TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

CEILING FAN LIGHT KITS

June 18, 2015 

U.S. Department of Energy

Assistant Secretary

Office of Energy Efficiency and Renewable Energy

Building Technologies Program

Appliances and Commercial Equipment Standards

Washington, DC 20585
This Document was prepared for the U.S. Department of Energy

by staff members of

Navigant Consulting, Inc.

and

Lawrence Berkeley National Laboratory
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CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

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

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

1.3 OVERVIEW OF APPLIANCE STANDARDS.............................................................. 1-6

1.4 OVERVIEW OF CURRENT CEILING FAN LIGHT KIT STANDARDS................... 1-7

1.5 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS ................... 1-7

1.6 STRUCTURE OF THE DOCUMENT............................................................................ 1-9

REFERENCES .......................................................................................................................... 1-13

LIST OF TABLES

Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed Energy 

Conservation Standards for CFLKs (TSL 2)* ............................................... 1-2

Table 1.2.2 Summary of National Economic Benefits and Costs of Proposed Energy 

Conservation Standards for CFLKs (TSL 2)* ............................................... 1-4

Table 1.5.1 Analyses under the Process Rule .............................................................................. 1-8
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CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

This notice of proposed rulemaking (NOPR) technical support document (TSD) is a 

standalone report that documents the technical analyses and results supporting the NOPR 

analysis for amending energy conservation standards for ceiling fan light kits (CFLKs). 

1.2 SUMMARY OF THE NATIONAL BENEFITS

DOE’s analyses indicate that the proposed energy conservation standards for CFLKs 

would save a significant amount of energy. Relative to the case where no amended energy 

conservation standard is set (hereinafter referred to as the “no-standard case”), the lifetime 

energy savings for CFLKs purchased in the 30-year period that begins in the anticipated year of 

compliance with the amended standards (2019–2048) amount to 0.047 quadrillion Btu (quads).a

This represents a savings of 3.6 percent relative to the energy use of these products in the nostandard case. 

The cumulative net present value (NPV) of total consumer costs and savings of the 

proposed standards for CFLKs ranges from $0.65 billion (at a 7-percent discount rate) to $0.82 

billion (at a 3-percent discount rate). This NPV expresses the estimated total value of future 

operating-cost savings minus the estimated change in product costs for CFLKs purchased in 

2019–2048. 

In addition, the proposed standards for CFLKs would have significant environmental 

benefits. DOE estimates that the proposed standards would result in cumulative emission 

reductions of 3.3 million metric tons (Mt)b

 of carbon dioxide (CO2), 3.5 thousand tons of sulfur 

dioxide (SO2), 4.7 thousand tons of nitrogen oxides (NOX), 11.2 thousand tons of methane 

(CH4), 0.037 thousand tons of nitrous oxide (N2O), and 0.011 tons of mercury (Hg).c The 

cumulative reduction in CO2 emissions through 2030 amounts to 3.08 Mt, which is equivalent to 

the emissions resulting from the annual electricity use of almost 400 thousand homes. 

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, or SCC) developed by a recent Federal 

interagency process.1 The derivation of the SCC values is discussed in chapter 14. Using 

discount rates appropriate for each set of SCC values (see Table 1.2.1), DOE estimates the 

present monetary value of the CO2 emissions reduction (not including CO2 equivalent emissions 

a A quad is equal to 1015 British thermal units (Btu).

b A metric ton is equivalent to 1.1 short tons. Results for emissions other than CO2 are presented in short tons. c DOE calculated emissions reductions relative to the no-standards case, which reflects key assumptions in the 

Annual Energy Outlook 2014 (AEO 2014) Reference case. AEO 2014 generally represents current legislation and 

environmental regulations for which implementing regulations were available as of October 31, 2013.
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of other gases with global warming potential) is between $0.038 billion and $0.41 billion, with 

a value of $0.14 billion using the central SCC case represented by $41.2/t in 2015. DOE also

estimates the present monetary value of the NOX emissions reduction to be $0.016 billion at a 7-

percent discount rate and $0.023 billion at a 3-percent discount rate.d

Table 1.2.1 summarizes the national economic benefits and costs expected to result from 

the proposed standards for CFLKs. 

Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed Energy 

Conservation Standards for CFLKs (TSL 2)*

Category Present Value

Billion 2014$ Discount Rate

Benefits

Consumer Operating-Cost Savings

0.56 7%

0.73 3%

CO2 Reduction Monetized Value ($12.2/t case)** 0.03 5%

CO2 Reduction Monetized Value ($41.2/t case)** 0.13 3%

CO2 Reduction Monetized Value ($63.4/t case)** 0.21 2.5%

CO2 Reduction Monetized Value ($121/t case)** 0.40 3%

NOX Reduction Monetized Value

0.02 7%

0.02 3%

Total Benefits†

0.71 7%

0.89 3%

Costs

Consumer Incremental Installed Costs

0.06 7%

0.07 3%

Total Net Benefits

Including EmissionsReduction Monetized Value†

0.65 7%

0.82 3%

* This table presents the costs and benefits associated with CFLKs shipped in 2019−2048. These results

include benefits to consumers that accrue after 2048 from the products purchased in 2019−2048. The 

results account for the incremental variable and fixed costs incurred by manufacturers due to the

standard, some of which may be incurred in preparation for the rule.

** The CO2 values represent global monetized values of the SCC, in 2014$, in 2015 under several 

scenarios of the updated SCC values. The first three cases use the averages of SCC distributions 

calculated using 5%, 3%, and 2.5% discount rates, respectively. The fourth case represents the 95th

percentile of the SCC distribution calculated using a 3% discount rate. The SCC time series incorporate 

an escalation factor.

† Total Benefits for both the 3% and 7% cases are derived using the series corresponding to average 

SCC with 3-percent discount rate ($41.2/t case).

d DOE is currently investigating valuation of avoided SO2 and Hg emissions.
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The benefits and costs of the proposed standards, for CFLKs sold in 2019-2048, can also 

be expressed in terms of annualized values. The annualized monetary values are the sum of: (1) 

the annualized national economic value of the benefits from consumer operation of products that 

meet the amended standards (consisting primarily of operating-cost savings from using less 

energy, minus any increase in product purchase prices, which is another way of representing 

consumer NPV), and (2) the annualized monetary value of the benefits of emission reductions, 

including CO2 emission reductions.e

Although combining the values of operating savings and CO2 emission reductions is 

relevant to DOE’s determination, 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, whereas 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 for 

the lifetime of CFLKs shipped in 2019–2048. Because CO2 emissions have a very long residence 

time in the atmosphere,f 2 the SCC values after 2050 reflect future climate-related impacts 

resulting from the emission of CO2 that continue beyond 2100.

Estimates of annualized benefits and costs of the proposed standards are shown in Table 

1.2.2. The results under the Primary Estimate are as follows. Using a 7-percent discount rate for 

benefits and costs other than CO2 reduction (for which DOE used a 3-percent discount rate along 

with the average SCC series that has a value of $41.2/t in 2015), the estimated cost of the 

standards proposed in this rule is $6.0 million per year in increased equipment costs, while the 

estimated annual benefits are $55 million in reduced equipment operating costs, $7.5 million in 

CO2 reductions, and $1.6 million in reduced NOX emissions. In this case, the net benefit amounts

to $59 million per year. Using a 3-percent discount rate for all benefits and costs and the average 

SCC series that has a value of $41.2/t in 2015, the estimated cost of the proposed CFLK 

standards is $4.0 million per year in increased equipment costs, while the estimated annual 

benefits are $41 million in reduced operating costs, $7.5 million in CO2 reductions, and $1.3

million in reduced NOX emissions. In this case, the net benefit amounts to $46 million per year. 

 e To convert the time-series of costs and benefits into annualized values, DOE calculated a present value in 2015, the 

year used for discounting the NPV of total consumer costs and savings. For the benefits, DOE calculated a present 

value associated with each year’s shipments in the year in which the shipments occur (e.g., 2020 or 2030), and then 

discounted the present value from each year to 2015. The calculation uses discount rates of 3 and 7 percent for all 

costs and benefits except for the value of CO2 reductions, for which DOE used case-specific discount rates, as 

shown in Table 1.2.1. Using the present value, DOE then calculated the fixed annual payment over a 30-year period, 

starting in the compliance year, that yields the same present value.

f The atmospheric lifetime of CO2 is estimated of the order of 30–95 years.
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Table 1.2.2 Summary of National Economic Benefits and Costs of Proposed Energy 

Conservation Standards for CFLKs (TSL 2)*

Discount Rate

Primary Estimate* Low Net Benefits 

Estimate*

High Net 

Benefits 

Estimate*

million 2014$/year

Benefits

Consumer Operating-Cost 

Savings

7% 55 36 59

3% 41 24 43

CO2 Reduction Monetized 

Value ($12.2/t case)* 5% 2.6 1.4 2.7

CO2 Reduction Monetized 

Value ($41.2/t case)* 3% 7.5 3.9 7.9

CO2 Reduction Monetized 

Value ($63.4/t case)* 2.5% 11 5 11

CO2 Reduction Monetized 

Value ($112.1/t case)* 3% 22 12 24

NOX Reduction Monetized 

Value

7% 1.6 0.90 1.6

3% 1.3 0.65 1.3

Total Benefits†

7% plus CO2

range

60 to 79 38 to 48 63 to 85

7% 65 40 69

3% plus CO2

range

45 to 64 26 to 36 47 to 68

3% 49 28 53

Costs

Consumer Incremental Installed 

Product Costs

7% 6.0 3.5 6.4

3% 4.0 2.3 4.2
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Discount Rate

Primary Estimate* Low Net Benefits 

Estimate*

High Net 

Benefits 

Estimate*

million 2014$/year

Net Benefits

Total†

7% plus CO2

range

54 to 73 34 to 44 57 to 78

7% 59 37 62

3% plus CO2

range

41 to 60 24 to 34 43 to 64

3% 46 26 48

* This table presents the annualized costs and benefits associated with CFLKs shipped in 2019−2048. These

results include benefits to consumers which accrue after 2048 from the products purchased in 2019−2048. The 

results account for the incremental variable and fixed costs incurred by manufacturers due to the standard, some of 

which may be incurred in preparation for the rule. The Primary Estimate assumes the reference case electricity 

prices and housing starts from AEO 2015 and decreasing product prices for LED CFLKs, due to price learning. 

The Low Benefits Estimate uses the Low Economic Growth electricity prices and housing starts from AEO 2015

and a faster decrease in product prices for LED CFLKs. The High Benefits Estimate uses the High Economic 

Growth electricity prices and housing starts from AEO 2015 and the same product price decrease for LED CFLKs 

as in the Primary Estimate.

** The CO2 values represent global monetized values of the SCC, in 2014$, in 2015 under several scenarios of the 

updated SCC values. The first three cases use the averages of SCC distributions calculated using 5%, 3%, and 

2.5% discount rates, respectively. The fourth case represents the 95th percentile of the SCC distribution calculated 

using a 3% discount rate. The SCC time series incorporate an escalation factor.

† Total Benefits for both the 3% and 7% cases are derived using the series corresponding to the average SCC with 

a 3-percent discount rate ($41.2/t case). In the rows labeled “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.

DOE has tentatively concluded that the proposed standards represent the maximum 

improvement in energy efficiency that is technologically feasible and economically justified, and 

would result in the significant conservation of energy. DOE further notes that products achieving 

these standard levels are already commercially available for all product classes covered by this 

proposal. Based on the analyses described above, DOE has tentatively concluded that the 

benefits of the proposed standards to the nation (energy savings, positive NPV of consumer 

benefits, consumer LCC savings, and emission reductions) would outweigh the burdens (loss of 

INPV for manufacturers and LCC increases for some consumers).

DOE also considered more- and less-stringent efficacy levels (EL)s as trial standard

levels, and is still considering them in this rulemaking. However, DOE has tentatively concluded 

that the potential burdens of the more-stringent ELs would outweigh the projected benefits. 

Based on consideration of the public comments DOE receives in response to this notice and

related information collected and analyzed during the course of this rulemaking effort, DOE 

may adopt ELs presented in the NOPR that are either higher or lower than the proposed 

standards. 
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1.3 OVERVIEW OF APPLIANCE STANDARDS

Title III of the Energy Policy and Conservation Act of 1975 (EPCA or “the Act”; 42 

U.S.C. 6291, et seq.) sets forth a variety of provisions designed to improve energy efficiency. 

(All references to EPCA refer to the statute as amended through the American Energy 

Manufacturing Technical Corrections Act (AEMTCA), Pub. L. 112-210 (Dec. 18, 2012).) Part B 

of title III, which for editorial reasons was redesignated as Part A upon incorporation into the 

U.S. Code (42 U.S.C. 6291–6309, as codified), establishes the “Energy Conservation Program 

for Consumer Products Other Than Automobiles.” These products include CFLKs - the focus of 

this rulemaking. 

Pursuant to EPCA, the U.S. Department of Energy (DOE) is required to design each 

standard for these products 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 must review comments on the proposed 

standard and 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; 

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. (42 U.S.C. 6295(o)(2)(B)(i); 6313(d)(4)) 

EPCA also directs that DOE may not prescribe an amended or new standard if the 

standard is likely to result in the unavailability in the United States of performance 

characteristics (including reliability), features, sizes, capacities, and volumes that are

substantially the same as those generally available in the United States at the time that the 

standard is prescribed. (42 U.S.C. 6295(o)(4) and 6313(d)(4)) For CFLKs, DOE is applying 

those factors in a manner consistent with its other energy conservation standards rulemakings to 
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ascertain the maximum improvement in energy efficiency that is technologically feasible and 

economically justified for these products.

1.4 OVERVIEW OF CURRENT CEILING FAN LIGHT KIT STANDARDS

Current energy conservation standards for CFLKs (42 U.S.C. 6295 (ff)) were established 

by the Energy Policy Act of 2005 (EPAct 2005; Title I, Subtitle C, section 135(c)), which were 

later amended by EPCA. Specifically, EPAct 2005 established individual energy conservation 

standards for three groups of CFLKs: (1) those having medium screw base sockets (hereafter 

“Medium Screw Base product class”); (2) those having pin-based sockets for fluorescent lamps 

(hereafter “Pin-Based product class”); and (3) any CFLKs other than those included in the 

Medium Screw Base product class or the Pin-Based product class (hereafter “Other Base Type 

product class”). (42 U.S.C. 6295(ff)(2)-(4)) In a technical amendment published on October 18, 

2005, DOE codified the statute’s requirements for the Medium Screw Base and Pin-Based 

product classes. 70 FR 60413. EPAct 2005 also specified that if DOE failed to issue a final rule 

on energy conservation standards for Other Base Type product class CFLKs by January 1, 2007, 

a 190 watt (W) limit would apply to those products. (42 U.S.C. 6295(ff)(4)(C)) Because DOE 

did not issue a final rule on standards for CFLKs by that date, on January 11, 2007, DOE 

published a technical amendment that codified the statute’s requirements for Other Base Type 

product class CFLKs, which applied to Other Base Type product class CFLKs manufactured on 

or after January 1, 2009. 72 FR 1270. Another technical amendment final rule published on 

March 3, 2009 (74 FR 12058) added a provision that CFLKs with sockets for pin-based 

fluorescent lamps must be packaged with lamps to fill all sockets. (42 U.S.C. 6295(ff)(4)(C)(ii))

These standards for CFLKs are codified in title 10 of the Code of Federal Regulations (CFR) part 

430.32 (s)(2).

1.5 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

DOE considers stakeholder participation a very important part of the standards-setting 

process. DOE encourages the participation of all stakeholders during the comment period of each 

rulemaking stage. Beginning with the rulemaking framework document for CFLKs (hereafter the 

“framework document”) and during subsequent comment periods, interactions among 

stakeholders provide a balanced discussion of the information that is required for the standards 

rulemaking.

After the publication of the framework document, the energy conservation standards 

rulemaking process involves three additional, formal public notices, which DOE publishes in the 

Federal Register. The first of the rulemaking notices is a notice of public meeting (NOPM), 

which is designed to publicly vet the models and tools used in the preliminary rulemaking and to 

facilitate public participation before the NOPR stage. The second notice is the NOPR, which 

presents a discussion of comments received in response to the NOPM and the preliminary 

analyses and analytical tools; analyses of the impacts of potential amended energy conservation 

standards on consumers, manufacturers, and the Nation; DOE’s weighting of these impacts of 

amended energy conservation standards; and the proposed energy conservation standards for 

each product. The third notice is the final rule, which presents a discussion of the comments 
1-8 

received in response to the NOPR; the revised analyses; DOE’s weighting of these impacts; the 

amended energy conservation standards DOE is adopting for each product; and the effective 

dates of the amended energy conservation standards. See Table 1.5.1 for a summary of what 

analyses DOE completes in which stage of the rulemaking. 

Table 1.5.1 Analyses under the Process Rule

Preliminary Analyses NOPR Final Rule

Market and technology assessment Revised preliminary analysis Revised analyses

Screening analysis Consumer subgroup analysis

Engineering analysis Manufacturer impact analysis

Energy use analysis Emissions analysis

Product price determination Monetization of emissions reduction benefits

Life-cycle cost and payback period analysis Utility impact analysis

Shipments analysis Employment impact analysis

National impact analysis Regulatory impact analysis

Preliminary manufacturer impact analysis

On March 15, 2013, DOE published in the Federal Register a NOPM announcing the 

availability of the framework document to initiate the energy conservation standard rulemaking 

for CFLKs. 78 FR 16443. DOE requested feedback from interested parties in the framework 

document on many issues. This document is available on regulations.gov under docket number 

EERE-2012-BT-STD-0045-0001. 

DOE held the framework public meeting on March 22, 2013. DOE originally scheduled 

the framework comment period to close on April 29, 2013. However, due to the large number 

and broad scope of questions and issues raised in the framework document (and during the 

public meeting), DOE subsequently published a notice in the Federal Register extending the 

comment period until June 14, 2013, to allow additional time for interested parties to submit 

comments. 78 FR 25626 (May 2, 2013). 

During the March 2013 public meeting, interested parties commented about numerous 

issues relating to the analyses. Comments from interested parties submitted during the 

framework document comment period elaborated on the issues raised during the public meeting. 

DOE addressed these issues during its preliminary analysis and summarized the comments and 

DOE’s responses in chapter 2 of the preliminary TSD. 

As part of the information gathering and sharing process, DOE organized and held 

interviews with manufacturers of the products covered in this rulemaking. DOE selected 

companies that represented production of all types of products, ranging from small to large 

manufacturers. DOE had four objectives for these interviews: (1) solicit manufacturer feedback 

on the draft inputs to the engineering analysis; (2) solicit feedback on topics related to the 

preliminary manufacturer impact analysis; (3) provide an opportunity, early in the rulemaking 
1-9 

process, to express manufacturers’ concerns to DOE; and (4) foster cooperation between 

manufacturers and DOE. DOE incorporated the information gathered during the interviews with 

manufacturers into its engineering analysis (chapter 5 of the preliminary TSD) and the 

preliminary manufacturer impact analysis (MIA, chapter 12 of the preliminary TSD). 

DOE published the preliminary analysis for this rulemaking in the Federal Register on 

October 31, 2014. 79 FR 64712. The preliminary analysis, as well as the complete preliminary 

TSD, is available at regulations.gov under docket number EERE-2012-BT-STD-0045. The 

preliminary TSD includes the results of the following DOE preliminary analyses: (1) market and 

technology assessment; (2) screening analysis; (3) engineering analysis; (4) energy use 

characterization; (5) product price determinations; (6) life-cycle cost (LCC) and payback period 

(PBP) analyses; (7) shipments analysis; and (8) national impact analysis (NIA). 

DOE held a public meeting on November 18, 2014, to present the methodologies and 

results for the preliminary analyses. Manufacturers, trade associations, and environmental 

advocates attended the meeting. The participants discussed multiple issues, including the 

methodology and results of the market and technology assessment, screening analysis, 

engineering analysis, product price determination, energy use, life-cycle cost analysis, 

shipments, and NIA. Additionally, the MIA and additional analyses that would be undertaken 

during the NOPR stage were discussed. The comments received during the public meeting, along 

with the written comments submitted to DOE since publication of the preliminary analysis, have 

contributed to DOE’s proposed resolution of the issues in this rulemaking. A detailed discussion 

of comments from interested parties is available in the NOPR Federal Register notice for this 

rulemaking. Following the publication of the preliminary analysis and the preliminary analysis 

public meeting, DOE held additional meetings with manufacturers as part of the consultative 

process for the MIA conducted during the NOPR phase. 

For the LCC, PBP, and NIA, DOE developed spreadsheets using Microsoft Excel®. The 

LCC and PBP spreadsheets calculate the economic impacts of making CFLKs more efficient. 

The NIA spreadsheet calculates the national energy savings (NES) and national net present value 

(NPV) at various trial standard levels (TSLs) and includes a model that forecasts the impacts of 

energy conservation standards at various levels on product shipments. These spreadsheets are 

available on regulations.gov, docket number EERE-2012-BT-STD-0045. 

1.6 STRUCTURE OF THE DOCUMENT

This NOPR TSD outlines the analytical approaches used in this rulemaking. The TSD 

consists of 17 chapters, as well as several appendices.

Chapter 1 Introduction: Provides an overview of the appliance standards 

program and how it applies to the CFLK rulemaking, and outlines the 

structure of the document 

Chapter 2 Analytical Framework: Describes the rulemaking process step by 

step and provides an overview of each analysis 
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Chapter 3 Market and Technology Assessment: Characterizes the CFLK

market and the technologies available for increasing product efficacy 

and outlines the product classes

Chapter 4 Screening Analysis: Determines which technology options are viable 

for consideration in the engineering analysis

Chapter 5 Engineering Analysis: Describes DOE’s approach to the engineering 

analysis, and establishes efficacy levels based on lamp efficacy

Chapter 6 Energy Use Analysis: Discusses the process used for generating 

energy use estimates of CFLKs for each efficacy level.

Chapter 7 Product Price Determination: Describes DOE’s methodology for 

establishing the end-user prices for lamps packaged in CFLKs

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

economic effects of standards on individual consumers and users of 

the products and compares the LCC and PBP of products with and 

without higher efficiency standards

Chapter 9 Shipments Analysis: Discusses the methods used for forecasting 

shipments with and without higher efficiency standards

Chapter 10 National Impact Analysis: Discusses the methods used for 

forecasting national energy consumption and national economic 

impacts based on annual product shipments and estimates of future 

product efficiency distributions in the absence and presence of higher 

efficiency standards

Chapter 11 Consumer Subgroup Analysis: Discusses the economic effects of 

standards on certain consumer subgroups and calculates the LCCs and 

PBPs for these consumers

Chapter 12 Manufacturer Impact Analysis: Discusses the effects of standards on 

the finances and profitability of product manufacturers

Chapter 13 Emissions Analysis: Discusses the effects of standards on airborne 

emissions of electric utilities

Chapter 14 Monetization of Emissions Reduction Benefits: Considers the 

estimated monetary benefits likely to result from reduced emissions of 

carbon dioxide and nitrogen oxides expected to result from the 

standards

Chapter 15 Utility Impact Analysis: Discusses the effects of standards on the 

installed generation capacity of electric utilities
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Chapter 16 Employment Impact Analysis: Discusses the effects of standards on 

national employment

Chapter 17 Regulatory Impact Analysis: Discusses the present regulatory 

actions as well as the impact of non-regulatory alternatives to setting 

energy efficiency standards

Appendix AA Acronyms and Abbreviations: Provides a set of acronyms and 

abbreviations used throughout this TSD

Appendix 7A Product Class Restructuring Sub-Baseline Engineering and 

Pricing Results: Provides detailed results for the engineering and 

pricing sub-baseline analyses conducted for product class restructuring

Appendix 8A User Instructions for Life-Cycle Cost and Payback Period 

Analysis Spreadsheet: Provides basic instructions for operating the 

LCC and PBP workbook, as well as descriptions of each worksheet 

included in the workbook

Appendix 8B Uncertainty and Variability in LCC Analysis: Discusses uncertainty 

and variability analyses and presents results of those uncertainty and 

variability analyses performed in the LCC and PBP analyses

Appendix 8C Discount Rate Distributions: Provides the distribution of discount 

rates used in the LCC analysis

Appendix 8D Electricity Prices: Provides the electricity pricing used in the LCC 

analysis

Appendix 9A Relative Price Elasticity of Demand for Appliances: Provides a 

summary of DOE’s study of the price elasticity of demand for 

consumer appliances

Appendix 10A User Instructions for Shipments and NIA Spreadsheets: Provides 

basic instructions for operating the NIA workbook, as well as 

descriptions of each worksheet included

Appendix 10B Full-Fuel-Cycle Multipliers: Summary of methods used to calculate 

full-fuel-cycle energy savings expected from potential standards

Appendix 10C Alternative NIA Scenarios: Provides the national energy 

consumption and national economic impacts in the absence and 

presence of higher efficiency standards using alternative shipment 

scenarios

Appendix 12A Manufacturer Impact Analysis Interview Guide

Appendix 12B Government Regulatory Impact Model Overview
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Appendix 14A Social Cost of Carbon for Regulatory Impact Analysis under 

Executive Order 12866

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

Analysis under Executive Order 12866

Appendix 17A Regulatory Impact Analysis: Supporting Materials
1-13

REFERENCES

1 Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under 

Executive Order 12866, Interagency Working Group on Social Cost of Carbon, U.S. 

Government (May 2013; revised November 2013) (Last accessed June 16, 2015.)

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf.

2 Jacobson, M. Z. Correction to “Control of Fossil-Fuel Particulate Black Carbon and Organic 

Matter, Possibly the Most Effective Method of Slowing Global Warming.” Journal of 

Geophysical Research: Atmospheres. 2005. 110(D14): p. D14105. (Last accessed June 

12, 2015.) http://onlinelibrary.wiley.com/doi/10.1029/2005JD005888/abstract.
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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 ........................................................ 2-5

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

2.3.2 Technology Assessment ....................................................................................... 2-6

2.3.3 Product Classes .................................................................................................... 2-6

2.4 SCREENING ANALYSIS .............................................................................................. 2-6

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

2.5.1 Representative Product Classes ........................................................................... 2-7

2.5.2 Baseline Lamps .................................................................................................... 2-7

2.5.3 More Efficacious Substitutes ............................................................................... 2-7

2.5.4 Efficacy Levels .................................................................................................... 2-7

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

2.7 PRODUCT PRICE DETERMINATION ........................................................................ 2-8

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

2.9 SHIPMENTS ANALYSIS............................................................................................... 2-9

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

2.10.1 National Energy Savings Analysis..................................................................... 2-10

2.10.2 Net Present Value Analysis ............................................................................... 2-11

2.11 CONSUMER SUBGROUP ANALYSIS ...................................................................... 2-11

2.12 MANUFACTURER IMPACT ANALYSIS.................................................................. 2-11

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

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

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

2.16 EMPLOYMENT IMPACT ANALYSIS ....................................................................... 2-15

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 Flow Diagram of Analyses for Typical Standards Rulemaking .............................. 2-2
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CHAPTER 2 ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Sections 6295(o)(2)(A) and (3) of Title 42 United States Code (42 U.S.C. 6295(o)(2)(A) 

and (3)) require that energy conservation standards set by the U.S. Department of Energy (DOE) 

be technologically feasible and economically justified, and achieve the maximum improvement 

in energy efficiency. This chapter describes the general analytical framework that DOE uses in 

developing such standards, and in particular, standards for ceiling fan light kits (CFLKs). The 

analytical framework is a description of the methodology, the analytical tools, and relationships 

among the various analyses that are part of this rulemaking. For example, the methodology that 

addresses the statutory requirement for economic justification includes analyses of life-cycle cost

(LCC); economic impact on manufacturers and users; national benefits; impacts, if any, on utility

companies; and impacts, if any, from lessening competition among manufacturers. DOE will 

also 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.

Figure 2.1.1 summarizes the analytical components of the standards-setting process. The 

central parts of this figure are the analyses contained in the boxes. The key inputs to the left and 

key outputs to the right show how the analyses fit into the rulemaking process, and how the

analyses relate to each other. Key inputs are the types of data and information that the analyses

require. Some key inputs exist in public databases; DOE collects other inputs from interested 

parties or persons with special knowledge. Key outputs are analytical results that feed directly 

into the standards-setting process. Dotted lines connecting analyses show types of information 

that feed from one analysis to another. While Figure 2.1.1 summarizes the inputs, outputs, and 

analyses of a typical standards rulemaking, individual inputs and outputs may vary by 

rulemaking.
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Figure 2.1.1 Flow Diagram of Analyses for Typical Standards Rulemaking
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The analyses performed in the notice of proposed rulemaking (NOPR) stage and reported 

in this technical support document (TSD) include:

• a market and technology assessment to characterize the CFLK market, identify 

technology options that improve efficacy, and develop product classes;

• a screening analysis to review each technology option and determine if it is 

technologically feasible; practical to manufacture, install, and service; would adversely 

affect lamp utility or lamp availability; or would have adverse impacts on health and 

safety;

• an engineering analysis to study representative lamp units used as substitutes for baseline 

products, to select efficacy levels (ELs), and to determine lamp power ratings;

• an energy use analysis that estimates annual energy use of CFLKs;

• a product price determination that develops consumer product prices for the 

representative lamp units identified in the engineering analysis;

• a LCC analysis to calculate, at the consumer level, the discounted savings in operating 

costs throughout the estimated average life of the lamp, and any change in the installed 

costs likely to result directly from imposition of the standard;

• a payback period (PBP) analysis to estimate the amount of time it takes consumers to 

recover any increase in purchase expense of more efficacious lamps through lower 

operating costs;

• an LCC subgroup analysis to evaluate the economic impacts on identifiable groups of 

consumers of CFLKs, including various categories of purchasers or owners who may 

experience disproportionate impacts from a national energy conservation standard;

• a shipments analysis to estimate shipments of CFLKs during the period considered in the 

analysis;

• a national impact analysis (NIA) to assess the aggregate impacts at the national level of 

potential standards as measured by the net present value (NPV) of total consumer 

economic impacts and national energy savings (NES);

• a manufacturer impact analysis (MIA) that estimates the financial impact of standards on 

CFLK manufacturers and calculates impacts on competition, employment at the 

manufacturing plant, and manufacturing capacity;

• an emissions analysis to provide estimates of the effects of proposed standards on 

emissions of carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxides (NOX), and 

mercury (Hg);
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• an emissions monetization to assign values to the benefits associated with emissions 

reductions;

• a utility impact analysis to estimate the effects of potential standards on electric utilities;

• an employment impact analysis to assess the aggregate impacts on national employment;

and

• a regulatory impact analysis (RIA) to examine major alternatives to amended energy 

conservation standards that potentially could achieve substantially the same regulatory 

goal at a lower cost.

In response to comments it receives on the NOPR, DOE may revise some of its analyses before 

publishing the final rule.

2.2 BACKGROUND

Title III, Part Ba of the Energy Policy and Conservation Act of 1975 (42 U.S.C. 6291 et 

seq.; EPCA), established the Energy Conservation Program for Consumer Products Other Than 

Automobiles, a program that covers most major household appliances and certain industrial and 

commercial equipment.b This program authorized DOE to establish technologically feasible, 

economically justified energy conservation regulations that would likely produce substantial 

NES. (42 U.S.C. 6295(o)(2)(B)(i)(I)-(VII)) Title III of EPCA, as amended, authorized DOE to 

regulate the energy efficiency of several additional products and equipment, including CFLKs, 

which are the focus of this document. (42 U.S.C 6295(ff))

Current energy conservation standards for CFLKs (42 U.S.C. 6295(ff)) were established 

by the Energy Policy Act of 2005 (EPAct 2005) (Title I, Subtitle C, section 135(c)), which were 

later amended by EPCA. Specifically, EPAct 2005 established individual energy conservation 

standards for three groups of CFLKs: (1) those having medium screw base sockets (hereafter 

“Medium Screw Base product class”); (2) those having pin-based sockets for fluorescent lamps 

(hereafter “Pin-Based product class”); and (3) any CFLKs other than those included in the 

Medium Screw Base product class or the Pin-Based product class (hereafter “Other Base Type 

product class”). (42 U.S.C. 6295(ff)(2)-(4)) In a technical amendment published on October 18, 

2005, DOE codified the statute's requirements for the Medium Screw Base and Pin-Based 

product classes. 70 FR 60413. EPAct 2005 also specified that if DOE failed to issue a final rule 

on energy conservation standards for Other Base Type product class CFLKs by January 1, 2007, 

a 190 watt (W) limit would apply to those products. (42 U.S.C. 6295(ff)(4)(C)) Because DOE 

did not issue a final rule on standards for CFLKs by that date, on January 11, 2007, DOE 

published a technical amendment that codified the statute’s requirements for Other Base Type 

product class CFLKs, which applied to Other Base Type product class CFLKs manufactured on 

or after January 1, 2009. 72 FR 1270. Another technical amendment final rule published on 

 

a For editorial reasons, upon codification in the U.S. Code, Part B was redesignated Part A.

b All references to EPCA in this document refer to the statute as amended through the Energy Efficiency 

Improvement Act of 2015, Pub. L. 114-11 (Apr. 30, 2015).
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March 3, 2009 (74 FR 12058) added a provision that CFLKs with sockets for pin-based 

fluorescent lamps must be packaged with lamps to fill all sockets. (42 U.S.C. 6295(ff)(4)(C)(ii))

These standards for CFLKs are codified in title 10 of the Code of Federal Regulations (CFR) part 

430.32(s)(2).

DOE issued the preliminary analysis for the CFLK energy conservation standards 

rulemaking on October 27, 2014, and published it in the Federal Register on October 31, 2014. 

78 FR 13563. DOE posted the preliminary analysis, as well as the complete preliminary TSD, on 

its website.c The preliminary TSD includes the results of the following DOE preliminary 

analyses: 1) market and technology assessment; 2) screening analysis; 3) engineering analysis; 4) 

energy use analysis; 5) product price determination; 6) LCC and PBP analyses; 7) shipments 

analysis; 8) NIA; and 9) preliminary MIA.

This NOPR, which is part of DOE’s first review cycle for CFLK standards, continues the 

rulemaking to determine whether the standards currently in effect for CFLKs (42 U.S.C 

6295(ff)) should be amended and presents ELs under consideration by DOE. It also addresses the 

requirement under 42 U.S.C. 6295(gg)(3) that DOE incorporate, if feasible, standby mode and 

off-mode energy use in any amended (or new) standard adopted after July 1, 2010, pursuant to 

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

2.3 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the relevant product markets,

identifies technology options to improve efficacy, and outlines the proposed product class. For 

more detail on the market and technology assessment, see chapter 3 of this NOPR TSD.

2.3.1 Market Assessment

When initiating a standards rulemaking, DOE develops information on the industry 

structure and market characteristics of the products concerned. This activity consists of both 

quantitative and qualitative efforts to assess the industry based on publicly available information.

As such, DOE addresses: 1) industry structure and manufacturer market shares; 2) existing

regulatory and non-regulatory efficiency improvement initiatives; and 3) trends in product

characteristics and retail markets. This information serves as resource material throughout the 

rulemaking.

DOE has used and will use the most reliable and accurate data available at the time of 

each analysis in this rulemaking. DOE welcomes and will consider any submissions of additional 

data.

 

c The preliminary analysis, preliminary TSD, and preliminary analysis public meeting information are available at 

regulations.gov under docket number EERE-2012-BT-STD-0045-0072.
2-6

2.3.2 Technology Assessment

DOE typically uses information relating to existing technology options to develop 

designs that are more efficient. DOE prepared a list of CFLK lighting technologies for 

consideration that could improve the efficacy of lamps packaged with CFLKs. To develop this 

list, DOE reviewed manufacturer catalogs, recent trade publications, and technical journals, and

consulted with technical experts.

2.3.3 Product Classes

When evaluating and establishing energy conservation standards, DOE divides covered 

products into classes by a) the type of energy used; b) the capacity of the product; or c) other 

performance-related features that justify different standard levels, such as features affecting 

consumer utility. (See 42 U.S.C. 6295(q)) DOE then conducts its analysis and considers standard 

levels for each product class. DOE applied the criteria of 42 U.S.C. 6295(q) to CFLKs to develop 

product classes.

2.4 SCREENING ANALYSIS

The screening analysis examines the technology options identified in the technology 

assessment to determine whether they: 1) are technologically feasible; 2) are practical to 

manufacture, install, and service; 3) do not have an adverse impact on product utility or 

availability; and 4) do not have adverse impacts on health and safety. As described in section 

2.3.2, DOE develops a list of technology options that can improve efficacy in consultation with 

industry, technical experts, and other interested parties. In the screening analysis, DOE reviews 

the list to determine if these technologies meet the screening criteria described above. In the 

engineering analysis, DOE only considers design options that meet all four of the screening 

criteria. For more detail on the screening analysis, see chapter 4 of this NOPR TSD.

2.5 ENGINEERING ANALYSIS

DOE derives ELs in the engineering analysis and consumer prices in the product price 

determination. By combining the results of the engineering analysis and the product price 

determination, DOE derives typical inputs for use in the LCC and NIA. The engineering analysis 

is generally based on commercially available lamps that incorporate the design options identified 

in the technology assessment and screening analysis. The methodology consists of the following 

steps: 1) selecting representative product classes, 2) selecting baseline lamps, 3) identifying more 

efficacious substitutes, and 4) developing ELs by directly analyzing representative product 

classes and then scaling those ELs to non-representative product classes. For more detail on the 

engineering analysis, see chapter 5 of this NOPR TSD.
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2.5.1 Representative Product Classes

When a product has multiple product classes, DOE selects certain classes as 

“representative” and concentrates its analytical effort on these classes.

2.5.2 Baseline Lamps

DOE selects a baseline lamp as a reference point against which to measure changes 

resulting from energy conservation standards. Typically, a baseline model is the most common, 

least efficacious lamp in a CFLK sold in a given product class. DOE also considers other lamp 

characteristics in choosing the most appropriate baseline for CFLKs, such as wattage, lumen 

output, and lifetime.

2.5.3 More Efficacious Substitutes

DOE selects higher efficacy lamps as replacements for the baseline model considered. 

When selecting higher efficacy lamps, DOE considers only design options that meet the criteria 

outlined in the screening analysis.

2.5.4 Efficacy Levels

Having identified the more efficacious substitutes, DOE develops ELs based on the 

consideration of several factors, including (1) the design options associated with the specific 

lamps studied; (2) the ability of lamps across wattages (or lumen outputs) to comply with the 

standard level of a given product class;d and (3) the maximum technologically feasible (“max 

tech”) EL.

2.6 ENERGY USE ANALYSIS

The energy use analysis provides estimates of annual energy consumption of CFLKs that 

DOE evaluates in the LCC and PBP analysis and the NIA. To develop annual energy 

consumption estimates, DOE multiplied annual usage (operating hours per year) by the CFLK

input power and a factor representing the impact of controls.

To determine annual operating hours in the residential sector, DOE primarily used data 

from various field metering studies, consistent with the approach taken in the General Service 

Lamp (GSL) preliminary analysis.

e For the commercial sector, DOE determined annual 

operating hours using data primarily from the 2003 Commercial Building Energy Consumption 

 

d ELs span multiple lamps of different lumen outputs. In selecting ELs, DOE considered whether these multiple 

lamps can meet the standard levels.

e DOE has published a framework document and preliminary analysis for amending energy conservation standards 

for general service lamps. Further information is available at www.regulations.gov under Docket ID: EERE-2013-

BT-STD-0051.
2-8

Survey (CBECS).

1 DOE developed its estimate of the power consumption of CFLKs by scaling 

the input power and lumen output of the representative lamp units characterized in the 

engineering analysis to account for the average lumen output of CFLKs in the market. To

account for the impact of controls, DOE assumed an average energy reduction of 30 percent. 

This estimate was based on a meta-analysis of field measurements of energy savings from 

commercial lighting controls by Williams, et al.

2 Because field measurements of energy savings 

from controls in the residential sector are very limited, DOE assumed that controls would have 

the same impact as in the commercial sector. DOE assumed that the fraction of CFLKs used with

controls in both the residential and commercial sectors is 11 percent. For more detail on the 

energy use analysis, see chapter 6 of this NOPR TSD.

2.7 PRODUCT PRICE DETERMINATION

Because the efficiency of a CFLK is based on the efficacy of the lamps it is packaged 

with, DOE developed a product price determination for the lamp component of the CFLK. 

Typically, DOE develops manufacturer selling prices for covered products and applies markups 

to create consumer prices to use as inputs to the LCC analysis and NIA. Because lamps are 

difficult to reverse-engineer (i.e., not easily disassembled), DOE directly derives consumer 

prices for the lamps in this rulemaking. DOE determined average shipment-weighted premiums

on distributor net prices of CFLKs and developed end user prices for CFLKs packaged with 

representative lamp units. DOE then determined the proportion of the CFLK end user price 

attributable to the lamp component to determine the end user price of the representative lamp 

unit sold with the CFLK. For representative lamp units that are not currently sold with CFLKs, 

DOE developed ratios of the lamp retail prices to end user prices of lamps packaged with CFLKs 

to determine the end user price of the representative lamp unit if it were sold in a CFLK. For 

more detail on the product price determination, see chapter 7 of this NOPR TSD.

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

DOE conducts LCC and PBP analyses to evaluate the economic impacts on individual 

consumers of potential energy conservation standards. The LCC is the total consumer expense 

over the life of a product. The PBP is the estimated amount of time (in years) it takes consumers 

to recover any increased first cost of a more efficacious product through lower operating costs.

Inputs to the calculation of the LCC for CFLKs are the total installed cost, the lifetime operating 

cost, the lamps’ disposal cost, and the lamps’ residual value. The total installed cost includes 

consumer product price and sales tax. DOE assumed that the installation costs did not vary by 

efficacy level, and therefore did not consider them in the analysis. Inputs to the calculation of the 

lifetime operating cost include the annual energy consumption (from the energy use analysis), 

electricity prices and electricity price trends, replacement lamp costs (for the lamps which 

replace the initially packaged lamps when they fail), product lifetime, discount rates, the market 

efficacy distribution for each standard case, and the year in which compliance with proposed 

standards would be required. For more detail on the LCC and PBP analysis, see chapter 8 of this 

NOPR TSD.
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2.9 SHIPMENTS ANALYSIS

DOE uses projections of product shipments to calculate the national impacts of standards 

on energy use, NPV, and future manufacturer cash flows. Future CFLK shipments are estimated 

by projecting historical CFLK shipments data throughout the analysis period (2019-2048). DOE 

modeled four market segments that account for all CFLK shipments: (1) replacements of retired 

CFLKs; (2) installation in new construction; (3) increased penetration of CFLKs into existing 

buildings; and (4) demolitions that erode building stock and demand for CFLKs. In practice, 

DOE’s model for CFLK shipments uses historical data for hugger and standard ceiling fans and 

assumes that 88 percent of hugger and standard ceiling fans are shipped with CFLKs. 

DOE adopted a consumer-choice-based model to estimate market shares by ELs. In the 

case of CFLKs, ELs correspond to lamp efficacies and CFLK manufacturers purchase the lamps 

that are packaged with the CFLKs. DOE modified the model to assume CFLK manufacturers are 

only sensitive to the first cost of lamps packaged with the CFLK and will primarily package a 

CFLK with the lowest first cost lamp.

The options available to the consumer also depend on the trial standard level (TSL) under 

consideration. In the absence of an amended standard (“the no-standard case”) all CFLK options 

are available. In each standards case only those CFLK options at or above the TSL are allowed.

DOE’s analysis of the current CFLK market estimates that less than 1 percent of products 

utilize light-emitting diode (LED) technology. However, information from manufacturers 

suggests rapid penetration of LED technology into the CFLK market within the next 5 years 

(approximately 15% by 2018). To account for this expected transition to LED technology, DOE 

modeled the initial growth of the LEDs into the CFLK market using LED incursion based on 

results from the report titled Energy Savings Potential of Solid-State Lighting in General 

Illumination Applications.

3

 DOE assumed a lag of 3.5 years for LED incursion into the CFLK 

market compared to the GSL market. DOE assumed that once the cost of LEDs reached parity 

with compact fluorescent lamps (CFLs), however, manufacturers would choose to package 

CFLKs with LEDs over more expensive, shorter-lived CFLs. 

DOE uses a price elasticity model to consider the impact that standards may have on 

shipments. The model estimates the reduction in shipments in response to any price increase 

attributable to an amended standard for CFLKs. Because most CFLKs are sold packaged with a 

ceiling fan, consumer price sensitivity is generally in response to the price of the combined 

package. DOE assumed no standard in place from the concurrent energy conservation standard 

for ceiling fans, because no standard has been proposed yet.f Only potential price increases from 

a CFLK standard are considered when calculating the impacts to CFLK shipments.

Because of the complexity of the consumer-choice-based shipments analysis, the 

shipments model was executed as a separate computer model written in the Python computer 

f The ceiling fans energy conservation standards rulemaking docket is available on regulations.gov under docket 

number EERE-2012-BT-STD-0045.
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programming language. The inputs and outputs of the shipments model were then embedded in 

the NIA spreadsheet model for all cases and scenarios.

For more detail on the shipments analysis, see chapter 9 of this NOPR TSD.

2.10 NATIONAL IMPACT ANALYSIS

The NIA assesses the NES and the NPV from a national perspective of total consumer 

costs and savings expected to result from new or amended energy conservation standards. 

Analyzing impacts of potential energy conservation standards for CFLKs requires comparing 

projections of U.S. energy consumption with amended energy conservation standards against 

projections of energy consumption without amended standards.

DOE analyzed the impacts of four TSLs, corresponding to each EL specified in the 

engineering analysis, chapter 5 of this TSD. DOE coded the NIA in a Microsoft Excel file 

available on regulations.gov, docket number EERE-2012-BT-STD-0045, allowing access to 

scenario assumptions for conducting sensitivity analyses. For more detail on the NIA, see 

chapter 10 of this NOPR TSD.

2.10.1 National Energy Savings Analysis

The inputs for determining the NES for CFLKs are: (1) shipments; (2) annual energy 

consumption per unit; (3) stocks of CFLKs in each year; (4) national energy consumption; and 

(5) site-to-primary energy and fuel full cycle (FFC) conversion factors. The stocks were 

calculated by the shipments model for each year of the analysis period from the prior year’s 

stock, minus retirements, plus new shipments, accounting for product lifetimes. DOE calculated 

the national electricity consumption in each year by multiplying the number of units at each EL 

in the stock by the corresponding power consumption and operating hours. The electricity 

savings are estimated from the difference in national electricity consumption between the nostandard case and the standards cases for CFLKs shipped during the 2019-2048 period.

DOE has historically presented the NES in terms of primary energy savings. In response 

to the recommendations of a committee on “Point-of-Use and Full-Fuel-Cycle Measurement 

Approaches to Energy Efficiency Standards” appointed by the National Academy of Science, 

DOE announced its intention to use FFC measures of energy use and greenhouse gas (GHG) and 

other emissions in the national impact analyses and emissions analyses included in future energy 

conservation standards rulemakings. 76 FR 51281 (August 18, 2011). 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 stated it would review alternative 

methods, including the use of the National Energy Modeling System (NEMS). 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 this analysis, DOE calculated FFC 

energy savings using the methodology described in appendix 10B of this NOPR TSD, which 

presents both the primary energy savings and the FFC energy savings for the considered TSLs.
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2.10.2 Net Present Value Analysis

The inputs for determining NPV are: (1) total annual installed cost; (2) total annual 

savings in operating costs; and (3) a discount factor to calculate the present value of costs and 

savings. DOE calculated net savings each year as the difference between the no-standard case

and each standard case in terms of total savings in operating costs versus total increases in 

installed costs. DOE calculated savings over the lifetime of products shipped in the 30-year 

analysis period. DOE calculated NPV as the difference between the present value of operatingcost savings and the present value of total installed costs. DOE used a discount factor based on 

real discount rates of 3 and 7 percent to discount future costs and savings to present values.

For the NPV analysis, DOE calculates any increase in total installed costs as the 

difference in total installed cost between the no-standard case and the proposed standard case 

(i.e., once an amended standard would take effect). Because the more efficacious products 

bought in the standards case usually cost more than products bought in the base case, cost 

increases appear as negative values in the NPV.

DOE expresses savings in operating costs as decreases associated with the lower energy 

consumption of products bought in a standard case compared to the no-standard case. Total 

savings in operating costs are the product of savings per unit and the number of units of each 

vintage that survive in a given year.

2.11 CONSUMER SUBGROUP ANALYSIS

A consumer subgroup comprises a subset of the population that could, for one reason or 

another, be affected disproportionately by new or amended energy conservation standards. DOE 

identified low-income consumers and small businesses as consumers that could be 

disproportionately impacted by the proposed standards. The LCC subgroup analysis evaluates 

the effects on these consumer subgroups by accounting for variations in key inputs to the LCC 

analysis. For more detail on the consumer subgroup analysis, see chapter 11 of this NOPR TSD.

2.12 MANUFACTURER IMPACT ANALYSIS

DOE conducted an MIA for CFLKs to estimate the financial impact of amended energy 

conservation standards on manufacturers of CFLKs. Also, DOE calculated the impact of such 

standards on domestic manufacturing employment and capacity. The MIA has both quantitative 

and qualitative aspects. The quantitative part of the MIAs primarily relies on Government 

Regulatory Impact Models (GRIMs)an industry-cash-flow model customized for this 

rulemaking. The key GRIM inputs are data on the industry cost structure, manufacturer product

costs, shipments, and assumptions about manufacturer markups and conversion costs. The key 

MIA output is industry net present value (INPV). DOE used the GRIM to calculate cash flows 

using standard accounting principles and to compare changes in INPV between a no-standards

case and various TSLs (the standards cases). The difference in INPV between the no-standards
2-12

case and standards cases represents the financial impact of amended energy conservation 

standards on CFLK manufacturers. Different sets of assumptions (scenarios) produce different 

INPV results. The qualitative part of the MIA addresses factors such as manufacturing capacity; 

characteristics of, and impacts on, any particular subgroup of manufacturers; and impacts on 

competition.

DOE conducted the MIA in three phases. Phase 1, “Industry Profile,” consisted of the 

preparation of an industry characterization. Phase 2, “Industry Cash Flow,” focused on the 

industry as a whole using industry financial parameters derived in Phase 1 and the shipment 

scenarios used in the NIA. In Phase 3, “Subgroup Impact Analysis,” DOE conducted structured, 

detailed interviews with a variety of CFLK manufacturers and discussed engineering, 

manufacturing, procurement, and financial topics specific to each company and obtained each 

manufacturer’s view of the CFLK industry as a whole. The interviews provided valuable 

information that DOE used to evaluate the impacts of amended standards on manufacturers’ cash 

flows, manufacturing capacities, and employment levels.

For more detail on the MIA, see chapter 12 of this NOPR TSD.

2.13 EMISSIONS ANALYSIS

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

carbon dioxide (CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and mercury (Hg) from 

potential energy conservation standards for the considered products. In addition, DOE will 

estimate emissions impacts in production activities (extracting, processing, and transporting 

fuels) that provide the energy inputs to power plants. These are referred to as “upstream” 

emissions. Together, these emissions account for the FFC. In accordance with DOE’s FFC 

Statement of Policy (76 FR 51282 [Aug. 18, 2011]), the FFC analysis includes impacts on 

emissions of methane and nitrous oxide, both of which are recognized as greenhouse gases.

DOE conducts the emissions analysis using emissions factors derived from data in the latest 

version of the U.S. Energy Information Administration’s (EIA’s) Annual Energy Outlook (AEO), 

supplemented by data from other sources. EIA prepares the AEO using NEMS. Each annual 

version of NEMS incorporates the projected impacts of existing air quality regulations on 

emissions. The text below refers to AEO 2014, which generally represents current legislation and 

environmental regulations, including recent government actions, for which implementing 

regulations were available as of October 2013.

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. On July 6, 2011 EPA issued a replacement for CAIR, 

the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 

2012, the D.C. Circuit issued a decision to vacate CSAPR, and ordered EPA to continue 
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administering CAIR.g On April 29, 2014, the U.S. Supreme Court reversed the judgment of the 

D.C. Circuit and remanded the case for further proceedings consistent with the Supreme Court's 

opinion.h On October 23, 2014, the D.C. Circuit lifted the stay of CSAPR.

i Pursuant to this 

action, CSAPR went into effect (and CAIR ceased to be in effect) as of January 1, 2015.

Because AEO 2014 was prepared prior to the Supreme Court's opinion, it assumed that 

CAIR remains a binding regulation through 2040. Thus, DOE’s analysis used emissions factors 

that assume that CAIR, not CSAPR, is the regulation in force. However, the difference between 

CAIR and CSAPR is not relevant for the purpose of DOE's analysis of emissions impacts from 

energy conservation standards.

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 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS 

rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air 

pollutants (HAP), and also established a standard for 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. AEO 

2014 assumes that, in order to continue operating, coal plants must have either flue gas 

desulfurization or dry sorbent injection systems installed by 2016. Both technologies, which are 

used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, 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 2016 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 CSAPR because excess NOX emissions allowances resulting from the 

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

 

g See EME Homer City Generation, LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). h See EPA v. EME Homer City Generation, 134 S.Ct. 1584, 1610 (U.S. 2014). The Supreme Court held in part that 

EPA's methodology for quantifying emissions that must be eliminated in certain States due to their impacts in other 

downwind States was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision 

that provides statutory authority for CSAPR.

i See Georgia v. EPA, Order (D. C. Cir. filed October 23, 2014) (No. 11-1302)
2-14

However, standards would be expected to reduce NOX emissions in the states not affected by 

CAIR, 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 emissions factors based on AEO 2015, which 

incorporates the MATS.

For more detail on the emissions analysis, see chapter 13 of this NOPR TSD.

2.14 MONETIZING CARBON DIOXIDE AND OTHER EMISSIONS REDUCTIONS

DOE estimates the 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 emissions, DOE uses the 

most current Social Cost of Carbon (SCC) values developed by an interagency process. 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. 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.

The Interagency Working Group on SCC released an update of its previous report in 2013.4 The 

most recent estimates of the SCC in 2015, expressed in 2013$, are $12.0, $40.5, $62.4, and $119 

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 multiplied the CO2 emissions reduction estimated for each year by the SCC value 

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

values, DOE discounted the values in 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 GHG to changes in the future global climate and the potential 

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

DOE also estimated the potential monetary benefit of reduced NOX emissions resulting 

from the standard levels it considers. Estimates of monetary value for reducing NOX from

stationary sources range from $476 to $4,893 per ton in 2013$.5 DOE calculated monetary 

benefits using a medium value for NOX emissions of $2,684 per short ton (2013$), and real 

discount rates of 3 percent and 7 percent. DOE is investigating appropriate valuation of Hg and 

SO2 emissions. DOE has not monetized estimates of SO2 and Hg reduction in this rulemaking
2-15

For more detail on the monetization of emissions reductions, see chapter 14 of this NOPR 

TSD.

2.15 UTILITY IMPACT ANALYSIS

In the utility impact analysis, DOE analyzes the changes in electric installed capacity and 

generation that result for each TSL. DOE uses a variant of NEMS, referred to as NEMS-BT,j to 

account for these impacts. NEMS-BT has several 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. In addition, 

the comprehensiveness of NEMS-BT permits the modeling of interactions among the various 

energy supply and demand sectors.

The utility impact analysis is a comparison between the NEMS-BT model results for the 

base case and standard cases. The utility impact analysis reports the changes in installed capacity 

and generation that result from each standard level by plant type. DOE models the anticipated 

energy savings impacts from potential amended energy conservation standards using NEMS-BT 

to generate forecasts that deviate from the AEO Reference Case.

Further detail is provided in chapter 15 of this NOPR TSD.

2.16 EMPLOYMENT IMPACT ANALYSIS

The adoption of energy conservation standards can affect employment both directly and 

indirectly. Direct employment impacts are changes in the number of employees at the plants that 

produce the covered products. DOE evaluates direct employment impacts in the MIA.

Indirect employment impacts may result from expenditures shifting between goods (the 

substitution effect) and changes in income and overall expenditure levels (the income effect) that 

occur due to standards. DOE defines indirect employment impacts from standards as net jobs

eliminated or created in the general economy as a result of increased spending driven by 

increased product prices and reduced spending on energy.

Indirect employment impacts are investigated in the employment impact analysis using 

the Pacific Northwest National Laboratory’s “Impact of Sector Energy Technologies” (ImSET) 

model.6 The ImSET model was developed for DOE’s Office of Planning, Budget, and Analysis 

to estimate the employment and income effects of energy-saving technologies in buildings, 

industry, and transportation. Compared with simple economic multiplier approaches, ImSET 

allows for more complete and automated analysis of the economic impacts of energy 

conservation investments.

 

j 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).
2-16

Further detail is provided in chapter 16 of this NOPR TSD.

2.17 REGULATORY IMPACT ANALYSIS

In the NOPR stage, DOE prepared an RIA. The RIA addresses the potential for nonregulatory approaches to supplant or augment energy conservation standards in order to improve 

the energy efficiency or reduce the energy consumption of the product covered under this 

rulemaking.

DOE recognizes that voluntary or other non-regulatory efforts by manufacturers, utilities, 

and other interested parties can substantially affect efficacy or reduce energy consumption. DOE 

bases its assessment on the actual impacts of any such initiatives to date, but also considers 

information presented by interested parties regarding the impacts existing initiatives might have 

in the future.

Further detail is provided in chapter 17 of this NOPR TSD.

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 U.S. Attorney General to make a determination of “the impact of any 

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

6295) Pursuant to this requirement, DOE will solicit the views of 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 economic justification of a proposed standard.
2-17

REFERENCES

1 U.S. Department of Energy–Energy Information Administration. 2003 CBECS Survey Data. 

(Last accessed June 15, 2015.) 

http://www.eia.gov/consumption/commercial/data/2003/index.cfm?view=microdata.

2 Williams, A., B. Atkinson, K. Garbesi, E. Page, and F. Rubinstein. “Lighting Controls in 

Commercial Buildings.” LEUKOS. 2012. 8(3): pp. 161–180.

3 Navigant Consulting, Inc. Energy Savings Potential of Solid-State Lighting in General 

Illumination Applications. 2012. U.S. Department of Energy. (Last accessed June 10, 

2015.) http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_energy-savingsreport_jan-2012.pdf.

4 Interagency Working Group on Social Cost of Carbon. Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866. 2013. (Last 

accessed June 10, 2015.) 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf.

5 U.S. Office of Management and Budget. 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. (Last accessed June 10, 2015.) 

https://www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb

_final_report.pdf.

6 Scott, M., J. Roop, O. Livingston, R. Schultz, and P. Balducci. ImSET 3.1: Impact of Sector 

Energy Technologies Model Description and User’s Guide. 2009. Pacific Northwest 

National Laboratory,: Richland, WA. (Last accessed June 10, 2015.) 

http://www.pnl.gov/main/publications/external/technical_reports/PNNL-18412.pdf.
3-i

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT 

TABLE OF CONTENTS

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

3.2 SCOPE OF COVERAGE ................................................................................................ 3-1

3.3 METRIC .......................................................................................................................... 3-1

3.4 MARKET ASSESSMENT .............................................................................................. 3-2

3.4.1 Trade Association ................................................................................................ 3-2

3.4.2 Manufacturers and Market Share ......................................................................... 3-3

3.4.2.1 Small Businesses ...................................................................................... 3-3

3.4.3 Regulatory Programs ........................................................................................... 3-4

3.4.3.1 CFLK Federal Energy Conservation Standards ....................................... 3-4

3.4.3.2 California Title 20 .................................................................................... 3-5

3.4.3.3 Canadian Energy Efficiency Standards ................................................... 3-6

3.4.3.4 Underwriters Laboratories Safety Requirements ..................................... 3-6

3.4.4 Non-Regulatory Initiatives................................................................................... 3-6

3.4.4.1 Pasadena Water and Power – 2015 Home Energy Rewards Program ..... 3-6

3.4.4.2 ENERGY STAR ...................................................................................... 3-7

3.4.5 Ceiling Fan Light Kit Market Trends .................................................................. 3-8

3.5 TECHNOLOGY ASSESSMENT .................................................................................... 3-8

3.5.1 Compact Fluorescent Lamps ................................................................................ 3-9

3.5.2 Light-Emitting Diode Lamps ............................................................................. 3-10

3.5.3 CFLK Technology Options................................................................................ 3-15

3.5.3.1 Highly Emissive Electrode Coatings ..................................................... 3-16

3.5.3.2 Higher-Efficiency Lamp Fill Gas Composition ..................................... 3-17

3.5.3.3 Higher-Efficiency Phosphors ................................................................. 3-18

3.5.3.4 Glass Coatings ....................................................................................... 3-20

3.5.3.5 Multi-Photon Phosphors ........................................................................ 3-20

3.5.3.6 Cold Spot Optimization ......................................................................... 3-20

3.5.3.7 Improved Ballast Components ............................................................... 3-22

3.5.3.8 Improved Ballast Circuit Design ........................................................... 3-22

3.5.3.9 Change to LEDs ..................................................................................... 3-22

3.5.3.10 Efficient Down Converters ................................................................. 3-22

3.5.3.11 Improved Package Architectures ........................................................ 3-23

3.5.3.12 Improved Emitter Materials ................................................................ 3-24

3.5.3.13 Alternative Substrate Materials ........................................................... 3-27

3.5.3.14 Thermal Interface Materials ................................................................ 3-28

3.5.3.15 Optimized Heat Sink Design .............................................................. 3-29

3.5.3.16 Active Thermal Management Systems ............................................... 3-29

3.5.3.17 Device-Level Optics ........................................................................... 3-30

3.5.3.18 Increased Light Utilization ................................................................. 3-30

3.5.3.19 Improved Driver Design ..................................................................... 3-30

3.5.3.20 AC LEDs ............................................................................................. 3-30

3.5.3.21 Reduced Current Density .................................................................... 3-31
3-ii

3.6 PRODUCT CLASSES................................................................................................... 3-31

REFERENCES.......................................................................................................3-33

LIST OF TABLES

Table 3.4.1 Historical Shipments and Market Shares of ENERGY STAR-Qualified Products..3-8

Table 3.5.1 Ceiling Fan Light Kit Technology Options............................................................ 3-15

Table 3.5.2 Substrate Comparison.............................................................................................3-28

LIST OF FIGURES

Figure 3.5.1 Photon Production through Electroluminescence .................................................3-11

Figure 3.5.2 LED Chip...............................................................................................................3-12

Figure 3.5.3 LED Module..........................................................................................................3-12

Figure 3.5.4 Exploded View of a LED Lamp............................................................................3-13

Figure 3.5.5 Light Conversion in a Phosphor-Converted LED Lamp.......................................3-13

Figure 3.5.6 Creating White Light in LEDs ..............................................................................3-14

Figure 3.5.7 Human Eye Photopic Spectral Luminous Efficiency Function, V(λ)...................3-18

Figure 3.5.8 Light Output versus Phosphor Thickness..............................................................3-19

Figure 3.5.9 T2 and T3 Spiral CFL Performance at 140 mA ....................................................3-21

Figure 3.5.10 Projected LED Package Efficacy Trends............................................................3-24

Figure 3.5.11 The “Green Gap”.................................................................................................3-26

Figure 3.5.12 Efficiency Droop .................................................................................................3-27
3-1

CHAPTER 3 MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter consists of four sections: scope of coverage, market assessment, the 

technology assessment, and product classes. The scope of coverage describes the products 

covered under this rulemaking. The market assessment provides an overall picture of the market 

for the products concerned, including the nature of the products, industry structure, and 

manufacturer market shares; regulatory and non-regulatory efficiency improvement programs; 

market trends; and quantities of products sold. The technology assessment identifies a 

preliminary list of technologies to consider in the screening analysis. The product classes section 

discusses the class-setting factors the U.S. Department of Energy (DOE) is proposing for this 

rulemaking.1

The information DOE gathers from the market and technology assessment serves as 

resource material for use throughout the rulemaking. DOE considers both quantitative and 

qualitative information from publicly available sources and interested parties.

3.2 SCOPE OF COVERAGE

In this section, DOE clarifies the scope of lamps included in this rulemaking. The Energy 

Policy and Conservation Act of 1975 (EPCA) defines a “ceiling fan light kit” (CFLK) as 

“equipment designed to provide light from a ceiling fan that can be integral, such that the 

equipment is attached to the ceiling fan prior to the time of retail sale; or attachable, such that at 

the time of retail sale the equipment is not physically attached to the ceiling fan, but may be 

included inside the ceiling fan at the time of sale or sold separately for subsequent attachment to 

the fan.” a (42 U.S.C. 6291(50)(A)-(B)) In the CFLK test procedure (TP) notice of proposed 

rulemaking (NOPR), DOE proposed to withdraw the current guidanceb on accent lighting and to 

consider all lighting packaged with any CFLK to be subject to energy conservation requirements.

79 FR 64688, 64692 (Oct. 31, 2014). Additionally, in the ceiling fan TP NOPR, DOE proposed

to reinterpret the definition of a ceiling fan to include hugger fans. 79 FR 62521, 62522 (Oct. 17, 

2014). For additional details on DOE’s reasoning for proposing these changes, please see the 

relevant NOPRs and supporting documents.

3.3 METRIC

DOE is proposing to use luminous efficacy as the efficiency metric for all CFLKs. DOE 

proposes lamp efficacy where possible and luminaire efficacy where the lamp component in the 

CFLK is not designed to be consumer replaceable from the CFLK. As proposed in the CFLK TP 

NOPR (79 FR 64688, 64694 [Oct. 31, 2014]), IES LM-79-08 would be used to test the luminaire 

 a Ceiling fan is defined as “a nonportable device that is suspended from a ceiling for circulating air via the rotation 

of fan blades.” (42 U.S.C. 6291(49))

b Guidance on accent lighting is available at 

www1.eere.energy.gov/guidance/detail_search.aspx?IDQuestion=470&pid=2&spid=1.
3-2

efficacy of CFLKs with integrated solid-state lighting (SSL) circuitry (i.e., light sources, drivers, 

or intermediate circuitry that is not consumer replaceable). DOE determined that for CFLKs with 

integrated SSL circuitry, luminaire efficacy was an appropriate metric because either destructive 

disassembly would be required to determine the lamp efficacy or, where non-destructive 

disassembly was possible, lamp efficacy measurements may not be consistent or accurate. 79 FR 

64688, 64693, 64703-64704 (Oct. 31, 2014).

3.4 MARKET ASSESSMENT

For CFLKs, the following market assessment identifies the relevant manufacturer trade 

association and domestic manufacturers; discusses manufacturer market share, regulatory 

programs, and non-regulatory initiatives; and provides historical shipment data and market 

trends.

3.4.1 Trade Association

The American Lighting Association (ALA) is the trade association for CFLKs. ALA is 

composed of a group of lighting and ceiling fan manufacturers, showroom retailers, 

manufacturer representatives, and designers. Currently, 25 CFLK manufacturers are active 

members of ALA. ALA oversees various types of light fixtures, including emergency lighting, 

wall and ceiling lighting, and integrated light-emitting diode (LED) fixtures. As an association, 

ALA’s objectives are organized into three categories – public affairs, education, and organized 

affairs. Some of their activities include:1

• “Promoting the value of the showroom distribution channel to consumers and interior 

designers

• Maintaining full involvement in U.S. and Canadian regulatory standards, code 

development, and legislation involving lighting and ceiling fans

• Supporting the proper application of good lighting solutions with energy-efficient 

products

• Focusing ALA's proactive legislative agenda on lighting and small business issues

• Developing and providing members with training and certification opportunities

• Offering networking opportunities through small groups to help members improve sales 

and their business operations

• Conducting a high-energy, high-powered conference that provides meaningful learning 

and networking opportunities that address industry and business issues

• Continuing to develop and provide services of bottom-line value

• Promoting the approved ALA data standard for the lighting industry to use in exchanging 

item data information”
3-3

3.4.2 Manufacturers and Market Share

The following list contains the names of some manufacturers that produce CFLKs:

• Allen + Roth

• Big Ass Fans

• Casablanca Fan Company

• Concord

• Craftmade Ceiling Fans

• DHI Corp.

• Ellington Ceiling Fans

• Emerson Electric Company

• Fanimation, Inc.

• Hampton Bay

• Harbor Breeze

• Hunter Fan Company

• Kichler Lighting

• King of Fans, Inc.

• Maxim Lighting International

• Minka Lighting, Inc.

• Modern Fan Company

• Monte Carlo Fans

• NICOR

• Broan-NuTone LLC

• The Period Arts Co.

• Progress Lighting

• Quorum International

• Regency Ceiling Fan Corporation

• Royal Pacific Ltd.

• Savoy House Lighting

• Sea Gull Lighting

• Sunway Fan Company

• Volume International Corp.

• Westinghouse Electric Corporation

• Yosemite Home Décor

3.4.2.1 Small Businesses

Small businesses may be particularly affected by the promulgation of minimum energy

conservation standards for CFLKs. The Small Business Administration lists small business size 

standards that are matched to industries as they are described in the North American Industry 

Classification System (NAICS). A size standard is the largest that a for-profit concern can be and 

still qualify as a small business for federal government programs. These size standards are 

generally the average annual receipts or the average employment of a firm. For CFLKs, the size 
3-4

standard is matched to NAICS code 335210, Small Electrical Appliance Manufacturing, which 

has a size standard of 750 employees or fewer.

DOE studies the potential impacts on these small businesses in detail as part of the 

manufacturer impact analysis.

3.4.3 Regulatory Programs

Several federal and international regulatory programs affect the markets for CFLKs. The 

following section summarizes U.S., Canadian, and European regulatory initiatives relevant to the 

CFLKs covered by this rulemaking. While the following discussion is not exhaustive in 

describing all regulatory action related to CFLKs, it provides detail on some notable initiatives 

that characterize recent developments in the market.

3.4.3.1 CFLK Federal Energy Conservation Standards

Title III, Part Bc of EPCAd (42 U.S.C. 6291, et. seq.) established the Energy Conservation 

Program for Consumer Products Other Than Automobiles, a program covering most major 

household appliances (collectively referred to as “covered products”), which includes the CFLKs 

that are the subject of this rulemaking. (42 U.S.C 6295(ff)) Specifically, the Energy Policy Act 

of 2005 (Title I, Subtitle C, section 135(c)) established standards for CFLKs, which were later 

amended by EPCA. The current energy conservation standards apply to CFLKs with medium 

screw base and pin-based sockets manufactured on and after January 1, 2007, and CFLKs with 

all other socket types manufactured on or after January 1, 2009, as follows:e

(2)(i) Ceiling fan light kits with medium screw base sockets manufactured on or after 

January 1, 2007, shall be packaged with screw-based lamps to fill all screw base sockets.

(ii) The screw-based lamps required under paragraph (2)(i) of this section shall—

(A) Meet the ENERGY STAR Program requirements for Compact Fluorescent Lamps, 

version 3; or

(B) Use light sources other than compact fluorescent lamps that have lumens per watt 

performance at least equivalent to comparable configured compact fluorescent lamps 

meeting the energy conservation standards described in paragraph (2)(ii)(A) of this 

section.

(3) Ceiling fan light kits with pin-based sockets for fluorescent lamps manufactured on or 

after January 1, 2007 shall—

(i) Meet the ENERGY STAR Program Requirements for Residential Light Fixtures 

version 4.0 issued by the Environmental Protection Agency; and

(ii) Shall be packaged to include the lamps described in paragraph (s)(3)(i) of this section 

with the ceiling fan light kits to fill all sockets.

 c For editorial reasons, upon codification in the U.S. Code, Part B was re-designated Part A.

d All references to EPCA in this document refer to the statute as amended through the Energy Efficiency 

Improvement Act of 2015, Pub. L. 114-11 (Apr. 30, 2015). e These standards were set forth in DOE’s regulations at 10 CFR 430.32(s).
3-5

(4) Ceiling fan light kits with socket types other than those covered in paragraphs (2) and 

(3) of this section, including candelabra screw base sockets, manufactured on or after 

January 1, 2009—

(i) Shall not be capable of operating with lamps that total more than 190 watts; and

(ii) Shall be packaged to include the lamps described in clause (i) with the ceiling fan 

light kits.

70 FR 60407, 60413 (Oct. 18, 2005)

EPCA, as amended, authorized DOE to conduct future rulemakings to determine whether 

to amend these standards. (42 U.S.C. 6295(ff)(5)-(6)) Under 42 U.S.C. 6295(m), DOE must also 

periodically review its already established energy conservation standards for a covered product.

This NOPR is part of DOE’s cycle of review to evaluate standards for CFLKs. This rulemaking

also addresses 42 U.S.C. 6295(gg)(3) in which DOE is directed to incorporate standby mode and 

off-mode energy use in any amended (or new) standard adopted after July 1, 2010. Specifically, 

when DOE adopts a standard for a covered product after that date, it must, if justified by the 

criteria for adoption of standards under EPCA (42 U.S.C. 6295(o)), incorporate standby mode 

and off-mode energy use into a single standard, or, if that is not feasible, adopt a separate 

standard for such energy use for that product. (42 U.S.C. 6295(gg)(3)(A)-(B)) In the CFLK TP 

NOPR, DOE proposed that the energy use from standby mode and off mode associated with 

CFLKs be attributed to the ceiling fan to which they are attached, and thus any standby mode 

energy use is accounted for in the ceiling fan test procedure. Therefore, the CFLK metric 

accounts for energy consumption only in active mode. 79 FR 64688 (Oct. 31, 2014). DOE will 

account for active mode energy use in any final amended energy conservation standards.

3.4.3.2 California Title 20

California’s Office of Administrative Law (OAL) enacts statewide regulatory programs 

published in the California Code of Regulations (CCR). CCR Title 20, Division 2, Chapter 4, 

Article 4, contains appliance efficiency regulations for CFLKs, and was last revised in August 

2013. This law applies to new CFLKs that are offered for sale or sold in California for use in 

California excluding use in mobile equipment such as recreational vehicles. This law requires 

CFLKs for fans that are not hugger fans to be tested following 10 CFR Section 430.23 (x) 

(Appendix V to subpart B of Part 430) (2008). Under this article, there are no federally or nonfederally regulated efficiency standards. Yet, there are federally and non-federally regulated 

energy design standards applicable to CFLKs manufactured on or after January 1, 2007. These 

design standards require CFLKs with medium screw base sockets or pin-based sockets be 

packaged the appropriate lamps that meet the most recent ENERGY STAR requirements, and 

shall not be capable of operating with lamps that total more than 190 watts (W).
3-6

3.4.3.3 Canadian Energy Efficiency Standards

The Natural Resources Canada (NRCan) Office of Energy Efficiency regulates the 

energy efficiency of CFLKs in Canada.f In 1992, the Canadian Parliament passed the Energy 

Efficiency Act (S.C. 1992, c. 36), which concerns minimum performance levels for energy-using 

products, effective February 1995. In January 2010, Canada’s Energy Efficiency Regulations

(SOR/94-651) were amended to include CFLKs.

Canada’s approach to the regulation of CFLKs harmonizes with the U.S. energy 

conservation standards established by the Energy Independence and Security Act (EISA)

discussed in section 3.4.3.1 in most ways.2 The NRCan standards that were published in 2007 

were made to align with the DOE 10 CFR 430.32 regulations, but were amended in 2010 with 

CSA C22.2 No. 9. This amendment changed the 190 W limiter requirement that NRCan had 

originally harmonized with the U.S. regulations such that ceiling fans with integral light kits that 

use less than or equal to 10 W of electrical power and have at least one socket that is not pinbased are not required to have a 190 W limiter.3

3.4.3.4 Underwriters Laboratories Safety Requirements

Underwriters Laboratories (UL) is a safety organization that develops standards for safety 

requirements for luminaires of all types, including CFLKs. CFLKs in this rulemaking are 

referred to as “ceiling-suspended fan light kits” in UL 507. UL 507 requires that the light fixture 

follow the appropriate luminaire UL certification for the lighting technology in use as applicable 

through the Standard for Luminaires, UL 1598, in addition to specific requirements for 

construction, performance, marking, and installation of the CFLK. The required markings 

include a clear combined electrical input rating of the fan and light kit if the light kit is packed 

separately from the fan. Additionally, a permanent marking is required indicating the maximum 

lamp wattage to be used.

3.4.4 Non-Regulatory Initiatives

DOE reviewed several national, regional, and local voluntary programs that promote the 

use of energy-efficient lighting in the United States. The following section summarizes these 

programs for the lamps covered by this rulemaking. While it is not an exhaustive list, the 

discussion provides detail on some notable initiatives that characterize voluntary energy 

efficiency efforts in the lighting market.

3.4.4.1 Pasadena Water and Power – 2015 Home Energy Rewards Program

Pasadena Water and Power (PWP), a utility company in Pasadena, California, offers a 

variety of incentives to help encourage energy conservation. These incentives apply to energyefficient heating, cooling, and appliances, and also to shade trees. Part of PWP’s incentive 

program is a rebate of $40 per ENERGY STAR rated ceiling fan with light kit, and a $5 bonus 

 f News and information on NRCan energy efficiency regulations and standards can be found at 

http://oee.nrcan.gc.ca/regulations/7957.
3-7

rebate for local purchases on the condition that the fan must come with the appropriate number 

of lamps.4

3.4.4.2 ENERGY STAR

To qualify for the ENERGY STAR label, CFLKs must meet the criteria specified in 

ENERGY STAR Program Requirements for Luminaires version 1.2, which became effective 

December 21, 2012, and is effective until June 1, 2016. These requirements have specific criteria 

for directional residential CFLKs under “accent lights” and for non-directional residential 

CFLKs under “indoor ceiling/close-to-ceiling mount luminaires.” To qualify for the ENERGY 

STAR label, directional residential products must be evaluated with luminaire photometry and 

non-directional residential products must be evaluated with source photometry requirements.g

Non-directional CFLKs that use fluorescent lamps, SSL, or high intensity discharge lamps 

(HIDs) must be packaged with lamps that have a minimum efficacy of 65 lm/W. ENERGY 

STAR additionally only recognizes fluorescent lamps that are non-integrated or have a GU24 

base, LED sources that have a GU24 base or are LED light engines, and CFLKs with HID lamps 

labeled for outdoor use. The non-directional CFLK must provide a minimum of 800 lumens, 

except in cases where the light kit has three of more heads. In this case, each head must provide a 

minimum of 450 lumens. Outdoor non-directional CFLKs with halogen incandescent lamps can 

pass ENERGY STAR regulations by utilizing specific controls that limit the time that the light 

kit will remain powered. In order for directional CFLKs to pass ENERGY STAR requirements, 

they must be packaged with lamps that are rated at or above 35 lm/W, deliver a minimum of 200 

lumens, and deliver a minimum of 80 percent of the total initial lumens within the 0-40 degree 

zone. ENERGY STAR also has requirements for other lamp characteristics in the CFLK such as 

CCT, CRI, lumen maintenance, light source life, color angle uniformity, color maintenance, start 

time, run-up time, power factor, transient protection, standby power, operating frequency, light 

source replicability, thermal performance, safety, dimming, labeling and packaging, warranty, 

lighting toxics reduction, and current crest factor.5

On June 1, 2016, CFLKs must meet the criteria specified in ENERGY STAR Program 

Requirements for Luminaires version 2.0. For these latest program requirements, coverage of 

lamps will no longer be limited to non-integrated or GU24 base fluorescent lamps for nondirectional CFLKs, and halogen incandescent lamps will no longer qualify. Additionally, the 

efficacy of directional CFLKs will be raised from 35 lm/W to 55 lm/W, and the zone for which 

the lamps must deliver 80 percent of their total lumens will be increased from 0-40 degrees, to 0-

60 degrees. Version 2.0 will also make changes to the lumen maintenance requirements, light 

source requirements, and color angle uniformity, color maintenance, start time, run-up factor, 

safety, dimming, labeling and packaging, and lighting toxics reduction requirements. These 

changes mainly to do with supporting the growth of LED light engines in CFLKs and with 

removing exemptions for certain types of fluorescent lamps.6

 g CFLKs that use screw base lamp holders (i.e., ANSI E26, E26d E12, E17, E39, E39d) without dedicated ballasts 

are not eligible to qualify for the ENERGY STAR label. The ENERGY STAR requirements are available at: 

http://www.energystar.gov/ia/partners/product_specs/program_reqs/Final_Luminaires_V1_2.pdf?3c71-128e.
3-8

3.4.5 Ceiling Fan Light Kit Market Trends

DOE evaluated CFLK market trends by considering data from ENERGY STARh on the 

number of ceiling fans sold with CFLKs and CFLKs sold alone for different years. As shown in 

Table 3.4.1, ENERGY STAR shipments and market share data are reported for two categories of 

CFLKs: ENERGY STAR-qualified CFLKs with ceiling fans and ENERGY STAR-qualified 

CFLKs only.

7

Table 3.4.1 Historical Shipments and Market Shares of ENERGY STAR-Qualified 

Products

CFLKs with Ceiling Fans CFLKs Only

Year

Reported 

Energy Star 

Shipments

% Energy 

Star Market 

Penetration

Reported 

Energy Star 

Shipments

% Energy 

Star Market 

Penetration

% of 

Respondents

2004 166,000 2 24,900 1 91

2005 368,355 4 130,276 6 100

2006 117,289 1 11,952 1 93

2007 132,100 1 21,120 1 97

2008 126,769 1 45,436 2 100

2009 219,000 2 61,000 3 100

2010 304,000 3 171,000 8 89

2011 581,000 6 67,000 3 86

2012 621,000 6 167,000 7 87

2013 576,000 6 68,000 3 89

Shipments of CFLKs purchased alone or with a ceiling fan reached a peak in 2007 and 

have since fluctuated slightly between about 13 million units and 14 million units, with a spike in 

2012. This spike falls in line with the hottest summer in the U.S. on record. ENERGY STAR’s 

historical shipment data indicate that CFLKs sold alone represent 17 percent of the ceiling fan 

market on average. In 2006 and 2007, when CFLKs had the highest total sales, CFLKs sold 

alone were a smaller proportion of total sales than in all other years.

3.5 TECHNOLOGY ASSESSMENT

The technology assessment identifies technology options that improve CFLK efficacy. 

This assessment provides the technical background and structure on which DOE bases its 

screening and engineering analyses. The following assessment provides a description of the basic 

 h ENERGY STAR takes partner-reported data to be representative of qualified ceiling fan shipments without 

adjusting for partners who do not report their shipments. This means that the ENERGY STAR data represent a 

conservative estimate, although the response rate among ceiling fan manufacturers is typically high (e.g., 89 percent 

in 2010). To estimate the market share of ENERGY STAR-qualified ceiling fans, ENERGY STAR estimates U.S. 

shipments based on modeled and documented data. Ceiling fan industry data are used as a direct input whenever 

possible and are compared to forecast assumptions to ensure data comparability. Industry sources may include 

ceiling fan manufacturer associations, publications, and commercially available market research.
3-9

construction and operation of compact fluorescent lamps (CFLs) and LED lamps, followed by 

options to improve efficacy within these lamp technologies.

3.5.1 Compact Fluorescent Lamps

CFLs are fluorescent lamps of bent-tube construction. The bent-tube construction of 

CFLs (e.g., spiral or U-shaped) allows them to fit into smaller spaces. CFLs consist of a glass 

envelope or tube with a phosphor coating on its interior surface, oxide coated electrodes on each 

end of the tube, small amounts of mercury or mercury amalgam, and an inert gas (e.g., argon). 

Voltage is applied to the two electrodes at the ends of the tube, emitting large quantities of 

electrons that collide with gaseous mercury atoms and excite the electrons within those atoms. 

As the mercury electrons return to their stable state, they release ultraviolet (UV) radiation. 

Because UV radiation is not visible to the human eye, phosphors are coated along the inner walls 

of the tube to absorb the UV radiation and emit visible light. CFLs are also operated with a 

current-limiting device, either a ballast integrated into the CFL (i.e., self-ballasted) or a ballast 

separate from the lamp (i.e., externally ballasted).8

Like general service fluorescent lamps (GSFLs), the two electrodes are hermetically 

sealed to both ends of the bulb, typically constructed from coiled tungsten wires coated with a 

mixture of alkaline oxides. Once a ballast applies the initial voltage to the electrodes, the 

tungsten coil and coatings emit large quantities of electrons after the coating has reached the 

appropriate temperature (800 degrees Celsius [°C]). Although the electrodes are actually in close 

proximity to each other at the base of the lamp (both tubes connect to the single base of the 

lamp), the electrons released by the coiled wires travel the full length of the tubes. These 

electrons collide with gaseous mercury atoms and the inert gas filling the rest of the tube. The 

inert gas is present to moderate the collisions of mercury ions and minimize evaporation of the 

electrode coating. The electrons from the electrodes excite those in the mercury atoms to release 

UV radiation that is absorbed by the phosphors and reemitted as visible light.9

A key to the miniaturization of GSFLs into CFLs in the 1980s was the development of 

new phosphor compositions that are more stable in intense UV radiation. A combination of rare 

earth phosphors is best suited for the miniaturization and strong radiation, while also providing 

better color rendering and increased efficacy (lumens per watt).10 CFLs use the same phosphors 

as GSFLs, but in different compositions. The five rare earth elements in the phosphors used in 

these lamps are lanthanum, cerium, europium, terbium, and yttrium. CFLs primarily use a 

Calcium Tungstate (CAT) phosphor that is higher in cerium, and GSFLs use a Lanthanum 

Phosphate (LAP) phosphor that is higher in lanthanum.11

Fluorescent lamp performance is influenced by the cold spot on a lamp and the ambient 

temperature of the operating environment. The cold spot is the area where excess mercury 

condenses, so the temperature of the cold spot defines the level of mercury vapor pressure and 

thus the luminous flux.12 CFLs fail when the emissive coating on the electrodes is completely 

dissipated by evaporation or sputtering. The inert gas is used to prevent this from occurring by 

protecting the electrodes from the bombardment of mercury ions, but loss of the emissive coating 

during starting is inevitable, albeit in small amounts.13
3-10

CFLs must be operated with magnetic or electronic ballasts, which supply the starting 

and operating voltages required by the lamp and limit the current while the lamp is in use. CFLs 

can be self-ballasted or externally ballasted (sometimes referred to as pin-based). The majority of 

CFLs in the marketplace are self-ballasted, and virtually all of these have electronic ballasts. 

These self-ballasted CFLs integrate a screw base with a ballast and can be directly fitted into a 

CFLK. Popular base types for self-ballasted CFLs include E12 (candelabra), E17 (intermediate), 

E26 (medium), E39 (mogul), or GU24 (twist and lock), with E26 being the most common 

because it is designed to fit standard luminaires and overhead light fixtures.9

External ballasts can be electronic or magnetic, though the market is shifting more toward 

electronic ballasts due to improved system efficacy and starting time; minimized flickering and

operating sounds; and lighter weight. Externally ballasted CFLs can be found with 2-pin or 4-pin 

socket configurations, with base types including (but not limited to) G23, 2G7, G24d, G24q, 

2G11, and GR10q.14 Base types that are 4-pin are generally paired with electronic ballasts that 

may be dimmable, while 2-pin bases are typically magnetic and seldom paired with electronic 

ballasts.9

CFL ballast starting modes, for both self-ballasted and externally ballasted CFLs, include 

preheat, rapid start (RS), instant start (IS), and programmed start (PS). The preheat mode 

requires an external starter or switch to strike the arc. RS ballasts produce a nearly instant start 

with almost full lumen output by having a short period of electrode heating, followed by the 

application of a higher voltage to initiate the arc between the two electrodes. IS ballasts bypass 

the initial electrode heating and apply high voltage to create an instant start of the arc between 

the two unheated electrodes. IS ballasts may sustain damage to the electrodes and reduced lamp 

life when they are started frequently, while PS ballasts are designed to prevent this damage by 

controlling the frequency of starting.9,13

CFL shapes differ depending on whether the lamp is self-ballasted or externally ballasted. 

Self-ballasted lamps can either be bare tubes (most often spiral-shaped) or tubes with an outer 

cover. These covered lamps primarily include A-shapes, globes, reflectors, posts, and candles.15

Externally ballasted CFLs typically consist of an arrangement of bare, bent tubes.14

3.5.2 Light-Emitting Diode Lamps

Generally, LED lamps utilize multiple LEDs inside of a housing that disperses or 

redirects the light from the point source LEDs to provide general illumination. LEDs work by 

using semiconductor material intentionally doped with an impurity to make a positive (p-type) 

and negative (n-type) region. The doping elements contain a similar atomic structure with either 

slightly fewer valence electrons to create a p-type region or slightly more valence electrons

creating an n-type region. This causes the p-type regions to have hole concentrations while the ntype regions have excess electrons. When a voltage is applied, excess electrons from the n-type 

region combine with holes from the p-type regions and the resulting recombination radiation 

produces photons. The semiconducting elements most commonly used in LEDs for lighting are 

group III (boron) and V (nitrogen) combinations such as gallium nitride (GaN). The different 

group III and group V pairings have different size bandgaps, and these different energy levels 

correspond to distinct parts of the visible spectrum. For instance, GaN LEDs usually emit violet 
3-11

light; however, their wavelength can be adjusted by mixing in other elements with different 

bandgaps. An added benefit of group III-V compositions is that they often have direct rather than 

indirect bandgaps, leading to more-efficient LEDs.16 An indirect bandgap is less efficient as it 

requires the electron to pass through an intermediate state and transfer momentum to the crystal 

lattice before it can recombine with a hole to produce a photon. Figure 3.5.1 displays this 

phenomenon known as electroluminescence.

Figure 3.5.1 Photon Production through Electroluminescence

Source: Digi-Key17

The photon production happens within the LED chip where the p and n-type regions, 

known as epitaxial layers, are built on top of a substrate, most commonly made out of sapphire.18

Often a reflective layer such as silver is used below the active region to help prevent absorption 

of emitted light into the substrate as seen in Figure 3.5.2. This is just one of many different 

approaches that are utilized to minimize absorption. The epitaxial layers, substrate, and a 

covering lens make up the LED module. LED modules or arraysi can be integrated into a lamp to 

provide illumination. If required, a phosphor can be mounted directly onto the LED die, as seen 

in Figure 3.5.3, or placed remotely to achieve a white light. The LEDs are mounted to the lamp 

housing which acts as a heat sink for the LEDs. The housing also accommodates the driver, 

which transforms the incoming power supply from AC to DC and controls the flow of power to 

the LEDs. LED circuitry employs capacitors (typically electrolytic capacitors) to store electric 

charges on the input AC stage to enable filtering of noise or on the output channel DC stage. 

Similar to CFLs, LED lamps are offered in a variety of lamp shapes and base types to serve as 

replacements for general service incandescent lamps (GSILs). Figure 3.5.4 shows the breakdown 

of the components for a typical A-shape LED GSL.

i The IESNA Lighting Handbook defines “LED array or module” as an assembly of LED packages (components), or 

dies on a printed circuit board or substrate. This assembly could also have optical elements and additional thermal, 

mechanical, and electrical interfaces that are intended to connect to the load side of a LED driver. However, the 

power source and ANSI standard base are not incorporated into the device and it cannot be connected directly to the 

branch circuit.
3-12

Figure 3.5.2 LED Chip

Source: LED Magazine19

Figure 3.5.3 LED Module

Source: WILA20
3-13

Figure 3.5.4 Exploded View of a LED Lamp

Source: EE Times21

White Light. LED lamps typically consist of an InGaN/GaN LED that emits blue light,

which then meets a yttrium aluminum garnet (YAG) phosphor coating before leaving the 

luminaire. That phosphor converts a portion of the blue light to longer wavelength (green 

through red) light to produce a combined white light as shown in Figure 3.5.5. Though phosphor 

coating a blue LED is the most common mode of producing white light in LED lamps, many 

other LED systems can achieve white light.

Figure 3.5.5 Light Conversion in a Phosphor-Converted LED Lamp

Source: Japanese Journal of Applied Physics22

Combining blue, green, and red LEDs can also produce a balanced white light and is 

theoretically the most efficient way to produce light since no light is lost during phosphor 

conversion; however, since green and amber LEDs lag behind blue LEDs in their respective 
3-14

efficiency, this method is uncommon in today’s LED lamp market.23 Hybrid LED architectures 

contain a mix of LED colors and use phosphor conversion on some of the LEDs. One example of 

such a product is the Philips L-Prize lamp. The lamp down shifts some blue light to the green 

spectrum to avoid the least efficient green LEDs and adds in red LEDs to produce white light at a 

warmer color temperature and a higher CRI. Figure 3.5.6 describes the three primary methods of 

creating white light with LEDs.

Figure 3.5.6 Creating White Light in LEDs

Source: U.S. Department of Energy24

Lifetime. Unlike GSILs and CFLs, LED lamps rarely fail entirely. Rather their light 

output drops throughout their lifetime. For this reason ENERGY STAR, the lighting industry, 

and standards organizations have approached lifetime ratings based on the amount of time 

expected before a 70 percent deterioration in total luminous flux (L70) rather than the expected 

lifetime of the components, which tend to be much longer.25 At this stage, two primary causes of 

premature failure of LED GSLs are overheated capacitors and faulty solder joints. Some 

electrolytic capacitors used in the lamps are not rated to withstand the high level of heat they 

endure which will shorten their lifetime. Low quality solder joints, particularly those that are 

hand soldered, as well as mechanical fatigue from thermal stresses, can also lead to broken 

contacts and lamp failure.26
3-15

3.5.3 CFLK Technology Options

This section outlines the technology options DOE is proposing for improving efficacy of 

CFLKs. DOE reviewed manufacturer catalogs, recent trade publications, technical journals, and 

patent filings to determine technology options. Table 3.5.1 presents the technology options DOE 

has identified to improve the efficacy of CFKLs. DOE notes in Table 3.5.1 the applicable lamp 

type for the technology options listed.

Table 3.5.1 Ceiling Fan Light Kit Technology Options

Lamp Type Name of Technology Option Description

CFL

Highly Emissive Electrode 

Coatings

Improved electrode coatings allow electrons to be more easily 

removed from electrodes, reducing lamp power and 

increasing overall efficacy.

Higher-Efficiency Lamp Fill 

Gas Composition

Fill gas compositions improve cathode thermionic emission 

or increase mobility of ions and electrons in the lamp plasma.

Higher-Efficiency Phosphors Techniques to increase the conversion of ultraviolet (UV) 

light into visible light.

Glass Coatings Coatings on inside of bulb enable the phosphors to absorb 

more UV energy, so that they emit more visible light.

Multi-Photon Phosphors Emitting more than one visible photon for each incident UV 

photon.

Cold Spot Optimization Improve cold spot design to maintain optimal temperature 

and improve light output.

Improved Ballast Components Use of higher-grade components to improve efficiency of 

integrated ballasts.

Improved Ballast Circuit 

Design

Better circuit design to improve efficiency of integrated 

ballasts.

Change in Technology Replace CFL with LED technology.
3-16

Lamp Type Name of Technology Option Description

LED

Efficient Down Converters

New high-efficiency wavelength conversion materials, 

including optimized phosphor conversion, quantum dots, and 

nano-phosphors, have the potential for creating warm-white 

LED emitters with improved spectral efficiency, high color 

quality, and improved thermal stability.

Improved Package 

Architectures

Novel package architectures such as RGB+, system-inpackage, hybrid color, and chip-on-heat-sink have the 

potential to improve thermal management, color-efficiency, 

and optical distribution, as well as electrical integration to 

greatly improve overall lamp and luminaire efficacy.

Improved Emitter Materials

The development of efficient red, green, or amber LED 

emitters, will allow for optimization of spectral efficiency 

with high color quality over a range of CCT, and color and 

efficiency stability with respect to operating temperature.

Alternative Substrate Materials

Alternative substrates such as gallium nitride (GaN), silicon 

(Si), GaN-on-Si, and silicon carbide to enable high-quality 

epitaxy for improved device quality and efficacy.

Improved Thermal Interface 

Materials (TIM)

Develop TIMs that enable high-efficiency thermal transfer for 

long-term reliability and performance optimization of the 

LED device and overall lamp product.

Optimized Heat Sink Design

Improve thermal conductivity and heat dissipation from the 

LED chip, thus reducing efficacy loss from rises in junction 

temperature.

Active Thermal Management 

Systems

Devices such as internal fans, vibrating membranes, and 

circulated liquid cooling systems to improve thermal 

dissipation from the LED chip.

Device-Level Optics

Enhancements to the primary optic of the LED package that 

would simplify or remove entirely the secondary optic, and 

thereby reduce losses due to absorption at interfaces.

Increased Light Utilization

Reduce optical losses from the lamp housing, diffusion, beam 

shaping, and color mixing to increase the efficacy of the LED 

lamp.

Improved Driver Design Increase driver efficiency through novel and intelligent circuit 

design.

AC LEDs Reduce or eliminate the requirements of a driver and 

therefore the effect of driver efficiency on lamp efficacy.

Reduced Current Density

Increase the number of LEDs in a lamp to reduce current 

density while maintaining lumen output. This reduces the 

efficiency losses associated with higher current density.

3.5.3.1 Highly Emissive Electrode Coatings

Fluorescent lamp electrodes are generally tungsten filaments coated with a mixture of 

alkaline earth oxides. The purpose of the electrodes is to emit a sufficient number of electrons to 

ionize the gas and maintain the lamp discharge. When electrons are more easily emitted from the 

electrodes, a lower voltage is needed to maintain the arc. Therefore, any improvement in 

electrode coating that would allow electrons to be more easily removed from the electrodes 
3-17

would reduce the lamp power and increase the overall efficacy of the lamp. Using highly 

emissive electrode coatings essentially addresses the efficiency of the conversion of electrical 

input power to visible radiation. In addition to raising efficacy, highly emissive electrode 

coatings can result in an increased lamp lifetime. Without sufficient electron emission from the 

electrode, a large voltage gradient is created in front of the electrode during the cathode cycle, 

which is emitting negatively charged ions and receiving positively charged ions. This high 

electric field accelerates these ions into the electrode at a high velocity, causing electrode 

damage. The damage shortens the lifetime of the lamp. Conventional emissive coatings include 

barium oxide (BaO), calcium oxide (CaO), and strontium oxide (SrO). Additional materials have 

been mixed with existing conventional oxides to coat fluorescent lamp electrodes. These 

materials include zirconium oxide (ZrO), which extends lamp lifetime, and silicon carbide (SiC), 

which more effectively removes electrons from the electrode.

3.5.3.2 Higher-Efficiency Lamp Fill Gas Composition

Fluorescent lamps contain mercury vapor, which when ionized produces UV radiation. 

They also contain a rare gas or combination of gases to facilitate ignition. These “lamp fill 

gases” affect the mobility of the mercury ions and electrons in the lamp plasma based on their 

molecular weight. Lower molecular weight gases generally result in higher lamp efficacy. As 

lighter gases are used, the mobility of mercury ions and electrons increases, allowing them to 

reach greater velocities. This causes a rise in electron temperature of the plasma, facilitating 

recombination and ultimately raising the UV radiation to saturation level. However, if the 

mobility of the ions and electrons exceeds a certain optimal point, they are then able to reach the 

lamp glass surface, which prevents emission and effectively reduces UV output.

Standard lamps generally use argon gas or a mixture of argon and neon. These gases are 

usually at low pressures, typically ranging from three to four torr. As the pressure exceeds a 

certain point, elastic scattering increases, decreasing the mobility of the ions and electrons. This 

decreased mobility lowers the total UV radiation of the lamp, thereby decreasing lamp efficacy. 

Because fluorescent lamp ballasts are often current-controlled devices, the use of higherefficiency lamp fill gases results in lower power.

Lamp fill gas composition can also affect lamp lifetime due to collisions between the 

lamp fill gas and the evaporated electrode coatings. As discussed earlier, one common emissive 

coating used on electrodes is barium oxide (BaO). During lamp operation, the BaO coating 

slowly evaporates from the surface of the electrode. However, some of these escaped barium 

atoms then collide with the lamp fill gas, propelling them back toward the electrode and 

redepositing them. The relative thermionic emission for the cathode, a measure of the number of 

electrons emitted, is much greater for a larger molecular weight gas than for a lighter gas. This is 

because a larger mass atom can more effectively collide with and change the trajectory of the 

barium atoms, providing a lower diffusion rate of the barium atoms away from the electrode. 

This redeposition of the electrode coating results in a longer lamp lifetime as less of the electron 

emissive material is lost.
3-18

3.5.3.3 Higher-Efficiency Phosphors

As described earlier, the main purpose of phosphor in a fluorescent lamp is to absorb the 

UV radiation and reemit it as visible radiation. Therefore, one method of improving lamp 

efficacy is to increase phosphor efficiency. As stated, a key to the miniaturization of GSFLs into 

CFLs in the 1980s was the development of new phosphor compositions that are more stable in 

intense UV radiation. Specifically, these narrow-band rare earth, or triband, phosphors 

(containing the rare earth elements terbium, europium, and yttrium), emit light in the short, 

middle, and long wavelength ranges of the visible spectrum. Compact fluorescent lamps on the 

market today exclusively use triband phosphors.

Lamp efficacy can be improved by using triband phosphors, which can increase UV 

absorption and emission of radiation in the visible spectrum relative to other phosphors. 

Typically, there are efficiency losses in the phosphor’s conversion of UV radiation to visible 

radiation, or light. Some of these losses are related to the extent to which the phosphors emit 

light in the visible spectrum (i.e., radiation with wavelengths between 400 and 750 nm) and the 

extent that they radiate at visually sensitive wavelengths. Triband phosphors allow a lamp to 

emit light at the wavelengths to which human eyes are most sensitive which increases lamp 

efficacy. To understand this effect it is important to note the relationship between the efficiency

losses in the phosphor’s conversion of light, wavelengths sensitive to the human eye, and 

measurement of lamp efficacy.

Lumens, used to calculate lamp efficacy, measure the radiometric energy emission from a 

light source weighted by the response function of the human eye, V(λ) (also referred to as the 

photopic luminous efficiency function). Figure 3.5.7 depicts V(λ). The human eye does not have 

the same level of sensitivity to all wavelengths of light. For example, the eye is highly sensitive 

to light emission around 550 nm, but less sensitive to emission at 450 nm or 650 nm.

Figure 3.5.7 Human Eye Photopic Spectral Luminous Efficiency Function, V(λ)

-

0.2

0.4

0.6

0.8

1.0

1.2

400 450 500 550 600 650 700 750

Wavelength (nanometers)

Relative Sensitivity
3-19

Therefore, the specific wavelengths that a lamp emits will affect the lamp’s calculated 

efficacy. In other words, because the human eye is less responsive to certain wavelengths of 

light, such as those in the blue spectrum, those lamps that contain these less sensitive 

wavelengths will have lower efficacies. As such, not every watt of radiometric energy emitted 

from a fluorescent lamp is equal under a lm/W metric. Therefore, by allowing a lamp to emit 

greater light at wavelengths to which the human eye is more sensitive, triband phosphors are able 

to increase lamp efficacy.

Lamp efficacy can also be improved by increasing the thickness of the phosphor layer, 

also called phosphor weight. Generally, as phosphor thickness increases, lamp light output 

increases until it slightly decreases or stays flat. Figure 3.5.8 illustrates this point, where coating 

weight is indicative of phosphor thickness.

Figure 3.5.8 Light Output versus Phosphor Thickness

Source: Journal of the Electrochemical Society 27

Different types of phosphor emit light with different spectral power distributions (SPDs), 

and the choice of phosphor or blend of phosphors greatly affects the color of the lamp. The SPD 

of light emitted from a lamp characterizes the amount of power radiated at each wavelength in 

the visible spectrum. SPD determines the CCT and the CRI, both important properties for 

measuring the color quality of light. CRI, a single value with no units, is a measure of the color 

rendering properties of a light source, or the ability of a light source to show the “true” color of 

an object as compared to a reference source.j 28 The maximum CRI is 100. Lower CRI values 

indicate greater variation in an object’s apparent color compared to when lit by the reference 

source. DOE has observed that higher-efficiency phosphors on the market typically offer higher 

CRIs. While CRI is not necessarily positively correlated to efficacy, the majority of phosphors 

offered in the market currently reflect this relationship. DOE has found that the vast majority of 

CFLs available on the market have a CRI of 80 or greater.

 j According to the IESNA Lighting Handbook, the CRI of a light source is “a measure of the degree of color shift 

objects undergo when illuminated by the light source as compared with those same objects when illuminated by a 

reference source of comparable color temperature.”
3-20

CCT, a single value with units of degrees Kelvin (K), is a measure of the color 

appearance of light emitted from a lamp.k l 29 Lower CCT values correspond to warmer light, 

with more red content in the spectrum, and higher CCTs correspond to cooler light, with more 

blue content. As the spectral emission from the lamp is modified to change the CCT, the light 

emitted often contains more red or blue light. Given the shift in the wavelengths of light emitted 

from lamps with different CCTs, and the fact that lumens account for the amount of light emitted 

at particular wavelengths, the efficacy of lamps with different CCTs can vary.

3.5.3.4 Glass Coatings

To increase the UV absorption by the phosphors, the bulb glass can be covered with an 

antireflective coating. This coating, also referred to as the undercoat or base coat layer, is a 

refractory oxide, such as aluminum oxide (Al2O3), silicon oxide (SiO2), and titanium oxide 

(TiO2). This layer is used to reflect any UV radiation that passes through the phosphor back onto 

the phosphor, allowing a greater portion of UV to be absorbed. As the phosphors absorb more 

UV radiation, they will emit more visible light, thereby increasing the overall efficacy of the 

lamp. When these coatings are used with rare earth phosphors, a good CRI can be achieved while 

minimizing the phosphor thickness necessary to absorb maximum UV radiation. A patent30 by 

Philips was issued in 2005 for an improved undercoat that reduces the amount of lamp 

contaminant resulting from the manufacture process. The improved coating covered under the 

patent contains an aluminum oxide material and a getter material, which reacts with 

contaminants. This reduction or elimination of contaminants results in improved lamp arc 

stability.

3.5.3.5 Multi-Photon Phosphors

The use of multi-photon phosphors, or quantum-splitting phosphors, can significantly 

improve lamp efficacy. Because these phosphors emit more than one visible photon for each 

incident UV photon, a lamp would be able to emit more light in the visible spectrum for the same 

amount of power. However, there are technical drawbacks with the technology such as light 

color output, color stability, and phosphor lifetime.31

3.5.3.6 Cold Spot Optimization

The point on a CFL with the lowest temperature is where the vaporized mercury 

condenses. The temperature of this “cold spot” has an impact on overall lamp efficacy as it 

influences the vapor pressure of gaseous mercury. With lower cold spot temperatures, there is 

insufficient gaseous mercury that reduces UV output; however, high cold spot temperatures 

cause UV radiation to become trapped in the tubing. Cold spot temperature is a function of 

 k According to the IESNA Lighting Handbook, the CCT of a light source is “the absolute temperature of a 

blackbody whose chromaticity most nearly resembles that of the light source.”

l While CCT is a single value, light with the same CCT value may have slightly different properties. Therefore, the 

lighting industry has defined elliptical regions in chromaticity space (called 4 MacAdam color steps) for specific 

CCT values (e.g., 2,700 K, 3,000 K, 3,500 K, 4,000 K/4,100 K, 5,000 K, and 6,500 K) in ANSI C78.376-2001.

These regions act as a tolerance for the color properties of the lamp.
3-21

current density, and light output increases with current density until it reaches a certain saturation 

level. As current density increases, the ionization rate of mercury atoms increases, causing UV 

radiation to increase, ultimately improving the transfer of UV radiation to visible light. 

Simultaneously, however, the electron temperature will drop due to a step-wise ionization at the 

higher current density and the chemical tendency to revert to a state of equilibrium. As electron 

temperature decreases and electrons experience de-excitation, the useful UV radiation intensity is 

unable to increase as quickly as the current density, so it will gradually approach a constant. 

Therefore, by finding the right cold spot temperature, one can balance these effects and optimize 

light output. In a study of commercially available T2 and T3 CFLs, Feng and Hu found that light 

output reaches a maximum at about 48 °C with a fixed current of 140 mA (see Figure 3.5.9). 

This conclusion is similar to the temperature of 50 °C for T2 tubes found by Han et al. Future 

CFL designs should ensure that the cold spot reach an optimal temperature in steady state 

operation to maximize efficacy.32

Figure 3.5.9 T2 and T3 Spiral CFL Performance at 140 mA

The lamp’s orientation (i.e., base up versus base down) also affects the cold spot 

temperature. A typical spiral CFL’s cold spot occurs at the top of the bulb where the two helical 

spirals meet. Due to rising heat, the cold spot temperature is higher when the lamp is operated 

base down rather than base up. The higher temperature can result in roughly 10 percent less light 

output. The use of amalgam in CFLs help alleviates efficacy issues related to lamp orientation 

but also increases warm up time over liquid Hg CFLs. In 2010, the United States granted General 

Electric (GE) a patent that involves a CFL with a secondary cold spot to maintain cold spot 

temperature for base up and base down applications. The secondary cold spot is positioned 

between the longitudinal end portions of the discharge tube arrangement, contrary to the first 

cold chamber, which is located in a longitudinal end portion of the tube arrangement. The second 

is positioned on the wall of the tube and has a cold chamber wall protruding substantially away 

from the central axis of the discharge tube arrangement. This location optimizes the cooling 

effect of ambient air to provide efficient cooling of the second cold chamber in base down 

positions.33
3-22

3.5.3.7 Improved Ballast Components

Integrated CFLs (i.e., self-ballasted CFLs) use electronic ballasts. The use of ballasts with 

high efficacy factors in integrated CFLs will increase lamp efficacy. Further, proper lamp-ballast 

combinations can affect lamp life up to 50 percent.34 New electronic ballast components and 

novel circuit designs can continue to improve overall lamp efficacy. A common way to increase 

the efficiency of ballasts is to improve the quality of their components. Magnetics (transformers 

and inductors), diodes, capacitors, and transistors are the main components that affect efficiency. 

For example, one way to reduce magnetic component power loss at light loads is to select 

magnetics with higher quality core materials. Another way to reduce the magnetic component’s 

power loss is to select materials with lower winding losses. The efficiency of the circuit can also 

be improved by using capacitors with low effective series resistance (ESR). Further, using 

transistors with drain-to-source resistance (low RDS_ON) can reduce losses.

3.5.3.8 Improved Ballast Circuit Design

Another method of increasing the efficiency of integrated CFLs is to improve the 

ballast’s circuit design. Examples of improved circuit design include cathode cutout technology, 

integrated circuits, improved starting method, and synchronous rectification.

Cathode cutout technology using an electronic circuit removes the power provided to the 

filament after the lamp has been started, thereby increasing the efficiency of both PS and RS 

ballasts. In certain cases, a ballast’s efficiency can be improved by substituting integrated circuits 

for discrete components.35 Further, RS and PS ballasts are inherently less efficient than IS 

ballasts even though IS ballasts use extra power to provide filament power to the ballast to 

increase its lifetime.

3.5.3.9 Change to LEDs

LED lamps can provide a higher level of efficacy than CFLs, and it is likely that LED 

lamp’s efficacy margin over CFLs will grow because of ongoing research and development 

(R&D). Therefore, DOE is considering a switch from CFL to LED technology as an option for 

improving lamp efficacy of GSLs.

3.5.3.10 Efficient Down Converters

The color of light produced by an LED lamp is dependent on the type of semiconductor 

material used in the lamp, and is not inherently white. As LEDs themselves emit a single, fairly 

narrow wavelength, some sort of manipulation of light or addition of light is necessary to 

produce a quality (high CRI) mixed white light source. Currently, manipulation of light in LED 

lamps is most commonly done through phosphor conversion, converting some light from a UV 

or blue LED to longer wavelength light (green through red) for a cumulative white light. The 

conversion of light from shorter wavelength photons to longer wavelength photons is a high to 

low energy conversion. The difference in energy between the high-energy UV or blue photons 

and the low energy green through red photons is lost in the conversion. The most commonly 

used phosphor today, with variations, is Y3Al5O12:Ce3+ (YAG:Ce) with a blue LED. The 
3-23

quantum efficiency (a component of overall phosphor efficiency) of YAG:Ce under excitation of 

a blue LED can exceed 85 percent.36 By improving down converter efficiencies, more light will 

be produced for the same input, and lamp efficacy will increase.

Novel compositions of phosphor can further improve down converter efficiencies. The 

United States recently granted GE a patent for a novel phosphor producing blue-green and green 

light. The quantum efficiency of the phosphor at 150 °C is 80 percent of quantum efficiency at 

ambient temperature. Most current phosphors’ quantum efficiencies at 150 °C degrade to 60 

percent of their efficiency at ambient temperature.37 This new composition would allow for 

greater package efficacy at higher system temperatures, which are characteristic of LED lamps.

Although phosphors are the most common type of down converters, other quantum dots 

can also improve LED efficacy. Colloidal quantum dot phosphors are nanocrystal emitters that 

can tune their emission wavelength by changing their size and contain no rare earth elements. 

These could be a potential replacement for traditional phosphors if they can overcome 

temperature performance issues as well as light flux issues. Researchers at the State University 

of New York at Buffalo have produced quantum dot phosphors emitting green through red 

wavelengths at efficiencies over 80 percent. Further, the phosphors are able to produce green 

through red wavelengths with only a five percent loss in efficiency at 150 °C, and have minimal 

losses at fluxes up to 38,000 W/cm. Researchers have also managed to reduce Auger 

recombinationm significantly through these new phosphors.38 Further, although no longer 

commercially available, a R30 LED lamp utilizing quantum dot phosphors was introduced by 

Nexxus Lighting, Inc. and QD Vision in 2010.39

3.5.3.11 Improved Package Architectures

LED packages are individual nodes that make up the LED and have their own efficacy 

which is driven by the method of generating white light, color quality attributes, and drive 

current. The package efficacy ultimately contributes to the LED lamp efficacy. Improved LED 

package architectures utilize color mixing with multiple monochromatic LEDs (e.g., red, blue, 

green) or hybrid systems that incorporate both phosphor-converted (PC) LEDs and multiple 

monochromatic LEDs. LED color mixing is one method to generate white light output from 

LEDs. It entails mixing the appropriate amount of light from multiple monochromatic LEDs 

(red, green, and blue) to yield white light. Additional LEDs, such as amber, can be added to 

potentially improve the color characteristics, but they may also lower efficacy. Because it avoids 

phosphor conversion losses, LED color mixing is theoretically the most efficient way to produce 

light from LEDs. However, the efficacy of color mixing package architecture lags behind 

packages using PC LEDs due to the inability to produce efficient green or amber LEDs, which 

has led to the prevalence of PC LED lamps.

Hybrid package architectures combine the use of multiple monochromatic LEDs as in 

color mixing along with PC LEDs. Configurations may involve efficient blue LEDs with a 

phosphor coating to emit green wavelengths and efficient red LEDs to provide combined white 

light. Such a design could prove to be more efficient than current designs utilizing only PC LEDs 

 m Auger recombination is a process by which electrons collide and produce heat rather than emit light.
3-24

by using phosphor to eliminate the loss during blue to red down conversion. In 2002, Philips 

filed a patent for such a device as well as other combinations of LEDs and phosphors to make 

white light with comparable color rendering and color temperature control to existing LED 

lamps but with potentially higher efficacies.40 Further, LED package efficacies tend to be lower 

at higher CRI as well as at lower CCTs (warm-white). High efficacy warm-white luminaires 

employing the hybrid approach have been on the market since 2009.41 In 2013, Philips reported 

on the development of a warm-white hybrid LED package for general illumination that could 

potentially achieve 131 lm/W at 85 °C with a CRI 90, a CCT of 2,955 K. Philips noted that the 

hybrid architecture could allow for more-efficacious warm-white LEDs and at high CRIs.42

Additionally, the driver current also affects efficacy of the package. A package 

architecture that increases driver current will increase the lumen output but also results in a 

proportional decrease in efficacy (an effect called the efficiency droop, which section 3.5.3.12

discusses).

Manufacturer feedback and DOE research indicates that these and various other ways for 

improving package architecture are being utilized in products and are the subject of further 

R&D. Cree, for example, produced a traditional PC LED in its lab with an efficacy of 276 lm/W 

in 2013 owing the improvement in part to advances in LED package architecture.43 Cree has 

since improved its lab LED performance to over 300 lm/W as of March 2014.44 Figure 3.5.10

shows the expected efficacy gains for the color mixing and PC methodologies.

Figure 3.5.10 Projected LED Package Efficacy Trends

Source: DOE’s Solid-State Lighting Technology Fact Sheet

3.5.3.12 Improved Emitter Materials

Advanced emitter materials and structures can improve the efficiency of LEDs. The basis 

of any LED lamp is the LED emitter, which actually produces the light. As mentioned 

previously, the predominant configuration in the LED lamp market is a PC blue LED, which 

produces white light by mixing the down converted light with some of the blue light, which 

passes through the phosphor unchanged. The color mixing methodology uses individual 

monochromatic LEDs (e.g., red, green, blue) to produce a mix of colors that generates white 
3-25

light. Because color mixing does not utilize PC LEDs, it avoids down conversion losses 

associated with the phosphor conversion methodology. While blue LEDs have made good 

progress toward their theoretical maximum efficiencies, green LEDs remain markedly behind 

their theoretical maximum of 683 lm/W for a monochromatic light source at 555 nm.45 Improved 

emitter materials for green and amber LEDs would allow for development of high efficacy colormixed LED lamps.

Figure 3.5.11 plots the lumens per watt of various researchers’ prototype LEDs versus 

their wavelength at a given current of 45 Acm-2 (note the y-axis is logarithmic). The additional 

lines show the theoretical maximum wall plug efficiency (WPE), a dimensionless efficiency 

metric of optical radiant flux (in watts) per watt of electricity supplied. Semiconducting materials 

such as group III nitrides (such as gallium nitride [GaN] and indium gallium nitride [InGaN]) 

and group III phosphides (such as Gallium Phosphide [GaP]) are efficient in producing shorter 

and longer wavelength light, respectively, but have much lower WPEs in the middle of the 

visible spectrum. This region has been dubbed the “green gap” by researchers because of the 

difficulty creating highly efficient LEDs in this part of the visible spectrum.
3-26

Figure 3.5.11 The “Green Gap”

Source: Compound Semiconductor46

In order to emit green light, a high forward voltage must be applied which reduces power 

conversion efficiency. In addition, green LEDs, even more so than other LEDs, fall victim to 

reduced efficiency with increased current, known as “efficiency droop.” At typical operating 

currents, the effect is large as seen in Figure 3.5.12.
3-27

Figure 3.5.12 Efficiency Droop

Source: Compound Semiconductor46

While there have been many different theories regarding the efficiency droop, in April 

2013, researchers at the University of California, Santa Barbara in collaboration with École 

Polytechnique in France identified Auger recombination as the primary source of efficiency 

droop. Auger recombination is a process by which electrons collide and produce heat rather than 

emit light. The rate at which this process occurs is proportional to third power of carrier density, 

which leads to low efficacy at high injection rates (i.e. high current).47 The problem is inherent 

with the technology and not solvable, but can be mitigated through either thicker quantum wells 

or growth of epitaxial layers along non-polar or semi-polar directions in order to lower carrier 

density.48, 49 Accordingly, RPI and other institutions such as the National Renewable Energy 

Laboratory (NREL) are continuing research efforts to reduce the effect through different 

polarities and even non-polar epitaxial structures. In another approach, Dr. Alexander Efros with 

the U.S. Naval Research Laboratory obtained a patent for a method that utilizes quantum wells 

with softened confinement electrostatic potential that reduce carrier momentum necessary for the 

Augur process as a means of reducing efficiency droop.50,51

3.5.3.13 Alternative Substrate Materials

One of the major barriers to producing efficient LEDs is the lack of proper substrate 

materials. The LED substrate is the foundation on which the emitting material is built. The key 

issues surrounding substrates for LEDs are price and efficiency. The difference between the 

spacing of the atoms in the crystal lattice of the substrate and epitaxial layer has a large influence 

on the efficiency of the LED. A mismatch between the substrate and the GaN epitaxial layer 

creates strain, which causes the materials to break away and fracture leading to decreased 

performance.52 Better-matched substrates will result in less strain in epitaxial layers leading to 

fewer defects and higher-efficiency LEDs, thereby increasing LED lamp efficacy as well.
3-28

The oldest and most common LED substrate material is sapphire. Because sapphire has 

been used for so long, manufacturers have been able to solve many performance issues and bring 

down price substantially. A few manufacturers are utilizing GaN on silicon-carbide substrates,

which are more costly but also offer higher performance over sapphire (see Table 3.5.2). 53

Other than silicon carbide, GaN on GaN LEDs have emerged as a potential alternative 

substrate option. The major advantage of using GaN on GaN LED material comes from a better 

crystal lattice match with fewer defects, which enables LEDs to emit more light per unit area of 

LED material. Further, GaN on GaN LEDs have greater optical transparency and high thermal 

and electrical conductivity that delivers improved light output and performance.54 The main 

issues with GaN substrates are high substrate cost, consistency, and a lack of large diameter 

wafers.

55 On February 24, 2014 Soraa, a company known for their GaN on GaN LEDs, 

announced their third generation GaN on GaN LED package achieving a 75 percent WPE at 35 

A/cm2 and 85 °C. It also renders with a CRI of 95 and R9

n of 95.56

Table 3.5.2 Substrate Comparison

Material

Lattice 

Mismatch

(%)

Strength Weakness

Sapphire 16 Low price, chemical 

stability Large lattice mismatch

GaN 0 Homogeneous 

substrates

Difficulty in high-quality crystal 

growth, high price; currently in basic 

research stage

SiC 3.5 Small lattice mismatch Price, difficulty in large substrate 

growth

Si 18 Low price, large 

substrate possible

Difficulty in high-brightness 

manufacturing

Source: Solid-State Technology57

3.5.3.14 Thermal Interface Materials

Thermal interface materials (TIMs) have the ability to improve cooling of the LED chip. 

LEDs produce not only light, but also heat during their operation since they are not perfectly 

efficient light emitters. Increasing heat at the LED junction can decrease LED efficacy.

58, 59

Continuously high junction temperatures are also known to hasten lumen depreciation over time 

due to LED component degradation, ultimately reducing lamp efficacy and lifetime.60 There are 

varying levels of thermal performance offered by commercially available TIMs. For example, 

Dow Corning offers thermal adhesives, thermal greases, and thermal pads, each available in a 

range of thermal conductivities for use in LED lamps.61 Additionally, DOE has identified novel 

approaches to TIMs for use in LEDs and continual R&D advancements in the field. For example,

chemical vapor deposition (CVD) diamond provides higher thermal conductivity than 

 n R9 is not used in the calculation of CRI. It is part of the special color rendering indices, referred to as R9 through 

R14, which are each based on single test colors. R9, the “strong red” color sample, is especially pertinent because the 

rendition of saturated red is important for the appearance of skin tones, among other materials. For more information 

please refer to the LED Color Characteristics Fact Sheet: 

http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/led-color-characteristics-factsheet.pdf.
3-29

conventional materials as the diamond acts as an effective heat spreader. Further, a titanium layer 

can be applied to the diamond allowing for use of platinum or gold enabling attachment via 

standard methods such as solders or epoxies.62 There is also research regarding use of carbon 

nanotubes, which have high thermal conductivity, in TIMs of high-brightness LEDs.63

3.5.3.15 Optimized Heat Sink Design

As mentioned in section 3.5.3.14 high LED junction temperatures can lead to a decrease 

in efficacy. The LED heat sink is the primary means by which LED lamps wick away heat from 

the LED chips. A well-designed heat sink helps reduce LED junction temperature. DOE has 

found that optimized heat sink designs are being used in commercially available lamps that have 

traditional A-shape form factors. For example, finned designs made out of materials with high 

thermal transfer coefficients have been utilized in commercially available A-shape lamps. 

Further, there are existing patents on optimized heat sinks for LEDs indicating that this is an area 

of ongoing research.64, 65

Sapa Extrusions North America recently unveiled a new method in manufacturing high 

ratio air cooled extruded aluminum heat sinks that are approximately eight percent more 

thermally efficient than the industry standard. The new technology allows them to achieve heat 

sink fin ratios, a ratio of the fin height to the spacing between the fins, in excess of 40:1 where 

they were previously restricted to ratios of 16:1 based on extrusion limitations.

66 This allows an 

increase in the number of fins on a finite footprint, leading to the boost in convection.

Another novel approach to thermal management in LED lamps is the use of a liquid filler 

inside the bulb to increase convection from the LED to the surface of the lamp through a liquid 

with much higher thermal mass than the conventional fluid, which is air. Follett Optoelectronic 

Co., Ltd. in China has announced liquid cooled LEDs at 6-8 W producing 90-100 lm/W.67 Cree 

also recently announced a new LED lamp that removed the need for a heat sink. It is designed 

with ventilation chambers at the top and bottom of the bulb, and when heat is generated by the 

LEDs, the heat causes air to circulate, which ultimately cools the components using 

thermodynamic properties of convection.68 Further improvements to heat sink manufacturing and 

materials will increase LED lamp performance and broaden application.

3.5.3.16 Active Thermal Management Systems

As mentioned in section 3.5.3.14 high LED junction temperatures can lead to a decrease 

in lamp efficacy. Active thermal management systems can provide higher performance 

alternatives to the conventional passively cooled convection heat sink designs. Active thermal 

management systems are specifically designed to provide cooling to LED components and 

theoretically, should provide net gains in overall efficiency even though they consume extra 

energy. Some active thermal management systems take the form of integral fans or vibrating 

membranes, increasing convection. The Philips MASTER LEDspot product line with integrated 

fans for cooling is an example of commercially available lamps that utilize active thermal 

management systems.69
3-30

3.5.3.17 Device-Level Optics

A primary optic is integrated onto the LED package that optimizes light extraction and/or 

beam shaping. Secondary optics help further shape light of the LED lamp and are additional 

components separate and outside of the LEDs itself (e.g., lens or diffuser). Reducing secondary 

interfaces through integration of a lens onto the LED, or removing the secondary optic 

altogether, will increase package efficacy of LED lamps. Secondary surfaces lower the efficacy 

of LED lamps by introducing another layer for light absorption. By removing secondary optics 

or integrating lens functionality at the package level, LED lamps will suffer fewer absorption 

losses at the same power input, and therefore have higher efficacy. DOE has found that there are 

research efforts addressing issues of optimizing extraction efficiency for small package sizes as 

well as improving beam shaping through only primary optics. An existing patent presents 

primary optic configurations that achieve more controlled beam shapes while allowing for a 

more simplified and efficient secondary optic.70

3.5.3.18 Increased Light Utilization

Once light is emitted from an LED, there are still a number of losses to the lamp and 

surrounding housing. Highly reflective coatings inside the lamp can help reduce light absorption 

losses, thereby increasing overall luminaire efficacy. WhiteOptics, in conjunction with DOE and 

University of Delaware’s composite materials department, has developed a new reflector 

composite coating demonstrated in LED luminaires that achieves a 97 percent reflectance as well 

as 15 percent optical efficiency improvement over benchmark LED luminaires. Further, when 

the diffuse coating is applied on luminaires, it allows them to produce a uniform light 

distribution.71 Other companies, such as SABIC, also offer plastics with reflectivities around 97 

percent.72 Dow Corning won an award at the 2014 LIGHTFAIR® International Innovation 

Awards for their MS-2002 Moldable White Reflector Silicon for use in LED devices that targets 

reflectivity up to 98 percent.73 With fewer reflective interfaces and highly reflective materials, 

LED lamps will be able to decrease light absorption and increase efficacy.

3.5.3.19 Improved Driver Design

Current LED drivers are typically 85 percent efficient with improvements expected.74

New designs will allow for smaller, more compact drivers with even higher-efficiency, which 

will boost overall LED lamp efficacy. Philips Light Sources and Electronics is developing 

drivers with the help of DOE funding. These drivers are expected to have efficiencies over 90 

percent and are also smaller and lower in cost. Philips’ new designs for 75 W drivers are 36 

percent smaller than existing drivers are, enable more precise dimming from 1V to 8V, and have 

added protection in the case of module overheating.75 Additionally, manufacturer feedback and 

DOE’s review of catalogs show a range of efficiencies associated with drivers, indicating the 

potential for improvement in driver design.

3.5.3.20 AC LEDs

By reducing conventional driver size or removing the driver component, complexity, and 

efficiency losses, AC-powered LEDs can increase performance of the LED lamp. AC LEDs 
3-31

integrate power conversion into the package, thus requiring few additional components though 

design specifics vary by manufacturer. In original AC LED setups, half of the LEDs would light 

up for the first half of the AC power sine wave cycle and only the other half would light up for 

the second half of the cycle.o Under this setup, LEDs were linked together in a series, forming 

strings. This design provides low efficacy solutions for niche applications, such as accent 

lighting. The latest AC LED designs use a high voltage architecture with simple control circuitry 

to operate on AC current. Seoul Semiconductor has a number of high voltage AC LED modules 

commercially available for integration into lamps. In July 2014, Seoul Semiconductor announced 

a new line of AC LED modules with improved AC drivers designed specifically for the 

omnidirectional lamps.76 The new AC driver improves compatibility with TRIAC dimmers and 

is able to effectively mitigate flicker issues that can arise during dimming of AC LEDs. 77, 78

Additionally, improvements in circuit design can increase LED utilization. For example, Texas 

Instruments (TI)'s TPS92411 MOSFET switch allows for a small capacitor to be placed across 

each LED segment on a circuit to store energy keeping all LEDs lit even when the AC line 

voltage is too low, thereby increasing LED utilization.79 Some challenges for AC LEDs 

functionality include total harmonic distortion, power-factor correction, and zonal dimming.

3.5.3.21 Reduced Current Density

As discussed previously, efficiency droop negatively influences the performance of LEDs 

at common current densities.80 Most LEDs peak in efficiency when driven at just tens of 

milliamps, whereas 350 mA is much more common. However, decreasing current also reduces 

lumen output, thus requiring more LEDs. Therefore, current density must be reduced in a way 

that increases LED efficacy while maintaining practical levels of lumen output per unit area. One 

method to achieve this benefit would be to design a lamp with several arrays of medium or low 

power LEDs in lieu of a small number of high-power LEDs. Cree has introduced mid-power 

LEDs at 154 lm/W that uses the ceramic substrate that is typically utilized in high-power LEDs 

rather than plastic packaging found in most mid-power LEDs. This product could potentially 

minimize lumen depreciation and offer lifetimes comparable to the high-power LEDs.81

3.6 PRODUCT CLASSES

When evaluating and establishing energy conservation standards, DOE divides covered

products into classes by (a) the type of energy used; (b) the capacity of the product; or (c) other 

performance-related features that justify different standard levels, such as features affecting 

consumer utility. (See 42 U.S.C. 6295(q)) DOE then conducts its analysis and considers separate 

standard levels for each product class. DOE applied the criteria of 42 U.S.C. 6295(q) to CFLKs 

to develop product classes in this NOPR. This section describes DOE’s examination of

developing product classes for CFLKs.

The current product class structure for CFLKs, which was established by EPACT 2005, 

divides CFLKs into three product classes: CFLKs with medium screw base (E26) sockets 

(Medium Screw Base product class), CFLKs with pin-based sockets for fluorescent lamps (Pin-

 o In the United States, wall socket power supplies operate at 120 V and 60 Hz.
3-32

Based product class), and any CFLKs other than those in the Medium Screw Base or Pin-Based 

product classes (Other Base Type product class).

DOE determined that CFLKs for high-volume CFs would not have distinctive 

characteristics compared to those for low-volume CFs and therefore concluded that a separate 

product class for CFLKs designed for high-volume CFs was not necessary. DOE examined

several possible characteristics or features of CFLKs that could warrant separation of covered 

products into different product classes, including lamp technology, lamp cover, dimmability, 

ballast location, socket type, lamp spectrum, correlated color temperature, and lumen package.

In evaluating CFLKs, DOE determined that CFLKs differing in these characteristics do 

not offer consumers any unique utilities or represent dissimilar capacities requiring different 

standard levels. Therefore, in this NOPR, DOE is considering establishing one CFLK product 

class: “all CFLKs.”
3-33

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4-i 

CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 SCREENED OUT TECHNOLOGIES ............................................................................ 4-2

4.2.1 Compact Fluorescent Lamps ................................................................................ 4-2

4.2.1.1 Multi-Photon Phosphors ............................................................................. 4-2

4.2.2 Light-Emitting Diode Lamps ............................................................................... 4-2

4.2.2.1 Colloidal Quantum Dot Phosphors ............................................................ 4-2

4.2.2.2 Improved Emitter Materials ....................................................................... 4-3

4.3 SUMMARY OF TECHNOLOGY OPTIONS SCREENED OUT .................................. 4-3

4.4 REMAINING TECHNOLOGIES ................................................................................... 4-3

4.4.1 All CFLKs Design Options .................................................................................. 4-4

REFERENCES ............................................................................................................................ 4-5

LIST OF TABLES

Table 4.3.1 GSL Technology Options Screened Out of the Analysis ......................................... 4-3
4-1

CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter discusses the U.S. Department of Energy’s (DOE) screening analysis of the 

technology options identified for lamps used in ceiling fan light kits (CFLKs). As discussed in 

chapter 3 of this notice of proposed rulemaking (NOPR) technical support document (TSD), 

DOE consults with industry, technical experts, and other interested parties in developing a list of 

technology options for consideration. The purpose of the screening analysis is to evaluate the list 

of options and determine which to consider further and which to screen out.

Section 325(o)(2) of the Energy Policy and Conservation Act (EPCA) provides that any 

new or revised standard must be designed to achieve the maximum improvement in energy 

efficiency that is determined to be technologically feasible and economically justified. (42 

U.S.C. 6295(o)(2)) In view of the EPCA requirements for determining whether a standard is 

technologically feasible and economically justified, Appendix A to Subpart C of Title 10, Code 

of Federal Regulations (CFR), Part 430, “Procedures, Interpretations, and Policies for 

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

Process Rule) sets forth procedures to guide DOE in its consideration and promulgation of new 

or revised efficiency standards. These procedures elaborate on the statutory criteria provided in 

42 U.S.C. 6295(o) and, in part, eliminate problematic technologies early in the process of 

prescribing or amending an energy efficiency standard. In particular, sections 4(b)(4) and 5(b) of 

the Process Rule provide guidance to DOE for determining which technology options are 

unsuitable for further consideration:

1. Technological feasibility. DOE will consider technologies incorporated in commercially

available products or in working prototypes to be technologically feasible.

2. Practicability to manufacture, install, and service. If mass production of a technology 

and reliable installation and servicing of the technology could be achieved on the scale 

necessary to serve the relevant market at the time the standard requires compliance, then 

DOE will consider that technology practicable to manufacture, install, and service.

3. Adverse impacts on product utility or product availability. If DOE determines a 

technology would have significant adverse impact on the utility of the product to 

significant subgroups of consumers, or would result in the unavailability of any covered 

product type with performance characteristics (including reliability), features, sizes, 

capacities, and volumes that are substantially the same as products generally available in 

the United States at the time, it will not further consider this technology.

4. Adverse impacts on health or safety. If DOE determines that a technology will have 

significant adverse impacts on health or safety, it will not consider this technology

further.
4-2

Section 4.2 of this chapter discusses the technology options DOE screened out from 

further consideration. Section 4.3 summarizes those options. Section 4.4 lists the remaining 

technology options not screened out and considered as design options in this NOPR analysis.

The market and technology assessment (see chapter 3 of this NOPR TSD) discusses in detail all 

technology options analyzed in this NOPR analysis.

4.2 SCREENED OUT TECHNOLOGIES

This section addresses the technologies that DOE screened out, having considered the 

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

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

health or safety.

4.2.1 Compact Fluorescent Lamps

4.2.1.1 Multi-Photon Phosphors

Theoretically, the use of multi-photon phosphors, or quantum-splitting phosphors, could 

significantly improve lamp efficacy of compact fluorescent lamps (CFLs). By emitting more 

than one visible photon for each ultraviolet photon, a lamp would be able to emit more light for 

the same amount of power. Researchers at Georgia Tech were issued a patent for an oxide-based 

phosphor doped with praseodymium and atoms of at least one activator that produces two visible 

light photons from a single ultraviolet light photon.1 However, development of this technology 

for use in lighting products remains in the research phase and DOE is unaware of any prototypes 

or commercialized products that incorporate multi-photon phosphors.2 Further, researchers have 

identified technical drawbacks to this technology, such as light color output, color stability, and 

phosphor lifetime.3 Thus, DOE screened out this technology option based on the first criterion, 

technological feasibility, and will not consider multi-photon phosphors as a design option for 

improving the efficacy of lamps in CFLKs.

4.2.2 Light-Emitting Diode Lamps

4.2.2.1 Colloidal Quantum Dot Phosphors

Improving the efficiency of down converters in light-emitting diodes (LEDs) can result in 

more light output with the same power input, thus improving lamp efficacy. One method of 

down conversion that can result in increased lamp efficacy is the use of colloidal quantum dot 

phosphors. These quantum dot phosphors are nanocrystal emitters formed from indium 

phosphide-based nanocrystals that have unique compositional structures enabling higher 

efficiencies at higher temperatures and minimal losses associated with light flux. While 

researchers at State University of New York (SUNY) have produced quantum dot phosphors 

emitting green through red wavelengths at efficiencies over 80 percent, they have been unable to 

incorporate these nanocrystal emitters into white LED products.4 Because quantum dot and

nanophosphor technologies are still in R&D and not currently commercially available in LED 

lamps, DOE screened out this technology option based on the first criterion, technological 
4-3

feasibility, and will not consider quantum dot and nanophosphor technologies as a design option 

for improving the efficacy of lamps in CFLKs.

4.2.2.2 Improved Emitter Materials

The LED emitter is the component of the LED that generates the actual light output. LED 

lamps are now able to use color mixing of red, green, and amber emitters to produce white light 

rather than phosphor coated LEDs. Increasing the efficiency of each of these emitters will 

increase the efficacy of the lamp. In particular, green LEDs are unable to achieve their 

theoretical maximum efficiency. Green LEDs experience an efficiency droop with increased 

operating currents. This effect is noticeably large at typical operating currents, compared to other 

LED colors, such as blue. Research has shown that the efficiency problem with green LEDs is 

inherent with the technology but it can be mitigated through thicker quantum wells or growth of 

epitaxial layers along non-polar or semi-polar directions in order to lower carrier density and 

thereby increase lamp efficacy (see chapter 3 of this NOPR TSD). However, because research in 

these mechanisms to reduce efficiency droop is ongoing, DOE screened out this technology 

option based on the first criterion, technological feasibility, and will not consider improved 

emitter materials as a design option for improving the efficacy of lamps in CFLKs.

4.3 SUMMARY OF TECHNOLOGY OPTIONS SCREENED OUT

The following table summarizes the technology options DOE is proposing to screen out 

and the associated screening criteria.

Table 4.3.1 GSL Technology Options Screened Out of the Analysis

Technology Design Option Excluded Screening Criteria

CFL Multi-Photon Phosphors Technological feasibility

LED

Colloidal Quantum Dot 

Phosphors Technological feasibility

Improved Emitter Materials Technological feasibility

4.4 REMAINING TECHNOLOGIES

After screening out technologies in accordance with the policies set forth in 10 CFR Part 

430, Subpart C, Appendix A, (4)(a)(4) and 5(b), DOE is proposing the design options in the 

following list as viable means for improving efficacy. These design options are being utilized in 

commercially available lamps and have demonstrated that they are technologically feasible, 

practicable to manufacture, install, and service, and do not result in adverse impacts on product 

utility/availability or health and safety. DOE confirmed this using feedback from manufacturers 

and DOE research. The market and technology assessment (see chapter 3 of this NOPR TSD) 

provides a detailed description of these design options.
4-4

4.4.1 All CFLKs Design Options

CFL Design Options

• Highly Emissive Electrode Coatings

• Higher-Efficiency Lamp Fill Gas Composition

• Higher-Efficiency Phosphors

• Glass Coatings

• Cold Spot Optimization

• Improved Ballast Components

• Improved Ballast Circuit Design

• Change in Technology

LED Design Options

• Efficient Down Converters (with the exception of colloidal quantum dots phosphors)

• Improved Package Architectures

• Alternative Substrate Materials

• Improved Thermal Interface Materials

• Optimized Heat Sink Design

• Active Thermal Management Systems

• Device-Level Optics

• Increased Light Utilization

• Improved Driver Design

• AC LEDs

• Reduced Current Density

DOE will consider these options in the engineering analysis as a viable means for 

improving lamp efficacy.
4-5

REFERENCES

1 Do, Young-rag (Seoul, KR); Park, Do-hyung (Suwon-city, KR); Park, Woun-jhang (Boulder, 

CO, US); Summers, Christopher J. (Dunwoody, GA, US); Yang, Hong-guen (SuwonCity, KR). (2003). U.S. Patent No. 6669867.Washington, DC: U.S. Patent and Trademark 

Office.

2 U.S. Department of Energy Office of Building Technologies. U.S. Lighting Market 

Characterization, Volume II: Energy Efficient Lighting Technology Options. September,

2005. (Last accessed June 11, 2015). 

<http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ee_lighting_vol2.pdf>

3 Scholand, Michael. Max Tech and Beyond: Fluorescent Lamps. Lawrence Berkeley National 

Laboratory. 2012. LBNL Paper LBNL-5403E. (Last accessed June 11, 2015).

<http://escholarship.org/uc/item/9569025t>

4 U.S. Department of Energy. SUNY/Buffalo Developing High-Efficiency Colloidal Quantum Dot 

Phosphors. July 11, 2013. (Last accessed June 11, 2015).

<http://energy.gov/eere/ssl/sunybuffalo-developing-high-efficiency-colloidal-quantumdot-phosphors>
5-i 

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

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

5.2 METHODOLOGY OVERVIEW .................................................................................... 5-1

5.3 ANALYSIS ...................................................................................................................... 5-2

5.3.1 Representative Product Classes ........................................................................... 5-2

5.3.2 Existing Standards ............................................................................................... 5-2

5.3.3 Baseline Lamps .................................................................................................... 5-4

5.3.4 Baseline Fixture ................................................................................................... 5-5

5.3.5 More-Efficacious Substitutes ............................................................................... 5-5

5.3.6 Efficacy Levels .................................................................................................... 5-9

5.3.7 Scaling to Other Product Classes ....................................................................... 5-13

REFERENCES .......................................................................................................................... 5-14

LIST OF FIGURES

Figure 5.3.1 Efficacy Levels for All CFLKs: Lamp Replacement Scenario ................... 5-11

Figure 5.3.2 Efficacy Levels for All CFLKs: Light Kit Replacement Scenario .............. 5-12

LIST OF TABLES

Table 5.3.1 Medium Screw Base Product Class Current Standard Efficacy 

Requirements ................................................................................................. 5-3

Table 5.3.2 Pin-Based Product Class Existing Standard Efficacy Requirements ............. 5-3

Table 5.3.3 Current Standard Efficacy Requirements for All CFLKs .............................. 5-4

Table 5.3.4 All CFLKs Baseline Lamp: Lamp Replacement ........................................... 5-5

Table 5.3.5 All CFLKs Product Class Design Options: Lamp Replacement 

Scenario.......................................................................................................... 5-7

Table 5.3.6 All CFLKs Product Class Design Options: Light Kit Replacement 

Scenario.......................................................................................................... 5-8

Table 5.3.7 ELs for All CFLKs Product Class ............................................................... 5-13
5-1

CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

The U.S. Department of Energy (DOE) performed engineering analyses for ceiling fan 

light kits (CFLKs). This chapter of the notice of proposed rulemaking (NOPR) technical support 

document (TSD) provides the analyses DOE used to study representative lamp units and to select 

efficacy levels (ELs). After selecting representative product classes, DOE then selects lamps 

packaged with CFLKs that represent the baseline efficacy, as well as more-efficacious 

substitutes for those baseline lamps. DOE uses these more-efficacious lamps to develop ELs.

In energy conservation rulemakings for other products, DOE often develops costefficiency relationships in the engineering analysis. However, for this CFLK rulemaking, DOE 

derives ELs in the engineering analysis and consumer prices in the product price determination. 

By combining the results of the engineering analysis and the product price determination, DOE 

derives typical inputs for use in the life-cycle cost (LCC) and national impact analysis (NIA). 

See chapter 7 of this NOPR TSD for discussion of DOE’s methodology for determining product 

prices.

5.2 METHODOLOGY OVERVIEW

To the extent possible, DOE bases the engineering analysis on commercially available 

lamps that incorporate the design options identified in the technology assessment and screening 

analysis (chapters 3 and 4 of this NOPR TSD). The engineering analysis for the CFLKs that 

DOE analyzes in this rulemaking follows a similar approach:

1. Select representative product classes: DOE first reviews CFLKs covered under the scope 

of the rulemaking and the associated product classes. When a product has multiple 

product classes, DOE selects certain classes as “representative” and concentrates its 

analytical effort on these classes. DOE selects representative product classes primarily 

because of their high market volumes and/or distinct characteristics. For those product 

classes that DOE does not analyze directly, DOE extrapolates the ELs from 

representative product classes.

2. Select baseline lamps: For each representative product class, DOE selects a baseline lamp 

as a reference point against which to measure changes resulting from energy conservation 

standards. Typically, a baseline model is the most common, least efficacious lamp in a 

CFLK sold in a given product class. DOE also considers other lamp characteristics in 

choosing the most appropriate baseline for each product class, such as wattage, lumen 

output, and lifetime.

3. Identify more-efficacious substitutes: DOE selects higher efficacy lamps as replacements 

for each of the baseline models considered. When selecting higher efficacy lamps, DOE 

considers only design options that meet the criteria outlined in the screening analysis (see 

chapter 4 of this NOPR TSD).
5-2

4. Determine Efficacy Levels. After identifying the more-efficacious substitutes for each 

baseline lamp, DOE develops ELs. DOE bases its analysis on three factors: (1) the design 

options associated with the specific lamps studied; (2) the ability of lamps across 

wattages (or lumen outputs) to comply with the standard level of a given product class;a

and (3) the maximum technologically feasible (“max tech”) EL. DOE then scales the ELs 

of representative product classes to any classes not directly analyzed.

The sections that follow discuss how DOE applies this methodology to each CFLK 

product class to create the engineering analysis.

5.3 ANALYSIS

5.3.1 Representative Product Classes

As discussed in the market and technology assessment (chapter 3 of this NOPR TSD), 

DOE is considering one product class for CFLKs: All CFLKs. Therefore, DOE analyzed this 

product class as representative.

5.3.2 Existing Standards

Existing standards for CFLKs, codified at 10 CFR 430.32(s), are currently divided into 

three product classes: (1) Ceiling fan light kits with medium screw base sockets (hereafter 

referred to as the “Medium Screw Base product class”); (2) Ceiling fan light kits with pin-based 

sockets for fluorescent lamps (hereafter referred to as the “Pin-Based product class”); and, (3) 

Ceiling fan light kits with socket types other than those covered in the previous two product 

classes, including candelabra screw base sockets (hereafter referred to as the “Other Base Type 

product class”).

Current standards require lamps in the Medium Screw Base product class to “meet the 

ENERGY STAR Program requirements for Compact Fluorescent Lamps, version 3.” 10 CFR 

430.32(s) Table 5.3.1 summarizes the minimum efficacy requirements specified in the ENERGY 

STAR Program requirements for Compact Fluorescent Lamps, version 3.

1

 a ELs span multiple lamps of different lumen outputs. In selecting ELs, DOE considered whether these multiple 

lamps meet the standard levels.
5-3

Table 5.3.1 Medium Screw Base Product Class Current Standard Efficacy Requirements

Configuration

Lamp Power Minimum Efficacy

W lm/W

Bare lamp

< 15 45.0

> 15 60.0

Covered lamp (no reflector)

< 15 40.0

15 < lamp power < 19 48.0

19 < lamp power < 25 50.0

> 25 55.0

With reflector

< 20 33.0

> 20 40.0

Current standards require lamps in the pin-based product class to “meet the ENERGY 

STAR Program Requirements for Residential Light Fixtures version 4.0.” 10 CFR 430.32(s) 

Table 5.3.2 summarizes the minimum efficacy requirements specified in the ENERGY STAR 

Program Requirements for Residential Light Fixtures version 4.0.

2

Table 5.3.2 Pin-Based Product Class Existing Standard Efficacy Requirements

Lamp Power Lamp Length System Efficacy per Lamp Ballast Platform

W In lm/W

< 30 All 50

> 30

< 24 60

> 24 70

Products in the All CFLKs product class are subject to either ENERGY STAR Program 

Requirements for Residential Light Fixtures version 4.0 or ENERGY STAR Program 

requirements for Compact Fluorescent Lamps, version 3. ENERGY STAR Program 

Requirements for Residential Light Fixtures version 4.0 minimum efficacy requirements are 

specific to wattage and length and ENERGY STAR Program requirements for Compact 

Fluorescent Lamps, version 3 are specific to wattage and whether the lamp is bare or covered. 

Because DOE is not proposing length or lamp cover as product class setting factors, DOE 

determined the minimum efficacy requirements for this product class by lamp wattage. DOE 

determined that, consistent with 42 U.S.C. 6295(o)(1), products in the All CFLKs product class 

are subject to the highest of the existing standards for each wattage bin. Therefore, for products 

less than 15 W, DOE set the minimum baseline efficacy at 50 lm/W. For products greater than or 

equal to 15 W and less than 30 W, DOE set the baseline efficacy at 60 lm/W. For products 

greater than or equal to 30 W, DOE set the baseline efficacy at 70 lm/W. Table 5.3.3 shows the 

combined minimum efficacy requirements.
5-4

Table 5.3.3 Current Standard Efficacy Requirements for All CFLKs

Lamp Power Minimum Efficacy

W lm/W

< 15 50.0

> 15 and < 30 60.0

> 30 70.0

5.3.3 Baseline Lamps

DOE selects baseline lamps that are typically the most common, least efficacious lamps 

in a CFLK that meet existing energy conservation standards. Specific lamp characteristics are 

used to characterize the most common lamps packaged with CFLKs today (e.g., wattage and 

lumen output). To identify baseline lamps, DOE reviews product offerings in catalogs and 

manufacturer feedback obtained during interviews. DOE uses performance data presented in 

lamp manufacturer catalogs to determine lamp efficacy.

In this NOPR analysis, DOE selected a lamp representative of the most common, least 

efficacious lamps that just meet existing CFLK standards. DOE selected a baseline lamp as a 

reference point from which to measure changes resulting from proposed energy conservation 

standards. To determine energy savings and changes in price, DOE compared representative 

lamp units at each higher efficacy level with the baseline unit.

DOE considered a number of criteria, such as lamp type, base type, wattage, color 

rendering index (CRI), correlated color temperature (CCT), and lifetime when selecting which 

commercially available lamp would serve as a baseline. DOE found that the most common lamps 

that just meet existing standards are medium base (E26), non-dimmable, bare spiral lamps with 

wattages of 13 W or 14 W. DOE selected a 14 W compact fluorescent lamp (CFL) baseline 

based on its lower efficacy compared to comparable 13 W products.

As noted, typically, a baseline product is a unit that just meets the existing energy 

conservation standard for the product (50.0 lm/W for less than 15 W products, see section 5.3.2

for further details). The corresponding lumen output for a 14 W CFL at the minimum efficacy 

requirement is 700 lm. However, market information indicated that many 14 W CFLs with low 

lumen outputs typically had an additional feature (e.g., a cover or a coating for rough service 

operation) that DOE did not find to be utilized in lamps packaged with CFLKs. DOE also 

received feedback that CFLK manufacturers typically purchase the least expensive lamp 

available and a rough service lamp, for example, would command a premium. Thus, in the 

NOPR analysis, DOE modeled a 14 W CFL as the baseline lamp without these additional 

features and a lumen output of 800 lumens, which is a common lumen output for this lamp. DOE 

assumed the modeled baseline lamp would have the same characteristics (spiral shape, 82 CRI, 

2,700 K CCT, and 10,000-hour lifetime) as the most common commercially available lamps.
5-5

Table 5.3.4 provides detailed information on the modeled baseline representative lamp unit used 

in the engineering analysis and subsequent analyses.

Table 5.3.4 All CFLKs Baseline Lamp: Lamp Replacement

Bulb Shape Base Type Lamp 

Type

Lamp 

Wattage

Initial 

Lumen 

Output

Efficacy Lamp 

Lifetime CRI

CCT

W lm lm/W hr K

Spiral E26 CFL 14 800 57.1 10,000 80 2,700

5.3.4 Baseline Fixture

As described in more detail in the discussion of the selection of more-efficacious 

substitutes (section 5.3.5), DOE selected a baseline and more-efficacious substitutes taking into 

consideration two different substitution scenarios: (1) a lamp replacement scenario and (2) a light 

kit replacement scenario. In the lamp replacement scenario, the more-efficacious substitute must 

have the same base type as the baseline lamp and no changes to the fixture are made. In the light 

kit replacement scenario, a more-efficacious fixture is chosen, allowing for a more-efficacious 

lamp substitute with a different base type than the baseline lamp and a change in number of 

sockets.

Both the lamp and light kit replacement scenario use the same baseline lamp. However, 

in the light kit replacement scenario, DOE also identified a baseline fixture consisting of the 

most common number of sockets, each containing the baseline lamp. Specifically, based on 

commercially available product information, DOE determined that the most common number of 

sockets in CFLKs is two.

5.3.5 More-Efficacious Substitutes

After choosing a baseline lamp, DOE identifies commercially available lamps that can 

serve as more-efficacious substitutes. When identifying higher efficacy replacement lamps, DOE 

used lamp efficacy values determined using catalog lumens and catalog wattage. DOE utilized a 

database of commercially available lamps and selected substitute lamps that both save energyb

and maintain comparable lumen output to the baseline lamp. Specifically, DOE ensured that 

potential substitutions maintained lumen output within 10 percent of the baseline lamp lumen 

output (for the lamp replacement scenario) and within 10 percent of the baseline light kit lumen 

output (for the light kit replacement scenario). Further, DOE considered only technologies that 

met all four criteria in the screening analysis.

Regarding the lamp characteristics of the substitutes, DOE selected replacement lamp 

units with lifetimes greater than or equal to the lifetime of the baseline lamp. DOE also selected 

 b DOE allows for substitutions that both save energy and do not save energy in the NIA.
5-6

replacement lamp units with a CRI, CCT, and bulb shape comparable to that of the baseline 

representative lamp unit.

As mentioned previously, for each representative product class, DOE considered moreefficacious lamps under two different substitution scenarios: (1) a lamp replacement scenario and 

(2) a light kit replacement scenario. In the lamp replacement scenario, DOE assumed that 

manufacturers would maintain the original fixture design, including the number of sockets, and 

only replace the lamp. Thus, DOE selected the base types of the more-efficacious substitutes to 

be the same as that of the baseline lamp.

In the light kit replacement scenario, DOE accounted for the possibility that 

manufacturers may change fixture designs. Thus, the base types of the more-efficacious 

substitutes were not required to be the same as that of the baseline lamp and the number of 

sockets could be changed. DOE determined that the most common number of sockets in CFLKs 

is two; thus, DOE selected a baseline fixture with two sockets, each containing the baseline 

lamp. DOE considered more-efficacious replacement fixtures with non-medium screw base types 

and between one and four sockets, which are the common socket counts in CFLKs on the 

market. Varying the number of sockets allows for a wider variety of lamp types, wattages, and 

lumen packages to be considered. DOE calculated the total lumen output and wattage of the 

fixture as the product of the lumen output of each lamp or wattage of each lamp, respectively, 

and the total number of lamps. More-efficacious substitutes were selected such that they have a 

both a lower fixture wattage and higher efficacy than the previous level and maintain fixture 

lumen output within 10 percent of the baseline fixture.

DOE analyzed two representative lamp units at EL 2: a CFL and light-emitting diode 

(LED) lamp. DOE found that efficacies for meeting this level were common for both CFLs and 

LED lamps, but there was a difference in price between the two options. Therefore, DOE 

analyzed both a CFL and LED lamp at EL 2.

DOE modeled the representative lamp unit at the max tech level. At the time of the 

NOPR analysis, DOE determined that a commercially available 3-way LED lamp when operated 

at its middle setting is more-efficacious than any other commercially available lamp that could 

be considered an adequate replacement for the baseline lamp (i.e., has a non-reflector shape, a 

lumen output within 10 percent of the baseline lamp, a CCT around 2,700 K, a CRI greater than 

or equal to 80, a lifetime greater than or equal to that of the baseline, and a medium screw base). 

Specifically, the 3-way lamp is 8 W at its middle setting, and has a lumen output of 820 lumens, 

an efficacy of 102.5 lm/W, and a lifetime of 25,000 hours. DOE concluded that the higher 

efficacy level achieved by the middle setting demonstrated the potential for a standard, non-3-

way, 8 W LED lamp to achieve this efficacy level. Therefore, DOE modeled an 8 W lamp with 

820 lumens and an efficacy of 102.5 lm/W. DOE assumed the modeled lamp would have similar 

characteristics to the most common commercially available LED lamps in the 800-lumen range. 

Hence, DOE modeled the lamp to have an A19 shape, medium base type, 25,000-hour lifetime, 

2,700 K CCT, 80 CRI, and dimming functionality. DOE set EL 4 according to the efficacy of the 

modeled 8 W lamp, but adjusted it to be slightly lower to allow for additional products to meet 

this level, such as consumer replaceable LED modules and driver systems that can provide 
5-7

flexibility in CFLK design options and 800-lumen LED lamps, which represent a common lumen 

output for 60 W equivalent LEDs.

Table 5.3.5 presents detailed information on the representative lamp units used in 

determining the ELs in the lamp replacement scenario for the All CFLKs representative product 

class. DOE based the identified ELs on lamp efficacy.

Table 5.3.5 All CFLKs Product Class Design Options: Lamp Replacement Scenario

Efficacy Level Lamp 

Type

Base 

Type

Bulb 

Shape

Wattage

Initial 

Lumen

Output

Efficacy CRICCT Lamp 

Lifetime

W lm lm/W K hr

Baseline CFL E26 Spiral 14 800 57.1 80 2,700 10,000

EL 1 CFL E26 Spiral 13 800 61.5 80 2,700 10,000

EL 2 

(Representative 

Lamp Unit 1)

CFL E26 Spiral 11 730 66.4 82 2,700 10,000

EL 2 

(Representative 

Lamp Unit 2)

LED E26 A19 12 800 66.7 82 2,700 25,000

EL 3 LED E26 A19 8.5 800 94.1 81 2,700 25,000

EL 4 LED E26 A19 8 820 102.5 80 2,700 25,000
5-8

Table 5.3.6 presents detailed information on the representative lamp units used in determining the ELs in the light kit

replacement scenario for the All CFLKs product class.

Table 5.3.6 All CFLKs Product Class Design Options: Light Kit Replacement Scenario

Efficacy 

Level

Lamp 

Type

Base 

Type

Bulb 

Shape

Fixture 

Sockets

Lamp 

Wattage

Fixture 

Wattage

Lamp 

Initial 

Lumen

Output

Fixture 

Initial 

Lumen

Output

Efficacy CRICCT Lamp Life

W W lm lm lm/W K hr

Baseline CFL E26 Spiral 2 14 28 800 1,600 57.1 80 2,700 10,000

EL 1 CFL E26 Spiral 3 9 27 520 1,560 57.8 80 2,700 10,000

EL 2 LED E26 G25 3 8 24 500 1,500 62.5 82 2,700 25,000

EL 3 LED E26 A21 1 16 16 1,600 1,600 100.0 80 2,700 25,000

EL 4 LED E26 A21 1 15 15 1,600 1,600 106.7 82 2,700 25,000
5-9

5.3.6 Efficacy Levels

DOE adopted an equation-based approach to establish ELs for CFLKs. To develop the 

general form of the equation (shown in Eq. 5.1), DOE evaluated lamps with similar 

characteristics, such as technology, bulb shape, and lifetime, across a range of lumen outputs.

EEEEEEEEEEEEEEEE = AA − 2222. 44 ∗ 00. 9999LLLLLLLLnnss

Eq. 5.1

For each representative lamp unit, DOE calculated the coefficient, AA, of the equation

from the lamp efficacy. DOE then evaluated the equation against lamps with the same design 

options as the representative lamp unit for each EL and made slight adjustments to capture the 

efficacy of lamps with those design options across a range of lumen packages.

The continuous equations specify a minimum lamp efficacy requirement across lumen 

packages and represent the efficacy a lamp achieves. Because the equations account for variation 

in efficacy due to lumen package, they can account for design options of new lamp lumen 

packages based on a technology curve.

As mentioned, for each representative product class, DOE considered two scenarios: a 

lamp replacement scenario and a light kit replacement scenario. DOE selected ELs that could be 

met by the more-efficacious substitutes identified in the lamp replacement scenario. DOE also 

identified more-efficacious lamp substitutes for the light kit replacement scenario that had 

efficacies equal to or greater than the efficacies of the corresponding EL based on the lamp 

replacement scenario.

DOE ensured that a wide variety of design options would be available at all ELs. DOE 

performed a survey of lamps with small bases (e.g., E12, E17, and G9) and small form factors 

(e.g., candle, flame tip, torpedo) based on catalog data and concluded that these lamp types are 

available at all ELs, including the max tech level, EL 4. For example, DOE identified a 3 W LED 

with a G9 base, a lumen output of 275 lm, and an efficacy of 91.7 lm/W, and also a 2 W LED 

with an E12 base, a lumen output of 200 lm, and an efficacy of 100 lm/W, with T4 and B11 

shapes, respectively.

Further, DOE determined that CFLKs with LED modules and driver systems could offer 

similar modular design options as CFLKs that use lamps with small bases. DOE applied thermal 

and driver losses estimated from the DOE Multi-Year Program Plan for Solid-State Lighting 

Research and Development3 to commercially available LED modules and drivers to determine 

their lamp efficacy if they were incorporated as a consumer replaceable system in a CFLK. Per 

the CFLK test procedure NOPR, lamp efficacy is used to measure the efficiency of SSL CFLKs 

unless a CFLK has any light sources, drivers, or intermediate circuitry, such as wiring between a 

replaceable driver and a replaceable light source, that are not consumer replaceable. (79 FR 

64688 [Oct. 31, 2014]). DOE determined that these CFLKs would meet the max tech level, EL 4.

In addition to the thermal and driver losses, DOE also applied fixture losses and determined that 

the efficacy of fixtures with integrated (non-consumer replaceable) LED modules and drivers 

would meet EL 2.
5-10

DOE also ensured that dimmable lamps and lamps with a range of CCTs and lumen 

packages were available at each EL.

In general terms, DOE based its ELs on the following described design options.

• EL 1: This efficacy level can be achieved in the lamp replacement scenario with an 

improved efficacy CFL with more efficient phosphors and improved ballast components.

This level can also be achieved in the light kit replacement scenario with a three-socket 

fixture and an improved efficacy CFL.

• EL 2: This efficacy level can be achieved in the lamp replacement scenario with a more 

improved efficacy CFL with phosphors that are more efficient and improved ballast 

components, or a basic LED lamp with an efficacy near the lowest performing LED 

lamps currently available on the market. This level can also be achieved in the light kit 

replacement scenario with a three-socket fixture and basic LED lamps.

• EL 3: This efficacy level can be achieved in the lamp replacement scenario with an 

advanced efficacy LED lamp with improved package architecture, high efficiency driver, 

and improved optics. This level can also be achieved in the light kit replacement scenario 

with a one-socket fixture and advanced efficacy LED lamp.

• EL 4: This is the maximum technologically feasible efficacy level and can be achieved in 

the lamp replacement scenario with an LED lamp with the combination of the most 

efficacious package architecture, driver, and optics on the market today. This level can 

also be achieved in the light kit replacement scenario with a one-socket fixture and an 

LED lamp with the combination of the most efficacious package architecture, driver, and 

optics on the market today.

DOE adjusted EL 1 and EL 2 to ensure that the efficacy remains above the current 

standards summarized in Table 5.3.3 to comply with 42 U.S.C. 6295(o)(1) , as described in 

Section 5.3.2. For lamps less than 15 W, the minimum efficacy is 50 lm/W. For a lumen output 

of less than 260 lumens, DOE found that the EL 1 equation could potentially allow lamps that 

are less than 50 lm/W to meet standards and therefore set the minimum efficacy requirement at 

50 lm/W for lamps in this lumen range. For a lumen output of less than 120 lumens, DOE found 

the EL 2 equation could potentially allow lamps that are less than 50 lm/W to meet standards and 

therefore set the minimum efficacy requirement at 50 lm/W for lamps in this lumen range. DOE 

determined that no adjustments to any ELs were necessary to meet the current standard of 60 

lm/W applicable to lamps greater than 15 W and less than 30 W.

For lamps greater than 30 W, the minimum efficacy to comply with 42 U.S.C. 6295(o)(1) 

is 70 lm/W. DOE found that the equation for EL 1 could potentially allow lamps that are less 

than 70 lm/W to meet standards. DOE determined that for a particular lumen output, lamps 

greater than 30 W would have lower efficacies than a 30 W lamp because efficacy is inversely 

proportional to wattage. Therefore, for this range of lumens, DOE set the standard above the 

highest efficacy that a 30 W lamp can achieve, which is (1/30) × lumens. At greater than 
5-11

2,100 lm, a 30 W lamp achieves greater than 70 lm/W, which is the minimum efficacy to comply 

with 42 U.S.C. 6295(o)(1). At less than 2,040 lumens, the EL 1 equation results in less than the 

efficacy a 30 W lamp could achieve. Hence, for lumens greater than 2,040 and less than 2,100, 

DOE set the minimum efficacy requirement at greater than (1/30) × lumens for EL 1.

Figure 5.3.1 illustrates the four proposed ELs on a plot with the representative lamp units

for the lamp replacement scenario. The square box identifies the baseline lamp and the triangles 

identify the representative lamp units, which are the more-efficacious substitutes that DOE 

considered as replacements for the baseline. As shown in Figure 5.3.1, two representative lamp 

units were analyzed for EL 2. These replacements were selected such that they have both a lower 

lamp wattage and higher efficacy than the previous level and maintain lamp lumen output within 

10 percent of the baseline lamp.

Figure 5.3.1 Efficacy Levels for All CFLKs: Lamp Replacement Scenario
5-12

Figure 5.3.2 illustrates the four proposed ELs on a plot with the representative lamp units

for the light kit replacement scenario. The square box identifies the baseline lamp and the circles 

identify the representative lamp units, which are the more-efficacious substitutes that DOE 

considered as replacements for the baseline. Because proposed standards are based on lamp 

efficacy, not fixture efficacy, the lamp efficacy of each representative lamp unit is plotted. 

However, DOE selected these replacements such that they have both a lower fixture wattage and 

higher fixture efficacy than the previous level and maintain fixture lumen output within 10 

percent of the baseline fixture.

Figure 5.3.2 Efficacy Levels for All CFLKs: Light Kit Replacement Scenario
5-13

Table 5.3.7 summarizes the efficacy requirements at EL 1 through EL 4 for the All 

CFLKs product class.

Table 5.3.7 ELs for All CFLKs Product Class

Representative Product Class Efficacy Level

Lumen Output Minimum Required 

Efficacy

lm lm/W

All CFLKs

EL 1

< 260 50

≥ 260 and ≤ 2040 69 - 29.42 × 0.9983lumens

> 2040 and < 2100 > (1/30) × lumens

≥ 2100 70

EL 2

< 120 50

≥ 120 74 - 29.42 × 0.9983lumens

EL 3 All 101 - 29.42 × 0.9983lumens

EL 4 All 106 - 29.42 × 0.9983lumens

5.3.7 Scaling to Other Product Classes

Typically DOE determines ELs for product classes that were not directly analyzed (“nonrepresentative product classes”) by scaling from the ELs of the representative product classes. As

DOE only identified one product class for CFLKs and analyzed it as a representative product 

class, no scaling was required.
5-14

REFERENCES

1 ENERGY STAR. ENERGY STAR Program Requirements for CFLs: Version 3.0. October 30, 

2003. (Last accessed June 11, 2015). 

http://www.energystar.gov/ia/partners/product_specs/program_reqs/archive/cfls_prog_re

q_V3.pdf?b9d3-4c25.

2 ENERGY STAR. ENERGY STAR Program Requirements for Residential Light Fixtures: 

Version 4.0. (Last accessed June 11, 2015). 

https://www.energystar.gov/ia/partners/prod_development/revisions/downloads/fixtures/

RLF_V4FINALSpec.pdf?38af-96ae.

3 U.S. Department of Energy. Solid-State Lighting Research and Development Multi-Year 

Program Plan. April 2013. (Last accessed June 11, 2015). 

http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_mypp2013_web.pdf
6-i 

CHAPTER 6. ENERGY USE ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.2 CONSUMER SAMPLES ................................................................................................ 6-1

6.3 OPERATING HOURS .................................................................................................... 6-2

6.3.1 Residential Sector ................................................................................................ 6-2

6.3.2 Commercial Sector............................................................................................... 6-6

6.4 INPUT POWER ............................................................................................................... 6-8

6.5 LIGHTING CONTROLS (DIMMING) ........................................................................ 6-13

6.6 RESULTS ...................................................................................................................... 6-14

REFERENCES .......................................................................................................................... 6-19

LIST OF TABLES

Table 6.3.1 CFL-to-All GSL HOU Adjustment Factor .................................................... 6-3

Table 6.3.2 CFL HOU Converted to All GSL HOU ........................................................ 6-3

Table 6.3.3 Distribution of Ceiling Fans and CFLKs by Room Type .............................. 6-4

Table 6.3.4 Daily CFLK HOU by Building Type in the Commercial Sector .................. 6-7

Table 6.3.5 CBECS Building Type Average HOU .......................................................... 6-8

Table 6.4.1 Weighted-Average Total Lumens for All CFLKs ....................................... 6-11

Table 6.4.2 Calculation of Input Power for All CFLKs: Lamp Replacement 

Scenario........................................................................................................ 6-12

Table 6.4.3 Calculation of Input Power for All CFLKs: Light Kit Replacement 

Scenario........................................................................................................ 6-12

Table 6.6.1 Annual Energy Use (kWh/year) for All CFLKs (Lamp Replacement 

Scenario) in the Residential Sector .............................................................. 6-15

Table 6.6.2 Annual Energy Use (kWh/year) for All CFLKs (Light Kit 

Replacement Scenario) in the Residential Sector ........................................ 6-16

Table 6.6.3 Annual Energy Use (kWh/year) for All CFLKs (Lamp Replacement 

Scenario) in the Commercial Sector ............................................................ 6-17

Table 6.6.4 Annual Energy Use (kWh/year) for All CFLKs (Light Kit 

Replacement Scenario) in the Commercial Sector ...................................... 6-18

LIST OF FIGURES

Figure 6.3.1 Average Hours of Use of CFLKs by Room Type.......................................... 6-6

Figure 6.4.1 Lumens Variation for All CFLKs with Efficacies above the 

Restructured Baseline .................................................................................. 6-11
6-1 

CHAPTER 6. ENERGY USE ANALYSIS

6.1 INTRODUCTION

This chapter describes the methodology the U.S. Department of Energy (DOE) followed 

to estimate the annual energy use of typical ceiling fan light kits (CFLKs) as used by consumers 

in homes and businesses. The results of this analysis are key inputs to the life-cycle cost and 

payback period analysis, described in chapter 8 of this notice of proposed rulemaking (NOPR) 

technical support document (TSD), and the national impact analysis (NIA), described in chapter 

10 of this NOPR TSD. DOE requires information on annual energy use to determine the 

potential energy and operating cost savings consumers would realize from the use of more 

efficient products.

DOE determined the annual energy use of CFLKs using information on their input power 

consumption and the way consumers use them (i.e., their operating hours per year and the impact 

of lighting controls). DOE derives the annual energy consumption of CFLKs by multiplying the 

input power by the number of hours of operation per year and a factor representing the impact of 

controls: 

AAAAAAAAAAAA EE EEEEEEEE UUUU = OOOO OOOO HHHH × II PP PP × IIII II oo CC

DOE has not included standby power consumption in its estimation of CFLK energy 

consumption for reasons described in the concurrent test procedure rulemaking for CFLKs.

1

The energy use analysis is developed for CFLKs using the representative lamps described 

in the engineering analysis (chapter 5 of this NOPR TSD). The CFLKs are assumed to have a 

total lumen output that is characterized by the current market and the individual CFLKs are 

assumed to use only a single lamp type, as is characteristic of the vast majority of the current 

market. The following sections describe the inputs and calculations DOE used to develop annual 

operating hours, input power, and annual energy use for the products considered in this analysis. 

6.2 CONSUMER SAMPLES

The goal of the energy use analysis is to generate energy use values that reflect actual use 

by consumers in the United States (U.S.). Calculating the average energy use of CFLKs therefore 

requires data on the variation in energy use across the nation. These data were applied to 

consumer samples obtained from energy consumption surveys for residences and commercial 

buildings administered by the Energy Information Administration (EIA). DOE did not consider 

the industrial sector in this analysis, because DOE’s research indicates that CFLKs are designed 
6-2 

almost solely for low-volume ceiling fans (where volume refers to airflow)a

. Low-volume ceiling 

fans generally are not suitable for the large spaces characteristic of most industrial buildings.

DOE used the EIA’s 2009 Residential Energy Consumption Survey (RECS)2

 and the 

2003 Commercial Buildings Energy Consumption Survey (CBECS)3 to develop consumer 

samples for CFLKs in the residential and commercial sectors, respectively. RECS and CBECS 

were constructed by the EIA to be nationally representative of the residential and commercial 

sectors in the United States, respectively. RECS is a national sample survey of housing units that 

collects statistical information on the consumption of, and expenditures for, energy in housing 

units, along with data on energy-related characteristics of the housing units and occupants. RECS 

2009 includes data from 12,083 housing units to represent almost 113.6 million households. 

CBECS 2003 includes data from 5,212 sample buildings out of an estimated U.S. commercial 

building stock of nearly 4.6 million buildings. 

For the residential sector, DOE used a subset of the 2009 RECS data that includes houses 

with ceiling fans (8,762 houses). For each of those houses, DOE applied a selection weight 

quantified by the RECS sampling weight (RECS variable NWEIGHT) multiplied by the number 

of ceiling fans in each household (RECS variable NUMCFAN). For the commercial sector, DOE 

first divided the 2003 CBECS sample into building types characterized by their principal 

building activity (CBECS variable PBA8) using a subset of the data that excluded vacant 

buildings (5,080 buildings). For that subset, DOE then applied a selection weight equal to the 

square footage of each sampled building (CBECS variable SQFT8) multiplied by the adjusted 

CBECS sampling weight (CBECS variable ADJWT8). 

6.3 OPERATING HOURS

This section discusses how DOE calculated operating hours for CFLKs used in the 

residential and commercial sectors.

6.3.1 Residential Sector

To determine the average hours of use (HOU) of CFLKs in the residential sector on a 

regional basis, DOE collected data from a number of sources. Consistent with the general service 

lamp (GSL) preliminary analysis,b

 DOE used data from various field metering studies of GSL 

operating hours in the residential sector. To account for any difference in CFLK HOU compared 

to GSL HOU, DOE considered the HOU associated with the specific room types in which 

CFLKs are located, and the relative HOU for GSLs installed in ceiling light fixtures compared to 

all GSLs. As in the GSL preliminary analysis, DOE assumed that CFLK operating hours do not 

vary by light source technology. The following sections discuss in detail the methodology DOE 

followed to estimate CFLK operating hours in the residential sector. 

 

a All ceiling fan data referred to in this chapter are for low-volume ceiling fans, as defined in the concurrent ceiling 

fans test procedures rulemaking, where volume refers to air flow. DOE has determined that CFLKs are designed

almost solely for the low-volume ceiling fan market. Hereafter in this document ceiling fans refers to low-volume 

ceiling fans.

b DOE has published a framework document and preliminary analysis for amending energy conservation standards 

for general service lamps. Further information is available at www.regulations.gov under Docket ID: EERE-2013-

BT-STD-0051.
6-3 

To estimate HOU by region, DOE collected GSL HOU data from metering studies in 

California,

4

 Georgia,5 North Carolina,

6

 South Carolina,7 Maryland,8 Ohio,9 Illinois,10 the 

Northwest,11 and the Northeast.12 For studies that only reported operating hours for CFLs, DOE 

used an adjustment factor to estimate operating hours for all GSLs.c This adjustment factor was 

calculated based on the population-weighted average fractional difference in operating hours 

between all GSLs and CFL GSLs. Table 6.3.1 shows the daily HOU for all GSLs compared to 

the HOU for CFL GSLs for five U.S. states, and the (population-weighted average) CFL-to-all 

GSL adjustment factor. 

Table 6.3.1 CFL-to-All GSL HOU Adjustment Factor 

State All GSL 

HOU CFL HOU Population Weight (2013)

HOU Ratio 

All GSLs/CFLs

CT12 2.8 3.1 5% 0.90

RI12 2.6 3.0 2% 0.87

MA12 2.7 3.0 10% 0.90

NY12 3.3 4.0 28% 0.83

CA4 1.6 1.9 55% 0.83

CFL-to All GSL Adjustment Factor (Population-Weighted Average) 0.84

For studies that report HOU for CFL GSLs only, DOE applied the population-weighted 

average CFL-to-all GSL adjustment factor, as shown in Table 6.3.2. 

Table 6.3.2 CFL HOU Converted to All GSL HOU

State Reported

CFL GSL HOU

Estimated

All GSLs HOU

IL10 2.7 2.3

OH9 2.8 2.4

MD8 3.0 2.5

GA5 2.8 2.4

NC6 2.9 2.5

SC7 2.7 2.3

Next, DOE estimated the representative operating hours for each RECS reportable 

domain (i.e., state, or group of states). To do this, DOE first grouped U.S. states by Census 

Division and large stated. Within each of these groupings, DOE examined whether HOU 

metering data were available. If metering data were available in a given Census Division or large 

state, DOE divided the region into RECS reportable domains and examined whether HOU 

metering data were included within each RECS reportable domain. For any RECS reportable 

 

c As discussed in the GSL preliminary analysis, DOE did not differentiate HOU by light source technology, but 

instead assumed that HOU would be the same across all light source technologies after the assumed compliance date 

(2019). For details, see chapter 7, Energy Use Characterization, of the GSL preliminary analysis TSD.

d The large states include California, Florida, New York, and Texas. 
6-4 

domain with metering data, DOE used the HOU data within that reportable domain. When a 

reportable domain included HOU data from one metering study, the HOU for the reportable 

domain were those of that one metering study; when multiple metering studies were available 

within a reportable domain, the HOU were the population-weighted average HOU of all the 

metering studies in the reportable domain. For RECS reportable domains without metering data, 

DOE used the population-weighted average HOU within the larger Census Division or large 

state. If metering data were not available in a particular Census Division or large state, DOE used 

data from surrounding regions based on proximity, where possible, or from regions having 

similar latitudes to the region in question (DOE assumed that regions of similar latitude would 

tend to have similar HOU). For more details on the methodology DOE used to map HOU data to 

each region, see chapter 7 (Energy Use Characterization) of the GSL preliminary analysis TSD.13 

Because HOU for lighting products typically vary by room type, in order to estimate 

CFLK HOU, DOE then considered the difference between the distribution of CFLKs by room 

type and the distribution of GSLs by room type. To determine the CFLK distribution by room 

type, DOE used data from a Lawrence Berkeley National Laboratory survey of ceiling fan and 

CFLK owners (LBNL survey),

14 in which respondents indicated which rooms in their house have 

ceiling fans, and whether those ceiling fans have CFLKs. Table 6.3.3 shows the national 

distribution of CFLKs by room type based on those data. 

Table 6.3.3 Distribution of Ceiling Fans and CFLKs by Room Type

LBNL

Survey Room 

Type

Number of Ceiling

Fans Reported in 

Survey Responses

Distribution of 

Ceiling Fans by 

Room Type (%)

Percentage of 

Fans with 

Light Kits (%)

Distribution of 

CFLKs by 

Room Type (%)

Bedroom 3,908 50 89 51

Dining Room 703 9 90 9

Living Room 1,587 20 83 20

Family Room 526 7 81 7

Kitchen 518 7 88 7

Outdoor/Patio 252 3 68 2

Bathroom 83 1 84 1

Other 183 2 73 3

Total 7,760 100 100

Source: Kantner et al. (2013)14 

DOE then used this distribution of CFLKs by room type and information from the 

Residential Lighting End-Use Consumption Study (RLEUCS)4

, which includes both lamp and 

ceiling light fixture HOU estimates by RECS reportable domain and room type, to convert the 

GSL HOU derived from the metering studies to CFLK HOU as follows: 

DOE combined the room-and-RECS-reportable-domain-specific HOU for ceiling light 

fixtures from RLEUCS with the distribution of CFLKs by room type (last column in Table 6.3.3) 

to derive the average CFLK HOU by RECS reportable domain d, according to RLEUCS 
6-5 

(HOUCFLK,RLEUCS,d). DOE then used this factor to adjust the average GSL HOU derived from the 

metering studies for each RECS reportable domain. Specifically, to determine the average CFLK 

HOU for each RECS reportable domain, DOE multiplied the GSL HOU from the metering 

studies by the ratio of the HOUCFLK,RLEUCS,d to the HOUAll_Lamps,RLEUCS,d, (where 

HOUAll_Lamps,RLEUCS,d corresponds to the average HOU reported in RLEUCS for all lamps in each 

RECS reportable domain). The CFLK energy use analysis was based on HOU data from the 

aforementioned metering studies, rather than HOU data directly from RLEUCS, because the 

metering studies were considered to be more representative of lamp usage across the U.S. 

To reflect actual energy use of CFLKs by individual consumers, DOE developed HOU 

distributions for each room type using data from the Northwest Energy Efficiency Alliance’s 

Residential Building Stock Assessment Metering Study (RBSAM).11 RBSAM is a metering 

study of 92 single-family houses across the Northwest (located in Idaho, Montana, Oregon, and 

Washington), which includes GSL HOU data by room type for each household. For each 

sampled HOU corresponding to an individual consumer, DOE sampled a room type, where the 

likelihood of sampling was based on the room-type distribution shown in Table 6.3.3. DOE 

mapped the RBSAM room types to the LBNL survey room types, and assigned a distribution of 

GSL HOU data to each room type in the LBNL survey dataset.e

 DOE then sampled from HOU 

distributions developed for each room type using data from the RBSAM.

11

To determine the room and geographic location-specific HOU distributions, DOE scaled 

the HOU distribution for a given room type from the RBSAM study by the average CFLK HOU 

in a given RECS reportable domain. Note that in cases where the resulting HOU exceeded 24

h/day, DOE capped the HOU to 24 h/day. DOE assumed that the shape of the HOU distribution 

for a particular room type would be the same across the U.S., even if the average HOU for that 

room type varied by geographic location. 

Figure 6.3.1 shows the distribution of daily HOU by room type, for all U.S. regions. DOE 

used the distribution of CFLKs by room type shown in Table 6.3.3 as a weighting factor to 

determine the national weighted-average HOU, which DOE used in the energy use analysis. 

Based on those calculations, DOE estimates that CFLKs are used an average of 2.0 hours per day 

in the residential sector.

 

e In cases where the RBSAM included HOU data for multiple lamps in the same room, DOE used a simple average 

of those HOU for that specific room type. DOE also applied a weighting factor based on the number of lamps 

identified in each room for each of the metered houses.
6-6 

Figure 6.3.1 Average Hours of Use of CFLKs by Room Type 

Note: The solid line in each box plot shows the median HOU, and the box delineates the HOU within the 25th

and 75th percentile of the sample for each room type. 

6.3.2 Commercial Sector

DOE developed average daily operating hours for CFLKs in commercial buildings using 

lighting data for 15 commercial building types obtained from the 2010 U.S. Lighting Market 

Characterization (LMC).

15 

For each commercial building type presented in the LMC, DOE determined the fraction 

of installed lamps (hereafter referred to as CFLK lamps) utilizing each of the light source 

technologies typically used in CFLKs (i.e., incandescent, halogen, and CFL)f

, which are reported 

in LMC Table 4-18. Using data on the commercial HOU for each of these light source 

technologies (LMC Table 4.20), and weighting those data by the percentage of CFLK lamps in 

each building type (LMC Table 4.18), DOE estimated the weighted-average HOU of CFLK 

lamps in each building type. The results are shown in Table 6.3.4. 

f Operating hours specifically for LEDs are not reported in the LMC.
6-7 

Table 6.3.4 Daily CFLK HOU by Building Type in the Commercial Sector

Building Type

Fraction of 

CFLK Lamps in 

Each Building 

Type (%)

CFLK WeightedAverage HOU 

(h/day)

Education 12 10.6

Food Service 29 10.3

Food Store 5 10.7

Health Care–Inpatient 15 10.5

Health Care–Outpatient 11 10.4

Lodging 45 10.4

Offices (Non-medical) 16 10.5

Public Assembly 30 10.4

Public Order and Safety 8 10.7

Religious Worship 13 10.4

Retail–Mall & Non-Mall 18 11.2

Services 6 10.8

Warehouse and Storage 8 10.8

Other 15 10.9

To estimate the national average HOU for the commercial sector, DOE mapped the HOU 

of the building types in the LMC to the building types in the 2003 CBECS sample (excluding 

vacant buildings). DOE used a weighting factor equal to the number of lamps per 1000 ft2

according to the LMC (Table 4.21), multiplied by the CBECS building weight and the area of 

each CBECS building (as discussed earlier in this chapter). Using this weighting factor, DOE 

calculated that, nationwide, CFLKs are used an average of 10.7 hours per day in the commercial 

sector, as shown in Table 6.3.5. 
6-8 

Table 6.3.5 CBECS Building Type Average HOU

CBECS Building Type LMC Building HOU/day Building 

Weight

Office Offices (Non-medical) 10.5 22%

Laboratory Other 10.9 1%

Nonrefrigerated warehouse Warehouse and Storage 10.8 4%

Food sales Food Store 10.7 1%

Public order and safety Public Order and Safety 10.7 1%

Outpatient health care Health Care – Outpatient 10.4 2%

Refrigerated warehouse Warehouse and Storage 10.8 0%

Religious worship Religious Worship 10.4 5%

Public assembly Public Assembly 10.4 10%

Education Education 10.6 7%

Food service Food Service 10.3 5%

Inpatient health care Health Care – Inpatient 10.5 3%

Nursing Lodging 10.4 3%

Lodging Lodging 10.4 11%

Strip shopping mall Retail - Mall & Non-Mall 11.2 10%

Enclosed mall Retail - Mall & Non-Mall 11.2 4%

Retail other than mall Retail - Mall & Non-Mall 11.2 9%

Service Services 10.8 2%

Other Other 10.9 1%

National Average 10.7 100% (total)

As presented in Table 6.3.5, each building’s weighted-average HOU were estimated to 

range between 10.3 HOU per day for food service buildings and 11.2 HOU per day for retail 

buildings. To capture the variability in HOU for individual consumers in the commercial sector, 

DOE applied a triangular distribution to each building type’s weighted-average HOU having a 

minimum of 80% and a maximum of 120% of the weighted-average HOU value. This additional 

variation did not result in any change to the estimated national average CFLK HOU for the 

commercial sector. 

6.4 INPUT POWER 

The engineering analysis (chapter 5 of this NOPR TSD) presents the power consumption 

data for representative lamp units in CFLKs. DOE utilized these data to estimate the power 

consumption of All CFLKs

g for each efficacy level (EL) considered in this rulemaking. 

DOE’s analysis of product energy use (and the subsequent analyses of life-cycle cost, 

shipments, and NIA) is intended to represent the energy use of products in the field. At any given 

 

g As described in chapter 3 of this NOPR TSD, the new product class structure effectively combines the current 

product classes into a single product class, called ‘All CFLKs’.
6-9 

efficacy level, a CFLK’s power consumption is directly proportional to lumen output. Given that 

lumen output is the primary service that CFLKs provide to consumers, CFLKs subjected to an 

amended standard should provide the same level of lumen output as current CFLKs on the 

market. Therefore, DOE developed its estimate of the power consumption of CFLKs to account 

for lumen output of CFLKs in the market. 

Stakeholders have indicated that the wattage limiter currently required for lamps in the 

current Other Base Type product classh constrains the lumen output of CFLKs with low-efficacy 

lamps in that product class to levels below those preferred by consumers. (ALA, No. 39 at p. 43; 

Monte Carlo, No. 41 at p.1; Westinghouse, No. 29 at p. 2)i DOE therefore did not consider the 

lumen output of CFLKs that are so constrained to be representative of the lumen output desired 

by consumers. DOE instead considered the lumen output of these CFLKs separately from the 

rest of the market when developing its assessment of CFLK lumen output. This approach is also 

consistent with DOE’s proposal to remove the wattage limiter requirement for current product 

class 3 CFLKs under the new product class structure proposed in this rulemaking, in which case 

that constraint on lumen output would no longer exist. 

Using the most recently available market data on CFLKs and the lamps therein, DOE 

estimated the average lumen output of All CFLKs on the market separately for CFLKs with 

candelabra sockets using low-efficacy (below baseline) lamps and CFLKs with other socket 

types. 

To do so, DOE first performed model counts of CFLKs (sold with a ceiling fan, or 

independently) using two data sources: in-store shelf surveys and product offerings on the 

Internet.jRecognizing that model counts do not reflect the fact that products typically sell in 

higher volumes at lower prices, DOE used a price-weighting scheme based on an analysis 

presented in the concurrent ceiling fans energy conservation standards rulemakingk and 

summarized here.

The relationship between ceiling fan market share and retail price is described by the 

following equation: 

MMMM( ) = AA ββFFFF∗pp

Eq. 6.1 

Where:

h

See chapter 3 of this NOPR TSD for details on the current product class structure.

i A notation in this form provides a reference for information that is in the docket of DOE’s rulemaking to develop 

energy conservation standards for CFLKs (docket number EERE–2012–BT–STD–0045), which is maintained at 

www.regulations.gov. For the ALA reference, for example, the notation indicates that the citation refers to

information contained in document number 39 in the docket for the CFLK energy conservation standards

rulemaking, and appears on page 43 of that document.

j Comprehensive shelf surveys were conducted in nine stores in May and June, 2014: 4 Home Depot stores, 4 

Lowe’s stores, and 1 Wal-Mart store. The stores were all located in California. The survey of Internet offerings 

included 1,340 samples from the Hansen Wholesale website (http://www.hansenwholesale.com).

k The preliminary analysis technical support document for the CFs energy conservation standards rulemaking

is available at www.regulations.gov under docket number EERE-2012-BT-STD-0045-0065. 
6-10 

MMMM( ) = Market share for a ceiling fan that costs , 

AA = Normalization factor,

= Retail price of a ceiling fan, and

ββFFFF = Consumer sensitivity to first cost, found to be equal to -0.028.l

Because CFLKs are sold primarily with ceiling fans, DOE uses the same equation to 

estimate CFLK market shares. DOE also accounted for the fact that different data sources 

represent distribution channels with different sales volumes.m 

Using the shelf survey and online data, DOE collected information on socket types, 

socket numbers, light source technologies, lamp wattages, and retail prices. However, lamp 

efficacy data are generally unavailable for the lamps packaged with CFLKs. DOE therefore

consulted online lamp retailers for data on the efficacy of lamps (by socket type, lamp wattage, 

and light source technology) found in CFLKs. Because DOE assumes that CFLKs are typically

packaged with low-cost lamps, DOE applied the price-weighting scheme described earlier. Using 

this method, DOE estimated the weighted-average efficacies of lamps packaged with CFLKs for 

the socket types, light source technologies, and representative wattages of lamps applicable to 

the proposed All CFLKs product class. 

Using these lamp efficacies, DOE then calculated the lumen output for each CFLK found 

in the shelf surveys. The results of this analysis are presented in Table 6.4.1, which also shows 

the market shares of the dominant socket types included in the sample. The table presents CFLK 

lumen output data for two main categories of CFLKs: 

1) CFLKs having lamps with candelabra base types, which have efficacies below the

baseline set for the new product class structure considered in this rulemaking. The

weighted-average lumen output for these CFLKs was estimated to be 1,622 lumens.

2) CFLKs having lamps with medium-screw and GU24 base types, which have efficacies

above the baseline set for the new product class structure considered in this rulemaking.

The weighted-average lumen output for these CFLKs was estimated to be 2,039 lumens.

Because all products presented in the engineering analysis have efficacies above the

restructured baseline, and because the wattage limit constraint would not exist under the 

amended standards proposed in this rulemaking, DOE excluded CFLKs with candelabra base 

sockets in its characterization of a representative CFLK for the All CFLKs product class subject 

to standards. DOE used data on CFLKs with medium-screw base and GU24 sockets because 

they constitute the majority of the CFLK market with efficacies above the restructured baseline. 

l

For further details on the derivation of equation 6.1 and its parameters, see the preliminary analysis TSD for ceiling 

fans. http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0045-0065 m Specifically, DOE weighted the shelf survey versus the online data based on the store (95.5%) and online (4.5%) 

market shares, and the shelf survey retailer market shares (37.7% for Home Depot, 31.9% for Lowe’s, and 8.9% for 

Wal-Mart) according to data from the NPD Group (2011). In addition, the data from each retailer in the shelf survey 

was weighted by the percent of occurrences of each model across the same retailer. For example, if a CFLK was 

found on the shelves of 2 out of 4 surveyed Home Depot stores, it would be weighted by 95.5% (the store market 

share), by 37.7% (the Home Depot store market share), and by 50% (the percent of occurrences in all Home Depot 

stores). In this analysis, DOE assumed that offerings at the largest retailers (Home Depot, Lowe’s, and Wal-Mart) 

are representative of other in-store sales.
6-11 

Therefore, DOE estimated that the weighted-average lumen output for All CFLKs (i.e., after 

amended standards, at or above the restructured baseline) is 2,039 lumens. Figure 6.4.1 shows 

the lumen output histogram of All CFLKs, excluding those with candelabra sockets.

Table 6.4.1 Weighted-Average Total Lumens for All CFLKs

Lamp Efficacies Base Type Market Share 

(2014) 

Weighted-Average 

CFLK Total Lumens

Below restructured baseline Candelabra 66% 1,622

Above restructured baseline 

Medium Screw 21% 

2,039

GU24 12%

Vary All Other 1% (excluded)

Total 100%

Figure 6.4.1 Lumens Variation for All CFLKs with Efficacies above the Restructured 

Baseline

DOE then used the weighted-average total lumen output representative of All CFLKs

(2,039 lumens) to scale the results of the engineering analysis, and to reflect the broader CFLK 

market. DOE scaled the power input of the EL 0 CFLK to reflect an average market lumen 

output of 2,039 lumens for each of the engineering scenarios. This scaling factor is equal to the 

ratio of the market-weighted average total lumen output to the baseline lamp lumen output. This

implies a scaling factor of 2.55 (2,039 lumens divided by 800 lumens) for the representative 

lamp lumens and wattages in the lamp replacement scenario, and a scaling factor of 1.27 (2,039 

lumens divided by 1,600 lumens) for the representative fixture lumens and wattage in the light 

kit replacement scenario. Then, DOE scaled the wattage and lumens for all other ELs with those 

same scaling factors to preserve the proportionality of lumens and wattages at the different ELs 
6-12 

of the representative lamp units in the engineering analysis. Table 6.4.2 and Table 6.4.3 show the 

results of input power calculations for the lamp replacement scenario and the light kit 

replacement scenario, respectively. 

Note that representative CFLKs with efficiencies below the restructured baseline 

(hereafter referred to as the ‘sub-baseline’) were assumed to be packaged with lamps with the 

properties described in appendix 7A: an efficacy of 7.3 lumens per watt, power consumption of 

60 watts, a 1,500-hour lifetime, and a price in 2014$ of $0.55 per lamp. CFLKs at the subbaseline are also assumed to have three sockets (because they operate with a 190-watt limiter), 

and have a lumen output of approximately 1,320 lumens. 

Table 6.4.2 Calculation of Input Power for All CFLKs: Lamp Replacement Scenario 

Candidate

Standard

Level

Lamp Input 

Power 

(watts)*

Lamp Light 

Output 

(lumens)*

Scaled CFLK 

Light Output

(lumens)

Scaled CFLK 

Input Power

(watts)

Sub-baseline 60 440 1,320 180

Baseline (EL 0) 14 800 2,039 35.7

EL 1 13 800 2,039 33.1

EL 2 – Rep. Unit 1 11 730 1,861 28.0

EL 2 – Rep. Unit 2 12 800 2,039 30.6

EL 3 8.5 800 2,039 21.7

EL 4 8 820 2,090 20.4

*Values come from chapter 5 of this NOPR TSD. 

Table 6.4.3 Calculation of Input Power for All CFLKs: Light Kit Replacement Scenario

Candidate

Standard

Level

Fixture 

Input 

Power*

(watts)

Fixture

Light 

Output*

(lumens)

Scaled CFLK 

Light Output

(lumens)

Scaled CFLK 

Input Power

(watts)

Sub-baseline 180 1,320 1,320 180

Baseline (EL 0) 28 1,600 2,039 35.7

EL 1 27 1,560 1,988 34.4

EL 2 24 1,500 1,912 30.6

EL 3 16 1,600 2,039 20.4

EL 4 15 1,600 2,039 19.1

*Values come from chapter 5 of this NOPR TSD. 
6-13 

6.5 LIGHTING CONTROLS (DIMMING)

In this analysis, DOE assumed that the only lighting controls used with CFLKs are 

dimmers. DOE accounted for the impact of dimmers on residential and commercial CFLK

energy use by reducing the lumen output by 30 percent for CFLKs that operate on dimmers. This 

estimate was based on a meta-analysis of field measurements of energy savings from commercial 

lighting controls by Williams, et al.16 Field measurements of energy savings from controls in the 

residential sector are very limited; DOE assumed that controls would have the same impact as in 

the commercial sector. DOE was able to find a single study17, which focuses on California, and 

suggests that energy savings from dimming may be larger than 30% in the residential sector. 

However, because of the small sample size of this study (the findings were based on metered 

data from two houses in California), DOE did not base its analysis on the findings of this study. 

In addition, following publication of the GSL preliminary analysis, NEMA commented that 30% 

energy savings due to dimming in the residential sector is a reasonable estimate.n

 

The unit energy consumption (UEC) of CFLKs used with dimmers can be computed 

using the following formula: 

UUUUCCcc = UUUU nn × �11 − ffdddddddddddd × ff ee�

Eq. 6.2 

Where:

 

UU cc= weighted-average annual energy consumption (kWh) of CFLKs considering use 

of dimmers

UU CCnn = annual unit energy consumption (kWh) of CFLKs, operating under full power, 

dddddddddddd= the fraction of lamps estimated to be operating on dimmers, and 

eeee ee = a parameter describing the effect of dimmers on energy consumption, i.e., the 

reduction on energy use due to dimmers, taken to be 0.3. 

To estimate the fraction of CFLKs used with lighting controls for each EL, DOE assumed 

that CFLs used in CFLKs are non-dimmableo and that all other light source technologies

(incandescent and LED) are dimmable. DOE has assumed that an equal fraction of LED and 

incandescent CFLKs are operated with dimmers, based on the increasing fraction of 

commercially available dimmers that are now compatible with LEDs, the increase in LED lamps 

that are being designed to operate properly on legacy dimmers, and the assumption that integral 

LEDs have built-in dimming capability with no compatibility issues. 

For the residential sector, DOE used data from the LBNL survey to determine (based on 

the respondents’ most-used and second-most-used ceiling fans) that the fraction of CFLKs used 

with dimmers is 11%. To estimate the fraction of CFLKs used with dimmers by room type, DOE 

used data from the LMC15 (Table 4.34, which reports the percentage of dimmers by room type in 

the residential sector) and multiplied each room’s dimming fraction in the LMC with a ratio 

 

n NEMA’s comment (NEMA, 34, at p.21) is available at the GSL rulemaking docket available at 

http://www.regulations.gov/#!documentDetail;D=EERE-2013-BT-STD-0051-0034 o According to manufacturers of GSLs, only 5% of CFLs are dimmable. DOE assumed that, because manufacturers 

typically package low-cost CFLs with CFLKs, all CFLs packaged with CFLKs are non-dimmable. 
6-14 

equal to the overall fraction of CFLKs used with dimmers divided by an adjustment factorp

. Due 

to lack of data to suggest otherwise, DOE assumed that the percentage of CFLKs used with 

dimmers is the same in the commercial sector as in the residential sector. 

6.6 RESULTS

The following tables present results of the energy use analysis for CFLKs in kilowatthours per year (kWh/yr). Table 6.6.1 and Table 6.6.2 present the energy use analysis results for 

All CFLKs in residential buildings, while Table 6.6.3 and Table 6.6.4 present the results for All 

CFLKs in commercial buildings.

 

p This adjustment factor was derived as the overall fraction of CFLKs with dimmers, divided by the weighted 

average of the product of each dimming fraction by room type in the LMC (Table 4.34) that was reported in the 

LBNL survey, with the distribution of CFLKs by room type as reported in Table 6.3.1. 
6-15 

Table 6.6.1 Annual Energy Use (kWh/year) for All CFLKs (Lamp Replacement Scenario) in the Residential Sector

RECS Reportable Domain SubBaseline EL 0 EL 1 EL 2a* EL 2b* EL 3 EL 4

01. CT, ME, NH, RI, VT 157 32.7 30.4 25.7 26.6 18.9 17.7

02. MA 204 41.4 38.4 32.5 34.7 24.6 23.1

03. NY 173 35.8 33.3 28.2 29.4 20.9 19.6

04. NJ 152 31.4 29.2 24.7 25.8 18.3 17.2

05. PA 152 31.0 28.8 24.4 25.8 18.2 17.2

06. IL 120 24.7 22.9 19.4 20.4 14.4 13.6

07. IN, OH 135 27.6 25.6 21.7 22.9 16.2 15.3

08. MI 125 25.6 23.8 20.1 21.3 15.1 14.2

09. WI 152 30.6 28.4 24.0 25.8 18.3 17.2

10. IA, MN, ND, SD 103 21.4 19.9 16.8 17.4 12.3 11.6

11. KS, NE 127 26.4 24.5 20.7 21.6 15.3 14.4

12. MO 145 30.4 28.2 23.9 24.6 17.4 16.4

13. VA 123 25.3 23.5 19.9 20.9 14.8 13.9

14. DE, DC, MD, WV 146 29.6 27.5 23.3 24.9 17.6 16.6

15. GA 133 26.9 25.0 21.2 22.6 16.0 15.1

16. NC, SC 116 23.9 22.2 18.7 19.8 14.0 13.2

17. FL 142 28.7 26.7 22.6 24.1 17.1 16.1

18. AL, KY, MS 126 25.5 23.7 20.1 21.4 15.2 14.3

19. TN 134 27.3 25.4 21.5 22.7 16.1 15.1

20. AR, LA, OK 127 25.9 24.0 20.3 21.6 15.3 14.4

21. TX 125 25.8 24.0 20.3 21.2 15.0 14.1

22. CO 88 18.9 17.6 14.9 14.9 10.6 10.0

23. ID, MT, UT, WY 98.2 19.8 18.4 15.6 16.7 11.8 11.1

24. AZ 81 16.4 15.2 12.9 13.8 9.7 9.2

25. NV, NM 101 20.7 19.2 16.3 17.2 12.2 11.5

26. CA 89.3 18.4 17.1 14.5 15.2 10.8 10.1

27. AK, HI, OR, WA 109 22.9 21.3 18.0 18.4 13.1 12.3

U.S. Average 127 26.1 24.3 20.5 21.7 15.3 14.4

*EL 2a, and EL2b show the energy use for EL 2 Rep Unit 1, and EL 2 Rep Unit 2, respectively.
6-16 

Table 6.6.2 Annual Energy Use (kWh/year) for All CFLKs (Light Kit Replacement Scenario) in the Residential Sector

RECS Reportable Domain SubBaseline EL 0 EL 1 EL 2 EL 3 EL 4

01. CT, ME, NH, RI, VT 157 32.7 31.5 26.6 17.7 16.6

02. MA 204 41.4 39.9 34.7 23.1 21.7

03. NY 173 35.8 34.6 29.4 19.6 18.4

04. NJ 152 31.4 30.3 25.8 17.2 16.1

05. PA 152 31.0 29.9 25.8 17.2 16.1

06. IL 120 24.7 23.8 20.4 13.6 12.7

07. IN, OH 135 27.6 26.6 22.9 15.3 14.3

08. MI 125 25.6 24.7 21.3 14.2 13.3

09. WI 152 30.6 29.5 25.8 17.2 16.1

10. IA, MN, ND, SD 103 21.4 20.6 17.4 11.6 10.9

11. KS, NE 127 26.4 25.4 21.6 14.4 13.5

12. MO 145 30.4 29.3 24.6 16.4 15.4

13. VA 123 25.3 24.4 20.9 13.9 13.0

14. DE, DC, MD, WV 146 29.6 28.6 24.9 16.6 15.5

15. GA 133 26.9 26.0 22.6 15.1 14.1

16. NC, SC 116 23.9 23.0 19.8 13.2 12.3

17. FL 142 28.7 27.7 24.1 16.1 15.1

18. AL, KY, MS 126 25.5 24.6 21.4 14.3 13.4

19. TN 134 27.3 26.4 22.7 15.1 14.2

20. AR, LA, OK 127 25.9 25.0 21.6 14.4 13.5

21. TX 125 25.8 24.9 21.2 14.1 13.2

22. CO 88 18.9 18.3 14.9 10.0 9.3

23. ID, MT, UT, WY 98.2 19.8 19.1 16.7 11.1 10.4

24. AZ 81 16.4 15.8 13.8 9.2 8.6

25. NV, NM 101 20.7 20.0 17.2 11.5 10.8

26. CA 89.3 18.4 17.7 15.2 10.1 9.5

27. AK, HI, OR, WA 109 22.9 22.1 18.4 12.3 11.5

U.S. Average 127 26.1 25.2 21.7 14.4 13.5
6-17 

Table 6.6.3 Annual Energy Use (kWh/year) for All CFLKs (Lamp Replacement Scenario) in the Commercial Sector

CBECS Building Types SubBaseline EL 0 EL 1 EL 2a* EL 2b* EL 3 EL 4

Education 663 139 129 109 115 81.4 76.6

Enclosed mall 683 146 136 115 120 85.3 80.3

Food sales 682 141 130 110 115 81.6 76.8

Food service 678 135 126 106 112 79.7 75.0

Inpatient health care 679 136 126 107 112 79.2 74.6

Laboratory 652 140 130 110 116 82.2 77.3

Lodging 702 136 126 107 112 79.2 74.5

Nonrefrigerated warehouse 662 140 130 110 116 82.1 77.2

Nursing 664 135 125 106 112 79.2 74.5

Office 676 136 126 107 113 79.8 75.1

Other 662 141 131 111 117 82.9 78.0

Outpatient health care 658 135 125 106 111 78.5 73.9

Public assembly 658 136 126 107 113 79.9 75.2

Public order and safety 658 139 130 110 115 81.7 76.9

Refrigerated warehouse 710 141 131 111 119 84.5 79.5

Religious worship 709 136 126 107 113 79.8 75.1

Retail other than mall 716 146 136 115 122 86.2 81.1

Service 687 141 131 110 117 82.7 77.9

Strip shopping mall 688 146 135 114 121 85.5 80.4

U.S. Average 676 139 129 109 115 81.4 76.6

*EL 2a, and EL2b show the energy use for EL.2 Rep Unit 1, and EL2 Rep Unit 2, respectively.
6-18 

Table 6.6.4 Annual Energy Use (kWh/year) for All CFLKs (Light Kit Replacement Scenario) in the Commercial Sector

CBECS Building Types SubBaseline EL 0 EL 1 EL 2 EL 3 EL 4

Education 663 139 134 115 76.6 71.8

Enclosed mall 683 146 141 120 80.3 75.3

Food sales 682 141 136 115 76.8 72.0

Food service 678 135 131 112 75.0 70.3

Inpatient health care 679 136 131 112 74.6 69.9

Laboratory 652 140 135 116 77.3 72.5

Lodging 702 136 131 112 74.5 69.9

Nonrefrigerated warehouse 662 140 135 116 77.2 72.4

Nursing 664 135 130 112 74.5 69.9

Office 676 136 131 113 75.1 70.4

Other 662 141 136 117 78.0 73.1

Outpatient health care 658 135 130 111 73.9 69.3

Public assembly 658 136 131 113 75.2 70.5

Public order and safety 658 139 134 115 76.9 72.1

Refrigerated warehouse 710 141 136 119 79.5 74.5

Religious worship 709 136 131 113 75.1 70.4

Retail other than mall 716 146 141 122 81.1 76.1

Service 687 141 136 117 77.9 73.0

Strip shopping mall 688 146 140 121 80.4 75.4

Education 663 139 134 115 76.6 71.8

U.S. Average 676 139 134 115 76.6 71.8
6-19 

REFERENCES

1. U.S. Department of Energy. Energy Conservation Program: Test Procedures for Certain 

Consumer Products and Certain Commercial and Industrial Equipment; Technical 

Amendment to Energy Conservation Standards for Certain Consumer Products and 

Certain Commercial and Industrial Equipment. Washington, DC. (Last accessed June 15, 

2015.) http://www.regulations.gov/#!documentDetail;D=EERE-2006-TP-0121-0003. 

2. U.S. Department of Energy–Energy Information Administration. 2009 RECS Survey 

Data. (Last accessed June 9, 2015.) 

http://www.eia.gov/consumption/residential/data/2009/. 

3. U.S. Department of Energy–Energy Information Administration. 2003 CBECS Survey 

Data. (Last accessed June 9, 2015.) 

http://www.eia.gov/consumption/commercial/data/2003/index.cfm?view=microdata. 

4. DNV KEMA Energy and Sustainability and Pacific Northwest National Laboratory. 

Residential Lighting End-Use Consumption Study: Estimation Framework and Baseline 

Estimates. 2012. (Last accessed June 15, 2015.) 

http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2012_residential-lightingstudy.pdf. 

5. Nexant, Inc. and Apex Analytics, LLC. Georgia Power Company Residential Lighting 

Saturation and Metering Study. 2013. Georgia Power Company: Louisville, CO. (Last 

accessed May 28, 2015.) 

http://www.psc.state.ga.us/factsv2/Document.aspx?documentNumber=150352. 

6. Navigant Consulting, Inc., Cadmus Group, Inc., and Apex Analytics, LLC. EM&V 

Report for the 2010-2011 Residential Energy Star Lighting Program. 2012. Duke Energy 

Progress: Boulder, CO. 

7. TecMarket Works and BuildingMetrics Inc. Evaluation of the Non-Residential Smart 

$aver® Prescriptive Program in North and South Carolina: Results of a Process and 

Impact Evaluation. 2011. Duke Energy. (Last accessed June 15, 2015.) 

http://dms.psc.sc.gov/pdf/matters/F0FA6EA2-155D-2817-10053D8B44C06C9D.pdf. 

8. Navigant Consulting, Inc. and Cadmus Group, Inc. EmPOWER Maryland 2010 Interim 

Evaluation Report. 2011. Baltimore Gas and Electric, Potomac Electric Power Company, 

Delmarva Power and Light (DPL), Southern Maryland Electric Cooperative (SMECO), 

and Allegheny Power (AP): Boulder, CO. 

9. Vermont Energy Investment Corporation. State of Ohio Energy Efficiency Technical 

Reference Manual. 2010. Public Utilities Commission of Ohio. (Last accessed June 15, 

2015.) http://www.lopdf.net/preview/Sb6XvMTluQXqUgcULDoOveBonWBSh5Nng3-

u8UYwxwk,/State-of-Ohio-Energy-Efficiency-Technical-ReferenceManual.html?query=The-System-Evaluation-Manual-and-Chiller. 
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10. Cadmus Group, Inc. Lighting and Appliance Evaluation - PY 2. 2010. Ameren, Illinois. 

(Last accessed June 9, 2015.) 

http://ilsagfiles.org/SAG_files/Evaluation_Documents/Ameren/AIU%20Evaluation%20R

eports%20EPY2/AIU%20EPY2%20Draft/AIU_Lighting_and_Appliance_PY2_Evaluati

on_DRAFT.pdf. 

11. Ecotope Inc. Residential Building Stock Assessment: Metering Study. 2014. Northwest 

Energy Efficiency Alliance: Seattle, WA. Report No. E14-283. (Last accessed June 15, 

2015.) http://neea.org/docs/default-source/reports/residential-building-stock-assessment--

metering-study.pdf?sfvrsn=6. 

12. NMR Group, Inc. and DNV GL. Northeast Residential Lighting Hours-of-Use Study. 

2014. Connecticut Energy Efficiency Board, Cape Light Compact, Massachusetts Energy 

Efficiency Advisory Council, National Grid Massachusetts, National Grid Rhode Island, 

New York State Energy Research and Development Authority. (Last accessed June 9, 

2015.) https://app.box.com/s/o1f3bhbunib2av2wiblu/1/1995940511/17399081887/1. 

13. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. Energy 

Conservation Program for Consumer Products: Preliminary Technical Support 

Document: Energy Efficiency Standards for General Service Lamps. December 2014. 

Washington, D.C. 

14. Kantner, C. L. S., S. J. Young, S. M. Donovan, and K. Garbesi. Ceiling Fan and Ceiling 

Fan Light Kit Use in the U.S.—Results of a Survey on Amazon Mechanical Turk. 2013. 

Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6332E. (Last 

accessed June 8, 2015.) http://www.escholarship.org/uc/item/3r67c1f9. 

15. Navigant Consulting, Inc. Final Report: 2010 U.S. Lighting Market Characterization. 

2012. U.S. Department of Energy. (Last accessed June 10, 2015.) 

http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2010-lmc-final-jan-2012.pdf. 

16. Williams, A., B. Atkinson, K. Garbesi, E. Page, and F. Rubinstein. Lighting Controls in 

Commercial Buildings. LEUKOS. 2012. 8(3): pp. 161–180. (Last accessed June 9, 2015.) 

http://eetd.lbl.gov/publications/lighting-controls-commercial-buildings. 

17. Consortium for Energy Efficiency. Residential Lighting Controls Market 

Characterization. 2014. Boston, MA. (Last accessed May 6, 2015.) 

http://library.cee1.org/sites/default/files/library/11458/CEE_LightingMarketCharacterizat

ion.pdf. 
7-i 

CHAPTER 7. PRODUCT PRICE DETERMINATION

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 METHODOLOGY .......................................................................................................... 7-1

7.2.1 Premium Determination ....................................................................................... 7-2

7.2.2 Consumer Price Determination ............................................................................ 7-2

7.3 RESULTS ........................................................................................................................ 7-3

7.3.1 Consumer Prices for All CFLKs .......................................................................... 7-4

LIST OF TABLES

Table 7.3.1 Consumer Prices for All CFLKs Product Class: Lamp Replacement 

Scenario.......................................................................................................... 7-4

Table 7.3.2 Consumer Prices for All Other CFLKs Product Class: Fixture 

Replacement Scenario .................................................................................... 7-5
7-1

CHAPTER 7. PRODUCT PRICE DETERMINATION

7.1 INTRODUCTION

This chapter describes the methodology the U.S. Department of Energy (DOE) followed 

in developing consumer prices for lamps sold with ceiling fan light kits (CFLKs) proposed in 

this rulemaking. It also provides the results for the consumer prices of lamps sold with CFLKs.

In energy conservation standards rulemakings, DOE often develops cost-efficiency 

relationships in the engineering analysis. Then, DOE applies markups to derive consumer prices. 

For this rulemaking, DOE derived efficacy levels (ELs) in the engineering analysis and prices in 

this chapter of the technical support document (TSD). The product price determination develops 

consumer prices directly because reverse engineering a lamp is impractical, as lamps are not 

easily disassembled. By combining the results of the engineering analysis and the product price 

determination, DOE derived typical inputs for use in the life-cycle cost (LCC) analysis, which is 

discussed in chapter 8 of this notice of proposed rulemaking (NOPR) TSD and the national 

impact analysis (NIA), which is discussed in chapter 10 of this NOPR TSD.

7.2 METHODOLOGY

Because the efficiency of the CFLK is based on the efficacy of the lamps with which it is 

packaged, DOE develops a product price determination for the lamp component of the CFLK. 

Typically, DOE develops manufacturera selling prices (MSPs) for covered products and applies 

markups to create consumer prices to use as inputs to the LCC analysis and NIA. Because lamps 

are difficult to reverse-engineer (i.e., not easily disassembled), DOE directly derives consumer

prices for the lamps in this rulemaking. Consumer price refers to the average product price a 

consumer pays before tax and installation.

As noted in chapter 5 of this NOPR TSD, DOE identified representative lamp units for 

each EL analyzed in the CFLK representative product class. In this product price determination,

DOE developed consumer prices for each representative lamp unit as a component of a CFLK 

with which it is packaged. To do so, DOE determined the consumer price of the CFLK and then 

determined the portion of that price attributable to the lamp packaged with the CFLK. DOE 

started with a consistent set of aggregated manufacturer-suggested distributor net prices for 

ceiling fans (CFs) sold with light kits and determined average shipment-weighted premiums.

DOE then applied the premiums to distributor net prices to obtain the consumer prices of 

CFLKs. Based on manufacturer feedback, DOE determined the lamp component of the consumer 

price of the CFLK to establish the consumer price of representative lamp units when packaged 

with CFLKs. The following sections detail the steps of this process.

 a DOE uses “manufacturer” to refer only to CFLK manufacturers unless otherwise noted.
7-2

7.2.1 Premium Determination

DOE based its product price determination on publicly available pricing data and 

aggregated information received in manufacturer interviews. In its review of publicly available 

prices, DOE observed a range of consumer prices for CFs and CFLKs, depending on the 

distribution channel through which they are purchased. Based on feedback from manufacturer 

interviews, DOE identified three main distribution channels for CFLKs: electrical/specialty 

centers, home centers, and lighting showrooms.

DOE first collected manufacturer-suggested distributor net price lists, which indicate

prices distributors should payb for CFs sold with CFLKs and CFLKs sold alone. Starting with 

these consistent sets of distributor net prices, DOE calculated an average premium corresponding 

to prices for CFs sold with CFLKs offered in electrical/specialty centers, home centers, and 

lighting showrooms. Specifically, DOE compared the distributor net prices of CFs sold with 

CFLKs to their consumer prices in each of the distribution channels. Because CFLKs are 

primarily sold with fans, DOE developed premiums only for CFLKs sold with CFs. In general, 

DOE found that the premiums did not differ significantly with the efficacy of the CFLK, and 

therefore did not vary premiums by efficacy.

DOE then developed an average premium weighted by estimated shipments that go 

through each distribution channel. Using manufacturer feedback from interviews, DOE 

determined an aggregated percentage of shipments that go through each of the channels: 

electrical/specialty channel at 12 percent, home center channel at 80 percent, and lighting 

showroom channel at 8 percent. DOE then applied these percentages to the average premiums 

calculated for the respective distribution channels. Because DOE did not have distributor net 

price lists from all manufacturers, DOE adjusted the premium to ensure that it reflected the 

majority of the CFLK market. DOE based this adjustment on a ratio of CFLK retail prices from 

manufacturers that represent a majority of the market to prices from the manufacturers for which 

DOE had distributor net prices.

DOE applied the average shipment-weighted premium to the distributor net prices of the 

CFs sold with CFLKs to obtain their consumer price. Based on manufacturer feedback, DOE 

determined that a basic light kit fixture comprises 20 percent of the consumer price of a CF sold 

with a light kit. Therefore, DOE then applied this percentage to the consumer price of the CF 

sold with the CFLK to obtain the consumer price of only the CFLK.

7.2.2 Consumer Price Determination

Once DOE had determined the CFLK consumer prices as described in section 7.2.1, DOE 

developed an average consumer price for each representative lamp unit when packaged with a 

CFLK. To do so, DOE determined the component of the CFLK consumer price that is 

attributable to the lamp with which it is packaged. DOE was able to apply this methodology to

representative lamp units that were sold with CFLKs and for which distributor net prices were 

available. Specifically, DOE applied this methodology to the 13 W spiral compact fluorescent 

 b These prices may be discounted for larger orders.
7-3

lamp (CFL) representative lamp unit at EL 1. Based on manufacturer feedback and stakeholder 

comments, DOE found that a CFL in a CFLK comprises 15 percent of the CFLK consumer

price. DOE applied this percentage to the consumer prices of CFLKs packaged with 13 W spiral 

CFLs to obtain an average consumer price of the lamp packaged with a CFLK.

While the representative lamp unit at the baseline is sold with CFLKs, distributor net 

prices for these CFLKs were not available and representative lamp units at all other ELs were not 

sold in CFLKs. In these cases, DOE developed a ratio of the consumer price of the 13 W spiral 

CFL when packaged with a CFLK to the consumer price of the same lamp when sold in the

home center channel. DOE then applied this ratio to the home center channel consumer prices of 

the representative lamp units to obtain their consumer prices if they were packaged with a CFLK. 

DOE used consumer prices from the home center channel because it carries the highest volume 

of CFLKs and, therefore, more closely reflects prices paid by CFLK consumers. Where home 

center consumer prices were not available for representative lamp units, DOE developed ratios 

based on lamp characteristics such as technology, wattage, and efficacy, or developed ratios 

using other distribution channels where prices for the representative lamp unit were available.

As mentioned, DOE also included the incremental cost due to changes in socket 

configuration when applicable. Based on manufacturer feedback, DOE estimated the cost of 

different socket types to the manufacturer and then applied the appropriate manufacturer and 

distributor markups to obtain the consumer price of the socket. Specifically, DOE estimated that 

medium screw base (E26) sockets cost $0.15 to the manufacturer and GU24 and pin-base sockets 

cost $0.35 to the manufacturer. The consumer prices for these socket types was determined by 

applying the appropriate manufacturer markup (see chapter 12 of this NOPR TSD) and the 

distribution markup for ceiling fan parts weighted by 2013 market shares (see chapter 6 of the

CF preliminary TSD).c

Once DOE calculated consumer prices for lamps packaged with CFLKs, DOE added 

sales tax and, if appropriate, installation costs to derive the total, installed consumer cost. The 

consumer prices derived for lamps packaged with CFLKs were used in the main LCC analysis 

(chapter 8 of this NOPR TSD) and NIA (chapter 10 of this NOPR TSD).

7.3 RESULTS

Section 7.3.1 summarizes the consumer prices for representative lamp units packaged in 

CFLKs. The results also indicate the remaining CFLK consumer price (i.e., CFLK price 

excluding the lamps and sockets) based on the lamp and socket prices and total CFLK consumer 

price determined for EL 1. DOE assumed that this remaining CFLK consumer price was the 

same at all levels, and the only changes in the total CFLK consumer price were a function of the 

lamp and socket consumer prices at a particular level. The tables below show the consumer 

prices for the lamp, socket, remaining CFLK consumer price, and total CFLK consumer price at 

each level.

 c The CF preliminary TSD is available on regulations.gov under docket number EERE-2012-BT-STD-0045.
7-4

7.3.1 Consumer Prices for All CFLKs

The following tables present the consumer prices for the All CFLKs product class. The prices presented do not include sales 

tax. The weighted average lamp consumer price is for a single lamp and the socket consumer price is for a single socket. The total 

price of the CFLK is calculated as the sum of the remaining price of the CFLK and the total lamps and sockets in the CFLK. In the 

lamp replacement scenario, each CFLK is assumed to have two sockets. (Note that these results are rounded.)

Table 7.3.1 Consumer Prices for All CFLKs Product Class: Lamp Replacement Scenario

EL Lamp 

Type

Base 

Type

Wattage

Lamp Initial 

Lumen 

Output

Lamp 

Efficacy

Lamp 

Lifetime

Weighted 

Average Lamp 

Consumer Price

Socket 

Consumer 

Price

Remaining 

Price of 

CFLK

Total Price of 

CFLK

W lm lm/W hr 2014$ 2014$ 2014$ 2014$

Baseline CFL E26 14 800 57.1 10,000 $1.70 $0.31 $22.15 $26.19

EL 1 CFL E26 13 800 61.5 10,000 $2.01 $0.31 $22.15 $26.80

EL 2

CFL E26 11 730 66.4 10,000 $2.23 $0.31 $22.15 $27.24

LED E26 12 800 66.7 25,000 $12.93 $0.31 $22.15 $48.63

EL 3 LED E26 8.5 800 94.1 25,000 $5.19 $0.31 $22.15 $33.17

EL 4 LED E26 8 820 102.5 25,000 $4.09 $0.31 $22.15 $30.96
7-5

Table 7.3.2 Consumer Prices for All Other CFLKs Product Class: Fixture Replacement Scenario

EL Lamp 

Type

Base 

Type

Bulb 

Shape

Fixture 

Sockets

Lamp 

Wattage

Fixture 

Wattage

Lamp 

Initial 

Lumen 

Output

Fixture 

Initial 

Lumen

Output

Lamp 

Efficacy

Lamp 

Lifetime

Weighted 

Average 

Lamp 

Consumer 

Price

Socket 

Consumer 

Price

Remaining 

Price of 

CFLK

Total 

Price 

of 

CFLK

W W lm lm lm/W hr 2014$ 2014$ 2014$ 2014$

Baseline CFL E26 Spiral 2 14 28 800 1,600 57.1 10,000 $1.70 $0.31 $22.15 $26.19

EL 1 CFL E26 Spiral 3 9 27 520 1,560 57.8 10,000 $2.45 $0.31 $22.15 $30.45

EL 2 LED E26 G25 3 8 24 500 1,500 62.5 25,000 $12.45 $0.31 $22.15 $60.43

EL 3 LED E26 A21 1 16 16 1,600 1,600 100.0 25,000 $16.12 $0.31 $22.15 $38.59

EL 4 LED E26 A21 1 15 15 1,600 1,600 106.7 25,000 $15.71 $0.31 $22.15 $38.18
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 ................................... 8-1

8.2 LIFE-CYCLE COST INPUTS ........................................................................................ 8-4

8.2.1 Overview .............................................................................................................. 8-4

8.2.2 Ceiling Fan Light Kit Lumen Output................................................................... 8-5

8.2.3 Market Distribution of Ceiling Fan Light Kits by Efficacy Level ....................... 8-6

8.2.4 Price Learning ...................................................................................................... 8-6

8.2.5 Total Installed Cost Inputs ................................................................................... 8-7

8.2.6 Operating Cost Inputs .......................................................................................... 8-9

8.2.6.1 Electricity Cost ........................................................................................... 8-9

8.2.6.2 Lifetime .................................................................................................... 8-11

8.2.6.3 Replacement Costs ................................................................................... 8-14

8.2.6.4 Discount Rates .......................................................................................... 8-16

8.2.7 Disposal Cost ..................................................................................................... 8-24

8.2.8 Lamp Residual Value ......................................................................................... 8-25

8.3 PAYBACK PERIOD INPUTS ...................................................................................... 8-25

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS ...................................... 8-25

8.4.1 Reference Approach Results .............................................................................. 8-27

8.4.2 “Roll-up” Approach Results .............................................................................. 8-31

8.4.3 Rebuttable Presumption Payback Period ........................................................... 8-33

REFERENCES .......................................................................................................................... 8-35

LIST OF TABLES 

Table 8.1.1 Summary of Inputs to Life-Cycle Cost and Payback Period ......................... 8-4

Table 8.2.1 CFLK Efficacy Distribution by Trial Standard Level in 2019 ...................... 8-6

Table 8.2.2 Average Sales Tax Rates by RECS Reportable Domain ............................... 8-8

Table 8.2.3 Lamp Replacement Options ......................................................................... 8-16

Table 8.2.4 Definitions of Income Groups ..................................................................... 8-17

Table 8.2.5 Types of Household Debt and Equity by Percentage Shares (%) ................ 8-18

Table 8.2.6 Data Used to Calculate Real Effective Mortgage Rates .............................. 8-19

Table 8.2.7 Average Real Effective Interest Rates for Household Debt ........................ 8-20

Table 8.2.8 Average Nominal and Real Interest Rates for Household Equity ............... 8-21

Table 8.2.9 Average Real Effective Discount ................................................................. 8-22

Table 8.2.10 Risk-Free Rate and Equity Risk Premium, 2011-2013 ................................ 8-23

Table 8.2.11 Weighted Average Cost of Capital for Analyzed Sectors ........................... 8-24

Table 8.4.1 Average LCC and PBP Results by Efficacy Level for All CFLKs ............. 8-27

Table 8.4.2 Average LCC Savings Relative to the No-Standard Case Efficacy 

Distribution for All CFLKs .......................................................................... 8-28
8-ii

Table 8.4.3 Average LCC and PBP Results by EL ......................................................... 8-31

Table 8.4.4 Average LCC Savings Relative to the No-standard-Case Efficacy 

Distribution for All CFLKs* ........................................................................ 8-32

Table 8.4.5 Rebuttable-Presumption Payback Period Results ........................................ 8-34

LIST OF FIGURES 

Figure 8.1.1 LCC Calculation Flow Diagram .................................................................... 8-3

Figure 8.1.2 PBP Calculation Flow Diagram ..................................................................... 8-3

Figure 8.2.1 Trends in Residential Electricity Prices ....................................................... 8-11

Figure 8.2.2 Trends in Commercial Electricity Prices ..................................................... 8-11

Figure 8.2.3 Survival Function for Ceiling Fans .............................................................. 8-13

Figure 8.4.1 Range of LCC Savings in Residential Sector for All CFLKs ...................... 8-29

Figure 8.4.2 Range of LCC Savings in Commercial Sector for All CFLKs .................... 8-29

Figure 8.4.3 LCC Savings Distribution in Residential Sector for All CFLKs at TSL 

2.................................................................................................................... 8-30

Figure 8.4.4 LCC Savings Distribution in Commercial Sector for All CFLKs at 

TSL 2 ........................................................................................................... 8-30

Figure 8.4.5 Range of LCC Savings in Residential Sector for All CFLKs (Roll-up) ...... 8-32

Figure 8.4.6 Range of LCC Savings in Commercial Sector for All CFLKs (Roll-up) .... 8-33
8-1 

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS 

8.1 INTRODUCTION

This chapter describes the methodology and metrics the U.S. Department of Energy 

(DOE) used to analyze the economic impacts of energy conservation standards for ceiling fan 

light kits (CFLKs) on consumers.a DOE used the following metrics for its analyses: 

• Life-cycle cost (LCC) is the total consumer expense over the life of a product, including

purchase, installation costs and operating costs (which are comprised of maintenance,

repair, and energy costs). After discounting the future operating costs to the time of

purchase, DOE sums the present value of all costs incurred over the lifetime of the

product.

• Payback period (PBP) is the amount of time it takes consumers to recover the assumed

higher purchase price of more energy-efficient products through lower operating costs.

Inputs to the LCC and PBP calculations are discussed in sections 8.2 and 8.3 of this

chapter, respectively. Calculations and results for the LCC and PBP analysis, for each different 

efficacy level (EL), are presented in section 8.4. The calculations discussed here are illustrated 

with a Microsoft Excel® spreadsheet, which is accessible on DOE’s rulemaking website for

CFLKs (http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/76).

Details on the spreadsheet, and instructions for using it, are included in appendix 8A of this 

notice of proposed rulemaking (NOPR) technical support document (TSD). 

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis 

Recognizing that several inputs to the determination of consumer LCC and PBP are 

either variable or uncertain, DOE conducts the LCC and PBP analysis by modeling both the 

uncertainty and variability in the inputs when performing calculations for a representative sample 

of individual consumers that purchase CFLKs. As discussed in chapter 6 of this NOPR TSD, 

DOE used the EIA’s 2009 Residential Energy Consumption Survey (RECS)1 and the 2003 

Commercial Buildings Energy Consumption Survey (CBECS)2 to develop consumer samples for 

CFLKs in the residential and commercial sectors, respectively. 

DOE displays the LCC results as distributions of impacts relative to the absence of an

amended energy conservation standard (hereafter referred to as the “no standard case”). DOE 

uses Monte Carlo simulationb

 and probability distributions for a sample of 10,000 consumers in 

the LCC and PBP analysis. Results are presented at the end of this chapter. 

8.1.2 Overview of Life-Cycle Cost and Payback Period Inputs

DOE categorizes inputs to the LCC and PBP analysis for each EL as follows: (1) inputs 

for establishing the total installed cost, and (2) inputs for calculating the operating cost. 

a For the purposes of the PBP and LCC analysis, a consumer is someone who purchases and uses a CFLK.

b For details on the Monte Carlo simulation, see appendix 8B of this NOPR TSD.
8-2 

The primary inputs for establishing the total installed cost arec

: 

• Consumer Product Price: The LCC analysis includes only the consumer, weightedaverage CFLK lamp prices from the engineering analysis, and socket prices. The 

remaining CFLK consumer price (i.e., the CFLK price excluding the lamps and sockets) 

is ignored, because this is assumed to be the same for all CFLKs regardless of efficacy. 

See chapter 7 of this NOPR TSD for details on the product price methodology. 

• Sales tax: The state and local retail sales tax.

The primary inputs for calculating the operating cost are:

• Energy use: The CFLK energy use, derived from chapter 6 of this NOPR TSD. For the 

residential sector, energy use varies by geographic location, and room type. For the 

commercial sector, energy use varies by building type. 

• Energy prices: The prices paid by consumers for electricity. For this analysis, DOE used 

marginal electricity prices. See section on electricity prices for details.

• Energy price trends: Forecasted electricity prices. DOE used electricity price forecasts 

reported in the Energy Information Administration’s Annual Energy Outlook 2015

(AEO2015).3

 

• Repair and maintenance costs: Repair costs are those associated with repairing or 

replacing components that have failed. Maintenance costs are those associated with 

maintaining the operation of the product. DOE considered lamp replacements for failed 

lamps during the lifetime of the CFLK as the only repair and maintenance cost associated 

with changes in product efficacy. 

• Disposal Cost: After CFLK lamps reach the end of their life, some consumers pay to 

recycle or discard those lamps. The CFLK disposal cost represents the cost of recycling 

these lamps and is only applicable to those consumers in the commercial sector. 

• Lamp Residual Value: The remaining value of the surviving lamp(s) at the end of the 

CFLK lifetime.

• Lifetime: The age at which the CFLK is retired from service.

• Discount rate: The rate at which DOE discounts future expenditures to establish their 

present value.

• Efficacy distribution: The market efficacy distribution, estimated by the shipments model. 

See chapter 9 of this NOPR TSD for details. 

The data inputs to the PBP are the total installed cost of the product to the consumer and the 

annual operating expenses, for each EL. In this NOPR, DOE used a “simple” PBP calculation, 

 

c DOE did not take into account installation cost as one of the total installed cost inputs. DOE assumes that the 

installation cost, which represents all costs required to install the CFLK, is not affected by changes in product 

efficiency and is therefore the same for all ELs for both the residential and commercial sectors. 
8-3 

which does not take into account changes in operating cost over time. Thus, the inputs to the 

total installed cost are the consumer product price, including the sales tax, and the inputs to the 

annual operating expenses are the CFLK energy use and the electricity price in the assumed

compliance year of this rulemaking (2019). 

Figure 8.1.1 and Figure 8.1.2 depict the relationships among inputs for installed cost and 

operating costs that DOE used to calculate the LCC and PBP. The yellow boxes indicate inputs, 

the green boxes indicate intermediate outputs, and the blue boxes indicate final outputs (the LCC 

and PBP). 

Figure 8.1.1 LCC Calculation Flow Diagram 

Figure 8.1.2 PBP Calculation Flow Diagram 
8-4 

Table 8.1.1 Summary of Inputs to Life-Cycle Cost and Payback Period 

Inputs Average or Typical Value Characterization

Total Installed Cost Inputs

Product Price Varies by efficacy level and 

product class

Single-point value

Sales Tax 7.11% Varies by region

Operating Cost Inputs

Power Rating Varies by efficacy level and 

product class

Single-point value

Operating Hours Residential sector: 2.0 hrs/day

Commercial sector: 10.7 hrs/day

Distribution (see chapter 6 of this 

NOPR TSD)

Electricity Prices Residential sector: 0.131 $/kWh

Commercial sector: 0.111 $/kWh

Vary by region and season for 

each sector

Electricity Price 

Trends AEO 2015 reference case Vary by AEO growth scenario 

Lamp Replacements 

For both sectors, lamp 

replacements have lumen outputs 

similar to initially packaged lamps

Vary by initially packaged lamps, 

lamp lumen range, and year of 

replacement

Lamp Disposal Costs

Residential sector: None

Commercial sector: $0.7 for 35% 

of compact fluorescent lamps 

(CFLs)

Single-point value

Product Lifetime Mean: 13.8 years

Median: 13.0 years

Weibull distribution

Discount Rate 

Residential sector: 4.4%

(weighted-average across income 

groups) 

Commercial sector: 5.0%

(weighted-average across building 

types)

Residential sector: Vary by 

income group 

Commercial Sector: Vary by 

building type 

(see appendix 8C of this NOPR 

TSD)

Assumed Date 

Standards Become 

Effective

2019 (3 years after expected 

publication of final rule) 

Single-point value

8.2 LIFE-CYCLE COST INPUTS

8.2.1 Overview

The LCC analysis calculates the average life-cycle cost of CFLKs for U.S. consumers at 

each EL based on the LCCs of purchases of representative products. LCC is the total consumer 

expense over the life of a product, including purchase expenses and operating costs (which 

include energy expenditures). DOE discounts future operating costs to the time of purchase and 

sums them over the lifetime of the product. 

 For an individual purchase, DOE defines LCC by the following equation: 
8-5 

LLLLLL = II + � OO yy

(1 + rr)yy − RV + DC

1

(1 + rr)NN

NN

yy=1

Eq. 8.1 

Where:

LLLLLL = life-cycle cost in dollars,

II = total installed cost in dollars,

Σ = sum over the lifetime, from year 1 to year N, 

NN = lifetime of product in years,

OO = annual operating cost in dollars, 

RRRR = residual value in dollars of CFLK lamps at the end of CFLK lifetime,

d

rr = discount rate,

DD = disposal cost, and 

tt = year for which operating cost is being determined. 

DOE expresses costs in 2014$. Total installed cost, operating cost, lifetime, and discount 

rate are discussed in the following sections. The year of product purchase is 2019, which is the 

assumed compliance date of this rulemaking. 

To determine the impact of standards on the average LCC for all U.S. CFLK consumers, 

DOE must address how all of the many inputs that affect the LCC vary across U.S. residential 

and commercial CFLK consumers and how the standard itself affects the LCC through its 

indirect effect on equipment and operating costs. The following sections develop the inputs 

necessary to address those factors.

8.2.2 Ceiling Fan Light Kit Lumen Output

 To account for the effect of efficacy levels on the LCC of an individual CFLK purchase, 

DOE ideally compares the LCC of a lower and a higher efficacy product that deliver the same 

level of consumer utility. DOE believes that the primary utility of CFLKs is their light output, 

measured in lumens. 

As discussed in chapter 6 of this NOPR TSD, DOE estimated the weighted-average 

CFLK lumen output based on CFLK market data and efficacy data for lamps packaged with 

CFLKs. DOE scaled the lumen output of each representative lamp by a factor equal to the ratio 

of the market-weighted average total lumen output to the baseline lamp lumen output for both 

lamp scenarios presented in the engineering analysis (lamp replacement and light kit replacement

scenarios). 

 

d The residual value is calculated by multiplying the fraction of the lamp’s original lifetime that is left at the year 

that the CFLK is retired by the price of the CFLK lamp at that year. For LED CFLK lamps, the price is adjusted due 

to price learning.
8-6 

8.2.3 Market Distribution of Ceiling Fan Light Kits by Efficacy Level

To accurately estimate the share of consumers that would be affected by a potential 

energy conservation standard at a particular efficacy level, DOE’s LCC analysis considered the 

projected distribution (i.e., market shares) of product efficacies that consumers purchase in the

no-standard case, and each of the standards cases (i.e., the cases where a standard would be set at 

each EL) in the assumed compliance year. The estimated market shares for the no-standard case 

and each standards case for CFLKs are determined by the shipments analysis and are shown in 

Table 8.2.1. See chapter 9 of this NOPR TSD for further information on the derivation of the 

market efficacy distributions.

Table 8.2.1 CFLK Efficacy Distribution by Trial Standard Level in 2019 

Trial Standard 

Level

SubBaseline

(%) 

EL 0

(%) 

EL 1

(%) 

EL 2

(%) 

EL 3

(%) 

EL 4

(%) 

Total

(%) 

No-standard 55.9 0.0 26.3 10.2 3.5 4.1 100

TSL 0 0.0 0.0 82.2 10.2 3.5 4.1 100

TSL 1 0.0 0.0 82.2 10.2 3.5 4.1 100

TSL 2 0.0 0.0 0.0 51.3 3.5 45.2 100

TSL 3 0.0 0.0 0.0 0.0 3.5 96.5 100

TSL 4 0.0 0.0 0.0 0.0 0.0 100.0 100

In this NOPR TSD, DOE assumed that consumers who, in the no-standard case would 

have purchased a CFLK with an efficacy less than a given EL, will in, the presence of a standard 

at that EL, purchase a CFLK that meets or exceeds that EL. DOE used this approach to account 

for those consumers who, under a particular TSL, would select more efficacious (and sometimes 

less expensive) CFLKs than the CFLKs corresponding to that EL, as estimated by the consumerchoice model in chapter 9 of this NOPR TSD. 

8.2.4 Price Learning

As discussed earlier, lamp prices in the compliance year are an input to the LCC analysis. 

DOE uses a price learning analysis to account for changes in lamp prices that are expected to 

occur between the time that it has data for lamp prices and the assumed compliance year of the 

rulemaking. Desroches et al. (2013) summarize the data and literature currently available that are

relevant to price learning forecasts for selected appliances and equipment.4

 Gerke et al (2014) 

have also documented the dramatic price decline in LED lamps in recent years.5 Price learning 

reflects reductions in price that frequently occur as a result of increased volume of production 

and distribution of a product as it matures in the market. In the early years, learning rates tend to 
8-7 

be fast. Because learning is a function of cumulative production, prices tend to become relatively 

stable as a product matures and the rate of growth in cumulative production slows. 

In this NOPR, DOE assumed that only LED lamps undergo price learning because of 

their recent introduction into the market. While traditional lamps and CFLs used in CFLKs are 

relatively mature technologies with relatively constant prices, LED lamps have been undergoing 

very rapid price reductions in the few years they have been on the market. Because lamps 

packaged with CFLKs represent a small fraction of the lighting market, DOE assumes that price 

learning of LED lamps used in CFLKs is driven by the entire LED lamp market, not just by the 

lamps packaged with CFLKs. As discussed in chapter 9 of this NOPR TSD, DOE did not 

develop LED price trends for lamps packaged with CFLKs; rather, it used the price trend 

developed in the General Service Lamps (GSL) Energy Conservation Standard Preliminary 

Analysis.6

 For further details on the price learning analysis, see chapter 9 of this NOPR TSD. 

8.2.5 Total Installed Cost Inputs

As discussed in chapter 5 of this NOPR TSD, the efficacy of the CFLK is based on the 

efficacy of the lamps with which it is packaged; therefore, DOE’s product price determination 

accounted for the difference in lamp (and associated socket) costs at the different ELs. These 

changes in incremental costs are accounted for in the LCC savings analysis. 

DOE also applied sales tax to the CFLK incremental costs. The sales tax is a 

multiplicative factor that represents state and local sales taxes applied to the consumer price. 

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse.7 DOE 

derived population-weighted average8 tax values for each RECS reportable domain, as shown in 

Table 8.2.2: 
8-8 

Table 8.2.2 Average Sales Tax Rates by RECS Reportable Domain 

RECS 

Rep. 

Domain

State(s) Population (2019) Tax Rate (2019)

1 Connecticut, Maine, New Hampshire, Rhode 

Island, Vermont 8,453,982 5.1%

2 Massachusetts 6,855,546 6.3%

3 New York 19,576,920 8.5%

4 New Jersey 9,461,635 7.0%

5 Pennsylvania 12,787,354 6.4%

6 Illinois 13,236,720 8.0%

7 Indiana, Ohio 18,271,066 7.1%

8 Michigan 10,695,993 6.0%

9 Wisconsin 6,004,954 5.5%

10 Iowa, Minnesota, North Dakota, South Dakota 10,353,316 6.9%

11 Kansas, Nebraska 4,693,244 7.2%

12 Missouri 6,199,882 7.5%

13 Virginia 8,917,395 4.0%

14 Delaware, District of Columbia, Maryland, 

West Virginia 9,742,487 5.4%

15 Georgia 10,843,753 7.0%

16 North Carolina, South Carolina 15,531,866 7.0%

17 Florida 23,406,525 6.7%

18 Alabama, Kentucky, Mississippi 12,198,158 7.3%

19 Tennessee 6,780,670 9.5%

20 Arkansas, Louisiana, Oklahoma 11,515,069 8.7%

21 Texas 28,634,896 8.0%

22 Colorado 5,278,867 6.1%

23 Idaho, Montana, Utah, Wyoming 6,285,110 5.3%

24 Arizona 8,456,448 7.2%

25 Nevada, New Mexico 5,536,624 7.5%

26 California 42,206,743 8.5%

27 Alaska, Hawaii, Oregon, Washington 13,879,323 5.3%

Population-Weighted Average 7.1%

DOE calculated the total installed cost based on the following equation:

II = �(LLLL + SS ) × NNSS� × TTTTTT

Eq. 8.2 

Where:
8-9 

LLLL = the consumer weighted-average CFLK lamp price, in dollars, 

SS = the CFLK socket price, in dollars, 

NNSS = the number of sockets in the CFLK, and 

TTTT = sales tax markup. 

8.2.6 Operating Cost Inputs

DOE defines the operating cost (OC) for CFLKs in year t by the following equation: 

OO tt = EE tt + RR tt

Eq. 8.3

Where:

EE = electricity cost associated with operating the CFLK, in dollars, 

RR = replacement cost for CFLK lamps that fail during the CFLK lifetime, in dollars, and

tt = year for which the operating cost is being determined. 

DOE used electricity price trends to forecast electricity prices into the future and, along 

with the product lifetime and discount rate, to establish the present value of lifetime energy costs.

8.2.6.1 Electricity Cost

DOE calculated the electricity cost in year t based on the following equation: 

EE tt = tt × EEPPtt

Eq. 8.4 

Where:

tt = annual energy consumption of the CFLK at year t, in kWh per year, and 

EEPPtt = electricity price at year t, in dollars per kWh. 

The annual energy consumption for each representative CFLK presented in the 

engineering analysis is calculated in chapter 6 of this NOPR TSD. The following sections 

discuss the electricity cost inputs in detail. 

Electricity Prices 

For the LCC analysis, DOE used marginal prices, which vary by season, region and 

baseline electricity consumption level. DOE estimated these prices using data published with the 

Edison Electric Institute (EEI) Typical Bills and Average Rates reports for summer and winter 

2014.9

 For the residential sector, the reports provide the total bill assuming household 

consumption levels of 500, 750 and 1,000 kWh for the billing period for most of the major 

investor-owned utilities (IOUs) in the country. For the commercial sector, the reports provide 

typical bills for several combinations of monthly electricity peak demand and total consumption. 

For the residential sector, DOE used the EEI data to define a marginal price as the ratio of 

the change in the bill to the change in energy consumption. For the commercial sector, marginal 

prices cannot be estimated directly from the EEI data. Commercial building marginal prices 
8-10 

depend on both the change in electricity consumption and the change in monthly peak-coincident 

demand. For a given change in electricity consumption, the corresponding change in demand is 

defined through the marginal load factor (MLF). The MLF is equal to the ratio of the average 

hourly change in electricity use during the billing period to the change in electricity use in the 

hour of the building peak load. For on-off loads such as lighting, the marginal load factor is 

equal to the ratio of the number of hours that the equipment is on divided by the total number of 

hours in the billing period. DOE used a previous analysis of commercial sector electricity 

prices10 to estimate the ratio of the marginal to the average price assuming an MLF of 0.5. 

DOE calculated regional weighted-average values for each type of price for the nine 

census divisions and four large states (CA, FL, NY and TX). Each EEI utility in a region was 

assigned a weight based on the number of consumers it serves; hence, the utility weight for the 

residential and commercial sectors may be slightly different. Consumer counts were taken from 

EIA Form 861 data (2012).11 DOE adjusted these regional weighted-average prices to account 

for systematic differences between IOUs and publicly-owned utilities (POUs), as the latter are 

not included in the EEI data set. For each region and sector, DOE estimated a correction factor 

based on the ratio of the average electricity price for IOUs to the average price charged by POUs 

(calculated using EIA form 861 data), and the percentage of consumers served by POUs. 

DOE assigned seasonal marginal prices to each household or commercial building in the 

LCC sample based on its location and its baseline monthly electricity consumption for an 

average summer or winter month. For a detailed discussion of the development of electricity 

prices, see appendix 8F of this NOPR TSD.

Electricity Price Trends 

To estimate operating costs over the lifetime of the CFLK, DOE requires a forecast of 

energy prices over the lifetime of the product. To arrive at future prices, DOE used the projected 

annual changes in average residential and commercial electricity prices in the Reference case 

projection in AEO 2015.

3 Note that the AEO price trends do not distinguish between marginal 

and average prices. DOE reviewed the EEI data for the years 2007 to 2014 and determined that 

there is no systematic difference in the trends for marginal vs. average prices in the data.

For each purchase sampled, DOE applied the projection for the Census division in which the 

purchase was located. 

The AEO reference case projection is a business-as-usual estimate, given known market, 

demographic, and technological trends. DOE also included AEO High Growth and AEO Low 

Growth scenarios in the analysis. The high- and low-growth cases show the projected effects of 

alternative growth assumptions on energy markets. To estimate the trends after 2040, DOE 

followed past guidelines provided to the Federal Energy Management Program (FEMP) by EIA 

and used the average rate of change during 2025–2040. 

Figure 8.2.1 and Figure 8.2.2 show residential and commercial electricity price trends 

based on the three AEO 2015 projections. For the LCC results presented in section 8.4, DOE 

used the electricity price forecasts from the AEO reference case only. Appendix 8B of this NOPR 

TSD, which discusses uncertainty and variability in LCC analysis, presents LCC results for the 

high- and low-economic growth scenarios. 
8-11 

Figure 8.2.1 Trends in Residential Electricity Prices

Figure 8.2.2 Trends in Commercial Electricity Prices

8.2.6.2 Lifetime

DOE uses ceiling fan lifetime as a proxy for CFLK lifetime. DOE used a lifetime 

distribution function to account for the variability in CFLK lifetime. This was developed by 

fitting a survival probability function (hereafter referred to as the survival function) to data of 

ceiling fan historical shipments and the 2012 age distribution of ceiling fan installed stock. The

age distribution data for the installed ceiling fan stock in 2012 were obtained from a Lawrence 

Berkeley National Laboratory (LBNL) study of ceiling fan owners.12 By combining data from 

the LBNL study with a history of ceiling fan shipments (see chapter 9 of this NOPR TSD for 
8-12 

more information on historical shipments), DOE estimated the percentage of ceiling fans of a 

given age still in operation. This survival function, which DOE assumed has the form of a 

cumulative Weibull distribution, provides an average and median appliance lifetime. 

The Weibull distribution is a probability distribution commonly used to measure failure 

rates. Its form is similar to an exponential distribution, which models a fixed failure rate, except 

that a Weibull distribution allows for a failure rate that changes over time in a particular fashion. 

The survival function, which is modeled by the cumulative Weibull distribution, takes the form:

PP(xx) = ee−�xx−θθ

αα �

ββ

for xx > θθ and 

PP(xx) = 1 for xx ≤ θθ

Eq. 8.5 

Where:

PP(xx) = probability that the appliance is still in use at age xx, 

xx = appliance age, 

αα = scale parameter, which would be the decay length in an exponential distribution, 

ββ = shape parameter, which determines the way in which the failure rate changes through 

time, and 

θθ = delay parameter, which allows for a delay before any failures occur.

When ββ = 1, the failure rate is constant over time, giving the distribution the form of a 

cumulative exponential distribution. In the case of appliances, ββ commonly is greater than 1, 

reflecting an increasing failure rate as appliances age.

DOE used the LBNL study12 as the source for the ceiling fan age distribution. The survey 

asked respondents to identify the age of the most-used ceiling fan in increments of two years. 

Households that did not know the age of their ceiling fans were allocated among the remaining 

age bins according to the distribution of respondents who did report ceiling fan age. 

DOE used the ceiling fan age distribution, combined with shipments history, to develop 

the survival function for ceiling fans. For example, DOE summed the total shipments from 4 to 6 

years before the LBNL study and then compared this number to the number of units still in use at 

the time of the LBNL study to approximate the percentage of surviving appliances within that 

age bin. DOE combined the data from all age bins in the LBNL study to build a fit to a Weibull 

distribution and find the parameters (αα, ββ, θθ) that best approximate the surviving units. DOE 

weighted each bin’s contribution to the sum of squares by the inverse of the variance estimated 

for the LBNL study results; this weighting controls for the changes in sample size between bins 

and through time. DOE added the simultaneous constraint that the shipments history multiplied 

by the survival function sum to the estimate of installed stock from 2009 RECS. The equation for 

the sum of squares that DOE minimized is:

�(SSSS SS yyii − SS vv ii)2

σσSSSSSSSSSSyyii

2

ii

+ �∑jj SSh iiii ii ii(jj) ∗ PP�aaaaeejj� − sssskkRRRRRR �

2

σσRRRRRR

2

Eq. 8.6 
8-13 

Where:

= identifier for an age bin from the LBNL study, 

SS rr yyii = number of appliances reported by the LBNL study in bin , 

SSSS vv ii = the number of surviving appliances in bin predicted by the Weibull 

distribution applied to the number of appliances shipped (a function of αα, ββ, and 

θθ), 

σσSSSSSSSSSSyyii = the standard error of the LBNL study data point for bin , 

jj = shipments year at the beginning of the historical time series and ending at 2009, 

SSh iiii ii ii(jj) = shipments in year jj, 

aaaaeejj = shipments vintage relative to the year 2009, 

PP�aaaaeejj� = survival probability of an appliance at aaaaeejj, 

sssskkRRRRRR = estimate of installed stock at year 2009, and

σσRRRRRR = the standard error for stock measurement from RECS.

The survival function, shown in Figure 8.2.3 is characterized by parameters (αα, ββ, θθ) =

(10.93, 2.0, 4.14). This distribution has a mean ceiling fan lifetime of 13.8 years and a median 

lifetime of 13.0 years. This estimate is consistent with the estimate from Appliance Magazine13, 

which estimates typical lifetimes within the range of 7 and 18 years, as well as estimates 

received from a technical expert and feedback from manufacturers. 

Figure 8.2.3 Survival Function for Ceiling Fans

DOE notes that the method used to calculate product lifetimes incorporates several 

assumptions, as detailed here. 
8-14 

• Appliance lifetime can be modeled by a survival function. In particular, a Weibull 

distribution is an appropriate survival function. 

• The appliance survival function does not change form through time. 

• The survival function is independent of household factors, such as household size or 

geographic region. 

• Survey respondents neither systematically overestimated nor underestimated the current 

age of their appliance.

• The historical shipment data are accurate.

• The shipped appliances are installed predominantly in residences. 

• A Weibull shape parameter, ββ ≥ 2. 

Three of these assumptions reflect analytical choices made by DOE. The first is the 

assumption that a Weibull distribution is the appropriate distribution to use for observed rates of 

appliance retirement, which include other factors outside of failure rate (e.g., retirement due to 

reasons other than product failure). This distribution has been used for this purpose in TSDs for 

other energy conservation standard rulemakings, but it is not guaranteed to reflect actual realworld experience. The second assumption is that consumer behavior and mechanical appliance 

lifetime have not changed over time. The third assumption concerns the Weibull failure rate 

parameter. DOE constrained the shape parameter (β) to values larger than two in order to avoid

potential unlikely occurrences, such as sharp changes in consumer behavior or appliance survival 

immediately following the delay period represented by the delay parameter (θ). 

8.2.6.3 Replacement Costs

While DOE considers the lifetime of a CFLK to be equivalent to the lifetime of the 

ceiling fan to which it is attached, DOE acknowledges that the lamps packaged with the CFLK 

may have a shorter lifetime than the lifetime of the CFLK. CFLK standards affect only the 

efficacy of lamps packaged with the CFLK, but the lifetime energy use of the CFLK is also

affected by the lamps that replace those that are shipped with the CFLK at the end of the shipped 

lamps’ lifetimes. In the calculation of the LCC of CFLKs, lamps replacement costs are 

considered maintenance costs and are included in CFLK operating costs.

In the LCC analysis, DOE assumes that in both of the commercial and residential sectors, 

lamps fail only at the end of the lamp service life. The service life (in years) is determined by 

dividing the lamps’ lifetime (in hours) by the lamps’ average operating hours per year. 

Replacement costs include, in principle, both the lamps and labor associated with 

replacing a CFLK lamp at the end of its lifetime. However, DOE assumes that labor costs for 

lamp replacements are not affected by changes in product efficacy and are, therefore, the same 

for all ELs for both the residential and commercial sectors. Therefore, DOE considers that the 

only costs associated with lamp replacements are lamp purchase costs to consumers. 

DOE assumed that consumers replace failed lamps with new lamps chosen from options 

available in the lighting market at the time of failure that have the same base-type and provide an 

equivalent lumen output. DOE modeled this decision using a consumer-choice model, which 

incorporates consumer sensitivity to first cost, lifetime, energy savings relative to an 
8-15 

incandescent, and mercury content, following the methodology used in the GSL preliminary 

analysis.6

 Details on the GSL consumer choice model can be found in chapter 9 of the GSL 

preliminary analysis TSD. DOE accounted for the first cost associated with purchasing a 

replacement lamp, the electricity consumption and operating costs depending on replacement 

lamp wattage, and the residual value of the lamp at the end of the CFLK lifetime. In this section, 

DOE describes the methodology to calculate the effects of relamping in the LCC analysis. 

Lamp Replacement Options 

In replacing a failed CFLK lamp, DOE considered different lamp options available to 

consumers; hereafter, these lamp options are referred to as "lamp replacement options". As 

discussed earlier, consumers were constrained to purchase lamps of the same socket type and 

equivalent lumen output. The model allows replacement lamps to have a different light source 

technology from those that the CFLK were packaged with. Due to the EISA 2007 backstop 

requirement, which DOE assumes will go into effect January 1, 2020, and because lamp 

replacements occur in 2020 or later, the only available lamp replacement options for consumers 

were CFL and LED lampse

. To determine lamp replacement options for each representative 

CFLK lamp, DOE first considered the representative lamp options from the engineering analysis. 

When more than one medium or high efficacy lamp was specified in a given lumen range in the 

engineering analysis, DOE used the least expensive lamp option of those lamps. In the case 

where there were no lamp options available from the engineering analysis with the same base 

type and similar lumen output to the originally packaged lamps, DOE used market research data 

to determine lamp replacement options. Table 8.2.3 shows the efficacies, wattages, lifetimes, and 

prices for every lamp replacement option. 

Note that to estimate consumer prices that are consistent with the prices of the 

representative lamp units presented in the engineering analysis, DOE adjusted the consumer

lamp prices to omit the CFLK manufacturer markup.

f Therefore, DOE estimated the price at 

which consumers purchase replacement lamps in Table 8.2.3 by dividing the lamp consumer 

prices from the engineering analysis data by 1.37, which is the CFLK manufacturer markup on 

the lamp. Furthermore, DOE applied price learning, as described in section 8.2.4, to the lamp 

price of high efficacy (LED) lamps. See chapter 12 of this TSD for details on the CFLK 

manufacturer markup. 

e In the preliminary analysis for the concurrent GSL energy conservation standards rulemaking, DOE considered

lamps that have base types specified by ANSI, have a lumen output of at least 310 lumens, and are intended to serve 

in general lighting applications to meet the GSL definition. Therefore, DOE considers candelabra-base lamps that 

meet the lumen output and general application requirements to meet the GSL definition, which available information 

indicates would include all candelabra-base lamps currently packaged with CFLKs. All lamps that meet the GSL 

definition would be subject to the EPCA requirement prohibiting the sale of any GSL that does not meet a minimum 

efficacy standard of 45 lm/W if the concurrent GSL rulemaking is not completed by January 1, 2017, or if the 

energy savings of the GSL final rule are not greater than or equal to the savings from a minimum efficacy standard 

of 45 lumens per watt. 42 U.S.C. 6295(i)(6)(A)(v) Because GSILs are not included in the scope of the GSL 

rulemaking, DOE assumed that any GSL final rule would not yield sufficient energy savings to avoid triggering the 

45 lm/W requirement in 2020. Accordingly, DOE has assumed in both the no-standard and the standards-case 

shipment projections that candelabra-base lamps with efficacy below the minimum requirement of 45 lm/W will no 

longer be an option available for packaging with CFLKs beginning January 1, 2020.

f Unlike the lamps packaged with the CFLK, which first receive a manufacturer markup and then a distributor 

markup, lamps purchased by consumers are assumed to only receive the distributor markup.
8-16 

Table 8.2.3 Lamp Replacement Options EL Lamp Technology Base Type Efficacy (lm/w) Wattage (W) Lifetime (hrs) Price ($2014)

CFL Replacement LED Replacement Efficacy (lm/W) Wattage (W) Lifetime (hrs) Price ($2014) Efficacy (lm/W) Wattage (W) Lifetime (hrs)

Price ($2014)

Subbaseline Inc. E12 7.3 60 1,000 0.40 48.4 8.3 8,667 5.09 65.4 5.2 22,667 11.3

EL 0 CFL E26 57.1 14 10,000 1.24 57.1 14.0 10,000 1.24 102.5 8 25,000 2.99

Lamp Replacement Scenario

EL 1 CFL E26 61.5 13 10,000 1.47 57.1 14.0 10,000 1.24 102.5 8 25,000 2.99

EL 2a CFL E26 66.4 11 10,000 1.63 57.1 14.0 10,000 1.24 102.5 8 25,000 2.99

EL 2b LED E26 66.7 12 25,000 9.44 57.1 14.0 10,000 1.24 102.5 8 25,000 2.99

EL 3 LED E26 94.1 8.5 25,000 3.79 57.1 14.0 10,000 1.24 102.5 8 25,000 2.99

EL 4 LED E26 102.5 8 25,000 2.99 57.1 14.0 10,000 1.24 102.5 8 25,000 2.99

Light Kit Replacement Scenario

EL 1 CFL E26 57.8 9 10,000 1.79 57.8 9.0 10,000 1.79 62.5 8 25,000 9.09

EL 2 LED E26 62.5 8 25,000 9.09 57.8 9.0 10,000 1.79 62.5 8 25,000 9.09

EL 3 LED E26 100 16 25,000 11.8 68.8 23.0 12,000 4.49 106.7 15 25,000 11.5

EL 4 LED E26 106.7 15 25,000 11.5 68.8 23.0 12,000 4.49 106.7 15 25,000 11.5

Relamping Matrices 

DOE developed a relamping matrix for each lamp packaged with a CFLK for both the 

residential and commercial sector. The relamping matrix determines the year a lamp used with a 

CFLK will fail and estimates the probability associated with a consumer choosing each possible 

lamp replacement option. The matrix tracks the probability that each lamp replacement option 

will replace failed CFLK lamps as a function of time, as prescribed by the consumer-choice 

model described previously. The lifetime of the lamps originally packaged with the CFLK 

dictates the year of the first lamp replacement based on the lamps’ lifetime. Subsequent 

relampings of the replacement lamps, should they fail before the CFLK is retired, are tracked 

over the lifetime of the CFLK. Each lamp option packaged with a CFLK will have a unique 

relamping matrix based on the lifetime of the packaged lamp option, the CFLK operation hours, 

and the characteristics of the replacement lamp options. The different usage patterns in the 

residential and commercial sector also require unique relamping matrices for each sector for each 

lamp packaged with a CFLK. 

8.2.6.4 Discount Rates

The discount rate is the rate at which future expenditures and savings are discounted to 

establish their present value. DOE estimates discount rates separately for residential and 

commercial consumers. For residential consumers, DOE calculates discount rates as the 

weighted average real interest rate across consumer debt and equity holdings. For commercial 
8-17 

consumers, DOE calculates commercial discount rates as the weighted average cost of capital 

(WACC), using the Capital Asset Pricing Model (CAPM).

Discount Rates for Residential Consumers 

The discount rate is the rate at which future savings and expenditures are discounted to 

establish their present value. DOE uses publicly available data (the Federal Reserve Board’s 

Survey of Consumer Finances (SCF)) to estimate a consumer’s opportunity cost of funds related 

to appliance energy cost savings and maintenance costs. The discount rate value is applied in the 

LCC to future year energy cost savings and non-energy operations and maintenance costs in 

order to present the estimated net LCC and LCC savings. DOE notes that the discount rate used 

in the LCC analysis is distinct from an implicit discount rate, as it is not used to model consumer 

purchase decisions. The opportunity cost of funds in this case may include interest payments on 

debt and interest returns on assets.

DOE estimates separate discount rate distributions for six income groups, divided based 

on income percentile as reported in the Federal Reserve Board’s SCF.

14 This disaggregation 

reflects the fact that low and high income consumers tend to have substantially different shares 

of debt and asset types and tend to face different rates on debts and assets. Summaries of shares 

and rates presented in this chapter are averages across the entire population.

Table 8.2.4 Definitions of Income Groups 

Income Group Percentile of Income

1 1st to 20th

2 21st to 40th

3 41st to 60th

4 61st to 80th

5 81st to 90th

6 91th to 99th

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Shares of Debt and Asset Classes 

DOE’s approach involved identifying all relevant household debt or asset classes in order 

to approximate a consumer’s opportunity cost of funds related to appliance energy cost savings 

and maintenance costs. The approach assumes that, in the long term, consumers are likely to 

draw from or add to their collection of debt and asset holdings approximately in proportion to 

their current holdings when future expenditures are required or future savings accumulate. DOE 

has included several previously excluded debt types (i.e., vehicle and education loans, 

mortgages, all forms of home equity loan) in order to better account for all of the options 

available to consumers.

The average share of total debt plus equity and the associated rate of each asset and debt 

type are used to calculate a weighted average discount rate for each SCF household (Table 

8.2.5). The household-level discount rates are then aggregated to form discount rate distributions 
8-18 

for each of the six income groups. Note that in previous rulemakings DOE performed 

aggregation of asset and debt types over households by summing the dollar value across all 

households and then calculating shares. Weighting by dollar value gave disproportionate 

influence to the asset and debt shares and rates of higher income consumers. DOE has shifted to 

a household-level weighting to more accurately reflect the average consumer in each income 

group. 

DOE estimated the average percentage shares of the various types of debt and equity 

using data from the Federal Reserve Board’s SCF for 1995, 1998, 2001, 2004, 2007, and 2010.g

DOE derived the household-weighted mean percentages of each source of financing throughout 

the five years surveyed. DOE posits that these long-term averages are most appropriate to use in 

its analysis. 

Table 8.2.5 Types of Household Debt and Equity by Percentage Shares (%) 

Type of Debt or Equity Income Group

1 2 3 4 5 6

Debt:

Mortgage 18.9% 24.1% 33.1% 38.1% 39.3% 25.0%

Home equity loan 3.1% 3.3% 2.6% 3.6% 4.5% 7.2%

Credit card 15.3% 13.0% 11.8% 8.7% 6.0% 2.7%

Other installment loan 25.1% 20.6% 17.3% 13.2% 9.6% 4.7%

Other residential loan 0.7% 0.6% 0.6% 0.7% 1.0% 1.2%

Other line of credit 1.6% 1.5% 1.3% 1.5% 2.1% 1.8%

Equity:

Savings account 18.5% 16.0% 12.7% 10.6% 10.4% 7.9%

Money market account 3.6% 4.5% 4.0% 4.5% 5.0% 8.6%

Certificate of deposit 7.0% 7.8% 5.5% 5.0% 4.4% 4.2%

Savings bond 1.8% 1.7% 1.9% 2.2% 1.7% 1.1%

Bonds 0.2% 0.4% 0.5% 0.7% 0.8% 3.8%

Stocks 2.3% 3.1% 4.4% 5.7% 7.6% 15.8%

Mutual funds 2.1% 3.5% 4.3% 5.7% 7.6% 15.9%

Total 100.0 100.0 100.0 100.0 100.0 100.0

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Rates for Types of Debt 

g Note that two older versions of the SCF are also available (1989 and 1992); these surveys are not used in this 

analysis, because they do not provide all of the necessary types of data (e.g., credit card interest rates, etc.). DOE 

feels that the 15-year span covered by the six surveys included is sufficiently representative of recent debt and 

equity shares and interest rates.
8-19 

DOE estimated interest rates associated with each type of debt. The source for interest 

rates for mortgages, loans, credit cards, and lines of credit was the Federal Reserve Board’s SCF

for 1995, 1998, 2001, 2004, 2007, and 2010, which associates an interest rate with each type of 

debt for each household in the survey. 

In calculating effective interest rates for home equity loans and mortgages, DOE 

accounted for the fact that interest on both such loans is tax deductible (Table 8.2.6). This rate 

corresponds to the interest rate after deduction of mortgage interest for income tax purposes and 

after adjusting for inflation (using the Fisher formula).h

 For example, a 6-percent nominal 

mortgage rate has an effective nominal rate of 4.5 percent for a household at the 25-percent 

marginal tax rate. When adjusted for an inflation rate of 2 percent, the effective real rate becomes 

2.45 percent. 

Table 8.2.6 Data Used to Calculate Real Effective Mortgage Rates 

Year Mortgage Interest Rates in Selected Years (%)

Average 

Nominal 

Interest Rate

Inflation Rate15

Applicable 

Marginal Tax 

Rate

16

Average Real Effective 

Interest Rate

1995 8.2 2.83 24.2 3.3

1998 7.9 1.56 25.0 4.3

2001 7.6 2.85 24.2 2.8

2004 6.2 2.66 20.9 2.2

2007 6.3 2.85 20.6 2.1

2010 5.7 1.64 20.0 2.9

Table 8.2.7 shows the household-weighted average effective real rates for different types 

of household debt. Because the interest rates for each type of household debt reflect economic 

conditions throughout numerous years and various phases of economic growth and recession, 

they are expected to be representative of rates in effect in 2020.

 

h Fisher formula is given by: Real Interest Rate = [(1 + Nominal Interest Rate) / (1 + Inflation Rate)] – 1.
8-20 

Table 8.2.7 Average Real Effective Interest Rates for Household Debt 

Type of Debt Income Group

1 2 3 4 5 6

Mortgage 6.6% 6.2% 6.1% 5.2% 5.0% 4.0%

Home equity loan 7.0% 6.9% 6.7% 5.9% 5.7% 4.3%

Credit card 15.2% 15.0% 14.5% 14.2% 14.0% 14.5%

Other installment loan 10.8% 10.3% 9.9% 9.4% 8.7% 8.6%

Other residential loan 9.8% 10.2% 8.9% 8.2% 7.7% 7.4%

Other line of credit 9.1% 10.9% 9.6% 8.8% 7.4% 6.1%

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Rates for Types of Assets 

No similar rate data are available from the SCF for classes of assets, so DOE derived 

asset interest rates from various sources of national historical data (1983-2013). The interest rates 

associated with certificates of deposit,

17 savings bonds,18 and bonds (AAA corporate bonds)19

were collected from Federal Reserve Board time-series data. Rates on money market accounts 

came from Cost of Savings Index data.20 Rates on savings accounts were estimated as one half of 

the rate for money market accounts, based on recent differentials between the return to each of 

these assets. The rates for stocks are the annual returns on the Standard and Poor’s.21 Rates for 

mutual funds are a weighted average of the stock rates (two-thirds weight) and the bond rates 

(one-third weight) in each year. DOE assumed rates on checking accounts to be zero. 

DOE adjusted the nominal rates to real rates using the annual inflation rate for each year. 

Average nominal and real interest rates for the classes of household assets are listed in Table 

8.2.8. Because the interest and return rates for each type of asset reflect economic conditions 

throughout numerous years, they are expected to be representative of rates that may be in effect 

in 2019. For each type, DOE developed a distribution of rates, as shown in appendix 8C of this 

NOPR TSD. 
8-21 

Table 8.2.8 Average Nominal and Real Interest Rates for Household Equity 

Type of Equity Average Real 

Rate 

%

Savings accounts 1.0

Money market accounts 1.9

Certificates of deposit 1.9

Savings bonds 3.4

Bonds 4.2

Stocks 9.4

Mutual funds 7.4

Discount RateCalculation and Summary 

Using the asset and debt data discussed previously, DOE calculated discount rate 

distributions for each income group as follows. First, DOE calculated the discount rate for each 

consumer in each of the six versions of the SCF, using the following formula: 

DD ii = � SShaa ii,jj

jj

× RR ii,jj

Eq. 8.7 

Where:

DD ii = discount rate for consumer i, 

SShaa ii,jj = share of asset or debt type j for consumer i, and

RR ii,jj = real interest rate or rate of return of asset or debt type j for consumer i.

The rate for each debt type is drawn from the SCF data for each household. The rate for 

each asset type is drawn from the distributions described previously. 

Once the real discount rate was estimated for each consumer, DOE compiled the 

distribution of discount rates in each survey by income group by calculating the proportion of 

consumers with discount rates in bins of 1 percent increments, ranging from 0-1 percent to 

greater than 30 percent. Giving equal weight to each survey, DOE compiled the six-survey 

distribution of discount rates. 

Table 8.2.9 presents the average real effective discount rate and its standard deviation for 

each of the six income groups. To account for variation among households, DOE sampled a rate 

for each RECS household from the distributions for the appropriate income group. (RECS 

provides household income data.) Appendix 8C of this NOPR TSD presents the full probability 

distributions for each income group that DOE used in the LCC and PBP analysis. 
8-22 

Table 8.2.9 Average Real Effective Discount 

Income Group Discount Rate (%)

1 4.85

2 5.12

3 4.75

4 4.04

5 3.80

6 3.57

Overall Average 4.49

Discount Rates for Commercial Sector Consumers 

The commercial discount rate is the rate at which future operating costs are discounted to 

establish their present value in the LCC analysis. The discount rate value is applied in the LCC to 

future year energy costs and non-energy operations and maintenance costs to calculate the 

estimated net life-cycle cost of products of various efficacy levels and life-cycle cost savings as 

compared to the baseline for a representative sample of commercial consumers. 

DOE’s method views the purchase of a higher efficiency appliance as an investment that 

yields a stream of energy cost savings. DOE derived the discount rates for the LCC analysis by 

estimating the cost of capital for companies that purchase CFLKs. The weighted average cost of 

capital (WACC) is commonly used to estimate the present value of cash flows to be derived from a 

typical company project or investment. Most companies use both debt and equity capital to fund 

investments, so their cost of capital is the weighted average of the cost to the firm of equity and 

debt financing, as estimated from financial data for publicly traded firms in the sectors that 

purchase CFLKs.

22

Damodaran Online, a widely used source of information about company debt and equity 

financing, was used as the primary source of data for this analysis.

23 Companies included in the 

Damodaran Online database were assigned to the aggregate categories listed below:

• Office

• Retail

• Lodging

• Education

• Food Services

• Other

DOE estimated the cost of equity using the CAPM.24 The CAPM assumes that the cost of 

equity (ke) for a particular company is proportional to the systematic risk faced by that company, 

where high risk is associated with a high cost of equity and low risk is associated with a low cost 

of equity. The systematic risk facing a firm is determined by several variables: the risk 

coefficient of the firm (β), the expected return on risk-free assets (Rf), and the equity risk 
8-23 

premium (ERP). The risk coefficient of the firm indicates the risk associated with that firm 

relative to the price variability in the stock market. The expected return on risk-free assets is 

defined by the yield on long-term government bonds. The ERP represents the difference between 

the expected stock market return and the risk-free rate. The cost of equity financing is estimated 

using the following equation, where the variables are defined as above: 

Rk fe ( ) β ×+= ERP

Eq. 8.8 

Where:

ke = cost of equity, 

Rf = expected return on risk-free assets,

β = risk coefficient of the firm, and

ERP = equity risk premium. 

Several parameters of the cost of capital equations can vary substantially over time; 

therefore, the estimates can vary with the time period over which data is selected and the 

technical details of the data averaging method. For guidance on the time period for selecting and 

averaging data for key parameters and the averaging method, DOE used Federal Reserve 

methodologies for calculating these parameters. In its use of the CAPM, the Federal Reserve 

uses a forty-year period for calculating discount rate averages, utilizes the gross domestic 

product price deflator for estimating inflation, and considers the best method for determining the 

risk-free rate as one where “the time horizon of the investor is matched with the term of the riskfree security.”

25 

By taking a forty-year geometric average of Federal Reserve data on annual nominal 

returns for 10-year Treasury bills, DOE found for this analysis the following risk-free rates for 

2011-2013 (Table 8.2.10).26 DOE also estimated the ERP by calculating the difference between 

risk-free rates and stock market return for the same time period.

27 

Table 8.2.10 Risk-Free Rate and Equity Risk Premium, 2011-2013 

Year Risk-free rate (%) ERP (%)

2011 6.61% 2.94%

2012 6.41% 3.99%

2013 6.24% 5.30%

The cost of debt financing (kd) is the interest rate paid on money borrowed by a company. 

The cost of debt is estimated by adding a risk adjustment factor (Ra) to the risk-free rate. This 

risk adjustment factor depends on the variability of stock returns represented by standard 

deviations in stock prices. So for firm i, the cost of debt financing is: 

+= RRk aifdi
8-24 

Eq. 8.9 

Where:

kd = cost of debt financing for firm, i, 

Rf = expected return on risk-free assets, and

Rai = risk adjustment factor to risk-free rate for firm, i. 

DOE estimates the WACC using the following equation: 

dee ×+×= wkwkWACC d

Eq. 8.10 

Where:

WACC = weighted average cost of capital,

we = proportion of equity financing, and 

wd = proportion of debt financing. 

By adjusting for the influence of inflation, DOE estimates the real WACC, or discount 

rate, for each company. DOE then aggregates the company real WACC to estimate the discount 

rate for each of the ownership types in the CFLK analysis. These values are presented in Table 

8.2.11. While WACC values for any category may trend higher or lower over substantial periods 

of time, these values represent a private sector cost of capital that is averaged over major 

business cycles. 

Table 8.2.11 Weighted Average Cost of Capital for Analyzed Sectors 

Sector Standard Deviation Market Share Mean Discount Rate

Office 1.3% 36% 5.1%

Retail 1.1% 24% 5.0%

Lodging 1.7% 14% 6.0%

Education 2.2% 7% 2.5%

Food Service 0.9% 5% 4.9%

Other 1.1% 9% 5.0%

Average Discount Rate: 5.0%

Source: Damodaran Online Data Page: Costs of Capital by Industry Sector, 2011, 2012, 2013.

8.2.7 Disposal Cost 

 When CFLK lamps fail, some consumers choose to recycle the lamps, incurring a 

disposal cost. According to the Association of Lighting and Mercury Recyclers, in 2003, 29% of 

mercury lamps (including linear fluorescent lamps) were being recycled nationwide in the 

commercial sector, while 2% were being recycled in the residential sector.28 DOE performed 

market research on the recycling costs of CFLs29 and found that, on average, disposing of a CFL 

costs about $0.70 per lamp. DOE did not find any data for LED bulb disposal costs, and 

assumed, because they do not contain mercury, that their disposal costs would be zero. Given 
8-25 

more recent state laws requiring recycling of mercury containing lamps, DOE assumed that 

commercial sector consumers pay recycling costs for 35% of CFL failures. Because few 

residential sector consumers recycle lamps, and because residential consumers generally do not 

pay to recycle CFLs, DOE did not apply disposal costs to this sector. 

8.2.8 Lamp Residual Value

The residual value, defined in section 8.2.1, represents the remaining dollar value of 

surviving lamps at the end of the CFLK lifetime. DOE assumed that all lamps with lifetimes 

shorter than the CFLK lifetime are replaced. To account for the value of lamps with remaining 

life to the consumer, the LCC model applies this residual value as a “credit” at the end of the 

CFLK lifetime and discounts it back to the start of the analysis period. Additionally, due to LED

lamps undergoing price learning, the residual value of these lamps is calculated based on the 

LED lamp price in the year the CFLK is retired.

8.3 PAYBACK PERIOD INPUTS 

The payback period is the amount of time it takes the consumer to recover the estimated 

higher purchase expense of more energy-efficient products as a result of lower operating costs. 

As is typical in DOE rulemaking analyses, DOE has used simple payback period as the metric. 

Simple PBP does not take into account changes in operating expense over time or the time value 

of money; the calculation is done at an effective discount rate of zero percent.

The equation for PBP is: 

PPPPPP = 

ΔΔ

ΔΔ

Eq. 8.11 

Where:

ΔIC = difference in the total installed cost between the more energy-efficient design and 

the baseline design, and 

ΔOC = difference in annual operating expenses.

Payback periods are expressed in years. Payback periods greater than the life of the 

product indicate that the increased total installed cost is not recovered with the reduced operating 

expenses.

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

This section presents the results of the LCC and PBP analysis for CFLKs. DOE 

calculated the average installed cost, first year operating cost, lifetime operating cost, and LCC 

for each EL, assuming that all of the sampled consumers purchase a product at that EL. This 

allows the installed costs, operating costs, and LCCs for each EL to be compared under the same 

conditions, across a variety of sampled consumers. DOE used these average values for installed 

cost and first year operating cost to calculate the PBP for each EL, relative to the sub-baseline 

product. 
8-26 

DOE also calculated the LCC savings of a standard set at each EL. For the reference 

scenario, DOE calculated the LCC savings that result for each TSL (when a standard is set at a 

given EL) and the efficacy distribution of products in the no-standard case is transformed into a 

different efficacy distribution in the standards case. In both the no-standard case and the 

standards cases, the efficacy distributions have been calculated in the shipments analysis. DOE 

took this approach when calculating LCC savings in order to more accurately reflect the impact 

of a potential standard on consumers. This approach is intended to account for those consumers 

who, under a particular standard, would select more efficient (and sometimes less expensive) 

lamp options than the lamps corresponding to that standard level, as estimated by the consumerchoice model in chapter 9 of this NOPR TSD. 

DOE first assigned CFLKs to consumers using the efficacy distribution in the nostandard case. Then, for each TSL analyzed, DOE re-assigned CFLKs to consumers using the 

efficacy distribution for the corresponding standards case. The efficacy distributions are 

presented in Table 8.2.1. When re-assigning products to consumers, DOE assumed that 

consumers would purchase a CFLK at least as efficient as the CFLK they would have purchased 

in the no-standard case. In addition, DOE calculated the LCC savings only for affected 

consumers. This means that consumers who would purchase the same product in the no-standard 

case and a particular TSL, were not included in the LCC savings calculation. 

As an alternative scenario, DOE used a “roll-up” approach to calculate the LCC savings. 

Like the reference case approach, DOE assigned products to consumers in the no-standard case 

using the corresponding efficacy that was calculated by the shipments analysis. However, in the 

“roll-up” approach, DOE assumed that all consumers who, in the no-standard case, would 

purchase a CFLK that was less efficient than the standard level being analyzed, would purchase a 

CFLK at the minimum efficacy level allowed in the standards case. DOE also assumed that all 

consumers who would purchase a CFLK that was as efficient as, or more efficient than, the 

standard level being analyzed in the no-standard case, would purchase the same CFLK in the 

standards case (i.e., at the same EL as in the no-standard case). 

Both the reference and “roll-up” approach to calculating LCC savings take into account 

the no-standardcase efficacy distribution. For this reason, in both cases, the average LCC savings 

are not equal to the difference between the LCC of a specific EL and the LCC of the least 

efficient product available on the market. 

In both approaches, DOE calculated the share of consumers receiving a net LCC cost for 

each TSL. DOE considered a consumer to receive a net LCC cost if the customer had negative 

LCC savings for a given TSL. 

LCC and PBP calculations were performed 10,000 times on the representative consumer 

sample of each sector. Each of the 10,000 LCC and PBP calculations was performed on a single 

consumer selected from the sample in each sector. A consumer was selected based on how 

representative that particular consumer was of other consumers in the distribution (i.e., selection 

weight), as discussed in chapter 6 of this NOPR TSD. Each LCC and PBP calculation also 
8-27 

sampled from the probability distributions that DOE developed to characterize many of the 

inputs to the analysis. 

The following sections present the key LCC and PBP findings, as well as figures that 

illustrate the range of LCC and PBP effects among sample consumers. 

8.4.1 Reference Approach Results

Table 8.4.1 shows the LCC and PBP results and Table 8.4.2 shows the LCC savings, for 

all ELs. 

Table 8.4.1 Average LCC and PBP Results by Efficacy Level for All CFLKs 

EL

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

Residential Sector

Sub* 2.8 17.4 70.3 71.3 -- 13.8

0 5.5 3.6 40.4 45.6 0.2 13.8

1 8.8 3.4 40.0 48.4 0.4 13.8

2 19.4 2.9 33.4 51.8 1.2 13.8

3 10.5 2.0 23.4 32.8 0.5 13.8

4 9.3 1.9 22.0 30.3 0.4 13.8

Commercial Sector

Sub* 2.8 76.9 194.5 196.7 -- 13.8

0 5.5 15.8 136.9 142.9 0.0 13.8 

1 8.8 14.9 157.2 167.3 0.1 13.8

2 19.4 12.8 140.8 160.6 0.3 13.8

3 10.5 9.0 107.7 117.8 0.1 13.8

4 9.3 8.5 104.9 113.8 0.1 13.8

* “Sub” corresponds to the sub-baseline (i.e., lamps that have efficacies below the baseline set for the

new product class structure proposed in this rulemaking).

Note: The results for each TSL are calculated assuming that all consumers use products at that 

efficacy level. The PBP is measured relative to the least efficient product currently available on the 

market.
8-28 

Table 8.4.2 Average LCC Savings Relative to the No-Standard Case Efficacy 

Distribution for All CFLKs 

TSL

Life-Cycle Cost Savings

% of Consumers that Experience Average Savings

Net Cost 2014$

Residential Sector

0 0.6% 23.0

1 0.6% 23.0

2 9.7% 24.3

3 7.6% 30.9

4 7.6% 30.9

Commercial Sector

0 10.5% 28.7

1 10.5% 28.7

2 1.9% 53.4

3 0.3% 67.7

4 0.3% 67.8

Note: The results for each TSL represent the impact of a standard set at that TSL, based on 

the no-standard case and standards-case efficacy distributions calculated in the shipments 

analysis. The calculation excludes consumers with zero LCC savings (no impact).

The positive average LCC savings for residential and commercial consumers at TSL 0 

represent a positive impact of the product class consolidation proposed in this rulemaking. These 

represent the estimated savings of a standard set at the baseline under product class 

consolidation. There are savings at TSL 0 because there are products currently on the market 

with efficacies below that efficacy level.i 

Figure 8.4.1 and Figure 8.4.2 show the range of LCC savings for all TSLs in the 

residential and commercial sector for All CFLKs. The top and the bottom of the box indicate the 

75th and 25th percentiles, respectively. The solid bar indicates the median; 50 percent of 

consumers have LCC savings above this value. The horizontal lines above and below each box 

indicate the 95th and 5th percentiles, respectively. The red dot shows the average LCC savings. 

 

i

Essentially all CFLKs currently on the market with candelabra sockets are packaged with lamps that have 

efficacies below TSL 0 (i.e., the products that occupy the market share of the sub-baseline level). 
8-29 

Figure 8.4.1 Range of LCC Savings in Residential Sector for All CFLKs

Figure 8.4.2 Range of LCC Savings in Commercial Sector for All CFLKs

Figure 8.4.3 and Figure 8.4.4 show the distribution of LCC savings for TSL 2 in the 

residential sector and commercial sector, respectively, for All CFLKs. The figures show the 

number of consumers with negative or positive impact from each sample of 10,000 consumers. 

DOE presents the distribution of impacts for TSL 2 because it is the proposed TSL in this NOPR.
8-30 

Figure 8.4.3 LCC Savings Distribution in Residential Sector for All CFLKs at TSL 2 

Figure 8.4.4 LCC Savings Distribution in Commercial Sector for All CFLKs at TSL 2
8-31 

8.4.2 “Roll-up” Approach Results

Table 8.4.3 and Table 8.4.4 present LCC and PBP using the “roll-up” approach. 

Table 8.4.3 Average LCC and PBP Results by EL 

EL

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

Residential Sector

Sub* 2.8 17.4 70.3 71.3 -- 13.8

0 5.5 3.6 40.4 45.6 0.2 13.8

1 8.8 3.4 40.0 48.4 0.4 13.8

2 19.4 2.9 33.4 51.8 1.2 13.8

3 10.5 2.0 23.4 32.8 0.5 13.8

4 9.3 1.9 22.0 30.3 0.4 13.8

Commercial Sector

Sub* 2.8 76.9 194.5 196.7 -- 13.8

0 5.5 15.8 136.9 142.9 0.0 13.8

1 8.8 14.9 157.2 167.3 0.1 13.8

2 19.4 12.8 140.8 160.6 0.3 13.8

3 10.5 9.0 107.7 117.8 0.1 13.8

4 9.3 8.5 104.9 113.8 0.1 13.8

* “Sub” corresponds to the sub-baseline (i.e., lamps that have efficacies below the baseline set for the

new product class structure proposed in this rulemaking).

Note: The results for each EL are calculated assuming that all consumers use products at that efficacy 

level. The PBP is measured relative to the least efficient product currently available on the market.
8-32 

Table 8.4.4 Average LCC Savings Relative to the No-standard-Case Efficacy 

Distribution for All CFLKs* 

TSL

Life-Cycle Cost Savings

% of Consumers that Experience Average Savings*

Net Cost 2014$

Residential Sector

-- 0.4% 25.7

1 0.7% 23.0

2 33.0% 9.7

3 3.6% 29.7

4 0.7% 31.0

Commercial Sector

-- 0.9% 53.5

1 9.9% 29.3

2 23.6% 20.5

3 0.0% 66.3

4 0.0% 67.8

Note: The results for each TSL represent the impact of a standard set at that TSL, based on 

the no-standard-case and standards-case efficacy distributions calculated in the shipments 

analysis. The calculation excludes consumers with zero LCC savings (no impact).

Figure 8.4.5 and Figure 8.4.6 show the range of LCC savings for each EL in the 

residential and commercial sector, respectively, for All CFLKs, using the roll-up approach. 

Figure 8.4.5 Range of LCC Savings in Residential Sector for All CFLKs (Roll-up)
8-33 

Figure 8.4.6 Range of LCC Savings in Commercial Sector for All CFLKs (Roll-up)

8.4.3 Rebuttable Presumption Payback Period 

EPCA establishes a rebuttable presumption that an energy conservation standard is 

economically justified if the increased purchase cost for a product that meets the standard is less 

than three times the value of the first year’s energy savings resulting from the standard. DOE 

expresses this criterion as having a simple payback period of less than three years. In calculating 

a rebuttable-presumption payback period for each of the considered TSLs, DOE based the energy 

use calculation on the DOE test procedures for CFLKs, as required by EPCA. Table 8.4.5 shows 

the results of this analysis for the considered TSLs. Note that because dimming is not included in 

the current or proposed DOE test procedures for CFLKs, the rebuttable presumption PBP does 

not take into account the effect of dimming on CFLK energy use. 
8-34 

Table 8.4.5 Rebuttable-Presumption Payback Period Results 

TSL Residential 

Sector

Commercial 

Sector

-- 0.2 0.4

1 0.4 0.1

2 1.1 0.2

3 0.5 0.1

4 0.4 0.1

 While DOE examined the rebuttable-presumption criterion, it considered whether the 

standard levels considered for this rule are economically justified through a more detailed 

analysis of the economic impacts of those levels, pursuant to 42 U.S.C. 6295(o)(2)(B)(i), that 

considers the full range of impacts to the consumer, manufacturer, nation, and environment. The 

results of that analysis serve as the basis for DOE to evaluate the economic justification for a 

potential standard level, thereby supporting or rebutting the results of any preliminary 

determination of economic justification.
8-35 

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11. U. S. Department of Energy, Energy Information Administration. Form EIA-861 Annual 

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12. Kantner, C. L. S., S. J. Young, S. M. Donovan, and K. Garbesi. Ceiling Fan and Ceiling 

Fan Light Kit Use in the U.S.—Results of a Survey on Amazon Mechanical Turk. 2013. 

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Theory of Investment. American Economic Review. 1958. 48(3): pp. 261–297. (Last 

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26. Board of Governors of the Federal Reserve System. Selected Interest Rates (Daily) - 

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27. Damodaran, A. Data Page: Historical Returns on Stocks, Bonds and Bills-United States. 

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28. Association of Lighting and Mercury Recyclers. National Mercury-Lamp Recycling Rate 

and Availability of Lamp Recycling Services in the U.S. 2004. (Last accessed June 10, 

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Compact Fluorescent Light Bulbs (CFLs). (Last accessed June 8, 2015.) 

http://www2.epa.gov/cfl/recycling-and-disposal-after-cfl-burns-out. 
9-i 

CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.1.1 Representative Lamp Units Analyzed in the Shipments Analysis ....................... 9-1

9.2 SHIPMENTS MODEL OVERVIEW .............................................................................. 9-3

9.2.1 Shipments Demand Model ................................................................................... 9-4

9.2.2 Stock Accounting Model ..................................................................................... 9-4

9.2.3 Consumer-Choice Model ..................................................................................... 9-5

9.3 MODEL IMPLEMENTATION ....................................................................................... 9-6

9.3.1 Historical Shipments ............................................................................................ 9-6

9.3.2 Shipments Projections by Market Segment ......................................................... 9-8

9.3.3 Current Efficacy Distribution ............................................................................ 9-10

9.3.4 Light-Emitting Diode Incursion and Distribution by Efficacy Level ................ 9-14

9.3.5 Consumer-Choice Model ................................................................................... 9-15

9.3.6 Impact of GSL Energy Conservation Standards Rulemaking ........................... 9-16

9.3.7 Summary ............................................................................................................ 9-17

9.4 IMPACT OF ENERGY CONSERVATION STANDARDS ON SHIPMENTS .......... 9-17

9.4.1 Application of Relative Price Elasticity ............................................................. 9-19

9.5 AFFECTED STOCK ..................................................................................................... 9-20

9.6 IMPACT OF A CEILING FAN STANDARD ON CFLK SHIPMENTS .................... 9-21

9.7 ALTERNATIVE SHIPMENTS SCENARIOS ............................................................. 9-21

9.8 SHIPMENTS PROJECTIONS ...................................................................................... 9-22

REFERENCES .......................................................................................................................... 9-25

LIST OF TABLES

Table 9.1.1 Characteristics of Representative CFLKs used in the Shipments 

Analysis.......................................................................................................... 9-3

Table 9.3.1 NPD Estimates of Historical Shipments of Ceiling Fans .............................. 9-7

Table 9.3.2 DOE Estimates of Historical Shipments of Ceiling Fans .............................. 9-7

Table 9.3.3 CFLK Market Share Distribution by Product Class in 2014 ....................... 9-12

Table 9.3.4 CFLK Market Share Distribution by Efficacy in 2014 ................................ 9-13

Table 9.3.5 CFLK Market Share Distribution by Efficacy Level................................... 9-13

Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price 

Change ......................................................................................................... 9-19

Table 9.8.1 Cumulative CFLK Shipments from 2019-2048 (millions of units) from 

a CFLK Standard ......................................................................................... 9-24
9-ii

LIST OF FIGURES

Figure 9.3.1 Historical Shipments of CFLKs ..................................................................... 9-8

Figure 9.3.2 CFLK Retirement Function ........................................................................... 9-9

Figure 9.3.3 LED Incursion into CFLK Market Assuming Trend Estimated from 

SSL Report ................................................................................................... 9-15

Figure 9.7.1 Reference and High LED Learning Price Index, taken from the GSL 

Preliminary Analysis Base-Case Reference Scenario .................................. 9-22

Figure 9.8.1 Total CFLK Shipments Projection in the No Standard Case by Market 

Segment in the Reference Scenario ............................................................. 9-23

Figure 9.8.2 CFLK Shipments Projection for all TSLs in the Reference Scenario ......... 9-23
9-1 

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

This chapter describes the data and methods the U.S. Department of Energy (DOE) used 

to project annual product shipments with and without amended standards and presents results 

for the ceiling fan light kit (CFLK) product class being considered in this analysis. Estimates of 

future product shipments are necessary inputs to the national energy savings (NES) and net 

present value (NPV) calculations, as well as to the manufacturer impact analysis (MIA). 

Because the vast majority of CFLKs are sold with ceiling fans, the shipments model for 

CFLKs is necessarily coupled to shipments of ceiling fans.a DOE developed a national stock 

accounting model to estimate annual shipments of ceiling fans, which was then used to estimate 

shipments of CFLKs. To apportion CFLK shipments into different efficacy levels (ELs), DOE 

developed a modified consumer-choice model based on the shipments modeling methodology 

developed by DOE for the general service fluorescent lamp and incandescent reflector lamp 

energy conservations standards rulemaking.1 

The shipments model was executed as a separate computer model written in the Python 

programming language. The inputs and outputs of the shipments models were embedded in the 

national impact assessment (NIA) model, which is implemented in a Microsoft Excel

spreadsheet, available on regulations.gov, docket number EERE-2012-BT-STD-0045 at

http://www.regulations.gov/#!docketDetail;D=EERE-2012-BT-STD-0045. Appendix 10A of 

this NOPR technical support document (TSD) discusses how to access and utilize the NIA 

spreadsheet. The rest of this chapter explains the shipments model in more detail. Section 9.2 

presents the methodology behind the model; section 9.3 describes the data inputs and calibration 

of the model; section 9.4 discusses impacts on shipments from standards; section 9.5 discusses 

the affected stock; section 9.6 discusses the impact of a ceiling fan standard of CFLK 

shipments; section 9.7 discusses alternative shipment scenarios; and section 9.8 presents the 

shipments projections for standards set at each efficacy level (EL). 

9.1.1 Representative Lamp Units Analyzed in the Shipments Analysis

As described in chapter 3 of this NOPR TSD, DOE is considering a single product class 

in this NOPR analysis. In chapter 5 of this NOPR TSD, DOE described the ELs it has 

developed, and it identified representative lamp units that could meet each EL. The 

representative lamp units are specified by the lamps that could be packaged with the CFLK to 

meet each EL. Included in the no-standard shipments case are CFLKs with efficacies below the 

restructured baseline (hereafter referred to as the ‘sub-baseline’).The properties of representative 

lamp units corresponding to the sub-baseline are described in appendix 7A of this NOPR TSD. 

a All ceiling fan data referred to in this chapter are hugger and standard ceiling fans, as defined in the concurrent 

ceiling fans test procedures rulemaking; for these purposes, volume refers to air flow. DOE has determined that 

CFLKs are designed almost solely for the hugger and standard ceiling fan market. Hereafter in this document the 

term ceiling fans refers to standard and hugger ceiling fans.
9-2 

As described in chapter 5 of this NOPR TSD, DOE assumed that two different scenarios 

were available to manufacturers to meet each EL. In the lamp replacement scenario, the 

manufacturer is assumed to meet each EL by using more efficacious packaged lamps, but is 

assumed to leave all other aspects of the fixture unchanged. In the CFLK replacement scenario, 

DOE assumes that the manufacturer may substitute the base type and number of sockets. 

Accordingly, at least two representative options were developed for each EL, as well as for the 

baseline. 

The lamp and socket prices associated with efficacy improvements for representative 

products are presented in chapter 7 of this NOPR TSD. These prices are key inputs to the 

shipments model. Table 9.1.1presents the characteristics of representative CFLKs based on the 

representative lamp units and sockets specified for the lamp and fixture replacement scenarios.b

Values for CFLK wattage and lumen output, and the portion of the CFLK end-user price that is 

associated with the lamps and sockets (“CFLK End-User Price for Lighting Components”), were 

based on the per-lamp and per-socket values for representative units described in chapter 7 of 

this NOPR TSD. For the baseline product (EL 0), those values were scaled to make them 

representative of the average CFLK on the market that is not constrained by a wattage limiter, 

which has an estimated average lumen output of 2039 lumens.c

For all other levels, except the 

sub-baseline, those values were scaled by the same amount as the baseline product, to preserve 

the percentage variations in lumen output between ELs that were identified for the 

representative lamp units and fixtures in the engineering analysis. The differences in the prices 

at the different ELs represent the incremental price of efficacy improvements, which are key 

inputs to the shipments and NIA analyses. 

b For example, $4.33 of the total cost of a baseline CFLK is assumed to be associated with the lamps. However, 

included in that $4.33 is both a manufacturer markup on the manufacturers cost of lamps and a distributor markup 

on top of that.

c Chapter 6 of this NOPR TSD describes how DOE obtained a value for the lumen output of the average CFLK not 

constrained by a wattage limiter. 
9-3 

Table 9.1.1 Characteristics of Representative CFLKs used in the Shipments Analysis

Efficacy

Level

CFLK Light Output

(lumens)

CFLK Input Power

(watts)

CFLK End-User 

Price for Lighting 

Components in 2019

(2014$)

All lamps

Sub-Baseline 1320 180 1.65 

Lamp Replacement Scenario* All lamps

EL 0 2039 36 4.33

EL 1 2039 33 5.12

EL 2a 1861 28 5.68

EL 2b 2039 31 18.61

EL 3 2039 22 7.47

EL 4 2090 20 5.89

Fixture Replacement Scenario** All lamps and sockets

EL 0 2039 36 5.12

EL 1 1988 34 10.55

EL 2 1912 31 28.06

EL 3 2039 20 11.99

EL 4 2039 19 11.70

* Representative lamp lumens, wattages, and prices presented in chapter 7 are each scaled by 2.7, to obtain values

representative CFLK, which has a light output of 2039 lumens. Lamp prices are projected to 2019 values assuming 

price learning for solid-state lighting.

** Representative fixture lumens, wattages, and prices given in chapter 7 are each scaled by 1.3, to obtain values 

representative of the average CFLK, which has a light output of 2039 lumens. Lamp and socket prices are then 

summed and multiplied by the number of sockets to obtain the total CFLK total end-user prices for lamps plus 

sockets. Similarly, the lamp and socket prices presented in chapter 7 were scaled by a factor of 1.3 and then

multiplied by the number of sockets in the representative CFLK. The socket price used in this table is determined 

using markup channels projected for 2019 (the starting period of the analysis). Lamp prices are projected to 2019 

values assuming price learning for solid-state lighting.

9.2 SHIPMENTS MODEL OVERVIEW

DOE developed a model to project CFLK shipments by efficacy level, over the 30-year 

analysis period (2019 – 2048). The model projects shipments for both the no-standard case and 

for each TSL. The shipments model consists of three main interacting elements: (1) a shipments 

demand model that determines the total new CFLKs purchased (hence shipped) in a given year, 

(2) a stock model that tracks the age distribution of the stock over the analysis period, and (3) a 

modified consumer-choice model, which determines the distribution (market shares) of those 
9-4 

purchases across efficacy level. This section describes these elements of the shipments model in 

general terms. Section 9.3 describes the calibration and implementation of the model.

9.2.1 Shipments Demand Model 

The shipments model considers four market segments in developing the total CFLK 

shipments projection: replacements for retired stock, additions due to new building 

construction, additions due to expanded demand in existing buildings, and demolitions, which 

decreases demand from replacements and new installations in existing buildings. Total 

shipments for CFLKs in a given year are given by:

SShiiii(jj) = RR RR(jj) + NNNN(jj) + EEEE(jj) − DDDDDD(jj) 

Eq. 9.1

Where:

SShiiii(jj) = total number of units shipped in year jj, 

RR RR(jj) = units retired and replaced in year jj, 

NNNN(jj) = number of new construction installations in year jj, 

EEEE(jj) = number of units installed in existing buildings in year jj, and

DDDDDD(jj) = number of replacement installations that did not occur due to demolitions of 

existing buildings in year jj. 

The details of how demand from replacements and new construction, expanded demand 

in existing building, and reduction in demand from demolitions are quantified in section 9.3.2. 

9.2.2 Stock Accounting Model

As indicated in Eq. 9.1, shipments projections depend in part on a projection of the 

number of units in the existing stock that need to be replaced in any given year because of stock 

retirements. Determining how many units are retired in a given year requires knowledge of the 

age distribution of the stock. A stock accounting model is used to track the age distribution of the 

stock, based on an accounting of additions to the stock (shipments) and retirements. The age 

distribution of the stock in 2012 is estimated using results of an online survey of ceiling fan

owners conducted by Lawrence Berkeley National Lab (LBNL).2The stock age distribution is 

updated in subsequent years using projected shipments and retirements determined by the stock 

age distribution and a product retirement function. The age distribution of the stock is also a key 

input to both the NES and NPV calculations, because the operating costs for any year depend on 

the age distribution of the stock. Older, less efficient units may have higher operating costs, 

while newer, more efficient units have lower operating costs. 

DOE calculates the total stock of products by integrating historical shipments beginning 

with the start year of the historical data. New units are then added to the stock to replace older 

units that are retired and to account for growth in demand. 

Assuming a low probability for retirements within the first year, the number of one-year 

old units is equal to the number of new units purchased the previous year: 
9-5 

SSSSSSSSSS(jj, aaaa = 1) = SShiiii(jj − 1)

Eq. 9.2 

Where:

SSSSSSSSSS(jj, aaaa ) = number of units of a particular aaaa , 

jj = year for which the stock is being estimated, and 

SShiiii(jj– 1) = number of units purchased in year jj– 1.

Over time, a fraction of the stock is removed due to retirements. That fraction is determined by a 

retirement probability function, pp ppRRRRRR(aaaa ), which is described in section 9.3.2. Accordingly, 

DOE tracks existing stock of units accounting for units that retire using the following: 

SSSSSSSSSS(jj + 1, aaaa + 1) = SSSSSSSSSS(jj, aaaa ) − SShiiii ii ii(jj − aaaa ) × pp ppRRRRRR(aaaa + 1))

Eq. 9.3 

Where:

SSSSSSSSSS(jj, aaaa ) = number of units of a particular age, 

jj = year for which the stock is being estimated, 

SShiiii(jj − aaaa ) = number of units purchased in year jj − aaaa , and 

pp RRRRRR(aaaa + 1) = the probability a unit is retired at aaaa + 1.

9.2.3 Consumer-Choice Model

The consumer-choice model determines the distribution (market shares) of purchases in 

any given year by estimating the likelihood that a consumer will purchase a particular CFLK 

option, based on sector-specific sensitivities to first cost and operation and maintenance (O&M) 

cost. The consumer-choice model is used to determine the market allocations for both the nostandard case and the standards cases considered. The consumer-choice model allows for 

voluntary conversions to more efficient technologies based on economic incentives. The model 

allocates the shipments to the representative products described in chapter 5 of this NOPR TSD, 

assuming the prices for those representative products described in chapter 7 of this NOPR TSD.

The consumer-choice model uses a conditional logit model to calculate the 

probability that a consumer will purchase product jj based on the logistic curve probability 

function of the form: 

PPjj(zz) = 

zzjj

∑nn zzii ii=1

Eq. 9.4 

Where:

PPjj(zz) = the probability a consumer will purchase product jj among possible options, and 

zz = the ‘logit’, which is defined as follows 

zzjj = ββFFFF ∗ FFCCjj + ββOOOO ∗ OOMMjj

Eq. 9.5 

Where:
9-6 

ββFFFF = consumer sensitivity to first cost,

FFCCjj = first cost of product option jj, 

ββOOOO = consumer sensitivity to operations and maintenance cost, and 

OOMMjj = operations and maintenance cost of product option jj. 

The regression coefficients (ββ values) represent the consumer’s sensitivity to first cost (ββFFFF) and 

O&M cost (ββOOOO). The coefficients are determined using historical shipments' market-share data. 

Consumer sensitivities are sector-specific and vary between residential and commercial

purchasers. Other parameters that influence consumer-purchasing behavior not captured by the 

logit model are calibrated using acceptance factors. Acceptance factors limit the total market 

share that can be assigned to a product and nominally are set to 1. 

In the case of CFLKs, the lamps packaged with the CFLK are purchased by the 

CFLK manufacturer. Thus, the consumer-choice model for CFLKs is modified to 

project lamp purchases by CFLK manufacturers, as those will be the options available 

to consumers. 

9.3 MODEL IMPLEMENTATION

9.3.1 Historical Shipments

 DOE’s analysis of the CFLK market indicates that a majority of CFLKs are packaged and 

sold with ceiling fans. DOE used historical shipments data of ceiling fans to calibrate its CFLK 

shipments model by assuming a constant fraction of ceiling fan shipments include a CFLK. 

DOE used three sources of data to establish historical shipments trends in the total 

number of ceiling fans: (1) data from Appliance Magazine’s Statistical Review from the period 

1991-2006,d

(2) data from ENERGY STAR Annual Reports from the period 2003-2011,e and (3) 

data purchased from the NPD Group from 2007-2011. DOE took an average value in cases 

where data from multiple sources overlap. 

Table 9.3.1 shows NPD estimates of total U.S. shipments of ceiling fans sold with and 

without CFLKs, along with shipments of ceiling fans for which it was not specified if the ceiling 

fan was sold with or without a CFLK. Using the data in Table 9.3.1 and assuming that the ratio 

of ceiling fans sold with and without CFLKs is the same for the customers who did and did not

d Appliance® Statistical Review, Annual Report, Appliance Magazine (2003-2006). Appliance Magazine sources 

may include the Air-Conditioning and Refrigeration Institute; Appliance Magazine, Association of Home Appliance 

Manufacturers; Consumer Electronics Association; Gartner; Gas Appliance Manufacturers Association; Hearth, 

Patio & Barbeque Association; International Data Corporation; Outdoor Power Equipment Institute; and the Water 

Quality Association. The data have not included exports since 1997. e ENERGY STAR takes partner-reported data to be representative of qualified ceiling fan shipments without 

adjusting for partners who do not report their shipments. This means that the ENERGY STAR data represent a 

conservative estimate, although the response rate among ceiling fan manufacturers is typically high, for example, 89 

percent in 2010. In order to estimate the market share of ENERGY STAR-qualified ceiling fans, ENERGY STAR 

estimates U.S. shipments based on modeled and documented data. Ceiling fan industry data are used as a direct 

input whenever possible and compared to forecast assumptions to ensure data comparability. Industry sources may 

include ceiling fan manufacturer associations, publications, and commercially available market research.
9-7 

specify, DOE estimated the total shipments of ceiling fans sold with and without CFLKs and 

the percentage of ceiling fans sold with CFLKs in each year. The results are shown in Table 

9.3.2. Based on this analysis NPD shipment data and an online survey of ceiling fan owners 

conducted by LBNL,2 DOE found 88% of ceiling fans purchased within the last four years 

included CFLKs. 

Table 9.3.1 NPD Estimates of Historical Shipments of Ceiling Fans

Number of Units 2007 2008 2009 2010 2011

With CFLK 14,899,594 15,485,300 16,015,248 14,414,909 14,597,241

Without CFLK 2,176,229 2,226,826 2,106,832 2,184,390 2,035,746

Not Specified 3,970,371 3,080,311 3,561,611 2,830,707 2,140,867

Source: NPD Group (2007-2011).

Table 9.3.2 DOE Estimates of Historical Shipments of Ceiling Fans

Number of Units 2007 2008 2009 2010 2011

With CFLK 18,360,000 18,180,000 19,160,000 16,870.000 16,480,000

Without CFLK 2,680,000 2,610,000 2,520,000 2,560,000 2,290,000

Total 21,050,000 20,790,000 21,680,000 19,430,000 18,770,000

% with CFLK 87% 87% 88% 87% 88%

Figure 9.3.1 presents DOE’s estimate of CFLK historical shipments from 1991-2011. 
9-8 

Figure 9.3.1 Historical Shipments of CFLKs

9.3.2 Shipments Projections by Market Segment

Section 9.2.1 describes how shipments are projected based on sales to the following 

market segments: product replacements due to retirements, increased demand from new 

building construction, increased demand in existing buildings, and decrease in demand due 

to building demolitions. This section describes how demand from each of these market 

segments is quantified. 

Replacement Shipments. Estimated sales due to replacements in a given year are equal 

to the total stock of the appliance minus the sum of the appliances sold in previous years that still 

remain in the stock. DOE assumed that the relevant lifetime in projecting replacements for

CFLKs is the lifetime of the ceiling fan to which it is attached. The following equation represents 

how DOE estimated replacement shipments:

RR RR(jj) = � SShiiii(ii) × pp ppRRRRRR

jj−1

ii=NN

(aaaa = jj − ii)

Eq. 9.6 

Where:

RR RR(jj) = Units retired and replaced in year jj, 

SShiiii(jj) = Total shipments in year jj, 

pp RRRRRR(aaaa ) = Probability that a ceiling fan of a particular aaaa will be retired,

NN = Start year when the model begins its stock accounting.
9-9 

The retirement function, pp RRRRRR(aaaa ), is based on the product lifetime distribution, as

discussed in chapter 8 of this NOPR TSD. The product lifetime distribution has an average value 

of 13.8 years, and minimum and maximum values of 4.1 years and 30 years, respectively. Figure 

9.3.2 shows the retirement function for a CFLK that DOE used to estimate replacement 

shipments. 

Figure 9.3.2 CFLK Retirement Function

Shipments to New Construction. The number of installations due to new construction is 

estimated using projections for new construction starts, product saturation, and the average 

number of CFLK units installed per building. DOE adopted the projections from the DOE’s

Energy Information Administration’s (EIA’s) Annual Energy Outlook 2014 (AEO 2014) for 

2010-2040.3 DOE estimated a current penetration rate of ceiling fans in homes of 73%, using 

data from Residential Energy Consumption Survey (RECS) 2009, with an average of 3.1 ceiling 

fans installed in a single new installation. DOE assumed 88% of these installations include a 

CFLK to determine shipments of CFLKs to new construction.

Installations into Existing Buildings. The number of new first-time owners was 

estimated using trends in the penetration of ceiling fans in existing buildings over time from 

RECS data from 1993, 1997, 2001, 2005, and 2009. DOE estimated the rate at which the 

number of buildings without ceiling fans decreased between RECS years. DOE determined that 

the number of buildings without ceiling fans decreased at an average rate of 2.8% each year. 

However, DOE found that using a first-time owner rate of 2.8% over-estimated shipments 

model estimates for 2010 and 2011 compared to historical data. To match the shipments data 

from 2010 and 2011, DOE assumed that market saturation occurs at a penetration rate of 75% 

for purchases of ceiling fans in all buildings. DOE also considered the addition of ceiling fans
9-10 

into buildings that already have ceiling fans and assumed that the average number of ceiling 

fans in buildings with ceiling fans does not exceed 3.1 ceiling fans per building. DOE assumed 

88% of ceiling fan installations into existing buildings included a CFLK. The market for CFLKs 

in existing buildings quickly decreases as the market reaches its saturation point, using the 

previously described assumptions. By the start of the analysis period (2019), installations into 

existing buildings make up 1% of CFLK shipments. 

Reduction in Demand Due to Demolitions. The demand for installations into existing 

buildings was decreased due to demolitions. Demolitions act to decrease the potential demand 

of ceiling fans in older housing stock that would have contributed to CFLK replacements. DOE 

estimated this demand reduction using a stock accounting model based on AEO 2015 residential 

housing stock and housing starts following Eq. 9.7 below.4

Demolition (j) = Stock (j+1) - Stock (j) - Starts (j)

Eq. 9.7 

Where:

DDDDDD (jj)=Housing demolitions in year jj, 

SSSSSSSSSS(jj) = Housing stock in year jj, and 

SSSS SS (jj)= New housing starts in year jj. 

DOE based its estimates of the penetration of ceiling fans in buildings being demolished 

using houses built before 1950 in RECS 2009. DOE found that 68% of buildings had ceiling 

fans with an average of 2.69 ceilings fans per building with a ceiling fan. DOE assumed 88% of 

ceiling fan installations included a CFLK. 

Commercial Sector Shipments. Based on feedback from manufacturers, DOE estimated 

that 5% of total CFLK shipments go to the commercial sector. DOE assumed this fraction 

remained fixed over the course of the analysis period. 

9.3.3 Current Efficacy Distribution

To obtain a better understanding of the CFLK market, DOE examined the market 

distribution of CFLKs broken down by current product class and by efficacy for 2014. Using 

these results, DOE derived the efficacy distribution for the ELs presented in the engineering 

analysis. 

DOE currently divides CFLKs into three products classes based on the light socket 

type: Medium -Screw Base product class, Pin-Based fluorescent product class, and the 

Other Base Type product class. To make estimates of CFLK market shares, DOE performed 

model counts of CFLKs (sold with a ceiling fan, or independently) using two data sources: 
9-11 

in-store shelf surveys and product offerings on the Internet.f Recognizing that model counts do 

not reflect the fact that products typically sell in higher volumes at lower prices, DOE used a 

price-weighting scheme based on an analysis presented in the concurrent ceiling fans energy 

conservation standards rulemakingg

and summarized here. 

The relationship between ceiling fan market share and retail price is described by the 

following equation: 

MM ( ) = AA ββFFFF∗pp

Where:

MM ( ) = Market share for a ceiling fan that costs p,

AA = Normalization factor,

= Retail price of a ceiling fan, and

ββFFFF = Consumer sensitivity to first cost, found to be equal to -0.028.g 

Because CFLKs are sold primarily with ceiling fans, DOE uses the same equation to 

estimate CFLK market shares by product class. DOE also accounted for the fact that 

different data sources represent channels with different sales volumes.h 

As shown in Table 9.3.3, the current Other Base Type product class constitutes 

approximately 66% of the entire market, largely with CFLKs with candelabra sockets that use 

low-efficacy lamps (15 lm/W). CFLKs with medium screw base sockets constitute the next 

largest product class, which in the survey year included only compact fluorescent lamps. The 

Pin-Based product class is insignificant at less than 1% of the market. 

f Comprehensive shelf surveys were conducted in 9 California stores in the summer of 2014: 4 Home Depot stores, 4 

Lowes stores, and 1 Wal-Mart store. The survey of Internet offerings was also conducted in the summer of 2014 and 

included 1,340 samples available from Hansen Wholesale, an online retailer that sells a wide variety of ceiling fan 

brands and models: http://www.hansenwholesale.com. g The specific consumer sensitivity to first cost is discussed in chapter 9 of the preliminary analysis technical support 

document for the ceiling fans energy conservation standards rulemaking, and is available at 

http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0045-0065. h Specifically, DOE weighted the data by three criteria, in addition to price: 1) Shelf survey versus online data, 2) 

Retailer market share, and 3) Percent of occurrences of each model across the same retailer. In this analysis DOE 

assumed that offerings at the largest retailers (Home Depot, Lowe’s, and Wal-Mart) are representative of other instore sales.
9-12 

Table 9.3.3 CFLK Market Share Distribution by Product Class in 2014 

Product 

class

Shelf Surveys Online

Weighted 

Total

Market Share 

by Product 

Class Base Type

Market 

Share by 

Base Type

Market Share 

by Product 

Class

Market 

Share by 

Base Type

MediumScrew 

Base

34.9% Medium 

screw base 34.9% 20.7% 20.7% 34.2%

Pin-Based 0.0% Pin-based 

fluorescent 0.0% 0.2% 0.2% 0.0%

Other 

Base Type 65.1% 

Candelabra 

(including 

mini 

candelabra)

60.2% 

79.2%

66.3%

65.7% GU24 3.9% 12.0%

Others (e.g.,

G9, GU 10, 

integral 

solid-state 

lighting)

0.9%

0.9%

100% 100% 100% 100% 100%

DOE also examined the 2014 market breakdown by product class efficacy based on the 

survey data, the results of which are shown in Table 9.3.4. From this table and Table 9.3.3, it is 

clear that the majority of the market was Other Base Type CFLKs that use relatively lowefficacy lamps, i

typically less than 15 lm/W. The remainder of the market was dominated by 

CFLKs with medium-screw base sockets which, based on the current efficiency standard, were 

constrained to higher efficacy lamps, typically greater than 55 lm/W. As shown in Table 9.3.3, a 

very small share of the Other Base Type CFLK market (estimated to be less than 0.5%) was 

made up of CFLKs with integral solid-state lighting, which are just entering the market; this 

market segment is expected to grow rapidly. 

i

The efficacy of lamps is measured in light output (lumens, lm) per unit of power input (watts, W).
9-13 

Table 9.3.4 CFLK Market Share Distribution by Efficacy in 2014

Market Share Distribution within Product Classes

Share of

Light Source Efficacy Medium Total Market -Screw 

Base Pin-Based Other Base Type

>70 lm/W 0.0% 0.0% 0.7% 0.5%

≥60 and <70 lm/W 84.5% 100.0% 8.7% 34.7%

≥45 and <60 lm/W 0.0% 0.0% 0.0% 0.0%

≥15 and <45 lm/W 0.0% 0.0% 0.9% 0.6%

<15 lm/W 15.5% 0.0% 89.6% 64.2%

TOTAL* 100.0% 100.0% 100.0% 100.0%

*Values in columns may not add to 100% because of rounding.

Based on the results of Table 9.3.3 and Table 9.3.4 for market shares by lighting 

technology and efficacy for the current product class structure, DOE developed market shares 

by efficacy level for the proposed single product class of All CFLKs. Results for the market 

distribution of efficacy level are presented in Table 9.3.5. Note, that DOE found negligible 

market share for products in the 45-60 lm/W range and thus attributed 0% market share to EL 0 

(which represents an efficacy of 57.1 lm/W). 

Table 9.3.5 CFLK Market Share Distribution by Efficacy Level

Efficacy Level Market Share

Sub-Baseline 64.7%

Lamp Replacement Scenario

EL 0 0.0%

EL 1 15.2%

EL 2a 4.7%

EL 2b 0.3%

EL 3 0.0%

EL 4 0.0%

Fixture Replacement Scenario

EL 0 0.0%

EL 1 15.2%

EL 2 0.3%

EL 3 0.0%

EL 4 0.0%

TOTAL* 100.0 %

*Values in columns may not add to 100% because of rounding.
9-14 

9.3.4 Light-Emitting Diode Incursion and Distribution by Efficacy Level

Although products with light-emitting diode (LED) technology currently make up less 

than 1% of shipments of CFLKs, feedback from CFLK manufacturers indicated that the 

penetration of LED technology into the CFLK market would likely reach 15% by 2018. DOE 

assumed that the CFLK market would follow the larger lighting market as a whole early in the 

analysis period, and estimated the market share of LED technologies using the incursion curved 

developed for general service lamps (GSLs) in its report Energy Saving Potential for Solid 

State Lighting in General Illumination Applications (SSL report).5 DOE recognizes that the 

CFLK market lags behind the GSL market and shifted the introduction year used in its analysis 

by 3.5 years compared to the introduction year presented in the SSL report to match 

manufacturer estimates of LED penetration in 2018. The remaining market share for non-LED 

lamp options was apportioned using the modified consumer-choice model, described in section 

9.3.5 below. 

DOE assumed the prices of LED lamps will decline over time due to learning associated 

with the cumulative production of LED lamps. Because lamps packaged with CFLKs represent a 

small portion of the total GSL market, instead of developing a separate LED lamp price trend for 

CFLKs, DOE used the price trend developed in the GSL Energy Conservation Standard 

Preliminary Analysis, which models the price learning associated with cumulative shipments of 

the GSL market. Specifically, DOE developed a time series index using prices for GSL LED 

lamps with a lumen output between 750-1049 lumens in the base case of the reference scenario

(in which shipments of LED GSLs are affected by the EISA 2007 backstop but not by a GSL 

final rule). That scenario assumed that LED GSLs would experience the same learning rate 

historically observed by CFLs. This index was used to scale the LED CFLK prices during the 

analysis period (2019-2048). 

To apportion the demand for LEDs among the different LED lamp options, DOE used 

the efficacy trend projection for decorative LED lamps presented in DOE’s report SSL Pricing 

and Efficacy Trend Analysis for Utility Program Planning.

6

 The decorative lamp category 

exhibits a slower rate of efficacy increase compared to medium-screw base lamps; therefore 

DOE believes this represents a conservative estimate of the efficacy trend. To determine the 

current mean and maximum efficacy of LED lamps, DOE analyzed data from 2012-2015 for 

lamps in its LED Lighting Facts Products Database.

j

 DOE assumed the ratio between the 

average LED efficacy and the maximum LED efficacy for 2015 would remain constant over the 

analysis period. DOE approximated the LED efficacy distribution for a given year as a normal 

distribution, with a mean given by the average LED efficacy and a standard deviation given by 

the difference between the max LED efficacy and the average LED efficacy divided by 3. In 

other words, the maximum LED efficacy represents the 3σσ upper confidence level (i.e., the 

99.9th percentile) of efficacy. DOE then determined the relative fraction of the LED efficacy 

distribution that would fall into each LED EL. By the start of the analysis period, 2019, 29% of 

LED market share would be at the highest LED efficacy level presented in chapter 5 of this 

NOPR TSD. DOE used its projected LED incursion curve and efficacy trend to estimate the 

market share for LED lamp options while the price of LEDs remains higher than other 

technology options. 

j

http://www.lightingfacts.com/products. Accessed on February 13, 2015.
9-15 

However, once the price of LEDs falls below the price of the other lighting technologies 

presented in the engineering analysis, DOE assumed that manufacturers will choose to package 

all CFLKs with LED lamps. DOE assumed that manufacturers would not pay a price premium to 

package with any of the CFL lamp options, which are more expensive, less efficacious, and have 

a shorter lifetime.

Figure 9.3.3 LED Incursion into CFLK Market Assuming Trend Estimated from SSL 

Report 

9.3.5 Consumer-Choice Model 

The total shipments must be allocated by efficacy level. To do so, the shipments model 

uses a hybrid approach. As described in section 9.3.4, until LEDs are first-cost competitive 

with other lighting technologies, they are accounted for separately using the incursion curve 

developed in the SSL report. Non-LEDs are allocated by the consumer-choice model. 

DOE implemented a consumer-choice model, as described in Section 9.2.3, with 

modifications to account for DOE’s observation that purchasers of CFLKs are not choosing the 

lamps packaged with CFLKs. Instead, CFLK manufacturers purchase the lamps for CFLKs, 

ultimately deciding which lamp options are available for consumers of CFLKs. The options 

available to the consumer-choice model for CFLKs correspond to the ELs shown in Table 9.1.1. 

A key assumption for the shipments model is that CFLK manufacturers will generally 

choose to package CFLK with the lowest first cost lamp options. However DOE recognizes 

that some manufacturers will package CFLKs with LED lamps for reasons other than cost 

alone, such as consumer desire for LED lamps in comparison to other available technology 

options. 
9-16 

DOE assumed that for a given socket type and lamp technology, CFLK manufacturers 

are primarily sensitive to the first cost of the lamp, as there is no economic incentive for CFLK 

manufacturers to purchase a more efficacious lamp to package with a CFLK. For example, a 

manufacturer who is planning to package a medium-screw base CFLK with a compact

fluorescent lamp is assumed to purchase the lowest first cost compact fluorescent lamp available. 

After the consumer-choice model identifies which options manufacturers will make available to

consumers, the model calculates the market share for ELs based on Eq. 9.4 and Eq. 9.5. Note that 

the logit value (defined in Eq. 9.5) for non- LED lamp options remains a constant value over the 

analysis period, as DOE assumes no price learning for non-LED lamp options. 

The first cost of the CFLK associated with any given lamp option is given by: 

PP CCCC = PPbbbbbbbb + PPll ll + PP ssssss

Eq. 9.8

Where:

PP CCCC = total CFLK price (2014$), 

PPbbbbbbbb = price of the CFLK not including sockets and lamps (2014$), 

PP ssssss = total price of sockets (2014$), and 

PPll ll = total price of lamps packaged with CFLK (2014$). 

The prices for the lamps, sockets, and remaining CFLK base are determined in the 

engineering analysis, chapter 5 of this NOPR TSD. 

The manufacturers of CFLKs have a multitude of ways to meet a standard while 

maintaining a fixed utility (in this case a suitable lumen output). In this analysis, DOE 

considered two possible scenarios to meet each EL: (1) replacing the packaged lamps in the 

baseline CFLK, and (2) changing the light kit fixture (including the number of sockets and/or 

base type) to meet the EL. DOE lacked the data necessary to calibrate the consumer-choice 

model to forecast how manufacturers and consumers will choose between these two scenarios. 

For ELs with two options, DOE split market share evenly between the lamp-replacement 

scenario and the CFLK replacement scenario such that each scenario would have 50% market 

share. For EL 2, current data indicate that a majority of the market share is in the CFL lamp 

replacement unit (EL 2a in Table 9.3.5). Accordingly, DOE apportioned a majority of the 

current market share in EL 2 to EL 2a. However, DOE splits market share that moves from EL 

2a to other ELs evenly between the two scenarios. 

9.3.6 Impact of GSL Energy Conservation Standards Rulemaking

In the preliminary analysis for the concurrent GSL energy conservation standards 

rulemaking, DOE considered lamps that have base types specified by ANSI, have a lumen 

output of at least 310 lumens, and are intended to serve in general lighting applications to meet 

the GSL definition. Therefore, DOE considers candelabra-base lamps that meet the lumen 

output and general application requirements to meet the GSL definition, which available 

information indicates would include all candelabra-base lamps currently packaged with 

CFLKs. All lamps that meet the GSL definition would be subject to the EPCA requirement
9-17 

prohibiting the sale of any GSL that does not meet a minimum efficacy standard of 45 lm/W if 

the concurrent GSL rulemaking is not completed by January 1, 2017, or if the energy savings 

of the GSL final rule are not greater than or equal to the savings from a minimum efficacy 

standard of 45 lumens per watt. 42 U.S.C. 6295(i)(6)(A)(v). Because GSILs are not included in 

the scope of the GSL rulemaking, DOE assumed that any GSL final rule would not yield 

sufficient energy savings to avoid triggering the 45 lm/W requirement in 2020. Accordingly, 

for this rulemaking, DOE has assumed in both the no-standard and the standards case shipment 

projections that candelabra-base lamps with efficacy below the minimum requirement of 45 

lm/W will no longer be an option available for packaging with CFLKs beginning January 1, 

2020. 

9.3.7 Summary

To summarize, the CFLK shipments model works in the following way: 

1. Total shipments are estimated using a stock-accounting methodology, which accounts for

shipments due to replacements, installation in new buildings, additional installations of

CFLKs in existing buildings, and reduction in housing stock from demolitions.

2. Until LED lamp options reach cost parity with available non-LED lamp options, the total

market share apportioned to LED lamp options is estimated using the LED incursion

curve. Market share between LED lamp options is determined from projections of the

average LED efficacy as a function of time.

3. The remaining market share is split between ELs that do not use LED technology as

determined by the modified consumer-choice model. The model assumes manufacturers

will choose the lowest price lamp option available on the market. Based on the impacts of

GSL rulemaking, after January 1, 2020, only those ELs that meet a minimum efficacy of

45 lm/W are apportioned market share.

4. Once LED lamp options are the lowest first-cost option available, all CFLKs are assumed

to be packaged with LEDs. Market share between LED lamp options is still determined

from projections of the average LED efficacy as a function of time.

5. Market share is divided between lamp-replacement and fixture-replacement scenarios.

Market share is split evenly between those ELs with two lamp options. For EL 2, market

share is initially apportioned to EL 2a and is split evenly as market share shifts out of that

lamp option.

9.4 IMPACT OF ENERGY CONSERVATION STANDARDS ON SHIPMENTS

DOE projects that appliance standards often result in an increase in the price of the 

product due to an incremental cost associated with efficiency improvements. Economic theory 

suggests that, all else being equal, an increase in the price of a normal good would lead to a 

decrease in demand for that good. DOE conducted a literature review and an analysis of 

appliance price and efficiency data to estimate the effects on product shipments from increases 

in product price. DOE also considered the decreases in operating costs from higher energy 

efficiency and changes over time in household income. Appendix 9A of this NOPR TSD 

provides a detailed explanation of the methodology DOE used to quantify the impacts of these 

variables on shipments. 
9-18 

In the literature, DOE found only a few studies of appliance markets that are relevant to 

the issue at hand. DOE identified no studies after 1980 that use time-series data of product price 

and shipments data to estimate a relative price elasticity of demand specific to appliances. The 

information that can be summarized from the literature suggests that the demand for appliances 

is price-inelastic. Other information in the literature suggests that appliances are a normal good, 

such that rising incomes increase the demand for appliances. Finally, the literature suggests that 

market behavior indicates relatively high “implicit discount rates” when comparing appliance 

prices and appliance operating costs. 

DOE found insufficient data on product purchase price and operating cost to perform a 

thorough analysis of dynamic changes in the appliance market. Instead, it used purchase price 

and efficiency data specific to residential refrigerators, clothes washers, and dishwashers over 

the period 1980–2002 to evaluate broad market trends and conduct simple regression analyses. 

These data indicate that there has been a rise in appliance shipments and a decline in appliance 

purchase price and operating costs over the time period. Household income has also risen during 

this time.

To simplify the analysis, DOE combined the available economic information into one 

variable, termed the relative price, and used this variable in an analysis of market trends, as 

well as to conduct a regression analysis. The relative price is defined with the following 

expression: 

RR = TT = PPPP + PPPP

Eq. 9.9 

Where:

RR = relative price,

TT = total price,

 = household income, 

PPPP = purchase price, and

PPPP = present value of operating cost.

DOE used an “implicit discount rate” of 37 percent to determine the present value of 

operating costs. This value is an average from values derived by several studies described in a 

1985 journal article. 

DOE’s regression analysis yields a relative price elasticity of demand, averaged over 

the three appliances, of -0.34. For example, a relative price increase of 10 percent results in a 

3.4 percent decrease in shipments. Because the relative price elasticity incorporates the 

impacts from three effects (i.e., purchase price, operating cost, and household income), the 

impact from any single effect is somewhat mitigated by changes from the other two effects. 

The relative price elasticity of -0.34 is consistent with estimates in the literature (see 

appendix 9A of this NOPR TSD for a summary). Nevertheless, DOE stresses that the measure 

is based on a small data set, using simple statistical analysis. More importantly, the measure is 

based on an assumption that economic variables, including purchase price, operating costs, 
9-19 

and household income, explain most of the trend in appliances per household in the United 

States since 1980. Changes in appliance quality and consumer preferences may have occurred 

during this period, but DOE did not account for them in this analysis. Despite these 

uncertainties, DOE believes that its estimate of the relative price elasticity of demand provides 

a reasonable assessment of the impact that purchase price, operating cost, and household 

income have on product shipments. 

DOE considers the relative price elasticity provided by the preceding analysis to be a 

short-run value. Because DOE’s projections of shipments and national impacts due to standards 

is over a 30-year time period, it needed to consider how the relative price elasticity is affected 

once a new standard takes effect. It was unable to identify sources specific to household durable 

goods, such as appliances, to indicate how short-run and long-run price elasticities differ. To 

estimate how the relative price elasticity changes over time, DOE relied on a study pertaining to 

automobiles. This study shows that the automobile price elasticity of demand changes in the 

years following a purchase price change. With increasing years after the purchase price change, 

the price elasticity becomes smaller (more inelastic) until it reaches a terminal value around the 

tenth year after the price change. Table 9.4.1 shows the relative change in the price elasticity of 

demand for automobiles over time. DOE developed a time series of relative price elasticities for 

home appliances based on the relative change in the automobile price elasticity of demand. For 

years not shown in the table, DOE performed a linear interpolation to obtain the relative price 

elasticity.

Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price Change

Years Following Price Change

1 2 3 5 10 20 

Change in 

Elasticity 

Relative to 1st

year

1.00 0.78 0.63 0.46 0.35 0.33 

Relative Price 

Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11 

9.4.1 Application of Relative Price Elasticity

DOE estimated shipments for each standard case using the relative price elasticities 

described in the previous section, along with the change in the relative price between a standard 

case and the no-standard case. Because CFLKs are predominantly sold with ceiling fans, the 

relative price reflects the combined price of a CFLK sold with the average price of a ceiling fan. 

The ceiling fan price was estimated using the engineering results, markups, and ceiling fan price 

trend for the base case published in the ceiling fan preliminary analysis energy conservation 

standards.7

Because household income is the same in each standard case and the no-standard case, it 

does not figure into the calculation of the change in the relative price. Note that, in the 

following equation, the relative price and the relative price elasticity are functions of the year 

because both change with time. 
9-20 

× (1 + RRRR(jj) ×

RRPPSSSSSS(jj) − RRPPBBBB BB(jj)

RRPPBBBB BB(jj) )

Eq. 9.10 

Where:

SShii SSSSSS (jj) = total shipments under the CFLK standards case in year jj, 

RR NN SSSSSS(jj) = units under the no CFLK standard case retired and replaced in year jj, 

NN NN SSSSSS(jj) = number of new construction installations under the no CFLK standard case in 

year jj, 

DDDD NN SSSSSS(jj) = number of replacement installations that did not occur due to demolitions 

of existing buildings in year jj, 

EEEENN SSSSSS(jj) = units installed in existing buildings under the no CFLK standard case in year 

jj, 

RRRR(jj) = relative price elasticity in year jj (equals -0.34 for year 1), 

RRPPSSSSSS(jj) = relative price of a ceiling fan and CFLK system after implementation of a CFLK 

standard level in year jj, and 

RR BBBB BB(jj) = average relative price of a ceiling fan and CFLK system in absence of CFLK

standard in year jj. 

In the case of CFLKs, DOE assumed consumers were only sensitive to purchase price 

and did not consider operating cost in its calculation of the relative price. In this case, only the 

purchase price contributes to the relative price. This is consistent with the assumption made for 

standard and hugger ceiling fans in the concurrent ceiling fan energy conservation standards 

rulemaking. 

9.5 AFFECTED STOCK

In addition to the projection of product shipments under both the no-standard case and 

the standards case, the affected stock is a key output of DOE’s shipments models. The affected 

stock (stock that is affected by a standards level) consists of those in-service units that are 

purchased in or after the year the standard has taken effect, as described by the following 

equation: 

AAAAAA SSSSSSSSSS(jj) = ∑ SShiiii(ii) × pp ss ss(aaaa = jj − ii) jj

ii=SS ddyyyy 

Eq. 9.11 

Where:

AAAAAA SSSSSSSSSS(jj) = affected stock of units of all vintages that are in service in year jj, 

SShiiii(ii) = shipments in year ii, 

pp ss ss(aaaa ) = probability a unit will survive to aaaa , 

aaaa = age of the units (years), and 

SSSSddyy = compliance year of the standard. 

For its analysis, DOE assumed that amended energy conservation standards will 

become effective in 2019. Thus, all appliances purchased starting in 2019 are affected by the 

standard level. 

SShiiiiSSSSSS(jj) = �RRiiRRNNOO SSSSSS(jj) + NNNNNNOO SSSSSS(jj) + EEEENNOO SSSSSS(jj) − DDDDDDNNOO SSSSSS(jj)�
9-21 

9.6 IMPACT OF A CEILING FAN STANDARD ON CFLK SHIPMENTS

DOE is concurrently pursuing an energy conservation standard rulemaking on ceiling 

fans. As CFLKs are primarily sold coupled to ceiling fans, a ceiling fan standard that increases 

the average price of a ceiling fan will increase the average price of a ceiling fan and CFLK 

system. This price increase will decrease the shipments of CFLKs in both a no-standard case and 

a standard case. DOE did not assume a standard on ceiling fans in its projections for CFLK 

shipments. DOE assumed no efficiency performance standard for ceiling fans in its CFLK NOPR

analyses because DOE has not yet proposed a ceiling fan standard. In any CFLK final rule, DOE 

will take into account the impact of a potential proposed ceiling fan standard on CFLK 

shipments and will consider taking comment on its revised analysis as appropriate. In the ECS 

NOPR for ceiling fans, DOE will consider the impact of the proposed CFLK standard in its

projections of ceiling fan shipments. 

9.7 ALTERNATIVE SHIPMENTS SCENARIOS

To estimate the range of potential impacts of an energy conservation standard for 

CFLKs, DOE developed two alternative shipments scenarios, a high- and a low-benefits 

scenario. In the high-benefits scenario DOE utilized projection from the AEO 2015 higheconomic growth scenario for the new housing starts rate and electricity prices. In the lowbenefits scenario, DOE utilized projections from the AEO 2015 low-economic growth scenario 

for new housing starts and electricity prices. Additionally DOE utilized a higher rate of decline 

for LED lamp prices in the low-benefits scenario. This scenario assumed that LED price 

learning will follow the trend recently observed for LED GSLs.8 A faster rate of decline for 

LED lamp prices yields lower benefits from a potential standard due to the faster adoption of 

LED CFLKs in the no-standard case, which results in consumers moving to CFLKs that 

already meet or exceed potential standards.
9-22 

Figure 9.7.1 Reference and High LED Learning Price Index, taken from the GSL 

Preliminary Analysis Base-Case Reference Scenario

9.8 SHIPMENTS PROJECTIONS 

This section presents the shipments projections for a standard set at each of the trial 

standard levels (TSLs) that DOE considered for CFLKs as well as the no-standard case. The 

TSLs developed by DOE correspond to enacting a standard at the efficacy associated with a 

given EL. These shipments are considered the reference scenario, and use the following 

assumptions: AEO 2015 reference projections for construction growth, reference LED incursion 

discussed in Section 9.3.4, and no ceiling fan standard. Figure 9.8.1 shows DOE’s projections 

for the total number of CFLK shipments in the reference scenario disaggregated by market 

segment in the absence of a CFLK standard. 

DOE generally expects that a standard on CFLKs that increases the average per unit 

price of a given product, which will lead to a decrease in shipments. Figure 9.8.2 shows DOE’s 

projections for shipments in the reference case. In some cases, the change in shipments between 

a trial standard case and the no-standard case is so small that it is indistinguishable on the 

figures. In that case, the higher standard level overwrites the lower standard level on the plot. 

Table 9.8.1 shows the cumulative CFLK shipments over the analysis period for the reference 

scenario for each TSL. 
9-23 

Figure 9.8.1 Total CFLK Shipments Projection in the No Standard Case by Market 

Segment in the Reference Scenario

Figure 9.8.2 CFLK Shipments Projection for all TSLs in the Reference Scenario
9-24 

Table 9.8.1 Cumulative CFLK Shipments from 2019-2048 (millions of units) from a 

CFLK Standard 

TSL

Cumulative 

Shipments (millions 

of units)

No Standard 596.7

1 596.6

2 596.6

3 596.5

4 596.6
9-25 

REFERENCES

1. U.S. Department of Energy- Office of Energy Efficiency and Renewable Energy. Notice

of Proposed Rulemaking Technical Support Document Energy Conservation Program for

Consumer Products and Certain Commercial and Industrial Equipment: General Service

Fluorescent Lamps and Incandescent Reflector Lamps. April 2014. Washington, D.C.

(Last accessed May 6, 2015.) http://www.regulations.gov/#!documentDetail;D=EERE2011-BT-STD-0006-0039.

2. Kantner, C. L. S., S. J. Young, S. M. Donovan, and K. Garbesi. Ceiling Fan and Ceiling

Fan Light Kit Use in the U.S.—Results of a Survey on Amazon Mechanical Turk. 2013.

Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6332E. (Last

accessed June 8, 2015.) http://www.escholarship.org/uc/item/3r67c1f9.

3. U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections

to 2040. 2014. Washington, D.C. Report No. DOE/EIA-0383(2014). (Last accessed June

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf.

4. U.S. Energy Information Administration. Annual Energy Outlook 2015 with Projections

to 2040. 2015. Washington, D.C. Report No. DOE/EIA-0383(2015). (Last accessed June

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2015).pdf.

5. Navigant Consulting, Inc. Energy Savings Potential of Solid-State Lighting in General

Illumination Applications. 2012. U.S. Department of Energy. (Last accessed June 10,

2015.) http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_energy-savingsreport_jan-2012.pdf.

6. Pacific Northwest National Laboratory. SSL Pricing and Efficacy Trend Analysis for

Utility Program Planning. 2013. Report No. PNNL-22908. (Last accessed June 15,

2015.) http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_trendanalysis_2013.pdf.

7. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. Energy

Conservation Program for Consumer Products: Preliminary Analysis Technical Support

Document: Energy Efficiency Standards for Consumer Products and Certain Commercial

and Industrial Equipment: Ceiling Fans. September 2014. Washington, D.C. (Last

accessed June 15, 2015.) http://www.regulations.gov/#!documentDetail;D=EERE-2012-

BT-STD-0045-0065.

8. Gerke, B., A. Ngo, A. Alstone, and K. Fisseha. The Evolving Price of Household LED

Lamps: Recent Trends and Historical Comparisons for the US Market. 2014. Lawrence

Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6854E. (Last accessed

June 15, 2015.) http://eetd.lbl.gov/publications/the-evolving-price-of-household-led-l.
10-i 

CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 ECONOMIC IMPACTS OF PRODUCT CLASS REDEFINITION ............................ 10-1

10.3 TRIAL STANDARD LEVEL DEFINITION ................................................................ 10-2

10.4 NATIONAL ENERGY SAVINGS ............................................................................... 10-2

10.4.1 Primary Energy Savings .................................................................................... 10-2

10.4.2 Full-Fuel-Cycle Energy Savings ........................................................................ 10-3

10.4.3 National Energy Savings Inputs......................................................................... 10-4

10.4.3.1 Shipments and Affected Stock ................................................................. 10-4

10.4.3.2 Average Unit Energy Consumption ......................................................... 10-4

10.4.3.3 Primary Energy Factors ............................................................................ 10-5

10.4.3.4 Full-Fuel Cycle Energy Factors ............................................................... 10-5

10.4.3.5 Rebound Rate Factor ................................................................................ 10-6

10.5 NATIONAL NET PRESENT VALUE ......................................................................... 10-6

10.5.1 Definition ........................................................................................................... 10-6

10.5.2 Net Present Value Inputs ................................................................................... 10-7

10.5.2.1 Total Annual Change in Installed Cost .................................................... 10-7

10.5.2.2 Total Annual Operating Cost Savings ...................................................... 10-8

10.5.2.3 Discount Factor ...................................................................................... 10-10

10.6 RESULTS .................................................................................................................... 10-10

10.6.1 National Energy Savings Results ..................................................................... 10-11

10.6.2 Net Present Value Results ................................................................................ 10-12

REFERENCES ........................................................................................................................ 10-14

LIST OF TABLES

Table 10.6.1 Reference Case Estimates of Cumulative Full-Fuel Cycle Energy 

Savings (quads) for CFLKs ....................................................................... 10-11

Table 10.6.2 Cumulative Net Present Value of Consumer Benefits for CFLKs at 3% 

and 7% Discount Rates .............................................................................. 10-12

Table 10.6.3 Cumulative Equipment Cost Increase of Consumer Benefits for 

CFLKs at 3% and 7% Discount Rate ......................................................... 10-13

Table 10.6.4 Cumulative Operating Cost Savings of Consumer Benefits for CFLKs 

at 3% and 7% Discount Rate ..................................................................... 10-13

LIST OF FIGURES

Figure 10.6.1 Annual FFC NES for CFLKs in the Reference Scenario (quads) ............. 10-11

Figure 10.6.2 Total NPV at 7% Discount for CFLKs in the Reference Scenario ............ 10-12
10-1 

CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method the U.S. Department of Energy (DOE) used to 

estimate national impacts of new energy conservation standards for ceiling fan light kits 

(CFLKs). To estimate those impacts, DOE calculated the quantity and net value to consumers of 

future national energy savings (NES) and the net present value (NPV) of consumer benefits 

resulting from various trial standard levels (TSLs). Results described include (1) national energy 

consumption and savings, (2) monetary value of national energy savings associated with each 

TSL, (3) change in total installed costs nationwide due to TSLs, and (4) the NPV of consumer 

benefits (which takes into account the value of operating cost savings and change in total 

installed costs from a potential standard). 

DOE determined both the NES and the NPV for all the TSLs considered for CFLKs. All 

calculations were performed using a Microsoft Excel spreadsheet model. The spreadsheet model, 

referred to as the national impact analysis (NIA) model, incorporates the calculations of NES 

and NPV, along with inputs from the shipments model described in chapter 9 of this notice of 

proposed rulemaking (NOPR) technical support document (TSD). Details and instructions for 

using the NIA model are provided in appendix 10A of this NOPR TSD. 

Chapter 9 of this NOPR TSD provides a detailed description of the shipments model 

DOE used to project future purchases of CFLKs through a stock-accounting approach coupled 

with a modified consumer-choice model. The model also estimates the impact of a CFLK 

standard on shipments based on sensitivities related to total installed cost, operating costs, and 

household income. 

10.2 ECONOMIC IMPACTS OF PRODUCT CLASS REDEFINITION

As described in chapter 3 of this NOPR TSD, the current energy conservation standards 

divide CFLKs into three product classes depending on the type of light socket used: the 

Medium-Screw Base product class, the Pin-Based Fluorescent product class, and the Other Base 

Type product class. In each case, the standards require that the CFLKs be packaged with lamps 

to fill all sockets. However, as described in chapter 3 of this NOPR TSD, DOE is proposing 

redefining CFLKs into a single product class. 

As required by law (42 U.S.C. 6295(o)(1)), DOE developed baselines for the new 

product class proposed in this rulemaking in a way that ensures that amended standards would 

not decrease the minimum required efficacy of any covered product. Because there are some

CFLKs currently on the market (CFLKs with low efficacy candelabra lamps in the Other Base 

Type product class) that are less efficacious than the redefined baseline, even a standard set at 

the new baseline would have energy and economic impacts with respect to the no-standard case

(i.e., in the absence of an energy conservation standards rulemaking that would amend the 

current standards). 
10-2 

10.3 TRIAL STANDARD LEVEL DEFINITION

The engineering analysis, chapter 5 of this NOPR TSD, DOE defines various 

efficacy levels for CFLKs. Because there is a single product class for all CFLKs, these 

efficacy levels correspond to the TSLs analyzed in the shipments and national impact 

analyses. 

10.4 NATIONAL ENERGY SAVINGS

This section describes DOE’s calculation of the NES for CFLKs for the TSLs considered 

in this NOPR analysis. In all cases, energy savings are calculated with respect to the no-standard 

case. As described in chapter 9 of this NOPR TSD, the shipments model projects a dynamically 

changing market in both the standards cases and the no-standard case, which accounts for an 

economically driven shift toward more efficient solid-state lighting throughout the analysis 

period. Additionally, the shipments analysis accounts for the impact of the expected triggering of 

the EISA 2007 backstop requirement, which would impact candelabra-based lamps above 310 

lumens. As described in chapter 9 of this NOPR TSD, DOE assumed a 45 lumen/W minimum 

efficacy requirement for candelabra-based general service lamps starting in January 1, 2020, and 

that candelabra-based lamps with efficacy below 45 lumen/W would not be available for 

packaging with CFLKs (or as replacements for the initially-packaged lamps) in both the nostandard and standards cases thereafter. 

10.4.1 Primary Energy Savings 

The annual NES quantifies the difference in energy use between the no-standard case and 

a higher TSL in a given year: 

NNNNSSyy = �AA CCnnnn ssssss,yy − AA CCssssss,yy� × RRRR

Eq. 10.1

Where:

NNNNSSyy = national energy savings in year yy (quads), 

AA CCnnnn ssssss,yy = annual national energy consumption in the no-standard case at the power plant 

for all affected stock in year yy (quads), 

AA CCssssss,yy = standards case annual national energy consumption at the power plant for all 

affected stock in year yy (quads), and 

RRRR = rebound rate factor = 1- rebound rate. 

Positive values of NNNN correspond to a net energy savings following a standards 

implementation, or, in other words, annual energy consumption (AA ) in the standards case is 

less than AA in the no-standard case. 

The cumulative national energy savings is the sum over the analysis period of each NNNNSSyy

for all affected stock. The analysis tracks the energy use of the stock shipped during the analysis 

period until the last unit is retired in 2077. The cumulative NES is computed as follows:
10-3 

NNNNSScccccc = �NNNNSSyy

yy

Eq. 10.2 

Where:

NNNNSScccccc = cumulative national energy savings (quads). 

DOE calculated the AA of the affected stock as follows:

AA CCyy = ��SSSSSS KKvv × UU CCvv

vv

� × SSPPyy

Eq. 10.3

Where:

SSSSSS KKvv = affected stock of vintage vv (number of units), 

UU CCvv = unit energy consumption of vintage vv, given by Eq. 10.6 (kWh/unit), and

SSPPyy = time variable site-to-power-plant energy consumed at the power plant (quads/kWh). 

10.4.2 Full-Fuel-Cycle Energy Savings 

DOE has historically presented the NES computed based on the primary energy savings 

at the power plant, as described in the previous section. Per DOE’s 2011 Statement of Policy for 

Adopting Full-Fuel-Cycle Analyses, DOE now uses full-fuel-cycle (FFC) measures of energy 

use and emissions in its energy conservation standards analyses.1 In addition to accounting for 

the primary energy used at the power plant to supply electricity to the site of use, and 

transmission and distribution losses, the FFC analysis also accounts for energy consumed upstream of the power plant in extracting, processing, and transporting or distributing the primary 

fuels to the power plant, as described in appendix 10B of this NOPR TSD. 

As shown in Eq. 10.4, the FFC national energy savings NNNNSSFFFFFF are obtained by 

multiplying the primary energy savings (Eq. 10.1) by the FFC multiplier computed for the same 

year, μμyy. 
10-4 

NNNNSSFFFFFF,yy = �AA CCnnnn ssssss,yy − AA CCssssss,yy� × RRRR × μμyy

Eq. 10.4

Where:

μμyy = the FFC multiplier in year yy, and 

all other variables as defined in Eq. 10.1. 

The cumulative FFC energy savings is calculated by summing the annual FFC energy 

saving from 2019-2077. 

NNNNSSFFFFFF,cccccc = �NNNNSSFFFFFF,yy

yy

Eq. 10.5 

10.4.3 National Energy Savings Inputs 

As described in the preceding sections, the following inputs are used for calculating the 

national energy savings: 

• shipments and stock shipped during the analysis period,

• average unit energy consumption (UEC),

• site-to-power-plant conversion factor (SP),

• primary energy to FFC multipliers (μ), and

• rebound rate factor (RF).

These inputs are further discussed in the following sections. 

10.4.3.1 Shipments and Affected Stock

The shipments model, described in chapter 9 of this NOPR TSD, calculated the 

shipments of CFLKs and market share distribution among representative units and efficacy 

levels for different scenarios, for each year of the 30-year (2019-2048) shipments analysis. Based 

on that analysis, the model developed a description of the stock affected by the rulemaking. 

10.4.3.2 Average Unit Energy Consumption 

For the NOPR analysis DOE calculated the UEC for CFLKs at each efficacy level using: 

(1) input power values from representative lamp units developed in the engineering analysis 

scaled to the market-weighted average lumen output of CFLKs, (2) hours of use calculated in 

chapter 6 of this NOPR TSD, and (3) a scaling factor to account of the effects of dimming on the 

input power. Therefore, as discussed in chapter 6 of this NOPR TSD, the UEC is given by: 
10-5 

UU CCpp,ss(yy) = IIIIIIIIII PPPPPPPPrrpp(yy) × HH UUss × (1 − ff dd dddd × ff eeee )

Eq. 10.6

Where:

UU CCpp,ss(yy) = annual energy consumption of product in year yy in sector ss (kWh/year), 

HH ss = operating hours (hours of use) in sector ss, and 

IIIIIIIIII PPPPPPPPrrpp(yy) = input power of product in year yy (kW)

ff dd dddd= the fraction of lamps estimated to be operating on dimmers, and 

ff eeee = a parameter describing the effect of dimmers on energy consumption, i.e., the 

reduction on energy use due to dimmers, taken to be 0.3 

The average input power of a CFLK may be time-dependent if the lamps packaged with 

the CFLK need to be replaced before the CFLK is retired. In this case, DOE assumes that CFLK 

owners will replace the failed lamp with a lamp chosen from possible lamp replacement options 

in the lighting market that provide a similar lumen output. The selection of a replacement lamp is 

determined using a consumer-choice model, following the methodology developed for the 

General Service Lamps (GSLs) energy conservation standards preliminary analysis. This model 

used sector specific parameters for consumer sensitivities to first cost, lamp lifetime, energy 

savings compared to an incandescent, and mercury content, for more information on this model 

see chapter 9 of the GSL preliminary TSD.

2 See chapter 8 of this NOPR for details on the 

replacement lamps available for each EL.

10.4.3.3 Primary Energy Factors 

For electricity use, the conversion from site kWh to power plant primary million Btu uses 

a marginal heat rate factor that accounts for losses associated with the generation, transmission,

and distribution of electricity. DOE derived these marginal factors using data published with the 

Energy Information Administration (EIA’s) Annual Energy Outlook 2014 (AEO 2014),3

following the methodology outlined in appendix 15A of this NOPR TSD. The factors depend on 

the sector and end-use, and also vary with time due to changes in the mix of fuels used for 

electric power generation. 

10.4.3.4 Full-Fuel Cycle Energy Factors

The full-fuel-cycle energy use is equal to the primary energy use plus the energy 

consumed "upstream" of the site in the extraction, processing and distribution of fuels. The FFC 

energy use was calculated by applying a fuel-specific FFC energy multiplier to the primary 

energy use. DOE developed FFC multipliers using the data and projections generated by the 

National Energy Modeling System (NEMS) used for AEO 2014. The AEO provides 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. The multiplier for electricity 
10-6 

represents the energy needed to produce and deliver the fuels that are consumed in electricity

generation. The multipliers are dimensionless numbers that express the upstream energy use as a 

percentage of the primary energy use. As with the NES, DOE calculated cumulative, national 

level energy savings in the full-fuel-cycle metric by calculating the difference relative to the nostandard case and summing over the analysis period. The method used to calculate FFC energy 

multipliers and the values for various fuels and end uses are described in appendix 10B of this 

NOPR TSD. 

10.4.3.5 Rebound Rate Factor

A rebound effect accounts for increased usage of an appliance by consumers after the 

implementation of a standard, reducing the energy savings attributed to a standard. DOE 

generally accounts for the direct rebound effect in its estimates of the NES. Based on the 

discussion of the rebound rate for general service fluorescent lamp/incandescent reflector lamp 

(GSFL/IRL)

a and feedback from manufacturers, DOE assumed a rebound rate of 0 for CFLKs in 

its reference case. The rebound rate factor is as an adjustable input in the NIA spreadsheet. 

10.5 NATIONAL NET PRESENT VALUE

10.5.1 Definition

The NPV is the difference between the present value of savings (PVS) in operating cost 

resulting from the use of more-efficient appliances under the standard and the present value of 

the change in total installed costs (PVC) of those appliances. The NPV is described by the 

following equation: 

NN NN = PP − PP

Eq. 10.7

Where:

PP = present value of savings in operating cost (2014$) for all CFLKs shipped during the 

analysis period, and 

PP = present value of change in total installed cost (including costs for equipment, 

installation, and disposal in 2014$) for all CFLKs shipped during the analysis period. 

Under this definition, reductions in operating costs would contribute positively to the 

NN NN, while increases in total installed costs would contribute negatively to the NN NN. 

DOE determined the PP and PP according to the following expressions: 

PP = � SSyy × DD yy

yy

a

 See appendix 10C: Rebound Rate of the GSFL/IRL energy conservation rulemaking.
10-7 

Eq. 10.8

PP = � IIyy × DD yy

yy

Eq. 10.9

Where:

SSyy = the total operating cost savings from the affected stock in year yy, relative to the nostandard case (2014$), 

IIyy = total equipment cost increase (negative values indicate a reduction in equipment 

costs) for shipments in year yy, relative to the no-standard case (2014$), and 

DD yy = discount factor in each year (defined in section 10.4.3.5). 

DOE calculated PP from 2019 to 2048 (the duration of the shipments analysis period) 

and PP from 2019 to 2077 (to account for the full lifetime of all affected stock). The 

contributions to PP and PP for each year were discounted to 2015. The following sections 

describe how DOE derives the variables in Eq. 10.8 and Eq. 10.9. 

10.5.2 Net Present Value Inputs

The inputs to DOE’s calculation of the NN NN of costs and savings for the nation are: 

• total annual installed cost ( ) in the no-standard case and the standards cases,

• total annual operating cost ( ) for the affected stock in the no-standard case and the

standards cases, and

• discount factor (DD ).

The computation of these inputs from the outputs of upstream analyses is detailed in the

following sections. 

10.5.2.1 Total Annual Change in Installed Cost

DOE calculated the total annual change in equipment cost of using more efficient CFLKs

in the standards cases relative to the no-standard case as the difference between the in the

standards case and the no-standard case:

IIyy = CCssssss,yy − CCnnnn ssssss,yy

Eq. 10.10

Where:

CCssssss,yy = the total installed cost of equipment shipped in year yy in the standards case 

(2014$), and 

CCnnnn ssssss,yy = the total installed cost of equipment shipped in year yy in the no-standard case 

(2014$). 
10-8 

Due to the higher costs generally associated with more-efficient products in the standards 

case versus the no-standard case, cost increases appear as negative values in the NN NN

calculation. In this rulemaking, the cost of a more efficient CFLK can be less than the cost of a 

less efficient option leading to a negative ECI associated with a standard. In this case, the CC is 

a positive contribution to the NN NN. 

For CFLKs, the per-unit installed cost only includes the first cost of the CFLK. DOE 

assumed the installation cost of a CFLK was attributed to the installation cost for a ceiling fan 

and, therefore, is not included in this analysis. Therefore, the is given by the following 

equation: 

= � ss ,yy × ,yy

Eq. 10.11 

Where:

ss ,yy = total shipments of CFLK option ii in year y (number of units), and 

,yy = purchase price of CFLK option ii in year y, including corrections for learning 

(2014$/unit). 

The CFLK shipments and purchase prices referenced in Eq. 10.11 are described in 

chapter 9 of this NOPR TSD. 

10.5.2.2 Total Annual Operating Cost Savings 

DOE expresses savings in operating costs as cost reductions associated with the lower 

energy consumption of products shipped in the standards cases compared to the no-standard

case. DOE calculated the total annual operating cost savings as the difference between the 

in the standards cases and the no-standard case. As for the computation of the NNNN , the 

needs to be calculated for the affected stock:

SSyy = CCnnnn ssssss,yy − CCssssss,yy

Eq. 10.12 

Where:

SSyy = the total operating cost saving of the affected stock in year yy (2014$), 

CCbbbb ,yy = the total operating cost of the affected stock in year yy in the no-standard case 

(2014$), and 

CCssssss,yy = the total operating cost of the affected stock in year yy in a standard case (2014$). 

For CFLKs, the annual operating cost is primarily the cost of electricity consumption. 

However, CFLK owners also incur a cost for any lamp replacement of failed lamps that occurs 

during the lifetime of the CFLK. The replacement lamp may also have a different wattage than 

the original lamp packaged with the CFLK, leading to different operating costs. As described in 

10.4.3.2, DOE accounted for relamping in its model using a consumer-choice model originally 

developed for the General Service Lamps (GSL) Preliminary Analysis.

2 The options available 

for lamp replacement are drawn from lamps with the same base and similar lumen output as the 
10-9 

lamp packaged with the CFLK. See chapter 8 of this NOPR TSD for more discussion regarding 

how DOE calculated the impacts to energy use and operating and maintenance costs due to 

relamping. 

The model credits CFLK owners for any residual life left in the lamp when the CFLK

fails. DOE derived a weighted-average residual lamp value for each CFLK based on the lamps 

included with the CFLK at time of purchase, subsequent lamps purchased to replace the initial 

lamps, and the vintage of the CFLK in the following way: DOE first calculated the average 

residual lamp value for a CFLK that fails for each year in the full range of lifetime values 

between 1 to 30 years. DOE then computed the weighted-average residual value using the 

retirement probability function as weights for each age. DOE then attributed the weightedaverage residual value as a savings 14 years (average lifetime of a CFLK) after the CFLK was 

shipped. Note that the residual value is a negative value in the context of the total operating cost. 

As described in the life-cycle cost (LCC) and payback period (PBP) analysis (chapter 8 

of this NOPR TSD), DOE calculated total annual operating costs based on national weightedaverage electricity prices. The national electricity price for 2014 was based on the average 2014 

marginal price of electricity associated with consumers in the LCC Monte Carlo purchaser 

sample. The LCC purchaser sample accounts for the geographic distribution of CFLK owners 

which may potentially differ from a population-weighted average electricity price. DOE then 

used the reference-case, sector-specific electricity price projections from AEO 20154 to establish 

the electricity price trend over the course of the analysis period. As AEO 2015 only projects 

prices through 2040, electricity prices in later years were extrapolated based on the trend from

2030-2040 and projected through 2077 for each sector. DOE performed sensitivity analyses 

using the high- and low-economic growth scenarios from AEO 2015 to establish electricity price

trends. 

DOE calculated annual for CFLKs by summing over the operating costs of all 

affected stock in the residential and commercial sectors, according to the following expression: 

CCyy = EE,yy × � SS ,yy × UU CC ,yy + �RR LL ,yy −�RR ,yy

Eq. 10.13 

Where:

CCyy = annual total operating cost in year yy for all affected stock of CFLKs (2014$), 

EE,yy = the price of electricity, in year yy, as forecasted using price trends from the AEO2015

reference scenario (2014$/kWh), 

SS ,yy = the affected stock of CFLKs in year yy (number of units), 

UU CC ,yy = the average unit energy consumption of a CFLK of type ii in year yy, as computed 

using Eq. 10.6 (kWh/unit), 

RR LL ,yy = the average relamping replacement costs for CFLK type ii in year yy (2014$), and 

RR ss ,yy = the residual value of lamps remaining in retired CFLKs of type ii in year yy (2014$). 

 
10-10 

The stock of a product depends on annual shipments and the lifetime of the product. DOE 

calculates the total stock of products by integrating historical shipments beginning with the start 

year of the historical data. New units are then added to the stock to replace older units that are 

retired and to account for growth in demand. As described in chapter 9 of this NOPR TSD, DOE 

projected product shipments under the no-standard case and the standards cases. 

In principle, the rebound effect also affects the energy costs to consumers in the standards 

case. However, the take-back in energy consumption associated with the rebound effect provides 

consumers with increased value. As described in the 2012 residential furnace fan preliminary 

analysis TSD,5 DOE believes that, if it were able to monetize the increased value to consumers 

of a rebound effect, this value would be similar in value to the forgone energy savings. 

Therefore, the economic impacts on consumers with or without the rebound effect are the same, 

so DOE does not adjust operating cost savings in the NIA based on rebound. 

10.5.2.3 Discount Factor

DOE multiplied monetary values in future years by a discount factor (DF) to determine 

the present value. The DF is described by the equation: 

DD = 1

(1 + rr)�yy−yypp�

Eq. 10.14 

Where:

rr = discount rate, 

yy = year of the monetary value, and 

yypp = year for which the present value is being determined. 

DOE estimated NPV using both a 3-percent and a 7-percent real discount rate, in 

accordance with the Office of Management and Budget’s guidance to Federal agencies on the 

development of regulatory analysis, particularly section E therein: Identifying and Measuring 

Benefits and Costs.

6 DOE defined the present year as 2015. 

10.6 RESULTS

This section presents the results of the NIA for CFLKs for the reference case. As 

discussed in chapter 9 of this NOPR TSD, DOE is concurrently pursing an energy conservation 

standard on ceiling fans, which could affect shipments of CFLKs. DOE assumed no efficiency 

performance standard for ceiling fans in its NOPR analyses because DOE has not yet proposed a 

ceiling fan standard. In any CFLK final rule, DOE will take into account the impact of a 

potential proposed ceiling fan standard on CFLK shipments and will consider taking comment 

on its revised analysis as appropriate. 

DOE conducted high- and low-benefits sensitivity scenarios to assess the impact of using 

different AEO 2015 growth scenarios and a different assumption for the price learning associated 
10-11 

with learning rate for solid-state lighting technologies. Results for the sensitivity scenarios 

are presented in appendix 10C of this NOPR TSD and are available in the published NIA 

spreadsheet. 

10.6.1 National Energy Savings Results

Table 10.6.1 shows the cumulative full-fuel cycle NES for the reference case which 

assumes no ceiling fan standard. 

Table 10.6.1 Reference Case Estimates of Cumulative Full-Fuel Cycle Energy Savings 

(quads) for CFLKs 

TSL FFC NES

(quads)

1 0.008

2 0.049

3 0.068

4 0.070

Figure 10.6.1 Annual FFC NES for CFLKs in the Reference Scenario (quads)

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

FFC Energy Savings (quads)

20192022202520282031203420372040204320462049205220552058206120642067207020732076

Year

TSL 1

TSL 2

TSL 3

TSL 4
10-12 

10.6.2 Net Present Value Results

Table 10.6.2 gives the results for cumulative net present value for CFLKs at 3-percent 

and 7-percent discount rates for the reference scenario. Figure 10.6.2 illustrates NPV using a 7-

percent discount rate over the analysis period. 

Table 10.6.2 Cumulative Net Present Value of Consumer Benefits for CFLKs at 3% and 

7% Discount Rates 

TSL

NPV 

3% discount 

(billion 2014$)

NPV 

7% discount 

(billion 2014$)

1 0.210 0.209

2 0.660 0.501

3 0.946 0.695

4 0.967 0.708

Figure 10.6.2 Total NPV at 7% Discount for CFLKs in the Reference Scenario

Table 10.6.3 give results for the cumulative equipment cost increase for CFLKs at 3-

percent and 7-percent discount rates. 

-0.05

0

0.05

0.1

0.15

0.2

20192022202520282031203420372040204320462049205220552058206120642067207020732076

NPV @ 7% (billion 2014$)

Year

TSL 1

TSL 2

TSL 3

TSL 4
10-13 

Table 10.6.3 Cumulative Equipment Cost Increase of Consumer Benefits for CFLKs at 

3% and 7% Discount Rate 

TSL

Equipment Cost Increase 

at 3% discount

(billion 2014$) 

Equipment Cost Increase 

at 7% discount

(billion 2014$) 

1 0.054 0.046

2 0.072 0.061

3 -0.034 -0.017

4 -0.042 -0.022

Table 10.6.4 gives the cumulative operating cost savings for CFLKs at 3-percent and 7-

percent discount rates. 

Table 10.6.4 Cumulative Operating Cost Savings of Consumer Benefits for CFLKs at 3% 

and 7% Discount Rate

TSL

Operating Cost Savings at 

3% discount

(billion 2014$) 

Operating Cost Savings at 

7% discount

(billion 2014$) 

1 0.264 0.255

2 0.731 0.562

3 0.913 0.678

4 0.926 0.686
10-14 

REFERENCES

1. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. Federal 

Register. August 18, 2011. vol. 76, no. 160: pp. 51245–51868. (Last accessed June 10, 

2015.) http://digital.library.unt.edu/ark:/67531/metadc52337/m1/44/. 

2. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. 

Preliminary Technical Support Document: Energy Efficiency Program for Consumer 

Products and Commercial and Industrial Equipment: General Service Lamps. December 

2014. Washington, D.C. (Last accessed June 15, 2015.) 

http://www.regulations.gov/#!documentDetail;D=EERE-2013-BT-STD-0051-0022. 

3. U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections 

to 2040. 2014. Washington, D.C. Report No. DOE/EIA-0383(2014). (Last accessed June 

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf. 

4. U.S. Energy Information Administration. Annual Energy Outlook 2015 with Projections 

to 2040. 2015. Washington, D.C. Report No. DOE/EIA-0383(2015). (Last accessed June 

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2015).pdf. 

5. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. Energy 

Conservation Program for Consumer Products: Preliminary Analysis Technical Support 

Document: Energy Efficiency Standards for Consumer Products and Certain Commercial 

and Industrial Equipment: Residential Furnace Fan. July 2012. Washington, D.C. (Last 

accessed June 4, 2015.) http://www.regulations.gov/#!documentDetail;D=EERE-2010-

BT-STD-0011-0037. 

6. U.S. Office of Management and Budget. Circular No. A-4, Regulatory Analysis. 2003. 

Washington, D.C. (Last accessed June 15, 2015.) 

http://www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a-4.pdf. 
11-i 

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 SUBGROUP DEFINITIONS ........................................................................................ 11-1

11.2.1 Low-Income Households ................................................................................... 11-1

11.2.1.1 Distribution of Subgroup Households with CFLKs ................................. 11-1

11.2.2 Small Businesses ................................................................................................ 11-2

11.3 RESULTS ...................................................................................................................... 11-4

REFERENCES .......................................................................................................................... 11-7

LIST OF TABLES

Table 11.2.1 Household Population Data for CFLKs ....................................................... 11-1

Table 11.2.2 Size Premium by Year ................................................................................. 11-3

Table 11.2.3 Discount Rate Difference between Small Company and Sector 

Average ........................................................................................................ 11-4

Table 11.3.1 LCC and PBP Results for Low-Income Households ................................... 11-5

Table 11.3.2 Comparison of Average LCC Savings and PBP Results for Consumer 

Subgroups and All Households in the Residential Sector ........................... 11-5

Table 11.3.3 LCC and PBP Results for Small Businesses................................................ 11-5

Table 11.3.4 Comparison of Average LCC Savings and PBP Results for Consumer 

Subgroups and All Buildings in the Commercial Sector ............................. 11-6
11-1 

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

The consumer subgroup analysis evaluates impacts on groups or consumers who may be 

disproportionately affected by any national energy conservation standard. The U.S. Department 

of Energy (DOE) evaluates impacts on particular subgroups of consumers by analyzing the lifecycle cost (LCC) impacts and payback period (PBP) for those consumers from the proposed 

efficacy levels. 

DOE determined the impact on ceiling fan light kit (CFLK) consumer subgroups based 

on a subset of the CFLK consumer samples from the residential and commercial sectors and 

the methodology discussed in chapter 8 of this notice of proposed rulemaking (NOPR) 

technical support document (TSD). DOE evaluated the impacts of the proposed efficacy levels 

for CFLKs on low-income households and small businesses for the residential and commercial 

sector, respectively. This chapter describes the subgroup identification in further detail and 

gives the results of the LCC and PBP analyses for the considered subgroups. 

11.2 SUBGROUP DEFINITIONS

11.2.1 Low-Income Households

As defined in the Energy Information Administration’s 2009 Residential Energy 

Consumption Survey (RECS 2009) survey1, low-income households are those at or below the 

“poverty line.” The poverty line varies with household size, head of household age, and family 

income. RECS includes a group of households with incomes below the poverty level in 2009 as 

defined by the U.S. Bureau of the Census2

. The RECS survey classifies approximately 15 

percent of U.S. households as low-income.

11.2.1.1 Distribution of Subgroup Households with CFLKs

Of the 12,083 households in RECS 2009, 8,762 have ceiling fans. In selecting the set of 

households to include in the LCC sample for the subgroup analysis, DOE assumes that all 

households with ceiling fans have CFLKs. Table 11.2.1 shows the household sample sizes for 

CFLKs. 

Table 11.2.1 Household Population Data for CFLKs

No. of Records

General Population 8,762

Low-Income Households 960
11-2 

11.2.2 Small Businesses

The Small Business Administration (SBA) defines a small business by its annual receipts 

or its number of employees. DOE assumed that CFLKs are used by small businesses with the 

same distribution of applications and sectors as all CFLKs used in the commercial sector, so 

DOE did not assign a different distribution of CFLK applications or sectors of the economy to 

this subgroup. 

The capital asset pricing model (CAPM) underestimates the cost of capital for small 

companies. In CAPM, the risk premium β is used to account for the higher returns associated 

with greater risk. However, for small companies, particularly very small companies, historic 

returns have been significantly higher than the CAPM equation predicts. This additional return 

can be accounted for by adding a size premium to the cost of equity for small firms: 

Rk ( ) ERP S fe β +×+=

Where:

ke = Cost of equity, 

Rf = Expected return on risk-free assets,

β = Risk coefficient of the firm, 

ERP = Equity risk premium, and 

S = Size Premium.

DOE obtained size premium data from Ibbotson Associates’ Stocks, Bonds, Bills, and 

Inflation Yearbook (Table 11.2.2).3 For each year, the size premium is the historical average 

difference in performance between small companies and the market average. For example, for 

the period of 1926-2008, the average size premium for the smallest companies in all industries is 

3.74%, implying that on average, historic performance of small companies was 3.74% higher 

than the CAPM estimate of the small company cost of equity over this period. 
11-3 

Table 11.2.2 Size Premium by Year

Year

Maximum Market 

Capitalization ($mil)

Size Premium 

(“Microcap” Companies)

a

2004 505 4.02%

2005 586 3.95%

2006 627 3.88%

2007 723 3.65%

2008 456 3.74%

2009 431 3.99%

2010 478 4.07%

2011 423 3.89%

2012 514 3.81%

2013b 514 3.81%

 DOE calculated the real weighted-average cost of capital (as described in chapter 8) 

using the cost of equity including a size premium for small companies instead of the CAPM cost 

of equity.4 Table 11.2.3 presents DOE’s estimates of the discount rates for entire sectors, small 

companies specifically, and the small company discount rate premium. To estimate the impact of 

standards specifically on small businesses, DOE applied the small company discount rate 

distributions for each sector in the LCC and PBP calculation, instead of the distributions 

described in chapter 8 of this NOPR TSD. 

 

a “Microcap” companies are defined as companies with market capitalization in the 9th decile or lower (i.e., the 

smallest two deciles of companies). DOE uses the microcap size premium as it results in a conservative estimate of 

small company NPV of energy cost savings. 

b As data were not yet available for 2013, DOE applies the 2012 size premium to WACC calculations.
11-4 

Table 11.2.3 Discount Rate Difference between Small Company and Sector Average

Sector

Discount Rate

Average Standard

Deviation

Small Company 

Discount Rate Premium

Retail

Entire Sector 5.00% 1.07%

2.21%

Small Companies 7.21% 2.06%

Medical 

Services

Entire Sector 4.97% 0.92%

2.72%

Small Companies 7.69% 1.88%

Lodging

Entire Sector 5.96% 1.65%

1.19%

Small Companies 7.15% 2.14%

Food Service

Entire Sector 4.90% 0.95%

2.59%

Small Companies 7.49% 1.86%

Office 

Entire Sector 5.08% 1.28%

1.46%

Small Companies 6.54% 2.10%

Other

Entire Sector 5.04% 1.07%

1.97%

Small Companies 7.01% 2.44%

11.3 RESULTS

Table 11.3.1 and Table 11.3.3 summarize the LCC and PBP results for low-income 

households and small business subgroups, respectively. Table 11.3.2 and Table 11.3.4 compare 

results from the sample of subgroup consumers to the results for the total sample of consumers

used in the overall LCC analysis. LCC results are provided by efficacy level and LCC savings

are provided by trial standard level (TSL). Note that the impact on small businesses may be 

overestimated; many small buildings may be owned by the same company, as is the case with 

retail and restaurant chains. 
11-5 

Table 11.3.1 LCC and PBP Results for Low-Income Households

EL

Average Costs (2014$) Simple 

Payback

(years) 

Average 

Lifetime

(years) Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

SubBaseline 2.8 17.3 69.5 70.4 -- 13.8

Baseline 5.5 3.5 39.6 44.8 0.2 13.8

1 8.9 3.4 39.2 47.6 0.4 13.8

2 19.4 2.9 32.7 51.1 1.2 13.8

3 10.6 2.0 22.9 32.3 0.5 13.8

4 9.3 1.9 21.5 29.8 0.4 13.8

Table 11.3.2 Comparison of Average LCC Savings and PBP Results for Consumer 

Subgroups and All Households in the Residential Sector

TSL

Average LCC Savings

2014$

Payback Period

years

All Low-Income All Low Income

0 23.0 23.0 0.2 0.2

1 23.0 23.0 0.4 0.4

2 24.3 24.1 1.2 1.2

3 30.9 30.6 0.5 0.5

4 30.9 30.7 0.4 0.4

Table 11.3.3 LCC and PBP Results for Small Businesses

EL

Average Costs (2014$) Simple 

Payback

(years) 

Average 

Lifetime

(years) Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

SubBaseline 2.8 76.9 182.5 184.8 -- 13.8

Baseline 5.5 15.8 124.8 130.9 0.0 13.8

1 8.8 14.9 142.6 152.7 0.1 13.8

2 19.4 12.8 127.4 147.2 0.3 13.8

3 10.5 9.0 97.1 107.3 0.1 13.8

4 9.3 8.5 94.4 103.4 0.1 13.8
11-6 

Table 11.3.4 Comparison of Average LCC Savings and PBP Results for Consumer 

Subgroups and All Buildings in the Commercial Sector

TSL

Average LCC Savings

(2014$)

Simple Payback Period

(years)

All Small Businesses All Small Businesses

0 28.7 31.7 0.0 0.0

1 28.7 31.7 0.1 0.1

2 53.4 51.9 0.3 0.3

3 67.7 65.4 0.1 0.1

4 67.8 65.5 0.1 0.1
11-7 

REFERENCES

1. U.S. Department of Energy–Energy Information Administration. 2009 RECS Survey 

Data. (Last accessed June 9, 2015.) 

http://www.eia.gov/consumption/residential/data/2009/. 

2. U.S. Department of Commerce - Bureau of the Census. Poverty Thresholds 2009 - U.S 

Census Bureau. (Last accessed June 10, 2015.) 

http://www.census.gov/hhes/www/poverty/data/threshld/thresh09.html. 

3. Ibbotson Associates. Stocks, Bonds, Bills, and Inflation Valuation Edition Yearbook. 

2004. Morningstar, Inc.: Chicago, IL.

4. Damodaran, A. Cost of Capital by Sector. January 2014. (Last accessed June 15, 2015.) 

http://people.stern.nyu.edu/adamodar/New_Home_Page/datafile/wacc.htm. 
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 Analysis and Interview Guide ............................ 12-2

12.2.2.1 Industry Cash-Flow Analysis ....................................................................... 12-2

12.2.2.2 Interview Guides .......................................................................................... 12-3

12.2.3 Phase III: Subgroup Analysis............................................................................. 12-3

12.2.3.1 Manufacturing Interviews ............................................................................ 12-3

12.2.3.2 Revised Industry Cash-Flow Analysis ......................................................... 12-4

12.2.3.3 Small Business Subgroup ............................................................................ 12-4

12.2.3.4 Manufacturing Capacity Impact .................................................................. 12-5

12.2.3.5 Employment Impact ..................................................................................... 12-5

12.2.3.6 Cumulative Regulatory Burden ................................................................... 12-5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES .......................................... 12-6

12.3.1 Duplicative Regulation ...................................................................................... 12-6

12.3.2 Shift to Air Conditioning ................................................................................... 12-6

12.4 GRIM INPUTS AND ASSUMPTIONS ........................................................................ 12-6

12.4.1 Overview of the GRIM ...................................................................................... 12-6

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 .............................................................................. 12-9

12.4.3 Financial Parameters .......................................................................................... 12-9

12.4.4 Corporate Discount Rate .................................................................................. 12-10

12.4.5 Trial Standard Levels ....................................................................................... 12-12

12.4.6 National Impact Analysis Shipment Forecast .................................................. 12-12

12.4.7 Production Costs .............................................................................................. 12-13

12.4.8 Capital and Product Conversion Costs ............................................................ 12-14

12.4.9 Markup Scenarios ............................................................................................ 12-15

12.4.9.1 Preservation of Gross Margin Markup Scenario ....................................... 12-15

12.4.9.2 Two-Tiered Markup Scenario .................................................................... 12-15

12.5 INDUSTRY FINANCIAL IMPACTS ........................................................................ 12-16

12.5.1 Impacts on Industry Net Present Value ........................................................... 12-16

12.5.2 Impacts on Annual Cash Flow ......................................................................... 12-18

12.6 IMPACTS ON MANUFACTURER SUBGROUPS ................................................... 12-20

12.6.1 Impacts on Small Business Manufacturers ...................................................... 12-20

12.6.1.1 Description and Estimated Number of Small Entities Regulated .............. 12-20

12.6.1.2 Description and Estimate of Compliance Requirements ........................... 12-21
12-ii

12.6.2 Employment.....................................................................................................12-22

12.6.3 Production Capacity.........................................................................................12-22

12.6.4 Cumulative Regulatory Burden .......................................................................12-22

12.6.4.1 DOE Regulations for Other Products Produced by CFLK Manufacturers 12-23

12.6.4.2 Other State and Federal Regulations on CFLK Manufacturers.................12-24

12.7 CONCLUSION............................................................................................................12-25

12.7.1 Ceiling Fan Light Kit Impacts.........................................................................12-25

REFERENCES ........................................................................................................................12-28

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classifications of Small Businesses Potentially Affected 

by This Rulemaking....................................................................................................... 12-4

Table 12.4.1 GRIM Financial Parameters Based on 2007–2013 Weighted Company

Financial Data ................................................................................................................ 12-9

Table 12.4.2 GRIM Revised CFLK Industry Financial Parameters........................................ 12-10

Table 12.4.3 Cost of Equity Calculation.................................................................................. 12-11

Table 12.4.4 Cost of Debt Calculation..................................................................................... 12-12

Table 12.4.5 Trial Standard Levels for CFLKs ....................................................................... 12-12

Table 12.4.6 Manufacturer Production Cost Breakdown (2014$) for Lamp Replacement 

Scenario........................................................................................................................ 12-14

Table 12.4.7 Manufacturer Production Cost Breakdown (2014$) for Light Kit 

Replacement Scenario.................................................................................................. 12-14

Table 12.4.8 Product and Capital Conversion Costs for all CFLKs by TSL........................... 12-15

Table 12.4.9 Two-Tiered Markups for the CFLK Lamp Replacement Scenario .................... 12-16

Table 12.4.10 Two-Tiered Markups for the CFLK Light Kit Replacement Scenario............. 12-16

Table 12.5.1 Manufacturer Impact Analysis for Ceiling Fan Light Kits – Preservation of 

Gross Margin Markup Scenario................................................................................... 12-17

Table 12.5.2 Manufacturer Impact Analysis for Ceiling Fan Light Kits – Two-Tiered 

Markup Scenario.......................................................................................................... 12-17

Table 12.6.1 Other DOE Regulations Potentially Affecting CFLK Manufacturers................ 12-24

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow............................................................ 12-7

Figure 12.5.1 Annual Industry Free Cash Flows for CFLKs – Preservation of Gross 

Margin Markup Scenario ............................................................................................. 12-19

Figure 12.5.2 Annual Industry Free Cash Flows for CFLKs – Two-Tiered Markup 

Scenario........................................................................................................................ 12-19
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. 6312(a)(6)(B)(i)) 

The law 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 amended energy conservation standards on manufacturers of 

ceiling fan light kits (CFLKs) 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 for the products in this rulemaking. The GRIM inputs include information on 

industry cost structure, shipments, and pricing strategies. The GRIM’s key output is the industry 

net present value (INPV). The model estimates the financial impact of more-stringent energy 

conservation standards for each product by comparing changes in INPV between the nostandards case and the various trial standard levels (TSLs) in the standards case. The qualitative 

part of the MIA addresses product characteristics, manufacturer characteristics, market and 

product trends, as well as the impact of standards on subgroups of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of 

preparing an industry characterization for the CFLK industry, including data on market share, 

sales volumes and trends, pricing, employment, and financial structure. In Phase II, “Industry 

Cash Flow,” DOE used a GRIM to assess the impacts of amended energy conservation standards 

on CFLKs.

In Phase II, DOE created a GRIM for CFLKs, as well as an interview guide for CFLK

manufacturers, to gather information on the potential impacts of amended energy conservation 

standards. DOE presented the MIA results for CFLKs based on a set of considered TSLs. Section 

12.4.5 provides descriptions of these TSLs.

In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers that account 

for more than 30 percent of CFLK sales. Interviewees included large and small manufacturers 

with various market shares and market focuses, providing a representative cross-section of the

industry. During interviews, DOE discussed financial topics specific to each manufacturer and 

obtained each manufacturer’s view of the relevant industry. The interviews provided DOE with 

valuable information for evaluating the impacts of amended energy conservation standards on 

manufacturer cash flows, investment requirements, and employment.
12-2

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the CFLK industry that built upon the 

market and technology assessment prepared for this rulemaking (see chapter 3 of this notice of 

proposed rulemaking [NOPR] technical support document [TSD]). Before initiating the detailed 

impact study, DOE collected information on the present and past structure and market 

characteristics of the industry. This information included market share data, unit shipments, 

manufacturer markups, and cost structures for various manufacturers. The industry profile 

includes: (1) further detail on the overall market and product characteristics; (2) estimated 

manufacturer market shares; (3) financial parameters such as net plant, property, and equipment

(PPE); selling, general and administrative (SG&A) expenses; cost of goods sold, etc.; and (4) 

trends in the number of firms, specific lighting markets, and general product characteristics. The 

industry profile included a top-down cost analysis of CFLK manufacturers that DOE used to 

derive preliminary financial inputs for the GRIM (e.g., revenues, depreciation, SG&A, and 

research and development [R&D] expenses).

DOE also used public information to calibrate its initial characterization of the CFLK

industry further, including Securities and Exchange Commission (SEC) 10–K reports,1 Standard 

& Poor’s (S&P) stock reports,2 and corporate annual reports. DOE supplemented this public 

information with data released by privately held companies.

12.2.2 Phase II: Industry Cash-Flow Analysis and Interview Guide

Phase II focused on the financial impacts of potential amended energy conservation 

standards on manufacturers of CFLKs. More-stringent energy conservation standards can affect 

manufacturer cash flows in three distinct ways: (1) create a need for increased investment, (2) 

raise production costs per-unit, and (3) alter revenue due to higher per-unit prices and/or possible 

changes in sales volumes. To quantify these impacts, DOE used the GRIM to perform a cashflow analysis for CFLKs. In performing these analyses, DOE used the financial values derived 

during Phase I and the shipment scenarios used in the national impact analysis (NIA). In Phase 

II, DOE performed these preliminary industry cash-flow analyses and prepared written guides 

for manufacturer interviews.

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. These factors include annual expected revenues, costs of sales, 

SG&A, taxes, and capital expenditures related to the amended standards. Inputs for the GRIM 

include manufacturing production costs, selling prices, and shipments forecasts developed in 

other analyses. DOE derived the end-user price as part of the product price determination 

analysis and back calculated the manufacturer costs based on information provided by the 

industry and estimates of typical manufacturer markups from public financial reports and 
12-3

interviews with manufacturers. DOE developed an alternative markup scenario for the GRIM 

based on discussions with manufacturers. DOE’s shipments analysis, presented in chapter 9 of 

this NOPR TSD, provided the basis for the shipment projections in the GRIM. DOE developed 

the financial parameters using publicly available manufacturer data and information submitted 

confidentially during manufacturer interviews. The GRIM results are compared to the nostandards case projections for the industry. The financial impact of amended energy conservation 

standards is the difference between the discounted annual cash flows in the no-standards case

and in the standards case at each TSL.

12.2.2.2 Interview Guides

During Phase III of the MIA, DOE interviewed manufacturers 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 for the CFLK industry. The interview guide provided a starting 

point to identify relevant issues and help identify the impacts of amended energy conservation 

standards on individual manufacturers or subgroups of manufacturers in the CFLK industry.

Information DOE received from these meetings is protected by non-disclosure agreements and 

resides with DOE’s contractors. Before each telephone interview or site visit, DOE provided 

company representatives with an interview guide that included the topics for which DOE sought 

input. The MIA interview topics included (1) key issues, (2) company overview and 

organizational characteristics, (3) markups and profitability, (4) financial parameters, (5) 

conversion costs, (6) cumulative regulatory burden, (7) direct employment impact assessment,

(8) manufacturing capacity and non-U.S. sales, (9) impact on competition, and (10) impacts on 

small businesses. The interview guides are presented in appendix 12A of this NOPR TSD.

12.2.3 Phase III: Subgroup Analysis

Using average cost and financial assumptions to develop an industry cash-flow model is 

not adequate for assessing potential differential impacts among a potential subgroup of 

manufacturers. Small manufacturers, niche players, or manufacturers exhibiting a cost structure 

that differs largely from the industry average could experience larger negative impacts. During 

interviews, DOE identified one potential manufacturer subgroup (small manufacturers) that 

could be disproportionately impacted by amended energy conservation standards. As a result, 

DOE will analyze small business manufacturers as a subgroup.

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.
12-4

DOE used these interviews to tailor the GRIM to reflect unique financial characteristics 

of CFLK manufacturers. DOE contacted companies from its database of manufacturers and 

interviewed small and large companies, subsidiaries and independent firms, as well as public and 

private corporations, to provide an accurate 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 helped clarify responses and identify additional issues. The 

resulting information provides valuable inputs to the GRIM developed for the CFLK industry.

12.2.3.2 Revised Industry Cash-Flow Analysis

In Phase II of the MIA, DOE provided manufacturers with preliminary GRIM input 

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.

12.2.3.3 Small Business Subgroup

As part of Phase III, DOE investigated whether small businesses should be analyzed as a 

subgroup. DOE used the Small Business Administration (SBA) small business size standards 

published on January 7, 2013, as amended, 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 this rulemaking.a For the CFLKs under review, the SBA bases its small business 

definition on the total number of employees for a business, its subsidiaries, and its parent 

companies. An aggregated business entity with fewer employees than the listed limit is 

considered a small business.

Table 12.2.1 SBA and NAICS Classifications of Small Businesses Potentially Affected by 

This Rulemaking

Industry Description Revenue 

Limit Employee Limit NAICS

Electric Small Appliance Manufacturing N/A 750 335210

DOE used the American Lighting Association (ALA)

3 member directory, DOE’s 

Compliance Certification Management System (CCMS), the SBA database, information from the 

previous rulemakings, individual company websites, and market research tools (e.g., 

Hoovers.com) to create a list of companies that potentially sell CFLKs covered by this 

rulemaking. Additionally, DOE asked interested parties and industry representatives if they were 

aware of other small businesses in the industry. DOE screened out companies that did not offer 

products covered by this rulemaking, did not meet the definition of a “small business,” or are 

foreign owned and operated.

 a The size standards are available on the SBA’s website at 

https://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf.
12-5

During its research, DOE identified 34 companies that sell CFLKs covered by this 

rulemaking and qualify as small businesses per the SBA employment threshold for this industry. 

During interviews, DOE solicited data from manufacturers on differential impacts that these 

small businesses might experience from amended energy conservation standards. Because DOE 

was not able to certify that the proposed rulemaking for CFLKs would not have a significant 

economic impact on a substantial number of small entities, DOE has analyzed small businesses

as a subgroup. Section 12.6 present the results of this subgroup analysis.

12.2.3.4 Manufacturing Capacity Impact

One significant outcome of amended energy conservation standards could be the 

obsolescence of existing manufacturing assets, including tooling and investment. The 

manufacturer interview guide has a series of questions to help identify impacts of amended 

standards on manufacturing capacity. These include questions regarding 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 potential stranded assets; and estimates for 

any one-time changes to existing PPE. DOE’s estimates of the one-time capital changes and 

stranded assets affect the cash-flow estimates in the CFLK GRIM. These estimates can be found 

in section 12.4.8; DOE’s discussion of the capacity impacts can be found in section 12.6.3.

12.2.3.5 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 CFLK 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 retraining employees. The employment 

impacts are reported in section 12.6.2.

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to amended energy 

conservation standards and other regulatory actions affecting the same products. DOE analyzed 

the impact on manufacturers of multiple, product-specific regulatory actions. Based on its own 

research and discussions with manufacturers, DOE identified regulations relevant to CFLK

manufacturers, such as state and other federal regulations that affect other products made by the 

same manufacturers. Discussion of the cumulative regulatory burden can be found in section

12.6.4.
12-6

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview begins by asking: “What are the key issues for your company 

regarding this energy conservation standard rulemaking?” This question prompts manufacturers 

to identify the issues they believe DOE should explore and discuss further during the interview. 

The following sections describe the most significant issues identified by manufacturers. These 

summaries are provided in aggregate to protect manufacturer confidentiality.

12.3.1 Duplicative Regulation

Some manufacturers commented that a separate regulation specifically for CFLKs was 

unnecessary, as most lamps placed in CFLKs would be covered by other lighting energy 

conservation standards, such as the ongoing general service lamps (GSLs) rulemaking.b These 

manufacturers claim that there would not be significant additional energy savings from separate 

CFLK energy conservation standards.

12.3.2 Shift to Air Conditioning

Manufacturers were also concerned about a potential technology shift in the CFLK 

market because of energy conservation standards. Manufacturers stated that CFLK standards 

might require more-efficacious lamps in CFLKs and significantly increase the price of the 

overall ceiling fan. Manufacturers pointed out that this could cause consumers to choose air 

conditioning systems rather than ceiling fans with light kits. These manufacturers claim this 

change could result in more energy use, as ceiling fans could be more efficient at cooling rooms 

than air conditioners.

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. 

DOE then feeds data and assumptions from these sources 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, 

investments, and associated margins. The GRIM spreadsheet uses a number of inputs to arrive at 

a series of annual cash flows, beginning with the base year of the analysis, 2015, and continuing 

 b Information regarding this general service lamp rulemaking, including links to notices and supporting material in 

the regulations.gov docket, may be found on DOE’s website at: 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=83.
12-7

to 2048. The model calculates the INPV by summing the stream of annual discounted cash flows 

during this period and adding a discounted terminal value.4

The GRIM projects cash flows using standard accounting principles and compares 

changes in INPV between the no-standards case and the standard case induced by amended 

energy conservation standards. The difference in INPV between the no-standards case and the 

standard case represents the estimated financial impact of the amended energy conservation 

standard on manufacturers. Appendix 12B of this NOPR TSD provides more technical details 

and user information for the GRIM.

Figure 12.4.1 Using the GRIM to Calculate Cash Flow

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, credit ratings, the 

shipments model, the engineering analysis, and manufacturer interviews.

12.4.2.1 Corporate Annual Reports

Corporate annual reports to the SEC (SEC 10-Ks) provided many of the initial financial 

inputs to the GRIM. These reports exist for publicly held companies and are freely available to 

the public. DOE developed initial financial inputs to the GRIM by examining the annual SEC 

10-K reports filed by publicly traded manufacturers that produce CFLKs, among other products. 

As these companies do not provide detailed information about their individual product lines, 

DOE used financial information at the parent company level as its initial estimates of the 

financial parameters in the GRIM analysis. These figures were later revised, using feedback from 

interviews, to be representative of CFLK manufacturing. DOE used corporate annual reports to 

derive the following initial inputs to the GRIM:

• Tax rate
12-8

• 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 cost of 

capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the NIA. 

The NIA shipment model relied on ceiling fan shipments to project CFLK shipments because the 

majority of ceiling fans are sold with a CFLK. Ceiling fan shipments and the growth of ceiling 

fan shipments were modeled using shipments due to replacements of failed units, new 

construction as projected by the Annual Energy Outlook 2015 (AEO 2015), and the number of 

additions to existing buildings due to expanding demand throughout the analysis period. DOE 

then determined that 88 percent of ceiling fan shipments included a CFLK; DOE used this 

determination as the basis for CFLKs shipped in this analysis. Chapter 9 of this NOPR TSD 

describes the methodology and analytical model DOE used to forecast shipments.

12.4.2.4 Engineering Analysis

The engineering and product price determination analyses establish the relationship 

between end-user price and efficacy level (EL) for all CFLKs covered in this rulemaking. The 

engineering analysis generally is based on commercially available lamps that incorporate the 

design options identified in the technology assessment and screening analysis. See chapters 3 and 

4 of this NOPR TSD for further information on technology and design options. The methodology 

consists of the following steps: 1) selecting representative product classes, 2) selecting baseline 

lamps, 3) identifying more-efficacious substitutes, and 4) developing ELs by directly analyzing 

representative product classes and then scaling those ELs to non-representative product classes. 

Typically, DOE develops manufacturer production costs (MPCs) for the covered products and 

uses the prices as an input to the life-cycle cost (LCC) analysis and NIA. However, because the 

proposed CFLK standard is based on the efficacy of the lamps with which it is packaged with 

and lamps are difficult to reverse-engineer, DOE directly derived end-user prices for CFLKs in 

this rulemaking. See chapter 5 of this NOPR TSD for a complete discussion of the engineering 

analysis and chapter 7 of this NOPR TSD for a discussion on product price determination.
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12.4.2.5 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative crosssection of manufacturers. DOE also interviewed manufacturers that account for a significant 

portion of sales in every product class. During these discussions, DOE obtained information to 

determine and verify GRIM input assumptions in for the CFLK industry. Key topics discussed 

during the interviews and reflected in the GRIM include:

• capital conversion costs (one-time investments in PPE);

• product conversion costs (one-time investments in research, product development, 

testing, certification, and marketing);

• product cost structure, or the portion of the MPCs related to materials, labor, overhead, 

and depreciation costs;

• possible profitability impacts;

• impacts on small businesses; and

• efficacy levels determine in the engineering analysis and associated consumer prices 

developed in the product price determination.

12.4.3 Financial Parameters

Table 12.4.1 provides financial parameters for seven public companies engaged in 

manufacturing and selling CFLKs. The values listed are averages over a six-year period (2007 to 

2013).

Table 12.4.1 GRIM Financial Parameters Based on 2007–2013 Weighted Company 

Financial Data

Parameter Weighted 

Average

Manufacturer

A B C D E F G

Tax Rate

% of taxable income 35.2% 26.2% 31.7% 37.4% 4.6% 36.1% 38.2% 20.4%

Working Capital

% of revenues 13.0% 1.3% 57.9% 3.9% 16.7% 4.9% 18.9% 14.4%

SG&A

% of revenues 23.3% 21.4% 21.9% 23.9% 20.6% 22.9% 30.3% 21.2%

R&D

% of revenues 0.5% 2.7% 2.1% 0.0% 1.1% 0.0% 0.0% 2.7%

Depreciation

% of revenues 2.6% 2.5% 2.3% 3.2% 2.2% 2.5% 2.0% 1.9%

Capital Expenditures

% of revenues 2.8% 2.0% 2.7% 3.4% 1.9% 2.2% 2.6% 1.2%

Net PPE

% of revenues 32.3% 14.7% 15.1% 44.2% 13.9% 34.9% 13.5% 10.6%
12-10

During interviews, CFLK manufacturers were asked to provide their own figures for the 

parameters listed in Table 12.4.1. Where applicable, DOE adjusted the parameters in the GRIM 

using this feedback. Table 12.4.2 presents the revised parameters used for CFLK manufacturers

for this NOPR.

Table 12.4.2 GRIM Revised CFLK Industry Financial Parameters

Parameter Revised Estimates

Tax Rate

% of taxable income 35.2%

Working Capital

% of revenues 16.9%

SG&A

% of revenues 17.3%

R&D

% of revenues 2.0%

Depreciation

% of revenues 1.3%

Capital Expenditures

% of revenues 2.2%

Net PPE

% of revenues 5.4%

12.4.4 Corporate Discount Rate

DOE used the weighted average cost of capital (WACC) as the discount rate to calculate 

the INPV. A company’s assets are financed by a combination of debt and equity. The WACC is 

the total cost of debt and equity weighted by their respective proportions in the capital structure 

of the industry. DOE estimated the WACC for the CFLK industry based on several 

representative companies, using the following formula:

WACC = After-Tax Cost of Debt × (Debt Ratio) + Cost of Equity × (Equity Ratio)

The cost of equity is the rate of return that equity investors (including, potentially, the 

company) expect to earn on a company’s stock. These expectations are reflected in the market 

price of the company’s stock. The 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 = Riskless Rate of Return + β × Risk Premium

Where:

Riskless Rate of Return = the rate of return on a “safe” benchmark investment, typically 

considered the short-term Treasury bill (T-bill) yield;

Risk Premium = the difference between the expected return on stocks and the riskless 

rate; and
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Beta (β) = the correlation between the movement in the price of the stock and that of the 

broader market. In this case, Beta equals one if the stock is correlated perfectly with 

the S&P 500 market index. A Beta lower than one means the stock is less volatile 

than the market index.

DOE determined that the industry average cost of equity for the CFLK industry is 

13.2 percent.

Table 12.4.3 Cost of Equity Calculation

Parameter

IndustryWeighted 

Average

%

Manufacturer

A B C D E F G

(1) Average Beta 1.27 1.25 1.25 1.16 1.64 1.37 1.04 0.85

(2) Yield on 10-Year T-Bill

(1928-2011) 5.1 - - - - - - -

(3) Market Risk Premium

(1928-2011) 6.4 - - - - - - -

Cost of Equity (2)+[(1)*(3)] 13.2 13.1 13.1 12.5 15.6 13.9 11.8 10.5

Equity/Total Capital 56.1 68.4 72.6 54.0 66.6 46.0 88.2 97.3

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 

method to calculate the cost of debt for all seven public manufacturers by using S&P ratings and 

adding the relevant spread to the risk-free rate.

In practice, investors use a variety of different maturity Treasury bonds to estimate the 

risk-free rate. DOE used the 10-year Treasury bond return because it captures long-term inflation

expectations and is less volatile than short-term rates. The risk-free rate is estimated to be 

approximately 5.1 percent, which is the average 10-year Treasury bond return between 1928 and 

2013.

For the cost of debt, S&P’s Credit Services provided the average spread of corporate 

bonds for the seven public manufacturers. DOE added the industry-weighted average spread to 

the average T-bill rate. Since proceeds from debt issuance are tax deductible, DOE adjusted the 

gross cost of debt by the industry average tax rate to determine the net cost of debt for the 

industry. Table 12.4.4 presents the derivation of the cost of debt and the capital structure of the 

industry (i.e., the debt ratio [debt ÷ total capital]).
12-12

Table 12.4.4 Cost of Debt Calculation

Parameter

IndustryWeighted 

Average

%

Manufacturer

A B C D E F G

S&P Bond Rating - A- A- A- BB+ A- AA B

(1) Yield on 10-Year T-Bill

(1928-2011) 5.1% - - - - - - -

(2) Gross Cost of Debt 6.5% 6.4% 6.4% 6.4% 8.1% 6.4% 5.8% 11.6%

(3) Tax Rate 35.2% 26.2% 31.7% 37.4% 16.2% 36.1% 38.2% 47.5%

Net Cost of Debt

(2) x (1-(3)) 4.2% 4.7% 4.4% 4.0% 6.8% 4.1% 3.6% 6.1%

Debt/Total Capital 43.9% 31.6% 27.5% 46.0% 33.4% 54.0% 11.8% 2.7%

Using public information for these seven companies, the initial estimate for the CFLK

industry WACC was approximately 9.0 percent. Subtracting an inflation rate of 3.1 percent 

between 1928 and 2013, the inflation-adjusted WACC, which was the initial estimate of the 

discount rate used in the straw-man GRIM, was 5.9 percent. During manufacturer interviews,

CFLK manufacturers were asked to provide feedback on this discount rate. Based on 

manufacturer feedback that the 5.9 percent discount was too low for the CFLK industry and 

needed to be raised, DOE revised the real discount rate to be 7.4 percent.

12.4.5 Trial Standard Levels

DOE developed TSLs for CFLKs consistent with the engineering analysis. DOE analyzed 

one representative product class for all CFLKs. Table 12.4.5 shows the ELs at each TSL for the 

CFLKs analyzed by DOE.

Table 12.4.5 Trial Standard Levels for CFLKs

Representative Product Class Description TSL 1 TSL 2 TSL 3 TSL 4

Ceiling Fan Light Kits EL 1 EL 2 EL 3 EL 4

TSL 1 sets the efficacy level at EL 1 for all CFLKs. TSL 2 sets the efficacy level at EL 2 

for all CFLKs. TSL 3 sets the efficacy level at EL 3 for all CFLKs. TSL 4 sets the efficacy level 

at EL 4 for all CFLKs.

12.4.6 National Impact Analysis Shipment Forecast

INPV, which is the key GRIM output, depends on industry revenue, which depends on 

the quantity and prices of CFLKs shipped in each year of the analysis period. Industry revenue 

calculations require forecasts of: (1) total annual shipment volume of CFLKs; (2) the distribution 

of shipments across the product class (because prices vary by product class); and, (3) the 

distribution of shipments across ELs (because prices vary with lamp efficacy).
12-13

As the vast majority of CFLKs are sold with ceiling fans, DOE modeled CFLK 

shipments based on ceiling fan shipments. DOE modeled ceiling fan shipments and the growth 

of ceiling fan shipments using replacements shipments of failed ceiling fan units, new 

construction as projected by AEO 2015, and the number of additions to existing buildings due to 

expanding demand throughout the analysis period. DOE included the impact of demolitions to 

old housing stock, which decreases the overall demand for ceiling fans. DOE then determined 

that 88 percent of ceiling fan shipments included a CFLK; DOE used this determination as the 

basis for CFLKs shipped in this analysis.

In the standards cases, the change in the number of shipments is driven by changes in 

average CFLK price due to a standard. DOE assumed that a CFLK does not fail before the 

ceiling fan to which it is attached and that a CFLK will be retired when the ceiling fan it is 

attached to is retired. Therefore, CFLK lifetime is estimated to be the same as the lifetime of a 

ceiling fan in this analysis. For a complete description of the shipments analysis, see chapter 9 of 

this NOPR TSD.

12.4.7 Production Costs

During the product price determination, DOE developed the consumer price for the 

representative product class at each EL analyzed. To get from the consumer price to the 

manufacturer selling price (MSP), the price at which manufacturers sell CFLKs to the consumer, 

DOE calculated the distributor markup for the CFLK industry. DOE determined the distributor 

markup by surveying distributor net prices in the three main CFLK distribution channels to 

estimate a distributor markup of 1.52 for CFLKs. DOE divided the end-user price for all CFLKs 

by this value to arrive at MSPs for all CFLKs.

DOE examined the SEC 10-Ks of publicly traded CFLK manufacturers to estimate the 

average CFLK manufacturer markup. DOE further refined this manufacturer markup based on 

manufacturer interviews. The manufacturer markup represents the markup CFLK manufacturers 

apply to their MPCs to arrive at the MSPs. This is different from the distributor markup, which is 

what retail stores use to mark up the MSP to arrive at the end-user price. Based on SEC 10-Ks

and feedback from manufacturer interviews, DOE found the typical manufacturer markup for 

CFLK manufacturers to be 1.37.

For CFLKs, DOE estimated the percentage of the MPC that corresponds to materials, 

labor, depreciation, and overhead necessary to produce CFLKs at each EL. DOE estimates that 

the percentages of the MPC allocated to materials, labor, depreciation, and overhead remain

constant for each EL. This means as the overall price of the CFLK fluctuates due to the 

components used, while the absolute dollar amount of materials, labor, depreciation and 

overhead fluctuates proportionally with the MPC.
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Table 12.4.6 Manufacturer Production Cost Breakdown (2014$) for Lamp Replacement 

Scenario

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $8.15 $2.26 $0.22 $1.91 $12.53 1.37 $17.17

EL 1 $8.47 $2.31 $0.23 $1.82 $12.83 1.37 $17.57

EL 2_CFL $15.36 $4.19 $0.41 $3.31 $23.28 1.37 $31.89

EL 2_LED $8.61 $2.35 $0.23 $1.85 $13.04 1.37 $17.86

EL 3 $10.48 $2.86 $0.28 $2.26 $15.88 1.37 $21.75

EL 4 $9.78 $2.67 $0.26 $2.11 $14.82 1.37 $20.30

Table 12.4.7 Manufacturer Production Cost Breakdown (2014$) for Light Kit Replacement 

Scenario

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $8.15 $2.26 $0.22 $1.91 $12.53 1.37 $17.17

EL 1 $9.62 $2.62 $0.26 $2.07 $14.57 1.37 $19.97

EL 2 $19.09 $5.21 $0.52 $4.11 $28.93 1.37 $39.63

EL 3 $12.19 $3.32 $0.33 $2.63 $18.47 1.37 $25.30

EL 4 $12.06 $3.29 $0.33 $2.60 $18.27 1.37 $25.03

12.4.8 Capital and Product Conversion Costs

DOE expects amended energy conservation standards to cause manufacturers to incur 

conversion costs by bringing their tooling and product designs into compliance with amended 

standards in the light kit replacement scenario. For the MIA, DOE classified these conversion 

costs into two major groups: (1) capital conversion costs and (2) product conversion costs. 

Capital conversion costs are investments in property, plant, and equipment necessary to adapt or 

change existing tooling equipment such that new product designs can be fabricated and 

assembled. Product conversion costs are investments in research, development, testing, 

marketing, certification, and other non-capitalized costs necessary to make product designs 

comply with amended standards.

Using feedback from manufacturer interviews, DOE conducted a bottom-up analysis to 

calculate the capital and product conversion costs for CFLK manufacturers for each product 

class at each EL. To conduct this bottom-up analysis, DOE used manufacturer input from 

manufacturer interviews regarding the types and dollar amounts of discrete capital and product 

expenditures that would be necessary to convert specific production lines for CFLKs at each EL.

DOE examined conversion costs for each replacement scenario separately. DOE assumed that 

there would be no capital or product conversion costs for the lamp replacement scenario. In this 

scenario, CFLK manufacturers comply with amended standards by replacing the lamps in the 

CFLKs with more-efficacious lamps that meet amended standards. In this scenario, CFLK 

manufacturers are not required to adjust the type or number of lamps in their CFLK, nor are they

required to adjust the existing light kit. In the light kit replacement scenario, manufacturers 
12-15

would incur product and capital conversion costs at ELs that require light-emitting diode (LED) 

lamps. Some CFLKs would need to be redesigned due to potential heat sinking issues associated 

with LEDs and the potentially larger size of LEDs, as compared to the compact fluorescent 

lamps (CFLs) currently being used. Manufacturers would also need to purchase tooling 

equipment necessary to produce these redesigned CFLKs. Once DOE had compiled these capital 

and product conversion costs, DOE then took average values (i.e., average number of hours or 

average dollar amounts) based on the range of responses given by manufacturers for each capital 

and product conversion cost at each EL. Table 12.4.8 provides DOE’s estimates of the capital 

and product conversion costs for all CFLKs.

Table 12.4.8 Product and Capital Conversion Costs for all CFLKs by TSL

Conversion Costs TSL 1

(2014$ millions)

TSL 2

(2014$ millions)

TSL 3

(2014$ millions)

TSL 4

(2014$ millions)

Product Conversion Costs - 0.6 0.8 0.8

Capital Conversion Costs - 1.4 1.7 1.8

Total - 1.9 2.5 2.6

12.4.9 Markup Scenarios

DOE modeled two standards-case markup scenarios for CFLKs to represent the 

uncertainty regarding the potential impacts on prices and profitability for CFLK manufacturers 

following the implementation of amended energy conservation standards. The two scenarios are: 

(1) a preservation of gross margin, or flat, markup scenario; and (2) a two-tiered markup 

scenario. Each scenario leads to different manufacturer markup values, which, when applied to 

the inputted MPCs, result in varying revenue and cash-flow impacts on CFLK manufacturers.

12.4.9.1 Preservation of Gross Margin Markup Scenario

Under the preservation of gross margin, or flat, markup scenario DOE applied a single 

uniform markup across all ELs for all CFLKs. As production costs increase with efficacy, this 

scenario implies that the absolute dollar markup will increase as well. Based on publicly 

available financial information for manufacturers of CFLKs and comments from manufacturer 

interviews, DOE assumed the non-production cost markup—which includes SG&A expenses; 

R&D expenses; interest; and profit—to be 1.37 for all CFLKs. Because this markup scenario 

assumes that manufacturers would be able to maintain their gross margin percentage as 

production costs increase in response to amended standards, it represents the upper bound to 

industry profitability under amended standards.

12.4.9.2 Two-Tiered Markup Scenario

DOE modeled the two-tiered markup scenario because multiple manufacturers stated in 

interviews that they offer tiers of product lines that are differentiated, in part, by EL. The higher 

efficacy tiers typically earn premiums (for the manufacturer) over the baseline efficacy tier. 
12-16

Several manufacturers suggested that amended standards would lead to a reduction in premium 

markups and reduce the profitability of higher efficacy products. During the MIA interviews, 

manufacturers provided information on the range of typical ELs in those tiers and the change in 

profitability at each level. DOE used this information to estimate markups for CFLKs under a 

two-tiered pricing strategy in the no-standards case. In the standards case, DOE modeled the 

situation in which standards result in less product differentiation, compression of the markup 

tiers, and an overall reduction in profitability.

Table 12.4.9 and Table 12.4.10 list the two-tiered markups for CFLKs.

Table 12.4.9 Two-Tiered Markups for the CFLK Lamp Replacement Scenario

Efficacy

Level

Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.352

EL 1 1.352 1.352

EL 2 1.352 1.352 1.352

EL 3 1.379 1.379 1.379 1.352

EL 4 1.379 1.379 1.379 1.352 1.352

Table 12.4.10 Two-Tiered Markups for the CFLK Light Kit Replacement Scenario

Efficacy 

Level

Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.349

EL 1 1.349 1.349

EL 2 1.377 1.377 1.349

EL 3 1.377 1.377 1.349 1.349

EL 4 1.377 1.377 1.349 1.349 1.349

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated 

indicators of financial impacts on the CFLK industry. The following sections detail additional 

inputs and assumptions for CFLKs. The main results of the MIA are also reported in this section. 

The MIA consists of two key financial metrics: INPV and annual cash flows.

12.5.1 Impacts on Industry Net Present Value

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs in the standards case. The INPV is different from DOE’s net present 

value, which is applied to the U.S. economy. The INPV is the sum of all net cash flows 
12-17

discounted at the industry’s cost of capital or discount rate. The CFLK GRIM estimates cash 

flows from 2015 to 2048. This timeframe models both the short-term impacts on the industry

from the announcement of the standard until the compliance date (2015 until an estimated 

compliance date of 2019) and a long-term assessment over the 30-year analysis period used in 

the NIA (2019 – 2048).

In the MIA, DOE compares the INPV of the no-standards case to that of each TSL in the 

standards case. The difference between the no-standards 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 CFLK industry, DOE examined the two markup scenarios described in section 

12.4.9, the preservation of gross margin markup scenario and the two-tiered markup scenario.

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for the two markup scenarios

analyzed for the CFLK industry.

Table 12.5.1 Manufacturer Impact Analysis for Ceiling Fan Light Kits – Preservation of 

Gross Margin Markup Scenario

Units

NoStandards 

Case

Trial Standard Levels

1 2 3 4

INPV (2014$ millions) 94.8 98.9 96.8 92.1 91.9

Change in INPV (2014$ millions) - 4.1 2.1 (2.6) (2.8)

(%) - 4.3 2.2 (2.8) (3.0)

Product Conversion 

Costs (2014$ millions) - - 0.6 0.8 0.8

Capital Conversion 

Costs (2014$ millions) - - 1.4 1.7 1.8

Total Conversion 

Costs (2014$ millions) - - 1.9 2.5 2.6

Table 12.5.2 Manufacturer Impact Analysis for Ceiling Fan Light Kits – Two-Tiered 

Markup Scenario

Units

NoStandards 

Case

Trial Standard Levels

1 2 3 4

INPV (2014$ millions) 94.8 97.9 86.8 74.9 74.7

Change in INPV (2014$ millions) - 3.1 (7.9) (19.9) (20.0)

(%) - 3.3 (8.4) (21.0) (21.1)

Product Conversion 

Costs (2014$ millions) - - 0.6 0.8 0.8

Capital Conversion 

Costs (2014$ millions) - - 1.4 1.7 1.8

Total Conversion 

Costs (2014$ millions) - - 1.9 2.5 2.6
12-18

12.5.2 Impacts on Annual 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 

cause investors to flee, even though recovery may be possible. Thus, a short-term disturbance 

can have long-term effects that the INPV cannot capture. To get an idea of the behavior of 

annual free cash flows, Figure 12.5.1 and Figure 12.5.2 present the annual free cash flows from 

2015 through 2048 for the no-standards case and different TSLs in the standards case.

Annual cash flows are discounted to the base year, 2015. Between 2015 and the 2019

expected compliance date of the amended energy conservation standards, cash flows are driven 

by the level of conversion costs and the proportion of these investments spent every year. After 

the standard announcement date (i.e., the publication date of the final rule), industry cash flows 

begin to decline as companies use their financial resources to prepare for the amended energy 

conservation standards. The more-stringent the amended energy conservation standards, 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.

Two competing factors drive free cash flow in the year the amended energy conservation 

standards potentially take effect. In addition to capital and product conversion costs, amended 

energy conservation standards could create stranded assets (i.e., tooling and equipment that 

would have enjoyed longer use if the energy conservation standards had not made them 

obsolete). In this year, manufacturers write down the remaining book value of existing tooling 

and equipment whose value is affected by the amended energy conservation standards. 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 is needed due to more costly 

production components and materials, higher inventories of more expensive products, and higher 

accounts receivable for more expensive products. Depending on these two competing factors, 

cash flow can be either positively or negatively affected in the year the standards takes effect.

In the years following the projected compliance date of the standards, the impact on cash 

flow depends on the operating revenue. In the preservation of gross margin markup scenario, the 

manufacturer markup is held constant to yield the same gross margin percentage in the standards 

case at each TSL as in the no-standards case in the year after the standards take effect. The 

implicit assumption is that manufacturers can freely pass on and mark up higher cost units. The 

result under this scenario is that operating cash flow increases (in absolute terms) as revenue 

increases. At TSLs where production costs increase substantially, this scenario drives large 

increases in operating cash flow relative to the no-standards case. The larger the production cost 

increase, the more likely it is that the increase in operating cash flow after the standards take 

effect will outweigh the initial conversion costs.
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Under the two-tiered markup scenario, cash flow decreases at each TSL in the standards 

case compared to the no-standards case because manufacturers reduce profit margins on high 

efficacy products as these products become the baseline, higher volume product. 

Figure 12.5.1 Annual Industry Free Cash Flows for CFs – Preservation of Gross Margin 

Markup Scenario 

Figure 12.5.2 Annual Industry Free Cash Flows for CFs – Two-Tiered Markup Scenario 
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12.6 IMPACTS ON MANUFACTURER SUBGROUPS

As described in Section 12.2.3, DOE identified one subgroup of CFLK manufacturers: 

small businesses. Section 12.6.1 describes the results of this subgroup analysis.

12.6.1 Impacts on Small Business Manufacturers

12.6.1.1 Description and Estimated Number of Small Entities Regulated

For manufacturers of CFLKs, the SBA has set a size threshold, which defines those 

entities classified as “small businesses” for the purposes of the statute. DOE used the SBA’s 

small business size standards to determine whether any small entities would be subject to the 

requirements of the rule. 65 FR 30836, 30848 (May 15, 2000), as amended at 65 FR 53533, 

53544 (Sept. 5, 2000) and codified at 13 CFR part 121. The size standards are listed by NAICS

code and industry description available at: 

www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf. CFLK manufacturing is 

classified under NAICS code 335210, “Small Electrical Appliance Manufacturing.” The SBA 

sets a threshold of 750 employees or less for an entity to be considered as a small business for 

this category.

To estimate the number of companies that could be small business manufacturers of 

CFLKs covered by this rulemaking, DOE conducted a market survey using publicly available 

information. DOE’s market research involved trade associations (e.g., ALA3

), information from 

previous rulemakings, and SBA’s database. DOE then estimated the total number of employees 

for each potential small business by reaching out directly to potential small businesses, and by 

using information from individual companies’ websites and external corporate information

websites (e.g., Hoovers.com, Glassdoor.com, Linkedin.com, etc.). DOE also asked stakeholders 

and industry representatives if they were aware of any small manufacturers during manufacturer 

interviews and DOE public meetings. DOE used information from these sources to create a list 

of companies that potentially manufacture or sell CFLKs and would be affected by this 

rulemaking. DOE screened out companies that do not offer products covered by this rulemaking, 

do not meet the definition of a “small business,” or are completely foreign owned and operated.

For CFLKs, DOE initially identified 67 potential companies that sell CFLKs in the 

United States. However, DOE only identified one manufacturer that also manufacturers the 

lamps sold with their CFLKs. All other CFLK manufacturers source the lamps packaged with 

their CFLKs. After reviewing publicly available information on these potential CFLK

businesses, DOE determined that 40 were either large businesses or businesses that were 

completely foreign owned and operated. DOE determined that the remaining 27 companies were 

small businesses that either manufacture or sell covered CFLKs in the United States. The one 

CFLK manufacturer that also sells lamps that DOE identified is also a small business. Based on 

manufacturer interviews, DOE estimates that these small businesses account for approximately 

25 percent of the CFLK market. One small business accounts for approximately 5 percent of the 

CFLK market, while all other small businesses account for 1 percent or less of the CFLK market

individually.
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12.6.1.2 Description and Estimate of Compliance Requirements

At TSL 2, the proposed standard in the NOPR, DOE projects that impacts on small 

businesses because of amended standards would be consistent with the overall CFLK industry 

impacts presented in section 12.5. Small businesses are not expected to experience differential 

impacts as a result of the amended CFLK standards due to the majority of large and small 

businesses sourcing the lamps used in their CFLKs from lamp manufacturers; small and large 

CFLK businesses typically outsourcing the manufacturing of the CFLKs they sell to original 

equipment manufacturers located abroad; the range of available options to replace non-complaint 

lamps with lamps on the market that can meet the proposed standard; and the potential standards 

from the GSL rulemaking and the EISA 2007 backstop requirement of a 45 lm/W standard for 

GSLs in 2020.

DOE identified only one CFLK small business that is also a lamp manufacturer. For this 

analysis, DOE refers to lamp manufacturers as entities that produce and sell lamps, as opposed to 

purchasing lamps from a third party. The majority of lamps packaged in CFLKs are purchased 

from lamp manufacturers, then inserted into a CFLK or packaged with a CFLK. Therefore, 

CFLK businesses will typically not be responsible for the costs associated with producing moreefficacious lamps packaged with CFLKs that comply with the proposed standards. Furthermore, 

because lamp manufacturers typically test and certify their lamps, CFLK businesses can chose to 

use the testing and certification data provided by the lamp manufacturer to comply with the 

CFLK standards. Thereby, both large and small CFLK businesses can significantly reduce their 

own testing and certification costs associated with compliance to proposed standards.

At the proposed standard level, CFLK businesses have the option to replace the lamps 

used in their CFLKs with more-efficacious lamps available on the market. This lamp 

replacement option allows most CFLK businesses to comply with the proposed CFLK standards 

without redesigning their existing CFLKs. DOE’s shipments analysis found that over 50 percent 

of CFLKs sold at TSL 2 will follow this lamp replacement option, allowing these CFLK 

businesses to avoid redesign and conversion costs. Based on manufacturer interviews, small 

businesses are just as likely to pursue the lamp replacement option as large businesses.

DOE expects CFLK businesses that choose to meet amended CFLK standards by 

redesigning CFLKs instead of replacing lamps to incur conversion costs driven by retooling 

costs, increased R&D efforts, product certification costs, and testing costs. DOE learned during

manufacturer interviews that the majority of the manufacturing of CFLKs sold by small and 

large CFLK businesses is outsourced to a limited number of original equipment manufacturers 

located abroad. CFLK businesses pay retooling costs to original equipment manufacturers 

located abroad, who operate and maintain machinery used to produce the CFLKs those CFLK 

businesses then sell.

DOE also learned from manufacturer interviews that, in some cases, multiple CFLK 

businesses, including small and large CFLK businesses, are outsourcing production to the same 

original equipment manufacturer located abroad. Small businesses are currently competing 

against large businesses despite purchasing components at lower volumes, and DOE expects that 

they will continue to compete after the adoption of standards, as the proposed standards will not 

significantly disrupt most CFLK manufacturers’ supply chain. DOE does not expect that small 
12-22

businesses would be disadvantaged compared to large businesses if they chose to redesign their 

CFLKs. Total estimated conversion costs for the industry at TSL 2 are $1.9 million, which is 

relatively small compared to an INPV of almost $95 million in the no-standards case.

Potential standards from the GSL standards rulemaking and the minimum efficacy of 

45 lumens per watt (lm/W) required for GSLs, expected to require compliance in 2020, will 

affect GSLs used in CFLKs (see section 12.6.4 for further details). Therefore, regardless of the 

standards proposed in this rulemaking, CFLK businesses will likely need to package moreefficacious lamps with CFLKs in 2020.

For the reasons outlined above, DOE has determined that most small businesses would 

not be disproportionally affected by the proposed CFLK energy conservation standard compared 

to large businesses. At TSL 2, overall impacts on CFLK INPV range from -8.4 percent to -2.2

percent (see section 12.5). DOE estimates that the overall percent change in INPV for the CFLK 

industry is reflective of the range of potential impacts for small businesses.

12.6.2 Employment

DOE determined that there was only one CFLK manufacturer with domestic production 

of CFLKs, and this manufacturer’s sales of ceiling fans packaged with CFLKs represents a very 

small portion of their overall revenue. During manufacturer interviews, manufacturers stated that 

the vast majority of manufacturing of the CFLKs they sell is outsourced to original equipment 

manufacturers located abroad. These original equipment manufacturers produce CFLKs based on 

designs from domestic CFLK manufacturers. Because of this feedback, DOE did not 

quantitatively assess any potential impacts on domestic production employment because of 

amended energy conservation standards on CFLKs.

12.6.3 Production Capacity

CFLK manufacturers stated that they did not anticipate manufacturing capacity 

constraints as a result of amended energy conservation standard. If manufacturers choose to 

redesign their CFLKs to comply with amended standards, the original equipment manufacturers 

of CFLKs would be able to make the changes necessary to comply with standards in the 

estimated three years from the publication of the final rule to the compliance date. Additionally, 

at the proposed level manufacturers can comply with standards for a significant portion of the 

CFLKs by replacing the lamps with existing products that are sold on the market today. DOE 

does not anticipate any impact on the manufacturing capacity at the proposed amended energy 

conservation standards in the NOPR.

12.6.4 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 industry as a whole. Assessing the impact of a 
12-23

single regulation may overlook the cumulative regulatory burden faced by manufacturers. For 

this cumulative regulatory burden analysis, DOE examines other significant product-specific 

regulations that could affect CFLK manufacturers or their parent companies that will take effect 

approximately three years before or three years after the effective date of the amended energy 

conservation standards for CFLKs. DOE also describes additional state and federal regulations 

with which manufacturers of CFLKs must comply.

Companies that produce a wide range of regulated products may be faced with more 

capital and product conversion costs than their competitors may. Additional regulations can 

prompt those companies to exit the market or reduce their product offerings, potentially reducing 

overall competition. Companies that could be affected by this rulemaking also typically 

manufacture ceiling fans, among other products and equipment, which are potentially subject to 

DOE energy conservations. DOE considers a proposed standard not to be economically justified 

if it contributes to an unacceptable level of cumulative regulatory burden.

12.6.4.1 DOE Regulations for Other Products Produced by CFLK

Manufacturers

In addition to the proposed amended energy conservation standards on CFLKs, several 

other existing and pending federal regulations may apply to other products produced by lamp

manufacturers and may subsequently influence CFLK manufacturers. These lighting regulations 

include the finalized metal halide lamp fixture standards (79 FR 7745 [Feb. 10, 2014]), the 

finalized general service fluorescent lamp standards (80 FR 4041 [Jan. 26, 2015]), and the 

ongoing high-impact discharge lamp standards (77 FR 18963 [Feb. 28, 2012]). DOE 

acknowledges that each regulation can affect a manufacturer’s financial operations. Multiple 

regulations affecting the same manufacturer can strain manufacturers’ profit and possibly cause 

them to exit particular markets. Table 12.6.1 lists the other DOE energy conservation standards 

that could also affect CFLK manufacturers in the three years leading up to and after the 

estimated compliance date of amended energy conservation standards for CFLKs.
12-24

Table 12.6.1 Other DOE Regulations Potentially Affecting CFLK Manufacturers

Regulation Approximate 

Compliance Date

Estimated Industry Total 

Conversion Expenses

Metal Halide Lamp Fixtures 2017 $25 million (2012$)c

General Service Fluorescent Lamps 2018 $26.6 million (203$)d

HID Lamps 2018* N/A†

Ceiling Fans 2019* N/A†

General Service Lamps 2019* N/A†

Candelabra-Base Incandescent Lamps 

and Intermediate-Base Incandescent 

Lamps

N/A‡ N/A†

Other Incandescent Reflector Lamps N/A‡ 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.

‡ These rulemakings are placed on hold due to the Consolidated and Further Continuing 

Appropriations Act, 2015 (Public Law 113-235, Dec. 16, 2014).

Note: For minimum performance requirements prescribed by the Energy Independence and 

Security Act of 2007 (EISA 2007), DOE did not estimate total industry conversion costs 

because an MIA was not completed as part of the final rule codifying these statutorilyprescribed standards. 

12.6.4.2 Other State and Federal Regulations on CFLK Manufacturers

General Service Lamp Rulemaking

DOE has initiated a rulemaking to evaluate the energy conservation standards of GSLs by 

publishing notices of availability for a framework document (78 FR 73737 [Dec. 9, 2013]) and 

preliminary analysis TSD (79 FR 73503 [Dec. 11, 2014]). GSLs covered by this rulemaking are 

commonly packaged with and used in CFLKs. In addition, if standards from the GSL standards 

rulemaking do not produce savings greater than or equal to the savings from a minimum efficacy 

standard of 45 lm/W, sales of GSLs that do not meet the minimum 45 lm/W standard would be 

prohibited as of January 1, 2020. (42 U.S.C. 6295(i)(6)(A)(v)) Any potential standards 

established by the GSL rulemaking are also projected to require compliance in 2020. Potential 

standards promulgated from the GSL standards rulemaking and/or the enactment of the GSL 45 

 c Estimated industry conversion expenses were published in the TSD for the February 10, 2014, metal halide lamp 

fixtures final rule. 79 FR 7745. The TSD for the 2014 metal halide lamp fixture final rule can be found at

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/16. d Estimated industry conversion expenses were published in the TSD for the January 26, 2015, general service 

fluorescent lamps final rule. 80 FR 4042. The TSD for the 2015 general service fluorescent lamps final rule can be 

found at http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/24.
12-25

lm/W provision will affect GSLs available to be packaged with CFLKs. Therefore, regardless of 

the standards proposed in this rulemaking, CFLK manufacturers will likely need to package 

more-efficacious lamps with CFLKs.

California Title 20

The California Title 20 requires that all lamps packaged with CFLKs must meet the 

ENERGY STAR Programe requirements, which are identical to DOE’s existing standards.

Further, to certify compliance with Title 20, all lamps packaged with CFLKs must be tested 

according to DOE test procedures.

Interstate Mercury Education and Reduction Clearinghouse

The Interstate Mercury Education and Reduction Clearinghouse (IMERC) supports 

legislation that lowers allowable mercury levels in certain products and seeks to eliminate certain 

products in participating states. CFLK manufacturers stated in manufacturer interviews that 

IMERC’s labeling requirements have increased labeling costs for CFLKs packaged with CFLs.

In participating states, IMERC requires that packaging containing CFLs must indicate that the 

product contains mercury. IMERC member states include California, Connecticut, Illinois, 

Louisiana, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New 

York, North Carolina, Rhode Island, Vermont, and Washington.

12.7 CONCLUSION

The following section summarizes the impacts for the scenarios DOE believes are most 

likely to capture the range of impacts on CFLK manufacturers due to amended energy 

conservation standards. DOE also notes that, while these scenarios bound the range of most 

plausible impacts on manufacturers, there potentially could be circumstances that cause 

manufacturers to experience impacts outside of this range.

12.7.1 Ceiling Fan Light Kit Impacts

TSL 1 sets the efficacy level at EL 1 for all CFLKs. At TSL 1, DOE estimates impacts on 

INPV range from $3.1 million to $4.1 million, or a change in INPV of 3.3 percent to 4.3 percent. 

At TSL 1, industry free cash flow (operating cash flow minus capital expenditures) is expected to 

remain constant at $5.0 million, which is the same as the no-standards case value in 2018, the 

year leading up to the standard.

Percentage impacts on INPV are slightly positive at TSL 1. DOE anticipates that most 

manufacturers would not lose any of their INPV at this TSL. DOE estimates that 100 percent of 

shipments will meet the efficacy standards at TSL 1 in 2019, the expected compliance year of the 

 e These requirements are available, in full, at: 

https://www.energystar.gov/ia/partners/product_specs/program_reqs/archive/cfls_prog_req_V3.pdf?e9b3-2397.
12-26

standard. As none of the shipments will be required to be converted at this EL, DOE projects that 

there will be no conversion costs at this TSL.

At TSL 1, the shipment-weighted average MPC decreases by 9 percent relative to the nostandards case MPC in 2019, the expected year of compliance. Manufacturers are able to 

maintain their manufacturer markups in both the preservation of gross margin and the two-tiered 

markup scenarios, resulting in slightly positive INPV impacts at TSL 1.

TSL 2 sets the efficacy level at EL 2 for all CFLKs. At TSL 2, DOE estimates impacts on 

INPV range from -$7.9 million to $2.1 million, or a change in INPV of -8.4 percent to 2.2 

percent. At this TSL, industry free cash flow is estimated to decrease by approximately 

15 percent to $4.2 million, compared to the no-standards case value of $5.0 million in 2018, the 

year leading up to the proposed standard.

Percentage impacts on INPV range from slightly negative to slightly positive at TSL 2. 

DOE anticipates that most manufacturers would not lose a significant portion of their INPV at 

TSL 2 because the ELs at this TSL can be met by purchasing replacement lamps that are 

currently available on the market. DOE projects that in 2019, 40 percent of all CFLK shipments 

would meet or exceed the EL required at TSL 2.

DOE expects that most manufacturers will not incur any conversion costs in the lamp 

replacement scenario at any of the TSLs. DOE expects that product conversion costs will rise 

from zero at TSL 1 to $0.6 million in the light kit replacement scenario at TSL 2. Manufacturers 

will incur product conversion costs, primarily driven by increased R&D efforts needed to 

redesign CFLKs to use LED lamps that meet the EL at TSL 2. As ELs rise with each TSL, 

product conversion costs will increase incrementally in proportion with the increasing amount of 

R&D needed to design more-efficacious CFLKs in the light kit replacement scenario. Capital 

conversion costs will increase from zero at TSL 1 to $1.4 million at TSL 2 in the light kit 

replacement scenario. Manufacturers will incur capital conversion costs driven by retooling costs 

associated with producing CFLKs using LEDs.

At TSL 2, under the preservation of gross margin markup scenario, the shipmentweighted average MPC increases by 27 percent relative to the no-standards case MPC in 2019. 

In this scenario, INPV impacts are slightly negative because the higher production costs are 

outweighed by the $1.9 million in conversion costs. Under the two-tiered markup scenario, the 

27 percent MPC increase is outweighed slightly by a lower average markup of 1.35 (compared to 

the preservation of gross margin markup scenario’s markup of 1.37) and $1.9 million in 

conversion costs, resulting in slightly negative impacts at TSL 2.

TSL 3 sets the efficacy level at EL 3 for all CFLKs. At TSL 3, DOE estimates impacts on 

INPV range from -$19.9 million to -$2.6 million, or a change in INPV of -21.0 percent to -

2.8 percent. At this level, industry free cash flow is estimated to decrease by approximately 

20 percent to $4.0 million, compared to the no-standards case value of $5.0 million in 2018.

Percentage impacts on INPV range from moderately negative to slightly negative at 

TSL 3. TSL 3 proposes the first EL that would potentially require manufacturers to use LED 
12-27

lamps. DOE projects that in 2019, 17 percent of all CFLKs shipments would meet or exceed the 

EL at TSL 3.

At TSL 3, DOE estimates manufacturers will incur product conversion costs of 

$0.8 million in the light kit replacement scenario. Product conversion costs are driven primarily 

by increased R&D efforts needed to redesign CFLKs to accommodate the more-efficacious 

LEDs. Manufacturers are estimated to incur $1.7 million in capital conversion costs as a result of 

retooling costs necessary to produce redesigned CFLKs that use LEDs at TSL 3.

At TSL 3, under the preservation of gross margin markup scenario, the shipmentweighted average MPC increases by 1 percent relative to the no-standards case MPC in 2019. In 

this scenario, INPV impacts are slightly negative because the slightly higher production costs are 

outweighed by the $2.5 million in conversion costs. Under the two-tiered markup scenario, the 

1 percent MPC increase is outweighed moderately by a lower average markup of 1.35 (compared 

to the preservation of gross margin markup scenario’s markup of 1.37) and $2.5 million in 

conversion costs, resulting in moderately negative impacts at TSL 3.

TSL 4 sets the efficacy level at max-tech, EL 4, for all CFLKs. At TSL 4, DOE estimates 

impacts on INPV to range from -$20.0 million to -$2.8 million, or a change in INPV of -

21.1 percent to -3.0 percent. At this level, industry free cash flow is estimated to decrease by 

approximately 21 percent to $4.0 million, compared to the no-standards case value of 

$5.0 million in 2018.

Percentage impacts on INPV are slightly negative to moderately negative at TSL 4. DOE 

projects that in 2019, 9 percent of all CFLK shipments would meet or exceed the ELs at TSL 4.

DOE expects total conversion costs in the light kit replacement scenario to increase from 

$2.5 million at TSL 3 to $2.6 million at TSL 4. DOE estimates manufacturers will incur product 

conversion costs of $0.8 million as they allocate more capital to R&D efforts necessary to 

redesign CFLKs that meet the max-tech EL. DOE estimates that manufacturers will incur 

$1.8 million in capital conversion costs due to retooling costs associated with the high number of 

models that will be redesigned in the light kit replacement scenario at TSL 4.

At TSL 4, under the preservation of gross margin markup scenario, the shipmentweighted average MPC increases by 1 percent relative to the no-standards case MPC in 2019. In 

this scenario, the INPV impacts are slightly negative because the slightly higher production costs 

are outweighed by $2.6 million in conversion costs. Under the two-tiered markup scenario, the 

1 percent MPC increase is outweighed by a lower average markup of 1.35 (compared to the 

preservation of gross margin markup scenario’s markup of 1.37) and $2.6 million in conversion 

costs, resulting in moderately negative impacts at TSL 4.
12-28

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3 ALA. Membership Directory and Buyer’s Guide 2015. Last Accessed June 9, 2015. 

<http://www.lightrays-digital.com/lightrays/2015_membership_directory#pg1>.

4 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies. 

Third Edition, eds. Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.
13-i 

CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS 

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS ............................... 13-1

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS ............................................. 13-3

13.4 UPSTREAM EMISSIONS FACTORS ......................................................................... 13-4

13.5 EMISSIONS IMPACT RESULTS ................................................................................ 13-6

REFERENCES ........................................................................................................................ 13-11

LIST OF TABLES 

Table 13.3.1 Power Plant Emissions Factors .................................................................... 13-3

Table 13.3.2 Natural Gas Site Combustion Emissions Factors ........................................ 13-4

Table 13.3.3 Fuel Oil Site Combustion Emissions Factors .............................................. 13-4

Table 13.4.1 Electricity Upstream Emissions Factors ...................................................... 13-5

Table 13.4.2 Natural Gas Upstream Emissions Factors.................................................... 13-5

Table 13.4.3 Fuel Oil Upstream Emissions Factors.......................................................... 13-6

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Ceiling 

Fan Light Kits .............................................................................................. 13-7

LIST OF FIGURES 

Figure 13.5.1 Ceiling Fan Light Kits: CO2 Total Emissions Reduction ........................... 13-8

Figure 13.5.2 Ceiling Fan Light Kits: SO2 Total Emissions Reduction ............................ 13-8

Figure 13.5.3 Ceiling Fan Light Kits: NOx Total Emissions Reduction ........................... 13-9

Figure 13.5.4 Ceiling Fan Light Kits: Hg Total Emissions Reduction .............................. 13-9

Figure 13.5.5 Ceiling Fan Light Kits: N2O Total Emissions Reduction ......................... 13-10

Figure 13.5.6 Ceiling Fan Light Kits: CH4 Total Emissions Reduction ......................... 13-10
13-1 

CHAPTER 13. EMISSIONS IMPACT ANALYSIS 

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector and site (where applicable) 

combustion emissions of carbon dioxide (CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and 

mercury (Hg). The second component estimates the impacts of potential standards 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 full-fuel-cycle (FFC), in accordance with DOE’s FFC Statement of 

Policy. 76 FR 51282 (Aug. 18, 2011). 

The analysis of power sector emissions uses marginal emissions factors calculated by the 

U.S. Department of Energy (DOE). As of 2014, DOE is using a new methodology based on 

results published for the Annual Energy Outlook 2014 (AEO 2014) reference case and a set of 

side cases that implement a variety of efficiency-related policies.

1 The new methodology is 

described in chapter 15 and in the report “Utility Sector Impacts of Reduced Electricity Demand” 

(Coughlin, 2014).2 

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 MWh or

MMBtu of site energy savings. Total emissions reductions are estimated using the energy 

savings calculated in the national impact analysis (chapter 10 of this notice of proposed 

rulemaking [NOPR] technical support document [TSD]). 

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

Each annual version of the AEO incorporates the projected impacts of existing air quality 

regulations on emissions. AEO 2014 generally represents current Federal and State legislation 

and final implementation regulations in place as of the end of October 2013. 

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 

a http://www.epa.gov/climateleadership/inventory/ghg-emissions.html. 
13-2 

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. On July 6, 2011 EPA issued a replacement for CAIR, 

the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 

2012, the D.C. Circuit issued a decision to vacate CSAPR, and ordered EPA to continue 

administering CAIR.b

 On April 29, 2014, the U.S. Supreme Court reversed the judgment of the 

D.C. Circuit and remanded the case for further proceedings consistent with the Supreme Court's 

opinion.c On October 23, 2014, the D.C. Circuit lifted the stay of CSAPR. d Pursuant to this 

action, CSAPR went into effect (and CAIR ceased to be in effect) as of January 1, 2015. 

Because AEO 2014 was prepared prior to the Supreme Court's opinion, it assumed that 

CAIR remains a binding regulation through 2040. Thus, DOE’s analysis used emissions factors 

that assume that CAIR, not CSAPR, is the regulation in force. However, the difference between 

CAIR and CSAPR is not relevant for the purpose of DOE's analysis of emissions impacts from 

energy conservation standards. 

The attainment of emissions caps is typically flexible among affected Electric Generating 

Units (EGUs) and is enforced through the use of emissions allowances and tradable permits. 

Under existing EPA regulations, any excess 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 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS 

rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air 

pollutants (HAP), and also established a standard for 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. AEO 

2014 assumes that, in order to continue operating, coal plants must have either flue gas 

desulfurization or dry sorbent injection systems installed by 2016. Both technologies, which are 

used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, emissions will 

b See EME Homer City Generation, LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012).

c See EPA v. EME Homer City Generation, 134 S.Ct. 1584, 1610 (U.S. 2014). The Supreme Court held in part that

EPA's methodology for quantifying emissions that must be eliminated in certain States due to their impacts in other 

downwind States was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision 

that provides statutory authority for CSAPR. 

d See Georgia v. EPA, Order (D. C. Cir. filed October 23, 2014) (No. 11-1302),
13-3 

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 2016 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 CSAPR 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 

CAIR, 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 emissions factors based on 

AEO 2015, which incorporates the MATS. 

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from analysis of the AEO 2014 reference and a number of side cases incorporating enhanced 

equipment efficiencies. To model the impact of a standard, DOE calculates factors that relate a 

unit reduction to annual site electricity demand for ceiling fan light kits to corresponding 

reductions to installed capacity by fuel type, fuel use for generation, and power sector emissions. 

Details on the approach used may be found in Coughlin (2014).2 

 

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 homes. The average factors for each year take into account the projected 

shares of each of the sources in total electricity generation. 

Table 13.3.2 presents the natural gas site combustion emissions factors for selected years 

and Table 13.3.3 presents fuel oil site combustion emissions factors for select years. 

Table 13.3.1 Power Plant Emissions Factors 

Unit* 2020 2025 2030 2035 2040

CO2 kg/MWh 723 642 579 529 483

SO2 g/MWh 718 560 471 395 353

NOx g/MWh 574 479 419 369 334

Hg g/MWh 0.00222 0.00173 0.00145 0.00122 0.00109

N2O g/MWh 7.2 7.1 6.9 6.6 6.4

CH4 g/MWh 50.2 49.4 47.9 46.4 44.8

* Refers to site electricity savings.
13-4 

Table 13.3.2 Natural Gas Site Combustion Emissions Factors 

Unit* 2020 2025 2030 2035 2040

CO2 kg/mcf 54.2 54.2 54.2 54.2 54.2

SO2 g/ mcf 0.271 0.271 0.271 0.271 0.271

NOx g/ mcf 69.9 69.9 69.9 69.9 69.9

N2O g/ mcf 0.102 0.102 0.102 0.102 0.102

CH4 g/ mcf 1.022 1.022 1.022 1.022 1.022

* Refers to site gas savings.

Table 13.3.3 Fuel Oil Site Combustion Emissions Factors 

Unit* 2020 2025 2030 2035 2040

CO2 kg/bbl 446 446 446 446 446

SO2 g/bbl 220 220 220 220 220

NOx g/bbl 11,530 11,530 11,530 11,530 11,530

N2O g/bbl 8.6 8.6 8.6 8.6 8.6

CH4 g/bbl 13.3 13.3 13.3 13.3 13.3

* Refers to site fuel oil savings.

13.4 UPSTREAM EMISSIONS FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10B of this NOPR TSD. See also Coughlin (2013)3

and 

Coughlin (2014).2 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 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. Fugitive emissions factors for 

methane from coal mining and natural gas production were estimated based on a review of recent 

studies compiled by Burnham (2011).4 This review includes estimates of the difference between 
13-5 

fugitive emissions factors for conventional production of natural vs. unconventional (shale or 

tight gas). These estimates rely in turn on data gathered by EPA under new GHG reporting 

requirements for the petroleum and natural gas industries.5,6 As more data are made available, 

DOE will continue to update these estimated emissions factors.

 For ease of application in its analysis, DOE developed all of the emissions factors using 

site (point of use) energy savings in the denominator. Table 13.4.1 presents the electricity 

upstream emissions factors for selected years. The caps that apply to power sector NOX

emissions do not apply to upstream combustion sources. 

Table 13.4.1 Electricity Upstream Emissions Factors 

Unit 2020 2025 2030 2035 2040

CO2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO2 g/MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 368 375 382 387 387

Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001

N2O g/MWh 0.25 0.25 0.24 0.23 0.23

CH4 g/MWh 2,149 2,195 2,216 2,248 2,255

Table 13.4.2 illustrates the natural gas upstream emissions factors for selected years. 

These were used to estimate the emissions associated with the increased gas use at some of the 

considered efficacy levels.

Table 13.4.2 Natural Gas Upstream Emissions Factors 

Unit 2020 2025 2030 2035 2040

CO2 kg/ mcf 7.1 7.2 7.3 7.4 7.4

SO2 g/ mcf 0.030 0.031 0.031 0.032 0.032

NOx g/ mcf 101 103 105 105 105

N2O g/ mcf 0.011 0.011 0.012 0.012 0.012

CH4 g/ mcf 659 665 666 670 670

Table 13.4.3 presents the fuel oil upstream emissions factors for selected years.
13-6 

Table 13.4.3 Fuel Oil Upstream Emissions Factors 

Unit 2020 2025 2030 2035 2040

CO2 kg/bbl 70.8 70.3 69.9 68.9 68.3

SO2 g/bbl 14.5 14.1 14.0 13.9 13.9

NOx g/bbl 765 742 737 733 732

Hg g/bbl 0.000007 0.000007 0.000007 0.000007 0.000007

N2O g/bbl 0.598 0.579 0.574 0.569 0.568

CH4 g/bbl 897 905 902 888 877

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

products sold in 2019-2048 for each TSL. Negative values indicate that emissions increase.
13-7 

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Ceiling Fan 

Light Kits 

TSL

1 2 3 4

Power Sector Emissions

CO2 (million metric 

tons) 0.654 3.213 4.398 4.488

SO2 (thousand tons) 0.953 3.458 4.583 4.664

NOX (thousand tons) 0.665 2.789 3.759 3.831

Hg (tons) 0.003 0.011 0.014 0.014

N2O (thousand tons) 0.035 0.251 0.354 0.362

CH4 (thousand tons) 0.005 0.036 0.051 0.052

Upstream Emissions

CO2 (million metric 

tons) 0.016 0.134 0.192 0.196

SO2 (thousand tons) 0.004 0.025 0.036 0.037

NOX (thousand tons) 0.213 1.876 2.690 2.756

Hg (tons) 0.000 0.000 0.000 0.000

N2O (thousand tons) 1.25 10.95 15.69 16.07

CH4 (thousand tons) 0.000 0.001 0.002 0.002

Total Emissions

CO2 (million metric 

tons) 0.670 3.346 4.590 4.684

SO2 (thousand tons) 0.956 3.483 4.619 4.701

NOX (thousand tons) 0.878 4.665 6.449 6.586

Hg (tons) 0.003 0.011 0.014 0.014

N2O (thousand tons) 1.281 11.197 16.045 16.435

CH4 (thousand tons) 35.86 313.53 449.25 460.17

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 products 

sold in 2019-2048. 
13-8 

Figure 13.5.1 Ceiling Fan Light Kits: CO2 Total Emissions Reduction

Figure 13.5.2 Ceiling Fan Light Kits: SO2 Total Emissions Reduction
13-9 

Figure 13.5.3 Ceiling Fan Light Kits: NOx Total Emissions Reduction

Figure 13.5.4 Ceiling Fan Light Kits: Hg Total Emissions Reduction
13-10 

Figure 13.5.5 Ceiling Fan Light Kits: N2O Total Emissions Reduction

Figure 13.5.6 Ceiling Fan Light Kits: CH4 Total Emissions Reduction
13-11 

REFERENCES 

1. U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections 

to 2040. 2014. Washington, D.C. Report No. DOE/EIA-0383(2014). (Last accessed June 

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf. 

2. Coughlin, K. Utility Sector Impacts of Reduced Electricity Demand. 2014. Lawrence 

Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6864E. (Last accessed 

February 17, 2015.) http://www.osti.gov/scitech/servlets/purl/1165372. 

3. Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013. 

Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6025E. (Last 

accessed September 26, 2014.) http://ees.lbl.gov/sites/all/files/lbnl6025e_ffc.pdf. 

4. Burnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, and I. Palou-Rivera. Life-Cycle 

Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum. 

Environmental Science & Technology. 2011. 46(2): pp. 619–627. (Last accessed June 10, 

2015.) http://dx.doi.org/10.1021/es201942m. 

5. U.S. Environmental Protection Agency. Fugitive Emissions Reporting from the 

Petroleum and Natural Gas Industry. 2009. Washington, D.C. 

6. U.S. Environmental Protection Agency. Oil and Natural Gas Sector: Standards of 

Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution, 

Background Supplemental Technical Support Document for the Final New Source 

Performance Standards. 2012. Washington, D.C. (Last accessed May 6, 2015.) 

http://www.epa.gov/airquality/oilandgas/pdfs/20120418tsd.pdf. 
14-i 

CHAPTER 14. MONETIZATION OF EMISSIONS REDUCTION BENEFITS 

TABLE OF CONTENTS 

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon ........................................................................................ 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ............................................................. 14-2

14.2.3 Current Approach and Key Assumptions .......................................................... 14-2

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.3.1 Results ................................................................................................................ 14-6

REFERENCES .......................................................................................................................... 14-9

LIST OF TABLES 

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010 – 2050 (in 

2007 dollars per metric ton) ......................................................................... 14-4

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 

2007 dollars per metric ton CO2) ................................................................ 14-4

Table 14.3.1 Summary of the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by Electricity Generating 

Units (2010$) ............................................................................................... 14-5

Table 14.3.2 Estimates of Global Present Value of CO2 Emissions Reduction for 

Potential Standards for Ceiling Fan Light Kits ............................................ 14-6

Table 14.3.3 Estimates of Domestic Present Value of CO2 Emissions Reduction for 

Potential Standards for Ceiling Fan Light Kits ............................................ 14-7

Table 14.3.4 Estimates of Present Value of NOX Emissions Reduction for Potential 

Standards for Ceiling Fan Light Kits ........................................................... 14-8
14-1 

CHAPTER 14. MONETIZATION OF EMISSIONS REDUCTION BENEFITS 

14.1 INTRODUCTION

As part of its assessment of energy conservation standards for ceiling fan light kits, 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 TSLs considered. 

This chapter summarizes the basis for the monetary values used for each of these emissions and 

presents the benefits estimates considered. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon 

The social cost of carbon (SCC) is an estimate of the monetized damages associated with 

an incremental increase in carbon emissions in a given year. It is intended to include (but is not 

limited to) changes in net agricultural productivity, human health, property damages from 

increased flood risk, and the value of ecosystem services. Estimates of the SCC are provided in 

dollars per metric ton of carbon dioxide. A domestic SCC value is meant to reflect the value of 

damages in the United States resulting from a unit change in carbon dioxide emissions, while a 

global SCC value is meant to reflect the value of damages worldwide.

Under section 1(b) of Executive Order 12866, agencies must, to the extent permitted by 

law, “assess both the costs and the benefits of the intended regulation and, recognizing that some 

costs and benefits are difficult to quantify, propose or adopt a 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 

14.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A report from the National Research 

Council1

 points out that any assessment will suffer from uncertainty, speculation, and lack of 

information about (1) future emissions of greenhouse gases, (2) the effects of past and future 

emissions on the climate system, (3) the impact of changes in climate on the physical and 

biological environment, and (4) the translation of these environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change will raise serious questions of science, economics, and ethics and should be viewed as 

provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Most Federal 

regulatory actions can be expected to have marginal impacts on global emissions. For such 

policies, the agency can estimate the benefits from reduced (or costs from increased) emissions 

in any future year by multiplying the change in emissions in that year by the SCC value 

appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions. 

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in 

how benefits are evaluated across 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.

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 (IAMs) 

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 

a The models are described in appendix 14A of this NOPR TSD. 
14-3 

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; 

(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.2(The 

2010 report is reproduced in appendix 14A of this notice of proposed rulemaking [NOPR] 

technical support document [TSD].) 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, as depicted in Table 

14.2.1. Additionally, the interagency group determined that a range of values from 7 percent to 

23 percent should be used to adjust the global SCC to calculate domestic effects,balthough 

preference is given to consideration of the global benefits of reducing CO2 emissions. 

The SCC values used for this NOPR were generated using the most recent versions of the 

three integrated assessment models that have been published in the peer-reviewed literature, as

described in the 2013 update from the interagency working group (revised November 2013).

3

Table 14.2.2 shows the updated sets of SCC estimates in five year increments from 2010 to 

2050. Appendix 14B of this NOPR TSD provides the full set of SCC estimates, as well as the 

2013 report from the interagency group. 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. 

b

It is recognized that this calculation for domestic values is approximate, provisional, and highly speculative. 

There is no a priori reason why domestic benefits should be a constant fraction of net global damages over time. 
14-4 

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010 – 2050 (in 2007 

dollars per metric ton) 

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 

dollars per metric ton CO2) 

Year

Discount Rate %

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Research Council report mentioned 

above points out that there is tension between the goal of producing 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. 
14-5 

In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, which is reprinted in appendix 

14B of this NOPR TSD, escalated to 2014$ using the GDP price deflator. For each of the four 

cases specified, the values used for emissions in 2015 were $12.0, $40.5, $62.4, and $119 per 

metric ton avoided. DOE derived values after 2050 using the relevant growth rates for the 2040-

2050 period in the interagency update. 

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

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

values, DOE discounted the values in 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

As noted in chapter 13 of this NOPR TSD, new or amended energy conservation 

standards would reduce NOX emissions in those 22 States that are not affected by caps. DOE 

considered the potential monetary benefit of reduced NOX emissions from the TSLs it 

considered. DOE estimated the monetized value of NOX emissions reductions resulting 

from each of the TSLs considered using sector-based benefit per ton estimates from EPA’s 

Office of Air Quality Planning and Standards Environmental Benefits Mapping and 

Analysis Program (BenMAP).4 The methodology is discussed in the Technical Support 

Document Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors, 

which includes values for 2016, 2020, 2025, and 2030.5 The values in the table are based on 

the mortality risk estimate that is considered more conservative by EPA. They refer to 

emissions from Electricity Generating Units. 

Table 14.3.1 Summary of the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by Electricity Generating Units 

(2010$) 

Year of 

emission NOx

3% discount rate 7% discount rate

2016 5200 4600

2020 5400 4900

2025 5800 5200

2030 6200 5600

For years after 2030, DOE extrapolated values based on the growth rate shown for 2016 

to 2030. To calculate present value of the total monetary sum from reduced NOX emissions, 

DOE applied real discount rates of 3 percent and 7 percent to the appropriate $/ton series. 

DOE is still evaluating appropriate values to use to monetize avoided SO2 and Hg 

emissions. The interagency group is investigating appropriate values to use to monetize avoided 

CH4 emissions. DOE did not monetize these emissions for the current analysis. 
14-6 

14.3.1 Results

Table 14.3.2 presents the global values of CO2 emissions reductions for each considered 

TSL. DOE calculated domestic values as a range from 7 percent to 23 percent of the global 

values, and these results are presented in Table 14.3.3. 

Table 14.3.2 Estimates of Global Present Value of CO2 Emissions Reduction for Potential 

Standards for Ceiling Fan Light Kits 

TSL 

SCC Case*

5% 

discount 

rate, 

average

3% 

discount 

rate, 

average

2.5% 

discount 

rate, 

average

3% discount 

rate, 95th

percentile

million 2014$

Power Sector Emissions

1 8.52 30.45 44.65 86.58

2 32.72 128.87 196.88 386.33

3 43.41 173.19 265.70 521.88

4 44.16 176.42 270.74 531.81

Upstream Emissions

1 0.24 0.83 1.18 2.28

2 1.35 5.34 8.17 16.04

3 1.86 7.47 11.48 22.57

4 1.90 7.64 11.74 23.07

Total Emissions

1 8.77 31.28 45.84 88.86

2 34.06 134.21 205.05 402.37

3 45.27 180.66 277.18 544.44

4 46.06 184.05 282.48 554.88

* For each of the four cases, the corresponding SCC value for emissions in 2015 are $12.2, $41.2, $63.4, and $121

per metric ton (2014$).
14-7 

Table 14.3.3 Estimates of Domestic Present Value of CO2 Emissions Reduction for 

Potential Standards for Ceiling Fan Light Kits 

TSL 

SCC Case*

5% discount 

rate, average

3% discount 

rate, average

2.5% discount 

rate, average

3% discount rate, 

95th percentile

million 2014$

Power Sector Emissions

1 0.6 to 2.0 2.1 to 7.0 3.1 to 10.3 6.1 to 19.9

2 2.3 to 7.5 9.0 to 29.6 13.8 to 45.3 27.0 to 88.9

3 3.0 to 10.0 12.1 to 39.8 18.6 to 61.1 36.5 to 120.0

4 3.1 to 10.2 12.3 to 40.6 19.0 to 62.3 37.2 to 122.3

Upstream Emissions

1 0.0 to 0.1 0.1 to 0.2 0.1 to 0.3 0.2 to 0.5

2 0.1 to 0.3 0.4 to 1.2 0.6 to 1.9 1.1 to 3.7

3 0.1 to 0.4 0.5 to 1.7 0.8 to 2.6 1.6 to 5.2

4 0.1 to 0.4 0.5 to 1.8 0.8 to 2.7 1.6 to 5.3

Total Emissions

1 0.6 to 2.0 2.2 to 7.2 3.2 to 10.5 6.2 to 20.4

2 2.4 to 7.8 9.4 to 30.9 14.4 to 47.2 28.2 to 92.5

3 3.2 to 10.4 12.6 to 41.6 19.4 to 63.8 38.1 to 125.2

4 3.2 to 10.6 12.9 to 42.3 19.8 to 65.0 38.8 to 127.6

* For each of the four cases, the corresponding SCC value for emissions in 2015 are $12.2, $41.2, $63.4, and $121

per metric ton (2014$).

Table 14.3.4 presents the present value of cumulative NOX emissions reductions for each 

TSL, calculated using seven-percent and three-percent discount rates.
14-8 

Table 14.3.4 Estimates of Present Value of NOX Emissions Reduction for Potential 

Standards for Ceiling Fan Light Kits 

TSL 3% Discount Rate 7% Discount Rate

million 2014$

Power Sector Emissions

1 3.81 3.54

2 13.51 9.54

3 17.76 12.01

4 18.06 12.16

Upstream Emissions

1 1.47 1.67

2 8.97 6.18

3 12.47 8.13

4 12.74 8.26

Total Emissions

1 5.28 5.21

2 22.48 15.73

3 30.24 20.13

4 30.80 20.42
14-9 

REFERENCES 

1. National Research Council. Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use. 2010. National Academies Press: Washington, D.C. (Last accessed 

June 10, 2015.) http://www.nap.edu/catalog/12794/hidden-costs-of-energy-unpricedconsequences-of-energy-production-and. 

2. Interagency Working Group on Social Cost of Carbon. Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866. 2010. United States 

Government. (Last accessed June 10, 2015.) 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-ofCarbon-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. 2013. 

(Last accessed June 10, 2015.) 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf. 

4. U.S. Environmental Protection Agency. Environmental Benefits Mapping and Analysis 

Program - Community Edition (BenMAP-CE). April 2, 2015. (Last accessed June 15, 

2015.) http://www2.epa.gov/benmap. 

5. U.S. Environmental Protection Agency. Sector-based PM2.5 Benefit Per Ton Estimates. 

April 2, 2015. (Last accessed June 15, 2015.) http://www2.epa.gov/benmap/sector-basedpm25-benefit-ton-estimates. 
15-1

CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-2

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-4

15.3.1 Installed Capacity............................................................................................... 15-4

15.3.2 Electricity Generation ........................................................................................ 15-7

15.3.3 Results Summary ............................................................................................. 15-10

REFERENCES ........................................................................................................................ 15-12

LIST OF TABLES

Table 15.7.1 Ceiling Fan Light Kits: Summary of Utility Impact Results..................... 15-11

LIST OF FIGURES

Figure 15.3.1 Ceiling Fan Light Kits: Total Electric Capacity Reduction......................... 15-5

Figure 15.3.2 Ceiling Fan Light Kits: Coal Capacity Reduction ....................................... 15-5

Figure 15.3.3 Ceiling Fan Light Kits: Nuclear Capacity Reduction.................................. 15-6

Figure 15.3.4 Ceiling Fan Light Kits: Gas Combined Cycle Capacity Reduction ............ 15-6

Figure 15.3.5 Ceiling Fan Light Kits: Peaking Capacity Reduction.................................. 15-7

Figure 15.3.6 Ceiling Fan Light Kits: Renewables Capacity Reduction ........................... 15-7

Figure 15.3.7 Ceiling Fan Light Kits: Total Generation Reduction................................... 15-8

Figure 15.3.8 Ceiling Fan Light Kits: Coal Generation Reduction.................................... 15-8

Figure 15.3.9 Ceiling Fan Light Kits: Nuclear Generation Reduction .............................. 15-9

Figure 15.3.10 Ceiling Fan Light Kits: Gas Combined Cycle Generation Reduction......... 15-9

Figure 15.3.11 Ceiling Fan Light Kits: Oil Generation Reduction.................................... 15-10

Figure 15.3.12 Ceiling Fan Light Kits: Renewables Generation Reduction...................... 15-10

CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes 

in electric installed capacity and power generation that result for each trial standard level (TSL). 
15-2

The utility impact analysis is based on output 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 an energy forecast for the United States, the Annual Energy Outlook (AEO). 

The EIA publishes a reference case, which incorporates all existing energy-related policies at the 

time of publication, and a variety of side cases which analyze the impact of different policies, 

energy price and market trends. As of 2014, DOE is using a new methodology based on results 

published for the Annual Energy Outlook 2014 (AEO 2014) Reference case and a set of side 

cases that implement a variety of efficiency-related policies.

2

The new approach retains key aspects of DOE’s previous methodology, and provides 

some improvements:

• The assumptions used in the AEO reference case and side cases are fully 

documented and receive detailed public scrutiny.

• NEMS is updated each year, with each edition of the AEO, to reflect changes in 

energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS permits the modeling of interactions among 

the various energy supply and demand sectors. 

• Using EIA published side cases to estimate the utility impacts enhances the 

transparency of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be 

reduced under the new approach.

The methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of 

Reduced Electricity Demand.”3

This chapter presents the results for ceiling fan light kits.

15.2 METHODOLOGY 

DOE estimates 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. 

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 

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

documentation. A useful summary is National Energy Modeling System: An Overview.

1
15-3

duration curves, which are then used to estimate the most cost-effective additions to capacity. 

When electricity demand deviates from the AEO reference case, in general there are three interrelated effects: the annual generation (TWh) from the stock of electric generating capacity 

changes, the total generation capacity itself (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. Changes in 

generation by fuel type lead in turn to changes in total power sector emissions of SO2, NOx, Hg 

and CO2.

DOE’s new approach examines a series of AEO side cases to estimate the relationship 

between demand reductions and the marginal energy, emissions and capacity changes. The 

assumptions for each side case are documented in Appendix E of the AEO. The side cases, or 

scenarios, that incorporate significant changes to equipment efficiencies relative to the Reference 

case are: 

• 2013 Technology (leaves all technologies at 2013 efficiencies);

• Best Available Technology (highest efficiency irrespective of cost);

• High Technology (higher penetration rates for efficiency and demand management);

• Extended Policies (includes efficiency standards that are not in the reference).

Scenarios that incorporate policies that directly affect the power sector without changes 

in energy demand (for example, subsidies for renewables, or high fuel price assumptions) are not 

appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply-side data on generation, capacity and emissions, and demand-side data on electricity 

use by sector and end-use, are extracted from each side case. The data are converted to 

differences relative to the AEO Reference case.

2. The changes in electricity use on the demand-side data are allocated to one of three 

categories: on-peak, shoulder, and off-peak. These categories are used in the utility sector to 

correlate end-use consumption with supply types. For each of the end-uses that are modeled 

explicitly in NEMS, load shape information is used to identify the fraction of annual 

electricity use assigned to each category. On-peak hours are defined as noon-5pm, June 

through September. Off-peak hours are nights and Sundays. All other hours are assigned to 

the shoulder period.

3. For each year and each side case, the demand-side reductions to on-peak, off-peak and 

shoulder-period electricity use are matched on the supply-side to reductions in generation by 

fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. The 

allocation is based on the following rules:

• All petroleum-based generation is allocated to peak periods;

• Natural gas generation is allocated to any remaining peak reduction, this is consistent 

with the fact that oil and gas steam units are used in NEMS to meet peak demand;

• Base-load generation (nuclear and coal) is allocated proportionally to all periods; and

• Remaining generation of all types is allocated to the remaining off-peak and shoulder 

reductions, proportionally.
15-4

4. The output of step 3 defines fuel-share weights giving the fraction of energy demand in each 

load category that is met by each fuel type as a function of time. These are combined with the 

weights that define the load category shares by end-use to produce coefficients that allocate a 

marginal reduction in end-use electricity demand to each of the five fuel types.

5. A regression model is used to relate reductions in generation by fuel type to reductions in 

emissions of power sector pollutants. The model produces coefficients that define the change 

in total annual emissions of a given pollutant resulting from a unit change in total annual 

generation for each fuel type, as a function of time. These coefficients are combined with the 

weights calculated in step 4 to produce coefficients that relate emissions changes to changes 

in end-use demand.

6. A regression model is used to relate reductions in generation by fuel type to reductions in 

installed capacity. The categories used for installed capacity are the same as for generation 

except for peak: NEMS uses two peak capacity types (combustion turbine/diesel and oil and 

gas steam) which are combined here into a single “peak” category. The model produces 

coefficients that define the change in total installed capacity of a given type resulting from a 

unit change in total annual generation for the corresponding fuel type. These coefficients are 

combined with the weights calculated in step 4 to produce coefficients that relate installed 

capacity changes to changes in end-use demand, as a function of time.

7. The coefficient time-series for fuel share, pollutant emissions and capacity for the 

appropriate end use are multiplied by the stream of energy savings calculated in the NIA to 

produce estimates of the utility impacts.

This analysis ignores pumped storage, fuel cells and distributed generation, as these 

generation types are not affected by the policy changes modeled in the EIA side cases. The 

methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of Reduced 

Electricity Demand.”3

15.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types.

15.3.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each TSL by major plant type for selected years. The changes have been calculated 

based on factors (megawatts (MW) of capacity reduction per gigawatt hours (GWh) of 

generation reduction) calculated using the methodology described in Section 15.2. Note that a 

negative number means an increase in capacity under a TSL.
15-5

Figure 15.3.1 Ceiling Fan Light Kits: Total Electric Capacity Reduction

Figure 15.3.2 Ceiling Fan Light Kits: Coal Capacity Reduction
15-6

Figure 15.3.3 Ceiling Fan Light Kits: Nuclear Capacity Reduction

Figure 15.3.4 Ceiling Fan Light Kits: Gas Combined Cycle Capacity Reduction
15-7

Figure 15.3.5 Ceiling Fan Light Kits: Peaking Capacity Reduction

Figure 15.3.6 Ceiling Fan Light Kits: 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 fuel type. The change by fuel type has been calculated based on factors calculated 

as described in Section 15.2.
15-8

Figure 15.3.7 Ceiling Fan Light Kits: Total Generation Reduction

Figure 15.3.8 Ceiling Fan Light Kits: Coal Generation Reduction
15-9

Figure 15.3.9 Ceiling Fan Light Kits: Nuclear Generation Reduction

Figure 15.3.10 Ceiling Fan Light Kits: Gas Combined Cycle Generation Reduction
15-10

Figure 15.3.11 Ceiling Fan Light Kits: Oil Generation Reduction

Figure 15.3.12 Ceiling Fan Light Kits: Renewables Generation Reduction

15.3.3 Results Summary 

Table 15.3.1 presents a summary of the utility impact results for ceiling fan light kits.
15-11

Table 15.3.1 Ceiling Fan Light Kits: Summary of Utility Impact Results

TSL

1 2 3 4

Installed Capacity Reduction (MW)

2020 186 254 272 272

2025 -27.5 33.2 54.5 56.1

2030 -21.1 26.8 51.0 53.0

2035 -8.82 8.51 24.1 25.7

2040 -2.84 2.88 11.4 12.4

Electricity Generation Reduction (GWh)

2020 1005 1346 1436 1438

2025 -138 171 283 290

2030 -96.7 127 239 248

2035 -36.9 35.9 102 109

2040 -11.0 11.4 44.6 48.9
15-12

REFERENCES

1. U.S. Department of Energy-Energy Information Administration. The National Energy 

Modeling System: An Overview 2009. 2009. Report No. DOE/EIA-0581. (Last accessed 

June 15, 2015.) http://www.eia.gov/oiaf/aeo/overview/.

2. U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections 

to 2040. 2014. Washington, D.C. Report No. DOE/EIA-0383(2014). (Last accessed June 

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf.

3. Coughlin, K. Utility Sector Impacts of Reduced Electricity Demand. 2014. Lawrence 

Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6864E. (Last accessed 

June 10, 2015.) http://www.osti.gov/scitech/servlets/purl/1165372.
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 IMPACTS .............................................................................................. 16-3

REFERENCES .......................................................................................................................... 16-4

LIST OF TABLES

Table 16.4.1 Net National Short-Term Change in Employment (Number of Jobs) ......... 16-3
16-1 

CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy’s (DOE’s) employment impact analysis is designed to 

estimate indirect national job creation or elimination resulting from possible standards, due to

reallocation of the associated expenditures for purchasing and operating ceiling fan light kits. Job 

increases or decreases reported in this chapter are separate from the direct manufacturing sector 

employment impacts reported in chapter 12 of this notice of proposed rulemaking (NOPR) 

technical support document (TSD) and reflect the employment impact of efficiency standards on 

all other sectors of the economy. 

16.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption and, 

therefore, to reduce energy expenditures. The savings in energy expenditures may be spent on 

new investment or not at all (i.e., they may remain “saved”). The standards may alter the 

purchase price of appliances, including the retail price plus sales tax, and alter 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 ne

t economic output and employment.

DOE intends this analysis to quantify the indirect employment impacts of these expenditure 

changes. It evaluated direct employment impacts at manufacturers’ facilities in the 

manufacturer impact analysis (see chapter 12 of this NOPR TSD). 

DOE notes that ImSET is not a general equilibrium forecasting model and understands 

the uncertainties involved in projecting employment impacts, especially changes in the later 

years of the analysis.1 Because ImSET does not incorporate price changes, the employment 

effects predicted by ImSET would over-estimate the magnitude of actual job impacts over the 

long run for this rule. As input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analyses. DOE, therefore, includes a qualitative 

discussion of how labor markets are likely to respond in the longer term. In future rulemakings, 

DOE may consider the use of other modeling approaches for examining long-run employment 

impacts. 

16.3 METHODOLOGY

The Department based its analysis on an input/output model of the U.S. economy that 

estimates the effects of standards on major sectors of the economy related to buildings and the 

net impact of standards on jobs. The Pacific Northwest National Laboratory developed the 

model, ImSET 3.1.12

 (Impact of Sector Energy Technologies), as a successor to ImBuild,

3 a 

special-purpose version of the IMPLAN4 national input/output model. ImSET estimates the 

employment and income effects of building energy technologies. In comparison with simple 

economic multiplier approaches, ImSET allows for a more complete and automated analysis of 

the economic impacts of energy efficiency investments in buildings.
16-2 

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

relationship of different sectors of the economy and the spending flows among them. Different 

sectors have different levels of labor intensity, and 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 affect the overall level of employment.

ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial building 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 appliances. Increased appliance costs lead to higher employment in the 

appliance manufacturing sectors and lower employment in other economic sectors; reduced 

appliance costs have the opposite effect. Second, commercial firm and residential spending are 

redirected from utilities toward other industries. Third, electric utility sector investment funds are 

released for use in other sectors of the economy. When consumers use less energy, electric 

utilities experience relative reductions in demand, which leads to reductions in utility sector 

investment and employment. 

DOE also notes that the employment impacts estimated with ImSET for the entire

economy differ from the employment impacts in the ceiling fan light kit manufacturing sector 

estimated in chapter 12 of this NOPR TSD 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 ceiling fan light kit standards relative to the 

base case. DOE disaggregated the impact of standards on employment into three component 

effects: altered capital investment costs, decreased energy costs, and changes in operations and 

maintenance costs. DOE presents the summary impact. 

Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors: the ceiling fan light kit 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 alters the purchase price of light kits, 

in turn altering employment in this sector. At the same time, the improvements in energy 

efficiency reduce consumer expenditures on electricity. The reduction in electricity demand 

causes a reduction in employment in that sector. Finally, based on the net impact of altered

expenditures on light kits and reduced expenditures on electricity, consumer expenditures on 
16-3 

everything else are either positively or negatively affected, increasing or reducing jobs in that 

sector accordingly. The model also captures any indirect jobs created or lost by changes in 

consumption due to changes in employment. (As more workers are hired, they consume more 

goods, generating more employment; the converse is true for workers who are laid off.) 

 Table 16.4.1 presents the modeled net employment impact from the rule in 2019, rounded 

to the nearest ten jobs. Virtually all ceiling fan light kits are imported. The net employment 

impact estimate is sensitive to assumptions regarding the return to the U.S. economy of money 

spent on imported light kits. The two scenarios bounding the ranges presented in Table 16.4.1 

represent situations in which none of the money spent on imported light kits returns to the U.S. 

economy and all of the money spent on imported light kits returns to the U.S. economy. The U.S. 

trade deficit in recent years suggests that between 50% and 75% of the money spent on imported 

light kits is likely to return, with employment impacts falling within the ranges presented below.

Table 16.4.1 Net National Short-Term Change in Employment (Number of Jobs) 

Trial Standard Level 2019 2024 

1 70 to 770 -100

2 -10 to 830 130

3 280 to 940 250 to 380

4 280 to 940 260 to 400

 For context, the Office of Management and Budget (OMB) currently projects that the 

official unemployment rate may decline to 5.4% in 2019.5 The unemployment rate in 2019 is 

projected 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, rather than enter or exit 

longer-term employment. 

16.5 LONG-TERM IMPACTS

 Due to the short payback period of energy efficiency improvements mandated by this 

rule, over the long term DOE expects the energy savings to consumers to increasingly dominate 

the increase in appliance costs, resulting in increased aggregate savings to consumers. As a 

result, DOE expects demand for electricity to decline over time and demand for other goods to 

increase. As 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 towards consumer goods. Note that, in a long-run equilibrium, there is no net effect 

on total employment, because wages adjust to bring the labor market into equilibrium.

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 

2024, are included in the second column of Table 16.4.1. 
16-4 

REFERENCES

1. Scott, M. J., 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. (Last accessed June 15, 

2015.) http://www.sciencedirect.com/science/article/pii/S0140988307001119. 

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

Technologies. 2005. Pacific Northwest National Laboratory: Richland, WA. (Last 

accessed June 10, 2015.) 

http://www.pnl.gov/main/publications/external/technical_reports/PNNL-15273.pdf. 

3. M. J. Scott, D. J. H. ImBuild: Impact of Building Energy Efficiency Programs. 1998. 

4. Minnesota IMPLAN Group, Inc. IMPLAN Professional: User’s Guide, Analysis Guide, 

and Data Guide. 1997. Stillwater, MN. 

5. U.S. Office of Management and Budget. Fiscal Year 2013 Mid-Session Review: Budget 

of the U.S. Government. 2012. Washington, D.C. (Last accessed June 15, 2015.) 

http://www.whitehouse.gov/sites/default/files/omb/budget/fy2013/assets/13msr.pdf. 
17-1 

CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION ......................................................................................................... 17-2

17.2 NON-REGULATORY POLICIES ................................................................................ 17-2

17.2.1 Methodology ...................................................................................................... 17-2

17.2.2 Assumptions Regarding Non-Regulatory Policies ............................................ 17-3

17.2.3 Policy Interactions ............................................................................................. 17-4

17.3 NON-REGULATORY POLICY ASSUMPTIONS ...................................................... 17-4

17.3.1 No New Regulatory Action ............................................................................... 17-4

17.3.2 Consumer Rebates ............................................................................................. 17-5

173.2.1.1 Methodology ............................................................................................ 17-5

173.2.1.2 Analysis .................................................................................................... 17-6

17.3.3 Consumer Tax Credits ....................................................................................... 17-8

17.3.4 Manufacturer Tax Credits .................................................................................. 17-9

17.3.5 Voluntary Efficacy Targets .............................................................................. 17-10

17.3.6 Bulk Government Purchases ............................................................................ 17-12

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES ......................................... 17-14

REFERENCES ........................................................................................................................ 17-16

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards ................................... 17-2

Table 17.2.1 Efficacy by TSL (% Efficacy Improvement) ............................................... 17-4

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates........................................... 17-6

Table 17.3.2 Market Penetration in 2019 Attributable to Consumer Rebates .................. 17-7

Table 17.3.3 Market Penetration in 2019 Attributable to Consumer Tax Credits ............ 17-9

Table 17.3.4 Market Penetration in 2019 Attributable to Manufacturer Tax Credits ..... 17-10

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Efficacy Targets 

(TSL 2) ....................................................................................................... 17-11

Table 17.3.6 Market Penetration in 2019 Attributable to Voluntary Energy 

Efficiency Targets ...................................................................................... 17-12

Table 17.3.7 Market Penetration in Selected Years Attributable to Voluntary 

Efficacy Targets for TSL 2 ........................................................................ 17-12

Table 17.3.8 Market Penetration in 2019 Attributable to Bulk Government 

Purchases.................................................................................................... 17-13

Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives (TSL 2) .......................... 17-15

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curve for Ceiling Fan Light Kits ................................. 17-7

Figure 17.4.1 Market Penetration of Efficacious CFLKs (TSL 2) ................................... 17-14
17-2 

CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

In this chapter, DOE evaluated non-regulatory alternatives to proposed standards by 

performing a regulatory impact analysis (RIA). To conduct this analysis, DOE used an integrated 

National Impact Analysis (NIA)-RIA model built on the NIA model discussed in chapter 10 of 

this notice of proposed rulemaking (NOPR) technical support document (TSD). DOE identified 

five non-regulatory policy alternatives that possibly could provide incentives for the same 

efficacy level as the one in the proposed trial standard level for the Ceiling Fan Light Kits 

(CFLKs) that are the subject of this rulemaking. The non-regulatory policy alternatives are listed 

in Table 17.1.1, which also includes the “no new regulatory action” alternative. DOE evaluated 

each alternative in terms of its ability to achieve significant energy savings at a reasonable cost, 

and compared the effectiveness of each to the effectiveness of the proposed standard for the 

CFLKs covered by this rulemaking. 

Table 17.1.1 Non-Regulatory Alternatives to National Standards 

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Efficacy Targets

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed in Table 17.1.1

(excluding the alternative of “No New Regulatory Action”). Section 17.4 presents the results of 

the policy alternatives. 

17.2 NON-REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost 

effectiveness of the non-regulatory policy alternatives for CFLKs. This section also describes the 

assumptions underlying the analysis. 

17.2.1 Methodology 

DOE used its integrated NIA-RIA spreadsheet model to calculate the national energy

savings (NES) and net present value (NPV) associated with each non-regulatory policy 

alternative. Chapter 10 of this NOPR TSD describes the NIA spreadsheet model. Appendix 

17A of this NOPR TSD discusses the NIA-RIA integrated model approach. 

DOE quantified the effect of each alternative on the purchase of equipment that meets the 

efficacy level corresponding to each TSL. After establishing the quantitative assumptions 

underlying each alternative, DOE appropriately revised inputs to the NIA-RIA spreadsheet 

model. The primary model inputs revised were market shares of equipment meeting the target 

efficacy level set for each TSL. The shipments of equipment for any given year reflect a 

distribution of efficacy levels. DOE assumed, for each TSL, that new energy efficiency standards 
17-3 

would affect 100 percent of the shipments of products that did not meet the TSL target level in 

the no-standard case,

a whereas the non-regulatory policies would affect a smaller percentage of 

those shipments. DOE made certain assumptions about the percentage of shipments affected by 

each alternative policy. DOE used those percentages to calculate the shipment-weighted average 

energy consumption and costs of CFLKs attributable to each policy alternative. 

Increasing the efficacy of a product often increases its average installed cost. However, 

operating costs generally decrease because energy consumption declines. DOE therefore 

calculated an NPV for each non-regulatory alternative in the same way it did for the proposed 

standards. In some policy scenarios, any increases in total installed cost are mitigated by 

government rebates or tax credits. Because DOE assumed that consumers would re-pay credits 

and rebates in some way (such as additional taxes), DOE did not include rebates or tax credits as 

a consumer benefit when calculating national NPV. DOE’s analysis also excluded any 

administrative costs for the non-regulatory policies; including such costs would decrease the 

NPVs slightly.

The following are key measures for evaluating the impact of each alternative. 

• National Energy Savings (NES), given in quadrillion Btus (quads), describes the 

cumulative national energy saved over the lifetime of equipment purchased during the 

30-year analysis period starting in the effective date of the policy (2019-2048). 

• Net Present Value (NPV), represents the value of net monetary savings in 2015, 

expressed in 2014$, from equipment purchased during the 30-year analysis period 

starting in the effective date of the policy (2019-2048). DOE calculated the NPV as the 

difference between the present values of installed equipment cost and operating 

expenditures in the no-standard case and the present values of those costs in each policy 

case. DOE calculated operating expenses (including energy costs) for the life of the 

product. 

17.2.2 Assumptions Regarding Non-Regulatory Policies

The effects of non-regulatory policies are by nature uncertain because they depend on 

program implementation, marketing efforts, and on consumers’ response to a program. Because 

the projected effects depend on assumptions regarding the rate of consumer participation, they 

are subject to more uncertainty than are the impacts of mandatory standards, which DOE 

assumes will be met with full compliance. To increase the robustness of the analysis, DOE 

conducted a literature review regarding each non-regulatory policy and consulted with 

recognized experts to gather information on similar incentive programs that have been 

implemented in the United States. By studying experiences with the various types of programs,

DOE sought to make credible assumptions regarding potential market impacts. Section 17.3

presents the sources DOE relied on in developing assumptions about each alternative policy and 

reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each 

alternative policy. 

 

a The no-standard case for the NIA is a market-weighted average efficacy calculated from units at several efficacy 

levels.
17-4 

Each non-regulatory policy that DOE considered would improve the average efficacy of 

new CFLKs relative to their no-standard case efficacy scenario (which involves no new 

regulatory action). The analysis considered that each alternative policy would induce consumers 

to purchase units having the same technology as required by standards (the target level), 

according to the minimum efficacy set for each TSL. As opposed to the standards case, however, 

the policy cases may not lead to 100 percent market penetration of units that meet the target 

level. 

Table 17.2.1 shows the efficacy from the technology stipulated for CFLKs for the 

proposed TSL. 

Table 17.2.1 Efficacy by TSL (% Efficacy Improvement) 

TSL 2

All CFLKs 16%

DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards—2019—through the end of the analysis period, which is 2048. 

17.2.3 Policy Interactions 

DOE calculated the effects of each non-regulatory policy separately from those of the 

other policies. In practice, some policies are most effective when implemented in combination, 

such as voluntary efficacy targets implemented with consumer rebates or tax credits. However, 

DOE attempted to make conservative assumptions to avoid double-counting policy impacts. The 

resulting policy impacts are therefore not additive, and the combined effect of several or all 

policies cannot be inferred from summing their results. 

Section 17.4 presents a graph that shows the market penetration estimated under each 

non-regulatory policy for CFLKs. 

17.3 NON-REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to the standard proposed for CFLKs. (Because the alternative of 

“No New Regulatory Action” has no energy or economic impacts, essentially representing the 

NIA no-standard case, DOE did not perform any additional analysis for that alternative.) DOE 

developed estimates of the market penetration of more efficacious products both with and 

without each of the non-regulatory policy alternatives. 

17.3.1 No New Regulatory Action 

The case in which no new regulatory action is taken with regard to the efficacy of CFLKs 

constitutes the no-standard case, as described in chapter 10 of this NOPR TSD. The no-standard 

case provides the basis of comparison for all other policies. By definition, no new regulatory 

action yields zero NES and an NPV of zero dollars. 
17-5 

17.3.2 Consumer Rebates

DOE considered the scenario in which the Federal government would provide financial 

incentives in the form of rebates to consumers for purchasing energy-efficient equipment. This 

policy provides a consumer rebate for purchasing CFLKs that operate at the same efficacy level

as stipulated in the proposed TSL. 

17.3.2.1 Methodology 

DOE based its evaluation methodology for consumer rebates on a comprehensive study 

of California’s potential for achieving energy efficiency. The study, performed by XENERGY, 

Inc.,b summarized experiences with various utility rebate programs.1 XENERGY’s analytical 

method utilized graphs, or penetration curves, that estimate the market penetration of a 

technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other 

methods of estimating the effect of consumer rebate programs on the market penetration of 

efficient technologies. The other methods, developed after the referenced XENERGY report was 

published,2, 3, 4, 5, 6, 7

 used different approaches: other economic parameters (e.g., payback 

period), expert surveys, or model calibration based on specific utility program data rather than 

multi-utility data. Some models in use by energy efficiency program evaluation experts were so 

client-specific that generic relationships between economic parameters and consumer response 

could not be established.5DOE decided that the most appropriate available method for this RIA 

was the XENERGY approach of penetration curves based on B/C ratio, which incorporates 

lifetime operating cost savings. 

XENERGY’s model estimates market impacts induced by financial incentives based on 

the premise that two types of information diffusion drive the adoption of new technologies. 

Internal sources of information encourage consumers to purchase new equipment primarily

through word-of-mouth from early adopters. External sources affect consumer purchase 

decisions through marketing efforts and information from outside the consumer group. Appendix 

17A of this NOPR TSD contains additional details on internal and external information 

diffusion. 

XENERGY’s model equation accounts for the influences of both internal and external 

sources of information by superimposing the two components. Combining the two mechanisms 

for information diffusion, XENERGY’s model generates a set of penetration (or implementation) 

curves for a policy measure. XENERGY calibrated the curves based on participation data from 

utility rebate programs. The curves illustrate the increased penetration (i.e., increased market 

share) of efficient equipment driven by consumer response to changes in B/C ratio induced by 

rebate programs. The penetration curves depict various diffusion patterns based on perceived 

market barriers (from no-barriers to extremely-high-barriers) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetration curves based on expert 

advice founded on more recent utility program experience.5, 8 

DOE modeled the effects of a consumer rebate policy for CFLKs by determining, for the 

proposed TSL, the increase in market penetration of equipment meeting the target level relative 

to their market penetration in the no-standard case. It used the interpolation method presented in 

b XENERGY is now owned by KEMA, Inc. (www.kema.com) 
17-6 

Blum et al (2011)9 to create a customized penetration curve based on relationships between 

actual no-standard case market penetrations and actual B/C ratios. To inform its estimate of B/C 

ratios provided by a rebate program DOE performed a thorough nationwide search for existing 

rebate programs for CFLKs. It gathered data on utility or agency rebates throughout the nation 

for this equipment, and used this data to calibrate the customized penetration curve it developed 

for the CFLKs covered by this RIA so it can best reflect the market barrier level that consumer 

rebates for CFLKs would face. Section 17.3.2.2 shows the resulting interpolated curve used in 

the analysis. 

17.3.2.2 Analysis 

DOE estimated the effect of increasing the B/C ratio of CFLKs via a rebate that would 

pay part of the increased installed cost of units that meet the target efficacy level compared to the 

least efficacious products on the market. To inform its estimate of an appropriate rebate amount, 

DOE performed a thorough nationwide search for existing rebate programs for CFLKs in the 

first quarter of 2015. It gathered data from a sample of utility and agency rebate programs that 

includes 10 rebates for ceiling fans with light kits initiated by 7 utilities or agencies in various 

States. DOE then estimated a market representative rebate value for the CFLKs covered by this 

RIA which it applied in the calculation of the B/C ratio of CFLKs under the effect of consumer 

rebates. (Appendix 17A, identifies the rebate programs and details the methodology DOE used to 

estimate a market representative rebate amount.) DOE assumed that rebates would remain in 

effect at the same level throughout the forecast period (2019-2048). 

DOE first calculated the B/C ratio of a CFLK without a rebate using the difference in 

total installed costs (C) and lifetime operating cost savingsc (B) between a unit meeting the target 

level and the least efficacious products on the market. It then calculated the B/C ratio given a 

rebate for the unit meeting the target efficacy level. Because the rebate reduced the incremental 

cost, the unit receiving the rebate had a larger B/C ratio. Table 17.3.1 shows the effect of 

consumer rebates for the proposed TSL on the B/C ratio of CFLKs shipped in the first year of the 

analysis period. 

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates

TSL 2

All CFLKs

B/C Ratio Without Rebate 33.7

Rebate Amount (2014$) 5.13

B/C Ratio With Rebate infinite

Estimated Market Barriers Extremely High

DOE used the B/C ratio along with the customized penetration curve shown in Figure 

17.3.1 to estimate the percentage of consumers who would purchase CFLKs that meet the target 

level both with and without a rebate incentive. The estimated level of market barriers

corresponding to the penetration curve DOE calculated to represent the market behavior for

c The cash flow of the operating cost savings is discounted to the purchase year using a 7 percent discount rate.
17-7 

CFLKs at the proposed TSL is indicated in Table 17.3.1. DOE assumed the estimated market 

barriers would remain the same over the whole analysis period. 

TSL 2

All CFLKs

Figure 17.3.1 Market Penetration Curve for Ceiling Fan Light Kits

DOE next estimated the percent increase represented by the change in penetration rate 

shown on the corresponding penetration curve. It then added this percent increase to the market 

share of units that meet the target level in the no-standard case to obtain the market share of units 

that meet the target level in the rebate policy case. 

Table 17.3.2 summarizes DOE’s assumptions for CFLKs regarding the market 

penetration of products in 2019 that meet the target level at the proposed TSL given a consumer 

rebate. 

Table 17.3.2 Market Penetration in 2019 Attributable to Consumer Rebates

TSL 2

All CFLKs 

No-Standard-Case Market 

Share 10.2% 

Policy Case Market Share 50.0%

Increased Market Share 39.8%
17-8 

DOE used the resulting annual increases in market shares as inputs to represent the rebate 

policy case scenario in its NIA-RIA model. Appendix 17A of this NOPR TSD shows the annual 

market share increases due to this policy for the whole forecast period. Section 17.4 presents the 

resulting market penetration trends for the policy case of consumer rebates for CFLKs. 

17.3.3 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous 

analysis of consumer participation in tax credits. DOE supported its approach using data from 

Oregon State’s tax credit program for energy-efficient appliances. DOE also incorporated 

previous research that disaggregated the effect of rebates and tax credits into a direct price effect, 

which derives from the savings in purchase price, and an announcement effect, which is 

independent of the amount of the incentive.10, 11 The announcement effect derives from the 

credibility that a technology receives from being included in an incentive program, as well as 

changes in product marketing and modifications in markup and pricing. DOE assumed that the 

rebate and consumer tax credit policies would encompass both direct price effects and 

announcement effects, and that half the increase in market penetration associated with either 

policy would be due to the direct price effect and half to the announcement effect. 

In estimating the effects of a tax credit on purchases of consumer products that meet new 

efficiency standards, DOE assumed the amount of the tax credit would be the same as the 

corresponding rebate amount discussed above. 

DOE estimated that fewer consumers would participate in a tax credit program than 

would take advantage of a rebate. Research has shown that the delay required for a consumer to 

receive a tax credit, plus the added time and cost in preparing the tax return, make a tax credit 

incentive less effective than a rebate received at the time of purchase. Based on previous 

analyses, DOE assumed that only 60 percent of the consumers who would take advantage of a 

rebate would take advantage of a tax credit.12

In preparing its assumptions to estimate the effects of tax credits on consumer purchases 

of CFLKs, DOE also reviewed other tax credit programs that have been offered at both the 

Federal and State levels for energy-efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005) included Federal tax credits for

consumers who purchase energy-efficient products.13 Those tax credits were in effect in 2006 

and 2007, expired in 2008, were reinstated for 2009–2010 by the American Recovery and

Reinvestment Act of 2009 (ARRA), extended by Congress for 2011 with some modifications, 

and expired at the end of 2011.14, 15 The American Taxpayer Relief Act of 2012 extended, with 

some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water 

heaters placed in service between January 1, 2012 and December 31, 2013.16 DOE reviewed

Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during 

tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation 

tax credit program in place in the 1980s. However, DOE did not find data specific enough to 

CFLKs to warrant adjusting its analysis method for the Consumer Tax Credits policy case. 

Appendix 17A of this NOPR TSD contains more information on Federal consumer tax credits. 
17-9 

DOE also reviewed its previous analysis of Oregon’s tax credits for clothes washers to

provide support for its assumptions.17 In that previous analysis, DOE compared the market

shares of ultra-high efficiency (UHE) residential clothes washers in Oregon, which offered both 

State tax credits and utility rebates, with those in Washington State, which offered only utility

rebates during the same period. Based on this analysis, DOE estimated that in Oregon the impact 

of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equivalent 

efficiency. This finding supports its original assumption that participation in a tax credit program 

would be about 60 percent of participation in a rebate program. Additional discussion of State 

tax credits for Oregon and other states is in appendix 17A of this NOPR TSD. 

DOE applied the assumed 60 percent participation described above to the increase in 

penetration rates estimated for the rebate policy to estimate penetration rates attributable to 

consumer tax credits. In doing so, DOE incorporated the assumptions for consumer response to 

financial incentives from the customized penetration curve it developed for CFLKs (See Figure 

17.3.1). 

Table 17.3.3 summarizes DOE’s assumptions for CFLKs regarding the market 

penetration of products in 2019 that meet the target level at the proposed TSL given a consumer 

tax credit. 

Table 17.3.3 Market Penetration in 2019 Attributable to Consumer Tax Credits

TSL 2

All CFLKs 

No-Standard-Case Market 

Share 10.2% 

Policy Case Market Share 34.1%

Increased Market Share 23.9%

The increased market shares attributable to consumer tax credits shown in Table 17.3.3 

were used as inputs in the NIA-RIA model. Appendix 17A of this NOPR TSD shows the annual 

market share increases due to this policy for the whole forecast period. Section 17.4 presents the 

resulting market penetration trends for the policy case of consumer tax credits for CFLKs that 

meet the efficacy level for the proposed TSL. Because the increase in market penetration for 

consumer tax credits is proportional to the increase in market penetration DOE calculated for 

consumer rebates, the former follows a similar increasing trend over the analysis period as the 

latter. 

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce CFLKs that meet the target efficacy level at the proposed TSL, DOE assumed that a 

manufacturer tax credit would lower the consumer’s purchase cost by an amount equivalent to 

that provided by the consumer rebates or tax credits described above. DOE further assumed that 

manufacturers would pass on some of their reduced costs to consumers, causing a direct price 

effect. DOE assumed that no announcement effect would occur, because the program would not 
17-10 

be visible to consumers.d Because the direct price effect is approximately equivalent to the 

announcement effect,10 DOE estimated that a manufacturer tax credit would induce half the 

number of consumers assumed to take advantage of a consumer tax credit to purchase more 

efficacious products. Thus the assumed participation rate is equal to 30 percent of the number of 

consumers who would participate in a rebate program.

DOE attempted to investigate manufacturer response to the Energy Efficient Appliance 

Credits for manufacturers mandated by EPACT 2005.18 Those manufacturer tax credits have 

been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009. 

DOE was unable to locate data from the Internal Revenue Service or other sources on 

manufacturer response to the Federal credits. Appendix 17A of this NOPR TSD presents 

details on Federal manufacturer tax credits. 

DOE applied the assumption of 30 percent participation to the increase in penetration 

rates predicted for the rebate policy to estimate the effects of a manufacturer tax credit policy. In 

doing so, DOE incorporated the assumptions for consumer response to financial incentives from 

the customized penetration curve it developed for CFLKs. (See Figure 17.3.1).

Table 17.3.4 summarizes DOE’s assumptions for CFLKs regarding the market 

penetration of products in 2019 that meet the target level at the proposed TSL given a 

manufacturer tax credit.

Table 17.3.4 Market Penetration in 2019 Attributable to Manufacturer Tax Credits

TSL 2

All CFLKs 

No-Standard-Case Market 

Share 10.2% 

Policy Case Market Share 22.2%

Increased Market Share 11.9%

The increased market shares attributable to a manufacturer tax credit shown in Table 

17.3.4 were used as inputs in the NIA-RIA model. Appendix 17A of this NOPR TSD shows the 

annual market share increases due to this policy for the whole forecast period. Section 17.4 

presents the resulting market penetration trends for the policy case of manufacturer tax credits 

for CFLKs. Because the increase in market penetration for manufacturer tax credits is 

proportional to the increase in market penetration DOE calculated for consumer rebates, the 

former follows a similar increasing trend over the analysis period as the latter. 

17.3.5 Voluntary Efficacy Targets 

DOE assumed that voluntary efficacy targets would lead manufacturers of CFLKs to 

gradually stop producing units that operate below the efficacy level set for the proposed TSL. 

d Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency 

programs might promote the models for which manufacturers increase production due to the tax credits, which in 

turn might induce some announcement effect. However, DOE found no data on such programs on which to base an 

estimate of the magnitude of this possible announcement effect on consumer behavior.
17-11 

DOE assumed that the impetus for phasing out production of low-efficacy units would be a 

program with impacts similar to those of the ENERGY STAR labeling program conducted by 

the Environmental Protection Agency (EPA) and DOE in conjunction with industry partners. 

The ENERGY STAR program specifies the minimum energy efficiencies that various products 

must have to receive the ENERGY STAR label. ENERGY STAR encourages consumers to 

purchase efficient products via marketing that promotes consumer label recognition, various 

incentive programs that adopt the ENERGY STAR specifications, and manufacturers’ promotion 

of their qualifying appliances. ENERGY STAR projects market penetration of compliant 

appliances and estimates the percentage of sales of compliant appliances that are attributable to 

the ENERGY STAR program. 

Researchers have analyzed the ENERGY STAR program’s effects on sales of several 

consumer products. Program efforts generally involve a combination of information 

dissemination and utility or agency rebates. The analyses have been based on State-specific data 

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The 

analyses generally have concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where ancillary promotional programs have been 

active.

19, 20, 21

DOE believes that informational incentive programs – like ENERGY STAR, or any other 

labeling program sponsored by industry or other organizations – are likely to reduce the market 

barriers to more efficient products over time. During the rebate analysis, when assessing the B/C 

ratio and market penetration in the no-standard case for CFLKs, DOE observed that the level of 

market barriers for more efficacious CFLKs extremely high. DOE estimates that voluntary 

efficacy targets could reduce these barriers to lower levels over 10 years. Table 17.3.5 presents 

the levels of market barriers DOE estimated for CFLKs in the no-standard case and in the policy 

case of voluntary efficacy targets. DOE followed the methodology presented by Blum et al 

(2011)22 to evaluate the effects that such a reduction in market barriers would have on the market 

penetration of efficacious CFLKs.

e

 The methodology relies on interpolated market penetration 

curves to calculate – given a B/C ratio – how the market penetration of more efficient units 

increases as the market barrier level to those units decreases.

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Efficacy Targets (TSL

2) 

No-Standard Case Voluntary Efficacy Targets

All CFLKs Extremely-High Moderate-to-High

* The policy reduces the market barriers from an upper to a lower level within the range of moderate barriers.

Table 17.3.6 summarizes DOE’s assumptions for CFLKs regarding the market 

penetration of products in 2019 that meet the target level at the proposed TSL given voluntary 

efficacy targets. Table 17.3.7 expands on Table 17.3.6 to include, for the proposed TSL, DOE’s 

assumptions regarding the market penetration of units in selected years. 

 

e For the calculation of B/C ratios DOE discounted the cash flow of the operating cost savings to the purchase year 

using a 7 percent discount rate.
17-12 

Table 17.3.6 Market Penetration in 2019 Attributable to Voluntary Energy Efficiency 

Targets

TSL 2

All CFLKs 

No-Standard-Case Market 

Share 10.2% 

Policy Case Market Share 30.9%

Increased Market Share 20.7%

Table 17.3.7 Market Penetration in Selected Years Attributable to Voluntary Efficacy

Targets for TSL 2 

2019 2028 2048

All CFLKs

No-Standard-Case Market 

Share

10.2% 1.9% 0.0%

Policy Case Market Share 30.9% 1.9% 0.0%

Increased Market Share 20.7% 0.0% 0.0%

The increased market share(s) attributable to voluntary efficacy targets shown in Table 

17.3.6 were used as inputs in the NIA-RIA model. Appendix 17A of this NOPR TSD shows the 

annual market share increases due to this policy for the whole forecast period. Section 17.4 

presents the resulting market penetration trends for the policy case of voluntary efficacy targets 

for CFLKs that meet the efficacy level for the proposed TSL. 

17.3.6 Bulk Government Purchases 

Bulk government purchases can lead to Federal, State, and local governments purchasing 

large quantities of products that meet a certain, target efficiency level. Combining the market 

demands of multiple public sectors can provide a market signal to manufacturers and vendors 

that some of their largest customers seek products that meet an efficiency target at favorable 

prices. Such a program also can induce “market pull,” whereby manufacturers and vendors 

would achieve economies of scale for high efficiency products. 

Most of the previous bulk government purchase (procurement) initiatives at the Federal, 

State, and municipal levels have not tracked data on numbers of purchases or degree of 

compliance with procurement specifications. In many cases, procurement programs are 

decentralized, being part of larger State or regional initiatives. DOE based its assumptions 

regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) 

performed regarding the savings potential of its procurement specifications for appliances and 

other products. FEMP, however, does not track purchasing data, because of the complex range of 

purchasing systems, large number of vendors, and so on. States, counties, and municipalities

have demonstrated increasing interest and activity in “green purchasing." Although many of the 

programs target office equipment, the growing infrastructure for developing and applying 
17-13 

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible.

23, 24

DOE assumed that government agencies would administer bulk purchasing programs for 

CFLKs. At the federal level, this type of program could modify the current FEMP procurement 

guidelines for CFLKs, which refer to the ENERGY STAR requirements for CFLKs.

25 DOE 

reviewed its own previous research on the potential for market transformation through bulk 

government purchases. Its major study analyzed several scenarios based on the assumption that 

20 percent of Federal equipment purchases in 2000 already incorporated energy efficiency

requirements based on FEMP guidelines. One scenario in the DOE report showed energy 

efficient purchasing ramping up during 10 years from 20 percent to 80 percent of all Federal 

purchases.26 Based on this study, DOE estimated that a bulk government purchase program 

instituted within a 10-year period would result in at least 80 percent of government-purchased 

CFLKs meeting the target efficacy level.

DOE assumed that bulk government purchases would affect a subset of housing units for 

which government agencies purchased or influenced the purchase of CFLKs. According to the 

2009 Residential Energy Consumption Survey (RECS 2009), about 1.2 percent of all U.S. 

households with one or more ceiling fans are housing units in public housing authority.27 DOE 

therefore estimated that 1.2 percent of the U.S. housing units with one or more ceiling fans 

constitute the population to which this policy would apply. DOE estimated that starting in 2019, 

each year of a bulk government purchase policy would result in an increasing percent of 

shipments of government-purchased units beyond the no-standard case that would meet the 

target efficacy level. 

Table 17.3.8 summarizes DOE’s assumptions for CFLKs regarding the market 

penetration of products in 2019 that meet the target level at the proposed TSL given bulk 

government purchases. 

Table 17.3.8 Market Penetration in 2019 Attributable to Bulk Government Purchases

TSL 2

All CFLKs 

No-Standard-Case Market 

Share 10.2% 

Policy Case Market Share 10.3%

Increased Market Share 0.1%

The increased market share attributable to bulk government purchases shown in Table 

17.3.8 were used as inputs in the NIA-RIA model. Appendix 17A of this NOPR TSD shows the 

annual market share increases due to this policy for the whole forecast period. Section 17.4 

presents the resulting market penetration trends for the policy case of bulk government purchases 

for CFLKs. 
17-14 

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES

Figure 17.4.1shows the effects of each non-regulatory policy alternative on the market 

penetration of more efficacious CFLKs. Relative to the no-standard case, the alternative policy 

cases increase the market shares that meet the target level. Recall the proposed standards (not 

shown in the figures) would result in a 100-percent market penetration of products that meet the 

more efficacious technology. 

Figure 17.4.1 Market Penetration of Efficacious CFLKs (TSL 2) 

Table 17.4.1shows the national energy savings and net present value for the five nonregulatory policy alternatives analyzed in detail for CFLKs. The target level for each policy 

corresponds to the same efficacious technology proposed for standards in TSL 2. The case in 

which no regulatory action is taken with regard to CFLKs constitutes the no-standard case (or 

"No New Regulatory Action" scenario), in which NES and NPV are zero by definition. For 

comparison, the tables include the impacts the proposed standards would have on the market if 

the market comprised by the CFLKs that do not meet the efficacy level corresponding to the 

proposed standard is offset to that efficacy level, and under the assumption of constant 

technology for the relamping of all CFLKs. Energy savings are given in quadrillion British 

thermal units (quads).f The NPVs shown in Table 17.4.1 are based on two discount rates, 7 

percent and 3 percent. 

f For the alternative policies whose market penetration depends on B/C ratio, the energy savings in Table 17.4.1

correspond to the case where the cash flow of the operating cost savings was discounted to the purchase year using a 

7 percent discount rate. 
17-15 

The policy with the highest projected cumulative energy savings is consumer rebates. 

Savings from tax credits and voluntary efficacy targets range from 29.2 to 14.6 percent of the 

savings from standards. Bulk government purchases has negligible cumulative energy savings. 

Overall, the energy saving benefits from the alternative policies, range from zero percent to 48.7 

percent of the benefits from an amended standard calculated under the assumptions above. 

Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives (TSL 2) 

Policy Alternative 

Energy Savings* 

quads

Net Present Value* 

million 2014$ 

7% Disc Rate 3% Disc Rate

Consumer Rebates 0.091 (48.7%) 457.7 732.0

Consumer Tax Credits 0.055 (29.2%) 274.6 439.2

Manufacturer Tax Credits 0.027 (14.6%) 137.3 219.6

Voluntary Efficacy Targets 0.049 (25.8%) 223.9 367.3

Bulk Government Purchases 0.000 (0.0%) 0.0 0.0

Proposed Standards 0.188 (100.0%) 962.1 1528.0

* For products shipped 2019-2048.

**The percentages show how the energy savings from each policy alternative compare to the primary 

energy savings from an amended standard (represented in the table as 100%) calculated under the 

assumptions that (a) the market comprised by the CFLKs that do not meet the efficacy level 

corresponding to the proposed standard is offset to that efficacy level, and (b) technology will be held 

constant when relamping CFLKs. 
17-16 

REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy 

Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.) 

<http://www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_

energy_efficiency>

2. ICF International, Arizona Public Service Energy Efficient Market Potential Study, 2007. 

Fairfax, VA. 

3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Optimal Energy. January, 2008. 

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Quantec, LLC. January, 2008. 

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Itron, Inc. January 2008 and March 2009. 

6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last 

accessed March, 2014.) <http://calmac.org/publications/PGE0264_Final_Report.pdf>

7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market 

Potential Study, 2010. (Last accessed March, 2014.) <www.ameren.com/-

/media/missourisite/Files/Environment/Renewables/AmerenUEVolume2MarketResearchReport2.pdf>

8. Coito, F., E-mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc. 

June, 2010. 

9. Blum, H., Atkinson, B., and Lekov, A., A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. Lawrence Berkeley National Laboratory. 

Berkeley, CA. Report No. LBNL-4749E. <http://escholarship.org/uc/item/4t32x6vr> 

10. Lawrence Berkeley National Laboratory, Energy End-Use Forecasting: Analysis of Tax 

Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and 

Assessment Group (Last accessed March, 2014.) 

<http://enduse.lbl.gov/Projects/TaxCredits.html>

11. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment 

Purchase Decisions, 1994. Prepared for Southern California Edison by Cambridge 

Systematics, Pacific Consulting Services, The Technology Applications Group, and 

California Survey Research Services. 
17-17 

12. U.S. Department of Energy: Office of Codes and Standards, Technical Support 

Document: Energy Efficiency Standards for Consumer Products: Refrigerators, 

Refrigerator-Freezers, and Freezers, Including Environmental Assessment and 

Regulatory Impact Analysis, November, 1995. Washington, DC. Report No. DOE/EE0064. 

13. Energy Policy Act of 2005, in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC 

25C note,. 2005. http://www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf

14. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed March, 2014.) <http://www.gpo.gov/fdsys/pkg/BILLS111hr1enr/pdf/BILLS-111hr1enr.pdf>

15. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed March, 2014.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed March, 2014.) <http://energytaxincentives.org/>

17. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support 

Document: Energy Efficiency Program for Consumer Products and Commercial and 

Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products, 

and Commercial Clothes Washers, 2008. Washington, DC. 

18. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed March, 

2014.) <http://energytaxincentives.org/builders/appliances.php>

19. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modeling the Effects of U.S. 

ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who 

Delivers?, May 30-June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La 

Napoule, France. 

<http://www.eceee.org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper

> 

20. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® 

Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy 

Program Evaluation Conference Proceedings. 

21. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy-Efficient Appliance 

Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program 

Evaluation Conference Proceedings. Seattle, WA. 

22. Blum, H., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. 
17-18 

23. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal 

communication. National Association of State Procurement Officials. April, 2008. 

24. Responsible Purchasing Network, (Last accessed March, 2014.) 

<www.responsiblepurchasing.org/>

25. ENERGY STAR, Ceiling Fans Key Product Criteria, (Last accessed May, 2015, 2015.) 

<http://www.energystar.gov/index.cfm?c=ceiling_fans.pr_crit_ceiling_fans>

26. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase. ACEEE: Summer Study on Energy Efficiency in 

Buildings, 2000: pp. 147-166 

<www.aceee.org/files/proceedings/2000/data/papers/SS00_Panel4_Paper13.pdf> 

27. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS), 2009. (Last accessed December, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata>
AA-1

APPENDIX AA. ACRONYMS AND ABBREVIATIONS

ACEEE The American Council for an Energy-Efficient Economy

AEMTCA American Energy Manufacturing Technical Corrections Act

AEO Annual Energy Outlook

ALA American Lighting Association

AP Allegheny Power

BaO Barium oxide

CAIR Clean Air Interstate Rule

CaO Calcium oxide

CAPM Capital Asset Pricing Model

CAT Calcium Tungstate

CBECS Commercial Building Energy Consumption Survey

CCMS Compliance Certification Management System

CCR California Code of Regulations

CCT Correlated color temperature

CF Ceiling fans

CFL Compact fluorescent lamp

CFLK Ceiling fan light kit

CFR Code of Federal Regulations

COSI Cost of Savings Index

CRI Color rendering index

CSAPR Cross-State Air Pollution Rule

CVD Chemical vapor deposition

DF Discount factor

DOE U.S. Department of Energy

DOJ Department of Justice

DPL Delmarva Power and Light

DSM Demand Side Management

EEI Edison Electric Institute

EGU Electric generating units

EIA U.S. Energy Information Administration

EISA Energy Independence and Security Act

EL Efficacy level

EPA U.S. Environmental Protection Agency

EPCA Energy Policy and Conservation Act

ERP Equity risk premium

ESR Effective series resistance

FEMP Federal Energy Management Program

FFC Full-fuel-cycle

GaN Gallium nitride

GDP Gross domestic product

GE General Electric

GHG Greenhouse gas

GREET Gases, Regulated Emissions, and Energy Use in Transportation
AA-2

GRIM Government Regulatory Impact Model

GSFL General service fluorescent lamp

GSIL General service incandescent lamp

GSL General service lamp

HAP Hazardous air pollutants

HID High-intensity discharge

HOU Hours of use

IAM Integrated assessment models

IMERC Interstate Mercury Education and Reduction Clearinghouse

ImSET Impact of Sector Energy Technologies

INPV Industry net present value

IOU Investor-owned utilities

IS Instant start

LBNL Lawrence Berkeley National Laboratory

LCC Life-cycle cost

LED Legacy dimmers

LED Light-emitting diode

LMC Lighting Market Characterization

MATS Mercury and Air Toxics Standards

MIA Manufacturer impact analysis

MLF Marginal load factor

MPC Manufacturer production costs

MSP Manufacturer selling price

NAICS North American Industry Classification System

NEMS National Energy Modeling System

NES National energy savings

NIA National Impact Analysis

NOPM Notice of public meeting

NOPR Notice of proposed rulemaking

NOX Nitrogen oxides

NPV Net present value

NREL National Renewable Energy Laboratory

O&M Operation and maintenance

OAL Office of Administrative Law

OC Operating cost

OMB Office of Management and Budget

PBP Payback period

PC Phosphor-converted

POU Publicly owned utilities

PPE Plant, property, and equipment

PS Programmed start

PVS Present value of savings

PWP Pasadena Water and Power

R&D Research and development

RBSAM Residential Building Stock Assessment Metering

RECS Residential Energy Consumption Survey
AA-3

RF Rate factor

RIA Regulatory Impact Analysis

RLEUCS Residential Lighting End-Use Consumption Study

RS Rapid start

S&P Standard & Poor

SBA Small Business Administration

SCC Social Cost of Carbon

SCF Survey of Consumer Finances

SG&A Selling, general, and administrative

SiC Silicon carbide

SMECO Southern Maryland Electric Cooperative

SPD Spectral power distributions

SrO Strontium oxide

SSL Solid-state lighting

TIM Thermal Interface Materials

TP Test procedure

TSD Technical support document

TSL Trial standard level

UEC Unit energy consumption

UHE Ultra-high efficiency

UL Underwriters Laboratories

UV Ultraviolet

WACC Weighted average cost of capital

WPE Wall plug efficiency

YAG Yttrium aluminum garnet

ZrO Zirconium dioxide
7A-i

APPENDIX 7A. PRODUCT CLASS RESTRUCTURING SUBBASELINE ENGINEERING AND PRICING RESULTS 

TABLE OF CONTENTS

7A.1 INTRODUCTION ........................................................................................................ 7A-1

7A.2 SUB-BASELINE ENGINEERING ANALYSIS ......................................................... 7A-1

7A.3 SUB-BASELINE PRICING ANALYSIS .................................................................... 7A-2

LIST OF TABLES

Table 7A.2.1 Other Base Type Product Class Sub-Baseline Lamp Engineering Results ........ 7A-1

Table 7A.3.1 Other Base Type Product Class Sub-Baseline Lamp Price Results .................... 7A-2
7A-1

APPENDIX 7A PRODUCT CLASS RESTRUCTURING SUB-BASELINE 

ENGINEERING AND PRICING RESULTS

7A.1 INTRODUCTION

When evaluating and establishing energy conservation standards, U.S. Department of 

Energy (DOE) divides covered products into classes by (a) the type of energy used; (b) the 

capacity of the product; or (c) other performance-related features that justify different standard 

levels, such as features affecting consumer utility. (See 42 U.S.C. 6295(q)) For this rulemaking, 

DOE reviewed the existing product class structure and determined that the current divisions did 

not meet DOE’s criteria for establishing product classes. Thus, as discussed in chapter 3 of this

notice of proposed rulemaking (NOPR) technical support document (TSD), DOE is proposing a 

new product class structure. Because the existing product classes each have different efficacy

levels, DOE selected an appropriate sub-baseline representative lamp unit to account for the 

impacts of the product class restructuring in the life-cycle cost (LCC) analysis and national 

impact analysis (NIA). See chapters 8 and 10 of this NOPR TSD for further detail. DOE also 

determined consumer prices of the sub-baseline representative lamp unit.

7A.2 SUB-BASELINE ENGINEERING ANALYSIS

Existing standards for the Other Base Type product class, which comprises ceiling fan 

light kits with sockets other than pin-bases for fluorescent lamps or medium screw bases, are not 

based on an efficacy requirement. DOE determined that lamps in the Other Base Type product 

class generally have the lowest efficacies and identified it as the sub-baseline product class. See 

chapter 5 of this NOPR TSD for further discussion of the existing standards.

DOE selected a sub-baseline lamp as a reference point against which to measure changes 

resulting from the new product class structure. DOE selected a baseline lamp that is the most 

common lamp used in ceiling fan light kits (CFLKs) with an efficacy at or near the current 

standard. DOE used performance data presented in manufacturer catalogs for commercially 

available lamps to determine lamp efficacy. DOE also considered other lamp characteristics in 

choosing the most appropriate sub-baseline, such as wattage (W), bulb shape, base type, lifetime, 

color rendering index (CRI), and correlated color temperature (CCT). Based on its analysis, DOE 

identified a 60 W E12 at 7.3 lumens (lm)/W as the sub-baseline representative lamp unit, as 

shown in Table 7A.2.1.

Table 7A.2.1 Other Base Type Product Class Sub-Baseline Lamp Engineering Results

Bulb 

Shape

Base 

Type

Lamp 

Wattage

Initial Lumen

Output Efficacy Lamp 

Lifetime

W lm lm/W hr

A15 E12 60 440 7.3 1,500
7A-2

7A.3 SUB-BASELINE PRICING ANALYSIS

In energy conservation standards rulemakings, DOE often develops cost-efficiency 

relationships in the engineering analysis. Then, DOE applies markups to derive consumer prices. 

For this rulemaking, DOE developed efficacy levels in the engineering analysis and the product 

price determination develops consumer prices directly because reverse engineering a lamp is 

impractical, as lamps are not easily disassembled. Because the efficiency of the CFLK is based 

on the efficacy of the lamps it is packaged with, DOE develops a product price determination for 

the lamp component of the CFLK. Consumer price refers to the average product price a 

consumer pays before tax and installation.

DOE based its product price determination on publicly available pricing data and 

aggregated information received in manufacturer interviews. DOE developed the average 

consumer price for the sub-baseline representative lamp unit when packaged a CFLK. To do so, 

DOE determined the component of the CFLK consumer price that is attributable to the lamp with 

which it is packaged. DOE started with a consistent set of aggregated manufacturer-suggested 

distributor net prices for ceiling fans (CFs) sold with light kits and determined average shipmentweighted premiums applied to distributor net prices to obtain the consumer prices of CFLKs. 

DOE then applied the average shipment-weighted premium to the distributor net prices of the 

CFs sold with CFLKs to obtain their consumer price. (See chapter 7 of this NOPR TSD for 

further details on how premiums were developed.) Based on manufacturer feedback, DOE 

determined that a basic light kit fixture comprises 20 percent of the consumer price of a CF sold 

with a light kit. DOE applied this percentage to the consumer price of the CF sold with the 

CFLK to obtain the average consumer price of only the CFLK.

DOE then determined the portion of the consumer price of the CFLK attributable to the 

lamp component. Based on manufacturer feedback, DOE determined that lower efficacy lamps 

comprise 10 percent of the CFLK consumer price. DOE applied this percentage to the CFLK 

consumer price to obtain the consumer price of the lamp packaged with the CFLK. Table 7A.3.1

shows the results of the product price determination.

Table 7A.3.1 Other Base Type Product Class Sub-Baseline Lamp Price Results

Bulb 

Shape

Base 

Type

Lamp 

Wattage

Initial 

Lumen 

Output

Efficacy Lamp Lifetime

Weighted Average 

Lamp Consumer 

Price

W lm lm/W hr 2014$

A15 E12 60 440 7.3 1,500 $0.55
8A-i 

APPENDIX 8A. USER INSTRUCTIONS FOR LCC AND PBP SPREADSHEET 

TABLE OF CONTENTS

8A.1 INTRODUCTION ........................................................................................................ 8A-1

8A.2 DESCRIPTION OF LIFE-CYCLE COST SPREADSHEET ...................................... 8A-1
8A-1 

APPENDIX 8A. USER INSTRUCTIONS FOR LCC AND PBP SPREADSHEET 

8A.1 INTRODUCTION

The detailed results of the life-cycle cost (LCC) and payback period (PBP) analysis are 

illustrated with a Microsoft Excel® spreadsheet, which is accessible on the U.S. Department of 

Energy’s (DOE) rulemaking website for CFLKs.

a The spreadsheet posted on the DOE website 

has been tested with Microsoft Excel 2010. 

8A.2 DESCRIPTION OF LIFE-CYCLE COST SPREADSHEET

For all of the product classes, DOE created a single LCC workbook file containing a 

collection of worksheets. The LCC workbook contains the following worksheets that present 

results and sample calculations: 

Summary This worksheet contains summary LCC and PBP results at each efficacy 

level (EL) and LCC savings results and box plots for each trial standard 

level (TSL), as well as results for all alternative scenarios. Users can 

select between the reference scenario or alternative scenarios to generate 

results.

Res Example,

Com Example

Each of these worksheets contains detailed results and sample 

calculations for a single consumer (i.e., a purchaser of a CFLK) for the

residential sector (Res Example) and the commercial sector (Com 

Example), respectively. Users can choose consumer characteristics with 

a series of drop-down menus and fillable cells. Users can also select the 

EL and scenario (lamp- or CFLK-replacement) of the CFLK the 

consumer would choose in the no-standard case (i.e., in the absence of 

an amended standard), the trial standard level, and the EL and scenario a 

consumer would choose if a standard is set at the selected TSL. The 

right side of each sheet shows LCC and LCC savings results for the 

selected parameters. 

The LCC workbook contains the following worksheets that present inputs used in the 

LCC and PBP analysis:

Res Sample, 

Com Sample

The Res Sample and Com Sample worksheets contain the samples of 10,000 

consumers for the residential and commercial sector, respectively. During a 

simulation, DOE uses these samples to derive results for the analysis.

Res Energy Use, 

Com Energy Use

This worksheet contains inputs used to calculate annual CFLK energy use by 

EL: CFLK power input values, operating hours, market data on controls.

a http://www.regulations.gov/#!docketDetail;D=EERE-2012-BT-STD-0045 (Last accessed June 8th, 2015). 
8A-2 

Disposal Cost This worksheet shows the disposal costs for CFLs in the commercial sector at 

the end of the CFLK lifetime.

Relamping This worksheet contains calculations for the costs associated with replacing 

the lamps of the light kit at the end of the lamp lifetime over the analysis 

period.

Residual Value This worksheet shows the estimates of the residual value of the surviving 

lamps packaged with the CFLK at the end of the CFLK lifetime.

Parameters This worksheet contains data from the engineering analysis used in this LCC 

and PBP analysis. Other parameters on the worksheet include: total light kit 

price, lumen output, replacement options, and LED price learning. The 

replacement options are determined from the engineering analysis and market 

data.

Elec Trends This worksheet shows the price trends used to estimate electricity price for 

each purchaser.

Discount Rates This worksheet contains the distributions of discount rates.

Lifetime This worksheet contains the CFLK lifetime distribution.

Market Distribution This worksheet contains the market distribution for all scenarios

Tax This worksheet contains projected tax rates in 2019.
8B-i 

APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN LCC AND PBP ANALYSIS

TABLE OF CONTENTS

8B.1 INTRODUCTION .........................................................................................................8B-1

8B.2 INTRODUCTION TO UNCERTAINTY AND VARIABILITY .................................8B-1

8B.2.1 Approaches to Address Uncertainty and Variability .........................................8B-1

8B.3 PROBABILITY ANALYSIS AND THE USE OF MONTE CARLO 

SIMULATION IN THE LCC AND PBP ANALYSIS .................................................8B-2

8B.4 ALTERNATIVE SCENARIOS CONSIDERED IN THIS RULEMAKING ...............8B-3

8B.4.1 High AEO Growth Scenario ..............................................................................8B-3

8B.4.2 Low AEO Growth Scenario ...............................................................................8B-5

REFERENCES ..........................................................................................................................8B-8

LIST OF TABLES

Table 8B.4.1 Average LCC and PBP Results by Efficacy Level ......................................8B-4

Table 8B.4.2 Average LCC Savings..................................................................................8B-5

Table 8B.4.3 Average LCC and PBP Results by Efficacy Level ......................................8B-6

Table 8B.4.4 Average LCC Savings for Integrated Low-Lumen GSLs............................8B-7

LIST OF FIGURES

Figure 8B.3.1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions .................................................................................................8B-3
8B-1 

APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN LCC AND PBP ANALYSIS

8B.1 INTRODUCTION

This appendix discusses uncertainty and variability and describes how the U.S. 

Department of Energy (DOE) incorporated these into the life-cycle cost (LCC) and payback 

period (PBP) analysis in this notice of proposed rulemaking (NOPR) technical support document 

(TSD) for the ceiling fan light kit (CFLK) energy conservation standards (ECS) rulemaking. The 

two key approaches are (1) to use distributions to capture uncertainties and variations in input 

variables when such distributions are reasonably well defined, and (2) to use scenarios that 

capture the bounds of uncertainty when the bounds are less well defined. 

8B.2 INTRODUCTION TO UNCERTAINTY AND VARIABILITY

DOE develops mathematical models to analyze the impacts of proposed energy 

conservation standards. The models generate outputs (e.g., the LCC impact of proposed 

standards) based on inputs that are often uncertain, variable, or both. 

Variability means that the quantity of interest takes on different values at different times 

or under different conditions. Variability may be caused by many factors. For example, the hours 

of use of a lamp depend on environmental factors (e.g., diurnal variations in light) and behavioral 

factors (e.g., the schedules and preferences of the inhabitants of a house). Manufacturing 

irregularities can also cause variability. For example, 10 lamps of the same model may each have 

slightly different power consumptions. DOE attempts to account for major sources of variability 

in its analyses. 

Uncertainty has many sources. Variability may lead to uncertainty in model inputs, 

because analysts frequently must estimate the values of interest based on samples of a variable 

quantity (for example, the hours of use of lighting in a home). Measurement uncertainty is 

another source of uncertainty, which may result from instrumental uncertainties (resulting, for 

example, from drift, bias, and precision of resolution) and human factors (e.g., variations in 

experimental setup, errors in instrument readings or recordings). Uncertainty can also arise when 

there is limited data available to estimate a particular parameter. DOE attempts to address the 

major sources of uncertainties in its analyses. 

8B.2.1 Approaches to Address Uncertainty and Variability 

This section describes two approaches to address uncertainty and variability in numerical 

modeling that in practice are often used in tandem, as they are in this rulemaking: (1) probability 

analysis and (2) scenario analysis.

Probability analysis considers the probability that a variable has a given value over its 

range of possible values. For quantities with variability (e.g., electricity rates in different 

households), data from surveys or other forms of measurement can be used to generate a 

frequency distribution of numerical values to estimate the probability that the variable takes a 

given value. By sampling values from the resulting distribution, it is possible to quantify the 

impact of known variability in a particular variable on the outcome of the analysis. In this 
8B-2 

rulemaking, DOE used probability distributions to estimate CFLK service lifetime, annual lamp 

energy use, consumer electricity prices, and other variables. 

Unlike probability analysis, which considers the impact of known variability, scenario 

analysis estimates the sensitivity of an analysis to sources of uncertainty and variability whose 

probability distribution is not well known. Certain model inputs are modified to take a number of 

different values, and models are re-analyzed, in a set of different model scenarios. Because only 

selected inputs are changed in each scenario, the variability in the results for each scenario helps 

to quantify the impact of uncertainty in the input parameters. Whereas it is relatively simple to 

perform scenario analyses for a range of scenarios, scenario analyses provide no information 

regarding the likelihood of any given scenario’s actually occurring. 

Scenario and probability analysis provide some indication of the robustness of the policy 

given the uncertainties and variability. A policy is robust when the impacts are acceptable over a 

wide range of possible conditions. 

8B.3 PROBABILITY ANALYSIS AND THE USE OF MONTE CARLO 

SIMULATION IN THE LCC AND PBP ANALYSIS 

To quantify the uncertainty and variability that exist in inputs to the LCC and PBP 

analysis, DOE used Monte Carlo simulation and probability distributions to conduct probability 

analyses.

Simulation refers to any analytical method meant to imitate a real-life system, especially 

when other analyses are too mathematically complex or too difficult to reproduce. Without the 

aid of simulation, a model will only reveal a single outcome, generally the most likely or average 

scenario. Probabilistic risk analysis uses both a spreadsheet model and simulation to 

automatically analyze the effect of varying inputs on the outputs of a modeled system. One type 

of simulation is Monte Carlo simulation, which repeatedly generates random values for uncertain 

variables, drawn from a probability distribution, to simulate a model. 

For each uncertain variable, the range of possible values is controlled by a probability 

distribution. The type of distribution selected is based on the conditions surrounding that 

variable. Probability distribution types include normal, triangular, uniform, and Weibull 

distributions, as well as custom distributions where needed. Example plots of these distributions 

are shown in Figure 8B.3.1. 
8B-3 

Figure 8B.3.1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions

During a simulation, multiple scenarios of a model are calculated by repeatedly sampling 

values from the probability distributions for the uncertain variables and using those values for 

that input. Monte Carlo simulations can consist of as many trials as desired, with larger numbers 

of trials yielding more accurate average results. During a single trial, the simulation randomly 

selects a value from the defined possibilities (the range and shape of the probability distribution) 

for each uncertain variable and then recalculates the result for that trial.

8B.4 ALTERNATIVE SCENARIOS CONSIDERED IN THIS RULEMAKING

As discussed in chapter 8 of this NOPR TSD, to estimate future electricity prices, DOE 

used the Reference case projection in Annual Energy Outlook 2015 (AEO 2015).1 The AEO 

reference case projection is a business-as-usual estimate, given known market, demographic, and 

technological trends. Because of the uncertainty associated with estimating future electricity 

prices, DOE also included two alternative scenarios for future electricity prices; the AEO High 

Growth and AEO Low Growth scenarios in the analysis. The high- and low-growth cases show 

the projected effects of alternative growth assumptions on energy markets. 

DOE used the Monte Carlo approach with 10,000 runs per alternative scenario to 

determine how the results of the LCC and PBP analysis change under different assumptions. The 

following section presents the results of the LCC and PBP for the alternative scenario analyses. 

8B.4.1 High AEO Growth Scenario

Table 8B.4.1 through Table 8B.4.2 present results for the high economic growth 

electricity price trends from AEO 2015. 

NORMAL TRIANGULAR UNIFORM

WEIBULL CUSTOM
8B-4 

Table 8B.4.1 Average LCC and PBP Results by Efficacy Level

EL

Average Costs (2014$) Simple 

Payback

(years) 

Average 

Lifetime

(years) Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

Residential Sector

Sub* 2.8 17.4 70.6 71.6 -- 13.8

0 5.5 3.6 40.8 45.9 0.2 13.8

1 8.8 3.4 40.4 48.7 0.4 13.8

2 19.4 2.9 33.7 52.1 1.2 13.8

3 10.5 2.0 23.6 33.0 0.5 13.8

4 9.3 1.9 22.2 30.5 0.4 13.8

Commercial Sector

Sub* 2.8 77.8 197.0 199.1 -- 13.8

0 5.5 16.0 139.4 145.5 0.0 13.8

1 8.8 15.1 160.0 170.1 0.1 13.8

2 19.4 13.0 143.5 163.3 0.3 13.8

3 10.5 9.1 109.7 119.9 0.1 13.8

4 9.3 8.6 106.9 115.8 0.1 13.8

* “Sub” corresponds to the sub-baseline (i.e., lamps which have efficacies below the baseline set for the new product 

class structure proposed in this rulemaking).

Note: The results for each TSL are calculated assuming that all consumers use products at that efficacy level. The 

PBP is measured relative to the least efficient product currently available on the market.
8B-5 

Table 8B.4.2 Average LCC Savings 

TSL

Life-Cycle Cost Savings

% of Consumers that 

Experience

Net Cost

Average Savings

(2014$) 

Residential Sector

0 0.7% 22.9

1 0.7% 22.9

2 9.7% 24.3

3 7.6% 31.0

4 7.6% 31.1

Commercial Sector

0 10.8% 28.2

1 10.8% 28.2

2 2.0% 53.7

3 0.2% 68.2

4 0.2% 68.4

Note: The results for each TSL represent the impact of a standard set at that TSL, based on the no-standard case and 

standards-case efficacy distributions calculated in the shipments analysis. The calculation excludes consumers with 

zero LCC savings (no impact).

8B.4.2 Low AEO Growth Scenario

Table 8B.4.3 and Table 8B.4.4 present results for the low economic growth electricity 

price trends from AEO 2015. 
8B-6 

Table 8B.4.3 Average LCC and PBP Results by Efficacy Level

EL

Average Costs (2014$) Simple 

Payback

(years) 

Average 

Lifetime

(years) Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

Residential Sector

Sub* 2.8 17.1 69.2 70.2 -- 13.8

0 5.5 3.5 39.3 44.5 0.2 13.8

1 8.8 3.3 38.9 47.3 0.4 13.8

2 19.4 2.9 32.5 50.9 1.2 13.8

3 10.5 2.0 22.7 32.2 0.5 13.8

4 9.3 1.9 21.4 29.7 0.4 13.8

Commercial Sector

Sub* 2.8 75.1 189.6 191.8 -- 13.8

0 5.5 15.4 131.6 137.7 0.0 13.8

1 8.8 14.6 151.4 161.5 0.1 13.8

2 19.4 12.5 135.5 155.3 0.3 13.8

3 10.5 8.8 103.5 113.7 0.1 13.8

4 9.3 8.3 100.9 109.7 0.1 13.8

* “Sub” corresponds to the sub-baseline (i.e., lamps which have efficacies below the baseline set for the new product

class structure proposed in this rulemaking).

Note: The results for each TSL are calculated assuming that all consumers use products at that efficacy level. The 

PBP is measured relative to the least efficient product currently available on the market.
8B-7 

Table 8B.4.4 Average LCC Savings for Integrated Low-Lumen GSLs

TSL

Life-Cycle Cost Savings

% of Consumers that 

Experience

Net Cost

Average Savings

(2014$) 

Residential Sector

0 0.6% 23.0

1 0.6% 23.0

2 9.9% 24.0

3 7.7% 30.5

4 7.7% 30.5

Commercial Sector

0 9.5% 29.6

1 9.5% 29.6

2 1.7% 52.7

3 0.3% 66.5

4 0.3% 66.7

Note: The results for each TSL represent the impact of a standard set at that TSL, based on the no-standard case and 

standards-case efficacy distributions calculated in the shipments analysis. The calculation excludes consumers with 

zero LCC savings (no impact).
8B-8 

REFERENCES

1. U.S. Energy Information Administration. Annual Energy Outlook 2015 with Projections 

to 2040. 2015. Washington, D.C. Report No. DOE/EIA-0383(2015). (Last accessed June 

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2015).pdf.
8C-i 

APPENDIX 8C. DISCOUNT RATE DISTRIBUTIONS

TABLE OF CONTENTS

8C.1 INTRODUCTION .........................................................................................................8C-1

8C.2 DISTRIBUTION OF RATES FOR DEBT CLASSES .................................................8C-1

8C.3 DISTRIBUTION OF RATES FOR EQUITY CLASSES .............................................8C-4

8C.4 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY INCOME 

GROUP ..........................................................................................................................8C-8

8C.5 DISTRIBUTIONS USED FOR COMMERCIAL DISCOUNT RATES ....................8C-11

REFERENCES ........................................................................................................................8C-12

LIST OF TABLES

Table 8C.4.1 Distribution of Real Discount Rates by Income Group .............................8C-10

Table 8C.5.1 Real Interest Rate Distribution for Commercial Buildings ........................8C-11

LIST OF FIGURES

Figure 8C.2.1 Distribution of Mortgage Interest Rates ......................................................8C-1

Figure 8C.2.2 Distribution of Home Equity Loan Interest Rates .......................................8C-2

Figure 8C.2.3 Distribution of Credit Card Interest Rates ...................................................8C-2

Figure 8C.2.4 Distribution of Installment Loan Interest Rates ...........................................8C-3

Figure 8C.2.5 Distribution of Other Residence Loan Interest Rates ..................................8C-3

Figure 8C.2.6 Distribution of Other Lines of Credit Loan Interest Rates ..........................8C-4

Figure 8C.3.1 Distribution of Annual Rate of Return on CDs ...........................................8C-5

Figure 8C.3.2 Distribution of Annual Rate of Return on Savings Bonds ...........................8C-5

Figure 8C.3.3 Distribution of Annual Rate of Return on Corporate AAA Bonds ..............8C-6

Figure 8C.3.4 Distribution of Annual Rate of Savings Accounts .......................................8C-6

Figure 8C.3.5 Distribution of Annual Rate of Money Market Accounts ...........................8C-7

Figure 8C.3.6 Distribution of Annual Rate of Return on S&P 500 ....................................8C-7

Figure 8C.3.7 Distribution of Annual Rate of Return on Mutual Funds ............................8C-8

Figure 8C.4.1 Distribution of Real Discount Rates by Income Group ...............................8C-9
8C-1 

APPENDIX 8C. DISCOUNT RATE DISTRIBUTIONS

8C.1 INTRODUCTION

The U.S. Department of Energy (DOE) derived discount rates for the life-cycle cost 

(LCC) analysis using data on interest or return rates for various types of debt and equity to 

calculate a real effective discount rate for each household in the Federal Reserve Board’s Survey 

of Consumer Finances (SCF) in 1995, 1998, 2001, 2004, 2007, and 2010. 1To account for 

variation among households in rates for each of the types, DOE sampled a rate for each 

household in its building sample from a distribution of discount rates for each of six income 

groups. This appendix describes the distributions used. 

8C.2 DISTRIBUTION OF RATES FOR DEBT CLASSES 

Figure 8C.2.1 through Figure 8C.2.6 show the distribution of real interest rates for 

different types of household debt. The data source for the interest rates for mortgages, home 

equity loans, credit cards, installment loans, other residence loans, and other lines of credit is the 

Federal Reserve Board’s SCF in 1995, 1998, 2001, 2004, 2007, and 2010.1

 DOE adjusted the 

nominal rates to real rates using the annual inflation rate in each year. 

Using the appropriate SCF data for each year, DOE adjusted the nominal mortgage 

interest rate and the nominal home equity loan interest rate for each relevant household in the 

SCF for mortgage tax deduction and inflation. In cases where the effective interest rate is equal 

to or below the inflation rate (resulting in a negative real interest rate), DOE set the real effective 

interest rate to zero.

Figure 8C.2.1 Distribution of Mortgage Interest Rates

0%

5%

10%

15%

20%

25%

30%

35%

0.5%1.6%2.5%3.5%4.4%5.4%6.3%7.4%8.5%9.4%

10.4%11.6%12.5%13.2%14.4%16.6%22.8%

Probability

Real Interest Rate
8C-2 

Figure 8C.2.2 Distribution of Home Equity Loan Interest Rates 

Figure 8C.2.3 Distribution of Credit Card Interest Rates

0%

5%

10%

15%

20%

25%

30%

0.4%1.5%2.5%3.5%

4.5%5.4%6.4%7.5%8.4%9.4%

10.5%11.5%12.5%13.8%14.7%

15.7%16.6%17.1%22.0%

Probability

Real Interest Rate

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

0.0%2.5%4.5%6.6%8.6%

10.5%12.5%14.7%16.4%18.4%20.4%22.7%24.7%26.8%28.5%

Probability

Real Interest Rate
8C-3 

Figure 8C.2.4 Distribution of Installment Loan Interest Rates

Figure 8C.2.5 Distribution of Other Residence Loan Interest Rates

0%

5%

10%

15%

20%

25%

30%

35%

40%

0.0%2.4%4.5%6.6%8.5%

10.5%12.5%14.7%16.4%18.4%20.5%22.5%24.6%26.8%28.5%

Probability

Real Interest Rate

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

0.2%1.5%2.5%3.5%4.5%5.5%6.5%7.5%8.6%9.4%

10.4%11.5%12.5%13.1%14.7%15.7%16.6%

Probability

Real Interest Rate
8C-4 

Figure 8C.2.6 Distribution of Other Lines of Credit Loan Interest Rates

8C.3 DISTRIBUTION OF RATES FOR EQUITY CLASSES

Figure 8C.3.1 through Figure 8C.3.7 show the distribution of real interest rates for 

different types of equity. Data for equity classes are not available from the Federal Reserve 

Board’s SCF, so DOE derived data for these classes from national-level historical data (1984-

2013). The interest rates associated with certificates of deposit (CDs),2 savings bonds,3

 and 

AAA corporate bonds4 are from Federal Reserve Board time-series data. DOE assumed rates on 

checking accounts to be zero. Rates on savings and money market accounts are from Cost of 

Savings Index data.5

 The rates for stocks are the annual returns on the Standard and Poor’s 

(S&P) 500.6 The mutual fund rates are a weighted average of the stock rates (two-thirds weight) 

and the bond rates (one-third weight) in each year. DOE adjusted the nominal rates to real rates 

using the annual inflation rate in each year. 

0%

2%

4%

6%

8%

10%

12%

0.1%2.5%4.5%6.6%8.6%

10.5%12.5%14.7%16.4%

18.4%20.2%

22.6%25.4%

Probability

Real Interest Rate
8C-5 

 Figure 8C.3.1 Distribution of Annual Rate of Return on CDs

Figure 8C.3.2 Distribution of Annual Rate of Return on Savings Bonds

-0.04

-0.02

0

0.02

0.04

0.06

0.08

19841986

19881990

1992199419961998200020022004200620082010

2012

Annual Rate of Return (%)

Year

CDs (Average of 12 and 30 month)

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Savings Bonds
8C-6 

Figure 8C.3.3 Distribution of Annual Rate of Return on Corporate AAA Bonds 

Figure 8C.3.4 Distribution of Annual Rate of Savings Accounts 

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

AAA Corporate Bonds

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Savings Accounts
8C-7 

Figure 8C.3.5 Distribution of Annual Rate of Money Market Accounts 

Figure 8C.3.6 Distribution of Annual Rate of Return on S&P 500

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Money Market Accounts

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Stocks (S&P 500)
8C-8 

Figure 8C.3.7 Distribution of Annual Rate of Return on Mutual Funds

8C.4 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY INCOME 

GROUP

Figure 8C.4.1 and Table 8C.4.1 present the distributions of real discount rates for each 

income group. 

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

1984198619881990199219941996

19982000

200220042006200820102012

Annual Rate of Return (%)

Year

Mutual Funds
8C-9 

Figure 8C.4.1 Distribution of Real Discount Rates by Income Group

0%

5%

10%

15%

20%

25%

0.0% 5.0% 10.0% 15.0% 20.0%

Share of Individuals in Group (%)

Real Effective Discount Rate (%)

Group 1

Group 2

Group 3

Group 4

Group 5

Group 6
8C-10 

Table 8C.4.1 Distribution of Real Discount Rates by Income Group 

DR Bin

Income Group 1 Income Group 2 Income Group 3 Income Group 4 Income Group 5 Income Group 6

(1-20 percentile) (21-40 percentile) (41-60 percentile) (61-80 percentile) (81-90 percentile) (90-99 percentile)

rate weight rate weight rate weight rate weight rate weight rate weight

0-1 0.5% 0.238 6% 0.152 0.6% 0.104 0.6% 0.077 0.6% 0.056 0.6% 0.057

1-2 1.6% 0.110 1.6% 0.120 1.6% 0.105 1.6% 0.146 1.6% 0.142 1.6% 0.185

2-3 2.5% 0.087 2.5% 0.112 2.6% 0.131 2.5% 0.205 2.5% 0.219 2.5% 0.207

3-4 3.5% 0.117 3.5% 0.137 3.5% 0.164 3.5% 0.173 3.5% 0.200 3.5% 0.178

4-5 4.5% 0.097 4.5% 0.113 4.5% 0.136 4.5% 0.129 4.5% 0.153 4.5% 0.144

5-6 5.5% 0.083 5.5% 0.084 5.5% 0.100 5.5% 0.093 5.5% 0.098 5.5% 0.120

6-7 6.5% 0.058 6.5% 0.062 6.5% 0.075 6.5% 0.067 6.5% 0.063 6.4% 0.079

7-8 7.5% 0.036 7.5% 0.051 7.6% 0.054 7.4% 0.041 7.4% 0.029 7.3% 0.011

8-9 8.5% 0.036 8.4% 0.039 8.4% 0.034 8.5% 0.015 8.4% 0.012 8.5% 0.005

9-10 9.5% 0.017 9.5% 0.018 9.5% 0.017 9.5% 0.010 9.5% 0.008 9.6% 0.005

10-11 10.5% 0.014 10.5% 0.019 10.5% 0.013 10.5% 0.011 10.6% 0.004 10.7% 0.004

11-12 11.5% 0.010 11.5% 0.015 11.5% 0.014 11.5% 0.007 11.4% 0.004 11.7% 0.001

12-13 12.5% 0.011 12.5% 0.012 12.5% 0.009 12.4% 0.005 12.4% 0.002 12.4% 0.002

13-14 13.6% 0.012 13.5% 0.008 13.5% 0.009 13.5% 0.004 13.5% 0.002 13.3% 0.001

14-15 14.6% 0.016 14.6% 0.014 14.6% 0.009 14.5% 0.005 14.6% 0.003 14.2% 0.001

15-16 15.5% 0.011 15.5% 0.010 15.5% 0.006 15.6% 0.004 15.6% 0.002 15.3% 0.000

16-17 16.5% 0.013 16.5% 0.009 16.5% 0.004 16.5% 0.003 16.5% 0.001 0.0% 0.000

17-18 17.5% 0.009 17.6% 0.006 17.5% 0.005 17.5% 0.003 17.6% 0.001 17.7% 0.001

18-19 18.4% 0.005 18.5% 0.005 18.6% 0.003 18.4% 0.001 18.2% 0.000 0.0% 0.000

19-20 19.4% 0.006 19.4% 0.004 19.4% 0.002 19.7% 0.000 19.7% 0.000 19.4% 0.000

20-21 20.6% 0.004 20.4% 0.002 20.5% 0.001 20.3% 0.001 20.5% 0.000 20.3% 0.000

21-22 21.4% 0.003 21.4% 0.002 21.4% 0.001 21.5% 0.001 0.0% 0.000 21.4% 0.000

22-23 22.5% 0.002 22.4% 0.001 22.6% 0.001 22.9% 0.000 22.8% 0.000 22.3% 0.000

23-24 23.6% 0.001 23.4% 0.001 23.6% 0.001 0.0% 0.000 0.0% 0.000 24.0% 0.000

24-25 24.6% 0.001 24.5% 0.000 24.6% 0.000 24.1% 0.000 24.3% 0.000 0.0% 0.000

25-26 25.4% 0.001 25.4% 0.001 25.5% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

26-27 26.5% 0.001 26.5% 0.000 26.4% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

27-28 27.8% 0.000 27.6% 0.000 27.8% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

28-29 28.2% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

29-23 29.9% 0.000 29.3% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

>30 59.1% 0.001 142.7% 0.002 0.0% 0.000 53.3% 0.000 0.0% 0.000 0.0% 0.000
8C-11 

8C.5 DISTRIBUTIONS USED FOR COMMERCIAL DISCOUNT RATES

Table 8C.4.1 shows the probability distribution used for commercial discount rates by different 

commercial sector groups:

Table 8C.5.1 Real Interest Rate Distribution for Commercial Buildings

Retail Medical Lodging Food Service Office Education Other

Rate

(%)

Weight

(%)

Rate

(%)

Weight

(%)

Rate

(%)

Weight

(%)

Rate

(%)

Weight

(%)

Rate

(%)

Weight

(%)

Rate

(%)

Weight

(%)

Rate

(%)

Weight

(%)

2.9 6.0 2.9 6.2 2.9 4.5 2.9 4.7 2.9 6.7 -1.3 17.1 2.9 6.4

3.8 11.8 3.6 4.3 3.9 7.1 3.7 14.5 3.7 12.6 0.7 7.3 3.8 5.3

4.5 32.1 4.6 41.9 4.6 15.8 4.6 37.1 4.5 31.2 1.5 14.6 4.6 41.0

5.5 31.9 5.5 38.3 5.6 30.7 5.6 35.7 5.5 27.8 2.7 7.3 5.4 31.3

6.5 15.9 6.3 7.3 6.4 20.8 6.5 7.7 6.4 15.4 3.6 31.7 6.4 11.9

7.3 1.8 7.5 1.7 7.4 9.2 7.3 0.3 7.5 3.5 4.2 9.8 7.3 2.6

8.2 0.4 8.6 0.3 8.4 5.1 8.3 0.0 8.4 1.5 5.4 9.8 8.3 1.0

9.3 0.0 9.6 0.1 9.3 3.7 9.2 1.2 6.1 2.4 9.5 0.4

10.0 0.0 10.6 0.0 10.6 3.2 10.3 0.0 10.4 0.1

11.4 0.0 11.6 0.0

12.3 0.0

13.0 0.0
8C-12 

REFERENCES

1. Board of Governors of the Federal Reserve System. Survey of Consumer Finances. 1989, 

1992, 1995, 1998, 2001, 2004, 2007, and 2010. (Last accessed June 15, 2015.) 

http://www.federalreserve.gov/econresdata/scf/scfindex.htm. 

2. Board of Governors of the Federal Reserve System. Federal Reserve Statistical Release, 

Selected Interest Rates, Historical Data, Instrument: CDs (secondary market), Maturity: 

6-month, Frequency: Annual, Description: Average rate on 6-month negotiable 

certificates of deposit (secondary market), quoted on an investment basis. December 19, 

2013. (Last accessed June 15, 2015.) 

http://www.federalreserve.gov/releases/H15/data.htm. 

3. Board of Governors of the Federal Reserve System. Federal Reserve Statistical Release, 

Selected Interest Rates, Historical Data, Instrument: State and local bonds, Maturity: 20-

year, Frequency: Monthly, Description: Bond buyer go 20-bond municipal bond index. 

(Last accessed June 15, 2015.) http://www.federalreserve.gov/releases/H15/data.htm. 

4. Board of Governors of the Federal Reserve System. Federal Reserve Statistical Release, 

Selected Interest Rates, Historical Data, Instrument: Corporate bonds/Moody’s Seasoned 

AAA, Frequency: Annual, Description: Moody’s yield on seasoned corporate bonds—all 

industries, AAA. (Last accessed June 15, 2015.) 

http://www.federalreserve.gov/releases/H15/data.htm. 

5. Mortgage-X. Cost of Savings Index (COSI): Historical Data. (Last accessed June 15, 

2015.) http://mortgage-x.com/general/indexes/cosi_history.asp. 

6. Damodaran, A. Data Page: Historical Returns on Stocks, Bonds and Bills-United States. 

January 5, 2015. (Last accessed June 15, 2015.) http://pages.stern.nyu.edu/~adamodar/. 
8D-i 

APPENDIX 8D. ELECTRICITY PRICES

TABLE OF CONTENTS

8D.1 INTRODUCTION ........................................................................................................ 8D-1

8D.2 DATA SOURCES ........................................................................................................ 8D-2

8D.3 RESIDENTIAL SECTOR ............................................................................................ 8D-5

8D.4 NON-RESIDENTIAL SECTOR .................................................................................. 8D-7

REFERENCES ....................................................................................................................... 8D-13

LIST OF TABLES

Table 8D.2.1 Summary of Data Sources for Electricity Price Information ...................... 8D-3

Table 8D.2.2 Consumption and Demand Levels Included in the EEI Typical Bills 

and Rates Reports ....................................................................................... 8D-5

Table 8D.3.1 Adjustment Factors by Region and Sector.................................................. 8D-7

Table 8D.3.2 Residential Sector Electricity Prices by Region ......................................... 8D-7

Table 8D.4.1 Average Price for the US for each Bill Type, Sorted by Summer Price ..... 8D-9

Table 8D.4.2 Definition of Consumption Tiers for Averaging Across Bill Types ......... 8D-11

Table 8D.4.3 Non-Residential Sector Prices by Region ................................................. 8D-12
8D-1 

APPENDIX 8D. ELECTRICITY PRICES 

8D.1 INTRODUCTION

The following sections discuss DOE’s general methodology to derive marginal and 

average electricity prices for the residential and commercial sectors. Based on stakeholder 

comments encouraging DOE to use marginal electricity prices, DOE used only marginal 

electricity prices in the CFLK life-cycle cost (LCC) and payback period (PBP) NOPR analyses. 

Energy prices are used to calculate the operating cost savings to consumers who purchase 

and operate a more efficacious appliance. The energy savings are estimated as the difference 

between the energy use in the reference case and at standards-cases. For a consumer using more 

efficacious equipment, total household energy use and therefore the total energy bill is reduced 

in the standards case. The value of the energy savings is defined by the marginal price, i.e., the 

cost of a unit increment or decrement in energy use relative to the consumer's bill in the 

reference case. If the utility bill were simply a commodity cost times the amount consumed, then 

the marginal and average price would be identical. However, utility tariffs can have complex 

structures, so in general the marginal price differs from the average price and may be higher or 

lower. For this reason, DOE uses utility tariff information to independently estimate marginal 

and average prices. Two examples are presented below to illustrate how marginal prices depend 

on the tariff structure. 

Example 1: in this example the tariff is defined by a fixed charge FF and a commodity 

charge AA. Let EE be the electricity use in the reference case, and ∆EE the decremented electricity 

use in the standards case.

The total utility bill BB (neglecting taxes) is

BB = FF + (AA × EE) 

Eq. 8D.1 

The average price pp is defined as the ratio of the total bill to total usage:

pp = BB

EE = FFEE + AA

Eq. 8D.2 

The marginal price is defined by considering an increment dd to total usage (dd may be 

negative), and calculating the ratio of the change in the bill dd to the change EE: 

∆BB = FF + [AA × (EE + ∆EE)] − [FF + (AA × EE)] = AA × ∆EE

mm = ∆BB

∆EE = AA

Eq. 8D.3 

For the simple tariff defined above mm < pp as long as the fixed charge FF is not zero. 
8D-2 

Example 2: in this example the tariff is defined by a fixed charge FF and two commodity 

charges AA1 and AA2. Charge AA1 applies for all electricity use up to EE1, while charge AA2 applies for 

any usage over EE1. This is an example of a tiered rate structure. In general AA2 may be larger or 

smaller than AA1, although most utilities use increasing tiers with AA2 > AA1. For a tariff of this type, 

the marginal price is either AA1 or AA2, depending on whether the household energy use is more or 

less than EE1 in the reference case. For EE < EE1 the average price is

PP = [FF + (AA1 × EE)]

EE

Eq. 8D.4 

which is higher than the marginal price AA1. However for EE ≥ EE1

PP = {FF + (AA1 × EE1) + [AA2 × (EE − EE1)]}

EE

Eq. 8D.5 

and it is impossible to say in general whether the average price is higher or lower than the 

marginal price without knowing the precise values of all parameters. This relatively simple case 

illustrates that, for a given tariff, both the average and marginal prices depend on the level of 

consumption in the reference case.

8D.2 DATA SOURCES

DOE has reviewed several data sources related to electricity pricing for use in its 

consumer impacts analyses. The available data sets, along with features such as the size of the 

sample, temporal and spatial resolution of the data, and coverage of different market segments 

are summarized in Table 8D.2.1. The five publicly available sources that have been reviewed are

1. The Energy Information Agency (EIA) form 861: annual data on revenues, sales and

consumer counts by sector for all utilities in the U. S.

2. The Energy Information Agency (EIA) form 826: monthly data on revenues, sales and

consumer counts by sector for a subset of utilities in the U. S.

3. The RECS and CBECS building energy use surveys performed by the EIA. These

include, for some survey years, monthly utility bills and consumption.

4. Edison Electric Institute "Typical Bills and Average Rates" biannual reports, which

provide the total utility bills for specific consumptions levels for most of the investorowned utilities (IOU's) in the U. S.

5. Utility tariffs are public information and are generally available on the internet. The

Tariff Analysis Project (TAP) at LBNL has compiled a database of residential and nonresidential sectors, for about 100 utilities.
8D-3 

Table 8D.2.1 Summary of Data Sources for Electricity Price Information 

Sector Data 

Source

Resolution Sample Time Resolution

TOU

Time Geographic Customer 

Type Size Control Annual Seasonal Annual Seasonal

Res EIA 861 annual by utility none complete n/a estimate no no no no

C&I EIA 861 annual by utility none complete n/a estimate no no no no

Res EIA 826 monthly by utility none medium ok? estimate estimate estimate estimate no

C&I EIA 826 monthly by utility none medium ok? estimate estimate no no no

Res Bill data 

(RECS)

multiyear by region high large good yes yes estimate estimate no

C&I Bill data 

(CBECS)

multiyear by region high large good yes yes no no no

Res

EEI 

Typical 

Bills

biannual by utility 3 types small poor yes yes estimate estimate no

C&I

EEI 

Typical 

Bills

biannual by utility 9 types small poor yes yes no no no

Res Tariffs multiyear by region high small good yes yes yes yes yes

C&I Tariffs multiyear by region high small good yes yes yes yes yes

The EIA 861 data are often used to estimate average prices by defining the price as the 

ratio of total revenues to total sales. This is equivalent to calculating a consumption-weighted 

average bill across all consumers for a given utility and sector. This approach doesn't allow for 

the fact that the price depends on the consumer's baseline electricity use. In addition, nonresidential tariffs generally define the utility bill as a function of both consumption and demand, 

so datasets that include only electricity consumption cannot account for how the demand affects 

price. 

The EIA 826 data can be used to estimate a monthly average price in the same way as the 

EIA 861. The EIA826 data can also be used to estimate a marginal price by plotting the revenues 

vs. sales for each month and calculating the slope of this relationship. Seasonal values can be 

estimated by segregating the data into summer and winter months. The slope is a single number 

that represents the marginal revenue per additional unit of electricity sold for the utility. As with 

the EIA 861 data, this approach doesn't allow for any distinction between consumer segments or 

account for the role that electricity demand plays in determining prices.

The monthly utility bill data compiled with RECS and CBECS can be used to calculate 

both average prices and an approximate marginal price for each building in the sample. The 

marginal price is estimated by plotting the total bill vs. consumption for each billing period and 

estimating the slope of this relationship. For residential prices this is a useful approach as 

residential tariffs generally consist of a fixed charge plus tiered rates, which can be captured in a 

simple regression. For the non-residential sector however this approach is problematic, primarily 

because it is not possible to explicitly account for the effect of demand charges. Moreover, 

CBECS data have not included complete billing information since 1995. 
8D-4 

The Edison Electric Institute publishes a "Typical Bills and Average Rates" report for 

summer and winter each year.

1 The data in these reports consist of the total consumer bill at a set 

of fixed usage levels for most of the major investor-owned utilities (IOU's) in the country. The 

commercial and industrial usage levels specify both the electricity consumption (EE) and the peak 

electricity demand (DD). Usage levels are summarized in Table 8D.2.2. The EEI data can be used 

to estimate average prices for each of the typical bills, which helps to distinguish the effect of 

baseline energy use on the price. The EEI data also provide some ability to estimate the impact at 

the margin of changes in electricity consumption, as bills are provided with several levels of 

consumption for a fixed level of demand. The effect of demand can be evaluated to some extent 

with these data, by comparing bills for customers with the similar consumption but different 

demand levels, but the information provided is qualitative rather than quantitative.

The "Tariff Analysis Project" (TAP) database and calculation tools developed at LBNL2,3

have also been used in some DOE rulemakings.

4 The TAP database consists of a complete set of 

residential and non-residential tariffs for approximately 100 utilities. The information in the 

tariffs is stored in a set of normalized data tables whose structure represents the most common 

tariff structures.

2 The tariff database is the only electricity price dataset that allows the marginal 

value of changes in electricity demand and consumption to be separately estimated for the nonresidential sector, and that can explicitly model time-of-use rate structures. Hence, it allows for 

the computation of exact marginal prices, assuming the consumer baseline energy use, and 

appropriate decrements to both consumption and demand are known. However, this database is 

infrequently updated. 

For this analysis, DOE used the EEI Typical Bills and Rates reports for 2014, as these 

provide the most up-to-date information. This allows separate calculation of rates for summer 

and winter. The EEI data were supplemented as needed with information from EIA and the TAP 

database. DOE's calculation methods for the residential and non-residential sectors are described 

in the next section
8D-5 

Table 8D.2.2 Consumption and Demand LevelsIncluded in the EEI Typical Bills and 

RatesReports 

Index Sector Consumption (EE) Demand (DD) Load Factor (LL)

1 residential 500 0 n/a

2 residential 750 0 n/a

3 residential 1,000 0 n/a

4 commercial 375 3 0.171

5 commercial 1,500 3 0.685

6 commercial 10,000 40 0.343

7 commercial 14,000 40 0.480

8 commercial 150,000 500 0.411

9 commercial 180,000 500 0.493

10 industrial 15,000 75 0.274

11 industrial 30,000 75 0.548

12 industrial 50,000 75 0.913

13 industrial 200,000 1,000 0.274

14 industrial 400,000 1,000 0.548

15 industrial 650,000 1,000 0.890

16 industrial 15,000,000 50,000 0.411

17 industrial 25,000,000 50,000 0.685

18 industrial 32,500,000 50,000 0.890

8D.3 RESIDENTIAL SECTOR

DOE used the EEI typical bills to calculate an average and a marginal price for each 

utility, consumption level and season. The average price is equal to the total bill divided by the 

consumption: 

ppii = BBii

EEii

Eq. 8D.6 

where:

ii is the index of the typical bill from Table 8D.2.1, 

BBii is the bill,

EEii is the electricity consumption, and 

ppii is the average price.
8D-6 

The marginal price was determined by comparing the bills at two different consumption 

levels:

mmiiii = (BBii − BB )

(EEii − EE )

Eq. 8D.7 

DOE used mm32 as the marginal price for consumers with baseline energy use above EE2 = 

750 kWh/month, and mm21 as the marginal price for consumers with baseline energy use below 

EE2. DOE used pp1 as the average price for consumers with baseline consumption below EE1, pp3 as 

the price for consumers whose baseline is above EE3, and pp2 for those in between. DOE created 

regional weighted-average values for ppii and mmiiii by using the utility consumer counts to weight 

the contribution of each utility in a region. The regions used are census division/large state as 

used in the RECS data. The consumer counts were taken from the most recent available EIA 861 

data (in this case 2012). 

The EEI data do not contain information about publicly-owned utilities (POUs). DOE 

used the EIA data to account for the possibility that prices for POUs might differ systematically 

from those for IOUs. To begin with, an estimated average price p' for each utility and sector was 

calculated as the ratio of revenues to sales. Next, two regional weighted averages of pp′ were 

calculated, one based on all utilities (ppaaaaaa

′ ), and one based on only IOUs (ppIIIIII

′ ). DOE then 

defined an adjustment factor for each region and sector as the ratio ppaaaaaa

′ /ppIIIIII

′ . This adjustment 

factor, (shown in Table 8D.3.1 for all sectors) was applied to the prices calculated from the EEI 

data. 

The result of this analysis is a set of average and marginal prices that vary by region and 

by baseline electricity consumption. DOE assigned an average and a marginal price to each of 

the households in the RECS 2009 database based on its location and average monthly energy 

use. The regional prices used for the residential sector, incorporating the adjustment factor, are 

provided in Table 8D.3.2.
8D-7 

Table 8D.3.1 Adjustment Factors by Region and Sector 

Region Commercial Industrial Residential

1. New England 1.002 0.988 0.994

2. Middle Atlantic 1.002 1.003 0.997

3. East North Central 1.008 1.042 1.001

4. West North Central 1.045 1.198 1.012

5. South Atlantic 1.044 1.016 1.006

6. East South Central 1.080 1.028 1.024

7. West South Central 1.056 1.021 1.016

8. Mountain 0.982 1.066 0.994

9. Pacific 0.857 0.820 0.855

10. New York 1.006 0.960 0.999

11. Florida 1.052 0.984 1.020

12. Texas 1.022 1.180 0.963

13. California 0.966 1.033 0.968

Table 8D.3.2 Residential Sector Electricity Prices by Region 

Region 

Summer Prices

(cents/kWh)

Winter Prices

(cents/kWh)

pp11 pp22 pp33 mm22 mm33 pp11 pp22 pp33 mm22 mm33

1. New England 18.4 17.8 17.5 16.6 16.8 18.7 18.1 17.8 16.8 17.1

2. Middle Atlantic 15.3 15.1 15.1 14.6 15.0 15.0 14.5 14.3 13.7 13.7

3. East North Central 14.0 13.5 13.2 12.6 12.3 13.3 12.7 12.3 11.6 10.9

4. West North Central 13.5 12.8 12.5 11.6 11.6 12.1 11.4 10.7 9.8 8.9

5. South Atlantic 12.7 12.1 12.0 11.1 11.7 12.4 11.7 11.3 10.4 10.0

6. East South Central 12.9 12.0 11.5 10.1 10.1 12.5 11.5 10.8 9.5 8.9

7. West South Central 11.1 10.9 10.6 10.4 9.7 10.3 9.7 9.2 8.7 7.5

8. Mountain 12.3 12.2 12.2 12.1 12.1 11.9 11.4 11.2 10.4 10.5

9. Pacific 14.5 14.2 14.0 13.4 13.6 14.5 14.1 13.9 13.3 13.5

10. New York 21.6 20.5 20.0 18.3 18.3 27.0 25.8 25.1 23.3 23.3

11. Florida 11.9 11.3 11.0 10.0 10.0 11.8 11.1 10.8 9.9 9.9

12. Texas 12.4 11.9 11.7 11.0 11.0 11.1 10.6 10.4 9.7 9.6

13. California 17.5 22.9 26.1 33.7 35.6 16.7 21.7 24.7 31.8 33.7

8D.4 NON-RESIDENTIAL SECTOR

Electricity tariffs for non-residential consumers can be very complex, with the principal 

difference from residential rates being the incorporation of demand charges. The presence of 

demand charges means that two consumers with the same monthly electricity consumption may 

have very different bills, depending on their peak demand. Utilities use a broad range of pricing 

schemes for demand,

2 so the simplest way to characterize the effect of demand charges at the 

margin is to use an empirical marginal price, defined below.
8D-8 

While the EIA and EEI data make a distinction between the commercial and industrial 

sectors, utility tariffs typically refer only to consumer usage characteristics (small, medium or 

large power, high load factor, etc.). Hence, the commercial/industrial distinction is somewhat illdefined in real tariff data. In this analysis DOE uses the commercial and the industrial bills with 

index ≤ 15 to represent the non-residential sector consisting of commercial buildings and the 

type of light industry that would typically take place in buildings. The EEI bills for industrial 

with index ≥ 16 are used to represent heavy industry.

The average prices ppii are calculated using the same method as for the residential sector, 

with ppii equal to the ratio of the bill to the electricity consumption. Average prices depend on the 

demand and consumption values that define the bill. Analysis of the data show that prices are 

also sensitive to the billing-period load factor LL. The load factor is defined as the ratio to the 

average hourly energy use to the peak demand for the billing period: 

LL = (

EE

DD) × (

1

HH)

Eq. 8D.8 

where:

H = the number of hours in the average monthly billing period (8,760/12). 

Values for LL for each commercial and industrial bill are included in Table 8D.2.2. 

Sensitivity of the average price to demand, consumption and load factor is illustrated in Table 

8D.4.1 which provides the national weighted-average (across all utilities in the EEI data) values 

of pi for summer and winter. The data are sorted on the value of the summer average price. The 

right-most column defines a bin for the load factor, with LL < 0.4 assigned to bin 1, 0.4 ≤ LL ≤ 0.6 

to bin 2, and LL > 0.6 in bin 3. While the ordering of the prices is generally consistent with large 

users at the low end and small users at the high end, it is most closely tied to the ordering of the 

load factors. This is especially significant for the mid-range of consumption. The point here is 

that it can be misleading to assign prices based only on customer size as measured by either 

consumption or demand; the load factor is an equally important determinant of price. 
8D-9 

Table 8D.4.1 AveragePrice for the US for each Bill Type,Sorted by Summer Price 

Sector Consumption 

(EE)

Demand 

(DD)

Load Factor 

(LL) Index Summer Winter L-bin

ind 32,500,000 50000 0.890 18 7.32 6.63 3

ind 25,000,000 50000 0.685 17 7.91 7.08 3

ind 650,000 1000 0.890 15 8.47 7.42 3

ind 50,000 75 0.913 12 9.31 8.23 3

ind 15,000,000 50000 0.411 16 9.52 8.34 2

ind 400,000 1000 0.548 14 10.09 8.62 2

ind 30,000 75 0.548 11 10.80 9.40 2

com 180,000 500 0.493 9 11.46 10.37 2

com 14,000 40 0.480 7 12.21 11.13 2

com 150,000 500 0.411 8 12.33 11.02 2

ind 200,000 1000 0.274 13 13.69 11.28 1

com 10,000 40 0.343 6 13.74 12.32 1

ind 15,000 75 0.274 10 14.00 11.88 1

com 1,500 3 0.685 5 14.38 13.24 3

com 375 3 0.171 4 18.14 16.92 1

Marginal prices are defined using the approach developed for the TAP data.2,4 In this 

approach, independent marginal consumption and marginal demand prices are defined based on 

the change in the bill induced by independently changing either one or the other variable. The 

marginal consumption price (also called the marginal energy price) is defined as:

ee = [BB(EE + ∆EE,DD) − BB(EE,DD)]

∆EE

Eq. 8D.9 

where:

BB is the bill expressed as a function of EE and DD,

EE is the electricity consumption, 

DD is the electricity demand, 

∆EE is the increment to electricity consumption (which may be negative), and

ee is the marginal energy price or marginal consumption price.

Similarly, a marginal demand price dd is defined by: 

dd = [BB(EE,DD + ∆DD) − BB(EE,DD)]

∆DD

Eq. 8D.10 

where:

∆DD is the increment to electricity demand (which may be negative), and

dd is the marginal demand price.
8D-10 

Typically an energy conservation measure will alter both the consumption and demand. 

This affects the price through a variable called the marginal load factor λλ. The equation for λλ is:

λλ = (

∆EE

∆DD) × (

1

HH)

Eq. 8D.11 

where:

HH = the number of hours in the average monthly billing period (8,760/12). 

The marginal load factor λλ is a dimensionless number is analogous to the billing period 

load factor LL, it measures the ratio of the average hourly decrement to the peak demand 

decrement. The ratio of these two is partly determined by the degree to which the load decrement 

is coincident with the overall building load shape. For on-off loads such as lighting, the marginal 

load factor is equal to the fraction of total hours that the load is on. For flat loads such as 

refrigeration, λλ is close to one, while for strongly peaking loads like air conditioning λλ is likely 

to be in the range 0.15-0.5. 

The values of ee and dd are determined by the tariff and the baseline consumer data (EE, DD), 

but λλ is a variable in the marginal price equation. The empirically-determined marginal price, 

defined as the change in the bill induced by the joint increment (EE + ∆EE, DD + ∆DD),2 can be 

written as a function of λλ a as

mm = ee + [�

dd

HH� × �

1

λλ

�]

Eq. 8D.12 

The value dd/HH has the same units as ee (dollars per kWh). With this definition, the change 

in the bill is equal to mm × ∆EE, which accords with the usual definition of a marginal price. When 

the demand charges are zero, this marginal price is equal to the energy-only marginal price. The 

minimum value of λλ is 1/HH, and the maximum value is 1. In real applications λλ is unlikely to fall 

below 0.1.3

The EEI data allow estimation of marginal energy prices based on the equation 

a The equation for mm is equivalent to setting mm × ∆EE = (ee × ∆EE) + (dd × ∆DD). 
8D-11 

eeiiii = BBii − BB

EEii − EE

Eq. 8D.13 

for pairs of indices (ii, jj) corresponding to constant demand and varying energy ((ii, jj) = (4,5), 

(6,7), (8,9), (10,11), (11,12), etc.). As the EEI data do not allow the marginal demand price dd to 

be estimated directly, DOE used previous analyses of commercial tariffs to estimate the marginal 

demand price by region.4 The marginal demand prices estimated based on earlier data were 

scaled to 2014 using AEO current and historical price indices. 

If data about building baseline energy use is available, then in principle prices can be 

assigned based on the typical bill the building most closely resembles. However, when these data 

are not available a method must be used to average across the typical bills to get a single regional 

value from the EEI data for the average prices (pp) and marginal energy prices (ee). For this 

analysis, DOE used the CBECS building samples used for the tariff work4 to estimate the relative 

weight of buildings that should be assigned to the different consumption tiers represented in the 

EEI data. For this averaging across bill types, DOE excluded very low and very high load factors 

as not representative of real buildings. Consumption tiers were defined using mid-points between 

the values used for the typical bills, as shown in Table 8D.4.2. For example, any building with 

monthly consumption between 22,000 kWh and 105,000 kWh was assigned to the bill with 

index=11. In creating the tiers, DOE mixed the commercial and industrial bills because, as noted 

above, these distinctions are typically not used in the utility tariffs. 

Table 8D.4.2 Definition of Consumption Tiers for Averaging Across Bill Types 

Tier E_min E_max Index Sector 

1 0 7,750 5 com

2 7,750 22,000 7 com

3 22,000 105,000 11 ind

4 105,000 290,000 9 com

5 290,000 7,700,000 14 ind

6 7,700,000 20,000,000 15 ind

7 20,000,000 ω 16 ind

Once the average across bill types is complete, for each region a summer and winter 

average price, marginal energy price and marginal demand price can be calculated. For a given 

value of the marginal load factor, the empirical marginal price can also be defined. For the 

commercial sector, as the only location data available in CBECS are census divisions, DOE used 

these to define the regions. The Mountain and Pacific census divisions (8 and 9) were further 

subdivided into north and south based on the CBECS climate zone. Region 8.1 includes CBECS 

climate zone 1, and subdivision 8.2 all other climate zones. Subdivision 9.1 includes CBECS 

climate zones 1, 2 and 3, while subdivision 9.2 includes climate zones 4 and 5. The state 

assignments are 8.1 = (MT, ID, WY), 8.2 = (NV, UT, CO, AZ, NM), 9.1 = (WA ,OR), and 9.2 = 

CA.
8D-12 

The results of the analysis are presented in Table 8D.4.3. The table includes the marginal 

price calculated for λλ = 0.5, which is a reasonable mid-range value for many end-uses. 

Table 8D.4.3 Non-Residential Sector Prices by Region 

Region

Average Price (pp) 

cents/kwh

Marginal Energy 

Price (ee) cents/kwh

Marginal Demand 

Price (dd) $/kWh

Marginal Price (mm) 

with λλ = 0.5

Summer Winter Summer Winter Summer Winter Summer Winter

1_NE 15.36 16.03 12.61 13.54 15.73 11.29 16.92 16.63

2_Mid-Atl 13.88 14.46 10.86 12.12 12.79 13.27 14.36 15.75

3_ENC 11.28 10.51 9.28 8.54 12.05 10.30 12.58 11.36

4_WNC 10.71 8.82 8.56 7.04 5.61 4.58 10.09 8.30

5_S-Atl 10.18 9.88 7.78 7.46 6.84 6.42 9.65 9.22

6_ESC 11.38 10.81 9.28 8.77 6.37 5.65 11.03 10.32

7_WSC 9.58 8.29 7.72 6.60 4.43 3.10 8.93 7.45

8.1_Mtn_N 9.46 8.80 7.72 7.21 3.65 3.73 8.72 8.23

8.2_Mtn_S 10.95 9.72 7.61 6.70 7.48 7.50 9.66 8.75

9.1_Pac_N 9.39 9.41 7.85 7.82 2.19 2.16 8.44 8.42

9.2_Pac_S 21.11 13.32 13.99 10.20 8.84 4.02 16.41 11.30
8D-13 

REFERENCES

1. Coughlin, K., R. M. White, C. A. Bolduc, D. C. Fisher, and G. J. Rosenquist. The Tariff 

Analysis Project: A Database and Analysis Platform for Electricity Tariffs. 2006. 

Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-55680. (Last 

accessed June 15, 2015.) http://eetd.lbl.gov/publications/tariff-analysis-project-databaseand-analysis-platform-electricity-tariffs. 

2. Coughlin, K., C. Bolduc, R. Van Buskirk, G. Rosenquist, and J. E. McMahon. Tariffbased Analysis of Commercial Building Electricity Prices. 2008. Lawrence Berkeley 

National Laboratory: Berkeley, CA. Report No. LBNL-55551. (Last accessed June 8, 

2015.) https://escholarship.org/uc/item/9dv1m729. 

3. Edison Electric Institute. Typical Bills and Average Rates Report. Winter 2014 published 

April 2014, Summer 2014 published October 2014: Washington, D.C. (Last accessed 

June 2, 2015.) http://www.eei.org/resourcesandmedia/products/Pages/Products.aspx. 

4. U.S. Department of Energy. Commercial Unitary Air Conditioners and Heat Pumps 

Technical Support Document: Life-Cycle Cost and Pay-back Period Analysis. July 2004. 

Washington, D.C. (Last accessed June 15, 2015.) 

http://www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cuac_tsd_c

hp_8.pdf. 
9A-i 

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES

TABLE OF CONTENTS

9A.1 INTRODUCTION ........................................................................................................ 9A-1

9A.2 LITERATURE REVIEW ............................................................................................. 9A-1

9A.2.1 Price .................................................................................................................. 9A-1

9A.2.2 Income............................................................................................................... 9A-1

9A.2.3 Appliance Efficiency and Discount Rates ........................................................ 9A-1

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS ........................................................ 9A-2

9A.3.1 Physical Household/Appliance Variables ......................................................... 9A-2

9A.3.2 Economic variables ........................................................................................... 9A-3

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS ................................................................................................................ 9A-4

9A.4.1 Broad Trends ..................................................................................................... 9A-5

9A.4.2 Specification of Model ...................................................................................... 9A-7

9A.4.3 Discussion of Model ......................................................................................... 9A-8

9A.4.4 Analysis Results ................................................................................................ 9A-8

9A.4.4.1 Individual Appliance Model .................................................................... 9A-8

9A.4.4.2 Combined Appliance Model ................................................................... 9A-9

9A.4.5 Additional Regression Specifications and Results ......................................... 9A-10

9A.4.5.1 Lower Consumer Discount Rate ........................................................... 9A-10

9A.4.5.2 Disaggregated Variables ....................................................................... 9A-10

9A.5 LONG RUN IMPACTS .............................................................................................. 9A-11

9A.6 SUMMARY ................................................................................................................ 9A-12

9A.7 DATA USED IN THE ANALYSIS ........................................................................... 9A-13

REFERENCES ....................................................................................................................... 9A-15

LIST OF TABLES

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on 

Automobile and Appliance Sales ................................................................ 9A-2

Table 9A.3.1 Physical Household/Appliance Variables ................................................... 9A-3

Table 9A.3.2 Economic Variables .................................................................................... 9A-4

Table 9A.4.1 Estimate of Total Price Elasticity of Demand............................................. 9A-6

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand ......... 9A-7

Table 9A.4.3 Individual Appliance Model Results .......................................................... 9A-9

Table 9A.4.4 Combined Appliance Model Result ............................................................ 9A-9

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate..................... 9A-10

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate .................... 9A-11

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change .............................................................................. 9A-12
9A-ii

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change .............................................................................. 9A-12

LIST OF FIGURES

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements ............ 9A-4

Figure 9A.4.1 Residual Shipments and Appliance Price ................................................... 9A-6
9A-1 

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES

9A.1 INTRODUCTION

This appendix summarizes DOE’s study of the price elasticity of demand for home 

appliances, including refrigerators, clothes washers and dishwashers. DOE chose this particular 

set of appliances because of the availability of data to determine a price elasticity. Section 9A.2 

reviews the existing economics literature describing the impact of economic variables on the sale 

of durable goods. Section 9A.3 describes the market for home appliances and the changes that 

have occurred over the past 20 years. In section 9A.4, DOE summarizes the results of its

regression analysis and presents estimates of the price elasticity of demand for the three 

appliances. In section 9A.5, DOE presents development of an ‘effective’ purchase price 

elasticity. DOE’s interpretation of its results is presented in section 9A.6. Finally, section 9A.7 

describes the data used in DOE’s analysis.

9A.2 LITERATURE REVIEW

Relatively few studies measure the impact of price, income and efficiency on the 

sale of household appliances. This section briefly reviews the literature that describes 

the likely importance of these variables on the purchase of household appliances.

9A.2.1 Price

DOE reviewed many studies that sought to measure the impact of price on sales in a 

dynamic market. One study of the automobile market prior to 1970 finds the price elasticity of 

demand to decline over time. The author explains this as the result of buyers delaying purchases 

after a price increase but eventually making the purchase (Table 9A.2.1).

1 A contrasting study of 

household white goods also prior to 1970, finds the elasticity of demand to increase over time as 

more price-conscious buyers enter the market.2 An analysis of refrigerator market survey data 

finds that consumer purchase probability decreases with survey asking price.3Estimates of the

price elasticity of demand for different brands of the same product tend to vary. A review of 41 

studies of the impact of price on market share found the average price elasticity to be -1.75.4The 

average estimate of price elasticity of demand reported in these studies is -0.33 in the appliance 

market and -0.47 in the combined automobile and appliance markets. 

9A.2.2 Income

Higher income households are more likely to own household appliance.5The impact of 

income on appliance shipments is explored in two econometric studies of the automobile and 

appliance markets.1,2 The average income elasticity of demand is 0.50 in the appliance study 

cited in the literature review, much larger in the automobile study (Table 9A.2.1).

9A.2.3 Appliance Efficiency and Discount Rates

Many studies estimate the impact of appliance efficiency on consumers’ choice of 

appliance. Typically, this impact is summarized by the implicit discount rate; that is, the rate 

consumers use to compare future savings in appliance operating costs against a higher initial 

purchase price of 
9A-2 

an appliance. One early and much cited study concludes that consumers use a 20 percent implicit 

discount rate when purchasing room air conditioners (Table 9A.2.1).6 A survey of several studies 

of different appliances suggests that the consumer implicit discount rate has a broad range and 

averages about 37 percent.

7

 

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile 

and Appliance Sales

Durable Good Price 

Elasticity

Income 

Elasticity

Brand 

Price 

Elasticity

Implicit 

Discount 

Rate

Model Data 

Years

Time 

Period

Automobiles1 -1.07 3.08 - - Linear Regression, stock 

adjustment - Short run

Automobiles1 -0.36 1.02 - - Linear Regression, stock 

adjustment - Long run

Clothes Dryers2 -0.14 0.26 - - Cobb-Douglas, diffusion 1947-1961 Mixed

Room Air 

Conditioners2 -0.378 0.45 - - Cobb-Douglas, diffusion 1946-1962 Mixed

Dishwashers2 -0.42 0.79 - - Cobb-Douglas, diffusion 1947-1968 Mixed

Refrigerators3 -0.37 - - 39% Logit probability, survey 

data 1997 Short run

Various4 - - -1.769 - Multiplicative regression - Mixed

Room Air 

Conditioners5 - - -1.72 - Non-linear diffusion 1949-1961 Short run

Clothes Dryers5 - - -1.32 - Non-linear diffusion 1963-1970 Short run

Room Air 

Conditioners6 - - - 20% Qualitative choice, survey data - - 

Household 

Appliances7 - - - 37%10 Assorted - - 

Sources: 1 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997; 4 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985.

Notes: 8 Logit probability results are not directly comparable to other elasticity estimates in this table.

9 Average brand price elasticity across 41 studies.

10 Averaged across several household appliance studies referenced in this work.

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS

In this section DOE evaluates variables that appear to account for refrigerator, clothes 

washer and dishwasher shipments, including physical household/appliance variables and 

economic variables. 

9A.3.1 Physical Household/Appliance Variables

 Several variables influence the sale of refrigerators, clothes washers and dishwashers. The 

most important for explaining appliance sales trends are the annual number of new households 

formed (housing starts) and the number of appliances reaching the end of their operating life 

(replacements). Housing starts influence sales because new homes are often provided with, or 
9A-3 

soon receive, new appliances, including dishwashers and refrigerators. Replacements are 

correlated with sales because new appliances are typically purchased when old ones wear out. In 

principle, if households maintain a fixed number of appliances, shipments should equal housing 

starts plus appliance replacements. 

9A.3.2 Economic variables

Appliance price, appliance operating cost and household income are important 

economic variables affecting shipments. Low prices and costs encourage household appliance 

purchases and a rise in income increases householder ability to purchase appliances. In 

principle, changes in economic variables should explain changes in the number of appliances 

per household. 

During a 1980–2002 study period, annual shipments grew 69 percent for clothes 

washers, 81 percent for refrigerators and 105 percent for dishwashers (Table 9A.3.1). This 

rising shipments trend is explained in part by housing starts, which increased 6 percent and by 

appliance replacements, which rose between 49 percent and 90 percent, depending on the 

appliance, over the period (Table 9A.3.1).

a

For mature markets such as these, replacements 

exceed appliance sales associated with new housing construction. 

Table 9A.3.1 Physical Household/Appliance Variables 

Shipmentsi(millions) Housing Startsii (millions) Replacementsiii (millions)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49%

Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50%

Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90% i

Shipments: Number of units sold. Sources: AHAM Fact Book and Appliance Magazine. iiHousing Starts: Annual number of new homes constructed. Source: U.S. Census. iiiReplacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, ± 5 years.

Shipments increased somewhat more rapidly than housing starts and replacements. This is shown 

by comparing the beginning and end points of lines that represent “starts plus replacements” 

(uppermost solid line in Figure 9A.3.1) and “shipments” (diamond linked line in Figure 9A.3.1). 

In 1980 the “shipment” line begins below the “starts plus replacements” line. In 2002, the 

“shipments” line ends above the “starts plus replacements” line. This more rapid increase in 

shipments, compared to housing starts plus replacements, suggests that the appliance per 

household ratio increased over the study period. 

a Appliance replacements are determined from the expected operating life of refrigerators (19 years), clothes 

washers (14 years), and dishwashers (12 years) and from past shipments. Replacements are further discussed in 

section 9A.3. The dishwasher lifetime used in this analysis does not match the dishwasher used in the primary 

analysis. 
9A-4 

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements

Economic variables, including price, cost and income, may explain this increase in 

appliances per household. Over the period, appliance prices decreased 40 percent to 50 

percent, operating costs fell between 33 percent and 72 percent, and median household 

income rose 16 percent (Table 9A.3.2). 

Table 9A.3.2 Economic Variables

Pricei(1999$) Operating Costii (1999$) Household Incomeiii (1999$)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 1208 726 -40% 333 94 -72% 37,447 43,381 16%

Clothes Washers 779 392 -50% 262 175 -33% 37,447 43,381 16%

Dishwashers 713 369 -48% 183 95 -48% 37,447 43,381 16% i

Price: Shipment weighted retail sales price. Sources: AHAM Fact Book and Appliance Magazine. iiOperating Cost: Annual electricity price times electricity consumption. Source: AHAM Fact Book. iiiIncome: Mean Household income. Source: U.S. Census.

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS

Few data are available to estimate the impact of economic variables on the demand for 

appliances. Industry operating cost data is incomplete—appliance energy use data are available 

for only 12 years of the 1980-2002 study period. Industry price data are also incomplete—

available for only 8 years of the study period for each of the appliances. 

The lack of data suggests that regression analysis can at best evaluate broad data trends, 

utilizing relatively few explanatory variables. This section begins by describing broad trends 

apparent in the economic and physical household data sets and then specifies a simple 

regression model to measure these trends, making assumptions to minimize the number of 

explanatory variables. Finally, results of the regression analysis are presented along with an 

estimate of the price elasticity of demand for appliances. In section 9A.4.5, DOE presents the 

results of regression 

Refrigerator Shipments

0

1

2

3

4

5

6

7

8

9

10

1980 1984 1988 1992 1996 2000

Millions 

New Housing

Retirements

Shipments

Clothes Washer Shipments

0

1

2

3

4

5

6

7

8

1980 1984 1988 1992 1996 2000

Millions 

Dishwasher Shipments

0

1

2

3

4

5

6

1980 1984 1988 1992 1996 2000

Millions 
9A-5 

analysis performed with more complex models, which are used to test assumptions underlying 

the simple model. These results support the specification of the simple model and the price 

elasticity of appliance demand estimated with that model. 

9A.4.1 Broad Trends 

 In this section DOE reviews trends in the physical household and economic data sets and 

posits a simple approach for estimating the price elasticity of appliance demand. As noted above, 

the physical household variables (housing starts and appliance replacements) explain most of the 

variability in appliance shipments during the study period (1980-2002).b DOE assumes the rest

of the variability in shipments (referred to as “residual shipments”) is explained by economic 

variables. Below, DOE presents a tabular method for measuring price elasticities.

To illustrate this tabular approach, DOE defines two new variables—residual shipments 

and total price. Residual shipments are defined as the difference between shipments and physical 

household demand (starts plus replacements). Total price, represented by the following equation, 

is defined as appliance price plus the present value of lifetime appliance operating cost:

c

TTTT = + PPPPPP

Eq. 9A.1 

Where:

TTTT =Total price,

= Appliance purchase price, and

PPPPPP = Present value of operating cost. 

Over the study period, residual shipments increased in proportion to total shipments by 

30 percent for refrigerators, 19 percent for clothes washers, and 23 percent for dishwashers. At 

the same time, total prices declined 47 percent, 45 percent and 48 percent for refrigerators, 

clothes washers, and dishwashers, respectively. Assuming that total price explains the entire 

change in per household appliance usage, a rough estimate is calculated of the total price 

elasticity of demand equal to -0.48 for refrigerators, -0.32 for clothes washers and -0.37 for 

dishwashers 

(Table 9A.4.1). 

b A log regression of the form: Shipments = a + b • Housing Starts + c • Retirements, indicates that these two 

variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer 

shipments, and 97 percent of the variation in dishwasher shipments.

c Present value operating cost is calculated assuming a 19-year operating life for refrigerators, 14-year operating life 

for clothes washers, and a 12-year operating life for dishwashers. A 37 percent discount rate is used to sum annual 

operating costs into a present value operating cost.
9A-6 

Table 9A.4.1 Estimate of Total Price Elasticity of Demand 

Residual Shipments (millions) Total Price (1999$)

Appliance 1980 2002 Difference Change 1980 2002 Change Elasticity

Refrigerators -0.5 1.6 2.1 30% 1541 820 -61% -0.48

Clothes Washers -1.0 0.2 1.1 19% 1042 567 -59% -0.32

Dishwashers -1.0 -0.01 1.0 23% 896 464 -64% -0.37

The negative correlation between total price and residual shipments suggested by these 

negative price elasticities is illustrated in a graph of residual shipments on the y-axis and total 

price on the x-axis (Figure 9A.4.1). 

Note: Yellow points are observed price data; red points are interpolated price data. 

Figure 9A.4.1 Residual Shipments and Appliance Price 

Household income rose during the study period, making it easier for households to 

purchase appliances. Assuming that a rise in income has a similar impact on shipments as a 

decline in price, the impact of income is incorporated by defining a third variable, termed 

relative price, which is calculated as total price divided by household income and represented 

by the following equation.d 

RR = 

TTTT

IIIIIIIIIIII

Eq. 9A.2 

Where:

RR = Relative price,

TTTT = Total price, and

IIIIIIIIIIII = Household income. 

d Recall that the income elasticity of demand cited in the literature review is 0.50 and the price elasticity of demand 

cited in the review averages -0.35. This suggests that combining the effects of income and price will yield an 

elasticity less negative than price elasticity alone.

Refrigerators

400

800

1200

1600

2000

-2.0 -1.0 0.0 1.0 2.0

Total Price

Residual Shipments 

Clothes Washers

400

600

800

1000

1200

-1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 

Dishwashers

200

400

600

800

1000

-2.0 -1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 
9A-7 

The percent decline in relative price for the three appliances divided by the percent 

decline in residual shipments suggests a rough estimate of relative price elasticity equal to 

-0.40 for refrigerators, -0.26 for clothes washers and -0.30 for dishwashers (Table 9A.4.2). 

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand 

Residual Shipments (millions) Relative Price (1999$)

Appliance 1980 2002 Change 1980 2002 Change Elasticity

Refrigerators -0.532 1.597 30% 0.041 0.019 -74% -0.40

Clothes Washers -0.953 0.174 19% 0.028 0.013 -72% -0.26

Dishwashers -0.974 -0.005 23% 0.024 0.011 -76% -0.30

9A.4.2 Specification of Model

The limited price data suggest it is appropriate to use a simple regression model to 

estimate the impact of economic variables on shipments, using few explanatory variables. The 

following equation, chosen for this analysis, includes one physical household variable (housing 

starts plus replacements) and one relative price variable (the sum of purchase price plus 

operating cost, divided by income). 

SShiiii = aa + bb × RR + × [SSSS SSSSSS + RR RR ]

Eq. 9A.3 

Where:

SShiiii = Quantity of appliance sold, 

RR = Relative price,

SSSS SSSSSS = Number of new homes, and 

RR RR = Number of appliances at the end of their operating life.

The natural logs are taken of all variables so that the estimated coefficients for each 

variable in the model may be interpreted as the percent change in shipments associated with 

the percent change in the variable. Thus, the coefficient b in this model is interpreted as the 

relative price elasticity of demand for the three appliances. 

DOE used the following combined regression equation to estimate an average price 

elasticity of demand across the three appliances, using pooled data in a single regression. A 

combined regression specification is justified, given the limited data available and the similarity 

in price and shipment behavior across appliances (see Figure 9A.4.1). Thus, the model 

represented by the combined regression equation is considered the basic model in DOE’s 

analysis of appliance shipments. 

SShiiii = aa + bb × RR + × [SSSS SSSSSS + RR RR ] + dd × CC + × DD 

Eq. 9A.4 

Where:

CC = Quantity of clothes washers sold, and 

DD = Quantify of dishwashers sold. 
9A-8 

9A.4.3 Discussion of Model 

The most important assumption used to specify this model is that changes in economic 

variables over the study period—income, price, and operating cost—are responsible for all 

observed growth in residual appliance shipments. In other words, DOE assumes no impact from 

other possible factors, such as changing consumer preferences or increases in the quality of 

appliances. This assumption seems unlikely, but without additional data, the impact of this 

assumption on the price elasticity of demand cannot be measured. DOE effectively assumes that 

changes in consumer preferences and appliance characteristics, while affecting which models are 

purchased, have relatively little impact on the total number of appliances purchased in a year. 

Three additional assumptions used to specify this model deserve comment. The 

relative price variable is specified in the model, assuming that (1) the correct implicit discount 

rate is used to combine appliance price and operating cost and that (2) rising income has the 

same impact on shipments as falling total price. The “starts + replacements” variable is 

specified, assuming (3) that starts and replacements have similar impacts on shipments. 

To investigate the first assumption about discount rates, DOE calculated “present value 

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in Eq. 9A.3 and Eq. 9A.4. The results of this analysis, 

presented in section 9A.4.5, indicate that the elasticity of relative price is fairly insensitive to 

changes in the discount rate. 

To investigate the second and third assumptions, DOE specified a regression model 

separating income from total price and replacements from starts, thereby adding two additional 

explanatory variables to the basic model as shown in the following equation: 

SShiiii = aa + bb × RR + × IIIIIIIIIIII + dd × SSSS SSSS + × RR RR + ff × CC + gg × DD

Eq. 9A.5 

The results of the regression analysis of this model are presented in section 9-A.4.5. 

These results suggest that the elasticity of total price (coefficient b) is relatively insensitive to 

changes in the treatment of income and “starts + replacements” in the model. 

9A.4.4 Analysis Results

The following sections describe results of analyses using both the individual and 

combined models for appliances and the effects of a lower consumer discount rate and 

disaggregated variables. 

9A.4.4.1 Individual Appliance Model

The individual appliance regression equations are specified in the following equation. 

SShiiii = aa + bb × RR + × [SSSS SSSSSS + RR RR ]

Eq. 9A.6 
9A-9 

In regression analysis of this model, the elasticity of relative price (b) is estimated to be 

-0.40 for refrigerators, -0.31 for clothes washers and -0.32 for dishwashers (Table 9A.4.3),

averaging -0.35. These elasticities are similar to those reported in the literature survey for

appliances (Table 9A.2.1). They are remarkably similar to the price elasticity calculated using a

tabular approach (Table 9A.4.2).

The estimated coefficient associated with the “starts + replacements” variable is close to 

one. A coefficient equal to one for this variable would imply that, holding economic variables 

constant, shipments increase in direct proportion to an increase in “starts + replacements.” The 

high R-squared values (above 95) and t-statistics (above 5) in the results provide a measure of 

confidence in this analysis, despite the very small data set.

Table 9A.4.3 Individual Appliance Model Results

Refrigerator Clothes Washer Dishwasher

Variable Coefficient t-stat Coefficient t-stat Coefficient t-stat

Intercept -1.51 -7.26 -1.47 -8.23 -2.08 -16.78

Relative Price -0.40 -6.60 -0.31 -5.69 -0.32 -7.03

Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46

R2 0.954 0.954 0.975

Observations 23 23 23

9A.4.4.2 Combined Appliance Model 

The combined appliance regression equation is specified in the following equation. 

SShiiii = aa + bb × RR + × [SSSS SSSSSS + RR RR ] + dd × CC + × DD

Eq. 9A.7 

This regression analysis indicates that the model fits the existing shipments data well 

(high R-squared) and that the variables included in the model are statistically significant (Table 

9A.4.4). Estimated with this model, the elasticity of relative price is -0.34, close to the average 

value estimated in the individual appliance models (-0.35). It is also similar to elasticity 

estimates reported in the literature survey and calculated using the tabular approach in Table 

9A.4.2. 

Table 9A.4.4 Combined Appliance Model Result

Variable Coefficient t-statistic

Intercept -1.60 -15.54

Relative Price -0.34 -10.74

Starts + Replacements 1.21 13.95

CW -0.20 -9.04

DW -0.32 -6.58

R2 0.983

Observations 69

Variable Coefficient t-statistic 
9A-10 

9A.4.5 Additional Regression Specifications and Results

As described in section 9A.4.3, DOE used three assumptions to specify its appliance 

models. The first, made to aggregate appliance price and operating cost, is that the implicit price 

variable in the basic regression model is specified using a 37 percent implicit discount rate. The 

second states that the implicit price variable is defined assuming that rising income has the same 

impact on shipments as falling total price. The third states that the “starts + replacements” 

variable is defined assuming that housing starts have a similar impact on shipments as appliance 

replacements.

9A.4.5.1 Lower Consumer Discount Rate

To investigate the first assumption about discount rates, DOE calculated “present value 

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in Eq. 9A.3 and Eq. 9A.4. The estimated coefficient 

associated with the relative price variable in these regressions is almost identical to the 

coefficients estimated for the same variable based on a 37 percent implicit discount rate. The 

elasticity of relative price calculated using a 20 percent discount rate is -0.33 in the combined 

regression and averages - 0.35 for the three appliances (Table 9A.4.5). The elasticity of price 

calculated using a 37 percent discount rate is -0.34 in the combined regression and averages -0.35 

for the three appliances. DOE concludes from this analysis that the elasticity of relative price is 

fairly insensitive to changes in the discount rate. 

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate

9A.4.5.2 Disaggregated Variables 

To investigate the second and third assumptions, DOE constructed a regression 

model that separates income from total price and replacements from starts, thus adding 

two additional explanatory variables to the basic model (as shown earlier as Eq. 9A.5 and 

shown below). 

Three Appliances

Variable Coefficient t-Stat

Intercept -1.53 -14.61

Total Price / Income -0.33 -10.69

Starts + Retirements 1.20 13.65

CW -0.18 -8.69

DW -0.32 -6.57

R2 0.982

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -1.36 -6.26 -1.41 -7.49 -2.04 -17.23

Total Price / Income -0.38 -6.50 -0.32 -5.29 -0.33 -7.30

Starts + Retirements 1.04 5.73 1.06 5.83 1.34 11.64

R2 0.953 0.950 0.977

Observations 23 23 23
9A-11 

SShiiii = aa + bb × RR + × IIII + dd × SSSS SSSS + × RR RR + ff × CC + gg × DD

Eq. 9A.8 

The estimated coefficient associated with the total price variable in these regressions is 

almost identical to the coefficients estimated for the relative price variable reported above. The 

elasticity of total price in the above equation is -0.36 in the combined appliance regression and 

averages - 0.35 for the three appliances (Table 9A.4.6). The elasticity of relative price based on 

the model described in Eq. 9A.4 is -0.34 in the combined regression (Table 9A.4.4) and averages 

-0.35 across the individual appliances (Table 9A.4.3). DOE concludes that the price elasticity 

calculated in this analysis is relatively insensitive to the specification of household income and 

“starts + replacements” variables in the model.

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate

9A.5 LONG RUN IMPACTS

As noted above in Table 9A.2.1, the literature review provides price elasticities over short 

and long time periods, also referred to as short run and long run price elasticities. As noted in the 

first two rows of Table 9A.2.1, one source (i.e., Hymans) shows that the price elasticity of 

demand is significantly different over the short run and long run for automobiles.1 Because 

DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time 

period, consideration must be given to how the relative price elasticity is affected once a new 

standard takes effect. 

DOE considers the relative price elasticities determined above in section 9A.4 to be 

short run elasticities. DOE was unable to identify sources specific to household durable goods, 

such as appliances, to indicate how short run and long run price elasticities differ. Therefore, to 

estimate how the relative price elasticity changes over time, DOE relied on the Hymans study 

pertaining to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the 

automobile price elasticity of demand changes in the years following a purchase price change. 

With increasing 

Three Appliances

Variable Coefficient t-Stat

Intercept -2.92 -1.26

Income 0.58 2.92

Total Price -0.36 -7.06

Housing Starts 0.44 10.02

Retirements 0.62 8.12

CW -0.24 -9.25

DW -0.46 -7.68

R2 0.985

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23

Income 0.89 3.80 0.87 2.31 0.20 0.52

Total Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18

Housing Starts 0.41 7.38 0.25 3.29 0.62 8.24

Retirements 0.56 6.06 0.56 2.09 0.65 5.86

R2 0.984 0.958 0.979

Observations 23 23 23
9A-12 

years after the price change, the price elasticity becomes more inelastic until it reaches a terminal 

value around the tenth year after the price change.

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Price Elasticity of Demand -1.20 -0.93 -0.75 -0.55 -0.42 -0.40

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33 

Source: Hymans, 1971.

Based on the relative change in the automobile price elasticity of demand shown in Table 

9A.5.1, DOE developed a time series of relative price elasticities for home appliances. Table 

9A.5.2 presents the time series. 

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33 

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

9A.6 SUMMARY

This appendix describes the results of a literature search, tabular analysis, and regression 

analyses of the impact of price and other variables on appliance shipments. In the literature, DOE

found only a few studies of appliance markets that are relevant to this analysis and no studies 

after 1980 using time series price and shipments data. The information that can be summarized 

from the literature suggests that the demand for appliances is price inelastic. Other information in 

the literature suggests that appliances are a normal good, such that rising incomes increase the 

demand for appliances. Finally, the literature suggests that consumers use relatively high implicit 

discount rates, when comparing appliance prices and appliance operating costs. 

There are too few price and operating cost data available to perform complex 

analysis of dynamic changes in the appliance market. In this analysis, DOE used data 

available for refrigerators, clothes washers, and dishwashers to evaluate broad market trends 

and perform simple regression analysis. 

These data indicate an increase in appliance shipments and a decline in appliance 

price and operating cost over the study period 1980-2002. Household income has also risen 

during this 
9A-13 

time. To simplify the analysis, DOE combined the available economic information into one 

variable, termed relative price, and used that variable in a tabular analysis of market trends and a 

regression analysis.

DOE’s tabular analysis of trends in the number of appliances per household suggests 

that the price elasticity of demand for the three appliances is inelastic. Our regression analysis 

of these same variables suggests that the relative price elasticity of demand is -0.34. The price 

elasticity is consistent with estimates in the literature. Nevertheless, DOE stresses that the 

measure is based on a small data set, using very simple statistical analysis. More important, 

the measure is based on an assumption that economic variables, including price, income and 

operating costs, explain most of the trend in appliances per household in the United States 

since 1980. Changes in appliance quality and consumer preferences may have occurred during 

this period, but they are not accounted for in this analysis. 

9A.7 DATA USED IN THE ANALYSIS

• Appliance Shipments are defined as the annual number of units shipped in millions. These

data were collected from the Association of Home Appliance Manufacturers (AHAM)

8,9 and

Appliance Magazine10 as annual values for each year, 1980–2002. AHAM was used for the

period 1989–2002 while Appliance Magazine was used for the period 1980–1988.

• Appliance Price is defined as the shipments weighted retail sales price of the unit in 1999

dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998, and 2002 were collected

from AHAM Fact Books.

11 Price values for other years were interpolated from these eight

years of data.

• Housing Starts data were collected from the U.S. Census construction statistics (C25

reports) as annual values for each year, 1980–2002.12

• Replacements, driven by equipment retirements, are estimated with the assumption that

some fraction of sales arise from consumers replacing equipment at the end of its useful life.

Since each appliance has a different expected lifespan (19 years for refrigerators,13 14 years

for clothes washers,14 12 years for dishwashers15), replacements are calculated differently for

each appliance type. Replacements are estimated as the average of shipments 14–24 years

previous for refrigerators, 9–19 years previous for clothes washers, and 7–17 years previous

for dishwashers. Historical shipments data were collected from AHAM and Appliance

Magazine.

• Annual Electricity Consumption (UEC) is defined as the energy consumption of the unit in

kilowatt-hours. Electricity consumption depends on appliance capacity and efficiency. These

data were provided by AHAM for 1980, 1990–1997 and 1999–2002.9 Data were interpolated

in the years for which data were not available.

• Operating Cost is the present value of the electricity consumption of an appliance over its

expected lifespan. The lifespans of refrigerators, clothes washers and dishwashers are
9A-14 

assumed to be 19, 14, and 12 years respectively. Discount rates of 20 percent6 and 37 

percent16 were used, producing similar estimates of price elasticity. A study by Hausman 

recommended a discount rate of “about 20 percent” in its introduction and presented results 

ranging from 24.1 percent to 29 percent based on his calculations for room air conditioners. 

A study by Train suggests a range of implicit discount rates averaging 35 percent for 

appliances.

• Income: Median annual household income in 2003 dollars. These data were collected for

each year, 1980–2002, from Table H-6 of the U.S. Census.17
9A-15 

REFERENCES

1. Hymans, S. H. Consumer Durable Spending: Explanation and Prediction. Brookings

Papers on Economic Activity. 1970. 1(2): pp. 173–206. http://www.brookings.edu/

/media/projects/bpea/1970 2/1970b_bpea_hymans_ackley_juster.pdf.

2. Golder, P. N. and G. J. Tellis. Beyond Diffusion: An Affordability Model of the Growth

of New Consumer Durables. Journal of Forecasting. 1998. 17(3-4): pp. 259–280.

http://doi.wiley.com/10.1002/(SICI)1099-131X(199806/07)17:3/4<259::AIDFOR696>3.0.CO;2-T.

3. Revelt, D. and K. Train. Mixed Logit with Repeated Choices: Households’ Choices of

Appliance Efficiency Level. Review of Economics and Statistics. 1998. 80(4): pp. 647–

657. (Last accessed September 24, 2014.) http://dx.doi.org/10.1162/003465398557735.

4. Tellis, G. J. The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric

Models of Sales. Journal of Marketing Research. 1988. 25(4): pp. 331–341. (Last

accessed September 24, 2014.) http://www.jstor.org/stable/3172944.

5. U.S. Energy Information Administration. The Effect of Income on Appliances in U.S.

Households. 2002. Washington, D.C. (Last accessed April 3, 2014.)

http://www.eia.doe.gov/emeu/recs/appliances/appliances.html.

6. Hausman, J. A. Individual Discount Rates and the Purchase and Utilization of EnergyUsing Durables. The Bell Journal of Economics. 1979. 10(1): pp. 33–54.

http://www.jstor.org/stable/info/3003318.

7. Train, K. Discount Rates in Consumers’ Energy-Related Decisions: A Review of the

Literature. Energy. 1985. 10(12): pp. 1243–1253. (Last accessed September 24, 2014.)

http://www.sciencedirect.com/science/article/pii/0360544285901355.

8. Association of Home Appliance Manufacturers. Fact Book 2000. 2000. AHAM:

Washington, D.C.

9. Association of Home Appliance Manufacturers. Fact Book 2003. 2003. AHAM:

Washington, D.C.

10. Statistical Review. Appliance Magazine. 1990.

11. Association of Home Appliance Manufacturers. Fact Books: 1987, 1988, 1993, 1995,

2000, and 2003. AHAM: Washington, D.C.

12. U.S. Dept. of Commerce, Bureau of the Census and U.S. Dept. of Housing and Urban

Development. Current Construction Reports. C25, New One-Family Houses Sold and

For Sale. Washington, D.C.

13. Duemling, R. Product Life of Refrigerators. Master’s thesis, University of California,

Berkeley. 1999.
9A-16 

14. Biermayer, P. J., J. E. McMahon, and B. Berringer. Final Rule Technical Support

Document (TSD): Energy Efficiency Standards for Consumer Products: Clothes

Washers. 2004. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No.

LBNL-47462. (Last accessed June 10, 2015.) http://eaei.lbl.gov/publications/final-ruletechnical-support-document.

15. U.S. Department of Energy-Office of Codes and Standards. Technical Support

Document: Energy Conservation Standards for Consumer Products: Dishwashers,

Clothes Washers, and Clothes Dryers including: Environmental Impacts, Regulatory

Impact Analysis. 1990. Washington, D.C. Report No. DOE/CE-0299P.

16. Train, K. E. and T. Atherton. Rebates, Loans, and Customers’ Choice of Appliance

Efficiency Level: Combining Statedand Revealed-Preference Data. The Energy Journal.

1995. 16(1): pp. 55–69. (Last accessed June 15, 2015.)

http://www.jstor.org/stable/41322585.

17. U.S. Census Bureau. Historical Income Data. September 16, 2014. The Census Bureau

reports income data from several major household surveys and programs. (Last accessed

June 10, 2015.) http://www.census.gov/hhes/www/income/data/historical/index.html.
10A-i 

APPENDIX 10A. USER INSTRUCTIONS FOR SHIPMENTS AND NIA 

SPREADSHEETS

TABLE OF CONTENTS

10A.1 INTRODUCTION ...................................................................................................... 10A-1

10A.2 BASIC INSTRUCTIONS ........................................................................................... 10A-2
10A-1 

APPENDIX 10A. USER INSTRUCTIONS FOR SHIPMENTS AND NIA 

SPREADSHEETS

10A.1 INTRODUCTION

The results obtained for the shipments analysis and the national impact analysis 

(NIA) can be examined and reproduced using the Microsoft Excel spreadsheet available on 

the U.S. Department of Energy Building Technologies website at: 

http://www.eere.energy.gov/buildings/appliance_standards/. 

The spreadsheets are posted on the DOE websitea and represent the latest version that 

has been tested with Microsoft Excel 2010. 

To execute the spreadsheet requires Microsoft Excel 2003 or a later version. The NIA 

spreadsheet performs calculations to project the change in national energy use and net present 

value (NPV) due to an energy conservation standard. The energy use and associated costs for a

given standard are determined first by calculating the shipments and then calculating the energy 

use and costs for all equipment shipped under that standard. The differences between the 

standards and no standards case can then be compared and the overall energy savings and present 

values determined. 

The NIA spreadsheet or workbook consists of the following worksheets: 

NIA options and Contains user input selections under “Options” and a summary

summary table with Cumulative Energy Savings, Operating Cost Savings, 

Increased Installed Cost and NPV for the selected shipments 

scenario, and electricity price scenario. The summary contains 

results for the trial standard levels considered for CFLKs. 

NIAres Contains calculations of energy use, operating cost, and installed 

cost in the residential sector for the no-standard case, the TSL0 

case and the standards cases. 

NIAcom Contains calculations of energy use, operating cost, and installed 

cost in the commercial sector for the no-standard case, the TSL0 

case and the standards cases. 

NIA Savings Calculated NIA savings for a potential standard for chosen TSL. 

Parameters and Contains parameters and assumptions used in the shipments and 

Assumptions NIA models. 

a http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/76. (Last accessed April 1, 

2014.) This material is available in Docket #EERE-2012-BT-STD-0045 at regulations.gov.
10A-2 

AEO New Construction Contains projections for new construction starts from AEO 2015. 

Full-Fuel Cycle Contains the annual site to power plant and upstream conversation 

Multipliers factors. 

Shipments Contains projected shipments of CFLKs in the no standard case 

and the standards cases for selected scenario. 

Price Contains price trends for CFLKs for selected scenario. 

Historical Shipments Contains estimated data for historical shipments of CFLKs. 

Electricity Prices Contains projected average energy prices for the three electricity 

price scenarios. 

Lifetime Contains the probability of survival of a CFLK at a given age and 

the average lifetime of a unit.

10A.2 BASIC INSTRUCTIONS 

Basic instructions for operating the NIA spreadsheets are as follows: 

1. Once the NIA spreadsheets have been downloaded from the World Wide Web, open the

file using Excel. At the bottom, click on the tab for the worksheet ‘Input and Summary’.

2. Use Excel’s View/Zoom commands at the top menu bar to change the size of the display

to make it fit your monitor.

3. The user can change the model parameters listed in the grey box labelled “User Inputs”.

The parameters are:

a. Discount Year: To change discount rate year, click on highlighted cell and enter

desired year.

b. Rebound Rate: To change the rebound rate, click on highlighted cell and enter

desired rebound rate.

c. TSL: To change TSL being analyzed, use the drop-down arrow and select the

desired TSL. Calculations on NIAres, NIAcom, and NIASavings worksheets

update accordingly.

d. Shipments Scenario: To change value, use the drop-down arrow and select the

desired shipments scenario.

e. Electricity Price Scenario: To the change value, use the drop-down arrow and

select the desired scenario (Reference, Low, or High).

4. The results are automatically updated and are reported in the summary table to the right

of the Options box. Results are shown for the selected inputs for each TSL.
10B-i

APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10B.1 INTRODUCTION .......................................................................................................10B-1

10B.2 METHODOLOGY ......................................................................................................10B-1

10B.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE.....................................10B-3

REFERENCES ........................................................................................................................10B-4

LIST OF TABLES

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs........................................10B-3

Table 10B.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014).............10B-3
10B-1

APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

10B.1 INTRODUCTION

This appendix summarizes the methods the U.S. Department of Energy (DOE) used to 

calculate the full-fuel-cycle (FFC) energy savings estimated from potential standards for ceiling 

fan light kits. 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 method of analysis 

previously encompassed only site energy and the energy lost through generation, transmission, 

and distribution of electricity. In 2011 DOE announced its intention, based on recommendations 

from the National Academy of Sciences, to use FFC measures of energy use and emissions when 

analyzing proposed energy conservation standards.1 This appendix summarizes the methods 

DOE used to incorporate impacts of the full fuel cycle into the analysis.

This analysis uses several terms to describe aspects of energy use. The physical sources 

of energy are primary fuels such as coal, natural gas, or liquid fuel. Primary energy is equal to 

the heat content (British thermal units [Btu]) of the primary fuel used to produce an end-use 

service. Site energy use is defined as the energy consumed at the point of use in a house or 

establishment. When natural gas or petroleum fuels are consumed at the site (for example in an 

on-site furnace), site energy is identical to primary energy, with both equal to the heat content of 

the primary fuel consumed.

For electricity generated by an off-site power plant, site energy is measured in kilowatthours (kWh). In such a case the primary energy is equal to the quads (quadrillion Btu) of primary 

energy required to generate and deliver electricity to the site. For the FFC analysis, 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 full-fuel-cycle analysis must distinguish between electricity generated by fossil 

fuels and uranium and electricity generated from renewable sources (wind, solar, and hydro). For 

the former, the upstream fuel cycle relates to the amount of fuel consumed at the power plant. 

There is no upstream component for the latter, because no fuel per se is used.

10B.2 METHODOLOGY

The mathematical approach to determining FCC is discussed in Coughlin (2012).2 Details 

on analyzing the fuel production chain are presented in Coughlin (2013).3 The methods used to 

calculate FFC energy use are summarized here. When all energy quantities are normalized to the 

same units, FFC energy use can be represented as the product of the primary energy use and an 

FFC multiplier. Mathematically the FFC multiplier is a function of a set of parameters that 

represent the energy intensity and material losses at each stage of energy production. Those 

parameters depend only on physical data, so the calculations require no assumptions about prices 

or other economic factors. Although the parameter values often differ by geographic region, this 

analysis utilizes national averages. 
10B-2

The fuel cycle parameters are defined as follows.

• ax is the quantity of fuel x burned per unit of electricity produced, on average, for grid 

electricity. The calculation of ax includes a factor to account for losses incurred 

through the transmission and distribution systems. 

• by is the amount of grid electricity used in producing fuel y, in MWh 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 (MBtu/physical unit).

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x

produced).

All the parameters are calculated as functions of an annual time step; hence, when 

evaluating the effects of potential new standards, a time series of annual values is used to 

estimate the FFC energy and emissions savings in each year of the analysis period and 

cumulatively. Fossil fuel quantities are converted to energy units using the heat content factor qx. 

To convert electricity in kWh to primary energy units, on-site electricity consumption is 

multiplied by the site-to-power-plant energy use factor, described in chapter 10. 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 generated each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. Also calculated is a multiplier for electricity that reflects the fuel mix used in its 

generation. The multipliers are dimensionless numbers 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.

When DOE estimates energy savings attributable to appliance standards, the method for 

performing the full-fuel-cycle analysis utilizes data and projections published in the Annual 

Energy Outlook (AEO); in the case of ceiling fan light kits, the AEO2014.

4 Table 10B.2.1

summarizes the AEO2014 data used as inputs to the calculation of various parameters. The 

column titled "AEO Table" gives the name of the table that provided the reference data.
10B-3

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter(s) Fuel(s) 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 energy consumption

by the power sector

bc, cnc, cpc Coal Coal production by region and 

type

Coal production by 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 Domestic crude oil 

production

cnn Natural gas

Oil and gas supply U.S. dry gas production

Natural gas supply, disposition, 

and prices Pipeline, lease, and plant fuel

zx All Electricity supply, disposition, 

prices, and emissions Power sector emissions

The AEO2014 does not provide all the information needed to estimate total energy use in 

the fuel production chain. Coughlin (2013) describes the additional data sources needed to 

complete the analysis. The time dependence in the FFC multipliers for ceiling fan light kits, 

however, arises exclusively from variables taken from the AEO2014.

10B.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE

FFC energy multipliers for selected years are presented in Table 10B.3.1. The 2040 value 

was held constant for the analysis period beyond 2040, which is the last year in the AEO2014

projection. The multiplier for electricity reflects the shares of various primary fuels in total 

electricity generation throughout the forecast period. 

Table 10B.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014)

2019 2020 2025 2030 2035 2040

Electricity 1.043 1.044 1.045 1.046 1.047 1.047

Natural gas 1.108 1.109 1.111 1.113 1.114 1.114

Petroleum fuels 1.176 1.176 1.176 1.174 1.172 1.170
10B-4

REFERENCES

1. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. Federal 

Register. August 18, 2011. vol. 76, no. 160: pp. 51245–51868. (Last accessed June 10, 

2015.) http://digital.library.unt.edu/ark:/67531/metadc52337/m1/44/.

2. Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy. 2012. 

37(1): pp. 698–708. (Last accessed June 15, 2015.) 

http://www.sciencedirect.com/science/article/pii/S0360544211006803.

3. Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013. 

Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6025E. (Last 

accessed June 10, 2015.) http://ees.lbl.gov/sites/all/files/lbnl6025e_ffc.pdf.

4. U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections 

to 2040. 2014. Washington, D.C. Report No. DOE/EIA-0383(2014). (Last accessed June 

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf.
10C-i 

APPENDIX 10C. ALTERNATIVE NIA SCENARIOS

TABLE OF CONTENTS

10C.1 INTRODUCTION .......................................................................................................10C-1

10C.1.1 High-Benefits Scenario ....................................................................................10C-1

10C.1.2 Low-Benefits Scenario.....................................................................................10C-1

10C.2 RESULTS ....................................................................................................................10C-1

REFERENCES ........................................................................................................................10C-3

LIST OF TABLES

Table 10C.2.1 High-Benefits Scenario NIA results ..........................................................10C-2

Table 10C.2.2 Low-Benefits Scenario NIA results ...........................................................10C-2
10C-1 

APPENDIX 10C. ALTERNATIVE NIA SCENARIOS

10C.1 INTRODUCTION

The reference scenario presented in chapter 10 of this notice of proposed rulemaking 

(NOPR) technical support document (TSD) represents the U.S. Department of Energy's 

(DOE’s) best estimate of the national impacts of amended standards for ceiling fan light kits 

(CFLKs). The reference scenario utilizes the Annual Energy Outlook 2015 (AEO 2015)

1

reference economic growth scenario for new housing starts and electricity price trends for 

shipments projections and NIA. This appendix presents two alternative scenarios that represent 

the range of potential impacts of amended standards. 

10C.1.1 High-Benefits Scenario

The high-benefits scenario represents an upper bound for the potential benefits obtained 

from amended standards. In this scenario, DOE used the AEO 2015 high economic growth 

scenario for projected new housing starts and electricity price trends. DOE used its reference 

price trend for LED, which assumes a learning rate parameter derived from the historical trend 

for other lighting technologies (i.e., CFLs). These assumptions result in higher CFLK shipments 

and generally more energy saving and higher NPV from an amended CFLK standard compared 

to the reference scenario. 

10C.1.2 Low-Benefits Scenario

The low-benefits scenario represents a lower bound for the benefits of amended 

standards. In this scenario, DOE used the AEO 2015 low-economic growth estimates for

projected new housing starts and electricity price trends. Additionally DOE assumed a faster 

decline in LED lamp prices based on the observed learning rate parameter estimated from 

historical LED lamp data. The projected trend is based on cumulative shipments of LED lamps

from the general service lamps preliminary analysis2 and assumes that LEDs are experiencing a 

faster learning rate than historically observed in other lighting technologies as measured using 

historical LED data.

3 This scenario results in lower energy savings and NPV from a potential 

standard compared the reference scenario due to more consumers adopting LED technologies in 

the no-standard case, limiting the savings from a potential standard.

10C.2 RESULTS

Table 10C.2.1 and Table 10C.2.2 present the Full-Fuel Cycle (FFC) energy savings and NPV 

results of the high-and low-benefits scenarios respectively, for all the TSLs under consideration.
10C-2 

Table 10C.2.1 High-Benefits Scenario NIA results 

TSL 1 TSL 2 TSL 3 TSL 4

FFC NES (quads) 0.009 0.052 0.072 0.074

NPV 3% discount rate 

(billion 2014$) 0.222 0.703 1.014 1.036 

NPV 7% discount rate 

(billion 2014$) 0.221 0.534 0.743 0.757 

Table 10C.2.2 Low-Benefits Scenario NIA results

TSL 1 TSL 2 TSL 3 TSL 4

FFC NES (quads) 0.023 0.023 0.035 0.036

NPV 3% discount rate 

(billion 2014$) 0.386 0.386 0.548 0.564 

NPV 7% discount rate 

(billion 2014$) 0.325 0.325 0.434 0.444 
10C-3 

REFERENCES

1. U.S. Energy Information Administration. Annual Energy Outlook 2015 with Projections 

to 2040. 2015. Washington, D.C. Report No. DOE/EIA-0383(2015). (Last accessed June 

5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2015).pdf. 

2. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. 

Preliminary Technical Support Document: Energy Efficiency Program for Consumer 

Products and Commercial and Industrial Equipment: General Service Lamps. December 

2014. Washington, D.C. (Last accessed June 15, 2015.) 

http://www.regulations.gov/#!documentDetail;D=EERE-2013-BT-STD-0051-0022. 

3. Gerke, B., A. Ngo, A. Alstone, and K. Fisseha. The Evolving Price of Household LED 

Lamps: Recent Trends and Historical Comparisons for the US Market. 2014. Lawrence 

Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6854E. (Last accessed 

June 15, 2015.) http://eetd.lbl.gov/publications/the-evolving-price-of-household-led-l. 
12A-i 

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE 

TABLE OF CONTENTS

12A.1 ENGINEERING ANALYSIS INTERVIEW GUIDE FOR CEILING FAN LIGHT KITS

..................................................................................................................................... 12A-1

12A.2 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE FOR CEILING FAN

LIGHT KITS ............................................................................................................. 12A-10

12A.3 ADDENDUM ........................................................................................................... 12A-21
12A-1

APPENDIX 12A MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

12A.1 ENGINEERING ANALYSIS INTERVIEW GUIDE FOR CEILING FAN LIGHT 

KITS

Spring 2015
12A-2

Purpose

To gather information on the U.S. ceiling fan and ceiling fan light kit market to assist in the 

Department of Energy’s energy conservation standards analysis.

Method

Navigant Consulting, Inc. (Navigant) is circulating this interview guide to manufacturers of 

ceiling fan light kits (CFLKs) who operate in the U.S. market. Navigant will combine all the 

responses from individual manufacturers to protect proprietary information of any one 

manufacturer. Individual responses and any other data provided will all be covered under a nondisclosure agreement, which Navigant will enter into with each participating manufacturer. 

Navigant will handle all individual company data in the strictest confidence.

Preliminary Analysis Follow-Up

On October 31, 2014 DOE published a Federal Register notice announcing the availability of the 

preliminary technical support document for CFLKs.1 The following questions expand upon 

topics discussed in these documents and at the November 18, 2014 public meeting for CFLK and 

November 19, 2014 public meeting for CF.

1 MARKET ANALYSIS

1.1 To calculate the aggregate national energy savings, DOE needs data on shipment 

trends. DOE would like sales information on CFLKs by lamp type used in CFLKs. For 

each of the CFLK types listed in the table below, please provide sales data.

 1 For more details about the analysis that has been done to date, please visit DOE’s website at: 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/66
12A-3

Table 12A-1 CFLK Sales Shares

Lamp Type and Socket

Sales share 

today

(2015)

Projected sales 

share 5 years 

from now

Incandescent with medium screw base

Incandescent with candelabra base

Incandescent with other bases

Halogen with medium screw base

Halogen with candelabra base

Halogen with other bases

Fluorescent with pin base (externally ballasted lamp)

Compact fluorescent lamp (CFL) with medium screw base

CFL with GU-24 base

CFL with candelabra base

CFL with other bases

Light-emitting diode (LED) with medium screw base

LED with GU-24 base

LED with candelabra base

LED with other socket types

Integral LED (not consumer separable)

Total 100% 100%

1.2 Please provide the percentage of sales and/or shipment for CFLKs sold alone and 

those CFLKs sold with a fan. Has this percentage changed from past years and do you 

expect it to change in future?

1.3 Please provide the CFLK sales and/or shipment percentages by number of sockets in a 

CFLK in the table below. Please also provide the common lamp lumen ranges 

associated with the number of sockets.
12A-4

Table 12A-2 CFLK Market Share by Socket Count

Product Class Socket Count % Sales or 

Shipments

Common Lumen Range 

per Lamp in Socket

All CFLKs

1

2

3

4

5

Other (please Specify)

Total 100%

2 PRODUCT CLASSES

2.1 DOE separates products into categories called product classes based on differences in 

efficacy and consumer utility (i.e., features valued by consumers). DOE then conducts 

its analyses and establishes separate standard levels for each product class. DOE is 

considering one product class for all CFLKs. Are there parameters by which CFLKs 

should be divided into separate product classes?

2.2 Are there incandescent/halogen lamps that offer a size or light output that is not 

available in CFLs or LED lamps? If so, please explain.

2.3 Do CFLs offer any utility to the consumer (i.e., light output, dimming, size etc.) that is 

not available in LED lamps? If so, please explain.

2.4 Is there a difference in utility to CFLK consumers offered by a T9 circline lamp versus 

a T5 circline lamp?

2.5 What are the issues, if any, associated with designing CFLK with controls that are 

compatible with dimmable CFLs and LED lamps?

2.6 Are there any scenarios in which consumers will likely replace a component of the 

CFLK, such as a ballast or driver, rather than the entire CFLK?

2.7 Are there any consumer utilities unique to CFLKs with Externally-Ballasted/Driven 

Lamps?

3 ENGINERING ANALYSIS

3.1 Table 12A-3 presents baseline and efficacy levels (ELs) achievable by packaging a 

more efficacious lamp with the CFLK. Table 12A-4 presents the baseline and efficacy 
12A-5

levels achievable by packaging a more efficacious lamp and changing the fixture 

design (i.e., socket type and numbers). DOE used lamp catalogs to identify 

representative lamp units and determine efficacy levels.

3.2 Please review the information on efficacy and typical characteristics of the 

representative lamp units and provide further information on design pathways for 

achieving higher efficacies. Please also indicate which of your product lines would be 

at each baseline level and efficacy level.
12A-6

Table 12A-3 All CFLKs Engineering Analysis: Lamp Replacement Scenario

Level Lamp 

Type

Wattage

Lamp 

Initial 

Light 

Output

Lamp 

Efficacy

Lamp 

Lifetime

Proposed Efficacy Level 

Equation

Consumer 

Lamp 

Price in 

CFLK*

Remaining 

Consumer 

Price of 

CFLK

Total 

Consumer 

Price of 

CFLK**

% 

Consumer 

Lamp 

Price of 

CFLK***

W lm lm/W hr lm/W 2014$ 2014$ 2014$ 2014$

Baseline CFL 14 800 57.1 10,000 N/A $6.05 $39.67 $51.77 23%

EL 1 CFL 13 800 61.5 10,000 69 - 29.42*0.9983lumens $3.50 $39.67 $46.67 15%

EL 2**** CFL 11 730 66.4 10,000 74 - 29.42*0.9983lumens $3.88 $39.67 $47.44 16%

LED 12 800 66.7 25,000 74 - 29.42*0.9983lumens $22.51 $39.67 $84.69 53%

EL 3 LED 11 800 72.7 25,000 80 - 29.42*0.9983lumens $18.67 $39.67 $77.00 48%

EL 4 LED 10 800 80.0 25,000 87 - 29.42*0.9983lumens $14.82 $39.67 $69.31 43%

EL 5 LED 9.5 800 84.2 25,000 91 - 29.42*0.9983lumens $12.89 $39.67 $65.44 39%

EL 6 LED 9 800 88.9 25,000 96 - 29.42*0.9983lumens $10.97 $39.67 $61.60 36%

EL 7 LED 8.5 800 94.1 25,000 101 - 29.42*0.9983lumens $9.05 $39.67 $57.76 31%

*Lamp prices are weighted by estimated shipments though distribution channels.

**Total price of CFLK reflects a two-socket fixture.

***Percentages reflect the lighting component of CFLK for two lamps.

****At EL 2, there are two different representative lamp units that can achieve this level.
12A-7

Table 12A-4 All CFLKs Engineering Analysis: Fixture Replacement Scenario

Level Lamp 

Type

Fixt

ure 

Soc

kets

Socket 

Type

Lamp 

Watta

ge

Fixt

ure 

Wat

tage

Lamp 

Initial 

Light 

Output

Fixture 

Initial 

Light 

Output

Lamp 

Effica

cy

Lamp 

Lifeti

me

Proposed Efficacy 

Level Equation

Consu

mer 

Lamp 

Price 

in 

CFLK

*

Consumer 

Socket 

Price in 

CFLK**

Remai

ning 

Consu

mer 

Price 

of 

CFLK

Total 

Consumer 

Price of 

CFLK

% 

Consu

mer 

Lamp 

Price 

of 

CFLK

***

W W lm lm lm/W hr lm/W 2014$ 2014$ 2014$ 2014$ 2014$

Baseli

ne

CFL 2 E26 14 28 800 1600 57.1 10,000 N/A $6.05 $0.35 $39.67 $52.48 24%

EL 1 CFL 3 E26 9 27 520 1560 57.8 10,000 69 - 29.42*0.9983lumens $4.27 $0.35 $39.67 $53.54 26%

EL 2 LED 3 E26 8 24 500 1500 62.5 25,000 74 - 29.42*0.9983lumens $21.67 $0.35 $39.67 $105.75 62%

EL 3 LED 3 E26 7 21 480 1440 68.6 30,000 80 - 29.42*0.9983lumens $19.17 $0.35 $39.67 $98.25 60%

EL 4 LED 2 GU24 10 20 800 1600 80.0 25,000 87 - 29.42*0.9983lumens $14.82 $0.83 $39.67 $70.97 44%

EL 5 LED 3 E26 6.5 19.5 530 1590 81.5 25,000 91 - 29.42*0.9983lumens $19.94 $0.35 $39.67 $100.56 61%

EL 6 LED 1 E26 17 17 1600 1600 94.1 25,000 96 - 29.42*0.9983lumens $28.78 $0.35 $39.67 $68.80 42%

EL 7 LED 1 E26 16 16 1600 1600 100.0 25,000 101 - 29.42*0.9983lumens $28.07 $0.35 $39.67 $68.09 42%

*Lamp prices are weighted by estimated shipments though distribution channels.

**Socket prices are based on estimated manufacturer production cost of $0.15 for E26; $0.35 for GU24.

***Percentage reflects the lighting component of CFLK based on number of lamps and sockets.
12A-8

3.3 DOE selects baseline models within a representative product class. The baseline model 

typically represents the most common, least efficacious lamp available. Do you agree 

with the baseline models selected in the tables above? Do you offer the baseline lamp 

in CFLKs in the wattage and other characteristics described in the tables above?

3.4 DOE is considering an equation-based approach to define the efficacy requirements at 

each EL. The general equation form is:

EEEEEEEEEEEEEEEE = AA − 2222. 44 ∗ 00. 9999LLLLLLLLLLLL

The numerator AA changes at each efficacy level. Do you agree with this equation 

form? If not, what equation would you recommend?

3.5 What is the likelihood of a manufacturer changing the design (i.e., number of sockets, 

socket type) of an existing CFLK product due to standards? What specific changes are 

likely to be implemented?

3.6 DOE received a comment stating that the lifetimes of LEDs used in CFLKs decrease 

because of excessive heat generated by the ceiling fan motor. By how much do LED 

lifetimes decrease when used in CFLKs? To what is this lifetime loss attributable?

3.7 Please quantify the efficacy losses from the fixture in your CFLKs in which the light 

source is not designed to be consumer replaceable, such as a CFLK with an LED 

module and driver system. Please also provide information on the LED module and 

driver system used (i.e., model number, manufacturer, efficacy).

4 MAX TECH

4.1 DOE is required to consider the maximum technologically feasible energy 

conservation standard. Table 12A-5 lists the most efficient level in each representative 

product class. Please provide any efficacy improvements that may be possible as well 

as the associated cost increase expected.

Table 12A-5 CFLK Max Tech Levels for Representative Product Classes

Product Class Max Tech Level Potential Efficacy 

Improvement

Design Pathway Associated Cost 

Increase

All CFLKs 96 - 29.42*0.9983lumens

(e.g., 8.5 W LED, 800 

lumens, 94.1 lm/W)

5 PRODUCT PRICE DETERMINATION

5.1 For CFLKs sold with a ceiling fan, what percentage of the consumer price of this 
12A-9

product (fan with CFLK) is attributable to only the CFLK? How does this percentage 

vary?

5.2 For CFLKs sold with a ceiling fan, what percentage of the consumer price of the 

CFLK is attributable for each of the following light sources packaged with the CFLK:

1) CFL;

2) Externally-ballasted CFL;

3) Circline fluorescent lamps;

4) LED lamps;

5) LED modules and driver system in a CFLK that is consumer replaceable; and

6) LED modules and driver system in a CFLK that is integrated in the CFLK and 

not consumer replaceable.

5.3 How do the percentages above change for a standalone CFLK?

5.4 DOE found that the manufacturer cost is $0.15 for an E26 socket and $0.35 for a 

GU24 socket. Do you agree with these estimates? Would prices for E12 and E17 bases 

be the same as an E26? Would the cost for other G or GU pin bases be the same as a 

GU24?

5.5 For CFLKs sold with a ceiling fan DOE determined that on average consumers pay a 

premium of about 2.0 on distributor net prices2 for the home center channel, 1.90 for 

the lighting showroom channel, and 1.81 for the electrical distributor channel. Do you 

agree with these estimates? How do they change for a CFLK sold alone?

5.6 In what distribution channel would you include online retailers such as Delmar and 

Build.com?

5.7 Are the consumer prices for lamps and sockets developed in the engineering analysis 

and displayed in Table 12A-3 through Table 12A-4 representative of your company’s 

products? If not, please provide a quantitative indication of the differences.

 2 Distributor net prices are prices at which the distributor purchases the product.
12A-10

12A.2 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE FOR 

CEILING FAN LIGHT KITS

Spring 2015
12A-11

Purpose

To gather information on the U.S. ceiling fan and ceiling fan light kit market to assist in the 

Department of Energy’s energy conservation standards analysis and measure the impacts of the 

energy conservation standards to the manufacturers (Manufacturer Impact Analysis, or MIA). 

Method

Navigant Consulting, Inc. (Navigant) is circulating this interview guide to manufacturers of 

ceiling fans (CFs) and ceiling fan light kits (CFLKs) who operate in the U.S. market. Navigant 

will combine all the responses from individual manufacturers to protect proprietary information 

of any one manufacturer. Individual responses and any other data provided will all be covered 

under a non-disclosure agreement, which Navigant will enter into with each participating 

manufacturer. Navigant will handle all individual company data in the strictest confidence.

1 KEY ISSUES

1.1 In general, what are the key issues for your company regarding amended energy 

conservation standards for ceiling fans and ceiling fan light kits?

1.2 For the issues identified, how significant are they for each efficiency or efficacy level? 

See Table 12A-3 and Table 12A-4 for ceiling fan light kits efficiency levels.

1.3 Are any of the issues more or less significant for different product classes of CFs or 

CFLKs? Please refer to Table 12A-6 for a list of product classes.

Table 12A-6 Product Classes Under Consideration

Product Classes for CFs Product Class for CFLKs

Standard Fans All CFLKs

Hugger Fans

High-Volume Small-Diameter (HVSD)

Large-Diameter

Very-Small Diameter Fans

2 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

DOE is interested in understanding manufacturer impacts at the plant or profit center level 

directly pertinent to CF and CFLK production. However, the context surrounding plant operation 

and details of plant production and costs are not always readily available from public sources. 

DOE is interested in gaining a more nuanced understanding of these issues and invites you to 

provide these details confidentially. Understanding the organizational setting around the CF and 

CFLK profit centers will help DOE assess the probable future of CF and CFLK manufacturing 

activity with and without amended energy conservation standards.
12A-12

2.1 Do you have a parent company, and/or any subsidiaries relevant to the CF and CFLK 

industry?

2.2 What is your company’s approximate market share of all CFs within the scope of this 

rulemaking that are sold domestically? Does this vary significantly for any particular 

product class that you manufacture?

2.3 What is your company’s approximate market share of all CFLKs within the scope of 

this rulemaking that are sold domestically (i.e., with or without a CF attached to the 

CFLK)?

2.4 Do you manufacture any products other than CFs and CFLKs? If so, what other 

products do you manufacture? What percentage of your total manufacturing revenue 

corresponds to CFs and CFLKs?

Table 12A-7 Ceiling Fan and Ceiling Fan Light Kit Revenue and Shipment Volumes

Product 2014 Shipments 2014 Revenue

Ceiling Fans

Ceiling Fans with Light Kits

Ceiling Fan Light Kits (Sold alone)

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. In this section, 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.

Manufacturer production costs include the direct materials, direct labor, production specific 

overhead, and depreciation of production related assets required to produce a given product. The 

manufacturer markup is a multiplier applied to manufacturer production costs to cover nonproduction costs, such as SG&A and R&D, as well as profit. It does not reflect a profit margin.

The manufacturer production cost times the manufacturer markup equals the manufacturer 

selling price. Manufacturer selling price is the price manufacturers charge their first customers, 

but does not include additional costs along the distribution channels.

DOE estimated a baseline markup of 1.37 for all CFs and CFLKs; this corresponds to a 

27% gross margin.

3.1 Is the 1.37 markup representative of an average CF and CFLK industry markup? Does 

your company’s manufacturer markup differ from this industry average?
12A-13

3.2 How are markups and margins determined in this industry? How would standards 

potentially impact this pricing/margin structure? Please indicate if profit levels vary 

between product classes or with additional features. If yes, please explain why for both 

CFs and CFLKs.

3.3 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 of CFs and CFLKs, do markups vary by the level of efficiency for a design 

option? If yes, please provide information about the markups at higher efficiency 

design options for each product.

3.4 What factors or product attributes besides the offered design options affect the 

profitability of CFs and CFLKs within their respective product class?

3.5 Do your markups change with selected design options for CFs and CFLKs? Are the 

markups on incremental costs for more efficient designs different than the markups on 

the baseline models?

3.6 Would you expect changes in your estimated profitability following an energy 

conservation standard? If so, please explain why for CFs and CFLKs. Can you suggest 

any scenarios that would model these expected changes for each product?

3.7 What is the structure of your distribution channels and how does that influence your 

markups for CFs and CFLKs? Does that change between product classes or across the 

industry?

4 FINANCIAL PARAMETERS

4.1 In order to accurately collect information about CF and CFLK manufacturing, please 

compare your financial parameters to the GRIM parameters below in Table 12A-8.
12A-14

Table 12A-8 Financial Parameters for CF and CFLK Manufacturers

GRIM Input Definition

Industry 

Estimated

Value

Your Actual (If 

Significantly 

Different from 

DOE’s Estimate)

Income Tax 

Rate

Corporate effective income tax paid (percentage of 

earnings before taxes, EBT) 35.2%

Discount Rate

Weighted average cost of capital (inflation-adjusted 

weighted average of corporate cost of debt and return on 

equity)

5.9%

Working 

Capital

Current assets less current liabilities (percentage of 

revenues) 13.0%

Net PPE Net plant property and equipment (percentage of revenues) 32.3%

SG&A Selling, general, and administrative expenses (percentage 

of revenues) 23.3%

R&D Research and development expenses (percentage of 

revenues) 0.5%

Depreciation Amortization of fixed assets (percentage of revenues) 2.6%

Capital 

Expenditures

Outlay of cash to acquire or improve capital assets 

(percentage of revenues, not including acquisition or sale 

of business units)

2.8%

Cost of Goods 

Sold

Includes material, labor, overhead, and depreciation 

(percentage of revenues) 64.8%

4.2 Do any of the financial parameters in Table 12A-8 change significantly based on 

product class for either CFs or CFLKs? Please describe any differences.

4.3 Do any of the financial parameters in Table 12A-8 change for a particular subgroup of 

CF and CFLK manufacturers? Please describe any differences.

4.4 How would you expect amended energy conservation standards to impact any of the 

financial parameters for the industry?

4.5 Can you please provide the breakdown in the total production costs for CFs in Table 

12A-9 and CFLKs in Table 12A-10 by the percentage for labor, materials, overhead, 

and depreciation?

Table 12A-9 Breakdown of Total Production Costs for Ceiling Fans

Labor % of full 

production costs

Materials % of full 

production costs

Overhead % of full 

production costs

Depreciation % of full 

production costs

Table 12A-10 Breakdown of Total Production Costs for Ceiling Fan Light Kits

Labor % of full 

production costs

Materials % of full 

production costs

Overhead % of full 

production costs

Depreciation % of full 

production costs
12A-15

5 CONVERSION COSTS

DOE understands that amended energy conservation standards may cause your company to incur 

capital and product conversion costs to redesign existing products and make changes to existing 

production lines. Understanding the nature and magnitude of the conversion costs is a critical 

part of the MIA. Depending on their magnitude, the conversion costs can have a substantial 

effect on the outputs used by DOE to evaluate the impact of amended energy conservation 

standards on the CF and CFLK industries. The MIA considers two types of conversion costs:

• Capital conversion costs are one-time investments in plant, property, and equipment 

(PPE) necessitated by amended energy conservation standards. These may be incremental 

additions to existing PPE or the replacement of existing PPE. Included are expenditures 

on buildings, equipment, and tooling.

• Product conversion costs are costs related to research, product development, testing, 

marketing, and other costs for redesigning products necessitated by amended energy 

conservation standards.

DOE is interested in understanding the nature and magnitude of conversion costs including 

research programs and changes to production lines and production facilities necessary to bring 

all products into compliance at each design option.

5.1 Please provide estimates for your conversion costs by product class for CFs in Table 

12A-11 and for CFLKs in Table 12A-12 and Table 12A-13 below. In your estimates, 

please consider such expenses as product development expenses, prototyping, testing, 

certification, and marketing. Where applicable, please describe the assumptions behind 

the estimates provided and note where proposed changes would require a complete 

platform change.
12A-16

Table 12A-11 Expected Product and Capital Conversion Costs for Ceiling Fans

Product Class EL

Total Product 

Conversion 

Costs ($)

Total Capital 

Conversion 

Costs ($)

Description

Standard Fans

1

2

3

4

Hugger Fans

1

2

3

4

High-Volume SmallDiameter (HVSD)

1

2

3

Large-Diameter

1

2

3

4

Very-Small Diameter 

Fans

More 

Efficient

Table 12A-12 Expected Product and Capital Conversion Costs for Ceiling Fan Light Kits 

Fixture Replacement Scenario

Product 

Class EL Lamp Type

Total Product 

Conversion 

Costs ($)

Total Capital 

Conversion 

Costs ($)

Description

All CFLKs

1 CFL

2 CFL/LED

3 LED

4 LED

5 LED

6 LED

7 LED
12A-17

Table 12A-13 Expected Product Conversion Costs for Ceiling Fan Light Kits Lamp 

Replacement Scenario

Product 

Class EL Lamp Type

Total Product 

Conversion 

Costs ($)

Total Capital 

Conversion 

Costs ($)

Description

All CFLKs

1 CFL N/A

2 CFL/LED N/A

3 LED N/A

4 LED N/A

5 LED N/A

6 LED N/A

7 LED N/A

5.2 Is it correct to assume there would not be any capital conversion costs for CFLK 

standards in the lamp replacement scenario, regardless of the efficacy level selected?

5.3 For the design options put forth, which ELs would require minor changes to 

production lines, major changes to production lines, substantial modifications to 

existing facilities, or the development of entirely new manufacturing facilities?

6 CUMULATIVE REGULATORY BURDEN

Cumulative regulatory burden refers to the burden that industry faces from overlapping effects of 

new or revised DOE standards and/or other regulatory actions affecting the same product or 

industry.

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 Below is a list of regulations that could affect manufacturers of CFs and CFLKs. 

Please provide any comments on the listed regulations and provide an estimate for 

your expected compliance cost.
12A-18

Table 12A-14 Other Regulations Identified by DOE

Regulation Estimated or Actual 

Effective Date(s)

Expected Expense 

for Compliance Comments

DOE’s Energy Conservation Standards 

for Other Products and Equipment

International Energy Efficiency 

Standards

Others

6.2 Are there any other recent or impending regulations that CF and CFLK manufacturers 

face (from DOE or otherwise)? If so, please identify the regulation, the corresponding 

effective dates, and your expected compliance cost.

6.3 Under what circumstances would you be able to coordinate any expenditure related to 

these other regulations with energy conservation standards?

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 CF and CFLK employment and solicit manufacturer views on how domestic 

employment patterns might be affected by energy conservation standards.

7.1 Where are your CF and CFLK manufacturing facilities that produce products for the 

United States located? What types of products are manufactured at each location? 

Please provide annual shipment figures for your company’s CF and CFLK 

manufacturing at each location by product class. Please also provide employment 

levels at each of these facilities.

Table 12A-15 Ceiling Fan and Ceiling Fan Light Kit Manufacturing Facilities

Facility Location Product Types Manufactured Employees Annual 

Shipments

Example Jackson, TN XX 650 300,000 for XX

1

2

3

4

5
12A-19

7.2 Would your domestic employment levels be expected to change significantly under 

amended energy conservation standards? If so, please explain how and why they 

would change if higher efficiency levels were required for CFs and CFLKs.

7.3 Would the workforce skills necessary under amended energy conservation standards 

require extensive retraining or replacement of employees at your CF and CFLK 

manufacturing facilities?

7.4 Would amended CF and CFLK 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 energy conservation 

standards for CFs and CFLKs?

8 MANUFACTURING CAPACITY AND NON-US SALES

8.1 How would amended energy conservation standards impact your company’s CF and 

CFLK manufacturing capacity?

8.2 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 

compliance date of the final rule for certain product classes?

8.3 What percentage of your company’s CFs and CFLKs sold within the United States is 

manufactured domestically?

8.4 What percentage of your U.S. production of CFs and CFLKs are exported?

8.5 Are there any foreign companies with North American CF or CFLK production 

facilities?

8.6 Would amended energy conservation standards impact your domestic vs. foreign 

manufacturing or sourcing decisions for CFs or CFLKs? Is there an efficiency level 

that would cause you to move existing domestic production facilities outside the U.S. 

for either CFs or CFLKs?

9 IMPACT ON COMPETITION

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 

energy conservation standard.
12A-20

9.1 How would amended energy conservation standards affect your ability to compete in 

the market place? Would the effects on your company be different than others in the 

CF and CFLK industry?

9.2 Would you expect your market share to change if amended energy conservation 

standards become effective for CFs and CFLKs?

9.3 Do you or any other firms hold intellectual property that gives them a competitive 

advantage following energy conservation standards?

9.4 How would CF and CFLK industry competition change as a result of amended energy 

conservation standards?

10 IMPACTS ON SMALL BUSINESSES

10.1 The Small Business Administration (SBA) denotes a small business in the CF and 

CFLK manufacturing industry as having less than 750 total employees, including the 

parent company and all subsidiaries (NAICS code 335210, Small Electrical Appliance 

Manufacturing). 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?
12A-21

12A.3 ADDENDUM

Manufacturers were instructed to use the following Table 12A-16, Table 12A-17 and 

Table 12A-18 instead of the previous Table 12A-3, Table 12A-4, and Table 12A-5.
12A-22

Table 12A-16 All CFLKs Engineering Analysis: Lamp Replacement Scenario

Level Lamp 

Type

Wattage

Lamp 

Initial 

Light 

Output

Lamp 

Efficacy

Lamp 

Lifetime

Proposed Efficacy Level 

Equation

Consumer 

Lamp 

Price in 

CFLK*

Remaining 

Consumer 

Price of 

CFLK

Total 

Consumer 

Price of 

CFLK**

% 

Consumer 

Lamp Price 

of CFLK†

W lm lm/W hr lm/W 2014$ 2014$ 2014$ 2014$

Baseline CFL 14 800 57.1 10,000 N/A $1.70 $22.15 $26.19 13%

EL 1 CFL 13 800 61.5 10,000 69 - 29.42 × 0.9983lumens $2.01 $22.15 $26.80 15%

EL 2‡

CFL 11 730 66.4 10,000 74 - 29.42 × 0.9983lumens $2.23 $22.15 $27.24 16%

LED 12 800 66.7 25,000 74 - 29.42 × 0.9983lumens $12.93 $22.15 $48.63 53%

EL 3 LED 11 730 72.7 25,000 80 - 29.42 × 0.9983lumens $10.72 $22.15 $44.22 48%

EL 4 LED 10 800 80.0 25,000 87 - 29.42 × 0.9983lumens $8.51 $22.15 $39.81 43%

EL 5 LED 9.5 800 84.2 25,000 91 - 29.42 × 0.9983lumens $7.40 $22.15 $37.58 39%

EL 6 LED 9 800 88.9 25,000 96 - 29.42 × 0.9983lumens $6.30 $22.15 $35.38 36%

EL 7 LED 8.5 800 94.1 25,000 101 - 29.42 × 0.9983lumens $5.19 $22.15 $33.17 31%

EL 8 LED 8 820 102.5 25,000 107 - 29.42 × 0.9983lumens $4.09 $22.15 $30.96 26%

*Lamp prices are weighted by estimated shipments though distribution channels.

**Total price of CFLK reflects a two-socket fixture. 

†

Percentages reflect the lighting component of CFLK for two lamps. 

‡

At EL 2, there are two different representative lamp units that can achieve this level.
12A-23

Table 12A-17 All CFLKs Engineering Analysis: Fixture Replacement Scenario

Level Lamp 

Type

Fixture 

Sockets

Socket 

Type

Lamp 

Wattage

Fixture 

Wattage

Lamp 

Initial 

Light 

Output

Fixture 

Initial 

Light 

Output

Lamp 

Efficacy

Lamp 

Lifetime

Proposed Efficacy Level 

Equation

Consumer 

Lamp 

Price in 

CFLK*

Consumer 

Socket 

Price in 

CFLK**

Remaining 

Consumer 

Price of 

CFLK

Total 

Consumer 

Price of 

CFLK

% 

Consumer 

Lamp 

Price of 

CFLK†

W W lm lm lm/W hr lm/W 2014$ 2014$ 2014$ 2014$ 2014$

Baseline CFL 2 E26 14 28 800 1600 57.1 10,000 N/A $1.70 $0.31 $22.15 $26.19 13%

EL 1 CFL 3 E26 9 27 520 1560 57.8 10,000 69 - 29.42 × 0.9983lumens $2.45 $0.31 $22.15 $30.45 24%

EL 2 LED 3 E26 8 24 500 1500 62.5 25,000 74 - 29.42 × 0.9983lumens $12.45 $0.31 $22.15 $60.43 62%

EL 3 LED 3 E26 7 21 480 1440 68.6 30,000 80 - 29.42 × 0.9983lumens $10.99 $0.31 $22.15 $56.06 59%

EL 4 LED 2 GU24 10 20 800 1600 80.0 25,000 87 - 29.42 × 0.9983lumens $8.51 $0.73 $22.15 $40.64 42%

EL 5 LED 3 E26 6.5 19.5 530 1590 81.5 25,000 91 - 29.42 × 0.9983lumens $11.45 $0.31 $22.15 $57.45 60%

EL 6 LED 1 E26 17 17 1600 1600 94.1 25,000 96 - 29.42 × 0.9983lumens $16.53 $0.31 $22.15 $39.00 42%

EL 7 LED 1 E26 16 16 1600 1600 100.0 25,000 101 - 29.42 × 0.9983lumens $16.12 $0.31 $22.15 $38.59 42%

EL 8 LED 1 E26 15 15 1600 1600 106.7 25,000 107 - 29.42 × 0.9983lumens $15.71 $0.31 $22.15 $38.18 41%

*Lamp prices are weighted by estimated shipments though distribution channels.

**Socket prices are based on estimated manufacturer production cost of $0.15 for E26; $0.35 for GU24.

†

Percentage reflects the lighting component of CFLK based on number of lamps and sockets.
12A-24

Table 12A-18 CFLK Max Tech Levels for Representative Product Classes

Product Class Max Tech Level Potential Efficacy 

Improvement Design Pathway Associated Cost Increase

All CFLKs

107 - 29.42 × 0.9983lumens

(e.g., 8 W LED,

820 lumens, 102.5 lm/W)
12B-i 

APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW 

TABLE OF CONTENTS

12B.1 INTRODUCTION AND PURPOSE .......................................................................... 12B-1

12B.2 CEILING FAN LIGHT KIT DESCRIPTION ............................................................ 12B-1

12B.3 CEILING FAN LIGHT KIT DETAILED CASH FLOW EXAMPLE ...................... 12B-4
12B-1

APPENDIX 12B GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

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 the value of the industry or manufacturers 

following a regulation or a series of regulations. The model structure also allows an analysis of 

multiple products with regulations taking effect over a period of time, and of multiple regulations 

on the same products.

Industry net present value is defined, for the purpose of this analysis, as the discounted 

sum of industry free cash flows plus a discounted terminal value. The model calculates the actual 

cash flows by 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) 

(i.e., the standards case).

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 CEILING FAN LIGHT KIT DESCRIPTION

DOE analyzed the impacts of standards on ceiling fan light kit (CFLK) manufacturers. 

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. The line 

items below relate to the CFLK manufacturers and are definitions of listed items on the printout 

of the output sheet (see section 12B.3).

(1) Revenues: Annual revenues - computed by multiplying products’ unit prices at each 

efficiency level by the appropriate manufacturer markup;

(2) Total Shipments: The total covered units shipped;

(3) Material: The portion of cost of goods sold (COGS) that includes materials;

(4) Labor: The portion of COGS that includes direct labor, commissions, dismissal pay, 

bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor 

up-time;

(5) Depreciation: The portion of COGS that includes an allowance for the total amount 

of fixed assets used to produce that one unit;

(6) Overhead: The portion of COGS that includes indirect labor, indirect material, energy 

use, maintenance, property taxes, and insurance related to assets;

(7) Standard SG&A: Selling, general, and administrative costs are computed as a 

percentage of Revenues (1);
12B-2

(8) R&D: GRIM separately accounts for ordinary research and development (R&D) as a 

percentage of Revenues (1);

(9) Product Conversion Costs: Product conversion costs are one-time investments in 

research, development, testing, marketing, and other costs focused on making product 

designs comply with amended energy conservation standards. The GRIM allocates these 

costs over the period between the standards’ announcement and compliance dates;

(10) Stranded Assets: In the year the standard becomes effective, a one-time write-off of 

stranded assets is accounted for;

(11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions for 

interest paid and taxes;

(12) Per Unit EBIT: The average amount of EBIT (11) per covered unit shipped;

(13) EBIT/Revenues: EBIT (11) as a percentage of sales to compare with the industry’s 

average reported in financial statements;

(14) Taxes: Taxes on EBIT (11) are calculated by multiplying the tax rate contained in 

the Discounted Cash Flow tab by EBIT (11);

(15) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Taxes (14)

from EBIT (11);

(16) NOPAT repeated: NOPAT (15) is repeated in the Statement of Cash Flows;

(17) Depreciation repeated: Depreciation (5) is added back in the Statement of Cash 

Flows because it is a non-cash expense;

(18) Stranded Assets repeated: Stranded Assets (10) is added back in the Statement of 

Cash Flows because it is a non-cash expense;

(19) Change in Working Capital: Change in cash tied up in accounts receivable, 

inventory, and other cash investments necessary to support operations is calculated by 

multiplying working capital (as a percentage of revenues) by the change in annual 

revenues;

(20) Cash Flow From Operations: Calculated by taking NOPAT (16), adding back noncash items such as Depreciation (17) and Stranded Assets (18), and subtracting the 

Change in Working Capital (19);

(21) Ordinary Capital Expenditures: Ordinary investments in property, plant, and 

equipment to maintain and replace existing production assets, computed as a percentage 

of Revenues (1);

(22) Capital Conversion Costs: Capital conversion costs are one-time investments in 

property, plant, and equipment to adapt or change existing production facilities so that 

new product designs can be fabricated and assembled under amended regulations; the 

GRIM allocates these costs over the period between the standards’ announcement and 

compliance dates;

(23) Free Cash Flow: Calculated by taking annual Cash Flow From Operations (20)

and subtracting Ordinary Capital Expenditures (21) and Capital Conversion Costs (22);

(24) Free Cash Flow repeated: Free Cash Flow (23) is repeated in the Discounted Cash 

Flow section;
12B-3

(25) Terminal Value: Estimate of the continuing value of the industry after the analysis 

period. Computed by growing the Free Cash Flow (24) at the beginning of 2048 at a 

constant rate in perpetuity;

(26) Present Value Factor: Factor used to calculate an estimate of the present value of an 

amount to be received in the future;

(27) Discounted Cash Flow: Free Cash Flow (23) multiplied by the Present Value 

Factor (26). For the end of 2048, the discounted cash flow includes the discounted 

Terminal Value (25); and

Industry Value thru the end of 2048: The sum of Discounted Cash Flows (27).
12B-4

12B.3 CEILING FAN LIGHT KIT DETAILED CASH FLOW EXAMPLE
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-13

14A.4.4 Equilibrium Climate Sensitivity ................................................................... 14A-13

14A.4.5 Socioeconomic and Emissions Trajectories .................................................. 14A-16

14A.4.6 Discount Rate ................................................................................................ 14A-19

14A.5 REVISED SCC ESTIMATES .................................................................................. 14A-26

14A.6 LIMITATIONS OF THE ANALYSIS ..................................................................... 14A-32

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS ............................................................................................................ 14A-34

14A.8 CONCLUSION ......................................................................................................... 14A-36

REFERENCES ..................................................................................................................... 14A-38

14A.9 ANNEX..................................................................................................................... 14A-43

14A.9.1 Other (Non-CO2) Gases................................................................................ 14A-44

14A.9.2 Extrapolating Emissions Projections to 2300 ............................................... 14A-46

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 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-18

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars) ...................... 14A-29

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 14A-31

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 14A-31

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation ............ 14A-35

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 14A-43

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-53

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-54
14A-ii

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-55

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates ............. 14A-56

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-11

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ....................................................................................... 14A-16

Figure 14A.5.1 Level of Global GDP across EMF Scenarios ......................................... 14A-30

Figure 14A.9.1 Sulfur Dioxide Emission Scenarios ........................................................ 14A-46

Figure 14A.9.2 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ...................................................................................................... 14A-48

Figure 14A.9.3 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) ........... 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) ................................................. 14A-49

Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) ............................................................................................... 14A-50

Figure 14A.9.6 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 14A-51

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-51

Figure 14A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 14A-55
14A-1 

APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

14A.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 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. 

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. 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. 
14A-2 

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) 

Discount Rate

5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

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. We report estimates of the SCC in dollars per 

metric ton of carbon dioxide throughout this document.a

 

 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive 

Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and 

the benefits of the intended regulation and, recognizing that some costs and benefits are difficult 

to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of 

the intended regulation 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 carbon dioxide 

emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts 

 

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 

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 

carbon dioxide emissions. For policies that have a large (non-marginal) impact on global 

cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for 

calculating the benefits of reduced emissions; we do not attempt to answer that question here. 

 An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of 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 $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the 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 

section 16-A.5 for the full range of annual SCC estimates from 2010 to 2050. 

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within two years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

raised in this document and consider public comments as part of the ongoing interagency 

process. 
14A-4 

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 carbon dioxide emissions. In the final model year 

2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of 

$2 per ton of CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(in 2007 dollars), 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 carbon dioxide emissions, while a global SCC 

value is meant to reflect the value of damages worldwide. 

 A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton 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 (in 2007 dollars). In 

addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified 

what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean 

values were $68 and $40 per ton CO2 for discount rates of approximately 2 percent and 3 

percent, respectively (in 2006 dollars for 2007 emissions). 

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide 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.

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-5 

14A.4 APPROACH AND KEY ASSUMPTIONS

 Since the release of the interim values, the interagency group has reconvened on a regular 

basis to generate improved SCC estimates. Specifically, the group has considered public 

comments and further explored the technical 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 

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. 

 

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 

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 socioeconomic (GDP and population) pathways. These emissions are translated into 

concentrations using the carbon cycle built into each model, and concentrations are translated 

into warming based on each model’s simplified representation of the climate and a key 

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, socioeconomic 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 socioeconomic 

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.

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-7 

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric carbon dioxide 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, carbon dioxide emissions are a function of global 

GDP and the carbon intensity of economic output, with the latter declining over time due to 

technological progress. The DICE damage function 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 

climate-related diseases, such as malaria and dengue fever, and pollution), non-market amenities 

(based on outdoor recreation), and human settlements and ecosystems. The DICE damage 

function also includes the expected value of damages associated with low probability, high 

impact “catastrophic” climate change. This last component is calibrated based on a survey of 

experts (Nordhaus 1994). The expected value of these impacts is then added to the other market 

and non-market impacts mentioned above. 

No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function. 

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its health impact estimates assume 

improvements in healthcare over time. In addition, the small impacts on forestry, water systems, 

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these 

sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level 

rise are incorporated into damage estimates, but their magnitude is not clearly reported.

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective 

adaptation, and largely ignores adaptation costs." 

Note that the damage function in DICE has a somewhat different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, 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

 

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 re-calibrated the exogenous path 

of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly 

matched the EMF scenarios.

The DICE Model 
14A-8 

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). 

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 

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 

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-9 

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.

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. But 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.4.2) and higher (Figure 14A.4.1) 

increases in global-average temperature. 
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

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.

 

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 Figures 1A and 1B are the outcome of default assumptions. For instance, under alternate assumptions, 

the damages from FUND may cross from negative to positive at less than or greater than 3 °C.
14A-11 

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE

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

 

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 

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 

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. 

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. 

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. 

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 

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, 

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 carbon dioxide 

emissions. 

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.i It is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric 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 

 

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).
14A-14 

‘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 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 included below gives summary statistics for the four calibrated distributions.

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Roe & Baker Log-normal Gamma Weibull

Pr(ECS < 1.5°C) 0.013 0.050 0.070 0.102

Pr(2°C < ECS < 4.5°C) 0.667 0.667 0.667 0.667

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 (see Hegerl et al. 2006, p. 

721). 

 

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.

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 

 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. 

 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. 
14A-16 

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C)

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 (above) overlays it on Figure 9.20 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

 

14A.4.5 Socioeconomic and Emissions Trajectories

 Another key issue considered by the interagency group is how to select the set of 

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic 

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, 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. 

 

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. 

(2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of 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-17 

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22. EMF-22 uses ten well-recognized models to 

evaluate substantial, 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, peer-reviewed, published, and publicly available. 

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socioeconomic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (see Table 14A.4.2

below). 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 (ii.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-18 

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 (using 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 socioeconomic pathways.

 

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). PPP takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the 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-19 

 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 

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 socioeconomic 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 carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 

gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 

and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, 

respectively. These projections are consistent with one or more EMF-22 scenarios. Likewise, the 

United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people 

in 2000 to 9.1 billion people 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 Annex 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 carbon 

dioxide emissions are long-lived, subsequent damages occur over many years. In calculating the 

SCC, we first estimate the future damages to agriculture, human health, and other market and 

non-market sectors from an additional unit of carbon dioxide emitted in a particular year in terms 

of reduced consumption (or consumption equivalents) due to the impacts of elevated 

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 

 

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-20 

the selected discount rate, which is intended to reflect society's marginal rate of substitution 

between consumption in different time periods. 

 For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, 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, 
14A-21 

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999). 

Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that underlies 

the 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. 

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 (see Lind 1990, Arrow et al 1996, and Arrow 2000). The 
14A-22 

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC. 

 Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

discount rate typically assumes a representative agent with perfect foresight and no credit 

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," that is the 

maximum certain amount that we would exchange for the uncertain amount. However, the 

calculation of the certainty-equivalent requires first estimating the correlation between the 

benefits of the policy and baseline consumption. 

If the IAM projections of future impacts represent expected values (not 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, which yields a real rate of roughly 5 percent.s

 

 

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-23 

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 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).t

These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.u

 In the simplest version of the Ramsey model, 

with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey 

discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors

articulate whether their choice is based on prescriptive or descriptive reasoning.v

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, because

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 et al. 2006). However, even in an intergenerational setting, it may make sense to use a small positive pure rate of time

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-24 

preference because of the small probability of unforeseen cataclysmic events (Stern et al. 

2006). 

• g. A commonly accepted approximation is around 2 percent per year. For the 

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5-2 percent to 2100. 

Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential 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 et al. 

(2006) applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 

percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent 

income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 

percent of their income.w

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is 

a case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur 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 of greater than 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. 

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 

Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on 

net present values. A main result from these studies is that if there is a persistent element to the 

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 (see Weitzman 1998, 1999, 2001; 

Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and 

Gollier and Weitzman 2009). 

The proper way to model discount rate uncertainty remains an active area of research.

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

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-25 

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 Groom et al. (2006), uses more 

general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility 

of interest rates depends on whether rates are currently low or high and the variation in the level 

of persistence over time. 

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further

out in time.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

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 

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. 
14A-26 

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 

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. 

 

z Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).
14A-27 

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.)

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 Annex.) 

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 IAM or socioeconomic scenario is given greater weight than 
14A-28 

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 socioeconomic 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 Annex.) 

As noted above, the 3 percent discount rate is the central value, and so the central value that 

emerges is the average SCC across models at the 3 percent discount rate. For purposes of 

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. 
14A-29 

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars) 

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 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 

comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.aa

 

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 
14A-30 

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. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 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 five-year increments from 2010 to 

2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per 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.

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-31 

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars)

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

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 Annex. 

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%

Avg Avg Avg 95th

2010-2020 3.6% 2.1% 1.7% 2.2%

2020-2030 3.7% 2.2% 1.8% 2.2%

2030-2040 2.7% 1.8% 1.6% 1.8%

2040-2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis.

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in the year 2020 that are 
14A-32 

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 

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. But it is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from 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 highimpact, 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 

 

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-33 

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. 

 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 

understate 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 

 

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-34 

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 

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.

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-35 

 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 (see 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 socioeconomic 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. 

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points

Duration before

effect is fully

realized (in years)

Additional Warming by 2100 

0.5-1.5 C 1.5-3.0 C 3-5 C

Reorganization of Atlantic Meridional 

Overturning Circulation about 100 0-18% 6-39% 18-67%

Greenland Ice Sheet collapse at least 300 8-39% 33-73% 67-96%

West Antarctic Ice Sheet collapse at least 300 5-41% 10-63% 33-88%

Dieback of Amazon rainforest about 50 2-46% 14-84% 41-94%

Strengthening of El Niño-Southern Oscillation about 100 1-13% 6-32% 19-49%

Dieback of boreal forests about 50 13-43% 20-81% 34-91%

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (that is, by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different 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-36 

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 

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). 

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 economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (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 $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the 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 
14A-37 

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 

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-38 

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14A-43 

14A.9 ANNEX

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) 

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non-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. 
14A-44 

14A.9.1 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, N20, 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 factorsee, decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N20, 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/m2 non-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-45 

 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 sulfur dioxide, black carbon and organic carbon, has improved in the 

post-SRES scenarios. Generally, these emissions are projected to be lower than reported in 

SRES. {WGIII 3.2, TS.3, SPM}.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, the figure below shows that the sulfur 

dioxide emissions peak over the short term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.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 lowerbound 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-46 

. 

Figure 14A.9.1 Sulfur 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 preSRES. 

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.9.2 Extrapolating Emissions Projections to 2300 

To run each model through 2300 requires assumptions about GDP, population, 

greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for 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 the year 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300.
14A-47 

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate over 2090-

2100 is maintained from 2100 through 2300. 

4. Net land use CO2 emissions decline linearly, reaching zero in the year 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 (figure below) 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. 

 Figures below 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-48 

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 the year 2300)

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]

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. 
14A-49 

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. 

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
14A-50 

Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 extrapolations 

assume emissions decline linearly, reaching zero in the year 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).

-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-51 

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. 

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
14A-52 

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) 

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

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. 
14A-53 

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
14A-54 

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-55 

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 

Note: 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-56 

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate Scenario

DICE PAGE FUND

5%

Mean 9 6.5 -1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3%

Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50%

Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30
14B-i 

APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14B.1 PREFACE ....................................................................................................................14B-1

14B.2 PURPOSE ....................................................................................................................14B-1

14B.3 SUMMARY OF MODEL UPDATES.........................................................................14B-2

14B.3.1 DICE ................................................................................................................14B-2

14B.3.1.1 Carbon Cycle Parameters .......................................................................14B-2

14B.3.1.2 Sea Level Dynamics ...............................................................................14B-3

14B.3.1.3 Re-calibrated Damage Function .............................................................14B-3

14B.3.2 FUND ...............................................................................................................14B-4

14B.3.2.1 Space Heating .........................................................................................14B-4

14B.3.2.2 Sea Level Rise and Land Loss ...............................................................14B-5

14B.3.2.3 Agriculture .............................................................................................14B-5

14B.3.2.4 Temperature Response Model ................................................................14B-6

14B.3.2.5 Methane ..................................................................................................14B-6

14B.3.3 PAGE ...............................................................................................................14B-6

14B.3.3.1 Sea Level Rise ........................................................................................14B-6

14B.3.3.2 Revised Damage Function to Account for Saturation ............................14B-7

14B.3.3.3 Regional Scaling Factors ........................................................................14B-7

14B.3.3.4 Probability of a Discontinuity ................................................................14B-7

14B.3.3.5 Adaptation ..............................................................................................14B-8

14B.3.3.6 Other Noteworthy Changes ....................................................................14B-8

14B.4 REVISED SCC ESTIMATES .....................................................................................14B-8

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ....................................14B-11

REFERENCES ......................................................................................................................14B-12

14B.6 ANNEX......................................................................................................................14B-14

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) ...........................................................................................................14B-9

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................14B-10

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) ..................................14B-14

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) .................14B-15

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-16
14B-ii

LIST OF FIGURES

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) ............14B-10
14B-1 

APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866 

14B.1 PREFACE

 The following text is reproduced almost verbatim from the May 2013 report of the 

Interagency Working Group on the Social Cost of Carbon of the United States Government. 

Minor changes were made to the working group's report to make it more consistent with the rest 

of this technical support document. 

14B.2 PURPOSE

 The purpose of this document is to update the schedule of social cost of carbon (SCC) a

estimates from the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010).1 E.O. 13563 commits the Administration to regulatory 

decision making “based on the best available science.”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 solely on the latest peer-reviewed version of the models, 

replacing model versions that were developed up to ten years ago in a rapidly evolving field. It 

does not revisit other assumptions with regard to the discount rate, reference case socioeconomic 

and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are 

modeled are confined to those that have been incorporated into the latest versions of the models 

by the developers themselves in the peer-reviewed literature. The Environmental Protection 

Agency (EPA), in collaboration with other Federal agencies such as the Department of Energy 

(DOE), continues to investigate potential improvements to the way in which economic damages 

associated with changes in 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. Section 14B.4 presents the updated schedule of SCC estimates for 2010 – 

2050 based on these versions of the models. 

 

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. 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).1

 
14B-2 

14B.3 SUMMARY OF MODEL UPDATES

 This section briefly summarizes changes integrated into the most recent versions of the 

three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on 

describing those model updates that are relevant to estimating the social cost of carbon. For 

example, both the DICE and PAGE models now include an explicit representation of sea level 

rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to 

ensure damages are constrained GDP, updated regional scaling of damages, and a revised 

treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, the 

model’s simple carbon cycle has been updated to be more consistent with a relatively more 

complex climate model. The FUND model includes updated damage functions for sea level rise 

impacts, the agricultural sector, and reduced space heating requirements, as well as changes to 

the response of temperature to the buildup of GHG concentrations and the inclusion of indirect 

effects of methane emissions. Changes made to parts of the models that are superseded by the 

interagency working group’s modeling assumptions – regarding climate sensitivity, discounting, 

and socioeconomic variables – are not discussed. 

14B.3.1 DICE

Changes in the DICE model 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)2

and on DICE2010 in Nordhaus (2010)3 and the associated on-line appendix containing 

supplemental information. 

14B.3.1.1 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).2,d

Carbon cycle transfer coefficient values in DICE2010 are based on re-calibration of the model to 

match the newer version of MAGICC (Nordhaus 2010 p 2).3

 For example, in DICE2010 in each 

decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains 

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 

2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each 

decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean. 

 

d MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research 

that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from 

much more sophisticated state of the art earth system simulation models (Randall et al. 2007).4

 
14B-3 

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. 

14B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea 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.4,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 o

C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in 

each period is proportional to the 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 to a maximum rate of 

0.025 meters per decade at a temperature anomaly of 6 °C. 

14B.3.1.3 Re-Calibrated Damage Function 

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future production, so each period’s climate damages will 

reduce consumption in that period and in all future periods due to the lost investment. The 

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)

5 and NAS (2011).6

 
14B-4 

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. The loss function in DICE2010 has been expanded 

by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the 

temperature anomaly coefficients have been recalibrated to avoid double-counting damages from 

sea level rise that were implicitly included in these parameters in DICE2007. 

 The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3),3

 who notes 

that “...damages in the uncontrolled (baseline) (i.e., reference) case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 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 (as downloaded from the homepage of William Nordhaus), annual damages are lower 

in most of the early periods but higher in later periods of the time horizon than would be 

calculated using the DICE2007 damage function. Specifically, the percent difference between 

damages in the base run of DICE2010 and those that would be calculated using the DICE2007 

damage function starts at +7 percent in 2005, decreases to a low of -14 percent in 2065, then 

continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), 

and to +160 percent by the end of the model time horizon in 2595. The large increases in the far 

future years of the time horizon are due to the permanence associated with damages from sea 

level rise, along with the assumption that the sea level is projected to continue to rise long after 

the global average temperature begins to decrease. The changes to the loss function generally 

decrease the interagency working group SCC estimates slightly, all else equal.

14B.3.2 FUND

 FUND version 3.8 includes a number of changes over the previous version 3.5 used in 

the interagency report. Documentation supporting FUND and the model’s source code for all 

versions of the model is available from the model authors.g

 Notable changes, due to their impact 

on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level 

rise damage functions in addition to changes to the temperature response function and the 

inclusion of indirect effects from methane emissions.h

 We discuss each of these in turn. 

14B.3.2.1 Space Heating

 In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for 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 

 

g http://www.fund-model.org/. This report uses version 3.8 of the FUND model, which represents a modest update 

to the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013).7 For the 

purpose of computing the SCC, the relevant changes are associated with improving consistency with IPCC AR4 by 

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.

h The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not the subject of significant updates.
14B-5 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

has been modified to ensure that the function is everywhere concave, meaning that for every 

simulation there will exist an upper bound on the benefits a region may receive from reduced 

space heating needs. The new formulation approaches a value of two in the limit as the 

temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the 

reduced expenditures on space heating at 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. 

14B.3.2.2 Sea Level Rise and Land Loss 

The FUND model explicitly includes damages associated with the inundation of dry land 

due to sea level rise. The amount of land lost within a region is dependent upon the proportion of 

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 

3.5 the function defining the 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 nonlinear function of sea level rise, thereby assuming that the slope of the shore line is not constant 

moving inland, with a positive first derivative. The effect of this change is to typically reduce the 

vulnerability of some regions to sea level rise based land loss, therefore having an effect of 

lowering the expected SCC estimate. The model has also been updated to assume that the value 

of dry land at risk of inundation is not uniform across a region but will be a decreasing function 

of protection measure, thereby implicitly assuming that the most valuable land will be protected 

first.

14B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is made up of three additively separable 

components that represent the effects from carbon fertilization, the rate of temperature change, 

and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the 

sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function 

with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the 

ratio of two normal distributions. Within this specification, as draws from the distribution in the 

denominator approached zero the share of the sector’s value “lost” approaches (+/-) infinity 

independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic 

coefficients are drawn directly from truncated normal distributions so that they remain in the 

range [0,∞) and (−∞,0], respectively, where the means for the new distributions are set equal to 

the ratio of the means from the normal distributions used in the previous version. In general the 

impact of this change has been to increase the likelihood that increases in the temperature level 

will have either larger positive or negative effects on the agricultural sector relative to the 

previous version (through eliminating simulations in which the “lost” value approached (+/-) 

infinity). The net effect of this change on the SCC estimates is difficult to predict. 
14B-6 

14B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

cumulative temperature anomaly is based on a mean reverting function where the mean equals 

the equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is 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. 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. 

14B.3.2.5 Methane

 The IPCC notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007).8

 FUND 3.8 now includes the same methods for incorporating the indirect 

effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been 

set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative 

forcing associated with atmospheric methane has also been increase by 40% to account for its net 

impact on ozone production and increase in stratospheric water vapor. The general effect of this 

increased radiative forcing will be to increase the estimated SCC values, where the degree to 

which this occurs will be dependent upon the relative curvature of the damage functions with 

respect to the temperature anomaly.

14B.3.3 PAGE

 PAGE09 (Hope 2012)9 includes a number of changes from PAGE2002, the version used 

in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates 

include: explicitly modeling the impacts from sea level rise, revisions to the damage function to 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 

the calculation of regional temperatures. More details on PAGE2009 can be found in three 

working papers (Hope 2011a, 2011b, 2011c).10–12 A description of PAGE2002 can be found in 

Hope (2006).13 

14B.3.3.1 Sea Level Rise

 While PAGE2002 aggregates all damages into two categories – economic and noneconomic impacts - PAGE2009 adds a third explicit category: damages from sea level rise. In the 

previous version of the model, damages from sea level rise were subsumed by the other damage 
14B-7 

categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea 

level based on the assumption that low-lying shoreline areas will be associated with higher 

damages than current inland areas. Damages from the economic and non-economic sector were 

adjusted to account for the introduction of this new category. 

14B.3.3.2 Revised Damage Function to Account for Saturation 

 In PAGE09, small initial economic and non-economic benefits (negative damages) are 

modeled for small temperature increases, but all regions eventually experience positive economic 

damages from climate change, where damages are the sum of additively separable polynomial 

functions of temperature and sea level rise. Damages transition from this polynomial function to 

a logistic path once they exceed a 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. 

14B.3.3.3 Regional Scaling Factors 

 As in the previous version of PAGE, the PAGE09 model calculates the damages for the 

European Union (EU) and then, assumes that damages for other regions are 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).11 Because of the long coastline in the EU, other 

regions are, on average, less vulnerable than the EU for the same sea level and temperature 

increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on 

four studies reported in the IPCC’s third assessment report, and allowed for benefits from 

temperature increase in Eastern Europe, smaller impacts in developing countries, and higher 

damages in developing countries. 

14B.3.3.4 Probability of a Discontinuity 

 In PAGE2002, the damages associated with a “discontinuity” were modeled as an 

expected value. That is, additional damages from an extreme event, such as extreme melting of 

the Greenland ice sheet, were multiplied by the probability of the event occurring and added to 

the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event 

for each year in the model. The damages for each model run are estimated 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. 
14B-8 

14B.3.3.5 Adaptation 

 As in PAGE2002, adaptation is available to increase the tolerable level of temperature 

change and can help mitigate any climate change impacts that still occur. In PAGE this 

adaptation is the same regardless of the temperature change or sea level rise and is therefore akin 

to what is more commonly considered a reduction in vulnerability. It is modeled by modifying 

the temperature change and sea level rise used in the damage function or by reducing the 

damages by some percentage. PAGE09 assumes a smaller decrease in vulnerability than the 

previous version of the model and assumes that it will take longer for this change in vulnerability 

to be realized. In the aggregated economic sector, at the time of full implementation, this 

adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature 

anomalies between 1°C and 3°C, it will reduce damages by 15-30 percent (depending on the 

region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation 

was assumed to reduce economic sector damages up to 3°C by 50-90 percent after 20 years. 

Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change. 

For the non-economic sector, in PAGE09 adaptation is available to reduce 15 percent of the 

damages due to a temperature 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)12 estimates 

that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea 

level rise via adaptation increase the SCC by approximately 30 percent.

14B.3.3.6 Other Noteworthy Changes

 Two other changes in the model are worth noting. A revised carbon cycle feedback is 

introduced to simulate decreased CO2 absorption by the terrestrial biosphere and ocean as the 

temperature rises. This feedback is linear in the average global and annual temperature anomaly 

but is capped at a maximum value. In the previous version of PAGE, an additional amount was 

added to the 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 the previous version of PAGE, the scaling was determined 

solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional 

temperature anomaly is further adjusted using an additive factor that is based on the average 

absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s 

landmass.

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.1

 The approach along with the inputs for the 

socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount 

rate remains the same. This includes the five reference scenarios based on the EMF-22 modeling 

exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth 

Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent. 
14B-9 

 As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD 

applied equal weight to each model and socioeconomic scenario in order to reduce the 

dimensionality down to three separate distributions representative of the three discount rates. The 

interagency group selected four values from these distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and 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 is 

available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central 

value, and so the central value that emerges is the average SCC across models at the 3 percent 

discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance and value of 

including all four SCC values. 

Table 14B.4.1 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all 

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. 

Values for the years in between are calculated using basic linear interpolation. The full set of 

annual SCC estimates between 2010 and 2050 is reported in the Annex. 

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

 The SCC estimates using the updated versions of the models are higher than those 

reported in the TSD due to the changes to the models outlined in the previous section. 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. 
14B-10 

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) 

As was the case in the original TSD, the SCC increases over time because future 

emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The approach taken by the 

interagency group is to allow the growth rate to be determined endogenously by the models 

through running them for a 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 5.0% 3.0% 2.5% 3.0%

Rate (%) Avg Avg Avg 95th

2010-2020 1.2% 3.2% 2.4% 4.3%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.8% 1.5% 2.0%

2040-2050 2.6% 1.6% 1.3% 1.5%

 The future monetized value of emission reductions in each year (the SCC in year t 

multiplied by the change in emissions in year t) must be discounted to the present to determine 

its total net present value for use in regulatory analysis. As previously discussed in the original 

TSD, damages from future emissions should be discounted at the same rate as that used to 

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from 

climate change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. 
14B-11 

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS

 The 2010 interagency SCC technical support report discusses a number of important 

limitations for which additional research is needed. In particular, the document highlights the 

need to improve the quantification of both non-catastrophic and catastrophic damages, the 

treatment of adaptation and technological change, and the way in which inter-regional and intersectoral linkages are modeled. It also discusses the need to more carefully assess the implications 

of risk aversion for SCC estimation as well as the inability to perfectly substitute between 

climate and non-climate goods at higher temperature increases, both of which have implications 

for the discount rate used. EPA, DOE, and other agencies continue to engage in long-term 

research work on modeling and valuation of climate impacts that we expect will inform 

improvements in SCC estimation in the future.
14B-12 

REFERENCES 

1. Interagency Working Group on Social Cost of Carbon. Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866. 2010. United States 

Government. (Last accessed June 10, 2015.) 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-ofCarbon-for-RIA.pdf. 

2. Nordhaus, W. A Question of Balance: Weighing the Options on Global Warming 

Policies. 2008. Yale University Press: New Haven. 

3. Nordhaus, W. D. Economic Aspects of Global Warming in a Post-Copenhagen 

Environment. Proceedings of the National Academy of Sciences. 2010. 107(26): pp. 

11721–11726. (Last accessed June 10, 2015.) http://www.pnas.org/content/107/26/11721. 

4. Randall, D. A., R. A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. 

Pitman, J. Shukla, J. Srinivasan, R. J. Stouffer, A. Sumi, and K. E. Taylor. Climate 

Models and their Evaluation. In Climate Change 2007: The Physical Science Basis. 

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Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, 

M. Marquis, K. Averyt, M. M. B. Tignor, and H. L. Miller Jr., Editors. 1st ed. 2007. 

Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA. 

(Last accessed June 10, 2015.) http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-

wg1-chapter8.pdf. 

5. Nicholls, R. J., N. Marinova, J. A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. 

Hinkel, and R. S. J. Tol. Sea-Level Rise and its Possible Impacts given a “Beyond 4°C 

World” in the Twenty-First Century. Philosophical Transactions of the Royal Society of 

London A: Mathematical, Physical and Engineering Sciences. 2011. 369(1934): pp. 161–

181. (Last accessed June 10, 2015.) 

http://rsta.royalsocietypublishing.org/content/369/1934/161. 

6. National Research Council. Climate Stabilization Targets: Emissions, Concentrations, 

and Impacts over Decades to Millennia. 2011. National Academies Press: Washington, 

D.C. (Last accessed May 26, 2015.) http://www.nap.edu/catalog/12877/climatestabilization-targets-emissions-concentrations-and-impacts-over-decades-to. 

7. Anthoff, D. and R. S. J. Tol. The Uncertainty about the Social Cost of Carbon: A 

Decomposition Analysis using FUND. Climatic Change. 2013. 117(3): pp. 515–530. 

(Last accessed June 10, 2015.) http://link.springer.com/article/10.1007/s10584-013-0706-

7. 

8. Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D. W. Fahey, J. Haywood, 

J. Lean, D. C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz, and R. Van 

Dorland. Changes in Atmospheric Constituents and in Radiative Forcing. In Climate 

Change 2007: The Physical Science Basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. 
14B-13 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. M. B. Tignor, and 

H. L. Miller, Jr., Editors. 1st ed. 2007. Cambridge University Press: Cambridge, United 

Kingdom and New York, NY, USA. (Last accessed June 10, 2015.) 

http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter2.pdf. 

9. Hope, C. Critical Issues for the Calculation of the Social Cost of CO2: Why the Estimates 

from PAGE09 are Higher than those from PAGE2002. Climatic Change. 2012. 117(3): 

pp. 531–543. (Last accessed June 10, 2015.) 

http://link.springer.com/article/10.1007/s10584-012-0633-z. 

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Cambride Judge Business School. (Last accessed June 10, 2015.) 

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http://journals.sfu.ca/int_assess/index.php/iaj/article/view/227/190. 

 
14B-14 

14B.6 ANNEX

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 33 52 90

2011 11 34 54 94

2012 11 35 55 98

2013 11 36 56 102

2014 11 37 57 106

2015 12 38 58 109

2016 12 39 60 113

2017 12 40 61 117

2018 12 41 62 121

2019 12 42 63 125

2020 12 43 65 129

2021 13 44 66 132

2022 13 45 67 135

2023 13 46 68 138

2024 14 47 69 141

2025 14 48 70 144

2026 15 49 71 147

2027 15 49 72 150

2028 15 50 73 153

2029 16 51 74 156

2030 16 52 76 159

2031 17 53 77 163

2032 17 54 78 166

2033 18 55 79 169

2034 18 56 80 172

2035 19 57 81 176

2036 19 58 82 179

2037 20 59 84 182

2038 20 60 85 185

2039 21 61 86 188

2040 21 62 87 192

2041 22 63 88 195

2042 22 64 89 198

2043 23 65 90 200

2044 23 65 91 203

2045 24 66 92 206

2046 24 67 94 209

2047 25 68 95 212

2048 25 69 96 215

2049 26 70 97 218

2050 27 71 98 221
14B-15 

Table 14B.6.2 202 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 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -17 -1 5 17 34 44 59 90 113 176

MERGE -7 2 7 16 30 35 49 72 91 146

MESSAGE -19 -4 2 12 27 32 46 70 87 135

MiniCAM Base -9 1 8 18 35 45 59 87 108 172

5th Scenario -30 -12 -5 6 19 24 35 57 72 108

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -14 -3 1 9 20 25 35 54 69 111

MERGE -8 -1 3 9 18 22 31 47 60 97

MESSAGE -16 -5 -1 6 16 18 28 43 55 88

MiniCAM Base -9 -1 3 10 21 27 35 53 67 107

5th Scenario -22 -10 -5 2 10 13 20 33 42 63
14B-16 

Table 14B.6.4 2020 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 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -3 -1 2 3 6 11 15 25

MERGE -6 -3 -2 0 3 4 7 12 16 27

MESSAGE -10 -6 -4 -1 2 2 5 9 13 23

MiniCAM Base -7 -3 -2 0 3 4 7 11 15 26

5th Scenario -11 -7 -5 -2 0 0 3 6 8 14
17A-i 

APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

TABLE OF CONTENTS

17A.1 INTRODUCTION ...................................................................................................... 17A-2

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY ....................................... 17A-3

17A.3 NIA-RIA INTEGRATED MODEL ............................................................................ 17A-4

17A.4 MARKET PENETRATION CURVES ...................................................................... 17A-4

17A.4.1 Introduction ..................................................................................................... 17A-4

17A.4.2 Adjustment of XENERGY Penetration Curves .............................................. 17A-6

17A.4.3 Interpolation of Penetration Curves ................................................................ 17A-7

17A.5 CONSUMER REBATE PROGRAMS ....................................................................... 17A-8

17A.6 FEDERAL AND STATE TAX CREDITS................................................................. 17A-9

17-A.1.1 Federal Tax Credits for Consumers .............................................................. 17A-10

17A.6.1 Federal Tax Credits for Manufacturers ......................................................... 17A-11

17A.6.2 State Tax Credits ........................................................................................... 17A-11

REFERENCES ..................................................................................................................... 17A-13

LIST OF TABLES

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for All CFLKs (TSL 2) ............................................................. 17A-3

Table 17A.5.1 Rebates Programs for Ceiling Fan Light Kits ........................................... 17A-9

LIST OF FIGURES

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies ................................................................ 17A-6
17A-2 

APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

17A.1 INTRODUCTION

 This appendix contains sections discussing the following topics: 

• Projections of annual market share increases for the alternative policies;

• NIA-RIA Integrated Model;

• Market penetration curves used to analyze consumer rebates and voluntary efficacy

targets, including: 

o Background material on XENERGY’s approach, 

o DOE’s adjustment of these curves for this analysis, and 

o The method DOE used to derive interpolated, customized curves;

• Detailed table of rebates offered for the considered product, as well as DOE’s approach 

to estimate a market representative rebate value for this RIA; and

• Background material on Federal and State tax credits for appliances. 
17A-3 

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17A.2.1 shows the annual increases in market shares of ceiling fan light kits 

(CFLKs) meeting the target efficacy level for the proposed TSL (TSL #2). DOE used these 

market share increases as inputs to the NIA-RIA spreadsheet model. 

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for All CFLKs (TSL 2) 

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2019 39.8% 23.9% 11.9% 20.7% 0.11%

2020 43.4% 26.0% 13.0% 33.1% 0.26%

2021 43.7% 26.2% 13.1% 0.0% 0.00%

2022 0.5% 0.3% 0.2% 0.0% 0.00%

2023 0.7% 0.4% 0.2% 0.0% 0.00%

2024 0.7% 0.4% 0.2% 0.0% 0.00%

2025 0.7% 0.4% 0.2% 0.0% 0.00%

2026 0.7% 0.4% 0.2% 0.0% 0.00%

2027 0.6% 0.4% 0.2% 0.0% 0.00%

2028 0.5% 0.3% 0.1% 0.0% 0.00%

2029 0.4% 0.2% 0.1% 0.0% 0.00%

2030 0.3% 0.2% 0.1% 0.0% 0.00%

2031 0.3% 0.2% 0.1% 0.0% 0.00%

2032 0.2% 0.1% 0.1% 0.0% 0.00%

2033 0.2% 0.1% 0.0% 0.0% 0.00%

2034 0.1% 0.1% 0.0% 0.0% 0.00%

2035 0.1% 0.1% 0.0% 0.0% 0.00%

2036 0.1% 0.0% 0.0% 0.0% 0.00%

2037 0.1% 0.0% 0.0% 0.0% 0.00%

2038 0.0% 0.0% 0.0% 0.0% 0.00%

2039 0.0% 0.0% 0.0% 0.0% 0.00%

2040 0.0% 0.0% 0.0% 0.0% 0.00%

2041 0.0% 0.0% 0.0% 0.0% 0.00%

2042 0.0% 0.0% 0.0% 0.0% 0.00%

2043 0.0% 0.0% 0.0% 0.0% 0.00%

2044 0.0% 0.0% 0.0% 0.0% 0.00%

2045 0.0% 0.0% 0.0% 0.0% 0.00%

2046 0.0% 0.0% 0.0% 0.0% 0.00%

2047 0.0% 0.0% 0.0% 0.0% 0.00%

2048 0.0% 0.0% 0.0% 0.0% 0.00%
17A-4 

17A.3 NIA-RIA INTEGRATED MODEL

 For this analysis, DOE used its integrated NIA-RIAa

 model approach that the Department 

built on the NIA model discussed in Chapter 10 and documented in Appendix 10-A.The 

resulting integrated NIA-RIA model features both the NIA and RIA inputs, analyses and results. 

It has the capability to generate results, by product class and TSL, for the mandatory standards 

and each of the RIA policies. Separate modules estimate increases in market penetration of more 

efficient equipment for consumer rebates, voluntary energy efficiency targets and bulk 

government purchases.b The consumer rebates module calculates benefit-cost (B/C) ratios and 

market barriers, and generates customized market penetration curves for each product class; the 

voluntary energy efficiency targets module relies on the market barriers calculated in the 

consumer rebates module to project a reduction in those barriers over the first ten years of the 

forecast period and estimate the market effects of such a reduction; and the bulk government 

purchases module scales down the market for CFLKs to housing units in public housing 

authority. A separate module summarizes the market impacts from mandatory standards and all 

policy alternatives, and an additional module produces all tables and figures presented in Chapter 

17 as well as the table of market share increases for each policy reported in Section 17A.2 of this 

Appendix.

17A.4 MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that 

DOE used to analyze the Consumer Rebates and Voluntary Efficacy Targets policies. Next it 

discusses the adjustments it made to the maximum penetration rates. It then refers to the method 

it used to develop interpolated penetration curves for CFLKs that meet the target efficacy level at 

the proposed TSL. The resulting curve is presented in Chapter 17. 

17A.4.1 Introduction

XENERGY, Inc.c, developed a re-parameterized, mixed-source information diffusion 

model to estimate market impacts induced by financial incentives for purchasing energy efficient 

appliances.

1 The basic premise of the mixed-source model is that information diffusion drives 

the adoption of technology. 

Extensive economic literature describes the diffusion of new products as technologies 

evolve. Some research focuses primarily on developing analytical models of diffusion patterns 

applicable to individual consumers or to technologies from competing firms.2, 3, 4

 One study 

records researchers’ attempts to investigate the factors that drive diffusion processes.5 Because a 

new product generally has its own distinct characteristics, few studies have been able to 

 

a NIA = National Impact Analysis; RIA = Regulatory Impact Analysis

b As mentioned in Chapter 17, the increase in market penetrations for consumer tax credits and manufacturer tax 

credits are estimated as a fraction of the increase in market penetration of consumer rebates. 

c XENERGY is now owned by KEMA, Inc. (www.kema.com) 
17A-5 

conclusively develop a universally applicable model. Some key findings, however, generally are 

accepted in academia and industry. 

 One accepted finding is that, regardless of their economic benefits and technological 

merits, new technologies are unlikely to be adopted by all potential users. For many products, a 

ceiling must be placed on the adoption rate. A second conclusion is that not all adopters purchase 

new products at the same time: some act quickly after a new product is introduced; others wait 

for the product to mature. Third, diffusion processes can be characterized approximately by 

asymmetric S-curves that depict three stages of diffusion: starting, accelerating, and decreasing 

(as the adoption ceiling is approached). 

A so-called epidemic model of diffusion is used widely in marketing and social studies. 

The epidemic model assumes that (1) all consumers place identical value on the benefits of a 

new product, and (2) the cost of a new product is constant or declines monotonically over time. 

What induces a consumer to purchase a new product is information about the availability and 

benefits of the product. In other words, information diffusion drives consumers’ adoption of a 

new product.

3

 The model incorporates information diffusion from both internal sources (spread 

by word of mouth from early adopters to prospective adopters) and external sources (the 

“announcement effect” produced by government agencies, institutions, or commercial 

advertising). The model incorporates both internal and external sources by combining a logistic 

function with an exponential function.4,5 

The relative degree of influence from the internal and external sources determines the 

general shape of the diffusion curve for a specific product.4,5 If adoption of a product is 

influenced primarily by external sources of information (the announcement effect), for instance, 

a high rate of diffusion occurs at the beginning of the process. In this scenario, external sources 

provide immediate information exposure to a significant number of prospective adopters. In 

contrast, internal sources (such as a network of prospective adopters) are relatively small in size 

and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, 

information diffusion dominated by external sources is represented by a concave curve (the 

exponential curve in Figure 17A.4.1). If adoption of a new product is influenced most strongly 

by internal sources of information, the number of adopters increases gradually, forming a convex 

curve (the logistic curve in Figure 17A.4.1). 
17A-6 

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on Adoption of 

New Technologies

17A.4.2 Adjustment of XENERGY Penetration Curves

In consultation with the primary authors of the 2002 XENERGY study who later 

conducted similar California studies, DOE made some adjustments to XENERGY’s original 

implementation (penetration) curves.6 The experiences with utility programs since the 

XENERGY study indicate that incentive programs have difficulty achieving penetration rates as 

high as 80 percent. Consumer response is limited by barriers created by consumer utility issues 

and other non-economic factors. DOE therefore adjusted the maximum penetration parameters 

for some of the curves from 80 percent to the following levels: 

Moderate Barriers: 70% 

High Barriers: 60% 

Extremely High Barriers: 50% 

The low barriers and no barriers curves (the latter used only when a product has a very 

high base-case-market share) remained, respectively, with 80 percent and 100 percent as their 

maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set 

the no barriers and extremely high barriers curves as the upper and lower bounds, respectively, 

for any benefit/cost ratio points higher or lower than the curves. It set another constraint such 

that the policy case market share cannot be great than 100 percent, as might occur for products 

with high no-standard case market shares of the target-level technology. 
17A-7 

17A.4.3 Interpolation of Penetration Curves 

 As discussed above, the XENERGY penetration (implementation) curves followed a 

functional form to estimate the market implementation rate caused by energy efficiency 

measures such as consumer rebates.d The XENERGY report presents five reference market 

implementation curves that vary according to the level of market barriers to technology 

penetration.1 Such curves have been used by DOE in the Regulatory Impact Analyses for 

rulemakings for appliance energy efficiency standards to estimate market share increases in 

response to rebate programs.e

 They provide a framework for evaluating technology penetration, 

yet require matching the studied market to the curve that best represents it. This approximate 

matching can introduce some inaccuracy to the analysis. 

 Blum et al (2011, Appendix A)7 presents an alternative approach to such evaluation: a 

method to estimate market implementation rates more accurately by performing interpolations of 

the reference curves. The referred report describes the market implementation rate function and 

the reference curves, the method to calibrate the function to a given market, and the limitations 

of the method. 

 DOE used the above referred method to interpolate market implementation curves, to 

generate customized curves that were used to estimate the effects of consumer rebates and 

voluntary efficacy targets for the product class covered by this RIA. For consumer rebates, DOE 

derived such curves based on an algorithm that finds the market implementation curve that best 

fits, for the first year of the analysis period, the B/C ratio of the target efficacy level and the 

market penetration of equipment with that level of efficacy in the no-standard case. For the 

analysis of voluntary efficacy targets, DOE departs from the market barriers level corresponding 

to the market implementation curve it derived for consumer rebates, to linearly decrease it over 

the ten initial years of the analysis period. For each year, as market barriers decline, the 

corresponding market implementation curve leads – for the same B/C ratio – to higher market 

penetrations. 

 

 

d The RIA chapter refers to these curves as penetration curves. This section, in references to the original source, uses 

the term implementation curve. e DOE has also used this method to estimate market share increases resulting from consumer tax credit and 

manufacturer tax credit programs, since the effects of tax credits on markets are considered in this RIA proportional 

to the impacts from rebates. 
17A-8 

17A.5 CONSUMER REBATE PROGRAMS 

DOE performed an internet search for rebate programs that offered incentives for CFLKs

in the first quarter of 2015. Some organizations nationwide, comprising electric utilities and 

regional agencies, offer rebate programs for ceiling fans with light kits. Table 17A.5.1 provides 

the organizations’ names, states, rebate amounts, and program websites (as they were available 

in the first quarter of 2015). If there is more than one entry for an organization, it offers different 

rebates in different states. When an organization offers rebates through several utilities, it is 

represented only once in that table. 

DOE relied on the data it gathered from the 10 rebate programs offered by the 7 

organizations listed in Table 17A.5.1 to estimate a market representative rebate amount for 

CFLKs. DOE estimated a market representative rebate amount for CFLKs in two steps. First 

DOE calculated the simple average of the rebate values offered by the programs listed in Table 

17A.5.1. Then, DOE scaled that average down using the proportion of the cost of a light kit 

relative to the cost of a ceiling fan with a light-kit, which DOE assumed to be 25 percent. The 

resulting representative rebate amount is $5.13 (in 2014$). 
17A-9 

Table 17A.5.1 Rebates Programs for Ceiling Fan Light Kitsf

Organization State Rebate* Website

Bright Energy Solutions 

(offered by 16 utilities) IA $25 

http://www.brightenergysolutions.com/_files/pdf/

generated/ENERGYSTARCeilingFanDenison 

MunicipalUtilities.pdf#zoom=100 

Coldwater Board of Public 

Utilities MI $15 

http://www.coldwater.org/Content/documents /

simply_efficient_residential_incentive_app.pd f 

Energy Optimization 

(offered by 11 utilities) MI $15 http://www.michiganenergy.org/algerproducts 

Lansing Board of Water & 

Light - Hometown Energy 

Savers 

MI $15 http://www.lbwl.com/energysavers.aspx 

Austin Utilities MN $15 http://www.austinutilities.com/pages/residenti alconserve-incentives/ 

Bright Energy Solutions 

(offered by 22 utilities) MN $25 http://www.brightenergysolutions.org/municip alities/?category=home&state=mn 

Bright Energy Solutions 

(offered by 5 utilities) ND $25 http://www.brightenergysolutions.com/munici 

palities/?category=home&state=nd 

First Energy OH $30 

http://energysaveohiohome.com/uploads/eepp/FE_OH_LightHH_Reb 

ate_final2_LR.pdf 

American Municipal 

Power - Efficiency Smart OH $15 http://www.efficiencysmart.org/Media/Docum ents/EfficiencySmart-Rebate-Form-2014.pdf 

Bright Energy Solutions 

(offered by 10 utilities) SD $25 http://www.brightenergysolutions.com/munici palities/?category=home&state=sd 

* In 2014$.

17A.6 FEDERAL AND STATE TAX CREDITS

This section summarizes the Federal and State tax credits available to consumers who 

purchase energy efficient appliances. This section also describes tax credits available to 

manufacturers who produce certain energy efficient appliances.

f This table is based on rebate programs DOE found to be available through an extensive internet search during the 

first quarter of 2015. Some of the programs referenced—and consequently their websites—may no longer be 

available by the time this document is published. 
17A-10 

17-A.1.1 Federal Tax Credits for Consumers

EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps; gas, oil and propane furnaces and boilers; furnace fans; and/or gas, 

oil, or electric heat pump water heaters in new or existing homes.8, 9 These tax credits were in 

effect in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by the American 

Recovery and Reinvestment Act (ARRA).10 There was a $1,500 cap on the credit per home, 

including the amount received for insulation, windows, and air and duct sealing. Congress 

extended this provision for 2011, with some modifications to eligibility requirements, and 

reductions in the cap to $500 per home. The American Taxpayer Relief Act of 2012 extended, 

with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and 

water heaters placed in service between January 1, 2012 and December 31, 2013.8, 11

The tax credit for furnace fans was $50 in 2011, after which it expired. 

The importance of the Federal tax credits has been emphasized in research in the 

residential heating industry on the impacts of the relatively large credits that were available for 

HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors 

conducted by Vermont Energy Investment Corporation, respondents indicated that the ample 

credit had had a notable impact on sales of higher-efficiency heating and cooling equipment. 

Some distributors combined the Federal tax credits with manufacturer rebates and utility 

program rebates for a greater consumer incentive. However, when the amount of the Federal tax 

credit was reduced, smaller utility rebate incentives had not induced the same levels of 

equipment sales increases. The decrease in incentive size from a $1,500 cap in 2009-2010 to a 

$500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a 

decline in total sales of residential HVAC products. Distributors stated that an incentive needed 

to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence 

consumer choice. The industry publication “2011 HVAC Review and Outlook” noted a decline 

in sales of air conditioning units with >14 SEER in 2011 and a return in sales of units with >16 

SEER to 2009 levels (after an increase in 2010). The large majority of distributors observed no 

impacts from the utility programs with their lower rebate amounts available in 2011. Distributors 

also commented on the advantages of the Federal tax credit being nationwide in contrast to 

utility rebate programs that target regional markets.12, 13

In an effort to evaluate the potential impact of a Federal appliance tax credit program, 

DOE reviewed Internal Revenue Service (IRS) data on the numbers of taxpayers who claimed 

the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed 

Form 5695, Residential Energy Credits.

14 It also estimated the percentage of taxpayers with 

entries under Form 5695’s section 3, Residential energy property costs, line 3b, qualified natural 

gas, propane, or oil furnace or hot water boiler. DOE reasoned that the percentage of taxpayers 

with an entry on Line 3b could serve as a rough indication of the potential of taxpayer 

participation in a Federal tax credit program for furnaces during the initial program years. DOE 

found that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 

2007, claimed a credit for a furnace or boiler. DOE further found that the percentages of those 

filing Form 5695 for any qualifying energy property expenditure (which also included 

installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 

respectively. 
17A-11 

 

DOE also reviewed data from an earlier Federal energy conservation tax credit program 

in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located 

data for only the first three years of the program.

15, 16, 17 For those three years - 1979, 1980, and 

1981 - the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 

percent. Given that the data from this earlier tax credit program were not disaggregated by type 

of energy property, this data series served only to indicate a possible trend of greater 

participation in the initial program year, followed by slightly smaller participation in subsequent 

years. However, DOE did not find detailed analysis of this program to indicate the possible 

reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 

2005 energy tax credit program data for its first two program years. 

 As discussed in Chapter 17, DOE analyzed the percentage of participation in consumer 

tax credit programs using its estimates of consumer participation in rebate programs that was 

based on benefit/cost data specific to the product class of CFLKs covered by this RIA. Hence it 

was difficult to compare these detailed estimates to the more general data analysis described 

above from the existing Federal tax credit program, or to use the IRS data analysis in its 

consumer tax credit analysis.

17A.6.1 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that 

produced high-efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007.18

The Emergency Economic Stabilization Act of 200819 amended the credits and extended them 

through 2010. The credits were extended again to 2011 with modifications in the eligibility 

requirements. Manufacturer tax credits were extended again, by the American Taxpayer Relief 

Act of 2012, for clothes washers, refrigerators, and dishwashers manufactured between January 

1, 2012 and December 31, 2013. 

Manufacturers who produce these appliances receive the credits for increasing their 

production of qualifying appliances. These credits had several efficiency tiers in 2011. For 2012-

2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers 

for clothes washers and dishwashers.

11 The credit amounts applied to each unit manufactured. 

The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers was 

capped at $75 million for the period of 2008-2010. However, the most efficient refrigerator 

(30%) and clothes washer (2.2 MEF/4.5 wcf) models was not subject to the cap. The credit to 

manufacturers was capped at $25 million for 2011, with the most efficient refrigerators (35%) 

and clothes washers (2.8 MEF/3.5 WCF) exempted from this cap.20

17A.6.2 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for efficient 

appliances for several years, and the States of Kentucky, Michigan and Indiana began offering 

such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on 

taxpayer participation in credits for eligible products. (See the discussion in Chapter 17, Section 

17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not 

disaggregate participation data by appliance, although DOE reviewed Montana's overall 
17A-12 

participation trends and found them congruent with its analysis of Oregon's clothes washer tax 

credits. 

Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon 

legislature expanded the RETC program in 1997 to include residential refrigerators, clothes 

washers, and dishwashers, which significantly increased participation in the program. The 

program subsequently added credits for high-efficiency heat pump systems, air conditioners, and 

water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, 

and renewable energy equipment. Beginning in 2012 a Tax Credit Extension Bill (HB3672) 

eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the 

RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters, 

and heat pump water heaters.21, 22 Those technologies recognized by the Oregon Department of 

Energy as “premium efficiency” are eligible for tax credit of $0.60 per kWh saved in the first 

year (up to $1,500).21, 23 

Montana has had an Energy Conservation Tax Credit for residential measures since 

1998.24 The tax credit covers various residential energy and water efficient products, including 

split system central air conditioning; package system central air conditioning; split system air 

source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water 

boilers; advanced main air circulating fans; heat recovery ventilators; gas, oil, or propane water 

heaters; electric heat pump water heaters; low-flow showerheads and faucets; light fixtures; and 

controls. In 2002 the amount of the credit was increased from 5 percent of product costs (up to 

$150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in 

new construction or remodeling projects. The tax credit covers only that part of the cost and 

materials that exceed established standards of construction. 
17A-13 

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Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.) 

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energy_efficiency>

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6. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal 

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7. Blum, H., Atkinson, B., and Lekov, A., A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. Lawrence Berkeley National Laboratory. 

Berkeley, CA. Report No. LBNL-4749E. <http://escholarship.org/uc/item/4t32x6vr> 

8. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

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