Technical ReportPDF Available

Life cycle primary energy and carbon analysis of recovering softwood framing lumber and hardwood flooring for reuse

Abstract and Figures

Within the green building fields is a growing movement to recover and reuse building materials in lieu of demolition and land fill disposal. However, they lack life-cycle data to help quantify environmental impacts. This study quantifies the primary energy and greenhouse gas (GHG) emissions released from the production of wood recovered from an old house and from new wood harvested from the forest and produced in a sawmill with both products ending up installed in a new house. In addition, the study quantifies the primary energy and GHG emissions released if the recovered wood is not reused but instead is either burned to replace coal or natural gas to generate electricity, landfilled with or without landfill gas capture equipment, ground into mulch, or some combination.
Content may be subject to copyright.
Life-Cycle Energy and
GHG Emissions for New
and Recovered Softwood
Framing Lumber and
Hardwood Flooring
Considering End-of-Life
Scenarios
Richard D. Bergman
Robert H. Falk
James Salazar
Hongmei Gu
Thomas R. Napier
Jamie Meil
United States
Department of
Agriculture
Forest Service
Forest
Products
Laboratory
Research
Paper
FPL–RP–672
April 2013
Bergman, Richard D.; Falk, Robert H.; Salazar, James; Gu, Hongmei;
Napier, Thomas R.; Meil, Jamie. 2013. Life-cycle energy and GHG emis-
sions for new and recovered softwood framing lumber and hardwood
ooring considering end-of-life scenarios. Research Paper FPL-RP-672.
Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Prod-
ucts Laboratory. 33 p.
A limited number of free copies of this publication are available to the
public from the Forest Products Laboratory, One Gifford Pinchot Drive,
Madison, WI 53726–2398. This publication is also available online at
www.fpl.fs.fed.us. Laboratory publications are sent to hundreds of libraries
in the United States and elsewhere.
The Forest Products Laboratory is maintained in cooperation with the
University of Wisconsin.
The use of trade or rm names in this publication is for reader information
and does not imply endorsement by the United States Department of
Agriculture (USDA) of any product or service.
The USDA prohibits discrimination in all its programs and activities on the
basis of race, color, national origin, age, disability, and where applicable,
sex, marital status, familial status, parental status, religion, sexual orienta-
tion, genetic information, political beliefs, reprisal, or because all or a part
of an individual’s income is derived from any public assistance program.
(Not all prohibited bases apply to all programs.) Persons with disabilities
who require alternative means for communication of program informa-
tion (Braille, large print, audiotape, etc.) should contact USDA’s TARGET
Center at (202) 720–2600 (voice and TDD). To le a complaint of discrimi-
nation, write to USDA, Director, Ofce of Civil Rights, 1400 Independence
Avenue, S.W., Washington, D.C. 20250–9410, or call (800) 795–3272
(voice) or (202) 720–6382 (TDD). USDA is an equal opportunity provider
and employer.
Abstract
Within the green building fields is a growing movement
to recover and reuse building materials in lieu of demoli-
tion and land fill disposal. However, they lack life-cycle
data to help quantify environmental impacts. This study
quantifies the primary energy and greenhouse gas (GHG)
emissions released from the production of wood recovered
from an old house and from new wood harvested from the
forest and produced in a sawmill with both products ending
up installed in a new house. In addition, the study quanti-
fies the primary energy and GHG emissions released if the
recovered wood is not reused but instead is either burned to
replace coal or natural gas to generate electricity, landfilled
with or without landfill gas capture equipment, ground into
mulch, or some combination.
Keywords: energy, carbon emissions, reuse, softwood fram-
ing lumber, hardwood flooring, life-cycle, life-cycle inven-
tory, LCI, end-of-life, EOL
Acknowledgments
U.S. Forest Service Global Change Research (Agreement
No. 09-JV-11111133) funded this work. The authors thank
the following reviewers: James L. Bowyer (Professor
Emeritus, Department of Bioproducts and Biosystems
Engineering, University of Minnesota), Brad Upton
(Principal Research Engineer, National Council on Air
Stream Improvement), and Dirk Wassink (President,
Second Use Building Materials).
Contents
Executive Summary ............................................................1
Comparison of Processes to Make New Wood
Products and Recovered Products ...................................2
Energy and Emissions for Discarded Old Wood
Products at Their End-of-Life .........................................2
Introduction .........................................................................3
Background .........................................................................3
Construction and Demolition Waste Management .........4
Life-Cycle Assessment ....................................................4
Environmental Assessment Tools ...................................5
Methodology .......................................................................6
Part 1—Cradle-to-Gate LCIs ..........................................6
Part 2—End-of-Life Scenarios .......................................9
Results and Discussion ..................................................... 12
Part 1—Cradle-to-Gate LCIs ........................................ 12
Part 2—End-of-Life Scenarios ..................................... 13
Conclusion ........................................................................ 16
References ......................................................................... 17
Appendix 1—Survey Instrument ...................................... 20
Part I—Wood Flooring.................................................. 20
Part I—Wood Flooring (Soft-Strip) .............................. 20
Part II—Framing Lumber ............................................. 22
Part II—Framing Lumber (Full Deconstruction) .......... 22
Appendix 2—Landfill Gas (LFG) Equations ................... 24
Appendix 3—Assumptions and Limitations ..................... 25
Appendix 4—Simapro Inputs ........................................... 27
Appendix 5—LCI Flows................................................... 31
Conversion Table
English unit Conversion factor SI unit
board feet 0.0023597 m3 (nominal)
mile (m) 1.6093 kilometer (km)
British thermal unit (Btu) 0.00105506 megajoule (MJ)
Life-Cycle Energy and GHG Emissions
for New and Recovered Softwood
Framing Lumber and Hardwood Flooring
Considering End-of-Life Scenarios
Richard D. Bergman1, Research Forest Products Technologist
Robert H. Falk1, Research General Engineer
James Salazar2, LCA Professional
Hongmei Gu1, Research Forest Products Technologist
Thomas R. Napier3, Research Architect
Jamie Meil2, Senior Associate
1Forest Products Laboratory, Madison, Wisconsin
2Athena Sustainable Materials Institute, Ottawa, Ontario, Canada
3Construction Engineering Research Laboratory, U.S. Army Corps of Engineers
Engineer Research and Development Center (ERDC), Champaign, Illinois
Executive Summary
Within the green building and sustainable construction elds
is a growing movement to recover and reuse building ma-
terials in lieu of demolition and landll disposal. Building
materials reuse has several benets including reducing car-
bon footprint, conserving resources, extending landll life,
and minimizing pollution. Additionally, recovering build-
ing materials for reuse in construction typically provides
greater economic benets than any alternative use. Building
professionals including architects, materials speciers, and
contractors are more interested in mitigating the environ-
mental impact of the buildings they create. However, they
lack life-cycle data that will help quantify the environmental
impact of the building materials they specify as well as the
project’s overall impact, including contribution to global
climate change.
The goal of this study was to use life-cycle assessment
(LCA) to quantify greenhouse gas (GHG) emissions and
primary energy use of new and reused wood products with
additional information on end-of-life (EOL) options. At the
end, we compare the impact on GHG emissions for two
alternatives: reusing old wood in recovered products or
discarding old wood with the attendant end-of-life emis-
sions and making new products. We use existing life-cycle
inventory (LCI) data and develop new LCI data from reus-
ing two recovered wood products, softwood framing lumber
and solid-strip hardwood ooring, production of new wood
products, and disposal of old wood products. Life-cycle
stages for new products include harvesting (i.e., resource ex-
traction or deconstruction), resource transportation, primary
product production, and product transportation. For reusing
recovered wood, the environmental burdens associated with
recovery include decay of waste wood generated, transpor-
tation, production, and product transportation. For disposal
of old wood, the EOL burdens include demolition, waste
transport, and processing of waste wood whether burning it
to replace fossil fuels for generation of electricity, mulching
it, or landlling it.
This study involved two parts. The rst developed life-cycle
data for the two recovered wood products and compared
these data with life-cycle data for their new wood product
counterparts. LCI data for new products are from forest
cradle to installation in a new building. LCI data for recov-
ered products includes the deconstruction process through
to installation in a new building. The second part estimates
emissions for various EOL scenarios when old wood is dis-
carded from old structures (i.e., burning to generate electric-
ity, grinding for mulch, landlling without methane capture,
and other disposal options for wood removed from old
buildings). The second part also compared GHG emissions
for cases where old wood is reused for products to cases in
which old wood is discarded to various EOL dispositions
and new products are produced.
The LCI for recovered wood excluded the environmental
impacts associated with the previous product production
of those products. The processes that were excluded from
the initial product system include raw material extraction,
resource transportation, product manufacturing, product
transportation, construction, and use. The focus of this
analysis was on comparing impacts of making new products
compared with making recovered wood products.
The study methodology followed ISO 14040 and 14044
guidelines. The present study used allocated emissions using
Conversion Table
English unit Conversion factor SI unit
board feet 0.0023597 m3 (nominal)
mile (m) 1.6093 kilometer (km)
British thermal unit (Btu) 0.00105506 megajoule (MJ)
Research Paper FPL–RP–672
2
an economic screen and mass allocation to assign emissions
to wood products and waste co-products. Emissions were
allocated on the basis of mass to primary products and co-
products if they each had some economic value (as is the
case for new products). If the waste has no economic value,
then all emissions are assigned to the primary products (as
is the case for recovered/reused products). Primary (annual
production) data for reused products were collected on the
residential sector from 13 deconstruction companies spread
across the United States in 2009. Using production weight-
averaged survey data, our study estimated emissions per
functional unit, which was 1 m3 of nal product at the con-
struction site where the reused product was installed.
Evaluation of the emissions associated with discarding
old wood products used a base case EOL scenario and ve
alternate scenarios. The base case approximates current
practice and was comprised of burning the wood for energy
to replace coal power (30%), grinding for mulch (10%), and
disposal in a construction and demolition landll (C&D)
without methane capture (60%). In landlls, wood breaks
down anaerobically into biogenic methane and biogenic
carbon dioxide. C&D landlls, unlike municipal solid waste
(MSW) landlls, typically do not have methane capture
technology installed. Biogenic methane, a potent GHG if
captured from landlls, avoids the necessity of producing
natural gas, although not all biogenic CH4 that is generated
can be captured. In the present study, the captured landll
methane was burned to generate electricity to replace natural
gas. The Global Warming Potential (GWP) for EOL GHG
emissions was calculated using the International Panel on
Climate Change (IPCC) 2007 100-year time horizon.
LCI data for recovered wood products includes cumulative
cradle-to-gate energy use and emissions obtained from the
survey of demolition/reuse businesses and from the U.S.
LCI Database. Fossil energy used to recover softwood fram-
ing lumber and hardwood ooring was 418 and 859 MJ/m3,
respectively. Crude oil was the largest energy component
due mostly to resource and product transportation with val-
ues of 178 and 437 MJ/m3, respectively. No biomass energy
was used to make recovered products.
Comparison of Processes to Make New
Wood Products and Recovered Products
Fossil and biomass energy used to make new softwood
framing lumber and hardwood ooring was 6,440 and
7,750 MJ/m3, about 15 and 9 times, respectively, the amount
used to make recovered wood products. One-half or more of
the energy used to make new softwood framing lumber
and hardwood ooring was from biomass: 4,360 and
3,880 MJ/m3, respectively.
Of the life cycle stages examined for both new and recov-
ered wood products, the highest energy consumed was asso-
ciated with new wood product production. Most energy for
new wood products came from burning of on-site biomass
(i.e., mill residues) to generate thermal energy. Burning bio-
mass such as mill residues emits biogenic CO2.
Fossil CO2 emitted for new framing lumber and new hard-
wood ooring were about four times greater than for re-
covered softwood framing lumber (109 vs 23.9 kg/m3) and
recovered hardwood ooring (228 vs 49.7 kg/m3). Primary
drivers for the higher fossil CO2 emissions for new products
were from new wood product production and transporta-
tion. New wood products typically travel much further to
their markets, urban centers, than recovered wood products.
Recovered wood products are primarily produced in urban
centers and have low transportation environmental burdens.
When biogenic CO2 emissions are added to fossil emissions,
the total new products’ CO2 emissions were 8 to 10 times
the emissions for the recovered products.
When estimating GWP, it is standard protocol not to include
biogenic CO2 emissions, as they are assumed to be recov-
ered by forest carbon sequestration over time. GWP without
biogenic CO2 emissions is lower for recovered wood prod-
ucts than for the new wood products. GWP without biogenic
CO2 emissions is also lower for recovered products if dis-
posal of wood waste during deconstruction is not
considered.
Energy and Emissions for Discarded Old
Wood Products at Their End-of-Life
EOL scenarios for old wood products can result in large
negative energy use and fossil CO2 emissions if discarded
wood is used to displace coal or natural gas in producing
electric power. In the base case EOL scenario, where 30% of
the discarded wood replaces coal power, the displaced coal
energy consumption was –2,300 and –2,920 MJ/m3 for the
discarded softwood framing lumber and hardwood oor-
ing, respectively. To put this into perspective, this negative
energy amount would offset 53% (2,300/4,360) and 75%
(2,920/3,880) of biomass energy consumed to make the new
softwood framing lumber and hardwood ooring.
Five other EOL scenarios that examined various alternatives
included 1) burning wood as a replacement for natural gas
to generate electricity, 2) turning 100% wood waste into
mulch, 3) disposing 100% to construction, and 4) demoli-
tion landlls with or 5) without methane capture and energy
recovery into electricity. When 100% of wood replaces
natural gas to generate electricity, energy use and GWP were
also negative but less negative than when offsetting coal.
All other EOL scenarios result in positive energy and GWP
values.
For the base case EOL scenario (approximate current prac-
tice) where we assumed making new products would require
discard of old wood to the current mix of proportions to
energy, landlls, and mulch, the reuse of both softwood
lumber and hardwood ooring produces less GWP than
making new products. The GWP benet is greatest for reus-
ing hardwood ooring instead of making of new ooring.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
3
This is true for GWP estimates that either exclude or include
biogenic CO2 emission. However, if we consider GWP that
excludes biogenic CO2 emissions (standard LCA practice),
reuse could be notably worse (higher GWP) than new wood
if all old wood could be burned to offset coal or natural gas
in making electric power. Reuse could be slightly worse
than new for softwood lumber if all old wood could be used
as mulch (unlikely if wood is contaminated). So a key point
is that if wood cannot be reused for products, the next best
step is to keep it out of landlls and use it for either energy
(top priority) or mulch (lower priority). There is a greater
benet from keeping hardwood out of landlls than keeping
softwood out of landlls.
Introduction
The role of carbon emissions on global climate and the pro-
jected negative impact on ecosystem sustainability and the
general health of our planet have never been more elevated
in the public’s consciousness. This awareness is particularly
evident in the building construction eld where green build-
ing concepts are becoming more prevalent. Green building
is dened as the practice of increasing the efciency with
which buildings use resources—energy, water, and materi-
als—while reducing building impacts on human health and
the environment. This is done through better siting, design,
material selection, construction, operation, maintenance,
and removal throughout the complete building life cycle.
Building professionals, including architects, materials speci-
ers, and contractors, are more interested in mitigating the
environmental impact of the buildings they create. However,
they lack life-cycle data that will help quantify the environ-
mental impact of the building materials they specify and a
given project’s contribution to global climate change.
Within the green building and sustainable construction elds
is a growing movement to recover and reuse building mate-
rials in lieu of demolition and landll disposal. Building ma-
terials reuse has several benets including reducing carbon
footprint, conserving resources, extending landll life, and
minimizing pollution (Smith and others 2001, Falk 2002,
Ericksson and others 2005, Heilmann and Winkler 2005,
Olofsson and others 2005, Thorneloe and others 2007). In
spite of these benets, there is currently no easy way for
building professionals to quantify the environmental impact
of incorporating reused building materials in new building
or remodeling construction.
The goal of this study was to use life-cycle analysis to
quantify greenhouse gas (GHG) emissions and primary en-
ergy use of new and reused wood products with additional
information on end-of-life (EOL) options or scenarios.
Incorporating existing and developed life-cycle inventory
(LCI) data, the environmental consequences of reusing two
recovered wood products—softwood framing lumber and
solid-strip hardwood ooring—relative to the virgin
counterparts were evaluated. A study by Bergman and
others (2010) developed cradle-to-gate LCI data for these
two recovered wood products and compared these data
with corresponding cradle-to-gate LCI data of their new
wood product counterparts (Puettmann and Wilson 2005,
Puettmann and others 2010). These studies found that the
new wood products consumed more energy and emitted
more GHGs than did the recovered wood products; how-
ever, those results considered neither product transportation
for the new wood from the manufacturing facility to the
construction site nor transportation of the recovered wood
from the resale facility to the construction site.
The results presented here include new wood product trans-
portation to the construction site. In addition, various EOL
scenarios (i.e., burning for energy, grinding for mulch, and
other disposal options) for the two wood products removed
from old buildings were studied to evaluate the impact on
global warming potential (GWP).
Background
The recovery and reuse of building materials from wood-
framed building removal is becoming more widely recog-
nized as a positive environmental alternative to demolition
and landlling. Using “deconstruction,” or dismantlement,
a building can be selectively dismantled and usable materi-
als recovered for reuse in construction (Falk 2002, Falk
and Guy 2007, Kibert 2003). This deconstruction and reuse
strategy is consistent with resource conservation efforts,
waste reduction, and green building certication programs.
Examples of such programs are United States Green
Building Council’s (USGBC) Leadership in Energy and
Environmental Design (LEED), and the Green Globe Green
Building Initiative (GBI).
Using conventional demolition, the wood-framed building is
cleared from the site by the most expedient means possible,
typically using a “track-hoe” or other heavy machinery
to reduce the building to the smallest pieces possible for
easy loading and transport to a construction and demolition
(C&D) landll. This process is by its nature destructive to
the building materials and typically results in nearly all
of the building ending up as unusable for reuse in new
construction.
As for deconstruction, two approaches are typically used in
salvaging building materials. At its simplest level, a non-
structural approach is taken (also known as “soft-stripping”)
and focuses on the recovery of easier to remove compo-
nents, such as nish ooring, wall nishes, doors, windows,
and other nish materials. A more involved approach, often
called “full deconstruction,” involves the dismantling of the
structural components of a building. As a result, it is more
time intensive. The material recovered typically includes
roof, wall, and oor framing, sheathing, and other building
frame components. Unusable wood material goes typically
to a local C&D landll.
Research Paper FPL–RP–672
4
Construction and Demolition Waste
Management
In 2003 (latest gures), the United States produced about
164 million metric tons of C&D waste from building-related
activities (EPA 2009a, 2012a). Of this, about 69 million
metric tons comes from residential construction (primar-
ily wood framed). Because wood contributes between 25%
and 40% of a C&D landll (EPA 1995, NWMOA 2009,
Falk and McKeever 2012), potentially a signicant amount
of reusable wood building materials can be diverted and
reused. Better materials management strategies for C&D
waste would result in conservation of natural resources,
reduced landll requirements and associated pollution,
and GHG emissions that result from such facilities. In the
United States, landlls are the third largest source of meth-
ane behind intestinal fermentation and natural gas systems
(EPA 2011).
Three recent events illustrate the importance of the need for
such strategies:
1) The USEPA has declared carbon dioxide and other
GHG emissions as air pollutants (EPA 2009b).
2) A requirement for 50% C&D waste diversion by 2015
for Federal agencies (Executive Order (EO) 13514,
Federal Leadership in Environmental, Energy, and
Economic Performance).
3) A requirement for Federal agencies to set goals in the
areas of energy efciency, acquisition, renewable energy,
toxics reductions, recycling, sustainable buildings, elec-
tronics managements, eets, and water conservation (EO
13423 Strengthening Federal Environmental, Energy,
and Transportation Management).
Not surprisingly, many studies indicate that increasing the
reuse and recycling of C&D materials results in correspond-
ing lower levels of landlling. Additionally, this utilization
lowers the need for new product production, lowering over-
all energy consumption and environmental impact (Blengini
2009, Smith and others 2001, Eriksson and others 2005,
Heilmann and Winkler 2005, Sunberg and others 2004,
Thorneloe and others 2007).
Reusing, or otherwise diverting, building materials fated
for landlls can help reduce energy use and mitigate GHG
emissions (e.g., biogenic methane) released from landlls
(EPA 2011). Thorneloe and others (2007) indicates that for
a typical U.S. landll accumulating 437,000 tonnes/y with
a recycling rate of 40% saved almost 8.44 million GJ
(8,000 trillion (1012) Btu (TBTU)) of energy compared to a
recycling rate of 30%, which saved about 2.11 million GJ
(2,000 TBTU) of energy. This effect seems counter-intui-
tive. Nevertheless, improving the recycling rate affects ma-
terials that may be more difcult to remove but require more
energy to produce as new. As a result, higher recycling rates
create higher energy savings per percentage increase.
Recycling and reuse have different environmental impacts
depending on types of materials recycled and reused, trans-
portation distances, and the remanufacturing processes
(Thorneloe and others 2007). Life-cycle research can play
an important role by examining various scenarios for their
environmental trade-offs (Borghi and others 2009).
Life-Cycle Assessment
Life-cycle assessment (LCA) is comprised of four stages
(phases) as dened by the International Organization for
Standardization (ISO). These are 1) goal and scope deni-
tion, 2) inventory analysis, 3) impact assessment, and
4) interpretation (Fig. 1). A LCA study includes all stages
but a LCI study does not include stage 3, the impact assess-
ment (SAIC 2006, ISO 2006a,b).
LCA is a well-established method for evaluating the envi-
ronmental impacts of processes and products. Among other
attributes of environmental performance, LCA quanties the
carbon impact of products. Performing a unit process based
LCA of a product is a detailed, data-intensive process. A
LCA is composed of life-cycle stages. Life-cycle stages for
building products include resource extraction, transporta-
tion, product manufacturing, construction, use, and nal
disposition (i.e., EOL) (Fig. 2).
Each life-cycle stage is evaluated by conducting LCIs. LCIs
quantify the material and energy inputs as well as the envi-
ronmental burdens within carefully dened system boundar-
ies for a given product, process, or service in relation to a
functional unit (ISO 2006a,b). LCIs track all the inputs and
outputs including emissions of a single life-cycle stage such
as harvesting or product manufacturing across the system
boundary (ISO 2006a). While LCAs have typically been
Figure 1. Life-cycle assessment phases.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
5
used to evaluate the environmental impact of producing vir-
gin materials, they can also be used to evaluate the environ-
mental impacts of reusing recovered materials. For example,
a study by Blengini (2009) indicated that the total energy
and GHG emissions associated with the reuse of recovered
building materials from a residential building shell located
in Italy are 29% and 18%, respectively, of the environmental
burdens of similar virgin materials.
Environmental Assessment Tools
Research on assessing the environmental impact of nal
product disposition for wood products draws on and must
integrate diverse literature, available U.S. LCI data for
virgin wood products, and analysis of various disposal
scenarios.
For LCA practitioners, obtaining transparent and consis-
tent U.S. LCI datasets on wood products was difcult until
the mid-2000s. As part of an on-going effort, the National
Renewable Energy Laboratory (NREL) working with LCA
experts manages a publically available LCI database called
the U.S. LCI Database. To aid in populating the U.S. LCI
Database, the Consortium for Research on Renewable
Industrial Materials (CORRIM) is developing critically re-
viewed LCI datasets on forestry and forest products for the
U.S. LCI database (CORRIM 2010, USDA 2013).
In the various geographical regions of the United States,
CORRIM has constructed cradle-to-gate LCI data based on
individual gate-to-gate LCIs for many new wood products
(Puettmann and Wilson 2005, Puettmann and others 2010).
The U.S. LCI Database has become a repository of many
wood building materials. Other environmental tools, such as
the U.S. Environmental Protection Agency’s (EPA) Waste
Reduction Model (WARM) are available to evaluate materi-
als for their carbon emissions and energy use.
Initial work on broad categories of waste disposal, including
dimensional lumber, are being evaluated through WARM
that was developed using a streamlined LCA approach (EPA
2012b). WARM’s streamlined LCA is limited to an inven-
tory of GHG emissions, carbon sinks, and energy impacts.
The model does not evaluate human health impacts or air,
water, or other environmental burdens that do not have
a direct impact on climate change. In addition, WARM
simplies the determination of emissions from life-cycle
stages that occur before a material reaches its end-of-life
(EPA 2010). WARM calculates GHG emissions and energy
benets of baseline and alternative waste management prac-
tices. Another LCA tool for building products, the ATHENA
Impact Estimator (IE, Athena 2012), includes evaluation of
several LCA impact categories including primary energy
consumption, air pollution index, water pollution index,
and GWP.
Napier and others (2007) used both the WARM and
ATHENA IE tools in a study to evaluate the GHG impact
of recovering wood products from deconstructed military
facilities. Results were limited to the effects of diverting
materials from landlls and did not include end-of-life ef-
fects. Results indicated that 15,000 tonnes of recovered di-
mensional lumber that was reused instead of being landlled
Figure 2. Complete life-cycle from regeneration of trees to disposal of wood materials
(based on Fava and others (1994)).
Research Paper FPL–RP–672
6
reduced carbon emissions by 3,257 tonnes of CO2 equiva-
lents and reduces energy use by approximately 8,750 GJ. As
for the ATEHNA IE, results did indicate signicant reduc-
tion for all six impact categories when recovering framing
lumber for reuse. These results suggested that a comparative
assessment of the environmental impacts of substituting
recovered wood for new wood building material in con-
struction could be made. This could include not only GHG
effects of the manufacturing process and transportation but
EOL effects as well.
Methodology
The rst step in this study was to develop cradle-to-gate unit
process life-cycle data for the two recovered wood products
and compare these data with cradle-to-gate life-cycle data of
their new wood product counterparts. No burdens of the pre-
vious life for the recovered wood products were assigned.
For recovered wood, the old building from which the
lumber was recovered was the cradle, whereas the forest
was the cradle for new wood products. Therefore, the loca-
tion from where the wood products are extracted is a critical
distinction between products because the old building from
which reused wood was removed was once built with new
wood harvested from forests. To further emphasize that the
LCI data developed was modular in nature, this study as-
signed impacts to each product as they happened, not for
any future impacts. The gate is dened as the installation
at the construction site for both new wood and recovered
wood. For recovered wood products, a transportation dis-
tance of 25 miles was assumed between the deconstruc-
tion site and the resale facility. For new wood products,
data from the United States Department of Transportation
(USDOT) were used to calculate an average transportation
distance from a manufacturing facility to a local wholesale
facility. For both new products, the transportation from the
sale facility to the construction site was assumed to be
24 km (15 miles).
The second part of this study evaluated various EOL scenar-
ios of old wood products discarded from old buildings (i.e.,
burning to generate electricity, grinding for mulch, and other
disposal options) and their environmental impacts.
Emission proles per functional unit of products were es-
timated using SimaPro 7 modeling software. Input data
averages from the survey of demolition and recovery busi-
nesses were production weighted and additional secondary
data used from U.S. LCI Database (PRé Consultants 2013,
USDA 2013). The modeling software provided a list of raw
materials consumed during the cradle-to-gate production
(i.e., LCI ows) that was used to calculate cumulative (pri-
mary) energy consumption. The study estimated the GWP
in kg CO2 equivalent using the IPCC 2007 100-year Method
(PRé Consultants 2013, IPCC 2007). Calculating values for
GWP typically does not include biogenic CO2 emissions, as
they are part of the natural carbon cycle and thus are
excluded as standard LCA practice. However, new wood
production burns wood waste (i.e., mill residue), a byprod-
uct of making new wood products, thus emitting biogenic
CO2. Therefore, GWP with and without biogenic CO2 was
calculated to provide insight on overall CO2 emissions in
relation to new and recovered wood products.
Part 1—Cradle-to-Gate LCIs
New Softwood Framing Lumber and Hardwood
Flooring
An analysis of the energy consumption and associated emis-
sions of new wood products was made by using the LCA
framework from cradle-to-gate and existing information
from the U.S. LCI Database and other resources. The two
products were evaluated for their environmental impact
from harvesting through manufacturing to product transpor-
tation to the construction site.
Figure 3 highlights the system boundaries for a cradle-to-
gate LCI for new framing lumber and hardwood ooring,
respectively. Within each system boundary, the individual
unit processes were identied for greater transparency and
identifying environmental “hot spots.” Unit processes for
producing softwood framing lumber include log yard, saw-
ing, drying, and planing operations (Milota and others 2005,
Bergman and Bowe 2010).
Assumptions of this analysis include that new wood mate-
rial transport from the manufacturing site to a wholesale
location occurs by rail and truck. Because wood products
production is regionally located in the United States, and
new lumber must be transported long distances to local
markets, an average distance of travel from manufacturer
to wholesaler based on data from USDOT (2010) for soft-
wood framing lumber (NAICS 321) and hardwood ooring
(NAICS 337) was calculated. Table 1 shows transportation
data for moving the new product from the manufactur-
ing site to wholesaler to the construction site. For product
transportation, we assumed a distance of 24 km (15 miles)
from wholesaler to construction site for the new lumber and
the same distance for moving the recovered wood products
from the resale facility to the construction site.
Recovered Softwood Framing Lumber and Hardwood
Flooring
An analysis analogous to the new wood products was per-
formed for the recovered products, however, because LCI
information did not exist for the recovery process, a survey
of the deconstruction industry was performed to collect nec-
essary primary data (Appendix 1—Survey Instrument). The
resultant survey data were weight-averaged before entering
into the LCA modeling software for estimating the emission
prole and raw material usage for the recovered material
deconstruction process.
Figure 4 shows system boundaries for recovered softwood
framing lumber and hardwood ooring cradle-to-gate LCI.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
7
The process begins with extraction of the installed mate-
rial (the raw material) from a building (i.e., the cradle), and
includes transportation of the recovered material to storage
and processing if necessary (product refurbishing) as well
as transportation of the nal product to the construction site
(i.e., the gate). This cradle-to-gate analysis included every-
thing within the “system boundary” that covers raw material
extraction and product manufacturing (refurbishing) with
the associated transportation up to but not including the use
phase. Unit processes upstream of extraction such as storage
of the recovered material were included in this analysis and
the storage LCI data were the same for storing new material.
For recovered wood, survey data provided the basis for
transporting the material from storage facility to construc-
tion site along with secondary sources (see Table 11 for new
wood products) and expert opinion assuming recovered
material reused locally. Surveying 13 U.S. building
deconstruction companies that regularly recover lumber and
wood ooring provided the primary (2009 annual produc-
tion) data. LCA modeling software using weight-averaged
production data along with secondary data from the U.S.
LCI Database estimated emissions and raw material usage.
Survey data included information as a basis for calculating
materials transportation from extraction (deconstruction
site) to resale location. The distance from resale location
to construction site was assumed to be the same as for new
wood products. In addition, because the deconstruction
process generated wood waste, estimated material lost were
17% and 11% for softwood framing lumber and hardwood
ooring, respectively. The deconstruction wood waste (i.e.,
unusable wood) was transported to a C&D landll with no
methane capture.
Many laboratory and eld studies have focused on de-
composition of wood (i.e., biomass) disposed in a landll.
Figure 3. Cradle-to-gate system boundaries for new softwood framing lumber and hardwood flooring.
Research Paper FPL–RP–672
8
The question pertains to what percentage of wood actually
breaks down to generate biogenic CO2 and biogenic CH4.
The IPCC (2006) recommends a carbon loss for wood prod-
ucts of 0.5 in a landll. Wang and others (2011) reported
a range of 0%–19.9% wood decomposition for a labora-
tory study conducted on solid wood and engineered wood
products. An Australian study (Ximenes and others 2008)
indicated that after 46 years, hardwoods and softwoods un-
der different waste management schemes lose on average
about 18% and 17%, respectively, of their original carbon
content. A study by Skog (2008) states an average 23% car-
bon loss for wood products disposed of in the landll and is
the basis for this study. A portion (23%) of this waste wood
was assumed to decompose anaerobically (in the absence of
oxygen) and be released as landll gas in the LCI analysis
whereas the remaining wood remained in the landll as is.
This study assumed that both types of landlls examined
(MSW and C&D) passed through the four typical phases
of a landll over time but spent the vast majority of time
in phase IV where biogenic methane and biogenic carbon
dioxide are produced. Anaerobic conditions prevent the full
decomposition of wood, unlike aerobic conditions. Wood
material completely breaks down in aerobic conditions such
as the forest oor and generates biogenic carbon dioxide and
water as a result (ATSDR 2001, Staley and Barlaz 2009)).
Future work will evaluate this phenomenon. In addition, we
assumed that landlls captured the biogenic methane during
decomposition and avoided natural gas production on 1:1
energy basis and was used to generate electricity. Biogenic
methane is a GHG included in calculating GWP because it
is human-made.
Deconstruction (extraction of recovered materials)This
unit process begins with installed softwood framing lumber
(structural deconstruction) and installed hardwood ooring
Figure 4. System boundaries for recovered framing lumber and hardwood flooring.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
9
(non-structural deconstruction) and includes the following
operations:
Recovered softwood framing lumber unit processes
• Transporting workers to the deconstruction site.
• Transporting forklifts, bobcats, or other energy-
consuming equipment to the jobsite.
• Removing surface materials such as roong, drywall,
subooring, and insulation that would interfere with
removal of framing lumber, either by hand or with ma-
chinery.
• Removing actual framing.
• Denailing framing, either by hand or pneumatic tool
(i.e., denailer).
• Loading framing onto trucks, either by hand or with
equipment.
• Transporting framing to a storage facility (i.e., resale
facility).
• Unloading and storing the material until sold.
Recovered solid-strip hardwood ooring unit processes
• Transporting workers to the deconstruction site.
• Removing any furniture or other materials such as
moulding that would interfere with the removal of
the ooring.
• Sawing oor to ease removal.
• Removing the wood ooring board by board (Fig. 5)
• Denailing the ooring, either by hand or pneumatic tool
(i.e., denailer).
• Loading the wood ooring onto trucks, either by hand or
with equipment.
• Transporting the wood ooring to a retail facility.
• Unloading and storing the material until sold
Inputs include transportation fuel for worker vehicles and
for material, fuel to run generators providing on-site elec-
tricity and/or grid electricity for tools to remove framing
lumber and ooring, and fuel to run heavy equipment used
for structural deconstruction and unloading material at stor-
age facility. Outputs include recovered softwood framing
lumber and recovered hardwood ooring. Emissions include
solid (wood) waste produced during the removal process, air
emissions from grid electricity, on-site generators, and other
equipment, and non-wood waste such as nails and drywall.
Solid waste was transported to a C&D landll.
Part 2—End-of-Life Scenarios
Various EOL strategies were also evaluated to assign the
environmental burdens for cases where wood is not reused.
EOL scenarios included burning wood for electric power
production (decreases fossil emissions), grinding for mulch,
and landlling (without or with methane capture for energy
production). A base-case scenario assumed that 30% of the
wood not reused would be burned to replace coal to generate
electricity, 10% would be ground for mulch, and the remain-
ing 60% would be disposed of in a C&D landll without
methane capture. Additional scenarios were evaluated that
extend the work done by Bergman and others (2012) and
Winistorfor and others (2005).
Table
1. New wood product transportation data
Transportation
Wood products
Softwood framing
lumber Hardwood flooring
tkm/m
3 b
tm/Mbf
c
tkm/m
3 b
tm/Mbf
c
Gate to wholesaler, by diesel truck
233
258
813
1,314
Gate to wholesaler, by rail
96
107
4
6
Wholesaler to construction site, by diesel truck
14
16
17
26
aSoftwood framing lumber and solid strip hardwood flooring are 1.63 and 2.36 m3
/thousand board feet (Mbf),
respectively.
b
tkm/m3is tonne-kilometer per cubic meter of wood.
c
tm/Mbf is ton-mile per thousand board feet of wood.
Figure 5. Hardwood flooring boards being removed during
deconstruction.
Research Paper FPL–RP–672
10
Figure 6 indicates expected EOL scenarios for wood ma-
terials (Bergman and others 2012). Equations that estimate
landll emissions of biogenic CH4 and biogenic CO2
from anaerobic decomposition of wood are provided in
Appendix 2—Landll Equations.
The base case was comprised of burning the wood for coal
power substitution (30%), grinding the wood into mulch
(10%), and landlling the wood without methane capture
(60%). This distribution of wood to energy and landlls is
in line with recent U.S. practices (Salazar and Meil 2009,
Kaplan and others 2009, EPA 2011). Roughly 30% of C&D
wood is burned for energy recovery: 11.2 million tons out of
39.4 million tons (EPA 2009c, EPA 2012b). C&D landlls
contain much more wood than a MSW landll on a volume
basis (25% to 40% vs 6%), according to the EPA (2011). In
addition, methane is not captured in C&D landlls. In alter-
nate scenarios, we considered biogenic methane captured
from C&D landlls and burning it to generate electricity to
replace natural gas. Table 2 shows the base case scenario
and the ve alternatives scenarios.
Analysis Considerations
ReuseThis study evaluated the direct effects of reusing
recovered wood products. Sathre and Gustavsson (2006)
conducted a somewhat broader study that evaluated energy
and carbon from a series of cascading uses where cascading
wood is the sequential use (i.e., reuse) for different purposes
and assumed that wood quality declines over time. In the
present study, no decline in wood quality was assumed.
This study also assumed that the lumber and ooring were
used in the same application for which they were originally
manufactured. We assume reused wood will be graded and
this tends to assure that its performance will be the same as
virgin wood that could have been used instead.
We assumed that carbon storage in recovered wood is the
same as carbon stored in virgin wood. The benets of reus-
ing recovered wood relative to use of virgin wood include
avoiding the environmental burdens associated with produc-
tion of virgin wood and avoiding the generation of biogenic
methane from the decay of discarded wood in landlls
(Salazar and Meil 2009).
Forest Carbon SequestrationWe assumed that the use
of wood harvested on a sustainable basis does not alter the
carbon storage of forests in the long run. The USFS Forest
Inventory Analysis indicates that forest area in the United
States has been relatively constant since 1910 (USFS 2011,
Oswalt and others 2009, Smith and others 2009). According
to the International Panel on Climate Change (IPCC) GWP
Figure 6. End-of-life disposal options for recovered wood materials.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
11
method found in SimaPro 7.3 (PRé Consultants 2013, IPCC
2007), the characterization factor for biogenic CO2 emis-
sions was not assigned a value in the calculation for GWP.
This approach also follows the ISO 14067 standard (ISO
2012). In addition, the issue of how to properly account for
the carbon in harvested wood products has not been devel-
oped in a consensus-based standard. Therefore, the system
boundary assumed for this study starts at the harvesting of
the tree and extends to the time where new or reused mate-
rial would be placed in a new use. This temporal end-point
is assumed to include all EOL emissions for wood that is
discarded when it is not reused.
Data Collection and TreatmentBuilding material recov-
ery and reuse data were collected for the United States only.
Primary data of deconstruction companies were collected
across the entire United States. Primary mill data for de-
construction and reuse businesses were production-weight-
averaged as required by CORRIM research guidelines to
maintain condentiality of surveyed facilities (CORRIM
2010). The following tasks are part of a standard LCA re-
search protocol.
Validation of DataThe reused wood materials considered
in this study were tracked through the entire process to en-
sure validation of raw and LCI data. No physical changes
occurred in the softwood framing lumber and hardwood
ooring recovered for reuse in new construction.
Data Quality StatementPrimary data quality was high
because of expert knowledge and complete responses ob-
tained from the extensive and comprehensive survey of
industry (Appendix 1—Survey Instrument). Annual produc-
tion data were collected for the years 2008 and 2009 from
deconstruction facilities across the United States that used
average technologies and produced 362 m3 (230,000 bf)
of recovered softwood framing and 25.4 thousand m2 (273
thousand ft2) of recovered hardwood ooring. Statistics for
total production of recovered softwood framing and hard-
wood ooring for the same time period were not available at
the time of completion of this study.
AggregationPrimary data on environmental burdens per
unit of reused wood were weighted as in previous CORRIM
reports (Milota and others 2004) using
where Pweighted is the weighted average of the survey values
reported by the mills, Pi is the reported mill value, and
xi is the ratio of the mill’s production to total production for
all surveyed facilities.
Modeling Procedure Including Allocation—The weighted-
average primary data on environmental burdens was es-
timated per functional unit using SimaPro LCA software
(PRé Consultants 2013). A wood mass balance and energy
consumption verication for the necessary unit processes
were performed and the weighted-average data were linked
together into SimaPro for each unit process. Secondary data
found in the U.S. LCI Database within SimaPro software
provided additional life-cycle data. The additional life-cycle
data includes generation and delivery of electricity and fos-
sil fuel use and emissions. LCI outputs from SimaPro in-
cluded raw material consumption, solid waste, and emission
to air, water, and land. The mass allocation method was used
to allocate environmental burdens to the primary product
and co-products.
The mass allocation method was chosen because the highest
volume product had the highest economic value. Emissions
were allocated based on mass to primary products and co-
products if they each had some economic value (as is the
case for new products). If the waste has no economic value,
then all emissions are assigned to the primary products (as is
the case for recovered/reused products). The cradle-to-gate
LCI data for recovered hardwood ooring and softwood
framing lumber were developed using a functional unit of
1 m3, the same functional unit as new wood products.
Elementary Flows—Nearly all individual wood products
ow through the system without changing shape. Softwood
framing lumber and hardwood ooring that suffered physi-
cal damage during deconstruction were not reused and were
sent as waste wood to a C&D landll with no methane
capture.
Assumptions—Assumptions and limitations associated
with making the LCI estimates are provided in
Appendix 3—Assumptions and Limitations.
Table 2. End-of-life scenarios by type and percentage
End
-of-life scenarios
Base case
(%)
Alt#1
(%)
Alt#2
(%)
Alt#3
(%)
Alt#4
(%)
Alt#5
(%)
Wood burned (substitute coal power)
30
100
0
0
0
0
Wood mulched
10
0
100
0
0
0
C&D landfill (no CH4capture)
60
0
0
100
0
0
Wood burned (substitute gas power)
0
0
0
0
100
0
C&D landfill (CH
4
capture)
0
0
0
0
0
100
Research Paper FPL–RP–672
12
Results and Discussion
Part 1—Cradle-to-Gate LCIs
LCI data for cumulative cradle-to-gate materials use, energy
use, and emissions per cubic meter of newly made softwood
framing lumber and hardwood ooring were generated us-
ing SimaPro modeling. Additional data were obtained on
energy and emissions per cubic meter for product transpor-
tation to the construction site from literature. LCI data were
estimated using primary data from surveys and secondary
data found in the U.S. LCI Database and literature. The
surveyed facilities provided detailed data on mass ow, and
energy consumption and emissions by type of fuel. Data
from surveys were weighted by facility production and were
input into SimaPro 7 to estimate average non-wood raw
material use and emissions. Input data collected by survey
are listed in Appendix 4—SimaPro Inputs. The total energy
to produce 1-m3 framing lumber and hardwood ooring
from new wood materials was 6,440 and 7,750 MJ/m3, re-
spectively (Table 3). Based on information obtained, at least
50% of the energy used to make new wood products came
from woody biomass.
Table 4 shows the major GHG emitted in making new soft-
wood framing lumber and hardwood ooring. Emissions
were consistently greater per cubic meter to make hardwood
ooring because of its higher density and greater energy
requirements in product production (principally drying) than
softwood framing lumber. Production of hardwood lumber
emits about twice as much fossil CO2 (228 kg/m3) as does
the production of softwood framing lumber (109 kg/m3).
Appendix 5—LCI Flows shows detailed LCI results for
newly made and recovered softwood lumber and hardwood
lumber including raw materials used, solid waste generated,
and emissions to air, water, and soil.
Table 5 shows cradle-to-gate LCI energy use data for re-
covered softwood framing lumber and hardwood ooring
per cubic meter of recovered wood and includes energy for
product transportation to the construction site. Recovered
softwood framing lumber and hardwood ooring consume
418 and 859 MJ/m3 of energy, respectively. Included in the
energy allocated to the recovered wood are those from wood
lost during deconstruction process (i.e., waste wood) that
was sent a C&D landll with no methane capture. Crude oil
was the largest energy component because of resource (i.e.,
raw material) and product transportation with values of 178
and 437 MJ/m3, respectively. Coal consumption was next at
145 and 235 MJ/m3. However, more energy was consumed
during hardwood ooring recovery because the ooring was
primarily stored in a closed, natural gas-heated building.
No biomass was burned for energy in recovering old wood
products for reuse.
Table 6 shows cradle-to-gate major GHG emissions for re-
covered softwood framing lumber and hardwood ooring.
Recovered hardwood ooring production emitted more fos-
sil CO2 than recovered softwood framing lumber did. This
was due to the higher wood density for hardwood versus
softwood and because hardwood ooring was stored inside
heated closed buildings unlike softwood framing lumber,
which was stored covered outside. In addition, more bio-
genic CO2 was emitted for softwood framing lumber
(24.8 kg/m3) than hardwood ooring (19.2 kg/m3) because
more lumber was lost during the recovery process for soft-
wood framing lumber than ooring. Therefore, a higher
percentage of removed softwood framing lumber later
Table
3. Cradle-to-
gate cumulative energy requirements
by fuel source allocated to 1
m3new wood productsa
Softwood framing
lumber Hardwood flooring
MJ/m
3
%MJ/m
3
%
Coal
462
7
816
11
Crude oil
826
13
1,980
26
Natural gas
592
9
790
10
Uranium
160
2
270
3
Biomass
4,360
68
3,880
50
Hydropower
37
1
12
0
Total
6,440
100
7,750
100
aEnergy values were determined using their higher heating values in
MJ/kg: 54.4 for natural gas
,and 20.9 for oven-dried wood, 26.2 for coal,
45.5 for crude oil, and 381,000 for uranium.
Table 4. Cradle-to-gate major GHG emissions allocated to 1 m3
new wood products
Quantity emitted
(kg/m3)
Softwood framing
lumber Hardwood flooring
Carbon dioxide, fossil 109 228
Methane, fossil 0.303 0.459
Methane, biogenic 0 0
N
itrous oxide 3.32 × 10–3 2.87 × 10–3
Carbon dioxide, biogenic 365 390
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
13
decomposed in a landll. Biogenic methane has a much
greater impact on climate change than carbon dioxide, 22
to 1 when calculating GWP (non-biogenic methane has a
GWP characterization factor of 25 for the IPCC 2007 100-
year time horizon) (PRé Consultants 2013, IPCC 2007).
Bergman and others (2010) provided LCI data for recovered
framing lumber and hardwood ooring without considering
disposal of the unusable wood from deconstruction. Total
methane production was 0.3 kg/m3 for both recovered wood
products when the analysis did not include biogenic meth-
ane emissions generated after landlling of unusable wood.
Biogenic methane emissions from the decomposition of
wood lost increased total methane emitted by a factor of 24
(7.31/0.3) and 19 (5.59/0.3), respectively, a huge increase in
GWP. Survey results estimated wood lost during removal at
17% and 11% for recovered framing lumber and wood oor-
ing, respectively. Wood decomposition in landlls was the
source for all the biogenic CO2 and biogenic CH4 emitted in
production of recovered wood products.
Tables 7 and 8 show that cradle-to-gate GWP including and
excluding biogenic CO2 for products and recovered prod-
ucts. For new products, GWP with biogenic CO2 emissions
included was two to four times greater than when biogenic
CO2 emissions were excluded. For recovered wood
products, GWP was marginally greater when biogenic CO2
emissions were included. Biogenic CO2 emitted came from
the decomposition in the landlled wood lost during the re-
covery process for recovered wood products.
GWP for new products is substantially higher when biogen-
ic CO2 is included because production includes burning on-
site biomass (i.e., mill residues) to provide thermal energy
for drying wood. Considerably lower GWPs were indicated
when biogenic CO2 emissions were not considered (standard
LCA practice).
Tables 7 and 8 show cumulative energy requirements for
the new framing lumber and ooring were 15 and 9.0 times
greater, respectively, than for the equivalent recovered wood
products. The GWP ratio between new and recovered prod-
ucts is much lower because it includes, for recovered prod-
ucts, the landll methane emissions generated from wood
lost during the recovery.
When GWP excludes biogenic CO2 emissions (standard
LCA practice), then GWP for new hardwood ooring is
greater than for recovered ooring (ratio is 1.4). However,
GWP is less for new softwood lumber than for recovered
softwood lumber (ratio is 0.6). GWP that includes biogenic
CO2 emissions is greater for new products than for recov-
ered products by factors of 2.3 and 3.2 for softwood lumber
and hardwood ooring, respectively. The doubling or more
of GWP when including biogenic CO2 indicates the impor-
tance of what emissions are included for wood products
production.
Part 2—End-of-Life Scenarios
The second part of this study examined various EOL scenar-
ios for the case where wood is not recovered and estimated
the associated cumulative energy use and GHG emissions.
This portion of the study is intended to answer the question,
“If recovered wood were not reused in construction and had
a different fate (e.g., burned to generate electricity, ground
for mulch, or landlled), what would the impact be on en-
vironmental burdens?” In addition, we answer the question,
“When are GHG emissions less for reuse of old wood com-
pared to discard of old wood under several EOL scenarios
and associated production of new products?”
The base case EOL scenario assumes a mix of dispositions
for discarded wood: burning wood for energy to replace
coal power (30%), grinding for mulch with open air decay
(10%), and disposal in a C&D landll without methane cap-
ture (60%). All EOL analyses used data from the U.S. LCI
Database. There are ve alternate scenarios.
Table 9 shows that for the base case (current U.S. practice)
cumulative EOL energy is negative because of decreased
coal burning with increased wood burning. Wood
burning allows for coal energy decreases of –2,300 and
–2,920 MJ/m3 of softwood framing lumber and hardwood
ooring, respectively. To put this into perspective,
Table 5. Cradle
-to-gate cumulative energy
requirements by fuel source allocated to 1
m3
recovered wood products
a
Softwood framing
lumber
Hardwood
flooring
MJ/m
3
%MJ/m
3
%
Coal
145
35
235
27
Crude oil
178
43
437
51
Natural gas
47
11
109
13
Uranium
43
10
70
8
Biomass
0
0
0
0
Hydropower
5
1
8
1
Total
418
100
859
100
a
Energy values were determined using their higher heating
values in MJ/kg: 54.4 for natural gas and 20.9 for oven
-dried
wood, 26.2 for coal, 45.5 for crude oil
,and 381,000 for
uranium.
Table 6. Cradle-to-gate major GHG emissions allocated to
1 m3 recovered wood products
Quantity emitted
(kg/m3)
Softwood framing
lumber
Hardwood
flooring
Carbon dioxide, fossil 23.9 49.7
Methane, fossil 4.73 × 10–3 9.94 × 10–3
Methane, biogenic 7.31 5.59
N
itrous oxide 3.32 × 10–3 7.99 × 10–3
Carbon dioxide, biogenic 24.8 19.2
Research Paper FPL–RP–672
14
53% (2,300/4,360) and 75% (2,920/3880) of the biomass
energy used in making new softwood framing lumber and
hardwood ooring is displaced by burning the old wood
at EOL to avoid coal power production. Wood burned for
energy recovery at EOL has a considerable effect, although
only 30% of the old wood is burned to replace coal.
Table 10 shows that for the base case, some major GHG
emissions were positive overall. Positive GHG emissions
occurred even though cumulative energy at EOL was nega-
tive (Table 9). Negative fossil CO2 emissions correspond to
negative energy consumption and result from the discarded
wood being burned to displace coal to generate electricity.
As a result, fossil CO2 values were –156 kg/m3 for softwood
framing lumber and –193 kg/m3 for hardwood ooring.
However, some positive GHG emissions occurred because
of the release of biogenic CO2 and biogenic methane from
wood burning and degradation. Wood degradation occurs
from two sources: the spreading of mulch and the landlling
of the discarded wood. Biogenic CO2 generated from
burning wood, mulch, and landlls was large for softwood
framing lumber (317 kg/m3) and hardwood ooring
(402 kg/m3). Biogenic methane emissions from landlls
were 21.5 and 27.2 kg for softwood framing lumber and
hardwood ooring, respectively. The discarded wood burned
to replace coal to generate electricity substantially offsets
fossil CO2 emissions but adds to biogenic CO2 emissions.
Tables 11 and 12 show cumulative energy use and GWP for
the base case and ve alternative EOL scenarios. GWP1 ex-
cludes biogenic CO2, the method consistent with TRACI 2
Method (PRé Consultants 2013, Bare 2011). GWP2 includes
biogenic CO2. Alternative scenario #1 (burn all the wood to
replace coal to generate electricity) had the lowest levels of
GWP1 and GWP2 across all the scenarios. Softwood framing
Table 7
.Summary environmental impact measures for producing
softwood framing lumber
Environmental
impact measures
(1)
New framing
lumber
(2)
Recovered
framing lumber
Ratio
(1)/(2)
Cumulative energy (MJ/m3)
6440
418
15
CO2total (kg/m3)
474
48.9
9.7
CO2less biogenic (kg/m3)
109
24.8
4.4
GWP including biogenic CO
2
(kg CO2-e/m
3
)
a
483
211
2.3
GWP less biogenic CO
2
(kg CO2-e/m3)
118
186
0.6
aGlobal warming potential (GWP) when biogenic CO2is given a characterization factor of 1.
Table 8. Summary environmental impact measures for producing
hardwood flooring
Environmental
impact measures
(1)
New
hardwood
flooring
(2)
Recovered
hardwood
flooring
Ratio
(1)/(2)
Cumulative energy (MJ/m3)
7,750
859
9.0
CO2total (kg/m3)
618
79.4
7.8
CO2less biogenic CO2(kg/m3)
228
48.7
4.7
GWP including biogenic CO
2
(kg CO2-e/m
3
)
a
630
195
3.2
GWP less biogenic CO
2
(kg CO2-e/m3)
240
175
1.4
a
Global warming potential (GWP) when biogenic CO2is given a characterization
factor of 1.
Table 9. Cumulative end
-of-life energy
consumption by fuel source allocated to 1 m
3
wood products (base
-case scenarioa,b)
Softwood framing
lumber Hardwood flooring
MJ/m3
%
MJ/m3
%
Coal
2,300
106
2,920
110
Crude oil
65
3
173
–7
Natural gas
31
1
41
2
Uranium
32
1
38
1
Biomass
0
0
0
0
Hydropower
5
0
7
0
Total
2,170
100
2,660
100
a
30% of wood waste from old structures goes to replace coal to
generate electricity, 10% gets ground into mulch, and the
remaining 60% transported to a construction and demolition
landfill with no methane capture
.
bEnergy values were determined using their higher heating values
in MJ/kg: 54.4 for natural gas and 20.9 for oven
-
dried wood, 26.2
for coal, 45.5 for crude oil and 381,000 for uranium.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
15
lumber and hardwood ooring had GWP1 values of –592
and –744 kg/m3, respectively. GWP2 values were –93 and
–113 kg/m3, respectively.
Cumulative energy was negative and lowest for alternative
scenario 1. Scenario 4 (burn wood to replace natural gas to
generate electricity) followed closely behind the rst alter-
native with values of –7,300 and –9,200 MJ/m3 for soft-
wood framing lumber and hardwood ooring, respectively.
Scenario 5 was next lowest but signicantly behind. In
Scenario 5, all wood goes to landlls and a small part is
emitted as biogenic methane (i.e., landll gas). Seventy-ve
% of the biogenic methane generated in the landll was
captured and burned to generate electricity to replace natu-
ral gas. Burning landll methane generates biogenic CO2.
The remaining 25% was considered fugitive emissions and
was emitted to the atmosphere. These results are consistent
with Kaplan and others (2009) who found burning wood for
energy provides substantially lower (negative) GHG emis-
sions than landlling and burning the captured landll meth-
ane for energy. When landll methane is not burned as in
Scenario 3, GHG emissions are much higher. For softwood
lumber, the effect of not burning landll methane for energy
when all wood is landlled was to increase GWP1 by a fac-
tor of four, from 199 to 808 kg CO2-equivalents/m3. The
effect was similar for hardwood ooring. Thus, methane
capture is critical for wood products when stored in a land-
ll to lower their impact on climate change.
Tables 13 and 14 compare GWP for cases where 1) old
wood products are discarded (six EOL cases) and new prod-
ucts are made or 2) old wood is reused to make products.
For the base case EOL scenario where we assume making
new products would require discard of old wood to the cur-
rent mix of proportions to energy, landlls, and mulch, then
the reuse of both softwood lumber and hardwood ooring
produces less GWP than making new products. The ben-
et is greatest for reuse of hardwood ooring (1,429/195
vs 1,110/211). This is true for GWP estimates that either
include or exclude biogenic CO2 emission. However, if we
Table 10. Major GHG emissions for the end-of-life (base-case
scenarioa)
Quantity emitted
(kg/m3)
Softwood framing
lumber
Hardwood
flooring
GHG
Carbon dioxide, fossil –156 –193
Methane, fossil –0.286 –0.353
Methane, biogenic 21.5 27.2
Nitrous oxide 1.78 × 10–3 2.40 × 10–3
Carbon dioxide, biogenic 317 402
a30% of wood waste from old structures goes to replace coal to generate electricity,
10% gets ground into mulch, and the remaining
60% is transported to a construction and
demolition landfill with no methane capture.
Table 11.Cumulative energy and GWP for the various end-of-life scenariosafor softwood framing
lumber
Environmental
impacts Base case Alt#1 Alt#2 Alt#3 Alt#4 Alt#5 Reuse New
Cumulative energy (MJ/m3)
2,160
–8,100
471
329
–7,300
1,420
418
6,440
GWP1(kgCO2-e/m3)g
310
592
28
808
379
199
186
118
GWP
2
(kgCO
2
-e/m3
)
h
627
93
980
928
120
394
211
483
a
Base case: 60% of wood waste into landfill with no methane capture/30% burned to replace coal power/10% ground into mulch;
Alt#1:
100% wood was
te burned to replace coal power;Alt#2: 100% wood waste ground into mulch;Alt#3: 100% wood waste into landfill
with no methane capture
;Alt#4: 100% wood waste burned to replace natural gas power;Alt#5: 100% wood waste into landfill with
methane capture; Global warming potential (GWP) with biogenic CO2having a characterization factor of 1.
Table 12
.Cumulative energy and GWP for the various end-of-life scenarios
a
for hardwood flooring
Environmental
impacts Base case Alt#1 Alt#2 Alt#3 Alt#4 Alt#5 Reuse New
Cumulative energy (MJ/m3)
2660
10,100
676
497
–9,200
1,720
859
7,750
GWP1(kgCO2-e/m3)g
397
744
40
1030
475
257
175
240
GWP
2
(kgCO
2
-e/m3)h
799
113
1,250
1180
156
504
195
630
a
Base case: 60% of wood waste into landfill with no methane capture/ 30% burned to replace coal power/ 10% ground into mulch;Alt#1:
100% wood waste burned to replace coal power
;Alt#2: 100% wood waste ground into mulch;
Alt#3: 100% wood waste into landfill with no
methane capture
;Alt#4: 100% wood waste burned to replace natural gas power;Alt#5: 100% wood waste into landfill with methane capture;
Global warming potential (GWP) with biogenic CO2having a characterization factor of 1.
Research Paper FPL–RP–672
16
consider GWP that excludes biogenic CO2 emissions (stan-
dard LCA practice) reuse could be notably worse (higher
GWP) than new wood if all old wood could be burned to
offset coal or natural gas in making electric power. Reuse
could be slightly worse than new for softwood lumber if
all old wood could be used as mulch (unlikely if wood is
contaminated (186/146)). So a key point is that if wood
cannot be reused for products the next best step is to keep it
out of landlls and use it for either energy (top priority) or
mulch (lower priority). There is a greater benet from keep-
ing hardwood out of landlls than keeping softwood out of
landlls.
Conclusion
Recovering softwood framing lumber and hardwood oor-
ing for reuse instead of making new products displaces a
considerable amount of production energy use and avoids
some GHG emissions, particularly biogenic CO2. Reusing
wood products for construction does not use biomass energy
during its production, unlike new wood products. Including
biogenic CO2 from burning biomass for energy adds con-
siderably to the estimate of GWP for new wood products.
Adding biogenic CO2 results in substantially higher GWP
for new wood products because over 50% of its primary en-
ergy is from mill residues (i.e., wood). However, if biogenic
CO2 is not included, then the GWP values for new products
are lower and reuse of wood does not avoid as much GWP
as new product production.
GWP values change considerably for the different cases of
discarding old wood (i.e., C&D waste). Standard GWP pro-
tocol excludes biogenic CO2 emissions whether generated
from burning wood, decaying mulch, decomposing wood
in landlls, or burning landll methane. Biogenic CO2
emissions are assume to be balanced out by the carbon se-
questration from trees over time as part of the natural carbon
cycle. Standard U.S. waste disposal practice includes burn-
ing wood for energy to replace coal power (30%), grinding
for mulch with open air decay (10%), and disposal in a
C&D landll without methane capture (60%). Therefore,
the following hierarchy in this study shows the lowest to the
highest GWP values for the six EOL scenarios investigated:
1) burning wood to generate electricity to replace coal,
2) burning wood to generate electricity to replace natural
gas, 3) grinding wood into mulch, 4) installing methane
capture equipment to capture most of the landll methane
and burning it to generate electricity to replace natural gas,
5) current U.S. practice, and 6) storing the wood in a land-
ll without methane capture. The two cases of burning old
wood to replace fossil fuels have greater GHG benets than
the two cases of landlling the discarded old wood with or
without methane capture with mulching the old wood falling
in between.
A downside to using C&D waste in energy generation to
offset coal or natural gas emissions is that C&D waste typi-
cally contains contaminants unless the material is separated
at the demolition or deconstruction site. Therefore, burning
C&D waste can result in other air emissions and unknown
materials that may require additional handling and associ-
ated energy consumption in the process than that already
noted.
Greater environmental benets tend to occur by keeping
hardwoods out of landlls than keeping softwoods out of
landlls. This is because hardwood ooring is produced
from a dense wood species, and if kept out of landlls,
could generate more energy per unit weight of wood than
softwoods. Alternately, hardwoods that are kept out of
Table 13. Scenariosa of GWP comparison of new softwood framing lumber plus the
various end-of-life scenarios to recovered softwood framing lumber
Environmental impacts Base caseaAlt#1bAlt#2cAlt#3dAlt#4e Alt#5fReuse
GWP1 (kgCO2-e/m3)g427 –474 146 926 –262 317 186
GWP2 (kgCO2-e/m3
)
h1,110 390 1,463 1,411 603 877 211
aBase case: 60% of wood waste into landfill with no methane capture/ 30% burned to replace coal power/ 10% ground
into mulch; Alt#1: 100% wood waste burned to replace coal power; Alt#2: 100% wood waste ground into mulch; Alt#3:
100% wood waste into landfill with no methane capture; Alt#4: 100% wood waste burned to replace natural gas power;
Alt#5: 100% wood waste into landfill with methane capture; Global warming potential (GWP) with biogenic CO2
having a characterization factor of 1.
Table 14. GWP comparison of new hardwood flooring plus the various EOL scenariosa to recovered
hardwood flooring
Environmental impacts Base case Alt#1 Alt#2 Alt#3 Alt#4 Alt#5 Reuse
GWP1 (kgCO2-e/m3) 637 –504 280 1,270 –235 497 175
GWP2 (kgCO2-e/m3) 1,429 518 1,880 1,810 787 1,134 195
aGlobal warming potential (GWP) with biogenic CO2 having a characterization factor of 1; Base case: 60% of wood waste into
landfill with no methane capture/30% burned to replace coal power/10% ground into mulch; Alt#1: 100% wood waste burned to
replace coal power; Alt#2: 100% wood waste ground into mulch; Alt#3: 100% wood waste into landfill with no methane capture;
Alt#4: 100% wood waste burned to replace natural gas power; Alt#5: 100% wood waste into landfill with methane capture.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
17
landlls and are then reused will avoid production of new
hardwood ooring, which requires more energy for drying
to a lower moisture content to improve dimensional stability
and to avoid degrade than softwood framing lumber does.
This translates to greater environmental benets of keeping
hardwoods out of landlls and reusing for ooring or using
it for energy than can be realized by recovery of softwood
species for reuse.
Product transportation can be a critical component of the
cradle-to-gate LCI. For new products, transportation often
requires long distances. For example, new wood ooring is
primarily produced in the eastern United States. Therefore,
transportation by both truck and rail is typically required to
deliver products to wholesale locations on the West Coast.
For reused wood products, transport distance can be shorter
because wood is recovered in large urban centers and can be
reused locally.
References
Athena. 2012. Athena Impact Estimator for Buildings.
Ottawa, Ontario: Athena Sustainable Materials Institute.
http://www.athenasmi.org/our-software-data/impact-estima-
tor/ (Accessed March 5, 2013).
ATSDR. 2001. Landll gas primer—an overview for en-
vironmental health professionals. Chapter 2: Landll gas
basics. Agency for Toxic Substances & Disease Registry
(ATSDR). http://www.atsdr.cdc.gov/HAC/landll/PDFs/
Landll_2001_ch2mod.pdf (Accessed March 5, 2013)
Bare, J.C. 2011. TRACI 2.0: the tool for the reduction and
assessment of chemical and other environmental impacts
2.0. Clean Technologies and Environmental Policy.
13: 687–696.
Bergman, R.D.; Bowe, S.A. 2010. Environmental im-
pact of manufacturing softwood lumber in northeastern
and north central United States. Wood and Fiber Science.
42(CORRIM Special Issue): 67–78.
Bergman, R.D.; Gu, H.; Falk R.H.; Napier, T.R. 2010.
Using reclaimed lumber and ooring in construction: mea-
suring environmental impact using life-cycle inventory
analysis. Society of Wood Science and Technology 53rd
International Convention. Geneva, Switzerland. October
11–15, 2010. WS-11. 1–11.
Bergman, Richard D.; Gu, Hongmei; Napier, Thomas
R.; Salazar, James; and Falk, Robert H. 2012. Life cycle
primary energy and carbon analysis of recovering soft-
wood framing lumber and hardwood ooring for reuse.
In: Proceedings, Instruments for Green Futures Markets,
American Center for Life Cycle Assessment XI Conference.
October 4-6, 2011. Vashon, WA: 44–51.
Blengini, G.A. 2009. Life-cycle of buildings, demolition
and recycling potential: a case study in Turin, Italy. Building
and Environment. (44): 319–330.
Borghi, A.D.; Gallo, M.; Borghi, M.D. 2009. A survey of
life-cycle approaches in waste management. International
Journal of Life Cycle Assessment. (14): 597–610.
CORRIM. 2010. Research guidelines for life-cycle inven-
tories. Consortium for Research on Renewable Industrial
Materials (CORRIM), Inc., University of Washington,
Seattle, WA. Updated 2010. 47 p.
EIA. 2013. Independent statistics and analysis: Glossary
(H). United States Energy Information Association.
http://www.eia.gov/tools/glossary/index.cfm?id=H
(Accessed March 5, 2013).
EPA. 1995. Construction and demolition waste landlls. ICF
Incorporated, Contract No. 68-W3-0008, February 1995.
39 p. http://www.epa.gov/osw/hazard/generation/sqg/const/
cdrpt.pdf (Accessed March 5, 2013).
EPA. 2009a. Estimating 2003: building-related construction
and demolition material amounts. Washington, D.C.: United
States Environmental Protection Agency. 60 p. http://www.
epa.gov/osw/conserve/imr/cdm/pubs/cd-meas.pdf (Accessed
March 5, 2013).
EPA. 2009b. 40 CFR Chapter I: endangerment and cause
or contribute ndings for greenhouse gases under Section
202(a) of the Clean Air Act; Final Rule. Federal Register
Docket ID No. EPA-HQ-OAR-2009-0171. Washington,
D.C.: United States Environmental Protection Agency. 52 p.
EPA. 2009c. Combustor survey database in support of the
2009 proposal of the Commercial and Industrial Solid Waste
Incinerator (CISWI) standards and the Industrial Boilers
Maximum Achievable Control Technology (MACT) stan-
dards, April 2009. Washington, D.C.: U.S. Environmental
Protection Agency.
EPA. 2010. WARM background and overview. http://epa.
gov/climatechange/wycd/waste/downloads/background-
and-overview10-28-10.pdf (Accessed March 5, 2013).
Washington, D.C.: U.S. Environmental Protection Agency.
25 p.
EPA. 2011. Inventory of U.S. greenhouse gas emissions
and sinks: 1990-2009, EPA-430-R-11-005. http://www.epa.
gov/climatechange/Downloads/ghgemissions/US-GHG-
Inventory-2011.pdf (Accessed March 13, 2013) 470 p.
EPA. 2012a. Materials characterization paper in support
of the nal rulemaking – identication of nonhazardous
secondary materials that are solid waste construction and
demolition materials–building-related C&D materials.
Washington, D.C.: U.S. Environmental Protection Agency.
15 p. http://www.regulations.gov/#!documentDetail;D=EPA-
HQ-RCRA-2008-0329-1811 (Accessed March 5, 2013).
EPA. 2012b. Waste reduction model. Updated February
2012. Washington, D.C.: U.S. Environmental Protection
Agency. http://www.epa.gov/climatechange/wycd/waste/cal-
culators/Warm_home.html (Accessed March 5, 2013).
Research Paper FPL–RP–672
18
Eriksson O.; Reich, C.M.; Frostell B.;Björklund, A.; Assefa,
G.; Sundqvist, J.-O.; Granath, J.; Baky, A.; Thyselius, L.
2005. Municipal solid waste management from a system
perspective. Journal of Cleaner Production. 13(3): 241–252.
Falk, R.H. 2002. Wood-framed building deconstruction: a
source of lumber for construction? Forest Products Journal.
52(3): 8–15.
Falk, R.H.; Guy B. 2007. Unbuilding: salvaging the archi-
tectural treasures of unwanted houses. Newtown, CT: The
Taunton Press Inc. 248 p.
Falk, R.H.; McKeever. 2012. Generation and recovery of
solid wood waste. BioCycle. August 2012: 30–32.
Fava J.; Jensen A.; Lindfors L.; Pomper, S., De Smet, B.,
Warren J, Vigon B. 1994. Life-cycle assessment data qual-
ity: a conceptual framework. Society for Environmental
Toxicology and Chemistry (SETAC) and SETAC
Foundation for Environmental Education. 179 p.
Heilmann, A.; Winkler, J. 2005. Inuence of the source
separation efciency of recyclable materials on the envi-
ronmental performance of municipal waste management
systems. Proceedings Sardinia 2005, Tenth International
Waste Management and Landll Symposium; S. Margherita
di Pula, Cagliari, 3–7 October 2005.
Hubbard, S.S.; Bowe S.A. 2010. A gate-to-gate life-cycle
inventory of solid strip hardwood ooring in the eastern
U.S. Wood and Fiber Science. 42(CORRIM Special Issue):
79–89.
ISO. 2006a. Environmental management—life-cycle as-
sessment—principles and framework. ISO 14040. Geneva,
Switzerland: International Organization for Standardization.
20 p.
ISO. 2006b. Environmental management—life-cycle assess-
ment—requirements and guidelines. ISO 14044. Geneva,
Switzerland: International Organization for Standardization.
46 p.
ISO. 2012. ISO 14067 Carbon footprint of products (draft).
ISO 14067. Geneva, Switzerland: International Organization
for Standardization. 52 pp.
IPCC. 2006. 2006 IPCC guidelines for national greenhouse
gas inventories. Prepared by the National Greenhouse Gas
Inventories Programme, Eggleston H.S., Buendia L., Miwa
K., Ngara T. and Tanabe K. (eds). Published: Institute
for Global Environmental Strategies, Japan. Geneva,
Switzerland: Intergovernmental Panel on Climate Change
(IPCC).
IPCC. 2007. Climate change 2007: The physical science ba-
sis. In: Solomon S., Qin D., Manning M., Chen Z., Marquis
M., Averyt K.B., Tignor M., Miller H.L. (eds.). Contribution
of working group to the fourth assessment report of the
Intergovernmental Panel on Climate Change (IPCC),
Cambridge, United Kingdom: Cambridge University Press.
996 p.
Kaplan, P.O.; DeCarolis, J.; Thorneloe, S. 2009. Is it bet-
ter to burn or bury waste for clean electricity generation?
Environmental Science and Technology. 43(6): 1711–1717.
Kibert, C.J. 2003. Deconstruction: the start of a sustainable
materials strategy for the built environment. UNEP Industry
and Environment. 26(2–3): 84–88.
Milota, M.R.; West, C.D.; Harley, I.D. 2004. Softwood lum-
ber—Southeast Region. In CORRIM Phase I Final Report
Module B. Life-cycle environmental performance of renew-
able building materials in the context of residential con-
struction. Seattle, WA: University of Washington. 75 p.
Milota, M.R.; West, C.D.; Hartley, I.D. 2005. Gate-to-gate
life inventory of softwood lumber production. Wood and
Fiber Science. 37: 47–57.
Napier, T.R.; McKay D.T.; Mowry N.D. 2007. A life-cycle
perspective on recycling construction materials (The most
sustainable materials may be the ones we already have).
In: Y.M. Chun, P. Claisse, T.R. Naik, E. Ganjian, eds.
Proceedings of the International Conference: Sustainable
construction materials and technologies, 11–13 June 2007
Coventry. London: Taylor and Francis. ISBN 13: 978-0-415-
44689-1: 563–573.
NWMOA. 2009, Construction & demolition waste man-
agement in the northeast in 2006. A report of the Northeast
Waste Management Ofcials Association (NWMOA),
June 30, 2009. 65 p. http://www.newmoa.org/solidwaste/
CDReport2006DataFinalJune302009.pdf (Accessed
March 5, 2013).
Ogden, C.L.; Fryar, C.D.; Carroll, M.D.; Flegal, K.M.
2004. Mean body weight, height, and body mass index,
United States. Advance Data No. 347, October 27, 2004.
Hyattsville, Maryland: National Center for Health Statistics.
18 p.
Olofsson, M.; Sunberg, J.; Sahlin, J. 2005. Evaluating
waste incineration as treatment and energy recovery
method from an environmental point of view. ASME
Conference Proceedings, 2005, 175 (2005), DOI:10.1115/
NAWTEC13-3168. May 23–25, 2005. Orlando, Florida.
175–192.
Oswalt S.N., Thompson M., Smith W.B. 2009. U.S. forest
resource facts and historical trends (metric unit brochure).
FS-901M. Revised September 2009. 60 p. http://a.fs.fed.
us/library/brochures/docs/Forest%20Facts%201952-
2007%20English.pdf. (Accessed March 5, 2013).
PRé Consultants. 2013. SimaPro 7 Life-Cycle assess-
ment software package, Version 7. Plotter 12, 3821 BB
Amersfoort, The Netherlands. http://www.pre.nl. (Accessed
March 5, 2013).
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
19
Puettmann, M.E.; Wilson, J.B. 2005. Life-cycle analysis
of wood products: cradle-to-grave LCI of residential wood
building materials. Wood and Fiber Science. (37): 18–29.
Puettmann, M.; Bergman, R.; Hubbard, S.; Johnson, L.;
Lippke, B.; Oneil, E.; Wagne, F.G. . 2010. Cradle-to-gate
life-cycle inventories of U.S. wood products production –
CORRIM Phase I and Phase II Products. Wood and Fiber
Science. 42(CORRIM Special Issue): 15–28.
SAIC. 2006. Life-cycle assessment: Code and practices.
Scientic Applications International Corporation (SAIC).
EPA/600/R-06/060 May 2006. 80 p.
Salazar, J.; Meil, J. 2009. Prospects for carbon-neutral
housing – the inuence of greater wood use on the carbon-
footprint of a single-family residence. Journal of Cleaner
Production. 17 (17): 1563–1571.
Sathre, R.; Gustausson L. 2006. Energy and carbon bal-
ances of wood cascade chains. Resource, Conservation and
Recycling 47(4): 332–355.
Skog, Kenneth E. 2008. Sequestration of carbon in har-
vested wood products for the United States. Forest Products
Journal. 58(6): 56–72.
Smith, A.; Brown, K.; Ogilvie, S.; Rushton, K.; Bates, J.
2001. Waste management options and climate change. Final
report to the European Commission, DG Environment. 205
p. http://ec.europa.eu/environment/waste/studies/pdf/cli-
mate_change.pdf (Accessed March 5, 2013).
Smith, B.W.; Miles, P.D.; Perry, C.H.; Pugh, S.A. 2009.
Forest Resources of the United States, 2007. Gen. Tech.
Rep. WO-78. Washington, D.C.: U.S. Department of
Agriculture, Forest Service, Washington Ofce. 336 pp.
Staley B.F.; Barlaz M.A. 2009. Composition of municipal
solid waste in the United States and implications for carbon
sequestration and methane yield. Journal of Environmental
Engineering. 135(10): 901–909.
Sunberg, J.; Olofsson, M.; Sahlin, J. 2004. Evaluating waste
incineration as treatment and energy recovery method from
an environmental point of view. Final version 2004-05-13.
Profu. Stockholm, Sweden. 81 p.
Thorneloe, S.A.; Weitz, K.A.; Jambeck, J. 2007. Application
of the U.S. decision support tool for materials and waste
management. Waste Management. 27(2007): 1006–1020.
USDA. 2013. Life-cycle inventory database project.
Washington, D.C.: United Department of Agriculture.
https://www.lcacommons.gov/nrel (Accessed March 5,
2013).
USFS. 2011. National report on sustainable forests—2010.
United States Department of Agriculture, Forest Service,
FS-979, June 2011. 214 p.
USDOT. 2010. Bureau of Transportation Statistics and U.S.
Census Bureau, 2007 Economic Census. Transportation.
2007 Commodity ow survey. United States Department of
Transportation (USDOT). 68 pp. http://www.census.gov/
prod/2010pubs/ec07tcf-ex.pdf (Accessed March 5, 2013).
Wang, X.; Padgett, J.M.; De la Cruz, F.B; Barlaz, M.A.
2011. Wood biodegradation in laboratory-scale landlls.
Environmental Science and Technolpgy. 45(16): 6864–6871.
Winistorfer, P.; Chen, Z.; Lippke, B.; Stevens, N. 2005.
Energy consumption and greenhouse gas emissions related
to use, maintenance, and disposal of a residential structure.
Wood and Fiber Science. 37(CORRIM Special Issue):
128–139.
Ximenes, F.A.; Gardner, W.D.; Cowie, A.L. 2008. The
decomposition of wood products in landlls in Sydney,
Australia. Waste Management. 28 (11): 2344–2354.
Research Paper FPL–RP–672
20
Appendix 1—Survey Instrument
Survey Instrument for Deconstruction Facility Operators
and Managers
This questionnaire is comprised of two parts: 1. wood
ooring and; 2. framing lumber. Some questions pertain to
companies or individuals who do deconstruction/demolish-
ing whereas some questions pertain to transportation to and
around a resale facility. We are looking to evaluate your
most current practices.
Part I—Wood Flooring
For this project, we are limiting our analysis to solid wood
ooring, including tongue and groove (T&G) and plank
ooring. We are not interested at this time in laminated (or
engineered) wood ooring. The goal of this section is to
determine all the energy inputs to remove, store, and sell the
ooring. We are assuming that the following represents a
typical sequence of ooring removal.
1. Transportation of workers to job-site.
2. Removal of any furniture or other materials such as
molding that would interfere with removal of the oor-
ing.
3. Removal ooring board by board.
4. Denailing of ooring either by hand or nail kicker.
5. Loading of ooring onto truck either by hand or with
equipment.
6. Transport the ooring to storage facility
7. Unload the wood ooring either by hand or equipment.
8. Store the wood ooring in a facility until sold
9. Electricity and fuel used to keep facility lighted and
heated.
10. Selling of wood ooring to customers
Part I—Wood Flooring (Soft-Strip)
1. What is the typical distance your crew travels to a job-
site to remove ooring? ________ miles
2. How many days do you typically stay on a deconstruc-
tion job to remove wood ooring? ____ days
3. How many hours per day spent removing ooring?
_____ hours
4. How many miles are driven per day while at the jobsite?
That is, driving to lunch, to the hardware store, picking
up equipment, back and forth from ofce, etc.
_______________ miles
5. Does your crew typically travel individually or as a
group to the jobsite? Circle one: individual, group.
6. If a group, how many typically ride in each vehicle?
______persons
7. Estimate how many square feet of ooring each person
can remove in an hour? _____sq.ft/hr/person
8. How many total sites (jobs) for wood oor removal are
completed on annual basis?
9. List tools used to remove ooring?
If no tools use fuel or electricity, go to question 11, other-
wise continue with question 10.
10. What tools do you use that require fuel or electricity,
such as a Nail Kicker, electric saw/etc. in the ooring
removal and denailing process?
What do you typically use for a power source? Circle
one: electrical outlet onsite or a jobsite generator
If you use a jobsite generator, what fuel does the genera-
tor use and how much fuel does it use per hour of opera-
tion?
___________ gallons per hour gasoline
___________ gallons per hour fuel oil
___________ gallons per hour propane
11. Estimate how many square feet of ooring each person
can denail and trim in an hour using the Nail Kicker or
other electrical tools?____________ sq.ft./hr/person
12. If you use plug-in electricity for your tools, do you
know how many Kw/hrs are used per square foot of
ooring removed?
____________ kw-hr/sq.ft. ooring
13. On average, how much ooring do you recover per
site? ____________ sq.ft.
14. Estimate how much ooring you lose in the removal and
trimming process? 5, 10, 25, 50%? ____%
15. What percentage of recovered ooring is hardwood or
softwood? _______% hardwood ________% softwood
16. Estimate how much ooring you recovered in the last
year? Please give your answer in square feet.
17. What is the average distance you must move the recov-
ered ooring to your resale facility? _______miles
18. Average number of trips to move the recovered ooring
to resale facility per job? ____
19. Please indicate what type of truck is used? Circle one:
pickup truck, box truck, semi, etc.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
21
20. Estimate what percentage of the load (by weight) is
ooring for a typical job? ______%
21. What type of fuel is used? Circle one: diesel or gas
22. We are trying to determine the energy costs associated
with storing and selling the wood ooring you recovere.
Can you estimate the holding costs (e.g., heating, light-
ing in BTU’s/KWH’s) associated with the retailing or
wholesaling of wood ooring in your store?
a. Store size _____________ sq. ft.
b. Typically, how much wood ooring is stored at one
time? ______________ sq. ft.
c. What would you estimate the percentage of wood oor-
ing (by weight) is in your store compared to overall
inventory _____%
d. Typically, how long is ooring stored before selling?
________days or weeks or months
e. Annual fuel consumption to heat store
___________ gallons heating oil
___________ gallons propane
___________1000 cubic feet natural gas
___________Kw-hr electricity
f. Annual electrical consumption for store (please provide
at least two of the following items)
____________ kilowatt-hours
____________ cents/Kw-hr
____________ monthly electric bill
23. If you use equipment such as a forklift to move ooring
around the facility, please list what type of equipment?
24. If the equipment uses fuel, what type of fuel? Circle
one: gas, diesel, LP, or electric
25. Estimate how much fuel (or electricity) is used per year
to move wood ooring around the facility and for load-
ing onto a customer’s vehicle? ____________ gallons
fuel / _______ kw-hr
26. If you sell, average selling price of wood ooring?
___________$/sq.ft
Research Paper FPL–RP–672
22
Part II—Framing Lumber
Framing lumber is the structural support of a building, not
the sheathing, trim, or other wood. In a house, it is usually
2 × 4s, 2 × 6s, 2 × 8s, and 2 × 10s. Once again, we are look-
ing to see what energy goes into removing, transporting, and
selling the framing lumber. However, if other re-useable ma-
terial is also removed when removing the framing lumber,
please indicate that. We expect that heavier equipment might
be used. We are assuming that the following represents a
typical sequence of framing removal.
1. Transportation of workers to jobsite.
2. Transportation of forklift, bobcat, or other energy using
equipment to jobsite.
3. Removal of surface materials such as roong, drywall,
suboors, and insulation that would interfere with the
removal of the framing lumber
4. Removal of actual framing.
5. Denailing of framing either by hand or nail kicker.
6. Loading of framing onto truck either by hand or with
equipment.
7. Transport the framing to storage facility
8. Unload the wood framing either by hand or equipment.
9. Store the wood framing in a facility until sold
10. Electricity and fuel used to keep facility lighted and
heated.
11. Selling of framing lumber to customer
Part II—Framing Lumber (Full Deconstruction)
What is the typical distance your crew travels to a jobsite to
remove framing? ________ miles.
1. How many miles are driven per day while at the jobsite?
That is, driving to lunch, to the hardware store, pick-
ing up equipment, back and forth from ofce, etc.
______________ miles
2. Does your crew typically travel individually or as a
group to the jobsite? Circle one: individual, group.
3. If a group, how many typically ride in each vehicle?
_____ persons
4. How many days do you typically stay on a deconstruc-
tion job to remove framing? _____days
5. How many hours per day spent removing framing?
_____ hours
6. Estimate how many linear feet or board feet of framing
each person can remove in an hour?
______ linear ft/hr/person or board ft/hr/person
7. How many total sites (jobs) for framing removal are
completed on annual basis?
8. List tools used to remove ooring.
If no tools use fuel or electricity, go to question 11, oth-
erwise continue with question 10.
9. What tools do you use that require fuel or electricity,
such as a power saw/etc. in the framing removal and
denailing process?
What do you typically use for a power source? Circle
one: electrical outlet onsite or a jobsite generator
If you use a jobsite generator, what fuel does the gen-
erator use and how much fuel does it use per hour of
operation?
___________ gallons per hour gasoline
___________ gallons per hour fuel oil
___________ gallons per hour propane
10. Please estimate how many lineal feet, board ft. or tons
of framing each person can denail and trim in an hour
using the Nail Kicker or other electrical tools?
11. If you use plug-in electricity for your tools, do you
know how many Kw/hrs are used per square foot of
framing removed?
12. On average, how much framing do you recovere per
site? ____________ board feet or lineal feet or tons
Can you estimate how much framing you loose in the re-
moval and trimming process? 10% 25% 50%
13. Do you use heavy equipment to recovere and move
framing, such as a telescoping forklift, Bob Cat, boom
lift, or other equipment? If so, please indicate how
many total hours you typically use each machine on
each job and how much fuel it burns per hour.
Machine Type Hours used Fuel type Fuel use per hour
Forklift
Bobcat
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
23
14. Can you estimate how much framing you recovered in
the last year? Please give your answer in either board
feet, lbs (tons), or lineal feet of each size.
15. What is the average distance you must move the recov-
ered framing to your resale facility? _______miles
16. Average number of trips to move the framing lumber to
resale facility per job? ____
17. Please indicate what type of truck is used? Circle one:
pickup truck, box truck, semi, etc.
18. Estimate what percentage of the load (by weight) is
framing for a typical job? _____%
19. What type of fuel is used in the truck? Circle one: diesel
or gas
20. We are trying to determine the energy costs associated
with storing and selling the wood framing you recovere.
Can you estimate the holding costs (e.g., heating, light-
ing in BTU’s/KWH’s) associated with the retailing or
wholesaling of wood framing in your store?
a. Store size _____________ sq. ft.
b. Typically, how much wood framing is stored at one
time? ______________ sq. ft.
c. Estimate the percentage of wood framing (by
weight) is in your store compared to overall inven-
tory _____%
d. Typically, how long is framing stored before selling?
________days or weeks or months
e. Annual fuel consumption to heat store
___________ gallons heating oil
___________ gallons propane
___________1000 cubic feet natural gas
___________kw-hr electricity
f. Annual electrical consumption for store (please pro-
vide at least two of the following items)
____________ kilowatt-hours
____________ cents/kw-hr
____________ monthly electric bill
21. If you use equipment such as a forklift to move ooring
around the facility, please list what type of equipment?
22. If the equipment uses fuel, what type of fuel? Circle
one: gas, diesel, LP, or electric
23. Estimate how much fuel (or electricity) is used per year
to move wood ooring around the facility and for load-
ing onto a customer’s vehicle? ____________ gallons
fuel / _______ kw-hr
24. If you sell framing lumber, average selling price of ma-
terial broken down by size if possible?
Size Price ($ per bf)
2x4’s
2x6’s
2x8’s
2x10’s
2x12’s
3x3’s
3x4’s
4x4’s
6x6’s
Other ___________________
Other ___________________
Other ___________________
Other ___________________
Other ___________________
If you have more to add please make as many additional
comments as necessary. We appreciate your time to help out
on this important research project. Also, please call Rick
Bergman (608) 231-9477 or email at rbergman@fs.fed.us if
you have any questions.
Please send the complete questionnaire to:
Rick Bergman
Mail: Forest Products Laboratory
One Gifford Pinchot Dr.
Madison, WI 53704
Fax: (608) 231-9508 (fax)
Email: rbergman@wisc.edu
Research Paper FPL–RP–672
24
Appendix 2—Landll Gas (LFG) Equations
Equation 1: Where GHGDE is GHGs directly emitted to atmosphere (kg CO2-eq):



 



 
12
16
GWPLFG1
12
44
LFG1GHG 442CHCCHkgCCOkgDE DCCWDCCW
Equation 2: Where GHGLFGR is GHG emitted from LFG energy recovery (kg CO2-eq):

   
12
44
LFGLFGGHG RCkgLFGR DCW
Equation 3: Where GHGLFGF is GHG emitted from LFG flaring (kg CO2-eq):

   
12
44
LFG1LFGGHG RCkgLFGF DCW
Equation 4: EOLFGR is the energy offset by LFG recovery (MJ/kg):


   
 
42CHCORCkgHHVLFGR 12
16
12
44
LFGLFGLFGEO CCDCW
Wkg wood mass (kg)
C carbon montent of mood = 50%
D decomposition of mood in mandfill = 23% (Skog 2008)
CCO2 carbon content of wood converted to CO2 at landfill surface= 55%
CCH4 carbon content of wood converted to CH4 at landfill surface= 45%
GWPCH4 global warming potential of CH4 = 25
LFGC landfill gas capture efficiency = 75%
LFGR landfill gas energy recovery efficiency = 70%
LFGHHV landfill gas higher heating value = 15.8 MJ/kg
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
25
Appendix 3—Assumptions and
Limitations
• The oven-dried (OD) density of solid-strip hardwood
ooring was assumed to be 657 kg/m3 based on several
hardwood species including hard maple, oak, cherry, ash,
and beech (Hubbard and Bowe 2010).
• Based calculation for the OD density of recovered fram-
ing lumber on a Southeast (SE) and Pacic Northwest
(PNW) softwood lumber study done by Milota and oth-
ers (2005). Milota and others reported 774 OD kg wood
per 1.623 m3 planed dry western lumber and 883 OD kg
per 1.623 m3 planed dry southwestern lumber. In addi-
tion, Milota and others reported approximately 21 mil-
lion cubic meters annual lumber production in the PNW
for Douglas r and western hemlock and 36 million
cubic meters in southern pine dimension lumber.
Density value for recovered framing lumber estimated
using weighted-averages as following: (774/1.623) ×
(21/(21+36)) + (883/1.623)x(36/(21+36)) =519 kg/m3
• Based calculation for onsite electricity use during the
reclaiming process on the assumption that the on-site
grid electricity has the same value as the electricity
per volume reclaimed material produced from on-site
generators. During a physical visit to a demolition site
(7/7/2009 in Madison, WI), data collected included time
and power of the generator powered by gasoline. Using
run-time data, results estimated the electricity used
from the grid per volume of material recovered.
Results estimated 20.09 kWh/Msf of ooring and
27.08 kWh/Mbf of framing lumber recovered. Although
removal of other materials such as wood doors, cast iron
sink, etc. occurred during deconstruction, the study as-
signed all of the material and energy inputs to the old
wood products.
• HHV represents the energy content of a fuel with the
combustion products such as water vapor brought to
25º C (77º F), whereas lower heating value (LHV) ig-
nores the energy produced by the combustion of hydro-
gen in fuel. HHV is the preferred method in the United
States (EIA 2013).
• For transportation of workers and materials to/from the
demolition site, survey results indicated transportation of
ve men during each building deconstruction daily with
an average weight 86 kg per worker (Ogden and others
2004).
• During deconstruction, survey results estimated weight-
ed average of 17% and 11% material loss for softwood
framing lumber and solid-strip hardwood ooring, re-
spectively.
• During the transportation, assumed the recovered materi-
als had 8% MC to account for the water weight.
• U.S. LCI Database typically provided LCI data for mate-
rials and energy including electricity (USDA 2012).
• The study modied a sanitary landll process in the U.S.
Ecoinvent database to alter waste and emissions to waste
treatment based on U.S. specic data, named specic
for our project: Disposal, wood untreated, 20% water,
to sanitary landll/CH with U.S. electricity U – USLCI.
To create a U.S.-specic process, removed all processes
associated with burning of sludge from wastewater treat-
ment of short-term leachate.
• Assumed 1kg of disposal wood produce 0.314 of LFG,
and 45% of it was methane at the surface on a molar
basis and the rest was CO2. On a mass basis, the landll
emits 0.072 kg CH4/kg and 0.242 kg CO2/kg. In addi-
tion, assumed approximately 75% of methane captured
leaving the landll and the remainder emitted directly to
the atmosphere. Of the LFG, collected, assumed
30% ared (without energy recovery) and 70% burned
for energy recovery (Salazar and Meil 2009).
• Assumed landll methane has the same characteristics as
natural gas.
• Survey questionnaire collected primary data on an an-
nual basis across the United States for 2009.
• Collected primary mill data through a critically reviewed
questionnaire in accordance with ISO 14040 and 14044
standards (2006a,b). Missing values were not weighted-
average for a particular process in accordance with ISO
standards.
• Background information1: recycled maple oor can
be sold for $1.50/ft2, Oak oor for $1.00/ft2, Birch for
$1.25/ft2, and Douglas-r for $1.25/ft2.
• Ideal reclaimed material cost is 5075% of virgin
materials.
• Size of the deconstruction industry is unknown.
• Changed wood conversion to electricity process in U.S.
LCI Database because less electricity was produced from
wood from steam turbine (standard U.S. practice) than
gasication systems provided in the dataset. Estimations
provided by actual wood power plants lowered the value
from 2.17 to 1.14 kWh/kg OD wood, a more conserva-
tive but realistic value. Calculation assuming HHV of
20.9 MJ/kg OD wood and 20% conversion of wood
to electricity conrmed the value of 1.14 kWh/OD kg
wood.
• Assumed energy use to store the virgin and recover
wood material the same on a per unit basis.
• The study derived new framing lumber and new wood
ooring transportation data from USDOT (2010).
1 Frank Bryne, Madison Restore, July 20, 2009
Research Paper FPL–RP–672
26
Assumed transportation data on Wood product manufac-
turing (NAICS code 321) estimated virgin wood fram-
ing and Furniture and related product manufacturing
(NAICS code 337) estimated new solid-strip hardwood
ooring.
• Future work will examine regional effects because insuf-
cient data were available.
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
27
Appendix 4— SimaPro Inputs
revised_appendix_4a.xls
Recovered wood framing lumber
CompositeRecoveredSoftwoodFramingLumberfortheUnitedStates
Removal
Output Amount Unit
Recovered softwoodframing lumber, at deconstruction site 519 kg 83%
Disposal, wood untreated, 20% water, to santitary lanfilled/CH with US
electricty - US LCI 106 kg 17%
Input
Installed softwood framing lumber, at deconstruction site 625 kg
Transport, Single unit truck, gasoline powered/US 0.62 tkm materials
Transport, Single unit truck, diesel powered/US 0.51 tkm materials
Gasoline, combusted in equipment/US 1.53 liter
Electricity, at grid, US/US 14.3 kWh
Storage
Output Amount Unit
Recovered softwood framing lumber, at storage facility 519 kg
Input
Recovered framing lumber, at deconstruction site 519 kg
Transport, combination truck, gasoline powered/US 0.67 tkm to storage facility
Transport, combination truck, diesel powered/US 0.55 tkm to storage facility
Electricity, at grid, US/US 2.86 kWh lighting
Residual fuel oil, combusted in industrial boiler/US 0.22 liter heating
Natural gas, combusted in industrial boiler/US 9.0E-05 m³heating
Gasoline, combusted in equipment/US 1.44 liter transporting on-site
ProductTransportation
Output Amount Unit
Recovered softwood framing lumber, at construction site 519 kg
Input
Recovered framing lumber, at storage facility 519 kg
Transport, single unit truck, diesel powered/US 12.5 tkm to construction site
LCI Inputs R 4/22/2013
Research Paper FPL–RP–672
28
revised_appendix_4a.xls
New wood framing LCI Inputs
CompositeSoftwoodFramingLumberMillfortheUnitedStates
Output Amount Unit
Surfaced dried lumber, at planer mill, US AVG, at mill 519 kg
Input
Surfaced dried lumber, at planer mill, US SE/kg/US - Modified 328 kg 63.2%
Surfaced dried lumber, at planer mill, US SE/kg/US - Modified 191 kg 36.8%
Storage
Output Amount Unit
Surfaced dried lumber, at planer mill, US AVG, at storage facility 519 kg
Input
Surfaced dried lumber, at planer mill, US AVG, at mill 519 kg
Transport, single unit truck, diesel powered/US 233 tkm to storage facility
Transport, train, diesel powered/US 96 tkm to storage facility
Electricity, at grid, US/US 2.86 kWh lighting
Residual fuel oil, combusted in industrial boiler/US 0.22 liter heating
Natural gas, combusted in industrial boiler/US 9.0E-05 m³heating
Gasoline, combusted in equipment/US 1.44 liter transporting on-site
ProductTransportation
Output Amount Unit
Surfaced dried lumber, at planer mill, US AVG, at construction site 519 kg
Input
Surfaced dried lumber, at planer mill, US AVG, at storage facility 519 kg
Transport, combination truck, diesel powered/US 14 tkm to construction site
LCI Inputs V 4/22/2013
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
29
Appendix 4b Flooring Recovery LCI - input for SimaPro.xls
Recovered solid strip wood flooring
CompositeRecoveredHardwoodFlooringtheUnitedStates
Removal
Output Amount Unit
Recovered hardwood flooring, at deconstruction site 657 kg 89%
Disposal, wood untreated, 20% water, to santitary lanfilled/CH with US
electricty - US LCI 81 kg 11%
Input
Installed hardwood flooring, at deconstruction site 738 kg
Transport, Single unit truck, gasoline powered/US 2.86 tkm materials
Transport, Single unit truck, diesel powered/US 3.84 tkm materials
Gasoline, combusted in equipment/US 4.57 liter
Electricity, at grid, US/US 18.6 kWh
Storage
Output Amount Unit
Recovered hardwood flooring, at storage facility 657 kg
Input
Recovered hardwood flooring, at deconstruction site 657 kg
Transport, combination truck, gasoline powered/US 2.86 tkm to storage facility
Transport, combination truck, diesel powered/US 3.84 tkm to storage facility
Electricity, at grid, US/US 1.17 kWh heating
Residual fuel oil, combusted in industrial boiler/US 1.12 liter heating
Natural gas, combusted in industrial boiler/US 0.659 heating
Liquefied petroleum gas, combusted in industrial boiler/US 0.831 liter heating
Electricity, at grid, US/US 7.55 kWh lighting
Electricity, at grid, US/US 0.558 kWh transporting on-site
Gasoline, combusted in equipment/US 2.47 liter transporting on-site
ProductTransportation
Output Amount Unit
Recovered hardwood flooring, at construction site 657 kg
Input
Recovered hardwood flooring, at storage facility 657 kg
Transport, single unit truck, diesel powered/US 17 tkm to construction site
LCI Inputs R 2/21/2013
Research Paper FPL–RP–672
30
Appendix 4b Flooring Recovery LCI - input for SimaPro.xls
New solid strip hardwood flooring
USSolidStripHardwoodFlooringComposite
Storage
Output Amount Unit
Solid strip and plank flooring, hardwood, US NE-NC, at storage facility 657 kg
Input
Solid strip and plank flooring, hardwood, US NE-NC, at mill 657 kg
Transport, combination truck, diesel powered/US 813 tkm to storage facility
Transport, train, diesel powered/US 4 tkm to storage facility
Electricity, at grid, US/US 1.17 kWh heating
Residual fuel oil, combusted in industrial boiler/US 1.12 liter heating
Natural gas, combusted in industrial boiler/US 0.659 m³ heating
Liquefied petroleum gas, combusted in industrial boiler/US 0.831 liter heating
Electricity, at grid, US/US 7.55 kWh lighting
Electricity, at grid, US/US 0.558 kWh transporting on-site
Gasoline, combusted in equipment/US 2.47 liter transporting on-site
ProductTransportation
Output Amount Unit
Solid strip and plank flooring, hardwood, US NE-NC, at construction site 657 kg
Input
Solid strip and plank flooring, hardwood, US NE-NC, at storage facility 657 kg
Transport, combination truck, diesel powered/US 17 tkm to construction site
LCI Inputs V 2/21/2013
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
31
Appendix 5—LCI Flows
No Substance Compartment Unit
Reclaimed
flooring, no EOL,
at construction
site
Solid strip and plank
flooring, hardwood,
US NE-NC, at
construction site
Reclaimed framing
lumber, no EOL,
at construction
site
Surfaced dried
lumber, at planer
mill, US AVG, at
construction site
1 Carbon dioxide, in air Raw kg 0.394989242 1115.391423 0.243722696 2461.63328
2 Coal, 26.4 MJ per kg, in ground Raw kg 8.900009534 30.9100888 5.491160187 17.49535827
3 Energy, from hydro power Raw MJ 7.716753336 11.60133582 4.861951228 37.02101266
4 Gas, natural, 46.8 MJ per kg, in ground Raw kg 0.003127892 0.586299379 x 0.655641422
5 Gas, natural, in ground Raw m3 2.254683069 17.52841005 0.974065359 12.73090649
6 Iron ore, in ground Raw kg x 0.000210417 x x
7 Limestone, in ground Raw kg 4.40529E-05 21.40715507 x 24.06784967
8 Nickel Raw m3 0.545879263 2.002789587 0.224457368 1.66269518
9 Occupation, forest, intensive, normal Raw m2a x 2400.896794 x x
10 Oil, crude, 42 MJ per kg, in ground Raw kg 0.044943599 2.676549027 x 2.958685658
11 Oil, crude, in ground Raw kg 9.555540097 41.13096704 3.910362129 15.43115058
12 Oxygen, in air Raw kg x 1.50374E-05 x x
13 Uranium oxide, 332 GJ per kg, in ore Raw kg 0.000211681 0.000751922 0.000130615 0.000412281
14 Uranium, 2291 GJ per kg, in ground Raw kg 3.10718E-09 8.86132E-06 x 9.95911E-06
15 Water, process, well, in ground Raw kg x 2.959882421 x x
16 Water, unspecified natural origin/m3 Raw m3 x x x 0.190482081
17 Water, well, in ground Raw m3 x 0.106907415 x x
18 Wood and wood waste, 20.9 MJ per kg, ovendry basis Raw kg x 185.6510361 x 208.7263515
19 Wood and wood waste, 9.5 MJ per kg Raw kg 3.21075E-05 3.21355E-05 x x
20 Wood, soft, US SE, standing/m3 Raw m3 x x x 1.056604878
21 2-Chloroacetophenone Air kg 1.3151E-11 4.56847E-11 8.11462E-12 4.64181E-11
22 5-methyl Chrysene Air kg 8.57664E-11 2.9567E-10 5.2921E-11 1.66486E-10
23 Acenaphthene Air kg 1.98817E-09 6.85399E-09 1.22677E-09 3.85934E-09
24 Acenaphthylene Air kg 9.74621E-10 3.3599E-09 6.01376E-10 1.89189E-09
25 Acetaldehyde Air kg 8.23735E-05 0.000728841 3.57812E-05 0.001719876
26 Acetophenone Air kg 2.81806E-11 9.78959E-11 1.73885E-11 9.94673E-11
27 Acrolein Air kg 1.10648E-05 0.000516431 5.01271E-06 0.007478123
28 Aldehydes, unspecified Air kg 0.000394706 0.001686594 0.000161184 0.000634206
29 Ammonia Air kg 0.000214002 0.000904373 9.15732E-05 0.000652492
30 Ammonium chloride Air kg 1.12352E-05 3.99092E-05 6.93253E-06 2.18823E-05
31 Anthracene Air kg 8.18683E-10 2.82232E-09 5.05157E-10 1.58919E-09
32 Antimony Air kg 7.03936E-08 1.22941E-06 4.32991E-08 1.48881E-05
33 Arsenic Air kg 1.86538E-06 2.41679E-05 1.06711E-06 4.44422E-05
34 Barium Air kg x 0.000763005 x x
35 Benzene Air kg 0.000105582 0.001295539 4.68139E-05 0.007918452
36 Benzene, chloro- Air kg 4.13316E-11 1.43581E-10 2.55031E-11 1.45885E-10
37 Benzene, ethyl- Air kg 1.76599E-10 6.93994E-10 1.08968E-10 6.23329E-10
38 Benzo(a)anthracene Air kg 3.11875E-10 1.07515E-09 1.92438E-10 6.05396E-10
39 Benzo(a)pyrene Air kg 1.48143E-10 5.10706E-10 9.14095E-11 2.87568E-10
40 Benzo(b,j,k)fluoranthene Air kg 4.28832E-10 1.47835E-09 2.64605E-10 8.32428E-10
41 Benzo(g,h,i)perylene Air kg 1.0526E-10 3.62872E-10 6.49492E-11 2.04326E-10
42 Benzyl chloride Air kg 1.3151E-09 4.56847E-09 8.11462E-10 4.64181E-09
43 Beryllium Air kg 9.21059E-08 5.22874E-07 5.48416E-08 2.2649E-06
44 Biphenyl Air kg 6.62746E-09 2.28474E-08 4.08938E-09 1.28649E-08
45 Bromoform Air kg 7.32697E-11 2.54529E-10 4.521E-11 2.58615E-10
46 BTEX (Benzene, Toluene, Ethylbenzene, and Xylene), unspecified ratio Air kg 0.000689952 0.004811743 0.00029527 0.003546035
47 Butadiene Air kg 4.19929E-06 5.34675E-06 1.82407E-06 2.71223E-06
48 Cadmium Air kg 3.27987E-07 1.76423E-06 1.68075E-07 8.36955E-06
49 Carbon dioxide Air kg x x x 0.387940417
50 Carbon dioxide, biogenic Air kg 19.18702515 389.9056033 24.8357227 365.0154064
51 Carbon dioxide, fossil Air kg 49.67036669 227.9076378 23.92201678 108.5247301
52 Carbon disulfide Air kg 2.44232E-10 8.48431E-10 1.507E-10 8.6205E-10
53 Carbon monoxide Air kg 0.027648663 2.498400905 2.24647E-05 1.162003941
54 Carbon monoxide, fossil Air kg 1.115974063 1.180377546 0.463712491 0.525031041
55 Chloride Air kg 3.02705E-10 1.05355E-09 1.8678E-10 6.78568E-10
56 Chlorine Air kg 8.97629E-09 0.001451336 x 0.001475193
57 Chloroform Air kg 1.10844E-10 3.85057E-10 6.83947E-11 3.91238E-10
58 Chromium Air kg 1.2409E-06 1.49109E-05 7.01312E-07 4.16248E-05
59 Chromium VI Air kg 3.07975E-07 1.06171E-06 1.90032E-07 5.97826E-07
60 Chrysene Air kg 3.89852E-10 1.34397E-09 2.40553E-10 7.56762E-10
61 Cobalt Air kg 1.5441E-06 4.31635E-06 5.78788E-07 1.36177E-05
62 Copper Air kg 8.76266E-09 1.7934E-07 5.10678E-09 9.22764E-08
63 Cumene Air kg 9.95716E-12 3.45899E-11 6.14393E-12 3.51451E-11
64 Cyanide Air kg 4.69677E-09 1.6316E-08 2.89808E-09 1.65779E-08
65 Dinitrogen monoxide Air kg 0.000798902 0.002866567 0.000326802 0.003324541
66 Dioxin, 2,3,7,8 Tetrachlorodibenzo-p- Air kg 5.80321E-12 2.08718E-07 2.64976E-12 3.11841E-06
67 Dioxins, measured as 2,3,7,8-tetrachlorodibenzo-p-dioxin Air kg 1.51906E-16 1.51906E-16 x x
68 Ethane, 1,1,1-trichloro-, HCFC-140 Air kg 9.33979E-10 3.98902E-09 3.90016E-10 1.58022E-09
69 Ethane, 1,2-dibromo- Air kg 2.25445E-12 7.83167E-12 1.39108E-12 7.95739E-12
70 Ethane, 1,2-dichloro- Air kg 7.51484E-11 2.61056E-10 4.63693E-11 2.65246E-10
71 Ethane, chloro- Air kg 7.89058E-11 2.74108E-10 4.86877E-11 2.78509E-10
72 Ethene, tetrachloro- Air kg 1.82195E-07 6.20452E-07 1.08039E-07 3.42634E-07
73 Ethene, trichloro- Air kg 2.62384E-11 2.71274E-11 x x
74 Fluoranthene Air kg 2.76794E-09 9.54217E-09 1.70792E-09 5.373E-09
75 Fluorene Air kg 3.54755E-09 1.22298E-08 2.18897E-09 6.88633E-09
76 Fluoride Air kg 3.34582E-07 1.18848E-06 2.06449E-07 5.167E-06
77 Formaldehyde Air kg 0.000167358 0.001923641 5.88787E-05 0.008319854
78 Furan Air kg 1.77491E-11 6.31152E-11 1.09518E-11 3.61766E-11
79 Heat, waste Air MJ 0.39771 x 0.52046 x
80 Hexane Air kg 1.25874E-10 4.37268E-10 7.76685E-11 4.44287E-10
81 Hydrazine, methyl- Air kg 3.19381E-10 1.10949E-09 1.97069E-10 1.1273E-09
82 Hydrocarbons, unspecified Air kg 6.48455E-05 0.000230341 4.0012E-05 0.000126297
83 Hydrogen chloride Air kg 0.004818197 0.018896858 0.002929338 0.044715943
84 Hydrogen fluoride Air kg 0.000584709 0.002015663 0.000360775 0.001134831
85 Hydrogen sulfide Air kg 9.78444E-12 3.40544E-11 6.03736E-12 2.19336E-11
86 Indeno(1,2,3-cd)pyrene Air kg 2.37814E-10 8.19838E-10 1.4674E-10 4.61634E-10
87 Iron Air kg x 0.000763005 x x
88 Isophorone Air kg 1.08965E-09 3.78531E-09 6.72354E-10 3.84607E-09
89 Isoprene Air kg 0.009921691 0.034532074 0.006122043 0.022241254
90 Kerosene Air kg 5.38147E-06 1.91142E-05 3.32018E-06 1.048E-05
91 Lead Air kg 1.98363E-06 0.00022075 1.12535E-06 9.32676E-05
92 Magnesium Air kg 4.28832E-05 0.000147835 2.64605E-05 8.32428E-05
93 Manganese Air kg 2.50896E-06 0.001768589 1.35847E-06 0.002991965
94 Mercaptans, unspecified Air kg 3.81527E-07 1.35651E-06 2.35416E-07 1.41564E-06
95 Mercury Air kg 3.63339E-07 1.76988E-06 2.14805E-07 7.28671E-06
96 Metals, unspecified Air kg 1.45013E-08 0.005345182 6.92614E-13 0.079867914
97 Methane Air kg 0.08692436 0.421212633 0.041932457 0.278714432
98 Methane, biogenic Air kg 5.589 x 7.314 x
99 Methane, bromo-, Halon 1001 Air kg 3.00594E-10 1.04422E-09 1.85477E-10 1.06098E-09
Research Paper FPL–RP–672
32
100 Methane, dichloro-, HCC-30 Air kg 2.09877E-06 4.28123E-05 9.94679E-07 0.000544782
101 Methane, dichlorodifluoro-, CFC-12 Air kg 1.10892E-09 4.77325E-09 4.53798E-10 1.79079E-09
102 Methane, fossil Air kg 0.012505587 0.037332056 0.005374387 0.024399024
103 Methane, monochloro-, R-40 Air kg 9.95716E-10 3.45899E-09 6.14393E-10 3.51451E-09
104 Methane, tetrachloro-, CFC-10 Air kg 2.21369E-10 5.6243E-06 4.53798E-11 8.40302E-05
105 Methanol Air kg x x x 0.000199253
106 Methyl ethyl ketone Air kg 7.32697E-10 2.54529E-09 4.521E-10 2.58615E-09
107 Methyl methacrylate Air kg 3.75742E-11 1.30528E-10 2.31846E-11 1.32623E-10
108 N-Nitrodimethylamine Air kg 5.80007E-12 5.99872E-12 x x
109 Naphthalene Air kg 2.9319E-07 0.000429068 1.05857E-07 0.000181492
110 Nickel Air kg 1.73382E-05 0.000135687 5.44088E-06 7.41356E-05
111 Nitrogen oxides Air kg 0.363888569 1.746210706 0.170237861 1.193315102
112 Nitrogen, total Air kg x x x 8.76702E-05
113 NMVOC, non-methane volatile organic compounds, unspecified origin Air kg 0.020800881 0.084952299 0.008191468 0.033816433
114 Organic acids Air kg 4.12865E-08 1.46656E-07 2.54753E-08 8.04118E-08
115 Organic substances, unspecified Air kg 2.6016E-05 0.028871248 1.49451E-05 4.70208E-05
116 PAH, polycyclic aromatic hydrocarbons Air kg 1.80433E-05 2.29731E-05 7.83758E-06 1.16535E-05
117 Particulates, < 10 um Air kg 4.46743E-05 0.0332225 x x
118 Particulates, > 2.5 um, and < 10um Air kg 0.004557214 0.091028294 0.002277674 0.947526756
119 Particulates, unspecified Air kg 0.01519143 0.874807804 0.008906701 0.343247833
120 Phenanthrene Air kg 1.0526E-08 3.62872E-08 6.49492E-09 2.04326E-08
121 Phenol Air kg 7.20543E-10 0.006936408 1.85477E-11 2.06475E-05
122 Phenols, unspecified Air kg 7.91815E-07 8.6026E-06 2.62337E-07 9.61544E-05
123 Phosphate Air kg x x x 2.00047E-06
124 Phthalate, dioctyl- Air kg 1.37146E-10 4.76427E-10 8.46239E-11 4.84074E-10
125 Potassium Air kg x 0.135259945 x x
126 Propanal Air kg 7.1391E-10 2.48003E-09 4.40508E-10 2.51984E-09
127 Propene Air kg 0.000277089 0.000352801 0.000120361 0.000178965
128 Propylene oxide Air kg x 7.29658E-11 x x
129 Pyrene Air kg 1.2865E-09 4.43505E-09 7.93815E-10 2.49728E-09
130 Radioactive species, unspecified Air Bq 220696.1287 760660.4401 136098.3141 428047.3597
131 Radionuclides (Including Radon) Air kg 0.000300902 0.001068848 0.000185667 0.000586052
132 Selenium Air kg 5.22194E-06 1.85198E-05 3.17861E-06 1.53823E-05
133 Sodium Air kg x 0.003121383 x x
134 Styrene Air kg 4.69677E-11 1.6316E-10 2.89808E-11 1.65779E-10
135 Sulfur dioxide Air kg 0.163796405 0.743235329 0.09183665 0.481662491
136 Sulfur oxides Air kg 0.040618974 0.172844458 0.016647482 0.108597679
137 Sulfuric acid, dimethyl ester Air kg 9.01781E-11 3.13267E-10 5.56431E-11 3.18295E-10
138 t-Butyl methyl ether Air kg 6.57548E-11 2.28424E-10 4.05731E-11 2.3209E-10
139 Tar Air kg 3.4046E-10 1.18496E-09 2.10076E-10 7.63202E-10
140 TOC, Total Organic Carbon Air kg x 0.000511133 x 0.007637393
141 Toluene Air kg 4.39265E-05 5.59305E-05 1.90807E-05 2.83724E-05
142 Toluene, 2,4-dinitro- Air kg 5.26039E-13 1.82739E-12 3.24585E-13 1.85672E-12
143 Vinyl acetate Air kg 1.42782E-11 4.96006E-11 8.81016E-12 5.03968E-11
144 VOC, volatile organic compounds Air kg 0.026182848 1.231171265 0.011134295 0.376528589
145 Xylene Air kg 3.06087E-05 3.8973E-05 1.32957E-05 1.97698E-05
146 Zinc Air kg 5.84177E-09 0.000763124 3.40452E-09 1.6771E-06
147 2-Hexanone Water kg 2.79726E-07 1.35871E-06 1.15556E-07 6.55918E-07
148 4-Methyl-2-pentanone Water kg 1.80037E-07 8.74494E-07 7.4374E-08 4.22162E-07
149 Acetone Water kg 4.28395E-07 2.08085E-06 1.76972E-07 1.00453E-06
150 Acidity, unspecified Water kg 4.90244E-11 4.90365E-11 x x
151 Acids, unspecified Water kg 6.35839E-09 2.21301E-08 3.92336E-09 1.42535E-08
152 Aluminium Water kg 0.003297335 0.014562978 0.001370755 0.00593024
153 Ammonia Water kg 0.000762179 0.003568032 0.000315077 0.001619134
154 Ammonia, as N Water kg 3.19325E-09 1.1114E-08 1.97035E-09 7.15824E-09
155 Ammonium, ion Water kg 2.40216E-06 6.61583E-06 1.48222E-06 4.67857E-06
156 Antimony Water kg 2.0029E-06 8.8875E-06 8.21503E-07 3.58594E-06
157 Arsenic, ion Water kg 1.14315E-05 5.4387E-05 4.73003E-06 2.91491E-05
158 Barium Water kg 0.044225109 0.197105639 0.018145317 0.080320868
159 Benzene Water kg 7.1867E-05 0.000349073 2.96886E-05 0.00016852
160 Benzene, 1-methyl-4-(1-methylethyl)- Water kg 4.28095E-09 2.07939E-08 1.76848E-09 1.00383E-08
161 Benzene, ethyl- Water kg 4.04269E-06 1.96366E-05 1.67005E-06 9.47963E-06
162 Benzene, pentamethyl- Water kg 3.21075E-09 1.55956E-08 1.32638E-09 7.52878E-09
163 Benzenes, alkylated, unspecified Water kg 1.75686E-06 7.79511E-06 7.20586E-07 3.14457E-06
164 Benzo(a)pyrene Water kg x 8.71687E-10 x x
165 Benzoic acid Water kg 4.3458E-05 0.00021109 1.79527E-05 0.000101904
166 Beryllium Water kg 6.15994E-07 2.88877E-06 2.53745E-07 1.30804E-06
167 Biphenyl Water kg 1.13749E-07 5.04701E-07 4.66549E-08 2.03599E-07
168 BOD5, Biological Oxygen Demand Water kg 0.007774409 0.28058594 0.003210139 3.648924516
169 Boron Water kg 0.000134624 0.000653275 5.55454E-05 0.000315288
170 Bromide Water kg 0.00917871 0.044585209 0.003791775 0.021524725
171 Cadmium, ion Water kg 1.717E-06 8.10037E-06 7.12535E-07 5.0297E-06
172 Calcium, ion Water kg 0.137634332 0.668566023 0.056857903 0.322781263
173 Chloride Water kg 1.547287631 7.515803026 0.639192781 3.628429016
174 Chromate Water kg 5.73102E-10 5.73873E-10 x x
175 Chromium Water kg 8.12708E-05 0.000349797 3.32549E-05 0.000136857
176 Chromium VI Water kg 3.41926E-07 1.47179E-06 1.39924E-07 5.52173E-07
177 Chromium, ion Water kg 9.76081E-06 5.40097E-05 4.07854E-06 3.15997E-05
178 Cobalt Water kg 9.49108E-07 4.61012E-06 3.92081E-07 2.22555E-06
179 COD, Chemical Oxygen Demand Water kg 0.014454992 0.069017324 0.00596115 0.032331671
180 Copper, ion Water kg 1.33145E-05 5.88711E-05 5.925E-06 2.92382E-05
181 Cyanide Water kg 3.12277E-09 1.14769E-07 1.28952E-09 7.29457E-09
182 Decane Water kg 1.24877E-06 6.06568E-06 5.15874E-07 2.92821E-06
183 Detergent, oil Water kg 3.71296E-05 0.00018343 1.536E-05 9.113E-05
184 Dibenzofuran Water kg 8.14577E-09 3.95666E-08 3.36505E-09 1.91009E-08
185 Dibenzothiophene Water kg 6.95088E-09 3.36151E-08 2.8704E-09 1.61042E-08
186 DOC, Dissolved Organic Carbon Water kg 1.95244E-11 6.79539E-11 1.20473E-11 4.37674E-11
187 Docosane Water kg 4.58418E-08 2.22669E-07 1.89375E-08 1.07494E-07
188 Dodecane Water kg 2.36933E-06 1.15086E-05 9.78782E-07 5.55581E-06
189 Eicosane Water kg 6.52339E-07 3.16862E-06 2.69484E-07 1.52966E-06
190 Fluorene, 1-methyl- Water kg 4.87556E-09 2.36821E-08 2.01412E-09 1.14326E-08
191 Fluorenes, alkylated, unspecified Water kg 1.01814E-07 4.51746E-07 4.17597E-08 1.82236E-07
192 Fluoride Water kg 3.9076E-05 0.000138827 2.41098E-05 0.011203709
193 Fluorine Water kg 5.07316E-08 2.26643E-07 2.08187E-08 9.28492E-08
194 Hexadecane Water kg 2.58613E-06 1.25616E-05 1.06834E-06 6.06415E-06
195 Hexanoic acid Water kg 8.99973E-06 4.37146E-05 3.71783E-06 2.11033E-05
196 Hydrocarbons, unspecified Water kg 2.44297E-11 8.50267E-11 1.5074E-11 5.47636E-11
197 Iron Water kg 0.006751376 0.030279538 0.002814926 0.012788917
198 Lead Water kg 2.31121E-05 0.000106797 9.57503E-06 4.92358E-05
199 Lead-210/kg Water kg 4.45123E-15 2.1621E-14 1.83882E-15 1.04376E-14
200 Lithium, ion Water kg 0.009561175 0.066582562 0.004091097 0.049011523
201 m-Xylene Water kg 1.29796E-06 6.30462E-06 5.36193E-07 3.04359E-06
202 Magnesium Water kg 0.026906192 0.13069937 0.01111512 0.06310174
203 Manganese Water kg 0.000111269 0.000451528 5.98552E-05 0.000237695
204 Mercury Water kg 3.82188E-08 1.66821E-07 1.63188E-08 1.2516E-07
Life-Cycle Energy and GHG Emissions for New and Recovered Softwood Framing Lumber and Hardwood Flooring Considering End-of-Life Scenarios
33
205 Metallic ions, unspecified Water kg 1.03602E-06 1.03702E-06 1.84046E-10 6.68634E-10
206 Methane, monochloro-, R-40 Water kg 1.72433E-09 8.37564E-09 7.1233E-10 4.04336E-09
207 Methyl ethyl ketone Water kg 3.44855E-09 1.67507E-08 1.42461E-09 8.08642E-09
208 Molybdenum Water kg 9.84803E-07 4.7835E-06 4.06826E-07 2.30924E-06
209 n-Hexacosane Water kg 2.85994E-08 1.38916E-07 1.18146E-08 6.70621E-08
210 Naphthalene Water kg 7.80116E-07 3.78142E-06 3.22261E-07 1.82772E-06
211 Naphthalene, 2-methyl- Water kg 6.78563E-07 3.29599E-06 2.80317E-07 1.59115E-06
212 Naphthalenes, alkylated, unspecified Water kg 2.87887E-08 1.27734E-07 1.18078E-08 5.15285E-08
213 Nickel Water kg 1.08905E-05 5.10226E-05 4.48575E-06 2.49356E-05
214 Nickel, ion Water kg x 2.31632E-10 x x
215 Nitrate Water kg 2.3E-10 2.44338E-10 1.32066E-12 4.79792E-12
216 Nitrate compounds Water kg 8.61713E-11 2.99916E-10 5.31708E-11 1.93168E-10
217 Nitric acid Water kg 1.93286E-07 6.72724E-07 1.19264E-07 4.33285E-07
218 Nitrogen, total Water kg 5.97993E-06 2.13503E-05 3.68984E-06 1.16468E-05
219 o-Cresol Water kg 1.23239E-06 5.98613E-06 5.09107E-07 2.88982E-06
220 Octadecane Water kg 6.38901E-07 3.10335E-06 2.63933E-07 1.49815E-06
221 Oils, unspecified Water kg 0.000981118 0.004650683 0.00040586 0.00218248
222 Organic substances, unspecified Water kg 4.97148E-07 4.99635E-07 x x
223 p-Cresol Water kg 1.32966E-06 6.4586E-06 5.49288E-07 3.11793E-06
224 Phenanthrene Water kg 1.0827E-08 4.96386E-08 4.45198E-09 2.14911E-08
225 Phenanthrenes, alkylated, unspecified Water kg 1.1937E-08 5.29639E-08 4.89601E-09 2.13658E-08
226 Phenol Water kg 1.51356E-05 6.51386E-05 6.19249E-06 2.44369E-05
227 Phenol, 2,4-dimethyl- Water kg 1.19997E-06 5.82865E-06 4.95714E-07 2.8138E-06
228 Phenols, unspecified Water kg 6.09505E-06 3.68337E-05 2.56861E-06 2.3834E-05
229 Phosphate Water kg 2.07145E-08 1.96866E-08 x 0.008376962
230 Phosphorus Water kg x 2.19489E-09 x x
231 Radioactive species, Nuclides, unspecified Water Bq 348.9136622 1239.39443 215.292391 679.5631383
232 Radium-226/kg Water kg 1.54861E-12 7.52204E-12 6.39736E-13 3.63126E-12
233 Radium-228/kg Water kg 7.92139E-15 3.84767E-14 3.27236E-15 1.85748E-14
234 Selenium Water kg 1.22962E-06 4.71283E-06 6.78303E-07 2.33496E-06
235 Silver Water kg 8.99456E-05 0.000436795 3.71562E-05 0.000210781
236 Sodium, ion Water kg 0.436307313 2.11937221 0.180242611 1.023216853
237 Solids, inorganic Water kg 4.91424E-10 1.71038E-09 3.03226E-10 1.10161E-09
238 Solved solids Water kg 1.908660079 9.270560665 0.788398128 4.47564447
239 Strontium Water kg 0.002335453 0.011344004 0.000964786 0.005476322
240 Sulfate Water kg 0.007997099 0.03248363 0.004294245 0.016827245
241 Sulfide Water kg 1.75587E-06 7.8514E-06 7.18554E-07 2.83556E-06
242 Sulfur Water kg 0.000113501 0.000551312 4.6888E-05 0.000266148
243 Sulfuric acid Water kg 4.07386E-08 4.13776E-08 x x
244 Suspended solids, unspecified Water kg 0.1001116 0.445249952 0.041257201 0.181847715
245 Tar Water kg 4.87021E-12 1.69506E-11 3.00509E-12 1.09174E-11
246 Tetradecane Water kg 1.03839E-06 5.04377E-06 4.28962E-07 2.43489E-06
247 Thallium Water kg 4.221E-07 1.87317E-06 1.73129E-07 7.55954E-07
248 Tin Water kg 8.61387E-06 3.96856E-05 3.54331E-06 1.73537E-05
249 Titanium, ion Water kg 3.07602E-05 0.000136499 1.26166E-05 5.50799E-05
250 Toluene Water kg 6.78984E-05 0.000329804 2.80491E-05 0.000159214
251 Vanadium Water kg 1.1633E-06 5.65052E-06 4.80564E-07 2.7278E-06
252 Water Water kg x 0.006119306 x x
253 Xylene Water kg 3.62544E-05 0.000175529 1.49728E-05 8.42605E-05
254 Yttrium Water kg 2.88705E-07 1.40232E-06 1.19265E-07 6.76972E-07
255 Zinc Water kg 7.8296E-05 0.000345884 3.29023E-05 0.000143119
256 Zinc, ion Water kg 3.79766E-09 -6.34299E-09 x x
257 Waste, solid Waste kg 0.000780247 0.000780247 x x
258 Bark Soil kg x x x 4.836418555
259 Cost, Total Non mat. $US x 6.236109334 x x
... Using forest carbon data compiled by the USFS [1] and cradle-to-gate LCI data on new and recovered softwood framing lumber [15], we developed a dynamic GHG inventory approach for two different end-of-life (EOL) scenarios for wood construction. The dynamic GHG inventory approach demonstrated what happens to GHG flows when recovered softwood lumber from old buildings is reused (recovered wood scenario) instead of landfilling it with a comparison to harvesting new wood from PNW-West and SE forests (new wood scenario) for construction. ...
... Cradle to Gate Manufacturing The fossil carbon emissions for the cradle-to-gate new and recovered softwood lumber are 118 and 76.4 kg/m 3 , respectively [15]. Cradle-to-gate emissions for new lumber include from harvesting (cradle) through production to product transportation to the building site (gate) whereas recovered lumber emissions account for removing wood from old buildings (cradle) through storage to product transportation to the building site (gate). ...
... Regardless based on our preliminary analysis, initially using recovered wood for reuse results in lower carbon emissions but eventually the harvested forests could overtake the recovered wood scenario primarily because of new tree growth. Recovered wood products definitely have an advantage with respect to lower cradle-to-gate life cycle emissions than new wood products [15]. However, the initial advantage of lower product manufacturing emissions may be offset eventually by the forest itself if no future harvesting occurred. ...
Conference Paper
Full-text available
Static life cycle assessment does not fully describe the carbon footprint of construction wood because of carbon changes in the forest and product pools over time. This study developed a dynamic greenhouse gas (GHG) inventory approach using US Forest Service and life-cycle data to estimate GHG emissions on construction wood for two different end-of-life scenarios. Biogenic and fossil GHG emissions sources included a growing forest, logging slash, softwood lumber manufacturing, residue decay and combustion, and product in the landfill. The two scenarios focused on 1) disposing of old wood and logging forests for new construction wood and 2) reusing the old construction wood instead of making new and landfilling the old wood. GHG emissions covered a 100-year time-period and were allocated to 1.0 m3 of softwood lumber produced for two different forests and harvesting rates. Reusing old construction wood had lower GHG emissions initially. However, using new wood would eventually have lower GHG emissions because logged forests regrow and absorb carbon faster and for a longer time than unlogged forests. The paper shows the critical time delay in forest carbon re-accumulating from logging forests may be problematic in mitigating climate change in the short-term but unlikely in the long-term.
... As mentioned previously, carbon in wood products may continue to be stored after its service (i.e., use) life in a building, or it may be emitted by burning or decay. Wood products may end up in landfills where most of the wood does not decompose, it may be recycled into new engineered products, it may be burned for its energy values, or it may be reused as is in new construction (Skog 2008, Bergman et al. 2013a). Specifically, for wood to be used in new construction, Bergman et al. (2013a) show fossil CO 2 emitted for new framing lumber and new hardwood flooring are about four times greater than for recovered softwood framing lumber and recovered hardwood flooring. ...
... Additionally , end-of-life (i.e., after first product use) scenarios for old wood products can result in large cumulative energy savings and fossil CO 2 emission reductions when discarded wood is used to displace coal or natural gas in producing electric power. In fact, for the base case end-of-life scenario developed by Bergman et al. (2013a), these energy savings would offset 53 and 75 percent of biomass energy consumed to make new softwood framing lumber and new hardwood flooring, respectively. ...
Article
Full-text available
Wood products have many environmental advantages over nonwood alternatives. Documenting and publicizing these merits helps the future competitiveness of wood when climate change impacts are being considered. The manufacture of wood products requires less fossil fuel than nonwood alternative building materials such as concrete, metals, or plastics. By nature, wood is composed of carbon that is captured from the atmosphere during tree growth. These two effects—substitution and sequestration—are why the carbon impact of wood products is favorable. This article shows greenhouse gas emission savings for a range of wood products by comparing (1) net wood product carbon emissions from forest cradle–to–mill output gate minus carbon storage over product use life with (2) cradle-to-gate carbon emissions for substitute nonwood products. The study assumes sustainable forest management practices will be used for the duration of the time for the forest to regrow completely from when the wood was removed for product production during harvesting. The article describes how the carbon impact factors were developed for wood products such as framing lumber, flooring, moulding, and utility poles. Estimates of carbon emissions saved per unit of wood product used are based on the following: (1) gross carbon dioxide (CO 2) emissions from wood product production, (2) CO 2 from biofuels combusted and used for energy during manufacturing, (3) carbon stored in the final product, and (4) fossil CO 2 emissions from the production of nonwood alternatives. The results show notable carbon emissions savings when wood products are used in constructing buildings in place of nonwood alternatives. Evaluating the environmental impact of product
Article
Full-text available
To perform a life-cycle analysis, a life-cycle inventory is needed. Data from surveys of manufacturers are presented for the energy and materials required to produce 1.623 m(3) (1 mbf) of planed, dry, dimension lumber from logs in the western and southern U.S. In the West and South, 53 and 41 % of the log volume (3.05 and 3.92 m(3)) leaves the mill as planed, dry dimension lumber, respectively. A much greater portion of the energy used for production in the South is produced on site from wood fuels. CO2 emissions were greater in the South because of the wood fuel, 574 kg versus 419 kg per 1.623 m(3) produced.
Article
Full-text available
This study compares the cradle-to-gate total energy and major emissions for the extraction of raw materials, production, and transportation of the common wood building materials from the CORRIM 2004 reports. A life-cycle inventory produced the raw materials, including fuel resources and emission to air, water, and land for glued-laminated timbers, kiln-dried and green softwood lumber, laminated veneer lumber, softwood plywood, and oriented strandboard. Major findings from these comparisons were that the production of wood products, by the nature of the industry, uses a third of their energy consumption from renewable resources and the remainder from fossil-based, non-renewable resources when the system boundaries consider forest regeneration and harvesting, wood products and resin production, and transportation life-cycle stages. When the system boundaries are reduced to a gate-to-gate (manufacturing life-cycle stage) model for the wood products, the biomass component of the manufacturing energy increases to nearly 50% for most products and as high as 78% for lumber production from the Southeast. The manufacturing life-cycle stage consumed the most energy over all the products when resin is considered part of the production process. Extraction of log resources and transportation of raw materials for production had the least environmental impact.
Conference Paper
Full-text available
Recovering wood for reuse in a new house affects energy and greenhouse gas emissions. This paper finds the energy and emissions for recovering softwood framing lumber and hardwood flooring from an old house for installation in a new house. Recovering wood displaces primary production of new wood products and avoids the end-of-life (EOL) burdens for the old house. We used a cradle-to-gate life cycle analysis and examined the EOL burdens for the wood products when disposed of including the effect of biogenic CO2. Results showed that recovering softwood framing lumber and hardwood flooring for reuse consumed 320 and 761 MJ/m3 while displacing primary production energy of 6,467 and 7,763 MJ/m3, respectively. Burning wood for energy at EOL avoided coal power production. GWP increased by two to four times when including biogenic CO2. Recovering softwood framing lumber and hardwood flooring for reuse displaced a considerable amount of primary production energy.
Conference Paper
Full-text available
With green building concepts becoming widespread in the construction field, building practices and materials are being examined for their environmental impact. Reusing building materials has a distinct advantage over using newly manufactured materials because these reclaimed materials avoid greenhouse gas emissions associated with new (virgin) material manufacturing. In a wood-framed building, building materials reclaimed during deconstructing (dismantling) may include framing lumber and wood flooring. This study quantified the energy impact of reusing these two wood materials in new construction or remodeling. This paper presents results of a deconstruction industry survey following Consortium for Research on Renewable Industrial Materials Research Guidelines. A life-cycle inventory approach was applied to track the energy consumption and emissions associated with reclaiming materials. This study showed how the material flowed through the various unit processes beginning at the deconstruction site and ending at a storage facility. We used weight-averaged material and energy production data to estimate the environmental impact of the two reclaimed materials. Results from this life-cycle inventory showed that cumulative energy consumed in producing virgin compared to reclaimed framing lumber and wood flooring was about 11 and 13 times greater, respectively. Global Warming Potential was about 3 and 5 times greater, respectively. These results indicate that reclaimed framing lumber and wood flooring have a significantly lower environmental impact than their two virgin alternatives.
Article
Full-text available
The Intergovernmental Panel on Climate Change (IPCC) provides guidelines for countries to report greenhouse gas removals by sinks and emissions from sources. These guidelines allow use of several accounting approaches when reporting the contribution of harvested wood products (HWP) under the United Nations Framework Convention on Climate Change. Using extensions of methods suggested by the IPCC and a software model called WOODCARB II in Microsoft Excel�, this paper presents estimates of the U.S. HWP contribution to annual greenhouse gas removals in the agriculture, forestry, land use, and land use change sector. In 2005, the contribution to removals was 30 Tg (million metric tons) C (carbon) and 31 Tg C for the Production and Atmospheric Flow Approaches, respectively, and 44 Tg C for the Stock Change Approach. This range is 17 to 25 percent of C removals by forests, or would offset 42 percent to 61 percent of residential natural gas C emissions in 2005. The contribution has declined under the Production and Atmospheric Flow Approaches since 1990 and has increased under the Stock Change Approach. The Stock Change estimate has increased because it explicitly includes C in increasing net imports of wood and paper products. The contribution estimates were validated by adjusting the half-life of products in use in order to match independent estimates of carbon in housing in 2001 and annual wood and paper discards to solid-waste disposal sites (SWDS) during 1990 to 2001. Estimates of methane emissions from wood and paper in landfills were also checked against independent estimates of total landfill methane emissions. A Monte-Carlo simulation used to assess the effect of uncertainty in inputs suggests the 90 percent confidence interval for removal contribution estimates under the three approaches is within –23% to +19%.
Article
Full-text available
This paper examines the energy and carbon balance of two residential house alternatives; a typical wood frame home using more conventional materials (brick cladding, vinyl windows, asphalt shingles, and fibreglass insulation) and a similar wood frame house that also maximizes wood use throughout (cedar shingles and siding, wood windows, and cellulose insulation) in place of the more typical materials used – a wood-intensive house. Carbon emission and fossil fuel consumption balances were established for the two homes based on the cumulative total of three subsystems: (1) forest harvesting and regeneration; (2) cradle-to-gate product manufacturing, construction, and replacement effects over a 100-year service life; and (3) end-of-life effects – landfilling with methane capture and combustion or recovery of biomass for energy production.
Article
Full-text available
Eleven statewide waste characterization studies were compared to assess variation in the quantity and composition of waste after separation of recyclable and compostable materials, i.e., discarded waste. These data were also used to assess the impact of varying composition on sequestered carbon and methane yield. Inconsistencies in the designation of waste component categories and definitions were the primary differences between study methodologies; however, sampling methodologies were consistent with recommended protocols. The average municipal solid waste MSW discard rate based on the statewide studies was 1.90 kg MSW person −1 day −1 , which was within the range of two national estimates: 2.35 and 1.46 kg MSW person −1 day −1 . Dominant components in MSW discards were similar between studies. Organics food waste, yard trimmings, paper, and plastic components averaged 23.6 4.9%, 28.5 6.5%, and 10.6 3.0% of discarded MSW, respectively. Construction and demolition C&D waste was 20.2 9.7% of total solid waste discards i.e., MSW plus C&D. Based on average statewide waste composition data, a carbon sequestration factor CSF for MSW of 0.13 kg C dry kg MSW −1 was calculated. For C&D waste, a CSF of 0.14 kg C dry kg C and D waste −1 was estimated. Ultimate methane yields L o of 59.1 and 63.9 m 3 CH 4 wet Mg refuse −1 were computed using EPA and state characterization study data, respectively, and were lower than AP-42 guidelines. Recycling, combustion, and other management practices at the local level could significantly impact CSF and L o estimates, which are sensitive to the relative fraction of organic components in discarded MSW and C&D waste.
Article
Disposal of buildings In most industrial and emerging industrial countries is wasteful and problematic. Waste from building demolition (partial demolition for renovation, or total demolition for building removal) represents 30-50% of total waste in most of these countries. Deconstruction is an alternative to demolition. It calls for buildings to be dismantled or disassembled, and for the components to be reused or recycled. A number of economic and social benefits can be realized by shifting towards better materials recovery practices in the construction sector. Deconstruction preserves the invested embodied energy of materials, thus reducing inputs of new embodied energy during materials reprocessing or remanufacturing. The concept of design for disassembly (DfD) of buildings emerged in the early 1990s. Closing construction materials loops will require including both product design and deconstruction in a process that might be called "design for deconstruction and disassembly" (DfDD).
Article
The tool for the reduction and assessment of chemical and other environmental impacts (TRACI) is described along with its history, the research and methodologies it incorporates, and the insights it provides within individual impact categories. TRACI; a stand-alone computer program developed by the U.S. Environmental Protection Agency, facilitates the characterization of environmental stressors that have potential effects, including ozone depletion, global warming, acidification, eutrophication, tropospheric ozone (smog) formation, ecotoxicity, human health criteria-related effects, human health cancer effects, human health noncancer effects, fossil fuel depletion, and land-use effects. TRACI was originally designed for use with life-cycle assessment (LCA), but it is expected to find wider application in the future. To develop TRACI, impact categories were selected, available methodologies were reviewed, and categories were prioritized for further research. Impact categories were characterized at the midpoint level for reasons including a higher level of societal consensus concerning the certainties of modeling at this point in the cause-effect chain. Research in the impact categories of acidification, smog formation, eutrophication, land use, human cancer, human noncancer, and human criteria pollutants was conducted to construct methodologies for representing potential effects in the United States. Probabilistic analyses allowed the determination of an appropriate level of sophistication and spatial resolution necessary for impact modeling for each category, yet the tool was designed to accommodate current variation in practice (e.g., site-specific information is often not available). The methodologies underlying TRACI reflect state-of-the-art developments and best-available practice for life-cycle impact assessment (LCIA) in the United States and are the focus of this article. TRACI's use and the impact of regionalization are illustrated with the example of concrete production in the northeastern United States.
Article
Virtual residential houses in Atlanta, Georgia, and Minneapolis, Minnesota, were analyzed to determine energy consumption and greenhouse gas emission during the building use, maintenance, and demolition phases of their life cycle. An analysis of Census data on housing stocks provided estimates for the useful life of a house. Home Energy Saver, an internet tool for energy analysis sponsored by the Department of Energy and available from the Lawrence Berkeley National Laboratory, was the primary tool used in assessing energy consumption for heating and cooling during the use phase of the buildings. A survey on the life span of house components by the National Association of Home Builders (NAHB) was used to estimate a maintenance/replacement schedule. Emissions during demolition and transport to the landfill were estimated based on the initial bill of materials in the house and distance to the landfill. The energy consumption over a 75-year life was estimated to be 4,575 GJ for the Atlanta wood frame, 4,725 GJ for the Atlanta concrete block structure, and 7,800 GJ for the Minneapolis wood frame. A steel-framed Home Energy Saver model was not available, but since the steel-framed house was designed to code for equal thermal properties with the wood frame house, we assume no difference. Energy consumption related to structural/exterior maintenance was estimated at 110.5 GJ for the Atlanta location and 73.3 GJ for Minneapolis, only 1 - 2% as large as used for heating and cooling. The energy needed for demolition and waste removal was even smaller. Carbon dioxide (CO2) emissions from the consumed energy were estimated using the regional energy grids in SimaPro at 227,000 kg (501,000 lbs) for the Atlanta wood frame, 235,000 kg (519,000 lbs) for the concrete frame, and 338,000 kg (856,000 lbs) for the Minneapolis wood frame. CO2 emissions related