NIST Technical Note 1878

Building Industry Reporting and 

Design for Sustainability (BIRDS) New 

Residential Database Technical 

Manual 

Joshua Kneifel

Priya Lavappa

Anne Landfield Greig

Sangwon Suh

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.TN.1878

 

NIST Technical Note 1878

Building Industry Reporting and 

Design for Sustainability (BIRDS) New 

Residential Database Technical 

Manual

Joshua Kneifel

Priya Lavappa

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.1878

April 2015

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

 Willie May, Acting Under Secretary of Commerce for Standards and Technology and Acting Director
ii

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 1878

Natl. Inst. Stand. Technol. Tech. Note 1878, 120 pages (April 2015) 

http://dx.doi.org/10.6028/NIST.TN.1878

CODEN: NTNOEF
iii

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. Whole 

building 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. The BIRDS new residential building database includes energy, 

environmental, and cost measurements for 9120 new residential buildings, covering 10 

single-family dwellings (5 one-story and 5 two-story of a various of conditioned floor area) in 

228 cities across all U.S. states for study period lengths ranging from 1 year to 40 years. The 

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

compared to their performance when meeting four alternative building energy standard editions 

to determine the impact of energy efficiency on sustainability performance. The impact of the 

building location and the investor’s time horizon on sustainability performance (economic and 

environmental) can also be measured.

Keywords

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

efficiency; residential buildings
iv
v

Preface

This documentation was developed by the Applied Economics Office in the Engineering 

Laboratory (EL) at the National Institute of Standards and Technology (NIST). The 

document explains how the BIRDS new residential database was developed, including 

the assumptions and data sources for the energy, environmental, and cost estimate 

calculations. The intended audience is BIRDS v2.0 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.
vi
vii

Acknowledgements

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

They include Dr. Eric O’Rear 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 life-cycle

assessments for BIRDS. 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 Mr. Brian Presser and Mr. Samuel Sharpe for assisting in 

developing the energy simulations and generating the new residential sustainability 

database. Thanks to Mr. Tarang Hirani for assisting in the development of BIRDS v2.0

web interface. Finally, the many Beta testers of BIRDS v2.0 deserve special thanks for 

contributing suggestions leading to substantial improvements in the tool.

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
viii

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
ix

Contents

Abstract.............................................................................................................................iii

Preface................................................................................................................................ v

Acknowledgements ......................................................................................................... vii

Author Information ........................................................................................................ vii

List of Acronyms............................................................................................................. xv

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........................................................................................... 5

3 Energy Performance Measurement ........................................................................ 7

3.1 Building Types..................................................................................................... 7

3.2 Building Designs.................................................................................................. 8

3.3 Energy Simulation Design ................................................................................. 12

3.3.1 Building Envelope ............................................................................................. 13

3.3.2 Heating, Ventilation, and Air Conditioning Equipment.................................... 20

3.3.3 Outdoor Air Ventilation and Infiltration ........................................................... 20

3.3.4 Domestic Hot Water.......................................................................................... 23

3.3.5 Lighting ............................................................................................................. 25

3.3.6 Appliances and Miscellaneous Electrical Loads ............................................... 27

3.3.7 Internal Mass..................................................................................................... 28

3.3.8 Occupancy ......................................................................................................... 29

3.3.9 Internal Heat Gains............................................................................................ 29

4 Environmental Performance Measurement......................................................... 31

4.1 Goal and Scope Definition ................................................................................. 31

4.2 Life-cycle Inventory Analysis............................................................................ 32

4.3 Life-cycle Impact Assessment ........................................................................... 36

4.3.1 BIRDS Impact Assessment ............................................................................... 37

4.3.2 BIRDS Normalization ....................................................................................... 40

4.4 Life-cycle Interpretation..................................................................................... 42

4.4.1 EPA Science Advisory Board Study ................................................................. 42

4.4.2 BEES Stakeholder Panel Judgments ................................................................. 44

4.5 BIRDS Residential Energy Technologies.......................................................... 48

4.5.1 General Information Regarding the Energy Technology LCIs ......................... 48

4.5.2 Wall and Ceiling Insulation............................................................................... 50

4.5.3 Windows............................................................................................................ 68

4.5.4 HVAC................................................................................................................ 76

4.5.5 Residential Electric and Gas Water Heaters...................................................... 85

4.5.6 Lighting ............................................................................................................. 87

4.5.7 Sealants.............................................................................................................. 95

5 Economic Performance Measurement.................................................................. 99

5.1 First Cost ............................................................................................................ 99
x

5.1.1 Approach ........................................................................................................... 99

5.1.2 Data ................................................................................................................. 100

5.2 Future Costs...................................................................................................... 102

5.2.1 Approach ......................................................................................................... 102

5.2.2 Data ................................................................................................................. 103

5.3 Residual Value ................................................................................................. 105

5.4 Life-Cycle Cost Analysis................................................................................. 106

6 Limitations and Future Research........................................................................ 107

References...................................................................................................................... 111

A Appendix................................................................................................................ 119
xi

List of Figures

Figure 2-1 BIRDS Sustainability Framework.................................................................... 5

Figure 3-1 State Residential Energy Codes..................................................................... 10

Figure 3-2 Locations and Climate Zones......................................................................... 11

Figure 3-3 Building Material Layers for Exterior Wall................................................... 14

Figure 3-4 Building Material Layers for Ceiling............................................................. 15

Figure 3-5 Building Material Layers for Roof................................................................. 15

Figure 3-6 Building Material Layers for Slab-on-Grade Foundation.............................. 16

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

Hour .................................................................................................................................. 24

Figure 3-8 Electrical Equipment Load Profiles as a Proportion of Peak Wattage, By Hour

........................................................................................................................................... 28

Figure 4-1 Compiling LCA Inventories of Environmental Inputs and Outputs.............. 33

Figure 4-2 Illustration of Supply Chain Contributions to U.S. Construction Industry.... 35

Figure 4-3 BEES Stakeholder Panel Importance Weights Synthesized across Voting 

Interest and Time Horizon ................................................................................................ 47

Figure 4-4 BEES Stakeholder Panel Importance Weights by Stakeholder Voting Interest

........................................................................................................................................... 47

Figure 4-5 BEES Stakeholder Panel Importance Weights by Time Horizon.................. 48

Figure 4-6 Insulation System Boundaries – Fiberglass Blanket Example....................... 51

Figure 4-7 Windows System Boundaries ........................................................................ 70

Figure 4-8 HVAC System Boundaries – Electric Furnace Example............................... 77

Figure 4-9 Lighting System Boundaries – CFL Example ............................................... 88

Figure 4-10 Sealants System Boundaries – Exterior Sealant Example ........................... 95

Figure 5-1 Baseline Construction Costs ........................................................................ 100

Figure 5-2 Baseline Maintenance and Repair Costs by Year ........................................ 104

Figure A-1 Conditioned Floor Area of New 1-Story Single-Family Housing .............. 119

Figure A-2 Conditioned Floor Area of New 2-Story Single-Family Housing .............. 120
xii
xiii

List of Tables

Table 3-1 Building Prototype Characteristics.................................................................... 8

Table 3-2 Energy Code by State ........................................................................................ 9

Table 3-3 2003 IECC Climate Zones............................................................................... 12

Table 3-4 Material Parameter Calculation Approach ...................................................... 13

Table 3-5 2003 IECC Energy Code Requirements for Exterior Envelope...................... 17

Table 3-6 2006, 2009, 2012 IECC Energy Code Requirements for Windows................ 18

Table 3-7 2006, 2009, 2012 IECC Energy Code Requirements for Exterior Envelope.. 19

Table 3-8 2006, 2009, 2012 IECC Energy Code Requirements for Foundation............. 19

Table 3-9 IECC Energy Code Requirements for Infiltration........................................... 20

Table 3-10 62.2-2010 Minimum Air Exchange Rate Requirements............................... 22

Table 3-11 Hot Water Consumption................................................................................ 23

Table 3-12 Domestic Hot Water Daily Internal Heat Gains............................................ 25

Table 3-13 Annual Lighting Electricity Consumption .................................................... 26

Table 3-14 Lighting Load Profile as Proportion of Peak Wattage in Use, By Hour....... 26

Table 3-15 Appliance and MEL Electricity Consumption .............................................. 27

Table 3-16 Occupancy Load Profile as a Proportion of Maximum Occupancy, and Total 

Occupants, by Hour .......................................................................................................... 29

Table 3-17 Daily Heat Gain Comparison-Reference....................................................... 30

Table 4-1 Construction Industry Outputs Mapped to BIRDS Building Types................ 35

Table 4-2 BIRDS Life-cycle Impact Assessment Calculations by Building Component 40

Table 4-3 BIRDS Normalization References .................................................................. 41

Table 4-4 Pairwise Comparison Values for Deriving Impact Category Importance 

Weights............................................................................................................................. 43

Table 4-5 Relative Importance Weights based on Science Advisory Board Study......... 44

Table 4-6 Relative Importance Weights based on BEES Stakeholder Panel Judgments 46

Table 4-7 Specified Insulation Types and R-Values ....................................................... 50

Table 4-8 Fiberglass Blanket Mass by Application......................................................... 52

Table 4-9 Fiberglass Insulation Constituents................................................................... 53

Table 4-10 Energy Requirements for Fiberglass Insulation Manufacturing ................... 53

Table 4-11 Blown Cellulose Insulation by Application .................................................. 54

Table 4-12 Cellulose Insulation Constituents.................................................................. 55

Table 4-13 B-Side Formulation – Material Constituent Percentages.............................. 57

Table 4-14 SPF Insulation Reference Unit Parameters for Original and BIRDS LCAs. 58

Table 4-15 Material Constituents for Open-Cell and Closed-Cell SPF Insulation.......... 59

Table 4-16 Mineral Wool Blanket Mass by Application................................................. 61

Table 4-17 Mineral Wool Insulation Constituents........................................................... 62

Table 4-18 Energy Requirements for Mineral Wool Insulation Manufacturing ............. 62

Table 4-19 XPS Foam Board Production Data................................................................ 64

Table 4-20 Raw Material Inputs to Produce Polyiso Foam............................................. 66

Table 4-21 Energy Inputs and Process Outputs for 1 Board-Foot Polyiso Foam............ 67

Table 4-22 Window Specifications.................................................................................. 69

Table 4-23 Dimensions and Main Parts of the Wood Clad Casement Window.............. 71

Table 4-24 Dimensions and Main Parts of the Aluminum Casement Window............... 71

Table 4-25 Dimensions and Main Parts of the Vinyl Casement Window....................... 71
xiv

Table 4-26 Dimensions and Main Parts of the Wood Clad Double Hung Window........ 72

Table 4-27 Dimensions and Main Parts of the Aluminum Double Hung Window......... 73

Table 4-28 Dimensions and Main Parts of the Vinyl Double Hung Window................. 73

Table 4-29 Natural Gas Furnace Bill of Materials........................................................... 78

Table 4-30 Electric Furnace Bill of Materials ................................................................. 79

Table 4-31 Furnace Manufacturing ................................................................................. 80

Table 4-32 Condenser Unit Bill of Materials .................................................................. 82

Table 4-33 Condenser Unit Masses................................................................................. 83

Table 4-34 Refrigerant Quantities ................................................................................... 83

Table 4-35 Electric Water Heater Bill of Materials......................................................... 85

Table 4-36 Gas Water Heater Bill of Materials............................................................... 86

Table 4-37 Performance of Lighting Technologies in BIRDS ........................................ 88

Table 4-38 Incandescent Light Bulb Bill of Materials .................................................... 89

Table 4-39 CFL Bill of Materials .................................................................................... 91

Table 4-40 LED Bill of Materials.................................................................................... 93

Table 4-41 Duct Tape Bill of Materials........................................................................... 96

Table 4-42 Exterior Sealant Bill of Materials.................................................................. 97

Table 5-1 Energy Efficiency Component Requirements for Alternative Building Designs

......................................................................................................................................... 102

Table 5-2 2009 SPV Discount Factors for Future Non-Fuel Costs, 3 % Real Discount 

Rate ................................................................................................................................. 103
xv

List of Acronyms

Acronym Definition

ABS acrylontrile-butadiene-styrene

ACH air changes per hour

AEO Applied Economics Office

AFUE annual fuel utilization efficiency

AHP Analytical Hierarchy Process

AHRI Air Conditioning, Heating, and Refrigeration Institute

AHS American Housing Survey

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers

BA Building America

BEA Bureau of Economic Analysis

BEES Building for Environmental and Economic Sustainability

BIRDS Building Industry Reporting and Design for Sustainability

C&D construction and demolition

CDD Cooling Degree Day

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
xvi

Acronym Definition

GDP gross domestic product

GWB Gypsum Wall Board

HBCD hexabromocyclododecane

HCFC hydrochlorofluorocarbon

HDD Heating Degree Day

HDPE high density polyethylene

HFC Pentafluoropropane

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

LBL Lawrence Berkeley 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 replacement

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

NBR number of bedrooms

NIST National Institute of Standards and Technology

N S Net Savings

PBDE polybrominated diphenyl ethers

PCR Product Category Rules
xvii

Acronym Definition

PCR Product Category Rules

PIB polyisobutylene

PIMA Polyisocyanurate Insulation Manufacturers Association

PM10 particulate matter less than 10 micrometers in diameter

pMDI polymeric methylene diphenyl diisocyanate

PNS Net LCC savings as a percentage of base case LCC

PP propylene

PUR Polyurethane

PV present value

PVC Polyvinyl chloride

SAB Science Advisory Board

SEER Seasonal Energy Efficiency Ratio

SHGC Solar Heat Gain Coefficient

SOC Survey of Construction

SPF spray polyurethane foam

SPV single present value

TCPP Tris(2-chloroisopropyl)phosphate

TRACI Tool for the Reduction and Assessment of Chemical and other environmental Impacts

UPV Uniform Present Value Discount Factor

UPV* Modified Uniform Present Value Discount Factor

VOC volatile organic compound

VT Visual Transmittance

XPS Extruded Polystyrene

XPSA Extruded Polystyrene Foam Association
xviii
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 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.

The approach developed for BEES has now been applied at the whole building 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 new sustainability measurement system to an extensive whole building 

performance database NIST has compiled for this purpose. The energy, environment, and cost 

data in BIRDS measure building operating energy use through 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 new 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. Similarly, 

the new residential building database incorporated into BIRDS v2.0 includes energy, 

environmental, and cost measurements for 9120 new residential buildings, covering 10 single 

family dwellings (5 one-story and 5 two-story of various conditioned floor area) in 228 cities 

across all U.S. states for study period length ranging from 1 year to 40 years. The sustainability 

performance of buildings designed to meet current state energy codes can be compared to their 

performance when meeting up to three user-selected alternative building energy standard 

editions to determine the impact of energy efficiency on sustainability performance. The impact 

of the building location and the investor’s time horizon on sustainability performance (economic 

and environmental) can also be evaluated.

1

 

1 A forthcoming user guide will include a detailed tutorial of how to use the BIRDS new residential database web 

interface to makes these different types of comparisons.
2

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 1987). In the context of sustainable 

development, needs can be thought to include the often-conflicting goals of environmental 

quality, economic well-being, 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.

This conflict is particularly apparent within the construction industry. Frequently, 

well-intentioned green development plans are not executed for economic reasons, and economic 

development plans 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 due to 

the current lack of applicable rigorous measurement methods.
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 problem-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, yet they would pay 

only $4000 to $5000, or about 2 %, more.2 More recent studies have shown an increase in this 

willingness to pay for more sustainable home designs. Kok and Kahn (2012) show that green 

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

non-labeled home. Aroul and Hansz (2012) estimate 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. There 

 

2

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

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 a different way of thinking about sustainability performance for 

buildings. In the BIRDS model, a unifying LCA framework developed for the U.S. economy is 

applied 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 wellestablished 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 bottom-up and top-down 

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

regularly-updated, top-down statistical data without truncation (Suh et al. 2004; Suh and Huppes

2005). The hybrid approach generally reduces the uncertainty of existing pure bottom up or pure 

top down systems: it helps reduce truncation error in the former and increases the resolution of 

the latter (Suh 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 the

newest energy code editions. As of November 2014, state energy code adoptions range across all 

editions of the International Energy Conservation Code (IECC) for Residential Buildings (2003

IECC, 2006 IECC, 2009 IECC, and 2012 IECC).3 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 residential building types, 

locations, and energy codes. The BIRDS database includes operating energy use predicted 

through energy simulation of 4 alternative building designs for 10 building types in 228 U.S. 

locations, with each design complying with some version of the IECC energy code. 

 

3

ICC (2003, 2006, 2009, 2012)
5

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

code 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 

database, as shown in Figure 2-1.

Environmental Energy Simulation

Life Cycle Assessment

Economic

Life Cycle Costing

Local Prices

•Construction

•Current Fuel Prices

•Fuel Price Projections

Function

Design

Size

Location

Energy Technologies

•HVAC

•Building Envelope

•Efficiency

Maintenance, Repair &

Replacement Schedules

Building Service Life

Energy Code

Climate

Building Type

•Low-Rise Residential

Sustainability

Performance

Economic

Performance

Environmental

Performance Energy

Performance

Materials Acquisition

Manufacturing

Transportation

Installation/Use

Service Life

End of Life

BIRDS Database

Fuel Type

•Heating

•Cooling

Building Specifications

Global Warming

Resource Use

Human Health

Water Pollution

Air Pollution

Figure 2-1 BIRDS Sustainability Framework

2.2 Establish Consistency

This new way of measuring building sustainability performance 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 9120 buildings included 

in the BIRDS new 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. 
6

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 period of time are the environmental and 

economic costs and benefits related to the capital investment decision of interest to the investor 

or policymaker? Since different stakeholders have different time perspectives, there is no one 

correct study period for developing a business case for 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 (Energy Independence and Security Act of 2007). Beyond 40 years, 

technological obsolescence becomes an issue, data become too uncertain, and the farther in the 

future, the less important the costs.

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 remaining 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 go into more detail regarding the modeling of the energy, environmental, 

and economic performance measures within the BIRDS new residential building database.
7

3 Energy Performance Measurement

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

of the BIRDS new residential database was built following the framework developed in Kneifel 

(2010) and further expanded in Kneifel (2011a) and Kneifel (2011b). The BIRDS new residential 

database includes the results of 9120 whole building energy simulations covering 4 energy 

efficiency designs for 10 single-family dwellings, 228 cities across the United States, and 40 

study period lengths.

3.1 Building Types

The building characteristics in Table 3-1 describe the 10 building types included in the BIRDS 

new residential database, which include 5 one-story and 5 two-story single-family detached 

dwellings of varying conditioned floor area to represent the distribution of new home 

construction in the United States.

The prototype buildings range in size from 111.9 m2(1205 ft2) to 420.2 m2(4523 ft2). The house 

dimension ratios are rectangular and the same at approximately 2.56:1 and 1.60:1 for the 1-story 

and 2-story prototypes, respectively. These alternative building sizes are based on the U.S. 

Census’ Survey of Construction (SOC) database (U.S. Census Bureau 2012). Figure A-1 and 

Figure A-2 show the percentile breakdown of the size of new single-family detached houses for 

one-story and two-story houses, respectively, both in frequency (left y-axis) and cumulative 

distribution (right y-axis).4 The building sizes selected for the residential prototype sizes attempt 

to represent the 10th, 30th, 50th, 70th, and 90th percentiles for each distribution.

All building prototypes are assumed to have wood-framing, 3 bedrooms, 2.4 m (8 ft) high 

ceilings, a roof slope of 4:12 (height:length) with 0.3 m (1 ft) overhangs on the north and south 

sides of the building, and no garage. The fraction of wall area covered by fenestration ranges 

from 13 % to 24 %.

 

4 Homes with less than 700 ft2

are assumed to have 700 ft2.
8

Table 3-1 Building Prototype Characteristics

Floors Conditioned Floor Area

m2(ft2)

Dimensions

m (ft) Fenestration

1 111.9 (1205) 6.61 x 16.89

(21.67x55.42) 15 %

1 148.6 (1600) 7.62 x 19.51

(25.0x64.0) 17 %

1 176.6 (1901) 8.31 x 21.26

(27.25x69.75) 18 %

1 215.8 (2323) 9.17 x 23.53

(30.1x77.21) 20 %

1 292.8 (3152) 10.67 x 27.43

(35.0x90.0) 24 %

2 148.8 (1602) 6.8x10.9

(22.37x35.8) 13 %

2 204.9 (2205) 8.00 x 12.80

(26.25x42.0) 15 %

2 251.2 (2704) 8.86 x 14.17

(29.07x46.5) 17 %

2 311.0 (3348) 9.85 x 15.78

(32.33x51.78) 19 %

2 420.2 (4523) 11.49 x 18.29

(37.7x60.0) 22 %

3.2 Building Designs

Current state energy codes are based on different editions of the International Energy 

Conservation Code (IECC), which have requirements that vary based on a building’s 

characteristics and the climate zone of the location. For the BIRDS new residential database, the 

IECC-equivalent design is used to meet current state energy codes and to define the alternative 

building designs. Table 3-2 shows that residential building energy codes as of November 2014 

vary by state. It is important to consider that local jurisdictions have adopted energy standard 

editions that are more stringent than the state energy codes.5

 

5 Local and jurisdictional requirements can be obtained from the Database of State Incentives for Renewables and 

Efficiency (DSIRE) (DSIRE 2014).
9

Table 3-2 Energy Code by State

Location Energy Code Location Energy Code Location Energy Code

AK None LA 2006 OH 2009

AL 2009 MA 2012 OK 2006

AR 2003 MD 2012 OR 2009

AZ None ME None PA 2009

CA 2012 MI 2009 RI 2012

CO 2003 MN 2006 SC 2009

CT 2009 MO None SD None

DE 2012 MS None TN 2006

FL 2009 MT 2009 TX 2009

GA 2009 NC 2009 UT 2009

HI 2006 ND 2009 VA 2012

IA 2012 NE 2009 VT 2009

ID 2009 NH 2009 WA 2012

IL 2012 NJ 2009 WI 2006

IN 2009 NM 2009 WV 2009

KS None NV 2009 WY None

KY 2009 NY 2009

Note: Some city ordinances require energy codes that exceed state energy codes.

Note: State codes as of December 1, 2011.

State energy codes vary from no state code to 2003 IECC to 2012 IECC with some regional 

trends shown in Figure 3-1. The states in the central U.S. tend to wait longer to adopt newer 

IECC editions. However, there are many cases in which energy codes of neighboring states vary 

drastically. For example, Missouri has no state energy code while of the 8 surrounding states, 1 

has no state energy code, 1 has adopted 2003 IECC, 2 have adopted 2006 IECC, 2 have adopted 

2009 IECC, and 2 have adopted 2012 IECC.
10

Figure 3-1 State Residential Energy Codes6

The prototype buildings are designed to meet the requirements for each of the editions of IECC

(2003, 2006, 2009, and 2012) in the 228 cities, which are shown in Figure 3-2 along with current 

climate zones used in defining IECC building requirements. These cities are selected for three 

reasons. First, the cities are spread out to represent the entire United States, and represent as

many climate zones in each state as possible. Second, the locations cover all the major 

population centers in the country. Third, multiple locations for a climate zone within a state are 

included to allow building costs to vary for each building design.

 

6 Figure was obtained from the DOE Building Technologies Program in November 2014 (DOE 2014).
11

Figure 3-2 Locations and Climate Zones

The climate zones implemented in the IECC were consolidated from the 17 climate zones shown 

in Table 3-3 for 2003 IECC into 8 climate zones in 2006 IECC. The consolidation is more 

complex than simply grouping the older climate zones together. The zones in Table 3-3 are based 

solely on a city’s heating degree days at a base of 18 °C (65 °F) (HDD65) while the zones in 

Figure 3-2 are based on the combination of a county’s heating degree days and cooling degree 

days at a base of 10 °C (50 °F) (CDD50). The change in the approach results in some cities 

located in the same climate zone in 2003 IECC being located in a different climate zone in 2006 

IECC. The new climate zones are further separated into subzones, “wet,” “dry,” and “marine,” as 

shown in Figure 3-2, for a total of 16 subzones.
12

Table 3-3 2003 IECC Climate Zones

Climate Zone

Zone HDD

1 0 to 499

2 500 to 999

3 1000 to 1499

4 1500 to 1999

5 2000 to 2499

6 2500 to 2999

7 3000 to 3499

8 3500 to 3999

9 4000 to 4499

10 4500 to 4999

11 5000 to 5499

12 5500 to 5999

13 6000 to 6499

14 6500 to 6999

15 7000 to 8499

16 8500 to 8999

17 9000 to 12 999

CDD50 = Annual Cooling Degree Days base 10 °C (50 °F)

HDD65 = Annual Heating Degree Days base 18 °C (65 °F)

3.3 Energy Simulation Design

The prototype residential building designs in this report are based on a number of sources, 

including Kneifel (2012) and Hendron and Engebrecht (2010). Additional resources are RS 

Means cost databases, U.S. Census and Energy Information Administration (EIA) housing stock 

data, and a collection of ASHRAE standards and IECC codes. The prototype buildings are 

designed in the E+ Version 7.1 simulation software.7

Ten prototypical residential building designs are documented in detail in this section: 5 one-story 

and 5 two-story single-family detached homes. The framework for these designs is IECC for 

Residential Buildings (ICC 2003, 2006, 2009, 2012). IECC code defines the thermostat control, 

window specifications, exterior envelope R-values, minimum lighting efficiency, maximum 

infiltration rates, minimum mechanical ventilation, internal and structural mass, and heating, 

ventilating, and air-conditioning (HVAC) system requirements.

 

7 Department of Energy (2012b)
13

Although IECC defines the general construction requirements, the code does not address the 

building’s plug loads, occupancy, or geometry. Also lacking are a number of small but important 

details and assumptions required to effectively simulate the energy use of a residential building.

One source of this additional information is the Building America House Simulation Protocols 

(Hendron and Engebrecht 2010), which is used for the annual loads, load profiles, and internal 

heat gains for lighting, occupancy, and domestic hot water.

Prototypical building sizes and geometries are selected based on three sources. The U.S. Census 

Bureau Survey of Construction (SOC) is used to determine the square footage for one-story and 

two-story residential detached buildings (SOC 2010). RSMeans Square Foot Costs (2011) are 

used to determine appropriate prototype building geometries.

Other codes and standards are needed to establish additional specifications: ASHRAE 90.2-2007

(ASHRAE 2007), ASHRAE 62.2-2010 (ASHRAE 2010), and the ASHRAE Fundamentals 

Handbook (ASHRAE 2009). Their use will be defined in detail where appropriate in the 

remainder of the document.

3.3.1 Building Envelope

Some building envelope assumptions are constant regardless of the building location’s climate 

while other assumptions are specific to the climate zones shown in Figure 3-2. The following 

two subsections will define each set of assumptions separately.

3.3.1.1 Non-Climate Zone-Specific Assumptions

The EnergyPlus 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 is shown in Table 3-4.

Table 3-4 Material Parameter Calculation Approach

Construction Pct. Frame Approach

Exterior Wall 23 % surface-weighted path fractions

Ceiling 11 % surface-weighted path fractions

Roof 11 % split into separate surfaces
14

The exterior wall is assumed to be 3.8 cm x 8.9 cm (1.5 in x 3.5 in) wood-framed 41 cm (16 in) 

on center (OC) with 9 cm (3.42 in) of fiberglass batt cavity insulation having a thermal resistance 

of 0.67 m2∙K/W (R 3.8 ft2∙0F∙h/Btu) per 2.5 cm (1 in). The material layers for the exterior wall 

are defined in Figure 3-3. The construction is made of 5 or 6 layers depending on IECC

requirements: wood siding, a felt air barrier, rigid insulation (if required), plywood sheathing, 3.8 

cm x 8.9 cm (1.5 in x 3.5 in) framing with batt insulation in wall cavity, and gypsum wall board 

(GWB).

Figure 3-3 Building Material Layers for Exterior Wall

The average conductance (U-factor) of the framing/insulation combination material is calculated 

by the Two-Dimensional U-Factor Calculation equation from 2009 ASHRAE Fundamentals 

Handbook (ASHRAE 2009). For this material, 23 % of the wall area is framing with the 

remaining 77 % being the wall cavity that is filled with cavity insulation.

For the 2-story prototypes, the interior floor/ceiling (first level ceiling/second level floor) 

construction is assumed to be 3.8 cm x 19.1 cm (1.5 in x 7.5 in) floor joists with 1.6 cm (5/8 in) 

plywood subflooring and 100 % carpet finish. The framing accounts for 13 % of the floor. No 

insulation is required for interior surfaces because there is no thermal benefit from restricting 

thermal transfer within the same zone.

The top floor ceiling is assumed to be 1.3 cm (0.5 in) GWB with 3.8 cm x 14.0 cm (1.5 in x 5.5 

in) ceiling joists. There is blown-in cellulose insulation in the open cavity plus any additional 

blown-in insulation as required by 2009 IECC. The amount of blown-in insulation varies by 

climate zone, and will be described in Section 3.3.1.2. The average material U-factor is 

calculated in the same manner as the exterior wall for the wood frame/cavity layer. Framing 

accounts for 11 % of the ceiling surface. The material layers for the ceiling are defined in Figure 

3-4.

Sheathing

Felt

Average of 

Insulation/ 

Wood Frame

GWB

Wood Siding

Rigid Insulation 

(if required)
15

Figure 3-4 Building Material Layers for Ceiling

The roof construction is assumed to be 3.8 cm x 14.0 cm (1.5 in x 5.5 in) rafters with 1.3 cm (0.5 

in) plywood sheathing, felt paper, and asphalt shingles. There is no insulation in the rafters, 

which makes it unnecessary to calculate an average U-factor. Instead the roof is split into two 

surfaces for energy simulation, one with framing (23 % of roof area) and one without framing 

(77 % of roof area). The material layers for the roof with framing are defined in Figure 3-5.

Figure 3-5 Building Material Layers for Roof

The foundation is a 10 cm (4 in) concrete slab. The floor finish is assumed to be 40 % carpet, 

40 % hardwood, 15 % vinyl tile, and 5 % ceramic tile (based on RSMeans Square Foot Costs

(2011)). Some climate zones require rigid insulation to be placed on the slab edge, which will be 

described in Section 3.3.1.2. The material layers for the slab are defined in Figure 3-6. The 

EnergyPlus Slab preprocessor is used to simulate the heat transfer between the ground and the 

slab, which recommends simulating horizontal insulation under the slab instead of vertical 

insulation on the edge of the slab for “slab-on-grade” foundations because the energy transfer 

that occurs on the vertical edges of the slab are significantly smaller than the energy transfer 

from the surface area contacting the ground. The Slab preprocessor assumes that the bottom of 

the slab is flush with the grade.

Blown-In 

Insulation

GWB

Average of 

Insulation/ 

Wood Frame

Shingles

Wood Frame

Sheathing

Felt
16

Figure 3-6 Building Material Layers for Slab-on-Grade Foundation

Window glazing area is assumed to be between 13 % and 24 % of conditioned floor area and is 

split between the four exterior walls based on wall area. Two 1.2 m (4 ft) high windows per story 

on each side of the house (8 in total) are assumed to be located equal distance from the wall 

edge. The width of the windows is based on the fraction of total wall area represented by each 

side. There are assumed to be two 0.9 m x 2.0 m (3 ft x 6 ft 8 in) x 4.4 cm (1.75 in) solid pine 

wood doors, located in the center of the wall on the north and south walls. Windows are defined 

in EnergyPlus using the “Simple Glazing System,” which requires only 3 parameters: U-factor 

(W/ (m2∙K) or Btu/ft2∙0F∙h), solar heat gain coefficient (SHGC), and visible transmittance (VT). 

These parameters vary by climate zone, and will be described in Section 3.3.1.2.

3.3.1.2 Climate Zone-Specific Assumptions

The exterior envelope performance requirements of IECC vary depending on the climate zone 

and edition of the code. The climate zones were redefined between 2003 IECC and 2006 IECC, 

which makes direct comparisons of building requirements difficult. The 2003 IECC requirements 

(shown in Table 3-5) for each location has been mapped to the 17 IECC climate zones defined in 

Section 3.2.

Floor Finish

Concrete

Rigid Insulation (IECC 

Requirement)

Rigid Insulation 

(Simulated in E+)
17

Table 3-5 2003 IECC Energy Code Requirements for Exterior Envelope

Building Envelope Requirements

Climate Zone Heating Degree Days U-Factor Ceiling

R-value

Wall

R-value

Slab

R-value

Slab

Depth

1 0 to 499 NR 2.3 (13) 1.9 (11) 0 (0) 0 (0)

2 500 to 999 5.1 (0.90) 3.4 (19) 1.9 (11) 0 (0) 0 (0)

3 1000 to 1499 4.3 (0.75) 3.4 (19) 1.9 (11) 0 (0) 0 (0)

4 1500 to 1999 4.3 (0.75) 4.6 (26) 2.3 (13) 0 (0) 0 (0)

5 2000 to 2499 3.7 (0.65) 5.3 (30) 2.3 (13) 0 (0) 0 (0)

6 2500 to 2999 3.4 (0.60) 5.3 (30) 2.3 (13) 0.7 (4) 0.6 (2)

7 3000 to 3499 3.1 (0.55) 5.3 (30) 2.3 (13) 0.7 (4) 0.6 (2)

8 3500 to 3999 2.8 (0.50) 5.3 (30) 2.3 (13) 0.9 (5) 0.6 (2)

9 4000 to 4499 (0.45) 6.7 (38) 2.3 (13) 0.9 (5) 0.6 (2)

10 4500 to 4999 (0.45) 6.7 (38) 2.8 (16) 1.1 (6) 0.6 (2)

11 5000 to 5499 (0.45) 6.7 (38) 3.2 (18) 1.1 (6) 0.6 (2)

12 5500 to 5999 2.3 (0.40) 6.7 (38) 3.2 (18) 1.6 (9) 1.2 (4)

13 6000 to 6499 2.0 (0.35) 6.7 (38) 3.2 (18) 1.6 (9) 1.2 (4)

14 6500 to 6999 2.0 (0.35) 8.6 (49) 3.7 (21) 1.9 (11) 1.2 (4)

15 7000 to 8499 2.0 (0.35) 8.6 (49) 3.7 (21) 2.3 (13) 1.2 (4)

16 8500 to 8999 2.0 (0.35) 8.6 (49) 3.7 (21) 2.5 (14) 1.2 (4)

17 9000 to 12 999 2.0 (0.35) 8.6 (49) 3.7 (21) 3.2 (18) 1.2 (4)

*NR = No Requirement

Note: R-value Units = m2∙K/W (ft2∙0F∙h/Btu); Depth Units = m (ft); U-factor Units = W/m2∙K (Btu/ft2∙0F∙h)

The values defined in 2003 IECC were simplified in Table 402.1.1 of Chapter 4: Residential 

Energy Efficiency of newer editions of IECC. Table 3-6 shows the window U-factor and solar 

heat gain coefficient (SHGC) requirements for 2006 IECC, 2009 IECC, and 2012 IECC. 

Although EnergyPlus requires three parameters for defining window performance (U-factor, 

SHGC, and visual transmittance), IECC only specifies two: U-factor and SHGC. In general, the 
18

U-factor and SHGC maximum requirements decrease in newer editions of IECC and as the 

climate zone gets colder.8

Table 3-6 2006, 2009, 2012 IECC Energy Code Requirements for Windows

Glazed Fenestration

U-Factor

(W/m2∙K)

SHGC

Climate Zone 2006 2009 2012 2006 2009 2012

1 6.8 (1.2) 6.8 (1.2) NR 0.40 0.30 0.25

2 4.3 (0.75) 3.7 (0.65) 2.3 (0.40) 0.40 0.30 0.25

3 3.7 (0.65) 2.8 (0.50) 2.0 (0.35) 0.40 0.30 0.25

4 except Marine 2.3 (0.40) 2.0 (0.35) 2.0 (0.35) NR* NR* 0.40

5 and 4 Marine 2.0 (0.35) 2.0 (0.35) 1.8 (0.32) NR* NR* NR

6 2.0 (0.35) 2.0 (0.35) 1.8 (0.32) NR* NR* NR

7 and 8 2.0 (0.35) 2.0 (0.35) 1.8 (0.32) NR* NR* NR

*NR = No Requirement

** Conversion: 5.678 W/m2∙K = 1 Btu/ft2∙0F∙h

Table 3-7 shows the ceiling and wall insulation R-value requirements for 2006 IECC, 

2009 IECC, and 2012 IECC. The minimum insulation R-value requirements for the exterior 

walls and ceilings increase for newer editions of IECC and as the climate zone gets colder. The 

exterior wall R-value ranges from RSI-2.3 (R-13) to RSI-3.5+0.9/ RSI-2.3+1.8 (R-20+5/R-13+10)

(wall cavity + exterior continuous). The first RSI-2.3 (R-13) is met with high density cavity 

insulation in the wall cavity. Additional R-value is met by adding rigid insulation to the exterior 

of the wall. The ceiling R-value requirement ranges from RSI-5.3 (R-30) to RSI-8.6 (R-49). 

Additional cellulose blown-in insulation is used to reach the required R-value.

 

8 Visible transmittance values are based on window characteristics defined in the ASHRAE Fundamentals 

Handbook (2009). 
19

Table 3-7 2006, 2009, 2012 IECC Energy Code Requirements for Exterior Envelope

Insulation R-Values

Ceiling Wall

Climate Zone 2006 2009 2012 2006 2009 2012

1 5.3 (30) 5.3 (30) 5.3 (30) 2.3 (13) 2.3 (13) 2.3 (13)

2 5.3 (30) 5.3 (30) 6.7 (38) 2.3 (13) 2.3 (13) 2.3 (13)

3 5.3 (30) 5.3 (30) 6.7 (38) 2.3 (13) 2.3 (13) 3.5 (20) or

2.3+0.9 (13+5)**

4 except 

Marine

6.7 (38) 6.7 (38) 8.6 (49) 2.3 (13)

2.3 (13) 3.5 (20)

or 2.3+0.9 (13+5)**

5 and 4 Marine 6.7 (38) 6.7 (38) 8.6 (49) (19) or

2.3+0.9 (13+5)**

3.5 (20) or

2.3+0.9 (13+5)**

3.5 (20)

or 2.3+0.9 (13+5)**

6 8.6 (49) 8.6 (49) 8.6 (49) (19) or

2.3+0.9 (13+5)**

3.5 (20) or

2.3+0.9 (13+5)**

3.5+0.9 (20+5)

or 2.3+1.8 (13+10)**

7 and 8 8.6 (49) 8.6 (49) 8.6 (49) 3.7 (21) 3.7 (21) 3.5+0.9 (20+5)

or 2.3+1.8 (13+10)**

*NR = No Requirement

**Internal Cavity Insulation + Exterior Continuous Insulation R-values

Note: R-value Units = m2∙K/W (ft2∙0F∙h/Btu)

Table 3-8 shows the slab edge insulation requirements (R-value and depth) for 2006 IECC, 

2009 IECC, and 2012 IECC, which are the same for all 3 editions of IECC. RSI-1.8 (R-10)

insulation under the foundation is required in Zone 4 through Zone 8 only, which is met by 

adding 5.1 cm (2 in) of extruded polystyrene (XPS) below the slab.

Table 3-8 2006, 2009, 2012 IECC Energy Code Requirements for Foundation

Insulation R-Values

Slab Edge Slab Depth

Climate Zone 2006 2009 2012 2006 2009 2012

1 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

2 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

3 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

4 except 

Marine

1.8 (10) 1.8 (10) 1.8 (10) 0.61 m (2 ft) 0.61 m (2 ft) 0.61 m (2 ft)

5 and 4 Marine 1.8 (10) 1.8 (10) 1.8 (10) 0.61 m (2 ft) 0.61 m (2 ft) 0.61 m (2 ft)

6 1.8 (10) 1.8 (10) 1.8 (10) 1.21 m (4 ft) 1.21 m (4 ft) 1.21 m (4 ft)

7 and 8 1.8 (10) 1.8 (10) 1.8 (10) 1.21 m (4 ft) 1.21 m (4 ft) 1.21 m (4 ft)

Note: R-value Units = m2∙K/W (ft2∙0F∙h/Btu)
20

3.3.2 Heating, Ventilation, and Air Conditioning Equipment

The HVAC system in the prototype building design is a single-speed unitary system with an air 

conditioner with a seasonal energy efficiency ratio (SEER) rating of 13,9and a gas furnace with 

annual fuel utilization efficiency (AFUE) of 78 % (both current minimum federal energy 

efficiency requirements). No dehumidification option or economizer is included in the system. 

The supply fan is assumed to cycle, and has a total efficiency of 70 %. The supply fan motor 

efficiency is 90 %. The ductwork is assumed to be within the conditioned space, which leads to 

zero energy loss from the ductwork. This assumption assists in simplifying the model, and will 

be relaxed in future research to account for split systems and the resulting duct leakage.10 For the 

2-story prototypes, the first and second floors are assumed to be a single zone, with the HVAC 

equipment located on the first floor. The thermostat setpoints for all conditioned floor area are 

based on the standard reference designs defined in 2012 IECC, which are 23.9 °C (75 °F) 

cooling and 22.2 °C (72 °F) heating.

3.3.3 Outdoor Air Ventilation and Infiltration

Table 3-9 shows the maximum allowable building envelope infiltration rates for 2003 IECC, 

2006 IECC, 2009 IECC, and 2012 IECC. The requirements are defined as the air changes per 

hour at 50 Pascal (Pa). There were no infiltration testing requirements for 2003 IECC and 2006 

IECC.

Table 3-9 IECC Energy Code Requirements for Infiltration

Infiltration (Effective Leakage Area – ACH50)

Climate Zone 2003 2006 2009 2012

1 NR NR 7.0 5.0

2 NR NR 7.0 5.0

3 NR NR 7.0 3.0

4 except Marine NR NR 7.0 3.0

5 and 4 Marine NR NR 7.0 3.0

6 NR NR 7.0 3.0

7 and 8 NR NR 7.0 3.0

*NR = No Requirement

 

9 The 13 SEER rating converts to a Coefficient of Performance (COP) of 3.28 for the EnergyPlus simulation based 

on the following equation: COP = (−0.02 ∗ SEER2 + 1.12 ∗ SEER)/3.412.

10 The 2009 IECC prescriptive requirements include a duct tightness post-construction test showing duct leakage 

less than or equal to 226.5 L/min (8 CFM) per 9.29 m2(100 ft2) at 25 Pascal (Pa).
21

The prescriptive maximum air changes per hour at 50 pascals (ACH50) specified in Section 

402.4.2.1 of the 2009 IECC and 2012 IECC is not defined under normal operating conditions. 

The air infiltration rate must be converted into effective leakage area (ELA) to represent 

infiltration in the E+ simulation, which requires two conversions. First, the ACH50 is converted 

into cubic feet per minute at 50 pascals (CFM50) based on the following equation from Lawrence 

Berkeley Laboratory (LBL) (2012).

CFM50 = ACH50 ∗

Volume[ft3

]

60

Second, the following equation converts the CFM50 into an ELA.

ELA =

CFM50

2700 ∗

cm2

ft2

Example: 2009 IECC requirement (7.0 ACH50) for 1-story house with 148.6 m2(1600 ft2) of 

conditioned floor area and 12 800 ft3of conditioned space volume.

CFM50 = ACH50 ∗

Volume[ft3

]

60 = 7 ∗

12800

60 = 1493

ELA =

CFM50

2700 = 0.55 ft2 = 520 cm2

Previous editions of the IECC do not specify the air infiltration rate. The air infiltration rate for 

these prototypes will use the following equation from Hendron and Engebrecht (2010), where the 

ELA is the specific leakage area (SLA) as defined in the 2012 IECC standard reference building 

design multiplied by the conditioned floor area (CFA).11

ELA = SLA ∗ CFA

Table 405.5.2(1) of the 2009 IECC specifies that for “residences with mechanical ventilation that 

are tested in accordance with ASHRAE 119, Section 5.1, the measured air exchange rate 

combined with the mechanical ventilation rate...shall not be less than” the ASHRAE 62.2-2010

minimum outdoor air flow rate. 

The minimum air exchange rates for the 10 prototypes shown in Table 3-10 are calculated using 

ASHRAE 62.2-2010 equation 4.1b (liters per second):

0.05 ∗ m2 + 3.5 ∗ (NBR + 1)

Where NBR = number of bedrooms

 

11 All values in the same units.
22

Table 3-10 62.2-2010 Minimum Air Exchange Rate Requirements

Floors

Conditioned 

Floor Area

m2(ft2)

Bedrooms

Conditioned 

Volume

m3(ft3)

62.2-2010

Air Exchange Rate

L/s CFM ACH

1 111.9 (1205) 3 273 (9636) 19.6 41.5 0.15

1 148.6 (1600) 3 362 (12 796) 21.4 45.4 0.13

1 176.6 (1901) 3 431 (15 207) 22.8 48.4 0.11

1 215.8 (2323) 3 526 (18 583) 24.8 52.5 0.10

1 292.8 (3152) 3 714 (25 213) 28.6 60.7 0.09

2 148.8 (1602) 3 363 (12 813) 21.4 45.4 0.13

2 204.9 (2205) 3 500 (17 644) 24.2 51.4 0.10

2 251.2 (2704) 3 613 (21 631) 26.6 56.3 0.09

2 311.0 (3348) 3 758 (26 781) 29.6 62.6 0.08

2 420.2 (4523) 3 1025 (36 184) 35.0 74.2 0.07

For a 148.6 m2 (1600 ft2), 3 bedroom house, the total outdoor air rate is 0.021 m3/s (45 CFM), 

which converts to 0.13 ACH. The 2009 IECC maximum air infiltration rate of 7.0 ACH50 can be 

converted into a relative natural air change per hour (ACHNAT) with the following equation from 

Environmental Protection Agency (EPA) (2001), where the LBL Factor ranges from 14 to 26 

and the Location Factor ranges from 0.72 to 1.00.

ACHNAT =

ACH50

LBL Factor ∗ Location Factor

The ACHNAT will range between 0.27 and 0.50 for a 2009 IECC design. Since the air infiltration 

is always greater than the minimum ventilation rate defined in ASHRAE 62.2-2010, no 

mechanical ventilation is required.

The 2012 IECC requirements of 5.0 ACH50 or 3.0 ACH50 lead to natural infiltration rates that are 

lower than the ASHRAE 62.2-2010 minimum outdoor air requirements (0.19 ACH or 0.50 ACH 

and 0.12 ACH to 0.30 ACH, respectively). As a result, the estimated infiltration from leakage is 

nearly always greater than the ASHRAE 62.2 required levels, and mechanical ventilation is not 

likely to be required to reach the outdoor air requirements.

Based on ASHRAE 90.2-2007, an additional 0.15 ACH is added to the model for occupant 

activity, such as window and door operation and running exhaust fans.12

Based on Hendron and Engebrecht (2010) and the 2012 IECC, the attic ventilation for a standard 

reference building design is used in the model, which is equivalent to 1 unit per 300 units of the 

ceiling area, which converts to an effective leakage area (ELA) of 4955 cm2(5.33 ft2) for a 

148.6 m2 (1600 ft2) house.

 

12 According to the ASHRAE Fundamentals Handbook, “In residences...air exchange associated with a door can be 

estimated based on air leakage through cracks between door and frame.”
23

3.3.4 Domestic Hot Water

The BA Protocol functions from Hendron and Engebrecht (2010), defined in Table 3-11, are 

used instead of IECC requirements for a standard reference building design to estimate the hot 

water consumption of each piece of equipment and activities. Total hot water consumption based 

on the BA Protocol is 228 liters (60 gallons) for all prototypes. These estimates are used for the 

analysis because they break down consumption by end use; the IECC estimate is for total hot 

water consumption only. The clothes washer and dishwasher use only hot water at 48.9°C 

(120°F) while the showers, baths, and sinks use hot water that is tempered to 43.3°C (110°F), as 

shown in Table 3-11.

Table 3-11 Hot Water Consumption

Equipment Water Temp. Function 

Liters (Gallons)

Liters (Gallons) 

Per Day

Clothes Washer 48.9 °C (120 °F) 8.9+3.0·NBR (2.35+0.78·NBR) 19 (5)

Dishwasher 48.9 °C (120 °F) 8.6+2.8· NBR (2.26+0.75·NBR) 19 (5)

Showers 43.3 °C (110 °F) 53.0+17.7· NBR (14+4.67·NBR) 68 (18)

Baths 43.3 °C (110 °F) 13.2+4.4· NBR (3.5+1.17·NBR) 27 (7)

Sinks 43.3 °C (110 °F) 47.3+15.7· NBR (12.5+4.16·NBR) 95 (25)

Total: 228 (60)

The BA Protocol total water use, daily water use load profiles, and sensible (temperature-based) 

and latent (humidity-based) loads for hot water use are used to estimate the real time hot water 

use and the resulting internal heat gains. The daily load profile, or proportion of peak flow rate 

by hour of the day, for each hot water use category can be seen in Figure 3-7.
24

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

Based on the BA Protocol, internal heat gains from hot water for the clothes washer and 

dishwasher are assumed to be zero for both sensible and latent loads. Any heat gains for this 

equipment are included in the equipment electricity use heat gains. The sensible and latent load 

fractions for shower, bath, and sinks are reverse engineered from internal heat gains estimated 

using the BA Protocol functions in Table 3-12.

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9 11 13 15 17 19 21 23

Clothes Washer

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9 11 13 15 17 19 21 23

Dish Washer

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9 11 13 15 17 19 21 23

Bath

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9 11 13 15 17 19 21 23

Shower

0.0

0.5

1.0

1 3 5 7 9 11 13 15 17 19 21 23

Sinks
25

Table 3-12 Domestic Hot Water Daily Internal Heat Gains

Equipment 

End Use

Sensible Load Latent Load

Heat Gain

Function

kWh (Btu)

Heat Gain

kWh (Btu)

Load 

Fraction

Heat Gain 

Function

kWh (Btu)

Heat Gain

kWh (Btu)

Load 

Fraction

Showers 0.22+0.07·NBR

(741+247·NBR)

0.4 (1482) 0.1947 0.21+0.07· NBR

(703+235·NBR)

0.4 (1408) 0.0055

Baths 0.05+0.02· NBR

(185+62·NBR)

0.4 (1371) 0.7262 0 (0) 0 (0) 0.0000

Sinks 0.09+0.03·NBR

(310+103·NBR)

0.2 (619) 0.0917 0.04+0.01· NBR

(140+47·NBR)

0.1 (281) 0.0012

Based on IECC, the water heater must meet federal energy efficiency requirements. The water 

heater is assumed to be a gas water heater with a perfectly mixed storage tank and an efficiency 

of 76 %,13 a firing rate based on the number of bedrooms and bathrooms,14 and a setpoint 

temperature of 48.9 °C (120 °F). The heat loss coefficient from the water heater to the ambient 

temperature in the internal space is assumed to be 2.1 W/K.15

3.3.5 Lighting

The lighting load profiles and electricity consumption are based on the simplest profile in the BA 

Protocol, which varies by time of day, but not from day to day. Total electricity use is estimated 

based on the two-stage process defined in the BA Protocol as expressed in Table 3-13. The 

baseline lighting estimate is based on 66 % incandescent, 21 % compact fluorescent (CFL), and 

13 % T-8 linear fluorescent use. The lighting requirements in 2009 IECC require 50 % high 

efficiency lights for all hard-wired lighting, which is met by shifting 16 % of all hard-wired 

lighting from incandescent to CFL. An additional 25 % of incandescent lighting must be shifted 

to high efficiency lights for all hard-wired lighting to meet the 2012 IECC requirement of 75 %. 

The BA Protocol adjustment functions shown on the right in Table 3-13 are used to adjust the 

total electricity consumption estimate to represent the greater efficiency required by 2009 IECC

(50 % efficient lighting fixtures) and 2012 IECC (75 % efficient lighting fixtures).

 

13 Minimum federal efficiency requirement for gas water heaters.

14 Based on the BA Benchmark for a 3 bedroom/2 bathroom house with a gas unit.

15 Based on NIST expert recommendation.
26

Table 3-13 Annual Lighting Electricity Consumption

Lighting Use 

Category

Baseline Efficiency Conversion

Function Function

Hard-wired 0.8·(CFA·0.542+334)

Baseline·{[(FINC+0.34) + (FCFL-0.21)·0.27 + 

(FLF-0.13)·0.17]·SAF·0.9+0.1}

Plug-In 0.2·(CFA·0.542+334)

Exterior CFA·0.145 Baseline·{[(FINC+0.34) + (FCFL-0.21)·0.27 + 

(FLF-0.13)·0.17]·0.9+0.1}

FINC = Fraction incandescent

FCFL = Fraction compact fluorescent

FLF = Fraction linear fluorescent

SAF = Smart replacement algorithm factor = 1.1·FInc

4

-1.9· FInc

3+1.5· FInc2

-0.7· FInc+1

The load profile (proportion of peak wattage in use) for interior lighting can be seen in Table 

3-14. The total wattage parameter for lighting in E+ is reverse engineered from the peak 

consumption load profile and the total electricity consumption estimate. The load profile for 

exterior lighting assumes the lights are on sunset to sunrise.

Table 3-14 Lighting Load Profile as Proportion of Peak Wattage in Use, By Hour

Hour Load Profile Hour Load Profile

1:00 0.067 13:00 0.119

2:00 0.067 14:00 0.119

3:00 0.067 15:00 0.119

4:00 0.067 16:00 0.206

5:00 0.187 17:00 0.439

6:00 0.394 18:00 0.616

7:00 0.440 19:00 0.829

8:00 0.393 20:00 0.986

9:00 0.172 21:00 1.000

10:00 0.119 22:00 0.692

11:00 0.119 23:00 0.384

12:00 0.119 24:00 0.16

Monthly multipliers defined in Hendron and Engebrecht (2010) are used to account for the 

variation across the year in lighting use. The load profile in Table 3-14 is adjusted by the 

multiplier for each month.
27

3.3.6 Appliances and Miscellaneous Electrical Loads

The electrical equipment in the house includes a refrigerator, clothes washer, clothes dryer, 

dishwasher, electric range, and a combination of variable miscellaneous electrical loads (MELs). 

MELs encompass electronics such as computers, televisions, stereos, and other plug loads. The 

electricity consumption for each category is based on the BA Protocol functions seen in Table 

3-15. Electricity consumption is usually a function of the number of bedrooms in the home 

(NBR). The only category that will see a change in electrical consumption over the 10 prototypes 

is MELs because it includes the conditioned floor area (CFA).

Table 3-15 Appliance and MEL Electricity Consumption

Equipment Function kWh

Refrigerator Constant 434

Clothes Washer 38.8+12.9·NBR 78

Clothes Dryer 538.2+179.4· NBR 1076

Dishwasher 87.6+29.2· NBR 175

Electric Range 250+83· NBR 499

Variable MELs 1703+ (266· NBR) + (0.454·CFA) Varies

Source: Hendron and Engebrecht (2010)

The BA Protocol load profiles in Figure 3-8 create a real-time variation in electricity 

consumption for each category. Appliances such as the electric range and dishwasher have a 

significant variation in time-of-day use while refrigerator use only varies slightly over a day. The 

variations are implemented through “schedules” in E+.
28

Figure 3-8 Electrical Equipment Load Profiles as a Proportion of Peak Wattage, By Hour

3.3.7 Internal Mass

Instead of defining individual interior surfaces and furniture, internal mass is estimated based on 

ASHRAE 90.2-2007 recommendations. There is assumed to be 3.6 kg (8 lb) of 5 cm (2 in) thick 

wood per square foot of conditioned space for contents. There is assumed to be 2.3 kg (5 lb) of 

1.3 cm (0.5 in) gypsum wall board (GWB) per square foot of conditioned space for the internal 

structure. 

E+ estimates internal mass in terms of square meters of material open to the zone air. Based on 

the total weight of the two materials (wood and GWB) and their weight per unit of thickness, it is 

possible to determine the comparable surface area needed to match the amount of internal mass 

required. Assuming the wood density is equivalent to that of white oak, 753 kg/m3(47 lb/ft3), the 

0.00

0.02

0.04

0.06

0.08

0.10

1 3 5 7 9 11 13 15 17 19 21 23

Clothes Washer

0.00

0.05

0.10

0.15

1 3 5 7 9 11 13 15 17 19 21 23

Dishwasher

0.00

0.02

0.04

0.06

1 3 5 7 9 11 13 15 17 19 21 23

Refrigerator

0.00

0.02

0.04

0.06

0.08

0.10

1 3 5 7 9 11 13 15 17 19 21 23

Clothes Dryer

0.00

0.05

0.10

0.15

0.20

1 3 5 7 9 11 13 15 17 19 21 23

Range

0.00

0.02

0.04

0.06

0.08

1 3 5 7 9 11 13 15 17 19 21 23

MELs
29

5 cm (2 in) thick wood weighs 38 kg/m2(7.833 lb/ft2). Assuming GWB density of 10 kg/m2

(2.08 lb/ft2), a total of 357.3 m2(3846 ft2) of structural mass is required to meet the standard 

recommendation.

3.3.8 Occupancy

Occupancy is based on the BA Protocol constant daily occupancy load profile seen in Table 

3-16. The profile changes over the hours of the day, but does not vary across days. The house is 

treated as a single zone with an average internal heat gain of 113 watts per person 

(385.8 Btu/person/hr). The average number of people varies between all three family members 

being at home during the night to only 0.73 people present during the middle of the day.

Table 3-16 Occupancy Load Profile as a Proportion of Maximum Occupancy and Total 

Occupants, by Hour

Hour Load Profile People Hour Load Profile People

1:00 1.000 3.00 13:00 0.242 0.73

2:00 1.000 3.00 14:00 0.242 0.73

3:00 1.000 3.00 15:00 0.242 0.73

4:00 1.000 3.00 16:00 0.242 0.73

5:00 1.000 3.00 17:00 0.295 0.89

6:00 1.000 3.00 18:00 0.553 1.66

7:00 1.000 3.00 19:00 0.897 2.69

8:00 0.883 2.65 20:00 0.897 2.69

9:00 0.409 1.23 21:00 0.897 2.69

10:00 0.242 0.73 22:00 1.000 3.00

11:00 0.242 0.73 23:00 1.000 3.00

12:00 0.242 0.73 24:00 1.000 3.00

3.3.9 Internal Heat Gains

Internal heat gains originate from domestic hot water use, electrical equipment use, lighting, and 

human occupancy. Previous sections have addressed specific internal heat gain categories, but it 

is of interest to summarize these heat gains and determine if they are comparable to the IECC

standard reference design specifications. The total internal heat gains specified by IECC are 

based on the following equation from the 2012 IECC standard reference design specifications:

IGain = 17 900 + 23.8 ∙ CFA + 29.565 ∙ (NBR + 1)

For the 1-story, 148.6 m2(1600 ft2) prototype, total internal heat gains based on IECC, IGain, 

should be 20.0 kWh/day (68 292 Btu/day) for the one-story prototype. The internal heat gains 

resulting from each category based on an EnergyPlus simulation of the prototype are shown in 

Table 3-17. The simulated internal heat gains of 19.0 kWh/day (64 905 Btu/day) are comparable, 
30

though slightly lower, than the IECC standard reference design (5 %). The minimal difference 

shows that the E+ model performs similar to the heat gain targets for IECC.

Table 3-17 Daily Heat Gain Comparison-Reference

Equipment BA Protocol

kWh (Btu)

2009 IECC

kWh (Btu)

Prototype Design 

kWh (Btu)

Lighting Not Specified Not Specified 2.7 (9266)

Equipment 11.2 (38 142) Not Specified 10.7 (36 511)

Occupancy 5.6 (19 140) Not Specified 5.6 (19 128)

Hot Water 1.2 (4161) Not Specified 1.3 (4420)

Total Not Specified 20.0 (68 292) 19.0 (64 905)

Note: Results are for the 1-story, 148.6 m2(1600 ft2) prototype
31

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 (ISO 2006). 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 next LCA step, impact assessment, 

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 combines 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, boundary setting 

requires expert judgment by the analyst. The construction of a building involves a number of unit 

processes (e.g., asphalt production for input to the manufacture 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 ever-expanding 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 non-negligible 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 found incomparable.

One important advantage of the BIRDS hybrid approach is that it addresses the bottom-up 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. 
32

Due to their long service lives, buildings are somewhat unique when it comes to the end-of-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, end-of-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 2002, the year of the most recent top-down data available from the 

U.S. Economic Census. For the bottom-up inventory data on building energy technologies, the 

most representative technology for which data are available is evaluated.

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. 
33

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 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 life-cycle

inventory data are gathered from the top-down and then systematically combined with bottom-up 

data into a comprehensive, hybrid life-cycle inventory 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, kg carbon dioxide, kWh primary energy consumption—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 buildings. The LCAs for the buildings are then completed by applying 

conventional methods of life-cycle impact assessment and interpretation to the hybrid inventory 

data. 

Top-Down Inventory Analysis. An economy’s accounting structure provides a cost-effective 

top-down approach to LCA inventory data collection. Many developed economies maintain 

economic input-output (I-O) 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 

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
34

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 does just 

that. 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). 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, and 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 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 environmentally-extended 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—the total value of the consumption of goods and services in a year—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” contribution to GDP, reflects its share of final demand.

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. 
35

U.S. 

Construction 

Industry

Inputs

Design & 

related 

services

Retail trade 

Wholesale trade Ready-mix concrete 

mfg Metal products

Trucking Plastics mfg 

Petroleum refining Machinery/ 

equipment rental

Fabricated 

structural prods

Etc.

Outputs

Offices

Hospitals 

Lodging

Education

SingleFamily

Highways

Etc.

Ready-mix 

concrete 

mfg 

Cement 

mfg

Mining 

machinery 

mfg

Sand, 

gravel, 

clay 

mining

Gypsum 

mfg

Lime mfg

Direct 

Inputs Indirect Inputs

Value 

Added 

Outputs

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 the residential new construction. The second corresponds 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 life-cycle environmental flows per 

dollar of construction.

Table 4-1 Construction Industry Outputs Mapped to BIRDS Building Types

Construction Type Industry Output

New Construction New Residential Construction

M&R Construction Residential maintenance and repair construction
36

One advantage of the BIRDS approach is the economic dimension built into the top-down 

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 lifecycle inventory. 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.

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 
37

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 TRACI (Tool for the Reduction and 

Assessment of Chemical and other environmental Impacts) 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 (Guinee 2002, Goedkoop 2009). 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. 

Some of the thermal radiation is absorbed by "greenhouse" gases in the atmosphere, principally 

water vapor, but also carbon dioxide, methane, the chlorofluorocarbons, and ozone. 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 otherwise. This phenomenon, which acts 

rather 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) 

attributable to humans. A general increase in temperature can alter atmospheric and oceanic 

temperatures, which can potentially lead to alteration of circulation and weather patterns. A rise 

in sea level is also predicted from an increase in temperature due to thermal expansion of the 

oceans and 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, 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 non-perfect markets (e.g., 

monopolies and subsidies). The primary energy consumption metric is used to account for the 

resource depletion aspect of fossil fuel extraction. 
38

Human Health—Criteria Air 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 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.

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 the potential of pollutants from industrial sources to harm landand water-based ecosystems. Its assessment is based on the global 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 

lack of oxygen and therefore death of species like fish. 

Land Use. This impact category measures the use of land resources 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—Noncancer 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 from 

industry and transportation are trapped at ground level where they react with sunlight. Smog 

leads to harmful impacts on human health and vegetation.
39

Acidification Potential. Acidifying compounds may in a gaseous state either dissolve in water or 

fix on solid particles. They reach ecosystems through dissolution in rain or wet deposition. 

Acidification affects trees, soil, buildings, animals, and humans. The two compounds principally 

involved in acidification are sulfur and nitrogen compounds. Their principal human source is 

fossil fuel and biomass combustion. Other compounds released by human sources, such as 

hydrogen chloride and ammonia, also contribute to acidification.

Ozone Depletion, or a 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.

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 life-cycle inventory 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.
40

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

ET=energy technology product

FU=functional unit*

i = building type, i=1 to 11

j=environmental impact, j=1 to 12

K=energy standard, K=1 to 5

n=study period length in years, n=1 

to 40

NG=natural gas

s=U.S. state, s=1 to 50

T = energy technology group, T=1 to 

6

*

x= building location, x=1 to 228

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 = (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 =  (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), roof insulation (ft2), windows (ft2), 

HVAC (no. of units), overhangs (ft2), and daylighting (ft2).

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, 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. 

The U.S. EPA has developed “normalization references” corresponding to its TRACI set of 

impact assessment methods (Bare et al. 2006). These U.S. data are updated and expanded for use 

in BIRDS. Shown in Table 4-3, these values quantify the U.S. economy’s annual contributions to 
41

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 buildingspecific 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.

Table 4-3 BIRDS Normalization References

Impact Category

Normalization 

reference

(U.S. total/yr)

Units

Global Warming 7.16E+12 kg carbon dioxide equivalents

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)

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 Noncancer 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)

Normalized BIRDS impact scores have powerful implications. By evaluating a building’s 

impacts with reference to their importance in a larger context, an impact to which one building 
42

design contributes little will not appear important when, by comparison, competing designs 

contribute even less to that impact.

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, 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 directly or as a starting point for 

developing user-defined weights. The alternative weights 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 1990, 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 noncancer effects)

• Medium-Risk Problems: ozone depletion, smog, acidification, eutrophication, human 

health—criteria air pollutants

The SAB did not explicitly consider primary energy consumption or water consumption. For this 

exercise, these impacts are assumed to be relatively medium-risk and low-risk problems, 

respectively, based on other relative importance lists. (Levin 1996).
43

Verbal importance rankings, such as “highest risk,” may be translated into numerical importance 

weights by following ASTM International standard guidance for applying a Multiattribute 

Decision Analysis method known as the Analytic Hierarchy Process (AHP) (ASTM 2011). The 

AHP methodology suggests the following numerical comparison scale:

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 process 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 noncancer 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
44

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 Noncancer 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.

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 
45

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 was evaluating the effect that this 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 these 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 ASTM (2011) and in Gloria et al. (2007).
46

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 Noncancer 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 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 the weights specific to 

panelist voting interest, and Figure 4-5 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.
47

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
48

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 life-cycle inventory 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 meet the 4 levels of energy efficiency based on the IECC for 

Residential Buildings simulated for each of 10 building types in 228 different U.S. locations. 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 ISO 14040 

(2006a) and the guidelines specified in ISO 14044 (2006b). 

4.5.1.2 Primary and Secondary Data Sources 
49

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 desire for organizations or companies to disclose 

data pertaining to their data. 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, company-specific 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 being 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 reader 

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 (NREL, 

2005-present, hereinafter referred to as “U.S. LCI Database”) and the ecoinvent Data v.2.2 

database (ecoinvent 2007) are the main sources of background data throughout the various lifecycle stages. Other sources of data are used where data were not available from 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 lifecycle 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:

 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. 
50

 Where 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 was 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-5.02

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).

Note 2: See the XPS foam documentation (Section 4.5.2.5) regarding the increase of thermal resistance 

with increasing foam thickness.
51

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 below. 

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 in BEES Online (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.
52

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.
53

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

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 come from ecoinvent. For the facing, 

Kraft paper comes 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 of 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)
54

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 Site through End-of-Life

Transportation of the insulation to the building site is modeled as an assumed 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 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. 

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 endof-life. End-of-life modeling includes transportation by heavy-duty diesel-fuel powered truck 

approximately 80 km (50 mi) to a construction & demolition (C&D) landfill. Insulation in a 

landfill is modeled based on ecoinvent end-of-life waste management process data. 

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 BEES Online (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
55

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, they are 

assumed to be 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. The raw materials are all assumed to 

be 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. Manufacturing energy is assumed to be 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 (1 800 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 
56

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. Insulation in the landfill 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 open-cell (lowdensity) and closed-cell (medium-density) SPF. Open-cell has a nominal density ranging from 

6.4 to 11.1 kg/m3(0.4 to 0.7 lb/ft3); 7.9 kg/m3(0.5 lb/ft3) has been assumed for the LCA. Opencell foam is formed using water as a reactive blowing agent. Water reacts with the A-side 

methylene diphenyl diisocyanate (MDI) to create 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 Rvalue ranging from RSI-0.07 to RSI-0.08 per cm (R-5.8 to R-6.8 per in); RSI-0.43 (R-6.2) has been 

assumed. Emissions from the HFC-245fa were included in the LCA, and the assumptions around 

its release are stated in this documentation. 

The information provided in this summary is based on a comprehensive LCA study on SPF 

(PE 2012). The quantitative data come mainly from a Spray Polyurethane Foam Alliance (SPFA)
57

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, Table 2-3) 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.

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.
58

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.
59

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. data compiled from PE International 

(2011). 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 comes from ecoinvent. Data for the HFC 245fa are based on 

stoichiometry and information from McCulloch (2010). 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.
60

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. 

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 B-side 

drums is modeled as a weighted industry average of 1287 km (800 mi) by heavy-duty dieselfueled truck. 

On-site application is nearly identical for all high-pressure SPF materials. The A- and B-side 

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 NA 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 as follows, according to the EPD: 

 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. 

SPF insulation is assumed to be disposed of in a landfill at end-of-lifeend-of-life. The foam is 

inert in the landfill, except for the release of the HFC-245fa, as stated above. End-of-life 
61

modeling includes transportation by heavy-duty diesel-fuel powered truck approximately 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. Some of the data on mineral wool 

insulation come from BEES Online (2010).

Thermal resistance values of RSI-2.3 (R-13) and RSI-3.4 (R-19) are used for BIRDS 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)). 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 added to seal the surface against dust production 

(Defra 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.
62

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, it 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. (BEES 

Online, 2010)

Table 4-18 Energy Requirements for Mineral Wool Insulation Manufacturing

Energy Source MJ/kg (Btu/lb)

Coke 6.38 (2 740)

Electricity 1.0 (430)

Total 7.38 (3 170)

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.
63

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 diesel-fuel powered 

truck approximately 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. 

4.5.2.5 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:16

 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 XPS foam board.

 

16 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.
64

Table 4-19 XPS Foam Board Production Data

Inputs Unit Quantity per kg (per lb)

Blowing agents HFC-134a kg (lb) 0.060 (0.060)

HFC-152a kg (lb) 0.017 (0.017)

CO2 kg (lb) 0.012 (0.012)

Solid additives PS resin kg (lb) 0.907 (0.907)

Additives kg (lb) 0.018 (0.018)

Energy Electricity kWh 1.00 (0.454)

Outputs

Air HFC-134a kg (lb) 0.0105 (0.0105)

HFC-152a kg (lb) 0.0029 (0.0029)

Waste Waste kg (lb) 1.0 E-4 (1.0 E-4)

The table presents the current representative blowing agent usage. It should be noted that HFCs 

began to replace 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 

exit 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.

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. (IPCC/TEAP 2005, Table 7.7) 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 are 805 km (500 mi) for 
65

polystyrene, HFC-134a, flame retardant, and CO2. 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.

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 year. (IPCC/TEAP, 

2005, Table 7.7) The blowing agent emission loss during the use phase is complex and nonlinear 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. 

(IPCC/TEAP 2005, Table 7.7)

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. Insulation 

in a landfill 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 %. 

(UNEP/TEAP 2005, Table 4.2)

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 
66

representatives at Bayer MaterialScience.17 The foam has a wet, or pre-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, 

2011). 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 Sec. 7), 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 

(wt)

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

 

17 Verbal communication with Bayer MaterialScience representatives, July 2013.
67

The MDI comes from the U.S. LCI Database. Data for the polyester polyol comes from an ecoprofile study of aromatic polyester polyols (PE International 2010). Data for Tris 

(2-chloroisopropyl) phosphate (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 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 an 

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 (1 500 mi) by rail; 

 Polyester polyol: 1 384 km (860 mi) by rail (90 %), 1 384 km (860 mi) by truck (10 %);

 Pentane: 2 414 km (1 500 mi) by truck; 

 Remaining materials: 1 609 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
68

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 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. Insulation in a landfill 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 

BIRDS residential tool evaluates double hung and casement windows with specifications 

complying with different energy code editions. Table 4-22 provides the U-factor 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.
69

Table 4-22 Window Specifications

U-Factor

W/m2∙K

(Btu /ft2∙0F∙h)

SHGC Window characteristics

Frame Type for 

Double Hung and 

Casement Windows

1.99 (0.35) Any 2 window panes, low-e coating, argon gas Wood-clad frame

2.27 (0.4) Any 2 window panes, low-e coating, argon gas Vinyl frame

2.56 (0.45) Any 2 window panes, tint Wood-clad frame

2.84 (0.5) 0.3 2 window panes, tint Wood-clad frame

2.84 (0.5) Any 2 window panes, tint Wood-clad frame

3.12 (0.55) Any 2 window panes, tint Wood-clad frame

3.41 (0.6) Any 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) Any 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) Any 1 window pane, no coating Wood-clad frame

5.11 (0.9) Any 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) Any 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 tool18 and individuals in industry. It is 

acknowledged that there are many window assembly options (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 Table 4-22 represent one of many options available for each. As such, the window assemblies 

presented here are not endorsed or preferred over any other type of window assembly for a 

residential home. 

 

18 Efficient Windows Collaborative tool, found at http://www.efficientwindows.org/selection.cfm.
70

BIRDS environmental performance data for the windows category was provided on a per 0.09 

m2(1 ft2) of a typical or common size of residential window. Since data were collected 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)
71

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 (10.76) 1 (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 (10.76) 1 (10.76) 1 (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 (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)
72

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 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
73

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 freighter and rail. The 
74

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, and data come from the U.S. 

LCI Database. The aluminum profile is modeled as 50 % primary and 50 % secondary 

aluminum, and data come 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 glass-reinforced polyamide, from ecoinvent, and PUR 

resin, 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, based on early 2000’s 

European processes and technologies. Due to 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 (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 % 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. 

Aluminum and PUR data come from the U.S. LCI Database. Data for the other IGU materials 

come from ecoinvent. Synthetic rubber is used as a general proxy for PIB. 
75

Other components raw materials. The jambliner, or the lining between the window sash and 

frame, is modeled as vinyl; data for PVC resin comes from the U.S. LCI Database and extrusion 

comes 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. Weatherstrip 

is described below. The sealant is assumed to be silicone-based. U.S. LCI Database provided the 

production data for PVC while the ecoinvent database provided the data for the remaining 

materials. 

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 is based on data from World Steel Association (2011), with steel profiles 

customized to U.S. using the U.S. electricity grid; steel product manufacturing, i.e. parts 

forming, comes from ecoinvent. Stainless steel is 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 comes 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. 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 this 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 rate of 0.1 % of the weight of glass.

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 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. 
76

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 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 later in this documentation in Section 4.5.7 and is modeled as being replaced 

every 15 years (Vigener 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 energyrelated 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 0-100 recycling methodology 

has been applied. For this, 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 lifecycle. 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. For more information on the 

approach to modeling metals at end-of-life, see Atherton (2006) 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 
77

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 

Proxy data were used because no bill of materials data were available for residential furnaces. 

Athena (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).19,20 These data were supplemented by 

Yang (2008), which provided data for a blower motor, replacing that of 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.

 

19 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. 

20 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
78

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.21 Data for the blower motor were provided by 

Yang (2008). Table 4-30 presents the bill of materials with data adjustments. 

 

21 Rheem Air Handlers, Form No. H11-524 REV. 8, Unit Dimensions & Weights, p.4, model 4221/4821.
79

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 which come from the U.S. LCI Database. Data for copper come from 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).

No manufacturing data were available for residential furnaces, so as proxy, an ecoinvent data set 

for a 10-kW (34-MBH) oil boiler was used (ecoinvent Report No. 6-IV 2007). The ecoinvent 

dataset description states that the 10 kW low-NOx or condensing boiler data may be applied for 

residential furnaces. (ecoinvent Report No. 6-IV, 2007, p.2) The production energy is stated to 
80

be estimated from a 1998 environmental report. In general, the data being estimated and of older 

vintage are considered to be not representative of current practice. Nonetheless, the use of these 

data was considered to be better than no data at all and 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). Data for a van come from ecoinvent. Unplanned service visits (i.e., 

unanticipated issues that require a service technician) are not included in the model, assuming 

that the home owner adequately follows the maintenance and care guidelines. 

A lifetime of 18 years has been assumed for both the gas and electric furnaces, based on NAHB 

(2007), a study on life expectancy of home components.22 

At the end-of-life, it is assumed that the air conditioners are sent for recycling to recover 

valuable metal (see Section 4.5.3.7 for the recycling methodology used). What cannot be 

recovered, i.e., the non-metal parts, are modeled as landfilled, and a distance of 48 km (30 mi) to 

the landfill in a heavy-duty diesel truck has been modeled. The landfill is modeled based on 

ecoinvent end-of-life waste management process data. 

 

22 NAHB (2007), Table 1, Section 15 gives furnaces a life expectancy range of 15 to 20 years.
81

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 Seasonal Energy Efficiency Ratios 

(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 watt-hours. (AHRI 2008, Def. 

3.9) 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 inside and outside units 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.
82

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 (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, Sec. 3.1.4)

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, Sec. 3.1.4) 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, Sec. 3.1.4)

Nylon polymer 0.10 0.22

Copper Wiring Copper wire 0.20 0.44

Total 54.20 119.49
83

De Kleine’s (2009) teardown was for a SEER 10, and he needed the bill of materials for the 

higher SEERs 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) Sec. 3.1.3 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, Sec 3.1.2) 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, Sec. 3.1.4). 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, Sec. 3.1.4).

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, assumed to be cold-rolled, and the galvanized steel come from World Steel 

Association (2011). Aluminum is modeled as a 50/50 mix of primary and secondary extruded 

aluminum which come from the U.S. LCI Database. Data for copper tube, sheet, and wire come 

from 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 (R125). Due to lack of available production data on both of these chemicals, proxies were 

implemented. Trifluoromethane (HFC-23) was used as a proxy for difluoromethane and 
84

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 an assumed average of 1 287 km 

(800 mi) by heavy-duty diesel fuel-powered truck. 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. (NAHB 2007, Table 1) During 

use, refrigerant is assumed to escape at a rate of 2 % per year of the total refrigerant. (De Kleine

2009, Sec. 3.3. 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 (see Section 4.5.3.7 for the recycling methodology used). 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. The landfill is based on ecoinvent waste 

management process data. A study prepared for Air-Conditioning, Heating, and Refrigeration 

Technology Institute (AHRTI, 2011) assumes an overall loss of 15% of the R-410a refrigerant. 

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, p.12)
85

4.5.5 Residential Electric and Gas Water Heaters

Electric and gas water heaters with 189-liter (50-gal) storage tanks were included in BIRDS. 

Upstream Materials Production through Manufacturing

Bill of materials data for both water heaters came from a comprehensive study by VHK (2007) 

on the eco-design of water heaters. Table B.9 in VHK (2007) provided bills of materials for 

several electric water heaters; the heater with 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, these data were supplemented by more detailed parts and materials provided 

by Table B.10 in VHK (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 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 VHK (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 

VHK (2007), the data for the 150 l (39.6 gal) tank was normalized on a mass basis. The data for 

the gas heating component was left the same.
86

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 storage 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 which come from the U.S. 

LCI Database. Data for copper sheet and wire come from 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), EPS, PUR foam, and enamel. 

Raw materials are modeled as transported to the manufacturing plant via diesel truck an assumed 

average distance of 805 km (500 mi). 
87

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.

Transportation to the Building Site through End-of-Life

Transportation of the equipment to the building site is modeled 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 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 gasolinepowered 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. (NAHB, 2007 Table 1, Section 1) At the end-of-life, it is assumed that the water 

heaters are sent for recycling to recover valuable metal (see Section 4.5.3.7 for the recycling 

methodology used). 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. 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. 
88

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 EERE (2012) were used to define the BIRDS’ lighting systems’ functional 

equivalency. Table 4.1 in 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, they used a lumen output available in 2011, 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

Upstream Materials Production through Manufacturing

The bills of materials for all three alternatives come from Defra (2009); these bills of materials 

are based on Ramroth (2008), with more detail/aggregation. 

Incandescent

The bill of materials shown in Table 4-38 was based on a 100 W (soft white) incandescent 

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
89

incandescents,23 but Table B-12 of Defra (2009) was used since its data are 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. 

Defra (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. 

Compact Fluorescent Lamp (CFL) 

The bill of materials in Table 4-39 was based on a 23 W Philips Marathon Mini CFL. Other data 

were available for an 18 W CFL24 which might have been preferable in terms of wattage 

 

23 See, for example, Parsons (2006). 

24 Ibid.
90

identified in Table 4-39, but Tables B.10-B.11 of Defra (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.
91

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 average mix of 
92

primary and secondary aluminum). Data for copper sheet and wire come from ICA (2012). The 

remaining data sets come from ecoinvent. Defra (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, Defra (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 Defra 

(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 Defra. 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 Defra (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.
93

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 ICA (2012). The 

remaining data sets, including the production of the LEDs, come from ecoinvent. Defra (2009) 

used proxy data for materials not available in ecoinvent or other publicly-available data or 
94

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 (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 (2009), pp.9-11, for more qualitative detail and schematics on the 

manufacture of the LED. Figure 6 in OSRAM (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.

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, 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. (EERE 2012, p.33; Defra 2009; Parsons 2006; 

RMI 2008) 

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 (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.” (EERE 2012, p.33) 

For Asia manufacturing, transportation includes the ocean freighter transport to a port in 

California, plus heavy duty diesel truck an assumed 2 414 km (1 500 mi), an average distance 

traveled from California to other parts of the United States. For U.S. manufacturing, transport is 

modeled an assumed average of 2 414 km (1 500 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 from the retail store 

has been included.
95

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), 80% landfill. A distance of 48 km (30 mi) in a heavy-duty diesel truck to the landfill 

or recycler has been assumed.

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.

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: 0.05-mm (2.0-mil) thick aluminum 

foil, synthetic rubber for the adhesive, and unbleached natural Kraft paper for the liner (Nashua, 

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, 

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
96

weighed 0.55 kg (1.2 lb), or 3.64 g (0.13 oz) per linear foot.25 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

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 one 

linear foot 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 quantities 

of each ingredient modeled were estimated based on best-guess assumptions (see Table 4-42).

 

25 Weight taken by Four Elements.
97

Table 4-42 Exterior Sealant Bill of Materials

Wt 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 electricity-only 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).
98

Transportation to the Building Site through End-of-Life

Transportation of the equipment to the building site is modeled an assumed average of 483 km 

(300 mi) by 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 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.
99

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 the 

life-cycle cost (LCC) method (ASTM 2012; Fuller and Petersen 1996). BIRDS follows the 

ASTM International standard method for life-cycle costing of building-related investments 

(ASTM 2010), which involves calculating a cost’s present value (PV) by discounting its future 

value into 2011 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, and data sources are 

described below.

5.1 First Cost

The first costs of a building are the total costs of constructing a building in a particular city. First 

costs include costs for labor, materials, equipment, overhead, and profit.

5.1.1 Approach

The construction costs for a prototype building are estimated by summing the costs of the 

baseline building (CNatAvg) and the changes in costs required to meet the prototype building 

design (ΔCx), adjusted for location-related cost variation as well as contractor and architectural 

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. A city construction cost index (IFG) (Faithful + Gould 2011) is used to adjust the sum of the 

costs for both the incremental building component costs and the baseline prototypical building. 

The formula below shows the indexed construction cost (CIndex) calculation.

CIndex = [(CNatAvg) + (ΔCHVAC) + (ΔCWall) + (ΔCCeiling) + (ΔCFoundation) + (ΔCLighting)

+ (ΔCSealant) + (ΔCWindow)] ∗ IFG

Where CIndex = Indexed construction costs

CNatAvg = National average construction costs

ΔCHVAC = Change in HVAC costs

ΔCWall = Change in wall insulation costs

ΔCCeiling = Change in ceiling insulation costs

ΔCFoundation = Change in foundation costs

ΔCLighting = Change in lighting costs

ΔCSealant = Change in air sealing costs

ΔCWindow = Change in window costs

IFG = “Faithful + Gould” residential construction cost index
100

Once the indexed construction costs of the building are calculated, it is necessary to adjust for 

the contractor and architect profits by multiplying the costs by the contractor “mark-up” rate 

(IM), assumed to be 25 %, and then the architectural fees rate (IA), assumed to be 7 %, as shown 

in the following equation. 

CFirst = (CIndexed ∗ (1 + IM)) ∗ (1 + IA

)

These mark-up rates are based on the default values used by the RS Means Square Foot Cost 

Estimator (SFCE). The marked-up, indexed construction costs are the first costs of constructing 

the prototype building in the particular city (CFirst).

5.1.2 Data

Building construction costs are obtained from two sources: RSMeans (2011) and 

Faithful + Gould (2012). The baseline costs of each prototypical building is estimated based on 

the average cost per unit of floor area for “average” construction quality in RSMeans (2011) for 

one-story and two-story single-family dwellings, which is a function of total floor area.

Figure 5-1 shows that the average cost per unit of floor area decreases as the total floor area 

increases for both one- and two-story single family dwellings. A power curve is fit to the 

available data points (base index of the cost per unit of floor area for the 1-story, 

148.6 m2(1000 ft2) house).26

Figure 5-1 Baseline Construction Costs27

 

26 Indexed to protect proprietary RSMeans data.

27 1 m2 = 10.764 ft2

50

60

70

80

90

100

110

120

130

140

150

500 1500 2500 3500 4500

Cost Index

Floor Area (ft2)

1-Story

2-Story
101

Incremental cost data from Faithful + Gould (2012) for each required energy efficiency measure 

are added to the baseline costs used to estimate the total first costs of a building that is compliant 

with each of the four energy efficiency design alternatives: 2003 IECC, 2006 IECC, 2009 IECC, 

and 2012 IECC. Six components – roof insulation, wall insulation, foundation insulation, air 

sealant, windows, and lighting – are changed to make the prototypical designs compliant with 

2003, 2006, 2009, and 2012 IECC. A summary of the requirement ranges (varying climate zone) 

for each building design are shown in Table 5-1. 

The cost data for windows is based on the cost per unit of area for “average casement window 

across sizes” in the F+G database (Faithful and Gould 2012). The lowest cost window that meets 

the maximum window characteristics (U-factor and solar heat gain coefficient (SHGC)) required 

by the building design is selected.

There are three different insulation values required for the BIRDS new residential prototypes: 

wall, ceiling, and foundation. The foundation insulation requirements include two values, the Rvalue of the insulation and the depth of the insulation. Based on these two values, a cost per 

linear unit is estimated and multiplied by the total perimeter of the prototype. The cost of ceiling 

insulation is estimated based on the cost per unit of area for a given R-value multiplied by the 

area of the top story ceiling. The wall insulation requirements are met using wall cavity 

insulation or a combination of wall cavity insulation and rigid exterior insulation. Costs per unit 

of floor area for installed insulation are treated as additive, and are multiplied by the net exterior 

wall area (gross wall area minus window area) to estimate the total installed cost of the 

insulation.

Infiltration rates defined in IECC are maximum requirements based on a blower door test at 

50 Pa. However, the cost data in Faithful+Gould (2011) are based on natural infiltration rates, 

which required conversion of ACH50 requirements to ACHNAT (see Section 3.3.3 for details). 

The cost data is mapped to the IECC requirements in the following manner:

ACHNAT = 0.35 → ACH50 = 7.0

ACHNAT = 0.16 to 0.25 → ACH50 = 5.0

ACHNAT ≤ 0.15 → ACH50 = 3.0

The fraction of hard-wired lighting that is high efficiency is adjusted to meet each edition’s 

requirements. The BA Protocol baseline lighting is assumed for IECC editions with no lighting 

requirement (see Section 3.3.5 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.
102

The capacity of the HVAC equipment varies based on the thermal load for a given building 

prototype, with all equipment meeting minimum federal requirements (SEER 13 for AC unit and 

80 % AFUE for gas furnace). Installed costs for AC units (2.0, 2.5, 3.0, 4.0, or 5.0 ton) and 

furnaces (40 MBH to 50 MBH, 60 MBH to 64 MBH, 78 MBH to 80 MBH, and 96 MBH to 100 

MBH) are selected based on the closest match to the “autosized” system in the E+ simulation.

Table 5-1 Energy Efficiency Component Requirements for Alternative Building Designs

Design 

Comp. Parameter Units 2003 IECC 2006 IECC 2009 IECC 2012 IECC

Ceiling 

Insulation R-Value m2

∙K/W 

(ft2∙°F∙h/Btu)

2.3 to 8.6

(13.0 to 49.0)

5.3 to 8.6

(30.0 to 49.0)

5.3 to 8.6

(30.0 to 49.0)

5.3 to 8.6

(30.0 to 49.0)

Wall 

Insulation R-Value m2

∙K/W 

(ft2∙°F∙h/Btu)

1.9 to 3.7

(11.0 to 21.0)

2.3 to 3.7

(13.0 to 21.0)

2.3 to 3.7

(13.0 to 21.0)

2.3 to 3.5+0.9/3.7+1.8

(13.0 to 20+5/13+10)

Foundation 

Insulation R-value m2

∙K/W 

(ft2∙°F∙h/Btu)

0.0 to 3.2

(0 to 18.0)

0 to 1.7

(0 to 10.0)

0 to 1.7

(0 to 10.0)

0 to 1.7

(0 to 10.0)

Depth m (ft) 0 to 1.2

(0 to 4.0)

0 to 1.2

(0 to 4.0)

0 to 1.2

(0 to 4.0)

0 to 1.2

(0 to 4.0)

Infiltration

Air 

Changes

Per Hour

ACH50 NR 7.0 7.0 3.0 to 5.0

Windows U-Factor

W/(m2∙K) 

(Btu/(h∙ft2∙°F))

1.99 to NR 

(0.35 to NR)

1.99 to 6.81

(0.35 to 1.2)

1.99 to 6.81

(0.35 to 1.2)

1.82 to NR

(0.32 to NR)

SHGC Fraction NR 0.30 to NR† 0.30 to NR† 0.25 to NR

Lighting

Fraction 

High 

Efficiency

Fraction NR NR 50 % 75 %

NR = No Requirement for one or more climate zones. By definition, the value of SHGC cannot exceed 1.0.

5.2 Future Costs

Future costs of a building include maintenance, repair, and replacement (MRR) costs as well as 

operational energy-related costs from electricity and natural gas consumption. Each of these is 

discussed below.

5.2.1 Approach

Building MRR costs are discounted to equivalent present values using the Single Present Value 

(SPV) factors for future non-fuel costs reported in Rushing, Kneifel, and Lippiatt (2011). These 

factors are calculated using the DOE Federal Energy Management Program (FEMP) 2011 real 
103

discount rate for federal energy conservation projects (3 %). Table 5-2 reports the SPV factors 

used to develop the BIRDS new residential database. The MRR costs for each year (CMRR,i) are 

multiplied by the SPV for that year and then summed and indexed to determine the total present 

value MRR costs (CMRR).

Table 5-2 2009 SPV Discount Factors for Future Non-Fuel Costs, 3 % Real Discount Rate

Yrs SPV Factor Yrs SPV Factor Yrs SPV Factor Yrs SPV Factor

1 0.971 11 0.722 21 0.538 31 0.400

2 0.943 12 0.701 22 0.522 32 0.388

3 0.915 13 0.681 23 0.507 33 0.377

4 0.888 14 0.661 24 0.492 34 0.366

5 0.863 15 0.642 25 0.478 35 0.355

6 0.837 16 0.623 26 0.464 36 0.345

7 0.813 17 0.605 27 0.450 37 0.335

8 0.789 18 0.587 28 0.437 38 0.325

9 0.766 19 0.570 29 0.424 39 0.316

10 0.744 20 0.554 30 0.412 40 0.307

The electricity and natural gas use predicted by the building’s energy simulation is used as the 

annual energy use of the building for each year of the selected study period. Electricity and 

natural gas prices are assumed to change over time according to U.S. Energy Information 

Administration forecasts from 2011 to 2041 (EIA 2013a, 2013b). These forecasts are embodied 

in the FEMP Modified Uniform Present Value Discount Factors for energy price estimates 

(UPV*) reported in Rushing, Kneifel, and Lippiatt (2011).28 Multiplying the annual electricity 

costs and natural gas costs by the associated UPV* value for the study period of interest 

estimates the present value total electricity costs (CElect) and natural gas costs (CGas). The 

discount factors vary by census region, end use, and fuel type.

Total present value future costs (CFuture) is the sum of present value location-indexed MRR costs 

and present value energy costs, as shown in the following equation:

CFuture = CMRR + CElect + CGas

5.2.2 Data

Residential building component and building lifetimes are based on three data sources: NAHB

(2007) for building components excluding lighting, Energy Star (2011) for light bulbs, and U.S. 

Census (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 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 2011). Additionally, NAHB (2007) 

 

28 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.
104

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 20 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 air conditioning units have assumed lifespans of 15 years

while the hot water heater is assumed to have a lifespan of 10 years. Incandescent and 

fluorescent light bulbs are assumed to have lifespans of 1 year and 7 years, respectively (Energy 

Star 2011).

MRR cost data are collected from two sources: U.S. Census (2011) and Faithful + Gould (2011). 

The total maintenance and repair costs per square foot of conditioned floor area 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 Table C-12-OO and Table C-15-OO in U.S. Census (2011), which reports median 

floor area and average maintenance and repair costs per unit of floor area for “Total OwnerOccupied Units” Housing Units and “New Construction the Past 4 Years” Housing Units, 

respectively. These two data points are 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. These costs are assumed to include all maintenance and repair costs associated with a 

single-family dwelling.

Figure 5-2 Baseline Maintenance and Repair Costs by Year

Faithful + Gould (2011) is the source of replacement costs for the individual components for 

which costs change across alternative building designs, which in this analysis are the HVAC 

system, lighting, and windows. The replacement cost of windows is assumed to be equal to the 

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

$/m2

Baseline M&R Costs [$/ft2]

...
105

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 (EPA 2012). 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.

Future MRR costs are discounted to equivalent present values using the Single Present Value 

(SPV) factors for future non-fuel costs reported in Rushing, Kneifel, and Lippiatt (2011), which 

are calculated using the U.S. Department of Energy's 2011 real discount rate for energy 

conservation projects (3 %). 

Annual energy costs are estimated by multiplying annual electricity and natural gas use predicted 

by the building’s energy simulation by the average state retail residential electricity and natural 

gas prices, respectively. Average state residential electricity and natural gas prices for 2011 are 

collected from the Energy Information Administration (EIA) Electric Power Annual State Data 

Tables (EIA 2013a) and Natural Gas Navigator (EIA 2013b), respectively. 

5.3 Residual Value

A building's residual value is its value remaining at the end of the study period. In life-cycle 

costing it is treated as a negative cost item. In BIRDS, it is estimated in four parts based on the 

approach defined in Fuller and Petersen (1996): the building (excluding HVAC, windows, and 

lighting), HVAC system, windows, and lighting. The building's residual value is calculated as 

the building's location-indexed first cost 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. For example, if a 

building has first costs (excluding HVAC, windows, and lighting) of $500 000, a 65 year service 

life, and the study period length is 10 years, the residual value of the building at the end of the 

study period (excluding HVAC, window, and lighting) is $500 000 ∙ (1 −

10

65) = $423 077.

Because they may be replaced during the study period, residual values for the HVAC, windows,

and lighting are computed separately. The remaining “life” of the HVAC equipment 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 residual value in year 20 is $12 000 ∙

10

15

= $8000. The residual 

value for the light bulbs and windows is computed in a similar manner.
106

The total residual value of the building and its HVAC, windows, and lighting, multiplied by the 

SPV factor for the number of years in the study period, 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:

CLCC = CFirst + CFuture − CResidual

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.

Two metrics are used to analyze changes in life-cycle costs: net LCC savings (ASTM 2009) and 

net LCC savings as a percentage of base case LCC. Net LCC savings (NS) is the difference 

between the base case LCC (CBase) and alternative design LCC (CAlt) as shown in the following 

equation:

NS = CBase − CAlt

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.

PNS =

NS

CBase

∙ 100
107

6 Limitations and Future Research

BIRDS is limited in scope and would be strengthened by including uncertainty analysis, 

expanding the database and metrics, and adding design flexibility to the tool.

Uncertainty analysis is needed for at least three elements of the analysis. First, consider the 

assumed discount rate. Although 3 % is a reasonable discount rate, in real terms, for federal 

government investment decisions, it may be too low of a value for an expected real return on an 

alternative investment in the private sector. Sensitivity analysis on the assumed discount rate is 

needed to determine the robustness of the cost results. Second, the current analysis assumes that 

building cooling loads are met by equipment running on electricity while heating loads are met 

by equipment running on natural gas, which is not the typical fuel mix for some areas of the 

nation. The database should be expanded to include alternative fuel source options, such as 

electricity in the South. Third, the BIRDS environmental impact scores do not incorporate 

uncertainty analysis as required by international standards (ISO 2006). 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.

Additional data are needed to refine and expand the BIRDS database. The 10 prototypical singlefamily dwellings analyzed in this study may not be representative of the entire building stock

because of the assumed simplicity of the building geometry. For this reason, the results should be 

considered as general magnitudes for making reasonable comparisons instead of precise 

quantitative analysis. Future research should include additional prototypes in the database. The 

state average energy cost rates do not control for local variation in energy tariffs. By using 

utility-level energy cost data, the accuracy of the estimates in BIRDS could be improved.

The analysis in this study ignores the impacts that variations in occupancy and behavior patterns, 

in terms of plug and process load use, thermostat setpoints, and number of occupants, and HVAC 

equipment efficiency have on the reductions in energy use. Homes with greater plug and process 

loads will realize smaller percentage changes in energy use because the energy efficiency 

measures considered in this study focus on the IECC requirements (building envelope and 

lighting) holding constant the efficiency of other equipment (HVAC equipment and appliances) 

used in the building and the number of occupants living in the building. Future research should 

consider the impact that improved equipment efficiency and occupant activity may have on the 

overall energy savings realized by energy efficiency improvements to buildings.

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 life-cycle inventory data for prototypical 

buildings. The baseline data for these buildings represent status quo building technologies as of 
108

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 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.

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., 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. 

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. 

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.

Analysis based on the energy performance is susceptible to the limitations of current whole 

building energy simulation models. As the underlying simulation software improves in accuracy 

and uncertainty, the sustainability analysis related to the modeling will improve as well.
109

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, which may affect energy, environmental, and economic considerations. 
110
111

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A Appendix

Figure A-1 Conditioned Floor Area of New 1-Story Single-Family Housing

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Figure A-2 Conditioned Floor Area of New 2-Story Single-Family Housing

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