.852 3% dr, average million 2012$ 0.411 0.541 0.134 3.043 1.228 0.014 1.193 0.911 0.310 0.248 0.399 4.221 12.655 2.5% dr, average million 2012$ 0.599 0.787 0.196 4.430 1.787 0.021 1.737 1.326 0.451 0.362 0.580 6.144 18.420 3% dr, 95th perc million 2012$ 1.268 1.668 0.415 9.382 3.786 0.045 3.679 2.809 0.956 0.766 1.229 13.013 39.016 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.002 0.002 0.000 0.011 0.005 0.000 0.004 0.003 0.001 0.001 0.001 0.016 0.047 At 2,639 2012$/ton million 2012$ 0.009 0.011 0.003 0.063 0.026 0.000 0.025 0.019 0.006 0.005 0.008 0.088 0.264 At 4,809 2012$/ton million 2012$ 0.016 0.021 0.005 0.116 0.047 0.001 0.045 0.035 0.012 0.009 0.015 0.160 0.480
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.001 0.002 0.001 0.011 0.005 0.000 0.004 0.005 0.001 0.001 0.001 0.017 0.050

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.002 0.003 0.001 0.013 0.006 0.000 0.005 0.006 0.001 0.001 0.002 0.020 0.059

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.002 0.003 0.001 0.015 0.006 0.000 0.005 0.006 0.002 0.001 0.002 0.022 0.065

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.003 0.004 0.001 0.020 0.008 0.000 0.007 0.008 0.002 0.002 0.002 0.029 0.08610E

- 6 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.010 0.009 0.001 0.077 0.030 0.000 0.025 0.018 0.006 0.006 0.007 0.096 0.286 Economic Impacts

Incremental Equipment Cost billion 2012$ 0.032 0.007 0.001 0.105 0.020 0.000 0.050 0.033 0.005 0.004 0.012 0.095 0.364 Operating Cost Savings billion 2012$ 0.089 0.061 0.009 0.499 0.199 0.003 0.240 0.150 0.035 0.039 0.041 0.748 2.114

NPV billion 2012$ 0.057 0.054 0.009 0.394 0.178 0.002 0.190 0.118 0.031 0.034 0.030 0.653 1.751 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.523 0.481 0.074 3.945 1.549 0.016 1.252 0.896 0.282 0.305 0.342 4.904 14.569

NOx kilo

- ton 0.312 0.287 0.044 2.354 0.924 0.009 0.747 0.535 0.168 0.182 0.204 2.926 8.692 Hg ton 0.001 0.001 0.000 0.007 0.003 0.000 0.002 0.002 0.000 0.001 0.001 0.008 0.025
SO 2 kilo

- ton 0.752 0.691 0.106 5.673 2.228 0.023 1.801 1.289 0.405 0.439 0.492 7.052 20.951 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 3.669 3.374 0.518 27.687 10.874 0.111 8.790 6.289 1.977 2.143 2.402 34.421 102.256 3% dr, average million 2012$ 16.600 15.266 2.342 125.261 49.196 0.504 39.767 28.454 8.945 9.693 10.868 155.724 462.620 2.5% dr, average million 2012$ 26.309 24.195 3.712 198.525 77.970 0.799 63.026 45.096 14.176 15.362 17.225 246.805 733.200 3% dr, 95th perc million 2012$ 51.180 47.067 7.221 386.199 151.679 1.554 122.607 87.727 27.578 29.885 33.508 480.120 1426.324 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.061 0.056 0.009 0.463 0.182 0.002 0.147 0.105 0.033 0.036 0.040 0.575 1.710 At 2,639 2012$/ton million 2012$ 0.346 0.318 0.049 2.610 1.025 0.010 0.828 0.593 0.186 0.202 0.226 3.244 9.638 At 4,809 2012$/ton million 2012$ 0.630 0.580 0.089 4.755 1.868 0.019 1.510 1.080 0.340 0.368 0.413 5.912 17.563
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.061 0.057 0.009 0.425 0.190 0.003 0.200 0.125 0.033 0.037 0.032 0.691 1.863

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.074 0.069 0.011 0.522 0.229 0.003 0.231 0.147 0.040 0.044 0.041 0.812 2.223

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.084 0.078 0.012 0.596 0.257 0.003 0.254 0.163 0.045 0.050 0.047 0.903 2.494

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.109 0.101 0.016 0.783 0.331 0.004 0.314 0.206 0.058 0.065 0.064 1.136 3.18710E

- 7 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.002 0.000 0.000 0.006 0.001 0.000 0.003 0.002 0.000 0.000 0.001 0.005 0.021 Operating Cost Savings billion 2012$ 0.005 0.004 0.001 0.029 0.011 0.000 0.014 0.009 0.002 0.002 0.002 0.043 0.121

NPV billion 2012$ 0.003 0.003 0.000 0.023 0.010 0.000 0.011 0.007 0.002 0.002 0.002 0.038 0.101 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.290 0.267 0.041 2.189 0.860 0.009 0.695 0.497 0.156 0.169 0.190 2.722 8.085 3% dr, average million 2012$ 0.953 0.877 0.134 7.193 2.825 0.029 2.284 1.634 0.514 0.557 0.624 8.942 26.565 2.5% dr, average million 2012$ 1.387 1.276 0.196 10.470 4.112 0.042 3.324 2.378 0.748 0.810 0.908 13.016 38.667 3% dr, 95th perc million 2012$ 2.939 2.703 0.415 22.177 8.710 0.089 7.040 5.037 1.584 1.716 1.924 27.570 81.903 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.004 0.003 0.000 0.027 0.010 0.000 0.008 0.006 0.002 0.002 0.002 0.033 0.098 At 2,639 2012$/ton million 2012$ 0.020 0.018 0.003 0.150 0.059 0.001 0.048 0.034 0.011 0.012 0.013 0.186 0.553 At 4,809 2012$/ton million 2012$ 0.036 0.033 0.005 0.273 0.107 0.001 0.087 0.062 0.019 0.021 0.024 0.339 1.008
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.004 0.003 0.001 0.025 0.011 0.000 0.012 0.007 0.002 0.002 0.002 0.040 0.109

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.004 0.004 0.001 0.030 0.013 0.000 0.013 0.008 0.002 0.003 0.002 0.047 0.128

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.005 0.004 0.001 0.033 0.014 0.000 0.014 0.009 0.003 0.003 0.003 0.051 0.140

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.006 0.006 0.001 0.045 0.019 0.000 0.018 0.012 0.003 0.004 0.004 0.065 0.18310E

- 8 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.010 0.014 0.002 0.100 0.046 0.000 0.038 0.022 0.008 0.008 0.007 0.124 0.380 Economic Impacts

Incremental Equipment Cost billion 2012$ 0.032 0.047 0.009 0.488 0.128 0.003 0.263 0.119 0.025 0.027 0.012 0.333 1.483 Operating Cost Savings billion 2012$ 0.089 0.088 0.016 0.651 0.302 0.004 0.325 0.181 0.052 0.044 0.041 0.929 2.722

NPV billion 2012$ 0.057 0.042 0.007 0.163 0.174 0.001 0.062 0.062 0.027 0.018 0.030 0.596 1.238 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.523 0.690 0.122 5.115 2.359 0.024 1.939 1.132 0.409 0.398 0.342 6.318 19.371

NOx kilo

- ton 0.312 0.412 0.073 3.052 1.408 0.014 1.157 0.675 0.244 0.237 0.204 3.769 11.557 Hg ton 0.001 0.001 0.000 0.009 0.004 0.000 0.003 0.002 0.001 0.001 0.001 0.011 0.033
SO 2 kilo

- ton 0.752 0.993 0.175 7.356 3.393 0.034 2.788 1.627 0.589 0.572 0.492 9.085 27.856 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 3.669 4.846 0.854 35.902 16.559 0.167 13.608 7.943 2.872 2.793 2.402 44.343 135.959 3% dr, average million 2012$ 16.600 21.924 3.865 162.427 74.916 0.756 61.563 35.937 12.995 12.636 10.868 200.613 615.099 2.5% dr, average million 2012$ 26.309 34.748 6.125 257.428 118.734 1.198 97.570 56.956 20.596 20.026 17.225 317.948 974.862 3% dr, 95th perc million 2012$ 51.180 67.596 11.915 500.785 230.977 2.331 189.807 110.798 40.066 38.957 33.508 618.518 1896.43
9 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.061 0.081 0.014 0.600 0.277 0.003 0.227 0.133 0.048 0.047 0.040 0.741 2.273 At 2,639 2012$/ton million 2012$ 0.346 0.457 0.081 3.384 1.561 0.016 1.283 0.749 0.271 0.263 0.226 4.179 12.815 At 4,809 2012$/ton million 2012$ 0.630 0.832 0.147 6.166 2.844 0.029 2.337 1.364 0.493 0.480 0.413 7.616 23.351
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.061 0.047 0.008 0.202 0.192 0.001 0.077 0.070 0.030 0.021 0.032 0.644 1.387

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.074 0.064 0.011 0.329 0.250 0.002 0.125 0.098 0.040 0.031 0.041 0.801 1.866

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.084 0.077 0.013 0.424 0.294 0.002 0.161 0.119 0.048 0.038 0.047 0.918 2.226

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.109 0.110 0.019 0.667 0.406 0.003 0.254 0.173 0.067 0.057 0.064 1.219 3.14810E

- 9 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.002 0.003 0.000 0.028 0.007 0.000 0.015 0.007 0.001 0.002 0.001 0.019 0.085 Operating Cost Savings billion 2012$ 0.005 0.005 0.001 0.037 0.017 0.000 0.019 0.010 0.003 0.003 0.002 0.053 0.156

NPV billion 2012$ 0.003 0.002 0.000 0.009 0.010 0.000 0.004 0.004 0.002 0.001 0.002 0.034 0.071 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.290 0.383 0.068 2.839 1.309 0.013 1.076 0.628 0.227 0.221 0.190 3.506 10.750 3% dr, average million 2012$ 0.953 1.259 0.222 9.327 4.302 0.043 3.535 2.064 0.746 0.726 0.624 11.520 35.321 2.5% dr, average million 2012$ 1.387 1.833 0.323 13.576 6.262 0.063 5.146 3.004 1.086 1.056 0.908 16.768 51.412 3% dr, 95th perc million 2012$ 2.939 3.882 0.684 28.756 13.263 0.134 10.899 6.362 2.301 2.237 1.924 35.517 108.899 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.004 0.005 0.001 0.034 0.016 0.000 0.013 0.008 0.003 0.003 0.002 0.043 0.131 At 2,639 2012$/ton million 2012$ 0.020 0.026 0.005 0.194 0.090 0.001 0.074 0.043 0.016 0.015 0.013 0.240 0.736 At 4,809 2012$/ton million 2012$ 0.036 0.048 0.008 0.354 0.163 0.002 0.134 0.078 0.028 0.028 0.024 0.437 1.341
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.004 0.003 0.000 0.012 0.011 0.000 0.005 0.004 0.002 0.001 0.002 0.038 0.083

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.004 0.004 0.001 0.019 0.014 0.000 0.007 0.006 0.002 0.002 0.002 0.046 0.107

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.005 0.004 0.001 0.023 0.016 0.000 0.009 0.007 0.003 0.002 0.003 0.051 0.123

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.006 0.006 0.001 0.038 0.023 0.000 0.015 0.010 0.004 0.003 0.004 0.070 0.18110E

- 10 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.013 0.015 0.003 0.107 0.048 0.000 0.038 0.022 0.012 0.008 0.012 0.124 0.401
Economic Impacts

Incremental Equipment Cost billion 2012$ 0.097 0.048 0.009 0.501 0.135 0.003 0.263 0.119 0.087 0.027 0.147 0.333 1.770 Operating Cost Savings billion 2012$ 0.107 0.095 0.017 0.691 0.314 0.004 0.325 0.181 0.059 0.044 0.033 0.929 2.801

NPV billion 2012$ 0.010 0.047 0.008 0.190 0.179 0.001 0.062 0.062

- 0.028 0.018

- 0.114 0.596 1.032 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.660 0.746 0.134 5.430 2.455 0.024 1.939 1.132 0.601 0.403 0.592 6.318 20.433

NOx kilo

- ton 0.394 0.445 0.080 3.239 1.464 0.014 1.157 0.675 0.359 0.240 0.353 3.769 12.190 Hg ton 0.001 0.001 0.000 0.009 0.004 0.000 0.003 0.002 0.001 0.001 0.001 0.011 0.035
SO 2 kilo

- ton 0.949 1.073 0.193 7.808 3.530 0.035 2.788 1.627 0.865 0.579 0.851 9.085 29.383 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 4.634 5.236 0.940 38.109 17.228 0.170 13.608 7.943 4.220 2.826 4.154 44.343 143.411 3% dr, average million 2012$ 20.966 23.689 4.252 172.412 77.944 0.768 61.563 35.937 19.093 12.785 18.792 200.613 648.812

2.5% dr, average million 2012$ 33.229 37.544 6.739 273.253 123.532 1.217 97.570 56.956 30.260 20.263 29.783 317.948 1028.29
3

3% dr, 95th perc million 2012$ 64.641 73.037 13.109 531.570 240.312 2.368 189.807 110.798 58.866 39.419 57.937 618.518 2000.38
1 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.077 0.088 0.016 0.637 0.288 0.003 0.227 0.133 0.071 0.047 0.069 0.741 2.398 At 2,639 2012$/ton million 2012$ 0.437 0.494 0.089 3.592 1.624 0.016 1.283 0.749 0.398 0.266 0.391 4.179 13.517 At 4,809 2012$/ton million 2012$ 0.796 0.899 0.161 6.545 2.959 0.029 2.337 1.364 0.725 0.485 0.713 7.616 24.631
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.015 0.053 0.009 0.232 0.198 0.001 0.077 0.070

- 0.023 0.021

- 0.109 0.644 1.189

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.031 0.071 0.012 0.366 0.259 0.002 0.125 0.098

- 0.008 0.031

- 0.094 0.801 1.694

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.043 0.085 0.015 0.467 0.304 0.002 0.161 0.119 0.003 0.038

- 0.083 0.918 2.074

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.075 0.121 0.021 0.725 0.421 0.003 0.254 0.173 0.032 0.058

- 0.055 1.219 3.04610E

- 11 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.006 0.003 0.001 0.029 0.008 0.000 0.015 0.007 0.005 0.002 0.008 0.019 0.102 Operating Cost Savings billion 2012$ 0.006 0.005 0.001 0.040 0.018 0.000 0.019 0.010 0.003 0.003 0.002 0.053 0.161

NPV billion 2012$ 0.001 0.003 0.000 0.011 0.010 0.000 0.004 0.004

- 0.002 0.001

- 0.007 0.034 0.059 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.366 0.414 0.074 3.013 1.362 0.013 1.076 0.628 0.334 0.223 0.328 3.506 11.340 3% dr, average million 2012$ 1.204 1.360 0.244 9.900 4.476 0.044 3.535 2.064 1.096 0.734 1.079 11.520 37.256 2.5% dr, average million 2012$ 1.752 1.980 0.355 14.411 6.515 0.064 5.146 3.004 1.596 1.069 1.571 16.768 54.230 3% dr, 95th perc million 2012$ 3.712 4.194 0.753 30.524 13.799 0.136 10.899 6.362 3.380 2.264 3.327 35.517 114.867 NOx (3% dr)

At 468 2012$/ton million 2012$ 0.004 0.005 0.001 0.037 0.017 0.000 0.013 0.008 0.004 0.003 0.004 0.043 0.138 At 2,639 2012$/ton million 2012$ 0.025 0.028 0.005 0.206 0.093 0.001 0.074 0.043 0.023 0.015 0.022 0.240 0.776 At 4,809 2012$/ton million 2012$ 0.046 0.052 0.009 0.376 0.170 0.002 0.134 0.078 0.042 0.028 0.041 0.437 1.414
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.001 0.003 0.001 0.014 0.012 0.000 0.005 0.004

- 0.001 0.001

- 0.006 0.038 0.071

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.002 0.004 0.001 0.021 0.015 0.000 0.007 0.006 0.000 0.002

- 0.005 0.046 0.097

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.002 0.005 0.001 0.026 0.017 0.000 0.009 0.007 0.000 0.002

- 0.005 0.051 0.114

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.004 0.007 0.001 0.042 0.024 0.000 0.015 0.010 0.002 0.003

- 0.003 0.070 0.17510E

- 12 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.002 0.006 0.001 0.017 0.013 0.000 0.002 0.002 0.002 0.001 0.001 0.025 0.073 Economic Impacts

Incremental Equipment Cost billion 2012$ 0.001 0.002 0.000 0.008 0.002 0.000 0.001 0.001 0.001 0.000 0.001 0.005 0.024 Operating Cost Savings billion 2012$ 0.007 0.017 0.004 0.051 0.040 0.001 0.005 0.006 0.005 0.005 0.004 0.076 0.221

NPV billion 2012$ 0.006 0.016 0.004 0.043 0.038 0.001 0.004 0.004 0.004 0.004 0.004 0.070 0.198 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.123 0.297 0.074 0.867 0.673 0.004 0.088 0.097 0.085 0.076 0.073 1.267 3.723

NOx kilo

- ton 0.073 0.177 0.044 0.517 0.402 0.002 0.053 0.058 0.051 0.046 0.043 0.756 2.221 Hg ton 0.000 0.001 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.006
SO 2 kilo

- ton 0.177 0.427 0.106 1.247 0.968 0.005 0.127 0.139 0.122 0.110 0.105 1.821 5.354 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.863 2.082 0.518 6.085 4.727 0.026 0.618 0.677 0.597 0.536 0.512 8.890 26.130 3% dr, average million 2012$ 3.906 9.419 2.342 27.530 21.383 0.118 2.798 3.064 2.700 2.423 2.314 40.219 118.217 2.5% dr, average million 2012$ 6.190 14.929 3.712 43.632 33.890 0.186 4.434 4.856 4.280 3.841 3.668 63.743 187.361 3% dr, 95th perc million 2012$ 12.042 29.041 7.221 84.879 65.928 0.363 8.626 9.447 8.325 7.471 7.135 124.002 364.481 NOx ( 7 % dr)

At 468 2012$/ton million 2012$ 0.005 0.011 0.003 0.032 0.025 0.000 0.003 0.004 0.003 0.003 0.003 0.047 0.138 At 2,639 2012$/ton million 2012$ 0.026 0.062 0.015 0.181 0.140 0.001 0.018 0.020 0.018 0.016 0.015 0.264 0.775 At 4,809 2012$/ton million 2012$ 0.047 0.113 0.028 0.329 0.256 0.001 0.033 0.037 0.032 0.029 0.028 0.481 1.413
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.007 0.018 0.004 0.049 0.042 0.001 0.005 0.005 0.005 0.005 0.004 0.080 0.224

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.010 0.025 0.006 0.071 0.059 0.001 0.007 0.007 0.007 0.007 0.006 0.111 0.317

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.012 0.031 0.008 0.087 0.072 0.001 0.009 0.009 0.009 0.008 0.007 0.134 0.386

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.018 0.045 0.011 0.128 0.104 0.001 0.013 0.014 0.013 0.012 0.011 0.195 0.56310E

- 13 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.003 Operating Cost Savings billion 2012$ 0.001 0.002 0.000 0.005 0.004 0.000 0.001 0.001 0.001 0.000 0.000 0.008 0.023

NPV billion 2012$ 0.001 0.002 0.000 0.005 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.021 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.068 0.165 0.041 0.481 0.374 0.002 0.049 0.054 0.047 0.042 0.040 0.703 2.066 3% dr, average million 2012$ 0.224 0.541 0.134 1.581 1.228 0.007 0.161 0.176 0.155 0.139 0.133 2.310 6.788 2.5% dr, average million 2012$ 0.326 0.787 0.196 2.301 1.787 0.010 0.234 0.256 0.226 0.203 0.193 3.362 9.881 3% dr, 95th perc million 2012$ 0.692 1.668 0.415 4.874 3.786 0.021 0.495 0.542 0.478 0.429 0.410 7.121 20.929 NOx (7 % dr)

At 468 2012$/ton million 2012$ 0.000 0.001 0.000 0.003 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.005 0.015 At 2,639 2012$/ton million 2012$ 0.003 0.007 0.002 0.019 0.015 0.000 0.002 0.002 0.002 0.002 0.002 0.028 0.082 At 4,809 2012$/ton million 2012$ 0.005 0.012 0.003 0.035 0.027 0.000 0.004 0.004 0.003 0.003 0.003 0.051 0.149
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.001 0.002 0.000 0.005 0.004 0.000 0.000 0.001 0.000 0.000 0.000 0.008 0.023

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.001 0.002 0.001 0.006 0.005 0.000 0.001 0.001 0.001 0.001 0.001 0.010 0.028

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.001 0.002 0.001 0.007 0.006 0.000 0.001 0.001 0.001 0.001 0.001 0.011 0.031

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.001 0.003 0.001 0.009 0.008 0.000 0.001 0.001 0.001 0.001 0.001 0.015 0.04210E

- 14 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.004 0.006 0.001 0.033 0.013 0.000 0.013 0.010 0.003 0.003 0.004 0.045 0.136 Economic Impacts

Incremental Equipment Cost billion 2012$ 0.003 0.002 0.000 0.020 0.002 0.000 0.009 0.008 0.001 0.001 0.003 0.011 0.060 Operating Cost Savings billion 2012$ 0.013 0.017 0.004 0.100 0.040 0.001 0.038 0.047 0.010 0.008 0.013 0.138 0.428

NPV billion 2012$ 0.011 0.016 0.004 0.080 0.038 0.001 0.029 0.039 0.009 0.007 0.009 0.127 0.368 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.226 0.297 0.074 1.669 0.673 0.008 0.655 0.500 0.170 0.136 0.219 2.315 6.940

NOx kilo

- ton 0.135 0.177 0.044 0.996 0.402 0.005 0.390 0.298 0.101 0.081 0.130 1.381 4.141 Hg ton 0.000 0.001 0.000 0.003 0.001 0.000 0.001 0.001 0.000 0.000 0.000 0.004 0.012
SO 2 kilo

- ton 0.324 0.427 0.106 2.400 0.968 0.011 0.941 0.719 0.245 0.196 0.314 3.329 9.980 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 1.583 2.082 0.518 11.714 4.727 0.056 4.594 3.507 1.194 0.956 1.535 16.247 48.711 3% dr, average million 2012$ 7.161 9.419 2.342 52.995 21.383 0.252 20.783 15.868 5.401 4.327 6.942 73.502 220.376 2.5% dr, average million 2012$ 11.349 14.929 3.712 83.991 33.890 0.399 32.938 25.150 8.559 6.858 11.003 116.492 349.271 3% dr, 95th perc million 2012$ 22.078 29.041 7.221 163.392 65.928 0.777 64.076 48.924 16.651 13.342 21.405 226.617 679.451 NOx ( 7 % dr)

At 468 2012$/ton million 2012$ 0.008 0.011 0.003 0.062 0.025 0.000 0.024 0.018 0.006 0.005 0.008 0.086 0.256 At 2,639 2012$/ton million 2012$ 0.047 0.062 0.015 0.348 0.140 0.002 0.136 0.104 0.035 0.028 0.046 0.482 1.445 At 4,809 2012$/ton million 2012$ 0.086 0.113 0.028 0.633 0.256 0.003 0.248 0.190 0.065 0.052 0.083 0.878 2.634
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.012 0.018 0.004 0.092 0.042 0.001 0.033 0.042 0.010 0.008 0.011 0.144 0.418

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.018 0.025 0.006 0.133 0.059 0.001 0.049 0.055 0.014 0.012 0.016 0.201 0.590

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.022 0.031 0.008 0.164 0.072 0.001 0.062 0.064 0.017 0.014 0.020 0.244 0.719

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.033 0.045 0.011 0.244 0.104 0.002 0.093 0.088 0.025 0.021 0.031 0.354 1.04910E

- 15 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.000 0.000 0.000 0.002 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.001 0.006 Operating Cost Savings billion 2012$ 0.001 0.002 0.000 0.011 0.004 0.000 0.004 0.005 0.001 0.001 0.001 0.015 0.045

NPV billion 2012$ 0.001 0.002 0.000 0.008 0.004 0.000 0.003 0.004 0.001 0.001 0.001 0.013 0.039 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.125 0.165 0.041 0.926 0.374 0.004 0.363 0.277 0.094 0.076 0.121 1.285 3.852 3% dr, average million 2012$ 0.411 0.541 0.134 3.043 1.228 0.014 1.193 0.911 0.310 0.248 0.399 4.221 12.655 2.5% dr, average million 2012$ 0.599 0.787 0.196 4.430 1.787 0.021 1.737 1.326 0.451 0.362 0.580 6.144 18.420 3% dr, 95th perc million 2012$ 1.268 1.668 0.415 9.382 3.786 0.045 3.679 2.809 0.956 0.766 1.229 13.013 39.016 NOx (7 % dr)

At 468 2012$/ton million 2012$ 0.001 0.001 0.000 0.007 0.003 0.000 0.003 0.002 0.001 0.001 0.001 0.009 0.027 At 2,639 2012$/ton million 2012$ 0.005 0.007 0.002 0.037 0.015 0.000 0.014 0.011 0.004 0.003 0.005 0.051 0.153 At 4,809 2012$/ton million 2012$ 0.009 0.012 0.003 0.067 0.027 0.000 0.026 0.020 0.007 0.005 0.009 0.093 0.278
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.001 0.002 0.000 0.009 0.004 0.000 0.003 0.004 0.001 0.001 0.001 0.015 0.043

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.002 0.002 0.001 0.012 0.005 0.000 0.004 0.005 0.001 0.001 0.001 0.018 0.052

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.002 0.002 0.001 0.013 0.006 0.000 0.005 0.005 0.001 0.001 0.002 0.020 0.057

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.002 0.003 0.001 0.018 0.008 0.000 0.007 0.007 0.002 0.002 0.002 0.026 0.07810E

- 16 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.010 0.009 0.001 0.077 0.030 0.000 0.025 0.018 0.006 0.006 0.007 0.096 0.286
Economic Impacts

Incremental Equipment Cost billion 2012$ 0.017 0.004 0.000 0.055 0.011 0.000 0.026 0.017 0.003 0.002 0.006 0.050 0.191 Operating Cost Savings billion 2012$ 0.041 0.028 0.004 0.233 0.093 0.001 0.111 0.070 0.016 0.018 0.019 0.347 0.982

NPV billion 2012$ 0.025 0.025 0.004 0.177 0.082 0.001 0.085 0.052 0.014 0.016 0.013 0.297 0.791 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.523 0.481 0.074 3.945 1.549 0.016 1.252 0.896 0.282 0.305 0.342 4.904 14.569

NOx kilo

- ton 0.312 0.287 0.044 2.354 0.924 0.009 0.747 0.535 0.168 0.182 0.204 2.926 8.692 Hg ton 0.001 0.001 0.000 0.007 0.003 0.000 0.002 0.002 0.000 0.001 0.001 0.008 0.025
SO 2 kilo

- ton 0.752 0.691 0.106 5.673 2.228 0.023 1.801 1.289 0.405 0.439 0.492 7.052 20.951 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 3.669 3.374 0.518 27.687 10.874 0.111 8.790 6.289 1.977 2.143 2.402 34.421 102.256 3% dr, average million 2012$ 16.600 15.266 2.342 125.261 49.196 0.504 39.767 28.454 8.945 9.693 10.868 155.724 462.620 2.5% dr, average million 2012$ 26.309 24.195 3.712 198.525 77.970 0.799 63.026 45.096 14.176 15.362 17.225 246.805 733.200 3% dr, 95th perc million 2012$ 51.180 47.067 7.221 386.199 151.679 1.554 122.607 87.727 27.578 29.885 33.508 480.120 1426.324 NOx ( 7 % dr)

At 468 2012$/ton million 2012$ 0.019 0.018 0.003 0.146 0.057 0.001 0.046 0.033 0.010 0.011 0.013 0.181 0.538 At 2,639 2012$/ton million 2012$ 0.109 0.100 0.015 0.822 0.323 0.003 0.261 0.187 0.059 0.064 0.071 1.021 3.034 At 4,809 2012$/ton million 2012$ 0.198 0.182 0.028 1.497 0.588 0.006 0.475 0.340 0.107 0.116 0.130 1.861 5.529
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.028 0.028 0.004 0.206 0.093 0.001 0.094 0.059 0.016 0.018 0.016 0.333 0.896

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.041 0.040 0.006 0.304 0.131 0.002 0.125 0.081 0.023 0.026 0.024 0.454 1.257

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.051 0.049 0.008 0.377 0.160 0.002 0.148 0.098 0.028 0.031 0.030 0.545 1.527

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.076 0.072 0.011 0.564 0.234 0.003 0.208 0.140 0.042 0.046 0.047 0.778 2.22010E

- 17 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.002 0.000 0.000 0.006 0.001 0.000 0.003 0.002 0.000 0.000 0.001 0.005 0.020 Operating Cost Savings billion 2012$ 0.004 0.003 0.000 0.025 0.010 0.000 0.012 0.007 0.002 0.002 0.002 0.037 0.104

NPV billion 2012$ 0.003 0.003 0.000 0.019 0.009 0.000 0.009 0.006 0.001 0.002 0.001 0.031 0.084 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.290 0.267 0.041 2.189 0.860 0.009 0.695 0.497 0.156 0.169 0.190 2.722 8.085 3% dr, average million 2012$ 0.953 0.877 0.134 7.193 2.825 0.029 2.284 1.634 0.514 0.557 0.624 8.942 26.565 2.5% dr, average million 2012$ 1.387 1.276 0.196 10.470 4.112 0.042 3.324 2.378 0.748 0.810 0.908 13.016 38.667 3% dr, 95th perc million 2012$ 2.939 2.703 0.415 22.177 8.710 0.089 7.040 5.037 1.584 1.716 1.924 27.570 81.903 NOx ( 7 % dr)

At 468 2012$/ton million 2012$ 0.002 0.002 0.000 0.015 0.006 0.000 0.005 0.003 0.001 0.001 0.001 0.019 0.057 At 2,639 2012$/ton million 2012$ 0.012 0.011 0.002 0.087 0.034 0.000 0.028 0.020 0.006 0.007 0.008 0.108 0.321 At 4,809 2012$/ton million 2012$ 0.021 0.019 0.003 0.158 0.062 0.001 0.050 0.036 0.011 0.012 0.014 0.197 0.584
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.003 0.003 0.000 0.021 0.010 0.000 0.010 0.006 0.002 0.002 0.002 0.034 0.092

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.004 0.003 0.001 0.026 0.012 0.000 0.011 0.007 0.002 0.002 0.002 0.040 0.110

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.004 0.004 0.001 0.029 0.013 0.000 0.012 0.008 0.002 0.002 0.002 0.045 0.123

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.006 0.005 0.001 0.041 0.017 0.000 0.016 0.011 0.003 0.003 0.003 0.059 0.16610E

- 18 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.010 0.014 0.002 0.100 0.046 0.000 0.038 0.022 0.008 0.008 0.007 0.124 0.380
Economic Impacts

Incremental Equipment Cost billion 2012$ 0.017 0.025 0.005 0.257 0.067 0.002 0.138 0.063 0.013 0.014 0.006 0.175 0.781 Operating Cost Savings billion 2012$ 0.041 0.041 0.007 0.303 0.141 0.002 0.151 0.084 0.024 0.021 0.019 0.432 1.266

NPV billion 2012$ 0.025 0.017 0.003 0.046 0.073 0.000 0.012 0.021 0.011 0.007 0.013 0.256 0.484 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.523 0.690 0.122 5.115 2.359 0.024 1.939 1.132 0.409 0.398 0.342 6.318 19.371

NOx kilo

- ton 0.312 0.412 0.073 3.052 1.408 0.014 1.157 0.675 0.244 0.237 0.204 3.769 11.557 Hg ton 0.001 0.001 0.000 0.009 0.004 0.000 0.003 0.002 0.001 0.001 0.001 0.011 0.033
SO 2 kilo

- ton 0.752 0.993 0.175 7.356 3.393 0.034 2.788 1.627 0.589 0.572 0.492 9.085 27.856 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 3.669 4.846 0.854 35.902 16.559 0.167 13.608 7.943 2.872 2.793 2.402 44.343 135.959 3% dr, average million 2012$ 16.600 21.924 3.865 162.427 74.916 0.756 61.563 35.937 12.995 12.636 10.868 200.613 615.099 2.5% dr, average million 2012$ 26.309 34.748 6.125 257.428 118.734 1.198 97.570 56.956 20.596 20.026 17.225 317.948 974.862 3% dr, 95th perc million 2012$ 51.180 67.596 11.915 500.785 230.977 2.331 189.807 110.798 40.066 38.957 33.508 618.518 1896.439 NOx ( 7 % dr)

At 468 2012$/ton million 2012$ 0.019 0.026 0.004 0.189 0.087 0.001 0.072 0.042 0.015 0.015 0.013 0.233 0.716 At 2,639 2012$/ton million 2012$ 0.109 0.144 0.025 1.065 0.491 0.005 0.404 0.236 0.085 0.083 0.071 1.316 4.034 At 4,809 2012$/ton million 2012$ 0.198 0.262 0.046 1.941 0.895 0.009 0.736 0.429 0.155 0.151 0.130 2.398 7.351
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.028 0.022 0.004 0.083 0.090 0.000 0.026 0.029 0.014 0.009 0.016 0.302 0.624

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.041 0.039 0.007 0.210 0.149 0.001 0.074 0.057 0.024 0.019 0.024 0.458 1.103

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.051 0.051 0.009 0.305 0.193 0.002 0.110 0.078 0.032 0.027 0.030 0.576 1.463

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.076 0.084 0.015 0.548 0.305 0.003 0.202 0.132 0.051 0.046 0.047 0.876 2.38510E

- 19 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.002 0.003 0.000 0.027 0.007 0.000 0.015 0.007 0.001 0.001 0.001 0.019 0.083 Operating Cost Savings billion 2012$ 0.004 0.004 0.001 0.032 0.015 0.000 0.016 0.009 0.003 0.002 0.002 0.046 0.134

NPV billion 2012$ 0.003 0.002 0.000 0.005 0.008 0.000 0.001 0.002 0.001 0.001 0.001 0.027 0.051 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.290 0.383 0.068 2.839 1.309 0.013 1.076 0.628 0.227 0.221 0.190 3.506 10.750 3% dr, average million 2012$ 0.953 1.259 0.222 9.327 4.302 0.043 3.535 2.064 0.746 0.726 0.624 11.520 35.321 2.5% dr, average million 2012$ 1.387 1.833 0.323 13.576 6.262 0.063 5.146 3.004 1.086 1.056 0.908 16.768 51.412 3% dr, 95th perc million 2012$ 2.939 3.882 0.684 28.756 13.263 0.134 10.899 6.362 2.301 2.237 1.924 35.517 108.899 NOx ( 7 % dr)

At 468 2012$/ton million 2012$ 0.002 0.003 0.000 0.020 0.009 0.000 0.008 0.004 0.002 0.002 0.001 0.025 0.076 At 2,639 2012$/ton million 2012$ 0.012 0.015 0.003 0.113 0.052 0.001 0.043 0.025 0.009 0.009 0.008 0.139 0.426 At 4,809 2012$/ton million 2012$ 0.021 0.028 0.005 0.205 0.095 0.001 0.078 0.045 0.016 0.016 0.014 0.253 0.777
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.003 0.002 0.000 0.008 0.009 0.000 0.002 0.003 0.001 0.001 0.002 0.031 0.062

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.004 0.003 0.001 0.014 0.012 0.000 0.005 0.004 0.002 0.001 0.002 0.039 0.087

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.004 0.004 0.001 0.019 0.014 0.000 0.006 0.005 0.002 0.002 0.002 0.044 0.103

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.006 0.006 0.001 0.034 0.021 0.000 0.012 0.009 0.003 0.003 0.003 0.063 0.16010E

- 20 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Cumulative Results Energy Savings

Full

- Fuel Cycle quads 0.013 0.015 0.003 0.107 0.048 0.000 0.038 0.022 0.012 0.008 0.012 0.124 0.401 Economic Impacts

Incremental Equipment Cost billion 2012$ 0.051 0.025 0.005 0.264 0.071 0.002 0.138 0.063 0.046 0.014 0.077 0.175 0.932 Operating Cost Savings billion 2012$ 0.050 0.045 0.008 0.322 0.146 0.002 0.151 0.084 0.028 0.021 0.015 0.432 1.302

NPV billion 2012$

- 0.002 0.019 0.003 0.058 0.075 0.000 0.012 0.021

- 0.018 0.007

- 0.062 0.256 0.370 Emissions Savings (physical)

Full

- Fuel Cycle

CO 2 million metric

ton 0.660 0.746 0.134 5.430 2.455 0.024 1.939 1.132 0.601 0.403 0.592 6.318 20.433

NOx kilo

- ton 0.394 0.445 0.080 3.239 1.464 0.014 1.157 0.675 0.359 0.240 0.353 3.769 12.190 Hg ton 0.001 0.001 0.000 0.009 0.004 0.000 0.003 0.002 0.001 0.001 0.001 0.011 0.035
SO 2 kilo

- ton 0.949 1.073 0.193 7.808 3.530 0.035 2.788 1.627 0.865 0.579 0.851 9.085 29.383 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 4.634 5.236 0.940 38.109 17.228 0.170 13.608 7.943 4.220 2.826 4.154 44.343 143.411 3% dr, average million 2012$ 20.966 23.689 4.252 172.412 77.944 0.768 61.563 35.937 19.093 12.785 18.792 200.613 648.812 2.5% dr, average million 2012$ 33.229 37.544 6.739 273.253 123.532 1.217 97.570 56.956 30.260 20.263 29.783 317.948 1028.29
3 3% dr, 95th perc million 2012$ 64.641 73.037 13.109 531.570 240.312 2.368 189.807 110.798 58.866 39.419 57.937 618.518 2000.38
1
NOx (7 % dr)

At 468 2012$/ton million 2012$ 0.024 0.028 0.005 0.201 0.091 0.001 0.072 0.042 0.022 0.015 0.022 0.233 0.755 At 2,639 2012$/ton million 2012$ 0.138 0.155 0.028 1.131 0.511 0.005 0.404 0.236 0.125 0.084 0.123 1.316 4.255 At 4,809 2012$/ton million 2012$ 0.251 0.283 0.051 2.061 0.932 0.009 0.736 0.429 0.228 0.153 0.225 2.398 7.754
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.003 0.025 0.004 0.098 0.093 0.000 0.026 0.029

- 0.014 0.010

- 0.058 0.302 0.518

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.020 0.043 0.007 0.232 0.154 0.001 0.074 0.057 0.001 0.020

- 0.043 0.458 1.023

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.032 0.057 0.010 0.333 0.199 0.002 0.110 0.078 0.012 0.027

- 0.032 0.576 1.403

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.063 0.092 0.016 0.591 0.316 0.003 0.202 0.132 0.041 0.046

- 0.004 0.876 2.37510E

- 21 

### 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 SCU

W

Large

B SCU

A

Small

B SCU

A

Large

B IMH

A

Small

C IMH

A

Large

C SCU

A

Small

C IMH

A

Large

B Total

Annualized Results Economic Impacts Incremental Equipment Cost billion 2012$ 0.005 0.003 0.001 0.028 0.007 0.000 0.015 0.007 0.005 0.001 0.008 0.019 0.098 Operating Cost Savings billion 2012$ 0.005 0.005 0.001 0.034 0.015 0.000 0.016 0.009 0.003 0.002 0.002 0.046 0.138

NPV billion 2012$ 0.000 0.002 0.000 0.006 0.008 0.000 0.001 0.002

- 0.002 0.001

- 0.007 0.027 0.039 Emissions Savings (monetized)

Full

- Fuel Cycle

CO 2 (global)

5% dr, average million 2012$ 0.366 0.414 0.074 3.013 1.362 0.013 1.076 0.628 0.334 0.223 0.328 3.506 11.340 3% dr, average million 2012$ 1.204 1.360 0.244 9.900 4.476 0.044 3.535 2.064 1.096 0.734 1.079 11.520 37.256 2.5% dr, average million 2012$ 1.752 1.980 0.355 14.411 6.515 0.064 5.146 3.004 1.596 1.069 1.571 16.768 54.230 3% dr, 95th perc million 2012$ 3.712 4.194 0.753 30.524 13.799 0.136 10.899 6.362 3.380 2.264 3.327 35.517 114.867
NOx (7 % dr)

At 468 2012$/ton million 2012$ 0.003 0.003 0.001 0.021 0.010 0.000 0.008 0.004 0.002 0.002 0.002 0.025 0.080 At 2,639 2012$/ton million 2012$ 0.015 0.016 0.003 0.119 0.054 0.001 0.043 0.025 0.013 0.009 0.013 0.139 0.449 At 4,809 2012$/ton million 2012$ 0.026 0.030 0.005 0.218 0.098 0.001 0.078 0.045 0.024 0.016 0.024 0.253 0.819
NPV
Consumer & Emissions Value
Consumers + CO 2 (1s t) + NOx (Med ) billion 2012$ 0.000 0.002 0.000 0.009 0.009 0.000 0.002 0.003

- 0.002 0.001

- 0.006 0.031 0.051

Consumers + CO 2 (2nd) + NOx (Med) billion 2012$ 0.001 0.003 0.001 0.016 0.012 0.000 0.005 0.004

- 0.001 0.001

- 0.005 0.039 0.077

Consumers + CO 2 (3rd) + NOx (Med) billion 2012$ 0.002 0.004 0.001 0.021 0.015 0.000 0.006 0.005 0.000 0.002

- 0.005 0.044 0.094

Consumers + CO 2 (4th) + NOx (Med ) billion 2012$ 0.004 0.006 0.001 0.037 0.022 0.000 0.012 0.009 0.001 0.003

- 0.003 0.063 0.15412A

- i 

### APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

12A.1 Manufacturer Impact Analysis Interview Guide

................................ ......................... 12A

- 112A

- 1 

### 12A.1 MANUFACTURER IMPACT ANALYSIS INTERVIEW G UIDE

The Department of Energy (DOE) conducts the manufacturer impact analysis (MIA) as part of the rulemaking process for amended energy conservation standards for automatic commercial ice makers. In this analysis, DOE uses publicly available information and in formation provided by manufacturers during interviews to assess possible impacts on manufacturers due to amended energy conservation standards.

DOE is requesting information on the product classes in Table 1.1.

Table 1. 1 Automatic Commercial Ice Makers Product Classes

Equipment Type Type of Cooling Harvest Capacity
Rate (lb/24 hours) Type of Ice
Maker Equipment Class
Nomenclature

Ice

- Making Head (IMH) Water ≥50 and <500 Batch IMH

- W

- Small

- B ≥500 and <1,436 Batch IMH

- W

- Medium

- B ≥1,436 and ≤4,000 Batch IMH

- W

- Large

- B

Air ≥50 and <450 Batch IMH

- A

- Small

- B ≥450 and ≤4,000 Batch IMH

- A

- Large

- B Remote Condensing, but not remote compressor (RCU

NRC) Air ≥50 and <1,000 Batch RCU

- NRC

- Small

- B

≥1,000 and ≤4,000 Batch RCU

- NRC

- Large

- B
Remote Condensing and remote compressor (RCU

RC) Air ≥50 and <934 Batch RCU

- RC

- Small

- B

≥934 and ≤4,000 Batch RCU

- RC

- Large

- B

Self

- Contained Unit (SCU) Water ≥50 and <200 Batch SCU

- W

- Small

- B ≥200 and ≤4,000 Batch SCU

- W

- Large

- B

Air ≥50 and <175 Batch SCU

- A

- Small

- B ≥175 and ≤4,000 Batch SCU

- A

- Large

- B

Ice

- Making Head (IMH) Water ≥50 and <1000 Continuous IMH

- W

- Small

- C ≥1,000 and ≤4,000 Continuous IMH

- W

- Large

- C

Air ≥50 and <1000 Continuous IMH

- A

- Small

- C ≥1,000 and ≤4,000 Continuous IMH

- A

- Large

- C Remote Condensing, but not remote compressor (RCU

NRC) Air ≥50 and <1000 Continuous RCU

- NRC

- Small

- C

≥1,000 and ≤4,000 Continuous RCU

- NRC

- Large

- C

Remote Condensing and remote compressor (RCU

RC) Air ≥50 and <1000 Continuous RCU

- RC

- Small

- C

≥1,000 and ≤4,000 Continuous RCU

- RC

- Large

- C

Self

- Contained Unit (SCU) Water ≥50 and <175 Continuous SCU

- W

- Small

- C
≥175 and ≤4,000 Continuous SCU

- W

- Large

- C

Air ≥50 and <175 Continuous SCU

- A

- Small

- C ≥175 and ≤4,000 Continuous SCU

- A

- Large

- C12A

- 2 

### 1 KEY ISSUES

1.1 In general, what are the key issues for your company regarding amended energy conservation standards for automatic commercial ice makers and this rulemaking?

1.2 Are any of the issues more or less significant for specific product classes?

1.3 Do any of the issues become more significant at higher efficiency levels?

1.4 Do you have any suggestions for incorporating any of these issues into the into DOE’s manufacturing i mpact model?12A

- 3 

### 2 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

DOE is interested in understanding the impact to manufacturers at the business unit level directly related to the production of automatic commercial ice makers.

2.1 What percentage of your automatic commercial ice maker manufacturing corresponds to each product class, both in terms of revenue and shipments? Please indicate if you do not manufacture products in any given product class, or if you purchase products from other
manufacturer s for resale under your own brand name (i.e. OEM), and whether the factory that supplies those products is located in the United States.

Table Error! No text of specified style in document.

. 2 Batch Commercial Ice Makers

- Revenue and Shipments by Product Class

Equipment Class 2010 Revenue 2010 Shipments % Private Label % Made in U.S.

IMH

- W

IMH

- A

RCU

- NRC

RCU

- RC

SCU

- W

SCU

- A



### Table Error! No text of specified style in document. .2 Continuous Commercial Ice Makers

- Revenue and Shipments by Product Class

Equipment Class 2010 Revenue 2010 Shipments % Private Label % Made in U.S.

IMH

- W

IMH

- A

RCU

- NRC

RCU

- RC

SCU

- W

SCU

- A



### 2.2 What is your company’s approximate market share in both the batch and continuous
automatic commercial ice maker market?12A

- 4 

### 3 MARKUPS AND PROFITABILITY

One of the primary objectives of the MIA is to assess the impact of amended energy
conservation standards on indust ry profitability. DOE would like to understand the current markup structure of the industry and how amended energy conservation standards would impact your company’s markup structure and profitability. Please consider the following definitions when prepa ring your response.

The Manufacturer Production Cost (MPC) is the sum of all materials, labor, overhead, and depreciation directly associated with manufacturing a product.

The Manufacturer Selling Price (MSP) is the price manufacturers charge their fi rst customers. This does not include additional costs incurred along the distribution channels and as such does not reflect the manufacturer suggested retail price (MSRP).

The Manufacturer Markup is the ratio of MSP to MPC. It covers non

- production cos ts such as SG&A and R&D, as well as profit, but does not reflect the profit margin.

DOE estimated a baseline markup of 1.27 for automatic commercial ice makers.

3.1 Baseline Markup

• Is the 1.27 baseline markup representative of an average industry markup?

• Please comment on the baseline markups DOE calculated as compared to your company’s baseline markup for each of the product classes listed in Table 3.1. If your response would vary by machine capacity within a given product class, please specify t he differences you expect and the range of product capacities to which this different response would apply.

Table 3. 3 Baseline Manufacturer Markups by Product Class

Product Classes

- Batch Machines Adjusted Markup Product Classes

- Continuous Machines Adjusted Markup

IMH

- W IMH

- W

IMH

- A IMH

- A

RCU

- NRC RCU

- NRC

RCU

- RC RCU

- RC

SCU

- W SCU

- W

SCU

- A SCU

- A



### • How are markups and margins determined in this industry? How would standards potentially impact the pricing or margin structure?12A

- 5 

### 3.2 Incremental Markups

• One of the possible scenarios DOE uses to model impacts on industry profitability is the impact of commoditization of premium products. Because the market disruption caused by standards can alter the pricing of premium products, DOE is interested in understanding if efficiency is a feature that earns a premium. Within each product class, does markup vary by efficiency level? If yes, please provide information about the markup at higher efficiencies.

• What factors or product attributes besides efficiency affect the markup of commercial ice makers within a product class?

• Does your markup change with se lected design options? Is the markup on incremental costs for more efficient designs different than the markup on the baseline models?

• What is the structure of your distribution channel and how does that influence your markup? Does that change between pr oduct classes or across the industry?

• Would you expect changes in your estimated profitability following an amended energy conservation standard? If so, please explain why. Can you suggest any scenarios that would model these expected changes?12A

- 6 

### 4 FINANCIAL PARAMETERS

Using publicly available data, DOE’s contractor has developed a discounted cash flow model of the automatic commercial ice maker manufacturing industry. However, publicly available information may not depict the cost or the financial performance of this industry as accurately as an aggregation of data provided by its constituents. This section attempts to define the financial parameters for this industry and how your company’s financial situation may differ from the aggregate picture.

4.1 Estimated Industry Average Financial Parameters

• Please compare the financial parameters for your company to those listed in Table 4.1.

Table Error! No text of specified style in document.

. 4 Estimated Industry Average Financial
Parameters

GRIM Input Definition Industry
Estimated Value (%) Your Actual (%)

Income Tax
Rate Corporate effective income tax paid (percentage of earnings before taxes, EBT) 33.6%

Discount

Rate Weighted average cost of capital (inflation

- adjusted) 9.2%

Working Capital Current assets less current liabilities (percentage of revenues) 9.3%

Net PPE Net plant property and equipment (percentage of revenues) 16.4%

SG&A Selling, general, and administrative expenses (percentage of revenues) 15.4%

R&D Research and development expenses (percentage of revenues) 1.5%

Depreciation Amortization of fixed assets (percentage of revenues) 2.1%

Capital Expenditures Outlay of cash to acquire or improve capital assets (percentage of revenues, not including acquisition or sale of business units) 1.5%

Cost of
Goods Sold Includes material, labor, overhead, and depreciation (percentage of revenues) 79.0%



### • Do any of the financial parameters in Table Error ! No text of specified style in
document.

. 4 change based on product class or for a particular subgroup of manufacturers ? Please describe any differences.12A

- 7 

### 5 CONVERSION COSTS

Energy conservation standards may cause your company to incur capital and product conversion costs to redesign existing products and make changes or upgrades to production lines in order to comply with the energy conservation standard. Depending on the magnitude, the conversion costs can have a substantial impact on the outputs used by DOE to evaluate the industry impacts and the economic viability of a particular standard level. Understanding the nature and magnitude of conversion costs is a critical portion of the MIA. In this analysis, DOE considers two types of conversion costs.

Capital conversion costs (CCCs) are one

- time investments in plant, property, and equipment (PPE) necessitated by an energy conservation standard. These may be incremental changes to existing PPE or the replacement of existing PPE. Inc luded are expenditures on buildings, equipment, and tooling.

Product conversion costs (PCCs) include the related research, product development, testing, marketing and other non

- capital costs necessary to bring products into compliance with an amende d energy conservation standard.

DOE is interested in understanding the nature and magnitude of conversion costs necessary to bring all products into compliance at each efficiency level. For each table in this section, the columns indicate each efficiency level as a reduction in energy consumption below that of a baseline unit. For batch type commercial ice makers, please consider the baseline to be the current DOE energy conservation standard. For continuous commercial ice makers, please see Chapter 5, Section 5.4.2.2 of the Preliminary Analysis Technical Support Document for baseline product specifications.

5.1 Capital Conversion Costs

• Please provide estimates for the capital conversion costs required to meet each improvement in efficiency as shown in Table 5.1 and Table 5.2. Where applicable, please provide information as to the nature of these expenditures, specifying the changes to bring products into compliance, the quantity and cost of n ew production equipment and where proposed levels would require a complete platform change. If your response to question 5.1 would vary by machine capacity within a given product class, please specify the differences you expect and the range of product cap acities to which this different response would apply.12A

- 8 

### Table 5. 5 Capital Conversion Costs

– Batch Machines

% Reduction in Energy Consumption Below Baseline (Current DOE Energy Conservation Standard)

Designation 10% 15% 20% 25% 30% Max. Tech

IMH

- W

IMH

- A

RCU

- NRC

RCU

- RC

SCU

- W

SCU

- A



### Table 5.2 Capital Conversion Costs

– Continuous Machines

% Reduction in Energy Consumption Below Baseline (See Chapter 5, Section 5.4.2.2 of the Preliminary Analysis TSD)

Designation 10% 15% 20% 25% 30% Max. Tech

IMH

- W

IMH

- A

RCU

- NRC

RCU

- RC

SCU

- W

SCU

- A



### • Are your answers to question 5.1 indicative of the changes required of all of your production lines and factories?

• Are there certain efficiency levels where the capital conversion costs increase significantly over the previous efficiency levels? Would your answer change for different product classes? Please describe these changes qualitatively.

• For each of the efficiency levels put forth, which design options would require only minor changes to production lines, major changes to production lines, substantial12A

- 9 

### modifications to existing facilities, or the development of entirely new facilities?

5.2 Product Conversion Costs

• Considering such expenses as product development, prototyping, testing, certification, and marketing, p lease provide estimates for the product conversion costs required to meet each efficiency level in Table 5.3 and Table 5.4. Where appli cable, please provide information as to the nature of these expenditures. If your response to question 5.2 would vary by machine capacity within a given product class, please specify the differences you expect and the range of product capacities to which this different response would apply.

Table 5.3 Product Conversion Costs

– Batch Machines

% Reduction in Energy Consumption Below Baseline (Current DOE Energy Conservation Standard)

Designation 10% 15% 20% 25% 30% Max. Tech

IMH

- W

IMH

- A

RCU

- NRC

RCU

- RC

SCU

- W

SCU

- A



### Table 5.4 Product Conversion Costs

– Continuous Machines

% Reduction in Energy Consumption Below Current Baseline (See Chapter 5, Section 5.4.2.2 of the Preliminary Analysis TSD)

Designation 10% 15% 20% 25% 30% Max. Tech

IMH

- W

IMH

- A

RCU

- NRC

RCU

- RC

SCU

- W

SCU

- A12A

- 10 

### • Are there certain efficiency levels where the product conversion costs increase significantly over the previous efficiency levels? Please describe these changes.

• Are there certain efficiency levels that would require relatively minor changes to existing products? For each of the product classes shown, which efficiency level changes could be made within existing platforms and which would result in major product redesigns?

• Please provide additional qualitative information to help DOE understand the types a nd nature of your investments, including the plant and tooling changes and the product development effort required at different efficiency levels.12A

- 11 

### 6 CUMULATIVE REGULATOR Y BURDEN

In assessing the impact to industry, DOE seeks to understand the cumulative regulatory burden facing manufacturers. Cumulative regulatory burden refers to the financial burden that stems from overlapping effects of new or revised DOE standards and/or other regulatory actions affecting the same product or industry. In this regard , DOE will consider other regulations (beyond efficiency) such as materials regulations and building codes that will adversely impact
the cost to either the manufacturer or consumer.

6.1 Have you made any R&D expenditures to comply with the automatic com mercial ice maker energy conservation standards as amended by the Energy Policy Act of 2005, which became effective in January 2010? What R&D and product testing expenses were required to make your products compliant? Did you incur any capital expenses to make your products comply? Will any of these changes be coordinated with the changes required by this rulemaking?

6.2 Below is a list of the other relevant regulations that could affect manufacturers of automatic commercial ice makers. Please provide any comments on the listed regulations and provide an estimate for your expected compliance cost.

Table 6. 6 Other Regulations Identified by DOE

Regulation Estimated or Actual Effective Date(s) Comments Expected Expense
for Compliance

Residential Refrigerators
and Freezers 2014

Commercial Refrigeration Equipment 2015

Walk

- In Coolers and
Walk

- In Freezers 2015

Miscellaneous Residential Refrigeration Products 2017

Cooking Products 2020



### 6.3 Are there any other recent or impending regulations that manufacturers of automatic commercial ice makers face (from DOE or otherwise)? If so, please identify the regulation, the corresponding effective dates, and your expected compliance cost.

6.4 Unde r what circumstances would you be able to coordinate any expenditure related to these other regulations with an amended energy conservation standard?12A

- 12 

### 7 DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of amended energy conservation standards on employment is an important consideration in the rulemaking process. This section of the interview guide seeks to explore current trends in manufacturing employment and to solicit manufacturer views on how domestic employment patterns might be affected by amended ener gy conservation standards.

7.1 Manufacturing Facilities

• Where are your manufacturing facilities that produce automatic commercial ice makers for the United States market? What types of products are manufactured at each location? Please provide annual shi pment figures for your company’s manufacturing at each location by product class in Table 7.1. Please also provide employment levels at each of
these facilities.

Table 7. 7 Manufacturing Facilities

Facility Location Product Types Manufactured Employees Annual Shipments
Example Brunswick, ME IMH

- A, IMH

- W 125 8,500

1

2

3

4

5



### 7.2 Impact to Employment

• Would your domestic employment levels be expected to change significantly under amended energy conservation standards? If so, please explain how they would change if higher efficiency levels are required.

• Would the workforce skills necessary under amended energy conservation standards require extensive retraining or replacement of employees at your manufacturing
facilities?

• Would amended energy conservation standards require extensive retraining of your service/field technicians? If so, could you expand on how your service infrastructure would be impacted in general as a result of amended energy co nservation standards?12A

- 13 

### 8 MANUFACTURING CAPACITY AND NON

- US SALES

• How would amended energy conservation standards impact your company’s manufacturing capacity?

• For any design changes that would require new production equipment, please describe
how much downtime would be required. What impact would downtime have on your business? Are there any design changes that could not be implemented before the anticipated compliance date of the final rule for certain product classes?

• What percentage of your company’ s automatic commercial ice maker sales is made
within the United States?

• What percentage of your automatic commercial ice makers is produced in the United
States?

• What percentage of your U.S. production of automatic commercial ice makers is exported ?

• Are there any foreign companies with North American production facilities?

• Would amended energy conservation standards impact your domestic manufacturing or sourcing decisions? Is there an efficiency level that would cause you to move exiting
domestic production facilities abroad?

9 IMPACT ON COMPETITIO N

Amended energy conservation standards can alter the competitive dynamics of the market. This can include prompting companies to enter or exit the market, or to merge. DOE and the Department of Justice are both interested in any potential reduction in competition that would result from an amended energy conservation standard.

• How would amended energy conservation standards affect your ability to compete in the marketplace? Would the effects on your comp any be different than others in the industry? Would you expect your market share to change following amended standards?

• Do any firms hold intellectual property that gives them a competitive advantage following amended energy conservation standards?

• How would industry competition change as a result of amended energy conservation
standards?12A

- 14 

### 10 IMPACTS ON SMALL BUS INESS

The Small Business Administration (SBA) denotes a small business in the automatic commercial
ice maker manufacturing industry as having less than 500 total employees, including the parent company and all subsidiaries. a



### 10.1 By this definition, is your company considered a small business?

10.2 Are there any reasons that a small business manufacturer might be at a disadvantage
relative to a larger business under amended energy conservation standards? Please consider such factors as technical expertise, access to capital, bulk purchasing power for materials/components, engineering resources, and any other relevant issues.

10.3 To your knowled ge, are there any small businesses for which the adoption of amended energy conservation standards would have a particularly severe impact? If so, why?

10.4 To your knowledge, are there any niche manufacturers or component manufacturers for which the adop tion of amended energy conservation standards would have a particularly severe impact? If so, why?

a 

### DOE uses the small business size standards amended January 1, 2012, and effective January 7,
2013 , as amended, by the SBA to determine whether a comp any is a small business. To be categorized as a small business, a manufacturer of “ Air

- Conditioning and Warm Air Heating Equipment and Commercial and Industrial Refrigeration Equipment ” (NAICS #333415, which includes automatic commercial ice maker manufac turers ) and its affiliates may employ a maximum of 750 employees. The 750 employee threshold includes all employees in a business’s parent company and any other subsidiaries.12B

- 1 

### APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL

TABLE OF CONTENTS

12B.1 INTRODUCTION AND PURPOSE

................................ ................................ ........... 12B

- 2
12B.2 MODEL DESCRIPTION

................................ ................................ ............................ 12B

- 2
12B.3 MODEL INDUSTRY INCOME STATEMENT, CASH FLOW STATEMENT, AND
DISCOUNTED CASH FLOW CALCULATION

................................ ....................... 12B

- 512B

- 2 

### APPENDIX 12 B . GOVERNMENT REGULATORY IMPACT MODEL

12B.1 INTRODUCTION AND PUR POSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the impacts of energy conservation standards and othe r regulations on manufacturers. The basic mode of analysis is to estimate the change in value of the industry or manufacturers(s) following a regulation or a series of regulations

. The model structure also allows an analysis of multiple equipment classes w ith regulations taking effect over a period of time, and of multiple regulations on the same equipment

.
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 va lue

. 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 (TSLs).

Output from the model con sists of summary financial metrics, graphs of major variables, and, when appropriate, access to the complete cash flow calculation.

12B.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses manufacturer selling pri ces, manufacturing costs, a shipments forecast, and financial parameters as inputs and accepts a set of regulatory conditions as changes in costs and investments

. The cash flow analysis is separated into two major blocks : income and cash flow

. The income calculation determines net operating profit after taxes

. The cash flow calculation converts net operating profit after taxes into an annual cash flow by including investment and non

- cash items

. Below are definitions of listed items on the printout of the o utput sheet (see s ection 12B.3 ). (1) Revenues includes a nnual revenues computed by multiplying equipment unit price at each efficiency level by the appropriate manufacturer markup

. (2) Material s inclu d es t he portion of cost of goods sold ( COGS ) that includes

mater ials

. (3) Labor includes the portion of COGS that includes direct labor, commissions, dismissal pay, bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor up

- time

. (4) Depreciation includes a nnual depreciation computed as a perc entage of COGS

. While typically included in overhead, the depreciation is shown as a separate line
item

. (5) Overhead includes t he portion of COGS that includes indirect labor, indirect material, energy use, maintenance, property taxes, and insurance related t o assets

. While typically included in overhead, the depreciation is shown as a separate line
item

. (6) Standard SG&A includes s elling, general, and administrative costs computed as a percentage of Revenues ( 1 )

. (7) R&D includes ordinary research and development (R &D) as a percentage of
Revenues ( 1 )

.12B

- 3 

### (8) Product Conversion Costs includes one

- time investments in research, development, testing, and marketing focused on making equipment designs comply with the amended energy conservation standard

. GRIM allocates these

cost s over the period between the standard’s announcement and effective dates

. (9) Stranded Assets includes a onetime write

- off of stranded assets i n the year the
standard becomes effective

. (10) Earnings Before Interest and Taxes (EBIT) includes profits before deduct ions for interest paid and taxes

. (11) EBIT as a Percentage of Revenues includes EBIT as a percentage of revenues ; this
is used to compare with the industry’s average re ported in financial statements

. (12) Taxes are calculated by multiplying the tax rate contained in Financial s by EBIT (1 0 )

. (13) Net Operating Profits After Taxes (NOPAT) are c omputed by subtracting COGS ( ( 2 ) to ( 5 ) )

, SG&A ( 6 )

, R&D ( 7 )

, Product Conversion Cos t ( 8 ) , and Taxe s (1 2 )
from Revenue s ( 1 )

. (14) NOPAT repeated is NOPAT (13)

, repeated in the Cash Flow Statement

. (15) Depreciation repeated is Depreciation (4)

, added back in to the Statement of Cash
Flows as a non

- cash expense

. (16) Loss on the Disposal of Stranded Assets is the r esidual un

- depreciated value of
stranded assets

, added back i n to the Statement of Cash Flows as a non

- cash

expense

. (17) Change in Working Capital includes c hange in cash tied up in accounts receivable, inventory, and other cash investments necessary to support operations is calculated by multiplying working capital as a percentage of R evenues (1) by the change in
annual revenues

. (18) Cash Flow From Operations is c alculated by taking NOPA T (1 4 ) , adding back

non

- cash items such as a Depreciation (1 5 ), Loss on the Disposal of Stranded
Assets (16) and subtracting the Change in Working Capital (17)

. (19) Ordinary Capital Expenditures includes o rdinary investments in property, plant, and equipment to maintain and replace existing production assets, computed as a percentage of Revenues ( 1 )

. (20) Capital Conversion Costs are one

- time investme nts in property, plant, and equipment to adapt or change existing production facilities so that new product designs can be fabricated and assembled under the new regulation

. (21) Free Cash Flow includes a nnual cash flow from operations and investments; computed by subtracting Ordinary Capital Expenditures (19) and Capital
Conversion Cost s (2 0 ) from Cash Flow from Operation s (18)

. (22) Free Cash Flow repeated includes the a nnual cash flow from operations and
investments repeated in the Discounted Cash Flow (25) calcul ations

. (23) Terminal Value is an e stimate of the continuing value of the industry after 204 7

,
c omputed by growing the Free Cash Flow (22) i n the year 204 7 at a constant rate in perpetuity

. (24) Present Value Factor is the factor used to calculate an estimate of the present value of an amount to be received in the future

.12B

- 4 

### (25) Discounted Cash Flow is Free Cash Flow (22) multiplied by the Present Value Factor (24)

. For 204 7 the discounted cash flow includes the discounted Terminal Value (23)

. (26) INPV at TSL is t he sum of Disc ounted Cash Flows (25).12B

- 5 

### 12B.3 MODEL INDUSTRY INCOM E STATEMENT, CASH FL OW STATEMENT, AND DI SCOUNTED CASH FLOW
CALCULATION

Base Yr Ancmt Yr Std Yr Industry Income Statement 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Revenues 285.0 $ 306.0 $ 311.1 $ 320.6 $ 331.9 $ 339.3 $ 353.6 $ 335.3 $ 312.8 $ 301.6 $ 305.9 $ 323.3 $ 350.5 $ 368.1 $

- Materials 177.6 $ 190.7 $ 193.9 $ 199.7 $ 206.8 $ 211.4 $ 223.6 $ 212.0 $ 197.7 $ 190.7 $ 193.4 $ 204.4 $ 221.6 $ 232.8 $

- Labor 13.9 $ 14.9 $ 15.2 $ 15.6 $ 16.2 $ 16.5 $ 16.4 $ 15.5 $ 14.5 $ 14.0 $ 14.2 $ 15.0 $ 16.2 $ 17.0 $ - Depreciation 6.0 $ 6.4 $ 6.5 $ 6.7 $ 7.0 $ 7.1 $ 7.4 $ 7.0 $ 6.6 $ 6.3 $ 6.4 $ 6.8 $ 7.4 $ 7.7 $
- Overhead 30.5 $ 32.8 $ 33.3 $ 34.4 $ 35.6 $ 36.4 $ 35.5 $ 33.7 $ 31.4 $ 30.3 $ 30.7 $ 32.5 $ 35.2 $ 37.0 $
- Standard SG&A 43.9 $ 47.1 $ 47.9 $ 49.4 $ 51.1 $ 52.3 $ 54.5 $ 51.6 $ 48.2 $ 46.5 $ 47.1 $ 49.8 $ 54.0 $ 56.7 $

- R&D 4.3 $ 4.6 $ 4.7 $ 4.8 $ 5.0 $ 5.1 $ 5.3 $ 5.0 $ 4.7 $ 4.5 $ 4.6 $ 4.8 $ 5.3 $ 5.5 $
- Product Conversion Costs

- $

- $

- $ 7.5 $ 13.1 $ 16.9 $ 0.8 $

- $

- $

- $

- $

- $

- $

- $
- Stranded Assets

- $

- $

- $

- $

- $

- $ 2.4 $

- $

- $

- $

- $

- $

- $

- $ Earnings Before Interest and Taxes (EBIT) 8.8 $ 9.5 $ 9.6 $ 2.4 $ (2.9) $ (6.4) $ 7.8 $ 10.4 $ 9.7 $ 9.4 $ 9.5 $ 10.0 $ 10.9 $ 11.4 $
EBIT/Revenues 3.1% 3.1% 3.1% 0.8% -0.9% -1.9% 2.2% 3.1% 3.1% 3.1% 3.1% 3.1% 3.1% 3.1%
- Taxes 3.0 $ 3.2 $ 3.2 $ 0.8 $

- $

- $ 2.6 $ 3.5 $ 3.3 $ 3.1 $ 3.2 $ 3.4 $ 3.7 $ 3.8 $ Net Operating Profit after Taxes (NOPAT) 5.9 $ 6.3 $ 6.4 $ 1.6 $ (2.9) $ (6.4) $ 5.2 $ 6.9 $ 6.4 $ 6.2 $ 6.3 $ 6.7 $ 7.2 $ 7.6 $

Cash Flow Statement
NOPAT 5.9 $ 6.3 $ 6.4 $ 1.6 $ (2.9) $ (6.4) $ 5.2 $ 6.9 $ 6.4 $ 6.2 $ 6.3 $ 6.7 $ 7.2 $ 7.6 $ + Depreciation 6.0 $ 6.4 $ 6.5 $ 6.7 $ 7.0 $ 7.1 $ 7.4 $ 7.0 $ 6.6 $ 6.3 $ 6.4 $ 6.8 $ 7.4 $ 7.7 $ + Loss on Disposal of Stranded Assets

- $

- $

- $

- $

- $

- $ 2.4 $

- $

- $

- $

- $

- $

- $

- $ - Change in Working Capital

- $ 1.9 $ 0.5 $ 0.9 $ 1.1 $ 0.7 $ 1.3 $ (1.7) $ (2.1) $ (1.0) $ 0.4 $ 1.6 $ 2.5 $ 1.6 $ Cash Flows from Operations 11.9 $ 10.8 $ 12.5 $ 7.5 $ 3.1 $ 0.1 $ 13.7 $ 15.6 $ 15.1 $ 13.6 $ 12.3 $ 11.8 $ 12.0 $ 13.7 $ - Ordinary Capital Expenditures 4.3 $ 4.6 $ 4.7 $ 4.8 $ 5.0 $ 5.1 $ 5.3 $ 5.0 $ 4.7 $ 4.5 $ 4.6 $ 4.8 $ 5.3 $ 5.5 $ - Capital Conversion Costs

- $

- $

- $ 0.8 $ 1.4 $ 1.8 $

- $

- $

- $

- $

- $

- $

- $

- $
Free Cash Flow 7.6 $ 6.2 $ 7.8 $ 1.9 $ (3.3) $ (6.8) $ 8.4 $ 10.6 $ 10.4 $ 9.1 $ 7.7 $ 7.0 $ 6.8 $ 8.1 $

Discounted Cash Flow
Free Cash Flow 7.6 $ 6.2 $ 7.8 $ 1.9 $ (3.3) $ (6.8) $ 8.4 $ 10.6 $ 10.4 $ 9.1 $ 7.7 $ 7.0 $ 6.8 $ 8.1 $

Terminal Value

- $

- $

- $

- $

- $

- $

- $

- $

- $

- $

- $

- $

- $

- $

Present Value Factor 0.000 1.000 0.916 0.839 0.768 0.703 0.644 0.590 0.540 0.495 0.453 0.415 0.380 0.348
Discounted Cash Flow

- $ 6.2 $ 7.1 $ 1.6 $ (2.5) $ (4.8) $ 5.4 $ 6.3 $ 5.6 $ 4.5 $ 3.5 $ 2.9 $ 2.6 $ 2.8 $

INPV at TSL 3 80.4 $

### 14A

- i 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

- ii 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

- 5114A

- 1 

### 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.14A

- 2 

### In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC 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.

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

- 3 

### Despite the serious limits of both quantification and monetization, SCC estimates can be
useful in estim ating 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 intended regulation justify its costs.” The purpose of the SCC estimates presented here is to make it possible for agencies to incorporate the social benefits from reducing CO 2 

### emissions into cost

benefit analyses of regulatory actions that have small or “marginal” impacts on cumula tive global emissions. Most F ederal regulatory actions can be expected to have marginal impacts on global emissions.

For such policies, the benefits from reduced (or costs from increased) emissions in any future year can be estimated by multiplying the change in emissions in that year by the SCC value appropriate for that year. The net present value of the benefits can then be calculated by multiplying each of these future benefits by an appropriate discount factor and summing across all affected years. This approach assumes that the marginal damages from increased emissions are constant for small departures from the baseline emissions path, an approximation that is reasonable for policies that have effects on emissions that are small relative to cumulati ve 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 ans wer 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 par ticipated in the interagency process include the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, Transportation, and Treasury. This process was convened by the Council of Economic Advisers and the Office of Management and Budget, with active participation and regular input from the Council on Environmental Quality, National Economic Council, Office of Energy and Climate Change, and Office of Science and Technology Policy. The main objective of this process was to
devel op 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 ru lemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $4.7, $21.4, $35.1, and $64.9 ( 2007$ ). The first three estimates are based on the average SCC across models and socio

- 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 us e the SCC value for the 95 th 

### percentile at a 3 percent discount rate. The central value is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the im portance 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 fro m 2010 to 2050.14A

- 4 

### It is important to emphasize that the interagency process is committed to updating these estimates as the science and economic understanding of climate change and its impacts on society improves over time. Specifically, we have set a prelim inary goal of revisiting 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
raised in this document and c onsider public comments as part of the ongoing interagency

process.

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY
ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to
estimate the benefits associated with reducing CO 2 

### emissions. In the final model year 2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of $2 per ton of CO 2 

### and a “global” SCC value of $33 per ton of CO 2 

### for 2007 emission reductions (2007 $ ), increasing bot h 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 m eant to reflect the value of damages worldwide.

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO 2 

### (in 2006 dollars) for 2011 emission reductions (with a range of $0

- $14 for sensitivity analysis), also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a domestic SCC range of $0 to $20 per ton CO 2 

### for 2007 emissio n reductions (2007 $ ). In addition, 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 rat es of approximately 2 percent and 3 percent, respectively ( 2006$ for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing CO 2 

### emissions. To ensure consistency in how benefits are evaluated across agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemaking process, to quantify avoided climate change damages from reduced CO 2 

### emissions. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the existing literature to use as interim values until a more comprehensive analysis could be conducted.

The outcome of the preliminary assessment by the interagency gr oup 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 values were derived by adjusting the published estimates for uncertainty in the discount rate (using factors developed by N ewell 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 increme ntal damages over time as the magnitude of climate change increases.14A

- 5 

### These interim values represent the first sustained interagency effort within the U.S. government to develop an SCC for use in regulatory analysis. The results of this preliminary
effort w ere presented in several proposed and final rules and were offered for public comment in connection with proposed rules, including the joint EPA

- DOT fuel economy and CO 2 

### tailpipe emission proposed rules.

14A.4 APPROACH AND KEY ASSUMPTIONS

Since the 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 reflect increasing knowledge of the science and economics of climate impacts, as well as improvements in modeling. In this context, statements recognizing the limitations of the analysis and calling for further research take on exceptional significance. T he 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) co mmonly 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 in mind their different limitations (discussed below).

These models are useful because they combine climate processes, economic growth, and feedbacks between the climate and the global economy into a single modeling framework. At the same time, they gain this advantage at the expense of a more detailed representation of the

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

- 6 

### 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 advantages of this approach). Other IAMs may better reflect the complexity of the science in their modeling frameworks but do not link physical impacts to economic damages. There is currently a limited amount of research linking climate impacts to economi c 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 e conomic 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 emi ssions 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, and concentrations are translated into warming based on each model’s simplified representation of the climate and a key parameter, climate sensitivity. Each model uses a different approach to translate warming into damages. Finally, transforming the strea m of economic damages over time into a single value requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result in changes in economic damages. In PAGE, for example, the consumption

- equivalent damages in each period are calculated as a fraction of GDP, depending on the temperature in that period relative to the pre

- industrial average temperature in each region. In FUND, damages in each period also depend on the rate of temperature ch ange 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 consistent e xploration of the three models while respecting the different approaches to quantifying damages taken by the key modelers in the field. An extensive review of the literature was conducted to select three sets of input parameters for these models: climate s ensitivity, 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

- econom ic 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 constan ts; in PAGE, most parameters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically.14A

- 7 

### The sensitivity of the results to other aspects of the models ( e.g. , the carbon cycle or damage funct ion) 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 mo del based on a global production function with an extra stock variable (atmospheric CO 2 

### concentrations). Emission reductions are treated as analogous to investment in “natural capital.” By investing in natural capital today through
reductions in emissions

— implying reduced consumption

— harmful effects of 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 l atter 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 exp ected 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 vulner able 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. Th e 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 impac ts 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 f unction. 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 impacts on forestry, water systems, construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these sectors (Nordhaus and Boyer, 2000; Warren et al

. , 2006). Costs of resettlement due to sea level rise are incorporated into damage estimates, but their magnitude is not clearly reported. Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective adaptation, and largely ignores adaptation costs. ”14A

- 8 

### Note that the damage function in DICE has a somewhat diff erent 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 FUND and PAGE, so damages in any given year do not propagate forward. c



### 14A.4.1.2 The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into economic, non

- economic, and catastrophic categories and calculates these impacts sep arately 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 t he damage 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 DIC E, PAGE2002 models these events probabilistically. The probability of a “discontinuity” ( i.e. , a catastrophic event) is assumed to increase with temperature above a specified threshold. The threshold temperature, the rate at which the probability of experi encing 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 l evel (2 °C for developed countries and 0 °C for developing countries for economic impacts, and 0 °C for all regions for non

- economic impacts), but adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond
the tolerable 2 °C increase and that developing countries can eventually eliminate 50 percent of their economic impacts. All regions are assumed to be able to mitigate 25 percent of the non

economic impacts through adaptation (Hope 2006).

14A.4.1.3 The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water,

energ y (based on heating and cooling demand), sea level rise (based on the value of land lost and the cost of protection), ecosystems, human health (diarrhea, vector

- borne diseases, and cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a different functional form, and is calculated separately for sixteen 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. Specifical ly, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it consistent with EMF GDP trajectories (see next section): we as sumed a fixed rate of savings of 20%, and we re

calibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly matched the EMF scenarios.14A

- 9 

### 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 ra te 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 climate scenarios, biodiversity loss, and effects on economic development and political violence. With

regar d 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 r elatively quickly, and if they did, the costs could be substantial. Only a few studies of climate change have examined
these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen in the agriculture and sea leve l 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
du e 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 negat ive 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 te mperatures, and sufficiently slow increases in temperature across these sectors can result in negative economic damages from climate change.

14A.4.1.4 Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of how to represent the effects of climate change (represented by the increase in global

- 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 temperature.

d In the deterministic version of FUND , the majority of damages are attributable to increased air conditioning demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in those regions at low to moderate levels of warming (Warren et al . 2 006).14A

- 10 

### 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 emissions par ameters 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

- 11 

### 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 es timated by DICE are roughly equal to the 95 th 

### percentile estimated by PAGE. This is significant because at higher discount rates we expect that a greater proportion of the SCC value is due to damages in years with lower temperature increases. For example, when the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to damages that occur in years when the temperature is less than or equal to 3 °C. This increases to approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions

— in particular, how the models incorporate adaptation, technological change, and catastrophic damages. Gaps in the literature make modifying these aspects of 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 G lobal 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 perm issible; the relevant statutory provisions are usually ambiguous and allow
selection of either measure. f



### 14A.4.2.1 Global SCC

Under current OMB guidance contained in Circular A

- 4, analysis of economically significant proposed and final regulations from the domestic perspective is required, while analysis from the international perspective is optional. However, the climate change problem is highly unusual in at least two respects. First, it involves a global externality: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in the United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages caused by GHG emissions. Second, climate change presents a problem th at the United States alone cannot solve. Even if the United States were to reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid substantial climate change. Other countries would also need to take action to reduce emissio ns 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

f It is true tha t 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 federal law and hence does not intrude on such interests.14A

- 12 

### considerations are taken as a whole, the interagency group concluded that a global measure of the benefits from reducing U.S. emissions is preferable.

When quantifying the damages associated with a change in emissions, a number of analysts ( e.g. , Anthoff et al . 2009a) employ “equity weighting” to aggregate changes in consumption across regions. This weighting takes into account the relative reductions in wealth in different regions of the world. A per

- capita loss of $500 in GDP, for instance, is weighted more heavily in a country with a per

- capita GDP of $2,000 than in one with a per

- capita GDP of $40,000. The main argument for this approach is that a los s of $500 in a poor country causes a greater reduction in utility or welfare than does the same loss in a wealthy nation. Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group concluded that this approach would not be 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 appropriate approach.

14A.4.2.2 Domestic SCC

As an empirical matte r, 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 model. The resulting estimates suggest that the rati o of domestic to global benefits of emission reductions varies with key parameter assumptions. For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7

– 10 percent of the global benefit, on average, across the scenarios analyzed. Alte rnatively, 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 int eragency workgroup determined that a range of values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic effects. Reported domestic values should use this range. It is recognized that these values are approximate, provisional , and highly speculative. There is no a priori reason why domestic benefits should be a constant fraction of net global damages over time. Further, FUND does not account for how damages in other regions could affect the United States ( e.g. , global migratio n, 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 p revalent 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 cos ts, 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

— for both the costs and benefits of emissions reductions

— is appropriate when considering the utility or welfare effects of international action, the interagency group concluded that it should not be used in developing an SCC for use in regulatory policy at this time.

h Based on 2008 GDP (in current US dollar s) from the World Bank Development Indicators Report

.14A

- 13 

### hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these

gases is commonly discussed in terms of their 100

- year global warming potential (GWP). GWP measures the ability of different gases to trap heat in the atmosphere ( i.e. , radiative forcing per unit of mass) over a particular timeframe relative to CO 2 

### . However, bec ause these gases differ in both radiative forcing and atmospheric lifetimes, their relative damages are not constant over time. For example, because methane has a short lifetime, its impacts occur primarily in the near
term and thus are not discounted as h eavily as those caused by longer

- lived gases. Impacts other than temperature change also vary across gases in ways that are not captured by GWP. For instance, CO 2 

### emissions, unlike methane and other greenhouse gases, contribute to ocean
acidification. Like wise, damages from methane emissions are not offset by the positive effect of
CO 2 

### fertilization. Thus, transforming gases into CO 2 

### - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accurate estimates of the
social costs of non

- CO 2 

### gases.

In light of these limitations, and the significant contributions of non

- CO 2 

### emissions to climate change, further research is required to link non

- CO 2 

### emissions to economic impacts. Such work would feed into efforts to develop a monetized value of reductions in non

- CO 2 

### greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the interagency group hopes to develop methods to value these other greenhouse gases. The goal is to develop these estimates by the time we issue revised SCC estimates for CO 2 

### emissions.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and
FUND models. i 

### It is defined as the long

- term increase in the annual global

- average surface temperature from a doubling of atmospheric CO 2 

### concentration relative to pre

- industrial levels (or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties in this important parameter have received substantial attention in the peer

- reviewed literature.

The most aut horitative statement about equilibrium climate sensitivity appears in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including
observed climate change and the strength of known feedbacks simulated in [global climate models], we conclude that the global mean equilibrium warming for doubling CO 2 

### , or ‘equilibrium climate sensitivity,’ is likely to lie in the range 2 °C to 4.5 °C, with a most likely value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. j



### For fundamental physical reasons as well as data limitations, values substantially 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 term (up to 100

– 200 years), but it does not include long

- term feedback effects due to possible large

- scale changes in ice sheets or the biosphere, which occur on a time scale of many hundreds to thousands of years ( e.g. , Hansen et al . 2007).
j This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of “likely” as greater than 66 percent probability (Le Treut et al

. 2007). “Very likely” indicates a greater than
90 percent probability.14A

- 14 

### worse for those high values than for values in the 2 °C to 4.5 °C ra nge. (Meehl et al . 2007,

p

. 799)

After 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 a nd Baker (2007), log

- normal, gamma, and Weibull.
Table 14A.4

. 1 gives summary statistics for the four calibrated distributions.

Table 14A.4

. 1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Rank Roe & Baker Log

- 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 tha t 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 bas ed 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 proportio nal to the change in surface temperature; and (3) uncertainties in feedback factors are normally distributed. There is widespread agreement on the first point and the second and third points are common assumptions.

k Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC report is not specific on this point. For the distributions we considered, the median was between the mode and the mean. For the Roe and Baker distribution, setting the median equal to 3 °C, rather than the mode or mean, gave a
95 th percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode equal to 3 °C produced 95 th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the range in the literature. Finally, the median is closer to 3 °C than is the mode for the truncated distributions selected by the IPCC (Hegerl et al . 2006); the average median is 3.1 °C and the 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

- 15 

### Second, the calibrated Roe and Baker dis tribution 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 t he mean and the median (7.2 °C) of the 95 th 

### percentiles of 21 previous studies summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl et al

. 2006)
than are the 95 th 

### percentiles of the three other calibrated distributions (5.2

– 6.0 °C).

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 sensitivity being greater than 1.5 °C is almost 99 percent, is not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it reflects a greater degree of certainty a bout 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 Rep ort. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate. l



### Figure 14A.4

. 3 Esti mates of the Probability Density Function for Equilibrium Climate Sensitivity

l The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed line, anthropogenic forcings only), Forest et al . (2006; solid line, anthropogenic and natural forcings), Gregory et al

. (2002), Knutti et al . (2002), Frame et al . (20 05), 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.14A

- 16 

### 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 in PAG E, 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,
C CSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed to select scenarios that span most of the plausible ranges of outcomes for these variables.

To accomplish this task in a transparent way, we decided to rely on the recent Stanford Energy Modeling Forum exercise, EMF

- 22 , which uses ten well

- recognized models to evaluate substantial, coordinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, and emission trajectories are internally consistent
for each model and scenario evaluated. The EMF

- 22 modeling effort also is preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES
scenarios are now extr eme outliers in one or more variables. Although the EMF

- 22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are recent, peer

reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect on global cumulative emissions, we use socio

- economic and emission trajectories that span a range of plausible scenarios. Five trajectories were selected from EMF

- 22 ( Table 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 exerc ise, we selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories from each of these four models. For the 550 ppm CO 2 

### e scenario, we averag ed 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 emissi ons, 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.14A

- 17 

### Table 14A.4

. 2 Socioeconomic and Emissions Projections from Select EMF

- 22 Reference
Scenarios

Reference Fossil and Industrial CO 2 Emissions
GtCO 2 /yr

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100
IMAGE 26.6 31.9 36.9 40.0 45.3 60.1 MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9
MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7
MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5 550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8
Reference GDP market exchange rates in trillion 2005$ n

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100
IMAGE 38.6 53.0 73.5 97.2 156.3 396.6 MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0
MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9
MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5 550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9 Global Population
billions

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100
IMAGE 6.1 6.9 7.6 8.2 9.0 9.1 MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7
MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4
MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7 550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1



### We explore how sensitive the SCC is to various assumptions about how the future will evolve without prejudging what is likely to occur. The interagency group considered formally assigning probability weights to different states of the world, but this proved challenging to do in an analytically rigorous way given the dearth of information on the likelihood of a full range of
futur e socio

- economic pathways.

There are a number of caveats. First, EMF BAU scenarios represent the modelers’ judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the

n While the EMF

- 22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use purchasing power parity (PPP) , which takes into account the different price levels across countries, so it more accurately describes relative standards of living across c ountries. 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 countrie s. 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 unreali stically 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 ef fect 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 quantit y 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, or the many geophysical uncertainties.14A

- 18 

### most likely outcome span a wide range, from the more optimistic ( e.g. , abundant low

- cost, low

carbon energy) to more pessimistic ( e.g. , constraints on the availability of nuclear and renewables). o 

### Second, the socio

- economic trajectories associated with a 550 ppm CO 2 

### e concentration scenario are not derived from an assessment of what policy is optimal from a
benefit

- cost standpoint. Rather, it is indicative of one possible future outcome. The emission trajectories underlying s ome 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 concentrations at this time, given the difficulty man y models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy
Outlook projected that global CO 2 

### emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion (2005 $ using market exchange rates) in 2010, 2020, and 2030, respectively. These projections
are consistent with one or more EMF

- 22 scenarios. Likewise, the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people in 2000 to 9.1 billion people in 2050, which is close to the population trajectories for the IMAGE, MiniCAM, and
MERGE models.

In addition to fossil and industrial CO 2 

### emissions, each EMF scen ario provides projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO 2 

### emissions out to 2100. These assumptions also are used in the three models while retaining the default radiative forcings due to other factors ( e.g. , ae rosols 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 year s. 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 consumption (or consumption equivalents) d ue 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 pe rcent 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

- 19 

### 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, “ I f your rule will have important intergenerational benefits or costs you might consider a further sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specifi c 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 investments. 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 use” (Arrow et al . 1996).

One theoretical foundation for the cost

- benefit analyses in which the social cost of carbon
will be us ed

— the Kaldor

- Hicks potential

- compensation test

— also suggests that market rates should be used to discount future benefits and costs, because it is the market interest rate that would govern the returns potentially set aside today to compensate future indi viduals for climate damages that they bear ( e.g. , Just et al

. 2004 )

. As some have noted, the word “potentially” is an important qualification; there is no assurance that such returns will actually be set aside to provide compensation, and the very idea of compensation is difficult to define in the intergenerational context. On the other hand, societies provide compensation to future generations through investments in human capital and the resulting increase in knowledge, as
well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the normative judgments that the decision

- maker wants explicitly to incorporate into the policy
evaluation

— e.g. , how inter

- personal comparisons of utility shoul d 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 ac ross 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 amen ities may not be the same as those today, making the current market rate on consumption an inappropriate metric by which to discount future climate

related damages. Others argue that the discount rate should be below market rates to correct for
market dist ortions 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, Weitz man 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 underlies14A

- 20 

### the prescriptive approach assumes a representative agent with perfect foresight and no credit constraints. This is an artificial rendering of the real world that misses many of the frictions that characterize individuals’ lives and indeed the available descriptive evidence supports this. For instance, many indi viduals smooth consumption by borrowing with credit cards that have relatively high rates. Some are unable to access traditional credit markets and rely on payday lending operations or other high cost forms of smoothing consumption. Whether one puts greate r 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 primar ily 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 bene fit

- cost analysis and with the approach required by OMB’s existing guidance. The logic of this framework also suggests that market rates should be used for discounting future consumption

- equivalent damages. Regardless of the theoretical approach used to de rive the appropriate discount rate(s), we note the inherent conceptual and practical difficulties of adequately capturing consumption trade

- offs over many decades or even centuries. While relying primarily on the descriptive approach in selecting specific discount rates, the interagency group has been keenly aware of the deeply normative dimensions of both the debate over discounting in the intergenerational context and the consequences of selecting one discount rate over another.

14A.4.6.1 Historically Observed Inte rest 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 im perfections drive a wedge
between the risk

- free rate of return on capital and the consumption rate of interest. Thus, the literature recognizes two conceptual discount concepts

— the consumption rate of interest and the opportunity cost of capital.

According to OMB’s Circular A

- 4, it is appropriate to use the rate of return on capital when a regulation is expected to displace or alter the use of capital in the private sector. In this case, OMB recommends Agencies use a discount rate of 7 percent. When regulat ion is expected to primarily affect private consumption

— for instance, via higher prices for goods and services

— a lower discount rate of 3 percent is appropriate to reflect how private individuals trade

- off current and future consumption.

The interagency g roup examined the economics literature and concluded that the consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a marginal change in carbon emissions (Lind 1990, Arrow et al

., 1996, Arrow 2000). The consumpt ion 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 th at vary with risk level, time horizon, and tax characteristics. The standard analytic framework used to develop intuition about the14A

- 21 

### discount rate typically assumes a representative agent with perfect foresight and no credit
constraints. The risk

- free rate is appropriate for discounting certain future benefits or costs, but the benefits calculated by IAMs are uncertain. To use the risk

- free rate to discount uncertain benefits, these benefits first must be transformed into “ certainty equivalents, ” i.e.

, the m aximum certain amount that we would exchange for the uncertain amount. However, the calculation of the certainty

- equivalent requires first estimating the correlation between the benefits of the policy and baseline consumption.

If the IAM projections of fut ure impacts represent expected values (not certainty

equivalent values), then the appropriate discount rate generally does not equal the risk

- free rate. If the benefits of the policy tend to be high in those states of the world in which consumption is low, then the certainty

- equivalent benefits will be higher than the expected benefits (and vice versa). Since many (though not necessarily all) of the important impacts of climate change will flow through market sectors such as agriculture and energy, and sinc e willingness to pay for environmental protections typically increases with income, we might expect a positive (though not necessarily perfect) correlation between the net benefits from climate policies and market returns. This line of reasoning suggests t hat the proper discount rate would exceed the riskless rate. Alternatively, a negative correlation between the returns to climate policies and market returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post

- tax riskless and risky rates can be used to capture individuals’ consumption

- equivalent interest rate. As a measure of the post

- tax riskless rate, we calculate the average real return from Treasury notes over the longest time period
avai lable (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 ap proach, 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 tha t 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).14A

- 22 

### 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
Ramse y 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 aut hors articulate whether their choice is based on prescriptive or descriptive reasoning. v 

### Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η equal to 1 suggests savings rates that do not conform to observed behavior.

• ρ. With respect to the pure rate of time preference, most papers in the climate change literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates tend to follow from moral judgments involving intergenerational neutrality. Some have argued that 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 pre ference because 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 2100.

t The parameter ρ measures the pure ra te 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 : consumpt ion 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 ge nerations. 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 inc ome 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 bench mark 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 st andard 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 22 OECD countries. They also estimate ρ = 1.08 percent per year using data on mortality rates. Anthoff et al . (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the bivariate probability distributions from their work and Evans and Sezer (2005 ) together, they find η = 1.47, and

ρ = 1.07.14A

- 23 

### 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 p referential treatment 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 cho ice 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 th eir income. w



### Recently, Stern (2008) revisited the values used in Stern (2006), stating that there is a case to be made for raising η due to the amount of weight lower values place on damages far in the future (over 90 percent of expected damages occur afte r 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 Pizer (2003) and Panipoulou et al. (2004) confirm empirically that discount rate uncertainty can have a large
effect on net present values. A main result from these studies is that if there is a persistent element to the uncertainty in the disc ount 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; N ewell 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 interest rates change over time to forecast future discount rates. Their model incorporates some of the basic features of how interest rates move over time, and its parameters are estimated based on historical observ ations 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 c urrently low or high and variation in the level of persistence over time.

w Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied savings rate and that η at or above 2 are inconsistent with observed behavior with regard to e quity . (At the same time, adding exogenous technical change

— all else equal

— would increase g as well.)14A

- 24 

### 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 A nalysis ). y



### 14A.4.6.4 The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in this context and uncertainty about how interest rates may change over time, we use three
discount rates t o span a plausible range of certainty

- equivalent constant discount rates: 2.5, 3, and 5 percent per year. Based on the review in the previous sections, the interagency workgroup determined that these three rates reflect reasonable judgments under both desc riptive and prescriptive approaches.

The central value ( 3 percent ) is consistent with estimates provided in the economics
literature and OMB’s Circular A

- 4 guidance for the consumption rate of interest. As previously mentioned, the consumption rate of inte rest 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 inc luded to represent the possibility that climate damages are positively correlated with market returns. Additionally, this discount rate may be justified by the high interest rates that many consumers use to smooth consumption across periods.

The low value ( 2.5 percent ) is included to incorporate the concern that interest rates are highly uncertain over time. It represents the average certainty

- equivalent rate using the mean

reverting and random walk approaches from Newell and Pizer (2003) starting at a disc ount rate of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random walk model and 2.8 percent using the mean reverting approach. 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 justified if climate investments are negatively correlated with the overall market rate of return. Use of this lower value also responds to certain judgments using the prescriptive

x For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31

– 75;
2.5 percent for years 76

– 125; 2 percent fo r years 126

– 200; 1.5 percent for years 201

– 300; and 1 percent after
300 years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over
time.

y Uncertainty in future damages is distinct from uncertainty in the discount rate . Weitzman (2008) argues that Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a low probability, catastrophic event far in the future dominate the effect of the discount rate i n a present value calculation and result in an infinite willingness

- to

- pay for mitigation today. Newbold and Daigneault, (2009) and Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, utility, an d 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 . usi ng the original simulation program from Newell and Pizer (2003).14A

- 25 

### or normative appr oach and to ethical objections that have been raised about rates of 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 distribution for the climate sensitivity parameter bounded between 0 and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of two

thirds.

• Five sets of GDP, population and carbo n 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 fina l output from each
model run is a distribution over the SCC in year t

.

For each of the IAMS, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF

- 22 scenarios, and the extrapolations based on these scenarios for post

- 2100 years.

2. Calculate the temperature effects and (consumption

- equivalent) damages in each year resulting from the baseline path of emissions.

a. In PAGE, the consumption

- equivalent damages i n each period are calculated as a fraction of the EMF GDP forecast, depending on the temperature in that period relative to the pre

- industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of

tem p erature change in that period.

c. In DICE, temperature affects both consumption and investment, so we first adjust the EMF GDP paths as follows: Using the Cobb

- Douglas production function with the DICE2007 parameters, we extract the path of exogenous technica l change implied by the EMF GDP and population paths, then we recalculate the baseline GDP path taking into account climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t

. (The exact unit varies by model.)

4. Recalculate the temperature effects and damages expected in all years beyond t resulting from this adjusted path of emissions, as in step 2.

5. Subtract the damages computed in step 2 from those in step 4 in each year. (DICE is run in 10 year time st eps, FUND in annual time steps, while the time steps in PAGE vary.)14A

- 26 

### 6. Discount the resulting path of marginal damages back to the year of emissions using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed in step 6, divided by the unit of carbon emissions used to shock the models in step 3.

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of CO 2 

### (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO 2 

### in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, climate damages are calculated as lost consu mption in each future year.

It is important to note that each of the three models has a different default end year. The default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.
This is an issue for the multi

- model appr oach 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 thes e data are available from the EMF

- 22 models. (A more detailed discussion of these assumptions is included in the Appendix.)

This exercise produces 45 separate distributions of the SCC for a given year, the product of 3 models, 3 discount rates, and 5 socio economic scenarios. This is clearly too many separate distributions for consideration in a regulatory impact analysis.

To produce a range of plausible estimates that still reflects the uncertainty in the estimation exercise, the distributions from each of the models and scenarios are equally weighed and combined to produce three separate probability distributions for SCC in a given year, one for
each assumed discount rate. These distributions are then used to define a range of point estimates for the globa l 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 a ppropriate rate to use in an intergenerational context, we present SCCs based on the average values across models
and socioeconomic scenarios for each discount rate.

The interagency group selected four SCC values for use in regulatory analyses. Three
val ues are based on the average SCC across models and socio

- economic and emissions scenarios at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higher

than

- expected economic impacts from climate change further out in th e 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 disco unt rate is the central value, and so the central value that emerges is the average SCC across models at the 3 percent discount rate. For purposes14A

- 27 

### 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 oth er two models), its probabilistic treatment of the equilibrium climate sensitivity parameter will directly affect the non

- catastrophic damages that are a function of the rate of temperature change.

In Table 14A.5

. 1 , we begin by presenting SCC estimates for 2010 by model, scenario, and discount rate to illustrate the variability in the SCC across each of these input parameters. As expected, higher discou nt rates consistently result in lower SCC values, while lower discount rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there
are differences in the SCC estimated across the three main models. For these estimates , FUND produces the lowest estimates, while PAGE generally produces the highest estimates.

Table 14A.5

. 1 Disaggregated Social Cost of 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 t he year 2010 (assuming, as we did for the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There
are far fewer estimates usi ng the latest versions of DICE and PAGE in the literature: Using similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE (based on Hope 20 06, 2008) close to $8 per ton for a 4 percent discount rate. Note that these14A

- 28 

### comparisons are only approximate since the literature generally relies on Ramsey discounting,
while we have assumed constant discount rates. aa



### The SCC estimates from FUND are sensi tive 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, whereas in FUND the fractional loss also increases with the rate of temperature change. Further, in FUND increases in income over time dec rease vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and PAGE. These structural differences among the models make FUND more sensitive to the path of emissions and less sensitive to GDP compared to DICE and PAGE.

Figure 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. Valu es for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using a simple linear interpolation.

aa Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) treats ρ and η as random parameters, specified using a triangular distribution such t hat the min, mode, and max = 0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC estimates for values of ρ = 0, 1, and 3 in many recent papers ( e.g

. , Anthoff et al . 2009). The path of per

- cap ita 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 out in the future are discounted at a lower rate than impacts that occur in the nearer term. 0 50 100 150 200 250 300 350 400 450

2000 2010 2020 2030 2050 2100 Global

GDP (trillions

of U.S. dollars) IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing14A

- 29 

### Ta ble 14A.5

. 2 Social Cost of CO 2 

### , 2010

– 2050 (2007$)

Discount Rate 5% 3% 2.5% 3%
Year Avg Avg Avg 95th
2010 4.7 21.4 35.1 64.9
2015 5.7 23.8 38.4 72.8
2020 6.8 26.3 41.7 80.7
2025 8.2 29.6 45.9 90.4
2030 9.7 32.8 50.0 100.0
2035 11.2 36.0 54.2 109.7
2040 12.7 39.2 58.4 119.3
2045 14.2 42.1 61.7 127.8
2050 15.7 44.9 65.0 136.2



### The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as
was done for th e interim estimates (using 3 percent). This helps to ensure that the estimates are internally consistent with other modeling assumptions

. Table 14A.5

. 3 illustrates how the growth
rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 2010 and 2050 is reported in the Appendix.

Table 14A.5

. 3 Changes in the Average Annu al Growth Rates of SCC Estimates between
2010 and 2050

Average Annual

Growth Rate 5% 3% 2.5% 3.0%

Year Range Avg Avg Avg 95th
2010

– 2020 3.6 2.1 1.7 2.2
2020

– 2030 3.7 2.2 1.8 2.2
2030

– 2040 2.7 1.8 1.6 1.8
2040

– 2050 2.1 1.4 1.1 1.3



### While the SCC estimate grows over time, the future monetized value of emissions reductions in each year (the SCC in year t multiplied by the change in emissions in year t ) must be discounted to the present to determine its total net present value for use i n regulatory analysis. Damages from future emissions should be discounted at the same rate as that used to calculate the SCC estimates themselves to ensure internal consistency

— i.e. , future damages from climate change, whether they result from emissions 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 disc ount rate. bb



### 14A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further refinement (and possibly significant change) in accordance with evolving scientific, economic, and ethical understandings. Durin g 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 deve lop the SCC estimates.14A

- 30 

### several areas in particular need of additional exploration and research. These caveats and additional observations in the following section are necessary to consider when interpreting and appl ying the SCC estimates.

Incomplete treatment of non

- catastrophic damages

. The impacts of climate change are expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain because of the inherent comple xity 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 im pacts of climate change recognized in the climate change literature (some of which are discussed above) because of lack of precise information on the nature of damages and because the science incorporated into these models understandably lags behind the

mo st 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 i s one example of a potentially large damage from CO 2 

### emissions not quantified by any of the three models. Species and wildlife loss is another example that is exceedingly difficult to monetize.)

Incomplete treatment of potential catastrophic damages

. Ther e has been considerable recent discussion of the risk of catastrophic impacts and how best to account for extreme scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West Antarctic Ice Sheet, or large releases of metha ne from melting permafrost and warming oceans. Weitzman (2009) suggests that catastrophic damages are extremely large

— so large, in fact, that the damages from a low probability, catastrophic event far in the future dominate the effect of
the discount rate in a present value calculation and result in an infinite willingness

- to

- pay for mitigation today. However, Nordhaus (2009) concluded that the conditions under which
Weitzman ’ s results hold “are limited and do not apply to a wide range of potential uncertai n scenarios. ”

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for large catastrophe risk premiums but also showed that the aggregate benefit estimates can be highly sensitive to the shapes of both the climate sensitivity dist ribution and the damage function at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high

- impact
low

- probability risks, using a right

- skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficient , but in most cases found only a modest risk premium. Given this difference in opinion, further research in this area is needed before its practical significance can be fully understood and a reasonable approach developed to account for such risks in regul atory 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 extrem e climate change scenarios.14A

- 31 

### 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 single benefit category in FUND is the reduced electricity costs from not having to run air conditioning as intensively (NRC 2009).

Climate change also will increase returns on investment to develop technologies that allow individuals to cope with adverse climate conditions, and IAMs to do not adequately account for this directed technological change. cc 

### For example, scientists may dev elop 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 (Mastr andrea 2009), they do not take into account technological changes that lower the cost of this adaptation over time. On the other hand, the calibrations do not account for increases in climate variability, pests, or diseases, which could make adaptation mor e difficult than assumed by the IAMs for a given temperature change. Hence, models do not adequately account for potential adaptation or technical change that might alter the emissions pathway and resulting damages. In this respect, it is difficult to dete rmine whether the incomplete treatment of adaptation and technological change in these IAMs under or overstate the likely damages.

Risk aversion

. A key question unanswered during this interagency process is what to
assume about relative risk aversion with regard to high

- impact 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 high er

- probability but lower

- impact damages with the same expected cost. (The
inclusion of the 95 th 

### percentile estimate in the final set of SCC values was largely motivated by this concern.) If individuals do show such a higher willingness to pay, a further qu estion is whether that fact should be taken into account for regulatory policy. Even if individuals are not
risk

- averse for such scenarios, it is possible that regulatory policy should include a degree of
risk

- aversion.

Assuming a risk

- neutral representati ve agent is consistent with OMB’s Circular A

- 4, which advises that the estimates of benefits and costs used in regulatory analysis are usually based on the average or the expected value and that “emphasis on these expected values is appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] analysis.”

Nordhaus (2008) points to the need to explore the relationship betwe en risk and income in the context of climate change across models and to explore the role of uncertainty regarding various parameters in the results. Using FUND, Anthoff et al. (2009) explored the sensitivity of the SCC to Ramsey equation parameter assumpt ions 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

- 32 

### assumption on risk p reference in regulatory analysis if it is adequately justified, we plan to continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE
FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the SCC may not capture the economic effects of all possible adverse consequences of climate change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, the models’ functional forms may not adequately capture: (1) potentia lly discontinuous “tipping point” behavior in Earth systems ; (2) inter

- sectoral and inter

- regional interactions, including global security impacts of high

- end warming ; and (3) limited near

- term substitutability between damage to natural systems and increased consumption.

It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regu latory 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

The damage functions in the models are c alibrated at moderate levels of warming and should therefore be viewed cautiously when extrapolated to the high temperatures found in the upper end of the distribution. Recent science suggests that there are a number of potential climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with potentially severe social and economic consequences ( e.g. , Lenton et al. 2008, Kriegler et al. 2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of
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 Oscillation, and the release of methane from m elting permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C and 5 °C (Lenton et al. 2008). Probabilities of several of these tipping points were assessed through expert elicitation in 2005

– 2006 by Kriegler et al. (20 09); results from this study are highlighted in Table 14A.7

. 1 . Ranges of probability are averaged across core experts on each topic.14A

- 33 

### Table 14A.7

. 1 Probabilities of Various Tipping Points from Expert Elicitation Possible Tipping Points Duration before effect is fully realized

years Additional Warming by 2100
%
0.5

– 1.5 C 1.5

– 3.0 C 3

– 5 C Reorganization of Atlantic Meridional Overturning Circulation about 100 0

– 18 6

– 39 18

– 67

Greenland Ice Sheet Collapse at least 300 8

– 39 33

– 73 67

– 96 West Antarctic Ice Sheet Collapse at least 300 5

– 41 10

– 63 33

– 88
Dieback of Amazon rainforest about 50 2

– 46 14

– 84 41

– 94 Strengthening of El Niño

- Southern
Oscillation about 100 1

– 13 6

– 32 19

– 49

Dieback of Boreal Forests about 50 13

– 43 20

– 81 34

– 91
Shift in Indian Summer Monsoon about 1 not formally assessed Release of Methane from Melting
Permafrost less than 100 not formally assessed



### As previously mentioned, FUND does not include potentially catastrophic effects. DICE assumes a small probability of catastrophic damages that increases with increased warming, but the damages from these risks are incorporated as expected values ( i.e. , ign oring 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 instance, at the 95 th 

### percentile and a 3 percent discount rate, the SCC estimated by PAGE across the five socio

- economic and emission trajectories of $113 per ton o f CO 2 

### is almost double the value estimated by DICE, $58 per ton in 2010. We cannot evaluate how well the three models account for catastrophic or non

catastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the
distr ibution 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 catastrophic event across the two models. For instance, PAGE places a probability near zero on a catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probabili ty 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 necessarily lead to an economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across
which some as pect of the Earth system starts to shifts into a qualitatively different state (for instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a catastrophe is a low

- probability environmental change with high economic im pact.

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 de pend on the modeler’s choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one14A

- 34 

### 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). Th ese inter

- regional interactions, though difficult to quantify, are the basis for climate

- induced national and economic security concerns ( e.g. , Campbell et al. 2007; U.S. Department of Defense 2010) and are particularly worrisome at higher levels of warmin g. High

- end warming scenarios, for instance, project water scarcity affecting 4.3

– 

### 6.9 billion people by 2050, food scarcity affecting about 120 million additional people by 2080, and the creation of millions of climate refugees (Easterling et al. 2007;
Cam pbell 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
Ther mal 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 plant s (Wing

et al. 2005), and dwarfing of both land mammals (Gingerich 2006) and soil fauna (Smith et al. 2009).

The three IAMs used here assume that it is possible to compensate for the econoic consequences of damages to natural systems through increased cons umption of non

- climate goods, a common assumption in many economic models. In the context of climate change, however, it is possible that the damages to natural systems could become so great that no increase in consumption of non

- climate goods would provid e complete compensation (Levy

et al. 2005). For instance, as water supplies become scarcer or ecosystems become more fragile
and less bio

- diverse, the services they provide may become increasingly more costly to replace. Uncalibrated attempts to incorporat e the imperfect substitutability of such amenities into IAMs (Sterner and Persson 2008) indicate that the optimal degree of emissions abatement can be considerably greater than is commonly recognized.

14A.8 CONCLUSION

The interagency group selected four SCC est imates 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 discoun t rates, respectively. The fourth value is included to represent the higher

than

- expected impacts from temperature change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th 

### percentile at a 3 percent discou nt 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 e stimates 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 impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of14A

- 35 

### 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 analys is by the Federal government will continue to evolve with improvements in modeling.14A

- 36 

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- 42 

### 14A.9.1 A NNEX

This A nnex provides additional technical information about the non

- CO 2 

### emission projectio ns used in the modeling and the method for extrapolating emissions forecasts through 2300, and shows the full distribution of 2010 SCC estimates by model and scenario combination. Annual SCC values for the next 40 years are provided in Table 14A.9

. 1

.

Table 14A.9

. 1 Annual SCC Values: 2010

– 2050 ( 2007$ )

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th
2010 4.7 21.4 35.1 64.9
2011 4.9 21.9 35.7 66.5
2012 5.1 22.4 36.4 68.1
2013 5.3 22.8 37.0 69.6
2014 5.5 23.3 37.7 71.2
2015 5.7 23.8 38.4 72.8
2016 5.9 24.3 39.0 74.4
2017 6.1 24.8 39.7 76.0
2018 6.3 25.3 40.4 77.5
2019 6.5 25.8 41.0 79.1
2020 6.8 26.3 41.7 80.7
2021 7.1 27.0 42.5 82.6
2022 7.4 27.6 43.4 84.6
2023 7.7 28.3 44.2 86.5
2024 7.9 28.9 45.0 88.4
2025 8.2 29.6 45.9 90.4
2026 8.5 30.2 46.7 92.3
2027 8.8 30.9 47.5 94.2
2028 9.1 31.5 48.4 96.2
2029 9.4 32.1 49.2 98.1
2030 9.7 32.8 50.0 100.0
2031 10.0 33.4 50.9 102.0
2032 10.3 34.1 51.7 103.9
2033 10.6 34.7 52.5 105.8
2034 10.9 35.4 53.4 107.8
2035 11.2 36.0 54.2 109.7
2036 11.5 36.7 55.0 111.6
2037 11.8 37.3 55.9 113.6
2038 12.1 37.9 56.7 115.5
2039 12.4 38.6 57.5 117.4
2040 12.7 39.2 58.4 119.3
2041 13.0 39.8 59.0 121.0
2042 13.3 40.4 59.7 122.7
2043 13.6 40.9 60.4 124.4
2044 13.9 41.5 61.0 126.1
2045 14.2 42.1 61.7 127.8
2046 14.5 42.6 62.4 129.4
2047 14.8 43.2 63.0 131.1
2048 15.1 43.8 63.7 132.8
2049 15.4 44.4 64.4 134.5
2050 15.7 44.9 65.0 136.214A

- 43 

### 14A.9.2 Other (non

- CO 2 

### ) G ases

In addition to fossil and industrial CO 2 

### emissions, each EMF scenario provides projections of methane (CH 4 

### ), nitrous oxide (N 2 

### O), fluorinated gases, and net land use CO 2 

### emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s default radiative forcings (RF) due to other factors ( e.g. , aerosols and other gases). Specifically,
to obtain the RF associated with the non

- CO 2 

### EMF emissions on ly, we calculated the RF associated with the EMF atmospheric CO 2 

### concentrations and subtracted them from the EMF
total RF. dd 

### This approach respects the EMF scenarios as much as possible and at the same time takes account of those components not included in the EMF projections. Since each model treats

non

- CO 2 

### gases differently ( e.g. , DICE lumps all other gases into one composite exogenous input), this approach was applied slightly differently in each of the models.

FUND : Rather than relying on RF for these g ases, the actual emissions from each
scenario were used in FUND. The model default trajectories for CH 4 

### , N 2 

### O , SF 6 

### , and the CO 2 

### emissions from land were replaced with the EMF values.

PAGE : PAGE models CO 2 

### , CH 4 

### , sulfur hexafluoride (SF 6 

### ), and aerosols and contains an

“ excess forcing ” vector that includes the RF for everything else. To include the EMF values, we
removed the default CH 4 

### 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 well as the model default values for aerosols and other factors. Net land use CO 2 

### emissions were added to the fossil and industrial CO 2 

### emissions pathway.

DICE : DICE presents the greatest challe nge 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 Interg overnmental 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 no n

- CO 2 

### sources is

- 0.06 W/m 2 

### in 2005, as reported in AR4, and increases linearly to
0.3 W/m 2 

### in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH 4 

### , N 2 

### 0, and halocarbons (approximately
similar to the F

- gases in the EMF

- 22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m 2 

### and RF
from total aerosols was

- 1.2 W/m 2 

### . Thus, the

- .06 W/m 2 

### non

- CO 2 

### forcing in DICE can be decomposed into: 0.98 W/m 2 

### due to the EMF non

- CO 2 

### gases,

- 1.2 W/m 2 

### due to aerosols, and the remainder, 0.16 W/m 2 

### , due to other residual forcing.

dd Note EMF did not provide CO 2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed the fossil, industrial and land CO 2 emissions into MAGICC (considered a “ neutral arbiter ” model, which is tuned to emulate the major global climate models) and the resulting CO 2 concentratio ns were used. Note also that MERGE assumes a neutral biosphere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario ( i.e. , we add up the land use emissions from the 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

- 44 

### For subsequent years, we calculated the DICE default RF from aerosols and other non

CO 2 

### gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR and then stays 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 aerosols in 2100 equals 66 percent of that in 2000, which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black carbon, and organic carbon) in 2100 v s. 2000 under the A1B SRES emissions scenario. Since the SRES marker scenarios were not updated for the AR4, the TAR provides the most recent IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides one of the lowe r aerosol forecasts among the SRES marker scenarios and is more consistent with the AR4 discussion of the post

- SRES literature on aerosols:

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor emissions, including su lphur dioxide, black carbon and organic carbon, has improved in the post

- SRES scenarios. Generally, these emissions are projected to be lower than reported in SRES. {WGIII 3.2, TS.3, SPM}. ff



### Assuming a simple linear decline in aerosols from 2000 to 2100 als o 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 som e 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 because of new information about present and planned sulfur legislati on in some developing countries, such as India and China. hh 

### The lower bound projections of the recent literature have also shifted downward slightly compared to the SRES scenario (IPCC 2007).

With these assumptions, the DICE aerosol forcing changes from

- 1.2 in 2005 to

- 0.792 in
2105 W/m 2 

### ; forcing due to other non

- CO 2 

### gases not included in the EMF scenarios declines from
0.160 to 0.153 W/m 2



### .

ff AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment

- report/ar4/syr/ar4_syr.pdf

gg See Smith, S.J., R. Andres, E. Conception, and J. Lurz

. 2004 . “ Historical sulfur dioxide emissions, 1850

- 2000:
methods and results. ” Joint Global Research Institute, College Park, 14 pp.

hh See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda

. 2002

. “ Changing trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. ” Environmental Science and Technology 36(22):4707

- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.

- Q. Zhang, D. Xu, M. Jacobson, and J. Hansen

. 2001

. “ Recent reductions in China’s greenhouse gas emissions. ” Science 294(5548):1835

- 1837.14A

- 45 

### . 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) illustrate s individual scenarios and the range of Smith et al. (2004). Dotted lines
indicate the minimum and maximum of SO 2 emissions scenarios developed pre

- SRES. Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3

- ens3

- 2

- 2

- 4.html

.


### Although other approaches to decomposing the DICE exogenous forcing vector are possible, initial sensitivity analysis suggests that the differences among reasona ble alternative approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values (for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 2100 increases average 2010 SCC values by 6

– 7 percent (or $0.50

– $3)

, depending on the discount rate. These differences increase slightly for SCC values in later years but are still well within 10 percent of eac h 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 Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which these projections are available from the EMF

- 22 models. These inputs were extrapolated from
2100 to 2300 as follows:

1. Population growth rate decli nes 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 2300.14A

- 46 

### 4. Net land use CO 2 

### emissions decline linearly, reaching zero in 2200.
5. Non

- CO 2 

### radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying assumption than a linear or exponential extrapolation of the pre

- 2100 economic growth rate of each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the degradation of environmental sinks available for assimilating pollution from economic

p roduction activities may eventually overtake the rate of technological progress. Thus, the overall rate of economic growth may slow over the very long run. The interagency group also considered allowing an exponential decline in the growth rate of GDP per capita. However, since this would require an additional assumption about how close to zero the growth rate would get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by
2300.

The population growth rate is also as sumed 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 resulti ng 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 growth rate ( i.e. , CO 2 

### per dollar of GDP) through 2300 assumes that technological improvements and innovations in the areas of energy efficiency and other carbon reducing technol ogies (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 2 300 will be between 5,000 and 12,000 GtC, which is within the range of the total potential global carbon stock estimated in the literature.

Net land use CO 2 

### emissions are expected to stabilize in the long run, so in the absence of any post 2100 projections , the group assumed a linear decline to zero by 2200. Given no a priori reasons for assuming a 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) r esulting from these assumptions.

ii United Nations. 2 004. World Population to 2300

. http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf

.14A

- 47 

### Figure 14A.9

. 2 Global Population, 2000

– 2300 ( p ost

- 2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200)

Note: In the fifth scenario, 2000

– 2100 population is equal to the average of the population under the 550 ppm
CO 2 e, full

- participation, not

- to

- exceed scenarios 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 s cenarios 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

- 48 

### 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

– 210 0 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 linearly, reaching zero in 2200 ) jj

Note: In the fifth scenario, 2000

– 2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, full

- participation, not

- to

- exceed scenarios considered by each of the four models.

jj MERGE assumes a neutral biosphere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario ( i.e. , we add up the land use emissions from the other three models and divide by 4). 0 50 100 150 200

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil&Industrial CO2 Emissions [GtCO2/decade]

Year

-3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario
ExtrapolationsNet land CO2 emissions [GtCO2/yr]

Year z14A

- 49 

### 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 the 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

– 2300 ( p ost

- 2100 extrapolations assume decline in CO 2 

### /GDP growth rate over 2090

– 2100 is maintained through 2300)

Note: In the fifth scenario, 2000

– 2100 emissions are equal to the average of the emissi ons 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 $]

Year14A

- 50 

### 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 53.614A

- 51 

### 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

- 52 

### 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 23,558.314B

- i 

### 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

- 1214B

- 1 

### 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

- 2 

### 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 Revised 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 = 4 4/12 = 3.67).14B

- 3 

### 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 interagen cy 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 evolving field. It does not revisit other assumptions with regard to the discount rate, reference case socioeconomic and emission scenarios, or equilibrium climate sensitivity

. Improvements in the way damages are
modeled are confined to those that have been incorporated into the latest versions of the models by the developers themselves in the peer

- reviewed literature. The agencies participating in the interagency working group continue to investigate potential improvements to the way in which economic damages associated with changes in CO 2 

### emissions are quantified.

Section 14B.3 summari zes the major updates relevant to SCC estimation that are
contained in the new versions of the integrated assessment models released since the 2010 interagency report

. 14B.4 presents the updated schedule of SCC estimates for 201 0

– 2050 based
on these versions of the models. Section 14B.5 provides a discus sion of other model li mitations and research gaps

.

14B.3 SUMMARY OF MODEL UPD ATES

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 updated to be more consistent with a more complex
climate model. The FUND model includes updated d amage functions for sea level rise impacts,
the agricultur al 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 e missions

. Changes made to parts of the models that are superseded by the interagency working group’s modeling assumptions

– regarding equilibrium climate sensitivity, 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 Cost of Carbon 2010)

.14B

- 4 

### 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 representation of SLR d amages, 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.



### 14B.3.1 DICE

DICE 2010 includes a number of changes over the previous 2007 version used in the 2010 interagency report. The model changes that are relevant for the SCC estimates developed by the interagency working group include: 1) updated parameter values for the car bon cycle model, 2) an explicit representation of sea level dynamics, and 3) a re

- calibrated damage function that includes an explicit representation of economic damages from sea level rise. Changes were also made to other parts of the DICE model

— including the equilibrium climate sensitivity parameter, the rate of change of total factor productivity, and the elasticity of the marginal utility of consumption

— but these components of DICE are superseded by the interagency working group’s assumptions and so wil l not be discussed here. 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 us es 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 Mo del for the Assessment 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). Fo r example, in DICE2010, in each decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains in the shallow ocean, and 0.5 perc ent 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 sophisticated state of the art earth system simulation models ( Randall et al. 2007).14B

- 5 

### decade

, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as a carbon sink and therefore a higher concentration of carbon in the atmosphere than in
DIC E2007, for a given path of emissions. All else equal, these changes will generally increase the level of warming and therefore the SCC estimates in DICE2010 relative to those from
DICE2007.

Sea Level Dynamics

A new feature of DICE2010 is an explicit repre sentation 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 mod el developer’s website. 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 (deca de) 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 glac iers and small ice caps occurs at a rate of 0.008 meters per decade per °C above the average global temperature in 1900.

The contribution to sea level rise from melting of the Greenland ice sheet is more complex. The equilibrium contribution to SLR is 0 m eters for temperature anomalies less than
1 o 

### C and increases linearly from 0 meters to a maximum of 7.3 meters for temperature anomalies
between 1 o 

### C and 3.5 °C. The contribution to SLR in each period is proportional to the difference between the previous period’s sea level anomaly and the equilibrium sea level anomaly, where the constant of proportionality increases with the temperature anomaly in the current period. The contribution to SLR from the melting of the Antarctic ice sheet is

- 0.001 meters per d ecade when the temperature anomaly is below 3 °C and increases linearly between 3 °C and 6 °C to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

Re

- calibrated Damage Function

Economic damages from climate change in the DICE mod el are represented by a fractional loss of gross economic output in each period. A portion of the remaining economic output in each period (net of climate change damages) is consumed and the remainder is invested in the physical capital stock to support fu ture economic production, 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://nordhaus.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

- 6 

### investment. The fraction of output in each period that is lost due to climate change impacts is represented as one m inus 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 tempera ture. 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 d ifference between damages in the base run of DICE2010 and those that would be calculated using the DICE2007 damage function starts at +7 percent in 2005, decreases
to a low of

- 14 percent in 2065, then continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), and to +160 percent by the end of the model time horizon in 2595. The large increases in the far future years of the time horizon are due to the permanence associated with damages from sea level rise, along with the assumption that the sea level is projected to continue to rise long after the global average temperature begins to decrease. The changes to the loss function generally decrease the interagency working group SCC estimates slightly given that relative in creases in damages in later periods are discounted more heavily, all else equal.

14B.3.2 FUND

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. Documentatio n 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 functi ons 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 heatin g 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 webpag e 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 represents 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 IPCC 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

- 7 

### based on the forecasted temperature anomaly’s deviation from the one degree benchmark and adjusted for changes in vulnerability due to economic and energy e fficiency 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 tempe rature 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 app roaches 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 degr ee 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 l evel 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 e xpected 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 effects from carbon fertilization, the rate of temperature change, and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function with an intercept of zero. In FUND 3.5, the 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 p arameter in the denominator approached zero or went negative. In FUND 3.8, the coefficients are drawn directly from truncated normal distributions so that they remain in the range [0, ) ∞ 

### and ( ,0]

− ∞ 

### , respectively, ens uring the correct sign and eliminating 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

- 8 

### 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 Respo nse

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 equ als the equilibrium temperature anomaly that would eventually be reached if that year’s level of radiative forcing were sustained. The rate of mean reversion defines the rate at which the transient temperature approaches the equilibrium. In FUND 3.5, the r ate 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 temperature response has been updated to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity of the rate of temperature response to the level of the equilibrium climate sensitivity, a relationship first noted by Hansen et al. (1985) based on the heat uptake of the deep ocean. Therefore in FUND 3.8, the temperature response will typically be faster than in the previous version. The overall effect of this change is likely to increase esti mates of the SCC as higher temperatures are reached during the timeframe analyzed and as the same damages experienced in the previous version of the model are now experienced earlier and therefore discounted less.

Methane

The IPCC AR4 notes a series of in direct effects of methane emissions, 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 average atmospheric lifetime of methane has been set to 12 years to account for the feedback of methane emissions on its own lifetime. The radiative 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.

14B.3.3 PAGE

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 damage function to

ensu re damages are constrained by GDP , a change in the regional scaling of damages, a revised treatment for the probability of a discontinuity within the damage function, and revised assumptions on adaptation. The model also includes revisions to the carbon cy cle feedback and14B

- 9 

### 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 into 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 level 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 ensu re that damages do not exceed 100 percent of GDP. This differs from PAGE2002, which allowed Eastern Europe to potentially experience large benefits from temperature increases, and which also did not bound the possible damages that could be experienced.

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 scaling 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 pos itive 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 higher damages in
dev eloping 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 multiplied by the dama ges 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 multiplied by the prob ability of the event occurring and added to the damage estimate. In PAGE09, the probability

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

.14B

- 10 

### 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 occu rring, rather than as an expected value. A large

‐ scale discontinuity becomes possible when the temperature rises beyond
some threshold value between 2 and 4°C. The probability that a discontinuity will occur beyond this threshold then increases by between 10 and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a mean of 15 percent) in addition to other damages, and ot her 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 anomaly and the dama ges 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.

Adaptation

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 reduction in vulnerabilit y. 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 e conomic 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 the 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 the damages over 20 y ears 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 optimistic assumptio ns 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

acco unts 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 PAGE2002, an addition al amount was
added to the CO 2 

### emissions each period to account for a decrease in ocean absorption and a loss of soil carbon. Also updated is the method by which the average global and annual temperature anomaly is downscaled to determine annual average re gional temperature anomalies to be used in the regional damage functions. In PAGE2002, the scaling was determined solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional temperature14B

- 11 

### anomaly is further adjusted using a n 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 latitude s.

14B.4 REVISED SCC ESTIMATE S

The updated versions of the three integrated assessment models were run using the same methodology detailed in the 2010 TSD ( Interagency Working Group on Social Cost of Carbon
2010 ). The approach along with the inputs for the socio economic emissions scenarios, equilibrium climate sensitivity distribution, and discount rate remains the same. This includes the
five reference scenarios based on the EMF

- 22 modeling exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the 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 d istributions for use in regulatory analysis. Three values are based on the average SCC across models and socio

- economic

- emissions scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to represent the higher

- than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent discount rate was chosen. (A detailed set of percentiles by model and scenario combina tion 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 models at the 3 percent d iscount 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 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 linear interpolation

. The full set of revised
annual SCC estimates between 2010 and 2 050 is reported in the Appendix

.14B

- 12 

### Table 14B.4

. 1 Revised 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 increas es 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 m arginal ton emitted in the future by running the models for a14B

- 13 

### set of perturbation years out to 2050. Table 14B.4

. 2 illustrates how the growth rate for these four
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 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.

Un der 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 pr oblem is highly unusual in at least two respects. First, it involves a global externality: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in the United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages caused by GHG emissions. Second, climate change presents a problem that the United States alone cannot solve. Even if the United States were to reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid substantial climate change. Other countries would also need to take action to reduce emissions if significant changes in the global climate are to be avoided. Emphasizing the need for a global solution to a glob al 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 concluded 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 LIMITATI ONS AND RESEARCH GAPS

The 2010 interagency SCC TSD disc usse s a number of important limitations for which
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 to14B

- 14 

### perfectly substitute between climate and non

- climate goods at higher temperature increases, both of which have implications for the discount rate used . EPA, DOE, and other agencies continue to engage in research on modeling and valuation of climate impacts that can potentially improve
SCC estimation in the future

.14B

- 15 

### 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 p ublication. doi:
10.1007/s10584

- 013

- 0959

- 1.

Fankhauser, S. 1995. Valuing 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 Margin al Impact of CO 2 

### from PAGE2002: An Integrated Assessment Model Incorporating 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” Cambridg e Judge Business School Working Paper No. 4/2011 (April). Accessed November 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/worki ng_papers/2011/wp1105.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.

Interagency 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. K nutti, 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 Intergovern mental14B

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### 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.

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 Millennia . 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 wor ld’ in the twenty

- first century. 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: Weighing 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 Models and Their
Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [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.14B

- 17 

### 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 22014B

- 18 

### 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

- 19 

### 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 1314B

- 20 

### 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 1493114B

- 21 

### 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.