# TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

ASHRAE Equipment

June 2015

U.S. Department of Energy Assistant Secretary Office of Energy Efficiency and Renewable Energy Building Technologies Program Appliances and Commercial Equipment Standards Washington, DC 20585This Document was prepared for the Department of Energy by staff members of Navigant Consulting, Inc.

and Lawrence Berkeley National Laboratory1

- i CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUMENT

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

- 1

1.2 PROCESS FOR AMENDED ENERGY CONSERVATION STANDARDS FOR ASHRAE EQUIPMENT

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

- 1

1.3 STRUCTURE OF THIS DOCUMENT

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

- 31

- 1 CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

This technical support document (TSD) is a stand

- alone report that documents the
technical analyses and results supporting the information presented in the Final Rule for
establishing amended energy conservation standards for certain commercial and industrial
equipment covered by the American National Standards Institute (ANSI)/ American Society of
Heating, Refrige rating and Air

- Conditioning Engineers (ASHRAE)/ Illuminating Engineering
Society of Nort h America (IESNA) Standard 90.1 2013 Energy Standard for Buildings Except

Low

- Rise Residential Buildings (ASHRAE Standard 90.1 2013 ).

1.2 PROCESS FOR AMENDED ENERGY CONSER VATION STANDARDS FOR
ASHRAE EQUIPMENT

Title III of the Energy Policy and Conservation Act (EPCA), Pub. L. 94

- 163, as
amended, sets forth a variety of provisions concerning energy efficiency. Part C a of Title III
created the energy conservation program for “Certain Industrial Equipment.” (42 U.S.C. 6311

- 6317)

. Of particular relevance for this rulemaking is 42 U.S.C 6313(a)(6) which directs DOE, in

the event that ASHRAE Standard 90.1 is amended for a c ertain type of commercial and
industrial equipment, to adopt that efficiency level unless clear and convincing evidence supports
a determination that adopting a more stringent level would produce significant additional energy
savings and is technologically feasible and economically justified.

On October 9, 201 3 , American Society of Heating, Refrigeration and Air

- Conditioning
Engineers (ASHRAE) officially released the ANSI/ASHRAE/IESNA Standard 90.1

- 201 3
(hereinafter referred to as ASHRAE Standard 90.1

- 201 3 ), which addressed efficiency levels for
certain categories of commercial heating, ventilating

, and air

- conditioning (HVAC) and water
heating equipment covered by EPCA. The new ASHRAE Standard 90.1 revised the efficiency

levels of the previous ASHRAE Stan dard 90.1

- 20 10 for certain equipment, including s mall

three

- phase commercial air

- cooled air conditioners (single package only) and heat pumps (single
package and split system) less than 65,000 Btu/h ; water

- source heat pumps ; oil

- fired storage

water heaters ; single package vertical units; and packaged terminal air conditioners

.

P ursuant to the EISA 2007 amendments to EPCA, under 42 U.S.C. 6313(a)(6)(C), the
agency must periodically review its already

- established energy conservation standards for
ASHRAE eq uipment. In December 2012, this provision was further amended by the American
Energy Manufacturing Technical Corrections Act (AEMTCA) to clarify that DOE’s periodic

a This Part was subsequ ently redesignated as Part A

- 1 for editorial reasons after Part C of Title III of EPCA was repealed by Pub. L. 109

- 58.1

- 2 review of ASHRAE equipment must occur “[e]very six years.” (42 U.S.C. 6313(a)(6)(C)(i))

D OE was triggered by ASHRAE for 3

- phase air

- cooled single package air conditioners less than
65,000 Btu/h, but not the split system variant, even though both types of units were included in a

more comp rehensive DOE equipment class. DOE is acting to prevent confusion by proposing to

re

- create separate product classes for the two types of systems, and DOE has decided to now

consider amended standards for 3

- phase air

- cooled split system air conditioners less than 65,000

Btu/h under this 6

- year lookback authorit y.

EPCA also requires DOE to amend the test procedures for certain ASHRAE equipment

, which manufacturers are required to use in order to certify compliance with energy conservation

standards mandated under EPCA, to be consistent with the amended industr y test procedure in

ASHRAE Standard 90.1 unless clear and convincing evidence suggests that the test procedure
does not reflect the energy efficiency, energy use, and estimated operating costs of the equipment
and is unduly burdensome to conduct (42 U.S.C 6314(a)(2)

- (4)). Additionally, EISA 2007
amended EPCA to require that at least once every 7 years, DOE must conduct an evaluation of
the test procedures for all covered equipment and either amend test procedures (if the Secretary

determines that amended te st procedures would more accurately or fully comply with the
requirements of 42 U.S.C. 6314(a)(2)

- (3)) or publish notice in the Federal Register of any

determination not to amend a test procedure. (42 U.S.C. 6314(a)(1)(A))

D OE has already amended its test procedures to adopt most changes in industry test
procedures specified by ASHRAE 90.1

- 2013 th at affect DOE’s test procedures. However, DOE
is proposing to incorporate by reference ANSI (American National Standards Institute) Z21.47

- 2012, Standard for Gas

- Fired Central Furnace , for its commercial warm

- air furnace test
procedure as specified by ASHRAE 90.1

- 2013

. In addition, DOE is proposing to update the
citations and incorporations by reference in DOE’s regulations for commer cial warm

- air

furnaces to the most recent version of ASHRAE 103

- 2007, Method of Testing for Annual Fuel
Utilization Efficiency of Residential Central Furnaces and Boiler

, under the 7 year look back.

On April 11 , 201 4 , DOE published a notice of data availa bility ( April 2014 NODA) and
request for public comment in the Federal Register as a preliminary step pursuant to EPCA’s
requirements for DOE to consider amended energy conservation standards for certain types of
commercial equipment covered by ASHRAE Stan dard 90.1. 7 9 FR 20114 . Specifically, the April

2014 NODA presented for public comment DOE’s analysis of the potential energy savings
estimates for amended national energy conservation standards for types of commercial
equipment based on: (1) the modified efficiency levels contained within ASHRAE Standard

90.1

- 201 3 ; and (2) more

- stringent efficiency levels. DOE has described these analyses and
preliminary conclusions and sought input from interested parties, including the submission of

data and other releva nt information

.1

- 3 After receiving public comment on the April 2014 NODA, DOE revised its analysis for

certain of the ASHRAE equipment under review ( i.e., small three

- phase commercial air

- cooled

air conditioners and heat pumps with less than 65,000 Btu/h coo ling capacity and water

- source heat pumps with less than 135,000 Btu/h cooling capacity ). For oil

- fired storage water heaters

, DOE determined that the potential for energy savings from adopting more

- stringent levels than
the ASHRAE Standard 90.1 levels was not significant, and, thus, DOE is proposing to adopt the
ASHRAE Standard 90.1 levels without further analysis. For single package vertical units and
packaged terminal air conditioners, DOE is perfo rming economic analyses in separate
rulemakings that were previously ongoing. b

On January 8, 2015, DOE published a notice of proposed rulemaking ( January 2015
NOPR) and request for public comment in the Federal Register

. 80 FR 1172. In the January
2015 NOPR, DOE addressed, in detail, the comments received in earlier stages of the
rulemaking, and proposed to adopt energy conservation standards for small three

- phase

commercial air

- cooled air conditioners and heat pumps with less than 6 5,000 Btu/h cooling
capacity and water

- source heat pumps with less than 135,000 Btu/h cooling capacity, that were

the same as the levels in ASHRAE 90.1

- 2013. In the NODA, DOE did not conduct analysis on
split system air conditioners because ASHRAE did not propose more stringent standards for that
equipment. After reviewing comments in response to the NODA, DOE decided to review split
system air conditioners under the 6 year look back in the NOPR

.

DOE held a public meeting on February 6, 2015 to discuss the methodologies and results
of the analyses set forth in the NOPR. Interested parties provided comments on the analysis. Key

comments from stakeholders included: (1) support of DOE’s proposal to adopt the efficiency

levels in ASHRAE 90.1

- 2013, ( 2 ) adopting higher standards for water

- source heat pumps

between 17,000 btu/h and 65,000 btu/h, and ( 3 ) revising the energy use analysis to account for
oversizing of equipment.

The assumptions and analysis results for small three

- phase commercial air cooled air
conditioners and heat pumps and water

- source heat pumps are present ed in this Final Rule TSD.
A list of the analyses and the corresponding c hapter number is presented in the next section.

1.3 STRUCTURE OF THIS DOCUMENT

This Final Rule TSD outlines the analytical approaches used in this rulemaking and
presents the analysis results . The TSD consists of 11 chapters with appendices :

b See Packaged Terminal Air Conditioners and Heat Pumps Standards Rulemaking webpage: www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/64 a nd Single Package Vertical Air Conditioners and Heat Pumps Standards Rulemaking webpage : www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=107

.1

- 4 Chapter 1 Introduction: Provides an overview of the process to amend energy

conser vation standards for ASHRAE equipment and outlines the

structure of the document.

Chapter 2 Market Assessment: Characterizes the market for small three

- phase

commercial air

- cooled air conditioners and heat pumps, and water

source heat pumps

.

Chapter 3 E ngineering Analysis: Discusses the methods used for developing the

manufacturer cost

- efficiency relationship for small three

- phase

commercial air

- cooled air conditioners and heat pumps, and water

source heat pumps

.

Chapter 4 Energy Use Analysis : Discusses the process used for generating energy

use estimates for small three

- phase commercial air

- cooled air
conditioners and heat pumps, and water

- source heat pumps a t the

considered efficiency levels

.

Chapter 5 Markups for Product Price Determination : Discusses the methods used
for establishing markups for converting manufacturer prices to customer
equipment prices

Appendix 5A Detailed Data for Equipment Price Markups

Chapter 6 Life

- Cycle Cost and Payback Period Analysi s: Discusses the economic

effect s of standards on commercial customers and users of the
equipment and compares the LCC and PBP of equipment with and
without higher efficiency standards.

Chapter 7 Shipments: Discusses the methods used for projecting shipments with
and without higher efficiency standards.

Chapter 8 National Impact Analysis: Discusses the methods used for project ing
national energy savings and national economic impacts based on annual
equipment shipments and estimates of future equipment efficiency

distributions in the absence and presence of higher energy efficiency

standards.

Appendix 8A Full Fuel Cycle1

- 5 Appendix 8B National Net Present Value of Customer Benefits Using Alternative
Equipment Price Projections and Economic Growth Scenarios

Chapter 9 Emissions Analysis : Discusses the environmental effect of amended
energy conservation standards, including the impact on emissions of six
pollutants or greenhouse gases: sulfur dioxide (SO2), nitrogen oxides
(NOx), carbon dioxide (CO2), mercury (Hg), methane (CH4), and

nitr ous oxide (N2O).

Chapter 10 Monetization of Emission Reductions Benefits : Discusses the

monetization of reductions in CO 2 and NO X emissions.

Appendix 10A Social Cost of Carbon for Regulatory Impact Analysis Under Executive

Order 12866

Appendix 10B Techn ical Update of Social Cost of Carbon for Regulatory Impact
Analysis Under Executive Order 12866

Chapter 11 Utility Impact Analysis: Discusses the effects of standards on the
installed generation capacity of electric utilities2

- i CHAPTER 2. MARKET ASSESSMENT

TABLE OF CONTENTS

2.1 BACKGROUND

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

- 1

2.1.1 Three

- Phase Air

- Cooled Commercial Air

- Conditioning and Heating Equipment

....... 2

- 1

2.1.2 Water

- Source Heat Pumps

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

- 2

2.1.3 Federal Energy Conservation Standards

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

- 2

2.1.4 ASHRAE Standard 90.1

- 2013 Efficiency Levels

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

- 3

2.2 MARKET ASSESSMENT

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

- 4

2.2.1 Trade Associations

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

- 4

2.2.2 Manufacturers

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

- 5

2.2.3 Ma rket Performance Data

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

- 7

2.2.3.1 Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps

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

- 7

2.2.3.2 Water

- Source Heat Pumps

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

- 12

REFERENCES

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

- 17

LIST OF TABLES

Table 2.1.1 Current Federal Energy Conservation Standards for Small Three

- Phase

Commercial Air

- Cooled Air

- Conditioning and Heating Equipment and

Water

- Source Heat Pumps

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

- 3

Table 2.1.2 Equipment Classes with Updated Efficiency Levels in ASHRAE

Standard 90.1

- 2 013

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

- 4

Table 2.2.1 Manufacturers of Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

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

- 6

Table 2.2.2 Manufacturers of Commercial Water

- Source Heat Pumps

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

- 7

LIST OF FIGU RES

Figure 2.2.1 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Single Package Air Conditioners <65,000 Btu/h

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

- 8 Figure 2.2.2 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Split System Air Conditioners < 65,000 Btu/h

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

- 9 Figure 2.2.3 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Single Package Heat Pumps <65,000 Btu/h

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

- 10 Figure 2.2.4 HSPF Histogram of Small Three

- Phase Commercial Air

- Cooled Single Package Heat Pumps <65,000 Btu/h

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

- 10 Figure 2.2.5 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Split System Heat Pumps <65,000 Btu/h

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

- 112

- ii Figure 2.2.6 HSPF Histogram of Small Three

- Phase Commercial Air

- Cooled Split System Heat Pumps <65,000 Btu/h

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

- 12 Figure 2.2.7 EER Histogram of Water

- Source Heat Pumps <17,000 Btu/h

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

- 13 Figure 2.2.8 COP Histogram of Water

- Source Heat Pumps <17,000 Btu/h

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

- 13 Figure 2.2.9 EER Histogram of Water

- Source Heat Pumps ≥17,000

– <65,000

Btu/h

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

- 14 Figure 2.2.10 COP Histogram of Water

- Source Heat Pumps ≥17,0 00

– <65,000

Btu/h

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

- 15 Figure 2.2.11 EER Histogram of Water

- Source Heat Pumps ≥65,000 Btu/h

– <135,00 Btu/h

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

- 16 Figure 2.2.12 COP Histogram of Water

- Source Heat Pumps ≥65,000 Btu/h

– <135,00 Btu/h

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

- 162

- 1 CHAPTER 2. MARKET ASSESSMENT

2.1 BACKGROUND

The American Society of Heating, Refrigeration, and Air

- Conditioning Engineers (ASHRAE) released an updated version of the American National Standards Institute (ANSI)/ASHRAE/Illuminating Engineering Society (IES) Standard 90.1 (hereinafter referred to

as ASHRAE Standard 90.1

- 2013 ) on October 9, 201 3 . ASHRAE Standard 90.1

- 2013 included increased efficiency levels for small three

- phase commercial air

- cooled air

- con ditioning and heating equipment with less than 65,000 Btu/h cooling capacity and water

- source heat pumps (triggering a review by the U.S. Department of Energy (DOE) of the minimum standard s for these equipment classes).

2.1.1 Three

- Phase Air

- Co oled Commercial Ai r

- Conditioning and Heating Equipment

Three

- phase air

- cooled air conditioners and heat pumps are part of a subset of commercial package air

- conditioning and heating equipment. The Energy Policy and

Conservation Act ( EPCA ) defines “commercial package air con ditioning and heating equipment”

as “air

- cooled, water

- cooled, evaporatively

- cooled, or water

- source (not including ground wate r

source) electrically operated, unitary central air conditioners and central air conditioning heat pumps for commercial applicat ion.” (42 U.S.C. 6311(8)(A))

Air conditioners use a refrigeration cycle to cool an indoor space by moving heat from the cold location to a hotter location by means of evaporating and condensing a fluid, typically a refrigerant. An air conditioner typical ly has four major components: a compressor, condenser coil, expansion valve, and evaporator coil. The compressor turns the refrigerant from a low pressure gas into a high pressure gas, which raises the temperature of the gas. At the next step,

the condens e r coil convects the heat from the hot refrigerant, which condenses into a liquid, still at a high temperature and high pressure. This liquid refrigerant then goes through an expansion valve that lowers its pressure and temperature before entering the evapo rator coil. The evaporator coil cools the indoor air by transferring heat from the indoor air to the low temperature refrigerant. Heat pumps can reverse the refrigeration cycle to draw heat from cooler external air and release it into the indoor space to h eat it.

The Federal energy conservation standards for air

- cooled air conditioners and heat pumps are differentiated based on the cooling capacity ( i.e., small, large, or very large). For small equipment, there is an additional disaggregation into: (1) equipment less than 65,000 Btu/h and (2) equipment greater than or equal to 65,000 Btu/h and less than 135,000 Btu/h. ASHRAE

Standard 90.1

- 2013 also differe ntiates the equipment that is less than 65,000 Btu/h into split system and single package subcategories . In the past, DOE has followed the same disaggregation. However, when the Energy Independence and Security Act ( EISA ) of 2007 increased the efficiency l evels to identical levels across single package and split system equipment, effective in 2008, DOE combined the equipment classes in the Code of Federal Regulations ( CFR ), resulting in only two equipment classes, one for air conditioners and one for heat p umps. Because ASHRAE has increased the standard for only single package air conditioners, and has increased the heating seasonal performance factor (HSPF) level to a more2

- 2 stringent level for split system heat pumps than for single package heat pumps, and D OE is obligated to adopt, at a minimum, the increased level in ASHRAE 90.1

- 2013 for that equipment class, DOE re

- created separate equipment classes for single package and split system equipment in the overall equipment classes of small commercial package air conditioners and heat pumps (three

- phase air

- cooled) less than 65,000 Btu/h. DOE reviewed standards for single package equipment under the ASHRAE trigger (42 U.S.C 6313(a)(6)(A)) and split system equipment under the 6 y ear look back provision (42 U.S.C. 6313(a)(6)(C)(i)) , as described in Chapter 1.

2.1.2 Water

- Source Heat Pumps

Water source heat pumps are also part of a subset of commercial package air

conditioning and heating equipment as defined by EPCA

. However, EPCA does not further define this equipment.

According to the ASHRAE Handbook, “A water

- source heat pump is a single

- package

reverse

- cycle heat pump that uses water as the heat source for heating and as the heat sink for cooling. The main components are a compresso r, refrigerant

- to

- water heat exchanger, refrigerant

- to

- air heat exchanger, refrigerant expansion devices, and refrigerant reversing valve

. ....A water

- loop heat pump uses a circulating water loop as the heat source and heat sink. When loop water temperature exceeds a certain level during cooling, a cooling tower dissipates heat

from the water loop into the atmosphere. When loop water temperature drops below a prescribed level during heating, heat is added to the circulating loop water, usually with a boiler. ” 1
Decentralized systems using multiple water source heat pumps on a shared circulating water loop

are common. Water

- loop heat pumps systems are popular in multi

- room buildings such as office buildings, where heat gains can be redistributed from interior t o perimeter zones. Water

- source heat pumps are manufactured in both horizontal and vertical configurations. 1

EPCA and ASHRAE Standard 90.1

- 2013 both further divide this equipment into three categories based on cooling capacity ranges: (1) <17,000 Btu/h ; (2) ≥17,000 and <65,000 Btu/h;

and (3) ≥65,000 and <135,000 Btu/h.

ASHRAE Standard 90.1

- 2013 changed the name of this equipment class from “wat er

source” to “water to air, water loop.” DOE believes this to be an editorial change only and that

this new nomenclature refers to the same water

- source heat pump equipment covered by Federal energy conservation standards. ASHRAE also changed the descript or for this equipment from

COP to COP H . DOE believes this is also an editorial change to clarify the difference between COP for refrigeration and COP for heat pumps.

2.1.3 Federal Energy Conservation Standards

On January 12, 2001, DOE published in the Federal Re gister a final rule promulgating

the current standards for all three equipment classes of water source heat pumps relevant to this rulemaking . 66 FR 3336. This final rul e adopted the energy conservation standards set by

ASHRAE Standard 90.1

- 1999.

The existing energy conservation standards for all classes of air

- cooled three

- phase air conditioners and heat pumps less than 65,000 Btu/h were established by the EISA 20072

- 3 amendments to EPCA. These standards were codified by DOE in a Technical Amendment published in the Federal Register on March 23, 2009. 74 FR 12058.

Table 2.1

. 1 shows the current F ederal energy conservation levels for equipment classes

where ASHR AE Standard 90.1

- 2013 increased the standa rd levels in comparison to the F ederal

levels.

Table 2.1

. 1 Current Federal Energy Conservation Standards for Small Three

- Phase

Commercial Air

- Cooled Air

- Conditioning and Heating Equipment and

Water

- Source Heat Pumps

Equipment Type Cooling Capacity Subcategory Federal Standard

Small Commercial Packaged Air

Conditioning and Heating Equipment, Air

- cooled 3

- Phase <65,000 Btu/h AC 13.0 SEER

HP 13.0 SEER

7.7 HSPF

Small Commercial Packaged Air Conditioning and Heating Equipment, Water Source <17,000 Btu/h HP 11.2 EER

4.2 COP

≥17,000 Btu/h and <65,000 Btu/h HP 12.0 EER

4.2 COP

≥65,000 Btu/h and <135,000 Btu/h HP 12.0 EER

4.2 COP

2.1.4 ASHRAE Standard 90.1

- 2013 Efficiency Levels

Table 2.1

. 2 shows the ASHRAE Standard 90.1

- 201 3 efficiency level s for the updated equipment classes along with the currently applicable Federal minimum efficiency standards for comparison.

ASHRAE Standard 90.1

- 201 3 raised the efficiency level s in comparison to the current Federal minimum energy conservation standards f or three equipment classes of small three

phase commercial air

- cooled air

- conditioners and heat pumps and three equipment classes of

water

- source heat pump. DOE assessed the current market for each equipment class. DOE also

assessed the current market for split system air conditioners even though ASHRAE Standard

90.1

- 2013 did not raise the efficiency level for that equipment. The market assessment for all equipment classes is described in the sections that follow

.2

- 4 Table 2.1

. 2 Equipment Classes with Updated Efficiency Levels in ASHRAE Standard

90.1

- 2013

ASHRAE Equipment Type Size Category Subcategory ASHRAE Standard

90.1

- 2013 Federal

Minimum

Energy

Conservation

Standards

Air conditioners, air

- cooled <65,000 Btu/h Split System 13.0 SEER* 13.0 SEER

Single package 14.0 SEER (as of 1/1/2015) 13.0 SEER

Heat pumps, air

- cooled <65,000 Btu/h Split System 14.0 SEER

8.2 HSPF (as of 1/1/2015) 13.0 SEER

7.7 HSPF

Single package 14.0 SEER

8.0 HSPF (as of 1/1/2015) 13.0 SEER

7.7 HSPF

Heat pump s , water to air, water loop <17,000 Btu/h N/A 12.2 EER

4.3 COP 11.2 EER

4.2 COP

≥17,000 and <65,000Btu/h N/A 13.0 EER

4.3 COP 12.0 EER

4.2 COP

≥65,000 and <135,000Btu/h N/A 13.0 EER

4.3 COP 12.0 EER

4.2 COP

*This efficiency level was not updated in ASHRAE Standard 90.1

- 2013 but is included here as it is part of the same equipment class in the current F ederal standards.

2.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade associations and

domestic and international manufacturers of small three

- phase commercial air conditioners and heat pumps, and water

- source heat pumps . The market assessment also summarizes the relevant market performance data for each equipment class where such data are available.

2.2.1 Trade Associations

DOE researched various trade groups who represent manufacturers, distributors, and installers of the various types of equipment being analyzed i n this rulemaking. The Air

Conditioning, Heating, and Refrigeration Institute (AHRI) is one of the largest trade associations for manufacturers of space

- heating

, cooling , and water

- heating equipment, representing more than 90% of the residential and commer cial air

- conditioning, space

- heating, water

- heating, and commercial refrigeration equipment manufactured in the U nited States

. 2 AHRI also develops and publishes test procedures standards for residential and commercial heating, ventilati ng , and air

conditioning (HVAC) equipment and coordinates with the International Organization for Standardization (ISO) to help harmonize U

. S

. standards with international standards, if feasible.

AHRI also maintains the AHRI Directory of Certified Product Performance that lists all the products and equipment that have been certified by AHRI.2

- 5 Heating, Air

- conditioning and Refrigeration Distributors International (HARDI) is a trade association that represents over 450 wholesale heating, ventilati ng , air

- conditioning, a nd refrigeration (HVACR) companies plus over 300 manufacturing associates and nearly 140 manufacturing representatives. HARDI estimates that 80% of the revenue of HVACR systems goes through its members. 3

Air Conditioning Contractors of America (ACCA) is another trade association whose members include over 4,000 contractors and 60,000 professionals. ACCA provides contractors technical, legal, and market resources, helping to promote good practices and to keep buildings safe, clean, and affordable. 4

2.2.2 Manufa cturers

DOE identified 23 parent companies ( comprising 61 manufacturers ) of small three

- phase

air

- cooled air conditioners and heat pumps and 18 parent companies ( comprising 21

manufacturers ) of water

- source heat pumps

. The manufacturers for each equipment class are

listed in Table 2.2

. 1 and Table 2.2

. 2

.2

- 6 Table 2.2

. 1 Manufacturers of Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

Aaon, Inc. Eair Llc Johnson Controls

• Coleman

• Evcon

• Fraser

• Guardian

• Johnson Controls

• Luxaire

• York Nordyne

• Broan

• Frigidaire

• Gibson

• Kelvinator

• Mammoth, Inc.

• Maytag

• Miller

• Nordyne

• Nutone

• Philco

• Tappan

• Westinghouse

Addison Ecotemp Kenmore Rheem Sales Company, Inc

• Rheem Manufacturing

• United Refrigeration Inc

– Thermal Zone

Aerosys Gd Midea Heating & Ventilating Equipment Co., Ltd. Klimaire Products Inc. United Cool Air

Air Green Corporation Heat Controller, Inc. Lennox International

• Airease

• Armstrong Air Conditioning, Inc

• Concord

• Ducane

• Lennox Industries, Inc. United Technologies Corporation

• Airquest

• Arcoaire

• Bryant Heating And Cooling Systems

• Carrier Air Conditioning

• Comfortmaker

• Day & Night

• Grandaire

• Heil

• Icp Commercial

• Keeprite

• Maratherm

• Payne Heating And Cooling

• Tempstar

Bard Manufacturing Company Ingersoll

- Rand

• American Standard

• Trane Msh Legacy

– Maintenance Supply Headquarters LP Xenon

Daikin Industries

• AAF International

• Daikin

• Daikin Americas

• Goodman Manufacturing Innovair Zamil Air Conditioners & Home Appliances Co. (L.L.C.)

– Zamil Industrial2

- 7 Table 2.2

. 2 Manufacturers of Commercial Water

- Source Heat Pumps

Addison

Bard Manufacturing Company

Climate Master, Inc.

Coldflo Inc.

Cold Point Corporation

Daikin

• McQuay International

Enertech Global,Llc.

Robert Bosch GMBH

• Fhp Manufacturing

First Operations Lp, Dba First Co.

Geomaster, Llc.

Geosmart Energy Inc.

Ingersoll

- Rand

• American Standard

• Trane

Johnson Controls, Inc.

• Johnson Controls

• York

Rheem Manufacturing

United Cool Air

United Technologies Corporation Carrier Air Conditioning

Waterfurnace International, Inc.

• Geostar

• Waterfurnace

The Whalen Company

2.2.3 Market Performance Data

For each equipment class analyzed, DOE gather ed market data to characterize the efficiency and performance of models currently on the market. DOE reviewed data for small

three

- phase commercial air

- cooled air conditioners and heat pumps, and water

- source heat

pumps currently on the market by examining the AHRI Directory of Certified Product

Performance

. 5

2.2.3.1 Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat

Pumps

Single Package Air Conditioners

. Small three

- phase commercial air

- cooled single package air condition ers have a cooling capacity of less than 65,000 Btu/h and include the2

- 8 condenser and evaporator in the same cabinet. The Federal energy conservation standard for this equipment is 13.0 SEER (seasonal energy efficiency r atio ) (10 CFR 431.97). ASHRAE Standard

90.1

- 2013 changes the efficiency rating to 14.0 SEER as of January 1, 2015

. Figure 2.2

. 1 shows

the distribution of SEER values for this equipment. The average SEER value for this equipment class is 14.3. Of the models identified by DOE, 1

, 450 (45 percent of the total models) have

SEERs rated below the ASHRAE Standard 90.1

- 2013 levels.

Figure 2.2

. 1 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Single Package Air Conditioners <65,000 Btu/h

Split System Air Conditioners

. Small three

- phase commercial air

- cooled split system air conditioners have a cooling capacity of less than 65,000 Btu/h . The outdoor cabinet contains

the condenser and the indoor cabinet contains the evaporator. The Federal energy conservation

standard f or this equipment is 13.0 SEER (seasonal energy efficiency r atio ) (10 CFR 431.97).

ASHRAE Standard 90.1

- 2013 did not change the efficiency rating for this equipment

. Figure

2.2

. 2 shows the distribution of SEER values for this equipment

. The average SEER value for this equipment class is 1 3.85

. 0 200 400 600 800 1000 1200

13 13.1 -

13.5 13.6 -

14 14.1 -

14.5 15.6 -

15 15.1 -

15.5 15.6 -

16 16.1 -

16.5 16.6 -

17 17.1 -

17.5 17.6 -

18 18.1 -

18.5 18.6 -

19.15Frequency

SEER2

- 9 Figure 2.2

. 2 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Split System Air Conditioners < 65,000 Btu/h

Single Package Heat Pumps

. Small three

- phase commercial air

- cooled single package heat pumps have a cooling capacity of less than 65,000 Btu/h and include the condenser and evaporator in the same cabinet. The F ederal energy conservation standard for this equipment is

13.0 SEER and 7.7 HSPF ( 10 CFR 431.97 ). ASHRAE Standard 90.1

- 2013 changes the efficiency rating s to 14.0 SEER and 8.0 HSPF as of January 1, 2015

. Figure 2.2

. 3 and Figure

2.2

. 4 show the distribution s of SEER and HSPF values for this equipment. The average SEER

v alue for this equipment class is 14. 0 . Of the models identified by DOE, 653 ( 54 percent of the total models) have SEERs rated below the ASHRAE Standard 90.1

- 2013 levels. The average HSPF value for this equipment class is 7.9. Of the models identified by DO E, 632 (52% of the total models) have HSPFs rated below the ASHRAE Standard 90.1

- 2013 levels. 0 5000 10000 15000 20000 25000 30000 35000 40000 45000

13 13.1 -

13.5 13.6 -

14 14.1 -

14.5 14.6 -

15 15.1 -

15.5 15.6 -

16 16.1 -

16.5 16.6 -

17 17.1 -

17.5 17.6 -

18 18.1 -

18.5 18.6 -

19.15Frequency

SEER2

- 10 Figure 2.2

. 3 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Single Package Heat Pumps <65,000 Btu/h

Figure 2.2

. 4 HSPF Histogram of Small Three

- Phase Commercial Air

- Cooled Single Package Heat Pumps <65,000 Btu/h

Split System Heat Pumps

. Small three

- phase commercial air

- cooled split system heat

pumps ha ve a cooling capacity of less than 65,000 Btu/h . The outdoor cabinet contains the compressor and condenser, while the indoor cabinet contains the evaporator . The Federal energy

conservation standard for this equipment is 13.0 SEER and 7.7 HSPF ( 10 CFR 431.97 ). 0 50 100 150 200 250 300 350 400

13 13. 1 - 14 14.1 - 15 15.1 - 16 16.1 - 17 17.1 - 18.05Frequency

SEER

0 100 200 300 400 500 600 700

7.7 7.75 - 8 8.05 - 8.3 8.35 - 8.6 8.65 - 8.9 8.95 - 9.2 9.25 - 9.3Frequency

HSPF2

- 11 ASHRAE Standard 90.1

- 2013 changes the efficiency rating s to 14.0 SEER and 8.2 HSPF as of January 1, 2015

. Figure 2.2

. 5 and Figure 2.2

. 6 show the distribution s of SEER and HSPF values for this equipment . The average SEER value for this equipment class is 13.7 . Of the models identified by DOE, 30,009 ( 64 percent of the total models) have SEERs rated below the

ASHRAE Standard 90.1

- 2013 levels. The average HSPF for this equipment class is 7.9. Of the models identified by DOE, 36,902 (79% of the total models) have HSPFs rated below the

ASHRAE Standard 90.1

- 2013 levels.

Figure 2.2

. 5 SEER Histogram of Small Three

- Phase Commercial Air

- Cooled Split System Heat Pumps <65,000 Btu/h 0 2000 4000 6000 8000 10000 12000 14000 16000

13 13.1 - 13.5 13.6 - 14 14.1 - 14.5 14.6 - 15 15.1 - 15.5 15.6 - 16.2Frequency

SEER2

- 12 Figure 2.2

. 6 HSPF Histogram of Small Three

- Phase Commercial Air

- Cooled Split System Heat Pumps <65,000 Btu/h

2.2.3.2 Water

- Source Heat Pumps

Water

- Source Heat Pumps <17,000 Btu/h . Small water

- source heat pumps are individual packaged units that transfer heat using a water loop. The Federal energy conservation

standard s for water

- source heat pumps with a cooling capacity less than 17,000 Btu/h are 11.2

EER (energy efficiency ratio) and 4.2 COP (coefficient of performance ) (10 CFR 431.97 ).

ASHRAE Standard 90.1

- 2013 changes the efficiency ratings to 12.0 EER and 4 .3 COP. Figure

2.2

. 7 and Figure 2.2

. 8 show the distribution of EERs and COPs for this equipment

. The average EER value for this equipment class is 13.8 . Of the models identified by DOE, 34 (six percent of the total models) have EERs rated below the ASHRAE Standard 90.1

- 201 3 levels. The average COP value for this equipment class is 4 .7 . Of the models identified by DOE, 30 ( f ive percent of the total models) have COPs rated below the ASHRAE Standard 90.1

- 201 3 levels. 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

7.7 7.75 -

7.95 8.0 - 8.2 8.25 -

8.45 8.5 - 8.7 8.75 -

8.95 9.0 - 9.2 9.25 -

9.45 9.5 - 9.7Frequency

HSPF2

- 13 Figure 2.2

. 7 EER Histogram of Water

- Source Heat Pumps <17,000 Btu/h

Figure 2.2

. 8 COP Histogram of Water

- Source Heat Pumps <17,000 Btu/h

Water

- Source Heat Pumps ≥17,000

– <65,000 Btu/h

. The Federal energy conservation

standard s for w ater

- source heat pumps with a cooling ca pacity greater than or equal to 17,000 0 20 40 60 80 100 120 140 160 180

11.2 11.25 - 12.2 12.3 - 13.2 13.3 - 14.2 14.3 - 15.2 15.3 - 16.2 16.3 - 17.2 17.3 - 18.1Frequency

EER

0 10 20 30 40 50 60 70 80Frequency

COP2

- 14 Btu/h and less than 65,000 Btu/h are 12.0 EER and 4.2 COP (10 CFR 431.97 ). ASHRAE

Standard 90.1

- 2013 changes the efficiency ratings to 1 3 .0 EER and 4.3 COP. Figure 2.2

. 9 and Figure 2.2

. 10 show the distribution of EERs and COPs for this equipment. The average EER

value fo r this equipment class is 15.2 . Of the models identified by DOE, 72 ( two percent of the total models) have EERs rated below the ASHRAE Standard 90.1

- 201 3 levels. The average COP value for this equipment class is 4.9 . Of the models identified by DOE, 133 ( f our percent of the total models) have COPs rated below the ASHRAE Standard 90.1

- 201 3 levels.

Figure 2.2

. 9 EER Histogram of Water

- Source Heat Pumps ≥17,000

– <65,000 Btu/h 0 100 200 300 400 500 600 700 800 900

12 12.1 -

13 13.1 -

14 14.1 -

15 15.1 -

16 16.1 -

17 17.1 -

18 18.1 -

19 19.1-

20 20.1 -

21 21.1 -

21.6Frequency

EER2

- 15 Figure 2.2

. 10 COP Histogram of Water

- Source Heat Pumps ≥17,000

– <65,000 Btu/h

Water

- Source Heat Pumps ≥65,000 Btu/h

– <135,000 Btu/h. The Federal energy

conservation standard s for water

- source heat pumps with a cooling capacity gr eater than or equal to 65,000 Btu/h and less than 135 ,000 Btu/h are 12.0 EER and 4.2 COP (10 CFR 431.97 ).

ASHRAE Standard 90.1

- 2013 changes the efficiency ratings to 1 3 .0 EER and 4.3 COP. Figure

2.2

. 11 and Figure 2.2

. 12 show the distribution of EERs and COPs for this equipment. The average EER value fo r this equipment class is 14. 7 . Of the models identified by DOE, five ( one percent of the total models) have EERs rated below the ASHRAE Standard 90.1

- 201 3 levels. The average COP value for this equipment class is 4.8. Of the models identified by DOE, two ( 0.5 percent of the total models) have COPs rated below the ASHRAE Standard 90.1

- 201 3

levels. 0 50 100 150 200 250 300 350 400 450Frequency

COP2

- 16 Figure 2.2

. 11 EER Histogram of Water

- Source Heat Pumps ≥65,000 Btu/h

– <135,00 Btu/h

Figure 2.2

. 12 COP Histogram of Water

- Source Heat Pumps ≥65,000 Btu/h

– <135,00 Btu/h 0 20 40 60 80 100 120 140 160

12 12.1 - 13 13.1 - 14 14.1 - 15 15.1 - 16 16.1 - 17 17.1 - 17.2Frequency

EER

0 20 40 60 80 100 120

4.2 4.21 -

4.30 4.31 -

4.40 4.41 -

4.50 4.51 -

4.60 4.61 -

4.70 4.71 -

4.80 4.81-

4.90 4.91 -

5.00 5.01 -

5.10 5.11 -

5.20 5.21 -

5.30 5.31 -

5.40 5.41 -

5.50 5.51 -

5.60Frequency

COP2

- 17 REFERENCES

1 2012 ASHRAE Handbook, Heating, Ventilating, and Air

- Conditioning Systems and Equipment . ASHRAE, Atlanta, GA. Chapter s 9 and 49.

2 Air

- Conditioning, Heating, and Refrigeration Institute Website. About Us . 2013. (Last accessed February 10, 2014 .) < http://ahrinet.org/site/318/About

- Us >

3 Heating, Air

- conditioning & Refrigeration Distributors International Website. About HARDI.

201 4 . (Last accessed February 10, 2014 .) < www.hardinet.org/about

- hardi

- 0 >

4 Air Conditioning Contractors of America Website. About ACC A. 201 4 . (Last accessed February 10, 2014 .) < www.acca.org/acca >

5 AHRI Directory of Certified Product Performance . 201 3 . (Last accessed November 11, 2013 .)

< www.ahridirectory.org >3

- i CHAPTER 3

. ENGINEERING ANALYSIS

TABLE OF CO NTENTS

3.1 INTRODUCTION

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

- 1

3.2 EQUIPMENT CLASSES ANALYZED

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

- 1

3.3 EFFICIENCY LEVELS

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

- 2

3.3.1 Baseline Units

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

- 2

3.3.2 Identification of Efficiency Levels for Analysis

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

- 3

3.4 METHODOLOGY OVERVIEW

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

- 5

3.4.1 Physical Teardown Analysis

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

- 7

3.4.2 Catalog Teardown Analysis

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

- 7

3.4.3 Selection of Units

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

- 7

3.4.4 Generation of Bill of Materials and Cost Model

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

- 7

3.4.5 Manufacturer Markup

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

- 9

3.4.6 Shipping Costs for Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps

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

- 10

3.4.7 Shipping Costs for Water

- Source Heat Pumps

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

- 12

3.5 ENGINEERING ANALYSIS RESULTS

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

- 12

3.5.1 Unit Operating Weight for Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pump Results

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

- 12

3.5.2 Cost

- Efficiency Results for Small Three

- Phase Commercial Air Conditioners and Heat Pump Results

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

- 13

3.5.3 Unit Operating Weight for Water

- Source Heat Pumps

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

- 15

3.5.4 Cost

- Efficiency Results for Water

- Source Heat Pumps

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

- 15

LIST OF TABLES

Table 3.2.1 Equipment Classes and ASHRAE 90.1

- 2013 Efficiency Levels for Small

Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

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

- 2

Table 3.3.1 Baseline Efficiency Levels for Small Three

- Phase Commercial Air

Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

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

- 3

Table 3.3.2 Baseline EER Efficiency Levels for Water

- Source Heat Pumps

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

- 3

Table 3.3.3 Efficiency Levels for Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps < 65,000 Btu/h

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

- 4

Table 3.3.4 Efficiency Levels for Engineering Analysis of Water

- Source Heat Pumps

........ 3

- 5

Table 3.4.1 Major Manufacturing Processes

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

- 8

Table 3.4.2 Manufacturer Shipping Costs for Three

- Ton Split

- System Three

- Phase

C ommercial Air

- Cooled Air Conditioners

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

- 11

Table 3.4.3 Manufacturer Shipping Costs for Three

- Ton Single

- Package Three

- Phase

Commercial Air

- Cooled Air Conditioner s

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

- 113

- ii Table 3.4.4 Manufacturer Shipping Costs for Three

- Ton Split

- System Three

- Phase

Commercial Air

- Cooled Heat Pumps

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

- 11

Table 3.4.5 Manufacturer Shipping Costs for Three

- Ton Single

- Package Three

- Phase

Commercial Air

- Cooled Heat Pumps

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

- 12

Table 3.4.6 Manufacturer Shipping Costs for Water

- Source Heat Pumps

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

- 12

Table 3.5.1 Weight

- Efficiency Data for Three

- Ton Three

- Phase Com mercial Air

Cooled Air Conditioners and Heat Pumps

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

- 13

Table 3.5.2 Manufacturer Production Costs for Three

- Ton Split

- System Three

- Phase

Commercial Air

- Cooled Air Conditioners

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

- 14

Table 3.5.3 Manufacturer Production Costs for Three

- Ton Single

- Package Three

Phase Commercial Air

- Cooled Air Conditioners

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

- 14

Table 3.5.4 Manufacturer Production Costs for Three

- Ton Split

- System Three

- Phase

Commercial Air

- Cooled Heat Pumps

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

- 14

Table 3.5.5 Manufacturer Production Costs for Three

- Ton Single

- Package Three

Phase Commercial Air

- Cooled Heat Pumps

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

- 15

Table 3.5.6 Weight

- Efficiency Data for Water

- Source Heat Pumps

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

- 15

Table 3.5.7 Manufacturer Production Cost

- Efficiency Data for Water

- Source He at Pumps

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

- 16

LIST OF FIGURE S

Figure 3.4.1 Manufacturing Cost Assessment Stages

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

- 83-1 CHAPTER 3. ENGINEERING ANALYSIS

INTRODUCTION 3.1

The engineering analysis establishes the relationship between the cost and efficiency of a piece of equipment DOE is evaluating for potential amended energy conservation standards. This relationship serves as the basis for cost

- benefit calculations for individual consumers and the Nation. The engineering analysis identifies representative baseline equipm ent, which is the starting point for analyzing possible energy efficiency improvements. For covered ASHRAE equipment, DOE sets the baseline at the ASHRAE Standard 90.1 efficiency level, because by statute, DOE cannot adopt any level below the revised ASHRA E level. The engineering analysis then identifies higher efficiency levels and the incremental increase in product cost associated with achieving higher efficiency levels. After identifying the baseline models and cost of achieving increased efficiency, DO E estimates the additional costs to the customer through an analysis of contractor costs and markups, and uses that information in the downstream analyses

to examine the costs and benefits associated with increased equipment efficiency. The

downstream ana lys e s include: th e building energy

- use and end

- use load characterization ( c hapter

4 )

, life

- cycle cost (LCC) and payback period (PBP) ( c hapter 6), shipment analysis (chapter 7), national impact analysis (chapter 8), emissions analysis (chapter 9), and utili ty impact analysis (chapter 11).

E QUIPMENT CLASSES ANALYZED 3.2

ASHRAE 90.1

- 2013 separates three

- phase commercial air

- cooled single

- package and split

- system air conditioners and heat pumps with a capacity less than 65,000 Btu/h into four equipment classes. Table 3.2

. 1 summarizes the four equipment classes and the efficiency levels required by ASHRAE 90.1

- 2013. All four equipment classes are currently at the DOE standard

le vel of 13.0 SEER.3-2 Table 3.2

. 1 Equipment Classes and ASHRAE 90.1

- 2013 Efficiency Levels for Small

Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

Equipment

Class Equipment Type Cooling Capacity Sub

Category Heating Type Minimum Efficiency

1 Single

- Package Three

- Phase

Commercial Air

- Cooled Air

Conditioning and Heating Equipment (Air

- Cooled) < 65,000 Btu/h AC All 13.0 SEER (before 1/1/2015)

14.0 SEER (as of 1/1/2015)

2 HP All 13.0 SEER and 7.7 HSPF (before 1/1/2015)

14.0 SEER and 8.0 HSPF (as of 1/1/2015)

3 Split

- System Three

- Phase

Commercial Air

- Cooled Air

Conditioning and Heating Equipment (Air

- Cooled) < 65,000 Btu/h AC All 13.0 SEER

4 HP All 13.0 SEER and 7.7 HSPF (before 1/1/2015)

14.0 SEER and 8.2 HSPF (as of 1/1/2015)

ASHRAE 90.1

- 2013 also separates water

- source heat pumps into three equipment classes based on cooling capacity. These three equipment classes are unchanged from those defined in EPCA: (1) <17,000 Btu/h; (2) ≥17,000 and <65,000 Btu/h; and (3) ≥65,000 and <135,000 Btu/h.

EFFICIENCY LEVELS 3.3

3.3.1 Baseline Units

DOE selected baseline efficiency levels as reference points for each equipment class, against which it measured changes resulting from potential amended energy conservation standards. DOE defined the baseline efficiency levels in the engineering analysis, as reference points to compare the technology, energy savings, and cost of equipment with higher energy efficiency levels. Typically, units at the baseline efficiency level just meet Federal energy conservation standards and provide basic consumer utility. However, EPCA requires that DOE must adopt either the ASHRAE Standard 90.1

- 201 3 levels or more

- stringent levels. Therefore,

because the ASHRAE Standard 90.1

- 201 3 levels were the lowest levels that DOE could adopt,

DOE used those levels as the reference p oints against which more

- stringent levels were

evaluated. The current baseline and ASHRAE efficiency levels for each equipment class of small

three

- phase commercial air

- cooled air conditioners and heat pumps <65,000 Btu/h are presented

below in Table 3.3

. 1

.3-3 Table 3.3

. 1 Baseline Efficiency Levels for Small Three

- Phase Commercial Air

- Cooled

Air Conditioners and Heat Pumps <65,000 Btu/h

DOE assigned the baseline units for water

- source heat pumps as those just meeting the

current federal standard. However, EPCA requires that DOE must adopt either the ASHRAE

Standard 90.1

- 2013 le vels or more stringent levels. Therefore, because the ASHRAE Standard

90.1

- 2013 levels were the lowest levels that DOE could adopt, DOE used those levels as the baseline efficiency level for the purpose of assessing economic impacts of raising the standards above these required levels. Table 3.3

. 2 shows the baseline efficiency level for each water

- source heat pump equipment class.

Table 3.3

. 2 Baseline EER Efficiency Levels for Water

- Sour ce Heat Pumps

Water

- Source Heat Pumps <17,000 Btu/h Water

- Source Heat Pumps

≥ 17,000 and <65,000 Btu/h Water

- Source Heat Pumps

≥ 65,000 and <135,000 Btu/h Efficiency Level (EER)

Baseline

- Current

Federal Standard 11.2 12.0 12.0

Baseline

-

ASHRAE Level 12.2 13.0 13.0

3.3.2 Identification of Efficiency Levels for Analysis

DOE established intermediate energy efficiency levels for each equipment class. The intermediate efficiency levels are representative of efficiency levels along the technology paths Split

System AC Single

Package AC Split

System HP Single

Package HP

SEER

Baseline

– Federal

Standard 13.0 13.0 1 3.0 13.0

Baseline

–

ASHRAE

Standard 13.0 14.0 14.0 14.0

HSPF

Baseline

– Federal

Standard - - 7.7 7.7

Baseline

–

ASHRAE

Standard - - 8.2 8.03-4 that manufacturers commonly use to maintain cost effective designs while increasing energy efficiency. DOE reviewed AHRI’s Directory of Certified Product Performance, manufacturer catalogs, and other publicly available literature to determine the most comm on efficiency levels for each representative equipment class. Then, DOE established intermediate energy efficiency levels for each of the equipment classes that are representative of efficiencies that are typically

available on the market.

For small thr ee

- phase commercial air

- cooled air conditioners and heat pumps, DOE analyzed several efficiency levels and obtained incremental cost data for the four equipment

classes. Table 3.3

. 3 presents the efficiency levels for each equipment class. As part of the engineering analysis, DOE considered up to six efficiency levels beyond the baseline for each equipment class. DOE derived the “max

- tech” level from the market maximum i n the AHRI Certified Directory, as of November 2013. The highest available efficiency level for split

- system heat pumps was 16.2, compared to 18.05 for single

- package heat pumps. DOE believes that split

system heat pumps are capable of reaching the same ef ficiency level as single

- package units because the technologies to increase efficiency can be employed in both equipment classes. As a result, the analyzed “max

- tech” level for single

- package and split

- system heat pumps was 18.05.

The “max

- tech” level for single

- package air conditioners was 19.15. 1 DOE rounded the “max

tech” levels to integer values of 18 for split

- system and single

- package heat pumps, and 19 for split

- system and single

- package air conditioners, respectively. The impact of this rounding is minimal, and it ensures consideration of efficiency levels that are whole

- number values of

SEER.

Table 3.3

. 3 Efficiency Levels for Small Three

- Phase Commercial Air

- Cooled Air Conditioners and Heat Pumps <65, 000 Btu/h

Split

System AC Split

- System HP Single

Package AC Single

- Package HP

Efficiency Level SEER SEER HSPF SEER SEER HSPF

Baseline

– Federal Standard 13 13 7.7 13 13 7.7

0

– ASHRAE

90.1 14 14 8.2 14 14 8

1 15 15 8.5 15 15 8.4

2 16 16 8.7 16 16 8.8

3 17 17 9 17 17 8.9

4 18 18 9.2 18 18 9.1

5

- Max Tech 19

- - 19

- - For water

- source heat pumps, five efficiency levels were chosen for the <17,000 Btu/h and 17,000

- 65,0000 Btu/h classes, but only four efficiency levels were chosen for the 65,000

- 135,000 Btu/h equipment class d ue to a small number of units on the market . The chosen levels range from the baseline to the max

- tech level, which repr esents the most efficient unit currently

1 www.gpo.gov/fdsys/pkg/FR

- 2014

- 04

- 11/pdf/2014

- 08214.pdf3-5 on the market for each equipment class. Only EER levels for water

- loop applications were considered in the analysis. The efficiency levels selected for analysis for each capacity class are

shown below in Table 3.3

. 4

.

Table 3.3

. 4 Efficiency Levels for Engineering Analysis of Water

- Source Heat Pumps

Water

- Source Heat Pumps <17,000 Btu/h Water

- Source Heat Pumps

≥ 17,000 and <65,000 Btu/h Water

- Source Heat Pumps

≥ 65,000 and <135,000 Btu/h
Efficiency Level (EER)

Baseline

- Current

Federal Standard 11.2 12.0 12.0

Baseline

- ASHRAE Level (0) 12.2 13.0 13.0

Efficiency Level 1 13.0 14.6 14.0 Efficiency Level 2 14.0 16.6 15.0
Efficiency Level 3 15.7 18.0 16.0
Efficiency Level 4 16.5 19.2

- Efficiency Level 5

– “Max

- Tech”

– 18.1 21.6 17.2

METHODOLOGY OVERVIEW 3.4

DOE typically structures its engineering analysis around one of three methodologies: (1) the design

- option approach, which calculates the incremental costs of adding specific design options to a baseline model; (2) the efficiency

- level approach, which calc ulates 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 or cost

- assessment approach,

which involves a “bottom

- up” manufa cturing cost assessment based on a detailed bill of

materials (BOM) derived from tear

- downs of the product being analyzed. DOE also uses a supplementary analysis called catalog teardowns for units that have not been physically torn

down

. Catalog teardowns combine published manufacturer product literature with physical teardown data to determine the manufacturer production cost (MPC). Section 3.4.2 provides

more detai l about catalog teardowns. For this analysis, DOE used a combination of the efficiency

- level and the cost

- assessment approach. DOE used the efficiency

- level approach to identify incremental improvements in efficiency for each equipment class and the cost

a ssessment approach to develop a cost for each efficiency level.

For small three

- phase commercial air conditioners and heat pumps, the efficiency levels that DOE considered in the engineering analysis were representative of units produced by

manufacturers at the time the engineering analysis was developed. DOE relied on data reported in the AHRI Directory of Certified Product Performance to select representative efficiency3-6 levels.

DOE generated a bill of materials for each representative product that i t disassembled. DOE did this for multiple manufacturers’ products that span a range of efficiency levels for the key equipment classes analyzed in this rulemaking. The BOMs describe the products i n detail, providing the costs to manufacture and assemble th e product. DOE also conducted catalog teardowns to supplement the information obtained directly from physical teardowns. Subsequently, DOE calculate d manufacturer production cost (MPC) for each product, based on the detailed BOM data. DOE develop ed the cos t

- efficiency curves which describe the relationship between calculated cost and efficiency level (based on rated efficiency) of the analyzed products.

DOE developed cost

- efficiency curves for small three

- phase commercial air

- conditioning and heat pump equipment with a representative capacity of three tons . DOE based the cost

efficiency relationship for small three

- phase commercial central air conditioners and heat pumps

on reverse

- engineering and catalog teardowns conducted for the 2011 Direct Final rul e (DFR) 2
for single

- phase central air conditioners and heat pumps. Through a nalyzing manufacturing specification sheets, DOE did not find significant differences between single

- phase and three

phase products, aside from the use of three

- phase compressors i n three

- phase units

. DOE also found that manufacturers use model numbers interchangeably between single

- phase and three

phase products

. To supplement the 2011 DFR data (29 physical teardowns and 12 catalog teardowns), DOE completed one physical teardown an d seven catalog teardowns.

For water

- source heat pumps, t he efficiency levels that DOE considered in the engineering analysis were representative of units currently produced by manufacturers at the time the engineering analysis was developed. DOE reli ed on data reported in the AHRI Directory of Certified Product Performance to select representative efficiency levels. After identifying representative efficiency levels, a catalog teardown or “virtual teardown” approach was used to estimate product costs at each level, in which published manufacturer catalog data and supplementary component data were used to estimate the major physical differences between

water

- source heat pumps and commercial heating and cooling products with similar components

that were previously disassembled. This approach was used instead of physical teardowns due to time constraints on the analysis imposed by EPCA.

DOE generated a cost model for water

- source heat pumps based on other previously

disassembled heating and cooling equi pment

, manufacturer

, and component data . This cost model generated manufacturer production costs (MPCs) and product weights for water

- source heat pumps at each capacity and efficiency level. These MPCs were used to generate cost

efficiency curves for each equipment class. DOE calculated the manufacturer selling price (MSP) by applying a manufacturer markup to the MSP and adding the estimated shipping costs.

2 www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf3-7 3.4.1 Physical Teardown Analysis

As part of the reverse engineering analyses for central air conditioners and heat pumps,

DOE conducted physical teardowns to develop a BOM to evaluate key design features ( e.g. , heat exchangers, compressors, fan and fan motors, control strategies, etc.) and determine the corresponding MPC of each unit

. DOE select ed units for physical teardowns based on the methodology described in Section 3.4.3 . Units selected for physical teardowns were disassembled, and each part was characterized according to weight, manufacturing process, dimensions, material, and quantity.

3.4.2 Catalog Teardown Analysis

DOE use d the catalog teardown method when physical teardowns could not be compl eted. Catalog teardowns use published manufacturer literature combined with physical teardown data to estimate the MPC of the catalog unit. DOE gathered the information from the catalog teardown analysis to adapt a BOM from a similar unit that was develope d using the reverse engineering methodology.

For water

- source heat pumps

, DOE used the catalog teardown analysis to identify the design pathways manufacturers typically use to increase the EER of units within each capacity

class. DOE calculated the MPC fo r products spanning the full range of efficiencies from the baseline to the maximum technology available at various levels. DOE determined the cost

effectiveness of each technology by comparing the increase in MPC to the increase in energy efficiency. DOE select ed units for catalog teardowns based on the methodology described in

Section 3.4.3

.

3.4.3 Selection of Units

DOE consider ed the following criteria when selecting un its for physical and catalog

teardowns :

• T he selected products should span the full range of efficiency levels for each equipment class

under consideration.

• T he selected products should come from manufacturer s with large market shares.

• T he selected products should have non

- efficiency

- related features that are the same as, or similar to, features of other equipment in the same class and at the same efficiency level

.

3.4.4 Generation of Bill of Materials and Cost Model

The BOM developed from physical and catalog te ardown analysis incorporates all materials, components, and value

- added steps a manufacturer takes to make a product. The cost

model uses the BOM to determine the MPC of a product by calculating the costs of raw materials, purchased parts, and labor requir ed to fabricate and assemble the product. Every part that makes up a product is cataloged and every value

- added step to make and assemble it in the3-8 manufacturing facility is captured. Table 3.4

. 1 describes some of these manufacturing processes. The cost model also calculates the required equipment, space needs, and other overhead costs such as insurance to run and maintain a factory. Thus, the cost model recreates the f actory electronically.

Table 3.4

. 1 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality Control Fixturing Washing Adhesive Bonding Inspecting & Testing Stamping/Pressing Powder Coating Spot Welding Turret Punching De

- burring Seam Welding Brake Forming Polishing Brazing

Cut ting and Shearing Tube Forming

To help inform the cost model inputs , DOE collected information on labor rates, tooling costs, raw material prices, and other factors. For purchased parts, the cost model estimates the purchase price based on volume

- variable price quotations and detailed discussions with

manufacturers and co mponent suppliers. For fabricated parts, the prices of raw metal materials (e.g., tube, sheet metal) are based on 5

- year averages. The cost of transforming the intermediate materials into finished parts is estimated based on current industry pricing. The c ost model outputs the MPCs and operating weights, which were used for generating cost

- efficiency curves and downstream economic analysis. Figure 3.4

. 1 shows the three major steps in generating manufacturing cost.

Figure 3.4

. 1 Ma nufacturing Cost Assessment Stages

For water

- source heat pumps, commercial heating and cooling equipment which operate

similar to water

- source heat pumps were previously physically torn down, and a bill of materials (BOM) was derived from multiple sources

. This BOM was adapted for water

- source heat pumps by combining features from indoor air handlers used in heat pump applications and condenser systems that use a water coil as a heat exchanger. Features such as multiple compressors, multiple sealed systems, etc. were also accounted for in the BOM by configuring a flexible piping, sensor, and control sub

- system.

Besides scaling indoor heat exch angers as a function of published manufacturer data, the

model for water

- source heat pumps also allows for the substitution of various capacity and3-9 efficiency

- related components (compressors, motors, thermal expansion valves, etc.), scaling of pipe sizes a s a function of sealed system capacity, as well as scaling the sheet metal to match published specifications from manufacturers. The flexibility of the model in turn allows a wide range of capacities to be examined as relevant inputs can be modified easily

. DOE subsequently conducted a ‘‘virtual” or catalog teardown analysis, using published manufacturer catalogs and supplementary component data to estimate the MPCs of water

- source heat pumps.

For supplementary virtual teardowns, DOE gathered product dat a such as dimensions, weight, and design features from publicly

- available information, such as manufacturer catalogs. DOE obtained information and data not typically found in catalogs and brochures, such as fan motor details, heat exchanger specifications, or assembly details, from the physical teardowns of a similar product and/ or through estimates based on industry knowledge. The teardown analysis for this engineering analysis included over 60 different water

- source heat pumps from multiple

manufacturers. The cost model used the BOM and the inputs to calculate multiple cost

- efficiency curves that detailed the cost of increasing energy efficiency across multiple equipment capacities.

3.4.5 Manufacturer Markup

DOE applies a non

- production cost multiplier (the manu facturer markup) to the full MPC to account for corporate non

- production costs and profit. The resulting manufacturer selling price (MSP) is the price at which the manufacturer can recover all production and nonproduction costs and earn a profit. To meet new or amended energy conservation standards, manufacturers often introduce design changes to their product lines that result in increased manufacturer production costs. Depending on the competitive environment for these particular products, some or all of the increased production costs may be passed from manufacturers to retailers and eventually to customers in the form of higher purchase p rices. As production costs increase, manufacturers typically incur additional overhead. The MSP should be high enough to recover the full cost of the product ( i.e. , full production and non

- production costs), and yield a profit. The manufacturer markup has an important bearing on profitability. A high markup under a standards scenario suggests manufacturers can pass through the increased variable costs and some of the capital and product conversion costs (the one

- time expenditures). A low markup suggests tha t manufacturers will not be able to recover as much of the necessary investment in plant and equipment.

The manufacturer markup is based on the 2011 Direct Final Rule (DFR) 3 for sing le

- phase central air conditioners and heat pumps . In the 2011 DFR analys is, DOE used U.S. Security and Exchange Commission (SEC) 10

- K reports from publicly owned residential cooling and heating product companies to estimate manufacturer markups. The SEC form 10

- K, filed by companies annually, provides a comprehensive overview of the company’s business and financial

conditions. The 10

- K report includes the company’s revenues along with the direct and indirect

costs. The income statement section of the 10

- K often lists the figures necessary for calculating the manufacturer markup

– net sales, cost of sales, and gross profit. DOE first calculated a five

3 www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf3-10 year average markup for each company. After calculating the markup for each manufacturer with

the 10

- K data, DOE then calculated a single market

- share weighted manufacturer markup t o represent the industry. The markup was further calibrated based on feedback received during manufacturer interviews. DOE used a manufacturer markup of 1.30 3 f or small three

- phase

commercial air conditioners and heat pumps <65,000 Btu/h

.

For water

- source heat pumps, DOE used the same manufacturer markup of 1.30 that was developed in the 2011 Direct Final Rule (DFR) 4 and used for small three

- phase commercial ai r

conditioners and heat pumps

.

3.4.6 Shipping Costs for Small Three

- Phase Commercial Air

- Cooled Air Conditioners

and Heat Pumps

Manufacturers of HVAC products typically pay for freight to the first step in the distribution chain. Freight is not a manufacturing c ost, but because it is a substantial cost incurred by the manufacturer, DOE accounted for shipping costs separately from the non

production costs that comprise the manufacturer markup. DOE calculated the MSP by multiplying the MPC ( from the cost model ) by the manufacturer markup and adding shipping costs. More specifically, DOE calculated shipping costs based on a typical 53

- foot straight frame trailer with a storage volume of 4,240 cubic feet

. DOE examined the sizes of representative units

and determined t he number of units that would fit in a trailer

.

For small three

- phase commercial air

- cooled air conditioners and heat pumps, DOE

assumed that units would be double

- stacked. During the 2011 and 2014 physical teardowns

, DOE observed that the stacking capabilities of certain units were specified on the equipment packaging. DOE assumed all units can be double

- stacked since there was no information provided on the equipment packaging to suggest otherwise. The shipping costs for small three

ph ase commercial air

- cooled air conditioners and heat pumps <65,000 Btu/h are shown in Table

3.4

. 2 through Table 3.4

. 5

.

4 www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf3-11 Table 3.4

. 2 Manufacturer Shipping Costs for Three

- Ton Split

- System Three

- Phase

Commercial Air

- Cooled Air Conditioners

SEER Split

- System AC Shipping Cost (201 4 $)

13 $23

14 $23

15 $25

16 $34

17 $34

18 $36

19 $37

Table 3.4

. 3 Manufacturer Shipping Costs for Three

- Ton Single

- Package Three

- Phase

Commercial Air

- Cooled Air Conditioners

SEER Single

- Package AC Shipping Cost (201 4 $)

13 $29

14 $31

15 $33

16 $39

17 $39

18 $39

19 $85

Table 3.4

. 4 Manufacturer Shipping Costs for Three

- Ton Split

- System Three

- Phase

Commercial Air

- Cooled Heat Pumps

SEER Split

- System HP Shipping Cost (201 4 $)

13 $23

14 $23

15 $25

16 $35

17 $46

18 $583-12 Table 3.4

. 5 Manufacturer Shipping Costs for Three

- Ton Single

- Package Three

- Phase

Commercia l Air

- Cooled Heat Pumps

SEER Single

- Package HP Shipping Cost (201 4 $)

13 $29

14 $31

15 $39

16 $39

17 $39

18 $78

3.4.7 Shipping Costs for Water

- Source Heat Pumps

For water

- source heat pumps, DOE assumed all units in the < 17,000 Btu/h and 17,000

- 65,000 Btu/h equipment classes could be double

- stacked in shipping, but not the units in the 65,000

- 135,000 Btu/h class because of their higher weights. The shipping costs calculated for

water

- source heat pumps are shown below in Table 3.4

. 6

.

Table 3.4

. 6 Manufacturer Shipping Costs for Water

- Source Heat Pumps

<17,000 Btu/h ≥17,000 and <65,000

Btu/h ≥65,000 and <135,000 Btu/h

EER Shipping

Cost ( 201 4 $) EER Shipping

Cost ( 201 4 $) EER Shipping

Cost ( 201 4 $)

ASHRAE

– Level 0 12.2 $10 13 $17 13 $78 Efficiency Level 1 13.0 $11 14.6 $17 14 $85 Efficiency Level 2 14.0 $15 16.6 $18 15 $94 Efficiency Level 3 15.7 $16 18 $18 16 $69 Efficiency Level 4 16.5 $16 19.2 $18 17.2 $78 Efficiency Level 5 18.1 $17 21.6 $21

- -

ENGINEERING ANALYSIS RESULTS 3.5

3.5.1 Unit Operating Weight for Small Three

- Phase Commercial Air

- Cooled Air
Conditioners and Heat Pump Results

Representative system weights for units at all SEER levels were used in the downstream analyses to estimate installation costs. DOE used the weight

- efficiency relationships developed for the 2011 DFR, which were based on operating weights reported in manu f acturer specification3-13 sheets. For each of the four equipment classes, data were collected across a number of manufacturers and a range of SEER levels. The system weights as listed by the manufacturers were plotted against rated SEER, and a best fit curve w as applied to the data. From the curve fit, typical unit weights were estimated for applicable SEER levels. Applying this methodology allowed for a single unit weight corresponding to a given SEER value to be used in each equipment class. For SEER levels n ot covered in the 2011 DFR, DOE extrapolated the operating unit weights based on the curve fits. DOE ’s calculated weight

- efficiency relationships for small

three

- phase commercial air conditioners and heat pumps <65,000 Btu/h are shown in Table

3.5

. 1

.

Table 3.5

. 1 Weight

- Efficiency Data for Three

- Ton Three

- Phase Commercial Air

- Cooled

Air Conditioners and Heat Pumps

Operating Weight (lbs)

SEER Split

- System

AC Single

- Package

AC Split

- System

HP Single

- Package

HP

13 323 341 358 379

14 346 361 393 411

15 367 382 426 444

16 387 402 456 476

17 405 422 485 508

18 422 443 512 541

19 439 463

- -

3.5.2 Cost

- Efficiency Results for Small Three

- Phase Commercial Air Conditioners and

Heat Pump Results

DOE’s calculated MPCs for small three

- phase commercial air conditioners and heat pumps <65,000 Btu/h are shown in Table 3.5

. 2 through Table 3.5

. 5 . DOE used the cost

efficiency curves from the engineering analysis as an input for the LCC and other downst ream analyses.3-14 Table 3.5

. 2 Manufacturer Production Costs for Three

- Ton Split

- System Three

- Phase

Commercial Air

- Cooled Air Conditioners

SEER MPC ( 2014$ )

13 $855

14 $937

15 $1,023

16 $1,115

17 $1,212

18 $1,316

19 $1,427

Table 3.5

. 3 Manufacturer Production Costs for Three

- Ton Single

- Package Three

- Phase

Commercial Air

- Cooled Air Conditioners

SEER MPC (2014$)

13 $1,003

14 $1,122

15 $1,241

16 $1,361

17 $1,480

18 $1,599

19 $1,719

Table 3.5

. 4 Manufacturer Production Costs for Three

- Ton Split

- System Three

- Phase

Commercial Air

- Cooled Heat Pumps

SEER HSPF MPC (2014$)

13 7.7 $1,068

14 8.2 $1,154

15 8.5 $1,244

16 8.7 $1,377

17 9.0 $1,486

18 9.2 $1,6013-15 Table 3.5

. 5 Manufacturer Production Costs for Three

- Ton Single

- Package Three

- Phase

Commercial Air

- Cooled Heat Pumps

SEER HSPF MPC (2014$)

13 7.7 $1,239

14 8.0 $1,372

15 8.4 $1,504

16 8.8 $1,637

17 8.9 $1,769

18 9.1 $1,902

3.5.3 Unit Operating Weight for Water

- Source Heat Pumps

The unit operating weights were calculated from the cost model spreadsheet. The inputs

for t hese calculations were the same as those for the cost calculations d escribed above in Section

3.4 . Representative unit weights for units at all EER levels were required as an input for the downstream analyses to estimate instillation costs. For each equipment class, unit weights for models examined in the catalog teardown analysis were plotted against rated cooling EER, and a best fit curve was appli ed to the data. From this curve fit, average unit weights were estimated

for each examined EER level. DOE’s calculated weight

- efficiency relationships for water

- source heat pumps are shown in Table 3.5

. 6

.

Table 3.5

. 6 Weight

- Efficiency Data for Water

- Source Heat Pumps

<17,000 Btu/h ≥17,000 and <65,000

Btu/h ≥65,000 and <135,000 Btu/h

EER Weight (lbs) EER Weight (lbs) EER Weight (lbs)

ASHRAE

– Level 0 12.2 13 1 13 27 2 13 737 Efficiency Level 1 13.0 14 1 14.6 290 14 811 Efficiency Level 2 14.0 154 16.6 313 15 885 Efficiency Level 3 15.7 17 7 18 329 16 959 Efficiency Level 4 16.5 187 19.2 343 17.2 1048 Efficiency Level 5 18.1 208 21.6 371

- -

3.5.4 Cost

- Efficiency Results for Water

- Source Heat Pumps

DOE calculated the manufacturer production costs (MPC s ) for water

- source heat pumps

in the same manner described above for unit operating weights, using outputs from the cost model from catalog teardown analysis and a trend line analysis. DOE used the cost

- efficiency curves from the engineering analysis as an input for downstream economic analyses. Representative MPC s for each EER level of each of the three capacity classes are shown below

in Table 3.5

. 7 . DOE did not estimate c osts of units at the current federal standard

— these were3-16 not required for the analysis, as EPCA requires DOE to comp are the costs and benefits of higher

efficiency levels to the level in ASHRAE 90.1.

Table 3.5

. 7 Manufacturer Production Cost

- Efficiency Data for Water

- Source Heat Pumps

<17,000 Btu/h ≥17,000 and <65,000

Btu/h ≥65,000 and <135,000 Btu/h

EER MPC (2014$) EER MPC (2014$) EER MPC (2014$)

ASHRAE

– Level 0 12.2 $860 13 $1,346 13 $3,274 Efficiency Level 1 13.0 $904 14.6 $1,463 14 $3,660 Efficiency Level 2 14.0 $960 16.6 $1,609 15 $4,045 Efficiency Level 3 15.7 $1,053 18 $1,711 16 $4,431 Efficiency Level 4 16.5 $1,097 19.2 $1,798 17.2 $4,893 Efficiency Level 5 18.1 $1,185 21.6 $1,974

- -4

- i CHAPTER 4. ENERGY USE ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION

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

- 1

4.2 BASE NATIONAL AVERAG E UEC DATA

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

- 1

4.2.1 Air

- Cooled Air Conditioners

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

- 1

4.2.1.1 Cooling UEC

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

- 1

4.2.1.2 Heating UEC

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

- 3

4.2.2 Water Sour ce Heat Pumps

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

- 4

4.2.2.1 Cooling UEC

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

- 4

4.2.2.2 Heating UEC

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

- 5

4.2.2.3 UEC Adjustment

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

- 7

4.3 VARIABILITY BY BUILD ING TYPE AND CENSUS DIVISION

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

- 8

4.4 RESULTS

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

- 10

REFERENCES

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

- 13

LIST OF TABLES

Table 4.2.1 Cooling UECs for Air

- Cooled Single

- Phase Air Conditioners <65,000

Btu/h

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

- 2

Table 4.2.2 Cooling UECs for Air

- Cooled Single

- Phase Heat Pumps <65,000

Btu/h

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

- 3

Table 4.2.3 Heating UECs for Air

- Cooled Single

- Phase Heat Pumps <65,000

Btu/h

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

- 4

Table 4.2.4 2000 Cooling UECs for Water Source Heat Pumps

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

- 5

Table 4.2.5 Commercial Unitary Heat Pump (≥65,000

- <135,000 Btu/h) Heating Energy Use Parameters

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

- 6

Table 4.2.6 Water

- Source Heat Pump COP to EER Relationship

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

- 6

Table 4.2.7 Heating UECs for Water Source Heat Pumps

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

- 7

Table 4.2.8 Factors Used to Represent Relative Changes in Required Cooling Energy

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

- 7

Table 4.2.9 Factors Used to Represent Relative Changes in Required Heating Energy

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

- 8

Table 4.3.1 Distribution of Small Commercial Air

- Cooled Air

- Conditioning and Heating Equipment by Building Type

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

- 8

Table 4.3.2 Distribution o f Water

- Source Heat Pumps by Building Type

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

- 8

Table 4.3.3 Distribution of Building Types by Census Division: Small Commercial

Ai r

- Cooled Air

- Conditioning and Heating Equipment

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

- 9

Table 4.3.4 Distribution of Building Types by Census Division: Water

- Source Heat Pumps

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

- 9

Table 4.4.1 Cooling UECs (kWh/year) for Small, Air

- Cooled, 3

- Phase Air

Conditioning and Heating Equipment less tha n 65,000 Btu/h

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

- 114

- ii Table 4.4.2 Cooling UECs for Water

- Source Heat Pumps

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

- 11

Table 4.4.3 Heating UECs for Water

- Source Heat Pumps

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

- 124

- 1 CHAPTER 4. ENERGY USE ANALYSIS

4.1 INTRODUCTION

This chapter describes the energy use analysis for small, air

- cooled, 3

- phase air

conditioning and heat pump equipment less than 65,000 Btu/h and water source heat pumps with cooling capacities less than 17,000 Btu/h, between 17,000 and 65,000 Btu/h, and between 65,000 and 135,000 Btu/h . DOE analyzed the energy savings consumers would realize from the standards set forth by the American Society of Heating, Refri geration, and Air

- Conditioning Engineers (A S HRAE) Standard 90.1

- 2013 for specific equipment classes

, as well as for higher energy efficiency levels.

4.2 BASE NATIONAL AVERAGE UEC DATA

4.2.1 Air

- Cooled Air Conditioners

4.2.1.1 Cooling UEC

To estimate the UEC for each class of small commercial packaged air

- conditioning and heating equipment less than 65,000 Btu/h (air

- cooled, three

- phase) , DOE began with the cooling UECs for single

- phase equipment installed in commercial buildings as presented in the national impact analysis associated with the 2011 direct final rule (DFR) for residential central air conditioners and heat pumps

. 1 Table 4.2

. 1 and Table 4.2

. 2 show these cooling UECs for the

United States. In the 2011 DFR analysis, the cooling UECs for split and single package systems were very similar, but UECs were available for higher efficiency level s for split systems tha n for single package equipment. As a result, DOE used the 201 1 UECs for split systems for all equipment classes analyzed for t his Final Rule , including both split and single package systems.
UECs were directly available for each of t he air conditioner and heat pump efficiency levels analyzed for this Final Rule

.4

- 2 Table 4.2

. 1 Cooling UECs for Air

- Cooled Single

- Phase Air Conditioners <65,000 Btu/h

Elec tric Comm ercial Cooling U.S. (kWh/yr )

SEER Split Packaged

13.0 2702.7 2716.0

13.5 2658.8 2669.1

14.0 2614.9 2622.2

14.5 2529.9 2539.3

15.0 2445.0 2456.4

15.5 2360.0 2373.5

16.0 2275.1 2290.6

16.5 2246.6 2260.5

17.0 2218.1

-

18.0 2161.2

-

19.0 2119.3

-

20.0 2077.5

-

21.0 2035.6

-

22.0 1993.7

-

23.0 1978.3

-

24.5 1955.1

- Source: U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Central Air Conditioners National Impact Analysis. 1
Note: UECs for new units were used, and these values were very similar to the UECs for replacement units.4

- 3 Table 4.2

. 2 Cooling UECs for Air

- Cooled Single

- Phase Heat Pumps <65,000 Btu/h

Elec tric Comm ercial Cooling U.S. (kWh/yr)

SEER Split Packaged

13.0 2741.4 2710.6

13.5 2701.4 2670.1

14.0 2661.3 2629.5

14.5 2582.1 2552.1

15.0 2502.8 2474.7

15.5 2423.5 2397.3

16.0 2344.2 2319.8

16.5 2310.8 2286.3

17.0 2277.4

-

18.0 2210.5

-

19.0 2161.5

-

20.0 2112.5

-

21.0 2063.5

-

22.0 2045.3

- Source: U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Central Air Conditioners National Impact Analysis. 1

4.2.1.2 Heating UEC

Although ASHRAE 90.1

- 2013 increased the HSPF levels for this equipment, DOE did not include heating UECs in this analysis. Table 4.2

. 3 shows that f or commercial installations in

the 201 1 analysis, the heating UECs did not scale proportionally with HSPF. Based on these data, DOE has determ ined that increasing HSPF for small commercial air conditioners will likely not result in significant energy savings.4

- 4 Table 4.2

. 3 Heating UECs for Air

- Cooled Single

- Phase Heat Pumps <65,000 Btu/h

SEER HSPF Elec tric Comm ercial Heating U.S. (kWh/yr)

Split Packaged Split Packaged

13.0 8.0 7.7 509.5 359.7

13.5 8.1 7.9 508.5 359.0

14.0 8.3 8.1 507.5 358.3

14.5 8.4 8.3 510.4 359.9

15.0 8.5 8.4 513.3 361.6

15.5 8.6 8.6 516.2 363.2

16.0 8.7 8.8 519.0 364.8

16.5 8.8 9.0 515.5 361.9

17.0 9.0

- 512.0

-

18.0 9.2

- 505.0

-

19.0 9.4

- 499.8

-

20.0 9.7

- 494.6

-

21.0 9.9

- 489.5

-

22.0 9.9

- 487.5

- Source: U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Central Air Conditioners National Impact Analysis. 1

4.2.2 Water Source Heat Pumps

4.2.2.1 Cooling UEC

To estimate the cooling UEC for each class of water source heat pump, DOE began with

the data provided for water source heat pumps published in Appendix D of the 2000 Screening Analysis for the Energy Policy Act of 2005 (EPACT). 2 Table 4.2

. 4 shows the input capacity and national average full load equivalent operating hours (FLEOH) for each equipment class and efficiency level. The UEC equals the input capacity times the FLEOH.4

- 5 Table 4.2

. 4 2000 Cooling UECs for Water Source Heat Pumps

WSHP <17,000

Btu/h EER 9.3 11.2 12.5 13.1 14 15.8

Input Capacity (kW) 1.29 1.071 0.96 0.916 0.857 0.759

National Avg FLEOH 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5

UEC (kwh/year) 2,093 1,738 1,558 1,486 1,390 1,231

WSHP ≥17,000

- <65,000

Btu/h EER 9.3 12 12.5 13.1 14 15.2

Input Capacity (kW) 3.871 3 2.88 2.748 2.571 2.368

National Avg FLEOH 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5

UEC (kwh/year) 6,281 4,868 4,673 4,459 4,171 3,842

WSHP ≥65,000

- <135,000

Btu/h EER 10.5 12 12.5 13 14

Input Capacity (kW) 8.571 7.5 7.2 6.923 6.429

National Avg FLEOH 1,537.1 1,537.1 1,537.1 1,537.1 1,537.1

UEC (kwh/year) 13,174 11,528 11,067 10,641 9,882

Source: U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Screening Analysis for

EPACT

- Covered Commercial HVAC and Water

- Heating Equipment. Volume 1

– Main Report . 2000.

Where identical efficiency levels were available, DOE us ed the input capacity and

FLEOH directly from the screening analysis. For additional efficiency levels, DOE used the same FLEOH (as it does not differ by efficiency level) and scaled the input capacities based on the ratio of EER, as was done in the origin al analysis and shown in the following equation:

IIIIIIIIIIIIIIIIIIIIIIIIII BB = EEEEEE AA
EEEEEE BB xx IIIIIIIIIIIIIIIIIIIIIIIIII AA
Eq. 4

. 1

DOE then multiplied the FLEOH by the scaled input capacity to calculate the UEC for each additional efficiency level.

4.2.2.2 Heating UEC

The 2000 screening analysis did not estimate heating UECs for water source heat pumps. DOE requested data on this in the Apri l 2014 N otice of Data Availability (N ODA ) ( 7 9 FR 20114) but did not receive any. In order to characterize heating

- side performance, DOE relied on an analysis of the Commercial Buildings Energy Consumption Survey ( CBECS ) 2003 data for air source heat pumps at larger capacities than two of the water source heat pump equipment classes in this rulemaking. 3 DOE assumed that this estimate was sufficiently representative of the heating energy use for all three classes of water source heat pumps.

In the air

- source heat pump analysis, DOE identified a representative building set consisting of 117 CBECS 2003 records. From this sub

- sample of buildings, DOE determined a weighted

- average annual heating energy consumption of 1.37 kWh/sq.ft. Because CBECS provides no infor mation on equipment efficiency, DOE assumed that this energy use value4

- 6 pertained to heat pump s with a heating efficiency, or coefficient of performance (COP), of 2.9. A heating efficiency of 2.9 COP corresponds to the minimum heating efficiency standard fo r air

source heat pumps in 2003, a value which DOE feels is representative of the heat pump stock characterized by CBECS.

DOE used the energy consumption per square foot value derived from CBECS and the tons per square foot value developed for commercial unitary air conditioners greater than or equal to 65,000 Btu/h and less than 135,000 Btu/h to calculate an annual energy use per ton value for a 2.9 COP unit ( Table 4.2

. 5 ). Because equipment energy use is a function of efficiency, DOE assumed that the annual heating energy consumption of a unit scales in direct proportion to the COP. Therefore, DOE multiplied the annual energy use per ton values by the representative tons for a given equipment class (1 ton, 3 tons, and 7.5 tons)

, and adjusted it by the ratio of 2.9 COP to the COP for that efficiency level. The COPs for each efficiency level were developed based on a relationship between EER and COP of units in the AHRI database a
( Table 4.2

. 6 ). Table 4.2

. 7 shows the heating

- side UEC values for water source heat pumps

.

Table 4.2

. 5 Commercial Unitary Heat Pump ( ≥65,000

- <135,000 Btu/h) Heating Energy

Use Parameters

Tons/SF 0.00260

Annual Energy Use/ SF (2.9 COP) 1.37

Annual Energy Use per Ton (2.9 COP) 527

Representative Tons 7.5

Table 4.2

. 6 W ater

- Source Heat Pump COP to EER Relationship

Equipment Class Slope Intercept
WSHP <17,000

Btu/h 0.2136 1.781

WSHP ≥17,000

- <65,000 Btu/h 0.2354 1.368

WSHP ≥65,000

- <135,000 Btu/h 0.1434 2.667

a www.ahridirectory.org/ahridirectory/pages/homeM.aspx4

- 7 Table 4.2

. 7 Heating UECs for Water Source Heat Pumps

WSHP <17,000

Btu/h EER N/A 11.2 12.2 13 .0 14 .0 15.7 16.5 18.1

COP 2.9 4.2 4.3 4.6 4.8 5.1 5.3 5.6

Heating UEC

(kWh/year) 527 364 355 335 320 298 288 271

WSHP ≥17,000

- <65,000

Btu/h EER N/A 12 .0 13 .0 14.6 16.6 18 .0 19.2 21.6

COP 2.9 4.2 4.3 4.8 5.3 5.6 5.9 6.5

Heating UEC

(kWh/year) 1581 1092 1066 954 869 818 779 711

WSHP ≥65,000

- <135,000

Btu/h EER N/A 12 .0 13 .0 14 .0 15 .0 16 .0 17.2 N/A

COP 2.9 4.2 4.3 4.7 4.8 5.0 5.1 N/A

Heating UEC

(kWh/year) 3953 2729 2666 2452 2379 2310 2233 N/A

4.2.2.3 UEC Adjustment

The cooling UECs for water source heat pumps are based on a study from 2000 and the heating UECs from the building stock in 2003. Over time, changes in building shell characteristics and internal loads can lead to a change in the energy required to meet a given cooling or heating load. The N ational Energy Modeling System (N EMS ) commercial demand module accounts for these trends by adjusting the cooling and heating energy use by factor s that

are a function of region and building activity. These factors can b e either less than one indicating a relative decrease in energy consumption, or positive, indicating an increase. DOE used the

factors from the version of NEMS underlying AEO 2014 4 to adjust the cooling and heating energy use from the values developed in 2 000 and 2003, respectively, to the start of the analysis period, 2020. To do this, DOE took the ratio of the factors in 2020 and 2004. Factors prior to 2004 were not available, so DOE assumed the 2004 factor was representative of earlier years as

well. Ide ally, DOE would have applied scalars based on building vintages but because DOE did not have a breakdown of the buildings by vintage represented by the available UECs, DOE applied the ratio of the 2020 and 2004 factors to the average UEC. The resulting fac tors make relatively modest adjustments to the UEC, as shown in Table 4.2

. 8 and Table 4.2

. 9

.

Table 4.2

. 8 Factors Used to Represent Relative Changes in Required Cooling Energy

Building Type Census Division

1 2 3 4 5 6 7 8 9

Office 0.96 0.95 0.96 0.96 0.93 0.94 0.92 0.94 0.90

Education 0.96 0.85 0.96 0.94 0.92 0.88 0.9 0.94 0.89 Lodging 0.97 0.96 0.98 0.98 0.94 0.93 0.93 0.95 0.96

Residential 0.97 0.96 0.98 0.98 0.94 0.93 0.93 0.95 0.96

Healthcare 0.96 0.95 0.96 0.95 0.94 0.95 0.95 0.97 0.91

Source: NEMS commercial demand module.4

- 8 Table 4.2

. 9 Factors Used to Represent Relative Changes in Required Heating Energy

Building Type Census Division

1 2 3 4 5 6 7 8 9

Office 0.96 0.94 0.96 0.96 0.93 0.92 0.90 0.95 0.80

Education 0.95 0.92 0.94 0.92 0.92 0.87 0.91 0.93 0.84 Lodging 0.95 0.94 0.95 0.95 0.95 0.93 0.94 0.97 0.94

Residential 0.96 0.94 0.95 0.95 0.95 0.92 0.93 0.96 0.83

Healthcare 0.95 0.94 0.95 0.94 0.94 0.93 0.94 0.98 0.86

Source: NEMS commercial demand module.

4.3 VARIABILITY BY BUILDING TYPE AND CENSUS DIVISION

DOE incorporates uncertainty and variability considerations in certain of the key parameters for the LCC analysis . In order to reflect variability of the base national average UEC estimates, DOE created dist ributions of UECs by building type and Census Division.

DOE determined that small, air

- cooled, 3

- phase air

- conditioning and heat pump equipment less than 65,000 Btu/h are used in the office and retail sectors and that water

- source heat pumps are used in th e office, lodging, education, multi

- family housing, and healthcare

sectors. Table 4.3

. 1 and Table 4.3

. 2 show the distribution s of small, air

- cooled air conditioning and heating equipment and water source heat pumps by building type. 3

, 5 Table 4.3

. 3 and Table

4.3

. 4 show the distribution s of building types by census division. 3

Table 4.3

. 1 Distribution of Small Commercial Air

- Cooled Air

- Conditioning and Heating Equipment by Building Type Building Type Market Share

Office 47%

Retail 53%

Source: CBECS 2003.

Table 4.3

. 2 Distribution of Water

- Source Heat Pumps by Building Type

Building Type Market Share

Offices 26%

Public 30%
Lodging 5%
Property owners 29%

Medical services 10%

Source: Adjusted from market shares for a broader set of building types identified in the 2014 CUAC NOPR.4

- 9 Table 4.3

. 3 Distribution of Building Types by Census Division: Small Commercial Air

Cooled Air

- Conditioning and Heating Equipment

Census Division Building Type

Office Retail
New England 4% 5%

Middle Atlantic 19% 11%

East North Central 18% 14%

West North Central 7% 7%

South Atlantic 17% 22%

East South Central 4% 6%

West South Central 11% 14%

Mountain 6% 7%

Pacific 14% 13%

Source: CBECS 2003.

Table 4.3

. 4 Distribution of Building Types by Census Division: Water

- Source Heat Pumps

Census Division Building Type

Office Education Lodging Multifamily Healthcare
New England 4% 3% 7% 7% 3%

Middle Atlantic 19% 14% 16% 20% 16%

East North Central 18% 20% 11% 15% 17%

West North Central 7% 6% 12% 5% 6%

South Atlantic 17% 25% 18% 17% 25%

East South Central 4% 3% 7% 3% 7%

West South Central 11% 12% 8% 10% 12%

Mountain 6% 6% 9% 4% 5%

Pacific 14% 10% 13% 19% 9%

Source: CBECS 2003.

In order to develop the distribution of UECs, DOE used estimates of Full Load Equivalent Operating Hours (FLEOH) provided in the 2000 Screening Analysis for commercial air conditioners and heat pumps in 7 building types and 11 cities across the United States. 2

The Screening Analysis had FLEOHs for five of the building types but did not have

FLEOHs for multi

- family and healthcare

. DOE determined that the lodging FLEOHs would be representative for multi

- family and th e office FLEOHs would be representative for healthcare. DOE used the total floor space by Census Division from the 2003 Commercial Building Energy Consumption Survey (CBECS) to determine the distribution of building types. 3 DOE then mapped the 11 cities to their respective C ensus D ivisions. The M ountain and P acific C ensus D iv isions had multiple cities which represented different climate zones within the C ensus D ivision. DOE weighted the FLEOHs by the population of that C ensus D ivision which lives in the climate zone represented by each city. 6

, 7 The mid

- Atlantic C ensus D ivision did not have a4

- 10 representative city in the screening analysis, so DOE used the same FLEO Hs as New England (whose representative city is Providence) for the Middle Atlantic.

DOE normalized the distributions of FLEOHs to the national average UECs used in this analysis to create distributions of UECs by building type and Census Division.

4.4 RESUL TS

Table 4.4

. 1 through Table 4.4

. 3 show the national average UECs by equipment class and efficiency level that were used as inputs to the LCC analysis

. These include the UEC adjustments

made to the data for water

- source heat pumps to account for changes in cooling and heating loads over time. These UECs do not include the effects of random sampling that takes into

accou nt variability by building type and Census Division. This variability is accounted for in the LCC outputs, which are used as inputs to the NIA. As a result, the UECs used in the NIA are not exactly the same as those shown here.4

- 11 Table 4.4

. 1 Cooling UECs (kWh/year) for S mall, Air

- Cooled , 3

- Phase Air

- C onditioning

and H eat ing E quipment less than 65,000 Btu/h

SEER Split AC Package AC Split HP Package HP

13 2,703 2,703 2,741 2,741

14 2,615 2,615 2,661 2,661

15 2,445 2,445 2,503 2,503

16 2,275 2,275 2,344 2,344

17 2,218 2,218 2,277 2,277

18 2,161 2,161 2,211 2,211

19 2,119 2,119 N/A N/A

Table 4.4

. 2 Cooling UECs for W ater

- S ource H eat P umps

<17,000 Btu/h ≥17,000 and <65,000 Btu/h ≥65,000 and <135,000 Btu/h

EER UEC (kWh/year) EER UEC (kWh/year) EER UEC (kWh/year)

11.2 1,631 12.0 4,568 12.0 10,819

12.2 1,497 13.0 4,217 13.0 9,987

13.0 1,405 14.6 3,755 14.0 9,274

14.0 1,305 16.6 3,302 15.0 8,655

15.7 1,163 18.0 3,045 16.0 8,114

16.5 1,107 19.2 2,855 17.2 7,548

18.1 1,009 21.6 2,538 N/A N/A4

- 12 Table 4.4

. 3 Heating UECs for W ater

- S ource H eat P umps

<17,000 Btu/h ≥17,000 and <65,000 Btu/h ≥65,000 and <135,000 Btu/h

COP UEC (kWh/year) COP UEC (kWh/year) COP UEC (kWh/year)

4.2 336 4.2 1,007 4.2 2,517

4.3 328 4.3 983 4.3 2,458

4.5 309 4.8 880 4.7 2,261

4.8 295 5.4 801 4.8 2,194

5.3 274 5.8 754 5.0 2,130

5.5 266 6.1 718 5.1 2,059

6.0 250 6.8 655 N/A N/A4

- 13 REFERENCES

1 U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. 2011

- 10

- 31 Energy Conservation Program: Energy Conservation Standards for Residential Furnaces and Residential Central Air Conditioners and Heat Pumps

< www.regulations.gov/#!docume ntDetail;D=EERE

- 2011

- BT

- STD

- 0011

- 0058 >

2 U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Screening Analysis for EPACT

- Covered Commercial HVAC and Water

- Heating Equipment. Volume 1

– Main Report . 2000. < www.regulations.gov/#!documentDetail;D=EERE

- 2006

- STD

- 0098

- 0015 >

3 U.S. Department of Energy

- Energy Information Administration, Commercial Buildings Energy Consumption Survey. 2003. < www.eia.gov/consumption/commercial/index.cfm >

4 U.S. Energy Information Administration, Commercial Demand Module of the National Energy Modeling System: Model Documentation 2014 . 2014.

< www.eia.gov/forecasts/aeo/nems/documentation/commercial/pdf/m066(2014).pdf >

5 U.S. Department of Energy. 20 1 4. Technical Support Document: Energy Efficiency Program for Commercial and Industrial Equipment: Small, Large, and Very Large Commercial Package Air Conditioning and Heating Equipment . Washington, D.C.

< www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/59 >

6 U.S. Department of Energy

– Energy Information Administration, Commercial Buildings Energy Consumption Survey

– Maps. 2003. < www.eia.gov/consumption/commercial/census

maps.cfm >

7 U.S. Census Bureau

– American Fact Finder. Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2013.

< www.census.gov/popest/data/counties/asrh/2013/index.html >5-i CHAPTER 5. MARKUPS FOR PRODUCT PRICE DETERMINATION

TABLE OF CONTENTS

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

5.1.1 Distribution Channels .......................................................................................... 5-2

5.2 MARKUP CALCULATION METHODOLOGY ........................................................... 5-3

5.3 APPROACH FOR CALCULATING OVERALL MARKUP ........................................ 5-4

5.3.1 Baseline Markup .................................................................................................. 5-4

5.3.2 Incremental Markups ........................................................................................... 5-5

5.4 APPROACH FOR CALUCLATING MANUFACTURER MARKUP .......................... 5-6

5.5 APPROACH FOR CALCULATING WHOLESALER AND CONTRACTOR

MARKUPS ...................................................................................................................... 5-6

5.5.1 Key Data and Assumptions of the Markups Methodology .................................. 5-7

5.5.2 Approach for Wholesaler Markup ....................................................................... 5-8

5.5.3 Approach for Mechanical and General Contractor Markup ................................ 5-9

5.6 DERIVATION OF MARKUP S .................................................................................... 5-10

5.6.1 Manufacturer Markups....................................................................................... 5-10

5.6.2 Wholesaler Markups .......................................................................................... 5-10

5.6.3 Mechanical Contractor Markups........................................................................ 5-12

5.6.3.1 Aggregate Baseline Markups for Mechanical Contractors ...................... 5-12

5.6.3.2 Baseline Markups for Mechanical Contractors in the Replacement

and New Construction Markets ................................................................ 5-13

5.6.3.3 Baseline Markups for Small and Large Mechanical Contractors ............. 5-13

5.6.3.4 Baseline Markups by Disaggregated Mechanical Contractor Type ......... 5-14

5.6.3.5 Incremental Markups by Mechanical Contractor Type ............................ 5-15

5.6.4 Estimation of General Contractor Markups ....................................................... 5-16

5.7 SALES TAX .................................................................................................................. 5-18

5.8 OVERALL MARKUPS ................................................................................................. 5-18

REFERENCES .......................................................................................................................... 5-20

LIST OF TABLES

Table 5.1.1 Shares of Market by Distribution Channel .................................................... 5-3

Ta ble 5.6.1 Wholesaler Expenses and Markups ............................................................. 5-11

Table 5.6.2 Baseline Markup, All Mechanical Contractors ............................................ 5-12

Table 5.6.3 Baseline Markups for the Replacement and New Construction Markets, All Mechanical Contractors .......................................................... 5-13

Table 5.6.4 Baseline Markups for Small and Large Mechanical Contractors ................ 5-14

Table 5.6.5 Baseline Markups by Mechanic al Contractor Type ..................................... 5-15

Table 5.6.6 Mechanical Contractor Disaggregated Expenses and Markups Based

on 2007 U.S. Census Bureau Data ............................................................... 5-15

Table 5.6.7 Incremental Markups by Mechanical Contractor Type ............................... 5-165- ii Table 5.6.8 General Contractor Expenses and Markups ................................................. 5-17

Ta ble 5.7.1 Average Sales Tax Rates by Census Division and Large State ................... 5-18

Table 5.8.1 Summary of Overall Markups (3- phase, air cooled, air conditioning and heat pumps <65,000 Btu/h and water source heat pumps) ................... 5-19

LIST OF FIGURES

Figure 5.1.1 Distribution Channels for New Construction Applications ........................... 5-2 Figure 5.1.2 Distribution Channels for Replacement Applications ................................... 5-35-1 CHAPTER 5. MARKUPS FOR PRODUCT PRICE DETERMINATION

5.1 INTRODUCTION

To carry out the life- cycle cost (LCC) calculations described in Chapter 6 , DOE needed

to determine the cost to the commercial consumer of baseline air

- cooled, air -conditioners and heat pumps, and water source heat pumps, along with the cost of the more- efficient unit s the consumer would purchase under potential standards. However, the commercial consumer price of such units is not generally known. By applying a multiplier called a “markup” to the known manufacturer’s price, DOE was able to estimate the commercial consumer ’s price. DOE based the markups for small commercial air

- cooled air -conditioning and heating equipment and watersource heat pumps on the analysis conducted for commercial unitary air conditioners and heat

pumps (CUAC/CUHP)

. This chapter describes how DOE derived those markups and the modifications made for today's Final Rule

.

The equipment price to the commercial consumer depends on how the consumer purchases the equipment. DOE defines two primary types of distribution channels to describe

how air -conditioners and heat pumps generally pass from the manufacturer to the consumer : (1) in the first distribution channel, the manufacturer sells the equipment to a wholesaler, who in turn sells it to a mechanical contractor, who in turn sells it to a general contractor, who in turn

sells it to the consumer; (2) in the second distribution channel, the manufacturer sells the equipmen t directly to the consumer through a national account. DOE has further subdivided the first distribution channel by mechanical contractor size (as measured in annual revenues).

DOE did not use the national accounts distribution channel in this rulemaking. National accounts are used by large commercial consumers of HVAC equipment, such as national retail chains. Due to the large volume of equipment purchased, the large commercial consumer s can buy directly from the manufacturer at lower prices than would be obtained in the typical distribution chain. Small, air -cooled, 3- phase air -conditioning and heat pump equipment less than 65,000 Btu/h and water source heat pumps with cooling capacities less than 135,000 Btu/h do not fit the profile of equipment which would be used by a large, national chain. Therefore,

DOE assumed all equipment uses a small or large mechanical contractor distribution channel to determine the markups in this rulemaking.

For wholesalers and contractors, DOE estimated a baseline markup and an incremental markup. DOE defines a baseline markup as a multiplier that converts the manufacturing selling price 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 to cover business cost and profit margin at each step in the distribution channel, both baseline and incremental markups are dependent on the particular distribution channel, as described in s ection

5.1.1

.5-2 5.1.1 Distribution Channels

The appropriate markups for determining consumer equipment prices depend on the type of distribution channels through which products move from manufacturers to purchasers. At each point in the distribution channel, companies mark up the price of the equipment to cover their business costs and profit margin. In this analysis, DOE considered a primary distribution

channel involving distributors and contractors. Within this primary channel, DOE distinguishes between new and replacement applications, as only new construction applications are expected to include a general contractor. DOE also distinguishes between small and large mechanical

contractors.

The d istribution channel for new construction applications is shown in

Figure 5.1.1 . In the new construction distribution channel, the manufacturer sells the equipment to a wholesaler, who in turn sells it to either a small or large mechanical contractor, who in turn sells it to a general contractor, who in turns sells it to the consumer and performs the

installation.

Figure 5.1.1 Distribution Channels for New Construction Applications

Distribution channels for replacement applications are shown in Figure 5.1.2 . In the replacement distribution channel, the manufacturer sells the equipment to a wholesaler, who in turn sells it to either a small or large mechanical contractor,

who in turns sells it to the consumer and performs the installation. Wholesaler Small

Mechanical

Contractor General

Contractor Consumer

Manufacturer

Large

Mechanical

Contractor General

Contractor Consumer5-3 Figure 5.1.2 Distribution Channels for Replacement Applications

Based on information that equipment manufacturers provided in the commercial unitary air conditioner and heat pump rulemaking , end

- use commercial consumers purchase 50 pe rcent of equipment through small mechanical contractors, 32.5 percent through large mechanical contractors, and the remaining 17.5 percent through national accounts. DOE excluded national accounts from this analysis, therefore of the contractor distributio n channel, small contractors represent 61 percent of sales and large contractors represent 39 percent of sales. In addition, 20 percent of commercial air -conditioning equipment is purchased for the new construction market while the remaining 80 percent is assumed to serve the replacement market. DOE used this same distribution for the equipment in today's Final Rule

. Table 5.1.1 lists each distribution channel’s

share of the full commercial air conditioner market for the original CUAC/CUHP analysis and today's analysis.

Table 5.1.1 Shares of Market by Distribution Channel

Distribution Channel CUAC/CUHP

Share of

Market Small 3

- Phase

AC/HP and

WSHP Share of

Market

New Construction, Small Mechanical Contractor 10.0 % 12.2 %

New Construction, Large Mechanical Contractor 6.5 % 7.8 %

New Construction, National Account 3.5 % N/A

Replacement, Small Mechanical Contractor 40.0 % 48.8 %

Replacement, Large Mechanical Contractor 26.0 % 3 1.2 %

Replacement, National Account 14.0 % N/A

5.2 MARKUP CALCULATION METHODOLOGY

At each point in the distribution channel, companies mark up the price of the equipment to cover their business costs and profit margin. In financial statements, gross margin is the difference between the company revenue and the company cost of sales or cost of goods sold Wholesaler Small

Mechanical

Contractor Consumer

Manufacturer

Large

Mechanical

Contractor Consumer5-4 ( CGS ). The gross margin includes the expenses of companies in the distribution channel— including overhead costs (sales, general, and administration); research and development (R&D) and interest expenses; depreciation and taxes —and company profits. In order fo r sales of a product to contribute positively to company cash flow, the product’s markup 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.

Equipment manufacturers sell most of their products directly to wholesalers. Wholesalers sell to contractors at the wholesale price. Wholesalers absorb short

- term imbalances in supply and demand, allowing manufacturers to operate mor e efficiently and satisfying consumer needs for fast deliveries. In addition, wholesalers are important sources for parts. Most contractors compete at the local level. Many carry more than one brand of air conditioner, and most install the products they s ell.

In addition to the wholesaler and mechanical contractor markups, in new construction the general contractor adds a markup. In retrofit installations, sales tax applies to the final consumer

cost.

5.3 APPROACH FOR CALCULATING OVERALL MARKUP

5.3.1 B aseline Markup

DOE used the overall baseline markup to estimate the consumer product price of baseline models, given the manufacturer cost of the baseline models. DOE considers baseline models to be product sold 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 in the first new construction distribution

channel.

CC CCCC BBBBBBBB = CCCCCCCC MMMMMM

× � MMMM MMMMMM × MM WWWWWWWW

_ BB BBBBBB × MMMM MMMM MM MMMM MM MM

_ BBBBBBBB × MMMM

_ BB BBBBBB �

= CCCCCCCC MMMMMM × MMMM OO

_ BB BBBBBB Eq

. 5.1

Where:

CPP BASE = Consumer product price for baseline models,

COST MFG = Manufacturer cost for baseline models,

MU MFG = Manufacturer markup,

MU WHOLE_BASE = Baseline wholesaler markup,

MUM CONTRACT_BASE = Baseline mechanical contractor markup,

MUM GENERAL_BASE = Baseline general contractor markup, and

MU OVERALL_BASE = Baseli ne overall markup.5-5 The following equation shows how DOE used the overall baseline markup to determine the product price for baseline models in the first replacement distribution channel.

CC CCCC BBBBBBBB = CCCCCCCC MMMMMM × � MMMM MMMMMM × MMMM WWWWWWWW

_ BB BBBBBB × MM MMMM MM MMMM MM MM

_ BBBBBBBB × TTTT �

= CCCCCCCC MMMMMM × MMMM OO

_ BB BBBBBB E q. 5.2

Where:

CPP BASE = Consumer product price for baseline models,

COST MFG = Manufacturer cost for baseline models,

MU MFG = Manufacturer markup,

MU WHOLE_BASE = Baseline wholesaler markup,

MUM CONTRACT_BASE = Baseline mechanical contractor markup,

TAX SALES = Sales tax, and

MU OVERALL_BASE = Baseline overall markup.

5.3.2 Incremental Markups

Similarly, DOE used the overall incremental markup to estimate changes in the consumer product price, given changes in the manufacturer cost from the baseline model cost resulting from an energy conservation standard to raise product energy efficiency. The total consumer product price for more energy-efficient 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 manufacturer cost to meet the new energy conservation standard. The following equation shows how DOE used the overall incremental markup to determine the product price for more energy -efficient models in the first new construction distribution channel.

CCCCCC = CCCCCC BBBBBBBB + CCCCCCCC MMMM

_ II

_ × � MMMM MMMMMM × MMMM WWWWWWWW

_ II × MMMM MMMM MM MMMM MMMM

_ II × MMMM

_ II �

= CCCCCC BBBBBBBB + CCCCCCCC MMMMMM

_ II × MMMM OO

_ II E q. 5.3

Where:

CPP EFF = Consumer product price for more

- efficient models

, CPP BASE = Consumer equipmen t price for baseline models,

COST MFG

_ INCR = Incremental manufacturer cost

, MU MFG = Manufacturer markup,

MU WHOLE_INCR = Incremental wholesaler markup,

MU M CONTRACT_INCR = Incremental mechanical contractor markup,

MU M GENERAL_INCR = Incremental general contractor markup, and

MU OVERALL_INCR = Incremental overall markup.5-6 The following equation shows how DOE used the overall incremental markup to determine the product price for more energy

- efficient models in the first replacement distribution

channel.

CC CCCC = CCCCCC BBBBBBBB + CCCCCCCC MMMMMM

_ II

× � MMMM MMMMMM × MMMM WWWWWWWW

_ II × MMMM MMMM MM MMMM MM MM

_ II × TTTT �

= CC CCCC BBBBBBBB + CCCCCCCC MMMMMM

_ II × MMMM OO

_ II E q. 5.4

Where:

CPP EFF = Consumer product price for more- efficient models

, CPP BASE = Consumer equipmen t price for baseline models,

COST MFG

_ INCR = Incremental manufacturer cost

, MU MFG = Manufacturer markup,

MU WHOLE_INCR = Incremental wholesaler markup,

MU M CONTRACT_INCR = Incremental mechanical contractor markup,

TAX SALES = Sales tax, and

MU OVERALL_INCR = Incremental overall markup.

5.4 APPROACH FOR CALUCLATING MANUFACTURER MARKUP

DOE uses manufacturer markups to transform a manufacturer’s product cost into a manufacturer sales price. Using the CGS and gross margin, the manufacturer markup can be

calculated as follows:

MMMM MMMMMM = CCCCCC MMMMMM + MMMMMM

CCCCCC MMMMMM Eq. 5.5

Where:

MU MFG = Manufacturer markup,

CGS MFG = Manufacturer cost of goods sold, and

GM MFG = Manufacturer gross margin.

5.5 APPROACH FOR CALCULATING WHOLESALER AND CONTRACTOR

MARKUPS

DOE examined the manner in which markups change by efficiency level and other

factors for wholesalers and contractors. DOE determined th at markups are neither fixed -dollar, nor proportional to all direct costs, which means that the selling price of a product may not be strictly proportional to the purchase price of the equipment. Using the available data, DOE has5-7 found measurable differenc es between incremental markups on direct equipment costs and the

average aggregate markup on direct business costs. Additionally, DOE discovered significant differences between average and incremental markups for heating, ventilation, air -conditioning and refrigeration (HVACR) wholesalers and for HVAC contractors.

The main reason that the selling price of a product may not be strictly proportional to the purchase price of the equipment is that businesses incur a wide variety of costs. When the purchase price of equipment and materials increases, only a fraction of the business expenses increase, while the remainder of the businesses’ expenses stays relatively constant. Certain business expenses are uncorrelated with the cost of equipment or cost of goods. For example, if the unit price of an air conditioner increases by 30 percent, it is unlikely that the cost of secretarial support in an administrative office will increase by 30 percent also.

5.5.1 Key Data and Assumptions of the Markups Methodology

DOE derive d the wholesaler and contractor markups from three key assumptions about

the costs associated with furnaces and central air conditioners and heat pumps. DOE based the wholesaler and mechanical contractor markups on firm

- level income statement data, while i t 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 & Refrigeration Distributors International (HARDI) 2010 Profit Planning Report and from the Air Conditioning Contractors of America (ACCA). 1 2 HARDI and ACCA are trade associations representing wholesalers and mechanical contractors, respectively. DOE used the financial data from the 2007 US Census of Business to develop general contractor markups in

the same form as the income statement data for wholesalers and mechanical contractors. 3 4 These income statements break down the components of all costs incurred by firms that supply and install air conditioning equipment. a

The key assumptions used to estimate markups using these financial data are:

• The firm income statements faithfully represent the various average costs incurred by firms distributing and installing HVAC products including commercial air conditioners.

• These costs can be divided into two categories: 1) costs that vary in proportion to the manufacturer selling price (MSP) of commercial air conditioners (variant costs); and 2) costs that do not vary with the MSP commercial air conditioners (invariant costs ).

• Overall , commercial air -conditioner wholesaler and contractor prices vary in proportion

to commercial air -conditioner wholesaler and contractor costs 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,

a Wholesaler s and mechanical contractors to which these reports refer handle multiple commodity lines, including

residential and commercial air

- conditioners.5-8 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 air conditioners and heat pumps, and boilers, the data provide the most accurate available indication of the expenses associated with commercial air conditioners.

Information obtained from the trade literature, and from selected heating, ventilating, and

air

- conditioning (HVAC) wholesalers, contractors, and consultants, tends to support the second assumption; this information indicates that wholesale and contractor markups vary accordin g to the quantity of labor and materials used to distribute and install appliances In its analysis, 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). This division of costs led to the estimate of wholesale and contractor markups described below.

In support of the third assumption, the HVAC wholesaler and contractor industry is competitive, and consumer demand for commercial heatin g and air conditioning is inelastic, i.e., the demand is not expected to decrease significantly with an increase in 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, 5 300 wholesalers of heat pumps and air-conditioning equipment, 6 more than 170,000 general residential contractors, 36,000 commercial and institutional building contractors, 7,8 and 91,000 HVAC contractors listed

in the 2007 Census. 9 Additionally, the 2007 Census estimated that the four firm concentration ratio (FFCR) for the HVAC wholesale sector is 29.7%. 10 The FFCR is the market share of the four largest firms in the industry; FFCR under 40% represents a competitive industry. 11

, 12
Following standard economic theory, competitive firms facing inelastic demand either set prices in line with costs or quickly go out of business. 13

5.5.2 Approach for Wholesaler Markup

Using the above assumptions, DOE developed baseline and incremental markups for wholesalers using the firm income statement from the Heating, Air -conditioning & Refrigeration Distributors International (HARDI) 2010 Profit Report (Appendix 5A.1). DOE used the baseline markups, which cover all of the wholesaler’s costs (both invariant costs and variant costs ), to determine the sales price of baseline models. Here variant costs were defined as costs that vary in proportion to the change in MSP induced by increased efficiency standards; in contrast, invariant costs were defined as costs that do not vary in proportion to the change in MSP due to increased efficiency standards. The baseline markup relates the manufacturer sales price to the wholesaler sales price. DOE calculated the baseline markup for wholesalers using the following equation:

MMMM WWWWWWWW

_ BB BBBBBB = CCCCCC WWWWWWWW + WWWWWWWW
CCCCCC WWWWWWWW = CCCCCC WWWWWWWW + ( IIII WWWWWWWW + WWWWWWWW )

CCCCCC WWWWWWWW Eq. 5.6

Where:

MU WHOLE_BASE = Baseline wholesaler markup,

CGS WHOLE = Wholesaler cost of goods sold,5-9 GM WHOLE = Wholesaler gross margin,

IVC WHOLE = Wholesaler invariant costs, and

VC WHOLE = Wholesaler variant costs.

Incremental markups are coefficients that relate the change in the manufacturer sales price of more energy -efficient models, or those products that meet the requirements of 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 manufacturer’s sales price (i.e., variant

costs, VC ). DOE calculated the incremental markup for wholesalers using the following equation:

MMMM WWWWWWWW

_ II = CCCCCC WWWWWWWW + WWWWWWWW
CCCCCC WWWWWWWW Eq. 5.7

Where:

MU WHOLE_INCR = Incremental wholesaler markup,

CGS WHOLE = Wholesaler cost of goods sold, and

VC WHOLE = Wholesaler variant costs.

5.5.3 Approach for Mechanical and General Contractor Markup

Similar financial data to that used to estimate wholesaler markups are also available for mechanical contractors and general contractors from ACCA and the 2007 Economic Census. 2

, 9 To estimate mechanical contractor markups for furnaces and central air conditioners and heat pumps, DOE collected financial data from ACCA and from the Plumbing and HVAC Contractors (NAICS 23822) series from the 2007 Economic Census. To estimate general contractor markups, DOE collected data from the Commercial and Institutional Building Construction series from the 2007 Economic Census (NAICS 236220).

ACCA financial data provides gross margin (GM) as percent of sales for the mechanical contractor industry; therefore, the mechanical contractor baseline markup is derived using the following equation:

MMMM MMMM MM MMMM MM MM

_ BBBBBBBB = SSSS (%)

SS SS ( % )

− (% ) Eq. 5.8

The 2007 Economic Census data include the number of establishments, payroll for construction workers, value of construction, cost of materials, and cost of subcontracted work at

both state and national level. DOE c alculated another estimate of the baseline markup for mechanical contractors and general contractors using the following equation:5-10 MMMM BBBBBBBB = CC
PP + MM + SS Eq. 5.9

W here:

MU BASE = Baseline mechanical contractor or general contractor markup,

V CONSTRUCT = Value of construction,

PAY = Payroll 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 considering those costs that scale with a change in the manufacturer’s sales price (variant costs, VC ) for higher energy efficiency products. As stated in

Section 5.5.1 , DOE assumes a division of costs between those that do not scale with the manufacturer price (labor and occupancy expenses), and those that do (oth er operating expenses and profit). Hence, DOE categorized the 2007 Economic Census cost data in each major cost category and estimated incremental contractor markups using the following equation:

MMMM

_ II = CCCCCC +

CCCCCC Eq. 5.10

Where:

MU CONTRACT_INCR = Incremental contractor markup,

CGS CONTRACT = Contractor cost of goods sold, and

VC CONTRACT = Contractor variant costs.

5.6 DERIVATION OF MARKUPS

5.6.1 Manufacturer Markups

For this rulemaking, DOE relied on the manufacturer markups developed for central air conditioners and heat pumps, which is a markup of 1.30. b DOE applied this markup to all equipment classes.

5.6.2 Wholesaler Markups

Wholesalers rep orted median data in a confidential survey that HARDI conducted across its members. In the survey, HARDI itemized revenues and costs into categories, including direct equipment expenses (cost of goods sold), labor expenses, occupancy expenses, other operat ing expenses, and profit. DOE presents these data in full in appendix 5A.1, Detailed Data for

b www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf5-11 Equipment Price Markups. Table 5.6.1 s ummarizes them in terms of cost

- pe r- dollar sales

revenue in the first data column and in terms of cost- per -dollar of cost of goods sold in the

second column.

Table 5.6.1 Wholesaler Expenses and Markups

Descriptions Per Dollar

Sales Revenue $ Per Dollar Cost

of Goods $ Direct Cost of Equipment Sales: Cost of goods sold 0.737 1.000 Labor Expenses: Salaries and benefits 0.151 0.205 Occupancy Expense: Rent, maintenance, and utilities 0.036 0.049 Other Operating Expenses: Depreciation, advertising, and

insurance. 0.055 0.075

Operating Profit 0.020 0.027 Baseline Revenue: Baseline revenue earned per dollar cost of goods sold 1.357 Wholesaler Baseline Markup (MU WHOLE BASE ) 1.357 Incremental Revenue: Increased revenue per dollar increase cost of goods

sold 1.103

Incremental Markup (MU WHOLE INCR ) 1.103 Source: Heating, Air Conditioning & Refrigeration Distributors International. 2012. 2012 Profit Report (2010 Data).

In this case, direct equipment expenses (cost of goods sold) represent about $0.74 per dollar sales revenue, so for every $1 wholesalers take in as sales revenue, $0.74 is used to pay the direct equipment costs. Labor expenses represent $0.15 per dollar sales revenue, occupancy expenses represent $0.04, other operating expenses represent $0.06, and profit accounts for $0.02 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 $0.74 (i.e., cost of goods sold per dollar of sales revenue). For every $1.00 the wholesaler spends on equipment costs, the wholesaler spends $0.205 to cover labor costs, $0.049 to cover occupancy expenses, $0.075 for other operating expenses, and $0.027 in profits. This totals to $1.357 in sales revenue earned for every $1.00 spent on equipment costs. Therefore, the wholesaler baseline markup ( MU WHOLE BASE ) is 1.357 ($1.357 ÷ $1.00).

DOE also used the data in column two to estimate the incremental markups. The incremental markup depe nds on which of the costs in Table 5.6.1 are variant or invariant with respect to MSP. For example, for a $1.00 increase in the MSP, if all of the oth er costs scale with the MSP (i.e., all costs are variant), the increase in wholesale price will be $1.357, implying that the incremental markup is 1.357, or the same as the baseline markup. At the other extreme, if no 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.

As stated in section 5.5 DOE believes that the labor and occupancy costs are invariant with respect to MSP, while other operating costs and profit are variant. In this case, for a $1.005-12 increase in the MSP, the wholesale price will increase in line with changes in the categories “other operating costs” and “operating profit”; these are approximately of $0.067, which when divided by $0.737 cents in cost of goods sold yields an increase of $0.103, giving a wholesaler incremental markup ( MU WHOLE INCR ) of 1. 103. See appendix 5.A for cost details.

5.6.3 Mechanical Contractor Markups

5.6.3.1 Aggregate Baseline Markups for Mechanical Contractors

The HVAC contractors, represented here as mechanical contractors, reported median cost data in an ACCA financial analysis of the HVAC industry. These data are shown in Table 5.6.2

Table 5.6.2 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 0.73 1.00 Gross Margin: Labor, occupancy, operating expenses, and profit 0.27 0.37

Revenue: Baseline revenue earned per dollar cost of goods 1.37 Baseline Markup (MU MECH CONT BASE ) 1.37
Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Data reported in this analysis are not as detailed as those provided by HARDI and the U.S. Census, containing only cost of goods sold and gross margin. Since the gross margin is the sum of labor, occupancy expenses, operating expenses, and profit, the data are sufficient to estimate the baseline markup. The first data column in Table 5.6.2 provides the cost of goods sold and gross margin, expressed as dollars per dollar of sales revenue. The only important difference is that the direct cost of equipment sales in this table includes installation costs as well as the cost of goods sold. As shown in the table, the direct cost of sales represents about $0.73 per dollar sales revenue to the contractor and the gross margin is $0.27 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 $0.73. 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 $0.37 in sales revenue to cover the other costs. This totals $1.37 in sales revenue earned for every $1.00 spent on equipment costs, for a baseline markup ( MU MECH

CONT BASE ) of 1.37 for mechanical contractors. This is the beginning point for the determination of mechanical contractor markups.

The baseline markup derived above for mechanical contractors is the average value for all contractors, including large and small contractors serving the replacement an d new construction markets. The following sections describe how DOE further derived the baseline and incremental markups for four distinct categories of mech anical contractors: (1) small contractors in the replacement market; (2) small contractors in the new construction market; (3) large contractors in the replacement market; and (4) large contractors in the new construction market.5-13 5.6.3.2 Baseline Markups for Mech anical Contractors in the Replacement and

New Construction Markets

As presented above for all mechanical contractors in aggregate , the ACCA financial data provided only gross margin data, which allows for the determination of only the baseline markup

for the four types of contractor, since the gross margin is the sum of all contractor labor and operating expenses plus profit.

Table 5.6.3 summarizes the gross margin a nd resulting baseline markup data for all mechanical contractors that serve the replacement and new construction markets. Note that while this data is disaggregated by the market served, it aggregates mechanical contractors of all sizes.

Table 5.6.3 Baseline Markups for the Replacement and 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 1.000 0.745 1.000

Gross Margin: Labor, occupancy, operating expenses, and profit 0.297 0.422 0.255 0.342

Baseline Markup (MU MECH CONT

BASE ): Revenue per dollar cost of goods NA 1.42 NA 1.34

% Difference from Aggregate

Mechanical Contractor Baseline

MU NA 3.6% NA -2.2%

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Using the baseline markup data for replacement and new construction contractors from

Table 5.6.3 , DOE calculated that the baseline markups for the replacement and new markets are 3.6 percent higher and 2.2 percent lower, respectively, than that for all mechanical contractors serving all markets shown in Table 5.6.2 . These percentage differences are later used, along

with those defined in section 5.6.3.3 to estimate separate markups for the four categories of

mechanical contractor of interest.

5.6.3.3 Baseline Markups for Small and Large Mechanical Contractors

ACCA provides data for con tracting businesses earning annual revenues in each of the four following categories: (1) below one million dollars; (2) from one to two million dollars; (3) from two to five million dollars; and (4) over five million dollars. DOE aggregated the data into two categories: contractors earning annual revenue below two million dollars and contractors earning annual revenue above two million dollars. The values reported for small contractors are5-14 the average of the categories: (1) below one million dollars, and (2) from one to two million dollars. The values reported for large contractors are the average of the categories: (3) from two to five million dollars, and (4) over five million dollars.

Table 5.6.4 Baseline Markups for Small and Large Mechanical Contractors

Description Contractor Expenses or Revenue by Market Type Small ( < $2 million) Large ( > $2 million)

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 0.653 1.000 0.753 1.000

Gross Margin: Labor, occupancy, operating expenses, and profit 0.347 0.532 0.247 0.328

Baseline Markup (MU MECH CONT

BASE ): Revenue per dollar cost of goods NA 1.53 NA 1.33

% Difference from Aggregate

Mechanical Contractor Baseline

MU NA 11.6% NA -3.2%

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Using the baseline markup data for small and large contractors from Table 5.6.4

, DOE calculated that the baseline markups for small and large are 11.6 percent higher and 3.2 percent lower, respectively, than that for all mechanical contractors serving all markets shown i n Table

5.6.2

.

5.6.3.4 Baseline Markups by Disaggregated Mechanical Contractor Type

Using the percentage difference from the aggregate mechanical contractor baseline markup ( Table 5.6.2 ), DOE then estimated the baseline markup for each of the four mechanical contractor types: (1) small contractors in the replacement market; (2) small contractors in the new construction market; (3) large contractors in the replacement market; and (4) large contractors in the new construction market. The baseline markup for each mechanical contractor type is estimated using the following equation:

MMMM MMMM MM MMMM MM MM

_ BBBBBBBB = MMMM + MMMM × μμ SS + MMMM × μμ MM MM MM MM E q. 5.11

Where:

MU AGGREGATE = Baseline markup for aggregate of all mechanical contractors,

μ SIZE = Size modifier (small or large contractor percentage difference from aggregate of all contractors’ markup),5-15 μ MARKET = Market modifier (replacement or new construction contractor percentage difference from aggregat e of all contractors’ markup), and,

MUM CONTRACT_BASE = Baseline markup for specific mechanical contractor type.

Table 5.6.5 Baseline Markups by Mechanical Contractor Type
Mechanical Contractor Type Size

Modifier Market

Modifier Baseline Markup Size Market

Small Replacement 11.6% 3.6% 1.58

New Construction 11.6%

- 2.2% 1.50

Large Replacement

- 3.2% 3.6% 1.38

New Construction

- 3.2%

- 2.2% 1.30

5.6.3.5 Incremental Markups by Mechanical Contractor Type

Calculation of the incremental markups for each mechanical contractor type required separation of the invariant and variant costs. Since this was not possible using the ACCA data,DOE used more detailed 2007 U.S. Census Bureau data for all plumbing, heating, and airconditioning contractors. The U.S. Census Bureau data were used to calculate the baseline and incremental markups as shown in Table 5.6.6 . Appendix 5A.2 contains the full set of data used

in this calculation.

Table 5.6.6 Mechanical Contractor Disaggregated Expenses and Markups Based on 2007

U.S. Census Bureau Data

Description 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 0.17 0.26 Occupancy Expense: Rent, maintenance, and utilities 0.02 0.03 Other Operating Expenses: Depreciation, advertising, and

insurance. 0.04 0.06

Net Profit Before Taxes 0.09 0.13 Baseline Markup (MU MECH BASE ): Revenue per dollar cost of goods 1.47 Incremental Markup (MU MECH INCR ): Increased revenue per dollar increase cost of goods sold 1.18

Percent difference between Baseline and Incremental Markup 20.0%
Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air

- Conditioning Contractors. Sector 23: 238220. Construction: Industry Series, Preliminary Detailed Statistics for Establishments, 2007.5-16 Using the data in Table 5.6.6 , the incremental markup is 20 percent lower than the baseline markup. By applying this 20 percent differential between baseline and incremental markup to the baseline markups estimated for the four specific mechanical contractor types, DOE estimates the incremental markup for each type ( Table 5.6.7 ).

Table 5.6.7 Incremental Markups by Mechanical Contractor Type
Mechanical Contractor Type Incremental Markup Size Market

Small Replacement 1.26

New Construction 1.20

Large Replacement 1.10

New Construction 1.04

5.6.4 Estimation of General Contractor Markups

DOE derived markups for general contractors from U.S. Census Bureau data for the commercial building construction sector. The commercial construction sector includes establishments primarily engaged in construction work, including new construction work, additions, alterations, and repairs.

The U.S. Census Bureau data for the construction sector include detailed statistics fo r 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 in total dollars for the construction industry as a whole, rather than in ty pical 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, represented revenues and expenses for an average or typical contra cting business. Similar to the data for wholesalers,

Table 5.6.8 summarizes the expenses for general contractors as expenses per dollar sales revenue

in the first dat a column (Appendix 5A.3 contains the full set of data).5-17 Table 5.6.8 General Contractor Expenses and Markups

Description Wholesale Firm Expenses or

Revenue

Per Dollar

Sales Revenue $ Per Dollar

Cost of Goods $ Direct Cost of Equipment Sales: Cost of goods sold 0.76 1 Labor Expenses: Salaries (indirect) and benefits 0.08 0.10 Occupancy Expense: Rent, maintenance, and utilities 0.01 0.01 Other Operating Expenses: Depreciation, advertising, and

insurance. 0.03 0.04

Net Profit Before Taxes 0.12 0.15 Baseline Markup (MU GEN CONT BASE ): Revenue per dollar cost of goods 1.31 Incremental Markup (MU GEN CONT INCR ): Increased revenue per dollar increase cost of goods sold 1.19

Source: U.S. Census Bureau. 2007. Sector 236220 (Commercial Building Construction). Construction: Industry Series: Preliminary Detailed Statistics for Establishments: 2007.

As shown in the first column , the direct cost of sales represents about $0.76 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 revenue, other operating expenses represent $0.03, and profit makes up $0.12 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 $0.76. 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, $0.10 to cover labor costs, $0.01 to cover occupancy expenses, $0.04 for other operating expenses, and $0.15 in profits. This totals to $1.31 in sales revenue earned for every $1.00 spent on equipment costs. Thus, the general contractor baseline markup ( MU GCONTRACT_ BASE ) is 1.31.

DOE was also able to use the data in column two of Table 5.6.8 to estimate the

inc remental 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

an incremental mar kup of 1.0. DOE believes the labor and occupancy costs are invariant, while the other operating costs and profit to 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 $1.19 , giving a general contractor incremental markup ( MU GCONTRACT_INCR ) of 1. 19.5-18 5.7 SALES TAX

The sales tax represents state and local sales taxes that are applied to the consumer price of the equipment. The sales tax is a multiplicative factor that increases the consumer equipment price.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse. 14
These data represent weighted averages that include county and city rates. DOE then derived population-weighted average tax values for each Census division and large state, as shown in

Table 5.7.1 below. The average sales ta x presented in the bottom row of the table is the weighted average accounting for commercial building floor space with packaged cooling by Census division, calculated using CBECS 2003. Detailed sales tax data by each state can be found in Appendix 5A.4.

Table 5.7.1 Average Sales Tax Rates by Census Division and Large State

Census Division/State Tax Rate
New England 6.91%

Mid Atlantic 8.01%

East North Central 7.47%

West North Central 6.44%

South Atlantic 5.68%

East South Central 7.65%

West South Central 6.50%

Mountain 7.09%

Pacific 8.13%

New York 8.40%

California 8.45%

Texas 7.90%

Florida 6.65%

Average 7.16%

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed March, 2014)

5.8 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 . These values are provided

in Table 5.8.1

.5-19 Table 5.8.1 Summary of Overall Markups (3-phase, air cooled, air conditioning and heat pumps <65,000 Btu/h and water source heat pumps)

Distribution Channel Segment Replacement New Construction

Baseline Markup Incremental Markup Baseline Markup Incremental Markup

Manufacturer 1. 30 1. 30 1. 30 1. 30

Wholesaler 1.36 1.10 1.36 1.10

Mechanical Contractor (large/small) 1.38 / 1.58 1.10 / 1.26 1.30 / 1.50 1.04 / 1.20

General Contractor (new construction only) -- -- 1.31 1.20

Sales tax (replacements only) 1.07 1.07

-- --
Overall Markup (large/small contractor) 2.60 / 2.99 1.70 / 1.94 3.00 / 3.47 1.78 / 2.065-20 REFERENCES

1 Heating Air Conditioning & Refrigeration Distributors International, 2010 Profit Report, 2010.

2 Air Conditioning Contractors of America, 2005 Financial Analysis for the HVACR Contracting Industry, 2005.

3 U.S. Census Bureau, Data set for Sector 23, EC0723A1, Construction: Geographic Area Series, Detailed Statistics for Establishments, 2007.

4 U.S. Census Bureau, Data set for Sector 23: EC0723I1, Construction: Industry Series, Preliminary Detailed Statistics for Establishments: 2007. 2007.

5 U.S. Census Bureau, Manufacturing Industry Series: 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.

6 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.

7 U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for Establishments: 2007. Commercial and Institutional Building Construction. , 2007.

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 Residential.,

2007.

9 U.S. Census Bureau, Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 238220. Construction: Industry Series, Preliminary Detailed Statistics for Establishments, 2007., 2007.

10 U.S. Census Bureau, Wholesale Trade: Sub ject Series: Estab and Firm Size: Summary Statistics by Concentration of Largest Firms for the United States: 2007. Data set for Sector

42: EC074373. 2007.

11 University of Maryland University College, Note on Industry Structure, 2002. (Last accessed

Febru ary, 2013.) < http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html

12 QuickMBA, Industry Concentration, 2010. (Last accessed February, 2013.)

< www.quickmba.com/econ/micro/indcon.shtml >

13 Pindyck, R. and D. Rubinfeld, Microeconomics. 2000. Prentice Hall.

14 The Sales Tax Clearinghouse, < https://thestc.com/STRates .stm >5 A

- i APPENDIX 5A. DETAILED DATA FOR EQ UIPMENT PRICE MARKUP S

TABLE OF CO NTENTS

5A.1 DETAILED WHOLESALER COST DATA

................................ ............................... 5 A

- 1

5A.2 DETAILED MECHANICAL CONTRACTOR DATA

................................ ............... 5 A

- 2

5A.3 DETAILED GENERAL CON TRACTOR COST DATA

................................ ............ 5 A

- 3

5A.4 STATE SALES TAX RATE S

................................ ................................ ...................... 5 A

- 5

REFERENCES

................................ ................................ ................................ ......................... 5 A

- 6

LIST OF TABLES

Table 5A.1.1 Disaggregated Costs and Expenses for Wholesalers

................................ .. 5 A

- 1

Table 5A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the Incremental Markups

................................ ................................ .................. 5 A

- 3

Table 5A.3.1 General Contractor Expenses and Markups

................................ ................ 5 A

- 4

Table 5A.4.1 State Sales Tax Rates (Percent)

................................ ................................ .. 5 A

- 55 A

- 1 APPENDIX 5A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

5A.1 DETAILED WHOLESALER COST DATA

Based on data provided by the Heating Air

- conditioning & Refrigeration Distributors International (HARDI), Table 5

. 6 .1 of chapter 5 shows wholesaler revenues and costs in aggregated form. 1 Table 5A.1

. 1 below provides the complete breakdown of costs and expenses

. The column labeled “Scaling” in Table 5A.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 5 , section 5 .5, only those expenses that scale with both baseline and incremental costs are marked up when there is an incremental change in equipment costs.

Table 5A.1

. 1 Disaggregated Costs and Expenses for W holesalers

Item Percent of Revenue

% Scaling

Cost of Goods Sold 73.7

Gross Margin 26. 3

Payroll Expenses 1 5.1 Baseline

Executive Salaries & Bonuses 1.7

Branch Manager Salaries and Commissions 1. 5

Sales Executive Salaries & Commissions 0.5

Outside Sales Salaries & Commissions 2. 1

Inside/Counter Sales/Wages 2. 8

Purchasing Salaries/Wages 0. 4

Credit Salaries/Wages 0. 2

IT Salaries/Wages 0. 1

Warehouse Salaries/Wages 1. 4

Accounting 0.5

Delivery Salaries/Wages 0. 7

All Other Salaries/Wages & Bonuses 0. 8

Payroll Taxes 1. 0

Group Insurance 1. 1

Benefit Plans 0. 3

Occupancy Expenses 3. 6 Baseline

Utilities: Heat, Light, Power, Water 0.4

Telephone 0.3

Building Repairs & Maintenance 0.2

Rent or Ownership in Real Estate 2. 75 A

- 2 Table 5 A.1.1 Continued Disaggregated Costs and Expenses for Wholesalers

Other Operating Expenses 5. 5 Baseline & Incremental

Advertising & Promotion 0. 9

Insurance (business liability & casualty) 0.2

Depreciation 0.4

Vehicle Expenses 1.4

Personal Property Taxes/Licenses 0.1

Collection Expenses 0.3

Bad Debt Losses 0.3

All Other Operating Expenses 1.7

Total Operating Expenses 24.2

Operating Profit 2.1 Baseline & Incremental

Other Income 0. 4

Interest Expense 0. 5

Other Non

- operating Expenses 0.0

Profit Before Taxes 2.0

Source: Heating, Air

- conditioning & Refrigeration Distributors International. 201 2 . 201 2 Profit Report (20 10 d ata). Note: The wholesaler costs and expenses are percentage values as opposed to the per

- dollar of sales revenue values shown in Table 6. 6 .1.

5A.2 DETAILED MECHANICAL CONTRACTOR DATA

Tables 5

. 6.2, 5 .6.3, and 5 .6 .4 of chapter 5 provide mechanical contractor revenues and costs in aggregated form by ‘Cost of Goods Sold’ and ‘Gross Margin.’ The tab les are based on

data in the 2005 edition of Financial Analysis for the HVACR Contracting Industry, published by

the Air Conditioning Contractors of America (ACCA). 2 The ACCA report did not provide a more disaggregate tabulation of these costs and expenses. T he gross margin category was

assumed to scal e only with the baseline markup.

The more disaggregated breakdown of costs used to scale the incremental markup are

shown in Table 5A.2

. 1 , presented in both dollar val ue and percentage terms from the 2007

Economic Census. 3 As the ACCA data were used to calculate the baseline markup, in Table

5A.2

. 1 only the categories in the ‘Scaling’ column that sc aled with both the baseline and incremental markups are marked when there is an incremental change in equipment costs.5 A

- 3 Table 5A.2

. 1 Mechanical Contractor Expenses and Markups Used To Scale the Incremental Markups

Item Dollar Value $ 1000 Percentage

% Scaling

Total Cost of Equipment Sales 107,144,428 67.80

Cost of materials, components, and supplies 59,023,964 37.35

Payroll, construction workers 31,373,558 19.85

Cost of construction work subcontracted out to others 13,646,192 8.64

Cost of selected power, fuels, and lubricants 3,100,714 1.96

Gross Margin 50,895,129 32.20

Payroll Expenses 27,626,376 17.48 Baseline

Fringe benefits, all employees 13,585,040 8.60

Payroll, other employees 14,041,336 8.89

Occupancy Expenses

Rental cost for machinery, equipment, and buildings + Cost of repairs to buildings and other structures +

Purchased communication services 3,436,208 2.17 Baseline

Other Operating Expenses 6,165,776 3.90 Baseline &

Incremental Depreciation charges during year 2,297,550 1.50

Computers + Insurance and other business services + Advertising and Promotions + Taxes and License Fees 3,868,226 2.40

Net Profit Before Income Taxes 13,666,769 8.60 Baseline &

Incremental

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air

- Conditioning Contractors: 2007. Sector 23: 238220. Construction: Geographic

Area Series. Detailed Statistics for Establishments: 2007.

Note: Mechanical contractor costs and expenses are first pre sented as total dollar values and then converted to percentage val ues.

5A.3 DETAILED GENERAL CON TRACTOR COST DATA

Based on U.S. Department of Census data, Table 5

. 6.8 of chapter 5 shows general contractor revenues and costs in aggregated form. Table 5A.3

. 1 below shows the complete breakdown of costs and expenses provided in the 2007 Economic Census

. 4 The column labeled “Scaling” in Table 5A.4

. 1 indicates which expenses DOE assumed to scale with only the baseline markup and which scaled with both the baseline and incremental markups. Only those expenses that scale with baseline and incremental costs are m arked up when there is an incremental change in equipment costs.5 A

- 4 Table 5A.3

. 1 General Contractor Expenses and Markups

Item Dollar Value $1000 Percentage

% Scaling

Total Cost of Equipment Sales 250,657,006 76.24

Total payroll, construction workers wages 16,449,830 5.00

Cost of materials, components, and supplies 74,148,280 22.55

Cost of construction work subcontracted out to others 157,873,840 48.02

Total cost of selected power, fuels, and lubricants 2,185,056 0.66

Gross Margin 78,113,967 23.76

Payroll Expenses 25,948,454 7.89

Baseline

Total payroll, other employees wages 16,652,791 5.07

Total fringe benefits 8,666,079 2.64

Temporary staff and leased employee expenses 629,584 0.19

Occupancy Expenses 3,301,046 1.00

Baseline

Rental costs of machinery and equipment 1,403,979 0.43

Rental costs of buildings 1,045,163 0.32

Communication services 385,109 0.12

Cost of repair to machinery and equipment 466,795 0.14

Other Operating Expenses 10,770,620 3.28

Baseline &

Incremental Purchased professional and technical services 1,121,644 0.34

Data processing and other purchased computer services 127,031 0.04

Expensed computer hardware and other equipment 219,601 0.07

Expensed p urchases of software 67,977 0.02

Advertising and promotion services 290,239 0.09

All other expenses 6,321,197 1.92

Refuse removal (including hazardous waste) services 233,831 0.07

Taxes and license fees 807,872 0.25

Total depreciation ($1,000) 1,581,228 0.48

Net Profit Before Income Taxes 38,093,847 11.59 Baseline &

Incremental

Source: U.S. Census Bureau. 2007. Commercial and Institutional Building Construction. Sector 236220 . 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.5 A

- 5 5A.4 STATE SALES TAX RATE S

Table 5A.4

. 1 State Sales Tax Rates (Percent)

State Combined

State and Local

Tax Rate State Combined

State and

Local Tax Rate State Combined

State and

Local Tax Rate

Alabama 8.55% Louisiana 8.75% Ohio 6.80%

Alaska 1.35% Maine 5.00% Oklahoma 8.35%

Arizona 8.15% Maryland 6.00% Oregon 0.00%

Arkansas 8.35% Massachusetts 6.25% Pennsylvania 6.40%

California 8.40% Michigan 6.00% Rhode Island 7.00%

Colorado 6.15% Minnesota 7.20% South Carolina 7.10%

Connecticut 6.35% Mississippi 7.00% South Dakota 5.40%

Delaware 0.00% Missouri 6.60% Tennessee 9.45%

Florida 6.65% Montana 0.00% Texas 7.95% Georgia 7.10% Nebraska 6.00% Utah 6.70%

Hawaii 4.40% Nevada 7.85% Vermont 6.05%

Idaho 6.05% New Hampshire 0.00% Virginia 5.00%

Illinois 8.05% New Jersey 6.95% Washington 8.90%

Indiana 7.00% New Mexico 6.60% West Virginia 6.05%

Iowa 6.85% New York 8.40% Wisconsin 5.45%

Kansas 8.00% North Carolina 6.85% Wyoming 5.35% Kentucky 6.00% North Dakota 5.90%
Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (A ccessed on February 2013 )5 A

- 6 REFERENCES

1. Heating Air Conditioning & Refrigeration Distributors International, 2012 Profit Report

, 2012. < www.hardin et.org/Profit

- Report >

2. Air Conditioning Contractors of America, 2005 Financial Analysis for the HVACR Contracting Industry , 2005.

3. U.S. Census Bureau, Plumbing, Heating, and Air

- Conditioning Contractors. Sector 23: 238220. Construction: Industry Series, Preliminary Detailed Statistics for Establishments, 2007. , 2007.

4. U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for Establishments: 2007. Commercial and Institutional Building Construction. , 2007.6

- i CHAPTER 6. LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION

................................ ................................ ................................ ........... 6

- 1

6.1.1 General Approach for Life

- Cycle Cost and Payback Period Analysis

................ 6

- 1

6.1.2 Overview of Life

- Cycle Cost and Payback Period Analysis Inputs

.................... 6

- 2

6.2 LIFE CYCLE COST ANAL YSIS INPUTS

................................ ................................ .... 6

- 4

6.2.1 Total Installed Cost Inputs

................................ ................................ ................... 6

- 5

6.2.1.1 Manufacturer Costs

................................ ................................ .................... 6

- 6

6.2.1.2 Markups

................................ ................................ ................................ ...... 6

- 6

6.2.1.3 Total Customer Price

................................ ................................ .................. 6

- 6

6.2.1.4 Future Equipment Prices

................................ ................................ ............ 6

- 7

6.2.1.5 Installation Cost

................................ ................................ .......................... 6

- 9

6.2.1.6 Total Installed Cost

................................ ................................ .................. 6

- 13

6.2.2 Operating Cost Inputs

................................ ................................ ........................ 6

- 14

6.2.2.1 Annual Energy Use Savings

................................ ................................ ..... 6

- 14

6.2.2.2 Energy Prices

................................ ................................ ............................ 6

- 14

6.2.2.3 Energy Price Trends

................................ ................................ ................. 6

- 15

6.2.2.4 Repair Cost

................................ ................................ ............................... 6

- 16

6.2.2.5 Maintenance Cost

................................ ................................ ..................... 6

- 18

6. 2.2.6 Lifetime

................................ ................................ ................................ .... 6

- 20

6.2.2.7 Discount Rates

................................ ................................ .......................... 6

- 21

6.2.2.8 Compliance Date of Standard

................................ ................................ .. 6

- 24

6.2.2.9 Base Case Distribution of Efficiency Levels

................................ ........... 6

- 25

6.3 PAYBACK PERIOD INPUT S

................................ ................................ ...................... 6

- 26

6.4 LCC AND PBP RESULTS

................................ ................................ ............................ 6

- 27

REFERENCES

................................ ................................ ................................ .......................... 6

- 35

LIST OF TABLES

Table 6.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP Analysis

................................ ................................ ................................ .......... 6

- 3

Table 6.2.1 Summary of Average Markups

................................ ................................ ...... 6

- 6

Table 6.2.2 Overall Markup for All Equipment Classes

................................ ................... 6

- 6

Table 6.2.3 Average Customer Price for Small Commercial Air

- cooled Air Conditioning and Heating Equipment ( 2014$ )

................................ .............. 6

- 7

Table 6.2.4 Average Customer Price for Water

- Source Heat Pumps ( 2014$ )

................. 6

- 7

Table 6.2.5 Installation Costs for Baseline Small, 3

- phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

................................ .............. 6

- 10

Table 6.2.6 Installation Costs f or Baseline Water

- Source Heat Pumps

.......................... 6

- 106

- ii Table 6.2.7 National Average Installation Costs for Small, 3

- phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h ( 2014$ )

................................ 6

- 11

Table 6.2.8 National Average Installation Costs for Water Source Heat Pump s ( 2014$ )

................................ ................................ ................................ ......... 6

- 11

Table 6.2.9 Installation Cost Indices by State (National Value = 100.0)

....................... 6

- 12

Table 6.2.10 Installation Cost Indices by Region (National Value = 100.0)

.................... 6

- 13

Table 6.2.11 Average Total Installed Cost for Small, 3

- phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h ( 2014$ )

................................ 6

- 13

Table 6.2.12 Average Total Installed Cost for Water

- Source Heat Pumps ( 2014$ )

........ 6

- 13

Table 6.2.13 Average and Marginal Tariff

- based Prices by Region

................................ 6

- 15

Table 6.2.14 Repair Costs for 3

- Phase, Air

- Cooled, Air Conditioners < 65,000

Btu/h

................................ ................................ ................................ ............. 6

- 17

Table 6.2.15 RS Means Costs Used to Derive Repai r Costs for 3

- Phase, Air

- Cooled Heat Pumps < 65,000 Btu/h and Water

- Source Heat Pumps

...................... 6

- 17

Table 6.2.16 Total Repair Costs for 3

- Phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h ( 2014$ )

................................ ............... 6

- 18

Table 6.2.17 Total Repair Costs for Water Source Heat Pumps ( 2014$ )

......................... 6

- 18

Table 6.2.18 Annual Repair Costs for 3

- Phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h ( 2014$ )

................................ ............... 6

- 18

Table 6.2.19 Annual Repair Costs for Water Source Heat Pumps ( 2014$ )

...................... 6

- 18

Table 6.2.20 Maintenance Costs for Air

- Cooled Air Conditioning and Heating Equipment

................................ ................................ ................................ .... 6

- 20

Table 6.2.21 Risk Free Rate and Equity Risk Premium (2011

- 2013)

.............................. 6

- 22

Table 6.2.22 Weighted Average Cost of Capital for Sectors that Purchase 3

- phase

Air

- Cooled, Air Conditioning and Heating Equipment

............................... 6

- 23

Table 6.2.23 Weighted Average Cost of Capital for Sectors that Purchase Water

Source Heat Pumps

................................ ................................ ...................... 6

- 23

Table 6.2.24 Base Case Efficiency Market Shares in 2020 for 3

- phase Air

- Cooled, Air Conditioning and Heating Equipment

................................ ................... 6

- 25

Table 6.2.25 Base Case Efficiency Market Shares in 2020 for Water

- Source Heat Pumps

................................ ................................ ................................ ........... 6

- 26

Table 6.4.1 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Split

System Air Conditioners <65,000 Btu/h

................................ ...................... 6

- 27

Table 6.4.2 LCC Savings Relative to the Base

- Case Efficiency Distribution for 3

- Phase Air

- Cooled Split

- System Air Conditioners <65,000 Btu/h

............... 6

- 28

Table 6.4.3 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Package Air Conditioners <65,000 Btu/h

................................ .................... 6

- 28

Table 6.4.4 LCC Savings Relative to the Base

- Case Efficiency Distribution for 3

- Phase Air

- Cooled Single

- Package Air Conditioners <65,000 Btu/h

........... 6

- 29

Table 6.4.5 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Split System Heat Pumps <65,000 Btu/h

................................ ............................. 6

- 29

Table 6.4.6 LCC Savings Relative to the Base

- Case Efficiency Distribution for 3

- Phase Air

- Cooled Split System Heat Pumps <65,000 Btu/h

....................... 6

- 30

Table 6.4.7 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Package Heat Pumps <65,000 Btu/h

................................ ............................ 6

- 306

- iii Table 6.4.8 LCC Savings Relative to the Base

- Case Efficiency Distribution for 3

- Phase Air

- Cooled Package Heat Pumps <65,000 Btu/h

.............................. 6

- 31

Table 6.4.9 Life Cycle and Payback Period Results for Water Source Heat Pumps <17,000 Btu/h

................................ ................................ .............................. 6

- 31

Table 6.4.10 LCC Savings Relative to the Base

- Case Efficiency Distribution for Water Source Heat Pumps <17,000 Btu/h

................................ .................. 6

- 32

Table 6.4.11 Life Cycle and Payback Period Results for Water Source Heat Pumps ≥17,000 to <65,000 Btu/h

................................ ................................ ............ 6

- 32

Table 6.4.12 LCC Savings Relative to the Base

- Case Efficiency Distribution for Water Source Heat Pumps ≥17,000 to <65,000 Btu/h

................................ . 6

- 33

Table 6.4.13 Life Cycle and Payback Period Results for Water Source Heat Pumps ≥65,000 to <135,000 Btu/h

................................ ................................ .......... 6

- 33

Table 6.4.14 LCC Savings Relative to the Base

- Case Efficiency Distribution for Water Source Heat Pumps ≥65,000 to <135,000 Btu/h

............................... 6

- 34

LIST OF FIGURES

Figure 6.2.1 Historical Nominal and Deflated Producer Price Indexes for Unitary Air Conditioners except Heat Pumps

................................ ............................. 6

- 8 Figure 6.2.2 Historical Nominal and Deflated Producer Price Indexes for All other Miscellaneous Refrigeration and Air Conditioning Equipment

.................... 6

- 9 Figure 6.2.3 AEO 2014 Electricity Price Projections by Census Division

...................... 6

- 166

- 1 CHAPTER 6. LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

6.1 INTRODUCTION

The effect of amended standards on individual customers 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 stand ards on individual commercial customers.

• Life

- cycle cost (LCC) is the total customer cost over the life of an appliance or product, including purchase costs and operating costs (which in turn include maintenance, repair, and energy costs). Future operatin g costs are discounted to the time of purchase and

summed over the lifetime of the equipment

.

• Payback period (PBP) measures the amount of time it takes customers to recover the assumed higher purchase price of more energy

- efficient equipment through reduce d 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 6.1.1

.

Inputs to the LCC and PBP analysis of 3

- phase, air

- cooled air conditioners, 3

- phase, air

cooled heat pumps, and water

- source heat pumps are discussed in sections 6.2 and 6.3 respectively. Results for each metric are presented in section 6.4 . Key variables and calculations are presented for each metric. The calculations discussed here were performed with a series of

Microsoft Excel spreadsheets.

6.1.1 General Approach for Life

- Cycle Cost and Payback Period Analysis

In recognition of the fact that the energy use of three

- phase, a ir

- cooled air conditioning and heating equipment and water

- source heat pumps varies by building type and climate , the

LCC and PBP calculations analyze energy use based on the distribution of end

- use rs (commercial buildings types) by census division . The r esults are expressed as the unit energy consumption for each equipment type . The LCC and PBP model was developed using Microsoft Excel spreadsheets combined with Crystal Ball.

The LCC analysis used the estimated energy use for each equipment type as descr ibed in the energy use analysis in chapter 4 . Energy use is sensitive to climate and therefore varies by location within the United States. Aside from energy use, other important factors influencing the LCC and PBP analysis include energy prices, installat ion costs, equipment distribution markups,

and sales tax es

. T he LCC spreadsheets explicitly modeled both the uncertainty and the variability in the model’s inputs

.6

- 2 As mentioned previously, DOE generated LCC and PBP results as probability

distributions usi ng 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 Carlo analysis can also be expressed as probabilit y 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 customers 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. The base case efficiency distribution is for 2019 for split s ystem air conditioners and 2020 for the other equipment classes and is defined as a mix of efficiency levels reflecting the expected distribution of efficiency levels by equipment class.

6.1.2 Overview of Life

- Cycle Cost and Payback Period Analysis Inputs

The LCC is the total customer cost over the life of the equipment, including purchase price (including retail markups, sales taxes, and installation costs) and operating cost (including repair costs, maintenance costs, and energy cost). Future operating costs are discounted to the time of purchase and summed over the lifetime of the equipment. The PBP is the increase in purchase cost of a higher efficiency unit divided by the change in annual operating cost of the unit. It represents the number of years that it will take the customer to recover the increased purchase cost through decreased operating costs. In the calculation of PBP, future costs are not

discounted.

Inputs to the LCC and PBP analysis are categorized as: (1) inputs for establishing the purchase co st, 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 bas eline manufacturer selling price (MSP) is the price charged by the manufacturer to a wholesaler for equipment meeting existing minimum efficiency (or baseline) standards. The MSP includes a markup that converts the cost of production ( i.e. , the manufacture r cost) to a MSP.

• Standard

- level manufacturer selling price increase : The standard

- level MSP is the incremental change in MSP associated with producing equipment at each of the higher

standard levels.

• Markups and sales tax : Markups and sales tax are the w holesaler and contractor margins and state and local retail sales taxes associated with converting the MSP to a customer price.

• Installation cost : Installation cost is the cost to the customer of installing the equipment. The installation cost represents all costs required to install the equipment but does not

include the marked

- up customer equipment price. The installation cost includes labor, overhead, and any miscellaneous materials and parts.6

- 3 The primary inputs for calculating the operating cost are:

• Equipment energy consumption : The equipment energy consumption is the site energy use associated with providing space conditioning to the building.

• Electricity Prices : Average and marginal electricity prices for commercial buildings are determined using a tariff

- based analysis. The average electricity price is used to estimate operating costs for the baseline, and marginal prices are used to value the operating cost savings at higher efficiency levels.

• Electricity price trends : The Energy Information Admi nistration’s (EIA’s) Annual Energy Outlook 201 4 ( AEO 201 4 ) 1 is used to project electricity prices into the future.

• Maintenance costs : The labor and material costs associated with maintaining the operation of the equipment.

• Repair costs : The labor and mat erial costs associated with repairing or replacing components that have failed.

• Lifetime : The age at which the equipment is retired from service.

• Discount rate : The rate at which future costs and savings are discounted to establish their present value.

Table 6.1

. 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 variabilit y in the input variables.

Table 6.1

. 1 Summary of Inputs and Key Assumptions Used in the LCC and PBP Analysis Inputs Description Affecting Installed Costs

Equipment Price Derived MSP for equipment (from the engineering analysis) and 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. 2 Overhead and materials costs and profits are assumed to be included in the contractor’s markup. Thus, the total installed cost equals the customer equipment price (manufacturer cost multiplied by the vari ous markups plus sales tax) plus the installation cost. Affecting Operating Costs Annual Energy Use See chapter 4

.

Energy Efficiency Seasonal energy efficiency ratio (SEER) is the cooling performance descriptor for small commercial air

- cooled air conditioners and heat pumps. E nergy efficiency ratio (EER) is the efficiency descriptor for cooling performance of water

- source heat pumps. Coefficient of performance (COP) is the performance descriptor for heating6

- 4 Inputs Description performance of water

- source heat pumps. U nit energy consumption data

is used to determine the annual energy consumption associated with a particular standard level.

Electricity Prices Average and marginal electricity prices for commercial buildings are determined using a tariff

- based analysis fr om a representative sample of

electric utilities . The average electricity price is used to estimate operating costs for the baseline, and marginal prices are used to value the operating cost savings at higher efficiency levels.

Maintenance Cost Maintenan ce costs came from RS Means. 3 The cost associated with maintaining the operation of the equipment (e.g., cleaning heat exchanger coils). Annual maintenance cost does not change as a function of MSP.

Repair Cost Repair costs were derived from RS Means. 3 In order to make the repair costs into an annual cost, DOE annualized the p resent value of the repair cost ( which take s place in year 10 ) and applied them over the lifetime of

the equipment

. Affecting Present Value of Annual Operating Cost Savings

Equipment Lifetime DOE used Weibull distribution s from the 2011 DFR to determine the

lifetimes of each equipment class in this rulemaking.

Discount Rate Mean real discount rates ranging from 6.57 percent to 7.21 percent for

various classes of commercial customers of three

- phase air

- cooled air conditioning and heating equipment less than 65,000 Btu/h

. Mean real

discount rates ranging from 3.74 percent to 5.57 percent for water

- source heat pumps . Discount rates were calculated using financial data from Damodaran Online. Probability distributions are used for the discount

rates.

Compliance Date 2020 ( 4 years after expected publication of the final rule ) for all equipment except Split System AC which has a compliance dat e of 2019 (3 years after expected publication of the final rule)

.

All of the inputs summarized in Table 6.1

. 1 ar e discussed in sections 6.2 and 6.3

.

6.2 LIFE CYCLE COST ANALYSIS INPUTS

Life

- cycle cost is the total customer cost over the life of a piece of equipment, including purchase cost and operating costs (which are composed of energy co sts, maintenance costs, and repair costs). Future operating costs are discounted to the time of purchase and summed over the lifetime of the equipment. Life

- cycle cost is defined by the following equation:6

- 5 Eq. 6

. 1

Where:

LCC = life

- cycle cost ($),

IC = total installed cost ($), ∑ = sum over the lifetime, from year 1 to year N, where N = lifetime of equipment (years),

OC = operating cost ($),

r = discount rate, and

t = year for which operating cost is being determined.

DOE expresses all the costs in 2014$ . Total installed cost, operating cost, lifetime, and discount rate are discussed in the following sections. In the LCC analysis, the year of equipment purchase is assumed to be 2020 the expected compliance date of energy conservation standards higher than the ASHRAE level for the triggered equipment classes, or 2019 for split system air

conditioners.

6.2.1 Total Installed Cost Inputs

The total installed cost to the customer is defined by the following equation:

IIII = EEEEEE + IIIIIIII

Eq. 6

. 2

Where:

EQP = equipment price ($) (i.e., customer price for the equipment only), and

INST = installation cost ($) (i.e., the cost for labor and materials).

The equipment price is based on the distribution channel through which the customer purchases the equipment. As discussed in chapter 5 , DOE defined one major distribution channel for new units to describe how the equipme nt passes from the manufacturer to the customer: the manufacturer sells the equipment to a wholesaler or distributor, who sells to a mechanical contractor hired by a general contractor. The general contractor purchases and installs the equipment on behalf of the customer and adds its markup to the mechanical contractor’s price. Replacement equipment follows 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

calculate the total installed cost 

# ∑

= 

### + + = N

t t

t 

### r OC IC LCC

1 

### ) 1 /(6

- 6 6.2.1.1 Manufacturer Costs

DOE developed the manufacturer costs as described in chapter 3 , Engineering Analysis.

6.2.1.2 Markups

For a given distribution channel, the overall markup is the value determined by multiplying all the associated markups and the applicable sales tax together to arrive at a single overall distribution chain markup value. The overall markup is multiplied tim es the baseline or

standard

- compliant manufacturer cost to arrive at the price paid by the customer. Because there are baseline and incremental markups associated with the wholesaler and mechanical and general contractors, the overall markup is also divide d into a baseline markup (i.e., a markup used to convert the baseline manufacturer price into a customer 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 customer price). Markups can differ depending on whether the equipment is being purchased for a new construction installation or is being purchased to replace existing equipment. DOE developed the overall baseline markups and incremental markup s for both new construction and replacement applications as a part of the markups analysis (chapter 5 of the TSD).

Table 6.2

. 1 display s baseline and incremental markups used to calculate customer price

from manufacturer cos t. Because the relative importance of new construction and replacements in total shipments varies among the equipment classes, the total markup varies as well ( Table

6.2

. 2 ).

Table 6.2

. 1 Summary of Average Markups
Baseline Markup Incremental Markup

Manufacturer 1.30 1.30

Wholesaler 1.36 1.10

Mechanical Contractor (new construction/replacement) 1.42/1.50 1.14/1.20

General Contractor (new construction only) 1.31 1.19

Sales Tax (replacement only) 1.07 1.07

Table 6.2

. 2 Overall M arkup for A ll E quipment C lasses Baseline Markup Incremental Markup

2.929 1. 867

6.2.1.3 Total Customer Price

DOE derived the customer equipment price for the efficiency levels above the baseline by taking the product of the baseline manufacturer cost and the baseline overall markup (including the sales tax) and adding to it the product of the incremental manufact urer cost and the incremental overall markup (including the sales tax). DOE followed the same process for6

- 7 shipping costs but in this case the manufacturer markup was not included in the overall markup.

DOE then added the marked

- up shipping cost to the cust omer equipment price. Markups and the sales tax all can take on a variety of values, depending on location, so the resulting total installed cost for a particular efficiency level (EL) is represented by a distribution of values. Table 6.2

. 3

and Table 6.2

. 4 present the average customer equipment price for each equipment class at ea ch efficiency level examined.

Table 6.2

. 3 Average Customer Price for Small Commercial Air

- cooled Air Conditioning and Heating Equipment ( 2014$ )

Equipment

Class Federal

Standard

Baseline ASHRAE

Baseline/

EL0 EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $ 2,555 $ 2,709 $ 2,872 $ 3,057 $ 3,239 $ 3,436 $ 3,645

Package AC N/A $ 3,358 $ 3,584 $ 3,814 $ 4,037 $ 4,260 $ 4,549

Split HP N/A $ 3,433 $ 3,602 $ 3,864 $ 4,068 $ 4,286 N/A

Package HP N/A $ 4,089 $ 4,347 $ 4,595 $ 4,842 $ 5,146 N/A

Table 6.2

. 4 Average Customer Price for Water

- Source Heat Pumps ( 2014$ )

Equipment Class ASHRAE

Baseline EL 1 EL 2 EL 3 EL 4 EL 5

Water Source HP <17,000

Btu/h $ 2,544 $ 2,627 $ 2,735 $ 2,911 $ 2,993 $ 3,160

Water Source HP ≥17,000 to <65,000 Btu/h $ 3,981 $ 4,199 $ 4,473 $ 4,664 $ 4,827 $ 5,159

Water Source HP ≥65,000 to 135,000 Btu/h $ 9,766 $ 10,497 $ 11,229 $ 11,914 $ 12,790 N/A

6.2.1.4 Future Equipment Prices

To develop an equipment price trend for small commercial air

- cooled air conditioners <65,000 Btu/h , DOE obtained an inflation

- adjusted index of the producer price index (PPI) for “unitary air

- conditioners, except air source heat pumps” from 1978 to 2013

. 4 Although the PPI

index shown in Figure 6.2

. 1 shows a long

- term declining trend, data for the last decade have

shown a flat

- to

- slightly rising trend. Given the uncertainty as to which of the trends will prevail in coming years, DOE cho se to apply a constant price trend (2013 levels) for the Final Rule

. Because PPI data for air

- source heat pumps was not available for very many years, DOE applied the same constant price trend to small commercial air

- cooled heat pumps

.6

- 8 Figure 6.2

. 1 Historical Nominal and Deflated Producer Price Indexes for Unitary Air

Conditioners except Heat Pumps

To develop an equipment price trend for water

- source heat pumps, DOE derived an

inflation

- adjusted index of the producer price index (PPI) for “all other miscellaneous refrigeration and air

- conditioning equipment” from 1990

- 2013. 5 Although the inflation

- adjusted

index shown in Figure 6.2

. 2 shows a declining trend from 1990 to 2004, data since 2008 have

shown a flat

- to

- slightly rising trend. Given the uncertainty as to which of the trends will prevail in coming years, DOE chose to apply a constant price trend (2013 levels) fo r each water

- source heat pump equipment class. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0197819801982198419861988199019921994199619982000200220042006200820102012Deflated PPI (2013=1)Nomianl PPI (1982=100)

Unitary air-conditioners, except air source heat pumps Nominal PPI

Unitary air-conditioners, except air source heat pumps Deflated PPI6

- 9 Figure 6.2

. 2 Historical Nominal and Deflated Producer Price Indexes for All other Miscellaneous Refrigeration and Air Conditioning Equipment

6.2.1.5 Installation Cost

The installation cost is the price to the customer of labor and materials (other than the actual equipment) needed to install equipment. DOE derived installation costs from 2013 RS

Means data. 2 RS Means provides estimates on the person

- hours required to install HVAC equipment and the labor rates associated with the type of crew required to install the equipment.

DOE calculated the installation cost by multiplying the number of person

- hours by the corresponding labor rate. RS Means provides specific person

- hour and labor rate data for the

installation of 3

- ton air conditioners , 3

- ton air to air heat pumps, and 1

- , 3

- , and 7.5

- ton wat er

source heat pumps . DOE decided that data for 3

- ton, single zone air conditioners and 3

- ton air to air split system heat pumps are representative of installation costs for 3 phase, air

- cooled air conditioners and heat pumps less than 65,000 Btu/h

. DOE d etermined the 1

- , 3

- , and 7.5

- ton

water

- source heat pumps were representative of the market for water

- source heat pumps less than 135,000 Btu/h.

Labor rates vary significantly among regions , and the RS Means data provide the necessary information to capt ure this regional variability. RS Means provides cost indices that

reflect the labor rates for 656 cities in the United States. Several cities in all 50 states of the

United States and the District of Columbia are identified in the RS Means data. DOE

incorporated these cost indices into the analysis to vary the installation cost depending on the

location of the customer. 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Deflated PPI (2013=1)Nomianl PPI (1989=100)

All other miscellaneous refrigeration & air-conditioning equipment Nominal PPI

All other miscellaneous refrigeration & air-conditioning equipment Defalted PPI6

- 10 Within a given equipment class, DOE chose to vary installation costs in direct proportion to the weight of the equipment. The weight of th e equipment in each equipment class and efficiency level was determined through the engineering analysis.

Table 6.2

. 5 and Table 6.2

. 6 summarize the nationally representative person

- hours and

labor rates associated with the installation of 3

- ton air conditioners , 3

- ton air to air heat pumps,

and 1

- , 3

- , and 7.5

- ton water

- source heat pumps as presented in RS Means 2013.

Table 6.2

. 5 and Table 6.2

. 6 provide both bare installation costs (i.e., costs before overhead and profit (O&P)) and installation costs including O&P. DOE decided that the installation costs that include O&P represent th e installation costs for baseline efficient systems (i.e., 13.0 S EER fo r small, 3

- phase air

- cooled air conditioners and heat pumps <65,000 Btu/h

, , 11.2 EER for water

- source heat pumps less than 17,000 Btu/h, and 12.0 EER for water

- source heat pumps of grea ter than or equal to 17,000 to less than 65,000 Btu/h and greater than or equal

to 65,000 and less than 135,000 Btu/h ).

Table 6.2

. 5 Installation Costs for Baseline Small, 3

- phase Air

- Cooled A ir Conditioners

and H eat P umps <65,000 Btu/h

System Type* Person

- hours 2013 Bare Costs Labor w/ O&P Cost per

Person

hour ( 2014$ ) Total

Labor

Cost ( 2014$ ) Cost per

Person

hour ( 2014$ ) Total

Labor

Cost ( 2014$ ) Rooftop Air Conditioners, single zone, electric cool only, 3 ton cooling 18.2 $ 52 $ 942 $ 78 $ 1

, 417

Air

- Source Heat Pumps, air to air, split system, 3 ton cooling 13.3 $ 52 $ 690 $ 78 $ 1

, 038

Air

- Source Heat Pumps, air to air, single package, 3 ton cooling 13.3 $ 52 $ 690 $ 78 $ 1

, 038

Table 6.2

. 6 Installation Costs for Baseline Water

- Source Heat Pumps

System Type* Person

- hours 2013 Bare Costs Labor w/ O&P Cost per

Person

hour ( 2014$ ) Total

Labor

Cost ( 2014$ ) Cost per

Person

hour ( 2014$ ) Total

Labor

Cost ( 2014$ )

Water

- Source Heat Pumps, water

source to air, single package, 1 ton cooling 8 $52 $414 $78 $623

Water

- Source Heat Pumps, water

source to air, single package, 3 ton cooling 11.4 $52 $591 $78 $890

Water

- Source Heat Pumps, water

source to air, single package, 7.5 ton cooling 26.6 $52 $1,379 $78 $2,0766

- 11 Table 6.2

. 7 and Table 6.2

. 8 show the national average total installation costs by equipment class and efficiency level. The total includes overhead and profit (O&P), which is calculated using labor markups from RS Means

. For efficiency levels above the baseline, the installation costs vary in direct proportion to equipment weight.

Table 6.2

. 7 National Average Installation Costs for Small, 3

- phase Air

- Cooled Air

Conditioners and Heat Pumps <65,000 Btu/h ( 2014$ )

Equipment Class Efficiency Level

Federal

Baseline ASHRAE

Baseline/

EL0 EL 1 EL 2 EL 3 EL 4 EL 5

Split AC Weight (lbs) 323 346 367 387 405 422 439

Installation Cost (2014$) $ 1,417 $ 1,518 $ 1,611 $ 1,698 $ 1,777 $ 1,852 $ 1,926

Package

AC Weight (lbs) 341 361 382 402 422 443 463

Installation Cost (2014$) $ 1,417 $ 1,501 $ 1,588 $ 1,671 $ 1,754 $ 1,841 $ 1,925

Split HP Weight (lbs) 358 393 426 456 485 512

Installation Cost (2014$) $ 1,038 $ 1,140 $ 1,235 $ 1,322 $ 1,407 $ 1,485

Package

HP Weight (lbs) 379 411 444 476 508 541

Installation Cost (2014$) $ 1,038 $ 1,126 $ 1,216 $ 1,304 $ 1,392 $ 1,482

Table 6.2

. 8 National Average Installation Costs for Water Source Heat Pumps ( 2014$ )

Equipment Class Efficiency Level

Federal

Baseline ASHRAE

Baseline EL 1 EL 2 EL 3 EL 4 EL 5

<17,000

Btu/h Weight (lbs) 118 131 141 154 177 187 208

Installation

Cost $623 $693 $749 $819 $937 $993 $1,105

≥17,000 to <65,000

Btu/h Weight (lbs) 260 272 290 313 329 343 371

Installation

Cost $890 $930 $993 $1,072 $1,127 $1,175 $1,270

≥65,000 to <135,000

Btu/h Weight (lbs) 663 737 811 885 959 1048

Installation

Cost $2,076 $2,309 $2,540 $2,772 $3,004 $3,282

Table 6.2

. 9 summarizes the cost indices for installations in each of the 50 States of the U.S., plus the District of Columbia, used to vary the nationally representative installation costs in

Table 6.2

. 7 and Table 6.2

. 8 . To arrive at an average index for each state , DOE weighted the city6

- 12 indices in each state by their population. It used population estimates for the year 2010 from the U.S. Census Bureau to calculate a weighted

- average index for each state. DOE then assigned

each state to a census division and calcul ated a weighted

- average index for each region in the same manner ( Table 6.2

. 10 ).

Table 6.2

. 9 Installation Cost Indices by State (National Value = 100.0)

State Index State Index State Index

Alabama 50.1 Kentucky 83.6 North Dakota 58.6

Alaska 107.4 Louisiana 62.5 Ohio 93.2

Arizona 79.5 Maine 75.9 Oklahoma 66.4

Arkansas 58.9 Maryland 83.8 Oregon 99.6

California 124.9 Massachusetts 120.6 Pennsylvania 116.4

Colorado 78.8 Michigan 101.4 Rhode Island 112.0

Connecticut 119.8 Minnesota 112.9 South Carolina 52.3

D.C 95.3 Mississippi 52.4 South Dakota 41.4

Delaware 119.5 Missouri 99.2 Tennessee 73.2

Florida 68.0 Montana 73.2 Texas 56.8

Georgia 68.9 Nebraska 80.5 Utah 70.8

Hawaii 112.9 Nevada 100.3 Vermont 75.1

Idaho 75.6 New Hampshire 83.0 Virginia 73.1

Illinois 130.6 New Jersey 126.5 Washington 107.2

Indiana 81.9 New Mexico 70.5 West Virginia 88.6

Iowa 80.5 New York 163.8 Wisconsin 97.7

Kansas 73.9 North Carolina 55.7 Wyoming 67.26

- 13 Table 6.2

. 10 Installation Cost Indices by Region (National Value = 100.0)
Region Index

New England 110.3

Mid Atlantic 141.0

East North Central 104.3

West North Central 91.2

South Atlantic 68.7

East South Central 66.3

West South Central 58.7

Mountain 79.1

Pacific 120.4

6.2.1.6 Total Installed Cost

The total installed cost is the sum of the equipment price and the installation cost. MSPs, markups, and sales taxes all can take on a variety of values, depending on location, so the resulting total installed cost for a particular efficiency level will no t be a single

- point value, but

rather a distribution of values. Table 6.2

. 11 and Table 6.2

. 12 present the average total installed cost for each equipment class at each efficiency level examined.

Table 6.2

. 11 Average Total Installed Cost for Small, 3

- phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h ( 2014$ )

Equipment

Class Efficiency Level

Federal

Baseline ASHRAE

Baseline/ EL0 EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $ 3,973 $ 4,227 $ 4,483 $ 4,756 $ 5,017 $ 5,288 $ 5,571
Package AC N/A $ 4,858 $ 5,171 $ 5,485 $ 5,791 $ 6,101 $ 6,473
Split HP N/A $ 4,573 $ 4,838 $ 5,187 $ 5,474 $ 5,771 N/A
Package HP N/A $ 5,214 $ 5,563 $ 5,899 $ 6,234 $ 6,628 N/A

Table 6.2

. 12 Average Total Installed Cost for Water

- Source Heat Pumps ( 2014$ )

Equipment Class Efficiency Level

ASHRA

E

Baseline EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $ 3,237 $ 3,376 $ 3,554 $ 3,849 $ 3,986 $ 4,264
≥17,000 to <65,000

Btu/h $ 4,911 $ 5,192 $ 5,545 $ 5,791 $ 6,002 $ 6,429

≥65,000 to

< 135,000 Btu/h $ 12,075 $ 13,037 $ 14,001 $ 14,918 $ 16,073 N/A6

- 14 6.2.2 Operating Cost Inputs

DOE defined the operating cost by the following equation:

OC = EC + MC

Eq. 6

. 3

Where:

OC = operating cost ($),

EC = energy cost associated with operating the equipment ($), and

MC = annual maintenance cost for maintaining equipment operation ($).

The remainder of this section provides information about the variables that DOE used to calculate the operating cost s by equipment class and efficiency level . The annual energy costs of the equipment are computed from energy consumption per unit for the baseline and standard

compliant cases (efficiency level 2, 3, and so on), combined with the energy prices. Equipment lifetime, discount rate, and complian ce date of the standard are required for determining the operating cost and for establishing the operating cost present value.

6.2.2.1 Annual Energy Use Savings

For each equipment class, DOE calculated the annual energy use savings for each product

at each effici ency level by taking the difference between a considered efficiency level and the baseline efficiency level.

6.2.2.2 Energy Prices

In this analysis, DOE used electricity prices ($/k W h) by census division which came

from the 2014 Central Unitary Air Conditioner Notice of Proposed R ulemaking

. 6 The prices by census division were in 2012$ in the CUAC rulemaking and were scaled to 2014$ for this analysis

. Electricity cost savings at efficiency levels above the ASHRAE baseline were calculated using the annual marginal price per kWh, as shown in Table 6.2

. 13 . For each efficiency level, the operating costs savings are calculated by multiplying the reduction in

electr icity consumption (in kWh) by the annual marginal price.6

- 15 Table 6.2

. 13 Average and M arginal T ariff

- based P rices by R egion

Census Division Average Marginal

2014 $/kWh

New England $0.16 $0.10

Mid

- Atlantic $0.16 $0.10

East North Central $0.13 $0.07

West North Central $0.08 $0.06

South Atlantic $0.11 $0.07

East South Central $0.09 $0.06

West South Central $0.08 $0.06

Mountain $0.11 $0.08

Pacific $0.15 $0.11

US Average $0.12 $0.08

6.2.2.3 Energy Price Trends

DOE projected future electricity prices using trends in national

- average commercial electricity price from AEO 201 4

, as shown in Figure 6.2

. 3

. The US average trend is shown as a heavy dashed line. DOE used AEO 201 4 Reference Case scenarios a for the nine census divisions. DOE applied the projected energy price for each of the nine census divisions based on the distribution of buildings across divisio ns , as discussed in Chapter 4

.

a The reference case is a business

- as

- usual estimate, given known market, demographic, and technological trends.6

- 16 Figure 6.2

. 3 AEO 2014 Electricity Price Projections by Census Division

6.2.2.4 Repair Cost

Repair costs are associated with repairing or replacing components that have failed. DOE

utilized RS Means to find th e repair costs associated with small commercial air

- cooled air

conditioning and heating equipment , and water

- source heat pumps . For air

- cooled air

conditioners , DOE used the repair costs for a 3 ton, single zone rooftop uni t. For air

- cooled heat

pumps , DOE took the repair costs for 1.5 ton, 5 ton, and 10 ton air to air heat pumps and linearly scaled the repair costs to derive a 3 ton repair cost. Because specific water

- source heat pump data were not available, f or water

- sour ce heat pumps , DOE also used the repair costs for 1.5 ton, 5 ton, and 10 ton air to air heat pumps and linearly scaled the repair costs to derive costs for 1 ton, 3 ton, and 7.5 ton water

- source heat pumps

. DOE assumed the air

- source costs would be

represe ntative of the water

- source costs.

RS Means breaks down repair costs into two components, material and labor. DOE assumed the repair costs in RS Means represented the current baseline efficiency level. DOE then scaled the material portion of the repair cos ts by the percentage increase in manufacturer production costs at each efficiency level. 0.06 0.08 0.10 0.12 0.14 0.16 0.18201120132015201720192021202320252027202920312033203520372039Electricity Price ($/kwh) New England

Mid-Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific

National6

- 17 Based on information in RS Means, DOE assumed the repair would take place in the 10th year of the equipment life

. In order to make the repair costs into an annual va lue, DOE annualized the present value of the repair costs in year 10 over the lifetime of the equipment.

Table 6.2

. 14 shows the r epair costs used for air

- cooled air conditioners , and Table 6.2

. 15 shows the repair costs for air to air heat pumps which were used to derive the repair c osts for air

source heat pumps and water

- source heat pumps

. Table 6.2

. 16 shows one

- time repair costs for

small commercial air

- cooled equipment , and Table 6.2

. 17 shows one

- time repa ir costs for water

source heat pumps

. Table 6.2

. 18 shows the annualized repair costs for small commercial air

cooled equipment , and Table 6.2

. 19 shows the annualized repair costs for water

- source heat

pumps

.

Table 6.2

. 14 Repair C osts for 3

- Phase, Air

- Cooled, Air Conditioners < 65,000 Btu/h

System Type Annual

Person

hours Bare Costs Labor and Materials w/O&P

Mat’l ( $2014 ) Cost

per Person

hour ( $2014 ) Annual

Labor

Cost ( $2014 ) Annual

Total

Cost ( $2014 ) Mat’l ( $2014 ) Cost

per Person

hour ( $2014 ) Annual

Labor

Cost ( $2014 ) Annual

Total

Cost ( $2014 )

Single

zone rooftop unit, 3

ton 18 $ 781 $ 58 $ 1,041 $ 1,822 $ 1,474 $ 87 $ 1,544 $ 3,018

Table 6.2

. 15 RS Means Costs U sed to D erive R epair C osts for 3

- Phase, Air

- Cooled Heat Pumps < 65,000 Btu/h and Water

- Source Heat Pumps

System Type Annual

Person

hours Bare Costs Labor and Materials w/O&P

Mat’l ( $2014 ) Cost

per Person

hour ( $2014 ) Annual

Labor

Cost ( $2014 ) Annual

Total

Cost ( $2014 ) Mat’l ( $2014 ) Cost

per Person

hour ( $2014 ) Annual

Labor

Cost ( $2014 ) Annual

Total

Cost ( $2014 )

1.5 ton

air to

air HP 16 $612 $57 $900 $1,512 $726 $87 $1,355 $2,081

5 ton

air to

air HP 18 $968 $57 $1,016 $1,984 $1,691 $87 $1,530 $3,221

10 ton

air to

air HP 24 $1,994 $52 $1,228 $3,222 $3,607 $78 $1,848 $5,4556

- 18 Table 6.2

. 16 Total Repair Costs for 3

- Phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h ( 2014$ )

Equipment Class Federal

Baseline ASHRAE

Baseline/

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $ 3,018 $ 3,159 $ 3,308 $ 3,467 $ 3,636 $ 3,815 $ 4,007 Package AC $ 3,018 $ 3,193 $ 3,193 $ 3,369 $ 3,545 $ 3,721 $ 3,897 Split HP $ 2,585 $ 2,673 $ 2,676 $ 2,811 $ 2,922 $ 3,040 N/A Package HP $ 2,585 $ 2,701 $ 2,701 $ 2,817 $ 2,933 $ 3,049 N/A

Table 6.2

. 17 Total Repair Costs for Water Source Heat Pumps ( 2014$ )

Equipment Class Federal

Baseline ASHRAE

Baseline/

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $1,784 $1,827 $1,818 $1,861 $1,934 $1,968 $2,036

≥17,000 to <65,000 Btu/h $2,585 $2,647 $2,713 $2,874 $3,052 $3,237 $3,529

≥65,000 to <135,000

Btu/h $4,387 $4,726 $4,726 $5,066 $5,406 $5,813 N/A

Table 6.2

. 18 Annual Repair Costs for 3

- Phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h ( 2014$ )

Equipment Class Federal

Baseline ASHRAE

Baseline/

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $ 148 $ 155 $ 162 $ 170 $ 178 $ 187 $ 196 Package AC N/A $ 157 $ 157 $ 165 $ 174 $ 182 $ 191 Split HP N/A $ 143 $ 143 $ 150 $ 156 $ 162 N/A Package HP N/A $ 144 $ 144 $ 150 $ 157 $ 163 N/A

Table 6.2

. 19 Annual Repair Costs for Water Source Heat Pumps ( 2014$ )

Equipment Class ASHRAE

Baseline EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $ 93 $ 92 $ 95 $ 98 $ 100 $ 104 ≥17,000 to <65,000 Btu/h $ 135 $ 138 $ 146 $ 155 $ 165 $ 179 ≥65,000 to <135,000 Btu/h $ 240 $ 240 $ 258 $ 275 $ 296 N/A

6.2.2.5 Maintenance Cost

The maintenance cost is the routine cost to the customer of maintaining equipment operation.

DOE took annualized maintenance costs for air conditioners and heat pumps from data in RS Means Online, which provides estimates on the person

- hours, labor rates, and materials required

to maintain air

- conditioning equipment. DOE took annualized maintenance costs for CUACs6

- 19 from data in RS Means Online, which provides estimates on the person

- hours, labor rates, and materials required to maintain CUAC equipment. RS Me ans specifies the following 11 actions that constitute required annual maintenance:

• Check with operating or area personnel for deficiencies.

• Check tensions, condition, and alignment of belts; adjust as necessary.

• Lubricate shaft and motor bearings.

• Replace air filters.

• Clean electrical wiring and connections; tighten loose connections.

• Clean coils, evaporator drain pan, blowers, fan motors, and drain piping as required.

• Perform operational check of unit; make adjustments on controls and other compone nts as required.

• During operation of unit, check refrigerant pressure; add refrigerant as necessary.

• Check compressor oil level; add oil as required.

• Clean area around unit.

• Fill out maintenance checklist and report deficiencies.

DOE calculated the annuali zed maintenance cost by multiplying the number of person

hours by the corresponding labor rate and adding to it the associated materials costs. RS Means provides specific cost data for the maintenance of rooftop air conditioners ranging in cooling capacity from 3 through 24 tons as well as for air

- cooled heat pumps up to 5 tons and over 5

tons

. RS Means did not provide maintenance costs specific to water

- source heat pumps. DOE took the 3 through 24 ton rooftop air conditioner data to be representative of ma intenance costs

for the small commercial air

- cooled air conditioner equipment classes . DOE took the up to 5 ton

air

- cooled heat pump data to be representative of maintenance costs for the small commercial

air

- cooled heat pump equipment classes and the wate r

- source heat pump classes less than 65,000

Btu/h

. DOE took the over 5 ton air

- cooled heat pump data to be representative of the water

source heat pump class greater than or equal to 65,000 Btu/h and less than 135,000 Btu/h.

Because data were not availabl e to indicate how maintenance costs vary with equipment efficiency, the Department decided to use costs that stay constant as equipment efficiency

increases.

Table 6.2

. 20 summarizes the nationally representative annualized maintenance costs for the relevant equipment as presented in RS Means Online. Table 6.2

. 20 provides both bare maintenance costs (i.e., costs before O&P) and maintenance costs including O&P. DOE uses maintenance costs including O&P in its analysis.6

- 20 Table 6.2

. 20 Maintenance Costs for Air

- Cooled Air Conditioning and Heating Equipment

System Type Annual

Person

hours Bare Costs Labor and Materials w/O&P

Mat’l ( $2014 ) Cost

per Person

- hour ( $2014 ) Annual

Labor

Cost ( $2014 ) Annual

Total

Cost ( $2014 ) Mat’l ( $2014 ) Cost

per Person

- hour ( $2014 ) Annual

Labor

Cost ( $2014 ) Annual

Total

Cost ( $2014 )

Packaged unit,

air

- cooled, 3

- tons through 24

- tons 2.4 $ 75 $ 57 $ 138 $ 213 $ 94 $ 87 $ 208 $ 302

Heat pump, air cooled, up to 5 ton 3.2 $ 45 $ 57 $ 184 $ 229 $ 56 $ 87 $ 277 $ 334

Heat pump, air cooled, over 5 ton 3.6 $ 75 $ 57 $ 206 $ 281 $ 94 $ 87 $ 310 $ 404

6.2.2.6 Lifetime

DOE defines lifetime as the age when equipment is retired from service. In the April 2014 Notice of Data Availability ( NODA ) , DOE identified the average lifetime as 15 years for 3

- phase, air

- cooled air conditioning and heating equipment <65,000 Btu/ and 19 years for water

source heat pumps

. ( 7 9 FR 20114) For this Final Rule

, DOE uses a "survival function" to more accurately represent equipment lifetime. DOE used the survival functions in the form of Weibull

distributions prese nted in the 2011 DFR for residential central air conditioners and heat pumps.

The Weibull distribution is a probability distribution function commonly used to measure

failure rates. 4 Its form is similar to an exponential distribution, which would model 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. 6

. 4

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 time, and
θ = the delay parameter, which allows for a delay before any failures occur.

The 2011 DFR include Weibull distributions for both air conditioners and heat pumps.

The air conditioner function uses an alpha of 20.29, a beta of 1.89, and a theta of 1. This resulted 

# e x



### x P β

α

θ 

    

−

−



### = ) (6

- 21 in a mean lifetime of 19.0 years and a median lifetime of 17.7 years

. This is a longer lifetime than the 15 years estimated in the NODA, which was for larger commercial equipment. DOE

believes that the estimated lifetime from the 2011 DFR for single

- phase air conditioners used in commercial settings is more representative of the lifetime for similar three

- phase air conditioners than is the estimate for larger equipment used in the NODA

.

The heat pump function used an alpha of 17.04, a beta of 1.64, and a theta of 1. This resulted in mean lifetime of 16.24 years and a median lifetime of 14.63 years.

DOE also applied the air conditioner function to water

- source heat pumps as they are estimated to have a mean lifetime of 19 years and DOE does not expect the shape of the function to be significantly different.

6.2.2.7 Discount Rates

The discount rate is the rate at which future expenditures and savings are discounted to establish their present value. The cost of capital is commonly used to estimate the present value of cash flows to be derived from a typical company project or investment

. Following financial theory, the finance cost of raising funds for expenditure can be interpreted as: (1) the financial cost of any debt incurred to maintain equipment, or (2) the opportunity cost of any equity used to maintain equipment. Similarly, the v alue of cost savings can be interpreted as: (1) the cost avoided by decreasing debt, or (2) the return earned from increasing equity holdings. Most companies use both debt and equity capital to fund investments, so their cost of capital is the weighted av erage of the cost to the firm of equity and debt financing. 7 DOE defines the discount rate as the weighted average cost of capital ( WACC ), calculated as the weighted average of the cost of equity financing and the cost of debt financing, as estimated from financial data for publicly traded firms in the sectors that purchase commercial air conditioners.

Damodaran Online is a widely used source of information about company debt and equity financing for most types of firms, and was the primary source of data f or this analysis. 8
Companies included in the Damondaran Online database were assigned to the categories listed

below for 3

- phase air

- cooled air conditioning and heating equipment :

• Small Office

• Small Retail

For water

- source heat pumps, companies were assigned to the following categories:

• Property owners

• Medical services

• Lodging

• Offices

• Public

DOE estimated the cost of equity using the capital asset pricing model (CAPM). 9 The CAPM assumes that the cost of equity ( k

e ) for a particular company is proportional to the6

- 22 systematic risk faced by that company, where high risk is associated with a high cost of equity and low risk is associated with a low cost of equity. The system atic risk facing a firm is determined by several variables: the risk coefficient of the firm ( β ), the expected return on risk

free assets ( R f ), and the equity risk premium ( ERP ). The risk coefficient of the firm indicates the

risk associated with that firm relative to the price variability in the stock market. The expected

return on risk

- free assets is defined by the yield on long

- term government bonds. The ERP represents the difference between the expected stock market return and the risk

- free rate. The

co st of equity financing is estimated using the following equation, where the variables are

defined as above:

kk
ee = RR ff + ( ββ × EE EE )
Eq. 6

. 5

Where:

K

e = cost of equity,

R

f = expected return on risk

- free assets, β = risk coefficient of the firm, and

ERP = equity risk premium.

Several parameters of the cost of capital equations can vary substantially over time, and therefore the estimates can vary with the time period over which data is selected and the technical details of the data averaging method. For guidance on the time period for selecting and averaging data for key parameters and the averaging method, DOE used Federal Reserve methodologies for calculating these parameters. In its use of the CAPM, the Federal Reserve uses a forty

- year period for calculating discount rate averages, utilizes the gross domestic product price deflator for estimating inflation, and considers the best method for determining the

risk free rate as one where “the time horizo n of the investor is matched with the term of the risk

free security.” 10

By taking a forty

- year geometric average of Federal Reserve data on annual nominal

returns for 10

- year Treasury bills, DOE found for this analysis the following risk free rates for

2011

- 2013 ( Table 6.2

. 21 ). DOE also estimated the ERP by calculating the difference between risk free rate and stock market return for the same time period. 11

Table 6.2

. 21 Risk Free Rate and Equity Risk Premium (2011

- 2013)

Year Risk free rate (%) ERP (%)

2011 6.61% 2.94%

2012 6.41% 3.99%

2013 6.24% 5.30%

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

a ) to the risk

- free rate. This risk adjustment factor depends on the variability of stock returns represented by standard deviations in stock prices. So for fir m i, the cost of debt financing is:6

- 23 kk dddd = RR ff + RR aa Eq. 6

. 6

Where:

k d i = cost of debt financing for firm, i,

R

f = expected return on risk

- free assets, and

R ai = risk adjustment factor to risk

- free rate for firm, i.

DOE estimates the WACC using the following equation:

d d e e 

## w k w k WACC × + × =
Eq. 6

. 7

Where:

WACC = weighted average cost of capital,

w e = proportion of equity financing, and

w d = proportion of debt financing.

By adjusting for the influence of inflation, DOE estimates the real weighted average cost of capital, or discount rate, for each company. DOE then aggregates the company real weighted average costs of capital to esti mate the discount rate for each of the ownership types in the analysis. An overall average is estimated by weighting each ownership type’s discount rate by its

estimated share of the equipment market. The values are presented in

Table 6.2

. 22 for small commercial air

- cooled air conditioners and heat pumps and Table

6.2

. 23 for water

- source heat pumps . While WACC values for any category may trend higher or lower over substantial periods of time, these values represent a private sector cost of capital that is averaged over major business cycles.

Table 6.2

. 22 Weighted Average Cost of Capital for Sectors that Purchase 3

- phase Air

Cooled, Air Conditioning and Heating Equipment

Sector Discount

Rate Standard

Deviation Market

Share

Small Office 6.57% 2.09% 47 %

Small Retail 7.21% 2.06% 53 %
Source: Damodaran Online Data Page: Costs of Capital by Industry Sector, 2011,2012,2013.

Table 6.2

. 23 Weighted Average Cost of Capital for Sectors that Purchase Water

- Source

Heat Pumps

Sector Discount

Rate Standard

Deviation Market

Share

Offices 5.13% 2.05% 26%

Public 3.74% 1.43% 30% Lodging 5.57% 1.87% 5%
Property owners 5.15% 1.20% 29%

Medical services 4.73% 1.66% 10%

Source: Damodaran Online Data Page: Costs of Capital by Industry Sector, 2011,2012,2013.6

- 24 6.2.2.8 Compliance Date of Standard

Generally, covered equipment to which a new or amended energy conservation standard applies must comply with the standard if such equipment is manufactured or imported on or afte r a specified date. In this Final Rule , DOE is evaluating whether more

- stringent efficiency levels

than those in ASHRAE Standard 90.1

- 2013 would be technologically feasible, economically justified, and result in a significant addition al amount of energy savings. If DOE were to propose a rule prescribing energy conservation standards at the efficiency levels contained in ASHRAE

Standard 90.1

- 2013 for the triggered equipment classes, EPCA states that compliance with any

such standards sh all be required on or after a date which is two or three years (depending on equipment size) after the compliance date of the applicable minimum energy efficiency requirement in the amended ASHRAE/IES standard. (42 U.S.C. 6313(a)(6)(D)) Given the equipme nt size at issue here, DOE has applied the two

- year implementation period to determine the compliance date of any energy conservation standard equal to the efficiency levels specified by ASHRAE Standard 90.1

- 2013 proposed by this rulemaking. Thus, if DOE d ecides to adopt the efficiency levels in ASHRAE Standard 90.1

- 2013, the compliance date of the rulemaking would be dependent upon the date specified in ASHRAE Standard 90.1

- 2013 or its publication date, if none is specified. In this case, the rule would ap ply to small commercial air

- cooled air conditioners and heat pumps less than 65,000 Btu/h manufactured on or after January 1, 2017, which is two years after the date specified in ASHRAE Standard 90.1

- 2013. The rule would apply to water

- source heat pumps ma nufactured on or after October 9, 2015, which is two years after the publication date of ASHRAE Standard 90.1

- 2013.

If DOE were to propose a rule prescribing energy conservation standards more stringent than the efficiency levels contained in ASHRAE Standard 90.1

- 2013, EPCA states that compliance with any such standards is required for products manufactured on or after a date which is four years after the date the final rule is published in the Federal Register. (42 U.S.C. 6313(a)(6)(D)) DOE has app lied this 4

- year implementation period to determine the compliance date for any energy conservation standard more stringent than the efficiency levels specified by

ASHRAE Standard 90.1

- 2013 that might be prescribed at the final rule stage for the equipment classes triggered by ASHRAE. Thus, for equipment for which DOE might adopt a level more stringent than the ASHRAE efficiency levels, the rule would apply to products manufactured on or after a date four years from the date of publication of the final rule , which the statute requires to be completed by April 9, 2016 (thereby resulting in a compliance date no later than April 9, 2020). b

Economic justification is not required for DOE to adopt the efficiency levels in ASHRAE

90.1

- 2013, as DOE is statutorily required to, at a minimum, adopt those levels. Therefore, DOE did not perform an LCC analysis on the ASHRAE Sta ndard 90.1

- 2013 levels. And t herefore, for purposes of the LCC analysis, DOE used 2020 as the first year of compliance with amended

standards.

b Since ASHRAE published ASHRAE Standard 90.1

- 2013 on October 9, 2013, EPCA requires that DOE publish a final rule adopting more

- stringent standards than those in ASHR AE Standard 90.1

- 2013, if warranted, within 30 months of ASHRAE action ( i.e., by April 2016). Thus, four years from April 2016 would be April 2020, which would be the anticipated compliance date for DOE adoption of more

- stringent standards.6

- 25 F or split system air

- cooled air conditioners, which DOE evaluated under the 6 year look back, DOE applied a different compliance date. Specifically, EPCA states that amended standards prescribed under this subsection shall apply to products manufactured aft er a date that

is the later of

— (I) the date that is 3 years after publication of the final rule establishing a new standard; or (II) the date that is 6 years after the effective date of the current standard for a covered product. (42 U.S.C. 6313(a)(6)(C)(i v)) Because DOE must publish a final rule by April 9, 2016, in the case that it adopts standards higher than those in ASHRAE 90.1 for the other equipment classes, DOE projected that the date under clause (I) would be April 2019, which is

later than the dat e under clause (II). For purposes of its analysis, DOE used 2019 as the first year of compliance with amended standards.

6.2.2.9 Base Case Distribution of Efficiency Levels

DOE created base case efficiency distributions for the compliance year based on model

avai lability in the AHRI certified directory. DOE bundled the efficiency levels into "efficiency ranges" and determined the percentage of models within each range. DOE applied the percentages of models within each efficiency range to the total unit shipments f or a given equipment class to estimate the distribution of shipments within the base case.

DOE has estimated a base

- case efficiency trend of approximately 1 S EER every 35 years,

based on the EER trend from 2012 to 2035 found in the Commercial Unitary Air Conditioner Advance Notice of Proposed Rulemaking

. 12 DOE applied this efficiency trend to estimate the base case efficiency distribution in the year in which compliance is expected for any standards adopted higher than the ASHRAE level. c Table 6.2

. 24 and Table 6.2

. 25 show the distributions.

Table 6.2

. 24 Base Case Efficiency Market Shares in the compliance year for 3

- phase A ir

Cooled, Air Conditioning and Heating Equipment Split System AC Single Package AC Split System HP Single Package HP

SEER Market

Share SEER Market

Share SEER Market

Share SEER Market Share

13.0 26% N/A N/A N/A N/A N/A N/A

14.0 50 % 14.0 52% 14.0 80% 14.0 69%

15.0 22% 15.0 30% 15.0 19% 15.0 21%

16.0 2% 16.0 7% 16.0 1% 16.0 9%

17.0 0% 17.0 4% 17.0 0% 17.0 1%

18.0 0% 18.0 7% 18.0 0% 18.0 1%

19.0 0% 19.0 0%

Note: The compliance year is 2019 for Split System AC and 2020 for Single Package AC, Split System HP, and Single Package HP.

c Chapter 8 con tains additional information about development of efficiency distributions for the ASHRAE level and

use of the base

- case efficiency trend.6

- 26 Table 6.2

. 25 Base Case Efficiency Market Shares in 2020 for Water

- Source Heat Pumps

WSHP <17,000 Btu/h WSHP ≥17,000 to <65,000 Btu/h WSHP ≥65,000 to <135,000 Btu/h

EER Market

Share EER Market

Share EER Market

Share

12.2 0.7% 13.0 7.6% 13.0 0.0%

13.0 49.7% 14.6 55.1% 14.0 29.8%

14.0 22.0% 16.6 25.0% 15.0 48.5%

15.7 20.5% 18.0 8.9% 16.0 20.1%

16.5 4.9% 19.2 2.5% 17. 2 1.7%

18.1 2.3% 21.6 1.0%

6.3 PAYBACK PERIOD INPUTS

The PBP is the amount of time it takes the customer 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 e fficient design to a more efficient design) to the decrease in first year annual operating expenditures.

The equation for PBP is:

PBP =∆IC/∆OC

Eq. 6

. 8

Where:

PBP = payback period in years,

∆ IC = difference in the total installed cost between the more efficient standard

- level equipment (efficiency levels 2, 3, etc.) and the baseline efficiency equipment, and

∆OC = difference in first year annual operating costs.

Payback periods are expressed in years. Paybac k 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 t he customer for each efficiency level and the annual (first year) operating costs for each efficiency level. The inputs to the total installed cost are the equipment price and the installation cost. The inputs to the operating costs are the annual energy c ost and the annual 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 and discount rates are not required. Since the PBP is a “simple” pa yback, the required electricity cost is only for the year in which a new efficien cy6

- 27 standard is to take effect

— in this case, 20 19 for split system air conditioners and 2020 for all other equipment classes

.

6.4 LCC AND PBP RESULTS

This se ction presents the results of the LCC and PBP analysis. As discussed previously, DOE used probability distributions to characterize the uncertainty in many of the inputs to the analysis. DOE used Monte Carlo simulation to perform the LCC calculations. LCC and PBP calculations were performed 10,000 times, sampling from the probability distributions that DOE developed to characterize many of the inputs to the analysis.

For each set of customers using the equipment in each equipment class, DOE calculated

the average LCC and LCC savings and the simple PBP for each efficiency level

. DOE calculated simple payback relative to the baseline equipment. DOE calculated LCC savings relative to the

base

- case efficiency distribution in the compliance year. Based on the Monte Carlo simulations that DOE performed, for each standard level DOE also calculated the share of customers receiving a net LCC cost.

Table 6.4

. 1 through Table 6.4

. 14 show the LCC and PBP results for each equipment

class. In general, the average LC C savings are negative for nearly all equipment classes and efficiency levels, with the exception of water

- source heat pumps greater than or equal to 17,000 Btu/h and less than 65,000 Btu/h, is has positive LCC savings for the first three efficiency levels

.

Table 6.4

. 1 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Split

- System Air Conditioners <65,000 Btu/h

Efficiency

Level Average Costs 2014$ Simple Payback

years Average

Lifetime

years Installed

Cost First Year’s Operating

Cost Lifetime Operating

Cost LCC

Federal

Baseline $3,901 $776 $7,532 $11,433 N/A 19

0 $4,150 $773 $7,497 $11,647 68 19

1 $4,401 $766 $7,433 $11,834 49 19

2 $4,670 $760 $7,373 $12,043 47 19

3 $4,927 $763 $7,409 $12,335 80 19

4 $5,194 $768 $7,449 $12,643 148 19

5 $5,474 $774 $7,507 $12,981 560 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that efficiency level. The PBP is measured relative to the baseline equipment.6

- 28 Table 6.4

. 2 LCC Savings Relative to the Base

- Case Efficiency Distribution for 3

- Phase

Air

- Cooled Split

- System Air Conditioners <65,000 Btu/h

Efficiency Level Life

- Cycle Cost Savings

% of Customers that Experience Average Savings*

Net Cost 2014$

0 26% ( $ 56 )

1 75% ( $ 198)

2 97% ( $ 402)

3 100% ( $ 695)

4 100% ( $ 1,002)

5 100% ( $ 1,341)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4

. 3 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Package Air Conditioners <65,000 Btu/h

Efficiency

Level Average Costs 2014$ Simple Payback

years Average

Lifetime

years Installed

Cost First Year’s Operating

Cost Lifetime Operating

Cost LCC

ASHRAE

Baseline $4,781 $772 $7,516 $12,297 N/A 19

1 $5,090 $758 $7,381 $12,471 22 19

2 $5,400 $753 $7,329 $12,729 32 19

3 $5,702 $757 $7,368 $13,070 61 19

4 $6,007 $761 $7,407 $13,414 110 19

5 $6,375 $766 $7,457 $13,833 270 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that efficiency level. The PBP is measured relative to the baseline equipment.6

- 29 Table 6.4

. 4 LCC Savings Relative to the Base

- Case Efficiency Distribution for 3

- Phase

Air

- Cooled Single

- Package Air Conditioners <65,000 Btu/h

Efficiency Level Life

- Cycle Cost Savings

% of Customers that Experience Average Savings*

Net Cost 2014$

1 49% ( $ 89)

2 81% ( $ 299)

3 89% ( $ 602)

4 93% ( $ 922)

5 100% ( $ 1,340)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4

. 5 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Split System Heat Pumps <65,000 Btu/h

Efficiency

Level Average Costs 2014$ Simple Payback

years Average

Lifetime

years Installed

Cost First Year’s Operating

Cost Lifetime Operating

Cost LCC

ASHRAE

Baseline $4,513 $796 $7,070 $11,584 N/A 16

1 $4,774 $783 $6,957 $11,731 20 16

2 $5,118 $777 $6,906 $12,024 33 16

3 $5,401 $778 $6,911 $12,312 49 16

4 $5,694 $778 $6,918 $12,612 69 16

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that efficiency level. The PBP is measured relative to the baseline equipment.6

- 30 Table 6.4

. 6 LCC Savings Rela tive to the Base

- Case Efficiency Distribution for 3

- Phase

Air

- Cooled Split System Heat Pumps <65,000 Btu/h

Efficiency Level Life

- Cycle Cost Savings

% of Customers that Experience Average Savings*

Net Cost 2014$

1 75% ( $ 118)

2 99% ( $ 410)

3 100% ( $ 697)

4 100% ( $ 997)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4

. 7 Life Cycle and Payback Period Results for 3

- phase, Air

- Cooled, Package Heat Pumps <65,000 Btu/h

Efficiency

Level Average Costs 2014$ Simple Payback

year s Average

Lifetime

years Installed

Cost First Year’s Operating

Cost Lifetime Operating

Cost LCC

ASHRAE

Baseline $5,155 $797 $7,084 $12,239 N/A 16

1 $5,499 $784 $6,969 $12,468 27 16

2 $5,830 $777 $6,909 $12,739 34 16

3 $6,161 $778 $6,916 $13,077 53 16

4 $6,550 $779 $6,923 $13,473 77 16

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that efficiency level. The PBP is measured relative to the baseline equipment.6

- 31 Table 6.4

. 8 LCC Savings Rela tive to the Base

- Case Efficiency Distribution for 3

- Phase

Air

- Cooled Package Heat Pumps <65,000 Btu/h

Efficiency Level Life

- Cycle Cost Savings

% of Customers that Experience Average Savings*

Net Cost 2014$

1 68% ( $ 158)

2 90% ( $ 402)

3 99% ( $ 735)

4 99% ( $ 1,128)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4

. 9 Life Cycle and Payback Period Results for Water Source Heat Pumps <17,000 Btu/h

Efficiency

Level Average Costs 2014$ Simple Payback

years Average

Lifetime

years Installed

Cost First Year’s Operating

Cost Lifetime Operating

Cost LCC

ASHRAE

Baseline $3,216 $654 $7,692 $10,908

-- 19

1 $3,354 $645 $7,578 $10,932 14 19

2 $3,530 $638 $7,492 $11,022 19 19

3 $3,822 $628 $7,377 $11,199 23 19

4 $3,958 $624 $7,334 $11,292 25 19

5 $4,233 $618 $7,263 $11,496 28 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that efficiency level. The PBP is measured relative to the baseline equipment.6

- 32 Table 6.4

. 10 LCC Savings Relative to the Base

- Case Efficiency Distribution for Water Source Heat Pumps <17,000 Btu/h

Efficiency Level Life

- Cycle Cost Savings

% of Customers that Experience Average Savings*

Net Cost 2014$

1 0% ( $ 0)

2 46% ( $ 46)

3 68% ( $ 175)

4 89% ($ 262)

5 95% ( $ 462)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4

. 11 Life Cycle and Payback Period Results for Water Source Heat Pumps ≥17,000 to <65,000 Btu/h

Efficiency

Level Average Costs 2014$ Simple Payback

years Average

Lifetime

years Installed

Cost First Year’s Operating

Cost Lifetime Operating

Cost LCC

ASHRAE

Baseline $4,882 $1,118 $13,169 $18,052

-- 19

1 $5,162 $1,075 $12,655 $17,817 6 19

2 $5,513 $1,039 $12,232 $17,745 8 19

3 $5,758 $1,023 $12,041 $17,799 9 19

4 $5,968 $1,013 $11,930 $17,898 10 19

5 $6,392 $997 $11,732 $18,124 12 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that efficiency level. The PBP is measured relative to the baseline equipment.6

- 33 Table 6.4

. 12 LCC Savings Relative to the Base

- Case Efficiency Distribution for Water Source Heat Pumps ≥17,000 to <65,000 Btu/h

Efficiency Level Life

- Cycle Cost Savings

% of Customers that Experience Average Savings*

Net Cost 2014$

1 2% $19

2 29% $64

3 52% $17

4 66% ( $ 78)

5 76% ( $ 303)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4

. 13 Life Cycle and Payback Period Results for Water Source Heat Pumps ≥65,000 to <135,000 Btu/h

Efficiency

Level Average Costs 2014$ Simple Payback

years Average

Lifetime

years Installed

Cost First Year’s Operating

Cost Lifetime Operating

Cost LCC

ASHRAE

Baseline $12,005 $2,202 $25,958 $37,963

-- 19

1 $12,961 $2,126 $25,065 $38,026 13 19

2 $13,919 $2,087 $24,599 $38,518 17 19

3 $14,830 $2,054 $24,213 $39,042 19 19

4 $15,977 $2,022 $23,834 $39,811 22 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that efficiency level. The PBP is measured relative to the baseline equipment.6

- 34 Table 6.4

. 14 LCC Savings Relative to the Base

- Case Efficiency Distribution for Water Source Heat Pumps ≥65,000 to <135,000 Btu/h

Efficiency

Level Life

- Cycle Cost Savings

% of Customers that Experience Average Savings*

Net Cost 2014$

1 0%** $0**

2 27% ( $ 148)

3 72% ( $ 560)

4 93% ( $ 1,315)

* The calculation includes households with zero LCC savings (no impact).

**The base case efficiency distribution has 0% market share at the ASHRAE baseline; therefore there are no savings for EL1

.6

- 35 REFERENCES

1 U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook 2014, April, 2014. Washingto n, DC. Report No. DOE/EIA

- 0383(2014). www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf

2 RS Means Mechanical Cost Data 2013. Reed Construction Data, LLC. 2012.

3 RS Means Facilities Ma intenance & Repair Cost Data 2013. Reed Construction Data, LLC.

2012

4 U.S. Department of Labor

- Bureau of Labor Statistics. Unitary air

- conditioners, except air

source. Series ID: PCU333415333415E. http://data.bls.gov/cgi

- bin/srgate . Accessed:

6/6/2013.

5 U.S. Department of Labor

- Bureau of Labor Statistics. All other miscellaneous refrigeration and air conditioning equipment. Series PCU3334153334159; www.bls.go v/ppi/

6 U.S. Department of Energy. 20 1 4. Technical Support Document: Energy Efficiency Program for Commercial and Industrial Equipment: Small, Large, and Very Large Commercial Package Air Conditioning and Heating Equipment . Washington, D.C. <

< www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/59 >

7 Modigliani, F. and M. Miller, The Cost of Capital, Corporations Finance and the Theory of

Investment

. American Economic Review , 1958. 48(3): pp. 261

- 297

< www.jstor.org/stable/1809766 >

8 Damodaran Online, Data Page: Costs of Capital by Industry Sector, 2011, 2012, 2013. Damodaran. (Last ac cessed February, 2014.) < http://pages.stern.nyu.edu/~adamodar/ >

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

10 Board of Governors of the Federal Reserve System, Federal Reserve Bank Services Private Sector Adjustment Factor . 2005: Washington, DC.

11 Damodaran Online, Data Page: Historical Returns on Stocks, Bonds and Bills

- United States,

201 3

. Damodaran. (Last accessed February, 201 4 .) < http://pages.stern.nyu.edu/~adamodar/ >

12 U.S. Department of Energy. 2004. Technical Support Document: Energy Efficiency Program for Commercial and Industrial Equipment: Commercial Unitary Air Conditioners and Heat Pumps . Washington, D.C.

< www1.eere.energy.gov/buildings/appliance_standards/commercial/cuac_tsd_060904.html >7

- i CHAPTER 7. SHIPMENTS

TABLE OF CONTENTS

7.1 INTRODUCTION

................................ ................................ ................................ ........... 7

- 1

7.2 INPUTS AND METHODOLO GY

................................ ................................ .................. 7

- 1

7.2.1 Commercial Air

- cooled Air Conditioners and Heat Pumps

................................ 7

- 1

7.2.2 Water Source Heat Pumps

................................ ................................ ................... 7

- 4

7.3 BASE CASE SHIPMENTS

................................ ................................ ............................. 7

- 6

7.4 STANDARDS CASE SHIPM ENTS

................................ ................................ ............... 7

- 8

REFERENCES

................................ ................................ ................................ .......................... 7

- 10

LIST OF TABLES

Table 7.2.1 Historical Shipments of Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

................................ ................................ ............ 7

- 1

Table 7.2.2 Census Shipments of Air

- Cooled Equipment <65,000 Btu/h (Including

Both S ingle

- and Three

- Phase Equipment

................................ ..................... 7

- 2

Table 7.2.3 Year Over Year Percentage Growth of Small, Air

- Cooled Air

Conditioners and Heat Pumps from 2005

- 2014 (AHRI)

............................... 7

- 4

Table 7.2.4 Census Shipments of Water Source Heat Pumps

................................ .......... 7

- 5

Table 7.2.5 Total Shipments of Water

- Source Heat Pumps by Equipment Class

............ 7

- 6

Table 7.3.1 Projected Shipments of Air

- Cooled Three

- Phase Equipment <65,000 Btu/h (2011

- 2046)

................................ ................................ .......................... 7

- 7

Table 7.3.2 Projected WSHP Shipments by Equipment Class (2011

- 2045)

.................... 7

- 8

LIST OF FIGURES

Figure 7.2.1 Air

- Cooled Equipment Shipping Trends 1999

- 2010

................................ ..... 7

- 3 Figure 7.2.2 Water Source Heat Pum p Shipment Trends 1990

- 2010

................................ 7

- 67

- 1 CHAPTER 7. SHIPMENTS

7.1 INTRODUCTION

Estimates of equipment shipments are a necessary input for calculating national energy savings (NES) and net present value (NPV), which are required to justify potential new or amended energy efficiency standards. Shipments also are a necessary input to th e manufacturer impact analysis. This chapter describes DOE’s method and results of projecting annual shipments for small 3

- phase air

- cooled air conditioners and heat pumps as well as water source heat pumps

.

The shipments models are part of the National I mpact Analysis models and are prepared as Microsoft Excel spreadsheets that are accessible on the Internet ( www.eere.energy.gov/buildings/appliance_standards/ ).

7.2 INPUTS AND METHODOLO GY

This section outlines the inputs and methodology used to develop the shipments model

for small, 3

- phase air

- cooled air conditioning and heat pump equipment and water source heat

pumps.

7.2.1 C ommercial Air

- cooled Air Conditioners and Heat Pumps

DOE estimated shipments of three

- phase air

- cooled equipment less than 65,000 Btu/h by equipment class for the year 1999 as part of the 2000 Screening Analysis for the Energy Policy

Act ( EPACT )

. 1 Table 7.2.1 shows the se data. DOE used U.S. Census data for similar equipment, including both single

- and three

- phase, from 1999 to 2010, 2 shown in Table 7.2

. 2 , to extrapolate

DOE’s 1999 estimated sh ipments data from 2000 to 2010. F or each year from 2000 to 2010, DOE multiplied the 1999 shipments by the indexed value for the relevant year from the C ensus data.

Table 7.2

. 1 Historical Shipments of Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h Equipment Class 1999 Split System AC 91,598 Single Package AC 213,728
Split System HP 11,903
Single Package HP 27,773

Source: U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Screening Analysis for

EPACT

- Covered Commercial HVAC and Water

- Heating Equipment. Volume 1

– Main Report. 2000.7

- 2 Table 7.2

. 2 Census Shipments of Air

- Cooled Equipment <65,000 Btu/h (Including Both Single

- and Three

- Phase Equipment
Split System AC Single Package AC Split System HP Single Package HP

Year Shipments Index (to 1999) Shipments Index (to 1999) Shipments Index (to 1999) Shipments Index (to 1999)

1999 4,255,509 1 .00 666,314 1 .00 1,011,525 1 202,608 1 .00

2000 4,181,538 0.98 686,448 1.03 1,041,287 1.03 204,324 1.01

2001 3,818,875 0.9 0 611,636 0.92 1,159,308 1.15 220,716 1.09

2002 4,033,357 0.95 553,728 0.83 1,159,507 1.15 180,521 0.89

2003 4,393,942 1.03 602,282 0.9 0 1,345,671 1.33 205,301 1.01

2004 4,988,640 1.17 614,937 0.92 1,634,521 1.62 214,886 1.06

2005 5,908,762 1.39 607,981 0.91 1,849,512 1.83 236,189 1.17

2006 4,456,324 1.05 575,916 0.86 1,873,980 1.85 232,762 1.15

2007 3,890,171 0.91 551,675 0.83 1,578,409 1.56 244,256 1.21

2008 3,534,140 0.83 453,055 0.68 1,530,443 1.51 197,024 0.97

2009 3,342,768 0.79 364,616 0.55 1,581,241 1.56 169,317 0.84

2010 3,235,467 0.76 330,965 0.5 0 1,571,177 1.55 172,816 0.85
Source: U.S.Census Bureau. Current Industrial Reports for Refrigeration, Air Conditioning, and Warm Air Heating Equipment, MA333M.

The census data showed a declining trend in shipments for Split System AC, Single Package AC and Single Package HP equipment classes and a slightly increasing trend for Split Sy s tem HP , as shown in Figure 7.2

. 1

. For the April 2014 N otice of Data Availability , DOE applied these trends to the single package and split system heat pumps and used a constant value for single package air conditioners based on the average value from 1999 to 2010. a ( 79 FR

20114 ) However, in response to comm ents on the NODA, DOE reviewed the inputs available for its shipments projections. Because the Census data end in 2010, DOE cannot use that data to determine whether shipments continue to decline past 2010. Therefore, DOE reviewed AHRI’s monthly shipments data for the broader category of central air conditioners and heat pumps 3 to
determine more recent trends.

a DOE did not analyze split system AC in the NODA.7

- 3 Figure 7.2

. 1 Air

- Cooled Equipment Shipping Trends 1999

- 2010

Table 7.2

. 3 shows the annual percentage growth in shipments of small air conditioners and heat pumps from 2005 to 201 4 as reported by AHRI

. DOE found that the average annual growth rate from 2005 to 2010 was

- 12% for air conditioners and

- 4% for heat pumps. However, the average annual growth rate from 2010 to 2014 was 7% for air conditioners and 8% for heat pumps. These data indicate that the decline in shipments through 2010 has stopped and has in

fa ct begun to reverse. Therefore, DOE used the AHRI reported growth rates from 2010 to 2011 (10% for air conditioners and 1% for heat pumps) to scale its projected 2010 shipments to 2011.

In order to project shipments into the future, DOE relied on Annual E nergy Outlook (AEO) 2014 forecasts for commercial floor space from 2011 through 2040. 4 DOE assumed that shipments of small commercial air conditioners and heat pumps would be related to the growth of commercial floor space. DOE used this projection, with a n average annual growth rate of 1%, to project shipments for each of the four equipment classes through 2040. For years beyond 2040, DOE also applied an average annual growth rate of 1%. y =

- 8738.8x + 2E+07
R2 = 0.7655

y =

- 185.13x + 399431
R2 = 0.0397

y = 720.37x

- 1E+06
R2 = 0.5542 y = 1E+133x

- 38.79

R2 = 0.1709

- 50,000 100,000 150,000 200,000 250,000

1998 2000 2002 2004 2006 2008 2010 2012 CUAC <65 single

CUHP <65 single

CUHP <65 split

CUAC <65 split7

- 4 Table 7.2

. 3 Year Ove r Year Percentage Growth of Small, Air

- Cooled Air Conditioners and Heat Pumps from 2005

- 201 4 (AHRI)

Year Central Air Conditioners Central Heat Pumps

2005 17% 13%

2006

- 23%

- 1%

2007

- 9%

- 10%

2008

- 12%

- 2%

2009

- 11%

- 12%

2010

- 3% 6%

2011 10% 1%

2012 5%

- 4%

2013 7% 16%

2014 (May) 6% 17%

Source: AHRI. 2012. HVACR & Water Heating Industry Statistical Profile. See also AHRI Monthly Shipments December 2013 and May 2014 releases.

7.2.2 Water Source Heat Pumps

DOE based its shipments analysis for water

- source heat pumps on data from the U.S. Census. The U.S. Census published historical (1980, 1983

- 1994, 1997

- 2006, and 2008

- 2010)

water

- source heat pump shipment data

. 2 Table 7.2

. 4 exhibits the complete available time series

. DOE analyzed data from the years 1990

- 2010 to establish a trend from which to project shipments beyond 2010. DOE used a linear trend , as shown in Figure 7.2

. 2 . Because the Census data do not distinguish between equipment capacities, DOE used the shipme nts data by equipment class provided by AHRI in 1999, and published in the 2000 Screening Analysis for

EPACT 1

, to distribute the total water

- source heat pump shipments to individual equipment

classes ( Table 7.2

. 5 )

.7

- 5 Table 7.2

. 4 Census Shipments of Water Source Heat Pumps

Year Product Code: 333415E181

1980 48,783

1981 N/A

1982 N/A

1983 69,280

1984 86,165

1985 119,164

1986 103,970

1987 142,033

1988 149,795

1989 157,080

1990 139,864

1991 111,746

1992 99,236

1993 105,159

1994 99,321

1995 N/A

1996 N/A

1997 112,052

1998 120,080

1999 120,545

2000 133,654

2001 130,351

2002 114,336

2003 123,974

2004 135,769

2005 141,410

2006 202,234

2007 N/A

2008 223,636

2009 180,101

2010 170,788

Source: U.S.Census Bureau. Current Industrial Reports for Refrigeration, Air Conditioning, and Warm Air Heating Equipment, MA333M.7

- 6 Figure 7.2

. 2 Water Source Heat Pump Shipment Trends 1990

- 2010

Table 7.2

. 5 Total Shipments of Water

- Source Heat Pumps by Equipment Class

Equipment Class 1999 Percent

<17

, 000 Btu/h 41,000 31%
≥17,000

- < 65

, 000 Btu/h 86,000 65%

≥ 65

, 000

- 135

, 000 Btu/h 5,000 4%

Source: U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Screening Analysis for

EPACT

- Covered Commercial HVAC and Water

- Heating Equipment. Volume 1

– Main Report . 2000.

7.3 BASE CASE SHIPMENTS

Table 7.3

. 1 and Table 7.3

. 2 show the resulting projected shipments for 3

- phase, air

cooled air conditioning and heat pump equipment and water source heat pumps, respectively. y = 4135.8x

- 8E+06
R2 = 0.5483

0 50000 100000 150000 200000 250000

1985 1990 1995 2000 2005 2010 20157

- 7 Table 7.3

. 1 Projected Shipments of Air

- Cooled Three

- Phase Equipment <65,000 Btu/h (2011

- 204 8 )

Year Split System AC Single Package AC Split System HP Single Package HP

2011 76,270 116,264 18,669 23,921

2012 76,855 117,157 18,813 24,104

2013 77,333 117,884 18,930 24,254

2014 77,878 118,715 19,063 24,425

2015 78,536 119,719 19,224 24,632

2016 79,307 120,894 19,413 24,873

2017 80,210 122,271 19,634 25,157

2018 81,187 123,759 19,873 25,463

2019 82,199 125,302 20,121 25,780

2020 83,175 126,790 20,360 26,086

2021 84,127 128,242 20,593 26,385

2022 85,053 129,653 20,819 26,675

2023 85,945 131,013 21,038 26,955

2024 86,806 132,325 21,248 27,225

2025 87,651 133,613 21,455 27,490

2026 88,471 134,864 21,656 27,748

2027 89,269 136,081 21,851 27,998

2028 90,053 137,275 22,043 28,244

2029 90,830 138,460 22,234 28,487

2030 91,610 139,649 22,424 28,732

2031 92,414 140,874 22,621 28,984

2032 93,279 142,193 22,833 29,256

2033 94,195 143,590 23,057 29,543

2034 95,154 145,051 23,292 29,844

2035 96,170 146,600 23,541 30,162

2036 97,255 148,253 23,806 30,502

2037 98,357 149,933 24,076 30,848

2038 99,458 151,612 24,345 31,193

2039 100,541 153,262 24,610 31,533

2040 101,593 154,867 24,868 31,863

2041 102,603 156,405 25,115 32,180

2042 103,622 157,959 25,365 32,499

2043 104,652 159,529 25,617 32,822

2044 105,692 161,114 25,871 33,148

2045 106,742 162,715 26,128 33,478

2046 107,802 164,332 26,388 33,810

2047 108,874 N/A N/A N/A

2048 109,955 N/A N/A N/A7

- 8 Table 7.3

. 2 Projected WSHP Shipments by Equipment Class (2011

- 2045)

Year Total <17,000 Btu/h ≥17,000

- <65,000

Btu/h ≥65,000

- 135,000

Btu/h

2011 181,937 56,511 118,535 6,892

2012 186,073 57,795 121,229 7,048

2013 190,209 59,080 123,924 7,205

2014 194,344 60,365 126,618 7,362

2015 198,480 61,649 129,313 7,518

2016 202,616 62,934 132,007 7,675

2017 206,752 64,218 134,702 7,832

2018 210,887 65,503 137,396 7,988

2019 215,023 66,788 140,091 8,145

2020 219,159 68,072 142,785 8,301

2021 223,295 69,357 145,480 8,458

2022 227,430 70,641 148,174 8,615

2023 231,566 71,926 150,869 8,771

2024 235,702 73,210 153,563 8,928

2025 239,838 74,495 156,258 9,085

2026 243,974 75,780 158,952 9,241

2027 248,109 77,064 161,647 9,398

2028 252,245 78,349 164,342 9,555

2029 256,381 79,633 167,036 9,711

2030 260,517 80,918 169,731 9,868

2031 264,652 82,203 172,425 10,025

2032 268,788 83,487 175,120 10,181

2033 272,924 84,772 177,814 10,338

2034 277,060 86,056 180,509 10,495

2035 281,195 87,341 183,203 10,651

2036 285,331 88,626 185,898 10,808

2037 289,467 89,910 188,592 10,965

2038 293,603 91,195 191,287 11,121

2039 297,739 92,479 193,981 11,278

2040 301,874 93,764 196,676 11,435

2041 306,010 95,049 199,370 11,591

2042 310,146 96,333 202,065 11,748

2043 314,282 97,618 204,759 11,905

2044 318,417 98,902 207,454 12,061

2045 322,553 100,187 210,148 12,218

7.4 STANDARDS CASE SHIPMENTS

As equipment purchase price and repair costs increase with efficiency, DOE recognizes that higher first costs and repair costs can result in a drop in shipments. However, DOE had no basis for estimating the elasticity of shipments for small commercial air

- cooled air conditioners and heat pumps less than 65,000 Btu/h or water

- source heat pumps as a function of first costs, repair costs, or operating costs. In addition, because air

- cooled air conditioners are likely the7

- 9 lowest

- cost option for air conditionin g small office and retail applications, DOE believes it is unlikely that air

- cooled shipments would change as a result of higher first costs and repair costs.

In contrast , because water

- source heat pumps are often installed for their higher efficiency as

c ompared to air

- cooled equipment , DOE believes it is unlikely that water

- source shipments would change as a result of higher first costs and repair costs. Therefore , DOE presumed that the shipments projection does not change with higher standard levels.7

- 10 REFERENCES

1 U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Screening Analysis for EPACT

- Covered Commercial HVAC and Water

- Heating Equipment. Volume 1

– Main Report . 2000. < www.regulations.gov/#!documentDetail;D=EERE

- 2006

- STD

- 0098

- 0015 >

2 U.S.Census Bureau. Current Industrial Reports for Refrigeration, Air Conditioning, and Warm Air Heating Equipment, MA333M.

< www.census.gov/manufacturing/cir/historical_data/ma333m/index.html >

3 AHRI. 2012. HVACR & Water Heating Industry Statistical Profile. See also AHRI Monthly Shipments: < ahrinet.org/site/498/Resources/Statistics/Monthly

- Shipments > ; especially

December 2013 release:

< ahrinet.org/App_Content/ahri/files/Statistics/Monthly%20Shipments/2013/December2013. pdf > ; May 2014 release:

< ahrinet.org/App_Content/ahri/files/Statistics/Monthly%20Shipments/2014/May2014.pdf >

.

4 U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook

201 4

, April , 201 4 . Washington, DC. Report No. DOE/EIA

- 0383(201 4 )

. < www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf

. >8

- i CHAPTER 8. NATIONAL IMPACT ANAL YSIS

TABLE OF CONTENTS

8.1 INTRODUCTION

................................ ................................ ................................ ........... 8

- 1

8.2 NATIONAL ENERGY SAVI NGS

................................ ................................ ................. 8

- 1

8.2.1 Definition

................................ ................................ ................................ ............. 8

- 1

8.2.2 NES Inputs

................................ ................................ ................................ ........... 8

- 5

8.2.2.1 Shipments

................................ ................................ ................................ ... 8

- 5

8.2.2.2 Equipment Lifetime

................................ ................................ .................... 8

- 5

8.2.2.3 National Annual Energy Consumption

................................ ...................... 8

- 5

8.2.2.4 Site

- to

- Power

- Plant Energy Use Factor

................................ ................... 8

- 16

8.2.2.5 Full

- Fuel

- Cycle Energy Factors

................................ ............................... 8

- 17

8.2.2.6 Compliance Dates and Analysis Period

................................ ................... 8

- 17

8.3 NET PRESENT VALUE OF CUSTOMER BENEFITS

................................ .............. 8

- 19

8.3.1 Net Present Value Definition

................................ ................................ ............. 8

- 19

8.3.2 Inputs to Calculation

................................ ................................ .......................... 8

- 20

8.3.2.1 Total Annual Installed Cost

................................ ................................ ...... 8

- 21

8.3.2.2 Equipment Price Projection

................................ ................................ ...... 8

- 21

8.3.2.3 Total Annual Operating Cost

................................ ................................ ... 8

- 22

8.3.2.4 Discount Factor

................................ ................................ ........................ 8

- 23

8.3.2.5 Present Value of Increased Installed Costs

................................ .............. 8

- 23

8.3.2.6 Present Value of Savings

................................ ................................ .......... 8

- 24

8.4 NES AND NPV RESULTS

................................ ................................ ........................... 8

- 24

8. 4.1 National Energy Savings

................................ ................................ .................... 8

- 24

8.4.2 Annual Costs and Savings

................................ ................................ ................. 8

- 25

8.4.3 Net Present Value Results

................................ ................................ .................. 8

- 26

8.4.4 Summary

................................ ................................ ................................ ............ 8

- 28

REFERENCES

................................ ................................ ................................ .......................... 8

- 29

LIST OF TABLES

Table 8.2.1 Efficiency Distributions for the Base and Standards Cases for Commercial Split

- System Air Conditioners < 65,000 Btu/h

.......................... 8

- 6

Table 8.2.2 Efficiency Distributions for the Base and Standards Cases for Commercial Package Air Conditioners < 65,000 Btu/h

................................ . 8

- 6

Table 8.2.3 Efficiency Distributions for the Base and Standards Cases for Commercial Split

- System Heat Pumps <65 ,000 Btu/h

................................ .. 8

- 7

Table 8.2.4 Efficiency Distributions for the Base and Standards Cases for Commercial Package Heat Pumps <65,000 Bt u/h

................................ ......... 8

- 7

Table 8.2.5 Efficiency Distributions for the Base and Standards Cases for Water

Source Heat Pumps <17,000 Btu/h

................................ ................................ 8

- 88

- ii Table 8.2.6 Efficiency Distributions for the Base and Standards Cases for Water

Source Heat Pumps ≥17,000 to ≤65,000 Btu/h

................................ .............. 8

- 8

Table 8.2.7 Efficiency Distributions for the Base and Standards Cases for Water

Source Heat Pumps ≥65,000 to ≤135,000 Btu/h

................................ ............ 8

- 9

Table 8.2.8 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO 2014 )

........................ 8

- 17

Table 8.3.1 Average Total Installed Costs for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h

................................ 8

- 21

Table 8.3.2 Average Total Installed Costs for Water

- Source Heat Pumps

..................... 8

- 21

Table 8.3.3 Average Annual Electricity Costs in the Compliance Year for 3

- phase,

Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h (2014$)

................................ ................................ ................................ ......... 8

- 22

Table 8.3.4 Average Annual Electricity Costs in 2020 for Water

- Source Heat Pumps (2014$)

................................ ................................ ............................. 8

- 22

Table 8.4.1 Cumulative Primary Energy Savings for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h (quads)

................... 8

- 24

Table 8.4.2 Full Fuel Cycle Energy Savings for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h (quads)

................... 8

- 25

Table 8.4.3 Cumulative Primary Energy Savings for Water

- Source Heat Pumps (quads)

................................ ................................ ................................ .......... 8

- 25

Table 8.4.4 Full Fuel Cycle Energy Savings for Water

- Source Heat Pumps (quads)

.... 8

- 25

Table 8.4.5 Cumulative Net Present Value for 3

- phase, Air

- Cooled Air Conditioning and Heating Equipment <65,000 Btu/h (7% Discount Rate)

................................ ................................ ................................ ............. 8

- 27

Table 8.4.6 Cumulative Net Present Value for 3

- phase, Air

- Cooled Air Conditioning and Heating Equipment <65,000 Btu/h (3% Discount Rate)

................................ ................................ ................................ ............. 8

- 27

Table 8.4.7 Cumulative Net Present Value for Water

- Source Heat Pumps (7% Discount Rate)

................................ ................................ ............................. 8

- 27

Table 8.4.8 Summary of Cumulative Net Present Value for Water

- Source Heat Pumps (3% Discount Rate)

................................ ................................ .......... 8

- 28

LIST OF FIGURES

Figure 8.2.1 Flow Diagram of National Energy Savings Calculation

................................ 8

- 2 Figure 8.2.2 Efficiency Trends for Small, Commercial Air

- Cooled Split

- System Air Conditioners <65,000 Btu/h

................................ ................................ ......... 8

- 10 Figure 8.2.3 Efficiency Trends for Small, Commercial Air

- Cooled Single Package Air Conditioners <65,000 Btu/h

................................ ................................ .. 8

- 11 Figure 8.2.4 Efficiency Trends for Small, Commercial Air

- Cooled Split System Heat Pumps <65,000 Btu/h

................................ ................................ .......... 8

- 12 Figure 8.2.5 Efficiency Trends for Small, Commercial Air

- Cooled Single Package Heat Pumps <65,000 Btu/h

................................ ................................ .......... 8

- 13 Figure 8.2.6 Efficiency Trends for Water

- Source Heat Pumps <17,000 Btu/h

............... 8

- 14 Figure 8.2.7 Efficiency Trends for Water

- Source Heat Pumps ≥17,000 to ≤65,000

Btu/h

................................ ................................ ................................ ............. 8

- 158

- iii Figure 8.2.8 Efficiency Trends for Water

- Source Heat Pumps ≥65,000 to ≤135,000

Btu/h

................................ ................................ ................................ ............. 8

- 16 Figure 8.4.1 3

- phase, Air

- Cooled, Single Package Air Conditioners <65,000 Btu/h: Installed Costs, Operating Cost Savings, and Net Savings; EL 1

................ 8

- 26 Figure 8.4.2 C umulative NES and NPV Results for 3

- Phase, Air

- Cooled Air Conditioning and Heating Equipment <65,000 Btu/h at a Seven

Percent Discount Rate

................................ ................................ .................. 8

- 288

- 1 CHAPTER 8. NATIONAL IMPACT ANALYSIS

8.1 INTRODUCTION

This chapter describes the method the U.S. Department of Energy (DOE ) used to estimate the impacts on national energy consumption of efficiency levels for both small

commercial air

- cooled air

- conditioning and heating equipment less than 65,000 Btu/h and water

source heat pumps . DOE evaluated the following impacts: (1) national energy savings (NES and NWS) attributable to each efficiency level , (2) monetary value of energy savings for customers of the equipment , (3) increased total installed cost of the equipment because of standards, and (4) the net present value (NPV) of energy savings ( i.e., the difference between the savings in operating costs a nd the increase in total installed costs).

DOE examined various standard levels for each class of equipment, including the

ASHRAE Standard 90.1

- 201 3 efficiency level, the max

- tech level (the highest efficiency level of equipment currently on the market), a nd several levels between. The base case for the ASHRAE

level consists of equipment at or above the current F ederal energy conservation standard. F or levels higher than ASHRAE Standard 90.1

- 201 3

, the base case consists of equipment at or above

the ASHRAE l evel . Because DOE by statute may not adopt standards at efficiency levels less stringent than those specified in ASHRAE Standard 90.1

- 201 3 , it reports all energy and economic impacts in this analysis as potential impacts compared with adoption of the ASHRA E

Standard 90.1

- 201 3 levels. Note that for split system air conditioners, for which ASHRAE did not raise the efficiency level, the ASHRAE 90.1

- 2013 levels are equivalent to the current Federal energy conservation standard.

DOE performed all calculations u sing a Microsoft Excel® spreadsheet, which is

accessible on the Internet ( www.regulations.gov/#!docketDetail;D=EERE

- 2014

- BT

- STD

- 0015 ).

8.2 NATIONAL ENERGY SAVI NGS

DOE calculated the national energy savings (NES) associated with the difference between the base case and each potential standards case for small commercial air

- cooled air

conditioning and heating equipment less than 65,000 Btu/h and water

- source heat pump s . DOE calculated cumulative energy savings throughout the lifetime of equipment shipped during the projection period, from 201 7 to 204 6 for air

- cooled equipment other than split system air conditioners, and from 2016 to 2045 for water

- source heat pumps

. F or both types of equipment, in standards cases above the ASHRAE level energy savings do not begin accruing until 2020. For split system air conditioners, DOE used a projection period of 2019 to 2048 for all cases.

8.2.1 Definition

DOE calculates annual NES as the difference between two projections: a base case (without new standards) and a standards case (with new standards). Figure 8.2

. 1 illustrates the inputs, interme diate values , and outputs of the NES calculation. The base case and standards case

distributions are discussed in section 8.2.2.1

.8

- 2 NES Inputs

Calculated Values

NES Output Historical Shipments

Equip

. Class Market

Shares

Equipment Lifetime

Baseline Unit Energy

Consumption

Unit Energy
Consumption ( at

each EER / SEER / Et

under

consideration )

Site to Primary and
Upstream Energy

Conversion

Analysis Period Base Case UEC

Market Shares

Standards Case UEC

Market Shares Projected Shipments

Projected Shipments by
Equip

. Class

Base Case Unit
Energy Consumption

Standards Case

Unit Energy
Consumption

Annual
Energy Savings

National
Energy Savings

Figure 8.2

. 1 Flow Diagram of National Energy Savings Calculation8

- 3 For each equipment class, DOE calculated the total energy savings using a stock accounting method. DOE projected shipments into the future bas ed on available information for each equipment class. The number of new shipments in any given year is assumed to remain in the commercial building stock based on a survival function, with a certain percentage of shipments dropping out of the stock over ti me until there are not any left. In this way, the total stock, or number of surviving units, in any given year is the sum of that year’s shipments plus the shipments remaining from previous years up to the maximum lifetime of the equipment

.

The annual bas e case total energy use in any given year is the sum product of the shipments for that year and th ose surviving from previous years and the market weighted average

base unit energy consumption ( UEC a ) in each relevant year.

AAAAAAAAAAAA BB BBBB CC TTTTTT EE EEEEEE UU yyyyyyyy = nn

= � SS SSSSSSSS SS h iiii yyyyyyyy × BB BBBB UU yyyyyyyy nn

yyyyyyyy = nn

− mm mm llllll llll

− 1

Eq. 8

. 1

As currently modeled, the base UEC remains constant throughout time. As a result, in the previous calculation, BB BBBB UU yyyyyyyy could be moved outside the summation terms as BB BBBB UU

.

The annual standards case total energy use in any given year is the sum product of the shipments for that year and those surviving from previous years and the market weighted average

standards case UEC b in each relevant year (multiplied by primary and upstream conversion

factor s ).

AAAAAAAAAAAA SS SS CC TTTTTT EE EEEEEE UU yyyyyyyy = nn

= � SS SSSSSSSS SS h iiii yyyyyyyy × SS SS UU yyyyyyyy nn

yyyyyyyy = nn

− mm mm llllll llll

− 1 Eq. 8

. 2

Prior to the analysis period, the S tandards C ase UEC is equal to the Base UEC, as standards have not yet become effective. I n the earlier part of the analysis period, energy savings accrue from only the newer units in the building stock. Later in the analysis period, all units in

the build ing stock will have the standards UEC (as they were all shipped within the analysis period) and will be providing energy savings.

a As will be discussed later, the market weighted average base UEC is based on the distribution of efficiencies under the current federal standards (base case). It includes equipment both meeting and exceeding the current federal minimum efficiency. It is n ot the UEC for a product at a specified EER or SEER.

b The market weighted average standards UEC is based on the distribution of efficiencies under a given amended federal standards scenario. It may include equipment both meeting and exceeding the given am ended federal standards level (with the exception of the Max Tech scenario, in which it is assumed that no equipment exceeds the amended federal standards level). It is not the UEC for a product at a specified EER or SEER.8

- 4 For water

- source heat pumps, energy savings for heating based on COP are also taken

into account:

AAAAAAAAAAAA BB BBBB CC TTTTTT EE EEEEEE UU yyyyyyyy = nn

= � SS SSSSSS SS h iiii yyyyyyyy × ( BB BBBB UU yyyyyyyy nn

yyyyyyyy = nn

− ( MM LL

− 1 ) + BB BBBB UU

_ h yyyyyyyy )

Eq. 8

. 3

AAAAAAAAAAAA SS SS SS CC TTTTTT EE EEEEEE UU yyyyyyyy = nn

= � SS SSSSSSSS SS h iiii yyyyyyyy × ( SS SS UU yyyyyyyy + SS SS UU

_ h yyyyyyyy ) nn

yyyyyyyy = nn

− ( MM LL

− 1 ) Eq. 8

. 4

For all equipment, t he annual energy savings is the difference between the base case total energy use and the standards case total energy use.

AAAAAAAAAAAA EE EEEEEE SS SSSS yyyyyyyy = nn

= AAAAAAAAAAAA BB BBBB CC TTTTTT EE EEEEEE UU yyyyyyyy = nn

− AAAAAAAAAAAA SS SS SS CC TTTTTT EE EEEEEE UU yyyyyyyy = nn Eq. 8

. 5

DOE calculated the cumulative energy saving s over the analysis period by summing the annual energy savings for each year in the analysis period. The analysis period differs by equipment , as discussed in the next section.

CC EE EEEEEE SS SSSS = � AAAAAAAAAA EE EEEEEE SS SSSS yyyyyyyy + 29

yyyyyyyy = Eq. 8

. 6

For the ASHRAE standard, the cumulative energy savings are calculated as above. For higher efficiency levels (except for split system air conditioners)

, DOE uses the market

- weighted average UEC under the ASHRAE case ( ASHRAE UEC ) instead of the Base UEC to calculate the annual total energy use to which the annual standards case total energy use is compared.8

- 5 8.2.2 NES Inputs

The inputs for calculating national en ergy savings are:

• shipments

,

• equipment lifetime,

• annual energy consumption for the Nation ( AEC ), and

• power plant primary energy use factor ( src_conv ) and full

- fuel

- cycle (FFC) multiplier

.

Shipments 8.2.2.1

An extensive description of the methodology for conducting and generating the shipments projections can be found in Chapter 9.

Equipment Lifetime 8.2.2.2

For this analysis, DOE used the equipment lifetimes and survival functions as discussed in Chapter 6.

National Annual Energy Consumption 8.2.2.3

The national annual e nergy consumption is the product of the per

- unit annual energy consumption and the stocks of units of each vintage. This approach accounts for differences in UEC from year to year.

DOE uses a market

- or shipment

- weighted average annual UEC in its calculation of the

national AEC. This takes into account that the markets include shipments of equipment with a broad range of efficiencies

, as was shown in chapter 2. To assess the impacts on the market from the introduction of higher standards, DOE calculates impacts based on efficiency

- distribution weighted annual UEC. Chapter 6 described how DOE estimated base case efficiency

distributions for 20 20 (or 2019 for split system air conditione rs) , the year that compliance with any higher standard would be required.

For each standard level analyzed, DOE has assumed that all equipment shipped in the base case with efficiencies below the standard level become shipments at that standard level in

t he standard

- level shipment distributions , starting with the assumed compliance date for

amended standards (i.e., 2015 or 2017 for the ASHR A E level and 2019 or 2020 for standards higher than ASHRAE 90.1

- 2013) . Shipments in the base

- case distribution that ar e above the efficiency defined by the standard level are assumed to be unaffected. DOE terms this a “rollup” scenario, because base

- case efficiency shipments below the proposed standard level “roll up” to

meet the standard.

The tables below present the eff iciency distributions for the base case and standards cases

for 3

- phase, air

- cooled, air conditioners and heat pumps and water

- source heat pumps by equipment class. Each standards case refers to a standard at the corresponding efficiency level.8

- 6 Table 8.2

. 1 Efficiency Distributions for the Base and Standards Cases for Commercial Split

- System Air Conditioners <65,000 Btu/h

SEER Market Share

Base

Year ( 2012 ) Base Case (20 19 ) Standards Case (20 19 )

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

13.0 41% 2 6 % 0% 0% 0% 0% 0% 0%

14.0 35% 5 0 % 76% 0% 0% 0% 0% 0%

15.0 22% 22% 22% 98% 0% 0% 0% 0%

16.0 2% 2% 2% 2% 100% 0% 0% 0%

17.0 0% 0% 0% 0% 0% 100% 0% 0%

18.0 0% 0% 0% 0% 0% 0% 100% 0%

19.0 0% 0% 0% 0% 0% 0% 0% 100%

Market Weighted Average

UEC 2

, 605 2

, 590 2

, 569 2

, 440 2

, 274 2

, 217 2

, 160 2

, 118

Table 8.2

. 2 Efficiency Distributions for the Base and Standards Cases for Commercial Package Air Conditioners <65,000 Btu/h

SEER Market Share

Base

Year ( 2012 ) ASHRAE (2017) ASHRAE

Base Case (2020) Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

13.0 42% 0% 0% 0% 0% 0% 0% 0%

14.0 18% 60% 52% 0% 0% 0% 0% 0%

15.0 22% 22% 30% 82% 0% 0% 0% 0%

16.0 7% 7% 7% 7% 89% 0% 0% 0%

17.0 4% 4% 4% 4% 4% 93% 0% 0%

18.0 7% 7% 7% 7% 7% 7% 100% 0%

19.0 0% 0% 0% 0% 0% 0% 0% 100%

Market Weighted Average

UEC 2

, 542 2

, 505 2

, 491 2

, 403 2

, 264 2

, 213 2

, 160 2

, 1188

- 7 Table 8.2

. 3 Efficiency Distributions for the Base and Standards Cases for Commercial Split

- System Heat Pumps <65,000 Btu/h

SEER Market Share

Base

Year ( 2012 ) ASHRAE (2017) ASHRAE

Base Case (2020) Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

13.0 52% 0% 0% 0% 0% 0% 0% 0%

14.0 37% 89% 80% 0% 0% 0% 0% 0%

15.0 11% 11% 19% 99% 0% 0% 0% 0%

16.0 1% 1% 1% 1% 100% 0% 0% 0%

17.0 0% 0% 0% 0% 0% 100% 0% 0%

18.0 0% 0% 0% 0% 0% 0% 100% 100%

Market Weighted Average

UEC 2

, 682 2

, 641 2

, 627 2

, 500 2

, 343 2

, 276 2

, 209 2

, 209

Table 8.2

. 4 Efficiency Distributions for the Base and Standards Cases for Commercial Package Heat Pumps <65,000 Btu/h

SEER Market Share

Base

Year ( 2012 ) ASHRAE (2017) ASHRAE

Base Case (2020) Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

13.0 42% 0% 0% 0% 0% 0% 0% 0%

14.0 36% 78% 69% 0% 0% 0% 0% 0%

15.0 12% 12% 21% 90% 0% 0% 0% 0%

16.0 9% 9% 9% 9% 99% 0% 0% 0%

17.0 1% 1% 1% 1% 1% 99% 0% 0%

18.0 1% 1% 1% 1% 1% 1% 100% 100%

Market Weighted Average

UEC 2

, 641 2

, 608 2

, 594 2

, 485 2

, 342 2

, 276 2

, 209 2

, 2098

- 8 Table 8.2

. 5 Efficiency Distributions for the Base and Standards Cases for Water

- Source Heat Pumps <17,000 Btu/h

EER Market Share

Base

Year ( 2012 ) ASHRAE (201 6 ) ASHRAE

Base Case (2020) Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

11.2 1% 0% 0% 0% 0% 0% 0% 0%

12.2 20% 15% 1% 0% 0% 0% 0% 0%

13 .0 29% 35% 50% 50% 0% 0% 0% 0%

14 .0 22% 22% 22% 22% 72% 0% 0% 0%

15.7 20% 20% 20% 20% 20% 93% 0% 0%

16.5 5% 5% 5% 5% 5% 5% 98% 0%

18.1 2% 2% 2% 2% 2% 2% 2% 100%

Market Weighted Average

UEC 1,630 1,622 1,606 1,606 1,548 1,431 1,370 1,259

Table 8.2

. 6 Efficiency Distributions for the Base and Standards Cases for Water

- Source

Heat Pumps ≥17,000 to ≤65,000 Btu/h

EER Market Share

Base Year ( 2012 ) ASHRAE (201 6 ) ASHRAE

Base Case (2020) Standards Case (2020)

EL 1 EL 2 EL 3 EL 4

12.0 0% 0% 0% 0% 0% 0% 0%

13.0 18% 15% 8% 0% 0% 0% 0%

14.6 45% 48% 55% 0% 0% 0% 0%

16.6 25% 25% 25% 88% 0% 0% 0%

18 .0 9% 9% 9% 9% 97% 0% 0%

19.2 2% 2% 2% 2% 2% 99% 0%

21.6 1% 1% 1% 1% 1% 1% 100%

Market Weighted Average

UEC 4,491 4,471 4,430 4,055 3,788 3,569 3,1938

- 9 Table 8.2

. 7 Efficiency Distributions for the Base and Standards Cases for Water

- Source Heat Pumps ≥65,000 to ≤135,000 Btu/h

EER Market Share

Base

Year ( 2012 ) ASHRAE (201 6 ) ASHRAE

Base Case (2020) Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

12.0 0% 0% 0% 0% 0% 0% 0% 0%

13 .0 13% 7% 0% 0% 0% 0% 0% 0%

14 .0 21% 27% 30% 30% 0% 0% 0% 0%

15 .0 44% 44% 48% 48% 78% 0% 0% 0%

16 .0 20% 20% 20% 20% 20% 98% 0% 0%

17.2 2% 2% 2% 2% 2% 2% 100% 100%

Market Weighted Average

UEC 11,058 11,006 10,911 10,911 10,707 10,234 9,607 9,607

To estimate a trend post

- 2020 , DOE applied the same rate of growth in efficiency as it did to get from 20 1 3 to 20 20 , which is also the same rate of growth used in the 2004 rulemaking for commercial unitary air conditioners, which is approximately 1 EER every 35 years. 1 Figure

8.2

. 2 through Figure 8.2

. 8 show the trend in market

- weighted efficienc y for each equipment

class.8

- 10 Figure 8.2

. 2 Efficiency Trends for Small, Commercial Air

- Cooled Split

- System Air Conditioners <65,000 Btu/h 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00

2012 2017 2022 2027 2032 2037 2042Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 58

- 11 Figure 8.2

. 3 Efficiency Trends for Small, Commercial Air

- Cooled Single Package Air Conditioners <65,000 Btu/h 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00

2012 2017 2022 2027 2032 2037 2042Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 58

- 12 Figure 8.2

. 4 Efficiency Trends for Small, Commercial Air

- Cooled Split System Heat Pumps <65,000 Btu/h 10 11 12 13 14 15 16 17 18 19

2012 2017 2022 2027 2032 2037 2042Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 48

- 13 Figure 8.2

. 5 Efficiency Trends for Small, Commercial Air

- Cooled Single Package Heat Pumps <65,000 Btu/h 10 11 12 13 14 15 16 17 18 19

2012 2017 2022 2027 2032 2037 2042Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 48

- 14 Figure 8.2

. 6 Efficiency Trends for Water

- Source Heat Pumps <17,000 Btu/h

Note: There is a minimal difference between ASHRAE and EL 1 as nearly all equipment sold after 2020 is projected

to be at or above EL 1. 10 11 12 13 14 15 16 17 18 19

2012 2017 2022 2027 2032 2037 2042Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 58

- 15 Figure 8.2

. 7 Efficiency Trends for Water

- S ource Heat Pumps ≥17,000 to ≤65,000 Btu/h

10 12 14 16 18 20 22 24

2012 2017 2022 2027 2032 2037 2042

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 58

- 16 Figure 8.2

. 8 Efficiency Trends for Water

- Source Heat Pumps ≥65,000 to ≤135,000 Btu/h

Note: There is no change in Shipment Weighted EER between ASHRAE and EL 1 because there is no market share projected below EL 1 after 2020.

Site

- to

- Power

- Plant Energy Use Factor 8.2.2.4

In determining national annual energy consumption, DOE initially calculated the a nnual energy consumption at the site (for electricity, the energy in kWh consumed at the household. DOE then applied a conversion factor to site energy consumption to account for losses associated with the generation, transmission, and distribution of elec tricity. This multiplicative site

- to

power

- plant conversion factor converts site energy consumption into primary energy consumption, expressed in quadrillion Btus (quads). 13.5

14 14.5

15 15.5

16 16.5

17 17.5

2012 2017 2022 2027 2032 2037 2042Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 48

- 17 DOE used annual site

- to

- power

- plant conversion factors based on the version of the National Energy Modeling System (NEMS) c that corresponds to DOE’s Energy Information Administration’s (EIA’s) Annual Energy Outlook 201 4 ( AEO201 4 ). 2 The factors are marginal values, which represent the response of the system to an incremental decrease in consumption. For electricity, the conversion factors change over time in response to projected changes in generation sources (that is, the types of power plants projected to provide electricity to the Nation). The value AEO201 4 reported for 2040 (the last year available in AEO201 4 ) was extrapolated through the end of the projection period (204 6 ).

Full

- Fuel

- Cycle Energy Factors 8.2.2.5

The full

- fuel

- cycle (FFC) 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 estimate the FFC by including the energy consumed in extracting, processing, and transporting o r distributing primary fuels, which we refer to as “upstream” activities, DOE developed FFC multipliers d using the data and projections generated by NEMS and used for AEO201 4 . The AEO201 4 provides extensive information about the energy system, including pr ojections of future oil, natural gas, and coal supplies; energy use for oil and gas field and refinery operations; and fuel consumption and emissions related to electric power production. This information can be used to define a set of parameters that repr esent the energy intensity of energy production.

Table 8.2

. 8 shows the FFC energy multipliers used for selected years. The method used to calculate FFC energy multipl iers is described in appendix 8A

.

Table 8.2

. 8 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO 2014 )

Energy Source 2020 2025 2030 2035 2040

Electricity (power plant energy use) 1.04 4 1.04 5 1.04 6 1.04 7 1.04 7

Compliance Dates and Analysis Period 8.2.2.6

Generally, covered equipment to which a new or amended energy conservation standard applies must comply with the standard if such equipment is manufactured or imported on or after a specified date. In t hi s Final Rule

, DOE is evaluating whether more

- stringent efficiency levels

than those in ASHRAE Standard 90.1

- 2013 would be technologically feasible, economically justified, and result in a significant additional amount of energy savings. If DOE were to propose

c For more information on NEMS, r efer to the U.S. Department of Energy, Energy Information Administration

documentation. A useful summary is National Energy Modeling System: An Overview 2000 , DOE/EIA

0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model

with no modification to code or data. Because this analysis entails minor code modifications

, and the model is run under policy scenarios that are variations on EIA assumptions, DOE refers to the model as NEMS

- BT (BT is DOE’s Building Technologies Program, under whose aegis this work was performed). NEMS

- BT previously was

called NEMS

- BRS.

d FFC multipliers discussed in this chapter relate to the upstream part of the FFC process.8

- 18 a rule pre scribing energy conservation standards at the efficiency levels contained in ASHRAE

Standard 90.1

- 2013 for the triggered equipment classes , EPCA states that compliance with any such standards shall be required on or after a date which is two or three years (depending on equipment size) after the compliance date of the applicable minimum energy efficiency requirement in the amended ASHRAE/IES standard. (42 U.S.C. 6313(a)(6)(D)) Given the equipment size at issue here, DOE has applied the two

- year implementation period to determine the compliance date of any energy conservation standard equal to the efficiency levels specified by ASHRAE Standard 90.1

- 2013 proposed by this rulemaking. Thus, if DOE decides to adopt

the eff iciency levels in ASHRAE Standard 90.1

- 2013, the compliance date of the rulemaking would be dependent upon the date specified in ASHRAE Standard 90.1

- 2013 or its publication date, if none is specified. In this case, t he rule would apply to small commercial air

- cooled air conditioners and heat pumps less than 65,000 Btu/h manufactured on or after January 1, 2017, which is two years after the date specified in ASHRAE Standard 90.1

- 2013. The rule would apply to water

- source heat pumps manufactured on or after October 9, 2015, which is two years after the publication date of ASHRAE Standard 90.1

- 2013.

If DOE were to propose a rule prescribing energy conservation standards more stringent than the efficiency levels contained in ASHRAE Standard 90.1

- 201 3, EPCA sta tes that compliance with any such standards is required for products manufactured on or after a date which is four years after the date the final rule is published in the Federal Register. (42 U.S.C. 6313(a)(6)(D)) DOE has applied this 4

- year implementat ion period to determine the compliance date for any energy conservation standard more stringent than the efficiency levels specified by

ASHRAE Standard 90.1

- 2013 that might be prescribed at the final rule stage for the equipment classes triggered by ASHRAE . Thus, for equipment for which DOE might adopt a level more stringent than the ASHRAE efficiency levels, the rule would apply to products manufactured on or after a date four years from the date of publication of the final rule, which the statute requires to be completed by April 9, 2016 (thereby resulting in a compliance date no later than April 9, 2020). e

For split system air

- cooled air conditioners, which DOE evaluated under the 6 year look back, DOE applied a different compliance date. Specifically, EPCA states that amended standards prescribed under this subsection shall apply to products manufactured after a date that

is the later of

— (I) the date that is 3 years after publication of the final rule establishing a new standard; or (II) the date that i s 6 years after the effective date of the current standard for a covered product. (42 U.S.C. 6313(a)(6)(C)(iv)) Because DOE must publish a final rule by April 9, 2016, in the case that it adopts standards higher than those in ASHRAE 90.1 for the other equi pment classes, DOE projected that the date under clause (I) would be April 2019, which is later than the date under clause (II). For purposes of its analysis, DOE used 2019 as the first year of compliance with amended standards.

e Since ASHRAE published ASHRAE Standard 90.1

- 2013 on Octo ber 9, 2013, EPCA requires that DOE publish a final rule adopting more

- stringent standards than those in ASHRAE Standard 90.1

- 2013, if warranted, within 30 months of ASHRAE action ( i.e., by April 2016). Thus, four years from April 2016 would be April 2020, which would be the anticipated compliance date for DOE adoption of more

- stringent standards.8

- 19 A 30

- year analysis period is used for the NES, extending from the compliance date based on adoption of the levels in ASHRAE Standard 90.1

- 201 3

. However, energy savings accrue until the last unit purchased at the end of the 30

- year analysis period is retired from service. For small

commercial air

- cooled air conditioners and heat pumps less than 65,000 Btu/h except split system air conditioners , the analysis period extends from 201 7 to 204 6 ; for water

- source heat pumps, the analysis period extends from 201 5 to 204 4 . The time period fo r energy savings for standards going higher than ASHRAE Standard 90.1

- 201 3 is from 20 20 to 2046 or 2044 , based

on the equipment classes described. This approach accounts for any lost energy savings from a delay incurred by DOE’s not adopting ASHRAE efficie ncy levels, but instead adopting standards at efficiency levels higher than those specified in ASHRAE Standard 90.1

- 201 3

. For split system air conditioners, which were not triggered by ASHRAE, energy savings accrue during the lifetime of equipment shipped in the period 2019 to 2048 in all cases.

8.3 NET PRESENT VALUE OF CUSTOMER BENEFITS

For equipment triggered by ASHRAE, DOE considers the ASHRAE level as the base minimum standard that it could set under Federal law. In the NIA, DOE establishes the NPV for going above the ASHRAE base case, but does not need to determine the NPV of adoption of that minimum base case. DOE only looked at subsequent higher standard levels to determine whether each higher level, when examined independently, has a positive NPV com pared with the

ASHRAE base case. For split system air conditioners, which were not triggered by ASHRAE,

DOE determined the NPV for all levels.

8.3.1 Net Present Value Definition

The NPV is the value in the present of a time

- series of costs and savings. The NPV is described by the equation:

NPV = PVS

- PVC Eq. 8

. 7

Where:

PVS = present value of savings in operating cost (including co sts for energy, repair, and maintenance); and

PVC = present value of increase in total installed cost (including costs for equipment, extended repairs, and installation).

DOE determined the PVS and PVC according to the following expressions.

Eq. 8

. 8

Eq. 8

. 9 

## ∑

y y DF OCS PVS × =



## ∑

y y DF TIC PVC × =8

- 20 DOE calculated the total annual savings in operating cost by multiplying the number or stock of a given equipment (by vintage) by its per

- unit operati ng cost savings (also by vintage). DOE calculated the total annual increase in installed cost by multiplying the number or stock of a given equipment (by vintage) by its per

- unit total installed cost increase (also by vintage). Total annual savings in oper ating cost and increases in installed cost are calculated using the following equations.

Eq. 8

. 10

Eq. 8

. 11

Where:

OCS = total annual savings in operating cost each year summed over vintages of the equipment stock, STOCK V ; TIC = total annual increase in installed cost each year summed over vintages of the equipment stock, STOCK V ;
DF = discount factor in each year;

STOCK V = stock of equipment (units) of vintage V that survive in the year for which DOE calculated annual energy 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 equ ipment was purchased as a new unit; and

y = year in the projection

.

DOE determined the PVC for each year from the compliance date of the standard until

204 4 (for water

- source heat pumps)

, 2046 (for small commercial air conditioners and heat pumps other than split system air conditioners ) , or 2048 (for split system air conditioners) . DOE

determined the PVS for each year from the compliance date of the standard until the year when

units purchased in the last year retire. DOE calculated costs and sav ings as the difference

between each standards case and the base case.

DOE calculated a discount factor from the discount rate and the number of years between the “present” (201 4 , the year to which the sum is being discounted) and the year in which the

c osts and savings occur. The NPV is the sum over time of the discounted net savings.

8.3.2 Inputs to Calculation

Listed below are the inputs to DOE’s calculati on of the NPV of costs and savings.

• total annual installed cost,

• total annual operating cost saving s,

• discount factor

, 

## ∑

V V y UOCS STOCK OCS × =



## ∑

V V y UTIC STOCK TIC × =8

- 21 • present value of total annual increases in installed cost,

• present value of savings

.

Total Annual Installed Cost 8.3.2.1

The increase in the total annual installed cost is equal to the annual change in the per

- unit

total installed cost (difference between base case and standards case) multiplied by the shipments projected in the standards case. f The total installed cost includes both the equipment cost and the

installation cost. Table 8.3

. 1 and Table 8.3

. 2 provide average total installed cost values by efficiency level for all equipment classes.

Table 8.3

. 1 Average Total Installed Costs for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h

Equipment Class Federal

Baseline ASHRAE

Baseline/

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $3,901 $4,150 $4,401 $4,670 $4,927 $5,194 $5,474

Package AC N/A $4,781 $5,090 $5,400 $5,702 $6,007 $6,375

Split HP N/A $4,513 $4,774 $5,118 $5,401 $5,694 N/A

Package HP N/A $5,155 $5,499 $5,830 $6,161 $6,550 N/A

Table 8.3

. 2 Average Total Installed Costs for Water

- Source Heat Pumps

Equipment Class ASHRAE

Baseline/

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $3,216 $3,354 $3,530 $3,822 $3,958 $4,233

≥17,000 to ≤65,000 Btu/h $4,882 $5,162 $5,513 $5,758 $5,968 $6,392

≥65,000 to ≤135,000 Btu/h $12,005 $12,961 $13,919 $14,830 $15,977 N/A

As discussed in section 8.2.2.2 , DOE developed base case and standards case projections of market share by efficiency level. DOE multiplied the market share by efficiency level in each year by the values in Table 8.3

. 1 and Table 8.3

. 2 to calculate annual shipment

- weighted average

total installed cos ts.

Equipment Price Projection 8.3.2.2

As discussed in chapter 6, DOE used a constant pr ice assumption for the default projection in the NIA. In order to investigate the impact of different equipment price forecasts on

f For this rulemaking, the base case and standards case shipments are identical.8

- 22 the NPV for t he considered efficiency levels

, DOE also considered two alternative price trends for each equipment type

.

For small commercial air

- cooled air conditioners and heat pumps, DOE used an exponential fit on the deflated price index for unitary air

- conditioners from 1978

- 2006 (low price case) and from 2007

- 2013 (high price case). For water

- source heat pumps, DOE used an exponential fit on the deflated price index for all other miscellaneous refrigeration and air

conditioning equipment, and the other is based on the deflator for industrial equi pment that was

forecasted for AEO201 4 . Details on how these alternative price trends we re developed are in Appendix 10 B, which also presents the results of the sensitivity analysis.

Total Annual Operating Cost 8.3.2.3

The annual operating cost savings are equal to the change in the annual operating costs (difference between base case and standards case) per unit multiplied by the shipments projected in the standards case. The annual operating cost includes electricity and repair/maintenance

costs.

Annual Electric ity Cost Savings

As explained in chapter 4 , DOE used unit energy consumption data for the energy use of each product at different efficiency levels . Chapter 6 describes how DOE calculat ed annual electricity prices

. For each efficiency level, the electricit y cost savings are calculated by multiplying the reduction in electricity consumption (in kWh) by the annual marginal electricity price. Table 8.3

. 3 and Table 8.3

. 4 show the electricity costs in 2020 by equipment type and TSL.

Table 8.3

. 3 Average Annual Electricity Costs in the Compliance Year for 3

- phase, Air

Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h (2014 $)

Equipment Class Federal

Baseline ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $312 $302 $288 $275 $270 $266 $263

Package AC N/A $302 $288 $275 $270 $266 $263

Split HP N/A $307 $295 $282 $277 $272 N/A

Package HP N/A $307 $295 $282 $277 $272 N/A

Note: 2019 is the compliance year for Split AC; 2020 is the compliance year for the other classes.

Table 8.3

. 4 Average Annual Electricity Costs in 2020 for Water

- Source Heat Pumps (2014 $)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $219 $210 $201 $188 $183 $174

≥17,000 to ≤65,000 Btu/h $625 $580 $537 $513 $495 $465

≥65,000 to ≤135,000 Btu/h $1,497 $1,424 $1,369 $1,321 $1,270 N/A8

- 23 As with the total installed cost data, DOE developed projected annual electricity expenses based on the annual projections of market share by efficiency level specified in the base case and standards cases. DOE multiplied the market share by efficiency lev el in each year by the electricity costs specific to each efficiency level to calculate annual shipment

- weighted average electricity costs. DOE then applied electricity price trends from AEO 201 4 to scale the electricity expenses moving forward.

Annual Mai ntenance Costs

As discussed in chapter 6, DOE determined that the maintenance costs are $ 302 for 3

- phase, air cooled air conditioners, $ 334 for 3

- phase, air

- cooled heat pumps, $ 334 for water

source heat pumps less than 17,000 Btu/h and between 17,000 and 65,000 Btu/h, and $ 404 for

water

- source heat pumps between 65,000 and 135,000 Btu/h. As the maintenance costs do not vary efficiency level, they have no impact on operating cost savings.

Ann ual Repair Costs

DOE based average annual repair costs on a base repair cost, which increased with the value of the equipment in 201 4 $ (see chapter 6 ). Using the efficiency distribution of the stock at each standard level analyzed, DOE developed a weighted average repair cost for the stock based

on the distribution at each standard level.

Discount Factor 8.3.2.4

DOE multiplied monetary values in future years by a discount factor to determine the present value. The discount factor (DF) is described by the equation:

Eq. 8

. 12

Where:

r = discount rate,

y = year of the monetary value, and

y p = year in which the present value is being determined.

Although DOE used consumer discount rates to determine the life

- cycle cost of equipment (chapter 8), it used national discount rates to calculate national NPV. DOE estimated NPV using both a 3

- percent and a 7

- percent real discount rate, in accordance with the Office of Management and Budget’s guidance to Federal agencies on the development of regulatory analysis, particularly section E therein: Identifying and Measuring Benefits and Costs

. 3 DOE defined the present year as 2014.

Present Value of Increased In stalled Costs 8.3.2.5

The present value of increased installed cost is the difference between installation cost in each standards case and the base case discounted to the present and summed throughout the period over which DOE is considering the installation of un its (from the co mpliance date of ) yp y (

_ ) r ( DF

+ 1 1

=8

- 24 standards, 2020 (or 2019 for split system air conditioners) , through 20 44, 2046, or 2048, depending on equipment class ). DOE calculated annual increases in installed cost as the difference in total installed cost for new eq uipment purchased each year between the base case

and each standards case.

Present Value of Savings 8.3.2.6

The present value of annual savings in operating cost is the difference between the base

case and each standards case discounted to the present and summed throughout the peri od from the compliance date, 2020 (or 2019 for split system air conditioners) , to the time when the last

unit installed in 20 44, 2046 , or 2048 is retired from service.

Savings represent decreases in operating cost (including electricity

, maintenance , and repair cost ) associated with the more energy efficient equipment purchased in each standards case compared to the base case. Total annual savings in operating cost are the savings per unit multiplied by the number of units of each vintag e that survive in a particular year.

8.4 NES AND NPV RESULTS

8.4.1 National Energy Savings

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 in the LCC analysis.

Table 8.4

. 1 Cumulative Primary Energy Savings for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h (quads)
Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

AC Split 0.0 2 0.0 8 0.1 3 0.1 6 0.1 8 0.1 9

AC Single 0.04 0.05 0.11 0.15 0.18 0.19

HP Split 0.01 0.01 0.02 0.03 0.03 N/A

HP Single 0.01 0.01 0.02 0.03 0.04 N/A

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard

90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3 standards were adopted.8

- 25 Table 8.4

. 2 Full Fuel Cycle Energy Savings for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h (quads)
Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

AC Split 0.02 0.0 8 0.1 4 0.1 7 0.1 9 0. 20

AC Single 0.04 0.06 0.12 0.15 0.18 0.20

HP Split 0.01 0.01 0.02 0.03 0.03 N/A

HP Single 0.01 0.01 0.02 0.03 0.04 N/A

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard

90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3 standards were adopted.

Table 8.4

. 3 Cumulative Primary Energy Savings for Water

- Source Heat Pumps (quads)
Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h

- * 0.00 0.02 0.06 0.08 0.11

≥17,000 to ≤65,000 Btu/h

- * 0.02 0.26 0.45 0.60 0.83

≥65,000 to ≤135,000 Btu/h

- *

- * 0.01 0.03 0.05 0.05

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard

90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3 standards were adopted.

* There are no savings for this level because there is no market share projected below this level in the base case

.

Table 8.4

. 4 Full Fuel Cycle Energy Savings for Water

- Source Heat Pumps (quads)
Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h

- * 0.00 0.02 0.06 0.08 0.11

≥17,000 to ≤65,000 Btu/h

- * 0.03 0.27 0.47 0.63 0.87

≥65,000 to ≤135,000 Btu/h

- *

- * 0.01 0.03 0.05 0.05

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard

90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3 standards were adopted.

* There are no savings for this level because there is no market share projected below this level in the base case

.

8.4.2 Annual Costs and Savings

As an example, Figure 8.4

. 1 show s the changes over time of the non

- discounted annual

total installed cost increases and the non

- discounted operating cost savings for 3

- phase, air

cooled single

- package air conditioners < 65,000 Btu/h at efficiency level 1

. The total net annual impact is the discounted value of the difference between annual equipment purchases and annual operating costs at a 7

- percent discount rate. The annual equipment price change is the increase in equip ment price for equipment purchased each year over the period from 2020 to 2046 . The annual operating savings is the savings in operating costs for equipment purchased, and which have not been retired, for each year over the time period from 2020 to 2080 . T he NPV (the8

- 26 discounted total net annual impact) is the difference between the cumulative annual discounted savings and the cumulative annual discounted costs

.

Figure 8.4

. 1 3

- phase, Air

- Cooled, Single Packag e Air Conditioners <65,000 Btu/h: Installed Costs, Operating C ost Savings, and Net Savings; E L 1

Figure 8.4

. 1 shows that annual operating cost savings are growing fa ster than the increased equipment price costs through 203 5 , after which annual operating cost savings begin to

stabilize while increased equipment price costs continue to rise

. After 2046, no additional 3

- phase, air

- cooled, packaged air conditioner sales a re included in the analysis; however the annual energy savings continues to accrue until the last of the 3

- phase, air

- cooled, packaged air

conditioners installed in 2046 are removed from the stock

.

8.4.3 Ne t Present Value Results

The following sectio n provides NPV results for the different efficiency levels. Results are

cumulative and are shown as discounted values in dollar terms.

Table 8.4

. 5 through show Table 8.4

. 8 show the NPV results for each equipment class

based on a seven

- percent or a three

- percent discount rate.

DOE also developed sensitivity analyses using an increasing equipment price trend and a decreasing equipment price trend coupled with AEO2014 Low Economic Growth and High

-25 -15

-5 5 15 25 3520202023202620292032203520382041204420472050205320562059206220652068207120742077208020832086208920922095Millions of 2014$ Incremental Equipment Cost

Operating Cost Savings

Net Savings8

- 27 Economic Growth cases, respectively. These equipment trends and AEO Economic Growth case are described in appendix 8 B, which also pres ents NPV results based on these alternative

sensitivities.

Table 8.4

. 5 Cumulative Net Present Value for 3

- phase, Air

- Cooled Air Conditioning and Heating Equipment <65,000 Btu/h (Billions of 2014$

- 7% Discount Rate)
Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 E L 5

AC Split ( $ 0.05) ( $ 0.18) ( $ 0.38) ( $ 0.66) ( $ 0.95) ( $ 1.17)

AC Single N/A * ( $ 0.14) ( $ 0.43) ( $ 0.82) ( $ 1.25) ( $ 1.63)

HP Split N/A * ( $ 0.03) ( $ 0.09) ( $ 0.15) ( $ 0.19) N/A **

HP Single N/A * ( $ 0.04) ( $ 0.11) ( $ 0.20) ( $ 0.28) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would res ult if ASHRAE Standard 90.1

- 2013 standards were adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0)

. ** The max

- tech level for this equipment class is efficiency level 4.

Table 8.4

. 6 Cumulative Net Present Value for 3

- phase, Air

- Cooled Air Conditioning and Heating Equipment <65,000 Btu/h ( Billions of 2014$

- 3% Discount Rate)
Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

AC Split ( $ 0.07) ( $ 0.27) ( $ 0.64) ( $ 1.15) ( $ 1.71) ( $ 2.09)

AC Single N/A * ( $ 0.21) ( $ 0.74) ( $ 1.47) ( $ 2.30) ( $ 2.96)

HP Split N/A * ( $ 0.05) ( $ 0.15) ( $ 0.26) ( $ 0.33) N/A **

HP Single N/A * ( $ 0.07) ( $ 0.19) ( $ 0.35) ( $ 0.48) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* Economic analysis was not conduc ted for the ASHRAE levels (Efficiency Level 0).

** The max

- tech level for this equipment class is efficiency level 4.

Table 8.4

. 7 Cumulative Net Present Value for Water

- Source Heat Pumps ( Billions of 2014$

- 7% Discount Rate)

Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h ( $ 0.00) ( $ 0.04) ( $ 0.14) ( $ 0.21) ( $ 0.33)

≥17,000 to ≤65,000 Btu/h $ 0.01 $ 0.00 ( $ 0.11) ( $ 0.27) ( $ 0.59)

≥65,000 to ≤135,000 Btu/h

- * ( $ 0.01) ( $ 0.06) ( $ 0.11) N/A **

Notes: Numbers in parentheses indicate negative NPV. The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case

. ** The max

- tech level for this equipment class is efficiency level 4.8

- 28 Table 8.4

. 8 Summary of Cumulative Net Present Value for Water

- Source Heat Pumps ( Billions of 2014$

- 3% Discount Rate)

Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h ( $ 0.00) ( $ 0.05) ( $ 0.20) ( $ 0.30) ( $ 0.49)

≥17,000 to ≤65,000 Btu/h $ 0.03 $ 0.26 $ 0.21 $ 0.03 ( $ 0.37)

≥65,000 to ≤135,000 Btu/h

- * ( $ 0.02) ( $ 0.08) ( $ 0.15) N/A **

Notes: Numbers in parentheses indicate negative NPV. The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were

calculated relative to the ef ficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* There are no results for EL1 because there is no market share projected below this level in the base case

. ** The max

- tech level for this equipment class is efficiency level 4.

8.4.4 Summary

Figure 8.4

. 2 shows the relationship of NPV results to NES results by efficiency level for an example equipment class

.

Figure 8.4

. 2 Cumulative NES and NPV Results for 3

- Phase, Air

- Cooled Air Conditioning and Heating Equipment <65,000 Btu/h at a Seven Percent Discount Rate -3.5000 -3.0000 -2.5000 -2.0000 -1.5000 -1.0000 -0.5000 0.0000

0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000

EL 1 EL 2 EL 3 EL 4 EL 5NPV (Billions of 2014$)Cumulative Energy Savings (Quads)

NES

NPV8

- 29 REFERENCES

1 U.S. Department of Energy. 2004. Technical Support Document: Energy Efficiency Program for Commercial and Industrial Equipment: Commercial Unitary Air Conditioners and Heat Pumps . Washington, D.C. www1.eere.energy.gov /buildings/appliance_standards/commercial/cuac_tsd_060904.html

2 U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook 2014, April, 2014. Washington, DC. Report No. DOE/EIA

- 0383(2014). www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf

.

3 U.S. Office of Management and Budget, Circular A

- 4: Regulatory Analysis, September 17,

2003. www.whitehouse.gov/omb/circulars_a004_a

- 4/8A

- i APPENDIX 8A. FULL FUEL CYCLE

TABLE OF CONTENTS

8A.1 INTRODUCTION

................................ ................................ ................................ ........ 8A

- 1

8A.2 METHODOLOGY

................................ ................................ ................................ ....... 8A

- 1

8A.3 ENERG Y MULTIPLIERS FOR TH E FULL FUEL CYCLE

................................ ...... 8A

- 3

REFERENCES

................................ ................................ ................................ ......................... 8A

- 4

LIST OF TABLES

Table 8A.2.1 Dependence of FFC Parameters on AEO Inputs

................................ ......... 8A

- 3

Table 8A.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014 )

.............. 8A

- 38A

- 1 APPENDIX 8A. FULL FUEL CYCLE

8A.1 INTRODUCTION

This appendix summarizes the methods the U.S. Department of Energy (DOE) used to

calculate the full

- fuel

- cycle (FFC) energy savings estimated from potential standards for 3

- phase,

air

- cooled air conditioning and heating equipment and water source heat pumps. The FFC measure includes point

- of

- use (site) energy; the energy losses associated with generation, transmission, and distribution of electricity; and the energy consumed in extracting, processing, and transporting or distributing primary fuels. DOE’s method of analysis previously encompassed only site energy and the energy lost through generation, transmission, and

d istribution of electricity. In 2011 DOE announced its intention, based on recommendations from the National Academy of Sciences, to use FFC measures of energy use and emissions when analyzing proposed energy conservation standards.

0F 1 This appendix summari zes the methods DOE used to incorporate impacts of the full fuel cycle into the analysis.

This analysis uses several terms to describe aspects of energy use. The physical sources of energy are primary fuels such as coal, natural gas, or liquid fuel. Prima ry energy is equal to the heat content (British thermal units [Btu]) of the primary fuel used to produce an end

- use service. Site energy use is defined as the energy consumed at the point of use in a house or establishment. When natural gas or petroleum fu els are consumed at the site (for example in an

on

- site furnace), site energy is identical to primary energy, with both equal to the heat content of the primary fuel consumed.

For electricity generated by an off

- site power plant, site energy is measured in kilowatt

hours (kWh). In such a case the primary energy is equal to the quads (quadrillion Btu) of primary energy required to generate and deliver electricity to the site. For the FFC analysis, upstream energy use is defined as the energy consumed in e xtracting, processing, and transporting or distributing primary fuels. FFC energy use is the sum of primary plus upstream energy use.

Both primary fuels and electricity are used in upstream activities. The treatment of electricity in full

- fuel

- cycle ana lysis must distinguish between electricity generated by fossil fuels and uranium and electricity generated from renewable sources (wind, solar, and hydro). For the former, the upstream fuel cycle relates to the amount of fuel consumed at the power plant.

T here is no upstream component for the latter, because no fuel per se is used.

8A.2 METHODOLOGY

The mathematical approach to determining FCC is discussed in Coughlin (2012).

1F 2 Details on analyzing the fuel production chain are presented in Coughlin (2013).

2F 3 The methods used to calculate FFC energy use are summarized here. When all energy quantities are normalized to the same units, FFC energy use can be represented as the product of the primary energy use and an FFC multiplier. Mathematically the FFC multipli er is a function of a set of parameters that represent the energy intensity and material losses at each stage of energy production. Those parameters depend only on physical data, so the calculations require no assumptions about prices8A

- 2 or other economic fac tors. Although the parameter values often differ by geographic region, this analysis utilizes national averages.

The fuel cycle parameters are defined as follows.

• a x is the quantity of fuel x burned per unit of electricity produced, on average, for grid electricity. The calculation of a

x includes a factor to account for losses incurred through the transmission and distribution systems.

• b y is the amount of grid electricity used in producing fuel y , in MWh per physical unit of

fuel y

.

• c xy is the amount of fuel x consumed in producing one unit of fuel y

.

• q x is the heat content of fuel x (MBtu/physical unit).

• z x (s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x produced).

All the parameters are calculated as functions of an annual time step; hence, when evaluating the effects of potential new standards, a time series of annual values is used to estimate the FFC energy and emissions savings in each year of the analysis perio d and cumulatively. Fossil fuel quantities are converted to energy units using the heat content factor q

x . To convert electricity in kWh to primary energy units, on

- site electricity consumption is multiplied by the site

- to

- power

- plant energy use factor, de scribed in chapter 10. The site

- to

power

- plant energy use factor is defined as the ratio of the total primary energy consumption by the electric power sector (in quads) divided by the total electricity generated each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel used on site. Also calculated is a multiplier for electricity that reflects the fuel mix used in its generation. The multipliers are dimensionless numbers applied to primary energy savings to

obtain t he FFC energy savings. The upstream component of the energy savings is proportional to (μ

- 1). The fuel type is denoted by a subscript on the multiplier μ.

When DOE estimates energy savings attributable to appliance standards, the method for performing the full

- fuel

- cycle analysis utilizes data and projections published in the Annual Energy Outlook (AEO); in the case of this rulemaking , the AEO2014.

3F 4 Table 8A.2

. 1 sum marizes the AEO2014 data used as inputs to the calculation of various parameters. The column titled "AEO Table" gives the name of the table that provided the reference data.8A

- 3 Table 8A.2

. 1 Dependence of FFC P arameters on AEO Inputs

Parameter(s) Fuel(s) AEO Table Variables

q x All Conversion factors MMBtu per physical unit

a x All Electricity supply, disposition, prices, and emissions Generation by fuel type

Energy consumption by sector

and source Electric energy consumption by the power sector

b

c , c nc , c pc Coal Coal production by region and
type Coal production by type and

sulfur content

b p , c np , c pp Petroleum Refining industry energy consumption Refining

- only energy use

Liquid fuels supply and disposition Crude supply by source

International liquids supply and disposition Crude oil imports

Oil and gas supply Domestic crude oil production

c nn Natural gas Oil and gas supply U.S. dry gas production Natural gas supply, disposition,

and prices Pipeline, lease, and plant fuel

z x All Electricity supply, disposition, prices, and emissions Power sector emissions

The AEO2014 does not provide all the information needed to estimate total energy use in the fuel production chain. Coughlin (2013) describes the additional data sources needed to complete the analysis. The time dependence in the FFC multipliers for dishwashers, however, arises exclusively from variables taken from the AEO2014.

8A.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE

FFC ene rgy multipliers for selected years are presented in Table 8A.3

. 1 . The 2040 value

was held constant for the analysis period beyond 2040, which is the last year in the AEO2014 projection. The multiplier for electricity reflects the shares of various primary fuels in total electricity generation throughout the forecast period.

Table 8A.3

. 1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014 )

2020 2025 2030 2035 2040

Electricity 1.044 1.045 1.046 1.047 1.047

Natural gas 1.109 1.111 1.113 1.114 1.114

Petroleum fuels 1.176 1.176 1.174 1.172 1.1708A

- 4 REFERENCES

1 U.S. Department of Energy

– O ffice of Energy Efficiency and Renewable Energy. Energy Conservation Program for Consumer Products and Certain Commercial and Industrial Equipment. Statement of Policy for Adopting Full Fuel Cycle Analyses into Energy

Conservation Sta ndards Program. Federal Register

. August 18, 2011. Vol. 76, n o. 160 :

pp. 51281

– 51289

.

2 Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use

. Energy. 2012. 37(1): pp. 698

– 708. (Last accessed June 22, 2014.)

< www.sciencedirect.com/science/article/pii/S0360544211006803 >

3 Coughlin, K. Projections of Full

- Fuel

- Cycle Energy and Emissions Metrics . 2013. Lawrence Berkeley National Labo ratory. Report No. LBNL

- 6025E.

4 U.S. Energy Information Administration. Annual Energy Outlook 201 4 with Projections to

2040 . April 201 4 . DOE/EIA

- 0383(201 4 ). Washington, D.C. (Last accessed June 22, 2014.) < www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >8B

- i APPENDIX 8B. NATIONAL NET PRESENT VALUE OF CUSTOMER BE NEFITS

USING ALTERNATIVE EQ UIPMENT PRICE PROJEC TIONS AND ECONOMIC

GROWTH SCENARIOS

TABLE OF CONTENTS

8B.1 INTRODUCTION

................................ ................................ ................................ ......... 8B

- 1

8B.2 DESCRIPTION OF ALTER NA TIVE EQUIPMENT PRICE PROJECTIONS

........... 8B

- 1

8B.2.1 Decreasing Price Scenario

................................ ................................ ................. 8B

- 2

8B.2.2 Increasing Price Scenario

................................ ................................ ................... 8B

- 3

8B.2.3 Summary

................................ ................................ ................................ ............ 8B

- 6

8B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

........................... 8B

- 8

8B.4 NET PRESENT VALUE RE SULTS USING ALTERNAT IVE EQUIPMENT

PRICE TRENDS

................................ ................................ ................................ ............ 8B

- 9

REFERENCES

................................ ................................ ................................ ........................ 8B

- 15

LIST OF TABLES

Table 8B.2.1 Small Commercial Air

- Cooled Air Conditioning and Heating Equipment Price Trend Sensitivities

................................ ............................ 8B

- 6

Table 8B.2.2 Water

- Source Heat Pump Price Trend Sensiti vities

................................ .... 8B

- 6

Table 8B.4.1 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning

and Heating Equipment Using Reference Equipment Price Forecast and AEO Reference Case Scenario, 3% Discount Rate (billion 2014$)

..... 8B

- 9

Table 8B.4.2 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Reference Equipment Price Forecast and AEO Reference Case Scenario, 7% Discount Rate (billion 2014$)

... 8B

- 10

Table 8B.4.3 Cumulative NPV Results 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Econom ic Growth Case Scenario, 3% Discount Rate (billion 2014$)

................................ ............................. 8B

- 10

Table 8B.4.4 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Cond itioning and Heating Equipment Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario, 7% Discount Rate (billion 2014$)

................................ ............................. 8B

- 11

Table 8B.4.5 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario, 3% Discount Rate (billion 2014$)

................................ ............................. 8B

- 11

Table 8B.4.6 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equ ipment Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario, 7% Discount Rate (billion 2014$)

................................ ............................. 8B

- 128B

- ii Table 8B.4.7 Cumulative NPV Results for Water Source Heat Pumps Equipment Using Reference Equipment Price Forecast and AEO Reference Case Scenario, 3% Discount Rate (billion 2014$)

................................ ............. 8B

- 12

Table 8B.4.8 Cumulative NPV Results for Water Source Heat Pumps Equipment Using Reference Equipment Price Forecast and AEO Reference Case Scenario, 7% Discount Rate (billion 2014$)

................................ ............. 8B

- 13

Table 8B.4.9 Cumulative NPV for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO Hig h Economic Growth Case Scenario, 3% Discount Rate (billion 2014$)

...... 8B

- 13

Table 8B.4.10 Cumulative NPV Results for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario, 7% Discount Rate (billion 2014$)

................................ ................................ ................................ ........ 8B

- 13

Table 8B.4.11 Cumulative NPV Results for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario, 3% Discount Rate (billion 2014$)

................................ ................................ ................................ ........ 8B

- 14

Table 8B.4.12 Cumulative NPV Results for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Increasing Trend) and AEO

Low Economic G rowth Case Scenario, 7% Discount Rate (billion 2014$)

................................ ................................ ................................ ........ 8B

- 14

LIST OF FIGURES

Figure 8B.2.1 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential

Fit from 1978 to 2006

................................ ................................ .................. 8B

- 3 Figure 8B.2.2 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential

Fit from 2007 to 2013

................................ ................................ .................. 8B

- 4 Figure 8B.2.3 Deflated All Other Miscellaneous Refrigeration and Air Conditioning Equipment PPI versus Year, with Exponential Fit from 2001 to 2013

....... 8B

- 5 Figure 8B.2.4 Small Commercial Air

- Cooled Air Conditioning and Heating Equipment Price Trend Indexes

................................ ................................ ... 8B

- 7 Figure 8B.2.5 Water

- Source Heat Pump Price Trend Indexes

................................ ........... 8B

- 7 Figure 8B.3.1 Electricity Price Projections for Reference Case, High, and Low

Economic Growth Scenarios

................................ ................................ ........ 8B

- 88B

- 1 APPENDIX 8B. NATIONA L NET PRESENT VALUE OF CUSTOMER BENEFITS USING ALTERNATIVE EQ UIPMENT PRICE PROJEC TIONS AND ECONOMIC

GROWTH SCENARIOS

8B.1 INTRODUCTION

This appendix presents National Impact Analysis (NIA) results using alternative equipment price projections and economic growth scenarios. As will be discussed below, DOE developed increasing and decreasing equipment price projections . In addition, alt ernative economic growth scenarios were utilized 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 ( AEO201 4 ). 1 DOE developed low and high NPV scenarios based on the

combination of equipment price projections and economic growth scenarios. DOE based the low

NPV scenario on the combination of an increasing equipment price projection and the Low Economic Growth case, while the high NPV scenario w as based on the combination of a decreasing equipment price projection and the High Economic Growth case. Results are presented for the above two combinations at the end of this appendix.

8B.2 DESCRIPTION OF ALTER NATIVE EQUIPMENT PRI CE

PROJECTIONS

DOE used a constant price a ssumption for the default projection in the NIA described in Chapter 8

. In order to investigate the impact of different equipment price projections on the net present value (NPV) for the considered efficiency levels , DOE also considered two alternative price trends for a sensitivity analysis. This appendix describes the alternative price trends and compares NPV results for these scenarios with the default projection

.

In recent rulemakings for several residential products, DOE has us ed the experience

c urve method to derive learning rates to project future prices . In the experience curve method, the real cost of production is related to the cumulative production, or experience, with a manufactured product. That experience usually is measured in terms of cumulative production.

A s experience (production) accumulates, the cost of prod ucing the next unit decreases. The percentage reduction in cost that occurs with each doubling of cumulative production is known as the learning rate . A recent report from Lawre nce Berkeley National Laboratory by Taylor and Fujita provides an overview of some of the major findings of the academi c literature on learning curves, and describes the application of a component

- based learning curve approach (by the

Environmental Protect ion Agency and the National Highway Transportation Safety

Administration ) and a price

- based learning curve approach (by DOE) in regulatory impact

assessment. 2

For some commercial and industrial equipment, there are insufficient data to apply a price

- bas ed learning curve approach , particularly with respect to cumulative production . In such cases, DOE used a constant price assumption for the default forecast in the NIA , but made use of price indexes that are relevant for the equipment in question to derive alternative price trends for8B

- 2 sensitivity analysis. a DOE is using this approach for small commercial air

- cooled air conditioning and heating equipment as well as for water

- source heat pumps

.

DOE considered two alternative price trends for a sensitivity analysis. For small

commercial air

- cooled air conditioning and heating equipment, o ne assumes decreasing prices used an exponential fit on the deflated price index for “unitary air

- conditioners, except heat pumps” during the period of 1978 to 2006 ; t he other assumes rising prices and is based on the same deflated price index between 200 7 and 201 3

. For water

- source heat pumps, one assumes decreasing prices based on the deflator for industrial equipment that was forecasted for

AEO201 4; the other assumes rising prices and is based on an exponential fit on the deflated price

index for all other miscellaneous refrigeration and air

- conditioning equipment during the period

of 2001 to 2013.

8B.2.1 Decreasing Price Scenario

Small Commercial Air

- Cooled Equipment For the decreasing price scenario, DOE used an inflation

- adjusted “unitary air

conditioners, except heat pumps” Producer Price Index (PPI) spanning the time period of 1978

- 2006 from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with yea r as the explanatory variable. b The PPI during this period of time showed a continuing downward trend, so the exponential fit based on this part of the historical PPI represents the decreasing price scenario of future price projection.

The PPI data refl ect nominal prices, adjusted for equipment quality changes. An

inflation

- adjusted (deflated) price index for “unitary air

- conditioners, except heat pumps” was calculated by dividing the PPI series by the Gross Domestic Product Chained Price Index

. In

this case, the exponential function takes the form of:

YY = aa ∙ ee bbbb

where Y is the small commercial air

- cooled air conditioning and heating equipment price index, X is the time variable, a is the constant and b is the slope parameter of the time variable.

To estimate these exponential parameters, a least

- square fit was performed on the

inflation

- adjusted small commercial air

- cooled air conditioning and heating equipment price

index versus year from 1978 to 2006. See Figure 8B.2

. 1

.

a See appendix 10C of the final rule TSD for distribution transformers. www.regulations.gov/#!documentDetail;D=EERE

- 2010

- BT

- STD

- 0048

- 0760

b Series ID PCU333415333415E ; www.bls.gov/ppi/8B

- 3 Figure 8B.2

. 1 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential Fit

from 1978 to 2006

The regression performed as an exponential trend line fit results in an R

- square of 0.95,

whi ch indicates a good fit to the data. The final estimated exponential function is:

YY = 3

. 29 × 10 11

∙ ee (

− 0

. 0133 )

DOE then derived a price factor index for this scenario, renormalized with 201 3 equal to 1, to project prices in each future year in the analysis period considered in the NIA. The index value in a given year is a function of the exponential parameter and year

.

Water

- Source Heat Pumps

For water

- source heat pumps, DOE examined a forecast based on the “chained price

index

— industrial equipment” that was forecasted for AEO201 4 out to 2040. This index is the most disaggregated category that includes water

- source heat pumps. To develop an inf lation

adjusted index, DOE normalized the above index with the “chained price index

— gross domestic product” forecasted for AEO201 4 . To extend the price index beyond 2040, DOE used the average annual price growth rate in 2031 to 2040.

8B.2.2 Increasing Price Sce nario

Small Commercial Air

- Cooled Equipment

DOE used an inflation

- adjusted “unitary air

- conditioners, except heat pumps” Producer

Price Index (PPI) spanning the time period of 2007

- 2013 from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the explanatory variable. The PPI during this period of time shows a continuing upward trend, so the exponential fit based on this part of the8B

- 4 historical PPI represents the increasing price scenario of future price projection. The PPI data reflect nominal prices, adjusted for equipment quality changes. An inflation

- adjusted (deflated) price index for “unitary air

- conditioners, except heat pumps” was calculated by dividing the PPI series by the Gross Domestic Product Chained Price Index

. In this case, the exponential function

takes the form of:

YY = aa ∙ ee bbbb

where Y is the small commercial air

- cooled air conditioning and heating equipment price index, X is the time variable, a is the constant and b is the slope parameter of the time variable.

To estimate these exponential parameters, a least

- square fit was performed on the

inflation

- adjusted small commercial air

- cooled air conditioning and heating equipment price

index versus year fr om 2007 to 2 013. See Figure 8B.2

. 2

.

Figure 8B.2

. 2 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential Fit

from 2007 to 2013

The regression performed as an exponential tr end line fit results in an R

- square of 0.3 0

. The final estimated exponential function is:

YY = 1

. 66 × 10 (

− 4 )

∙ ee 0

. 0043

DOE then derived a price factor index for this scenario, renormalized with 201 3 equal to 1, to project prices in each future year in the analysis period considered in the NIA. The index value in a given year is a function of the exponential parameter and year

.8B

- 5 Water

- Source Heat Pumps

DOE used an inflation

- adjusted “all other miscellaneous refrigeration an d air conditioning equipment” Producer Price Index (PPI) spanning the time period of 2001

- 2013 from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the explanatory variable. The PPI during this period of time shows a continui ng upward trend, so the exponential fit based on this part of the historical PPI represents the increasing price scenario of future price projection. The PPI data reflect nominal prices, adjusted for equipment quality changes. An inflation

- adjusted (deflat ed) price index for “all other miscellaneous refrigeration and air conditioning equipment” was calculated by dividing the PPI series by the Gross Domestic

Product Chained Price Index

. In this case, the exponential function takes the same form

described abo ve for small commercial air

- cooled air conditioning and heating equipment.

To estimate the exponential parameters, a least

- square fit was performed on the inflation

adjusted water

- source heat pump price index versus year from 2001 to 2013. See Figure 8B.2

. 3

.

Figure 8B.2

. 3 Deflated All Other Miscellaneous Refrigeration and Air Conditioning Equipment PPI versus Year, with Exponential Fit from 2001 to 2013

The regression performed as an exponential trend line fit results in an R

- square of 0.75. The final estimated exponential function is:

YY = 8

. 12 × 10 (

− 13 )

∙ ee 0

. 0138

DOE then derived a price factor index for this scenario, renormalized with 201 3 equal to 1, to project pric es in each future year in the analysis period considered in the NIA. The index value in a given year is a function of the exponential parameter and year

.8B

- 6 8B.2.3 Summary

Table 8B.2

. 1 and Table 8B.2

. 2 show the summar ies of the average annual rates of change for the equipment price index in each scenario. Figure 8B.2

. 4 and Figure 8B.2

. 5 show the resulting price trends.

Table 8B.2

. 1 Small Commercial Air

- Cooled Air Conditioning and Heating Equipment

Price Trend Sensitivities Sensitivity Price Trend Average Annual Rate of Change %

Medium (Default) Constant Price Projection 0.0 Decreasing Price

Scenario Exponential Fit using data from 1978 to 2006

- 1.32

Increasing Price

Scenario Exponential Fit using data from 2007 to 2013 0.4 3

Table 8B.2

. 2 Water

- Source Heat Pump Price Trend Sensitivities

Sensitivity Price Trend Average Annual Rate of Change % Medium (Default) Constant Price Projection 0.0 Decreasing Price

Scenario AEO2014

-- “chained price index

— industrial equipment” - 0.39

Increasing Price

Scenario Exponential Fit using data from 2001 to 2013 1.398B

- 7 Figure 8B.2

. 4 Small Commercial Air

- Cooled Air Conditioning and Heating Equipment

Price Trend Indexes

Figure 8B.2

. 5 Water

- Source Heat Pump Price Trend Indexes8B

- 8 8B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

To generate NIA results reported in chapter 8 , DOE uses t he Reference case energy price projection s from AEO201 4

. The reference case is a business

- as

- usual estimate, given known market, demographic, and technological trends. For AEO201 4 , EIA explored the impacts of alternative assumptions in other scenarios wit h 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 AEO201 4 uses High and Low Economic Growth scenarios to project the possib le impacts of alternative economic growth assumptions on energy markets. 3

Figure 8B.3

. 1 shows electricity price projections based on the different AEO 201 4

scen arios.

Figure 8B.3

. 1 Electricity Price Projections for Reference Case, High, and Low Economic

Growth Scenarios8B

- 9 8B.4 NET PRESENT VALUE RE SULTS USING ALTERNA TIVE EQUIPMENT

PRICE TRENDS

This section presents the NPV results for low and high scenarios using the alternative equipment price projections in combination with the alternative economic growth scenarios. DOE based the low NPV scenario on the combination of an increasing equipment price projection and the AEO Low Economic Growth case, while the high NPV scenario was based on the combination of a decreasing equipment price projection and the AEO High Economic

Growth case. Results are presented below for each equipment class.

Table 8B.4

. 1 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Reference Equipment Price Forecast and AEO

Reference Case Scenario, 3% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

0 1 2 3 4 5

Sp lit System Air

Conditioners <65,000 Btu/h (0.07) (0.27) (0.64) (1.15) (1.71) (2.09)

Packaged Air Conditioners <65,000 Btu/h N/A * (0.21) (0.74) (1.47) (2.30) (2.96)

Split System Heat Pumps <65,000 Btu/h N/A * (0.05) (0.15) (0.26) (0.33) N/A **

Packaged Heat Pumps <65,000 Btu/h N/A * (0.07) (0.19) (0.35) (0.48) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* Economic analysis was not conducted for the A SHRAE levels (Efficiency Level 0)

. ** The max

- tech level for this equipment class is efficiency level 4.8B

- 10 Table 8B.4

. 2 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Reference Equipment Price Forecast and AEO

Reference Case Scenario

, 7% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

0 1 2 3 4 5

Sp lit System Air

Conditioners <65,000 Btu/h (0.05) (0.18) (0.38) (0.66) (0.95) (1.17)

Packaged Air Conditioners <65,000 Btu/h N/A * (0.14) (0.43) (0.82) (1.25) (1.63)

Split System Heat Pumps <65,000 Btu/h N/A * (0.03) (0.09) (0.15) (0.19) N/A **

Packaged Heat Pumps <65,000 Btu/h N/A * (0.04) (0.11) (0.20) (0.28) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0)

. ** The max

- tech level for this equipment class is efficiency level 4.

Table 8B.4

. 3 Cumulative NPV Results 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario, 3% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air Conditioners <65,000 Btu/h (0.05) (0.19) (0.47) (0.91) (1.39) (1.73)

Packaged Air Conditioners <65,000 Btu/h N/A * (0.14) (0.56) (1.18) (1.88) (2.46)

Split System Heat Pumps <65,000 Btu/h N/A * (0.03) (0.11) (0.20) (0.26) N/A **

Packaged Heat Pumps <65,000 Btu/h N/A * (0.05) (0.14) (0.28) (0.38) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0)

. ** The max

- tech level for this equipment class is efficiency level 4.8B

- 11 Table 8B.4

. 4 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario

, 7%

Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air Conditioners <65,000 Btu/h (0.04) (0.14) (0.31) (0.55) (0.80) (1.00)

Packaged Air Conditioners <65,000 Btu/h N/A * (0.10) (0.34) (0.68) (1.05) (1.39)

Split System Heat Pumps <65,000 Btu/h N/A * (0.02) (0.07) (0.12) (0.16) N/A **

Packaged Heat Pumps <65,000 Btu/h N/A * (0.03) (0.09) (0.17) (0.23) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 20 13 standards were adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0)

. ** The max

- tech level for this equipment class is efficiency level 4.

Table 8B.4

. 5 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario

, 3%

Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air Conditioners <65,000 Btu/h (0.08) (0.31) (0.71) (1.26) (1.86) (2.25)

Packaged Air Conditioners <65,000 Btu/h N/A * (0.24) (0.82) (1.59) (2.48) (3.17)

Split System Heat Pumps <65,000 Btu/h N/A * (0.06) (0.17) (0.28) (0.36) N/A **

Packaged Heat Pumps <65,000 Btu/h N/A * (0.08) (0.21) (0.39) (0.52) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* Economic analysis was not conducted for the A SHRAE levels (Efficiency Level 0)

. ** The max

- tech level for this equipment class is efficiency level 4.8B

- 12 Table 8B.4

. 6 Cumulative NPV Results for 3

- phase, Air

- Cooled, Air Conditioning and Heating Equipment Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario

, 7%

Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air Conditioners <65,000 Btu/h (0.05) (0.19) (0.42) (0.70) (1.01) (1.24)

Packaged Air Conditioners <65,000 Btu/h N/A * (0.15) (0.47) (0.87) (1.33) (1.73)

Split System Heat Pumps <65,000 Btu/h N/A * (0.03) (0.10) (0.16) (0.21) N/A **

Packaged Heat Pumps <65,000 Btu/h N/A * (0.05) (0.12) (0.22) (0.29) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0)

. ** The max

- tech level for this equipment class is efficiency level 4.

Table 8B.4

. 7 Cumulative NPV Results for Water Source Heat Pumps Equipment Using Reference Equipment Price Forecast an d AEO Reference Case Scenario

, 3% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.05) (0.20) (0.30) (0.49)

≥17,000 to ≤65,000 Btu/h 0.03 0.26 0.21 0.03 (0.37)

≥65,000 to ≤135,000 Btu/h

- * (0.02) (0.08) (0.15) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* There are no savings for EL1 because there i s no market share projected below this level in the base case.

** The max

- tech level for this equipment class is efficiency level 4.8B

- 13 Table 8B.4

. 8 Cumulative NPV Results for Water Source Heat Pumps Equipment Using Reference Equipment Price Forecast and AEO Reference Case Scenario

, 7% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.04) (0.14) (0.21) (0.33)

≥17,000 to ≤65,000 Btu/h 0.01 0.00 (0.11) (0.27) (0.59)

≥65,000 to ≤135,000 Btu/h

- * (0.01) (0.06) (0.11) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.

** The max

- tech level for this equipment class is efficiency level 4.

Table 8B.4

. 9 Cumulative NPV for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Econo mic

Growth Case Scenario

, 3% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.04) (0.17) (0.26) (0.43)

≥17,000 to ≤65,000 Btu/h 0.04 0.35 0.38 0.26 (0.03)

≥65,000 to ≤135,000 Btu/h

- * (0.02) (0.06) (0.13) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE St andard 90.1

- 2013 standards were adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.

** The max

- tech level for this equipment class is efficiency level 4.

Table 8B.4

. 10 Cumulative NPV Results for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic

Growth Case Scenario

, 7% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.04) (0.13) (0.19) (0.30)

≥17,000 to ≤65,000 Btu/h 0.01 0.04 (0.04) (0.18) (0.46)

≥65,000 to ≤135,000 Btu/h

- * (0.01) (0.05) (0.10) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.8B

- 14 ** The max

- tech level for this equipment class is efficiency level 4.

Table 8B.4

. 11 Cumulative NPV Results for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic

Growth Case Scenario

, 3% Discount Rate (billion 201 4 $)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.08) (0.28) (0.42) (0.65)

≥17,000 to ≤65,000 Btu/h 0.02 0.05 (0.17) (0.51) (1.17)

≥65,000 to ≤135,000 Btu/h

- * (0.03) (0.12) (0.22) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if A SHRAE Standard 90.1

- 2013 standards were adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.

** The max

- tech level for this equipment class is efficiency level 4.

Table 8B.4

. 12 Cumulative NPV Results for Water Source Heat Pumps Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic

Growth Case Scenario

, 7% Discount Rate (billion 2014 $)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.05) (0.18) (0.26) (0.41)

≥17,000 to ≤65,000 Btu/h (0.00) (0.09) (0.28) (0.51) (0.96)

≥65,000 to ≤135,000 Btu/h

- * (0.02) (0.07) (0.14) N/A **

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1

- 2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 2013 standards were adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.

** The max

- tech level for this equipment class is efficiency level 4.8B

- 15 REFERENCES

1 Energy Information Administration, Annual Energy Outlook 201 4 with Projections to 2040

, 201 4 . Washington, DC. www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf

2 Taylor, Margaret, and Sydny K. Fujita. Accounting for Technological Change in Regulatory Impact Analyses: The Learnin g Curve Technique . Berkeley: Lawrence Berkeley National Laboratory, 2013. LBNL

- 6195E

3 Energy Information Administration, Macroeconomic Activity Module for Annual Energy

Outlook 201 4 , 201 4 . Washington, DC.

< www.eia.gov/forecasts/aeo/assumptions/pdf/macroeconomic.pdf >9

- i CHAPTER 9. EMISSIONS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

................................ ................................ ................................ ........... 9

- 1

9.2 AIR QUALITY REGULATI ONS AND EMISSIONS IMPACTS

................................ . 9

- 2

9.3 POWER SECTOR AND SIT E EMISSIONS FACTORS

................................ ............... 9

- 3

9.4 UPSTREAM FACTORS

................................ ................................ ................................ . 9

- 4

9.5 EMISSIONS IMPACT RES ULTS

................................ ................................ .................. 9

- 5

9.5.1 Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000

Btu/h

................................ ................................ ................................ ..................... 9

- 5

9.5.2 Water

- Source Heat Pumps

................................ ................................ ................. 9

- 10

REFERENCES

................................ ................................ ................................ .......................... 9

- 14

LIST OF TABLES

Table 9.3.1 Power Pl ant Emissions Factors (Small Commercial Air

- Cooled Air Conditioners and Heat Pumps)

................................ ................................ ...... 9

- 4

Table 9.3.2 Power Plant Emissions Factors (Water

- Source Heat Pumps)

........................ 9

- 4

Table 9.4.1 Electricity Upstream Emissions Factors

................................ ........................ 9

- 5

Table 9.5.1 Cumulative Emissions Reduction for Potential Standard for Small

Three

- Phase Air

- Cooled Air Condi tioners and Heat Pumps <65,000

Btu/h

................................ ................................ ................................ ............... 9

- 6

Table 9.5.2 Cumulative Emissions Reduction for Potential Standard for Water

Source Heat Pumps

................................ ................................ ...................... 9

- 10

LIST OF FIGURES

Figure 9.5.1 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps:

CO2 Total Emissions Reduction

................................ ................................ .... 9

- 7 Figure 9.5.2 Small Commercial A ir

- Cooled Air Conditioners and Heat Pumps: SO2

Total Emissions Reduction

................................ ................................ ............ 9

- 7 Figure 9.5.3 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps:

NOx Total Emissions Reduction

................................ ................................ ... 9

- 8 Figure 9.5.4 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps: Hg

Total Emissions Reduction

................................ ................................ ............ 9

- 8 Figure 9.5.5 Small Commercial A ir

- Cooled Air Conditioners and Heat Pumps:

N2O Total Emissions Reduction

................................ ................................ ... 9

- 9 Figure 9.5.6 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps:

CH4 Total Emissions Reduction

................................ ................................ .... 9

- 9 Figure 9.5.7 Water

- Source Heat Pumps: CO2 Total Emissions Reduction

..................... 9

- 11 Figure 9.5.8 Water

- Source Heat Pumps: SO2 Total Emissions Reducti on

..................... 9

- 119

- ii Figure 9.5.9 Water

- Source Heat Pumps: NOx Total Emissions Reduction

..................... 9

- 12 Figure 9.5.10 Water

- Source Heat Pumps: Hg Total E missions Reduction

........................ 9

- 12 Figure 9.5.11 Water

- Source Heat Pumps: N2O Total Emissions Reduction

..................... 9

- 13 Figure 9.5.12 Water

- Source Heat Pumps: CH4 Total Emissions Reduction

..................... 9

- 139

- 1 CHAPTER 9. EMISSIONS ANALYSIS

9.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the effect of potential energy conservation standards o n power sector and site combustion emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ) and merc ury (Hg). The second component estimates the impacts of 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 margin al 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 poli cies

. 1 The new methodology is described in c hapter

1 1 and in the report “Utility Sector Impacts of Reduced Electricity Demand” ( Coughlin

, 2014). 2

Site emissions of CO 2 and NO X are estimated using emissions intensity fac tors 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 publis hed 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 atmosphere) 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 analy sis (chapter 8 ).

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 o n the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 5 DOE used GWP values o f 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.9

- 2 9.2 AIR QUALITY REGULATI ONS AND EMISSIONS IM PACTS

Each annual version of the AEO incorporates the projected impacts of existing air quality regulations on emissions. AEO 2014 generally represents current Federal and State legis lation 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 District of Columbia (D.C.). SO 2 emissions from 28 eastern states and D.C. were also limited under the Clean Air Interstate Rule (CAIR ) , which created an allowance

- based trading program that that operates along with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). 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 ef fect. On July 6, 2011 EPA issued a replacement for CAIR,

the Cross

- State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011 ). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. 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 emissions allowances and tradable permits. Under existing EPA regulations, any excess SO 2 emissions allowances resulting from the lower electricity demand caused by the impo sition of an efficiency standard could be used to permit offsetting increases in SO 2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions covered by the ex isting cap

- and

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

emissions would occur for SO 2 as a result of standards.

Beginning in 201 6 , however, SO 2 emissions will fall as a result of the Mercury and Air Toxics Standards (MATS) for pow er 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 equiv alent surrogate standard for acid gas HAP. The same controls are used to reduce HAP and

non

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

installed on coal

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

201 4 assumes that, in order to continue operating, coal plants must have either flue gas

c On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circui t 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 st ates 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). Because DOE is using emissions factors based on AEO 2014 , 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.9

- 3 desulfurization or dry sorbent injection systems installed by 201 6 . Both technologies, which are used to reduce acid gas emissions, also reduce SO 2 e missions. 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 ha ve 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 re duce 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 ener gy conservation standards would likely reduce Hg emissions. DOE estimated mercury emissions reductions using emissions factors based on AEO

2014 , which incorporates the MATS.

9.3 POWER SECTOR AND SIT E EMISSIONS FACTORS

The analysis of power sector emissions uses marginal emissions intensity factors derived

from analysis of the AEO 2014 reference and a number of side cases incorporating enhanced equipment efficiencies. To model the impact of a standard, DOE calculates factors that relate a unit reduction to an nual 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 ).

T able 9.3

. 1 and Table 9.3

. 2 present the average power plant emissions factors for selected years. These power plant emissions factors are derived from the emissions factors of the plant types used to supply electricity to commerci al buildings for space cooling and space heating. The average factors for each year take into account the projected shares of each of the sources in total electricity generation. For mall commercial air

- cooled air

- conditioning and heating equipment , the em issions factors refer to the power plants that supply electricity for space cooling. For water

- source heat pumps , the factors are calculated as the weighted average of emissions factors of plants that supply electricity for space cooling (80%) and space he ating (20%).9

- 4 Table 9.3

. 1 Power Plant Emissions Factors ( Small Commercial Air

- Cooled Air Conditioners and Heat Pumps )

Unit* 202 0 2025 2030 2035 2040

CO 2 kg/MWh 695 620 560 514 472

SO 2 g/MWh 649 507 426 357 319

NOx g/MWh 536 450 394 348 316

Hg g/MWh 0.00200 0.00156 0.00132 0.00110 0.00098

N 2 O g/MWh 7.2 7.1 6.9 6.6 6.4

CH 4 g/MWh 50 49 48 46 45

* Refers to site electricity savings

.

Table 9.3

. 2 Power Plant Emi ssions Factors (W ater

- Source Heat Pumps )

Unit* 2020 2025 2030 2035 2040

CO 2 kg/MWh 705 628 567 519 477

SO 2 g/MWh 671 524 441 369 330

NOx g/MWh 549 460 402 355 322

Hg g/MWh 0.00207 0.00162 0.00136 0.00114 0.00102

N 2 O g/MWh 7.2 7.1 6.9 6.6 6.4

CH 4 g/M Wh 50 49 48 46 45

* Refers to site electricity savings.

9.4 UPSTREAM FACTORS

The upstream emissions accounting uses the same approach as the upstream energy accounting described in appendix 8A

. 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 emi ssions. These upstream emissions are defined to include the combustion emissions from the fuel used upstream, the fugitive emissions associated with the fuel used upstream, and the fugitive emissions associated

with the fuel used on site.

Fugitive emiss ions of CO 2 occur during oil and gas production, but are small relative to

combustion emi 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 emissions (particularly for gas production) may be relatively large. Hence, fugitive emissions make up over 99 percent
of total methane emis sions for natural gas, about 95 per cent for coal, and 93 percent for petroleum fuels.9

- 5 U pstream emissions factors account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primary fuels. Fugitive emissions factors for methane from coal mining and natural gas production were estimated based on a review of recent studies compiled by Burnham (2011). 6 This review includes estimates of the difference between fugitive emissions factors for conventional production of natural vs. unconventional (shale or tight gas). These estimates rely in turn on data gathered by EPA under new GHG reporting requirements for the petroleum and natural gas industries. 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 9.4

. 1 presents the electricity upstream emissions factors for selected years

. These were used to estimate the emissions associated with the decreased electricity use. The caps that apply to power sector NO X emissions do not apply to upstream combustion sources.

Table 9.4

. 1 Electricity Upstream Emissions Factors

Unit 202 0 2025 2030 2035 2040

CO 2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO 2 g /MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 368 375 382 387 387

Hg g/MWh 0.000012 0.000011 0.000011 0.000010 0.000010

N 2 O g/MWh 0.252 0.247 0.241 0.234 0.228

CH 4 g/MWh 2149 2195 2216 2248 2255

* Refers to site electricity savings.

9.5 EMISSIONS IMPACT RES ULTS

9.5.1 Sma ll Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

Table 9.5

. 1 presents the estimated cumulative emissions reductions for the lifetime of equipment sold in 2017

- 2046 for the ASHRAE level and 2020

- 204 6 for eac h additional efficiency level for the equipment classes triggered by ASHRAE, and 2019

- 2048 for split system

air conditioners . Negative values indicate that emissions increase.9

- 6 Table 9.5

. 1 Cumulative Emissions Reduction for Potential Standard for Small Three

Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h Efficiency Level

ASHRAE/

0 1 2 3 4 5

Power Sector Emissions

CO 2 (million metric tons) 3.7 8.9 16.8 20.8 24.3 25.9

SO 2 ( thousand ton s ) 2.9 6.9 13.0 16.1 18.8 20.1

NO X (thousand tons) 2.8 6.7 12.6 15.6 18.2 19.4 Hg ( tons) 0.01 0.02 0.04 0.05 0.06 0.06

N 2 O ( thousand tons ) 0.05 0.13 0.24 0.30 0.35 0.37

CH 4 ( thousand tons ) 0.38 0.90 1.69 2.10 2.45 2.61 Upstream Emissions

CO 2 (million metric tons) 0.22 0.54 1.00 1.24 1.45 1.54

SO 2 ( thousand tons ) 0.04 0.09 0.17 0.22 0.25 0.27

NO X (thousand tons) 3.2 7.6 14.3 17.7 20.7 22.0 Hg ( tons) 0.0001 0.0002 0.0004 0.0005 0.0006 0.0006

N 2 O ( thousand tons ) 0.002 0.005 0.009 0.011 0.012 0.013

CH 4 ( thousand tons ) 19 45 83 103 121 128

Total FFC Emissions

CO 2 (million metric tons) 4.0 9.5 17.8 22.1 25.8 27.4

SO 2 ( thousand tons ) 2.9 7.0 13.2 16.4 19.1 20.3

NO X (thousand tons) 6.0 14.3 26.8 33.4 38.9 41.4 Hg ( tons) 0.01 0.02 0.04 0.05 0.06 0.06

N 2 O ( thousand tons ) 0.06 0.13 0.25 0.31 0.36 0.39

CH 4 ( thousand tons ) 19 45 85 105 123 131

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE

Standa rd 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3 standards were adopted.

Figure 9.5

. 1 through Figure 9.5

. 6 show the annual reductions for to tal emissions for each type of emission from each efficiency level . The reductions reflect the lifetime impacts of equipment sold in 2017

- 2046 for the ASHRAE level and 2020

- 2046 for each additional efficiency level for the equipment classes triggered by AS HRAE, and 2019

- 2048 for split system

air conditioners

.9

- 7 Figure 9.5

. 1 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : CO2 Total

Emissions Reduction

Figure 9.5

. 2 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : SO2 Total

Emissions Reduction9

- 8 Figure 9.5

. 3 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : NOx Total

Emissions Re duction

Figure 9.5

. 4 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Hg Total

Emissions Reduction9

- 9 Figure 9.5

. 5 Small Commercial Air

- Cooled Ai r Conditioners and Heat Pumps : N2O Total

Emissions Reduction

Figure 9.5

. 6 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : CH4 Total

Emissions Reduction9

- 10 9.5.2 Water

- Source Heat Pumps

Table 9.5

. 2 presents the estimated cumulative emissions reductions for the lifetime of equipment sold in 20 16

- 20 45 for the ASHRAE level and 2020

- 2045 for each additional efficiency level . Negative values indicate that emissions increase.

Table 9.5

. 2 Cumulative Emissions Reduction for Potential Standard for Water

- Source Heat Pumps Efficiency Level

ASHRAE/

0 * 1 2 3 4 5

Power Sector Emissions

CO 2 (million metric tons)

- 1.4 16.3 30.5 41.5 56.7

SO 2 ( thousand tons )

- 1.1 12.9 24.1 32.9 44.9

NO X (thousand tons)

- 1.1 12.3 23.1 31.4 42.9 Hg ( tons)

- 0.003 0.040 0.074 0.101 0.139

N 2 O ( thousand tons )

- 0.02 0.23 0.44 0.60 0.81

CH 4 ( thousand tons )

- 0.14 1.63 3.06 4.16 5.68 Upstre am Emissions

CO 2 (million metric tons)

- 0.08 0.97 1.81 2.47 3.36

SO 2 ( thousand tons )

- 0.01 0.17 0.32 0.43 0.59

NO X (thousand tons)

- 1.2 13.8 25.9 35.2 48.0 Hg ( tons)

- 0.00003 0.00037 0.00070 0.00095 0.00129

N 2 O ( thousand tons )

- 0.0 01 0.008 0.016 0.021 0.029

CH 4 ( thousand tons )

- 7.0 80.4 150.7 205.0 279.6

Total FFC Emissions

CO 2 (million metric tons)

- 1.5 17.3 32.3 44.0 60.1

SO 2 ( thousand tons )

- 1.1 13.1 24.5 33.3 45.5

NO X (thousand tons)

- 2.3 26.1 48.9 66.6 90.9 Hg ( tons)

- 0.004 0.040 0.075 0.102 0.140

N 2 O ( thousand tons )

- 0.02 0.24 0.45 0.62 0.84

CH 4 ( thousand tons )

- 7.2 82.0 153.8 209.1 285.3

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASH RAE

Standard 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3 standards were adopted.

* There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in

the base case.

Figure 9.5

. 7 through Figure 9.5

. 12 show the annual reductions for total emissions for each type of emission from each efficiency level . The reductions reflect the lifetime impacts of equipment sold in 201 6

- 204 5 for the ASHRAE level and 2020

- 2045 for each additional efficiency level.9

- 11 Figure 9.5

. 7 Water

- Source Heat Pumps : CO2 Total Emissions Reduction

Figure 9.5

. 8 Water

- Source Heat Pumps : SO2 Total Emissions Reduction9

- 12 Figure 9.5

. 9 Water

- Source Heat Pumps : NOx Total Emissions Reduction

Figure 9.5

. 10 Water

- Source Heat Pumps : Hg Total Emissions Reduction9

- 13 Figure 9.5

. 11 Water

- Source Heat Pumps : N2O Total Emissions Reduction

Figure 9.5

. 12 Water

- Sou rce Heat Pumps : CH4 Total Emissions Reduction9

- 14 REFERENCES

1. Energy Information Administration, Annual Energy Outlook 201 4 with Projections to

20 40 , 201 4 Washington, DC. < www.eia.gov/forecasts/aeo/pdf/0383(201 4 ).pdf >

2. Coughlin, K., Utility Sector Imacts 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: Stationa ry 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 Climate 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. Stoc ker, D. Qin, G.

- K. Plattner, M. Tignor, S.K. Allen, J. Boschung, 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. Dun n, 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 Protection 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 the Final New Source Performance Standards , 2012. U.S. Environmental Protection Agency. Washington, DC.10

- i CHAPTER 10. MONETIZATION OF EMIS SION REDUCTIONS BENE FITS

TABLE OF CONTENTS

10.1 INTRODUCTION

................................ ................................ ................................ ......... 10

- 1

10.2 MONETIZING CARBON DI OXIDE EMISSIONS

................................ ..................... 10

- 1

10.2.1 Social Cost of Carbon

................................ ................................ ........................ 10

- 1

10.2.2 Monetizing Carbon Dioxide Emissions

................................ ............................. 10

- 2

10.3 DEVELOPMENT OF SOCIA L COST OF CARBON VAL UES

................................ . 10

- 2

10.3.1 Current Approach and Key Assumptions

................................ .......................... 10

- 2

10.3.2 Limitations of Current Estimates

................................ ................................ ....... 10

- 4

10.4 VALUATION OF OTHER E MISSIONS REDUCTIONS

................................ ........... 10

- 5

10.5 RESULTS

................................ ................................ ................................ ...................... 10

- 5

REFERENCES

................................ ................................ ................................ ........................ 10

- 12

LIST OF TABLES

Table 10.3.1 Annual SCC Values for 2010

- 2050 from 2010 Interagency Report (in 2007$ per Metric Ton)

................................ ................................ ................. 10

- 3

Table 10.3.2 Annual SCC Values for 2010

– 2050 from 2013 Interagency Update (in 2007$ per Metric Ton of CO2)

................................ ................................ .... 10

- 4

Table 10.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under

TSLs for Small Three

- Phase Air

- Cooled Air Cond itioners and Heat Pumps <65,000 Btu/h

................................ ................................ .................. 10

- 6

Table 10.5.2 Estimates of Global Present Value of CO2 Emissions Reduction under

TSLs f or Water

- Source Heat Pumps

................................ ............................ 10

- 7

Table 10.5.3 Estimates of Domestic Present Value of CO2 Emissions Reduction

under TSLs for Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

................................ ................................ .......... 10

- 8

Table 10.5.4 Estimates of Domestic Present Value of CO2 Emissions Reduction

under TSLs for Water

- Source Heat Pumps

................................ ................. 10

- 9

Table 10.5.5 Estimates of Present Value of NOX Emissions Reduction under TSLs

for Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

................................ ................................ ............................ 10

- 10

Table 10.5.6 Estimates of Presen t Value of NOX Emissions Reduction under TSLs

for Water

- Source Heat Pumps

................................ ................................ ... 10

- 1110

- 1 CHAPTER 10. MONETIZA TION OF EMISSION RED UCTIONS BENEFITS

10.1 INTRODUCTION

As part of its assessment of the effe cts of potential energy conservation standards for

small commercial air

- cooled air

- conditioning and heati ng equipment less than 65,000 Btu/h and

water

- source heat pumps , 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 result from each trial standard level ( TSL ) considered for this rulemaking . This chapter summarizes the basis for the monetary values assigned to emissions and presents the

modeled benefits of e stimated reductions

.

10.2 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 so cial cost of carbon (SCC) to represent the broad cost or value associated with producing

— or reducing

— a quantifiable amount of CO 2 emissions

.

10.2.1 Social Cost of Carbon

The SCC is an estimate of the monetized damages associated with an incremental

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 estimates 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 cos ts and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose

of the SCC estimates required by the Executive Order is to enable agenc ies to incorporate the monetized social benefits of reducing CO 2 emissions into cost

- benefit analyses of regulatory actions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a clear understanding that they wil l 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 lite rature 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 assumptions regarding model inputs that was grounded in t he scientific and economic literature. In this way,10

- 2 key uncertainties and model differences transparently and consistently inform the range of SCC

estimates developed for use in the rulemaking process.

10.2.2 Monetizing Carbon Dioxide Emissions

When attempting t o assess the incremental economic impacts of carbon dioxide emissions, the analyst faces several serious challenges. A report from the National Research

Council 2 points out that any assessment will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the effects of changes in climate on the physical and biological environment, and (4) the translation of those environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change raise s serious questions of scien ce, economics, and ethics and should 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 values appropriate for that year. The n the net present value of the benefits can be calculated by multiplying each of the future b enefits 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 reasonabl e for policies that have effects on emissions that are small relative to cumulative global carbon dioxide emissions.

10.3 DEVELOPMENT OF SOCIA L COST OF CARBON VAL UES

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 Federal agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemakin g 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 literature to use as interim values until a more comprehensive anal ysis could be conducted. The outcome of the preliminary assessment was a set of five interim values: global SCC estimates for 2007 (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 results of th at preliminary effort were presented in several proposed and final rules.

10.3.1 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 group considered public comments and further explored the technical l iterature in relevant fields. The interagency group relied on three 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

-10

- 3 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 slightly different approach to calculating how increases in e missions 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 modelers 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 developers’ best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses

. Three values are based on the average SCC from the three integrated assessment models, at discount

rates of 2.5 percent , 3 percent , and 5 percent. The fourth value, which represents the 95 th
percenti le 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 distribution. The values increase in real terms over time

. Addit ionally, 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 emissi ons

. Table 10.3

. 1 presents the values in the 2010 interagency group report

. 4

Table 10.3

. 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 update from the interagency working group (revised November 2013). Table 10.3

. 2 shows the updated

sets of SCC estimates in 5

- year increments from 2010 to 2050. The full set of annual SCC10

- 4 estimates for 2010

– 2050 is presented in appendix 10B 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 10.3

. 2 Annual SCC Values for 2010

– 2050 from 2013 Interagency Update (in 2007 $

per Metric Ton of CO2)

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

10.3.2 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 summary, 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 2014 $. For the four SCC values, the values of emissions in 2015 were $ 12.2 , $ 41.2 , $ 63.4 , and $ 121 per metric ton avoided (values expressed in 2014 $). DOE derived values after 2050 using the relevant growth rates for 2040

– 2050 in the interagency update.

DOE multiplied the CO 2 emissions reduction est imated for each year by the SCC value for that year under each discount rate . To calculate a present value of the stream of monetary10

- 5 values, DOE discounted the values in each of the four cases using the same discount rate that had

been used to obtain the S CC values in each case.

10.4 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 9 , 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 damage found in the scienti fic literature.

E stimates suggest a wide range of monetary values, from $ 484 to $ 4,971 per ton ( in 2014 $). 5

DOE calculated monetary benefits using a medi an value for NO X emissions of $ 2,727 per short ton (in 201 4 $), 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

.

10.5 RESULTS

Table 10.5

. 1 and Table 10.5

. 2 p resent the global values of CO 2 emissions reductions from

standards for small commercial air

- cooled air conditioners and heat pumps and water

- source heat

pumps for each efficiency level

.10

- 6 Table 10.5

. 1 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs

for Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

Efficiency

Level SC C Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate,

95 th percentile*

Million 2014 $

Primary Energy Emissions

ASHRAE/0 $ 24 $ 115 $ 184 $ 356

1 $ 57 $ 273 $ 437 $ 846

2 $ 110 $ 521 $ 832 $ 1

, 613

3 $ 136 $ 646 $ 1

, 031 $ 1

, 999

4 $ 159 $ 754 $ 1

, 204 $ 2

, 334

5 $ 170 $ 804 $ 1

, 283 $ 2

, 489

Upstream Emissions

ASHRAE/0 $ 1 $ 7 $ 11 $ 21

1 $ 3 $ 16 $ 26 $ 50

2 $ 6 $ 31 $ 49 $ 95

3 $ 8 $ 38 $ 61 $ 118

4 $ 9 $ 44 $ 71 $ 138

5 $ 10 $ 47 $ 76 $ 147

Full

- Fuel

- Cycle Emissions

ASHRAE/0 $ 25 $ 122 $ 195 $ 377

1 $ 60 $ 289 $ 463 $ 896

2 $ 116 $ 552 $ 881 $ 1

, 708

3 $ 144 $ 684 $ 1

, 092 $ 2

, 117

4 $ 168 $ 799 $ 1

, 275 $ 2

, 472

5 $ 179 $ 851 $ 1

, 359 $ 2

, 635

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE

Standard 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3

standards were adopted.

* For each of the four cases , the corresponding global SCC value for emissions in 2015 is $ 12.2, $41.2, $63.4, and $121 per metric ton (2014$)10

- 7 Table 10.5

. 2 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs

for Water

- Source Heat Pumps

Efficiency

Level SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate,

95 th percentile*

Million 2014 $

Primary Energy Emissions

ASHRAE/0 **

- - - -
1 $ 9 $ 44 $ 71 $ 137

2 $ 106 $ 504 $ 805 $ 1

, 560

3 $ 198 $ 943 $ 1

, 507 $ 2

, 922

4 $ 270 $ 1

, 285 $ 2

, 052 $ 3

, 979

5 $ 370 $ 1

, 758 $ 2

, 808 $ 5

, 446

Upstream Emissions

ASHRAE/0 **

- - - -
1 $ 1 $ 3 $ 4 $ 8

2 $ 6 $ 30 $ 47 $ 92

3 $ 12 $ 55 $ 89 $ 172

4 $ 16 $ 75 $ 121 $ 234

5 $ 21 $ 103 $ 165 $ 320

Full

- Fuel

- Cycle Emissions

ASHRAE/0 **

- - - - 1 $ 10 $ 47 $ 75 $ 145

2 $ 112 $ 533 $ 852 $ 1

, 652

3 $ 209 $ 999 $ 1

, 596 $ 3

, 094

4 $ 285 $ 1

, 360 $ 2

, 173 $ 4

, 213

5 $ 391 $ 1

, 862 $ 2

, 973 $ 5

, 765

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE

Standard 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3

standards were adopted. * For each of the four cases, the corresponding global SCC value for emissions in 2015 is $ 12.2, $41.2, $63.4, and $121 per metric ton (2014$)

. ** There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in

the base case.

After calculating global values of CO 2 emissions reductions for each efficiency level

, 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 10.5

. 3 and Table 10.5

. 4

.10

- 8 Table 10.5

. 3 Estimates of Domestic Present Value of CO2 Emissions Reduction under

TSLs for Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

Efficiency

Level SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate,
95 th percentile*

Million 2014 $

Primary Energy Emissions

ASHRAE/0 $ 1.7 to $ 5.5 $ 8.1 to $ 26.5 $ 12.9 to $ 42.3 $ 24.9 to $ 81.9

1 $ 4.0 to $ 13.1 $ 19.1 to $ 62.8 $ 30.6 to $ 100.5 $ 59.2 to $ 194.5

2 $ 7.7 to $ 25.3 $ 36.5 to $ 119.9 $ 58.2 to $ 191.3 $ 112.9 to $ 371.0

3 $ 9.5 to $ 31.2 $ 45.2 to $ 148.5 $ 72.2 to $ 237.1 $ 139.9 to $ 459.7

4 $ 11.1 to $ 36.5 $ 52.8 to $ 173.5 $ 84.3 to $ 276.9 $ 163.4 to $ 536.8

5 $ 11.9 to $ 39.0 $ 56.3 to $ 184.9 $ 89.8 to $ 295.2 $ 174.2 to $ 572.4

Upstream Emissions

ASHRAE/0 $ 0.1 to $ 0.3 $ 0.5 to $ 1.6 $ 0.8 to $ 2.5 $ 1.5 to $ 4.9

1 $ 0.2 to $ 0.8 $ 1.1 to $ 3.7 $ 1.8 to $ 6.0 $ 3.5 to $ 11.6

2 $ 0.4 to $ 1.5 $ 2.1 to $ 7.1 $ 3.4 to $ 11.3 $ 6.7 to $ 21.9

3 $ 0.6 to $ 1.8 $ 2.7 to $ 8.8 $ 4.3 to $ 14.0 $ 8.3 to $ 27.1

4 $ 0.6 to $ 2.1 $ 3.1 to $ 10.2 $ 5.0 to $ 16.4 $ 9.6 to $ 31.7

5 $ 0.7 to $ 2.3 $ 3.3 to $ 10.9 $ 5.3 to $ 17.4 $ 10.3 to $ 33.7

Full

- Fuel

- Cycle Emissions

ASHRAE/0 $ 1.8 to $ 5.8 $ 8.5 to $ 28.0 $ 13.6 to $ 44.8 $ 26.4 to $ 86.7

1 $ 4.2 to $ 13.9 $ 20.3 to $ 66.6 $ 32.4 to $ 106.5 $ 62.7 to $ 206.0

2 $ 8.1 to $ 26.7 $ 38.6 to $ 126.9 $ 61.7 to $ 202.6 $ 119.6 to $ 392.8

3 $ 10.1 to $ 33.1 $ 47.9 to $ 157.3 $ 76.4 to $ 251.2 $ 148.2 to $ 486.9

4 $ 11.8 to $ 38.6 $ 55.9 to $ 183.7 $ 89.3 to $ 293.3 $ 173.0 to $ 568.5

5 $ 12.6 to $ 41.3 $ 59.6 to $ 195.8 $ 95.1 to $ 312.6 $ 184.5 to $ 606.2

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE

Standard 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3

standards were adopted.

* For each of the four cases , the corresponding SCC value for emissions in 2015 is 12.2, $41.2, $63.4, and $121 per metric ton (2014$)

.10

- 9 Table 10.5

. 4 Estimates of Domestic Present Value of CO2 Emissions Reduction under

TSLs for Water

- Sou rce Heat Pumps

Efficiency

Level SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate,

95 th percentile*

Million 2014 $

Primary Energy Emissions

ASHRAE/0 **

- - - -
1 $ 0.6 to $ 2.1 $ 3.1 to $ 10.2 $ 4.9 to $ 16.2 $ 9.6 to $ 31.5

2 $ 7.4 to $ 24.3 $ 35.3 to $ 115.8 $ 56.3 to $ 185.0 $ 109.2 to $ 358.8

3 $ 13.8 to $ 45.5 $ 66.0 to $ 217.0 $ 105.5 to $ 346.6 $ 204.5 to $ 672.1

4 $ 18.9 to $ 62.0 $ 89.9 to $ 295.5 $ 143.6 to $ 472.0 $ 278.5 to $ 915.1

5 $ 25.9 to $ 85.0 $ 123.1 to $ 404.4 $ 196.5 to $ 645.8 $ 381.2 to $ 1

, 252.5

Upstream Emissions

ASHRAE/0 **

- - - -
1 $ 0.0 to $ 0.1 $ 0.2 to $ 0.6 $ 0.3 to $ 1.0 $ 0.6 to $ 1.8

2 $ 0.4 to $ 1.4 $ 2.1 to $ 6.8 $ 3.3 to $ 10.9 $ 6.4 to $ 21.1

3 $ 0.8 to $ 2.6 $ 3.9 to $ 12.8 $ 6.2 to $ 20.4 $ 12.0 to $ 39.5

4 $ 1.1 to $ 3.6 $ 5.3 to $ 17.4 $ 8.5 to $ 27.8 $ 16.4 to $ 53.8

5 $ 1.5 to $ 4.9 $ 7.2 to $ 23.7 $ 11.6 to $ 38.0 $ 22.4 to $ 73.5

Full

- Fuel

- Cycle Emissions

ASHRAE/0 **

- - - - 1 $ 0.7 to $ 2.3 $ 3.3 to $ 10.8 $ 5.2 to $ 17.2 $ 10.1 to $ 33.3

2 $ 7.8 to $ 25.7 $ 37.3 to $ 122.6 $ 59.6 to $ 195.9 $ 115.6 to $ 379.9

3 $ 14.7 to $ 48.2 $ 69.9 to $ 229.7 $ 111.7 to $ 367.1 $ 216.6 to $ 711.6

4 $ 20.0 to $ 65.6 $ 95.2 to $ 312.8 $ 152.1 to $ 499.7 $ 294.9 to $ 968.9

5 $ 27.4 to $ 90.0 $ 130.3 to $ 428.1 $ 208.1 to $ 683.7 $ 403.6 to $ 1

, 326.0

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE

Standard 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3

standards were adopted. * For each of the four cases, the corresponding SCC value for emissions in 2015 is 12.2, $41.2, $63.4, and $121 per metric ton (2014$)

. ** There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in

the base case.

Table 10.5

. 5 and Table 10.5

. 6 present the present

- value s of the c umulative NO X emissions reductions from standards for small commercial air

- cooled air conditioners and heat pumps and water

- source heat pumps for each efficiency level

. 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.10

- 10 Table 10.5

. 5 Estimates of Present Value of NOX Emissions Reduction under TSLs for

Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000

Btu/h

Efficiency

Level 3% Discount

Rate 7% Discount

Rate

million 2014 $ Primary Energy Emissions

ASHRAE/

0 $ 3.5 $ 1.5

1 $ 8.2 $ 3.5

2 $ 16 $ 7.0

3 $ 20 $ 8.6

4 $ 23 $ 10

5 $ 25 $ 11 Upstream Emissions

ASHRAE/

0 $ 3.8 $ 1.5

1 $ 9.0 $ 3.6

2 $ 17 $ 7.2

3 $ 22 $ 8.9

4 $ 25 $ 10

5 $ 27 $ 11

Full

- Fuel

- Cycle Emissions

ASHRAE/

0 $ 7.3 $ 3.0

1 $ 17 $ 7.1

2 $ 33 $ 14

3 $ 41 $ 17

4 $ 48 $ 20

5 $ 51 $ 22

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE

Standard 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3

standards were adopted.10

- 11 Table 10.5

. 6 Estimates of Present Value of NOX Emissions Reduction under TSLs for

Wate r

- Source Heat Pumps

Efficiency

Level 3% Discount

Rate 7% Discount

Rate

million 2014 $ Primary Energy Emissions

ASHRAE/

0 * - -

1 $ 1.4 $ 0.6

2 $ 15 $ 6.6

3 $ 29 $ 12

4 $ 39 $ 17

5 $ 54 $ 23 Upstream Emissions

ASHRAE/

0 * - -

1 $ 1.5 $ 0.6

2 $ 17 $ 6.7

3 $ 31 $ 13

4 $ 42 $ 17

5 $ 58 $ 24

Full

- Fuel

- Cycle Emissions

ASHRAE/

0 * - -

1 $ 2.8 $ 1.2

2 $ 32 $ 13

3 $ 60 $ 25

4 $ 82 $ 34

5 $ 112 $ 47

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE

Standard 90.1

- 201 3 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1

- 201 3

standards were adopted.

* There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in

the base case.10

- 12 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 Use , 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, February 2010.

United States Governmen t. 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 , 2006. Washington, DC.

< www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_fina l_report.pdf >10A

- i APPENDIX 10A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866

TABLE OF CO NTENTS

10A.1 EXECUTIVE SUMMARY

................................ ................................ .................... 10A

- 1

10A.2 MONETIZING CARBON DIOXIDE EMISS IONS

................................ .............. 10A

- 2

10A.3 SOCIAL COST OF CARBO N VALUES USED IN PAS T REGULATORY

ANALYSES

................................ ................................ ................................ ............................ 10A

- 4

10A.4 APPROACH AND KEY ASS UMPTIONS

................................ ........................... 10A

- 5

10A.4.1 Integrated Assessment Models

................................ ................................ ........... 10A

- 5

10A.4.2 Global versus Domestic Measures of SCC

................................ ....................... 10A

- 11

10A.4.3 Valuing Non

- CO2 Emissions

................................ ................................ ........... 10A

- 13

10A.4.4 Equilibrium Climate Sensitivity

................................ ................................ ....... 10A

- 13

10A.4.5 Socioeconomic and Emissions Trajectories

................................ ...................... 10A

- 16

10A.4.6 Discount Rate

................................ ................................ ................................ .... 10A

- 19

10A.5 REVISED SCC ESTIMATE S

................................ ................................ .............. 10A

- 26

10A.6 LIMITATIONS OF THE A NALYSIS

................................ ................................ . 10A

- 32

10A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPA CTS AND DAMAGE

FUNCTIONS

................................ ................................ ................................ ........................ 10A

- 34

10A.8 CONCLUSION

................................ ................................ ................................ ..... 10A

- 37

REFERENCES

................................ ................................ ................................ ..................... 10A

- 38

10A.9 ANNEX

................................ ................................ ................................ ................. 10A

- 43

10A.9.1 Other (non

- CO 2 ) gases

................................ ................................ ...................... 10A

- 44

10A.9.2 Extrapolating Emissions Projections to 2300

................................ ................... 10A

- 46

LIST OF TABLES

Table 10A.1.1 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ .. 10A

- 2

Table 10A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity

Distributions

................................ ................................ ............................ 10A

- 14

Table 10A.4.2 Socioeconomic and Emissions Projections from Select EMF

- 22

Reference Scenarios

................................ ................................ ................ 10A

- 18

Table 10A.5.1 Disaggregated Social Cost of CO 2 Values by Model, Socioeconomic Trajectory, and Discount Rate for 2010 (in 2007 dollars)

...................... 10A

- 29

Table 10A.5.2 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ 10A

- 31

Table 10A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates

between 2010 and 2050

................................ ................................ .......... 10A

- 31

Table 10A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

............ 10A

- 35

Table 10A.9.1 Annual SCC Values: 2010

– 20 50 (in 2007 dollars)

................................ 10A

- 43

Table 10A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 10A

- 54

Table 10A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 10A

- 5510A

- ii Table 10A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 10A

- 56

Table 10A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates

............. 10A

- 57

LIST OF FIGURE S

Figure 10A.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

................................ ................................ .......................... 10A

- 10 Figure 10A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE

................................ ................................ .................. 10A

- 11 Figure 10A.4.3 Estimates of the Probability Density Function for Equilibrium Climate Sensitivity (°C)

................................ ................................ ....................... 10A

- 16 Figure 10A.5.1 Level of Global GDP across EMF Scenarios

................................ ......... 10A

- 30 Figure 10A.9.2 Sulfur Dioxide Emission Scenarios

................................ ........................ 10A

- 46 Figure 10A.9.3 Global Population, 2000

- 230 0 (Post

- 2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.)

................................ ................................ ................................ ...... 10A

- 48 Figure 10A.9.4 World GDP, 2000

- 2300 (Post

- 2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in the year 2300)

........... 10A

- 49 Figure 10A.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)

................................ ................. 10A

- 50 Figure 10A.9.6 Global Net Land Use CO 2 Emissions, 2000

- 2300 (Post

- 2100 extrapolations assume emissions decline linearly, reaching zero in the year 2200)

................................ ................................ ............................... 10A

- 51 Figure 10A.9.7 Global Non

- CO 2 Radiative Forcing, 2000

- 2300 (Post

- 2100 extrapolations assume constant non

- CO 2 radiative forcing after 2100)

. 10A

- 52 Figure 10A.9.8 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000

- 2300 (Post

- 2100 extrapolations assume de cline in CO 2 /GDP growth

rate over 2090

- 2100 is maintained through 2300)

................................ .. 10A

- 53 Figure 10A.9.9 Histogram of Global SCC Estimates i n 2010 (2007$/ton CO 2 ), by

discount rate

................................ ................................ ............................ 10A

- 5610A

- 1 APPENDIX 10A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECUTIVE ORDER 1286 6 a

10A.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the social benefits of reducing carbon dioxide (CO 2 ) emissions into cost

- benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a clear understanding that they should be updated over time to reflect increasing knowledge of the

science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, hu man health, property damages from increased flood risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagency process that developed these SCC estimates. Technical experts from numerous agencies met on a regular basis to consider public comments, explore the technical literature in relevant fields, and discuss key model inputs and assumptions. The main objective of this process was to develop a range of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic literatures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

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 Budg et Office of Science and Technology Policy Department of the Treasury10A

- 2 The interagency group select ed four SCC values for use in regulatory analyses. Three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95 th percentile SCC estimate across all three models at a 3 percent discount rate, is included to represent higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution.

Table 10A.1.1 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollar s)

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

10A.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

Academ ies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact o f 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 que stions of science, economics, and ethics and should be viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive

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).10A

- 3 Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to make it possible for agencies to incorporate the social benefits from reducing carbon dioxide

emissions i nto 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 eac h 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 approximati on that is reasonable for policies that have effects on emissions that are small relative to cumulative global carbon dioxide emissions. For policies that have a large (non

- marginal) impact on global cumulative emissions, there is a separate question of w hether the SCC is an appropriate tool for calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore

the technical literature in rel evant fields, and discuss key inputs and assumptions in order to generate SCC estimates. Agencies that actively participated in the interagency process include the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy,

Tran sportation, and Treasury. This process was convened by the Council of Economic Advisers and the Office of Management and Budget, with active participation and regular input from the Council on Environmental Quality, National Economic Council, Office of En ergy 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 uncert ainties and model differences can more transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $5, $ 21, $35, and $65 (in 2007 dollars). The first three estimates are based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, respectively. The fourth value is included to represent the hig her

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 incre ases to $24 per ton of CO 2 in 2015 and $26 per ton of CO 2 in 2020. See

section 16

- A .5 for the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the interagency process is committed to updating these

estimates as the science and economic understanding of climate change and its impacts on10A

- 4 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.

10A.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 resulting from a unit change in carbon dioxide emissions, while a global SCC value is meant to reflect the value of damages worldwide.

A 2008 regulation propos ed by DOT assumed a domestic SCC value of $7 per ton CO 2 (in 2006 dollars) for 2011 emission reductions (with a range of $0

- $14 for sensitivity analysis), also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a

dom estic 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 revisio n. 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 ho w 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 comprehe nsive 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 versions of three integrated assessment

models

— DICE, PAGE, and FUND

— at approximately 3 and 5 percent discount rates. The $55 and $10 values were derived b y adjusting the published estimates for uncertainty in the discount rate (using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, respectively. The $19 value was chosen as a central value between the $5 and $33 per ton

estima tes. All of these values were assumed to increase at 3 percent annually to represent growth in incremental damages over time as the magnitude of climate change increases.10A

- 5 These interim values represent the first sustained interagency effort within the U.S. government to develop an SCC for use in regulatory analysis. The results of this preliminary effort were presented in several proposed and final rules and were offered for public comment in connection with proposed rules, including the joint EPA

- DOT fuel economy and CO 2 tailpipe emission proposed rules.

10A.4 APPROACH AND KEY ASS UMPTIONS

Since the release of the interim values, the interagency group has reconvened on a regular basis to generate improved SCC estimates. Specifically, the group has considere d 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 uncertainti es remain, and that current SCC estimates should be treated as provisional and revisable

, since they will evolve with improved scientific and economic understanding. The interagency group also recognizes that the existing models are imperfect and incomplet e. The National Academy of Science (2009) points out that there is tension between the goal of producing quantified estimates of the economic damages from an incremental ton of carbon and the limits of existing efforts to model these effects. Throughout this document, we highlight a number of concerns and problems that should be addressed by the research community, including research programs housed in many of the agencies participating in the interagency process to estimate the SCC

.

The U.S. Governm ent 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 t he limitations of the analysis and calling for further research take on exceptional significance. The interagency group offers the new SCC values with all due humility about the uncertainties embedded in them and with a sincere promise to continue work to improve them.

10A.4.1 Integrated Assessment Models

We rely on three integrated assessment models (IAMs) commonly used to estimate the SCC: the FUND, DICE, and PAGE models. 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).

c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolv ed 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 m arginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, originally to study international capital transfers in climate policy . is now widely used to study climate impacts ( e.g. , Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).10A

- 6 These models are useful because they combine climate processes, economic growth, and

feedbac ks 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 sty lized, reduced

form approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the possible advantages of this approach). Other IAMs may better reflect the complexity of the science in their modeling frameworks but do not link physical impacts to economic damages. There is currently a limited amount of research linking climate impacts to economic damages, which makes this exercise even more difficult. Underlying the three IAMs selected for this exercise are a number of simplifying assu mptions and judgments reflecting the various modelers’ best attempts to synthesize the available scientific and economic research characterizing these relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse

concentrations , atmospheric concentrations into changes in temperature, and changes in temperature into economic damages. The emissions projections used in the models are based on specified socioeconomic (GDP and population) pathways. These emissions are translated int o 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 t ranslate warming into damages. Finally, transforming the stream of economic damages over time into a single value requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result

in ch anges in economic damages. In PAGE, for example, the consumption

- equivalent damages in each period are calculated as a fraction of GDP, depending on the temperature in that period relative to the pre

- industrial average temperature in each region. In FUND, damages in each period also depend on the rate of temperature change from the prior period. In DICE, temperature affects both consumption and investment. We describe each model in greater detail here. In a later section, we discuss key gaps in how the models account for various scientific and economic processes ( e.g. the probability of catastrophe, and the ability to adapt to climate change and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key objective of the interagency process was to enable a consistent exploration of the three models while respecting the different approaches to quantifying damages taken by the key modelers in

the field. An extensive review of the literature was conduct ed to select three sets of input parameters for these models: climate sensitivity, socioeconomic and emissions trajectories, and discount rates. A probability distribution for climate sensitivity was specified as an input into all three models. In addition, the interagency group used a range of scenarios for the socioeconomic parameters and a range of values for the discount rate. All other model features were left unchanged, relying on the model developers’ best estimates and judgments. In DICE, these parameters are handled deterministically and represented by fixed constants; in PAGE, most

param eters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically.10A

- 7 The sensitivity of the results to other aspects of the models ( e.g. the carbon cycle or damage function) is also important to explore in the context of future revisions to the SCC but has not been incorporated into these estimates. Areas for future research are highlighted at the

end of this document.

The DICE Model

T he 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 natu ral capital today through reductions in emissions

— implying reduced consumption

— harmful effects of climate change can be avoided and future consumption thereby increased.

For purposes of estimating the SCC, carbon dioxide emissions are a function of glo bal GDP and the carbon intensity of economic output, with the latter declining over time due to technological progress. The DICE damage function links global average temperature to the overall impact on the world economy. It varies quadratically with tem perature change to capture the more rapid increase in damages expected to occur under more extreme climate change, and is calibrated to include the effects of warming on the production of market and nonmarket goods and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vulnerable market sectors” (based primarily on changes in energy use), human health (based on

climate

- related diseases, such as malaria and dengue fever, and pollution), non

- market amenities (base d on outdoor recreation), and human settlements and ecosystems. The DICE damage function also includes the expected value of damages associated with low probability, high impact “catastrophic” climate change. This last component is calibrated based on a survey of experts (Nordhaus 1994). The expected value of these impacts is then added to the other market

and non

- market impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is included implicitl y through the choice of studies used to calibrate the aggregate damage function. For example, its agricultural impact estimates assume that farmers can adjust land use decisions in response to changing climate conditions, and its health impact estimates assume improvements in healthcare over time. In addition, the small impacts on forestry, water systems, construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these sectors (Nordhaus and Boyer, 2000; Warren et al., 2006 ). Costs of resettlement due to sea level rise are incorporated into damage estimates, but their magnitude is not clearly reported. Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective adaptation, and largely ignores adapta tion costs."

Note that the damage function in DICE has a somewhat different meaning from the damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because damages in a given year reduce investment in that year, damages propagate forwar d in time and10A

- 8 reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages 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. Dama ges are expressed as power functions of temperature change. The exponents of the damage function are the same in all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage sub

function. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a “discontinuity” ( i.e. , a catastrophic event) is assumed to increase with temperature above a specifie d threshold. The threshold temperature, the rate at which the probability of experiencing a discontinuity increases above the threshold and the magnitude of the resulting catastrophe are all modeled probabilistically.

Adaptation is explicitly included i n PAGE. Impacts are assumed to occur for temperature increases above some tolerable level (2°C for developed countries and 0°C for developing countries for economic impacts, and 0°C for all regions for non

- economic impacts), but adaptation is assumed to r educe these impacts. Default values in PAGE2002 assume that the developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of

th eir economic impacts. All regions are assumed to be able to mitigate 25 percent of the non

economic impacts through adaptation (Hope 2006).

The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately

calibrated dama ge functions for eight market and nonmarket sectors: agriculture, forestry, water, energy (based on heating and cooling demand), sea level rise (based on the value of land lost and the cost of protection), ecosystems, human health (diarrhea, vector

- borne d iseases, and cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a different functional form, and is calculated separately for sixteen geographic regions. In some impact sectors, the fraction of output lost or gained du e to climate change depends not only on

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 section): we assumed a fixed rate of savings of 20%, and we re

calibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the absence of warming that ex actly matched the EMF scenarios.10A

- 9 the absolute temperature change but also on the rate of temperature change and level of regional

income. e In the forestry and agricultural sectors, economic damages also depend on CO 2 concentrations.

Tol (2009) di scusses 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

scenar ios, biodiversity loss, and effects on economic development and political violence. With regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of changes or what effects they would have are not well

- understood, althoug h the chance of any one of them happening seems low. But they do have the potential to happen relatively quickly, and if they did, the costs could be substantial. Only a few studies of climate change have examined

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as energy and human health, where wealthier populations are assumed to be less vulnerable to climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those due to the rate of temperature change (damages are always positive); (2) those due to the level of temperature change (damages can be positive or negative depending on region and temperature); and (3) those from CO 2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate change happens more slowly. The combined effect of CO 2 fertilization in the agricultural sector, positive impacts to some regions from higher temperatures, and sufficiently slow increases in temperature across these sectors can result in negative economic damages from c limate change.

Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of how to represent the effects of climate change (represented by the increase in global

- average surface temperature) on the consumption

- equivalent value of both market and non

- market goods (represented as a fraction of global GDP). We recognize that these representations are incomplete and highly uncertain. But given the paucity of data linking the physical impacts to economic damages, we were not able to identify a better way to translate changes in climate into net economic damages, short of launching our own research program.

The damage functions for the three IAMs are presented in Figure 1 0A.4.1 and Figure

10A.4.2 , using the modeler’s default scenarios and mean input assumptions. There are significant differences between the three models both at lower ( Figure 10A.4.2 ) and higher ( Figure 10A.4.1 ) increases in global

- average temperature.

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 benefit s in those regions at low to moderate levels of warming (Warren et al., 2006).10A

- 10 Figure 10A.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

The lack of agreement among the models at lower temperature increases is underscored by the fact that the damages from FUND are well below the 5 th percentile estimated by PAGE, while the damages estimated by DICE are roughly equal to the 95 th percentil e 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 perce nt of the 2010 SCC value in DICE is due to damages that occur in years when the temperature is less than or equal to 3 °C. This increases to approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences und erscore 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 highlight s the need for additional research. As knowledge improves, the Federal government is

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 spec ific combination of climate sensitivity,

socioeconomic , and emissions parameters will produce a different realization of damages for each IAM. The damage functions represented in Figures 1A and 1B are the outcome of default assumptions. For instance, und er alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater than 3

°C.10A

- 11 committed to exploring how these (and other) models can be modified to incorporate more accurate estimates of damages.

Figure 10A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE

10A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current attention on a global measure of SCC. This approach is the same as that taken for the interim values, but it otherwise represents a departure from past practices, which tended to put greater emphasis on a domestic measure of SCC (limited to impacts of climate change experienced within U.S. borders). As a matter of law, consideration of both global and domestic values is generally permissible; the relevant statutory provisions are usually ambiguous and allow

selection of either measure. g

Global SCC

Under current OMB guidance contained in Circular A

- 4, analysis of economically significant propose d and final regulations from the domestic perspective is required, while analysis from the international perspective is optional. However, the climate change problem is highly unusual in at least two respects. First, it involves a global externality: emi ssions of most greenhouse gases contribute to damages around the world even when they are emitted in the

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.10A

- 12 United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages caused by GHG emissions. Second, climate change presents a problem that the United States alone cannot solve. Even if the United States were to reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid substantial climate change. Other countries would also need to take action to reduce emissions if significant changes in the global climate are to be avoided. Emphasizing the need for a global solution to a global problem, the United States has been actively involved in seeking international agreem ents 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 benef its from reducing U.S. emissions is preferable.

When quantifying the damages associated with a change in emissions, a number of analysts ( e.g. , Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in consumption across regions. This weighting takes into account the relative reductions in wealth in different regions of the world. A per

- capita loss of $500 in GDP, for instance, is weighted more heavily in a country with a per

- capita GDP of $2,000 than in one with a per

- capita GDP of $4 0,000. The main argument for this approach is that a loss of $500 in a poor country causes a greater reduction in utility or welfare than does the same loss in a wealthy nation. Notwithstanding the theoretical claims on behalf of equity weighting, the in teragency 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 t he appropriate approach.

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the relatively few region

- or country

- specific estimates of the SCC in the literature. One potential

source of estimates comes fro m the FUND model. The resulting estimates suggest that the ratio of domestic to global benefits of emission reductions varies with key parameter assumptions. For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7

- 10 percent of th e global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP lost due to climate change is assumed to be similar across countries, the domestic benefit would be proportional to the U.S. share of global GDP, which is currently about 23 percent. 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

h It is plausible that a loss of $X inflicts more serious harm on a poo r nation than on a wealthy one, but development of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor nation than on a wealthy one. Even if equity weighting

— for both the costs and benefits of emissions reductions

— is appropriate when considering the utility or welfare effects of international action, the interagency group concluded that it should not be used in developing an SCC for use in regulatory policy at this time.

i Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report

.10A

- 13 effects. Reported domestic values should use this r ange. 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 t o update its approach.

10A.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, perf luorocarbons, and sulfur hexafluoride. The climate impact of these gases is commonly discussed in terms of their 100

- year global warming potential (GWP). GWP measures the ability of different gases to trap heat in the atmosphere ( i.e. , radiative forcing per unit of mass) over a particular timeframe relative to CO 2 . However, because these gases differ in both radiative forcing and atmospheric lifetimes, their relative damages are not constant over time. For example, because methane has a short lifetime, its impacts occur primarily in the near term and thus are not discounted as heavily as those caused by longer

- lived gases. Impacts other than temperature change also vary across gases in ways that are not captured by GWP. For instance, CO 2 emissions, unl ike methane and other greenhouse gases, contribute to ocean acidification. Likewise, damages from methane emissions are not offset by the positive effect of

CO 2 fertilization. Thus, transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accurate estimates of the

social costs of non

- CO 2 gases.

In light of these limitations, and the significant contributions of non

- CO 2 emissions to climate change, further research is required to link non

- CO 2 emissions to economic impacts. Such work would feed into efforts to develop a monetized value of reductions in non

- CO 2 greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the interagency group hopes to develop methods to value these other greenhouse gases. The goal is to develop these estimates by the time we issue revised SCC estimates for carbon dioxide

emissions.

10A.4.4 Equilibrium Climate Sensiti vity

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 t o pre

- industrial levels (or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties in this important parameter have received substantial attention in the peer

- reviewed literature.

j The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas concentrations over the short to medium term (up to 100

- 200 years), but it does not include long

- term feedback effects due to possible large

- scale changes in ice sheets or the biosphere, which occur on a time scale of many hundreds to thousands of years ( e.g. Hansen et al. 2007).10A

- 14 The most authoritative statement ab out equilibrium climate sensitivity appears in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including observed climate change and the strength of known feedbacks simulated in [global climate models], we conclude that the global mean equilibrium warming for doubling CO 2 , or ‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C, with a most likely value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. 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

g enerally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et al., 2007, p 799)

After consulting with several lead authors of this chapter of the IPCC report, the interagency workgroup selected four candidate probability distributions and calibrated them to be consistent with the above statement: Roe and Baker (2007), log

- normal, gamm a, and Weibull.

Table 10A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 10A.4.1 Summary Statistics for Four Calibrated Climate Sensit ivity 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

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

“l ikely” as greater than 66 percent probability (Le Treut et al.2007). “Very likely” indicates a greater than 90 percent probability.10A

- 15 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 Baker distribution from the four candidates for two reasons. First, the Roe and Baker distribution is the only one of the four that is based on a theoretical understanding of the response of the climate system to increased greenhou se gas concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general shape. The Roe and Baker distribution results from three assumptions about climate response: (1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback factors are proportional to the change in surface temperature; and (3) uncertainties in feedback factors are normally dis tributed. There is widespread agreement on the first point and the second and third points are common assumptions.

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that “values substantially higher than 4.5°C still can not 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 Newbo ld and Daigneault (2009). It is also closer to the mean (7.5 °C) and median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006)

than are the 95 th percentiles of the three other calibrated distributions (5.2

- 6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the probability of equilibrium climate sensitivity being greater than 1.5 °C is almo st 99 percent, is not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it reflects a greater degree of certainty about very low values of ECS than was expressed by the IPCC.

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 on this point. For the distributions we considered, the median was between the mode and the

mean. For the Roe and Baker distributi on, setting the median equal to 3°C, rather than the mode or mean, gave a 95 th
percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode equal to 3°C produced 95 th percentiles of 5.6 and 8.6 °C, resp ectively, 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.10A

- 16 Figure 10A.4.3 Estimates of the Probability D ensity 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 10A.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

10A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of

socioeconom ic 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 p ay 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 poli cy simulations ( e.g. , SRES 2000, CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed to select scenarios that span most of the plausible ranges of outcomes for these variables.

m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed line, anthropogenic forcings onl y), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. (2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown

are the 5

- 95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.10A

- 17 To accomplish this task in a trans parent way, we decided to rely on the recent Stanford Energy Modeling Forum exercise, EMF

- 22. EMF

- 22 uses ten well

- recognized models to evaluate substantial, coordinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, and emission trajectories are internally consistent for each model and scenario evaluated. The EMF

- 22 modeling effort also is preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the

EMF

- 22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are
recent, peer

- reviewed, published, and publicly available.

To esti mate the SCC for use in evaluating domestic policies that will have a small effect on global cumulative emissions, we use socioeconomic and emission trajectories that span a range of plausible scenarios. Five trajectories were s e lected from EMF

- 22 (see Table 10A.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) conc entrations 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 exercise, we selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories

from each of these fou r models. For the 550 ppm CO 2 e scenario, we averaged 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 al l the models converge: a 550 ppm, not to exceed, full participation scenario.10A

- 18 Table 10A.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 prov ed challenging to do in an analytically rigorous way given the dearth of information on the likelihood of a full range of

future socioeconomic 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 differen t price levels across countries, so it more accurately describes relative standards of living across countries. MERs tend to make low

- income countries appear poorer than they actually are. Because many models assume convergence in per capita income over t ime, 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 ec onomic growth and hence a significant upward bias in projections of greenhouse gas emissions, and unrealistically high future temperatures ( e.g. , Castles and Henderson 2003). Others argue that convergence of the emissions

- intensity gap across countries at least partially offset the overstated income gap so that differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., u sing 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

exchan ge

- rate

- conversion issues are as important as uncertainties about population, technological change, or the many geophysical uncertainties.10A

- 19 T here are a number of caveats. First, EMF BAU scenarios represent the modelers’ judgment of the mos t likely pathway absent mitigation policies to reduce greenhouse gas emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the most likely outcome span a wide range, from the more optimistic ( e.g. , abundant low

- cost, lo w

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 optim al from a

benefit

- cost standpoint. Rather, it is indicative of one possible future outcome. The emission trajectories underlying some BAU scenarios ( e.g. , MESSAGE’s 612 ppm) also are consistent with some modest policy action to address climate change. 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 Annua l Energy Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion (in 2005 dollars using market exchange rate s) in 2010, 2020, and 2030, respectively. These projections are consistent with one or more EMF

- 22 scenarios. Likewise, the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people in 2000 to 9.1 billion people in 20 50, which is close to the population trajectories for the IMAGE, MiniCAM, and MERGE models.

In addition to fossil and industrial CO 2 emissions, each EMF scenario provides projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land u se CO 2 emissions out to 2100. These assumptions also are used in the three models while retaining the default radiative forcings due to other factors ( e.g. , aerosols and other gases). See the Annex for greater detail.

10A.4.6 Discount Rate

The choice of a discount rate, especially over long periods of time, raises highly contested and exceedingly difficult questions of science, economics, philosophy, and law. Although it is well understood that the discount rate has a large influence on the current value of future damages, there is no consensus about what rates to use in this context. Because carbon dioxide emissions are long

- lived, subsequent damages occur over many years. In calculating the SCC, we first estimate the future damages to ag riculture, human health, and other market and

non

- market sectors from an additional unit of carbon dioxide emitted in a particular year in terms of reduced consumption (or consumption equivalents) due to the impacts of elevated temperatures, as represented in each of the three IAMs. Then we discount the stream of future damages to its present value in the year when the additional unit of emissions was released using

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 primary energy in 2000 to 54 percent in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100.

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.10A

- 20 the selected discount rate, which is intended to reflect society's marginal rate of substit ution between consumption in different time periods.

For rules with both intra

- and intergenerational effects, agencies traditionally employ constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A

- 4.

As Circular A

- 4 acknowledges, however, the choice of discount rate for intergenerational problems raises distinctive problems and presents considerable challenges. After reviewing those challenges, Circular A

- 4 states, “ If your rule will have important intergenerational benefits or costs you might consider a further sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific purpose of developing the SCC, we adapt and re vise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive approach reflects a positive (non

- normative) persp ective based on observations of people’s

actual choices

— e.g. , savings versus consumption decisions over time, and allocations of savings among more and less risky investments. Advocates of this approach generally call for inferring

the discount rate from market rates of return “because of a lack of justification for choosing a social welfare function that is any different than what decision makers [individuals] actually use” (Arrow et al. 1996).

One theoretical foundation for the cost

- benefit analyses in which the social cost of carbon

will be used

— the Kaldor

- Hicks potential

- compensation test

— also suggests that market rates should be used to discount future benefits and costs, because it is the ma rket interest rate that would govern the returns potentially set aside today to compensate future individuals for climate damages that they bear ( e.g. , Just et al. 2004)

. As some have noted, the word “potentially” is an important qualification; there is n o 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 in vestments in human capital and the resulting increase in knowledge, as well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the normative judgments that the decision

- maker wants explicitly to incorporate into the policy

evaluation

— e.g. , how inter

- personal comparisons of utility should be made, and how the welfare of future generations should be weighed against that of the present generation. Ramsey (1928), for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time preference to discount values across generations, and many agree with this view.

Other concerns also motivate making adjustments to descriptive discount rates. In particular, i t has been noted that the preferences of future generations with regard to consumption versus environmental amenities may not be the same as those today, making the current market rate on consumption an inappropriate metric by which to discount future clim ate

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,10A

- 21 which in the Kaldor

- Hicks logic are presumed to compensat e 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 heterogen eity in the population. The utility function that underlies the prescriptive approach assumes a representative agent with perfect foresight and no credit constraints. This is an artificial rendering of the real world that misses many of the

frictions that characterize individuals’ lives and indeed the available descriptive evidence supports this. For instance, many individuals smooth consumption by borrowing with credit cards that have relatively high rates. Some are unable to access traditional credit ma rkets and rely on payday lending operations or other high

- cost forms of smoothing consumption. Whether one puts greater weight on the prescriptive or descriptive approach, the high interest rates that

credit

- constrained individuals accept suggest that som e 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 b e 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 ra tes should be used for discounting future consumption

- equivalent damages. Regardless of the theoretical approach used to derive the appropriate discount rate(s), we note the inherent conceptual and practical difficulties of adequately capturing consumptio n trade

- offs over many decades or even centuries. While relying primarily on the descriptive approach in selecting specific discount rates, the interagency group has been keenly aware of the deeply normative dimensions of both the debate over discounting in the intergenerational context and the consequences of selecting one discount rate

over another.

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on investment, and the market rate of interest would be the appropriate choice for the social discount rate. In the real world risk, taxes, and other market imperfections drive a wedge

between the risk

- free rate of return on capital and the consumption rate of interest. Thus, the

li terature recognizes two conceptual discount concepts

— the consumption rate of interest and the opportunity cost of capital.

According to OMB’s Circular A

- 4, it is appropriate to use the rate of return on capital when a regulation is expected to displace or alter the use of capital in the private sector. In this case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected to primarily affect private consumption

— for instance, via higher prices for goods and services

— a lower discount rate of 3 percent is appropriate to reflect how private individuals trade

- off current and future consumption.

The interagency group examined the economics literature and concluded that the consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a10A

- 22 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 (

- equiva lent) 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 fore sight and no credit

constraints. The risk

- free rate is appropriate for discounting certain future benefits or costs, but the benefits calculated by IAMs are uncertain. To use the risk

- free rate to discount uncertain benefits, these benefits first must be transformed into "certainty equivalents," that is the maximum certain amount that we would exchange for the uncertain amount. However, the calculation of the certainty

- equivalent requires first estimating the correlation between the benefits of the polic y and baseline consumption.

If the IAM projections of future impacts represent expected values (not certainty

equivalent values), then the appropriate discount rate generally does not equal the risk

- free rate. If the benefits of the policy tend to be high in those states of the world in which consumption is low, then the certainty

- equivalent benefits will be higher than the expected benefits (and vice versa). Since many (though not necessarily all) of the important impacts of climate change will

flow through market sectors such as agriculture and energy, and since willingness to pay for environmental protections typically increases with income, we might expect a positive (though not necessarily perfect) correlation between the net benefits from climate policies and market returns. This line of reasoning suggests that the proper discount rate would exceed the riskless rate. Alternatively, a negative correlation between the returns to climate policies and market returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post

- tax riskless and risky rates can be used to capture individuals’ consumption

- equivalent interest rate. As a measure of the post

- tax riskless rate, we calculate the avera ge real return from Treasury notes over the longest time period available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal rate from tax years 2003 through 2006 is around 27 percent). r This calculation produces a real interest rate of about 2.7 percent, which is roughly consistent with Circular A

- 4’s recommendation to use 3 percent to represent the consumption rate of interest. s A measure of the post

- tax risky rate for investments whose returns are positively correlat ed with overall equity

r The literatur e argues for a risk

- free rate on government bonds as an appropriate measure of the consumption rate of interest. Arrow (2000) suggests that it is roughly 3

- 4 percent. OMB cites evidence of a 3.1 percent pre

- tax rate for

10

- year Treasury notes in the A

- 4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 percent for 30

- year Treasury securities.

s The positive approach reflects how individuals make allocation choices across time, but it is important to keep in

mind that we wish t o reflect preferences for society as a whole, which generally has a longer planning horizon.10A

- 23 market returns can be obtained by adjusting pre

- tax rates of household returns to risky investments (approximately 7 percent) for taxes , which yields a real rate of roughly 5 percent. 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 rate 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 representative agent with perfect foresight, what we are calling the “Ramsey discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e. , the market

in terest rate.

A review of the literature provides some guidance on reasonable parameter values for the Ramsey discounting equation , based on both prescriptive and descriptive approaches.

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 (Weitzman cites plausible values as those ranging from 1 to 4), although not all authors articulate whether their choice is based on prescriptive or descriptive reasoning. w Dasgupta (2008) argues that η should be greater than 1 a nd may be as high as 3, because

η equal to 1 suggests savings rates that do not conform to observed behavior.

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 19 50

– 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 genera tions. 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.

w Empirical estimates of η span a wide range of values. A benchmar k value of 2 is near the middle of the range of values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman (2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η us ing data on labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and

concludes that the sta ndard expected utility model cannot generate high levels of risk aversion without contradicting established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the bivariate probability distributions from their work and Evans and Sezer (20 05) together, they find η = 1.47, and ρ = 1.07.10A

- 24 • ρ. With respect to the pure rate of time preference, most papers in the climate change literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates tend to follow from moral judgments involving intergenerational neutrality. Some have argued that to use any value other than ρ = 0 would unjustly discriminate against future generations ( e.g. , Arrow et al. 1996, Stern et al. 2006). However, even in an inter

generational setting, it may make sense to use a small positive pure rate of time preference because of the small probability of unforeseen cataclysmic events (Stern et al. 2006).

• g. A com monly accepted approximation is around 2 percent per year. For the

socioeconomic scenarios used for this exercise, the EMF models assume that g is about

1.5

- 2 percent to 2100.

Some economists and non

- economists have argued for constant discount rates below 2 percent based on the prescriptive approach. When grounded in the Ramsey framework, proponents of this approach have argued that a ρ of zero avoids giving preferential treatment t o one generation over another. The choice of η has also been posed a s an ethical choice linked to the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. (2006) applies this perspective through his choice of ρ = 0.1 percent per year

, η = 1 and g = 1.3 percent per year, which yields a n annual discount rate of 1.4 percent. In the context of permanent income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent of their income. x

Recently, Stern (2008) revisited the values used in Stern et al. (2 006), 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, combi ned with a η of 1.5 to 2 and his original growth rate, yields a discount rate of greater than 2 percent.

We conclude that arguments made under the prescriptive approach can be used to justify discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most appropriate value for η , we find it difficult to justify rates at the lower end of this range under the Ramsey framework.

Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is uncertain over time. Ideally, we would formally model this uncerta inty, just as we do for climate sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on net present values. A main result fro m these studies is that if there is a persistent element to the

x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity . (At the same time, adding exogenous technical change

— all else equal

— would increase g as well.)10A

- 25 uncertainty in the discount rate ( e.g. , the rate follows a random walk), then it will result in an effective (or certainty

- equivalent) discount rate that declines over time. Consequently, low er discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and Gollier and Weitzman 2009).

The proper way to model discount rate uncer tainty remains an active area of research. Newell and Pizer (2003) employ a model of how long

- term interest rates change over time to forecast future discount rates. Their model incorporates some of the basic features of how

interest rates move over time , and its parameters are estimated based on historical observations of long

- term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more general models of interest rate dynamics to allow for better forecasts. Specifically, the v olatility of interest rates depends on whether rates are currently low or high and the variation in the level of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model uncertainty in the discount rate, othe rs argue for a declining scale of discount rates applied over time ( e.g. , Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach uses a higher discount rate initially, but applies a graduated scale of lower discount rates further

out in time. y A key question that has emerged with regard to both of these approaches is the

trade

- off between potential time inconsistency and giving greater weight to far future outcomes (see the EPA Science Advisory Board’s recent comments on this top ic as part of its review of

their Guidelines for Economic Analysis ). z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in this context and uncertainty about how inte rest rates may change over time, we use three discount rates to span a plausible range of certainty

- equivalent constant discount rates: 2.5, 3, and 5 percent per year. Based on the review in the previous sections, the interagency workgroup

determined that these three rates reflect reasonable judgments under both descriptive and prescriptive approaches.

The central value, 3 percent, is consistent with estimates provided in the economics

literature and OMB’s Circular A

- 4 guidance for the consumption rate o f interest. As previously mentioned, the consumption rate of interest is the correct discounting concept to use when future

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 perc ent 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 unce rtainty in the discount rate . Weitzman (2008) argues that Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a low probability, catastrophic event far in the future dominate the effect of the disco unt rate in a present value calculation and result in an infinite willingness

- to

- pay for mitigation today. Newbold and Daigneault, (2009) and Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity,

u tility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required.10A

- 26 damages from elevated temperatures are estimated in consumption

- equivalent units. Further, 3 percent roughly corresponds to the af ter

- tax riskless interest rate. T he upper value of 5 percent is included to represent the possibility that climate damages are positively correlated with market returns. Additionally, this discount rate may be justified by the high interest rates that ma ny consumers use to smooth consumption across periods.

The low value, 2.5 percent, is included to incorporate the concern that interest rates are highly uncertain over time. It represents the average certainty

- equivalent rate using the mean

reverting an d 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 investments are negatively correlated with the overall market

rate of return. Use of thi s 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.

10A.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 cumulati ve 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.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and FUND incorporate uncertainty in other model parameters, the final output from each model run is a distribution over the SCC in year t

.

For each of the 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 ea ch year resulting from the baseline path of emissions.

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).10A

- 27 a. In PAGE, the consumption

- equivalent damages in each period are calculated as a fraction of the EMF GDP forecast, depending on the temperature in that period relative to the pre

- industrial average

te mperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of temperature change in that period.

c. In DICE, temperature affects both consumption and investment, so we first adjust the EMF GDP paths as follows: Using th e Cobb

- Douglas production function with the DICE2007 parameters, we extract the path of exogenous technical change implied by the EMF GDP and population paths, then we recalculate the baseline GDP path taking into account climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by model.)

4. Recalculate the temperature effects and damages expected in all years beyond t resulting from this adjusted path of emissions, as in step 2.

5. Subtract the damages computed in step 2 from those i n step 4 in each year. (DICE is run in 10

- year time steps, FUND in annual time steps, while the time steps in PAGE vary.)

6. Discount the re sulting path of marginal damages back to the year of emissions using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed in step 6, divided by the unit of carbon emissions used to sho ck the models in step 3.

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of

CO 2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO 2 in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.

This is an issue for the multi

- model approach because differences i n SCC estimates may arise simply due to the model time horizon. Many consider 2200 too short a time horizon because it could miss a significant fraction of damages under certain assumptions about the growth of marginal damages and discounting, so each mod el is run here through 2300. This step required a small adjustment in the PAGE model only. This step also required assumptions about GDP, population, and greenhouse gas emission trajectories after 2100, the last year for which these10A

- 28 data are available fr om 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. Thi s 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 scenar ios are equally weighed and combined to produce three separate probability distributions for SCC in a given year, one for each assumed discount rate. These distributions are then used to define a range of point estimates for the global SCC. In this way, n o 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 intergeneration al 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 mo dels 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 SCC distribution. For this p urpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. ( The full set of distributions by model and scenario combination is included in the Annex .) As noted above, the 3 percent discount rate is the central value, and so the ce ntral value that emerges is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivi ty. Treating climate sensitivity probabilistically results in more high

- temperature outcomes, which in turn lead to higher projections of damages. Although FUND does not include catastrophic damages (in contrast to the other two models), its probabilisti c 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 10A.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 disco unt 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 estima tes, FUND produces the lowest estimates, while PAGE generally produces the highest estimates.10A

- 29 Table 10A.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 f or the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE (based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these comparisons are only approximate since the literature generally relies on Ramsey discounting,

while we have assumed constant discount rates. 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 distribu tion such that the min, mode, and max = 0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC estimates for values of ρ = 0, 1, and 3 in many recent papers ( e.g. Anthoff et al. 2009). The pat h of per

- capita consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is10A

- 30 The SCC estimates from FUND are sensitive to differences in emissions paths but relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for DICE and PAGE. This likely occurs because of several structural differences among the models. Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages increases with the level of temperature alone, whereas in FUND the fractional loss also increases with the rate of temperature change. Furthermor e, in FUND increases in income over time decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and PAGE. These structural differences among the models make FUND more sensitive to the path of emissions and les s sensitive to GDP compared to DICE and PAGE.

Figure 10A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result that is to be expected given its less direct relationship between its damage function and G DP.

Figure 10A.5.1 Level of Global GDP across EMF Scenarios

Table 10A.5.2 shows the four selected SCC values in five

- year increments from 2010 to 2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using a simple linear interpolation.

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate change that occ ur far out in the future are discounted at a lower rate than impacts that occur in the nearer term. 0 50 100 150 200 250 300 350 400 450

2000 2010 2020 2030 2050 2100 Global

GDP (trillions

of U.S. dollars) IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing10A

- 31 Table 10A.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

directl y using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are internally consistent with other modeling assumption s. Table 10A.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 10A.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 t oday or emissions in a later year, should be discounted using the same rate. For example, climate damages in the year 2020 that are10A

- 32 calculated using a SCC based on a 5 percent discount rate also should be discounted back to the analysis year using a 5 perc ent discount rate. cc

10A.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 wh en interpreting and applying the SCC estimates.

Incomplete treatment of non

- catastrophic damages. The impacts of climate change are expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain because of the inherent complexity of climate processes, the economic behavior of current and future p opulations, and our inability to accurately forecast technological change and adaptation. Current IAMs do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature (so me of which are discussed above) because of lack of precise information on the nature of damages and because the science incorporated into these models understandably lags behind the most recent research. Our ability to quantify and monetize impacts will undoubtedly improve with time. But it is also likely that even in future applications, a number of potentially significant damage categories will remain non

- monetized. (Ocean acidification is one example of a potentially large damage from CO 2 emissions not quantified by any of the three models. Species and wildlife loss is another example that is exceedingly difficult to monetize.)

Incomplete treatment of potential catastrophic damages. There has been considerable

recent discussion of the risk of catastr ophic impacts and how best to account for extreme scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. Weitzman (2009) suggests that catastrophic damages are extremely large

— so large, in fact, that the damages from a low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value calculation and result in an infinite willin gness

- to

- pay for mitigation today. However, Nordhaus (2009) concluded that the conditions under which Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain

scenarios."

Using a simplified IAM, Newbold and Daigneaul t (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 thos e used to develop the SCC estimates.10A

- 33 impact

, low

- probability risks, using a right

- skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficient, but in most cases found only a modest risk premium.

Giv en this difference in opinion, further research in this area is needed before its practical significance can be fully understood and a reasonable approach developed to account for such risks in regulatory analysis. (The next section discusses the scientif ic 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 cal ibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming that damages increase as some power of the temperature change. Hence, estimated damages are far more uncertain under more extreme climate change scenarios.

Incomplete treatment of adaptation and technological change: Each of the three integrated assessment models used here assumes a certain degree of low

- or no

- cost adaptation. For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on

air c onditioning; so much so, that the largest single benefit category in FUND is the reduced electricity costs from not having to run air conditioning as intensively (NRC 2009).

Climate change also will increase returns on investment to develop technologie s that allow individuals to cope with adverse climate conditions, and IAMs to 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 temperatu res. Although DICE and FUND have both calibrated their agricultural sectors under the assumption that farmers will change land use practices in response to climate change (Mastrandrea, 2009), they do not take into account technological changes that lower the cost of this adaptation over time. On the other hand, the calibrations do not account for increases in climate variability, pests, or diseases, which could make adaptation more difficult than assumed by the IAMs for a given temperature change.
Hence, models do not adequately account for potential adaptation or technical change that might alter the emissions pathway and resulting damages. In this respect, it is difficult to determine whether the incomplete treatment of adaptation and technological cha nge 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 in to 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

risk

- averse for such scenarios, it is possible that regulatory policy should include a degree of

risk

- aversion.

dd However these research dollars will be diverted from whatever their next best use would have been in the absence of climate change (so productivity/GDP would have been still higher).10A

- 34 Assuming a risk

- neutral representative agent is consistent with OMB’s Circular A

- 4,

which advi ses that the estimates of benefits and costs used in regulatory analysis are usually based on the average or the expected value and that “emphasis on these expected values is appropriate as long as society is ‘risk neutral’ with respect to the regulatory a lternatives. While this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] analysis.”

Nordhaus (2008) points to the need to explore the relationship between risk and income in the context of climate change acro ss models and to explore the role of uncertainty regarding various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude

that “the assumed rate of risk aversion is at least as important as the assumed rate of time preference in determining the social cost of carbon.” Since Circular A

- 4 allows for a different assumption on risk preference in regulatory analysis if it is adequately jus tified, we plan to continue investigating this issue.

10A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPA CTS 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 pos sible 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 sy stems, (2) inter

- sectoral and inter

- regional interactions, including global security impacts of high

- end warming, and (3) limited near

- term substitutability between damage to natural systems and increased consumption.

It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling. In the meantime, we

discuss some of the avai lable evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at moderate levels of warming and should therefore be viewed cautiously when extrapolated to the high temperatures found in the upper end of the distribution. Recent science suggests that there are a number of potential climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with potentially severe social and economic consequences ( e.g. , Lenton et al , 2008, Kriegler et al., 2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West Antarctic Ice Sheet, reorganization of the Atla ntic Meridional Overturning Circulation, strengthening of El Niño

- Southern Oscillation, and the release of methane from melting10A

- 35 permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C and 5 °C (Lenton et al., 2008). P robabilities 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 10A.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 10A.4.1 ), so the high

- end output from PAGE potentially offers the best insight into the SCC if the world were to experience catastrophic climate change. For instance, at the 95 th percentile and a 3 percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission trajectories of $113 per ton of CO 2 is almost double the value estimate d by DICE, $58 per ton in

2010. We cannot evaluate how well the three models account for 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 10A.7.1 Probabilities of Various Tipping Points from Exp ert Elicitation

Possible Tipping Points Duration before effect is fully realized (in years) Additional Warming by 2100

0.5

- 1.5 C 1.5

- 3.0 C 3

- 5 C

Reorganization of Atlantic Meridional Overturning Circulation about 100 0

- 18% 6

- 39% 18

- 67%

Greenland Ice Sheet collapse at least 300 8

- 39% 33

- 73% 67

- 96%

West Antarctic Ice Sheet collapse at least 300 5

- 41% 10

- 63% 33

- 88%

Dieback of Amazon rainforest about 50 2

- 46% 14

- 84% 41

- 94%

Strengthening of El Niño

- Southern Oscillation about 100 1

- 13% 6

- 32% 19

- 49%

Dieback of boreal forests about 50 13

- 43% 20

- 81% 34

- 91%

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it deterministically (that is, by adding the expected value of the damage from a catastrophe to the aggregate damage function). In part, this results in different probabilities being assigned to a catastrophic event ac ross 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

- 3 6 percent of10A

- 36 crossing at least one of their primary climatic tipping points in a scenario with temperatures

about 2

- 4 °C warmer than pre

- Industrial levels in 2100.

It is important to note that crossing a climatic tipping point will not necessarily lead to an economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across which some aspect of the Earth system starts to shifts into a qualitatively different state (for instance, one with dramatically reduced ice sheet vol umes 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 damage functions do not fully incorporate either inter

- sectoral or inter

- regional interactions. For instance, while damages to the agricultural sector are incorporated, the effects of changes in food supply on human health are not fully captured and depend on the modeler’s

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one region of the world on another region are not included in some of the models (FUND includes the effects of migration from sea level rise). These inter

- regional interactions, though difficult to

qua ntify, 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 w ater scarcity affecting 4.3

- 6.9 billion people by 2050, food scarcity affecting about 120 million additional people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; Campbell et al., 2007).

Imperfect substitutability of e nvironmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming may have severe consequences for natural systems. For instance, during the Paleocene

- Eocene Thermal Maximum about 55.5 million years ago, when t he Earth experienced a geologically rapid release of carbon associated with an approximately 5 °C increase in global mean temperatures, the effects included shifts of about 400

- 900 miles in the range of plants (Wing et al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 2009).

The three IAMs used here assume that it is possible to compensate for the economic consequences of damages to natural systems through increased consumption of non

- climate goods, a common assumption in many economic models. In the context of climate change, however, it is possible that the damages to natural systems could become so great that no increase in consumption of non

- climate goods would provide complete compensation (Levy et al., 2 005). For instance, as water supplies become scarcer or ecosystems become more fragile

and less bio

- diverse, the services they provide may become increasingly more costly to replace. Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs (Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be considerably greater than is commonly recognized.10A

- 37 10A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analy ses. 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 fourt h 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 t he 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 integrated assessment models capture catastrophic and no n

- catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impacts to economic damages makes this modeling exercise even more difficult. It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal

governm ent will continue to evolve with improvements in modeling.10A

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- 43 10A.9 A NNEX

Table 10A.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.10A

- 44 10A.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 i n all three IAMs while retaining each model’s default radiative forcings (RF) due to other factors ( e.g. , aerosols and other gases). Specifically,

to obtain the RF associated with the non

- CO 2 EMF emissions only, we calculated the RF associated with the EMF atmospheric CO 2 concentrations and subtracted them from the EMF

total RF. 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 relyi ng 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 expl ained 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.

Accordi ng 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 c oncentrations 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 th e 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.10A

- 45 decompo sed into: 0.98 W/m 2 due to the EMF non

- CO 2 gases,

- 1.2 W/m 2 due to aerosols, and the remainder, 0.16 W/m 2 , due to other residual forcing.

For subsequent years, we calculated the DICE default RF from aerosols and other non

CO 2 gases based on the follow ing two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR and then stays constant thereafter ; and (2) With respect to RF from non

- CO 2 gases not included in the EMF

- 22 scenarios, the share

of non

- aeroso l RF matches the share implicit in the AR4 summary statistics cited above and

remains constant over time.

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since the SRES marker scenarios were not updated for the AR4, the TAR provides the most recent IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides one of the lower aerosol forecasts among the SRES marker scenarios and is more consistent with the AR4 discussion of the post

- SRES literature on aerosols:

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the

post

- SRES scenarios. Generally, these emissions are projected to be lower than reported in SRES. {WGIII 3.2, TS.3, SPM}. gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent with the recent literature on these emissions. For example, the f igure below shows that the sulfur dioxide emissions peak over the short term of some SRE S scenarios above the upper bound

estimates of the more recent scenarios. hh Recent scenarios project sulfur emissions to peak earlier and at lower levels compared to the SRES in part because of new information about present and planned sulfur legislation in some developing countries, such as India and China. ii The lower

bound projections of the recent literature have also shifted downward slightly compared to the SRES scenario (IPCC 2007).

gg AR4 Synthesis Report, p. 44, www.ipcc.ch/pdf/assessment

- report/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. Sinton, X.

- Q. Zhang, D. Xu, M. Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835

- 1837.10A

- 46 With these assumptions, the DICE aerosol forcing changes from

- 1.2 in 2005 to

- 0.792 in

2105 W/m 2 ; forcing due to other non

- CO 2 gases not included in the EMF scenarios declines from

0.160 to 0.153 W/m 2

.

.

Figure 10A.9.1 Sulfur Dioxide Emission Scenarios

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines show the median, 5 th

, and

95 th 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 approaches to decomposing the DICE exogenous forcing vector are possible, initial sensitivity analysis suggests that the differences among reasonable alternative approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values (for 2010) by approximately 3 percent (or less than $2); assuming all aerosol s are phased out by 2100 increases average 2010 SCC values by 6

- 7 percent (or $0.50

- $3)

– depending on the discount rate. These differences increase slightly for SCC values in later years but are still well within 10 percent of each other as far out as 2050

.

Finally, as in PAGE, the EMF net land use CO 2 emissions are added to the fossil and

industrial CO 2 emissions pathway.

10A.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, greenhou se gas emissions, and radiative forcing trajectories after 2100, the last year for which these projections are available from the EMF

- 22 models. These inputs were extrapolated from

2100 to 2300 as follows:10A

- 47 1. Population growth rate declines linearly, re aching 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 forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying assumption than a linear or exponenti al 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 ac tivities may eventually overtake the rate of technological progress. Thus, the overall rate of economic growth may slow over the very long run. The interagency group also considered allowing an exponential decline in the growth rate of GDP per capita. H owever, since this would require an additional assumption about how close to zero the growth rate would get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by

2300.

The population growth rate is also assumed t o decline linearly, reaching zero by 2200. This assumption is reasonably consistent with the United Nations long run population forecast, which estimates global population to be fairly stable after 2150 in the medium scenario (UN 2004). jj The resulting range o f EMF population trajectories (f igure below ) also encompass the UN medium scenario forecasts through 2300

— global population of 8.5 billion by 2200, and 9 billion by 2300.

Maintaining the decline in the 2090

- 2100 carbon intensity growth rate ( i.e. , CO 2 per dollar of GDP) through 2300 assumes that technological improvements and innovations in the areas of energy efficiency and other carbon reducing technologies (possibly including currently unavailable methods) will continue to proceed at roughl y the same pace that is projected to occur towards the end of the forecast period for each EMF scenario. This assumption implies that total cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range of the total potential global carbon stock estimated in the literature.

Net land use CO 2 emissions are expected to stabilize in the long run, so in the absence of any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori

reasons for a ssuming a long run increase or decline in non

- CO 2 radiative forcing, it is assumed to remain at the 2100 levels for each EMF scenario through 2300.

Figures below show the paths of global population, GDP, fossil and industrial CO 2 emissions, net land CO 2 emissions, non

- CO 2 radiative forcing, and CO 2 intensity (fossil and

industrial CO 2 emissions/GDP) resulting from these assumptions.

jj Un ited Nations. 2004. World Population to 2300

. www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf10A

- 48 Figure 10A.9.2 Global Population, 2000

- 2300 (Post

- 2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.)

Note: In the fifth scenario, 2000

- 2100 population is equal to the average of the population under the 550 ppm CO 2 e, full

- participation, not

- 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]

Year10A

- 49 Figure 10A.9.3 World GDP, 2000

- 2300 (P ost

- 2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in 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 consi dered 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]10A

- 50 Figure 10A.9.4 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]

Year10A

- 51 Figure 10A.9.5 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 z10A

- 52 Figure 10A.9.6 G lobal 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

- participatio n, 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]

Year10A

- 53 Figure 10A.9.7 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP),

2000

- 2300 (P ost

- 2100 extrapolations assume decline in

CO 2 /GDP growth rate over 2090

- 2100 is maintained through

2300 )

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 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 $]

Year10A

- 54 Table 10A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rat e (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 96.810A

- 55 Table 10A.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 53.610A

- 56 Table 10A.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 10A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by discount rate

* The distribution of SCC values ranges from

- $5,192 to $66,116

, but the X

- axis has been truncated at approximately the 1 st and 99 th percentiles to better show the data. 0 2000 4000 6000 8000 10000 12000 14000 16000-25-12114274053667992105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%10A

- 57 Table 10A.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 23,558.3010B

- i APPENDIX 10B. T ECHNICAL U PDATE OF SOCIAL COST OF CARBO N FOR

REGULATORY IMPACT AN ALYSIS UNDER EXECUTI VE ORDER 12866

TABLE OF CONTENTS

10B.1 PREFACE

................................ ................................ ................................ ............... 10B

- 1

10B.2 PURPOSE

................................ ................................ ................................ ............... 10B

- 1

10B.3 SUMMARY OF MODEL UPD ATES

................................ ................................ .... 10B

- 2

10B.3.1 Dice

................................ ................................ ................................ ..................... 10B

- 2

10B.3.1.1 Carbon Cycle Parameters

................................ ................................ ............ 10B

- 2

10B.3.1.2 Sea Level Dynamics

................................ ................................ ................... 10B

- 3

10B.3.1.3 Re

- calibrated Damage Function

................................ ................................ . 10B

- 4

10B.3.2 F und

................................ ................................ ................................ .................... 10B

- 4

10B.3.2.1 Space Heating

................................ ................................ ............................. 10B

- 5

10B.3.2.2 Sea Level Rise and Land Loss

................................ ................................ .... 10B

- 5

10B.3.2.3 Agriculture

................................ ................................ ................................ .. 10B

- 5

10B.3.2.4 Temperature Response Model

................................ ................................ .... 10B

- 6

10B.3.2.5 Methane

................................ ................................ ................................ ....... 10B

- 6

10B.3.3 P age

................................ ................................ ................................ ..................... 10B

- 6

10B.3.3.1 Sea Level Rise

................................ ................................ ............................. 10B

- 7

10B.3.3.2 Revised Damage Function to Account for Saturation

................................ 10B

- 7

10B.3.3.3 Regional Scaling Factors

................................ ................................ ............ 10B

- 7

10B.3.3.4 Probability of a Discontinuity

................................ ................................ ..... 10B

- 7

10B.3.3.5 Adaptation

................................ ................................ ................................ ... 10B

- 8

10B.3.3.6 Other Noteworthy Changes

................................ ................................ ......... 10B

- 8

10B.4 REVISED SCC ESTIMATE S

................................ ................................ ................ 10B

- 9

10B.5 OTHER MODEL LIMITATI ONS OR RESEARCH GAPS

............................... 10B

- 11

REFERENCES

................................ ................................ ................................ ..................... 10B

- 12

10B.6 ANNEX

................................ ................................ ................................ ................. 10B

- 14

LIST OF TABLES

Table 10B.4.1 Revised Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars per ton of

CO 2 )

................................ ................................ ................................ ......... 10B

- 10

Table 10B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and

2050

................................ ................................ ................................ .......... 10B

- 11

Table 10B.5.1 Annual SCC Values: 2010

- 2050 (2007$/ton CO 2 )

................................ .. 10B

- 14

Table 10B.5.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ......... 10B

- 15

Table 10B.5.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

................. 10B

- 15

Table 10B.5.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ......... 10B

- 1610B

- ii LIST OF FIGURES

Figure 10B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 )

............ 10B

- 1010B

- 1 APPENDIX 10B. T ECHNICAL U PDATE OF SOCIAL COST OF CARBO N FOR

REGULATORY IMPACT AN ALYSIS UNDER EXECUTI VE ORDER 12866

10B.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 consistent with the rest of this technical support document.

10B.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 u pdates that reflect the growing body of scientific and economic knowledge become available. c New versions of the three integrated assessment models used by the U.S. government to estimate the SCC (DICE, FUND, and PAGE), are now available and have been p ublished in the peer reviewed literature. While acknowledging the continued limitations of the approach taken by the interagency group in 2010 (documented in the original 2010 TSD), this document provides an update of the SCC estimates based solely on the latest peer

- reviewed version of the models, replacing model versions that were developed up to ten years ago in a rapidly evolving field. It does not revisit other assumptions with regard to the discount rate, reference case socioeconomic

and emission scen arios, 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.

S ection 10B.3 summarizes the major updates relevant to SCC estimation that are contained in the new versions of the integrated assessment models rele ased since the 2010 interagency report. Section 10B.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/de 5 fault/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). 110B

- 2 10B.3 SUMMARY OF MODEL UPD ATES

This section briefly summarizes changes integrated into the most recent versions of the three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on describing those model updates that are relevant to estimating the social cost of carbon. For example, both the DICE and PAGE models now include an explicit representation of sea level rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to ensure damages are constrained GDP, updated regional scaling of damages, and a revised treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, the model’s simple carbon cycle has been updated to be more consistent with a relatively mor e 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 an d 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 di scussed.

10B.3.1 DICE

Changes in the DICE model relevant for the SCC estimates developed by the interagency working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit representation of sea level dynamics, and 3) a re

- calibrated damage function that includes an explicit representation of economic damages from sea level rise. Changes were also made to other parts of the DICE model

— including the equilibrium climate sensitivity parameter, the rate of change of total factor productivi ty, and the elasticity of the marginal utility of consumption

— but these components of DICE are superseded by the interagency working group’s assumptions and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008) 2

and on D ICE2010 in Nordhaus (2010) 3 and the associated on

- line appendix containing supplemental information.

10B.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

perce nt 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 t o the shallow ocean each

d MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from much more sophisticated state of the art earth system simulation models (Randall et al. 2007). 410B

- 3 decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as a carbon sink and therefore a higher concentration of carbon in the atmosphere than in DICE2007, for a given path of emissions. All else equal, these changes will generally increase the level of warming and therefore the SCC estimates in DICE2010 relative to those from

DICE2007.

10B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global average sea level anomaly to be used in the updated damage function (discussed below). This section contains a brief description of the sea level rise (SLR) module; a more detailed description can be found on the model developer’s website. e The average global sea level anomaly is modeled as the sum of four terms that represent contributio ns 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

con sensus results from the IPCC’s Fourth Assessment Report. 4

, f The rise in sea level from thermal expansion in each time period (decade) is 2 percent of the difference between the sea level in the previous period and the long run equilibrium sea level, which is 0.5 meters per degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above the average global temperature in 1900.

The contribution to sea level rise from melting of the G reenland 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 diff erence 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 ic e sheet is

- 0.001 meters per decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: www.econ.yale.edu/~nordhaus/homepage/documents/SLR_021910.p df

. f For a review of post

- IPCC AR4 research on sea level rise, see Nicholls et al. (2011 ) 5 and NAS (2011). 610B

- 4 10B.3.1.3 Re

- calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a fractional loss of gross economic output in each period. A portion of the remaining economic output in each period (net of climate change damages) is consumed and the remainder is invested in the physical capital stock to su pport 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

mi nus 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 temperatu re. 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 per cent of global output, for a global temperature increase of 3.4 o C above 1900 levels.” This compares to a loss of 3.2 percent of global output at 3.4 o C in DICE2007. However, in DICE2010 (as downloaded from the homepage of William Nordhaus), annual damage s 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 c alculated 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 t he model time horizon in 2595. The large increases in the far future years of the time horizon are due to the permanence associated with damages from sea level rise, along with the assumption that the sea level is projected to continue to rise long after

t he global average temperature begins to decrease. The changes to the loss function generally decrease the interagency working group SCC estimates slightly, all else equal.

10B.3.2 F UND

FUND version 3.8 includes a number of changes over the previous version 3.5 u sed in the interagency report. Documentation supporting FUND and the model’s source code for all

versions of the model is available from the model authors. g Notable changes, due to their impact on the estimates of expected SCC, are adjustments to the spa ce 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 consi stency with IPCC AR4 by adjusting the atmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along with making minor stability improvements in the sea wall construction algorithm.

h The other damage sectors (water resou rces, space cooling, land loss, migration, ecosystems, human health, and extreme weather) were not the subject of significant updates.10B

- 5 10B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are based on the estimated impact due to one degree of warming. These baseline damages are scaled based on the forecasted temperature anomaly’s deviation from the one degree benchmark and adjuste d for changes in vulnerability due to economic and energy efficiency growth. In FUND 3.5, the function that scales the base year damages adjusted for vulnerability allows for the possibility that in some simulations the benefits associated with reduced hea ting 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 be nefits 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 heati ng 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 esti mated SCC. This update accounts for a significant portion of the difference in the expected SCC estimates reported by the two versions of the model when run probabilistically.

10B.3.2.2 Sea Level Rise and Land Loss

The FUND model explicitly includes damages associa ted with the inundation of dry land due to sea level rise. The amount of land lost within a region is dependent upon the proportion of the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 3.5 the function defining t he 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 m oving inland. In FUND 3.8 the function defining the potential land lost has been changed to be a non

linear 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 e ffect of this change is to typically reduce the vulnerability of some regions to sea level rise based land loss, therefore having an effect of lowering the expected SCC estimate. The model has also been updated to assume that the value of dry land at risk of inundation is not uniform across a region but will be a decreasing function of protection measure, thereby implicitly assuming that the most valuable land will be protected

first.

10B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as proportional to the sector’s value. The fraction is made up of three additively separable components that represent the effects from carbon fertilization, the rate of temperature change, and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function with an intercept of zero. In FUND 3.5, the linear and q uadratic coefficients are modeled as the ratio of two normal distributions. Within this specification, as draws from the distribution in the10B

- 6 denominator approached zero the share of the sector’s value “lost” approaches (+/

- ) infinity independent of the tem perature anomaly itself. In FUND 3.8, the linear and quadratic coefficients are drawn directly from truncated normal distributions so that they remain in the

range [0, ) ∞ and ( ,0]

− ∞ , respectively, where the means for the new distributions are set equal to the ratio of the means from the normal distributions used in the previous version. In general the impact of this change has been to increase the likelihood that increases in the temperature level

will have either lar ger 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.

10B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing into the current expected temperature anomaly. In FUND, a given year’s increase in the cumulative temperature anomaly is based on a mean re verting function where the mean equals the equilibrium temperature anomaly that would eventually be reached if that year’s level of radiative forcing were sustained. The rate of mean reversion defines the rate at which the transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore in FUND 3.8, the temperature response will typically be faster than in the previous version. The overall effect of this change is likely to increase estimates of the SCC as higher temperatures are reached during t he timeframe analyzed and as the same damages experienced in the previous version of the model are now experienced earlier and therefore discounted less.

10B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emissions, and has developed

methods f or proxying such effects when computing the global warming potential of methane (Forster et al. 2007). 8 FUND 3.8 now includes the same methods for incorporating the indirect effects of methane emissions. Specifically, the average atmospheric lifetime of methane has be en set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative forcing associated with atmospheric methane has also been increase by 40% to account for its net impact on ozone production and increase in stratospheric wat er vapor. The general effect of this increased radiative forcing will be to increase the estimated SCC values, where the degree to which this occurs will be dependent upon the relative curvature of the damage functions with respect to the temperature anoma ly.

10B.3.3 PAGE

PAGE09 (Hope 2012) 9 includes a number of changes from PAGE2002, the version used in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates10B

- 7 include: explicitly modeling the impacts from sea level rise, revisions to the damage function to ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised treatment for the probability of a discontinuity with in the damage function, and revised assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and the calculation of regional temperatures. More details on PAGE2009 can be found in three working papers (Hope 2011a, 2011b, 20 11c). 10

, 11

, 12 A description of PAGE2002 can be found in Hope (2006). 13

10B.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 fr om 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 cur rent inland areas. Damages from the economic and non

- economic sector were adjusted to account for the introduction of this new category.

10B.3.3.2 Revised Damage Function to Account for Saturation

In PAGE09, small initial economic and non

- economic benefits (negati ve damages) are modeled for small temperature increases, but all regions eventually experience positive economic damages from climate change, where damages are the sum of additively separable polynomial functions of temperature and sea level rise. Damages transition from this polynomial function to a logistic path once they exceed a certain proportion of remaining Gross Domestic Product (GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, which allowed Eastern Europe to potentially experience large benefits from temperature increases, and which also did not bound the possible damages that could be experienced.

10B.3.3.3 Regional Scaling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the

Eu ropean Union (EU) and then, assumes that damages for other regions are proportional based on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s coastline relative to the EU (Hope 2011b). 11 Because of the long coastline in the EU, other regions are, on average, less vulnerable than the EU for the same sea level and temperature

increas e, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on four studies reported in the IPCC’s third assessment report, and allowed for benefits from temperature increase in Eastern Europe, smaller impacts in developing countr ies, and higher damages in developing countries.

10B.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 of10B

- 8 t he 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 be yond this threshold then increases by between 10 and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a

mean of 15 percent) in addition to other damages, and other regions lose an amount determined by the regional scaling factor. The threshold value for a possible discontinuity is lower than in PAGE2002, while the rate at which the probability of a discontinuity incre ases with the temperature anomaly and the damages that result from a discontinuity are both higher than in PAGE2002. The model assumes that only one discontinuity can occur and that the impact is phased in over a period of time, but once it occurs, its eff ect is permanent.

10B.3.3.5 Adaptation

As in PAGE2002, adaptation is available to increase the tolerable level of temperature change and can help mitigate any climate change impacts that still occur. In PAGE this adaptation is the same regardless of the temperature change or sea level rise and is therefore akin to what is more commonly considered a reduction in vulnerability. It is modeled by modifying the temperature change and sea level rise used in the damage function or by reducing the damages by some percentage . PAGE09 assumes a smaller decrease in vulnerability than the previous version of the model and assumes that it will take longer for this change in vulnerability to be realized. In the aggregated economic sector, at the time of full implementation, this

ad aptation will mitigate all damages up to a temperature increase of 1°C, and for temperature anomalies between 1°C and 3°C, it will reduce damages by 15

- 30 percent (depending on the region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation was assumed to reduce economic sector damages up to 3°C by 50

- 90 percent after 20 years. Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change.

For the non

- economic sector, in PAGE09 adaptation is available to reduce 15 percent of the damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. Similarly, adaptation is assumed to alleviate 25

- 50 percent of the damages from the first 0.20 to 0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c) 12 estimates that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea level rise via adaptation increase the SCC by approximately 30 percent.

10B.3.3.6 Other Noteworthy Chan ges

Two other changes in the model are worth noting. A revised carbon cycle feedback is

introduced to simulate decreased CO 2 absorption by the terrestrial biosphere and ocean as the temperature rises. This feedback is linear in the average global and annu al 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 loss10B

- 9 of soil carbon. Also updated is the method by which the average global and annual temperature anomaly is downscaled to determine annual average regional temperature anomalies to be used in the regional damage functions. In the previous version of PAGE, the scaling was determined solely based on regio nal difference in emissions of sulfate aerosols. In PAGE09, this regional temperature anomaly is further adjusted using an additive factor that is based on the average absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s

landmass.

10B.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 The approach along with the inputs for the socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount

rate remains the same. This includes the five reference scenarios based on the EMF

- 22 modeling exercise, the Roe and Baker equilibr ium 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 scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD applied equal weight to each model and socioeconomic scenario in order to reduce the dimensionality down to three separate distributions representative of the three discount rates. Th e interagency group selected four values from these distributions for use in regulatory analysis. Three values are based on the average SCC across models and socio

- economic

- emissions scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to represent the higher

- than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent discount rate was chosen. (A deta iled 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 10B.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.10B

- 10 Table 10B.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

10B.4.1 illustrates where the four SCC values for 2020 fall within the full distribution for each discount rate based on the combined set of runs for each model and scenario (150,000 estimates

in total for each discount rate). In general, the distributions are skewed to the right and have long tails. The Figure also shows that the lower the discount rate, the longer the right tail of the

distribution.

Figure 10B.4.1 Distribution of SCC Estimates for 2020 (in 2 007$ per ton CO 2 )

As was the case in the original TSD, the SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. T he approach taken by the interagency group is to allow the growth rate to be determined endogenously by the models10B

- 11 through running them for a set of perturbation years out to 2050. Table 10B.4.2 illustrates how the growth rate for these four SCC estimates varies over time.

Table 10B.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.

10B.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 needed. In particular, the document highlights the need to improve the quantification of both non

- catastrophic and catastrophic damages, the treatment of adaptation and technological change, and the way in which inter

- regional and inter

sectoral linkages a re modeled. It also discusses the need to more carefully assess the implications of risk aversion for SCC estimation as well as the inability to perfectly substitute between

climate and non

- climate goods at higher temperature increases, both of which have implications for the discount rate used. EPA, DOE, and other agencies continue to engage in long

- term research work on modeling and valuation of climate impacts that we expect will inform improvements in SCC estimation in the future.10B

- 12 REFERENCES

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. < 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 Haver.

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. S hukla, 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. Brown, P. Velling a, 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 Stabilization 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 in Radiative Forc ing . 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.

Mi ller 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

. Climatic Change

, 2012(Forthcoming)10B

- 13 10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description . 2011, Cambridge Judge Business School www.jbs.cam.ac.uk/filea dmin/user_upload/research/workingpapers/wp1104.pdf

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model . 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 . 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 Reasons for Concern

. The Integrated Assessment

Journal , 2006. 6(1): pp. 19

- 5610B

- 14 10B.6 ANNEX

Table 10B.6.1 Annual SCC Values: 2010

- 2050 (2007$/ton CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 33 52 90
2011 11 34 54 94
2012 11 35 55 98
2013 11 36 56 102
2014 11 37 57 106
2015 12 38 58 109
2016 12 39 60 113
2017 12 40 61 117
2018 12 41 62 121
2019 12 42 63 125
2020 12 43 65 129
2021 13 44 66 132
2022 13 45 67 135
2023 13 46 68 138
2024 14 47 69 141
2025 14 48 70 144
2026 15 49 71 147
2027 15 49 72 150
2028 15 50 73 153
2029 16 51 74 156
2030 16 52 76 159
2031 17 53 77 163
2032 17 54 78 166
2033 18 55 79 169
2034 18 56 80 172
2035 19 57 81 176
2036 19 58 82 179
2037 20 59 84 182
2038 20 60 85 185
2039 21 61 86 188
2040 21 62 87 192
2041 22 63 88 195
2042 22 64 89 198
2043 23 65 90 200
2044 23 65 91 203
2045 24 66 92 206
2046 24 67 94 209
2047 25 68 95 212
2048 25 69 96 215
2049 26 70 97 21810B

- 15 2050 27 71 98 221

Table 10B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95 th 99th
Scenario PAGE
IMAGE 6 11 15 27 58 129 139 327 515 991
MERGE 4 6 9 16 34 78 82 196 317 649
MESSAGE 4 8 11 20 42 108 107 278 483 918
MiniCAM Base 5 9 12 22 47 107 113 266 431 872
5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE
IMAGE 25 31 37 47 64 72 92 123 139 161
MERGE 14 18 20 26 36 40 50 65 74 85
MESSAGE 20 24 28 37 51 58 71 95 109 221
MiniCAM Base 20 25 29 38 53 61 76 102 117 135
5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND IMAGE

- 17

- 1 5 17 34 44 59 90 113 176
MERGE

- 7 2 7 16 30 35 49 72 91 146
MESSAGE

- 19

- 4 2 12 27 32 46 70 87 135
MiniCAM Base

- 9 1 8 18 35 45 59 87 108 172
5th Scenario

- 30

- 12

- 5 6 19 24 35 57 72 108

Table 10B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th
Scenario PAGE
IMAGE 4 7 10 18 38 91 95 238 385 727
MERGE 2 4 6 11 23 56 58 142 232 481
MESSAGE 3 5 7 13 29 75 74 197 330 641
MiniCAM Base 3 5 8 14 30 73 75 184 300 623
5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE IMAGE 16 21 24 32 43 48 60 79 90 102
MERGE 10 13 15 19 25 28 35 44 50 58
MESSAGE 14 18 20 26 35 40 49 64 73 83
MiniCAM Base 13 17 20 26 35 39 49 65 73 85
5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND IMAGE

- 14

- 3 1 9 20 25 35 54 69 111
MERGE

- 8

- 1 3 9 18 22 31 47 60 97
MESSAGE

- 16

- 5

- 1 6 16 18 28 43 55 88
MiniCAM Base

- 9

- 1 3 10 21 27 35 53 67 107
5th Scenario

- 22

- 10

- 5 2 10 13 20 33 42 6310B

- 16 Table 10B.6.4 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 1411

- i CHAPTER 11. UTILITY IMPACT ANALY SIS

TABLE OF CONTENTS

11.1 INTRODUCTION

................................ ................................ ................................ ......... 11

- 1

11.2 METHODOLOGY

................................ ................................ ................................ ........ 11

- 2

11.3 UTILITY IMPACT RESUL TS

................................ ................................ ..................... 11

- 4

11.3.1 Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps < 65,000

Btu/h

................................ ................................ ................................ ................... 11

- 4

11.3.1.1 Installed Capacity

................................ ................................ ..................... 11

- 4

11.3.1.2 Electricity Generation

................................ ................................ .............. 11

- 7

11.3.1.3 Results Summary

................................ ................................ .................... 11

- 11

11.3.2 Water

- Source Heat Pumps

................................ ................................ ............... 11

- 11

11.3.2.1 Installed Capacity

................................ ................................ ................... 11

- 11

11.3.2.2 Electricity Generation

................................ ................................ ............ 11

- 14

11.3.2.3 Results Summary

................................ ................................ .................... 11

- 17

REFERENCES

................................ ................................ ................................ ........................ 11

- 19

LIST OF TABLES

Table 11.3.1 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps: Summary of Utility Impact Results

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

- 11

Table 11.3.2 Water

- Source Heat Pumps: Summary of Utility Impact Results

............... 11

- 18

LIST OF FIGURES

Figure 11.3.1 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps: Total Electric Capacity Reduction

................................ ............................... 11

- 4 Figure 11.3.2 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps: Coal Capacity Reduction

................................ ................................ ............. 11

- 5 Figure 11.3.3 Small Commercial A ir

- Cooled Air Conditioners and Heat Pumps: Nuclear Capacity Reduction

................................ ................................ ........ 11

- 5 Figure 11.3.4 Small Commercial Air

- Cooled Air Conditio ners and Heat Pumps: Gas Combined Cycle Capacity Reduction

................................ .......................... 11

- 6 Figure 11.3.5 Small Commercial Air

- Cooled Air Conditioners and Hea t Pumps: Peaking Capacity Reduction

................................ ................................ ........ 11

- 6 Figure 11.3.6 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps:

Renewables Capacity Reduction

................................ ................................ .. 11

- 7 Figure 11.3.7 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps:

Total Generation Reduction

................................ ................................ ......... 11

- 8 Figure 11.3.8 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps:

Coal Generation Reduction

................................ ................................ .......... 11

- 811

- ii Figure 11.3.9 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps:

Nuclear Generation Reduction

................................ ................................ ..... 11

- 9 Figure 11.3.10 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps: Gas Combined Cycle Generation Reduction

................................ ...................... 11

- 9 Figure 11.3.11 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps: Oil

Generation Reduction

................................ ................................ ................ 11

- 10 Figure 11.3.12 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps

Renewables Generation Reduction

................................ ............................ 11

- 10 Figure 11.3.13 Water

- Source Heat Pumps: Total Electric Capacity Reduction

................ 11

- 12 Figure 11.3.14 Water

- Source Heat Pumps: Coal Capacity Reduction

............................... 11

- 12 Figure 11.3.15 Water

- Source Heat Pumps: Nuclear Capacity Reduction

......................... 11

- 13 Figure 11.3.16 Water

- Source Heat Pumps: Gas Combined Cycle Capacity Reduction

.... 11

- 13 Figu re 11.3.17 Water

- Source Heat Pumps: Peaking Capacity Reduction

......................... 11

- 14 Figure 11.3.18 Water

- Source Heat Pumps: Renewables Capacity Reduction

................... 11

- 14 Figure 11.3.19 Water

- Source Heat Pumps: Total Generation Reduction

.......................... 11

- 15 Figure 11.3.20 Water

- Source Heat Pumps: Coal Generation Reduction

........................... 11

- 15 Figure 11.3.21 Water

- Source Heat Pumps: Nuclear Generation Reduction

...................... 11

- 16 Figure 11.3.22 Water

- Source Heat Pumps: Gas Combined Cycle Generation

Reduction

................................ ................................ ................................ ... 11

- 16 Figure 11.3.23 Water

- Source Heat Pumps: Oil Generation Reduction

............................. 11

- 17 Figure 11.3.24 Water

- Source Heat Pumps: Renewables Generation Reduction

............... 11

- 1711

- 1 CHAPTER 11. UTILITY IMPACT ANALYSIS

11.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes in electric installed capacity and power generation that result for each trial standard level (TSL).

The utility impact analysis is based on output of the DOE/Energy Information Administration (EIA)’s National Energy Modeling System (NEMS). a NEMS is a public domain,

multi

- sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO)

. The EIA publishes a reference case, which incorporates all existing energy

- related policies at the time of publication, and a variety of side cases which analyze the impact of different policies, energy price and market trends. As of 2014, DOE is using a new methodology based on results published for the Annual Energy Outlook 2014 ( AEO 2014 ) 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 compr ehensiveness of NEMS permits the modeling of interactions among the various energy supply and demand sectors.

• Using EIA published side cases to estimate the utility impacts enhances the transparency of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be reduced under the new approach.

On the average, however, 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 Sy stem: An Overview

. 111

- 2 11.2 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 pr ojection of the total electric system load growth . The system load shapes are converted internally to load duration curves, which are then used to estimate the most cost

- effective additions to capacity. When electricity demand deviates from the AEO referen ce 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 th ese 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. Chang es 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 AEO. The side cases, or scenarios, that incorporate significant changes to equipment efficiencies relative to the R eference

case are:

• 2013 Technology (leaves all technologies at 2013 efficiencies) ; • Best Available Technology (highest efficiency irrespective of cost) ; • High Technology (higher penetration rates for efficiency and demand management) ; • Extended Policies (includes efficiency standards that are no t in the reference)

.

Scenarios that incorporate policies that directly affect the power sector without changes in energy demand (for example

, subsidies for renewables, or high fuel price assumptions) are not appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply

- side data on generation, capacity and emissions, and demand

- side data on electricity use by sector and end

- use, are extracted from each side case . The data are converted to

differences relative to the AEO R ef erence case.

2. The changes in electricity use on the demand

- side data are a llocated to one of three categories: on

- peak, shoulder, and off

- peak. These categories are used in the utility sector to

correlate end

- use consumption with supply types. For each of t he 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 Sunda ys. All other hours are assigned to the shoulder period.11

- 3 3. For each year and each side case , the demand

- side reductions to on

- peak, off

- peak and

shoulder

- period electricity use are matched on the supply

- side to reductions in generation by fuel type. The fue l types are petroleum fuels, natural gas, renewables, nuclear and coal. The

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 I s Consistent With The Fact That Oil And Gas Steam Units Are Used In Nems To 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 A nd 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 marginal reduction in end

- use electricity demand to each of the five fuel types

.

5. A regression model is used to relate reductions in generation by fuel type to reductions in

emissions of power sector pollutants. The model produces coefficients that define the change in total annual emissions of a given pollutant resulting from a unit change in total annual generation for each fuel type , as a function of time . These coefficients are combine d with the weights calculated in step 4 to produce coefficients that relate emissions changes to changes

in end

- use demand.

6. A regression model is used to relate reductions in generation by fuel type to reductions in installed capacity. The categories used for installed capacity are the same as for generation except for peak : NEMS uses two peak capacity types (combustion turbin e/diesel and oil and gas steam) which are combined here into a single “peak” category. The model produces

coefficients that define t he 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 install ed capacity changes to changes in end

- use demand , as a function of time

.

7. The coefficient time

- series for fuel share, pollutant emissions and capacity for the appropriate end use are multiplied by the stream of energy savings calculated in the NIA to produ ce estimates of the utility impacts.

This analysis ignores p umped storage , fuel cells and distributed generation , as t hese generation types are not affected by the policy changes modeled in the EIA side cases. The methodology is described in more detail i n K. Coughlin, “Utility S ector I mpacts of E lectricity

D emand R eductions”

. 311

- 4 11.3 UTILITY IMPACT RESUL TS

This section presents results of the analysis for all of the capacity types used to supply electricity to operate small commercial air

- cooled air

- conditioning and heating equipment less than 65,000 Btu/h and water

- source heat pumps in commercial buildings

.

11.3.1 Small Three

- Phase Air

- Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

11.3.1.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that

result for each efficiency level by major plant type for selected years. Note that a negative

number means an increase in capacity under an efficiency level

.

Figure 11. 3

. 1 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Total Electric Capacity Reduction11

- 5 Figure 11.3

. 2 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Coal Capacity Reduction

Figure 11.3

. 3 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Nuclear Capacity Reduction11

- 6 Figure 11.3

. 4 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Gas Combined Cycle Capacity Reduction

Figure 11.3

. 5 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Peaking Capacity Reduction11

- 7 Figure 11.3

. 6 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Renewables Capacity Reduction

11.3.1.2 Electricity Generation

The figures in this section show the annual change in electricity generation that resul t for

each efficiency level by fuel type. Note that a negative number means an increase in generation

under an efficiency level

.11

- 8 Figure 11.3

. 7 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Total

Generation Reduction

Figure 11.3

. 8 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Coal

Generation Reduction11

- 9 Figure 11.3

. 9 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Nuclear
Generation Reduction

Figure 11.3

. 10 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Gas Combined Cycle Generation Reduction11

- 10 Fig ure 11.3

. 11 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps : Oil

Generation Reduction

Figure 11.3

. 12 Small Commercial Air

- Cooled Air Conditioners and Heat Pumps

Renewables Generation Reduction11

- 11 11.3.1.3 Results Summary

Table 11.3

. 1 presents a summary of the utility impact results for small commercial air

cooled air

- conditioning and heating equipment less than 65,000 Btu/h

.

Table 11.3

. 1 Small Commercial Air

- Cooled Air Conditioners and H eat Pumps : Summary of Utility Impact Results

ASHRAE

Level/ EL0 EL 1 EL 2 EL 3 EL 4 EL 5

Installed Capacity Reduction (MW)

2020 9.78 15.69 35.10 42.19 49.37 53.91

2025 35.65 90.44 198.97 241.48 283.41 307.82

2030 65.55 162.17 342.56 417.99 489.80 529.42

2035 96.39 231.28 467.68 574.52 672.11 722.42

2040 124.02 292.01 561.97 696.29 813.12 867.95 Electricity Generation Reduction (GWh)

2020 27.99 44.90 100.46 120.74 141.31 154.29

2025 80.39 203.96 448.70 544.56 639.11 694.16

2030 142.12 351.61 742.73 906.28 1061.98 1147.89

2035 197.52 473.91 958.30 1177.23 1377.19 1480.30

2040 242.25 570.38 1097.69 1360.06 1588.25 1695.36

11.3.2 Water

- Source Heat Pumps

11.3.2.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that

result for each efficiency level by major plant type for selected years. Note that a negative number means an increase in capacity under an efficiency level

.11

- 12 Figure 11.3

. 13 Water

- Source Heat Pumps : Total Electric Capacity Reduction

Figure 11.3

. 14 Water

- Source Heat Pumps : Coal Capacity Reduction11

- 13 Figure 11.3

. 15 Water

- Source Heat Pumps : Nuclear Capacity Reduction

Figure 11.3

. 16 Water

- Source Heat Pumps : Gas Combined Cycle Capacity Reduction11

- 14 Figure 11.3

. 17 Water

- Source Heat Pumps : Peaking Capacity Reduction

Figure 11.3

. 18 Water

- Source Heat Pumps : Renewables Capacity Reduction

11.3.2.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for

each efficiency level by fuel type. Note that a negative number means an increase in generation

under an efficiency level

.11

- 15 Figure 11.3

. 19 Water

- Source Heat Pumps : Total Generation Reduction

Figure 11.3

. 20 Water

- Source Heat Pumps : Coal Generation Reduction11

- 16 Figure 11.3

. 21 Water

- Source Heat Pumps : Nuclear Generation Reduction

Figure 11.3

. 22 Water

- Source Heat Pumps : Gas Combined Cycle Generation Reduction11

- 17 Figure 11.3

. 23 Wa ter

- Source Heat Pumps : Oil Generation Reduction

Figure 11.3

. 24 Water

- Source Heat Pumps : Renewables Generation Reduction

11.3.2.3 Results Summary

Table 11.3

. 2 presents a summary of the utility impact results for water

- source heat

pumps

.11

- 18 Table 11.3

. 2 Water

- Source Heat Pumps : Summary of Utility Impact R esults

ASHRAE

Level/ EL0 * EL 1 EL 2 EL 3 EL 4 EL 5

Installed Capacity Reduction (MW)

2020

- 2.08 19.96 36.77 50.42 71.04

2025

- 13.45 146.57 272.27 373.37 523.72

2030

- 23.73 271.34 506.24 693.22 966.08

2035

- 34.10 394.49 738.04 1008.29 1393.85

2040

- 43.63 504.36 945.74 1288.17 1763.56 Electricity Generation Reduction (GWh)

2020

- 6.65 63.66 117.28 160.79 226.58

2025

- 34.83 379.64 705.24 967.11 1356.54

2030

- 58.74 671.65 1253.09 1715.92 2391.31

2035

- 79.48 919.62 1720.51 2350.51 3249.31

2040

- 96.60 1116.59 2093.74 2851.85 3904.29

* There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in

the base case.11

- 19 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 Imacts of Reduced Electricity Demand , 2014. Lawrence Berkeley National Laboratory. Report No. LBNL

- 6864E

.