# TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

CEILING FAN LIGHT KI TS

June 1 8

, 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 20585This Document was prepared for the U.S. Department of Energy by staff members of Navigant Consulting, Inc.

and Lawrence Berkeley National Laboratory1

- i CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUM ENT

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

- 1

1.2 SUMMARY OF THE NATIO NAL BENEFITS

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

- 1

1.3 OVERVIEW OF APPLIANC E STANDARDS

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

- 6

1.4 OVERVIEW OF CURRENT CEILING FAN LIGHT KI T STANDARDS

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

- 7

1.5 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

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

- 7

1.6 STRUCTURE OF THE DOC UMENT

................................ ................................ ............ 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 CFLK s (TSL 2)*

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

- 4 Table 1.5.1 Analyses under the Process Rule

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

- 81-1 CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUM ENT

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 NATIO NAL 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 c ase”), 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). aThis represents a savings of 3.6 percent relative to the energy use of these products in the nostandard case.

T he 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.

I n 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 (CO 2 ), 3.5 thousand tons of sulfur

dioxide (SO 2 ), 4.7 thousand tons of nitrogen oxides (NO X ), 11.2 thousand tons of methane (CH 4 ), 0.037 thousand tons of nitrous oxide (N 2 O), and 0.011 tons of mercury (Hg). c The

cumulative reduction in CO 2 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 va lue of t he C O 2 reductions is c alculated using a r ange of values pe r m etric t on of

C O 2 ( o th erwi se known as the Social Cost of Carbon, or SCC) developed by a recent Federal interagency p r o cess. 1 The derivation of the SCC values is discussed in chapter 14. Using

d iscount rates appropriate for each s et o f

S C C v alues (see Table 1.2.1), DOE est imates the

present monetary value of the CO 2 emissions reduction (not including CO 2 equivalent emissions

a A quad is equal to 10 15 British thermal units (Btu).

b A metric ton is equivalent to 1.1 short tons. Results for emissions other than CO 2 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, 2013.1-2

of other gases with global warming potential) is between $0.038 billion and $0.41 billion, w ith

a va lue of $ 0.14 billi on using the central S CC c ase r epresented by $41.2/t i n 2015. D OE a lso estimates the presentm onetary v alue of t he NO X emissions r educti on to b e $0.016 bi llion a t a 7

- percent di scount r ate and $0.023 billion a t a 3

- per cent discount rate. d

T able 1.2.1 summarizes the national economic benefits and costs expected to result from the proposed standards for CFLKs.

Ta ble 1.2.1 Summary of National E conomic 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%

CO 2 Reduction Monetized Value ($12.2/t case)** 0.03 5%

CO 2 Reduction Monetized Value ($41.2/t case)** 0.13 3%

CO 2 Reduction Monetized Value ($63.4/t case)** 0.21 2.5%

CO 2 Reduction Monetized Value ($121/t case)** 0.40 3%

NO X 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 Emissions Reduction Monetized Value† 0.65 7%

0.82 3%

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

includ e benefits t o consumers that accrue after 204 8 from the products purchase d in 2019 −2 048. T he

results account for th e incremental variable and fixed costs incurre d by manufacturers due t o the standard, s ome of whic h may be incurre d in prep ar ation for the rule.

** The CO 2 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 95 th 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 serie s corresponding to average

SCC with 3

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

d DOE is currently investigating valuation of avoided SO 2 and Hg emissions.1

- 3 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 CO 2 emission reductions. e

Although combining the values of operating savings and CO 2 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 CO 2 reduc tions is based on a global value. Second, the assessments of operating

- cost savings and CO 2 savings are performed with different methods that use different time frames for analysis. The national operating

- cost savings is measured for

the lifetime of CFLKs shipped in 20 19

– 20 48

. Because CO 2 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 CO 2 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. U sing a 7

- percent discount rate for

benefits and costs other than CO 2 reduction (for which DOE used a 3

- percent discount rate along with the average SCC series that has a value of $41.2/t in 2015), the estimated cost of the standards proposed in this rule i s $ 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

CO 2 reductions, and $ 1.6 million in reduced NO X 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, wh ile the estimated annual
benefits are $ 41 million in reduced operating costs, $ 7.5 million in CO 2 reductions, and $ 1.3

million in reduced NO X emissions. In this case, the net benefit amounts to $ 46 million per year.

e T o 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 CO 2 red uctions, 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 CO 2 is estimated of the order of 30

– 95 years.1

- 4 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

CO 2 Reduction Monetized

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

CO 2 Reduction Monetized

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

CO 2 Reduction Monetized

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

CO 2 Reduction Monetized

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

NO X Reduction Monetized

Value 7% 1.6 0.90 1.6

3% 1.3 0.65 1.3

Total Benefits† 7% plus CO 2
range 60 to 79 38 to 48 63 to 85

7% 65 40 69

3% plus CO 2
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 4.21-5 D iscount Rate Primary Estimate* Low Net Benefits

Estimate* High Net

Benefits

Estimate*

million 2014$/year

Net Benefits

Total† 7% plus CO 2
range 54 to 73 34 to 44 57 to 78

7% 59 37 62

3% plus CO 2
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

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

results account for the incremental variab le 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 f or 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 CO 2 values represent global monetized values of the SCC, in 2014$, in 2015 under several scenarios of the updated SCC v alues. The first three cases use the averages of SCC distributions calculated using 5%, 3%, and 2.5% discount rates, respectively. The fourth case represents the 95 th percentile of the SCC distribution calculated using a 3% discount rate. The SCC time series 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 disc ount rate ($41.2/t case). In the rows labeled “7% plus CO 2 range” and “3% plus CO 2 range,” the operating

- cost and NO X benefits are calculated using the labeled discount rate, and those values are added to the full range of CO 2 values.

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 product s 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 a lso considered more- and l ess

- stringent efficacy l evels (EL) s as trial st andard levels, a nd i s s till considering them in thi s ru lemaking. However, DOE ha s t entativel y concluded

that the pot ential bur dens of t he m ore-stringent ELs w ould outweigh the projected benefits.

Based on considerati on of t he publ ic c omments D OE receives in r esponse to t his notice and related information collected and analyzed during the course of this rulemaking effort, DOE may adopt ELs presented in th e NOPR that are either higher or lower than the proposed

standards.1

- 6 1.3 OVERVIEW OF APPLIANC E 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 th e 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), esta blishes 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 prod ucts 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 w hether 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. T he economic impact of the standard on the manufacturers and on the consumers of the products subject to such standard;

2. T he savings in operating costs throughout the estimated average life of the covered product in the type (or class) compared to any incre ase 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. T he total projected amount of energy savings likely to result directly from the imposition

of t he standard;

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

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

6. T he need for national energy conservation; and

7. O ther 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 to1

- 7 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 KI T STANDARDS

Current energy conservation standards for CFLKs (42 U.S.C. 6295 (ff)) were established by the Energy Policy Act of 2005 ( EPA ct 2005 ; Title I, Subtitle C, section 135(c)), which were later amended by EPCA. Specifically, EPAct 2005 established individual energy conservation standards for three group s 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 stat ute ’ s requirements for the Medium Screw Base and Pin

- Based product classes . 70 FR 60413. EPA ct 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 w att (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 sock ets 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 STANDAR DS

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 f ramework 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 f ramework d ocument, 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 th e 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 comments1-8 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 comple tes in which stage of the rulemaking.

Ta ble 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 publ ishe d in the Federal R egister a N OPM announcing the

a vailability of the framework document t o initiate t he e nergy c onservation standar d rulemaking

f or CFLKs

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

document on many issues. T h is documenti s available on regulations.gov under docket numb er EERE-2012-BT-STD-0045-0001

.

D OE 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 exten ding the

comme nt period until June 14, 2013, to allow additional time for interested parties to submit comments. 78 FR 25626 (May 2, 2013).

Dur ing 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 t hese issue s during its preliminary analysi s and summarized the comments and DOE’s responses in chapter 2 of the preliminary TSD.

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

interviews with manufacturers of the products co vered 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 engineeri ng analysis; (2) solicit feedback on topics related to the preliminary manufacturer impact analysis; (3) provide an opportunity, early in the rulemaking1-9 process, to express manufacturers’ concerns to DOE; and (4) foster cooperation between

manufacturers an d DOE. DOE incorporated the information gathered during the interviews with

manufacturers into its engineering analysis (c hapter 5 of the preliminary TSD ) and the preliminary manufacture r impact analysis (MIA, c hapter 12 of the preliminary TSD).

DOE pub lished 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 ).

D OE held a public meeting on November 18, 2014, to present the methodologies and results for the preliminary analyses. Manufacturers, trade associatio ns, 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 u se, life

- cycle cost analysis, shipments, and NIA. Additionally , the MIA and additional analyses that would be undertaken

durin g 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 comme nts 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.

F or 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 s preadsheets are

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

- STD -0045.

1.6 STRUCTURE OF THE DOC UMENT

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

consists of 1 7 chapters, as well as several appendices.

C hapt er 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 : D escribes the rulemaking process step by

step and provides an overview of each analysis1

- 10 Chapter 3 Market and Technology Assessment : C haracterizes the CFLK market and the technologies available for increasing product efficacy and outlines the product class es

Chapter 4 Screening Analysis : D etermines which technology op tions are viable for consideration in the engineering analysis

Chapter 5 Engineering Analysis : D escribes DOE’s approach to the engineering analysis, and establishes efficacy level s based on lamp efficacy

Chapter 6 Energy Use Analysis : D iscusses the process used for generating energy use estimates of CFLKs for each efficacy level.

Chapter 7 Product Price Determination: D escribes DOE’s methodology for establishing the end

- user prices for lamps packaged in CFLKs

Chapter 8 Life

- Cycle Cost and Payback Period Analysi s : D iscusses 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 : D iscusses t he methods used for forecasting shipments with and without higher efficiency standards

Chapter 1 0 National Impact Analysis : D iscusses the methods used for forecasting national energy consumption and national economic impacts based on annual product shipmen ts 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 1 2 Manufacturer Impact Analysis : D iscusses the effects of standards on the finances and profitability of product manufacturers

Chapter 1 3 Emissions Analysis : D iscusses the effects of standards on airborne

emissions of electric utilities

Chapter 1 4 Mone tization of Emissions Reduction Benefits: C onsiders the estimated monetary benefits likely to result from reduced emissions of carbon dioxide and nitrogen oxides expected to result from the

standards

Chapter 1 5 Utility Impact Analysis : D iscusses the effects of standards on the installed generation capacity of electric utilities1

- 11 Chapter 1 6 Employment Impact Analysis : D iscusses the effects of standards on national employment

Chapter 1 7 Regulator y Impact Analysis : D iscusses the present regulatory

actions as well as the impact of non

- regulatory alternatives to setting energy efficiency standards

Appendix AA Acronyms and Abbreviations: P rovides a set of acronyms and abbreviations used throughout this TSD

Appendix 7A Product Cl ass Restructuring Sub

- Baseline Engineering and

Pric ing 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 anal yses 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 a nalysis

Appendix 8D Electricity Prices: Prov ides the electricity pricing used in the LCC analysis

Appendix 9 A Relative Price Elasticity of Demand for Appliances : Provides a summary of DOE ’ s study of the price elasticity of demand for consumer appliances

Appendix 1 0 A User Instructions for Shipments a nd NIA Spreadsheets : Provides basic instructions for operating the NIA workbook, as well as descriptions of each worksheet included

Appendix 1 0 B 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 effi ciency standards using alternative shipment

scenarios

Appendix 12 A Manufacturer Impact Analysis Interview Guide

Appendix 1 2 B Government Regulatory Impact Model Overview1

- 12 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 Materials1

- 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

- social

cost

- of

- carbon

- for

- regulator

- impact

- analysis.pdf

.

2 Jacobson, M. Z. Correction to “Control of Fossil

- Fuel Particulate Blac k 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

.2-i CHAPTER

2

. ANALYTICAL FRAMEWORK

TAB LE OF CONTENTS

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

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

2.3 MARKET AND TECHNOLOG Y 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 La mps .................................................................................................... 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 EMIS SIONS REDUCTIONS ... 2-14

2.15 UTILITY IMPACT ANALY SIS ................................................................................... 2-15

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

2.17 REGULATORY IMPAC T ANALYSIS ........................................................................ 2-16

2.18 DEPARTMENT OF JUSTIC E REVIEW ..................................................................... 2-16

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

LI ST OF FIGURES

Figure 2.1.1 Flow Diagram of Analyses for Typical Standards Rulemaking .............................. 2-22

- 1 CHAPTER 2 ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section s 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 )

. T he analytical framework is a description of the methodology, the analytical t ools, 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 manufacture rs 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 i nputs to the left and

k ey outputs to the right show how the analyses fit into the rulemaking process, and how the analyses relate to each other

. K ey inputs are the types of data and information that the analyses require

. S ome key inputs exist in public databases; DOE collects other inputs fro m interested parties or persons with special knowledge

. K ey outputs are analytical results that feed directly

into the standards

- setting process

. D otted 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.2

- 2 Figure 2.1

. 1 Flow Diagram of Analyses for Typical Standards Rulemaking2

- 3 The analyses performed in the notice of proposed r ulemaking (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

safe ty ;

• a n engineering analysis to study representative lamp units used as substitutes for baseline products , to select efficacy level s ( E Ls), and to determine lamp power ratings;

• a n energy use analysis that estimates annual energy use of CFLKs ;

• a product price determination that develops consumer product prices for the representative lamp unit s identified in the engineering analysis;

• a LCC analysis t o 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 t o 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 t o estimate shipments of CFLKs during the period considered in the analysis ;

• a national impact analysis (NIA) t o assess the aggregate impacts at the national level of potential standards as measured by the net pre sent 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 (CO 2 ), sulfur dioxide (SO 2 ), nitrogen oxides (NO X ), and

mercury (Hg) ;2

- 4 • a n 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 ;

• a n 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 B a of the Energy Polic y and Conservation Act of 1975 (42 U .S.C. 6291 et

seq. ; EPCA )

, established the E nergy C onservation P rogram 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 technologi cally 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 amen d ed, a uthorized DOE to regulate the energy efficiency of severa l 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 ( EPA ct 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. EPA ct 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 d ocument refer to the statute as amended through the Energy Efficiency Improvement Act of 2015, Pub. L. 114

- 11 (Apr. 30, 2015).2

- 5 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 follo wing DOE preliminary analyses: 1) mar ket and technology assessment; 2) screening analysis; 3) e ngineering 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 CFLK s (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 A ND TECHNOLOGY ASSESS MENT

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 th is NOPR TSD.

2.3.1 Market Assessment

When initiating a standards rulemaking, DOE develops inf ormation on the industry

structure and market characteristics of the products concerned

. T his 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 initi atives ; and 3 ) trends in product

characteristics and retail markets

. T his 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

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- 6 2.3.2 Technology Assessment

DOE typically uses information relating to ex isting technology options to develop designs that are more efficient

. DOE prepared a l ist of CFLK lighting technologies for

consideration that could improve the efficacy of lamps packaged with CFLKs

. To develop this list, DO E reviewed manufacturer catalog s, 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) t he cap acity 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 condu cts 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 feasib le; 2 ) are practical to
manuf acture, 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

. A s 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

t he list to determine if these technologies meet the screening criteria described above

. I n the engineering analysis, DOE only consider s design options that meet all four of the screening

criteria

. For more detail on the screening analysis, see chapter 4 of th is NOPR TSD.

2.5 ENGINEERING ANALYSIS

DOE derives E Ls 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 f or 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) s electing 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

en gineering analysis, see chapter 5 of th is NOPR TSD.2

- 7 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 classe s.

2.5.2 Baseline Lamps

DOE select s 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 consider s 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 c onsidered. When selecting higher efficacy lamps, DOE consider s only design options that meet the criteria

outl ined in the screening analysis.

2.5.4 Efficacy Level s

Having identified the more efficacious substitutes, DOE develop s E Ls based on the

consideration of several facto rs , 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”) E L.

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

cons umption 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 E Ls span multiple lamps of different lumen outputs . In selecting E Ls, DOE con sidered 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

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- 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 DETERM INATION

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

- en gineer ( i.e. , not easily disassembled), DOE directly derives consumer prices for the lamps in this rulemaking. DOE determined average shipment

- weighted premium s on distributor net prices of CFLKs and developed end user prices for CFLK s packaged with

repres entative lamp unit s

. 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 unit s that are not currently s old 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 , se e chapter 7 of th is NOPR TSD.

2.8 LIFE

- CYCLE COST A ND PAYBACK PERIOD AN ALY SIS

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 t he 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 lamp s’ disposal cost, and the lamp s’ 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 theref ore 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 trend s , replacement lamp cost s (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.2-9 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 CFL K 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 perc ent 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 t o 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 t he current C FLK marketest imates that l ess than 1 p ercent of pr oducts

utilize light

- emitting di ode ( LED ) t echnology. However, information from manufacturer s suggests rapid penetration of LED technology into the CFLK market within the next 5 years ( approximately 1 5% by 2018). To account for this expected transition to LED technology, DOE

modeled the in itial growth o f th e L ED s into the CFLK market using LED incursion b ased on

r esults f rom th e 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 i nto th e CFLK

market compared to the GSL market. D OE assu med t hat o nce the cost

of LEDs reached

p arity

w ith compactf

luorescentl amps ( CFLs ), however, manufacturers would choose to package

CFLKs with LEDs over more expensive, shorter-l ived 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 o f 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 ceili ng fans energ y conservatio n standards rulemaking docket is available on regulations.gov under docket

number EERE-2012-BT-STD-0045.2

- 10 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 ANAL YSIS

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 compa ring projections of U.S. energy consumption with amended energy conservation standards against projections of energy consumption without amended standards.

DOE analyzed the impacts of f our TSLs, corresponding to each EL specified in the engineering analy sis, chapter 5 of this TSD

. DOE coded the NIA in a Microsoft Excel file available on regulations.gov, docket number EERE

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2.10.1 National Energy Savings Analysis

The inputs for determining the NES for CFLKs are : (1) shipments; (2) annual energy consumptio n per unit; (3) stocks of CFLKs in each year; (4) national energy consumption; and (5) site

- to

- primary energy and fuel full cycle (F FC) 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 n ational 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 consumptio n between the no

standard case and the standard s cases for CFLKs shipped during the 201 9

- 204 8 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

- Fu el

- 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 emi ssions 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 t o 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 appl ication. 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 meth odology described in appendix 1 0 B of this NOPR TSD, which presents both the primary ene rgy savings and the FFC energy savings for the con sidered TSLs

.2

- 11 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 l ifetime of products shipped in the 30

- year analysis period. DOE calculated NPV as the difference between the present value of operating

cost 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 inst alled 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 nu mber 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 i n 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 calculate d the impact of such standards on domestic manufacturing employment and capacity. The MIA has both quantitative and qualitative aspects

. The quantitative part of the MIA s 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, shipmen ts, 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

- standar ds case and various TSLs (the standards case s ). The difference in INPV between the no

- standards2

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

standards on CFLK manufacturers. Different sets of assumptions (scenario s) produce different INPV results. The qualitative part of the MIA addresses factors such as manufacturing capacity; characteristics of, and impacts on, any particular sub group of manufactur ers; 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 us ing industry financial parameters derived in Phase 1 and the shipment

scenarios used in t he 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

manufa cturer’s view of the CFLK industr y 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 d etail on the MIA, see chapter 12 of th is NOPR TSD.

2.13 EMISSIONS ANALYSIS

In the emissions analysis, DOE estimates the reduction in power sector emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ) and mercury (Hg) from potential energy conserv ation 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”

emis sions. 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 ga ses. DOE conducts the emissions analysis using emissions factors derived from data in the latest

version of the U.S. En ergy 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 incorporat es the projected impacts of existing air quality regulations on

emissions. The text below refers to AEO 201 4 , which generally represents current legislation and environmental regulations, including recent government actions, for which implementing regulations were available as of October 201 3

.

SO 2 emissions from affected electric generating units (EGUs) are sub ject to nationwide and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual emissions cap on SO 2 for affected EGUs in the 48 contiguous states and the District of Columbia (D.C.). SO 2 emissions from 28 eastern states an d 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 s tates 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 is sued a decision to vacate CSAPR, and ordered EPA to continue2

- 13 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 opini on, 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 E GUs and is enforced through the use of emissions allowances and tradable permits. Under existing EPA regulations, any excess SO 2 emissions allowances resulting from the lower electricity demand caused by the imposition of an efficiency standard could be used to permit offsetting increases in

SO 2 emissions by any regulated EGU. In past r ulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions covered by the existing

cap

- and

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

for SO 2 as a result of standards.

Beginning in 2016, however, SO 2 emissions will fall as a result of the Mercury and Air Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air pollutants (HAP), and also established a standard for SO 2 (a non

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

non

- HAP acid gas; thus, SO 2 emissions will be reduced as a result of the control technologies

installed on coal

- fired power plants to comply with the MATS requirements for acid gas. AEO

201 4 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 SO 2 emissions. Under the MATS, emissions will be far below the cap established by CAIR, so it is unlikely that excess SO 2 emissions allowances resulting from the lower electricity demand would be needed or used to permit offsetting

increases in SO 2 emissions by any regulated EGU. Therefore, DOE believes that efficiency

standards will reduce SO 2 emissions in 2016 and beyond.

CAIR esta blished a cap on NO X emissions in 28 eastern s tates and the District of

Columbia. Energy conservation standards are expected to have little effect on NO X emissions in

those s tates covered by CSAPR because excess NO X emissions allowances resulting from the lower electricity demand could be used to permit offsetting increases in NO X emissions.

g See EME Homer City Generation , LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 201 2).

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 NO X emissions in the s tates not affected by CAIR, so DOE estimated NO X emissions reductions from potential standards for those s tates.

The MATS limit mercury emissions from power plants, but they do not include emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. DOE 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 1 3 of this NOPR TSD.

2.14 MON ETIZING CARBON DIOXI DE A ND OTHER EMISSIONS R EDUCTIONS

DOE estimates the monetary benefits likely to result from the reduced emissions of CO 2 and NO X that are expected to result from each of the standard levels considered. To estimate the monetary value of benefits resulting from reduced emissions of CO 2 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 yea r. 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

mon etization, 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 2 013$, are $12.0, $40.5, $62.4, and $119 per metric ton of CO 2 avoided. For emissions reductions that occur in later years, these values grow in real terms over time. Additionally, the interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although DOE gives preference to consideration of the global benefits of reducing CO 2 emissions.

DOE multiplied the CO 2 emissions reduction estimated for each year by the SCC value for that year in each of the four cases. To calculate a present value of the stream of monetary values, DOE discounted the values in each of the four cases using the discount rates that had

been used to obtain the SCC values in each case.

DOE recogn izes that scientific and economic knowledge continues to evolve rapidly as to

the contribution of CO 2 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 NO X emissions resulting from the standard levels it considers. Estimates of monetary value for reducing NO X from stationary sources range from $476 to $4,893 per ton in 2013$. 5 DOE calculated mo netary benefits using a medium value for NO X 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

SO 2 emissions. DOE has not monetized estimates of SO 2 and Hg reduction in this rulemaking2

- 15 For more detail on the monetization of emissions reductions, see chapter 1 4 of this NOPR

TSD.

2.15 UTILITY IMPACT ANALY SIS

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

gener ation 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.

Th e 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 m odels 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 EMP LOYMENT IMPACT ANALY SIS

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 t he 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 us ing 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 technolog ies 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 e ntails 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 NEMS

BT (BT is DOE’s Building Technologies Program, under whose aegis this work has been perfo rmed).2

- 16 Further detail is provided in chapter 16 of th is NOPR TSD.

2.17 REGULATORY IMPACT AN ALYSIS

In the NOPR stage, DOE prepared a n RIA . The RIA addresses the potential for non

regulatory approaches to supplant or augment energy conservation standards in order to improve the energy efficiency or reduce the energy consumptio n 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 assessmen t 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 th is NOPR TSD.

2.18 DEPARTMENT O F 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_ene rgy

- savings

report_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

- social

cost

- of

- carbon

- for

- regul ator

- 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.)

htt ps://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://w ww.pnl.gov/main/publications/external/technical_reports/PNNL

- 18412.pdf

.3

- i CHAPTER

3

. MARKET AND TECHNOLOG Y ASSESSMENT

TAB LE OF CONTENTS

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

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

3.3 ME TRIC .......................................................................................................................... 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 ASSESSMEN T .................................................................................... 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 .................................................................... 3-313

- ii 3.6 PRODUCT CLASSES

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

- 31

REFERENCE S

.................... .......... ......................................................................... 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 Figu re 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

- 273

- 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 c onsider 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

reso urce 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 guidance b on accent lighting and to consider all lig hting 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 , 6 2522 (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 lam p 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 lum inaire

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 CFLK s, t he 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 CFLK s

. ALA is composed of a group of lighting and ceiling fan manufacturers, showroom retailers, manufacturer rep resentatives, and designers. C urrently

, 25 CFLK manufacturers are active

member s of ALA

. ALA oversees v arious types of light fixtures

, including emergency lighting, wall and ceiling lighting, and integrated light

- emitting diode ( LED ) fixtures. As an associ ation, ALA’s objectives are organized into three categories

– public affairs, education, and organized

a ffairs. 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

• Support ing the proper application of good lighting solutions with energy

- efficient products

• Focus ing ALA's proactive legislati ve agenda on lighting and small business issues

• Develop ing and providing members with training and certification opportunities

• Offer ing networking opportunities through small groups to help members improve sales and their business operations

• Conduct ing a h igh

- 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 li ghting 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

- Nu Tone 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 f ederal g overnment programs. These size standards are generally the average an nual receipts or the average employment of a firm. For CFLKs , the size3

- 4 standard is matched to NAICS code 335 210

, Small Electrical Appliance Manufacturing , which

has a size standard of 750 employees or fewer.

DOE studies the potential impacts on these sma ll 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 initiati ves 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 B c of EPCA d ( 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 soc kets manufactured on and after January 1, 2007, and CFLKs with all other socket types manufactured on or after January 1, 2 009

, as follows: e

(2)(i) Ceiling fan light kits with medium screw base sockets manufactured on or after January 1, 2007, shall be pa ckaged 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, includi ng 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 est ablished 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)), incorpo rate 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 i n a ctive 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 Calif ornia 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 non

federally 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 CFLK s 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 Sta ndards

The Natural Resources Canada (NRCan) Office of Energy Efficiency regula tes 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 prod ucts, 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 es tablished 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 k its that use less than or equal to 10 W of electrical power and have at least one socket that is not pin

based are not required to have a 190 W limiter. 3

3.4.3.4 Underwriters Laboratories Safety Requirements

Underwriters Laboratories ( UL ) is a safety organizatio n that develop s 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 luminair e 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 cle ar 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 lam ps covered by this rulemaking. While it is not an exhaustive list, the

disc ussion 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 energy

efficient 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 reside ntial 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 a re 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 thre e 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, standb y 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 non

directional CFLKs, and halogen incandescent lamps will no longer q ualify. 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 grow th 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 C eiling F an L ight K it Market Trends

DOE evaluated CFLK market trends by considering data from ENERGY STAR h 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 an d 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 T ECHNOLOGY ASSESSMENT

The technology assessment identifies technology options that improve CFLK effic acy

. 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 t heir 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 f ans, 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

ceili ng 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 ef ficacy 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 a n inert gas ( e.g., argon). Voltage is applied to the two electrodes at the ends of the tube, emittin g 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

separat e 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 e mit 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 ion s 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

C alcium 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 dissipat ed 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 inev itable, albeit in small amounts. 133

- 10 CFLs must b e 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 C FLs 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 G R 10q. 14 Base types that are 4

- pin are generally paired wi th 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 require s 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 e lectrodes. 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 frequ ently, 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 oute r

cover

. These c overed 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 L ight

- E mitting D iode 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 a n n

- type region. This causes the p

- type regions to have hole concentrations while the n

type regions have excess electrons. When a voltage is applied, excess electrons from the n

- type region combine with holes from the p

- ty pe 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 g allium n itride (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 violet3

- 11 light; however, their wavelength can be adjusted by mixing in other elements with di fferent 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 know n as electroluminescence.

Fi gure 3.5.1 Photon Production through Electroluminescence
Source: Digi

- Key 17

T he 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 arrays i 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 CFL s, 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 assem bly could also have optical elements and additional thermal, mechanical, and electrical interfaces that are intended to con nect to the load side of a LED driver. However, the

p ower source and ANSI standard base are not incorporated into the device and it c annot be connected directly to the

branch circuit.3

- 12 Figure 3.5

. 2 LED Chip
Source: LED Magazine 19

Figure 3.5

. 3 LED Module

Source: WILA 203

- 13 Figure 3.5

. 4 Exploded View of a LED Lamp

Source: EE Times 21

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 Physics 22

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 respective3

- 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 s ome 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 h igher 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 Energy 24

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 befo re a 70 percent deterioration in total luminous flux ( L 70 ) 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 jo ints. 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 t hermal stresses, can also lead to broken contacts and lamp failure. 263

- 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, te chnical 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 C eiling F an L ight K it 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 em ission 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

- in

package , 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, di ffusion, 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 mor e 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 electrodes3

- 17 would reduce the lamp power and increase the overa ll 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 in creased 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 hi gh 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 f rom 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, allow ing 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 opt imal 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 higher

efficiency 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 el ectrode 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 colli de 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 exclusive ly 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 c onversion 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 whic h 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 functio n 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 Sensitivity3

- 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 m etric. 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 maj ority 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 co lor 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 co ntent 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, t he 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 (Al 2 O 3 ), silicon oxide (SiO 2 ), and titanium oxide (TiO 2 ). This layer is used to r eflect 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

l amp. 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 patent 30 by Philips was issued in 2005 for an improved undercoat that reduces the amo unt 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 i n 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 lam p 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

insuff icient 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 v alue 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 reg ions 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 temperatu re 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 t emperature 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 h eat, 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 orientat ion 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 secondar y 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. 333

- 22 3.5.3.7 I mproved 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 u p 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 indu ctors), 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 mag netic 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 (lo w R DS_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

balla sts. 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 filame nt 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 no t 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, conve rting 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 e nergy 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 Y 3 Al 5 O 12 :Ce 3+ (YAG:Ce) with a blue LED. The3

- 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 temperat ure. 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 lamp s.

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 ra re 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 phosp hors 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

recombination m 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 Vis ion 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 ult imately contributes to the LED lamp efficacy. Imp roved 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

p otentially imp rove 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 mixin g 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 onl y 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 eliminat e 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 temper ature 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 o n the market since 2009. 41 In 2013, Philips reported

on the d evelop ment 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 t hrough the phosphor unchanged. The color mixing methodology uses individual monochromatic LEDs ( e.g. , red, green, blue) to produce a mix of colors that generates white3

- 25 light. Because color mixing does not utilize PC LEDs, it avoids down conversion losses

a ssociated 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 color

mixed LED lamps.

Figure 3.5.11 plots the lumen s 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 op tical radiant flux (in watts) per watt of electricity supplied. Semiconducting materials

such as g roup III nitrides (such as g allium n itride [GaN] and i ndium g allium n itride [InGaN])

and g roup 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 Semiconductor 46

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 Semiconductor 46

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 Auge r 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 l eads 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 direction s 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 p rocess 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 buil t. 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 t he 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

- e fficiency 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 hig h 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/cm 2 and 85 °C. It also renders with a CRI of 95 and R 9 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 ; c urrently 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 Technology 57

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 r educing 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 conducti vities 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 R 9 is not used in the calculation of CRI. It is part of the special color rendering indices, referred to as R 9 through

R 14 , which are each based on single test colors. R 9 , 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/buildi ngs/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 regardin g use of carbon

nanotubes , which have high thermal conductivity, in TIM s 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 n ovel 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 bul b, 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 performan ce 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 decrea se 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 comp onents 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. 693

- 30 3.5.3.17 Device

- Level Optics

A p rimary optic is integrated onto the LED package that optimizes light extraction and/or beam shaping. Se condary 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 th e 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 l evel, 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 improvin g 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 e mitted 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 l uminaires. 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 d ecrease 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 bo ost 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 d rivers 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 LEDs3

- 31 integrate power conversion into the package, thus requir ing few additional component s 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 w ere linked together in a series

, forming strings . This design provide s 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. I n 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 i ssues 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 o n 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 Reduce d 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, decrea sing 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 examin ation 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 fo r low

- volume CFs and therefore concluded that a separate product class for CFLKs designed for high

- volume CF s 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 characte ristics do

not offer consumers any unique utilit ies or rep resent dissimilar capacities requiring different

standard levels . Therefore, in this NOPR , DOE is considering establishing one CFLK product

class : “all CFLKs

. ”3

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- intro >4-i CHAPTER 4. SCREENING ANALYSIS

TAB LE OF CONTENTS

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 SCREENED OUT TECHNOL OGIES ............................................................................ 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 TECHNOLOG Y OPTIONS SCREENED O UT .................................. 4-3

4.4 REMAINING TECHNOLOGI ES ................................................................................... 4-3

4.4.1 All CFLKs Design Opti ons .................................................................................. 4-4

REFERENCES ............................................................................................................................ 4-5

LI ST OF TABLES

Table 4.3.1 GSL Technology Options Screened Out of the Analysis ......................................... 4-34

- 1 CHAPTER 4. SCREENING AN ALYSIS

4.1 I NTRODUCTION

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 ( CFLK s )

. As discussed in

c hapter 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 th e list of options and deter mine 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 determine d 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, A ppendix A to S ubpart C of Title 10, Code

of Fed eral 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 promulgatio n 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 commercial ly

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

nec essary 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 i mpacts on product utility or product availability

. If DOE determines a

techn ology 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,

ca pacities, 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 signifi cant adverse impacts on health or safety, it will not consider this technology

further

.4

- 2 Section 4.2 of this chapter discusses the technology option s 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 analys is

. The market and technology assessment ( see c hapter 3 of this NOPR TSD ) discusses in detail all technology options analyzed in this NOPR analysis

.

4.2 SCREENED OUT TECHNOLO GIES

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 availabil ity ; 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 (CF Ls) . 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, d evelopment of this technology for use in lighting products remains in the research phase and DOE is unaware of any prototypes

or commercial ized 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 f or 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 conv erters 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 qu antum 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 S tate 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, technological4

- 3 feasibility, and will not consider quantum dot and nanophosphor technolog ies as a design option for improving the efficacy of lamps in CF LKs.

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 emitter s 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 experien ce 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 technol ogy 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 researc h 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 summarize s the technology options DOE is proposing to screen out
and the associated screening criteria.

Table 4.3

. 1 GS L Technolo gy 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 TECHNOLOGI ES

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. T hese 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 no t result in adverse im pacts on product

utilit y /availability or health and safety

. DOE confirmed this using feedback from manufacturers

and DOE research

. The market and technology a ssessment ( see c hapter 3 of this NOPR TSD ) provides a detailed description of these design options.4

- 4 4.4.1 All CFLK s 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 th e 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 the se options in the engineering analysis as a viable means for improving lamp efficacy

.4

- 5 REFEREN CES

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 (Suwon

City, 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 Colloi dal Quantum Dot Phosphors. July 11, 2013. (Last accessed June 11 , 2015 )

. < http://energy.gov/eere/ssl/sunybuffalo

- develop ing

- high

- efficiency

- colloidal

- quantum

dot

- phosphors >5-i CHAPTER 5. ENGINEERING ANALYSIS

TAB LE 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

LI ST 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

LI ST 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 ............................................................... 5-135

- 1 CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

The U.S. Department of Energy (DOE) performed engineering analyses for ceiling fa n 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 unit s and to select efficacy levels ( E Ls). After selecting representative product cl asses, 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 E Ls.

In energy conservation rulemakings for other p roducts, DOE often develops cost

efficiency relationships in the engineering analysis. However, for this CFLK rulemaking, DOE

derives E Ls in the engineering analysis and consumer prices in the product price determination. By combining the results of the en gineering 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 eng ineering 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 ha s 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 E Ls 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 mea sure 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 baseli ne 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

consider s 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 E Ls. DOE bases its analysi s 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 (“m ax tech”) E L . DOE then scales the E Ls 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 Repre sentative 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

Exis ting 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

- B ased product class”) ; and, (3)

C eiling 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 t he minimum efficacy requirements specified in the ENERGY STAR Program requirements for Compact Fluorescent Lamps, version 3

. 1

a E Ls span multiple lamps of different lumen outputs . In selecting E Ls, DOE considered whether these multiple lamps meet the standard levels.5

- 3 Table 5.3

. 1 Medium Screw Base Product Class C urrent 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 t he 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 S ystem Efficacy per Lamp Ballast Platform

W I n 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 Resident ial Light Fixtures version 4.0 or ENERGY STAR Program

req uirements for Compact Fluorescent Lamps, version 3. ENERGY STAR Program Requirements for Resident ial Light Fixtures version 4.0 minimum efficacy requirements are specific to wattage and length and ENERGY STAR Program requirements for Compact

Fluorescent La mps, 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. Ther efore, 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 t he

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 char acterize 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

la mp 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 whic h to measure changes resulting from p roposed 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 lamp s 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, t ypically, 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 C FL 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 10,0 00

- 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 8 0 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

co nsideration 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 t o 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 li ght 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, b ased on

comme rcially 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 subs titutes. 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 energy b

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 base line 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

uni ts with lifetimes greater than or equal to the lifetime of the baseline lamp. DOE also selected

b DOE allows for substitutions that both sa ve 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 more

efficacious 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 woul d 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. DO E determined that the most common number of sockets in CFLKs is two; thus

, DOE selected a baseline fixture with two socket s, each containing the baseline lamp. DOE considered more

- efficacious replacement fixtures with non

- medium screw base types

and betwee n 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 t he 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 s ubstitu t es were selected such that they have a both a lower fixture wattage and higher efficacy than the prev ious 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 C FLs and LED lamps

, but there was a difference in price between the two options . Therefore, DOE analyzed b oth 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 anal ysis, 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

- ref lector 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 mid dle 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 additiona l products to meet this level, such as consumer replaceable LED modules and driver systems that can provide5

- 7 flexibility in CFLK design options and 800

- lumen LED lamps, which represent a common lumen

o utput 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 t he identified EL s 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 CRI CCT 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 25,0005

- 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 CFLK s 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

Lum en Output Fixture

Initial

Lumen Output Efficacy CRI CCT 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 25,0005

- 9 5.3.6 Efficacy Levels

DOE adopted an equation

- based approach to establish EL s for CFLKs. To develop the general form of t he 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

. 9999 LLLLLLLL nn ss 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 rep resentative 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 repr esent 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 cl ass, DOE considered two scenarios: a lamp replacement scenario and a light kit replacement scenario. DOE selected EL s 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 leve l, 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.

Furth er, 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 Ligh ting Research and Development 3 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 N OPR , 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 module s 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 EL s on the following described design options.

• EL 1 : This efficacy level c an be achieved in the lamp replacement scenario with a n 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 e fficacy 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 performi ng 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 a n advan ced 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 maximu m 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 th e 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 require ment 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

great er than 30 W would have lower efficacies than a 30 W lamp because efficacy is inversely proportional to wattage. Therefore, for th is range of lumens , DOE set the standard above the highest efficacy that a 30 W lamp can ach ieve, which is (1/30) × lumens

. At greater than5

- 11 2

, 100 lm, a 30 W lamp achieves grea ter 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 EL s 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 b aseline lamp

.

Figure 5.3

. 1 Efficacy Levels for All CFLKs: Lamp Replacement Scenario5

- 12 Figure 5.3

. 2 illustrates the four proposed EL s on a plot with the representat ive 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

. Beca use proposed standards are based on lamp efficacy, not fixture efficacy, the lamp efficacy of each representative lamp unit is plotted.
However, DOE selected t hese replacements such that they have both a lower fixture wattage and higher fixture efficacy th an 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 Scenario5

- 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 EL s for All CFLKs Product Class

Representative Product Class Efficacy Level L ume n Output Minimum Required Efficacy

lm lm/W

All CFLKs EL 1 < 260 50

≥ 260 and ≤ 2040 69

- 29.42 × 0.9983 lumens

> 2040 and < 2100 > (1/30) × lumens

≥ 2100 70

EL 2 < 120 50

≥ 120 74

- 29.42 × 0.9983 lumens

EL 3 All 101

- 29.42 × 0.9983 lumens

EL 4 All 10 6

- 29.42 × 0.9983 lumens

5.3.7 Scaling to Other Product Classe s

Typically DOE determines EL s for product classes that were not directly analyzed (“non

representative product classes”) by scaling from the EL s 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 Prog ram Requirements for Residential Light Fixtures:

Version 4.0 . (Last accessed June 11 , 2015). https://www.energystar.go v/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.pdf6-i CHAPTER 6. ENERGY USE ANALYSIS

TAB LE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.2 CONSUMER SAMPLES ................................................................................................ 6-1

6.3 O PERATING 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

LI ST OF TABLES

Table 6.3.1 CFL

- to

- All GSL HOU Adjustment Factor .................................................... 6-3

Tabl e 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 To tal 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

LI ST 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 .................................................................................. 6-116-1 CHAPTER 6. ENERGY US E ANALYSIS

6.1 IN TRODUCTION

This chapter describes the methodology the U.S. Department of Energy (DOE) followed

to estima te 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 analys i s, 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 consum ers 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 CFLK s. 1

The energy use analysis is developed for CFLKs using the representative l amps 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 characteristi c of the vast majority of the current market. The following sections d escribe 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 an alysis 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 (EI A). DOE did not consider the industrial sector in this analysis, because DOE’s research indicates that CFLKs are designed6-2 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 sector s , respectively

. RECS and CBECS

were constructed by the EIA to be nationally representative of the residential and commercial

sector s in the United States , respectively. RECS is a national sample survey of housing units that collects statistical information on the consumption of, and expenditure s 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 methodolog y 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 servic e 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

CT 12 2.8 3.1 5% 0.90

RI 12 2.6 3.0 2% 0.87

MA 12 2.7 3.0 10% 0.90

NY 12 3.3 4.0 28% 0.83

CA 4 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 t o All GSL HOU

State Reported

CFL GSL HOU Estimated

All GSLs HOU

IL 10 2.7 2.3

OH 9 2.8 2.4

MD 8 3.0 2.5

GA 5 2.8 2.4

NC 6 2.9 2.5

SC 7 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 s tate d . 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 in to 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, b ut 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 latitude s 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 N umber of Ceiling Fans Reported in Survey Re sponses Distribution of Ceiling Fans by Room Type (%) P ercentage of

Fans with Light Kits (%) Distribution of CFLKs by Room Type (%)

Bedroom 3

, 908 50 89 5 1

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 -reporta ble -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 RLEUCS6-5 (HOU CFLK, 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 eac h RECS reportable domain, DOE multiplied the GSL HOU from the metering studies by the ratio of the HOU CFLK,RLEUCS,d to the HOU All_Lamps,RLEUCS,d , (where

HOU All_Lamps,RLEUCS,d corresponds to the average HOU reported in RLEUCS for all lamps in each RECS repo rtable 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 ass umed 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 tho se 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

identifi ed in each room for each of the metered houses.6-6

Figure 6.3.1 Average Hours of U se of C FLKs by Room Type

Note: The soli d line i n eac h bo x plot shows the media n HOU, and the bo x delineates the HOU withi n the 25 th

an d 75 th p ercentil e of th e sampl e for each room type.

6.3.2 Commercial Sector

DOE developed average daily operating hours for CFLKs in c ommercial building s 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 th o se 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 Weighted

Average 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 t he number of lamps per 1000 ft 2
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, CFLK s 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

W eight

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 CFLK s 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 p roducts in th e field. At any given

g As described i n chapter 3 of this NOPR TSD, the new product class structure effectively combines the current product classes into a singl e 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, CFLK s 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 limit er currently required for lamps in the

current Other Base Type product class h constrains the lumen output of CFLKs with low

- efficacy lamps in th at 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 th e rest of the market when developing its assessment of CFLK lumen output. This approach is also

consistent with DOE ’s propos al to remov e 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 th e 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 p roduct offerings on the

Internet. j 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 rulemaking k and

summarized here.

The relationship between ceiling fan market share and retail price is described by the following equation:

MMMM ( ) = AA ββ FFFF ∗pp Eq. 6.1

Where:

h See chapter 3 of this NOPR TSD f or details o n the current product class structure.

i A notati on i n this form provides a reference for informati on that is i n the docket of DOE’s rulemaki ng t o develop

energy conservati on standards for CFLKs ( d ocket number EERE –2 012 –B T –S TD –0 045 ), which is maintained at www.regulations.gov

. For the ALA r eference, for example, the notation indicates that the citation refers to

information contained in document number 39 i n the docket for the CFLK energy conservation standards rulemaking, an d appears o n page 43 of that document.

j Comprehensive shelf surveys were conducte d in nine stores in Ma y an d June, 2 014 : 4 Home Depo t stores, 4 Lowe’s stores, and 1 Wal -M art store. The stores were all located in California. The survey of Internet offerings

include d 1,340 s amples from the Hanse n Wholesale website ( http://www.hansenwholesale.com ).

k The prelim inary analysis technical support do cument for the CF s energy conservation standards rulemakin g is av ailable at www.regulations. go v u nder do cket n umber EERE-2012-BT-STD-0045-0 065

.6-10 MMMM ( ) = Market shar e for a ceiling fan that costs

, AA = Normali zation factor,

= Retail pr ice of a ceiling fan, and ββ FFFF = Consumer sensitivity to first cost, found to be equal to -0.028. l

B ecause 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 surv ey 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 onli ne 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 l amp 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 1,6 22 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.

B ecause all products presented in the engineering analysis have efficacies above the

restructured b aseline, 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 subj ect

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 perc ent 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 (201 4) Weighted

- Average

CFLK Total Lumens

Below restructured baseline Candelabra 66 % 1,622

Above restructured baselin e 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 CF LK to reflect an average market lumen output of 2,039 lumens for eac h 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 lume ns and wattage in the light

kit replacement scenario

. Then , DOE scaled the wattage and lumens for all other EL s with those same scaling factors to preserve the proportionality of lumens and wattages at the different EL s6-12 of the representative lamp units in the engineering analysis. Table 6.4.2 and Table 6.4.3 show the

results of inp ut 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 sub

baseline are also assumed t o have three so ckets (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

L evel Lamp Input

Power (watts)* Lamp Light Output (lumens) * Scaled CFLK Ligh t Outp ut (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 R eplacement Scenario

Candidate

Standard

L evel Fixture Input

Power * (watts) Fixture Light Output* (lumens) Scaled CFLK Light Outp ut (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 (D IMMING)

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 CFLK s 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 study 17 , 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:

UUUU CC
cc = UUUU
nn × �11 − ff dddddddddddd × ff ee �

Eq. 6.2

Wh ere:

UU
cc = weighted -average annual energy consumption (kWh) of CFLKs considering use

of dimmers

UU CC
nn = 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 estimat e the fraction of CFLKs used with lighting controls for each EL , DOE assumed

that CFLs used in CFLKs are non- dimmable o 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 assump tion 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 fan s ) 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 LMC 15 (Table 4.34, which reports the percentage of dimmers by room type in the residential sector) an d multiplied each room’s dimming fraction in the LMC with a ratio

n NEMA’s comment (NEMA, 34, at p.21) is availa ble at the GSL rulemaking docket available at http://www.regulations.gov/#!documentD etail;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 factor p

. 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 kilowatt

hours per year (kWh/yr)

. Table 6.6.1 and Table 6.6 .2 present the energy use analysis results for

All CFLKs in residential building s, 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 C FLKs 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 Sub

Baseline 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 Sub

Baseline 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 13.56-17 Table 6.6.3 Annual Energy Use (kWh/year) for All CFLKs (Lamp Replacement Scenario) in the Commercial Sector

CBECS Building Types Sub

Baseline 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 Sub

Baseline 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 71.86-19 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 Commerc ial 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 M ay 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 an d 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

- Reference

Manual.html?query=The- System -Evaluation- Manual -and- Chiller

.6-20 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. Re sidential 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

- stu dy.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 an d 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. Navigan t 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

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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 DETERM INATION

TAB LE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 METHODOLOGY .......................................................................................................... 7-1

7.2.1 Premium Determination ....................................................................................... 7-2

7.2.2 Consumer Price Dete rmination ............................................................................ 7-2

7.3 RESULTS ........................................................................................................................ 7-3

7.3.1 Consumer Prices for All CFLKs .......................................................................... 7-4

LI ST 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 .................................................................................... 7-57

- 1 CHAPTER 7. PRODUCT P RICE 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 combinin g 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 impac t 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 C FLK. Typically, DOE develops manufacturer a 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 represe ntative 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 determi ned 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 CF LK to establish the consumer price of representative lamp units when packaged

with CFLKs. T he following sections detail the steps of this process

.

a DOE uses “m anufacturer ” to refer only to CFLK manufacturer s unless otherwise noted.7

- 2 7.2.1 Premium Determination

DOE based its product price determination on publicly available pricing data and

aggre gated 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 man ufacturer 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 pay b for CFs sold with CFLKs and CFLKs sold alone. Starting with

these consistent set s of distributor net prices, DOE calculated an average premium corresponding

to prices for CF s sold with CFLKs offered in electrical/specialty centers, home centers, and lighting showrooms. Specifically, DOE compared the distributor net prices of CFs sold with

CFLK s to their consumer price s in each of the distribution channels. Because CFL Ks are primarily sold with fans, DOE developed premiums only for CFLKs sold with CFs. In general, DOE found that the premiums did n ot 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 cent er 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 manufacturer s, 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 ha d distributor net prices.

DOE applied the average shipment

- weighted premium to the distributor net price s of the

CF s sold with CFLK s 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 price s as described in section 7.2.1 , DOE developed an average consumer price for each representative lamp unit when packaged with a

C FLK. 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 p rices 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 comprise s 15 percen t of the CFLK consumer price. DOE applied this percentage to the consumer prices of CFLK s 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 price s of the representative lamp unit s to obtain their consumer price s if they were packaged with a CFLK.

DOE used consum er 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 develop ed rat ios 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 ch anges 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 so cket. 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 manufa cturer 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 th e CF p reliminary 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 thi s NOPR TSD).

7.3 RESULTS

Section 7.3.1 summarize s the consumer prices for representative lamp units packaged in

CFLKs

. The results also indicate the remaining CFLK co nsumer 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 t he 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 price s 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 th at 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 20 14 $ 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 $30.967

- 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

L umen 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 $38.188-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 INPUT S ...................................................................................... 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 .......................................................................... 8-288- 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 S avings 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) .... 8-338-1

CHAPTER 8. L IFE -C YCL E COST A ND PAYBACK P ERIOD 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 ( CFLK s) on consumers. a DOE used the following metrics for its analyses:

• L ife -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 r esults for the LCC and P BP analysis, for each different efficacy level (E L), 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/produc tid/76 ). Details on the spreadsheet, and instructions for using it, are included in a ppendix 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 CFLK s. 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

.

D OE 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 simulation b 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 pur poses 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 inst alled cost are c :

• 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 socke ts)

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 s tate and local retail sales tax.

The primary inputs for calculating t he 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 pr ices : 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 : F orecasted electricity prices. DOE used electricity price forecasts reported in the Energy Information Administration’s Annual Energy Outlook 2015 ( AEO201 5 ). 3

• Repair and maintenance costs : Repair costs are those associated with repairing or replacing components that have failed. Main tenance 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 c ost 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 Resid ual 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.

• Effic acy 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 Diagram8-4 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 l evel and

p roduct c lass Single

- point value

Sales Tax 7.1 1 % Varies by region Operating Cost Inputs

Power Rating Varies by effi cacy level and product class Single

- point value

Operating Hours Residential sector : 2 .0 hrs/day

Commercial sector : 10. 7 hrs/day D istribution (see chapter 6 of this

NOPR TSD)

Electricity Prices Residential sector: 0.1 31 $/kWh

Commercial sector: 0.1 11 $ / kW h Var y 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 pack aged 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 d istri bution

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 8 C of t his 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 tim e 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 = to tal installed cost in dollars,

Σ = sum o ver the lifetime, from year 1 to year N

, NN = lifetime of product in years,

OO = a nnual operating cost in dollars,

RRRR = re sidual value in dollars of CFLK lamps at the end of CFLK lifetime

, d

rr = discount rate,

DD = dis posal cost, and

tt = ye ar for which operating cost is being determined.

DOE expresses costs in 201 4$. 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 impac t 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 effec t 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 e ffect of efficacy level s 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, D OE 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 val ue 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 eac h 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 show n in

Table 8.2.1 . See chapter 9 of th is NOPR TSD for further information on the derivation of the market efficacy distributions.

Table 8.2.1 CFLK Efficacy Distribution by Trial Stand ard Level in 2019

Trial Standard

Level Sub

Baseline

( % ) 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 tha n a given EL , will in

, the presence of a sta ndard

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 consumer

choice model in chapter 9 of this NOPR TSD.

8.2.4 Price Learning

As d iscussed earlier, lamp prices in the compliance year are an input to the LCC analysis.

DOE uses a price learning a nalysis 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 to8-7 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 rec ent 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. B ecause 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 th e 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 EL s. 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 fr om data provided by the Sales Tax Clearinghouse. 7 DOE derived population-weighted average 8 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) Populati on (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 Ind iana, 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, Dis trict 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 Arkansa s, 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

- W eighted A verage 7.1%

DOE calculated the total installed cost based on the following equation:

II = � ( LLLL + SS ) × NN SS � × TTTTTT
Eq. 8.2

Where:8-9 LLLL = the consumer weighted- average CFLK lamp price , in dollars,

SS = the C FLK socket price , in dollars,

NN

SS = the number of sockets in the CFLK, and

TTTT = sales t ax markup.

8.2.6 Oper ating 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 = ele ctricity cost associated w ith operating the CFLK , in dollars,

RR = rep lacement cost for CFLK lamps that fail during the CFLK lifetime , in dollars, and

tt = yea r for which the operating cost is being determined.

D OE used electricity price trends to forecast electricity prices int o 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 × EEPP tt E q. 8.4

W here:

tt = annual energy consumption of the CFLK at year t, in kWh per year, and

EEPP tt = electricity price at year t

, in dollars per kWh

.

T he 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

.

E lectricity 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

in vestor

- 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 marg inal 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 prices8-10 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 prices 10 to estimate the ratio of the marginal to the averag e price assuming an MLF of 0.5.

DOE calculated r egional 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 consume rs 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 electri city price for IOUs to the average price charged by POUs (calculated using EIA form 861 data), and the percentag e 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.

E lectricity 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 t he 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 tre nds 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 u ncertainty and v ariability in LCC analysis, presents LCC results for the high- and low -economic growth scenarios.8-11 Fi gure 8.2.1 Trends in Residential Electricity Prices

Fi gure 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 functio n 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 insta lled 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 c eiling fan shipments (see chapter 9 of this NOPR TSD for8-12 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 failur e 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 cumu lative 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 t he 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 consta nt 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 study 12 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.

Hou seholds 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 yy ii − SS vv ii ) 2

σσ

SSSSSSSSSS yy ii 2

ii + � ∑ SSh iiii ii ii ( jj ) ∗ PP� aaaa ee jj � jj − ssss kk RRRRRR � 2

σσ

RRRRRR 2

Eq. 8.68-13 Where:

= i dentifier for an age bin from the LBNL study,

SS rr yy ii = number of appliances reported by the LBNL study in bin

, SSSS vv ii = the number of surviving appliances in bin pre dicted by the Weibull distribution applied to the number of appliances shipped (a function of αα
, ββ , and

θθ )

,

σσ

SSSSSSSSSS yy ii = the standard error of the LBNL study data point for bin

, jj = shi pments year at the beginning of the historical time series and ending at 2009,

SSh iiii ii ii ( jj ) = shi pments in year jj

, aaaa ee jj = shipments vintage relative to the year 2009, PP� aaaa ee jj � = survival probability of an appliance at aaaa ee jj ,

ssss kk RRRRRR = estimate of installed stock at year 2009, and

σσ

RRRRRR = 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 Magazine 13

, 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

assumpt ions , 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 in stalled 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

a ppliance 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 no t guaranteed to reflect actual real

world experience. The second assumption is that consumer behavior and mechanical appliance

lifetime have not ch anged over time. The third assumption concerns the Weibull failure rate

parameter . DOE constrain ed 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

Whil e 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 standard s 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 (i n 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 replacement s are not affected by changes in product efficacy and are, therefore

, the same

for all EL s 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 an8-15 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

.

La mp R ep la cement O p tions

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 lamps e . 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, watt ages, lifetimes, and prices for every lamp replacement option.

Note that t o estimate consumer prices that are consistent with the prices of the representative lamp unit s presented in the engineering analysis, DOE adjusted the consumer lamp prices to omit the CFLK manufacturer mark up. 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. Fu rthermore, 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 considere d

l amps 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 d efinition. 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 CFLK s. 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

ene rgy 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 cons umers are assumed to only receive the distributor markup.8-16 Table 8.2.3 Lamp Replacement OptionsELLamp TechnologyBase TypeEfficacy (lm/w)Wattage (W)Lifetime (hrs)Price ($2014) CFL Replacement LED ReplacementEfficacy (lm/W)Wattage (W)Lifetime (hrs)Price ($2014)Efficacy (lm/W)Wattage(W)Lifetime (hrs)Price ($2014)

Sub

baseline 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 2 5

, 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

R elamping 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 probabi lity 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

dictate s the year of the first lamp re placement 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 commercial8-17 consumers, DOE calculates commercial discount rates as the weighted average cost of capital (WACC), usi ng the Capital Asset Pricing Model (CAPM).

Discount R

a tes f or R esidential C onsumers

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 Reser ve Board’s Survey of Consumer Finance s (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 o pportunity cost of funds in this case may include interest payments on

debt and interest returns on assets.

D OE estimates separate discount rate distributions for six income groups, divided based on income percentile as reported in the Federal Reserve Boa rd’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

I ncome Group Percentile of Income

1 1 st to 20 th

2 21 st to 40 th

3 41 st to 60 th

4 61 st to 80 th

5 81 st to 90 th

6 91 th to 99 th

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Shares of D ebt an d A sset C lasse s

DOE’s approach involved identifying all relevant household debt or asset classes in order

t o 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 previo usly 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.

T he average share of total debt plus equity and the associated rate of each a sset 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 distributions8-18 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.

D OE estimated the average percentage shares of the various types of debt and equi ty 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.

R a tes f or T y pes o f D ebt

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. , credi t 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 associ ates 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 nom inal 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 Rate 15 Applicable
Marginal Tax

Rat e 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 T yp es of A sset s

N o 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 Feder al 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

- thi rds weight) and the bond rates (one-third weight) in each year. DOE assumed rates on checking accounts to be zero.

D OE 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 b e 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 A verage Nominal and Real Interest Rates for Household Equity

Type of Equity Av erage 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 R ate C a lculation a nd Summary

Using the asset and debt data discussed previously, DOE calculated discount rate

di stributions 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 = � SSh

aa ii

, jj

jj × RR ii

, jj

Eq. 8.7

Wh ere:

DD ii = discount rate for consumer i,

SSh aa 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 ra te 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.

O nce the real discount rate was estimated for each consumer, DOE compiled the

distrib ution 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.

T able 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 ra te 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 analy sis.8-22 Table 8.2.9 A verage 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 R ates f or C ommercial S ecto r C onsumers

The co mmercial 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 cost s 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

.

D OE’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

D amodaran 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:

• O ffice

• Retail

• Lodging

• Education

• Food Services

• Other

D OE estimated the cost of equity using the CAPM. 24 The C APM assumes that the cost of equity (k

e ) 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 ri sk facing a firm is determined by several variables: the risk coefficient of the firm (β), the expected return on risk

- free assets (R f ), and the equity risk8-23 premium (ERP). The risk coefficient of the firm indicates the risk associated with that firm

relati ve 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 e quity financing is estimated using the following equation, where the variables are defined as above:



## ( ) ERP Rk

fe



### ×+= β

Eq. 8.8

Where:

k

e = cost of equity,

R

f = 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 det ails 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 inves tor is matched with the term of the risk

free security. ” 25

By t aking 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 differenc e 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, 201 1-2013

Ye ar Risk

- free rate (%) ERP (%)

2011 6.61% 2.94%

2012 6.41% 3.99%

2013 6.24% 5.30%

The cost of debt financing ( k d

) is the interest rate paid on money borrowed by a company. The cost of debt is estimated by adding a risk adjustment factor ( R

a ) to the ris k- 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:

aifdi 

### RRk +=8-24 Eq. 8.9

Where:

k

d = cost of debt financing for firm, i

, R

f = expected return on risk- free assets, and

R ai = risk adjustment factor to risk

- free rate for firm, i

.

DOE estimates the WACC using the following equation:

d dee 

## wkwkWACC ×+×=

Eq. 8.10

Where:

WACC = weighted average cost of capital,

w e = proportion of equity financing, and

w d = 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 Analy zed 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 ) we re 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 CFLs 29 and found that, on average, disposi ng 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. Given8-25 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 cos ts 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. A dditionally, 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 INPUT S

The payback period is the amount of time it takes the consumer to r ecover 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 = d iffere nce 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 y ear operating cost, lifetime operating cost, and LCC

for each EL, assuming that all of the sample d 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 E L, relative to the sub- baseline product.8-26 DOE also calculated the LCC savings of a standard set at each EL. F or 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 standar ds case. In both the no- standard case and the

standards case s, 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 stan dard 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 purcha sed

in the no-standard c ase. 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 calculati on.

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 consumer s 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

consumer s 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 T SL . 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 calculation s 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 also8-27 samp led from the probability distributions that DOE developed to characterize many of the inputs to the analysis.

T he 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 show s 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

- b aseline ( i.e. , lamps that have efficacies below the baseline set for th e

n ew product clas s 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

- stan dard 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 t he 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 TSL s 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, respect ively. 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 Saving s 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 28-31 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 Averag e 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 1 3.8

* “Sub” corresponds to the sub

- b aseline ( i.e., lamps that have efficacies below the baseline set for th e

n ew 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 fo r 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 wit h 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 A ll 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 (Ro ll

- 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 valu e 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 te st 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 C ommercial

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

anal ysis 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 e conomic justification for a potential standard level, thereby supporting or rebutting the results of any preliminary determination of economic justification.8-35 REFERENCES

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3. U.S. Energy Information Administration. Annual Energy Outlook 2015 with Projections

t o 2040 . 2015. Washington, D.C. Repo rt No. DOE/EIA -0383(2015). (Last accessed June 5, 2015.) http://www.eia.gov/forecasts/aeo/pdf/0383(2015).pdf

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4. D esroches, L. -B., K. Garbesi, C. Kantner, R. Van Buskirk, and H.-C. Yang. Incorporating experience curves in appliance standards analysis. Ener gy Policy . 2013. 52 pp. 402–416. (Last accessed June 15, 2015.) http://www.sciencedirect.com/science/article/pii/S0301421512008488

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5. Gerke, B., A. Ngo, A. Alstone, and K. Fisseha. The Evolving Price of Household LED

L amps: Recent Trends and Historical Co mparisons 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

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6. U.S. Department of Energy– Office of Ene r gy 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

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7. Sales Tax Clearinghouse, Inc. State Sales Tax Rates Along with Combined Average City

and C ounty Rates . 2014. (Last accessed June 15, 2015.) http://thestc.com/STrates.stm

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8. U.S. Census Bureau. Annual Estimates of the Resident Population for the United States,

R egions, States, and Puerto Rico: April 1, 2013 to July 1, 2013. (Last accessed June 15, 2015.) http://www.census.gov/popest/data/national/totals/2013/index.html

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9. Edison Elect ric 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

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10. Coughlin, K., C. Bolduc, R. Van Buskirk, G. Rosenquist, and J. E. McMahon. Tariff

b ased Analysis of Commercial Building Electricity Prices . 2008. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-55551. (Last accessed June 8, 2015.) https://escholars hip.org/uc/item/9dv1m729

.8-36 11. U. S. Department of Energy, Energy Information Administration. Form EIA -861 Annual Electric Power Industry Report Database. (Last accessed June 8, 2015.) http://www.eia.gov/electricity/data/eia861/index.html

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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. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6332E. (Last accessed June 8, 2015.) http://www.escholarship.org/uc/item/3r67c1f9

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13. 22nd Portrait of the U.S. Appliance Industry. Appliance Magazine . 1999.

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19. Board of Governors of the Fed eral 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 acces sed June 15, 2015.) http://www.federalreserve.gov/releases/H15/data.htm

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.8-37 22. Modigliani, F. and M. H. Miller. The Cost of Capital, Corporations Finance and th e Theory of Investment. American Economic Review . 1958. 48(3): pp. 261–297. (Last accessed June 15, 2015.) http://www.jstor.org/stable/1809766

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23. 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

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24. Ibbotson Associates. Cost of Capital 2001 Yearbook . 2001. Chicago, IL.

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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

. January 5, 2015. (Last accessed June 15, 2015.) http://pages.stern.nyu.edu/~adamodar/

.

28. Ass ociation 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, 2015.) http://www.lamprecycle.org/wp

content/uploads/2014/02/ALMR_capacity_statement.2004.-pdf.pdf

.

29. U.S. Environmental Protection Agency. Recycling and Disposal After a CFL Burns Out | 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

TAB LE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.1.1 Representative Lamp Units Analyzed in the Shipments Analysis ....................... 9-1

9.2 SHIPMENTS MODEL OVER VIEW .............................................................................. 9-3

9.2.1 Shipments Demand Model ................................................................................... 9-4

9.2.2 Stock Accounting Model ..................................................................................... 9-4

9.2.3 Consumer-Choice Mod el ..................................................................................... 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 C urrent 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 SHIPMENT S SCENARIOS ............................................................. 9-21

9.8 SHIPMENTS PROJECTION S ...................................................................................... 9-22

REFERENCES .......................................................................................................................... 9-25

LI ST 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 ......................................................................................... 9-249- 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 ......... 9-239-1 CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

This chapter

d escribes the data and

m ethods the U.S. D epartmento f E nergy ( DOE) u sed

t o project annual product shipments with and without amended s tandards a nd p resents resu l ts
f o r the ceiling f

an lig ht kit (C FLK ) product class being considered in this analysis. E stimates of

future product shipments are necessary inputs to the national energy savings (NES) and net

presentv alue (NPV) calculations, as w ell as to t he manufacturer i mpact a nalysi s (MIA).

Because th e vast majority of CFLKs are sold with ceiling fans, the shipments model for

CFLKs is nece ssarily coupled to shipments of ceiling f an s. a DOE developed a national stock accounting m odel t o est imate annual sh ipments of ceiling fans, which w a s then used t o est imate shipments o f

CF LKs

. To apportion CFLK shipments into different efficacy l evels (EL s), DOE developed a mo dified consumer-choice model based on the shipments modeling methodology developed by D OE for

t he general ser vice fluor escentl amp an d i ncandescentr eflector lamp energy conservations standards rulemaking. 1

The shipments mod el w as executed as a separate computer model written in the Python programming language

. T he input s a nd output s o f t he s hipment s m odel s w er e e mbedded in the

n ational i mpactass essment ( NIA) m od el, w hich i s i mplemented i n a MicrosoftE x cel spreadsheet

, a vailabl e on r egulations.gov, docke t nu mbe r EERE -2012- BT

- STD -0045 a t http://www.regulations.gov/#!docketDetail;D=EERE-2012-B T -S TD -0045 . Appendix 10A of this NOPR technical support document (TSD) discusses how t o access and ut ili ze the N IA

sp r eadshee t. Th e r estof t hi s chapter expl ains t he shipments model in more detail. Section 9.2

pre sents t he methodology behind the model; section 9.3 des cribes the da ta inputs and calibration

of the model; section 9.4 dis cusses impacts o n shipments fro m st andards; sec tio n 9.5 discusses

the a ffected

s tock; s ection 9.6 dis cu sses t he impact of a ceiling f an standard of CFLK shipments; section 9.7 dis cusse s a lternativ e shipment scen arios ; and section 9.8 presents the shipments pr ojections for standard s seta t each e fficacy level (EL ).

9.1.1 R epresentative Lamp Units Analyzed in the Shipments Analysis

As described in chapter 3 of this NOPR TS D , DOE is considering a s ingle product cl ass i n th is NOPR analysis. In chapter 5 of this NOPR T SD, D O E described t he EL s it has developed, an d it id entifiedre presentative lamp units that co uld meet e ac h EL . The

r epresentative l amp units are specified by the lamps that could be packaged with the CFLK to

me et each EL . Included in the no-st andard sh ipments case are CFLKs with e f ficaci es below t he

r estructured

b aselin e ( hereaf

ter r eferred to as t he ‘sub-basel ine’)

. The properties of representative lamp units corresponding to the sub-ba seline 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

i n ch apter 5 of this NOPR T SD

, DOE assu med t hat two d ifferent

scen arios

were a vailable to m anufacturer s t o meet each E L . I n th e lamp r eplacement scen ario, the

manufacturer is assumed

t o meeteach EL by using more efficacious packaged lamps, but is

assumed t o l eave all other aspects of the fixture unchanged. In the CFLK r eplacementscen ario,

DOE assumes that the manufacturer may substitute the base type and number of sockets. Accordingly, a t

l east two representative options were developed for each E L

, as well as f or t he

b aseline.

The lamp an d

so cketp rices associated

w ith ef f icacy improvements for representative products ar e presented in chapter 7 of this NOPR TSD. These prices are key inputs to the shipments model. Table 9.1.1presents th e character istics of representative CFLKs based o n the representative l amp uni t s and sock ets specified f o r t h e lamp and f

ixtur e replacemen t scena rios. b

Value s f or C FL K w atta ge a nd lumen output, a nd t he por tion o f t he CFLK en d- u ser p rice thati s associated

w ith t he lamps a nd soc kets ( “CFLK End -U ser P rice f or Lighting Components”), we re based on the per-lamp and per-s o cketv alues for r epresentative units described i n chapter 7 of

this NOPR TSD

. F or the b aselin e p roduct( EL 0), t hos e values were scaled to m ake t hem

r epresentative o f the a verage CFLK on the market that is not constrained by a wattage limiter

,

w hich ha s an est imated

ave rage lu

men output of 2039 lumens. c For a ll o ther l evels , except the

sub-b aseline, t hose values were scaled by t he s ame amount as the baseline product, to pr eserve the p ercentage va riations in l umen out put b e tween ELs thatw ere id entified for t he

r e presentative lamp units and f ixtur es in the e ngineerin g ana lysis. T he di fferences in the prices

at t he differen t ELs represent the i ncremental p rice o f e fficacy improvements, which are key inputs to the shipments and NIA analyses.

b For example, $ 4.33 of the t otal 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 DO E 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

L evel CFLK Ligh t Outp ut (lumens) CFLK Input Power (watts) CFLK End

- User Price for Lighting Components in 2019 ( 2014 $)

A ll 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 2 a 1861 28 5.68

EL 2b 2039 31 18.61

EL 3 2039 22 7.47

E L 4 2090 20 5.89

Fixture Replacement Scenario** All lamps and sockets

EL 0 2039 36 5.12

E L 1 1988 34 10.55

E L 2 1912 31 28.06

E L 3 2039 20 11.99

E L 4 2039 19 11.70

* Representative lamp lumens, wattages, and prices p resented in chapter 7 are each scaled by 2.7 , to obta in 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 num ber 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 s ocket 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 OVER VIEW

DOE developed a model to project CFLK shipments by e f f icacy level, over the 30-y ear

an alysis period (2019 – 2048). The model projects shipments for both the no-s tandard cas e and

f or each TSL . The shipments model consists o f t hree main interacting elements: (1) a shipments

demand model th at d etermines t h e to tal 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 m odel, which de termines t he d istr ibution (m arket shares) of those9-4 purchases across efficacy level

. This section describe s these elements of the shipments model in general terms . Section 9.3 describes the calibration and implementation of the model.

9.2.1 Shipments D emand Model

The shipments model considers four market segments in developing the total C FLK shipments projection: r eplacements f or r etired s tock , additions due to new building construction, additions due to expanded demand in existing buildings, and demolitions, w h ich

d ecreases demand

f rom replacements and new installations in existing buildings. Total shipments for CFLKs in a given year are given by:

SSh iiii ( jj ) = RR RR ( jj ) + NNNN ( jj ) + EEEE ( jj )

− DDDDDD ( jj ) Eq. 9.1

Where:

SS h iiii ( jj ) = total number of units shipped in year jj

, RR RR ( jj ) = uni ts retired and replaced in year jj

, NNNN ( jj ) = numbe r 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 q uantified in section 9.3.2.

9.2.2 Stock Accounting Model

As in dicatedin 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. D etermining 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, ba sed on an accounting of a dditions t o the s tock (shipments) a nd retirements. T he age

distribution of the s toc k in 2012 is e stimated using results of a n online survey of c e iling fa n owners conducted

by L awrence Berkeley N ational L ab (LBNL). 2 The s tock age di stribution is updated i n subsequent years us ing projected shipments a nd retirements de termined by the s tock

age di stribution and a pr oduct r etirement f unction. The a ge d istribution of t he s tock i s a lso a k ey input t o both t he N ES a nd NPV calculations , because t he ope rating costs f or a ny year depend on

the a ge d istribution of t he s toc k. O lder

, le ss e fficient units m ay have hi gher ope rati ng costs,
while newer , more e fficient uni ts ha ve l ower ope rating costs.

DOE cal culat es the total st ock o f p roduct s b y in tegrating h istorical shipments b eginning

w ith th e start year of

th e h istorical data. New units are then added to the stock to replace older

units that are retired and to account for growth in demand.

Assu min g a lo w p robability f or retirements w ithin th e f ir st year, t he number of one-year old units is equal to the number of new units purc h ased the previous year:9-5 SSSSSSSSSS ( jj

, aaaa = 1) = SSh iiii ( jj

− 1) Eq. 9.2

W here:

SSSSSSSSSS ( jj

, aaaa ) = number of units of a particular aaaa
, jj = year for which the stock is being estimated, and

SSh iiii ( jj – 1) = number of units purchased in year jj – 1

.

Over ti me, a fraction of the stock is removed due to retirements . That fraction is determined by a retirement probability function, pp pp RRRRRR ( aaaa ) , which is described in section 9.3.2 . Acc ordingly,

DOE tracks existing stock of units accounting for units that retire using the following:

SSSSSS SSSS ( jj + 1

, aaaa + 1 ) = SSSSSSSSSS ( jj

, aaaa ) − SSh iiii ii ii ( jj − aaaa ) × pp pp RRRRRR ( aaaa + 1 ) ) Eq. 9.3

W here:

SSSSSSSSSS ( jj

, aaaa ) = number of units of a particular age, jj = year for which the stock is being estimated,

SSh iiii ( jj − aaaa ) = number of units purchased in yea r jj − aaaa , and

pp RRRRRR ( aaaa + 1 ) = the pro bability a unit is retired at aaaa + 1

.

9.2.3 Consum er

- Choice Model

The consumer-c hoice model d eter mines the distrib u tion ( marketsh ares) o f p urchases in

an y given y ear

b y e stimating th e l ikelihoodth at a consumer will pu rchase a p articular CFLK option, based on sector-s pecific sensitivities to fir stco stan d operat io n and m ain tenance (O&M)

co st. The consumer-c h o ice m odel is u sed to d etermine th e m ar ket allocations f or both th e n o

standard c ase and the st andards cases co nsidered. T he co n sumer -choice model allows for voluntary c o nversion s to m ore efficient technologies based on economic incentives. The model

allocates the sh ipments to t he rep r esentative prod u cts described

i n ch apter

5 o f t hi s NOP R TSD, assuming t h e prices for

t ho se representativ e products descr ibed i n ch apter 7 o f

t hi s NOP R TSD.

The consumer-c hoice model u se s a co nditional logit model to c alculate the

p r o bability th at a consumer will purchase product jj based on the logistic curve probability

function of the form:

PP jj ( zz ) = zz jj
∑ zz ii

nn ii=1 Eq. 9.4

Where:

PP

jj ( zz ) = the probability a consumer will purchase product jj among possibl e options, and

zz = the ‘l ogit’, which is defined as follows

zz

jj = ββ FFFF ∗ FFCC jj + ββ OOOO ∗ OOMM jj Eq. 9.5

W here:9-6 ββ FFFF = consumer sensitivity to first cost,

FFCC jj = first cost of product option jj

, ββ OOOO = consumer sensitivity to operations and maintenance cost, and

OOMM jj = operations and maintenance cost of product option jj

.

Th e regression coefficients ( ββ values) represent the c onsumer’s sensitivity to first cost ( ββ FFFF ) and O&M cost ( ββ OOOO ). The coefficients are determined using historical shipments ' market

- share data.

Consumer sensitivities are sector -specific and vary bet ween residential and commercial purchasers. Other para meters 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.

I n t he case of

C FLKs, t he lamps packaged with the CFLK are purchased by the

CFLK manufacturer. Thus, the consumer-choice model for CFLKs is modified to project lamp p urchases by C FLK manufacturers, as those will b e the options available

to consu mers.

9.3 M ODEL IMPLEMENTATION

9.3.1 Historical Shipments

DOE’s analysis o f t he CFLK marketi ndicat es that a m ajority o f C FLKs a r e p a ckaged and

sold with ceiling fans. D OE u sed h istorical sh ipments data of cei ling f ans to ca librat e its CFLK shipments model by a ssuming a constant fraction of ceiling fan shipments include a CFLK.

DOE u sed

t hree so u rces o f

d ata to es tablish h istorical sh ipmen t s trends in th e to tal

n u mb er of ceiling fa ns: (1) d ata fr om Appliance Magazine’s Statistical Review from the period
1991-2006, d (2) d ata

fr om E NER GY S TAR Annual Reports from the period 2003-2011, e and ( 3)

d ata purchased from the NPD Group from 2007-2011. DOE took an average value in cases

where d ata from multiple sources overlap

.

Table 9.3.1 shows NP D estimates of total U.S. shipments of ceiling fans sold with and

without C FLKs , 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 d ata in Table 9.3.1 a n d assuming that the ratio

of ceiling fans sold with and without CFLKs is th e same f or 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 I nstitute; 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 ceili ng fan manufacturer associations, publications, and commercially available market research.9-7

specify, D OE e stimatedth e tota l shipments o f ceiling f ans sold with and without CFLKs and

the percentage of

cei ling f ans sold w ith CFLKs in each y ear. T he r esults are sho wn i n T able

9.3.2. B ased

o n t his analysis NPD sh ipmentd at a and a n online survey of ceiling fan owners

conducted b y L BNL, 2 DOE found 88% of ceiling fans purchased within the last four years included C FLKs.

Tab le 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).

Tab le 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 estima te o f CF LK hi storical shipments from 1991-2011.9-8 Fig ure 9.3.1 Historical Shipments of CFLKs

9.3.2 Shi pments Projections by Market Segment

Section 9.2.1 d escrib es how sh ipments are projected b ased o n sales to the following

m arket segments: product r eplacements due to r etirements , i n creas ed demand from new building construc tion , i n creased demand in existing buildings, and decrease in demand due to building demolitions. T his section d escribes how d emand from each o f t hese market

seg ments is quantified.

R ep lacement Shipments

. Estimated sales due to replacements in a given year are equal

to the t otal st ock

o f t he appl iance min us t he sum o f t he appl iances sold

i n pr evious y e ar s thatst ill

remain i n t he stock. D OE assum ed t hatt he relevantl ifetime in projecting replacements for

CFLKs is the lif

etime of t h e c eiling f

an t o whic h i t i s attached. T he f ollowing equati on r epresents
h ow D OE es timated

r eplacement sh i pments:

RR RR ( jj ) = � SSh iiii ( ii ) × pp pp RRRRRR jj−1

ii=NN ( aaaa = jj − ii )

Eq. 9.6

W here:

RR RR ( jj ) = Unit s retired and replaced in year jj

, SSh iiii ( jj ) = Tot al shipments in year jj

,

pp RRRRRR ( aaaa ) = Probability that a ceiling fan of a particular aaaa will be re tired,

NN = Start year when the model begins its stock accounting.9-9 The retir ement fu nction, pp RRRRRR ( aaaa ) , is based on the product lifetime distribution , as discu ssed in chapter 8 of this NOPR TSD . The product lifetime distribution has an average value

of 1 3.8 years, and minimum and maximum values of 4.1 years and 30 years , respe ctively

. Figure

9.3.2 shows the retirement function for a CFLK that DOE used to estimate replacement shipments.

Figur e 9.3.2 CFLK Retirement Function

Shipments t

o N ew Construction

. The number of installations d ue to new construction is estimated using projections f or new c onstructi on starts , product s aturation, and the a verage number of C FLK uni ts i nstalled per b uilding. DOE a dopted the pr ojections f rom the DOE ’s Energy I nf

ormation A dministration’s (EIA’s ) Annual E nergy O utlook 2 014 (

AEO 2014 ) for

2010-2040. 3 DOE est imated a current p enetration rate of ceiling f ans in h omes of 73 %, using

data f rom Residen tial E nergy Consumption Survey ( R ECS ) 2009, w ith a n a ve rage of 3.1 ceiling

f ans installedin a single new installation. D OE a ssumed 88% of t hese i nstallations include a C FLK to determine s hipments of CFLKs t o new c onstruction.

Installations in to E

xisting B uildings

. The number of new f irst -t ime owners was

estimated u sing trends in the penetration of ceiling fans in existing buildings over time from

R ECS d ataf

rom 1993, 1997, 2001, 2005, and 2009. DOE estimated the rate at which the

number of buildings witho ut ce iling f ans decr eased

b etween R EC S y ear s. 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 fans9-10

into buildings that already h ave ceiling fan s and ass umed t h att he average number

o f cei ling

f ans in buildings with ceiling f ans does not exceed 3.1 ceiling fans per building. DOE assumed

88% of ceiling fan i n stallations into existing buildings included a CFLK. The market for CFLKs in existing buildings quickly d ecreases as t he marketr each es its s aturation point, using t h e previously described assumptions. By the start of the analysis period (2019), installations into existing buildings make up 1% of CFLK sh ipments.

R eduction in Demand Du e to D emolitions

. The demand for installations into e xisting

b u ildings 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. D OE

est imated this demand reduction using a stock accounting model based on AEO 2 01 5 residential housing stock and housing starts following E q

. 9.7 below. 4

Demolition (j) = Stock (j+1) - Stock (j) - Starts (j) Eq. 9.7

Wh ere:

DDDDDD ( jj ) =Hous ing demolitions in year jj

, SSSSSSSSSS ( jj ) = Housing stock in year jj , and

SSSS SS ( jj ) = Ne w 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 i n stallations in cludeda CFLK.

C ommercial Sector Shipments

. B ased on feedback from manufacturers , DOE estimated

th at 5% of total CFLK shipments go to the commercial sector. DOE assumed this f r action

r emained fixed o ver the course of the analysis per io d

.

9.3.3 C urrent Efficacy Distribution

To obtain a better understanding of the CFLK market, DOE examined th e m arket

d istribution o f 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 c urrently d ivides CFLKs into th ree p rod ucts c l asses b ased on the light socket

type: Medium -Sc r ew Ba se p r oductcl ass, Pin -Ba sed f luorescen tproduct class

, and t he

Other B ase 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 ty pically s ell in h igher volumes a t lower prices, DOE u sed a

p r ice -weighting scheme based on an analysis presented in the concurrent ceiling fans energy

conservation standards rulemaking g and s ummarized h er e.

The relationship between ceiling fan market share and retail price is described by the following equation:

MM ( ) = AA ββ FFFF ∗pp

W here:

MM ( ) = Market share for a ceiling fan that costs p,

AA = Nor malization factor,

= Retai l price of a ceiling fan, and ββ FFFF = Consumer sensitivity to first cost, found to be equal to -0.028. g

Because CFLKs are sold

p rimarily w ith ce iling f

ans, D OE u ses th e same equation to

es timate CFLK marketsh ares by productc lass. D OE al so acco unted f or t he fact that

d ifferent data s o urces r epresentch annels with d ifferents ales volumes. h

As shown in T able 9.3.3, the current Other Base Type product class constitutes approximately 66% of the entire marke t, largely w ith C FLKs with can d elabra so cket s thatu se low -e fficacy lamps (15 lm/W). CFLKs with m ed ium scr ew b ase sock et s constitut e the next

l argestp roduct class, w hich i n t he survey y ear i ncluded o nly co mpactf

luorescent lamps. The

Pi n -Based product class is in signif ican t at less th an 1 % of

th e m arket.

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 s ensitivity to first cost is discussed in chapter 9 of t he 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 offe rings at the largest retailers (Home Depot, Lowe’s, and Wal

- Mart) are representative of other in

store 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

Medium

Screw

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 al so ex amin ed

t he 2014 market breakdown by product class efficacy based on the survey data, the results of which are shown in T a ble 9.3.4. F rom th is ta ble and Table 9.3.3, i ti s clear that the major ity o f th e mar k etw as O ther Base Type C FLKs th at use re latively lo w

efficacy lamps, i typically less than 15 lm/W. The remainder of the market was dominated b y C F LKs wi th mediu m -s crew b ase sockets which, based o n t he cu rrentef

ficiency s tandard, w ere constrained

t o higher efficacy lamps, typically greater than 55 lm/W. As shown in T able 9.3.3, a very small share of

t h e Oth er B ase Type CFLK market( estimated t o b e less than 0 .5%) w as made up o f CFLKs w ith in tegral 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 Ta ble 9.3.4 CFLK Market Share Distribution by Efficacy in 2014

Market Share Distribution within Product Classes Share of

Total Market Light Source Efficacy Medium

- 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 o n t he results of Table 9.3.3 and T a ble 9

. 3 .4 f or market shares by lighting technology and efficacy for the current product clas s structure, D OE d eveloped m arket sh ares by efficacy level for the proposed single product class of All CFLKs. Results for the market

distribution of efficacy l evel ar e presente d in T abl e 9.3.5. N ote, th at DOE f oundn egligible

m arket share f

or products in the 45-60 lm/W range and thus attributed 0% market share to EL 0 ( which r epresen ts an efficacy of 57.1 lm/W).

Ta ble 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-e mitting d iode ( LED ) technology currently make up less

than 1% of shipments of

CF LKs , f

eedback f rom CFLK manufacturers indicated t h at the penetration of LED technology into the CFLK marketwould likely reach 15% by 2018. D OE

assu med t hat the C FLK market would follow the la rger lighting m arke t as a w hole early in th e

a nalysis p eriod, and e stimated t he marketsh are of

L ED t echnologies using the incursion curved developed f

o r g eneral ser vice lamps ( GSLs) in its r eport Energy Saving Potential for Solid

State Lighting in G eneral I llu mination A

pplications (SSL re port). 5 DOE r ecogni zes that the

CFLK market lags behind the GSL marketand s h iftedth e introductio n year used in its analysis by 3. 5 y ears compared to the introduction year presented in the SSL report to match

manufacturer

e stimates of LED penetration in 2018. The remaining marketsh are f or non-L ED lamp options was apportioned using the modified consume r -choice model, described in section

9.3.5 below.

DOE assume d 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 d eveloped a time series index using prices for GSL LED lamps with a lumen output between 750-1049 lumen s 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 sca le the LED CFLK prices during the analysis period (2019-2048).

To apportion the demand for LEDs among the different LED lamp options, D OE used

the e fficacy tre ndp rojection f or d ecorative LED l amp s p resented in D OE’s re port SSL Pricing and Efficacy Trend Analysis for Utility Program Planning

. 6 The decorative lamp category

exhibits a s lower

r at e o f ef ficacy i ncrease compared to medium-s crew b ase lamps; t herefore

DOE b elieves this represents a c onservativ e estimate of t he efficacy t ren d

. T o d etermine th e

current mean and m aximum e f ficacy o f L ED l amps , DOE analyzed data from 2012-2015 for lamps in its L ED Lighting Facts P rod u cts Database

. j D OE assu med t he ratio b etween t he average LED e f

f icacy 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 m aximum LED ef f icacy r epresents th e 3 σσ upper confidence level ( i.e., the

99.9 th p ercentile) o f efficacy . DOE then determined the relative fraction of the LED e fficacy

distribution that would f

all into each L ED EL . By the start of the analysis period, 2019, 29% o f

L ED marketsh are would be at the highest LED efficacy l evel p r esented i n ch apter

5 o f this

NOP R 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 th e p rice o f L EDs falls below the price of the other lighting technologies presented i n the engineering ana lysis, D OE assu med thatm anufacturers will choose to pa ckage

all CFLKs w ith L ED la mps. D OE a ssumed that m anufacturers w ould not pa y a pr ice premium t o package w ith any of t he C FL l amp options, which a re more expensive, less efficacious, and have

a s horter lifetime.

Fig ure 9.3.3 LED Incursion into CFLK Market Assuming Trend Estimated from SSL Report

9.3.5 C onsumer

- Choice Model

The total sh ip ments mustb e allocated b y ef ficacy level. To do so, the shipments model

uses a hybrid approach. A s d escribed in sec tion 9.3.4, until LEDs are first -co st comp etitive

w ith other lighting technologies, they are accounted for separately using the incursion curve developed in the SSL report. Non-L EDs are allocated b y t he consumer -choice model.

DOE implemented a consumer -choice model, as described i n S ectio n 9.2.3, with

modifications to account for DOE’s observation t hat p urchasers of

C FLKs are not choosing t h e lamps packaged with CFLKs. Instead, C FLK manufacturers p urchase th e lamps f or CFLKs,

u ltimately deciding which lamp options are available for consumers of CFLKs. The options

available to the consumer-choice model f o r C F LKs correspond to the ELs shown in T ab le 9.1.1.

A key assumption for the shipments model is that CFLK manufacturers will generally

choose to p ackage CFLK w ith t h e lowestf

irst costl amp o ptions. H owever D OE r ecogni zes that some manufacturers will p ackage CFLKs with L ED l amps for r easons other than co st

a lone, su ch as consumer desire for LED lamps in comparison to other available technology options.9-16

DOE assu med

t h atfor a given socket type and lamp technology, C FLK manufactu r er s

ar e prim arily s ensitive to th e first cost of th e la mp , a s there is no economic incentive for CFLK

manufacturers to pur chase a more efficacious lamp to package w ith a C FLK. For e xample, a

manufacturer who is pl anning to package a medium

- screw ba se C FLK w ith a comp act
fluorescent lamp is assumed t o purchase the lowestf

irst cos t compact

f luorescent lamp available

. After t he consumer -choice m odel i dentifies w hich options manufacturers w ill make a vailable to

consumers , t he model cal culates th e market share for ELs based on Eq. 9.4 and Eq. 9.5. N ote tha t
the l ogit va lue ( defined in Eq. 9.5) f or non- L ED l amp options remains a constant va lue over t he analysis pe riod, a s D OE assum e s no price learning for non- LED lamp options.

T he first cost of the CFLK associated with any given lamp option is given by:

PP CCCC = PP bbbbbbbb + PP ll ll + PP ssssss Eq. 9.8

Where:

PP CCCC = t otal CFLK p rice (2014$),

PP

bbbbbbbb = price of the CFLK not including sockets and lamps (2014$),

PP ssssss = total p rice of socket s ( 201 4$), and

PP

ll 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 th is NOP R TSD.

The manufacturers of CFLK s have a multitude of ways to meeta standard w hile

m aintaining a fixedu tility (in this case a suitable lumen output). In this analysis, DOE

considered two possible scenario s t o m eet each EL : (1) replacing the p ack aged lamps in t he

b aseline CFLK , and (2) changing the light kit fixture (including the number of sockets and/or

b ase type) t o meett he E L . D OE l acked t he d ata necessary to ca librate th e co n sumer -choice

model to f

orecast how manufactu rers and consumer s will choose b etween t h ese two scen arios

. For ELs with tw o options, D OE sp litm arket sh are evenly b etween the lamp -r eplacement

sc enario and

t he C FLK r ep lacementscen ario such t hat each scenario w ould h ave 50% m ar k et
sh are . For EL 2, current data in dicate th at a majority o f t he market sh ar e i s in t h e CFL lamp

r eplacementu nit( EL 2 a in Table 9.3.5). Accordingly, D OE a pportioneda m ajority o f th e

c urr en t market share in E L 2 t o E L 2a

. H owever, D OE sp lits market share that moves from EL

2 a to other ELs ev enly b etween the two scen arios.

9.3.6 Impact of GSL Energy Conservation Standards Rulemaking

In the preliminary analysis for the concurrent GSL energy cons er v ation st andar d s

r ulemaki ng, 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-b ase lamp s thatm eett he lumen

output and g eneral ap plication r equirements to m eet t he GS L definition, which available

information i nd icates w ouldin clude all candelabra -b ase lamps currently packaged w ith

C FLKs. All l amp s t h at meet the G SL d efinition w ould be s ub ject to th e EPCA requirement9-17

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 n ot greater than o r equal to the savings from a minimum efficacy

standard of 45 lumens per watt. 42 U.S.C. 6295(i)(6)(A)(v). Be cause G SILs 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, D OE h as assumed in both the no-s tandard and t he standards case shipment

p rojections t hat can delabra -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 EL s 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 thos e 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 p rojections 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 IM PACT OF ENERGY CONSERVATION STANDARDS ON SHIP MENTS

DOE projects that appliance standard s often r esulti n an increase in the price of t h e

p roductdue to an i ncremental costa ssociated w ith ef ficiency improvements . Economic theory

suggests that, all else being equal, an increase in the price of a normal good woul d l ead to a d ecrease in demand for that good. DOE conducted a literature review and an analysis of appliance price and e fficiency d ata to estimate th e effects on p rodu ct shipments from i ncreases in p roduct price. DOE also con sidered t he decrea ses in o pera ting 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 stu dies of ap plian ce markets thatar e relevantt o

t h e issue at hand. DOE identified no studies after 1980 thatu se time -series data of product price and shipments data to e stimate a r elative price e lasticity of d emand sp ecifi c to ap pliances . 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 e fficiency d ata specific to r esidential r efrigerators, cl othes washers, an d dishwashers o ver

t he period 1980–2002 to evaluate broad market trends and conduct simple regr ession an alyses.

These dat a indicate that there has been a rise in a ppliance shipments and a decline in appliance purchase p rice a nd operating costs ov er t he time pe riod. Household income ha s a ls o risen during

thi s t ime.

To simplify the analysis, DOE combined the available economic information into one variable, t ermed

t he relative price, an d

u sed t his variable in an an alysis of markett rends, 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 = r elative price,

TT = t otal pri ce,

= household i ncome,
PPPP = p urch ase price, and

PPPP = p resent 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 an alysis yields a relative pri ce elasticity of demand, averaged over

the three appliances, o f -0 .34. F or ex ample, a relative price increase of 1 0 p ercent

r esult s in a 3.4 p ercent decrease in s hipments. B ecause the r elative p rice e lasticity in corporates the

im pacts f rom th ree effects ( i.e., purchase price, operating cost, and household income), the impact from any single effect i s somewhatm itigated

b y ch anges from t he o ther two ef fects.

Th e relative p rice e lasticity of -0. 3 4 is c onsisten t with e stimates in th e li teratur e (see appendix 9A of this NOPR TSD for a s ummary)

. N ev ertheless, DOE st r esses that the measure

is based o n a small data set, usin g si mple statistical a nalysis. More importantly, t he measure is

b ased o n an assu mption t hat 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 n o t accoun t for them in th is a nalysis. D espite th ese uncertainties, D OE b elieves thati ts estimate of t he relative price elasticity o f d emand p rovid es a reasonable assessm ent o f t he impactt hat purchase price, operating cost, and household

income have on product shipments.

DOE co nsiders th e relative pri ce elasticity provided by the preceding analysis to be a short-run v alue. B ecause DO E’s projections of shipments and national impacts due to standards

is over a 30-y ea r time p eriod, it neededto c onsider how th e re lati v e p r i c e e lasticity is a ffected

o n c e 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-r un p rice e lasticities d iffer. T o

e stimate h ow the relativ e pri ce elasticity ch anges over t ime, D OE r e lied on a study pertaining to
automobiles. This study shows that the automobile price elasticity of demand changes in the years following a purchase price chan ge. With i ncreasing y ear s after t he purchase price change,

t he price elasticity becomes smal ler (more i nela s t i c ) until it reaches a te rminal value a round the

t e n t h y e a r afte r the price change. T able 9.4.1 shows the relative change in t he price elasticity of

d ema nd for automobiles over t ime. DOE d eveloped a time series of r elative price elasticities for home appliances b ased

o n t h e relative change in t h e automobile price elasticity o f d emand. F or

y ears not shown in the table, DOE performed a linear interpolation to o b t a i n t he re lative p rice

ela sticity.

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 1 st

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 Applica tion of Relative Price Elasticity

DOE est imated

sh ipments f

o r each s tandard

ca se using t h e relative price elasticities

described in the previous section, al ong w ith t h e change in the relative price between a standard

c ase and t he no-s tandard

case . B ecause CFLKs are predominantly sold w ith c eiling f ans, th e

r e lative p rice reflects the comb ined price of a CFLK sold with the average price of a ceiling fan. The ceiling fan p rice was estimated using the engineering results, markups, and ceiling fan price

trend f o r the base case published in the ceiling fan preliminary analysis energy conservation

standards. 7

Because household

i ncome is the same in each standard case an d t he no-s tandard

cas e , 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 ) × RRPP SSSSSS ( jj ) − RRPP BBBB BB ( jj )

RRPP BBBB 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 replaceme nt installations that did not occur due to demolitions of existing buildings in year jj

, EEEE NN 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),

RRPP SSSSSS ( 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

.

I n t he case of C FLKs, D OE assu med co nsumers were o n ly se nsitive to p urchase pric e and d id not co nsider o peratin g c ost

i n i ts calculation o f t he relative price. In t hi s case, o nly the

p urchase p r ice contributes to the r elative p r ice. T his is c onsistent 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-s tandard case and

the standards case, t he affected st ock is a k ey o utput of

D OE’s shipment s models. T he affected

s tock (stock that is af

fected b y a standards level) consists of t hose in -s ervice units that are purchased in or

af ter th e year

t he standard

h as taken e ffect, as described by the following equation:
AAAAAA SSSSSSSS SS ( jj ) = ∑ SSh iiii ( ii ) × pp ss ss ( aaaa = jj − ii ) jj

ii= SS dd yyyy Eq. 9.11

Where:

AAAAAA SSSSSSSS SS ( jj ) = affected stock of units of all vintages that are in service in year jj

, SSh iiii ( ii ) = shipments in year ii

,

pp ss ss ( aaaa ) = probability a unit will survive to aaaa
, aaaa = age of the uni ts (years), and

SSSS dd yy = compliance year of the st andard.

For i ts analysis, D OE assu med t hata mended

en ergy co nservation s tandards will

b ecome effective in 2019. Thus, all appliances purchased starting in 2019 a r e affected b y t he

standard level. SSh iiii SS SS SS ( jj ) = � RRii RR NNOO SSSSSS (

jj ) + NNNN

NNOO SSSSSS ( jj ) + EEEE NNOO SSSSSS (

jj )

− DDDD DD NNOO SSSSSS (

jj ) �9-21 9.6 IMPACT OF A CEILING FAN STANDARD ON CFLK SHIPMENTS

DOE i s c oncurrently pursuing an energy conservation standard rulemaking on ceiling

fans. As CFLKs are primarily so ld co upled to cei ling f

ans, a ceiling f

an st andard

t hati ncreases the average price of a ceiling f an w ill i ncrease the average price o f a ceiling fan and CFLK system. T his price increase will d ecrease th e shipments of CFLKs in both a no-standard case and

a standard

cas e. DOE did not assume a standard on ceiling fans in its projections f or CFLK

s hipments. D OE assu med no efficiency performance s tandard f or ceiling f

ans in its C FLK

N O PR analyses because DOE ha s not ye t pr oposed a ceiling f

an st andard . In a ny CFLK final r ule, DOE

will t ake i nto account the i mpact of a pot ential p roposed ceiling fan standard on CFLK shipments a nd will c onsider taking comme nt on i ts revised

an alysi s as appropriate. In t he EC S

N OPR for c eiling fans, DOE w ill c onsider the i mpact of t he pr oposed CFLK s tandard i n its projections of c eiling fan shipments.

9.7 ALT ERNATIVE SHIPMENT S SCENARIOS

To est imate the range of

p otential impacts of an energy conservation standard for CFLKs, DOE developed two al ternative shipments scenarios, a h igh- and a low-b enefits

scenario. In the high-benefits s cenario D OE u tilizedp rojection from the AEO 2015 higheconomic growth scen ario for the new housing st arts rate and e lectricity p rices. I n th e lo w

b enefits scenar io

, D OE u tilized projections from the AEO 2015 low-economic growth scenario

for new housing starts and el ectricity p rices. A dditionally D OE u tilized a h igher r ate o f d ecline

f o r LED la mp p rices in the low-be n efits scenario

. T his s cenario a ssumedth at LED p rice learning w ill follow th e tre nd recently observed for LED GSLs. 8 A faster r ate of d ecline for

L ED l amp p rices yields lower benefits f rom a p otential standard d u e to th e faster adoption of

LED CFLKs in the no-standard ca se, w hich resu lts in consumer s moving t o C FLKs that

a lready meeto r

e xceed p otential standards.9-22 Figur e 9.7.1 Reference and High LED L earning P rice I ndex, taken from the GSL

P reliminary A nalysis Base

- Case R eference S cenario

9.8 SH IPMENTS PROJECTION S

This section p r esents th e shipments projections for a standard

set at each o f

t he t rial

standard lev el s ( TSL s) that DOE considered for CFLKs as well as the no-st andard case. The

TSLs developed by DOE correspond to enacting a standard at the efficacy a sso ciated w ith a

g iven E L. These shipments are con sidered th e ref erence s cenario , and use the following assumptions: AEO 2015 re f erence projectio ns for

co ns truction growth, re feren ce LED incursion

d iscussed i n Section 9.3.4, and no ce iling f an standard . Figure 9.8.1 sh ows D OE’s p rojections

f or the to tal n umb er

o f C FLK sh ipments in t he refer en ce scenario d isaggregated b y m arket

seg ment i n t h e absence of a C FLK st andard.

DOE generally expects that a standard on C FLKs tha t increase s the average per unit price of a given product, which will lead to a decrease in sh ipments. Figure 9.8.2 s how s DOE’s

p rojections for

sh ipments in t h e reference case. I n some cases, t he change in sh ipments between

a tri al s tandard

cas e and the no-s tandard ca se i s s o s mall that it is in distinguishable o n th e f igure s. In t hatc ase, t he higher s tandard l evel o verwrites the lower standard l evel on the plot.

Table 9.8.1 shows the cumulative CFLK shipments over th e analysis period f or t he reference

scenario for each TSL

.9-23 Fi gure 9.8.1 Total CFLK Shipments Projection in the No Standard Case by Market Segment in the Reference Scenario

Fi gure 9.8.2 CFLK Shipments Projection for all TSL s in the Reference Scenario9-24 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

4 596.69-25 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

Fluo rescent 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

F

an 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

t o 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 Infor m ation 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. N avigant 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- savings

report_jan -2012.pdf

.

6. P acific Northwe st 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

C onservation 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

L amps: R ecent 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

T AB LE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 ECONOMIC IMPACTS OF PRODUCT CLASS REDEFI NITION ............................ 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 Prim ary 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

LI ST 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

LI ST 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

............ 10-1210-1 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 ( T SLs ). Results described include (1) national energy consumption and savings, (2) monetary value of national energ y savings associated with each

T SL , (3) change in total installed costs nationwide due to T SLs , and (4) the NPV of consumer benefits (which takes into accoun t the value of operating cost savings and change in total

installed costs from a potential standard).

DOE de termined bot h the N ES a nd the N PV f or all the T SLs considered for CFLKs

. A ll

calculations were performed us ing a MicrosoftE xcel spr eadsheet model. The s preadsheet m odel,

ref er r ed to a s th e n ational impact analysis (NI A ) model, in corporates the c alculations o f N ES

a nd NPV, along with inputs from the shipments model described in chapter 9 of th is notice of

p r o posed rulemaking ( N OPR ) te chnical support document (TSD). D etails an d instructions for using the NIA model are provided in appendix 10A of this NOPR TSD

.

C hapter 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 REDEFI NITION

As described in chapter 3 of t his NOPR T SD, the c urr ent e nergy conservation standards

divide CFLKs into three product classes depending on the type of light socketu sed: t h e Medium -S crew B ase prod u ctc lass , t h e P in -B ased

F l uo rescentp roductcl ass , and the O th er B ase Type product class. I n each case, t he standards requir e that the CFLKs be packaged w ith l amps

to f ill all so ckets. However, as descri bed i n chapter 3 of this NOPR T SD, DOE is proposing redefining C FLKs i nto a single product cl ass

.

As required by l aw ( 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 a ny 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 ba seline 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 f

or C FLKs. Becau se ther e is a single productcl ass for a ll CFLKs , t h ese ef ficacy levels correspond to the TSLs analyzed in the shipments and n ational i mpact

an alyses

.
10.4 NATIONAL ENERGY SAVI NGS

This section describes DOE’s calculation of t he NES f or CFLKs for t he T SLs c onsidered

in thi s NOPR

a nalysis. In al l c ases, e nergy savi ngs are calculated w ith respectt o the no- standard

case. A s described in ch apter 9 of t his N OPR T SD, the s hipments model pr ojects a dynamically changing market in both t he s tandards c a ses and the no-standard case, w hich acco unt s for a n economically driven s hift towardm ore e fficient solid-state l ighting t hroughout t he a nalysis period. Additionally, the shipments a nalysis accounts f or t he impact of t he e xpecte d triggering of

the EISA 2007 backstop requirement, which would impact candelabra -based lamps above 310

lumens. A s described

i n chapter

9 of this NOPR TSD

, D OE assu med a 45 l umen/W minimum

ef ficacy r equ irement for

can d elabra -based g en eral s ervice 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 f or the in itially -packaged lamps) in both the nostandard an d st andards cases thereafter

.

10.4.1 Primary Energy Savings

The annual N ES quantifies the difference in energy use between the no- standard cas e and a higher T SL in a given year:

NNNN SS yy = � AA CC
nnnn ssssss

, yy − AA CC ssssss

, yy � × RRRR
Eq. 10.1

Where:

NNNN SS yy = national energy savings in year yy (quads),

AA CC
nnnn ssssss

, yy = annual national energy consumption in the no- standard case at the power plant

for all affected stock in year yy (quads),

AA CC ssssss

, yy = standards case annual national energy consumption a t the power plant for all

affected stock in year yy (quads), and

RRRR = rebo und rate factor = 1 - rebound rate.

Posit ive values of NNNN corr espond 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 s avings is the sum over the analysis period of each NNNN SS yy for all affected stock. The analysis tracks t he 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 NNNN SS
cccccc = � NNNN SS yy

yy Eq. 10.2

Where:

NNNN SS
cccccc = cumulative national energy savings (quads).

DOE cal culated the AA of t he affected stock as follows:

AA CC yy = � � SSSSSS KK
vv × UU CC
vv

vv �

× SSPP yy

Eq. 10.3

Where:

SSSSSS KK
vv = affected stock of vintage vv (number of units),

UU CC
vv = unit energy consumption of vintage vv , given by Eq. 10.6 (kW h/unit), and

SSPP yy = 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, andtransmission 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

.

A s shown in Eq. 10.4 , the FFC national energy savings NNNN SS FFFFFF are obtained by multiplying the primary energy savings ( Eq. 10.1 ) by the FFC multiplier computed for the same

year, μμ yy .10-4 NNNN SS FFFFFF

, yy = � AA CC
nnnn ssssss

, yy − AA CC ssssss

, yy � × RRRR × μμ yy Eq. 10.4

Where:

μμ yy = the FFC multiplier in year yy , and

a ll other variables as defined in Eq. 10 .1

.

T he cumulative FFC energy savings is calculated by summing the annual FFC energy saving from 2019-2077.

NNNN SS FFFFFF

, cccccc = � NNNN SS FFFFFF

, 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:

• s hipments 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).

T hese 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

level s for different scenarios, for each year of the 30- year (2019 -2048) s hipments 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 valu es 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 CC pp

, ss ( yy ) = IIIIIIIIII PPPPPPPPrr pp ( yy ) × HH UU
ss × (1 − ff dd dddd × ff eeee )

Eq

. 10.6

W here:

UU CC pp

, ss ( yy ) = annual energy consumption of product in yea r yy in sector ss (kWh/year),

HH
ss = operating hours (hours of use) in sector ss , and IIIIIIIIII PPPPPP PPrr pp ( yy ) = input power of product in yea r 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

T he ave rage 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 outpu t . 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 e lectricity use, the c onversion from s ite kW h t o power pl ant pr imary million Btu uses a marginal he atr ate factor t hatacco unts for l osses associated

w ith t he generation, t ransmission,

and distribution of e lectricity. DOE de rive d these marginal f actors us ing data publ ishe d with the

En ergy In formation A dministration (EIA’s) Annual Energy Outlook 2014 ( AEO 2014 ), 3

f o llowing th e m eth odology 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 electricity10-6

represent s the energy ne eded t o pr oduce and de liver

t he fuels that ar e consumed i n el ectricity generation. The m

ultipliers are dimensionless numbers that express the upstream ene rgy us e as a percentage of

t he primary ene rgy us e. As w ith the N ES, D OE calculated cumulative, n ational level energy s avings i n the f ull- f uel

- cycle metric by cal culating t he difference relat ive t o the no

standard case and summing over the analysis period. T he method u sed t o calculate FFC en ergy multipliers and the values for various fuels and end use s are described i n a ppendix 1 0 B 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 fo r 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 an d the present value of the change in total installed costs (PVC) o f those appliances. The NPV is described by the following equation:

NN NN = PP − PP Eq. 10.7

Where:

PP = presen t value of savings in ope rating cost (2014 $) for all CFLKs shipped during the analysis period, and

PP = p resent 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 t his definition, reductions in o perating costs would contribute positively to the

NN NN , wh ile increases in total installed costs would contribute negatively to the NN NN

.

DOE de termined the PP and PP acc ording to the following expressions:

PP = � SS yy × DD yy

yy

a S ee appendix 10C: Rebound Rate of the GSFL/IRL energy conservation rulemaking.10-7 Eq. 10.8

PP = � II yy × DD yy

yy Eq. 10.9

Where:

SS

yy = the total operating cost savings from the affected stock in year yy , relative to the nostandard case (201 4$),

II yy = total equipment cost increase (negative values indicate a reduction in equipment
costs) for shipments in year yy , relative to the no- standard case (201 4$), and

DD yy = discount factor in each year (defined in section 10.4.3.5 ).

DOE cal culated PP fro m 2019 to 2048 (the duration of the shipments analysis period)

and PP fro m 2019 to 2077 (to account for the full lifetime of all affected stock ). The

contributions to PP and PP for e ach 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 input s to DOE’s calculation of the NN NN of cos ts and savings for the nation are:

• total annual installed cost ( ) in the no-standard case and the standards cases,

• tota l annual operating cost ( ) for the affected stock in the no-standard case and the standards cases, and

• disc ount factor ( DD ).

The com putation of these inputs from the outputs of upstream analyses is deta iled in the following sections.

10.5.2.1 Total Annual Change in Installed Cost

DOE cal culated 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:

II yy = CC ssssss

, yy − CC
nnnn ssssss

, yy Eq. 10.10

Where:

CC

ssssss

, yy = the total installed cost of equipment shipped in ye ar yy in the standards case (2014$), and

CC

nnnn ssssss

, yy = the total installed cost of equipment shipped in year yy in the no- standard case (2014$).10-8 Due t o the higher cost s 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

calcul ation

. 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, includin g corrections for learning (2014$/unit).

The CFLK shipments and purchase price s 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 t he standards case s and the no-standard case. As for the computation of the NNNN , the

needs t o be calculated for the affected stock:

SS

yy = CC
nnnn ssssss

, yy − CC ssssss

, yy Eq. 10.12

Where:

SS

yy = the total operating cost saving of the affected stock in year yy (2014$),

CC

bbbb

, yy = the total operating cost of the affected stock in year yy in the no-standard case (2014$), and

CC

ssssss

, 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 , D OE 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 the10-9 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 lamp s 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 ran ge of lifetime values

between 1 to 30 years. DOE then computed the weighted

- average residual value using the retirement probability function as weights f or each age. DOE then attributed the weighted

average 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 calculatedtotal 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

sa mple . 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 electricit y price projections from A EO 2015 4 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 wer e 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 calcu lated annual for CFLKs by summing over the ope rating costs of all

affected stock in the residential and commerci al sectors , according to the following expression:

CC

yy = EE

, yy × � SS

, yy × UU CC

, yy + � RR LL

, yy −� RR

, yy

Eq. 10.13

Where:

CC yy = 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 AEO201 5 reference scenario (201 4$/kWh),

SS

, yy = the affected stock of CFLKs in year yy (number of units),

UU CC

, yy = the averag e unit energy consumption of a CFLK of type ii in year yy , as computed using Eq. 10.6 (kWh/unit),

RR LL

, yy = th e average re lamping replacement costs for CFLK type ii in year yy (2014$), and

RR ss

, yy = the residual value of lamps remaining in retired CFLK s 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 eff ect 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−yy pp �

Eq. 10.14

Where:

rr = discount rate,

yy = year of the monetary value, and

yy

pp = 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 Me asuring Benefits and Costs

. 6 DOE defined the present year as 201 5.

10.6 RESULTS

This section presents the results of the NIA for CFLKs for the r eference 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 associated10-11

with le arning ra te f

or solid -s tate lighting te chnologies. R esults f or th e s ensitivity s cenarios

a re presented

i n appendix 1 0C of th is NOPR TSD an d ar e available in the published NI A

sp readsheet.

10.6.1 N ational Energy Savings Results

Table 10.6.1 shows the cumulative full

- fuel cycle NES for the reference case which

assumes no ceiling fan standard.

Tab le 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

Fig ure 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 0.02520192022202520282031203420372040204320462049205220552058206120642067207020732076FFC Energy Savings (quads)

Year TSL 1

TSL 2

TSL 3

TSL 410-12 10.6.2 Net Present Value Results

Table 10.6.2 gives the result s 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.

Ta ble 10.6.2 Cumulative Net Present Value of Consumer Benefits for CFLKs at 3% and

7% Discount Rate s

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

Fi gure 10.6.2 Total NPV at 7% Discount for CFLKs in the Reference Scenario

T able 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

0.220192022202520282031203420372040204320462049205220552058206120642067207020732076NPV @ 7% (billion 2014$)

Year TSL 1

TSL 2

TSL 3

TSL 410-13 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 0.68610-14 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

TAB LE 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

LI ST 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 ............................. 11-611-1 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. Departmentof Energy (DOE) evaluates impacts on particular subgroups of consumers by analyzing the lifecycle cost (LCC) impacts and payback period (PBP) for tho se consumers from the proposed efficacy levels.

DOE de termined

t he impacton ceiling f

an light kit (CFLK) consumer s ubgroups ba sed

o n a subset of t he CFLK

co nsumer

sam ples from t he residential an d commercial sect ors and

the methodology discussed in chapter 8 of this notice of proposed rulemaking ( NOPR ) technical support document ( TSD )

. DOE ev aluated the impact s of t he proposed ef f icacy levels

f or CF LKs o n l ow -income households and small businesses for the r esidential a nd co mmercial

s ector, r espectively. This chapter d escribes the subgroup identification in f urthe r d etail an d

g ives the results of

t he LCC an d

P BP a nalyses for t he 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) survey 1 , 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 Census 2 . The RECS survey classifies approximately 15 percent of U.S. households as low- income.

11.2.1.1 D istribution 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 f or CFLK s.

Ta ble 11.2.1 Household Population Data for CFLKs

No. of Records

General Population 8,762

Low

- Income Households 96011-2 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 CFLK s 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. Howev er, for small companies, particularly very small companies, historic returns have been significantly higher than the CAPM equation predicts. T his additional return

can be accounted for by adding a size premium to the cost of equity for small firms:



## ( ) S ERP Rk

fe



### +×+= β

Where:

k

e = Cost of equity,

R

f = 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

Y ear M ax imum Market Capitalization ($mil) S ize Premium ( “M icrocap ” 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%

2013 b 514 3.81%

D OE 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 distri butions described in chapter 8 of this NOPR TSD

.

a “Microcap” companies are defined as companies with market capitalization in the 9 th 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 c ost 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 St andard

Dev iation 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 subgro ups, respectively. Table 11.3.2 and Table 11.3.4 compare results from the samp le 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

Sub

Baseline 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

Sub

Baseline 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 13.811-6 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 0.111-7 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

TAB LE OF CONTENTS

12.1 INTRODUCTION ......................................................................................................... 12-1

12.2 METHODOLOGY ........................................................................................................ 12-1

12.2.1 Pha se I: Industry Profile ..................................................................................... 12-2

12.2.2 Phase II: Industry Cash -Flow 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 ........................... 12-2112

- 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 Regul ations 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 La mp 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 Fl ows 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

- 1912

- 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 ( CFLK s) and assessed the impact of such standards on direct employm ent 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 info rmation 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 com paring ch anges in INPV between the no

standards 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 impac t of standards on subgroups of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of preparing an in dustry characterization for the CFLK industr y , including data on market share,

sales volumes and tre nds, pricing, employment, and financial structure. In Phase II, “In dustry Cash Flow,” DOE used a GRIM to assess the impacts of amended energy conservation standards

on CFLK s

.

In Phase II, DOE created a GRIM for CFLK s , as well as an interview guide for CF LK

manufacturer s , 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. S ection

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 focus es , 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 r elevant 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 M IA, DOE prepared a profile of the CFLK industry that built upon the market and technology assessment prepared for this rulemaking (see c hapter 3 of this notice of proposed rulemaking [ NOPR ] t e chnical s upport d ocument [ TSD ] ). Before initia ting the detailed impact study , DOE collected information on the present and past structure an d market

characteristics of the industry. This information included market share data, unit shipments, manufacturer markups, and cost structure s for various manufacturers. The indu stry 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) exp enses; cost of goods sold, etc.; and (4) trends in the number of firms, specific lighting market s , and general product characteristics. The industry profile included a top

- down cost analysis of CFLK manufacturers that DOE used to derive preliminary financi al 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 Commissio n (SEC) 10

– K reports, 1 Standard & Poor’s (S&P) stock reports, 2 and corporate annual reports. DOE s upplemented 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 CFLK s . More

- stringent energy conservation standards can affect

manufacturer cas h 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 cash

flow analysis for CFLK s . In performing these analy ses, 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 guid es for manufacturer interview s.

12.2.2.1 Industry Cash

- Flow Analysis

The GRIM use s 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 an nual 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 estimate s of typical manufacturer markups from public financial reports and12

- 3 interviews with man ufacturers. DOE developed an alternative markup scenario for the GRIM

based on discussions with manufacturers. DOE’s sh ipments analysis, presented in c hapter 9 of

th is NOPR TSD, provided the basis for the shipment projections in the GRIM. DOE developed

t he financial parameters using publicly available man ufacturer data and information submitted confidentially during manufacturer interviews. The GRIM r esults are compared to the no

standards 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 I nterview Guides

During Phase III of t he 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 intervi ew 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

. I nformation 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 ) k ey issues

, ( 2 ) company overview and

or ganizational characteristics

, ( 3 ) markups and profitability

, ( 4 ) financial parameters

, ( 5 )

conversion costs

, ( 6 ) cumulative regulatory burden

, ( 7 ) direct employment impact assessme nt

, ( 8 ) manufacturing capacity and non

- U

. S

. sales

, ( 9 ) impact on competition, and ( 1 0 ) impacts on

small business es . The interview guides are presented in a ppendix 1 2 A of this NOPR TSD

.

12.2.3 Phase III: Subgroup Analysis

Using a verage 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 st ructure that differs largely from the industry average could experience larger negative impact s

. During interviews, DOE identified one potential manufacturer subgroup (small manufacturers) that could be disproportionately impacted by amended energy conserv ation standards. As a result, DOE will analyze small business manufacturers a s 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 consid ered in the rulemaking process.12

- 4 DO E 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 a n 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 questionna ire was acceptable, DOE sought interactive interviews, which help ed 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 P hase 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 bas ed 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 business es 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 Class ification 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 Classification s of Small Businesses Potentially Affected by This Rulemaking

Industry Description Revenue

Limit Employee Limit NAICS

Elec tric Small Appliance Manufacturing N/A 750 33 52 10

DOE used the American Lighting Association (ALA ) 3 member directory

, DOE’s Compliance Certification Management System (CCMS) , the SBA database, information from the previous r ulemakings

, individual company websites, and market research tools ( e.g. , Hoover s.com ) to create a list of companies that potentially sell CFLK s covered by this rulemaking. Additionally, DOE asked interested parties and industry representatives if they were

aware of other small business es in the industry . DOE screened out comp anies 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 av ailable on the SBA’s website at https://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf

.12

- 5 During its researc h, DOE identified 34 companies that sell CFLK s 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 CFLK s would not have a significant economic impact on a substantial number of small entities, DOE has analyzed small business es as a subgroup. S ection 12.6 present t he results of this subgroup analysis

.

12.2.3.4 Manufacturing Capacity Impact

One significant outcome of amended ene rgy conservation standards could be the obsolescence of existing manufacturing assets, including tooling and investment. The

ma nufacturer interview guide has a series of questions to help identify impacts of amended standards on manufacturing capacity

. The se include questions regarding capacity utilization and plant location decisions in the U nited S tates 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 cap ital changes and

stranded assets affect the cash

- flow estimates in the CFLK GRIM. These estimates can be found

in s ection 12.4.8 ; DOE’s discussion o f the capacity impact s can be found in s ection 12.6.3

.

12.2.3.5 Employment Impact

The impact of amended energy conservation standards on employment is an important

consid eration 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 empl oyment impacts are reported in s ection 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 product s . DOE analyzed the impact on manufacturers of mul tiple, product

- specific regulatory actions. Based on its own research and discussions with manufacturers, DOE identified regulations relevant to CFLK manufacturers, such as s tate and other f ederal regulations that affect other product s made by the

same man ufacturers. Discussion of the cumulative reg ulatory burden can be found in s ection

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 th is energy conservation standard rulemaking?” This question prompts manufacturers to identify the issues they believe DOE should explore and discuss further during th e 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 r egulation 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 b e 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. Manufact urers 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 m anufacturers 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 ASSU MPTIONS

The GRIM serves as the main tool for assessing the i mpacts on industry due to amended energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM.

DOE then feeds d ata and assumptions from these sources into an accounting model that calculates the industry cash flow both with a nd 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 a s 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, 201 5 , and continuing

b Information regarding this gen eral service lamp rulemaking, including links to notices and supporting material in the regulations.gov docket, may be found on DOE’s website at: http://www 1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=83

.12

- 7 to 204 8 . The model calculates the INPV by s umming 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 12 B of this N OPR 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 profil es, credit ratings, the shipments model, the engineering analysis, and manufacturer interviews.

12.4.2.1 Corporate Annual Reports

Corporate annual reports to t he 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 report s filed by publicly traded manufacturers that produce CFLK s , 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 estima tes 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 i nputs to the GRIM:

• Tax rate12

- 8 • Working capita l

• 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 d ebt 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 201 5 )

, a nd the number of additions to existing buildings due to expanding demand t hroughout 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 th is 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 analys e s establish the relationship

between end

- user price and effic acy level (EL) for all CFLK s 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 cha pters 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 in put 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.12

- 9 12.4.2.5 Manufacturer Interviews

D uring the course of the MIA, DOE conducted interviews with a representative cross

section 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 inpu t assumptions in for the CFLK industry. Key topics discussed during the interviews and reflected in the GRIM include:

• c apital 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 provid es financia l parameters for seven public companies engaged in manufacturing and selling CFLK s . The values listed are ave rages over a six

- year period (200 7 to

201 3 ).

Table 12.4

. 1 GRIM Fi nancial Parameters Based on 200 7

– 20 1 3 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 m arket 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 P remium

W here:

Riskless R ate of R eturn = the rate of return on a “safe” benchmark investment, typically

considered the short

- te rm Treasury b ill (T

- b ill) yield ; Risk P remium = the difference between the expected return on stocks and the riskless

rate ; and12

- 11 B eta (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 Industry

Weighted Average

% M anufacturer

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 ri sk

- 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 relevan t spread to the risk

- free rate.

In practice, investors use a variety of different maturity Treasury bonds to es timate the

risk

- free rate. DOE used the 10

- year Treasury bond return because it captures long

- term in flation 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

- y ear Treasury b ond return between 1928 and

201 3

.

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

- b ill 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 Ca lculation

Parameter Industry

Weighted 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

industr y WACC was approximately 9.0 percent. Subtracting an inflation rate of 3.1 percent
between 1928 and 201 3 , the in flation

- adjusted WACC

, which was the initial estimate of the

discount rate used in the stra w

- 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 CFLK s c onsistent with the engineering analysis

. DOE analyzed

one representative product class for all CFLK s

. Table 12.4.5 show s the ELs at each TSL for the

CFLK s analyzed by DOE.

Table 12.4

. 5 Trial Standard Levels for CFLK s

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 CFLK s 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

dis tribution 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 f an 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 demolitio ns 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 case s, 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 determi nation , 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 a t which manufacturers sell CFLKs to the consumer,

DOE calculated the distr ibutor 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 distrib utor 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 ove rhead necessary to produce CFLKs at each EL. DOE estimates that

the percentage s 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 du e to the

components u sed

, while the absolute do llar amount of materials, labor, depreciation and

overhead fluctuates proportiona lly with the MPC.12

- 14 Table 12.4

. 6 Manufacturer Production Cost Breakdown (201 4 $ ) 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 manufactur ers to incur conversion costs by bring ing 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 analysi s to calculate the capital and product conversion costs for CFL K 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 c onvert 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, manufacturers12

- 15 would incur product and capital conversion costs at ELs that require light

- emitting diode ( LED ) lamp s. Some CFLKs would need to be redesigned due to potential heat sink ing issues associated

with LEDs and the potentially larger size of LEDs

, as compared to the compact fluorescent lamps (CFLs) currently b eing used . Manufacturers would also need to purchase tooling equi pment 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 manufacture rs 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 CF L Ks by TSL

Conversion Costs TSL 1 (2014 $ millions) TSL 2 (201 4 $ millions) TSL 3 (201 4 $ millions) TSL 4 (201 4 $ 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 m odeled two standards

- case markup scenarios for CFLK s 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 m arkup 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 G ross M argin Markup Scenario

Under the preservation of gross margin

, or flat

, markup scenario DOE applied a sin gle uniform markup across all EL s for all CFLKs . As production costs increase with effic a cy, this scenario implies that the absolute dollar markup will increase as well. Based on publicly

available financial information for manufacturers of CFLK s and comme nts 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 f or 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 standard s , it represents the upper bound to industry profitability under amended standard s

.

12.4.9.2 Two

- Tiered Markup Scenario

DOE model ed the two

- tiered markup scenario because multiple manufacturers stated in interviews that they offer tier s of product lines that are differ entiated, in part, by EL . The high er efficacy tier s typically earn premiums (for the manufacturer) over the baseline efficacy tier

.12

- 16 Several manufacturers sugg e sted 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 tier s and the change in

p rofitability at each level. DOE used this information to estimate markups fo r 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, co mpression 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 fo r the CFLK Lamp Replacement Scenario

Efficacy

L evel Markups by Selecte d 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

L evel 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 I MPACTS

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 CFLK s . 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 e conomic 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 flows12

- 17 discounted at the industry’s cost of capital or discount rate. The CFLK GRIM estima te s cash

flows from 201 5 to 204 8 . This timeframe models both the short

- term impacts on the industry

from the announcement of the standard until the compliance date (201 5 until an estimated compliance date of 201 9 ) and a long

- term assessment over the 3 0

- year analysis period used in the NIA (201 9

– 204 8 ).

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 o f the economic impacts that implementing that particular TSL would have on the industry. For the CFLK industry , DOE examined the two m arkup 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 Manufactu rer Impact Analysis for Ceiling Fan Light Kits

– Preservation of Gross Margin Markup Scenario

Units No

Standards

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 Manufactu rer Impact Analysis for Ceiling Fan Light Kits

– Two

- Tiered Markup Scenario

Units No

Standards

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 2.612

- 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

- te rm 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

201 5 through 204 8 for the no

- standards case and differ ent TSLs in the standards case.

Annual cash flows are discounted to the base year, 201 5 . Between 201 5 and the 20 1 9 expected compliance date of the amended energy conservation standard s , cash flows are driven by the level of conversion costs an d 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

c onservation standard s . The more

- stringent the amended energy conservation standard s , 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 out flows for capital expenditures.

Two competing factors drive f ree cash flow in the year the amended energy conservation

standards potentially take effect. In addition to capital and product conversion costs, amended

energ y 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 exist ing tooling and equipment whose value is affected by the amended energy conservation standards. This one

time write

- down acts as a tax shield that alleviates decreases in cash flow from operations in the year of the write

- down. In this year, there is also an increase in working capital that reduces cash flow from operations. A large increase in working capital is needed due to more costly production components and materials, higher inventories of more expensive products, and higher

accounts receivable for m ore 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 standard s , the impact on cash flow depends on the operating revenue. In the preservation of gross margin markup scenario , the

manufacture r markup i s 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 t he standard s 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 productio n 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 standard s take

e ffect will outweigh the initial conversion costs.12-19 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

efficac y products as these products become the baseline, higher volume product.

Figure 12.5.1 A nnual I ndustry Fr ee C ash Flo ws f or CFs – P reservation o f G ross Margin Markup S cenario

Figure 12.5.2 A nnual I ndustry Fr ee C ash Flows f or CFs – T wo -T iered Markup Scenario1 2

- 20 12.6 IMPACTS ON MANUFACTU RER SUBGROUPS

As described in S ection 12.2.3 , DOE identified one subgroup of CFLK manufacturers:

small business es

. S ection 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 S BA has set a size threshold, which defines those entities classified as “small businesses” for the purposes of the statut e. 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 12 1. The size standards are listed by N AICS

code and ind ustry 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., ALA 3 ), 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 manuf acture 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 CFLK s, DOE initially identified 67 potential companies that sell CFLK s in the

United States . However, DOE only identified one manufacturer that also manufacturers the lamps sold with their CFLKs. All othe r CFLK manufacturers source the lamps packaged with

their CFLKs. After reviewing publicly available inf ormation on these potential CFLK businesses, DOE determined that 40 were either large businesses or businesses that were completely foreign owned an d ope rated. DOE determined that the remaining 27 companies were

small businesses that either manufacture or sell covered CFLK s in the United States. The one CFLK manufacturer that also sells lamps that DOE identified is also a small business. Based on

manufactu rer 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

CFL K market, while all other small businesses account for 1 percent or less of the CFLK market individually

.12

- 21 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 standard s would be consistent with the overall CFLK indus try 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 ma jority 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 rang e 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 ide ntified 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 a re 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 more

efficacious lamps packaged with CFLKs that comply with the pr oposed 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 a nd 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 mo re

- 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 e xpects CFLK businesses that choose to meet amended CFLK standards by redesigning CFLK s 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 busin esses, 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 compe te after the adoption of standards, as the proposed standards will not significantly disrupt most CFLK manufacturers’ supply chain. DOE does not expect that small12

- 22 businesses would be disadvantaged compared to large businesses if they chose to redesign thei r 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 effi cacy 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 more

efficacious lamps with CFLKs in 2020

.

For the reason s 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 im pacts 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 CFL K manufacturer with domestic production of CFLKs, and this manufacturer’s sales of ceiling fans packaged with CFLKs represents a very small po rtion of their overall revenue. During manufacturer interviews, manufacturers stated that the vast majority of man ufacturing 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 c onservation standards on CFLKs.

12.6.3 Production Capacity

CFLK manufacturers stated that they did not anticipate manufacturing capacity

constraints as a result of a mende d energy conservatio n standard. If manufacturers choose to redesign their CFLKs to comply wi th amended standards, the original equipment manufacturers of CFLKs would be able to make the changes necessary to comply with standards in the estimated three year s 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 to day. 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

combi ned 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 a12

- 23 single regulation may overlook the cumulative regulatory burden faced by manufa cturers. For this cumulative regulatory burden analysis, DOE examines other significant product

- specific regulation s 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

conserva tion standards for CFLK s. DOE also describes additional s tate and f ederal regulations

with which manufacture r s of CFLK s 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 b y this rulemaking also typically

manuf acture 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 unacceptabl e level of cumulative regulatory burden.

12.6.4.1 DOE Regulations for Other P roducts Produced by CFLK

Manufacturers

In addition to the proposed amended energy conserva tion standards on CFLKs, seve ral other existing and pending f ederal regulations may apply to other p roducts 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 la mp 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 m anufacturer can strain manufacturers’ profit and possibly cause them to exit particular markets

. Table 12.6.1 lists the other DOE energy conservation standards

tha t could also affect CFLK manufacturers in the three years leading up to and after the

estimated compliance date of amended energy conserva tion standards for CFLKs

.12

- 24 Table 12.6

. 1 Other DOE Regulations Pot entially 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, 201 5 (Public Law 11 3

- 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 statutorily

prescribed standards.

12.6.4.2 Other S tate 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 prelimin ary 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 min imum 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 c ompliance 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 la mp fixture final rule can be found at http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/16

. d Estimated industry conversion e xpenses 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 tha t all lamps packaged with CFLKs must m eet the ENERGY STAR Program e 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 Clea ringhouse ( IMERC ) supports legislation that lowers allowable mercury levels in certain products and seeks to eliminate certain produc ts in participating states. CFLK manufacturers stated in manufacturer interviews that IMERC’s labeling requirements have in creased labeling costs for CFLK s packaged with CFLs

. In participating states, IMERC requires that packaging containing CFLs must indicate that the product contains mercury. IMERC member state s include California, Connecticut, Illinois, Louisiana, Maine, Ma ssachusetts, 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 mo st plausible impacts on manufacturers, there potentially could be circumstances that cause manufacturers to experience impacts outside of this range.

12.7.1 C eiling F an L ight K it Impacts

TSL 1 sets the efficacy level at EL 1 for all CFLKs . At TSL 1, DOE estimat es 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 s ame as the no

- standards case value in 2018, the year leading up to the standard.

Percentage impacts o n INPV are slightly positive at TSL 1. DOE anticipate s that most
manufacturers would not lose any of their INPV at t his TSL. DOE estimates that 100 perce nt 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 no

standards 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 IN PV 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 ava ilable 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 p roduct conve rsion costs, primarily drive n 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 LED s

.

At TSL 2, under the preservation of gross margin markup scenario, the shipment

weighted 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 ar e 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 negati ve to slightly negative at

TSL 3. TSL 3 proposes the first EL that would potentially require manufacturers to use LED12

- 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 manufa cturers 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 e stimated 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 shipment

weighted average MPC in creases 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 scenar io, 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 esti mated 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 conversi on 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 shipment

weighted average MPC increase s 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, th e 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 REFERENCES

1 Securities and Exchange Commission

. Annual 10

- K Reports, Various Years

. Washington

, DC. < www.sec.gov >.

2 Standard and Poors Financial Services LLC

. Company Credit Ratings, Various Companies

. New York, NY. < http://www.standardandpoors.com >.

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 IM PACTS ............................... 13-1

13.3 POWER SECTOR AND SIT E EMISSIONS FACTORS ............................................. 13-3

13.4 UPSTREAM EMISSIONS F ACTORS ......................................................................... 13-4

13.5 EMISSIONS IMPACT RES ULTS ................................................................................ 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 To tal 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 ......................... 13-1013-1 CHAPTER 1 3. E MISSION S I MPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. T he first component estimates the effect of potential energy conservation standards on power sector and site (where applicable) combustion emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ) and

merc ury (Hg). The second component estimates the impacts of potential standard s on emissions

of tw o additional greenhouse gases, methane (CH 4 ) and nitrous oxide (N 2 O), as well as the

reductions to emissions of all species due to “upstream” activities in the fuel production chain.

T hese upst ream activities comprise extraction, processing, and transporting fuels to the site of

combustion. T he associated emissions are referred to as upstream emissions. T ogether, these

emissions account for the full- fuel

- cycle (FFC) , i n 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 CH 4 and N 2 O are estimated using emissions intensity factors published by the EPA , GHG Emissions Factors Hub. a The FFC upstream emissions a re estimated based on the methodology developed by Coughlin (2013). 3 The upstream emissions include both emissions from fuel combustion during extraction, processing and transportation of fuel, and “fugitive” emissions (direct leakage to the atmosphere) of CH 4 and CO 2

.

The emissions intensity factors are expressed in t erms of phy sical uni ts per MW h or MMBtu of site energy savings. Total emissions reductions ar e estimated u sing t he energ y savings calculated in the national impact analysis (chapter 10 of this notice of proposed rulemaking [NOPR] technical support document [TSD] ).

13.2 AI R QUALITY REGULATIONS AND EMISSIONS IM PACTS

Each annual version of the AEO incorporates the projected impacts of existing air quality regulations on emissions

. A EO 2014 generally represents current Federal and State legislation and final implementation regulations in place as of the end of October 2013.

SO 2 emissions from affected electric generating units (EGUs) are subject to nationwide and regional emissions cap and trading programs. T itle IV of the Clean Air Act sets an annual

a http://www.epa.gov/climateleadership/inventory/ghg

- emissions.html

.13-2 emissions cap on SO 2 for affected EGUs in the 48 contiguous states and the District of Columbia (D .C.). SO 2 emissions from 28 eastern states and D.C. were also limited under the Clean Air Interstate Rule (CAIR ) , which created an 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). C AIR 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 48 208 (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.

B ecause AEO 2014 wa s 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. H owever, the difference between

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

T he attainment of emissions caps is typically flexible among affected Electric Generating Units (EGUs) and is enforced through the use of emissions allowances and tradable permits

. Under existing EPA regulations, any excess SO 2 emissions allowances resulting from the lower electricity demand caused by the imposition of an efficiency standard could be used to permit offsetting increases in SO 2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions covered by the existing cap-and-trade system, but it concluded that no reductions in power sector

emissions would occur for SO 2 as a result of standards.

B eginning in 2016, however, SO 2 emissions will fall as a result of the Mercury and Air

Tox ics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air pollutants (HAP), and also established a standard for SO 2 (a non- HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. T he same controls are used to reduce HAP and

non- HAP acid gas; thus, SO 2 emissions will be reduced as a result of the control technologies

installed on coal -fired power plants to comply with the MATS requirements for acid gas

. A EO

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 SO 2 emissions . Under the MATS, e missions 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 SO 2 emissions allowances resulting from the lower electricity demand would be needed or used to permit offsetting

i ncreases in SO 2 emissions by any regulated EGU. T herefore, DOE believes that efficiency

standards will reduce SO 2 emissions in 2016 and beyond.

CAIR established a cap on NO

x emissions in 28 eastern States and the District of Columbia. Energy conservation standards are expected to have little effect on NO

x emissions in those States covered by CSAPR because excess NO

x emissions allowances resulting from the lower electricity demand could be used to permit offsetting increases in NO

x emissions

. However, standards would be expected to reduce NO

x 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 c aps 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 ana lysis 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

. D etails on the approach use d may be found in Coughlin (2014 ). 2

Table 13.3.1 presents the average power plant emissions factors for selected years . The se power plant emissions factors are derived from the emissions factors of the plant 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

CO 2 kg/MWh 723 642 579 529 483

SO 2 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

N 2 O g/MWh 7.2 7.1 6.9 6.6 6.4

CH 4 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

CO 2 kg/mcf 54.2 54.2 54.2 54.2 54.2

SO 2 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

N 2 O g/ mcf 0.102 0.102 0.102 0.102 0.102

CH 4 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

CO 2 kg/bbl 446 446 446 446 446

SO 2 g/bbl 220 220 220 220 220

NOx g/bbl 11,530 11,530 11,530 11,530 11,530

N 2 O g/bbl 8.6 8.6 8.6 8.6 8.6

CH 4 g/bbl 13.3 13.3 13.3 13.3 13.3

* Refers to site fuel oil savings.

13.4 UPSTREAM EMISSIONS F ACTORS

The upstream emissions acco unting uses th e same approach as the u pstream 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 u se for upstream activities leads to f urther r ed u ctions

in em issions . Th ese upstream emissions are defined to include the combustion emissions from

the fuel used upstream, the fugitive emissio n s associated w ith t he fuel u sed

upstream, an d t he

f ugitiv e emissions associated with the fuel used on site.

Fugitive emissions of CO 2 occur during oil and gas production, but are small relative to combustion emissions. They comprise about 2.5 percent of total CO 2 emissions for natural gas and 1.7 percent for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal production. Combustion emissions of CH 4 are very small, while fugitive emissions (particularly for gas production) may be relatively large . Hence, fugitive emissions make up over 99 percent
of total methane emis sions for natural gas, about 95 percent for c oal, and 93 percent for petroleum fuels.

U pstream emissions factors account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primary fuels. F ugitive emissions factors for methane from coal mining and natur al gas production were estimated based on a review of recent studies compiled by Burnham (2011). 4 This review includes estimates of the difference between13-5 fugitive emissions factors for conventional production of natural vs. unconven tional (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 fac tors.

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 . T he caps that apply to power sector NO X emissions do not apply to upstream combustion sources.

Table 13.4.1 Electricity Upstream Emissions Factors

Unit 2020 2025 2030 2035 2040

CO 2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO 2 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

N 2 O g/MWh 0.25 0.25 0.24 0.23 0.23

CH 4 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

. T hese 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

CO 2 kg/ mcf 7.1 7.2 7.3 7.4 7.4

SO 2 g/ mcf 0.030 0.031 0.031 0.032 0.032

NOx g/ mcf 101 103 105 105 105

N 2 O g/ mcf 0.011 0.011 0.012 0.012 0.012

CH 4 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

CO 2 kg/bbl 70.8 70.3 69.9 68.9 68.3

SO 2 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

N 2 O g/bbl 0.598 0.579 0.574 0.569 0.568

CH 4 g/bbl 897 905 902 888 877

13.5 EMISSIONS IMPACT RES ULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of products sold in 2019-2048 for each TSL . N egative 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

CO 2 (million metric tons) 0.654 3.213 4.398 4.488

SO 2 (thousand tons) 0.953 3.458 4.583 4.664

NO X (thousand tons) 0.665 2.789 3.759 3.831 Hg (tons) 0.003 0.011 0.014 0.014

N 2 O (thousand tons) 0.035 0.251 0.354 0.362

CH 4 (thousand tons) 0.005 0.036 0.051 0.052
Upstream Emissions

CO 2 (million metric tons) 0.016 0.134 0.192 0.196

SO 2 (thousand tons) 0.004 0.025 0.036 0.037

NO X (thousand tons) 0.213 1.876 2.690 2.756 Hg (tons) 0.000 0.000 0.000 0.000

N 2 O (thousand tons) 1.25 10.95 15.69 16.07

CH 4 (thousand tons) 0.000 0.001 0.002 0.002

Total Emissions

CO 2 (million metric tons) 0.670 3.346 4.590 4.684

SO 2 (thousand tons) 0.956 3.483 4.619 4.701

NO X (thousand tons) 0.878 4.665 6.449 6.586 Hg (tons) 0.003 0.011 0.014 0.014

N 2 O (thousand tons) 1.281 11.197 16.045 16.435

CH 4 (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 e ach TSL . T he reductions reflect the lifetime impacts of products

sold in 2019-2048.13-8 Fi gure 13.5.1 Ceiling Fan Light Kits : CO2 Total Emissions Reduction

Fi gure 13.5.2 Ceiling Fan Light Kits : SO2 Total Emissions Reduction13-9 Fi gure 13.5.3 Ceiling Fan Light Kits : NOx Total Emissions Reduction

Fi gure 13.5.4 Ceiling Fan Light Kits: Hg Total Emissions Reduction13-10 Fi gure 13.5.5 Ceiling Fan Light Kits : N2O Total Emissions Reduction

Fi gure 13.5.6 Ceiling Fan Light Kits : CH4 Total Emissions Reduction13-11 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 BENE FITS

TABLE OF CONTENTS

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MO NETIZING CARBON DIOX IDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon ........................................................................................ 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ............................................................. 14-2

14.2.3 Curr ent 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 ........................................................... 14-814-1

CHAPTER

1 4. MONE TIZ ATION O F E MISSIONS R EDUCTION 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 (CO 2 ) and nitrogen oxides (NO X ) that are expected to result from each of the TSLs considered

. T his chapter summarizes the basis for the monetary values used for each of these emissions and presents the benefits estimates considered.

14.2 M ONETIZING 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 . E stimates o f 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.

U nder 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 CO 2 emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions . T he 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 p art 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 assumption s 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

Council 1 points out that any assessment will suffer from uncertainty, speculation, and lack of information about (1) future emissio ns 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 . A s 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.

D espite the serious limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. M ost 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. T he 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 . T his 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.

I n 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 CO 2 emissions. The interagency group did not undertake any original analysis . I nstead, 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

p reliminary assessment by the interagency group was a set of five interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per ton of CO 2

. 2 These interim values represented the first sustained interagency effort within the U.S. government to develop

an SCC for use in regulatory analysis. The results of this preliminary effort were presented in several proposed and final rules.

14.2.3 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 . T he 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 Th e models are described in app endi x 14A of th is NOPR TSD.14-3 Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC values that were developed.

E ach 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. A n extensive review of the literature was conducted to select three sets of input parameters for these models: (1) climate sensitiv ity; (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 analys es. 2 ( The 2010 report is reproduced in appendix 14A of this notice of proposed rulemaking [NOPR] technical support document [TSD] .) Th ree values are based o n t he aver age SCC f

rom three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The fourth value, w h ich

r epresents the 9 5 th p ercentil e SCC est imate acr o ss all t hree models at a 3 p er centd iscoun t rate,
is in cluded

to re present higher -t han -e x pected i mpacts f rom t emperatu r e change further o ut in th e tails o f th e S CC d istribution . Th e values grow i n r eal terms over t ime, as depicted

i n 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, b although

p reference i s g iven to co nsideration of the global benefits of reducing CO 2 emissions.

The S CC va lues us ed for t hi s N OPR w ere generated

us ing the mostr ecent

ve rsions of the three integrated assessment m odels t hat ha ve be en publishe d in the pe er- reviewed l iter ature

, as described i n the 2013 update f rom t he i nteragency working group (revised November 2013)

. 3
Table 14.2.2 shows the updated se t s of S CC est imates in f

ive year in crements from 2 0 10 t o 2050. Appendix 14B of this NOPR TSD provides the full seto f S CC est imates, as well as the

2 013 r eportf

rom t he interagency group. T he central v alu e thatem erg es i s th e averag e SCC

acr oss models at t he 3 percent discoun t r ate . However, for purposes of capturing the uncertainties involved in regulatory impact analysis, the interagency group emphasizes the importance of including all f our set s of S CC v alues.

b It is recognize d that this calculati on for domestic values is approximate, p rovisional, an d highly speculative.

There i s no a priori reaso n 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 Ra t e %

5 3 2.5 3

Average Average Average 95 th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007 dollars per metric ton CO2)

Year Discount Ra te %

5 3 2.5 3

Average Average Average 95 th 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 . T he 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

thes e 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 CO 2 emissions, DOE used the values from the 2013 interagency report, which is reprinted in appendix

14B of this NOPR TSD, escalated to 201 4$ 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.

D OE multiplied the CO 2 emissions reduction estimated for each year by the SCC value for that year in each of the four cases. T o 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 NO X emissions in tho se 22 States that are not affected by cap s . DOE

considered the p o tential monetary benefit of reduced NO X emissions from the TSLs it

considered . DO E estimated the monetized value o f NOX emissions r eductions resulting

from each of th e TSLs consid ered using sector-based benefit per ton estimates from EPA’s Office of Air Quality Plannin g and Standard s Environmental B enefits Mappin g and Analysis Progr am (BenMAP).4 Th e 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, an d 2030.5 The values in the tab le are based on the mortality r isk estimate that is consider ed more conservative by EPA. Th ey refer to emissions from Electricity Gen erating 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. T o calculate present value of the total monetary sum from reduced NO X 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 SO 2 and Hg

emissions . The interagency group is investigating appropriate values to use to monetize avoided

CH 4 emissions. D OE did not monetize these emissions for the current analysis

.14-6 14.3.1 Results

Table 14.3.2 p resent s the global values of CO2 emissions reductions for each considered

TSL . D OE 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, 95 th
percentile

millio n 2 01 4$

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 a re $12. 2 , $4 1.2 , $6 3.4 , and $1 21 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,

95 th percentile

millio n 2 01 4$

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 ar e $12. 2 , $4 1.2 , $6 3 .4, and $1 21 per metric ton ( 2014 $).

Table 14.3.4 presents the present value of cumulative NO X emissions reductions for each TSL, calculated using seven

- percent a nd 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

millio n 2 01 4$

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 20.4214-9 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. In teragency 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/infore g/technical-update- social

cost -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://w ww2.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- based

pm25 -benefit-ton- estimates

.15

- 1 CHAPTER 15. UTILITY IMPACT ANALY SIS

TABLE OF CONTENTS

15.1 INTRODUCTION

................................ ................................ ................................ ......... 15

- 1

15.2 METHODOLOGY

................................ ................................ ................................ ........ 15

- 2

15.3 UTILITY IMPACT RESUL TS

................................ ................................ ..................... 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 Cy cle 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 Gen eration 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 201 4 (AEO 201 4 ) R eference 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 t he 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 estimate s the marginal impacts of re duction in energy demand on the energy supply sector. In principle, marginal value s 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, re fer to the U.S. Department of Energy, Energy Information Administration documentation. A useful summary is National Energy Modeling System: An Overview

. 115

- 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 inter

related 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 u se. 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 cha nges in total power sector emissions of SO 2 , NO
x , Hg

and CO 2

.

DO E ’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 s ide case are documented in Appendix E of the AEO . The side cases, or scenarios, that incorporate significant changes to equipment efficiencies relative to the R eference

case are:

• 2013 Technology (leaves all technologies at 2013 efficiencies) ; • Best Av ailable 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 p olicies 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 gener ation, 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 R eference case.

2. The changes in electricity use on the demand

- side data are a llocated 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 , l oad 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 p eriod.

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, renewabl es, nuclear and coal. The

allocation is based on the following rules:

• A ll petroleum

- based generation is allocated to peak periods;

• N atural gas generation is allocated to any remaining peak reduction, this is consistent with the fact that oil and gas steam units are use d in N EMS to meet peak demand ;

• B ase

- load generation (nuclear and coal) is allocated proportionally to all periods ; and

• R emaining generation of all types is allocate d 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 coefficient s 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 coeffic ients 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 generat ion except for peak : NEMS uses two peak capacity types (combustion turbin e/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 corres ponding 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, pollut ant 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 p umped storage , fuel cells and distributed generation , as t hese 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 RESUL TS

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 Reduction15

- 6 Figure 15.3

. 3 Ceiling Fan Light Kits : Nuclear Capacity Reduction

Figure 15.3

. 4 Ceiling Fan Light Kits : Gas Combined Cycle Capacity Reduction15

- 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 Reduction15

- 9 Figure 15.3

. 9 Ceiling Fan Light Kits : Nuclear Generation Reduction

Figure 15.3

. 10 Ceiling Fan Light Kits : Gas Combined Cycle Generation Reduction15

- 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 resu lts 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 48.915

- 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

TA BLE 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

LI ST OF TABLES

Table 16.4.1 Net National Short-Term Change in Employment (Number of Jobs) ......... 16-316-1 CHA PTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

The U .S. Department of Energy’s ( DOE’s ) employment impac t analysis is de signedto
estimate indi rect national job c reation or elimination r esulting f rom possible s tandards, du e to
reallocation of t he a ssociated expenditures f or pur chasing and operating ceiling f an li ght kits . Job

increases o r

d ecreases r eported

i n t his ch apter

a r e separate from t he d irect manufacturing sector employment impacts r eportedin ch ap ter 1 2 of this notice of proposed rulemakin g (NOPR) technical support document (TSD) an d r eflect t h e emplo y men t

i mpact o f ef ficiency standards on

all other sectors of the economy.

16.2 ASSUMPTIONS

DOE expects energy c onservation 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 a n input/output e conometric m odel of t he U.S. economy, this a nalysis e stimated

the s hort- term e ffect of t hese e xpenditure i mpacts on ne t e conomic out put and e mployment.

DOE i ntends t his a nalysis t o quantify t he i ndirect employment i mpacts of t hese e xpenditure

ch anges. I t evaluatedd irect employment impacts at manufacturers’ f acilities in th e manufacturer impact an alysis ( s ee ch apter 1 2 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 rel ated to buildings and the net impact of standards on jobs. The Pacific Northwest National Laboratory developed the model, ImSE T 3.1.1 2 ( Im pact of S ector E nergy Technologies), as a successor to ImB uild

, 3 a special -purpose version of the IMPLAN 4 national input/output model. Im SET estimates the employment and income effects of building energy technologies. In comparison with simple economic multiplier approaches, Im SET allows for 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 hav e 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.

I mSET uses a 1 87-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

resulti ng 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.

E nergy 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 appli ance 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 consumer s use less energy, electric utilities experience relative reductions in demand

, which leads to reductions in utility sector investment and employment.

DOE a lso notes that the e mployment impacts e stimated w ith ImSET f or the e ntire

economy differ from the employment impacts in the ceiling f an li ght kit m anufacturing s ector
estimated in chapter 12 of this NOPR TSD using the Government Regulatory Impact Model (GRIM). The methodologies used and t he sectors an aly zed

i n t h e ImSET and

G RIM m odels

ar e 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 dis aggregated 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 good s sector (as mentioned previously, ImSET’s calculations are made at a much more disaggregate level) . By r aising 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 employ ment in that sector. Finally, based on the net impact of altered expenditures on light kit s and reduced expenditures on electricity, consumer expenditures on16-3 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. (A s 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 r eturn to the U.S. economy of money spent on imported light kit s. The two scenarios bounding the ranges presented in Table 16.4.1 represent situations in which none of the money spent on imported light kit s returns to the U.S. economy and all of the money spent on imported light kit s 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 b elow.

Table 16.4.1 Net National Short-T erm Change in Employment ( N umber 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 project s that the

offic ial 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 expect s the energy savings to consumers to increasingly dominate the increase in appliance costs, resulting in increased aggregate savings to consumers. As a

re sult, DOE expect s 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 good s sector, the net effect will be an increase in labor de mand. 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 anticipate s that net labor market impacts will be negligible over time due to the small magnitude of the short- term effects presented in Table 1 6.4.1. 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 Profe ssional: 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.white house.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 AS SUMPTIONS ...................................................... 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 M arket 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) ................................... 17-1417-2 CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

In this c hapter, DOE e valuated non-regulatory a lternatives t o proposed standards b y

p erforming a regulatory i mpact an alysis ( RIA). To conduct this an aly sis

, DOE used a n in teg rated

Nationa l I mpact Analysis ( NIA )-R I A model built on the NIA model discussed in c hapter 10 of this notice of proposed rulemaking (NOPR) technical support document (TSD) . D OE identified

five non-regulatory policy alternatives that possibly could provide incentives for the same

ef f icacy l evel as t he one in the proposed tr ial standardle ve l for th e Ceiling F an Light K its ( C FLKs) th at are th e s ubject o f this rulemaking. The non-r egulatory p olicy a lternatives a re listed

in Table 17.1.1, which also includes the “no new regulatory action” alternative. DOE evaluated

ea ch alternative in te rms o f its a bility to a chieve s ignificant e nergy s avings at a reasonable cost,

an d co mpared

t h e ef f ectiveness o f

each t o t h e effectiveness o f t he proposed standard for t h e C FLK s 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 “N o N ew Regulatory A ction ” ). Section 17.4 present s 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 us ed i ts integrated NIA

- RIA spreadsheet m odel to cal culate the national ene rgy savings (NES) and net present value (NPV) associated with each non-regulatory policy

alter native. Chapter 10 of th is NOPR TSD describes the NIA spreadsheet model. Appendix

17A of this NOPR TSD discusse s the N I A -RIA integrated model approach.

D OE quantified the effect of each alternative on the purchase of equipment that meet s 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 standard s17-3 would affect 100 percent of the shipments of produc ts 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 efficac y 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

standard s. 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) , giv en in quadrillion Btus (quads), describes the cumulative national energy sav ed 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 va lue 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 value s of installed equipment cost and operating expenditures in the no- standard case and the present values of th o se 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 Polic ies

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

- regulator y 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 regardin g 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 effica cy 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 h aving the same te chnology as required by standard s (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

.

T able 17.2.1 Efficacy by TSL ( % Efficacy Improvement )

TSL 2

All CFLKs 16%

D OE 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 AS SUMPTIONS

The following subsection s 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 estimate s 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 C FLKs

constitutes th e no-s tandard case, as d escribed i n c hapter 10 of this NOPR TSD . T h e no-standard

case provides the basis of comparison for all other policies. By definition, no new regulatory

action yields zero N ES and an NPV of zero dollars.17-5 17.3.2 Consumer Rebates

DOE considered th e scenario in which the Federal government would provide financial

incentives in the form of rebate s to consumers for purchasing energy- efficient 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

DO E based its evaluation methodology for consumer rebates on a comprehensive study of California’s potential for achieving energy efficiency

. Th e study, performed by XENERGY,
Inc., b summarize d experiences with various utility rebate programs. 1 XENE RGY’s analytical

method utilized graphs, or penetration curves , that estimate the market penetration of a technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other methods of estimating the effect of consumer rebate programs on the market penetration of efficient technologies. The other methods, developed after the referenced XENERGY report wa s published, 2

, 3

, 4

, 5

, 6

, 7 used different approaches: other economic parameters (e.g., payback period), expert surveys, or model calibration based on specific utility program data rather than

multi

- utility data . Some models in use by energy efficiency program evaluation experts were so

client

- specific that generic relationships between economic parameters and consumer res ponse
could not be established. 5 DOE decided that the most appropriate available method for this RIA was the XENERGY approach of penetration curves based on B/C ratio, which incorporates lifetime operating cost savings.

XENERGY’s m odel es timates m arket i mpacts i nduced

b y f inancial i ncentives ba sed

on the pr emise t hat t wo types of i nformation diffusion drive t he a doption of n ew t echnologies.

Internal s ources of i nformation encourage c onsumers t o purchase ne w equipment primarily through word-of-mouth from e arly a dopters. External s ources affect cons umer pur chase

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 info rmation 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 an alysis

.

17

. 3.2.2 Analysis

D OE estimated t he effect of increasing the B/ C ratio of CFLKs via a rebate that would

pay part of the increased installed cost of units that me et 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 re bates 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 17 A, 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 savings c (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 l arger B/C ratio. Table 17.3.1 show s 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

.

Ta ble 17.3.1 Benefit/Cost Ratios Without and W ith Rebates

TSL 2

All CFLKs

B/C Ratio Without Rebate 33.7 Rebate Amount ( 2014 $) 5.13

B/C Ratio With Rebate infinite

Estim ated Market Barrier s E x tremely High

DO E 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 2All 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 R ebates

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 r esulting annual in crease s in m arket s hares as inputs to r epresent t he rebate

p olicy c ase scenario in it s NIA -RIA model. Appendix 17A of this NOPR TSD shows the annual

market share increases due to this policy for the whole forecast period. S ection 17.4 p r esents th e

r esulting market

p enetrationtrends for the policy case of consumer rebates f or 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 effe ct of rebates and tax credits into a direct price effect

, which deriv es from the savings in purchase price

, and an announcement effect , which is independent of the amount of the incentive. 10

, 11 The announcement effect derives from the credibility that a technology receives from being included in an incent ive program, as well as changes in product marketing and modifications in markup and pricing. DOE assumed that the rebate and consumer tax credit policies would encompass both dire ct price effect s and

announcement effect s, and that half the increase in market penetration associated with either policy would be due to the direct price effect and half to the announcement effect.

In estimat ing the effects of a tax credit on purchases of consumer products that meet new efficiency standards, DOE 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 E nergy P olicy A ct o f 2005 (EPACT 2005) included F ederal t ax cr edits f

or

consumers w ho pur chase ene rgy

- efficient products. 13 T hose t ax c redits w ere i n effect i n 2006 and 2007, expired in 2008, were reinstated for 20 09–2010 by the AmericanR ecovery and

Reinvestment A ct of 200 9 ( AR RA ), extended by C ongress f or 2011 w ith s ome modi fications

, and expired at t he e nd of 2011. 14

, 15 The A merica n Taxpayer R elief A ct of 2 012 extended, with

some m odifications, residential t ax c redits f or a ir conditioners, heat pum ps, furnaces, and water

heaters pl aced

i n s ervi ce betweenJ anua ry 1, 2012 and December 31, 2013. 16 DOE r eviewed
Internal R evenue S ervice da ta on the num bers of t axpayers who claimed the t ax c redits dur ing

tax years 2006 and 2007. DOE al so r eviewed d ata f rom ane arlier Federal e n ergy c onservation

tax c redit pr ogram i n place i n the 1980s . However, DOE di d not f ind data s pecific e nough to

C FLKs 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 a lso reviewed its pr evious a nalysis of Oregon’s t ax cr edits f or cl othes w ashers t o provide s upport f

or i ts a ssumptions. 17 I n t hat pr evious analysis, D OE com pared t he market
shares of ultra

- high ef

ficiency ( UHE) r esi dential c lothes w ashers i n Oregon, which offered both

State ta x c redits a nd ut ility rebates, w ith thos e in Washington S tate, w hich of fered onl y utility

rebates dur ing t he s ame p eriod. Based on this a nalysis, D OE estimated tha t i n Oregon t he impa ct
of t ax c redits was 62 pe rcent of t he impact

o f r ebates f or U HE cl othes w ashers having e quivalent efficiency. This f

inding s upports its original assumption tha t participation in a ta x c redit program

would be about 60 percent of participation i n a rebate program. A d ditional discussion of State

tax cr edits f

or O regon and

other states is in a ppendix 17A of this NOPR TSD

.

D OE 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 devel oped 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.

T able 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 T able 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. S ection 17.4 p r esents the

r esulting market

p enetratio n trends for the policy case of consumer tax cr edits f or CFLKs that

meet t he e ffica cy l evel for t h e proposed T SL

. B ecause the increase inm ar k et p enetrationf

or

co nsumer t ax credits is proportional to the increase in market penetration DOE calculated for

consumer r ebates, the former follows a s imilar increasing t rend o ver t h e analysis p eriod

as t he

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 red uced costs to consumers, causing a direct price effect. DOE assumed that no announcement effect would occur, because the program would not17-10 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 reba te program.

DOE at tempted

t o i nvestigate manufacturer r esponse to t he Energy E fficient A ppliance

Credits f or m anufacturers m andated by E PACT 2005. 18 T hose manufacturer t ax cr edits ha ve beeni n ef fect f or di shwashers, cl othes w ashers an d refrigerators pr oduce d beginning i n 2009.

DOE was unable to locate data from the Internal Revenue Service or other sources on manufacturer

r esponse to t he Federal credits. Appendix 17A of this NOPR TSD presents
d etails o nF ederal man

u f acturer t ax cr edits.

D OE 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 incen tives 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.

T able 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

m arket s h ares at tributable to a manufacturer t ax cr ed it shown in T able

17.3.4 were used as inputs in t h e N I A -R IA

model. Appendix 17A of this NOPR TSD shows t he

an nual m arket

s hare increases due to this policy for the whole forecast period. S ectio n 17.4

p r esents the r esultin g market p enetratio n trends for the policy c ase of manufacturer tax cr edits

f o r CFLKs . B ecause the increase inm ark et p enetration

f o r manufacturer tax credits is proportio n al t o t he increase in market p enetration D OE calculated for co nsu mer

r ebates, t he

f o rmer follows a s imilar increas in g trend over the analysis period a s t he 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 f ound 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 specifie s the minimum energy efficienc ies that various products

must have to receive the ENERGY STAR l abel . 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 severa l 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 spe cifications. 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 bel ieves 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 ass essing 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 low er leve ls 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 tar gets. 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 E x tremely

- High Mod erate

- 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 T able 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%

T able 17.3.7 Market Penetration in Selected Years Attributable to Voluntary Efficacy

T argets 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

m arket s h are (s) attributable to voluntary efficacy targets shown in T able

17.3.6 were used as inputs in the NIA-RIA model. Appendix 17A of this NOPR TSD s hows t he

an nual m arket

s hare increases due to this policy for the whole forecast p eriod . S ectio n 17.4

p r esents the r esultin g market p enetratio n trends for the policy c ase o f voluntary efficacy targets
for C FLKs that meet t he eff

ica cy l evel f

or t h e proposed T SL

.

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 mu ltiple 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

p rices. Such a program also can induce “market pull,” whereby manufacturers and vendors

would achieve econo mies 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 municipalit ies have demonstrated increasing interest and activity in “green purchasing." Although many of the programs target office equipment, the growing infrastructure for developing and applying17-13 efficient purchasing specifications indicates that bulk government p urchase programs are

feasible

. 23

, 24

D OE 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 purcha ses. Its major study analyzed several scenarios based on the assumption that 20 percent of Federal equipment purchas es in 2000 already incorporated energy efficiency requirements based on FEMP guidelines. One scenario in the DOE report showed energy efficient purchasing ram ping up during 10 years from 20 percent to 80 percent of all Federal purchases. 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.

T able 17.3.8 Market Penetration in 2019 Attributable to Bulk Government Pu rchases

TSL 2

All CFLKs

No

- Standard

- Case Market

Share 10.2%

Policy Case Market Share 10. 3 %

Increased Market Share 0. 1 %

The increased

m arket s h are attributab le to bulk government purchases shown in T able

17.3.8 were used as inputs in t h e N I A -R IA

model. Appendix 17A of this NOPR TSD s hows t he

an nual m arket

s hare increases due to this policy for the whole forecast period. S ection 17.4

p r esents the r esultin g market penetration trends for the policy c ase o f bulk government purchases

for C FLKs

.17-14 17.4 IMPACTS OF NON

- REGULATORY ALTERNATI VES

Figure 17.4.1 shows the effects of each non-regulatory policy alternative on the market penetration of more efficacious CFLKs . R elative 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.

F igure 17.4.1 Market Penetration of Efficacious CFLKs (TSL 2)

T able 17.4.1 shows the national energy savings and net present value for the five nonregulatory polic y alternatives analyzed in detail for CFLKs . T he target level for each policy corresponds to the same e fficacious technology proposed for standards in TSL 2. Th e case in which no regulatory action is taken with regard to CFLKs constitute s 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 standard s 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 efficac y le vel, 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 NPV s 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 polic y 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.

Ta ble 17.4.1 Impacts of Non-Regulatory Policy Alternatives (TSL 2)

Policy Alternative Energy Savings* quads Net Present V alue*

mi llion 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 e fficac y level corresponding to the proposed standard is offset to that effica cy 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 Pub lic 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.c om/-

/media/missouri

site/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. Law rence 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 Consultin g 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, Refrigerat or-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 accesse d 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 Incentive s 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 Ef ficiency 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 Confere nce 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 ABBREVI ATIONS

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 Ind ex 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 TransportationAA

- 2 GRIM Government Regulatory Impact Model

GSFL G eneral servi ce fluorescent lamp

GSIL Ge neral 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 star t

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

NO X Nitrogen oxide s
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 SurveyAA

- 3 RF Rate factor

RIA Regulatory I mpact A nalysis

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 Cooperativ e 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 Weight ed average cost of capital

WPE Wall plug efficiency

YAG Yttrium aluminum garnet

ZrO Zirconium dioxide7A

- i APPENDIX 7 A

. PRODUCT CLASS RE ST RUCTURING S UB

BASELINE E NGINEERING AN D PRIC ING RE SULTS

TAB LE OF CONTENTS

7A .1 INTRODUCTION

........................................................................................................ 7A-1

7A.2 SUB

- BASELINE ENGINEERING ANALYSIS

......................................................... 7A-1

7A.3 SUB

- BASELINE PRICING ANALYSIS

.................................................................... 7A-2

LI ST OF TABLES

T able 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-27A

- 1 APPENDIX 7A PRODUCT CLASS RESTRU CTURING SUB

- BASELINE

ENGINEERING AND PRIC ING 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 uti lity . (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 p roposed rulemaking (NOPR) technical support document ( TSD ) , DOE is proposing a new product class structure. Because the existing product classes each have different effic acy levels , DOE sel ected 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 als o determined consumer prices of the sub

- baseline represen tative lamp unit

.

7A.2 SUB

- BASELINE ENGINEERING ANALYSIS

E xisting 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 efficac y 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 s elect ed 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 t he current
standard . DOE use d performance data presented in manufacturer catalogs for commercially available lamps to determine lamp efficacy. DOE also consider ed 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 1,5007A

- 2 7A.3 SUB

- BASELINE PRICING ANA LYSIS

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 produ ct 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 av erage shipment

weighted 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 percent age 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 determ ined 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 l m lm/W hr 201 4 $

A15 E12 60 440 7.3 1,500 $0.558A-i APPENDIX 8A. USER INSTRUCTIONS FOR LCC AND PBP SPREADSHEET

TA BLE OF CONTENTS

8A.1 INTRODUCTION ........................................................................................................ 8A-1

8A.2 DESCRIPTION OF LIFE

- CYCLE COST SPREADSHE ET ...................................... 8A-18A-1

APPE NDIX 8 A. US E R INSTRUCTIONS F OR LCC A ND PBP S PREADSHEET

8A.1 INTRODUCTION

The detailed results of the life

- cycle cost ( LCC ) and payback period ( PBP ) analys is 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 post ed on the DOE website

has b een 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 calcula tions:

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 work sheets 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 show s LCC and LCC savings results for the selected parameters

.

T he 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 de rive results for the analysis

.

Res Energy Use

, Com Energy Use Th is 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 a ccessed 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 calculat ions for the costs associated with replacing

t he 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 resid ual 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 Th is worksheet shows the price trends used to estimate electricity price for each purchaser

.

Discount Rate s Th is worksheet contains the distributions of di scount rates.

Lifetime Th is worksheet contains the CFLK lifetime distribution.

Market Distribution This worksheet contains the market distribution for all scenario s

Tax This worksheet contains projected tax rates in 2019.8B-i APPENDIX 8B. UNCERTAINTY AND VARI ABILITY IN LCC AND PBP ANALY SIS

TA BLE OF CONTENTS

8B.1 INTRODUCTION .........................................................................................................8B-1

8B.2 INTRODUCTION TO UNCERTAINTY AND VARIABIL ITY .................................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 SCENARIO S 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

LI ST 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

LI ST OF FIGURES

Figure 8B.3.1 Normal, Triangular, Uniform, Weibull, and Custom Probability

Distributions .................................................................................................8B-38B-1 APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN LCC AND PBP ANALY SIS

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) analys i s 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 UNCE RTAINTY 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.

Variabilit y 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 consumption s . DOE a ttempts to account for major sources of variability in its analyses.

Uncertainty has many sources. Variability may lead to uncertainty in model inputs, because analysts fre quently must estimate the values of interest based on samples of a variable quantit y (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 instrumen t 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 analys es.

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 i n 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., electri city 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 this8B-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 LC C AND PBP ANALYSIS

To quantify the uncertainty and variability that exist in inputs to the LCC and PBP analys is, 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 sing le 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 Ca rlo 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 SCENARIO S CONSIDERED IN THIS RULEMAKING

As discussed in chapter 8 of this NOPR TSD, t o 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

tec hnological 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 ass umptions. Th e 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 20 15. NORMAL UNIFORM TRIANGULAR

WEIBULL CUSTOM8B-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 relati ve 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 ” c orresponds t o the s ub -b aseline ( i.e.

, l amps which have efficacies b elow t he baseline set for t he new p roduct

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 Ener gy 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

TA BLE 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 DISTRIBU TIONS USED FOR COMMERCIAL DISCOUNT RATES ....................8C-11

REFERENCES ........................................................................................................................8C-12

LI ST 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

LI ST 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-98C-1 APPENDIX 8C. DISCOUNT RATE DISTRIBUTIONS

8C.1 INTRODUCTION

The U.S. Department of Energy (DOE) derived discount rates for the life-c y cle cost ( LCC) analysis using data on interest or return rates for various types of debt and equity to
calculate a r eal e ffective discount r ate for e ach h ousehold

i n t he Federal R ese rve Board’s S u rvey

o f Consumer Finances (SCF) in 1995, 1998, 2001, 2004, 2007, and 2010. 1 To 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 RATE S FOR DEBT CLASSES

Figure 8C.2.1 through Figure 8C.2.6 show the distribution of real interest rates for

differ ent 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 b elow the inflation rate (resulting in a negative real interest rate), DOE set the real effective

interest rate to zero.

F igure 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 Rate8C-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 Rate8C-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 Rate8C-4 Figure 8C.2 .6 Distribution of Other Lines of Credit Loan Interest Rates

8C.3 DISTRIBUTION OF RATE S FOR EQUITY CLASSES

Figure 8C.3.1 through Figure 8C.3.7 show the distribution of real interest rates for

differen t 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 bonds 4 are from Federal Reserve Board time

- series data . DOE assumed rates on checking accounts to be zer o. 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)

an d 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 Rate8C-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 0.08198419861988199019921994199619982000200220042006200820102012Annual 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%198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year Savings Bonds8C-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%198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year AAA Corporate Bonds

-0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year Savings Accounts8C-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%198419861988199019921994199619982000200220042006200820102012Annual 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%198419861988199019921994199619982000200220042006200820102012Annual 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%198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year Mutual Funds8C-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 68C-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% 0.0008C-11 8C.5 DISTRIBUTIONS USED F OR COMMERCIAL DISCOU NT RATES

Table 8C.4.1 shows the probability distribution used for commercial discount rates by different commercial sector groups:

Table 8C.5.1 R eal 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 0.08C-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

certi ficates 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 Re lease, 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/da ta.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

TA BLE 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

LI ST 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-128D-1 APPE NDIX 8 D. E LECTRICITY P RICES

8D.1 INTRODUCTION

The following sections discuss DOE’s general methodology to derive marginal and average electricity prices for the residential and commercial sectors. Ba sed 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 savi ngs 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 c onsumer 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

referen ce 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 pri ce 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 thi s 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.

T he total utility bill BB (neglecting taxes) is

BB = FF + ( AA × EE )

E q

. 8 D .1

T he average price pp is defined as the ratio of the total bill to total usage:

pp = BB

EE = FF

EE + AA

E q

. 8 D .2

The mar ginal price is defined by considering an increment dd to tota l usage ( dd may b e 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

E q

. 8 D .3

F or the simple tariff defined above mm < pp as long as the fixed charge FF is not zero.8D-2 E

xample 2 : in this example the tariff is defined by a fixed charge FF and two commodity charges AA 1 and AA 2 . Charge AA 1 applies for all electric ity use up to EE 1 , while charge AA 2 applies for

any usage over EE 1 . This is an example of a tiered rate structure. In general AA 2 may be larger or

smaller than AA 1 , although most utilities use increasing tiers with AA 2 > AA 1 . For a tariff of this type, the marginal price is either AA 1 or AA 2 , depending on whether the household energy use is more or

less than EE 1 in the reference case. For EE < EE 1 the average price is

PP = [ FF + ( AA 1 × EE )]

EE E q

. 8 D .4 which is higher than the marginal price AA 1 . However for EE ≥ EE 1

PP = { FF + ( AA 1 × EE 1 ) + [ AA 2 × ( EE − EE 1 )]}

EE E q

. 8 D .5

a nd 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. T he 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, whichprovide 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 non

residential se ctors, for about 100 utilities.8D-3

T a bl e 8 D.2 .1 Summary

of Da t a So urces f or El ect ricity Pr ic e In form a t ion

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) multi

year by region high large good yes yes estimate estimate no

C&I Bill data (CBECS) multi

year by region high large good yes yes no no no

Res EEI Typical

Bills bi

annual by utility 3 types small poor yes yes estimate estimate no

C&I EEI Typical

Bills bi

annual by utility 9 types small poor yes yes no no no

Res Tariffs multi

year by region high small good yes yes yes yes yes

C&I Tariffs multi

year by region high small good yes yes yes yes yes

T he 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 forthe 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.

T he 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 est imate 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 th e 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.

Th e 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 bil ling 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 publis hes 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 industria l 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 bi lls 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 inform ation provided is qualitative rather than quantitative.

The "Tariff Analysis Project" (TAP) database and calc ulation tools developed at LBNL 2,3

have also been u sed 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 st ored in a set of normalized data tables whose structure represents th e 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 esti mated 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 section8D-5

T a bl e 8 D.2 .2 Consumption and Dema nd Le v els In cluded i n t he E EI T ypical B ills a nd

Ra t es Re po rts

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:

pp ii = BB ii

EE ii E q

. 8 D .6

where:

ii is the index of the typical bill from Table 8D.2.1

, BB ii is the bill,

EE ii is the electricity consumption, and

pp ii is the average price.8D-6 The marginal price was determined by comparing the bills at two different consumption

levels:

mm iiii = ( BB ii − BB )

( EE ii − EE )

E q

. 8 D .7

DOE used mm 32 as the marginal price for consumers with baseline energy use above EE 2 = 750 kWh/month, and mm 21 as the marginal price for consumers with baseline energy use below

EE 2 . DOE used pp 1 as the average price for consumers with baseline consumption below EE 1 , pp 3 as the price for consumers whose baseline is above EE 3 , and pp 2 for those in between. DOE created regional weighted -average values for pp ii and mm iiii 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).

T he 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 ( pp aaaaaa ′ ), and one based on only IOUs ( pp IIIIII ′ ). DOE then defined an adjustment factor for each region and sector as the ratio pp aaaaaa ′ / pp IIIIII ′ . This adjustment factor, (shown in Table 8D.3 .1 for all sectors) was applied to the prices calculated from the EEI

data.

T he result of this analysis is a set of average and marginal prices that vary by reg ion 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, incorpo rating the adjustment factor, are provided in Table 8D.3.2

.8D-7

T a bl e 8 D.3 .1 Adjustment Fa ctors by R egion 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

T a bl e 8 D.3 .2 Resident ia l Sector E lectricity Prices b y R egion

Region Summer Prices (cents/kWh) Winter Prices (cents/kWh)

pp 11 pp 22 pp 33 mm 22 mm 33 pp 11 pp 22 pp 33 mm 22 mm 33 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 Wh ile the EIA and EEI data make a distinction between the commercial and industrial sectors, utility tariffs typically refer only to consumer usage characteristics (small, medi um or large power, high load factor, etc. ). Hence, the commercial/industrial distinction is somewhat ill

defined 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.

Th e average prices pp ii are calculated using the same method as for the residential sector,

with pp ii 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 )

E q

. 8 D .8

whe re:

H = the number of hours in the average monthly billing period (8,760/12).

Val ues 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 util ities 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

T a bl e 8 D.4 .1 Av

er age Pr ice for t he US f or ea ch Bi ll Ty pe, So rted by Su mmer Pr ice

Sector Consumption ( EE ) Demand ( DD ) Load Factor ( LL ) I ndex 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

M arginal 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 i nduced 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 E q

. 8 D .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.

S imil arly, a marginal demand price dd is defined by:

dd = [ BB ( EE

, DD + ∆ DD ) − BB ( EE

, DD )]

∆DD E q

. 8 D .1 0

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 )

E q

. 8 D .1 1

where:

HH = the number of hours in the average monthly billing period (8,760/12).

T he 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 lightin g, 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.

T he 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

writte n as a function of λλ a as

mm = ee + [ � dd

HH � × � 1

λλ � ]

E q

. 8 D .1 2

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

T he 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 ee iiii = BB ii − BB

EE ii − EE

E q

. 8 D .1 3

f or 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

deman d price by region. 4 The marginal demand prices estimated based on earlier data were scaled to 2014 using AEO current and historical price indices.

I f data a bout 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 samp les used for the tariff work 4 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 b ecause, as noted above, these distinctions are typically not used in the utility tariffs.

T a bl e 8 D.4 .2 Def init io n o f

C o nsumption T iers f or A veraging A cross B ill Types

T ier E_min E_max Index Sect o r

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

O nce 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 de fined. 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

t he analysis ar e presented i n Tabl e 8D.4.3. The t able i ncludes t he m arginal

p r ice calculated

f o r λλ = 0.5, which is a reasonable mid-r ange v alue for

m any end -u ses.

T a bl e 8 D.4 .3 Non

-R esidential S ector Prices b y R egion

Region Average Price ( pp )

cents/kwh Marginal Energy Price ( ee ) cents/kwh Marginal D emand Price ( dd ) $/k W h 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 11.308D-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

acce ssed June 15, 2015.) http://eetd.lbl.gov/publications/tariff

- analysis

- project

- database

and

- analysis

- platform

- electricity

- tariffs

.

2. Coughlin, K., C. Bolduc, R. Van Buskirk, G. Rosenquist, and J. E. McMahon. Tariff

based 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

Te chnical 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 ELAST ICITY 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 REFRI GERATORS, 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 AFFECTI NG 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 ANA LYSIS ........................................................................... 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 discoun t rate .................... 9A-11

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a Purchase Price Change .............................................................................. 9A-129A- 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-69A-1 APPENDIX 9A. RELATIV E PRICE ELASTICITY O F 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. D OE chose t his p articular

s et of appliances b ecause of t he av ailability o f

d ata t o determine a p rice e lasticity

. S ectio n 9A.2

review s the existing e conomics lite rature de scribing the impa ct of e conomic va riables on the s a le

of dur able goods. Section 9A.3 de scribes the market f or hom e a ppliances a nd t he changes that

have occurred

ove r t he p ast 20 years . I n s ection 9A.4, D OE s ummarizes the r esults of its regression an alysis and

p resent s estimates of t he price elasticity of d emand for t he three appliances. In section 9A.5, D OE pr esents de velopment o f an ‘ effective’ p urchase price elasticity. D OE’s interpretation of i ts re sults i s pr esented in section 9A.6. Finally, section 9A.7

describes t he data used

i n D OE’s an alysis.

9A .2 LITERATURE REVIEW

Re latively few studies measu r e th e impact of price, inco me and efficiency on the

sale of household appliances. Th is section b riefly r ev iew s the literatur e that describes

th e likely imp ortan ce of these variables on the purchase of household appliances.

9A .2.1 Price

DOE reviewed many studies that sought to m easure the impact o f p r ice on sales i n a dynamic market. One study of the automobile market prior to 1970 finds the price elasticity of

demand to d ecline over

t ime. T he auth o r

ex plains t his as t h e result o f b uyers d elayin g purchases

after a price increase b u t ev entua lly making the purchase (T able 9A.2.1)

. 1 A contrasting study of

household white g oods also prior t o 1970, finds t he e lasticity o f de mand t o increase ove r t ime a s more price

- conscious bu yers ent er t he market. 2 A n a nalysis o f r efrigerator market s urvey d ata
finds t hat c onsumer pur chase pr obability de creases w ith survey asking p rice. 3 Estimates of the price e lasticity of d emand for di fferent br ands of t he s ame pr oduct t end to vary. A r eview of 41

studies of the impa ct of

p ric e on m arket

s hare found

t he average price elasticity t o be

- 1.75. 4 The average estimate of p rice elasticity of d emand r eportedin the se s tudies is -0.33 in the a ppliance

market and -0.47 in the combined automobile a nd appliance m arkets.

9A .2.2 Income

Higher income households are more likely to own household appliance. 5 The impact o f

i ncome on appliance shipments is explored in two econometric studies of the automobile and appliance mar

kets. 1,2 The average income elasticity o f demand is 0.50 in the appliance study

cited in the literature review, m uch l arger in th e automobile study (T able 9A.2.1)

.

9A .2.3 Appliance Efficiency and Discount Rates

Many s tudies estimate th e impact o f ap pliance efficiency o nco nsum er s’ ch oice of appliance. Typically, th is imp act is summarized b y th e imp licit discount r ate; that is

, t he rat e

consumers u se to compare future savings in a p pliance operating c osts ag ainst a higher initial purchase price of9A-2 an appliance. One early and much cited study co ncludes 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 disco unt rate has a broad range and averages about 37 percent

. 7

Ta ble 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile and Appliance Sales

Durable Good Price Elasticity Income Elasticity B rand

Price Elasticity Implicit

Discount

Rate Model Data

Years Time

Period

Automobiles 1

- 1.07 3.08

- - Linear Regression, stock adjustment - Short run

A utomobiles 1

- 0.36 1.02

- - Linear Regression, stock adjustment - Long run

Clothes Dryers 2

- 0.14 0.26

- - Cobb

- Douglas, diffusi on 1947

- 1961 Mixed

Room Air

Conditioners 2 - 0.37 8 0.45

- - Cobb

- Douglas, diffusion 1946

- 1962 Mixed

Dishwashers 2

- 0.42 0.79

- - Cobb

- Douglas, diffusion 1947

- 1968 Mixed

Refrigerators 3

- 0.37

- - 39% Logit probability, survey

data 1997 Short run

Various 4

- - - 1.76 9

- Multiplicative regression

- Mixed

Room Air

Conditioners 5 - - - 1.72

- Non

- linear diffusion 1949

- 1961 Short run

Clothes Dryers 5

- - - 1.32

- Non

- linear diffusion 1963

- 1970 Short run

Ro om Air

Conditioners 6 - - - 20% Qualitative choice, survey

data - -

Household Appliances 7 - - - 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. Tra in, 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 REFRI GERATORS,

CLOTHES WASHERS, AND DISHWASHERS

In this section D OE evaluate s variables t hat ap pear t o ac count

f or r efrigerator, cl othes

w asher and dishwasher shipments, including physical household/appliance variables and

economic variables.

9A.3.1 Physical Household/Appliance Variables

Several v ariables i nfluence the sale of r efrigerato r s, cl othes w ashers and d ishwashers. The

mo st important for explaining appliance sales trends are the annual number of new households formed ( h o using starts) and the number of appliances reaching the end of their operating life ( r eplacements). Housing star ts i nfluence sales b ecau se n ew homes are often provided with, or9A-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 ini ncome increases h ouseholder ab ility t o p urchas e applian ces. 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 p ercent for refrigerators a nd 105 percent for dishwashers (T able 9A.3.1)

. This

r ising s hipments trend is explained in part by housing starts, which increased 6 p ercent and

b y

ap pliance repl acements, which rose between 49 percent and 90 percent, depending on the appl iance, o ver the per io d (T able 9A.3.1)

. a For m ature markets s uch as t hese, r eplacements

ex ceed ap pliance sales associated with new housing construction.

T able 9A.3.1 Physical Household/Appliance Variable s Shipments i (millions) Housing Starts ii (millions) Replacements iii (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.

ii Housing Starts: Annual number of new homes constructed. Source: U.S. Census.

iii Replacements: 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 th at r epresent “s tarts p lus r eplacements” (uppermost solid line in F igure 9A.3.1) and “shipments ” (diamo nd linked line in F igure 9A.3.1)

. In 1980 the “shipment” line begins below the “starts plus replacements” line. In 2002, the “shipments” line ends ab o v e th e “starts p lus r eplacements” 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 anal ysis 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, ap pliance prices decreased 40 percent to 50
percent, o p erating costs fell between 33 percent and 72 percent, and median household

income rose 16 percent (T ab le 9A.3.2)

.

T able 9A.3.2 Economic Variables

Price i (1999$) Operating Cost ii (1999$) Household Income iii (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.

ii Operating Cost: Annual electricity price times electricity consumption. Source: AHAM Fact Book.

iii In come: Mean Household income. Source: U.S. Census.

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTI NG APPLIANCE

SHIPMENTS

Few data are a vailable t o estimat e the impact of economic variables on the demand for appliances. Industry op erating c ost da ta i s i ncomplete—a ppliance energy us e data a re available

for onl y 12 years of t he 1 980-2002 study pe ri od. Industry pr ice da ta are a lso inc omplete

— available f

or onl y 8 years of t he s tudy p eriod for each of t he appliances.

The lack o f d ata suggests t hat

r egressionan aly si s can at best evaluate broad data trends, utilizing relatively f

ew ex planatory variables. This s ection beg in s by describing broad trends

apparent in the economic and physical household data sets and then s p ecif ies a s imp le

r egression mo del to measure th ese trends, making assumptions to minimize the number of explanatory variables. Finally, results of the regression analysis are presented al ong with an estimate of

t he price elasticity o f

d emand f or a pp liances. In section 9A.4.5, D OE present s the

results of regression Refrigerator Shipments

0 1 2 3 4 5 6 7 8 9 10

1980 1984 1988 1992 1996 2000Millions New Housing

Retirements

Shipments Clothes Washer Shipments

0 1 2 3 4 5 6 7 8

1980 1984 1988 1992 1996 2000Millions Dishwasher Shipments

0 1 2 3 4 5 6

1980 1984 1988 1992 1996 2000Millions9A-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 est imated with that model.

9A.4.1 Broad Trends

In this section D OE review s trends in the physical household and economic data sets and posits a simple approach f or es timating t he price elasticity of appliance demand. As noted above, the physical household variables ( housing starts and appliance replacements) explain most of the variability in a

ppliance s hipments during t he s tudy period (1980-2002). b DOE assume s the rest

of the va riability in s hipments ( referred t o as “ residual s hipments” ) i s ex plained b y econom ic

va riables

. Below, DOE present s a ta bular me thod for

me asuring p rice e lasticities.

To illustrate this tabular approach, DOE defines two new variables—residual shipments

and total p r ice. Residual s hipments ar e d efined as t he differen ce betweens hipments and physical household demand (starts pl us r eplacements). Total pr ice, represented by t he f ollowing equation,

is de fined as appl iance price plus t he present v alue of l ifetime appliance operating cost : c

TTTT = + PPPPPP Eq. 9A.1

W h ere:

TTTT = T otal price, = App liance 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. A t
th e sam e time, total prices declined 47 percent, 45 percent and 48 percent for r efrigerators,

cl othes washers

, and d ishwashers, r espectively

. A ssuming t hat t o tal p rice explains t he en tir e change in per household appliance usage, a rough estimate is cal culated of th e to tal price

e lasticity o f demand equal to -0. 4 8 for refrigerators, -0.32 for clothes washers and -0.37 for

dishwashers (T ab le 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 per cent 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$)

Elasticity Appliance 1980 2002 Difference Change 1980 2002 Change

Refrigerators

- 0.5 1.6 2.1 30% 1541 820

- 61%

- 0.48

Clot hes 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 p rice e lasticities is illu strated in a g raph o f

r esidual s hipments on the y-a xis an d t otal

p r ice ont he x-axis ( Figure 9A.4.1)

.

Note: Yellow p oints ar e observed p rice data; r ed p oints ar e interpolated p rice data. Figure 9 A.4 .1 Residual Sh ipments a nd Appliance P rice

Household income rose during the study period, making it easier for households to purchase ap pliances. Assuming th at a r ise in in come h as a similar imp act on s hipments a s a d ecline in price, th e imp act of in come is incorporated b y 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

W here:

RR = Re lative 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 d emand 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 PriceResidual Shipments Clothes Washers

400 600 800 1000 1200

-1.5 -1.0 -0.5 0.0 0.5

Total PriceResidual Shipments Dishwashers

200 400 600 800 1000

-2.0 -1.5 -1.0 -0.5 0.0 0.5

Total PriceResidual Shipments9A-7

The percent

d ecline in relative p r ice for t he thre e applian ces d ivided b y t he p er cent
d ecline in residual shipments suggests a rough estimate of relative price elasticity eq ual t o -0.40 fo r refrigerators, -0.26 for clothes washers and -0.30 for dishwashers (T able 9 A.4 .2)

.

T able 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand

Residual Shi pments (millions) Relative Price (1999$)

Elasticity Appliance 1980 2002 Change 1980 2002 Change

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

p rice data suggest it is a ppr o p riate to use a s imple r egression mo del to

e stimate th e impact of economic variables on shipments, using few explanatory variables. T h e following equation, chosen for this analysis, includes one physical household variable (housing starts plus r eplacements) an d o ne relative price variable (th e sum o f p urch ase price plus

oper ating c ost, divided by income).

SS h iiii = aa + bb × RR + × [ SSSS SSSSSS + RR RR ] Eq. 9A.3

W h ere:

SS h iiii = Quantity of appliance sold,

RR = Relat ive price,

SSSS SSSSSS = Number of new homes, and

RR RR = N umber of appliances at the end of t heir operating life.

The natural l ogs a re tak en o f al l v ariables s o t hat

t he estimated

co efficients for

ea ch

v ariable in the model m ay b e interpreted as t he percent

ch ange in

s hip men ts as sociated

w ith

t he percent change in t he variable. Thus, the coefficie n t b i n th is mo del is in terpreteda s th e relative price elasticity o f d emand f

or t he thr ee appliances.

DOE used the following c o mbined r egression

eq uation t o es timate an

av erage pri ce elasticity o f demand across the three appliances, using poole d d ata i n a single regression. A combined regression specification is justified, given t he l imited

d ata a vailable and

the similarity

in p r ice and shipment behavior across appliances (s ee Figure 9A.4.1)

. Thus, the model represented by the combined regression equation is considered the basic model in D OE’s

an alysis o f

ap pliance shipments.

SSh iiii = aa + bb × RR + × [ SSSS SSSSSS + RR RR ] + dd × CC + × DD Eq. 9A.4

W h ere:

CC = Q uantity of clothes washers sold, and

DD = Qua ntify of dishwashers sold.9A-8 9A.4.3 Discussion of Model

Th e mo st important assumptio n 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, D OE assume s no impact from

other possible fa cto r s, s uch as changing co nsu mer p references or incr eases i nt he q uality o f

ap pliances. This assumption seems unlikely, but without additional data, the impact of this assumption on the price elasticity of demand cannot be measured. D OE ef f ectively as sume s that

ch anges i n consumer p r eferences an d ap plian ce ch aracteristics, w hile af f ecting which models ar e

p urchased, have relativ ely l ittle impact o nt h e to tal n umber

o f ap plian ces p urchased

i n a year.

Three additional assumptions used to specify this model de serve comment. The

relative pr ice v ar iab le is s pecifiedin th e mo del, a ssuming th at (1) th e c orrect implicit discount

r ate is u sedto combine appliance price and operating cost and that (2) rising income has the same impact on s hipments a s f alling to tal p rice. Th e “s tarts + replacements ” variable is specified, assuming (3) th at starts a ndr eplacements h ave s imilar imp acts o n s hipments.

To investigate the first assumption about discount rates, D OE calculated “p resent v alue

o perating cost” using a 20 percent implicit discount rate and performed a second regression analysis based on the mode ls d escribed i n Eq. 9A.3 and Eq. 9A.4. The results of this analysis, presented in s ecti on 9A.4.5, in dicate t hat the e lasticity o f rela tive price is fairly insensitive to changes in the discount rate.

To investigate the second and third assumptions, D OE specified a regr essio n m odel

s eparating income f

rom total p r ice andr eplacements f rom starts, th ereby adding two additional explanatory variables t o t he basic mo del as shown in the following equation:

SS h iiii = aa + bb × RR + × IIIIIIIIII II + dd × SSSS SSSS + × RR RR + ff × CC + gg × DD Eq. 9A.5

The results of the regression analysis of this model ar e presented i n s ection9 -A.4.5.

Th ese r esu lts s uggest that the elasticity o f to tal p r ice ( coefficient b ) i s r elatively i nsensitive to

ch anges in

t he treatment

o f i ncome and

“s tarts + replacemen ts ” 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

d isaggregated variables.

9A .4.4.1 Individual Appliance Model

The individual appliance regression equations are specified in the following equation.

SS h iiii = aa + bb × RR + × [ SSSS SSSSSS + RR RR ] Eq. 9A.69A-9

In regression analysis of this model, the elasticity of relative price ( b ) is e stimated to b e -0.40 f or refrigerators, -0.31 for c lothes w ashers a nd -0.32 for di shwashers ( Table 9A .4.3 ), averagin g -0.35. T hese elasticities a re s imilar to thos e r eported

in the lite ratur e surve y for appliances ( Table 9A .2.1 ). They ar e remarkably s imilar t o t he price elasticity calculated us ing a

t abular a pproach ( Tabl e 9A.4.2)

.

The estimated

co efficient

as sociated

w ith t he “s tarts + re p lacements ” v ariable is cl ose to

o ne. A coefficient equal to one for this variable would imply that, holding econom ic variables

constant , shipments increase in direct proportion to an increase in “ s tarts + re p lacements.” The high R-squared values (above 95) and t-statistics (above 5) in the results provide a measure of

confidence i n this a nalysis, despite t he ve ry s ma ll data s e t.

Ta ble 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

- 2.0 8

- 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

R 2 0.954 0.954 0.975

Observations 23 23 23

9A.4.4.2 Co mbined Appliance M odel

The combined appliance regression equation is specified in the following equation.

SSh iiii = aa + bb × RR + × [ SSSS SSSSSS + RR RR ] + dd × CC + × DD Eq. 9A.7

This regression a nalysis i ndicates th at the mo del fits th e e xisting s hipments data w ell ( h igh R -squared) and that the variables included in t h e model ar e st atistically s ignificant ( Table

9 A.4 .4)

. Estimated

w ith th is mo del, t he elasticity o f relative price is -0.34, close to the average

value estimatedin th e in dividual appliance mo dels ( -0.35). It is a lso s imilar to elasticity

e stimates reported in the literature survey and calculated using the tabular approach i n 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

R 2 0.983

Observations 69 Variable Coefficient t-statistic9A-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, ma de to aggregate appliance price and operating cost, is th at the imp licit price

v ariable in the basic regressio n model is specified using a 37 percent implicit discount ra te. The

second s tates t hat the imp licit price v ariable is d efined assuming th at r ising in co me h as th e s ame imp act on s hipments a s f

alling t otal price. The th irds tates th at the “ starts + re p lacements ”

v ariable is defined assuming that housing starts have a similar imp act on s hipments a s a ppliance replacements.

9A .4.5.1 Lower Consumer Discount Rate

To investigate the first assumption about discount rates, D OE calculated “p resent v alue

o perating 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 co efficient

as sociated

w ith t he relative pr ice variable in these regressio ns i s al most i dentical t o t he

coefficient s e stimatedf

or the same variable based on a 37 p ercen t implicit d iscount rate. Th e elasticity o f relative price calcu lated u sing a 20 p ercent

discount rate is -0.33 in the combined regression and averages

- 0.35 fo r t he three appliances ( Tab le 9A.4.5)

. The e lasticity o f p r ice

calculated u sing a 37 p ercent discount rate is -0.34 in the combined regression and averages -0.35

for the three appliances. D OE concludes from this a naly sis th at the elasticity of relative price is

f airly inse n sitiv e to chan g es i n t he discount r ate.

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 th at sep arates income from total price and replacements from starts, thus adding two additional explanatory variables to the basic model (a s 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

R 2 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

R 2 0.953 0.950 0.977

Observations 23 23 239A-11 SSh iiii = aa + bb × RR + × IIII + dd × SSSS SSSS + × RR RR + ff × CC + gg × DD Eq. 9A.8

Th e e stimatedc oefficient a ssociatedw ith th e to ta l p rice v ariable in th ese regressions is

a lmost identical to th e c oefficients e stimatedf or th e rela tive price variable reported ab ove. The elasticity of

to tal price in the above equation i s -0.36 in the combined appliance regression and

averages - 0.35 fo r

t he three appliances ( Table 9A.4.6)

. The e lasticity o f relative pri ce bas ed on

the model described in Eq. 9A.4 i s -0.34 in the combined regression (T able 9 A.4 .4) a n d av erages -0.35 a cross t he individual appliances (T able 9A.4.3)

. DOE concludes that th e p r ice elasticity

calculatedin thi s a nalysis is r elatively ins ensitive to the s pecifi cation of ho usehold income a nd

“s tarts + r eplacements ” variables in the mode l.

T able 9A.4.6 Disaggregated Regression Results, 37 percent discount rate

9A.5 LONG RUN IMPACTS

As noted above in T abl e 9A.2.1, t he literatur e review p r o v ides p rice elasticities o ver s hort

an d long time periods, also referred to as short run and long run price elasticities. As noted in the

first two rows of Tab le 9 A. 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 f orecasts of shipments and national impacts due to standards is over a 30-year time period, consideration must be given to how the relative pri ce elasticity i s af fected o nce a new

s tandard t akes effect.

DOE considers the relative price e lasticities d etermined above in section 9 A.4 to be

short run elasticities. DOE was unable to identify sources specific to household durable goods,

such as ap p liance s, to indicate how short run and long run price elasticities differ. Therefore, to

estimate how the relative price e lasticity changes o ver time

, D OE r eliedo n th e Hymans s tudy

p ertaining to automobiles. Based on the Hymans study, T able 9A.5.1 shows how the

automobile price elasticity o f d emand ch anges i nt h e years following a purchase price change.

W ith i ncreasing 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

R 2 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

R 2 0.984 0.958 0.979

Observations 23 23 239A-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.

T able 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 Deman d

- 1.20

- 0.93

- 0.75

- 0.55

- 0.42

- 0.40
Relative Change in Elasticity to 1 st 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 T able 9A.5.1, D OE d eveloped a time series o f relative price elasticities f o r h o me appliances. Table 9A.5.2 p resents t he time series.

T able 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 1 st 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 descr ib es t he results o f a liter ature search, t abular analysis , and regression analys es of t he i mpact of pr ice a nd other va riables on appliance s hipments

. In the lite rature, DOE

found onl y a few s tudies of appl iance markets t hat ar e relevant t o t his ana lysis a nd no studies

after 1980 us ing t ime series pr i ce and s hipments da ta. T he i nformation that c an be s ummarized

from the lite rature s uggests tha t the de mand f or a ppliances is pr ice ine lastic. O ther i nformation in the literature suggests t hat appl iances a re a n ormal g ood, such that r ising i ncomes i ncrease t he demand for appliances

. Finally, the lite rature s uggests tha t consumers us e r elatively hi gh impl icit

discount r ates, whe n comparing appliance pr ices and appliance op erating c osts.

Ther e are too f

ew price an d

o perating co st

d at a available to p erform co mplex

an alysis o f dynami c ch an g es i nt he appliance market. In this analysis, D OE used d at a

av ailable for refrigerato rs, clothes w ashers , and dishwashers to evaluate broad market trends and perform simple regres sion analysis.

These data indicate a ni ncrease in appliance shipments and a decline in appliance price and o perating co st over the study period 1980-2002. Household income has also risen during this9A-13 time. To simplify the analysis, DOE combined the available economic information into one variable, termed relative price, and used th at 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 p r ice elasticity o f d emand f

or t he three appliances i s i nelastic. O ur r egression an alysis

o f t hese same variables s u g g ests t hat t he relative price e lasticity o f d emandis -0.34. The p r i ce elasticity is c onsistent with e stimates in th e lite rature. Nevertheless, DO E stress es that the

me asure is basedo n a s mall data s et, u sing v ery s imple s tatistical 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 ANA LYSIS

• Appliance Shipments are defined a s the annual number of units shipped in millions. These data were collected from the Association of Home Appliance Manufacturers ( AHAM ) 8,9 and Appliance Magazine 10 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.

• A ppliance 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 Book s. 11 Price values for other years were interpolated from these eight years of data.

• H ousing Starts data were collected from the U.S. Census construction statistics (C25 reports) as annual values for each year, 1980–2002. 12

• R eplacements , 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 dishwashers 15 ), 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 colle cted from AHAM and Appliance Magazine.

• A nnual 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.

• O perating Cost is the present value of the electricity consumption of an appliance over its expected lifespan. The lifespans of refrigerators, clothes washers and dishwashers are9A-14 assumed to be 19, 14, and 12 years respectively. Discount rates of 20 percent 6 and 37

percent 16 were used, producing similar estimates of price elasticity. A study by Hausman

recomme nded a discount rate of “about 20 percent” in its introduction and pres ented 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 perc ent for appliances.

• I ncome : Median annual household income in 2003 dollars. Th ese data were collected for each year, 1980–2002, from Table H-6 of the U.S. Census. 179A-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. T ellis, 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 Inform a tion 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 U tilization of Energy

Using 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- R elated 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

- rule

technical -support-document

.

15. U.S. Department of Energy-Office of Codes and Standards. Technical Support

D ocument: 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: Combi ning 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

r eports 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

TAB LE OF CONTENTS

10A.1 INTRODUCTION ...................................................................................................... 10A-1

10A.2 BASIC INSTRUCTIONS ........................................................................................... 10A-210A-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 b e 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/a ppliance_standards/

.

Th e spreadsheet s are post ed on t h e DOE we bsite a and represent the latest version that

has be en tested with Microsoft Excel 2010.

To ex ecut e th e spreadsheetr equir es Microsoft Excel 2003 or a later version. T he N IA spreadsheet performs calculations to project t he change i n national e nergy us e a nd ne t pr esent
value (NPV) due t o an energy conservation standard. The e nergy use a nd associated costs for a given st andard

ar e determined

f irst by cal culating t he shipments an d the n calculating the e nergy use and cos ts for al l equ ipment s hipped under t hat s tandard. The di fferences be twee n the

standards and no standards case can t hen be compared a nd the ove rall e nergy savings a nd present

values de termined.

The N IA spreadsheetor workbook consists of the following worksheets:

N IA options and Contains user input selections under “Options” an d a s ummary

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.

N IAres Contains calculations of energy use, operating cost, and installed

cost in the residential sector fo r the no -standard case, the T SL0

case and the standards case s.

NI Acom Contains calculations of energy use, operating cost, and installed

cost in the commercial sector for the no -standard case, the T SL0

case and the standards case s.

NI A Savings Calculated NIA savings for a potential standard for chosen TSL.

P arameters 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 a ccessed April 1,

2014 .) This material is available in Docket # EERE- 201 2- BT

- STD

- 004 5 at regulations.gov.10A-2 AEO New Construction Contains projections for new co nstruction starts from AEO 2015.

Ful l

- Fuel Cycle Contains the annual site to power plant and upstream conversation Multipliers factors

.

Shi pments Contains projected shipments of CFLKs in the no standard case and the standards case s for selected scenario

.

P rice Contains price trends for CFLKs for selected scenario

.

His torical Shipmen ts Contains estimated data for historical shipments of CFLKs

.

E lectricity Prices Contains projected average energy prices for the three electricity price scenarios.

L ifetime Contains the probability of survival of a CFLK at a given age and the average lifetime of a unit.

10A.2 BASIC INSTRUCTIONS

Basic in structions f

or operating th e NIA s preadsheet s ar e a s follows:

1. O nce the N IA spreadsheet s have been downloaded from the World Wide Web, open the file using Excel. A t the bottom, click on the tab for the worksheet ‘Input and Summary’.

2. U se Excel’s View/Zoom commands at the top menu bar to change the size of the display to make it fit your monitor.

3. T he 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 highligh ted 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. T he results are automatically updated and are reporte d in th e 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 O F CONTENTS

10B.1 INTRODUCTION

................................ ................................ ................................ ....... 10B

- 1

10B.2 METHODOLOGY

................................ ................................ ................................ ...... 10B

- 1

10B.3 ENERGY MULTIPLIERS F OR THE FU LL FUEL CYCLE

................................ ..... 10B

- 3

REFERENCES

................................ ................................ ................................ ........................ 10B

- 4

LIST O F 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

- 310B

- 1 APPENDIX 10B. FULL

- FUEL

- CYCLE MULTIPLIERS

10B.1 INTRODUCTION

This appendix summarizes the methods the U.S. Department of Energy (D OE ) 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) e nergy ; the energy losses associated with generation, transmission, and distribution of electricity ; and the en ergy consumed in extracting, processing, and transporting or distributing primary fuels. DOE’s method of analysis previously encompassed only site energy and the energy los t through generation, transmission, and distribution of electricity. In 2011 DOE ann ounced 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 o f the ful l 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 [ B tu ] ) 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 . Whe n natural gas or petroleum fuels are consumed at the site (for example in a n

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 a n off

- site power plant , site energy is measured in kilowatt

hours ( 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 transpor ting 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 elec tricity 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.

T here is no upstream component for t he latter, because no fuel per se is used

.

10B.2 METHODOLOGY

The mathematical approach to determining FCC is discussed in Coughlin (2012). 2 D etails

on analy zing 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 t h e FFC multiplier is a function of a set of parameters that represent the energy intensity and material losses at each stage of energy production. Th o se parameters depend only on physical data, so the calculations require no assumptions about prices

or othe r economic factor s

. Although the parameter values often differ by geographic region, this analysis utilizes national averages.10B

- 2 The fuel cycle parameters are defined as follows.

• a x is the quantity of fuel x burned per unit of electricity produced , on average, for grid electricity. The calculation of a
x includes a factor to account for losses incurred through the transmission and distribution system s

.

• b y is the amount of grid electricity used in producing fuel y , in MWh per physical unit

of fuel y

.

• c xy is the amount of fuel x consumed in producing one unit of fuel y

.

• q x is the heat content of fuel x (MBtu/physical unit)

.

• z x (s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x produced )

.

All t he parameters are calculated as function s 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 . Fos sil fuel quantities are converted to energy units using the heat content factor q
x
. 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

- to

power

- plant energy use factor is defined as the ratio of the total primary energy consumption by the electric power sector (in quad s ) 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 . A lso 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 FF C 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 AEO201 4

. 4 Table 10B.2

. 1

summa rizes the AEO 201 4 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

q x All Conversion factors MMBtu per physical unit

a x All Electricity supply, disposition, prices, and emissions Generation by fuel type

Energy consumption by sector

and source Electric energy consumption by the power sector

b c , c nc , c pc Coal Coal production by region and

type Coal p roduction by type and

sulfur content

b p , c np , c pp Petroleum Refining industry energy consumption Refining

- only energy use

Liquid fuels supply and disposition Crude supply by source

International liquids supply and disposition Crude oil imports

Oil and gas supply Domestic c rude oil production

c nn Natural gas Oil and gas supply U

. S

. dry gas production

Natural gas supply, disposition, and prices Pipeline, lease, and plant fuel

z x All Electricity supply, disposition, prices, and emissions Power sector emissions

The AEO 201 4 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 AEO201 4

.

10B.3 ENERGY MULTIPLIERS F OR THE FULL FUEL CYC LE

FFC energy multipliers for selected years are presented in Table 10B.3

. 1

. The 2040 value

was held constant f or the analysis period beyond 2040

, which is the last year in the AEO 201 4 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 o n AEO201 4 )

2019 2020 2025 2030 2035 2040

Electricity 1.04 3 1.04 4 1.04 5 1.04 6 1.04 7 1.04 7

Natural gas 1.10 8 1.10 9 1.1 11 1. 113 1. 114 1.1 14

Petroleum fuels 1.1 76 1.1 76 1.1 76 1.1 74 1.1 72 1.1 7010B

- 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/artic le/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-210C-1 APPENDIX 10C. ALTERNATIVE NIA SCENARIOS

10C.1 INTRODUCTION

Th e r eferenc e s cenario presented in chapter 10 o f thi s n o tice of proposed rulemaking ( NOPR ) technical support docu ment ( TSD ) represent s the U.S. Department of Energ y 's ( DOE’s ) b est estimat e o f th e nationa l impact s o f amended standard s f o r ceiling fan light kits ( C FLKs )

. Th e r eferen ce s cenario utili zes th e Annual Energy Outlook 2015 ( AEO 2015 ) 1

r eferen ce e conomi c g rowth scenario f or new housing starts and electricity price trends for shipments projections and NIA. This appendix p r esent s tw o alternativ e s cenario s t hat r epresent
th e r ang e o f p otential impact s o f 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 e lectricity 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 mor e 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- eco nomic 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 analysis 2 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 l ower 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 (b illion 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 0.44410C-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. Wa shington, D.C. (Last accessed June 15, 2015.) http://www.regulations.gov/#!documentD etail;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 N o. LBNL

- 6854E. (Last accessed June 15, 2015.) http://eetd.lbl.gov/publications/the -evolving- price -of-household- led -l

.12A-i

APPE NDIX 12A

. MANUFACTURER IMP ACT ANALYSIS I NT ERVIEW G UIDE

TAB LE OF CONTENTS

12A .1 ENGINEERING ANALYSIS INTERVIEW GUIDE FOR CEILING FAN LIGHT KI TS

................................................................................................ ..................................... 12A-1

12A.2 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE FOR CEILING FAN

LIGHT KITS ............................................................................................................. 12A-10

12A.3 ADDENDUM ........................................................................................................... 12A-2112A

- 1 APPENDIX 12A MANUFACTURER IMPACT ANALYSIS INTERVIEW G UIDE

12A.1 ENGINEERING AN ALYSIS INTERVIEW GUI DE FOR CEILING FAN L IGHT

KITS

Spring 201512A

- 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 cir culating this inte rview 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 re sponses 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.

Preliminary Analys is Follow

- Up

On October 31, 201 4 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 th ese document s and at the November 18 , 2 01 4 public meeting for CFLK and November 19, 2014 public meeting for CF.

1 MARKET ANALYSIS

1.1 T o 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

eac h of the CFLK types listed in the table below, please provide sales data.

1 For more details about the analysis tha t has been done to date, please visit DOE’s website at: http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/6612A

- 3 Table 12A

- 1 CFLK Sales Shares

Lamp Type and Socket Sales share today (2015) Projected s ales share 5 years

from now

Incandescent with m edium 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)

Compa ct 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 so cket 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 ch ange 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 l umen ranges associa ted 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 prod ucts 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 p roduct 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 LE D 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 LE D lamps? If so, please explain.

2.4 Is there a difference in utility to CFLK consumers offered by a T9 circline l amp versus a T5 circline lamp?

2.5 What ar e the issues, if any, associated with designing CFLK with controls that are compatible wi th 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 drive r, rather than the e ntire 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 ef ficacious lamp with the CFLK. Table 12A

- 4 presents the baseline and efficacy12A

- 5 levels achievable by packaging a more efficacious lamp and changing the fixture design (i.e., socket type and numbers). DOE used lamp cat alogs to identify representative lamp units and determine efficacy levels.

3.2 P lease review the information on efficacy and typical characteristics of the representative lamp units and provide further information on design pathways for achieving higher effic acies. 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

I nitial 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 l m lm/W hr lm/W 2014$ 2014$ 2014$ 2014$

Bas eline 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*0.9983 lumens $3.50 $ 39.67 $46.67 15%

EL 2 **** CFL 11 730 66.4 10,000 74

- 29.42*0.9983 lumens $3.88 $ 39.67 $47.44 16%

LED 12 800 66.7 25,000 74

- 29.42*0.998 3 lumens $22.51 $ 39.67 $84.69 53%

EL 3 LED 11 800 72.7 25,000 80

- 29.42*0.9983 lumens $18.67 $ 39.67 $77.00 48%

EL 4 LED 10 800 80 .0 25,000 87

- 29.42*0.9983 lumens $14.82 $ 39.67 $69.31 43%

EL 5 LED 9.5 800 84.2 25,000 91

- 29.42*0.9983 lumens $12.89 $ 39.67 $ 65.44 39%

EL 6 LED 9 800 88.9 25,000 96

- 29.42*0.9983 lumens $10.97 $ 39.67 $61.60 36%

EL 7 LED 8.5 800 94.1 25,000 101

- 29.42*0.9983 lumens $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 Equat ion 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 l m 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*0.9983 lumens $4.27 $0.35 $39.67 $53.54 2 6 %

EL 2 LED 3 E26 8 24 500 1500 62.5 25,000 74

- 29.42*0.9983 lumens $21.67 $0.35 $ 39.67 $105.75 62%

EL 3 LED 3 E26 7 2 1 480 1440 68.6 30,000 80

- 29.42*0.9983 lumens $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*0.9983 lumens $14.82 $0.83 $ 39.67 $70.97 4 4 %

EL 5 LED 3 E26 6.5 19.5 530 1590 81.5 25,000 91

- 29.42*0.9983 lumens $19.94 $0 .35 $ 39.67 $100.56 6 1 %

EL 6 LED 1 E26 17 17 1600 1600 94.1 25,000 96

- 29.42*0.9983 lumens $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*0.9983 lumens $28.07 $0.35 $ 39.67 $68.09 42% *Lamp prices are weighted by est imated 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 b aseline models within a representative product class. The baseline model typically represents the most common, least efficacious lamp available. Do you agree

with the baseline model s 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

. 9999 LLLLLLLLLLLL

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 replaceabl e, 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 techn olog ically feasible ener gy 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 expec ted.

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*0.9983 lumens ( e.g., 8 .5 W LED, 800 lumens, 94.1 lm/W)

5 PRODUCT PRICE DETERM INATION

5.1 For CFLKs sold with a ceiling fan, what percentage of the consumer price of this12A

- 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 wi th 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 p in bases be the same as a

GU24?

5.5 For CFLKs sold with a ceiling fan D OE determined that on average consumers pay a premium of about 2.0 on distributor net prices 2 for the home center channel, 1.90 for the lighting showroom channel, and 1.81 for the electrical distributor channel. Do y ou agree with these estimates? How do th ey ch ange 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 ne t prices are prices at which the distributor purchases the product.12A

- 1 0 12A.2 MANUFACTURER IMPACT ANALYSIS INTERVIEW G UIDE FOR

CEI LING FAN LIGHT KITS

Spring 201512A

- 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 impac ts 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 a greement, 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

conserv ation 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 lig ht 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 Class es for CFs Product Class for CFLKs

Standard Fans All CFLK s
Hugger Fans

High

- Volume Small

- Diameter (HVSD)

Large

- Diameter

Very

- Small Diameter Fans

2 COMPANY OVERVIEW AND ORGANIZATIONAL CHARA CTERISTICS

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 he lp 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 CF s 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 CF LKs within the scope of

t his 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

corre sponds 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 Fan s with Light Kits

Ceiling Fan Light K its (Sold alone)

3 MARKUPS AND PROFITAB ILITY

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 prod uction related assets required to produce a given product. The manufacturer markup is a multiplier applied to manufacturer production costs to cover non

production costs, such as SG&A and R&D, as well as profit. It does not reflect a profit margin.

The ma nufacturer 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 a nd 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 intereste d 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 t he 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

ear nings 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 Amortizati on 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, ov erhead, 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 fina ncial 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 cost s

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 costs12A

- 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. Und erstanding 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

sta ndards 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 incrementa l 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 redesignin g 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 c hanges 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 Small

Diameter (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

Al l CFLKs 1 CFL

2 CFL /LED

3 LED

4 LED

5 LED

6 LED

7 LED12A

- 17 Table 12A

- 13 Expected Product Conversion Costs for Ceiling Fan Light Kits Lamp Replacement Scenario

Product

Class EL Lam p 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 m odifications to existing facilities, or the development of entirely new manufacturing facilities?

6 CUMULATIVE REGULATOR Y 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 s tems 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 t hat 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 com pliance 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, th e 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 IM PACT 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 domes tic 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 a nnual 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 F an 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

512A

- 19 7.2 Woul d your domestic employment levels be expected to change signi ficantly 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

ser vice infrastructure would be impacted in general as a result of energy conservation

standards for CFs and CFLKs?

8 MANUFACTURING CAPACI TY AND NON

- US SALES

8.1 How would amended energy conservation standards impact your company’s CF and CFLK manufacturing capa city?

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 complian ce 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 forei gn 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 COMPETITIO N

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 intel lectual 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 BUS INESSES

10.1 The Small Business Adm inistration (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 definitio n, 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 expert ise, 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 pa rticularly 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

Manufa cturers 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$

Base line 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 × 0.9983 lumens $2.01 $22.15 $26.80 15%

EL 2 ‡ CFL 11 730 66.4 10,000 74

- 29.42 × 0.9983 lumens $2.23 $22.15 $27.24 16%

LED 12 800 66.7 25,000 74

- 29.42 × 0.9 983 lumens $12.93 $22.15 $48.63 53%

EL 3 LED 11 730 72.7 25,000 80

- 29.42 × 0.9983 lumens $10.72 $22.15 $44.22 48%

EL 4 LED 10 800 80.0 25,000 87

- 29.42 × 0.9983 lumens $8.51 $22.15 $39.81 43%

EL 5 LED 9.5 800 84.2 25,000 91

- 29.42 × 0.9983 lumens $7.40 $22.15 $37.58 39%

EL 6 LED 9 800 88.9 25,000 96

- 29.42 × 0.9983 lumens $6.30 $22.15 $35.38 36%

EL 7 LED 8.5 800 94.1 25,000 101

- 29.42 × 0.9983 lumens $5.19 $22.15 $33.17 31%

EL 8 LED 8 820 102.5 25,000 107

- 29.42 × 0.9983 lumens $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 lam p 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 Outp ut 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 l m 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 × 0.9983 lumens $2.45 $0.31 $22.15 $30.45 24%

EL 2 LED 3 E26 8 24 500 150 0 62.5 25,000 74

- 29.42 × 0.9983 lumens $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 × 0.9983 lumens $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 × 0.9983 lumens $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 × 0.9983 lumens $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 × 0.9983 lumens $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 × 0.9983 lumens $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 × 0.9983 lumens $15.71 $0.31 $22.15 $38.18 41%

*Lamp prices are weighted by estimated shipments though distribution channels.
**Socket prices are ba sed 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 f or Representative Product Classes

Product Class Max Tech Level Potential Efficacy Improvement Design Pathway Associated Cost

Increase

All CFLKs 107

- 29.42 × 0.9983 lumens

( e.g ., 8 W LED, 820 lumens, 102.5 lm/W)12B-i

APPE NDIX 12B

. GOVERNMENT REGULATORY IMPACT M ODE L OVE RVIEW

TAB LE OF CONTENTS

12B .1 INTRODUCTION AND PURPOSE .......................................................................... 12B-1

12B.2 CEILING FAN LIGHT KI T DESCRIPTION ............................................................ 12B-1

12B.3 CEILING FAN LIGHT KIT DETAIL ED CASH FLOW EXAMPLE ...................... 12B-412B

- 1 APPENDIX 12B GOVERNMENT REGULATOR Y IMPACT MODEL OVERV IEW

12B.1 I NT R ODUCTION AND P URPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the impacts of energy conservation standards and ot her 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 re gulations 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. T he 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).

Out put from the model consists of summary financial metrics, graphs of major variables, and, when appropriate, access to the complete cash flow calculation.

12B.2 C E ILING FAN LIGHT KIT D ESCRIPTION

DOE analyze d the impacts of standards on c eiling f an l ight k it ( 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 p ortion 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 asset s 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 standard s . The GRIM allocates these

costs ov er the period between the standard s ’ announcement and compliance dates;

( 10 ) Stranded Assets: In the year the standard becomes effective, a one

- time write

- off of

s tranded assets is accounted for ;

(1 1 ) 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 ;

(1 3 ) EBIT/Revenues : EBIT (11) as a percentage of sales to compare with the industry’s average reported in financi al statements;

(1 4 ) Taxes: Taxes on EBIT (1 1 ) are calculated by multiplying the tax rate contained in

the Discounted Cash Flow tab by EBIT (1 1 ) ;

(1 5 ) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Taxes (1 4 )

from EBIT (11 ) ;

(1 6 ) NOPAT r epeated: NOPAT (1 5 ) is repeated in the Statement of Cash Flows;

(1 7 ) Depreciation repeated : Depreciation (5 ) is added back in the Statement of Cash

Flows because it is a non

- cash expense;

(1 8 ) Stranded Assets repeated : Stranded Assets ( 10 ) is added back in the Statement of

Cash Flows because it is a non

- cash expense;

(1 9 ) 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 (1 6 ) , adding back non

cash items such as Depreciation (1 7 ) and Stranded Assets (1 8 ) , and subtracting the Change in Working C apital (1 9 ) ;

(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

) ;

(2 2 ) Capital Conversion Costs : Capital conversion costs ar e 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 regulation s ; t he GRIM allocates these costs over the period between the sta ndard s ’ announcement and compliance dates;

(2 3 ) Free Cash Flow: Calculated by taking a nnual Cash Flow From Operations (20 ) and subtracting Ordinary Capital Expenditures (21) and Capital Conversion Costs (22 ) ;

(2 4 ) Free Cash Flow repeated: Free Cash Flow ( 2 3 ) is repeated in the Discounted Cash Flow section;12B

- 3 (2 5 ) Terminal Value: Estimate of the continuing value of the industry after the analysis period. Computed by growing the Free Cash Flow (2 4 ) at the beginning of 204 8 at a constant rate in perpetuity;

(2 6 ) Present Value Factor: Factor used to calculate an estimate of the present value of an amount to be received in the future;

(2 7 ) Discounted Cash Flow: Free Cash Flow (2 3 ) multiplied by the Present Value Factor (2 6 ) . For the end of 204 8 , the discounted cash flow includes the discounted Terminal Value (2 5 ) ; and

Industry Value thru the end of 204 8 : The sum of Discounted Cash Flows (2 7 )

.12B

- 4 12B.3 CEILING FAN LIGHT KI T D ETAILED C ASH F LOW E XAMPLE14A-i APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXECUTIVE ORDER 12866

TAB LE OF CONTENTS

14A.1 EXECUTIVE SUMMARY ........................................................................................ 14A-1

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS .................................................. 14A-2

14A.3 SOCIAL COST OF CARBO N 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 ESTIMATE S .................................................................................. 14A-26

14A.6 LIMITATIONS OF THE A NALYSIS ..................................................................... 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 ( N on-C O 2 ) G ases

.................................................................... . ........... 14A-44

14A.9.2 Extrapolating Emissions Projections to 2300 ............................................... 14A-46

LI ST 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$/ton CO2) ........................................................................................................ 14A-5414A- ii Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/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-5514A-1 APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXECUTIVE ORDER 12866

14A.1 E XECUTIVE 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 dioxi de (CO 2 ) emissions into cost- benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a clear understanding that they should be updated over time to reflect increasing knowledge of the science 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 interagen cy 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 objecti ve 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 95 th percentile SCC estimate across all three models at a 3 percent discount rate, is included to represent higher

- than -expected impacts from temperature change further out in the tails of the SCC distribu tion.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 unc ertainty, 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 j ustify 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 CO 2 emissions. Alternatively, one could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of

CO 2 and the mass of carbon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12 = 3.67).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 estimat ed 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 acro ss 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 sm all 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 Econom ic 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 ma in 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 th e 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 95 th percentile at a 3 percent discount rate. The central value is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range. These SCC estimates also grow over time. For instance, the central value increases to $24 per ton of CO 2 in 2015 and $26 per ton of CO 2 in 2020. See

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 c ontinue 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 CO 2 and a “global” SCC value of $33 per ton of CO 2 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 CO 2

. A domestic SCC value is meant to reflect the value of damages in

the Unite d 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 CO 2 (in 2006 dollars) for 2011 emission reductions (with a range of $0-$14 for sensitivity analysis), also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a domestic SCC range of $0 to $20 per ton CO 2 for 2007 emission reductions (in 2007 dolla rs). In addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean values were $68 and $40 per ton CO 2 for discount rates of appro ximately 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 CO 2 emissions. Th e 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 t he interagency group was a set of five interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per

ton of CO 2. The $33 and $5 values represented model- weighted means of the published estimates produced from the most r ecently 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 CO 2 tailpipe emission proposed rules.14A-5 14A.4 APPROACH AND KEY ASS UMPTIONS

S ince 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. T his 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 revisab le

, 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 peer

reviewed literature and used in the IPCC assessment. Each model is given equal weight in the SCC values developed through this process, bearing in mind their different limitations (discussed below).

Thes e 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 representa tion of the underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reduced

form approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the possible advantages of this approach). Other IAMs may better refl ect 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 Effec t) model was developed by Chris Hope in 1991 for use by European decision

makers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (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.

T he 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

s pecified 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 cli mate 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 s lightly different approac h t o model how c hanges i n emissions r esult

in cha nge s in econom ic damages. In P AGE

, for e xample, the c onsumption-equivalent da mages in each period are c alculated as a f raction of G DP, depending on the t emperature i n that pe riod

r elative to th e pr e

- industrial av erage temperature in each r egion. In F UND, da mages in each period als o depend on the r ate of t emperature change f rom t he pr ior p eriod. I n DI CE, temperature affects bot h consumption and investment

. We describe each model in gr eater detail

h ere. In a later

sec tion, w e discu ss key g aps in h ow t he models account f or v arious scientific and

economi c p rocesses ( e.g.

, the probability of catastrophe, and the ability to adapt to climate change and the physical changes it cau ses).

T he 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 distribu tion 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 i n a mode in which parameters were treated probabilistically.

T he 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 b een 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 nat ural 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 te mperature 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 implicit ly 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 Model14A-8

The PAG E Mo del

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. Dam ages 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 sub

function. 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 specifi ed threshold. The threshold temperature, the rate at which the probability of experiencing a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature increases above some tolerable level (2°C for developed countries and 0°C for developing countries for economic impacts, and 0°C for all regions for non- economic impacts), but adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of their economic impacts. All regions are assumed to be able to mitigate 25 percent of the non

economic impacts through adaptation (Hope 2006).

The F UND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately

calibrated da mage 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 CO 2 concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a relatively small effect on damage estimates (both positive and negative). However, he characterizes several omitted impacts as “big unknowns”: for instance, extreme climate

scen arios, biodiversity loss, and effects on economic development and political violence. With

d In the deterministic version of FUND, the majority of damages are attrib utable 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 CO 2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate change happens more slowly. The combined effect of CO 2 fertilization in the agricultural sector, positive impacts to some regions from higher temperatures, and sufficiently slow increases in temperature across these sectors can result in negative economic damages from climate change.

Damage Functions

T o 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

models e

The lack of agreement among the models at lower temperature increases is underscored by the fact that the damages from FUND are well below the 5 th percentile estimated by PAGE, while the damages estimated by DICE are roughly equal to the 95 th percentile estimated by

PAGE. This is significant because at higher discount rates we expect that a greater proportion of the SCC value is due to damages in 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 posit ive at less than or greater than 3 °C.14A-11 Fi gure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DIC E, 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 o therwise 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 va lues 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 th e 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

consi derations 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

mo re 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.

Do mestic S CC

A s 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 c ountries, 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 con stant 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 weigh t" 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 CO 2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 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

m easures the ability of different gases to trap heat in the atmosphere ( i.e. , radiative forcing per unit of mass) over a particular timeframe relative to CO 2

. However, because these gases differ in both radiative forcing and atmospheric lifetimes, their re lative 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, CO 2 emissions, unlike methane and other greenhouse gases, contribute to ocean

acidification. Likewise, damages from methane emissions are not offset by the positive effect of

CO 2 fertilization. Thus, transforming gases into CO 2 -equivalents using GWP, and then multiplying the carbon -equivalents by the SCC, would not result in accurate estimates of the

social costs of non- CO 2 gases.

In light of these limitations, and the significant contributions of non

- CO 2 emissions to climate change, further research is required to link non

- CO 2 emissions to economic impacts. Such work would feed into efforts to develop a monetized value of reductions in non- CO 2 greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the interagency group hopes to develop methods to value these other greenhouse gases. The goal is to develop these estimates by the time we issue revised SCC estimates for 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 CO 2 concentration relative to pre

- industrial levels (or stabilization at a concentration of approximately 550 parts per million (ppm)) . Uncertainties in this important parameter have received substantial attention in the peer

- reviewed literature.

The most authoritative statement about equilibrium climate sensitivity appears in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including observed climate change and the strength of known feedbacks simulated in [global climate models], we conclude that the global mean equilibrium warming for doubling CO 2 , or

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- norma l, 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

5 th percentile 1.72 1.49 1.37 1.13

10 th percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90 Median (50 th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90 th percentile 5.86 5.14 4.93 4.69

95 th 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 sp eaking, “most likely” refers to the mode of a distribution rather than the median, but common usage would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC report is not specific on this point. For the distributions we considered, the median was between the mode and the mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode or mean, gave a 95 th

percentile that is more consistent with IPCC judgments and the lit erature. For example, setting the mean and mode equal to 3°C produced 95 th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most consistent with a Roe and Baker distribution with the median set equal to 3 °C.14A-15 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

the oretical 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 95 th percentile of the calibrated Roe & Baker distribution (7.1 °C) is much closer to the mean and the median (7.2 °C) of the 95 th percentiles of 21 previous studies summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006)

than are the 95 th percentiles of the three other calibrated distributions (5.2 -6.0 °C).

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 th e 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 lo w 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. Soc ioeconomic 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 h ow to model several input parameters in tandem: GDP, population, CO 2 emissions, and non- CO 2 radiative forcing. A wide variety of scenarios have been developed and used for climate change policy simulations ( e.g. , SRES 2000, CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed to select scenarios that span most of the plausible ranges of outcomes for these variables.

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 decid ed 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 sc enarios 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 s e lected 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 CO 2 (only) concentrations ranging from 612 to 889 ppm

in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO 2 e ( i i.e.

, CO 2 -only concentrations of 425 – 484 ppm or a radiative forcing of 3.7 W/m 2 ) in 2100, a lowerthan- BAU trajectory. m Out of the 10 models included in the EMF- 22 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 CO 2 e scenario, we averaged the GDP, population, and emission trajectories implied by these same four models.

m Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions,

tho ugh 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 CO 2 Emissions (GtCO 2 /yr)

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1 MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7
MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5 550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP (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 m arket 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. The re 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 unrealistica lly 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 in dexes 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

man y geophysical uncertainties.14A-19 T here are a number of caveats. First, EMF BAU scenarios represent the modelers’ judgment of the mos t 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 CO 2 e concentration scenario are not derived from an assessment of what policy is optimal from a

benefit- cost standpoint. Rather, it is indicative of one possible future outcome. The emission trajectories underlying some BAU scenarios ( e.g. , MESSAGE’s 612 ppm) also are consistent with some modest policy action to address climate change. p We chose not to include

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 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 CO 2 emissions, each EMF scenario provides projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land u se CO 2 emissions out to 2100. These assumptions also are used in the three models while retaining the default radiative forcings due to other factors ( e.g. , aerosols and other gases). See the 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 ag riculture, 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 percen t
in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100.

p For example, MiniCAM projects if all non

- US OECD countries reduce CO 2 emissions to 83 percent below 2005 levels by 2050 (per the G

- 8 agreement) but all other countries continue along a BAU path CO 2 concentrations in 2100 would drop from 794 ppmv in its reference case to 762 ppmv.14A-20 the selected discount rate, which is intended to reflect society's marginal rate of substit ution 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 re vise 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 potentia lly 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 thecurrent 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 underl ies 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-equ ivalent 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.

Historicall

y Observed

I nterest R ates

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.

A ccording 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 ch ange in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The14A-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 assessmen t 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 fore sight 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 certainty

equivalent values), then the appropriate discount rate generally does not equal the risk- free rate. If the benefits of the policy tend to be high in those states of the world in which consumption is low, then the certainty -equivalent benefits will be higher than the expected benefits (and vice versa). Since many (though not necessarily all) of the important impacts of climate change will

flow through market sectors such as agriculture and energy, and since willingness to pay for environmental protections typically increases with income, we might expect a positive (though not necessarily perfect) correlation between the net benefits from climate policies and market

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless

rate. Alternatively, a negative correlation between the returns to climate policies and market returns would imply that a discount rate below the riskless rate is appropriate.

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 avera ge 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 correlat ed 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

i nterest. 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 Treasu ry 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 avers ion 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 revie w 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

D asgupta (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 inter

generational 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 fut ure. 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 consumpt ion 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 soci oeconomic 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 Jone s (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 la bor 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

establis hed 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, combin ed 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 uncerta inty, 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, low er 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 uncer tainty 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 techn ical 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 v olatility 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, othe rs 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 top ic 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 inte rest 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.

T he 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 a fter

- 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 m any consumers use to smooth consumption across periods.

T he 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) argu es 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 (2 008) 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 ESTIMATE S

Our general approach to estimating SCC values is to run the three i ntegrated assessment models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds.

• Five sets of GDP, population, and carbon emissions trajectories based on EMF -22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and FUND incorporate uncertainty in other model parameters, the final output from each model run is a distribution over the SCC in year t

.

For each of the IAMs , the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 scenarios, and the extrapolations based on these scenarios for post- 2100 years.

2. Calculate the temperature effects and (consumption -equivalent) damages in each year resulting from the baseline path of emissions.

a. In PAGE, the consumption- equivalent damages in each period are calculated as a fraction of the EMF GDP forecast, depending on the temperature in that period relative to the pre

- industrial average

te mperature 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 th e 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 i n 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

CO 2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO 2 in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.

This is an issue for the multi -model approach because differences in SCC estimates may arise simply due to the model time horizon. Many consider 2200 too short a time horizon because it could miss a significant 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 a vailable 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 scen arios. 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 than14A-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

inte rgenerational 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 scenariosat the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount

rate. (The full set of distributions by model and scenario combination is included in the 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 sens itivity. 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 probabi listic 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, w hile 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 95thDICE 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 50.8PAGE 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 42.9 104.7FUND 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. An thoff 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 is14A-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. Furthermor e, 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 les s 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 forcing14A-31 Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollar s)

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 assumption s. 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 are14A-32 calculated using a SCC based on a 5 percent discount rate also should be discounted back to the analysis year using a 5 perc ent discount rate. bb

14A.6 LIMITATIONS OF THE A NALYSIS

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 under standably 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 CO 2 emissions not quantified by any of the three models. Species and wildlife loss is another example that is exceedingly difficult to monetize.)

Incomplete treatment of p otential 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 domin ate 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 a s 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 CO 2 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 a re 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 c alibrated 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 po tential adaptation or technical change that might alter the emissions pathway and resulting damages. In this respect, it is difficult to determine whether the incomplete treatment of adaptation and technological change in these IAMs

under state or overstat e 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 95 th percen tile 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 c osts 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 b e 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 unc ertainty 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 investigatin g 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 mod elers 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

T he damage functions in the models are calibrated at moderate levels of warming and should therefore be viewed cautiously when extrapolated to the high temperatures found in the upper end of the distribution. Recent science suggests that there are a number of potential climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with potentially severe social and economic consequences ( e.g. , Lenton et al, 2008, Kriegler et al., 2009). These tipping points include the disruption of 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 tippin g 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 probabil ity 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 95 th percentile and a 3 percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission trajectories of $113 per ton of CO 2 is almost double the value estimate d by DICE, $58 per ton in

2010. We cannot evaluate how well the three models account for cata strophic 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 Probabiliti es 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, Kriegl er 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 (f or 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

Th e 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). Th ese inter -regional interactions, though difficult to quantify, are the basis for climate- induced national and economic security concerns ( e.g. , Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at higher levels of 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).

Imp erfect Substitutability o f 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- Eoc ene 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).

T he 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 attem pts 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 se lected 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 95 th percentile at a 3 percent discount rate. The central value is the average SCC across models at the 3 percent discount rate. For purposes of14A-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 CO 2 in 2015 and $26 per ton of CO 2 in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which

the int egrated 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

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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

- CO 2 emission projections used in the modeling and the method for extrapolating emissions forecasts through 2300 and shows the full distribution of 2010 SCC estimates by model and scenario combination.14A-44 14A.9.1 Ot her ( N on -CO 2 ) G ases

In addition to fossil and industr ial CO 2 emissions, each EMF scenario provides projections of methane (CH 4 ), nitrous oxide (N 2 O), fluorinated gases, and net land use CO 2 emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s default radiative forcings (RF) due to other factors ( e.g. , aerosols and other gases). Specifically,

to obtain the RF associated with the non- CO 2 EMF emissions only, we calculated the RF associated with the EMF atmospheric CO 2 concentrations and subtracted them from the EMF

total RF. dd This approach respects the EMF scenarios as much as possible and at the same time takes account of those components not included in the EMF projections. Since each model treats

non- CO 2 gases differently ( e.g. , DICE lumps all other gases into one composite exogenous input), this approach was applied slightly differently in each of the models.

F UND : Rather than relying on RF for these gases, the actual emissions from each

scenario were used in FUND. The mode l default trajectories for CH 4 , N 2 0, SF 6 , and the CO 2 emissions from land were replaced with the EMF values.

P AGE : PAGE models CO 2 , CH 4 , sulfur hexafluoride (SF 6 ), and aerosols and contains an "excess forcing" vector that includes the RF for everything else. To include the EMF values, we

removed the default CH 4 and SF 6 factors ee , decomposed the excess forcing vector, and constructed a new excess forcing vector that includes the EMF RF for CH 4 , N 2 0, and fluorinated gases, as well as the model default values for aerosols and other factors. Net land use CO 2 emissions were added to the fossil and industrial CO 2 emissions pathway.

DI CE : DICE presents the greatest challenge because all forcing due to factors other than

industrial CO 2 emissions is embedded in an exogenous non- CO 2 RF vector. To decompose this exogenous forcing path into EMF non- CO 2 gases and other gases, we relied on the references in DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report (TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing

from all non

- CO 2 sources is -0.06 W/m 2 in 2005, as reported in AR4, and increases linearly to

0.3 W/m 2 in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH 4 , N 2 0, and halocarbons (approximately

similar to the F

- gases in the EMF

- 22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m 2 and RF

from total aerosols was

- 1.2 W/m 2 . Thus, the - .06 W/m 2 non- CO 2 forcing in DICE can be decomposed into: 0.98 W/m 2 due to the EMF non- CO 2 gases, -1.2 W/m 2 due to aerosols, and the remainder, 0.16 W/m 2 , due to other residual forcing.

dd No te EMF did not provide CO 2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed the fossil, industrial

, and land CO 2 emissions into MAGICC (considered a "neutral arbiter" model, which is tuned to emulate the major global climate models) and the resulting CO 2 concentrations were used. Note also that MERGE assumes a neutral biosphere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use emissions from the other three models and divide by 4).

ee Both the model default CH 4 emissions and the ini tial atmospheric CH 4 is set to zero to avoid double counting the effect of past CH 4 emissions.14A-45For subsequent years, we calculated the DICE default RF from aerosols and other nonCO 2 gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by t he TAR and then stays constant thereafter ; and (2) With respect to RF from non

- CO 2 gases not included in the EMF -22 scenarios, the share

of non- aerosol RF matches the share implicit in the AR4 summary statistics cited above and

remains constant over time.

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000,

which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black carbon, and organic carbon) in 2100 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 carbo n 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

wit h 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/m 2 ; forcing due to other non- CO 2 gases not included in the EMF scenarios dec lines from

0.160 to 0.153 W/m 2

.

ff AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment

- report/ar4/syr/ar4_ syr.pdf

gg See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850

- 2000: methods and results. Joint Global Research Institute, College Park, 14 pp.

hh See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Ja cobson, 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, 5 th , and 95 th per centile o f th e f requency d istributio n f

o r th e f ull ensemble of 40 SRES scenarios.

The blue area (and the thin dashed lines in blue) illustrates individual scenarios and the range of Smith et

al. (2004). Dotted lines indicate the minimum and maximum of SO 2 emissions scenarios developed p re

SRES.

Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-e ns3 -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 pe rcent (or $0.50-$3)–depending on the

discount rate. These differences increase slightly for SCC values in later years but are still well within 10 percent of each other as far out as 2050.

Finally, as in PAGE, the EMF net land use CO 2 emissions are a dded to the fossil and

industrial CO 2 emissions pathway.

14A.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which these projections are available from the EMF -22 models. These inputs were extrapola ted from

2100 to 2300 as follows:

1. P opulation 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 (CO 2 /GDP) growth rate over 2090- 2100 is maintained from 2100 through 2300.

4. Net land use CO 2 emissions decline linearly, reaching zero in the year 2200.

5. Non

- CO 2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capi ta 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 e nvironmental 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 o f 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. , CO 2 per dollar of GDP) through 2300 assumes that technological improvements and innovations in the areas of energy efficiency and other carbon reducing technologies (possibly including currently unavailable methods) will continue to proceed at roughly the same pace that is projected to occur towards the end of the forecast period for each EMF scenario. This assumption implies that total cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range of the total potential global carbon stock estimated in the literature.

Net land use CO 2 emissions are expected to stabilize in the long run, so in the absence of any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori reasons for assuming a long run increase or decline in non- CO 2 radiative forcing, it is assumed to remain at the 2100 levels for each EMF scenario through 2300.

Figures below show the paths of global population, GDP, fossil and industrial CO 2 emissions, net land CO 2 emissions, non- CO 2 radiative forcing, and CO 2 intensity (fossil and

industrial CO 2 emissions/GDP) resulting from these assumptions.

ii United Nations. 2004. World Population to 2300

. http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf14A-48 Fig ure 14A.9.2 Global Population, 2000-2300 (Post-2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.)

Note: In the fifth scenario, 2000-2100 population is equal to the average of the population under the 550 ppm

CO 2 e, full-participation, not-t o -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

ExtrapolationsGlobal Population [billion]

Year

0 500 1000 1500 2000

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

YearWorld GDP [$trillion]

Note: In the fifth scenario, 2000-2100 GDP is equal to the average of the GDP under the 550 p pm CO 2 e, fullparticipation, not-t o -e xceed sc enarios considered

b y ea ch o f

t he four m odels.14A-49 Figur e 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)

No te : I n th e f

ifth scenario, 2000-2100 emissions are equal to the average of the emissions under the 550 ppm

CO 2 e, full-participation, not-t o -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

ExtrapolationsFossil&Industrial CO2 Emissions [GtCO2/decade]

Year14A-50 Fi gure 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

CO 2 e, full-participation, not-t o -exceed scenarios considered by each of the four models.

jj MERGE assumes a neutral biosphere so net land CO 2 emissions are set to ze ro 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

ExtrapolationsNet land CO2 emissions [GtCO2/yr]

Year z14A-51 Fig ure 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

CO 2 e, full-participation, not-t o -exceed scenarios considered by each of the four models. 0.00 0.75 1.50 2.25 3.00 3.75

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsnonCO2 RF [W/m^2]

Year14A-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

ExtrapolationsFossil & Industrial CO2 intensity [GtCO2/trillion $]

Year

Note: In the fifth scenario, 2000-2100 emis sions are equal t o the averag e o f t he emissions under the 550 ppm

CO 2 e, full-participation, not-t o -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 96.814A-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 53.614A-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

Fi gure 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-a x is has been

truncated at approximately the 1 st and 99 th p ercentiles to b etter sh ow t h e data. 0 2000 4000 6000 8000 10000 12000 14000 16000-25-12114274053667992105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%14A-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 23,558.3014B-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 D amage 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 Agric ulture .............................................................................................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 Ada ptation ..............................................................................................14B-8

14B.3.3.6 Other Noteworthy Changes ....................................................................14B-8

14B.4 REVISED SCC ESTIMATE S .....................................................................................14B-8

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ....................................14B-11

REFERE NCES ......................................................................................................................14B-12

14B.6 ANNEX ......................................................................................................................14B-14

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO 2 , 2010 – 2050 (in 2007 dollars per ton of

CO 2 ) ...........................................................................................................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 CO 2 ) ..................................14B-14

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 ) .........................................................................................................14B-15

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2 007$/ton CO2) .................14B-15

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 ) .........................................................................................................14B-1614B- ii LIST OF FIGURES

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 ) ............14B-1014B-1 APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 1286 6

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

re viewed 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 ve rsion 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 c limate 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 CO 2 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 CO 2 emissions. Alternatively, one could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of

CO 2 and the mass of carbon is 3.67

. 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). 114B-2 14B.3 SUMMARY OF MODEL UPD ATES

This section briefly summarizes changes integrated into the most recent versions of t he 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 rep resentation 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 da mages. 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 se ctor, 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

decad e, 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). 414B-3 T he 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 glacie rs and small ice caps occurs at a rate of 0.008 meters per decade per °C above the average global temperature in 1900.

T he 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 se a level anomaly, where the constant of proportionality increases with the temperature anomaly in the current period.

T he 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

- C alibrated D amag e Function

E conomic 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 consump tion 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). 614B-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 temperatu re 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 t o avoid double-counting damages from sea level rise that were implicitly included in these parameters in DICE2007.

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3), 3 who notes that “...damages in the uncontrolled (baseline) ( i.e. , reference) case ... in 2095 are $12 trillion, or 2.8 percent of global output, for a global temperature increase of 3.4 o C above 1900 levels.” This compares to a loss of 3.2 percent of global output at 3.4 o C in DICE2007. However, in DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages 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 he ating, 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 associa ted 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 ne eds 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, lan d 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 a ny 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 S CC. 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 wit h 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 pote ntial 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 movinginland. 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 o f 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 distri butions. Within this specification, as draws from the distribution in the denominator approached zero the share of the sector’s value “lost” approaches (+/- ) infinity independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic

coef ficients are drawn directly from truncated normal distributions so that they remain in the

range [0, ) ∞ and ( ,0] −∞ , respectively, where the means for the new distributions are set equal to the ratio of the means from the normal distributions used in the previous version. In general the impact of this change has been to increase the likelihood that increases in the temperature level will have either larger positive or negative effects on the agricultural sector relativ e 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 d efined as a decreasing linear function of equilibrium climate sensitivity to capture the fact that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the equilibrium climate sensitivity. In FUND 3.8, the rate of tempe rature response has been updated to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore in FUND 3.8, the temperature response will typically be faster than in the previous version. The overall effect of this change is likely to increase estimates of the SCC as higher temperatures are reached during the timeframe analyzed and as the same damages experienced in the pr evious version of the model are now experienced earlier and therefore discounted less.

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 potentia l of methane (Forster et al. 2007). 8 FUND 3.8 now includes the same methods for incorporating the indirect effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative forcing associated with atmospheric methane has also been increase by 40% to account for its net 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 l evel 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 Hop e (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 damage14B-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 larg e 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

t he 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

full y 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 CO 2 absorption by the terrestrial biosphere and ocean as the temperature rises. This feedback is linear in the average global and annual temperature anomaly but is capped at a maximum value. In the previous version of PAGE, an additional amount was

added to the CO 2 emissions each period to account for a decrease in ocean absorption and a loss of soil carbon. Also updated is the method by which the average global and annual temperature anomaly is downscaled to determine annual average regional temperature anomalies to be used in the regional damage functions. In the previous version of PAGE, the scaling was determined solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional temperature anomaly is further adjusted using an additive factor that is based on the average

absolut e latitude of a region relative to the area weighted average absolute latitude of the Earth’s

landmass.

14B.4 REVISED SCC ESTIMATE S

The updated versions of the three integrated assessment models were run using the same methodology detailed in the 2010 TSD. 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 distrib ution 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 mode l 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 CO 2 , 2010 – 2050 (in 2007 dollars per ton of CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 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 CO 2 )

As was the case in the original TSD, the SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. The approach taken by the interagency group is to allow the growth rate to be determined endogenously by the models 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 emissi ons today or emissions in a later year, should be discounted using the same rate.14B-11 14B.5 OTHER MODEL LIMITATI ONS OR RESEARCH GAPS

The 2010 interagency SCC technical support report discusses a number of important

limitations for which additional research is neede d. In particular, the document highlights the need to improve the quantification of both non- catastrophic and catastrophic damages, thetreatment of adaptation and technological change, and the way in which inter-regional and intersectoral linkages are mo deled. 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 impli cations 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. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change . S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. 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 giv en 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 t he 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

.

10. Hope, C. The PAGE09 Integrated Assessment Model: A Technical Description. 2011. University of Cambride Judge Business School. (Last accessed June 10, 2015.) http://www.jbs.cam.ac.uk/fileadmin/user_u pload/research/workingpapers/wp1104.pdf

.

11. Hope, C. The Social Cost of CO2 from the PAGE09 Model. 2011. University of Cambride Judge Business School. (Last accessed June 10, 2015.) http://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1105.pdf

.

12. Hope, C. New Insights from the PAGE09 Model: The Social Cost of CO2. 2011. University of Cambride Judge Business School. (Last accessed June 10, 2015.) http://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1108.pdf

.

13. Hope, C. The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern. The Integrated Ass essment
Journal . 2006. 6(1): pp. 19–56. (Last accessed June 10, 2015.) http://journals.sfu.ca/i nt_assess/index.php/iaj/article/view/227/190

.14B-14 14B.6 ANNEX

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 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 22114B-15 Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95 th 99th
Scenario PAGE
IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918
MiniCAM Base 5 9 12 22 47 107 113 266 431 872
5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE
IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221
MiniCAM Base 20 25 29 38 53 61 76 102 117 135
5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND
IMAGE

- 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 6314B-16 Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th
Scenario PAGE
IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216
MiniCAM Base 1 1 2 3 8 21 21 54 94 190
5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE
IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25
MiniCAM Base 5 6 7 8 11 12 14 18 20 22
5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND
IMAGE

- 9

- 5

- 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 1417A-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-617A-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 m ethod 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 S tate tax credits for appliances

.17A-3 17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

T able 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 t hese market share increases as inputs to the NIA -RIA spreadsheet model.

T able 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for All CFLKs (TSL 2)

Year C onsumer
Rebates Consumer

Tax Credits Ma nufacturer

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

- RIA a model appro ach that the Department built on the NIA model discussed in Chapter 10 and documented in Appendix 10-A.The resulting integrated NIA

- RIA model feature s both the NIA and RIA inputs, analys es and results

. It has the capability to generate results , by product class and TSL, for the mandatory standards and each of the RIA polic ies. Separate modules estimate increases in marke t 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 ; t he 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 estimat e the market effects of such a reduction; and t he 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 S ection 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 a nalyze the Consumer Rebates and Voluntary Efficacy Targets polic ies . 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 C hapter 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 purchasi ng energy efficient appliance s. 1 The basic premise of the mixed

- source model is that informati on 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 wo rd 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 log istic 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 s ources 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 Fi gure 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 respo nse 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:

Mo derate Barriers: 70% High Barrier s: 60% Extremely High Barriers: 50%

Th e low barriers and no barriers curves (the latter used only when a product has a very high base- case -market share) remained, respectively, with 80 percent and 100 percent as their maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set

the no barri ers and extremely high barriers curves as the upper and lower bounds, respectively, for any benefit/cost ratio points higher or lower than the curves. It set another constraint such that the policy case market share cannot be great than 100 percent, as mig ht 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 implem entation 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 interpolati ons 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 interpolat e 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 increase s resulting from consumer tax credit and manufacturer tax credit programs, since the effects of tax credits on market s are considered in this RIA proportional

to the impacts from rebate s

.17A-8 17A.5 CONSUMER REBATE PROGRAMS

DOE performed an internet search for rebate programs that offered incentive s for CFLKs in the first quarter of 2015. Some organizations nationwide, comprising elect ric utilities and regional agencies, offer rebat e 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. Firs t
DOE calculated the simple average of the rebate value s 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 T able 17A.5.1 Rebates Programs for Ceiling Fan Light Kits f

Organization State Rebate * Website

B right E nergy S olutions ( o f f er ed b y 1 6 u tilities) I A $ 25 h ttp://www.brightenergysolutions.com/_files/pdf/

g en erated/ENERGYSTARCeilingFanDenison

Mu n icipalUtilities.pdf#zoom=100

C o ldwate r Board

o f Public

Utilities MI $ 15 h ttp://www.cold water.org/Content/documents / simply_efficient_residential_incentive_app.pd f

Energy O ptimization (offered b y 1 1 u tilities) MI $ 15 h ttp://www.michigan

en er gy.org/algerproducts

Lansing Board of Water & Light

- Ho metown Energy

Sav ers MI $ 15 h ttp://www.lbwl. com/energysaver s.aspx

Au stin Ut ilities MN $ 15 h ttp://www.austinutilities.com/p ages/residenti a l

conserve -i n centives/

B right E nergy S olutions ( o f f er ed b y 2 2 u tilities) MN $ 25 h ttp://www.brig h tenergysolutio n s.org/municip alities/?category=home&state=mn

Bright E nergy S olutions (offered b y 5 u tilities) ND $ 25 h ttp://www.brig h tenergysolutio n s.com/munici

p alities/?category=home&state= n d

Fir st E nergy OH $ 30 h ttp://energysaveohio

h o me.com/uploads/eepp/FE_OH_LightHH_Reb ate_final2_LR.pdf

American M unicipal

Po wer - Ef

f iciency S mart OH $ 15 h ttp://www.efficiencysmart.org/Media/Docum ents/ EfficiencySmart -R eb ate -F o r m -2 014.pdf

B right E nergy S olutions ( o f f er ed b y 1 0 u tilities) SD $ 25 h ttp://www.brig h tenergysolutio n s.com/munici

p alities/?category=home&state= sd

* I n 2014$.

17A .6 FEDERAL AND STATE TAX CR EDITS

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 eli gibility 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 J anuary 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 impac ts of the relatively large credits that were available for HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors conducted by Vermont Energy Investment Corporation, respondents indicated that the ample

credit had had a notable impact on sal es of higher-efficiency heating and cooling equipment.

Some distributors combined the Federal tax credits with manufacturer re bates and ut ility

program rebates for a greater consumer incentive. However, when the amount of the Fed eral tax

credit was reduced , smaller utility rebate incentives had not ind uced the same levels of equipment sales increases . Th e decrease in incentive size from a $1,500 cap in 2009-2010 to a $500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a decline in total sales of residential HVAC 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 l arge majority of distributor s 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 En ergy Credits

. 14 It also estimated the percentage of taxpayers with entries under Form 5695’s section 3, Residential energy property costs , line 3b, qua lified 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 furnace s 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 pr ogram 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 da ta 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

re asons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 2005 energy tax credit program data for its first two program years.

As discussed in C hapter 17, DOE analyzed the percentage of participation in consumer

tax cr edit 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 manufactu rers that produced high-efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007. 18

The Emergency Economic Stabilization Act of 2008 19 amended the credits and extended them through 2010. The credits were extended again to 2011 with modifications in the eligibility requirements. Manufacturer tax credits were extended again, by the American Taxpayer Relief Act of 2012, for clothes washers, refrigerators, and dishwashers manufactured between January 1, 2012 and December 31, 2013.

Manufacturers who pr oduce these appliances receive the credits for increasing their production of qualifying appliances. These credits had sev eral efficiency tiers in 2011. For 2012- 2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers

for cloth es washers and dishwashers

. 11 The credit amounts applied to each unit manufactured. The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers wa s capped at $75 million for the period of 2008-2010. However, the most efficient refrigerator (30%) and clothes washer (2.2 MEF/4.5 wcf) models was not subject to the cap. The credit to

manufacturers wa s capped at $25 million for 2011, with t he most 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 offe ring such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on taxpayer participation in credits for eligible products. (See the discussion in Chapter 17, S ection 17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not disaggregate participation data by appliance, although DOE reviewed Montana's overall17A-12 participation trends and found them congruent with its analysis of Oregon's clothes washer tax

credits.

Oregon’s Residential Energy Tax Credit (RE TC) was created in 1977. The Oregon legislature expanded the RETC program in 1997 to include residential refrigerators, clothes washers, and dishwashers, which significantly increased participation in the program . The program subsequently added c redits 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 ta x 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 REFERENCES

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