Article
pubs.acs.org/est
Functional Unit, Technological Dynamics, and Scaling Properties for
the Life Cycle Energy of Residences
Stephane Frijia,† Subhrajit Guhathakurta,‡ and Eric Williams*,§
†
Greater Phoenix Economic Council, Phoenix, Arizona, United States
School of City & Regional Planning, Georgia Institute of Technology, Atlanta, Georgia, United States
§
Golisano Institute of Sustainability, Rochester Institute of Technology, Rochester, New York, United States
‡
S Supporting Information
*
ABSTRACT: Prior LCA studies take the operational phase to include all energy use within
a residence, implying a functional unit of all household activities, but then exclude related
supply chains such as production of food, appliances, and household chemicals. We argue
that bounding the functional unit to provision of a climate controlled space better focuses
the LCA on the building, rather than activities that occur within a building. The second
issue explored in this article is how technological change in the operational phase affects life
cycle energy. Heating and cooling equipment is replaced at least several times over the
lifetime of a residence; improved efficiency of newer equipment affects life cycle energy use.
The third objective is to construct parametric models to describe LCA results for a family of
related products. We explore these three issues through a case study of energy use of
residences: one-story and two-story detached homes, 1,500−3,500 square feet in area,
located in Phoenix, Arizona, built in 2002 and retired in 2051. With a restricted functional
unit and accounting for technological progress, approximately 30% of a building’s life cycle
energy can be attributed to materials and construction, compared to 0.4−11% in previous studies.
■
Functional Units for Buildings. The definition of the
functional unit is fundamental to any life cycle assessment. The
functional unit is the unit of functionality associated with a
product or service in question.1 To illustrate the concept, a
functional unit to compare light bulb technologies could be
chosen as 10,000 h of 1,800 lm light. The reference flow is the
associated product/service systems needed to deliver the
functional unit, e.g. one 23 W compact fluorescent light bulb
in this example. We argue that for theoretical and practical
reasons there is a need to revisit the definition of functional unit
and reference flows for buildings. Prior studies analyze a
reference flow of embodied materials and construction
combined with entire operational energy within a building.5,15,16 The functional unit for such a reference flow is
unclear. To clarify this statement, Table 1 shows functions
associated with residences and how building systems and other
supply chains connect with these services.
Depending on the purpose of the LCA study, different sets of
functions could be chosen. A key point shown in the table is
that equating total energy use of a home with the operational
phase implies a functional unit choice of all home functions.
Delivering these functions includes supply chains for appliances,
electronics, lights, food, and various household products, supply
chains that are excluded from building LCA studies.
INTRODUCTION
Life Cycle Assessment and Urban Systems. Life Cycle
Assessment (LCA) is a set of methods, tools, and data designed
to estimate materials flows and assess environmental impacts
over the life cycle of a product or service.1,2 At the sub-building
scale, LCA is used to assess building level technologies such as
water heaters3 and energy systems.4 At the building level, LCA
has been used to study residences2,5−7 and office buildings.8,9
Most of these studies indicate that the operational energy use
overwhelmingly dominates energy use for materials and
construction (e.g., 90−95% of energy use in operation versus
5−10% for materials and construction). Keoleian7 found that
materials/construction share increased from 9.4% for a
standard home in Michigan to 26% for an energy efficient
home.
At a larger scale, Norman10 compared life cycle energy of low
and high population density areas in Toronto and found a
substantial reduction in carbon overhead for high density living.
LCA has also been used to assess urban transportation
systems11 and work/lifestyle models such as telecommuting.12,13 At the level of a complete urban system, researchers
have been working to assess the life cycle environmental
footprints of an urban area that include impacts from producing
imported goods.14
In this analysis we focus on the building level and propose
methodological developments in three aspects of conducting
LCA: defining the functional unit, incorporating technological
progress, and parametrization.
© 2011 American Chemical Society
Received:
Revised:
Accepted:
Published:
1782
July 1, 2011
December 19, 2011
December 20, 2011
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Table 1. Functions Associated with Residences with Associated Building Systems and Supply Chainsa
function
enclosed space
heating/cooling
lighting
cleanliness
food
ICT and
entertainment
a
building systems
equipment/appliances
other products
structural
structural/electrical
electrical
electrical/plumbing
electrical/plumbing
electrical
HVAC
light fixtures
water heater, clothes washer, dishwasher, dryer
oven, microwave, refrigerator
TV, computer, telephone, ....
light bulbs
soap, detergent, cleaners
food and beverages
paper, printer ink
HVAC = heating ventilation and cooling, ICT = Information and Communication Technology.
involves carrying out a set of studies for discrete parameter
values and fitting a regression model.
We thus propose that the functional unit and boundaries for
reference flows be chosen in a consistent way. In this case study
we consider a functional unit including enclosed space and
heating/cooling. We argue that this functional unit is a
reasonable choice for residential LCA, since these two functions
closely connect with most building design and engage most
building systems. It is not the only choice, depending on the
purpose of the study, lighting, cleanliness, or other functions
could be added. Note also that our scope of operational energy
is narrower than prior definitions; heating cooling and
ventilation (HVAC) equipment in the Unites States accounts
for 52.2% of the operational energy end-use in residences.17
The share of energy used in building manufacturing relative to
operation will increase with this new definition.
Technological Change. Technological change presents a
challenge for environmental systems assessment using LCA. LCA
is usually retrospective; recommendations based on the past can
be overturned by technological development. Processes evolve
over time. For example, the embodied carbon in manufacturing
silicon photovoltaic modules fell by 65% between 2002 and
2010.18 Products and their use also change over time. Trends in
life cycle impact per functionality delivered can be dramatically
different from trends in impact per typical product.19 It is thus
important to attempt to account for technological progress in life
cycle assessment. One approach to address dynamics is forecasting
of retrospective trends in materials flows.20 Another approach is to
construct future technology scenarios and then relating such
scenarios to materials flows.21
Buildings are particularly interesting from the perspective of
technological change. While the core of a building is generally very
long-lived (e.g., 50−100 years), key elements of the technology
system from an energy perspective, such as HVAC equipment,
have a much shorter lifespan. Efficiency improvements in HVAC
equipment have been substantial in the past decade,22 thus
replacement of equipment can significantly affect life cycle energy.
Parameterization of LCA. LCA is data and labor-intensive.
To address this challenge there is a stream of work in the LCA
community to make LCA easier to implement, such as
streamlined LCA,23 scoping LCA, and development of
standardized databases.24,25 Another strategy involves the use
of parametric models. Within a given category of products,
rather than conduct a new LCA study for each different model,
a parametric model could in principle map product characteristics to life cycle impacts, i.e.
■
CASE STUDY: SCALING BEHAVIOR OF LIFE CYCLE
ENERGY OF PHOENIX RESIDENCES
We undertake a case study analyzing life cycle energy use of
residences that incorporates our definition of the functional
unit, accounts for technological progress, and estimates scaling
behavior of typical residential units in Phoenix. By scaling we
mean how energy used to construct and operate a residence
changes as a function of its area. To control for the effects of
climate and technology, we limit the study to the Phoenix
metropolitan area and to a standard set of materials and
technologies typically used in residential structures within this
region, in compliance with local building code and the 2009
International Energy Conservation Code for Arizona’s climate
zone 2. For this analysis we consider houses typical in Phoenix:
average construction quality, without basement, built on a
cement slab foundation, exterior walls made of stucco on a wood
frame, with a cement tiled roof. Utility infrastructure and access
related developments (e.g., roads, driveways) are not included in
the analysis. We consider one- and two-story detached houses
with area ranging from 1,500 to 3,500 square feet.
We apply the following process to parametrize our LCA model:
1 Define the functional unit as delivering climate
controlled lighted spaces over the assumed lifespan of
the home (50 years).
2 Using the hybrid LCA method described in the next
section, we estimate life cycle energy and carbon
associated with materials, construction, and operation
of the residence and associated equipment built in 2002.
3 Forecast technological progress in HVAC technology
from 2002 to 2051 and integrate into estimation of life
cycle energy.
■
4 Create a parametric model that extrapolates life cycle
energy and carbon for any home in Phoenix in the
1,500−3,500 square feet range and one- or two-stories.
METHOD: COST-BREAKDOWN ECONOMIC
INPUT-OUTPUT LCA
LCA is a quantitative method designed to assess the
environmental impacts of a product or service including
relevant phases of the entire life cycle from mining, materials
production, assembly, and distribution to use and disposal.
LCA can be conceptually divided into inventory and assessment phases. The inventory phase includes a description of the
materials and their energy use and emissions over the life cycle.
This set of material and energy quantities is termed the life
cycle inventory (LCI). The three main methods for estimating
parametric model: product characteristics
→ life cycle inventory
There are continuous (e.g., area of building) and discrete (e.g.,
materials type) parameters that describe a product or service.
The general method for constructing a parametric model
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approaches to achieve this. The simplest is additive hybrid in
which economic data are identified covering processes for
which materials data are unavailable and associated with sectors
in an EIO model.20 An economic-balance hybrid calculates the
value-added covered in a materials process model, subtracts this
from the total price, and estimates impacts associated with the
remaining value using EIOLCA.32 A mixed unit hybrid model
constructs a matrix with both physical and economic
quantities.33
We use a variant of the additive method that relies entirely
on the EIO model to estimate supply chains for manufacturing.
We term this method cost-breakdown EIOLCA and is based on
economic analysis to account for the full cost of a product, that
is, costs of different materials and basic manufacturing
processes.34 Each cost element associated with an EIO sector
and the total energy to manufacture a residence is given by
life cycle inventories of material and energy used are processsum, economic input-output, and their combination, known as
hybrid analysis.
The process-sum method, upon which most existing LCI are
based, includes both a calculation method and the type of data
normalization used.1 The method starts with a process network
diagram, for which materials input-output data has been
collected for each element in the network. The flows between
processes are usually described in material terms, e.g. kg of
emissions per unit mass of product output. The sources of data
are often facility based, though sometimes reflect industry or
even national averages. The net materials use and emissions
associated with a unit output of a product or service being
studied is obtained using a linear increment of materials flow
associated with each process.
Economic input-output life cycle assessment (EIOLCA) is
based on Wassily Leontief’s formulation of an economy as a
matrix describing economic transactions between sectors.26 The
core of the model is the input-output matrix, usually denoted
by Zmn, which describes the economic purchases and sales
between economic sectors. This matrix, being a national
aggregate of results of (confidential) firm-level surveys, is
normally formulated by a government agency, such as the
Bureau of Economic Analysis in the U.S. The most detailed
tables divide an economy into 400−500 sectors. While originally formulated to address economic questions, the model can
also be supplemented with environmental information to
estimate supply chain materials use and emissions for products.
This method has been used since the 1970s to estimate the net
energy cost of products and facilities and more recently expanded to cover a broad variety of emissions and impacts.27−29
The basic formula used to calculate the net materials use or
emissions associated with a unit of economic output for
economic sectors is
ESC = ED(1 − A)−1
Eproduction =
∑ Ci·Esc , i
i
(2)
where the subscript i refers to the ith line item in the cost
model, Ci ($) refers to the cost of line item i, and Esc,i (MJ/$)
refers to the corresponding supply chain energy intensity from
EIO-LCA.29 The energy use in operation is estimated using the
process-sum method.
Technological Progress. One of the objectives of the
analysis is to estimate how changes in the efficiency of HVAC
equipment replaced periodically over the building lifespan
would affect operational energy use. Since we consider a
residence built in 2002 with a lifetime of 50 years, this
estimation necessarily involves technological forecasting. There
are many possible paths to approach this forecasting; here we
take a scenario approach that relies on expert judgment on the
future of efficiency improvements. In particular, in the Annual
Energy Outlook (AEO), the Energy Information Administration of the U.S. Department of Energy report (DOE)
estimates for home technology efficiencies to the year 2030.22
Forecast improvements in electric heat pumps and air
conditioners are nearly the same, starting with 2.3% annual
improvement in 2003, decreasing to 1.1% in 2015, then down
to a 0.16% annual improvement in 2030. We use this scenario,
extending it to 2052 by assuming a 0.15% annual improvement
reported from 2030 until 2052. The SEER rating of the Air
Conditioner stock in 2052 would be 14.4, which in our view is a
pessimistic forecast. A more sophisticated treatment is a topic
for future research; our purpose here is simply to scope the
importance of incremental technological progress as it is
currently viewed by the Department of Energy.
Data and Analysis. To estimate life cycle energy and
carbon associated with materials, construction, and operation of
the residence and associated equipment we organized the data
in the following three categories:
1 construction materials;
(1)
The result, ESC, is the vector of sector level supply chain
energy use intensities (MJ/$). A is the requirements matrix
built from transaction matrix (Amn = Zmn/Total economic
output of sector n). ED represents direct energy use of a sector
and is constructed from national (or sometimes process level)
information by LCA practitioners. In the U.S., researchers at
Carnegie Mellon have developed and maintained a public use
model based on the 428-sector Benchmark U.S. IO tables.29
The LCA result for energy use for manufacturing the target
product is found by
1 identifying its representative sector in the input-output
table,
2 calculating ESC for the materials/emissions of interest,
3 multiplying by the producer price of the product (or the
consumer price, depending on the formulation of the
input-output model)
Process-sum LCA inevitably excludes some processes in the
supply chain for which materials input-output data are
unavailable, leading to truncation-error. EIOLCA is a coarse
grain model often combining many different processes into
economic sectors, leading to aggregation error. Hybrid LCA
combines the process-sum model with EIOLCA30−32 with the
goal of reducing cutoff error in the former and aggregation in
the latter.19 The term hybrid generically refers to any method
combining process and EIOLCA; there are a number of
2 construction processes; and
3 operational data.
The next section provides more detail about these
components, their respective data sources, and the resulting
analyses.
Construction Materials. Construction material and labor
costs are affected by a number of variables. These include
design, building quality, area, type of materials, project location,
market conditions, among other variables. For this analysis the
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business done and spent $673 million in energy purchases.
Using 2002 average industrial prices and EIA’s energy
conversion factors we estimated that the total energy use in
this sector is equivalent to 112 PJ (petajoules), which is equal
to 1.81 mega joules of primary energy per dollar of business
done (see the Supporting Information).
To estimate the energy used in the construction processes of
a single-family house the following equation was used
economic data associated with material quantities, labor, and
equipment costs for a standard building design of average
construction quality is prepared using a construction estimation
software.
RSMeans CostWorks 2010 is used as the primary source of
economic data for materials and processes associated with
construction. RSMeans is a standard source of data in North
America for construction cost information for both new
building construction and renovation projects. The reported
costs are adjusted to reflect local material costs and labor rates
with the help of RSMeans’ own city Construction Cost Index
(CCI) and open-shop labor rates.35 The output produced
includes an up-to-date bill of material and unit costs for each
material assembly of the residential structures (e.g., foundation,
framing, roofing, etc.).
In addition to the standard material list provided by
RSMeans, we added a line item to the output to account for
the manufacturing of the tools and equipment used during the
construction phase. Typically, tools and equipment are not
purchased and completely used-up during the construction of
just a single home, but are used in multiple projects. Thus, the
economic value associated with the manufacturing of the tools,
based on available industry survey data of general overhead
expenditures, is estimated as 1.5% of total material cost plus
5.9% of total labor cost.35
Since the EIO model is based on the 2002 Benchmark U.S.
producer sector tables, we adjusted each line item in the output
to reflect only the producer prices in 2002 real dollar values by
multiplying each output line by a US Producer Price Index
(PPI) and by a producer/purchaser ratio. We interpreted costs
items such as profit, overhead, and markups as part of the
wholesale portion of the purchaser price and so subtracted
these to obtain the producer price. To reflect the above
adjustments, eq 2 for each cost element associated with an EIO
sector and the total energy to manufacture a residence is now
given by
Ematerial =
Econstr . process = (BVhouse / BVsector )· ∑ Fi , sector · ki
i
where the subscript i refers to a specific fuel type used in
construction sector, BVhouse ($) refers to the business value of
the home, BVsector ($) refers to the entire business value the new
single-family general contractors businesses, Fi ($) refers to
total fuel purchases, and ki refers to the consumption heat rate
of a given fuel type.
Operational Data. The energy required for space heating
and cooling was calculated using the Home Energy Saver
(HES) Web-application, which uses the DOE-2 program
developed by the U.S. Department of Energy for building
energy analysis.37 The program provides hourly energy use and
energy cost of a building given hourly weather information and
a description of the building envelope and its HVAC
equipment. For our analysis we customized the building
envelope to match the dimensions, materials, and other
prescriptive building requirement commonly found in the
Phoenix area. Other general characteristics and values relating
to behavior and user preferences were left in the default setting.
The default settings are based on results of the Residential
Energy Consumption Survey (RECS) from the Energy
Information Administration.38 The RECS survey represents
national household energy consumption and expenditures
based on a national area-probability weighted sample of more
than four thousand households. The data include detailed
physical characteristics of the housing unit, appliances information, heating and cooling equipment, socio-demographics
characteristics, fuel types, and quantities used.
The output of the HES model is reported as on-site
consumption and does not include the overhead energy input
needed to produce electricity from multiple energy sources nor
transmission losses. We convert all energy to primary energy to
account for losses in electricity generation.39 Since the mix of
primary energy used to generate electricity varies greatly across
the U.S., we chose to calculate primary energy of electricity
using the Western Electricity Coordinating Council (WECC) a
primary energy factor per unit of delivered electricity of 2.894.40
As with prior studies we work only with average operational
energy. It is important to note however that there is significant
variability household by household. For example, 2005 RECS
microdata from several thousand survey respondents for
electricity use in a 1,500 sq. ft. home varies from 7,500 to
24,000 kWh.38
∑ (C2010, i)·(PPI2002 / PPI2010)·PPR i·Esc , i
(3)
i
(4)
th
where the subscript i refers to the i line item in the cost
model, and Ci ($) refers to the cost in the year 2010 from
RSMeans, after subtracting installation related overhead, profits,
markups, and fees in construction. PPI refers to the average US
Producer Price Index for a cost item in a given year, and PPRi
refers to the producer/purchaser ratio for the corresponding
material producing industry that allows to estimate the cost at
the material factory gate. E sc,i (MJ/$) refers to the
corresponding supply chain energy intensity from EIO-LCA.29
Construction Processes. The second input to our hybrid
EIOLCA model accounts for the energy utilized on-site by
power tools and machinery during the construction phase. The
economic value of the onsite energy use accounts for fuel
consumption including gasoline, diesel fuel, and lubricants, and
electric energy purchased from other companies or received
from other establishments of the company. Also included are
costs for natural gas, manufactured gas, fuel oil, and coal and
coke products. Aggregate data on energy and resources used in
the construction phase are available from the 2002 Economic
Census report.36 Detailed statistics for new single-family
general contractors businesses grouped in NAICS sector
236115.
According to the Census data the new single-family general
contractors businesses reported $62.2 billion in value of
■
RESULTS
Table 2 provides a summary of the results for materials and
construction for one-story dwellings of different areas, along
with space heating and cooling, and operational primary
consumption. We found that the total energy use in the material manufacturing and construction processes decreases, as the
area of the unit increases, from 6.45GJ/m2 to 5.34 GJ/m2,
for a one-story unit, and from 5.79 GJ/m2 to 4.85 GJ/m2,
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Table 2. Embedded Primary Energy in Operation, Materials, and Construction for a One-Story Residence
annual operational phase
material mfg. and construction phase
area [sq.ft.]
total primary energy use [GJ]
heating and cooling [GJ]
material mfg. [GJ]
construction process [GJ]
total [GJ]
intensity [GJ/m2]
1,500
2,000
2,450
3,000
3,500
180
195
211
228
244
73.2
89.9
105
121
138
702
878
1,030
1,210
1,370
197
241
284
330
367
899
1,120
1,310
1,540
1,730
6.45
6.02
5.78
5.54
5.34
Figure 1. Embedded energy in manufacturing materials for one- and two-story 2,450 ft2 residences.
Figure 2 provides a direct comparison between three
methodological variants in LCA: 1) including all primary
for a two-story unit. As dwelling area increases, the total life
cycle embedded energy in the construction and material
processes grows nearly linearly. These results are consistent
with the observation that material requirements, such as cement
used for the foundations, scale proportionally with the overall
area of the building, while other materials such as drywall, paint,
wood, and stucco scale as a function of the perimeter or
volume.
Figure 1 shows embedded energy in materials used in oneand two-story residences of the same area: 2,450 square feet.
Note that 10−15% energy savings can be obtained when
building a two- rather than a one-story unit. The smaller energy
footprint of the two-story house is because lower energy materials
such as wood substitute for more energy intensive materials such
as cement. Materials included in the “others” category contribute
far less individually to the total embedded energy and did not vary
significantly in quantities for single- and two-storied dwellings.
With our definition of functional unit and after accounting
for technological progress we find that between 19% and 30%
of the total life cycle energy can be attributed to materials and
construction processes, as opposed to the 0.4−11% found in
other studies.5,10,41−43 Note that our results for total energy to
manufacture a 2,450 square foot residence (1,310 GJ) are
similar to those found in prior studies (1,435 GJ for a 2,450
square feet standard home)7,16 Thus the difference in share is
explained by the functional unit choice and not difference in
method.
Figure 2. Comparison between definitions of functional unit for a onestory 2,450 ft2 residence.
energy end-use in the functional unit (with no technological
improvements); 2) including only HVAC primary energy uses
(with no technological improvements); and 3) our proposed
functional unit. Using our definition of functional unit and our
methodology, the embedded energy in construction and
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energy is associated with residences and products used in
residences, it is only natural that manufacturing should
represent a reasonable share of life cycle energy.
The reinterpretation of functional unit has implications for
planning sustainable, energy-efficient communities. In particular
there is a stronger case to manage the embedded energy in
building materials in building codes. Such codes will go a long
way in reducing total energy demands in a community. In
addition, subdivision regulations can take advantage of specific
volumetric considerations that incentivize builders to build up
rather than horizontally, once they hit a particular building
footprint threshold. Future research needs to examine the
scaling behavior of high-rise buildings with respect to energy
given that there are expected discontinuities in such relationships.
While this article showed only a simple parametric model
mapping one product characteristic − house area − to life cycle
energy, the approach is general and potentially of great utility in
LCA. While LCA has been mainly approached from a case
study perspective, new software-based models are being
developed to allow users input individual data. For example,
for buildings the e-Quest and DOE-2 models generate
operational energy use based on a variety of user input data
on building, equipment, and usage .45 One can envision a suite
of LCA models for different products that allow the user to
customize product supply chain, design, and operation
characteristics.
material manufacturing processes accounts for 27.3% of the
total embodied energy for a 2,450 square feet one-story
dwelling. Specifically, the embedded energy used during
construction corresponds to 5.7% and in the material
manufacturing 21.6% of the total life cycle energy. This result
is in contrast to the more typical 11.4% when all primary energy
end-use are included in the functional unit, without accounting
for technological improvement.
Lastly, we build a parametric model mapping house area to
life cycle energy. This is done by regressing the five areas
studied for different functional forms on estimated total lifecycle energy. While higher order polynomials give a
marginally better fit, a linear regression model was sufficient
to reproduce energy for the house areas examined in our
study. We found that the fit of our linear regression models
for both single-story and two-story units were extremely good
with R-squares in the high 90s. This result suggests that the
embedded energy in residential units scale linearly with area
of the unit after controlling for the number of stories. The
coefficients of the area parameter suggests that for each
additional square foot increase in area of livable space, for a
one-story unit, the embedded energy goes up by 419 MJ. For
two-story residential units this marginal increase per square
foot is 360 MJ.
Figure 3 shows that the linear scaling behavior across
different unit areas for total embedded energy is also reflected
in the disaggregated components of the total as well.
■
ASSOCIATED CONTENT
* Supporting Information
S
Tables S-1−S-6. This material is available free of charge via the
Internet at http://pubs.acs.org.
■
AUTHOR INFORMATION
Corresponding Author
*E-mail: [email protected].
■
ACKNOWLEDGMENTS
This research was supported by the Civil Infrastructure Systems
program at the National Science Foundation (CMMI grant #
1031690). The authors thank Ariane Middel for helpful input.
■
Figure 3. Life cycle primary energy embedded in materials,
construction, and HVAC for one-story residence of different areas,
with linear parametrizations.
■
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DISCUSSION
This study finds that by designating a different functional unit,
the contribution of materials and construction to the life cycle
energy of a residence is far higher than previous studies. Prior
studies excluded potentially important supply chains (e.g.,
appliances, food, household chemicals) associated with the
implicit functional unit. We argue that the conventional wisdom
that operational energy use overwhelmingly dominates the life
cycle of buildings needs to be reconsidered. This higher share is
also more intuitive from a macroperspective. Since industry
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