TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

AUTOMATIC COMMERCIAL ICE MAKERS

March 2014

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 Laboratory
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CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

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

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

1.3 OVERVIEW OF APPLIANCE STANDARDS .............................................................. 1-4

1.4 OVERVIEW OF AUTOMATIC COMMERCIAL ICE MAKERS STANDARDS....... 1-5

1.4.1 Framework and Analysis Methodology............................................................... 1-6

1.5 STRUCTURE OF THE DOCUMENT............................................................................ 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 ........................................................................................................ 1-7
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CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

This technical support document (TSD) is a stand-alone report that provides the technical 

analyses and results in support of the information presented in the notice of proposed rulemaking 

(NOPR) for automatic 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. Department 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-making (ACIM) equipment

purchased in the 30-year period that begins in the year of the compliance with amended 

standards (2018–2047) amount to 0.286 quadrillion British thermal units (quads) of cumulative 

energy.

The cumulative national net present value (NPV) of total customer costs and savings of 

the proposed standards for automatic commercial ice makers in 2012$ ranges from $0.791 billion 

(at a 7-percent discount 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 2018–2047, discounted to 2013.

DOE also studied the potential impact on the ACIM equipment manufacturers. The 

industry net present value (INPV) is the sum of the discounted cash flows to the industry from 

the base year (2013) through the end of the analysis period (2047). Using a real discount rate of 

9.2 percent,a DOE estimates that the INPV of ACIM manufacturers is $101.8 million in 2012$. 

Under the proposed standards, DOE expects that manufacturers may lose up to 23.5 percent of 

their INPV, or approximately $23.9 million. 

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

on energy savings from equipment purchased over the period of 2018–2047, the energy savings 

would result in cumulative greenhouse gas emission reductions of 14.6 million metric tonsb of 

carbon dioxide (CO2), 75.8 thousand tons of methane, 0.3 thousand tons of nitrous oxide, 

21.0 thousand tons of sulfur dioxide (SO2), 8.7 thousand tons of nitrogen oxides (NOx), and 

0.02 tons of mercury (Hg).

c DOE estimates that the net present monetary value of the CO2 

emissions reduction would be between $0.102 and $1.426 billion, expressed in 2012$ and 

discounted to 2013. DOE also estimates the present monetary value of the NOx emissions 

 a This is the rate used to discount future cash flows in the manufacturer impact analysis. A discount rate of 10 

percent was calculated based on SEC filings and feedback from manufacturer interviews about the current cost of 

capital in the industry. For more information, refer to chapter 12 of the NOPR TSD. b A metric ton is equivalent to 1.1 U.S. short tons. Results for NOx and Hg are presented in short tons. c DOE calculated emissions reductions relative to the Annual Energy Outlook 2013 (AEO2013) Reference Case, 

which generally represents current legislation and environmental regulations for which implementing regulations 

were available as of December 31, 2012.
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reduction would be between $0.54 and $5.53 million 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 2018 and 2047, 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 CO2 emission reductions.e

The value of the CO2 reductions is calculated using a range of values per metric ton of CO2

(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$ to 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 CO2 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 CO2 reductions is based on a global value. Second, the 

assessments of operating cost savings and CO2 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 makers shipped in 2018–2047. 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 CO2 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 estimates, are based on the low and high electricity price scenarios from the AEO2013

forecast. At a 7-percent discount rate 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 CO2 reductions (note 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 CO2 emissions 

reductions), and $0.32 million in reduced NOx emissions. In this case, the annualized net benefit 

 d DOE is currently investigating valuation of avoided Hg and SO2 emissions. e DOE used a two-step calculation process to convert the time-series of costs and benefits into annualized values. 

First, DOE calculated a present value in 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 

except for the value of CO2 reductions. For the latter, DOE used a range of discount rates, as shown in Table 1.2.1. 

From the present value, DOE then calculated the fixed annual payment over a 30-year period (2018 through 2047) 

that yields the same present value. The fixed annual payment is the annualized value. Although DOE calculated 

annualized values, this does not imply that the time-series of cost and benefits from which the annualized values 

were determined is a steady stream of payments.
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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 equipment costs. The benefits are $121 million per year in reduced operating costs, 

$27 million in CO2 reductions, and $0.55 million in reduced NOx emissions. In this case, the net 

benefit amounts to $128 million per year. 

Table 1.2.1 Annualized Benefits and Costs of Proposed Standards for Automatic 

Commercial Ice Makers

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

CO2 Reduction 

Monetized Value (at 

$11.8/Metric Ton)**

5% 8 8 8

CO2 Reduction 

Monetized Value (at 

$39.7/Metric Ton)**

3% 27 26 27

CO2 Reduction 

Monetized Value (at 

$61.2/Metric Ton)**

2.5% 39 38 40

CO2 Reduction 

Monetized Value (at 

$117.0/Metric 

Ton)**

3% 82 80 84

NOx 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, CO2

Reduction and NOx

Reduction)†

7% 131 124 139

3% 149 139 160

Costs

Total Incremental 

Installed Costs

7% 20 21 20

3% 21 22 20

Net Benefits Less Costs

Total Benefits Less 

Incremental Costs 

7% 110 103 120

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 CO2 values represent global monetized values of the SCC, in 2012$, in 2015 under several scenarios of the 

updated SCC values. The values of $11.8, $39.7, and $61.2 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 NOx, 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 NOx and CO2

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 high 
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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 Appliance Energy Conservation Amendments of 1988 (NAECA 1988), 

Pub. L. 100-357; and the Energy Policy Act of 1992 (EPACT 1992), Pub. L. 102-486, 

established the Energy Conservation Program for Consumer Products other than Automobiles. 

(42 U.S.C. 6291–6309) Part 3 of Title IV of NECPA amended EPCA to add Part A-1 of Title III, 

which established an energy conservation program for certain industrial equipment.

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, updated 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. 6313(d)(4)) To determine whether a proposed standard is economically justified, DOE 

will, after receiving comments on the proposed standard, determine whether the benefits of the 

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

factors:

1. the economic impact of the standard on the manufacturers and on the consumers of 

the products subject to such standard;

2. the savings in operating costs throughout the estimated average life of the covered 

product in the type (or class) compared to any increase in the price of, or in the initial 

charges for maintenance expenses of, the covered products that are likely to result 

from the imposition of the standard; 

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

imposition of the standard; 

4. any lessening of the utility or the performance of the covered products likely to result 

from the imposition 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.
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5. the impact of any lessening of competition, as determined in writing by the Attorney 

General, that is likely to result from the imposition of the standard; 

6. the need for national energy conservation; and

7. other factors the Secretary considers relevant.

(See 42 U.S.C. 6295(o)(2)(B)(i); 6313(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 technologically 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 lb ice/24 hours. These standards appear at Title 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 of 

Cooling

Harvest Rate

lb ice/24 hours

Maximum Energy 

Use

kWh/100 lb ice

Maximum Condenser 

Water Use*

gal/100 lb ice

Ice-Making Head

Water

<500 7.8-0.0055H** 200-0.022H**

≥500 and <1,436 5.58-0.0011H 200-0.022H

≥1,436 4.0 200-0.022H

Air <450 10.26-0.0086H Not Applicable

≥450 6.89-0.0011H Not Applicable

Remote Condensing 

(but not remote 

compressor)

Air

<1,000 8.85-0.0038H Not Applicable

≥1,000 5.10 Not Applicable

Remote Condensing 

and Remote 

Compressor

Air

<934 8.85-0.0038H Not Applicable

≥934 5.30 Not Applicable

Self-Contained

Water <200 11.4-0.019H 191-0.0315H

≥200 7.60 191-0.0315H

Air <175 18.0-0.0469H Not Applicable

≥175 9.80 Not Applicable

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

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

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

interested parties and facilitate their involvement in the rulemaking process. The analytical 

framework presented at the public meeting described different analyses, such as the engineering 

analysis and the LCC and PBP analyses, the methods proposed for conducting them, and the 

relationships among the various analyses.

After the December 2010 framework public meeting, as part of the information gathering 

and sharing process for the preliminary manufacturer impact analysis (MIA), DOE organized 

and held interviews with ACIM manufacturers. DOE selected companies that represented 

production of all types of automatic commercial ice makers, ranging from small to large 

manufacturers. DOE had four objectives for these interviews: (1) solicit feedback on the draft 

engineering analysis (including methodology, production costs, manufacturing processes, and 

findings); (2) solicit feedback on topics related to the preliminary MIA; (3) provide an 

opportunity, early in the rulemaking process, for manufacturers to express specific concerns to 

DOE; and (4) foster cooperation between the manufacturers and DOE.

In January 2012, DOE published a notice of public meeting and availability of the 

preliminary TSD (January 2012 preliminary analysis) for the ongoing rulemaking to potentially

amend energy conservation standards for automatic commercial ice makers. 77 FR 3404 (Jan. 

24, 2012).

DOE held a public meeting on February 16, 2012 (February 2012 preliminary analysis 

public meeting) to give stakeholders the opportunity to comment on the proposed equipment 

classes DOE is considering; the analytical framework, models, and tools (e.g., LCC and NIA

spreadsheets) that DOE has been using to perform analyses of the impacts of energy 

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

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

NIA 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 2012

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 Consumer Products (the “Process Rule”). 61 FR 36974 (July 15, 

1996). The LCC spreadsheet calculates national distributions of LCC savings at all efficiency 

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

national NPVs at all efficiency levels, and includes a module that forecasts shipments for the 

various equipment classes of automatic commercial ice makers at different efficiency levels.

1.5 STRUCTURE OF THE DOCUMENT

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

TSD. 

Chapter 1 Introduction: provides an overview of the appliance standards program and how 

it applies to the ACIM rulemaking, provides a history of DOE’s actions to date, 

and outlines the structure of this document.

Chapter 2 Analytical Framework: describes the rulemaking process.
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Chapter 3 Market and Technology Assessment: provides DOE’s definition of automatic 

commercial ice makers, discusses the proposed equipment classes, and names 

the major industry players. This chapter also provides an overview of ACIM

technology, including 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 prices.

Chapter 6 Markups for Equipment Price Determination: presents the methodology used to 

determine the distribution channel markups that are used to convert 

manufacturer selling prices into customer purchase price of the equipment.

Chapter 7 Energy and Water Use Analysis: DOE used the energy consumption model in 

the engineering analysis to estimate ACIM energy use. DOE did not conduct a 

separate energy use analysis for this rulemaking.

Chapter 8 Life-Cycle Cost and Payback Period Analysis: presents the 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 historical

and future shipments of automatic commercial ice makers. The estimated 

shipments numbers are used as inputs to NIA and other downstream analyses.

Chapter 10 National Impact Analysis: presents the methodology used to estimate national 

impacts by calculating national energy savings and NPV at all higher efficiency 

levels.

Chapter 11 Customer Subgroup Analysis: evaluates impacts on identifiable customer 

subgroups that may be disproportionately disadvantaged by the proposed new or 

amended standards.

Chapter 12 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 13 Emissions Analysis: presents the assessment of the impacts of proposed ACIM

standard levels on emissions of certain pollutants.
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Chapter 14 Monetization of Emission Reductions Benefits: presents the methodology to 

estimate monetary benefits likely to result from the reduced emissions of certain 

pollutants.

Chapter 15 Utility Impact Analysis: analyzes the effects of proposed new or amended 

ACIM standard levels on the electric utility industry.

Chapter 16 Employment Impact Analysis: estimates national job creation or elimination 

(indirect effects) resulting from possible amended standards due to reallocation 

of the associated commercial expenditures for purchasing and operating 

equipment.

Chapter 17 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 markups 

analysis.

Appendix 8A User Instructions for Life-Cycle Cost Spreadsheet: presents user instructions for 

the LCC spreadsheet.

Appendix 8B Detailed Life-Cycle Cost and Payback Period Analysis Results: presents 

detailed results from the LCC analysis.

Appendix 8C Uncertainty and Variability in Life-Cycle Cost Analysis: presents brief 

discussion on the uncertainty and variability analysis used in the LCC analysis.

Appendix 8D Estimation of Potential Equipment Price Trend for Automatic Commercial Ice 

Makers: presents experiential learning analysis in the LCC analysis.

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

spreadsheet.

Appendix 10B National Net Present Value Using Alternative Sensitivity Analyses: presents 

experiential learning sensitivity analysis results, electric price sensitivity 

analysis results, and 9-year analysis results.

Appendix 10C Trial Standard Levels and Standards Equations: presents the criteria for trial 

standard levels (TSLs) selection and the proposed standards equation at each 

TSL.

Appendix 10D Full-Fuel-Cycle Multipliers: presents the development of full-fuel-cycle 

coefficients.
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Appendix 10E RISC & OIRA Consolidated Information System (ROCIS) Tables: presents the 

ROCISg tables.

Appendix 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: presents an overview of the model used 

in the MIA.

Appendix 14A Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866: presents the SCC analysis.

Appendix 14B Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866: Technical Model Update: presents updated SCC analysis.

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

Combined Information System.
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REFERENCES

1. U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 

2013. 2013. Washington D.C. DOE/EIA-0383(2013).
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CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

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

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

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

2.3.1 Market Assessment .............................................................................................. 2-4

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 PAYBACK PERIOD ANALYSES .................................... 2-8

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

2.10 NATIONAL IMPACT ANALYSIS.............................................................................. 2-10

2.11 CUSTOMER SUBGROUP ANALYSIS....................................................................... 2-11

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

2.13 EMISSIONS ANALYSIS.............................................................................................. 2-13

2.14 MONETIZING CARBON AND OTHER EMISSIONS REDUCTIONS BENEFITS. 2-14

2.15 UTILITY IMPACT ANALYSIS................................................................................... 2-15

2.16 EMPLOYMENT IMPACT ANALYSIS....................................................................... 2-16

2.17 REGULATORY IMPACT ANALYSIS ....................................................................... 2-16

2.18 DEPARTMENT OF JUSTICE REVIEW ..................................................................... 2-16

REFERENCES .......................................................................................................................... 2-17

LIST OF FIGURES

Figure 2.1.1 Analyses for Automatic Commercial Ice-Making Equipment Energy Conservation 

Standards.............................................................................................................. 2-3
2-1

CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

This chapter describes the analytical framework that the U.S. Department of Energy 

(DOE) used in developing standards and assessing the impact for automatic commercial ice 

makers. The chapter describes the methodology, analytical tools, and relationships among the 

various analyses that are part of this rulemaking. The objective of the rulemaking process is to 

determine minimum efficiency standards for automatic commercial ice-making (ACIM)

equipment that are technologically feasible and economically justified. In this context, economic 

justification includes consideration of the economic impact 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 standards-setting process. The 

focus of this figure is the third column, identified as “Analysis.” The columns labeled “Key 

Inputs” and “Key Outputs” indicate how the analyses fit into the rulemaking process, and how 

the analyses relate to each other. Key outputs are analytical results that feed directly into the 

standards-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 

standards-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 review 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 analysis to estimate the relationship between the manufacturing cost 

of an ACIM unit and its performance;

• a markup analysis to convert manufacturer selling prices (MSPs) 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 national 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 potential 

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 (CO2), sulfur dioxide (SO2), 

nitrogen oxides (NOx), and mercury (Hg); 

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

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

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 purchase 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 publishing 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 Standards
2-4

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 associations, 

state agencies utilities, and other interested parties to provide input to this effort. As a result of 

this combined effort, DOE published Procedures, Interpretations and Policies for Consideration 

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

10 CFR part 430, subpart C, appendix A. The Process Rule outlined the procedural 

improvements identified by the interested parties, and included a review of the (1) economic 

models; (2) analytical tools; (3) methodologies; (4) non-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, the following sections provide a general 

description of the analytical components for this rulemaking.

DOE developed the analytical framework pertaining to automatic commercial icemakers 

in the Rulemaking Framework for Automatic Commercial Ice Making Equipment (Nov. 4, 2010). 

DOE announced the availability of the Framework document in a notice of public meeting and 

availability of a Framework 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 

on December 16, 2010.1 DOE used comments gathered during the Framework public meeting as 

well as additional information during the preliminary analysis stage, at the completion of which,

DOE announced the notice of public meeting and the availability of the preliminary TSD.

2 77 FR 

3404 (Jan. 24, 2012).

The following sections provide a brief overview of the analytical 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 from interested parties during the NOPR phase of the rulemaking process.

2.3 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the ACIM equipment market and 

existing technology options for making a unit of ACIM equipment more efficient. 

2.3.1 Market Assessment

DOE reviewed relevant literature and interviewed manufacturers to develop an overall 

picture of the ACIM industry in the United States. Industry publications and trade journals, 

government agencies, and trade 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 

performance-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 and equipment based on 

publicly available information. 

2.3.2 Technology Assessment

The function of the technology assessment is to develop a preliminary list of technologies 

that could potentially be used to improve the efficiency of ACIM equipment, as well as to 

highlight the developments within those technology categories and their applicability to these 

equipment classes. The result of technology assessment is a list of technology options to be 

analyzed in the screening analysis. Chapter 3 of the NOPR TSD includes a detailed list of all 

technology options DOE identified for this rulemaking.

2.4 SCREENING ANALYSIS

The purpose of the screening analysis is to evaluate the technologies identified in the 

technology assessment to determine which options to consider further in the engineering analysis

and which options to screen out. As stated 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 adverse impacts on health and safety. Chapter 4 of the

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

For the efficiency-enhancement technologies that are not eliminated 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 NOPR TSD) is to determine the 

relationship between manufacturer production cost (MPC) and energy and/or water consumption 

for automatic commercial ice makers. In determining this relationship, DOE estimates the 

increase in MPC associated with design changes that 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 by 
2-6

increasing 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 appropriate method to determine 

the manufacturing cost of these design options. DOE recognizes that some of the design options 

will reduce both energy and water consumption, but others may reduce one and increase the 

other. To address this issue, DOE developed an approach to consider both energy use and 

condenser water use in the engineering analysis in a way that maintains the rulemaking’s focus 

on energy use reduction while appropriately accounting for the cost implications of changing 

water use. This MPC-efficiency relationship developed in the engineering analysis is used in the 

LCC analysis.

DOE develops MPC estimates for the engineering analysis disaggregated into the cost 

contributions of materials (including purchased parts), labor, and overhead. 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 creates an industry-wide analysis 

based on a variety of data sources, including manufacturer-supplied data. 

2.5.1 Analysis Approach

DOE has developed 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 combinations of design 

options, to identify increases in efficiency. Under this approach, cost estimates are based on 

manufacturer or component supplier data or engineering computer simulation models. Individual 

design options, or combinations of design options, are added to the baseline model in ascending 

order of cost-effectiveness. Also, the efficiency levels addressed in the analyses, including the 

downstream analyses, are based on groups of design options.

An efficiency-level approach establishes the relationship between manufacturer cost and 

increased efficiency at predetermined efficiency levels above the baseline. Under this approach, 

manufacturers typically provide manufacturer cost data for incremental increases in efficiency, 

without identifying the technology 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 based on a “bottoms-up” manufacturing cost assessment. The efficiency levels addressed in 

the analyses for such an approach are based on the efficiency levels of the reverse-engineered 

products.

DOE used a combination of all three of these approaches to determine the cost-efficiency 

relationship for this rulemaking. DOE obtains 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 reverse 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 Framework 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 available data on continuous type 

equipment, including newly 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 classes within each 

continuous equipment type.

2.6 MARKUPS TO DETERMINE EQUIPMENT PRICE

DOE uses the markup analysis to determine distribution channel markups that are used to 

convert the MSP of the equipment into customer purchase price. DOE uses customer purchase 

price to analyze the impact of potential energy conservation standard levels from the consumer 

perspective. To estimate the cost to customers buying ice makers, DOE adds markups to reflect 

the costs added by the wholesale, 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 added to the markup associated with distribution channels. 

DOE calculated baseline markups that were applied to baseline MSPs and incremental 

markups that were applied to MSP increments at higher efficiency levels. See TSD chapter 6 for 

details on the markups analysis. 

2.7 ENERGY AND WATER USE ANALYSIS 

In the energy and water use analysis, DOE typically reviews the engineering results to 

assess their use in the downstream analyses and whether additional analyses are required. For 

automatic commercial ice makers, DOE concluded no additional analyses were necessary. 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 information 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 alternative cycles of freezing 

and harvesting. Continuous type equipment, on the other hand, produces ice continuously. Continuous type ice 

makers include flake and nugget machines.
2-8

2.8 LIFE-CYCLE COST AND PAYBACK 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 standards 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 

operating 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 

as a potential new standard level. 

The first efficiency level (level 1) in each equipment class is the least efficient and the 

least expensive equipment in that equipment class. For the equipment classes that are currently 

covered by DOE’s standards, level 1 equipment is equipment that just meets the efficiency 

standards. For equipment not currently covered by standards, level 1 reflects the least efficient 

equipment currently available in the ACIM market. The higher efficiency levels (level 2 and up) 

have progressive increases in efficiency and equipment cost from level 1. The highest efficiency 

level in each equipment class corresponds to the maximum efficiency level obtainable with nonproprietary 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 

(customer 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 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 these costs over the life of the 

equipment. To facilitate cost comparisons, DOE discounted operating costs to the discount year 

(2018, the assumed effective date of the standards established through this rulemaking). The sum 
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of the installed cost and the operating costs, discounted to reflect the present value, is termed the 

life-cycle cost or LCC.

Generally, customers incur higher installed costs when they purchase higher efficiency 

equipment, and these cost increments will be 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 less energy over the lifetime 

of the equipment. DOE calculated the change in LCC for each efficiency level of each equipment 

class.

DOE obtained the PBP of higher efficiency equipment by dividing the increase in the 

installed cost by the decrease in annual operating cost. For this calculation, DOE used the sum of 

the first year operating cost changes as the estimate of the annual decrease in operating cost, 

noting that some of the repair and maintenance costs used herein are annualized estimates of 

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

The LCC and PBP model are developed using Microsoft Excel spreadsheets combined 

with Crystal Ball, a commercially available add-in for modeling uncertainty. DOE estimated 

many inputs for the LCC and PBP analysis from the best available data, and in some cases DOE 

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 carried out the LCC and 

PBP analysis in the form of Monte Carlo simulations using ranges of values and probability 

distributions for certain inputs that account for the uncertainties. DOE presents the 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 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 base-case scenario is 

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

conservation standard. A standards-case scenario is a scenario in which customers purchase 

equipment after the hypothetical energy conservation standard goes 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 level 1 represents a potential 

new standard, or a candidate standard level (CSL). Usually, the market will offer equipment at 

various efficiencies. Therefore, for both 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 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. 
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 unique to each equipment class. Therefore, DOE assumed 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 presented 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, and 

the MIA. Shipments analysis is used to estimate future ACIM equipment shipments over the 

national impact analysis period. 

DOE calculated the historical 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 

Outlook3 (AEO2013) forecasts of commercial floor space additions, by business type, to 

calculate the future shipments. 

TSD chapter 9 presents the mathematical formulation of the shipment analysis model and 

the methodology used to estimate historical and future shipments of ACIM equipment.

2.10 NATIONAL IMPACT ANALYSIS

DOE quantifies national impacts of energy conservation standards using two metrics: 

(1) NES; and (2) NPV of future savings. NES and NPV impacts are the cumulative energy and 

economic effects of an amended ACIM equipment energy conservation standard (TSD 

chapter 10). The NES values, expressed in quads (quadrillion British thermal units), are the 

cumulative energy savings as a result of the new standards for equipment purchased over the 30-

year time frame of DOE’s analysis, 2018–2047. The NPV of future savings is also calculated for 

equipment purchased between 2018 and 2047. NPV is expressed in constant 2012$ discounted to 

the present year (2013).

DOE analyzed 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 market 
2-11

influences, national economic growth, and electricity consumption. DOE then derived energy 

savings for various CSLs from the cost-efficiency schedules.

To make the analysis more accessible and transparent to all stakeholders, DOE used an 

Excel spreadsheet model to calculate the NES and the NPV. Users can change input quantities 

within the spreadsheet to test the impact of alternative input assumptions. Unlike the LCC 

analysis, the NES spreadsheet does not use distributions for inputs or outputs. Users can 

demonstrate sensitivities by running different scenarios using the spreadsheet. 

As discussed in TSD chapter 10, the national impact analysis 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 consumption with and without amended standards. The 

base case, which is the projected U.S. energy consumption without standard, includes the mix of 

efficiencies being sold at the time the standard becomes effective.

DOE estimated national energy consumption for each year beginning with the expected 

effective date of the standard. DOE calculated national annual energy savings as the difference 

between two projections: a base case and a standards case. 

DOE has 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 the 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. 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 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 is 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 averaging effect of the LCC and PBP analysis. Therefore, 

DOE carried out the customer subgroup analysis by using the LCC and PBP analysis 

spreadsheet, but applying the inputs that are applicable only to the identified subgroups. 

In general, the subgroups that face higher cost of capital and lower electricity price rates 

are more disadvantaged than others. Higher cost of capital imposes burden on the businesses 

because they have to borrow additional capital to purchase equipment that meets new or 

amended standards, compared to the case of where there are no new or amended standards. 

Lower electricity price rates result in lower savings in energy costs and, consequently, lower 

LCC savings and 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 impacts include financial effects, both quantitative and 

qualitative, that might lead to changes in the manufacturing practices for this equipment. DOE 

identified these potential impacts through interviews with manufacturers and other interested 

parties. 

DOE conducted the MIA in three phases, and further tailored the analytical framework 

based on interested parties’ comments. In Phase I, an industry profile was created to characterize 

the industry, and a preliminary MIA was conducted to identify important issues that required 

consideration. In Phase II, an industry cash flow model and an interview questionnaire were 

prepared to guide subsequent discussions. In Phase III, manufacturers were interviewed, and the 

impacts of standards were assessed both quantitatively and qualitatively. Industry and subgroup 

cash flow and NPV were assessed through use of the Government Regulatory Impact Model 

(GRIM). Then impacts on competition, manufacturing capacity, employment, and 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. 
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2.13 EMISSIONS ANALYSIS 

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

CO2, NOx, SO2, and Hg from potential energy conservation standards for ACIM equipment. In 

addition, DOE estimated emissions impacts in production activities (extracting, processing, and 

transporting fuels) that 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 (CH4) and nitrous 

oxide (N2O), 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 

separate emissions factors for power sector emissions and upstream emissions. The method that 

DOE used to derive emissions factors is described in chapter 13 of the NOPR TSD.

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

projected 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, 2012.

SO2 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 SO2 for affected EGUs in the 48 contiguous States and the District of Columbia 

(D.C.). SO2 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. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, LP v. 

EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering CAIR. 

The AEO2013 emissions factors used for the NOPR assume that CAIR remains a binding 

regulation through 2040.

The attainment of emissions caps is typically flexible among EGUs and is enforced 

through the use of emissions allowances and tradable permits. Under existing EPA regulations, 

any excess SO2 emissions allowances resulting from the lower electricity demand caused by the 

adoption of an efficiency standard could be used to permit offsetting increases in SO2 emissions 

by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about 

the effects of efficiency standards on SO2 emissions covered by the existing cap-and-trade 

system, but it concluded that no reductions in power sector emissions would occur for SO2 as a 

result of standards.
2-14

Beginning in 2015, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 

2011. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for HCl 

as a surrogate for acid gas hazardous air pollutants (HAP), and also established a standard for 

SO2 (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, SO2 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 

SO2 emissions. Under the MATS, NEMS shows a reduction in SO2 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 SO2 emissions allowances resulting 

from the lower electricity demand would be needed or used to permit offsetting increases in SO2

emissions by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce 

SO2 emissions in 2015 and beyond.

CAIR established a cap on NOx emissions in eastern States and the District of Columbia. 

Energy conservation standards are expected to have little or no physical effect on these emissions 

in those States covered by CAIR because excess NOx emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOx emissions. 

However, standards would be expected to reduce NOx emissions in the States not affected by 

caps, so DOE estimated NOx emissions reductions from the standards considered in the NOPR 

for these States.

The MATS limit mercury emissions from power plants, but they do not include emissions 

caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. DOE 

will estimate mercury emissions reduction using emissions factors based on AEO2013, which 

incorporates the MATS.

2.14 MONETIZING CARBON AND OTHER EMISSIONS REDUCTIONS BENEFITS

DOE considered the estimated monetary benefits likely to result from the reduced 

emissions of CO2 and NOx that are expected to result from each of the standard levels 

considered.

To estimate the monetary value of benefits resulting from reduced emissions of CO2, 

DOE used the most current Social Cost of Carbon (SCC) values developed and/or agreed to by 

an interagency process. The SCC is intended to be a monetary measure of the incremental 

damage resulting from GHG emissions, including, but not limited to, net agricultural 

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 questions of science, economics, and ethics. But with full regard for the 

limits of both quantification and monetization, the SCC can be used to provide estimates of the 

social benefits of reductions in GHG emissions. 
2-15

The Interagency Working Group on SCC 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 CO2 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 CO2 emissions. 

DOE multiplies the CO2 emissions reduction estimated for each year by the SCC value 

for that year in each of the four cases. To calculate a present value of the stream of monetary 

values, DOE 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 CO2 and other GHGs 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 NOx emissions resulting 

from the standard levels it considers. For NOx 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 NOx and real discount rates of 3 percent and 7 percent. 

DOE is evaluating appropriate valuation of avoided SO2 and Hg emissions. Whether 

monetization of reduced Hg emissions will occur in this rulemaking is yet to be determined.

2.15 UTILITY IMPACT ANALYSIS

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 (NEMSBT)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 States Government, May 2013. 

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

d For additional information, refer to U.S. Office of Management and Budget, Office of Information and Regulatory 

Affairs, 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on 

State, Local, and Tribal Entities, Washington, 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 NEMSBRS.
2-16

changes in electricity consumption. See NOPR TSD chapter 15 for more information on DOE’s 

utility impact analysis for this rulemaking. 

2.16 EMPLOYMENT IMPACT ANALYSIS

The imposition 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 evaluates direct 

employment impacts in the MIA. Indirect employment impacts may result from customers 

shifting expenditures between goods (the substitution effect) and from changes in income and 

overall expenditure levels (the income effect). DOE used Pacific 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 employment 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 investments.

2.17 REGULATORY IMPACT ANALYSIS

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 Regulatory Affairs at the OMB. The RIA 

addressed the potential for non-regulatory approaches to supplant or augment energy 

conservation standards to improve the energy efficiency or reduce the energy consumption of the 

ACIM equipment covered under this rulemaking.

2.18 DEPARTMENT OF JUSTICE REVIEW

Section 325(o)(2)(B)(i)(V) of the Energy Policy and Conservation Act states that before 

the Secretary of Energy may prescribe a new or amended energy conservation standard, the 

Secretary shall ask the Attorney General to make a determination of “the impact of any lessening 

of competition...that is likely to result from the imposition of the standard.” (42 U.S.C. 

6295(o)(2)(B)(i)(V); 6314(d)(4)) Pursuant to this requirement, DOE 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 at 

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/buildings/appliance_standards/rulemaking.aspx/ruleid/29 >

3. U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 

2013, 2013. Washington, DC. DOE/EIA-0383(2012). 
3-i

CHAPTER 3. MARKET AND TECHNOLOGY 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 ASSESSMENT..................................................................................... 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 ............................................................................................................................ 3-1
3-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 Shipments of Open Refrigerated Display Cases (Remote Condensing and SelfContained).......................................................................................................... 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 (oF (oC))................................................................ 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. Harvest Rate (Air-Cooled Cube Remote Condensing Units 

[Remote Compressor and Non-Remote Compressor])...................................... 3-35

Figure 3.3.9 Energy Consumption vs. Harvest Rate (Air-Cooled Cube Self-Contained Units)3-36

Figure 3.3.10 Energy Consumption vs. Harvest Rate (Water-Cooled Cube 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).......................................... 3-40
3-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 SelfContained 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 SelfContained Units)................................................................................................ 3-45

Figure 3.3.19: Condenser Water Consumption vs Harvest Rate (Water-Cooled Flake/Nugget IceMaking Head Units)........................................................................................... 3-46

Figure 3.3.20: Condenser Water Consumption vs Harvest Rate (Water-Cooled Flake/Nugget 

Self-Contained Units) ........................................................................................ 3-46
3-1

CHAPTER 3. MARKET AND TECHNOLOGY 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 airor water-cooled condensers; ice-making heads with air- or water-cooled 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 types,

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

Energy Policy and Conservation Act (42 U.S.C. 6311–6317; 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), standards 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-making (ACIM) equipment industry and market structure based on publicly 

available information and information submitted by manufacturers and other stakeholders.

Manufacturer characteristics and market shares, existing regulatory and non-regulatory 

efficiency improvement initiatives, equipment classes, and trends in markets and equipment 

characteristics are addressed. The purpose of the technology assessment is to develop a 

preliminary list of technologies that could improve the efficiency of automatic commercial ice 

makers.

Automatic commercial ice makers are used in several types of commercial sectors, 

including health care, lodging, foodservice, retail, education, food sales, and office buildings.

Definitions

EPCA defines “automatic commercial ice maker” as a factory-made assembly (not 

necessarily shipped in one package) that:

1. consists of a condensing unit and ice-making section operating as an integrated unit, 

with means for making and harvesting ice; and

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

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

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

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

added the definition 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-making 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-contained automatic commercial ice makers (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 integral cabinet. Self-contained automatic commercial ice 

makers tend to have lower harvest capacities than other equipment types, are typically 

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

counters.

2. Ice Making Head (IMH): An ice-making head is a category in which all of the icemaking machinery, including freezing, harvesting, and condensing components, are

integrated into one unit. These units do not include a means for storing ice. Icemaking heads are generally mounted on top of storage bins (which are sold 

separately).

3. Remote Condensing Ice Makers (RCU): For automatic commercial ice makers, two 

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 containing the ice 

freezing and harvesting equipment, typically with the condenser in a remote 

condensing unit. This configuration can also be designed for connection to an existing 

remote compressor rack and is referred to as “remote condensing and remote 

compressor” in this rulemaking. Ice makers equipped with remote 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 loops 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 

systems that use water one time for cooling and then release it into the wastewater 

system. 
3-3

Most remote condensing ice makers feature 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 maker can produce in 24 hours. 

DOE current defines 13 different product classes for automatic commercial ice makers in 

10 CFR 431 Subpart H (42 U.S.C. 6313(d)(1)). These are shown in Table 3.1.1 below.

Table 3.1.1: Current Equipment Classes for Automatic Commercial Ice Makers

Equipment Type Type of 

Cooling

Harvest Rate

lb ice/24 hours

Ice-Making Head

Water

<500

≥500 and <1,436

≥1,436

Air <450

≥450

Remote Condensing (but not remote 

compressor) Air <1,000

≥1,000

Remote Condensing and Remote 

Compressor Air <934

≥934

Self-Contained

Water <200

≥200

Air <175

≥175

3.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade association, domestic 

manufacturers of automatic commercial ice makers, manufacturer market share, regulatory 

programs, and non-regulatory initiatives; defines equipment classes; provides historical shipment 

data, shipment projections, and equipment lifetime estimates; and summarizes market 

performance data.

3.2.1 Trade Association

The Air-Conditioning, Heating, and Refrigeration Institute (AHRI)) is the U.S. trade 

association for automatic commercial ice maker manufacturers. A merger by the former AirConditioning and Refrigeration Institute, or ARI, and the former Gas Appliance Manufacturers 

Association, or GAMA resulted in the creation of AHRI. AHRI coordinates with member 

manufacturers to establish the Automatic Commercial Ice Makers and Ice Storage Bins division
3-4

within AHRI. This division serves to develop standards and implement a certification program 

for automatic commercial ice makers and storage bins.

The technical activities of AHRI includeb

:

• working to harmonize international equipment standards;

• developing industry performance standards for automatic commercial ice makers;

• updating industry guidelines for operation and installation of ACIM equipment;

• communicating with refrigerant suppliers and government agencies about 

environmentally acceptable refrigerants; and,

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

3.2.2 Manufacturers and Market Share

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

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

1

• Hoshizaki America, Inc.

• IMI Cornelius, Inc.

• ITV Ice Makers SA

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

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

• Mile High Equipment LLC/Ice-O-Matic (Scotsman Industries)

• Scotsman Ice Systems (Scotsman Industries)

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

are shown in parentheses if applicable.

• A&V Refrigeration

• Arctic-Temp/Holiday Ice, Inc.

• Brema Ice Makers

• Follett Ice Makers (VGI Holdings Corp)

• Howe Corporation

• North Star Ice

• Orien U.S.A.

• Summit Appliance (Felix Storch, Inc.)

• Vogt Ice, LLC (Vogt Power International)

According to a recent study 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 LLC/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.aspx
3-5

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 published in 1996. At about the same time that the data on Figure 3.2.1 was compiled, the 

parent company of Mile High Equipment LLC/Ice-O-Matic was acquired by Enodis, PLC. 

Enodis then acquired Scotsman industries in 1999.3 Thus, these two companies no longer existed 

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. The Manitowoc purchase of Enodis, 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 sold

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

would use the Scotsman name.4,5

Thus, the ice-making companies have been consolidated since this data was collected.

Based on DOE’s latest NOPR information, the three largest firms—Manitowoc, Scotsman/IceO-Matic, and Hoshizaki—are believed to have increased their market share to approximately 95

percent of the market in the United States.

Manitowoc

30%

Scotsman

25%

Hoshizaki

20%

Mile High 

Equipment 

LLC/Ice-OMatic

15%

IMI Cornelius 

and Others

10%
3-6

3.2.2.1 Small Businesses

DOE has considered 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 studied 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 small 

business manufacturers 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, several states (Arizona, California, Oregon, and Washington)

have established efficiency regulations for automatic commercial ice makers. The state standards 

all regulate cube type ice makers only. California’s standards were adopted in 2004, and were

based on the Consortium for Energy Efficiency (CEE) Tier 1 and Federal guidelines 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 Federal standards set forth in EPACT 2005 and effective 

January 1, 2010. 

3.2.2.3 Natural Resources Canada

The Natural Resources Canada (NRCan) Office of Energy Efficiency established energy

efficiency standards for automatic commercial ice makers on December 31, 1998. The NRCan 

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 equipment 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 produce other than cube type ice. The NRCan efficiency 

standards also establish minimum storage effectiveness levels for ice storage bins on selfcontained 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

automatic commercial ice makers, with harvest capacity converted to lb/ 24 hours. 

NRCan does not require adjustments of continuous ice maker usage for ice hardness. Nor 

does the continuous ice maker efficiency standard differentiate by the type of equipment (i.e., 

ice-making head, self-contained, units with remote condensing). The NRCan website indicates 

the continuous process efficiency standard went into effect January 1, 2000.

Table 3.2.1 NRCan Efficiency Standard for Continuous Automatic Commercial Ice Makers

Equipment Type Type of 

Cooling

Ice-Making Capacity

kg/24 hours

(lb/24 hours*)

Maximum Energy

Use

kJ/kg

Maximum Energy

Use

kWh/100 lb**,***

All Continuous Machines Air < 300 (660)

≥ 300 (660)

875.2 – 1.122 x C

538.6

11.03 – 0.0064 x C

6.79

All Continuous Machines Water < 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 capacity (ice 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 have 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 consumption per 

100 kg of ice. The Australian standards categorize equipment by the following criteria:

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

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

• split system, air-cooled, remote condensing but not remote compressor

• split system, air-cooled, remote condensing and remote compressor

• storage systems
3-8

The standards apply to cube, flake, and nugget machines without distinguishing between 

the machines and, apparently, 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 promoting energy efficient automatic

commercial ice makers in the United States, ENERGY STAR®, CEE, and the Federal Energy 

Management Program (FEMP) procurement program. DOE also reviewed various rebate 

programs offered by utilities.

3.2.3.1 ENERGY STAR

Since the publication of the preliminary analysis, the U.S. Environmental Protection 

Agency (EPA) has issued an updated set of ENERGY STAR standards. The ENERGY STAR

Version 2.0 standards issued by the EPA have been effective since February 1, 2013 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 between efficiency and harvest capacity.

Table 3.2.2 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for AirCooled 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 ≤20.0

RCU 

400 ≤ H ≤ 1600 ≤ 22.95 * H -0.258 + 

1.00 ≤20.0

1600 ≤ H ≤ 4000 ≤ -0.00011 * H + 

4.60 ≤20.0

SCU 50 ≤ H ≤ 450 ≤ 48.66 * H -0.326 + 

0.08 ≤25.0

kWh = kilowatt-hours, gal = gallons

Source: www.energystar.gov
3-9

Table 3.2.3 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for AirCooled 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 -0.349 + 

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).

Table 3.2.4 Performance Requirement for Federal Purchases of Water-Cooled Ice Makers

Type Ice Harvest Rate

lb/24 hours

Energy Use

kWh/100 lb*

Self-Contained Unit ≤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 Management Program Covered Products website, 

www1.eere.energy.gov/femp/technologies/eep_purchasingspecs.html. * Measured in accordance with 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 for cube type machines. CEE set Tier 1 to be equivalent to FEMP standards, most 

recently the FEMP standards effective as of January 1, 2010. CEE set Tier 2 to be 10 percent 
3-10

below Tier 1, and Tier 3 to be 15 percent below Tier 1. ENERGY STAR standards corresponded

approximately with Tier 2 (for air-cooled machines). CEE standards set limits on both energy 

and water use. The water use is separated into potable water use and cooling water use for watercooled machines.

Beginning on July 1, 2011, CEE revised their system, making the Tier 1 equal to the 

previous Tier 2 levels, and setting the new Tier 2 to 10 percent lower than Tier 1.10 The new 

CEE efficiency requirements for air-cooled ice makers are shown in Table 3.2.5, while the new 

requirements for water-cooled ice makers are shown in 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: RemoteCondensing without 

remote compressor

<1,000 8.05 - 0.0035H ≤25

≥,1000 4.64 ≤25

Cube Type Machines 

Only: RemoteCondensing 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 (pre7/1/2011)

Cube Type Machines

Only: Ice-Making 

Head

<500 7.02 – 0.0049H ≤25

≥500 and <1436 5.13 – 0.0010H ≤25

≥1436 3.68 ≤25

Cube Type Machines

Only: Self-Contained

<200 10.6 – 0.0177H ≤35

≥200 7.07 ≤35

Tier 2

Approximately

10% More 

Efficient than 

Average 

Performance

Cube and Nugget

Type Ice Machines

<175 10.6 - 0.0241H ≤30

≥175 and <450 7.1 - 0.0062H ≤20

≥450 and <1000 4.7 - 0.0011H ≤20

≥1,000 3.7 - 0.0002H ≤20

Flake Type Ice

Machines

<1,000 4.8 - 0.0017H ≤20

≥1,000 3.1 ≤20

3.2.3.3 Rebate Programs

Numerous organizations and entities throughout the United States offer rebate programs 

for customers who purchase and install qualified automatic commercial ice makers. Twenty three

utilities, governmental agencies, and other entities operate rebate programs specifically to 

provide incentives to customers who purchase ENERGY STAR compliant ice makers. The 

rebate programs, which can be found on the ENERGY STAR website by using the “Commercial 
3-12

Food Service Equipment Incentive Finder,” offer rebates ranging from $50 to $600 for 

qualifying purchases. 

The EnergySmart Grocer Program is funded by California utility ratepayers under the 

auspices of the California Public Utilities Commission. Eligible participants include grocery and 

convenience stores, food processors, and refrigerated warehouses operating in the Pacific Gas 

and Electric Company (PG&E) electric service territory. The program offers rebates for use 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 specifications to $500 

for large equipment meeting CEE Tier 3 specifications.12

3.2.4 Equipment Classes

Automatic commercial ice makers are divided into equipment classes categorized by 

physical characteristics that affect commercial application, equipment utility, and equipment 

efficiency: (1) the ice-making process; (2) the configuration of the ice-making and refrigeration 

systems; (3) the type of cooling media used; and (4) the capacity of the unit. The following list 

shows the key characteristics of automatic commercial ice makers that DOE is proposing to use

for this rulemaking to distinguish equipment classes:

1. ice-making process

• continuous

• batch

2. equipment configuration

• ice-making head

• remote condensing (but not remote compressor)

• remote condensing and remote compressor

• self-contained

3. condenser cooling media

• air-cooled

• water-cooled

4. capacity range

• various

Table 3.2.7 shows the automatic commercial ice-making equipment classes 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

≥1,000 and <4,000 Continuous

Air ≥50 and <1000 Continuous

≥1,000 and <4,000 Continuous

Remote Condensing 

(but not remote compressor) Air ≥50 and <1000 Continuous≥1,000 and <4,000 Continuous

Remote Condensing and Remote 

Compressor Air ≥50 and <1000 Continuous≥1,000 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 Framework document, AHRI submitted 2010 shipment 

data by equipment class for its member companies, and historical information on total shipments. 

DOE used this shipments data as part of the basis for this analysis. DOE understands that this

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

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

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

Table 3.2.8 shows 2010 annual shipments reported by AHRI for each category of 

automatic commercial ice maker by equipment class. Note that the AHRI data have been 

aggregated to match the primary equipment groupings, meaning that for batch and continuous ice 

maker types the remote condensing unit ice makers have been aggregated into one group, 

distinguished only by harvest rates. AHRI’s data include information to distinguish between 

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

remote compressors.
3-14

Table 3.2.8 AHRI 2010 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 2010 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 2009. NAFEM 

published similar study reports for shipments in 200814 and 2006.15

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 provided 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 Bureau Data

The U.S. Census Bureau’s Current Industrial Report (CIR) series publishes statistics on 

the quantity and value of shipments based on a survey of manufacturers. The CIR has been 

published since 1904, and DOE collected CIR data going back to the 1940s for purposes of 

evaluating prices and shipments. The Census has not updated its 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 Condensing and SelfContained)

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. Census Bureau data contain some limited shipments data 

that would be useful for conducting technical analyses, not enough detail is available to provide 

specific assessments for shipments within each of the primary categories covered in this 

rulemaking.

3.2.6 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 in 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 lifetime of 7 to 10 years, with some equipment being retired after 

as short a period as 5 years in cases of building remodels and as long as 20 years in cases where 

the equipment owners did not care about the appearance of the equipment. 
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 Reference

7 to 10 years Arthur D. Little, 199616

8.5 years California Energy Commission, 200417

8.5 years Fernstrom, G., 200418

8.5 years Koeller J., and H. Hoffman, 20082

7 to 10 years Navigant Consulting, Inc. 200919

3.3 TECHNOLOGY ASSESSMENT

This section provides a technology assessment for automatic commercial ice makers.

Contained in this technology assessment are details about product operations and components 

(section 3.3.1), an examination of possible technological improvements 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. Ice makers that have no integral storage bin generally deliver ice by gravity (i.e., dropping 

the ice) into a bin or other equipment, such as a beverage/ice dispenser, on which the ice maker 

is mounted when in use. The refrigeration system may be entirely contained in a single unit, or 

the condenser and possibly the compressor may be located remotely. 

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

heads (IMHs), remote condensing units (RCUs), and self-contained units (SCUs). IMHs have the 

highest sales levels and are available in the widest range of capacities. They are generally 

mounted on top of a separate storage bin or other equipment that uses the ice. The indoor 

sections of RCUs are similar to IMHs, except that they are mated with remote condensers 

intended to be located outdoors, rejecting heat directly to the outside air without adding to the 

interior air-conditioning load. SCUs have their own integral ice storage bins. They generally 

have smaller harvest capacities than IMHs and RCUs, and most of these units are designed for 

under-counter placement.

There are two distinct ice-making processes that are used by commercial ice makers:

• The batch process involves alternate freezing and harvesting periods, producing 

batches of solid ice by allowing water to flow over an evaporator and freeze into 
3-17

cubes, tubes, or another shape depending on the evaporator geometry. Once the ice is 

fully formed, the refrigeration system switches into harvest mode, in which 

compressor heat is transferred to the evaporator to release the ice, which is 

subsequently moved to storage.

• The continuous process makes flake and nugget-shaped ice by continuously scraping 

ice as it freezes on a cylindrical evaporator surface. Most commercial flake and 

nugget ice makers use a stationary evaporator that freezes incoming water on the 

enclosed inner surface. These evaporators contain a rotating auger that scrapes the ice 

off the evaporator and extrudes it upward. The auger forces the ice through exit 

orifices past a cutter, whose specific geometry dictates the ice format: in nugget ice 

makers, the flakes are converted into nuggets using an extrusion head with smaller 

openings that compresses the flakes to form nuggets. After passing through the entire 

evaporator, the flakes or nuggets typically drop down into the ice bin.

All commercially available ice makers use vapor compression refrigeration systems to 

provide the refrigeration needed for ice production. Table 3.3.1 provides descriptions for the key 

refrigeration components. Aside from the evaporator, ice makers use the same refrigeration 

components that are used in other medium-temperature commercial refrigeration applications. 

Ice makers use either air-cooled or water-cooled condensers. Air-cooled condensers are used in 

roughly 90 percent of units shipped.

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 higher water consumption because condenser water is most often drained after it is used to 

cool the condenser. Water-cooled ice makers can, however, be used with closed-water-loop 

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.

WaterCooled

• Concentric tube heat exchanger with steel outer and copper or cupronickel inner 

tube, possibly enhanced surface area using spiral grooving. Water flow 

controlled by the supply valve to maintain a constant preset condensing

temperature.

Expansion Device • Most ice makers use conventional thermostatic expansion valves. Electronic 

valves are rarely used. Smaller-capacity ice makers may use capillary tubes.

Evaporator • Copper tubing attached to copper or stainless steel ice-making surfaces (see 

specific descriptions of batch and continuous evaporators below).

Liquid Line/Suction Line 

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 batch process ice machines.

Figure 3.3.1 shows key components of a generic refrigeration cycle used for batch icemaking. 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 Type Ice Makers
3-19

There are five basic evaporator designs used by manufacturers in the ice maker industry 

today for ice makers with harvest capacities in the commercial range (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 capacities 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 cubeshaped cavities to form ice cubes.

Hoshizaki 

Style Hoshizaki

Crescent Cube Vertically oriented pair of stainless steel surfaces 

shaped to ensure 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 facing 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 sources:

Vertical Grid – Manitowoc: www.manitowocice.com

Hoshizaki Style: www.ccfse.com/Hoshizaki-Crescent-Ice-Cuber-p/km-650mah.htm

Horizontal Grid – Kold-Draft: www.kold-draft.com/ice-making-technology/ice-sizes.php

Tube Ice: www.prithvinigen.com/tube_ice.jpg
3-20

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 the 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 sump 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. The continuous fill technique is common in selfcontained 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 the 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 compressor 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) measuring 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 

drained. Depending on ice maker design details, the liquid may or may not be 

completely drained, and there may or may not be fresh potable water flowing during 

Water 

Pump

Sump

Water Distributor

Water 

Supply

Purge Drain 
3-22

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

from 18–40 gallons 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 the incoming water for the next batch of ice. Harvest times range from less than 

1 minute up to 2 minutes.

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

into small pieces as it falls. 

3.3.1.3 Continuous Process

This section describes the “continuous” ice-making process, including the refrigeration 

cycle, the evaporator designs, and the water supply systems currently used for continuous icemaking in the ice maker industry. As previously stated, the continuous process is used to make 

flake and nugget ice.

Figure 3.3.4 shows the typical refrigeration cycle used for continuous ice-making. The 

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

hot-gas bypass line, because there is no need for a harvest cycle.
3-23

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 refrigerant. 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-type 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 the evaporator.

Compressor

Air or Water Condenser

Evaporator

Thermostatic 

Expansion Valve

Liquid-line 

Filter Drier

Suction-Line 

Heat Exchanger
3-24

(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-Section 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 discussed 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 identified 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 condenser 

fans 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 water thermal exchange √

3.3.2.1 Compressor

The compressor 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 reciprocating compressors are the most commonly 

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

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

use three-phase motors.

Ice maker compressor capacities range from approximately 1,500 British thermal units 

(Btu) per hour to 40,000 Btu/hr. Three organizations have established conditions for rating the 

performance of compressors: Air-conditioning, Heating, and Refrigeration 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 ratings are based on the premise 

that refrigerant tubing connecting system components are adiabatic (i.e., that they undergo no 

thermal exchange with the environment) and that they don’t impose refrigerant pressure drop. 

Note that the actual operating conditions for compressors in ice makers under DOE energy test 

conditions can be different than these rating conditions. In fact, batch ice maker compressors 

operate over a range of conditions during each ice-making cycle.

Table 3.3.4 Compressor Rating Conditions (oF (oC))

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 = medium back pressure *

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 compressor 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 called 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 not 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-Load Operation

Part-load operation may be achieved using variable-speed, variable-capacity, or dualcapacity 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 variable-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 rows across the direction of air flow. These measures can be 

limited by the geometry of the ice maker. Increasing tube width can also result in an increased 

refrigerant pressure drop across the condenser, so there is a tradeoff between improving heat 

transfer to reduce condensing temperature and increasing condenser inlet pressure due to the 

higher pressure drop. Similarly, adding tube rows can increase the air pressure drop through the 

condenser, so there is a tradeoff between improving heat transfer and increasing fan work.

The surface area of water-cooled ice makers can also be increased through the use of 

larger coaxial condenser designs.

Increasing condenser size also has implications associated with limits on equipment size. 

Increasing the size of an ice maker to allow for a condenser size increase may not be acceptable 

because many ice makers have standard sizes. Such limitations may be less important for remote 

condensers, however. There is often greater potential for increase in the size of 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 fan/motor assemblies, and space for air flow 

generally take up much more space than water-cooled condensers.

One issue to consider when implementing condenser size increase is the added refrigerant 

charge associated with such a design change. The added refrigerant can add cost, but also can 

increase the challenges associated with refrigerant management throughout both the freeze and 
3-28

harvest cycles of batch ice makers. While few IMH-style batch ice makers have refrigerant 

receivers, an increase of condenser size can potentially lead to required use of receivers to ensure 

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 Chapter 5 of the NOPR TSD.

Enhanced Fin Surfaces (Air-Cooled 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 makers already take advantage of 

such enhanced fin surfaces. 

Increased Air Flow (Air-Cooled Models)

Increasing the air flow rate through the condenser will increase the heat removal rate and 

reduce condensing temperature. However, increasing air flow generally requires increased fan 

input power, so there are limits to 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-Cooled Models)

Increasing the condenser water flow rate for water-cooled ice makers will improve heat 

transfer. However, this measure can significantly increase costs associated with water 

consumption in installations where condenser water is drained to a building’s 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-Cooled 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 are used to draw or blow air through air-cooled condensers. Condenser fans used in 

ice makers almost exclusively are axial design. 

One source of inefficiency for axial fans lies in their tendency to throw air outward. The 

Pax GroupTM has developed a fan (PAX fan) that employs streamlined blades with patented 

geometrical shapes that reportedly provide better air flow direction and improved efficiency. 

Though a prototype has not yet been developed for ice makers, Pax is working to optimize the 

fans for other commercial refrigeration equipment, including supermarket display cases and 

vending machines. Tests performed with the PAX fan have demonstrated a reduction in fanmotor power of 15 to 20 percent.21 At this point, because the PAX fan is proprietary, the 

widespread use of the design is highly uncertain.

Most condenser fan motors used in products today have shaded pole induction designs 

with efficiencies between 20 and 30 percent, or permanent split capacitor (PSC) designs, which 

reach efficiencies of 40 to 65 percent. Brushless DC motors can have efficiencies up to 80 

percent, thus representing an option for further 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 maker pumps use shaded-pole motors. However, PSC or brushless-DC 

motors could also be used with ice maker pumps.

3.3.2.6 Demand Response Controls

Ice makers can be integrated into communication infrastructures that allow electric utility 

controllers to broadcast demand response events to customer electric meters, which then 

communicate with the target appliance. The ice maker must be configured with appropriate 

hardware and software to enable two-way communication. According to demand response 

studiese

, 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 demand 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 and continuous ice makers.

Batch ice maker evaporator design requires finding a careful balance between the ice 

growth behavior, water flow rate over the evaporator, localized water distribution, materials 

selection, and harvest performance (e.g., successful ice detachment with little meltage). 

Evaporator design is a complex process not amenable to analysis, and developing a successful 

evaporator design requires many hours of laboratory testing. Manufacturers are very reluctant to 

change the evaporator design once a successful design has been developed.

Increased Evaporator Size

A larger evaporator size would allow for a 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. These 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 alternative designs to reduce 

this loss. Numerous patents suggest evaporator designs with reduced thermal mass or otherwise 

improved performance. However, performance data is publicly available for few of these 

designs, perhaps just for one: the evaporator design used in most Hoshizaki cube ice machines. 

DOE requested 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 

harvest capacity because it reduces total cycle time associated with a given batch weight. Harvest 

meltage has been reduced in cube ice makers by ensuring smooth evaporator surfaces, allowing 

buildup of ice bridges connecting the cubes, and employing harvest assist. 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 at 
3-31

once, which can help ensure harvest of individual 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 side23 (this method is used by Scotsman and Ice-OMatic ice makers) or by blowing air behind the ice sheet from the rear of the evaporator to create 

pressure to remove the ice24 (this method is used by Manitowoc ice makers). The speed 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 potable water to help in warming the ice. 

Additional design adjustments may be possible to further improve harvest to reduce harvest 

meltage or reduce harvest time, but information is not publicly available detailing such additional 

options and/or their effectiveness.

3.3.2.8 Improved or Thicker Insulation

Insulation can be used around the evaporator compartment and the sump to minimize the 

thermal load through the walls of these parts of the ice maker. Ice maker manufacturers use 

varying amounts of insulation. Some manufacturers simply place a piece of batt insulation 

between the ice-making and compressor compartments, while other manufacturers 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 consumption. 

3.3.2.9 Larger Diameter Suction Line (Remote Compressor Models)

Remote condenser units must be tested with at least 25 feet of interconnecting refrigerant 

piping between the condensing 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 additional 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 velocities. 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 freeze 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 drain 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 

function 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 contained 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 this 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 standard 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 figures 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 chapter 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 energy use ratings (less than 3 

kWh/100 lb). DOE expects that these data points represent models that are intended to be 
3-33

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 Consumption 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 specified by the DOE Test 

Procedure.
3-39

Figure 3.3.12 Energy Consumption vs. Harvest Rate (Water-Cooled Flake/Nugget IceMaking Heads)

Note: The ice makers in this figure 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)

Note: 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 SelfContained 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 SelfContained 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 region 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

The 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 Condenser 

Water Standard

IMH-W-B 200 – 0.022 * H 200 – 0.022 * H

SCU-W-B 191 – 0.0315 * H 191 – 0.0315 * H

IMH-W-C No current standard 160 – 0.0176 * H

SCU-W-C No current standard 153 – 0.0252 * H
3-1

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<www.warburgpincus.com/PDF/Enodis%20PRESS%20RELEASE%205-15-

09%20final.pdf>

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<www.sba.gov/gopher/Financial-Assistance/Size-Standards/sizetablestandards.txt> The 

June 25, 2007 material from this website is available in Docket EE-2006-STD-0126. For 

more information, contact Brenda Edwards-Jones at (202) 586-2945.

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 4865:3:2008.

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 dated 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_purchasingspecs.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%20EnergySmartGrocer%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 Association 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 Regulations. 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. 2004. 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. Submitted to the U.S. Department of 

Energy’s Energy Efficiency and Renewable Energy Building Technologies Program, 

Washington, D.C. 

<http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/commercial_refrig_rep

ort_10-09.pdf>

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, IL. 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 TECHNOLOGIES ...................................................................................... 4-4

LIST OF TABLES

Table 4.2.1 Screening Justification.............................................................................................. 4-4
4-1

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-making (ACIM) equipment.

In the market and technology assessment—chapter 3 of the notice of proposed 

rulemaking (NOPR) technical support document (TSD)—DOE presents an initial list of 

technologies that can improve the energy efficiency of automatic commercial ice makers. The 

purpose of the screening analysis is to evaluate the technologies that improve equipment 

efficiency to determine which technologies to consider further and which to screen out. DOE 

consulted with a range of parties, including ACIM industry representatives, technical experts, 

and others, to develop a list of technologies for consideration. DOE evaluated the technologies

pursuant to the criteria set out in the Energy Policy and Conservation Act (42 U.S.C. 6311–6317; 

EPCA), as amended.

EPCA establishes criteria for prescribing new or amended standards designed to achieve 

the maximum improvement in energy efficiency. Further, EPCA directs the Secretary of Energy 

to determine whether a standard is technologically feasible and economically justified. (42 

U.S.C. 6295(o)(2)(A)–(B) and 6313(d)(4))

Appendix A to subpart C of 10 CFR part 430, “Procedures, Interpretations and Policies 

for Consideration of New or Revised Energy Conservation Standards for Consumer Products”

(the Process Rule), sets forth procedures to guide DOE in its consideration and promulgation of 

new or revised equipment energy conservation standards. These procedures elaborate 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 guide DOE in determining 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 6313(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 volumes that are 
4-2

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 consideration. If a technology fails to meet one or more of the four criteria, it 

will be screened out. Section 4.2 documents the reasons for eliminating the technologies that

were screened out.

4.2 SCREENED-OUT TECHNOLGIES

This section describes the technologies that DOE eliminated for failure to meet one of the 

following four criteria: (1) technological feasibility; (2) practicability to manufacture, install, and 

service; (3) impacts on equipment utility or equipment availability; and (4) adverse impacts on 

health or safety. The discussion also points out which technologies have not been analyzed in the 

engineering and downstream analyses for other reasons, including that they don’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 

benefits associated with operation of an ice maker compressor 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 that 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 represent an option for improved 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 watercooled 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 during the NOPR phase; these analyses are 

described in chapter 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 proprietary. The evaporators used by Hoshizaki America, Inc. contain 

proprietary elements that would make it difficult for others to replicate the design. Hence, DOE 

is screening out this option because of its proprietary status.

4.2.5 Technology Options that Reduce Harvest Meltage or Reduce Harvest Time

DOE has not changed its position from the preliminary analysis. Manufacturers have 

implemented design changes to reduce harvest meltage and reduce harvest time in batch ice 

makers. DOE has been unable to identify additional design changes that are not already in use in 

the industry to 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 TSDa

), 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 Diameter Suction Line (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 

equipment with remote compressors. However, the reduced 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 horizontally to the 

compressor; hence, they are conservatively sized to provide adequate oil return for a wide range 

of installation conditions. DOE has not considered increase in suction line size because of

reliability concerns associated with potential oil hold-up and compressor failure associated with 

larger-diameter line sets.

4.2.8 Demand Response Controls

While there may be energy demand benefits associated with use of “smart technologies” 

in ice makers in that they reduce energy demand (e.g., shift the refrigeration system operation to 

a time of utility lower demand), DOE is not aware of any commercialized products or prototypes

that also demonstrate improved energy efficiency in automatic commercial ice makers. Demand 

savings alone do not impact energy efficiency, 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-0037-0025
4-4

commercial ice makers, not how they operate, for example, in relation to utility demand, this 

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 Screening Justification

Design Option

EPCA Criteria for Screening Not Considered in the Analysis 

for Other Reasons

Technological 

Feasibility

Practicability to 

Manufacture, Install, 

and Service

Adverse Impacts on 

Product Utility

Adverse Impacts on 

Health and Safety

No 

Energy 

Savings or 

Savings not 

Measurable

Test 

Procedure 

Efficiency 

Metric 

Does 

Not 

Capture 

Savings

Proprietary 

Technology

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 REMAINING 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 

considered the following technologies in the NOPR analysis:

• improved compressor efficiency

• increased condenser surface area

• increased condenser air flow

• increased condenser water flow

• higher efficiency condenser fans and fan motors

• improved auger motor efficiency

• improved pump motor efficiency

• increased evaporator surface area

• reduced portable water flow

• use of drain water thermal exchange
5-i

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 EQUIPMENT CLASSES ANALYZED.......................................................................... 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 MODELING................................................................... 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 ........................................................................... 5-26
5-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 Reverse 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

............................................................................................................................ 5-31
5-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 Approach..... 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 Than 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-Cooled IMH Batch 

Ice Machines with Capacities between 500 and 1436 lb/24 hours.................... 5-42

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) .. 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 with Capacities Greater Than 700 lb/24 hours (Smaller 

Sizes).................................................................................................................. 5-46

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............. 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 5-48
5-1

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, technologically 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 lifecycle cost (LCC) and payback period analyses (chapter 8 of the NOPR TSD) as well as the 

national impacts analyses (chapter 10 of the NOPR TSD).

This chapter covers the equipment classes 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 analysis phase of the rulemaking, as well as the results of 

these analyses. 

5.2 EQUIPMENT CLASSES ANALYZED

In the NOPR engineering analysis, DOE directly analyzed 12 of the 26 proposed 

equipment classes for batch and continuous ice-making machines. This means 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 for 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 classes analyzed in the 

NOPR engineering analysis, and Table 5.2.2 lists the continuous ice-making machine equipment 

classes analyzed.

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

automatic commercial ice makers. To perform the analysis as efficiently as possible, DOE did 

not directly analyze all covered equipment classes. 

DOE did not conduct full separate analyses for remote condensing unit ice makers with 

and without remote compressors. DOE considered analyses for the remote condensing without 

remote compressor equipment to be representative of equipment classes both with and without 

remote compressors. This 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 Classes 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-Small-B 

Medium (≥500 and <1,436) IMH-W-Med-B

Large (≥1,436) IMH-W-Large-B 

Air

Small (<450) IMH-A-Small-B 

Large (≥450 and <875) IMH-A-Large-B 

Extended (≥875)† IMH-A-Extended-B 

Remote Condensing 

(but not remote 

compressor)

Air Small (<1,000) RCU-NRC-Small-B

Large (≥1,000) RCU-NRC-Large-B 

Remote Condensing 

and Remote 

Compressor*

Air Small (<1,000) RCU-RC-Small-B

Large (≥1,000) RCU-RC-Large-B 

Self-Contained Unit

Water Small (<200) SCU-W-Small-B

Large (≥200) SCU-W-Large-B 

Air Small (<175) SCU-A-Small-B 

Large (≥175) SCU-A-Large-B 

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

**Abbreviation notation: system type (batch (B) or continuous (C)). Equipment type (ice-making head (IMH), remote condenser 

unit (RCU), self-contained unit (SCU)). Condenser cooling (air (A) or water (W)) or compressor location for RCUs (remote 

compressor (RC), non-remote compressor (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 NOPR TSD.

Table 5.2.2 Continuous Equipment Classes 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-Small-C

Large (≥900) IMH-W-Large-C

Air Small (<700) IMH-A-Small-C 

Large (≥700) IMH-A-Large-C 

Remote Condensing 

(but not remote 

compressor)

Air Small (<850) RCU-NRC-Small-C

Large (≥850) RCU-NRC-Large-C

Self-Contained Unit

Water Small (<900) SCU-W-Small-C

Large (≥900) SCU-W-Large-C *

Air Small (<700) SCU-A-Small-C 

Large (≥700) SCU-A-Large-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 efficiency 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 reverse engineering of products to 

develop a baseline MPC and to support calculation 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 component 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 terms of component 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 questionnaires provided 

input to the manufacturing cost model. DOE determined incremental costs associated with 

specific design options both from vendor information and using the cost model. DOE modeled 

energy use reduction using the FREEZE program, which was developed in the 1990s, upgraded 
5-4

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 LEVELS

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 makers, shown in 

Table 5.4.1, effective January 1, 2010. (42 U.S.C. 6313(d)(1)) These efficiency 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 classes are tabulated in

Table 5.4.2. This information is based on a survey of product databases and manufacturer 

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-1900SAH3

Remote 

Condensing 

(but not remote 

compressor)

Air

Small (<1,000) 25% 550 Cornelius CCM0630RF2,H2

Large (≥1,000) 40% 1,804 Vogt-Ice HES-20 / 02KAPHB426AC

Remote 

Condensing 

and Remote 

Compressor

Air

Small (<934) 19% 520 Manitowoc ID-0686C-161

Large (≥934) 15% 1,550 Manitowoc SD-1872C,

Manitowoc SY-1874C

Self-Contained 

Unit

Water Small (<200) 23% 141 Hoshizaki KM-201BWH

Large (≥200) 28%** 285 Scotsman CU3030MW, SW-1#

Air Small (<175) 36% 55 Hoshizaki AM-50BAE

Large (≥175) 33% 436 Hoshizaki DKM-500BAH

* Percent energy use lower than the baseline efficiency level (i.e., the current DOE standard)

** Maximum efficiency level available for units that are not ice makers + dispensers

5.4.1.3 Incremental Efficiency Levels

DOE established for each of the 11 analyzed batch ice equipment classes 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 available products, as discussed in section 5.4.1.2. 

Table 5.4.3 shows the selected 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 to 
5-6

publication of the updated standards, and so the 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 Classes 

Equipment Type*

Representative Harvest 

Capacity Rate

lb/24 hours

EL2**

(2012 ENERGY 

STAR)

EL3 EL4 EL5 EL6

IMH-W-Small-B 300 10% 15% 20% 25%

IMH-W-Med-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% 20%

RCU-NRC/RCSmall-B 700 Not Directly Analyzed‡

RCU-NRC/RCLarge-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 levels 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 

technology 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 classes. The maximum technology efficiency levels for the analyzed 

batch ice maker equipment classes 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 capacity 

rates.

Also, the table does not show maximum technology levels separately for remote 

condenser units with and without remote compressors. During the preliminary analysis, DOE 

considered whether the efficiency improvements associated with the design options used in the 

engineering analysis would be significantly different for remote compressor ice makers as 

compared with non-remote compressor models. DOE concluded that there would be little 

differences in the magnitudes or costs of these improvements as the design options are applied to 

these two types of ice makers. In the 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 Levels for Batch Ice Maker Equipment Classes

Equipment Class Energy Use Lower than Baseline

IMH-W-Small-B 30%

IMH-W-Med-B 22%

IMH-W-Large-B 17% (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-Small-B Not analyzed – similar to IMH-A-Large-B (1500)

RCU-Large-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 ice hardness (for a 32 °F ice product, the 

percentage of frozen ice) is directly related to the measured energy consumption of these 

machines. To account for this impact on energy use, the test procedure final rule for automatic 

commercial ice makers states that the measured energy use of continuous machines must be 

adjusted based on the ice hardness. 77 FR 1591 (Jan. 11, 2012).
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 calorimeter test results as follows:

Ice Quality Adjustment Factor = 

⎣

⎢

⎢

⎢

⎢

⎡

144Btu

lb � + 38Btulb �

� 144Btu

lb �

calorimeter constant�+ 38Btu

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 AirConditioning Engineers (ASHRAE) Standard 29-2009, would 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 analysis 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 baseline 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 efficiency levels are tabulated in 

Table 5.4.5, and they are plotted with the ice maker energy use data in chapter 3 of the NOPR 

TSD.
5-9

Table 5.4.5 Baseline Efficiency Levels for Continuous Ice Maker Equipment Classes

Equipment Type Type of Cooling Harvest Capacity Rate 

lb/24 hours

Energy Use

kWh/100 lb ice*

Ice-Making Head

Water Small (<900) 8.1–0.00333H

Large (≥900) 5.1

Air Small (<700) 11.0–0.00629H

Large (≥700) 6.6

Remote Condensing Air Small (<850) 10.2–0.00459H

Large (≥850) 6.1

Self-Contained Unit

Water Small (<900) 9.1–0.00333H

Large (≥900) 6.1

Air Small (<700) 11.5–0.00629H

Large (≥700) 7.1

* H = Harvest capacity in lb/24 hours.

5.4.2.3 Maximum Available Efficiency Levels

Maximum available efficiency levels for the analyzed equipment classes 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 incremental 

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 17% 1,100 Hoshizaki F-1500MAH

Remote 

Condensing 

(but not remote 

compressor)

Air

Small 18% 835 Scotsman N0922R-32#

Large 19% 1,050 Scotsman F1222R-32#

Remote 

Condensing 

and Remote 

Compressor

Air Small 8% 713 Manitowoc RN1078C-161

Air Large 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 classes 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 equipment, as discussed 

in section 5.4.2.3. Table 5.4.7 shows the selected incremental efficiency levels. The efficiency 

levels are defined by the percent energy use less than the trial baseline energy use.

Table 5.4.7 Incremental Efficiency Levels for Continuous Ice Maker Equipment Classes 

Equipment 

Type*

Harvest 

Capacity

lb/24 hours

EL2** EL3 EL4 EL5 EL6

IMH-W-S-C 800 Not Directly Analyzed†

IMH-W-L-C 1000 Not Directly Analyzed†

1800 Not Directly Analyzed†

IMH-A-S-C 310 10% 15% 20% 25% 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 Maximum Technology Level

The maximum technology efficiency levels for the analyzed batch ice maker equipment 

classes are presented in Table 5.4.8. The design options considered in the analysis 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 representative of analyses conducted for two very different harvest 

capacity rates

For two equipment classes, 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 classes. DOE expects 

to set energy standards for those equipment classes at the levels 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 Baseline
5-11

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 COLLECTION

5.5.1 Component Vendor Data

DOE contacted major suppliers of key commercial ice maker components to obtain 

performance and cost data to support its design option analysis. This effort consisted of phone 

interviews and email correspondence. Table 5.5.1 lists the vendors contacted.

DOE also obtained from the compressor vendors or their websites’ complete performance 

data for compressors used in the energy analyses, including analyses for baseline and improvedefficiency configurations.
5-12

Table 5.5.1 Component Vendors Contacted 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 automatic commercial ice makers 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 were 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 thoroughly describes 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 conducted energy tests (see 

section 5.5.4 for more on energy testing). DOE aimed to select a range of models that would 

cover a broad spectrum of equipment classes and capacities based on the California Energy 

Commission (CEC), ENERGY STAR, and AHRI databases, as well as manufacturer websites.
5-13

Table 5.5.2 Selected Units for Reverse Engineering and Energy Testing during Preliminary 

Analysis

Type Equipment Class

Harvest 

Capacity

lb/24 hours

Energy 

Use

kWh/

100 lb

Potable 

Water Use

gal/100 lb

Energy 

Test

Reverse 

Engineering

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-Large-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 during NOPR 

Analysis

Type Equipment Class

Harvest 

Capacity

lb/24 

hours

Energy 

Use

kWh/

100 lb

Potable 

Water Use 

gal/100 lb

Energy 

Test

Reverse 

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, the test laboratory 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 water 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 component-level power input for fans, water pumps, and air pumps for 

the products that had these components. For some 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 boards.

DOE made air flow measurements for all air-cooled condensers using a hot wire 

anemometer. The location of these measurements varied, depending on the heat exchanger type 

and configuration. The determination of air flows based on these measurements is not very 

reliable, so this measurement was used as an indication of air flow trends more than exact 

indication of air flow for the various products.

Details of the cabinet size, heat exchanger dimensions, insulation thickness, etc. were 

based on direct physical measurements made during the teardown process. DOE noted condenser 

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 component manufacturer and model data for key components such as 

compressors, fans, and controls.

5.5.3 Manufacturer Interviews

The manufacturer interviews conducted during the preliminary analysis are discussed in 

chapter 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:

• input on the cost of compressors, heat exchangers, motors, and chassis pieces

• input on the overall cost of the product and the percentage of total cost allocated to 

materials, labor, depreciation, and overhead

• input on which compressors on the market are considered acceptable for 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 Testing

DOE conducted energy testing to verify energy use of several of the ice makers obtained 

for reverse engineering, and to provide information about refrigeration system operating 

conditions during different phases of operation to support energy use modeling. 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

• potable water use

• refrigeration circuit temperatures

• refrigerant pressures

• potable water circuit temperatures

• air temperatures, including the evaporator compartment, compressor/condenser 

compartment, condenser fan air outlet (for air-cooled and remote air-cooled 

machines)

• cooling water inlet and outlet temperatures

• machine power input raw data and accumulated energy use

• Air temperatures required by the test procedure

5.6 MANUFACTURING COST MODELING

5.6.1 Generation of Bills of Materials

Each 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 order in which 

manufacturers assembled them. The BOMs describe each fabrication and assembly operation in 

detail, including the type of equipment needed (e.g., presses, drills), the process cycle times, and 

the labor associated with each manufacturing step. The result is a thorough and explicit model of 

the production process, which includes space, conveyor, and equipment requirements by planned 

production 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 manufacturers 

(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 section 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 Spreadsheet 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 step 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 

incorporated all materials, components, and fasteners with estimates of raw material costs and 

purchased part costs. Assumptions about the sourcing of parts and in-house fabrication were 
5-17

based on industry experience, information in trade publications, and discussions with 

manufacturers. Interview and plant visits were conducted with manufacturers to add industry 

experience on the methodology and pricing.

The last step was to convert this information into dollar values. To perform this task, 

DOE collected information on labor rates, tooling costs, raw material prices, and other factors. 

DOE assumed values for these parameters using internal expertise and confidential information 

available to DOE contractors. Although most of the assumptions are manufacturer specific and 

cannot be revealed, section 5.6.4.2 discusses the values used for each assumption.

In summary, DOE assigned costs of labor, materials, and overhead to each part, whether 

purchased or produced in-house. DOE then aggregated single-part costs into major assemblies 

(e.g., heat exchanger assembly, potable water system, packaging, controls, fan/motor assembly,

refrigerant circuit, wiring harnesses) and summarized these costs in a worksheet. 

5.6.3 Cost Model and Definitions

Once DOE disassembled selected units, gathered information from manufacturer catalogs 

on additional products, and identified technologies, DOE created an appropriate manufacturing 

cost model that could translate physical information into MPCs. The cost model is based on 

production activities and divides factory costs into the following categories:

• Materials: purchased parts (compressor, fan motors, control boards, etc.), raw 

materials (cold rolled steel, copper tube, etc.), and indirect materials that are used for 

processing and fabrication.

• Labor: fabrication, assembly, indirect, and supervisor labor. Fabrication and assembly 

labor costs are burdened with benefits and supervisory costs.

• Overhead: equipment, 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: purchased parts (out-sourced) plus manufactured parts (made inhouse from raw materials);

• Indirect material: material used during manufacturing (e.g., welding rods, adhesives);

• Fabrication labor: labor associated with in-house piece manufacturing;

• Assembly labor: labor associated with final assembly;
5-18

• Indirect labor: labor costs that scaled with fabrication and assembly labor—including

the cost of technicians, manufacturing engineering support, stocking, etc. that were 

assigned on a span basis;

• Equipment and plant depreciation: money allocated to pay for initial equipment 

installation and replacement as the production equipment wears out;

• Tooling depreciation: cost for initial tooling (including nonrecurring engineering and 

debugging of the tools) and tooling replacement as tools wear out;

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

conveyors that feed and/or make up the assembly line;

• Utilities: electricity, gas, telephones, etc.;

• Maintenance: money spent annually on maintaining tooling and equipment;

• Insurance: appropriated as a function of unit cost; and

• Property tax: appropriated as a function of unit cost.

5.6.4 Cost Model Assumptions Overview

As discussed in the previous section, assumptions about manufacturer practices and cost 

structure were important to the final product cost estimate. 

In converting physical information about the product into cost information, DOE 

reconstructed manufacturing processes for each component using internal expertise and 

knowledge of the methods used by the industry. DOE used assumptions regarding the 

manufacturing process parameters (e.g., equipment use, labor rates, tooling depreciation, and 

cost of purchased raw materials) to determine the value of each component. DOE then summed 

the values of the components into assembly 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 equipment 

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. Manufacturer-specific assumptions are 

presented as industry averages to prevent disclosure of confidential information.
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, interviews 

with manufacturers, and manufacturing facilities site visits. The major manufacturer processes 

identified and developed for the spreadsheet model are listed in Table 5.6.1. Fabrication process 

cycle times were estimated and entered into the BOM. 

Table 5.6.1 Cost Model In-House 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 factory parameters 

before the manufacturer interviews. DOE then revised the assumptions using comments and 

information gathered during the interviews. Table 5.6.2 lists DOE’s assumptions for ice maker

manufacturers.

During manufacturer interviews in the NOPR analyses, DOE found that some larger 

batch equipment classes were produced in much smaller volumes than other batch equipment 

classes. To more accurately 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/year) 25,000 5,000 6,000

Actual Annual Production Volume (units/year) 12,000 2,500 3,000

Fabrication Labor Wages ($/hour) 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 makers include cold rolled steel 

(CRS), stainless steel, copper tubing, and aluminum. There have been large fluctuations in metal 

prices over the last few years. To account for these fluctuations, DOE used a 5-year average of 

metal prices from the Bureau of Labor Statistics Producer Price Indices (PPIs) spanning from 

2007 to 2012 with an adjustment to 2012 dollars (2011$).

2 DOE used the PPIs for copper rolling, 

drawing, and extruding and steel mill products, and made the adjustments to 2012$ using the 

gross domestic 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, and direct overhead used to 

manufacture a product in order to calculate the manufacturing production 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 includes 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 estimating the incremental costs 

varied, depending on the design option. Details in this calculation that are specific to individual 

design options are discussed in section 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 changes. 

The appropriate price increases are discussed in section 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 option. This approach was used in particular for 

condenser size increases.

Manufacturer 

Selling Price

Full Cost of 

Product

Manufacturer 

Production Cost

Direct 

Labor

Direct 

Material Overhead

Indirect Labor

Indirect Material

Maintenance

Depreciation

Taxes

Insurance related 

to assets

Non-Production 

Cost

Selling

Market 

Research

Advertising

POS Promotion

Salesperson 

Comp & Travel

Logistics 

(Warehousing, 

Delivery, RecordKeeping, Order 

Processing)

General & 

Admin.

Costs of 

Service & 

Staff Units

General 

Corporate 

Costs 

(Comp, etc)

R & D

Costs associated 

with finding new 

and improved 

products or 

processes

Code 

Compliance

Modernization

Interest

Cost of 

borrowing 

funds

Profit
5-22

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 

adder was applied to all of the MPC estimates in order to determine MSP numbers. This markup 

is described in more detail in section 5.7.

5.7 MANUFACTURER MARKUP

Manufacturers 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 

estimated 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 energy modeling. Energy model input was determined for 

these products from the data collected during the reverse engineering work, described in section 

5.5.2. Additional data, used both as input and for calibration of individual product energy 

models, was provided by energy testing as described in section 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 this model briefly. A more detailed 

description of the program and its recent development is presented in appendix 5A.

5.8.1 FREEZE Model Overview

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

ice makers. As applied to batch ice makers, FREEZE is a transient model that can calculate 

changes in operating conditions as a function of time. This feature is important for batch-type 

machines for which refrigerant temperatures and pressures undergo large changes during the 

freeze cycle because of ice buildup on the evaporator. The development of the FREEZE model 

during the preliminary analysis is detailed in chapter 5 of the preliminary analysis TSD.

During the NOPR analysis, 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-disclosure agreements. These changes 

included:

• adjusting 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 the 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, section 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 

against 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 the 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:

• increases in both the evaporator face area and condenser volume, and an increase in 

the chassis size to accommodate these growths

• an increase in condenser fan size and a change from a shaded pole motor (SPM)

motor to a permanent split capacitor (PSC) motor

• an 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 evaporator area (with chassis size increase)

23.51% Switch to PSC condenser fan motor

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 compressor 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 market.
5-24

DOE believes that these changes help ensure that the energy model results accurately reflect 

technology behavior in the market.

5.9 DESIGN 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 Class

Compressor UpgradeCondenser Fan MotorsPump MotorsAuger MotorsAir-Cooled CondensersHarvest 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 Analysis

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 modified 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), but the ordering of 

different design configurations in this curve would be based on minimizing the representative 

LCC calculated for the candidate design configurations at each successive efficiency level.
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 evaluation are shown in Table 5.9.2 through Table 5.9.4. The 

analysis assumed that in the field half of the ice makers would be used in open systems and half 

in closed-loop systems, 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 conducted 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 available 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

gal/100 lb 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

gal/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

gal/100 lb 92.3 64.9 52.2
5-26

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

considered growth in condenser size for water-cooled condensers and use of ECM pump motors.

5.9.2 Improved Compressor Efficiency

DOE considered the substitution of higher efficiency compressors for all equipment 

classes. DOE acquired compressor performance data from compressor manufacturers for use in 

the energy analysis, including capacity and power input for scroll and reciprocating compressors 

with capacities of 1,000 to 36,000 British thermal units per hour (Btu/h) rated at AHRI 540 

medium temperature testing conditions. Table 5.9.2 lists the AHRI 540 medium temperature 

operating test conditions. Not all compressor vendors provided compressor performance data at 

these operating conditions. 

Table 5.9.5 AHRI 540 Medium Temperature Compressor Rating Test Conditions

Condensing Temperature 120 oF

Evaporating Temperature 20 oF

Suction Temperature 40 oF

Subcooling 0 oF

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 

compressor 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 families 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 highefficiency compressors that were considered in the preliminary analysis, DOE also added 

additional compressor families to its database, 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 compressor 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 mediumtemperature compressors as part of the commercial refrigeration equipment (CRE) rulemaking, 

relating the compressor cost to its capacity. DOE assigned the cost derived using the cost 
5-27

correlation to median-EER compressors of a given capacity. The cost curve applicable for 

median-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, based 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.

Figure 5.9.1 NOPR Cost Curves for Medium-Temperature Compressors Used in 

Commercial Ice Makers 

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 Condenser Surface Area

The condenser transfers heat from the refrigerant to the ambient air. A larger surface area 

allows the heat transfer to occur more efficiently, allowing the refrigerant circuit to operate with 

a lower condensing temperature. DOE considered an increase in condenser surface area for all 
5-28

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 products 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.

Condenser size increases were implemented in the energy analysis through direct 

adjustment of the input parameters describing the condenser geometry. DOE increased either 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 drop 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 

stakeholders. The 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 

section 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, instead 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 sufficient 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 Increase

The evaporator transfers heat from the freezing liquid water to the refrigerant. Larger 

surface area allows the heat transfer to occur more efficiently, allowing the refrigeration system 

to transfer the same amount of heat with a higher evaporating temperature. 
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 showed 

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 strongly 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 sufficiently 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 moreefficient 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-making head that is placed 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 updated 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 shaded pole (SP) or

permanent split capacitor (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 permanent magnet DC (PMDC) fan motors are more efficient than SP or 

PSC motors currently used in baseline commercial ice makers. 

In the NOPR analyses, DOE kept the same range of motor efficiencies as was used in the 

preliminary analysis. DOE did update the costs associated with these motors based on 
5-31

stakeholder feedback. Table 5.9.3 lists the shaft output power range and efficiency ranges of 

different types of motors 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 

efficiencies 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 the efficiencies at different 

shaft power output levels scale similarly. 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 Efficiencies 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. The 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 StandardEfficiency PSC

HighestEfficiency PSCPermanent 

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 Auger Motors

Augers are integrated into the evaporators of continuous commercial ice makers, as 

discussed in chapter 3 of the NOPR TSD. Auger assemblies incorporate a motor and a gear drive

to reduce the motor speed to that of the auger. The motors typically used for augers in ice makers 

using single-phase power are PSC or capacitor start induction run (CSIR) motors with shaft 

power output between 200 and 400 W. 
5-32

For the NOPR engineering analysis, DOE assumed that the cost of an efficiency 

improvement 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 Pump Motors

Water pumps are used in batch commercial ice makers to transport water from the 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 evaluate pump motor design options, 

assuming that this information is also representative of the potential for performance 

improvement and cost increase for pump motors. See section 5.9.7 for more information on 

condenser fan motors.

5.9.10 Drain Water Heat Exchanger

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 concludes 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 requirements 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 hours

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 energy 
5-33

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-inchdiameter version of the heat exchanger (for products with harvest capacity larger than 1,000 

lb/day) and a cost of $210 for a smaller, 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 ENGINEERING APPROACHES

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

modeling and cost-modeling 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 notes 

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 with 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 increase 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 cost 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 websites. 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 470 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 relationship 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 values 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 presented 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 metric
5-36

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 establish 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 effects. 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 costefficiency model. In addition, DOE was unable to establish a significant correlation 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 classes, 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 

makers. 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 combined 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 indicate 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 Extrapolation Approach

Equipment 

Class

Representative 

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 24.0 153 Direct Analysis

Medium 850 $1,536 $1,920 19.7 153 Direct Analysis
5-37

Large 1,500* $1,732 $2,165 22.3 NA IMH-A-Large-B (1500)

2,600 $3,725 $4,656 19.7 174 Direct Analysis

IMH-A

Small 300 $910 $1,138 25.6

NA

Direct Analysis

Large 800 $1,485 $1,856 22.7 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 30.0 132 Direct Analysis

SCU-A

Small 110 $1,045 $1,306 34.0 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 makers

Equipment 

Class

Max 

Available

Representative 

Harvest 

Capacity 

lb/24 hours

EL2* EL3 EL4 EL5 EL6

Max Tech

Level Cost

IMH-W 10% 15% 20% 25%

Small 22% 300 $20 $37 $72 $136 29% $370

Medium 16% 850 $26 $53 $279 21% $288

Large 8% 1,500** $46 $359 17% $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 29% $356 

17% 1,500 $35 $118 $429 20% $429

RCU 9% 15% 20%

Small 25% 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 22% 110 Not Directly Analyzed

Large 28% 300 $30 $48 $62 $88 $304 30% $305

SCU-A 7% 15% 20% 25% 30%

Small 27% 110 $14 $32 $44 $56 $90 39 $340

Large 26% 200 $19 $40 $68 $79 $118 35% $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 adjustment 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

Equipment 

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 Makers

Equipment 

Class

Max 

Available

Representative 

Harvest 

Capacity 

lb/24 hours

EL2* EL3 EL4 EL5 EL6

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 $39 $57 $190 $495 31% $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 28% $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 product 
5-39

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 options.

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 classes 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 

Consumption 

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: 300 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: 800 lb/24 hours
5-41

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 Successively 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

Representative Harvest Capacity: 1500 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

Representative Harvest Capacity: 300 lb/24 hours
5-42

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

Representative Harvest Capacity: 850 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: 2600 lb/24 hours
5-43

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 Successively 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

Representative Harvest Capacity: 1500 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

Representative Harvest Capacity: 2400 lb/24 hours
5-44

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

Representative Harvest Capacity: 110 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

Representative Harvest Capacity: 200 lb/24 hours
5-45

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

Representative Harvest Capacity: 300 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

Representative Harvest Capacity: 310 lb/24 hours
5-46

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

Representative Harvest Capacity: 820 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

Representative Harvest Capacity: 110 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 independent of harvest capacity, as is the case for all other high-harvestcapacity equipment classes. The leveling of the standard is subject to the EPCA anti-backsliding 
5-47

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:

• ensuring that the new standard levels are shifted appropriately (from the existing 

standard) at each analyzed equipment size

• ensuring the existence of one continuous standard without discontinuities, despite 

having to lower the standard different 

• creating a constant value standard for the largest product sizes

• ensuring 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 value 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 used the calculated value as the proposed standard 

until it intersected 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, DOE set the proposed standard equal to the current standard 

at 2500 lb/day, which is 4.1 kWh/100 lb of ice.

• For the capacity range between 50 lb/day and 450 lb/day (designated IMH-A-SmallB), 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 portion 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 extended 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 defines the boundaries of these two proposed 

standard equations.

• To distinguish 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 

Classes
5-49

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/nipaweb/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, INCREMENTAL, AND OVERALL MARKUPS........................................ 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 USED TO ESTIMATE WHOLESALER AND 

MECHANICAL CONTRACTOR MARKUPS ................................................................. 6-5

6.4 ESTIMATION OF WHOLESALER MARKUPS ............................................................. 6-6

6.5 ESTIMATION OF MECHANICAL CONTRACTOR MARKUPS.................................. 6-7

6.6 ESTIMATION OF NATIONAL ACCOUNT MARKUPS................................................ 6-9

6.7 SALES TAX ..................................................................................................................... 6-10

6.8 OVERALL MARKUPS.................................................................................................... 6-11

REFERENCES .......................................................................................................................... 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 Overall Markup .... 6-11

LIST OF FIGURES

Figure 6.5.1 Cumulative Distribution of Mechanical Contractor Markups for Commercial 

Refrigeration Equipment...................................................................................... 6-9
6-1

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 includes 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 equipment. The manufacturer markup, projected as 

part of the engineering analysis (chapter 5 of the notice of proposed rulemaking (NOPR)

technical support document (TSD)), converts the MPC into manufacturer selling price (MSP). 

Distribution channel markups are also applied to the MSP based on the distribution channels

through which customers currently purchase the equipment. 

DOE determined that customers purchase automatic commercial ice makers through three 

major distribution channels:

Manufacturer  Customer (National Account Channel)

Manufacturer  Wholesaler  Customer (Wholesaler Channel)

Manufacturer  Wholesaler  Local 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 the three distribution channel markup values using the market share of 

each distribution channel to obtain weighted-average distribution channel markups (see section 

6.2.3 for market share estimates). Based on suggestions from stakeholders at the Framework and 

preliminary analysis public meetings, DOE used the same weighted-average distribution channel 

markup values for all ACIM equipment classes.

DOE calculated a weighted-average sales tax using the population of each state as the 

corresponding weight. DOE then multiplied the weighted-average sales tax by the weightedaverage distribution channel markups to estimate overall distribution channel markup values for 

automatic commercial ice makers.

DOE notes that Framework and preliminary analysis 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 calculating the three distribution channel 

markups, weighted-average sales tax, and overall distribution channel markups.
6-2

6.2 BASELINE, INCREMENTAL, AND OVERALL MARKUPS

Baseline markups are markups that convert the MSP of equipment at the baseline 

efficiency level (efficiency level 1) to customer purchase price. Incremental markups are 

markups that convert the projected increase in MSP of equipment at efficiency level 2 or higher 

to a projected increase in customer purchase price compared to the price of equipment at 

efficiency level 1. Overall markups include the weighted-average values of markups based on the 

estimated market share for each distribution channel and the sales tax. 

6.2.1 Baseline Markups

If the baseline equipment is sold through the National Account Channel, the following 

equation defines the estimated equipment price:

EQPNATL ACCT BASE = MFGBASE × MUNATL ACCT BASE × (1 + ST)

Eq. 6.1

Where:

MFGBASE = MSP of baseline equipment ($/unit), 

EQPNATL ACCNT BASE = national account price to the customer of baseline equipment ($/unit), 

MUNATL ACCT BASE = national account markup on baseline equipment, and

ST = weighted-average sales tax rate.

If the baseline equipment is sold through the Wholesaler Channel:

EQPWHOLE BASE = MFGBASE × MUWHOLE BASE × (1 + ST)

Eq. 6.2

Where:

EQPWHOLE BASE = wholesaler price of baseline equipment ($/unit), and

MUWHOLE BASE = wholesaler markup on baseline equipment.

If the baseline equipment is sold to a customer through the Contractor Channel:

EQPCONT BASE = MFGBASE × MUWHOLE BASE × MUMECH CONT BASE × (1 + ST)

Eq. 6.3

Where:

EQP CONT BASE = contractor or installer price of baseline equipment ($/unit), and

MUMECH CONT BASE = mechanical contractor markup on baseline equipment.
6-3

6.2.2 Incremental Markups

Incremental markups are cost multipliers that relate increments in the MSP of equipment 

at higher efficiency levels to the corresponding increments in customer purchase price. DOE 

projects the increment in MSP of equipment at higher efficiency levels by subtracting the MSP 

of equipment at the baseline efficiency level (MFGBASE) from the projected MSP of equipment at 

higher efficiency levels:

MFGINCR = MFGHIGHER EFF LEVEL−MFGBASE

Eq. 6.4

Where:

MFG INCR = projected increment in MSP of higher efficiency equipment ($/unit), and

MFGHIGHER EFF LEVEL = projected MSP of equipment at higher efficiency level ($/unit).

If the equipment is sold through the National Account Channel, DOE projects the 

increase in equipment price using the following equation:

EQPNATL ACCT INCR = MFGINCR × MUNATL ACCT INCR × (1 + ST)

Eq. 6.5

Where:

EQPNATL ACCNT INCR = projected increment in customer purchase price to the national account

customer ($/unit), and

MUNATL ACCT INCR = national account incremental markup.

If the customer acquires the higher efficiency equipment through the Wholesaler or 

Contractor Channels:

EQPWHOLE INCR = MFGINCR × MUWHOLE INCR × (1 + ST)

Eq. 6.6

Where:

EQPWHOLE INCR = projected incremental wholesaler price ($/unit), and

MUWHOLE INCR = incremental wholesaler markup.

EQPCONT INCR = MFGINCR × MUWHOLE INCR × MUMECH CONT INCR × (1 + ST)

Eq. 6.7

Where:

EQPCONT INCR = projected incremental contractor or installer price ($/unit), and

MUMECH CONT INCR = incremental mechanical contractor 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 the 

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 preliminary analysis 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 preliminary analysis public meetings indicated that 

the majority of automatic commercial ice makers are purchased through a three-step distribution 

channel. DOE’s estimates are listed on Table 6.2.1.

Table 6.2.1 Distribution Channel Market Shares for Baseline and Higher Efficiency Level 

Automatic Commercial Ice Makers

National Account Channel Wholesaler Channel Contractor Channel

Automatic Commercial Ice 

Makers, All 0% 38% 62%

Source: U.S. Census Bureau, Sector 42: EC0742SXSB01: Wholesale Trade: Subject Series - Misc Subjects: Sales by Class of 

Customer for the United States: 2007, data for NAICS code 423740, Refrigeration equipment and supplies merchant wholesalers. 

Downloaded March 4, 2011.

6.2.4 Overall Markups

DOE estimated overall markup values separately for both baseline and incremental 

markups by combining the weighted-average sales tax with the weighted-average distribution 

channel markups. DOE estimated overall baseline markup values using:

EQPCUST BASE = MFGBASE

× (WTNATL ACCT BASE × MUNATL ACCT BASE + WTWHOLE BASE × MUWHOLE BASE

+ WTCONT BASE × MUWHOLE BASE × MUMECH CONT BASE)

× (1 + ST)

= MFGBASE × MUOVERALL BASE

Eq. 6.8

Where:

EQPCUST BASE = customer purchase price for baseline equipment ($/unit),

WTNATL ACCNT BASE = estimated market share of baseline equipment sold through the National 

Account Channel (%),
6-5

WTWHOLE BASE = estimated market share of baseline equipment sold through the Wholesaler 

Channel (%),

WTCONT BASE = estimated market share of baseline equipment sold through the Contractor Channel 

(%), and

MUOVERALL BASE = estimated overall baseline markup.

DOE projected overall incremental markups using:

EQPCUST INCR = MFGINCR

× (WTNATL ACCT INCR × MUNATL ACCT INCR + WTWHOLE INCR × MUWHOLE INCR

+ WTCONT INCR × MUWHOLE INCR × MUMECH CONT INCR) × (1 + ST)

 = MFGINCR × MUOVERALL INCR

Eq. 6.9

Where:

EQPCUST INCR = projected increment in customer purchase price of equipment at a higher 

efficiency level compared to baseline equipment ($/unit), 

WTNATL ACCNT INCR = projected market share of higher efficiency level equipment sales through the 

National Account Channel (%),

WTWHOLE INCR = projected market share of higher efficiency level equipment sales through the 

Wholesaler Channel (%), 

WTCONT INCR = projected market share of higher efficiency level equipment sales through the 

Contractor Channel (%), and

MUOVERALL INCR = overall incremental markup.

EQPCUST = EQPCUST BASE + EQPCUST INCR

Eq. 6.10

DOE used the overall baseline and overall incremental markups to estimate the baseline 

customer price (EQPCUST BASE) and to project the incremental customer price (EQPCUST INCR). For a 

particular piece of equipment at a higher efficiency level, the projected total price of that 

equipment to the customer (EQPCUST) is the sum of the baseline customer price (EQPCUST BASE) and 

the projected incremental customer price (EQPCUST INCR).

6.3 BASIC ASSUMPTIONS USED TO ESTIMATE WHOLESALER AND 

MECHANICAL CONTRACTOR MARKUPS

DOE based the wholesaler markups on industry balance sheet data and based the 

mechanical contractor markups on U.S. Census Bureau data for the plumbing, heating, and airconditioning (PHAC) industry.

1 DOE obtained the wholesale industry balance sheet data from 

Heating, Air-conditioning & Refrigeration Distributors International (HARDI), the trade 

association representing wholesalers of refrigeration and heating, ventilating, and airconditioning (HVACR) equipment.

2 DOE compiled the U.S. Census Bureau PHAC data 
6-6

following the same format as used for the balance sheet data for wholesalers. These balance 

sheets itemize the costs incurred by firms that supply 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 makers.

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. Wholesaler 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 the industry balance sheets itemize costs

incurred by a firm by a number of cost categories, including direct costs to purchase or install the 

equipment, operating labor and occupancy costs, and other operating costs and profit. Although 

wholesalers and contractors tend to handle multiple equipment lines (including refrigerators, 

freezers, beverage dispensers, and other related equipment), the data provide the most credible

available information for ACIM distribution costs.

The basis for the second assumption is that firms must incur some costs regardless of the 

cost of the goods being sold—costs such as sales, accounting and management staff, office 

expenses, depreciation, and many other operating costs do not vary with the MSP. For purposes 

of this analysis, DOE refers to these costs as fixed. Other costs, such as uncollectable accounts, 

bad debts, and profit margins, vary as a function of the total cost of sales, including the cost of 

goods sold. For purposes of this analysis, DOE refers to these costs as variable.

The basis for the third assumption is that the HVACR wholesaler and contractor industry 

is competitive, while customer demand for ACIM equipment is assumed to be inelastic (i.e., the 

demand is not expected to decrease significantly with an increase in price of equipment). The 

large number of HVACR firms listed in the 2007 economic census indicates the competitive 

nature of the market. For example, the 2007 census lists more 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 WHOLESALER MARKUPS

Annually, HARDI conducts a confidential survey of its member firms in which

wholesalers 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 expenses 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 represents $0.737 per dollar of sales revenue. In 

other words, for every $1.00 the typical wholesaler takes in as sales revenue, it pays $0.737 in
6-7

direct costs. Labor expenses account for $0.151 per dollar of sales revenue, occupancy expenses 

account for $0.036, other operating expenses account for $0.055, and profit accounts for $0.021

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 $0.737 $1.000

Labor Expenses: Salaries and benefits $0.151 $0.205

Occupancy Expenses: Rent, maintenance, and utilities $0.036 $0.049

Other Operating Expenses: Depreciation, advertising, and insurance $0.055 $0.075

Profit $0.021 $0.028

Baseline Markup (MUWHOLE BASE): Revenue per dollar cost of goods sold * 1.357

Incremental Markup (MUWHOLE INCR): Increased revenue per dollar increase cost of goods sold* 1.103

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2012. 2012 Profit Report (2011 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 $0.737 (i.e., equipment expenditure per dollar 

revenue). The data in this column show that, for every $1.00 the wholesaler spends on equipment 

costs, the wholesaler earns $1.00 in sales revenue to cover the equipment cost, $0.205 to cover 

labor costs, $0.049 to cover occupancy expenses, $0.075 for other operating expenses, and 

$0.028 in profits. This totals to $1.357 in sales revenue earned for every $1.00 spent on 

equipment costs. Therefore, the wholesaler baseline markup (MUWHOLE BASE) is 1.357 ($1.357 ÷ 

$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 $1.357, implying that the incremental markup is 1.357, or the same as the baseline

markup. However, if none of the other costs is variable, then a $1.00 increase in the MSP will 

lead to a $1.00 increase in the wholesaler price, for an incremental markup of 1.0. DOE assumed

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 $1.103. Therefore, the wholesaler incremental markup 

(MUWHOLE INCR) is 1.103 ($1.103 ÷ $1.00). See appendix 6A for additional details and data used 

for markup calculations.

6.5 ESTIMATION OF MECHANICAL CONTRACTOR MARKUPS

DOE derived markups for mechanical contractors from U.S. Census Bureau data for

PHAC contractors. This sector includes establishments primarily engaged in installing and 

servicing PHAC equipment, which may include installations, additions, alterations, maintenance, 

and repairs. The U.S. Census Bureau data for the PHAC sector include detailed statistics for 

establishments with payrolls, similar to the data reported by HARDI for wholesalers. The 

primary difference is that the U.S. Census Bureau reports itemized revenues and expenses for the 

PHAC industry as a whole in total dollars, rather than in typical values for the average or 
6-8

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 6A contains the full set of data). The direct cost of sales represents about 

$0.678 per dollar sales revenue to the mechanical contractor. Labor expenses account for $0.175

per dollar sales revenue, occupancy expenses account for $0.022 per dollar sales revenue, other 

operating expenses account for $0.039, 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 (MUMECH CONT BASE): Revenue per dollar cost of goods sold * 1.475

Incremental Markup (MUMECH CONT INCR): Increased revenue per dollar increase cost of goods sold 1.185

Sources: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 238220. Construction: Geographic Area 

Series: Detailed Statistics for Establishments: 2007; DOE estimates.

* Numbers are rounded and presented so the totals match. The actual markups 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 $0.678. The data in the last column of 

Table 6.5.1 show that, for every $1.00 the mechanical contractor spends on equipment costs, the 

typical mechanical contractor earns $1.00 in sales revenue to cover the equipment cost, $0.258 to 

cover labor costs, $0.032 to cover occupancy expenses, $0.058 for other operating expenses, and 

$0.127 in profits. This totals $1.475 in sales revenue earned for every $1.00 spent on equipment 

costs. This results in a mechanical contractor baseline markup (MUMECH CONT BASE) of 1.475

($1.475÷ $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 separating the fixed and variable costs. For example, if all of the other 

costs scale with the equipment price (i.e., all costs are variable), the increase in mechanical

contractor price will be $1.475, an incremental markup of 1.475, or the same as the baseline 

markup. However, if none of the other costs are variable, then a $1.00 increase in the equipment 

price will lead to a $1.00 increase in the mechanical contractor price, for an incremental markup 

of 1.0. DOE assumed the labor and occupancy costs are fixed and the other operating costs and 

profit scale with the equipment price (i.e., are variable). In this case, for a $1.00 increase in the 

equipment price, the typical mechanical contractor price will increase by $1.185, giving a 

mechanical contractor incremental markup (MUMECH CONT INCR) of 1.185 ($1.185 ÷ $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 state, DOE characterized the markups 

developed from U.S. Census Bureau data with a probability distribution based on a state-by-state
6-9

analysis of U.S. Census Bureau data for PHAC contractors. The state-by-state analysis provided 

a distribution on the relative markups of mechanical contractors in the United States.

Figure 6.5.1 shows the cumulative probability distribution of the state markup index that 

DOE used to characterize the mechanical contractor baseline and incremental markups. As 

mentioned in section 6.1, the Contractor Channel markup index was varied by state. The baseline 

and incremental markups in Table 6.5.1 are the national average markup values for the 

Contractor Channel. In the LCC analysis (TSD chapter 8), these national average values were 

multiplied by the state markup index to obtain the Contractor Channel markups for a particular 

state. DOE used the state-level markup data in combination with state-level electricity and water 

prices in the LCC and PBP analysis 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 forAutomatic 

Commercial Ice Making Equipment

6.6 ESTIMATION OF NATIONAL ACCOUNT MARKUPS

National accounts are generally large accounts—large enough to circumvent the 

Wholesaler Channel and negotiate directly with, and purchase directly from, manufacturers to 

achieve significantly lower equipment prices. The manufacturers, in turn, must cover additional 

expenses related to the distribution of the equipment, but gain in terms of negotiating agreedupon sales volumes with each national account.

To estimate the price savings realized from equipment purchased through the national

account distribution chain, DOE assumed that the resulting equipment price increase is one half 

of that realized from distribution through the Wholesaler Channel. In other words, if the price 

markup resulting from the wholesaler markups is $100, the national account markup is $50.

Because DOE does 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 1.9

Cumulative Percentages (%)

Markup

Mechanical Contractor Markup by State
6-10

For example, using a baseline MSP of $1,000 for an automatic commercial ice maker

delivered to a restaurant, and a baseline wholesaler markup of 1.357, the resulting baseline 

customer equipment price for sales through a wholesaler (without sales taxes) is $1,357 ($1,000

× 1.357). The dollar value increase due to the above distribution channel markups is $357

($1,357 − $1,000). Under the assumption that national account customers realize equipment 

price increases equal to one-half of that through the wholesaler distribution channel, the dollar 

value of the estimated equipment price increase under the national account is $178.50. The 

resulting equipment price is $1,178.50 ($178.50 + $1,000), which results in a national account 

baseline markup of 1.178 ($1,178 ÷ $1,000). Using a value of 1.103 for the wholesaler 

incremental markup and a similar calculation results in a total national account incremental 

markup of 1.052 ($1,051.50 ÷ $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 multiplied 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 the 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 sales tax rates range from zero to 9.45 percent, with a mean value of 

6.24 percent. DOE applied sales taxes 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 State Sales Tax State Sales Tax

Alabama 8.55% Kentucky 6.00% North Dakota 5.95%

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 5.60%

Illinois 8.05% New Jersey 6.95% Washington 8.90%

Indiana 7.00% New Mexico 6.60% West Virginia 6.05%

Iowa 6.85% New York 8.40% Wisconsin 5.45%

Kansas 7.85% North Carolina 6.90% Wyoming 5.50%

Source: The Sale Tax Clearinghouse, www.thestc.com/STrates.stm. Last accessed July 10, 2013
6-11

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 presents

the incremental markup values for each distribution channel and the overall incremental markup 

values. Note that the markups shown for wholesaler/contractor 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 1.756

Sales Tax 1.071 1.071 1.071 1.071

Overall Multiplier* 1.454 2.143 1.263 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 1.229

Sales Tax 1.071 1.071 1.071 1.071

Overall Multiplier* 1.182 1.400 1.127 1.317

*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 unit 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 incremental 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 an 

incremental customer purchase price of $131.70. The customer purchase price of this higher

efficiency equipment is the sum of the baseline price and the increment, or $2,012.70 ($1,881+ 

$131.70).
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/tableservices/jsf/pages/productview.xhtml?pid=ECN_

2007_US_23A1&prodType=table>

2. Heating, Airconditioning & Refrigeration Distributors International. 2012 Profit Report 

(2011 Data). 2012. Columbus, OH.

3. U.S. Census Bureau. 2007 Economic Census, Release Date: 10/30/2009, Sector 31: 

EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United 

States: 2007. Air-Conditioning, Refrigeration, and Forced Air Heating Manufacturing. 

(Last accessed 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 Economic Census, Release Date: 11/24/2009, Sector 42: 

EC0742I1: Wholesale Trade: Industry Series: Preliminary Summary Statistics for the 

United States: 2007. HVAC equip. merchant wholesalers. (Last accessed October 29, 

2011.) 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_

EC0742I1_FTP&prodType=document>

5. U.S. Census Bureau. 2007 Economic Census, Release Date: 8/14/2009, Sector 23: 

EC0723I1: Construction: Industry Series, Preliminary Detailed Statistics for 

Establishments: 2007. Plumbing, heating, and air-conditioning contractors. (Last accessed 

October 29, 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 CONSUMPTION............................................................................. 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 CONSUMPTION............................................................................... 7-3

7.4 WEIGHTING FACTORS FOR LARGE BATCH EQUIPMENT 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.................................................... 7-5
7-1

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 consumption (AEC) in kilowatt-hours per year (kWh/yr) and 

annual water consumption (AWC) in gallons of water per year are key inputs to the life-cycle 

cost and payback period analysis (chapter 8 of the notice of proposed rule (NOPR) technical 

support document (TSD)) and national impacts analysis (chapter 10 of the NOPR TSD).

This chapter discusses 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 estimated the AEC for every combination of equipment 

class and efficiency level considered in the analysis. To estimate the AEC, estimated energy use 

per 100 lb of ice developed in the engineering analysis (see TSD chapter 5), assumed equipment 

utilization factors, 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 usage is also added to yield a total 

energy consumption value for a single 24-hour period. Multiplying the sum of the daily icemaking 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: 

AECi,j = (Ei,j ÷ 100× Hi× Ui + SEi × (1 – Ui)) × 365

Eq. 7.1

Where: 

AECi,j = calculated annual energy consumption for equipment class i, at efficiency level j

(kWh/year),

Ei,j = estimated energy use for equipment class i, at efficiency level j (kWh/100 lb ice),

Hi = harvest rate for equipment class i (pounds of ice produced per 24 hours),

Ui = assumed utilization for equipment class i (percent of time producing and harvesting ice), and

SEi = 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 level 1 is the initial value against which energy saving percentages for higher 

efficiency levels are estimated. A more complete breakdown of how the incremental percentage 

savings values are derived is described in chapter 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 class. The values used as estimates
7-2

for energy use (Ei,j) and harvest rate (Hi) are taken from the engineering analysis presented in 

chapter 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 (Ui) 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 

equipment 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 actively 

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 value of 5 W for all equipment classes, but 

believes the average value may actually be lower. The standby 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 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.
7-3

7.3 ANNUAL WATER CONSUMPTION

DOE estimated the AWC for ice-making equipment similarly to the way it estimated the 

AEC. To calculate the AWC, first the estimated potable water use and condenser water 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 assumed 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: 

AWCij = (PWij + CWij) ÷ 100× Hi × Ui × 365

Eq. 7.2

Where: 

AWCij = calculated total annual water consumption for equipment class i, at efficiency level j 

(gal/yr),

PWij = estimated potable water use for equipment class i, at efficiency level (gal/100 lb ice),

CWij = estimated condenser water use for equipment class i, at efficiency level (gal/100 lb ice),

Ui = assumed utilization for equipment class i (percent of time producing and harvesting ice),

and

Hi = harvest rate for equipment class i (pounds of ice produced per 24 hours).

Estimates of the annual combined potable water and condenser water usage are 

summarized in Table 7.3.1.
7-4

Table 7.3.1 Total Annual 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 FACTORS FOR LARGE BATCH 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 6.3
8-i

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK 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 INPUTS......................................................................................... 8-28

8.3.1 Definition .............................................................................................................. 8-28

8.3.2 Inputs..................................................................................................................... 8-28

8.4 LIFE-CYCLE COST AND PAYBACK 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

LIST 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........................................ 8-9
8-ii

Table 8.2.3 Baseline Energy Consumption Levels and Estimated MSP Values for the 12 Primary 

Equipment Classes................................................................................................ 8-10

Table 8.2.4 Baseline and Incremental Manufacturer Selling Prices for the 12 Primary Equipment 

Classes* ................................................................................................................ 8-11

Table 8.2.5 Energy Consumption Values for the 12 Primary Equipment Classes, by Efficiency 

Level* ................................................................................................................... 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 (2012$/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 Rates 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

.............................................................................................................................. 8-32
8-1

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 makers on individual commercial customers, henceforth referred to as 

customers. The effect of standards on customers includes changes in operating costs (usually 

decreased) and changes in purchase costs (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 the estimated amount of time it takes 

customers to recover the assumed higher purchase price of more energy efficient 

equipment through lower (undiscounted) operating costs.

An efficiency improvement in automatic commercial ice makers that is financially 

attractive to a customer will typically have a low PBP and a low LCC associated with it. 

The remainder of this section outlines the general approach and provides an overview of 

the inputs to the LCC and PBP analysis of automatic commercial ice makers. Inputs to the LCC 

and PBP analysis are discussed in detail in sections 8.2 and 8.3. Results for the LCC and PBP 

analysis are presented in sections 8.4 and 8.5.

DOE performed the calculations discussed in this chapter using a series of Microsoft 

Excelspreadsheets, which are available at www1.eere.energy.gov/buildings/appliance_standards/

commercial/automatic_ice_making_equipment.html. Appendix 8A includes instructions for 

using the spreadsheets. Appendix 8B presents the detailed results. Appendix 8C discusses 

uncertainty in the LCC modeling process.

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis

This section summarizes DOE’s approach to the LCC and PBP analysis for automatic 

commercial ice makers.

As part of the engineering analysis (chapter 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, increasing manufacturer selling price 

(MSP) values. For the LCC and PBP analysis, DOE chooses 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

referred to herein as efficiency levels. DOE treats each higher efficiency level as a potential new 

standard level. 

The first efficiency level (level 1) in each equipment class is the least efficient and the 

least expensive equipment in that equipment class. For many batch-type equipment classes, level 

1 equipment is equipment that just meets the efficiency standards for cube-type equipment set by 

the Energy Policy Act of 2005 (EPACT 2005). For equipment not currently covered by 

standards, level 1 reflects the least efficient equipment currently available. The higher efficiency 

levels (level 2 and up) have progressive increases in efficiency and equipment cost from level 1.

The highest efficiency level in each equipment class corresponds to the maximum 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 this value as part of the 

engineering analysis (chapter 5 of the NOPR TSD). DOE then applied 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). Installation 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 these costs over the life of the 

equipment. To facilitate cost comparisons, DOE discounted operating costs to the discount year

(2018, 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 costs, discounted to reflect 

the present value, is termed the life-cycle cost or LCC.

Generally, customers incur higher installed costs when they purchase higher efficiency 

equipment, and these cost increments will be 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 less energy over the lifetime 

of the equipment. The LCC of higher efficiency equipment can be lower compared to lower

efficiency equipment. DOE calculated the change in LCC for each efficiency level of each 

equipment class.

DOE obtained the PBP of higher efficiency equipment by dividing the increase in the 

installed cost by the decrease in annual operating cost. For this calculation, DOE used 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.

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 many inputs for the LCC and PBP analysis from the best available data,

and in some cases DOE 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 

carried out the LCC and PBP analysis in the form of Monte Carlo simulations using ranges of 

values and probability distributions for certain inputs that account for the uncertainties. DOE 

presents the 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 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 base-case scenario is 

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

conservation standard. A standards-case scenario is a scenario in which customers purchase 

equipment after the hypothetical energy conservation standard goes 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 efficiency level 1 represents a 

potential new standard, or a candidate standard level. Usually, the market will offer equipment at 

various efficiencies. Therefore, for both 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 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 assumed a distribution 

of equipment lifetimes that is defined by Weibull survival functions, with an average value of 8.5 

years (see section 8.2.3.7).

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. 

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 Life-Cycle Cost and Payback Period Inputs

DOE categorized inputs to the LCC and PBP analysis as: (1) inputs for establishing the 

total installed cost; and (2) inputs for calculating operating costs.

The primary inputs for establishing the total installed cost are:

• Baseline manufacturer selling price: The 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 converts the 

MPC to MSP.

• Price trends (learning): A method of adjusting the MSP over time to account for 

increasing 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 

appendix 8D.

• Candidate standard level manufacturer selling price increase: The incremental 

change in MSP associated with producing equipment at a given higher efficiency

level. 

• Markups and sales tax: The distribution chain markups and sales tax used to convert 

the MSP to a customer purchase price. NOPR TSD chapter 6 presents the 

methodology used to determine markups and sales taxes.

• Installation cost: The cost to the customer of installing the equipment, not including 

the equipment cost. DOE assumed the cost of installation as a one-time cost, and it is 

intended to represent the cost of labor, overhead, and other miscellaneous materials 

and parts.

The primary inputs for calculating the operating costs are:

• Equipment energy consumption: The energy consumed by an automatic commercial 

ice maker 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: The 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 were used to 

calculate annual water consumption in the energy and water usage analysis (NOPR 

TSD chapter 7).

• Electricity price: The price per kilowatt-hour, in cents or dollars, paid by each 

customer for electricity. DOE used average commercial electricity prices in each 

state, as determined from the Energy Information Administration (EIA) form 826 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 from 
8-5

average commercial prices. Section 8.2.3.1 details the development of electricity 

prices and the data sources used.

• Electricity price trends: DOE used the EIA’s Annual Energy Outlook 2013

(AEO2013)

1 to forecast electricity prices. For the results presented in this chapter, 

DOE used the regional prices from the AEO2013 reference case to forecast future 

electricity price

• Water and wastewater prices: DOE combined the prices for water and wastewater 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 disaggregated water prices for 

individual building types, so DOE varied prices by state only and not by building type 

within a state. Section 8.2.3.1 details the development of prices and the data sources 

used.

• Water and wastewater price trends: DOE used the Consumer Price Index (CPI) data 

for water related consumption (1973–2012) in developing a real growth rate for water 

and wastewater price forecasts. 

• Maintenance costs: DOE calculated maintenance costs as an annual expense 

representing the estimated labor and materials costs associated with maintaining the 

operation of the equipment. 

• Repair costs: DOE calculated the cost for repairs as an annual expense, representing

the estimated labor and materials costs associated with repairing or replacing 

components that have failed.

• Equipment lifetime: The typical age at which the automatic commercial 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 Rates

Electricity and 

Water Price 

Trends

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

Lifetime

Operating

Expense

Payback

Period

Life -Cycle

Cost

Water Prices

Annual Energy 

and Water Cost
8-7

Table 8.1.1 Summary Information of Inputs for the Determination of Life-Cycle Cost and 

Payback Period

Input Description TSD Chapter Reference

Total Installed Cost Primary Inputs

Baseline MSP Varies with equipment class. Chapter 5

Candidate standard level 

MSP increases

Vary with equipment class and candidate standard level

within an equipment class.

Chapter 5

Markups and sales tax Markups vary with distribution channel and sales tax 

varies with location (state) where equipment is installed.

Chapter 6

Installation price Varies 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 an equipment class.

Chapters 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 Vary with equipment class and candidate standard level 

within an equipment class.

Chapter 8

Lifetime Mean assumed to be 8.5 years for all equipment. Chapters 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 

sections 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 future 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 LCC IC OC r

1

/(1 )

Eq. 8.1

Where:

LCC = life-cycle cost ($),

IC = total installed cost ($),

N = lifetime of equipment (years),

b

OCt = 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 2012, DOE expressed all 

costs in 2012$. 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 2018, the presumed compliance date for standards set as a result of this rulemaking.

8.2.2 Total Installed-Cost Inputs

The following equation defines the total installed cost:

IC = EQP + INST

Eq. 8.2

Where:

EQP = customer purchase price for the equipment ($), and

INST = installation cost or the customer price to install equipment ($).

DOE based the equipment price on the distribution channel through which the customer 

purchases the equipment, as 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 inputs used to determine total installed cost.

Table 8.2.1 Inputs for Total Installed Costs

Baseline manufacturer selling price ($)

Price trend factor

Candidate standard level manufacturer selling price increases ($)

Wholesaler markup

Contractor 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 standards). The MSP includes a markup that is applied to convert

MPC to an MSP. DOE developed MSP values for the 12 primary equipment classes (see NOPR 

TSD chapter 5). Table 8.2.2 lists the 12 primary equipment classes 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-Making

Equipment Standard Life-Cycle Cost and Payback Period Analysis

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 chapter 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 was combined into one class. The classes were disaggregated to develop proposed 

standards by inclusion of an allowance related to lines carrying refrigerants.

Nine of 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 set 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 chapter 7.

Table 8.2.3 presents the baseline energy consumption values and the baseline MSPs used 

in the LCC and PBP analysis for the representative sizes for each of the 12 primary equipment 

classes and the typical units for the 3 large batch classes. NOPR TSD chapter 5 explains how 

energy use and MSP vary by harvest capacity. Table 8.2.3 also identifies whether DOE obtained 

the baseline from the engineering analysis, for equipment with no existing standards, or set it at 

the standards baseline, for equipment covered under existing standards. As discussed in chapter 

5, for new equipment on the market today that is not covered by existing U.S. standards, DOE 
8-10

set the baseline at levels approximating the least-efficient equipment that could potentially be 

available. Baseline energy consumption values presented in this section provide energy use per 

100 lb of ice produced. Chapter 7 discusses the methodology to calculate annual energy 

consumption using these values. 

Table 8.2.3 Baseline Energy Consumption Levels and Estimated MSP Values for the 12

Primary Equipment Classes

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 $2,165 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 5.89 $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 increases in MSP as a function 

of equipment efficiency for each of the primary equipment classes. The engineering 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 makers 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 12 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 chapter 7.

Table 8.2.5 Energy Consumption Values for the 12 Primary Equipment Classes, by

Efficiency Level*

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 chapter 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 

analysis for calculation of baseline and higher efficiency equipment price to customers. 

8.2.2.4 Installation Cost

Most automatic commercial ice makers are installed in fairly standard configurations, 

which helps in estimating the cost of installation. DOE defines installation cost as a one-time 

fixed cost that incorporates the labor and materials required to fully install an automatic 

commercial ice maker. For the NOPR analysis, DOE assumed 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. Installation cost 

may vary from one equipment class to another. If engineering design options change, DOE can

vary installation cost with efficiency level if technologies warrant an adjustment. DOE estimated 

material and labor cost to install equipment based on RS Means cost estimation data3 and on 

telephone conservations with contractors. Estimated installation costs vary by equipment class 

and by state. 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 

SCU-A-Small-C $300 
8-13

Table 8.2.7 shows installation cost indices for installation costs 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 to reflect differences by state. 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-tototal population values, using the 2012 population in each city.

5 DOE used state-level population 

weights for 2012 from the U.S. Census Bureau6 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 notice of data availability (NODA) titled Equipment Price 

Forecasting in Energy Conservation Standards Analysis. 76 FR 9696 (Feb. 22, 2011). In the 

NODA, DOE proposed a methodology for determining whether equipment prices have 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 experiential 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 (2018). The price trend

factor used in the LCC and PBP analysis was 0.9962, 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 MSPs, 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 rather a distribution of values.

The 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 the 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 the baseline and incremental markups, the sales tax, and installation 

cost to convert the MSPs into total installed costs for cases 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 Determination 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** 0.9962

* Installation cost applied to the baseline unit, with no incremental installation cost.

** Price trend (learning) factor for 2018. See appendix 8D.
8-15

The calculation below for the baseline (level 1) and for a higher efficiency level (level 4) IMHA-Small-B equipment class illustrates how DOE derived the weighted average total installed cost 

based on the data shown in Table 8.2.8. For the baseline product, DOE calculated the total 

installed cost at national average conditions as follows:

c

ICBASE IMH−A−Small−B = EQPBASE IMH−A−Small−B + INSTBASE IMH−A−Small−B × ISTINDEX

= MFGBASE IMH−A−Small−B × Price Trend FactorAnalysis Year

× MUBASE IMH−A−Small−B + INSTBASE IMH−A−Small−B

× ISTINDEX

= $1,137.59 × 0.9962 × 1.8813 + $300 × 1.00

= $2,132.02+ $300

= $2,432.02

Eq. 8.3

Where:

ICBASE IMH-A-Small-B = total installed cost of IMH-A-Small-B equipment at baseline efficiency level 

($), 

EQPBASE IMH-A-Small-B = equipment purchase price of IMH-A-Small-B equipment at baseline 

efficiency level ($),

INSTBASE IMH-A-Small-B = installation cost of IMH-A-Small-B equipment at baseline efficiency level 

($),

MFGBASE IMH-A-Small-B = MSP of IMH-A-Small-B equipment at baseline efficiency level ($), 

Price Trend Factor Analysis Year = Price trend factor for the 2012–2018 period, or 0.9962,

MUBASE 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 calculated the total installed cost for the higher efficiency level (efficiency level 4)

using an incremental MSP. DOE applied an incremental markup factor to the incremental 

increases in MSP. The level 4 price is equal to the baseline price calculated above, plus the 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, level 4, total installed cost (ICIMH-ASmall-BLEVEL4) as follows:d

ICIMH−A−Small−B LEVEL4

= EQPIMH−A−Small−B LEVEL4 + INSTIMH−A−Small−B LEVEL4 × ISTINDEX

=

MFGBASE IMH−A−Small−B × Price Trend FactorAnalysis Year ×

MUBASE IMH−A−Small−B +

∆MFGIMH−A−Small−B LEVEL4 × Price Trend Factor Analysis Year ×

MUIMH−A−Small−B LEVEL4 + INSTIMH−A−Small−BLEVEL4 × ISTINDEX

= $1,137.59 × 0.9962 × 1.8813 + $79.74 ×0.9962 × 1.3171 +

$300 × 1.000

= $2,536.64

Eq. 8.4

Where:

ICIMH-A-Small-B LEVEL4 = total installed cost of IMH-A-Small-B equipment at efficiency level 4 ($), 

EQP IMH-A-Small-BLEVEL4 = equipment price of IMH-A-Small-B equipment at efficiency level 4 ($), 

INST IMH-A-Small-BLEVEL4 = installation cost of IMH-A-Small-B equipment at efficiency level 4 ($),

ΔMFG IMH-A-Small-BLEVEL4 = incremental increase in MSP of IMH-A-Small-B equipment at 

efficiency level 4 compared to equipment at baseline efficiency level ($), and

MU IMH-A-Small-BLEVEL4 = incremental markup for equipment class IMH-A-Small-B, including state 

tax factor.

Table 8.2.9 presents the weighted average equipment price, installation cost, and total

installed costs for the IMH-A-Small-B equipment classes 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 Conditions (2012$)

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 defines the operating cost as the sum of energy cost, water cost, repair cost, and 

maintenance cost, or:

OC = EC+ WC+ RC+ MC

Eq. 8.5

Where:

OC = operating cost ($), 

EC = energy cost ($),

WC= water cost ($),

RC = repair cost ($), and

MC = maintenance cost ($).

The remainder of this section describes the variables that DOE used to calculate 

operating costs for automatic commercial ice makers. Table 8.2.10 shows the inputs used to

determine operating costs.

Table 8.2.10 Inputs Used to Determine Operating Costs

Electricity price (cents/kWh)

Water/wastewater price ($/1,000 gallons)

Electricity and water price trends

Repair cost ($)

Maintenance cost ($)

Lifetime (years)

Discount rate (%)

Effective date of standard

Baseline electricity consumption (kWh/100 lb ice)

Baseline water consumption (gallons of water/100 lb ice)

Standard case electricity consumption (kWh/100 lb ice)

Standards water consumption (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 considered regional 

differences in electricity prices. DOE divided the United States into the 50 states and the District 

of Columbia. DOE used average effective commercial electricity prices at the state level from 

the EIA web page titled Sales (Consumption), Revenue, Prices and Customers.

7 At the time 

DOE developed the LCC/PBP model, the latest available data was for calendar year 2012, 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 (2012 cents/kWh)

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 Buildings 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 the prices paid 

by the seven types of businesses in each state or district:

EPRICECOM BLDGTYPE STATE 2012 = EPRICECOM STATE 2012 × �

EPRICEBLDGTYPE US 2003

EPRICECOM US 2003

�

Eq. 8.6

Where:

EPRICECOM BLDGTYPE STATE 2012 = 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 BLDGTYPE 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 this analysis.
8-19

Table 8.2.12 Derived National Average Commercial Electricity Prices 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%

Foodservice $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 future 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 chapter 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 2047. Estimating future electricity prices is difficult, especially considering that there are 

efforts in many states throughout the country to restructure the electricity supply industry.

DOE applied a projected trend in regional average electricity prices to each customer’s 

energy prices based on the AEO2013 price scenarios.

f The discussion in this chapter refers to the 

reference price scenario. In the LCC analysis, DOE can analyze the following four scenarios:

1. Constant (real) energy prices at 2012 values (i.e., a constant index of 1.0)

2. AEO2013, High Economic Growth (“AEO2013 High Growth” in Figure 8.2.1)

3. AEO2013, Reference Case (“AEO2013 Reference” in Figure 8.2.1)

4. AEO2013, Low Economic Growth (“AEO2013 Low Growth” in Figure 8.2.1)

Figure 8.2.1 shows the trends for the AEO2013 reference case, high growth, and low 

growth price projections at the national level. DOE extrapolated the values for the later years, 

after 2040—the last year of the AEO2013 forecast. DOE used the price trend from 2031 to 2040

of each forecast scenario to establish prices for the years 2041 to 2047.

 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 2047

The default electricity price trend scenario used in the LCC analysis is the trend from the

AEO2013 reference case, shown in Figure 8.2.1. The LCC model spreadsheets have the 

capability to analyze the AEO2013 high growth trends, AEO2013 low growth 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 2045

US Weighted Average Real Cost/kWh (2012$)

Year

Electricity Price Projections

AEO 2013 Low Growth AEO 2013 Reference AEO 2013 High Growth
8-21

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 water 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 rate 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 average 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 that 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 commercial ice maker that have 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 equipment 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 from 
8-22

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 estimated 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 Class

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 chapter 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 cost does not include the costs associated with the replacement or repair of 

components that have failed (as discussed above).

In the NOPR analyses, DOE estimated material and labor costs for preventative 

maintenance based on RS Means cost estimation data3 and on telephone conservations with 

contractors. DOE assumed 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 chapter 7.

8.2.3.7 Lifetime

As discussed in section 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 concluded that a typical lifetime of 8.5 years is 

appropriate for most automatic commercial ice makers. 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 impact 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 maker analysis by 

estimating the cost of capital for the types of companies that purchase automatic commercial ice

makers. The cost of capital is commonly used to estimate the present value of cash flows to be 

derived from for a project or investment. Most companies use both debt and equity capital to 

fund investments, so their overall cost of capital is the weighted average of the cost to the 

company of equity and debt financing. 

DOE estimated the cost of equity financing by using the Capital Asset Pricing Model 

(CAPM).

9 The CAPM, among the most widely used of models that estimate the cost of equity 

financing, assumes that the cost of equity is proportional to the amount of systematic risk 

associated with a particular company. The cost of equity financing tends to be high when a 

company faces a large degree of systematic risk and it tends to be low when the company faces a 

small degree of systematic risk.

DOE determined the cost of equity financing by using several variables, including the 

risk coefficient of a company, β (beta), the expected return on “risk free” assets (Rf), and the 

additional return expected on assets facing average market risk, also known as the equity risk 

premium or ERP. The risk coefficient of a company, β, indicates the degree of risk associated 

with a given firm relative to the level of risk (or 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:

ke = Rf + (β x ERP)

Eq. 8.7

Where:

ke = the cost of equity for a company (%), 

Rf = 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 the risk-free rate using Federal Reserve data for the period 

1973 to 2012,

10 with a resulting rate of 6.41 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 & Poors

500 Index calculated using data downloaded from the Damodaran Online11 site and the risk-free 

rate.

The cost of debt financing (kd) is the interest rate paid on money a company borrows. 

DOE estimated the cost of debt by adding a risk adjustment factor (Ra) to the risk-free rate. 

d Rf Ra k = + ,

Eq. 8.8

Where:

kd = the cost of debt financing for each firm (%),

Rf = the expected return on risk-free assets (%), and 

Ra = the risk adjustment factor to risk-free rate for each firm (%). 

The risk adjustment factor depends on the variability of stock returns represented by 

standard deviations in stock prices. DOE calculated stock prices using values from Damodaran 

Online individual company cost of capital worksheets.11
8-26

The weighted average cost of capital (WACC) for a company is the weighted average 

cost of debt and equity financing:

k =ke x we+ kd x wd

Eq. 8.9

Where:

k = the (nominal) cost of capital (%),

ke = the expected rate of return on equity (%),

kd = the expected rate of return on debt (%),

we = the proportion of equity financing in total annual financing, and

wd = 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 calculated 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 maker purchasers, DOE used a

dataset of companies involved in each of the building types being analyzed, drawn from a 

database of U.S. companies given on the Damodaran Online individual company worksheet cited 

earlier. The Damodaran database includes most of the publicly traded companies in the United 

States.

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 likeliest to use 

automatic commercial ice makers. 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 facilities. The cost of capital for small, 

independent grocers; convenience store franchisees; gasoline station owner-operators; small 

hotel owner-operators; and others with more limited access to capital is more difficult to 

determine than for publicly traded companies. 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 small 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 / NontradedState & Local 

Government US Government Weighted 

Average, 

Real

No.

of

WACC Obs. % of 

Mkt

Rate

With

Adder6

% 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 5.10% 25% 0.0% 0% 2.34% 75.0% 0.0% 0.0% 3.03% 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% 4.63% 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 Federal Reserve Bank of St. Louis, and Federal Reserve H15 data (for 2012 only). Value shown on the 

table is 40-year geometric average, mixed quality bonds, based on annual average data. Non-seasonally adjusted. Data for 1973–

2011 from the Federal Reserve Bank 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. Articles did not 

indicate the percentage of branded hotel rooms that are franchisees essentially indistinguishable 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 commercial refrigeration energy conservation standards rulemaking. 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 not public schools. For the education sector, DOE obtained a 

40-year series of municipal bond rates from the Federal Reserve.

13 As shown in the discount rate 

calculation, DOE assumed 75 percent of schools are public schools facing the average municipal 

bond rates calculated from the Federal Reserve data. The same municipal bond rate was used in 

the calculation of office and foodservice 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-owned 

offices.

8.2.3.9 Compliance Date of Standard

The compliance date is the future date when a new standard will become operative (i.e., 

the date on which 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 makers 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 year of equipment purchase is 2018. However, all dollar values are expressed 

in 2012$.
8-28

8.3 PAYBACK PERIOD INPUTS

8.3.1 Definition

As stated in section 8.1, the PBP is the amount of time it takes the customer 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 

it does not take into account changes in operating cost over time or the time value of money, that 

is, the calculation is done at an effective discount rate of zero percent.

The equation for PBP is:

PBP =∆IC/∆OC

Eq. 8.10

Where:

PBP = payback period in years,

∆IC = difference in the total installed cost between the more efficient standard level and the 

baseline equipment, and

∆OC = difference in annual (first year) operating costs.

PBPs greater than the life of the product mean that the increased total installed cost of the 

more energy efficient equipment is not recovered in reduced operating costs over the life of the 

equipment, even when no discount rate is applied.

8.3.2 Inputs

The data inputs to PBP are the total installed cost of the equipment to the customer for 

each efficiency level and the annual (first year) operating costs for each efficiency level. The 

inputs to the total installed cost are the customer’s equipment price and the installation cost. The 

inputs to the operating costs are the annual energy and water costs, 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 because the PBP is a “simple” (undiscounted) payback and the required electricity and 

water prices are only for the year in which a new efficiency standard is to take effect—in this 

case, the year 2018. The electricity and water prices used in the PBP calculation were the prices

projected for 2018, expressed in 2012$. 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-ASmall-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 levels in an equipment class. The 

installed costs increase steadily from the baseline to the highest possible efficiency level and the 

LCCs decrease from level 1 to the highest possible efficiency level. 

Figure 8.4.1 LCC and Installed Cost Variation over Efficiency Levels for IMH-A-Small-B

Equipment Class

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. Appendix 8B includes similar plots of LCC 

savings for all equipment classes analyzed. Table 8.4.1 presents the numerical values associated 

with Figure 8.4.2. Figure 8.4.2 illustrates the mean and median values on the plot using red and 

blue markers, respectively. The elongated, large rectangular box represents the 25th and 75th

percentile values. The lower edge of the elongated rectangle represents 25th percentile, which 

means that 25 percent of the customers would experience LCC savings of $201 or less if the 

standard were set at level 2, $116 or less in LCC savings if the standard were set at level 3, and 

so on. The median value of LCC savings is equal to the 50th percentile. The upper edge of the 

elongated rectangle represents the 75th percentile. The two ends of the vertical black line for each 

efficiency level represent the 5th percentile (lower end) and 95th 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 Costs
8-30

Figure 8.4.2 LCC Savings Ranges by Efficiency Level for Equipment Class IMH-ASmall-B

Table 8.4.1 LCC Savings Distribution Results for Equipment Class IMH-A-Small-B* LCC Savings 2012$

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 mean and median LCC savings, respectively,

for all equipment classes analyzed.

-$200

$0

$200

$400

$600

$800

$1,000

 2 3 4 5 6 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8-31

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 calculated for this 

efficiency level because design options were unavailable to constitute an additional efficiency level. Values in parenthesis 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 chapter 7.

Table 8.4.3 Median LCC Savings for All Equipment Classes and Efficiency Levels

Equipment Class

Median 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 calculated for this 

efficiency level because design options were unavailable to constitute an additional efficiency level. Values in parenthesis 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 chapter 7.
8-32

8.4.2 Payback Period Results

Figure 8.4.3 presents the distribution of the PBP results for efficiency levels above the 

baseline for the equipment class IMH-A-Small-B. Table 8.4.4 presents the numerical values 

associated with this plot. The red marker represents the mean and the blue marker represents the 

median PBP for each efficiency level. The lower edge of the elongated rectangular box 

represents the 25th percentile, which means that 25 percent of the customers would experience a 

PBP of 0.89 years or less if the energy conservation standard were set at level 2, 1.02 years or 

less if the energy conservation standard were set at level 3, and so on. The upper edge of the 

rectangular box represents the 75th percentile. The two ends of the vertical line represent the 5th

percentile (lower end) and 95th percentile (upper end). Table 8.4.4 and Table 8.4.5 summarize the 

mean and median PBPs, respectively, for all efficiency levels for all the analyzed equipment 

classes.

Figure 8.4.3 Payback Period for All Efficiency Levels for the Equipment Class IMH-ASmall-B

Table 8.4.4 Payback Period Distribution Results for IMH-A-Small-B Payback Period years

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 7

Years

Efficiency Level

Payback Period 

Median Mean
8-33

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 establishes a rebuttable presumption for automatic commercial ice-making

equipment. (42 U.S.C. 6295(o)(2)(B)(iii) and 6313(d)(4)) The rebuttable presumption states that 

a standard is economically justified if the Secretary of Energy finds that the additional cost to the 

customer of purchasing a product complying with an energy conservation standard level will be 

less than three times the value of the energy savings 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, standards-compliant equipment, and compared this cost to the value of the energy 

saved during the first year of operation of the equipment. DOE interprets that the increased cost 

of purchasing standards-compliant equipment includes the cost of installing the equipment for 

use by the purchaser. DOE calculated the rebuttable presumption payback period (RPBP), or the 

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. This 

payback period calculation does not include other components of the annual operating cost of the 

equipment (i.e., water costs, maintenance costs, and repair costs). The RPBPs calculated can thus 

be different from the PBPs calculated in section 8.4.2.

DOE calculated the RPBPs for the range of installed costs and energy prices discussed in 

sections 8.4.1 and 8.4.2, which are representative of the same seven building types and all 50 
8-34

states plus the District of Columbia. DOE calculated the RPBP for each higher efficiency level 

within each equipment class.

Table 8.4.6 shows the RPBPs calculated for all equipment classes 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

Appendix 8B presents detailed results from the LCC analysis. Plots similar to 

Figure 8.4.2 and Figure 8.4.3 are presented in the appendix for all equipment classes. In addition, 

summary tables with all the necessary data in one table for each equipment class are presented. 

Table 8.5.1 is the summary table for IMH-A-Small-B equipment class. This table presents the 

mean values of installed costs, annual operating costs, LCC, LCC savings, and median PBP 

values for all the efficiency levels. It also presents the percentage of customers who experience 

net cost, no 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 (level 1). As 

discussed in chapter 10, DOE assumes that some 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 customers with 

no impact in the base case are already buying more-efficient equipment, so the efficiency level in 

question would not affect those customers.
8-35

Table 8.5.1 Summary of Results of LCC and PBP Analysis for IMH-A-Small-B Equipment 

Class

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 1.07

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 
8-36

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 Company, Inc. 2013 RS Means Electrical Cost Data. 2013. Kingston, MA.

4. RS Means 2013. City Cost Indexes, RJ1030-010 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.html>

6. U.S. Census Bureau. Annual Estimates of the Population for the United States, Regions, 

States, and Puerto Rico: April 1, 2010 to July 1, 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-826 data from 

the EIA web page Sales (Consumption), Revenue, Prices 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 20, 2013.) 

<www.federalreserve.gov/releases/h15/data.htm>

11. Damodoran Online. 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 Advocacy. The Small Business Economy 

2012. 2012. (Last accessed June 4, 2013.) <http://www.sba.gov/advocacy/7540/6282>. See 

also: U.S. Small Business 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 Stock 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 ............................................. 9-1
9-1

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Automatic commercial ice-making (ACIM) equipment shipments are key inputs to the 

national energy savings, net present value, and manufacturer impact analyses. This chapter 

describes the U.S. Department of Energy’s (DOE’s) methodology for estimating ACIM

equipment shipments and annual equipment stocks for 2018 through 2047.

DOE’s shipments model predicts ACIM equipment stock flows as a function of year and 

age. Historical shipment data for 12 primary and 13 secondary proposed ACIM equipment 

classes are key inputs to the shipments model. Figure 9.1.1 outlines the structure of the 

shipments model.

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 

makers either ages by 1 year or fails, and that failed equipment is replaced. It also assumes that

new equipment can be installed in new commercial floor space and old equipment can be 

removed through demolitions (not shown in the diagram).

Section 9.2 presents the mathematical formulation of the model, section 9.3 describes the 

data input to the model, and section 9.4 presents the results for the base-case energy conservation 

standard-level scenario and discusses the development of higher standard scenarios.

Initial Shipments to 

New Construction

Initial Stock

Projections

Initial Replacement 

Shipments

Shipments to 

Construction

NEMS Commercial 

Construction Forecast

Initial Stock

Total Shipments by 

Equipment Class

Replacement 

Shipments

Estimated Lifetime of 

ACIM Equipment

Stock by Equipment 

Class
9-2

9.2 SHIPMENTS MODEL

DOE examined all 25 proposed equipment classes using the shipments model. DOE

assumed no interdependence between the shipments of the various equipment classes; thus, the 

equations are applicable to each equipment class independently.

As described in sections 9.3.1 and 9.3.2, DOE collected historical ACIM equipment 

shipment data from 1994 through 2010. Using the historical data aggregated over 8.5-year 

periods, DOE developed a multi-year series of estimates of ACIM equipment stocks. DOE used

the estimates of existing stock and the expected life of the equipment as a basis for calculating

the portion of historical shipments that replaced 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 historical shipments for new installations, DOE subtracted the estimated

replacement sales from historical total sales. DOE then combined the estimated historical sales 

for new installations with indexes of historical and forecasted construction activity in seven 

building types to estimate future shipments for new installations. DOE forecasted 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 applied this 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 doing so, DOE projected 

sales of new and replacement equipment by the year sold for the forecast period. In the final step, 

DOE allocated sales 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 shipments model equations to update the stock from year t to year 

t+1 as a function of age a. 

As discussed in chapter 8 of this notice of proposed rulemaking (NOPR) technical 

support document (TSD), DOE estimates that the average lifetime of a unit is 8.5 years. DOE 

assumes 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

Where:

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). By 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, three things could happen to the stock of

automatic commercial ice makers:

1. existing equipment could fail, or be removed during a building renovation and be 

replaced,

2. existing equipment could be retired permanently, generally in a building demolition 

or renovation during which the business type(s) situated in the building changes, or

3. the stock could simply age by 1 year.

In the shipments model, DOE did not consider early replacements (i.e., existing 

equipment that is replaced before it fails) and assumed all failed equipment was replaced. The 

following sections present the equations used to represent each possible event.

9.2.2.1 Replacing Equipment

DOE determined the probability that ACIM equipment of age a from stock U0 will fail or 

will be replaced using a Weibull survival distribution function. The Weibull function 

describes a series of probabilities of equipment failure in given years. 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 results 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 year set equal to an 

average of the values over a range of the calendar year (e.g., 9.0) plus or minus 0.5 years.
9-4

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%

DOE defined the quantities of replaced equipment as follows:

UB(t,a) = PB0(a) × U0(t,a)

Eq. 9.2

Where:

UB(t,a) = quantity of units of age a in year t replaced,

PB0(a) = probability that stock of existing units of age a will fail or will be replaced,

U0(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

Where:

EFS(t) = existing floor space in year t (ft2),

NFS(t) = new floor space added in year t (ft2), and

DFS(t) = floor space demolished in year t (ft2).
9-5

The number of units installed in new buildings is:

UN(t) = UN(t-1) × NFS(t)/ NFS(t-1)

Eq. 9.4

Where:

UN(t) = the number of units installed in new buildings in year t.

DOE has little information on the variation in the market saturation of automatic 

commercial ice makers 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. The Air-Conditioning, Heating, 

and Refrigeration Institute (AHRI) provided ACIM shipments data for 2010. The AHRI data

provided a distribution of shipments by equipment class. AHRI also provided a 10-year history 

of total shipments. Additionally, DOE collected the U.S. Census Bureau’s Current Industrial 

Reports (CIR) data for the 1973 to 2009 period, including total ice maker shipments. 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 were associated with net exports. DOE identified one source 

with identifiable export information, the North American Association of Food Equipment 

Manufacturers (NAFEM). NAFEM data for two relatively recent calendar years (2007 and 2008) 

showed approximately 20 percent of total ice maker shipments associated with food service 

equipment as exports. Applying a 20-percent export factor to the CIR shipments data brought the 

CIR data into approximate agreement with the AHRI data. Given the similarity between the data 

after adjusting the CIR data and given that the AHRI data is better understood than the CIR data, 

for the NOPR analysis, DOE relied on the AHRI shipment values.

Using AHRI data, DOE created a rolling estimate of total existing stock by aggregating

historical shipments across 8.5-year historical periods. Using 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 the split between shipments to new buildings and shipments for stock 

replacement to derive new shipments using the 2010 historical shipments as a starting point.

Starting with the 2010 values, DOE began adding shipments, subtracting estimated stock 

retirements, and developing a forecast of surviving stock for 2011 to 2047.

To identify appropriate building construction estimates for future growth in the forecast 

step, DOE distributed the estimated total 2010 existing ice maker stock across building types 

using distributions from a 2009 commercial refrigeration equipment report from Navigant 

Consulting, Inc.

1 Thus divided, the projections of new construction activity for each building 
9-6

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 across building types by year, and

the totals ultimately were distributed across all proposed 25 equipment classes using distributions 

derived from the AHRI 20102 data.

Table 9.3.1 Distribution of Ice Maker Stock 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 percentages of 

total shipped units of automatic commercial ice makers for use in the shipments model. This

established the ACIM equipment market share attributed to each equipment class for a base, 

historical data year.

DOE developed the projections of building construction from the 2013 Annual Energy 

Outlook (AEO2013).3 Table 9.3.2 shows the forecast square footage of new construction used to 

scale annual new ACIM equipment shipments. As the data in Table 9.3.2 show, forecast squarefootage additions to the building stocks vary from year to year. Therefore, the 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 the growth rates over the 

last 10 years of the AEO2013 forecast (2031–2040) to extrapolate the AEO forecast through the 

year 2047 to develop the full 30-year forecast needed for the national impacts analysis (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 forecast of specific equipment classes. In the case of ACIM equipment, DOE currently has no 

specific data on such specific linkages between types of buildings and equipment classes. However, because 

building stocks for each of the seven building types are projected to grow at varying rates, projecting shipments by 

building type enables DOE to capture as much information as possible from the NEMS commercial building 

construction projections.
9-7

Table 9.3.2 AEO2013 Forecast of Commercial Floor Space Additions by Building Type

Year

New Construction of Commercial Floor Space

million ft2

Health 

Care 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 2013, data file kdbout.

DOE used the AHRI shipments 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 25 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: Watercooled; 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 rulemaking.

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 shipments model. The model divides commercial floor space 

into 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 AEO2013. DOE extracted annual estimates of new floor space additions 

from an AEO2013 data file (kdbout) for the 2009 through 2040 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 2047.

DOE projected the shipments of ice makers for new buildings by multiplying the 

estimated shipments to new buildings in 2010 by future indices of new construction for each

building type. DOE developed the index for each building type by dividing floor space 

projections in each future year by the prior year’s floor space projection.

Future 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 Shipments

DOE combined the shipments for new buildings and replacement of existing stock to 

yield total shipments in year t. DOE used total shipments to estimate shipments for each ACIM

equipment class y, MKTSHIP(t,y), as shown with Eq. 9.5. Because DOE assumed that the 

percent of shipments in each class was constant over time and the lifetime of each class was the

same, the ratio of shipments of a specific equipment class indicates the market share for that 

class, MKT(y). DOE assumed 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 these percentages.

MKTSHIP(t, y) = MKT(y) × (UN(t) + UB(t))

Eq. 9.5

Where:

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 shows DOE’s shipments forecast for the ACIM equipment classes at the 

base-case energy consumption level (or efficiency level 1) by equipment type.
9-10

Table 9.4.1 Forecast Shipments of New and Replacement Automatic Commercial Ice 

Makers by Building Type, 2018−2047 (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

Foodservice

– 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.

Equipment 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 cumulative 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 of Automatic Commercial Ice Makers, 2018−2047, by 

Equipment Type (Base Case)*

Equipment Class

Number of Units by Equipment Class (for selected years and cumulative)

2018 2020 2025 2030 2035 2040 2045 Cumulative

(2018 – 2047)

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 Opportunities for 

Commercial Refrigeration. September 23, 2009. Prepared for the U.S. Department of 

Energy, Washington, D.C.

2. Air-Conditioning, Heating, and Refrigeration Institute. 2010 shipments data submittal. 

February 2011. 

3. DOE. 2013. Annual Energy Outlook 2013. 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 SAVINGS .................................................................................. 10-1

10.2.1 National Energy Savings Definition ..................................................................... 10-1

10.2.2 National Energy Savings Inputs............................................................................ 10-2

10.2.2.1 Annual Unit Energy Consumption......................................................... 10-2

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 SAVINGS AND NET PRESENT VALUE RESULTS........... 10-18

10.4.1 National Energy Savings and Net Present Value Input 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 ........................................................................................................................ 10-34

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 Site-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 Cost per Unit by Efficiency Level ............................... 10-11
10-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 Maintenance Cost per Unit...................................................... 10-17

Table 10.4.1 NES and NPV Inputs.......................................................................................... 10-19

Table 10.4.2 Cumulative Primary National Energy Savings, by 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.............................. 10-26
10-1

CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method used to estimate 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 standards. Results of the 

national impact analysis (NIA) described in this chapter include national energy consumption 

and savings, monetary value of energy savings to the nation as a result of standards, increased 

total installed costs to the nation 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 12 primary equipment classes of automatic 

commercial ice makers examined in this notice of proposed rulemaking (NOPR). Sections 10.2

and 10.3 present the definitions and the inputs to NES and NPV, respectively. Section 10.4

presents the result of the NIA. DOE performed all calculations using a Microsoft Excel

spreadsheet, which is available at www.eere.energy.gov/buildings/appliance_standards/ 

commercial_products.html. Details and instructions for using the spreadsheet are discussed in 

appendix 10A of this technical support document (TSD).

10.2 NATIONAL ENERGY SAVINGS

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).

NESt = AECbase,t − AECstandards,t

Eq. 10.1

Where:

NESt = national energy savings in the year t (quadrillion British thermal units (quads)),

AECbase,t = annual national energy consumption in the year t in the base-case scenario (quads),

and

AECstandards,t = annual national energy consumption in the year t in a standards-case scenario 

(quads).

DOE calculates cumulative NES as the sum of annual NES from each year, starting with 

the year in which the standards that are the subject of this rulemaking become effective (2018), 

and through the end of the study period (2047). As discussed below, the NES also includes 

energy savings for equipment purchased in the final study year, and through the year in which 

the final year purchases are retired. Cumulative NES is explained by Eq. 10.2.
10-2

NEScum = ∑t NESt

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:

AECt = ∑ (STOCKV × UECV V ) × ffc_convt

Eq. 10.3

Where:

AECt = national annual energy consumption in the year t (quads), summed over vintages of 

ACIM stock,

STOCKV = stock of automatic commercial ice makers (millions of units) of vintage V surviving 

in the year for which DOE calculated AEC (vintages range from 1 to approximately 

14 years),

UECV = annual unit energy consumption in kilowatt-hours (kWh) for equipment of vintage V,

ffc_convt = time-dependent conversion factors to convert site energy to full-fuel-cycle (FFC) 

energy by converting site energy to source energy (Btu/kWh) and adding the energy 

consumed in extracting, processing, and transporting or distributing primary fuels (calculated 

as a percent of site energy)

t = year of forecast.

The ACIM stock depends 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 could cause some customers to forego or delay

discretionary equipment purchases.

10.2.2 National Energy Savings Inputs

Table 10.2.1 lists the inputs for the determination of NES.

Table 10.2.1 National Energy Saving Inputs

Input

Annual unit energy consumption (UEC)

Shipments

Equipment stock (STOCKV)

Site-to-source conversion factor (src_conv)

10.2.2.1 Annual Unit Energy Consumption

The annual UEC is the site energy consumed by an automatic commercial ice maker 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 combined 2010 automatic commercial ice maker 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 estimated the market shares based on the number of available ice maker models 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 created a 

shipment-weighted distribution of units by equipment class and efficiency level.

The market share for each equipment class by efficiency 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 12 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 levels.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than 

modeling these as one class. The UECs for these three 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 

level 1, but is rather, a shipment-weighted average UEC value for the base-case efficiency 

distribution. The same holds true for standards-case distributions. To develop standards-case 

distributions, DOE used the so-called “roll-up” method of aggregating shipments. In the roll-up 

method, when a standard is contemplated that eliminates 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 shows, in 

the base case, 39 percent of IMH-W-Small-B machines were shipped at level 1 efficiency. If 

level 1 is eliminated, those shipments would roll up into level 2. For each equipment class, DOE 

used the roll-up method to calculate shipment-weighted average UEC for the efficiency 

distribution at each efficiency level. Table 10.2.4 shows the resulting shipment-weighted average 

AEC 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 levels.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than 

modeling these as one class. The UECs for these three 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, Shipments Analysis. 

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 makers

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 2047, any units still remaining at the 

end of 2055 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 the automatic commercial ice makers). DOE then calculated primary energy consumption by 

applying a conversion factor to account for losses associated with the generation, transmission, 

and distribution of electricity.

Historically, DOE reported NES in 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 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 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 average site-to-power plant factors based on the version of the NEMS that 

corresponds to EIA’s Annual Energy Outlook 2013 (AEO2013).1 The factors change over time in 

response to projected changes in the types of power plants projected to provide electricity to the 

nation. Table 10.2.5 shows the site-to-power plant factors from 2017 to the end of the projection 

period. For years after 2040 (the last year in the AEO), DOE extrapolated the trend from 2031

through 2040.

 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 distributing primary fuels, which DOE refers 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 extensive 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 AEO2013)

Fuel 2017 2020 2025 2030 2035 2040 to 2060

Electricity

(power plant 

energy use)

1.042 1.041 1.040 1.040 1.041 1.040
10-8

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 certain 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 levels.

** 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.
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 that efficiency level because insufficient design options were 

identified to specify additional efficiency levels.

** 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.

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

Where:

PVS = present value of operating cost savings (electricity, waterb

, 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 the PVS and PVC according to the following expressions:

PVS = ∑t OCSt × DFt

Eq. 10.5

PVC = ∑t TICt × DFt

Eq. 10.6

Where:

OCSt = total annual operating cost savings in the year t ($),

TICt = total annual installed cost increases in the year t ($),

DFt = discount factor for the year t, and

t = year (PVS is summed over 2018−2055, and PVC is summed over 2018−2047).

DOE determined the contribution to PVC for each year, from the effective date of the

standard (2018) to the year 2047, discounted to the year 2013. DOE determined the contribution 

to PVS for each year, from the effective date of the standard to the year when units purchased in 

2047 would be retired. 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 which the sum is being discounted) and the year in 

which the costs and savings occur. DOE calculated the NPV as the sum over time of the 

discounted net savings.

10.3.2 Net Present Value Inputs

Table 10.3.1 summarizes the inputs to the NPV calculation.

Table 10.3.1 Net Present Value Inputs

Input

Total annual installed cost (TICt)

Total annual operating cost savings (OCSt)

Discount factor

Present value of costs (PVC)

Present value of savings (PVS)

10.3.2.1 Total Annual Installed Cost 

The increase in the total annual installed cost 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 costs. (See 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 makers by efficiency

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 levels.

** 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 Average 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 levels.

** 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 in Table 10.3.3 depend on the forecasted annual shipments by efficiency 

level. If a standard were set at level 1, customers would select equipment from all of the 

available levels. If the standard were set at level 2, customers would select from equipment at 

level 2 or higher. As the standard levels increase, the range of available efficiency levels is 

reduced. The 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

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 notice of data availability (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 production costs through innovations in technology and process.
10-13

To account for increased efficiency in the ACIM manufacturing process over time, DOE 

used a price forecast methodology based on experiential learning (see appendix 8D). To project

the manufacturer selling price of a unit, DOE will multiply the selling price for the first year of 

the analysis period by a coefficient specific to the year of purchase relative to the year in which 

manufacturer selling price was 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, 

constant 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 the remaining three 

scenarios—the high, reference, and low learning scenarios—for this rulemaking from historical 

ice maker shipments and Producer Price Index data. Appendix 8D of this TSD documents the 

development of the price learning scenarios. For the NOPR, DOE used the historically derived 

reference scenario. The total cost values reported in Table 10.3.2 include the effects of 

experiential learning for the period between 2012 and 2018. The other scenarios were used in 

sensitivity analyses, included in appendix 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, Life-Cycle Cost and 

Payback Period Analysis, DOE calculated annual electricity costs based on average state-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 multiplied the weighted-average energy 

consumption at each efficiency level from Table 10.3.3 by the number of units in the equipment

class in each year, and then by the sales-weighted national average electricity prices for the seven

building types in chapter 8. To determine energy savings, DOE then subtracted the national 

energy costs at each efficiency level (level 2 and higher) from the national energy costs at the 

baseline level (level 1).

Annual Water Cost Savings. DOE used average state-level commercial water/wastewater 

prices to calculate water/wastewater cost savings. To calculate annual water/wastewater cost 

savings for a particular equipment class in a given year, DOE first calculated the annual 

water/wastewater costs in each forecast year at each efficiency level by multiplying the 

weighted-average consumption at each efficiency level by the number of units in the equipment 

class in each year. To determine cost savings, DOE subtracted the national water costs at each 

efficiency level (level 2 and higher) from the national water costs at the baseline level (level 1). 

Chapter 8 discusses in more detail the 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 is discussed in chapter 8.
10-15

Table 10.3.5 Average 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 levels.

** 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 levels.

** 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.

Annual Maintenance Costs. The estimation of maintenance costs is described in chapter 

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 efficiency 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 levels.

** 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:

DF = 1

(1+r)�t−tp�

Eq. 10.7

Where:

r = discount rate,

t = year in which the cost or savings are realized, and

tp = 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 these 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,” therein. 

DOE defined the present year as 2011 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 difference 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 automatic commercial ice makers purchased in 

the standards case compared to the base case. DOE calculated annual installed costs as the 

difference in total installed cost for new equipment purchased each year, multiplied by the 

shipments in the standards case.

10.3.2.6 Present Value of Savings 

The present value of the operating cost savings is the annual operating cost savings 

(i.e., the difference between the base case and standards case) discounted to 2013, and summed 

over the period from the effective date of the standard, 2018, to the time when the last unit 

installed in 2047 is retired from service. Savings result from net decreases in total operating costs 

(including electricity, water, repair, and maintenance) associated with the automatic commercial 

ice makers purchased in the standards case compared to the base case. Total annual operating 

cost savings are the savings per unit multiplied by the number of units of each vintage surviving 

in a particular year. Equipment consumes energy and water over its entire lifetime. For example, 

for units purchased in 2045, 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 NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE 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 the inputs to the NES spreadsheet.

The NES spreadsheet model is available at

www1.eere.energy.gov/buildings/appliance_standards/commercial/automatic_ice_making_equip

ment.html. Appendix 10A provides details and instructions for using the model.

For this NOPR analysis, DOE selected five trial standard level (TSL) groupings 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 National Energy Savings and Net Present Value 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 date of standard 2018

Base-case efficiencies Distribution of base-case shipments by efficiency level.

Standards-case efficiencies Distribution of shipments by efficiency level for each standards 

case. Standards case annual market shares by efficiency level 

remain constant over time for the base case and each standards 

case.

Annual energy and water 

consumption per unit

Annual weighted-average values are a function of efficiency level 

(established in the engineering analysis, TSD chapter 5).

Total installed cost per unit Annual weighted-average values of installed cost are a function of 

efficiency level (see TSD chapter 8).

Repair cost per unit Annual weighted-average values of repair costs vary with 

efficiency level (see TSD chapter 8).

Maintenance cost per linear foot Annual weighted-average values of maintenance costs are constant 

with efficiency level (see TSD chapter 8).

Escalation of electricity prices 2013 EIA AEO2013 forecasts (to 2040) and extrapolation for 2041

and beyond (see TSD chapter 8).

Escalation of water prices Historic Consumer Price Index for water-related expenditures 

extrapolated out to 2055.

Electricity site-to-source 

conversion

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 rate 3 and 7 percent real.

Present year Future costs are discounted to year 2013.

10.4.2 National Energy Savings Results

The following section provides NES results for each energy consumption level

considered for the 12 primary equipment classes of automatic commercial ice makers 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 inputs to the NES spreadsheet model on 

weighted-average values, yielding results that are discrete point values, rather than a distribution 

of values as in the life-cycle cost analysis (see 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 Cumulative 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 levels.

** 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 RCULarge-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 10.4.4 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 2018-2055 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 NES results for the efficiency levels and for each equipment class, 

including the FFC adjustment. FFC energy savings are the standard savings reported by DOE. 

Table 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 levels.

** 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-LargeB, rather than modeling these as one class. The values for these three classes are the totals of the B1 and B-2 typical units.

10.4.3 National Water Impacts

For the NOPR, DOE analyzed one design option that resulted in changes in potable water 

usage. Table 10.4.8 presents 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 through 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 3,699 3,699 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 20753

 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 levels.

** 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 levels.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-LargeB, 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.

10.4.4 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 savings at the national level.

Figure 10.4.1 shows the changes over time of the non-discounted annual equipment price 

increases and the non-discounted operating cost savings at level 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 

annual operating costs at a 7-percent discount rate. The figure also shows the net annual impact, 

which is the difference between the savings and costs for each year. The annual equipment price 

change is the increase in equipment price between base and standards cases for equipment 

purchased each year over the period 2018–2047. The annual operating savings are the savings in 

operating costs for equipment purchased but not retired for each year over the time period 

2018−2055. DOE determined the annual costs and savings based on the AEO2013 reference case

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 the inputs to the NES spreadsheet 

model on weighted-average values yielding results that are discrete point values, rather than a 

distribution of values as in the life-cycle cost analysis (see TSD chapter 8). The present value of 

increased total installed costs is the total installed cost increase discounted to 2013 and summed 

over the 2018–2047 period. The installed costs embodied in the results below are based on 

reference case price trends.

Savings result from decreases in operating costs associated with the higher energy and 

water efficiency of automatic commercial ice maker units purchased in the standards case 

compared to the base case. Total operating cost savings are the savings per unit, multiplied by 

the number of units of each vintage. The operating cost includes energy and water consumed and 

maintenance and repair costs incurred until all units purchased during the analysis period (2018–

2047) 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

30.0

Millions 2012$

Year

Net Savings

Undiscounted Annual Equipment Costs Savings

Undiscounted Total O&M Cost Savings

Discounted Total Net Annual Impact
10-27

Table 10.4.9 Cumulative NPV Results, by CSL, Based on a 7-Percent Discount Rate

Equipment Class

Cumulative NPV by CSL at 7% 

billion 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 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 levels.

** 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 NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-LargeB, 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 AEO2013 reference case. 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-Percent Discount Rate

Equipment Class

Cumulative NPV by CSL at 3%

billion 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 levels.

** 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.
10-30

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 benefits 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 operation of equipment 

that meet the proposed standards (consisting primarily of operating cost savings from using less 

energy, minus increases in equipment purchase and installation costs, which is another way of 

representing customer NPV) and (2) the annualized monetary value of the benefits of emission 

reductions, including carbon dioxide (CO2) 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 CO2 reductions, otherwise known as the Social Cost of Carbon (SCC), is calculated 

using a range of values per metric ton of CO2 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 CO2 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 transactions, while the 

value of CO2 reductions is based on a global value. Second, the assessments of operating cost 

savings and CO2 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 
10-31

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 CO2 in 

each year. These impacts go well beyond 2100.

10.5.1 Calculation Method

DOE uses a two-step calculation process to convert each time series of costs and benefits 

into annualized values. First, DOE calculates a present value in the “present” year used in 

discounting the NPV of total 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 CO2 reductions. 

For the latter, DOE uses the discount rate appropriate for each SCC time series (see TSD chapter 

14 for discussion).

PVx = � (x(t) ∙ (1 + rx)yNPV−t)

t=y1,yT

Eq. 10.1

Where:

x(t)= time series under evaluation,

PVx= present value of the time series x,

y1= first year in the analysis period,

yT = last year in the analysis period,

yNPV= year to which the NPV of customers’ costs and savings are being discounted, and

rx = discount rate used to discount the annual values of time series x to year yNPV.

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.

Annx,r = PVx ∙ fy1−yNPV,r ∙ a30,r = PVx ∙ (1 + r)y1−yNPV ∙

r ∙ (1 + r)30

(1 + r)30 − 1

Eq. 10.2

Where:

Annx,r= annualized value of the time series x,

fn,r = factor to project a value n years aheadd with r discount rate, and

a30,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 determined 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 Annualized 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

CO2 Reduction at $11.8/t** 5% 8 8 8

CO2 Reduction at $39.7/t** 3% 27 26 27

CO2 Reduction at $61.2/t** 2.50% 39 38 40

CO2 Reduction at $117.0/t** 3% 82 80 84

NOX Reduction at $2,639/ton** 7% 0.32 0.31 0.33

3% 0.55 0.53 0.58

Total (Operating Cost Savings, 

CO2 Reduction and NOX

Reduction)†

7% plus CO2 range 112 to 186 106 to 178 120 to 196

7% 131 124 139

3% 130 to 204 121 to 193 141 to 217

3% plus CO2 range 149 139 160

Costs

Incremental Equipment Costs 7% 20 21 20

3% 21 22 20

Net Benefits/Costs

Total (Operating Cost Savings, 

CO2 Reduction and NOX

Reduction, Minus Incremental 

Equipment Costs)†

7% plus CO2 range 92 to 166 85 to 157 101 to 177

7% 110 103 120

3% plus CO2 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 equipment costs 

reflect a medium decline rate for projected product price trends in the Primary Estimate, a low decline rate for projected equipment 

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

Estimate. The methods used to derive projected price trends are explained in appendix 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 AEO High and Low Economics cases. The three cases also 

include reference, high, and low water prices. Water price projections are described in TSD chapter 8 while the complete set of 

projections is contained in TSD appendix 10B.

** The CO2 values represent global monetized values (in 2012$) of the social cost of CO2 emissions in 2015 under several 

scenarios. The values of $11.8, $39.7, and $61.2 per metric ton are the averages of SCC distributions calculated using 5-percent, 3-

percent, and 2.5-percent discount rates, respectively. The value of $117.0/t represents the 95th percentile of the SCC distribution 

calculated using a 3-percent discount rate. The value for NOx (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-percent discount rate, 

which is $39.7/t in 2015 (in 2012$). In the rows labeled as “7% plus CO2 range” and “3% plus CO2 range,” the operating cost and 

NOx benefits are calculated using the labeled discount rate, and those values are added to the full range of CO2 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 CUSTOMER SUBGROUPS......................................................... 11-1

11.3 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR 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 Census Data for Lodging* ............................... 11-6

Table 11.3.1 Comparison of Mean LCC Savings for the Small Business Subgroup in the 

Foodservice Sector with the National Average Values ..................................... 11-8

Table 11.3.2 Percentage Change in Mean LCC Savings for the Small Business Subgroup 

in the Foodservice Sector Compared to National Average Values*.................. 11-9

Table 11.3.3 Comparison of Median Payback Periods for the Small Business Subgroup in 

the Foodservice Sector with National Median Values..................................... 11-10

Table 11.3.4 Comparison of LCC Savings for the Small Business Subgroup in the 

Lodging Sector with the National Average Values ......................................... 11-11

Table 11.3.5 Percentage Change in the Mean LCC Savings for the Small Business 

Subgroup in the Lodging Sector Compared to the National Average 

Values .............................................................................................................. 11-12

Table 11.3.6 Comparison of Median Payback Periods for the Small Business Subgroup in 

the Lodging Sector with the National Median Values..................................... 11-13
11-1

CHAPTER 11. CUSTOMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

The life-cycle cost (LCC) customer subgroup 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 chapter 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 

reflect the results experienced by certain customer subgroups. In other words, some of the

adverse impacts on businesses that are disproportionately disadvantaged may be masked by the 

averaging effect of the LCC and PBP analysis. Therefore, the U.S. Department of Energy (DOE) 

carried out the LCC subgroup analysis by using the LCC and PBP analysis spreadsheet, but 

applying the inputs that are applicable only to the identified subgroups. The LCC spreadsheet 

model is accessible at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/

ruleid/29.

11.2 IDENTIFYING THE CUSTOMER SUBGROUPS

DOE identified small businesses as a subgroup that could potentially be affected 

disproportionately by the amended energy conservation standard for 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 educational 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, includes the so-called “big box” retailers that sell everything from beds, to 
11-2

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 close-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 calculation of discount rates. (See chapter 8 of the 

TSD for the development of discount rates.) For these reasons, DOE confined the research for 

identifying small businesses to the remaining three business types—lodging, foodservice, and 

food sales.

To identify small businesses, DOE used size standards from the Small Business 

Administration (SBA) to define which business entities are considered to be small. The SBA 

established size standards for types of economic activity, or industry, under the North American 

Industry Classification System (NAICS).3 Table 11.2.2 presents the size standards established by 

SBA for various businesses that use ACIM equipment.

Table 11.2.2 SBA Size Standards for Businesses that Use Automatic Commercial ice 

Makers

Business Type

SBA Size Standard 

Annual Sales 

millions

Supermarkets and Other Grocery (except 

Convenience) Stores $30.0 

Convenience Stores $27.0 

Meat Markets $7.0 

Fish and Seafood Markets $7.0 

All Other Specialty Food Stores $7.0 

Beer, Wine and Liquor Stores $7.0 

Gasoline Stations with Convenience Stores $27.0 

Warehouse Clubs and Superstores $27.0 

Hotels (except Casino Hotels) and Motels $30.0 

Casino Hotels $30.0 

Full-Service Restaurants $7.0 

Limited-Service Restaurants $10.0 

Cafeterias, Grill Buffets, and Buffets $25.5 

Snack and Nonalcoholic Beverage Bars $7.0 

Food Service Contractors $35.5 

Caterers $7.0 

Mobile Food Services $7.0 

Drinking Places (Alcoholic Beverages) $7.0 

Source: Size Standards Used To Define Small Business Concerns (13 CFR 121.201); 

also available at www.sba.gov/content/table-small-business-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 2007 economic census.

4 Table 11.2.3, 

Table 11.2.4, and Table 11.2.5 present the census data for single-unit and multiunit firms for 

three different types of businesses that constitute a dominant share of the ACIM equipment 

market.

Table 11.2.3 presents the data for supermarkets and other grocery (except convenience)

stores. The data show that 98.9 percent of the firms in this category fall under the SBA definition 
11-3

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 of 

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 Stores

Description Number 

of Firms

Total 

Employment

Number of 

Establishments

Sales per 

Firm 

1000$

Is 

Average 

Firm a 

Small 

Business?

Gasoline Stations with Convenience Stores*

All firms 53,375 719,108 97,508 6,300 Yes

Single-unit firms 49,010 287,220 49,010 2,249 Yes

Multiunit firms 4,365 431,888 48,498 51,788 No

Firms with 1 establishment 1,160 11,836 1,160 3,363 Yes

Firms with 2 establishments 1,168 21,386 2,336 6,622 Yes

Firms with 3 or 4 establishments 802 24,023 2,697 11,713 Yes

Firms with 5 to 9 establishments 573 30,488 3,709 22,945 Yes

Firms with 10 to 24 establishments 402 47,037 5,978 51,678 No

Firms with 25 to 49 establishments 150 45,235 5,020 128,070 No

Firms with 50 to 99 establishments 58 31,187 3,865 272,955 No

Firms with 100 establishments or 

more

52 220,696 23,733 2,616,567 No

Fraction of firms classed as small business 0.988

Fraction of establishments that belong to firms 

classified as small businesses 0.604

Fraction of employment in small businesses 0.521

Fraction of sales in small businesses 0.429

*Small business concerns in this category are firms with annual sales less than $27 million.

Table 11.2.6 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 percent 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 0.993

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 $30 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 meets 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, consequently, lower LCC 

savings and higher PBPs.

Discount rates and average electricity price rates associated with different types of 

businesses are presented in TSD chapter 8. Of the three business types, lodging has the highest 

estimated discount rate at 6.76 percent (compared to 5.85 percent for foodservice and 5.14

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 

commercial 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. Foodservice faces a higher cost of capital than food

sales and the highest electricity prices. Foodservice 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 also that they would value future savings less relative to the first cost.

DOE carried out two LCC subgroup analyses using the LCC spreadsheet described in 

TSD chapter 8, but with certain modifications. The input for business type was fixed to the 

identified subgroup, which ensured that the discount rates and electricity price rates associated 

with only that subgroup were selected in the Monte Carlo simulations (see TSD chapter 8). In the 

calculation of discount rates it was assumed that 100 percent of customers face the small 

business interest rate premium when calculating discount rates. Apart from these changes, all 

other inputs for LCC subgroup analysis are the same as those in the LCC analysis described in 

TSD chapter 8. 

11.3 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR SMALL

BUSINESS SUBGROUPS

Table 11.3.1 presents the comparison of mean LCC savings for the small business 

subgroup in the foodservice sector with the national 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 foodservice establishments. For all but three

equipment classes in Table 11.3.2, the percentage decrease in LCC savings is less than 10 

percent for all TSLs. For SCU-W-Large-B, the TSL 4 and 5 differences are -11 percent; for
11-8

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 period—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 average 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 13 percent lower. The differences are even greater at TSLs 4 and 5. The reason 

for this is that the energy price for lodging is slightly lower than the average of all commercial 

business types (97 percent of the average for all commercial business types) while the discount 

rate is higher. The lower electricity price reduces the nominal value of future operating and 

maintenance benefits while the higher discount rate reduces the present value of the benefits, 

thus decreasing the LCC savings.
11-10

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 19.12 
11-11

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 

Subgroup 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%) (13%) (13%)

 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 higher 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 sectors would perceive lower LCC savings due to their higher cost of capital. 

Foodservice has a lower PBP than average because of higher-than-average electricity prices. 

Payback periods for lodging 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 30, 2013.) 

<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/consumption/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 Phase I: Industry Profile.................................................................................. 12-2

12.2.2 Phase II: Industry Cash-Flow Model and Interview Guide ............................ 12-2

12.2.2.1 Industry Cash-Flow Analysis......................................................... 12-2

12.2.2.2 Interview Guide.............................................................................. 12-3

12.2.3 Phase III: Subgroup Analysis.......................................................................... 12-3

12.2.3.1 Manufacturing Interviews.............................................................. 12-3

12.2.3.2 Revised Industry Cash-Flow Analysis........................................... 12-3

12.2.3.3 Manufacturer Subgroup Analysis .................................................. 12-4

12.2.3.4 Small-Business Manufacturers ...................................................... 12-4

12.2.3.5 Manufacturing Capacity Impact..................................................... 12-5

12.2.3.6 Employment Impact....................................................................... 12-5

12.2.3.7 Cumulative Regulatory Burden...................................................... 12-5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES.......................................... 12-5

12.3.1 Price Sensitivity .............................................................................................. 12-6

12.3.2 Enforcement.................................................................................................... 12-6

12.3.3 Reliability 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 ........................................................................ 12-35
12-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 Classification 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 Automatic 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 Equipment 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.......................... 12-21
12-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........................... 12-23

Table 12.4.34 Product Conversion Costs for Batch Automatic Commercial Ice Makers by EL12-24

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 EL12-26

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 of 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-C12-34

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 Impact Analysis for Automatic Commercial Ice Makers –

Preservation of Gross Margin Percentage Markup Scenario........................... 12-36
12-iv

Table 12.5.2 Manufacturer Impact Analysis for Automatic Commercial Ice Makers –

Preservation of EBIT Markup Scenario........................................................... 12-36

Table 12.6.1 Comparison of Small Business Manufacturers of Automatic Commercial Ice Maker 

INPV to that of the Industry at Large by TSL under the Preservation of Gross 

Margin Markup Scenario. ................................................................................ 12-40

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............................................................................................................. 12-43
12-1

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-stringent energy conservation standards 

on manufacturers of automatic commercial ice makers, 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 pricing strategies. The GRIM’s 

key output is the industry net present value (INPV), which is the sum of discounted industry cash 

flow over the analysis period. The model estimates the financial impact of more-stringent energy 

conservation standards by comparing changes in INPV 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 automatic commercial ice maker

manufacturing 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 analyze the economic impact of amended energy conservation standards on the 

automatic commercial ice maker 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 automatic commercial ice maker market. Interviewees included 

manufacturers with various market shares and product focus, providing a representative crosssection of the industry. During interviews, DOE discussed financial topics specific to each 

manufacturer and obtained each manufacturer’s view of the industry. The interviews provided 

DOE with valuable information for evaluating the impacts of 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 automatic commercial ice maker

industry that built upon the market and technology assessment prepared for this rulemaking. (See 

chapter 3 of the notice of proposed rulemaking (NOPR) technical support document (TSD).)

Before initiating the detailed impact studies, DOE collected information on the present and past 

market structure and characteristics of the industry, tracking trends in market share, equipment 

attributes, shipments, manufacturer markups, and the cost structure for various manufacturers.

The profile also included a top-down analysis of manufacturers in the industry using 

Security and Exchange Commission (SEC) 10-K filings,

a Standard & Poor (S&P) stock reports,b

and corporate annual reports released by both public and privately held companies. DOE used 

this and other publicly available information to derive preliminary financial inputs for the GRIM 

(e.g. revenues; cost of goods sold; depreciation; selling, general 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 automatic commercial ice maker 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 

revenue 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 preparing 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 developing 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 date. 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

industry average financial parameters developed in Phase I. DOE derived the manufacturing 

costs from the engineering analysis as presented in chapter 5 of this NOPR TSD, information 

provided by the industry, publicly available financial reports, and interviews with manufacturers. 

To examine the range of possible impacts, DOE developed alternative markup scenarios based 

on discussions with manufacturers. DOE’s shipments analysis, presented in 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 base case 

 a Available online at www.sec.gov. b Available online at www2.standardandpoors.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 manufacturers of automatic commercial

ice makers 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 subgroups 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 and life cycle cost 

follow-up; (3) manufacturer markups and profitability; (4) financial parameters; and (5) 

conversion costs. The interview guide is presented in TSD appendix 12A.

12.2.3 Phase III: Subgroup Analysis

While conducting the MIA, DOE interviewed a representative cross-section of automatic 

commercial ice maker 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 issues and concerns. During interviews, DOE identified 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 information to be considered in the 

rulemaking process.

DOE used these interviews to tailor the GRIM to reflect the unique financial 

characteristics of the automatic commercial ice maker manufacturing industry. Companies with 

various market shares and product focus were interviewed to provide a representation of the 

industry. Interviews were scheduled well in advance to provide every opportunity for key 

individuals to be available for comment. Although a written response to the questionnaire was 

acceptable, DOE sought interactive interviews, which help clarify responses and identify 

additional issues. The resulting information provides valuable inputs to the 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 

adequately 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 average could be 

more negatively affected. DOE used the results of the industry characterization to group 

manufacturers exhibiting similar characteristics. During the interviews, DOE discussed the 

potential subgroups and subgroup members it identified for the analysis. DOE asked 

manufacturers and other interested parties to suggest what subgroups or characteristics are the 

most appropriate to analyze. Through the course of the rulemaking, DOE identified small 

business manufacturers as a subgroup that requires separate analysis.

12.2.3.4 Small-Business Manufacturers

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 American 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 333415

DOE used the Air-Conditioning, Heating, and Refrigeration Institute (AHRI)

1 certified 

product directory as well as public certification databases from the California Energy 

Commission (CEC)2 and ENERGY STAR3 to identify manufacturers of automatic commercial

ice makers. DOE then checked this list of ice maker manufacturers against the employee limit 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 manufacturers of 

automatic commercial ice makers that qualify as small businesses 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-standards
12-5

12.2.3.5 Manufacturing Capacity Impact

Outcomes 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 

decisions 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 one-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 onetime capital changes and stranded assets affect the projections 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 assess how domestic direct employment patterns 

might be affected, the interviews explored current employment trends in the automatic 

commercial ice maker industry. The interviews also solicited manufacturer views on changes in 

employment patterns that may result from more-stringent standards. The employment impacts 

section of the interview guide focused on current employment levels associated with 

manufacturers at each production facility, expected future employment levels with and without 

amended energy conservation standards, and differences in workforce skills and issues related to 

the retraining of employees. The employment impacts are reported in section 12.7.1.

12.2.3.7 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 

automatic commercial ice maker 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 issues 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. Additionally, 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 for 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 units 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 market 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 concern 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 investments 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 of 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 to 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 the 
12-7

R&D effort required for compliance is intermittent, innovation is impacted without adding to 

overall employment. 

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to amended 

energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM. 

Data and assumptions from these sources are then fed into an accounting model that calculates 

the industry cash flow both with and without amended energy conservation standards.

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4.1, is an annual cash flow 

analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial 

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, 2013, and continuing to 2047, 30 years after the compliance year of the rulemaking. 

The model calculates the INPV by summing the stream of annual discounted cash 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 scenarios. 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 

and user information for the GRIM.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash 

flow. These sources include corporate annual reports, company profiles, U.S. Census data, credit 

ratings, the shipments 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 Form 10-K. Additionally, some privately held companies 
12-8

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 automatic commercial ice makers. 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 automatic commercial ice makers. 

These figures were later revised using feedback from interviews. DOE used corporate annual 

reports to derive the following initial inputs to the GRIM:

• tax rate

• working capital

• SG&A

• R&D

• depreciation

• capital expenditures

• net 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 weightedaverage 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 relied on historical shipments data for automatic 

commercial ice makers. 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 analysis for this rulemaking using the efficiency-level

and cost-assessment approach, to develop a cost estimate for each efficiency level of each 

equipment class of automatic commercial ice makers. During this analysis, DOE used a 

manufacturing cost model to develop manufacturer production cost (MPC) estimates for 

automatic commercial ice makers. 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 determine 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 verify 
12-9

GRIM input assumptions. Key topics discussed during the interviews and reflected in the GRIM 

include the following:

• capital conversion costs (one-time investments in PPE);

• product conversion costs (one-time investments in research, product development, 

testing, and marketing);

• equipment cost structure, or the portion of the MPCs related to materials, labor, 

overhead, and depreciation costs;

• projected total shipment and shipment distribution mix; and

• MPCs estimated in the engineering analysis.

12.4.3 Financial Parameters

In the manufacturer interviews, DOE used the financial parameters from 2005 to 2011 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 automatic commercial ice maker

industry financial parameters. The industry financial parameters were determined by weighting 

each manufacturer’s individual financial parameters by their respective market share, and 

correcting for the 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 Parameters 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 debt and equity obligations 

manufacturers use to finance operations. The WACC is the total cost of debt and equity weighted 

by their respective proportions in the capital structure of the company. 

DOE estimated the WACC for the automatic commercial ice maker 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 the rate of return that equity investors (including, potentially, the 

company) expect to earn on a company’s stock. These expectations are reflected in the market 

price of the company’s stock. The capital asset pricing model (CAPM) provides one widely used 

means to estimate the cost of equity. According to the CAPM, the cost of equity (expected 

return) is:

Cost of Equity = Risk-free Rate of Return + (β x Risk Premium)

Eq. 12.2

Where:

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 

because it captures long-term inflation expectations and is less volatile than short-term rates. 

The risk-free rate is estimated to be approximately 5.1 percent, which is the average 10-year 

T-Bill return between 1928 and 2012.

Risk premium = the difference between the expected return on stocks and the risk-free rate of 

return. DOE used the average annual return on the S&P 500 between 1928 and 2012 as the 

expected return on stocks to arrive at an estimated market risk premium of 6.2 percent. 

β = the correlation between the movement in the price of the stock and that of the broader 

market. In this case, β (beta) 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 index.

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 automatic commercial ice maker industry is 19.4 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 (1928-2010) 5.1 - -

(3) Market Risk Premium (1928-2010) 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 risk-free rate. DOE used this 
12-11

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 deductible, DOE adjusted the gross cost of 

debt by the industry-average tax rate to determine the net cost of debt for the industry. DOE 

determined that the after-tax industry average cost of debt for the automatic commercial ice 

maker industry is 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 

(1928-2010) 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 automatic commercial ice maker 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 automatic commercial ice makers. For 

equipment classes with existing energy conservation standards, efficiency 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 previously 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 level 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 makers. 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 

number of efficiency levels available for each equipment class, there is often overlap between 

TSL levels. Thus, TSL 2 includes levels that overlap with both TSLs 1 and 3. The intent of TSL 

2 is to provide an intermediate level to preclude big jumps in efficiency 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

Table 12.4.4 Trial Standard Levels for Batch Automatic Commercial Ice Makers

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% 30.0% 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 Makers

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% 25.0% 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 shipments 

forecasts for automatic commercial ice makers in 2018.
12-14

Table 12.4.6 Total Base-Case 2018 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 automatic commercial ice makers. 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 automatic commercial ice maker equipment classes.
12-15

Table 12.4.7 Base-Case Distribution of Shipments for Batch Automatic Commercial Ice 

Makers in 2018

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 Makers in 2018

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 makers. 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 dated

and thereafter. 

As in the shipments analysis, DOE assumed no relative price elasticity in the automatic 

commercial ice maker 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 automatic commercial ice makers would not impact the number of total 

shipments.

12.4.7 Production Costs

Changes in the MPCs of automatic commercial ice makers can affect revenues, gross 

margins, and cash flow of the industry, making equipment production cost data key GRIM inputs 

for DOE’s analysis. 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 based on the aggregate cost of 

constituent components from analogous product classes and the design options identified. 

Generally, manufacturing 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 depreciation. 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 manufacturing 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 estimates 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 NOPR 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 

automatic commercial ice makers 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 equipment classes of automatic commercial ice makers.
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 12.4.11 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 $2,346.04
12-19

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 $1,461.55
12-20

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 $3,249.47
12-21

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 $2,387.33
12-22

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 $0.00
12-23

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 equipment designs into compliance with 

new regulations. For the MIA, DOE classified these one-time conversion costs 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 the 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 Costs

DOE based its estimates of the conversion costs required to meet each TSL on 

confidential information received during manufacturer interviews. DOE asked manufacturers to 

estimate their investments in product development and new manufacturing capital and 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 upgrade 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 

estimates to the remaining products in the database to develop estimates of the compliance costs 

required by the remainder of industry. These figures were then added to the disaggregated 
12-24

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 automatic 

commercial ice maker equipment class at each efficiency level identified.

Table 12.4.34 Product Conversion Costs for Batch Automatic Commercial Ice Makers by 

EL

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 $0.691
12-25

Table 12.4.35 Product Conversion Costs for Continuous Automatic Commercial Ice 

Makers by EL

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 Makers by 

EL

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 $0.194
12-27

Table 12.4.37 Capital Conversion Costs for Continuous Automatic Commercial Ice Makers 

by EL

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 $0.000 
12-28

Table 12.4.38 Stranded Assets for Batch Automatic Commercial Ice Makers by EL

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 $0.122
12-29

Table 12.4.39 Stranded Assets for Continuous Automatic Commercial Ice Makers by EL

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 

represent 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 interest 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 evaluated publicly available 

financial information for manufacturers whose equipment offerings include automatic 

commercial ice makers. During manufacturer interviews, DOE received feedback supporting the 

calculated 1.25 baseline manufacturer markup. For both GRIM 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 efficiencies.

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 1.25 is maintained for all equipment in the standards case. As production costs 

increase with efficiency, 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 that in the standards case, minimally compliant equipment

loses 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 year 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.

While all compliant equipment receives 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 1.237
12-31

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 

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
12-32

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 1.243
12-33

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

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 1.247 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 Class 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 1.244
12-34

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 1.239
12-35

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 IMPACTS

Using the inputs and scenarios described in the previous sections, DOE used the GRIM to 

estimate the financial impacts on the automatic 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 INPV

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs in the standards case. The INPV is different from DOE’s NPV, which 

is applied to the U.S. economy at large. The INPV is specific to the automatic commercial ice 

maker manufacturing industry, and is the sum of all net cash flows discounted at the industry’s 

weighted average cost of capital. The GRIM for the automatic commercial ice maker industry 

models cash flows from 2013 to 2047. This timeframe models both the short-term impacts on the 

industry from the announcement of the standard until the compliance date in 2018, and a longterm 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 automatic commercial ice maker industry, DOE 

examined the two markup scenarios described in section 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 provide the INPV estimates for the automatic commercial

ice maker manufacturing industry. 

Table 12.5.1 Manufacturer Impact Analysis for Automatic Commercial Ice Makers –

Preservation of Gross Margin Percentage Markup Scenario

Base Case Trial Standard Level

1 2 3 4 5

INPV (2012$ 

millions) $101.8 $93.4 $89.0 $80.9 $82.2 $81.9

Change 

in INPV

(2012$ 

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 Makers –

Preservation of 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 changes 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 could 
12-37

cause investors 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, 2013. From 2013 to 2018, cash flows 

are driven by the level of conversion costs and the portion of these investments made each 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 

energy 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 capital conversion costs 

increase cash outflows for capital expenditures.

Free cash flow in the year the amended energy conservation standards take effect is 

driven by two competing factors. In addition to capital and product conversion costs, amended 

energy conservation standards could create stranded assets, i.e., 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 onetime write-down acts as a tax shield that alleviates decreases in cash flow from operations in the 

year of the write-down. In this year, there is also an increase in working capital that reduces cash 

flow from operations. A large increase in working capital can be attributed to more costly 

production components and materials, higher inventory 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 the years following the compliance date of the standard, the impact on cash flow 

depends on the operating revenue. Under the preservation of gross margin percentage markup 

scenario, more-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 scenario 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 standards take effect. In this scenario production costs 

increase, but EBIT remains approximately 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 2013 through 

2047 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 Makers 

(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 IMPACTS ON SMALL BUSINESS MANUFACTURERS OF AUTOMATIC 

COMMERCIAL ICE MAKERS

To estimate the number of small businesses 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 interested 

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 determine whether they met the 

SBA’s definition of a small business manufacturer of covered automatic commercial ice makers. 

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 automatic commercial ice makers are currently manufactured by 

three companies that together account for approximately 95 percent of the total market. The 

remaining 5 percent of the market (approximately 10,000 shipments annually) is supplied by a 

combination of 11 international and domestic companies, all of which have small market shares. 

While 4 of these companies are either foreign owned and operated or exceed the SBA’s 

employment threshold of 750 employees, 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 ice 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. While small 

business manufacturers of automatic commercial ice makers have small overall market share, 

some hold substantial market share in specific equipment classes. Several of these smaller firms 

specialize in producing industrial ice machines, and the covered equipment they manufacture are 

extensions of 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 scope of this rulemaking, but are still considered small businesses based 

on the SBA limits for number of employees.

In confidential interviews, manufacturers indicated that many design options evaluated in 

the engineering analysis (e.g., higher efficiency motors and compressors) would require them to 

purchase more-expensive components. In many industries, small manufacturers typically pay 

higher prices for components 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 have 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 of 
12-40

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 expected 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 

capital 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, contributing 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-industry GRIM using information about the product portfolios of small 

businesses and the estimated market share of these businesses in each equipment class. DOE 

used this information in the GRIM to estimate the annual revenue, EBIT, R&D expense, and 

capital expenditures 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 in 

Table 12.6.1 and Table 12.6.2.

Table 12.6.1 Comparison of Small Business Manufacturers of Automatic Commercial Ice 

Maker INPV to that of the Industry at Large by TSL under the Preservation of Gross 

Margin Markup Scenario. 

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 Preservation 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 impacts 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 automatic commercial ice makers, 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 2013 to 2047. DOE used statistical data from the most recent U.S Census Bureau’s 2011

Annual Survey of Manufacturers (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 of 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 engineering analysis to estimate the annual 

labor expenditures in the automatic commercial ice maker industry. DOE used census 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 linesupervisor level who are directly involved in fabricating and assembling covered automatic 

commercial ice-making equipment within an original equipment manufacturer (OEM) facility. 

Workers performing services that are closely associated with production operations, such as 

material handling with a forklift, are also included as production labor. 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 equipment covered by this 

rulemaking.

The employment impacts shown in Table 12.7.1 represent the potential production 

employment that could result following 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 assumed 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 decisions 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. 

While 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 various 

TSLs.

DOE forecasts that the domestic labor expenditure for automatic commercial ice maker

production labor in 2018 will be approximately $13.6 million. Using the $25.97 hourly wage rate

including fringe benefits and 1,955 production hours per year per employee found in the 2011

ASM, DOE estimates there will be approximately 268 domestic production workers involved in 

the manufacturing of automatic commercial ice makers in 2018, the year in which amended 

standards would go into effect for TSL 1, TSL 3, and TSL 4. In addition, DOE estimates that 95

non-production employees in the United States will support automatic commercial ice maker

production.f Approximately 84 percent of automatic commercial ice makers sold in the United 

States are manufactured 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 impacts of amended energy conservation 

standards on domestic production employment levels at each TSL for the automatic commercial

ice maker market.

 f As defined in the 2011 ASM, the production workers number includes “workers (up through the linesupervisor level) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, 

packing, warehousing, shipping (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 establishment 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-supervisor 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, purchasing, 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 utilized as a separate work force.” 
12-43

Table 12.7.1 Potential Changes in the Total Number of Domestic Automatic Commercial

Ice Maker Production Workers in 2018

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 options 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 labor). 

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 complies 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 TSL 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 TSLs 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 the 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 efficiencies, 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 equipment, 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 suppliers 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 sufficient 

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, one manufacturer cited 

the possibility of a 3-month to 6-month shutdown in the event that amended standards were set 

high enough to require retooling of their entire production 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 Regulatory Burden 

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of several impending regulations may have serious consequences for some 

manufacturers, groups of manufacturers, or an entire industry. Regulatory 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 equipmentspecific regulations that will take effect 3 years before or after the 2018 compliance date of the

amended energy conservation standards for automatic commercial ice makers. In addition to 

energy conservation standards regulations, several other Federal regulations apply to automatic 

commercial ice makers. 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 recognizes 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 automatic commercial ice makers also produce

other appliances and commercial equipment. In addition to the amended energy conservation 

standards for automatic commercial ice makers, these manufacturers contend with several other 

Federal regulations and pending regulations that apply to other products and equipment. DOE 

recognizes that each regulation can significantly 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 automatic commercial ice makers in the 3 years 

leading up to and after the compliance date of amended energy conservation standards for this 

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 2017* 2 N/A**

Walk-In Coolers and Freezers 2017* 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 describes a number of other regulations that could also impact 

the automatic commercial ice makers 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 automatic commercial ice 

makers. Various building, electrical, mechanical, and plumbing codes in California affect ice 

makers, 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 electrical and electronic equipment.

International Energy Conservation Standards

Automatic commercial ice maker manufacturers that 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, beginning 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 Switzerland. 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 International (formerly the National Sanitation 

Foundation) is in the process of amending criteria for food service equipment sanitation 

certification pertaining to automatic commercial ice makers. New standards would specify lowlead 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 climate 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 9th, 

Congressman Scott Peters introduced the Super 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, these 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 flammability. 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) regulations. 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 summarizes the different impacts for the scenarios that DOE 

believes are most likely to capture the range of impacts on automatic commercial ice maker

manufacturers at each TSL in the standards case. While these scenarios bound the range of the 

most plausible impacts on manufacturers, some circumstances could cause manufacturers to 

experience impacts outside this range.

12.8.1 Automatic Commercial Ice Makers

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 approximately 
12-48

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 makers 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 specified at TSL 1.

At TSL 1, the majority of efficiency gains could be made through swapping purchased 

components for higher efficiency equivalents. It is expected that very few evaporators and 

condensers are affected at TSL 1, leading to very low expected industry 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 million, 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 makers, 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 TSL 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 these 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 from -$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 of $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 approximately 
12-49

88 percent of all 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 specified 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. 

The majority of redesigns necessary 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 million 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 million in the year before 

the compliance date (2017).

At TSL 4, total conversion costs grow to $51.2 million. Relative to the change between 

TSLs 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 makers 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 these 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 decrease 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 produce 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 TSL 5. As 
12-50

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. Air-Conditioning, Heating, and Refrigeration Institute. (Last accessed October 2012.)

<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. McKinsey & 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 REGULATIONS AND EMISSIONS IMPACTS.................................. 13-1

13.3 POWER SECTOR EMISSIONS FACTORS ................................................................... 13-3

13.4 UPSTREAM AND GREENHOUSE GAS EMISSIONS FACTORS.............................. 13-3

13.5 EMISSIONS IMPACT RESULTS................................................................................... 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: CO2 Total Emissions Reduction ............... 13-6

Figure 13.5.2 Automatic Commercial Ice Makers: SO2 Total Emissions Reduction................ 13-6

Figure 13.5.3 Automatic Commercial Ice Makers: NOx 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: N2O Total Emissions Reduction............... 13-8

Figure 13.5.6 Automatic Commercial Ice Makers: CH4 Total Emissions Reduction ............... 13-8
13-1

CHAPTER 13. EMISSIONS ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector and site combustion emissions 

of carbon dioxide (CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), and mercury (Hg). The 

second component estimates the impacts of a potential standard on emissions of two additional 

greenhouse gases, methane (CH4) and nitrous oxide (N2O), 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 fullfuel-cycle (FFC), 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-BT)

model, described in chapter 14. DOE used the version of NEMS based on the Annual Energy 

Outlook 2013 (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 2012. 

Combustion emissions of CH4 and N2O are estimated using emissions intensity factors 

published by the EPA, GHG Emissions Factors Hub.a The FFC upstream emissions are 

estimated based on the methodology developed by Coughlin (2013).3 The upstream emissions 

include both emissions from fuel combustion during extraction, processing, and transportation of 

fuel, and “fugitive” emissions (direct leakage to the atmosphere) of CH4 and CO2.

The emissions intensity factors are expressed in terms of physical units per megawatthours (MWh) or million British thermal units (MMBtu) of site energy savings. Total emissions 

reductions are estimated using the energy savings calculated in the national impact analysis 

(chapter 10).

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

SO2 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 SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that that operates 

along with the Title IV 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 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). 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.html
13-2

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 SO2 emissions allowances resulting from the lower electricity demand 

caused by the imposition of an efficiency standard could be used to permit offsetting increases in 

SO2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was

uncertainty about the effects of efficiency standards on SO2 emissions covered by the existing 

cap-and-trade system, but it concluded that no reductions in power sector emissions would occur 

for SO2 as a result of standards.

Beginning in 2015, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 

2011. 77 FR 9304 (Feb. 16, 2012).b In the final MATS rule, EPA established a standard for 

hydrogen chloride as a surrogate for acid gas hazardous air pollutants (HAP), and also 

established a standard for SO2 (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, SO2 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 SO2 emissions. Under the MATS, NEMS shows a reduction in SO2

emissions when electricity demand decreases (e.g., as a result of energy efficiency standards). 

Emissions will be far below the cap that would be established by CAIR, so it is unlikely that 

excess SO2 emissions allowances resulting from the lower electricity demand would be needed 

or used to permit offsetting increases in SO2 emissions by any regulated EGU. Therefore, DOE 

believes that efficiency standards will reduce SO2 emissions in 2015 and beyond.

CAIR established a cap on NOx emissions in 28 eastern states and the District of 

Columbia. Energy conservation standards are expected to have little effect on NOx emissions in 

those states covered by CAIR because excess NOx emissions allowances resulting from the lower 

electricity demand could be used to permit offsetting increases in NOx emissions. However, 

standards would be expected to reduce NOx emissions in the states not affected by the caps, so

DOE estimated NOx emissions reductions from potential standards for those states.

The MATS limit mercury emissions from power plants, but they do not include emissions 

caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. DOE

estimated mercury emissions reductions using the NEMS-BT based on 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 POWER SECTOR EMISSIONS FACTORS

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from runs of DOE’s NEMS-BT model, using the version updated to the 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, fuel use, and power sector emissions. A 

marginal emissions intensity factor is defined by dividing the reduction in the total emissions of 

a given pollutant by the reduction in total generation (in billion kilowatt-hours). DOE uses the 

site energy savings multiplied by a transmission and distribution (T&D) loss factor to estimate 

the reduction in generation for each trial standard level (TSL). Details on the approach used may 

be found in Coughlin (2013).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 NOx is an average for the entire U.S. The marginal 

calculation based on the NEMS-BT model accounts for the fact that NOx emissions are capped in 

some states. 

Table 13.3.1 Power Plant Emissions Factors

Unit 2018 2020 2025 2030 2035 2040

CO2 kg/MWh 598 598 559 524 497 309

SO2 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

N2O g/MWh 7.1 7.2 7.2 7.1 7.1 6.9

CH4 g/MWh 49 50 50 50 49 48

13.4 UPSTREAM AND GREENHOUSE GAS EMISSIONS FACTORS

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10D. See also Coughlin (2013).3 When demand for a particular 

fuel is reduced, there is a corresponding reduction in the emissions from combustion of that fuel 

at either the building site or the power plant. The associated reduction in energy use for upstream 

activities leads to further reductions in emissions. These upstream emissions are defined to 

include the combustion emissions from the fuel used upstream, the fugitive emissions associated 

with the fuel used upstream, and the fugitive emissions associated with the fuel used on site. 

Fugitive emissions of CO2 occur during oil and gas production, but are small relative to 

combustion emissions. They comprise about 2.5 percent of total CO2 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 CH4 are very small, while fugitive emissions 

(particularly for gas production) may be relatively large. Hence, fugitive emissions make up over 
13-4

99 percent of total methane emissions for natural gas, about 95 percent for coal, and 93 percent

for petroleum fuels. 

Upstream emissions factors account for both fugitive emissions and combustion 

emissions in extraction, processing, and transport of primary fuels. For ease of application in its 

analysis, DOE developed all of the emissions factors using site (point of use) energy savings in 

the denominator. Table 13.4.1 presents the electricity upstream emissions factors for selected 

years. The caps that apply to power sector NOx emissions do not apply to upstream combustion 

sources. 

Table 13.4.1 Electricity Upstream Emissions Factors

Unit 2018 2020 2025 2030 2035 2040

CO2 kg/MWh 27.6 27.3 26.9 26.8 26.9 26.3

SO2 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

N2O g/MWh 0.25 0.25 0.25 0.25 0.24 0.24

CH4 g/MWh 2,054 2,025 2,008 2,025 2,057 1,999

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

equipment sold in 2018-2047 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

 CO2 (million metric tons) 3.50 6.52 13.68 18.19 19.19

 NOx (thousand tons) -0.89 -1.66 -3.49 -4.64 -4.89

 Hg (tons) 0.01 0.01 0.02 0.03 0.03

 N2O (thousand tons) 0.08 0.15 0.31 0.41 0.43

 CH4 (thousand tons) 0.47 0.88 1.84 2.45 2.58

 SO2 (thousand tons) 5.31 9.89 20.76 27.60 29.12

Upstream Emissions

 CO2 (million metric tons) 0.23 0.42 0.89 1.18 1.24

 NOx (thousand tons) 3.11 5.80 12.18 16.19 17.08

 Hg (tons) 0.000 0.000 0.000 0.001 0.001

 N2O (thousand tons) 0.00 0.00 0.01 0.01 0.01

 CH4 (thousand tons) 18.89 35.22 73.93 98.30 103.68

 SO2 (thousand tons) 0.05 0.09 0.19 0.25 0.27

Total Emissions

 CO2 (million metric tons) 3.72 6.94 14.57 19.37 20.43

 NOx (thousand tons) 2.22 4.14 8.69 11.56 12.19

 Hg (tons) 0.01 0.01 0.02 0.03 0.03

 N2O (thousand tons) 0.08 0.15 0.32 0.42 0.45

 CH4 (thousand tons) 19.36 36.09 75.77 100.75 106.27

 SO2 (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 2018-2047.
13-6

Figure 13.5.1 Automatic Commercial Ice Makers: CO2 Total Emissions Reduction

Figure 13.5.2 Automatic Commercial Ice Makers: SO2 Total Emissions Reduction

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

900.0

1000.0

201820232028203320382043204820532058

kt

CO2 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

1400.0

201820232028203320382043204820532058

ton

SO2 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
13-7

Figure 13.5.3 Automatic Commercial Ice Makers: NOx 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

800.0

201820232028203320382043204820532058

ton

NOx 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

0.0

201820232028203320382043204820532058

ton

Hg Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
13-8

Figure 13.5.5 Automatic Commercial Ice Makers: N2O Total Emissions Reduction

Figure 13.5.6 Automatic Commercial Ice Makers: CH4 Total Emissions Reduction

0.0

5.0

10.0

15.0

20.0

25.0

201820232028203320382043204820532058

ton

N2OEmissions 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

4500.0

201820232028203320382043204820532058

ton

CH4 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
13-9

REFERENCES

1. U.S. Energy Information Administration. Annual Energy Outlook 2013 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 EMISSION REDUCTIONS BENEFITS 

TABLE OF CONTENTS

14.1 Introduction.................................................................................................................... 14-1

14.2 Monetizing Carbon Dioxide Emissions......................................................................... 14-1

14.2.1 Social Cost of Carbon ........................................................................................ 14-1

14.2.2 Monetizing Carbon Dioxide Emissions............................................................. 14-1

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 CO2)............................................................................................ 14-4

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007$ 

per metric ton CO2)............................................................................................ 14-4

Table 14.4.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

Automatic Commercial Ice Makers Trial Standard Levels ............................... 14-6

Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Automatic Commercial Ice Makers Trial Standard Levels ............................... 14-6

Table 14.4.3 Estimates of Present Value of NOx Emissions Reduction under Automatic 

Commercial Ice Makers Trial Standard Levels................................................. 14-7
14-1

CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of energy conservation standards for automatic commercial ice 

makers, the U.S. Department of Energy (DOE) estimated the monetary benefits likely to result 

from the reduced emissions of carbon dioxide (CO2) and nitrogen oxides (NOx) 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 DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon 

The social cost of carbon (SCC) is an estimate of the monetized damages associated with 

an incremental increase in carbon emissions in a given year. It is intended to include (but is not 

limited to) changes in net agricultural productivity, human health, property damages from 

increased flood risk, and the value of ecosystem services. Estimates of the SCC are provided in 

dollars per metric ton of CO2. A domestic SCC value is meant to reflect the value of damages in 

the United States resulting from a unit change in CO2 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 CO2 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 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 when attempting to assess the incremental 

economic impacts of CO2 emissions. A report from the National Research Council1 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 the 
14-2

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 

effort to quantify and monetize the harms associated with climate change will raise serious 

questions of science, economics, and ethics and should be viewed as provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing CO2 emissions. Most Federal regulatory 

actions can be expected to have marginal impacts on global emissions. For such policies, the 

agency can estimate the benefits from reduced (or costs from increased) emissions in any future 

year by multiplying the change in emissions in that year by the SCC value appropriate for that 

year. The net present value of the benefits can then be calculated by multiplying each of these 

future benefits by an appropriate discount factor and summing across all affected years. This 

approach assumes that the marginal damages from increased emissions are constant for small 

departures from the baseline emissions path, an approximation that is reasonable for policies that 

have effects on emissions that are small relative to cumulative global CO2 emissions.

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing CO2 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 CO2 emissions. The interagency group did not undertake any 

original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. The outcome of the 

preliminary assessment by the interagency group was a set of five interim values: global SCC 

estimates for 2007 (in 2006$) of $55, $33, $19, $10, and $5 per ton of CO2.

2 These interim 

values represented the first sustained interagency effort within the U.S. government to develop 

an SCC for use in regulatory analysis. The results of this preliminary effort were presented in 

several proposed and final rules.

14.2.3 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular 

basis to generate improved SCC estimates, which were considered for this 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 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: (1) climate sensitivity; 

 a The models are described in appendix 14A of this technical support document (TSD).
14-3

(2) socio-economic and emissions 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 values for the 

discount rate. All other model features were left unchanged, relying on the model developers’ 

best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates 

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC 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 CO2

emissions. Table 14.2.1 presents the values in the 2010 interagency group report,b which is 

reproduced in appendix 14A 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 increments from 2010 to 2050.

The full set of annual SCC estimates between 2010 and 2050 is reported in appendix 14B of this 

TSD. The central value that emerges is the average SCC across models at the 3-percent discount 

rate. However, for purposes of capturing the uncertainties involved in regulatory impact analysis, 

the interagency group emphasizes the importance of including all four sets of SCC values. For 

the years after 2050, DOE applied the average annual growth rate of the SCC estimates in 

2040−2050 associated with each of the four sets of values.

 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 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. November 2013. 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf
14-4

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007$ per 

metric ton CO2)

Year

Discount Rate 

%

5 3 2.5 3

Average Average Average 95th 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 CO2)

Year

Discount Rate 

%

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 32 51 89

2015 11 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

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Research Council report 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 process to estimate the SCC. The interagency group intends to 

periodically review and reconsider estimates of the SCC used for cost-benefit analyses to reflect 

increasing knowledge of the science and economics of climate impacts, as well as improvements 

in modeling.

In summary, in considering the potential global benefits resulting from reduced CO2

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 $11.8, $39.7, $61.2, and $117 per metric ton avoided.

3
14-5

DOE multiplied the CO2 emissions reduction estimated for each year by the SCC value 

for that year in each of the four cases. To calculate a present value of the stream of monetary 

values, 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 internal 

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 NOx emissions from the TSLs 

it considered. As noted in chapter 13, new or amended energy conservation standards would 

reduce NOx emissions in those States that are not affected by caps. DOE estimated the monetized 

value of NOx 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 NOx and real discount rates of 3 and 

7 percent.5

DOE is still evaluating appropriate values to use to monetize avoided SO2 and Hg 

emissions. It did not monetize these emissions for this analysis.

14.4 RESULTS

Table 14.4.1 presents the global values of CO2 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 CO2 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, 

95th percentile

million 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 $11.8, $39.7, 

$61.2, and $117.0 per metric ton (2012$).

Table 14.4.2 Estimates of Domestic Present Value of CO2 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, 

95th percentile

million 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 $11.8, $39.7, 

$61.2, and $117.0 per metric ton (2012$).
14-7

Table 14.4.3 presents the present value of cumulative NOx emissions reductions for each 

TSL, calculated using the average dollar-per-ton values and 7- and 3-percent discount rates.

Table 14.4.3 Estimates of Present Value of NOx Emissions Reduction under Automatic 

Commercial Ice Makers Trial Standard Levels

TSL 3% discount rate 7% discount rate

million 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 4.3
14-8

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 

Government. <www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/SocialCost-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.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>

4. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs and 

Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal 

Entities, 2006. Washington, D.C. 

<www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_final_r

eport.pdf>

5. U.S. Office of Management and Budget (OMB), Circular A-4: Regulatory Analysis, 2003.

(Posted September 17, 2003) (Last accessed April 2013.) 

<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 RESULTS ..................................................................................... 15-2

15.3.1 Installed Capacity............................................................................................... 15-2

15.3.2 Electricity Generation ........................................................................................ 15-5

15.3.3 Results Summary ............................................................................................... 15-8

REFERENCES ........................................................................................................................ 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............ 15-9
15-1

CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis, U.S. Department of Energy (DOE) analyzes the changes in 

electric installed capacity and generation that result for each trial standard 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 uses 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 the analysis of standards 

on automatic commercial ice makers, DOE used the version of NEMS based on AEO2013.

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 reduction 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 intrinsic uncertainties in the 

AEO.

NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. In 

general, when electricity demand deviates from the AEO Reference Case, 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 variations on DOE/EIA assumptions, DOE refers to it by the name NEMSBT. (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed.) 
15-2

generating capacity changes, the total generation capacity itself (gigawatts (GW)) may change, 

and the mix of capacity by fuel type may change. Each of these effects can vary for different 

types of end use. The change in total generating capacity is sensitive to the degree to which the 

end-use is peak coincident, while the capacity mix is sensitive to the hourly load shape 

associated with the end use.

To model the impact of a standard, DOE inputs a reduction to annual energy demand for 

the corresponding end use in the appropriate start year. The NEMS-BT model is run with the 

decremented energy demand to determine the modified build-out of capacity and total 

generation. Regional effects of a standard can be accounted for by defining the energy demand 

decrement as a function of census division.

The output of the NEMS-BT analysis includes the effective marginal heat rate (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 type 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 

reductionc 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 used may be found in Coughlin (2013).2

NEMS-BT provides output for the following capacity types: coal, nuclear, combined

cycle (natural gas), renewable sources, oil and natural gas steam, combustion turbine/diesel, 

pumped storage, fuel cells, and distributed generation (natural gas). DOE grouped oil and natural

gas steam and combustion turbine/diesel into a peaking category, and grouped pumped storage, 

fuel cells, and distributed generation (natural gas) into an “other” category.

In general, energy conservation standards impact primarily fossil combustion (i.e., coal,

natural gas and diesel) and renewables. Pumped storage and nuclear power are very insensitive 

to small changes in demand, while fuel cells and distributed generation 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. electricity installed capacity that 

result for each TSL 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 Case.
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

450

201820232028203320382043204820532058

MW

Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

5

10

15

20

25

30

35

40

45

50

201820232028203320382043204820532058

MW

Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
15-4

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

9

201820232028203320382043204820532058

MW

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

10

20

30

40

50

60

70

80

201820232028203320382043204820532058

MW

NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
15-5

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 electricity 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

8

201820232028203320382043204820532058

MW

Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

50

100

150

200

250

300

201820232028203320382043204820532058

MW

Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
15-6

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

1800

201820232028203320382043204820532058

GWh

Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

50

100

150

200

250

300

350

400

450

201820232028203320382043204820532058

GWh

Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
15-7

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

70

201820232028203320382043204820532058

GWh

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

200

400

600

800

1000

1200

201820232028203320382043204820532058

GWh

NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
15-8

Figure 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

60

201820232028203320382043204820532058

GWh

Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

100

200

300

400

500

600

700

800

900

201820232028203320382043204820532058

GWh

Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
15-9

Table 15.3.1 Automatic Commercial Ice Makers: Summary of Utility Impact Results

TSL

1 2 3 4 5

Installed Capacity Reduction 

MW

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

GWh

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 1612.83
15-10

REFERENCES

1. U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 

2013. 2013. Washington, DC. (Last accessed May 1, 2013.) 

<http://www.eia.gov/forecasts/aeo/>.

2. Coughlin, K. 2013. Projections 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 METHODOLOGY ........................................................................................................... 16-1

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.............................................................. 16-3
16-1

CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy’s (DOE) employment impact analysis is designed to 

estimate indirect national job creation or elimination resulting from proposed standards due to 

reallocation of associated expenditures for purchasing and operating automatic commercial ice 

making (ACIM) equipment. Job increases or decreases reported in this chapter are separate from 

the direct ACIM sector employment impacts reported in chapter 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 ASSUMPTIONS

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 

investments or not spent at 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 econometric model of the U.S. economy, this analysis estimated 

the short-term effect of these expenditure impacts on net economic output and employment. 

DOE intends this analysis to quantify the indirect employment 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 impacts over the long run for this rule. Because input/output models 

do not allow prices to bring markets into equilibrium, they are best used for short-run analysis. 

Therefore, DOE included a qualitative discussion of how labor markets are likely to respond in 

the longer term. In future rulemakings, DOE may consider the use of other modeling approaches 

for examining long-run employment impacts.

16.3 METHODOLOGY

DOE based its analysis on an input/output model of the U.S. economy that estimates the 

effects of standards on major sectors of the economy related to buildings and the net impact of 

standards on jobs. Pacific Northwest National Laboratory (PNNL) developed the model using

ImSET 3.1.12 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. ImSET estimates the employment and income effects of building energy technologies. 

Compared with simple economic multiplier approaches, ImSET 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 

relationships of different sectors of the economy and spending flows among them. Different 

sectors have different levels of labor intensity and so changes in the level of spending (e.g., due 

to the effects of an efficiency standard) in one sector of the economy will affect flows in other 

sectors, which affects the overall level of employment.

ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial buildings’ technologies. ImSET collects estimates of initial 

investments, energy savings, and economic activity associated with spending the savings 

resulting from standards (e.g., changes in final demand in personal consumption, business 

investment and spending, and government spending). It provides overall estimates of the change 

in national output for each input/output sector. The model applies estimates of employment and

wage income per dollar of economic output for each sector and calculates impacts on national 

employment and wage income.

Energy-efficiency technology primarily affects the U.S. economy along three spending 

pathways. First, general investment funds are diverted to sectors that manufacture, install, and 

maintain energy-efficient 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 sectors of the economy. 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 the entire economy 

differ from the employment impacts in the automatic commercial ice maker manufacturing 

sector estimated in chapter 12 using the Government Regulatory Impact Model (GRIM). The 

methodologies used and the sectors analyzed in the ImSET and GRIM models are different.

16.4 SHORT-TERM RESULTS

The results in this section refer to impacts of ACIM standards relative to the base case. 

DOE disaggregated the impact of standards on employment into three component effects: 

increased capital investment costs, decreased energy and water costs, and changes in operations 

and maintenance costs. 

Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors: the ACIM production sector, the energy generation sector, and the general consumer 

goods 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 energy 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 reduced expenditures on electricity, expenditures on 

everything else are either positively or negatively affected, increasing or reducing jobs in that 
16-3

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 16.4.1 presents the modeled net employment impact from the rule in 2018 and 

2021. As mentioned in chapter 12, 85 percent of automatic commercial ice makers are produced 

domestically and 15 percent are imported. The net employment impact estimate is sensitive to 

assumptions regarding the return to the U.S. economy of money 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 automatic 

commercial ice makers returns to the U.S. economy (low and high bounds, respectively). The 

U.S. trade deficit in recent years suggests that between 50 and 75 percent of the money spent on 

imported automatic commercial ice makers is likely to return, with employment impacts falling 

within the ranges presented below.

Table 16.4.1 Net Short-Term Change in Employment 

Trial Standard Level 2018* 2022

1 19 to 20 100 to 101

2 36 to 40 192 to 196

3 75 to 87 431 to 442

4 44 to 91 506 to 552

5 34 to 90 518 to 572

* Compliance date of standard levels is 2018.

16.5 LONG-TERM RESULTS

Due to the short payback period of energy-efficiency improvements mandated by this 

rule, over the long term DOE expects the energy savings to customers to increasingly dominate 

the increase in equipment costs, resulting in increased aggregate savings to customers. As a 

result, DOE expects 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 

2018.

5 The unemployment rate in 2018 is generally considered to be close to “full 

employment.” When an economy is at full employment, any effects on net employment are 

likely to be transitory as workers change jobs (e.g., some workers may need to be retrained or 

require time to search for new jobs) rather than enter or exit longer-term unemployment.

Nonetheless, even to the extent that markets are slow to adjust, DOE anticipates 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 

2022, 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. PNNL11884. 

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 FOR NON-REGULATORY ALTERNATIVES ............ 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

Table 17.4.2 Cumulative NPV of Non-Regulatory Alternatives Compared to the Proposed 

Standard ............................................................................................................. 17-5
17-1

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 evaluated each alternative’s ability to achieve significant energy savings at a reasonable 

cost, and compared 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. In support of DOE’s notice of proposed rulemaking, DOE includes 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 national impact analysis (NIA) spreadsheet models to calculate the national 

energy savings (NES) and the net present value (NPV) corresponding to each non-regulatory 

alternative. The NIA model is discussed in chapter 10 of this TSD. To compare each alternative 

quantitatively to the proposed energy conservation standards, DOE quantifies the effect of each 

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 models to estimate 

energy savings compared to the base-case scenario. Key inputs that DOE typically revises in 

these models include the following:

• energy prices and escalation factors;

• implicit market discount rates for trading off purchase price against operating expense 

when choosing product efficiency;

• business purchase prices and operating costs;

• purchase price-versus-efficiency relationships; and

• product stock data.
17-2

The key measures of the impact of each alternative include the following:

• Energy use reflects the cumulative energy use of the product from the effective date 

of the new standard to the year 2047.

• NES represents the cumulative national energy use from the base-case projection 

minus the alternative policy case projection, given in quadrillion British thermal units 

(quads).

• NPV represents 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

The base-case scenario is the one in which no new regulatory action is taken with regard 

to the energy efficiency of ACIM equipment, as described in the NIA (chapter 10 of this TSD). 

The base-case scenario provides the basis of comparison for all other non-regulatory alternatives. 

By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 Customer Rebates

Customer rebates cover a portion of the difference in incremental product price between 

products meeting baseline efficiency levels and those meeting higher efficiency levels, resulting 

in a higher percentage of consumers purchasing 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-efficient machine. Machines 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 $15 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 Portland 

Energy Conservation, Inc. (PECI), operate an EnergySmart Grocer program offering a series of 

rebates ranging from $40 to $300. Grant County Public Utility District also works with PECI to 

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).4
17-3

DOE used the amounts of 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 costs 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 ranging from 42 percent of an IMH-A-Small-C unit to 71 percent of an IMH-ASmall-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 chapter 10 of this TSD) that would 

result from such a rebate, DOE examined available information on efficiency choices in 

equipment classes covered by both ENERGY STAR and rebate programs. Information given to 

DOE indicates that, on average, 60 percent of total shipments in those equipment classes meet or 

exceed the ENERGY STAR level. For determining the potential impact of rebate programs 

targeting TSL 3, DOE developed a shift scenario. In the shift scenario, market share was shifted 

upward such that the total market share of shipments at or above TSL 3 was 60 percent. To do 

this, DOE calculated the existing market shares of shipments above TSL 3, if any, and the 

remainder of 60 percent minus existing market shares was assigned to the TSL 3 efficiency level.

Shipments below TSL 3 were distributed across efficiency levels in proportion to base-case 

efficiency distributions. In cases (if any) where the 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 compares 

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 2047.

Table 17.3.1 Customer Rebate NES and NPV Comparison to TSL 3

Policy Alternatives

Cumulative Primary 

Energy Savings*

quads

Net Present Value**

billion 2012$

7% Discount Rate 3% Discount Rate

No New Regulatory Action 0 0 0

Consumer Rebates (60% of price increments) 0.190 0.678 1.319

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.
17-4

17.3.3 Consumer Tax Credits

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 (EPACT 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 refund 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. Consequently, DOE estimated a response rate that 

is 80 percent of that for rebate programs (60 percent) and, therefore, a corresponding shift of 48 

percent in market shares to or above TSL 3 with no change in total shipments. In cases (if any) 

where the existing cumulative base-case market share at or above TSL 3 exceeded 48 percent, 

the distribution was left at the base-case distribution. DOE estimated NPV and NES values under 

these assumptions and the results are presented in Table 17.3.2

From a societal perspective, tax credits (like rebates) do not change the installed cost of 

the equipment, but rather transfer a portion of the cost from the consumer to taxpayers as a 

whole. DOE, therefore, assumed that equipment costs in the consumer tax credits scenario were 

identical to the NIA base case.

DOE assumed that tax credits would remain in effect for the duration of the analysis 

period. Table 17.3.2 presents the NES and NPV values for the tax credit scenario and compares 

them against the NES and NPV values at TSL.

Table 17.3.2 Tax Credit NES and NPV Comparison to TSL 3

Policy Alternatives

Cumulative Primary 

Energy Savings*

quads

Net Present Value**

billion 2012$

7% Discount Rate 3% Discount Rate

No New Regulatory Action 0 0 0

Customer Tax Credits 0.145 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 could 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 Replacement

Early replacement refers to the replacement of equipment before the end of its useful life.

The purpose of this policy is to retrofit or replace old, inefficient equipment with high-efficiency 
17-5

units. DOE studied the feasibility of a Federal program to promote early replacement of 

appliances and equipment under EPACT 1992. In this study, DOE identified Federal policy 

options for early replacement that include a direct national program, replacement of Federally

owned equipment, promotion through equipment manufacturers, customer incentives, incentives 

to utilities, market behavior research, 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 RESULTS FOR NON-REGULATORY ALTERNATIVES

Table 17.4.1 and Table 17.4.2 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 definition. 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 could 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 0.145

Customer rebates 0.190

Voluntary energy efficiency targets 0

Early replacement 0

Proposed standards (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 2012$

7% Discount 3% Discount

No new regulatory action 0 0

Consumer tax credits 0.520 1.011

Customer rebates 0.678 1.319

Voluntary energy efficiency targets 0 0

Early replacement 0 0

Proposed standards (TSL 3) 0.791 1.751
17-6

REFERENCES

1. Eugene Water and Electric Board. Commercial Food Service Equipment Catalog. 2013.

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, 2013.) 

<www.energysmartonline.org/rebates/index.html> 

4. Saving Water Partnership. Rebates. Seattle, 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 DEVIATIONS ..................................................................... 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-3
6A-1

APPENDIX 6A. DATA FOR EQUIPMENT PRICE MARKUPS

6A.1 INTRODUCTION

This appendix provides further details on information presented in chapter 6, Markups for 

Equipment Price Determination.

6A.2 DETAILED WHOLESALER COST DATA

Chapter 6 presents wholesaler revenues and costs in aggregated form, based on the

Heating, Air-conditioning & Refrigeration Distributors International (HARDI) 2012Profit 

Report (2011 Data). Table 6A.2.1 provides the complete breakdown of costs and expenses from

the 2012 Profit Report (2011 Data). The column labeled “Scaling” indicates which expenses 

were assumed to scale with only the baseline markup and which were assumed to scale with both 

the baseline and the incremental markups. As described in chapter 6, only those expenses that 

scale with baseline and incremental costs are marked 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 Expense (collection, credit card fees) 0.3

Bad Debt Losses 0.2
6A-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 form, based 

on U.S. Census Bureau data. Table 6A.3.1 shows the complete breakdown of costs and expenses 

provided by the U.S. Census Bureau. The column labeled “Scaling” 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%

Fringe benefits, all employees $13,585,040 8.60% Baseline

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 WHOLESALER AND MECHANICAL CONTRACTOR 

MARKUP STANDARD DEVIATIONS

The U.S. Department of Energy (DOE) used the U.S. Census Bureau’s Economic Census 

data to estimate mechanical contractor markup distributions. For mechanical contractors, the 

2007 census data included state-level plumbing, heating, and air-conditioning data for total value 

of work, number of firms, cost of goods sold, cost of subcontract work, cost of materials, and 

construction payroll, as shown in Table 6A.4.1.

DOE used the state-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 LCC analysis. By “selecting” a state during the Monte 

Carlo analysis, DOE varied the installation costs widely. Looking at contractor markups on a 

relative basis, the lowest state is 90 percent of the average markup while the highest state is 

119 percent of the average. Using population as a weighting factor, HVAC was combined with 

other factors that vary by state to create one of the key sets of cost components varied during the 

analysis.

Table 6A.4.1 Mechanical Contractor Baseline Markups by State, 2007*

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 1.470
6A-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/searchresults.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 where a 

D appears under the headings for Subcontractor Costs, Materials & Supplies, or Construction Payroll, data was withheld. States 

missing one or more variables were set equal to an average of neighboring states’ baseline markup.
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-1
8A-1

APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST SPREADSHEET

8A.1 INTRODUCTION

Detailed 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 FOR LIFE-CYCLE COST SPREADSHEET

The spreadsheet allows the user to perform LCC analyses of any of the primary

equipment classes 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 2010 and with Oracle Crystal 

Ball, Fusion Edition, Release 11.1.2.1.000. 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 payback.

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 electricity and water price forecasts for use in the NIA model . Also contains

experiential (price) learning data inputs used in the LCC and NIA model.

Markups Contains data used to derive industry baseline and incremental markups.

Engineering Contains the manufacturer price data for units at each efficiency level. Also contains data on 

harvest rates, utilization rates, and energy and water consumption, and summarizes other 

engineering derived data. 

Discounted Costs Contains annual operating costs in cash flow tables. 

Discount Rate Contains all data used to calculate discounts rates for each building type.

Electricity Prices Contains electricity prices by state, Annual Energy Outlook 2013 (AEO2013)

1 electricity price 

projections, and electricity price ratios derived for the various building 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 costs by state. Derived from data published by 

RS Means Publishers and Consultants and U.S. Census Bureau population estimates.

Sales Tax Contains data on sales tax rates and population data by state.

Population Contains 2012 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. Select Yes when asked if you want to enable macros.

b. At the bottom of the Excel window, click the tab for the Summary worksheet. 

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 escalation; (11) repair cost escalation; (12) analysis type; (13) price 

learning toggle; and (14) discounting toggle. 

a. You can start a complete Crystal Ball simulation by clicking the “Monte Carlo”

button.

b. All inputs, with the exception of some conservation program inputs, are either 

drop-down option boxes or toggle boxes (checked boxes make the statement true).

c. Equipment classes are defined as particular combinations of equipment families, 

cooling methods, harvest rate bins, and ice-making processes. 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. However, 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 an 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 

representative levels.
8A-3

5. This spreadsheet gives the user two 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 changed 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 

clicked, 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 model 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 efficiency levels. 

The Monte Carlo 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 

2013. 2013. Washington D.C. DOE/EIA-0383(2013).
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 Results 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 Life-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 (RCULarge-B-1)........................................................................................................8B-16

8B.2.9 Life-Cycle Cost Results for Remote Condensing Unit, Large, Batch-2 (RCULarge-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-WSmall-B).............................................................................................................8B-2

Table 8B.2.2 LCC Results for Ice Making Head, Water-Cooled, Medium, Batch (IMH-WMed-B)...............................................................................................................8B-4
8B-ii

Table 8B.2.3 LCC Results for Ice Making Head, Water-Cooled, Large, Batch-1 (IMH-WLarge-B-1)..........................................................................................................8B-6

Table 8B.2.4 LCC Results for Ice Making Head, Water-Cooled, Large, Batch-2 (IMH-WLarge-B-2)..........................................................................................................8B-8

Table 8B.2.5 LCC Results for Ice Making Head, Air-Cooled, Small, Batch (IMH-A-SmallB)......................................................................................................................8B-10

Table 8B.2.6 LCC Results for Ice Making Head, Air-Cooled, Large, Batch-1 (IMH-A-LargeB-1) ..................................................................................................................8B-12

Table 8B.2.7 LCC Results for Ice Making Head, Air-Cooled, Large, Batch-2 (IMH-A-LargeB-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-WLarge-B)...........................................................................................................8B-20

Table 8B.2.11 LCC Results for Self-Contained Unit, Air-Cooled, Small, Batch (SCU-ASmall-B)...........................................................................................................8B-22

Table 8B.2.12 LCC Results for Self-Contained Unit, Air-Cooled, Large, Batch (SCU-ALarge-B)...........................................................................................................8B-24

Table 8B.2.13 LCC Results for Ice Making Head, Air-Cooled, Small, Continuous (IMH-ASmall-C)...........................................................................................................8B-26

Table 8B.2.14 LCC Results for Ice Making Head, Air-Cooled, Large, Continuous (IMH-ALarge-C)...........................................................................................................8B-28

Table 8B.2.15 LCC Results for Self-Contained Unit, Air-Cooled, Small, Continuous (SCUA-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 8B.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 Results for Ice Making Head, Air-Cooled, Small, Batch (IMHA-Small-B).......................................................................................................8B-10
8B-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, Batch-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 (RCULarge-B-1)........................................................................................................8B-16

Figure 8B.2.16 PBP Statistical Results for Remote Condensing Unit, Large, Batch-1 (RCULarge-B-1)........................................................................................................8B-17

Figure 8B.2.17 LCC Statistical Results for Remote Condensing Unit, Large, Batch-2 (RCULarge-B-2)........................................................................................................8B-18

Figure 8B.2.18 PBP Statistical Results for Remote Condensing Unit, Large, Batch-2 (RCULarge-B-2)........................................................................................................8B-19

Figure 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 Results for Self-Contained Unit, Air-Cooled, Small, Batch 

(SCU-A-Small-B) ............................................................................................8B-22

Figure 8B.2.22 PBP Statistical Results for Self-Contained Unit, Air-Cooled, Small, Batch 

(SCU-A-Small-B) ............................................................................................8B-23

Figure 8B.2.23 LCC Statistical Results for Self-Contained Unit, Air-Cooled, 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-31
8B-1

APPENDIX 8B. DETAILED LIFE-CYCLE COST AND PAYBACK PERIOD 

ANALYSIS RESULTS

8B.1 INTRODUCTION

This appendix contains detailed output from the life-cycle cost (LCC) and payback period 

(PBP) analysis for automatic commercial ice makers, as developed using the LCC spreadsheet.

LCC and PBPs are presented for each of the primary equipment classes for each efficiency level.

See chapter 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. Department of Energy (DOE) calculated summary statistics—average values for 

LCC, product installation costs and operating costs; a distribution of customers who: (1) receive 

benefits in the form of lower LCC results; (2) are not affected by the standards; or (3) experience 

a cost in the form of increased LCC from equipment ownership. Results of the LCC and 

accompanying PBP analyses are shown in the tables in this 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 rectangular boxes 

used to represent the 25th and 75th percentile values. The lower edge of the elongated rectangle 

represents 25th percentile, which means that 25 percent of the customers will experience LCC 

savings below the value represented by the bottom of the rectangle if the standard were set at 

level in question. The upper edge of the elongated rectangle represents the 75th percentile. 

Finally, each of the rectangles includes two “tails,” or two vertical black lines, extending 

downward to represent the 5th percentile (lower end) and upward to represent the 95th percentile 

(upper end).

To simplify the presentation, all efficiency levels are presented as numbered levels. 

Therefore, the efficiency level referred to as the baseline in other chapters of this TSD is 

presented as level 1. All higher efficiency levels are numbered sequentially, from level 1 to level 

7.

The remainder of this appendix presents the tabular and graphical results for the 

equipment classes analyzed for the automatic commercial ice maker rulemaking.
8B-2

8B.2 LIFE CYCLE COST RESULTS

8B.2.1 LIFE-CYCLE COST RESULTS FOR ICE MAKING HEAD, WATERCOOLED, SMALL, BATCH (IMH-W-SMALL-B)

Table 8B.2.1 LCC Results for Ice Making Head, Water-Cooled, Small, Batch (IMH-WSmall-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-3

Figure 8B.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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-4

8B.2.2 LIFE-CYCLE COST RESULTS FOR ICE MAKING HEAD, WATERCOOLED, MEDIUM, BATCH (IMH-W-MED-B)

Table 8B.2.2 LCC Results for Ice Making Head, Water-Cooled, Medium, Batch (IMH-WMed-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-6

8B.2.3 LIFE-CYCLE COST RESULTS FOR ICE MAKING HEAD, WATERCOOLED, LARGE, BATCH-1 (IMH-W-LARGE-B-1)

Table 8B.2.3 LCC Results for Ice Making Head, Water-Cooled, Large, Batch-1 (IMH-WLarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-8

8B.2.4 LIFE-CYCLE COST RESULTS FOR ICE MAKING HEAD, WATERCOOLED, LARGE, BATCH-2 (IMH-W-LARGE-B-2)

Table 8B.2.4 LCC Results for Ice Making Head, Water-Cooled, Large, Batch-2 (IMH-WLarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-10

8B.2.5 LIFE-CYCLE COST RESULTS FOR ICE MAKING 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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-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-ALarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-14

8B.2.7 LIFE-CYCLE COST RESULTS FOR 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-ALarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-16

8B.2.8 LIFE-CYCLE COST RESULTS FOR 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 (RCULarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-17

Figure 8B.2.16 PBP Statistical Results for Remote Condensing Unit, Large, Batch-1 (RCULarge-B-1)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

 2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-18

8B.2.9 LIFE-CYCLE COST RESULTS FOR 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 (RCULarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-19

Figure 8B.2.18 PBP Statistical Results for Remote Condensing Unit, Large, Batch-2 (RCULarge-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-20

8B.2.10 LIFE-CYCLE COST RESULTS FOR SELF-CONTAINED UNIT, WATERCOOLED, LARGE, BATCH (SCU-W-LARGE-B)

Table 8B.2.10 LCC Results for Self-Contained Unit, Water-Cooled, Large, Batch (SCU-WLarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-22

8B.2.11 LIFE-CYCLE COST RESULTS FOR SELF-CONTAINED UNIT, AIRCOOLED, SMALL, BATCH (SCU-A-SMALL-B)

Table 8B.2.11 LCC Results for Self-Contained Unit, Air-Cooled, Small, Batch (SCU-ASmall-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-24

8B.2.12 LIFE-CYCLE COST RESULTS FOR SELF-CONTAINED UNIT, AIRCOOLED, LARGE, BATCH (SCU-A-LARGE-B)

Table 8B.2.12 LCC Results for Self-Contained Unit, Air-Cooled, Large, Batch (SCU-ALarge-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-26

8B.2.13 LIFE-CYCLE COST RESULTS FOR ICE MAKING HEAD, AIR-COOLED, 

SMALL, CONTINUOUS (IMH-A-SMALL-C)

Table 8B.2.13 LCC Results for Ice Making Head, Air-Cooled, Small, Continuous (IMH-ASmall-C)

Figure 8B.2.25 LCC Statistical Results for Ice Making Head, 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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-28

8B.2.14 LIFE-CYCLE COST RESULTS FOR ICE MAKING HEAD, AIR-COOLED, 

LARGE, CONTINUOUS (IMH-A-LARGE-C)

Table 8B.2.14 LCC Results for Ice Making Head, Air-Cooled, Large, Continuous (IMH-ALarge-C)

Figure 8B.2.27 LCC 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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8B-30

8B.2.15 LIFE-CYCLE COST RESULTS FOR SELF-CONTAINED UNIT, AIRCOOLED, SMALL, CONTINUOUS (SCU-A-SMALL-C)

Table 8B.2.15 LCC Results for Self-Contained Unit, Air-Cooled, Small, Continuous (SCUA-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 7

Cost Savings (2012$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median
8B-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 7

Years

Efficiency Level

Payback Period 

Median Mean
8C-i

APPENDIX 8C. UNCERTAINTY AND VARIABILITY IN LIFE-CYCLE COST 

ANALYSIS

8C.1 INTRODUCTION .........................................................................................................8C-1

8C.2 UNCERTAINTY ...........................................................................................................8C-1

8C.3 VARIABILITY..............................................................................................................8C-1

8C.4 APPROACHES TO UNCERTAINTY AND VARIABILITY .....................................8C-1

8C.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL® ......................8C-2
8C-1

APPENDIX 8C. UNCERTAINTY AND VARIABILITY IN LCC ANALYSIS

8C.1 INTRODUCTION

The U.S. Department of Energy’s (DOE’s) approach to analysis of an energy-efficiency 

standard also includes analyzing the range of potential impacts of higher efficiency equipment on 

consumers. DOE uses the consumer life-cycle cost (LCC) as the key metric for this analysis and 

examines the variability of the LCC of higher efficiency equipment by varying a wide range of 

input assumptions. To perform the calculation, analysts must first: (1) specify the equation or 

model that will be used; (2) define the quantities in the equation; and (3) provide numerical 

values for each quantity. In the simplest case, the equation is unambiguous (contains all relevant 

quantities and no others), each quantity has a single numerical value, and the calculation results 

in a single value. However, in most cases, the model and/or the numerical values for each

quantity in the model are not completely known (i.e., there is uncertainty), and/or the numerical 

values for each quantity in the model depend upon other conditions (i.e., there is variability).

Thorough analysis involves accounting for uncertainty and variability. While the simplest 

analysis involves a single numerical value for each quantity in a calculation, arguments can arise 

about what the appropriate value is for each quantity. Explicit analysis of uncertainty and 

variability is intended to provide more complete information to the decision-making process for 

establishing standards for automatic commercial 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 an ice maker used in 

a restaurant is not available at the time of purchase, but rather estimated based upon available 

historical information. When estimating numerical values expected for quantities at some future 

date, the exact outcome is rarely known in advance.

8C.3 VARIABILITY

Variability means that different applications or situations produce different numerical

values when calculating a quantity. Specifying an exact value for a quantity may be difficult

because the value depends on another parameter. For example, the electricity price 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 analysis 

and probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then

changes one (or more) of the numerical values and repeats the calculation. A number of

calculations are done, which provides some indication of the extent to which the result depends

on the assumptions. The advantages of scenario analysis are that each calculation is simple, a 

range of estimates is used, and crossover points can be identified. (An example of a crossover 
8C-2

point is 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 prices paid by different users), surveys can be used to generate a

frequency distribution of numerical values (e.g., the number of 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 energy efficiency to some level may be estimated to be 

$100 ± $20). The major disadvantage of the probability approach is that it requires more 

information, namely information about the shapes and magnitudes of the variability and 

uncertainty of each quantity. The advantage of the probability approach is that it provides greater 

information about the outcome of the calculations (i.e., it provides the probability that the 

outcome will be in a particular range). Scenario and probability analysis provide some indication 

of the robustness of the policy given the uncertainties and variability. A policy is robust when the 

impacts are acceptable over a wide range of possible conditions.

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 analysis 

is carried out in the form of Monte Carlo simulations. Simulation refers to any analytical method 

meant to imitate a real-life system, especially when other analyses are mathematically complex 

or difficult to reproduce. Without the aid of simulation, a spreadsheet model will only reveal a 

single outcome, generally the most likely or average scenario. 

Monte Carlo simulations are carried out by performing the LCC analysis multiple times 

(10,000 times for each equipment class for this rulemaking). Each simulation has one particular 

set of inputs. The inputs are chosen from their respective probability distributions. For example, 

there are 50 states in the United States and the probability of choosing one state for a simulation 

is based on the population of the state. The higher the population of a state, the higher the 

probability of choosing that state for a particular simulation. Similarly, the equipment lifetime is 

specified by Weibull distribution curves with mean equipment lifetime of 8-1/2 years. Crystal 

Ball chooses the equipment lifetime for any particular simulation based on the Weibull 

probability distribution curve. Thus, each Monte Carlo simulation is a random combination of 

input values, with each input value chosen according to its respective probability distribution.

That is, during a single trial, Crystal Ball selects a value from the defined possibilities (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-4
8D-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 2012 (the year for which the engineering analysis estimated costs) and

throughout the period of the analysis. In its notice of data availability (NODA) published on 

February 22, 2011, DOE stated that it may consider improving regulatory analysis by addressing 

equipment price trends. 76 FR at 9696. Consistent with the NODA, DOE examined historical 

producer price indices for automatic commercial ice makers.

DOE stated in the NODA that examination of 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). Economic literature and 

historical data suggest that the real costs of these products may in fact trend downward over time 

according to “learning” or “experience” curves, or alternatively that the price trends for certain 

sectors of the U.S. economy may be different than the price trends for the economy as a whole.

A draft paper, Using the Experience Curve Approach for Appliance Price Forecasting, available

at www1.eere.energy.gov/buildings/appliance_standards/pdfs/experience_curve_appliance

_price_forecasting_3-16-11.pdf, summarizes the data and literature currently available to DOE 

that is relevant to price forecasts for selected appliances and equipment. 

The extensive literature on the “learning” or “experience” curve phenomenon is typically 

based on observations in the manufacturing sector.1 In the experience curve method, the real cost 

of production is related to the cumulative production or “experience” with a manufactured 

product. To explain the empirical relationship, DOE would use the theory of technology learning

to substantiate a decline in the cost of producing a given product as firms accumulate experience 

with the technology. A common functional relationship used to model the evolution of 

production costs in this case is:

Y = aX-b

Eq. 8D.1

Where:

a = an initial price (or cost), 

b = a positive constant known as the learning rate parameter, 

X = cumulative production, and 

Y = the price as a function of cumulative production. 

Thus, as experience (production) accumulates, the cost of 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 historical prices using the Bureau of Labor Statistics’ (BLS) Producer 

Price Index (PPI) and gross domestic product (GDP) deflator, available from the Bureau of 

Economic Analysis (BEA). The PPI data for commercial refrigerators and related equipment is 

available for 1978–2012 and is used to represent aggregate automatic commercial ice makers’

prices. Figure 8D.1.1 shows the PPI data series used. 

Figure 8D.1.1 PPI Data 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 2012, but shows a decrease in the real PPI during two 

significant periods of time: 1982 to 1988 and1992 to 2004. Based on these price trends, DOE 

expects that the PPI is likely to resume a downward trend in the future.
8D-3

To perform an experience curve fit, DOE assembled a time-series of annual shipments 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 impact analysis (see chapter 10 of this technical support document). Projected 

annual shipments are depicted in Figure 8D.2.1.

Figure 8D.2.1 Projected Annual Shipments for Automatic Commercial Ice Makers

To estimate potential product price trends, DOE performed a least-squares power-law fit 

on the commercial refrigeration equipment price index versus cumulative shipments. The form 

of the fitting equation is: 

P(X) = PoX-b

Eq. 8D.3

Where:

b = the learning rate parameter, and 

Po = the price or cost of the first unit of production.

Both b and Po 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 2046

Shipments (units)

Year
8D-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 of a simple least-squares fit is equivalent to an assumption of no 

significant first price elasticity effects in the cumulative shipments variable. 

After modeling the data to the Eq. 8D.3, DOE estimated the learning rate (defined as the 

fractional reduction in price expected from each doubling of cumulative production) as 1.3

percent.

With cumulative shipments through 2047 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 2012 prices in the economy as a whole. Figure 8D.2.2 shows the model fit for 

the projected values for the period after 2010. 

Figure 8D.2.2 Model Fit for Automatic Commercial Ice Makers

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 25

Fitted 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-1
10A-1

APPENDIX 10A. USER INSTRUCTIONS FOR NIA SPREADSHEET

10A.1 INTRODUCTION

The results obtained in this analysis can be examined and reproduced using a Microsoft 

Excel spreadsheet available on the U.S. Department of Energy (DOE) Building Technologies 

website at 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 designed 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 standards for automatic commercial ice-making (ACIM) equipment, and 

the net present value of the change in energy usage.

a The energy usage 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 equipment 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 worksheets listed in Table 10A.2.1 .

Some of the worksheets are hidden to make it easier for users to find and examine final results 

quickly. Hidden worksheets can be retrieved in Excel 2010 by right-clicking on any worksheet

tab, selecting the “Unhide” option, and then choosing a specific worksheet to unhide from the list 

of hidden worksheets.

Table 10A.2.1 Description of Tabs in the NIA Spreadsheet

Worksheet Description

Instructions Provides a description of the NIA spreadsheet.

Labels Used 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 stocks for the complete analysis period.

Econ Trends Contains electricity and water price forecasts and experiential (price) learning data inputs.

LCC Input Contains data derived in the life-cycle cost (LCC) analysis spreadsheet. Some data is 

replicated in the NIA model to allow variations defined explicitly in the NIA.

NIA Compiled Data Contains data from several other worksheets that have been organized in a way that allows 

for discounting and output table creation.

 

a Note that the NIA model also forecasts impacts arising from changing water usage.
10A-2

Table 10A.2.1 (cont)

Worksheet Description

TSD Tables Contains output tables 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 installation costs from the LCC Inputs worksheet along with shipment data.

Annual Operating 

Costs

Contains data summed from the Annual Repair & Maint, Annual Water Cost, and Annual 

Elec Cost worksheets.

Annual Repair & 

Maint

Contains maintenance and repair cost data summed from the maintenance costs worksheet

and either the 3% or 7% repair cost worksheets, depending on the user-defined discount 

rate. Repair 3% and 7% contain the same levelized costs. The existence of the two different 

input streams is a legacy that has been since been superseded. 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 perunit basis.

Annual Water Cost Contains aggregated water cost data using inputs from the LCC Inputs worksheet and stock 

data.

Annual Elec Cost Contains aggregated electricity cost data using inputs from the LCC Inputs worksheet and 

stock data.

Electricity Use Contains aggregated water usage data using inputs from the LCC Input worksheet and 

stock data.

Water Use Contains aggregated electricity usage data using inputs from the LCC Input worksheet 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 input into the manufacturer impact 

analysis.

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 worksheets.

Healthcare Contains historical and projected floor space data from the Annual Energy Outlook 20131

(AEO2013) to account for growth rates in healthcare buildings.

Lodging Contains historical and projected floor space data from the AEO2013 to account for growth 

rates in lodging buildings.

Food Service Contains historical and projected floor space data from the AEO2013 to account for growth 

rates in foodservice buildings.

Retail Contains historical and projected floor space data from the AEO2013 to account for growth 

rates in retail buildings.

Education Contains historical and projected floor space data from the AEO2013 to account for growth 

rates in education buildings.
10A-3

Table 10A.2.1 (cont)

Worksheet Description

Food Sales Contains historical and projected floor space data from the AEO2013 to account for growth 

rates in food sales buildings.

Office Contains historical and projected floor space data from the AEO2013 to account for growth 

rates in office buildings.

Shipment-Weighted 

Data

Contains output tables 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 the Excel window, 

click on the worksheet tab for the Shipments Summary worksheet.

2. Use Excel’s View/Zoom commands at the top menu bar to size the display to your 

monitor.

3. 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 discounted.

b. Equipment Class: 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 

default 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) AEO2013 scenarios.

f. Water Price Scenario: Selects the desired growth level (reference, low, or high). 

g. Discount Rate: Selects the desired discount 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 

Shipments, 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 macro, click the “Build Shipment/Stock Tables” button. Please note that output values 

are not correctly updated until the Build Shipment/Stock Tables macro has 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 must be loaded into the LCC Input

worksheet. To do this:

a. Open the LCC workbook and execute as desired (see appendix 8A).

b. Once results are compiled 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 tables, in the Shipment-Weighted Data worksheet. 

Although many of the tables are formula-based, and will automatically update with a 

change in the user-defined parameters, some are reliant on a macro to update table values. 

To update the table values:

• Click the “Build Tables” button located at the top right portion of the ShipmentWeighted Data worksheet.
10A-5

REFERENCES

1. U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 

2013. 2013. Washington D.C. DOE/EIA-0383(2013).
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 Price Scenario ..............................................10B-4

Table 10B.1.3 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) at a 

3-Percent Discount Rate – 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 Commercial Ice Makers: Net Present Value in Billions (2012$) at a 

7-Percent Discount Rate – High Electric Price Scenario...............................10B-11

Table 10B.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 

Discount Rate for 9-Year Analysis Period for Equipment Purchased in 2018–

2026................................................................................................................10B-18
10B-ii

LIST OF FIGURES

Figure 10B.1.1 Automatic Commercial Ice Makers Price Factor Indices for Default Case and 

Sensitivity Cases..............................................................................................10B-2
10B-1

APPENDIX 10B. 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 shipments 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 2018–2047 analysis period is 0.04 percent and is based on historical PPI data 

for refrigeration equipment in the 1978–2012 period as discussed in appendix 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 confidence interval for an exponential fit to the inflation-adjusted PPI series of

1978 through 2012. The low price forecast case is based on the lower end of the 95 percent 

confidence interval for an exponential fit to the nominal PPI series of 1978 through 2012. The 

average annual rate of price decline over the analysis 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 experience 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 the 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 1978–2012; (2) a low price case based on the PPI 

trend in 1978–2012; and (3) a constant real price case. Each price scenario is expressed in terms 

of a price factor index time series, which is applied to the 2012 price estimate to forecast per unit 

prices over the 2018–2047 analysis period. Figure 10B.1.1 shows the equipment price factor 

indices for the default case and the three additional price decline forecasts considered in this 

sensitivity analysis. Table 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 and 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 commercial ice makers NPV results using the 

default price decline scenario shown in chapter 10 of this TSD.
10B-2

Figure 10B.1.1 Automatic Commercial Ice Makers Price Factor Indices for Default Case 

and Sensitivity Cases
10B-3

Table 10B.1.1 Price Factor Indices Used in Default Case and Sensitivity Cases

Year

Price Factor Index

2012$

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 1.0000
10B-4

Table 10B.1.2 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) 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-LargeB, 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 Billions (2012$) 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-LargeB, rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.
10B-6

Table 10B.1.4 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) 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-LargeB, rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.
10B-7

Table 10B.1.5 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) 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-LargeB, 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 Billions (2012$) at 

a 7-Percent Discount 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-LargeB, 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-9

Table 10B.1.7 Automatic Commercial Ice Makers: Net Present Value in Billions (2012$) 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-LargeB, rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B.1.2 Electric and Water Price Sensitivity Analyses 

For the NPV sensitivity, DOE considered three electric and water price forecast 

sensitivity cases: (1) a high electric and water prices case; (2) a low electric and water prices

case; and (3) a constant electric and water prices case. Table XXX displays the high, low, and 

reference water price scenarios used in the sensitivity cases. Table 10B.1.8 and Table 10B.1.9

provide NPV results for automatic commercial ice makers at each TSL based on the high electric 

and water price forecast case for 7- and 3-percent discount rates, respectively. Table 10B.1.10

and Table 10B.1.11 provide NPV results for automatic commercial ice makers based on the low

electric and water price forecast case at 7- and 3-percent discount rates, respectively. Table 

10B.1.12 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 Case
10B-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 $7.1230
10B-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 Billions (2012$) 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 

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-LargeB, 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-LargeB, 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 Discount 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-LargeB, 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-LargeB, 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, and RCU-LargeB, 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-LargeB, 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 revised standards.a This timeframe 

may not be statistically relevant with regard to the product lifetime, product manufacturing 

cycles, or other factors specific to automatic commercial ice makers. Thus, this information is 

 a For automatic commercial ice makers, DOE is required to review standards at least every 5 years after the effective 

date of any amended standards. (42 U.S.C. 6313(d)(3)(B)) If new standards are promulgated, EPCA requires DOE 

to provide manufacturers a minimum of 3 years and a maximum of 5 years to comply with the standards. (42 U.S.C. 

6313(d)(3)(C)) In addition, for certain other types of commercial equipment that are not specified in 42 U.S.C. 

6311(1)(B)-(G), EPCA requires DOE to review its standards at least once every 6 years (42 U.S.C. 6295(m)(1) and 

6316(a)), and either a 3-year or a 5-year 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, and 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 NPV results based on a 9-year analysis period are presented in 

Table 10B.1.14 and Table 10B.1.15 at 7-percent and 3-percent discount rates, respectively. The 

impacts are counted over the lifetime of equipment purchased in 2018–2026.

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-LargeB, 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-LargeB, 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.
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-10
10C-1

APPENDIX 10C. TRIAL STANDARD LEVELS AND STANDARDS EQUATIONS

10C.1 INTRODUCTION

The U.S. Department of Energy (DOE) carried out the life-cycle cost (LCC) analysis and 

national impact analysis (NIA) by defining efficiency levels within each equipment class of 

automatic commercial ice makers. These efficiency levels generally define pre-set efficiency 

improvements over base efficiencies and are generally consistent across equipment classes. 

However, the percentage energy reductions set for efficiency level 1 do differ between product 

classes. For example, efficiency level 1 for five batch type equipment classes is set to a 10 

percent energy-usage reduction from the baseline level while efficiency level 1 for the 3 selfcontained batch equipment classes is set to 7 percent. For all equipment classes, the highest 

efficiency level listed corresponds to the maximum savings level. The 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 levels across all equipment classes such that the efficiency levels 

belonging to a TSL grouping all have a certain uniform characteristic or criterion associated with 

them. DOE evaluates the national impacts (e.g., national energy savings (NES), net present value 

(NPV), water savings, employment impacts, impacts on manufacturers, and environmental 

impacts) at each TSL. Based on the results, DOE proposes to set the standard at a particular TSL.

This appendix describes DOE’s method for selecting TSLs for automatic commercial ice 

makers. The following sections describe the criteria used for TSL selection and standard level 

equations associated with each TSL.

10C.2 TRIAL STANDARD LEVEL SELECTION CRITERIA

DOE selected five TSLs for this rulemaking based on the following criteria:

1. TSL 5 was set at the maximum technologically feasible (max-tech) level for each 

equipment class.

2. TSL 4 was chosen as an intermediate step between TSL 5 (the max-tech level) and 

TSL 3 (the maximum customer NPV level), subject to the requirement that the TSL 4 

NPV must be positive. Where a sufficient number of efficiency levels allow it, TSL 4 

is set at least one level below TSL 5 and one level above TSL 3. In one case, the TSL 

4 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 represent 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 levels available for each 

equipment class, there is often overlap between TSL levels. Thus, TSL 2 includes 

levels that overlap with both TSLs 1 and 3. TSL 2 provides an intermediate level that 

compromises between TSLs 1 and 3.

5. TSL 1 was set equal to efficiency 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 MaxTech

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 “efficiency level” or alternative candidate standard level (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 noted as B1 and the larger as B2.

†DOE analyzed impacts for the B1 and B2 typical units and aggregated impacts to the equipment class level.
10C-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 TSL
10C-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-LargeB1

BW Fill, PSC 

PM

Increase Comp EER, 

Increase Cond

Same as 

previous Same as previous

Increase Cond, 

ECM PM, 

DWHX

DWHX

IMH-W-LargeB2

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 Exchanger
10C-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 setting standards to a 20 percent across the board efficiency improvement. This 

criterion was discarded because not all equipment classes exhibit desirable customer results at 20 

percent. 

10C.3 TRIAL STANDARD LEVEL EQUATIONS

Current energy conservation standards for automatic commercial ice makers specify 

maximum energy consumption levels per 100/lb of ice per 24-hour period. (42 U.S.C. 

6313(d)(1)) Similarly, for automatic commercial ice makers using water for cooling in the 

condenser, water limits are established in terms of gallons of water per 100/lb ice per 24-hour 

period. In this notice, DOE has retained these metrics for specifying energy and water 

conservation standards.

General Methodology
10C-6

Developing trial standard level equations required several steps. In developing TSLs, 

DOE analyzed each equipment class separately, and attributed a percentage reduction with each 

portion of the equipment standard curve (small/medium/large). To ensure that the standard curve 

was continuous (i.e., no gaps at the breakpoints between size categories), DOE developed a 

method for expressing the consumption standards. The method relied on two techniques:

1) adjusting the high-capacity equipment classes (whose equations were constant with 

respect to capacity) by a factor equal to the expected percentage reduction, 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-Batch, IMH-A-Batch, 

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 largecapacity equipment class (which is set at a constant value for all equipment types except IMH-ALarge-B) and applied the allocated percentage reduction (i.e., the percentage reduction associated 

with the TSL for that equipment class). For example, for IMH-W-Large-B, the baseline level is 

set at 4.0. If the TSL allocated a 10-percent reduction for IMH-W-Large-B, then the next level 

was set at 4.0 × (1-10 percent) = 3.6 kWh/100 lb of ice.

Next, for the small equipment classes, DOE applied the allocated percentage reduction at 

a designated median capacity in that harvest rate range. The medium capacity was selected based 

on shipment levels and where the median fell within the shipments data. For example, if the 

median capacity for the small equipment class was at 300 lb ice/24 hours, DOE would calculate 

the baseline energy usage and then apply the allocated percentage reduction to obtain a point at 

300 lb ice/24 hours. DOE would then draw a line between the start of the large equipment class 

and this median capacity point to obtain the equation for the small equipment class, ensuring that 

there were no gaps between small and large-capacity equipment class standards. This method 

generally produced trial standard level equations that were consistent with the expected 

percentage reduction for the small equipment classes.

Exceptions

For the IMH-W-Batch equipment classes, this equipment type has small, medium, and 

large equipment classes. In this case, for the small equipment class, DOE applied the allocated 

percentage reduction to the whole equation. So if the percentage reduction was 10 percent, the 

new equation for the small equipment class would be (1-10 percent) × (7.80 – 0.0055H) = 7.02 –

0.00495H. The method for treating the large equipment class would remain the same. DOE 

would then draw a line between the end of the small equipment class and the start of the large 

equipment class to obtain the equation for the medium equipment class.
10C-7

For the IMH-A-Batch equipment classes, DOE sought to obtain a constant efficiency 

level for the largest equipment classes. In order to achieve this, for the small equipment class, 

DOE applied the allocated percentage reduction to the whole equation. DOE then analyzed two 

different sizes of equipment in the IMH-A-Large-B equipment class. 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 efficiency 

level equal to this value. For the smaller size of the IMH-A-Large-B equipment, DOE simply 

drew a line between the IMH-A-Small-B trial standard level and the trial standard level for the 

largest IMH-A-Large-B equipment. Further details are available 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 ice differential discussed in chapter 5 (for suction in the 

lines connecting the ice-making portion of the machine to the remote condenser) to arrive at the 

standard levels. For small RCU classes, remote 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 

directly analyzed in the engineering analysis (Table 10C.3.3). 

Table 10C.3.1 Energy Consumption by TSL for Representative Units Analyzed in the 

Engineering Analysis for Each Primary Equipment Class

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 5.3
10C-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 7.8
10C-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 5.1
10C-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 with 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 condenser 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 90
10D-i

APPENDIX 10D. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10D.1 INTRODUCTION ...................................................................................................... 10D-1

10D.2 METHODOLOGY ..................................................................................................... 10D-1

10D.3 FULL-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-3
10D-1

APPENDIX 10D. 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 distribution 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 generation, transmission, and distribution of electricity. Per DOE’s 

2011 Statement of Policy for Adopting Full Fuel Cycle Analyses, DOE now uses FFC measures of 

energy use and emissions in its energy conservation standards analyses.76 FR 51281 (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 electricity, 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 

multiplying the site kilowatt-hours by the site-to-power plant energy use factor, given in chapter 

10. For the FFC analysis, the upstream energy use is defined as the energy consumed in 

extracting, processing, and transporting or distributing primary fuels. FFC energy use is the sum 

of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels 

and uranium, and electricity generated from renewable fluxes (wind, solar, and hydro). For the 

former, the 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 Emissions Metrics.

2 The following discussion

provides a brief summary of the methods used to calculate FFC energy. 

When all energy quantities are normalized to the same units, the FFC energy use can be 

represented as the product of the primary energy use and an FFC multiplier. The FFC multiplier 

is defined mathematically as a function of a set of parameters representing the energy intensity 

and material losses at each production stage. These parameters depend only on physical data, so 

the calculations do not require any assumptions about prices or other economic data. While in 
10D-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: 

• ax is the quantity of fuel x burned per unit of electricity output, on average, for grid 

electricity. The calculation of ax includes a factor to account for transmission and 

distribution system losses. 

• by is the amount of grid electricity used in production of fuel y, in megawatt-hours per 

physical unit of fuel y. 

• cxy is the amount of fuel x consumed in producing one unit of fuel y. 

• qx is the heat content of fuel x (million British thermal units/physical unit) 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x) 

The parameters are calculated as a function of time with an annual time step; hence, a 

time series of annual values is used to estimate the FFC energy and emissions savings in each 

year of the analysis period. Fossil fuel quantities are converted to energy units using the heat 

content factors qx. To convert electricity in kilowatt-hours to primary energy units, on-site 

electricity consumption is multiplied by the site-to-power plant energy use factor. The site-topower plant energy use factor is defined as the ratio of the total primary energy consumption by 

the electric power sector (in quads) divided by the total electricity generation in each year. 

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. A multiplier is also calculated for electricity reflecting the fuel mix used in its 

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 production 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 2013 (AEO2013).

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 MULTIPLIERS 

FFC energy multipliers are presented in Table 10D.3.1 for selected years. To extend the 

analysis period beyond 2040, the last year in the AEO2013 projection, the multipliers are 

assumed constant through 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 AEO2013)

2015 2020 2025 2030 2035 2040

Electricity (power plant primary 

energy use) 1.042 1.041 1.040 1.040 1.041 1.040

Natural Gas (site) 1.103 1.101 1.100 1.098 1.099 1.100

Petroleum Fuels (site) 1.132 1.140 1.148 1.158 1.166 1.168
10D-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 No. LBNL-6025E.

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

2013. April 2013. 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 Annualized 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 Shipped 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 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 3, 7 Percent Discount 

Rate)...............................................................................................................10E-16

Table 10E.1.9 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 4, 7 Percent Discount 

Rate)...............................................................................................................10E-18

Table 10E.1.10 Cumulative and Annualized Benefits and Costs of Considered Standard 

Levels for ACIM Units Shipped in the Period 2018–2047 (TSL 5, 7 Percent 

Discount Rate) ...............................................................................................10E-20
10E-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 presents the NPV values that would result if the U.S. Department of 

Energy (DOE) were to add the estimates of the potential economic benefits resulting from 

reduced carbon dioxide (CO2) and nitrogen oxide (NOx) 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 CO2 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 CO2 reductions is based on a global value. Second, the assessments of operating cost 

savings and CO2 savings are performed with different methods that use quite different time 

frames for analysis. The national operating cost savings is measured for the lifetime of products 

shipped in the 30-year analysis period. The social cost of carbon values, on the other hand, 

reflect the present value of future climate-related impacts resulting from the emission of one ton 

of CO2 in each year. These impacts go well beyond 2100.

The benefits and costs of the considered standard levels, for products sold in 2018

through 2047, 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 2012 dollars (2012$), of the benefits from customer 

operation of products that meet the considered standard levels (consisting primarily of operating 

cost savings from using less energy, minus increases in equipment purchase and installation 

costs, which is another way of representing customer NPV) and (2) the annualized monetary 

value of the benefits of emission reductions, including CO2 emission reductions.
10E-2

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)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.797
10E-3

Table 10E.1.1 (cont)

Units

IMHWSmallBIMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallCIMHALargeB 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.046
10E-4

Table 10E.1.2 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 2, 3 Percent Discount Rate)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 1.490
10E-5

Table 10E.1.2 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.086
10E-6

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)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallCIMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 3.187
10E-7

Table 10E.1.3 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.183
10E-8

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)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 3.148
10E-9

Table 10E.1.4 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.181
10E-10

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)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 1028.293

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 2000.381

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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 3.046
10E-11

Table 10E.1.5 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.175
10E-12

Table 10E.1.6 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 1, 7 Percent Discount Rate)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.563
10E-13

Table 10E.1.6 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.042
10E-14

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)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 1.049
10E-15

Table 10E.1.7 (cont)

Units

IMHWSmallBIMHWMed-BIMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallCIMHALargeB 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.078
10E-16

Table 10E.1.8 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 3, 7 Percent Discount Rate)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 2.220
10E-17

Table 10E.1.8 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.166
10E-18

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)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 2.385
10E-19

Table 10E.1.9 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.160
10E-20

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)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeBIMHASmallBRCU-

***-

LargeB

SCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCSCUASmallC

IMHALargeB

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

CO2

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

SO2 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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 2.375
10E-21

Table 10E.1.10 (cont)

Units

IMHWSmallB

IMHWMed-B

IMHWLargeB

IMHASmallB

RCU-

***-

LargeB

SCUWLargeB

SCUASmallB

SCUALargeB

IMHASmallC

IMHALargeC

SCUASmallC

IMHALargeB

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

CO2 (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 + CO2 (1st) + 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 + CO2 (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 + CO2 (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 + CO2 (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 0.154
12A-i

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

12A.1 Manufacturer Impact Analysis Interview Guide......................................................... 12A-1
12A-1

12A.1 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

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 information 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 (RCUNRC) 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 (RCURC) 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 (RCUNRC) Air

≥50 and <1000 Continuous RCU-NRC-Small-C

≥1,000 and ≤4,000 Continuous RCU-NRC-Large-C

Remote Condensing and 

remote compressor (RCURC) 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-C
12A-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 impact 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 

manufacturers 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 industry 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 preparing 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 first 

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 costs 

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 the 

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 selected 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 product 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. Included 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 amended 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 new 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 capacities 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, substantial 
12A-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, please provide estimates for the product conversion costs required to meet 

each efficiency level in Table 5.3 and Table 5.4. Where applicable, 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-A
12A-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 and 

nature of your investments, including the plant and tooling changes and the product 

development effort required at different efficiency levels.
12A-11

6 CUMULATIVE REGULATORY 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 commercial 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 Under 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 energy 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 shipment 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 conservation 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 COMPETITION

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 company 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 BUSINESS

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 knowledge, 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 adoption 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 company 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 manufacturers)

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-5
12B-2

APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL

12B.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the 

impacts of energy conservation standards and other regulations on manufacturers. The basic 

mode of analysis is to estimate the change in 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 with 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 value. The model calculates the actual 

cash flows by year and then determines the present value of those cash flows both without an 

energy conservation standard (i.e., the base case) and under different trial standard levels (TSLs).

Output from the model consists of summary financial metrics, graphs of major variables, 

and, when appropriate, access to the complete cash flow calculation.

12B.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses 

manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters 

as inputs and accepts a set of regulatory conditions as changes in costs and investments. The cash 

flow analysis is separated into two major blocks: income and cash flow. The income calculation 

determines net operating profit after taxes. The cash flow calculation converts net operating 

profit after taxes into an annual cash flow by including investment and non-cash items. Below 

are definitions of listed items on the printout of the output sheet (see section 12B.3).

(1) Revenues includes annual revenues computed by multiplying equipment unit price 

at each efficiency level by the appropriate manufacturer markup.

(2) Materials includes the portion of cost of goods sold (COGS) that includes 

materials.

(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 annual depreciation computed as a percentage of COGS. 

While typically included in overhead, the depreciation is shown as a separate line 

item.

(5) Overhead includes the portion of COGS that includes indirect labor, indirect 

material, energy use, maintenance, property taxes, and insurance related to assets. 

While typically included in overhead, the depreciation is shown as a separate line 

item.

(6) Standard SG&A includes selling, 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 

costs over the period between the standard’s announcement and effective dates.

(9) Stranded Assets includes a onetime write-off of stranded assets in the year the 

standard becomes effective.

(10) Earnings Before Interest and Taxes (EBIT) includes profits before deductions 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 reported in financial statements.

(12) Taxes are calculated by multiplying the tax rate contained in Financials by EBIT 

(10).

(13) Net Operating Profits After Taxes (NOPAT) are computed by subtracting COGS

((2) to (5)), SG&A (6), R&D (7), Product Conversion Cost (8), and Taxes (12)

from Revenues (1).

(14) NOPAT repeated is NOPAT (13), repeated in the Cash Flow Statement.

(15) Depreciation repeated is Depreciation (4), added back into the Statement of Cash 

Flows as a non-cash expense.

(16) Loss on the Disposal of Stranded Assets is the residual un-depreciated value of 

stranded assets, added back into the Statement of Cash Flows as a non-cash 

expense.

(17) Change in Working Capital includes change in cash tied up in accounts receivable, 

inventory, and other cash investments necessary to support operations is calculated 

by multiplying working capital as a percentage of Revenues (1) by the change in 

annual revenues.

(18) Cash Flow From Operations is calculated by taking NOPAT (14), adding back 

non-cash items such as a Depreciation (15), Loss on the Disposal of Stranded 

Assets (16) and subtracting the Change in Working Capital (17).

(19) Ordinary Capital Expenditures includes ordinary 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 investments 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 annual cash flow from operations and investments; 

computed by subtracting Ordinary Capital Expenditures (19) and Capital 

Conversion Costs (20) from Cash Flow from Operations (18).

(22) Free Cash Flow repeated includes the annual cash flow from operations and 

investments repeated in the Discounted Cash Flow (25) calculations.

(23) Terminal Value is an estimate of the continuing value of the industry after 2047, 

computed by growing the Free Cash Flow (22) in the year 2047 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 2047 the discounted cash flow includes the discounted Terminal 

Value (23).

(26) INPV at TSL is the sum of Discounted Cash Flows (25).
12B-5

12B.3 MODEL INDUSTRY INCOME STATEMENT, CASH FLOW STATEMENT, AND DISCOUNTED 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-CO2 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-CO2) Gases............................................................................. 14A-43

14A.9.3 Extrapolating Emissions Projections to 2300 ........................................... 14A-45

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010–2050 (2007$) ........................................................ 14A-2

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions.. 14A-14

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios....................................................................................................... 14A-17

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$).......................................... 14A-27

Table 14A.5.2 Social Cost of CO2, 2010–2050 (2007$) ...................................................... 14A-29

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050............................................................................................... 14A-29

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation.................. 14A-33

Table 14A.9.1 Annual SCC Values: 2010–2050 (2007$) .................................................... 14A-42

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2)14A-50

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2).. 14A-50

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2).. 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 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity ..................................................................................................... 14A-15

Figure 14A.5.1 Level of Global GDP Across EMF Scenarios............................................. 14A-28

Figure 14A.9.1 Sulphur Dioxide Emission Scenarios.......................................................... 14A-45

Figure 14A.9.2 Global Population, 2000–2300 (post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 2200)14A-47

Figure 14A.9.3 World GDP, 2000-2300 (post-2100 extrapolations assume GDP per capita 

growth declines linearly, reaching zero in 2300).......................................... 14A-47

Figure 14A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 2090–

2100 is maintained through 2300) ................................................................ 14A-48

Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000–2300 (post-2100 extrapolations 

assume emissions decline linearly, reaching zero in 2200).......................... 14A-48

Figure 14A.9.6 Global Non-CO2 Radiative Forcing, 2000–2300 (post-2100 extrapolations 

assume constant non-CO2 radiative forcing after 2100)............................... 14A-49

Figure 14A.9.7 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000–2300 

(post-2100 extrapolations assume decline in CO2/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 CO2), by Discount 

Rate* ............................................................................................................. 14A-51
14A-1

APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

Prepared by

Interagency Working Group on Social Cost of Carbon, United States Government

With participation by

Council of Economic Advisers 

Council on Environmental Quality 

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Environmental Protection Agency

National Economic Council

Office of Energy and Climate Change

Office of Management and Budget

Office of Science and Technology Policy 

Department of the Treasury

14A.1 EXECUTIVE SUMMARY

Under Executive Order (E.O.) 12866, agencies are required, to the extent permitted by 

law, “to assess both the costs and the benefits of the intended regulation and, recognizing that 

some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a 

reasoned determination that the benefits of the intended regulation justify its costs.” The purpose 

of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate 

the social benefits of reducing carbon dioxide (CO2) 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 and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include but is not limited to

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagency process that developed these SCC 

estimates. Technical experts from numerous agencies met on a regular basis to consider public 

comments, explore the technical literature in relevant fields, and discuss key model inputs and 

assumptions. The main objective of this process was to develop a 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 values for use in regulatory analyses

(Table 14A.1.1. Three values are based on the average SCC from three integrated assessment 

models, at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 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.

Table 14A.1.1 Social Cost of CO2, 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 increased flood 

risk, and the value of ecosystem services. We report estimates of the social cost of carbon in 

dollars per metric ton of CO2 throughout this document.a

When attempting to assess the incremental economic impacts of CO2 emissions, the 

analyst faces a number of serious challenges. A recent report from the National Academies of 

Science (NRC 2009) points out that any assessment will suffer from uncertainty, speculation, 

and lack of information about (1) future emissions of greenhouse gases; (2) the effects of past 

and future emissions on the climate system; (3) the impact of changes in climate on the physical 

and biological environment; and (4) the translation of these environmental impacts into 

economic damages. As a result, any effort to quantify and monetize the harms associated with 

climate change will raise serious questions of science, economics, and ethics and should be 

viewed as provisional.

 

a In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 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 estimating the social benefits of reducing CO2 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 CO2 emissions into costbenefit analyses of regulatory actions that have small or “marginal” impacts on cumulative 

global emissions. Most Federal regulatory actions can be expected to have marginal impacts on 

global emissions. 

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be 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 cumulative global 

CO2 emissions. For policies that have a large (non-marginal) impact on global cumulative 

emissions, there is a separate question of whether the SCC is an appropriate tool for calculating 

the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was to 

develop a range of SCC values using a defensible set of input assumptions that are grounded in 

the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. See 

Appendix A for the full range of annual SCC estimates from 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 preliminary 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 consider public comments as part of the ongoing interagency 

process.

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to 

estimate the benefits associated with reducing CO2 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 CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(2007$), increasing both values at 2.4 percent per year. It also included a sensitivity analysis at 

$80 per ton of CO2. A domestic SCC value is meant to reflect the value of damages in the United 

States resulting from a unit change in CO2 emissions, while a global SCC value is meant to 

reflect the value of damages worldwide.

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO2

(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 $20 per ton CO2 for 2007 emission reductions (2007$). 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 CO2 for discount rates of approximately 2 percent and 3 percent, 

respectively (2006$ for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing CO2 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 CO2 emissions. The interagency group did not undertake any 

original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. 

The outcome of the preliminary assessment by the interagency group was a set of five 

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per 

ton of CO2. 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 Newell and Pizer (2003)) at 3 and 5 percent discount rates, 

respectively. The $19 value was chosen as a central value between the $5 and $33 per ton 

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases.
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 were presented in several proposed and final rules and were offered for public comment in 

connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 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 section details the 

several choices and assumptions that underlie the resulting estimates of the SCC. 

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Academy of Science (2009) points 

out that there is tension between the goal of producing quantified estimates of the economic 

damages from an incremental ton of carbon and the limits of existing efforts to model these 

effects. Throughout this document, we highlight a number of concerns and problems that should 

be addressed by the research 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. The 

interagency group offers the new SCC values with all due humility about the uncertainties 

embedded in them and with a sincere promise to continue work to improve them.

14A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.b These models are frequently cited in the peerreviewed 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 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy 

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate 

Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, 

originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g., 

Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).
14A-6

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

science in their modeling frameworks but do not link physical impacts to economic damages.

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions 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 stream of 

economic damages over time into a single value requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

economic processes (e.g., the probability of catastrophe, and the ability to adapt to climate 

change and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key 

objective of the interagency process was to enable a consistent exploration of the three models 

while respecting the different approaches to quantifying damages taken by the key modelers in 

the field. An extensive review of the literature was conducted to select three sets of input 

parameters for these models: climate sensitivity, socio-economic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socio-economic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these 

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by 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 function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document.

14A.4.1.1 The DICE Model

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric CO2 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, CO2 emissions are a function of global GDP and the 

carbon intensity of economic output, with the latter declining over time due to technological 

progress. The DICE damage function links global average temperature to the overall impact on 

the world economy. It varies quadratically with temperature change to capture the more rapid 

increase in damages expected to occur under more extreme climate change, and is calibrated to 

include the effects of warming on the production of market and nonmarket goods and services. It 

incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vulnerable 

market sectors” (based primarily on changes in energy use), human health (based on climaterelated diseases, such as malaria and dengue fever, and pollution), non-market amenities (based 

on outdoor recreation), and human settlements and ecosystems. The DICE damage function also 

includes the expected value of damages associated with low probability, high impact 

“catastrophic” climate change. This last component is calibrated based on a survey of experts 

(Nordhaus 1994). The expected value of these impacts is then added to the other market and nonmarket impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function.

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its 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 different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, damages propagate forward in time and 

reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.c

14A.4.1.2 The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with 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 subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2 °C for developed countries and 0 °C for developing 

countries for economic impacts, and 0 °C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2 °C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006).

14A.4.1.3 The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

 c Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is 

endogenous. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period 

chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it 

consistent with EMF GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we recalibrated 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 rate of temperature change and level of regional 

income.d In the forestry and agricultural sectors, economic damages also depend on CO2

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 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen 

in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as 

energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

14A.4.1.4 Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-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. Given 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. 2006).
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 Modelse

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, socioeconomic, and emissions parameters will produce a different realization of damages for each IAM. The damage 

functions represented in Figure 14A.4.1 and Figure 14A.4.2are 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 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th 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 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.f

14A.4.2.1 Global SCC

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

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 that Federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a 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 loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation.

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be 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 matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter assumptions. For 

example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7–10 percent of the 

global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP 

lost due to climate change is assumed to be similar across countries, the domestic benefit would 

be proportional to the U.S. share of global GDP, which is currently about 23 percent.h

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach.

14A.4.3 Valuing Non-CO2 Emissions

While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

 g It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development 

of the appropriate “equity weight” is challenging. Emissions reductions also impose costs, and hence a full account 

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor 

nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is 

appropriate when considering the utility or welfare effects of international action, the interagency group concluded 

that it should not be used in developing an SCC for use in regulatory policy at this time. 

h Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report.
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 CO2. However, because these gases differ in 

both radiative forcing and atmospheric lifetimes, their relative damages are not constant over 

time. For example, because methane has a short lifetime, its impacts occur primarily in the near 

term and thus are not discounted as heavily as those caused by longer-lived gases. Impacts other 

than temperature change also vary across gases in ways that are not captured by GWP. For 

instance, CO2 emissions, unlike methane and other greenhouse gases, contribute to ocean 

acidification. Likewise, damages from methane emissions are not offset by the positive effect of 

CO2 fertilization. Thus, transforming gases into CO2-equivalents using GWP, and then 

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the 

social costs of non-CO2 gases.

In light of these limitations, and the significant contributions of non-CO2 emissions to 

climate change, further research is required to link non-CO2 emissions to economic impacts.

Such work would feed into efforts to develop a monetized value of reductions in non-CO2

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 CO2 emissions.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.iIt is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 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 authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, 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 range. (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 and Baker (2007), log-normal, gamma, and Weibull.

Table 14A.4.1 gives summary statistics for the four calibrated distributions.

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Rank Roe & Baker Log-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

5th Percentile 1.72 1.49 1.37 1.13

10th Percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90

Median (50th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90th Percentile 5.86 5.14 4.93 4.69

95th Percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by applying three constraints from the IPCC:

1. a median equal to 3 °C, to reflect the judgment of “a most likely value of about 

3 °C;”k

2. two-thirds probability that the equilibrium climate sensitivity lies between 2 and 

4.5 °C; and

3. zero probability that it is less than 0 °C or greater than 10 °C (Hegerl et al. 2006, 

p. 721).

We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007; Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: 

(1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) 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 

95th percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and 

mode equal to 3 °C produced 95th 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 distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th 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 95th 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 about very low values of ECS than was 

expressed by the IPCC. 

To show how the calibrated Roe and Baker distribution compares to different estimates 

of the probability distribution function of equilibrium climate sensitivity in the empirical 

literature, Figure 14A.4.3 overlays it on Figure 14A.9.2 from the IPCC Fourth Assessment 

Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal 

bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate.l

Figure 14A.4.3 Estimates of the Probability Density 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. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown 

are the 5–95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 

2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.
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 socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socio-economic 

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed 

to select scenarios that span most of the plausible ranges of outcomes for these variables. 

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22, which uses ten well-recognized models to evaluate 

substantial, coordinated global action to meet specific stabilization targets. A key advantage of 

relying on these data is that GDP, population, 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 extreme outliers in one or more variables. Although the EMF-22 scenarios 

have not undergone the same level of scrutiny as the SRES scenarios, they are recent, peerreviewed, 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 CO2 (only) concentrations ranging from 612 to 889 ppm in 

2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO2e (i.e., 

CO2-only concentrations of 425–484 ppm or a radiative forcing of 3.7 W/m2) in 2100, a lowerthan-BAU trajectory.m Out of the 10 models included in the EMF-22 exercise, we selected the 

trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. 

For the BAU pathways, we used the GDP, population, and emission trajectories from each of 

these four models. For the 550 ppm CO2e scenario, we averaged the GDP, population, and 

emission trajectories implied by these same four models.

 m Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.
14A-17

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios

Reference Fossil and Industrial CO2 Emissions

GtCO2/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 

future socio-economic pathways.

There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

 n While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP), which takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as 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, lowcarbon energy) to more pessimistic (e.g., constraints on the availability of nuclear and 

renewables).o Second, the socio-economic trajectories associated with a 550 ppm CO2e 

concentration scenario are not derived from an assessment of what policy is optimal from a 

benefit-cost standpoint. Rather, it is indicative of one possible future outcome. The emission 

trajectories underlying some BAU scenarios (e.g., MESSAGE’s 612 ppm) also are consistent 

with some modest policy action to address climate change.p We chose not to include socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 

difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global CO2 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 CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g., aerosols and other gases). See the Appendix 

for greater detail.

14A.4.6 Discount Rate

The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law.

Although it is well understood that the discount rate has a large influence on the current value of 

future damages, there is no consensus about what rates to use in this context. Because CO2

emissions are long-lived, subsequent damages occur over many years. In calculating the SCC, 

we first estimate the future damages to agriculture, human health, and other market and nonmarket sectors from an additional unit of CO2 emitted in a particular year in terms of reduced 

consumption (or consumption equivalents) due to the impacts of elevated temperatures, as 

represented in each of the three IAMs. Then we discount the stream of future damages to its 

present value in the year when the additional unit of emissions was released using the selected 

discount rate, which is intended to reflect society’s marginal rate of substitution between 

consumption in different time periods.

 o For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and 

non-biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent 

in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100. 

p For example, MiniCAM projects if all non-US OECD countries reduce CO2 emissions to 83 percent below 2005 

levels by 2050 (per the G-8 agreement) but all other countries continue along a BAU path CO2 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, “If your rule will have important intergenerational benefits or 

costs you might consider a further sensitivity analysis using a lower but positive discount rate in 

addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on 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 used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view.

Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above; Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that underlies 
14A-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 individuals smooth consumption by borrowing with credit cards that have 

relatively high rates. Some are unable to access traditional credit markets and rely on payday 

lending operations or other high cost forms of smoothing consumption. Whether one puts greater 

weight on the prescriptive or descriptive approach, the high interest rates that credit-constrained 

individuals accept suggest that some account should be given to the discount rates revealed by 

their behavior. 

We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. 

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While relying 

primarily on the descriptive approach in selecting specific discount rates, the interagency group 

has been keenly aware of the deeply normative dimensions of both the debate over discounting 

in the intergenerational context and the consequences of selecting one discount rate over another.

14A.4.6.1 Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital.

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (Lind 1990, Arrow et al., 1996, Arrow 2000). The 

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC.

Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 
14A-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 maximum 

certain amount that we would exchange for the uncertain amount. However, the calculation of 

the certainty-equivalent requires first estimating the correlation between the benefits of the 

policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate. 

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless 

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

rate from tax years 2003 through 2006 is around 27 percent).q This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.r A measure of the 

post-tax risky rate for investments whose returns are positively correlated with overall equity 

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes yields a real rate of roughly 5 percent.s

14A.4.6.2 The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

 q The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 

4 percent for 30-year Treasury securities. r The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon. s Cambell et al. (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950–2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20–40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).
14A-22

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).tThese are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.

u In the simplest version of the Ramsey model, 

with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey 

discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.v

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η

equal to 1 suggests savings rates that do not conform to observed behavior. 

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued 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 preference because of 

the small probability of unforeseen cataclysmic events (Stern 2006).

• g. A commonly accepted approximation is around 2 percent per year. For the socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5–2 percent to 2100.

 t

The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an increase 

in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The 

parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than consumption 

today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller 

reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar 

increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income 

is equally valuable no matter the level of income; and if η > 1, then a one percent increase in income is less valuable 

to wealthier individuals. 

u In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

v Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

Evans and Sezer (2005) estimate η = 1.49 for 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 preferential 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 choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 percent 

per year, which yields an annual discount rate of 1.4 percent. In the context of permanent income 

savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent 

of their income.w

Recently, Stern (2008) revisited the values used in Stern (2006), stating that there is a 

case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate greater 2 percent.

We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end 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 discount rate (e.g., the rate follows a random walk), then it will 

result in an effective (or certainty-equivalent) discount rate that declines over time. 

Consequently, lower discount rates tend to dominate over the very long term (Weitzman 1998, 

1999, 2001; Newell and Pizer 2003; Panipoulou et al. (2004); Gollier 2008; Summers and 

Zeckhauser 2008; and Gollier and Weitzman 2009). 

The proper way to model discount rate uncertainty remains an active area of research.

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Panipoulou et al. (2004), uses 

more general models of interest rate dynamics to allow for better forecasts. Specifically, the 

volatility of interest rates depends on whether rates are currently low or high and variation in the 

level of persistence over time.

 w Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same 

time, adding exogenous technical change—all else equal—would increase g as well.)
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 Analysis).y

14A.4.6.4 The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

The central value (3 percent) is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 

3 percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent 

is included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods.

The low value (2.5 percent) is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.z Without giving 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 for years 126–200; 1.5 percent for years 201–300; and 1 percent after 

300 years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over 

time. 

y Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that 

Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a 

low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value 

calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and 

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, 

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in 

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required. 

z Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).
14A-25

or normative approach 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 twothirds.

• Five sets of GDP, population and carbon emissions trajectories based on EMF-22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because 

PAGE and FUND incorporate uncertainty in other model parameters, the final output from each 

model run is a distribution over the SCC in year t. 

For each of the IAMS, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 scenarios, 

and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each year 

resulting from the baseline path of emissions.

a. In PAGE, the consumption-equivalent damages in each period are calculated as a 

fraction of the EMF GDP forecast, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period.

c. In DICE, temperature affects both consumption and investment, so we first adjust the 

EMF GDP paths as follows: Using the Cobb-Douglas production function with the 

DICE2007 parameters, we extract the path of exogenous technical change implied by 

the EMF GDP and population paths, then we recalculate the baseline GDP path 

taking into account climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by model.)

4. Recalculate the temperature effects and damages expected in all years beyond t resulting 

from this adjusted path of emissions, as in step 2. 

5. Subtract the damages computed in step 2 from those in step 4 in each year. (DICE is run 

in 10 year time steps, FUND in annual time steps, while the time steps in PAGE vary.)
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 CO2

(2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 in PAGE).

The steps above were repeated in each model for multiple future years to cover the time 

horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three 

IAMs, climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Appendix.)

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no integrated assessment model or socioeconomic scenario is 

given greater weight than another. Because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate to 

use in an intergenerational context, we present SCCs based on the average values across models 

and socioeconomic scenarios for each discount rate. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socio-economic and emissions scenarios 

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount 

rate. (The full set of distributions by model and scenario combination is included in the 

Appendix.) As noted above, the 3 percent discount rate is the central value, and so the central 

value that emerges is the average SCC across models at the 3 percent discount rate. For purposes 
14A-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 other two models), its probabilistic treatment of the equilibrium climate sensitivity 

parameter will directly affect the non-catastrophic damages that are a function of the rate of 

temperature change.

In Table 14A.5.1, we begin by presenting SCC estimates for 2010 by model, scenario, 

and discount rate to illustrate the variability in the SCC across each of these input parameters. As 

expected, higher discount rates consistently result in lower SCC values, while lower discount 

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there 

are differences in the SCC estimated across the three main models. For these estimates, FUND 

produces the lowest estimates, while PAGE generally produces the highest estimates. 

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th

DICE IMAGE 10.8 35.8 54.2 70.8

MERGE 7.5 22.0 31.6 42.1

Message 9.8 29.8 43.5 58.6

MiniCAM 8.6 28.8 44.4 57.9

550 Average 8.2 24.9 37.4 50.8

PAGE IMAGE 8.3 39.5 65.5 142.4

MERGE 5.2 22.3 34.6 82.4

Message 7.2 30.3 49.2 115.6

MiniCAM 6.4 31.8 54.7 115.4

550 Average 5.5 25.4 42.9 104.7

FUND IMAGE -1.3 8.2 19.3 39.7

MERGE -0.3 8.0 14.8 41.3

Message -1.9 3.6 8.8 32.1

MiniCAM -0.6 10.2 22.2 42.6

550 Average -2.7 -0.2 3.0 19.4

These results are not surprising when compared to the estimates in the literature for the 

latest versions of each model. For example, adjusting the values from the literature that were 

used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for 

the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or 

near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There 

are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using 

similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on 

Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE 

(based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these 
14A-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 sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models. 

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Further, in FUND increases in income over time decrease 

vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and 

PAGE. These structural differences among the models make FUND more sensitive to the path of 

emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 14A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE 

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the 

rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result 

that is to be expected given its less direct relationship between its damage function and GDP.

Figure 14A.5.1 Level of Global GDP Across EMF Scenarios

Table 14A.5.2 shows the four selected SCC values in 5-year increments from 2010 to 

2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using a simple linear interpolation.

 aa Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC 

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g., Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

0

50

100

150

200

250

300

350

400

450

2000 2010 2020 2030 2050 2100

Global 

GDP

(trillions

of U.S. 

dollars)

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing
14A-29

Table 14A.5.2 Social Cost of CO2, 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 the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 14A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Appendix.

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050

Average Annual 

Growth Rate 

5% 3% 2.5% 3.0%

Year Range Avg Avg Avg 95th

2010–2020 3.6 2.1 1.7 2.2

2020–2030 3.7 2.2 1.8 2.2

2030–2040 2.7 1.8 1.6 1.8

2040–2050 2.1 1.4 1.1 1.3

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis. 

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in 2020 that are calculated using a 

SCC based on a 5 percent discount rate also should be discounted back to the analysis year using 

a 5 percent discount rate.bb 

14A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

 bb However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop 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 

applying the SCC estimates.

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. It is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. 

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman’s results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios.”

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high-impact 

low-probability risks, using a right-skewed gamma distribution for climate sensitivity as well as 

an uncertain damage coefficient, but in most cases found only a modest risk premium. Given this 

difference in opinion, further research in this area is needed before its practical significance can 

be fully understood and a reasonable approach developed to account for such risks in regulatory 

analysis. (The next section discusses the scientific evidence on catastrophic impacts in greater 

detail.)

Uncertainty in extrapolation of damages to high temperatures. The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme 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 develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change.

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs under 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 higher-probability but lower-impact damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that 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 representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al. (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude 

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

 cc However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).
14A-32

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the 

SCC may not capture the economic effects of all possible adverse consequences of climate 

change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, 

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping 

point” behavior in Earth systems; (2) inter-sectoral and inter-regional interactions, including 

global security impacts of high-end warming; and (3) limited near-term substitutability between 

damage to natural systems and increased consumption. 

It is the hope of the interagency group that over time researchers and modelers will work 

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. In the meantime, we 

discuss some of the available evidence.

14A.7.1 Extrapolation of Climate Damages to High Levels of Warming

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al. 2008, Kriegler et al.

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

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 melting 

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al. 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); 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., ignoring potential risk 

aversion). PAGE models catastrophic impacts in a probabilistic framework (Figure 14A.4.1), so 

the high-end output from PAGE potentially offers the best insight into the SCC if the world were 

to experience catastrophic climate change. For instance, at the 95th percentile and a 3 percent 

discount rate, the SCC estimated by PAGE across the five socio-economic and emission 

trajectories of $113 per ton of CO2 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 noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the 

distribution to the assumptions made about catastrophic impacts. 

PAGE treats the 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 probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16–36 percent of 

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2–4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact.

14A.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 
14A-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). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al. 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3–6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al. 2007; 

Campbell et al. 2007).

14A.7.3 Imperfect Substitutability of Environmental Amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400–900 miles in the range of plants (Wing 

et al. 2005), and dwarfing of both land mammals (Gingerich 2006) and soil fauna (Smith et al.

2009).

The three IAMs used here assume that it is possible to compensate for the econoic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (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 incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 

14A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 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 of 
14A-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 analysis by the Federal 

government will continue to evolve with improvements in modeling. 
14A-36

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14A.9.1 ANNEX

This Annex provides additional technical information about the non-CO2 emission 

projections used in the modeling and the method for extrapolating emissions forecasts through 

2300, and shows the full distribution of 2010 SCC estimates by model and scenario combination. 

Annual SCC values for the next 40 years are provided in Table 14A.9.1. 

Table 14A.9.1 Annual SCC Values: 2010–2050 (2007$)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2
14A-43

14A.9.2 Other (non-CO2) Gases

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane (CH4), nitrous oxide (N2O), fluorinated gases, and net land use CO2

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-CO2 EMF emissions only, we calculated the RF 

associated with the EMF atmospheric CO2 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-CO2 gases differently (e.g., DICE lumps all other gases into one composite exogenous 

input), this approach was applied slightly differently in each of the models. 

FUND: Rather than relying on RF for these gases, the actual emissions from each 

scenario were used in FUND. The model default trajectories for CH4, N2O, SF6, and the CO2

emissions from land were replaced with the EMF values. 

PAGE: PAGE models CO2, CH4, sulfur hexafluoride (SF6), and aerosols and contains an 

“excess forcing” vector that includes the RF for everything else. To include the EMF values, we 

removed the default CH4 and SF6 factors,

ee decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N2O, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2

emissions were added to the fossil and industrial CO2 emissions pathway. 

DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment 

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing 

from all non-CO2 sources is -0.06 W/m2 in 2005, as reported in AR4, and increases linearly to 

0.3 W/m2 in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2non-CO2 forcing in DICE can be 

decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2

, due to other residual forcing. 

 dd Note EMF did not provide CO2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed 

the fossil, industrial and land CO2 emissions into MAGICC (considered a “neutral arbiter” model, which is tuned to 

emulate the major global climate models) and the resulting CO2 concentrations were used. Note also that MERGE 

assumes a neutral biosphere so net land CO2 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 CH4 emissions and the initial atmospheric CH4 is set to zero to avoid double counting the 

effect of past CH4 emissions.
14A-44

For subsequent years, we calculated the DICE default RF from aerosols and other nonCO2 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-CO2 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 vs. 2000 under the A1B SRES emissions scenario. 

Since the SRES marker scenarios were not updated for the AR4, the TAR provides the most 

recent IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because 

it provides one of the lower aerosol forecasts among the SRES marker scenarios and is more 

consistent with the AR4 discussion of the post-SRES literature on aerosols: 

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor 

emissions, including sulphur 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 also is more consistent 

with the recent literature on these emissions. For example, Figure 14A.9.1 shows that the sulfur 

dioxide emissions peak over the short-term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.gg Recent scenarios project sulfur emissions to peak earlier 

and at lower levels compared to the SRES in part because of new information about present and 

planned sulfur legislation in some developing countries, such as India and China.hh The lower 

bound projections of the recent literature have also shifted downward slightly compared to the 

SRES scenario (IPCC 2007). 

With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in 

2105 W/m2

; forcing due to other non-CO2 gases not included in the EMF scenarios declines from 

0.160 to 0.153 W/m2

. 

 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, 5th

and 95th percentile of the frequency distribution for the full ensemble of 40 SRES scenarios. The blue area (and 

the thin dashed lines in blue) illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines 

indicate the minimum and maximum of SO2 emissions scenarios developed pre-SRES.

Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2-2-

4.html.

Although other approaches to decomposing the DICE exogenous forcing vector are 

possible, initial sensitivity analysis suggests that the differences among reasonable alternative 

approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to 

assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values 

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 

2100 increases average 2010 SCC values by 6–7 percent (or $0.50–$3), depending on the 

discount rate. These differences increase slightly for SCC values in later years but are still well 

within 10 percent of each other as far out as 2050. 

Finally, as in PAGE, the EMF net land use CO2 emissions are added to the fossil and 

industrial CO2 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 declines linearly, reaching zero in 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in 2300.

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate 

over 2090-2100 is maintained from 2100 through 2300.
14A-46

4. Net land use CO2 emissions decline linearly, reaching zero in 2200.

5. Non-CO2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the 

degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, since 

this would require an additional assumption about how close to zero the growth rate would get 

by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300. 

The population growth rate is also assumed to decline linearly, reaching zero by 2200. 

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario 

(UN 2004).ii The resulting range of EMF population trajectories (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., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature.

Net land use CO2 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-CO2 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 CO2 emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2

intensity (fossil and industrial CO2 emissions/GDP) resulting from these assumptions. 

 ii United Nations. 2004. World Population to 2300.

http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf. 
14A-47

Figure 14A.9.2 Global Population, 2000–2300 (post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 2200)

Note: In the fifth scenario, 2000–2100 population is equal to the average of the population under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

Figure 14A.9.3 World GDP, 2000-2300 (post-2100 extrapolations assume GDP per capita 

growth declines linearly, reaching zero in 2300)

Note: In the fifth scenario, 2000–2100 GDP is equal to the average of the GDP under the 550 ppm CO2e, fullparticipation, not-to-exceed scenarios considered by each of the four models. 

5.5

6.5

7.5

8.5

9.5

10.5

11.5

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Global Population [billion]

Year

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]
14A-48

Figure 14A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 2090–2100 is 

maintained through 2300)

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000–2300 (post-2100 extrapolations 

assume emissions decline linearly, reaching zero in 2200)

jj

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

 jj MERGE assumes a neutral biosphere so net land CO2 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

Extrapolations

Fossil&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

Extrapolations

Net land CO2 emissions [GtCO2/yr]

Year

z
14A-49

Figure 14A.9.6 Global Non-CO2 Radiative Forcing, 2000–2300 (post-2100 extrapolations 

assume constant non-CO2 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 CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000–2300 

(post-2100 extrapolations assume decline in CO2/GDP growth rate over 2090–2100 is 

maintained through 2300)

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, 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

Extrapolations

nonCO2 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

Extrapolations

Fossil & Industrial CO2 intensity [GtCO2/trillion $]

Year
14A-50

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2)

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 CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3

MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3

Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6
14A-51

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2)

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 CO2), 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 1st and 99th percentiles to better show the data.

0

2000

4000

6000

8000

10000

12000

14000

16000

-25-12

1

14274053667992

105118131144157170183196209222235248261274

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 23,558.3
14B-i

APPENDIX 14B. TECHNICAL UPDATE OF THE SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS - UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14B.1 EXECUTIVE SUMMARY.......................................................................................14B-1

14B.2 PURPOSE .................................................................................................................14B-2

14B.3 SUMMARY OF MODEL UPDATES......................................................................14B-3

14B.3.1 DICE.............................................................................................................. 14B-4

14B.3.2 FUND............................................................................................................ 14B-6

14B.3.3 PAGE ............................................................................................................ 14B-8

14B.4 REVISED SCC ESTIMATES ................................................................................14B-11

14B.5 OTHER MODEL LIMITATIONS AND RESEARCH GAPS...............................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 

CO2) .................................................................................................................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 

CO2) ...............................................................................................................14B-12

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050..14B-13

LIST OF FIGURES

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per metric ton CO2) .....14B-12
14B-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 benefits of reducing carbon dioxide (CO2) 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 the 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 regulatory analyses. Three values are based on the average SCC from 

three integrated assessment models (IAMs), at discount rates of 2.5, 3, and 5 percent. The fourth 

value, which represents the 95th percentile SCC estimate across all three models at a 3 percent 

discount rate, is included to represent higher-than-expected impacts from temperature change 

further out in the tails of the SCC distribution.
14B-2

While acknowledging the continued limitations of the approach taken by the interagency 

group in 2010, this document 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 the 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 GDP, 

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 following 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 

CO2)

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 CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67).
14B-3

decision making “based on the best available science.”b Additionally, the interagency group 

recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates 

that reflect the growing body of scientific and economic knowledge become available.

c New 

versions of the three integrated assessment models used by the U.S. government to estimate the 

SCC (DICE, FUND, and PAGE), are now available and have been published in the peer 

reviewed literature. While acknowledging the continued limitations of the approach taken by the 

interagency group in 2010 (documented in the original 2010 TSD), this document provides an 

update of the SCC estimates based on the latest peer-reviewed version of the models, replacing 

model versions that were developed up to ten years ago in a rapidly 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 CO2 emissions are quantified. 

Section 14B.3 summarizes the major updates relevant to SCC estimation that are 

contained in the new versions of the integrated assessment models released since the 2010 

interagency report. 14B.4 presents the updated schedule of SCC estimates for 2010–2050 based 

on these versions of the models. Section 14B.5 provides a discussion of other model limitations 

and research gaps.

14B.3 SUMMARY OF MODEL UPDATES

This section briefly summarizes changes to the most recent versions of the three 

integrated assessment models (IAMs) used by the interagency group in 2010. We focus on 

describing those model updates that are relevant to estimating the social cost of carbon, as 

summarized in Table 14B.4.2. For example, both the DICE and PAGE models now include an 

explicit representation of sea level rise damages. Other revisions to PAGE include: updated 

adaptation assumptions, revisions to ensure damages 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 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. 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 damages, revisions to 

damage function to ensure damages do not exceed 

100% of GDP, change in regional scaling of damages, 

revised treatment of potential abrupt damages, and 

updated adaptation assumptions. 

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 carbon cycle 

model, 2) an explicit representation of sea level dynamics, and 3) a re-calibrated damage 

function that includes an explicit representation of economic damages from sea level rise. 

Changes were also made to 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 will 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 uses a three-box model of carbon stocks and flows to represent the accumulation 

and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and 

the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the 

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). For example, in DICE2010, in 

each decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains 

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 

2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each 

 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 

DICE2007, for a given path of emissions. All else equal, these changes will generally increase 

the level of warming and therefore the SCC estimates in DICE2010 relative to those from 

DICE2007.

Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea level rise (SLR) module; a more detailed 

description can be found on the model developer’s 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 (decade) is 2 percent of the difference between the sea 

level in the previous period and the long run equilibrium sea level, which is 0.5 meters per 

degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the 

melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above 

the average global temperature in 1900. 

The contribution to sea level rise from melting of the Greenland ice sheet is more 

complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 

1 oC 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 decade 

when the temperature anomaly is below 3 °C and increases linearly between 3 °C and 6 °C to a 

maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

Re-calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future economic production, so each period’s climate 

damages will reduce consumption in that period and in all future periods due to the lost 

 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 minus a fraction, which is one divided by a quadratic function of the 

temperature anomaly, producing a sigmoid (“S”-shaped) function.g The loss function in 

DICE2010 has been expanded by adding a quadratic function of SLR to the quadratic function of 

temperature. In DICE2010 the temperature anomaly coefficients have been recalibrated to avoid 

double-counting damages from sea level rise that were implicitly included in these parameters in 

DICE2007. 

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3), who notes 

that “...damages in the uncontrolled (baseline) [i.e., reference] case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 oC above 1900 levels.”

This compares to a loss of 3.2 percent of global output at 3.4 oC in DICE2007. However, in 

DICE2010, annual damages are lower in most of the early periods of the modeling horizon but 

higher in later periods than would be calculated using the DICE2007 damage function. 

Specifically, the percent difference between damages in the base run of DICE2010 and those that 

would be calculated using the DICE2007 damage function starts at +7 percent in 2005, decreases 

to a low of -14 percent in 2065, then continuously increases to +20 percent by 2300 (the end of 

the interagency analysis time horizon), and to +160 percent by the end of the model time horizon 

in 2595. The large 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 increases 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. Documentation supporting FUND and the model’s 

source code for all versions of the model is available from the model authors.h Notable changes, 

due to their impact on the SCC estimates, are adjustments to the space heating, agriculture, and 

sea level rise damage functions in addition to changes to the temperature response function and 

the inclusion of indirect effects from methane emissions.i

We discuss each of these in turn.

Space Heating

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

 g The model and documentation, including formulas, are available on the author’s

webpage at http://www.econ.yale.edu/~nordhaus/homepage/RICEmodels.htm. h http://www.fund-model.org/. This report uses version 3.8 of the FUND model, which 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 CH4 and N2O and incorporating the indirect forcing 

effects of CH4, along with making minor stability improvements in the sea wall construction algorithm. i

The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not 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 efficiency growth. In FUND 

3.5, the function that scales the base year damages adjusted for vulnerability allows for the 

possibility that in some simulations the benefits associated with reduced heating needs may be an 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

has been modified to ensure that the function is everywhere concave and that there will exist an 

upper bound on the benefits a region may receive from reduced space heating needs. The new 

formulation approaches a value of two in the limit 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 degree of 

warming. Since the reduced need for space heating represents a benefit of climate change in the 

model, or a negative damage, this change will increase the estimated 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 level rise for that year. This assumption implicitly assumes that all regions are 

well represented by a homogeneous coastline in length and a constant uniform slope moving 

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 expected 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 components that represent the effects from carbon fertilization, the rate of 

temperature change, and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, 

the fraction of the sector’s value lost due to the level of the temperature anomaly is modeled as a 

quadratic function with an intercept of zero. In FUND 3.5, the coefficients of this loss function 

are modeled as the ratio of two random normal variables. This specification had the potential for 

unintended extreme behavior as draws from the parameter in the denominator approached zero 

or went negative. In FUND 3.8, the coefficients are drawn directly from truncated normal 

distributions so that they remain in the range [0,∞) and (−∞,0], respectively, ensuring the correct 

sign and eliminating the potential for divide by zero errors. 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 level 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 Response 

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

temperature anomaly is based on a mean reverting function where the mean equals the 

equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact 

that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the 

equilibrium climate sensitivity. In FUND 3.8, the rate of 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 estimates of the SCC as higher 

temperatures are reached during the timeframe analyzed and as the same damages experienced in 

the previous version of the model are now experienced earlier and therefore discounted less.

Methane

The IPCC AR4 notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007). 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 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 
14B-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 – economic and noneconomic 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 ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, 

which allowed Eastern Europe to potentially experience large benefits from temperature 

increases, and which also did not bound the possible damages that could be experienced.

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 positive scaling factor. PAGE2002 based its scaling factors on four studies 

reported in the IPCC’s third assessment report, and allowed for benefits from temperature 

increase in Eastern Europe, smaller impacts in developed countries, and higher damages in 

developing countries. 

Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” (nonlinear extreme event) 

were modeled as an expected value. Specifically, a stochastic probability of a discontinuity was 

multiplied by the damages associated with a discontinuity to obtain an expected value, and this 

was added to the economic and non-economic impacts. That is, additional damages from an 

extreme event, such as extreme melting of the Greenland ice sheet, were multiplied by the 

probability 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 isolation 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 occurring, rather than as an 

expected value. A large‐scale discontinuity becomes possible when the temperature rises beyond 

some threshold value between 2 and 4°C. The probability that a discontinuity will occur beyond 

this threshold then increases by between 10 and 30 percent for every 1°C rise in temperature 

beyond the threshold. If a discontinuity occurs, the EU loses an additional 5 to 25 percent of its 

GDP (drawn from a triangular distribution with a mean of 15 percent) in addition to other 

damages, and other regions lose an amount 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 damages that 

result from a discontinuity are both higher than in PAGE2002. The model assumes that only one 

discontinuity can occur and that the impact is phased in over a period of time, but once it occurs, 

its effect is permanent.

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 vulnerability. It is 

modeled by reducing the damages by some percentage. PAGE09 assumes a smaller decrease in 

vulnerability than the previous version of the model and assumes that it will take longer for this 

change in vulnerability to be realized. In the aggregated economic sector, at the time of full 

implementation, this adaptation will mitigate all damages up to a temperature increase of 1°C, 

and for temperature anomalies between 1°C and 2°C, it will reduce damages by 15-30 percent 

(depending on 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 years 

assumed in PAGE2002. Similarly, adaptation is assumed to alleviate 25-50 percent of the 

damages from the first 0.20 to 0.25 meters of sea level rise but is assumed to be ineffective 

thereafter. Hope (2011c) estimates that the less optimistic assumptions regarding the ability to 

offset impacts of temperature and sea level rise via adaptation increase the SCC by 

approximately 30 percent.

Other Noteworthy Changes

Two other changes in the model are worth noting. There is a change in the way the model 

accounts for decreased CO2 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 CO2, capped at a maximum level. In PAGE2002, an additional amount was 

added to the CO2 emissions each period to account for a decrease in ocean absorption and a loss 

of soil carbon. Also updated is the method by which the average global and annual temperature 

anomaly is downscaled to determine annual average regional temperature anomalies to be used 

in the regional damage functions. In PAGE2002, the scaling was determined solely based on 

regional difference in emissions of sulfate aerosols. In PAGE09, this regional temperature 
14B-11

anomaly is further adjusted using an additive factor that is based on the average absolute latitude 

of a region relative to the area weighted average absolute latitude of the Earth’s landmass, to 

capture relatively greater changes in temperature forecast to be experienced at higher latitudes.

14B.4 REVISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD (Interagency Working Group on Social Cost of Carbon 

2010). The approach along with the inputs for the socioeconomic emissions scenarios, 

equilibrium climate sensitivity distribution, and discount rate remains the same. This includes the 

five reference scenarios based on the EMF-22 modeling exercise, the Roe and Baker equilibrium 

climate 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 distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and socio-economic-emissions 

scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to 

represent the higher-than-expected economic impacts from climate change further out in the tails 

of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent 

discount rate was chosen. (A detailed set of percentiles by model and scenario combination and 

additional summary statistics for the 2020 values is available in the 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 discount rate” (Interagency Working Group on 

Social Cost of Carbon 2010, p. 25). However, for purposes of capturing the uncertainties 

involved in regulatory impact analysis, the interagency group emphasizes the importance and 

value of including all four SCC values.

Table 14B.4.2 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 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 2050 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 

CO2)

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 the 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, the SCC increases over time because future emissions 

are expected to produce larger incremental damages as physical and economic systems become 

more stressed in response to greater climatic change. The approach taken by the interagency 

group is to compute the cost of a marginal ton emitted in the future by running the models for a 
14B-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. 

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group 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 LIMITATIONS AND RESEARCH GAPS

The 2010 interagency SCC TSD discusses a number of important limitations for which 

additional research is needed. In particular, the document highlights the need to improve the 

quantification of both non-catastrophic and 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 discusses the need to more 

carefully assess the implications of risk aversion for SCC estimation as well as the inability to 
14B-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 publication. 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 Marginal Impact of CO2 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” 

Cambridge 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 CO2 from the PAGE09 Model” Cambridge Judge 

Business School Working Paper No. 5/2011 (June). Accessed November 23, 2011: 

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf.

Hope, Chris. 2011c “New Insights from the PAGE09 Model: The Social Cost of CO2” 

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 CO2: 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-Carbonfor-RIA.pdf.

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Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. 

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Narita, D., R. S. J. Tol and D. Anthoff. 2010. Economic costs of extratropical storms under 

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R.S.J. Tol. 2011. Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the 

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Nordhaus, W. 2010. Economic aspects of global warming in a post-Copenhagen environment. 

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Nordhaus, W. 2008. A Question of Balance: Weighing the Options on Global Warming Policies. 

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Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. 

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Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working 

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USA. 
14B-17

APPENDIX A

Table A1 Annual SCC Values: 2010-2050 (2007$/metric ton CO2)

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 220
14B-18

Table A2 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/metric ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenariol 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 CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 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 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/metric ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE Optimistic 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE Optimistic 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -4 -1 2 3 6 10 14 24

MERGE Optimistic -6 -4 -2 0 3 4 6 11 15 26

MESSAGE -10 -6 -4 -1 1 2 5 9 12 21

MiniCAM Base -7 -4 -2 0 3 4 6 11 14 25

5th Scenario -11 -7 -5 -3 0 0 3 5 7 13
14B-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 14931
14B-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 misspecified in the model’s computer 

code. This correction is covered in an erratum to Anthoff and Tol (2013) published in the same 

journal (Climatic Change) in October 2013 (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 variance 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 

revision are all one dollar or less.