TECHNICAL SUPPORT DOCUMENT: ENERGY CONSERVATION PROGRAM FOR

CONSUMER PRODUCTS : ENERGY CONSERVATION STANDARDS FOR HEARTH

PRODUCTS

January 30, 2015

U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Building Technologies Program

Appliances and Commercial Equipment Standards

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

by the staff members of

Navigant Consulting, Inc.

and

Lawren ce Berkeley National LaboratoryCHAPTER 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 STANDARD S FOR HEARTH PRODUCT S

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

- 6

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

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

- 7

1.5 STRUCTURE OF THE DOC UMENT

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

- 9

LIST OF TABLES

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

Energy Conservation Standards for Hearth Products (Trial Standard Level

1)*

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

- 3

Table 1.2.2 Annualized Benefits and Costs of Proposed Energy Conservation

Standards for Hearth Products (Trial Standard Level1 )

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

- 5

Table 1.4.1 Analyses Under the Process Rule

.......................... Error! Bookmark not defined.

1

- iCHAPTER 1. INTRODUCTION

1.1 P URPOSE OF THE D OCUMENT

This technical support document ( TSD ) is a stand

- alone report that provides the technical

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

(NO PR ) for hearth products

.

1.2 SUMMARY OF THE NATIO NAL BENEFITS

The U.S. Department of Energy’s ( DOE ) analyses indicate that the proposed energy

conservation standard to disallow the use of continuously burning pilots ( i.e. , standing pilots) for

hearth products would save a significant amount of energy. The lifetime energy savings for

hearth products purchased in the 30

- year period that begins in the first full year of compliance

with new standards (202 1

– 20 50 ) amount to .6 9 quads a of full

- fuel

- cycle energy. This represents

a savings of about 77 percent relative to the energy use of the ignition syste ms in the base case

, i.e., without the proposed energy conservation standards.

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

proposed hearth products energy conservation standard ranges from $ 1.0 3 billion to $ 3.12 bill ion

at 7

- percent and 3

- percent discount rates, respectively. This NPV expresses the estimated total

value of future operating

- cost savings minus the estimated increased product costs for hearth

products purchased in 202 1

– 20 50

.

In addition, the proposed hearth products pilot standard would have significant

environmental benefits. The energy savings described above are expected to result in cumulative

full

- fuel cycle emission reductions of 37.0 million metric tons (Mt) b of carbon dioxide (CO 2 ),

486 thousand tons of methane (CH 4 ), .01 thousand tons of nitrous oxide (N 2 O), .26 thousand tons

of sulfur dioxide (SO 2 ), 125 thousand tons of nitrogen oxides ( NO x ) , and 0. 01 tons of mercury

(Hg). c The cumulative reduction in CO 2 emissions through 2030 amounts to 1 1.1 Mt.

The value of the CO 2 reductions is calculated using a range of values per metric ton of

CO 2 (otherwise known as the s ocial c ost of c arbon, or SCC) developed by a recent f ederal

interagency process. d The derivation of the SCC values is discussed in chapter 14 of the NOPR

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 Annual Energy Outlook 2013 ( AEO 2013 ) Reference case,

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

were available as of December 31, 2012.

d Technical Update of the Social Cost of Carbon f or Regulatory Impact Analysis Under Executive Order 12866

, Interagency Working Group on Social Cost of Carbon, U

. S

. Government ( May 2013; revised November 2013 )

(Available at: http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical

- update

- social

- cost

- of

carbon

- for

- regulator

- impact

- analysis.pdf )

.

1-1TSD

. Using discount rates appropriate for each set of SCC values, DOE estimates the present

monetary value of the CO 2 emissions reduction is between $0. 2 billion and $ 3.4 billion , wit h the

value of $1.1 billion using the central SCC case represented by $40.5 per ton in 2015

. Additionally, DOE estimates the present monetary value of the NO x emissions reduction to be

$0. 06 b illion to $ 0. 15 b illion at 7

- percent and 3

- percent discount rat es, respectively. e

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

the proposed pilot standards for hearth products

.

e DOE is currently investigating valuatio n of avoided Hg and SO 2 emissions.

1-2Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed Pil ot

Energy Conservation Standards for Hearth Products (T rial Standard

Level 1 )*

Category Present

Value

Billion 2013 $ Discount

Rate

%

Benefits

Consumer Operating Cost Savings 1.5 7

4.1 3

CO 2 r eduction m onetized v alue ($12.0/t case)** 0.2 5

CO 2 r eduction m onetized v alue ($40.5/t case)** 1.1 3

CO 2 d eduction m onetized v alue ($62.4/t case)** 1.8 2.5

CO 2 r eduction m onetized v alue ($119/t case)** 3.4 3

NO x r eduction m onetized v alue (at $2,684/ton)** 0.1 7

0 .1 3

Total b enefits† 2.7 7

5.4 3

Costs

Consumer i ncremental i nstalled c osts 0. 5 7

1

. 0 3

Total Net Benefits

Including e missions r eduction m onetized v alue† 2.2 7

4.4 3

* This table presents the costs and benefits associated with hearth products shipped in 202 1 −20 50 . These results

include benefits to consumers which accrue after 20 50 from the products purchased in 202 1 −20 50

. The results

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

may be incurred in prepar ation for the rule.

* * The CO 2 values represent global monetized values of the SCC, in 2013$, 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 used by DOE incorporate an escalation factor. The value for NO x is the

average of the low and high values used in DOE’s analysis.

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

- percent discount rate ($40.5/t in 2015)

.

The benefits and costs of the proposed energy conservation standards for hearth products

sold in 202 1

– 20 50 , 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 proposed ne w or amended standards (consisting

primarily of operating cost savings from using less energy, minus increases in product purchase

and installation costs, which is another way of representing consumer NPV), and (2) the

1-3annualized monetary value of the bene fits of emission reductions, including CO 2 emission

reductions. f

Although combining the values of operating savings and CO 2 emission reductions

provides a useful perspective, two issues should be considered. First, the national operating

savings are dom estic U.S. consumer monetary savings that occur as a result of market

transactions, whereas the value of CO 2 reductions is based on a global value. Second, the

assessments of operating cost savings and CO 2 savings are performed with different methods that

use different timeframes for analysis. The national operating cost savings is measured for the

lifetime of hearth products shipped in 20 2 1

– 20 50

. The SCC values, on the other hand, reflect

the present value of some future climate

- related impacts resulting from the emission of one ton

of carbon dioxide in each year. These impacts continue well beyond 2100.

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

Table 1.2.2

. The results under the primary estimate are as follows. Using a 7

- percent discount

rate for benefits and cos ts other than CO 2 reduction ( for which DOE used a 3

- percent discount

rate along with the average SCC series that uses a 3

- percent discount rate ($40.5 per ton in

2015)) , cost of the hearth standards proposed in today’s rule is $ 61.1 million per year in

inc reased equipment costs, while the estimated benefits are $ 186 million per year in reduced

equipment operating costs, $ 67 million in CO 2 reductions, and $ 7.0 million in reduced NO x

emissions. In this case, the net benefit would amount to $ 199 million per year. Using a 3

- percent discount rate for all benefits and costs and the average SCC series that uses a 3

- percent

discount rate ($40.5 per ton in 2015) , the estimated cost of the hearth products standards

proposed in today’s rule is $ 61.2 million per year in increased equipment costs, while the

estimated benefits are $ 251 million per year in reduced equipment operating costs, $ 67 million in

CO 2 re ductions, and $ 9.0 million in reduced NO x emissions. In this case, the net benefit would

amount to $ 266 millio n per year.

f DOE used a two

- step calculation process to convert the time

- series of costs and benefits into annualized values.

First, DOE calculated a present value in 2014 , the year used for discounting the NPV of total consumer costs and

savings, for the time

- series of costs and benefits using discount rates of 3 and 7 percent for all costs and benefits

except for the value of CO 2 reductions. For the latter, DOE used a ran ge of discount rates, as shown in Table I.7

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

- year period ( 202 1 through 20 50 )

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

annualized values, this does not imply that the time

- series of cost and benefits from which the annualized values

were determined is a steady stream of payments.

1-4Table 1.2.2 Annualized Benefits and Costs of Proposed Energy Conservation

Standards for Hearth Products (T rial Standard Level 1 )

Discount Rate

% Primary

Estimate* Low Net

Benefits

Estimate* High Net

Benefits

Estimate*

million 2013$/year

Benefits

Consumer o perating

c ost s avings 7 186 175 195

3 251 235 265

CO 2 r eduction

m onetized v alue

($12.0/t case)** 5 20 20 20

CO 2 r eduction

m onetized v alue

($40.5/t case)** 3 67 67 67

CO 2 r eduction

m onetized v alue

($62.4/t case)** 2.5 98 98 98

CO 2 r eduction

m onetized v alue

($119/t case)** 3 207 207 207

NO x r eduction

m onetized v alue (at

$2,684/ton) 7 7.00 7.00 7.00

3 8.99 8.99 8.99

Total b enefits† 7 plus CO 2 range 212 to 400 202 to 389 222 to 410

7 260 249 269

3 plus CO 2 range 280 to 468 264 to 452 294 to 482

3 327 311 341

Costs

Consumer

i ncremental i nstalled

c osts 7 61.1 61.1 61.1

3 61.2 61.2 61.2

Net b enefits

Total† 7 plus CO 2 range 151 to 399 141 to 328 161 to 349

7 199 188 208

3 plus CO 2 range 219 to 407 203 to 390 233 to 420

3 266 250 280

1-5* This table presents the annualized costs and benefits associated with hearth products shipped in 2021

- – 2050.

These results include benefits to consumers which accrue after 2050 from the products purchased in 2021

- 2050.

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

of which may be incurred in preparation for the rule. The p rimary, l ow b enefits, and h igh b enefits e stimates utilize

projections of energy prices from the AEO 2014 Reference case, Low Estimate, and High e stimate, respectively.

Incremental product costs are the same for each Estimate.

** The CO 2 values represent global monetized values of the SCC, in 2013$, 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 distri bution calculated using a

3% discount rate. The SCC time series used by DOE incorporate an escalation factor. The value for NOx is the

average of high and low values found in the literature.

† Total b enefits for both the 3% and 7% cases are derived usin g the series corresponding to the average SCC with a

3

- percent discount rate ($40.5/t in 2015). In the rows labeled “7% plus CO 2 range” and “3% plus CO 2 range,” the

operating cost and NOx benefits are calculated using the labeled discount rate, and those values are added to the full

range of CO 2 values.

The Energy Policy and Conservation Act of 1975 (EPCA), as amended, requires the U.S.

Department of Energy (DOE) to establish energy conservation standards that are technologically

feasible and economically justified, and would achieve the maximum improvement in energy

efficiency. See 42 U.S.C. 6295(o)(2)(B). DOE has tentatively concluded that the proposed

standards represent the maximum improvement in energy efficiency that is technologically

feasible and e conomically justified, and would result in the significant conservation of energy.

DOE further notes that for the products covered by this proposal, products achieving these

standard levels ( i.e. hearth products that do not use a standing pilot ignition system) are already

commercially available. Based on the analyses described previously, DOE has tentatively

concluded that the benefits of the proposed standards to the Nation (energy savings, pos itive

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).

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

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

may adopt the standard proposed in this notice, or some combination of options that incorporate

the proposed standards in part.

1.3 OVERVIEW OF STANDARD S FOR HEARTH PRODUCTS

T here are currently no f ederal energy conservation standards for hearth products.

On December 31, 2013, DOE published a notice of proposed determination of coverage to

classify hearth products as covered products under EPCA. 78 FR 79638. In the proposed

determination of coverage, DOE presented its preliminary findings relating to the energy use of

hearth products to determine whether they could be classified as a type of covered product under

the requirements of 42 U.S.C. 6292(b)(1)(A) and (B), and whether they would meet the criteria

for DOE to prescribe an energy conservation standard under 42 U.S.C. 6295(l)(1)(A)

- (D). (See

section II.A of the NOP D for a discussion of these statutory criteria.) DOE also proposed to

1-6define a “hearth product” as “a gas

- fired appliance that simulates a solid

- fueled fireplace or

presents a flame pattern (for aesthetics or other purpose) and that may provide space heating

directly to the space in which it is installed .” 78 FR 79638, 79640 (Dec. 31, 2013). The

proposed determination is still pending, but as discussed in section IV.A of the NOPR , DOE is

using th at proposed definition to delineate the scope of this proposed rulemaking

. In addition,

DOE has considered so me of the comments submitted in response to the proposed coverage

determination, which are relevant to the development of proposed energy conservation standards

for hearth products and addresses those comments as applicable in this proposed rulemaking

.

1.4 P R OCESS FOR S ETTING E NERGY C ON S ERVATION S TANDARDS

Under the Energy Policy and Conservation Act of 1975 (EPCA), as amended , when DOE

is studying new or amended standards, it must consider, to the greatest extent practicable, the

following seven factors (42 U .S.C. 6295 (o)(2)(B)(i)):

1) the economic impact of the standard on the manufacturers and consumers of the affected

products;

2) the savings in operating costs throughout the estimated average life of the product

compared to any increases in the initial cost or maintenance expense;

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

of the standard;

4) any lessening of the utility or the performance of the products likely to result from the

imposition of the standard;

5) the impact of any lessening of competition, as determined in writing by the Attorney

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

6) the need for national energy conservation; and

7) other factors the Secretary considers relevant.

Other statutory requirements are set forth in 42 U.S.C. 6295 (o)(1)

– (2)(A), (2)(B)(ii)

– (iii), and (3)

– (4) and 42 U.S.C. 6316(e).

DOE considers interested party participation to be a very important part of the process for

setting energy conservation stand ards. Through formal public notifications ( i.e.

, Federal Register

notices) , DOE actively encourages the participation and interaction of all interested partie s

during each stage of the rulemaking.

1-7B efore DOE determines whether to adopt a proposed energy conservation standard, it

must first solicit comments on the proposed standard. (42 U.S.C. 6 295 ( p )( 2 )) Any new or

amended standard must be designed to achieve the maximum improvement in energy efficiency

and be technologically feasible and economic ally justified. (42 U.S.C. 6 295 ( o )( 2 )(A)) To

determine whether economic justification exists, DOE must review comments on the proposal

and determine that the benefits of the proposed standard exceed its burdens to the greatest extent

practicable, weighing the seven factors listed above. (42 U.S.C. 6295 (o)(2)(B)(i))

The energy conservation standards rulemaking process usually involve s three formal

public notices, which DOE publishes in the Federal Register . The first of the rulemaking notices

is typically a notice of public meeting ( NO PM ) , which is designed to publicly vet the models and

tools used in the rulemaking and to facilitate public participation before the NOPR stage. The

second notice is usually the NOPR, which presents a discussion of : (1) c omme nts received in

response to the NOPM analyses ; (2) analyses of the impacts of potential amended energy

conservation standards on consumers, manufacturers, and the Nation; (3) DOE’s weighting of

these impacts of amended energy conservation standards; and (4 ) the proposed energy

conservation standards for each product. The third notice is usually the final rule, which presents

a discussion of : (1) the comments received in response to the NOPR; (2) the revised analyses; (3)

DOE’s weighting of these impacts; (4) the amended energy conservation standards DOE is

adopting for each product; and (5) the effective dates of the amended energy conservation

standards. However, due to the close proximity between this NOPR and prior rulemaking

activities for some hearth products, this energy conservation standards rulemaking did not

include a publication of a notice of public meeting ( NOPM ) or preliminary analysis. Instead, the

NOPR analysis included all analyses typically included in the preliminary analysis. Table 1.4.1

shows the analyses that typically occur during each phase of the rulemaking process.

1-8Table 1.4.1 Rulemaking Analysis Stages

Preliminary Analyses* NOPR Final Rule

Market and technology assessment Revised analyses Revised NOPR analyses

Screening analysis Life

- cycle cost sub

- group

analysis

Engineering analysis Manufacturer impact analysis

Energy use determination Utility impact analysis

Markups for equipment price

determination Emissions analysis

Life

- cycle cost and payback period

analys e s Employment impact analysis

Shipments analysis Regulatory impact analysis

National impact analysis

Preliminary m anufacturer i mpact a nalysis

* The analyses typically performed for the preliminary analysis w ere performed at the NOPR stage for this

rulemaking

.

1.5 STRUCTURE OF THE DOC UMENT

This document outlines the analytical approaches used in this rulemaking. This document

consists of 17 chapters and 15 appendices.

Chapter 1 Introduction: provides an overview of this rulemaking and outlines the

structure of the document.

Chapter 2 Analytical Framework: describ es an overview of the rulemaking

process , methodology, analytical tools, and relationships among the

various analyses.

Chapter 3 Market and Technology Assessment: characte rizes the market for the

considered products and the technologies available for increasing

product efficiency.

Chapter 4 Screening Analysis: identifies all the design options that improve

efficiency of the considered products and determines which technol ogy

options are viable for consideration in the engineering analysis.

Chapter 5 Engineering Analysis: discusses the methods used for developing the

relationship between manufacturer production cost and increased

product efficiency.

1-9Chapter 6 Markups Analysis : discusses the methods used for establishing markups

for converting manufacturer prices to customer product cost s

.

Chapter 7 Energy Use Analysis : discusses the pro cess used for generating energy

use estimates for the considered products as a fun ction of standard

levels.

Chapter 8 Life

- Cycle Cost and Payback Period Analys e s: discusses the methods

used to analyze effects of standards on individual customers and users of

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

wi thout higher efficiency standards.

Chapter 9 Shipments Analysis: estimate s shipments of the products over the 30

- year analysis period that i s used in performing the national impact

analysis (NIA) , including how shipments may vary under alternative

standa rd levels

.

Cha pter 10 National Impact Analysi s: assesse s the national energy savings and the

national net present value of total consumer costs and savings expected

to result from specific, potential energy conservation standards

.

Chapter 1 1 Consumer Subgroup Analysis: d iscusses the effect s of standards on

subgroups of hearth products consumers and compares the LCC and

PBP of products with and without higher efficiency standards for these

consumers.

Chapter 12 Manufacturer Impact Analysis: d iscusses the effects of amended

standards on the finances and profitability of manufacturers.

Chapter 1 3 Emissions Impact Analysis: d iscusses the effects of standards on air

borne emissions, including the impact of emissions of six pollutants or

greenhous e gases: sulfur dioxide (SO 2 ), nitrogen oxides (NOx), carbon

dioxide (CO 2 ), mercury (Hg), methane (CH 4 ), and nitrous oxide (N 2 O).

Chapter 14 Monetization of Emission Reduction Benefits: d iscusses the

monetization of reductions in CO 2 and NOx emissions.

Cha pt er 15 Utility Impact Analysis: d iscusses the effects of standards on the

installed generation capacity of electric utilities.

Chapter 16 Employment Impact Analysis: d iscusses the effects of standards on

n ational employment.

1-10Chapter 17 Regulatory Impact Analysis: d iscusses the present regulatory actions as

well as the impact of non

- regulatory alternatives to setting energy

conservation standards.

Appendix 6 A Detailed Data for Equipment Price Markups

Appendix 7A Building Variables

Appendix 7B Mapping of Weather Station Data to RECS Households

Appendix 8A User Instructions for Life

- Cycle Cost Analysis Spreadsheet for Hearth

Products

Appendix 8B Uncertainty and Variability in the LCC Analysis

Appendix 8C Energy Price Calculations for Hearth Products

Appendix 8D Distributions Used for Discount Rates

Appendix 8E Life

- Cycle Cost Analysis Using Alternative Economic Growth

Scenarios for Hearth Products

Appendix 9A Relative Price Elasticity of Demand for Appliances

Appendix 10A User Instructions National Impact Analysis Spreadsheet Model

Appendix 1 0B Full

- Fuel

- Cycle Multipliers

Appendix 10C National Impact Analysis U sing Alternative Economic Growth

Scenarios for Hearth Products

Appendix 12A Governme nt Regulatory Impact Model Overview

Appendix 14A Social Cost of Carbon for Regulatory Impact Analysis U nder Executive

Order 12866

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

Analysis U nder Executive Order 12866

Appendix 17A Regulatory Impact Analysis: Supporting Materials

1-11CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION

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

- 1

2.2 BACKGROUND

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

- 4

2.3 MARKET A ND TECHNOLOGY ASSESS MENT

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

- 4

2.3.1 Definition and Scope of Coverage

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

- 6

2.3.2 Product Classes

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

- 6

2.3.3 Market Assessment

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

- 7

2.3.4 Technology Assessment

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

- 7

2.4 SCREENING ANALYSIS

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

- 7

2.5 ENGINEERING ANALYSIS

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

- 8

2. 6 MARKUPS ANALYSIS

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

- 9

2.7 ENERGY USE ANALYSIS

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

- 10

2.8 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSES

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

- 11

2.9 SHIPMENTS ANALYSIS

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

- 12

2.10 NATIONAL IMPACT ANAL YSIS

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

- 13

2.10.1 National Energy Savings Analysis

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

- 14

2.10.2 Net Present Value Analysis

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

- 14

2.11 CONSUMER SUBGROUP AN ALYSIS

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

- 15

2.12 MANUACTURER IMPACT A NALYSIS

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

- 15

2.13 EMISSIONS IMPACT ANA LYSIS

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

- 16

2.14 MONETIZING REDUCED C O2 AND OTHER EMISSIO NS

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

- 18

2.15 UTILITY IMPACT ANALY SIS

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

- 19

2.16 EMPLOYMENT IMPACT AN ALYSIS

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

- 20

2.17 REGULATORY IMPACT AN ALYSIS

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

- 21

2

- iCHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

T he Energy Policy and Conservation Act (EPCA) , as amended ( 42 USC 6291 et.

seq. )

, requires that when prescribing new or amended energy conservation standards for

covered products, the U.S. Department of Energy (DOE) must promulgate standards that

achieve the maximum improvements in energy efficiency that are technologically feasible

and economically justified

. (42 U.S.C. 6295(o)(2)(A)) This chapter provides a description

of the analytical framework that DOE is using to evaluate amended energy conservation

standards for hearth product s

. Th is chapter sets forth the methodology, analytical tools,

and relationships among the various analyses that are part of this rulemaking.

Figure 2.1.1 summarizes the analytical components of the standards

- setting

process. The focus of this figure is the center column, identified as “Analyses.” The

columns labeled “Key Inputs” and “Key Outputs” show how the analyses fit into the

rulemaking process, and how the analyses relate to each other. Key inputs are the types of

data and information that the analyses require. Some key inputs exist in public databases;

DOE collects other inputs from interested parties or persons with special knowledge. Key

outputs are analytical results that feed directly into the standards

- setting process. Arrows

connecting analyses show types of information that feed from one analysis to another.

2

- 1Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Process a

a Note: This rulemaking did not include a preliminary analysis, and all analyses typically performed for the

preliminary analysis were performed at the NOPR stage of the rulemaking.

2

- 2In this technical support document ( TSD )

, DOE presents results of the following

analyses , which were performed for the development of this NOPR for hearth products :

• A market and technology assessment to characterize the relevant products, their

markets

, and technology options for reducing their energy consumption

.

• A screening analysis to review technology option s and determine if they are

technologically feasible; are practica b l e to manufacture, install, and service; would

adversely affect product utility or product availability; or would have adverse

impacts on health and safety.

• An engineering analysis to develop the cost difference between the manufacturer

selling price (MSP ) of standing pilot and electronic ignition systems

.

• A markups analysis to develop distribution channel markups that relate the MSP to

the cost to the consumer.

• An energy use analysis to determine the annual energy use of the considered

products in a representative set of users

.

• L ife

- cycle cost ( LCC ) and payback period (PBP) analys e s to calculate the savings

in operating costs at the consumer level throughout the life of the covered products

compared with any increase in the installed cost for th e products likely to result

directly from imposition of a standard.

• A shipments analysis to forecast product shipments, which are then used to

calculate the national impacts of standards on energy, net present value (NPV), and

future manufacturer cash flow s.

• A national impact analysis (NIA) to assess the aggregate impacts at the national

level of potential energy conservation standards for the considered products, as

measured by the NPV of total consumer economic impacts and the national energy

savings (NES ).

• An LCC subgroup analysis to evaluate variations in c onsumer characteristics that

might cause a standard to disproportionately affect particular c onsumer

subpopulations.

• A manufacturer impact analysis (MIA) to assess the potential impact of energy

conser vation standards on manufacturers’ capital conversion expenditures,

marketing costs, shipments, and research and development costs.

• A utility impact analysis to estimate the effects of potential standards on electric,

gas, or oil utilities.

• An employment impact analysis to assess the aggregate impacts on national

employment.

• An emissions analysis to assess the impacts of amended energy conservation

standards on the environment.

2

- 3• An emissions monetization to assess the benefits associated with emissions

redu ctions.

• A regulatory impact analysis to examine major alternatives to amended energy

conservation standards that potentially could achieve substantially the same

regulatory goal at a lower cost.

2.2 BACKGROUND

Amendments to T itle III of EPCA have given DOE the authority to set forth

various provisions designed to improve energy efficiency for residential products . In

addition to specifying a list of covered residential and commercial products, EPCA

contains provisions that ena ble the Secretary of Energy to classify additional types of

consumer products as covered products. DOE previously published a proposed

determination of coverage to classify hearth products as covered consumer products under

the provisions outlined in EPCA

. There are currently no f ederal energy conservation

standards for hearth products.

On December 31, 2013, DOE published a notice of proposed determination

(NOPD) of coverage to classify hearth products as covered products under EPCA. 78 FR

79638. In th at proposed determination of coverage, DOE presented its preliminary

findings relating to the energy use of hearth products to determine whether they could be

classified as a type of covered product under the requirements of 4 2 U.S.C

. 6292(b)(1) (A)

and (B), and whether they would meet the criteria for DOE to prescribe an energy

conservation standard under 42 U.S.C. 6295(l)(1)(A)

- (D). (See section II.A o f the NOPR

for a discussion of these statutory criteria.) DOE also proposed to define a “hearth

product” as “ a gas

- fired appliance that simulates a solid

- fueled fireplace or presents a

flame pattern (for aesthetics or other purpose) and that may provide sp ace heating directly

to the space in which it is installed. ” 78 FR 79638, 79640 (Dec. 31, 2013). The proposed

determination is still pending, but as discussed in section IV.A of the NOPR , DOE is

using the proposed definition to delineate the scope of this NOPR. In addition, DOE has

considered some of the comments submitted in response to the proposed coverage

determination, which are relevant to the d evelopment of proposed energy conservation

standards for hearth products and addresses those comments as applicable in this NOPR.

The following sections provide a general description of the different analytical

components of the rulemaking analytical fram ework. DOE has used 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 during the rulemaking process.

2.3 MARKET AND TECHNOLOG Y ASSESSMENT

When DOE b egins an energy conservation standards rulemaking, it develops

information that provides an overall picture of the market for the products considered,

2

- 4including the nature of the products, market characteristics, and industry structure

. This

activity consists of both quantitative and qualitative efforts based primarily on publicly

available information

. The market assessment examine s manufacturers

, trade associations

, and the quantities and types of products offered for sale.

DOE recognizes that there may be limited public information on national

shipments, manufacturing costs, channels of distribution, and manufacturer market shares

of hearth products

. This type of data is an important input for analyses that determine if

energy conservation standards are economically justified and will result in significant

energy savings

. Therefore, DOE encourages interested parties to submit data that will

improve DOE’s understanding of the hearth products market

. These data may be provided

under a confidentiality ag reement with DOE’s contractor responsible for this part of the

rulemaking analysis, Navigant Consulting, Inc. (NCI)

. As i n other rulemakings, NCI

works with confidential data provided by manufacturers and other organizations in

preparing aggregated results for DOE’s analysis

. These aggregated results do not divulge

the sensitive, individual raw data, but enable other interested parties to comment on the

aggregated dataset.

Alternatively, interested parties may submit confidential data to DOE, indicating in

writing which data should remain confidential

. Interested parties must submit confidential

information to DOE according to the procedures outlined in 10 CFR 1004.11

. Pursuant to

10 CFR 1004.11, any person submitting information that he or she believes to b e

confidential and exempt by law from public disclosure should submit two copies

. One

copy of the document shall include all the information believed to be confidential, and the

other copy shall have the information believed to be confidential deleted

. DOE will make

its own determination about the confidential status of the information and treat it

accordingly. b

DOE reviewed relevant literature and interviewed manufacturers to develop an

overall picture of the hearth products industry in the United States

. Industry publications

and trade journals, governmen t agencies, and trade organizations provided the bulk of the

information, including : (1) manufacturers and their approximate market shares ; (2)

shipments by product type ; (3) product information ; and (4) industry trends

.

The analyse s developed as part of the market and technology assessment are

described in chapter 3 of this TSD

.

b Factors that DOE considers when evaluating requests to treat submitted information as confidential

include: (1) a description of the items; (2) whether and why such items are customarily treated as

confidential within the industry; (3) whether the informa tion is generally known by or available from other

public sources; (4) whether the information has previously been made available to others without obligation

concerning its confidentiality; (5) an explanation of the competitive injury to the submitting pe rson which

would result from public disclosure; (6) a date after which such information might lose its confidential

character; and (7) why disclosure of the information would be contrary to the public interest.

2

- 52.3.1 Definition and Scope of Coverage

There is currently no statutory definition of hearth products as hearth products are

not covered consumer products. In the December 2013 NOP D , DOE proposed to adopt a

definition of “ hearth product ” that was used to define the scope of this rulemaking. In the

December 2013 NOPD, DOE suggested several common hearth product types that would

be covered under the proposed definition, including vented decorative hearth products,

vented heater hearth products, vented gas logs, gas stoves, outdoor hearth products, and

ventless hearth products . DOE used the definition proposed in the December 2013 NOPD

(as stated above) to determine the scope of this proposed rulemaking

.

As described in section III. B of t he NOPR, DOE has tentatively determined to

focus the current rulemaking on reduc ing standby mode energy consumption

. Whereas a n

energy conservation standard could fractionally reduce the energy use of hearth products

in active mode, DOE found that all standby mode fossil fuel consumption could be

eliminated with a prescriptive design requirement.

In some instances, hearth products may ha ve standby mode and/or off mode

electrical energy consumption. However, DOE has tentatively determined that the standby

and off mode electrical energy consumption of the ignition module is de minimis , and did

not analyze energy conservation standards to re gulate electrical standby mode and off

mode energy consumption. c

2.3.2 Product Classes

As described in III.C of the NOPR , DOE has tentatively determined not to

establish separate product classes for a standby mode energy conservation standard. The

criteria for separation into different product classes are: (1) energy source and (2) capacity

or other performance

- related features such as those that provide utility to the consumer or

others deemed appropriate by the Secretary that would justify the establishment o f a

separate energy conservation standard. (42 U.S.C. 6295 (q) and 6316(a)) DOE found

substantial similarity in the function, components used, and energy use of hearth products

with regard to standby mode. By reviewing manufacturer product literature, cond ucting

teardown analyses, and interviewing manufacturers, DOE found that the same or similar

ignition system components, including manual, millivolt, and electronic gas control

valves, pilot assemblies, and electronic control modules for electronic ignitio ns, were used

across a wide range of hearth products. DOE has tentatively determined should standing

pilot ignitions be disallowed, most hearth products styles will switch to similar electronic

ignition components. See section III.C of the NOPR for further detail regarding DOE’s

tentative determination that product classes do not need to be established for hearth

products in light of the focus on standby energy mode.

c See section III.I of the NOPR for more details regarding the tentative determination that standby electrical

consumption for hearth products is de minimis

.

2

- 62.3.3 Market Assessment

As part of the market and technology assessment, DOE developed informati on that

provides an overall picture of the market for the products considered, including the nature

of the products, market characteristics, and industry structure. DOE collected quantitative

and qualitative information, primarily from publicly available s ources. The market

assessment examined manufacturers, trade associations, and the quantities and types of

products sold and offered for sale. DOE reviewed relevant literature and interviewed

manufacturers to develop an overall picture of the hearth product s industry in the United

States. Industry publications, government agencies, and trade organizations provided the

bulk of the information, including: (1) manufacturers and their estimated market shares;

(2) shipments by product type; (3) product informatio n; and (4) industry trends. The

analyses developed as part of the market assessment are described in chapter 3 of this

TSD

.

2.3.4 Technology Assessment

DOE typically uses information relating to existing and past technology options

and prototype designs as inputs to determine what technologies manufacturers use to

attain higher performance levels. In consultation with interested parties , DOE develops a

list of technologies for consideration. Initially, these technologies encompass all those

DOE believes are technologica lly feasible.

DOE developed its list of technologically feasible design options for the

considered products through consultation with manufacturers of components and systems

. P roduct literature and direct examination provided additional inf ormation. The

technologies examined in the technology assessment are described in detail in chapter 3 of

th is TSD

.

2.4 SCREENING ANALYSIS

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

the technology assessment to determin e which technologies to consider further and which

technologies to screen out

. DOE consulted with industry, technical experts, and other

interested parties in developing a list of energy

- saving technologies for the technology

assessment

. DOE then applied t he screening criteria to determine which technologies were

unsuitable for further consideration in this rulemaking

. Chapter 4 of this TSD , the

screening analysis, contains details about DOE’s screening criteria.

As presented in further detail below, t he sc reening analysis examines whether

various technologies : (1) are technologically feasible; (2) are practicable to manufacture,

install, and service; (3) have an adverse impact on product utility or availability; and (4)

have adverse impacts on health and safety

. In consultation with interested parties, DOE

revi ewed the list of hearth products technologies according to these criteria

. In the

2

- 7engineering analysis, DOE further considers the efficiency

- enhancement technologies that

it did not eliminate in the screening analysis.

1. Technological feasibility. DOE screen s out technologies that are not

incorporated in commercially available products or working prototypes.

2. Practicability to manufacture, install, and service

. If DOE determines that mass

production of a technology in commercial products and reliabl e installation and

servicing of the technology c an not be achieved on the scale necessary to serve

the relevant market by the time of the compliance date of the standard, it will

not consider that technology further.

3. Adverse impacts on product or equipment utility or availability. If DOE

determines a technology has a significant adverse impact on the utility of the

product for significant consumer subgroups or results in the unavailability of

any covered product type with performance characteristics (includi ng

reliability), features, size, capacities, and volumes that are substantially the

same as products generally available in the United States at the time, it will not

consider that technology further.

4. Adverse impacts on health or safety. If DOE determines that a technology will

have significant adverse impacts on health or safety, it will not consider that

technology further.

Chapter 4 contains additional details about the screening analysis and the

justification for screening out certain technologies.

2.5 E NGINEERING ANALYSIS

The engineering analysis (chapter 5 of this TSD ) establishes the difference in

manufacturing production cost between standing pilot and electronic ignition systems

. This cost difference serves as the basis for cost

- benefit calculations in terms of individual

consumers, manufacturers, and the n ation. Chapter 5 discusses the representative units

analyzed

, methodology used to develop manufacturing production costs and manufacturer

markups

, and results of the analysis

. To determine the cost to consumers of hearth

products with different types of ignition systems , DOE estimated manufacturing costs,

markups in the distribution chain, installation costs, and maintenance costs product

teardowns and manufacturer interviews.

In the engineering analysis, DOE evaluated the different ignition systems found in

hearth products and the associated manufacturing costs. The purpose of the analysis is to

estimate the incremental increase to selling prices that would result from disallo wing the

use of standing pilot ignition systems and replacing them with electronic ignition systems

. The engineering analysis considers the electronic ignition system since it was not

eliminated in the screening analysis

. C ertain other technologies were no t analyzed due to

2

- 8other reasons, such as negligible incremental efficiency improvements , lack of

information on efficiency improvement, or inapplicability to certain hearth categories

( e.g., gas log sets)

. DOE considers the remaining technologies (i.e. the electronic ignition

system) in developing the cost

- efficiency relationship , which is subsequently used for the

LCC and PBP analyses.

DOE typically structures its engineering analysis around one of three

methodologies: (1) the design

- option approach, whic h calculates the incremental costs of

adding specific design options to a baseline model; (2) the efficiency

- level approach,

which calculates the relative costs of achieving increases in energy efficiency levels

without regard to the particular design opti ons used to achieve such increases; and/or (3)

the reverse

- engineering or cost

- assessment approach, which involves a “bottom

- up”

manufacturing cost assessment based on a detailed bill of materials derived from tear

downs of the product being analyzed.

For this analysis, DOE conducted the engineering analysis for hearth product s using

a combination of the design option and cost

- assessment approaches . DOE selected for

teardown hearth models that represent a range of hearth configurations ( e.g., vented

fireplaces, vented fireplace inserts, unvented fireplace inserts, vented gas log sets,

unvented gas log sets)

. In light of the analytical focus on standby mode energy

consumption, DOE examined the implementation of an electronic ignition system as a

design option and chose representative models for analysis that would allow a direct

comparison between standing pilot and electronic ignition systems. DOE gathered

information using reverse

- engineering methodologies

, product informa tion from

manufacture r catalogs and manuals, and discussions with manufacturers and other experts

on hearth products

.

DOE generated bill s of materials (BOM s ) by disassembling products representing

a range of hearth configurations, including vented and unvented fireplaces, ins erts, and

stoves, vented and unvented gas log sets, and outdoor products. The BOMs describe each

unit analyzed in detail, including all manufacturing steps required to make and/or

assemble each part. Subsequently, DOE developed a cost model that converted the BOMs

into manufacturer production costs (MPCs). By applying derived manufacturer markups to

the MPCs, DOE calculated the manufacturer selling prices

.

More information regarding the engineering analysis can be found in chapter 5 of

the TSD.

2.6 MARKUPS ANALYSIS

DOE uses manufacturer

- to

- customer markups to convert the manufacturer selling

price estimates in the engineering analysis to customer prices , which include markups

throughout the distribution chain (wholesalers, retailers, etc.) These are then us ed in the

LCC and PBP analys e s and in the manufacturer impact analysis. Retail prices are

2

- 9necessary for the baseline efficiency level (standing pilot) and the energy conservation

standard case being considered (electronic ignition system)

.

Before developing markups, DOE defines key market participants and identifies

distribution channels. Generally, the hearth products distribution chain includes five

market participants: (1) manufacturer; (2) wholesaler; (3) mechanical contractors; (4)

general con tractors; and (5) consumers. For the markups analysis, DOE used two types of

distribution channels to describe how most hearth products pass from the manufacturer to

the consumer : ( 1) replacement /new owner market; and ( 2) new construction

. These

channels a re explained in detail in c hapter 6 of this TSD

.

A fter defining the participants and channels, DOE develops baseline and

incremental markups to transform the manufacturer selling price into a consumer product

price. DOE uses the baseline markups, which c over all of a distributor’s costs, to

determine the sales price of baseline models. Incremental markups are coefficients that

DOE applies to the incremental cost of models of electronic ignition systems

. Because

companies mark up the price at each point in the distribution channel, both baseline and

incremental markups are dependent on the particular distribution channel.

2.7 ENERGY U S E A NALYSIS

The purpose of the energy use analysis is to determine the annual energy

consumption of hearth products used in representative U.S. single

- family homes and

multi

- family residences. Additionally, the energy use analysis assess es the energy savings

potential of increased hearth product efficiency. DOE estimated the annual energy

consumption of residential hea rth products across a range of climate zones. The annual

energy consumption includes the natural gas, liquid petroleum gas, oil fuel , and/or

electricity use by the hearth products

. The annual energy consumption of hearth products

is used in subsequent anal yses, including the LCC and PBP analys e s and the national

impact analysis.

DOE used Residential Energy Consumption Survey ( RECS ) 2009 data and

weather data from NOAA to estimate weather

- normalized energy use. The RECS 2009

data provide information on the home characteristics , as well as heating energy use in each

hou sehold. The survey includes household information such as the physical characteristics

of housing units, household demographics, information about other heating and cooling

products installed in the household , fuel type s used, energy consumption and

expenditures, a nd other relevant data.

To estimate the annual energy consumption of hearth products meeting higher

efficiency levels, DOE calculated the heating load based on the RECS estimates of the

annual energy consumption of the hearth products for each household.

Chapter 7 of this TSD details the energy use analysis methodology.

2

- 102.8 LIFE

- CYCLE COST AND PAYBA CK PERIOD A N ALYS E S

In determining whether an energy conservation standard is economically justified,

DOE considers the economic impact of potential standards on consumers

. The effect of

new or amended standards on individual consumers usually includes a reduction in

operating cost and an increase in purchase cost. DOE use d the fol lowing two metrics to

measure consumer impacts:

• L ife

- cycle cost ( L CC ) is the total c onsu mer cost of an appliance or product,

generally over the life of the a ppliance or product, including purchase and

operating costs. The latter consist of maintenance, repair, and energy costs. Future

operating costs are discounted to the time of purchase and summed over the

lifetime of the appliance or product.

• P ayback period ( PBP ) measures the amount of time it takes consumers to recover

the assumed higher purchase price of a more energy

- efficient product through

reduced operating costs.

DOE analyzed the net effect of potential hearth products standards on consumers

by determining the LCC and PBP using engineering performance data, energy

- use data,

and markup data

. Inputs to the LCC calculation include the installed cost to the consumer

(purchase price plus installation cost), operating expenses (energy expenses, repair costs

, and maintenance costs), the lifetime of the product, and a discount rate. Inputs to the

payback period calculation include the installed cost to the consumer and first

- year

operating costs.

DOE generated LCC and PBP results as probability distributions using a

simulation approach based on Monte Carlo analysis methods, in which certain key inputs

to the analysis consist of probability distributions rather than single

- point values.

Therefore, the outcomes of the Monte Carlo analysis can also be expressed as prob ability

distributions. As a result, the analysis produces a range of LCC and PBP results that allow

DOE to identify the fraction of customers achieving LCC savings or incurring net cost at

the considered efficiency levels.

DOE examined expected maintenance, repair, and installation costs for the

products covered in this rulemaking. DOE used the most recent RS Means Facilities

Maintenance & Repair Cost Data d to develop appropriate repair and maintenance costs

for baseline units (standing pilot models) and units including electronic ignition systems

. DOE concluded that while repair costs are likely to increase in proportion to the initial

size and cost of hearth products , maintenance costs for more

- efficient products were not

likely to be s ignificantly higher than those for baseline units.

DOE estimated all the installation costs associated with installing a hearth product

in a new housing unit or as a replacement for an existing hearth product using RS Means

d RS Means

, 2013 Facilities Maintenance & Repair Cost Data ( Available at :

http://rsmeans.reedconstructiondata.com/60303.aspx ) (Last accessed April 10, 2013).

2

- 112013 Residential Cost Data

, e m anufacturer literature, and information from expert

consultants

. This include s any additional costs

, such as venting modifications that would

be required to install various hearth products

.

Based on a search of industry studies and literature, DOE concluded that hearth

products typically have an approximate average lifetime of 1 5 years

.

DOE used discount rate s to determine the present value of lifetime operating

expenses. The discount rate u sed in the LCC analysis represents the rate from an

individual consumer’s perspective. f Much of the data used for determining consumer

discount rates comes from the Federal Reserve Board’s triennial Survey of Consumer

Finances. g

To estimate the share of consumers affected by a standard, DOE’s LCC and PBP

analys e s consider the projected distribution ( i.e. , market shares) of product s with

electronic ignition systems that consumers will purchase in the first compliance year under

the base case (the case with out amended energy conservation standards disallowing use of

standing pilots ).

Chapter 8 of this TSD provides more details on the LCC and PBP analys e s

methodology

.

2.9 SHIPMENTS ANALYSIS

DOE used forecasts of product shipments to calculate the national impacts of

standards on energy use, NPV, and future manuf acturer cash flows. DOE developed these

shipment forecasts based on an analysis of key market drivers for each product.

DOE estimated heart h product shipments by projecting shipments in three market

segments: (1) replacements; (2) new housing; and (3) new owners in buildings that did not

previously have a hearth product

. DOE also considered whether standards that disallow

standing pilot ignit ions would have an impact on hearth product shipments.

To project hearth product replacement shipments, DOE developed retirement

functions for hearth product s from the lifetime estimates and applied them to the existing

products in the housing stock. The existing stock of products is tracked by vintage and

developed from historical shipments data.

To project shipments to the new housing market, DOE utilized a forecast of new

housing construction and historic saturation rates of hearth product types in ne w housing.

e RS Means Company Inc., RS Means Residential Cost Data . 32nd Annual Edition ed. 2013: Kingston, MA

f The consumer discount rate d iffers from the discount rates used in the national impact analysis, which are

intended to represent the rate of return on capital in the U.S. economy, as well as the societal rate of return

on private consumption.

g Available at www.federalreserve.gov/econresdata/scf/scfindex.htm

.

2

- 12DOE used the Annual Energy Outlook ( AEO ) 201 4 for forecasts of new housing. Hearth

product saturation rates in new housing are provided by the U.S. Census Bureau’s

Characteristics of New Housing

. h

See c hapter 9 of this TSD for more details regarding the projection of hearth

product shipments.

B ecause the standards

- case projections take into account the increase in purchase

price and the decrease in operating costs caused by amended standards, project ed

shipments for a stand ards case typically deviate from those for the base case. Because

purchase price tends to have a larger impact than operating cost on appliance purchase

decisions, standards

- case projection s typically show a d ecrease in product shipments

relative to the ba se case.

C onsistent with economic theory, it is reasonable to expect that standards that

result in higher hearth product prices will have some dampening effect on sales. To

estimate the impact of the projected price increase for the considered efficiency levels

, DOE modeled developed a price elasticity model

. This approach gives some weight to the

operating cost savings from higher efficiency products. The impact of higher hearth

products prices is expressed as a percentage drop in market share for each year during the

analysis period

.

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 e nergy conservation standards.

DOE determined the NPV and NES for the standard level considered (disallowing

standing pilot ignitions) for the hearth products analyzed. To make the analysis more

accessible and transparent to all interested parties, DOE prep ared a Microsoft Excel

spreadsheet that uses typical values (as opposed to probability distributions) as inputs. To

assess the effect of input uncertainty on NES and NPV results, DOE has developed its

spreadsheet model to conduct sensitivity analyses by ru nning scenarios on specific input

variables.

Analyzing impacts of potential energy conservation standards for hearth products

requires comparing projections of U.S. energy consumption with new energy conservation

standards against projections of energy c onsumption without new standards. The forecasts

include projections of annual product shipments, the annual energy consumption of new

products , and the purchase price of new products

.

A key component of DOE’s NIA is the energy efficiencies forecasted over time for

the base case (without new standards) and the standards case (disallowing standing pilot

use)

. The forecasted efficiencies represent the annual shipment

- weighted energy efficiency

h Available at: http://www.census.gov/const/www/charindex.html

.

2

- 1 3of the products under consideration during the forecast period ( i. e. , from the assumed

compliance date of a new standard to 30 years after compliance is required).

DOE developed a distribut ion of standing pilot and electronic ignition systems in the

base

- case for 202 1 (the assumed compliance date for new standa rds)

. In the standards

case, all hearths were assumed to include an electronic ignition system for 202 1

. DOE

assume d : (1) product s in the base case that would have their standing pilot ignition

systems replaced wit h an electronic ignition system to meet the new standard; and (2)

product s that are match lit or already include an electronic ignition system would not be

affected.

Chapter 10 of th is TSD provides additional details on the national impact analysis.

2.10.1 National Energy Savings Analysis

The inputs for de termining the national energy savings for each product analyzed

are: (1) annual energy consumption per unit; (2) shipments; (3) product stock ; (4) national

energy consumption; and (5) site

- to

- source conversion factors. DOE calculated the

national energy co nsumption by multiplying the number of units (stock) of each product

(by vintage or age) by the unit energy consumption (also by vintage). Vintage represents

the age of the product. DOE calculated annual NES based on the difference in national

energy consu mption for the base case and for the standard case (disallowing standing

pilots)

. DOE estimated energy consumption and savings based on site energy and

converted the electricity consumption and savings to source (primary) energy using annual

conversion fac tors derived from the most recent version of the National Energy Modeling

System (NEMS). Cumulative energy savings are the sum of the NES for each year over

the timeframe of the analysis.

DOE has historically presented NES in terms of primary energy savi ngs. DOE

published a Statement of Policy regarding its intent to incorporate full

- fuel

- cycle (FFC)

metrics into its analyses, and outlining a proposed approach. DOE stated that it intends to

calculate FFC energy and emission impacts by applying conversion factors generated by

the g reenhouse g ases, r egulated e missions, and e nergy u se in t ransportation (GREET)

model to the NEMS

- based results currently used by DOE. 76 FR 51281 (Aug. 18, 2011).

Additionally, DOE will review alternative approaches to estimating these factors and may

decide to use a model other than GREET to estimate the FFC energy and emission impacts

in any particular future appliance energy conservation standards rulemaking. For this

analysis, DOE calculated FFC energy savings using the NEMS

- based methodology

described in appendix 10

- B of this TSD

. Chapter 10 of this TSD presents both the primary

NES and the FFC energy savings for the analyzed efficiency levels

.

2.10.2 Net Present Va lue Analysis

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

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

savings; (4) present value of costs; and (5) present value of s avings. DOE determined the

2

- 14net savings for each year as the difference between the base case and standards case in

terms of total savings in operating costs versus total increases in installed costs. DOE

calculated savings over the lifetime of products shi pped in the forecast 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 fut ure costs and savings to present values.

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

difference in total installed cost between the base case and standards case ( i.e. , once the

standards take effect). Because the produc ts bought in the standards case usually cost

more than products bought in the base case (due to the presence of electronic ignition)

, cost increases appear as negative values in the NPV.

DOE expresses savings in operating costs as decreases associated wi th the lower

energy consumption of products bought in the standards case compared to the base case.

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

of each vintage that survive in a given year.

DOE used the AEO 201 4 as the source of projections for future energy prices.

DOE estimates the NPV of consumer benefits using both a 3

- percent and a 7

- percent real discount rate. DOE uses these discount rates in accordance with guidance

provided by the Office of Manageme nt and Budget (OMB) to f ederal agencies on the

development of regulatory analysis. (OMB Circular A

- 4 (Sept. 17, 2003), section E,

“Identifying and Measuring Benefits and Costs”)

2.11 CONSUMER SUBGROUP AN ALYSIS

In analyzing the potential impacts of new standard s on consumers , DOE evaluates

the potential impact of new standards on identifiable groups of consumers ( i.e.

, subgroups), such as small businesses, that may be disproportionately affected by a

national standard. Accordingly, DOE evaluated impacts on low

- i ncome households and

senior

- only households using the LCC and PBP spreadsheet model, using inputs

appropriate to these subgroups to the extent possible. The hearth products subgroup

analysis is discussed in detail in chapter 11 of this TSD.

2.12 MANUACTURER IMPACT ANALYSIS

The MIA assesses the impacts of new energy conservation standards on

manufacturers of the considered product

. Potential impacts include financial effects, both

quantitative and qualitative, that might lead to changes in the manufacturing practices for

these products. DOE identified these potential impacts through interviews with

manufacturers and other interested parties.

2

- 15DOE conduct ed the MIA in three phases. In Phase I, DOE created an industry

profile to characterize the industry, and conducted a preliminary MIA to identify

important issues that required consideration. In Phase II, DOE prepared an industry cash

flow model and an interview questionnaire to guide subsequent discussions. In Phase III,

DOE interviewed manufacturers , and the impacts of standards were assessed both

quantitatively and qualitatively. Industry and subgroup cash flow and NPV were assessed

through use of the Government Regulatory Impact Model (GRIM). Then impacts on

competition, manufacturing capacity, employment, and cumulative regulatory burden

were assessed based on manufacturer interview feedback and discussions. DOE discusses

its findings from the MIA in chapter 12 of this TSD.

2.13 EMISSIONS IMPACT ANA LYSIS

In the emissions analysis, DOE estimate d 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 conservation standards for the considered products. In addition,

DOE estimate d emissions impacts in production activities (extracting, processing, and

transporting fuels) that provide the energy inputs to power plants. These are referred to as

“upstream” emissions. Together, these emissions account for the FFC. In accordance with

DOE’s FFC Statement of Policy (76 FR 51282 (Au g. 18, 2011)), the FFC analysis

includes impacts on emissions of methane and nitrous oxide, both of which are recognized

as greenhouse gases.

DOE primarily conduct ed the emissions analysis using emissions factors for CO 2 and most of the other gases derived from data in the latest version of AEO

. Combustion

emissions of CH 4 and N 2 O were estimated using emissions intensity factors published by

the Environmental Protection Agency ( EPA ) GHG Emissions Factors Hub

. i

The En e rgy Information Administration ( EIA ) prepares the A EO using NEMS.

Each annual version of NEMS incorporates the projected impacts of existing air quality

regulations on emissions. Th is discussion refers to AEO 201 4

, which generally represents

current legislation and environmental regulat ions, including recent government actions,

for which implementing regulations were available as of December 31, 201 3

.

Be cause the on

- site operation of hearth products requires use of fossil fuels and

results in emissions of CO 2 , NO X , and SO 2 at the sites where these appliances are used,

DOE also account ed for the reduction in these site emissions and the associated upstream

emissions due to potential standards.

SO 2 emissions from affected electric generating units (EGUs) are subject to

natio nwide and regional emissions cap and trading programs. Title IV of the Clean Air

Act sets an annual emissions cap on SO 2 for affected EGUs in the 48 contiguous states

and the District of Columbia (D.C.). SO 2 emissions from 28 eastern states and D.C. were

i http://www.epa.gov/climat eleadership/guidance/ghg

- emissions.html

2

- 16a lso limited under the Clean Air Interstate Rule (CAIR), which created an allowance

based trading program that operates along with the Title IV program in those States and

D.C. 70 FR 25162 (May 12, 2005). CAIR was remanded to EPA by the U.S. Court of

Appeal s for the District of Columbia Circuit (D.C. Circuit), but it remained in effect. j On

July 6, 2011

, EPA issued a replacement for CAIR, the Cross

- State Air Pollution Rule

(CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 2012, the D.C. Circuit issued a

d ecision to vacate CSAPR. k The court ordered EPA to continue administering CAIR. AEO

201 4 assumes that CAIR remains a binding regulation through 2040. l

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

through the use of emi ssions allowances and tradable permits. Under existing EPA

regulations, any excess SO 2 emissions allowances resulting from the lower electricity

demand caused by the adoption of an efficiency standard could be used to permit

offsetting increases in SO 2 emi ssions by any regulated EGU. In past rulemakings, DOE

recognized that there was uncertainty about the effects of conservation standards on SO 2 emissions covered by the existing cap

- and

- trade system, but it concluded that no

reductions in power sector emiss ions would occur for SO 2 as a result of standards.

Beginning in 201 6 , however, SO 2 emissions will fall as a result of the Mercury and

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

MATS rule, EPA established a standard for hydrochloric acid 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 2013 assumes that, in order to continue operating,

coal plants must have either flue gas desulfurization or dry sorbent injection systems

installed by 201 6 . Both technologies, which are used to reduce acid gas emissions, also

reduce SO 2 emissions. Under the MATS, e missions will be far below the cap that would

be established by CAIR , so it is unlikely that excess SO 2 emissions allowances resulting

from th e lower electricity demand would be needed or used to permit offsetting increases

in SO 2 emissions by any regulated EGU. Therefore, energy conservation standards will

reduce SO 2 emissions in 201 6 and beyond.

j See North Carolina v. EPA , 550 F.3d 1176 (D.C. Cir. 2008); North Carolina v. EPA , 531 F.3d 896 (D.C.

Cir. 2008).

k See EME Homer City Generation, LP v. EPA

, 696 F.3d 7, 38 (D.C. Cir. 2012).

l 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. The Supreme Court held in part

that EPA's methodology for quantifying emissions that must be eliminate d 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. See EPA v. EME Homer City

Generation , No 12

- 11 82, s lip o p. at 32 ( U.S. April 29, 2014). B ecause DOE is using emissions factors based

on AEO 2013, the analysis assumes that CAIR, not CSAPR, is the regulation in force. The difference

between CAIR and CSAPR is not relevant for the purpose of DOE's analys is of SO 2 emissions.

2

- 17CAIR established a cap on NO x emissions in eastern s tates and the District of

Columbia. Energy conservation standards are expected to have little or no physical effect

on these emissions in those s tates covered by CAIR because excess NO x emissions

allowances resulting from the lower el ectricity demand could be used to permit offsetting

increases in NO x emissions. However, standards would be expected to reduce NO x

emissions in the S t ates not affected by the caps, so DOE estimate d NO x emissions

reductions from potential standards in the s tates where emissions are not capped.

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 estimate d mercury emissions reduction using emissions factors based

on AEO 20 14 , which incorporates the MATS.

Power plants may emit particulates from the smoke stack, which are known as

direct particulate matter (PM) emissions. NEMS does not account for direct PM emissions

from power plants. DOE is investigating the possibility of using other methods to estimate

reduction in PM emissions due to standards. The great majority of ambient PM associated

with power plants is in the form of secondary sulfates and nitrates, which are produced at

a si gnificant distance from power plants by complex atmospheric chemical reactions that

often involve the gaseous emissions of power plants, mainly SO 2 and NO x . The mone tary

benefits that DOE estimated for reductions in SO 2 and NO x emissions resulting from

sta ndards are in fact primarily related to the health benefits of reduced ambient PM.

Further detail is provided in chapter 13 of the TSD.

2.14 MONETIZING REDUCED C O2 AND OTHER EMISSIO NS

DOE consider ed the estimated monetary benefits likely to result from the reduced

emissions of CO 2 and NO x that are expected to result from each of the standard levels

considered.

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

CO 2 , DOE use d the most current SCC values developed and/or agreed to by an

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

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

productivity loss, h uman health effects, property damage from sea level rise, and changes

in ecosystem services. Any effort to quantify and to monetize the harms associated with

climate change will raise serious questions of science, economics, and ethics. But with full

regar d for the limits of both quantification and monetization, the SCC can be used to

provide estimates of the social benefits of reductions in GHG emissions.

2

- 18The Interagency Working Group on Social Cost of Carbon released in 2013 an

update of its previous re port. m The most recent estimates of the SCC in 2015, expressed in

201 3 $, 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 inter agency 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 give s preference to consideration of the global benefits of reducing CO 2 emissions.

DOE multip lied 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 discount ed the values in each of the four cases using the discount

rates t hat had been used to obtain the SCC values in each case.

DOE recognizes that scientific and economic knowledge continues to evolve

rapidly as to the contribution of CO 2 and other GHG to changes in the future global

climate and the potential resulting dama ges 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 201 3 $

. n DOE

calculated monetary benefits using a medium value for NO x emissions of $2,6 84 per short

ton (201 3 $), 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 rulemaking.

Further detail on the emissions monetization is provided in chapter 14 of th is TSD.

2.15 UTILITY IMPACT ANALY SIS

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

capacity and generation that result for each trial standard level (TSL). DOE uses a variant

of NEMS, referred to as NEMS

- BT, o to account for these impacts. NEMS

- BT has several

m Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Ord er

12866 . Interagency Working Group on Social Cost of Carbon, United States Government ; revised

November 2013.

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical

- update

- social

- cost

- of

- carbon

- for

regulator

- impact

- analysis.pdf

n U.S. Office of Management and Budget , Office of Information and Regulatory Affairs, 2006 Report to

Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and

Tribal Entities , Was hington, DC

.

o DOE/EIA approves use of the name NEMS to describe only an official version of the model without any

modification to code or data. Because this analysis entails some minor code modifications and the model is

run under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the

2

- 19advantages that have led to its use in the an alysis 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 am ong the various energy supply and demand

sectors.

The utility impact analysis is a comparison between the NEMS

- BT model results

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

installed capacity and generation that result from each standard level by plant type . DOE

models the anticipated energy savings impacts from potential amended energy

conservation standards using NEMS

- BT to generate forecasts that deviate from the AEO

r eference c ase.

Further detail is provided in chapter 15 of th is TSD.

2.16 EMPLOYMENT IMPACT AN ALYSIS

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 cover ed products. DOE evaluates direct

employment impacts in the MIA.

Indirect employment impacts may result from expenditures shifting between goods

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

effect) that occur d ue 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 impact s are investigated in the employment impact analysis

using the Pacific Northwest National Laboratory’s “Impact of Sector Energy

Technologies” (ImSET) model. p The ImSET model was developed for DOE’s Office of

Planning, Budget, and Analysis to estimate the e mployment and income effects of energy

saving technologies in buildings, industry, and transportation

. Compared with simple

economic multiplier approaches, ImSET allows for more complete and automated ana lysis

of the economic impacts of energy conservation investments. Further detail is provided in

chapter 16 of th is TSD.

name NEMS

- BT (BT is DOE’s Building Technologies Program, under whose aegis this work has been

performed).

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

Energy Technologies , PNNL

- 18412, Pacific Northwest National Laboratory (2009) (Available at:

www.pnl.gov/main/publications/external/techni cal_reports/PNNL

- 18412.pdf )

.

2

- 202.17 REGULATORY IMPACT AN ALYSIS

DOE prepared a regulatory impact analysis (RIA) pursuant to Executive Order

12866, Regulatory Planning and Review, 58 FR 51735, October 4, 1993, which is subje ct

to review by the Office of Information and Regulatory Affairs at the Office of

Management and Budget. The RIA addresses the potential for non

- regulatory approaches

to supplant or augment energy conservation standards in order to improve the energy

effic iency or reduce the energy consumption of the product covered under this

rulemaking.

DOE recognizes that voluntary or other non

- regulatory efforts by manufacturers,

utilities, and other interested parties can substantially affect energy efficiency or redu ce

energy consumption. DOE bases its assessment on the actual impacts of any such

initiatives to date, but also considers information presented by interested parties regarding

the impacts existing initiatives might have in the future. Further detail is pro vided in

chapter 17 of th is TSD.

2

- 21CHAPTER 3. MARKET AND TECHNOLOG Y ASSESSMENT

TABLE OF CONTENT S

3.1 INTRODUCTION

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

- 1

3.1.1 Description of Products

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

- 1

3.1.2 Definitions

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

- 2

3.1.3 Product Classes

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

- 3

3.1.4 Test Procedures

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

- 3

3.2 MA RKET ASSESSMENT

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

- 4

3.2.1 Trade Associations

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

- 4

3.2.1.1 Hearth, Patio and Barbecue Association

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

- 4

3.2.1.2 Air

- Conditioning, Heating, and Refrigeration Institute

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

- 5

3.2.2 Manufacturer Information

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

- 5

3.2.2.1 Manufacturers

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

- 5

3.2.2.2 Small Business Impacts

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

- 6

3.2.3 Distribution Channels

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

- 7

3.2.4 Regulatory Programs

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

- 8

3.2.4.1 Current Federal Energy Conservation Standards

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

- 8

3.2.4.2 State Energy Conservation Standards

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

- 8

3.2.4.3 Canadian Standards Association

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

- 9

3.2.5 Voluntary Programs

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

- 9

3.2.6 Historical Shipments

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

- 9

3.2.6.1 New Home Starts

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

- 9

3.2.6.2 Shipments

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

- 11

3.2.7 In dustry Cost Structure

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

- 13

3.2.8 Equipment Lifetime

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

- 13

3.2.9 Market Performance Data

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

- 14

3.3 TECHNOLOGY ASSESSMENT

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

- 14

3.3.1 Baseline Equipment Components and Operation

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

- 15

3.3.2 Technology Options

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

- 17

3.3.3 Technology Options That Do Not Affect Energy Use

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

- 19

LIST OF TABLES

Table 3.1.1 Hearth Product Groups for DOE Analysis

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

- 3

Table 3.2.2 List of Sample Hearth Industry Standards

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

- 8

Table 3.2.3 Sample Rebate Programs

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

- 9

Table 3.2.4 HPBA Product Categories and Relative Market Shar es

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

- 12

Table 3.2.5 DOE Market Shares for Hearth Product Groups

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

- 13

Table 3.2.6 Industry Cost Structure

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

- 13

Table 3.2.7 Average Ma rket Share of Ignition Types by Analysis Group

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

- 14

3

- iLIST OF FIGURES

Figure 3.2.1 New Privately Owned Single

- Family and Multi

- Family Housing Unit Starts

from 2005 to 2013

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

- 10

Figure 3.2.2 HPBA Shipments Data

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

- 11

3

- iiCHAPTER 3. MARKET AND TECHNOLOG Y ASSESSMENT

3.1 INTRODUCTION

This chapter details the market and technology assessment that the U.S.

Department of Energy (DOE) cond ucted in support of the energy conservation standards

rulemaking for hearth products

.

This chapter consists of the market assessment and the technology assessment.

The goal o f the market assessment is to develop a qualitative and quantitative

characterization of hearth industry and market structures based on publicly available data

and other information that DOE received directly from manufacturers and other

i nterested parties

. The market and technology assessment addresses manufacturer

characteristics and market shares, existing regulatory and non

- regulatory efficiency

improvement initiatives, product classes, and trends in product markets and

characteristics. DOE performs the technology assessment to develop a preliminary list of

technologies (referred to as technology options) that could be used to improve the

efficiency or reduce the fuel consumption of hearth products

.

3.1.1 Description of Products

Gas

- fired h earth products prese nt a visible flame that typically provides aesthetic

appeal and may also provide supplemental space heating. Gas

- fired hearth products are

often designed to simulate wood

- burning hearth products, but come in a variety of

designs, including “modern” styles consisting of only a flame pattern, or a flame

surrounded by glass, stones, or other media. Consumers of gas

- fired hearth products may

select a product for décor, ambiance, heat, or some combination thereof. Hearth product

consumers are typically homeowner s or contractors purchasing a hearth product for

installation in a new home or for replacement or upgrade in an existing home. Some

consumers purchas e a gas

- fired hearth product to retrofit a wood

- burning fireplace

.

Hearth products covered by this rulema king use natural gas or propane, can be

vented or unvented , and may be designed for indoor or outdoor use

. Hearth products take

a variety of forms, including but not limited to fireplaces, fireplace inserts, freestanding

stoves, and gas log sets. Outdoor h earths may take the form of a fireplace, fire pit or patio

heater. Section 3.3.1 describes these hearth products and the compone nts that typically

comprise them

.

There are three general types of ignition types for hearth products: match lit,

constant burning or “standing” pilot, and electronic ignition. Match

- lit burners do not use

a pilot

- type ignition system. The user manually turns on the gas valve and lights gas

flowing to the main burner , typically with a match although sometimes a piezo

- electric

spark igniter is prov ided

. DOE notes that match

- lit hearth products would be unaffected

by the proposed prescriptive requirement disallowing use of a constant burning pilot (see

the notice of proposed rulemaking for further details on the proposal)

.

3

- 1The most common form of el ectronic ignition system found in hearth products is

the intermittent pilot ignition. Both standing pilot and intermittent pilot ignitions use a

pilot light to ignite the main burner. In both ignition systems, a small pilot light is first lit

and proven be fore gas can flow to the main burner. A “ constant burning pilot ” system is

so called because when gas flow to the main burner is discontinued, the pilot light

continues to consume fuel unless it is extinguished by the user. In such systems, the pilot

is no t designed to extinguish automatically, and in many cases the user will leave the

pilot on either year ’ round or during an entire heating season. In an intermittent pilot

ignition, the pilot light is only lit when there is a call for heat , and the pilot li ght is

automatically extinguished after the burner is turned off

. In order to ignite the pilot light,

these systems require an outside power source

, often supplied by either a battery or an

electrical connection

.

3.1.2 Definitions

Currently, there is no statutory definition of hearth products

, and hearth products

are not covered consumer products under the Energy Policy and Conservation Act et seq

. (EPCA)

. In December 2013

, DOE published a n otice of p roposed d etermination (NOPD )

that proposed a definition of hearth products for coverage. 78 FR 79638. In the December

2013 NOPD, DOE proposed the following definition of hearth product:

Hearth product means a gas

- fired appliance that simulates a solid

- fueled fireplace or

presents a flame pattern (for aestheti cs or other purpose) and that may provide space

heating directly to the space in which it is installed. 78 FR 79638, 79640

.

DOE tentatively determined that

, according to the proposed definition, hearth

products would meet the relevant statutory criteria so as to justify coverage as a consumer

product under EPCA, and provided the relevant justifications in the December 2013

NOPD

. Specifically, DOE estimated that hearth products exceeded the 100 k ilowatt

- hour

average household annual energy consumption thr eshold established by EPCA to defin e

product coverage. 78 FR 79638. Also i n the December 2013 NOPD, DOE suggested

se veral common styles of hearth product s that would be covered under the proposed

definition, including vented decorative hearth products, ven ted heater hearth products,

vented gas logs, gas stoves, outdoor hearth products, and ventless hearth products. DOE

used the definition proposed in the December 2013 NOPD (as stated above) for

determining the scope of the analysis contained in this technic al support document

( TSD )

. If the proposed determination process result s in coverage of hearth products, this

rulemaking process would form the basis for establishing energy conservation standards

for them

.

3

- 23.1.3 Product Classes

The criteria for separation of a product type into different classes are the type of

energy used, capacity, and other performance

- related features

, such as those that provide

utility to the consumer or others deemed appropriate by the Secretary that would justify

the establishment o f a separate energy conservation standard (42 U.S.C. 6295(q))

. For

hearth products, DOE has tentatively determined that product classes do not need to be

established for a prescriptive requirement for hearth products to disallow the use of

continuously bur ning pilots, as discussed in section III.C of the notice of proposed

rulemaking ( NOPR )

.

Although DOE tentatively determined that product classes do not need to be

established, DOE acknowledges the wide variety of hearth products styles available

. According ly DOE also recognizes that the impacts of the proposed prescriptive

requirement could depend on the types of hearth product s a manufacturer produces. For

instance, the impact of disallowing constant burning pilots on the manufacturer of vented

fireplaces may differ from the impact on a gas log set or outdoor patio heater

manufacturer. Additionally, DOE received information during manufacturer interviews

and from the Hearth, Patio and Barbecue Association ( HPBA ) that often pertained to

particular groups of hearth products rather than all products. T o assess the differences in

impact according to type of hearths produced and to provide the most accurate analysis

possible using available information, DOE opted to maintain some level of

disaggregation by hearth product type in its analysis of the hearth industry.

The analysis examined the five hearth product groups shown in Table 3.1

. 1

. Based on inf ormation from manufacturers obtained during interviews

, data provi ded by

HPBA (described further in section 3.2.6.2 ) , and the research presented in this TSD, these

hearth product groups adequately capture the differences among he arth produc t styles and

configurations. Section 3.3.1 and c hapter 5 of th is TSD provide descriptions of the hearth

products found in each of these analysis groups.

Table 3.1

. 1 Hearth Product Groups for DOE Analysis

Product Analysis Group Example Products

Vented Fireplaces/Inserts/Stoves Vented fireplaces, vented fireplace

inserts, vented freestanding stoves

Unvented Fireplaces/Inserts/Stoves Unvented fireplaces, unvented fireplace

inserts, unvented freestanding stoves

Vented Gas Log Sets Vented gas log sets for installation in

existing masonry fireplace

Unvented Gas Log Sets Unvented gas logs for installation in

exi sting masonry fireplace

Outdoor Products Outdoor fireplaces, outdoor fireplace

inserts, outdoor fire pits, outdoor gas

lamps, patio heaters

3.1.4 Test Procedures

3

- 3Currently, there is no statutory definition of hearth products

, and hearth products

are not cove red consumer products under EPCA

. Accordingly, there is no DOE test

procedure for measuring the energy efficiency or consumption of hearth products.

EPCA states , in relevant part, that an amended or new standard may not be

adopted if a test procedure h as not been established for the relevant product type or class

(42 U.S.C. 6295(o)(3)(A)) However, later sections of EPCA acknowledge that DOE may

establish prescriptive design requirements that by nature would not require a test

procedure. For determining compliance with standards, EPCA requires use of the test

procedures and criteria prescribed in 42 U.S.C. 6293, except for design standards. (42

U.S.C. 6295(s)) EPCA also states that a test procedure need not be prescribed if one

cannot be designed to reaso nably measure energy efficiency, energy use, water use, or

annual operating cost, and not be unduly burdensome to conduct. (42 U.S.C. 6293(d)(1))

EPCA requires that a determination be published in the Federal Register providing

justification for such case. Id.

Because the NOPR proposes to adopt a prescriptive design requirement for hearth

products, in the NOPR DOE tentatively concluded that a test procedure is unnecessary,

and thus, DOE is not developing a test method for these products. See section III.D of the

NOPR for more information regarding test procedures for hearth products.

3.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade associations

, domestic and international manufacturers of products

, and regulatory and non

- regulatory

programs. The market assessment also provides historical shipment data

, describes the

cost structure for the hearth industry

, and summarizes relevant market performance data

for each product type.

3.2.1 Trade Associations

DOE r ecognizes the importance of trade groups in disseminating information and

providing growth to the industry they support. To gain insight into hearth industry , DOE

researched various associations available to manufacturers, suppliers, and users of hearth

products . DOE also used the member lists of these groups to construct a database of

domestic manufacturers.

DOE identified two trade groups that support or have an interest in the hearth

industry: the Air

- Conditioning, Heating, and Refrigeration Institute ( AHRI ) and the

HPBA.

3.2.1.1 Hearth, Patio and Barbecue Association

HPBA is a n international trade association that promotes the hearth industry

through marketing and education, administers surveys and compiles statistics, and serves

its members through governmen t relations and advocacy

. HPBA has approximately 2,700

members 1 including “ manufacturers, retailers, distributors, manufacturers ’

3

- 4representatives, service and installation firms, and other companies and individuals

, all

having business interests in and rel ated to the hearth, patio, and barbecue products

industries.” 2

3.2.1.2 Air

- Conditioning, Heating, and Refrigeration Institute

AHRI a is a national trade association representing manufacturers of air

conditioning, heating, ventilation and commercial refrigeration eq uipment and

components. AHRI was established in January 2008, when the Air

- Conditioning and

Refrigeration Institute (ARI) merged with the Gas Appliance Manufacturers Association

(GAMA). AHRI has more than 300 member companies that account for more than 90

percent of the residential and commercial air

- conditioning, space heating, water heating,

and commercial refrigeration equipment manufac tured and sold in North America

. 3

AHRI serves many functions, including advocating for the heating, ventilation, air

con ditioning and refrigeration (HVACR) industry ; certifying product performance;

developing performance standards for equipment; compiling statistical reports of industry

data; sponsoring HVACR research programs; and supporting HVACR technician

education programs

. 4

AHRI maintains the AHRI Efficiency Certification Program. AHRI also

maintains on its website a database of products and equipment tested under its

certification program. While the director y lists a “vented hearth heater” product type in

its direct heating equipment directory, no products are listed under that category.

3.2.2 Manufacturer Information

The following section provides information about manufacturers of hearth

products , potential small business impacts, and product distribution ch annels.

3.2.2.1 Manufacturers

DOE identified 77 domestic manufacturers of hearth products as well as 13

foreign

- owned manufacturers of gas hearth products sold in the U nited S tates . The

majority of the domestic market is controlled by six manufacturers: Hearth an d Home

Technologies, Innovative Hearth Products, Vermont Castings Group, Travis Industries,

Regency, and Napoleon.

Table 3.2.1 lists all identified ma nufacturers of products potentially affected by

this rulemaking. Domestic small business manufacturers, defined as having 500

employees or fewer, are noted in the table

.

Table 3.2

. 1 Hearth Product Manufactur ers

Acucraft Fireplace

Systems ** Fire Features (Colombo

Construction Corp) ** O.W. Lee **

a For more information, please visit www.ahrinet.org.

3

- 5Adobelite LLC ** Firegear LLC ** Ortal Heating Solutions **

American Fireglass ** Firetaiment Inc ** Outdoor Entertainment

Designs **

American Gas Log, LLC ** Formation Creation Inc. ** Pacific Energy Fireplace*

American Heating

Technologies ** GHP Group, Inc ** Portland Willamette **

Appalachian Stove ** Golden Blount, Inc. ** Pride Family Brands **

Archgard Industries Ltd.* Hargrove Manufacturing ** Procom

Architectural Pottery ** Hearth & Home

Technologies Pyrotek, Inc.

Big Woods Hearth

Products ** Hearth Innovations ** Rasmussen Gas Logs &

Grills **

Blaze King Industries ** Hearth Products Controls

Co. ** Raw Urth Design **

Blue Rhino HearthStone Quality Home

Heating Produc ts, Inc. ** Regency

Blue Rooster Company,

The ** Heatmaster, Inc. ** Robert H. Peterson

Company **

Bobe Water & Fire ** Infrared dynamics** Sherwood Industries Ltd.*

Bond Manufacturing ** Innovative Hearth Products Sierra Products Inc **

Buck Stove Corporation ** IronHaus ** Skytech Products Group **

Burley Appliances Limited* J.A. Roby * Solus Décor*

Cal Flame ** Jotul* Spark Modern Fires **

California Outdoor

Concepts ** Kingsman Fireplaces* Steelog Artistic Metals **

Camp Chef ** Kozy Heat ** Stone Forest, Inc. **

Cast Classics Lava Heat ** Sun Star Heaters **

Cooke Furniture ** Louisville Tin and Stove

Co. ** Superior Radiant Products*

Crown Verity* Lynx ** Sure Heat Manufacturing **

Designing Fire ** Malm Fireplace ** Thelin **

Diamond Fire Glass ** Mason

- Lite ** Travis Industries, Inc.

Eiklor Flames Inc. ** Mendota Hearth Products Tropitone

Empire Comfort Systems ** Modern Home Products

Corporation ** Valor Fireplaces*

Empire Pre

- Cast ** Moderustic Fire ** Vermont Castings

Enerco Group, Inc ** Montigo ** Warming Trends, LLC **

Fire On Glass ** Napoleon Fireplaces* William Smith **

Fireboulder ** Nibe Stoves* World Marketing of

America, Inc. **

*Foreign

- owned

**Small business (500 employees or fewer)

3.2.2.2 Small Business Impacts

3

- 6S mall businesses may be disproportionately affected by the promulgation of

energy conservation standards for hearth products. The Small Business Administration

(SBA) defines small business manufacturing enterprises for residential hearth products as

those having 500 employees or fewer. 5 SBA lists small business size standards for

industries as they are described in the North American Industry Classification System

(NAICS). The size standard for an industry establishes the largest size that a for

- profit

entity can be while still qu alifying as a small business for f ederal g overnment programs.

These size standards are generally expressed in terms of the average annual receipts or

the average employment of a firm. H earth manufacturing is classified under NAICS

335228, “Other Major Hous ehold Appliance Manufacturing,” and under NAICS 333414,

“Heating Equipment (except warm air furnaces) Manufacturing.” The size standard is

500 employees or fewer for both NAICS codes.

DOE identified 66 domestic small business manufacturers of hearth produc ts

covered by this rulemaking (denoted in Table 3.2

. 1 above). DOE studied the potential

impacts on these small businesses as a part of the manufacturer impact analysis (chapter

12 of this TSD ).

3.2.3 Distribution Channels

Analysis of the distribution channels of products covered by this rulemaking is an

important facet of the market assessment. DOE gathered information from publicly

available sources and manufacturer intervi ews regarding the distribution channels for

hearth products

. DOE uses distribution channel markups ( e.g. , manufacturer markups,

retailer markups, distributor markups, contractor markups) and sales taxes (where

appropriate) to convert the manufacturer produ ction cost estimates from the engineering

analysis to consumer prices, which are then used in the LCC and PBP analys e s and in the

manufacturer impact analysis. The markups are multipliers that are applied to the

purchase cost at each stage in the distribut ion channel for hearth products. Before

developing markups, DOE defines key market participants and identifies distribution

channels.

DOE characterized two distribution channels to describe how hearth products pass

from the manufacturer to consumers: (1) r eplacement market and (2) new construction.

The replacement market channel is characterized as follows:

Manufacturer  Wholesaler  Mechanical contractor  Consumer

The new construction distribution channel is characterized as follows:

Manufacturer  Wholesaler  Mechanical contractor  General

contractor  Consumer

Chapter 6 of the NOPR TSD provides further detail on the estimation of markups.

3

- 73.2.4 Regulatory Programs

The following section details current regulatory programs mandating energy

conservation standards for hearth products . Section 3.2.4.1 discusses cur rent f ederal

energy conservation standards, and section 3.2.4.2 provides an overview of existing s tate

standards. Sections 3.2.4.3 review s standards in Canada that may affect companies

servicing the domestic market. No energy conservation standards have been implemented

for gas hearth products in Mexico. 6

3.2.4.1 Current Federal Energy Conservation Standards

As described in section 3.1.1

, there are currently no energy conservation standards

for hearth products as hearth pro ducts are not currently covered products

. In the

December 2013 NOPD, DOE proposed a definition of coverage for hearth products.

Should the December 2013 NOPD result in coverage of hearth products, this rulemaking

would establish energy conservation standar ds

, if it is determined that such standards

would meet the requirements of EPCA

. b

3.2.4.2 State Energy Conservation Standards

DOE notes that state and local jurisdictions may require certification to certain

safety standards. A sample list of these standards is provided in Table 3.2

. 2

. However,

none of the heater standards set forth a minimum efficiency requirement, and none of the

standards establish a design requirement int ended to reduce fuel consumption. DOE is

not aware of any s tate requirements that regulate the energy use or energy efficiency of

hearth products.

Table 3.2

. 2 List of Sample Hearth Industry Standards

ANSI Z21.50 Vented Gas Fireplaces

ANSI Z21.60 Decorative gas appliances for installation in solid

- fuel

burning fireplaces

ANSI Z21.84 Standard for manually lighted, natural gas, decorative

gas appliances for installation in solid

- fuel burning fireplaces

ANSI Z21.88 Vented gas fireplace heaters

ANSI Z21.11.1 Gas

- fired room heaters, volume II, unvented room

heaters

RGA #2

- 72 Standard for Decorative Log Sets for Installation in

Wood

- Burning Fireplaces

b Any new or amended standard for a covered product must be designed to achieve the maximum

improvement in energy efficiency that is technologically feasible and economically justified. (42 U.S.C.

6295(o)(2)(A) and (3)(B))

3

- 83.2.4.3 Canadian Standards Association

The Canadian Standards Association (CSA) is an independent standards

- setting

agency that establishes test procedures and efficiency standards that are typically adopted

by the Canadian g overnment.

Canada has product classes for vented gas fireplaces and fireplace heaters. Vente d

gas fireplaces are primarily used for aesthetic purposes, whereas vented gas fireplace

heaters are designed to provide heat to the space where they are installed. Canada has not

issued energy conservation standards for either of these products classes. 7

Since September 2003, all gas fireplaces sold in Canada must be tested and

certified to the Canadian test standard CSA P.4.1

- 02 under the EnerGuide program. This

standard determines a fireplace efficiency rating tha t is used in certification and labeling. 8

3.2.5 Voluntary Programs

DOE reviewed voluntary programs promoting energy efficient gas hearth

products in the United States

. H earth products are not currently covered under the

ENERGY STAR ® appliance program 9 DOE’s Federal Energy Management Program. 10

Several utilities offer rebate programs for gas fireplaces

. A sample of these

programs is listed in Table 3.2

. 3

.

Table 3.2

. 3 Sample Rebate Programs

State Utility Product Requirement Value

Washington Puget Sound Energy 11 Gas Fireplaces Must meet minimum fireplace

efficiency (CSA P.4.1

- 02 rating) of

70% and have electronic ignition $200

Minnesota CenterPoint Energy 12 Gas Fireplaces,

Inserts, Stoves Must have electronic ignition $ 75

Utah,

Wyom ing,

Idaho City Water Light and

Power 13 Gas Fireplaces

(Direct

- Vent) Must have minimum heater

efficiency annual fuel utilization

efficiency 70%, must be direct

- vent,

thermostatically controlled, include

blower, and electronic ignition $200

3.2.6 Historical Shipments

Annual product shipment trend data are an important aspect of the market

assessment and development of the standards rulemaking. Suc h data are used in the

shipments analysis ( c hapter 9 of this TSD ). The number of unit shipments is expected to

follow a trend similar to that of new home starts. This relationship is further detailed in

c hapter 9 of this TSD

.

3.2.6.1 New Home Starts

Figure 3.2

. 1 presents the total number of new single

- family and multifamily

housing units started in the United States from 2005 to 2013

. Between 2005 and 2009,

3

- 9total housing starts decreased by 73 percent

. Since 2009, total housing starts have

increased slightly

, and as of 2013, were at 45 percent of 2005 total housing starts. 14

C ertain hearth product types, specifically indoor fireplaces, a re more strongly tied

to housing starts as these products are more often purchased and installed at the time of

new home construction. Inserts and gas logs are less directly tied to housing starts as

these products are typically purchased for remodeling or u pgrad ing existing homes with

wood

- burning fireplaces

. However, remodeling existing homes can be assumed to

correlate with the overall state of the economy, an indicator of which is housing unit

starts. The relationship between housing starts and hearth pro duct shipments is discussed

further in c hapter 9 of this TSD

.

Figure 3.2

. 1 New Privately Owned Single

- Family and Multi

- Family Housing Unit

Starts from 2005 to 2013 15 0.00 500.00 1,000.00 1,500.00 2,000.00 2,500.00

2004 2006 2008 2010 2012 2014Housing Starts (thousands)

3

- 103.2.6.2 Shipments

Information about annual equi pment shipment trends allows DOE to estimate the

impacts of energy conservation standards on the hearth products industry . DOE has

examined unit shipments and value of shipments for various hearth products using data

provided by HPBA.

Data for the total nu mber of gas hearth appliances shipped are available on HPBA’s

website for the years 1998 to 2013

. 16 These total shipments include all types of gas hearth

products shipped by member manufacturers, including fireplaces, inserts, stoves,

fireboxes, and gas logs, among other types. HPBA also provided DOE with additional

data for the rulemaking process

, 17 includ ing ranges of market shares for each of nine

hearth p roduct styles from 2005 to 2013

, and the total annual shipments for these nine

categories. HPBA did not provide the relative market shares for each year, but rather

provide d a range of market shares over the corresponding period. Figu re 3.2

. 2 shows the

total shipments data from the HPBA website and those provided by HPBA f rom 2005 to

2014.

Figure 3.2

. 2 HPBA Shipments D ata

Table 3.2

. 4 shows the product categories and market shares provided to DOE by

HPBA. 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000

2004 2006 2008 2010 2012 2014Total Shipments Provided by HPBA - Total

for 9 Hearth Categories

Provided"

Total Gas Hearths from

HPBA Website

3

- 11Table 3.2

. 4 HPBA Product Categories and Relative Market Shares 17

HPBA Product Category Shipments, 2005

-

2013

%

Vented Fireplace 40 58

Unvented Fireplace 2 6

Vented Stove 3 5

Unvented Stove 0 1

Vented Fireplace Insert 4 10

Unvented Fireplace Insert 0 0.3

Vented Gas Logs 3 10

Unvented Gas Logs 16 23

Outdoor Fireplaces 0.4 24 *

*HPBA subsequently clarified via email that the range 4% to 9% was typical for outdoor fireplaces over

the period 2010

- 2013.

DOE confirmed with HPBA that the difference between the total shipments

available on the HPBA website and the total shipments provided for the nine categorie s is

due primarily to fireboxes and patio heaters. HPBA was unable to provide additional data

for p atio heaters. HPBA also clarified that the outdoor fireplaces category included

shipments for fire pits , and that a substantial portion of other outdoor products are from

non

- HPBA manufacturers and are therefore not captured in these data

.

As described i n section 3.1.3 , DOE elected to use five hearth product groups for

analysis. The shipment percentage ranges provided by HPBA were aggregated into the

five product groups for the rulemaking analysis. In order to derive a set of five average

shipment ratios for its product groups, DOE started by taking the midpoint of the ranges

provided and proportionally adjusting so that the total of the resulting percentages

equaled 100 percent

. DOE used the 4

- 9 percent range for outdoor fireplaces because

HPBA acknowledged that the range initially provided was large r than would be expected

in a typical year

.

T o determine a useful and accurate market share for outdoor produc ts and in light

of HPBA’s suggestions, DOE deemed it necessary to capture non

- HPBA shipments for

this product group. Using a product listing database compiled for the rulemaking

(discussed in section 3.2.9 ) , DOE found that there were 12 percent as many HPBA patio

heater models listed compared to the number of all other outdoor product models listed

for HPBA members

. DO E used this ratio as a proxy to estimate the num ber of patio

heater shipments from HPBA members. For patio heaters, DOE assumed that non

- HPBA

shipments were three times those of HPBA members. For all other outdoor products,

DOE assumed that the number of non

- HPBA shipments was equal to the HPBA

shipment s. These additional shipments estimates were added to the average number of

shipments from 2010 to 2013, and the shares for each of the five hearth product groups

were then recalculated. These values are provided in Table 3.2

. 5

.

3

- 12Table 3

. 2.5 DOE Market Shares for Hearth Product Groups

Product Group Market Share

% *

Vented Fireplace/Insert/Stove 56.2

Unvented Fireplace/Insert/Stove 4.4

Vented Gas Log Sets 6.1

Unvented Gas Log Sets 18.3

Outdoor 15.0

*These shipment weights include match

- lit hearth products. These products are later removed becase they

would be unaffected by a proposed rule disallowing standing pilot ignitions. See c hapter 9 of th is TSD for

more information regarding the shipments analysis.

3.2.7 Industry Cost Structure

DOE developed the hearth industry cost structure using publicly available

information ( e.g., Securities a nd Exchange Commission 10

- K reports, 18 corporate annual

reports, the U.S. Census Bureau’s 20 11 Annual Survey of Manufacturers 19 ) as well as

data obtained directly from manufacturers through interviews. Table 3.2

. 6 presents key

industry financial metrics, each of which is estimated as a percentage of total revenue.

The manufacturer impact analysis (chapter 12 of this TSD) includes a more detailed

discussion of the industry cost structure and the potential fin ancial impacts of an energy

conservation standard.

Table 3.2

. 5 Industry Cost Structure

Parameter Percent of Total

Revenue

%

Working Capital 2.2

Net Property, Plant, and

Equipment 10.8

Selling, General and

Administrative Expenses 25.0

R esearch and Development 2.3

Depreciation 2.1

Capital Expenditures 2.1

3.2.8 Equipment Lifetime

DOE reviewed available literature and consulted with manufacturers to establish

typical equipment lifetimes. (See the life

- cycle cost analysis, chapter 8 of this TSD, for

additional details and sources used to determine the typical equipment lifetimes.) DO E

combined these sources to develop an average estimated lifetime of the equipm ent

covered by this rulemaking

. DOE tentatively determined the average lifetime of hearth

products to be 15 years.

3

- 13Chapter 8 of the TSD provides more information about hearth pr oduct lifetimes.

3.2.9 Market Performance Data

As previously mentioned, DOE compiled a product listing database by conducting

a review of product literature. Fields captured in the database include the hearth product

type, fuel type

, and ignition type. For gas l og sets, DOE found this type of data

aggregation was not us eful, as model numbers are not used to reflect all possible

permutations of burners, log styles, and ignition types.

In conjunction with manufacturer estimates provided during the interview

process, DOE also used the product listing database to estimate the portion of shipments

that use each of the three general ignition types by product group. These estimates are

shown in Table 3.2

. 7

.

Table 3.2

. 6 Average Market Share of Ignition Types by Analysis Group

Product Group Match

Lit

% Standi

ng

Pilot

% I ntermi

ttent

P ilot

I gnition

% Source

Vented

Fireplace/Insert/Stove 5 40 55 Manufacturer Interviews

Unvented

Fireplace/Insert/Stove 0* 12 88 Product Listing Database

Vented Gas Log Sets 50* 44 6 Manufacturer Interviews

Unvented Gas Log Sets 0 94 6 Manufacturer Interviews

Outdoor 50** 26 24 Product Listing Database

* DOE assumed that there are no shipments of unvented match

- lit hearth products because the industry

safety standard ANSI Z21.11.2 requires the use of an oxygen depletion sensor and therefore a safety pilot.

* *The product listing database showed that the number of match

- lit outdoor models was lower, however

DOE assumed that 50% of outdoo r hearth products are match lit.

DOE found that efficiency data for heater

- rated hearth products are inconsistent

and frequently not fully specified. Thermal efficiency, annual fuel utilization efficiency

(AFUE), and fireplace efficiency (CSA P.4.1

- 02) are used inconsistently throughout the

industry, and efficiency ratings in product literature often do not specify which metric is

used or the test procedure by which the rating is obtained.

3.3 TECHNOLOGY ASSESSMENT

The purpose of the technology assessment is to dev elop a list of technology

options manufacturers can u se reduce the energy consumption of hearth products . The

following assessment provides descriptions of those technology options that apply to all

hearth product groups or specific hearth product groups

.

3

- 14In preparation for the screening and engineering analyses, DOE identified several

possible technology optio ns for improving the energy efficiency or reducing the energy

consumption of hearth products . These options provide insight into the design

improvements typically used to reduce the gas consumption of hearth products

.

3.3.1 Baseline Equipment Components and Operation

DOE typically defines the baseline model as a product having an efficiency that

just meets the existing f ederal energy conservation st andards. DOE also typically defines

baseline models as having commonly available features. The baseline models for hearth

products serve as a reference point s for measuring changes resulting from energy

conservation standards.

I n the case of hearth produc ts, there are no f ederal en ergy conservation standards

. For all hearth product groups

, DOE assumes the baseline model uses a constant burning

pilot ignition system

. Because the inclusion of a constant burning pilot ignition would

lead to the highest possi ble standby mode energy use, this ignition type represents the

baseline for comparison to other technologies that would reduce standby mode energy

use. Hearth products that are match lit were not considered baseline as these consume no

energy during standb y mode.

Furthermore, DOE’s selection of baseline models focused primarily on the types

of ignitions typically employed in each hearth product group because the standby mode

energy use is mostly attributable to the ignition type (see section III.B of the NOPR for

rationale)

. As noted in section 3.1.3 , DOE has tentatively concluded that within each

hearth product group and for each ignition type (standing pilot and electronic ignition)

t he primary ignition components (gas valve, pilot assembly, and contro l module for

electronic ignition models) are largely interchangeable. The paragraphs below describe

the baseline model for each hearth product group analyzed

.

Vented Fireplace/Insert /Stove

. A vented gas fireplace simulates a wood

- burning

fireplace and may be installed into a wall or other framing structure

. An insert is designed

for installation int o an existing solid fuel burning fireplace

. A stove is a free

- st anding unit

that does not require installation into a surrounding structure. Vented units include pipes

that carry combustion gases out of the building

.

For this product group, the typical constant burning pilot ignition system included

a millivolt gas co ntrol valve and a pilot assembly

. T he millivolt valve uses the pilot light

to generate a small voltage potential

, allow ing use with a remote control or thermostat to

open the gas valve without requiring an additional source of power

. The pilot assembly

includes a gas line connecting the gas valve to the pilot, a thermocouple

, and a

thermopile. The thermocouple is heated by the pilot and used to prove that the pilot is

burning gas, while t he thermopile provides the volt age potential for use with a remote

control or thermostat.

Unvented Fireplace/Insert/Stove

. Unvented fireplaces, inserts, and stoves are

similar to those found in the vented group, but are not designed to vent combustion gases.

3

- 15Rather, these products are designed to safely allow the byproduc ts of combustion into the

dwelling

. The ignition systems on these products differ from their vented counterparts in

the ir use of an oxygen depletion sensor (ODS)

. The ODS is a precision pilot light and

calibrated thermocouple that shuts the gas control val ve when the oxygen in the room is

below a threshold. These components are included with the pilot assembly.

Vented Gas Log Sets

. Vented gas log sets are designed for installation directly

into existing solid fuel burning fireplaces

. These products are op en flame devices and do

not i nclude the metal heat shield or enclosure found in fireplaces, inserts, and stoves. Gas

log sets are meant as a replacement for wood as a fuel source in existing fire places, and

are therefore subject to the physical space const raints of the existing fireplace.

Additionally, they rely on the existing chimney or ventilation system to remove the

combustion byproducts from the building. Vented gas log sets typically consist of a

burner, a grate, and ceramic or cement imitation logs.

In jurisdictions that do not require a safety pilot ignition system, the burner is

commonly lit manually with a match or lighter. These types of gas log sets do not

maintain a constant b urning pilot and are therefore unaffected by the proposed

prescriptive requirement contained in the NOPR

.

In jurisdictions that require a safety pilot specifically or certification to a safety

standard that requires one

, manufacturers may offer or include a manual safety pilot

system, a millivolt pilot system, or an electronic ignition system. Manual gas valves are

typically less expensive and smaller in size than millivolt gas valves or gas valves for

electronic ignition systems. DOE selected the manual sa fety pilot system as the baseline

model due to the space and cost constraints consumers of these products often face.

Unvented Gas Log Sets

. Unvented gas log sets are similar to their vented

counterparts except that the burner is designed so that the bypro ducts of combustion enter

the occupied space rather than being vented outside of the building. Unvented gas log

sets also incorporate an ODS for safe operation.

Outdoor Products. Outdoor products may take several forms. DOE identified

fireplaces, fire pits or burners, and patio heaters as the most prominent styles of outdoor

hearth products. Outdoor fireplaces are similar in design to indoor fir eplaces , but are

specially designed with materials that make them suitable for installation outdoors

. Fire

pits an d burners are open flame devices that do not incorporate heat shi elding or an

enclosure. These products are comprised of a burner and an ignition system consisting of

a gas valve and pilot.

Patio heaters may come in two styles, radiant and pyramid

. Radiant patio heaters

provide primarily radiant heat to an outdoor space. A steel emitter screen that surrounds

the combustion zone glows when heated and provides rad iant heat to its surroundings.

These patio heaters typically consist of a base or stand, a gas connection and regulator, a

post, and a head unit

, wh ich contains the gas valve and pilot assembly

, a burner, the

emitter screen, and a reflector shield. These patio heaters may use natural gas or propane;

in the case of propane, the base or stand is often large enough to store a propane tank.

3

- 16P yramid style patio heaters create a very large flame through a glass tube. These

heaters consist of a base, gas connections and regulator, gas valve and pilot assembly,

burner, glass tube , and reflector.

3.3.2 Techno logy Options

Air

- to

- Fuel Ratio. The mixture of air and fuel for combustion determines key

flame aspects for hearth products, in particular the flame color, height, and heat output

from the hearth product. As described previously, gas

- fired hearth products are often

designed to simulate the burning of wood. In order to achieve flame characteristics that

mimic wood

- burning flames, gas

- fired hearth products utilize a “rich ” mixture, that is

, the ratio of air to fuel is low. For many natural gas products, prima ry air is in fact not pre

mixed, and what is burned is nearly 100 percent natural gas. T his results in a tall yellow

flame. For propane products, air is pre

- mixed with fuel prior to combustion. Optimizing

the air

- to

- fuel ratio would improve the active mode energy use of hearth products , but

may reduce the flame aesthetic appeal

. Manufacturers indicated during interviews that

becasue the aesthetic appeal of the flame must be maintained, there would be no room to

reduce the fuel consumption by adjusting the air

- to

- fuel ratio.

Burner port design. Gas burners for hearth products typically comprise tubes

with holes or slots through which the gas exits and combusts. The holes or slots are

designed with particular sizes and patterns in order to achieve the desired flame pattern or

aesthetic. While the primary objective of optimizing gas burner ports is to achieve the

desired flame pattern, the p orts could also be optimized to deliver an acceptable flame

aesthetic while reducing the amount of fuel consumed. This design would have the

potential to improve the active mode energy use of hearth products , but may reduce

aesthetic appeal

. DOE is not awa re of any products on the market using advanced burner

port designs as a means of reducing energy consumption. During manufacturer

interviews, most manufacturers stated that they expected that adjusting the burner port

design would insignificant ly reduc e f uel consumption .

Simulated log design. Many gas hearth products incorporate cement, fiber, or

ceramic logs that are designed to simulate the look of wood logs. The log shapes are

optimized in conjunction with the burner design

. The combination of the burn er design

and log shape, size

, and placement results in the overall aesthetic for the product.

Additionally, logs must be design ed in conjunction with the burner to ensure that flames

d o not impinge on the logs themselves, as this causes th e flame to cool and form soot. For

contemporary style hearth products, simulated logs may be replaced with other shapes or

materials or may be removed entirely.

For products that incorporate simulated logs or other objects

, the combination of

the logs or objects with the flame pattern (burner design) result s in the complete design.

A key design objective for this log and flame pattern combination is to fill the firebox

into which they are installed as much as possible. For a given size, the logs could

therefore be optimize d to reduce the amount of fuel needed while sufficiently filling the

3

- 17firebox. This design option would potentially improve the active mode energy efficiency

of hearth products. However, DOE is not aware of any product designs on the market that

have proven to be more efficient methods for arranging the simulated log. M anufacturer

indicated during interviews that insignificant reductions in energy use would be achieved

by adjusti ng the simulated log design.

Pan burner media/bead or glowing ember type. M any hearth products include an

ember material that glows and radiates when heated. In pan type burners, sand is used to

cover the burner and results in a flame pattern. In contempor ary hearth products and in

outdoor fire pits, glass beads may be used in place of simulated logs for effect. These

media could potentially be selected to produce a satisfactory flame pattern while reducing

the required gas consumption. This design option w ould reduce the active mode energy

use of hearth products. During manufacturer interviews, DOE inquired about the use of

media, beads

, or glowing embers for reducing fuel consumption while providing an

adequate aesthetic flame . M anufacturers indicated tha t this technology would not result

in measurable energy savings. Additionally, this technology would only apply to the

subset of hearth products that use media, glass

, or other beads, or glowing embers.

Reflective walls and/or other components in firebox/c ombustion zone. For

hearth products that include a firebox or other enclosure, the interior walls could

potentially be painted with a reflective coating. This could potentially give the illusion of

more or taller flames, thereby reducing the amount of fuel required to achieve a

satisfactory aesthetic. This design option would only apply to fireplaces, inserts, and

stoves, as other hearth products do not incorporate a firebox. T his design option may

reduce the amount of fuel used in active mode. Manufacturers indicated during

interviews that reflective walls would not substantially reduce the fuel needed to produce

an aesthetic flame, and that the reflective coating would only serve as a supplemental

aesthetic effect. Also, this technology would only apply to the subset of hearth products

that include an enclosure surrounding the flame.

Air c irculating fan

. Air circulati ng fans improve the efficiency of heater hearth

products by increasing the air flow rate through the heat exchanger section of a fire place,

insert

, or stove

. For vented products, this results in more heat being provided to the

occupied space rather than lost through the vent. An air circulating fan would only be an

option for vented fireplaces

, inserts , and stoves

. These hearth prod ucts already

incorporate the heat exchanger for which the circulating fan would be of use, and many

are already available with circulating fans as an option. A circulating fan would not be of

use to or feasible for products such as gas logs, outdoor fire p its, and patio heaters

because these pr oducts do not incorporate a heat exchanger and do not have sufficient

cabinetry or enclosure to house the fan

. A circulating fan would not improve the

efficiency of an unvented hearth product, as all of the heat from an unvented hearth

already remains in the occupied space. A circulating fan would primarily improve the

heating efficiency during the active mode for those hearth products for which a fan could

be integrated, namely fireplaces, inserts, and stoves. It woul d not substantially affect the

standby mode energy use of hearth products.

3

- 18Condensing heat exchanger. For hearth products that prov ide space heating, the

combustion gases can be passed through a larger or improved heat exchanger to extract as

much heat as possible. Such a heat exchange can remove sufficient energy from the

combustion gases to reduce the combustion gas temperature below its condensing point

. This type of heater is typically termed a condensing heater and is highly efficient at

converting the energy in combustion gases into space heating.

The flue

- gas condensate is often acidic and corrosive. Therefore, special

corrosion

- resistant heat exchangers and vent linings are required for safe and reliable

operation. Corrosion due to condensation of combustion gases limits the heating

efficiency of a hearth product with a standard flue and vent system. Using corrosion

resistant heat exchangers or sidewall venting, and lining the vent/masonry systems with

corrosion

- resistant material can extend the hea ting efficiency.

Condensing systems require some means to collect and drain the condensate that

develops within the heat exchangers. Condensing systems can be designed to use

secondary heat exchangers and air circulation fans as well. This technology would only

apply to hearth products designed to provide space heating and only improves heating

efficiency during active mode operation.

Electronic Ignition

. As shown in Table 3.2

. 7 , a substantial portion of the hearth

industry uses standing pilot ignition systems. For hearth products, DOE found that there

are primarily two types of electronic ignition systems that do not rely on a continuously

burning pilot : intermittent pilot ignition and hot surface (or wire) ignition. The

intermittent pilot ignition is a device that generates a spark to light a pilot which in turn

lights the main burner. The pilot then automatically extinguishes after the main burner is

lit

. The hot su rface or hot wire ignition system lights the main burner directly via a

sufficiently hot surface.

Both hot surface and intermittent pilot electronic ignitions require an outside

source of electricity to operate. The gas valve for each electronic ignition system

typically only consumes power during a call for heat. The control module, which houses

the electronic circuitry required to control the entire ignition system, typically only

consumes power during a call for heat. Finally, the electronic thermostat

, if present,

draws power continuously (regardless of whether heat is needed) . These power

requirements in total are typically much less than the pilot burn rate

.

An electronic ignition would eliminate most standby mode energy consumption

since these ignitions do not rely on a continuously burning pilot light. This design option

would not affect active mode energy use.

3.3.3 Technology Options That Do Not Affect Energy Use

After reviewing the technology options, DOE found that the circulating fan,

condensing heat exchanger, and electronic ignition may substantially reduce energy

consumption of hearth products

. DOE understands that altering the air

- to

- fuel ratio,

burner port design, simulated log design, burner pan media, bead, or em ber type, and

3

- 19reflective combustion chamber walls in order to achieve fuel savings would result in

negatively impacting the design aesthetic of hearth products. When maintaining an

aesthetic flame, these technologies would not be means for achieving signif icant fuel

savings. During manufacturer interviews, DOE found that regulations focused on or

requiring any of these five technology options would hinder manufacturers’ ability to

innovate and produce aesthetic products. However, DOE does not discourage

man ufacturers from using these design options or strategies since they may reduce annual

energy consumption.

3

- 20REFERENCES

1 HPBA. Join HPBA. Last accessed August 2014

< http://www.hpba.org/members/members/join

- hpba >.

2 HPBA. HPBA Overview. Last accessed August 2014.

< www.hpba.org/index.php?id=46 >.

3 AHRI. About Us. Last accessed January 2015.

< http://www.ahrinet.org/site/318/About

- Us >.

4 I bid.

5 U.S. Census Bureau, 2012 NAICS Definitions . Last accessed August 2014.

<

http://www.census.gov/eos/www/naics/2012NAICS/2012_Definition_File.pdf >.

6 Comisi ó n N acional P ara El U so E ficiente D e L a E nerg í a . Last Accessed August

2014.

< http://www.conu ee.gob.mx/wb/CONAE/CONA_1002_nom_publicadas_vigen >.

7 National Resources Canada. Last accessed August 8, 2014.

< http://www.nrcan.gc.ca/energy/regulations

- codes

- standar ds/products/6865 >.

8 Ibid.

9 ENERGY STAR All Certified Products. Last accessed August 2014.

< http://www.energystar.gov/certified

- products/certified

- products >.

10 Federal Energy Management Program. Covered Product Categories. Last

accessed August 2014. < http://energy.gov/eere/femp/covered

- product

categories >.

11 Puget Sound Energy. Fireplace Rebate. Last accessed August 2014.

< https://pse.com/savingsandenergycenter/ForHomes/Pages/Fireplace

Rebate.aspx >.

12 CenterPoint Energy 2014 Fireplace Rebates. Last accesse d August 2014.

< http://www.centerpointenergy.com/services/naturalgas/residential/efficiencyreba

tesandprograms/firepl aceandhearthrebates/MN/ >.

13 THERMWISE High

- Efficiency Gas Fireplace Rebates. Last accessed August

2014. < http://www.thermwise.com/home/Firesidechat.php >

14 U

. S

. Census. New Privately Owned Housing Units Started. Last a ccessed August

2015. < https://www.census.gov/construction/nrc/pdf/st artsan.pdf >.

3

- 2115 Ibid.

16 HPBA U

. S

. Hearth Statistics. Last accessed August 2014.

< http://www.hpba.org/statistics/hpba

- us

- hearth

- statistics >.

17 HPBA Hearth Product Categories, Market Shares, and Total Shipments Data.

Submitted on August 18, 2014.

18

. U.S. Securities and Exchange Commission. Annual 10

- K Reports. Various Years.

< http://www.sec.gov/ >

19

. U.S. Census Bureau, Annual Survey of Manufacturers: General Statistics:

Statistics for Industry Groups and Industries (2010)

< http://www.census.gov/manufacturing/asm/index.html >

.

3

- 22CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION

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

- 1

4.2 TECHNOLOGY OPTIONS

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

- 2

4.3 SCREENED OUT TECHNOL OGIES

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

- 2

4.4 REMAINING TECHNOLOGI ES

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

- 2C H APTER 4

. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter discusses the screening analysis conducted by the U.S. Department of

Energy (DOE) of the technology options identified in the market and technology assessment for

hearth products (chapter 3 of this technical support document; TSD ). In the market and

technology assessmen t, DOE presented an initial list of technology options that can be used to

improve the energy efficiency and/or reduce energy consumption of hearth product s . The goal of

the screening analysis is to identify any technology o ptions that will be eliminated f rom further

consideration in the rulemaking analyses.

The candidate technology options are assessed based on information gathered during

DOE research, as well as inputs from inter e sted parties . Technology options that are judged to be

vi able approaches for improving energy efficiency or reducing annual energy consumption are

retained as inputs to the subsequent engineering analysis. Technology options that are not

incorpor ated in commercial ly available products or in working prototypes, that fail to meet

certain criteria pertaining to practicability to manufacture, install

, and service, that have adverse

impacts on product utility or availability, or have adverse impacts on health or safety will be

e liminated from consideration

, in accordance with 10 CFR 430, subpart C, appendix A, section

4(a)(4)(i

- iv).

DOE uses the following four screening criteria to determine which technology options

are suitable for further consideration in an energy conservation standards rulemaking:

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 and reliable

installation and servicing of a technology in commercial products could be ach ieved on the scale

necessary to serve the relevant market at the time the standard comes into effect, then DOE will

consider that technology practicable to manufacture, install, and service.

3. Adverse impacts on product utility or product availability

. If DOE determines a

technology would have a significant adverse impact on the utility of the product to significant

subgroups of c onsumers , or would result in the unavailability of any covered product type with

performance characteristics (including reliab ility), features, sizes, capacities, and volumes that

are substantially the same as products generally available in the United States at the time, it will

not consider this technology further.

4. Adverse impacts on health or safety

. If DOE determines that a technology will have

significant adverse impacts on health or safety, it will not consider this technology further.

4

- 14.2 TECHNOLOG Y OPTIONS

As described in c hapter 3 of this TSD

, DOE develop ed a list of tech nology options

manufacturers could use to reduce the energy consumption of hearth products. The following

t echnology options were considered in the technology assessment.

• Air

- to

- fuel ratio

• Burner port design

• Simulated log design

• Pan burner media/bead type

• Reflective walls and/or other components inside combustion zone

• Air c irculating fan

• Electronic ignition

• Condensing heat exchanger

S everal of these technologies were not considered further in the screening analysis ( see

c hapter 3 of this TSD for details). The air circulating fan, condensing heat exchanger, and

electronic ignition were considered as part of the screening analysis because DOE concluded that

these three option s may substantially the reduce energy consumption of hearth products.

4.3 SCREENED OUT TECHNOLOGIES

DOE has tentatively concluded that the se three technologies , namely the air circulating fan,

the condensing heat exchanger, and the electronic ignition, would not be screened out by any of

the four screening criteria listed above. T hese technologies are curre ntly commercially available

for hearth product s and do not result in adverse impacts on health or safety. Therefore, they do

not fail the first, second or fourth screening criteria. With regard to impact on product utility and

availability, DOE notes that an electronic ignition provides the same functionality as a millivolt

standing pilot gas valve, specifically the ability to be used with a remote control or thermostat.

DOE has also tentatively determined that electronic igni tion components are available for a wide

range of gas

- fired equipment be yond hearth products, and that the ability of hearth manufacturers

to comply with the standard will not be restricted for lack of available components.

4.4 REMAINING TECHNOLOGI ES

DOE pass ed the circulating fan, condensing heat exchanger, and electronic ignition to the

engineering analysis for further consideration

. See chapter 5 of this TSD for the results of that

analysis

.

4

- 2CHAPTER 5 ENGINEERING ANALYSIS

TABLE OF CONTENTS

CHAPTER 5 ENGINEERING ANALYSIS

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

- i

5.1 INTRODUCTION

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

- 1

5.2 PRODUC T CLASSES

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

- 2

5.2.1 Product Groups Analyzed

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

- 2

5.3 DESIGN OPTIONS

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

- 3

5.3.1 Ba seline Model

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

- 4

5.3.2 Electronic Ignition

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

- 5

5.4 METHODOLOGY

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

- 5

5.5 TEARDOWN ANALYSIS

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

- 6

5.5.1 Selection of Units

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

- 6

5.5.2 Baseline Units

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

- 7

5.6 COST MODEL

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

- 7

5.6.1 Generation of Bills of Materials

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

- 7

5.6.2 Cost Structure of the Spreadsheet Models

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

- 7

5.6.3 Cost Model and Definitions

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

- 8

5.6.4 Cost Model Assumptions Overview

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

- 9

5.6.5 Manufacturing Production Cost

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

- 12

5.7 MPC BREAKDOWN

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

- 13

5.8 MANUFACTURER MARKUP

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

- 13

5.9 ENGINEERING ANALYSIS SUMMARY OF RESULTS

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

- 14

5.10 REFERENCES

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

- 16

LIST OF TABLES

Table 5.2.1 Hearth Product Analysis Groups

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

- 3

Table 5.3.1 Gas Control Valve Types Used for Constant Burning Pilot Units in

Engineering Analysis

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

- 4

Table 5.3.2 Electronic Ignition Types Used in Engineering Analysis

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

- 5

Table 5.6.1 Cost Model In

- House Manufacturing Operation Assumptions

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

- 10

Table 5.6.3 Five

- Year Material Prices (2009 to 2014)

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

- 12

Table 5.7.1 Total Product Manufacturer Production Cost Breakdown for Hearth Product

Groups

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

- 13

Table 5.9.1 Baseline Full Assembly Manufacturer Production Costs and Incremental

Electronic Ignition System Costs for Hearth Products

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

- 15

LIST OF FIGURES

Figure 5.6.1 Full Production Costs

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

- 12

5

- iCHAPTER 5. ENGINEERING ANALYSIS

5.1 I NTRODUCTION

T he U.S. Department of Energy (DOE) performed an engineering analysis to

establish the relationship between manufacturer production cost (MPC) and reduced

energy consumption in hearth products

. Specifically, DOE analyzed the change in MPC

resulting from implementat ion of electronic ignition systems that would replace a

constant burning pilot. The change in MPC resulting from this requirement serves as the

basis for cost

- benefit calculations in terms of individual consumers , manufacturers, and

the n ation. Th is chapte r provides an overview of the engineering analysis (section 5.1 ),

discusse s product classes (section 5.2 ), establishes baselin e unit specifications (section

5.5.2 ), discusses incremen tal efficiency levels (section 5.3 ), explain s the methodology

used during data gathering (section 5.4 ), and discusses the analysis and results (section

5.9 ). DOE completed a separate engineering analysis for each of the hearth product

groups identified for analysis: vented fireplaces/inserts/stoves, unvented

fireplaces/inserts/stoves, vented gas log sets, unvented gas log se ts, and outdoor hearth

products.

The primary inputs of the engineering analysis are baseline information from the

market and technology assessment (MTA ; c hapter 3 of this TSD ) and the technolog ies

that are passed through the screening analysis ( c hapter 4 of this TSD ). Additional inputs

include cost data derived from the physical teardown analysis and interviews with

manufacturers. The primary output of the engineering analysis is the estimated change in

MPC associated with reduced energy consumption from disallowing constant burning

pilots by hearth product group.

DOE typically structures its engineering analysis around one of three

methodologies: (1) the design

- option approach, which calculates the incremental cost of

adding specific design options to th e baseline model; (2) the efficiency

- level approach,

which calculates the relative costs of achieving increases in energy efficiency levels

without regard to the particular design options used to achieve such increases; and/or (3)

the reverse engineering c ost

- assessment approach, which involves a “bottom

- up”

manufacturing cost assessment based on a detailed bill of materials derived from

teardowns of product s being analyzed. Deciding which methodology to use for the

engineering analysis depends on the produ ct, the technologies under study, and any

historical data DOE can draw upon.

To establish the industry incremental cost associated with implementing an

electronic ignition system , DOE used both the design

- option approach and the cost

assessment approach. A fter identifying hearth products that represent a cross section of

the market, DOE gathered additional information using reverse engineering

methodologies, product information from manufacturer catalogs, and discussions with

experts and manufacturers of he arth products

. DOE generated bill s of materials (BOM s )

5

- 1by disassembling manufacturers’ products. The BOMs describe the product in detail,

including all manufacturing steps required to make and/or assemble each part.

Subsequently, DOE developed a cost model that converted the BOMs into MPCs. By

applying derived manufacturer markups to the MPCs, DOE calculated the manufacturer

selling prices (MSPs) and developed the incremental MPCs associated with the

prescriptive requirement.

In a subsequent life

- cycle cost analysis ( c hapter 8), DOE used the MPCs and

MSPs to determine consumer prices for hearth products by applying the appropriate

distribution channel markups.

5.2 PRODUCT CLASSES

As described in section III.C of the NOPR , DOE has tentatively determined not to

establish product classes for a standby mode energy conservation standard. The criteria

for separation into different product classes are: (1) energy source and (2) capacity or

other performance

- related features

, such as those that provide utility to the consumer, or

others deemed appropriate by the Secretary that would justify the establishment of a

separate energy conservation standard. (42 U.S.C. 6295 (q) and 6316(a)) DOE found

substantial similarity in the function , components used, and energy use of h earth products

with regard to ignition systems and their standby mode energy consumption

. By

reviewing manufacturer product literature

, DOE found that the same or similar ignition

system components, including manual, millivolt, and electronic gas control v alves ; pilot

assemblies ; and electronic control modules for electronic ignitions ; were used across a

wide range of hearth products. DOE has tentatively determined that if standing pilot

ignitions are ultimately disallowed, most hearth products styles curre ntly using standing

pilot ignitions will switch to similar electronic ignition components. However,

recognizing the need to account for differences in product design and manufacturing

production volumes, DOE analyzed several specific subgroups of hearths s eparately, to

determine the impacts individually. This is explained in more detail in section 5.2.1

.

5.2.1 Product Groups Analyzed

While DOE has tentatively concluded th at product classes are not necessary to

distinguish hearth products as a result of focusing on standby mode operation (see section

III.C of the NOPR) , DOE acknowledges that the impact on manufacturers and consumers

could differ depending on the hearth product. T o investigate these differences, DOE

selected five hearth product groups for analysis based on product literature review and

manufacturer interviews.

Unvented hearth products consume air from the occupied space for combustion

, and release all b yproducts of combustion into th e occupied space. For safety, unvented

ignition systems on indoor hearth products use an oxygen depletion sensor that closes the

gas control valve when oxygen in the room drops below a threshold

. This sensor consists

of a pre cision pilot light and thermocouple connected to the gas control valve. The pilot

light heats the thermocouple; as the oxygen in the room decreases, the pilot flame “lifts”

5

- 2farther from the thermocouple to seek out sufficient oxygen for combustion. This co ols

the thermocouple, and once the thermocouple cools to a certain temperature

(corresponding to an unsafe room oxygen level) the gas control valve closes, preventing

further depletion of oxygen in the room. To account for this difference in ignition syste m

components, DOE separated unvented from vented products.

DOE also acknowledges differences between ga s log sets and other indoor hearth

products. In terms of standing ignition systems, DOE tentatively determined that manual

safety pilot valves were more likely to be found on gas log sets , rather than using larger,

more expensive millivolt valves. Gas log sets also lack the enclosure and integrated heat

shielding found in fireplaces, inserts, and stoves that would more readily house and

protect the additi onal components necessary for ignition systems of any kind. DOE

therefore used separate analysis groups for gas log sets.

Outdoor fireplaces often use similar ignition system components to indoor hearth

products. However, DOE found that certain types of o utdoor hearth products

, such as

patio heaters and fire pits, may use different ignition components than indoor hearth

products. For example, patio heaters do not typically use millivolt gas valves, and fire

pits often use hot wire igniters to ignite the pi lot light. For these reasons, DOE separated

outdoor hearth products as well.

Table 5.2

. 1 presents t he product groups used for analysis

.

Table 5.2

. 1 Hearth Product Analysis Groups

Vented Fireplaces, Inserts, and Stoves

Unvented Fireplaces, Inserts, and Stoves

Vented Gas Log Sets

Unvented Gas Logs Sets

Outdoor Hearth Products

5.3 DESIGN OPTIONS

For each of the hearth product groups listed in Table 5.2.1 , DOE analyzed

representative models a nd estimated the manufacturer production costs

. As described in

the market and technology assessment (chapter 3 of this TSD ) and the screening analysis

(chapter 4 of this TSD ), additional design options for hearth products were considered

. Because this rulemaking has focused on standby mode energy consumption for the

reasons descri b ed in section III.B of the NOPR, DOE did not consider technologies

affecting active mode energy consumption in the engineering analysis ( i.e., condensing

heat exchanger and ai r circulating blower). Rather, DOE focused i ts analysis on the

impacts of removing the standing p ilot ignition system and replacing it with a system that

does not use a continuously burning pilot.

5

- 3The following subsections describe the models selected for each product group

for comparison between the baseline (standing pilot) and design option (electronic

ignition)

.

5.3.1 Baseline Model

DOE selected baseline units ( standing pilot; i.e., without new energy standards) as

reference points for each product group , aga inst which DOE measured changes resulting

from potential energy conservation standards. The baseline unit s in each hearth product

group use a constant burning pilot ignition system to allow for comparison to the

electronic ignition design option. DOE used the baseline units in the engineering analysis

and the life

- cycle

- cost and payback

- period analys e s. DOE determined the increase in

MPC associated with switching to an electronic ignition

. DOE notes that less expensive

ignition systems are available

– namely match lit burner

– but those systems would not be

affected by this rulemaking because they do not employ a continuously burning pilot

light. DOE did not consider match lit burners as a viable alternative to standing pilot

ignition systems because many municipalities and product safety standards require an

ignition system be present for gas

- fired hearth products. However, DOE notes that for

cases where match lit burners are permissible, the MPC for the product would be lower

for the match lit hearth product than for those with standing pilot ignition systems.

DOE considered that there are two main standing pilot valve types: manual and

millivolt. The manual valve requires the user to manually open and close the valve and is

therefore smaller, simpler , and cheaper. The millivolt gas valve uses a thermopile to

generate a voltage difference such that the valve can be coupled with additional control

systems, for example

, a remote control or thermostat. Because gas log sets are subject to

physical space co nstraints that fireplaces, inserts, and stoves are not, DOE selected gas

log sets with manual valves as representative of gas log sets with standing pilots. DOE

selected models with millivolt gas valves as being representative of the fireplace, insert

, and stove vented and unvented categories as millivolt gas valves are common for these

products

.

Table 5.3

. 1 Gas Control Valve Types Used for Constant Burning Pilot Units in

Engineering Analysis

Hearth Product Group Standing Pilot

Valve

Vented Fireplaces, Inserts, and

Stoves Millivolt

Unvented Fireplaces, Inserts, and

Stoves Millivolt

Vented Gas Log Sets Manual

Unvented Gas Logs Sets Manual

Outdoor Manual

The market baseline units identified in the engineering analysis represent the cost

of the typical products on the market that utilize a constant burning pilot

. DOE used these

5

- 4baseline models for the subsequent analyses ( e.g.

, life

- cycle cost ( LCC )

, payback period

( PBP )

, national impact analysis ( NI A )

, manufacturer impact analysis ( MIA).

5.3.2 Electronic Ignition

The results of the screening analysis (chapter 4) are used as inputs to the

engineering analysis. As described above in section 5.3

, three technologies passed the

screening analysis (condensing heat exchanger, air circulating blower, and electronic

ignition), only the electronic ignition is being considered in the engineering analysis due

to the rulemak ing focus on standby energy use.

DOE selected models for each hearth product group that represented typical

implementations of electronic ignitions. Based on product literature review and

confirmation during manufacturer interviews, DOE determined that t he intermittent pilot

(IPI) is by far the most common type of electronic ignition used for hearth products. DOE

also found that in some outdoor products with high

- input capacities, a hot wire igniter

(HWI) is used to light the pilot intermittently rather than a spark igniter. DOE therefore

considered both types of units for its outdoor product analysis.

Table 5.3

. 2 Electronic Ignition Types Used in Engineering Analysis

Hearth Product Group EIS

Vented Fireplaces, Inserts, and

Stoves IPI

Unvented Fireplaces, Inserts, and

Stoves IPI

Vented Gas Log Sets IPI

Unvented Gas Logs Sets IPI

Outdoor IPI, HWI

5.4 METHODOLOGY

This section describes the analytical methodology used in the eng ineering

analysis.

DOE first identified units with standing pilot ignitions and with electronic

ignitions to represent each hearth product group

. DOE gathered the information from the

physical teardown analysis to create a BOM using reverse engineering methods (see

section 5.5 ). DOE calculated the MPC for both the standing pilot model and the

electronic ignition model for each product type

.

During the preparation of t he MPCs, DOE held interviews with manufacturers to

gain insight into hearth product industry and request comments regarding its cost

estimations for various components, particular ly those associated with the ignition

systems

. DOE used the information gathe red from these interviews to refine assumptions

5

- 5in the cost mode l

. N ext, DOE converted the MPCs into MSPs (see section 5.8 ) using

publicly available industry financial data, in addition to manufacturers’ feedback.

5.5 TEARDOWN ANALYSIS

To assemble BOMs and calculate the manufacturing costs of the different

components in hearth products , DOE disassembled multiple units into their components

and estimated t he material and labor cost of each individual component. This process is

referred to as a “physical teardown.”

5.5.1 Selection of Units

Because the engineering analysis is designed to assess the difference in MPC

between baseline (standing pilot) and the proposed design option (electronic ignition),

DOE was primarily concerned with selecting units for teardown that represented typical

implementations of standing pilot ignition systems and electronic ignition systems. DOE

determined the typical characterist ics of hearth products based on the market analysis

done for the market and technology assessment (chapter 3 of this TSD). In order to

provide direct comparison between standing pilot and electronic ignition costs , DOE

selected models within each hearth pr oduct group from the same manufacturer and within

the same product line

.

T o compare only the cost difference between standing pilot and electronic

ignition models in each product group, DOE considered only those subassemblies

necessary to cons titute the baseline unit and both ignition types

. Items that were not

common between ignition types and necessary to determining the cost difference for the

ignition system only were excluded. For example, remote controls and accompanying

remote receivers may be fou nd on electronic ignition systems but not with manual safety

pilot ignition systems. Because the goal of the analysis is to directly compare the change

in MPC related to the ignition system only, remote controls and remote receivers were

not included , as t hey are often included as a premium feature but are not required for

implementing an electronic ignition

. If, in the case of electronic ignition systems, the

electronic control module for the intermittent pilot ignition system was found to also

perform rem ote receiver functions, the components associated with the remote receiver

functions were not considered in the analysis.

Using the data gathered from the physical teardowns, DOE characterized each

component according to its weight, dimensions, material, q uantity, and the manufacturing

processes used to fabricate and assemble it. DOE collected additional component

information during the manufacturer interviews.

DOE did not explicitly identify the model number or manufacturer of the units it

tore down becaus e this could expose sensitive information about individual

manufacturers’ products.

5

- 65.5.2 Baseline Units

DOE selected baseline units for the teardown analysis as a comparison for more

efficient designs . Typically, DOE defines baseline products as those with energy

efficiencies equal to the current f ederal energy conservation standards , and which are

representative of the minimum technology and lowest costing product that a

manufacturer can produce that provides basic functionality and utility to the consumer

. In

this case, because there is currently no f ederal energy standard for hearth products , DOE

selected as baseline units those with continuously burning pilot designs. DOE then used

the baseline units as reference point s to compare the technology and cost of products with

electronic ignition systems

. The characteristics of the baseline units are described further

in section 5.3.1

.

5.6 COST MODEL

5.6.1 Generation of Bills of Materials

The end result of each teardown is a structured BOM. DOE developed structured

BOMs for each of the teardowns

. Structured BOMs describe each product part and its

relationship to the other parts in the estimated order in which manufacturers assembled

them. The BOMs describe each fabrication and assembly operation in detail, including

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

associated with each manufacturing step. The res ult is a thorough and explicit model of

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

planned production level.

The BOMs incorporate all materials, components, and fasteners classified as

either raw materials or purc hased parts and assemblies. The classifications into raw

materials or purchased parts were based on DOE’s previous industry experience, recent

information in trade publications, and discussions with high

- and low

- volume original

equipment manufacturers (OE Ms). DOE also visited manufacturing plants to reinforce its

understanding of the industry’s current manufacturing practices for hearth products.

For purchased parts, the purchase price is estimated based on volume

- variable

price quotations and detailed dis cussions with manufacturers and component suppliers.

For fabricated parts, the prices of “raw” materials ( e.g. , tube, sheet metal) are estimated

on the basis of 5

- year averages (see section 5.6.4.4 ). The cost of transforming the

intermediate materials into finished parts is estimated based on current industry pricing.

5.6.2 Cost Structure of the Spreadsheet Models

The last step was to convert the BOM information into doll ar values. To perform

this task, DOE collected information on labor rates, tooling costs, raw material prices,

and other factors. DOE assumed values for these parameters using internal expertise and

confidential information available to DOE contractors. Al though most of the assumptions

are manufacturer specific and cannot be revealed, section 5.6.4.3 provides a discussion of

the values used for each assumption.

5

- 7In su mmary, DOE assigned costs of labor, materials, and overhead to each part

whether purchased or produced in house. DOE then aggregated single

- part costs into

major assemblies ( e.g.

, ignition assembly , controls, burner, packaging .) and summarized

these costs in a worksheet. During interviews with manufacturers , DOE showed key

estimates from the cost model and asked for feedback. Because this engineering analysis

is focused on ignition components, DOE was particularly interested in feedback

regarding purchase p rices for key ignition components. DOE considered any information

manufacturers gave that was relevant to the cost model and incorporated it into the

analysis as appropriate.

5.6.3 Cost Model and Definitions

Once DOE disassembled selected units, gathered inform ation from manufacturer

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

manufacturing cost model that could translate physical information into MPCs. The cost

model is based on production activities and divides fact ory costs into the following

categories:

• Materials: Purchased parts ( e.g.,

. gas valves, pilot assemblies ), raw materials

( e.g., cold rolled steel), and indirect materials that are used for processing and

fabrication

.

• Labor: Fabrication, assembly, indirec t , and supervisor labor

. Fabrication and

assembly labor cost are burdened with benefits and supervisory costs.

• Overhead: Equipment, tooling, and building depreciation, as well as utilities,

equipment and tooling maintenance, insurance, and property taxes.

5.6.3.1 Cost Definitions

Because there are many different accounting systems and methods to monitor

costs, DOE defined the above terms as follows:

• Direct material: Purchased parts (outsourced) plus manufactured parts (made in

house from raw materials).

• Indirect material: Material used during manufacturing ( e.g. , welding rods,

adhesives).

• Fabrication labor: Labor associated with in

- house piece manufacturing.

• Assembly labor: Labor associated with final assembly.

• Indirect labor: Labor costs that scale d with fabricat ion and assembly labor. This

include d the cost of technicians, manufacturing engineering support, stocking,

etc.

, that were assigned on a span basis.

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

installation and replacemen t as the production equipment wears out.

• Tooling depreciation: Cost for initial tooling (including non

- recurring engineering

and debugging of the tools) and tooling replacement as it wears out.

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

conveyors that f e ed and/or ma k e up the assembly line.

5

- 8• Utilities: Electricity, gas, telephones, etc.

• Maintenance: Annual money spent on maintaining tooling and equipment.

• Insurance: Appropriated as a function of unit cost.

• Property Tax: Appropriated as a function as unit cost.

5.6.4 Cost Model Assumptions Overview

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

reconstructed manufacturing processes for each component using internal expertise and

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

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

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

then summed the va lues of the components into assembly costs and, finally, the total

product cost. The product cost included the material, labor, and overhead costs associated

with the manufacturing facility. The labor costs included fabrication, assembly, indirect

, direct

, and supervisor labor rates

, including the associated overhead. The labor costs were

de termined by the type of product manufactured at the factory. Overhead costs included

equipment depreciation, tooling depreciation, building depreciation, utilities, equi pment,

tooling maintenance, insurance, property, and taxes.

Usin g the information gathered during manufacturer interviews, DOE update d

the cost model to address manufacturer comments , particularly with respect to purchased

parts for ignition assemblies

. These changes involved updating component and material

pricing and production volumes. DOE used a continuous refinement process to update

information. Changes to the cost model were made immediately after interviews so that

refined data could be presented to the next manufacturer. Positive feedback from

manufacturers presented with refined data confirmed the accuracy of the changes.

The next sections discuss specific assumptions about outsourcing, factory

parameters, production volumes, and material prices

. When the assumptions are

manufacturer specific, they are presented as industry averages to prevent disclosure of

confidential information.

5.6.4.1 Fabrication Estimates

DOE characterized parts based on whether manufacturers purchase d them from

outside suppliers or fabricate d them in house. For purchased parts, DOE estimated the

purchase price. For fabricated parts, DOE estimated the price of raw materials ( e.g. , tube,

sheet metal) and the cost of transforming them into finished parts. Whenever possible,

DOE obtai ned price quotes directly from the manufacturers ’ suppliers

.

DOE based the manufacturing operations assumptions on internal expertise,

interviews with manufacturers, and manufacturing facilities site visits. The major

manufacturer processes identified and developed for the spreadsheet model are listed in

Table 5.6

. 1

.

5

- 9Table 5.6

. 3 Cost Model In

- House Manufacturing Operation Assumptions

Fabrication Finishing Assembly/Joining Quality Control

Fixturing Painting Adhesive Bonding Inspecting and Testing

Stamping/Pressing Powder Coating Spot Welding

Turret Punch Seam Welding

Tube Forming Packaging

Brake Forming Clinching

Cutting and Shearing

Hand Bending

Drilling

Concrete

5.6.4.2 Production Volumes Assumptions

A manufacturer’s production volumes vary depending on several factors,

including market share, the type of product produced ( e.g.

, fireplace, gas log set ), and if

the manufacturer produces other similar products ( e.g.

, direct heating equipment

, barbeque grills ). For hearth products, DOE based pr oduction volume assumptions on

data provided by the Hearth, Patio, and Barbecue Associat ion ( HPBA ) and information

obtained during manufacturer interviews.

The data provided by HPBA included ranges of the market share for nine product

categories. DOE used these data to determine average market s izes for each of its product

groups used in its analysis. Based on these weights, the historical shipments data also

provided by HPBA, the number of manufacturers identified for each product group, and

feedback from manufacturers, DOE selected representative p roduction volumes. The

production volumes are presented in Table 5.6

. 2

5.6.4.3 Factory Parameters Assumptions

DOE used information gathered from publicly available liter ature and analysis of

common industry practices to formulate factory parameters for each type of

manufacturer. Table 5.6

. 2 list s DOE’s assumptions for manufacturers of hear th products

.

5

- 10Table 5.6

. 4 Hearth Product Production Volumes and Factory Parameter

Assumptions

Parameter Plant

Capacity

units /year Actual

Annual

Production

Volume

units/year Fabrication

Labor

Wages

$/

hr Fringe

Benefits

Ratio

Estimate Vented

Fireplaces,

Inserts,

Stoves 12,000 10,000 16 50%

Unvented

Fireplaces,

Inserts,

Stoves 2,400 2,000 16 50%

Vented Gas

Log Sets 2,400 2,000 16 50%

Unvented

Gas Log

Sets 6,000 5,000 16 50%

Outdoor 3,600 3,000 16 50%

The main difference among the assumptions for all hearth products is the

production volumes. Labor rates were assumed to be the same across hearth products

. Approximate labor rates are based on published labor rates for the hearth industry from

the U.S. Department of Labor

. 1

5.6.4.4 Material Prices Assumptions

DOE determined the cost of raw materials using publicly available information

such as the American Metals Market, 2 interviews with manufacturers, and direct

discussions with materia l suppliers. Common metals used in the fabrication of hearth

products include plain cold rolled steel (CRS), CRS tubing, and stainless steel

. To

account for fluctuations, DOE used a 5

- year average of metal prices from the Bureau of

Labor Statistics Produce r Price Indices (PPIs) spanning 200 9 to 20 14 with an adjustment

to 2014 $. 3 DOE used the PPIs for copper rolling, drawing, and extruding and steel mill

products, and made the adjustments to 2014 $ using the gross domestic product implicit

price deflator. 4 DO E also used a 5

- year average in material prices from 2009 to 2014 to

normalize changes to some extent to better represent long

- term material price averages.

Table 5.6

. 3 shows the 5

- year average metal prices DOE used for the analysis.

5

- 11Table 5.6

. 5 Five

- Year M aterial Prices ( 200 9 to 2014 )

Metals Cost $/lb

2014$

Plain Cold Rolled Steel 0.4 32

Aluminized CRS 0.5 28

CRS Tube 0. 803

CRS Wire 1.148

Aluminized CRS Tube 0.867

Stainless Steel 1.507

Stainless Steel Tube 2.377

Brass (Sand Cast) 2.111

Concrete 0.028

5.6.5 Manufacturing Production Cost

Once the cost estimate for each teardown unit was finalized, a detailed summary

was prepared for relevant components, subassemblies, and processes. The BOM thus

details all aspects of unit costs. DOE total ed the cost of materials, labor, and direct

overhead used to manufacture a product to calculate the MPC. a Figure 5.6.1 shows the

general breakdown of costs associated with manufacturing a product.

Figure 5.6

. 1 Full Production Costs

a When viewed from the company

- wide perspective, the sum of all material, labor, and overhead costs

equals the company’s sales cost, also referred to as the cost of goods sold (COGS).

5

- 12The full cost of product is broken down into two main costs, the full production

cost or MPC, and the non

- production cost. The non

- production cost is equal to the

manufacturer markup minus profits

.

Following the development of the MPCs, D OE reviewed its major cost estimates

by conducting interviews with hearth products manufacturers. DOE presented the data

and estimates for purchased ignition components to manufacturers who provided

feedback and va lidation. DOE used a continuous refinement process by incorporating

each manufacturer’s feedback before each set of interviews

. As a result, DOE developed

MPCs for use in the engineering analysis and subsequent analyses

.

5.7 MPC BREAKDOWN

After DOE incorporated all of the assumptions into the cost model, the different

production cost percentages were calculated. The product cost percentages are used to

validat e the assumptions by comparing them to manufacturers’ actual financial data

published in ann ual reports

, where possible , along with feedback from manufacturers

during interviews. DOE also used these figures in the MIA (see chapter 12 of this TSD ).

DOE calculated the average product cost percentages by product group due to the

variations in produc tion volumes, fabrication and assembly costs, and other assumptions

that affect the calculation of a unit’s total MPC. Table 5.7.1 show s the different

percentages for the production costs that make up the total product MPC.

Table 5.7

. 1 Total Product Manufacturer Production Cost Breakdown for Hearth

Product Groups

Percentage Cost Breakdown by Hearth Product Group

( Baseline )

Materials

% Labor

% Depreciation

% Overhead

%

Vented Fireplaces,

Inserts, and Stoves 43.4% 22.4% 22.1% 12.1%

Unvented Fireplaces,

Inserts, and Stoves 38.2% 25 .0 % 24.8% 12.0%

Vented Gas Log Sets 29.5% 23.5% 32.8% 14.3%

Unvented Gas Log Sets 38.9% 28.9% 21.2% 11.0%

Outdoor Products 55.6% 15.8% 18.7% 9.9%

5.8 MANUFACTURER MARKUP

DOE applies a manufacturer markup to calculated MPCs in order to estimate

MSPs. The manufacturer markup is a multiplier that covers both non

- production costs

( e.g., selling, general and administrative expenses, research and development , interest

expenses) and profit. The manufacturer markup can be thought of as:

5

- 13Markup = Gross Profit______

Cost of Goods Sold (COGS)

Where:

Gross Profit = Revenu e

– COGS

The manufacturer markup is distinct from, and greater than, a manufacturer profit

margin, or gross margin percentage, which is calculated as:

Gross Margin Percentage = Gross Profit

Revenue

For the hearth industry, DOE estimated a set of base case manufacturer markups

using data developed as part of the 2010 Energy Conservation Standard for Residential

Water Heaters, Direct Heating Equipment, and Pool Heaters (75 FR 20112). DOE then

solicited feedback on its markup estimates during confidential manufacturer interviews.

Based on manufacturer feedback, DOE calculated and applied an average baseline

markup of 1.45 percent for all gas hearth products. The MSPs estimated using this

markup reflect t he price at which manufacturers recover both production and non

production costs and earn a profit. Additional markups across the distribution chain ( e.g.

, distributors, retailers, contractors) account for the final consumer price.

For further discussion of manufacturer markups, see the manufacturer impact analysis

(chapter 12 of this TSD ). For further discussion of final consumer prices, see the life

cycle cost analysis and payback period analysis (chapter 8 of this TSD ), and the national

impact analysis (chapter 10 of this TSD ).

5.9 ENGINEERING ANALYSIS SUMMARY OF RESULTS

Throughout the rulemaking process, the results from the engineering analysis are

used in the LCC analysis to determine consumer prices for hearth products. The

manufacturer production cos ts for the baseline assembly by product group are listed in

Table 5.9.1

. DOE also determined the incremental cost to each product group associated

with the switch to the electronic ignition design ; these are also presented in Table 5.9

. 1

.

5

- 14Table 5.9

. 2 Baseline Full Assembly Manufacturer Production Costs and

Incremental Electronic Ignition System Cost s for Hearth Products

Hearth Product Analysis Group Baseline

MPC

$ Incremental

Cost for EIS

$

Vented Fireplaces, Inserts, and Stoves 322 28

Unvented Fireplaces, Inserts, and Stoves 281 32

Vented Gas Log Sets 190 70

Unvented Gas Log Sets 208 56

Outdoor Products 210 55

5

- 155.10 REFERENCE S

1 U.S. Department of Labor, Bureau of Labor Statistic s, Wages by Area and

Occupation . Last accessed August 2014. < http://www.bls.gov/bls/blswage.htm >.

2 American Metals Market. Last accessed June 2014. < http:// www.amm.com >

.

3 U.S. Department of Labor, Bureau of Labor Statistics, Producer Price Indices

. Last accessed June 2014. < http://www.bls.gov/ppi >.

4 U.S. Department of Commerce, Bureau Economic Analysis, Gross Domestic

Product Implicit Price Deflator . Last accessed June 2014.

< http://www.bea.gov/iTable/iTable.cfm?reqid=9&step=3&isuri=1&903=13#reqid=9&ste

p=3&isuri=1&903=13 >

.

5

- 166

- i CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION

................................ ................................ ................................ ........... 6

- 1

6.2 DISTRIBUTION CHANNEL S

................................ ................................ ....................... 6

- 1

6. 3 APPROACH FOR MANUFAC TURER MARKUP

................................ ....................... 6

- 3

6.4 APPROACH FOR WHOLESA LER AND CONTRACTOR M ARKUPS

..................... 6

- 3

6.4.1 Wholesaler Markup

................................ ................................ ........................ 6

- 4

6.4. 2 Mechanical and General Contractor Markups

................................ ............... 6

- 5

6.5 DERIVATION OF MARKUP S

................................ ................................ ...................... 6

- 6

6.5.1 Manufacturer Markup

................................ ................................ .................... 6

- 6

6. 5.2 Wholesaler Markup

................................ ................................ ........................ 6

- 7

6.5.3 Mechanical Contractor Markups

................................ ................................ .... 6

- 8

6.5.3.1 Aggregate Markups for Mechanical Contractors

................................ ........... 6

- 8

6.5.3.2 Markups for Mechanical Contractors in the Replacement and New

Construction Markets

................................ ................................ ..................... 6

- 9

6.5.4 Ge neral Contractor Markups

................................ ................................ ........ 6

- 11

6.6 DERIVATION OF CENSUS REGIONS MARKUPS

................................ .................. 6

- 13

6.6.1 Estimation of Wholesaler Markups

................................ ............................. 6

- 13

6.6.2 Estimation of Mechanical Contractor Markups

................................ ........... 6

- 14

6.6.3 Estimation of General Contractor Markups

................................ ................. 6

- 16

SALES TAX

................................ ................................ ................................ .................. 6

- 18 6.7

6.7 OVERALL MARKUPS

................................ ................................ ................................ . 6

- 20

LIST OF TABLES

Table 6.5.1 Wholesaler Expenses and Markups

................................ ................................ ..... 6

- 7

Table 6.5.2 Mechanical Contractor Expenses and Markups Based on Census Bureau

Data

................................ ................................ ................................ ...................... 6

- 8

Table 6.5.3 Baseline Markup, All Mechanical Contractors

................................ .................. 6

- 10

Table 6.5.4 Baseline Markups for the Replacement and New Construction Markets,

All Mechanical Contractors

................................ ................................ ............... 6

- 10

Table 6.5.5 Markups for the Replacement and New Construction Markets

......................... 6

- 11

Table 6.5.6 Residential Building General Contractor Expenses and Markups

..................... 6

- 12

Table 6.6.1 Wholesaler Markups for Hearth Products

................................ .......................... 6

- 14

Table 6.6.2 Mechanical Contractor Markups Weighted by Housing Projections in

2021 for Hearth Products

................................ ................................ ................... 6

- 16

Table 6.6.3 General Contractor Markups Weighted by Housing Projections in 2021 for

Hearth Products

................................ ................................ ................................ .. 6

- 18

Table 6.6.4 Average Sales Tax Rates by RECS 2009 Region

................................ .............. 6

- 20

Table 6.7.2 Markups for Hearth Products

................................ ................................ ............. 6

- 22

Table 6.7.3 Summary of Overall Markup by Hearth Product Class

................................ ..... 6

- 226

- ii LIST OF FIGURES

Figure 6.2.1 Distribution Channels for Hearth Products

................................ .......................... 6

- 26

- 1 CHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

To carry out its analyses, the U.S. Department of Energy (DOE) needed to determine the

cost to the consumer of baseline products and the cost of more efficient units the consumer

would purchase under new energy conservation standards. DOE calculated such costs based on

engineering estimates of manufacturing product costs (see chapter 5) plus appropriate markups

for the various distribution channels for hearth products

.

Generally, companies mark up the price of a product to cover their business costs and

profit margin. In financial statements, the gross margin is the difference between the company

revenue and the company cost of sales or cost of goods sold ( CGS ). The gross margin takes

account of the expenses of companies in the distribution channel, incl uding overhead costs

(sales, general, and administration); research and development (R&D) and interest expenses;

depreciation; and taxes

— and company profits. In order for sales of a product to contribute

positively to company cash flow, the product’s marku p must be greater than the corporate gross

margin. Products command lower or higher markups, depending on company expenses

associated with the product and the degree of market competition.

For wholesalers and contractors , DOE estimated a baseline markup a nd an incremental

markup. DOE define s a baseline markup as a multiplier that convert s the manufacturer selling

price (MSP) of equipment with baseline efficiency to the consumer purchase price for the

equipment at the same baseline efficiency level. An incremental markup is defined as the

multiplier to convert the incremental increase in manufacturer selling price of higher efficiency

equipment to the consumer purchase price for the same equipment. Because companies mark up

the price at each point in the distribution channel, b oth baseline and incremental markups are

dependent on the distribution channe l, as described in section 6.2

.

The components used to pr oduce a hearth product are usually purchased by hearth

product manufacturers who install the ignition device in a hearth product

. F rom this point, the

ignition devices are passed along the distribution channels as part of hearth products . Essentially,

vari ous markups applied to these products by different market participants are also the markups

applied to hearth product ignition devices, whose manufacturing costs account for a portion of

the total manufacturing costs of the finished products

. Therefore , DO E developed the markup

analysis for hearth product ignition devices based on hearth products

.

6.2 DISTRIBUTION CHANNEL S

The appropriate markups for determining the consumer product price depend on the type

of distribution channel throug h which products move from manufacturers to purchasers. At each

point in the distribution channel, companies mark up the price of the product to cover their

bus iness costs and profit margin.6

- 2 There are two primary distribution channel s describing the way most products pass from

the manufacturer to the consumer, one applying to hearth products installed in replacement

markets or by new owners and the other applying to hearth products that are installed in new

construction. For replaceme nt applications, most sales go through wholesalers to mechanical

contractors, and then to consumers. The new construction distribution channel includes an

additional link in the chain

— the general contractor. Thus, DOE defined two distribution

channels for the purposes of estimating markups for hearth products , as shown in Figure 6.2.1

.

Replacement and New Owner :

New Construction:

Figure 6.2.1 Distribution Channels for Hearth Products

Based on information provided from manufacturer interviews, there is another possible

distribution channel that includes a retail store instead of a wholesaler. In this case, the

manufacturer sells the equipment to a retailer , who in turn sells it to a mechanical contractor,

who in turn sells it to the consumer. However, DOE does not have enough information at this

point to make a separate markup estimation for this distribution channel. DOE assume d that the

retailer markup is similar to the wholesaler m arkup. DOE is also aware that there may be two

additional distribution channels for hearth products : (1) an online distribution channel where

manufacturers sell the products to online retailers who in turn sell them directly to consumers,

and (2) a rebrand ing distribution channel where wholesalers or retailers negotiate good pricing

from the hearth product manufacturer based on high volumes and have the product customized to

carry their name

, and then send it through their normal distribution channel to the contractors.

The former one mainly applies to the do

- it

- yourself (DIY) installation representing around two

percent of the total HVAC shipments, which implies an even smaller fraction of the total hearth

product shipments

. For the latter one, DOE assumes that it would have the same overall markups

as the conventional distribution channels. Although manufacturers may have lower margin,

wholesalers and retailers would redistribute the profit throughout the distribution channel to have

the final retail price comparable with products sold through conventional distribution channels.

Due to the reasons mentioned above, DOE did not consider them in this analysis. Manufacturer Wholesaler Mechanical

Contractor Consumer

Wholesaler Mechanical

Contractor General

Contractor Consumer Manufacturer6

- 3 6.3 APPROACH FOR MANUFAC TURER MARKUP

DOE uses manufacturer markups to transform a manufacturer’s product cost into a

manufacturer sales price. Detailed methodology to derive manufacturer markups were described

in chapter 5 ( Engineering Analysis ).

6.4 APPROACH FOR W HOLESALER AND C ONTRACTOR MARKUPS

DOE examined the manner in which wholesaler and contractor markup s may change in

response to changes in hearth product ignition system efficiency and other factors. Using the

available data, DOE estimated that there are differences between incremental markups on

incremental equipment costs of higher efficiency products and the baseline markup on direct

business costs of products with baseline efficiency

.

DOE derived the wholesaler and contractor markups from three key assumptions about

the costs associated with hearth products . DOE based the wholesaler and mechanical co ntractor

markups on firm

- level income statement data, and based the general contractor markups on U.S.

Census Bureau data for the residential building construction industry. DOE obtained the firm

income statements from the Heating, Air

- conditioning & Refri geration Distributors International

(HARDI) 2013 Profit Report and from the Air Conditioning Contractors of America (ACCA)

2005 Financial Analysis. 1

, 2 HARDI and ACCA are trade associations representing wholesalers

and mechanical contractors, respectively. DOE used the financial data from the 2007 U.S.

Census of Business for developing general contractor mark ups in the same form as the income

statement data for wholesalers and mechanical contractors. These income statements break down

the components of all costs incurred by firms that supply and install heating and air

- conditioning

equipment. a The key assumpti ons used to estimate markups using these financial data are:

1. The firm income statements faithfully represent the various average costs incurred by

firms distributing and installing hearth products

.

2. These costs can be divided into two categories: 1) costs that vary in proportion to the

MSP of hearth products (variant costs ); and 2) costs that do not vary with the MSP of

hearth products (invariant costs).

3. Overall

, wholesale and contractor prices for hearth products vary in proportion to the

wholesale r and contractor costs for hearth products included in the income statements.

In support of the first assumption, the income statements itemize firm costs into a number

of expense categories, including direct costs to purchase or install the equipment, operating labor

and occupancy costs, and other operating costs and profit. Although wholesalers and contractors

tend to handle multiple commodity lines, including room air conditioners, furnaces, central a ir

a Wholesaler s and mechanical contractors to which these reports refer handle multiple commodity lines.6

- 4 conditioners and heat pumps , and boilers, the data provide the most accurate available indication

of the expenses associated with hearth products

.

Information obtained from the trade literature, selected HVAC wholesalers, contractors,

and consultants tends to support the second assumption

. T his information indicates that

wholesale and contractor markups vary according to the quantity of labor and materials used to

distribute and install appliances

. In the following discussion, DOE assumes a division of costs

between those that do not scale with the manufacturer price (labor and occupancy expenses) and

those that do (operating expenses and profit).

In support of the third assumption, the HVAC wholesaler and contractor industry is

competitive, and consum er demand for heating and air conditioning is inelastic, i.e., the demand

is not expected to decrease significantly with an increase in the price of equipment. The large

number of HVAC firms listed in the 2007 Census indicates the competitive nature of the market.

For example, there are more than 700 HVAC manufacturers, 3 5,300 wholesalers of heat pumps

and air

- conditioning equipment, 4 more than 170,000 general residential contractors, and 91,000

HVAC contractors 5 listed in the 2007 Census. Following standard econom ic theory, competitive

firms facing inelastic demand either set prices in line with costs or quickly go out of business. 6

DOE concluded that markups for more

- efficient products are unlikely to be proportional

to all direct costs. When the wholesaler’s purchase price of equipment increases, for example,

only a fraction of a business’ expenses increases, while the remainder may stay relatively

constant. For example, if the unit price of a hearth product unit increases by 30 percent due to

improved efficiency of the ignition system , it is unlikely that the cost of secretarial support in an

administrative office will increase by 30 percent also. Therefore, DOE assumed that i ncremental

markups cover only those costs that scale with a change in the MSP (variant costs ).

6.4.1 Wholesaler Markup

Using the abo ve assumptions, DOE developed baseline and incremental markups for

wholesalers using the firm income statement from the HARDI 2013 Profit Report ( appendix 6 A ).

The baseline markups cover all of the wholesaler’s costs ( both invariant costs and variant costs ).

Here, variant costs were defined as costs that likely vary in proportion to the change in MSP

induced by increased efficiency standards; in contrast, invariant costs were defined as costs that

are unlikely to vary in proportion to the change in MSP due to increased efficiency standards.

DOE calculated the baseline markup for wholesalers using the following equation:

WHOLE WHOLE WHOLE WHOLE

WHOLE WHOLE WHOLE

BASE CGS VC IVC CGS

CGS GM CGS

MU ) ( + +

= +

=

Eq. 6.1

Where:

MU BASE = b aseline wholesaler markup,6

- 5 CGS WHOLE = w holesaler cost of goods sold,

GM WHOLE = w holesaler gross margin,

IVC WHOLE = w holesaler invariant costs, and

VC WHOLE = w holesaler variant costs.

Incremental markups are coefficients that relate the change in the MSP of more energy

efficient models , or those products that meet the requirements o f new energy conservation

standards

, to the change in the wholesaler sales price. Incremental markups cover only those

costs that scale with a change in the MSP (variant costs, VC ). DOE calculated the incremental

markup ( MU INCR ) for wholesalers using the f ollowing equation:

WHOLE WHOLE WHOLE

INCR 

### CGS VC CGS MU +

=

Eq. 6.2

Where:

MU INCR = i ncremental wholesaler markup,

CGS WHOLE = w holesaler cost of goods sold, and

VC WHOLE = w holesaler variant costs.

6.4.2 Mechanical and General Contractor Markups

The type of financial data used to estimate markups for wholesalers is also available for

mechanical contractors and general contractors from the 2007 Economic Census and ACCA

2005 Financial Analysis. To estimate mechanical contractor markups for hearth products , DOE

collected financial dat a from the Plumbing and HVAC Contractors (NAICS 23822) series from

the 2007 Economic Census and from ACCA 2005 Financial Analysis. To estimate general

contractor markups for hearth products , DOE collected data from the Residential Building

Construction ser ies from the 2007 Economic Census, which is the aggregation of New Single

Family General Contractors (NAICS 236115), New M ultifamily H ousing C onstruction (NAICS

236116), New H ousing O perative B uilders (NAICS 236117), and Residential R emodelers

(NAICS 23611 8).

ACCA financial data provide GM as percent of sales for the mechanical contractor

industry. For mechanical contractors, t he baseline markup can be derived from the ACCA data

with the following equation:



### (%) (%) (%)

GM Sales Sales MU

BASE



### − =

Eq. 6.3

The U.S. Census data include the number of establishments, payroll for construction

workers, value of construction, cost of materials, and cost of subcontracted work at both state6

- 6 and national levels. DOE calculated the baseline markup for mechanical contract ors and general

contractors using the following equation:



### SubCost MatCost Pay V MU CONSTRUCT

BASE 

### + + =

Eq. 6.4

Where:

BASE MU = baseline mechanical contractor or general contractor markup,

CONSTRUCT V = value of construction,

Pay = payrol l for construction workers,

MatCost = cost of materials, and

SubCost = cost of subcontracted work.

Analogously, DOE estimated the incremental mechanical contractor and general

contractor markups by only marking up those costs that scale with a change in the MSP (variant

costs

, V C

) for more energy

- efficient products. As stated above, DOE assumes a division of costs

between those that do not scale with the manufacturer price (labor and occupancy expenses), and

those that do (othe r operating expenses and profit). Hence, DOE categorized the Census cost data

in each major cost category and estimated markups using the following equation:

CONT CONT CONT

INCR 

### CGS VC CGS MU +

=

Eq. 6.5

Where:

MU INCR = incremental contractor markup,

CGS CONT = contractor cost of goods sold, and

VC CONT = c ontractor variant costs.

6.5 DERIVATION OF MARKUP S

6.5.1 Manufacturer Markup

The methodology DOE used to determine the manufacturer markup for hearth products is

similar to the methodology described in chapter 5 of the Residential Furnace and Central Air

Conditioning products Direct Final Rule technical support document (TSD). b DOE used U.S.

Security and Exchange Commission (SEC) 10

- K report s from publicly owned hearth product

b The TSD for the direct final rule for HVAC products is available at the following website:

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

- 2011

- BT

- STD

- 0011

- 0012

.6

- 7 manufacturing companies to estimate manufacturer markup s. The estimated manufacturer

markup for hearth products is 1. 42

.

6.5.2 Wholesaler Markup

Wholesalers reported median data in a confidential survey that HARDI conducted of

member firms. In the survey, HARDI itemized revenues and costs into cost categories, incl uding

direct equipment expenses (cost of goods sold ), labor expenses, occupancy expenses, other

operating expenses, and profit. DOE presents these data in full in a ppendix 6 A

. Table 6.5.1

summarizes them at the national aggregated level as cost

- per

- dollar sales revenue in the first data

column. These wholesaler markups are applicable to hearth products

.

Table 6.5.1 Wholesaler Expenses a nd Markups

Descriptions Per Dollar

Sales Revenue

$ Per Dollar Cost

of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.739 1.000

Labor Expenses: Salaries and benefits 0.151 0.204

Occupancy Expense: Rent, maintenance, and utilities 0.035 0.047

Other Operating Expenses: Depreciation, advertising, and

insurance. 0.052 0.070

Operating Profit 0.023 0.031

Wholesaler Baseline Markup ( MU WHOLE BASE ) 1.353

Incremental Markup ( MU WHOLE INCR ) 1.101

Source: Heating, Air Conditioning & Refrigeration Distributors International. 201 3 . 201 3 Profit Report (201 2 Data).

In this case, direct equipment expenses (cost of goods sold) represent about $0.7 4 per dollar sales revenue, so for every $1 wholesalers take in as sales revenue, $0.7 4 is used to pay

the direct equipment costs. Labor expenses represent $0.1 5 per dollar sales revenue, occupancy

expenses represent $0.04, other operating expenses represent $0.0 5 , and profit accounts for $0.0 2

per dollar sales revenue.

DOE converted the expenses per dollar sales into expenses per dollar cost of goods sold,

by dividing each figure in the first data column by $0.7 4 ( i.e., cost of goods sold per dollar of

sales revenue). The data in column two show that, for every $1.00 the wholesaler spends on

equipmen t costs, the wholesaler allocates $0. 204 to cover labor costs, $0.0 47 to cover occupancy

expenses, $0.07 0 for other operating expenses, and $0.0 31 in profits. This totals to $1.3 53 in

sales revenue earned for every $1.00 spent on equipment costs. Therefore , the wholesaler

baseline markup ( MU WHOLE BASE ) is 1.3 53 ($1.3 53 ÷ $1.00).

DOE used the data in column two to estimate the incremental markup. The incremental

markup depends on which of the costs in Table 6.5.1 are variant and which are invariant with

MSP. For example, for a $1.00 increase in the MSP, if all of the other costs scale with the MSP

( i.e., all costs are variant), the increase in wholesa le price will be $1.3 53 , implying that the

incremental markup is 1.3 53 , or the same as the baseline markup. At the other extreme, if none6

- 8 of the other costs are variant, then a $1.00 increase in the MSP will lead to a $1.00 increase in

the wholesale price, for an incremental markup of 1.0. DOE believes that the labor and

occupancy costs will be invariant and that the other operating costs and profit will scale with the

MSP ( i.e., be variant). In this case, for a $1.00 increase in the MSP, the wholesale pric e will

increase to match changes in ”other” operating costs and operating profit of $0.0 75 , which when

divided by 73. 9 cents in cost of goods sold yields an increase of $0. 101 , giving a wholesaler

incremental markup ( MU WHOLE INCR ) of 1. 101

. See appendix 6 A for cost details.

6.5.3 Mechanical Contractor Markups

6.5.3.1 Aggregate Markups for Mechanical Contractors

The 2007 Economic Census provides Geographic Area Series for the Plumbing and

HVAC Contractors (NAICS 23822) sector, which contains national average sale s and c ost data

, including value of construction, cost of subcontract work, cost of materials, and payroll for

construction workers. It also provides the cost breakdown of gross margin, including labor

expenses, occupancy expenses, other operating expenses, and p rofit. The gross margin provided

by the U.S. Census is disaggregated enough that DOE was able to determine the invariant (labor

and occupancy expenses) and variant (other operating expenses and profits) costs for this

particular sector. By using the equati on mentioned above, baseline and incremental markups

were estimated. The markup results representing the plumbing and HVAC contractor industry at

the national aggregated level are presented in Table 6.5.2

. ( Appendix 6 A contains the full set of

data.)

Table 6.5.2 Mechanical Contractor Expenses and Markups Based on Census Bureau

Data

Description Mechanical Contractor Expenses

or Revenue

Per Dollar

Sales Revenue

$ Per Dollar

Cost of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.68 1.00

Labor Expenses: Salaries (indirect) and benefits 0.18 0.26

Occupancy Expense: Rent, maintenance, and utilities 0.02 0.03

Other Operating Expenses: Depreciation, advertising,

and insurance. 0.08 0.12

Net Profit Before Taxes 0.04 0.06

Baseline Markup ( MUMECH BASE ): Revenue per dollar cost of goods 1.48

Incremental Markup ( MUMECH INCR ): Increased revenue per dollar

increase in cost of goods sold 1.18

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air

- Conditioning Contractors. Sector 23: 238220.

Construction: Industry Series, Preliminary Detailed Statistics for Establishments, 2007.6

- 9 The first data column in Table 6.5 .2 provides the cost of goods sold and a list of gross

margin components as expenses per dollar of sales revenue. As shown in the table, the direct cost

of sales represents about $0.68 per dollar sales revenue to the mechanical contractor, and the

gross margin totals $0.32 per dollar sales revenue. DOE converted these expenses per dollar

sales into revenue per dollar cost of goods sold by dividing each figure in the first data column

by $0. 68. For every $1.00 the mechanical contractor spends on equipment costs, the mechanical

contractor earns $1.00 in sales revenue to cover the equipment cost and $0.48 to cover the other

costs. This totals $1.48 in sales revenue earned for every $1.00 spent on equipment costs. This is

equivalent to a baseline markup ( MU MECH CONT BASE ) of 1.48 for mechanical contractors.

DOE use d the data in column two in Table 6.5.2 t o estimate the incremental markups,

after classifying the costs as either invariant or variant. At one extreme, if all of the other costs

scale with the equipment price ( i.e., all costs are variant), the increase in general contractor price

will be $1.48, implying that the incremental markup is 1. 48 or the same as the baseline markup.

At the other extreme, if none of the other costs are variant, then a $1.00 increase in the

equipment price will lead to a $1.00 increase in the general contractor price, for a n incremental

markup of 1.0. DOE believe s the labor and occupancy costs are invariant and the other operating

costs and profit scale with the equipment price ( i.e., are variant). In this case, for a $1.00 increase

in the equipment price, the general contractor price will increase by $1. 18 , giving a general

contractor incremental markup ( MU MECH CONT INCR ) of 1. 18

.

6.5.3.2 Markups for Mechanical Contractors in the Replacement and New

Construction Markets

DOE derived the baseline and incremental markups for both replacement and new

construction markets using the 2007 Economic Census industrial cost data 7 supplemented with

the most recent ACCA 2005 financial data. 2 The 2007 Economic Census provides sufficient

detailed cost breakdown for the Plumbing and HVAC Contractors (NAICS 23822) sector so that

DOE was able to estimate baseline and incremental markup s for mechanical contractors.

However, the 2007 Economic Census does not separate the mechanical contractor market into

replacement and new construction markets. In order to calculate markups for these two markets,

DOE utilized 2005 ACCA financial data, wh ich reports gross margin data for the entire

mechanical contractor market and for both the replacement and new construction markets.

The HVAC contractors, defined here as mechanical contractors, reported median cost

data in an ACCA 2005 financial analysis of the HVAC industry. These data are shown in Table

6.5.3

.6

- 10 Table 6.5.3 Baseline Markup, All Mechanical Contractors

Description Contractor Expenses or Revenue

Per Dollar

Sales Revenue

$ Per Dollar

Cost of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.7 286 1.00

Gross Margin: Labor, occupancy, operating

expenses, and profit 0.27 14 0.37 2

Revenue: Baseline revenue earned per dollar cost of goods 1.37 2

Baseline Markup ( MU MECH CONT BASE ) 1.37 2

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Table 6.5.4 summarizes the gross margin and resulting baseline markup data for all

mechanical contractors that serve the replacement and new construction markets

.

Table 6.5.4 Baseline Markups for the Replacement a nd New Construction Markets, All

Mechanical Contractors

Description Contractor Expenses or Revenue by Market Type

Replacement New Construction

Per Dollar

Sales

Revenue

$ Per Dollar

Cost of

Goods

$ Per Dollar

Sales

Revenue

$ Per Dollar

Cost of

Goods

$

Direct Cost of Equipment Sales:

Cost of goods sold 0.703 1 1.000 0.745 1.000

Gross Margin: Labor, occupancy,

operating expenses, and profit 0.29 69 0.422 0.255 0.342

Baseline Markup ( MUMECH

CONT BASE ): Revenue per

dollar cost of goods NA 1.42 2 NA 1.34 2

% Difference from Aggregate

Mechanical Contractor Baseline

MU NA 3.6 3 % NA

- 2.2 0 %

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Using the baseline markup data from Table 6.5.4 and results from Table 6.5.3

, DOE

calculated that the baseline markups for the replacement and new construction markets are 3.63

percent higher and 2. 20 percent lower, respectively, than for all mechanical contractors serving

all markets

.

The markup deviations ( i.e., 3.63 percent higher and 2.20 percent lower for the

replacement and new construction m arkets, respectively) derived for all mechanical contractors

were then applied to the baseline markup of 1.48 and the incremental markup of 1.18 estimated

for the Plumbing and HVAC Contractors (NAICS 23822) sector in Table 6.5.2 . DOE assumed6

- 11 that this deviation applies equally to the baseline and incremental markups calculated from the

2007 Economic Census. The results of the baseline and incremental markups for the replacement

and new construction markets served by mechanical contractors are shown in Table 6.5.5

.

Table 6.5.5 Markups for the Replacement and New Construction Markets

Baseline Markup Incremental Markup

Replacement Market 1.53 1.22

New Construction Market 1.44 1.16

6.5.4 General Contractor Markups

DOE derived markups for general contractors from U.S. Census Bureau data for the

residential building construction sector to re flect application of hearth products

. 8 The residential

construction sector includes establishments primarily engaged in construction work, including

new construction work, additions, alterations, and repairs of residential buildings

. 9 The U.S.

Census Bureau data for the cons truction sector include detailed statistics for establishments with

payrolls, similar to the data reported by HARDI for wholesalers. The primary difference is that

the U.S. Census Bureau reports itemized revenues and expenses for the construction industry as a

whole in total dollars rather than in typical values for an average or representative business.

Because of this, DOE assumed that the total dollar values that the U.S. Census Bureau reported,

once converted to a percentage basis, represent revenues an d expenses for an average or typical

contracting business. Similar to the data for wholesalers, Table 6.5.6 summarizes the expenses

for general contractors in resi dential building construction at the national aggregated level as

expenses per dollar sales revenue in the first data column . ( A ppendix 6 A contains the full set of

data.)6

- 12 Table 6.5.6 Residential Building General Contractor Expenses and Markups

Description General Contractor Expenses

or Revenue

Per Dollar

Sales Revenue

$ Per Dollar

Cost of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.68 1.00

Labor Expenses: Salaries (indirect) and benefits 0.08 0.12

Occupancy Expense: Rent, maintenance, and utilities 0.01 0.01

Other Operating Expenses: Depreciation, advertising, and

insurance. 0.06 0.09

Net Profit Before Taxes 0.17 0.25

Baseline Markup ( MUGEN CONT BASE ): Revenue per dollar cost of goods 1.47

Incremental Markup ( MUGEN CONT INCR ): Increased revenue per dollar

increase in cost of goods sold 1.34

Source: U.S. Census Bureau. 2007. Residential Building Construction. Sector 23: 236115

- 236118. Construction:

Industry Series: Preliminary Detailed Statistics for Establishm ents: 2007.

As shown in the first column , the direct cost of sales represents about $0.68 per dollar

sales revenue to the general contractor. Labor expenses represent $0.08 per dollar sales revenue,

occupancy expenses represent $0.01 per dollar sales reven ue, other operating expenses represent

$0.03, and profit makes up $0.20 per dollar sales revenue.

DOE converted these expenses per dollar sales into revenue per dollar cost of goods sold,

by dividing each figure in the first data column by $0.68. The data in column two show that, for

every $1.00 the general contractor spends on equipment costs, the general contractor earns $1.00

in sales revenue to cover the equipment cost, $0.12 to cover labor costs, $0.01 to cover

occupancy expenses, $0.09 for other opera ting expenses, and $0.25 in profits. This totals to

$1.47 in sales revenue earned for every $1.00 spent on equipment costs. Thus, the general

contractor baseline markup ( MU GEN CONT BASE ) is 1.47.

DOE use d the data in column two in Table 6.5.6 to estimate the incremental markups,

after classifying the costs as either invariant or variant. At one extreme, if all of the other costs

scale with the equipment price ( i.e., all costs are variant), the increase in general contractor price

will be $1.4 7 , implying that the incremental markup is 1. 4 7 , or the same as the baseline markup.

At the other extreme, if none of the other costs are variant, then a $1.00 increase in the

equ ipment price will lead to a $1.00 increase in the general contractor price, for an incremental

markup of 1.0. DOE believe s the labor and occupancy costs are invariant and the other operating

costs and profit scale with the equipment price ( i.e., are varian t). In this case, for a $1.00 increase

in the equipment price, the general contractor price will increase by $1.3 4 , giving a general

contractor incremental markup ( MU GEN CONT INCR ) of 1.3 4

.6

- 13 6.6 DERIVATION OF CENSUS REGIONS MARKUPS

In this analysis, DOE assumed a market saturation rate for hearth products that varies by

geographical region defined by the 2009 Residential Energy Consumption Survey (RECS

2009 )

, 10 based on the housing projections for the year 2021 . Therefore , regional markups were

calculated for hearth products

.

Wholesalers and mechanical and general contractors in the hearth products indust r y were divided into the 30 regions c provided by RECS 2009 . Regional baseline and incremental

markups were derived using the region/state level data from the 2013 HARDI Profit Report and

the 2007 Economic Census.

6.6.1 Estimation of Wholesaler Markups

Based on the regional income statement from the 2013 HARDI Profit Report, DOE

estimated baseline and incremental markups for the seven HARDI regions (Northeastern, Mid

Atlantic, Southw estern, Great Lakes, Central, Southwestern, and Western) using the

methodology shown in s ection 6.4.1

. Next, each state in each region was assigned the HARDI

regional baseline and incremental markups for the region to which it belongs. Th en

, DOE

assigned all states to one of the 30 RECS 2009 regions used in the analysis and then calculated

2021 housing projections

- weighted baseline and incremental markup averag es for each region.

The r esults are summarized in Table 6.6.1

.

c RECS 2009 provides 27 regions (also called reportable domains). The 27 th region includes Oregon, Washington

, Alaska, and Hawaii. DOE subdivided Alaska and Hawaii into separate regions (28 and 29, respectively) based on

cooling and heating degree days. In addition, West Virginia, which is in RECS region 14

, was disaggregated into

region 30 based on coolin g and heating degree days. Se e appendix 7 B for more details.6

- 14 Table 6.6.1 Wholesaler Markups for Hearth Products

RECS

Regions State(s) Baseline

MU Incremental

MU

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont 1.366 1.072

2 Massachusetts 1.366 1.072

3 New York 1.366 1.072

4 New Jersey 1.355 1.092

5 Pennsylvania 1.354 1.095

6 Illinois 1.364 1.115

7 Indiana, Ohio 1.353 1.097

8 Michigan 1.353 1.097

9 Wisconsin 1.364 1.115

10 Iowa, Minnesota, North Dakota, South Dakota 1.364 1.115

11 Kansas, Nebraska 1.364 1.115

12 Missouri 1.364 1.115

13 Virginia 1.355 1.092

14 Delaware, District of Columbia, Maryland 1.355 1.092

15 Georgia 1.330 1.097

16 North Carolina, South Carolina 1.330 1.097

17 Florida 1.330 1.097

18 Alabama, Kentucky, Mississippi 1.338 1.097

19 Tennessee 1.330 1.097

20 Arkansas, Louisiana, Oklahoma 1.348 1.112

21 Texas 1.348 1.112

22 Colorado 1.364 1.115

23 Idaho, Montana, Utah, Wyoming 1.400 1.110

24 Arizona 1.404 1.110

25 Nevada, New Mexico 1.380 1.111

26 California 1.404 1.110

27 Oregon, Washington 1.404 1.110

28 Alaska 1.404 1.110

29 Hawaii 1.404 1.110

30 West Virginia 1.353 1.097

6.6.2 Estimation of Mechanical Contractor Markups

The 2007 Economic Census provides Geographic Area Series for the Plumbing and

HVAC Contractors (NAICS 23822) sector, which contains state

- level sale and cost data

, including value of construction, cost of subcontract work, cost of materials, and payroll for

construction workers. By using the equation s in section 6.4.2

, DOE was able to estimate baseline

markups for each state. Because the Census does not pr ovide more disaggregated cost data, DOE

was not able to differentiate between invariant and variant cost

.6

- 15 Alternatively, DOE calculated the national baseline and incremental markups ( Table

6.6.2 ) and found that the incremental markup is around 20 percent lower than the baseline

markups. DOE further derived the state

- level incremental markups by applying this ratio to the

baseline markup in each state, assuming that this deviation applies equally to all states.

( Appendix 6 A contains the full set of data .)

T o estimate the baseline and incremental markups for both replacement and new

construction markets for each state, DOE applied the markup deviations ( i.e., 3.6 percent higher

and 2. 2 percent lower for the replacement and new construction markets, respectively) derived in

section 6.5.3.2 to the statewide baseline and incre mental markups. DOE assumed that this

deviation of replacement and new construction markets applies equally to the baseline and

incremental markups.

Lastly, DOE divided all states among the 30 RECS 2009 regions and then calculated

average baseline and inc remental markups for mechanical contractors weighted by housing

projections in 2021 for each region, as shown in Table 6.6.2

.6

- 16 Table 6.6.2 Mechanical Contractor Markups Weighted by Housing Projections in 2021

for Hearth Products

6.6.3 Estimation of General Contractor Markups

T o derive regional general contractor markups for hearth products from the 2007 RECS

Region s State(s) Replacement

Baseline MU Replacement

Incremental

MU New

Construction

Baseline MU New

Construction

Incremental

MU

1 Connecticut, Maine, New

Hampshire, Rhode Island, Vermont 1.557 1.246 1.449 1.159

2 Massachusetts 1.538 1.231 1.431 1.145

3 New York 1.600 1.280 1.488 1.191

4 New Jersey 1.583 1.267 1.473 1.178

5 Pennsylvania 1.479 1.183 1.375 1.100

6 Illinois 1.577 1.262 1.467 1.173

7 Indiana, Ohio 1.563 1.250 1.453 1.163

8 Michigan 1.530 1.224 1.423 1.138

9 Wisconsin 1.510 1.208 1.404 1.123

10 Iowa, Minnesota, North Dakota,

South Dakota 1.531 1.224 1.423 1.139

11 Kansas, Nebraska 1.460 1.168 1.358 1.086

12 Missouri 1.479 1.183 1.376 1.101

13 Virginia 1.557 1.246 1.448 1.158

14 Delaware, District of Columbia,

Maryland 1.491 1.193 1.386 1.109

15 Georgia 1.474 1.179 1.371 1.096

16 North Carolina, South Carolina 1.501 1.201 1.396 1.117

17 Florida 1.512 1.210 1.407 1.125

18 Alabama, Kentucky, Mississippi 1.526 1.220 1.419 1.135

19 Tennessee 1.477 1.182 1.374 1.099

20 Arkansas, Louisiana, Oklahoma 1.541 1.233 1.434 1.147

21 Texas 1.498 1.198 1.393 1.115

22 Colorado 1.531 1.225 1.424 1.139

23 Idaho, Montana, Utah, Wyoming 1.491 1.193 1.387 1.110

24 Arizona 1.580 1.264 1.470 1.176

25 Nevada, New Mexico 1.537 1.230 1.430 1.144

26 California 1.607 1.286 1.495 1.196

27 Oregon, Washington 1.579 1.263 1.469 1.175

28 Alaska 1.766 1.413 1.642 1.314

29 Hawaii 1.835 1.468 1.707 1.366

30 West Virginia 1.528 1.222 1.421 1.1376

- 17 Economic Census, DOE combined four Geographic Area Series: (1) New Single

- Family General

Contractors (NAICS 236115), (2) New M ultifamily H ousing C onstruction (NAICS 236116), (3)

New H ousing O perative B uilders (NAICS 236117), and (4) Residential R emodelers (NAICS

236118)

.

Each series consist s of statewide cost data required to calculate baseline markups for each

state

, as illustrated in s ection 6.4.2

. Although there is only a new construction (no replacement)

channel for general contractors, the same technique shown for mechanical contractors can still be

employed to estimate regional baseline and incremental markups. First, DOE estimated the

statewide incremental markups by ap plying the ratio of national baseline and incremental

markups ( i.e., the national incremental markup is around 8.84 percent lower than the national

baseline markup) to the baseline markups for each state

. Lastly, DOE divided all states among

the 30 RECS re gions ; then calculated average baseline and incremental markups for general

contractors weighted by 2021 housing projections for each region. The final results are

summarized in Table 6.6.3 ( Appendix 6 A contains the full set of data.)6

- 18 Table 6.6.3 General Contractor Markups Weighted by Housing Projections in 2021 for

Hearth Products

RECS

Regions State(s) Baseline

MU Incremental

MU

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont 1.404 1.278

2 Massachusetts 1.343 1.222

3 New York 1.393 1.267

4 New Jersey 1.503 1.368

5 Pennsylvania 1.362 1.239

6 Illinois 1.589 1.446

7 Indiana, Ohio 1.378 1.254

8 Michigan 1.537 1.399

9 Wisconsin 1.340 1.219

10 Iowa, Minnesota, North Dakota, South Dakota 1.368 1.244

11 Kansas, Nebraska 1.351 1.229

12 Missouri 1.325 1.206

13 Virginia 1.450 1.320

14 Delaware, District of Columbia, Maryland 1.419 1.291

15 Georgia 1.428 1.300

16 North Carolina, South Carolina 1.390 1.265

17 Florida 1.528 1.391

18 Alabama, Kentucky, Mississippi 1.355 1.233

19 Tennessee 1.353 1.231

20 Arkansas, Louisiana, Oklahoma 1.372 1.248

21 Texas 1.499 1.364

22 Colorado 1.499 1.364

23 Idaho, Montana, Utah, Wyoming 1.303 1.186

24 Arizona 1.707 1.553

25 Nevada, New Mexico 1.637 1.490

26 California 1.717 1.562

27 Oregon, Washington 1.465 1.333

28 Alaska 1.854 1.687

29 Hawaii 1.417 1.289

30 West Virginia 1.545 1.406

SALES TAX 6.7

The sales tax represents state and local sales taxes that are applied to the consumer price

of the product . The sales tax is a multiplicative factor that increases the consumer product price.

DOE only applied the sales tax to the consumer price of the products in the replacement market,

not the new construction m arket. The common practice for selling larger residential appliances

like hearth products in the new construction market is that general contractors (or builders) bear6

- 19 the added sales tax for product , in addition to the cost of product , and then mark up th e entire

cost in the final listing price to consumers. Therefore, no additional sales tax is necessary to

calculate the consumer product price for the new construction market.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse. 11

These data represent weighted averages that include county and city rates. DOE then derived

average tax values weighted by housing projections in 2021 for each RECS 2009 region to mat ch

the regional markups for wholesalers and mechanical and general contractors, as shown in Table

6.6.4 . Detailed sales tax data by each state can be found in appe ndix 6 A

.6

- 20 Table 6.6.4 Average Sales Tax Rates by RECS 2009 Region

RECS

Regions State(s) Fraction of Housing

Projections in 2021 % Tax Rate

(2014) %

1 Connecticut, Maine, New Hampshire, Rhode Island,

Vermont 0.17 5.16

2 Massachusetts 0.69 6.25

3 New York 2.96 8.40

4 New Jersey 1.87 6.95

5 Pennsylvania 3.38 6.40

6 Illinois 6.24 8.05

7 Indiana, Ohio 5.00 7.06

8 Michigan 5.06 6.00

9 Wisconsin 3.17 5.45

10 Iowa, Minnesota, North Dakota, South Dakota 1.84 6.83

11 Kansas, Nebraska 1.29 7.12

12 Missouri 3.44 7.45

13 Virginia 1.85 5.60

14 Delaware, District of Columbia, Maryland 2.03 5.19

15 Georgia 4.49 7.05

16 North Carolina, South Carolina 1.91 7.00

17 Florida 0.74 6.65

18 Alabama, Kentucky, Mississippi 1.22 7.27

19 Tennessee 1.92 9.45

20 Arkansas, Louisiana, Oklahoma 1.77 8.65

21 Texas 9.88 7.90

22 Colorado 2.19 6.05

23 Idaho, Montana, Utah, Wyoming 0.90 5.12

24 Arizona 1.18 7.15

25 Nevada, New Mexico 0.81 7.31

26 California 10.52 8.45

27 Oregon, Washington 1.65 5.72

28 Alaska 0.07 1.30

29 Hawaii 0.03 4.40

30 West Virginia 0.37 6.10

2021 Housing Projection

- Weighted National Average 7.13

6.7 OVERALL MARKUPS

The overall markup for each distribution channel is the product of the appropriate

markups, as well as sales tax in the case of replacement applications.6

- 21 DOE used the overall baseline markup to estimate the consumer product price of baseline

models, given the manufacturer cost of the baseline models. As stated above, DOE considers

baseline models to be product so ld under existing market conditions ( i.e. , without new energy

conservation standards). The following equation shows how DOE used the overall baseline

markup to determine the product price for baseline models.



### ( )

BASE OVERALL MFG SALES BASE MFG MFG BASE MU COST Tax MU MU COST CPP

_ × = × × × =

Eq. 6.6

Where:

CPP BASE = consumer product price for baseline models,

COST MFG = manufacturer cost for baseline models,

MU MFG = manufacturer markup,

MU BASE = baseline replacement or new home channel markup,

Tax SALES = sales tax (replacement applications only), and

MU OVERALL_BASE = baseline overall markup.

Similarly, DOE used the overall incremental markup to estimate changes in the consumer

product price , given changes in the manufacturer cost above the basel ine model cost resulting

from an energy conservation standard to raise product energy efficiency. The total consumer

product price for higher energy efficien t models is composed of two components: the consumer

product price of the baseline model and the change in consumer product price associated with the

increase in manufac turer cost to meet the new energy conservation standard. The following

equation shows how DOE used the overall incremental markup to determine the consumer

product price for higher energy efficient models ( i.e. , models meeting new energy conservation

stand ards).



## ( )

INCR OVERALL MFG BASE SALES INCR MFG MFG BASE OVERALL MFG STD

MU COST CPP Tax MU MU COST MU COST CPP

_ _

× D + = × × × D + × =

Eq. 6.7

Where:

CPP STD = consumer product price for models meeting new energy conservation standards,

CPP BASE = consumer product price for baseline models,

COST MFG = manufacturer cost for baseline models,

Δ COST MFG = change in manufacturer cost for more energy

- efficient models,

MU MFG = manufacturer markup,

MU INCR = incremental replacement or new home channel markup,

Tax SALES = sales tax (replacement applications only),

MU OVERALL_BASE = baseline overall markup (produ ct of manufacturer markup, baseline

replacement or new home channel markup, and sales tax), and

MU OVERALL_INCR = incremental overall markup.6

- 22 National weighted

- average baseline and incremental markups for each market participant

are summarized in Table 6.7.2 for hearth products. These values represent the weighted average

markups based on the state

- level markup values and 2021 housing projections by state. Based on

hearth product shipment forecasts for the year 20 21 (see chapter 9), DOE estimated that 2 5

percent of hearth products go to new construction and 75 percent go to the replacement market.

By weigh t ing the markups by the market shares for each type of hearth product and market,

overall markups are listed in Table 6.7.3

.

Table 6.7.2 Markups for Hearth Products

Replacement New Construction

Baseline Incremental Baseline Incremental

Manufacturer 1.45 1.45

Wholesaler 1.36 1.10 1.36 1.10

Mechanical Contractor 1.53 1.23 1.43 1.14

General Contractor

- 1.46 1.33

Sales Tax 1.07

-

Total 3.25 2.10 4.11 2.43

Table 6.7.3 Summary of Overall Markup by Hearth Product Class

Product Class Baseline Markup Incremental Markup

Hearth Products 3.43 2.176

- 23 REFERENCES

1. Heating, Air Conditioning & Refrigeration Distributors International 2013 Profit Report,

2013. < www.hardinet.org/ >

2. Air Conditioning Contractors of America (ACCA), Financial Analysis for the HVACR

Contracting Industry: 2005, 2005. < www.acca.org/store/ >

3. U.S. Census Bureau, Manufacturing Industry Seri es: Detailed Statistics for the United

States: 2007. Air

- Conditioning and Warm Air Heating Equipment and Commercial and

Industrial Refrigeration Equipment Manufacturing. Data set for Sector 31: EC073111,

2007. < w ww.census.gov/econ/ >

4. U.S. Census Bureau, Wholesale Trade: Industry Series: Preliminary Summary Statistics

for the United States: 2007. HVAC Equipment Merchant Wholesaler. Data set for Sector

42: EC0742111, 2007. < www.census.gov/econ/ >

5. U.S. Census Bureau, Data set for Sector 23, EC0723A1: 238220 (Plumbing, Heating and

Air

- Conditioning Contractors), Construction: Geographic Area Series, Detailed

Statistics for Establishments, 2007. < www.census.gov/econ/ >

6. Pindyck, R. S. and D. L. Rubinfeld, Microeconomics . 5th ed. 2000. Prentice Hall: New

Jersey.

7. U.S. Census Bureau, Plumbing, Heating, and Air

- Conditioning Contractors. Sector 23:

23822 0. Construction: Industry Series, Preliminary Detailed Statistics for

Establishments, 2007, 2007. < www.census.gov/econ/ >

8. U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for

Establishments: 2007. New Single

- Family General Contractors, New Multifamily

Housing Construction (Except Operative Builders), New Housing Operative Builders,

and Residential Remodelers, 2007. < www.census.gov/ec on/ >

9. U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for

Establishments: 2007. Sector 23, EC0723I1:236200 (Commercial Building

Construction), 2007. < www.census.gov/econ/ >

10. U.S. Department of Energy: Energy Information Administration, Residential Energy

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7,

2015.) < www.eia.g ov/consumption/residential/data/2009/ >

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

and County Rates, 2014. (Last accessed January 10, 2014.)

< http://thestc.com/ST rates.stm >CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION

................................ ................................ ................................ ........... 7

- 1

7.2 HOUSEHOLD SAMPLE

................................ ................................ ................................ 7

- 1

7.3 HE ARTH PRODUCT IGNITIO N ENERGY CONSUMPTION

................................ .... 7

- 3

7.3.1 Determination of Standing Pilot Energy Consumption

................................ ................... 7

- 3

7.3.1.1 Determination of Hearth Product Main Burner Operating Hours

................. 7

- 6

7.3.1.2 Determination of Secondary Effects

................................ .............................. 7

- 9

7.3.2 Intermittent Pilot Ignition El ectricity Consumption

................................ ...................... 7

- 11

7.4 SUMMARY OF ENERGY US E RESULTS

................................ ................................ . 7

- 12

LIST OF TABLES

Table 7.1.1 Hearth Product Groups Analyzed

................................ ................................ ........ 7

- 1

Table 7.2.1 Selection of RECS 2009 Records for Hearth Products

................................ ........ 7

- 2

Table 7.3.1 Distribution of Hearth Product Standing Pilot Operation Behavior

.................... 7

- 6

Table 7.3.2 Overall Range of Standing Pilot Light Operating Hours

................................ ..... 7

- 6

Table 7.3.3 Hearth Product Standing Pilot Representative Inputs

................................ .......... 7

- 6

Table 7.3.4 Hearth Product Representative Input Capacities

................................ ................. 7

- 7

Table 7.3.5 Range of Burner Operating Hours for Hearth Products

................................ ....... 7

- 9

Table 7.3.6 Hearth Product Standing Pilot Representative Secondary Effect Fraction

.......... 7

- 9

Table 7.3.7 Main Heating Appliance Effic iencies

................................ ................................ 7

- 10

Table 7.3.8 Main Cooling Appliance Efficiencies

................................ ................................ 7

- 11

Table 7.3.9 Range of Intermittent Pilot Ignition Hours

................................ ........................ 7

- 12

Table 7.4.1 Average Annual Energy Consumption and Savings for Hearth Products

......... 7

- 13

7

- iCHAPTER 7. ENERGY USE ANALYSIS

7.1 I NTRODUCTION

The purpose of the energy use analysis is to determine the annual energy consumption of

hearth product ignitio n device s in use in the United S tates and to assess the energy savings

potential in switching from standing pilot lights to intermittent pilot ignition

. DOE used survey

data, tear downs, manufacturer literature, and consultant input to establish representa tive energy

consumption for each hearth product group and pilot light option (see chapter 5)

. DOE estimated

the annual energy consumption of hearth product ignition systems across a range of climate

zones

, building characteristics , and heating applications

.

DOE developed energy consumption estimates for the key product groups listed in Table

7.1.1

. The hearth product s analyzed utilize gas for a stand ing pilot or electric ity to power an

intermittent pilot ignition

.

Table 7.1.1 Hearth Product Groups Analyzed

Product

Group Description

1 Vented Fireplace (Fireplace/Insert/Stove)

2 Unv ented Fireplace (Fireplace/Insert/Stove)

3 Vented Gas Logs

4 Unvented Gas Logs

5 Outdoor

DOE estimated the energy consumption of hearth products by developing a building

sample for each of the five product groups based on the Energy Information Administration’s

(EIA) 2009 Residential Energy Consumption Survey (RECS 2009 )

. 1 This is the latest available

survey for residential households

. a This sample is further described in section 7.2

.

DOE used RECS 2009 reported hearth product heating energy consumption and

household characteri stics to calculate the energy use of the hearth product ignition system for

each household. RECS 2009 also provide d weather data for the sample households , which was

used to character iz e the heating season for each household

. In addition, DOE made adjustments

based on historical weather data, projections of building shell efficiency and building square

footage, and for household’s primary heating equipment type . To complete the analysis, DOE

calculated the energy consumption of alternative (more energy

- efficient) ignition systems if they

replaced existing ignition system in each housing unit.

7.2 HOUSEHOLD SAMPLE

DOE’s calculation of the annual energy use of residential b hearth products relied on data

from RECS 2009

. RECS 2009 inclu ded energy

- related data from 12,083 housing units that

a EIA is currently working on the 2015 version of RECS, which is not expected to be available until 2017.

b DOE did not consider hearth products in commercial applications for this analysis

7

- 1represent almost 113.6 million households. Of these units, 712 of them contained gas fireplaces,

representing 6.49 million households. For this analysis, DOE assume d th at , on average, the

characteristics of households with gas fireplaces do not significantly differ from those of

households with other types of hearth products.

DOE divided the hearth product subset into further subsets that include households that

use either vented or ventless hearth products. For vented gas fireplaces , t he subset of RE CS 2009

records used in the analysis met all of the following criteria:

• used a fireplace for secondary or primary space heating,

• used a heating fuel that is natural gas or liquefied pet roleum gas (LPG)

, and

• had a flue on the gas fireplace to the outside.

To identify households with ventless gas fireplaces

, DOE used the same criteria as for

vented gas fireplaces except for criteri on (3), which was replaced with:

• did not have a flue on the gas fireplace to the outside.

DOE used the vented and ventless sample to more accurately assign hearth products to

households. In DOE’s product group s listed in Table 7.1.1 , groups 1 and 3 include only vented

hearth products, and groups 2, 4, and 5 include only ventless hearth products. DOE assigned

vented hearth products to the households in RECS 2009 listed as having vented gas fireplaces.

Similarly , DOE assigned ventless hearth products from groups 2, 4, and 5 to households listed as

having ventless gas fireplaces

. This sampling method takes into account that ventless hearth

products are not allowed to be installed in some states

.

The RECS 2009 we ighting indicates how commonly each household configuration

occurs in the general population in 2009. Appendix 7 A presents the variables included in the

analysis and their definitions

. Table 7.2.1 lists the number of records and representative

population of households in the RECS 2009 selected for the hearth products replacement and

new construction sample s

. Based on hearth product shi pment forecasts for the year 2021 (see

chapter 9), DOE estimated that 25 percent of hearth products go to new construction and 75

percent go to the replacement market. For the new construction sample, DOE only selected

homes built on or after 2000 that wou ld better represent the new construction market.

Table 7.2.1 Selection of RECS 2009 Records for Hearth Products

Product Class Replacements Sample

(All homes in RECS2009) New Construction Sample

( H omes built on or after 2000 )

No. of

Records Number of

Households

million No. of

Records Number of

Households

million

Vented Gas Fireplaces 541 4.67 182 1.56

Ventless Gas Fireplaces 171 1.83 70 0.72

7

- 27.3 H EARTH PRODUCT IGNITION ENERGY CONSUMPTION

To c alculate the energy use of hearth product ignition device s in each of the product

groups , DOE determined either the annual natural gas or LPG consumption associate d with a

standing pilot light or the annual electricity use of an intermittent pilot ignition

. DOE estimated

the input capacity and operating hours of both the existing hearth product and hearth product

ignition device using household and hearth product characteristics. In addition, because hearth

products ( excluding outdoor units ) are installed in conditioned spaces, a portion of the standing

pilot fuel input is useful heat that slightly reduces the load of the primary heating appliance

during the heating season

. Conversely, if the pilot light is left on during the cooling season , this

heat increases the cooling load, resulting in additional electric ity consumption by the primary

cooling appliance

.

To calculate the natural gas or LPG consumption of a standing pilot light

, DOE

determined the standing pilot operating hours, the input capacity of the standing pilot, and the

secondary space conditioning effect s of heat from the standing pilot en tering the conditioned

space

. The electrical consumption of intermittent pilot ignitions was calculated from the

operating hours and input capacity of the intermittent pilot ignition

.

The sum of the fuel and electrical energy consumption (when applicable) represents the

estimated annual energy use of a sampled ignition device

. Additional details used for

determining the total energy use can be found in the following sections.

The calculation used for the det ermination of the total annual energy use for hearth

product ignition device s ( EnergyUse Total ) is:

EEEEEEEEEEEEEEEEEE TTTTTTTTTT = EEEEEEEEEEEEEEEEEE PPPP

, TTTTTTTT + SS SSSS SSSS SSSS SSSS

Eq. 7.1

Where:

EEEEEEEEEEEEEEEEEE PPPP

, TTTTTTTT = t otal annual fuel consumption as a result of standing pilot light operation

or the total annual e lectrical consumption as a result of intermittent pilot ignition operation,

and

SS SSSS SSSS SSSS SSSS = total annual energy consumed by the primary heating or cooling

appliance as a result of the heat input from the standing pilot into the conditioned space

(when applicable)

.

7.3.1 Determination of Standing Pilot Energy Consumption

DOE calculated the annual fuel consumption ( Energy Use Pilot ,Standing ) for each standing

pilot light using the following formula :

EEEEEEEEEEEEEEEEEE PPPP

, SS SSSS SSSS = OOOO PPPP

, SS SSSS SSSS × QQ PP Eq. 7.2

7

- 3Where:

OH Pilot ,Standing = a nnual s tanding p ilot operating hours (hr/yr), and

Q P = input capacity of hearth product standing pilot light (kBtu/hr).

DOE derived a range of possible operating hours from field studies

. 2

, 3 The operating

hours for each household were determined based on typical behavior patterns and the

household’s characteristics

, such as heating load, length of heating season, and primary heating

appliance

. These ranges correspond to three modes of behavior :

1. Mode 1: c onsumers closely monitor the standing pilot light operation and only use it

when starting the hearth product ; therefore

, their standing pilot operating hours are

almost zero ;

2. Mode 2: c onsumers leave the standing pilot light on for the entirety of the heating season

but turn it off for the remaining part o f the year ; or

3. Mode 3: c onsumers leave the standing pilot light on for the entire year.

DOE represented each of these three scenarios with a continuous distribution of standing

pilot operating hours , not including the pilot operating hours when the burne r is also operating

. These distributions are shown in Figure 7.3.1 through Figure 7.3.3

. Note that Figure 7.3.3 shows

the number of hours the standing pilot light is off.

Figure 7.3.1 Distribution of Standing Pilot Light Operating Hours for Mode 1

7

- 4Figure 7.3.2 Distribution of Standing Pilot Light Operating Hours for Mode 2

Figure 7.3.3 Distribution of Hours when the Standing Pilot Light is not Operating for

Mode 3

DOE then weighted these probability distributions by the likelihood that a consumer

would adopt them. The likelihood was determined from a field study of fireplace standing pilot

light gas usage. 2 The study found that 30 of 68 households (44 percent) left the pilot on year

round, while 14 of 68 (21 percent) had almost no pilot light usage. These households were

repr esented with the exponential distributions in Figure 7.3.1 and Figure 7.3.3

. The remaining

households are represented by a distribution of behavior when the standing pilot light is on only

during the heating season, which DOE represented as a triangular distribution

, as shown in

Figure 7.3.2

.

7

- 5For all LPG units, it wa s assumed that consumers do not leave the standing pilot light on

for the entire heating season, but rather o perate the pilot light proportionally to the main burner

of the hearth product because of the high cost of LPG fuel

. From the RECS 2009 household

sample, DOE determined that 23 percent of all gas fireplaces use LPG.

The percentages of households operati ng their standing pilot light in each mode are listed

in Table 7.3.1 . These probabilities are derived from a field study

. 2

Table 7.3.1 Distribution of Hearth Product Standing Pilot Operation Behavior

Behavior

Mode Description Percentage

1 Closely Monitored 40%

2 Heating Season 40%

3 Year

- Round 20%

Table 7.3.2 shows the resulting range of hearth product standing pilot light operating

hours among sample households

.

Table 7.3.2 Overall Range of Standing Pilot Light Operating Hours

Min Max Average Percentiles

5% 25% 50% 75% 95%

18.8 8760 3708 156 544 2691 7718 8760

Table 7.3.3 shows the representative pilot light inputs that DOE determined from the

tear

- down analysis (chapter 5) . These values are used to calculate the fuel used by the standing

pilot as in Eq. 7.2

.

Table 7.3.3 Hearth Product Standing Pilot Representative Inputs

Hearth Product Group Input Capacity

kBtu/h

Vented Fireplace (fireplace/insert/stove) 1

Unvented Fireplace (fireplace/insert/stove) 1.2

Vented G as L og S ets 0.7

Unvented G as L og S ets 0.8

Outdoor 1

7.3.1.1 Determination of Hearth Product Main Burner Operati ng Hours

In creating the ranges of standing pilot operating hours

, DOE also determine d the main

burner operating hours ( BOH ) of the hearth product. This allowed DOE to establish the

minimum of the range of standing pilot operating hours ( i.e. , the hours that the hearth product is

operating)

. To calculate the BOH for hearth products used for heating

, DOE used the building

heat ing load met by the hearth product ( BHL Hearth ), and the representative main burner input. The

7

- 6BHL Hearth is determined from the annual space heating energy consumption provided by RECS

2009 for vented and unvented gas fireplaces ( fireplace/insert/stove )

.

The BOH of the hearth product ( B OH Hearth ) is calculated as :

BB HH HH h = BB BB HH HH h

QQ IIII

, HH HH h × η HH HH h Eq. 7.3

Where:

BHL Hearth = building heating load served by a single hearth product (kBtu/yr)

, Q IN, Hearth = representative input capacity of the hearth product main burner (kBtu/h), and

η Hearth = average efficiency of newly installed gas hearth product

.

DOE assumed that the resulting BOH Hearth value for vented and unvented fireplaces

( fireplace/insert/stove ) would be similar for the other hearth product groups.

Table 7.3.4 shows the representative input capacity ( Q IN, Hearth ) that DOE determined from

the tear

- down analysis (chapter 5).

Table 7.3.4 Hearth Product Representative Input Capacities

Hearth Product Group Input Capacity

kbtu/h

Vented Fireplaces (fireplace/insert/stove) 35

Unvented Fireplaces (fireplace/insert/stove) 30

Vented G as L og S ets 35

Unvented G as L og S ets 25

Outdoor 50

The annual BHL is the total amount of heat output from the hearth product that the house

needs during the heating season. This includes heat from the burner. DOE determined projected

BHL Hearth in 2021 for each sampled housing unit based on the input capacity of the assigned

existing hearth product , using the following calculati on:

BB BB HH HH h = QQ YYYY × η HH HH h × HH HH HH × AA jj FF FF Eq. 7.4

Where:

Q YR = annual total space heating fuel consumption reported by RECS 2009 ( k Btu/yr),

η Hearth ,ex = average efficiency of existing gas hearth product

, assumed to be 64 percent for vented

and 100 percent for ventless

, HeatingLoadFraction = fraction of the total building heating load met by the hearth product, and

Adj Factor = adjustment factor (discussed below)

.

7

- 7RECS 2009 report s space heating energy use ( Q YR ) for each of the sampled households.

RECS 2009 also reports the fraction of heat provided by the main heating equipment. DOE

assumed that the remainder of the heat not provided by the main heating equipment was

provided by th e hearth product.

DOE adjusted the BHL to reflect the expectation that the houses in 2021 will have a

somewhat different BHL than the households in the RECS 2009 hearth product sample. The

adjustment involves multiplying the calculated BHL for each RECS 2009 household by the

building shell efficiency index c based on information from the National Energy Modeling

System (NEMS) simulation associated with EIA’s Annual Energy Outlook ( AEO 2014)

. 4 This

factor differs for new construction and replacement buildings. The factor a pplied in the analysis

is 0.9 1 for replacements and 0.92 for new construction. This means that households on average

will have lower space heating load compared to households in 2009.

DOE also adjusted BHL to reflect historical average climate conditions using heating

degree days (HDD) reported in RECS 2009 for each household and NOAA HDD data by

region

. 5 The adjustment factor s are calculated using the following equation :

AA jj FF FF aa cccccccccccccc = HHHH 10 yy rr aaaa

HHHH bb 2009 Eq. 7.5

Where:

HDD bldg _200 9 = HDD in 200 9 for the specific region where the housing unit is located, and

HDD 10 _yr_avg = 10

- year average HD D (200 4

– 20 1 3 ) based on NOAA data for the specific region

where the housing unit is located.

The adjustment factors range from 0.91 to 1.02 and on average 0.95 for the hearth

product household sample ( i.e.

, 2009 was in general colder than the 10

- year average )

.

DOE also account for future climate trends base d on AEO 201 4 HDD projections , which

show a decline in HDD, leading to lower projected BHL Hearth in 2021 relative to the non

- climate

trend

- adjusted BHL Hearth values

. On average, this decreases the heating load by 7.25% in 2021.

Table 7.3.5 presents the calculated hearth product burner operating hours among sample

households

.

c The building shell efficiency index sets the heating load value at 1.00 for an average home in 2009 (by type) in

each census division. The values listed below represent th e change in heating load based on the difference in

physical size and shell attributes for homes in the future (which takes into account physical size difference and

efficiency gains from better insulation and windows).

7

- 8Table 7.3.5 Range of Burner Operating Hours for Hearth Products

Min Max Av erage Percentiles

5% 25% 50% 75% 95%

1.95 3518 157 21.02 54.04 106.3 194.1 443.7

7.3.1.2 Determination of Secondary Effects

DOE included the seasonal secondary effects of the operation of a standing pilot light on

space conditioning energy use

. During the heating season, a fraction of th e standing pilot light

heat input ( HeatInput Pilot, Heating Season ) contributes to space heat ing that would otherwise be

provided by a main heating appliance, such as a furnace

. Therefore, DOE add ed a fraction of the

standing pilot light energy consumption that contributes to space heating to the intermittent pilot

fuel energy consumption

. Based on the results of a field study that quantified the percentage of

fireplace standing pilot fuel energy converted into useful heat in a home, DOE estimated th e

fraction of the heat output from the hearth product standing pilot light that contributes to space

heating for each hearth product group as listed in Table 7.3.6

. 6 For the decorative vented

fireplaces and vented gas logs, which are not intended for space heating, DOE assumed that the

fraction of the heat transferred to the space is half of the fraction for the vented fireplaces. For

the ventless hearth products, DOE assumed that the fraction of the heat transferred to the space is

double the fraction for the vented fireplaces. The large range in the ventless hearth products

fraction accounts for increased heat transfer to the space due to the combustion products going

into the room for a fraction of installations

, as well as for units installed in space without a

thermostat , where t he heat input from the hearth product has no impact on the operation of the

main heating equipment (see Table 7.3.6 )

.

Table 7.3.6 Hearth Product Standing Pilot Representative Secondary Effect Fraction

Hearth Product Group Fraction

%

Vented Fireplace (fireplace/insert/stove) 1 to 47

Vented Fireplaces (Decorative) 0 to 23

Unvented Fireplace (fireplace/insert/stove) 2 to 94

Vented G as L og S ets 0 to 23

Unvented G as L og S ets 2 to 94

Outdoor Not Applicable

The operation of standing pilot lights during the cooling season contributes some heat to

the conditioned space ( HeatInput Pilot,CoolingSeason )

, resulting in an additional cooling load and

increased electrical energy consumption. To account for the increased cooling energy use, DOE

subtracted the additional electrical energy consumption of the cooling appliance from the

electricity consumption of the interm ittent pilot ignition.

Eq. 7.6 through Eq. 7.8 describe the calculation approach of the effects

( SecondaryEffects ) of the heat input of the pilot during the heating and cooling season.

7

- 9HH HHHH PPPP

, HH HH = BB PPPP

, HH HH QQ PP η PPPP

ηη MM Eq. 7.6

HH HHHH PPPP

, CC = BB PPPP

, HH HH QQ PP η PPPP

CC CC CC Eq. 7.7

SS SSSS SSSS EE EE EE = PPPP

, CC − HH HHHH PPPP

, HH HH Eq. 7.8

Where:

HH HHHH PPPP

, HH HH = a nnual heat input of the pilot light into the space during the

heating season ( B tu) adjusted for main heating appliance operation

, Q P = as previously defined,

η Pilot = e fficiency of the pilot light, as listed in Table 7.3.6

,

η MainHeat = national average efficiency of the main heating appliance

, as listed in AEO 2014

, HH HHHH PPPP

, CC = a nnual heat input of the pilot light into the space during the

cooling season (kWh) adjusted for main cooling appli ance operation , and

COP Cool = national a verage Coefficient of Performance of the main cooling appliance based on

AEO 2014

.

RECS 2009 contain s information about several primary heating and cooling appliances.

DOE applied the AEO 2014 projections of the average efficiency ratings of these products in

2021. The listed efficiencies for each type of main heating appliance ( η MainHeat ) are listed in Table

7.3.7

. T h e listed efficiencies for each main cooling appliance ( COP Cool ) are listed Table 7.3.8

.

Table 7.3.7 Main Heating Appliance Efficiencies

Main Heating Appliance Description Efficiency

Rating

Natural Gas Furnace (AFUE, %) * 80 or 9 2

Gas DHE (%) 64

Electric Heat (%) 98

Electric Heat Pumps ( HSPF ) ** 8.01 or 8.52

No Primary Heating Appliance Not Applicable

* In the replacement hearth market, the average stock AFUE in 2021 from AEO2014 is used, which is 55 percent at

80

- percent AFUE and 45 percent at 92

- percent AFUE. In the new construction hearth market, the average shipment

AFUE in 2021 from DOE’s 2011 Direct Final Rule 7 is used, which is 43 percent at 80

- percent AFUE and 57 percent

at 92

- percent AFUE.

** In replacement hearth market, the average stock HSPF in 2021 from AEO2014 is used (8.01 HSPF). In new

construction hearth market, the average shipment HSPF in 2021 from DOE’s 2011 Direct Final Rule is used (8.52

HSPF).

7

- 10Table 7.3.8 Main Cooling Appliance Efficiencies

Main Cooling Appliance Description Efficiency

Rating

Central Air Conditions (SEER) 14.2

Electric Heat Pump (SEER) 13.7

Room Air Conditioner (EER) 11.0

No Primary Cooling Ap p liance Not Applicable

7.3.2 Intermittent Pilot Ignition Electricity Consumption

DOE calculated intermittent pilot ignition energy consumption with a similar

methodology as for standing pilot lights (section 7.3.1 ). The annual energy consumption of the

hearth product intermittent pilot ignition ( EnergyUs e Pilot,Intermittent ) is:

EEEEEEEEEEEEEEEEEE PPPP

, II II = OOOO PPPP

, II II × IIII Eq. 7.9

Where:

OH Pilot,Intermittent = operating hours of the intermittent pilot ignition, and

PE IG = input capacity of the intermittent pilot ignition, conservatively determined to be 50 watts

(see chapter 5)

. d

The distribution of BOH of hearth products with intermittent pilot ignition s was assumed

to be the same as for hearth products with standing pilots, a s listed in Table 7.3.2

. Because hearth

products are a secondary heating appliance similar to direct heating equipment, DOE applied the

average on

- time per cycle used in t he DHE test procedure to hearth products, which is 20

minutes. 11 D OE assumed a 30

- second operating time per cycle for the intermittent pilot ignition.

The operating hours of intermittent pilot ignitions ( OH Pilot

, Intermittent ) is determined by:

OOOO PPPP

, II II = BB HH HH h TT OO

TT

CC Eq. 7.10

Where:

BB HH HH h = hearth product b urner operating hours ( Eq. 7.3 )

, TT

OO = o n

- time per cycle of the intermittent pilot ignition , assumed to be 30 seconds , and

TT

CC = on

- t ime per cycle of the hearth product , assumed to be 20 minutes

.

Table 7.3.9 lists the resulting annual national average intermittent pilot operating hours.

d DOE surveyed several intermittent ignition systems and found the typical electrical requirements of the largest of

these units was approximately 24 volts, 2 amps, or 48 watts. 8

, 9

, 10

7

- 11Table 7.3.9 Range of Intermittent Pilot Ignition Hours

Min Max Average Percentiles

5% 25% 50% 75% 95%

0.05 87.95 3.94 0.53 1.35 2.66 4.85 11.09

7.4 SUMMARY OF E NERGY U SE R ESULTS

This section presents the average annual energy use and the average energy savings for

each considered energy efficiency level (EL 1) compared to the baseline energy efficiency (EL

0) for each hearth product ignition device

. e The LCC and PBP analysis uses the results calculated

as the national average from all sampled household s

. Table 7.4.1 lists the average annual energy

use for hearth product ignition devices and the average energy savings for each considered

energy efficiency level compared to the baseline standing pilot light for each hearth product

group

.

e The derivation of the base case energy consumption, which represents the energy consumption without the

standard, includes households that already include EL1 products as explained in chapter 8.

7

- 12Table 7.4.1 Average Annual Energy Consumption and Savings for Hearth Product s

EL Hearth Product

Group Annual Fuel Use Annual Electricity

Consumption Overall Energy

Savings **

Total Savings Total Savings Savings

MMBtu/yr MMBtu/yr kWh/yr kWh/yr MMBtu /yr

0 Vented Fireplace 3.99

-- 0.000

-- --

1 Vented Fireplace 0.499 3.49 13.60 (13.60) 3.44

0 Unvented Fireplace 3.52

-- 0.000

-- --

1 Unvented Fireplace 1.30 2.22 99.38 (99.38) 1.88

0 Vented Gas Logs 3.13

-- 0.000

-- --

1 Vented Gas Logs 0.289 2.84 5.79 (5.79) 2.82

0 Unvented Gas Logs 2.29

-- 0.000

-- --

1 Unvented Gas Logs 0.924 1.36 70.61 (70.61) 1.12

0 Outdoor 3.91

-- 0.000

-- --

1 Outdoor 0.000 3.91 0.175 (0.175) 3.91

0 All Hearth Products 3.58

-- 0.000

-- --

1 All Hearth Products 0.58 3.00 28.54 (28.54) 2.90

* Parentheses () indicate negative values

** Savings include both fuel and electrical energy consumption, reported in MMBtu /yr

.

7

- 13REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy

Consumption Survey (RECS): 2009 RECS Survey Data. 2009. (Last accessed January 7,

2015.) < www.eia.gov/consumption/residential/data/2009/ >

2. Hayden, A. C. S., Fireplace Pilots Take Gas Use Sky High. 1997. (Last accessed August

8, 2014.) < http://www.homeenergy.org/show/article/nav/hvac/page/28/id/1264 >

3. Menkedick, J., P. Hartford, S. Collins, S. Shumaker, and D. Wells, Hearth Products

Meter Study (1995

- 1997). 1997. Gas Research Institute. Report No. GRI

- 97/0298.

4. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040. 20 14. Washington, DC. (Last accessed May 2014.)

< www.eia.gov/forecasts/aeo/ >

5. National Oceanic and Atmospheric Administration, NNDC Climate Data Online. (Last

accessed March 15, 2013.) <www7.ncdc.noaa.gov/ CDO/CDODivisionalSelect.jsp>

6. Armstrong, M. M., M. C. Swinton, and F. Szadkowski, Assessment of the Impact of a

Natural Gas Fireplace on Heating Energy Consumption and Room Temperatures at the

Canadian Centre for Housing Technology. 2010. National Rese arch Council Canada.

7. U.S. Department of Energy

- Energy Efficiency & Renewable Energy, Technical Support

Document: Energy Efficiency Standards for Consumer Products: Residential Furnaces,

Central Air Conditioners, and Heat Pumps. 2011. Washington, DC.

8. Johnson Controls, G67 Intermittent Pilot Ignition Controls Manual. (Last accessed

August 28, 2014.) < http://www.engineeredair.com/parts/VaQdUhG16.pdf >

9. BASO Gas Products LLC, BG1600 M Intermittent Pilot Ignition Controls Manual. (Last

accessed August 28, 2014.)

< http://www.baso.com/lib/subdomain/baso/content/products/b2a6e982af38c9e7e453490

b7ed40e45/BASO

- IGN

- INS

BG1600M_Intermittent_Pilot_Ignition_Control_Instructions.pdf >

10. Honeywell, Honeywell Intermittent Pilot Modules S8600, S8610, S8660, S8670. (Last

accessed August 28, 2014.)

< http://www.hvacwebconnection.com/images/S8610Installation.pdf >

11. Department Of Energy, Appendix O to Subpart B of Part 430

- Uniform Test Method For

Measuring The Energy Consumption Of Vented Home Heating Equipment.

7

- 14CHAPTER 8. LIFE

- CYCLE COST AND PAYPA CK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION

................................ ................................ ................................ ........... 8

- 1

8.1.1 General Approach for Life

- Cycle Cost and Payback Period Analysis

............................ 8

- 1

8.1.2 Overview of Life

- Cycle Cost and Payback Period Analysis Inputs

................................ 8

- 2

8.1.3 Use of Residential Energy Consumption Survey in Life

- Cycle Cost and Payback

Period Analysis

................................ ................................ ................................ ................ 8

- 5

8.2 LIFE

- CYCLE COST ANALYSIS INPUTS

................................ ................................ .... 8

- 6

8.2.1 Total Insta lled Cost Inputs

................................ ................................ ............................... 8

- 6

8.2.1.1 Manufacturer Costs

................................ ................................ ........................ 8

- 7

8.2.1.2 Markups

................................ ................................ ................................ ......... 8

- 7

8.2.1.3 Total Consumer Price

................................ ................................ .................... 8

- 8

8.2.1.4 Future Product Prices

................................ ................................ ..................... 8

- 9

8.2.1.5 Installation Cost

................................ ................................ ........................... 8

- 10

8.2.1.6 Total Installed Cost

................................ ................................ ...................... 8

- 13

8.2.2 Operating Cost Inputs

................................ ................................ ................................ .... 8

- 13

8.2.2.1 Annual Energy Use Savings

................................ ................................ ........ 8

- 13

8.2.2.2 Energy Pric es

................................ ................................ ............................... 8

- 13

8.2.2.1 Maintenance Cost

................................ ................................ ......................... 8

- 19

8.2.2.2 Repair Cost

................................ ................................ ................................ ... 8

- 19

8.2.2.3 Lifetime

................................ ................................ ................................ ........ 8

- 21

8.2.2.4 Discount Rates

................................ ................................ ............................. 8

- 22

8.2.2.5 Compliance Date of Stan dard

................................ ................................ ...... 8

- 27

8.2.2.6 Base Case Distribution of Efficiency Levels

................................ ............... 8

- 27

8.3 PAYBACK PERIOD INPUT S

................................ ................................ ...................... 8

- 28

8.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS

................................ ...... 8

- 29

8.5 REB UTTABLE PAYBACK PERI OD

................................ ................................ .......... 8

- 31

8.5.1 Inputs

................................ ................................ ................................ .............................. 8

- 32

8.5.2 Results

................................ ................................ ................................ ............................ 8

- 32

LIST OF TABLES

Table 8.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP

Analysis

................................ ................................ ................................ ................ 8

- 5

Table 8.2.1 Manufacturer Production Cost for Hearth Product Ignitions Systems by

Hearth Product Group

................................ ................................ .......................... 8

- 7

Table 8.2.2 Summary of National Average Markups on Hearth Product Ignition

Devices

................................ ................................ ................................ ................. 8

- 8

Table 8.2.3 Overall Markup for Hearth Product Ignition Devices

................................ ......... 8

- 8

Table 8.2.4 Average Consumer P rice for Hearth Product Ignition Devices (2013$)

............. 8

- 9

Table 8.2.5 Installation Cost Household Sample Assumptions

................................ ............ 8

- 10

Table 8.2.6 RS Means National Installation Labor and Material Costs (2013$)

.................. 8

- 10

8

- iTable 8.2.7 RS M eans 2013 National Average Residential Labor Costs by Crew

............... 8

- 11

Table 8.2.8 RS Means Labor Costs Markups by Trade (Residential)

................................ .. 8

- 11

Table 8.2.9 Material and Labor Cost Factors by Geographical Area (for RECS 2009

Sample)

................................ ................................ ................................ .............. 8

- 12

Table 8.2.10 Average Installation Cost for Hearth Product Ignition Systems (2013$)

.......... 8

- 12

Table 8.2.11 Average Total Installed Cost for Hearth Product Ignition Devices (2013$)

..... 8

- 13

Table 8.2.12 Residential Average Monthly Natural Gas Prices for 2012 Using Monthly

Price Factors (2013$/MMBtu)

................................ ................................ ........... 8

- 15

Table 8.2.13 Residential Marginal Monthly Natural Gas Prices for 2012 Using Monthly

Price Factors (2013$/MMBtu)

................................ ................................ ........... 8

- 16

Table 8.2.14 Residential Average Electricity Prices for 2012 Using Monthly Price

Factors (2013$/kWh)

................................ ................................ ......................... 8

- 17

Table 8.2.15 Residential Marginal Electricity Prices for 2012 Using Monthly Price

Factors (2013$/kWh)

................................ ................................ ......................... 8

- 18

Table 8.2.16 Hearth Product Average Repair Cost Components (2013$)

.............................. 8

- 20

Table 8.2.17 RS Means 2013 National Average Residential Labor Costs by Crew

............... 8

- 20

Table 8.2.18 RS Means Labor Costs Markups by Trade (Residential)

................................ .. 8

- 20

Table 8.2.19 Annualized Repair Cost for Hearth Product Ignition Devices (2013$)

............. 8

- 21

Table 8.2.20 Lifetime Parameters for Hearth Products

................................ .......................... 8

- 22

Table 8.2.21 Definitions of Income Groups

................................ ................................ ........... 8

- 23

Table 8.2.22 Types of Household Debt and Equity by P ercentage Shares (%)

...................... 8

- 24

Table 8.2.23 Data Used to Calculate Real Effective Mortgage Rates

................................ .... 8

- 25

Table 8.2.24 Average Real Effective Interest Rates for Household Debt

.............................. 8

- 25

Table 8.2.25 Average Nominal and Real Interest Rates for Household Equity

..................... 8

- 26

Table 8.2.26 Average Real Effective Discount

................................ ................................ ....... 8

- 27

Table 8.2.27 Market Shares of Intermittent Ignition in 2021 by Product Group

.................... 8

- 28

Table 8.2.28 Market Shares of Match

- Lit Units in 2021 by Product Group

.......................... 8

- 28

Table 8.2.29 Market Shares of Hearth Products Ignition in 2021 by Product Group

............. 8

- 28

Table 8.4.1 Average LCC and PBP Results by Efficiency Level for Hearth Product

Ignition Devices

................................ ................................ ................................ . 8

- 30

Table 8.4.2 Average LCC Savings Relative to the Base Case Efficiency Distribution

for Hearth Product Ignition Devices

................................ ................................ .. 8

- 30

Table 8.5.1 Rebuttable Payback Period for Hearth Product Ignition Devices

...................... 8

- 32

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP

....................... 8

- 4

Figure 8.2.2 Historical Deflated Producer Price Indexes for Floor and Wall Furnaces

........... 8

- 9

Figure 8.2.3 Projected National Commercial Energy Price Factors, AEO 2014

(Reference Case)

................................ ................................ ................................ 8

- 19

Figure 8.2.1 Lifetime Distribution of Hearth Products (whole unit)

................................ ...... 8

- 22

Figure 8.4.2 Distribution of LCC Savings for Hearth Product Ignition Devices

................... 8

- 31

8

- iiCHAPTER 8. LIFE

- CYCLE COST AND PAYPA CK PERIOD ANALYSIS

8.1 INTRODUCTION

The effect of amended standards on individual consumer s usually includes a reduction in

operating cost and an increase in purchase cost. This chapter describes two metrics used in the

analysis to determine the economic impact of standards on individual consumers

.

• Life

- cycle cost (LCC) is the total consumer cost over the life of an appliance or product,

including purchase costs and operating costs (which in turn include main tenance, repair,

and energy costs). Future operating costs are discounted to the time of purchase and

summed over the lifetime of the appliance or product.

• Payback period (PBP) measures the amount of time it takes consumer s to recover the

assumed higher pu rchase price of more energy

- efficient products through reduced

operating costs.

The U.S. Department of Energy (DOE) conducted the LCC and PBP analysis using a

spreadsheet model developed in Microsoft Excel. When combined with Crystal Ball (a

commercially available software program), the LCC and PBP model generates a Monte Carlo

simulation to perform the analysis by incorporating uncertainty and variability considerations in

certain of the key parameters as discussed further in section 8.1.1

.

Inputs to the LCC and PBP analysis of hearth product ignition device s are discussed in

sections 8.2 and 8.3

, respectively. Results for each metric are presented in section s 8.4 and 8.5

. Key variables and calculations are presented for each metric. The calculations discussed h ere

were performed with a series of Microsoft Excel spreadsheets that are accessible over the Internet

( www1.eere.energy.gov/buildings/appliance_standards/p roduct.aspx?productid=83 ).

Details of the spreadsheets and instruct ions for using th em are discussed in appendix 8 A.

8.1.1 General Approach for Life

- Cycle Cost and Payback Period Analys i s

In recognition of the fact that each residential building using hearth product s is unique,

variability and uncertainty are analyzed by performing the LCC and PBP calculations detailed

here for a representative sample of individual households. The results are expressed as the

number of buildings experiencing economic impacts o f different magnitudes. The LCC and PBP

model was developed using Microsoft Excel spreadsheets combined with Crystal Ball. The LCC

and PBP analysis explicitly model both the uncertainty and the variability in the model’s inputs

using Monte Carlo simulation and probability distributions (see appendix 8 B)

.

The LCC analysis used the estimated energy use for each hearth product unit as described

in the energy use analysis in chapter 7. Energy use of hearth product s is sensitive to climate and

therefore varies b y location within the United States. Aside from energy use, other important

8

- 1factors influencing the LCC and PBP analysis include energy prices, installation costs, product

distribution markups, and sales tax es

.

As mentioned previously , DOE generated LCC a nd PBP results as probability

distributions using a simulation based on Monte Carlo analysis methods, in which certain key

inputs to the analysis consist of probability distributions rather than single

- point values.

Therefore, the outcomes of the Monte Car lo analysis can also be expressed as probability

distributions. As a result, the Monte Carlo analysis produces a range of LCC and PBP results. A

distinct advantage of this type of approach is that DOE can identify the percentage of consumer s

achieving LCC savings or attaining certain PBP values due to an increased efficiency level, in

addition to the average LCC savings or average PBP for that efficiency level.

The LCC results are displayed as distributions of impacts compared to a base case

. T he

base case efficiency is for 2021 and reflect s the expected distribution of efficiency levels by

product class. The PBP results are displayed compared to the baseline efficiency level.

8.1.2 Overview of Life

- Cycle Cost and Payback Period Analys i s Inputs

The LCC is the total consumer cost over the life of the product, including purchase price

(including retail markups, sales taxes, and installation costs) and operating cost (including repair

costs, maintenance costs, and energy cost). Future operating cost s are discounted to the time of

purchase and summed over the lifetime of the product. The PBP is the increase in purchase cost

of a higher efficiency product divided by the change in annual operating cost of the product. It

represents the number of years t hat it will take the consumer to recover the increased purchase

cost through decreased operating costs. In the PBP calculation , future costs are not discounted.

Inputs to the LCC and PBP analysis are categorized as: (1) inputs for establishing the

purchase cost, otherwise known as the total installed cost; and (2) inputs for calculating the

operating cost ( i.e. , energy, maintenance, and repair costs).

The primary inputs for establishing the total installed cost are:

• Baseline manufacturer selling price : The baseline manufacturer selling price (MSP) is

the price charged by the manufacturer to a wholesaler for product meeting existing

minimum efficiency (or baseline) standards. The MSP includes a markup that converts

the cost of production ( i.e. , the manufactur er cost) to a n MSP.

• Standard

- level manufacturer selling price increase : The standard

- level MSP is the

incremental change in MSP associated with producing product at each of the higher

efficiency standard levels.

• Markups and sales tax : Markups and sales ta x are the wholesaler and contractor margins

and state and local retail sales taxes associated with converting the MSP to a consumer

price.

8

- 2• Installation cost : Installation cost is the cost to the consumer of installing the product.

The installation cost re presents all costs required to install the product but does not

include the marked

- up consumer product price. The installation cost includes labor,

overhead, and any miscellaneous materials and parts.

The primary inputs for calculating the operating cost are:

• Product energy consumption : The product energy consumption is the site energy use

associated with the use of the hearth product ignition device to start the heart h product

.

• Energy Prices : Electricity, natural gas, and liquid petroleum gas (LPG) prices are

determined using average and marginal monthly energy prices.

• Electricity , LPG and natural gas price trends : The Energy Information Administration’s

(EIA’s) Annual Energy Outlook 201 4 ( AEO 201 4 ) 1 is used to forecast energy prices into

the future. For the results presented in this chapter, DOE used the AEO 201 4 R eference

case to forecast future energy prices.

• Maintenance costs : The labor and material costs associated with maintaining the

operation of the product

.

• Repair costs : The labor and material costs associated with repair ing or replacing

components that have failed.

• Lifetime : The age at which the hearth product is retired from service.

• Discount rate : The rate at which future costs and savings are discounted to establish their

present value.

Figure 8.1.1 graphically depicts the relationships between the installed cost and operating

cost inputs for the calculation of the LCC and PBP.

8

- 3Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP

Table 8.1.1 provides descriptions of the various inputs to the calculation of the LCC and

PBP. As noted earlier, most of the inputs are characterized by probability distributions that

capture variability in the input variables. Manufacturer Sales

Price Standard i= 1

... n

Manufacturer Sales

Price Standard i= 1

... n

Installation Costs Installation Costs

Repair Costs

Repair Costs Lifetimes Lifetimes Annual Maintenance Electricity

Consumption

Electricity

Consumption

Manufacturer Base

Price

Total Installed Cost

(Std i

– Base) Manufacturer Base

Price

Contractor Markups Wholesaler/

Distributor Markup

Electricity Prices Equipment Prices

Sales Tax

Manufacturer Selling

Price (Base)

Installation Costs Manufacturer Selling

Price Standard i= 1

... n

Electricity

Consumption Annual Energy Costs

Annual Maintenance

Repair Costs Annual Operating

Cost (Std i

- Base) Payback

Period

Discount Rate

Lifetimes Lifetime

Operating

Cost (Std

i

– Base) Lifecycle

Cost

Annual Maintenance

Costs Manufacturer Sales

Price Standard i= 1

... n

Manufacturer Sales

Price Standard i= 1

... n

Installation Costs Installation Costs

Repair Costs

Repair Costs Lifetimes Lifetimes Annual Maintenance Electricity

Consumption

Electricity

Consumption

Manufacturer Base

Price

Total Installed Cost

(Std i

– Base) Manufacturer Base

Price

Contractor Markups Wholesaler/

Distributor Markup

Electricity Prices Equipment Prices

Sales Tax

Manufacturer Selling

Price (Base)

Installation Costs Manufacturer Selling

Price Standard i= 1

... n

Electricity

Consumption Annual Energy Costs

Annual Maintenance

Repair Costs Annual Operating

Cost (Std i

- Base) Payback

Period

Discount Rate

Lifetimes Lifetime

Operating

Cost (Std

i

– Base) Lifecycle

Cost

Annual Maintenance

Costs

8

- 4Table 8.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP

Analysis

Inputs Description

Affecting Installed Costs

Product Price Derived from MSP for hearth product group s (from the engineering analysis

, chapter 5 ) multiplied by wholesaler markups and contractor markups plus sales

tax (from markups analysis). Used the probability distribution for the different

markups to describe their variability.

Installation Cost Includes installation labor derived from RS Means Residential Cost Data 201 3

. 2

Overhead and materials co sts and profits are assumed to be included in the

contractor’s markup. Thus, the total installed cost equals the consumer product

price (manufacturer cost multiplied by the various markups plus sales tax) plus

the installation cost.

Affecting Operating Costs

Energy Use Determined from the input capacity and operating hours of the hearth product

ignition system

. See chapter 7.

Energy Prices C osts were calculated for RECS 2009 households from monthly marginal

average electricity and natural gas or LPG prices in each of 30 states and groups

of states in RECS 2009. a Residential prices were escalated by the AEO 2014

forecasts to estimate future electricity prices. Escalation was performed at the

census division level and aggregated to the regions used in the study.

Maintenance Cost N o maintenance cost is applied in th is analysis.

Repair Cost Estimated the annualized repair cost for hearth product ignition systems based

on the cost of replacement.

Affecting Present Value of Annual Operating Cost Savings

Product Lifetime Used the probability distribution of lifetimes developed for hearth product s

.

Discount Rate Mean real discount rates ranging from 3.57 percent to 5.12 percent for various

classes of residential consumer s based on Federal Reserve Board’s Survey of

Consumer Finances . Probability distributions are used for the discount rates.

Date Standard

Become s Effective 2021 (5 years after expected publication of the final rule)

All of the inputs depicted in Figure 8.1.1 and summarized in Table 8.1.1 ar e discussed in

section 8.2

.

8.1.3 Use of Residential Energy Consumption Survey in Life

- Cycle Cost and Payback

Period Analysis

The LCC and PBP calculations detailed here are for a representativ e sample of individual

hearth product users

. As explained in chapter 7, t he Energy Information Administration (EIA) ’s

2009 Residential Energy Consumption Survey ( RECS 2009 ) 3 serves as the basis for determining

a RECS 2009 provides 27 regions (also called reportable domains). The 27 th region includes Oregon, Washington

, Alaska, and Hawaii. DOE subdivided Alaska and Hawaii into separate regions (28 and 29, respectively) based on

cooling and heating degree days. In addition, West Virginia, which is in RECS region 14

, was disaggregated into

region 30 based on cooling and heating degree days. See appendix 7 A for more details

.

8

- 5the representative residential sample

. RECS collects energy

- related data for occupied primary

housing units in the United States. RECS 2009 included data from 12,083 housing units that

represent almost 113.6 million households.

Appendix 7 A presents the variables used and their definitions, as well as further

information about the derivation of the household samples.

8.2 LIFE

- CYCLE COST ANALYSIS INPUTS

Life

- cycle cost is the total consumer cost over the life of a product, including purchase

cost and operating costs (which are composed of energy costs, maintenance costs, and repair

costs). Future operating costs ar e discounted to the time of purchase and summed over the

lifetime of the product. Life

- cycle cost is defined by the following equation:

Eq. 8.1

Where:

LCC

= life

- cycle cost ($),

IC

= total installed cost ($),

∑ = sum over the lifetime, from year 1 to year N,

where N = lifetime of product (years),

OC

= operating cost ($),

r = discount rate, and

t = year for which operating cost is being determined

.

DOE expresse d all the costs in 2013$ . Total installed cost, operating cost, lifetime, and

discount rate are discussed in the following sections. In the LCC analysis, the year of product

purchase is assumed to be 2021 , the assumed effective date of energy conservation standards for

hearth pro duct s

.

8.2.1 Total Installed Cost Inputs

The total installed cost to the c on s u mer is defined by the following equation:

Eq. 8.2

Where:

EQP

= product price ($) ( i.e.

, consumer price for the product only), and

INST

= installation cost ($) ( i.e. , the cost for labor and materials). 

# ∑

= 

### + + = N

t t

t 

### r OC IC LCC

1 

### ) 1 /(

INST EQP IC + =

8

- 6The product price is based on the distribution channel through which the consumer

purchases the product. As discussed in chapter 6, DOE defined two major distribution channel s

for new units to describe how a hearth product passes from the man ufacturer to the consumer

, one applying to hearth products installed in replacement markets or by new owners and the other

applying to hearth products that are installed in new construction

. In the new construction

channel, the manufacturer sells the produ ct to a wholesaler or distributor, who sells to a

mechanical cont ractor hired by a general contractor. The general contractor purchases a nd

installs the product on behalf of the consumer and adds its markup to the mechanical contractor’s

price. Replacement products follow the same distribution channel, except that there is no general

contractor. Instead, the mechanical contractor takes on the general contractor’s function.

The remainder of this section provides information about the variables DOE used to

c alculate the total installed cost for hearth products.

8.2.1.1 Manufacturer Costs

DOE developed the manufacturer costs for hearth product s ignition systems as described

in chapter 5, Engineering Analysis. The manufacturer costs for a baseline ignition system ( i.e.

, standing pilot light) for each product group and the incremental cost of a n intermittent pilot

ignition system are shown in Table 8.2.1

.

Table 8.2.1 Manufacturer Production Cost for Hearth Product Ignitions Systems by

Hearth Product Group

Product

Group Description Standing Pilot

MPC Intermittent

Pilot

MPC

1 Vented Fireplaces/Stoves/Inserts $49.13 $76.83

2 Unvented Fireplaces/Stoves/Inserts $69.17 $101.44

3 Vented Log Sets $30.79 $100.63

4 Unvented Log Sets $29.04 $85.46

5 Outdoor $84.83 $139.96

8.2.1.2 Markup s

For a given distribution channel, the overall markup is the value determined by

multiplying all the associated markups a nd the applicable sales tax together to arrive at a single

overall distribution chain markup value. The overall markup is multiplied by the baseline or

standard

- compliant manufacturer cost to arrive at the price paid by the consumer

. Because there

are base line and incremental markups associated with the wholesaler and mechanical contractor,

the overall markup is also divided into a baseline markup ( i.e. , a markup used to convert the

baseline manufacturer price into a consumer price) and an incremental markup ( i.e. , a markup

used to convert a standard

- compliant manufacturer cost increase due to an efficiency increase

into an incremental consumer price). Markups can differ depending on whether the product is

being purchased for a new construction installation or is being purchased to replace an existing

8

- 7product. DOE developed the overall baseline markups and incremental markups for both new

construction and replacement applications as a part of the markups analysis (chapter 6).

Table 8.2.2 display s the average markups and their associated components weighted by

housing projections in 2021

, 16,17,18 for the baseline and incremental markups fo r hearth product

ignition devices , respectively. DOE calculated the projected number of houses in 2021 by state

by accounting for the growth in population by region from 2008 to 2012 based on historical U.S.

census population numbers by state and the numbe r of people per house in each state based on

historical U.S. census population and housing numbers.

Table 8.2.2 Summary of National Average Markups on Hearth Product Ignition

Device s

Replacement New Construction

Baseline

Markup Incremental

Markup Baseline

Markup Incremental

Markup

Manufacturer 1.45 1.45

Wholesaler 1.36 1.10 1.36 1.10

Mechanical Contractor 1.53 1.23 1.43 1.14

General Contractor

- 1.46 1.33

Sales Tax 1.07

- Total Markup 3.25 2.10 4.11 2.43

Note: Components may not multiply to the overall markup due to rounding.

Table 8.2.3 presents the total markup for hearth product ignition device s b ased on the

percentage of the market attributed to each distri bution channel

.

Table 8.2.3 Overall Markup for Hearth Product Ignition Device s

Product Class Baseline Markup Incremental Markup

Hearth Products 3.43 2.17

8.2.1.3 Total Consumer Price

DOE derived the consumer product price for the baseline product by multiplying the

baseline manufacturer cost by the baseline overall markup (including the sales tax). For each

efficiency level above the baseline , DOE derived the consumer product price by taking baseline

product consumer price and adding to it the product of the inc remental manufacturer cost and the

incremental overall markup (including the sales tax). Markups and sales tax can take on a variety

of values depending on location, so the resulting total installed cost for a particular efficiency

level is represented by a distribution of values.

Table 8.2.4 presents the average consumer product price for hearth product ignition

device s at each efficiency level examined.

8

- 8Table 8.2.4 Average Consumer Price for Hearth Product Ignition Device s ( 2013$ )

Product Class Efficiency Level Average Consumer Price Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $166.46

-

Intermittent Pilot $248.14 $81.68

8.2.1.4 Future Product Prices

DOE examined the historical price trend of hearth products by looking at the producer

price index ( PPI ) data for floor and wall furnaces from 19 99

- 201 3 from the Bureau of Labor

Statistics’ (BLS). b The PPI for floor and wall furnaces is the most representative price index for

hearth products , as the products in this PPI index are generally similar to hearth products

. The

PPI data reflect nominal prices, adjusted for product quality changes. The inflation

- adjusted

(deflated) price index for floor and wall furnace s is calculated by dividing the PPI seri es by the

Gross Domestic Product Chained Price Index ( see Figure 8.2.1 ).

Figure 8.2.2 Historical Deflated Producer Price Indexes for F loor and Wall Furnaces

In Figure 8.2.1 , the deflated PPI for floor and wall furnaces has remained relatively

constant throughout the past decade. Even though there is a sign of downward trend beginning in

2012, DOE does not have s ufficient evidence t hat the downward trend will continue in the

future. Given the uncertainty, DOE chose to apply a constant price trend (in real dollars) to the

manufacturer selling price of hearth products

.

b Floor and wall furnaces PPI series ID: PCU 3334143334147; www.bls.gov/ppi/

8

- 98.2.1.5 Installation Cost

The installation cost is the cost to the consumer of installing a hearth product ignition

device

. Because the ignition device is a component of the hearth product, the only installation

costs considered in the analysis were the labor and material costs associated with electrical

retrofits for the fraction of households for which this is necessary

. DOE assumed that in new

construction there would be no additional installation cost of the intermittent pilot compared to

the standing pilot. DOE estimated the cost of installing a new electrical connection or installing

electrical grounding when applicable for each sample household based the age of the household

given in RECS 2009

. In addition, DOE assumed that the cost of electrical retrofits for ventless

gas logs

, which are easier to move closer to the location of the electrical outlet, is half of the cost

applied to the other hearth product groups. Table 8.2.5 presents the assumptions DOE used to

determine the households that require electrical retrofits.

Table 8.2.5 Installation Cost Household Sample Assumptions

Criteria Fraction of Households that Require

Electrical Outlet Electrical Grounding

Household built before 1960 50% 50%

Household built before 1990 25% 0%

Household built after 1990 10% 0%

New construction 0% 0%

Table 8.2.6 presents the national average basic installation cost based on RS Means 2013

Residential Cost Data. 2

Table 8.2.6 RS Means National Installation Labor and Material Costs (2013$)

Labor

Hours Bare Material

Cost *

Electricity Connection (Retrofit)

Trip Charge 0.5 $0.00

Receptacle Devices (Duplex Outlet,15

amp recpt., EMT & wire) (20' avg. runs

and #14/2 wiring included) 1.501 $29.50

Electricity Grounding (Retrofit)

Clamp, bronze,1/2" diameter 0.25 $5.20

Bare copper wire, #14 solid 0.571 $7.80

*Does not include sales tax or RS Means markups by trade

.

RS Means provides average national labor costs for different trade groups as shown in

Table 8.2.7

. La bor costs including overhead and profit (O&P) are the bare costs provided by RS

Means multiplied by the RS Means markups by trade shown in Table 8.2.8

.

8

- 10Table 8.2.7 RS Means 2013 National Average Residential Labor Costs by Crew

Crew Type Crew Description Laborers

per Crew Cost per Labor

- Hour

Bare Costs Incl. O&P*

1 Elec 1 Electrician 1 35.10 57.42

* O&P includes markups in Table 8.2.8

.

Table 8.2.8 RS Means Labor Costs Markups by Trade (Residential)

Trade Workers

Comp. Avg. Fixed

Overhead Overhead Profit Total

Electrician 5.7% 17.9% 30.0% 10.0% 63.6%

DOE’s analysis of installation costs accounts for regional differences in labor and

material costs. RS Means provides material and labor cost factors for 295 cities and towns in the

U.S. To derive average installation labor cost values by state, DOE weight ed the se price factors

by housing projections in 2021 . DOE used the average material and labor cost factors for cost s

associated with electrical labor

. Table 8.2.9 shows the final regional material and labor price

factors used in the analysis by geographical area.

8

- 11Table 8.2.9 Material and Labor Cost Factors by Geographical Area (for RECS 2009

Sample)

Geographical Area Electrical

Material Labor

Connecticut, Maine, New Hampshire, Rhode

Island, Vermont 1.01 0.97

Massachusetts 1.02 1.16

New York 1.02 1.68

New Jersey 1.02 1.37

Pennsylvania 0.96 1.25

Illinois 0.95 1.27

Indiana, Ohio 0.98 0.89

Michigan 0.97 0.99

Wisconsin 1.02 0.95

Iowa, Minnesota, North Dakota, South Dakota 1.01 0.91

Kansas, Nebraska 0.99 0.77

Missouri 1.01 0.95

Virginia 0.97 0.71

Delaware, District of Columbia, Maryland 0.98 0.97

Georgia 0.99 0.69

North Carolina, South Carolina 0.97 0.48

Florida 0.99 0.68

Alabama, Kentucky, Mississippi 0.99 0.69

Tennessee 1.00 0.63

Arkansas, Louisiana, Oklahoma 1.00 0.62

Texas 0.95 0.61

Colorado 1.01 0.84

Idaho, Montana, Utah, Wyoming 0.96 0.70

Arizona 0.98 0.66

Nevada, New Mexico 0.94 0.93

California 1.00 1.21

Oregon, Washington 1.02 0.97

Alaska 1.34 1.17

Hawaii 1.06 1.27

West Virginia 0.96 0.90

Table 8.2.10 presents the average installation cost for all hearth product ignition systems

.

Table 8.2.10 Average Installation Cost for Hearth Product Ignition Systems (2013$)

Product Class Efficiency Level Average

I nstallation Cost Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $0.00

-

Intermittent Pilot $ 19.53 $ 19.53

8

- 128.2.1.6 Total Installed Cost

T he total installed cost is the sum of the product price and the installation cost. M arkups,

sales tax es

, and labor and material costs all can take on a variety of values, depending on

location, so the resulting total installed cost for a particular efficiency level will not be a single

point value, but rather a distribution of values

. Table 8.2.11 present s the average total installed

cost for hearth product s ignition devices

.

Table 8.2.11 Average Total Installed Cost for Hearth Product Ignition Device s ( 2013$ )

Product Class Efficiency Level Total Installed Cost Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $166.46

-

Intermittent Pilot $267.67 $101.21

8.2.2 Operating Cost Inputs

DOE defined the operating cost by the following equation:

OC = EC+ RC+ MC

Eq. 8.3

Where:

OC

= operating cost ($),

EC

= energy cost associated with operating the product ($),

RC

= repair cost associated with component failure ($), and

MC

= annual maintenance cost for maintaining product operation ($).

The remainder of this section provides information about the variables that DOE used to

calculate the operating cost for hearth product s

. The annual energy costs of the product are

computed from energy consumptio n per unit for the baseline and standard

- com pliant cases,

combined with the energy prices. Product lifetime, discount rate, and compliance date of the

standard are required for determining the operating cost and for establishing the operating cost

present value.

8.2.2.1 Annual Energy Use Savings

DOE calcu lated the annual energy use savings for each sample household at each

efficiency level as described in chapter 7. DOE believes that consumers will not use a hearth

product with an intermittent pilot more frequently than a heart h product with a standing pilot

light, and did not include a rebound effect in the energy use calculations

.

8.2.2.2 Energy Prices

DOE derived average and marginal monthly energy prices for a number of geographic

areas in the United States u sing the latest data fro m EIA and monthly energy price factors that it

8

- 13developed

. Average energy prices are applied to the base case energy use, while marginal prices

are applied to the differential energy use from the other efficiency options. DOE then assign ed an

appropriate en ergy price to each household in the sample, depending on its location.

Derivation of Average and Marginal Monthly Prices

Derivation of Average Annual Energy Prices using EIA data

. DOE obtained the data

for natural gas price s from EIA’s Natural Gas Navigator

, 4 which includes monthly natural gas

prices by s tate for residential, commercial, and industrial consumer s. DOE derived 2012 annual

electricity prices from EIA Form 8 26 data

, 5 which includes electricity prices by state f or

residential, commercial, and industrial consumer s

. DOE calculated annual state electricity and

natural gas prices by averaging monthly energy prices by state

.

DOE collected 20 12 average LPG prices from EIA’s 20 12 State Energy Consumption,

Price, and Expenditures Estimates (SEDS). 6 SEDS includes annual LPG prices for residential,

commercial, industrial, and transportation consumers by state.

For a RECS region with more than one state, DOE weight ed each stat e ’s average energy

price by its number of homes in 2021

. See appendix 8C for the calculated annual energy prices in

2012.

Derivation of Average Monthly Energy Factors using EIA data

. To determine monthly

prices for use in the analysis, DOE developed mont hly energy price factors for each fuel based

on long

- term price data

. S ee appendix 8C for a description of the method. DOE multiplied the

average 2012 annual prices by the monthly price factors for each fuel to derive prices for each

month.

Seasonal Elec tricity and Natural Gas Marginal Price Factors using EIA data . Monthly

electricity and natural gas prices were adjusted using seasonal marginal price factors to

determine monthly marginal electricity and natural gas prices. These marginal energy prices

were used to determine the cost to the consumer of the change in energy consumed. Because

marginal price data is only available for electricity and natural gas, DOE only developed

marginal monthly prices for these fuels. For LP G, DOE used average monthly prices. For a

detailed discussion of the development of marginal energy price factors, see appendix 8C

.

Table 8.2.12

, Table 8.2.13

, Table 8.2.14 , and Table 8.2.15 show the average and marginal

monthly natural gas and electricity prices. Average LPG prices are shown in appendix 8C

.

8

- 14Table 8.2.12 Res idential Average Monthly Natural Gas Prices for 2012 Using Monthly

Price Factors (2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine,

New Hampshire, Rhode

Island, Vermont 14.4 14.5 14.6 14.9 15.6 16.9 18.4 18.7 18.2 16.3 15.5 15.1

Massachusetts 12.9 12.9 12.8 13.1 12.3 12.6 13.9 14.7 14.2 12.3 13.3 13.2

New York 12.8 12.6 12.8 13.3 14.8 16.9 17.8 16.8 16.4 14.4 12.9 12.1

New Jersey 10.9 10.7 10.7 11.0 11.7 12.9 13.4 13.4 13.2 12.1 11.5 11.2

Pennsylvania 11.4 11.6 11.8 12.3 13.8 15.9 17.6 18.1 17.2 14.0 12.4 11.8

Illinois 8.1 8.2 8.2 8.7 10.6 12.2 13.1 13.3 12.3 9.7 8.6 8.1

Indiana, Ohio 11.3 11.4 11.8 12.6 14.1 16.2 17.7 17.9 16.6 13.3 11.8 11.5

Michigan 10.1 10.1 10.3 10.8 12.0 13.7 15.2 15.7 14.5 12.1 10.9 10.5

Wisconsin 9.6 9.3 9.5 9.6 9.7 11.1 11.5 11.7 10.7 8.8 9.8 9.6

Iowa, Minnesota, North

Dakota, South Dakota 8.3 8.1 8.2 8.4 9.6 11.2 12.1 12.5 11.6 9.2 8.6 8.2

Kansas, Nebraska 9.5 9.5 9.5 10.4 11.7 13.7 14.7 15.4 15.0 12.9 10.4 9.8

Missouri 12.2 12.2 12.3 13.8 15.9 19.4 22.2 23.5 21.9 18.6 14.6 13.0

Virginia 12.1 11.6 11.5 12.8 15.1 17.6 18.9 18.6 18.6 15.1 12.4 12.0

Delaware, District of

Columbia, Maryland 11.9 11.8 12.1 13.2 15.0 16.9 18.0 17.9 17.6 14.6 12.8 12.2

Georgia 13.4 14.2 14.9 16.5 20.5 22.8 24.0 23.8 23.1 19.9 15.3 14.3

North Carolina, South

Carolina 12.6 12.5 12.9 13.9 15.9 18.8 19.9 20.7 19.8 16.5 13.8 13.3

Florida 15.4 15.7 16.7 17.7 19.3 20.5 21.2 21.6 21.3 20.9 19.2 17.0

Alabama, Kentucky,

Mississippi 11.7 11.7 12.1 13.3 15.2 16.9 17.3 17.7 17.3 15.7 13.4 12.4

Tennessee 9.9 10.0 10.0 10.9 12.0 13.7 14.6 15.0 14.3 13.0 11.0 10.4

Arkansas, Louisiana,

Oklahoma 10.6 10.5 10.8 11.9 14.1 15.6 16.6 17.1 16.6 15.5 13.1 11.2

Texas 9.5 9.5 9.8 11.2 12.9 14.4 14.8 15.2 15.1 13.6 11.2 9.9

Colorado 7.8 7.9 8.2 8.4 9.3 11.8 11.9 12.6 11.7 9.3 8.4 8.0

Idaho, Montana, Utah,

Wyoming 8.2 8.2 8.4 8.2 8.5 9.2 10.0 10.5 9.8 8.6 8.5 8.3

Arizona 13.5 14.0 14.5 15.8 17.7 19.6 21.4 22.1 21.4 20.0 16.9 14.6

Nevada, New Mexico 8.7 8.9 9.1 9.8 11.3 13.1 12.8 13.3 12.8 11.3 9.6 8.7

California 9.2 9.1 8.8 8.8 9.2 9.6 9.7 9.6 9.3 9.4 9.0 9.0

Oregon, Washington 11.0 11.2 11.2 11.5 12.0 12.6 13.9 14.4 14.0 12.7 11.7 11.4

Alaska 8.1 8.2 8.3 8.4 8.8 9.1 9.8 9.6 8.9 8.4 8.2 8.4

Hawaii 49.6 50.7 50.8 51.0 51.8 52.3 53.6 54.9 54.8 54.5 53.7 52.6

West Virginia 10.8 10.8 10.9 11.3 12.5 15.1 16.9 16.6 15.3 12.5 11.5 11.1

United States 10.5 10.5 10.6 11.2 12.3 13.8 14.6 14.9 14.3 12.3 11.2 10.7

8

- 15Table 8.2.13 Residential Marginal Monthly Natural Gas Prices for 2012 Using

Monthly Price Factors (2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New

Hampshire, Rhode Island,

Vermont 13.21 13.32 13.42 12.40 12.97 14.03 15.26 15.52 15.11 13.52 14.27 13.84

Massachusetts 13.30 13.29 13.19 11.75 10.98 11.30 12.45 13.13 12.71 11.00 13.66 13.60

New York 11.44 11.24 11.38 10.01 11.12 12.67 13.38 12.61 12.35 10.85 11.47 10.81

New Jersey 10.33 10.20 10.22 9.23 9.82 10.78 11.23 11.18 11.03 10.13 10.89 10.64

Pennsylvania 10.58 10.70 10.90 8.93 10.02 11.55 12.78 13.11 12.45 10.16 11.50 10.91

Illinois 7.93 7.96 8.01 5.91 7.17 8.26 8.88 8.99 8.33 6.58 8.40 7.91

Indiana, Ohio 10.43 10.53 10.88 9.17 10.20 11.78 12.82 12.99 12.08 9.65 10.91 10.65

Michigan 9.40 9.41 9.55 8.38 9.33 10.68 11.82 12.25 11.30 9.41 10.19 9.81

Wisconsin 9.37 9.16 9.33 7.66 7.67 8.78 9.11 9.26 8.48 7.02 9.64 9.39

Iowa, Minnesota, North

Dakota, South Dakota 8.04 7.89 8.03 6.03 6.83 8.01 8.65 8.92 8.30 6.59 8.39 8.02

Kansas, Nebraska 8.81 8.86 8.87 7.21 8.12 9.51 10.18 10.69 10.39 8.92 9.70 9.08

Missouri 9.98 9.98 10.11 8.20 9.50 11.59 13.25 14.03 13.07 11.08 12.00 10.67

Virginia 11.23 10.82 10.69 8.65 10.23 11.95 12.83 12.63 12.59 10.21 11.53 11.20

Delaware, District of

Columbia, Maryland 11.03 10.94 11.20 9.32 10.59 11.89 12.65 12.64 12.38 10.28 11.83 11.25

Georgia 11.58 12.31 12.86 9.19 11.42 12.74 13.40 13.30 12.88 11.08 13.22 12.39

North Carolina, South

Carolina 11.26 11.22 11.53 9.20 10.56 12.46 13.22 13.70 13.15 10.93 12.31 11.94

Florida 12.65 12.90 13.78 11.41 12.42 13.21 13.62 13.91 13.71 13.48 15.83 14.05

Alabama, Kentucky,

Mississippi 10.15 10.13 10.46 9.95 11.33 12.59 12.92 13.19 12.89 11.69 11.61 10.76

Tennessee 9.31 9.44 9.44 8.04 8.84 10.11 10.78 11.12 10.61 9.63 10.39 9.80

Arkansas, Louisiana,

Oklahoma 8.88 8.84 9.07 7.70 9.16 10.09 10.74 11.08 10.78 10.05 10.96 9.43

Texas 8.01 8.06 8.28 6.68 7.66 8.54 8.80 9.02 8.97 8.10 9.45 8.35

Colorado 7.09 7.18 7.43 5.78 6.41 8.12 8.17 8.67 8.02 6.42 7.66 7.28

Idaho, Montana, Utah,

Wyoming 7.84 7.87 8.01 6.82 7.13 7.71 8.38 8.76 8.18 7.22 8.13 7.97

Arizona 11.51 11.87 12.29 10.04 11.27 12.47 13.57 14.08 13.62 12.72 14.33 12.39

Nevada, New Mexico 7.69 7.88 8.08 7.10 8.13 9.46 9.20 9.56 9.24 8.15 8.50 7.77

California 9.94 9.84 9.48 7.45 7.77 8.17 8.22 8.10 7.89 7.95 9.75 9.77

Oregon, Washington 10.35 10.47 10.52 9.67 10.05 10.56 11.64 12.11 11.77 10.65 11.01 10.72

Alaska 7.82 7.89 7.95 7.20 7.55 7.81 8.41 8.25 7.63 7.25 7.87 8.12

Hawaii 45.38 46.34 46.42 39.47 40.05 40.49 41.44 42.49 42.37 42.15 49.08 48.09

West Virginia 10.26 10.30 10.42 9.02 9.99 12.08 13.44 13.28 12.20 9.95 10.91 10.59

United States 9.80 9.85 9.97 8.31 9.14 10.24 10.86 11.09 10.61 9.14 10.47 10.03

8

- 16Table 8.2.14 Residential Average Electricity Prices for 2012 Using Monthly Price

Factors (2013$/kWh)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine,

New Hampshire, Rhode

Island, Vermont 0.16 0.16 0.16 0.16 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16

Massachusetts 0.15 0.15 0.15 0.15 0.15 0.16 0.15 0.15 0.15 0.15 0.15 0.15

New York 0.17 0.17 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.18 0.18 0.17

New Jersey 0.15 0.15 0.15 0.15 0.16 0.17 0.17 0.17 0.17 0.16 0.16 0.16

Pennsylvania 0.12 0.12 0.12 0.13 0.13 0.14 0.14 0.14 0.13 0.13 0.13 0.12

Illinois 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Indiana, Ohio 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Michigan 0.14 0.14 0.14 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.14 0.14

Wisconsin 0.13 0.13 0.13 0.13 0.14 0.14 0.13 0.13 0.14 0.14 0.13 0.13

Iowa, Minnesota, North

Dakota, South Dakota 0.10 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Kansas, Nebraska 0.09 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.10 0.10

Missouri 0.09 0.09 0.09 0.10 0.11 0.12 0.12 0.12 0.11 0.10 0.10 0.09

Virginia 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Delaware, District of

Columbia, Maryland 0.12 0.12 0.12 0.12 0.14 0.15 0.15 0.15 0.14 0.13 0.12 0.12

Georgia 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.13 0.12 0.11 0.11 0.10

North Carolina, South

Carolina 0.11 0.11 0.11 0.11 0.12 0.11 0.12 0.12 0.12 0.12 0.11 0.11

Florida 0.11 0.11 0.12 0.12 0.12 0.11 0.12 0.12 0.12 0.12 0.12 0.12

Alabama, Kentucky,

Mississippi 0.10 0.10 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10

Tennessee 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.11 0.10

Arkansas, Louisiana,

Oklahoma 0.08 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.09 0.09

Texas 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Colorado 0.11 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Idaho, Montana, Utah,

Wyoming 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Arizona 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Nevada, New Mexico 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12

California 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15 0.15 0.16

Oregon, Washington 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

Alaska 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.19 0.18 0.18 0.18 0.18

Hawaii 0.36 0.37 0.37 0.37 0.38 0.38 0.38 0.39 0.39 0.39 0.39 0.39

West Virginia 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.10 0.10

United States 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

8

- 17Table 8.2.15 Residential Marginal Electricity Prices for 2012 Using Monthly Price

Factors (2013$/kWh)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New Hampshire,

Rhode Island, Vermont 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Massachusetts 0.15 0.16 0.16 0.14 0.15 0.15 0.14 0.15 0.15 0.15 0.16 0.16

New York 0.15 0.15 0.15 0.20 0.20 0.21 0.21 0.21 0.21 0.20 0.15 0.15

New Jersey 0.15 0.15 0.15 0.19 0.19 0.20 0.21 0.21 0.21 0.19 0.15 0.15

Pennsylvania 0.10 0.10 0.10 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.11 0.10

Illinois 0.07 0.08 0.08 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.08 0.08

Indiana, Ohio 0.08 0.08 0.08 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.09 0.08

Michigan 0.13 0.14 0.13 0.16 0.16 0.17 0.17 0.17 0.17 0.16 0.14 0.14

Wisconsin 0.11 0.12 0.12 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.12 0.12

Iowa, Minnesota, North Dakota,

South Dakota 0.09 0.09 0.09 0.12 0.12 0.13 0.13 0.13 0.12 0.12 0.09 0.09

Kansas, Nebraska 0.07 0.07 0.07 0.12 0.13 0.14 0.14 0.14 0.14 0.13 0.08 0.07

Missouri 0.07 0.07 0.07 0.12 0.13 0.15 0.14 0.14 0.13 0.12 0.07 0.07

Virginia 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Delaware, District of Columbia,

Maryland 0.11 0.11 0.11 0.14 0.16 0.17 0.17 0.17 0.16 0.15 0.11 0.11

Georgia 0.09 0.09 0.09 0.13 0.13 0.14 0.14 0.15 0.14 0.13 0.09 0.09

North Carolina, South Carolina 0.09 0.09 0.09 0.11 0.11 0.11 0.11 0.11 0.11 0.12 0.09 0.09

Florida 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Alabama, Kentucky, Mississippi 0.08 0.08 0.08 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.08

Tennessee 0.08 0.08 0.08 0.10 0.10 0.10 0.10 0.09 0.10 0.10 0.09 0.09

Arkansas, Louisiana, Oklahoma 0.06 0.06 0.06 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.07 0.06

Texas 0.09 0.09 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.10 0.10

Colorado 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.13 0.09 0.09

Idaho, Montana, Utah, Wyoming 0.09 0.09 0.09 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.09

Arizona 0.08 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Nevada, New Mexico 0.10 0.10 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.13 0.11 0.10

California 0.17 0.17 0.17 0.18 0.19 0.19 0.20 0.20 0.19 0.18 0.18 0.18

Oregon, Washington 0.08 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.09 0.09

Alaska 0.16 0.16 0.16 0.15 0.16 0.16 0.16 0.16 0.16 0.16 0.17 0.16

Hawaii 0.33 0.33 0.33 0.54 0.55 0.55 0.56 0.56 0.56 0.57 0.35 0.35

West Virginia 0.08 0.08 0.08 0.09 0.10 0.09 0.09 0.09 0.09 0.10 0.09 0.08

United States 0.09 0.09 0.09 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.10 0.09

Household Energy Price Adjustment Factor

RECS 2009 reports the total annual consumption and expenditure of each energy use

type. To take into account that household energy prices may vary inside a geographical area,

DOE developed an adjustment factor based on the reported average RECS 2009 energy price for

each household divided by the average energy price in the RECS 2009 geographical region. This

factor was then multiplied by the mon thly price developed above to determine the household

energy price. Appendix 8 C includes more details.

8

- 18Energy Price Trends by Census Division

To arrive at prices in future years, DOE multiplied the prices described in the preceding

section by the forecast s of a nnual average price changes in AEO 2014

. 1 Figure 8.2.2 show s the

national residential energy price factors with a 2012 base year (2012=1)

. To estimate the trend

after 2040 , DOE used the average rate of change during 2030

– 20 40

.

DOE applied the projected energy price for each of the nine census divisions to each

building in the sample based on the building’s location

. Appendix 8 C includes more details

.

Figure 8.2.3 Projected National Commercial Energy Price Factors, AEO 2014

(Reference Case)

8.2.2.1 Maintenance Cost

The maintenance cost is the routine cost to the consumer of maintaining equipment

operation. DOE assume d that hearth product ignition systems do not require any maintenance.

8.2.2.2 Repair Cost

The repair cost is the cost of materials and labor to the consumer for replacing an ignition

device that ha s failed

. Repair occurs if the ignition system fails before the end of the hearth 0.9

1 1.1 1.2 1.3 1.4 1.5 1.6

2012 2017 2022 2027 2032 2037Price Factor

Year Natural Gas

LPG

Electricity

8

- 19product life. The repair costs are assumed to be equal to the retail price for replacement

applications

. DOE accounts for regional differences in labor and material costs

.

The failure year distribution of the hearth product ignition device is assumed to be a

Weibull function (see section 8.2.3.3 )

. DOE estimated that the ignition system in 14 percent of

all hearth products would fail and be repaired over the course of the product lifetime. DOE

observed that typical warranties for hearth products cover the ignition sys tem parts for 5 year s

. If

the ignition system failed before the warranty period expired, it was assumed that the consumer

only incurred the cost of labor for the repair ; otherwise, the consumer incurred the cost of both

parts and repair for the repair

. The ignition system lifetime is determined as described in section

8.2.3.3

.

Table 8.2.16 presents the repair cost components that DOE derived from RS Means and

the repair cost of h earth product ignition devices.

Table 8.2.16 Hearth Product Average Repair Cost Component s (2013$)

Repair Descrip tion Bare Material Cost (2013$)* Total Labor Hours

Repair Standing Pilot Ignition $43.72 1.50

Repair Intermittent Pilot Ignition $142.89 1.50

*Does not include sales tax or RS Means markups by trade

.

RS Means provides average national labor costs for different trade groups as shown in

Table 8.2.17 . Labor costs including overhead and profit (O&P) are the bare cos ts provided by RS

Means multiplied by the RS Means markups by trade shown in Table 8.2.18

.

Table 8.2.17 RS Means 2013 National Average Residential Labor Costs by Crew

Crew Typ e Crew Description Laborers

per Crew Cost per Labor

- Hour

Bare Costs Incl. O&P*

Q1 1 Plumber, 1 Plumber Apprentice 2 33.18 54.61

* O&P includes markups in Table 8.2.8

.

Table 8.2.18 RS Means Labor Costs Markups by Trade (Residential)

Trade Workers

Comp. Avg. Fixed

Overhead Overhead Profit Total

Plumber 6.7% 17.9% 30.0% 10.0% 63.6%

DOE’s analysis of repair costs accounts for regional differences in labor and material

costs. RS Means provides material and labor cost factors for 295 cities and towns in the U.S. To

derive average repair labor cost values by state, DOE weighted these pri ce factors by housing

projections in 2021. DOE used the average material and labor cost factors for costs associated

with plumbing and heating, ventilation, and air conditioning (HVAC)

. Table 8.2.9 shows the

final regional material and labor price factors used in the analysis by geographical area.

8

- 20Table 8.2.19 show s the annualized repair cost estimates for hearth product ignition

device s

.

Table 8.2.19 Annualized Repair Cost for Hearth Product Ignition Device s (2013 $)

Product Class Efficiency Level Annualized Repair Cost Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $1.83

-

Intermittent Pilot $2.40 $0.57

8.2.2.3 Lifetime

DOE defines lifetime as the age when a product is retired from service. DOE used

warranty information and the lifetimes of similar appliances to estimate the distribution of both

hearth product and hearth product ignition system lifetimes.

Hearth product warranties typically cover the hearth product for 5 years and the ignition

system for 1 year for both labor and parts. Therefore, because any repairs performed during the

wa rranty period would be free to the consumer, DOE assumed that the minimum lifetime of both

the hearth product and the ignition system corresponded to the end of the respective warranty

period. In the analysis

, a unit was only repaired if the ignition syste m failed before the overall

hearth product did.

A Weibull distribution is a probability distribution function commonly used to measure

failure rates. 7 Its form is similar to an exponential distribution, which would m odel a fixed

failure rate, except that it allows for a failure rate that changes over time in a particular fashion.

The cumulative distribution takes the form:

for x > θ and P(x) = 1 for x ≤ θ

,

Eq. 8

- 1

Where:

P(x) = probability that the appliance is still in use at age x

,

x = appliance age,

α = the scale parameter, which is the decay length in an exponential distribution,

β = the shape parameter, which determines the way in which the failure rate changes in tim e, and

θ = the delay parameter, which allows for a delay before any failures occur.

When β = 1, the failure rate is constant over time, and this distribution takes the form of a

cumulative exponential distribution. For the case of appliances

, β is commonly greater than 1,

which results from a rising failure rate as the appliance ages. A plot of a Weibull distribution

(DOE’s calculated hearth product survival function) is shown as Figure 8.2.1

. 

# e x



### x P β

α

θ 

    

−

−



### = ) (

8

- 21Figure 8.2.1 Lifetime D istribution of H earth P roducts (whole unit)

Table 8.2.20 shows the average and minimum lifetime of both the hearth product and

hearth product ignition system

.

Table 8.2.20 Lifetime Parameters for Hearth Product s

Product Class Lifetime

Minimum Average

Hearth Product (Whole Unit) 5 15.0

Hearth Product (Ignition System Only) 1 7.3

8.2.2.4 Discount Rate s

The discount rate is the rate at which future savings and expenditures are discounted to

establish their present value. DOE uses publicly available data (the Federal Reserve Board’s

Survey of Consumer Finance s ( SCF ) 8 ) 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 life

- cycle cost and life

- cycle cost savings. DOE notes that the

discount rate used in the LCC analysis is distinct from an implicit disco unt rate, as it is not used

to model consumer purchase decisions. The opportunity cost of funds in this case may include

interest payments on debt and interest returns on assets.

DOE estimated separate discount rate distributions for six income groups, di vided based

on income percentile as reported in the Federal Reserve Board’s SCF

. This disaggregation

8

- 22reflects the fact that low and high income consumers tend to have substantially different shares

of debt and asset types, as well as facing different rates on debts and assets. Summaries of shares

and rates presented in this chapter are averages across the entire population.

Table 8.2.21 Definitions of Income Groups

Income 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 Debt and Asset Classes

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

to approximate a consumer’s opportunity cost of funds related to appliance energy cost savings

and maintenance costs. The approach assumes that in the long term, cons umers 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 acc umulate. DOE has

included several previously excluded debt types ( i.e., vehicle and education loans, mortgages, all

forms of home equity loan) in order to better account for all of the options available to

consumers.

The average share of total debt plus equity and the associated rate of each asset and debt

type ar e used to calculate a weighted average discount rate for each SCF household ( Table

8.2.22 ). The household

- level discount rates are then aggregated to form discount rate

distributions for each of the six income groups. Note that previously 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 disp roportionate influence to the asset

and debt shares and rates of higher income consumers. DOE has shifted to a household

- level

weighting to more accurately reflect the average consumer in each income group.

DOE estimated the average percentage shares of t he various types of debt and equity

using data from the Federal Reserve Board’s SCF for 1995, 1998, 2001, 2004, 2007, and 2010. c

DOE derived the household

- weighted mean percentages of each source of financing throughout

the 5 years surveyed. DOE posits tha t these long

- term averages are most appropriate to use in its

analysis.

c Note that although two older versions of the SCF are also available (1989 and 1992); these surveys are not used in

thi s analysis because they do not provide all of the necessary types of data ( e.g. , credit card interest rates, etc. ). DOE

feels that the 15

- year span covered by the six surveys included is sufficiently representative of recent debt and

equity shares and inte rest rates.

8

- 23Table 8.2.22 Types of Household Debt and Equity by Percentage Shares (%)

Type of Debt or Equity Income Group

1 2 3 4 5 6

Debt:

Mortgage 18.9% 24.1% 33.1% 38.1% 39.3% 25.0%

Home equity loan 3.1% 3.3% 2.6% 3.6% 4.5% 7.2%

Credit card 15.3% 13.0% 11.8% 8.7% 6.0% 2.7%

Other installment loan 25.1% 20.6% 17.3% 13.2% 9.6% 4.7%

Other residential loan 0.7% 0.6% 0.6% 0.7% 1.0% 1.2%

Other line of credit 1.6% 1.5% 1.3% 1.5% 2.1% 1.8%

Equity:

Savings account 18.5% 16.0% 12.7% 10.6% 10.4% 7.9%

Money market account 3.6% 4.5% 4.0% 4.5% 5.0% 8.6%

Certificate of deposit 7.0% 7.8% 5.5% 5.0% 4.4% 4.2%

Savings bond 1.8% 1.7% 1.9% 2.2% 1.7% 1.1%

Bonds 0.2% 0.4% 0.5% 0.7% 0.8% 3.8%

Stocks 2.3% 3.1% 4.4% 5.7% 7.6% 15.8%

Mutual funds 2.1% 3.5% 4.3% 5.7% 7.6% 15.9%

Total 100.0 100.0 100.0 100.0 100.0 100.0

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

Rates for Types of Debt

DOE estimated interest rates associated with each type of debt. The source for interest

rates for mortgages, loans, credit cards, and lines of credit was the Federal Reserve Board’s SCF

for 1995, 1998, 2001, 2004, 2007, and 2010, which associates an intere st 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 .23 ). 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). d For example, a 6 percent nominal

mortgage rate has an effective nominal rate o f 5 .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.

d Fisher formula is given by: Real Interest Rate = [(1 + Nominal Interest Rate) / (1 + Inflation Rate)]

– 1.

8

- 24Table 8.2.23 Data Used to Calculate Real Effective Mortgage Rates

Year Mortgage Interest Rates in Selected Years ( %)

Average Nominal

Interest Rate Inflation Rate 9 Applicable Marginal

Tax Rate 10 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 8.2.24 shows the household

- weighted average effective real rates in each year and

the mean rate across years. Because the interest rates for each type of household debt reflect

economic conditions t hroughout numerous years and various phases of economic growth and

recession, they are expected to be representative of rates in effect in 2021

.

Table 8.2.24 Average Real Effective Interest Rates for Household Debt

Type of Debt Income Group

1 2 3 4 5 6

Mortgage 6.6% 6.2% 6.1% 5.2% 5.0% 4.0%

Home equity loan 7.0% 6.9% 6.7% 5.9% 5.7% 4.3%

Credit card 15.2% 15.0% 14.5% 14.2% 14.0% 14.5%

Other installment loan 10.8% 10.3% 9.9% 9.4% 8.7% 8.6%

Other residential loan 9.8% 10.2% 8.9% 8.2% 7.7% 7.4%

Other line of credit 9.1% 10.9% 9.6% 8.8% 7.4% 6.1%

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

Rates for Types of Assets

No similar rate data are available from the SCF for classes of assets, so DOE derived

asset interest rates from various sources of national historical data (1983

- 2013). The interest rates

associated with certificates of deposit, 11 savings bonds, 12 and bonds (AAA corporate bonds) 13

were collected from Federal Reserve Board time

- series data. Rates on money market accounts

came from Cost of Savings Index data. 14 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. 15 Rates for

mutual funds are a weighted average of the stock rates (two

- thirds weight) and the bond rates

(one

- third weight) in each year. DOE assumed rates on checking accounts to be zero.

8

- 25D 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

8.2.25 . Because the interest and return rates for each type of asset reflect economic conditions

throughout numerous years, they are expected to be representative of rates that may be in effect

in 2 021 . For each type, DOE developed a distribution of rates, as shown in appendix 8 D

.

Table 8.2.25 Average Nominal and Real Interest Rates for Household Equity

Type of Equity Average Real

Rate

%

Savings accounts 1.0

Money market accounts 1.9

Certificates of deposit 1.9

Savings bonds 3.4

Bonds 4.2

Stocks 9.4

Mutual funds 7.4

Discount Rate Calculation and Summary

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

for each income group as follows. First, DOE calculated the discount rate for each consumer in

each of the six versions of the SCF , using the following formula:

DDDD ii = � SS h aaaaaa ii

, jj

jj × RR ii

, jj

Eq. 8.4

Where:

DDDD ii = discount rate for consumer i

, SS h aaaaaa ii

, jj = share of asset or debt type j for consumer i

, and

RR ii

, jj = real interest rate or rate of return of asset or debt type j for consumer i

.

The rate for eac h debt type is drawn from the SCF data for each household. The rate for

each asset type is drawn from the distributions described above.

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

distribution of discount rates in each survey by income group by calculating the proportion of

consumers with discount rates in bins of 1 percent increments, ranging from 0

- 1 percent to

greater than 30 percent. Giving equal weight to each survey, DOE compiled the six

- survey

distribution of disc ount rates.

8

- 26Table 8.2.26 presents the average real effective discount rate and its standard deviation

for each of the six income groups. To account for variatio n among households, DOE sampled a

rate for each RECS household from the distributions for the appropriate income group. (RECS

provides household income data.) Appendix 8 D presents the full probability distributions for

each income group that DOE used in th e LCC and PBP analysis.

Table 8.2.26 Average Real Effective Discount

Income Group Discount Rate (%)

1 4.85

2 5.12

3 4.75

4 4.04

5 3.80

6 3.57

Overall Average 4.49

8.2.2.5 Compliance Date of Standard

Pursuant to 42 U.S.C. 6295( m ), the compliance date of any new energy efficiency

standard for hearth product s is 5 years after the final rule is published. Consistent with its

published regulatory agenda, DOE assumed that the final rule would be issued by the end of

20 1 5 and that, therefore, the new s tandards would require compliance beginning in 2021 . DOE

calculated the LCC and PBP for all consumers considered as if they each would purchase a new

hearth product with an intermittent pilot light in 2021

.

8.2.2.6 Base Case Distribution of Efficiency Levels

DOE e stimat ed the market share of standing pilots and intermittent pilots in each hearth

product group using information from the tear

- down analysis (see chapter 5) and consultant

input. There is currently not enough publically available data to establish a trend in the adoption

of intermittent pilots in hearth products , so the base case market share of each ignition system is

projected to be the same through 2021. Table 8.2.27 lists the estimated percentage of intermittent

pilot ignition systems by product group

. In addition, DOE recognizes that match

- lit hearth

products comprise a third potential ignition system. These units were excluded from DOE’s

a nalysis as they are not covered in this rulemaking , and provide different functionality than the

other systems

. However, b ecause they are included in HPBA shipment information

, these units

were separated by product group to isolate covered products

. Table 8.2.28 presents the market

shares of match

- lit units. The match

- lit percentage shown in Table 8.2.28 and the initial

disaggregation shown in Table 8.2.27 w ere used to determine the fi nal base case pilot system

distributions, as shown in Table 8.2.29

.

8

- 27Table 8.2.27 Market Shares of Intermittent Ignition in 2021 by Product Group

Product Group Fraction of Model s

Vented Heater 55%

Vented Decorative 55%

Unvented 12%

Vented Gas Log Sets 6%

Unvented Gas Log Sets 6%

Outdoor 24%

Table 8.2.28 Market Shares of Match

- Lit Units in 2021 by Product Group

Product Group Fraction of Models

Vented Heater 5%

Vented Decorative 5%

Unvented

- Vented Gas Log Sets 50%

Unvented Gas Log Sets

-

Outdoor 50%

Table 8.2.29 Market Shares of Hearth Products Ignition in 2021 by Product Group

Product Group Intermittent Pilot Standing Pilot

Vented Heater 58% 42%

Vented Decorative 58% 42%

Unvented 12% 88%

Vented Gas Log Sets 12% 88%

Unvented Gas Log Sets 6% 94%

Outdoor 48% 52%

8.3 PAYBACK PERIOD INPUT S

Th e PBP is the amount of time it takes the consumer to recover the assumed higher

purchase cost of more energy

- efficient equipment as a result of lower operating costs.

Numerically, the PBP is the ratio of the increase in purchase cost ( i.e ., from a less efficient

design to a more efficient design) to the decrease in first year annual operating expenditures.

The equation for PBP is:

PBP =∆ IC/ ∆ O C

Eq. 8.5

Where:

PBP = payback period in years,

∆ IC = difference in the total installed cost between the more efficient standard

- level equipment

and the baseline efficiency equipment, and

8

- 28∆ OC = difference in first year annual operating costs.

Payback periods are expressed in years. Payback periods can be greater than the life of

the equipment if the increased total installed cost of the more efficient equipment is not

recovered fast enough in reduced operating costs.

The data inputs to PBP are the total installed cost of the equipment to the consumer for

each efficiency level and the annual (first year) operating costs for each efficiency level. The

inputs to the t otal installed cost are the equipment price and the installation cost. The inputs to

the operating costs are the annual energy cost, the annual ized repair cost, and the annual ized

maintenance cost (or

, in the case of rebuttable PBP, only the annual energy cost) . The PBP uses

the same inputs as the LCC analysis, except that electricity price trends are not required. Because

the PBP is a “simple” payback, the required electricity cost is only for the year in which a new

efficient standard is to take effect

— in this case, 2021

.

8.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS

As discussed previously

, DOE’s approach for conducting the LCC and PBP analysis

relied on developing samples of households that use each of the considered products. DOE also

used probability distributions to characterize the uncertainty in many of the inputs to the

analysis. DOE used a Monte Carlo simulation technique to perform the LCC and PBP

calculations o n the households in the sample.

LCC and PBP calculations were performed 10 ,000 times on the sample of households

established for each residential product. Each LCC and PBP calculation was performed on a

single household that was selected from the sample of the residential users. The selection of a

household was based on its samp le weight ( i.e. , how representative a particular household is of

other households in the distribution

— either regionally or nationally)

, as described in chapter 7.

Each LCC and PBP calculation also sampled from the probability distributions that DOE

develop ed to characterize many of the inputs to the analysis.

DOE calculated LCC savings relative to the ba se case product it assigned to the

households. DOE accounted for households that already have intermittent ignition pilots in the

base case. For this reason , the average LCC impacts are not equal to the difference between the

LCC of the new standard level and the LCC of the baseline product. The calculation of average

LCC savings includes households with zero LCC savings (no impact from a standard). DOE

consi dered a household to receive no impact at a given efficiency level if DOE assigned it a

base

- case product having an efficiency equal to or greater than the efficiency level in question.

Table 8.4.1 and Table 8.4.2 show the LCC and PBP results for all efficiency levels

considered for hearth product ignition device s

. In Table 8.4.1 , the simple payback is measured

relative to the baseline product. In Table 8.4.2 , the LCC savings ar e measured relative to the base

case efficie ncy distribution in the compliance year. No impacts occur when the base case

efficiency for a specific consumer equals or exceeds the efficiency at a given E L ; a standard

8

- 29would have no effect on the individual consumer because the product installed would a lready

have intermittent pilot ignition without amended standards.

Table 8.4.1 Average LCC and PBP Results by Efficiency Level for Hearth Product

Ignition Device s

TSL Efficiency

Level Average Costs 2013$ Simple

Payback

years Average

Lifetime

years Installed

Cost First Year’s

Operating Cost Lifetime

Operating Cost LCC

-- 0 $166 $50 $602 $769

-- 1 5.0

1 1 $ 268 $ 15 $ 174 $442 2.9 1 5.0

Note: The results for each TSL are calculated assuming that all consumers use products with that efficiency level.

The PBP is measured relative to the baseline product.

Table 8.4.2 Average LCC Savings Relative to the Base Case Efficiency Distribution

for Hearth Product Ignition Device s

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Consumers that Experience Net Cost Average Savings* 2013$

1 1 23% $165

* The calculation includes households with zero LCC savings (no impact).

Figure 8.4.2 show s the range of LCC savings for the efficiency level considered for

hearth product ignition device s

. For each standard level, the top and the bottom of the box

indicate the 75 th and 25 th percentiles, respectively. The bar at the middle of the box indicates the

median; 50 percent of the households have lifecycle cost savings above this value. The

“ whiskers ” at the bottom and the top of the box indicate the 5 th and 95 th percentiles. The sma ll

box shows the average LCC savings for the new standard level.

8

- 30Figure 8.4.2 Distribution of LCC Savings for Hearth Product Ignition Device s

8.5 REBUTTABLE PAYBACK P ERIOD

DOE presents rebuttable PBPs to provide the legally established rebuttable presumption

that an energy efficiency standard is economically justified if the additional product costs

attributed to the standard are less than three times the value of the first

- year energy cost savings.

(42 U.S.C. §6295 (o)(2)(B)(iii))

The basic equation for rebuttable PBP is the same as that shown for the PBP in section

8.3 . Unlike the analyses described in section 8.3 , however, the rebuttable PBP is not based on the

use of household samples and probability distributions, and it is based not on distributions but on

discrete single

- point values. For example, wh ereas DOE uses a probability distribution of energy

prices in the distributional PBP analysis, it uses only the nat ional average energy price to

determine the rebuttable PBP.

Numerically, the rebuttable PBP is the ratio of the increase in purchase cost ( i.e. , from a

less

- efficient design to a more

- efficient design) to the decrease in annual energy expenditures;

that i s, the difference in first year annual energy cost as calculated from the DOE test procedure

. Because no DOE test procedure exists for hearth products, the test procedure direct heating

equipment ( 10 CFR 430, s ubpart B, a ppendix O) was used to calculate en ergy use because direct

heating equipment are similar secondary heating appliances to hearth products, and in the past

the two products shared the same test procedure. The calculation excludes repair costs and

maintenance costs.

8

- 318.5.1 Inputs

Inputs for the rebuttable PBP differ from the distribution PBP in that the calculation uses

discrete values, rather than distributions. Note that for the calculation of distribution PBP,

because inputs for the determination of total installed cost were bas ed on single

- point values,

only the variability and/or uncertainty in the inputs for determining operating cost contributed to

variability in the distribution PBPs. The following summarizes the single

- point values that DOE

used in determining the rebuttabl e PBP:

• Manufacturing costs, markups, sales taxes, and installation costs were all based on the

single

- point values used in the distributional LCC and PBP analysis.

• Energy prices were based on national average values for the year that new standards

will t ake effect.

• An average discount rate or lifetime is not required in the rebuttable PBP calculation.

• The effective date of the standard is assumed to be 2021

.

8.5.2 Results

DOE calculated rebuttable PBPs for each standard level relative to the distribution of

product energy efficiencies estimated for the base case. Table 8.5.1 present s the rebuttable PBPs

for hearth product ignition device s.

Table 8.5.1 Rebutt able Payback Period for Hearth Product Ignition Device s

EL Technology Option Rebuttable Payback Period years

1 Intermittent Pilot 2.31

8

- 32REFERENCES

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< www.eia.gov/forecasts/aeo/ >

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Annual, Description: Average rate on 6

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12. The Federal Reserve Board, Federal Reserve Statistical Rel ease, Selected Interest Rates,

Historical Data, Instrument: State and local bonds, Maturity: 20

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8

- 33Monthly, Description: Bond buyer go 20

- bond municipal bond index. 2013.

< ww w.federalreserve.gov/releases/H15/data.htm >

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Historical Data, Instrument: Corporate bonds/Moody’s Seasoned AAA, Frequency:

Annual, Description: Moody’s yield on seasoned corporate bonds

— all industries, AAA.

2013. < www.federalreserve.gov/releases/H15/data.htm >

14. Mortgage

- X

- Mortgage Information Service, Cost of Savings Index (COSI) Index

History. 2013. < h ttp://mortgage

- x.com/general/indexes/default.asp >

15. Damodaran Online Data Page, The Data Page: Historical Returns on Stocks, Bonds and

Bills

- United States. 2013. Damodaran. (Last accessed June, 2014.)

< http://pages.stern.nyu.edu/~adamodar/ >

8

- 34CHAPTER 9. S HIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

................................ ................................ ................................ ........... 9

- 1

9.2 FUNDAMENTAL MODEL EQ UATIONS

................................ ................................ .... 9

- 1

9.3 DATA INPUTS

................................ ................................ ................................ ............... 9

- 2

9.3.1 Historical Shipments

................................ ................................ ................................ ........ 9

- 2

9.3.2 Historical and Projected Housing Starts

................................ ................................ .......... 9

- 4

9.3.3 Calculation of the Fraction of New Construction Homes with Hearth Products

............. 9

- 4

9.4 IMPACT OF STANDARDS ON SHIPMENTS

................................ ............................. 9

- 5

9.4.1 Purchase Price Elasticity

................................ ................................ ................................ .. 9

- 5

9.4.2 Impact from Increase in Relative Price

................................ ................................ ............ 9

- 7

9.5 MODELING APPROACH

................................ ................................ .............................. 9

- 7

9.6 RESULTS

................................ ................................ ................................ ...................... 9

- 10

LIST OF TABLES

Table 9.3.1 HPBA Shipments Data

................................ ................................ ........................ 9

- 2

Table 9.3.2 Hearth Products Shipments Disaggregated by Product Group

............................ 9

- 3

Table 9.3.3 Historical Hearth Product Shipments

................................ ................................ ... 9

- 4

Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price Change

........... 9

- 7

Table 9.5.2 Calculation of Fraction of Hearth Product Shipments to Housing Starts

............ 9

- 9

Table 9.6.1 Trial Standard Levels for Hearth Product Ignition Devices

.............................. 9

- 10

LIST OF FIGURES

Figure 9.3.1 Historical Shipments of Hearth Products, 2005

- 2013

................................ ......... 9

- 3

Figure 9.3.2 Historical and Projected Housing Starts

................................ .............................. 9

- 4

Figure 9.5.2 Normalized Historical Data of Hearth Product Shipments and Housing

Starts

................................ ................................ ................................ .................... 9

- 8

Figure 9.6.1 Historical and Projected Base Case Shipments of Hearth Products

.................. 9

- 10

Figure 9.6.2 Projected Hearth Products Groups Market Shares, 2021

- 2050

......................... 9

- 11

Figure 9.6.3 Hearth Products Shipments by product type in Base Case and Standards

Case, 2021

- 2050

................................ ................................ ................................ 9

- 11

9

- iCHAPTER 9. S HIPMENTS A NALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a necessary input to calculations of the national

energy savings (NES) and net present value (NPV) , as well as to the manufacturer impact

analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy

( DOE ) used to project an nual product shipments and presents results for hearth product types

considered in this analysis.

The shipments model divides the shipments of hearth product s into specific market

segments. The model starts from a historical base year and calculates ret irements and shipments

by market segment for each year of the analysis period

. Th is approach produces an estimate of

the total product stock, broken down by age or vintage , in each year of the analysis period. In

addition, t he product stock efficiency distribution is calculated for the base case and for each

standards case for each product class. The stock distribution is used in the n ational i mpact

a nalysis (NIA) to estimate the total costs and benefits associated with each efficiency level

.

The shi pments m odel was developed as a Microsoft Excel spreadsheet that is accessible

on DOE’s Appliance and Commercial Equipment Standards website

( www1.eere.ener gy.gov/buildings/appliance_standards/product.aspx?productid=83 )

. Appendix

10 A discusses how to access and utilize the shipments model spreadsheet , which is integrated

into the spreadsheet for the NIA . This chapter explains how the shipments model is constructed

and provides some summary output. Sections 9.2 through 9.5 describe the methodological

approach.

9.2 FUNDAMENTAL MODEL EQUATIONS

The fundamental dependent variable in the shipments model is the product stock

, which

is represented as a function of analysis year ( inde xed by j )

, and product vintage or age (the

product age is noted as a

, and is equal to the analysis year minus the vintage). The stock function

is adjusted in each year of the analysis period by new shipments coming in and broken or

demolished products bein g taken out.

For existing stock:



### ( ) ( ) ( ) ( ) 1

, 1

, 1

, 1

, − − +

− − − = a j Ship a j Rem a j Stock a j Stock

Eq. 9.1

and for new shipments :



### ( ) ( ) 1 1

, − = = j Ship a j Stock

9

- 1Eq. 9.2

Where:

Stock (j, a )

= number of units of age a in analysis year j

, Rem (j, a )

= number of units of age a removed in analysis year j

, and

Ship (j)

= number of units of shipped in year j

.

Removals due to product failure contain a survival function f

p (a) that is used to represent

the probability that a unit of age a will survive in a given year; equivalently, the probability that

this unit will fail i s 1

- f

p (a)

.

Total removals in the base case are then:



### ( ) ( ) 

## [ ] ( )

a j Stock a f a j Rem

p , 1

, ×

− =

Eq. 9.3

9.3 DATA INPUTS

The primary data inputs to the hearth products shipments model are the historical hearth

products shipments data, and historical and projected housing starts.

9.3.1 Historical Shipments

DOE used historical shipments data to populate its shipments model for hearth product s.

DOE use d historical shipment data provided by t he Hearth, Patio and Barbecue Association

(HPBA ) 1 for a ll hearth products between 2005 and 20 13

. Table 9.3.1 shows the initial shipments

data provided by HPBA , which in cludes all hearth products

.

Table 9.3.1 HPBA Shipments Data

2005 2006 2007 2008 2009 2010 2011 2012 2013

Shipments(millions) 1.54 1.184 1.027 0.715 0.421 0.443 0.385 0.397 0.534

DOE also estimated additional shipments from non

- HPBA members

. The magnitude of

thes e non

- HPBA shipments w as determined to be equal to the HPBA shipments for outdoor

units . However, the HPBA shipments do not include patio heaters, whic h are a type of outdoor

hearth product

. To account for all patio heaters shipments , DOE determined the nu mber of patio

heater models based on the HPBA

- member hearth product model data and comments received

from manufacturers during manufacturer interviews. DOE then applied a three

- to

- one ratio

between non

- HPBA and HPBA shipments to develop the total patio hea ter shipments

. In total,

patio heater shipments account for an additional 9 percent of hearth product shipment s relative to

the HPBA member hearth products shipments. DOE added these shipments to the data HPBA

provided to determine final shipment values. Finally, through manufacturer interviews and

analysis of the HPBA model database, DOE determined the fraction of match

- lit units for each

9

- 2hearth product group, which was subtracted from the hearth product shipment s

. HPBA also

provided market share s of each hearth product group relative to all hearth products. Because

HPBA provided a range for the market shares, DOE used the midpoint of this range. Table 9.3.2

lists t he disaggreg ated shipment s data provided by HPBA

, the value DOE determined , the

match

- lit units by product group, and the final base case market share relative to all hearth

products

.

Table 9.3.2 Hearth Products Shipments Disaggregated by Product Group

Product Group HPBA

- only

Shipment Data

(%) All Hearth

Products

Shipments * (%) Match

- Lit

Shipments

(%) Final Base Case

Disaggregation

(%)

Vented Fireplace 47 to 73 56.2 5 62

Unvented Fireplace 2 to 7 4.4 0 5

Vented Gas Logs 3 to 10 6.1 50 4

Unvented Gas Logs 16 to 23 18.3 0 21

Outdoor 4 to 9 15.0 50 9

* I nclud es match

- lit units and non

- HPBA hearth product shipments

The base case market shares are used to determine the yearly shipments for each hearth

product group. In the analysis period (2021

- 2050)

, these market shares by product group are

assumed to remain constant in the base case, as DOE does not have evidence of a chang e in the

trend of market shares

. Figure 9.3.1 and Table 9.3.3 show the historical hearth products

shipmen ts used in the analysis, including non

- HPBA shipments and excluding match

- lit

shipments.

Figure 9.3.1 Historical Shipments of Hearth P roduct s , 2005

- 2013 0 0.5

1 1.5

2Hearth Product Shipments (Millions)

9

- 3Table 9.3.3 Historical Hearth Product Shipments

2005 2006 2007 2008 2009 2010 2011 2012 2013

Shipments

(millions) 1.69 1.30 1.13 0.785 0.462 0.487 0.423 0.436 0.586

9.3.2 Historical and Projected Housing Starts

DOE used projected housing starts to estimate the shipments of hearth products . DOE

determined new residential housing starts by using Census data through 2013 2 and projections

from the Energy Information Administration’s (EIA ) Annual Energy Outlook 20 14 ( AEO 2014 ). 3

Figure 9.3.2 shows AEO 2014 historical data as well as projections of housing starts through

2040.

Figure 9.3.2 Historical and Proje cted Housing Starts

9.3.3 Calculation of the Fraction of New Construction Homes with Hearth Products

DOE estimated the new construction fraction by using historical hearth products

shipments data and the number of gas fireplaces in newer homes from EIA ’ s 2009 Re sidential

Energy Consumption Survey (RECS 2009 ) data. 4 RECS 20 09 data shows that there are 1.068

million homes built from 2005

- 2009 with vented and ventless gas fireplaces , which are the

hearth products most likely to be installed in a new construction home. DOE assumed that these

households would on average have 1.2 gas fireplaces per home 5 and that only half of the homes

built in 2009 were surveyed in RECS 2009

. Thi s resulted in 1. 335 million gas firepla ces in

homes built between 2005 and 2009. During this same period there were 5.367 million hearth 0 0.5

1 1.5

2 2.5Housing Starts (Millions)

Projected

Historic

9

- 4products shipped (see Table 9.3.3 )

. This result ed in 25 percent market share for hearth products

in new construction.

9.4 IMPACT OF S TANDARDS ON S HIPMENTS

C onsumer purchase decisions are influenced by the purchase price and operating cost of

the product, and therefore may be different in the base case and under the standard case . These

decisions are modeled by estimating the purchase price elasticity for hearth products . The

purchase price elasticity is defined as the change in the percentage of consumers acquiring a

hearth product divi ded by a change in the relative price (defined below) for that product. This

elasticity, along with information obtained from the life

- cycle cost (LCC) and payback period

(PBP) analysis on the change in purchase price and operating costs at different ELs, are used in

the shipments model to estimate the change in shipments under standards at different ELs.

9.4.1 Purchase Price Elasticity

DOE conducted a literature review and an analysis of appliance price and efficiency data

to estimate the combined effects on product shipments from increases in product purchase price,

decreases in product operating costs, and changes to household income

. Appendix 9A provides a

detailed explanation of the methodology DOE used to quantify the impacts from these variables.

Existing studies of appliance markets suggest that the demand for appliances is price

inelastic. Other information in the literature sug gests that appliances are a normal good, s o that

rising incomes incre ase the demand for appliances, and that consumer behavior reflects relatively

high implicit discount rates a when comparing appliance prices and appliance operating costs.

DOE used the available data for the period 1980

- 2002 on large appliance purchase s t o

evaluate broad market trends and conduct simple regression analyses. These data indicate that

there has been a rise in appliance shipments and a decline in appliance purchase price and

operating costs over the time period. Household income has also risen during this time

. Because

purchase decisions are sensitive to income, as well as to potential savings in the operating cost of

the appliance, DOE combined the available economic informa tion into one variable, termed the

relative price

. This variable was used in a regression analysis to parameterize historical market

trends

. The relative price is defined with the following expression:

Income PVOC PP

Income TP RP +

= =

Eq. 9.4

W here:

RP

= r elative price,

TP

= t otal price,

9

- 5Income

= h ousehold income,

PP

= a ppliance purchase price, and

PVOC

= p resent value of operating cost.

In Eq. 9.4

, DOE used real prices, as opposed to nominal, and an implicit discount rate of

37 percent to estimate the present value of operating costs. The rate of 37 percent is based on a

survey of several studies of different appliances that suggests that the consume r implicit discount

rate has a broad range and averages about 37 percent

. 6

DOE’s regression analysis suggests that the relative price elasticity of demand is

- 0.34

. Th is implies that a r elative price increase of 10 percent results in a 3 .4 percent decrease in

shipments

. Note that the relative price elasticity incorporates the impacts from purchase price,

operating cost, and household income, so the impact from any single effect can be mit igated by

changes in the other two effects

.

The relative price elasticity of

- 0.34 is consistent with estimates in the literature

. Nevertheless, DOE stresses that the measure is based on a small data set, using simple statistical

analysis. More important ly , the measure is based on an assumption that economic variables,

including purchase price, operating costs, and household income, explain most of the trend in

appliances per household in the United States since 1980. Changes in appliance quality and

cons umer preferences may have occurred during this period, but DOE did not account for them

in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price

elasticity of demand provides a reasonable assessment of the impact that purchase price,

operating cost, and household income have on product shipments.

Because projection s of shipments and national impacts attributable to standards are

calculated for a lengthy time period, DOE needed to consider how the relative price elasticity is

affected after a new standard takes effect. DOE considered the relative price elasticity, described

above, to be a short

- term value. It was unable to identify sources specific to household durable

goods, such as appliances, to indicate how s hort

- run and long

- run price elasticities differ.

Therefore, to estimate how the relative price elasticity changes over time, DOE relied on a study

pertaining to automobiles. 7 This study shows that the automobile price elasticity of demand

changes in the years fo llowing a purchase price change, becom ing smaller (more inelastic) unt il

it reaches a terminal value around the tenth year after the price change. Table 9.4.1 shows the

relative change in the price elasticity of demand for automobiles over time. DOE developed a

time series of relative price elasticities based on the relative change in the automobil e price

elasticity of demand. For years not shown in Table 9.4.1 , DOE performed a linear interpolation

to obtain the relative price elasticity.

9

- 6Table 9.4.1 Cha nge in Relative Price Elasticity F ollowing 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

9.4.2 Impact from Increase in Relative Price

Using the r elative price elasticity, DOE was able to estimate the impact of the increase in

relative price f rom a particular standard level . The impact , as shown in the equation below, is

expressed as a percentage drop in market share for each year, dMS p j

.



## ( )

( ) ( ) j e j base RP j std RP

dMS

RP

p p p

j ×  

 

 

 

 

 

 

 

− =

_ _ 1

Eq. 9.5

Where:

dMS p j = percentage market share drop for class p, year j

, RP_std

p (j)= relative price in the standards case for product group p, year j

, RP

p (j)= relative price in the base case for product group p, year j

, and

e RP (j) = relative price elasticity in year j

.

The model calculates the relative percentage market drop, dMS p j , due to the product price

increase from a particular standard level

.

9.5 MODELING APPROACH

For the years where hearth product shipments data are available ( 2005

- 2013), DOE found

that historical shipments and new housing starts we re highly correlated

. b DOE believes this

correlation reflects the relationship between hearth product install ations in new homes, and is

also because housing starts and hearth products shipments are both sensitive to the broader

housing market and general economic activity. DOE applied this correlation to all hearth

products shipments. The correlation coefficient ( Correl ) is determined as in the equation below

. Figure 9.5.2 shows the historical correlation of hearth product shipments and housing starts,

normalized for the average magnitude differences. c The correlation factor is calculated as:

b Historical shipments and new housing starts had a correlation factor of 0.98 (where 1 indicates perfect correlation).

c Because the graph in Figure 9.5.2 is a normalized representation of the relationship between hearth product

shipments and housing starts, it contains unit

- less quantities.

9

- 7CCCCCCCCCCCC ( XX

, YY )

= ∑ ( xx

− xx � ) ( yy

− yy � )

∑ ( xx

− xx � ) 22 ( yy

− yy � ) 22

Eq. 9.6

Where:

Correl(X,Y) = the correlation factor between X and Y

, X = a set of data being correlated,

Y = a set of data being correlated,

x = an element of set X

,

y = an element of set Y

,

xx ̅ = the global average of set X

, and

yy � = the global average of set Y

.

Figure 9.5.2 Normalized Historical Data of Hearth Product Shipment s and

Housing Starts

For the years between 2040 and 2050, for which AEO 2014 does not provide projected

housing start data, DOE used a trailing 10

- year average of housing starts to estimate housing

starts during this period. Th is approach emphasizes more recent values and pres erve s the cyclical

nature observed in historical trends.

The average ratio o f hearth product shi pments to housing starts from 2005

- 2013 is 0.754

(see Table 9.5.1 )

. This ratio is multiplied by the yearly projected housing starts in the years

2014

- 2050, as shown in Figure 9.3.2

, and the overall fraction of non

- match

- lit ship ments to

obtain the total projected hearth products shipments for these years ( SHP Proj,total (j) ) :

9

- 8SSSSSS PPPPPPPP

, tt tttttt ( jj )

= 0

. 754 ∗ PPPPPPPP ( jj ) ∗ MMMM

Eq. 9.7

Where:

0.754 = h istorical ratio of housing starts to hearth products shipments

, HS Proj = p rojected housing starts in year (j)

, and

ML = h istorical Fraction of non

- match

- lit hearth products shipments, determined to be 86.64

percent

.

Table 9.5.2 Calculation of Fraction of Hearth Product Shipments to Housing Starts

Year Housing Starts * Hearth Product

Shipments ** Calculated

Fraction

2005 2.068 1.691 0.818

2006 1.801 1.300 0.722

2007 1.355 1.128 0.832

2008 0.906 0.785 0.867

2009 0.554 0.462 0.835

2010 0.587 0.487 0.829

2011 0.609 0.423 0.695

2012 0.781 0.436 0.559

2013 0.925 0.586 0.634

Average 0.754

* Based on Census data through 2013. 2 See section 9.3.2

. ** Based on HPBA shipments data. 1 See section 9.3.1

.

T o calculate the yearly projected shipments for each hearth product gr oup, SHP Proj,total is

multiplied by the market share of that group and the yearly price elasticity value in the standards

case, as below:

SSSSSS PPPPPPPP

, gg gggg ( ii

, jj )

= SSSSSS PPPPPPPP

, tt tttttt ( jj ) ∗ MM ( ii

, jj ) ∗ PP ( ii

, jj )

Eq. 9

. 1

Where:

SSSSSS PPPPPPPP

, gg gggg ( ii

, jj ) = p rojected hearth products shipments for product group i in year j

, MS(j,i) = m arket share as a percent of product group i of the total hearth product shipments in

year j

, and

PE(j,i) = p rice elasticity of product group i in year j

. PE = 1 in the base case, and is equal to (1

-

p

j 

### dMS ) in the standard case as calculated in Eq. 9.5

.

9

- 99.6 R ESULTS

As detailed in c hapter 10, DOE created a trial standard level (TSL) that adopts

intermittent pilot ignition for all product groups

. Table 9.6.1 shows the ana lyzed TSL.

Table 9.6.1 Trial Standard Levels for Hearth Product Ignition Devices

Product Class TSL Description

Hearth Products 1 Adoption of Intermittent Pilot Ignition

Figure 9.6.1 shows the historic al and projected shipments for hearth product s. The

shipments of hearth product s decline after 2005 due to the economic recession . Shipments

bottom in 2011

- 2012, and recove ry begins in 2013

, wh en shipments increased by 36 percent

compared to 2012. The increase in shipments correlat es directly with an increase in housing

starts and i s expected to continue the trend as the economy continues to recover

. Hearth product

shipments are projected to recover to near pre

- recession levels by 2021 , which is the compliance

year of the standard

, and are expected to continue to closely correlate with the relatively stable

housing start projections through 2050. Th e s hipment s data and the market shares of each

product group are primary input s into the NIA analysis, which is described in c hapter 10. Figure

9.6.2 shows each hearth product group’s projected market share under the proposed standard.

Figure 9.6.3 shows each hearth product group’s total shipments in the base case and in the

standards case.

Figure 9.6.1 Historic al and Projected Base Case Shipments of Hearth Products 0 0.5

1 1.5

2Shipments (Millions) Historical Shipments

Projected Shipments

9

- 10Figure 9.6.2 Projected Hearth Products Groups Market Shares , 2021

- 2050

Figure 9.6.3 Hearth Products Shipments in Base Case and Standards Case, 2021

- 2050 0.9 0.95 1 1.05

1.1Shipments (Millions) All Hearth Products (Base Case)

All Hearth Products (Standards Case)

9

- 11REFERENCES

1. Hearth, Patio and Barbecue Association, HPBA US Hearth Statistics, August 2014. (Last

accessed August 28, 2014.) < www.hpba.org/statistics/hpba

- us

- hearth

- statistics >

2. U.S. Department of Commerce

- Bureau of the Census, New Privately Owned Housing

Units Started: Annual Data 1959

- 2012, 2013. (Last accessed March 15, 2013.)

< www.census.gov/construction/nrc/historical_data/ >

3. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

4. U.S. Department of Energy: Energy Information Administration, Residential Energy

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7,

2015.) < www.eia.gov/consumption/residential/data/2009/ >

5. Houck, J. E., Residential Decorative Gas Fireplaces Usage Characteristics . 2010.

Prepared for Hearth, Patio, & Barbecue Association.

6. Train, K., Discount Rates in Consumers' Energy

- Related Decisions

. Energy: The

International Journal , 1985. 10(12): pp. 1243

- 1253

7. Hymans, S., Consumer Durable Spending: Explanation and Prediction

. Brookings Papers

on Economic Activity , 1971. 1971(1): pp. 234

- 239

9

- 1210

- i CHAPTER 10. NATIONAL IMPACT ANAL YSES

TABLE OF CONTENTS

10.1 INTRODUCTION

................................ ................................ ................................ ......... 10

- 1

10.2 TRIAL STANDARD LEVEL S

................................ ................................ ..................... 10

- 1

10.3 OVERVIEW OF THE NATI ONAL IMPACTS ANALYSI S

................................ ....... 10

- 1

10.3.1 National Energy Savings

................................ ................................ ................................ 10

- 1

10.3.2 Net Present Value of Consumer Benefit

................................ ................................ ........ 10

- 2

10.4 PROJECTED EFFICIENCY TRENDS

................................ ................................ ......... 10

- 4

10.5 NATIONAL ENERGY SAVI NGS

................................ ................................ ............... 10

- 4

10.5.1 Annual Energy Consumption per Unit

................................ ................................ .......... 10

- 5

10.5.2 Shipments

................................ ................................ ................................ ....................... 10

- 6

10.5.3 Product Stock

................................ ................................ ................................ ................. 10

- 6

10.5.4 Annual Energy Consumption

................................ ................................ ......................... 10

- 7

10.5.5 Site

- to

- Power Plant Energy Use Factor

................................ ................................ ......... 10

- 7

10.5.6 Full

- Fuel

- Cycle Energy Factors

................................ ................................ ..................... 10

- 8

10.6 NET PRESENT VALUE OF CONSUMER BENEFITS

................................ .............. 10

- 8

10.6.1 Total Installed Cost per Unit

................................ ................................ .......................... 10

- 9

10.6.2 Annual Operating Cost per Unit

................................ ................................ .................... 10

- 9

10.6.3 Product Stock

................................ ................................ ................................ ............... 10

- 10

10.6.4 Increases in Total Annual Installed Cost

................................ ................................ ..... 10

- 10

10.6.5 Savings in Total Annual Operating Cost

................................ ................................ ..... 10

- 10

10.6.6 Discount Factor

................................ ................................ ................................ ............ 10

- 11

10.6.7 Present Value of Increased Installed Cost and Savings

................................ ............... 10

- 11

10.7 RESULTS

................................ ................................ ................................ .................... 10

- 12

10.7.1 National Energy Savings

................................ ................................ .............................. 10

- 12

10.7.2 Net Present Value of Consumer Benefit

................................ ................................ ...... 10

- 12

LIST OF TABLES

Table 10.2.1 Trial Standard Levels for Hearth Product Ignition Syst ems

.............................. 10

- 1

Table 10.4.1 Hearth Products: Ignition System Distributions for the Base and Standards

Cases in 2021

................................ ................................ ................................ ..... 10

- 4

Table 10.5.1 Average Annual Hearth Product Ignition System Energy Use for the Base

and Standard Cases in 2021

................................ ................................ ............... 10

- 5

Table 10.5.2 Upstream Energy Multipliers (Based on AEO 2014)

................................ ........ 10

- 8

Table 10.6.1 Average Installed Cost of Hearth Product Ignition Systems in 2021 for the

Base and Standard Cases (2013$)

................................ ................................ ...... 10

- 9

Table 10.6.2 Average Annual Operating Cost of Hearth Product Ignition Systems in

2021 for the Base and Standard Cases (2013$)

................................ ............... 10

- 10

Table 10.7.1 Primary and Full

- Fuel

- Cycle National Energy Savings for Hearth Product

Ignition Systems (quads)

................................ ................................ .................. 10

- 1210

- ii Table 10.7.2 Net Present Value of Consumer Benefit for Hearth Product Ignition

Devices (billion 2013$)

................................ ................................ .................... 10

- 12

LIST OF FIGURES

Figure 10.5.1 Combined Adjustment Factor for Hearth Products Energy Use

........................ 10

- 6

Figure 10.5.2 Primary to Site Energy Use Factor for Hearth Product Electricity Use

............ 10

- 710

- 1 CHAPTER 10. NATIONAL IMPACT ANAL YSES

10.1 INTRODUCTION

This chapter examines selected national impacts attributable to each trial standard level

( TS L ) considered for hearth product ignition systems . The results presented here include: (1)

national energy savings (NES); (2) operating cost savings; (3) increased total installed costs; and

(4) the net present value (NPV) of the difference between the value of operating cost savi ngs and

in creased total installed costs.

The calculations were performed using a Microsoft Excel spreadsheet model, which is

accessible on the Internet

( w ww1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83 ) . The

spreadsheet model, termed the National Impact Analysis (NIA) model, calculates energy savings

and NPV for the nation. Details regarding and instructions for using the NIA mod el are provided

in appendix 10 A.

The NIA model incorporates the shipments model that DOE used to project future

purchases of hearth product s.

10.2 T RIAL S TANDARD L EVELS

For hearth products, there are currently two dominant ignition systems on the market,

standing pilots and intermittent pilot ignition s. Standing pilot ignitions are the baseline

technology for hearth product ignition devices. Trial standard level ( TSL ) 1 consists of the

technology option of intermittent pilot ignitio ns for all hearth produc t groups ( Table 10.2.1 )

.

Table 10.2.1 Trial Standard Levels for Hearth Product Ignition Systems

Product Class TSL 1

Hearth Product Adoption of Intermittent Pilot

10.3 O VERVIEW OF THE N ATIONAL I MPACTS A NALYSIS

10.3.1 National Energy Savings

DOE calculates annual national energy savings ( NES ) as the difference between two

projections: the base case (without new standards) and a standard case (with a new standard).

The calculatio n of annual nation energy savings ( NES

y ) are represented by the following

expression :10

- 2 Eq. 10

. 1

Cumulative energy savings are the sum of each annual NES over the lifetime of products

shipped in the period th at extends from a standard’s assumed compliance date for 30 years. This

calculation is represented by the following equation:

Eq. 10

. 2

DOE calculated AEC by multiplying the number or stock of a given product (by vintage)

by its unit energy consumption (also by vintage). The calculation of the national and each

regional AEC is represented by the following equation:

Eq. 10

. 3

Where:

AEC = annual energy consumption each year for the Nation in quadrillion British thermal units

(Btus), or quads, summed over vintages of the product stock

, STOCK V

, NES

y = national annual energy savings (quads)

, NES

cum = national cumulative energy savings (quads)

, STOCK V = stock of product (millions of units) of vintage V that survive in the year for which

DOE calculated annual energy consumption

, UEC V = annual energy consumption per product in kilowatt

- hours (kWh); electricity

con sumption is converted from site energy to power plant energy (quads) by applying a time

dependent conversion factor

, natl

= designates the quantity corresponding to the Nation

, base

= designates the quantity corresponding to the base case

, std

= designates the quantity corresponding to the standard case

,

y = year in the projection,

cum = cumulative over the projection period

, and

V = year in which the product was purchased as a new unit.

The stock of products depends on annual shipments and the lifetime of the given product.

As described in chapter 9, DOE projected shipments for the base case and the standard case.

10.3.2 Net Present Value of Consumer Benefit

The NPV is the value in the present of a time

- series of costs and savings. The NPV is

described by the equation: std natl base natl y AEC AEC NES

− − =

_



# ∑

= y cum NES NES



# ∑ × = V V UEC STOCK AEC



### PVC PVS NPV

_ 

### =10

- 3 Eq. 10

. 4

Where:

PVS

= present value of savings in operating cost (including costs for energy, repair, and

maintenance)

, and

PVC

= present value of increase in total installed cost (including costs for product and

installation).

DOE determined the PVS and PVC according to the following expressions :

Eq. 10

. 5

Eq. 10

. 6

DOE calculated the total annual savings in operating cost by multiplying the number or

stock of a given product (by vintage) by its per

- unit operating cost savings (also by vintage).

DOE calculated the total annual increase in installed cost by multiplying the number or stock of a

given product (by vintage) by its per

- unit total installed cost increase (also by vintage). Total

annual savings in operating cost and increases in installed cost are calculated using the following

equations.

Eq. 10

. 7

Eq. 10

. 8

Where:

OCS

= total annual savings in operating cost each year summed over vintages of the product

stock, STOCK V , TIC = total annual increase in installed cost each year summed over vintages of the product

stock, STOCK V , DF = discount factor in each year

, STOCK V = stock of product (millions of units) of vintage V that survive in the year for which

DOE calculated annual en ergy consumption

, UOCS V = annual per

- unit savings in operating cost

, UTIC V = annual total per

- unit increase in installed cost

, V = year in which the product was purchased as a new unit

, and

y = year in the projection

. 

## ∑

y y DF OCS PVS × =



## ∑

y y DF TIC PVC × =



## ∑

V V y UOCS STOCK OCS × =



## ∑

V V y UTIC STOCK TIC × =10

- 4 DOE determined the PVC for each yea r from the compliance date of the standard through

2050 . DOE determined the PVS for each year from the compliance date of the standard until the

year when units purchased in 2021

− 2050 retire. DOE calculated costs and savings as the

difference between the s tandard case and the base case.

DOE calculated a discount factor from the discount rate and the number of years between

the “present” ( 2014 , the year to which the sum is being discounted) and the year in which the

costs and savings occur. The NPV is the sum over time of the discounted net savings.

10.4 P ROJECTED E FFICIENCY T RENDS

A key component of the NIA is the distribution of hearth product ignition systems

projected over time for the base case (without the new standard ) and for the standard case (with

the potential new standard ).

DOE developed a distribution of ignition system technologies in the base case for 2021

(the assumed compliance date for the new standa rd ), as described in chapter 8

. DOE did not have

sufficient data to establish a trend of adoption of intermittent pilot units. Therefore, DOE

assumed constant distributions for the ignition systems in the base case.

In the standard case, DOE assumed a “roll

- up” scenario to establish the distribution fo r

2021 . Products in the base case that did not meet the standard under consideration would “roll

up” t o meet the new standard level ( Table 10.4.1 )

. After the compl iance year, the distribution

remains constant.

Table 10.4.1 Hearth Products : Ignition System Distributions for the Base and

Standards Cases in 202 1

Product Group Market Share (percent)

Base Case TSL 1

Standing Pilot Intermittent

Ignition Standing Pilot Intermittent

Ignition

Vented Fireplaces 42 58 0 100

Unvented Fireplaces 88 12 0 100

Vented Gas Logs 87 13 0 100

Unvented Gas Logs 94 6 0 100

Outdoor 52 48 0 100

All Hearth Products 58 42 0 100

10.5 NATIONAL ENERGY SAVI NGS

The inputs for calculating national energy savings are:

• average annual energy consumption per unit ( UEC ),10

- 5 • shipments,

• product stock ( STOCK V ),

• annual energy consumption for the Nation ( AEC ), and

• power plant primary energy use factor ( src_conv ).

10.5.1 Annual Energy Consumption per Unit

For hearth product ignition device s , DOE presented the per

- unit annual energy

consumption as a function of product efficiency in chapter 7 , Energy Use Analysis . DOE used

the shipments

- weighted energy consumption of the base and standard case s presented in section

10.4 , along with the annual energy use data presented in chapter 7, to estimate the shipment

weighted average annual per

- unit energy consump tion (UEC) under the base and standard cases.

Table 10.5.1 present s the base case and standard case shipment

- weighted annual UECs

for hearth product ignition device s in 2021

. Due to the constant product group market shares and

ignition system distrib ution in each case, the annual energy use remain s the same throughout the

analysis period.

Table 10.5.1 Average Annual Hearth Product Ignition System Energy Use for the Base

and Standard Cases in 2021

Product Group Standing Pilot Intermittent Pilot

Fuel Use

(MMBtu/ yr) Elec Use

(kWh/yr) Fuel Use

(MMBtu/yr) Elec Use

(kWh/yr)

Vented Fireplaces 3.99 0.000 0.499 13.60

Unvented Fireplaces 3.52 0.000 1.30 99.38

Vented Gas Logs 3.13 0.000 0.289 5.79

Unvented Gas Logs 2.29 0.000 0.924 70.61

Outdoor 3.91 0.000 0.000 0.175

All Hearth Products 3.58 0.000 0.579 28.54

DOE also considered the effects of changes in climate and building shell efficiency on

the building heating load (BHL) of the hearth product, which determines the operating hours of

the hearth product and ignition system and consequent energy use of the hearth product ignition

system . The climate adjustment factor , which is based on the fore cast of heating degree days

(HDD) by region from Annual Energy Outlook 201 4 ( AEO 2014 )

, 1 shows a declining trend due

to warmer weather. Regional building shell efficiency factor s are also from AEO 2014

. For both

factors, DOE applied regional weights to make the factors specific to users of hearth product s.

Figure 10.5.1 show s the adjustment factor for hearth product ignition system energy use.10

- 6 Figure 10.5.1 Combined Adjustment Factor for Hearth Products Energy Use

10.5.2 Shipments

DOE project ed shipments for each product group under the base case and standard case

(see chapter 9)

. These shipments are used in conjunction with the LCC results to calculate

national impacts from the standard , such as the NES and the NPV. Several factors impact

pro jected shipments, including total installed costs, operating cost, household income, and

equipment lifetime. As noted earlier, the increas ed total installed cost of more

- efficient products

is expected to cause some consumers to forgo product purchases. Consequently, shipmen ts

projected under the standard case are lower than under the base case. DOE believes it would be

inappropriate to count energy savings that result from re duced shipments due to a standard

. Therefore, DOE d id not calculate annual energy consumption for the base case using the base

case shipments projection. Instead, for each comparison of a standard case with the base case,

DOE used shipments associate d with that particular standard case. As a result, all of the

calculated energy savings are due to lower energy consumption in the standard case.

10.5.3 Product Stock

The stock of product in any given year depends on annual shipments and the lifetime of a

given product class. The NIA model keeps track of the number of units shipped each year. The

lifetime of a unit determines how many units shipped in previous years survive in the given year. 

### 0.0 0.2 0.4 0.6 0.8 1.0 1.2

2010 2015 2020 2025 2030 2035 2040 2045Factor = 1 at 202010

- 7 DOE assumes that products have an increasing probability of retiring as they age. The

probability of survival as a function of y ears since purchase is termed the survival function.

Refer to chapter 8 for further details on the survival functions that DOE used in its analysis.

10.5.4 Annual Energy Consumption

For each product group , DOE calculated the total national site ( i.e. , the energ y consumed

at the household or establishment) annual energy consumption (AEC). Total a nnual energy

consumption is the product of the AEC per unit ( also termed the unit energy consumption

(UEC) ) and the number of units of each vintage. This method accounts for differ ences in UEC

from year to year.

10.5.5 Site

- to

- Power Plant Energy Use Factor

For hearth products, the considered TSL increases electricity use. DOE calculated

primary energy use (power plant consumption) from site electricity use by applying a factor t o

account for losses associated with the generation, transmission, and distribution of electricity.

DOE derived annual average site

- to

- power plant factors based on the version of the National

Energy Modeling System (NEMS) that corresponds to AEO 2014

. The factors change over time

in response to projected changes in the types of power plants projected to provide electricity to

the country. Figure 10.5.2 shows the site

- to

- power plant factors from 2021 to 2040 . For years

after 20 40 (the last year in the AEO ), DOE maintained the 2040 value

.

Figure 10.5.2 Primary to Site Energy Use Factor for Hearth P roduct Electricity Use 

### 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

2015 2020 2025 2030 2035 2040 2045Primary Btu/Site kWh10

- 8 10.5.6 Full

- Fuel

- Cycle Energy Factors

The full

- fuel

- cycle (FFC) measure includes point

- of

- use (site) energy, the energy losses

associated with generation, transmission, and distribution of electricity, and the energy consumed

in extracting, processing, and transporting or distributing primary fuels. To complete the full

fuel

- cycle by encompassing the energy consumed in extracting, processing, and transporting or

distributing primary fuels, which are refer red to as “upstream” activities, DOE developed

multipliers using the data and projections g enerated by the National Energy Modeling System

(NEMS) used for AEO 2014

. T he AEO provide s 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 operat ions, 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 method used to calculate FFC energy multipliers is describ ed

in appendix 10 B.

Table 10.5.2 s hows the upstream energy multipliers used for hearth product s for selected

years. The multipliers are applied to site energy. Fo r years after 2040 (the last year in the AEO ),

DOE maintained the 2040 value

.

Table 10.5.2 Upstream Energy Multipliers (Based on AEO 2014 )

2021 2025 2030 2035 2040 2045 2050

Elec tricity 1.04 4 1.045 1.046 1.047 1.047 1.047 1.047

Natural Gas 1.1 10 1.111 1.113 1.114 1.114 1.114 1.114

10.6 NET PRESENT VALUE OF CONSUMER BENEFITS

Listed below are t he inputs to DOE’s calculati on of the NPV of costs and savings.

• Total installed cost per unit,

• annual per

- unit savings in operation cost,

• shipments,

• product stock ( STOCK V ),

• total annual increases in installed cost ( TIC ),

• total annual operating cost ( OCS ),

• discount factor ( DF ),

• present value of costs ( PVC ), and

• present value of savings ( PVS ).

The total annual increase in installed cost is equal to the annual change in the total per

unit installed cost (difference between the base case and standard case) multiplied by the

shipments projected for each TSL . As with calculating energy savings, DOE did not use base

case shipments to calculate total annual installed costs for all of the product groups . DOE used

the projected shipments and stock for each TSL to calculate costs.10

- 9 The annual operating cost inclu des energy , repair, and maintenance costs. The total

annual savings in operating cost are equal to the change in the annual operating costs (difference

between the base case and standard case) per unit multiplied by the shipments projected for each

candida te standard level. As with calculating total annual installed costs, DOE use d standards

case shipments to calculate savings in operating cost.

10.6.1 Total Installed Cost per Unit

DOE described the total per

- unit installed cost for each product group as a func tion of

product efficiency in chapter 8 , Life

- Cycle Cost and Payback Period Analysis . Because the total

per

- unit annual installed cost depends directly on efficiency, DOE used the shipment

- weighted

efficiencies for the base and standard cases, combined wit h the total installed cost presented in

chapter 8, to estimate the shipment

- weighted total per

- unit average annual installed cost under

the base and standard cases. Table 10.6.1 shows the average installed cost of hearth product

ignition device s in 2021 for the base and standard case s

.

For reasons discussed in chapter 8, DOE used a constant product price assumption for the

default projection in the NIA. To investigate the impact of different product price projections on

the consumer net present value (NPV) for different efficiency levels, DOE also considered two

alternative price trends

. Details on how these alternative price trends were developed are in

app endix 10 C, which also presents the results of the sensitivity analysis.

Table 10.6.1 Average Installed Cost of Hearth Product Ignition Systems in 2021 for

the Base and Standard Case s ( 2013$ )

Product Group Base Case TSL 1

Fireplace (vented) $218.37 $253.55

Fireplace (ventless) $244.32 $323.17

Logs (vented) $128.67 $278.70

Logs (ventless) $104.02 $225.71

Outdoor $358.71 $432.34

All Hearth Products $208.43 $268.82

10.6.2 Annual Operating Cost per Unit

The per

- unit annual operating cost includes costs for energy, repair, and maintenance.

DOE determined the per

- unit annual savings in energy costs by multiplying the per

- unit annual

savings in energy consumption developed for hearth products by the appropri ate energy price.

Estimates of the per

- unit annual energy consumption for the base case and the standard

case were presented in section 10.5.1

. DOE projected the per

- unit annual energy consumption for

the base case for all product groups by applying a growth trend in efficiency.10

- 10 Energy prices and trends in energy prices are described in chapter 8. DOE projected

energy prices based on annual cha nges in average residential energy prices in EIA’s AEO 201 4

reference case scenario

.

DOE described the total per

- unit repair and maintenance costs for each product group as

a function of product efficiency in chapter 8. Because the per

- unit repair costs depend directly on

efficiency, DOE used the efficiencies for the base and standard case s presented in section 10.4

, combined with the repair costs presented in chapter 8, to estimate the per

- unit average repair and

maintenance costs under the base and standard case s

.

Table 10.6.2 shows the average operating cost of hearth product ignition device s in 2021

for the base and standard case s . The operating costs change over time, depending on change in

annual energy use and energy prices.

Table 10.6.2 Average Annual Operating Cost of Hearth Product Ignition Systems in

2021 for the Base and Standard C ase s ( 2013$ )

Product Group Base Case TSL 1

Fireplace (vented) $227.04 $72.46

Fireplace (ventless) $506.44 $305.43

Logs (vented) $299.19 $39.95

Logs (ventless) $338.33 $214.81

Outdoor $246.11 $2.99

All Hearth Products $267.80 $105.67

10.6.3 Product Stock

The product stock in any given year depends on annual shipments and the lifetime of a

given product group . The NIA model keeps track of the number of units shipped each year. The

lifetime of a unit determines how many units shipped in previous years survive in the given year.

DOE assumes that products have an increasing probability of retiring as they age. Th e

probability of survival as a function of years since purchase is termed the survival function.

Refer to chapter 8 for further details on the survival functions that DOE used in its analysis.

10.6.4 Increases in Total Annual Installed Cost

The increase in tota l annual installed cost for a product under the standard case is the

product of the increase in total installed cost per unit attributable to the standard and the number

of units of each vintage. This method accounts for differences in total installed cost from year to

year.

10.6.5 Savings in Total Annual Operating Cost

The savings in total annual operating cost for any given trial standard level is the product

of the annual per

- unit savings in operating cost attributable to the standard and the number of10

- 11 units of each vintage. This method accounts for the year

- to

- year differences in annual operating

cost savings.

10.6.6 Discount Factor

DOE multiplied monetary values in future years by a discount factor to determine the

present value. The discount factor (DF) is descr ibed by the equation:

Eq. 10

. 9

Where:

r = discount rate,

y = year of the monetary value, and

y

P = year in 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

. 2 DOE defined the present year as 201 4

.

10.6.7 Present Value of Increased Installed Cost and Savings

The present va lue of increased installed cost is the difference between installation cost in

the standard case and the base case discounted to the present and summed throughout the period

over which DOE is considering the installation of units (from the compliance date of standards,

2021 , through 2050 ). DOE calculated annual increases in installed cost as the difference in total

installed cost for new product purchased each year, multiplied by the shipments in the standard

case.

The pres ent value of annual savings in operating cost is the difference between t he base

case and the standard case discounted to the present and summed throughout the period from the

compliance date, 2021 , to the time when the last unit installed in 2021

− 2050 is retired from

service.

Savings represent decreases in operating cost (including electricity, repair, and

maintenance) associated with the more energy

- efficient product purchased in each standard case

compared to the base case. Total annual savings in oper ating cost are the savings per unit

multiplied by the number of units of each vintage that survive in a particular year. ) yp y (

_ ) r ( DF

+ 1 1

=10

- 12 10.7 RESULTS

10.7.1 National Energy Savings

This section provides the national energy savings that DOE calculated for each of the

TSLs analyzed for hearth product ignition systems. See Table 10.7.1 for both primary energy

savings and FFC energy savings . DOE based the inputs to the NIA model on we ighted

- average

values, producing results that are discrete point values, rather than a distribution of values as is

generated by the life

- cycle cost and payback period analysis.

Table 10.7.1 Primary and Full

- Fuel

- Cycle National Energy Savings for Hearth

Product Ignit ion Systems (quads)

Product Class Energy Savings Trial Standard Level

1

quads

Hearth Products Primary 0.6 2

Full

- Fuel

- Cycle 0.69

10.7.2 Net Present Value of Consumer Benefit

This section provides results of calculating the NPV for each trial standard level

considered for hearth product ignition device s. Results, which are cumulative, are shown as the

discounted dollar value of the net savings . See Table 10.7.2 for NPV results with both 3

- percent

and 7

- percent discount rates . DOE based the inputs to the NIA model on weighted

- average

values, yielding results that are discrete point values, rather than a distribution of values as is

produced by the life

- cy cle cost and payback period analyses.

Table 10.7.2 Net Present Value of Consumer Benefit for Hearth Product Ignition

Devices (billion 2013$ )

Product Class Discount Rate

% Trial Standard Level

1

billion 2013$

Hearth Products 3% 3.12 4

7% 1.03 110

- 13 REFERENCES

1. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

2. U.S. Office of M anagement and Budget (OMB), Circular A

- 4: Regulatory Analysis,

2003. (Posted September 17, 2003) (Last accessed April, 2013.)

< www.whitehouse.gov/sites/ default/files/omb/assets/regulatory_matters_pdf/a

- 4.pdf >11

- i CHAPTER 11. CONSUMER SUBGROUP AN ALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION

................................ ................................ ................................ ......... 11

- 1

11.2 APPROACH

................................ ................................ ................................ .................. 11

- 1

11.2.1 Subgroup Definition

................................ ................................ ................................ ....... 11

- 1

11.2.2 Distribution of Subgroup Households with Hearth Products

................................ ........ 11

- 2

11.2.3 Estimation of Impacts

................................ ................................ ................................ .... 11

- 2

11.3 RESULTS

................................ ................................ ................................ ...................... 11

- 2

LIST OF TABLES

Table 11.2.1 RECS 2009 Definitions of Low

- Income Households by Yearly Income

.......... 11

- 1

Table 11.2.2 Household Population Data for Hearth Products

................................ ............... 11

- 2

Table 11.3.1 Average LCC and PBP Results by Efficiency Level for Low

- Income

Households

................................ ................................ ................................ ......... 11

- 3

Table 11.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for Low

Income Households

................................ ................................ ............................ 11

- 3

Table 11.3.3 Average LCC and PBP Results by Efficiency Level for Senior

- Only

Households

................................ ................................ ................................ ......... 11

- 3

Table 11.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for Senior

Only Households

................................ ................................ ................................ 11

- 3

Table 11.3.5 Summary of Average LCC Savings and Simple Payback Period Results

for Consumer Subgroups and All Households

................................ ................... 11

- 311

- 1 CHAPTER 11. CONSUMER SUBGROUP AN ALYSIS

11.1 INTRODUCTION

The consumer subgroup analysis evaluates impacts on groups or consumer s who may be

disproportiona tely affected by any national en ergy conservation standard

. The U.S. Department

of Energy (DOE) evaluates impacts on particular subgroups of consumers by analyzing the life

cycle cost (LCC) impacts and payback period (PBP) for those consum ers from the considered

energy efficiency levels

. DOE determine d the impact on consumer subgroups using the LCC

spreadsheet models for hearth product s

. Chapter 8 explains in detail the inputs to the models

used in determining LCC impacts and PBPs

.

DOE eva luated impacts on low

- income households and households occupied solely by

senior citizens (senior

- only households)

.

11.2 A PPROACH

11.2.1 Subgroup Definition

As defined in the RECS 2009 survey, 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 and in RECS encompasses a group of households with incomes below the poverty level

in 2009 as defined b y the U.S. Bureau of the Census 1 (see Table 11.2.1 ). The RECS 2009 survey

classifies approximately 15 percent of U.S. households as low

- income.

Table 11.2.1 RECS 2009 Definitions of Low

- Income Households by Yearly Income

Household Size Weighted Average Threshold

2009$

1 10,956

2 13,991

3 17,098

4 21,954

5 25,991

6 29,405

7 33,372

8 37,252

9+ 44,366

Senior

- only households have occupants who are all at least 65 years of age. Based on the

Energy Information Administration’s 2009 Residential Energy Consumption Survey (RECS

2009), 2 senior

- only households comprise 17 percent of the country’s ho useholds.11

- 2 11.2.2 Distribution of Subgroup Households with Hearth Products

Of the 12,083 households in the 2009 RECS database, 4 .7 million have vented hearth

heaters and 1. 8 million have ventless hearth heaters

. Table 11. 2.2 shows the household sample

sizes for hearth product subgroups

. The low

- income sample included only 21 households

representing approximately 235,000 households, and the senior

- only house hold included 99

households representing approximately 900,000 households.

Table 11.2.2 Household Population Data for Hearth Products

Product Type General Population Low

- Income Households Senior

- Only Households

No. of

Records Number of

Houses No. of

Records Number of

Houses No. of

Records Number of

Houses

Vented Hearth

Heaters 541 4,666,601 13 124,196 77 662,864

Ventless Hearth

Heaters 171 1,825,134 8 96,271 22 237,811

All Hearth

Heaters 712 6,491,734 21 220,468 99 900,676

11.2.3 Estimation of Impacts

To calculate the subgroup results, DOE extracted the results of low

- income and senior

only households from the national LCC results . Then DOE calculated the LCC and PBP statistics

for the subgroups from the individual households

.

In the LCC analysis in c hapter 8, the national sample is separated into replacement and

new construction samples. For the subgroup analysis, because the number of households in each

subgroup is small, DOE chose to only use a single sample r ather than disaggregating

replacement an d new construction markets.

11.3 R ESULTS

Table 11.3.1 through Table 11.3.4 summarize the LCC and PBP results for low

- income

and senior

- only households

. Table 11.3.5 compares average LCC savings and simple PBP for the

consumer subgroups with those for all households

. For hearth product s, t he low

- income have

lower LCC savings but shorter PBP f or the intermittent pilot ignition , while senior

- only

households have higher LCC savings but longer PBP than average. This may be due to both the

higher disco unt rates in these demographics and the smaller sample size, which may include

more outliers by percentage than the overall sample.11

- 3 Table 11.3.1 Average LCC and PBP Results by Efficiency Level for Low

- Income

Households

EL Efficiency Level Average Costs Simple

Payback

years 2013$

Installed

Cost First Year’s

Operating Cost Lifetime

Operating Cost LCC

0 Standing Pilot 159 110 1,234 1,393

--

1 Intermittent Pilot 255 31 356 611 1.2

Note: The results for each EL are calculated assuming that all consumers use products with that efficiency level. The

PBP is measured relative to the baseline product.

Table 11.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for Low

Income Households

EL Efficiency Level Life

- Cycle Cost Savings

% of Consumers that

Experience Net Cost Average Savings*

2013$

1 Intermittent Pilot 21% 557

* The calculation includes buildings with zero LCC savings (no impact).

Table 11.3.3 Average LCC and PBP Results by Efficiency Level for Senior

- Only

Households

EL Efficiency Level Average Costs Simple

Payback

years 2013$

Installed

Cost First Year’s

Operating Cost Lifetime

Operating Cost LCC

0 Standing Pilot 168 43 505 673

--

1 Intermittent Pilot 270 13 155 425 3.5

Note: The results for each EL are calculated assuming that all consumers use products with that efficiency level. The

PBP is measured relative to the baseline product.

Table 11.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for Senior

Only Households

EL Efficiency Level Life

- Cycle Cost Savings

% of Consumers that

Experience Net Cost Average Savings*

2013$

1 Intermittent Pilot 26% 121

* The calculation includes buildings with zero LCC savings (no impact).

Table 11.3.5 Summary of Average LCC Savings and Simple Payback Period Results

for Consumer Subgroups and All Households

Technology Option Efficiency

Level Average LCC Savings Simple Payback Period

2013$ Years

Low

Income Senior

Only All Low

Income Senior

Only All

Intermittent Pilot 1 $557 $121 $165 1.2 3.5 2.911

- 4 REFERENCES

1. U. S. Department of Commerce

- Bureau of the Census, Poverty Thresholds 2009. 2009.

U.S. Census. (Posted June 2, 2010) (Last accessed March 15, 2013.)

< www.census.gov/hhes/www/poverty/data/threshld/thresh09.html >

2. U.S. Department of Energy: Energy Information Administration, Residential Energy

Consumption Survey (RECS): 2009 RECS Survey Data. 2009. (Last accessed January 7,

2015.) < www.eia.gov/consumption/residential/data/2009/ >CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION

................................ ................................ ............................. 12

- 1

12.2 METHODOLOGY

................................ ................................ ............................ 12

- 1

12.2.1 Phase I: Industry Profile

................................ ................................ ............. 12

- 1

12.2.1.1 Manufacturer Interviews

................................ ............................... 12

- 2

12.2.2 Phase II: Industry Cash

- Flow Analysis

................................ ...................... 12

- 3

12.2.3 Phase III: Manufacturer Subgroup Analysis

................................ ............... 12

- 3

12.2.3.1 Manufacturers of Gas Log Sets Subgroup

................................ .... 12

- 3

12.2.3.2 Small Business Manufacturer Subgroup

................................ ...... 12

- 4

12.2.4 Manufacturing Capacity Impact

................................ ................................ . 12

- 5

12.2.5 Employment Impact

................................ ................................ .................... 12

- 5

12.2.6 Cumulative Regulatory Burden

................................ ................................ .. 12

- 5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

.............................. 12

- 5

12.3.1 Impacts on Profitability

................................ ................................ .............. 12

- 6

12.3.2 Impacts on Industry Competition

................................ ............................... 12

- 6

12.3.3 Impacts on Product Performance

................................ ................................ 12

- 6

12.4 GRIM INPUTS AND ASSU MPTIONS

................................ ............................ 12

- 7

12.4.1 Overview of the Government Regulatory Impact Model

........................... 12

- 7

12.4.2 Sources for GRIM Inputs

................................ ................................ ............ 12

- 8

12.4.2.1 Final Rule for Residential Water Heaters, Direct Heating Equipment,

and Pool Heaters

................................ ................................ ............. 12

- 8

12.4.2.2 Shipment Model

................................ ................................ ............ 12

- 9

12 .4.2.3 Engineering Analysis

................................ ................................ .... 12

- 9

12.4.2.4 Manufacturer Interviews

................................ ............................... 12

- 9

12.4.3 Trial Standard Levels

................................ ................................ .................. 12

- 9

12.4.4 NIA Shipments

................................ ................................ ......................... 12

- 10

12.4.5 Production Costs

................................ ................................ ....................... 12

- 10

12.4.6 Conversion Costs

................................ ................................ ...................... 12

- 12

12 .4.6.1 Capital Conversion Costs

................................ ........................... 12

- 12

12.4.6.2 Product Conversion Costs

................................ ........................... 12

- 12

12.4.7 Markup Scenarios

................................ ................................ ..................... 12

- 13

12.4.7.1 Preservation of G ross Margin Percentage Scenario

................... 12

- 14

12.4.7.2 Preservation of Operating Profit Scenario

................................ .. 12

- 14

12.5 INDUSTRY FINANCIAL I MPACTS

................................ ............................ 12

- 14

12.5.1 Introduction

................................ ................................ ............................... 12

- 15

12.5.2 Hearth Industry Financial Impacts

................................ ............................ 12

- 15

12.6 IMPACTS ON SUBGROUPS OF MANUFACTURERS

.............................. 12

- 17

12.6.1 Impacts on Manufacturers of Gas Log Sets

................................ .............. 12

- 17

12.6.2 Impacts on Small Business Manufacturers

................................ ............... 12

- 19

12.7 OTHER IMPACTS

................................ ................................ .......................... 12

- 21

12.7.1 Direct Employment

................................ ................................ ................... 12

- 21

12.7.1.1 Methodology

................................ ................................ ............... 12

- 21

12

- i12.7.1.2 Direct Employment Impacts

................................ ....................... 12

- 22

12.7.2 Production Capacity

................................ ................................ .................. 12

- 23

12.7.3 Cumulative Regulatory Burden

................................ ................................ 12

- 23

12.8 CONCLUSION

................................ ................................ ................................ 12

- 24

LIST OF TABLES

Table 12.2.1 Classification of Small Business Manufacturers Potentially Affected by This

Rulemaking

................................ ................................ ................................ .................... 12

- 4

Table 12.4.1 Financial Parameters for Gas Hearth Manufacturers

................................ 12

- 8

Table 12.4.2 Trial Standard Levels for Analysis of Gas Hearth Products

................... 12

- 10

Table 12.4.3 Manufacturer Production Cost Breakdown (2013$) for Vented Fireplaces,

Inserts, and Stoves

................................ ................................ ................................ ....... 12

- 11

Table 12.4.4 Manufacturer Production Cost Breakdown (2013$) for Unvented Fireplaces,

Inserts, and Stoves

................................ ................................ ................................ ....... 12

- 11

Table 12 .4.5 Manufacturer Production Cost Breakdown (2013$) for Vented Gas Log Sets

................................ ................................ ................................ ................................ ...... 12

- 11

Table 12.4.6 Manufacturer Production Cost Breakd own (2013$) for Unvented Gas Log

Sets

................................ ................................ ................................ ............................... 12

- 11

Table 12.4.7 Manufacturer Production Cost Breakdown (2013$) for Outdoor Hearth

Product s

................................ ................................ ................................ ........................ 12

- 11

Table 12.4.8 Industry Cumulative Capital Conversion Costs (2013$ Millions)

......... 12

- 12

Table 12.4.9 Industry Cumulative Product Conversion Costs (2013$ Millions)

........ 12

- 13

Table 12.5.1 Preservation of Gross Margin Percentage Scenario Changes in INPV for

Hearth Products

................................ ................................ ................................ ............ 12

- 16

Table 12.5.2 Preservation of Operating Profit Scenario Changes in INPV for Hearth

Products

................................ ................................ ................................ ........................ 12

- 16

Table 12 .6.1. Added Cost of Electronic Ignition Systems vs. Standing Pilot Lights

.. 12

- 18

Table 12.6.2 Added Cost of Electronic Ignition Systems at Representative Production

Volumes

................................ ................................ ................................ ....................... 12

- 21

Table 12.7.1 Potential Changes in the Total Number of Production Workers in th e Gas

Hearth Industry in 2021

................................ ................................ ............................... 12

- 22

Table 12.8.1 Manufacturer Impact Analysis Results

................................ ................... 12

- 24

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow

................................ ................ 12

- 7

Figure 12.5.1 Annual Industry Net Cash Flows under Preservation of Gross Margin

Percentage Markup Scenario

................................ ................................ ....................... 12

- 16

Figure 12.5.2 Annual Industry Net Cash Flows under Preservation of Operating Profit

Markup Scenario

................................ ................................ ................................ .......... 12

- 17

12

- iiCHAPTER 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. 6313(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 an

energy conservation standard on manufacturers of hearth products and to assess the

impact such standards would have on direct employment and manufacturing capacity.

The MIA has both quantitativ e 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 each product in this rulemaking. The GRIM inputs

include information on industry cost str ucture, shipments, and pricing strategies. The

GRIM’s key output is the industry net present value (INPV). The model estimates the

financial impact of more stringent energy conservation standards for each product by

comparing changes in INPV between a base case (without new standards) and the various

trial standard levels (TSLs) in the standards case . The qualitative part of the MIA

addresses product characteristics, manufacturer characteristics , market and product

trends, as well as the impact of standards on subgroups of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, Industry Prof ile, consisted of

preparing a characterization of the gas hearth industry, including data on sales volumes,

pricing, employment, and financial structu re. As part of this phase, DOE conducted

interviews with a broad cross

- section of hearth manufacturers to gather information on

the industry as well as the potential impacts of an energy conservation standard . In Phase

II, Industry Cash Flow Analysis , DOE used the GRIM to assess the potential impacts of

an energy conservation standard on manufacturers. DOE used financial inputs derived

from a combination of sources

, including manufacturer interviews conducted in Phase I

as well as public sources of informat ion . In Phase III, Subgroup Impact Analysis, DOE

develo ped additional analyses for sub groups that required special consideration and

incorporated qualitative data from interviews into its analysis.

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepa red a profile of the gas hearth industry

. D OE

developed its industry profile using a combination of sources, including: public

information , such as Securities a nd Exchange Commission (SEC) 10

- K reports, 1 market

research tools ( e. g. , Hoovers 2 ), corporate annual reports, the U.S. Census Bureau’s 20 11

Annual Survey of Manufacturers (ASM) 3 , and the 2010 Energy Conservation Standard

Final Rule for Residential Water Heaters, Direct Heating Equipment, and Pool Heaters

12

- 1(75 FR 20112 (April 16, 2010) ); i nformation obtained through DOE’s engineerin g

analysis, life

- cycle cost analysis , and market and technology assessment prepared for this

rulemaking; and information obtained directly from manufacturers through interviews.

The industry profile includes an a nalysis of overall market and product

characteristics ( e.g., market structure , sales trends, competition ) as well as estimation of

financial parameters for the industry ( e.g., typical product markups, costs of goods sold

(COGS) , selling, general and admini strative expenses (SG&A), net plant, property, and

equipment (PPE), expenditures on research and development (R&D) )

. The financial

parameters developed as par t of the industry profile we re subsequently used to develop

the industry cash flow analysis conduc ted during Phase II

.

12.2.1.1 Manufacturer Interviews

As part of Phase I , DOE conducted structured, detailed interviews with a

representative cross

- section of manufacturers

. During these interviews, DOE discussed

engineering, manufacturing, procurement, and financial topics . The interviews were

designed to identify manufacturers’ key concerns regarding a potential energy

conservation standard and to gather information on the potential eff ects of a standard on

manufacturer finances, direct employment, capital assets, and industry competitiveness.

The interviews also presented an opportunity to discuss industry structure and market

segmentation and to identify subgroups of manufacturers that could be disproportionately

affected by an energy conservation standard

.

DOE scheduled interviews and distributed interview guide s to manufacturers well

in advance of conduct ing interviews . In doing so, DOE sought to provide every

opportunity for key ind ividuals to be available for comment and to afford manufacturers

sufficient time to prepare . Although a written response to the interview guide was

acceptable, DOE requested interactive interviews, which help to clarify responses and

identify additional is sues. Topics addressed during interviews included: (1) key issues to

this rulemaking; (2) engineering analysis; (3) company overview; (4) manufacturer

markups and profitability; (5) financial parameters ; (6) conversion costs; (7) industry

projections ; (8) direct employment impact assessment; ( 9 ) exports, foreign competition,

and outsourcing; (1 0 ) consolidation; (11) cumulative regulatory burden; and (1 2 ) impacts

on small businesses

.

Information obtained during interviews is protected by non

- disclosure agreements

(NDAs) and resides with DOE’s contractors. This allows manufacturers to express their

views on important issues privately and to share confidential or sensitive information for

consideration as part of the rulemak ing process. The opportunity to exchange confidential

information enables DOE to refine its engineering and manufacturer impact analyses in a

manner that would not be possible if relying solely on publicly available information. For

instance, c onfidential financial data obtained under NDA allows DOE to better tailor the

GRIM to reflect financial characteristics specific to the gas hearth industry

.

12

- 212.2.2 Phase II: Industry Cash

- Flow Analysis

Phase II of the MIA focused on the financial impacts of potential energy

conservation standards on manufacturers of gas hearth products

. In general, energy

conservation standards can affect manufacturer cash flow in three distinct ways: (1)

c reate a need for increased investment ; (2) raise production costs per unit ; and (3) al ter

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 the

hearth industry. In performing these analyses, DOE used financial values derived during

Phase I and shipment projections used in the national impact analysis (NIA).

The GRIM uses several factors to determine a series of annual cash flows from

the announcement year of energy conservation standards until 30 years after the

standard s’ complian ce date. These factors include annual expected revenues, COGS

, SG&A, taxes, and capital expenditures related to the standards. Inputs to the GRIM

include manufactur er production costs, markup assumptions, and shipments forecasts

developed in other analyses. DOE derived the manufacturing costs from the engineering

analysis and information provided by the industry

. It estimated typical manufacturer

markups from public fi nancial reports and interviews with manufacturers. DOE

developed alternative markup scenarios based on discussions with manufacturers. DOE’s

shipments analysis, presented in chapter 10 of the TSD, provide s the basis for the

shipment projections used in the GRIM. The financial parameters were developed using

publicly available manufacturer data and were revised with information submitted

confidentially during manufacturer interviews. The GRIM results for the standards case

are compared to results for the bas e

- case scenario for the industry. The financial impact

of an energy conservation standard is then evaluated as the difference between the

discounted annual cash flows in the base case and the discounted annual cash flows in the

standards case.

The results of the industry cash

- flow analysis are presented in section 12.5

.

12.2.3 Phase III: Manufacturer Subgroup Analysis

For its GRIM analysis, DOE presented impacts on the hearth industry as a whole.

However, u sing average cost ass umptions to develop an industry cash

- flow estimate may

not adequately assess differential impacts of an energy conservation standard among

manufacturer subgroups. For example, small manufacturers, niche players, or

manufacturers exhibiting a cost structure that largely differs from the industry average

could be more negatively affected. To address this possible impact, DOE used the results

of the industry characterization analysis in Phase I to grou p manufacturers that exhibit

similar characteristics. DOE identified two subgroups of hearth manufacturers that could

be disproportionately affected by an energy conservation standard and therefore

warranted a separate impact analysis: (1) manufacturers of gas log sets ; and (2) small

bu siness manufacturers

.

12.2.3.1 Manufacturers of Gas Log Sets Subgroup

12

- 3During interviews, multiple manufacturers commented that gas log sets represent

a distinct market segment within the gas hearth industry. These manufacturers indic ated

that gas log sets serve a different market niche and face different space constraints than

other gas hearth products. Additionally, manufacturers of gas log sets indicated that an

energy conservation standard disallowing the use of standing pilot ligh ts could have a

proportionally greater impact on the manufacturing costs and, by extension, the retail

prices, of gas log sets relative to other gas hearth products. Given the nature of the gas

log set market, manufacturers indicated that the proposed ener gy conservation standard

could trigger a decline in demand from price

- sensitive consumers and, in turn, negatively

affect manufacturer profitability. DOE reports further on the potential impact of this

rulemaking on the subgroup of gas log set manufacturer s i n section 12.6.1

.

12.2.3.2 Small Business Manufacturer Subgroup

DOE investigated whether small business manufacturers should be analyzed as a

manufacturer subgroup. DOE used the Small Business Administration (SBA) small

business size standards effective on January 1, 2012 , as amended, and the North

American Industry Classification System (NAICS) code s , presented in Table 12.2

. 1 , to

determine whether any small entities would be affected by the rulemaking. 4 For the

products under review, the SBA bases its small business definition on the total number of

employees for a business, its su bsidiaries, and its parent companies. An aggregated

business entity with fewer employees than the listed limit is considered a small business.

M anufacturing of hearth products is classified under NAICS code 33341 4

, “ Heating

Equipment (Except Warm Air Furna ces) Manufacturing

, ” and under NAICS code

335228, “Other Major Household Appliance Manufacturing.” For both NAICS codes, the

SBA sets a threshold of 500 employees or fewer for an entity to be considered a small

business.

Table 12.2

. 1 Classification of Small Business Manufacturer s Potentially Affected by

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS Code

Heating Equipment (Except Warm Air

Furnaces) Manufacturing N/A 500 33341 4

Other Major Household Appliance

Manufacturing N/A 500 335228

DOE used publicly available and proprietary information to identify potential

small business manufactures of products covered by this rulemaking . DOE’s research

involved industry trade ass ociation membership directories ( e.g.

, Hearth, Patio &

Barbecue Association ( HPBA ) ), individual company websites, and market research tools

( e.g.

, Hoovers.com) to create a list of small companies that manufacture products that

would be covered by this rule making. In interviews, DOE presented its initial list of

identified small business manufacturers and asked interviewees if they were aware of any

small manufacturers not included on the list. DOE also reached out to other interested

parties and industry re presentatives for information on small business manufacturers.

DOE screened out companies that do not manufacture products potentially covered by

this rulemaking, do not meet the definition of a small business, or are foreign owned and

operated. Based on this analysis

, DOE identified 66 domestic small businesses that

12

- 4manufacture gas hearth products affected by this rulemaking. DOE reports the potential

impact of this rulemaking on small businesses in section 12.6.2

.

12.2.4 Manufacturing Capacity Impact

One significant outcome of energy conservation standards could be the

obsolescence of existing manufacturing assets, including tooling and investment. The

manufacturer interv iew guides include a series of questions to help identify impacts of

standards on manufacturing capacity . S pecifically , these are : capacity utilization and

plant location decisions in the United States , with and without standards; the ability of

manufactur ers to upgrade or remodel existing facilities to accommodate the new

requirements; the nature and value of any stranded assets; and estimates of any one

- time

changes to existing plant, property, and equipment (PPE). DOE’s estimates of the one

time capital changes and stranded assets affect the cash flow estimates in the GRIM.

These estimates can be found in section 12.4.6

. DOE’s discussion of the capacity impact

can be found in section 12.7.2

.

12.2.5 Employment Impact

The impact of energy conservation standards on employment is an important

consideration in the rulemaking process. To assess how domestic direct employment

patterns might be affected, the interview er s explored with interested parties the current

employment trends in the hearth industry. The interview er s also solicited manufacturer

views on changes in employment pat terns that may result from an energy conservation

standard . The employment impacts section of the interview guide focuse s on current

employment levels associated with manufacturers at each production facility, expected

future employment levels with and without e nergy conservation standards, and

differences in workforce skills and issues related to the retraining of employees. The

employment impacts are reported in section 12.7.1

.

12.2.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to energy

conservation standards and other regulatory actions affecting the same products. DOE

analyzed the impact on manufacturers of multiple, product

- specific r egulatory actions.

Discussion of the cumulative regulatory burden can be found in section 12.7.3

.

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company

regarding the energy conservation standard rulemaking?” This question prompts

manufacturers to identify the issues they consider important for DOE to explore and

discuss further during t he interview s

. The following sections describe the most significant

issues identified by manufacturers. These summaries are provided in aggregate to protect

manufacturer confidentiality.

12

- 512.3.1 Impacts on Profitability

According to manufacturers, units with elec tronic ignition systems are more

expensive to manufacture than units with standing pilot lights. Manufacturers indicated

that purchasing components for electronic ignition systems increases per

- unit production

costs and, by extension, raises the retail price of products. Manufacturers stated that by

driving up their cost of goods sold as well as the end

- user purchase price, a standard

eliminating standing pilot lights could lead to a drop in consumer demand. Because gas

hearth products are not typically purchased exclusively for heating purposes but rather

are valued by customers for their aesthetic appeal, manufacturers indicated that higher

prices could depress demand if customers decide the decorative benefit of gas hearth

products does not merit the h igher costs. A fall in sales could, in turn, affect industry

profitability.

Additionally, manufacturers stated that shipments of gas hearth products declined

significantly over the last decade, in part due to the economic recession and a related

decline i n new

- home construction. Several manufacturers forecast steady or declining

shipments in future years absent an energy conservation standard. Those interviewed

generally argued that if an energy conservation standard raises the price of gas hearth

products , depresses demand, and reduces profitability, it could drive manufacturers to

exit the market.

12.3.2 Impact s on Industry Competition

Small manufacturers expressed concern that an energy conservation standard for

gas hearth products could alter the competitive dynamics of the market, favoring a subset

of large manufacturers over their small

- business competitors. Based on economies of

scale, manufacturers that produce gas hearth products at high volumes are typically able

to source components at lower per

- unit pr ices than manufacturers that produce at lower

volumes. In general, manufacturers of gas hearth products do not manufacture the

components used for electronic ignition systems in house. Rather, they source them from

component suppliers. In interviews, manuf acturers indicated that large manufacturers

with high production volumes are able to source these components at relatively low cost.

Small manufacturers with lower production volumes, in contrast, noted that the

comparatively high cost they would incur to purchase electronic ignition system

components would exacerbate the pricing advantage of large manufacturers and could

lead to loss of price competitiveness for smaller players in the market.

12.3.3 Impacts on Product Performance

Multiple manufacturers stated t hat electronic ignition systems represent a more

complicated and less reliable technology than standing pilot lights. These manufacturers

indicated that units with electronic ignition systems often require more effort to repair

and maintain. One manufactur er stated that electronic ignition systems account for a

small fraction of their sales but the vast majority of their service calls, and several

manufacturers suggested higher costs of maintaining units with electronic ignition

systems compared to standing pilot lights. Additionally, several manufacturers suggested

12

- 6that electronic ignition systems are not as well suited to cold climates, where standing

pilot lights may help to maintain buoyancy through the flue and to prevent condensation

from building up o n glass.

12.4 GRIM INPUTS AND ASSU MPTIONS

The GRIM serves as the main tool for assessing the impact s on industry resulting

from energy conservation standards. DOE relies on several sources (as detailed in section

12.4.2 ) to obtain inputs for the GRIM. DOE then feeds the d ata and assumptions from

these sources into an accounting model that calculates industry cash flow s both with and

without energy conservation standards

.

12.4.1 Overview of the Government Regulatory Impact Model

The GRIM is an annual cash flow model that uses manufacturer production costs,

m anufacturer selling prices, shipment projection s, and industry financial information as

inputs to arrive at a series of annual cash flows, beginning with the base year of the

analysis, 2014, and continuing to 2050

. As illustrated in Figure 12.4

. 1

, t he model

calc ulates INPV by summing the stream of annual discounted cash flows during this

period and adding a discounted terminal value. 5 Imposing different conditions

, such as

changes in manufacturing costs, investment requirement s, and anticipated markups

, enables D OE to analyze the potential effects of an energy conservation standard on

industry finances.

Figure 12.4

. 1 Using the GRIM to Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares

changes in INPV between the base case and the standard

- case scenario s induced by

energy conservation standards. The difference in INPV between the ba se case and the

standard case represents the estimated financial impact of the ener gy conservation

1 2

- 7standard on manufacturers. Appendix 12A provides more technical details and user

information for the GRIM.

DOE presents MIA results relative to a base case that assumes no energy

conservation standard for gas hearth products

. Accordingly , w hen comparing the INPV

impacts of the GRIM model, the baseline assumes continued production and sale of

hearth products with standing pilot lights

, while the standard case assumes the

elimination of all standing pilot lights and their replacement with elec tronic ignition

systems

.

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 ann ual reports, company profiles, c ensus data,

credit ratings, the shipments model, the engineering analysis, and manufacturer

interviews.

12.4.2.1 Final Rule for Residential Water Heaters, Direct Heating

Equipment, and Pool Heaters

The 2010 Energy Conservation Standard for Residential Water Heaters, Direct

Heating Equipment, and Pool Heaters (75 FR 20112 (April 16, 2010) ) provided many of

the initial financial inputs to the GRIM. As part of th at r ule, DOE derived a series of

financial parameters for vented gas hearth s b ased on a review of SEC public filings,

corporate annual reports, company profiles , credit ratings, and manufacturer interviews

. DOE used the se parameters as a starting point for analysis , presenting them to

manufacturers for review and comment during inte rviews conducted under Phase I of this

rulemaking

. Based on manufacturer feedback , DOE then revised its estimated financial

parameters to better reflect the current gas hearth industry. Table 12.4

. 1 presents both the

initial estimates and the revised financial parameters used as inputs to the GRIM

.

Table 12.4

. 1 Financial Parameters for Gas Hearth Manufacturers

Parameter Initial Estimate

% Revised

Estimate

%

Tax Rate (% of t axable i ncome) 35 36

Discount Rate 8.5 8.7

Working Capital (% of r evenue) 11.4 2.2

Net Property, Plant, and Equipment (% of

Revenue) 13 10.8

SG&A (% of r evenue) 20.9 25

R&D (% of r evenue) 1.5 2.3

Depreciation (% of r evenue) 2.2 2.1

Capital Expenditures (% of r evenue) 2.4 2.1

12

- 812.4.2.2 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in

the national impact analysis (NIA). Chapter 10 of th is TSD describes the methodology

and analytical model DOE used to forecast shipments.

12.4.2.3 Engineering Analysis

During the engineering analysis, DOE used a manufacturing cost model to

develop manufacturing production cost ( MPC ) estimates. The analysis provided t he

labor, materials, overhead

, and total production costs for different design option s for gas

hearth products

. DOE estimated a manufacturer markup and applied this to the MPC to

arrive at the manufacturer selling price (MSP).

12.4.2.4 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative

cross

- section of manufacturers. During these discussions, DOE obtained information to

determine and verify GRIM input assumptions. Key topics discussed during the

interviews and r eflected in the GRIM include:

• C apital conversion costs (one

- time investments in PPE);

• P roduct conversion costs (one

- time investments in research, product

development, testing, and marketing);

• P roduct cost str ucture ;

• Industry financial parameters; and

• P ossible profitability impacts.

12.4.3 Trial Standard Levels

DOE typically considers multiple t rial s tandard l evels ( TSLs ) for a standards

rulemaking. However, the hearth products rulemaking is proposing a prescriptive

standard that would dis allow the use of continuously burning pilots. There is currently

only one other established alternative to a standing pilot, which is an intermittent pilot.

Other options that are present in other combustion appliances, such as hot surface

ignition, are vi rtually non

- existent in the hearth product market primarily due to the

increased cost and additional engineering challenges. Therefore, hearth products have

only one TSL, which reflects a standard that would disallow the use of a standing pilot.

For the pu rposes of this analysis, TSL1 assumes that all covered hearth products would

use an intermittent pilot.

12

- 9Table 12.4

. 2 Trial Standard Levels for Analysis of Gas Hearth Products

Ignition Type TSL 1

%

Standing Pilot 0

Intermittent Pilot 100

12.4.4 NIA Shipment s

The GRIM estimates manufacturer revenues based on total

- unit

- shipment

forecasts and the distribution of these values by product group and ignition type. For this

analysis, the GRIM applied the NIA shipments forecasts for the period 2014 ( the base

year of the MIA analysis ) to 2050 (the end year of the analysis)

. As part of the shipments

forecasts , DOE estimated the base

- case shipment distribution by igniti on type

. In the

standards case, the shipments analysis assumes a roll

- up scenario, where all shipments in

the base case that do not meet the standard ( i.e. , use standing pilots) would instead ship at

the new standard level ( i. e

. , with electronic ignition s ystems)

. The shipments forecasts

also assume price elasticity of demand, whereby shipment volumes in the standards case

decline relative to the base case as MPCs rise and, in doing so, drive up end

- user

purchase prices. The key assumptions and methodology used to forecast shipments can

be found in chapter 10 of th is TSD.

12.4.5 Production Costs

Changes in production costs affect revenues and gross profits. Products that are

more efficient typically cost more to produce than baseline products (as shown in chapter

5 of the TSD). For the MIA, DOE used the MPCs derived in the engineering analysis

.

The engineering analysis developed MPCs for representative gas hearth units in

five product groups : (1) vented fireplaces, inserts, and stoves ; (2) unvented fireplaces,

inserts, and stoves; (3) vented gas log sets; (4) unvented gas log sets; and (5) outdoor

hearth products. The engineering analysis also determine d labor, materials, overhead

, and

depreciation percentages that constitute the full MPC for each product group. DOE uses

these MPCs in combination with shipment projections derived as part of the national

impact analysis to evaluate industry financials in both the base case and the standards

case. DOE also applies a manufacturer markup to the MPCs in order to calcu late MSPs

for each product group. DOE adjusts this markup under multiple markup scenarios in

order to analyze a range of potential financial impacts of an energy conservation standard

on manufacturers.

Table 12.4

. 3 through Table 12.4

. 5 show the manufacture production cost and

manufacturer selling price estimates used in the GRIM for each analyzed product group

. A baselin e markup of 1. 45 was applied to all product groups

. As explained in section

12.4.7 , this marku p varies under alternative markup scenarios.

12

- 10Table 12.4

. 3 Manufacturer Production Cost Breakdown ( 201 3 $ ) for Vented

Fireplaces, Inserts, and Stoves

Materials

$ Labor

$ Depreciation

$ Overhead

$ MPC

$ Markup

% MSP

$

Standing

Pilot 140 72 71 39 322 1.45 468

Intermittent

Pilot 167 72 71 40 350 1.45 508

Table 12.4

. 4 Manufacturer Production Cost Breakdown ( 201 3 $ ) for Unvented

Fireplaces, Inserts, and Stoves

Materials

$ Labor

$ Depreciation

$ Overhead

$ MPC

$ Markup

% MSP

$

Standing

Pilot 107 70 70 34 281 1.45 407

Intermittent

Pilot 134 75 69 35 313 1.45 454

Table 12.4

. 5 Manufactu rer Production Cost Breakdown ( 2013 $ ) for Vented Gas Log

Sets

Materials

$ Labor

$ Depreciation

$ Overhead

$ MPC

$ Markup

% MSP

$

Standing

Pilot 56 45 62 27 190 1.45 275

Intermittent

Pilot 121 47 62 29 260 1.45 376

Table 12.4

. 6 Manufactu rer Production Cost Breakdown ( 2013 $ ) for Unvented Gas

Log Sets

Materials

$ Labor

$ Depreciation

$ Overhead

$ MPC

$ Markup

% MSP

$

Standing

Pilot 81 60 44 23 208 1.45 301

Intermittent

Pilot 126 59 52 28 264 1.45 383

Table 12.4

. 7 Manufactu rer Production Cost Breakdown ( 2013 $ ) for Outdoor

Hearth Products

Materials

$ Labor

$ Depreciation

$ Overhead

$ MPC

$ Markup

% MSP

$

Standing

Pilot 117 33 39 21 210 1.45 304

Intermittent

Pilot 168 37 37 22 265 1.45 384

12

- 1112.4.6 Conversion Costs

E nergy conservation standards typically cause manufacturers to incur one

- time

conversion costs to bring their production facilities and product designs into compliance

with new regulations. For the MIA, DOE classified these one

- time conversion costs into

two major groups: capital conversion costs and product conversion costs. Capital

conversion costs are one

- time investments in plant, property, and equipment to adapt or

change existing production facilities in order to fabric ate and assemble new product

design s that comply with energy conservation standards . Product conversion costs are

one

- time investments in research, development, testing, marketing

, and other costs to

make product designs comply with energy conservation sta ndards. DOE based its

estimates of the conversion costs for each efficiency level on information obtain ed from

manufacturer interviews and the design pathways analyzed in the engineering analysis.

12.4.6.1 Capital Conversion Costs

To estimate the level of capital conversion costs manufacturers would likely incur

to comply with an energy conservation standard , DOE relied on information obtained

through manufacturer interviews as well as the engineering analysis.

Based on both the engineering analysis and conversations with manufact urers,

DOE has determined that the proposed standard eliminating standing pilot lights would

primarily entail a component swap

, in which manufacturers would assemble hearth

product s using a different set of purchased parts for t he ignition system . Accordingly,

capital investment by manufacturers to re

- tool or reconfigure production facilities likely

would be limited . Consistent with this expectation, interviewed manufacturers stated that

they did not anticipate incurring signific ant capital conversion costs in order to comply

with the proposed standard. As a result , DOE assigned a nominal capital conversion cost

per manufacturer , equivalent to $10,000, to account for any one

- time capital investments

or reorganization of production lines that a standard eliminating standing pilot l ights

could potentially entail. DOE assigned this capital conversion cost estimate to each of the

90 hearth product manufacturers identified. Table 12.4

. 8 presents estimated capital

conversion costs for the industry resulting from an energy conservation standard

.

Table 12.4

. 8 Industry Cumulative Capital Conversion Costs ( 201 3 $ Millions)

TSL Capital Conversion

Costs (2013$ Millions)

1 0.9

12.4.6.2 Product Conversion Costs

As with capital conversion costs, DOE relied on manufacturer interviews and the

engineering analysis to eva luate product conversion costs. Because most hearth product

manufacturers already offer models with electronic ignition systems, and because the

12

- 12proposed standard would primarily entail a change in purchased parts rather than an

entire product redesign, many manufacturers indicated in interviews that they d id not

expect to incur significant product conversi on costs under the proposed standard . DOE

based its product conversion cost estimates on the assumption that manufacturers would

incur limited costs related to research and development , testing and certifi cation, and

development of marketing materials in order to bring into compliance models not

currently offered with an electronic ignition system option

.

During interviews, some manufacturers expressed concern that an energy

conservation standard could entail significant product conversion cost s related to labeling

requirements. Under Canadian law, manufacturers must test and label gas fireplaces,

stoves and inserts with a fireplace efficiency rating. If a federal energy conservation

standard mandated an alternative efficiency metric for hearth products ( e.g., annual fuel

utilization efficiency ), manufacturers indicated they could be required to hold separate

stock

- keeping units ( SKUs ) for the Canadian and U.S. markets to comply with each

jurisdiction’s r equirements. B ecause the proposed standard is a prescriptive design

requirement and does not establish a minimum efficiency rating or require products to be

labeled with a particular efficiency metric, DOE did not factor the cost of holding

duplicate SKUs into its estimated product conversion costs

.

To analyze potential product conversion costs, DOE reviewed publicly available

product literature to estimate an average number of models offered per manufacturer

, as

well as the average percentage of models o ffered only with standing pilot lights and

without t he option of electronic ignition . DOE scaled up these assumptions to arrive at an

estimated total of 781 models for the industry that could require product conversion costs

in the form design engineering, testing, certification, etc. DOE then assigned a fixed cost

per model, equivalent to $10,000, to arrive at total estimated product con version costs.

This methodology assumes all non

- compliant models ( i.e., models currently offered only

with standing pilot lights) would be redesigned to accommodate electronic ignition

systems. This represents a conservative assumption, as manufacturers may choose to

discontinue some models with standing pilot lights. Models already available with the

option of electronic ig nition would not require any one

- time conversion costs by the

manufacturer in order to achieve compliance.

Table 12.4

. 9 presents estimated product conversion costs f or the industry

resulting from a design standard eliminating standing pilot lights.

Table 12.4

. 9 Industry Cumulative Product Conversion Costs ( 2013 $ Millions)

TSL Product Conversion

Costs (2013$ Millions)

1 7 .8

12.4.7 Markup Scenarios

DOE modeled multiple standards

- case markup scenarios to represent uncertainty

surrounding the potential impacts of energy conservation stand ard s on prices and

12

- 13profitability . In the base case, DOE used the same markups applied in the engineering

analysis

. In the standards case, DOE modeled two markup scenarios to capture a range of

potential impacts on manufacturers following implementation of energy conservation

standards: (1) a preservation of gross margin percentage scenario ; an d (2) a preservation

of operating profit scenario . These scenarios lead to different markup values that , when

applied to the inputted MPCs, result ed in varying revenue and cash flow impacts.

12.4.7.1 Preservation of Gross Margin Percentage Scenario

Under the preser vation of gross margin percentage scenario, DOE applied a single

uniform gross margin percentage markup, which assumes that manufacturers would be

able to maintain the same amount of profit as a percentage of revenues under an energy

conservation standard

. As production costs increase with implementation of energy

conservation standard s , this scenario implies that the absolute dollar markup will increase

as well. Based on publicly available financial information for manufacturers of hearth

products as well as comments from manufacturer interviews, DOE assumed the average

markup

— which includes SG&A expenses, R&D expenses, interest, and profit

— to be

1.45 for all hearth products

. Because this markup scenario assumes that manufacturers

would be able to maintain their gross margin percentage markups as production costs

increase in response to an energy conservation standard, it represents a high bound to

industry profitability.

12.4.7.2 Preservation of Operating Profit Scenario

Under the preservation of per unit operating profit scenario, manufacturer markups

are set so that operating profit one year after the compliance date of the energy

conservation standard is the sa me as in the base case on a per

- unit basis. In this scenario,

as production costs increase with implementation of energy conservation standards

, manufacturers are generally required to reduce their markups to a level that maintains

base

- case operating profit per unit. The implicit assumption behind this markup scenario

is that the indu stry can only maintain its operating profit in absolute dollars per unit after

compliance with the new standard. Therefore, operating margin in percentage terms is

reduced between the base case and standards case. To analyze this markup scenario, DOE

adjus ted the manufacturer markups in the GRIM to yield approximately the same

earnings before interest and taxes in the standards case as in the base case. This markup

scenario represents a low bound to industry profitability under the proposed energy

conservat ion standard.

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 hearth industr y . The following sections

detail addit ional inputs and assumptions for the a nalysis of industry financial impacts

. 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

- 1412.5.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the

economic impacts of an energy conservation standard ( the standards case ) to a base case

that assumes no energy conservation standard . The INPV is different from DOE’s NPV,

which app lie s to the U.S. economy. The INPV is the sum of annual net cash flows over

the analysis period discounted at the industry’s cost of capital

, or discount rate. The

GRIM for this rulemaking estimates cash flows from 201 4 to 20 50

. This timeframe

models both the short

- term impacts on the industry from the announcement of the

standard until the compliance date (201 5 to 20 2 1 )

, and a l ong

- term assessment over the

30

- year analysis period used in the NIA (20 2 1 to 2050 ).

In the MIA, DOE compares the INPV of the base case to that of the standards

case. The difference between the base case and the standards case INPV is an estimate of

the economic impacts that implementing the standard would have on the industry

. While

INPV is useful for evaluating the long

- term effects of 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

ind ustry’s access to capital. Consequently, the sharp drop in financial performance could

cause investors to flee, even though recovery may be possible. Thus, a short

- term

disturbance can have long

- term effects that the INPV cannot capture. Figure 12.5

. 1 and

Figure 12.5

. 2 present the expected behavior of annual net cash flow s over the analysis

period. A s the figures illustrate, industry cash flows begin to decline after the publication

date of the final rule as companies use their financial resources to prepare for the energy

conservation standard. Cas h flows between the publication date and the compliance date

are driven by the level of conversion costs and by the proportion of these investments

made each year. All cash flows are discounted to the base year, 2014.

Free cash flow in the year the energy conservation standards take effect is driven

by two competing factors. In addition to capital and product conversion costs, energy

conservation standards could create (1) stranded assets, i.e

., tooling and equipment that

would have enjoyed longer use if t he energy conservation standard had not made them

obsolete. In this year, manufacturers write down the remaining book value of existing

tooling and equipment whose value is affected by the energy conservation standard. This

one

- time write down acts as a ta x shield that alleviates decreases in cash flow from

operations in the year of the write down. In this year, there is also (2) an increase in

working capital , which reduces cash flow from operations. A large increase in working

capital is needed due to mor e costly production components and materials, higher

inventory carrying to sell more expensive products, and higher accounts receivable for

more expensive products. Depending on these two competing factors, cash flow can

either be positively or negatively affected in the year the standard takes effect.

12.5.2 Hearth Industry Financial Impacts

Table 12.5

. 1 and Table 12.5

. 2 provide INPV estimates for the hearth industry

under the two markup scenarios analyzed

. Figure 12.5

. 1 and Figure 12.5

. 2 present annual

industry net cash flows under the two markup scenarios. As described in section 12.4.7

,

12

- 15the preservation of gross margin percentage scenario presents an upper bound to industry

profitability under an energy conservation standard while the preservation of operating

profit scenario presents a lower bound to industry profitability

.

Table 12.5

. 1 Preservation of Gross Margin Percentage Scenario Changes in INPV

for Hearth Products

Units Base Case Standards

Case

INPV 2013$M 125.3 125.8

Change in INPV 2013$M

- 0.5

% Change

- 0.4

Table 12.5

. 2 Preservation of Operating Profit Scenario Changes in INPV for Hearth

Products

Units Base Case Standards

Case*

INPV 2013$M 125.3 122.0

Change in INPV 2013$M

- ( 3.3 )

% Change

- ( 2.6 )

*Parentheses indicate negative values

Under an energy conservation standard, DOE estimates impacts on INPV to range

from

- $ 3.3 million to $ 0.5 million, or a change of

- 2.6 percent to 0.4 percent

. See section

12.8 for further discussion of results.

Figure 12.5

. 1 Annual Industry Net Cash Flows under Preservation of Gross Margin

Percentage Markup Scenario

$0.0 $2.0 $4.0 $6.0 $8.0 $10.0 $12.0

2013 2018 2023 2028 2033 2038 2043 20482013$M

Base Case

TSL 1

12

- 16Figure 12.5

. 2 Annual Industry Net Cash Flows under Preservation of Operating

Profit Markup Scenario

12.6 IMPACTS ON SUBGROUPS OF MANUFACTURERS

As discussed above, using average cost assumptions to develop an industry cash

flow estimate is not adequate for assessing differential impacts among subgroups of

manufacturers. Small manufacturers, niche players, or manufacturers exhibiting a cost

structure that differs largely from the industry average could be aff ected differently. DOE

used the results of the industry characterization to group manufacturers exhibiting similar

characteristics. Specifically, DOE identified two subgroups of manufacturers for separate

impact analyses: (1) manufacturers of gas log sets

, and (2) small business manufacturers.

12.6.1 Impacts on Manufacturers of Gas Log Sets

During interviews, multiple manufacturers commented that gas log sets represent

a distinct market segment within the gas hearth industry. These manufacturers indicated

that ga s log sets serve a different market niche and face different space constraints than

other gas hearth products. Additionally, gas log sets often sell at lower prices than other

gas hearth products. As a result, an increase in manufacturing costs and, by ext ension,

retail price resulting from an energy conservation standard

, could have a proportionally

greater impact on gas log sets relative to other gas hearth products.

Gas log sets are typically designed for use in existing wood

- burning fireplaces.

During interviews, manufacturers of gas log sets stated that unlike other gas hearth $0.0 $2.0 $4.0 $6.0 $8.0 $10.0 $12.0

2013 2018 2023 2028 2033 2038 2043 20482013$M

Base Case

TSL 1

12

- 17products, gas log sets compete with wood, coal, and wood/wax logs. These alternatives

are typically in expensive to purchase, such that consumers could feasibly substitute away

f rom gas log sets and toward wood and/or wood/wax logs if an energy conservation

standard leads to higher prices. According to these manufacturers, i f design constraints

specific to gas log sets cause an energy conservation standard to alter product aesthet ics,

it could further d rive consumer s to switch product s

.

Because gas log sets are designed to fit within existing wood

- burning fireplaces,

manufacturers indicated that design options for gas log sets are constrained by the

geometric configurations of pr e

- existing fireplaces. Manufacturers stated that electronic

ignition systems take up more space than standing pilot lights

, and that accommodating

electronic ignition systems inside existing fireplaces could, in turn, reduce the size of the

gas log sets co nsumers could purchase for their fireplaces. Manufacturers also indicated

that electronic ignition system components can be difficult to conceal within a gas log

set’s design. Unlike other gas hearth products, gas log sets are not sold as part of a

package d unit, leaving manufacturers with limited options for obscuring the gas valve,

pilot assembly, control module, wiring, and other components that make up an electronic

ignition system. As a result, these components may be more exposed when used with gas

lo g sets compared to other gas hearth products. Manufacturers also stated that electric

outlets may not be situated in close enough proximity to wood

- burning fireplaces to

enable ready installation of units with electronic ignition systems. In such cases, th e need

for extension cords could impact the aesthetic appeal of products. Alternatively, hiring an

electrician could raise installation costs and potentially deter price

- sensitive consumers.

Alongside aesthetic impacts, manufacturers expressed concern reg arding the cost

implications of a potential standard eliminating standing pilot lights. As discussed

previously, purchasing components for electronic ignition systems typically costs

manufacturers more than purchasing components for standing pilot lights. Higher

manufacturing costs, in turn, lead to higher retail prices. To estimate the potential

difference in cost resulting from a standard eliminating standing pilot lights, DOE

modeled the MPCs for both vented and unvented gas log sets using both standing pilot

lights and electronic ignition systems. DOE similarly modeled MPCs for other categories

of gas hearth products. Table 12.6

. 1 presents the relative increase in MPC for products

manufactured with electronic ignition systems as opposed to standing pilot lights. See

chapter 5 of the TSD for a more detailed discussion o f how MPCs were calculated

.

Table 12.6

. 1

. Added Cost of Electronic Ignition Systems vs. Standing Pilot Lights

Product Group Estimated Increase in

MPC of Switching from

Standing Pilot to

Electronic Ignition*

$ Increase in Cost

of Ignition

System

% Increase in

Overall MPC

%

Vented

Fireplace/Insert/Stove 28 56 9

Unvented

Fireplace/Insert/Stove 32 47 11

12

- 18Vented Gas Logs 70 227 37

Unvented Gas Logs 56 194 27

Outdoor 55 65 26

* S tanding pilot ignitions largely use two styles of gas valves: manual and millivolt. The incremental costs

of switching from standing pilot lights to electronic ignition systems presented here assume gas fireplaces,

inserts, and stoves use standing pilot lights with millivolt gas valves w hile gas log sets and outdoor hearth

products use standing pilot lights with manual gas valves. The millivolt gas valve uses a thermopile place

in the pilot light to generate a voltage difference, so that a remote control can be used to turn the burner on

and off. These valves are larger and more expensive than manual gas valves, which are operated by

hand. Based on public comments on previous rulemakings and manufacturer interviews, DOE recognizes

the importance of space constraints and cost burden associa ted with control systems for gas log sets. For

the purposes of analysis, DOE chose to represent gas log sets with standing pilots using manual gas valves.

Fireplaces, inserts, and stoves provide more opportunity to package and conceal larger, more complex

ignition systems. Accordingly, DOE chose to represent the standing pilot variation of this product category

with models using millivolt gas valves.

As the results above indicate, DOE estimates that the cost of switching from a

standing pilot light to an electronic ignition system could disproportionately affect gas

log set manufacturers. These results are driven by two primary factors. First, they are

based on the assumption that gas log sets use standing pilot lights with manual gas

valves, which are smaller and less expensive than standing pilot lights with millivolt gas

valves. Under this assumption, the higher cost of purchasing electronic ignition system

components would represent a more significant expenditure both in absolute dollars and

in percentage terms for manufacturers of gas log sets using manual standing pilot lights

relative to manufacturers of products using more expensive millivolt sta nding pilot lights.

Second, the overall cost of manufacturing gas log sets is often lower than the overall cost

of manufacturing other types of gas hearth products. This means the same increase in

MPC in absolute dollars would result in a higher proportion al increase in MPC for gas

log sets. Assuming, as described, that manufacturers of gas log sets are likely to see a

greater increase in MPC in absolute dollars compared to manufacturers of other products,

this would imply an even greater proportional incre ase in overall MPC of gas log sets. If

retail prices scale with MPCs, manufacturers indicated that demand for gas log sets from

price

- sensitive consumers could decline and, in turn, negatively affect manufacturer

profitability.

12.6.2 Impacts on Small Business Manufacturers

To better assess the potential impacts of this rulemaking on small entities, DOE

conducted a more focused inquiry of companies that could be small business

manufacturers of products covered by this rulemaking. For hearth products, the Small

B usiness Administration (SBA) has set a size threshold of 500 employees or fewer for an

entity to be considered a small business. This 5 0 0

- employee threshold includes all

employees in a business’s parent company and any other subsidiaries and applies to bot h

heating and decorative hearth products, categorized respectively under North American

Industry Classification System (NAICS) code 333414, “Heating Equipment (Except

Warm Air Furnaces) Manufacturing” and NAICS code 335228, “Other Major Household

Appliance Manufacturing.” Based on this threshold, DOE used p ublic ly available

12

- 19information to identify potential small manufacturers. DOE’s research involved industry

trade association membership directories ( e.g.

, HPBA ), information from previous

rulemakings

, individual company websites, and market research tools ( e.g.

, Hoover’s

reports) to create a list of companies that manufacture hearth products covered by this

rulemaking. DOE also asked stakeholders and i ndustry representatives if they were aware

of any additional small manufacturers during manufacturer interviews . DOE reviewed

publicly available data and contacted various companies on its complete list of

manufacturers to determine whether they met the SB A’s definition of a small business

manufacturer

. DOE screened out companies that do not manufacture products impacted

by this rulemaking, do not meet the definition of a “small business,” or are foreign owned

and operated.

DOE identified 90 manufacturers of gas hearth products that would be affected by

today’s proposal. Of these, DOE identified 66 as domestic small business manufacturers

. DOE contacted a subset of identified small businesses to invite them to take part in a

manufacturer impact analysis interview. Of 25 small businesses contacted, DOE was able

to reach and discuss potential standards with five. DOE also obtained information about

small businesses and potential impacts on small businesses while interviewing large

manufacturers.

In intervi ews, small manufacturers expressed concern regarding the impact of a

standard eliminating standing pilot lights on their ability to compete with larger

manufacturers. Manufacturers stated that gas hearth products with electronic ignition

systems cost more to produce than gas hearth products with standing pilot lights, as the

components purchased for electronic ignition systems tend to be more expensive. Since

large manufacturers often produce at higher volumes, they may be able to source

components at lower per

- unit prices than small manufacturers that produce at lower

volumes. Because small manufacturers may not benefit from the same economies of scale

as large manufacturers, an energy conservation standard eliminating standing pilot lights

could disproport ionately affect their production costs and, in turn, the prices at which

they sell their products. This anticipated change in MPCs drove small manufacturer

concerns surrounding the impact of an energy conservation standard on their ability to

remain compet itive in the gas hearth market.

To evaluate small manufacturers’ concerns regarding the competitive implications

of a standard eliminating standing pilot lights, DOE modeled the difference in cost small

manufactures might face when sourcing components at lower volumes. Due to limited

available information on the relative sales volumes of small and large manufacturers

, DOE selected volumes of 1,000 units ( used to represent small manufacturers) and 10,000

units ( used to represent large manufacturers) for eac h product group analyzed. DOE

developed its analysis based on the engineering teardown analysis and cost model as well

as manufacturer feedback on the costs of ignition systems.

Table 12.6

. 2 presents the estimated added per

- unit cost of an electronic ignition

system compared to a standing pilot system at the two representative production volumes

modeled. As the results indicate, manufacturers likely would pay less pe r unit when

12

- 20producing 10,000 units versus 1,000 units. Estimated costs would decline further as

production volumes climb higher.

Table 12.6

. 2 Added Cost of Electronic Ignition Systems at Representative

Production Volumes

Product Group Baseline

MPC

$ Added Cost of

EIS at 1,000

units

$ Added Cost of

EIS at 10,000

units

$

Vented Fireplace/Insert/Stove 322 31 26

Unvented

Fireplace/Insert/Stove 281 33 24

Vented Log Sets 190 70 58

Unvented Log Sets 208 69 51

Outdoor 210 65 42

DOE’s analysis suggests that a standard eliminating standing pilot lights would

increase the per

- unit MPCs of gas hearth products by a greater amount for small

- volume

producers than for large

- volume producers. Higher MPCs, in turn, typically lead to

higher end

- user purchase prices. If products manufactured by small businesses cannot

compete with products manufactured by large businesses at lower cost, small businesses

could potentially experience a decline in profits and /or choose to exit the market.

DOE provides additional analysis in section VI.B of the NOPR

, Review Under

the Regulatory Flexibility Act

.

12.7 OTHER IMPACTS

12.7.1 Direct Employment

12.7.1.1 Methodology

To quantitatively assess the impacts of an energy conservation standard on

employment, DOE used the GRIM to estimate the domestic labor expenditures and

number of employees in the bas e case and the standards case from 201 4 through 2050

. DOE used statistical data f rom the U.S. Census Bureau’s 2011 Annual Survey of

Manufacturers (ASM) , the results of the engineering analysis, and interviews with

manufacturers to d etermine the inputs necessary to calculate industry

- wide labor

expenditures and domestic employment levels. Labor expenditures related to

manufacturing of the product are a function of the labor intensity of the product, the sales

volume, and an assumption that wages remain fixed in real terms over time. The total

labor expenditures in each year are calculated by multiplying the MPCs by the labor

percentage of MPCs.

The total labor expenditures in the GRIM were then converted to domestic

production employ ment levels by dividing production labor expenditures by the annual

payment per production worker (production worker hours times the labor rate found in

12

- 21the ASM ). The production worker estimates in this section cover workers up to the line

supervisor level who are directly involved in fabricating and assembling a product within

the o riginal equipment manufacturer facility. Workers performing services that are

closely associated with production operations, such as materials handling tasks using

forklifts, ar e also included as production labor. DOE’s estimates only account for

production workers who manufacture the specific products covered by this rulemaking.

To estimate an upper bound to employment change, DOE assumes all domestic

manufacturers would choose to continue producing products in the U.S. and would not

move production to foreign countries. To estimate a lower bound to employment, DOE

estimates the maximum portion of the industry that would choose to leave the industry or

relocate production overse as rather than make the necessary conversions at domestic

production facilities.

12.7.1.2 Direct Employment Impacts

In the absence of energy conservation standards , DOE estimates that the hearth

industry would employ 1,565 domestic production workers in 2021

. This estimate

assumes U.S. production labor accounts for 86 percent of the industry total, a figure

based on the percentage of domestic manufacturers identified as a share of total

manufacturers identified for the industry.

Table 12.7

. 1 show s the range of impacts of potential energy conservation

standards on U.S. production workers of gas hearth products . The potential changes to

direct employment presented suggest that the industry could experience anything from a

slight loss in domestic direct employment to a loss of more than 900 domestic jobs

.

Table 12.7

. 1 Potential Changes in the Total Number of Production Workers in the

Gas Hearth Industry in 2021

Base Case Standards Case

Domestic Production

Workers in 2021 1,565 657 to 1,514

Potential Changes in

Domestic Production

Workers in 2021* - (908) to (51)

*Parentheses indicate negative values

The less severe end of the range of potential employment impacts estimates a loss

of 51 domestic production jobs in 2021 in the standards case. This assumes

manufacturers would continue to produce the same scope of c overed products within the

United States. However, because the shipment model predicts a decline in shipment

volumes under an energy conservation standard, DOE estimates a related reduction in

labor inputs and employment.

The more severe end of the range represents the maximum decrease in total

number of U.S. production workers that could be expected to result from an energy

12

- 22conservation standard. For the hearths industry, DOE assumed a worst

- case scenario in

which all products sold with standing pilot li ghts in the base case would be eliminated in

the standards case and would not be replaced by any additional sales of compliant

products. DOE then assumed industry labor requirements would shrink in proportion to

lost sales volumes. The NIA shipments analys is forecasts that 58 percent of base

- case

shipments would consist of units with standing pilot lights in 2021. Based on the worst

case scenario assumptions above, DOE modeled a 58

- percent decline in direct production

employment. As a result, DOE estimates a loss of up to 908 domestic production jobs in

2021 resulting from a design standard that eliminates standing pilot lights.

The direct employment impacts discussed here do not include indirect

employment impacts on the broader U.S. economy, which are doc umented in the

Employment Impact Analysis, c hapter 16 of the TSD.

12.7.2 Production Capacity

According to gas hearth manufacturers interviewed

, a standard eliminating

standing pilot lights would not likely constrain manufactur ing production capacity

. Converting a gas hearth product’s ignition system from a standing pilot light to electronic

ignition is primarily a matter of purchasing and assembling different ignition system

components. While this may entail higher costs for purchased parts and chang es in

assembly, it is not likely to impede manufacturers’ capacity to continue producing gas

hearth products in line with demand. Moreover, several manufacturers stated that the

higher costs of producing products with electronic ignition systems could lead to a

decline in demand, potentially leaving them with excess production capacity. In that

light, the proposed standard is not likely to trigger capacity constraints.

12.7.3 Cumulative Regulatory Burden

While any one regulation may not impose a significant burd en on manufacturers,

the combined effects of several impending regulations may have serious consequences

for some manufacturers, groups of manufacturers, or an entire industry. Assessing the

impact of a single regulation may overlook this cumulative regula tory burden. Multiple

regulations affecting the same manufacturer can strain profits and can lead companies to

abandon product lines or markets with lower expected future returns than competing

products. For these reasons, DOE conducts an analysis of cumul ative regulatory burden

as part of its rulemakings pertaining to appliance efficiency.

Companies that produce a wide range of regulated products may be faced with

more capital and product development expenditures than competitors with a narrower

scope of p roducts. Regulatory burdens can prompt companies to exit the market or reduce

their product offerings, potentially reducing competition. Smaller companies in particular

can be affected by regulatory costs

, as these companies have lower sales volumes over

w hich they can amortize the costs of meeting new regulations. A proposed standard is not

12

- 23economically justified if it contributes to an unacceptable level of cumulative regulatory

burden.

For the cumulative regulatory burden analysis, DOE looks at other pr oduct

specific federal regulations that could affect gas hearth products and that will take effect

approximately three years before or after the 20 21 compliance date of the proposed

energy conservation standard . In interviews, manufacturers cited a Consume r Product

Safety Commission regulation requiring barrier screens on gas hearth products. However,

this requirement is set to take effect in January 2015 and therefore is not considered in

this analysis. DOE did not identify any other federally mandated pro duct

- specific

regulations that will take effect in the three years before or after the 2021 compliance

date for this rulemaking

, and therefore has not presented any other regulations in this

analysis of cumulative regulatory burden.

12.8 CONCLUSION

The follo wing section summarize s the range of financial impacts gas hearth

manufacturers are likely to experience as a result of energy conservation standards. W hile

these scenarios bound the range of most plausible impacts on manufacturers, there

potentially could be circumstances that cause manufacturers to experience impacts

outside this range.

Table 12.8

. 1 presents a range of results reflecting both the preservation of gross

margin percentage markup scenario and the preservation of operating profit markup

scenario. As explained in section 12.4.7 , the preservation of operating profit scenario

accounts for the more severe impacts presented. Estimated conversion costs and free cash

flow in the year prior to the effective date of stand ards do not vary with the markup

scenario.

Table 12.8

. 1 Manufacturer Impact Analysis Results

Units Base Case

$ Standards Case*

$

INPV 2013$M 125.3 122.0 to 125.8

Change in INPV 2013$M

- (3.3) to 0.5

% Change

- (2.6) to 0.4

Product Conversion

Costs 2013$M

- 7.8

Capital Conversion

Costs 2013$M

- 0.9

Total Conversion Costs 2013$M

- 8.7

Free Cash Flow (2020) 2013$M 10.9 8.2

% Change

- (24.0 )

*Parentheses indicate negative values

12

- 24Under a standards case that eliminates standing pilot lights , DOE estimates the

impacts on INPV to range from

- $ 3.3 million to $ 0.5 million, or a change of

- 2.6 percent

to 0.4 percent. Industry free cash flow is estimated to decrease by $ 2.6 million, or a

change of 24.0 percent compared to the base

- case value of $ 10.9 million in the year

before the compliance date (2020).

DOE estimates that in the year of compliance (20 21 ), 42 percent of all gas hearth

shipments will already be sold with electronic igniti on systems. Because most

manufacturers already offer units with electronic ignition systems, and because the

conversion from standing pilot to electronic ignition primarily entails a change in

purchased parts, DOE estimates limited capital conversion costs to achieve compliance

with a new standard. Product conversion costs in the form of testing and certification

costs and potential redesign requirements account for the bul k of estimated conversion

costs. DOE estimates total i ndustry conversion costs of $8. 7 million under the proposed

energy conservation standard.

12

- 25REFER E NCES

1

. U.S. Securities and Exchange Commission

. Annual 10

- K Reports

. Various Years

.

< www.sec.gov >

2

. Hoovers Inc. Company Profiles

. Various Companies. < www.hoovers.com/ >

3

. U.S. Census Bureau, Annual Survey of Manufacturers: General Statistics: Statistics

for Industry Groups and Industries (2010) (Available at:

< http://www.census.gov/manufacturing/asm/index.html >)

4

. U.S. Small Business Association. Table of Small Business Size Standards

. 2012.

< http://www.sba.gov/content/table

- small

- business

- size

- standards >.

5

. McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of

Companies , 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons,

2000.

12

- 2613

- i CHAPTER 13. E MISSIONS I MPACT A NALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION

................................ ................................ ................................ ......... 13

- 1

13.2 AIR QUALITY REGULATI ONS AND EMISSIONS IM PACTS

............................... 13

- 2

13.3 POWER SECTOR AND SIT E EMISSIONS FACTORS

................................ ............. 13

- 3

13.4 UPSTREAM FACTORS

................................ ................................ ............................... 13

- 4

13.5 EMISSIONS IMPACT RES ULTS

................................ ................................ ................ 13

- 5

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 13.4.1 Electricity Upstream Emissions Factors

................................ ............................ 13

- 5

Table 13.4.2 Natural Gas Upstream Emissions Factors

................................ .......................... 13

- 5

Table 13.5.1 Cumulative Emissions Reductio n for Potential Standard for Hearth

Product Ignition Devices

................................ ................................ .................... 13

- 6

LIST OF FIGURES

Figure 13.5.1 Hearth Product Ignition Devices: CO 2 Total Emissions Reduction

.................. 13

- 7

Figure 13.5.2 Hearth Product Ignition Devices: SO 2 Total Emissions Reduction

................... 13

- 7

Figure 13.5.3 Hearth Product Ignition Devices: NO

x Total Emissions Reduction

.................. 13

- 8

Figure 13.5.4 Hearth Product Ignition Devices: Hg Total Emissions Reduction

.................... 13

- 8

Figure 13.5.5 Hear th Product Ignition Devices: N 2 O Total Emissions Reduction

.................. 13

- 9

Figure 13.5.6 Hearth Product Ignition Devices: CH 4 Total Emissions Reduction

.................. 13

- 913

- 1 CHAPTER 13. E MISSIONS I MPACT A NALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the

effect of potential energy conservation standards on power sector and site combustion emissions

of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ) and me rc ury (Hg). The

second component estimates the impacts of a potential standard on emissions of tw o additional

greenhouse gases, methane (CH 4 ) and nitrous oxide (N 2 O), as well as the reductions to emissions

of all species due to “upstream” activities in the fuel production chain. These upstream activities

comprise extraction, processing, and transporting fuels to the site of combustion . T he associated

emissions are referred to as upstream emissions. Together, 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 intensity factors

calculated by 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 c hapter

15 and in the report “Utility Sector Impacts of Reduced Electricity Demand” ( Coughlin

, 201 4). 2

Site emissions of CO 2 and NO X are estimated using emissions in tensity factors from a

publication of the Environmental Protection Agency (EPA)

. 3

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 methodolo gy developed by Coughlin (2013). 4 The upstream emissions

include both emissions from fuel combustion during extraction, processing and transportation of

fuel, and “fugitive” emissions (direct leakage to the atmosphe re) of CH 4 and CO 2

.

The emissions intensity factors are expressed in terms of physical units per MWh or

MMBtu of site energy savings. Total emissions reductions are estimated using the energy

savings calculated in the national impact analysis (chapter 10).

For CH 4 and N 2 O, DOE also presents results in terms of units of carbon dioxide

equivalent (CO 2 e). Gases are converted to CO 2 e by multiplying the physical units by the gas

global warming potential (GWP) over a 100 year time horizon. Based on the Fifth Assessment

Report of the Intergovernmental Panel on Climate Change, 5 DOE used GWP values of 28 for

CH 4 and 265 for N 2 O. b

a www.epa.gov/climateleadership/inventory/ghg

- emissions.html

b The values are without inclusion of climate

- carbon feedbacks in response to emissions of the indicated non

- CO 2

gases.13

- 2 13.2 AIR QUALITY REGULATI ONS AND E MISSIONS I MPACTS

Each annual version of the AEO incorporates the projecte d impacts of existing air quality

regulations on emissions. AEO 2014 generally represents current Federal and State legislation

and final implementation regulations in place as of the end of October 2013.

SO 2 emissions from affected electric generating units (EGUs) are subject to nationwide

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual

emissions cap on SO 2 for affected EGUs in the 48 contiguous states and the Dis trict 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). 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 Ru le (CSAPR). 76 FR 48208 (August 8, 2011 ). On August 21,

2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, LP

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

CAIR. The AEO 201 4 emissions factors used for the present analysis assume that CAIR remains

a binding regulation through 2040. c

The attainment of emissions caps is typically flexible among affected Electric Generating

Units (EGUs) and is enforced through the use of emissi ons 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 emis sions 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

emission s would occur for SO 2 as a result of standards.

Beginning in 201 6 , however, SO 2 emissions will fall as a result of the Mercury and Air

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

n on

- 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

c 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. 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 stat es was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision

that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation , No 12

- 1182, slip op. at 32

(U.S. April 29, 2014). ). On October 23, 2014 , the D.C. Circuit lifted the stay of CSAPR and CSAPR went into

effect (and the CAIR sunset) in January 1, 2015. Because DOE is using emissions factors based on AEO 201 4 , the

analysis assumes that CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not

relevant for the purpose of DOE's analysis of SO 2 emissions.13

- 3 201 4 assumes that, in order to continue operating, coal plants must hav e either flue gas

desulfurization or dry sorbent injection systems installed by 201 6 . Both technologies, which are

used to reduce acid gas emissions, also reduce SO 2 emissions. Under the MATS, e missions 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 201 6 and beyond.

CAIR established a cap on NO

x emissions in 28 eastern States and the District of

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

x emissions in

those States covered by CSAPR because excess NO

x emissions allowances resulting from the

lower electricity demand could be used to permit 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 caps and, as such, DOE’s energy conservation standards would likely reduce Hg

emissions. DOE estimated mercury emi ssions reductions using the NEMS

- BT based on AEO

2014 , which incorporates the MATS.

13.3 P O WER S E CTOR AND S ITE E MISSIONS F ACTORS

The analysis of power sector emissions uses marginal emissions intensity factors derived

from analysis of the AEO 2014 reference and a number of side cases incorporating enhanced

equipment efficiencies. To model the impact of a standard, DOE calculates factors that relate a

unit reduction to annual site electricity demand for a given end use to corresponding reductions

to installed capacity by fuel type , fuel use for generation, and power sector emissions. Details on

the approach use d may be found in Coughlin ( 2014 ).

Table 13.3.1 present s 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 for space heating in homes. The average factors for each year take into account

the projected shares of each of the source s in total electricity generation

.

Table 13.3.2 presents the natural gas site combustion emissions factors for selected years.

Table 13.3.1 Power Plant Emissions Factors

Unit* 202 1 2025 2030 2035 2040

CO 2 kg/MWh 7 38 656 592 540 491

SO 2 g/MWh 7 31 590 496 417 373

NOx g/MWh 5 85 497 434 382 345

Hg g/MWh 0. 002 25 0.00182 0.00153 0.00129 0.00115

N 2 O g/MWh 7.2 7.1 6.9 6.6 6.4

CH 4 g/MWh 50. 1 49.4 47.9 46.4 44.813

- 4 * Refers to site electricity savings.

Table 13.3.2 Natural Gas Site Combustion Emissions Factors

Unit* 202 1 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.

13.4 UPSTREAM F ACTORS

The upstream emissions accounting uses the same approach as the upstream energy

acco unting described in appendix 10 B

. See also Coughlin (201 3 ) and Coughlin (2014). When

demand for a particular fuel is reduced, there is a corresponding reduction in the emissions from

combustion of that fuel at either the building site or the power plant. The associated reduction in

energy use for upstream activities leads to further reductions in emissions. These upstream

emissions are defined to include the combustion emissions from t he fuel used upstream, the

fugitive emissions associated with the fuel used upstream, and the fugitive emissions associated

with the fuel used on site.

Fugitive emissions of CO 2 occur during oil and gas production, but are small relative to

combustion e mi ssions. They comprise about 2.5 percent of total CO 2 em issions 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 emissi ons (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 coal, and 93 percent for

petroleum fuels.

U pstream emissions factors accoun t for both fugitive emissions and combustion

emissions in extraction, processing, and transport of primary fuels. Fugitive emissions factors for

methane from coal mining and natural gas production were estimated based on a review of recent

studies compiled by Burnham (2011). 6 This review includes estimates of the difference between

fugitive emissi ons factors for conventional production of natural vs. unconventional (shale or

tight gas). These estimates rely in turn on data gathered by EPA under new GHG reporting

requirements for the petroleum and natural gas industries. 7

, 8 As more data are made available,

DOE will continue to update these estimated emissions factors.

For ease of application in its analysis, DOE developed all of the emissions factors using

site (point of use) energy savings in the denominator. Table 13.4.1 presents the electricity

upstream emissions factors for selected years . The caps that apply to power sector NO X emissions do not apply to upstream combustion sources.13

- 5 Table 13.4.1 Electricity Upstream E missions Factors

Unit 202 1 2025 2030 2035 2040

CO 2 kg/MWh 29. 2 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 3 70 375 382 387 387

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, 1 57 2,195 2,216 2,248 2,255

Table 13.4.2 illustrates the natural gas upstream emissions factors for selected years.

These were used to estimate the emissions associated with the increased gas use at some of the

considered efficiency levels.

Table 13.4.2 Natural Gas Upstream Emissions Factors

Unit 202 1 2025 2030 2035 2040

CO 2 kg/ mcf 7. 2 7.2 7.3 7.4 7.4

SO 2 g/ mcf 0. 03 1 0.031 0.031 0.032 0.032

NOx g/ mcf 10 2 103 105 105 105

N 2 O g/ mcf 0.011 0.011 0.012 0.012 0.012

CH 4 g/ mcf 6 61 665 666 670 670

13.5 E MISSIONS I MPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of

products sold in 2021

- 2050 for each TSL. Negative values indicate that emissions increase.13

- 6 Table 13.5.1 Cumulative Emissions Reduction for Potential Standard for Hearth

Product Ignition Devices

TSL

1

Power Sector and Site Emissions

CO 2 (million metric tons) 32.3

SO 2 (thousand tons)

- 4.23

NO X (thousand tons) 49.2

Hg (tons)

- 0.0137

N 2 O (thousand tons) 0.279

CH 4 (thousand tons) 0.00634

Upstream Emissions

CO 2 (million metric tons) 4.78

SO 2 (thousand tons)

- 0.0275

NO X (thousand tons) 75.8

Hg (tons)

- 0.00011

N 2 O (thousand tons) 485

CH 4 (thousand tons) 0.00629

Total Emissions

CO 2 (million metric tons) 37.0

SO 2 (thousand tons)

- 4.26

NO X (thousand tons) 125

Hg (tons)

- 0.0138

N 2 O (thousand tons) 486

CH 4 (thousand tons) 0.0126

Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for

each type of emission from each TSL. The reductions reflect the lifetime impacts of products

sold in 2021

- 2050.13

- 7 Figure 13.5.1 Hearth Product Ignition D evice s: CO 2 Total Emissions

Reduction

Figure 13.5.2 Hearth Product Ignition Device s : SO 2 Total Emissions

Reduction 0 200 400 600 800 1,000 1,200 1,4002021202620312036204120462051205620612066207120762081208620912096kt CO

2 Emissions Reduction

TSL 1

-140 -120 -100

-80 -60 -40 -20

02021202620312036204120462051205620612066207120762081208620912096ton

SO

2 Emissions Reduction

TSL 113

- 8 Figure 13.5.3 Hearth Product Ignition Device s: NO

x Total Emissions

Reduction

Figure 13.5.4 Hearth Product Ignition Device s : Hg Total Emissions

Reduction 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,5002021202620312036204120462051205620612066207120762081208620912096ton NO

x Emissions Reduction

TSL 1

-0.0005 -0.0004 -0.0004 -0.0003 -0.0003 -0.0002 -0.0002 -0.0001 -0.0001 0.00002021202620312036204120462051205620612066207120762081208620912096ton

Hg Emissions Reduction

TSL 113

- 9 Figure 13.5.5 Hearth Product Ignition Device s : N 2 O Total Emissions

Reduction

Figure 13.5.6 Hearth Product Ignition Device s : CH 4 Total Emissions

Reduction 0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5 0.52021202620312036204120462051205620612066207120762081208620912096ton N

2 O Emissions Reduction

TSL 1

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,0002021202620312036204120462051205620612066207120762081208620912096ton CH

4 Emissions Reduction

TSL 113

- 10 REFERENCES

1. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

2. Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand , 2014. Lawrence

Berkeley National Laboratory. Report No. LBNL

- 6864E.

3. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors,

AP

- 42, Fifth Edition, Volume I: Stationary Point and Area Sources . 1998.

www.epa.gov/ttn/chief/ap42/index.html

4. Coughlin, K., Projections of Full

- Fuel

- Cycle Energy and Emissions Metrics , 2013.

Lawrence Berkeley National Laboratory. Report No. LBNL

- 6025E.

5. Intergovernmental Panel On C limate Change, Chapter 8 . In Climate Change 2013: The

Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report

of the Intergovernmental Panel on Climate Change , T.F. Stocker, D. Qin, G.

- K. Plattner,

M. Tignor, S.K. Allen, J. B oschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley,

Editor. 2013. Cambridge University Press: Cambridge, United Kingdom and New York,

NY, USA

6. Burnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, I. Palou

- Rivera, Life

- Cycle

Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum

. Environmental Science & Technology , 2011. 46(2): pp. 619

- 627

7. U.S. Environmental Protection Agency, Fugitive Emissions Reporting from the

Petroleum and Natural Gas Industry , 2009. U.S. Environmental Prote ction Agency.

Washington, DC.

8. 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 t he Final New Source

Performance Standards , 2012. U.S. Environmental Protection Agency. Washington, DC.14

- i CHAPTER 14. MONETIZATION OF EMIS SION REDUCTIONS BENE FITS

TABLE OF CONTENTS

14.1 Introduction

................................ ................................ ................................ .................... 14

- 1

14.2 Monetizing Carbon Dioxide Emissions

................................ ................................ ......... 14

- 1

14.2.1 Social Cost of Carbon

................................ ................................ ................................ .... 14

- 1

14.2.2 Monetizing Carbon Dioxide Emissions

................................ ................................ ......... 14

- 2

14.3 Development of Social Cost Of Carbon Values

................................ ............................ 14

- 2

14.3.1 Current Approach and Key Assumptions

................................ ................................ ...... 14

- 2

14.3.2 Limitations of Current Estimates

................................ ................................ ................... 14

- 4

14.4 Valuation of Other Emissions Reductions

................................ ................................ ..... 14

- 5

14.5 Results

................................ ................................ ................................ ............................ 14

- 5

LIST OF TABLES

Table 14.3.1 Annual SCC Values for 2010

- 2050 from 2010 Interagency Report (in

2007$ per Metric Ton)

................................ ................................ ....................... 14

- 3

Table 14.3.2 Annual SCC Values for 2010

– 2050 from 2013 Interagency Update (in

2007$ per Metric Ton of CO 2 )

................................ ................................ ........... 14

- 4

Table 14.5.1 Estimates of Global Present Value of CO 2 Emissions Reduction under

TSLs for Hearth Product Ignition Devices

................................ ........................ 14

- 5

Table 14.5.2 Estimates of Domestic Present Value of CO 2 Emissions Reduction under

TSLs for Hearth Product Ignition Devices

................................ ........................ 14

- 6

Table 14.5.3 Estimates of Present Value of NO X Emissions Reduction under TSLs for

Hearth Product Ignition Devices

................................ ................................ ........ 14

- 614

- 1 CHAPTER 14. MONETIZATION OF EMIS SION REDUCTIONS BENE FITS

14.1 INTRODUCTION

As part of its assessment of the effe cts of potential energy conservation standards for

hearth product ignition device s , the U.S. Department of Energy ( DOE ) estimated the monetary

benefits of the reduced emissions of carbon dioxide (CO 2 ) and nitrogen oxides (NO X ) that would

be expected to res ult from each trial standard level ( TSL ) considered for hearth product s . This

chapter summarizes the basis for the monetary values assigned to emissions and presents the

modeled benefits of e stimated reductions

.

14.2 MONETIZING CARBON DI OXIDE EMISSIONS

One challenge for anyone attempting to calculate the monetary benefits of reduced

emissions of CO 2 is what value to assign to each unit eliminated. The value must encompass a

broad range of p h ysical, economic, social, and political effects. Analysts developed the concept

of the social cost of carbon (SCC) to represent the broad cost or value associated with

producing

— or reducing

— a quantifiable amount of CO 2 emissions

.

14.2.1 Social Cost of Carbon

The SCC is an estimate of the monetized damages associated with an inc remental

increase in carbon emissions in a given year. The SCC 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. SCC estim ates are provided in dollars per metric

ton of carbon dioxide

. A value for the domestic SCC is meant to re present the damages in the

United States resulting from a unit change in carbon dioxide emissions, whereas a global SCC is

meant to reflect the value of damages worldwide.

Under section 1(b) (6) of Executive Order 12866

, 1 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 tha t the benefits of the intended regulation justify its costs.” The purpose

of the SCC estimates required by the Executive Order is to enable agencies to incorporate the

monetized social benefits of reducing CO 2 emissions into cost

- benefit analyses of regula tory

actions. The estimates are presented with an acknowledgement of the many uncertainties

involved and with a clear understanding that they will need updating in response to increasing

knowledge of the science and economics of climate impacts.

As part of the interagency process that developed the SCC estimates, technical experts

from numerous agencies met regular ly to explore the technical literature in relevant fields,

discuss key model inputs and assumptions, and consider public comments. The primary objective

of the process was to develop a range of SCC values using a defensible set of assumptions14

- 2 regarding model inputs that was grounded in the scientific and economic literature. In this way,

key uncertainties and model differences transparently and c onsistently inform the range of SCC

estimates developed for use in the rulemaking process.

14.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic impacts of carbon dioxide

emissions, the analyst faces several serious challe nges. A report from the National Research

Council 2 points out that any assessment will suffer from uncertainty, speculation, and lack of

information about (1) futur e emissions of greenhouse gases, (2) the effects of past and future

emissions on the climate system, (3) the effects of changes in climate on the physical and

biological environment, and (4) the translation of those environmental impacts into economic

dama ges. As a result, any effort to quantify and monetize the harms associated with climate

change raise s serious questions of science, economics, and ethics and s hould be viewed as

provisional.

Despite the limits of both quantification and monetization, SCC estimates can be useful

in estimating the social benefits of reducing CO 2 emissions. An 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 val ues appropriate for that year. The n the net present value of

the benefits can be calculated by multiplying each of the future benefits by an appropriate

discount factor and summing across all affected years. This approach assumes that the marginal

damages from increased emissions are constant for small departures from the baseline emissions

path, an approximation that is reasonable for policies that have effects on emissions that are

small relative to cumulative global carbon dioxide emissions.

14.3 DEVELOPMENT OF SOCIAL COST OF CA RBON VALUES

In 2009, an interagency process was initiated to develop a preliminary assessment of how

best to quantify the benefits from reducing carbon dioxide emissions. To provide consistency in

how benefits are evaluated across Fede ral 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 ori ginal analysis. Instead, it combined SCC estimates from the literature to use as

interim values until a more comprehensive analysis could be conducted. The outcome of the

preliminary assessment was a set of five interim values: global SCC estimates for 200 7 (in

2006 $ ) of $55, $33, $19, $10, and $5 per ton of CO 2

. 3 Those interim values represented the first

sustained interagency effort within the U.S. government to develop an SCC for use in regulatory

analysis. The resu lts of th at preliminary effort were presented in several proposed and final rules.

14.3.1 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened regular ly to

improve the SCC estimates. Speci fic ally, the gro up considered public comments and further

explored the technical literature in relevant fields. The interagency group relied on three14

- 3 integrated assessment models commonly used to estimate the SCC . The models are known by

their acronyms of FUND, DICE, and PAGE. Those three models frequently are cited in the peer

reviewed literature and were used in the most recent assessment of the Intergovernmental Panel

on Climate Change. Each model was given equal weight in developing SCC values.

Each model takes a sli ghtly different approach to calculating how increases in emissions

produce economic damages. A key objective of the interagency process was to enable a

consistent exploration of the three models while respecting the different approaches taken by the

key mo delers in the field. An extensive review of the literature identified three sets of input

parameters for the models: climate sensitivity; socioeconomic and emissions trajectories; and

d iscount rates

. A probability distribution for climate sensitivity was specified as an input to 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 develop ers’ best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses

. Three

values are based on the average SCC from the three integrated assessment models, at discount

rates of 2.5 percent , 3 percent , and 5 pe rcent. The fourth value, which represents the 95 th

percentile of the SCC estimate across all three models at a 3

- percent discount rate, is included to

represent larger

- than

- expected effects from temperature change s f a rther out in the tails of the

SCC distr ibution. The values increase 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 preference is given to consideration of the

global benefits of reducing CO 2 emissions

. Table 14.3.1 presents the values in the 2010

interagency group report

. 4

Table 14.3.1 Annual SCC Values for 2010

- 2050 from 2010 Interagency Report (in

2007 $ per Metric Ton )

Year Discount Rate ( % )

5 3 2.5 3

Average Average Average 95 th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC values used for the analysis of the effects of po tential standards for hearth

product s were generated using the most recent versions of the three integrated assessment

models that have been published in the peer

- reviewed literature, as described in the 2013 update14

- 4 from the interagency working group (revised November 2013). Table 14.3.2 shows the updated

sets of SCC estimates in 5

- year increments from 2010 to 2050. The full set of annual SCC

estimates for 2010

– 2 050 is presented in appendix 14 B of this TSD. The central value that

emerges is the average SCC across models at a 3

- percent discount rate. To capture the

uncertainties involved in regulatory impact analysis, however, the interagency group emphasizes

the importance of including all four sets of SCC values.

Table 14.3.2 Annual SCC Values for 2010

– 2050 from 2013 Interagency Update (in

2007 $ per Metric Ton of CO 2 )

Year Discount Rate ( % )

5 3 2.5 3

Average Average Average 95 th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

14.3.2 Limitations of Current Estimates

The interagency group recognizes that current models are imperfect and incomplete.

Because k e y uncertainties remain

, current SCC estimates should be treated as provisional and

revisable . Estimates doubtless will evolve in response to improved scientific and economic

understanding. The 2009 National Research Council report points out the tension between

producing quantif ied estimates of the economic damages from an incremental ton of carbon and

the limits of current modeling efforts. Several analytic challenges are being addressed by the

research community, some by research programs housed in many of the Federal agencies

participating in the interagency process. The interagency group intends to review and reconsider

SCC estimates periodically to incorporate expanding knowledge of the science and economics of

climate impacts, as well as improvements in modeling.

In summar y, in considering the potential global benefits resulting from reduced CO 2 emissions, DOE used the values from the 2013 interagency report , applying the GDP price

deflator to adjust the values to 2013 $. For the four SCC values, the values of emissions in 2 015

were $1 2

. 0 , $ 40.5 , $6 2

. 4 , and $11 9 per metric ton avoided (values expressed in 2013 $). DOE

derived values after 2050 using the relevant growth rates for 2040

– 2050 in the interagency

update.

DOE multiplied the CO 2 emissions reduction estimated for each year by the SCC value

for that year under each discount rate . To calculate a present value of the stream of monetary14

- 5 values, DOE discounted the values in each of the four cases using the same discount rate that had

b een used to obtain the SCC values in each case.

14.4 VALUATION OF OTHER E MISSIONS REDUCTIONS

DOE considered the potential monetary benefit s of reduced NO X emissions attributable to

the TSLs considered for hearth product ignition device s . As noted in chapter 13, new or amended

energy conservation standards would reduce NO X emissions in those States that are not affected

by emissions caps. DOE estimated the monetized value of NO X emissions reductions resulting

from each TSL based on estimates of environmental dama ge found in the scientific literature.

E stimates suggest a wide range of monetary values, from $4 7 6 to $4,89 3 per ton ( in 2013 $). 5

DOE calculated monetary benefits using a medi an value for NO X emissions of $2,684 per short

ton (in 2013$), at real discount rates of 3 percent and 7 percent.

DOE continues to evaluate appropriate values for monetizing avoided SO 2 and Hg

emissions. DOE did not monetize those emissions for this analysis

.

14.5 RESULTS

Table 14.5.1 p resent s the global values of CO 2 emissions reductions for each considered

TSL.

Table 14.5.1 Estimates of Global Present Value of CO 2 Emissions Reduction under

TSLs for Hearth Product Ignition Device s

TSL SCC Case*

5% discount

rate, average* 3% discount

rate, average* 2.5% discount

rate, average* 3% discount rate,

95 th percentile*

Million 201 3 $

Primary Energy Emissions

1 196 956 1535 2966

Upstream Emissions

1 29 142 228 440

Full

- Fuel

- Cycle Emissions

1 226 1098 1763 3405

* For each of the four cases , the corresponding global SCC value for emissions in 2015 is $ 12.0

,

$ 40.5 , $ 62.4 , and $ 119 per metric ton ( 2013 $).

After calculating global values of CO 2 emissions reductions for each considered TSL,

DOE calculated domestic values as a range of from 7 percent to 23 percent of the global values.

Results for domestic values are presented in Table 14.5.2

.14

- 6 Table 14.5.2 Estimates of Domestic Present Value of CO 2 Emissions Reduction under

TSLs for Hearth Product Ignition Device s

TSL SCC Case*

5% discount

rate, average* 3% discount

rate, average* 2.5% discount

rate, average* 3% discount rate,

95 th percentile*

Million 2013 $

Primary Energy Emissions

1 13.7 to 45.2 66.9 to 220.0 107.5 to 353.1 207.6 to 682.1

Upstream Emissions

1 2.0 to 6.7 9.9 to 32.6 15.9 to 52.3 30.8 to 101.1

Full

- Fuel

- Cycle Emissions

1 15.8 to 51.9 76.9 to 252.6 123.4 to 405.4 238.4 to 783.2

* For each of the four cases , the corresponding SCC value for emissions in 2015 is $ 12.0 , $ 40.5

,

$ 62.4 , and $ 119 per metric ton ( 2013 $).

Table 14.5.3 presents the present value of cumulative NO X emissions reductions for each

TSL

. Monetary v alues are calculated using the average dollar

- per

- ton values assigned to NO X

emissions at 7

- percent a nd 3

- percent discount rates.

Table 14.5.3 Estimates of Present Value of NO X Emissions Reduction under TSLs for

Hearth P roduct Ignition Device s

TSL 3% discount rate 7% discount rate

Million 2013$

Primary Energy Emissions

1 58 23

Upstream Emissions

1 90 35

Full

- Fuel

- Cycle Emissions

1 148 5814

- 7 REFERENCES

1. Presidential Documents

-- Executive Order 12866

-- Regulatory Planning and Review

, October 4, 1993. < www.archives.gov/federal

- register/executive

- orders/pdf/12866.pdf >

2. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Us e , 2010. National Academies Press. Washington, DC.

< www.nap.edu/catalog.php?record_id=12794 >

3. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for

Regulatory Impact Analysis under Executive Order 12866 , February 2010. United States

Government. Washington, D.C.

< www.whitehouse.gov/sites/default/files /omb/inforeg/for

- agencies/Social

- Cost

- of

Carbon

- for

- RIA.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 , May

2013; revised November 2013. United States Government. Washington, D.C.

< www.whitehouse.gov/sites/default/files/omb/assets/inforeg/ technical

- update

- social

cost

- of

- carbon

- for

- regulator

- impact

- analysis.pdf >

5. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs

and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal

Entities , 2 006. Washington, DC.

< www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_fina

l_report.pdf >15

- i CHAPTER 15. UTILITY I MPACT A NALYSIS

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

- 11

LIST OF TABLES

Table 15.3.1 Hearth Product Ignition Devices: Summary of Utility Impact Results

........... 15

- 11

LIST OF FIGURES

Hearth Product Ignition Devices: Total Electric Capacity Reduction

............... 15

- 4 Figure 15.3.1

Hearth Product Ignition Devices: Coal Capacity Reduction

............................. 15

- 5 Figure 15.3.2

Hearth Product Ignition Devices: Nuclear Capacity Reduction

........................ 15

- 5 Figure 15.3.3

Hearth Product Ignition Devices: Gas Combined Cycle Capacity Figure 15.3.4

Reduction

................................ ................................ ................................ ........... 15

- 6

Hearth Product Ignition Devices: Peaking Capacity Reduction

........................ 15

- 6 Figure 15.3.5

Hearth Product Ignition Devices: Renewables Capacity Reduction

.................. 15

- 7 Figure 15.3.6

Hearth Product Ignition Devices: Total Generation Redu ction

......................... 15

- 8 Figure 15.3.7

Hearth Product Ignition Devices: Coal Generation Reduction

.......................... 15

- 8 Figure 15.3.8

Hearth Product Ignition Devices: Nuclear Generation Reduction

..................... 15

- 9 Figure 15.3.9

Hearth Product Ignition Devices: Gas Combined Cycle Generation Figure 15.3.10

Reduction

................................ ................................ ................................ ........... 15

- 9

Hearth Product Ignition Devices: Oil Generation Reduction

.......................... 15

- 10 Figure 15.3.11

Hearth Product Ignition Devices: Renewables Generation Reduction

............ 15

- 10 Figure 15.3.1215

- 1 CHAPTER 15. UTILITY I MPACT A NALYSIS

15.1 INTRODUCTION

In the utility impact analysis for hearth product ignition devices , the U.S. Dep artment of

Energy (DOE) analyzed the changes in electric installed capacity and power generation that

result for each trial standard level (TSL).

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 incor porates 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 t he 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 an d demand sectors.

• Using EIA published side cases to estimate the utility impacts enhances the

transparency of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be

reduced under the new approach.

On average, howe ver, over the full analysis period, the results from the new approach are

comparable to results from the old approach.

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration

documentation. A useful summary is National Energy Modeling System: An Overview

. 115

- 2 15.2 METHODOLOGY

DOE estimate s the marginal impacts of re duction in energy demand on the energy supply

sector. In principle, marginal values should provide a better estimate of the actual impact of

energy conservation standards

.

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 internal ly to load

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 use. The change in

total generating capacity is sensitive to the degree to which the end

- use is peak coincident, while

the capacity mix is sensitive to the hourly load shape associated with the end use. Changes in

generation by fuel type lead in turn to changes in total power sector emissions of 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 side case are documented in Appendix E of the AE O. 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 Available Technology (highest efficiency irrespec tive of cost) ; • High Technology (higher penetration rates for efficiency and demand management) ; • Extended Policies (includes efficiency standards that are not in the reference)

.

Scenarios that incorporate policies that directly affect the power sector without changes

in energy demand (for example

, subsidies for renewables, or high fuel price assumptions) are not

appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply

- side data on generation, c apacity 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 l located to one of three

categories: on

- peak, shoulder, and off

- peak. These categories are used in the utility sector to

correlate end

- use consumption with supply types. For each of the end

- uses that are modeled

explicitly in NEMS , load shape information is used to identify the fraction of annual

electricity use assigned to each category. On

- peak hours are defined as noon

- 5pm, June

through September. Off

- peak hours are nights and Sundays. All other hours are assigned to

the shoulder period.15

- 3 3. For each year an d each side case , the demand

- side reductions to on

- peak, off

- peak and

shoulder

- period electricity use are matched on the supply

- side to reductions in generation by

fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. Th e

allocation is based on the following rules:

3.1. All petroleum

- based generation is allocated to peak periods ;

3.2. Natural gas generation is allocated to any remaining peak reduction; this is consistent

with the fact that oil and gas steam units are used in NEMS t o meet peak demand ;

3.3. Base

- load generation (nuclear and coal) is allocated proportionally to all periods ;

3.4. The remaining generation of all types is allocated to the remaining off

- peak and shoulder

reductions proportionally

.

4. The output of step 3 defines fuel

- share weights giving the fraction of energy demand in each

load category that is met by each fuel type as a function of time . These are combined with the

weights that define the load category shares by end

- use to produce coefficients that allocate a

m arginal reduction in end

- use electricity demand to each of the five fuel types

.

5. A regression model is used to relate reductions in generation by fuel type to reductions in

emissions of power sector pollutants. The model produces coefficients that define t he 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 r elate emissions changes to changes

in end

- use demand.

6. A regression model is used to relate reductions in generation by fuel type to reductions in

installed capacity. The categories used for installed capacity are the same as for generation

except for peak : NEMS uses two peak capacity types (combustion 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 corresponding fuel type. These coefficients are

combined with the weights calculated in step 4 to produce coefficients that relate installed

capacity changes to changes in end

- use demand , as a function of tim e

.

7. The coefficient time

- series for fuel share, pollutant emissions and capacity for the

appropriate end use are multiplied by the stream of energy savings calculated in the NIA to

produce estimates of the utility impacts.

This analysis ignores p umped sto rage , 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 S ector I mpacts of E lectricity

D emand R eduction s”

. 315

- 4 15.3 U TILITY I MPACT RESULTS

This section presents results of the analysis for all of the capacity types.

15.3.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that

result for each TSL by major plant type for selected years. Note that a negative number means an

increase in capacity under a TSL

.

Hearth Product Ignition Device s : Total Electric Capacity Figure 15.3.1

Reduction -90 -80 -70 -60 -50 -40 -30 -20 -10

02021202620312036204120462051205620612066207120762081208620912096MW

Total

TSL 115

- 5 Hearth Product Ignition Device s : Coal Capacity Reduction Figure 15.3.2

Hearth Product Ignition Device s : Nuclear Capacity Reduction Figure 15.3.3 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0

-8.0 -6.0 -4.0 -2.0 0.02021202620312036204120462051205620612066207120762081208620912096MW

Coal

TSL 1

-2.50 -2.00 -1.50 -1.00 -0.50 0.002021202620312036204120462051205620612066207120762081208620912096MW

Nuclear

TSL 115

- 6 Hearth Product Ignition Device s : Gas Combined Cycle Figure 15.3.4

Capacity Reduction

Hearth Product Ignition Devices : Peaking Capacity Reduction Figure 15.3.5 -40 -35 -30 -25 -20 -15 -10

-5 02021202620312036204120462051205620612066207120762081208620912096MW

NGCC

TSL 1

-1.20 -1.00 -0.80 -0.60 -0.40 -0.20 0.002021202620312036204120462051205620612066207120762081208620912096MW

Peaking

TSL 115

- 7 Hearth Product Ignition Device s : Renewables Capacity Figure 15.3.6

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. Note that a negative number means an increase in generation under a

TSL

. -20.0 -18.0 -16.0 -14.0 -12.0 -10.0

-8.0 -6.0 -4.0 -2.0 0.02021202620312036204120462051205620612066207120762081208620912096MW

Renewable

TSL 115

- 8 Hearth Product Ignition Device s : Total Generation Reduction Figure 15.3.7

Hearth Product Ignition Device s : Coal Generation Reduction Figure 15.3.8 -400 -350 -300 -250 -200 -150 -100

-50

02021202620312036204120462051205620612066207120762081208620912096GWh

Total

TSL 1

-120 -100

-80 -60 -40 -20

02021202620312036204120462051205620612066207120762081208620912096GWh

Coal

TSL 115

- 9 Hearth Product Ignition Device s : Nuclear Generation Figure 15.3.9

Reduction

Hearth Product Ignition Device s : Gas Combined Cycle Figure 15.3.10

Generation Reduction -16.0 -14.0 -12.0 -10.0

-8.0 -6.0 -4.0 -2.0 0.02021202620312036204120462051205620612066207120762081208620912096GWh

Nuclear

TSL 1

-200 -180 -160 -140 -120 -100

-80 -60 -40 -20

02021202620312036204120462051205620612066207120762081208620912096GWh

NGCC

TSL 115

- 10 Hearth Product Ignition Device s : Oil Generation Reduc tion Figure 15.3.11

Hearth Product Ignition Device s : Renewables Generation Figure 15.3.12

Reduction -0.025 -0.020 -0.015 -0.010 -0.005 0.0002021202620312036204120462051205620612066207120762081208620912096GWh

Oil

TSL 1

-70 -60 -50 -40 -30 -20 -10

02021202620312036204120462051205620612066207120762081208620912096GWh

Renewable

TSL 115

- 11 15.3.3 Results Summary

Table 15.3.1 presents a summary of the utility impact results for hearth product ignition

device s

.

Table 15.3.1 Hearth Product Ignition Device s : Summary of Utility Impact Results

Year TSL

1

Installed Capacity Reduction (MW)

2021

- 3.58

2025

- 17.8

2030

- 40.3

2035

- 61.0

2040

- 74.6

Electricity Generation Reduction ( G Wh)

2021

- 21.6

2025

- 111

2030

- 226

2035

- 312

2040

- 34915

- 12 REFERENCES

1. U.S. Department of Energy

- Energy Information Administration, National Energy

Modeling System: An Overview , 2009. Report No. DOE/EIA

- 0581(2009).

< www.eia.gov/oiaf/aeo/overview/ >

2. U.S. Department of En ergy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

3. Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand , 2014. Lawrence

Berkeley National Laboratory. Report No. LBNL

- 6864E.16

- i CHAPTER 16. EMPLOYMENT IMPACT AN ALYSIS

TABLE OF CO NTENTS

16.1 INTRODUCTION

................................ ................................ ................................ ......... 16

- 1

16.2 ASSUMPTIONS

................................ ................................ ................................ ............ 16

- 1

16.3 METHODOLOGY

................................ ................................ ................................ ........ 16

- 1

16.4 SHORT

- TERM RESULTS

................................ ................................ ............................ 16

- 2

16.5 LONG

- TERM RESULTS

................................ ................................ .............................. 16

- 3

LIST OF TABLES

Table 16.4.1 Net National Short

- term Change in Employment (Number of Jobs)

................ 16

- 316

- 1 CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

DOE’s employment impact analysis is designed to estimate indirect national job creation

or elimination resulting from possible standards due to reallocation of the associated

expenditures for purchasing and operating hearth pr oduct s

. Job increases or decreases reported in

this chapter are separate from the direct heart h product production sector employment impacts

reported in the manufacturer impact analysis ( Chapter 12 ) , and reflect the employment impact of

efficiency standard s on all other sectors of the economy.

16.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption, and

therefore to reduce energy expenditures. The savings in energy expenditures may be spent on

new investment or not at all ( i.e

. , they may remain “saved”). The standards may increase the

purchase price of products , including the retail price plus sales tax, and increase installation

costs.

Using an input/output econometric model of the U.S. economy, this analysis estimated

the short

- term effect of these expenditure impacts on net economic output and employment.

DOE intends for this analysis to quantify the indirect employment impacts of these expenditure

changes. It evaluated direct employment impacts at manufacturers’ facilitie s in the manufacturer

impact analysis (see c hapter 12).

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. Because input/output mode ls do not allow prices to bring markets into

equilibrium, they are best used for short

- run analysis. DOE therefore include a qualitative

discussion of how labor markets are likely to respond in the longer term. In future rulemakings,

DOE may consider the u se of other modelin g approaches for examining long run employment

impacts

.

16.3 METHODOLOGY

The Department based its analysis on an input/output model of the U.S. economy that

estimates the effects of standards on major sectors of the economy related to buildi ngs 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 T echnologies) as a successor to ImB uild

, 3 a

special

- purpose version of the IMPLAN 4 national input/output model. Im SET esti mates the

employment and income effects of building energy technologies. In comparison with simple

economic multiplier approaches, Im SET allows for more complete and automated analysis of the

economic impacts of energy

- efficiency investments in buildings.16

- 2 In an input/output model, the level of employment in an economy is determined by the

relationship of different sectors of the ec onomy and the spending flows among them. Different

sectors have different levels of labor intensity and so changes in the level of spending ( e.g. , due

to the effects of an efficiency standard) in one sector of the economy will affect flows in other

sectors , which affects the overall level of employment.

ImSET uses a 187

- sector model of the national economy to predict the economic effects

of residential and commercial buildings technologies. ImSET collects estimates of initial

investments, energy savings, and economic activity associated with spending the savings

resulting from standards ( e.g. , changes in final demand in personal consumption, business

investment and spending, and government spending). It provides overall estimates of the change

in national output for each input

- output sector. The model applies estimates of employment and

wage income per dollar of economic output for each sector and calculates impacts on national

employment and wage income.

Energy

- efficiency technology primarily affects the U.S. economy along three spending

pathways. First, general investment funds are diverted to sectors that manufacture, install, and

maintain energy

- efficient product s. The increased cost of product s leads to higher employment in

the product manufacturing s ectors and lower employment in other economic sectors. Second,

commercial firm and residential spending are redirected from utilities toward firms that supply

production inputs for energy

- efficient products . Third, investment funds are released from the

ut ility and energy production sectors for use in other sectors of the economy. When consumers

use less energy, utilities and energy producers experience relative reductions in demand which

leads to reductions in employment in those sectors

.

DOE also notes that the employment impacts estimated with ImSET for the entire

economy differ from the employment impacts in the hearth product manufacturing sector

estimated in Chapter 12 using the Government Regulatory Impact Model (GRIM). The

methodologies used and th e sectors analyzed in the ImSET and GRIM models are different.

16.4 SHORT

- TERM RESULTS

The results in this section refer to impacts of a hearth product ignition device standard

relative to the base case. DOE dis aggregated the impact of standards on employmen t into three

component effects : increased capital investment costs, decreased energy costs, and changes in

operations and maintenance costs

. This section presents the summary impact.

Conceptually, one can consider the impact of the rule in its first yea r on three aggregate

sectors, the hearth product production sector, the energy generation sector, and the general

consumer good s sector (as mentioned above

, ImSET’s calculations are made at a much more

disaggregate level) . By r aising energy efficiency, t he rule generally increas es the purchase price

of hearth product s ; this increase in expenditures causes an increase in employment in this sector.

At the same time, the improvements in energy efficiency reduce consumer expenditures on16

- 3 energy , freeing up this money to be spen t in other sectors . The reduction in energy demand

causes a reduction in employment in that sector. Finally, based on the net impact of increased

expenditures on hearth product s and reduced expenditures on energy , consumer expenditures on

e verything else are either positively or negatively affected, increasing or reducing jobs in that

sector accordingly. The model also captures any indirect jobs created or lost by changes in

consumption due to changes in employment (as more workers are hired they consume more

goods, which generates more employment , the converse is true for workers laid off ).

Table 16.4.1 present the modeled net employment impact fro m the rule in 20 2 1 , rounded

to the nearest ten jobs

. The proposed standard is projected to slightly increase employment from

2021 to 2026. Virtually all hearth product s are domestically produced, so DOE does not consider

imports in this analysis

.

Table 16.4.1 Net Nat ional Short

- term Change in Employment ( Number of J obs)

Trial Standard Level 20 2 1 202 6

TSL 1 80 1 50

For context, the Office of Management of Budget currently assumes that the

unemployment rate may decline to 6. 0 percent during 2014 an d drop further to 5. 4 percent by

2017. 5 The unemployment rate in 202 1 is projected to be 5.4 percent, which is close to “full

employment.” When an economy is at full employment any effects on net employment are likely

to be tr ansitory as workers change jobs, rather t han enter or exit longer

- term employment.

16.5 LONG

- TERM RESULTS

Over the long term DOE expect s the energy savings to consumers to increasingly

dominate the increase in product costs, resulting in increased aggregate savings to consumers. As

a result, DOE expect s demand for energy to decline over time and demand for other goods to

increase. Because the utility and energy production sector s are relatively capital intensive

compared to the consumer good s sector, the net effect will be an increase in labor demand. In

equilibrium, this should lead to upward pressure on wages and a shift in employment away from

utilities and energy producers towards consumer goods. Note that in long

- run equilibrium there

is no net effect on total employment because wages adjust to bring the labor market into

equilibrium. Nonetheless, even to the extent that markets are slow to adjust, DOE anticipate s that

net labor market impacts will in general be negligible over time due to the small magnit ude of

the short

- term effects presented in Table 16.4.1 for most product classes and TSLs

. The ImSET

model projections, assuming no price or wage effects until 202 6 , are included in the second

column of Table 16.4.1

.16

- 4 REFERENCES

1. Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Bui lding

Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment

. Energy Economics , 2008. 30(5): pp. 2283

- 2301

2. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy

Technologies , 2005. Pacific Northwest Nat ional Laboratory. Richland, WA. Report No.

PNNL

- 15273. < www.pnl.gov/main/publications/external/technical_reports/PNNL

15273.pdf >

3. Scott, M. J., D. J. Hostick , and D. B. Belzer, ImBuild: Impact of Building Energy

Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. Richland, WA.

Report No. PNNL

- 11884. Prepared for the U.S. Department of Energy under Contract

DE

- AC06

- 76RLO 1830.

4. Minnesot a IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide,

Data Guide , 1997. Stillwater, MN.

5. Office of Management and Budget, Fiscal Year 2014 Mid

- Session Review: Budget of the

U.S. Government,

< www.whitehouse.gov/sites/default/files/omb/budget/fy2014/assets/14msr.pdf >17

- i CHAPTER 1 7

. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION

................................ ................................ ................................ ... 17

- 1

17.2 NON

- REGULATORY POLICIES

................................ ................................ .......... 17

- 1

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

- 5

17.3.1 No New Regulatory Action

................................ ................................ ..................... 17

- 5

17.3.2 Consumer Rebates

................................ ................................ ................................ ... 17

- 5

17.3.2.1 Methodology

................................ ................................ .......................... 17

- 5

17.3.2.2 Analysis

................................ ................................ ................................ .. 17

- 6

17.3.3 Consumer Tax Credits

................................ ................................ ........................... 17

- 12

17.3.4 Manufacturer Tax Credits

................................ ................................ ...................... 17

- 14

17.3.5 Voluntary Energy Efficiency Targets

................................ ................................ .... 17

- 15

17.3.6 Bulk Government Purchases

................................ ................................ .................. 17

- 17

17.4 IMPACTS OF NON

- REGULATORY ALTERNATI VES

................................ ... 17

- 18

LIST OF TABLES

Table 17.1.1 Non

- Regulatory Alternatives to National Standards

................................ ...... 17

- 1

Table 17.2.1 Relative Energy Savings for Trial Standard Level 1 for Hearth Product

Ignition Devices

................................ ................................ .............................. 17

- 4

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Hearth Product

Ignition Devices

................................ ................................ .............................. 17

- 7

Table 17.3.2 Market Penetrations in 2021 Att ributable to Consumer Rebates for

Hearth Product Ignition Devices

................................ ................................ ... 17

- 11

Table 17.3.3 Market Penetrations in 2021 Attributable to Consumer Tax Credits for

Hearth Product Ignition Devices

................................ ................................ ... 17

- 13

Table 17.3.4 Market Penetrations in 2021 Attributable to Manufactu rer Tax Credits

for Hearth Product Ignition Devices

................................ ............................. 17

- 15

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Energy Ef ficiency

Targets for Hearth Product Ignition Devices (TSL1)

................................ ... 17

- 16

Table 17.3.6 Market Penetrations Attributable to Voluntary Energ y Efficiency

Targets for Hearth Product Ignition Devices (TSL1)

................................ ... 17

- 17

Table 17.4.1 Impacts of Non

- Regulatory Alternatives for Hearth Product Ignition

Devices (TSL 1)

................................ ................................ ............................ 17

- 21

LIST OF FIGURES

Figure 17. 3.1 Market Penetration Curves for Hearth Product Ignition Devices (TSL 1)

... 17

- 11

Figure 17.4.1 Market Penetration of Efficient Hearth Product Ignition Devices:

Fireplace (vented) (TSL 1)

................................ ................................ ........... 17

- 18

Figure 17.4.2 Market Penetration of Efficient Hearth Product Ign ition Devices:

Fireplace (ventless) (TSL 1)

................................ ................................ ......... 17

- 1917

- ii Figure 17.4.3 Market Penetration of Efficient Hearth Product Ignition Devices: L ogs

(vented) (TSL 1)

................................ ................................ ........................... 17

- 19

Figure 17.4.4 Market Penetration of Efficient Hearth Product Ignition Devices: Logs

(ventless) (TSL 1)

................................ ................................ ......................... 17

- 20

Figure 17.4.5 Market Penetration of Efficient Hearth Product Ignition Devices:

Outdoor (TSL 1)

................................ ................................ ........................... 17

- 2017

- 1 CHAPTER 17. REGULATORY IMPACT AN ALYSIS

17.1 I NTRODUCTION

The U.S. Department of Energy (DOE) has determined that the regulatory action

described in the Federal Register notice associated with this TSD constitute s an “economically

significant regulatory action” under Executive Order (E.O.) 12866

, Regulatory Plann ing and

Review. 58 FR 51735 (October 4, 1993). For such actions, E.O. 12866 requires Federal agencies

to provide “a n assessment, including the underlying analysis, of costs and benefits of potentially

effective and reasonably feasible alternatives to the p lanned regulation, identified by the agencies

or the public (including improving the current regulation and reasonably viable non

- regulatory

actions), and an explanation why the planned regulatory action is preferable to the identified

potential alternativ es. ” 58 FR 51735, 51741.

To conduct this analysis , DOE used an integrated National Impact Analysis ( NIA )

- RIA

model built on the NIA model discussed in C hapter 10

. DOE identified five non

- regulatory

policy alternatives that possibly could provide incent ives for the same energy efficiency level as

the one in the proposed trial standard level for the hearth product ignition devices that are the

subject of this rulemaking. The non

- regulatory policy alternatives are listed in Table 17.1.1

, which also includes the “no new regulatory action” alternative . DOE evaluated each alternative

in terms of its ability to achieve sign ificant e nergy savings at a reasonable cost, and compared

the effectiveness of each to the effectiveness of the proposed standard for each of five types of

hearth product ignition devices covered by this rulemaking.

Table 17.1.1 Non

- Regulatory Alternatives to Nationa l Standards

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Energy Efficiency Targets

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed above

( excluding the alternative of no new regulatory action ) . Section 17.4 present s the results of the

policy alternatives

.

17.2 N ON

- REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost

effectiveness of the non

- regulatory policy alternatives for hearth product ignition devices

. This

section also describes the assumptions underlying the analysis.17

- 2 17.2.1 Methodology

DOE used its integrated NIA

- RIA spreadsheet model to calculate the national energy

savings (NES) and net present value (NPV) associated with each non

- regulatory policy

alternative . Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet

model

. Appendix 17A discusse s the NIA

- RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet the

efficiency level corresponding to the proposed TSL . After establishing the quantitat ive

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 efficiency level set for the proposed TSL . The shipments of equipment for any given

year reflect a distribution of efficiency levels. DOE assumed , for the proposed TSL, that new

energy efficiency standard s would affect 100 percent of the shipments of products that did not

meet the TSL target level in the base 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 hearth product ignition devices

attributable to each policy alternative.

Increasing the efficiency of a product often increases its average installed co st. 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, increases in total install ed 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 nation al NPV. DOE’s analysis also excluded any administrative costs

for the non

- regulatory policies; including such costs would decrease the NPVs slightly.

The following are key measures for evaluating the impact of each alternative.

• National Energy Savings (NES) , given in quadrillion Btus (quads), describes the

cumulative national energy sav ed over the lifetime of equipment purchased during the

30

- year analysis period starting in the effective date of the policy (2021

- 2050)

.

• Net Present Value (NPV)

, repres ents the value of net monetary savings in 2014

, expressed in 2013$, from equipment purchased during the 30

- year analysis period

starting in the effective date of the policy (2021

- 2050)

. DOE calculated the NPV as

the difference between the present value of installed equipment cost and operating

expenditures in the base case and the present value of th o se costs in each policy case.

DOE calculated operating expenses (including energy costs ) for the life of the

product

.

a The base case for the NIA is a market

- weighted average energy efficiency calculated from units at several

efficiency levels.17

- 3 17.2.2 Assumptions Regarding Non

- Regulatory Policies

The effects of non

- regulatory policies are by nature uncertain because they depend on

program implementation

, marketing efforts

, and on consumers’ responses to a program. Because

the projected effects depend on assumptio ns 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 re view regarding each non

- regulatory policy and consulted with

recognized experts to gather information on similar incentive programs that have been

implemented in the United States. By studying experiences with the various types of programs,

DOE sought to m ake credible assumptions regarding potential market impacts . Section 17.3

presents the sources DOE relied on in developing assumptions about each alternative policy and

reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each

alternative policy.

Each non

- regulatory policy that DOE considere d would improve the average efficiency

of new hearth product ignition devices relative to their base case efficiency scenario (which

involve s no new regulatory action). The analysis considered that each alternative policy would

induce consumers to purchase units h aving the same technology as required by the proposed

standard s (the target level ) set for the proposed 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

le vel

.

Table 17.2.1 show s the relative energy savings from the technology stipulated in the

proposed standard for hearth product ignition devices

.17

- 4 Table 17.2.1 Relative Energy Savings for Trial Standard Level 1 for Hearth Product

Ignition Devices

Technology Annual Energy Consumption

Natural Gas

mcf Electricity

kWh

Fireplace (vented)

Standing Pilot 3.9 0.0

Intermittent Pilot 0.5 13.6

Relative Energy Savings 87.5% n/a

Fireplace (ventless)

Standing Pilot 3.4 0.0

Intermittent Pilot 1.3 99.4

Relative Energy Savings 63.0% n/a

Logs (vented)

Standing Pilot 3.1 0.0

Intermittent Pilot 0.3 5.8

Relative Energy Savings 90.8% n/a

Logs (ventless)

Standing Pilot 2.2 0.0

Intermittent Pilot 0.9 70.6

Relative Energy Savings 59.6% n/a

Outdoor

Standing Pilot 3.8 0.0

Intermittent Pilot 0.0 0.2

Relative Energy Savings 100.0% n/a

DOE assumed that the effects of non

- regulatory policies would last from the effective

date of standards

— 2021

— through the end of the analysis period, which is 2050

.

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 efficiency 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 not additive ; the combined effect of several or all policies cannot be

inferr ed from summing their results.

Section 17.4 presents graphs that show the market penetration estimated under each non

regulatory policy for hearth product ignition devices

.17

- 5 17.3 NON

- REGULATORY POLIC Y A SSUMPTIONS

The following subsections d escribe DOE’s analysis of the impacts of the five non

regulatory policy alternatives to proposed standard s for hearth product ignition devices

. (Because

the alternative of No New Regulatory Action has no energy or economic impacts, essentially

representing the NIA base case, DOE did not perform any additional analysis for that

alternative.) DOE developed estimate s of the market penetration of high

- efficiency 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 energy efficiency

of hearth product ignition devices constitutes the base case, as described in c hapter 10

, National

Impact Analysis. The base case provides the basis of compari son for all other policies. By

definition, no new regulatory action yields zero NES and an NPV of zero dollars.

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 appliances

. This

policy provides a consumer rebate for purchasing hearth product ignition devices that operate at

the same efficiency as stipulated in the proposed TSL.

17.3.2.1 Methodology

DOE based its evaluation methodo logy 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 penetrat ion 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 th e referenced XENERGY report was

published, 2

, 3

, 4

, 5

, 6

, 7 used different approaches: other economic parameters ( e.g. , payback

period), expert surveys, or model calibration based on specific utility program data rather than

multi

- utility data

. Some models in use by energy efficiency program evaluation experts were so

client

- specific that generic relationships between economic parameters and consumer res ponse

could not be established. 5 DOE decided that the most appropriate available method for this RIA

analysis was the XENERGY approach of penetration curves based on B/C ratio, which

incorporates lifetime operating cost savings

.

XENERGY’s model estimates market impacts induced by financial incentives based on

the premise that two types of information diffusion drive the adoption of new technologies.

Internal source s of information encourage consumers to purchase new equipment primarily

b XENERGY is now owned by KEMA, Inc. ( www.kema.com )17

- 6 through word

- of

- mouth from early adopters. External sources affect consumer purchase

decisions through marketing efforts and information from outside the consumer group. Appendix

17A 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 in formation 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

- hi gh

- barriers ) to consumer purchase of high

efficiency 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 hearth product ignition devices

by determining , for the proposed TSL, the increase in market penetration of equipment meeting

the target level relative to their market penetration in the base case. It used the interpolation

method presented in Blum et al (2011) 9 to c reate customized penetration curves based on

relationships between actual base 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 hearth product ignition devices . 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 hearth product ignition devices so they can best

reflect the market barrier level faced by hearth product ignition devices

. Section 17.3.2.2 shows

the interpolated curve used in the analysis

.

17.3.2.2 Analysis

DOE estimated t he effect of increasing the B/ C ratio of hearth product ignition devices

via a rebate that would pay part of the incr eased installed cost of a unit that met the target

efficiency level compared to one meeting the baseline efficiency l evel

. c To inform its estimate of

an appropriate rebate amount, DOE performed a thorough nationwide search for existing rebate

programs for hearth product ignition devices

. It gathered data from a sample of utility and

agency rebate programs that includes 8 rebates for hearth product ignition devices initiated by 6

utilities or agencies in various States. ( Appendix 17A identifies the rebate programs .) DOE then

estimated a market average rebate value as the simple average calculated over the rebate amounts

offered by the existing rebate programs. Since the existing rebate programs target hearth product

ignition device units as a whole, DOE further scaled down the market average rebate value it

calculated using the ratio of the price of a n ignition module and the (full) price of a hearth

c The baseline technology for hearth products is defined in the e ngineering a nalysis

, Chapter 5, as the technology

that represents the basic characteristics of products in that class. A baseline unit typically is one that just meets

current Federal energy conservation standards and provides basic consumer utility

.17

- 7 product ignition device

. DOE assumed th at rebates would remain in effect at the same level

throughout the forecast period (2021

- 2050)

.

DOE first calculated the B/C ratio of a hearth product ignition device without a rebate

using the difference in total installed costs (C) and lifetime operating cost savings d (B) between

the unit meeting the target level and the baseline unit. It then calculated the B/C ratio given a

rebate for the unit meeting the target efficiency 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

con sumer rebates for the proposed TSL on the B/C ratio of hearth product ignition devices

shipped in the first year of the analysis period

. Note that because ventless hearth product ignition

devices present negative benefits and benefit

- cost ratios, DOE did not assess the impacts from

consumer rebates for those types of hearth product ignition devices

.

Table 17.3.1 Benefit/Cost Ratios Without and W ith Rebates for Hearth P roduct

Ignition Devices

TSL 1

Fireplace (vented)

B/C Ratio Without Rebate 4.2

Rebate Amount (201 3 $) 37.03

B/C Ratio With Rebate 7.3

Estim ated Market Barrier s No

- Low

Fireplace (ventless)

B/C Ratio Without Rebate 4.0

Rebate Amount (201 3 $) 37.03

B/C Ratio With Rebate 6.5

Estim ated Market Barrier s Mod

Logs (vented)

B/C Ratio Without Rebate 1.6

Rebate Amount (201 3 $) 37.03

B/C Ratio With Rebate 2.1

Estim ated Market Barrier s Low

- Mod

Logs (ventless)

B/C Ratio Without Rebate 1.6

Rebate Amount (201 3 $) 37.03

B/C Ratio With Rebate 2.2

Estim ated Market Barrier s Low

- Mod

Outdoor

B/C Ratio Without Rebate 3.2

Rebate Amount (201 3 $) 37.03

B/C Ratio With Rebate 4.2

Estim ated Market Barrier s No

- Low

d The cash flow of the operating cost savings is discounted to the purchase year using a 7 percent discount rate.17

- 8 DOE used the B/C ratio along with the penetration curve s shown in Table 17.3.1 to

estimate the percentage of consumers who would purchase hearth product ignition devices that

meet the target level both with and without a rebate incentive. The estimated level of market

barriers corresponding to the penetration curve s DOE calculated to represent the market behavior

for hearth product ignition devices at the proposed TSL are indicated in Tab le 17.3.1

. DOE

assumed the estimated market barriers would remain the same over the whole analysis period.17

- 9 TSL 1Fireplace (vented)Fireplace (ventless)17

- 10Logs (vented)Logs (ventless)Outdoor17

- 11 Figure 17.3.1 Market Penetration Curve s for Hearth Product Ignition

Devices (TSL 1 )

DOE next estimated the percent increase represented by the change in penetration rate

shown on the corresponding penetration curve. It then added this percent i ncrease to t he market

share of units that meet the target level in the base 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 hearth product ignition devices

regarding the market penetration of products in 2021 that meet the target level at the proposed

TSL given a consumer rebate

.

Table 17.3.2 Market Penetrations in 2021 Attributable to Consumer R ebates for

Hearth Product Ignition Devices

TSL 1

Fireplace (vented)

Base

- Case Market Share 57.8%

Policy Case Market Share 66.6%

Increased Market Share 8.8%

Fireplace (ventless)

Base

- Case Market Share 12.3%

Policy Case Market Share 23.5%

Increased Market Share 11.2%

Logs (vented)

Base

- Case Market Share 12.7%

Policy Case Market Share 17.3%

Increased Market Share 4.6%

Logs (ventless)

Base

- Case Market Share 5.7%

Policy Case Market Share 10.0%

Increased Market Share 4.3%

Outdoor

Base

- Case Market Share 47.8%

Policy Case Market Share 53.6%

Increased Market Share 5.8%

DOE used the resulting annual increase s in market shares as inputs t o rep resent the rebate

policy case scenario in its NIA

- RIA model. Appendix 17A shows the annual market share

increases due to this policy. Section 17.4 presents the resulting market penetration trends for the

policy case of consumer rebates for hearth product ignition devices

. Because energy prices

increase over the analysis period and equipment price is assumed constant, the B/C ratio

increases over time. With increasing B/C ratio and constant market barriers, the increase in

market penetration of a more efficient technology also increases over the analysis period.17

- 12 17.3.3 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous

analysis of consumer parti cipation 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 effe ct

, 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 mar ket penetration associated with either

policy would be due to the direct price effect and ha lf 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 amou nt 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 con sumers 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 hearth product ignition devices , DOE also reviewed other tax credit programs t hat have been

offered at both the Federal and State levels for energy

- efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005 ) included Federal tax credits for

consumers who purchase energy

- efficient products

. 13 Those tax credits were in effect in 2006

and 2007, expired in 2008, were reinstated for 2009

– 2010 by the American Recovery and

Reinvestment Act of 2009 ( ARRA ), extended by Congress for 2011 with some modifications

, and expired at the end of 2011

. 14

, 15 The American Taxpayer Relief Act of 2012 extended, with

some modifications, residential tax credits for air conditioners, heat pumps, furnaces, an d water

heaters placed in service between January 1, 2012 and December 31, 2013. 16 DOE reviewed

Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during

tax years 2006 and 2007. DO E also reviewed data from an earlier Federal energy conservation

tax credit program in place in the 1980s. However, DOE did not find data specific enough to

hearth product ignition devices to warrant adjusting its analysis method for the Consumer Tax

Credits policy case. Appendix 17A contains more information on Federal consumer tax credits.

DOE also reviewed its previous analysis o f Oregon’s tax credits for clothes washers to

provide sup port for its assumptions. 1 7 In that previous analysis, DOE compared the market

shares of ultra

- high efficiency (UHE) residential clothes washers in Oregon, which offered both17

- 13 State tax credits and utility rebates, with those in Washington State, which offered only utility

rebate s during the same period. Based on this analysis, DOE estimated that in Oregon the impact

of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equivalent

efficiency. This finding supports its original assumption that partic ipation in a tax credit program

would be about 60 percent of participation in a rebate program. Additional discussion of State

tax credits for Oregon and other states is in Appendix 17A

.

DOE applied the assumed 60 percent participation described above to the 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 penetration curves selected for h earth product ignition devices

.

Table 17.3.3 summarizes DOE’s assumptions for hearth product ignition devices

regarding the market penetration of units in 2021 that meet the efficiency level at the proposed

TSL given a consumer tax credit.

Table 17.3.3 Market Penetrations in 2021 Attributable to Consumer Tax Credits for

Hearth Product Ignition Devices

TSL 1

Fireplace (vented)

Base

- Case Market Share 57.8%

Policy Case Market Share 63.1%

Increased Market Share 5.3%

Fireplace (ventless)

Base

- Case Market Share 12.3%

Policy Case Market Share 19.0%

Increased Market Share 6.7%

Logs (vented)

Base

- Case Market Share 12.7%

Policy Case Market Share 15.5%

Increased Market Share 2.8%

Logs (ventless)

Base

- Case Market Share 5.7%

Policy Case Market Share 8.3%

Increased Market Share 2.6%

Outdoor

Base

- Case Market Share 47.8%

Policy Case Market Share 51.3%

Increased Market Share 3.5%

The increased market shares attributable to consumer tax credits shown in Table 17.3.3

were used as inputs in the NIA

- RIA model. Appendix 17A shows the annual marke t share

increases due to this policy. Section 17.4 presents the resulting market penetratio n trends for the

policy case of consumer tax credits for hearth product i gnition devices that meet the efficiency17

- 14 level for the proposed TSL

. Because the increase in market penetration for consumer tax credits

is proportional to the increase in market penetration DOE calculated for consumer rebates, the

former follows a similar increasing trend over the analysis period as the latter.

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that

produce hearth product ignition devices that meet the target efficiency level at t he proposed TSL,

DOE assumed that a manufacturer tax credit would lower the consumer’s purchase cost by an

amount equivalent to that provided by the consumer rebates or tax credits described above. DOE

further assumed that manufacturers would pass on some of their reduced costs to consumers,

causing a direct price effect. DOE assumed that no announcement effect would occur, because

the program would not be visible to consumers. e Because the direct price effect is approximately

equivalent to the announcement effect, 10 DOE estimated that a manuf acturer tax credit would

induce half the number of consumers assumed to take advantage of a consumer tax credit to

purchase more efficient products . Thus the assumed participation rate is equal to 30 percent of

the number of consumers who would participate in a rebate program.

DOE attempted to investigate manufacturer response to the Energy Efficient Appliance

Credits for manufacturers mandated by EPACT 2005. 18 Those manufacturer tax credits have

been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009.

DOE was unable to locate data from the Internal Revenue Service or other sources on

manufacturer response to the Federal credits. Appendix 17A presents details on Federal

manufacturer tax credits.

DOE applied the assumption of 30 percent participation to the penetration rates predicted

for the rebate policy to estimate the effects of a manufacturer tax credit policy. In doing so, the

Department incorporated the assumptions for consumer response to fina ncial incentives from the

penetration curves calculated for hearth product ignition devices

.

Table 17.3.4 summarize DOE’s assumptions for hearth product ignition devices

regarding the market penetration of units in 2021 meeting the efficiency level the proposed TSL

given a manufacturer tax credit.

e 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 effe ct. However, DOE found no data on such programs on which to base an

estimate of the magnitude of this possible announcement effect on consumer behavior.17

- 15 Table 17.3.4 Market Penetrations in 2021 Attributable to Manufacturer Tax Credits

for Hearth Product Ignition Devices

TSL 1

Fireplace (vented)

Base

- Case Market Share 57.8%

Policy Case Market Share 60.5%

Increased Market Share 2.6%

Fireplace (ventless)

Base

- Case Market Share 12.3%

Policy Case Market Share 15.7%

Increased Market Share 3.3%

Logs (vented)

Base

- Case Market Share 12.7%

Policy Case Market Share 14.1%

Increased Market Share 1.4%

Logs (ventless)

Base

- Case Market Share 5.7%

Policy Case Market Share 7.0%

Increased Market Share 1.3%

Outdoor

Base

- Case Market Share 47.8%

Policy Case Market Share 49.6%

Increased Market Share 1.7%

The increased market shares attributab le to a manufacturer tax credit shown in Table

17.3.4 were used as inputs in the NIA

- RIA model. Appendix 17A shows the annual market share

increases due to this policy

. Section 17.4 presents the resulting market penetratio n trends for the

policy case of manufacturer tax credits for hearth product ignition devices

. Because the increase

in market penetration for manufacturer tax credits is proportional to the increase in market

penetration DOE calculated for consumer rebates, the former follows a similar increas ing trend

over the analysis period as the latter

.

17.3.5 Voluntary Energy Efficiency Targets

DOE assumed that voluntary en ergy efficiency targets would be achieved as

manufacturers of hearth product ignition devices gradually stopped producing units that operated

below the efficiency level set for the proposed TSL . DOE assumed that the impetus for phasing

out production of lo w

- efficiency 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 ener gy efficienc ies that various products must have to receive the ENERGY STAR

label . ENERGY STAR encourages consumers to purchase efficient products via marketing that

promotes consumer label recognition, various incentive programs that adopt the ENERGY

STAR specifications, and manufacturers’ promotion of their qualifying appliances. ENERGY17

- 16 STAR projects market penetration of compliant appliances and estimates the percentage of sales

of compliant appliances that are attributab le to the ENERGY STAR program.

Researchers have analyzed the ENERGY STAR program’s effects on sales of several

consumer products. Program efforts generally involve a combination of information

dissemination and utility or agency rebates. The analyses have been based on State

- specific da ta

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The

analyses generally have concluded that the market penetration of ENERGY STAR

- qualifying

appliances is higher in regions or States where ancillary promotional pro grams have been

active

. 19

, 20

, 21

DOE believes that informational incentive programs

– like ENERGY STAR, or any other

labeling program sponsored by industry or other organizations

– are likely to reduce the market

barriers to more efficient pr oducts over time. During the rebate analysis, when assessing the B/C

ratio and market penetration in the base case for hearth product ignition devices , DOE observed

that the level of market barriers for more efficient hearth product ignition devices are in the range

of no

- to

- low barriers to a moderate level of market barriers . DOE estimates that voluntary energy

efficiency targets could reduce these barriers to low er leve l s over 10 years

. Table 17.3.5 presents

the levels of market barriers DOE estimated for hearth product ignition devices in the base case

and in the policy case of voluntary energy efficiency targets. DOE followed the methodology

presented by Blum e t al ( 2011) 22 to evaluate the effects that such a reduction in market barriers

would have on the market penetration of efficient hearth product ignition devices

. f 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 Energy Efficiency

Targets for Hearth Product Ignition Devices (TSL1)

Base Case Voluntary Energy Efficiency Targets

Fireplace (vented) No

- Low No

Fireplace (ventless) Mod erate Mod erate

Logs (vented) Low

- Mod erate Low

Logs (ventless) Low

- Mod erate Low

Outdoor No

- Low No

Table 17.3.6 summarizes DOE’s assumptions for hearth product ignition devices

regarding the market penetration of units in selected years with the same efficient technology as

the one in the proposed TSL given voluntary energy efficiency targets. Because of the decrease

in the market barriers level over the first 10 years of the analysis period , the market increase of

more efficient hearth product ignition devices significantly increases over that period

. For the

remaining 20 years of the analysis period, the increase in market penetration keeps growing as a

result of increas ing energy prices, which

– with constant, same market barriers level as in 2030

–

f For the calculation of B/C ratios DOE discounted the cash flow of the operating cost savings to the p urchase year

using a 7 percent discount rate.17

- 17 eventually lead to increasing B/C ratio s and higher market penetration. Notice, however, that

since ventless hearth product ignition devices present negative benefits and benefit

- cost ratios,

DOE did not assess the impacts from voluntary energy efficiency targets for those types of

hearth product ignition d evices and rather assumed zero increased market share.

Table 17.3.6 Market Penetrations Attributable to V oluntary Energy E fficiency

T argets for Hearth Product Ignition Devices ( TSL1 )

2021 2030 2050

Fireplace (vented)

Base

- Case Market Share 57.8% 57.8% 57.8%

Policy Case Market Share 60.3% 81.2% 85.0%

Increased Market Share 2.5% 23.4% 27.2%

Fireplace (ventless)

Base

- Case Market Share 12.3% 12.3% 12.3%

Policy Case Market Share 13.2% 20.2% 20.2%

Increased Market Share 0.9% 7.9% 7.9%

Logs (vented)

Base

- Case Market Share 12.7% 12.7% 12.7%

Policy Case Market Share 15.3% 35.2% 41.4%

Increased Market Share 2.6% 22.5% 28.7%

Logs ( ventless )

Base

- Case Market Share 5.7% 5.7% 5.7%

Policy Case Market Share 9.4% 31.6% 31.6%

Increased Market Share 3.7% 25.9% 25.9%

Outdoor

Base

- Case Market Share 47.8% 47.8% 47.8%

Policy Case Market Share 51.2% 76.3% 80.8%

Increased Market Share 3.4% 28.5% 33.0%

The increased market shares attributable to voluntary energy efficiency targets shown in

Table 17.3.6 were used as inputs in the NIA

- RIA model. Appendix 17A shows the annual market

share increases due to this policy

. Section 17.4 presents the resulting market penetratio n trends

for the policy case of voluntary energy efficiency targets for hearth product ignition devices that

meet the efficiency level fo r the proposed TSL

.

17.3.6 Bulk Government Purchases

B ulk government purchases can lead to Federal, State, and local governments purchasing

large quantities of products that meet the target efficiency level . Combining the market demands

of mu ltiple public sectors also 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

.17

- 18 DOE assumed that government agencies would administer bulk purchasing programs for

hearth product ignition devices , and that bulk government purchases would affect a subset of

housing units for which government agencies purchased or influenced the purchase of hearth

product ignition devices . This subset would consist primarily of public housing and housing on

military bases . According to the 2009 Residential Energy Consumption Survey (RECS 2009), no

housing units in public housing authority use a gas fueled fireplace either as a primary or a

secondary source of heating

. 23 DOE therefore estimated that there is no market for this

alternative policy and, consequently , the inc rease in market share of more efficient hearth

product ignition devices due to this alternative policy is zero.

17.4 IMPACT S OF NON

- REGULATORY A LTERNATIVE S

Figure 17.4.1 through Figure 17.4.5 show the effects of each non

- regulatory policy on the

market penetration of more efficient hearth product ignition devices

. R elative to the base case,

the alternative policy cas es

– excluding bulk government purchases

– increase the market sha res

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 efficient technology

.

Figure 17.4.1 Market Penetration of Efficient Hearth Product Ignition

Devices : Fireplace (vented) (TSL 1 )17

- 19 Figure 17.4.2 Market Penetration of Efficient Hearth Product Ignition

Devices : Fireplace (ventless) (TSL 1)

Figure 17.4.3 Market Penetration of Efficient Hearth Product Ignition

Devices : Logs ( vented) (TSL 1)17

- 20 Figure 17.4.4 Market Penetration of Efficient Hearth Product Ignition

Devices : Logs (ventless) (TSL 1)

Figure 17.4.5 Market Penetration of Efficient Hearth Product Ignition

Devices : Outdoor (TSL 1)

Table 17.4.1 show s the national energy savings and net present value for five non

regulatory policies analyzed in detail for hearth product ignition devices . T he target level for

each policy corresponds to the same efficien t technology proposed for standard s in TSL 1

. Th e

case in which no regulatory action is taken with regard to hearth product ignition devices17

- 21 constitute s the base case (or "No New Regulatory Action" scenario ) , in which NES an d NPV are

zero by definition. For comparison, the tables include the impacts of the proposed standard s.

Energy savings are given in quadrillion British thermal units (quads). g The NPV s shown in Table

17.4.1 are based on two discount rates, 7 percent and 3 percent

.

The polic y with the highest projected cumulative energy savings is voluntary energy

efficiency targets

. Savings from rebates and t ax credits range from one third to less than on tenth

of the savings from voluntary energy efficiency targets

. Bulk government purchases, due to the

lack of market for gas fueled hea r th products in housing units in the public housing authority

, lead to zero benefits. Overall, the energy saving benefits from the alternative policies

, range from

zero percent to 49.2 percent of the benefits from the proposed standards.

Table 17.4.1 Impacts of Non

- Regulatory Alternatives for Hearth Product Ignition

Devices ( TSL 1 )

Polic y Alternative Energy Savings *

quads Net Present Value*

m illion 201 3 $

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.107 ( 15.9 % )** 182.3 483.0

Consumer Tax Credits 0.0 59 ( 8.7 % ) 109.4 289.8

Manufacturer Tax Credits 0.0 29 ( 4.4 % ) 54.7 144.9

Voluntary Energy Efficiency Targets 0. 331 ( 49

. 2 % ) 418

. 0 1 553

. 1

Bulk Government Purchases 0.000 ( 0.0 % ) 0.0 0.0

Proposed Standards *** 0.674 ( 100.0 % ) 1031.1 3123.8

* For products shipped 2021

– 2050

**The percentages show how the energy savings from each alternative policy compare to the (site) energy savings

from the proposed standards (represented in the table as 100%).

*** Refers to site energy savings.

g For the alternative policies whose market penetration depends on B/C ratio, the energy savings in Table 17.4.1

corre spond 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

- 22 REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy

Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.)

< www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_energy

_efficiency >

2. ICF International, Arizona Public Service Energy Efficient Market Potential Study , 2007.

Fairfax, VA.

3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Optimal Energy

. January, 2008.

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Quantec, LLC

. January, 2008.

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Itron, Inc

. January 2008 and March 2009.

6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last

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7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market

Potential Study, 2010. (Last accessed March, 2014.) < www.ameren.com/

-

/media/missouri

site/Files/Environment/Renewables/AmerenUEVolume2MarketResearchReport2.pdf >

8. Coito, F., E

- mail to Barbar a Atkinson of LBNL, personal communication. KEMA, Inc

. June, 2010.

9. Blum, H., B. Atkinson, and A. Lekov, A Framework for Comparative Assessments of

Energy Efficiency Policy Measures, May, 2011. Report No. LBNL

- 4749E.

< http://escholarship.org/uc/item/4t32x6vr >

10. Lawrence Berkeley National Laboratory, Energy End

- Use Forecasting: Analysis of Tax

Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and

Assessment Group (Last accessed March, 2014.)

< http://enduse.lbl.gov/Projects/TaxCredits.html >

11. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment

Purchase Decisions , 1994. Prepared for Southern California Edison by Cambridge

Systematics, Pacific Consulting Services, The Technology Applications Group, and

California Survey Research Services.17

- 23 12. U.S. Department of Energy: Office of Codes and Standards, Technical Sup port

Document: Energy Efficiency Standards for Consumer Products: Refrigerators,

Refrigerator

- Freezers, and Freezers, Including Environmental Assessment and

Regulatory Impact Analysis, November, 1995. Washington, DC. Report No. DOE/EE

0064.

13. Energy Po licy Act of 2005 , in 119 STAT. 594 Public Law 109

- 58. Section 1333, 26 USC

25C note, . 2005. www.gpo.gov/fdsys/pkg/BILLS

- 109hr6pcs/pdf/BILLS

- 109hr6pcs.pdf

14. American Rec overy and Reinvestment Act of 2009, 2009. Government Printing Office.

(Last accessed March, 2014.) < www.gpo.gov/fdsys/pkg/BILLS

- 111hr1enr/pdf/BILLS

111hr1enr.pdf >

15. T ax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling

Equipment., 2011. (Last accessed March, 2014.)

< www.energytaxincentives.org/consumers/heatin g

- cooling.php >

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed March, 2014.) < http://energytaxincentives.org/ >

17. U.S. Department of Energy

- Energy Efficiency & Renewable Energy, Technical Support

Document: Energy Efficiency Program for Consumer Products and Commercial and

Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products,

and Commerc ial Clothes Washers , 2008. Washington, DC.

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- Wright, and A. Li, Modelling the Effects of U.S.

ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who

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- June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La

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- 22, 2003. Seattle, WA. Published in the 2003 International Energy

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Programs in the U.S

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Consumption Survey (RECS), 2009. (Last accessed December, 2013.)

< http://www.eia.gov/ consumption/residential/data/2009/index.cfm?view=microdata >APPENDIX 6A. DETAILED DATA FOR PR ODUCT PRICE MARKUPS

TABLE OF CO NTENTS

6A.1 DETAILED WHOLESALER COST DATA

................................ ......................... 6A

- 1

6A.2 DETAILED MECHANICAL CONTRACTOR DATA

................................ ......... 6A

- 3

6A.3 DETAILED GENERAL CON TRACTOR COST DATA

................................ ...... 6A

- 5

6A.4 ESTIMATION OF CONTRA CTOR MARK

- UP BY STATE

.............................. 6A

- 6

6A.5 STATE SALES TAX RATE S

................................ ................................ .............. 6A

- 11

LIST OF TABLES

Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers

................................ .. 6A

- 2

Table 6A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the

Incremental Markups

................................ ................................ .................. 6A

- 4

Table 6A.3.1 Residential General Contractor Expenses and Markups

............................. 6A

- 6

Table 6A.4.1 Mechanical Contractor Markup Estimation by State, 2007

........................ 6A

- 6

Table 6A.4.2 Residential Building General Contractor Baseline Markups by State

........ 6A

- 9

Table 6A.5.1 State Sales Tax Rates

................................ ................................ ................ 6A

- 11

6A

- iAPPENDIX 6A. DETAILED DATA FOR PRODUCT PRICE MARKUPS

6A.1 D ETAILED WHOLESALER COST DATA

Based on data provided by the Heating Air

- conditioning & Refrigeration Distributors

International (HARDI), Table 6.5

. 1 of chapter 6 shows wholesaler revenues and costs in

aggregated form. Table 6A.1.1 in this appendix provides the complete breakdown of costs and

expenses

. The column labeled “Scaling” in Table 6A.1.1 indicates which expenses the U.S.

Department of Energy (DOE) assumed to scale with only the baseline markup and which with

both the baseline and incremental markups. As described in chapter 6, section 6.4 .1

, o nly those

expenses that scale with both baseline and incremental costs are marked up when there is an

incremental change in equipm ent costs.

6A

- 1Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers

Item Percent of Revenue

% Scaling

Cost of Goods Sold 73. 9

Gross Margin 2 6

. 1

Payroll Expenses 1 5. 1 Baseline

Executive Salaries & Bonuses 1. 6

Branch Manager Salaries and Commissions 1. 3

Sales Executive Salaries & Commissions 0.5

Outside Sales Salaries & Commissions 2. 3

Inside/Counter Sales/Wages 2. 6

Purchasing Salaries/Wages 0. 5

Credit Salaries/Wages 0.2

IT Salaries/Wages 0. 2

Warehouse Salaries/Wages 1.4

Accounting 0.5

Delivery Salaries/Wages 0. 8

All Other Salaries/Wages & Bonuses 0.8

Payroll Taxes 1.0

Group Insurance 1. 0

Benefit Plans 0. 4

Occupancy Expenses 3. 5 Baseline

Utilities: Heat, Light, Power, Water 0.4

Telephone 0.3

Building Repairs & Maintenance 0. 3

Rent or Ownership in Real Estate 2. 5

Other Operating Expenses 5. 2 Baseline & Incremental

Sales Expenses (incl. a dvertising & p romotion ) 0. 9

Insurance (business liability & casualty) 0.2

Depreciation 0.4

Vehicle Expenses 1. 2

Personal Property Taxes/Licenses 0.1

Collection Expenses 0.3

Bad Debt Losses 0.2

Data Processing 0.3

All Other Operating Expenses 1. 6

Total Operating Expenses 2 3.8

Operating Profit 2. 3 Baseline & Incremental

Other Income 0. 4

Interest Expense 0. 4

Other Non

- operating Expenses 0.0

6A

- 2Item Percent of Revenue

% Scaling

Profit Before Taxes 2. 3

Source: Heating, Air

- co nditioning & Refrigeration Distributors International. 201 3

. 201 3 Profit Report ( 201 2 Data).

6A.2 DETAILED MECHANICAL CONTRACTOR DATA

Table 6.5

. 3 and Table 6.5

. 4 of chapter 6 provide mechanical contractor revenues and

costs in aggregated form by ‘Cost of Goods Sold’ and ‘Gross Margin.’ The tables are based on

data in the 2005 edition of Financial Analysis for the HVACR Contracting Industry

, published by

the Air Conditioning Con tractors of America (ACCA). The ACCA report did not provide a

more disaggregate d tabulation of these costs and expenses. As in section 6A.1 , the gross margin

categ ory was assumed to scale only with the baseline markup.

A further disaggregated breakdown of costs used to scale the incremental markup as

presented in Table 6.5.2 of chapter 6 are shown in Table 6A.2.1 by both dollar value and

percentage terms from the 2007 Census of Business. As the ACCA data were used to calculate

the baseline markup, in Table 6A.2.1 only the categories in the ‘Scaling’ column that are scaled

with both the baseline and incremental markups are marked when there is an incremental change

in equipment costs.

6A

- 3Table 6A.2.1 Mechanical Contra ctor Expenses and Markups Used To Scale the

Incremental Markups

Item Dollar Value

$1,000 Percentag e

% Scaling

Total Cost of Equipment Sales 107,144,428 67.80

Total payroll, construction workers wages 31,373,558 19.85

Cost of materials, components, and supplies 59,023,964 37.35

Cost of construction work subcontracted out to others 13,646,192 8.63

Total cost of selected power, fuels, and lubricants 3,100,714 1.96

Gross Margin 50,895,129 32.20

Payroll Expenses 28,065,632 17.76

Baseline Total payroll, other employee wages 14,041,336 8.88

Total fringe benefits 13,585,040 8.60

Temporary staff and leased employee expenses 439,256 0.28

Occupancy Expenses 3,436,208 2.17

Baseline Rental costs of machinery and equipment 1,047,026 0.66

Rental costs of buildings 1,231,263 0.78

Communication services 640,851 0.41

Cost of repair to machinery and equipment 517,068 0.33

Other Operating Expenses 12,671,194 8.02

Baseline &

Incremental Purchased professional and technical services 843,641 0.53

Data processing and other purchased computer services 98,016 0.06

Expensed computer hardware and other equipment 255,474 0.16

Expensed purchases of software 64,195 0.04

Advertising and promotion services 1,018,265 0.64

All other expenses 6,944,674 4.39

Refuse removal (including hazardous waste) services 153,241 0.10

Taxes and license fees 996,138 0.63

Total depreciation ($1,000) 2,297,550 1.45

Net Profit Before Income Taxes 6,722,095 4.25 Baseline &

Incremental

Source: U.S. Census Bureau. 200 7 . Plumbing, Heating, and Air

- Conditioning Contractors: 200 7

. Sector 23: 238220

. Construction: Geographic

Area Series. Detailed Statistics for Establishments: 200 7

. Note: Mechanical contractor costs and expenses are first presented as total dol lar values and th e n converted to percentage values. This is in

contrast to the c ost per dollar of sales revenue values shown in Table 6

. 5

. 2

.

6A

- 46A.3 DETAILED GENERAL CON TRACTOR COST DATA

Based on U.S. Department of Census data, Table 6.5

. 6 of chapter 6 show residential

building general contractor revenues and costs in aggregated form. Table 6A.3.1 shows the

complete breakdown of costs and expenses of residential building contractor provided by the

U.S. Department of Census. The column labeled “Scaling” indicates which expenses DOE

assu med to scale with only the baseline markup and which are scaled with both the baseline and

incremental markups. O nly those expenses that scale with baseline and incremental costs are

marked up when there is an incremental chang e in equipment costs.

6A

- 5Table 6A.3.1 Resid ential General Contractor Expenses and Markups

Item Dollar Value

$1,000 Percentage

% Scaling

Total Cost of Equipment Sales 238,431,389 67.55

Total payroll, construction workers wages 16,629,321 4.71

Cost of materials, components, and supplies 126,764,975 35.91

Cost of construction work subcontracted out to others 90,956,668 25.77

Total cost of selected power, fuels, and lubricants 4,080,425 1.16

Gross Margin 114,558,247 32.45

Payroll Expenses 28,806,792 8.16

Baseline Total payroll, other employee wages 20,843,029 5.90

Total fringe benefits 7,464,670 2.11

Temporary staff and leased employee expenses 499,093 0.14

Occupancy Expenses 3,558,796 1.01

Baseline

Rental costs of machinery and equipment 572,783 0.16

Rental costs of buildings 1,532,841 0.43

Communication services 810,436 0.23

Cost of repair to machinery and equipment 642,736 0.18

Other Operating Expenses 21,341,175 6.05

Baseline &

Incremental Purchased professional and technical services 1,834,816 0.52

Data processing and other purchased computer services 141,344 0.04

Expensed computer hardware and other equipment 261,701 0.07

Expensed purchases of software 105,338 0.03

Advertising and promotion services 2,544,687 0.72

All other expenses 10,840,757 3.07

Refuse removal (including hazardous waste) services 520,907 0.15

Taxes and license fees 1,791,539 0.51

Total depreciation ($1,000) 3,300,086 0.93

Net Profit Before Income Taxes 60,851,484 17.24 Baseline &

Incremental

Source: U.S. Census Bureau. 2007. Residential Building Construction. Sector 23 , EC0723I1 : 236115 through 236118. Construction, Industry

Series, Preliminary Detailed Statistics for Establishments: 2007.

Note: G eneral contractor costs and expenses are first presented as total dollar values and then converted to percentage values. This is in contrast

to the cost per dollar of sales revenue values shown in Table 6.5.6.

6A.4 ESTIMATION OF CONTRA CTOR MARK

- UP BY STATE

Table 6A.4.1 Mechanical Contractor Markup Estimation by State, 2007

State Value of

Const

. $1,000 Cost of

Goods

Sold

$ 1, 000 Baseline

MU Incremental

MU Replacem

ent

Baseline

MU Replacement

Incremental

MU New Const

. Baseline

MU New Const

. Incremental

MU

Alabama 2,010,305 1,401,223 1.435 1.148 1.498 1.198 1.393 1.114

6A

- 6State Value of

Const

. $1,000 Cost of

Goods

Sold

$ 1, 000 Baseline

MU Incremental

MU Replacem

ent

Baseline

MU Replacement

Incremental

MU New Const

. Baseline

MU New Const

. Incremental

MU

Alaska 583,171 344,729 1.692 1.353 1.766 1.413 1.642 1.314

Arizona 3,522,116 2,326,475 1.514 1.211 1.580 1.264 1.470 1.176

Arkansas 1,065,754 743,395 1.434 1.147 1.496 1.197 1.392 1.113

California 16,726,969 10,865,201 1.539 1.232 1.607 1.286 1.495 1.196

Colorado 3,056,988 2,084,454 1.467 1.173 1.531 1.225 1.424 1.139

Connecticut 1,704,668 1,135,871 1.501 1.201 1.566 1.253 1.457 1.166

Delaware 481,900 D 1.421 1.137 1.483 1.186 1.379 1.104

District of

Columbia 34,600 D 1.458 1.167 1.522 1.218 1.416 1.133

Florida 9,061,426 6,254,391 1.449 1.159 1.512 1.210 1.407 1.125

Georgia 4,700,799 3,329,842 1.412 1.129 1.474 1.179 1.371 1.096

Hawaii 800,221 455,122 1.758 1.407 1.835 1.468 1.707 1.366

Idaho 900,698 617,165 1.459 1.168 1.523 1.219 1.417 1.133

Illinois 7,641,642 5,058,047 1.511 1.209 1.577 1.262 1.467 1.173

Indiana 4,002,323 2,605,238 1.536 1.229 1.604 1.283 1.491 1.193

Iowa 1,868,483 1,305,883 1.431 1.145 1.493 1.195 1.389 1.111

Kansas 1,395,359 966,707 1.443 1.155 1.507 1.205 1.401 1.121

Kentucky 1,747,925 1,157,360 1.510 1.208 1.576 1.261 1.466 1.173

Louisiana 1,997,044 1,317,429 1.516 1.213 1.582 1.266 1.472 1.177

Maine 580,816 394,847 1.471 1.177 1.535 1.228 1.428 1.142

Maryland 5,329,135 3,739,560 1.425 1.140 1.487 1.190 1.383 1.107

Massachusetts 4,099,301 2,781,377 1.474 1.179 1.538 1.231 1.431 1.145

Michigan 4,420,638 3,015,948 1.466 1.173 1.530 1.224 1.423 1.138

Minnesota 3,402,921 2,315,330 1.470 1.176 1.534 1.227 1.427 1.141

Mississippi 1,025,452 715,571 1.433 1.146 1.496 1.197 1.391 1.113

Missouri 3,335,124 2,353,598 1.417 1.134 1.479 1.183 1.376 1.101

Montana 483,578 345,458 1.400 1.120 1.461 1.169 1.359 1.087

Nebraska 1,004,296 755,338 1.330 1.064 1.388 1.110 1.291 1.033

Nevada 2,327,842 1,600,555 1.454 1.164 1.518 1.214 1.412 1.130

New

Hampshire 620,761 D 1.472 1.178 1.537 1.230 1.429 1.144

New Jersey 5,062,336 3,337,013 1.517 1.214 1.583 1.267 1.473 1.178

New Mexico 891,914 595,659 1.497 1.198 1.563 1.250 1.454 1.163

New York 10,364,779 6,760,337 1.533 1.227 1.600 1.280 1.488 1.191

North

Carolina 5,111,396 3,631,802 1.407 1.126 1.469 1.175 1.366 1.093

6A

- 7State Value of

Const

. $1,000 Cost of

Goods

Sold

$ 1, 000 Baseline

MU Incremental

MU Replacem

ent

Baseline

MU Replacement

Incremental

MU New Const

. Baseline

MU New Const

. Incremental

MU

North Dakota 360,683 255,057 1.414 1.131 1.476 1.181 1.373 1.098

Ohio 5,618,591 3,809,806 1.475 1.180 1.539 1.231 1.432 1.145

Oklahoma 1,352,943 924,264 1.464 1.171 1.528 1.222 1.421 1.137

Oregon 1,893,678 1,237,956 1.530 1.224 1.597 1.277 1.485 1.188

Pennsylvania 6,487,476 4,579,367 1.417 1.133 1.479 1.183 1.375 1.100

Rhode Island 631,202 410,653 1.537 1.230 1.604 1.284 1.492 1.194

South

Carolina 1,991,303 1,326,690 1.501 1.201 1.567 1.253 1.457 1.166

South Dakota 386,186 239,017 1.616 1.293 1.686 1.349 1.569 1.255

Tennessee 2,595,613 1,834,242 1.415 1.132 1.477 1.182 1.374 1.099

Texas 10,810,308 7,532,064 1.435 1.148 1.498 1.198 1.393 1.115

Utah 1,746,398 1,235,004 1.414 1.131 1.476 1.181 1.373 1.098

Vermont 294,806 D 1.472 1.178 1.537 1.230 1.429 1.144

Virginia 4,623,151 3,099,329 1.492 1.193 1.557 1.246 1.448 1.158

Washington 4,111,543 2,734,093 1.504 1.203 1.570 1.256 1.460 1.168

West Virginia 655,100 D 1.464 1.171 1.528 1.222 1.421 1.137

Wisconsin 2,926,545 2,023,634 1.446 1.157 1.510 1.208 1.404 1.123

Wyoming 289,391 198,105 1.461 1.169 1.525 1.220 1.418 1.135

Sources: U.S. Bureau of the Census. American Factfinder : 2007. Sector 23: Plumbing, Heating, and Air

- Conditioning Contractors ( NAICS

238220 ), Detailed Statistics for Establishments: 2007

http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I1&prodType=table and

Geographic Area Series: Detailed Statistics for Establishments: 2007.

Notes: The Census Bureau withheld data for some states.

Markups may v ary across states for several reasons, including differences in firm size.

Due to sample size and/or magnitude of reporting error relative to the mean, disaggregated information not provided for all o f the Subcontract,

Materials, and Fuels fields. In these cases, the state markup ratio is calcul a ted as an average of neighboring states (ex. Delaware, District of

Columbia, New Hampshire, Vermont

, and West Virginia)

6A

- 8Table 6A.4.2 Residential Building General Contractor Baseline Markups by State

State Va lue of Residential

Construction

$ 1, 000 Cost of

Goods Sold

$1,000 Baseline

Markup Incremental

Markup

Alabama 4,232,349 3,106,308 1.363 1.234

Alaska 598,572 322,897 1.854 1.678

Arizona 14,743,264 8,636,727 1.707 1.546

Arkansas 821,493 638,546 1.287 1.165

California 49,325,592 28,727,843 1.717 1.555

Colorado 9,711,667 6,478,218 1.499 1.357

Connecticut 2,835,015 1,914,706 1.481 1.341

Delaware 912,121 714,609 1.276 1.156

District of Columbia 177,004 115,545 1.532 1.387

Florida 33,290,091 21,780,175 1.528 1.384

Georgia 12,492,752 8,745,668 1.428 1.293

Hawaii 2,739,122 1,933,143 1.417 1.283

Idaho 2,565,176 2,014,522 1.273 1.153

Illinois 13,035,923 8,206,105 1.589 1.438

Indiana 4,637,976 3,418,576 1.357 1.228

Iowa 1,846,602 1,449,114 1.274 1.154

Kansas 1,940,745 1,443,265 1.345 1.217

Kentucky 3,074,656 2,244,283 1.370 1.240

Louisiana 2,429,529 1,650,884 1.472 1.332

Maine 821,980 630,393 1.304 1.181

Maryland 6,616,960 4,635,717 1.427 1.292

Massachusetts 7,693,991 5,728,767 1.343 1.216

Michigan 5,383,752 3,501,797 1.537 1.392

Minnesota 5,558,816 3,847,679 1.445 1.308

Mississippi 1,241,083 939,692 1.321 1.196

Missouri 4,754,552 3,588,694 1.325 1.200

Montana 1,148,453 919,206 1.249 1.131

Nebraska 577,746 424,822 1.360 1.231

Nevada 6,697,489 4,026,111 1.664 1.506

New Hampshire 292,227 228,854 1.277 1.156

New Jersey 8,492,015 5,649,618 1.503 1.361

New Mexico 2,236,262 1,395,073 1.603 1.451

New York 16,958,113 12,176,837 1.393 1.261

North Carolina 16,254,736 11,579,895 1.404 1.271

6A

- 9State Va lue of Residential

Construction

$ 1, 000 Cost of

Goods Sold

$1,000 Baseline

Markup Incremental

Markup

North Dakota D D 1.331 1.205

Ohio 6,788,825 4,883,462 1.390 1.259

Oklahoma 1,419,859 1,075,586 1.320 1.195

Oregon 5,519,819 4,019,693 1.373 1.243

Pennsylvania 9,971,624 7,323,399 1.362 1.233

Rhode Island 309,403 205,383 1.506 1.364

South Carolina 5,921,453 4,350,205 1.361 1.232

South Dakota 297,424 228,839 1.300 1.177

Tennessee 5,243,037 3,874,974 1.353 1.225

Texas 32,123,700 21,429,103 1.499 1.357

Utah 4,201,276 3,095,214 1.357 1.229

Vermont 527,837 387,905 1.361 1.232

Virginia 12,761,751 8,799,880 1.450 1.313

Washington 11,158,559 7,361,497 1.516 1.372

West Virginia 348,291 225,500 1.545 1.398

Wisconsin 3,820,533 2,850,921 1.340 1.213

Wyoming 524,809 418,215 1.255 1.136

Sources: U.S. Bureau of the Census, American Factfinder. 2007 Economic Census. Sector 23: Subsectors 236115 (residential sing le

- family),

236116 (residential multifamily), 236117 (operative builders), and 236118 (residential remodelers). Sector 23: EC0723A 1: Construction:

Geographic Area Series: Detailed Statistics for Establishments: 2007.

Notes: The Census Bureau withheld data for some states.

Markups may vary across states for several reasons, including differences in firm size.

Due to sample size and/or magnitude of reporting error relative to the mean, disaggregated information not provided for all of the Subcontract,

Materials, and Fuels fields. In these cases, the state markup ratio is calcul a ted as an average of neighboring states (ex. North Dakota)

.

6A

- 106A.5 STATE SALES TAX RATE S

Table 6A.5.1 State Sales Tax Rates

State Combined State

and Local Tax

Rate

% State Combined State

and Local Tax

Rate

% State Combined State

and Local Tax

Rate

%

Alabama 8.55 Kentucky 6.00 North Dakota 5. 9 0

Alaska 1.3 0 Louisiana 8.75 Ohio 7. 10

Arizona 7 .15 Maine 5.5 0 Oklahoma 8. 35

Arkansas 8. 90 Maryland 6.00 Oregon

--

California 8. 45 Massachusetts 6.25 Pennsylvania 6.40

Colorado 6. 05 Michigan 6.00 Rhode Island 7.00

Connecticut 6.35 Minnesota 7. 2 0 South Carolina 7. 20

Delaware

-- Mississippi 7.00 South Dakota 5. 40

Dist. of Columbia 5.75 Missouri 7.45 Tennessee 9. 4 5

Florida 6.65 Montana

-- Texas 7.9 0

Georgia 7. 05 Nebraska 6.00 Utah 6.70

Hawaii 4. 40 Nevada 7.85 Vermont 6.05

Idaho 6.05 New Hampshire

-- Virginia 5. 60

Illinois 8.05 New Jersey 6.95 Washington 8. 90

Indiana 7.00 New Mexico 6. 60 West Virginia 6. 10

Iowa 6.85 New York 8. 40 Wisconsin 5.45

Kansas 7. 85 North Carolina 6. 90 Wyoming 5. 50

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (A cc essed on January 22 , 201 4 )

.

6A

- 11APPENDIX 7A. BUILDING VARIABLES

TABLE OF CO NTENTS

7A.1 INTRODUCTION

................................ ................................ ................................ .. 7A

- 1

7A.2 RECS SAMPLE DETERMIN ATION

................................ ................................ .... 7A

- 1

7A.3 RECS 2009 DATABASE V ARIABLE RESPONSE COD ES

............................... 7A

- 3

LIST OF TABLES

Table 7A.2.1 List of RECS 2009 Variables Used for Hearth Products

...................... 7A

- 2

Table 7A.3.1 Definitions of RECS 2009 Variables Used in Life

- Cycle Cost

Analysis

................................ ................................ ................................ . 7A

- 3

7A

- iAPPENDIX 7A. BUILDING VARIABLES

7A.1 IN TRODUCTION

DOE created a database containing a subset of the records and variables from DOE’s

Energy Information Administration (EIA)’s 2009 Residential Energy Consumption Survey

( RECS 2009 ) using Microsoft ACCESS

. 1 DOE used this RECS 2009 subset in the life

- cycle cos t

(LCC) analysis of the Hearth P roduct s Rulemaking. This appendix explains the variable name

abbreviations and provides definitions of the variable values.

For the entire RECS 2009 dataset, refer to

www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata

.

7A.2 RECS SAMPLE DETERMIN ATION

The RECS consists of three parts:

• Personal interviews with households for information about energy used, how it is us ed,

energy

- using appliances, structural features, energy efficiency measures, and

demographic characteristics of the household.

• Telephone interviews with rental agents for households that have any of their energy use

included in their rent. This informatio n augments information collected from those

households that may not be knowledgeable about the fuels used for space heating or

water heating.

• Mail questionnaires sent to energy suppliers (after obtaining permission from households)

to collect the actual bi lling data on energy consumption and expenditures.

For vented hearth heaters, t he subset of RECS 2009 records used in the analysis met all

of the following criteria:

• used a fireplace for secondary or primary space heating,

• used a heating fuel that is natural gas or liquefied petroleum gas (LPG), and

• had a flue on the gas fireplace to the outside.

For ventless hearth heaters, t he subset of RECS 2009 records used in the analysis met all

of the following criteria:

• used a fireplace for secondary space he ating,

• used a fuel for a fireplace that is used for secondary space heating that is natural gas or

LPG , and

• did not have a flue on the gas fireplace to the outside.

The RECS 2009 weighting indicates how commonly each household configuration

occurs in the general population.

7A

- 1Table 7A.2.1 lists the variables use in the analysis.

Table 7A.2.1 List of RECS 2009 Variables Used for Hearth Product s

Variable Description

Location Variables

REGIONC Census Region

DIVISION Census Division

REPORTABLE_DOMAIN Reportable states and groups of states

HDD65 Heating degree days in 2009, base temperature 65F

CDD65 Heating degree days in 2009, base temperature 65F

Househol d Characteristics Variables

NWEIGHT Final sample weight

DOEID Unique identifier for each respondent

TYPEHUQ Type of housing unit

YEARMADE Year housing unit was built

BTUNGSPH Natural Gas usage for space heating, in thousand BTU, 2009

BTULPSPH LPG/Propane usage for space heating, in thousand BTU, 2009

CHIMNEY Fireplace used for secondary space heating

NGFPFLUE Flue on gas fireplace

USENGFP Frequency gas fireplace used

ROOMHEAT Built

- in room heaters used for secondary space heating

EQUIPM Type of main space heating equipment used

FUELHEAT Main space heating fuel

HEATOTH Main space heating equipment heats other homes, business, or

farm

MAINTHT Routine service or maintenance performed on main space heating

equipment

EQUIPAGE Age of main space heating equipment

MONEYPY 2009 gross household income

RMHTFUEL Fuel used by built

- in electric units for secondary space heating

FPFUEL Fuel used by fireplace for secondary space heating

EQMAMT Portion of space heating provided by main space heating

equipment (for homes with main and secondary heating only)

NHSLDMEM Number of household members

Seniors * Number of household members age 65 or older

POVERTY100 Household income at or below 100% of poverty line

StationID * ID number of weather station identified with household (See

Appendix 7 B )

TOTSQFT Total square footage (includes all attached garages, all basements,

and finished/heated/cooled attics)

TOTSQFT_EN Total square footage (includes heated/cooled garages, all

basements, and finished/heated/cooled attics). Used for EIA data

tables.

TOTHSQFT Total heated square footage

CENACHP Central air conditioner is a heat pump

COOLTYPE Type of air condit ioning equipment used

7A

- 2* Not part of RECS 2009 variables.

7A.3 RECS 2009 D ATABASE VARIABLE RES PONSE CODES

Table 7A.3.1 provides the response codes for all RECS 2009 variables used in the hearth

products samples.

Table 7A.3.1 Definitions of RECS 2009 Variables Used in Life

- Cycle Cost Analysis

Variable Response Codes

BTULPSPH Thousand BTU

BTUNGSPH Thousand BTU

CDD65 Cooling degree days in 2009, base temperature 65F

CENACHP 0

1

- 2 No

Yes

Not Applicable

CHIMNEY 0

1

- 2 No

Yes

Not Applicable

COOLTYPE 1

2

3

- 2 Central system

Window/wall units

Both a central system and window/wall units

Not Applicable

ROOMHEAT 0

1

- 2 No

Yes

Not Applicable

DIVISION 1

2

3

4

5

6

7

8

9

10 New England Census Division (CT, MA, ME, NH, RI, VT)

Middle Atlantic Census Division (NJ, NY, PA)

East North Central Census Division (IL, IN, MI, OH, WI)

West North Central Census Division (IA, KS, MN, MO, ND,

NE, SD)

South Atlantic Census Division (DC, DE, FL, GA, MD, NC,

SC, VA, WV)

East South Central Census Division (AL, KY, MS, TN)

West South Central Census Division (AR, LA, OK, TX)

Mountain North Sub

- Division (CO, ID, MT, UT, WY)

Mountain South Sub

- Division (AZ, NM, NV)

Pacific Census Division (AK, CA, HI, OR, WA)

DOEID 00001

-

12083 Unique identifier for each respondent

EQMAMT 1

2

3

- 2 Almost all

About three

- fourths

Closer to half

Not Applicable

EQUIPAGE 1

2

3

41

42 Less than 2 years old

2 to 4 years old

5 to 9 years old

10 to 14 years old

15 to 19 years old

7A

- 35

- 2 20 years or older

Not Applicable

EQUIPM 2

3

4

5

6

7

8

9

10

11

12

21

- 2 Steam or Hot Water System

Central Warm

- Air Furnace

Heat Pump

Built

- In Electric Units

Floor or Wall Pipeless Furnace

Built

- In Room Heater

Heating Stove

Fireplace

Portable Electric Heaters

Portable Kerosene Heaters

Cooking Stove

Other Equipment

Not Applicable

FPFUEL 1

2

7

21

- 2 Natural Gas

Propane/LPG

Wood

Other Fuel

Not Applicable

FUELHEAT 1

2

3

4

5

7

8

9

21

- 2 Natural Gas

Propane/LPG

Fuel Oil

Kerosene

Electricity

Wood

Solar

District Steam

Other Fuel

Not Applicable

HDD65 Heating degree days in 2009, base temperature 65F

HEATOTH 0

1

- 2 No

Yes

Not Applicable

MAINTHT 0

1

- 2 No

Yes

Not Applicable

MONEYPY 1

2

3

4

5

6

7

8

9

10

11

12 Less than $2,500

$2,500 to $4,999

$5,000 to $7,499

$7,500 to $9,999

$10,000 to $14,999

$15,000 to $19,999

$20,000 to $24,999

$25,000 to $29,999

$30,000 to $34,999

$35,000 to $39,999

$40,000 to $44,999

$45,000 to $49,999

7A

- 413

14

15

16

17

18

19

20

21

22

23

24 $50,000 to $54,999

$55,000 to $59,999

$60,000 to $64,999

$65,000 to $69,999

$70,000 to $74,999

$75,000 to $79,999

$80,000 to $84,999

$85,000 to $89,999

$90,000 to $94,999

$95,000 t o $99,999

$100,000 to $119,999

$120,000 or More

NGFPFLUE 1

2

- 2 Flue to the outside

Flueless

Not Applicable

NHSLDMEM 0

- 15 Number of household members

NWEIGHT Final sample weight

POVERTY100 0

1 No

Yes

REGIONC 1

2

3

4 Northeast Census Region

Midwest Census Region

South Census Region

West Census Region

REPORTABLE_DOMAIN 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26 Connecticut, Maine, New Hampshire, Rhode Island, Vermont

Massachusetts

New York

New Jersey

Pennsylvania

Illinois

Indiana, Ohio

Michigan

Wisconsin

Iowa, Minnesota, North Dakota, South Dakota

Kansas, Nebraska

Missouri

Virginia

Delaware, District of Columbia, Maryland, West Virginia

Georgia

North Carolina, South Carolina

Florida

Alabama, Kentucky, Mississippi

Tennessee

Arkansas, Louisiana, Oklahoma

Texas

Colorado

Idaho, Montana, Utah, Wyoming

Arizona

Nevada, New Mexico

California

7A

- 527 Alaska, Hawaii, Oregon, Washington

RMHTFUEL 1

2

3

4

- 2 Natural Gas

Propane/LPG

Fuel Oil

Kerosene

Not Applicable

Seniors 0

1 No

Yes

StationID Three character identifier for weather station

TOTHSQFT Square Feet

TOTSQFT Square Feet

TOTSQFT_EN Square Feet

TYPEHUQ 1

2

3

4

5 Mobile Home

Single

- Family Detached

Single

- Family Attached

Apartment in Building with 2

- 4 Units

Apartment in Building with 5+ Units

USE NGFP 1

2

3

- 2 Most days

About once a week

Fewer than 4 times each month

Not Applicable

YEARMADE 1600

-

2009 Year housing unit was built

* Not part of RECS 2009 variables.

7A

- 6REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7,

2015.) < www.eia.gov/co nsumption/residential/data/2009/ >

7A

- 7APPENDIX 7B. MAPPING OF WEATHER S TATION DATA TO RECS

HOUSEHOLDS

TABLE OF CO NTENTS

7B.1 INTRODUCTION

................................ ................................ ................................ ... 7B

- 1

7B.2 MAPPING METHODOLOGY

................................ ................................ ............... 7B

- 1

7B.3 MAPPING RESULTS

................................ ................................ ............................. 7B

- 2

7B.3.2 Developing Monthly Heating Degree Day Fractions

................................ .............. 7B

- 9

LIST OF TABLES

Table 7B.3.1 Weather Station Mapping Statistics, Heating and Cooling ODT,

and Average Outdoor Temperature

................................ ....................... 7B

- 2

Table 7B.3.2 Weather Station Monthly Heating Degree Day Data (10

- Year

Average)

................................ ................................ ................................ . 7B

- 9

Table 7B.3.3 Weather Station Monthly Heating Degree Day Data Fractions (10

- Year Average)

................................ ................................ ...................... 7B

- 16

7B

- iAPPENDIX 7B. MAPPING OF W EATHER S TATION D ATA TO RECS

HOUSEHOLDS

7B.1 IN TRODUCTION

Energy Information Administration’s (EIA) 2009 Residential Energy Consumption

Survey (RECS 2009) 1 provide s annual data on heating and cooling degree

- days but not on other

weather parameters needed for the analysis such as length of the heating and cooling sea s on,

mont h ly heating degree days (HDD) and monthly cooling degree days (CDD)

. Energy price data

used in this analysis are available on a monthly basis. Monthly HDD are used to disaggregate the

annual energy use provided by RECS by month. M onthly energy use is combined with monthly

energy prices to find the monthly operating cost.

7B.2 M APPING M ETHODOLOGY

To derive the additional weather data that is needed for the analysis ( e.g.

, HDD

, C DD),

for each building in the sample, DOE developed an approach to assign a physical location to

each RECS household. a The methodology consists of th e f ollowing steps:

1. DOE assembled monthly weather data from 360 weather stations from the National

Oceanic a nd Atmospheric Administration ( NOAA ) that provide the heating and cooling

degree

- days at base temperature 65 o F for year 2009 for these weather stations. 2 The 2009

heating and cooling degree days match the period u sed to determine the degree

- days in

RECS 2009

. 2. RECS report s both HDD and CDD to base temperature 65 o F for each household record.

DOE assigned each household to one of the 339 weather stations by calculating which

weather station (within the appropriate reg ion) was the closest using the best linear least

squares fit of the RECS data to the weather data

. The following equation calculate s the

U.S. weather station closest (or with minimum “distance”) to the RECS household :



### " " ( ) ( )

Distance

=

− +

− HDD HDD CDD CDD

2 1 2

2 1 2

Eq. 7B.2

. 1

Where:

HDD 1 = heating degree days from U.S. weather data,

HDD 2 = he ating degree days from RECS data,

CDD 1 = cooling degree days from U.S. weather data, an d

CDD 2 = co oling degree days from RECS data.

a For confidentiality, h eating and cooling degree day values were altered slightly by EIA to mask the exact

geographic location of the housing unit.

7 B

- 17B.3 MAPPING RESULTS

Table 7B.3.1 shows the imputation results for all RECS 2009 locations

. Note that some

U.S. weather station data match with several of the RECS weather data. The number of RECS

households that were matched to the specified weather station is indicated in the column

“Count”. Table 7B.3.1 shows the data matches ( 321 weather stations) including the heating and

cooling O DT as well as annual average outdoor temperature for the weather stations.

.

Table 7B.3.1 Weather Station Mapping Statistics, Heating and Cooling ODT, and

Average Outdoor Temperature

Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

AK Anchorage ANC 10335 2 8

- 18 68 36

AK Bethel BET 12530 0 1

- 24 68 30

AK Cold Bay CDB 9668 0 2 10 57 38

AK Cordova CDV 9511 0 2 1 67 38

AK Homer HOM 9817 0 10 4 62 38

AK Juneau JNU 8536 6 2 1 70 42

AK Kenai ENA 10423 0 1

- 14 65 36

AK Ketchikan KTN 7359 68 2 20 68 38

AK King Salmon AKN 11088 0 1

- 19 67 35

AK Kodiak ADQ 8903 0 1 13 65 41

AK Sitka SIT

- - - 21 64 42

AK St Paul Island SNP 11420 0 4 3 52 35

AK Talkeetna TKA

- - - - 21 73 34

AK Valdez VWS 7074 23 2 7 66 38

AK Yakutat YAK 9295 1 1 2 63 40

AL Birmingham BHM 2605 1958 25 21 93 62

AL Huntsville HSV 2982 1863 26 16 92 61

AL Mobile MOB 1594 2681 59 29 92 67

AL Montgomery MGM 2137 2367 3 25 94 65

AL Muscle Shoals MSL 2948 1773 12 21 93 61

AL Tuscaloosa TCL 2349 2136 10 23 93 64

AR Fayetteville FYV 3957 1185 48 12 93 58

AR Fort Smith FSM 3174 1906 3 17 96 61

AR Little Rock LIT 2946 1943 27 20 95 62

AR Texarkana TXK 2573 2006 10 23 96 61

AZ Douglas DUG 2160 2204 27 31 98 61

AZ Flagstaff FLG 6741 176 2 4 83 46

AZ Phoenix PHX 807 4942 26 34 108 74

AZ Tucson TUS 1268 3626 85 32 104 69

AZ Winslow INW 4233 1395 4 10 93 55

AZ Yuma NYL 671 4757 82 39 109 75

CA Bakersfield BFL 1873 2644 177 32 101 65

CA Blythe BLH 968 4580 8 33 112 71

CA Eureka EKA 5137 2 2 33 65 53

CA Fresno FAT 2239 2390 50 30 101 63

CA Los Angeles LAX 1294 569 117 43 80 63

7B

- 2Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

CA Mt Shasta MHS 5474 433 5 21 88 49

CA Paso Robles PRB 2676 1095 144 29 98 58

CA Red Bluff RBL 2452 2122 70 32 102 62

CA Redding RDD 2750 2086 63 31 102 62

CA Sacramento SAC 2531 1357 30 32 98 61

CA San Diego SAN 1050 813 540 44 81 64

CA San Francisco SFO 2614 220 278 38 78 57

CA Stockton SCK 2451 1468 122 30 98 62

CO Alamosa ALS 8229 49 27

- 16 82 41

CO Colorado Spring COS 6301 356 90 2 88 48

CO Denver DEN 5988 541 69 1 92 50

CO Eagle EGE 7593 124 15

- 7 86 41

CO Pueblo PUB 5427 818 77 0 96 52

CO Trinidad TAD 5323 719 17 3 90 51

CT Bridgeport BDR 5484 669 57 9 85 52

CT Hartford BDL 6072 610 94 7 88 50

DC Washington DCA 4124 1427 39 17 93 58

DE Wilmington ILG 4789 1031 14 14 89 54

FL Daytona Beach DAB 753 3321 99 35 91 71

FL Fort Myers FMY 294 4151 63 44 93 75

FL Ft Lauderdale FLL 118 4839 30 46 91 75

FL Gainesville GNV 1181 2789 118 31 92 69

FL Jacksonville JAX 1339 2772 60 32 93 68

FL Key West EYW 108 5017 11 57 89 78

FL Melbourne MLB 526 3718 80 43 91 73

FL Miami MIA 109 4914 2 47 91 77

FL Orlando MCO 588 3620 103 38 93 73

FL Pensacola PNS 1443 2729 44 29 92 68

FL Tallahassee TLH 1574 2802 31 30 94 68

FL Tampa TPA 496 3876 112 40 91 73

FL Vero Beach VRB 477 3604 26 43 91 73

FL West Palm Beach PBI 239 4314 169 45 90 75

GA Albany ABY 1767 2686 5 29 95 66

GA Athens AHN 2882 1903 253 22 93 62

GA Atlanta ATL 2813 1838 87 22 92 62

GA Augusta AGS 2475 2068 55 23 95 63

GA Brunswick SSI

- - - 32 91 69

GA Columbus CSG 2183 2194 2 24 94 65

GA Macon MCN 2288 2133 17 25 94 64

GA Savannah SAV 1739 2497 21 27 93 66

GA Waycross AYS 29 94 67

HI Hilo

- Hawaii ITO 0 3050 14 62 85 74

HI Honolulu

- Oahu HNL 0 4816 14 63 89 78

HI Kahului

- Maui OGG 1 3746 21 61 88 76

HI Lihue

- Kauai LIH 2 3611 5 62 85 76

IA Burlington BRL 5687 810 24

- 3 91 50

IA Cedar Rapids CID 6977 419 15

- 5 89 47

7B

- 3Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

IA Des Moines DSM 6124 898 33

- 5 90 50

IA Dubuque DBQ 7204 345 1

- 7 86 47

IA Mason City MCW 7856 338 15

- 11 88 47

IA Ottumwa OTM 6317 588 43

- 4 92 50

IA Sioux City SUX 6913 678 75

- 7 90 48

IA Waterloo ALO 7253 448 58

- 10 89 47

ID Boise BOI 5592 1199 9 10 95 52

ID Burley BYI 6697 397 1 2 90 47

ID Idaho Falls IDA

- - - - 6 89 47

ID Lewiston LWS 5386 1008 3 6 94 52

ID Pocatello PIH 7463 321 17

- 1 91 47

IL Chicago ORD 6417 585 40 0 90 49

IL Moline MLI 6250 636 35

- 4 91 50

IL Peoria PIA 5841 752 62

- 4 90 51

IL Quincy UIN 5460 849 12 3 90 51

IL Rockford RFD 6738 433 58

- 4 89 48

IL Springfield SPI 5234 933 41 2 91 53

IN Evansville EVV 4397 1283 13 9 92 56

IN Fort Wayne FWA 6077 601 41 1 88 50

IN Indianapolis IND 5203 953 22 2 89 53

IN South Bend SBN 6426 545 54 1 88 50

IN West Lafayette LAF 5436 826 32 3 90 5 0

KS Concordia CNK 5558 1094 18 3 96 54

KS Dodge City DDC 4975 1257 27 5 97 55

KS Garden City GCK 5014 1154 31 4 97 55

KS Goodland GLD 6016 722 11 0 94 51

KS Russell RSL 5298 1194 46 4 96 54

KS Salina SLN

- - - 5 98 56

KS Topeka TOP 4968 1195 9 4 94 54

KS Wichita ICT 4552 1506 68 7 97 56

KY Bowling Green BWG 3808 1407 52 10 91 57

KY Jackson JKL 4237 984 15 14 87 56

KY Lexington LEX 4670 1020 40 8 89 55

KY Louisville SDF 4155 1316 29 10 91 57

KY Paducah PAH 4198 1239 39 12 93 57

LA Baton Rouge BTR 1404 2985 24 29 93 67

LA Lafayette LFT 1296 3086 3 30 93 68

LA Lake Charles LCH 1380 2980 10 31 93 68

LA Monroe MLU 2118 2547 11 25 95 66

LA New Orleans MSY 1156 3221 35 33 92 69

LA Shreveport SHV

- - - 25 95 66

MA Boston BOS 5694 581 243 9 87 52

MA Worcester ORH 6699 370 258 4 83 47

MD Baltimore BWI 4745 1088 34 13 91 55

MD Salisbury SBY 4345 1149 19 16 90 57

ME Augusta AUG 7487 276 18 23 95 63

ME Bangor BGR 8098 246 19

- 6 84 46

7B

- 4Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

ME Caribou CAR 9415 149 13

- 13 82 39

ME Houlton HUL 9316 178 24

- 13 85 40

ME Portland PWM 7107 294 108

- 1 83 46

MI Alpena APN

- - - - 6 84 43

MI Detroit DTW 6224 588 81 6 87 50

MI Flint FNT 7068 328 40 1 86 47

MI Grand Rapids GRR 6580 444 35 5 86 48

MI Houghton Lake HTL

- - - 1 85 43

MI Jackson JXN 6585 420 11 14 87 56

MI Lansing LAN 6830 372 36 1 86 47

MI Marquette MQT

- - - - 8 83 39

MI Muskegon MKG 6719 371 38 6 83 47

MI Saginaw MBS 6960 350 19 4 87 47

MI Sault St Marie SSM

- - - - 8 80 40

MI Traverse City TVC 7695 253 14 1 86 47

MN Alexandria AXN 8922 340 8

- 16 86 42

MN Duluth DLH 9517 118 10

- 16 81 39

MN Hibbing HIB 10159 64 4

- 20 81 37

MN Int'l Falls INL 10648 72 8

- 25 83 37

MN Minneapolis MSP 7613 646 48

- 12 88 45

MN Rochester RST 7884 321 9

- 12 85 43

MN Saint Cloud STC 8704 301 74

- 11 88 42

MO Columbia COU 4999 958 125 4 92 54

MO Joplin JLN 4216 1382 98 10 94 56

MO Kansas City MCI 5084 1093 213 6 93 54

MO Saint Louis STL 4438 1457 70 6 93 56

MO Springfield SGF 4596 1114 180 2 91 53

MS Greenwood GWO 2376 2250 1 20 94 61

MS McComb MCB 1833 2472 34 26 92 64

MS Tupelo TUP 2842 1947 20 19 94 61

MT Billings BIL 6948 627 9

- 10 90 47

MT Butte BTM

- - - - 17 84 40

MT Cut Bank CTB

- - - - 20 84 40

MT Great Falls GTF 7941 300 1

- 15 89 44

MT Havre HVR

- - - - 11 90 44

MT Helena HLN 7704 444 1

- 16 87 44

MT Kalispell FCA

- - - - 7 86 43

MT Lewistown LWT

- - - - 16 86 44

MT Miles City MLS 7700 716 1

- 15 93 46

MT Missoula MSO 7588 355 2

- 6 88 45

NC Asheville AVL 4194 768 23 14 86 55

NC Cape Hatteras HAT

- - - 29 86 63

NC Charlotte CLT 3346 1611 71 22 91 61

NC Greensboro GSO 3605 1510 41 18 90 58

NC Hickory HKY 3593 1353 42 18 90 58

NC New Bern EWN 2769 1788 16 24 92 64

NC Raleigh Durham RDU 3164 1865 55 20 92 60

7B

- 5Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

NC Wilmington ILM 2521 1937 14 14 89 54

ND Bismarck BIS 9130 332 16

- 19 90 42

ND Devil's Lake P11 10245 236 8

- 21 87 40

ND Fargo FAR 9304 362 17

- 18 88 42

ND Grand Forks GFK 9928 269 8

- 22 89 40

ND Minot MOT 9559 314 9

- 20 89 41

ND Williston ISN 9721 297 8

- 21 92 41

NE Grand Island GRI 6431 788 26

- 3 93 50

NE Lincoln LNK 6159 912 14

- 2 94 51

NE Norfolk OFK 6789 643 4

- 4 92 49

NE North Platte LBF 6946 534 14

- 4 92 49

NE Omaha OMA 6288 851 32

- 3 90 51

NE Scottsbluff BFF 6689 579 6

- 3 92 48

NE Valentine VTN 7279 527 2

- 8 94 47

NH Concord CON 7462 325 5

- 3 87 46

NH Lebanon LEB 7312 371 18

- 3 86 46

NJ Atlantic City ACY 4693 994 57 13 89 54

NJ Newark EWR 4790 1021 147 14 91 55

NM Albuquerque ABQ 3823 1435 17 16 93 57

NM Carlsbad CNM 2398 2376 2 19 98 63

NM Clayton CAO 4517 1143 31 9 91 53

NM Gallup GUP 6134 442 6 5 87 53

NM Roswell ROW 3098 1961 7 18 96 61

NV Elko EKO 6948 450 1

- 2 92 46

NV Ely ELY 7925 125 4

- 4 87 45

NV Las Vegas LAS 1882 3818 66 28 106 68

NV Lovelock LOL

- - - 12 97 50

NV Reno RNO

- - - 10 92 51

NV Tonopah TPH 5298 874 5 10 92 51

NV Winnemucca WMC 6236 611 2 3 94 49

NY Albany ALB 6644 433 149 29 95 66

NY Binghamton BGM 7067 261 59 1 82 46

NY Buffalo BUF 6651 361 54 6 84 48

NY Glens Falls GFL 7612 285 26

- 5 85 46

NY Massena MSS 7980 298 2

- 8 84 46

NY New York LGA 4647 1041 469 15 89 55

NY Rochester ROC 6765 315 46

- 12 85 43

NY Syracuse SYR 6687 439 23 2 86 47

NY Utica UCA

- - - - 6 85 47

NY Watertown ART 7707 298 11

- 6 83 46

OH Akron Canton CAK 6131 497 6 6 86 50

OH Cincinnati CVG 4950 874 13 6 90 54

OH Cleveland CLE 5833 664 44 5 87 50

OH Columbus CMH 5243 874 32 24 94 65

OH Dayton DAY 5602 732 45 4 88 52

OH Findlay FDY 5901 698 34 3 87 50

OH Mansfield MFD 6214 468 10 5 85 49

7B

- 6Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

OH Toledo TOL 6283 592 32 1 88 50

OH Youngstown YNG 6239 443 8 4 86 49

OK Hobart HBR 3392 2034 1 16 101 60

OK McAlester MLC 3136 1845 6 19 96 60

OK Oklahoma City OKC 3519 1849 37 13 96 60

OK Tulsa TUL 3608 1885 24 13 97 61

OR Astoria AST 4871 39 4 29 72 51

OR Baker BKE 7529 220 2 6 91 45

OR Eugene EUG 4999 331 89 22 88 52

OR Medford MFR

- - - 23 95 54

OR Pendleton PDT 5713 720 6 5 93 52

OR Portland PDX 4357 635 32

- 1 83 46

OR Redmond RDM 6737 313 17 9 90 44

OR Salem SLE 4660 457 50 23 88 53

PA Allentown ABE 5725 622 22 9 88 51

PA Altoona AOO 6109 433 17 5 86 50

PA Bradford BFD

- - - - 1 80 50

PA Du Bois DUJ 6753 254 5 5 84 50

PA Erie ERI 6183 423 9 9 84 50

PA Harrisburg CXY 5097 866 111 11 90 53

PA Philadelphia PHL 4557 1219 46 14 90 55

PA Pittsburgh PIT 5661 617 6 5 87 51

PA Williamsport IPT 5636 644 69 7 87 50

RI Providence PVD 5717 579 69 9 86 51

SC Charleston CHS 1941 2390 13 27 93 65

SC Columbia CAE 2561 2220 19 4 92 54

SC Florence FLO 2541 2061 13 25 94 64

SC Greenville GSP 3116 1735 42 22 91 60

SD Aberdeen ABR 8872 329 13

- 15 91 44

SD Huron HON 8070 469 105

- 14 91 45

SD Pierre PIR 7738 577 36

- 10 95 48

SD Rapid City RAP 7738 362 12

- 7 91 47

SD Sioux Falls FSD 7670 481 42

- 11 90 45

TN Bristol TRI 4267 930 28 14 87 55

TN Chattanooga CHA 3168 1808 35 18 92 60

TN Crossville CSV 4100 940 33 15 87 54

TN Jackson MKL 3379 1597 22 14 87 56

TN Knoxville TYS 3643 1392 91 19 90 58

TN Memphis MEM 2906 2091 3 18 94 62

TN Nashville BNA 3615 1558 37 14 92 59

TX Abilene ABI 2359 2494 217 20 97 64

TX Alice ALI 738 4832 23 34 99 72

TX Amarillo AMA 4034 1340 33 11 95 57

TX Austin AUS 1722 3214 45 28 96 69

TX Brownsville BRO 525 4300 20 39 94 73

TX College Station CLL 1404 3476 29 29 96 69

TX Corpus Christi CRP 811 4058 8 35 94 72

7B

- 7Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

TX Dallas

- Ft. Worth DFW 2097 2745 61 22 98 66

TX Del Rio DRT 1252 3807 29 31 98 70

TX El Paso ELP 2106 2783 43 24 98 65

TX Galveston GLS 907 3640 3 36 91 71

TX Houston IAH 1267 3410 170 32 96 69

TX Laredo LRD 602 5330 1 36 101 73

TX Lubbock LBB 3178 1965 10 15 96 60

TX Lufkin LFK 1803 2839 64 29 95 69

TX McAllen MFE 393 5387 3 39 99 73

TX Midland Odessa MAF 2495 2445 81 21 98 63

TX San Angelo SJT 2020 2814 56 22 97 65

TX San Antonio SAT 1270 3598 28 30 97 69

TX Victoria VCT 1123 3608 35 32 95 70

TX Waco ACT 1927 3086 18 26 99 67

TX Wichita Falls SPS 2838 2394 14 18 100 63

UT Cedar City CDC 6058 645 56 5 91 52

UT Salt Lake City SLC 5716 1147 29 8 95 52

VA Lynchburg LYH 4433 1003 159 16 90 55

VA Norfolk ORF 3330 1659 41

- 4 92 49

VA Richmond RIC 3781 1564 47 17 92 58

VA Roanoke ROA 3931 1173 34 16 90 56

VT Burlington BTV

- - - - 3 91 50

VT Montpelier MPV 7998 237 12

- 6 83 45

WA Bellingham BLI 5568 115 8 15 76 51

WA Olympia OLM 5614 178 24 22 83 50

WA Quillayute UIL 5869 44 7 27 74 49

WA Seattle Tacoma SEA 4879 319 94 26 81 52

WA Spokane GEG 6942 599 5 2 89 47

WA Walla Walla ALW 5062 1144 12 7 95 54

WA Yakima YKM 6204 699 25 5 92 49

WI Eau Claire EAU 8208 333 23

- 11 87 44

WI Green Bay GRB 8005 275 55

- 9 85 44

WI Lacrosse LSE 7334 536 16

- 9 89 47

WI Madison MSN 7343 368 66

- 7 87 46

WI Milwaukee MKE 6816 474 28

- 4 87 48

WI Wausau AUW 8337 277 54

- 12 85 44

WV Beckley BKW 5325 404 16 4 84 52

WV Charleston CRW 4443 960 3 27 93 65

WV Elkins EKN 5993 284 3 6 83 50

WV Huntington HTS 4557 922 3 10 89 55

WV Martinsburg MRB 5046 854 63 10 91 54

WV Morgantown MGW 4957 836 15 8 87 54

WV Parkersburg PKB 4910 850 19 11 88 54

WY Casper CPR

- - - - 5 91 45

WY Cheyenne CYS 7390 203 11

- 1 86 45

WY Cody COD 7551 410 2

- 13 87 45

WY Lander LND 7743 351 1

- 11 87 45

7B

- 8Station Location Code RECS 2009 Heating

ODT Cool ing

ODT Average

Outdoor

State City HDD CDD Count Temp

WY Rock Springs RKS 8204 230 3

- 3 84 43

WY Sheridan SHR 7844 287 2

- 8 90 45

WY Worland WRL 7757 467 2

- 13 93 45

7B.3.2 Developing Monthly Heating Degree Day Fractions

Table 7B.3.2 show s the 10

- year average monthly HDD data based on NOAA data for

each weather station. 2 This data was then used to determine t he monthly fractions of HDD as

shown in Table 7B.3.3 . Mont hly HDD are used to disaggregate the annual energy use provided

by RECS 2009 by month. The monthly energy use is then combined with monthly energy prices

to find the monthly operating cost (see appendix 8 C for more details).

Table 7B.3.2 Weather Station Monthly Heatin g Degree Day Data (10

- Year Average)

Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AK Anchorage ANC 1558 1202 1238 824 510 292 184 234 464 855 1316 1421

AK Bethel BET 1914 1447 1684 1155 661 355 273 318 547 1004 1477 1690

AK Cold Bay CDB 1167 971 1177 939 755 559 440 388 497 744 944 1068

AK Cordova CDV 1225 1001 1074 797 594 413 321 330 490 762 1064 1127

AK Homer HOM 1314 1052 1163 829 622 436 320 337 487 786 1127 1201

AK Juneau JNU 1138 971 1006 720 492 310 248 280 444 707 980 1110

AK Kenai ENA 1608 1223 1285 838 560 362 248 292 478 828 1303 1439

AK Ketchikan KTN 930 823 861 666 479 298 212 203 349 600 805 924

AK King Salmon AKN 1632 1199 1427 932 620 406 299 307 505 886 1342 1474

AK Kodiak ADQ 1090 928 1063 806 621 442 313 294 454 724 969 1059

AK Sitka SIT 895 787 868 670 513 354 258 233 345 577 779 876

AK St Paul Island SNP 1277 1131 1314 1086 910 671 552 489 572 802 965 1127

AK Talkeetna TKA 1636 1267 1281 836 494 234 155 239 504 909 1394 1529

AK Valdez VWS 1212 912 952 622 371 194 136 154 319 594 996 1036

AK Yakutat YAK 1144 966 1045 796 602 404 297 316 462 719 987 1086

AL Birmingham BHM 635 502 261 105 21 0 0 0 4 114 312 563

AL Huntsville HSV 722 578 324 135 25 0 0 0 9 145 371 641

AL Mobile MOB 427 331 143 45 4 0 0 0 0 55 201 385

AL Montgomery MGM 543 417 198 70 9 0 0 0 2 80 265 481

AL Muscle Shoals MSL 716 576 317 135 25 0 0 0 7 149 366 640

AL Tuscaloosa TCL 606 477 234 95 16 0 0 0 2 110 299 539

AR Fayetteville FYV 859 724 446 230 76 2 0 1 32 233 458 803

AR Fort Smith FSM 745 599 313 120 26 0 0 0 4 122 343 685

AR Little Rock LIT 708 579 310 114 22 0 0 0 3 122 341 654

AR Texarkana TXK 606 488 240 84 15 0 0 0 1 96 274 559

AZ Douglas DUG 555 426 298 118 14 0 0 0 0 70 307 571

AZ Flagstaff FLG 1066 946 828 640 389 136 13 36 202 533 797 1085

AZ Phoenix PHX 234 172 63 11 0 0 0 0 0 4 68 283

AZ Tucson TUS 342 280 143 44 2 0 0 0 0 15 140 381

7B

- 9Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AZ Winslow INW 918 716 526 311 97 4 0 0 20 256 586 913

AZ Yuma NYL 148 128 46 13 0 0 0 0 0 2 54 227

CA Bakersfield BFL 484 327 217 133 21 2 0 0 1 51 274 491

CA Blythe BLH 271 191 73 13 0 0 0 0 0 5 105 337

CA Eureka EKA 557 513 527 496 414 314 276 258 308 365 467 579

CA Fresno FAT 527 367 250 146 25 1 0 0 2 52 289 518

CA Los Angeles LAX 215 220 200 152 68 13 2 2 4 33 124 251

CA Mt Shasta MHS 876 754 707 568 322 126 11 20 114 414 704 908

CA Paso Robles PRB 507 418 341 257 89 22 2 2 17 123 344 535

CA Red Bluff RBL 530 408 325 218 54 7 0 0 4 86 343 558

CA Redding RDD 551 423 351 241 61 9 0 0 5 100 374 583

CA Sacramento SAC 544 397 307 211 56 8 0 0 6 84 340 549

CA San Diego SAN 214 196 157 107 47 14 2 0 0 15 105 243

CA San Francisco SFO 438 344 308 262 187 105 61 52 53 104 266 427

CA Stockton SCK 549 393 299 190 45 5 0 0 3 72 327 541

CO Alamosa ALS 1432 1202 914 655 390 139 25 63 283 642 1012 1438

CO Colorado Spring COS 1025 942 712 503 259 57 7 19 133 443 736 1074

CO Denver DEN 1016 946 687 487 252 54 4 10 107 422 727 1075

CO Eagle EGE 1322 1086 854 621 367 112 8 26 223 567 918 1305

CO Pueblo PUB 1020 895 623 393 151 17 1 4 71 377 698 1062

CO Trinidad TAD 960 861 626 412 176 26 2 6 79 357 665 1005

CT Bridgeport BDR 1076 920 758 432 190 27 0 1 35 283 544 886

CT Hartford BDL 1204 1013 811 441 195 32 1 5 74 376 649 1007

DC Washington DCA 885 753 520 245 68 3 0 0 9 191 438 755

DE Wilmington ILG 1000 867 639 341 124 13 0 1 26 274 529 849

FL Daytona Beach DAB 240 173 75 21 1 0 0 0 0 11 60 172

FL Fort Myers FMY 119 65 24 3 0 0 0 0 0 3 15 75

FL Ft Lauderdale FLL 61 34 11 1 0 0 0 0 0 0 4 37

FL Gainesville GNV 345 249 116 37 1 0 0 0 0 28 120 279

FL Jacksonville JAX 385 287 140 51 3 0 0 0 0 32 148 313

FL Key West EYW 42 19 5 0 0 0 3 0 0 0 1 16

FL Melbourne MLB 181 117 49 15 0 0 0 0 0 9 35 116

FL Miami MIA 54 29 8 1 0 0 0 0 0 0 3 31

FL Orlando MCO 196 124 48 11 0 0 0 0 0 9 40 134

FL Pensacola PNS 394 306 128 36 1 0 0 0 0 36 159 344

FL Tallahassee TLH 424 328 149 53 2 0 0 0 0 42 189 368

FL Tampa TPA 183 117 46 10 0 0 0 0 0 6 32 129

FL Vero Beach VRB 165 107 47 13 0 0 0 0 0 6 26 102

FL West Palm Beach PBI 92 53 20 4 0 0 0 0 0 3 11 55

GA Albany ABY 469 370 173 56 2 0 0 0 0 51 217 418

GA Athens AHN 662 522 304 131 23 0 0 0 4 126 352 602

GA Atlanta ATL 650 519 292 118 21 0 0 0 3 110 325 581

GA Augusta AGS 598 475 265 106 11 0 0 0 2 100 314 532

GA Brunswick SSI 411 328 151 58 3 0 0 0 0 36 154 333

GA Columbus CSG 543 421 204 67 5 0 0 0 1 68 240 472

GA Macon MCN 563 445 239 91 10 0 0 0 2 89 295 510

GA Savannah SAV 478 373 191 69 6 0 0 0 0 50 215 399

GA Waycross AYS 412 317 143 50 2 12 0 0 0 40 180 369

7B

- 10Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

HI Hilo

- Hawaii ITO 0 0 0 0 0 0 0 0 0 0 0 0

HI Honolulu

- Oahu HNL 0 0 0 0 0 0 0 0 0 0 0 0

HI Kahului

- Maui OGG 0 0 0 0 0 0 0 0 0 0 0 0

HI Lihue

- Kauai LIH 0 0 0 0 0 0 0 0 0 0 0 0

IA Burlington BRL 1233 1043 667 337 125 8 1 5 71 339 617 1078

IA Cedar Rapids CID 1404 1197 807 441 187 21 5 14 128 450 763 1259

IA Des Moines DSM 1310 1109 716 360 128 6 0 4 72 359 697 1159

IA Dubuque DBQ 1421 1215 846 475 219 31 6 16 141 466 774 1275

IA Mason City MCW 1514 1288 924 514 230 33 6 23 153 505 865 1370

IA Ottumwa OTM 1310 1110 724 395 159 15 2 9 103 396 699 1152

IA Sioux City SUX 1393 1177 792 424 166 17 2 8 112 435 807 1282

IA Waterloo ALO 1449 1225 841 468 192 19 3 14 128 458 802 1304

ID Boise BOI 985 785 611 430 220 54 0 6 62 356 722 993

ID Burley BYI 1119 945 747 559 339 124 5 24 170 496 817 1106

ID Idaho Falls IDA 1379 1179 885 610 385 159 11 36 220 585 934 1270

ID Lewiston LWS 867 718 593 416 211 57 1 4 58 376 699 937

ID Pocatello PIH 1229 1046 806 589 366 128 5 22 182 535 887 1196

IL Chicago ORD 1256 1078 773 451 208 30 2 6 79 371 678 1109

IL Moline MLI 1280 1095 716 375 147 13 1 5 82 369 675 1145

IL Peoria PIA 1221 1034 666 346 134 10 0 5 64 348 646 1086

IL Quincy UIN 1178 996 628 328 120 8 0 5 66 332 611 1041

IL Rockford RFD 1330 1137 794 437 192 22 4 9 96 403 724 1193

IL Springfield SPI 1155 972 606 308 111 9 0 5 61 323 586 1006

IN Evansville EVV 981 828 505 242 73 4 0 1 27 247 516 863

IN Fort Wayne FWA 1247 1086 751 414 174 18 1 7 86 388 669 1074

IN Indianapolis IND 1131 968 620 316 120 10 1 3 46 314 592 987

IN South Bend SBN 1253 1090 783 442 210 29 2 11 96 404 685 1079

IN West Lafayette LAF 1178 1006 653 350 145 14 0 5 63 345 612 1022

KS Concordia CNK 1117 938 595 329 121 7 0 2 57 306 641 1067

KS Dodge City DDC 979 847 555 317 110 5 0 2 45 276 609 978

KS Garden City GCK 1010 868 572 336 112 6 1 2 43 294 624 1007

KS Goodland GLD 1059 946 668 436 196 24 3 5 89 394 720 1078

KS Russell RSL 1074 911 582 328 116 7 0 2 50 297 638 1050

KS Salina SLN 1043 872 532 279 87 3 0 1 36 256 570 997

KS Topeka TOP 1079 892 545 271 86 3 0 2 46 274 574 972

KS Wichita ICT 985 810 487 240 69 2 0 0 24 227 528 924

KY Bowling Green BWG 877 727 438 220 59 3 0 1 21 215 467 779

KY Jackson JKL 924 774 479 230 88 7 1 2 37 242 464 802

KY Lexington LEX 993 842 547 278 99 6 0 2 42 278 540 874

KY Louisville SDF 932 780 473 210 58 3 0 0 19 213 462 809

KY Paducah PAH 933 779 484 240 71 5 0 2 35 253 515 842

LA Baton Rouge BTR 398 310 128 40 3 0 0 0 0 48 182 376

LA Lafayette LFT 380 289 116 29 1 0 0 0 0 40 149 349

LA Lake Charles LCH 383 299 121 32 1 0 0 0 0 33 153 348

LA Monroe MLU 548 439 208 63 7 0 0 0 1 80 257 501

LA New Orleans MSY 347 263 99 23 0 0 0 0 0 24 124 309

LA Shreveport SHV 521 418 193 58 7 0 0 0 0 69 236 490

MA Boston BOS 1111 947 792 467 230 56 3 5 55 315 574 905

7B

- 11Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

MA Worcester ORH 1273 1083 892 514 268 79 9 16 107 435 682 1057

MD Baltimore BWI 969 837 602 309 109 8 0 0 26 265 511 836

MD Salisbury SBY 929 806 599 328 141 14 1 1 29 254 481 775

ME Augusta AUG 1410 1181 992 594 317 101 12 23 148 484 769 1168

ME Bangor BGR 1475 1245 1039 633 333 106 14 30 177 510 808 1212

ME Caribou CAR 1680 1420 1228 766 423 153 36 79 268 628 944 1401

ME Houlton HUL 1630 1393 1184 756 438 172 48 84 272 620 907 1359

ME Portland PWM 1318 1116 950 598 332 103 11 19 138 463 727 1089

MI Alpena APN 1419 1264 1076 685 387 118 31 54 209 545 826 1219

MI Detroit DTW 1226 1077 812 445 192 22 2 5 77 378 665 1049

MI Flint FNT 1306 1148 884 513 252 47 11 19 130 452 733 1116

MI Grand Rapids GRR 1264 1118 853 488 229 34 5 11 108 434 713 1077

MI Houghton Lake HTL 1433 1280 1044 629 335 103 37 65 223 562 836 1225

MI Jackson JXN 1286 1126 842 483 238 45 9 17 131 451 713 1100

MI Lansing LAN 1303 1148 873 510 250 46 10 19 130 455 729 1110

MI Marquette MQT 1515 1339 1152 765 441 162 64 82 264 616 945 1356

MI Muskegon MKG 1215 1097 875 528 268 54 8 17 119 432 701 1043

MI Saginaw MBS 1323 1172 917 540 261 48 10 20 129 450 738 1129

MI Sault St Marie SSM 1526 1324 1144 715 404 152 51 61 217 571 869 1299

MI Traverse City TVC 1312 1186 980 630 342 90 21 26 155 486 763 1132

MN Alexandria AXN 1675 1443 1085 589 298 66 10 29 183 561 967 1497

MN Duluth DLH 1676 1419 1140 713 430 156 45 69 256 630 1004 1506

MN Hibbing HIB 1780 1506 1170 737 454 181 75 126 334 695 1073 1599

MN Int'l Falls INL 1853 1586 1229 734 443 167 69 125 328 695 1099 1660

MN Minneapolis MSP 1512 1267 921 466 203 25 2 8 116 460 842 1353

MN Rochester RST 1531 1308 944 512 236 38 8 25 147 489 851 1377

MN Saint Cloud STC 1625 1385 1036 565 288 56 10 29 184 553 949 1463

MO Columbia COU 1077 896 547 274 96 5 0 3 48 287 557 955

MO Joplin JLN 914 751 439 218 70 3 0 1 28 217 445 821

MO Kansas City MCI 1104 918 564 280 92 5 0 1 46 281 575 993

MO Saint Louis STL 1002 835 498 230 67 3 0 1 26 227 498 883

MO Springfield SGF 970 800 498 258 89 4 0 2 39 261 511 881

MS Greenwood GWO 628 506 255 98 17 0 0 0 5 123 282 565

MS McComb MCB 490 384 174 61 9 0 0 0 1 71 230 446

MS Tupelo TUP 692 555 294 124 23 0 0 0 6 134 343 621

MT Billings BIL 1148 1004 776 536 332 93 3 13 146 499 829 1170

MT Butte BTM 1349 1201 996 783 564 301 67 117 366 724 1096 1424

MT Cut Bank CTB 1288 1157 981 709 487 238 50 88 300 671 984 1319

MT Great Falls GTF 1189 1070 889 641 431 188 22 50 231 575 900 1211

MT Havre HVR 1435 1273 990 608 380 142 13 42 234 612 1003 1418

MT Helena HLN 1252 1035 837 594 370 140 7 30 188 566 935 1285

MT Kalispell FCA 1145 946 764 513 281 132 20 45 216 517 882 1158

MT Lewistown LWT 1254 1139 939 702 490 235 41 72 275 631 956 1279

MT Miles City MLS 1368 1191 866 519 312 82 2 12 155 535 918 1331

MT Missoula MSO 1201 979 807 598 384 168 15 33 205 600 960 1246

NC Asheville AVL 857 712 500 267 89 6 0 0 33 272 521 786

NC Cape Hatteras HAT 440 383 263 90 21 0 0 0 1 24 102 292

NC Charlotte CLT 734 598 373 172 40 0 0 0 9 169 412 658

7B

- 12Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

NC Greensboro GSO 780 648 415 192 55 2 0 0 14 187 429 700

NC Hickory HKY 780 647 412 190 53 3 0 0 15 189 427 706

NC New Bern EWN 659 548 352 148 29 0 0 0 2 117 315 552

NC Raleigh Durham RDU 729 599 384 170 43 1 0 0 9 163 385 642

NC Wilmington ILM 617 510 324 128 24 0 0 0 1 103 297 517

ND Bismarck BIS 1595 1394 1044 589 328 80 8 28 198 594 1015 1472

ND Devil's Lake P11 1771 1580 1237 670 373 117 21 45 227 630 1091 1612

ND Fargo FAR 1718 1498 1119 575 288 56 12 31 185 557 992 1522

ND Grand Forks GFK 1803 1593 1226 639 355 83 22 46 223 618 1078 1609

ND Minot MOT 1606 1438 1119 609 360 101 17 37 204 624 1051 1512

ND Williston ISN 1635 1447 1089 620 381 119 13 36 226 654 1090 1553

NE Grand Island GRI 1231 1042 698 398 160 17 2 5 86 389 739 1172

NE Lincoln LNK 1261 1055 680 377 137 10 1 5 83 371 717 1169

NE Norfolk OFK 1313 1106 752 426 176 22 3 8 109 417 777 1223

NE North Platte LBF 1210 1048 752 487 234 37 3 10 135 477 821 1219

NE Omaha OMA 1292 1088 705 363 133 10 1 5 75 360 714 1179

NE Scottsbluff BFF 1138 1000 742 510 263 53 5 12 133 482 811 1190

NE Valentine VTN 1262 1117 816 510 260 51 4 12 143 496 848 1247

NH Concord CON 1380 1159 965 571 282 81 9 26 151 487 770 1150

NH Lebanon LEB 1439 1217 1000 598 285 81 9 28 152 511 794 1199

NJ Atlantic City ACY 975 847 663 366 151 20 0 1 31 263 512 817

NJ Newark EWR 1016 856 651 338 113 11 0 1 20 253 510 844

NM Albuquerque ABQ 821 668 469 236 58 1 0 0 15 199 536 859

NM Carlsbad CNM 617 470 255 86 21 0 0 0 4 94 349 638

NM Clayton CAO 853 788 564 343 126 11 2 5 52 278 558 888

NM Gallup GUP 1078 901 765 535 279 52 1 4 124 464 801 1090

NM Roswell ROW 701 545 314 123 27 0 0 0 7 127 418 740

NV Elko EKO 1221 990 795 593 349 109 2 16 156 516 863 1180

NV Ely ELY 1215 1032 864 663 434 151 6 33 218 569 884 1214

NV Las Vegas LAS 470 350 165 58 6 0 0 0 0 28 228 522

NV Lovelock LOL 987 771 617 431 183 35 0 4 71 373 741 1039

NV Reno RNO 886 727 585 431 190 43 0 3 47 303 637 912

NV Tonopah TPH 955 807 675 482 211 42 0 3 62 347 697 1011

NV Winnemucca WMC 1053 859 727 564 317 96 2 15 151 498 826 1097

NY Albany ALB 1303 1106 885 489 215 37 2 11 99 438 713 1092

NY Binghamton BGM 1328 1145 942 561 274 77 12 26 150 487 743 1133

NY Buffalo BUF 1242 1122 911 551 247 49 4 10 94 424 674 1041

NY Glens Falls GFL 1437 1236 996 581 282 74 12 34 177 518 789 1193

NY Massena MSS 1531 1318 1083 622 302 81 10 36 169 537 835 1287

NY New York LGA 976 834 657 338 118 12 0 1 11 206 456 786

NY Rochester ROC 1241 1106 888 537 254 54 6 15 107 427 692 1038

NY Syracuse SYR 1271 1125 908 523 232 46 3 12 99 417 690 1069

NY Utica UCA 1198 1011 784 386 142 32 2 11 57 307 599 991

NY Watertown ART 1434 1269 1007 607 308 89 14 35 157 486 757 1159

OH Akron Canton CAK 1208 1066 800 439 192 39 3 8 91 406 664 1036

OH Cincinnati CVG 1063 908 603 304 110 9 1 2 44 309 577 930

OH Cleveland CLE 1166 1030 795 451 197 32 2 5 71 365 619 994

OH Columbus CMH 1109 963 665 326 124 11 0 2 47 329 586 950

7B

- 13Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

OH Dayton DAY 1171 1010 697 368 151 15 1 4 67 358 628 1013

OH Findlay FDY 1212 1055 755 412 166 19 1 5 81 371 639 1041

OH Mansfield MFD 1229 1079 799 447 203 42 6 11 100 415 664 1053

OH Toledo TOL 1231 1079 797 446 189 22 1 6 86 389 678 1067

OH Youngstown YNG 1215 1068 820 464 219 55 7 16 108 425 666 1037

OK Hobart HBR 769 642 367 174 41 0 0 0 6 149 401 753

OK McAlester MLC 731 588 308 140 33 0 0 0 9 135 335 673

OK Oklahoma City OKC 785 640 347 148 35 1 0 0 9 144 390 744

OK Tulsa TUL 809 663 363 155 37 1 0 0 11 151 388 751

OR Astoria AST 646 584 578 493 365 222 127 112 184 362 547 682

OR Baker BKE 1176 964 817 649 426 214 38 62 256 611 923 1191

OR Eugene EUG 723 629 558 455 305 147 22 23 100 358 595 763

OR Medford MFR 760 595 527 391 188 52 1 3 39 276 606 790

OR Pendleton PDT 910 745 606 454 248 85 5 8 88 399 715 972

OR Portland PDX 718 594 523 388 217 89 8 8 59 290 554 755

OR Redmond RDM 946 835 760 640 414 214 42 60 214 540 801 1029

OR Salem SLE 712 616 554 443 269 121 14 14 86 336 583 755

PA Allentown ABE 1134 974 739 413 168 23 1 4 61 362 623 966

PA Altoona AOO 1176 1037 788 440 203 42 5 12 108 413 659 1028

PA Bradford BFD 1367 1213 991 621 350 138 58 75 229 574 797 1176

PA Du Bois DUJ 1276 1125 867 509 255 78 16 27 141 473 714 1103

PA Erie ERI 1167 1068 869 529 252 47 5 8 78 372 619 976

PA Harrisburg CXY 1049 918 670 355 133 15 0 2 44 318 573 908

PA Philadelphia PHL 986 848 613 308 94 7 0 1 15 238 490 822

PA Pittsburgh PIT 1141 997 731 386 165 27 2 5 76 381 631 981

PA Williamsport IPT 1165 1011 766 414 175 26 1 6 71 380 649 1001

RI Providence PVD 1104 946 781 453 218 44 2 3 57 331 585 916

SC Charleston CHS 504 393 205 72 7 0 0 0 1 62 226 412

SC Columbia CAE 606 478 269 103 12 0 0 0 3 101 321 543

SC Florence FLO 624 498 285 113 18 0 0 0 3 106 301 524

SC Greenville GSP 688 567 344 145 32 1 0 0 6 147 375 632

SD Aberdeen ABR 1624 1399 1024 572 285 57 7 28 192 573 993 1471

SD Huron HON 1494 1288 919 516 245 45 5 15 147 505 909 1378

SD Pierre PIR 1384 1215 887 513 267 51 3 9 129 495 893 1316

SD Rapid City RAP 1228 1116 840 559 332 88 6 16 154 509 863 1228

SD Sioux Falls FSD 1479 1265 900 502 239 35 4 15 141 488 872 1356

TN Bristol TRI 903 748 497 258 82 4 0 1 28 266 531 820

TN Chattanooga CHA 737 589 338 141 30 0 0 0 6 144 393 668

TN Crossville CSV 898 742 482 252 89 6 1 2 37 256 489 793

TN Jackson MKL 797 660 373 173 41 1 0 1 17 186 418 712

TN Knoxville TYS 808 654 397 182 46 1 0 0 11 191 444 738

TN Memphis MEM 700 566 296 111 18 0 0 0 3 115 329 623

TN Nashville BNA 813 665 392 175 45 2 0 0 12 175 422 718

TX Abilene ABI 584 469 230 79 20 0 0 0 3 87 279 570

TX Alice ALI 242 176 58 9 0 0 0 0 0 9 64 224

TX Amarillo AMA 820 717 457 242 81 2 0 1 25 218 501 821

TX Austin AUS 470 356 165 46 3 0 0 0 0 44 192 436

TX Brownsville BRO 144 109 28 7 0 0 0 0 0 1 28 145

7B

- 14Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

TX College Station CLL 417 316 127 33 2 0 0 0 0 32 145 375

TX Corpus Christi CRP 252 177 58 11 0 0 0 0 0 9 66 226

TX Dallas

- Ft. Worth DFW 550 438 195 53 9 0 0 0 0 53 213 506

TX Del Rio DRT 384 253 80 16 1 0 0 0 0 18 135 380

TX El Paso ELP 560 416 216 62 7 0 0 0 1 52 303 584

TX Galveston GLS 291 224 73 12 0 0 0 0 0 7 62 233

TX Houston IAH 362 271 106 23 0 0 0 0 0 23 129 332

TX Laredo LRD 216 146 31 6 0 0 0 0 0 5 47 199

TX Lubbock LBB 710 584 337 150 45 0 0 0 10 143 405 718

TX Lufkin LFK 474 368 165 49 4 0 0 0 0 53 202 433

TX McAllen MFE 160 117 22 10 0 0 0 0 0 3 31 149

TX Midland Odessa MAF 622 480 254 83 19 0 0 0 4 87 329 614

TX San Angelo SJT 548 422 196 67 10 0 0 0 1 72 259 524

TX San Antonio SAT 374 275 105 24 2 0 0 0 1 28 135 354

TX Victoria VCT 337 243 90 21 1 0 0 0 0 22 116 306

TX Waco ACT 521 404 184 55 6 0 0 0 0 54 211 485

TX Wichita Falls SPS 665 540 267 95 23 0 0 0 2 94 306 641

UT Cedar City CDC 1100 910 731 535 268 58 0 3 103 436 771 1110

UT Salt Lake City SLC 1092 857 641 432 218 45 0 3 58 350 718 1055

VA Lynchburg LYH 904 773 526 269 101 7 0 2 34 271 523 822

VA Norfolk ORF 737 632 440 201 55 1 0 0 3 129 349 605

VA Richmond RIC 821 694 463 213 60 2 0 0 10 181 409 704

VA Roanoke ROA 846 719 478 229 78 4 0 0 27 224 474 765

VT Burlington BTV 1427 1226 1002 581 263 61 5 23 133 495 768 1188

VT Montpelier MPV 1501 1280 1065 652 335 114 24 52 200 568 842 1269

WA Bellingham BLI 767 672 628 492 341 187 78 78 209 442 647 792

WA Olympia OLM 772 686 636 510 342 186 67 68 180 433 658 817

WA Quillayute UIL 702 639 651 549 430 282 179 160 237 441 619 742

WA Seattle Tacoma SEA 706 611 586 451 285 142 34 29 122 368 597 745

WA Spokane GEG 1094 904 769 556 320 141 17 23 147 511 879 1144

WA Walla Walla ALW 869 701 532 383 195 51 1 3 51 335 662 930

WA Yakima YKM 1029 785 662 476 242 84 8 15 124 456 817 1102

WI Eau Claire EAU 1546 1309 977 533 258 49 7 26 169 524 878 1386

WI Green Bay GRB 1452 1253 979 576 289 60 12 23 165 494 813 1274

WI Lacrosse LSE 1452 1226 870 457 197 27 2 11 117 442 790 1303

WI Madison MSN 1404 1202 887 510 240 36 6 17 140 462 784 1247

WI Milwaukee MKE 1288 1111 858 550 289 64 7 9 100 397 713 1135

WI Wausau AUW 1547 1312 1019 585 294 67 13 31 192 549 892 1369

WV Beckley BKW 1050 897 647 355 171 36 9 9 87 368 607 938

WV Charleston CRW 942 810 537 257 100 7 0 1 36 273 519 838

WV Elkins EKN 1130 996 750 455 225 62 17 11 122 436 693 1005

WV Huntington HTS 956 811 533 253 95 6 0 1 39 280 528 846

WV Martinsburg MRB 1020 884 642 341 138 15 0 1 48 323 569 906

WV Morgantown MGW 1040 904 633 337 138 19 3 2 52 333 558 890

WV Parkersburg PKB 1040 891 605 302 124 14 0 2 50 329 567 904

WY Casper CPR 1191 1088 864 644 408 129 12 28 208 574 880 1244

WY Cheyenne CYS 1084 1037 826 630 390 120 14 30 201 556 837 1163

WY Cody COD 1171 1048 793 600 382 139 14 36 196 551 869 1209

7B

- 15Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

WY Lander LND 1301 1128 842 605 380 121 7 22 171 563 923 1328

WY Rock Springs RKS 1320 1173 941 691 444 159 8 35 219 598 980 1336

WY Sheridan SHR 1220 1081 843 612 404 138 11 30 197 555 908 1272

WY Worland WRL 1416 1161 795 529 321 85 5 16 167 553 934 1415

Table 7B.3.3 Weather Station Monthly Heating Degree Day Data Fractions (10

- Year

Average)

Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AK Anchorage ANC 15% 12% 12% 8% 5% 3% 2% 2% 5% 8% 13% 14%

AK Bethel BET 15% 12% 13% 9% 5% 3% 2% 3% 4% 8% 12% 13%

AK Cold Bay CDB 12% 10% 12% 10% 8% 6% 5% 4% 5% 8% 10% 11%

AK Cordova CDV 13% 11% 12% 9% 6% 4% 3% 4% 5% 8% 12% 12%

AK Homer HOM 14% 11% 12% 9% 6% 5% 3% 3% 5% 8% 12% 12%

AK Juneau JNU 14% 12% 12% 9% 6% 4% 3% 3% 5% 8% 12% 13%

AK Kenai ENA 15% 12% 12% 8% 5% 3% 2% 3% 5% 8% 12% 14%

AK Ketchikan KTN 13% 12% 12% 9% 7% 4% 3% 3% 5% 8% 11% 13%

AK King Salmon AKN 15% 11% 13% 8% 6% 4% 3% 3% 5% 8% 12% 13%

AK Kodiak ADQ 12% 11% 12% 9% 7% 5% 4% 3% 5% 8% 11% 12%

AK Sitka SIT 13% 11% 12% 9% 7% 5% 4% 3% 5% 8% 11% 12%

AK St Paul Island SNP 12% 10% 12% 10% 8% 6% 5% 4% 5% 7% 9% 10%

AK Talkeetna TKA 16% 12% 12% 8% 5% 2% 1% 2% 5% 9% 13% 15%

AK Valdez VWS 16% 12% 13% 8% 5% 3% 2% 2% 4% 8% 13% 14%

AK Yakutat YAK 13% 11% 12% 9% 7% 5% 3% 4% 5% 8% 11% 12%

AL Birmingham BHM 25% 20% 10% 4% 1% 0% 0% 0% 0% 5% 12% 22%

AL Huntsville HSV 24% 20% 11% 5% 1% 0% 0% 0% 0% 5% 13% 22%

AL Mobile MOB 27% 21% 9% 3% 0% 0% 0% 0% 0% 3% 13% 24%

AL Montgomery MGM 26% 20% 10% 3% 0% 0% 0% 0% 0% 4% 13% 23%

AL Muscle Shoals MSL 24% 20% 11% 5% 1% 0% 0% 0% 0% 5% 12% 22%

AL Tuscaloosa TCL 25% 20% 10% 4% 1% 0% 0% 0% 0% 5% 13% 23%

AR Fayetteville FYV 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 21%

AR Fort Smith FSM 25% 20% 11% 4% 1% 0% 0% 0% 0% 4% 12% 23%

AR Little Rock LIT 25% 20% 11% 4% 1% 0% 0% 0% 0% 4% 12% 23%

AR Texarkana TXK 26% 21% 10% 4% 1% 0% 0% 0% 0% 4% 12% 24%

AZ Douglas DUG 24% 18% 13% 5% 1% 0% 0% 0% 0% 3% 13% 24%

AZ Flagstaff FLG 16% 14% 12% 10% 6% 2% 0% 1% 3% 8% 12% 16%

AZ Phoenix PHX 28% 21% 8% 1% 0% 0% 0% 0% 0% 1% 8% 34%

AZ Tucson TUS 25% 21% 11% 3% 0% 0% 0% 0% 0% 1% 10% 28%

AZ Winslow INW 21% 16% 12% 7% 2% 0% 0% 0% 0% 6% 13% 21%

AZ Yuma NYL 24% 21% 7% 2% 0% 0% 0% 0% 0% 0% 9% 37%

CA Bakersfield BFL 24% 16% 11% 7% 1% 0% 0% 0% 0% 3% 14% 25%

CA Blythe BLH 27% 19% 7% 1% 0% 0% 0% 0% 0% 1% 11% 34%

CA Eureka EKA 11% 10% 10% 10% 8% 6% 5% 5% 6% 7% 9% 11%

CA Fresno FAT 24% 17% 11% 7% 1% 0% 0% 0% 0% 2% 13% 24%

CA Los Angeles LAX 17% 17% 16% 12% 5% 1% 0% 0% 0% 3% 10% 20%

CA Mt Shasta MHS 16% 14% 13% 10% 6% 2% 0% 0% 2% 7% 13% 16%

CA Paso Robles PRB 19% 16% 13% 10% 3% 1% 0% 0% 1% 5% 13% 20%

7B

- 16Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

CA Red Bluff RBL 21% 16% 13% 9% 2% 0% 0% 0% 0% 3% 14% 22%

CA Redding RDD 20% 16% 13% 9% 2% 0% 0% 0% 0% 4% 14% 22%

CA Sacramento SAC 22% 16% 12% 8% 2% 0% 0% 0% 0% 3% 14% 22%

CA San Diego SAN 19% 18% 14% 10% 4% 1% 0% 0% 0% 1% 10% 22%

CA San Francisco SFO 17% 13% 12% 10% 7% 4% 2% 2% 2% 4% 10% 16%

CA Stockton SCK 23% 16% 12% 8% 2% 0% 0% 0% 0% 3% 13% 22%

CO Alamosa ALS 17% 15% 11% 8% 5% 2% 0% 1% 3% 8% 12% 18%

CO Colorado Spring COS 17% 16% 12% 9% 4% 1% 0% 0% 2% 7% 12% 18%

CO Denver DEN 18% 16% 12% 8% 4% 1% 0% 0% 2% 7% 13% 19%

CO Eagle EGE 18% 15% 12% 8% 5% 2% 0% 0% 3% 8% 12% 18%

CO Pueblo PUB 19% 17% 12% 7% 3% 0% 0% 0% 1% 7% 13% 20%

CO Trinidad TAD 19% 17% 12% 8% 3% 1% 0% 0% 2% 7% 13% 19%

CT Bridgeport BDR 21% 18% 15% 8% 4% 1% 0% 0% 1% 5% 11% 17%

CT Hartford BDL 21% 17% 14% 8% 3% 1% 0% 0% 1% 6% 11% 17%

DC Washington DCA 23% 19% 13% 6% 2% 0% 0% 0% 0% 5% 11% 20%

DE Wilmington ILG 21% 19% 14% 7% 3% 0% 0% 0% 1% 6% 11% 18%

FL Daytona Beach DAB 32% 23% 10% 3% 0% 0% 0% 0% 0% 2% 8% 23%

FL Fort Myers FMY 39% 21% 8% 1% 0% 0% 0% 0% 0% 1% 5% 25%

FL Ft Lauderdale FLL 41% 23% 7% 1% 0% 0% 0% 0% 0% 0% 3% 25%

FL Gainesville GNV 29% 21% 10% 3% 0% 0% 0% 0% 0% 2% 10% 24%

FL Jacksonville JAX 28% 21% 10% 4% 0% 0% 0% 0% 0% 2% 11% 23%

FL Key West EYW 49% 22% 6% 0% 0% 0% 4% 0% 0% 0% 1% 18%

FL Melbourne MLB 35% 22% 9% 3% 0% 0% 0% 0% 0% 2% 7% 22%

FL Miami MIA 43% 23% 7% 0% 0% 0% 0% 0% 0% 0% 3% 25%

FL Orlando MCO 35% 22% 9% 2% 0% 0% 0% 0% 0% 2% 7% 24%

FL Pensacola PNS 28% 22% 9% 3% 0% 0% 0% 0% 0% 3% 11% 24%

FL Tallahassee TLH 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 24%

FL Tampa TPA 35% 22% 9% 2% 0% 0% 0% 0% 0% 1% 6% 25%

FL Vero Beach VRB 35% 23% 10% 3% 0% 0% 0% 0% 0% 1% 6% 22%

FL West Palm Beach PBI 39% 22% 8% 2% 0% 0% 0% 0% 0% 1% 5% 23%

GA Albany ABY 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 24%

GA Athens AHN 24% 19% 11% 5% 1% 0% 0% 0% 0% 5% 13% 22%

GA Atlanta ATL 25% 20% 11% 5% 1% 0% 0% 0% 0% 4% 12% 22%

GA Augusta AGS 25% 20% 11% 4% 0% 0% 0% 0% 0% 4% 13% 22%

GA Brunswick SSI 28% 22% 10% 4% 0% 0% 0% 0% 0% 2% 10% 23%

GA Columbus CSG 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 23%

GA Macon MCN 25% 20% 11% 4% 0% 0% 0% 0% 0% 4% 13% 23%

GA Savannah SAV 27% 21% 11% 4% 0% 0% 0% 0% 0% 3% 12% 22%

GA Waycross AYS 27% 21% 9% 3% 0% 1% 0% 0% 0% 3% 12% 24%

HI Hilo

- Hawaii ITO 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8%

HI Honolulu

- Oahu HNL 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8%

HI Kahului

- Maui OGG 0% 50% 50% 0% 0% 0% 0% 0% 0% 0% 0% 0%

HI Lihue

- Kauai LIH 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0% 0%

IA Burlington BRL 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 20%

IA Cedar Rapids CID 21% 18% 12% 7% 3% 0% 0% 0% 2% 7% 11% 19%

IA Des Moines DSM 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IA Dubuque DBQ 21% 18% 12% 7% 3% 0% 0% 0% 2% 7% 11% 19%

IA Mason City MCW 20% 17% 12% 7% 3% 0% 0% 0% 2% 7% 12% 18%

7B

- 17Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

IA Ottumwa OTM 22% 18% 12% 6% 3% 0% 0% 0% 2% 7% 12% 19%

IA Sioux City SUX 21% 18% 12% 6% 3% 0% 0% 0% 2% 7% 12% 19%

IA Waterloo ALO 21% 18% 12% 7% 3% 0% 0% 0% 2% 7% 12% 19%

ID Boise BOI 19% 15% 12% 8% 4% 1% 0% 0% 1% 7% 14% 19%

ID Burley BYI 17% 15% 12% 9% 5% 2% 0% 0% 3% 8% 13% 17%

ID Idaho Falls IDA 18% 15% 12% 8% 5% 2% 0% 0% 3% 8% 12% 17%

ID Lewiston LWS 18% 15% 12% 8% 4% 1% 0% 0% 1% 8% 14% 19%

ID Pocatello PIH 18% 15% 12% 8% 5% 2% 0% 0% 3% 8% 13% 17%

IL Chicago ORD 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

IL Moline MLI 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 19%

IL Peoria PIA 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IL Quincy UIN 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IL Rockford RFD 21% 18% 13% 7% 3% 0% 0% 0% 2% 6% 11% 19%

IL Springfield SPI 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 20%

IN Evansville EVV 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IN Fort Wayne FWA 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

IN Indianapolis IND 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

IN South Bend SBN 21% 18% 13% 7% 3% 0% 0% 0% 2% 7% 11% 18%

IN West Lafayette LAF 22% 19% 12% 6% 3% 0% 0% 0% 1% 6% 11% 19%

KS Concordia CNK 22% 18% 11% 6% 2% 0% 0% 0% 1% 6% 12% 21%

KS Dodge City DDC 21% 18% 12% 7% 2% 0% 0% 0% 1% 6% 13% 21%

KS Garden City GCK 21% 18% 12% 7% 2% 0% 0% 0% 1% 6% 13% 21%

KS Goodland GLD 19% 17% 12% 8% 3% 0% 0% 0% 2% 7% 13% 19%

KS Russell RSL 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 13% 21%

KS Salina SLN 22% 19% 11% 6% 2% 0% 0% 0% 1% 5% 12% 21%

KS Topeka TOP 23% 19% 11% 6% 2% 0% 0% 0% 1% 6% 12% 20%

KS Wichita ICT 23% 19% 11% 6% 2% 0% 0% 0% 1% 5% 12% 22%

KY Bowling Green BWG 23% 19% 11% 6% 2% 0% 0% 0% 1% 6% 12% 20%

KY Jackson JKL 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 20%

KY Lexington LEX 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

KY Louisville SDF 24% 20% 12% 5% 1% 0% 0% 0% 0% 5% 12% 20%

KY Paducah PAH 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

LA Baton Rouge BTR 27% 21% 9% 3% 0% 0% 0% 0% 0% 3% 12% 25%

LA Lafayette LFT 28% 21% 9% 2% 0% 0% 0% 0% 0% 3% 11% 26%

LA Lake Charles LCH 28% 22% 9% 2% 0% 0% 0% 0% 0% 2% 11% 25%

LA Monroe MLU 26% 21% 10% 3% 0% 0% 0% 0% 0% 4% 12% 24%

LA New Orleans MSY 29% 22% 8% 2% 0% 0% 0% 0% 0% 2% 10% 26%

LA Shreveport SHV 26% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 25%

MA Boston BOS 20% 17% 15% 9% 4% 1% 0% 0% 1% 6% 11% 17%

MA Worcester ORH 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

MD Baltimore BWI 22% 19% 13% 7% 2% 0% 0% 0% 1% 6% 11% 19%

MD Salisbury SBY 21% 19% 14% 8% 3% 0% 0% 0% 1% 6% 11% 18%

ME Augusta AUG 20% 16% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

ME Bangor BGR 19% 16% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

ME Caribou CAR 19% 16% 14% 8% 5% 2% 0% 1% 3% 7% 10% 16%

ME Houlton HUL 18% 16% 13% 9% 5% 2% 1% 1% 3% 7% 10% 15%

ME Portland PWM 19% 16% 14% 9% 5% 1% 0% 0% 2% 7% 11% 16%

MI Alpena APN 18% 16% 14% 9% 5% 2% 0% 1% 3% 7% 11% 16%

7B

- 18Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

MI Detroit DTW 21% 18% 14% 7% 3% 0% 0% 0% 1% 6% 11% 18%

MI Flint FNT 20% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Grand Rapids GRR 20% 18% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Houghton Lake HTL 18% 16% 13% 8% 4% 1% 0% 1% 3% 7% 11% 16%

MI Jackson JXN 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 11% 17%

MI Lansing LAN 20% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Marquette MQT 17% 15% 13% 9% 5% 2% 1% 1% 3% 7% 11% 16%

MI Muskegon MKG 19% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

MI Saginaw MBS 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Sault St Marie SSM 18% 16% 14% 9% 5% 2% 1% 1% 3% 7% 10% 16%

MI Traverse City TVC 18% 17% 14% 9% 5% 1% 0% 0% 2% 7% 11% 16%

MN Alexandria AXN 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 18%

MN Duluth DLH 19% 16% 13% 8% 5% 2% 0% 1% 3% 7% 11% 17%

MN Hibbing HIB 18% 15% 12% 8% 5% 2% 1% 1% 3% 7% 11% 16%

MN Int'l Falls INL 19% 16% 12% 7% 4% 2% 1% 1% 3% 7% 11% 17%

MN Minneapolis MSP 21% 18% 13% 6% 3% 0% 0% 0% 2% 6% 12% 19%

MN Rochester RST 21% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 18%

MN Saint Cloud STC 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 18%

MO Columbia COU 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

MO Joplin JLN 23% 19% 11% 6% 2% 0% 0% 0% 1% 6% 11% 21%

MO Kansas City MCI 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

MO Saint Louis STL 23% 20% 12% 5% 2% 0% 0% 0% 1% 5% 12% 21%

MO Springfield SGF 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

MS Greenwood GWO 25% 20% 10% 4% 1% 0% 0% 0% 0% 5% 11% 23%

MS McComb MCB 26% 21% 9% 3% 0% 0% 0% 0% 0% 4% 12% 24%

MS Tupelo TUP 25% 20% 11% 4% 1% 0% 0% 0% 0% 5% 12% 22%

MT Billings BIL 18% 15% 12% 8% 5% 1% 0% 0% 2% 8% 13% 18%

MT Butte BTM 15% 13% 11% 9% 6% 3% 1% 1% 4% 8% 12% 16%

MT Cut Bank CTB 16% 14% 12% 9% 6% 3% 1% 1% 4% 8% 12% 16%

MT Great Falls GTF 16% 14% 12% 9% 6% 3% 0% 1% 3% 8% 12% 16%

MT Havre HVR 18% 16% 12% 7% 5% 2% 0% 1% 3% 8% 12% 17%

MT Helena HLN 17% 14% 12% 8% 5% 2% 0% 0% 3% 8% 13% 18%

MT Kalispell FCA 17% 14% 12% 8% 4% 2% 0% 1% 3% 8% 13% 17%

MT Lewistown LWT 16% 14% 12% 9% 6% 3% 1% 1% 3% 8% 12% 16%

MT Miles City MLS 19% 16% 12% 7% 4% 1% 0% 0% 2% 7% 13% 18%

MT Missoula MSO 17% 14% 11% 8% 5% 2% 0% 0% 3% 8% 13% 17%

NC Asheville AVL 21% 18% 12% 7% 2% 0% 0% 0% 1% 7% 13% 19%

NC Cape Hatteras HAT 27% 24% 16% 6% 1% 0% 0% 0% 0% 1% 6% 18%

NC Charlotte CLT 23% 19% 12% 5% 1% 0% 0% 0% 0% 5% 13% 21%

NC Greensboro GSO 23% 19% 12% 6% 2% 0% 0% 0% 0% 5% 13% 20%

NC Hickory HKY 23% 19% 12% 6% 2% 0% 0% 0% 0% 6% 12% 21%

NC New Bern EWN 24% 20% 13% 5% 1% 0% 0% 0% 0% 4% 12% 20%

NC Raleigh Durham RDU 23% 19% 12% 5% 1% 0% 0% 0% 0% 5% 12% 21%

NC Wilmington ILM 24% 20% 13% 5% 1% 0% 0% 0% 0% 4% 12% 21%

ND Bismarck BIS 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 18%

ND Devil's Lake P11 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 17%

ND Fargo FAR 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 12% 18%

ND Grand Forks GFK 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 17%

7B

- 19Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

ND Minot MOT 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 17%

ND Williston ISN 18% 16% 12% 7% 4% 1% 0% 0% 3% 7% 12% 18%

NE Grand Island GRI 21% 18% 12% 7% 3% 0% 0% 0% 1% 7% 12% 20%

NE Lincoln LNK 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

NE Norfolk OFK 21% 17% 12% 7% 3% 0% 0% 0% 2% 7% 12% 19%

NE North Platte LBF 19% 16% 12% 8% 4% 1% 0% 0% 2% 7% 13% 19%

NE Omaha OMA 22% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

NE Scottsbluff BFF 18% 16% 12% 8% 4% 1% 0% 0% 2% 8% 13% 19%

NE Valentine VTN 19% 17% 12% 8% 4% 1% 0% 0% 2% 7% 13% 18%

NH Concord CON 20% 16% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NH Lebanon LEB 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NJ Atlantic City ACY 21% 18% 14% 8% 3% 0% 0% 0% 1% 6% 11% 18%

NJ Newark EWR 22% 19% 14% 7% 2% 0% 0% 0% 0% 5% 11% 18%

NM Albuquerque ABQ 21% 17% 12% 6% 2% 0% 0% 0% 0% 5% 14% 22%

NM Carlsbad CNM 24% 19% 10% 3% 1% 0% 0% 0% 0% 4% 14% 25%

NM Clayton CAO 19% 18% 13% 8% 3% 0% 0% 0% 1% 6% 13% 20%

NM Gallup GUP 18% 15% 13% 9% 5% 1% 0% 0% 2% 8% 13% 18%

NM Roswell ROW 23% 18% 10% 4% 1% 0% 0% 0% 0% 4% 14% 25%

NV Elko EKO 18% 15% 12% 9% 5% 2% 0% 0% 2% 8% 13% 17%

NV Ely ELY 17% 14% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

NV Las Vegas LAS 26% 19% 9% 3% 0% 0% 0% 0% 0% 2% 12% 29%

NV Lovelock LOL 19% 15% 12% 8% 3% 1% 0% 0% 1% 7% 14% 20%

NV Reno RNO 19% 15% 12% 9% 4% 1% 0% 0% 1% 6% 13% 19%

NV Tonopah TPH 18% 15% 13% 9% 4% 1% 0% 0% 1% 7% 13% 19%

NV Winnemucca WMC 17% 14% 12% 9% 5% 2% 0% 0% 2% 8% 13% 18%

NY Albany ALB 20% 17% 14% 8% 3% 1% 0% 0% 2% 7% 11% 17%

NY Binghamton BGM 19% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY Buffalo BUF 19% 18% 14% 9% 4% 1% 0% 0% 1% 7% 11% 16%

NY Glens Falls GFL 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY Massena MSS 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY New York LGA 22% 19% 15% 8% 3% 0% 0% 0% 0% 5% 10% 18%

NY Rochester ROC 19% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY Syracuse SYR 20% 18% 14% 8% 4% 1% 0% 0% 2% 7% 11% 17%

NY Utica UCA 22% 18% 14% 7% 3% 1% 0% 0% 1% 6% 11% 18%

NY Watertown ART 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 10% 16%

OH Akron Canton CAK 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 17%

OH Cincinnati CVG 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

OH Cleveland CLE 20% 18% 14% 8% 3% 1% 0% 0% 1% 6% 11% 17%

OH Columbus CMH 22% 19% 13% 6% 2% 0% 0% 0% 1% 6% 11% 19%

OH Dayton DAY 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

OH Findlay FDY 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

OH Mansfield MFD 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 17%

OH Toledo TOL 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

OH Youngstown YNG 20% 18% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

OK Hobart HBR 23% 19% 11% 5% 1% 0% 0% 0% 0% 5% 12% 23%

OK McAlester MLC 25% 20% 10% 5% 1% 0% 0% 0% 0% 5% 11% 23%

OK Oklahoma City OKC 24% 20% 11% 5% 1% 0% 0% 0% 0% 4% 12% 23%

OK Tulsa TUL 24% 20% 11% 5% 1% 0% 0% 0% 0% 5% 12% 23%

7B

- 20Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

OR Astoria AST 13% 12% 12% 10% 7% 5% 3% 2% 4% 7% 11% 14%

OR Baker BKE 16% 13% 11% 9% 6% 3% 1% 1% 3% 8% 13% 16%

OR Eugene EUG 15% 13% 12% 10% 7% 3% 0% 0% 2% 8% 13% 16%

OR Medford MFR 18% 14% 12% 9% 4% 1% 0% 0% 1% 7% 14% 19%

OR Pendleton PDT 17% 14% 12% 9% 5% 2% 0% 0% 2% 8% 14% 19%

OR Portland PDX 17% 14% 12% 9% 5% 2% 0% 0% 1% 7% 13% 18%

OR Redmond RDM 15% 13% 12% 10% 6% 3% 1% 1% 3% 8% 12% 16%

OR Salem SLE 16% 14% 12% 10% 6% 3% 0% 0% 2% 7% 13% 17%

PA Allentown ABE 21% 18% 14% 8% 3% 0% 0% 0% 1% 7% 11% 18%

PA Altoona AOO 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 17%

PA Bradford BFD 18% 16% 13% 8% 5% 2% 1% 1% 3% 8% 11% 15%

PA Du Bois DUJ 19% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

PA Erie ERI 19% 18% 15% 9% 4% 1% 0% 0% 1% 6% 10% 16%

PA Harrisburg CXY 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

PA Philadelphia PHL 22% 19% 14% 7% 2% 0% 0% 0% 0% 5% 11% 19%

PA Pittsburgh PIT 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

PA Williamsport IPT 21% 18% 14% 7% 3% 0% 0% 0% 1% 7% 11% 18%

RI Providence PVD 20% 17% 14% 8% 4% 1% 0% 0% 1% 6% 11% 17%

SC Charleston CHS 27% 21% 11% 4% 0% 0% 0% 0% 0% 3% 12% 22%

SC Columbia CAE 25% 20% 11% 4% 1% 0% 0% 0% 0% 4% 13% 22%

SC Florence FLO 25% 20% 12% 5% 1% 0% 0% 0% 0% 4% 12% 21%

SC Greenville GSP 23% 19% 12% 5% 1% 0% 0% 0% 0% 5% 13% 22%

SD Aberdeen ABR 20% 17% 12% 7% 3% 1% 0% 0% 2% 7% 12% 18%

SD Huron HON 20% 17% 12% 7% 3% 1% 0% 0% 2% 7% 12% 18%

SD Pierre PIR 19% 17% 12% 7% 4% 1% 0% 0% 2% 7% 12% 18%

SD Rapid City RAP 18% 16% 12% 8% 5% 1% 0% 0% 2% 7% 12% 18%

SD Sioux Falls FSD 20% 17% 12% 7% 3% 0% 0% 0% 2% 7% 12% 19%

TN Bristol TRI 22% 18% 12% 6% 2% 0% 0% 0% 1% 6% 13% 20%

TN Chattanooga CHA 24% 19% 11% 5% 1% 0% 0% 0% 0% 5% 13% 22%

TN Crossville CSV 22% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

TN Jackson MKL 24% 20% 11% 5% 1% 0% 0% 0% 0% 6% 12% 21%

TN Knoxville TYS 23% 19% 11% 5% 1% 0% 0% 0% 0% 6% 13% 21%

TN Memphis MEM 25% 21% 11% 4% 1% 0% 0% 0% 0% 4% 12% 23%

TN Nashville BNA 24% 19% 11% 5% 1% 0% 0% 0% 0% 5% 12% 21%

TX Abilene ABI 25% 20% 10% 3% 1% 0% 0% 0% 0% 4% 12% 25%

TX Alice ALI 31% 23% 7% 1% 0% 0% 0% 0% 0% 1% 8% 29%

TX Amarillo AMA 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 13% 21%

TX Austin AUS 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 11% 25%

TX Brownsville BRO 31% 24% 6% 2% 0% 0% 0% 0% 0% 0% 6% 31%

TX College Station CLL 29% 22% 9% 2% 0% 0% 0% 0% 0% 2% 10% 26%

TX Corpus Christi CRP 32% 22% 7% 1% 0% 0% 0% 0% 0% 1% 8% 28%

TX Dallas

- Ft. Worth DFW 27% 22% 10% 3% 0% 0% 0% 0% 0% 3% 11% 25%

TX Del Rio DRT 30% 20% 6% 1% 0% 0% 0% 0% 0% 1% 11% 30%

TX El Paso ELP 25% 19% 10% 3% 0% 0% 0% 0% 0% 2% 14% 27%

TX Galveston GLS 32% 25% 8% 1% 0% 0% 0% 0% 0% 1% 7% 26%

TX Houston IAH 29% 22% 8% 2% 0% 0% 0% 0% 0% 2% 10% 27%

TX Laredo LRD 33% 22% 5% 1% 0% 0% 0% 0% 0% 1% 7% 31%

TX Lubbock LBB 23% 19% 11% 5% 1% 0% 0% 0% 0% 5% 13% 23%

7B

- 21Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

TX Lufkin LFK 27% 21% 9% 3% 0% 0% 0% 0% 0% 3% 12% 25%

TX McAllen MFE 33% 24% 5% 2% 0% 0% 0% 0% 0% 1% 6% 30%

TX Midland Odessa MAF 25% 19% 10% 3% 1% 0% 0% 0% 0% 3% 13% 25%

TX San Angelo SJT 26% 20% 9% 3% 0% 0% 0% 0% 0% 3% 12% 25%

TX San Antonio SAT 29% 21% 8% 2% 0% 0% 0% 0% 0% 2% 10% 27%

TX Victoria VCT 30% 21% 8% 2% 0% 0% 0% 0% 0% 2% 10% 27%

TX Waco ACT 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 11% 25%

TX Wichita Falls SPS 25% 21% 10% 4% 1% 0% 0% 0% 0% 4% 12% 24%

UT Cedar City CDC 18% 15% 12% 9% 4% 1% 0% 0% 2% 7% 13% 18%

UT Salt Lake City SLC 20% 16% 12% 8% 4% 1% 0% 0% 1% 6% 13% 19%

VA Lynchburg LYH 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

VA Norfolk ORF 23% 20% 14% 6% 2% 0% 0% 0% 0% 4% 11% 19%

VA Richmond RIC 23% 20% 13% 6% 2% 0% 0% 0% 0% 5% 11% 20%

VA Roanoke ROA 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

VT Burlington BTV 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 17%

VT Montpelier MPV 19% 16% 13% 8% 4% 1% 0% 1% 3% 7% 11% 16%

WA Bellingham BLI 14% 13% 12% 9% 6% 4% 1% 1% 4% 8% 12% 15%

WA Olympia OLM 14% 13% 12% 10% 6% 3% 1% 1% 3% 8% 12% 15%

WA Quillayute UIL 12% 11% 12% 10% 8% 5% 3% 3% 4% 8% 11% 13%

WA Seattle Tacoma SEA 15% 13% 13% 10% 6% 3% 1% 1% 3% 8% 13% 16%

WA Spokane GEG 17% 14% 12% 9% 5% 2% 0% 0% 2% 8% 14% 18%

WA Walla Walla ALW 18% 15% 11% 8% 4% 1% 0% 0% 1% 7% 14% 20%

WA Yakima YKM 18% 14% 11% 8% 4% 1% 0% 0% 2% 8% 14% 19%

WI Eau Claire EAU 20% 17% 13% 7% 3% 1% 0% 0% 2% 7% 11% 18%

WI Green Bay GRB 20% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

WI Lacrosse LSE 21% 18% 13% 7% 3% 0% 0% 0% 2% 6% 11% 19%

WI Madison MSN 20% 17% 13% 7% 3% 1% 0% 0% 2% 7% 11% 18%

WI Milwaukee MKE 20% 17% 13% 8% 4% 1% 0% 0% 2% 6% 11% 17%

WI Wausau AUW 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 11% 17%

WV Beckley BKW 20% 17% 12% 7% 3% 1% 0% 0% 2% 7% 12% 18%

WV Charleston CRW 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

WV Elkins EKN 19% 17% 13% 8% 4% 1% 0% 0% 2% 7% 12% 17%

WV Huntington HTS 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

WV Martinsburg MRB 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 12% 19%

WV Morgantown MGW 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

WV Parkersburg PKB 22% 18% 13% 6% 3% 0% 0% 0% 1% 7% 12% 19%

WY Casper CPR 16% 15% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

WY Cheyenne CYS 16% 15% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

WY Cody COD 17% 15% 11% 9% 5% 2% 0% 1% 3% 8% 12% 17%

WY Lander LND 18% 15% 11% 8% 5% 2% 0% 0% 2% 8% 12% 18%

WY Rock Springs RKS 17% 15% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

WY Sheridan SHR 17% 15% 12% 8% 6% 2% 0% 0% 3% 8% 12% 17%

WY Worland WRL 19% 16% 11% 7% 4% 1% 0% 0% 2% 7% 13% 19%

7B

- 22REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7,

2015.) < www.eia.gov/co nsumption/residential/data/2009/ >

2. National Oceanic And Atmospheric Administration, Degree Days Archives, 1997

-

Present.

< ftp://ftp.cpc.ncep.noaa. gov/htdocs/products/analysis_monitoring/cdus/degree_days/archi

ves/ >

7B

- 23APPENDIX 8A. USER INSTRUCTIONS FO R THE LIFE

- CYCLE COST ANALYSIS

SPREADSHEET FOR HEARTH PRODUCTS

TABLE OF CO NTENTS

8A.1 USER INSTRUCTIONS

................................ ................................ .............................. 8A

- 1

8A.2 STARTUP

................................ ................................ ................................ ..................... 8A

- 1

8A.3 DESCRIPTION OF THE L IFE

- CYCLE COST WORKSHEET

................................ . 8A

- 1

8A.4 BASIC INSTRUCTIONS F OR OPERATING THE LI FE

- CYCLE COST

SPREADSHEET

................................ ................................ ................................ ........... 8A

- 4

LIST OF FIGURES

Figure 8A.3.1 LCC and Payback Calculation Process

................................ ....................... 8A

- 3

8A

- iAPPENDIX 8A. USER INSTRUCTIONS FO R THE LIFE

- CYCLE COST ANALYSIS

SPREADSHEET FOR HEARTH PRODUCTS

8A.1 U S ER INSTRUCTIONS

The results obtained in this analysis can be examined and reproduced using the Microsoft

Excel spreadsheets available on the Department of Energy’s (DOE's) hearth product rulemaking

website : www1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83

. From that page, follow the links to the notice of data availability rulemaking phase and then to

Analytical Tools.

8A.2 STARTUP

DOE’s spreadsheet s enables users to perform life

- cycle cost (LCC) and payback period

(PBP) analyses for each product class. A spreadsheet labeled LCC exist s for the hearth which

contains the data inputs and calculations for the LCC analysi s

.

To examine the spreadsheet, DOE assumes that the user has access to a personal

computer with a hardware configuration capable of running Windows XP or later

. The LCC

spreadsheet require s Microsoft Excel 2003 or later installed under the Windows operat ing

system. Because certain variables inside the spreadsheets are defined as distributions, a copy of

Crystal Ball a (a commercially available add

- on program) is required to view them.

8A.3 DESCRIPTION OF THE LIFE

- CYCLE COST WORKSHEET

For the hearth product s a nalysis , DOE created a single LCC spreadsheet containing a

collection of worksheets. Each worksheet represents a conceptual component within the LCC

calculation. To facilitate navigability and identify how worksheets are related, each worksheet

contains an area on the extreme left showing variables imported to and exported from the current

worksheet. The LCC spreadsheet contains the following worksheets:

Introduction The Introduction worksheet contains an overview of each worksheet and a

flow chart of the inputs and outputs of the spreadsheet,

Statistics The Statistics worksheet contains the statistics of key parameters from the

outcome of the Monte Carlo simulations for the sample o f households

Summary The Summary worksheet contains a user interface to manipulate energy price

trends and start year inputs, and to run the Crystal Ball simulation. LCC and

PBP simulation results for each efficiency level are also displayed here.

a See www.oracle.com/us/products/applications/crystalball/overview/index.html

8A

- 1LCC&PB

Calcs * The LCC&PB Calcs worksheet shows LCC calculation results for different

efficiency levels for a single Residential Energy Consumption Survey (RECS)

2009 household

. 1 During a Crystal Ball simulation, the spreadsheet records

the LCC and PBP values for every sampled household

.

Rebuttable

Payback The manufacturer costs, retail pri ces, installation costs, repair and

maintenance costs, energy use calculations, and the simple PBP calculations

for each efficiency level. DOE ’s direct heating equipment test procedure is

used to calculate parameters Rebuttable Payback worksheet contains the total

and incremental used in energy use calculations.

Equip Price * The Equip Price worksheet calculates retail price values used as input s in the

LCC calculations in the Summary worksheet.

Markups * The Markups worksheet calculates markup values used as inputs in the Equip

Price worksheet. DOE applied baseline and incremental markups to calculate

final retail prices. DOE calculated the markups differently for replacement

units and new units.

Installation

Cost * The Installation Cost worksheet provides the weighted average install ation

cost for each design option. These results are used to calculate the total

installed prices of the design options.

Maintenance

and Repair

Cost * The Maintenance and Repair Cost worksheet provides the maintenance and

repair costs for each design optio n. These results are used to determine

operating costs for the design options.

Labor Costs * The Labor Cost worksheet provides the labor cost by region as used to

determine the installation and repair/maintenance costs.

Base Case EL The Base Case Efficiency Level worksheet provides the fraction of hearth

product types that have an intermittent pilot ignition under the base case.

Building

Sample * The Building Sample worksheet contains the RECS 2009 household data for

each product class. During a Cr ystal Ball simulation, DOE uses these

household characteristics to determine the analysis parameters.

Energy Use * The Energy Use worksheet calculates annual energy use by fuel type. The

annual energy use calculations for each design option are inputs to t he

LCC&PB Calcs worksheet to calculate the annual operating cost of the LCC.

Energy Use

(Calcs) * The Energy Use ( Calcs ) worksheet displays intermediate energy use

calculations . The intermediate energy use calculations for each design option

are inputs to the Energy Use worksheet to calculate the annual energy use by

fuel type, depending on product class

.

Energy Price * The Energy Price worksheet shows the estimated monthly natural gas, liquid

pe troleum gas (LPG)

, electricity, and oil prices.

8A

- 2Energy Price

Trends * The Energy Price Trends worksheet shows the future price trends of the

different fuels. DOE used energy price data and forecasts from the Energy

Information Administration’s (EIA's) Annu al Energy Outlook 2014 for the

period until 20 40 and extrapolated beyond 2040. 2

Discount Rate * The Discount Rate worksheet contains the distributions of discount rates for

replacement and new units.

Lifetime * The Lifetime worksheet contains the distribution of lifetimes for equipment of

that product class.

Energy Use

Adjustment

Factors * The Energy Use Adjustment Factors worksheet contains adjustment factors

for normal heating degree days and cooling degree days, as well a s building

shell efficiency index.

Weather Data * The Weather Data worksheet contains heating degree days, cooling degree

days, heating and cooling outdoor design temperature, and annual mean

temperature by weather station.

* R esults displayed in these wo rksheets are for only one household, not the entire population.

Figure 8A.3.1 depicts how these various inputs are used in order to generate the LCC and

PBP outp uts.

Figure 8A.3.1 LCC and Payback Calculation Process

8A

- 38A.4 BASIC INSTRUCTIONS F OR OPERATING THE LIF E

- CYCLE

COST SPREADSHEET

Basic instructions for operating the LCC spreadsheet are as follows:

1. Once the LCC spreadsheet has been downloaded, open the file using Excel. Click

“Enable Macro” when prompted and then click on the tab for the Summary worksheet.

2. Use Excel's View/Zoom commands at the top menu bar to change the size of the display

to fit your monitor.

3. The user can change the parameters listed under USER OPTIONS on the Summary

worksheet. There are three drop

- down boxes and one command button. The default

parameters are:

a. Energy Price Trend: Defaults to “AEO 201 4

- Reference Case.” To ch ange the

input, use the drop

- down menu and select the desired trend (Reference, Low, or

High).

b. Start Year: Defaults to “ 202 1 .” To change the value, use the drop

- down menu and

select the desired year (2018, 2019, 2020, 2021, or 2022)

.

c. # of Trials: Defaults to “10,000.” To change the value, use the drop

- down menu

and select the desired number of trials (1,000, 2,000, 3,000, 5,000, 10,000 , or

20,000 ).

d. Equipment Price Trend: Defaults to “Constant”, which is the only option in this

analysis.

4. To run t he Crystal Ball simulation, click the “run” button (you must re

- run after changing

any parameters). The spreadsheet will then be minimized. You can monitor the progress

of the simulation by watching the count of iterations at the left bottom corner. When t he

simulation is finished, the worksheet named Summary will reappear with the results.

8A

- 4REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7,

2015.) < www.eia.gov/consumption/residential/data/2009/ >

2. U.S. Department of Energy: Energy Information Administration, Annual Energy Outlook

201 4 wit h Projections to 2040 , 201 4 . Washington, DC.

< http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >

8A

- 5APPENDIX 8B. U NCERTAINTY AND V ARIABILITY IN THE LCC ANALYSIS

TABLE OF CO NTENTS

8B.1 INTRODUCTION

................................ ................................ ................................ ......... 8B

- 1

8B.2 UNCERTAINTY

................................ ................................ ................................ ........... 8B

- 1

8B.3 VARIABILITY

................................ ................................ ................................ .............. 8B

- 1

8B.4 APPROACHES TO UNCERT AINTY AND VARIABILIT Y

................................ ..... 8B

- 1

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYST AL BALL

......................... 8B

- 2

LIST OF FIGURES

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability

Distributions

................................ ................................ ................................ . 8B

- 3

8B

- iAPPENDIX 8B. U NCERTAINTY AND V ARIABILITY IN THE LCC ANALYSIS

8B.1 INTRODUCTION

Analysis of energy conservation standards involves calculations of impacts, for example,

the impact of a standard on consumer life

- cycle cost (LCC). In order to perform the calculation,

the analyst must: 1) specify the equation or model that will be used; 2) define the quantities in

the equation; and 3) provide numerical values for each quantity. In the simplest case, th e

equation is unambiguous (contains all relevant quantities and no others), each quantity has a

single numerical value, and the calculation results in a single value. However, unambiguity and

precision are rarely the case. In almost all cases, the model an d/or the numerical values for each

quantity in the model are not completely known ( i.e. , there is uncertainty) or the model and/or

the numerical values for each quantity in the model depend upon other conditions ( i.e. , there is

variability).

Thorough ana lysis involves accounting for uncertainty and variability. While the simplest

analysis involves a single numerical value for each quantity in a calculation, arguments can arise

about what the appropriate value is for each quantity. Explicit analysis of unc ertainty and

variability is intended to provide more complete information to the decision

- making process.

8B.2 UNCERTAIN TY

When making observations of past events or speculating about the future, imperfect

knowledge is the rule rather than the exception. For e xample, the energy actually consumed by a

particular appliance type (such as the average U.S. water heater, direct heating equipment, or

pool heater) is not directly recorded, but rather estimated based upon available information. Even

direct laboratory me asurements have some margin of error. When estimating numerical values

expected for quantities at some future date, the exact outcome is rarely known in advance.

8B.3 V ARIABILITY

Variability results when different applications or situations produce different n umerical

values when calculating a quantity. Specifying an exact value for a quantity may be difficult

because the value depends on something else. For example, water heater energy consumption

depends upon the specific circumstances and behaviors of the oc cupants ( e.g. , number of

persons, length and temperature of showers

, etc. ). Variability makes specifying an appropriate

population value more difficult inasmuch as any one value may not be representative of the

entire population. Surveys can be helpful her e, and analysis of surveys can relate the variable of

interest ( e.g. , hours of use) to other variables that are better known or easier to forecast ( e.g.

, persons per household).

8B.4 A PPROACHES TO U NCERTAINTY AND V ARIABILITY

This section describes two approach es to uncertainty and variability:

8B

- 1• scenario analysis, and

• probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then

changes one (or more) of the numerical values and repeats the calculation. A numbe r of

calculations are done, which provide some indication of the extent to which the result depends

upon the assumptions. For example, the life

- cycle cost of an appliance could be calculated for

energy rates of 2, 8, and 14¢ per kWh.

The advantages of scenario analysis are that each calculation is simple; a range of

estimates is used and crossover points can be identified. (An example of a crossover point is the

energy rate above which the life

- cycle cost is reduced, holding all other inputs constant. T hat is,

the crossover point is the energy rate at which the consumer achieves savings in operating

expense that more than compensate for the increased purchase expense.) The disadvantage of

scenario analysis is that there is no information about the likeli hood of each scenario.

Probability analysis considers the probabilities within a range of values. For quantities

with variability ( e.g. , electricity rates in different households), surveys can be used to generate a

frequency distribution of numerical val ues ( e.g. , the number of households with electricity rates

at particular levels) to estimate the probability of each value. For quantities with uncertainty,

statistical or subjective measures can be used to provide probabilities ( e.g. , manufacturing cost t o improve energy efficiency to some level may be estimated to be $10 ± $3).

The major disadvantage of the probability approach is that it requires more information,

namely information about the shapes and magnitudes of the variability and uncertainty of each

quantity. The advantage of the probability approach is that it provides greater information about

the outcome of the calculations ; that is, it provides the probability that the outcome will be in a

particular range.

Scenario and probability analysis provide some indication of the robustness of the policy

given the uncertainties and variability. A policy is robust when the impacts are acceptable over a

wide range of possible conditions.

8B.5 P ROBABILITY A NALYSIS AND THE U SE OF C RYSTAL B ALL

To qua ntify the uncertainty and variability that exist in inputs to the engineering, LCC,

and payback period (PBP) analyses, DOE used Microsoft Excel spreadsheets combined with

Crystal Ball, a commercially available add

- in, to conduct probability analyses. The p robability

analyses used Monte Carlo simulation and probability distributions.

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. Wi thout the

aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most

likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and

8B

- 2simulation to automatically analyze the effect of varying inputs on outputs of the modeled

system. One type of spreadsheet simulation is Monte Carlo simulation, which randomly

generates values for uncertain variables again and again to simulate a model. Monte Carlo

simulation was named for Monte Carlo, Monaco, where the primary attractions are casinos

containing games of chance. Games of chance

, such as roulette wheels, dice, and slot machines,

exhibit random behavior. The random behavior in games of chance is similar to how Monte

Carlo simulation selects variable values at random to simulate a model. When you roll a die, you

know that a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular roll.

T he same applies to the variables that have a known range of values but an uncertain value for

any par ticular time or event ( e.g. , equipment lifetime, discount rate, and installation cost).

For each uncertain variable (one that has a range of possible values), possible values are

defined with a probability distribution. The type of distribution selected is based on the

conditions surrounding that variable. Probability distribution types include:

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability

Distributions

During a simulation, multiple scenarios of a model are calculated by repea tedly sampling

values from the probability distributions for the uncertain variables and using those values for the

cell. Crystal Ball simulations can consist of as many trials (or scenarios) as desired

— hundreds or

even thousands. During a single trial, Cr ystal Ball randomly selects a value from the defined

possibilities (the range and shape of the probability distribution) for each uncertain variable and

then recalculates the spreadsheet.

8B

- 3 NORMAL UNIFORM TRIANGULAR

WEIBULL CUSTOMAPPENDIX 8C. ENERGY PRICE CALCULA TIONS FOR HEARTH PRODUCT S

TABLE OF CO NTENTS

8C.1 INTRODUCTION

................................ ................................ ................................ ......... 8C

- 1

8C.2 RECS SAMPLE MAPPING PROCESS

................................ ................................ ....... 8C

- 1

8C.3 AVERAGE MARGINAL MON THLY PRICES

................................ .......................... 8C

- 1

8C.3.1 Average Annual Prices Determination

................................ ................................ .... 8C

- 1

8C.3.1.1 Annual Electrical Prices

................................ ................................ ........ 8C

- 1

8C.3.1.2 Annual Natural Gas Prices

................................ ................................ .... 8C

- 4

8C.3.1.3 Annual LPG Prices

................................ ................................ ............... 8C

- 7

8C.3.2 Monthly Energy Price Factors Determination

................................ ......................... 8C

- 9

8C.3.2.1 Monthly Residential Electricity Price Factor Calculations

................. 8C

- 10

8C.3.2.2 Monthly Residential Natural Gas Price Factor Calculations

.............. 8C

- 14

8C.3.2.3 Monthly Residential Liquid Petroleum Gas Price Factor

Calculations

................................ ................................ ......................... 8C

- 18

8C.3.3 Seasonal Marginal Price Factors Determination

................................ .................... 8C

- 20

8C.3.3.1 Marginal Price Factor Calculation for Electricity and Natural Gas

.... 8C

- 20

8C.3.4 Results

................................ ................................ ................................ .................... 8C

- 22

8C.4 HOUSEHOLD ENERGY PRI CE ADJUSTMENT FACTOR

................................ .... 8C

- 27

8C.5 ENERGY PRICE TRENDS

................................ ................................ ........................ 8C

- 28

8C.5.1 Residential Energy Price Trends

................................ ................................ ............ 8C

- 28

LIST OF TABLES

Table 8C.3.1 2012 Monthly Residential Electricity Prices by State (2012¢/kWh)

........... 8C

- 2

Table 8C.3.2 Average Residential Electricity Prices by Region in 2012

.......................... 8C

- 4

Table 8C.3.3 2012 Monthly Residential Natural Gas Prices by State (2012$/tcf)

............ 8C

- 5

Table 8C.3.4 Average Residential Natural Gas Prices by Region in 2012

....................... 8C

- 7

Ta ble 8C.3.5 2012 Residential Average LPG Prices by State (2012$/MMBtu)

............... 8C

- 7

Table 8C.3.6 Average Residential LPG Prices by Region in 2012

................................ ... 8C

- 9

Table 8C.3.7 1990

- 2011 Average Residential Electricity Prices for New York

(nominal cents/kWh)

................................ ................................ .................. 8C

- 11

Table 8C.3.8 Monthly Electricity Price Factors for 1990

- 2011 for New York

............... 8C

- 12

Table 8C.3.9 Monthly Residential Electricity Price Factors

................................ ........... 8C

- 13

Table 8C.3.10 1989

- 2010 Average Residential Natural Gas Prices for New York

($/tcf)

................................ ................................ ................................ .......... 8C

- 14

Table 8C.3.11 1989

- 2011 Monthly Natural Gas Price Factors for New York

.................. 8C

- 15

Table 8C.3.12 Monthly Residential Natural Gas Energy Price Factors

............................ 8C

- 17

Table 8C.3.13 Average LPG Prices for the Northeast (nominal cents/gallon)

................. 8C

- 18

Table 8C.3.14 Monthly LPG Price Factors for 1995

- 2009 for the Northeast

................... 8C

- 19

Table 8C.3.15 Monthly Residential LPG Energy Price Factors

................................ ........ 8C

- 20

Table 8C.3.16 Residential Marginal Electricity Price Factors using EIA 2003

- 2012

Data

................................ ................................ ................................ ............ 8C

- 21

8C

- iTabl e 8C.3.17 Residential Marginal Natural Gas Price Factors using EIA 2003

- 2012

Data

................................ ................................ ................................ ............ 8C

- 22

Table 8C.3.18 Residential Average Monthly Electricity Prices for 2012 Using

Monthly Price Factors (2013$/kWh)

................................ ......................... 8C

- 23

Table 8C.3.19 Residential Average Monthly Natural Gas Pr ices for 2012 Using

Monthly Price Factors (2013$/MMBtu)

................................ .................... 8C

- 24

Table 8C.3.20 Residential Marginal Monthly Electricity Prices for 2012 Using

Marginal Price Factors (2013$/MMBtu)

................................ ................... 8C

- 25

Table 8C.3.21 Residential Marginal Monthly Natural Gas Prices for 2012 Using

Marginal Price Factors (2012$/MMBtu)

................................ ................... 8C

- 26

Table 8C.3.22 Residential Monthly LPG Prices for 2012 Using Average Price Factors

(2013$/MMBtu)

................................ ................................ ......................... 8C

- 27

LIST OF FIGURES

Figure 8C.1.1 Energy Price Calculation Process

................................ ................................ 8C

- 1

Figure 8C.3.1 Monthly Electricity Price Factors for 1990

- 2011 for New York

............... 8C

- 12

Figure 8C.3.2 1989

- 2011 Monthly Natural Gas Price Factors for New York

.................. 8C

- 15

Figure 8C.3.3 Monthly LPG Factors for 1995

- 2009 for the Northeast

............................ 8C

- 19

Figure 8C.5. 1 Projected Residential National Electricity Price

................................ ........ 8C

- 28

Figure 8C.5.2 Projected Residential Division Electricity Prices

................................ ...... 8C

- 29

Figure 8C.5.3 Projected Residential National Natural Gas Price

................................ ..... 8C

- 29

Figure 8C.5.4 Projected Residential Division Natural Gas Prices

................................ .... 8C

- 30

Figure 8C.5.5 Projected Residential National LPG Prices

................................ ............... 8C

- 30

Figure 8C.5.6 Projected Residential Division LPG Prices

................................ ............... 8C

- 31

8C

- iiAPPENDIX 8C. ENERGY PRICE CALCULA TIONS FOR HEARTH PRODUCT S

8C.1 INTRODUCTION

Figure 8C.1.1 depicts the energy price calculation process , which also encompasses

average energy price, seasonal marginal price factor, and monthly price factor calculations

.

Figure 8C.1.1 Energy Price Calculation Process

8C.2 RECS SAMPLE M APPING PROCESS

To match the state data from EIA to the RECS 2009 household samples, DOE used the

housing projections in 2021 for each geographic area

. See a ppendix 7A for more details. RECS

2009 utilizes 27 regions (also called reportable domains). DOE further subdivided the RECS

2009 regions into 30 regions based on climate data to disaggregate northern and rest of country

states. The 27 th RECS region includes Oregon, Washington , Alaska, and Hawaii. DOE

subdivided Alaska and Hawaii into separate regions (28 and 29, respectively), based on cooling

and heating degree days. In addition, West Virginia, which is in RECS region 14, was

disaggregated into region 30 based on cooling and heating degree days.

8C.3 AV E RAGE MARGINAL MONTHL Y PRICES

8C.3.1 Average Annual Prices Determination

8C.3.1.1 Annual Electrical Prices

DOE derived 201 2 annual electricity prices from EIA Form 8 26 data

. 1 The EIA Form EIA Hist orical

Data Seasonal

Marginal Price

Factors Energy

Prices Average

Marginal

Monthly Prices

Energy Price

Trends by

Census Division Location

Monthly Price

Factors RECS Sample

AEO Energy

Price Trends Average Energy

Prices

8C

- 18 26 data include energy prices by s tate

. DOE calculated residential annual electricity prices for

each geographical area by averaging monthly energy prices by state to get state electricity prices

. For areas with more than one state

, DOE weight ed each state ’s average price by its 2021 housing

projection

. Table 8C.3.1 shows the monthly residential electricity prices for each state

. Note that

all energy prices were converted from 2012$ to 2013$ in the LCC spreadsheet using the

consumer price index (CPI). a

Table 8C.3.1 201 2 Monthly Residential Electricity Prices by State (201 2 ¢ / kWh )

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg.

201 2

United States 11.41 11.51 11.70 11.92 11.90 12.09 12.00 12.17 12.30 12.03 11.75 11.62 11.87

Alabama 10.91 11.21 11.48 11.56 11.14 11.56 11.58 11.71 11.76 11.71 11.04 10.96 11.39

Alaska 18.04 17.44 17.97 17.64 18.43 18.10 19.44 19.07 17.34 17.82 17.10 17.06 17.95

Arizona 10.01 10.26 10.44 11.17 11.88 11.90 11.86 11.83 11.66 11.36 10.73 10.41 11.13

Arkansas 8.41 8.68 8.99 9.35 9.14 9.58 9.62 9.69 9.80 9.39 9.46 9.07 9.27

California 15.29 14.56 14.70 14.66 15.02 15.92 15.22 16.46 16.82 14.22 14.88 15.49 15.27

Colorado 10.61 10.76 10.84 11.04 11.27 12.01 12.32 12.15 12.11 11.33 11.38 11.07 11.41

Connecticut 17.32 17.09 17.16 17.64 17.71 17.30 17.08 17.12 17.24 18.08 17.82 17.06 17.39

Delaware 13.03 13.17 13.52 13.93 14.37 13.98 13.32 13.57 13.74 14.53 13.63 13.03 13.65

District of Columbia 11.78 12.21 12.20 12.49 12.39 13.18 12.26 12.23 12.22 12.35 12.21 11.98 12.29

Florida 11.55 11.33 11.09 11.42 11.00 11.49 11.31 11.49 11.60 11.53 11.82 11.38 11.42

Georgia 10.23 10.61 10.58 10.77 11.08 11.88 12.13 12.18 11.87 10.89 10.28 10.29 11.07

Hawaii 36.25 36.80 37.05 37.51 38.21 40.16 37.84 37.18 37.21 36.96 36.81 36.33 37.36

Idaho 8.15 8.09 8.18 8.16 8.39 9.04 9.75 9.77 8.31 9.11 8.67 8.54 8.68

Illinois 11.21 11.45 11.97 12.45 12.73 11.24 10.81 10.78 11.36 11.91 11.35 10.62 11.49

Indiana 10.00 10.18 10.85 11.56 10.85 10.54 9.94 10.44 10.92 11.09 10.45 10.49 10.61

Iowa 9.74 9.77 10.26 11.23 10.98 11.32 11.54 12.03 11.38 10.99 10.34 10.16 10.81

Kansas 10.23 10.71 10.81 11.15 11.28 11.69 11.74 11.72 11.52 11.29 10.96 10.90 11.17

Kentucky 8.98 9.07 9.47 9.63 9.72 9.48 9.27 9.45 9.83 9.72 9.41 9.46 9.46

Louisiana 8.15 8.40 8.40 8.38 8.48 8.01 8.38 8.29 8.56 8.68 8.39 8.47 8.38

Maine 15.02 15.07 14.28 14.46 14.41 14.21 14.63 14.66 14.81 14.76 14.83 14.70 14.65

Maryland 12.48 12.55 12.86 12.90 12.95 13.14 12.88 13.09 13.17 13.03 12.40 12.67 12.84

Massachusetts 15.10 15.41 15.72 14.80 15.39 15.48 14.58 14.16 15.20 14.38 13.77 15.19 14.93

Michigan 13.48 13.48 13.63 13.69 14.39 14.13 15.06 14.53 14.37 14.22 13.98 14.08 14.09

Minnesota 10.76 10.71 10.94 11.14 11.30 11.79 11.84 12.18 12.09 11.54 11.03 10.95 11.36

Mississippi 10.03 10.43 10.62 10.66 10.37 10.26 10.14 9.95 9.98 10.31 10.65 10.29 10.31

Missouri 8.66 8.90 9.33 10.06 10.90 11.53 11.31 11.49 10.46 9.89 9.20 9.03 10.06

Montana 9.57 9.59 9.70 9.86 10.13 10.43 10.60 10.52 10.74 10.54 10.04 9.89 10.13

Nebraska 8.61 8.86 9.16 9.95 9.68 10.71 11.38 11.24 11.50 10.43 9.43 9.13 10.01

Nevada 11.38 12.55 12.16 12.40 12.10 11.80 11.52 11.46 11.58 12.04 12.45 11.82 11.94

New Hampshire 16.17 16.12 16.33 16.45 16.53 16.51 15.81 15.59 15.88 16.03 15.90 15.83 16.10

New Jersey 16.07 16.22 15.86 15.91 15.93 15.67 16.12 15.81 15.57 15.17 15.24 15.49 15.76

New Mexico 10.79 10.62 10.71 10.77 11.04 12.08 12.19 12.49 11.98 11.51 10.69 10.71 11.30

New York 16.79 16.51 16.64 16.70 17.33 18.31 18.38 18.12 18.52 18.44 17.44 17.47 17.55

North Carolina 10.09 10.76 11.00 11.42 10.80 11.00 11.03 11.23 11.52 11.41 10.53 10.41 10.93

a http://www.bls.gov/cpi/

8C

- 2North Dakota 7.65 8.12 8.43 9.22 9.60 10.57 10.00 10.27 10.47 9.42 8.61 8.61 9.25

Ohio 11.08 11.04 11.35 11.93 11.85 12.24 12.16 12.14 12.27 12.06 11.67 11.39 11.77

Oklahoma 8.70 9.48 9.87 10.31 9.53 9.50 9.12 9.70 10.13 10.09 9.57 8.71 9.56

Oregon 9.58 9.63 9.66 9.73 9.86 9.92 10.01 10.01 10.00 10.00 9.82 9.74 9.83

Pennsylvania 12.82 12.79 12.77 12.97 12.99 12.89 12.62 12.68 12.57 12.80 12.60 12.62 12.76

Rhode Island 14.75 14.94 14.82 13.15 14.41 15.32 13.90 14.00 15.09 13.66 13.06 15.70 14.40

South Carolina 11.15 11.64 12.04 11.94 11.40 12.32 11.48 12.14 11.85 11.80 11.67 11.87 11.78

South Dakota 9.22 9.26 9.49 10.22 10.61 10.47 10.59 10.60 10.85 10.66 10.01 9.59 10.13

Tennessee 9.80 9.64 9.83 10.11 10.24 10.24 10.14 10.00 10.18 10.41 10.33 10.48 10.12

Texas 10.77 11.05 11.01 11.02 10.84 10.97 10.87 10.96 11.11 11.16 11.11 10.99 10.99

Utah 9.23 9.33 9.36 9.47 9.87 10.35 10.68 10.66 10.23 9.59 9.70 9.73 9.85

Vermont 16.52 17.07 16.76 16.87 16.41 16.93 16.77 16.38 16.38 17.51 19.26 17.20 17.01

Virginia 10.45 10.89 11.09 11.38 11.50 11.84 11.42 11.25 11.10 11.03 10.71 10.51 11.10

Washington 8.37 8.28 8.35 8.46 8.52 8.70 8.70 8.72 8.79 8.68 8.61 8.53 8.56

West Virginia 9.49 9.68 9.85 9.84 10.48 9.92 9.88 9.85 10.04 10.18 9.85 9.63 9.89

Wisconsin 12.66 12.88 13.10 13.36 13.35 13.36 13.28 13.50 13.83 13.43 13.16 12.61 13.21

Wyoming 9.12 9.16 9.40 9.74 10.08 10.22 10.38 10.27 10.38 10.36 10.01 9.89 9.92

All prices in 201 2 $ were converted to 201 3 $ to be consistent with the rest of the prices

used in the analysis. This conversion was performed using the Consumer Price Index

. Table

8C.3.2 shows the average residential electricity prices weighted by housing projections in 2021

for each geographic area.

8C

- 3Table 8C.3.2 Average Residential Electricity Prices by Region in 201 2

Geographic Area 201 3 $ / MMBtu

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $0.16

2 Massachusetts $0.15

3 New York $0.18

4 New Jersey $0.16

5 Pennsylvania $0.13

6 Illinois $0.12

7 Indiana, Ohio $0.12

8 Michigan $0.14

9 Wisconsin $0.13

10 Iowa, Minnesota, North Dakota, South Dakota $0.11

11 Kansas, Nebraska $0.11

12 Missouri $0.10

13 Virginia $0.11

14 Delaware, District of Columbia, Maryland $0.13

15 Georgia $0.11

16 North Carolina, South Carolina $0.11

17 Florida $0.12

18 Alabama, Kentucky, Mississippi $0.11

19 Tennessee $0.10

20 Arkansas, Louisiana, Oklahoma $0.09

21 Texas $0.11

22 Colorado $0.12

23 Idaho, Montana, Utah, Wyoming $0.10

24 Arizona $0.11

25 Nevada, New Mexico $0.12

26 California $0.15

27 Oregon, Washington $0.09

28 Alaska $0.18

29 Hawaii $0.38

30 West Virginia $0.10

31 U.S. Average $0.12

8C.3.1.2 Annual Natural Gas Prices

DOE obtained the data for natural gas price s from EIA’s Natural Gas Navigator

, 2 which

includes monthly natural gas prices by state for residential, commercial, and industrial

customers. For areas with more than one state

, DOE weight ed each state ’s average pric e by its

8C

- 42021 housing projection

. Table 8C.3.3 shows the monthly residential natural gas prices for each

state

.

Table 8C.3.3 201 2 Monthly Residential Natural Gas Prices by Sta te (201 2 $/ tcf )

Geographical

Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg.

201 2

United States 9.67 9.52 10.45 11.01 12.66 14.25 15.2 15.89 14.81 11.78 10.06 9.75 12.09

Alabama 14.27 14.41 15.25 18.67 19.77 21.34 21.77 21.96 21.04 20.05 15.9 15.25 18.31

Alaska 8.26 8.48 8.17 8.47 8.93 9.57 9.7 10.07 8.74 8.21 7.86 8.57 8.75

Arizona 13.3 14.09 14.68 15.94 18.55 20.39 21.99 22.43 21.8 19.34 15.74 14.94 17.77

Arkansas 10.57 10.78 11.84 15.02 13.59 15.26 16.64 17.47 16.27 13.67 10.96 10.71 13.57

California 9.27 8.36 8.69 8.48 9.04 9.7 9.97 10.12 10.07 9.79 9.09 9.33 9.33

Colorado 7.68 7.79 8.65 8.23 10.36 12.9 12.92 12.93 11.44 8.62 7.64 7.06 9.69

Connecticut 12.11 12.16 12.54 14.53 16.24 20.41 20.85 21.47 20.45 17.85 14.74 13.08 16.37

Delaware 13.87 14.03 14.52 16.12 17.76 22.2 23.55 24.59 24.14 20.49 14.52 12.8 18.22

District of

Columbia 11.92 11.11 12.64 11.93 17.28 17.62 17.96 14.31 13.35 12.38 11 11.58 13.59

Florida 16.19 15.93 16.65 18.02 19.39 21.14 21.43 22.42 21.81 21.43 17.98 16.29 19.06

Georgia 14 14.66 14.86 16.65 20.53 21.52 22.29 23.82 23.03 21.8 16.39 15.09 18.72

Hawaii 49.97 54.89 57.26 54.93 50.72 51.23 51.92 54.81 56.21 52.76 49.59 49.76 52.84

Idaho 8.43 8.27 8.45 8.37 8.6 9.07 8.8 9.28 9.15 8.01 7.71 7.81 8.50

Illinois 7.09 6.66 8.25 7.62 11.49 12.67 15.31 15.68 13.14 9.12 7.83 7.56 10.20

Indiana 8.06 8.31 11.24 11.18 12.18 12.89 14.84 14.7 12.23 8.04 7.04 7.63 10.70

Iowa 8.26 7.97 10.34 9.04 10.86 13.41 17.34 17.16 14.6 11.78 8.84 8.23 11.49

Kansas 8.16 8.66 8.92 12.31 14.22 16.09 17.69 19.11 18.15 13.67 9.87 9.07 12.99

Kentucky 8.62 9.2 11.41 11.97 15.02 16.95 19.34 18.81 17.68 11.16 8.36 8.44 13.08

Louisiana 10.24 10.91 11.63 12.3 12.24 13.48 14.12 14.92 13.86 13.51 10.84 10.06 12.34

Maine 15.87 16.65 16.44 17.12 16.33 16.62 16.47 17.93 16.55 13.28 13.41 15.81 16.04

Maryland 11.33 11.06 13.63 13.91 16.1 17.83 18.44 18.5 15.81 12.91 10.01 10.74 14.19

Massachusetts 13.69 12.59 12.83 12.75 12.54 12.18 14.1 14.84 14.9 12.74 13.77 13.28 13.35

Michigan 10.32 10.35 11.12 10.64 13.95 14.34 15.99 16.16 13.66 10.79 9.99 9.67 12.25

Minnesota 7.53 7.42 8.2 7.41 9.34 10.56 11.47 11.86 9.95 7.85 7.81 7.55 8.91

Mississippi 7.55 9.41 10.28 12.28 12.26 12.21 12.59 13.05 12 11.6 8.52 8.39 10.85

Missouri 9.41 9.6 11.07 17.24 18.56 22.53 24.82 26.15 23.73 16.25 11.86 9.9 16.76

Montana 8.05 7.78 7.82 7.99 8.03 8.48 10.61 10.81 11.02 8.06 7.61 7.56 8.65

Nebraska 7.57 7.37 7.47 10.34 10.64 12.06 12.4 15.01 14.43 11.49 8.61 8.08 10.46

Nevada 9.07 9.43 9.77 10.62 12.08 12.75 13.36 13.8 13.37 12.43 9.44 8.46 11.22

New Hampshire 13.02 13.25 13.56 15.41 13.94 16.03 18.18 19.98 18.85 14.63 12.74 12.19 15.15

New Jersey 10.29 9.82 10.46 11.11 12.01 13.21 13.77 14.34 13.83 12.51 11.61 11.27 12.02

New Mexico 7.41 7.41 7.48 8.65 10.23 11.82 13.71 14.31 14.3 12.59 9.32 8.17 10.45

New York 11.67 11.69 12.99 13.06 15.13 18 17.4 18.78 18.16 15.26 11.35 11.97 14.62

North Carolina 10.95 10.44 13.92 15.21 16.87 18.85 21.56 21.41 20.48 14.26 10.32 10.84 15.43

North Dakota 7.18 6.53 7.28 7.2 8.78 12.45 13.95 14.17 11.99 7.1 6.55 6.69 9.16

Ohio 8.56 8.05 8.53 10.74 13.9 22.17 28.1 29.98 25.43 14.67 9.93 9.27 15.78

Oklahoma 8.33 8.07 9.03 14.4 17.92 19.96 22.84 24.51 23.52 17.7 11.78 9.16 15.60

Oregon 11.47 11.53 10.7 10.56 12.33 12.25 12.53 13.18 14.12 11.79 10.49 10.27 11.77

Pennsylvania 11 11.12 11.34 12.04 13.42 18.09 18.65 19.7 18.56 13.9 11 10.68 14.13

Rhode Island 13.1 13.96 14.16 16.06 18.05 17.88 18.91 19.36 19.58 14.84 12.33 12.01 15.85

8C

- 5South Carolina 11.28 11.18 13.94 17.96 19.81 23.52 25.17 22.38 24.25 15.79 10.63 11.78 17.31

South Dakota 7.84 7.64 9.14 7.78 8.55 10.89 13.37 13.71 12.44 8.82 7.81 7.66 9.64

Tennessee 8.97 8.73 9.48 12.53 13.79 14.54 15.55 17.35 15.22 12.3 9.13 8.87 12.21

Texas 8.56 8.2 9.7 12.05 13.18 14.18 14.51 16.21 15.67 14.54 11.35 10.11 12.36

Utah 7.97 8.5 8.66 9.13 8.87 9.09 9.92 10.24 10.13 9.88 8.76 8.47 9.14

Vermont 15.64 15.45 15.79 16.53 17.4 21.35 23.4 24.02 23.23 19.28 16.62 15.21 18.66

Virginia 11.86 11.14 13.19 13.43 15.77 19.29 19.93 20.13 19.31 12.97 9.94 11.04 14.83

Washington 11.25 11.38 11.45 11.9 12.7 13.53 14.85 15.59 15.63 13.1 11.41 10.56 12.78

West Virginia 10.32 10.29 10.81 11.1 14.33 18 18.38 17.85 14.51 12.02 9.65 9.35 13.05

Wisconsin 8.63 8.73 10.05 9.73 9.58 11.44 13.35 13.6 10.83 8.25 9.03 8.93 10.18

Wyoming 7.87 7.7 7.59 8.06 8.74 10.58 14.37 14.9 14.23 9.7 7.87 7.51 9.93

All prices in 201 2 $ were converted to 201 3 $ to be consistent with the rest of the prices

used in the analysis. This conversion was performed using the Consumer Price Index. DOE also

used a conversion factor (1.02 5 ) to convert from c ubic feet of natural gas to MMBtu. b Table

8C.3.4 displays the annual average residential natural gas prices weighted by housing projections

in 2021 by geographic region

.

b www.eia.gov/tools/faqs/faq.cfm?id=45&t=7

8C

- 6Table 8C.3.4 Average Residential Natural Gas Prices by Region in 201 2

Geographic Area 201 3 $/ MMBtu

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $16.10

2 Massachusetts $13.21

3 New York $14.47

4 New Jersey $11.89

5 Pennsylvania $13.99

6 Illinois $10.09

7 Indiana, Ohio $13.85

8 Michigan $12.16

9 Wisconsin $10.07

10 Iowa, Minnesota, North Dakota, South Dakota $9.67

11 Kansas, Nebraska $11.88

12 Missouri $16.64

13 Virginia $14.69

14 Delaware, District of Columbia, Maryland $14.51

15 Georgia $18.56

16 North Carolina, South Carolina $15.88

17 Florida $18.88

18 Alabama, Kentucky, Mississippi $14.57

19 Tennessee $12.08

20 Arkansas, Louisiana, Oklahoma $13.63

21 Texas $12.24

22 Colorado $9.62

23 Idaho, Montana, Utah, Wyoming $8.88

24 Arizona $17.63

25 Nevada, New Mexico $10.77

26 California $9.23

27 Oregon, Washington $12.30

28 Alaska $8.67

29 Hawaii $52.52

30 West Virginia $12.94

31 U.S. Average $16.10

8C.3.1.3 Annual LPG Prices

DOE collected 20 1 2 average LPG prices from EIA’s 20 12 State Energy Consumption,

Price, and Expenditures Estimates (SEDS). 3 SEDS includes annual LPG prices for residential,

commercial, industrial, and transportation consumers by state. For areas with more than one

state

, DOE weight ed each state ’s average price by its 2021 housing projection

. Table 8C.3.5

shows the annual residential LPG prices for each state

.

Table 8C.3.5 201 2 Residential Average LPG Prices by State (201 2 $/MMBtu)

Geographical Area Avg. 201 2

United States 28.18

Alabama 29.73

8C

- 7Alaska 38.46

Arizona 35.22

Arkansas 30.25

California 33.84

Colorado 21.93

Connecticut 34.13

Delaware 33.37

District of Columbia 35.37

Florida 42.42

Georgia 28.42

Hawaii 64.01

Idaho 22.59

Illinois 23.02

Indiana 24.78

Iowa 22.97

Kansas 23.03

Kentucky 28.49

Louisiana 29.68

Maine 36

Maryland 38.13

Massachusetts 38.04

Michigan 22.97

Minnesota 23.16

Mississippi 30.74

Missouri 22.58

Montana 21.31

Nebraska 22.86

Nevada 35.45

New Hampshire 34.83

New Jersey 37.58

New Mexico 28.8

New York 35.44

North Carolina 30.28

North Dakota 22.75

Ohio 28.19

Oklahoma 22.64

Oregon 28.85

Pennsylvania 31.96

Rhode Island 43.86

South Carolina 31.84

South Dakota 22.53

Tennessee 29.9

Texas 30.76

Utah 22.58

Vermont 34.44

Virginia 26.67

Washington 29.24

West Virginia 35.78

8C

- 8Wisconsin 20.8

Wyoming 22.16

All prices in 201 2 $ were converted to 201 3 $ to be consistent with the rest of the prices

used in the analysis. This conversion was performed using the Consumer Price Index

. Table

8C.3.6 shows the housing

- projection

- weighted average residential LPG prices for each

geographic area.

Table 8C.3.6 Average Residential LPG Prices by Region in 201 2

Geographic Area 201 3 $/MMBtu

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $32.55

2 Massachusetts $34.94

3 New York $33.08

4 New Jersey $34.31

5 Pennsylvania $31.19

6 Illinois $21.69

7 Indiana, Ohio $25.35

8 Michigan $23.30

9 Wisconsin $21.51

10 Iowa, Minnesota, North Dakota, South Dakota $22.34

11 Kansas, Nebraska $21.47

12 Missouri $22.09

13 Virginia $24.48

14 Delaware, District of Columbia, Maryland $33.32

15 Georgia $26.61

16 North Carolina, South Carolina $26.64

17 Florida $27.55

18 Alabama, Kentucky, Mississippi $23.62

19 Tennessee $26.99

20 Arkansas, Louisiana, Oklahoma $20.33

21 Texas $15.30

22 Colorado $24.35

23 Idaho, Montana, Utah, Wyoming $23.36

24 Arizona $29.91

25 Nevada, New Mexico $24.77

26 California $28.49

27 Oregon, Washington $26.18

28 Alaska $28.45

29 Hawaii $38.22

30 West Virginia $31.69

31 U.S. Average $18.28

8C.3.2 Monthly Energy Price Factors Determination

For hearth product s, the Department of Energy (DOE) developed monthly energy price

factors and used monthly energy consumption data for the life

- cycle cost and payback period

8C

- 9calculation. DOE developed monthly energy price factors to capture robust seasonal trends in

monthly energy prices. T o convert annual energy prices into monthly energy prices, DOE

determined monthly energy price factors.

8C.3.2.1 Monthly Residential Electricity Price Factor Calculations

DO E collected historical electricity prices from 199 3 to 201 2 from EIA’s Form 826. 1

These data are published annually and include annual electricity sales, revenues from electricity

sales, and average price for the residential, commercial, industrial, and transportation sectors by

state . DOE aggregated the data into 30 geographical areas described in Chapter 8 (section 8.2. 3 .2

Energy Prices).

For each geographic region, DOE determined average electricity prices from 199 3 to

201 2 by weighting the average residential electricity prices for each state by the hous ing

project ions in 20 21 in each state.

As an example, to illustrate the methodology for producing monthly price factors, the

following tables and charts show the calculation of monthly average electricity price factors,

based on New York historic electr icity price data. Table 8C.3.7 shows the average residential

electricity prices for New York

.

8C

- 10Table 8C.3.7 1990

- 2011 Average Residential Electricity Prices for New York (nomin al

cents/kWh)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg

1990 10.71 11.12 11.19 11.02 11.31 11.64 11.80 11.89 11.98 11.71 11.62 11.48 11.46

1991 11.48 11.44 11.63 11.58 11.82 12.41 12.38 12.63 12.57 12.02 11.84 11.77 11.96

1992 11.43 11.42 11.49 11.60 12.21 13.09 13.31 13.46 13.46 12.95 12.71 12.55 12.47

1993 12.17 12.14 12.35 12.71 13.35 13.93 13.94 13.92 13.90 13.28 13.35 13.10 13.18

1994 12.92 12.74 13.01 13.19 13.61 14.11 14.19 14.30 14.37 13.43 13.50 13.15 13.54

1995 13.16 13.11 13.34 13.54 14.06 14.63 14.69 14.58 14.51 13.76 13.81 13.50 13.89

1996 13.39 13.46 13.71 13.80 14.00 14.54 14.67 14.78 14.59 13.97 13.83 13.75 14.04

1997 13.75 13.67 13.83 13.69 13.84 14.70 14.80 14.68 14.56 14.01 13.93 13.84 14.11

1998 13.87 13.73 13.77 13.84 14.05 13.78 13.78 13.65 13.66 13.29 13.04 12.92 13.62

1999 12.85 12.75 12.95 13.34 12.85 13.44 13.44 13.54 13.74 13.64 13.44 13.24 13.27

2000 12.90 13.18 13.33 13.52 13.54 14.22 15.40 14.77 14.52 14.12 13.94 13.98 13.95

2001 13.89 13.93 13.58 13.44 14.01 14.41 14.99 14.61 14.23 14.22 13.53 13.25 14.01

2002 12.95 13.00 12.81 12.69 13.30 14.01 14.19 14.16 14.42 13.87 13.37 13.19 13.50

2003 12.77 13.30 13.91 14.55 14.77 14.98 15.14 14.94 14.92 14.75 14.23 13.63 14.32

2004 13.32 14.02 13.98 14.03 14.20 14.99 15.36 15.32 15.10 14.93 14.88 14.29 14.53

2005 14.05 14.53 14.40 14.64 15.36 15.58 15.63 16.16 16.69 17.36 17.57 16.53 15.71

2006 16.61 16.66 15.89 16.36 16.56 17.33 17.56 17.74 17.92 17.22 16.33 15.88 16.84

2007 16.09 15.89 16.83 17.14 17.50 18.17 17.27 17.96 17.15 17.48 16.94 16.66 17.09

2008 16.86 17.31 16.92 18.08 18.79 19.42 19.66 20.93 19.49 17.57 16.95 16.61 18.22

2009 16.83 16.72 16.40 16.57 16.86 18.22 18.79 18.21 18.75 18.12 16.72 17.47 17.47

2010 17.29 18.04 17.55 18.92 19.21 19.41 20.11 19.35 20.09 18.36 18.25 17.72 18.69

2011 17.25 17.45 17.58 17.63 18.30 19.07 19.22 19.25 18.84 18.78 17.93 17.26 18.21

DOE then calculated monthly energy price factors by dividing the monthly prices by the

annual average for each year. Table 8C.3.8 and Figure 8C.3.1 sh ow the calculated results for

New York

.

8C

- 11Table 8C.3.8 Monthly Electricity Price Factors for 1990

- 2011 for New York

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1990 0.94 0.97 0.98 0.96 0.99 1.02 1.03 1.04 1.05 1.02 1.01 1.00

1991 0.96 0.96 0.97 0.97 0.99 1.04 1.03 1.06 1.05 1.00 0.99 0.98

1992 0.92 0.92 0.92 0.93 0.98 1.05 1.07 1.08 1.08 1.04 1.02 1.01

1993 0.92 0.92 0.94 0.96 1.01 1.06 1.06 1.06 1.05 1.01 1.01 0.99

1994 0.95 0.94 0.96 0.97 1.00 1.04 1.05 1.06 1.06 0.99 1.00 0.97

1995 0.95 0.94 0.96 0.97 1.01 1.05 1.06 1.05 1.04 0.99 0.99 0.97

1996 0.95 0.96 0.98 0.98 1.00 1.04 1.04 1.05 1.04 0.99 0.98 0.98

1997 0.97 0.97 0.98 0.97 0.98 1.04 1.05 1.04 1.03 0.99 0.99 0.98

1998 1.02 1.01 1.01 1.02 1.03 1.01 1.01 1.00 1.00 0.98 0.96 0.95

1999 0.97 0.96 0.98 1.01 0.97 1.01 1.01 1.02 1.04 1.03 1.01 1.00

2000 0.92 0.94 0.96 0.97 0.97 1.02 1.10 1.06 1.04 1.01 1.00 1.00

2001 0.99 0.99 0.97 0.96 1.00 1.03 1.07 1.04 1.02 1.02 0.97 0.95

2002 0.96 0.96 0.95 0.94 0.99 1.04 1.05 1.05 1.07 1.03 0.99 0.98

2003 0.89 0.93 0.97 1.02 1.03 1.05 1.06 1.04 1.04 1.03 0.99 0.95

2004 0.92 0.96 0.96 0.97 0.98 1.03 1.06 1.05 1.04 1.03 1.02 0.98

2005 0.89 0.93 0.92 0.93 0.98 0.99 0.99 1.03 1.06 1.11 1.12 1.05

2006 0.99 0.99 0.94 0.97 0.98 1.03 1.04 1.05 1.06 1.02 0.97 0.94

2007 0.94 0.93 0.98 1.00 1.02 1.06 1.01 1.05 1.00 1.02 0.99 0.97

2008 0.93 0.95 0.93 0.99 1.03 1.07 1.08 1.15 1.07 0.96 0.93 0.91

2009 0.96 0.96 0.94 0.95 0.97 1.04 1.08 1.04 1.07 1.04 0.96 1.00

2010 0.93 0.97 0.94 1.01 1.03 1.04 1.08 1.04 1.07 0.98 0.98 0.95

2011 0.95 0.96 0.97 0.97 1.00 1.05 1.06 1.06 1.03 1.03 0.98 0.95

Figure 8C.3.1 Monthly Electricity Price Factors for 1990

- 2011 for New York 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecElectricity Price Factors 1990 1991 1992

1993 1994 1995

1996 1997 1998

1999 2000 2001

2002 2003 2004

2005 2006 2007

2008 2009 2010

2011

8C

- 12DOE then averaged the monthly energy price factors for 199 3 to 201 2 to develop an

average energy price factor for each month. DOE performed the same calculations for each

geographic region to develop the average monthly energy price factors weighted by 2021

housing projections shown in Table 8C.3.9 , which include s the results for New York

.

Table 8C.3.9 Monthly Residential Electricity Price Factors

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine,

New Hampshire, Rhode

Island, Vermont 0.98 0.98 0.99 1.00 1.01 1.01 1.00 1.01 1.01 1.01 1.00 1.00

Massachusetts 0.98 0.99 0.99 0.99 1.00 1.03 1.00 1.00 1.02 1.01 0.99 1.01

New York 0.95 0.96 0.96 0.98 1.00 1.04 1.05 1.05 1.05 1.02 0.99 0.97

New Jersey 0.95 0.95 0.96 0.96 0.98 1.06 1.08 1.08 1.06 0.97 0.97 0.97

Pennsylvania 0.93 0.94 0.95 0.98 1.02 1.06 1.07 1.06 1.04 1.02 0.98 0.95

Illinois 0.89 0.93 0.96 1.01 1.05 1.07 1.06 1.04 1.05 1.06 0.97 0.91

Indiana, Ohio 0.90 0.92 0.95 1.01 1.05 1.06 1.03 1.04 1.05 1.05 1.01 0.94

Michigan 0.97 0.97 0.97 0.98 0.99 1.03 1.05 1.05 1.02 0.99 0.98 0.99

Wisconsin 0.96 0.99 0.98 1.00 1.02 1.03 1.00 1.01 1.01 1.02 1.00 0.98

Iowa, Minnesota, North

Dakota, South Dakota 0.91 0.93 0.95 0.98 1.03 1.07 1.08 1.07 1.05 1.02 0.97 0.94

Kansas, Nebraska 0.87 0.91 0.94 0.98 1.02 1.09 1.10 1.11 1.10 1.00 0.96 0.91

Missouri 0.84 0.86 0.90 0.96 1.08 1.18 1.17 1.16 1.06 0.98 0.94 0.87

Virginia 0.91 0.93 0.95 0.99 1.04 1.06 1.07 1.07 1.04 1.02 0.98 0.93

Delaware, District of

Columbia, Maryland 0.90 0.90 0.92 0.94 1.03 1.12 1.11 1.11 1.08 1.00 0.95 0.93

Georgia 0.90 0.93 0.96 0.97 1.01 1.09 1.10 1.11 1.06 1.00 0.95 0.91

North Carolina, South

Carolina 0.94 0.96 0.98 1.01 1.01 1.00 1.02 1.02 1.03 1.05 1.01 0.97

Florida 0.98 0.99 1.00 1.01 1.00 0.99 1.00 1.00 1.00 1.01 1.02 1.00

Alabama, Kentucky,

Mississippi 0.92 0.94 0.97 1.02 1.03 1.03 1.02 1.03 1.02 1.03 1.01 0.96

Tennessee 0.96 0.96 0.98 1.01 1.02 1.01 0.99 0.99 1.00 1.04 1.03 1.01

Arkansas, Louisiana,

Oklahoma 0.90 0.93 0.97 1.00 1.02 1.05 1.05 1.05 1.07 1.04 0.99 0.93

Texas 0.92 0.93 0.97 0.99 1.02 1.05 1.05 1.05 1.04 1.04 0.98 0.96

Colorado 0.95 0.96 0.97 0.99 1.02 1.03 1.02 1.02 1.03 1.02 1.00 0.97

Idaho, Montana, Utah,

Wyoming 0.95 0.96 0.96 0.97 1.01 1.04 1.04 1.04 1.03 1.03 0.99 0.98

Arizona 0.88 0.91 0.93 0.98 1.09 1.08 1.07 1.06 1.06 1.06 0.94 0.95

Nevada, New Mexico 0.96 0.98 1.00 1.01 1.01 1.00 0.99 1.00 1.00 1.03 1.02 0.99

California 1.00 0.97 0.97 0.97 1.00 1.02 1.05 1.05 1.01 0.96 1.00 1.01

Oregon, Washington 0.98 0.99 0.99 0.98 0.98 1.00 1.00 1.01 1.01 1.02 1.02 1.02

Alaska 0.95 0.96 0.98 0.99 1.02 1.02 1.04 1.03 1.01 1.01 1.00 0.99

Hawaii 0.96 0.97 0.96 0.97 0.99 1.00 1.01 1.02 1.02 1.03 1.03 1.02

West Virginia 0.94 0.95 0.98 1.01 1.04 1.02 1.00 1.00 1.02 1.05 1.02 0.97

United States 0.93 0.94 0.96 0.99 1.02 1.04 1.04 1.05 1.04 1.02 0.99 0.96

8C

- 138C.3.2.2 Monthly Residential Natural Gas Price Factor Calculations

DOE collected historical natural gas prices from 19 93 to 201 2 from the Energy

Information Administration’s (EIA’s) Natural Gas Navigator. 2 The Natural Gas Navigator

includes annual and monthly natural gas prices for residential, commercial, and industrial

consumers by state . DOE aggregated the data into 30 geographical areas for residential buildings

and 9 census division for commercial buildings as described in Chapter 8.

For each geographic area , DOE determined average natural gas prices from 19 93 to 201 2

by weighting the average residential natural gas prices for each state by the housing project ions

in 20 21 in each state.

Again, as an example for how DOE determined monthly natural gas price factors, the

methodology used to determine monthly average price factors can be seen below. Table 8C.3.10

shows the historic average residential gas prices for New York

.

Table 8C.3.10 1989

- 2010 Average Residential Natural Gas Prices for New York ($/tcf)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg

1990 6.60 6.66 7.46 6.87 7.11 8.31 9.01 9.36 9.18 7.98 7.58 6.77 7.74

1991 6.77 7.18 6.91 7.08 7.64 8.13 9.02 9.61 9.01 8.38 7.41 7.35 7.87

1992 6.96 6.97 6.82 6.91 7.53 8.91 9.53 9.79 8.90 8.03 7.44 7.14 7.91

1993 7.10 6.83 6.89 6.79 7.40 8.70 9.70 10.28 9.92 8.62 8.68 7.74 8.22

1994 7.57 7.20 7.14 7.71 9.17 9.83 10.95 11.43 10.88 9.33 8.38 8.43 9.00

1995 7.69 7.99 8.33 8.87 9.20 10.64 11.64 12.00 11.42 9.70 9.36 8.64 9.62

1996 8.10 7.66 7.66 7.88 8.70 10.20 11.64 12.00 11.81 11.09 8.70 7.77 9.43

1997 8.01 8.22 8.12 8.39 8.80 10.03 11.08 0.00 0.00 0.00 0.00 0.00 5.22

1998 9.88 9.55 8.86 8.51 9.01 10.83 12.70 11.62 12.59 11.38 9.93 9.34 10.35

1999 9.17 9.09 8.90 9.56 10.73 11.99 7.08 13.24 12.66 11.62 9.50 9.30 10.24

2000 8.21 8.49 8.05 8.74 10.10 11.79 12.65 12.01 11.93 10.29 9.66 9.01 10.08

2001 7.97 8.49 9.55 9.82 11.66 13.30 14.81 14.68 15.05 12.15 10.16 9.02 11.39

2002 12.47 11.24 10.53 11.43 13.50 13.84 14.25 14.41 13.14 11.57 11.16 10.89 12.37

2003 9.35 8.65 9.17 9.34 9.96 11.66 13.04 13.18 13.21 11.84 10.08 9.48 10.75

2004 9.63 9.88 11.69 12.22 12.93 14.71 16.01 16.17 15.58 13.01 12.02 11.36 12.93

2005 11.41 11.33 11.48 11.51 13.07 15.34 16.29 16.89 16.22 14.41 13.44 13.19 13.72

2006 12.80 12.65 12.42 13.45 14.49 16.16 17.62 18.48 20.78 22.24 20.21 17.44 16.56

2007 16.61 15.11 13.99 14.58 16.09 16.69 18.04 18.91 18.43 13.37 14.75 14.97 15.96

2008 15.24 14.43 15.08 15.47 17.33 19.59 19.95 18.94 18.53 18.64 16.04 14.83 17.01

2009 14.99 14.91 15.21 16.76 19.95 22.88 24.96 24.20 21.66 18.42 16.48 16.26 18.89

2010 15.46 14.84 14.63 14.19 15.13 16.82 18.24 17.81 17.74 14.71 14.97 14.02 15.71

2011 12.97 13.01 13.60 15.08 15.82 18.42 20.00 20.17 18.54 16.47 13.88 12.09 15.84

DOE then calculated monthly energy price factors for each year by dividing the

residential natural gas prices for each month by the natural gas annual average price for each

year. Table 8C.3.11 and Figure 8C.3.2 show the calculated results for New York

.

8C

- 14Table 8C.3.11 1989

- 2011 Monthly Natural Gas Price Factors for New York

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1990 0.85 0.86 0.96 0.89 0.92 1.07 1.16 1.21 1.19 1.03 0.98 0.87

1991 0.86 0.91 0.88 0.90 0.97 1.03 1.15 1.22 1.14 1.06 0.94 0.93

1992 0.88 0.88 0.86 0.87 0.95 1.13 1.20 1.24 1.13 1.02 0.94 0.90

1993 0.86 0.83 0.84 0.83 0.90 1.06 1.18 1.25 1.21 1.05 1.06 0.94

1994 0.84 0.80 0.79 0.86 1.02 1.09 1.22 1.27 1.21 1.04 0.93 0.94

1995 0.80 0.83 0.87 0.92 0.96 1.11 1.21 1.25 1.19 1.01 0.97 0.90

1996 0.86 0.81 0.81 0.84 0.92 1.08 1.23 1.27 1.25 1.18 0.92 0.82

1997 0.95 0.92 0.86 0.82 0.87 1.05 1.23 1.12 1.22 1.10 0.96 0.90

1998 0.90 0.89 0.87 0.93 1.05 1.17 0.69 1.29 1.24 1.14 0.93 0.91

1999 0.81 0.84 0.80 0.87 1.00 1.17 1.26 1.19 1.18 1.02 0.96 0.89

2000 0.70 0.75 0.84 0.86 1.02 1.17 1.30 1.29 1.32 1.07 0.89 0.79

2001 1.01 0.91 0.85 0.92 1.09 1.12 1.15 1.16 1.06 0.94 0.90 0.88

2002 0.87 0.80 0.85 0.87 0.93 1.08 1.21 1.23 1.23 1.10 0.94 0.88

2003 0.74 0.76 0.90 0.94 1.00 1.14 1.24 1.25 1.20 1.01 0.93 0.88

2004 0.83 0.83 0.84 0.84 0.95 1.12 1.19 1.23 1.18 1.05 0.98 0.96

2005 0.77 0.76 0.75 0.81 0.87 0.98 1.06 1.12 1.25 1.34 1.22 1.05

2006 1.04 0.95 0.88 0.91 1.01 1.05 1.13 1.18 1.15 0.84 0.92 0.94

2007 0.90 0.85 0.89 0.91 1.02 1.15 1.17 1.11 1.09 1.10 0.94 0.87

2008 0.79 0.79 0.81 0.89 1.06 1.21 1.32 1.28 1.15 0.98 0.87 0.86

2009 0.98 0.94 0.93 0.90 0.96 1.07 1.16 1.13 1.13 0.94 0.95 0.89

2010 0.82 0.82 0.86 0.95 1.00 1.16 1.26 1.27 1.17 1.04 0.88 0.76

2011 0.77 0.78 0.81 0.86 1.01 1.26 1.26 1.26 1.26 1.05 0.89 0.80

Figure 8C.3.2 1989

- 2011 Monthly Natural Gas Price Factors for New York 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecNatural Gas Price Factors 1989 1990 1991

1992 1993 1994

1995 1997 1998

1999 2000 2001

2002 2003 2004

2005 2006 2007

2008 2009 2010

2011

8C

- 15DOE then averaged the monthly energy price factors for 19 93 to 201 2 to develop an

average energy price factor for each month. DOE performed the same calculations for each

geographic area to develop the average monthly energy price factors weighted by housing

projections in 2021 shown in Table 8C.3 .12 , which also includes the monthly energy price factor

results calculated for New York

.

8C

- 16Table 8C.3.12 Monthly Residential Natural Gas Energy Price Factors

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine,

New Hampshire, Rhode

Island, Vermont 0.89 0.90 0.91 0.93 0.97 1.05 1.14 1.16 1.13 1.01 0.97 0.94

Massachusetts 0.98 0.98 0.97 0.99 0.93 0.96 1.05 1.11 1.07 0.93 1.00 1.00

New York 0.88 0.87 0.88 0.92 1.02 1.16 1.23 1.16 1.13 1.00 0.89 0.84

New Jersey 0.91 0.90 0.90 0.93 0.98 1.08 1.12 1.12 1.10 1.01 0.96 0.94

Pennsylvania 0.82 0.82 0.84 0.88 0.99 1.14 1.26 1.29 1.23 1.00 0.89 0.84

Illinois 0.80 0.81 0.81 0.86 1.04 1.20 1.29 1.31 1.21 0.96 0.85 0.80

Indiana, Ohio 0.82 0.82 0.85 0.91 1.02 1.17 1.28 1.29 1.20 0.96 0.85 0.83

Michigan 0.83 0.83 0.84 0.89 0.99 1.13 1.25 1.29 1.19 0.99 0.90 0.87

Wisconsin 0.95 0.93 0.94 0.96 0.96 1.10 1.14 1.15 1.06 0.88 0.97 0.95

Iowa, Minnesota, North

Dakota, South Dakota 0.85 0.84 0.85 0.87 0.99 1.16 1.25 1.29 1.20 0.95 0.89 0.85

Kansas, Nebraska 0.80 0.80 0.80 0.88 0.99 1.15 1.24 1.30 1.26 1.08 0.88 0.82

Missouri 0.73 0.73 0.74 0.83 0.96 1.17 1.34 1.42 1.32 1.12 0.88 0.78

Virginia 0.82 0.79 0.78 0.87 1.03 1.20 1.29 1.27 1.26 1.02 0.84 0.82

Delaware, District of

Columbia, Maryland 0.82 0.81 0.83 0.91 1.04 1.16 1.24 1.24 1.21 1.01 0.88 0.84

Georgia 0.72 0.77 0.80 0.89 1.10 1.23 1.29 1.28 1.24 1.07 0.82 0.77

North Carolina, South

Carolina 0.79 0.79 0.81 0.87 1.00 1.18 1.25 1.30 1.25 1.04 0.87 0.84

Florida 0.81 0.83 0.88 0.94 1.02 1.09 1.12 1.14 1.13 1.11 1.02 0.90

Alabama, Kentucky,

Mississippi 0.80 0.80 0.83 0.92 1.04 1.16 1.19 1.22 1.19 1.08 0.92 0.85

Tennessee 0.82 0.83 0.83 0.90 0.99 1.13 1.20 1.24 1.19 1.08 0.91 0.86

Arkansas, Louisiana,

Oklahoma 0.77 0.77 0.79 0.87 1.04 1.14 1.21 1.25 1.22 1.13 0.96 0.82

Texas 0.77 0.78 0.80 0.92 1.05 1.17 1.21 1.24 1.23 1.11 0.91 0.80

Colorado 0.81 0.82 0.85 0.88 0.97 1.23 1.24 1.31 1.22 0.97 0.88 0.83

Idaho, Montana, Utah,

Wyoming 0.92 0.93 0.94 0.92 0.96 1.04 1.13 1.18 1.10 0.97 0.96 0.94

Arizona 0.77 0.79 0.82 0.90 1.01 1.11 1.21 1.26 1.22 1.14 0.96 0.83

Nevada, New Mexico 0.80 0.82 0.84 0.91 1.04 1.21 1.18 1.23 1.19 1.05 0.89 0.81

California 1.00 0.99 0.95 0.95 0.99 1.04 1.05 1.03 1.01 1.01 0.98 0.98

Oregon, Washington 0.90 0.91 0.91 0.93 0.97 1.02 1.12 1.17 1.14 1.03 0.95 0.93

Alaska 0.94 0.94 0.95 0.96 1.01 1.05 1.13 1.10 1.02 0.97 0.94 0.97

Hawaii 0.95 0.97 0.97 0.97 0.99 1.00 1.02 1.05 1.05 1.04 1.02 1.00

West Virginia 0.83 0.84 0.84 0.87 0.97 1.17 1.30 1.29 1.18 0.96 0.88 0.86

United States 0.85 0.86 0.87 0.91 1.00 1.12 1.19 1.22 1.16 1.00 0.91 0.87

8C

- 178C.3.2.3 Monthly Residential Liquid Petroleum Gas Price Factor Calculations

DOE collected historical liquid petroleum gas (LPG) prices from 1995 to 2009 from

EIA’s Short

- Term Energy Outlook. 4 The Short

- Term Energy Outlook includes monthly LPG

prices by Census Region (Northeast, South, Midwest, and West). c

The same process as used for electricity and natural gas price factors was used for

calculating the monthly LPG price factors. These monthly price factors were calculated below,

using data from the Northeast region. Table 8C.3.13 shows the Northeast residential LPG prices

from 1995 to 2009.

Table 8C.3.13 Average LPG Prices for the Northeast (nominal cents/gallon)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1995 119 118 120 121 124 126 126 125 122 121 118 117

1996 123 125 128 125 130 131 129 127 127 133 135 145

1997 143 137 131 131 130 130 130 127 126 127 123 122

1998 121 120 120 123 124 124 122 121 119 118 115 114

1999 112 113 114 118 122 124 126 129 127 129 128 128

2000 132 148 148 145 148 151 155 154 157 159 156 160

2001 176 170 162 160 162 160 156 152 150 150 144 139

2002 139 138 139 143 142 144 143 141 141 142 142 142

2003 150 166 182 164 161 161 159 156 155 155 155 158

2004 169 173 171 168 170 173 173 176 181 187 193 187

2005 186 186 190 197 199 200 202 205 217 224 220 217

2006 221 220 220 225 231 237 242 244 240 232 229 228

2007 227 229 235 239 247 252 253 252 254 260 274 275

2008 282 280 284 292 306 320 333 329 324 305 280 267

2009 268 267 267 263 258 255 255 251 249 250 252 255

DOE then calculated monthly energy price factors for each year by dividing the prices for

each month by the average price for each year. Table 8C.3.14 and Figure 8C.3.3 show the

calculated results for the Northeast.

c Refer to www.census.gov/geo/www/us_regdiv.pdf

.

8C

- 18Table 8C.3.14 Monthly LPG Price Factors for 1995

- 2009 for the Northeast

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1995 0.98 0.97 0.99 1.00 1.02 1.03 1.04 1.03 1.00 1.00 0.97 0.96

1996 0.94 0.97 0.99 0.96 1.00 1.01 0.99 0.98 0.98 1.02 1.04 1.12

1997 1.10 1.06 1.01 1.01 1.01 1.00 1.00 0.98 0.97 0.98 0.95 0.94

1998 1.01 1.00 1.00 1.02 1.03 1.03 1.02 1.01 0.99 0.98 0.96 0.95

1999 0.92 0.92 0.93 0.96 1.00 1.02 1.03 1.05 1.04 1.06 1.05 1.04

2000 0.87 0.98 0.98 0.96 0.98 1.00 1.03 1.02 1.04 1.05 1.03 1.06

2001 1.12 1.08 1.03 1.02 1.04 1.02 0.99 0.97 0.96 0.96 0.92 0.89

2002 0.98 0.97 0.98 1.01 1.01 1.02 1.01 1.00 1.00 1.01 1.00 1.00

2003 0.94 1.04 1.13 1.03 1.01 1.00 1.00 0.97 0.97 0.97 0.97 0.99

2004 0.95 0.98 0.96 0.95 0.96 0.98 0.98 1.00 1.02 1.06 1.09 1.06

2005 0.91 0.91 0.93 0.97 0.98 0.98 0.99 1.01 1.07 1.10 1.08 1.07

2006 0.96 0.95 0.95 0.98 1.00 1.03 1.05 1.06 1.04 1.01 0.99 0.99

2007 0.91 0.92 0.94 0.96 0.99 1.01 1.01 1.01 1.02 1.04 1.10 1.10

2008 0.94 0.93 0.95 0.97 1.02 1.06 1.11 1.10 1.08 1.02 0.93 0.89

2009 1.04 1.04 1.04 1.02 1.00 0.99 0.99 0.97 0.97 0.97 0.98 0.99

Avg 0.97 0.98 0.99 0.99 1.00 1.01 1.02 1.01 1.01 1.01 1.00 1.00

Figure 8C.3.3 Monthly LPG Factors for 1995

- 2009 for the Northeast

DOE then averaged the monthly energy price factors for 1995 to 20 09 to develop an

average energy price factor for each month. DOE performed the same calculations for each

Census Region to develop t he average monthly energy price factors weighted by housing

projections in 2021 shown in Table 8C.3.15 , which includes the calculated Northeast region

monthly LPG e nergy price factors from 1995 to 2009

. 0.85 0.90 0.95 1.00 1.05 1.10 1.15

1 2 3 4 5 6 7 8 9 10 11 12 1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Avg

8C

- 19Table 8C.3.15 Monthly Residential LPG Energy Price Factors

Census Regions Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Northeast 0.97 0.98 0.99 0.99 1.00 1.01 1.02 1.01 1.01 1.01 1.00 1.00

South 1.04 1.04 1.03 1.01 1.00 0.97 0.94 0.93 0.96 0.98 1.03 1.07

Midwest 1.04 1.04 1.03 1.01 0.99 0.97 0.95 0.93 0.96 1.00 1.03 1.06

West 1.05 1.05 1.03 1.01 0.99 0.96 0.92 0.91 0.95 1.01 1.04 1.08

U.S. 1.02 1.03 1.02 1.02 1.02 1.00 0.95 0.93 0.96 0.99 1.02 1.05

8C.3.3 Seasonal Marginal Price Factors Determination

Marginal energy prices are the prices consumers pay for the last unit of energy used.

DOE used the marginal energy prices for each building for the cost of saved energy associated

with the use of higher

- efficiency equipment. Because marginal prices reflect a change in a

consumer’s bill associated with a change in energy consumed, such prices are appropriate for

determining energy cost savings associated with possible change s to efficiency standards.

EIA provides historical monthly consumption and expenditures by state. This data were

used to determine 10 year average marginal prices for the RECS 2009 geographical areas, which

are then used to convert average monthly energy prices into marginal monthly energy prices.

Because a heart h product operates during both the heating and cooling season s , DOE determined

summer and winter marginal price factors. EIA provided RECS 2009 billing data that had been

gathered from a subset of RECS housing records. For each household with billing data, the

following are provided for each billing cycle: the start and end date, the electricity consumption

in kWh, the electricity cost in dollars, the natural gas bill in dollars, and the gas consumption in

hundreds of cubic feet. This data was used to valid ate marginal energy price factors by RECS

2009 geographical area.

For LPG

- fired hearth products , DOE used the average LPG prices for each house for both

base case products and higher

- efficiency products , as the data necessary for estimating marginal

pric es were not available.

8C.3.3.1 Marginal Price Factor Calculation for Electricity and Natural Gas

EIA provides historical monthly consumption and expenditures by state. This data was

used to determine 10 year average marginal prices for the RECS 2009 geographical areas DOE

interpreted the slope of the regression line (consumption vs. expenditures) for each state as the

marginal energy price for that state.

Table 8C.3.16 and Table 8C.3.17 show the resulting marginal electricity and natural gas

marginal price factors.

8C

- 20Table 8C.3.16 R esidential Marginal Electricity Price Factors using EIA 200 3

- 2 012 Data

Geographical Area Summer Winter

Connecticut, Maine, New Hampshire, Rhode Island, Vermont 0.95 0.99

Massachusetts 0.96 1.04

New York 1.13 0.87

New Jersey 1.21 0.98

Pennsylvania 1.08 0.83

Illinois 0.98 0.72

Indiana, Ohio 1.00 0.75

Michigan 1.14 0.97

Wisconsin 1.01 0.89

Iowa, Minnesota, North Dakota, South Dakota 1.06 0.84

Kansas, Nebraska 1.16 0.74

Missouri 1.21 0.76

Virginia 1.08 0.85

Delaware, District of Columbia, Maryland 1.16 0.91

Georgia 1.16 0.84

North Carolina, South Carolina 0.97 0.83

Florida 1.01 0.93

Alabama, Kentucky, Mississippi 1.00 0.82

Tennessee 0.93 0.84

Arkansas, Louisiana, Oklahoma 1.04 0.74

Texas 1.05 0.90

Colorado 1.08 0.79

Idaho, Montana, Utah, Wyoming 1.10 0.93

Arizona 1.05 0.84

Nevada, New Mexico 1.04 0.88

California 1.21 1.13

Oregon, Washington 0.89 0.95

Alaska 0.85 0.91

Hawaii 1.46 0.89

West Virginia 0.92 0.84

United States 1.07 0.81

8C

- 21Table 8C.3.17 Residential Marginal Natural Gas Price Factors using EIA 2 003

- 201 2

Data

Geographical Area Summer Winter

Connecticut, Maine, New Hampshire, Rhode Island, Vermont 0.83 0.92

Massachusetts 0.89 1.03

New York 0.75 0.89

New Jersey 0.84 0.95

Pennsylvania 0.73 0.93

Illinois 0.68 0.97

Indiana, Ohio 0.73 0.92

Michigan 0.78 0.93

Wisconsin 0.79 0.98

Iowa, Minnesota, North Dakota, South Dakota 0.72 0.97

Kansas, Nebraska 0.69 0.93

Missouri 0.60 0.82

Virginia 0.68 0.93

Delaware, District of Columbia, Maryland 0.70 0.92

Georgia 0.56 0.87

North Carolina, South Carolina 0.66 0.89

Florida 0.64 0.82

Alabama, Kentucky, Mississippi 0.75 0.87

Tennessee 0.74 0.94

Arkansas, Louisiana, Oklahoma 0.65 0.84

Texas 0.59 0.85

Colorado 0.69 0.91

Idaho, Montana, Utah, Wyoming 0.84 0.96

Arizona 0.64 0.85

Nevada, New Mexico 0.72 0.89

California 0.85 1.08

Oregon, Washington 0.84 0.94

Alaska 0.86 0.96

Hawaii 0.77 0.91

West Virginia 0.80 0.95

United States 0.74 0.94

8C.3.4 Results

DOE applied the regional monthly energy price factors to develop residential and

commercial monthly energy prices for 201 2 for electricity and natural gas ( Table 8C.3.18 and

Table 8C.3.19 ) . Each geographical area was matched with the a ppropriate Census Region.

8C

- 22Table 8C.3.18 Residential Average Monthly Electricity Prices for 2012 Using Mo nthly

Price Factors (2013$/kWh)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine,

New Hampshire, Rhode

Island, Vermont 0.16 0.16 0.16 0.16 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16

Massachusetts 0.15 0.15 0.15 0.15 0.15 0.16 0.15 0.15 0.15 0.15 0.15 0.15

New York 0.17 0.17 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.18 0.18 0.17

New Jersey 0.15 0.15 0.15 0.15 0.16 0.17 0.17 0.17 0.17 0.16 0.16 0.16

Pennsylvania 0.12 0.12 0.12 0.13 0.13 0.14 0.14 0.14 0.13 0.13 0.13 0.12

Illinois 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Indiana, Ohio 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Michigan 0.14 0.14 0.14 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.14 0.14

Wisconsin 0.13 0.13 0.13 0.13 0.14 0.14 0.13 0.13 0.14 0.14 0.13 0.13

Iowa, Minnesota, North

Dakota, South Dakota 0.10 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Kansas, Nebraska 0.09 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.10 0.10

Missouri 0.09 0.09 0.09 0.10 0.11 0.12 0.12 0.12 0.11 0.10 0.10 0.09

Virginia 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Delaware, District of

Columbia, Maryland 0.12 0.12 0.12 0.12 0.14 0.15 0.15 0.15 0.14 0.13 0.12 0.12

Georgia 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.13 0.12 0.11 0.11 0.10

North Carolina, South

Carolina 0.11 0.11 0.11 0.11 0.12 0.11 0.12 0.12 0.12 0.12 0.11 0.11

Florida 0.11 0.11 0.12 0.12 0.12 0.11 0.12 0.12 0.12 0.12 0.12 0.12

Alabama, Kentucky,

Mississippi 0.10 0.10 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10

Tennessee 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.11 0.10

Arkansas, Louisiana,

Oklahoma 0.08 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.09 0.09

Texas 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Colorado 0.11 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Idaho, Montana, Utah,

Wyoming 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Arizona 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Nevada, New Mexico 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12

California 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15 0.15 0.16

Oregon, Washington 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

Alaska 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.19 0.18 0.18 0.18 0.18

Hawaii 0.36 0.37 0.37 0.37 0.38 0.38 0.38 0.39 0.39 0.39 0.39 0.39

West Virginia 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.10 0.10

United States 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

8C

- 23Table 8C.3.19 Residential Average Monthly Natural Gas Prices for 2012 Using Monthly

Price Factors (201 3 $/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine,

New Hampshire, Rhode

Island, Vermont 14.4 14.5 14.6 14.9 15.6 16.9 18.4 18.7 18.2 16.3 15.5 15.1

Massachusetts 12.9 12.9 12.8 13.1 12.3 12.6 13.9 14.7 14.2 12.3 13.3 13.2

New York 12.8 12.6 12.8 13.3 14.8 16.9 17.8 16.8 16.4 14.4 12.9 12.1

New Jersey 10.9 10.7 10.7 11.0 11.7 12.9 13.4 13.4 13.2 12.1 11.5 11.2

Pennsylvania 11.4 11.6 11.8 12.3 13.8 15.9 17.6 18.1 17.2 14.0 12.4 11.8

Illinois 8.1 8.2 8.2 8.7 10.6 12.2 13.1 13.3 12.3 9.7 8.6 8.1

Indiana, Ohio 11.3 11.4 11.8 12.6 14.1 16.2 17.7 17.9 16.6 13.3 11.8 11.5

Michigan 10.1 10.1 10.3 10.8 12.0 13.7 15.2 15.7 14.5 12.1 10.9 10.5

Wisconsin 9.6 9.3 9.5 9.6 9.7 11.1 11.5 11.7 10.7 8.8 9.8 9.6

Iowa, Minnesota, North

Dakota, South Dakota 8.3 8.1 8.2 8.4 9.6 11.2 12.1 12.5 11.6 9.2 8.6 8.2

Kansas, Nebraska 9.5 9.5 9.5 10.4 11.7 13.7 14.7 15.4 15.0 12.9 10.4 9.8

Missouri 12.2 12.2 12.3 13.8 15.9 19.4 22.2 23.5 21.9 18.6 14.6 13.0

Virginia 12.1 11.6 11.5 12.8 15.1 17.6 18.9 18.6 18.6 15.1 12.4 12.0

Delaware, District of

Columbia, Maryland 11.9 11.8 12.1 13.2 15.0 16.9 18.0 17.9 17.6 14.6 12.8 12.2

Georgia 13.4 14.2 14.9 16.5 20.5 22.8 24.0 23.8 23.1 19.9 15.3 14.3

North Carolina, South

Carolina 12.6 12.5 12.9 13.9 15.9 18.8 19.9 20.7 19.8 16.5 13.8 13.3

Florida 15.4 15.7 16.7 17.7 19.3 20.5 21.2 21.6 21.3 20.9 19.2 17.0

Alabama, Kentucky,

Mississippi 11.7 11.7 12.1 13.3 15.2 16.9 17.3 17.7 17.3 15.7 13.4 12.4

Tennessee 9.9 10.0 10.0 10.9 12.0 13.7 14.6 15.0 14.3 13.0 11.0 10.4

Arkansas, Louisiana,

Oklahoma 10.6 10.5 10.8 11.9 14.1 15.6 16.6 17.1 16.6 15.5 13.1 11.2

Texas 9.5 9.5 9.8 11.2 12.9 14.4 14.8 15.2 15.1 13.6 11.2 9.9

Colorado 7.8 7.9 8.2 8.4 9.3 11.8 11.9 12.6 11.7 9.3 8.4 8.0

Idaho, Montana, Utah,

Wyoming 8.2 8.2 8.4 8.2 8.5 9.2 10.0 10.5 9.8 8.6 8.5 8.3

Arizona 13.5 14.0 14.5 15.8 17.7 19.6 21.4 22.1 21.4 20.0 16.9 14.6

Nevada, New Mexico 8.7 8.9 9.1 9.8 11.3 13.1 12.8 13.3 12.8 11.3 9.6 8.7

California 9.2 9.1 8.8 8.8 9.2 9.6 9.7 9.6 9.3 9.4 9.0 9.0

Oregon, Washington 11.0 11.2 11.2 11.5 12.0 12.6 13.9 14.4 14.0 12.7 11.7 11.4

Alaska 8.1 8.2 8.3 8.4 8.8 9.1 9.8 9.6 8.9 8.4 8.2 8.4

Hawaii 49.6 50.7 50.8 51.0 51.8 52.3 53.6 54.9 54.8 54.5 53.7 52.6

West Virginia 10.8 10.8 10.9 11.3 12.5 15.1 16.9 16.6 15.3 12.5 11.5 11.1

United States 10.5 10.5 10.6 11.2 12.3 13.8 14.6 14.9 14.3 12.3 11.2 10.7

DOE then applied the marginal price factors to the monthly energy price s to develop

marginal residential monthly energy prices for 201 2 for electricity and natural gas ( Table

8C.3.20 and Table 8C.3 .21 ).

8C

- 24Table 8C.3.20 Residential Marginal Monthly Electricity Prices for 2012 Using Marginal

Price Factors (2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New Hampshire,

Rhode Island, Vermont 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Massachusetts 0.15 0.16 0.16 0.14 0.15 0.15 0.14 0.15 0.15 0.15 0.16 0.16

New York 0.15 0.15 0.15 0.20 0.20 0.21 0.21 0.21 0.21 0.20 0.15 0.15

New Jersey 0.15 0.15 0.15 0.19 0.19 0.20 0.21 0.21 0.21 0.19 0.15 0.15

Pennsylvania 0.10 0.10 0.10 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.11 0.10

Illinois 0.07 0.08 0.08 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.08 0.08

Indiana, Ohio 0.08 0.08 0.08 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.09 0.08

Michigan 0.13 0.14 0.13 0.16 0.16 0.17 0.17 0.17 0.17 0.16 0.14 0.14

Wisconsin 0.11 0.12 0.12 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.12 0.12

Iowa, Minnesota, North Dakota,

South Dakota 0.09 0.09 0.09 0.12 0.12 0.13 0.13 0.13 0.12 0.12 0.09 0.09

Kansas, Nebraska 0.07 0.07 0.07 0.12 0.13 0.14 0.14 0.14 0.14 0.13 0.08 0.07

Missouri 0.07 0.07 0.07 0.12 0.13 0.15 0.14 0.14 0.13 0.12 0.07 0.07

Virginia 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Delaware, District of Columbia,

Maryland 0.11 0.11 0.11 0.14 0.16 0.17 0.17 0.17 0.16 0.15 0.11 0.11

Georgia 0.09 0.09 0.09 0.13 0.13 0.14 0.14 0.15 0.14 0.13 0.09 0.09

North Carolina, South Carolina 0.09 0.09 0.09 0.11 0.11 0.11 0.11 0.11 0.11 0.12 0.09 0.09

Florida 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Alabama, Kentucky, Mississippi 0.08 0.08 0.08 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.08

Tennessee 0.08 0.08 0.08 0.10 0.10 0.10 0.10 0.09 0.10 0.10 0.09 0.09

Arkansas, Louisiana, Oklahoma 0.06 0.06 0.06 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.07 0.06

Texas 0.09 0.09 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.10 0.10

Colorado 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.13 0.09 0.09

Idaho, Montana, Utah, Wyoming 0.09 0.09 0.09 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.09

Arizona 0.08 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Nevada, New Mexico 0.10 0.10 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.13 0.11 0.10

California 0.17 0.17 0.17 0.18 0.19 0.19 0.20 0.20 0.19 0.18 0.18 0.18

Oregon, Washington 0.08 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.09 0.09

Alaska 0.16 0.16 0.16 0.15 0.16 0.16 0.16 0.16 0.16 0.16 0.17 0.16

Hawaii 0.33 0.33 0.33 0.54 0.55 0.55 0.56 0.56 0.56 0.57 0.35 0.35

West Virginia 0.08 0.08 0.08 0.09 0.10 0.09 0.09 0.09 0.09 0.10 0.09 0.08

United States 0.09 0.09 0.09 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.10 0.09

8C

- 25Table 8C.3.21 Residential Marginal Mon thly Natural Gas Prices for 2012 Using

Marginal Price Factors (2012$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New

Hampshire, Rhode Island,

Vermont 13.21 13.32 13.42 12.40 12.97 14.03 15.26 15.52 15.11 13.52 14.27 13.84

Massachusetts 13.30 13.29 13.19 11.75 10.98 11.30 12.45 13.13 12.71 11.00 13.66 13.60

New York 11.44 11.24 11.38 10.01 11.12 12.67 13.38 12.61 12.35 10.85 11.47 10.81

New Jersey 10.33 10.20 10.22 9.23 9.82 10.78 11.23 11.18 11.03 10.13 10.89 10.64

Pennsylvania 10.58 10.70 10.90 8.93 10.02 11.55 12.78 13.11 12.45 10.16 11.50 10.91

Illinois 7.93 7.96 8.01 5.91 7.17 8.26 8.88 8.99 8.33 6.58 8.40 7.91

Indiana, Ohio 10.43 10.53 10.88 9.17 10.20 11.78 12.82 12.99 12.08 9.65 10.91 10.65

Michigan 9.40 9.41 9.55 8.38 9.33 10.68 11.82 12.25 11.30 9.41 10.19 9.81

Wisconsin 9.37 9.16 9.33 7.66 7.67 8.78 9.11 9.26 8.48 7.02 9.64 9.39

Iowa, Minnesota, North

Dakota, South Dakota 8.04 7.89 8.03 6.03 6.83 8.01 8.65 8.92 8.30 6.59 8.39 8.02

Kansas, Nebraska 8.81 8.86 8.87 7.21 8.12 9.51 10.18 10.69 10.39 8.92 9.70 9.08

Missouri 9.98 9.98 10.11 8.20 9.50 11.59 13.25 14.03 13.07 11.08 12.00 10.67

Virginia 11.23 10.82 10.69 8.65 10.23 11.95 12.83 12.63 12.59 10.21 11.53 11.20

Delaware, District of

Columbia, Maryland 11.03 10.94 11.20 9.32 10.59 11.89 12.65 12.64 12.38 10.28 11.83 11.25

Georgia 11.58 12.31 12.86 9.19 11.42 12.74 13.40 13.30 12.88 11.08 13.22 12.39

North Carolina, South

Carolina 11.26 11.22 11.53 9.20 10.56 12.46 13.22 13.70 13.15 10.93 12.31 11.94

Florida 12.65 12.90 13.78 11.41 12.42 13.21 13.62 13.91 13.71 13.48 15.83 14.05

Alabama, Kentucky,

Mississippi 10.15 10.13 10.46 9.95 11.33 12.59 12.92 13.19 12.89 11.69 11.61 10.76

Tennessee 9.31 9.44 9.44 8.04 8.84 10.11 10.78 11.12 10.61 9.63 10.39 9.80

Arkansas, Louisiana,

Oklahoma 8.88 8.84 9.07 7.70 9.16 10.09 10.74 11.08 10.78 10.05 10.96 9.43

Texas 8.01 8.06 8.28 6.68 7.66 8.54 8.80 9.02 8.97 8.10 9.45 8.35

Colorado 7.09 7.18 7.43 5.78 6.41 8.12 8.17 8.67 8.02 6.42 7.66 7.28

Idaho, Montana, Utah,

Wyoming 7.84 7.87 8.01 6.82 7.13 7.71 8.38 8.76 8.18 7.22 8.13 7.97

Arizona 11.51 11.87 12.29 10.04 11.27 12.47 13.57 14.08 13.62 12.72 14.33 12.39

Nevada, New Mexico 7.69 7.88 8.08 7.10 8.13 9.46 9.20 9.56 9.24 8.15 8.50 7.77

California 9.94 9.84 9.48 7.45 7.77 8.17 8.22 8.10 7.89 7.95 9.75 9.77

Oregon, Washington 10.35 10.47 10.52 9.67 10.05 10.56 11.64 12.11 11.77 10.65 11.01 10.72

Alaska 7.82 7.89 7.95 7.20 7.55 7.81 8.41 8.25 7.63 7.25 7.87 8.12

Hawaii 45.38 46.34 46.42 39.47 40.05 40.49 41.44 42.49 42.37 42.15 49.08 48.09

West Virginia 10.26 10.30 10.42 9.02 9.99 12.08 13.44 13.28 12.20 9.95 10.91 10.59

United States 9.80 9.85 9.97 8.31 9.14 10.24 10.86 11.09 10.61 9.14 10.47 10.03

DOE applied the regional monthly energy price factors to the annual LPG data presented

in chapter 8 to develop residential energy prices for 201 2 ( Table 8C.3.22 ). Each geographical

area was matched with the a ppropriate Census Region.

8C

- 26Table 8C.3.22 Residen tial Monthly LPG Prices for 2012 Using Average Price Factors

(201 3 $/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New

Hampshire, Rhode Island,

Vermont 31.63 31.96 32.13 32.15 32.64 32.97 33.07 32.87 32.84 32.99 32.66 32.64

Massachusetts 33.96 34.31 34.49 34.51 35.04 35.39 35.51 35.29 35.26 35.42 35.06 35.04

New York 32.15 32.48 32.65 32.67 33.17 33.50 33.61 33.40 33.38 33.53 33.19 33.17

New Jersey 33.35 33.69 33.87 33.89 34.41 34.75 34.86 34.65 34.62 34.78 34.43 34.40

Pennsylvania 30.32 30.63 30.79 30.81 31.28 31.59 31.70 31.50 31.48 31.62 31.30 31.28

Illinois 22.60 22.59 22.32 21.97 21.70 20.94 20.31 20.25 20.72 21.34 22.31 23.29

Indiana, Ohio 26.41 26.40 26.08 25.67 25.36 24.47 23.74 23.67 24.22 24.93 26.07 27.22

Michigan 24.27 24.26 23.97 23.60 23.30 22.49 21.82 21.75 22.26 22.91 23.96 25.02

Wisconsin 22.40 22.40 22.13 21.78 21.51 20.76 20.14 20.08 20.54 21.15 22.12 23.09

Iowa, Minnesota, North

Dakota, South Dakota 23.27 23.26 22.98 22.62 22.34 21.56 20.92 20.85 21.34 21.97 22.97 23.99

Kansas, Nebraska 22.37 22.36 22.09 21.75 21.48 20.72 20.10 20.05 20.51 21.12 22.08 23.06

Missouri 23.01 23.00 22.72 22.37 22.09 21.32 20.68 20.62 21.10 21.72 22.71 23.72

Virginia 25.45 25.53 25.14 24.66 24.27 23.81 23.27 22.83 23.40 24.37 25.12 25.94

Delaware, District of

Columbia, Maryland 34.64 34.75 34.23 33.56 33.03 32.41 31.67 31.07 31.85 33.17 34.20 35.31

Georgia 27.67 27.75 27.33 26.80 26.38 25.89 25.29 24.82 25.43 26.49 27.31 28.20

North Carolina, South

Carolina 27.70 27.78 27.36 26.83 26.41 25.91 25.32 24.84 25.46 26.52 27.34 28.23

Florida 28.63 28.72 28.29 27.74 27.31 26.79 26.18 25.68 26.33 27.42 28.27 29.19

Alabama, Kentucky,

Mississippi 24.55 24.63 24.26 23.79 23.41 22.97 22.44 22.02 22.57 23.51 24.24 25.02

Tennessee 28.05 28.14 27.72 27.18 26.75 26.25 25.65 25.16 25.79 26.86 27.70 28.59

Arkansas, Louisiana,

Oklahoma 21.13 21.20 20.88 20.48 20.15 19.77 19.32 18.96 19.43 20.24 20.86 21.54

Texas 15.90 15.95 15.71 15.40 15.16 14.88 14.54 14.26 14.62 15.22 15.70 16.21

Colorado 25.58 25.63 25.19 24.62 24.02 23.34 22.50 22.21 23.09 24.47 25.26 26.24

Idaho, Montana, Utah,

Wyoming 24.54 24.59 24.17 23.62 23.05 22.39 21.58 21.31 22.15 23.48 24.23 25.18

Arizona 31.43 31.48 30.94 30.24 29.51 28.67 27.63 27.29 28.36 30.06 31.03 32.24

Nevada, New Mexico 26.03 26.07 25.63 25.04 24.44 23.74 22.89 22.60 23.49 24.90 25.70 26.70

California 29.93 29.99 29.48 28.81 28.11 27.31 26.32 25.99 27.02 28.63 29.56 30.71

Oregon, Washington 27.50 27.56 27.08 26.47 25.83 25.09 24.19 23.88 24.83 26.31 27.16 28.21

Alaska 29.89 29.95 29.43 28.76 28.07 27.27 26.28 25.95 26.98 28.59 29.51 30.66

Hawaii 40.16 40.24 39.55 38.65 37.72 36.64 35.32 34.87 36.25 38.42 39.65 41.20

West Virginia 32.94 33.04 32.55 31.92 31.41 30.82 30.11 29.55 30.28 31.54 32.52 33.57

United States 18.70 18.81 18.69 18.56 18.58 18.20 17.38 16.98 17.50 18.07 18.65 19.21

8C.4 HOUSEHOLD ENERGY PRI CE ADJUSTMENT FACTOR

RECS 2009 reports the total annual consumption and expenditure of each energy use

type. From this data DOE determined average energy prices per geographical area. To take into

account that household energy prices vary inside a geographical area, DOE developed an

8C

- 27adjustment factor based on the reported average energy price in RECS 2009 divided by the

average ene rgy price of the geographical region. This factor was then multiplied times the

monthly marginal energy prices (for natural gas and electricity) or the monthly price developed

above to come up with the household energy price.

8C.5 ENERGY PRICE TRENDS

8C.5.1 Residenti al Energy Price Trends

DOE us ed AEO 2014 Reference Case scenarios for the nine census divisions

. DOE

applied the projected energy price for each of the nine census divisions to each household in the

sample based on the household’s location.

To arrive at prices in future years, DOE multiplied the prices described in the preceding

section by the forecast of annual average price changes in EIA’s AEO 2014

. 5 Figure 8C.5.1

shows the national residential electricity price tren d. To estimate the trend after 20 40 , DOE

followed past guidelines provided to the Federal Energy Management Program (FEMP) by EIA

and used the average rate of change during 202 5

– 20 40 for electricity, natural gas, and LPG.

Figure 8C.5.1 Projected Residential National Electricity Price

Figure 8C.5.2 shows the residential national electricity price trends, disaggregated by the nine

census divisions.

8C

- 28Figure 8C.5.2 Projected Residential Di vision Electricity Prices

Figure 8C.5.3 shows the residential national natural gas price trends.

Figure 8C.5.3 Projected Residential National Natural Gas Price

Figure 8C.5.4 shows the residential national natural gas price trends, disaggregated by the nine

census divisions.

8C

- 29Figure 8C.5.4 Projected Residential Division Natural Gas Prices

Figure 8C.5.5 shows the residential national LPG price trends.

Figure 8C.5.5 Projected Residential National LPG Prices

Figure 8C.5.6 shows the residential national LPG price trends, disaggregated by the nine census

divisions.

8C

- 30Figure 8C.5.6 Projected Residential Division LPG Prices

8C

- 31REFERENCES

1. U.S. Department of Energy

- Energy Information Administration, Form EIA

- 826

Database Monthly Electric Utility Sales and Revenue Data, 2013.

< www.eia.gov/electricity/data/eia826/ >

2. U.S. Department of Energy

- Energy Information Administration, Natural Gas

Navigator . 2013. www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm

3. Energy Information Administration, 2012 State Energy Consumption, Price, and

Expenditure Estimates (SEDS) , 2013. Washington, DC. < www.eia.gov/state/seds/ >

4. U.S. Department of Energy

- Energy Information Administration, Short

- Term Energy

Outlook . 2009. http://eia.doe.gov/steo

5. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < w ww.eia.gov/forecasts/aeo/ >

8C

- 32APPENDIX 8D. DISTRIBUTIONS U SED FOR DISCOUNT R ATES

TABLE OF CONTENTS

8D.1 INTRODUCTION

................................ ................................ ................................ .. 8D

- 1

8D.2 DISTRIBUTIONS USED F OR RESIDENTIAL CONSU MER DISCOUNT

RATES

................................ ................................ ................................ .................... 8D

- 1

8D.2.1 Distribution of Rates for Debt Classes

................................ ................................ .... 8D

- 1

8D.2.2 Distribution of Rates for Equity Classes

................................ ................................ . 8D

- 5

8D.3 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT R ATES BY INCOME

GROUP

................................ ................................ ................................ ................... 8D

- 9

8D.4 DISTRIBUTIONS USED F OR COMMERCIAL DISCOUNT RATES

............. 8D

- 10

LIST OF TABLES

Table 8D.3.1 Distribution of Real Discou nt Rates by Income Group

............................ 8D

- 10

LIST OF FIGURES

Figure 8D.2.1 Distribution of Mortgage Interest Rates

................................ ..................... 8D

- 2

Figure 8D.2.2 Distribution of Home Equity Loan Interest Rates

................................ ...... 8D

- 2

Figure 8D.2.3 Distribution of Credit Card Interest Rates

................................ .................. 8D

- 3

Figure 8D.2.4 Distribution of Installment Loan Interest Rates

................................ .......... 8D

- 3

Figure 8D.2.5 Distribution of Ot her Residence Loan Interest Rates

................................ . 8D

- 4

Figure 8D.2.6 Distribution of Other Lines of Credit Loan Interest Rates

......................... 8D

- 4

Figure 8D.2.7 Distribution of Annual Rate of Return on CDs

................................ .......... 8D

- 5

Figure 8D.2.8 Distribution of Annual Rate of Return on Savings Bonds

.......................... 8D

- 6

Figure 8D.2.9 Distribution of Annual Rate of Return on Corporate AAA Bonds

............. 8D

- 6

Figure 8D.2.10 Distribution of Annual Rate of Savings Accounts

................................ ...... 8D

- 7

Figure 8D.2.11 Distribution of Annual Rate of Money Market Accounts

.......................... 8D

- 7

Figure 8D.2.12 Distribution of Annual Rate of Return on S&P 500

................................ ... 8D

- 8

Figure 8D.2.13 Distribution of Annual Rate of Return on Mutual Funds

........................... 8D

- 8

Figure 8D.3.1 Distribution of Real Discount Rates by Income Group

.............................. 8D

- 9

Figure 8D.4.1 Distribution of Commercial Discount Rates: Retail

................................ . 8D

- 11

Figure 8D.4.2 Distribution of Commercial Discount Rates: Property

............................. 8D

- 12

Figure 8D.4.3 Distribution of Commercial Discount Rates: Medical

.............................. 8D

- 12

Figure 8D.4.4 Distribution of Commercial Discount Rates: Industrial

........................... 8D

- 13

Figure 8D.4.5 Distribution of Commercial Discount Rates: Lodging

............................. 8D

- 13

Figure 8D.4.6 Distribution of Commercial Discount Rates: Food Service

..................... 8D

- 14

Figure 8D.4.7 Distribution of Commercial Discount Rates: Office

................................ 8D

- 14

Figure 8D.4.8 Distribution of Commercial Discount Rates: State and Local

Government

................................ ................................ ............................... 8D

- 15

Figure 8D.4.9 Distribution of Commercial Discount Rates: Federal Government

.......... 8D

- 15

Figure 8D.4.10 Distribution of Commercial Discount Rates: Other

................................ .. 8D

- 16

8D

- iAPPENDIX 8D. DISTRIBUTIONS USED F OR DISCOUNT R ATES

8D.1 I NTRODUCTION

The Department of Energy ( DOE) estimated discount rate distributions by consumer

type: residential and commercial consumers. This appendix describes the distributions used.

8D.2 DISTRIBUTIONS USED F OR RESIDENTIAL CONSU MER DISCOUNT

RATES

The Department of Energy ( DOE) derived consumer discount rates for the life

- cycle cost

(LCC) analysis using data on interest or return rates for various types of debt and equity to

calculate a real effective discount rate for each h ousehold in the Federal Reserve Board’s Survey

of 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 distrib ution of discount rates for each of six income

groups. This appendix describes the distributions used.

8D.2.1 Distribution of Rates for Debt Classes

Figure 8D.2.1 through Figure 8D.2.6 show the distribution of real interest rates for

different types of household debt. The data source for the interest rates for mortgages, hom e

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 i nterest rate is equal

to or below the inflation rate (resulting in a negative real interest rate), DOE set the real effective

interest rate to zero.

8D

- 1Figure 8D.2.1 Distribution of Mortgage Interest Rates

Figure 8D.2.2 Distribution of Home Equity Loan Interest Rates 0% 5% 10% 15% 20% 25% 30% 35%0.5%1.6%2.5%3.5%4.4%5.4%6.3%7.4%8.5%9.4%10.4%11.6%12.5%13.2%14.4%16.6%22.8%Probability

Real Interest Rate

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

8D

- 2Figure 8D.2.3 Distribution of Credit Card Interest Rates

Figure 8D.2.4 Distribution of Installment Loan Interest Rates 0% 1% 2% 3% 4% 5% 6% 7% 8% 9%0.0%2.5%4.5%6.6%8.6%10.5%12.5%14.7%16.4%18.4%20.4%22.7%24.7%26.8%28.5%Probability

Real Interest Rate

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

8D

- 3Figure 8D.2.5 Distribution of Other Residence Loan Interest Rates

Figure 8D.2.6 Distribution of Other Lines of Credit Loan Interest Rates 0% 2% 4% 6% 8% 10% 12% 14% 16% 18%0.2%1.5%2.5%3.5%4.5%5.5%6.5%7.5%8.6%9.4%10.4%11.5%12.5%13.1%14.7%15.7%16.6%Probability

Real Interest Rate

0% 2% 4% 6% 8% 10% 12%0.1%2.5%4.5%6.6%8.6%10.5%12.5%14.7%16.4%18.4%20.2%22.6%25.4%Probability

Real Interest Rate

8D

- 48D.2.2 Distribution of Rates for Equity Classes

Figure 8D.2.7 through Figure 8D.2.13 show the distribu tion of real interest rates for

different types of equity. Data for equity classes are not available from the Federal Reserve

Board’s SCF , so DOE derived data for these classes from national

- level historical data (1984

- 2013) . The interest rates associated with certificates of deposit (CDs), 2 savings bonds, 3 and AAA

corporate bonds 4 are from Federal Reserve Board time

- series data . DOE assumed rates on

checking accounts to be zero. Rates on savings and money market accounts are from Cost of

Savings Index data

. 5 The rates for stocks are the annual returns on the Standard and Poor’s

(S&P) 500

. 6 The mutual fund rates are a weighted average of the stock rates (two

- thirds weight)

and the bond rates (one

- third weight ) in each year . DOE adjusted the nominal rates to real rates

using the annual inflation rate in each year.

Figure 8D.2.7 Distribution of Annual Rate of Return on CDs -0.04 -0.02 0 0.02 0.04 0.06 0.08198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year CDs (Average of 12 and 30 month)

8D

- 5Figure 8D.2.8 Distribution of Annual Rate of Return on Savings Bonds

Figure 8D.2.9 Distribution of Annual Rate of Return on Corporate AAA Bonds 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 Bonds

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

8D

- 6Figure 8D.2.10 Distribution of Annual Rate of Savings Accounts

Figure 8D.2.11 Distribution of Annual Rate of Money Market Accounts -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year Savings Accounts

-1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year Money Market Accounts

8D

- 7Figure 8D.2.12 Distribution of Annual Rate of Return on S&P 500

Figure 8D.2.13 Distribution of Annual Rate of Return on Mutual Funds -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)

-30.0% -20.0% -10.0%

0.0% 10.0% 20.0% 30.0%198419861988199019921994199619982000200220042006200820102012Annual Rate of Return (%)

Year Mutual Funds

8D

- 88D.3 D ISTRIBUTION OF REAL EFFECTIVE DISCO UNT R ATES BY

INCOME GROUP

Figure 8D.3.1 and Tabl e 8D.3.1 present the distributions of real discount rates for each

income group.

Figure 8D.3.1 Distribution of Real Discount Rates by Income Group 0% 5% 10% 15% 20% 25%

0.0% 5.0% 10.0% 15.0% 20.0%Share of Individuals in Group (%)

Real Effective Discount Rate (%) Group 1

Group 2

Group 3

Group 4

Group 5

Group 6

8D

- 9Table 8D.3.1 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 0.6% 0.152 0.6% 0.104 0.6% 0.077 0.6% 0.056 0.6% 0.057

1

- 2 1.6% 0.110 1.6% 0.120 1.6% 0.105 1.6% 0.146 1.6% 0.142 1.6% 0.185

2

- 3 2.5% 0.087 2.5% 0.112 2.6% 0.131 2.5% 0.205 2.5% 0.219 2.5% 0.207

3

- 4 3.5% 0.117 3.5% 0.137 3.5% 0.164 3.5% 0.173 3.5% 0.200 3.5% 0.178

4

- 5 4.5% 0.097 4.5% 0.113 4.5% 0.136 4.5% 0.129 4.5% 0.153 4.5% 0.144

5

- 6 5.5% 0.083 5.5% 0.084 5.5% 0.100 5.5% 0.093 5.5% 0.098 5.5% 0.120

6

- 7 6.5% 0.058 6.5% 0.062 6.5% 0.075 6.5% 0.067 6.5% 0.063 6.4% 0.079

7

- 8 7.5% 0.036 7.5% 0.051 7.6% 0.054 7.4% 0.041 7.4% 0.029 7.3% 0.011

8

- 9 8.5% 0.036 8.4% 0.039 8.4% 0.034 8.5% 0.015 8.4% 0.012 8.5% 0.005

9

- 10 9.5% 0.017 9.5% 0.018 9.5% 0.017 9.5% 0.010 9.5% 0.008 9.6% 0.005

10

- 11 10.5% 0.014 10.5% 0.019 10.5% 0.013 10.5% 0.011 10.6% 0.004 10.7% 0.004

11

- 12 11.5% 0.010 11.5% 0.015 11.5% 0.014 11.5% 0.007 11.4% 0.004 11.7% 0.001

12

- 13 12.5% 0.011 12.5% 0.012 12.5% 0.009 12.4% 0.005 12.4% 0.002 12.4% 0.002

13

- 14 13.6% 0.012 13.5% 0.008 13.5% 0.009 13.5% 0.004 13.5% 0.002 13.3% 0.001

14

- 15 14.6% 0.016 14.6% 0.014 14.6% 0.009 14.5% 0.005 14.6% 0.003 14.2% 0.001

15

- 16 15.5% 0.011 15.5% 0.010 15.5% 0.006 15.6% 0.004 15.6% 0.002 15.3% 0.000

16

- 17 16.5% 0.013 16.5% 0.009 16.5% 0.004 16.5% 0.003 16.5% 0.001 0.0% 0.000

17

- 18 17.5% 0.009 17.6% 0.006 17.5% 0.005 17.5% 0.003 17.6% 0.001 17.7% 0.001

18

- 19 18.4% 0.005 18.5% 0.005 18.6% 0.003 18.4% 0.001 18.2% 0.000 0.0% 0.000

19

- 20 19.4% 0.006 19.4% 0.004 19.4% 0.002 19.7% 0.000 19.7% 0.000 19.4% 0.000

20

- 21 20.6% 0.004 20.4% 0.002 20.5% 0.001 20.3% 0.001 20.5% 0.000 20.3% 0.000

21

- 22 21.4% 0.003 21.4% 0.002 21.4% 0.001 21.5% 0.001 0.0% 0.000 21.4% 0.000

22

- 23 22.5% 0.002 22.4% 0.001 22.6% 0.001 22.9% 0.000 22.8% 0.000 22.3% 0.000

23

- 24 23.6% 0.001 23.4% 0.001 23.6% 0.001 0.0% 0.000 0.0% 0.000 24.0% 0.000

24

- 25 24.6% 0.001 24.5% 0.000 24.6% 0.000 24.1% 0.000 24.3% 0.000 0.0% 0.000

25

- 26 25.4% 0.001 25.4% 0.001 25.5% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

26

- 27 26.5% 0.001 26.5% 0.000 26.4% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

27

- 28 27.8% 0.000 27.6% 0.000 27.8% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

28

- 29 28.2% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

29

- 23 29.9% 0.000 29.3% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

>30 59.1% 0.001 142.7% 0.002 0.0% 0.000 53.3% 0.000 0.0% 0.000 0.0% 0.000

8D.4 DISTRIBUTIONS USED F OR COMMERCIAL DISCOU NT RATES

DOE derived commercial discount rates ( i.e. weighted average cost of capital) for the

life

- cycle cost (LCC) analysis using the capital asset pricing model and firm

- level data provided

by Damodaran Online. 7 State and local government discount rates were estimated using the rate

8D

- 10of return on 20

- year municipal bonds, as provided by the Federal Reserve Board. 3 Separate

distributions were constructed for each major industry. Figure 8D.4.1 through Figure 8D.4.10

show the probability distributions of commercial discount rates by industry.

Figure 8D.4.1 Distribution of Commercial Discount Rates: Retail

8D

- 11Figure 8D.4.2 Distribution of Commercial Discount Rates: Property

Figure 8D.4.3 Distribution of Commercial Discount Rates: Medical

8D

- 12Figure 8D.4.4 Distribution of Commercial Discount Rates: Industrial

Figure 8D.4.5 Distribution of Commercial Discount Rates: Lodging

8D

- 13Figure 8D.4.6 Distribution of Commercial Discount Rates: Food Service

Figure 8D.4.7 Distribution of Commercial Discount Rates: Office

8D

- 14Figure 8D.4.8 Distribution of Commercial Discount Rates: S tate and Local

Government

Figure 8D.4.9 Distribution of Commercial Discount Rates: Federal Go vernment

8D

- 15Figure 8D.4.10 Distribution of Commercial Discount Rates: Other

8D

- 16REFERENCES

1. The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998,

2001, 2004, 2007, 2010

. < www.federalreserve.gov/pubs/oss/oss2/scfindex.html >

2. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates,

Historical Data, Instrument: CDs (secondary market), Maturity: 6

- month, Frequency:

Annual, Description: Average rate on 6

- month negotiable certificates of deposit

(secondary market), quoted on a n investment basis, 2013.

< www.federalreserve.gov/releases/H15/data.htm >

3. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates,

Historical Data, Inst rument: State and local bonds, Maturity: 20

- year, Frequency:

Monthly, Description: Bond buyer go 20

- bond municipal bond index, 2013.

< www.federalreserve.gov/releases/H15/data.htm >

4. The Federal Reserve Board, 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,

2013. < www.federalreserve.gov/releases/H15/data.htm >

5. Mortgage

- X

- Mortgage Information Service, Cost of Savings Index (COSI) Index

History, 2013. < http://mortgage

- x.com/general/indexes/default.asp >

6. Damodaran Online Data Page, The Data Page: Historical Returns on Stocks, Bonds and

Bills

- United States, 2013. Damodaran. (Last accessed June, 2014.)

< http://pages.stern.nyu.edu/~adamodar/ >

7. Damodaran Online, Data Page: Costs of Capital by Industry Sector, 2012. Damodaran.

(Last accessed July, 2013.) < http://pages.ste rn.nyu.edu/~adamodar/ >

8D

- 17APPENDIX 8E. LIFE

- CYCLE COST ANALYSIS USING ALTERNATIVE EC ONOMIC

GROWTH SCENARIOS FOR HEARTH PRODUCTS

TABLE OF CO NTENTS

8E.1 INTRODUCTION

................................ ................................ ................................ ... 8E

- 1

8E.2 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

..................... 8E

- 1

8E.3 RESULTS

................................ ................................ ................................ ................ 8E

- 3

LIST OF TABLES

Table 8E.3.1 Average LCC and PBP Results by Efficiency Level for Hearth Product

Ignition Systems: High Economic Growth Scenario

................................ ... 8E

- 3

Table 8E.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for

Hearth Product Ignition Systems: High Economic Growth Scenario

.......... 8E

- 4

Table 8E.3.3 Average LCC and PBP Results by Efficiency Level for Hearth Prod uct

Ignition Systems: Low Economic Growth Scenario

................................ .... 8E

- 4

Table 8E.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for

Hearth Product Ignition Systems: Low Economic Growth Scenario

.......... 8E

- 4

Table 8E.3.5 Comparison of Average LCC Savings and Simple Payback Period

Results for Reference Case and High and Low Economic Growth

Scenarios

................................ ................................ ................................ ...... 8E

- 4

LIST OF FIGURES

Figure 8E.2.1 Electricity Price Forecasts for Reference Case and High and Low

Economic Growth Scena rios (National)

................................ ...................... 8E

- 2

Figure 8E.2.2 Natural Gas Price Forecasts for Reference Case and High and Low

Economic Growth Scenarios (Nat ional)

................................ ...................... 8E

- 2

Figure 8E.2.3 LPG Price Forecasts for Reference Case and High and Low Economic

Growth Scenarios (National)

................................ ................................ ....... 8E

- 3

8E

- iAPPENDIX 8E. L IFE

- C YCLE C OST A NALYSIS U SING A LTERNATIVE E CONOMIC

G ROWTH S CENARIOS FOR HEARTH PROD UCTS

8E.1 I NTRODUCTION

This appendix presents life

- cycle cost ( LCC ) results using energy price projections from

alternative economic growth scenarios. The scenarios are based on the High Economic Growth

case and the Low Economic Growth case from Energy Information Administration (EIA ) ’s

Annual Energy Outlook 201 4 ( AEO 2014 )

. 1

This appendix describes the High and Low Economic Growth scenarios in further detail.

See appendix 8 A for details about how to generate LCC results for High Economic Growth and

Low Economic Growth scenarios using the LCC spreadsheet.

8E.2 D ESCRIPTION OF HIGH AND LOW ECONOMI C SCENARIOS

To generate LCC results reported in chapter 8, DOE uses t he Reference case energy price

projection s from AEO 2014

. The reference case is a business

- as

- usual estimate, given known

market, demographic, and techn ological trends. For AEO 2014 , EIA explored the impacts of

alternative assumptions in other scenarios with different macroeconomic growth rates, world oil

prices, rates of technology progress, and policy changes.

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO 2014 uses

High and Low Economic Growth scenarios to project the possible impacts of alternative

economic growth assumptions on energy markets. The High Economic Growth scenario

incorporates population, labor force and pr oductivity growth rates that are higher than the

Reference scenario , while these values are lower for the Low Economic Growth scenario

. Economic output as measured by real GDP increases by 2. 4 percent per year from 201 2 through

2040, in the Reference case , 1.9 percent per year in the Low Economic Growth case, and 2. 8

percent per year in the High Economic Growth case

. 2

In general, e nergy prices are hig her in the High Economic Growth scenario and lower in

the Low Economic Growth scenario than they are in the Reference Case . The energy price

forecasts affect the operating cost savings at different efficiency levels. Figure 8E.2.1 t hrough

Figure 8E.2.3 show the national residential energy price trends for the Reference, High

E conomic Growth, and Low Economic G rowth scenarios

. AEO 2014 projections stop in 2040.

T o estimate energy prices after 2040 in the high and low scenarios, DOE used the growth rate

between 2030 and 2040 , which a re re presented with a dashed line in the charts

.

Because AEO 2014 provides the pri ce trends by census division, e ach sampled household

is matched to the appropriate census division price trend. See appendix 8 C for details about how

energy pr ice trends by census division are applied in the LCC analysi s.

8E

- 1Figure 8E.2.1 Electricity Price Forecasts for Reference Case and High and Low

Economic Growth Scenarios (National)

Figure 8E.2.2 Natural Gas Price Forecasts for Reference Case and High and Low

Economic Growth Scenarios (National) 10.00 12.00 14.00 16.00 18.00 20.00 22.00

2010 2015 2020 2025 2030 2035 2040 2045 2050 Reference Case

High Growth

Low Growth

32.00 34.00 36.00 38.00 40.00 42.00

2010 2015 2020 2025 2030 2035 2040 2045 2050 Reference Case

High Growth

Low Growth

8E

- 2Figure 8E.2.3 LPG Price Forecasts for Reference Case and High and Low Economic

Growth Scenarios (National)

8E.3 R ESULTS

Table 8E.3.1 and Table 8E.3.2 summarize the LCC and PBP results for the High

Economic Growth scenario by effici ency level (EL) for hearth products

. Table 8E.3.3 and Table

8E.3.4 summarize the LCC and PBP results for the Low Economic Growth scenario by EL for

hearth products

. Table 8E.3.5 compares average LCC savin gs and simple payback for these

scenarios to the Reference case

.

Table 8E.3.1 Average LCC and PBP Results by Efficiency Level for Hearth Product

Ignition Systems : High Economic Growth Scenario

TSL Efficiency

Level Average Costs 2013$

Simple

Payback

years Average

Lifetime

years Installed

Cost First

Year’s

Operating

Cost Lifetime

Operating

Cost LCC

-- 0 $166 $52 $625 $792

-- 15.0

1 1 $268 $15 $179 $447 2.8 15.0

Note: The results for each TSL are calculated assuming that all consumers use products with that efficiency level.

The PBP is measured relative to the baseline product. 22.00 23.00 24.00 25.00 26.00 27.00 28.00 29.00 30.00 31.00

2010 2015 2020 2025 2030 2035 2040 2045 2050 Reference Case

High Growth

Low Growth

8E

- 3Table 8E.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for Hearth

Product Ignition Systems : High Economic Growth Scenario

TSL Efficiency Level Life

- Cycle Cost Savings

% of Consumers that

Experience Net Cost Average Savings* 2013$

1 1 22% $175

* The calculation includes buildings with zero LCC savings (no impact).

Table 8E.3.3 Average LCC and PBP Results by Efficiency Level for Hearth Product

Ignition Systems : Low Economic Growth Scenario

TSL Efficiency

Level Average Costs 2013$

Simple

Payback

years Average

Lifetime

years Installed

Cost First

Year’s

Operating

Cost Lifetime

Operating

Cost LCC

-- 0 $16 6 $ 49 $ 584 $7 50

-- 15.0

1 1 $268 $1 4 $17 0 $4 38 2. 9 15.0

Note: The results for each TSL are calculated assuming that all consumers use products with that efficiency level.

The PBP is measured relative to the baseline product.

Table 8E.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for Hearth

Product Ignition Systems : Low Economic Growth Scenario

TSL Efficiency Level Life

- Cycle Cost Savings

% of Consumers that

Experience Net Cost Average Savings* 2013$

1 1 2 3 % $1 57

* The calculation includes buildings with zero LCC savings (no impact).

Table 8E.3.5 Comparison of Average LCC Savings and Simple Payback Period Results

for Reference Case and High and Low Economic Growth Scenarios

EL Efficiency Level Average LCC Savings Simple Payback Period

2013$ Years

High

Growth Low

Growth Reference

Case High

Growth Low

Growth Reference

Case

1 Intermittent Pilot $1 75 $ 1 57 $ 165 2. 8 2. 9 2 .9

8E

- 4REFERENCES

1. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

2. Energy Information Administration, Macroeconomic Activity Module for Annual Energy

Outlook 2014 , 2014. Washington, DC.

< www.eia.gov/forecasts /aeo/assumptions/pdf/macroeconomic.pdf >

8E

- 5APPENDIX 9A. R ELATIVE P RICE E LASTICITY OF D EMAND FOR A PPLIANCE S

TABLE OF CO NTENTS

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

- 2

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS,

CLOTHES WASHERS, AND DISHWASHERS

................................ .................. 9A

- 2

9A.3.1 Physical Household/Appliance Variables

................................ ............................... 9A

- 3

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

- 11

9A.5 LONG RUN IMPACTS

................................ ................................ ........................ 9A

- 12

9A.6 SUMMARY

................................ ................................ ................................ .......... 9A

- 13

9A.7 DATA USED IN THE ANA LYSIS

................................ ................................ ..... 9A

- 14

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

- 10

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate

..................... 9A

- 11

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount ra te

.................... 9A

- 12

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a

Purchase Price Change

................................ ................................ .............. 9A

- 12

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a

Purchase Price Change

................................ ................................ .............. 9A

- 13

9A

- iLIST OF FIGURE

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements

............ 9A

- 4

Figure 9A.4.1 Residual Shipments and Appliance Price

................................ ................... 9A

- 6

9A

- iiAPPENDIX 9A. R ELATIVE P RICE E LASTICITY OF D EMAND FOR A PPLIANCE S

9A.1 INTRODUCTION

This appendix summarizes DOE’s study of the price elasticity of demand for home

appliances, including refrigerators, clothes washers and dishwashers. DOE chose t his particular

set of appliances because of the availability of data to determine a price elast icity. Section 9A.2

review s the existing economics literature describing the impact of economic variables on the sa le

of durable goods. Section 9A.3 describes the market for home appliances and the changes that

have occurred over the past 20 years . In section 9A.4 , DOE summarizes the results of its

regression analysis and present s estimates of the price elasticity of demand for the three

appliances. In section 9A.5 , DOE presents development of an ‘effective’ purchase price

elasticity. DOE’s interpretation of its re sults is presented in section 9A.6

. Finally, section 9A.7

describes the data used in DOE’s analysis.

9A.2 LITERATURE REVIEW

R elatively few studies measur e the impact of price, income and efficiency on the sale of

household appliances. T his section briefly review s the literature that describes the likely

importance of these variables on the purchase of household appliances

.

9A.2.1 Price

DOE reviewed many studies that sought to measure the impact of price on sales in a

dynamic market. One study of the automobile market prior to 1970 finds the price elasticity of

demand to decline over time. The author explains this as the result of buyers delaying purchases

after a price incre ase but eventua lly making the purchase ( Table 9A.2.1 ). 1 A contrasting study of

household white goods also prior to 1970, finds the elasticity of demand to increase over time as

more price

- conscious buyers enter the market. 2 A n analysis of refrigerator market survey data

finds that consumer purchase probability decreases with survey asking price. 3 Estimates of the

price elasticity of demand for different brands of the same product tend to vary. A review of 41

studies of the impact of price on market share found the average price elasticity to be

- 1.75. 4 The

average estimate of price elasticity of demand reported in these studies is

- 0 .33 in the appliance

market and

- 0 .47 in the combined automobile and appliance markets.

9A.2.2 Income

Higher income households are more li kely to own household appliance

. 5 The impact of

income on appliance shipments is explored in two econometric studies of the automobile and

appliance markets. 1

, 2 The average income elasticity of demand is 0 .50 in the appliance study

cited in the literature review, much larger in the automobile study ( Table 9A.2.1 ).

9A

- 19A.2.3 Appliance Efficiency and Discount Rates

Many studies estimate the impact of appliance efficiency on consumer s’ choice of

appliance. Typically, this impact is summarized by the implicit discount rate ; that is , the rate

consumers use to compare future savings in appliance operating costs against a higher initial

purchase price of a n appliance. One early and much cited study co ncludes that consumers use a

20 percent implicit discount rate when purchasin g room air cond itioners ( Table 9A.2.1 ). 6 A

survey of several studies of different appliances suggests that the consumer implicit discount rate

has a broad range and averages about 37 percent

. 7

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile

and Appliance Sales

Durable Good Price

Elasticity Income

Elasticity Brand

Price

Elasticity Implicit

Discount

Rate Model Data

Years Time

Period

Automobiles 1

- 1.07 3.08

- - Linear Regression, stock

adjustment - Short run

Automobiles 1

- 0.36 1.02

- - Linear Regression, stock

adjustment - Long run

Clothes Dryers 2

- 0.14 0.26

- - Cobb

- Douglas, diffusion 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

Room Air

Conditioners 6 - - - 20% Qualitative choice, survey

data - -

Household

Appliances 7 - - - 37% 1 0 Assorted

- -

Sources: 1 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997;

4 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985.

Notes: 8 Logit probability results are not directly comparable to other elasticity estimates in this table.

9 Average brand price elasticity across 41 studies.

10 Averaged across several household appliance studies referenced in this work.

9A.3 VARIABLES DESCRIBING THE MARKE T FOR REFRIGERATORS,

CLOTHES WASHERS, AND DISHWASHERS

In this section DOE evaluate s variables that appear to account for refrigerator, clothes

washer and dishwasher shipments, including physical household / appliance variables and

econom ic variables.

9A

- 29A.3.1 Physical Household/Appliance Variables

Several variables influence the sale of refrigerators, clothes washers and dishwashers.

The most important for explaining appliance sales trends are the annual number of new

households formed (housing s tarts) and the number of appliances reaching the end of their

operating life (replacements). Housing starts influence sales because new homes are often

provided with, or soon receive, new appliances, including dishwashers and refrigerators.

Replacements ar e correlated with sales because new appliances are typically purchased when old

ones wear out. In principle, if households maintain a fixed number of appliances, shipments

should equal housing starts plus appliance replacements.

9A.3.2 Economic variables

Appliance price, appliance operating cost and household income are important economic

variables affecting shipments. Low prices and costs encourage household appliance purchases

and a rise in income increases householder ability to purchase appliances. In principle, changes

in economic variables should explain changes in the number of appliances per household.

During a 1980

– 2002 study p eriod, annual shipments grew 69 percent for clothes washers,

81 percent for refrigerators a nd 105 percent for dishwashe rs ( Table 9A.3.1 ). This rising

shipments trend is explained in part by housing starts, which increased six percent and by

appliance repl acements, which rose between 49 percent and 90 percent, depending on the

appliance, over the period ( Table 9A.3.1 ). a For mature markets such as these, replacements

exceed appliance sales asso ciated with new housing construction.

Table 9A.3.1 Physical Household/Appliance Variables

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.

S hipments increased somewhat more rapidly than housing starts and replacements. This

is shown by comparing the beginning and end points of lines that represent “starts plus

replacements” (uppermost solid line in Figure 9A.3.1 ) an d “shipment s” (diamond linked line in

Figure 9A.3.1 ). In 1980

, the “shipment” line begins below the “starts plus replacements” line. In

2002, the “shipments” line ends above the “starts plus replacements” line. This more rapid

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 9

- A. 3 . The dishwasher lifetime used in this analysis does not match the dishwasher used in the primary

analysis.

9A

- 3increase in shipments, compared to housing starts plus replacements, suggests that the appliance

per household ratio increased over the study period.

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and

Replacements

Economic variables, including price, cost and income, may explain this increase in

appliances per household. Over the period , appliance prices decreased 40 percent to 50 percent

, operating costs fell between 33 percent and 72 perc ent, and median household income rose 16

percent ( Table 9A.3.2 ).

Table 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 Income: Mean Household income. Source: U.S. Census.

9A.4 REGRESSION ANALYSIS OF VARIABLE S AFFECTING APPLIANC E

SHIPMENTS

Few data are available to estimat e the impact of economic variables on the demand for

appliances. Industry operating cost data is incomplete

— appliance energy use data are available

for only 12 years of t he 1980

- 2002 study period. Industry price data are also incomplete

— available for only 8 years of the study period for each of the appliances.

The lack of data suggests that regression analysis can at best evaluate broad data trends,

utilizing relatively few explanatory variables. This section begin s by describing broad trends 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 2000Millions

9A

- 4apparent in the economic and physical household data sets and then specifies a simple regression

model to measure these trends, making assumptions to minimize the number of explanat ory

variables. Finally, results of the regression analysis are presented along with an estimate of the

price elasticity of demand for appliances. In section 9A.4.5

, DOE present s the results of

regression analysis performed with more complex models, which are used to test assumptions

underlying the simple model. These results support the specification of the simple model and the

price elasticity of appliance demand estimated with that model.

9A.4.1 Broad Trends

In this section

, DOE review s trends in the physical household and economic data sets and

posit s a simple approach for estimating the price elasticity o f appliance demand. As noted above,

the physical household variables ( housing starts and appliance replacements) explain most of the

variability in appliance shipments during the study period (1980

- 2002)

. b DOE assume s the rest

of the variability in shipmen ts ( referred to as “ residual shipments ” ) is explained by economic

variables . Below, DOE present s a tabular method for measuring price elasticities.

To illustrate this tabular approach, DOE define s two new variables

— residual shipments

and total price. Residual shipments are defined as the difference between shipments and physical

household demand (starts plus replacements). Total price , represented by the following equation,

is defined as appliance price plus the present value of lifetime appliance oper ating cost : c



### PVOC PP TP + =

Eq. 9A

. 1

W here:

TP = t otal price,

PP = a ppliance purchase price, and

PVOC = p resent 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

, a nd 23 percent for dishwashers. At

the sam e time, total prices declined 47 percent, 45 percent and 48 percent for refrigerators,

clothes washers

, and dishwas hers, respectively. Assuming that total price explains the entire

change in per household appliance usage, a rough estimate is calculated of the total price

elasticity of demand equal to

- 0 .48 for refrigerators,

- 0 .32 for clothes washers a nd

- 0.37 for

dish washers ( Table 9A.4.1 ).

b A log regression of the form: Shipments = a + b • Housing Starts + c • Retire ments, indicates that these two

variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer

shipments, and 97 percent of the variation in dishwasher shipments.

c Present value operating cost is cal culated 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

- 5Table 9A.4.1 Estimate of Total Price Elasticity of Demand

Residual Shipments (millions) Total Price (199 9 $)

Elasticity Appliance 1980 2002 Difference Change 1980 2002 Change

Refrigerators

- 0.5 1.6 2.1 30% 1541 820

- 61%

- 0.48

Clothes Washers

- 1.0 0.2 1.1 19% 1042 567

- 59%

- 0.32

Dishwashers

- 1.0

- 0.01 1.0 23% 896 464

- 64%

- 0.37

The negative correlation between total price and residual shipments suggested by these

negative price elasticities is illustrated in a graph of residual shipments on the y

- axis and to tal

price on the x

- axis ( Figure 9A.4.1 ).

Yellow points are observed price data; red points are interpolated price data. Figure 9A.4.1 Residual Shipments and Appliance Price

Household income rose during the study period, making it easier for households to

purchase appliances. Assuming that a rise in income has a similar impact on shipments as a

decline in price, the impact of income is incorporated by defining a third variable, termed

relative price, which is calculated as total price divided by household income and represented by

the following equation. d

Income TP

RP

=

Eq. 9A

. 2

W here:

R P = r elative price,

T

P = t otal price, and

Income = h ousehold income.

d R ecall that the income elasticity of demand cited in the literature review is 0.50 and the price elasticity of demand

cited in the review averages

- 0.35. This suggests that combining the effects of income and price will yield an

elasticity less negative tha n 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 Shipments

9A

- 6The percent decline in relative price for the three appliances divided by the percent

decline in residual shipments suggests a rough estimate of relative price elasticity equal to

- 0 .40

for refrigerators,

- 0 .26 for clothes washers a nd

- 0.30 for dishwashers ( Tabl e 9A.4.2 ).

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand

Residual Shipments (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 price data suggest it is appropriate to us e a simple regression model to

estimate the impact of economic variables on shipments, using few explanatory variables. The

following equation

, chosen for this analysis

, includes one physical household variable ( housing

starts plus replacements) and one rel ative price variable (the sum of purchase price plus

ope rating cost, divided by income).



## [ ] Rplc Starts c RP b a Ship + × + × + =

Eq. 9A

. 3

W here:

Ship = q uantity of appliance sold,

R P = r elative price,

Starts = n umber of new homes, and

Rplc = n umber of appliances at the end of their operating life.

The natural logs are taken of all variables so that the estimated coefficients for each

variable in the model may be interpreted as the percent change in shipments associated with the

percent change in the variable. Thus, the coefficient b in this model is interpreted as the relative

price elasticity of demand for the three appliances.

DOE used the following combined regression equation to estimate an average price

elasticity of demand across the three appliances, using pool ed data in a single regression. A

combined regression specification is justified, given the limited data availab le and the similarity

in price and shipment behavior across appliances ( see Figure 9A.4.1 ). Thus, the model

represented by the combined regression equation is considered the basic model in DOE’s

analysis of appliance shipments.



## [ ] DW e CW d Rplc Starts c RP b a Ship × + × + + × + × + =

Eq. 9A

. 4

W here:

CW = q uantity of clothes washers sold, and

9A

- 7DW = quantit y of dishwashers sold.

9A.4.3 Discussion of Model

The most important assumption used to specify this model is that changes in economic

variables over the study period

— income, price

, and operating cost

— are responsible for all

observed growth in residual appliance shipments. In other words, DOE assume s no impact from

other possible fact ors , such as changing consumer preferences or increases in the quality of

appliances. This assumption seems unlikely

, but without additional data, the impact of this

assumption on the price elasticity of demand cannot be measured. DOE effectively assume s t hat

changes in consumer preferences and appliance characteristics, while affecting which models are

purchased, have relatively little impact on the total number of appliances purchased in a year.

Three additional assumptions used to specify this model de serve comment. The relative

price variable is specified in the model, assuming that (1) the correct implicit discount rate is

used to combine appliance price and operating cost and that (2) rising income has the same

impact on shipments as falling total pr ice. The “ starts + replacements ” variable is specified,

assuming (3) that starts and replacements have similar impacts on shipments.

To investigate the first assumption about discount rates, DOE calculated “present v alue

operating cost” using a 20 percent implicit discount rate and performed a second regression

analysis based on the models described in Eq. 9A

. 1 and Eq. 9A

. 2

. The results of this analysis,

presented in section 9A.4.5 , indicate that the elasticity of relative price is fairly insensitive to

changes in the discount rate.

To investigate the second and third assumptions, DOE specified a regression model

separating income from total price and replacements from starts, th ereby adding two additional

explanatory variables to the basic mo del as shown in the following equation:

DW g CW f Rplc e Start d Incone c TP b a Ship × + × + × + × + × + × + =

Eq. 9A

. 5

The results of the regression analysis of this model are presented in section 9A.4.5

. These

results suggest that the elasticity of total price (coefficient b ) is relatively insensitive to changes

i n the treatment of income and “starts + r eplacements ” in the model.

9A.4.4 Analysis Results

The following sections describe results of analyses using both the individual and

combined models for appliances and the effects of a lower consumer discount rate and

disaggregated variables.

9A.4.4.1 Individual Appliance Model

The individual appliance regression eq uations are specified in Eq. 9A

. 6

.

9A

- 8

## [ ] Rplc Starts c RP b a Ship + × + × + =

Eq. 9A

. 6

In regression analysis of this model, the elastici ty of relative price ( b ) is estimated to be

- 0 .4 0 for refrigerators,

- 0 .31 for clothes washers and

- 0 .32 for dishwashers ( Table 9A.4.3 ),

averaging

- 0 .35. These elasticities are similar to those reported in the literatur e survey for

appliances ( Table 9A.2.1 ). They are remarkably similar to the price elasticity calculated using a

tabular a pproach ( Table 9A.4.2 ).

The estimated coefficient assoc iated with the “s tarts + r eplacements ” variable is close to

one. A coefficient equal to one for this variable would imply that, holding economic variables

constant

, shipments increase in direct proportion to an increase in “s tarts + r eplacements. ” The

high R

- squared values (above 95) and t

- statistics (above 5) in the results provide a measure of

confidence in this analysis, despite the very small data set.

Table 9A.4.3 Individual Appliance Model Results

Refrigerator Clothes Washer Dishwasher

Variable Coefficient t

- stat Coefficient t

- stat Coefficient t

- stat

Intercept

- 1.51

- 7.26

- 1.47

- 8.23

- 2.08

- 16.78

Relative Price

- 0.40

- 6.60

- 0.31

- 5.69

- 0.32

- 7.03

Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46

R 2 0.954 0.954 0.975

Observations 23 23 23

9A.4.4.2 Combined Appliance Model

The combined appliance regression equation is specified in Eq. 9A

. 7

.



## [ ] DW e CW d Rplc Starts c RP b a Ship × + × + + × + × + =

Eq. 9A

. 7

This regression analysis indicates that the model fits the existing shipments data well

(high R

- squared) and that the variables included in the model are st atistically significant ( Table

9A.4.4 ). Estimated with this model, t he elasticity of relative price is

- 0 .34, close to the average

value estimated in the individual appliance models (

- 0 .35). It is also similar to elasticity

estimates reported in the literature survey and calculated using the tabular approach in Table

9A.4.2

.

9A

- 9Table 9A.4.4 Combined Appliance Model R esult

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

9A.4.5 Additional Regression Specifications and Results

As described in section 9A.4.3 , DOE used three assumptions to specify its appliance

models. The first, made to aggregate appliance price and operating cost , is that the implicit price

variable in the basic regressio n model is specified using a 37 percent implicit discount rate. The

second states that the implicit price variable is defined assuming that rising income has the same

impact on shipments as falling total p rice. The third states that the “s tarts + r eplacements ”

variable is defined assuming that housing starts have a similar impact on shipments as appliance

replacements.

9A.4.5.1 Lower Consumer Discount Rate

To investigate the first assumption about discount rates, DOE calculated “present value

operating cost” using a 20 percent implicit discount rate and performed a second regression

analysis based on the models described in Eq. 9A

. 3 and Eq. 9A

. 4

. The estimated coefficient

associated with the relative price variable in these regressions is almost identical to the

coefficients estimated for the same variable based on a 37 percent implicit discount rate. The

elasticity of relative price calculated using a 20 percent discount rate is

- 0 .33 in the c ombined

regression and averages

- 0 .35 f or the three appliances ( Table 9A. 4.5 ). The elasticity o f price

calculated using a 37 percent discount rate is

- 0 .34 in the combined regression and averages

- 0.35 for the three appliances. DOE conclude s from this analysis t hat the elasticity of relative

price is fairly insensitive to changes in the discount rate.

9A

- 10Table 9A.4.5 Combined and Individual Results, 20 percent discount rate

9A.4.5.2 Disaggregated Variables

To investigate the second and third assumptions, DOE constructed a regression model

that separat es income from total price and replacements from starts, thus adding two additional

explanatory variables to the basic model ( as shown earlier as Eq. 9A

. 5 and shown below ).

DW g CW f Rplc e Start d Income c TP b a Ship × + × + × + × + × + × + =

Eq. 9A

. 8

The estimated coefficient associated with the total price variable in these regressions is

almost identical to the coefficients estimated for the relative price variable reported above. The

elasticity of total price in the above equation is

- 0 .36 in the combined appliance regression and

averages

- 0 .35 f or the three appliances ( Table 9A.4.6 ). The elasticity of relative price based on

the model described in Eq. 9A

. 4 is

- 0 .34 in the combined regression ( Table 9A.4.4 ) and averages

- 0 .35 across t he individual appliances ( Table 9A.4.3 ). DOE conclude s that the price elasticity

calculated in this analysis is relatively insensitive to the specifi cation of household income and

“s tarts + r eplacements ” variables in the model. 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 23

9A

- 11Table 9A.4.6 Disaggregated Regression Results, 37 percent discount ra te

9A.5 LONG RUN IMPACTS

As noted above in Table 9A.2.1 , the literature review provides price elasticities over short

and long time periods, also referred to as shor t run and long run price elasticities. As noted in the

first two rows of Table 9A.2.1 , one source ( i.e. , Hymans) shows that the price elasticity of

demand is significantly different over the short run and long run for automobiles. 1 Because

DOE’s forecasts of shipments and national impacts due to standards is over a 30

- year time

period, consideration must be given to how the relative price elasticity is affected once a new

standard takes effect.

DOE considers the relative price elasticities determined above in section 9A.4 to be short

run elasticities . DOE was unable to identify sources specific to household durable goods, such as

appliances, to indicate how short run and long run price elasticities differ. Therefore, to estimate

how the relative price elasticity changes over time, DOE relied on the Hymans study pertaini ng

to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the automobile price

elasticity of demand changes in the years following a purchase price chang e. With increasing

years after the price change, the price elasticity becomes more inelastic until it reaches a terminal

value around the tenth year after the price change.

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Price Elasticity of Demand

- 1.20

- 0.93

- 0.75

- 0.55

- 0.42

- 0.40

Relative Change in

Elasticity to 1 st year 1.00 0.78 0.63 0.46 0.35 0.33

Source: Hymans, 1971. 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 23

9A

- 12Based on the relative change in the automobile price elasticity of demand shown in Table

9A.5.1 , DOE developed a time series of relative price elasticities for hom e appliances. Table

9A.5.2 presents the time series.

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Relative Change in

Elasticity to 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 describes the results of a literature search, tabular analysis

, and regression

analys e s of the impact of price and other variables on appliance shipments. In the literature, DOE

f ound only a few studies of appliance markets that are relevant to this analysis and no studies

after 1980 using time series price and shipme nts data. The information that can be summarized

from the literature suggests that the demand for appliances is price inelastic. Other information in

the literature suggests that appliances are a normal good, such that rising incomes increase the

demand for appliances. Finally, the literature suggests that consumers use relatively high implicit

discount rates, when comparing appliance prices and appliance operating costs.

There are too few price and operating cost data available to perform complex anal ysis of

dynamic changes in the appliance market. In this analysis, DOE use d data available for

refrigerators, clothes washers

, and dishwashers to evaluate broad market trends and perform

simple regression analysis.

These data indicate an increase in app liance shipments and a decline in appliance price

and operating cost over the study period 1980

- 2002

. Household income has also risen during this

time. To simplify the analysis, DOE combined the available economic information into one

variable, termed rela tive 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 price elasticity of demand for the three appliances is inela stic. Our regression analysis of

these same variables suggests that the relative price elasticity of demand is

- 0.34

. The price

elasticity is consistent with estimates in the literature. Nevertheless, DOE stress es that the

measure is based on a small data set, using very simple statistical analysis. More important, the

measure is based on an assumption that economic variables, including price, income and

operating costs, explain most of the trend in appliances per household in the United States since

1980. Changes in appliance quality and consumer preferences may have occurred during this

period, but they are not accounted for in this analysis.

9A

- 139A.7 DATA USED IN THE ANA LYSIS

• Appliance Shipments are defined as the annual number of units shipped in millions. These

data were collected from the Association of Home Appliance Manufacturers ( AHAM ) 8

, 9 and

Appliance 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

– 198 8.

• Appliance Price is defined as the shipments weighted retail sales price of the unit in 1999

dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998 , and 2002 were collected

from AHAM Fact B ook s. 11 Price values for other years were interpolated from these eight

years of data.

• Housing Starts data were collected from the U.S. Census construction statistics (C25

reports) as annual values for each year, 1980

– 2002. 12

• Repl acements , 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 refrigerat ors, 13 14 years

for clothes washers, 14 12 years for dishwashers 15 ), replacements are calculated differentl y for

each appliance type. Replacements are estimated as the average of shipments 14

– 24 years

previous for refrigerators, 9

– 19 years prev ious for clothes washers, and 7

– 17 years previous

for dishwashers. Historical shipments data were collected from AHAM and Appliance

Magazine.

• Annual Electricity Consumption (UEC) is defined as the energy consumption of the unit in

kilowatt

- hours. Electricity consumption depend s on appliance capacity and efficiency. These

data were provided by AHAM for 1980, 1990

– 1997 and 1999

– 2002. 9 Data were interpolat ed

in the years for which data were not available.

• Operating Cost is the present value of the electricity consumption of an appliance over its

expected lifespan. The lifespans of refrigerators, clothes washers and dishwashers are

assumed to be 19, 14, and 12 years resp ectively. Discount rates of 20 percent 6 and 37

percent 16 were used, producing similar estimates of price elasticity. A study by Hausman

re commend ed 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 impli cit discount rates averaging 35 percent for

appliances.

• Income : Median annual household income in 2003 dollars. Th ese data w ere collected for

each year, 1980

– 2002, from Table H

- 6 of the U.S. Census

. 17

9A

- 14REFERENCES

1. Hymans, S., Consumer Durable Spending: Explanation and Prediction

. Brookings Papers

on Economic Activity , 1971. 1971(1): pp. 234

- 23 9

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. D. Revelt and K. Train, Mixed Logit with Repeated Choices: Household's Choice of

Appliance Efficien cy Level

. Review of Economics and Statistics , 1997

4. Tellis, G., The Price Elasticity of Selective Demand: A Meta

- Analysis of Econometric

Models of Sales

. Journal of Marketing Research , 1988

5. U.S. Department of Energy

- Energy Information Agency, The Effect of Income on

Appliances in U.S. Households , 2002. Washington, DC.

< www.eia.doe.gov/emeu/recs/appliances/appliances.html >

6. Hausman, J., Individual discount rates and the purchase and utilization of energy using

durables

. The Bell Journal of Econom ics , 1979

7. Train, K., Discount Rates in Consumers' Energy

- Related Decisions

. Energy: The

International Journal , 1985. 10(12)

8. Association of Home Appliance Manufacturers, AHAM 2000 Fact Book . 2000.

Washington, DC.

9. Association of Home Appliance Manufacturers, AHAM 2003 Fact Book . 2003.

Washington, DC.

10. Statistical Review

. Appliance Magazine , April, 1990

11. Association of Home Appliance Manufacturers, AHAM Fact Books , 1987, 1988, 1993,

1995, 2000, and 2003. W ashington, DC.

12. U.S. Census Bureau, Current Construction Reports, C25

– New One

- Family Houses

Sold . Washington, DC. < www.census.gov/construction/nrc/pdf/startsan.pdf >

13. Duemling, R., Energy and Resources Group, University of California. Berkeley, 1999.

14. U.S. Department of Energy

- Office of Building Research and Standards, Final Rule

Technical Support Document (TSD): Energy Efficiency Standards for Consumer

Products: Clothes Washers , 2000. U.S. Department of Energy. Washington, DC. Report

No. LB NL

- 47462.

< www.eren.doe.gov/buildings/codes_standards/reports/cwtsd/index.html >

9A

- 1515. U.S. Department of Energy

- Office of Codes and Standards, Technical Support

Document: Energy Conservation Standards for Consumer Products: Dishwashers,

Clothes Washers, and Clothes Dryers including: Environmental Impacts, Regulatory

Impact Analysis, December, 1990. Washington, DC. Report No. DOE/CE

- 0299P.

16. K.E. Train and T. Atherton, Rebates, loans, and customers’ choice of appliance efficiency

level: combining stated

- and revealed

- preference data

. The Energy Journal , 1995

17. U.S. Census Bureau, Income, Historical Income Tables

– Households, Table H

- 6

. Washington, DC. < www.census.gov/hhes/www/income/data/historical/household/ >

9A

- 16APPENDIX 10A. U SER INSTRUCTIONS FOR NATIONAL IMPACT ANAL YSIS

SPREADSHEET MODEL

TABLE OF CO NTENTS

10A.1 USER INSTRUCTIONS

................................ ................................ ...................... 10A

- 1

10A.2 STARTUP

................................ ................................ ................................ ............. 10A

- 1

10A.3 DESCRIPTION OF NATIO NAL IMPACT ANALYSIS WORKSHEETS

........ 10A

- 1

10A.4 BASIC INSTRUCTIONS F OR OPERATI NG THE NATIONAL IMPA CT

ANALYSIS SPREADSHEET S

................................ ................................ ............ 10A

- 2

LIST OF FIGURES

Figure 10A.4.1 The default user input parameters

................................ ............................. 10A

- 3

Figure 10A.4.2 Set the spreadsheet to automatic calculation mode

................................ ... 10A

- 3

10A

- iAPPENDIX 10A. U SER INSTRUCTIONS FOR NATIONAL IMPACT ANAL Y SIS

SPREADSHEET MODEL

10A.1 U SER INSTRUCTIONS

The results obtained in this analysis can be examined and reproduced using the Microsoft

Excel ® spreadsheets accessible on the Internet from the Department of Energy’s ( DOE's ) hearth

products r ulemaking page:

www1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83

. From that

page, follow the links to the Preliminary Analysis phase of the rulemaking and then to the

a nalytical t ools.

10A.2 STARTUP

The NIA spreadsheet s enab le the user to perform a National Impact Analysis (NIA) for

hearth product ignition device s

. To utilize the spreadsheet, the Department assumes that the user

has access to a PC with a hardware configuration capable of running Windows 2003 or later. To

use the NIA spreadsheets, the user requires Microsoft Excel ® 2003 or later installed under the

Windows operating system

.

10A.3 DESCRIPTION OF NATIONAL IMPACT ANAL YSIS WORKSHEETS

The NIA spreadsheets perform calculations to project the change in national energy use

and net present val ue of financial impacts due to revised energy efficiency standard s

. The energy

use and associated costs for a given standard level are determined by calculating the shipments

and then calculating the energy use and costs for all hearth p roducts shipped under that standard.

The differences between the standards and base case can then be compared and the overall

energy savings and net present values determined. The NIA spreadsheets consist of the following

worksheets:

10A

- 1All Scenarios Contains NPV, NES, and intermediate results for all product types

, AEO economic scenario s, and discount rates. Also contains a

summary pivot table of NES and NPV results by TSL for user

selected AEO economic scenario and discount rate parameters.

Summary Contains a summary of disaggregated NIA and site NES results for all

product types

Fireplace (vented) Contains vented fireplace NIA calculations.

Fireplace (ventless) Contains ventless fireplace NIA calculations.

Logs (vented) Contains vented log set NIA calculations.

Logs (ventless) Contains ventless log set NIA calculations.

Outdoor Contains outdoor hearth product NIA calculations.

PC Inputs Contains energy use, electricity use, retail price, installation cost

, annual repair costs, and annual maintenance costs, for each product

class.

Shipments Includes historical and projected shipments data for each product type.

Price_Elasticity Includes the price elasticity to account for the change in the

percentage of consumers acquiring a hearth product divided by a

change in the relative price.

Base

- Case Shipments Contains shipments projections by product type under the base case

(no standard).

Lifetime Includes the lifetime and the retirement function for each product type.

Energy Price Contains energy prices for each product type by year.

10A.4 BASIC INSTRUCTIONS F OR OPERATING THE NATIONAL IMPACT

ANALYSIS SPREADSHEETS

Basic instructions for operating the NIA spreadsheets are as follows:

1. Once the NIA spreadsheet f ile has been downloade d from the Department’s web site

, open the file using MS Excel

. Click “Enable Macro” when prompted and then click on

the tab for the worksheet User Inputs

. 2. Use MS Excel's View/Zoom commands at the top menu bar to change the size of the

display to make it fit your monitor.

3. The user can change the parameters in the sheet “ Summary ”. The default parameters

(shown in Figure 10A.4.1 ) are:

10A

- 2Figure 10A.4.1 The default user input parameters

a) Economic Growth Scenario: Set to “Reference” (AEO 2014 Reference Case) . To

change value, click on t he pull down menu next to cell D3 “Economic Growth

Scenario” and change to desired scenario (Reference, Low, or High)

. b) Number of Years: Set to 30. To change value, click on the pull down menu next

to cell D5 and change to desired analysis period (30 or 9 years) . The year that

analysis end s is automatically calculated based on the year standards in effect and

analysis period.

c) Discount Rate s : Set to 7%

. To change value, click on the pull down menu next to

cell D 4 and change to desired value ( 7% or 3% ).

d) TSL: Set to 1.

4. The spreadsheet automa tically updates the analysis results based on user inputs: National

Energy Savings

, Net Present Values and intermediate results by product type , in cells H4

to M9

.

Note: Make sure that the spreadsheet is in automatic calculation mode. The calculation

mode could be change d by (shown in Figure 10A.4.2 ) :

1. In Excel 20 10 and later, go to the tab “Formulas” in the Office ribbon.

2. Clic k on the button “Calculation Options” and select “Automatic”.

The results are automatically updated and are reported in the source energy savings

matrix, net present value matrix, and summary table for each product class

.

Figure 10A.4.2 Set the spreadsheet to automatic calculation mode

10A

- 3APPENDIX 10B. FULL F UEL C YCLE M ULTIPLIERS

TABLE OF CO NTENTS

10B.1 INTRODUCTION

................................ ................................ ................................ . 10B

- 1

10B.2 METHODOLOGY

................................ ................................ ................................ 10B

- 1

10B.3 FULL

- FUEL

- CYCLE ENERGY MULTIPL IERS

................................ ............... 10B

- 3

LIST OF TABLES

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs

................................ ....... 10B

- 3

Table 10B.3.1 Upstream Energy Multipliers (Based on AEO 2014)

................................ 10B

- 3

10B

- iAPPENDIX 10B. FULL F UEL CYCLE MULTIPLIER S

10B.1 I NTRODUCTION

This appendix summarizes the methods used to calculate full

- fuel

- cycle (FFC) energy

savings expected to result from proposed standards. The FFC measure includes point

- of

- use

(site) energy, the energy losses associated with generation, transmission, and dis tribution of

electricity, and the energy consumed in extracting, processing, and transporting or distributing

primary fuels. DOE’s traditional approach encompassed only site energy and the energy losses

associated with generation, transmission, and distrib ution of electricity. Per DOE’s 2011

Statement of Policy for Adopting Full Fuel Cycle Analyses , DOE now uses FFC measures of

energy use and emissions in its energy conservation standards analyses. 1 This appen dix

summarizes the methods used to incorporate the full

- fuel

- cycle impacts into the analysis.

This analysis uses several different terms to reference energy use. The physical sources of

energy are the primary fuels such as coal, natural gas, liquid fuels

, etc . Primary energy is equal to

the heat content ( Btu ) of the primary fuels used to provide an end

- use service. Site energy use is

defined as the energy consumed at the point

- of

- use in a building or industrial process. Where

natural gas and petroleum fue ls are consumed at the site (for example in a hearth product ), site

energy is identical to primary energy, with both equal to the heat content of the primary fuel

consumed. For electricity, site energy is measured in kWh. In this case the primary energy is

equal to the quads of primary energy required to generate and deliver the site electricity. This

primary energy is calculated by multiplying the site kWh times the site

- to

- power plant energy

use factor

, given in chapter 10. For the FFC analysis, the upstr eam energy use is defined as the

energy consumed in extracting, processing, and transporting or distributing primary fuels. FFC

energy use is the sum of primary energy at the site or power plant plus upstream energy use.

Both primary fuels and electric ity are used in upstream activities. The treatment of

electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels

and uranium, and electricity generated from renewable fluxes (wind, solar and hydro). For the

former, th e upstream fuel cycle impacts are derived based on the amount of fuel consumed at the

power plant. For the latter, no fuel per se is used, so there is no upstream component.

10B.2 METHODOLOGY

The mathematical approach is discussed in the paper A Mathematical Analysis of Full

Fuel Cycle Energy Use

, 2 and details on the fuel production chain analysis are presented in the

paper Projections of Full Fuel Cycle Energy and Emissions Metrics

. 3 The text below provides a

brief summary of the methods used to calculate FFC energy.

When all energy quantities are normalized to the same units, the FFC energy use can be

represented as the product of the primary energy use and an FFC multiplier . The FF C multiplier

is defined mathematically as a function of a set of parameters representing the energy intensity

and material losses at each production stage. These parameters depend only on physical data, so

10B

- 1the calculations do not require any assumptions a bout prices or other economic data. While in

general these parameter values may vary by geographic region, for this analysis national

averages are used.

In the notation below, the indices x and y are used to indicate fuel type, with x = c for coal,

x = g fo r natural gas, x = p for petroleum fuels, x = u for uranium and x = r for renewable fluxes. The

fuel cycle parameters are:

• a x is the quantity of fuel x burned per unit of electricity output, on average, for grid

electricity. The calculation of a x includes a factor to account for transmission and

distribution system losses.

• b

y is the amount of grid electricity used in production of fuel y , in 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 )

The parameters are calculated as a function of time with an annual time step; hence, a

time series of annual valu es is used to estimate the FFC energy and emissions savings in each

year of the analysis period. Fossil fuel quantities are converted to energy units using the heat

content factors q x . To convert electricity in kWh to primary energy units, on

- site electric ity

consumption is multiplied by the power sector primary energy use factor indicated in chapter 10.

The power sector primary energy use factor is defined as the ratio of the total primary energy

consumption by the electric power sector (in quadrillion BTU s) divided by the total electricity

generation in each year.

The FFC multiplier is a dimensionless number denoted μ (mu). The upstream component

of the energy savings is proportional to ( μ

- 1). A separate multiplier is calculated for each fuel

used on sit e. The fuel type is denoted by a subscript on the multiplier μ

. A multiplier is also

calculated for electricity reflecting the fuel mix used in its generation.

For DOE’s appliance standards energy savings estimates, the FFC analysis methodology

is desig ned to make use of data and projections published in the Annual Energy Outlook (AEO).

Table 10B.2.1 provides a summary of the AEO data used as inputs to the different parameter

calculations. The AEO does not provide all the information needed to estimate total energy use

in the fuel production chain. The Projections of Full Fuel Cycle Energy and Emission s Metrics

paper 3 describes the additional data sou rces used to complete the analysis. However, the time

dependence in the FFC multipliers arises exclusively from variables taken from the AEO. The

FFC analysis for this rulemaking used data from AEO 201 4

. 4

10B

- 2Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables

q x all Conversion Factors MMB TU per physical unit

a x all Electricity Supply, Disposition,

Prices, and Emissions Generation by fuel type

Energy Consumption by Sector and

Source Electric power sector energy

consumption

b

c , c nc , c pc coal Coal Production by Region and

Type Production by coal 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 Crude oil domestic production

c nn natural

gas Oil and Gas Supply US 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

10B.3 FULL

- F UEL

- C YCLE E NERGY M ULTIPLIERS

Upstream energy multipliers are presented in Table 10B.3.1 for selected years. For years

after 2040 (the last year in the AEO ), DOE maintained the 2040 value

. The multipliers are

applied to site energy. The multiplier for electricity reflects the shares of various primary fuels in

total electricity generation over the forecast period.

Table 10B.3.1 Upstream Energy Multipliers (Based on AEO 201 4 )

202 1 2025 2030 2035 2040 2045 2050

Electricity 1.044 1.045 1.046 1.047 1.047 1.047 1.047

Natural Gas 1.110 1.111 1.113 1.114 1.114 1.114 1.114

10B

- 3REFERENCES

1. United States

- Office of the Federal Register, Federal Register, Volume 76, Number 160,

August 18, 2011 . UNT Digital Library. Washington D.C. .

2. Coughlin, K., A Mathematical Analysis of Full Fuel Cycle Energy Use

. Energy , 2012.

37(1): pp. 698

- 708 < www.sciencedirect.com/science/article/pii/S0360544211006803 >

3. Coughlin, K., Projections of Full

- Fuel

- Cycl e Energy and Emissions Metrics , 2013.

Lawrence Berkeley National Laboratory. Report No. LBNL

- 6025E.

4. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

10B

- 4APPENDIX 10C. NATIONAL IMPACT ANAL YSIS USING A LT ERNATIVE

E CONOMIC G ROWTH S CENARIOS FOR HEARTH PRODUCT S

TABLE OF CO NTENTS

10C.1 INTRODUCTION

................................ ................................ ................................ . 10C

- 1

10C.2 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

................... 10C

- 1

10C.3 RESULTS

................................ ................................ ................................ .............. 10C

- 2

10C.3.1 Nati onal Energy Savings

................................ ................................ ........................ 10C

- 2

10C.3.2 Net Present Value of Consumer Impacts

................................ ............................... 10C

- 3

10C.3.3 Summary

................................ ................................ ................................ ................ 10C

- 4

LIST OF TABLES

Table 10C.3.1 Full Fuel Cycle National Energy Savings (Quads)

– Reference Case

...... 10C

- 2

Table 10C.3.2 Full Fuel Cycle National Energy Savings (Quads)

– High Economic

Growth

................................ ................................ ................................ ....... 10C

- 2

Table 10C.3.3 Full Fuel Cycle National Energy Savings (Quads)

– Low Economic

Growth

................................ ................................ ................................ ....... 10C

- 3

Table 10C.3.4 Net Present Value, Discounted at 3 Percent (Billion 2013$)

–

Reference Case

................................ ................................ ........................... 10C

- 3

Table 10C.3.5 Net Present Value, Discounted at 7 Percent (Billion 2013$)

–

Reference Case

................................ ................................ ........................... 10C

- 3

Table 10C.3.6 Net Present Value, Discounted at 3 Percent (Billion 2013$)

– High

Economic Growth

................................ ................................ ...................... 10C

- 3

Table 10C.3.7 Net Present Value, Discounted at 7 Percent (Billion 2013$)

– High

Economic Growth

................................ ................................ ...................... 10C

- 3

Table 10C.3.8 Net Present Value, Discounted at 3 Percent (Billion 2013$)

– Low

Economic Growth

................................ ................................ ...................... 10C

- 4

Table 10C.3.9 Net Present Value, Discounted at 7 Percent (Billion 2013$)

– Low

Economic Growth

................................ ................................ ...................... 10C

- 4

Table 10C.3.10 Comparison of Energy Savings and Net Present Value Results for

Reference Case and High and Low Economic Growth Scenarios

............. 10C

- 4

LIST OF FIGURES

Figure 10C.2.1 Shipment Projections for Reference Case and High and Low

Economic Growth Scenarios (Base Case)

................................ ................. 10C

- 2

10C

- iAPPENDIX 10C. NATIONAL IMPACT ANAL YSIS USING ALTERNATI VE

ECONOMIC GROWTH SCEN ARIOS FOR HEARTH PRODUCT S

10C.1 I NTRODUCTION

This appendix presents National Impact Analysis ( NIA ) results using energy price

forecasts from alternative economic growth scenarios. The scenarios are based on the High

Economic Growth case and the Low Economic Growth case from Energy Information

Administration’s (EIA’s) Annual Energy Outlook 201 4 ( AEO 2014 ). 1 To estimate energy prices

after 2040 in the high and low scenarios, DOE u sed the growth rate between 2021 and 20 40

. See

appendix 8C for details about alternative economic growth scenarios.

This appendix also describes the High and Low Economic Growt h scenarios in further

detail. See appendix 10 A for details about how to generate NIA results for High Economic

Growth and Low Economic Growth scenarios using the NIA spreadsheet.

10C.2 D ESCRIPTION OF HIGH A ND LOW ECONOMIC SCEN ARIOS

To generate NIA results reported in chapter 10, DOE uses t he Reference case energy

price and housing projection s from AEO 2014

. The reference case is a business

- as

- usual

estimate, given known market, demographic, and technological trends. For AEO 2014 , EIA

explored the impacts of alternative assumptions in other scenarios with different macroeconomic

growth rates, world oil p rices, rates of technology progress, and policy changes.

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO 2014 uses

High and Low Economic Growth scenarios to project the possible impacts of alternative

economic growth assumpti ons on energy markets. 2

In general, e nergy prices are higher in the High Economic Growth scenario and lower in

the Low Economic Growth scenario. See appendix 8 E for details about the effect of these

alternative economic scenarios on energy price s

.

Because AEO 2014 provides the price trends by census division, each sampled household

is then matched to the appropriat e census division price trend. See chapter 10 for details about

how energy price trends by census division are applied in the NIA analysis.

In addition , the High and Low Economic Growth scenarios provide different housing

starts projections that affect the hearth product shipments projections. Figure 10C.2.1 shows the

shipments projections based on the different AEO 2014 scenarios.

10C

- 1Figure 10C.2.1 Shipment Projections for Reference Case and High and Low Economic

Growth Scenarios (Base Case)

10C.3 R ESULTS

10C.3.1 National Energy Savings

Table 10C.3.1 through Table 10C.3.3 sh ow the national energy savings (NES) results for

the Trial Standard Levels ( TSL ) analyzed for hearth product ignition systems using the

Reference case

, High Economic Growth scenario , a nd Low Economic Growth scenario

.

Table 10C.3.1 Full Fuel Cycle National Energy Savings (Q uads)

– Reference Case

Product Classes Trial Standard Levels

1

Hearth Products 0.6 9

Table 10C.3.2 Full Fuel Cycle National Energy Savings (Q uads)

– High Economic

Growth

Product Classes Trial Standard Levels

1

Hearth Products 1.01

10C

- 2Table 10C.3.3 Full Fuel Cycle National Energy Savings (Q uads)

– Low Economic

Growth

Product Classes Trial Standard Levels

1

Hearth Products 0. 44

10C.3.2 Net Present Value of Consumer Impacts

Table 10C.3.4 through Table 10C.3.9 show the national present value (NPV) results for

the TSLs analyzed for hearth product ignition systems using the Reference case

, High Economic

Growth scenario

, a nd Low Economic Growth scenar io

.

Table 10C.3.4 Net Present Value, Discounted at 3 Percent (B illion 201 3 $)

– Reference

Case

Product Classes Trial Standard Levels

1

Hearth Products 3.1

Table 10C.3.5 Net Present Value, Discounted at 7 Percent (B illion 201 3 $)

– Reference

Case

Product Classes Trial Standard Levels

1

Hearth Products 1.0

Table 10C.3.6 Net Present Value, Discounted at 3 Percent (B illion 201 3 $)

– High

Economic Growth

Product Classes Trial Standard Levels

1

Hearth Products 4.9

Table 10C.3.7 Net Present Value, Discounted at 7 Percent (B illion 201 3 $)

– High

Economic Growth

Product Classes Trial Standard Levels

1

Hearth Products 1. 6

10C

- 3Table 10C.3.8 Net Present Value, Discounted at 3 Percent (B illion 201 3 $)

– Low

Economic Growth

Product Classes Trial Standard Levels

1

Hearth Products 1.8

Table 10C.3.9 Net Present Value, Discounted at 7 Percent (B illion 201 3 $)

– Low

Economic Growth

Product Classes Trial Standard Levels

1

Hearth Products 0. 6

10C.3.3 Summary

Table 10C.3.10 shows the NES and NPV results for each of the TSL for the Reference

case and the High Economic Growth and Low Economic Growth scenarios. NES and NPV

results are larger for High Economic Growt h scenario and smaller for Low Economic Growth

scenario compared to Reference case.

Table 10C.3.10 Comparison of Energy Savings and Net Present Value Results for

Reference Case and High and Low Economic Growth Scenarios

Trial Standard Level

1

Reference Full Fuel Cycle Energy Savings (quads) 0.6 9

NPV 3% (billion 201 3 $) 3.1

NPV 7% (billion 201 3 $) 1.0

High Economic Growth Full Fuel Cycle Energy Savings (quads) 1.01

NPV 3% (billion 201 3 $) 4.9

NPV 7% (billion 201 3 $) 1. 6

Low Economic Growth Full Fuel Cycle Energy Savings (quads) 0. 44

NPV 3% (billion 201 3 $) 1.8

NPV 7% (billion 201 3 $) 0. 6

10C

- 4REFERENCES

1. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

2. Energy Information Administration, Model Documentation Report: Macroeconomic

Activity Module (MAM) of the National Energy Modeling System (AEO 2012) , 2013.

Washington, DC.

< www.eia.gov/forecasts/aeo/nems/documentation/macroeconomic/pdf/m065(2013).pdf >

10C

- 5APPENDIX 12 A. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12A.1 INTRODUCTION A ND PURPOSE

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

12A.2 MODEL DESCRIPTION

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

LIST OF TABLES

Table 1 2 A.2.1 Detailed Cash Flow Example

................................ ................................ .......... 12 A

- 4

1 2 A

- iAPPENDIX 1 2 A

. GOVERNMENT REGULATOR Y IMPACT MODEL OVERVIEW

12 A.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the

impacts of energy conservation standards and other regulations on manufacturers. The basic

mode of analysis is to estimate the change in the value of the industry or manufacturers(s)

following a regulation or a series of regulations. The model structure also allows an anal ysis of

multiple products with regulations taking effect over a period of time, and of multiple regulations

on the same products.

Industry net present value is defined, for the purpose of this analysis, as the discounted

sum of industry free cash flows plu s a discounted terminal value. The model calculates the actual

cash flows by year and then determines the present value of those cash flows both without an

energy conservation standard ( i.e. , the base case) and under different trial standard levels ( i.e.

, the standards case).

Output s from the model consist of summary financial metrics, graphs of major variables,

and, when appropriate, access to the complete cash flow calculation.

1 2 A.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses

manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters

as inputs and accepts a set of regulatory conditions as changes in costs and investments. The cash

flow analysis is separated into two major blocks: income and cash flow. The income calculation

determines net operating profit after taxes. The cash flow calculation converts net operating

profit after taxes into an annual cash flow by including investment and non

- cash items. Below

are definitions of listed items on the printout of the output sheet of the GRIM

.

(1) Unit s ales: Total annual shipments for the industry were obtained from the n ational i mpact

a nalysis s preadsheet.

(2) Revenues: Annual revenues are computed by multiplying products’ un it prices at each

efficiency level by the appropriate manufacturer markup

.

(3) Material: The portion of cost of goods sold ( COGS ) that includes materials.

(4) Labor: The portion of COGS that includes direct labor, commissions, dismissal pay,

bonuses, vacation, sic k leave, social security contributions, fringe, and assembly labor up

time

.

(5) Depreciation: The portion of overhead that includes an allowance for the total amount of

fixed assets used to produce that one unit. Annual depreciation is computed as a percentag e

of COGS

. While included in overhead, the depreciation is shown as a separate line item

.

1 2 A

- 1(6) Overhead: The portion of COGS that includes indirect labor, indirect material, energy use,

maintenance, depreciation, property taxes, and insurance related to assets. While included

in overhead, depreciation is shown as a separate line item.

(7) Standard SG&A: Selling, general, and administrative costs are computed as a percentage of

r evenues (2)

.

(8) R&D: GRIM separately accounts for ordinary research and development as a percentage of

r evenues (2)

.

(9) Product c onversion c osts : Product conversion costs are one

- time investme nts in research,

development, testing, marketing, and other costs focused on making products designs

comply with the new energy conservation standard. The GRIM allocates these costs over

the period between the standard’s announcement and compliance dates

.

(10) Stranded a ssets: In the year the st andard becomes effective, a one

- time write

- off of

stranded assets is accounted for

.

(11) Earnings b efore i nterest and t axes (EBIT): Includes profits before deductions for interest

paid and taxes

.

(12) EBIT as a p ercentage of s ales (EBIT/ r evenues) : GRIM calculates EBIT as a percentage of

sales to compare with the industry’s average reported in financial statements

.

(13) Taxes: Taxes on EBIT (11) are calculated by multiplying the tax rate contained in m ajor

a ssumptions by EBIT (11)

.

(14) Net o perating p rofits a fter t axes (NOPAT): Computed by subtracting COGS ((3) to (6))

, SG&A ( 7 )

, R&D ( 8 )

, p roduct c onversion c osts (9 ) , and t axes (13) from r evenues ( 2 )

.

(15) NOPAT repeated : NOPAT is repeated in the s tatement of c ash f lows

.

(16) Depreciation repeated : De preciation and s tranded a ssets are added back in to the s tatement

of c ash f lows because they are non

- cash expenses

.

(17) Change in w orking c apital : 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.

(18) Cash f low f rom o perations : Calculated by taking NOPAT (15) , adding back non

- cash

items such as a d epreciation (16) , and subtracting the c hange in w orking c apital (17)

.

(19) Ordinary c apital e xpenditures: Ordinary investments in property, plant, and equipment to

maintain and replace exist ing production assets, computed as a percentage of r evenues (2)

.

(20) Capital c onversion c osts : Capital conversion costs are one

- time investments in property,

plant, and equipment to adapt or change existing production facilities so that new product

1 2 A

- 2designs can be fabricated and ass embled under the new regulation. The GRIM allocates

these costs over the period between the standard’s announcement and compliance dates

.

(21) Capital i nvestment: Total investments in property, plant, and equipment are computed by

adding o rdinary c apital e xpenditures (19) and c apital c onversion c osts (20)

.

(22) Free c ash f low: Annual cash flow from operations and investments; computed by

subtracting c apital i nvestment (21) from c ash f low from o perations (18)

.

(23) Terminal v alue: Estimate of the continuing value of the industry after the analysis period.

Computed by growing the f ree c ash f low at a constant rate in perpetuity

.

(24) Present v alue f actor: Factor used to calculate an estimate of the present value of an amount

to be receive d in the future

.

(25) Discounted c ash f low: Free c ash f lows (22) multiplied by the p resent v alue f actor (24) . For

the end of 20 50 , the discounted cash flow includes the discounted t erminal v alue (23)

.

(26) Industry v alue thr o u gh the end of 2050 : The sum of d iscounted c ash f lows (25)

.

1 2 A

- 3Table 12 A.2

. 1 Detailed Cash Flow Example

1 2 A

- 4APPENDIX 14A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866

TABLE OF CO NTENTS

14A.1 EXECUTIVE SUMMARY

................................ ................................ .................. 14A

- 1

14A.2 MONETIZING CARBON DIOXIDE EMISS IONS

................................ ............ 14A

- 2

14A.3 SOCIAL COST OF CARBO N VALUES USED IN PAS T REGULATORY

ANALYSES

................................ ................................ ................................ .......... 14A

- 4

14A.4 APPROACH AND KEY ASS UMPTIONS

................................ ......................... 14A

- 5

14A.4.1 Integrated Assessment Models

................................ ................................ ............. 14A

- 6

14A.4.2 Global versus Domestic Measures of SCC

................................ ......................... 14A

- 11

14A.4.3 Valuing Non

- CO 2 Emissions

................................ ................................ .............. 14A

- 13

14A.4.4 Equilibrium Climate Sensitivity

................................ ................................ ......... 14A

- 14

14A.4.5 Socioeconomic and Emissions Trajectories

................................ ........................ 14A

- 17

14A.4.6 Discount Rate

................................ ................................ ................................ ...... 14A

- 19

14A.5 REVISED SCC ESTIMATE S

................................ ................................ ............ 14A

- 26

14A.6 LIMITATIONS OF THE A NALYSIS

................................ ............................... 14A

- 33

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPA CTS AND

DAMAGE FUNCTIONS

................................ ................................ ................... 14A

- 35

14A.8 CONCLUSION

................................ ................................ ................................ ... 14A

- 38

14A.9 ANNEX

................................ ................................ ................................ ............... 14A

- 45

14A.9.1 Other (non

- CO 2 ) gases

................................ ................................ ........................ 14A

- 46

14A.9.2 Extrapolating Emissions Projections to 2300

................................ ..................... 14A

- 48

LIST OF TABLES

Table 14A.1.1 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ .. 14A

- 2

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity

Distributions

................................ ................................ ............................ 14A

- 15

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF

- 22

Reference Scenarios

................................ ................................ ................ 14A

- 18

Table 14A.5.1 Disaggregated Social Cost of CO 2 Values by Model, Socioeconomic

Trajectory, and Discount Rate for 2010 (in 2007 dollars)

...................... 14A

- 30

Table 14A.5.2 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ 14A

- 32

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates

between 2010 and 2050

................................ ................................ .......... 14A

- 32

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

............ 14A

- 36

Table 14A.9.1 Annual SCC Values: 2010

– 2050 (in 2007 dollars)

................................ 14A

- 45

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 14A

- 56

Ta ble 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 14A

- 57

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 14A

- 58

14A

- iTable 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates

............. 14A

- 59

LIST OF FIGURE S

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

- 31

Figure 14A.9.2 Sulfur Dioxide Emission Scenarios

................................ ........................ 14A

- 48

Figure 14A.9.3 Global Population, 2000

- 2300 (Post

- 2100 extrapolations assume the

population growth rate changes linearly to reach a zero growth rate by

2200.)

................................ ................................ ................................ ...... 14A

- 50

Figure 14A.9.4 World GDP, 2000

- 2300 (Post

- 2100 extrapolations assume GDP per

capita growth declines linearly, reaching zero in the year 2300)

........... 14A

- 51

Figure 14A.9.5 Global Fossil and Industrial CO 2 Emissions, 2000

- 2300 (Post

- 2100

extrapolations assume growth rate of CO 2 intensity (CO 2 /GDP) over

2090

- 2100 is maintained through 2300)

................................ ................. 14A

- 52

Figure 14A.9.6 Global Net Land Use CO 2 Emissions, 2000

- 2300 (Post

- 2100

extrapolations assume emi ssions decline linearly, reaching zero in the

year 2200)

................................ ................................ ............................... 14A

- 53

Figure 14A.9.7 Global Non

- CO 2 Radiative Forcing, 2000

- 2300 (Post

- 2100

extrapolations assume constant non

- CO 2 radiative forcing after 2100)

. 14A

- 54

Figure 14A.9 .8 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000

- 2300 (Post

- 2100 extrapolations assume decline in CO 2 /GDP growth

rate over 2090

- 2100 is maintained through 2300)

................................ .. 14A

- 55

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by

discount rate

................................ ................................ ............................ 14A

- 58

14A

- iiAPPENDIX 14A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866 a

14A.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to

assess both the cos ts 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 pu rpose of the

“social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the

social benefits of reducing carbon dioxide (CO 2 ) emissions into cost

- benefit analyses of

regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The

estimates are presented with an acknowledgement of the many uncertainties involved and with a

clear understanding that they should be updated over time to reflect increasing knowledge of the

science and economics of clim ate 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 d amages from increased flood

risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagency process that developed these SCC

estimates. Technical experts from numerous agencies met on a regular basis to consider public

comments, explore the technical literature in relevant fields, and discuss key model inputs and

assumptions. The main objective of this process was to develop a range of SCC values using a

defensible set of input assumptions grounded in the existing scientific and economic literatures.

In this way, key uncertainties and model differences transparently and consistently inform the

range of SCC estimates used in the rulemaking process.

a Prepared by Interagency Working Group on Social Cost of Carbon, United States Government.

With participation by:

Council of Economic Advisers

Council on Environmental Quality

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Environmental Protection Agency

National Economic Council

Office of Energy and Climate Change

Office of Management and Budget

Office of Science and Technology Policy

Department of th e Treasury

14A

- 1The 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 pe rcent discount rate, is included to represent higher

- than

- expected

impacts from temperature change further out in the tails of the SCC distribution.

Table 14A.1.1 Social Cost of CO 2 , 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 EMISS IONS

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. b

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 un certainty,

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.

b 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

- 2Despite 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 jus tify 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

on cumulative global emissions. Most federal regulatory actions can be expected to have

marginal impacts on global emissions.

For such policies, the benefits from reduced (or costs from increased) emissions in any

future year can be estimated by multiplying the change in emissions in that year by the SCC

value appropriate for that year. The net present value of the benefits can then be calculated by

multiplying each of these future benefits by an appropriate discount factor and summing across

all affected years. This approach assumes that the marginal damages from increased emissions

are constant for small departures from the baseline emissions path, an approximation that is

reasonable for policies that have effects on emissions that are small rel ative 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 emissio ns; 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 estimat es. Agencies that actively participated in the interagency process include

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy,

Transportation, and Treasury. This process was convened by the Council of Economic Adviser s

and the Office of Management and Budget, with active participation and regular input from the

Council on Environmental Quality, National Economic Council, Office of Energy and Climate

Change, and Office of Science and Technology Policy. The main objective of this process was to

develop a range of SCC values using a defensible set of input assumptions that are ground ed in

the existing literature. In this way, key uncertainties and model differences can more

transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are

based on the av erage 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 higher

than

- expected impacts from temperature change further out in the tails of the SCC distribution.

For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. The

central value is the average SCC across models at the 3 percent discount rate. For purposes of

capturing the uncertainties involved in regula tory impact analysis, we emphasize the importance

and value of considering the full range. These SCC estimates also grow over time. For instance,

14A

- 3the 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 f or the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the interagency process is committed to updating these

estimates as the science and economic understanding of climate change and its impacts on

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC

values within two years or at such time as substantially updated models become available, and to

continue to support research in this area. In the meantime, we will continue to explore the issues

raised in this document and consider public comments as part of the ongoing interagency

process.

14A.3 SOCIAL COST OF CARBO N VALUES USED IN PAS T 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 v alues at 2.4 percent per year. It also included a sensitivity

analysis at $80 per ton of CO 2

. A domestic SCC value is meant to reflect the value of damages in

the United States result ing 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 reducti ons (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 dollars). In

addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified

what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean

values were $68 and $40 per ton CO 2 for discount rates of approximately 2 percent and 3

percent, respectively (in 2006 dollars for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in

how benefits are evaluated across agencies, the Administration sought to develop a transparent

and defensible method, specifically designed for the rulemaking process, to quantify avoided

climate change damages from reduced CO 2 emissions. The interagency group did not undertake

any original analysis. Instead, it combined SCC estimates from the existing literature to use as

interim values until a more comprehensive analysis could be conducted.

The outcome of the preliminary assessment by the interagency group was a set of five

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per

ton of CO 2. The $33 and $5 values represented model

- weighted means of the published estimates

produced from the most recently available ver sions of three integrated assessment models

—

14A

- 4DICE, 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 Newe ll 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 incrementa l 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 we re presented in several proposed and final rules and were offered for public comment in

connection with proposed rules, including the joint EPA

- DOT fuel economy and CO 2 tailpipe

emission proposed rules.

14A.4 APPROACH AND KEY ASS UMPTIONS

Since the release of th e interim values, the interagency group has reconvened on a regular

basis to generate improved SCC estimates. Specifically, the group has considered public

comments and further explored the technical literature in relevant fields. This section details the

several choices and assumptions that underlie the resulting estimates of the SCC.

It is important to recognize that a number of key uncertainties remain, and that current

SCC estimates should be treated as provisional and revisable

, since they will evol ve 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 quant ified 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

intera gency 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

- 514A.4.1 Integrated Assessment Models

We rely on three integrated assessment models (IAMs) commonly u sed to estimate the

SCC: the FUND, DICE, and PAGE models. c These models are frequently cited in the peer

reviewed literature and used in the IPCC assessment. Each model is given equal weight in the

SCC values developed through this process, bearing in mind their different limitations (discussed

below).

These models are useful because they combine climate processes, economic growth, and

feedbacks between the climate and the global economy into a single modeling framework. At the

same time, they gain this advantage at the expense of a more detailed representation of the

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 adva ntages of this approach). Other IAMs may better reflect the complexity of the

science in their modeling frameworks but do not link physical impacts to economic damages.

There is currently a limited amount of research linking climate impacts to economic dam ages,

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 econom ic research characterizing these

relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse

concentrations, atmospheric concentrations into changes in temperature, and changes in

temperature into economic damages. The emissi ons projections used in the models are based on

specified socioeconomic (GDP and population) pathways. These emissions are translated into

concentrations using the carbon cycle built into each model, and concentrations are translated

into warming based on each model’s simplified representation of the climate and a key

parameter, climate sensitivity. Each model uses a different approach to translate warming into

damages. Finally, transforming the stream of economic damages over time into a single value

requi res judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result

in changes in economic damages. In PAGE, for example, the consumption

- equivalent damages

in each period are calculated as a fraction of GDP, depending on the temperature in that period

relative to the pre

- industrial average temperature in each region. In FUND, damages in each

period also depend on the rate of temperature change from the prior period. In DICE,

temperature affects both consumption and investment. We describe each model in greater detail

here. In a later section, we discuss key gaps in how the models account for various scientific and

c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of

energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decision

makers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). Th e 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 2002 a, Tol 2002b, Anthoff et al. 2009, Tol 2009).

14A

- 6economic processes ( e.g. the probability of catastrophe, and the ability to adapt to climate change

and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key

objective of the interagency process was to enable a consistent exploration of the three models

while respecting the different approaches to quantifying damages taken by the key modelers in

the field. An extensive review of the literature was conducted to select three sets of input

parameters for these models: climate sensitivity, socioeconomic and emissions trajecto ries, and

discount rates. A probability distribution for climate sensitivity was specified as an input into all

three models. In addition, the interagency group used a range of scenarios for the socioeconomic

parameters and a range of values for the discou nt 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 pr obability distributions. FUND was also run in a mode in which

parameters were treated probabilistically.

The sensitivity of the results to other aspects of the models ( e.g. the carbon cycle or

damage function) is also important to explore in the context of future revisions to the SCC but

has not been incorporated into these estimates. Areas for future research are highlighted at the

end of this document.

The DICE Model

The DICE model is an optimal growth model based on a global production function with

an extra stock variable (atmospheric carbon dioxide concentrations). Emission reductions are

treated as analogous to investment in “natural capital.” By investing in natural capital today

through reductions in emissions

— implying reduced consumption

— harmfu l 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 ti me due to

technological progress. The DICE damage function links global average temperature to the

overall impact on the world economy. It varies quadratically with temperature change to capture

the more rapid increase in damages expected to occur under mo re 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” (b ased 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.

14A

- 7No structural components of the DICE model represent adaptation explicitly, though it is

included implicitly through the choice of studies used to calibrate the aggregate damage function.

For example, its agricultural impact estimates assume that farmers can adjust land use decisions

in response to changing climate conditions, and its health impact estimates assume

improvements in healthcare over time. In addition, the small impacts on forestry, water systems,

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these

sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level

rise are incorporated into damage estimates, but thei r 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

da mage 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 dama ges in

any given year do not propagate forward. d

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into

economic, non

- economic, and catastrophic categories and calculates these impacts separately for

eight geographic regions. Damages in each region are expressed as a fraction of output, where

the fraction lost depends on the temperature change in each region. Damages are expressed as

power functions of temperature change. The exponents of the damage function are the same in

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at

2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage sub

function. Unlike DICE, PAGE2002 model s these events probabilistically. The probability of a

“discontinuity” ( i.e. , a catastrophic event) is assumed to increase with temperature above a

specified threshold. The threshold temperature, the rate at which the probability of experiencing

a disconti nuity 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 d eveloped countries and 0°C for developing

countries for economic impacts, and 0°C for all regions for non

- economic impacts), but

d 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 secti on): we assumed a fixed rate of savings of 20 percent , and we re

- calibrated the exogenous

path of total factor productivity so that DICE would produce GDP projections in the absence of warming that

exactly matched the EMF scenarios.

14A

- 8adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the

developed countries can ultimately el iminate 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 im pacts through adaptation (Hope 2006).

The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water,

energy (based on hea ting 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

d ifferent 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 temperatu re change and level of regional

income. e In the forestry and agricultural sectors, economic damages also depend on CO 2

concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a

relatively small effect on damage estimates (both positive and negative). However, he

characterizes several omitted impacts as “big unknowns”: for instance, extreme climate

scenarios, biodiversity loss, and effects on economic development and political violence. With

regard to poten tially 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 s ectors. 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 tempera tures, and sufficiently slow increases in

temperature across these sectors can result in negative economic damages from climate change.

e In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning

demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in

those regions at low to moderate levels of warming (Warren et al., 2006).

14A

- 9Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of

how to represent the effects of climate change (represented by the increase in global

- average

surface temperature) on the consumption

- equivalent value of both market and non

- market goods

(represented as a fraction of global GDP). We recognize that these represent ations 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 rese arch 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.

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 f

f 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

,

a nd emissions parameters will produce a different realization of damages for each IAM. The damage functions

represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under alternate assumptions,

the damages from FUND may cross from negative to positive at less than or greater than 3 °C.

14A

- 10The lack of agreement among the models at lower temperature increases is underscored

by the fact that the damages from FUND are well belo w 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 t o

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.

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE,

FUND, and PAGE

14A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we c enter our current

attention on a global measure of SCC. This approach is the same as that taken for the interim

values, but it otherwise represents a departure from past practices, which tended to put greater

14A

- 11emphasis on a domestic measure of SCC (limited to impacts of climate change experienced

within U.S. borders). As a matter of law, consideration of both global and domestic values is

generally permissible; the relevant statutory provisions are usually ambiguous and allow

selection of either measure. g

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 cl imate 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 th e 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 emi ssions 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

sol ution to a global problem, the United States has been actively involved in seeking

international agreements to reduce emissions and in encouraging other nations, including

emerging major economies, to take significant steps to reduce emissions. When these

considerations are taken as a whole, the interagency group concluded that a global measure of

the benefits from reducing U.S. emissions is preferable.

When quantifying the damages associated with a change in emissions, a number of

analysts ( e.g. , Anthof f, et al. 2009a) employ “equity weighting” to aggregate changes in

consumption across regions. This weighting takes into account the relative reductions in wealth

in different regions of the world. A per

- capita loss of $500 in GDP, for instance, is weighted

more heavily in a country with a per

- capita GDP of $2,000 than in one with a per

- capita GDP of

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a

greater reduction in utility or welfare than does the sam e 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. h For this reason, the group concluded that using the global (rather

than domestic) value, without equity weighting, is the appropriate approach.

g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give

extraterritorial effect to federal law and hence does not intrude on such interests.

h 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 app ropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor

nation than on a wealthy one. Eve n 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.

14A

- 12Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the

relatively few region

- or country

- specific estimates of the SCC in the literature. One potential

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio

of domestic to global benefits of emission reductions varies with key parameter assumpti ons. 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 acro ss countries, the domestic benefit would

be proportional to the U.S. share of global GDP, which is currently about 23 percent. i

On the basis of this evidence, the interagency workgroup determined that a range of

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic

effects. Reported domestic values should use this range. It is recognized that these values are

approximate, provisional, and highly speculative. There is no a priori reason why domestic

benefits should be a constant fraction of net global damages over time. Further, FUND does not

account for how damages in other regions could affect the United States ( e.g. , global migration,

economic and political destabilization). If more accurate methods for calculating the domestic

SCC become available, the Federal government will examine these to determine whether to

update its approach.

14A.4.3 Valuing Non

- CO 2 Emissions

While CO 2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S.

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide,

hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact o f these

gases is commonly discussed in terms of their 100

- year global warming potential (GWP). GWP

measures the ability of different gases to trap heat in the atmosphere ( i.e. , radiative forcing per

unit of mass) over a particular timeframe relative to CO 2

. However, because these gases differ in

both radiative forcing and atmospheric lifetimes, their relative damages are not constant over

time. For example, because methane has a short lifetime, its impacts occur primarily in the near

term and thus are not d iscounted 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

acidi fication. 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 est imates 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 e fforts 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

i Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report

.

14A

- 13interagency group hopes to develop methods to value these other greenhouse gases. The goal is

to develo p 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. j 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 importan t 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):

Basi ng 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 , o r ‘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. k

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 sever al lead authors of this chapter of the IPCC report, the

interagency workgroup selected four candidate probability distributions and calibrated them to

be consistent with the above statement: Roe and Baker (2007), log

- normal, gamma, and Weibull.

Table 14A.4.1 included below gives summary statistics for the four calibrated distributions.

j The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas

concentrations ov er 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).

k 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

probabi lity.

14A

- 14Table 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”; l

(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).

We selected the calibrated Roe and Bake r distribution from the four candidates for two

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a

theoretical understanding of the response of the climate system to increased greenhouse gas

concentrations (Roe an d 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 resp onse: (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 a greement 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

l Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC

report is not specific o n 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 jud gments and the literature. For example, setting the mean and mode

equal to 3°C produced 95 th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the

range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected

by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most

consistent with a Roe and Baker distribution with the median set equal to 3 °C.

14A

- 15median (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 judgme nt that the equilibrium climate sensitivity “is very

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the

probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is

not inconsiste nt with the IPCC definition of “very likely” as “greater than 90 percent

probability,” it reflects a greater degree of certainty about very low values of ECS than was

expressed by the IPCC.

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. m

m The estimates bas ed 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 t he 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

- 1614A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic

pathways are closely tied to climate damages because, all else equal, more and wealthier people

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid

climate disruptions. For this reason, we consider how to model several input parameters in

tandem: GDP, population, CO 2 emissions, and non

- CO 2 radiative forcing. A wide variety of

scenarios have been developed and used for climate change policy simulations ( e.g. , SRES 2000,

C CSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed

to select scenarios that span most of the plausible ranges of outcomes for these variables.

To accomplish this task in a transparent way, we decided to rely on t he 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, a nd emission trajectories are

internally consistent for each model and scenario evaluated. The EMF

- 22 modeling effort also is

preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3

of 4 of the SRES scenarios are now e xtreme 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 dome stic 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 lower

than

- BAU trajectory. n Out of the 10 models included in the EMF

- 22 ex ercise, 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 ave raged the GDP, population, and

emission trajectories implied by these same four models.

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions,

though it could also result from technological advances. It was chosen because it represents the most stringent case

analyzed by the EMF

- 22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

14A

- 17Table 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$) o

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 socioeco nomic pathways.

o While the EMF

- 22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use

purchasing power parity (PPP). PPP takes into account the different price levels across countries, so it more

accurately describes relative standa rds of living across countries. MERs tend to make low

- income countries appear

poorer than they actually are. Because many models assume convergence in per capita income over time, use of

MER

- adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas

emissions, and unrealistically high future temperatures ( e.g. , Castles and Henderson 2003). Others argue that

convergence of the emissions

- intensity gap across countries at least partially offset the overstated income gap so that

differences in exchange ra tes have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006).

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts ( i.e. , using cross

- sectional PPP

measures for relative incomes and outputs and national accounts price and quantity indexes for time

- series

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that

exchange

- rate

- conversion issues are as important as uncertainties about population, te chnological change, or the

many geophysical uncertainties.

14A

- 18T here are a number of caveats. First, EMF BAU scenarios represent the modelers’

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas

emissions, rather than the wider range of possible outcomes. Neverthe less, these views of the

most likely outcome span a wide range, from the more optimistic ( e.g.

, abundant low

- cost, low

carbon energy) to more pessimistic ( e.g.

, constraints on the availability of nuclear and

renewables). p 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 s ome BAU scenarios ( e.g.

, MESSAGE’s 612 ppm) also are consistent

with some modest policy action to address climate change. q 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, r espectively, 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 Na tions’ 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 use CO 2

emissions out to 2100. These assumptions also are used in the three models while retaining the

default radiative forcings due to other factors ( e.g.

, 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, subsequ ent damages occur over many years. In calculating the

SCC, we first estimate the future damages to agriculture, human health, and other market and

non

- market sectors from an additional unit of carbon dioxide emitted in a particular year in terms

p 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 prima ry energy in 2000 to 54 percent

in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100.

q 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

- 19of 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 releas ed using

the selected discount rate, which is intended to reflect society's marginal rate of substitution

between consumption in different time periods.

For rules with both intra

- and intergenerational effects, agencies traditionally employ

constant dis count 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 revie wing 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 disco unt rates of 3 and 7 percent.” For the specific

purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for

climate change analysis, which they labeled “descri ptive” 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] actu ally 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 benefi ts and costs, because it is the market interest rate that

would govern the returns potentially set aside today to compensate future individuals for climate

damages that they bear ( e.g. , Just et al. 2004)

. As some have noted, the word “potentially” is an

im portant 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 judgment s 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 dis count rates. In

particular, it has been noted that the preferences of future generations with regard to

14A

- 20consumption versus environmental amenities may not be the same as those today, making the

current market rate on consumption an inappropriate metric by which to discount future climate

related damages. Others argue that the discount rate should be below market rates to correct for

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth,

which in the Kaldor

- Hicks log ic are presumed to compensate future generations for damage (a

potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that

they tend to obscure important heterogeneity in the population. The utility function that underlies

the prescriptive approach assumes a representative agent with perfect foresight and no credit

constraints. This is an artificial rendering of the real world that misses many of the frictions that

characterize individuals’ lives and indeed the available descriptive evidence supports this. For

instance, many individuals smooth consumption by borrowing with credit cards that have

relatively high rates. Some are unable to ac cess 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

individual s accept suggest that some account should be given to the discount rates revealed by

their behavior.

We draw on both approaches but rely primarily on the descriptive approach to inform the

choice of discount rate. With recognition of its limitations, we find this approach to be the most

defensible and transparent given its consistency with the standard contemporary theoretical

foundations of benefit

- cost analysis and with the approach required by OMB’s existing guidance.

The logic of this framework also suggests that market rates should be used for discounting future

consumption

- equivalent damages. Regardless of the theoretical approach used to derive the

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of

adequatel y 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 deb ate over discounting

in the intergenerational context and the consequences of selecting one discount rate over another.

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on

investment, and the market rate of interest would be the appropriate choice for the social

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge

between the risk

- free rate of return on capital and the consumption rate o f interest. Thus, the

literature recognizes two conceptual discount concepts

— the consumption rate of interest and the

opportunity cost of capital.

According to OMB’s Circular A

- 4, it is appropriate to use the rate of return on capital

when a regulation is expected to displace or alter the use of capital in the private sector. In this

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected

to primarily affect private consumption

— for instance, via higher prices for goods and services

—

14A

- 21a 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 t he correct concept to use in evaluating the benefits and costs of a

marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The

consumption rate of interest also is appropriate when the impacts of a regulation are measured in

consumption (

- equivalent) units, as is done in the three integrated assessment models used for

estimating the SCC.

Individuals use a variety of savings instruments that vary with risk level, time horizon,

and tax characteristics. The standard analytic framework used to develop intuition about the

discount rate typically assumes a representative agent with perfect foresight and no credit

constraints. The risk

- free rate is appropriate for discounting certain future benefits or costs, but

the benefits calc ulated 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 incom e, 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 neg ative 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 individu als’ consumption

- equivalent interest rate. As a measure of the post

- tax riskless

rate, we calculate the average real return from Treasury notes over the longest time period

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the aver age marginal

rate from tax years 2003 through 2006 is around 27 percent). r This calculation produces a real

interest rate of about 2.7 percent, which is roughly consistent with Circular A

- 4’s

r The literature argues for a risk

- free rate on government bonds as an appropriate measure of th e consumption rate of

interest. Arrow (2000) suggests that it is roughly 3

- 4 percent. OMB cites evidence of a 3.1 percent pre

- tax rate for

10

- year Treasury notes in the A

- 4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4

percen t for 30

- year Treasury securities.

14A

- 22recommendation to use 3 percent to represent the consumption rat e of interest. s A measure of the

post

- tax risky rate for investments whose returns are positively correlated with overall equity

market returns can be obtained by adjusting pre

- tax rates of household returns to risky

investments (approximately 7 percent) f or taxes , which yields a real rate of roughly 5 percent. t

The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount

rate. Under this approach, the analyst applies either positive or normative judgments in selecting

values for the key parameters of the Ramsey equation: η ( coefficient of relative risk aversion or

elasticity of the marginal utility of consumption) and ρ ( pure ra te of time preference). u 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. v In the simplest version of the Ramsey model,

with an optimizing represent ative agent with perfect foresight, what we are calling the “Ramsey

discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e. , the market interest rate.

A review of the literature provides some guidance on reasonable parameter values for the

Ramsey discounting equation , based on both prescriptive and descriptive approaches.

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3

(Weitzman cites plausible values as those ranging from 1 to 4), althou gh not all authors

s 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.

t 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).

u The parameter ρ measures the pure rate of time preference : people’s behavior reveals a preference for an increase

in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The

parameter η captures diminishing marginal utility : consumption in the future is likely to be higher than consumption

today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller

reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar

increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income

is equally valuable no matter the level of income; and if η > 1, then a one percent increase in income is less valuable

to wealthier individuals.

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the

rate of consumption growth.

14A

- 23articulate whether their choice is based on prescriptive or descriptive reasoning. w

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, because

η equal to 1 suggests savings rates that do not conform to observed behavior.

• ρ. With respect to the pure rate of time preference, most papers in the climate change

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates

tend to follow from moral judgments involving intergenerational neutrality. Some have

argued that to use any value other than ρ = 0 would unjustly discriminate against future

generations ( e.g. , Arrow et al. 1996, Stern et al. 2006). However, even in an int er

generational setting, it may make sense to use a small positive pure rate of time

preference because of the small probability of unforeseen cataclysmic events (Stern et al.

2006).

• g. A commonly accepted approximation is around 2 percent per year. For t he

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 t o

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 p oorer 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

in come savings behavior, however, Stern’s assumptions suggest that individuals would save 93

percent of their income. x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is

a case to be made for raising η due to the amount of weight lower values place on damages far in

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate,

yields a discount rate of greater than 2 percent.

w 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 (198 6), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on

labor supply behavior. He shows that existing evidence of the effects of wag e changes on labor supply imposes a

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and

concludes that the standard expected utility model cannot generate high levels of risk aversion without contrad icting

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation.

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year

using data on mortality rates . Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ =

1.07.

x Stern (2008) argues that building in a po sitive rate of exogenous technical change over time reduces the implied

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity . (At the same

time, adding exogenous technical change

— all else equal

— would increase g as well.)

14A

- 24We 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 i mportant driver of the benefits estimate, it is

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate

sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and

Groom et al. (2006) co nfirm 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 i t will result in an

effective (or certainty

- equivalent) discount rate that declines over time. Consequently, lower

discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001;

Newell and Pizer 2003; Groom et al. 2006; Gollier 200 8; Summers and Zeckhauser 2008; and

Gollier and Weitzman 2009).

The proper way to model discount rate uncertainty remains an active area of research.

Newell and Pizer (2003) employ a model of how long

- term interest rates change over time to

forecast fut ure 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. (20 06), uses more

general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility

of interest rates depends on whether rates are currently low or high and the variation in the level

of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over

time ( e.g. , Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further

out in time. y A key question that has emerged with regard to both of these approaches is the

trade

- off between potential time inconsistency and g iving greater weight to far future outcomes

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of

their Guidelines for Economic Analysis ). z

y 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.

z Uncertainty in future damages is distinct from uncertainty in the discount rate . Weitzman (2008) argues that

Stern’s choice of a low discount ra te 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 miti gation today. Newbold and Daigneault, (2009) and

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity,

14A

- 25The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in

this context and uncertainty about how interest rates may change over time, we use three

discount rates to span a plausible range of certainty

- equivalent constant discount rates: 2.5, 3,

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup

determined that these three rates reflect reasonable judgments under both descriptive and

prescriptive approaches.

The central value, 3 percent, is consistent wi th 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 te mperatures are estimated in consumption

- equivalent units. Further, 3

percent roughly corresponds to the after

- tax riskless interest rate. T he upper value of 5 percent is

included to represent the possibility that climate damages are positively correlated w ith market

returns. Additionally, this discount rate may be justified by the high interest rates that many

consumers use to smooth consumption across periods.

The low value, 2.5 percent, is included to incorporate the concern that interest rates are

high ly uncertain over time. It represents the average certainty

- equivalent rate using the mean

reverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate

of 3 percent. Using this approach, the certainty equivalent is about 2. 2 percent using the random

walk model and 2.8 percent using the mean reverting approach. aa 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 in vestments 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 integrated assessment

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of two

thirds.

• Five sets of GDP, population

, and carbon emissions trajectories based on EMF

- 22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damag es can also work in

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required.

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A

- 26Because 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

.

F or each of the IAM s , 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, dependin g on the

temperature in that period relative to the pre

- industrial average

temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of

temperature change in that period.

c. In DICE, temperature affects both consumpti on and investment, so we

first adjust the EMF GDP paths as follows: Using the Cobb

- Douglas

production function with the DICE2007 parameters, we extract the path of

exogenous technical change implied by the EMF GDP and population

paths, then we recalculate the baseline GDP path taking into account

climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by

model.)

4. Recalculate the temperature effects and damages expected in all years beyond t

resulting from this adjusted path of emissions, as in step 2.

5. Subtract the damages computed in step 2 from those i n step 4 in each year. (DICE

is run in 10

- year time steps, FU ND 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 da mages

computed in step 6, divided by the unit of carbon emissions used to shock the

models in step 3.

14A

- 278. 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 ton s 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

- mo del 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 dam ages 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 wh ich these

data are available from the EMF

- 22 models. (A more detailed discussion of these assumptions is

included in the Annex .)

This exercise produces 45 separate distributions of the SCC for a given year, the product

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate

distributions for consideration in a regulatory impact analysis.

To produce a range of plausible estimates that still reflects the uncertainty in the

estimation exercise, the distributions from each of the models and scenarios are equally weighed

and combined to produce three separate probability distributions for SCC in a given year, one for

each assumed discount rate. These distributions are then used to define a range of point estimates

for th e global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than

another. Because the literature shows that the SCC is quite sensitive to assumptions about the

discount rate, and because no consensus exists on the appropriate rate to use in an

intergenerational context, we present SCCs based on the average values across models and

socioeconomic scenarios for each discount rate.

The interagency group selected four SCC values for use in regulatory analyses. Three

values are based on the average SCC across models and socioeconomic and emissions scenarios

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higher

than

- expected economic impacts from climate change further out in the tails of the SC C

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 cent ral 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 f ull range.

14A

- 28As previously discussed, low probability, high impact events are incorporated into the

SCC values through explicit consideration of their effects in two of the three models as well as

the use of a probability density function for equilibrium climate sensitivity. Treating climate

sensitivity probabilistically results in more high

- temperature outcomes, which in turn lead to

higher projections of damages. Although FUND does not include catastrophic damages (in

contrast to the other two models), i ts probabilistic treatment of the equilibrium climate sensitivity

parameter will directly affect the non

- catastrophic damages that are a function of the rate of

temperature change.

In Table 14A.5.1 , we begin by presenting SCC estimates for 2010 by model, scenario,

and discount rate to illustrate the variability in the SCC across each of these input parameters. As

expected, higher discou nt rates consistently result in lower SCC values, while lower discount

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there

are differences in the SCC estimated across the three main models. For these estimates , FUND

produces the lowest estimates, while PAGE generally produces the highest estimates.

14A

- 29Table 14A.5.1 Disaggregated Social Cost of CO 2 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 PA GE 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. bb

bb 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, respecti vely. The FUND default value for η is 1, and Tol generates SCC

14A

- 30The SCC estimates from FUND are sensitive to differences in emissions pat hs but

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for

DICE and PAGE. This likely occurs because of several structural differences among the models.

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages

increases with the level of temperature alone, whereas in FUND the fractional loss also increases

with the rate of temperature change. Furthermore, in FUND increases in income over time

decrease vulnerability to climate c hange (a form of adaptation), whereas this does not occur in

DICE and PAGE. These structural differences among the models make FUND more sensitive to

the path of emissions and less sensitive to GDP compared to DICE and PAGE.

Figure 14A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the

rank ordering of SCC for PAGE and DIC E. 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 scena rios and models for a given discount rate. Values

for the years in between are calculated using a simple linear interpolation.

estimates for values of ρ = 0, 1, and 3 in many recent papers ( e.g. Anthoff et al. 2009). The path of per

- capita

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer ter m. 0 50 100 150 200 250 300 350 400 450

2000 2010 2020 2030 2050 2100 Global

GDP

(trillions

of U.S.

dollars) IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

14A

- 31Table 14A.5.2 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger

incremental damages as physical and economic systems become more stressed in response to

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC

directly using DICE, PAGE, a nd 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 Ann ual 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 i n regulatory analysis.

Damages from future emissions should be discounted at the same rate as that used to calculate

the SCC estimates themselves to ensure internal consistency

— i.e. , future damages from climate

change, whether they result from emissions to day or emissions in a later year, should be

discounted using the same rate. For example, climate damages in the year 2020 that are

14A

- 32calculated using a SCC based on a 5 percent discount rate also should be discounted back to the

analysis year using a 5 perce nt discount rate. cc

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 inhere nt 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 ec onomic impacts of climate change recognized in the climate change literature

(some of which are discussed above) because of lack of precise information on the nature of

damages and because the science incorporated into these models understandably lags behi nd 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 ac idification 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 potential catastrophi c 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 re leases of methane from melting permafrost and warming oceans.

Weitzman (2009) suggests that catastrophic damages are extremely large

— so large, in fact, that

the damages from a low probability, catastrophic event far in the future dominate the effect of

the discount rate in a present value calculation and result in an infinite willingness

- to

- pay for

mitigation today. However, Nordhaus (2009) concluded that the conditions under which

Weitzman's results hold “are limited and do not apply to a wide range of pot ential uncertain

scenarios."

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high

cc 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

- 33impact

, low

- probability risks, using a right

- skewed gamma distribution for climate sensitivity as

well as an uncertain damage coefficient, but in most cases found only a modest risk premium.

Given this difference in opinion, further research in this area is neede d before its practical

significance can be fully understood and a reasonable approach developed to account for such

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in

these IAMs are typically calibrated by estimating damages at moderate temperature increases

( e.g. , DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assum ing

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 ass essment 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 i s 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 t o do not adequately

account for this directed technological change. dd For example, scientists may develop crops that

are better able to withstand higher and more variable temperatures. Although DICE and FUND

have both calibrated their agricultural sectors u nder the assumption that farmers will change land

use practices in response to climate change (Mastrandrea, 2009), they do not take into account

technological changes that lower the cost of this adaptation over time. On the other hand, the

calibrations do not account for increases in climate variability, pests, or diseases, which could

make adaptation more difficult than assumed by the IAMs for a given temperature change.

Hence, models do not adequately account for potential adaptation or technical change t hat 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 overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to

assume about relative risk aversion with regard to high

- impact outcomes. These calculations do

not take into account the possibility that individuals may have a higher willingness to pay to

reduce the likelihood of low

- probability, high

- impact damages than they do to reduce the

likelihood of higher

- probability

, but lower

- impact

, damages with the same expected cost. (The

inclusion of the 95 th percentile estimate in the final set of SCC values was largely motivated by

this concern.) If individuals do show such a higher willingness to pay, a further question is

whether that fact should be taken into account for regulatory policy. Even if individuals are not

dd However these research dollars will be diverted from what ever their next best use would have been in the absence

of climate change (so productivity/GDP would have been still higher).

14A

- 34risk

- averse for such scenarios, it is possible that regulatory policy should include a degree of

risk

- aversion.

Assuming a risk

- neutral representative agent is consistent with OMB’s Circular A

- 4,

which advises that the estimates of benefits and costs used in regulatory analysis are usually

b ased on the average or the expected value and that “emphasis on these expected values is

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While

this may not always be the case, [analysts] should in general assum e ‘risk neutrality’ in [their]

analysis.”

Nordhaus (2008) points to the need to explore the relationship between risk and income

in the context of climate change across models and to explore the role of uncertainty regarding

various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude

that “the assumed rate of risk aversion is at least as important as the assumed rate of time

pr eference in determining the social cost of carbon.” Since Circular A

- 4 allows for a different

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to

continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTRO PHIC 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 im pacts of high

- end warming, and (3) limited near

- term substitutability between

damage to natural systems and increased consumption.

It is the hope of the interagency group that over time researchers and modelers will work

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal

government will continue to evolve with improvements in modeling. In the meantime, we

discuss some of the available evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at moderate levels of warming and

should therefore be viewed cautiously when extrapolated to the high temperatures found in the

upper end of the distribution. Recent science suggests that there are a number of potent ial

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

14A

- 35the 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, an d the release of methane from melting

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C

and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed

through expert elicitation in 2005

– 2006 by Kriegler et al. (2009); results from this study are

highlighted in Table 14A.7.1

. Ranges of probability are averaged across core experts on each

topic

.

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 potentiall y 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 $11 3 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 catastrophic or non

catastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the

distribution to the assumptions made about catastrophic impacts.

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

14A

- 36catastrophic event across the two models. For instance, PAGE places a probability near zero on a

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16

- 36 percent of

crossing at least one of their primary climatic tipping points in a scenario with temperatures

about 2

- 4 °C warmer than pre

- Industrial levels in 2100.

It is important to note that crossi ng a climatic tipping point will not necessarily lead to an

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across

which some aspect of the Earth system starts to shifts into a qualitatively different state (for

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a

catastrophe is a low

- probability environmental change with high economic impact.

Failure to incorporate inter

- sectoral and inter

- regional interactions

The d amage functions do not fully incorporate either inter

- sectoral or inter

- regional

interactions. For instance, while damages to the agricultural sector are incorporated, the effects

of changes in food supply on human health are not fully captured and depend on the modeler’s

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one

region of the world on another region are not included in some of the models (FUND includes

the effects of migration from sea level rise). These i nter

- 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;

Campb ell et al., 2007).

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming

may have severe consequences for natural systems. For instance, during the Paleocene

- Eocene

Th ermal 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 pla nts (Wing et

al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al.,

2009).

The three IAMs used here assume that it is possible to compensate for the economic

consequences of damages to natural systems through increased consumption of non

- climate

goods, a common assumption in many economic models. In the context of climate change,

however, it is possible that the damages to natural systems could become so great that no

increase in consumption of non

- climate good s 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.

14A

- 37Uncalibrated attempt s 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 sele cted 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 higher

than

- expected impacts from temperature change further out in the tails of the SCC distribution.

For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. The

central value is the average 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.

We noted a number of limitations to this analysis, including the incomplete way in which

the integrate d assessment models capture catastrophic and non

- catastrophic impacts, their

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of

damages to high temperatures, and assumptions regarding risk aversion. The limited amount of

research linking climate impacts to economic damages makes this modeling exercise even more

difficult. It is the hope of the interagency group that over time researchers and modelers will

work to fill these gaps and that the SCC estimates used f or regulatory analysis by the Federal

government will continue to evolve with improvements in modeling.

14A

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Environmental and Resource Economi cs 21:47

- 73.

Tol, R. 2002b. “Estimates of the damage costs of climate change. Part II: dynamic estimates.”

Environmental and Resource Economics 21:135

- 160.

Tol, R. 2006. “Exchange Rates and Climate Change: An Application of FUND.” Climatic

Change 75(1

- 2): 59

- 80.

Tol, R. 2009. “An analysis of mitigation as a response to climate change.” Copenhagen

Consensus on Climate. Discussion Paper.

U.S. Department of Defense. 2010. Quadrennial Defense Review Report. February.

Warren, R., et al. 2006. “Spotlight Impact Functions in Integrated Assessment.” Tyndall Center

for Climate Change Research, Working Paper 91.

Weitzman, M. 2009. “On modeling and interpreting the economics of catastrophic climate

change.” Review of Economics and Statistics 91:1

- 19

.

Weitzman, M. 2007. “A review of The Stern Review of the Economics of Climate Change .”

Journal of Economic Literature 45:703

- 724.

14A

- 43Weitzman, M. 1999. In Portney P.R. and Weyant J.P. (eds.), Discounting and Intergenerational

Equity , Resources for the Futu re, Washington, D.C.

Weitzman, M. 1998. “Why the Far

- Distant Future Should Be Discounted at Its Lowest Possible

Rate.” Journal of Environmental Economics and Management 36 (3): 201

- 208.

Wing, S. et al. 2005. “Transient floral change and rapid global warm ing at the Paleocene

- Eocene

boundary.” Science 310: 993

- 996.

14A

- 4414A.9 ANNEX

Table 14A.9.1 Annual SCC Values: 2010

– 2050 (in 2007 dollars)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non

- 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

- 4514A.9.1 Other (non

- CO 2 ) gases

In addition to fossil and industrial CO 2 emissions, each EMF scenario provides

projections of methane (CH 4 ), nitrous oxide (N 2 O), fluorinated gases, and net land use CO 2

emissions to 2100. These assumptions are used in all three I AMs 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. ee This approach respects the EMF scenarios as much as possible and at the same time

takes account of those components not included in the EMF projections. Since each model treats

non

- CO 2 gases differently ( e.g. , DICE lumps all other gases into one composite exogenous

input), this approach was applied slightly differently in each of the models.

FUND : Rather than relyin g on RF for these gases, the actual emissions from each

scenario were used in FUND. The model default trajectories for CH 4 , N 2 0, SF 6 , and the CO 2 emissions from land were replaced with the EMF values.

PAGE : PAGE models CO 2 , CH 4 , sulfur hexafluoride (SF 6 ), and aerosols and contains an

"excess forcing" vector that includes the RF for everything else. To include the EMF values, we

removed the default CH 4 and SF 6 factors ff , 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.

DICE : DICE presents the greatest challenge because all forcing due to factors other than

industrial CO 2 emissions is embedded in an exogenous non

- CO 2 RF vector. To decompose this

exogenous forcing path into EMF non

- CO 2 gases and other gases, we relied on the references in

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing

from all non

- CO 2 sources is

- 0.06 W/m 2 in 2005, as reported in AR4, and 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

ee Note EMF did not provide CO 2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed

the fossil, industrial

, and land CO 2 emissions into MAGICC (considered a "neutral arbiter" model, which is tuned to

emulate the major global climate models) and the resulting CO 2 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).

ff Both the model default CH 4 emissions and the initial atmospheric CH 4 is set to zero to avoid double counting the

effect of past CH 4 emissions.

14A

- 46decomposed into: 0.98 W/m 2 due to the EMF non

- CO 2 gases,

- 1.2 W/m 2 due to aeroso ls, and the

remainder, 0.16 W/m 2 , due to other residual forcing.

For subsequent years, we calculated the DICE default RF from aerosols and other non

CO 2 gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2 100 at the rate projected by the TAR

and then stays constant thereafter ; and

(2) With respect to RF from non

- CO 2 gases not included in the EMF

- 22 scenarios, the share

of non

- aerosol RF matches the share implicit in the AR4 summary statistics cited above a nd

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 low er 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 s ulfur dioxide, black carbon and organic carbon, has improved in the

post

- SRES scenarios. Generally, these emissions are projected to be lower than reported in

SRES. gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent

w ith the recent literature on these emissions. For example, the f igure 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. hh Recent scenarios project sulf ur 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. ii The lower

bound projections of the recent literatu re 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

.

gg AR4 Synthesis Report, p. 44, www.ipcc.ch/pdf/assessment

- rep ort/ar4/syr/ar4_syr.pdf

hh 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.

ii See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing

trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental

Science and Technology, 36(22):4707

- 4713; Streets, D., K. Jiang, X. Hu, J. Sint on, 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

- 47Figure 14A.9.2 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 percentile of the frequency distribution for the full

ensemble of 40 SRES scenarios. The blue area (and the thin dashed lines in blue)

illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines indicate

the minimum and maximum of SO 2 emissions scenarios developed pre

- SRES.

Source: IPCC (2007), AR4 WGIII 3.2,

www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3

- ens3

- 2

- 2

- 4.html

.

Although other appro aches 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

assum e that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by

2100 increases average 2010 SCC values by 6

- 7 percent (or $0.50

- $3)

– d epending 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 added to the fossil and

i ndustrial 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

thes e projections are available from the EMF

- 22 models. These inputs were extrapolated from

2100 to 2300 as follows:

14A

- 481. Population growth rate declines linearly, reaching zero in the year 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300.

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 for cing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying

assumption than a linear or exponential extrapolation of the pre

- 2100 economic growth rate of

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the

degradation of environmental sinks available for assimilating pollution from economic

production activities may eventually overtake the rate of technological progress. Thus, the

overall rate of eco nomic 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 wou ld 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 Na tions long run population forecast,

which estimates global population to be fairly stable after 2150 in the medium scenario (UN

2004). jj The resulting range o f EMF population trajectories (f igure below ) also encompass the

UN medium scenario forecasts throug h 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 th e 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 f or 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.

jj United Nations. 2004. World Population to 2300

. www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf

14A

- 49Figure 14A.9.3 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 equa l to the average of the population under

the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 5.5 6.5 7.5 8.5 9.5 10.5 11.5

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsGlobal Population [billion]

Year

14A

- 50Figure 14A.9.4 World GDP, 2000

- 2300 (Post

- 2100 extrapolations assume GDP per capita

growth declines linearly, reaching zero i n the year 2300)

Note: In the fifth scenario, 2000

- 2100 GDP is equal to the average of the GDP under the 550

ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four models. 0 500 1000 1500 2000

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

YearWorld GDP [$trillion]

14A

- 51Figure 14A.9.5 Global Fossil and Industrial CO 2 Emissions, 2000

- 2300 (Post

- 2100

extrapolations assume growth rate of CO 2 intensity (CO 2 /GDP) over

2090

- 2100 is maintained through 2300 )

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under

the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 0 50 100 150 200

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil&Industrial CO2 Emissions [GtCO2/decade]

Year

14A

- 52Figure 14A.9.6 Global Net Land Use CO 2 Emissions, 2000

- 2300 (Post

- 2100

extrapolations assume emissions decline linearly, reaching zero in the

year 2200 ) kk

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under

the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models.

kk 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). -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

ExtrapolationsNet land CO2 emissions [GtCO2/yr]

Year z

14A

- 53Figure 14A.9.7 Global Non

- CO 2 Radiative Forcing, 2000

- 2300 (Post

- 2100 extrapolations

assume constant non

- CO 2 radiative forcing after 2100 )

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under

the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 0.00 0.75 1.50 2.25 3.00 3.75

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsnonCO2 RF [W/m^2]

Year

14A

- 54Figure 14A.9.8 Global CO 2 Intensity ( fossil & industrial CO 2 emissions/GDP), 2000

- 2300

(P ost

- 2100 extrapolations assume decline in CO 2 /GDP growth rate over

2090

- 2100 is maintained through 2300 )

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under

t he 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 0 0 0 0 0 1 1 1 1

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil & Industrial CO2 intensity [GtCO2/trillion $]

Year

14A

- 55Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8

MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3

Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0

MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3

Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE

- 33.1

- 18.9

- 13.3

- 5.5 4.1 19.3 18.7 43.5 67.1 150.7

MERGE optimistic

- 33.1

- 14.8

- 10

- 3 5.9 14.8 20.4 43.9 65.4 132.9

Message

- 32.5

- 19.8

- 14.6

- 7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base

- 31.0

- 15.9

- 10.7

- 3.4 6 22.2 21 46.4 70.4 152.9

5th scenario

- 32.2

- 21.6

- 16.7

- 9.7

- 2.3 3 6.7 20.5 34.2 96.8

14A

- 56Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE

- 25.2

- 15.3

- 11.2

- 5.6 0.9 8.2 10.4 25.4 39.7 90.3

MERGE optimistic

- 24.0

- 12.4

- 8.7

- 3.6 2.6 8 12.2 27 41.3 85.3

Message

- 25.3

- 16.2

- 12.2

- 6.8

- 0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base

- 23.1

- 12.9

- 9.3

- 4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario

- 24.1

- 16.6

- 13.2

- 8.3

- 3

- 0.2 2.9 11.2 19.4 53.6

14A

- 57Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2

MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4

Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1

MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4

Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE

- 11.7

- 8.4

- 6.9

- 4.6

- 2.2

- 1.3 0.7 4.1 7.4 17.4

MERGE optimistic

- 10.6

- 7.1

- 5.6

- 3.6

- 1.3

- 0.3 1.6 5.4 9.1 19.0

Message

- 12.2

- 8.9

- 7.3

- 4.9

- 2.5

- 1.9 0.3 3.5 6.5 15.6

MiniCAM base

- 10.4

- 7.2

- 5.8

- 3.8

- 1.5

- 0.6 1.3 4.8 8.2 18.0

5th scenario

- 10.9

- 8.3

- 7

- 5

- 2.9

- 2.7

- 0.8 1.4 3.2 9.2

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by discount

rate

* The distribution of SCC values ranges from

- $5,192 to $66,116

, but the X

- axis has been

truncated at approximately the 1 st and 99 th percentiles to better show the data. 0 2000 4000 6000 8000 10000 12000 14000 16000-25-12114274053667992105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%

14A

- 58Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates

Discount

Rate Scenario

DICE PAGE FUND

5% Mean 9 6.5

- 1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3% Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50% Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30

14A

- 59APPENDIX 14B. TECHNICAL U PDATE OF SOCIAL COST OF CARBO N FOR

REGULATORY IMPACT AN ALYSIS UNDER EXECUTI VE ORDER 12866

TABLE OF CONTENTS

14B.1 PREFACE

................................ ................................ ................................ .............. 14B

- 1

14B.2 PURPOSE

................................ ................................ ................................ .............. 14B

- 1

14B.3 SUMMARY OF MODEL UPD ATES

................................ ................................ ... 14B

- 2

14B.3.1 DICE

................................ ................................ ................................ ...................... 14B

- 2

14B.3.1.1 Ca rbon Cycle Parameters

................................ ............................... 14B

- 2

14B.3.1.2 Sea Level Dynamics

................................ ................................ ....... 14B

- 3

14B.3.1.3 Re

- calibrated Damage Function

................................ ..................... 14B

- 4

14B.3.2 FUND

................................ ................................ ................................ ..................... 14B

- 4

14B.3.2.1 Space Heating

................................ ................................ ................. 14B

- 5

14B.3.2.2 Sea Level Rise and Land Loss

................................ ....................... 14B

- 5

14B.3.2.3 Agriculture

................................ ................................ ...................... 14B

- 5

14B.3.2.4 Temperature Response Model

................................ ........................ 14B

- 6

14B.3.2.5 Methane

................................ ................................ .......................... 14B

- 6

14B.3.3 PAGE

................................ ................................ ................................ ..................... 14B

- 6

14B.3.3.1 Sea Level Rise

................................ ................................ ................ 14B

- 7

14B.3.3.2 Revised Damage Function to Account for Saturation

.................... 14B

- 7

14B.3.3.3 Regional Scaling Factors

................................ ................................ 14B

- 7

14B.3.3.4 Probability of a Discontinuity

................................ ........................ 14B

- 7

14B.3.3.5 Adaptation

................................ ................................ ...................... 14B

- 8

14B.3.3.6 Other Noteworthy Changes

................................ ............................ 14B

- 8

14B.4 REVISED SCC ESTIMATE S

................................ ................................ ............... 14B

- 9

14B.5 OTHER MODEL LIMITATI ONS OR RESEARCH GAPS

.............................. 14B

- 11

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars per ton of

CO 2 )

................................ ................................ ................................ ......... 14B

- 10

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and

2050

................................ ................................ ................................ .......... 14B

- 11

Table 14B.5.1 Annual SCC Values: 2010

- 2050 (2007$/ton CO 2 )

................................ .. 14B

- 14

Table 14B.5.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ......... 14B

- 15

Table 14B.5.3 SCC Estimates at 3 Percent Dis count Rate (2007$/ton CO2)

................. 14B

- 15

Table 14B.5.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ......... 14B

- 16

LIST OF FIGURES

Figure 14B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 )

............ 14B

- 10

14B

- iAPPENDIX 14B. TECHNICAL U PDATE OF SOCIAL COST OF CARBO N FOR

REGULATORY IMPACT AN ALYSIS UNDER EXECUTI VE ORDER 12866

14B.1 P REFACE

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 consisten t with the rest

of this technical support document.

14B.2 P URPOSE

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

ver sions of the three integrated assessment models used by the U.S. government to estimate the

SCC (DICE, FUND, and PAGE), are now available and have been published in the peer

reviewed literature. While acknowledging the continued limitations of the approach taken by the

interagency group in 2010 (documented in the original 2010 TSD), this document provides an

update of the SCC estimates based solely on the latest peer

- reviewed version of the models,

replacing model versions that were developed up to ten year s ago in a rapidly evolving field. It

does not revisit other assumptions with regard to the discount rate, reference case socioeconomic

and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are

modeled are confined to those that have been incorporated into the latest versions of the models

by the developers themselves in the peer

- reviewed literature. The Environmental Protection

Agency (EPA), in collaboration with other Federal agencies such as the Department of Energy

(DOE), continues to investigate potential improvements to the way in which economic damages

associated with changes in 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 www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

c See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010). 1

14B

- 114B.3 SUMMARY OF MODEL UPD ATES

This section briefly summarizes changes integrated into the most re cent versions of the

three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on

describing those model updates that are relevant to estimating the social cost of carbon. For

example, both the DICE and PAGE models now inclu de an explicit representation of sea level

rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to

ensure damages are constrained GDP, updated regional scaling of damages, and a revised

treatment of potentially abrupt sh ifts in climate damages. In the most recent version of DICE,

the model’s simple carbon cycle has been updated to be more consistent with a relatively more

complex climate model. The FUND model includes updated damage functions for sea level rise

impacts, the agricultural sector, and reduced space heating requirements, as well as changes to

the response of temperature to the buildup of GHG concentrations and the inclusion of indirect

effects of methane emissions. Changes made to parts of the models that are superseded by the

interagency working group’s modeling assumptions

– regarding climate sensitivity, discounting,

and socioeconomic variables

– are not discussed.

14B.3.1 DICE

Changes in the DICE model relevant for the SCC estimates developed by the interagency

w orking 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. Chan ges 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 superse ded 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

d MAGI CC 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 ar t earth system simulation models (Randall et al. 2007). 4

14B

- 2decade, 9.7 percent of the carbon in the shallow ocean is transferred to th e atmosphere, 85.3

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as

a carbon sink and therefore a higher concentration of carbon in the atmosphere than in

DICE2007, for a given path of emissions. All else equal, these changes will generally increase

the level of warming and therefore the SCC estimates in DICE2010 relative to those from

DICE2007.

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

descri ption 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 diffe rence between the sea

level in the previous period and the long run equilibrium sea level, which is 0.5 meters per

degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the

melting of glaciers and small ice caps occur s at a rate of 0.008 meters per decade per °C above

the average global temperature in 1900.

The contribution to sea level rise from melting of the Greenland ice sheet is more

complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1

o C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in

each period is proportional to the difference between the previous period’s sea level anomaly and

the equilibrium sea level anomaly, where the constant of proportionality increases with the

temperature anomaly in the current period.

The contribution to SLR from the melting of the Antarctic ice sheet is

- 0.001 meters per

decade when the temperature anomaly is below 3 °C and increases linearl y to a maximum rate of

0.025 meters per decade at a temperature anomaly of 6 °C.

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at:

www.econ.yale.edu/

. f For a review of post

- IPCC AR4 research on sea level rise, see Nicholls et al. (2011 ) 5 and NAS (2011). 6

14B

- 314B.3.1.3 Re

- calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a

fractional loss of gross economic output in each period. A portion o f the remaining economic

output in each period (net of climate change damages) is consumed and the remainder is invested

in the physical capital stock to support future production, so each period’s climate damages will

reduce consumption in that period and in all future periods due to the lost investment. The

fraction of output in each period that is lost due to climate change impacts is represented as one

minus a fraction, which is one divided by a quadratic function of the temperature anomaly,

producing a sigmoid (“S”

- shaped) function. The loss function in DICE2010 has been expanded

by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the

temperature anomaly coefficients have been recalibrated to avoid double

- counting damages from

sea level rise that were implicitly included in these parameters in DICE2007.

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3), 3 who notes

that “...damages in the uncontrolled (baseline) ( i.e. , reference) case ... in 2095 are $12 trillion,

or 2.8 percent of global output, for a global temperature increase of 3.4 o C above 1900 levels.”

This compare s 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 d ue 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

decrea se the interagency working group SCC estimates slightly, all else equal.

14B.3.2 F UND

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

ver sions of the model is available from the model authors. g Notable changes, due to their impact

on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level

rise damage functions in addition to changes to the temperature response function and the

inclusion of indirect effects from methane emissions. h We discuss each of these in turn.

g 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 atmosph eric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along with making

minor stability improvements in the sea wall construction algorithm.

h The other damage sectors (water resources, space cooling, land loss, migration, ec osystems, human health, and

extreme weather) were not the subject of significant updates.

14B

- 414B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are

based on the estimated impact due to on e 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 t hat scales the base year damages adjusted for vulnerability allows for the

possibility that in some simulations the benefits associated with reduced heating needs may be an

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling

has been modified to ensure that the function is everywhere concave, meaning that for every

simulation there will exist an upper bound on the benefits a region may receive from reduced

space heating needs. The new formulation approaches a value of two in the limit as the

temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the

reduced expenditures on space heating at any level of warming will not exceed two times the

reductions experienced at one degree of warming . Since the reduced need for space heating

represents a benefit of climate change in the model, or a negative damage, this change will

increase the estimated SCC. This update accounts for a significant portion of the difference in the

expected SCC estimate s reported by the two versions of the model when run probabilistically.

14B.3.2.2 Sea Level Rise and Land Loss

The FUND model explicitly includes damages associated with the inundation of dry land

due to sea level rise. The amount of land lost within a region is de pendent upon the proportion of

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND

3.5 the function defining the potential land lost in a given year due to sea level rise is linear in

the rate of sea level rise for that year. This assumption implicitly assumes that all regions are

well represented by a homogeneous coastline in length and a constant uniform slope moving

inland. In FUND 3.8 the function defining the potential land lost has been changed to be a non

lin ear function of sea level rise, thereby assuming that the slope of the shore line is not constant

moving inland, with a positive first derivative. The effect of this change is to typically reduce the

vulnerability of some regions to sea level rise based la nd 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, t hereby 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 separa ble

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 anomal y is modeled as a quadratic function

with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the

ratio of two normal distributions. Within this specification, as draws from the distribution in the

14B

- 5denominator approached zero the share of the sector’s value “lost” approaches (+/

- ) infinity

independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic

coefficients are drawn directly from truncated normal distributions so that they remain in the

range [0, ) ∞ and ( ,0]

− ∞ , respectively, where the means for the new distributions are set equal to

the ratio of the means from the normal distributions used in the previous version. In general the

impact of this change has been to increase the likelihood that increases in the tem perature level

will have either larger positive or negative effects on the agricultural sector relative to the

previous version (through eliminating simulations in which the “lost” value approached (+/

- )

infinity). The net effect of this change on the SCC estimates is difficult to predict.

14B.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 temper ature 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 th e

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact

that the progressive heat uptake of the deep ocean causes th e rate to slow at higher values of the

equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated

to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity

of the rate of temper ature 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 highe r temperatures are

reached during the timeframe analyzed and as the same damages experienced in the previous

version of the model are now experienced earlier and therefore discounted less.

14B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emi ssions, and has developed

methods for proxying such effects when computing the global warming potential of methane

(Forster et al. 2007). 8 FUND 3.8 now includes the same methods for incorporating the indirect

effects of methane emissions. Specifically, the average atm ospheric 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 productio n 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 wi th

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 m ost directly affect the SCC estimates

14B

- 6include: explicitly modeling the impacts from sea level rise, revisions to the damage function to

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised

treatment for the probabil ity 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 pa pers (Hope 2011a, 2011b, 2011c). 10

, 11

, 12 A description of PAGE2002 can be found

in Hope (2006). 13

14B.3.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories

– economic and non

economic impacts

- PAGE2009 adds a third explicit category: damages from sea level rise. In the

previous version of the model, damages from sea level rise were subsumed by the other damage

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

- e conomic 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 a llowed Eastern Europe to potentially experience large benefits from temperature

increases, and which also did not bound the possible damages that could be experienced.

14B.3.3.3 Regional Scaling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the

European Union (EU) and then, assumes that damages for other regions are proportional based

on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s

coastline relative to the EU (Hope 2011b ). 11 Because of the long coastline in the EU, other

regions are, on average, less vulnerable than the EU for the sam e 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, s maller 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

14B

- 7the 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 triang ular 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 p robability 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 o f time, but once it occurs, its effect is permanent.

14B.3.3.5 Adaptation

As in PAGE2002, adaptation is available to increase the tolerable level of temperature

change and can help mitigate any climate change impacts that still occur. In PAGE this

adaptation is the same regardless of the temperature change or sea level rise and is therefore akin

to what is more commonly considered a reduction in vulnerability. It is modeled by modifying

the temperature change and sea level rise used in the damage function or by redu cing 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 t ime 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

- ec onomic sector, in PAGE09 adaptation is available to reduce 15 percent of the

damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to

fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE200 2.

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 lin ear 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

14B

- 8of soil car bon. 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 wa s determined

solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional

temperature anomaly is further adjusted using an additive factor that is based on the average

absolute latitude of a region relative to the area wei ghted average absolute latitude of the Earth’s

landmass.

14B.4 R E VISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same

methodology detailed in the 2010 TSD. 1 T he 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 Bak er equilibrium climate sensitivity distribution calibrated to the Fourth

Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five

scenari os 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 discoun t 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, respect ively. 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 chos en. (A detailed set of percentiles by model and scenario combination is

available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central

value, and so the central value that emerges is the average SCC across models at the 3 percent

discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in

regulatory impact analysis, the interagency group emphasizes the importance and value of

including all four SCC values.

Table 14B.4.1 shows the four selected SCC estimates in five year increments from 2010

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate.

Values for the years in between are calculated using basic linear interpolation. The full set of

annual SCC estimates between 2010 and 2050 is reported in the Annex.

14B

- 9Table 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.4.2 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 (150,00 0 estimates

in total for each discount rate). In general, the distributions are skewed to the right and have long

tails. The Figure also shows that the lower the discount rate, the longer the right tail of the

distribution.

Figure 14B.4.2 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 cli matic change. The approach taken by the

interagency group is to allow the growth rate to be determined endogenously by the models

14B

- 10through 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 disc ussed 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.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, the

treatment of adaptation and technological change, and the way in which inter

- regional and inter

sectoral 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

- 11REFERENCES

1. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for

Regulatory Impact Analysis under Executive Order 12866 , February 2010. United States

Government. Washington, D.C.

< www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

- Cost

- of

Carbon

- for

- RIA.pdf >

2. Nordhaus, W., A Question of Balance . 2008. Yale University Press: New Hav er.

3. Nordhaus, W., Economic aspects of global warming in a post

- Copenhagen environment

. Proceedings of the National Academy of Sciences , 2010. 107(26): pp. 11721

- 11726

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.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University

Press: Cambridge, United Kingdom and New York, NY, USA

5. Nicholls, R. J., N. Marinova, J.A. Lowe, S. B rown, P. Vellinga, D. de Gusmão, J. Hinkel

and R.S.J. Tol, Sea

- level rise and its possible impacts given a ‘beyond 4°C world’ in the

twenty

- first century

. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161

- 181

6. National Academy of Sciences, Climate Stabil ization Targets: Emissions,

Concentrations, and Impacts over Decades to Millennia . 2011. National Academies

Press, Inc: Washington, DC.

7. Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a

decomposition analysis using FUND

. Climatic Change , 2013(Forthcoming)

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 i n 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.

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Ti gnor and H.L.

Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New

York, NY, USA

9. Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates

from PAGE09 are higher than those from PAGE2002

. C limatic Change

, 2012(Forthcoming)

14B

- 1210. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description, April,

2011. Cambridge Judge Business School

< www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1104.pdf >

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model, June, 2011. Cambridge

Judge Business School

< 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, July, 2011.

Cambridge Judge Business School

< 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 Reas ons for Concern

. The Integrated Assessment

Journal , 2006. 6(1): pp. 19

- 56

14B

- 13ANNEX

Table 14B.5.1 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 98 221

14B

- 14Table 14B.5.2 202 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.5.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE

- 14

- 3 1 9 20 25 35 54 69 111

MERGE

- 8

- 1 3 9 18 22 31 47 60 97

MESSAGE

- 16

- 5

- 1 6 16 18 28 43 55 88

MiniCAM Base

- 9

- 1 3 10 21 27 35 53 67 107

5th Scenario

- 22

- 10

- 5 2 10 13 20 33 42 63

14B

- 15Table 14B.5.4 20 2 0 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 14

14B

- 16APPENDIX 17A. REGULATORY IMPACT AN ALYSIS: SUPPORTING M ATERIALS

TABLE OF CO NTENTS

17A.1 INTRODUCTION

................................ ................................ ................................ 17A

- 1

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

................................ . 17A

- 1

17A.3 NIA

- RIA INTEGRATED MODEL

................................ ................................ ...... 17A

- 7

17A.4 CONSUMER REBATE POLI CY MARKET PENETRATIO N CURVES

......... 17A

- 7

17A.4.1 Introduction

................................ ................................ ................................ ........... 17A

- 7

17A.4.2 Adjustment of XENERGY Penetration Curves

................................ .................... 17A

- 9

17A.4.3 Interpolation of Penetration Curves

................................ ................................ .... 17A

- 10

17A.5 CON SUMER REBATE PROGRAM S

................................ ............................... 17A

- 11

17A.6 FEDERAL AND STATE TA X CREDITS

................................ ......................... 17A

- 12

17A.6.1 Federal Tax Credits for Consumers

................................ ................................ .... 17A

- 12

17A.6.2 Federal Tax Credits for Manufacturers

................................ ............................... 17A

- 14

17A.6. 3 State Tax Credits

................................ ................................ ................................ . 17A

- 14

LIST OF TABLES

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products: Fireplace (vented) (TSL 1)

...................... 17A

- 2

Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products: Fireplace (ventless) (TSL 1)

................... 17A

- 3

Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products: Logs (vented) (TSL 1)

............................ 17A

- 4

Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products: Logs (ventless) (TSL 1)

.......................... 17A

- 5

Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products: Outdoor (TSL 1)

................................ ..... 17A

- 6

Table 17A.5.1 Calculation Steps of the Representative Rebate Value

........................... 17A

- 11

Table 17A.5.2 Rebates for Hearth Products

................................ ................................ ... 17A

- 1 2

LIST OF FIG URE S

Figure 17A.4.1 S

- Curves Showing Effects of External and Internal Sources on

Adoption of New Technologies

................................ ................................ 17A

- 9

17A

- iAPPENDIX 17A. REGULATORY IMPACT AN ALYSIS: SUPPORTING M ATERIALS

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;

• XENERGY penetration curves used to analyze consumer rebates, including:

o Background material,

o DOE’s adjustment of these curves for this analysis, and

o The m ethod DOE used for interpolating the curves;

• Detailed tables of rebates offered for the considered products; and

• Background material on Federal and state tax credits for appliances

.

17A.2 MARKET S HARE A NNUAL I NCREASES B Y P OLICY

Table 1 7A.2.1 through Table 17A.2.5 show the annual increases in market shares of

hearth products meeting the target efficiency levels for the proposed TSL (TSL 1 )

. DOE used

these market share increases as inputs to the NIA

- RIA spreadsheet model.

17A

- 1Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products : Firepla ce (vented) (TSL 1)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary

Energy

Efficiency

Targets Bulk

Government

Purchases

2021 7.9% 4.8% 2.4% 3.2% 0.0%

2022 7.9% 4.8% 2.4% 6.3% 0.0%

2023 7.9% 4.8% 2.4% 9.2% 0.0%

2024 7.9% 4.8% 2.4% 12.1% 0.0%

2025 7.9% 4.8% 2.4% 14.8% 0.0%

2026 7.9% 4.8% 2.4% 17.4% 0.0%

2027 7.9% 4.8% 2.4% 19.9% 0.0%

2028 7.9% 4.8% 2.4% 22.4% 0.0%

2029 7.9% 4.8% 2.4% 24.7% 0.0%

2030 7.9% 4.8% 2.4% 27.0% 0.0%

2031 7.9% 4.8% 2.4% 27.2% 0.0%

2032 7.9% 4.8% 2.4% 27.3% 0.0%

2033 7.9% 4.8% 2.4% 27.6% 0.0%

2034 7.9% 4.8% 2.4% 27.7% 0.0%

2035 7.9% 4.8% 2.4% 27.9% 0.0%

2036 7.9% 4.8% 2.4% 28.1% 0.0%

2037 7.9% 4.8% 2.4% 28.3% 0.0%

2038 7.9% 4.8% 2.4% 28.4% 0.0%

2039 7.9% 4.8% 2.4% 28.6% 0.0%

2040 7.9% 4.8% 2.4% 28.7% 0.0%

2041 7.9% 4.8% 2.4% 28.9% 0.0%

2042 7.9% 4.8% 2.4% 29.0% 0.0%

2043 7.9% 4.8% 2.4% 29.2% 0.0%

2044 7.9% 4.8% 2.4% 29.3% 0.0%

2045 7.9% 4.8% 2.4% 29.4% 0.0%

2046 7.9% 4.8% 2.4% 29.6% 0.0%

2047 7.9% 4.8% 2.4% 29.7% 0.0%

2048 7.9% 4.8% 2.4% 29.8% 0.0%

2049 7.9% 4.8% 2.4% 30.0% 0.0%

2050 7.9% 4.8% 2.4% 30.1% 0.0%

17A

- 2Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products : Fireplace (ventless) (TSL 1)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary

Energy

Efficiency

Targets Bulk

Government

Purchases

2021 10.8% 6.5% 3.2% 1.7% 0.0%

2022 10.8% 6.5% 3.2% 3.4% 0.0%

2023 10.8% 6.5% 3.2% 5.0% 0.0%

2024 10.8% 6.5% 3.2% 6.5% 0.0%

2025 10.8% 6.5% 3.2% 7.9% 0.0%

2026 10.8% 6.5% 3.2% 9.3% 0.0%

2027 10.8% 6.5% 3.2% 10.6% 0.0%

2028 10.8% 6.5% 3.2% 11.8% 0.0%

2029 10.8% 6.5% 3.2% 13.0% 0.0%

2030 10.8% 6.5% 3.2% 14.1% 0.0%

2031 10.8% 6.5% 3.2% 14.1% 0.0%

2032 10.8% 6.5% 3.2% 14.1% 0.0%

2033 10.8% 6.5% 3.2% 14.1% 0.0%

2034 10.8% 6.5% 3.2% 14.1% 0.0%

2035 10.8% 6.5% 3.2% 14.1% 0.0%

2036 10.8% 6.5% 3.2% 14.1% 0.0%

2037 10.8% 6.5% 3.2% 14.1% 0.0%

2038 10.8% 6.5% 3.2% 14.1% 0.0%

2039 10.8% 6.5% 3.2% 14.1% 0.0%

2040 10.8% 6.5% 3.2% 14.1% 0.0%

2041 10.8% 6.5% 3.2% 14.1% 0.0%

2042 10.8% 6.5% 3.2% 14.1% 0.0%

2043 10.8% 6.5% 3.2% 14.1% 0.0%

2044 10.8% 6.5% 3.2% 14.1% 0.0%

2045 10.8% 6.5% 3.2% 14.1% 0.0%

2046 10.8% 6.5% 3.2% 14.1% 0.0%

2047 10.8% 6.5% 3.2% 14.1% 0.0%

2048 10.8% 6.5% 3.2% 14.1% 0.0%

2049 10.8% 6.5% 3.2% 14.1% 0.0%

2050 10.8% 6.5% 3.2% 14.1% 0.0%

17A

- 3Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products : Logs (vented) (TSL 1)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary

Energy

Efficiency

Targets Bulk

Government

Purchases

2021 4.7% 2.8% 1.4% 4.1% 0.0%

2022 4.7% 2.8% 1.4% 7.9% 0.0%

2023 4.7% 2.8% 1.4% 11.3% 0.0%

2024 4.7% 2.8% 1.4% 14.3% 0.0%

2025 4.7% 2.8% 1.4% 17.0% 0.0%

2026 4.7% 2.8% 1.4% 19.5% 0.0%

2027 4.7% 2.8% 1.4% 21.9% 0.0%

2028 4.7% 2.8% 1.4% 24.1% 0.0%

2029 4.7% 2.8% 1.4% 26.3% 0.0%

2030 4.7% 2.8% 1.4% 28.3% 0.0%

2031 4.7% 2.8% 1.4% 28.7% 0.0%

2032 4.7% 2.8% 1.4% 29.0% 0.0%

2033 4.7% 2.8% 1.4% 29.4% 0.0%

2034 4.7% 2.8% 1.4% 29.7% 0.0%

2035 4.7% 2.8% 1.4% 30.0% 0.0%

2036 4.7% 2.8% 1.4% 30.3% 0.0%

2037 4.7% 2.8% 1.4% 30.6% 0.0%

2038 4.7% 2.8% 1.4% 30.9% 0.0%

2039 4.7% 2.8% 1.4% 31.2% 0.0%

2040 4.7% 2.8% 1.4% 31.5% 0.0%

2041 4.7% 2.8% 1.4% 31.8% 0.0%

2042 4.7% 2.8% 1.4% 32.0% 0.0%

2043 4.7% 2.8% 1.4% 32.3% 0.0%

2044 4.7% 2.8% 1.4% 32.6% 0.0%

2045 4.7% 2.8% 1.4% 32.8% 0.0%

2046 4.7% 2.8% 1.4% 33.1% 0.0%

2047 4.7% 2.8% 1.4% 33.4% 0.0%

2048 4.7% 2.8% 1.4% 33.6% 0.0%

2049 4.7% 2.8% 1.4% 33.9% 0.0%

2050 4.7% 2.8% 1.4% 34.1% 0.0%

17A

- 4Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products : Logs (ventless) (TSL 1)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary

Energy

Efficiency

Targets Bulk

Government

Purchases

2021 4.2% 2.5% 1.3% 0.8% 0.0%

2022 4.2% 2.5% 1.3% 1.8% 0.0%

2023 4.2% 2.5% 1.3% 8.2% 0.0%

2024 4.2% 2.5% 1.3% 13.4% 0.0%

2025 4.2% 2.5% 1.3% 17.5% 0.0%

2026 4.2% 2.5% 1.3% 21.0% 0.0%

2027 4.2% 2.5% 1.3% 24.1% 0.0%

2028 4.2% 2.5% 1.3% 26.8% 0.0%

2029 4.2% 2.5% 1.3% 29.4% 0.0%

2030 4.2% 2.5% 1.3% 31.7% 0.0%

2031 4.2% 2.5% 1.3% 31.7% 0.0%

2032 4.2% 2.5% 1.3% 31.7% 0.0%

2033 4.2% 2.5% 1.3% 31.7% 0.0%

2034 4.2% 2.5% 1.3% 31.7% 0.0%

2035 4.2% 2.5% 1.3% 31.7% 0.0%

2036 4.2% 2.5% 1.3% 31.7% 0.0%

2037 4.2% 2.5% 1.3% 31.7% 0.0%

2038 4.2% 2.5% 1.3% 31.7% 0.0%

2039 4.2% 2.5% 1.3% 31.7% 0.0%

2040 4.2% 2.5% 1.3% 31.7% 0.0%

2041 4.2% 2.5% 1.3% 31.7% 0.0%

2042 4.2% 2.5% 1.3% 31.7% 0.0%

2043 4.2% 2.5% 1.3% 31.7% 0.0%

2044 4.2% 2.5% 1.3% 31.7% 0.0%

2045 4.2% 2.5% 1.3% 31.7% 0.0%

2046 4.2% 2.5% 1.3% 31.7% 0.0%

2047 4.2% 2.5% 1.3% 31.7% 0.0%

2048 4.2% 2.5% 1.3% 31.7% 0.0%

2049 4.2% 2.5% 1.3% 31.7% 0.0%

2050 4.2% 2.5% 1.3% 31.7% 0.0%

17A

- 5Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Hearth Products : Outdoor (TSL 1 )

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary

Energy

Efficiency

Targets Bulk

Government

Purchases

2021 5.5% 3.3% 1.6% 1.7% 0.0%

2022 5.5% 3.3% 1.6% 3.6% 0.0%

2023 5.5% 3.3% 1.6% 6.7% 0.0%

2024 5.5% 3.3% 1.6% 11.3% 0.0%

2025 5.5% 3.3% 1.6% 15.4% 0.0%

2026 5.5% 3.3% 1.6% 19.3% 0.0%

2027 5.5% 3.3% 1.6% 23.0% 0.0%

2028 5.5% 3.3% 1.6% 26.5% 0.0%

2029 5.5% 3.3% 1.6% 29.8% 0.0%

2030 5.5% 3.3% 1.6% 32.8% 0.0%

2031 5.5% 3.3% 1.6% 33.1% 0.0%

2032 5.5% 3.3% 1.6% 33.3% 0.0%

2033 5.5% 3.3% 1.6% 33.5% 0.0%

2034 5.5% 3.3% 1.6% 33.8% 0.0%

2035 5.5% 3.3% 1.6% 34.0% 0.0%

2036 5.5% 3.3% 1.6% 34.2% 0.0%

2037 5.5% 3.3% 1.6% 34.4% 0.0%

2038 5.5% 3.3% 1.6% 34.6% 0.0%

2039 5.5% 3.3% 1.6% 34.8% 0.0%

2040 5.5% 3.3% 1.6% 34.9% 0.0%

2041 5.5% 3.3% 1.6% 35.1% 0.0%

2042 5.5% 3.3% 1.6% 35.3% 0.0%

2043 5.5% 3.3% 1.6% 35.5% 0.0%

2044 5.5% 3.3% 1.6% 35.6% 0.0%

2045 5.5% 3.3% 1.6% 35.8% 0.0%

2046 5.5% 3.3% 1.6% 36.0% 0.0%

2047 5.5% 3.3% 1.6% 36.1% 0.0%

2048 5.5% 3.3% 1.6% 36.3% 0.0%

2049 5.5% 3.3% 1.6% 36.4% 0.0%

2050 5.5% 3.3% 1.6% 36.6% 0.0%

17A

- 617A.3 NIA

- RIA I NTEGRATED M ODEL

For this analysis, DOE used its integrated NIA

- RIA a model appro ach that built on the

NIA model discussed in Chapter 10 and documented in Appendix 10 A .The resulting integrated

NIA

- RIA model featured both the NIA analysis inputs and results and the RIA inputs and had

the capability to generate results for each of the RIA polic ies. A separate module produced

result s summaries for the tables and figures in the RIA document. For the RIA methodology

documentation in C hapter 17, t he module created summaries of parameters calculated by the

model for the consumer rebates policy, generated its penetration curves (discussed in section

17A.4.3 below)

, and reported market s hare impacts for the rebate and tax cred it policies for

hearth products

. For the RIA results reported in C hapter 17 , the module produced graphs of the

market sh are increases resulting from each of the policies analyzed and created summary tables

for the national energy savings (NES) and net present value (NPV) results

. This mod ule also

generated tables of market share increases for each policy reported in section 17A.2 of this

a

ppendix

.

17A.4 C ONSUMER R EBATE POLICY M ARKET P ENE TRATION C URVE S

This section first discusses the theoretical basis for the market penetration curves that

DOE used to analyze the Consumer Rebates policy. Next it discusses the adjustments it made to

the maximum penetration rates. It then refers to the method it used to develop an interpolated

penetration curve for hearth products that meet the target efficiency level . The resulting curve for

hearth products is in C hapter 17

.

17A.4.1 Introduction

XENERGY, Inc. b , developed a re

- parameterized, mixed

- source in formation diffusion

model to estimate market impacts induced by financial incentives for purchasing energy efficient

appliance s. 1 The basic premise of the mixed

- source model is that information diffusion drives

the adoption of technology.

Extensive economic literature describes the diffusion of new products as technologies

evolve. Some research focuses primarily on developing analytical models of diffusion patterns

applicable to individual consumers or to technologies from competing firms

. 2

, 3

, 4 One study

records researchers’ attempts to investigate the factors that drive diffusion processes

. 5 Because a

new product generally has its own distinct characteristics, few studies have been able to

conclusively develop a universally applicable model. Some key findings, however, generally are

accepted in academia and industry.

a NIA = national impact analysis; RIA = regulatory impact analysis

b XENERG Y is now owned by KEMA, Inc. ( www.kema.com )

17A

- 7One 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 o f 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 o n 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, informa tion diffusion drives consumers’ adoption of a

new product

. 3 The model incorporates information diffusion from both internal sources (spread

by word of mouth from early adopters to prospective adopters) and e xternal sources (the

“announcement effect” produced by government agencies, institutions, or commercial

advertising). The model incorporates both internal and external sources by combining a logistic

function with an exponential function. 4

, 5

The relative degree of influence from the internal and external sources determines the

general shape of the diffusion curve for a specific product. 4

, 5 If adoption of a product is

influenced primarily by external sources of information (the announcement effect), for instance,

a high rate of diffusion occurs at the beginning of the process. In this scenari o, 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

- 8Figure 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 la ter

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 pene tration rates as

high as 80 percent. Consumer response is limited by barriers created by consumer utility issues

and other non

- economic factors. DOE therefore adjusted the maximum penetration parameters

for some of the curves from 80 percent to the followi ng levels:

Moderate Barriers: 70%

High Barriers: 60%

Extremely High Barriers: 50%

The low barriers and no barriers curves (the latter used only when a product has a very

high base

- case

- market share) remained, respectively, with 80 percent and 100 percent as their

maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set

the no barriers and extremely high barriers curves as the upper and lower bounds, respectively,

17A

- 9for 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, a s might occur for products

with high base case market shares of the target

- level technology.

17A.4.3 Interpolation of Penetration Curves

As discussed above, the XENERGY penetration (implementation) curves followed a

functional form to estimate the market implement ation rate caused by energy efficiency

measures such as consumer rebates. c 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 e stimate market share increases in

response to rebate programs. d 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 inacc uracy to the analysis.

Blum et al (2011, Appendix A) 7 presents an alternative approach to such evaluation: a method

to estimate market implementation rates more accurately by performing interpolations of the

r eference 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.

c Chapter 17 refers to these curves as penetration curves . This section, in references to the original source, uses the

term implementation curve

. d 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 can be considered proportional to the

rebate impacts

.

17A

- 1017A.5 CONSUMER REBATE PROGRAMS

DOE performed a search for rebate programs that offered incentive s for hearth products

. Some organizations nationwide, comprising elect ric utilities and regional agencies, offer rebat e

programs for this equipment

. Table 17A.5.2 provide s the organizations’ names, states, rebate

amounts

, and program websites. If there is more than one e ntry for an organization, it offers

different rebates in different states

. When an organization offers rebates through several utilities,

it is represented only once in each table.

DOE relied on the data it gathered from 8 rebates programs offered by 6 organizations

(see Table 17A.5.2 ) to calculate a market representative rebate amount for hearth product

ignition modules

. First DOE calculated a market average rebate value for hearth products as the

simple average of the re bate amounts offered by the programs. Then

– because the programs

target consumers shopping for a whole hearth product unit

– DOE scaled down the market

average rebate value it calculated from the available programs

, to estimate a rebate amount that

would be offered to consumers willing to purchase a hearth product unit with intermittent pilot

. DOE estimate d that amount by multiplying the market average rebate value by the ratio of the

price of the efficient ignition module divided by the price of the ( enti re ) hearth product. Table

17A.5.1 presents the values DOE used for this calculation.

Table 17A.5.1 Calculation Steps of the Representative Rebate Value

2013$

Market average rebate value 140.62

Hearth product unit price 308.00

Ignition module price 81.10

Rebate amount 37.03

17A

- 11Table 17A.5.2 Rebates for Hearth Products

Organization State Rebate Website

Columbia Gas of

Kentucky KY $100 www.columbiagasky.com/en/ways

- to

save/warmwise

- energy

- saving

- solutions/natural

gas

- appliance

- rebate

- program

Columbia Gas of

Kentucky KY $100 www.columbiagasky.com/en/ways

- to

save/warmwise

- energy

- saving

- solutions/natural

gas

- appliance

- rebate

- program

Austin Utilities MN $75 www.austinutilities.com/pages/residential

conserve

- incentives/

CenterPoint

Energy MN $75 www.centerpointenergy.com/services/naturalgas

/buildersandtradeallies/residentialdealersanddistr

ibutors/rebateprograms/7ea9 71be83d63410Vgn

VCM10000026a10d0aRCRD/HO/

Owatonna Public

Utilities MN $75 www.owatonnautilities.com/residential

customers/residential

- rebates

Cascade Natural

Gas WA $200 www.cngconserve.com/savings

- for

- your

- home

Cascade Natural

Gas WA $300 www.cngconserve.com/savings

- for

- your

- home

Pu get Sound

Energy WA $200 https://pse.com/savingsandenergycenter/ForHom

es/Pages/Fireplace

- Rebate.aspx

17A.6 FEDERAL AND STATE TA X CREDITS

This section summarizes the Federal and State tax credits available to consumers who

purchase energy efficient appliances. This section also describes tax credits available to

manufacturers who produce certain energy efficient appliances.

17A.6.1 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 pe r home,

including the amount received for insulation, windows, and air and duct sealing. Congress

extended this provision for 2011, with some modifications to eligibility requirements, and

reductions in the cap to $500 per home

. The American Taxpayer Relief Act of 2012 extended,

with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and

water heaters placed in service between January 1, 2012 and December 31, 2013. 8

, 11

The tax credit for furnace fans was $50 in 20 1 1, after which it expired.

17A

- 12The importance of the Fed eral tax credits has been emphasized in research in the

residential heating industry on the impacts of the relatively large credits that were available for

HVAC (h eating, ventilating, and air conditioning) equipment

. I n a survey of HVAC distributors

conducted by Vermont Energy Inve stment Corporation, respondents indicated that the ample

credit had had a notable impact on sal es of higher

- efficiency heating and coolin g 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 Federal tax

credit was reduced , smaller utility rebate incentives had no t ind uced the same levels of

equipment sales increases

. T h e decrease in incentive size from a $1,500 cap in 2009

- 20 10 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 condi tioning 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 observed no

impacts from the utility p rograms 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 (IR S) data on the numbers of taxpayers who claimed

the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed

Form 5695, Residential Energy Credits

. 14 It also estimated the percentage of taxpayers with

entries under Form 5695’s section 3, Residential energy property costs , line 3b, qualified natural

gas, propane, or oil furnace or hot water boiler . DOE reasoned that the percentage of taxpayers

with an entry on Line 3b could serve as a rough indication of the potential of taxpayer

participation in a Federal tax credit program for furnaces during the initial program years. DOE

found that of all resid ential 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.

DOE also reviewed data from an earlier Federal energy conservation tax credit program

in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located

data for only the first three years of the program

. 15

, 16

, 17 For those three years

- 1979, 1980, and

1981

- the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9

percent. Given that the data from this earlier tax credit program were not disaggregate d by type

of energy property, this data series served only to indicate a possible trend of greater

participation in the initial program year, followed by slightly smaller participation in subsequent

years. However, DOE did not find detailed analysis of thi s program to indicate the possible

reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct

2005 energy tax credit program data for its first two program years.

17A

- 13As discussed in C hapter 17, DOE analyzed the percentage of participation in consumer

tax credit programs using its estimates of consumer participation in rebate programs that was

based on benefit/cost data specific to hearth products . 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.6.2 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that

produced high

- efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007. 18

The Emergency Economic Stabilization Act of 2008 19 amended the credits and extended them

through 2010. The credits were extended again to 2011 with modifications in the eligibility

requirements. Manufacturer t ax credits were extended again, by the American Taxpayer Relief

Act of 2012 , for clothes washers, refrigerators, and dishwashers manufactured between January

1, 2012 and December 31, 2013.

Manufacturers who produce these appliances receive the credits for increasing their

production of qualifying appliances. These credits had sev eral efficiency tiers in 2011. F or 2012

- 2013, credits for the higher tiers remain but were eliminated for the lowest (least efficie nt) 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 millio n 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 ca p. The credit to

manufacturers wa s capped at $25 million for 2011, with t he most efficient refrigerators (35%)

and clothe s washers (2.8 MEF/3.5 WCF) exempted from this cap. 20

17A.6.3 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 beg an offering

such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on

taxpayer participation in credits for eligible products. ( See the discussion in C hapter 17 on tax

credit data for clothes washers.) Montana’s Department of R evenue does not disaggregate

participation data by appliance, although DOE reviewed Montana's overall participation trends

and found them congruent with its analysis of Oregon's clothes washer tax credits.

Oregon’s Residential Energy Tax Credit (RE TC) wa s created in 1977. T he Oregon

legislature expanded the RETC program in 1997 to include residential refrigerators, clothes

washers, and dishwashers, which significantly increased participation in the program . The

program subsequently added credits for high

- efficiency heat pump systems, air conditioners, and

water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery,

and renewable energy equipment. Beginning in 2012 a Tax Credit Extension Bill (HB3672)

eliminated refrig erators, 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

17A

- 14Energy as “premium efficiency” are eligible fo r tax credit of $0.60 per kWh saved in the first

year (up to $1,500). 21

, 23

Montana has had an Energy Conservation Tax Credit for residential measures since

1998. 24 The tax credit covers various residential energy and water efficie nt 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 fa ns; 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

- 15REFERENC ES

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_efficiency >

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- cooling.php >

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- 17