NIST Technical Note 1918

Building Industry Reporting and 

Design for Sustainability (BIRDS) 

Low-Energy Residential Database 

Technical Manual 

Joshua Kneifel

Priya Lavappa

Eric O’Rear

Anne Landfield Greig

Sangwon Suh

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.TN.1918

NIST Technical Note 1918

Building Industry Reporting and 

Design for Sustainability (BIRDS) 

Low-Energy Residential Database 

Technical Manual 

Joshua Kneifel

Priya Lavappa 

 Eric O’Rear

Applied Economics Office 

Engineering Laboratory 

Anne Landfield Greig 

Four Elements Consulting, LLC

Sangwon Suh

Industrial Ecology Research Services, LLC

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.TN.1918

May 2016

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

 Willie May, Under Secretary of Commerce for Standards and Technology and Director
Certain commercial entities, equipment, or materials may be identified in this

document in order to describe an experimental procedure or concept adequately.

Such identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1918

Natl. Inst. Stand. Technol. Tech. Note 1918, 125 pages (May 2016) 

CODEN: NTNOEF

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.TN.1918
i

Abstract

Building stakeholders need practical metrics, data, and tools to support decisions related to 

sustainable building designs, technologies, standards, and codes. The Engineering Laboratory of 

the National Institute of Standards and Technology (NIST) has addressed this high priority 

national need by extending its metrics and tools for sustainable building products, known as 

Building for Environmental and Economic Sustainability (BEES), to whole-buildings. Wholebuilding sustainability metrics have been developed based on innovative extensions to life-cycle 

assessment (LCA) and life-cycle costing (LCC) approaches involving whole-building energy 

simulations. The measurement system evaluates the sustainability of both the materials and the 

energy used by a building over time. It assesses the “carbon footprint” of buildings as well as 11 

other environmental performance metrics, and integrates economic performance metrics to yield 

science-based measures of the business case for investment choices in high-performance green 

buildings.

Building Industry Reporting and Design for Sustainability (BIRDS) applies the new 

sustainability measurement system to an extensive whole-building performance database NIST 

has compiled for this purpose. Based on the NIST Net-Zero Energy Residential Test Facility 

(NZERTF), the BIRDS Low-Energy Residential building database includes energy, 

environmental, and cost measurements for 240 000 design variations of the NZERTF for 

Gaithersburg, MD for study period lengths ranging from 1 year to 40 years. Focusing on a single 

location allows for the consideration of incremental building energy efficiency measures, both 

those specified in the most recent International Energy Conservation Code (IECC) as well as 

those adopted in the NZERTF design that lead to net-zero energy performance for a one-year 

period of experimental operation. The sustainability performance of buildings designed to meet 

current energy codes can be compared to a number of alternative building designs to determine 

the impacts of improving building energy efficiency on overall sustainability performance. This 

report describes the latest version of BIRDS v3.0 including additional options that were not 

included in previous versions such as varying discount rates, home purchase financing, and 

quality of construction options. 

Keywords

Building economics; economic analysis; life-cycle costing; life-cycle assessment; energy 

efficiency; residential buildings; low-energy buildings; net-zero energy performance
ii
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Preface

This documentation was developed by the Applied Economics Office (AEO) in the 

Engineering Laboratory (EL) at the National Institute of Standards and Technology 

(NIST). The document explains how the BIRDS low-energy residential database was 

developed, including the assumptions and data sources for the energy, environmental, and 

cost estimate calculations. The intended audience is BIRDS v3.0 users, researchers and 

decision makers in the residential building sector, and others interested in building 

sustainability.

Disclaimers

The policy of the National Institute of Standards and Technology is to use metric units in 

all of its published materials. Because this report is intended for the U.S. construction 

industry that uses U.S. customary units, it is more practical and less confusing to include 

U.S. customary units as well as metric units. Measurement values in this report are 

therefore stated in metric units first, followed by the corresponding values in U.S. 

customary units within parentheses.
iv
v

Acknowledgements

The authors wish to thank all those who contributed ideas and suggestions for this report. They 

include Mr. David Webb and Dr. Robert Chapman of EL’s Applied Economics Office, Stuart 

Dols of EL’s Energy and Environment Division, and Dr. Nicos S. Martys of EL’s Materials and 

Structural Systems Division. A special thanks to the Industrial Ecology Research Services team 

of Shivira Tomar, Christine Chen, and Matthew Leighton for their superb technical support in 

developing whole-building environmental life-cycle assessments for BIRDS. Thanks go to 

researchers at the Institute for Software Integrated Systems of the School of Engineering at 

Vanderbilt University for assisting in generating and compiling the energy simulation results. 

Thanks goes to our industry contacts that were instrumental in advising on the assumptions used 

to develop the product-level life-cycle impact assessments: Daniel Lea of the Cellulose 

Insulation Manufacturers Association, Dr. Richard S. Duncan of the Spray Polyurethane Foam 

Alliance (SPFA), Anders Schmidt of dk-Teknik Energy & Environment, and members of the 

Extruded Polystyrene Foam Association (XPSA). Thanks to Ms. Shannon Craig for assisting in 

developing the low-energy residential sustainability database. Finally, the many Beta testers of 

BIRDS v3.0 deserve special thanks for contributing suggestions leading to substantial 

improvements in the tool.
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vii

Author Information

Joshua Kneifel

Economist

National Institute of Standards and Technology

Engineering Laboratory

100 Bureau Drive, Mailstop 8603

Gaithersburg, MD 20899 8603

Tel.: 301-975-6857

Email: joshua.kneifel@nist.gov

Priya Lavappa

Computer Specialist

National Institute of Standards and Technology

Engineering Laboratory

100 Bureau Drive, Mailstop 8603

Gaithersburg, MD 20899 8603

Tel.: 301-975-4522

Email: priya.lavappa@nist.gov

Eric O’Rear

Economist

National Institute of Standards and Technology

Engineering Laboratory

100 Bureau Drive, Mailstop 8603

Gaithersburg, MD 20899 8603

Tel.: 301-975-4570

Email: eric.orear@nist.gov

Anne Landfield Greig

Principal 

Four Elements Consulting, LLC

Seattle, WA

Tel: 206-935-4600

Sangwon Suh

Director and Founder

Industrial Ecology Research Services (IERS), LLC

5951 Encina Rd, Suite 206

Goleta, CA 93117

Tel: 805-324-4674

Email: suh@iersweb.com
viii
ix

Contents

ABSTRACT................................................................................................................................................. I

PREFACE................................................................................................................................................. III

ACKNOWLEDGEMENTS ...................................................................................................................... V

AUTHOR INFORMATION ..................................................................................................................VII

LIST OF ACRONYMS ....................................................................................................................... XVII

1 INTRODUCTION...............................................................................................................................1

1.1 PURPOSE........................................................................................................................................1

1.2 BACKGROUND ...............................................................................................................................2

2 BIRDS APPROACH...........................................................................................................................3

2.1 RETHINK SUSTAINABILITY MEASUREMENT .................................................................................3

2.2 ESTABLISH CONSISTENCY.............................................................................................................6

3 ENERGY PERFORMANCE MEASUREMENT............................................................................9

3.1 BACKGROUND ...............................................................................................................................9

3.2 GEOMETRY AND BUILDING ENVELOPE.........................................................................................9

3.3 OCCUPANCY................................................................................................................................14

3.4 LIGHTING ....................................................................................................................................14

3.5 PLUG LOADS ...............................................................................................................................15

3.6 HEATING, VENTILATION, AND AIR CONDITIONING (HVAC) .....................................................16

3.6.1 Temperature, Humidity, and Ventilation Controls .................................................................16

3.6.2 Heating and Cooling Equipment.............................................................................................17

3.7 DOMESTIC HOT WATER ..............................................................................................................19

3.7.1 Domestic Hot Water Heater Equipment .................................................................................19

3.7.2 Domestic Hot Water End Use .................................................................................................20

3.8 SOLAR PHOTOVOLTAICS .............................................................................................................21

4 ENVIRONMENTAL PERFORMANCE MEASUREMENT.......................................................23

4.1 GOAL AND SCOPE DEFINITION....................................................................................................23

4.2 LIFE-CYCLE INVENTORY ANALYSIS............................................................................................25

4.3 LIFE-CYCLE IMPACT ASSESSMENT..............................................................................................28

4.3.1 BIRDS Impact Assessment ......................................................................................................29

4.3.1.1 Impact Categories ...........................................................................................................29

Global Warming..........................................................................................................29

Primary Energy Consumption.....................................................................................30

Human Health – Criteria Air Pollutants......................................................................30

Human Health – Cancer Effects..................................................................................30

Water Consumption ....................................................................................................30

Ecological Toxicity.....................................................................................................30

Eutrophication Potential..............................................................................................31

Land Use .....................................................................................................................31

Human Health – Non-cancer Effects ..........................................................................31

Smog Formation.........................................................................................................31
x

Acidification Potential ...............................................................................................31

Ozone Depletion ........................................................................................................31

4.3.1.2 Computational Algorithms..............................................................................................32

4.3.2 BIRDS Normalization .............................................................................................................33

4.4 LIFE-CYCLE INTERPRETATION ....................................................................................................35

4.4.1 EPA Science Advisory Board Study ........................................................................................36

4.4.2 BEES Stakeholder Panel Judgments.......................................................................................38

4.5 BIRDS RESIDENTIAL ENERGY TECHNOLOGIES..........................................................................42

4.5.1 General Information Regarding the Energy Technology LCIs...............................................42

4.5.1.1 Standards Used................................................................................................................42

4.5.1.2 Primary and Secondary Data Sources.............................................................................43

4.5.1.3 Data Sources Used for the Background Data..................................................................43

4.5.2 Wall and Ceiling Insulation ....................................................................................................44

4.5.2.1 Fiberglass Blanket...........................................................................................................45

Upstream Materials Production through Manufacturing ............................................46

Transportation to the Building through End-of-Life...................................................47

4.5.2.2 Blown Cellulose Insulation.............................................................................................48

Upstream Materials Production through Manufacturing ............................................48

Transportation to the Building Site through End-of-Life............................................49

4.5.2.3 Spray Polyurethane Foam Insulation ..............................................................................49

Upstream Materials Production through Manufacturing ............................................50

Transportation to the Building Site through End-of-Life............................................54

4.5.2.4 Mineral Wool Blanket Insulation....................................................................................55

Upstream Materials Production through Manufacturing ............................................55

Transportation to the Building Site through End-of-Life............................................57

4.5.2.5 XPS Foam Insulation ......................................................................................................57

Upstream Materials Production through Manufacturing ............................................57

Transportation to the Building Site through End-of-Life............................................59

4.5.2.6 Polyisocyanurate Foam Insulation..................................................................................59

Upstream Materials Production through Manufacturing ............................................60

Transportation to the Building Site through End-of-Life............................................62

4.5.3 Windows..................................................................................................................................62

4.5.3.1 Introduction.....................................................................................................................62

4.5.3.2 Casement Windows Bill of Materials.............................................................................64

4.5.3.3 Double Hung Window Bill of Materials.........................................................................66

4.5.3.4 Production Data for the Materials...................................................................................67

4.5.3.5 Manufacturing.................................................................................................................69

4.5.3.6 Transportation to the Building Site, Use and Maintenance ............................................70

4.5.3.7 End-of-Life .....................................................................................................................70

4.5.4 HVAC......................................................................................................................................71

4.5.4.1 Natural Gas and Electric Furnaces..................................................................................71

Upstream Materials Production through Manufacturing ............................................71

Transportation to the Building Site through End-of-Life............................................74

4.5.4.2 Residential Air Conditioners...........................................................................................75

Upstream Materials Production through Manufacturing ............................................75

Transportation to the Building Site through End-of-Life............................................78

4.5.5 Residential Electric and Gas Water Heaters..........................................................................79
xi

4.5.5.1 Upstream Materials Production through Manufacturing ................................................79

4.5.5.2 Transportation to the Building Site through End-of-Life................................................81

4.5.6 Lighting...................................................................................................................................81

4.5.6.1 Upstream Materials Production through Manufacturing ................................................82

Incandescent................................................................................................................82

Compact Fluorescent Lamp (CFL) .............................................................................84

Light-Emitting Diode (LED).......................................................................................86

4.5.6.2 Transportation to the Building Site through End-of-Life................................................88

4.5.7 Sealants...................................................................................................................................89

4.5.7.1 Upstream Materials Production through Manufacturing ................................................90

Ductwork Sealant........................................................................................................90

Exterior Sealant...........................................................................................................90

4.5.7.2 Transportation to the Building Site through End-of-Life................................................92

5 ECONOMIC PERFORMANCE MEASUREMENT ....................................................................93

5.1 FIRST COST..................................................................................................................................93

5.1.1 Total Construction Costs ........................................................................................................93

5.1.2 Purchase Financing ................................................................................................................95

5.1.3 Building Components..............................................................................................................96

5.1.3.1 Baseline Building Components.......................................................................................96

5.1.3.2 Building Envelope Components .....................................................................................97

5.1.3.3 Building System Components.........................................................................................99

5.2 FUTURE COSTS..........................................................................................................................101

5.2.1 Operating Energy..................................................................................................................102

5.2.2 Maintenance, Repair, and Replacement (MRR) Costs..........................................................103

5.3 RESIDUAL VALUE .....................................................................................................................105

5.4 LIFE-CYCLE COST ANALYSIS....................................................................................................106

6 SOFTWARE DEVELOPMENT AND DESIGN..........................................................................109

6.1 DATABASE.................................................................................................................................109

6.1.1 Database Management – Sql Server.....................................................................................109

6.1.2 Database Development - Python...........................................................................................109

6.2 APPLICATION.............................................................................................................................109

6.2.1 Software Programming Language – C# ...............................................................................109

6.2.2 Software Framework - .NET .................................................................................................109

6.2.3 Web Development Technologies – HTML, CSS, JavaScript, JQuery, jqChart.....................110

6.2.4 Application Design – Visual Studio ......................................................................................110

7 LIMITATIONS AND FUTURE RESEARCH.............................................................................113

REFERENCES........................................................................................................................................117
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List of Figures

FIGURE 2-1 BIRDS SUSTAINABILITY FRAMEWORK ......................................................................................................6

FIGURE 3-1 BSC ARCHITECTURAL MASSING MODEL AND NZERTF AS BUILT ..........................................................10

FIGURE 3-2 GOOGLE SKETCHUP 3-D REPRESENTATION OF THE E+ MODEL................................................................10

FIGURE 3-3 OCCUPANCY DENSITY ..............................................................................................................................14

FIGURE 3-4 ELECTRICAL EQUIPMENT LOAD PROFILES AS A PROPORTION OF PEAK WATTAGE, BY HOUR..................16

FIGURE 3-5 HVAC SYSTEM LAYOUT FOR (A) NZERTF (B) STANDARD EFFICIENCY..................................................18

FIGURE 3-6 DOMESTIC HOT WATER HEATING SYSTEMS (A) HEAT PUMP WATER HEATER WITH SOLAR THERMAL (B)

ELECTRIC WATER HEATER WITH SOLAR THERMAL (C) HEAT PUMP WATER HEATER (D) ELECTRIC WATER 

HEATER ...............................................................................................................................................................20

FIGURE 3-7 DOMESTIC HOT WATER LOAD PROFILES AS A PROPORTION OF PEAK FLOW RATE, BY HOUR .................21

FIGURE 4-1 COMPILING LCA INVENTORIES OF ENVIRONMENTAL INPUTS AND OUTPUTS ...........................................25

FIGURE 4-2 ILLUSTRATION OF SUPPLY CHAIN CONTRIBUTIONS TO U.S. CONSTRUCTION INDUSTRY ..........................27

FIGURE 4-3 BEES STAKEHOLDER PANEL IMPORTANCE WEIGHTS SYNTHESIZED ACROSS VOTING INTEREST AND TIME 

HORIZON .............................................................................................................................................................41

FIGURE 4-4 BEES STAKEHOLDER PANEL IMPORTANCE WEIGHTS BY STAKEHOLDER VOTING INTEREST ...................41

FIGURE 4-5 BEES STAKEHOLDER PANEL IMPORTANCE WEIGHTS BY TIME HORIZON ................................................42

FIGURE 4-6 INSULATION SYSTEM BOUNDARIES – FIBERGLASS BLANKET EXAMPLE...................................................45

FIGURE 4-7 WINDOWS SYSTEM BOUNDARIES .............................................................................................................64

FIGURE 4-8 HVAC SYSTEM BOUNDARIES – ELECTRIC FURNACE EXAMPLE ...............................................................71

FIGURE 4-9 LIGHTING SYSTEM BOUNDARIES – CFL EXAMPLE ...................................................................................82

FIGURE 4-10 SEALANTS SYSTEM BOUNDARIES – EXTERIOR SEALANT EXAMPLE .......................................................89

FIGURE 5-1 RS MEANS ONLINE SQUARE FOOT COST ESTIMATOR (SFCE) .................................................................94

FIGURE 5-2 BASELINE MAINTENANCE AND REPAIR COSTS BY YEAR ........................................................................104

FIGURE 6-1 APPLICATION INFORMATION FLOW .........................................................................................................110
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List of Tables

TABLE 3-1 MATERIAL PARAMETER CALCULATION APPROACH...................................................................................11

TABLE 3-2 CONSTRUCTIONS – ROOF, CEILING, WALL AND FOUNDATION...................................................................12

TABLE 3-3 WINDOW SPECIFICATIONS .........................................................................................................................13

TABLE 3-4 AIR EXCHANGE RATES ..............................................................................................................................13

TABLE 3-5 OCCUPANT ACTIVITY LEVEL .....................................................................................................................14

TABLE 3-6 LIGHTING TOTAL WATTAGE BY FLOOR BY DESIGN OPTIONS ....................................................................15

TABLE 3-7 DAILY DOMESTIC HOT WATER USE AND TARGET TEMPERATURES...........................................................21

TABLE 4-1 CONSTRUCTION INDUSTRY OUTPUTS MAPPED TO BIRDS BUILDING TYPES .............................................28

TABLE 4-2 BIRDS LIFE-CYCLE IMPACT ASSESSMENT CALCULATIONS BY BUILDING COMPONENT............................33

TABLE 4-3 BIRDS NORMALIZATION REFERENCES......................................................................................................35

TABLE 4-4 PAIRWISE COMPARISON VALUES FOR DERIVING IMPACT CATEGORY IMPORTANCE WEIGHTS ..................37

TABLE 4-5 RELATIVE IMPORTANCE WEIGHTS BASED ON SCIENCE ADVISORY BOARD STUDY....................................38

TABLE 4-6 RELATIVE IMPORTANCE WEIGHTS BASED ON BEES STAKEHOLDER PANEL JUDGMENTS ..........................40

TABLE 4-7 SPECIFIED INSULATION TYPES AND R-VALUES..........................................................................................44

TABLE 4-8 FIBERGLASS BLANKET MASS BY APPLICATION .........................................................................................46

TABLE 4-9 FIBERGLASS INSULATION CONSTITUENTS..................................................................................................46

TABLE 4-10 ENERGY REQUIREMENTS FOR FIBERGLASS INSULATION MANUFACTURING ............................................47

TABLE 4-11 BLOWN CELLULOSE INSULATION BY APPLICATION .................................................................................48

TABLE 4-12 CELLULOSE INSULATION CONSTITUENTS.................................................................................................48

TABLE 4-13 B-SIDE FORMULATION – MATERIAL CONSTITUENT PERCENTAGES .........................................................51

TABLE 4-14 SPF INSULATION REFERENCE UNIT PARAMETERS FOR ORIGINAL AND BIRDS LCAS.............................52

TABLE 4-15 MATERIAL CONSTITUENTS FOR OPEN-CELL AND CLOSED-CELL SPF INSULATION .................................53

TABLE 4-16 MINERAL WOOL BLANKET MASS BY APPLICATION.................................................................................55

TABLE 4-17 MINERAL WOOL INSULATION CONSTITUENTS .........................................................................................56

TABLE 4-18 ENERGY REQUIREMENTS FOR MINERAL WOOL INSULATION MANUFACTURING......................................56

TABLE 4-19 XPS FOAM BOARD PRODUCTION DATA...................................................................................................58

TABLE 4-20 RAW MATERIAL INPUTS TO PRODUCE POLYISO FOAM ............................................................................60

TABLE 4-21 ENERGY INPUTS AND PROCESS OUTPUTS FOR 1 BOARD-FOOT POLYISO FOAM .......................................61

TABLE 4-22 WINDOW SPECIFICATIONS .......................................................................................................................63

TABLE 4-23 DIMENSIONS AND MAIN PARTS OF THE WOOD CLAD CASEMENT WINDOW.............................................65

TABLE 4-24 DIMENSIONS AND MAIN PARTS OF THE ALUMINUM CASEMENT WINDOW...............................................65

TABLE 4-25 DIMENSIONS AND MAIN PARTS OF THE VINYL CASEMENT WINDOW.......................................................65

TABLE 4-26 DIMENSIONS AND MAIN PARTS OF THE WOOD CLAD DOUBLE HUNG WINDOW ......................................66

TABLE 4-27 DIMENSIONS AND MAIN PARTS OF THE ALUMINUM DOUBLE HUNG WINDOW ........................................67

TABLE 4-28 DIMENSIONS AND MAIN PARTS OF THE VINYL DOUBLE HUNG WINDOW ................................................67

TABLE 4-29 NATURAL GAS FURNACE BILL OF MATERIALS ........................................................................................72

TABLE 4-30 ELECTRIC FURNACE BILL OF MATERIALS ................................................................................................73

TABLE 4-31 FURNACE MANUFACTURING....................................................................................................................74

TABLE 4-32 CONDENSER UNIT BILL OF MATERIALS ...................................................................................................76

TABLE 4-33 CONDENSER UNIT MASSES ......................................................................................................................77

TABLE 4-34 REFRIGERANT QUANTITIES ......................................................................................................................77

TABLE 4-35 ELECTRIC WATER HEATER BILL OF MATERIALS .....................................................................................79

TABLE 4-36 GAS WATER HEATER BILL OF MATERIALS ..............................................................................................80

TABLE 4-37 PERFORMANCE OF LIGHTING TECHNOLOGIES IN BIRDS .........................................................................82

TABLE 4-38 INCANDESCENT LIGHT BULB BILL OF MATERIALS ..................................................................................83

TABLE 4-39 CFL BILL OF MATERIALS ........................................................................................................................85

TABLE 4-40 LED BILL OF MATERIALS ........................................................................................................................87
xvi

TABLE 4-41 FOIL TAPE BILL OF MATERIALS ...............................................................................................................90

TABLE 4-42 EXTERIOR SEALANT BILL OF MATERIALS................................................................................................91

TABLE 5-1 BASELINE BUILDING ASSUMPTIONS...........................................................................................................97

TABLE 5-2 WALL, ATTIC, AND FOUNDATION COSTS ($/FT2)........................................................................................98

TABLE 5-3 AIR LEAKAGE RATES AND COSTS ..............................................................................................................99

TABLE 5-4 WINDOW COSTS (PER FT2) .........................................................................................................................99

TABLE 5-5 LIGHTING COSTS......................................................................................................................................100

TABLE 5-6 HVAC EQUIPMENT COSTS (3 TON) .........................................................................................................100

TABLE 5-7 DHW COSTS............................................................................................................................................101

TABLE 5-8 2009 SPV DISCOUNT FACTORS FOR FUTURE NON-FUEL COSTS, 8 % AND 3 % REAL DISCOUNT RATE...103

TABLE 5-9 BUILDING COMPONENT AVERAGE MAINTENANCE AND REPAIR COSTS...................................................105
xvii

List of Acronyms

BEA Bureau of Economic Analysis

BEES Building for Environmental and Economic Sustainability

BIRDS Building Industry Reporting and Design for Sustainability

BSC Building Science Corporation

C&D construction and demolition

CFA conditioned floor area

CFC-11 trichlorofluoromethane

CFL compact fluorescent lamp

CFM cubic feet per minute

CO2 carbon dioxide

CO2e carbon dioxide equivalent

COP coefficient of performance

E+ EnergyPlus

EERE Office of Energy Efficiency & Renewable Energy

eGDP environmental gross domestic product

EIA Energy Information Administration

EL Engineering Laboratory

ELA effective leakage area

EPA Environmental Protection Agency

EPD environmental product declaration

EPDM ethylene propylene diene monomer

EPS expanded polystyrene
xviii

Acronym Definition

GDP gross domestic product

HBCD hexabromocyclododecane

HCFC hydrochlorofluorocarbon

HDPE high density polyethylene

HFC hydrochlorofluorocarbon

HSPF heating seasonal performance factor

HVAC heating, ventilation, and air-conditioning

IECC International Energy Conservation Code

IGU insulated glass unit

I-O input-output

IPCC Intergovernmental Panel on Climate Change

ISO International Organization for Standardization

LBNL Lawrence Berkeley National Laboratory

LCA life-cycle assessment

LCC life-cycle cost

LCI life-cycle inventory

LCIA life-cycle impact assessment

LED light-emitting diode

Low-E low-emissivity

M&R maintenance and repair

MBH million Btu per hour

MDI methylene diphenyl diisocyanate

MRR maintenance, repair, and replacement

MSDS material safety data sheet

NAHB National Association of Home Builders

NIST National Institute of Standards and Technology

NS net savings

NZERTF Net-Zero Energy Residential Test Facility

PBDE polybrominated diphenyl ethers

List of Acronyms
xix
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1

1 Introduction

1.1 Purpose

Building stakeholders need practical metrics, data, and tools to support decisions related 

to sustainable building designs, technologies, standards, and codes. The Engineering 

Laboratory (EL) of the National Institute of Standards and Technology (NIST) has

addressed this high priority national need by extending its metrics and tools for 

sustainable building products, known as Building for Environmental and Economic 

Sustainability (BEES), to whole-buildings. Whole-building sustainability metrics have 

been developed based on innovative extensions to environmental life-cycle assessment 

(LCA) and life-cycle costing (LCC) approaches involving whole-building energy 

simulations. The measurement system evaluates the sustainability of both the materials 

and energy used by a building over time. It assesses the “carbon footprint” of buildings as 

well as 11 other environmental performance metrics, and integrates economic 

performance metrics to yield science-based measures of the business case for investment 

choices in high-performance green buildings.

The approach previously developed for BEES has now been applied at the wholebuilding level to address building sustainability measurement in a holistic, integrated 

manner that considers complex interactions among building materials, energy 

technologies, and systems across dimensions of performance, scale, and time. Building 

Industry Reporting and Design for Sustainability (BIRDS) applies the sustainability 

measurement system to an extensive whole-building performance database NIST has 

compiled for this purpose. The energy, environment, and cost data in BIRDS provide

measures of building operating energy use based on detailed energy simulations, building 

materials use through innovative life-cycle material inventories, and building costs over 

time. BIRDS v1.0 included energy, environmental, and cost measurements for 12 540 

commercial and non-low rise residential buildings, covering 11 building prototypes in 

228 cities across all U.S. states for 9 study period lengths. See Lippiatt et al. (2013) for 

additional details. BIRDS v2.0 included both a commercial and residential database 

which incorporated the energy, environmental, and cost measurements for 9120 

residential buildings, covering 10 single family dwellings (5 one-story and 5 two-story of 

various conditioned floor area) in 228 cities for study period lengths ranging from 1 year 

to 40 years. 

Similar to the previous databases, the low-energy residential database incorporated into 

BIRDS v3.0 includes the energy, environmental, and cost measurements. However, 

instead of considering locations across the country with minimal building design options, 

BIRDS v3.0 allows for detailed incremental energy efficiency measure analysis for a 

single location, 240 000 variations in residential building designs based on the NIST NetZero Energy Residential Test Facility (NZERTF) specifications and varying requirements 

across International Energy Conservation Code (IECC) editions. Again, study period 

lengths from 1 year to 40 years are included in the low-energy residential database. The 
2

sustainability performance of buildings designed to meet current energy codes can be 

compared to a number of alternative building designs to determine the impacts of 

improving building energy efficiency as well as varying the investor time horizon and 

other assumptions affecting overall sustainability performance.1 

1.2 Background

A wave of interest in sustainability gathered momentum in 1992 with the Rio Earth 

Summit, during which the international community agreed upon a definition of 

sustainability in the Bruntland report: “meeting the needs of the present generation 

without compromising the ability of future generations to meet their own needs”

(Brundtland Commission 1987). In the context of sustainable development, needs can be 

thought to include the often-conflicting goals of environmental quality, economic wellbeing, and social justice. While the intent of the 1992 summit was to initiate 

environmental and social progress, it seemed to have instead brought about greater debate 

over the inherent conflict between sustainability and economic development (Meakin 

1992).

This conflict is particularly apparent within the construction industry. Well-intentioned 

green development plans may not be executed for economic reasons, and economic 

development plans may fail to materialize over concerns for the environment and public 

health. Thus, an integrated approach to sustainable construction – one that simultaneously 

considers both environmental and economic performance – lies at the heart of reconciling 

this conflict. For this reason, the BIRDS approach considers both the environmental and 

economic dimensions of sustainability. BIRDS, however, does not consider the social 

dimension of sustainability at this time due to the current lack of applicable rigorous 

measurement methods.

 1 A forthcoming user guide will include a detailed tutorial of how to use the BIRDS low-energy residential 

database web interface to make these different types of comparisons.
3

2 BIRDS Approach

2.1 Rethink Sustainability Measurement

One standardized and preferred approach for scientifically measuring the environmental 

performance of industrial products and systems is life-cycle assessment (LCA). LCA is a 

“cradle-to-grave” systems approach for measuring environmental performance. The 

approach is based on two principles. First, the belief that all stages in the life of a product 

generate environmental impacts and must be analyzed, including raw materials 

acquisition, product manufacture, transportation, installation, operation and maintenance, 

and ultimately recycling and waste management. An analysis that excludes any of these 

stages is limited because it ignores the full range of upstream and downstream impacts of 

stage-specific processes. LCA broadens the environmental discussion by accounting for 

shifts of environmental problems from one life-cycle stage to another. The second 

principle is that multiple environmental impacts must be considered over these life-cycle 

stages in order to implement a trade-off analysis that achieves a genuine reduction in 

overall environmental impact, rather than a simple shift of impact. By considering a 

range of environmental impacts, LCA accounts for impact-shifting from one 

environmental medium (land, air, water) to another. 

The LCA method is typically applied to products, or simple product assemblies, in a 

“bottom up” manner. The environmental inputs and outputs to all the production 

processes throughout a product’s life-cycle are compiled. These product life-cycle 

“inventories” quantify hundreds, even thousands, of environmental inputs and outputs. 

This is a data-intensive, time-consuming, and expensive process that must be repeated for 

every product.

The bottom-up approach becomes unwieldy and cost prohibitive for complex systems, 

such as buildings, that involve potentially hundreds of products. Furthermore, a 

building’s sustainability is not limited to the collective sustainability of its products. The 

manner in which designers integrate these products and systems at the whole-building 

level has a large influence on another major dimension of its sustainability performance, 

operating energy use.

The many dimensions of a building’s environmental performance are ultimately balanced 

against its economic performance. Even the most environmentally conscious 

policymaker, building designer, or potential homeowner will ultimately weigh 

environmental benefits against economic costs. A 2006 poll by the American Institute of 

Architects showed that 90 % of U.S. consumers would be willing to pay more to reduce 

their home’s environmental impact, but only an additional $4000 to $5000, or about 2 %, 

more.

2 More recent studies have shown that U.S. home buyers are willing to pay more for 

sustainable building designs or homes with green attributes like solar photovoltaic (PV).

Dastrup, Zivin et al. (2012) used a hedonic pricing approach to find that solar panels are 

 2 January 2006 survey cited in Green Buildings in the Washington Post (Cohen 2006).
4

capitalized, on average, at a premium of roughly 3.5 % based on a sample of homes 

located in San Diego, California and Sacramento, California. Hoen (2011) investigated 

the effects of solar photovoltaic on the sales prices of homes based on a much larger 

share of the California housing market. He found that average premiums for residential 

solar photovoltaic systems range from roughly $3.90 to $5.80 per watt.

A study conducted by Pfleger, Perry et al. (2011) discovered that ENERGY STAR

certified homes in North Carolina, on average, sold for $5,566 more than similar codecompliant homes when differences between the homes were minimized. They also sold 

for $2.99 more per square foot on average. Griffin, Kaufman et al. (2009) found that 

third-party certified homes in the Portland, Oregon area sold for 4.2% more on average 

than non-certified homes. Kok and Kahn (2012) showed that green labeled homes in 

California realize a sales price that is $34 800 or 9 % (+/- 4 %) higher than a non-labeled 

home. In a later study, the same authors found that green labeled homes in California that 

have been certified by the U.S. Green Building Council (USGBC), EPA, or Build It 

Green rating agency, realize an average premium between 2.0 % to 4.0 % (Kahn and Kok 

2014). Moreover, they discover that premium values tend to vary based on local climatic 

conditions and environmental ideologies. Aroul and Hansz (2012) estimated a more 

modest increase of 2.1 % to 2.4 % in home transaction prices for green-rated homes for 

two Texas cities, Frisco and McKinney. More recently, Adomatis (2015) discovered that 

the average and median premium captured by high-performance homes in Washington, 

D.C. were approximately 3.46 % and 2.91 %, respectively. There appears to be 

significant variation across locations in the value placed on green-rated homes, which 

may be driven by consumer preferences or knowledge. To satisfy stakeholders, the green 

building community needs to promote and design buildings with an attractive balance of 

environmental and economic performance. These considerations require innovative 

means to address sustainability performance for buildings. 

The BIRDS model applies a unifying LCA framework developed for the U.S. economy 

to the U.S. construction sector and its constituent building types. Through this “topdown” LCA approach, a series of baseline sustainability measurements are made for 

prototypical buildings, yielding a common yardstick for measuring sustainability with 

roots in well-established national environmental and economic statistics. Using detailed 

“bottom-up” data compiled through traditional LCA approaches, the baseline 

measurements for prototypical buildings are then “hybridized” to reflect a range of 

improvements in building energy efficiency, enabling assessment of their energy, 

environmental, and economic benefits and costs. The idea is to provide a cohesive 

database and measurement system based on sound science that can be used to prioritize 

green building issues and to track progress over time as design and policy solutions are 

implemented. “Bottom-up” and “top-down” data sources and approaches will be 

discussed in further detail in Chapter 4.

The BIRDS hybrid LCA approach combines the advantages of both the bottom-up and 

top-down approaches—namely the use of higher-resolution, bottom-up data and the use 
5

of regularly-updated, top-down statistical data without truncation (Suh, Lenzen et al. 

2004, Suh and Huppes 2005). The hybrid approach generally reduces the uncertainty of 

existing pure bottom-up or pure top-down systems by reducing truncation error in the 

former and increasing the resolution of the latter (Suh, Lenzen et al. 2004). The hybrid 

approach will be discussed in further detail in Chapter 4.

Operating energy use—a key input to whole-building LCAs—is assessed in BIRDS using 

the bottom-up approach. Energy use is highly dependent upon a building’s function, size, 

location, and the efficiency of its energy technologies. Energy efficiency requirements in 

current energy codes for residential buildings vary across states, and many states have not 

yet adopted a code based on the newest energy code editions. As of November 2015, 

state energy code adoptions range across all editions of the International Energy 

Conservation Code (IECC) for Residential Buildings (International Code Council (ICC) 

2006, International Code Council (ICC) 2009, International Code Council (ICC) 2012, 

International Code Council (ICC) 2015). Some states do not have a code requirement for 

energy efficiency, leaving it up to the locality or jurisdiction to set its own requirement. 

To address these issues, operating energy use in BIRDS is tailored to building types, 

locations, and energy codes. The current low-energy residential BIRDS database is 

constructed based on the NZERTF’s current location, Gaithersburg, MD, and includes all 

combinations of building component specifications defined for each climate zone in each 

of the IECC editions as well as additional incremental energy efficiency measures 

(EEMs) based on the NZERTF’s building specifications.

Like operating energy use, a building’s economic performance is dependent upon a 

building’s design and location. Construction material and labor costs vary by location, as 

do maintenance, repair, and replacement costs over time. Energy technologies for 

compliance with a given IECC edition vary across U.S. climate zones, as do their costs. 

Finally, a building’s operating energy costs vary according to the quantity and price of 

energy use, which depend upon the building’s location and fluctuate over time. All these 

variables are accounted for in the BIRDS databases, as shown in Figure 2-1. The lowenergy residential BIRDS database currently includes a single location and including the 

variations across climate zones will simplify future expansion of the database to include 

other locations starting with those within the same climate zone as the NZERTF (Kneifel 

and O'Rear 2015).
6

Figure 2-1 BIRDS Sustainability Framework

2.2 Establish Consistency

The new way of measuring building sustainability performance in BIRDS requires that 

special attention be paid to establishing consistency among its many dimensions. While 

BIRDS develops separate performance metrics for building energy, environmental, and 

economic performance, they are all developed using the same parameters and 

assumptions. For each of the 240 000 building designs included in the BIRDS low-energy 

residential database, consistent design specifications are used to estimate its operating 

energy use, environmental life-cycle impacts, and life-cycle costs. The building energy 

simulation, for example, specifies the same building envelope and HVAC technologies as 

do the bottom-up energy technology LCAs and cost estimates. 

One of the most important dimensions requiring BIRDS modeling consistency is the 

study period. The study period is the number of years of building operation over which 

energy, environmental, and economic performance are assessed. In economic terms, the 

study period represents the investor’s time horizon. Over what time period are investors 

or policymakers interested in the environmental and economic costs and benefits related 

to the capital investment decision? Since different stakeholders have different time 

perspectives, there is no one correct study period for developing a business case for 
7

sustainability. For this reason, 40 different study period lengths are offered in BIRDS, 

ranging from 1 year to 40 years.

Forty study period lengths are chosen to represent the wide cross section of potential 

investment time horizons. A 1-year study period is representative of a developer that 

intends to sell a property soon after it is constructed. A 5-year to 15-year study period 

best represents the typical length of time a homeowner is in a given house. The 20-year to 

40-year study periods better represent homeowners that intend to be a permanent resident 

of a particular house. BIRDS sets the maximum study period at 40 years for consistency 

with requirements for federal building life-cycle cost analysis (U.S. Congress 2007). 

Beyond 40 years, technological obsolescence becomes an issue, data become too 

uncertain, and the present value cost implications become less important.

Once the BIRDS user sets the length of the study period, the energy, environmental, and 

economic data are all normalized to that period of time. This involves adjustments to a 

building’s operating, maintenance, repair, and replacement data as well as to its residual 

value at the end of the study period. This assures consistency and comparability among 

the three metrics, and is one of the strengths of the BIRDS approach. 

The next 3 chapters present more detail regarding the modeling of the energy, 

environmental, and economic performance measures within the BIRDS low-energy 

residential building database.
8
9

3 Energy Performance Measurement

The operating energy component (i.e. energy consumed during use of the building by 

occupants) of the BIRDS low-energy residential database was built following the 

framework developed in Kneifel (2010), further expanded in Kneifel (2011a) and Kneifel 

(2011b) and implemented in previous versions of BIRDS (Lippiatt, Kneifel et al. 2013, 

Kneifel and Lavappa 2015). The BIRDS low-energy residential database includes the 

results of 240 000 whole-building energy simulations covering all combinations of 

building component requirements defined in the most recent editions of IECC and the 

energy efficiency measures implemented in the NZERTF during a one-year 

demonstration phase during which net-zero energy performance was achieved (Kneifel, 

Payne et al. 2015).

3.1 Background

The National Institute of Standards and Technology (NIST) received funding through the 

American Recovery and Reinvestment Act (ARRA) to construct a NZERTF. The initial 

goal of the NZERTF was to demonstrate that a net-zero energy residential design could 

“look and feel” like a typical home in the Gaithersburg area. The demonstration phase of 

the project was from July 2013 through June 2014, during which it successfully 

demonstrated the house performed at “net zero,” or produced as much electricity as it 

consumed over the entire year.

The EnergyPlus (E+) software was chosen to simulate the whole-building energy 

performance (U.S. Department of Energy (DOE) 2015). The general assumptions 

required by E+ are described in detail in Kneifel (2012). Kneifel et al. (2015) documents 

the validated model for the NZERTF based on the measured performance of the facility 

over the initial year of demonstration (July 2013 through June 2014), which is the basis 

for the 240 000 simulations in the BIRDS low-energy residential database. In order to 

obtain a prediction of a more representative years’ worth of energy performance, a 

Typical Meteorological Year 3 (TMY3) weather file was used for the simulations instead 

of the Actual Meteorological Year (AMY) weather file used during the model validation 

process. As in Kneifel et al (2015), the selected weather data is for the KGAI weather 

station (Weather Analytics 2014) located less than 6 km (4 miles) from the NIST campus. 

The validated model was simplified to allow for a 15-minute timestep instead of the oneminute timestep used in Kneifel et al. (2015) due to the time constraints of simulating 

240 000 designs.

3.2 Geometry and Building Envelope

The building dimensions specified by BSC (2009) along with the architectural massing 

model and image of the constructed NZERTF shown in Figure 3-1, were used along with 

Google SketchUp and National Renewable Energy Laboratory’s (NREL) Legacy Open 

Studio plug-in to construct the building geometry of the NZERTF. Total conditioned 

floor area (CFA) of the E+ model is 284.6 m2 (3063 ft2). Actual CFA of the NZERTF is 
10

251.7 m2 (2709 ft2). There are two reasons the CFA of the simulation model is 32.9 m2

(354 ft2) greater than the actual house design. First, the E+ model does not account for 

the open foyer/stairway, which adds approximately 19.0 m2 (204 ft2). Second, the gable 

walls (west wall and east wall) of the 2nd floor have built in storage under the gable, 

which decreases the modeled CFA by approximately 14.3 m2 (154 ft2). These two aspects 

of the model account for approximately 33.3 m2 (358 ft2), which decreases the CFA to 

251.3 m2 (2705 ft2) or a difference of only 0.4 m2 (4 ft2). Even though these two aspects 

of the house are not considered finished floor area, their volume of space will be 

conditioned.

Figure 3-1 BSC Architectural Massing Model and NZERTF as Built

Figure 3-2 shows the Google SketchUp three-dimensional geometry of the E+ model for 

the NZERTF. The model includes seven separate zones with three actively conditioned 

zones (1st floor, 2nd floor, and basement), three inactively conditioned zones – a.k.a. 

within the conditioned space without ductwork to the space (open web joist space 

between the 1st and 2nd floors, main attic, and living room attic) –, and one unconditioned 

zone (patio). The front porch and detached garage with the covered walkway are all 

treated as shading surfaces, which block sunlight but do not impact the thermal 

performance of the building envelope.

Figure 3-2 Google SketchUp 3-D Representation of the E+ Model
11

The E+ parameters for the materials used in the exterior envelope (excluding the roof) are 

estimated using an average transmittance with the surface-weighted path fractions. In 

other words, using a simple weighted average of parameter values based on the 

percentage of framing in the surface. The roof controls for varying materials by splitting 

the roof into two separate surfaces. The framing percentage for each surface type across 

different wall and roof constructions is shown in Table 3-1, which varies by construction 

and framing approach.

Table 3-1 Material Parameter Calculation Approach

Construction Framing Pct. Frame Approach

Exterior Wall Advanced 15 % surface-weighted path fractions

Typical 23 % surface-weighted path fractions

Ceiling Advanced 11 % surface-weighted path fractions

Typical 11 % surface-weighted path fractions

Roof Advanced 11 % split into separate surfaces

Typical 11 % split into separate surfaces

The NZERTF design adds energy efficiency measures to each aspect of the building 

envelope listed in Table 3-2, Table 3-3, and Table 3-4: framing, wall, roof, foundation, 

fenestration, and infiltration. The NZERTF is constructed using “advanced framing,” 

which uses 5.1 cm x 15.2 cm (2 in x 6 in) 61.0 cm (24 in) on center (OC) framing instead 

of the common practice or “typical,” of 5.1 cm x 10.2 cm (2 in x 4 in) 40.6 cm (16 in) OC 

framing (Lstiburek 2010). The thicker framing allows for greater levels of insulation 

within the wall cavity while decreasing the amount of wood required for framing the 

house, making it easier to increase the thermal performance of the building envelope.

Five exterior wall design options shown in Table 3-2(a) are included in the low-energy 

residential database. The first two represent construction requirements defined in 

different editions of the IECC and assume “typical” framing and include RSI-2.3 (R-13)

fiberglass batt insulation in the wall cavity, with Option 2 including R-5 continuous rigid 

insulation on the exterior. Option 3, Option 4, and the NZERTF design switch to 

advanced framing with RSI-3.5 (R-20) of blow-in cellulose in the wall cavity. Option 4 

and the NZERTF design (Option 5) add RSI-2.1 (R-12) and RSI-4.2 (R-24) of continuous 

rigid insulation to the exterior of the wall, respectively. 
12

Table 3-2 Constructions – Roof, Ceiling, Wall and Foundation

Constructions Option 1 Option 2 Option 3 Option 4 Option 5

(a) Exterior 

Wall

Framing Typical Typical Advanced Advanced Advanced

Insulation RSI-2.3 RSI-2.3+0.9 RSI-3.5 RSI-3.5+3.9 RSI-3.5+4.2

(R-13) (R-13+5) (R-20) (R-20+12) R-20+24

Note 1: Interior + Exterior R-Value

Constructions Option 1 Option 2 Option 3 Option 4

(b) Basement Wall R-8 R-10 RSI-3.9 (R-22) RSI-3.9 (R-22)

Slab R-0 R-0 R-0 RSI-1.8 (R-10)

Constructions Option 1 Option 2 Option 3 Option 4 Option 5

(c) Attic Roof R-0 R-0 R-45+4 R-45+15 RSI-7.9 + 5.3

(R-45 + 30)

Ceiling R-38 R-49 R-0 R-0 R-0

Three levels of energy efficiency are included for the basement designs (see Table 

3-2(b)). Option 1 includes R-8 continuous rigid insulation on the interior of the basement 

wall with no insulation under the basement slab. Option 2 increases continuous rigid 

insulation on the interior of the basement wall to R-10 while still having no insulation 

under the basement slab. Option 3 increases the thermal performance of the basement by 

including RSI-3.9 (R-22) of rigid insulation on the basement wall. The NZERTF design 

(Option 4) includes the RSI-3.9 (R-22) of rigid insulation on the basement wall from 

Option 3 and includes R-10 rigid insulation under the basement slab. 

Five attic constructions are included in the low-energy residential database (see Table 

3-2(c)). Option 1 and Option 2 include naturally vented attics with R-38 and R-49 of 

blown-in cellulose insulation on the attic floor, respectively. Option 3, Option 4, and the 

NZERTF attic construction shift the cellulose insulation into the rafter cavity (RSI-7.9 (R45)). Option 4 and the NZERTF design (Option 5) add continuous rigid insulation to the 

exterior roof to reach an additional RSI-5.3 (R-30) and RSI-2.7 (R-15), respectively.

The fenestration surface construction materials for windows are defined based on three 

simple parameters: U-factor, Solar Heat Gain Coefficient (SHGC), and Visible 

Transmittance (VT). This approach allows the rated window performance to be modeled 

while simplifying window “materials” and “constructions” in the simulation. The 

window parameters can be seen in Table 3-3 and are based on the Climate Zone 4

requirements for 2006, 2009, 2012, and 2015 IECC and the NZERTF window 

specifications.3 VT is assumed to be the same for all options (0.4) because the IECC does 

not specify this value.

 3 These parameters assume no difference in performance of the windows regardless of the window type 

(awning or double hung).
13

Table 3-3 Window Specifications

Blower door tests of the NZERTF measured an air exchange rate of 0.223 m3/s (470 cfm) 

or 0.63 air changes per hour at 50 Pa (ACH50). The infiltration rate is converted to 

effective leakage area (ELA) using the following formula from Chapter 16 in American 

Society for Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) (2012).

AALL = 10 000 ∗ QQrr ∗ �(ρρ/2∆pprr)

CCDD

Where AALL = eeeeeeeeeeeeeeeeeeee oooo eeeeeeeeeeeeeeeeee ll ll ( cc2)

QQrr = pp pp pp aa ∆pprr ( 3⁄ss)

ρρ = dd ( 3 ⁄ )

∆pprr = pp (PP )

CCDD = h

ASHRAE (2012) states that air exchange rates at 4 Pa, as used in E+, typically assumes a 

discharge coefficient (CCDD) of 1.0. The reference pressure difference (∆pprr) used in the 

blower door test is 50 Pa. The air density (ρρ) is assumed to be 1.2922 3 ⁄ . 

Substituting into the equation leads to an estimated ELA of 253.5 cm2

. Infiltration was 

defined using the ZoneInfiltration:EffectiveLeakageArea in the E+ model, and the 

measured ELA is split between the 1st floor (132.6 cm2

) and 2nd floor (120.9 cm2) based 

on total volume. Stack and wind coefficients of the ZoneInfiltration are set to 0.00029 

and 0.000325, respectively. Other specified ELA values in Table 3-4 are linearly 

extrapolated based on the ratio of the target ACH50 to the NZERTF ACH50 to meet the 

maximum air exchange rate values specified in different editions of the IECC.

Table 3-4 Air Exchange Rates

Assumed ELA (cm2)

Value ACH50 1st Floor 2nd Floor

2003/2006 IECC No Max 1473.3 1343.3

2009 IECC 7.0 1473.3 1343.3

2012/2015 IECC 3.0 403.6 368.1

NZERTF 0.63 132.6 120.9

Field Units Option 1 Option 2 Option 3 Option 4 Option 5

U-Factor W/m2-K

(Btu/h·ft2-F)

2.57

(0.45)

2.28

(0.40)

2.00

(0.35)

2.00

(0.35)

1.14

(0.20)

Solar Heat Gain 0.60 0.60 0.60 0.40 0.25

Visible Transmittance 0.40 0.40 0.40 0.40 0.40
14

3.3 Occupancy

A family of four – two parents and two children (14 years old and 8 years old) – is the 

assumed occupancy for all building designs. The assumed occupant activity levels and 

the resulting sensible and latent heat gains shown in Table 3-5 are based on Hendron and 

Engebrecht (2010) and Omar and Bushby (2013). The loads are assumed to be a constant

115 W, which should be representative of the occupancy impacts, on average.

Table 3-5 Occupant Activity Level

Occupant

Internal Load

Watts

1st Floor 2nd Floor

Sensible 70.15 70.15

Latent 44.85 44.85

Total 115.00 115.00

Occupancy schedules for each of the four family members are based on a meticulously 

detailed seven-day narrative defined in Omar and Bushby (2013). Figure 3-3 condenses 

the occupancy schedules to create an occupancy density (number of occupants) in the 

NZERTF by hour of each day of the week. For greater detail, see Omar and Bushby 

(2013), Kneifel (2012), and Kneifel, Payne et al. (2015). 

Figure 3-3 Occupancy Density

3.4 Lighting

Electricity use and internal loads from interior lighting are estimated based on Hendron 

and Engebrecht (2010), which varies from the operation of the NZERTF as defined in 

Omar and Bushby (2013), in order to simplify and speed up the simulation runs. Rooms 

on each floor are aggregated and simulated by thermal zone (1st and 2nd floors). The sum 

of lighting wattage by floor is shown in Table 3-6, which varies by the fraction of light 

fixtures that are high efficiency. Four different levels of efficient lighting are 

implemented in the 240 000 simulations based on the NZERTF design, requirements 

defined in IECC editions, and a “typical/baseline” lighting mix from Hendron and 
15

Engebrecht (2010). The monthly lighting schedules in terms of fraction of peak wattage 

(FPW) can be found in Hendron and Engebrecht (2010). For comparison purposes, the 

estimated lighting-based electricity consumption from the formulas in Hendron and 

Engebrecht (2010) is similar (417 kWh) to both the predicted (428 kWh) and measured 

performance (435 kWh) from the initial demonstration phase of the NZERTF (Kneifel, 

Payne et al. 2015).

Table 3-6 Lighting Total Wattage by Floor by Design Options

Fraction of High-efficiency Lighting

2003/2006 2009 2012/2015 NZERTF

Floor 34% 50% 75% 100%

1st 142 113 94 80

2nd 147 118 98 83

Only some of the rooms in the conditioned space are assumed occupied during the 

narrative. For example, lights in the office, hallways, and unfinished basement are never 

turned on. Based on the narrative, the use of these areas should be minimal (i.e., a few 

seconds at a time). Exterior lighting for the patio, garage, and the outdoor lights has been 

excluded. Garage and exterior lighting are not of a major concern because the lighting 

does not impact the thermal load of the NZERTF, and would only slightly increase 

electricity use if included in the model. 

3.5 Plug Loads

Plug loads are attributed to large appliances and any miscellaneous electrical loads 

(MELs), such as televisions, computers, hair dryers, etc., i.e., not space conditioning or 

water heating equipment. The same appliances and MELs operation are assumed for all 

240 000 building designs. Measured electricity consumption during the demonstration 

phase combined with the load profiles from Hendron and Engebrecht (2010) in Figure 

3-4 are used to reverse engineer the peak wattage for the operation of the refrigerator, 

clothes washer, clothes dryer, range (combination of oven, cooktop, and range hood), 

dishwasher, and MELs (split across the 1st and 2nd floor).
16

Figure 3-4 Electrical Equipment Load Profiles as a Proportion of Peak Wattage, By 

Hour

Although this method varies from the detailed narrative defined in Omar and Bushby 

(2013) and the associated assumptions made in (Kneifel, Payne et al. 2015), the estimated 

annual electricity consumption due to plug loads is 4214 kWh, which is similar to the 

predicted (4231 kWh) and measured (4307 kWh) consumption in Kneifel, Payne et al. 

(2015)

3.6 Heating, Ventilation, and Air Conditioning (HVAC)

All aspects of an HVAC system and the indoor environmental conditions that the system 

must maintain must be specified within the E+ representation of the building model, 

including the thermostat setpoints, ventilation rates, humidity controls, and HVAC 

equipment specifications. Each of these variables is defined in this section.

3.6.1 Temperature, Humidity, and Ventilation Controls

The temperature controls are the same for all 240 000 simulations while the humidity 

controls vary depending on which of the two HVAC system options, referred to as 

standard efficiency and high efficiency and described in Section 3.6.2, is being 

implemented in a given simulation. The thermostat setpoints are based on those used 

during the initial demonstration phase (Phase I) of the NZERTF (Kneifel, Payne et al. 

2015), which assume 21.9 °C (70.0 °F) for heating and 23.9 °C (75.0 °F) for cooling. 

Heating and cooling are both made available year-round. Relative humidity is not 

controlled when the standard efficiency equipment is installed because there is no control 

technology included in that system. The high efficiency system includes dehumidification 

control, which operates when the humidity level rises above 50 %.

Mechanical ventilation requirements defined in editions of the IECC vary based on the 

air exchange rate through the building envelope as measured by blower door testing
17

(ASTM 2010). If the measured air exchange rate is greater than 3 ACH50, no mechanical 

ventilation is required. For buildings with an air exchange rate of 3 ACH50 or less, 

mechanical ventilation is required to meet ASHRAE Standard 62.2-2007. Based on 

ASHRAE Standard 62.2, the minimum required mechanical outdoor air flow rate for the 

entire building is 0.0392 m3/s (83 CFM). E+ allows for the provision of a mechanical 

ventilation rate for each thermal zone. The 1st floor has 52.3 % of the volume while the 

2nd floor has 47.7 % of the volume of the occupied space. Based on these values, the 

required minimum outdoor air flow rates for each zone can be calculated based on a 

weighted fraction of the whole house mechanical ventilation as 0.02048 m3

/s (43.4 CFM) 

for the 1st floor, and 0.0187 m3

/s (39.6 CFM) for the 2nd floor.

The two types of HVAC systems meet the outdoor air ventilation rate requirements as 

follows. The standard efficiency system model provides mechanical ventilation via a 

constant flow of outdoor air through the outdoor air intake system of the heat pump 

model. The high efficiency HVAC system is based on the NZERTF specifications, which 

includes a heat recovery ventilator (HRV) with its own dedicated ductwork independent 

of the heating and cooling equipment. The HRV system in the NZERTF is a Venmar 

AVS HRV EKO 1.5 air-to-air heat exchanger. The HRV transfers heat between the 

exhaust air and supply air to decrease the heating and cooling load impact of the 

ventilation air. The HRV is the sole source of mechanical ventilation and operates yearround, 24 hours a day. The effectiveness of the HRV varies by the air flow rate and 

temperature differences across the heat exchange core, but is assumed to be the same for 

heating and cooling in the E+ model. Exhaust air recirculation is used to control for frost. 

The outdoor air flow rate for the HRV system was set to the actual operating conditions 

from Phase I of the NZERTF demonstration, which is 27 % higher than that required by 

ASHRAE 62.2 at 0.04956 m3/s (49.6 L/s or 105 CFM). The E+ software does not allow 

more than one whole-house air loop, which forces the splitting of the HRV system into 

two zone-level HRV systems. The same volume-based approach as in the standard 

efficiency HVAC system is used to allocate the air flow rates through each zone level 

HRV: 0.02592 m3/s (54.9 CFM) for the 1st floor and 0.02364 m3/s (23.6 L/s or 50.1 

CFM) for the 2nd floor. For more information on the HRV system, see Kneifel, Payne et 

al. (2015).

3.6.2 Heating and Cooling Equipment

Two heating and cooling system options are included in the low-energy residential 

database, one that meets minimum federal efficiency and IECC requirements (“standard 

efficiency”) and another based on the system installed in the NZERTF. Figure 3-5 shows 

the HVAC system modeled in the E+ simulation. The standard efficiency system 

includes a single-speed air-to-air heat pump with a rating of Seasonal Energy Efficiency 

Ratio (SEER) 13 for cooling mode and Heating Seasonal Performance Factor (HSPF) 7.7 

for heating mode with dedicated outdoor ventilation (as necessary) as shown in Figure 

3-5(a). The NZERTF system includes a multispeed air-to-air heat pump with a rating of 

SEER 16.5 for cooling mode, HSPF 9.1 for heating mode, and a dehumidification-only 
18

mode to maintain humidity below the 50 % maximum level. As described previously, the 

NZERTF system utilizes a separate HRV system (see Figure 3-5(b)). 

Figure 3-5 HVAC System Layout for (a) NZERTF (b) Standard Efficiency

The version of E+ used for these simulations does not provide a multispeed air-to-air heat 

pump model that can run in dehumidification-only mode, requiring modeling of separate 

equipment as shown in Figure 3-5(b) to dehumidify the occupied space. The 

dehumidifiers remove humidity directly from the zone. Both system options have an 

electric resistance heating element with an efficiency of 0.98 to supplement the heat 

pump when the heat pump cannot meet the heating loads. For more detail on the 

NZERTF HVAC system including information on zone sizing, coils, dehumidifiers, 

HRV, and HVAC fans, see Kneifel (2012) and Kneifel, Payne et al. (2015).

1st Floor (Control Zone)

2nd Floor

Building Envelope

Cool Heat Supp.

Heat Fan

(a)

HRV 1

1st Floor (Control Zone)

2nd Floor

Dehumidifier

Dehumidifier

Building Envelope

Cool Heat Supp.

Heat Fan

HRV 2

(a) (b)
19

3.7 Domestic Hot Water

Four different domestic hot water (DHW) systems were modeled including two with 

solar hot water. These systems as well as the DHW loads are described in this section.

3.7.1 Domestic Hot Water Heater Equipment

The DHW systems simulated in the model are shown in Figure 3-6. The system shown in 

Figure 3-6 (a) is based on the NZERTF design. It is a two-tank system located in the 

basement of the home, with two solar thermal panels located over the front porch 

providing water for indirect heating an 303 L (80 gal) storage tank and an air-to-water 

heat pump downstream of the storage tank. The closed-loop solar thermal system uses a 

50/50 water/glycol mix. The heat pump (rated COP of 2.36) draws water from the storage 

tank and further heats the water if necessary to meet the target exit temperature. There is 

an auxiliary heating element in the 189 L (50 gal) heat pump water heater tank that assists 

the heat pump in the event that the heat pump cannot reach the target setpoint.

The system shown in Figure 3-6 (b) is the same system as that in (a) except that the heat 

pump water heater is replaced with a “standard” electric water heater with an efficiency 

of 0.95. The system shown in Figure 3-6 (c) includes the heat pump from (a), but 

excludes the solar thermal system. The system shown in Figure 3-6 (d) is the “standard” 

electric water heater with an efficiency of 0.95. For additional details on the DHW 

system components including assumed parameters for tanks, solar collectors, heat 

exchanger, and coils, see Kneifel (2012) and Kneifel, Payne et al. (2015).
20

Figure 3-6 Domestic Hot Water Heating Systems (a) Heat Pump Water Heater with 

Solar Thermal (b) Electric Water Heater with Solar Thermal (c) Heat Pump Water 

Heater (d) Electric Water Heater

3.7.2 Domestic Hot Water End Use

There are five categories of DHW end use in the NZERTF: bath, shower, sinks, 

dishwasher, and clothes washer. The peak load fraction use schedules shown in Figure 

3-7 are the same for all 240 000 simulations and based on those defined in Hendron and 

Water

 Heater

 Tank

Water Main

Heat 

Exchanger

DHW

End Uses

Tempering

Valve

Solar 

Collectors

Heat Pump

(a)

Electric Water

 Heater Tank

Water Main

Heat 

Exchanger

DHW

End Uses

Tempering

Valve

Solar 

Collectors

(b)

Water

 Heater

Water Main

DHW

End Uses

Heat Pump

(c)

Electric Water

 Heater Tank

Water Main

DHW

End Uses (d)
21

Engebrecht (2010), which varies from the detailed schedule defined in Omar and Bushby 

(2013) and implemented in Kneifel, Payne et al. (2015).

Figure 3-7 Domestic Hot Water Load Profiles as a Proportion of Peak Flow Rate, 

By Hour

Table 3-7 defines DHW use in terms of daily water use and water temperature, which are 

based on measured performance (Kneifel, Payne et al. 2015). Although the load profiles 

differ, the measured water usage (317 L/day or 83.7 gal/day) is used to reverse engineer 

the peak flow rate for each category in order to get similar results in all 240 000 

simulations (318 L/day or 84.0 gal/day). 

Table 3-7 Daily Domestic Hot Water Use and Target Temperatures

DHW Use 

Category

Water Temp.

°C (°F)

Daily 

L (Gal)

Clothes Washer 19.4 (67) 48.5 (12.8)

Dishwasher 40.6 (105) 4.9 (1.3)

Shower 38.9 (102) 160.5 (42.4)

Bath 44.4 (112) 33.3 (8.8)

Sinks 37.7 (100) 71.2 (18.8)

3.8 Solar Photovoltaics

There are five solar photovoltaic system options included in the low-energy residential 

database: 0 kWh, 2.5 kWh, 5.1 kWh, 7.6 kWh, and 10.2 kWh. When included in a 

simulation, PV panels are installed on the roof. The solar photovoltaic system is based on 

the system installed on the NZERTF, which includes 4 horizontal rows of eight 320 W 

panels with two inverters. The five options are based on the removal of one string of 

panels at a time until no strings are installed. The inverter(s) specifications are adjusted 
22

accordingly to appropriately meet the wattage requirements. For additional details on the 

solar photovoltaic system, see Kneifel (2012) and Kneifel, Payne et al. (2015).
23

4 Environmental Performance Measurement

BIRDS evaluates the environmental performance of whole-buildings using a life-cycle 

assessment (LCA) approach, which is based on International Organization for 

Standardization (ISO) standards. The general LCA methodology involves four steps

(International Organization for Standardization (ISO) 2006a, International Organization 

for Standardization (ISO) 2006b).

1. Goal and scope definition

2. Inventory analysis

3. Impact assessment

4. Interpretation

The goal and scope definition step spells out the purpose of the study and its breadth and 

depth. The inventory analysis step identifies and quantifies the environmental inputs and 

outputs associated with a building over its entire life-cycle. Environmental inputs include 

water, energy, land, and other resources. Outputs include releases to air, land, and water. 

However, it is not these inputs and outputs, or inventory flows, which are of primary 

interest. We are more interested in their consequences, or impacts on the environment. 

Thus, the impact assessment step characterizes these inventory flows in relation to a set 

of environmental impacts. For example, the impact assessment step might relate carbon 

dioxide emissions, a flow, to global warming, an impact. Finally, the interpretation step 

examines the environmental impacts in accordance with the goals of the LCA study.

4.1 Goal and Scope Definition

The goal of BIRDS LCAs is to generate environmental performance scores for a range of 

U.S. building types, each designed to meet four alternative levels of operating energy 

efficiency. These results will be reported alongside economic performance scores to help 

designers, investors, and policymakers develop business cases for high-performance 

green buildings.

The scoping phase of any LCA involves defining the boundaries of the product system –

or, in the case of BIRDS, the building – under study. In traditional bottom-up LCAs, the 

setting of the boundary conditions requires expert judgment by the analyst because 

consideration must be given to the various unit processes related to the construction of 

the building (e.g. asphalt production for input to the manufacturing of facing for 

fiberglass batt insulation). Each unit process involves many inventory flows, some of 

which themselves involve other, subsidiary unit processes. Because including an everexpanding number of unit processes in LCAs is not feasible, the product system’s supply 

chain links are truncated at some point to include only those judged to make nonnegligible contributions to the product system. The analyst typically uses mass, energy, 

and/or cost contributions as decision criteria. Use of different boundary setting criteria is 

one of the main reasons LCA results from different studies are often incomparable.
24

One important advantage of the BIRDS hybrid approach is that it addresses the bottomup issue of truncation in supply chain links, thereby improving consistency in system 

boundary selections. Through the hybridization process, truncated supply chain links are 

connected to the background U.S. economic system represented by the top-down data. 

These linkages follow the metabolic structure of the U.S. economy, thereby benefitting 

from a more complete system definition including potentially thousands of supply chain 

interactions. 

Due to their long service lives, buildings are somewhat unique when it comes to the endof-life stage of the life-cycle. For most non-consumable product LCAs, end-of-life waste 

flows are included in the inventory analysis for full coverage of the life-cycle. If there is 

an active recycling market that diverts some of the product from the waste stream, that 

portion of the product’s end-of-life flows can be ignored. In BIRDS, however, a 

residential building lifetime is 65 years, much longer than the 40-year maximum length 

for the study period considered in BIRDS. Therefore, 100 % of each building is 

considered to be “recycled” at the end of the study period and there are no end-of-life 

waste flows allocated to the building at the end of the BIRDS study period. Rather, endof-life waste flows should be allocated to a different “product,” representing use of the 

building from the end of the study period to the end of the building service life. Similarly, 

the environmental burdens from building construction are allocated only to its first use 

(equally distributed across each year of the selected study period); LCAs for all 

subsequent uses should be treated as free of these initial construction burdens. This 

effectively credits the use of existing buildings over new construction and ensures there 

will be no double counting once existing building LCAs are included in future versions of 

BIRDS as planned.

Defining the unit of comparison is an important task in the goal and scoping phase of 

LCA. The basis for all units of comparison is the functional unit, defined so that the 

systems compared are true substitutes for one another. In the BIRDS model, the 

functional unit is construction and use of one building prototype over a user-defined 

study period. The functional unit provides the critical reference point to which the LCA 

results are scaled. 

Scoping also involves setting data requirements. With respect to geographic coverage, the 

BIRDS inventory data are generally U.S. average data. An exception is made for the 

electricity production inventory data applied to a building’s use of electricity. These data 

are customized to each U.S. state. In terms of technology coverage, the top-down 

inventory data represent the mix of technologies in place as of 20024

. For the bottom-up 

inventory data on building energy technologies, the most representative technology for 

which data are available is evaluated.

 4 More recent data is not available due to funding limitations of developing environmental inventory data 

for the most recent U.S. Economic Census.
25

4.2 Life-cycle Inventory Analysis

BIRDS applies a hybridized life-cycle assessment approach. The approach is hybridized 

in the sense that a mixture of top-down and bottom-up data are collected and 

systematically integrated in the inventory analysis LCA step. Traditional process-based 

LCAs gather data by modeling all the in-scope industrial processes involved in a 

product’s production (raw materials acquisition, materials processing, manufacture, 

transportation), use, and waste management. For each industrial process, the analyst 

collects very detailed, bottom-up data on all its inputs from the environment (e.g., 

materials, fuel, water, land) and outputs to the environment (e.g., products, water 

effluents, air emissions, waste). This process is summarized in Figure 4-1. 

To address the complexities of a whole building, BIRDS takes a new, multi-layered 

approach to inventory analysis. Since a building’s operating energy efficiency has an 

important influence on its sustainability performance, and energy efficiency is largely 

driven by the building’s energy technologies, BIRDS pays special attention to the 

materials used in them. Specifically, BIRDS uses detailed life-cycle inventory (LCI) data 

for a range of energy technology packages that have been analyzed at the traditional, 

bottom-up LCA level. These energy technology packages are used to meet the 4 levels of 

energy efficiency simulated for each building type in 228 different U.S. locations. The 

bottom-up approach is also used to gather inventory data for a building’s use of 

electricity and natural gas over the study period. These bottom-up BIRDS data were 

developed under contract to NIST by Four Elements Consulting, LLC, of Seattle, 

Washington, and are documented in Section 4.5. For all other building constituents, 

industry average LCI data are gathered from the top-down and then systematically 

combined with bottom-up data into a comprehensive, hybrid LCI for a whole building.

Figure 4-1 Compiling LCA Inventories of Environmental Inputs and Outputs

The inventory data items collected through the bottom-up and top-down approaches are 

identical – for example, kilogram (kg) carbon dioxide, kWh primary energy consumption 

recycle remanufacture reuse

INPUTS:

OUTPUTS: Products, Water Effluents, Air Emissions, Waste, Other Outputs

Materials, Fuel, Water, Other Inputs

raw material 

acquisition

material 

processing manufacturing use

waste 

management
26

– but some of the data sources are quite different. The systematic hybridization of the 

data sets bridges these differences to yield coherent and consistent BIRDS life-cycle 

inventories for a wide variety of new commercial and residential buildings. The LCAs for 

the buildings are then completed by applying conventional methods of life-cycle impact 

assessment (LCA) and interpretation to the hybrid inventory data. 

An economy’s accounting structure provides a cost-effective top-down approach to LCA 

inventory data collection. Many developed economies maintain economic input-output (IO) accounts that trace the flow of goods and services throughout industries. Much the 

same way that a product’s production can be traced upstream through its supply chain, an 

industry’s production can be traced upstream through its supply chain. The U.S. Census 

Bureau conducts an Economic Census of U.S. industry every five years that establishes 

industry linkages. Covering 97 % of business receipts, the census reaches nearly all U.S. 

business establishments. Based on the detailed data collected, the U.S. Bureau of 

Economic Analysis (BEA) creates what are called Input-Output Accounts, or “I-O 

tables,” for the U.S. economy. 

The U.S. I-O tables show how around 500 industries provide input to, and use output 

from, each other to produce Gross Domestic Product (GDP) – the total value of the 

consumption of goods and services in a year. These tables, for example, can show how 

$100 million of U.S. economic output in the residential building construction sector 

traces back through its direct monetary inputs—from the construction process itself—to 

its indirect inputs from contributing sectors such as the steel, concrete, lumber, and 

plastics industries. Economic output from the steel, concrete, lumber, and plastics 

industries, in turn, can be traced back through those supply chains such as mining, 

forestry, fuel extraction, and so on. 

While BEA provides these I-O tables in purely monetary terms, academics have 

successfully developed “environmentally-extended” I-O tables (Suh 2005, Hendrickson, 

Lave et al. 2006, Suh 2010). These top-down tables tap into a wide range of national 

environmental statistics to associate environmental inputs and outputs with economic 

activity in industry sectors, including use of raw materials, fuel, water, and land and 

releases of water effluents, air emissions, and waste. BIRDS uses environmentallyextended I-O tables for the U.S. construction industry developed under contract to NIST 

by Industrial Ecology Research Services of Goleta, California. These tables are based on 

the 2007 release of the 2002 BEA I-O data, the latest available, and quantify 6204 

environmental inputs and outputs occurring throughout production supply chains.

To understand the contribution of building construction to the nation’s environmental 

footprint (impact), it is useful to focus on the concept of “final demand.” The BEA’s 

monetary I-O tables use GDP to measure final demand. This value consists of spending 

and investment by consumers, businesses, and government, as well as net exports. Since 

final demand is satisfied through annual production – goods and services need to be 

produced before they can be bought – each industry’s value-added, or “direct” 
27

contribution to GDP, reflects its share of final demand, and will be referred to as GDP for 

the remainder of this document.

The environmentally-extended I-O tables translate economic activity into environmental 

terms, or monetary GDP into environmental GDP (eGDP). In LCA terms, the 

construction industry’s contribution to eGDP is not limited to the direct impact from 

value-added construction processes and activities. Its contribution to eGDP also includes 

the indirect impacts stemming from contributions by upstream construction supply chains 

up to and including raw materials acquisition. The supply chain relationships built into

the environmentally-extended I-O tables enable estimation of construction industry 

impacts on this cumulative, life-cycle basis. Figure 4-2 illustrates these supply chain 

relationships for some of the inputs to ready-mix concrete manufacturing, an indirect 

construction industry input.

Figure 4-2 Illustration of Supply Chain Contributions to U.S. Construction 

Industry

The environmentally extended I-O tables classify U.S. construction into 42 distinct 

industry outputs. In this new residential database of BIRDS, top-down inventory data 

represent the extended I-O table level of detail for the construction, maintenance, and 

repair associated with the two residential construction industry outputs shown in Table 

4-1. The first output corresponds to new residential construction. The second corresponds 
28

to maintenance and repair (M&R) activities in residential buildings. For both construction 

industry outputs, the baseline top-down inventory data are expressed in terms of lifecycle environmental flows per dollar of construction.

Table 4-1 Construction Industry Outputs Mapped to BIRDS Building Types

One advantage of the BIRDS approach is the economic dimension built into the topdown inventory data. These data are directly associated with U.S. economic data, 

permitting seamless integration of the economic dimension in the BIRDS sustainability 

measurement system. The top-down inventory values on a per-dollar basis are multiplied 

by the corresponding BIRDS construction, maintenance, and repair costs to translate 

them into the LCA functional unit representing the whole-building over a user-defined 

study period.

For more information on the mathematics, accounting structure, and step-by-step process 

under which the BIRDS hybrid environmental database is built, see Suh and Lippiatt 

(2012).

4.3 Life-cycle Impact Assessment

Environmental impacts from building construction and use derive from the 6204 inputs 

and outputs occurring throughout production supply chains, as quantified in the hybrid 

BIRDS LCI. The impact assessment step of LCA quantifies the potential contribution of 

these inventory items to a range of environmental impacts. The approach preferred by 

most LCA practitioners and scientists today involves a two-step process: 

• Classification of inventory flows that contribute to specific environmental 

impacts. For example, greenhouse gases such as carbon dioxide, methane, and 

nitrous oxide are classified as contributing to global warming.

• Characterization of the potential contribution of each classified inventory flow to 

the corresponding environmental impact. This results in a set of indices, one for 

each impact, which is obtained by weighting each classified inventory flow by its 

relative contribution to the impact. For instance, the Global Warming Potential 

index is derived by expressing each greenhouse gas in terms of its equivalent 

amount of carbon dioxide heat trapping potential.

Construction Type Industry Output

New Construction New Residential Construction

M&R Construction Residential maintenance and repair construction
29

There are two general applications of this life-cycle impact assessment (LCIA) approach: 

midpoint-level and endpoint-level analyses. An endpoint-level analysis attempts to 

measure the ultimate damage that each environmental input and output in the inventory 

will have along the cause-effect chain. Methods of this type include just a few impact 

categories, such as damage to human health, ecosystems, and resource availability that 

are easier to interpret in the final step of life-cycle assessment. This approach is criticized 

for the numerous assumptions, value judgments, and gaps in coverage of the underlying 

damage models. A midpoint-level analysis, on the other hand, selects points along the 

cause-effect chain at which more certain and comprehensive assessments may be carried 

out. While this approach generates many impact categories and makes life-cycle 

interpretation more difficult, it is more scientifically defensible. Even so, a midpoint-level 

analysis does not offer the same degree of relevance for all environmental impacts. For 

global and regional effects (e.g., global warming and acidification) the method provides 

an accurate description of the potential impact. For impacts dependent upon local 

conditions (e.g., smog), it may result in an oversimplification of the actual impacts 

because the indices are not tailored to localities. Note that some impact assessments apply 

a mix of midpoint and endpoint approaches.

4.3.1 BIRDS Impact Assessment

BIRDS uses a midpoint-level analysis to translate its 6204 environmental inputs and 

outputs into a manageable set of science-based measurements across 12 environmental 

impacts. BIRDS primarily uses the U.S. Environmental Protection Agency’s (EPA) Tool 

for the Reduction and Assessment of Chemical and other environmental Impacts 

(TRACI) version 2.0 set of state-of-the-art, peer-reviewed U.S. life-cycle impact 

assessment methods (Bare 2011). Since TRACI 2.0 does not include land and water use, 

these two important resource depletion impacts are assessed using other characterization 

methods (Guinée 2002, Goedkoop, Heijungs et al. 2008). Together these methods are 

used to develop BIRDS performance metrics indicating the degree to which construction 

and use of a building contributes to each environmental impact. Following are brief 

descriptions of the 12 BIRDS impact categories.

4.3.1.1 Impact Categories

Global Warming

The Earth absorbs radiation from the Sun, mainly at the surface. This energy is then 

redistributed by the atmosphere and ocean and re-radiated to space at longer wavelengths. 

Greenhouse gases in the atmosphere, principally water vapor, but also carbon dioxide, 

methane, chlorofluorocarbons, and ozone, absorb some of the thermal radiation. The 

absorbed energy is re-radiated in all directions, downwards as well as upwards, such that 

the radiation that is eventually lost to space is from higher, colder levels in the 

atmosphere. The result is that the surface loses less heat to space than it would in the 

absence of the greenhouse gases and consequently stays warmer than it would be 
30

otherwise. This phenomenon, which acts like a ‘blanket’ around the Earth, is known as 

the greenhouse effect.

The greenhouse effect is a natural phenomenon. The environmental issue is the change in 

the greenhouse effect due to emissions (an increase in the effect) and absorptions (a 

decrease in the effect) attributable to humans. A general increase in temperature can alter 

atmospheric and oceanic temperatures, which can potentially lead to alteration of natural 

circulations and weather patterns. A rise in sea level is also predicted from an increase in 

temperature due to thermal expansion of the oceans and the melting of polar ice sheets.

Primary Energy Consumption

Primary energy consumption leads to fossil fuel depletion when fossil fuel resources are 

consumed at rates faster than nature renews them. Some experts believe fossil fuel 

depletion is fully accounted for in market prices. That is, market price mechanisms are 

believed to take care of the scarcity issue, with price being a measure of the level of 

depletion of a resource and the value society places on that depletion. However, price is 

influenced by many factors other than resource supply, such as resource demand and nonperfect markets (e.g., monopolies and subsidies). The primary energy consumption metric 

is used to account for the resource depletion aspect of fossil fuel extraction. 

Human Health – Criteria Air Pollutants

These pollutants can arise from many activities including combustion, vehicle operation, 

power generation, materials handling, and crushing and grinding operations. They 

include coarse particles known to aggravate respiratory conditions such as asthma, and 

fine particles that can lead to more serious respiratory symptoms and disease.

Human Health – Cancer Effects

These effects can arise from exposure to industrial and natural substances, and can lead to 

illness, disability, and death. Its assessment is based on the global consensus model 

known as USEtox, which describes the fate, exposure and effects of thousands of 

chemicals (Rosenbaum, Huijbregts et al. 2011).

Water Consumption

Water resource depletion has not been routinely assessed in LCAs to date, but researchers 

are beginning to address this issue to account for areas where water is scarce, such as the 

Western United States. While some studies use water withdrawals to evaluate this 

impact, a more refined analysis considers that a portion of water withdrawn may be 

returned through evapotranspiration (the sum of evaporation from surface water, soil, and 

plant leaves). BIRDS uses the latter approach to measure water consumption, or water 

withdrawn net of evapotranspiration. BIRDS evaluates water consumption from cradle to 

grave, including water consumption during building use.

Ecological Toxicity

Measures of ecological toxicity consider the potential of pollutants from industrial 

sources to harm land- and water-based ecosystems. Its assessment is based on the global 
31

consensus model known as USEtox, which describes the fate, exposure and effects of 

thousands of chemicals. 

Eutrophication Potential

Eutrophication is the addition of mineral nutrients to the soil or water. In both media, the 

addition of large quantities of mineral nutrients, such as nitrogen and phosphorous, 

results in generally undesirable shifts in the number of species in ecosystems and a 

reduction in ecological diversity. In water, it tends to increase algae growth, which can 

lead to a lack of oxygen and subsequent death of species like fish. 

Land Use

This impact category measures the use of land resources in hectares by humans which 

can lead to undesirable changes in habitats. Note that the BIRDS land use approach does 

not consider the original condition of the land, the extent to which human activity 

changes the land, or the length of time required to restore the land to its original 

condition. As impact assessment science continues to evolve, it is hoped that these 

potentially important factors will become part of BIRDS land use assessment.

Human Health – Non-cancer Effects

The effects can arise from exposure to industrial and natural substances, and range from 

transient irritation to permanent disability and even death. Its assessment is based on the 

global consensus model known as USEtox, which describes the fate, exposure and effects 

of thousands of chemicals. 

Smog Formation

Smog forms under certain climatic conditions when air emissions (e.g. NOX, VOCs) from 

industry and transportation are trapped at ground level where they react with sunlight. 

Smog leads to harmful impacts on human health and vegetation.

Acidification Potential

Acidifying compounds may, in a gaseous state, either dissolve in water or fix on solid 

particles. These compounds reach ecosystems through dissolution in rain or wet 

deposition and can affect trees, soil, buildings, animals, and humans. The two compounds 

principally involved in acidification are sulfur and nitrogen compounds, with their 

principal human source being fossil fuel and biomass combustion. Other compounds 

released by human sources, such as hydrogen chloride and ammonia, also contribute to 

acidification.

Ozone Depletion

Ozone depletion is the thinning of the stratospheric ozone layer, allows more harmful 

short wave radiation to reach the Earth’s surface, potentially causing undesirable changes 

in ecosystems, agricultural productivity, skin cancer rates, and eye cataracts, among other 

issues.
32

4.3.1.2 Computational Algorithms

There are six building components represented in the BIRDS life-cycle impact 

assessment (LCIA) calculations for whole-buildings: 

• Baseline building: new construction (Base_New)

• Baseline building: maintenance and repair over study period (Base_M&R)

• Energy technology package: new construction (ETP_New)

• Energy technology package: annual maintenance and repair (ETP_M&R)

• Annual operating energy use: electricity (ELEC)

• Annual operating energy use: natural gas (NG)

The hybridized LCI data for each component are expressed in different units. For 

example, the baseline building inventories are given on a per-dollar basis, the energy 

technology package inventories on a per-physical unit basis (usually area), and the 

operating energy use inventories on a per-unit of energy basis. Thus, each requires its 

own LCIA computational algorithm as shown in Table 4-2. These calculations ensure 

that after adjusting for study period length, all LCIA results are expressed in the 

consistent functional unit defined for BIRDS: construction and use of one building 

prototype over a user-defined study period. In the case of the low-energy residential 

database, a number of the variables have a single value (e.g. building location, state, 

building type).
33

Table 4-2 BIRDS Life-cycle Impact Assessment Calculations by Building 

Component

Building 

Component LCIA Equation Notation

Base_New LCIAi,j,c=1 = (LCIAi,j,c=1/$)·$i,c=1

LCIA=classified and 

characterized life-cycle 

inventories

c=construction type code, 

1=new, 2=M&R

E=electricity

ETP=energy technology 

product

FU=functional unit*

i = building type

j=environmental impact, j=1 to 

12

K=building design

n=study period length in years, 

n=1 to 40

NG=natural gas

s=U.S. state,

T = energy technology group, 

T=1 to 10

x= building location

Base_M&R LCIAi,j,c=2,yr = (LCIAi,j,c=2/$)·$i,c=2,yr

ETP_New LCIAi,j,c=1,x,K,T

= Σ(LCIAj,c=1,ET(i,x,K,T)/FU) ·FUi,T

from T=1 to 6

ETP_M&R LCIAi,j,c=2,x,K,T/yr

=

Σ (LCIAj,c=2,ET(i,x,K,T)/FU/yr) 

·FUi,T

from T=1 to 6

ELEC LCIAi,j,x,K/yr = (LCIAj,s/BTUE) · (BTUE,i,x,K/yr)

NG LCIAi,j,x,K/yr = (LCIAj/BTUNG) · 

(BTUNG,i,x,K/yr)

*Energy technology groups and their functional units are wall insulation (ft2), foundation insulation 

(ft2), attic insulation (ft2), air leakage rate (ft2), windows (ft2), lighting (watt) heating, cooling, and 

ventilation equipment (no. of units), solar thermal (no. of units), and solar photovoltaic (no. of units).

4.3.2 BIRDS Normalization

Once impacts have been classified and characterized, the resulting LCIA metrics are 

expressed in incommensurate units. Global warming is expressed in carbon dioxide 

equivalents (CO2e), acidification in hydrogen ion equivalents, eutrophication in nitrogen 

equivalents, and so on. In order to assist in the next LCA step, interpretation, these 

metrics are often placed on the same scale through normalization. 
34

The EPA has developed “normalization references” corresponding to its TRACI set of 

impact assessment methods (Bare, Gloria et al. 2006). These U.S. data are updated and 

expanded for use in BIRDS. Shown in Figure 4-3, these values quantify the U.S. 

economy’s annual contributions to each impact category. As such, they represent a “U.S. 

impact yardstick” against which to evaluate the significance of building-specific impacts. 

Normalization is accomplished by dividing BEES building-specific impact assessment 

results by the fixed U.S.-scale normalization references, expressed in the same units, 

yielding an impact category score for a building that has been placed in the context of 

annual U.S. contributions to that impact. By placing each building-specific impact result 

in the context of its associated U.S. impact result, the measures are all reduced to the 

same scale, allowing comparison across impacts.

The environmental impact of a single building is small relative to the total U.S. emissions 

in an impact category, leading to normalized values that are small fractions of a percent. 

In order to improve the user experience, the low-energy residential database adjusted 

these normalized values by multiplying by the U.S. population (~309 million), creating a 

normalized value that represents the fraction of emissions per capita for each impact 

category.
35

Table 4-3 BIRDS Normalization References

Impact Category

Normalization 

reference

(U.S. total/year)

Units

Global Warming 7.16E+12 kg CO2e

Primary Energy 

Consumption 

3.52E+13

(1.20E+14)

kWh

(kBTU)

HH Criteria Air 2.24E+10 kg particulate matter 10 equivalents

(PM10 = particulate matter <10 

microns in diameter)

HH Cancer 1.05E+04 comparative human toxicity units

Water Consumption 1.69E+14 L

Ecological Toxicity 3.82E+13 comparative ecotoxicity units

Eutrophication 1.01E+10 kg nitrogen equivalents

Land Use 7.32E+08

(1.81E+09)

hectare

(acre)

HH Non-cancer 5.03E+05 comparative human toxicity units

Smog Formation 4.64E+11 kg ozone equivalents

Acidification 1.66E+12 moles hydrogen ion equivalents

Ozone Depletion 5.10E+07 kg CFC-11 equivalents (CFC-11 = 

trichlorofluoromethane)

4.4 Life-cycle Interpretation

At the BIRDS LCA interpretation step, a building’s normalized impact scores are 

evaluated. The midpoint-level impact assessment yields scores for twelve impact 

categories, making interpretation at this level difficult. To enable comparisons across 

buildings, the scores across impact categories may be synthesized. Note that in BIRDS, 

the synthesis of impact scores is optional.

Impact scores may be synthesized by weighting each impact category by its relative 

importance to overall environmental performance, then computing the weighted average 

impact score. In the BIRDS software, the set of importance weights is selected by the 

user. Several alternative weight sets are provided as guidance, and may be either used 
36

directly or as a starting point for developing user-defined weights. The alternative weight 

sets are based on an EPA Science Advisory Board study, a BEES Stakeholder Panel’s 

structured judgments, a set of equal weights, and a set exclusively focusing on the global 

warming impact, representing a spectrum of ways in which people value diverse aspects 

of the environment.

4.4.1 EPA Science Advisory Board Study

In 1990 and again in 2000, EPA’s Science Advisory Board (SAB) developed lists of the 

relative importance of various environmental impacts to help EPA best allocate its 

resources (U.S. EPA Science Advisory Board 1990, U.S. EPA Science Advisory Board 

2000). The following criteria were used to develop the lists:

• The spatial scale of the impact

• The severity of the hazard

• The degree of exposure

• The penalty for being wrong

Ten of the twelve BIRDS impact categories were covered by the SAB lists of relative 

importance:

• Highest-Risk Problems: global warming, land use

• High-Risk Problems: ecological toxicity, human health (cancer and non-cancer 

effects)

• Medium-Risk Problems: ozone depletion, smog, acidification, eutrophication, 

and human health – criteria air pollutants

The SAB did not explicitly consider primary energy consumption or water consumption. 

For BIRDS, these impacts are assumed to be relatively medium-risk and low-risk 

problems, respectively, based on other relative importance lists (Levin 1996).

Verbal importance rankings, such as “highest risk,” may be translated into numerical 

importance weights by following ASTM International standard guidance for applying a 

Multi-attribute Decision Analysis method known as the Analytic Hierarchy Process 

(AHP) (ASTM 2011). The AHP methodology suggests the following numerical 

comparison scale:
37

1 Two impacts contribute equally to the objective (in this case environmental 

performance)

3 Experience and judgment slightly favor one impact over another

5 Experience and judgment strongly favor one impact over another

7 One impact is favored very strongly over another, its dominance demonstrated in 

practice

9 The evidence favoring one impact over another is of the highest possible order of 

affirmation

*2, 4, 6, and 8 can be selected when compromise between values of 1, 3, 5, 7, and 9, is 

needed.

Through an AHP known as pairwise comparison, numerical comparison values are 

assigned to each possible pair of environmental impacts. Relative importance weights can 

then be derived by computing the normalized eigenvector of the largest eigenvalue of the 

matrix of pairwise comparison values. Table 4-4 and Table 4-5 list the pairwise 

comparison values assigned to the verbal importance rankings, and the resulting SAB 

importance weights computed for the BIRDS impacts, respectively. Note that the 

pairwise comparison values were assigned through an iterative process based on NIST’s 

background and experience in applying the AHP technique. Furthermore, while the SAB 

evaluated cancer and non-cancer effects as a group, the resulting 13 % weight was 

apportioned between the two based on the relative judgments of the BEES Stakeholder 

Panel discussed in the next section.

Table 4-4 Pairwise Comparison Values for Deriving Impact Category Importance 

Weights

Verbal Importance 

Comparison

Pairwise 

Comparison Value

Highest vs. Low 6

Highest vs. Medium 3

Highest vs. High 1.5

High vs. Low 4

High vs. Medium 2

Medium vs. Low 2
38

Table 4-5 Relative Importance Weights based on Science Advisory Board Study

Impact Category Relative Importance 

Weight (%)

Global Warming 18

Primary Energy 

Consumption 

7

HH Criteria Air 7

HH Cancer 8

Water Consumption 3

Ecological Toxicity 12

Eutrophication 5

Land Use 18

HH Non-cancer 5

Smog Formation 7

Acidification 5

Ozone Depletion 5

4.4.2 BEES Stakeholder Panel Judgments

While the derived EPA SAB-based weight set is helpful and offers expert guidance, 

several interpretations and assumptions were required in order to translate SAB findings 

into numerical weights for interpreting LCA-based analyses. A more direct approach to 

weight development would consider a closer match to the context of the application; that 

is, environmentally preferable purchasing in the United States based on life-cycle impact 

assessment results, as reported by BIRDS.

In order to develop such a weight set, NIST assembled a volunteer stakeholder panel that 

met at its facilities in Gaithersburg, Maryland, for a full day in May 2006. To convene the 

panel, invitations were sent to individuals representing one of three “voting interests:” 

producers (e.g., building product manufacturers), users (e.g., green building designers), 

and LCA experts. Nineteen individuals participated in the panel: seven producers, seven 

users, and five LCA experts. These “voting interests” were adapted from the groupings 

ASTM International employs for developing voluntary standards, in order to promote 

balance and support a consensus process.
39

The BEES Stakeholder Panel was led by Dr. Ernest Forman, founder of the AHP firm 

Expert Choice Inc. Dr. Forman facilitated panelists in weighting the BEES impact 

categories using the AHP pairwise comparison process. The panel weighted all impacts 

in the Short Term (0 years to 10 years), Medium Term (10 years to 100 years), and Long 

Term (>100 years). One year’s worth of U.S. flows for each pair of impacts was 

compared, with respect to their contributions to environmental performance. For 

example, for an impact comparison over the Long Term, the panel evaluated the effect 

that the current year’s U.S. emissions would have more than 100 years hence.

Once the panel pairwise-compared impacts for the three time horizons, its judgments 

were synthesized across the selected time horizons. Note that when synthesizing 

judgments across voting interests and time horizons, all panelists were assigned equal 

importance, while the short, medium, and long-term time horizons were assigned by the 

panel to carry 24 %, 31 %, and 45 % of the weight, respectively.

The environmental impact importance weights developed through application of the AHP 

technique at the facilitated BEES Stakeholder Panel event are shown in Table 4-6. These 

weights reflect a synthesis of panelists’ perspectives across all combinations of 

stakeholder voting interest and time horizon. The weight set draws on each panelist’s 

personal and professional understanding of, and value attributed to, each impact category. 

While the synthesized weight set may not equally satisfy each panelist’s view of impact 

importance, it does reflect contemporary values in applying LCA to real world decisions. 

This synthesized BEES Stakeholder Panel weight set is offered as an option in BIRDS 

online.

The panel’s application of the AHP process to derive environmental impact importance 

weights is documented in an appendix to Gloria, Lippiatt et al. (2007) and ASTM (2011).
40

Table 4-6 Relative Importance Weights based on BEES Stakeholder Panel 

Judgments

Impact Category Relative Importance 

Weight (%) 

Global Warming 29.9

Primary Energy Consumption 10.3

HH Criteria Air 9.3

HH Cancer 8.2

Water Consumption 8.2

Ecological Toxicity 7.2

Eutrophication 6.2

Land Use 6.2

HH Non-cancer 5.2

Smog Formation 4.1

Acidification 3.1

Ozone Depletion 2.1

Note: Since BIRDS does not currently include an Indoor 

Air Quality impact category, its 3 % BEES Stakeholder 

Panel weight has been redistributed by proportion among 

the remaining 12 impacts.

The three figures below display in graphical form the BEES Stakeholder Panel weights 

used in BIRDS. Figure 4-3 displays the synthesized weight set. Figure 4-4 displays the 

weights specific to panelist voting interest, and Figure 4-5 displays the weights specific to 

time horizon. The BIRDS user is free to interpret results using either of the weight sets 

displayed in Figure 4-4 and Figure 4-5 by entering them as a user-defined weight set.
41

Figure 4-3 BEES Stakeholder Panel Importance Weights Synthesized across Voting 

Interest and Time Horizon

0%

10%

20%

30%

40%

50%

Producers

Users

LCA Experts

Figure 4-4 BEES Stakeholder Panel Importance Weights by Stakeholder Voting 

Interest

0%

5%

10%

15%

20%

25%

30%

35%

Impact Category
42

Figure 4-5 BEES Stakeholder Panel Importance Weights by Time Horizon

4.5 BIRDS Residential Energy Technologies

Since buildings have very long lives, operating energy efficiency has an important 

influence on their sustainability performance. Energy efficiency is largely driven by a 

building’s energy technologies, but top-down inventory data are not readily available at 

this level of resolution. Therefore, BIRDS includes detailed LCI data for a range of 

energy technology packages that have been compiled at the traditional, bottom-up LCA 

level. These energy technology packages are used to match 240 000 different building 

designs based on the NZERTF for Gaithersburg, MD. The bottom-up BIRDS data were 

developed under contract to NIST by Four Elements Consulting, LLC, of Seattle, 

Washington. Energy technologies include wall and ceiling insulation, windows, HVAC 

equipment, water heaters, lighting, and interior and exterior sealants.

4.5.1 General Information Regarding the Energy Technology LCIs

4.5.1.1 Standards Used

The LCAs in BIRDS have been built based on the principles and framework in the 

International Organization for Standardization (ISO) (2006a) and the guidelines specified 

in International Organization for Standardization (ISO) (2006b).
43

4.5.1.2 Primary and Secondary Data Sources 

Both primary data (collected directly from a manufacturing facility) and secondary data 

(publicly-available literature sources) can be used to build LCAs, and it is common to see 

a combination of both data types based on the level of disclosure organizations or 

companies prefer related to the information pertaining to their products. Sources of data 

on the energy technologies in BIRDS vary from one category to the next, and within 

categories themselves for the different products. Data were based on one or more of the 

following:

• Primary data from a group of companies and/or an industry association, compiled 

into an industry average product; 

• Primary data on a product provided by one company;

• Secondary data that represent an average or typical product; and/or

• Secondary data that represent one product in a category.

For optimal data quality, the preference is to have the most representative data –

temporally, technologically, and geographically – on a product or system, so that the 

model produced most closely represents the product. But this is often not possible to 

achieve due to data constraints. It is also not always possible to have a data set that 

represents an entire category of products. For example, high quality, current, companyspecific data might be collected and used to build the LCA for a given product. Likewise, 

data for another product might be compiled from literature sources due to lack of other 

available data. In both cases, the LCI profiles may be used to represent the full product 

category, even though they may not be representative of all products within the category, 

based on market share, technology, geographical location, etc. The user should be aware 

of this limitation. 

4.5.1.3 Data Sources Used for the Background Data 

Secondary data have been applied to production of material inputs, production and 

combustion of fuels used for process energy, and transportation processes. The U.S. LCI 

Database (National Renewable Energy Laboratory (NREL) 2012) and the ecoinvent v.2.2 

database (Ecoinvent 2007) are the main sources of background data throughout the 

various life-cycle stages. Other sources of data were used where data were not available 

from the U.S. LCI Database or ecoinvent, and/or where they were deemed to be of better 

quality than these sources. 

The following subsections describe modeling, assumptions, and data sources of the 

product life-cycle data. Data for the production of material inputs for each product are 

described in the subchapters since these may vary for different industries. The following 

data aspects are consistent for all products except where noted differently in the 

subchapters:
44

• All energy production, including production of fuels and conversion into energy 

and electricity production come from the U.S. LCI Database. 

• All transportation data come from the U.S. LCI Database. 

• Whenever possible, where ecoinvent or other non-North American data sets were 

used, they were customized into North American processes by switching out 

foreign energy, electricity, transportation, and other processes for comparable 

North American based data sets from the U.S. LCI databases. Exceptions to this 

are noted.

• Data for parts forming (e.g., forming of metal parts and pieces, injection molding 

of plastic parts, etc.), are modeled with raw materials production. Most of these 

data come from ecoinvent.

4.5.2 Wall and Ceiling Insulation

The insulation categories considered for the residential walls and ceilings are presented in 

Table 4-7. BIRDS performance data for the insulation types were provided on the basis 

of 2.54 cm (1 in) or 0.09 m2 (1 ft2

), which was then multiplied by the area and required 

thickness to meet the requirements of the home’s design and energy code edition. Table 

4-7 presents the thermal resistance in RSI-value per-cm (R-value per in) for each.

Table 4-7 Specified Insulation Types and R-Values

R-value (SI) R-value (IP)

Insulation Type m2 K/W per 1 cm ft2 °F hr/Btu per 1 in

Kraft faced fiberglass blanket – RSI-2.3 (R-13) 0.26 R-3.7

Kraft faced fiberglass blanket - RSI-3.4 (R-19) 0.21 R-3.0

Blow-in cellulose - RSI-2.3 (R-13) 0.26 R-3.7

Blow-in cellulose - RSI-6.7 (R-38) 0.25 R-3.5

Spray polyurethane foam (open cell) 0.25 R-3.6

Spray polyurethane foam (closed cell) 0.42 R-6.2

Mineral wool blanket - RSI-2.6 (R-15)Note 1 0.30 R-4.3

Mineral wool blanket - RSI-4.1 (R-23)Note 1 0.30 R-4.2

XPS foam board 0.35 R-5.0Note 2

Polyisocyanurate foam board 0.46 R-6.5

Note 1: Thermal resistance values RSI-2.3 (R-13) and RSI-3.4 (R-19) are used for BIRDS wall insulation; 

mineral wool blankets have been modeled according to R-values more commonly offered for this 

insulation material (i.e., RSI-2.6 (R-15) and RSI-4.1 (R-23)). For BIRDS, the data have been normalized 

to RSI-2.3 (R-13) and RSI-3.4 (R-19).
45

Note 2: See the XPS foam documentation (Section 4.5.2.5) regarding the increase of thermal resistance 

with increasing foam thickness.

Figure 4-6 presents the general system boundaries for the insulation category as it is 

modeled for BIRDS. 

Figure 4-6 Insulation System Boundaries – Fiberglass Blanket Example

The modeling and assumptions for each type of insulation are presented in the following 

sections. 

4.5.2.1 Fiberglass Blanket 

Fiberglass blanket, or batt, insulation is made by forming spun-glass fibers into batts. At 

an insulation plant, the product feedstock is weighed and sent to a melting furnace. The 

raw materials are melted in a furnace at very high temperatures. Streams of the resulting 

vitreous melt are either spun into fibers after falling onto rapidly rotating flywheels or 

drawn through tiny holes in rapidly rotating spinners. This process shapes the melt into 

fibers. Glass coatings are added to the fibers that are then collected on conveyers. The 

structure and density of the product is continually controlled by the conveyer speed and 

height as it passes through a curing oven. The cured product is then sawn or cut to the 

required size. Off-cuts and other scrap material are recycled back into the production 

process. 

Thermal resistance values of RSI-2.3 (R-13) and RSI-3.4 (R-19) are used for wall 

insulation, and Table 4-8 specifies fiberglass insulation by type and R-value. Most of the 

fiberglass insulation data is based on the model presented in the online documentation of 

the BEES software (NIST 2010).

Use Phase EOL (landfilling)

Fiberglass Blanket 

assembly

Glass fiber 

production 

Facing 

production

Raw Materials 

Production

Transport to building site

Process 

Energy

Process aids

Emissions to air, 

water, soil

Raw Materials 

Production

Production

Transport 

to manuf.

Transport 

to manuf.
46

Table 4-8 Fiberglass Blanket Mass by Application

Application

Thickness 

cm (in)

Density kg/m3

(lb/ft3)

Mass per 1 in 

Functional Unit 

kg/m2 (oz/ft2)

R-Value per 

Reference Flow 

(m2 K/W per 1 cm)

R-Value per 

Reference Flow 

(ft2 °F hr/Btu per 1 in)

Wall – RSI-2.3 

(R-13)

8.9 (3.5) 12.1 (0.76) 0.31 (1.01) R-0.26 R-3.7

Wall – RSI-3.4 

(R-19)

15.9 (6.25) 7.0 (0.44) 0.18 (0.58) R-0.21 R-3.0

Upstream Materials Production through Manufacturing

Fiberglass insulation is made with a blend of sand, limestone, soda ash, and recycled 

glass cullet. Recycled window, automotive, or bottle glass is used in the manufacture of 

glass fiber; it accounts for 30 % to 50 % of the raw material input. The recycled content 

is limited by the amount of usable recycled material available in the market – not all glass 

cullet is of sufficient quality to be used in the glass fiber manufacturing process. The use 

of recycled material has helped to steadily reduce the energy required to produce 

insulation products. The raw materials used to produce fiberglass insulation are broken 

down by the glass and facing contents, shown in Table 4-9.

Table 4-9 Fiberglass Insulation Constituents

Glass Constituent Mass Fraction (%)

Soda Ash 9.0

Borax 12.0

Glass Cullet 34.0

Limestone 9.0

Phenolic resin (binder coating) 5.0

Sand 31.0

Total 100

Facing Mass Fraction (%)

Kraft paper 25.0

Asphalt 75.0

Total 100
47

The production data for the soda ash, limestone, and phenol formaldehyde resin come 

from the U.S. LCI Database. The borax, glass cullet, and silica sand data come from 

ecoinvent. For the facing, Kraft paper data come from ecoinvent and the asphalt comes 

from U.S. LCI Database. 

The raw materials are transported to the manufacturing plant via diesel truck. Materials 

are sourced domestically, and transportation distances range on average from 161 km 

(100 mi) to 805 km (500 mi).

The energy requirements for melting the glass constituents into fibers and drying off the 

completed blanket involve use of natural gas and electricity, shown in Table 4-10.

Table 4-10 Energy Requirements for Fiberglass Insulation Manufacturing

Energy Carrier MJ/kg (Btu/lb)

Natural Gas 1.99 (857)

Electricity 1.37 (591)

Total 3.36 (1 448)

Besides combustion emissions from fuel usage at manufacturing, particulates are emitted 

at a rate of 2.38 g/kg (4.76 lb/ton) of bonded blankets and volatile organic compounds 

(VOCs) are emitted at a rate of 0.76 g/kg (1.52 lb/ton) of bonded blankets.

All waste produced during the cutting and blending process is either recycled into other 

insulation materials or added back into the glass mix. Thus, no solid waste is assumed to 

be generated during the production process.

Transportation to the Building through End-of-Life 

Transportation of the insulation from the manufacturer to the building site is assumed to 

be an average of 805 km (500 mi) by heavy-duty diesel-fueled truck.

Installing fiberglass blanket insulation is primarily a manual process, with no energy or 

emissions included in the model. During installation, any waste material is added into the 

building shell where the insulation is installed – there is effectively no installation waste. 

Fiberglass insulation has a functional lifetime of over 50 years so no replacement is 

needed during the 40-year study period. How this product affects operating energy during 

the home’s use phase is addressed in other sections of this report.

While fiberglass insulation is recyclable, it is assumed that it is disposed of in a landfill at 

end-of-life. End-of-life modeling includes transportation by heavy-duty diesel-fuel 

powered truck approximately 80 km (50 mi) to a construction and demolition (C&D) 

landfill. Landfilled insulation is modeled based on ecoinvent end-of-life waste 

management process data. 
48

4.5.2.2 Blown Cellulose Insulation 

Thermal resistance values of RSI-3.4 (R-13) for a wall application and RSI-6.7 (R-38) for 

a ceiling application of blown cellulose have been used in BIRDS. The models for 

BIRDS are largely based on the blown cellulose in NIST (2010). Table 4-11 specifies 

blown cellulose insulation by type and R-value.

Table 4-11 Blown Cellulose Insulation by Application

Application

Thickness 

cm (in)

Density 

kg/m3

(lb/ft3)

Mass per 1 in 

Functional Unit 

kg/m2 (lb/ft2)

R-Value per 

Reference Flow 

(m2 K /W per 1 

cm)

R-Value per 

Reference Flow 

(ft2 °F hr/Btu per 1 in)

Wall – RSI-2.3 

(R-13)

8.9 (3.5) 35.3 (2.20) 0.89 (0.18) RSI-0.26 R-3.7

Ceiling – RSI-6.7 

(R-38)

27.6 

(10.9)

27.2 (1.70) 0.69 (0.14) RSI-0.24 R-3.5

Upstream Materials Production through Manufacturing

Cellulose insulation is essentially shredded, recovered wastepaper that is coated with fire 

retardants. Blown cellulose insulation is produced primarily from post-consumer wood 

pulp (newspapers), typically accounting for roughly 85 % of the insulation by weight. 

Ammonium sulfate, borates, and boric acid are the fire retardants used most commonly 

and account for the other 15 % of the cellulose insulation by weight. The mix of these 

materials is provided in Table 4-12; while the relative proportions of the fire retardants 

vary among manufacturers, it is assumed that they are mixed in equal proportions for 

BIRDS.

Table 4-12 Cellulose Insulation Constituents

Constituent Mass Fraction (%)

Recovered Newspaper 85

Ammonium Sulfate 7.5

Boric Acid 7.5

Total 100

BIRDS recovered newspaper data includes impacts from wastepaper collection, sorting, 

and subsequent transportation to the insulation manufacturer. Since it is a recovered 

product, the impacts from upstream production of the pulp are not included in the system 

boundaries. Data for ammonium sulfate and boric acid come from ecoinvent. It is 
49

assumed that the raw materials are shipped 161 km (100 mi) to the manufacturing plant 

via diesel truck.

The manufacturing process includes shredding the wastepaper and blending it with the 

different fire retardants. It is assumed that the manufacturing energy is purchased 

electricity in the amount of 0.35 MJ/kg (150 Btu/lb). Any waste produced during the 

production process is recycled back into other insulation materials. Therefore, no solid 

waste is generated during the production process.

Transportation to the Building Site through End-of-Life

Transportation of the insulation to the building site is modeled an assumed average of 

805 km (500 mi) by heavy-duty diesel-fueled truck.

At installation, a diesel generator is used to blow the insulation material into the space. 

For one hour of operation, a typical 18 kW (25 hp) diesel engine can blow 818 kg (1800 

lb) of insulation. The emissions and energy use for this generator are included in the 

system boundaries for this product. No other installation energy is required. Any waste 

material during installation is added into the building shell where the insulation is 

installed, so there is effectively no installation waste. 

Cellulose insulation has a functional lifetime of over 50 years so no replacement is 

needed during the 40 year study period. How this product affects operating energy during 

the home’s use phase is addressed in other sections of this report.

While cellulose insulation is mostly recyclable, it is assumed that all of the insulation is 

disposed of in a landfill at end-of-life. End-of-life modeling includes transportation 

approximately 80 km (50 mi) to a C&D landfill. Landfilled insulation is modeled based 

on ecoinvent end-of-life waste management process data.

4.5.2.3 Spray Polyurethane Foam Insulation

Spray polyurethane foam (SPF) is an insulation and roofing material that is formulated at 

the building installation site using a combination of an isocyanate, or “A-side,” with an 

equal volume of a polyol blend, or “B-side.” For SPF, the A-side is a blend of monomeric 

and polymeric methylene diphenyl diisocyanate (pMDI). The B-side formulation varies 

based on formulator and desired properties, and includes at least five different types of 

chemical ingredients: polyols, blowing agents, flame retardants, catalysts, and 

surfactants. When the A and B side are mixed, it expands in place providing both 

insulation and an air barrier; some SPF types deliver other performance properties 

including a vapor retarder, water resistance and structural enhancement. 

Two classifications for SPF insulation used in the U.S. construction industry are opencell (low-density) and closed-cell (medium-density) SPF. Open-cell has a nominal 

density ranging from 6.4 kg/m3 to 11.1 kg/m3 (0.4 lb/ft3 to 0.7 lb/ft3

); 7.9 kg/m3

(0.5 lb/ft3

) has been assumed for the LCA. Open-cell foam is formed using water as a 

reactive blowing agent. Water reacts with the A-side methylene diphenyl diisocyanate 
50

(MDI) to create carbon dioxide (CO2) gas that expands the curing liquid into a cellular 

foam material. Thermal resistance per inch is in the range of RSI-0.25 to RSI-0.28 per cm 

(R-3.6 to R-4.0 per inch); RSI-0.25 (R-3.6) has been assumed. 

Closed-cell foam has a nominal density ranging from 27.0 to 36.5 kg/m3 (1.7 to 2.3 

lb/ft3

); 31.8 kg/m3 (2.0 lb/ft3) has been assumed. Fluorocarbon (physical) blowing agents 

are used to expand closed-cell foams: the fluorocarbon liquid in the B-side converts to a 

gas from the heat of the reaction to expand the cells. The low thermal conductivity 

fluorocarbon gas yields an R-value ranging from RSI-0.07 to RSI-0.08 per cm (R-5.8 to R6.8 per in); RSI-0.43 (R-6.2) has been assumed. Emissions from the Pentafluoropropane 

(HFC-245fa) were included in the LCA, and the assumptions around its release are stated 

later in this documentation. 

The information provided in this summary is based on a comprehensive LCA study on 

SPF (PE International 2012). The quantitative data come mainly from a Spray 

Polyurethane Foam Alliance (SPFA) summary report (SPFA 2012) and SPFA’s 

Environmental Product Declaration (EPD), both based on PE International (2012).

Upstream Materials Production through Manufacturing

SPF’s A-side is MDI. The B-side is made up of a number of combinations and types of 

chemicals. Primary data for the year 2010 were collected from six formulation plants to 

attain an industry average. Table 4-13 provides the material constituent percentages of 

the B-side formulations (SPFA 2012). These data are assumed to be representative of 

SPF in the U.S., based on input and consensus by SPFA members. Nonetheless, these 

formulations are generic and thus do not represent one specific producer’s formulation.
51

Table 4-13 B-Side Formulation – Material Constituent Percentages

Constituent Low density (open 

cell foam) %

Medium density 

(closed cell foam) %

Polyol – polyester n/a 45.0

Polyol – Mannich n/a 30.0

Polyol – compatibilizer 10.0 n/a

Polyol – polyether 35.0 n/a

Fire retardent – TCPP 25.0 4.0

Fire retardent – brominated n/a 6.0

Blowing agent - reactive (de-ionized H2O) 23.5 2.0

Blowing agent - physical (HFC-245fa) n/a 8.5

Catalyst – amine 6.0 3.0

Catalyst – metal n/a 0.5

Surfactant – silicone 0.5 1.0

Total 100 100

The reference flows used for the original LCA were based on the functional unit defined 

by UL Environment (2011) Product Category Rules (PCR) on U.S. insulation: one square 

meter of foam provides an R-value of 1.00 m2 K/W (5.68 h·ft2

·°F/Btu). The 

specifications for the original LCA and the BIRDS LCA are shown together in Table 

4-14.
52

Table 4-14 SPF Insulation Reference Unit Parameters for Original and BIRDS 

LCAs

Unit Low-density 

Open-Cell

Medium-density 

Closed-Cell

Foam R-Value m2 K/W (h·ft2

·°F/Btu) per 

cm (per in)

0.25 (3.6) 0.43 (6.2)

Original LCA Foam area m2 (ft2

) 1.0 (10.76) 1.0 (10.76)

Target R-value m2 K/W (h·ft2·°F/Btu) 1.00 (5.68) 1.00 (5.68)

Thickness per 

reference unit

cm (in) 4.0 (1.58) 2.3 (0.92)

Mass per 

reference unit

kg (lb) 0.59 (1.31) 1.16 (2.55)

BIRDS LCA Foam area m2 (ft2

) 0.09 (1.0) 0.09 (1.0)

R-value per cm 

(per in)

m2 K/W (h·ft2·°F/Btu) 0.25 (3.6) 0.43 (6.2)

Thickness per 

reference unit

cm (in) 2.54 (1.0) 2.54 (1.0)

Mass per 

reference unit

kg (lb) 0.035 (0.077) 0.117 (0.26)

Based on the percentages of the B-side material constituents (presented in Table 4-13) 

and the mass per reference unit for the BIRDS LCA, the masses of materials in the foam 

shown in Table 4-15 were modeled for BIRDS. The quantities of the pMDI were 

calculated based on SPFA (2012), which provided the masses of A-side and B-side from 

a drum set at installation.
53

Table 4-15 Material Constituents for Open-Cell and Closed-Cell SPF Insulation

Constituent

Low Density 

(Open-Cell) Foam

kg lb

Medium Density 

(Closed-Cell) Foam

kg lb

Polyol – polyester n/a n/a 0.026 0.057

Polyol – Mannich n/a n/a 0.017 0.038

Polyol – compatibilizer 0.002 0.004 n/a n/a

Polyol – polyether 0.006 0.013 n/a n/a

Fire retardent – TCPP 0.004 0.009 0.002 0.005

Fire retardent – Brominated n/a n/a 0.003 0.008

Blowing agent – reactive (deionized H2O) 0.004 0.009 0.001 0.003

Blowing agent – physical (HFC-245fa) n/a n/a 0.005 0.011

Catalyst – amine 0.001 0.002 0.002 0.004

Catalyst – metal n/a n/a 0.0003 0.001

Surfactant – silicone n/a n/a 0.001 0.001

pMDI – A-Side 0.018 0.040 0.060 0.131

Total 0.035 0.077 0.117 0.258

Data for the MDI comes from the U.S. LCI Database. Data for the polyester polyol 

comes from PE International (2010). Due to lack of other available data, proxies were 

used for the other polyols: polyether polyol was used for the Mannich polyol and 

ethylene glycol was used for compatibilizer polyol. Both come from the U.S. LCI 

Database.

Data for Tris(2-chloroisopropyl) phosphate (TCPP) are U.S. compiled from PE 

International (2012). Due to lack of specific data on the brominated fire retardant, 

ecoinvent’s data set on diphenyl ether compounds was used as a proxy, since brominated 

fire retardants may be part of a group called polybrominated diphenyl ethers (PBDEs). 

Deionized water-blowing agent data come from ecoinvent. Data for the HFC-245fa are 

based on stoichiometry and information from McCulloch (2009). Dimethylamine from 

ecoinvent was used for the amine catalyst, and the silicone surfactant was modeled using 

tetrachlorosilane as a proxy. Both these data sets come from ecoinvent. No data were 

available to include the metal catalyst.
54

Transport distances and modes of transportation of raw materials to the formulation 

plants were provided in the original LCA; these distances were used in the BIRDS LCA 

but are not included in this text due to non-disclosure requests.

Weighted average process energy data from the six formulation sites were provided in 

Table 3.1 of PE International (2012). Data included electric and other fuel energy 

sources, waste quantities and their fates, packaging in steel drums, plastic drums and 

totes, plastic wrap, and pallets, plus direct process emissions from foam reactions. These 

data were included in the BIRDS LCA but are not provided in this summary to protect 

confidentiality.

Transportation to the Building Site through End-of-Life

According to PE International (2012), transportation to the building site of the A- and Bside drums is modeled as a weighted industry average of 1287 km (800 mi) by heavyduty diesel-fueled truck. 

On-site application is nearly identical for all high-pressure SPF materials. The A- and Bside chemicals are delivered to the SPF contractor in pairs (sets) of 208-liter (55-gal) 

drums from the formulator. These unpressurized drum sets are stored at the contractor’s 

facility at room temperature conditions until taken to the jobsite in a spray rig. 

At the jobsite, the chemicals are heated and pressurized by specialized equipment. The 

chemicals are aerosolized by a spray gun and combined by impingement mixing during 

application. Weighted average data from six installation contractors from across North 

America were provided in Table 3.3 of PE International (2012). Data included electricity, 

diesel fuel, and waste materials. These data were included in BIRDS but are not provided 

in this summary to protect confidentiality. According to the UL Environment (2013)

EPD, it is assumed that 50 % of the original HFC-245fa blowing agent in the closed-cell 

foam will stay with the product. The remaining original amount of the blowing agent is 

released according to the EPD as follows: 

• 10 % emitted at installation;

• 24 % emitted over its lifetime in the building; 

• 16 % emitted at end-of-life.

SPF insulation has a functional lifetime of 60 years so no replacement is needed during 

the BIRDS’ 40-year study period. Installed SPF insulation normally requires no 

maintenance and has no impacts associated with it besides the release of blowing agent as 

mentioned above. How insulation in the building affects operating energy during the use 

phase is addressed in other sections of this report. 

It is assumed that SPF insulation is disposed of in a landfill at end-of-life. The foam is 

inert in the landfill, except for the release of the HFC-245fa, as stated above. End-of-life 

modeling includes transportation by heavy-duty diesel-fuel powered truck approximately 
55

80 km (50 mi) to a C&D landfill. Insulation in a landfill is modeled based on ecoinvent 

end-of-life waste management process data. 

4.5.2.4 Mineral Wool Blanket Insulation

Mineral wool insulation is made by spinning fibers from natural rock like diabase or 

basalt (rock wool) or iron ore blast furnace slag (slag wool). Rock wool and slag wool are 

manufactured by melting the constituent raw materials in a cupola. A molten stream is 

created and poured onto a rapidly spinning wheel or wheels. The viscous molten material 

adheres to the wheels and the centrifugal force throws droplets of melt away from the 

wheels, forming fibers. A binder, used to stabilize the fibers, and a de-dusting agent to 

reduce free, airborne wool during use, are applied during this process. The material is 

heated to cure the binder and stabilize the material, and is then cooled. The blankets are 

then cut to size and packaged. A portion of the data on mineral wool insulation come 

from NIST (2010).

Thermal resistance values of RSI-2.3 (R-13) and RSI-3.4 (R-19) are used in BIRDS for 

wall insulation but data used for this LCA have been modeled according to thermal 

resistances more commonly offered for mineral wool blankets (i.e., RSI-2.6 (R-15) and 

RSI-4.1 (R-23)) for each of comparison. Table 4-16 specifies mineral wool blanket 

insulation by type and R-value (ROXUL 2013).

Table 4-16 Mineral Wool Blanket Mass by Application

Application

Thickness

cm (in)

Density 

kg/m3 (lb/ft3)

Mass per 1 in 

Functional Unit 

kg/m2 (lb/ft2)

R-Value per 

Reference Flow

m2 K/W per cm 

(h·ft2·°F/Btu per in)

Wall – RSI-2.6 (R-15) 8.9 (3.5) 31.5 (2.0) 0.074 (0.16) 0.30 (4.3)

Wall – RSI-4.1 (R-23) 14.0 (5.5) 34.3 (2.1) 0.081 (0.18) 0.30 (4.2)

Upstream Materials Production through Manufacturing

Mineral wool can be manufactured using slag wool or rock wool. Some products contain 

both materials; about 80 % of North American mineral wool is manufactured using iron 

ore slag. The binder is modeled as phenol formaldehyde resin. Quantities of resin vary for 

different manufacturers and performance characteristics; eight percent was modeled for 

BIRDS. Additionally, a small amount of mineral oil was modeled as added to seal the 

surface against dust production (U.K. Department for Environment 2008). Table 4-17

presents the weighted mix of the different types of mineral wool feedstock representing 

production in North America, plus the additional materials used in blankets.
56

Table 4-17 Mineral Wool Insulation Constituents

Constituent Mass Fraction (%)

Diabase Rock/Basalt 20.2

Iron Ore Slag 71.5

Phenol formaldehyde resin 8.0

Mineral oil 0.3

Total 100

Data for rock mining and grinding come from ecoinvent. Slag is produced during steel 

production, when molten iron and slag are produced together in the blast furnace. The 

slag is recovered and can be used to produce mineral wool, as an additive to cement, etc. 

For mineral wool, the production of slag is considered to be environmentally “free” of 

upstream impacts. However, the LCA accounts for the transportation to fiber production 

and the processing into fiber. The data for phenol formaldehyde resin and mineral oil 

come from the U.S. LCI database. 

The raw materials are transported to the manufacturing plant via diesel truck. Materials 

are sourced domestically, and transportation distances range on average from 161 km 

(100 mi) to 805 km (500 mi).

The energy requirements for melting the product constituents into fibers and drying of the 

fibers involve a mixture of coke and electricity. The energy demands are outlined in 

Table 4-18 (NIST 2010).

Table 4-18 Energy Requirements for Mineral Wool Insulation Manufacturing

Energy Source MJ/kg (Btu/lb)

Coke 6.38 (2740)

Electricity 1.0 (430)

Total 7.38 (3170)

The manufacturing process generates air emissions from energy use, particulates and 

fluorides during melting and spinning of the mineral feedstocks, and phenol and 

formaldehyde during melting and binding. These emissions are included in the model. All 

waste produced during the production process is either recycled into other insulation 

materials or added back into the melt. Therefore, no solid waste is generated during 

production.
57

Transportation to the Building Site through End-of-Life

Transportation of the insulation to the building site is modeled an assumed average of 

805 km (500 mi) by heavy-duty diesel-fueled truck. Installation is primarily a manual 

process; no energy or emissions are included in the model. During installation, any waste 

material is added into the building shell where the insulation is installed – there is 

effectively no installation waste. 

Mineral wool insulation has a functional lifetime of over 50 years so no replacement is 

needed during the 40-year study period. How this product affects operating energy during 

the home’s use phase is addressed in other sections of this report.

While mineral wool insulation may be recyclable, it is assumed that it is disposed of in a 

landfill at end-of-life. End-of-life modeling includes transportation by heavy-duty dieselfuel powered truck approximately 80 km (50 mi) to a C&D landfill. Landfilled insulation

is modeled based on ecoinvent end-of-life waste management process data. 

4.5.2.5 XPS Foam Insulation

Extruded Polystyrene (XPS) foam insulation has been modeled for the residential exterior 

wall. Type IV and Type XPS may be used for residential applications; the data in this 

document is representative of Type IV XPS, which has a typical average density of 

26.2 kg/m3 (1.63 lb/ft3). The foam has the following R-values, depending on thickness:5

• 2.5 cm (1 in): RSI = 0.9 (R = 5.0) 

• 5.1 cm (2 in): RSI = 1.9 (R = 10.6)

• 7.6 cm (3 in): RSI = 2.9 (R = 16.2)

• 10.2 cm (4 in): RSI = 3.9 (R = 22.0)

Upstream Materials Production through Manufacturing

Extruded Polystyrene Foam Association (XPSA) member companies provided 

representative industry average production data on XPS foam boards. XPSA represents 

the three largest producers in North America and accounts for over 95 % of XPS products 

produced and sold. Table 4-19 provides a 2010 representative average of the raw material 

and processing energy inputs and process outputs to produce one kg of XPS foam board.

The table presents the current representative blowing agent assumptions for XPS. It 

should be noted that HFCs began to replace 1-Chloro-1, 1-difluoroethane (HCFC-142b) 

as the principal blowing agent in 2009, as the industry complied with U.S. EPA and 

Environment Canada ODS phase-out regulations requiring the XPS sector to discontinue

HCFC use by the end of 2009. By 2010, all XPSA members had converted to non-HCFC 

blowing agents and have been using only HFC materials ever since.

 5 Extruded Polystyrene Foam Association (XPSA) website, found at http://www.xpsa.com. Values are 

based on a round-robin study in 2003 using the CAN/ULC S770-00 LTTR standard.
58

Table 4-19 XPS Foam Board Production Data

Inputs Quantity per kg Quantity per lb

Blowing agents HFC-134a 0.060 kg 0.060 lb

HFC-152a 0.017 kg 0.017 lb

CO2 0.012 kg 0.012 lb

Solid additives PS resin 0.907 kg 0.907 lb

Additives 0.018 kg 0.018 lb

Energy Electricity 1.00 kWh 0.454 kWh

Outputs

Air HFC-134a 0.0105 kg 0.0105 lb

HFC-152a 0.0029 kg 0.0029 lb

Waste Waste 1.0 E-4 kg 1.0 E-4 lb

The additives in the table include the flame retardant widely used in all polystyrene 

foams (hexabromocyclododecane (HBCD)) and colorants or dyes/pigments used to 

produce the characteristic color of each XPSA member’s foam. Additives may also 

include a nucleation control agent, process lubricant, acid scavenger, or others.

The blowing agent conversion/trim losses during manufacturing are assumed to be on 

average 17.5 % for North American XPS foam production (Intergovernmental Panel on 

Climate Change (IPCC) 2005). All of the polystyrene trim waste at the manufacturing 

plant is reused internally in the process. Only a very small amount of foam and other 

materials are occasionally sent off-site for disposal in a landfill. 

Data for polystyrene come from the U.S. LCI Database. Data for all three blowing agents 

and some of the additives come from ecoinvent. Data were not available for all of the 

additives; where data were not available proxy data were implemented. 

All the raw materials are produced in the U.S., and most of the raw materials are centrally 

located, as are the XPS manufacturer's largest plants. The estimated weighted average 

distance from the main suppliers to the majority of XPS manufacturing plants is 805 km 

(500 mi) for polystyrene, Tetrafluoroethane (HFC-134a), flame retardant, and CO2. 1,1-

Difluoroethane (HFC-152a) and other additives are transported an average distance of 

1609 km (1000 mi) to manufacturing plants. All but the blowing agents and polystyrene 

are transported by diesel truck; the blowing agents and polystyrene are transported by 

rail.
59

Transportation to the Building Site through End-of-Life

Transportation of the insulation to the building site is modeled as 563 km (350 mi), an 

average factoring in the various plants around the United States. Transportation is by 

heavy-duty diesel truck.

Foam boards are installed with installation tape, but tape is excluded since it is 

considered negligible. Scrap XPS foam board generated at installation is assumed to be 2 

% of the total, consistent with other foam products in this category. While the product 

may be recyclable, it is modeled as being sent to a landfill 32 km (20 mi) from the 

building site. Data for the landfill come from waste management datasets in ecoinvent. 

Blowing agent escape during installation is insignificant. Minimal cutting to size on the 

jobsite is done and, even then, a sharp tool is typically used so that very few cells are 

opened.

XPS insulation has a functional lifetime of over 40 years so no replacement is needed 

during the 40-year study period. How insulation in the buildings affects operating energy 

during the prototype buildings’ use phase is addressed in other sections of this report. 

The diffusion of HFC-134a from XPS during use is 0.75 % +/- 0.25 % per 

(Intergovernmental Panel on Climate Change (IPCC) 2005). The blowing agent emission 

loss during the use phase is complex and non-linear but can be represented for simplicity 

as a linear function after the first year. The rate is a function of the product thickness, 

properties (density, cell size, skins), blowing agent type(s) and transport properties 

(solubility, diffusion coefficient), and the installed application details (mean temperature, 

permeability of applied facings). The diffusion rate of HFC-152a is 15 % per year 

(Intergovernmental Panel on Climate Change (IPCC) 2005).

Reuse of the foam is possible after building decommissioning, but the model assumes 

that at end-of-life the foam is disposed of in a landfill. End-of-life modeling includes 

transportation by heavy-duty diesel-fuel powered truck approximately 80 km (50 mi) to a 

C&D landfill. Landfilled insulation is modeled based on ecoinvent end-of-life waste 

management process data. For a typical North America building demolition followed by 

disposal in a landfill, it is reasonable to assume an initial blowing agent end-of-life loss of 

20 % followed by annual losses of 1 % (United Nations Environment Programme 

(UNEP) 2005).

4.5.2.6 Polyisocyanurate Foam Insulation 

Polyisocyanurate (polyiso) foam insulation has been modeled for the exterior wall 

application. The thermal resistance value for wall polyiso board is 6.5 which includes its 

impermeable board facer. This R-value is based on a 6-month accelerated aging test and 

was provided by representatives at Bayer MaterialScience6

. The foam has a wet, or pre-

 6 Verbal communication with Bayer MaterialScience representatives, July 2013.
60

yield, density of 29.2 kg/m3 (1.82 lb/ft3). The final product, which includes the weight of 

the facers, has a nominal density of 32.0 kg/m3 (2.0 lb/ft3).

Upstream Materials Production through Manufacturing

Upstream Materials Production

Cradle-to-gate data on production through manufacturing is based on a 2010 study 

performed for the Polyisocyanurate Insulation Manufacturers Association (PIMA) (Bayer 

MaterialScience 2008). The scope of this study included collecting and compiling mostly 

2007 production data from the six PIMA member companies and compiling it into an 

industry average polyiso insulation board. Process energy data came from 29 out of 31 

polyiso plants in the U.S. and Canada, representing approximately 94 % of production in 

those geographies. 

The chemicals to produce polyiso foam make up an “A” side (MDI) and a “B” side 

(polyester polyol with various additives such as catalysts, surfactants and flame 

retardants) plus a blowing agent (pentane). Table 4-20 presents the raw material inputs 

associated with polyiso foam production (Bayer MaterialScience 2011), provided on the 

basis of 2.54 cm (1 in) in thickness.

Table 4-20 Raw Material Inputs to Produce Polyiso Foam

Inputs % in foam

kg per 0.09 m2, 

2.54 cm thick

lb per 1 ft2, 

1 in thick

MDI 55.5 0.0382 0.0842

Polyester Polyol 31 0.0213 0.0470

TCPP 3.4 0.0023 0.0051

Catalyst K15 1.4 0.0010 0.0022

Catalyst PC46 0.16 1.38 E-04 0.0003

Catalyst PV 0.08 6.90 E-05 0.0002

Surfactant 0.63 5.51 E-04 0.0012

Pentane (blowing agent) 7.5 0.0052 0.0115

Process water 0.1 0.0001 0.0002

The MDI data come from the U.S. LCI Database. Data for the polyester polyol comes 

from an eco-profile study of aromatic polyester polyols (PE International 2010). Data for 

TCPP are U.S. data compiled from literature sources (PE International 2011). Pentane 

data come from ecoinvent. No data were available to include the three catalysts or 
61

silicone surfactant; they total 2.3 % of the total input, so a total of 97.7 % of the inputs 

were included in the model. 

Polyiso wall applications normally use aluminum Kraft paper (foil) for the facer. The foil 

facer raw materials include paper, aluminum foil, adhesives and coatings, and has a mass 

of 0.098 kg/m2 (0.02 lb/ft2) (Bayer MaterialScience 2008). Data on material composition 

come from a material safety data sheet (MSDS). Based on this limited data source, the 

facer is modeled as 77 % foil and 23 % Kraft (Atlas Roofing Corporation 2012). Data for 

foil is modeled as 50/50 primary and secondary aluminum from the U.S. LCI Database, 

plus sheet rolling (ecoinvent). Data for Kraft paper come from ecoinvent.

Raw materials are transported to the manufacturing plant via diesel truck or rail. The 

following distances and modes of transport were modeled: 

• MDI: 2 414 km (1500 mi) by rail; 

• Polyester polyol: 1384 km (860 mi) by rail (90 %), 1384 km (860 mi) by truck 

(10 %);

• Pentane: 2414 km (1 500 mi) by truck; 

• Remaining materials: 1609 km (1 000 mi) by truck. 

Manufacturing

According to (Bayer MaterialScience 2011), polyiso plants consume primarily electricity 

and natural gas used to operate the laminator and associated operations support 

equipment, such as thermal oxidizers, storage areas, packaging machines, raw material 

pumps, offices, etc. A small amount of propane is used for fork lift trucks. Table 4-21

presents energy inputs and process outputs to produce 1 board-foot of foam, or 0.09 m2 (1 

ft2) of 2.54 cm (1 in) thick polyiso foam.

Table 4-21 Energy Inputs and Process Outputs for 1 Board-Foot Polyiso Foam

Energy inputs Unit Quantity

Electricity MJ (kWh) 0.0497 (0.0138)

Nat gas MJ (Btu) 0.0913 (86.55)

Propane kg (lb) 0.00015 (0.00031)

Outputs Unit Quantity

Pentane to air kg (lb) 0.00013 (0.00030)

Waste scrap board-foot 0.01

Based on review with polyiso plant manufacturers, approximately 2.5 % of the pentane 

added to the foam is lost to air during manufacturing. Depending on the plant and local 
62

regulatory requirements, pentane is emitted directly to the atmosphere or to a thermal 

oxidizer for combustion. Only 13 plants out of 31 use thermal oxidizers to combust the 

pentane emissions. Since the majority of polyiso plants in North America do not use 

thermal oxidizers, the pentane is modeled as going directly to atmosphere (Bayer 

MaterialScience 2011).

Transportation and disposal of manufacturing waste scrap was modeled as going to an 

industrial landfill. It is assumed that a landfill for such non-hazardous waste is within 32 

km (20 mi) of the polyiso plant.

Transportation to the Building Site through End-of-Life

Transportation to the building site is modeled as 400 km (250 mi) by heavy-duty diesel 

truck (Bayer MaterialScience 2011).

Installation tape is used but is excluded since it is considered negligible. Scrap polyiso 

generated at installation is assumed to be 2 % of the total, consistent with other foam 

products in this category. While the product may be recyclable, it is modeled as being 

sent to a landfill 32 km (20 mi) from the building site. Data for the landfill come from 

waste management datasets in ecoinvent. Pentane release at installation is negligible.

Polyiso insulation has a functional lifetime of over 40 years so no replacement is needed 

during the 40 year study period. How insulation in the buildings affects operating energy 

during the prototype buildings’ use phase is addressed in other sections of this report. 

Polyiso insulation is modeled as disposed of in a landfill at end-of-life. End-of-life 

modeling includes transportation by heavy-duty diesel truck approximately 80 km (50 

mi) to a C&D landfill. Landfilled insulation is modeled based on ecoinvent end-of-life 

waste management process data. According to Bayer MaterialScience (2011), 50 % of the 

total pentane in the product will have been released by end-of-life and 50 % remains in 

the product.

4.5.3 Windows

4.5.3.1 Introduction 

The BIRDS residential tool evaluates double hung and casement windows with 

specifications complying with different energy code editions. Table 4-22 provides the Ufactor and solar heat gain coefficients (SHGCs) used to address different climate zone 

and code edition requirements, plus the window characteristics and frame type modeled 

for each. The window that is the closest match to the thermal performance of the window 

options in BIRDS is used as a basis for calculating the window LCIA data.
63

Table 4-22 Window Specifications

U-Factor

W/m2∙K

(Btu /ft2∙F∙h)

SHGC Window characteristics

Frame Type for 

Double Hung and 

Casement Windows

1.99 (0.35) No Req. 2 window panes, low-e coating, argon gas Wood-clad frame

2.27 (0.4) No Req. 2 window panes, low-e coating, argon gas Vinyl frame

2.56 (0.45) No Req. 2 window panes, tint Wood-clad frame

2.84 (0.5) 0.3 2 window panes, tint Wood-clad frame

2.84 (0.5) No Req. 2 window panes, tint Wood-clad frame

3.12 (0.55) No Req. 2 window panes, tint Wood-clad frame

3.41 (0.6) No Req. 2 window panes, low-e coating, argon gas Aluminum frame

3.69 (0.65) 0.3 2 window panes, low-e coating, argon gas Aluminum frame

3.69 (0.65) 0.4 2 window panes, tint Aluminum frame, 

thermal break

3.69 (0.65) No Req. 2 window panes, low-e coating, argon gas Aluminum frame

4.26 (0.75) 0.4 1 window pane, tint Wood-clad frame

4.26 (0.75) No Req. 1 window pane, no coating Wood-clad frame

5.11 (0.9) No Req. 1 window pane, tint Wood-clad frame

6.81 (1.2) 0.3 1 window pane, low-e coating, tint Aluminum frame

6.81 (1.2) 0.4 2 window panes, low-e coating, tint Aluminum frame

6.81 (1.2) No Req. 1 window pane, no coating Aluminum frame

The compilation of window characteristics and frame types come from a variety of 

sources, including an online residential window selection tool7 and individuals in 

industry. In reality there are many possible window assembly possibilities (combination 

of frame material, glass in-fill, and operability) for residential structures in any climate 

zone. Multiple window assembly options can be nearly identical in performance. The 

window assembly types presented in Figure 4-7 represent one of many options available 

to meet each U-SHGC combination. As such, the window assemblies presented here are 

not endorsed or preferred over any other type of window assembly for a residential home. 

BIRDS environmental performance data for the windows category was provided per 0.09 

m2 (1 ft2

) of a typical or common size of residential window. Since data were collected 

 7 Efficient Windows Collaborative tool, found at http://www.efficientwindows.org/selection.cfm.
64

on a full window basis, windows were modeled as full windows and then normalized to 

0.09 m2 (1 ft2). Figure 4-7 presents the general system boundaries for the window 

category as it is modeled for BIRDS.

Figure 4-7 Windows System Boundaries

The next sections present the modeling and assumptions for the assemblies of wood clad, 

vinyl, aluminum, and aluminum with thermal break frames, for casement and double 

hung windows. 

4.5.3.2 Casement Windows Bill of Materials

One North American manufacturer of residential windows provided primary data on 

wood clad, aluminum, and vinyl casement windows. Data were based on 2012 

production, and material data were based on averaged information for all final assembly 

facilities located in North America. A generic casement window was modeled for each 

frame type, and the weight of each component was averaged between the plants. The data 

for the casement windows include: 

• Frame and sash - quantities of each material;

• Insulated glass unit (IGU), including quantities and type(s) of glass, spacer, 

sealants, and gas if applicable; 

• Hardware, weatherstrip, fasteners, and other components – quantities and 

types of each material.

Table 4-23, Table 4-24, and Table 4-25 provide dimensions and masses of the main parts 

of the casement windows. Due to the proprietary nature of the data, the details of the 

specific windows have been removed. Note that a thermal break is a material that 

separates the interior and exterior of a metal frame. The low thermal conductivity of the 

thermal break material reduces temperature transfer, making the metal window more 

energy efficient.

Maintenance Use Phase

Window Assembly

Window components 

production (cutting 

lineals, etc)

IGU Production

Raw Materials 

Production

Transport to building site

Process 

Energy

Process aids

Emissions to air, 

water, soil

Raw Materials 

Production

Production

Transport 

to manuf.

Transport 

to manuf.

EOL (recycling, 

landfilling)
65

Table 4-23 Dimensions and Main Parts of the Wood Clad Casement Window

Description Units Single pane Double pane

Dimension of the window m2 (ft2) 1.0 (10.76) 1.0 (10.76)

Weight of the finished sealed unit kg (lb) 17.9 (39.4) 23.4 (51.6)

Wood and extruded aluminum (frame) kg (lb) 10.4 (22.9) 10.4 (22.9)

IGU kg (lb) 5.0 (11)* 10.5 (23.2)

Other components kg (lb) 2.5 (5.5) 2.5 (5.5)

* only glass

Table 4-24 Dimensions and Main Parts of the Aluminum Casement Window

Description Units Single pane Double pane Double pane w/ 

thermal break

Dimension of the window m2 (ft2) 1.0 (10.76) 1.0 (10.76) 1.0 (10.76)

Weight of the finished sealed unit kg (lb) 16.4 (36.2) 20.9 (46.1) 21.6 (47.6)

Extruded aluminum (frame) kg (lb) 9.7 (21.4) 9.7 (21.4) 9.7 (21.4)

IGU kg (lb) 3.9 (8.6)* 8.4 (18.5) 8.4 (18.5)

Other components kg (lb) 2.8 (6.2) 2.8 (6.2) 3.6 (7.9)

* only glass

Table 4-25 Dimensions and Main Parts of the Vinyl Casement Window

Description Units Double pane

Dimension of the window m2 (ft2) 1.0 (10.76)

Weight of the finished sealed unit kg (lb) 20.1 (44.4)

Vinyl (frame) kg (lb) 8.0 (17.6)

IGU kg (lb) 10.0 (22.1)

Other components kg (lb) 2.1 (4.6)
66

4.5.3.3 Double Hung Window Bill of Materials

One North American manufacturer of residential windows provided primary data on 

wood clad double hung windows. Data were based on 2011 North American production, 

and material data were based on a typical construction of an average sized window. The 

data for double hung windows include:

• Frame and sash – quantities of each material;

• Insulated glass unit (IGU), including quantities and type(s) of glass, spacer, 

sealants, and gas if applicable; 

• Weatherstrip, fasteners, coatings, and other components – quantities and types 

of each material, where applicable.

Table 4-26, Table 4-27, and Table 4-28 provide dimensions and masses of the main parts 

of the double hung windows modeled for BIRDS. Due to the proprietary nature of the 

data, the details of the specific windows have been removed. Note that data for the 

aluminum frame windows are based on extrapolation from casement windows data. The 

mass of framing material per linear foot of the casement’s frame was used to calculate the 

estimated mass of that material in a double-hung window frame, using the perimeters of 

the double hung window. The same was done for the IGU sealants and spacer, using the 

perimeters of the IGUs. To estimate other IGU materials and glass, the glass area was 

used. Most of the casement hardware was removed from the data set. The resulting 

double hung window is therefore comprised of estimated data. Data for the vinyl 

windows are based on extrapolations as described above.

Table 4-26 Dimensions and Main Parts of the Wood Clad Double Hung Window

Description Units Single pane Double pane

Dimension of the window m2 (ft2) 1.3 (14.0) 1.3 (14.0)

Weight of the finished sealed unit kg (lb) 35.0 (77.2) 43.8 (96.5)

Wood and extruded aluminum (frame) kg (lb) 24.7 (54.4) 24.7 (54.4)

IGU kg (lb) 6.2 (13.7)* 15.0 (33.0)

Other components kg (lb) 4.1 (9.1) 4.1 (9.1)

* only glass
67

Table 4-27 Dimensions and Main Parts of the Aluminum Double Hung Window

Description Units Single pane Double pane Double pane 

w/ thermo-br

Dimension of the window m2 (ft2) 1.3 (14.0) 1.3 (14.0) 1.3 (14.0)

Weight of the finished sealed unit kg (lb) 20.8 (45.9) 25.4 (56.0) 26.7 (58.9)

Extruded aluminum (frame) kg (lb) 15.6 (34.4) 15.6 (34.4) 15.6 (34.4)

IGU kg (lb) 4.0 (8.8)* 8.6 (19.0) 8.6 (19.0)

Other components kg (lb) 1.2 (2.7) 1.2 (2.7) 2.5 (5.5)

* only glass

Table 4-28 Dimensions and Main Parts of the Vinyl Double Hung Window

Description Units Double pane

Dimension of the window m2 (ft2) 1.3 (14.0)

Weight of the finished sealed unit kg (lb) 23.6 (52.0)

Vinyl (frame) kg (lb) 12.9 (28.4)

IGU kg (lb) 10.2 (22.5)

Other components kg (lb) 0.5 (1.1)

4.5.3.4 Production Data for the Materials

Greater than 99 % of the mass of materials in each window were included and modeled 

for these window systems. While data in the tables represent masses of materials in the 

finished windows, manufacturers quantified amounts of unusable (i.e. waste) materials 

and materials recovered, recycled, or reused, including wood and extruded aluminum. 

Manufacturers noted which materials were reused, recycled, landfilled, or disposed of in 

another way. Where materials were reused or recycled, the production of that overage 

was not included in the model. Where material was landfilled or incinerated, the overage 

was included with the window model and the appropriate waste disposal treatment 

method was modeled. Some of the information used for the windows modeling was 

supplemented by Salazar (2007).

Supplier distances to the manufacturing or assembly plant were provided by the 

manufacturers. Modes of transportation included heavy-duty diesel-fueled truck, ocean 
68

freighter and rail. The appropriate distances and modes were modeled as such, using data 

from the U.S. LCI Database. Where transport distances were not given for smaller 

contributing materials, an average of 965 km (600 mi) was modeled as transported by 

diesel truck. 

Window frame raw materials. The wood clad frame consists of planed, kiln-dried 

lumber and extruded aluminum profile. The wood part of the frame is modeled as having 

equal amounts of planed, kiln-dried wood from the Southeast and Pacific Northwest 

using data from the U.S. LCI Database. The aluminum profile is modeled as 50 % 

primary and 50 % secondary aluminum, and uses data from the U.S. LCI Database. Data 

for extrusion come from an average of primary data from one window manufacturer and 

ecoinvent. The thermal break (used in the aluminum windows only) is made up of glassreinforced polyamide, using data from ecoinvent, and polyurethane (PUR) resin, using 

data from U.S. LCI Database and ecoinvent. Where applicable, stains and external 

coatings were modeled. The paint and primer are modeled as alkyd oil based, and stain is 

assumed to be latex-based; these data are built using ecoinvent data sets. 

IGU raw materials. The data for float glass come from ecoinvent and is based on early 

2000’s European processes and technologies. Due to the lack of available data on U.S. 

float glass production, older European data were implemented. Processes in the data set 

include melting, cullet addition, forming (on a float bath), annealing by cooling in an 

oven (know as a lehr), cutting of the glass, and storage. While this data set may not be 

representative of current U.S. production, it has been customized using U.S. energy and 

transportation data sets. Also, some transportation impacts have been removed, including 

transport between manufacturing plants and coating facilities, which, according to U.S. 

windows industry representatives, exists for European operations but not for U.S. 

operations. The next version of BIRDS hopes to have more representative data on glass 

production. 

The spacer, which separates the two panes of glass, can be made of an array of materials, 

including aluminum, stainless steel, and tin-plated steel. A hypothetical mix of equal 

parts of these three materials was used. Salazar (2007) reported a loss of approximately 

10 % of the spacer; this has been factored in to the model. However, the loss is recycled 

and is, therefore, not waste. The inner primary sealant, commonly polyisobutylene (PIB), 

is used to prevent leakage of the argon gas as well as penetration of water vapor into the 

space between the panes. An assumption of 50/50 polysulphide polymer and 

polyurethane as secondary sealants were implemented. The desiccant in the spacer, a 

silicone based product, is used to absorb moisture. Salazar (2007) reported a loss of 

approximately 0.7 % of the desiccant and this has been factored into the model. Salazar 

(2007) reported an escape of argon when the cavity is flushed prior to being sealed –

approximately 95 % more than the quantity in the finished unit. The manufacturers did 

not provide data on material losses. Since it is unknown if the reported quantity of 

escaped argon is high or low, a 50 % escape is assumed for this model. 
69

Aluminum and PUR data come from the U.S. LCI Database. Data for all other IGU 

materials come from ecoinvent. Synthetic rubber is used as a general proxy for PIB. 

Other components raw materials. The jambliner, or the lining between the window 

sash and frame, is modeled as polyvinyl chloride (PVC). Data for PVC resin comes from 

the U.S. LCI Database and extrusion data come from ecoinvent. Other components may 

include a combination of clips, gaskets, hardware, weatherstrip, and sealant. Clips, and 

gaskets are modeled as PVC and rubber parts. The sealant is assumed to be siliconebased. U.S. LCI Database provided the production data for PVC while the ecoinvent 

database provided the data for the remaining materials. Weatherstrip is described in 

section XXX.

Hardware is custom ordered and may vary with the window. For BIRDS, a hypothetical 

mix of equal parts of stainless steel, cold rolled steel, and bronze has been modeled. The 

steel in screws and other steel parts are based on data from the World Steel Association 

(2011), with steel profiles customized to the U.S. using the U.S. electricity grid; steel 

product manufacturing, i.e. parts forming, data come from ecoinvent. Stainless steel is 

modeled from a chromium steel (18/8 grade) stainless steel data set from ecoinvent with a 

steel product manufacturing data set to account for part forming. Bronze data come from 

ecoinvent. 

Coatings. Low-emissivity (low-E) coatings and tinted windows have been included in 

the windows modeling to meet different performance characteristics of the windows. 

Coatings are used to improve the insulation properties of the glass by reflecting visible 

light and infrared radiation. The BIRDS Low-E coating is modeled using the coating 

details of ecoinvent’s “flat glass, coated” data set as a starting point. The technology used 

at the particular plant is based on a cathodic sputtering technology which involves 

depositing thin silver and other layer(s) on the glass. According to the ecoinvent 

documentation, the raw materials used for sputtering are bismuth, silver and nickelchrome. The quantity of 1.19 E-4 kg (2.62 E-4 lb) metals per kg was divided into three to 

account for 1/3 nickel, 1/3 chromium, and 1/3 silver. These data are approximate. 

Tint is obtained by adding small amounts of metal oxides during glass manufacturing, 

coloring the glass uniformly. For BIRDS, iron oxide has been assumed to be the mineral 

additive for the tint, and it is modeled as applied at an assumed proportion of 0.1 % of the 

glass weight.

4.5.3.5 Manufacturing

Manufacturing data is representative of the year 2011 based on total windows 

manufactured that year. Data include process energy and water consumption. Electricity 

was reported for material preparation and window assembly, and natural gas was reported 

for heating and drying ovens. The facility totals were normalized to one window. These 

data are an average and not specific for the exact window(s) being modeled. For 
70

confidentiality purposes, the data are not shared in this documentation, however, they 

have been included in the models for all of the window types. 

4.5.3.6 Transportation to the Building Site, Use and Maintenance

Transportation of the window to the building site is modeled an average of 805 km (500 

mi) by heavy-duty diesel fuel-powered truck. 

Installing windows is primarily a manual process; no energy or emissions are included in 

the model. Windows come to the construction site fully assembled and custom-ordered to 

fit so there is generally no installation waste. 

Maintenance of the windows includes weatherstripping and sealing. Weatherstrip is 

modeled as a thermoplastic elastomer. Data for the thermoplast, as ethylene propylene 

diene monomer (EPDM) rubber, come from ecoinvent (as synthetic rubber). For BIRDS, 

an EPDM weatherstrip has been modeled in the amount of 0.0064 kg per 0.3 m (0.014 lb 

per ft). Weatherstrip is assumed to perform at its optimal level an average of 7.5 years 

(Vigener and Brown 2012), so is modeled as replaced every 7.5 years. Different 

perimeter sealants can be used for different applications. For BIRDS, an acrylic latex 

sealant with silicone has been modeled in the amount of 0.029 kg per m (0.31 oz per ft), 

which is based on a 0.47 cm (0.19 in) diameter bead (DAP 2011). Data for the sealant is 

described in Section 4.5.7 and is modeled as being replaced every 15 years (Vigener and 

Brown 2012). 

Other maintenance, such as glass and/or window frame cleaning, frame repainting or 

recoating, hardware adjustment or replacement, are not included in the analysis. All 

operational energy-related aspects of the window are addressed in other sections of this 

report. 

The windows are modeled as having a lifetime of 30 years. Lifetime was set based on 

Earthsure’s (2013) draft window industry PCR, even though it is acknowledged that 

realistically, the lifetime of windows can vary (based on frame type, weather conditions, 

maintenance, etc.), and that often windows have a longer service life than 30 years.

4.5.3.7 End-of-Life

Wood clad and vinyl windows are modeled as landfilled at end-of-life. The frame of the 

aluminum window is modeled as recycled at end-of-life, and the “0 to 100 recycling 

methodology” has been applied. For this methodology, system expansion is applied; the 

production of the same amount of virgin aluminum that is in the frame is subtracted out 

of the system, crediting the system with an avoided burden based on the reduced 

requirement for virgin material production in the next life-cycle. Likewise, recycled 

content in the aluminum adds some of the burden to the product system in order to share 

the burden with the previous life-cycle. The “0 to 100 recycling methodology” is applied 

to valuable metal recovery in other product categories as well. For more information on 

the “0 to 100 recycling methodology” approach to modeling metals at end-of-life, see 
71

Atherton (2007) and World Steel Association (2011). The remaining parts of the window, 

including the IGU, are disposed of in a landfill. 

End-of-life modeling includes transportation by diesel-fuel powered truck approximately 

80 km (50 mi) to a C&D landfill or to recycling. The portions of the window going to 

landfill are modeled based on ecoinvent end-of-life waste management process data.

4.5.4 HVAC

Residential BIRDS evaluates air conditioning systems and gas furnaces. BIRDS 

environmental performance data for these was provided on a per-unit (or per-system) 

basis as well as electric furnaces for future updates of the BIRDS new residential 

database. Figure 4-8 presents the general system boundaries for these as they are modeled 

for BIRDS.

Figure 4-8 HVAC System Boundaries – Electric Furnace Example

4.5.4.1 Natural Gas and Electric Furnaces

Upstream Materials Production through Manufacturing

LCA practitioners often seek bill of materials data when conducting life-cycle 

assessments. However, there was no bill of materials data available for residential 

furnaces so proxy data were used. Athena Sustainable Materials Institute (2003) provided 

teardown data for a medium efficiency Lennox sealed combustion furnace, manufactured 

in 1985. These data were used with adjustments. The mass of the Lennox, 91 kg (200 lb), 

was normalized to the mass of an efficient natural gas furnace currently available on the 

market that weighs approximately 56 kg (124 lb)8,9

. These data were supplemented by

Yang, Zmeureanu et al. (2008), which provided data for a blower motor, replacing that of 

 8 Weight of the Standard Rheem Classic 95 % efficiency natural gas furnace (RGRC-07-RBGS) from 

Rheem Classic Series, Upflow Gas Furnaces, Physical Data and Specifications, Form No. G11-527, p.4. 9 This source states a total shipping weight of 62.1 kg (137 lb). The mass of the equipment itself was 

assumed to be 91 % of the total weight, based on other Rheem product published weights relative to their 

shipping materials.

Use Phase EOL (recycling, 

landfilling) Maintenance

Electric Furnace 

Production

Raw Materials 

Production

Transport to building site

Process 

Energy

Process aids

Emissions to air, 

water, soil

Transport to manufacturing
72

the older Lennox, to account for the more efficient furnace. Table 4-29 provides the 

materials in the natural gas furnace; the notes column identifies the adjustments made.

Table 4-29 Natural Gas Furnace Bill of Materials

Mass

Material kg lb Notes

Aluminum 1.02 2.25 Yang (2008) air blower data, replacing the Al in 

Athena (2003)

Brass 0.05 0.11

Ceramic 0.04 0.08

Circuit board, transistors 0.05 0.11

Copper 2.20 4.85 Yang (2008) air blower data, replacing the Cu 

in Athena (2003)

Fiberglass insulation (foillined) 0.27 0.60

Galvanized Steel 21.86 48.19 Steel paired down to meet the Rheem weight

PET 0.38 0.83

PVC 0.45 0.99

Powder coating 0.19 0.42

Rubber 0.02 0.04

Steel 29.79 65.66 Yang (2008) air blower and Athena (2003). 

Steel paired down to meet the Rheem weight

Total 56.3 124.1

For the electric furnace, the mass of the Lennox was normalized down to the mass of the 

electric furnace in BIRDS, a unit currently on the market.10 Data for the blower motor 

were provided by Yang (2008). Table 4-30 presents the bill of materials with data 

adjustments. 

 10 Rheem Air Handlers, Form No. H11-524 REV. 8, Unit Dimensions & Weights, p.4, model 4221/4821.
73

Table 4-30 Electric Furnace Bill of Materials

Mass

Material kg lb Notes

Aluminum 1.02 2.25 Yang (2008), replacing the Al in Athena (2003)

Brass 0.05 0.11

Ceramic 0.04 0.08

Circuit board, transistors 0.05 0.11

Copper 2.20 4.85 Yang (2008), replacing the Cu in Athena (2003)

Fiberglass insulation (foillined) 0.27 0.60

Galvanized Steel 34.04 75.04 Steel paired down to meet the Rheem weight

PET 0.38 0.83

PVC 0.45 0.99

Powder coating 0.32 0.71 Model assumes 0.095 kg powder per m2 (from 

ecoinvent)

Rubber 0.02 0.04

Steel 29.17 64.31 Yang (2008) air blower and Athena (2003). Steel 

paired down to meet the Rheem weight

Total 68.00 149.9

The steel is assumed to be cold-rolled; this and the galvanized steel come from World 

Steel Association (2011). Aluminum is modeled as a 50/50 mix of primary and secondary 

extruded aluminum using data from the U.S. LCI Database. Data for copper come from 

International Copper Association (ICA) (2012). Data for PET and PVC come from the 

US LCI database. Ecoinvent provided the data for brass, ceramics, integrated circuit 

boards, rubber (as synthetic rubber), and powder coating. The fiberglass insulation was 

modeled as described in the insulation section of this report. 

Raw materials are modeled as transported to the manufacturing plant via diesel truck an 

assumed average distance of 805 km (500 mi).

Because no manufacturing data were available for residential furnaces, an ecoinvent data 

set for a 10-kW (34-MBH) oil boiler was used as a proxy (Ecoinvent 2007). The 

ecoinvent dataset description states that the 10 kW low-NOx or condensing boiler data 

may be applied for residential furnaces (Ecoinvent 2007). The production energy is stated 
74

to be estimated from a 1998 environmental report. Even though this proxy is considered 

not to be representative of current practice; however its use is considered sufficient for 

comparison purposes.

Table 4-31 shows data for a boiler of approximately 150 kg (331 lb). Since 

manufacturing the smaller units is assumed to require less energy, these data were 

normalized based on the total weights of the natural gas and electric furnaces.

Table 4-31 Furnace Manufacturing

Energy source Quantity for 10 kW boiler

Electricity, medium voltage, MJ (Btu) 294 (81.7) 

Natural gas in industrial furnace, MJ (Btu) 424 (401 874)

Light fuel oil in industrial furnace, MJ (Btu) 236 (223 685)

Tap water, liter (gal) 182 (48)

Transportation to the Building Site through End-of-Life

Transportation of the equipment to the building site is modeled as an assumed average of 

644 km (400 mi) by heavy-duty diesel fuel-powered truck. 

It is assumed that a qualified service technician comes to the building site to check and/or 

service the unit one time every three years to ensure optimal performance and lifetime. It 

is assumed that the qualified technician is within a 24 km (15 mi) service radius. This 

distance, driven in a gasoline-powered van, is allocated amongst other service visits, 

assuming that the same technician is making more than one service call during that trip. 

Assuming the technician makes 5 service calls in one day, one-fifth of the impacts from 

driving 24 km (15 mi) are allocated to the product, or 4.8 km (3 mi). Environmental flow

data associated with the operation of the van comes from ecoinvent. Unplanned service 

visits (i.e., unanticipated issues that require a service technician) are not included in the 

model, assuming that the homeowner adequately follows the maintenance and care 

guidelines. 

A lifetime of 16 years has been assumed for both gas and electric furnaces, based on the 

National Association of Home Builders (NAHB) Research Center (2007), a study on life 

expectancy of home components.11 

At the end-of-life, it is assumed that the furnaces are sent for recycling to recover 

valuable metal using the “0 to 100 recycling methodology” discussed previously in 

Section 4.5.3.7. What cannot be recovered, i.e., any non-metallic parts, are modeled as 

landfilled, assuming a distance of 48 km (30 mi) to the land fill with transport via heavy-

 11 NAHB (2007), Table 1, Section 15 gives furnaces a life expectancy range of 15 to 20 years.
75

duty diesel truck . The landfill is modeled based on ecoinvent end-of-life waste 

management process data. 

4.5.4.2 Residential Air Conditioners 

Residential air conditioners for BIRDS are modeled as split systems with outdoor and 

indoor components that provide a condenser and evaporative heat exchanger, 

respectively. BIRDS includes LCA data for 3-ton residential air conditioners with SEERs

of 13, 14, 18, and 21. SEER is defined by the Air Conditioning, Heating, and 

Refrigeration Institute (AHRI) as total heat removed from the conditioned space during 

the annual cooling season, expressed in Btu’s, divided by the total electrical energy 

consumed by the air conditioner or heat pump during the same season, expressed in watthours (AHRI 2008). Eighteen SEER is considered to be a high rating relative to other 

models; based on informal discussions with industry members, only a small percentage of 

the market is currently at 18 SEER. An even smaller percentage is beyond 18 SEER, so 

21 SEER is considered to be exceptionally high, providing an extremely efficient system.

Upstream Materials Production through Manufacturing

Bill of materials for the outside unit (condenser) shown in Table 4-32 came from De 

Kleine (2009). The condensing unit is based on a tear-down of a 3-ton, 10 SEER unit 

manufactured in approximately 2001. The components’ materials were assumed by Four 

Elements, except where noted.
76

Table 4-32 Condenser Unit Bill of Materials

Mass

Main Component Material Breakdown kg Lb Notes

Compressor Aluminum 0.53 1.16 Compressor breakdown from (Biswas and 

Rosano 2011), Fig 1.

Cast iron 24.74 54.54

Copper 1.32 2.90

Steel 2.72 6.00

Coil Assembly Copper (tubing) 3.95 8.71 Approx. 50 % copper tubing, 50 % aluminum 

fins. (De Kleine, 2009)

Aluminum (fins) 3.95 8.71

Fan Motor Copper wire 1.08 2.37 Assumed to have a composition of 25 % copper 

wire, 72 % steel, and 3 % polyamide 

(De Kleine 2009) Steel 3.10 6.83

Polyamide 0.13 0.28

Unit Wall Galvanized steel 3.10 6.83

Base Galvanized steel 2.70 5.95

Top Cover Galvanized steel 1.90 4.19

Fan Guard Stainless steel 1.60 3.53

Refrigerant Line 

service valve

Brass (service valves) 0.75 1.65

Rubber 0.25 0.55

Wire Guard Stainless steel 0.80 1.76

Fan Blade Steel 0.60 1.32

Misc Fasteners Steel 0.30 0.66

Capacitor Sheet metal (steel) 0.30 0.66

Relay Switch Copper wiring 0.10 0.22 Assumed to be 50 % copper wiring and 50 % 

nylon polymer (De Kleine 2009)

Nylon polymer 0.10 0.22

Copper Wiring Copper wire 0.20 0.44

Total 54.20 119.49
77

De Kleine’s teardown was for a SEER 10 unit, but the bill of materials for the higher 

SEERs were required for his study. To do this, De Kleine created a weight function using 

survey data and manufacturer specification sheets on condenser units from several 

different brands ranging in efficiency from 10 SEER to 18 SEER and ranging in capacity 

from 1.5 tons to 5 tons of cooling. This weight function was used to calculate the mass of 

each of the condenser units in BIRDS as follows, enabling the adjustment of the bill of 

materials to the different masses as shown in Table 4-33. See De Kleine (2009) for more 

detail.

Table 4-33 Condenser Unit Masses

SEER 10 SEER 13 SEER 14 SEER 18 SEER 21

Factor 1 1.36 1.49 1.98 2.34

Mass in kg (lb) 54.2 (119.5) 74.1 (163.5) 80.8 (178.1) 107.4 (236.7) 127.3 (280.7)

These values corresponded with De Kleine (2009), and are consistent with 

manufacturers’ products of the same SEER ratings. 

The evaporator coil indoor unit was modeled as being housed in the electric furnace. De 

Kleine (2009) approximated the inside coil assembly for a 13 SEER system to weigh 15.9 

kg (35 lb) and be composed of 50 % copper tubing and 50 % aluminum fins. The 

refrigerant line, connecting the outdoor and indoor units, was modeled as 20 feet of 

copper tubing weighing 4.5 kg (9.9 lb) (De Kleine 2009). The air conditioning system 

uses R-410a refrigerant, and data for the quantity of refrigerant used in each of the 

SEERs studied (shown in Table 4-34) was calculated using refrigerant mass function 

developed by De Kleine (2009).

Table 4-34 Refrigerant Quantities

SEER 13 SEER 14 SEER 18 SEER 21

R-410a in kg (lb) 3.3 (7.3) 3.6 (7.9) 5.0 (10.9) 6.5 (14.4)

The steel data, assumed to be cold-rolled, and the galvanized steel data come from the 

World Steel Association (2011). Aluminum is modeled as a 50/50 mix of primary and 

secondary extruded aluminum using data from the U.S. LCI Database. Data for copper 

tube, sheet, and wire come from International Copper Association (ICA) (2012). 

Ecoinvent provided the data for the cast iron, stainless steel, brass, rubber (as synthetic 

rubber), and polyamide.

R-410a data are based on a 50/50 share of difluoromethane (R-32) and pentafluoroethane 

(R-125). Due to lack of available production data on both of these chemicals, proxies 
78

were implemented. Trifluoromethane (HFC-23) was used as a proxy for difluoromethane 

and 1,1,difluoroethane (HFC-152a) was used as proxy for R-125. Note: while proxies 

were used for the production aspect of the chemicals, any release of these was based on 

the release of R-32 and R-125, not the proxy chemicals, so that ozone depletion impact 

remains zero and global warming potential impact is calculated appropriate to R-410a.

Raw materials are modeled as transported to the manufacturing plant via diesel truck an 

assumed average distance of 805 km (500 mi). 

No manufacturing data were available for residential air conditioners, so as proxy, an 

ecoinvent data set for a 10 kW (34 MBH) oil boiler was used. Since manufacturing the 

smaller units is assumed to require less energy and resources than the 150 kg (331 lb) 

boiler, these data were normalized based on the total weights of the air conditioners.

Transportation to the Building Site through End-of-Life

Transportation of the equipment to the building site is modeled assuming a heavy-duty, 

diesel-powered truck is used and an average distance of 1287 km (800 mi) is travelled. 

This distance was estimated by De Kleine (2009) and is based on five manufacturing 

locations of major residential air conditioner manufacturers. 

It is assumed that a qualified service technician comes to the building site to check and 

service the unit every three years to ensure optimal performance and lifetime. It is 

assumed that the qualified technician is within a 24 km (15 mi) service radius. This 

distance is driven in a gasoline-powered van and is shared amongst other service visits 

for that technician, assuming that the same technician is making more than one service 

call during that trip. Assuming the technician makes 5 service calls in one day, one-fifth 

of the impacts from driving 24 km (15 mi) are allocated to the product, or 4.8 km (3 mi). 

Data for a van come from ecoinvent. Unplanned service visits (i.e., unanticipated issues 

that require a service technician) are not included in the modeling assuming that the home 

owner adequately follows the maintenance and care guidelines.

A lifetime of 15 years has been assumed for the air conditioners (National Association of 

Home Builders (NAHB) Research Center 2007). During use, refrigerant is assumed to 

escape at a rate of 2 % per year of the total refrigerant (De Kleine 2009). It is recharged 

every three years during the maintenance visit. 

At the end-of-life, it is assumed that the air conditioners are sent for recycling to recover 

valuable metal using the “0 to 100 recycling methodology discussed previously in

Section 4.5.3.7. What cannot be recovered, i.e., any non-metallic parts, are modeled as 

landfilled assuming a distance of 48 km (30 mi) to the landfill with transport via heavyduty diesel truck. The landfill is based on ecoinvent waste management process data. A 

study prepared for Air-Conditioning, Heating, and Refrigeration Technology Institute

(AHRTI) assumes an overall loss of 15% of the R-410a refrigerant (AHRTI 2011). This 

is based on “recovering 90 % of the charge from 95 % of the field units, but allowing for 

a 100 % charge loss from about 5 % of the field stock” (AHRTI 2011).
79

4.5.5 Residential Electric and Gas Water Heaters

Electric and gas water heaters with 189-liter (50-gal) storage tanks are included in 

BIRDS. 

4.5.5.1 Upstream Materials Production through Manufacturing

Bill of materials data for both water heaters came from a comprehensive study by Van 

Holsteijn en Kemna (2007) on the eco-design of water heaters. Table B.9 in Van 

Holsteijn en Kemna (2007) provided bills of materials for several electric water heaters; 

the heater with a 200 L (53 gal) storage tank was used, as its size is closest to the 189 L 

(50 gal) needed for BIRDS. Since the Material Groups listed in Table B.9 were fairly 

general, more detailed parts and materials supplemented these data provided by Table 

B.10 in Van Holsteijn en Kemna (2007) to develop the data shown in Table 4-35. Any 

assumptions made are noted.

Table 4-35 Electric Water Heater Bill of Materials

Mass

B.9 Material Group kg lb Parts, Materials & Assumptions 

3-Ferro 40.5 89.2 Tank, mounting plate, screws. Tank assumed to be 

stainless steel

2-TecPlastics 1.5 3.3 Rubber gasket (assume 25 %), PVC pipe protection and 

pipe collar, PVC thermal control (assume 75%)

1-BlkPlastics - 1.2 2.7 Assume expanded polystyrene (EPS) insulation

7-Misc 1.2 2.7 Portion of the Misc. category; part unknown. Assuming 

packaging, based on Table B.10. Not included in this 

analysis.

7-Misc 0.3 0.7 Portion of the Misc. category; Assume resistance heater

5-Coating 3.1 6.7 Assume enamel

4-Non-ferro 0.2 0.5 Assume copper

6-Electronics 0.06 0.1 Assume wiring, thermal control

TOTAL 48.1 106.0

Bill of materials for the gas water heater and its storage tank shown in Table 4-36 are 

based on Table B.8 in Van Holsteijn en Kemna (2007), which contains materials for a 

generic water storage tank and the natural gas component of a water heater. Since a 189 L 

(50 gal) storage tank was not offered by Van Holsteijn en Kemna (2007), the data for the 
80

150 L (39.6 gal) tank was normalized on a mass basis. The data for the gas heating 

component was left the same.

Table 4-36 Gas Water Heater Bill of Materials

Parts Mass Materials 

Generic tank kg lb

Tank 26.0 57.3 Galvanized steel

Enamel 4.7 10.3 Enamel

Insulation 9.1 20.0 Rigid PUR foam

Mantle 11.7 25.9 Galvanized steel

Top/bottom 0.3 0.6 Polypropylene

Diptube 2.1 4.7 Galvanized steel

Fitting 1.3 2.8 Brass

Mounting 0.6 1.4 Galvanized steel

Subtotal 55.8 122.9

Gas heating component

Burner 1.5 3.3 Galvanized steel

H Ex 3.5 7.7 Galvanized steel

Flue parts 2.7 6.0 Galvanized steel

Gas valve 1.4 3.1 Aluminum diecast

Brass parts 1.0 2.2 Brass

Various steel parts 41.0 90.4 Galvanized steel

Subtotal 51.1 112.7

Total 106.9 235.6

All steel and galvanized steel data come from World Steel Association (2011). 

Aluminum is modeled as a 50/50 mix of primary and secondary extruded aluminum 

using data from the U.S. LCI Database. Data for copper sheet and wire come from

International Copper Association (ICA) (2012). U.S. LCI Database provided the 

production data for PVC and PP. Ecoinvent provided the data for the stainless steel, 

brass, rubber (as synthetic rubber), expanded polystyrene (EPS), PUR foam, and enamel. 
81

Raw materials are modeled as transported to the manufacturing plant via diesel truck an 

assumed average distance of 805 km (500 mi). 

No manufacturing data were available for residential water heaters, so as proxy, the 

ecoinvent data set for a 10 kW (34 MBH) oil boiler was used. Since manufacturing the 

smaller units is assumed to require less energy and resources than the 150 kg (331 lb) 

boiler, these data were normalized based on the total weights of the water heaters.

4.5.5.2 Transportation to the Building Site through End-of-Life

Transportation of the equipment to the building site is modeled assuming an average of 

644 km (400 mi) using a heavy-duty diesel fuel-powered truck for transport. 

It is assumed that a qualified service technician comes to the building site to check and 

service the unit every three years to ensure optimal performance and lifetime. It is 

assumed that the qualified technician is within a 24 km (15 mi) service radius. This 

distance, driven in a gasoline-powered van, is shared amongst other service visits for that 

technician, assuming that the same technician is making more than one service call 

during that trip. Assuming the technician makes 5 service calls in one day, one-fifth of 

the impacts from driving 24 km (15 mi) are allocated to the product, or 4.8 km (3 mi). 

Data for a van come from ecoinvent. Unplanned service visits (i.e., unanticipated issues 

that require a service technician) are not included in the modeling assuming that the home 

owner adequately follows the maintenance and care guidelines.

Lifetimes of 11 and 10 years have been assumed for the electric and gas-fired water 

heaters, respectively (National Association of Home Builders (NAHB) Research Center 

2007). At the end-of-life, it is assumed that the water heaters are sent for recycling to 

recover valuable metal using the 0 to 100 recycling methodology as discussed previously 

in Section 4.5.3.7. What cannot be recovered, i.e., the non-metal parts, are modeled as 

landfilled. A distance of 48 km (30 mi) to the landfill in a heavy-duty diesel truck has 

been modeled for waste transport. The landfill is based on ecoinvent waste management 

process data.

4.5.6 Lighting

Three lighting alternatives were included in BIRDS: incandescent, compact fluorescent 

lamp (CFL), and light-emitting diode (LED). These were calculated on a per one W/h 

basis so that BIRDS could calculate the energy needed for lighting based on time used. 

Figure 4-9 presents the lighting system boundaries for a CFL example. 
82

Figure 4-9 Lighting System Boundaries – CFL Example

In order to model the lighting alternatives on the basis of functional equivalency, the 

model must account for an equivalent or comparable lumen output, i.e., measure of 

brightness. The lighting characteristics from Energy Efficiency and Renewable Energy 

(EERE) (2012) were used to define the BIRDS’ lighting systems’ functional equivalency. 

Table 4.1 in Energy Efficiency and Renewable Energy (EERE) (2012), reproduced in 

part in Table 4-37, summarizes the average characteristics of the lamps analyzed within 

existing lighting LCA literature (at the time) in terms of lumen output, wattage, and 

lifetime. For the continuously-improving LED technology, a lumen output available in 

2011 was used, which was higher than lumens from earlier studies.

Table 4-37 Performance of Lighting Technologies in BIRDS

Lamp Type Watts Lumens Operating 

Lifetime (hr)

Number of Equiv. 

Systems

Incandescent 60 900 1 000 25

CFL 15 900 8 500 2.9

LED (2011) 12.5 800 25 000 1

4.5.6.1 Upstream Materials Production through Manufacturing

The bills of materials for all three alternatives come from U.K. Department for 

Environment (2009). These bills of materials are based on Ramroth (2008) but with more 

detail/aggregation provided. 

Incandescent

The bill of materials shown in Table 4-38 is based on a 100 W (soft white) incandescent 

bulb manufactured by General Electric. Other sources provided data available for 60 W 

Use Phase EOL (recycling & 

landfilling)

CFL Production/ 

Assembly

Raw Materials 

Production

Transport to building site

Process 

Energy

Process aids

Emissions to air, 

water, soil

Transport to manufacturing

Driving to/from 

store
83

incandescent bulbs12, but Table B-12 of U.K. Department for Environment (2009) was 

used since its data is based on a detailed tear-down for a widely available product. 

Furthermore, when factoring in use phase energy, the BOM differences between 60 W 

and 100 W are negligible.

Table 4-38 Incandescent Light Bulb Bill of Materials

Parts and Materials Mass Notes

Ballast g lb

Black glass insulation 2.0 4.4 E-3 Modeled as foam glass

Internal filler 1.0 2.2 E-3 Modeled as foam glass

Lamp

Tin plate base 2.0 4.4 E-3

Tungsten filament 2.0 E-2 4.4 E-5 Modeled as chromium

Internal glass 2.0 4.4 E-3 Modeled as borosilicate glass tube

Lens

Globe (glass) 20.0 4.4 E-2 Modeled as borosilicate glass tube

Packaging

Corrugated board 4.0 8.8 E-3

Total 31.0 6.8 E-2

The data sets used to model the incandescent system’s parts and materials come from 

ecoinvent. U.K. Department for Environment (2009) used proxy data for materials not 

available in ecoinvent or other publicly-available data or databases. These proxies were 

also used for BIRDS, and are indicated in the table notes. Raw materials are modeled as 

transported to the manufacturing plant via diesel truck an assumed average distance of 

805 km (500 mi).

No manufacturing data were available, but the parts forming (wire drawing, injection 

molding, extrusion, etc.) have been included with the upstream raw materials, accounting 

for at least some of the production energy. 

 12 See, for example, Parsons (2006). 
84

Compact Fluorescent Lamp (CFL)

The bill of materials in Table 4-39 is based on a 23 W Philips Marathon Mini CFL. Other 

data were available for an 18 W CFL13 which might have been preferable in terms of 

wattage identified in Table 4-39, but Tables B.10-B.11 of U.K. Department for 

Environment (2009) were used since the tear-down was quite detailed and for a widely 

available product. Furthermore, the difference in mass between the two sources was 3 % 

to 5 %, with a similar bill of materials. Factoring in use phase energy, this difference 

becomes negligible.

 13 Ibid.
85

Table 4-39 CFL Bill of Materials

Parts and Materials Mass Notes

Ballast g lb

PUR foam (rigid) 3.0 6.6 E-3

Plastic base (Polyvinyl chloride, PVC) 17.0 3.7 E-2

Printed wiring board 4.0 8.8 E-3

Printed board assembly (PBA) –

Polypropylene (PP) caps 4.0 8.8 E-3

PBA- inductor (cast iron) 7.0 1.5 E-2

PBA- inductor (copper) 4.5 9.9 E-3

PBA - transistor (ABS copolymer) 1.0 2.2 E-3

PBA - transistor (Aluminum) 3.5 7.7 E-3

PBA - resistors, diodes, HV capacitor 1.0 2.2 E-3

Modeled as a logic type 

integrated circuit

PBA - torus magnet (cast iron) 1.0 2.2 E-3

Lamp

Electrode assembly - mercury gas 4.0 E-3 8.8 E-6

Electrode assembly - chromium 2.0 4.4 E-3

Copper pins 2.0 4.4 E-3

Tin base plate 5.0 1.1 E-2

Black glass insulation 5.0 1.1 E-2 Modeled as foam glass

Lens

Glass tube 34.0 7.5 E-2

Modeled as borosilicate 

glass tube

Packaging

Corrugated board 4.0 8.8 E-3

Total 98.0 2.2 E-1

The U.S. LCI Database provided data for the polyvinyl chloride base, polypropylene 

caps, acrylontrile-butadiene-styrene (ABS) copolymer transistor, and aluminum (as an 
86

average mix of primary and secondary aluminum). Data for copper sheet and wire come 

from International Copper Association (ICA) (2012). The remaining data sets come from 

ecoinvent. U.K. Department for Environment (2009) used proxy data for materials and/or 

parts not available in ecoinvent or other publicly-available data or databases. These 

proxies were also used for BIRDS, and are indicated in the table notes. Raw materials are 

modeled as transported to the manufacturing plant via diesel truck an assumed average 

distance of 805 km (500 mi).

No manufacturing data were available, but the parts forming (wire drawing, injection 

molding, extrusion, etc.) and manufacture of the individual electronics components have 

been included with the upstream raw materials, accounting for at least some of the 

production energy. For assembly, U.K. Department for Environment (2009) used data 

provided by ecoinvent – assembly of an LCD screen – which the authors determined to 

be a suitable proxy for CFL manufacturing. According to U.K. Department for 

Environment (2009), this surrogate was selected because an LCD screen is also a 

complex electrical product, involving circuits and components that are assembled, and 

the impacts were expressed on a per kg basis so the assembly of the lighting systems 

could be modeled based on their respective weights. This was a conservative assumption 

on the part of U.K. Department for Environment (2009) Four Elements tested this 

assumption with sensitivity analysis, which showed that it did not make a significant 

difference to the overall results. BIRDS used this same assumption, applying the 

assembly data to a 98.0 g (0.22 lb) system.

Light-Emitting Diode (LED)

The integrated LED system from Table B.2 and Table B.3 in U.K. Department for 

Environment (2009) was modeled, giving the LED system the opportunity to be 

retrofitted into existing lighting infrastructure. The LED inventory shown in Table 4-40

factored in 10 LED die.
87

Table 4-40 LED Bill of Materials

Parts and Material Mass Notes

Ballast g lb

PUR foam (rigid) 3.0 6.6 E-3

Inductor (cast iron) 6.0 1.3 E-2

Inductor (Cu) 4.0 8.8 E-3

Zener Diodes 0.1 2.2 E-4 Modeled as an unspecified diode

Capacitors (aluminum) 5.0 1.1 E-2

Resistor 10.0 2.2 E-2

Transistor 3.0 6.6 E-3

PCB (aluminum machined tooled block) 100.0 2.2 E-1 Modeled as aluminum

Wiring (Cu) 2.0 4.4 E-3

Solder paste (used for electronics) 1.0 2.2 E-3

Polypropylene (PP) housing 35.0 7.7 E-2

Integrated circuit 1.0 2.2 E-3

Polyethylene terephthalate (PET) film 2.0 4.4 E-3

Lamp

Black glass insulation 6.0 1.3 E-2 Modeled as foam glass

Tinplate base 3.0 6.6 E-3 Modeled as a low-alloyed steel

Copper pins 0.1 2.2 E-4

Base contacts (Cu) 0.4 8.8 E-4

Base contacts (solder paste for electronics) 0.2 4.4 E-4

Plastic base (PVC) 16.0 3.5 E-2

Light emitting diodes (LED, 10 total) 19.0 4.2 E-2

Lens

Glass 20.0 4.4 E-2 Modeled as borosilicate glass tube

Coating (aluminum) 1.0 2.2 E-3

Packaging

Corrugated board 3.0 6.6 E-3

Total 240.8 5.3 E-1

The U.S. LCI Database provided data for the PVC base, PP housing, PET film, and 

aluminum (as average production mix). Data for copper sheet and wire come from

International Copper Association (ICA) (2012). The remaining data sets, including the 

production of the LEDs, come from ecoinvent. U.K. Department for Environment (2009)
88

used proxy data for materials not available in ecoinvent or other publicly-available data 

or databases. These proxies were also used for BIRDS, and are indicated in the table 

notes. Raw materials are modeled as transported to the manufacturing plant via diesel 

truck an assumed average distance of 805 km (500 mi).

Data for LED die manufacturing comes from OSRAM Opto Semiconductors GmbH. 

(2009). Primary data were collected on the two main process stages to produce 

OSRAM’s Golden Dragon Plus: the front end, where the 1mm2 (1.6 E-3 in2) 

semiconductor chip is fabricated, and back end, where the chip is contacted and 

packaged. See OSRAM Opto Semiconductors GmbH. (2009), pp.9-11, for more 

qualitative detail and schematics on the manufacture of the LED. Figure 6 in OSRAM 

Opto Semiconductors GmbH. (2009) presents the primary energy to produce one LED: 

approximately 0.41 kWh. Given that the other categories of data in the figure include 

materials production, an assumption was made that “common consumption” is energy at 

manufacturing, amounting to approximately 0.27 kWh per LED, or 2.7 kWh for 10 

LEDs. No other manufacturing data could be gleaned from this study, but the ecoinvent 

data set on assembly of an LCD screen was used for assembly (see above discussion), 

and parts forming data were applied to other parts and materials listed in the table.

4.5.6.2 Transportation to the Building Site through End-of-Life

A number of the LCA studies mentioned China or Asia in general as being the main 

manufacturing location for incandescents. For their study, Energy Efficiency and 

Renewable Energy (EERE) (2012), p.32, assumed that an incandescent lamp is either 

manufactured in the northeastern U.S. or Shanghai, China, while also acknowledging that 

production occurs all over the world. BIRDS assumed 25 % of production of 

incandescents to be produced in the northeast U.S., and 75 % in Shanghai, China. 

BIRDS modeled CFLs as manufactured in China (Parsons 2006, Ramroth 2008, U.K. 

Department for Environment 2009, Energy Efficiency and Renewable Energy (EERE) 

2012).

Energy Efficiency and Renewable Energy (EERE) (2012) stated that LED lighting 

market is highly fragmented, with several firms focusing on a specific part within the 

LED supply chain (this was seen with OSRAM Opto Semiconductors GmbH. (2009)). 

BIRDS adopted EERE’s approach to simplifying the sourcing logistics as follows: “the 

complete LED package is produced in Taiwan and then is assembled into the finished 

LED lamp product in Taiwan or the United States. In the second scenario, LED packages

are produced in Taiwan and then shipped to the southeast region of the U.S. where they 

are assembled into complete LED lamp products” (Energy Efficiency and Renewable 

Energy (EERE) 2012).

For Asia manufacturing, transportation includes the ocean freighter transport to a port in 

California, plus heavy duty diesel truck an assumed 2414 km (1500 mi), an average 

distance traveled from California to other parts of the United States. For U.S. 
89

manufacturing, transport is modeled an assumed average of 2414 km (1500 mi) by 

heavy-duty diesel fuel-powered truck. Intermediate transportation (i.e., from LED 

production to lamp assembly) is also included. 

For every light bulb purchased and needing to be replaced, customer driving to and from 

the retail store has been included.

How these lighting systems affect operating energy during the home’s use phase is 

addressed in other sections of this report.

At end-of-life, each of the lighting alternatives was modeled as 20 % recycling (and 

material recovery), and 80 % landfill. A distance of 48 km (30 mi) traveled via a heavyduty diesel truck to the landfill or recycler has been assumed for waste and recycling 

transport.

4.5.7 Sealants

LCAs of an interior and exterior sealant have been included in BIRDS. The interior 

sealant is modeled as an aluminum foil-backed multi-purpose tape designed for taping 

joints and seams against moisture and vapor on ductwork. The exterior sealant, an acrylic 

latex caulk with silicone, is used to minimize air infiltration around windows, door 

frames, and other areas on the building envelope. The LCAs are based on one linear foot 

of each type of sealant (boundaries shown in Figure 4-10). 

Figure 4-10 Sealants System Boundaries – Exterior Sealant Example

Both types of sealants have been modeled based on products that are readily available 

and whose published product literature provided sufficient data to model their production 

without the need to make too many assumptions. It should be reminded that there are 

many products on the market that can perform the same sealing functions, and that the 

products described herein are not considered endorsements.

Use Phase EOL (landfilling)

Sealant Production 

(Blending, 

Packaging)

Raw Materials 

Production

Transport to building site

Process 

Energy

Process aids

Emissions to air, 

water, soil

Transport to manufacturing

Maintenance
90

4.5.7.1 Upstream Materials Production through Manufacturing

Ductwork Sealant

The ductwork sealant modeled for BIRDS is modeled as a multi-purpose foil-backed 

tape. The 0.13-mm (5.0-mil) thick tape contains three distinct layers: a 0.05-mm (2.0-mil) 

thick aluminum foil layer, a synthetic rubber layer for the adhesive, and an unbleached 

natural Kraft paper layer for the liner (Nashua Tape Products 2013). One roll of 46 m 

(151 ft) long by 48 mm (0.16 ft) wide tape, or 2.2 m2 (23.8 ft2) of tape, weighed 0.55 kg 

(1.2 lb), or 3.64 g (0.13 oz) per linear foot.14 Based on this weight, the material 

components were broken down as shown in Table 4-41, with assumptions noted.

Table 4-41 Foil Tape Bill of Materials

Tape material Mass Notes

g oz

Aluminum 2.01 0.071 Based on 0.05 mm (2.0 mil) & density of 2.7 g/cm3

Unbleached Kraft 0.16 5.8 E-3 Assumed 10 % of remaining weight

Synthetic rubber 1.39 0.049 Assumed 85 % of remaining weight

Cardboard core 0.08 2.9 E-3 Assumed 5 % of remaining weight

LDPE* film (packaging) negl. negl.

Total 3.64 0.13

*LDPE = Low Density Polyethylene Film

Production data for the foil is modeled as 50/50 primary and secondary aluminum from 

the U.S. LCI Database, plus sheet rolling (ecoinvent). Data for the remaining materials 

were supplied by ecoinvent. Assembly of the tape was assumed to consist of layering and 

laminating the materials, rolling, and cutting. The electricity-only portion of the 

ecoinvent data set for “laminating foil (with an acrylic binder)” was used, which was 

reported as 0.0183 kWh/m2 (0.0017 kWh/ft2) laminated. This value is likely 

underestimated since cutting and rolling are not included in this dataset.

Exterior Sealant

The exterior sealant was modeled based on composition information for an MSDS of a 

commonly used acrylic latex sealant with silicone (DAP 2005). The amount modeled for 

0.3 m (1 ft) was calculated to be 28.9 g/m (0.31 oz/ft), based on a specific gravity of 1.65, 

coverage of 17 m (56 ft) per 298 cm3 (10.1 fl oz), and a bead diameter of 0.47 cm (0.19 

in) (DAP 2011). Since composition was provided in the customary percentage ranges, the 

 14 Weight taken by Four Elements.
91

quantities of each ingredient modeled were estimated based on best-guess assumptions 

(see Table 4-42).

Table 4-42 Exterior Sealant Bill of Materials

Weight Percentage

Components Low % High % % Modeled Notes

Non-hazardous polymer 10 30 30 Acrylic polymer assumed

Water 10 30 18 “82 % solids” (DAP 2011) 

Calcium carbonate 40 70 46

Phthalate ester 1 5 3

Titanium dioxide 0.1 1 0.55

Ethylene glycol 0.1 1 0.55

Crystalline silica 0.1 1 1

Carbon black 0 1.5 0.75

Ammonia 0.01 0.01

Formaldehyde 0.02 0.02

Ethyl acrylate 0.009 0.009

Acetaldehyde 0.002 0.002

Acrylonitrile 0.0003 0.0003

Total 100

The mass of the high density polyethylene (HDPE) squeeze tube packaging was 

calculated by subtracting the quantity of 10.1 fl oz sealant (493 g (17.3 oz)) from the 

weighed product (539 g (19.0 oz)). Thus, HDPE amounted to 46 g (1.6 oz), or 2.7 g/m 

(0.029 oz/ft).

The U.S. LCI Database provided production data for HDPE, calcium carbonate, and 

ethylene glycol. Ecobilan (2001) provided the inventory data on the phthalate ester, and 

ecoinvent provided the remaining data. Manufacturing data were limited; the electricityonly portion of an ecoinvent data set for acrylic filler production – likely mixing energy –

were used, which amounted to 1.8 E-3 kWh/kg (1.6 E-5 kWh/ft). 

Raw materials for both products are modeled as transported to the manufacturing plant 

via diesel truck an assumed average distance of 805 km (500 mi).
92

4.5.7.2 Transportation to the Building Site through End-of-Life

Transportation of the equipment to the building site is modeled assuming an average of 

483 km (300 mi) traveled via heavy-duty diesel fuel-powered truck. 

The tape is assumed to be replaced every 10 years. The exterior sealant is modeled as 

being replaced every 15 years (Vigener and Brown 2012). At the end of each of their 

respective lives, it is assumed that they are landfilled. Data on the landfill is based on 

ecoinvent end-of-life waste management process data. A distance of 48 km (30 mi) to the 

landfill in a heavy-duty diesel truck has been assumed.
93

5 Economic Performance Measurement

When a decision maker wants a dollar measure of cost-effectiveness and cash flows are 

primarily costs, the most appropriate method for measuring the economic performance of 

a building is LCC (Fuller and Petersen 1996, ASTM 2012). BIRDS follows the ASTM 

International standard method for LCC of building-related investments (ASTM 2010), 

which involves calculating a present value of a cost by discounting its future value into 

Year 0, 2015 for the current version of BIRDS, dollars based on the year the cost occurs 

and the assumed discount rate. The formulas and discount factors used to calculate the 

present values will vary depending on the type of cost. The different cost types and 

related formulas, discount factors, data sources, and adjustment factors are described 

below.

5.1 First Cost

In the case of LCC analysis of a building, the first costs of a building are the total costs 

realized at the point of transfer of ownership. In the case of a newly constructed house, 

these are the out-of-pocket costs at the time of the home purchase (in Year 0) such as 

down payment and closing costs. Many closing costs are similar regardless of the sale 

price of a home, so they are excluded from the analysis. The down payment is a function 

of two variables: the financing option selected by a home purchaser and the total 

construction costs of the house.

5.1.1 Total Construction Costs

Total construction costs include all of the costs associated with constructing a building in 

a particular location, such as contractor labor, materials, equipment, overhead, and 

profit.

15 The approach used to estimate the total construction costs is to first estimate the 

costs of constructing a building to minimum levels of energy efficiency, referred to 

moving forward as the “baseline” building design, and then adding the incremental costs 

of increasing the energy efficiency performance of each building component to meet the 

desired building design for each of the 240 000 building simulations. 

The national average baseline building costs are estimated using RSMeans Online Square 

Foot Cost Estimator (SFCE) based on the basic characteristics and features of the 

NZERTF as described in Chapter 3 (RS Means 2015). Since the costs are already in 2015 

dollars, there is no need to adjust for inflation. A screenshot of the SFCE is shown in 

Figure 5-1. Two levels of construction quality (average and luxury) – based on

architectural design and interior finishes – are considered in this initial version of the 

low-energy residential database.

 15 Does not include closing costs, such as realtor commissions.
94

Figure 5-1 RS Means Online Square Foot Cost Estimator (SFCE)

Incremental national average cost data from Faithful+Gould (2012) for each required 

energy efficiency measure are added to the baseline building costs to estimate the total 

first costs of a building design. The costs of 10 building components as defined in 

Chapter 3 – attic insulation, exterior wall insulation, foundation insulation, air sealant, 

windows, lighting, DHW heater, solar thermal system, HVAC system, and solar 

photovoltaic system – are adjusted to estimate the construction cost for the specific 

building design. The approaches to estimating the incremental costs of the alternative 

options for each building component will be discussed in greater detail in Section 5.1.3.

The location-specific construction costs for a building design are estimated by summing 

the costs of the baseline building (CNatAvg) and the changes in costs required to meet the 

prototype building design (ΔCx) using national average cost data, and adjusting for 

location-related cost variation as well as contractor and architect profits. Both the 

baseline building costs and component cost estimates are based on national average 

construction cost data, and must be adjusted with the city level cost indexes to control for 

local material and labor price variations in the 228 locations for which energy simulations 

are run. Two city construction cost indices are used to adjust for location-specific prices:

IFG (Faithful+Gould 2011), a cost index developed along with the cost database and used 

to adjust the sum of the costs for the incremental building component costs and IRS (RS 

Means 2015), a cost index developed by RS Means for location specific adjustment for 

their cost databases and used to adjust the baseline building costs. The formula below 

shows the indexed construction cost (CIndex) calculation.
95

CCIIIIIIIIII = �CCNNNNNNNNNNNN� ∗ IIRRRR

+ �(ΔΔCCWW WWWW) + (ΔΔCC AAAA) + (ΔΔCCFFFFFF FF ) + (ΔΔCC ) + (ΔΔCCWW WW)

+ �ΔΔCCLL h � + (ΔΔCCDDDD ) + (ΔΔCC TTh ee ) + (ΔΔCC HH HH)

+ (ΔΔCC PP )� ∗ IIFFFF

Where CIndex = Indexed construction costs

CNatAvg = Baseline building construction costs

ΔCWall = Change in wall insulation costs

ΔCAttic = Change in attic insulation costs

ΔCFoundation = Change in foundation costs

ΔCSealant = Change in air sealing costs

ΔCWindow = Change in window costs

ΔCLighting = Change in lighting costs

ΔCDHW Heater = Change in DHW Heater costs

ΔCSolar Thermal = Change in Solar Thermal system costs

ΔCHVAC = Change in HVAC system costs

ΔCSolar Photovoltaic = Change in solar photovoltaic costs

IRS = RS Means city construction cost index

IFG = Faithful+Gould city construction cost index

Once the indexed construction costs of the building are calculated, it is necessary to 

adjust for the contractor fees, which include general conditions, overhead, and profit, by 

multiplying the costs by the “mark-up” rate (IM), assumed to be 15 % based on the default 

value set within the SFCE. 

CCFF FF = CCIIIIIIIIII ∗ (1 + IIMM)

The marked-up, indexed construction costs are the total construction costs to a home 

purchaser for the specific building design in the particular city (CFirst) of interest, which is 

assumed to be Washington, D.C., the closest city in the cost index database to the 

NZERTF’s actual location in Gaithersburg, MD.

5.1.2 Purchase Financing

The other key factor in estimating the total first costs to the home purchaser is the 

financing method in which the building is purchased. Few new home purchases are 

completed with a 100% cash payment. Instead most home purchasers use some form of 

loan financing, which can vary in the terms of the loan such as the interest rate of the 

loan, length of the loan, and amount of the loan that must be paid up-front (down 

payment). An all cash purchase makes the total first costs equal to the total construction 

costs. A financed purchase makes the total first costs equal to the down payment. At this 

point, the low-energy residential database includes two financing options: (1) 100 % all 

cash and (2) a fixed rate loan with 20 % down payment (80/20 loan). There are two 

reasons for considering an 80/20 loan in the analysis. First, it is the most common type of 

loan for home purchases. Second, a down payment of 20 % or greater eliminates the need 
96

for any mortgage insurance, which simplifies the future mortgage payment calculations. 

The selected mortgage interest rate (4.375 %) was the national average mortgage interest 

rate for an 80/20 loan at the time of the database development. There are a variety of 

other loan options available to home purchasers, including changes in interest rates over 

time and additional costs, which should be considered. Homebuyers can make down 

payments of 10 % or less, but lower down payments tend to lead to higher interest rates. 

Borrowers can also pay additional money upfront to reduce their interest rate or to 

eliminate mortgage insurance in cases of a down payment less than 20 %. The most 

important of these and other factors will be incorporated into the low-energy residential 

database in future versions of BIRDS. Although it is important to note that inclusion of 

all potential loan options given the wide variety of potential variables associated with a 

given home mortgage make it impossible to include all options.

5.1.3 Building Components

The incremental costs of increasing the energy performance of different building 

components are calculated primarily using a publically available database developed for 

the Pacific Northwest National Laboratory (PNNL) (Faithful+Gould 2012) although 

some data points were developed by cost estimating firms through contracts with NIST

and the solar photovoltaic cost data is obtained from Barbose and Darghouth (2015). The 

cost estimation approaches for each building component alternative, or energy efficiency 

measure (EEM), is described in this section. All source cost data except for the solar 

photovoltaic cost data are in 2011 dollars, and are inflated using the general CPI into 

2015 dollars as shown in this section. The solar photovoltaic system cost data is from 

2014 and is not inflated to 2015 dollars due to the installed cost trends of solar 

photovoltaic systems, which will be explained in further detail in Section 5.1.3.3.

5.1.3.1 Baseline Building Components

In order to estimate the incremental costs of each building component relative to the 

baseline, it is necessary to identify the baseline building components included in the 

baseline building costs in order to not over- or under-estimate total costs. Table 5-1

shows the baseline building components. In some cases, the baseline is the same as the 

least efficient alternative for a building component (e.g. air exchange rate) while in other 

instances it is less efficient than any of the alternatives considered in the database (e.g. 

windows). For the user’s benefit, the cost of each baseline is included in the cost tables 

throughout this section to facilitate comparison to the alternatives given as options in 

BIRDS v3.0.
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Table 5-1 Baseline Building Assumptions 

Building Component Baseline

Exterior Wall Framing Typical

Insulation R-13

Basement Wall Wall R-0

Slab R-0

Attic Roof R-0

Ceiling R-30

Windows U-factor* 6.84 (1.2)

SHGC No Requirement

Air Exchange Rate ACH50 7.0

Lighting Fraction Efficient Typical

Heating and Cooling System 3 Ton Heat Pump

Efficiency SEER 13 / HSPF 7.7

Mechanical Ventilation No Requirement

Water Heater Efficiency 0.90

Solar Thermal None

Solar Photovoltaic None

*Units: W/m2-K (Btu/h·ft2-F)

5.1.3.2 Building Envelope Components

The building envelope accounts for 5 of the 10 building components that change across 

the 240 000 building designs. The thermal performance of the wall, attic, and foundation 

assemblies is changed by altering the materials in those assemblies, such as the framing 

and insulation. The installed costs associated with these changes are found in 

Faithful+Gould (2012) and are estimated on the basis of per unit of area (e.g. wall area).

There are five exterior wall assembly options based on requirements defined in different 

editions of IECC and the specifications of the NZERTF as defined in Section 3.2. 

Faithful+Gould (2012) includes installed assembly cost per unit of surface area based on 

different framing approaches and insulation levels based on IECC requirements. For wall 

assemblies that require additional insulation (Option 4 and Option 5), the additional 

installed costs per unit area of the insulation is added to the per unit area costs of the 

assembly to get the total cost of the assembly. The difference between the building 

design’s wall assembly costs and the baseline wall assembly costs (Option 1) is the 

change in wall assembly costs per unit of surface area. The change in the cost per unit 

area is multiplied by the amount of exterior wall area to get the total change in costs of 

the wall assembly relative to the baseline building design (ΔΔCCWW WWWW).
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Table 5-2 Wall, Attic, and Foundation Costs ($/ft2)

(a) Constructions Baseline Option 1 Option 2 Option 3 Option 4 Option 5

Exterior 18.99 18.99 19.82 19.83 21.64 23.03

(b) Constructions Baseline Option 1 Option 2 Option 3 Option 4

Basement Wall 1.34 1.34 1.60 3.22 3.22

Slab 0.00 0.00 0.00 0.00 1.70

(c) Constructions Baseline Option 1 Option 2 Option 3 Option 4 Option 5

Attic Roof 0.00 0.00 0.00 2.64 4.46 6.95

Ceiling 1.16 1.40 1.69 0.00 0.00 0.00

Conversion: Cost Per m2 = 10.76391 * $/ft2

The basement foundation alternatives are a combination of two assemblies, the wall 

assembly and floor/slab assembly, which vary in terms of the amount of rigid insulation 

installed in each. There are four design options defined in Section 3.2, two of which are 

based on IECC requirements and include insulation in the wall assembly only, and two

based on the NZERTF specifications that includes additional insulation in the wall 

assembly and under the basement floor/slab. The difference between the building 

design’s foundation costs per unit area and that of the baseline costs (Option 1) shown in 

Table 5-2 is the change in basement foundation costs per unit area. The change in per unit 

installed costs for the basement wall and floor/slab are multiplied by the associated area, 

and then added together to get the change in foundation costs (ΔΔCCFFFFFF FF ).

The thermal performance of the attic assembly considers two approaches to insulating the 

space. Common construction practice is to treat the attic as unconditioned space and 

cellulose is blown onto the attic floor while the NZERTF treats the attic as conditioned 

space, with no insulation between the attic and 2nd floor while incorporating insulation 

into the roof assembly. The baseline and Option 1 and Option 2 use the first approach, for 

which the cost per unit of area is multiplied by the attic floor area to calculate the roof 

assembly costs. Option 3, Option 4, and Option 5 (NZERTF) use the second approach, 

for which the costs per unit of area is multiplied by the roof area to calculate the roof 

assembly costs. The difference between the building design’s attic assembly costs and the 

baseline attic assembly costs (Option 1) is the total change in costs of the attic assembly 

relative to the baseline building design (ΔΔCC AAAA).

The overall thermal performance of the building envelope is dependent on the air leakage 

rate of the building envelope. The air leakage rate defined in IECC as well as the 

performance of the NZERTF are based on air changes per hour estimated using a blower 

door test at 50 Pa (ACH50). However, the cost data in Faithful+Gould (2012) are based on 

natural air leakage rates that assume 4 Pa (ACHNAT), which require conversion from the 

ACH50 requirements to ACHNAT (see Section 3.2 for details). Table 5-3 maps the cost per 

unit of area to the IECC requirements of 3.0 ACH50 and 7.0 ACH50, and extrapolates 
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those data points to estimate the cost of meeting the NZERTF performance of 0.63 

ACH50. Additional blower door tests are assumed to be required to ensure that the air 

leakage rate is reduced to the target ACH50 levels shown in Table 5-3.

Table 5-3 Air Leakage Rates and Costs

ACHNAT ACH50 Blower 

Door Tests

Cost Per ft2 Testing 

Costs

Baseline 7.00 1 0.14 289

> 0.35 7.00 1 0.14 289

< 0.15 3.00 2 0.40 579

NZERTF 0.63 3 0.53 868

Conversion: Cost Per m2 = 10.76391 * Cost Per ft2

The installed cost data for windows shown in Table 5-4 is based on the cost per unit area 

for “average casement window across sizes” in the F+G database (Faithful+Gould 2012). 

The lowest cost window that meets the minimum performance specification allowed 

(U-factor and SHGC) by the building design is selected.

Table 5-4 Window Costs (Per ft2)

Cost Baseline Option Option Option Option Option 5

Option 31.97 36.10 36.42 36.72 36.72 37.64

Conversion: Cost Per m2 = 10.76391 * Cost Per ft2

5.1.3.3 Building System Components

The building lighting, HVAC and DHW systems account for 5 building components that 

can be varied across the 240 000 building designs. The installed costs associated with 

these changes are provided in Faithful+Gould (2012) and are estimated on a per system 

basis except for lighting, which is estimated on a per-watt basis.

The fraction of hard-wired lighting that is high efficiency is adjusted to meet each IECC 

edition’s requirements and the NZERTF specifications. The baseline lighting from 

Hendron and Engebrecht (2010) is assumed for IECC editions with no lighting 

requirement (see Section 3.1.4 for details). The cost of lighting is estimated using the cost 

data for comparable incandescent and spiral CFL bulbs, which is converted into a 

normalized cost per-watt using incandescent wattage as the basis for the normalization to 

allow for simple cost change estimates based on wattage values. The linear fluorescent 

(LF) fixtures assumed in the BA Protocol baseline are treated as CFLs in the cost 

estimates. Total lighting cost estimates are shown in Table 5-5.
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Table 5-5 Lighting Costs

Lighting 

Costs ($) by 

Alternative

Fraction of High Efficiency Fixtures

Baseline/ 

2003/2006

2009 2012/2015 NZERTF

34% 50% 75% 100%

Total 108 137 184 230

Incandescent 30 22 11 0.00

CFL/LF 78 115 172 230

The HVAC equipment includes two components, the heating and cooling equipment and 

mechanical ventilation. The ductwork for the heating and cooling system is assumed to 

be incorporated into the baseline building design. There are currently two levels of 

efficiency available for the heating and cooling equipment, both of which use an air-to-air 

heat pump with electric supplemental heat but may have different mechanical ventilation 

requirements. The capacity of the HVAC equipment varies based on the thermal load for 

a given building design, which impacts the costs of the system. Installed costs for the heat 

pump (1.5, 2.0, 2.5, 3.0, 4.0, or 5.0 ton) are selected based on the closest match to the 

“autosized” system in the E+ simulation. Table 5-6 shows the costs of the two system 

designs assuming a 3.0-ton heat pump, which is the size of the baseline system.

Table 5-6 HVAC Equipment Costs (3 Ton)

Heating System

Baseline Federal Min. 

Efficiency

NZERTF 

System

Heat Pump 3846 3846 5267

Outdoor Air/HRV 0 0 12 197

Total 3846 3846 17 464

The less efficient system is designed to meet current federal minimum efficiency levels, 

SEER 13 for cooling and HSPF 7.7 for heating. The dedicated outdoor air ventilation 

component of the HVAC system varies depending on the air leakage rate of the building 

envelope. IECC requirements state that a building with air leakage of less than 3 ACH50

is required to have dedicated outdoor air ventilation to adhere to ASHRAE 62.2-2010. As 

a result, only building designs with the less efficient system and an air leakage rate of 

3 ACH50 or less will have a dedicated outdoor air component to the HVAC system.

Building designs with an air leakage rate of 3 ACH50 or lower and no dedicated outdoor 

air ventilation are not included in BIRDS because they do not meet ASHRAE 62.2 

requirements. Building designs with an air leakage rate of 7 ACH50 and dedicated 

outdoor air ventilation are not included in the database because IECC does not require 

mechanical ventilation. For designs with the higher efficiency system, the separate HRV 

system is always installed regardless of the air leakage rate. This assumption will lead to 

building designs with an air leakage rate of 7 ACH50 to over-ventilate the building 

relative to the ASHRAE 62.2 requirements due to the air leakage. Regardless of the 

building design, the cost of including the ductwork for the mechanical ventilation of the 
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less efficient system is considered negligible and is assumed to be zero ($0) as is shown 

in Table 5-6.

The DHW system has four alternative designs as described in Section 3.7, and includes 

two separate components: a water heater and a solar thermal system. There are two water 

heater options considered, an electric water heater of federal minimum required 

efficiency and a heat pump water heater based on the NZERTF specifications. The solar 

thermal system is either installed or not installed. The cost of these two different 

components is considered to be independent and, therefore, can be treated as additive as 

shown in Table 5-7. The cost of the plumbing/piping from the water heater to the fixtures 

is assumed to be incorporated into the baseline costs.

Table 5-7 DHW Costs

DHW System

Baseline Option 1 Option 2 Option 3 NZERTF

Water Heater – Electric 721 792 792 n/a n/a

Water Heater – Heat Pump n/a n/a n/a 876 876

Solar Thermal System n/a n/a 6278 n/a 6278

Total 721 792 7070 876 7154

n/a = Not Applicable

The solar photovoltaic system cost data is sourced from Tracking the Sun VIII published 

by Lawrence Berkeley National Laboratory (LBNL) and SunShot (Barbose and 

Darghouth 2015), which is updated annually and includes installed cost data from across 

the U.S. for PV system installations. The installed cost is assumed to be $3.90/W, which 

is the median installed price of PV systems in the state of Maryland. Given the trend of 

consistently decreasing installation costs, this is a conservative estimate (high) for the 

cost of installing a solar photovoltaic system as of 2015 – therefore, it is assumed to be in 

2015 dollars.

5.2 Future Costs

Future building costs include the maintenance, repair, and replacement (MRR) costs as 

well as operating energy-related costs of electricity and natural gas consumption. The 

total present value of future costs (CFuture) is the sum of present value location-indexed 

MRR costs (CMRR) and present value energy costs (sum of electricity costs (CElect) and 

natural gas costs((CGas))) based on location-specific energy prices and projections, as 

shown in the following equation:

CCFFFF FF = CCMM + CCEE + CC

Each of these is discussed below.
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5.2.1 Operating Energy

Energy consumption in each of the 240 000 building designs is 100% electricity because 

both space and water heating needs are met with equipment that runs on electricity.16

Therefore, natural gas consumption is zero. Note that future versions of the low-energy 

residential database will include natural gas options. The electricity use predicted by each 

building design’s energy simulation is used as the annual energy use of the building for 

each year of the selected study period. A key assumption is that the MRR of building 

components maintains the level of energy consumption predicted by the E+ simulation 

model. The electricity production predicted by the simulation model is used as a basis for 

the PV system performance, with a linear annual production degradation rate of 0.5 % 

over the its lifetime. With a constant consumption and decreasing production, net 

electricity consumption increases slightly year-over-year as the solar photovoltaic system 

degrades.

For some building designs in the database, a building would exhibit negative annual 

consumption resulting from the combination of lower energy consumption from energy 

efficiency measures and electricity production from the solar photovoltaic system. Due to 

the design of a state’s net metering program, the cost to the homeowner of consuming 

electricity may not be the same as the value earned from producing electricity. Based on a 

local utility’s rate schedule, the estimated cost of consuming electricity including all 

charges, taxes, and fees is assumed to be 14.0¢/kWh (average cost) while the value of 

excess production is assumed to be 8.4¢/kWh, which is the generation charge for one 

kWh of electricity. As a result, each unit of excess production is worth 60 % of the value

of a unit reduction in consumption, which encourages sizing a solar photovoltaic system 

to exactly match annual production to annual consumption. Along with per unit costs of 

electricity, there is also an assumed monthly fixed fee of $7.91(including Gross Receipts 

tax), which aggregates to $95 annually. Given that these electricity prices are based on 

rate schedules for 2015, electricity costs are assumed to be in 2015 dollars.

Electricity prices are assumed to change over time according to U.S. Energy Information 

Administration (EIA) forecasts from 2015 to 2045 (U.S. Energy Information 

Administration (EIA) 2015). These forecasts are embodied in the DOE Federal Energy 

Management Program (FEMP) Modified Uniform Present Value Discount Factors

(UPV*) for energy price estimates reported in Lavappa and Kneifel (2015)17, which are 

based on a 3 % real discount rate. Multiplying the annual electricity costs (positive or 

negative) by the associated UPV* value for the study period of interest estimates the 

present value total electricity costs (CElect). The discount factors vary by census region, 

end use, and fuel type. The same methodology is implemented to develop UPV* factors 

assuming an 8 % real discount rate. Since natural gas consumption is zero, the natural gas 

costs (CGas) are zero for all building designs. 

 16 The assumption of 100 % all electric equipment is based on the design of the NZERTF. 17 Since the U.S. Energy Information Administration forecasts end at year 30, the escalation rates for years 

31 through 40 are assumed to be the same as for year 30.
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5.2.2 Maintenance, Repair, and Replacement (MRR) Costs 

Building MRR costs are discounted to equivalent present values using the methodology 

for calculating Single Present Value (SPV) factors for future non-fuel costs reported in 

Lavappa and Kneifel (2015). These factors are calculated using the FEMP 2015 real 

discount rate for federal energy conservation projects (3 %) and a value that represents 

the long-run real return from the stock market (8 %). Table 5-8 reports the SPV factors 

for the 3 % and 8 % discount rates included in the BIRDS low-energy residential 

database. The MRR costs for each year (CMRR,i) are multiplied by the SPV for that year 

and assumed discount rate and then summed and indexed to determine the total present 

value of MRR costs (CMRR).

Table 5-8 2009 SPV Discount Factors for Future Non-Fuel Costs, 8 % and 3 % 

Real Discount Rate

Yrs SPV Factor Yrs SPV Factor Yrs SPV Factor Yrs SPV Factor

8 % 3 % 8 % 3 % 8 % 3 % 8 % 3 %

1 0.926 0.971 11 0.429 0.722 21 0.199 0.538 31 0.092 0.400

2 0.857 0.943 12 0.397 0.701 22 0.184 0.522 32 0.085 0.388

3 0.794 0.915 13 0.368 0.681 23 0.170 0.507 33 0.079 0.377

4 0.735 0.888 14 0.340 0.661 24 0.158 0.492 34 0.073 0.366

5 0.681 0.863 15 0.315 0.642 25 0.146 0.478 35 0.068 0.355

6 0.630 0.837 16 0.292 0.623 26 0.135 0.464 36 0.063 0.345

7 0.583 0.813 17 0.270 0.605 27 0.125 0.450 37 0.058 0.335

8 0.540 0.789 18 0.250 0.587 28 0.116 0.437 38 0.054 0.325

9 0.500 0.766 19 0.232 0.570 29 0.107 0.424 39 0.050 0.316

10 0.463 0.744 20 0.215 0.554 30 0.099 0.412 40 0.046 0.307

Residential building component, and building, lifetimes are based on three data sources: 

National Association of Home Builders (NAHB) Research Center (2007) for building 

components excluding lighting, ENERGY STAR (2011) for light bulbs, and U.S. Census 

Bureau (2011) for building lifetime. A residential building’s service lifetime is assumed 

constant across climate zones at 65 years because, when well maintained, a building can 

remain in use for up to or beyond 65 years. This assumption is supported by the data in 

Table C-010AH of the 2011 American Housing Survey (AHS), which shows that about 

half of all owner-occupied housing units are 40 years of age or older and 6 % are 96 years 

of age or older (U.S. Census Bureau 2011). Additionally, the National Association of 

Home Builders (NAHB) Research Center (2007) estimates the lifetime of a number of 

house components (e.g. foundations, chimneys) to be greater than 65 years, which 

implies that the house structure lasts over 65 years. Insulation and air barriers are 

assumed to have a lifespan greater than 40 years and have no maintenance or repair 

requirements. Windows have an assumed lifespan of 25 years with costs that vary 

depending on the required window specifications. Windows are assumed to have no 

maintenance costs or repair costs. The heating and cooling unit (heat pump with 

supplemental electric element) is assumed to have a lifespan of 16 years while the HRV 

unit is assumed to last 20 years. The electric resistance and heat pump hot water heater 

with additional solar thermal system are assumed to have lifespans of 12 years and 20 
104

years, respectively. The solar photovoltaic panels are assumed to have a lifetime of 25 

years while the inverters last 10 years. Incandescent and fluorescent light bulbs are 

assumed to have lifespans of 1 year and 7 years, respectively (ENERGY STAR 2011).

LEDs are not included in the analysis at this time.

MRR cost data are collected from two primary sources: U.S. Census Bureau (2011) and

Faithful+Gould (2011). The total maintenance and repair costs per square foot of CFA

represent the baseline (non-energy related) MRR costs per unit of floor area, which occur 

for a building type regardless of the energy efficiency measures incorporated into the 

design. These data are collected from The 2011 AHS National Table C-12-OO and Table 

C-15-OO (U.S. Census Bureau 2011), which reports median floor area (177 m2 or 1900 

ft2) and average maintenance and repair costs for “Total Owner-Occupied Units” 

Housing Units ($33/month) and “New Construction the Past 4 Years” Housing Units

($8/month), respectively. These two data points are adjusted to 2015 dollars and used to 

interpolate and extrapolate for all years considered (1 through 40) as shown in Figure 5-2. 

Costs are assumed to remain constant after 10 years. Additional MRR costs are calculated 

for all the building systems and windows.

Figure 5-2 Baseline Maintenance and Repair Costs by Year

Annual maintenance and repair costs related to each building system component are 

shown in Table 5-9. All other components are assumed to be accounted for in the 

baseline maintenance and repair costs.
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Table 5-9 Building Component Average Maintenance and Repair Costs

System Component Annual Cost

Heat Pump 200

HRV 60

Water Heater 100

Solar Thermal 47

Solar photovoltaic (per kW) 21

Faithful + Gould (2012) is the source of replacement costs for the individual components 

for which costs change across alternative building designs, which in this analysis are the 

windows and the building systems (lighting, HVAC, DHW, solar thermal, and solar 

photovoltaic. The replacement cost of windows is assumed to be equal to the initial 

installation costs of the same window. The cost of replacing CFL light bulbs is greater 

than the cost of replacing incandescent light bulbs. However, CFLs last at least 7 times 

longer than incandescents, which may lead to the replacements costs of light bulbs to be 

lower for the more efficient CFLs (U.S. Environmental Protection Agency (EPA) 2012). 

The HVAC system assumes the same initial installation costs for the heating and cooling 

equipment (i.e. heat pump unit). The HVAC system capacity size varies based on the 

thermal performance of the building design, which results in varying replacement costs 

(based on first cost estimates) because smaller capacity systems are relatively cheaper.

The high-efficiency system based on the NZERTF specifications includes the HRV unit 

that must be replaced ($3592), which costs less than the initial system because the 

ductwork does not need to be replaced. The replacement cost of the solar photovoltaic 

inverters is assumed to be $420/kW.

5.3 Residual Value

A building's residual value is its value remaining at the end of the study period. In lifecycle costing it is treated as a negative cost item. In BIRDS, it is estimated in two major 

parts based on the approach defined in Fuller and Petersen (1996): the building excluding

components that get replaced, and the building components that get replaced (HVAC, 

DHW, solar thermal, solar photovoltaic system windows, and lighting). The building's 

residual value is calculated as the building's location-indexed first cost (excluding 

HVAC, DHW, solar thermal, solar photovoltaic system windows, and lighting costs) 

multiplied by one minus the ratio of the study period to the service life of the building, 

discounted from the end of the study period using the single present value factor (SPV) 

for the last year of the study period (T). 

RRRR = FF CC ∙ �1 − SS PP

SS LL � ∙ SS TT

For example, if a building has first costs (excluding HVAC, DHW, solar thermal, solar 

photovoltaic, windows, and lighting) of $500 000, a 65-year service life, a 3 % discount 

rate, and the study period length is 10 years, the discounted residual value of the building 
106

at the end of the study period (excluding HVAC, DHW, solar thermal, solar photovoltaic 

system windows, and lighting) is:

 $500 000 ∙ �1 − 10

65� ∙ 0.744 = $314 769

Because they may be replaced during the study period, residual values for the excluded 

HVAC, DHW, solar thermal, solar photovoltaic, windows, and lighting are computed 

separately. The remaining “life” of a building component is determined by taking its 

service life minus the number of years since its last installation (as of the end of the study 

period), whether it occurred during building construction or replacement. The ratio of 

remaining life to service life is multiplied by the location-indexed installed cost of the 

system and discounted from the end of the study period.

For example, assume an HVAC system’s installed costs are $12 000 with a service life of 

15 years, and a 20-year study period. After one replacement, the system is 5 years old at 

the end of the study period, leaving 10 years remaining in its service life. The discounted

residual value in year 20 is:

$12 000 ∙

10

15 ∙ 0.744 = $5952

The residual value for the DHW, solar thermal, solar photovoltaic system windows, and 

lighting is computed in a similar manner.

The total residual value of the building and its HVAC, DHW, solar thermal, solar 

photovoltaic system windows, and lighting, multiplied by the SPV factor for the number 

of years in the study period and assumed discount rate, estimates the present value of the 

residual value (CResidual).

5.4 Life-Cycle Cost Analysis

The total life-cycle cost of a prototype building (CLCC) is the sum of the present values of 

first cost and future costs minus the residual value as shown in the following equation:

CCLL = CCFF FF + CCFFFF FF − CCRR

LCC analysis of buildings typically compares the LCC for a “base case” building design 

to the costs for alternative, more energy efficient building design(s) to determine if future 

operational savings justify higher initial investments. For BIRDS, total life-cycle costs 

are calculated as described above for all building design options for all study periods. The 

user of the tool has the option to select any of the building designs as the “base case,” and 

compare it to any of the alternative designs. For an investor comparing mutually 

exclusive design alternatives, the same study period must be used for all alternatives. For 

those interested in the sensitivity of LCC results to the assumed study period length, 

BIRDS permits the study period length for a given building design to vary.
107

Two metrics are used to analyze changes in life-cycle costs: net LCC savings (ASTM 

2012) and net LCC savings as a percentage of base case LCC. Net Savings (NS) is the 

difference between the base case LCC (CBase) and alternative design LCC (CAlt) as shown 

in the following equation:

NN = CCBB − CC

Net LCC savings as a percentage of base case LCC (PNS) is the net LCC savings divided 

by the base case LCC. This metric, shown in the equation below, allows for comparisons 

across building types that vary significantly in terms of floor area.

PP = NN

CCBB

∙ 100
108
109

6 Software Development and Design

BIRDS is a data-driven web application that enables access to a NIST-developed lowenergy residential building database with approximately 75 million records. The database 

is based on 240 000 energy simulations as described in previous sections of this report.

Comparisons of life-cycle costs, operating energy consumption, or environmental 

impacts for different building designs can be evaluated using the data visualization 

features in the application. Technologies were selected for this project based on their 

utility in developing this comprehensive system. A summary of each technology is 

described below.

6.1 Database

6.1.1 Database Management – Sql Server

Microsoft Sql Server relational database management system is used to store the 

low-energy residential building database. The data set used by the application is large, 

containing over 75 million records. Creating efficient indexes is important for reducing 

fetch times for records so the application will display data quickly.

6.1.2 Database Development - Python

Python is a widely used, general purpose programming language. For the BIRDS project, 

Python programs have been created for generating the final data set which contains over 

75 million records. Python scripts have been developed to generate LCC data, operating 

energy data, and environmental impact data from location specific building simulation 

data.

6.2 Application

6.2.1 Software Programming Language – C#

C# is an object-oriented programming language developed by Microsoft. It is based on 

the C++ programming language, has many similarities with Java, and was developed to 

work with the .Net framework. C# is used primarily for developing the server side code 

of BIRDS v3.0, including modules to process results data for visualization and data 

retrieval.

6.2.2 Software Framework - .NET

The .Net Framework is a Microsoft developed framework, which contains the common 

language runtime, in addition to several common class libraries. The common language 

runtime can be thought of as the foundation of the framework that manages processes at 

execution time. BIRDS targets the .Net Framework version 4.5.
110

6.2.3 Web Development Technologies – HTML, CSS, JavaScript, JQuery, jqChart

Several web technologies were used in the creation of the user interface. Hypertext 

Markup Language (HTML) is the primary language used for displaying web content. 

Cascading Style Sheet (CSS) is the definition file used by web pages for formatting. 

JavaScript is a light-weight scripting language used to programmatically manipulate the 

input, output or display of a web page. JQuery is a JavaScript library that facilitates 

Document Object Model (DOM) manipulation and simplifies partial web page data 

refreshing through asynchronous JavaScript and XML (AJAX) requests. jqChart is a 

html5 charting library used to render charts based on the data for specific comparisons in 

BIRDS v3.0.

6.2.4 Application Design – Visual Studio

The BIRDS application is developed using Visual Studio’s MVC 5 project template with 

the “database first method.” Model, view, and controller functions are all placed in 

different code files to keep each entity separate. The general flow of the application can 

be seen in Figure 6-1. A user makes a request through the browser. The controller gets 

the request and passes the request parameters to the model, which retrieves necessary 

data from the database. The model passes back the data, which is merged with the view 

and then passed back to the browser by the controller.

Figure 6-1 Application Information Flow

In developing the BIRDS low-energy application, the database first method is used 

because the BIRDS low-energy residential database had been developed prior to 

development of the user interface module. The model containing fourteen tables was 
111

created based on the database. Controller methods were then developed to retrieve data 

based on specific parameters from the model, which was then combined with the view 

and passed back to the browser to be displayed to the user. Comparison results are 

displayed by the chart module, which contains functions for ordering, formatting, and 

displaying data for different chart types. The capability to download the data in a .csv file 

is included so that data can be analyzed by the user according to their preferences.
112
113

7 Limitations and Future Research

The databases in the previous version of BIRDS (v2) allowed for analysis of a limited 

number of combinations of energy efficiency measures based on building energy 

standard and code editions across multiple (hypothetical and simplified) building 

prototypes located in 228 U.S. cities. Although some assumptions were allowed to vary 

(e.g. study period length), a number of key assumptions were held constant across the 

analysis (e.g. discount rate). The analysis that can be completed with such a database 

design could be considered an “inch-deep, mile-wide” approach. 

The approach used to develop the BIRDS v3.0 low-energy residential database focuses

on a single location (Gaithersburg, MD) and building prototype (NIST’s NZERTF), but 

includes many more building design options (variation from four or five building designs 

per building prototype-location combination to 240 000 building designs in one location) 

and additional variation across assumptions that had previously been held constant in 

BIRDS (e.g. discount rate, construction quality, financing). In addition, the simulation 

model for which the database is developed has been validated using in-situ energy 

performance from the demonstration phase of the NZERTF. This approach allows for a 

“deep-dive” into the sustainability performance of the building to determine the optimal 

design based on the assumptions, preferences, and goals of the BIRDS v3.0 user.

However, in order to get these additional features, the database is currently limited in 

scope. Including additional locations, building prototypes, uncertainty analysis, metrics, 

and flexibility into the tool would greatly strengthen the depth of its coverage.

Uncertainty analysis is needed for at least three elements of the analysis. First, consider 

the assumed discount rates, construction quality, and financing options. Although two 

options are included for each of these assumptions, additional alternatives should be 

included to better match potential user preferences. Second, the current analysis assumes 

all building designs operate on all-electric building system components (HVAC and 

DHW systems). However, given the lower cost of space heating and water heating with 

natural gas relative to electricity, the database should be expanded to include systems of 

varying energy efficiency that operate on natural gas. Third, the analysis in this study 

assumes the occupancy and load profiles of the “family of four” simulated in the 

NZERTF (Omar and Bushby 2012), which ignores the impacts from variations in 

occupancy and behavior patterns, in terms of plug and process load use, thermostat 

setpoints, and number of occupants. Future research should consider the impact that 

improved equipment efficiency and changes in occupant activity have on the overall 

energy savings realized by energy efficiency improvements to buildings. Fourth, the 

BIRDS environmental impact scores do not incorporate uncertainty analysis as required 

by international standards (International Organization for Standardization (ISO) 2006a, 

International Organization for Standardization (ISO) 2006b). While incorporating 

uncertainty analysis is problematic due to a lack of underlying uncertainty data, this 

omission should be brought into the interpretation of the BIRDS results in the future.
114

Additional locations and building prototypes should be incorporated into the BIRDS lowenergy residential database. Initially, the 240 000 building designs should be simulated in 

other locations in the Building America (BA) Mixed-Humid Climate Zone for which the 

NZERTF architectural design is common because it is possible that the optimal design 

may be different across the climate zone. Local energy tariffs should be included to 

capture the variation in energy costs across the climate zone. Alternative architectural 

designs, such as a single-story ranch/rambler style home, should be simulated and 

incorporated into the database to allow users to better match their desired home 

prototype. Alternative foundations should also be included because even within the 

Mixed-Humid Climate Zone, foundation type can vary. For example, locations in the 

central U.S. tend to have more slab and crawlspace foundations instead of the basement 

foundations that are typical in Maryland. The three elements here are merely examples of 

areas where additional uncertainty analysis is needed. Other sources to be considered in 

the future are financing rates, solar photovoltaic output, electricity price, degradation 

rates, and unscheduled MRR. 

Analysis based on the energy performance is susceptible to the limitations and variations 

among current whole building energy simulation tools as well as variations among 

different users in creating building representation with the tools. As the development and 

application of simulation software improves in accuracy and uncertainty, the 

sustainability analysis related to the modeling should improve as well.

Properly interpreting the BIRDS environmental performance results requires placing 

them in perspective. The environmental impact scores assess the life-cycle impacts of 

operating energy use based on inventories of localized energy simulation results and 

regional electricity grids. All other elements of the scores – including a building’s use of 

materials and its water consumption over the study period – are based on U.S. average 

LCI data for prototypical buildings. The baseline data for these buildings represent status 

quo building technologies as of 2002, the year of the latest available input-output data 

from the U.S. Bureau of Economic Analysis at the time of the database development. To 

account for evolution in status quo technologies over time, future versions of BIRDS 

should incorporate newer releases of these data as they become available.

The BIRDS LCAs use selected inventory flows converted to selected local, regional, and 

global environmental impacts to assess environmental performance. Those inventory 

flows which currently do not have scientifically proven or quantifiable impacts on the 

environment are excluded, such as mineral extraction and wood harvesting, which are 

qualitatively thought to lead to loss of habitat and an accompanying loss of biodiversity. 

Any new or improved data and/or modeling approaches should be brought into the 

interpretation of the BIRDS results. 

The Environmental Problems approach that BIRDS uses for impact assessment does not 

offer the same degree of relevance for all environmental impacts. For global and regional 

effects (e.g., global warming and acidification) the method may result in an accurate 

description of the potential impact. For impacts dependent upon local conditions (e.g., 
115

smog, ecological toxicity, and human health impacts) it may result in an 

oversimplification of the actual impacts because the indices are not tailored to localities. 

During the interpretation step of the BIRDS LCAs, environmental impact results are 

optionally combined into a single environmental performance score using relative 

importance weights. These weights necessarily incorporate values and subjectivity. 

BIRDS users should routinely test the effects on the environmental impact scores of 

changes in the set of importance weights by completing their analysis with more than one 

weighting approach.

Life-cycle impact assessment is a rapidly evolving science. Assessment methods unheard 

of a decade ago have since been developed and are now being used routinely in LCAs. 

While BIRDS incorporates state-of-the-art impact assessment methods, the science will 

continue to evolve and methods in use today – particularly those for land and water use –

are likely to change and improve over time. Future versions of BIRDS should incorporate 

these improved methods as they become available. 

The current economic analysis does not include all potential factors that could impact 

costs. Federal, state, and local incentives (i.e. federal and state tax credits, state and utility 

rebate programs) are available in many locations for energy efficient, green, and 

renewable products, systems, and buildings. More recent studies have shown that some 

homeowners have been able to capture “green premiums” at resale, but it is unclear 

whether those premiums match the residual values calculated using LCCA methodology. 

These, as well as many other cost-related factors, should be incorporating into future 

LCC analysis within BIRDS.

Energy, environmental, and economic performance are but three attributes of building 

performance. The BIRDS model assumes that its building prototypes all meet minimum 

technical performance requirements. However, there may be significant differences in 

technical performance not evaluated in BIRDS, such as acoustic, fire, or indoor 

environmental quality performance (e.g. air quality, comfort, health), which may affect 

energy, environmental, and economic considerations.

The BIRDS sustainability results do not apply to buildings constructed in other countries 

where industry practices, fuel mixes, environmental regulations, transportation distances, 

and labor and material markets may differ. Furthermore, all buildings of a given type are 

not created equal. Building designs, sizes, useful lives, materials compositions, and costs 

will all vary for an individual building. The BIRDS results for a building prototype do not 

necessarily represent the performance of an individual building of that type. Future 

versions of the tool should permit flexibility in building design and use of materials.
116
117

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