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R E S E A R C H A N D A N A L Y S I S
Input-Output-based Life Cycle Inventory
Development and Validation of a Database for the German
Building Sector
Bodo Muller and Liselotte Schebek
Summary
An input-output-based life cycle inventory (IO-based LCI) is grounded on economic en-
vironmental input-output analysis (IO analysis). It is a fast and low-budget method for
generating LCI data sets, and is used to close data gaps in life cycle assessment (LCA). Due
to the fact that its methodological basis differs from that of process-based inventory, its
application in LCA is a matter of controversy. We developed a German IO-based approach
to derive IO-based LCI data sets that is based on the German IO accounts and on theGerman environmental accounts, which provide data for the sector-specific direct emissions
of seven airborne compounds. The method to calculate German IO-based LCI data sets
for building products is explained in detail. The appropriateness of employing IO-based LCI
for German buildings is analyzed by using process-based LCI data from the Swiss Ecoinvent
database to validate the calculated IO-based LCI data.
The extent of the deviations between process-based LCI and IO-based LCI varies
considerably for the airborne emissions we investigated. We carried out a systematic
evaluation of the possible reasons for this deviation. This analysis shows that the sector-
specific effects (aggregation of sectors) and the quality of primary data for emissions from
national inventory reporting (NIR) are the main reasons for the deviations. As a rule,
IO-based LCI data sets seem to underestimate specific emissions while overestimating
sector-specific aspects.
Keywords:
building material
generic data
industrial ecology
input-output analysis (IOA)
life cycle assessment (LCA)
validation
Supporting information is available
on theJIE Web site
Introduction
Life cycle assessment (LCA), according to the Interna-
tional Organization for Standardization (ISO) 14040/14044
(ISO 2006a, 2006b), is an established methodology for assessing
resource consumption and the environmental impact of goods
and services. In an LCA study, the phase of compiling an in-
ventory of all the relevant flows for the product system (life
cycle inventory [LCI]) is generally the most time-consumingand labor-intensive one. For a complex product such as a car
or a building, up to several thousand individual processes may
be involved. Input and output data for each of these processes
Address correspondence to: Bodo Muller, KIT,Institutefor TechnologyAssessment andSystems Analysis,Hermann-von-Helmholtz-Platz1, 76344Eggenstein-Leopoldshafen,Germany.Email:[email protected]
2013 by Yale UniversityDOI: 10.1111/jiec.12018
Volume 17, Number 4
are usually acquired from databases, the literature, or research
projects, or they are calculated from specific measurements, re-
sulting in a process-based LCI. Often some of the data cannot
be provided because of time and budget limitations or they are
not accessible for the investigator and the public for reasons of
confidentiality.
In order to close data gaps in an LCA study, several proce-
dures have been proposed in the literature (Frischknecht et al.
2004b; Guinee et al. 2002; Suh and Huppes 2005). The easiestway to treat data gaps is to use cutoff rules. Inputs and outputsin
a unit process are then set to zero, leading to the corresponding
process or product being disregarded. This is done when experts
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R E S E A R C H A N D A N A L Y S I S
conclude that the respective environmental burden is insignifi-
cant. It is, however, hardly possible to verify if the emissions of
these processes and products are environmentally relevant if all
the information is lacking (Lichtenvort2004). Lenzen(2000)
criticizes that the extent of cutoffs in process-based LCI could
add up to 50% of all the environmental impacts. The environ-
mental burdens should thus be estimated if it is not possible to
prove their insignificance. Two possible ways could be used tomake these estimates:
Substitution: Closing data gaps by using similar unit pro-
cesses, where the inputs and outputs are known (Guinee
et al. 2002; Huijbregts et al.2001). Generation of LCI data sets with the help of an
input-output-based life cycle inventory (IO-based LCI)
(Hendrickson et al.2006; Suh and Huppes2005)
Environmental input-output (IO) analysis is the foundation
of IO-based LCI using economic IO models derived from sta-
tistical data. In IO-based LCI, the cumulative emissions are
calculated using the corresponding sales price of a final demand
good (i.e., IO-LCI data set). By generating the cumulativeemis-sions of the goods for final demand, IO-based LCI comprises all
the intermediate inputs of an economy. The claim is actually
made in the literature that someof the additional benefits of IO-
based LCI are that it overcomes the problem that there is a finite
boundary in process-based LCI resulting from the disregard of
services such as financial services or research and development
(Lenzen2000;Lenzen and Treloar2002), that it could prevent
erroneous decisions (Suh et al.2004), and that it could be used
to streamline data collection (Treloar1997).
Therefore IO-based LCI seems in principle to be an attrac-
tive way to calculate generic LCI data sets for virtually all
products and services of an economy in a fast and low-budget
manner. Due to its different methodological basis, its applica-tion in LCA has, however, been an object of controversy in the
literature. Suh and Huppes (2005) and Rebitzer (2005) argue
that there are weaknesses in IO-based LCI such as a low level
of detail due to aggregated statistical data, the data age, or the
use of monetary units (i.e., the direct correlation of calculated
emissions with the price of a product). In order to make the
use of IO-LCI data more reliable, a deeper understanding of the
reasons for possible deviations between process-based LCA and
IO-based LCI is needed.
Methodological Approach
Outline
To investigate the IO-based LCI approach, we chose as a
case study the German building sector. Two reasons support
this choice. First, the building sector is of crucial importance
with regard to the environmental impact of the economy, and
LCA has been increasingly used in the last few years to support
the development of green buildings in order to mitigate this
impact. In this regard, Kreissig and Binder (2007) identified for
Germany the lack of a comprehensive database for LCA in the
building sector. Second, the building sector is characterized by
a larger share of domestic production compared to other sec-
tors such as the automotive industry. This seems to make it
more appropriate for the development of IO-based data sets de-
rived from national accounting, althoughas shall be outlined
lateradequate methodological treatment of imports is also of
importance in this sector.
In order to derive IO-based data setsfor the Germanbuildingsector, we developed an IO-LCI model relevant to recent IO
tables for Germany and used data fromthe national accounting.
IO-LCI data sets were calculated for 284 generic building prod-
ucts delivered to the German market. To validate the results,
a systematic comparison between IO-based and process-based
data sets was carried out. To do this, the types of building prod-
ucts identified in the IO tables were matched to 106 building
products available in the process-based LCA database Ecoin-
vent. Pairs were matched for these 106 buildings products, for
which the results for different emissions were compared and
analyzed as to the specific reasons for deviations.
Case Study: The German Building Sector
In Germany, up to 21% of the total global warming gases car-
bon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)
and of the gases sulfur dioxide (SO2), nitrogen oxides (NOx),
ammonia (NH3), and nonmethane volatile organic compounds
(NMVOCs) named in the Gothenburg Protocol have been
shown to be emitted during the life cycle of civil engineer-
ing (construction, use, and end of life) (Kohler1999). For this
reason, the importance of green buildings, driven by pub-
lic building projects, has increased in the last decade both in
Germany and worldwide (see, e.g., EPA2012;UKGBC2012;
WorldGBC2012). Energy savings in the use phase of buildings
will become more and more important due to the rapid increasein the percentage of completed passive houses in the German
building stock. The energy demand and the environmental im-
pact of a building will consequently slide to the construction
and end-of-life phases in the life cycle. Mosle (2010) prognos-
ticates that in the year 2020 up to 42% of the primary energy
demand of a building over a period of 50 years will be needed
solely for the manufacture and disposal of building products.
Public authorities using theCodeof Practice forSustainable
Building Construction (BMVBS2001) request formal project
bids or certification activity by the German Sustainable Build-
ing Council (DGNB). This requires the inclusion of a building
LCA and fosters the need for a comprehensive database for the
German building sector. As such, the Okobau.dat database has
been developed (BMVBS 2011). A recentreviewcommissioned
by the Federal Ministry of Transport, Building and Urban De-
velopment concluded, however, that the available LCI data sets
in Okobau.dat (BMVBS2011) do not sufficiently cover all the
relevant building products manufactured in Germany (Kreissig
and Binder2007). A total of 178 building products were iden-
tified that were classified as relevant for an LCA of buildings,
but for which no LCI data were available (i.e., not even data
on global warming gas emissions).
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We took into account all 178 building products mentioned
above. Furthermore, in order to make a sample for validation,
we also included 106 building products for which data sets
are available in the Ecoinvent database. An excerpt of the
284 IO-LCI data sets that were to be calculated either to fill
data gaps in Okobau.dat or to be used for validation purposes
with the Ecoinvent data is given in table S1 in the supporting
information available on the Journals Web site.
State of the Art
As to an IO-based LCI, there are two tools for generating
data sets: the economic input-output lifecycle assessment (EIO-
LCA) model developed by Carnegie Mellon (2011) and the
missing inventory tool (MIET) that has become part of the
LCA software SimaPro (Weidema et al. 2005). These tools
have been evaluated with regard to their suitability for the
German building sector.
Due to the fact that building materials are produced region-
ally, current statistical data from Germany should be used for a
German building LCA. The EIO-LCA model includes Germanstatistical data from 1995, representing the situation shortly af-
ter German reunification. At that time the economy was going
through dramatic structural and technological changes, which
also affected airborne emissions. This is the reason that data
from 1995 can be regarded as outdated, especially in the case of
the building industries. Moreover, using EIO-LCA to generate
IO-LCI data sets that are valid for Germany does not seem ad-
equate due to the low number of sectors (presently only 58 are
included in the German statistics while the economy is subdi-
vided into 71 sectors). MIET, on the other hand, is based on
foreign national statistical data, namely from the United States
and the Netherlands. The underlying IO tables comprise re-
cent data, which are subdivided into a large number of sectors.The question arises of whether these IO models could be used
instead of German IO data.
Loerincik(2006) compared German and U.S. CO2 emis-
sions calculated with German and U.S. IO data. The result
showed significant deviations between the data sets on the scale
of one order of magnitude, where the German emissions were
lower than those for the United States. The reason for this re-
sult could be different production conditions, laws, or prices for
fuels and energy (Loerincik2006). This identified a need for a
current German IO model for calculating IO-LCI data sets.
Procedure for Calculating Input-OutputLife Cycle Inventory Data Sets
Databases
Two databases from official statistics form the basis for com-
piling a German IO-based LCI:
IO tables from national accounts (Destatis2007b) Tables of direct emissions from environmental account-
ing (Destatis2007a)
Data from both tables refer to the same sectors and goods.
Goods are distinguishedclearly by their registration numberand
are therefore categorized based on the European Classification
of Products by Activity (CPA classification), and the sectors are
classified according to the Statistical Classification of Economic
Activities in the European Community (NACE classification).
Using these classificationsystems, it is possible to cover all of the
goods and sectors completely and to identify explicitly whichgood is assigned to which sector.
The tables of direct emissions from German national ac-
counting provide data for only seven airborne compounds cov-
ered by international reporting obligations. Consequently, all
the generated German IO-LCI data sets only cover these seven
emissions that are reported in environmental accounting. This
is of course a restriction for impact assessment since environ-
mental indicators such as biodiversity or land use are system-
atically disregarded. The use of IO-based data sets still seems
reasonable taking into account that data gaps can be filled at
least with information on important categories such as climate
change. Furthermore, it can be expected that in the future
information on additional emissions will be included in envi-ronmental accounting that will make IO-based data sets more
comprehensive.
Calculation Methodology
The procedure for calculating IO-LCI data sets for building
products comprises the following steps:
Associating a building product with its CPA registration
number Determining the corresponding sector (according to
NACE) Determining the average price of the product over a year Calculating the cumulative emissions according to the
average price of a product
Equation(1) presents the mathematical form of this calcu-
lation, which follows the general IO approach (Leontief1970;
Miller and Blair1985;Holub and Schnabl1994):
zu =Bu (I A)1
y, (1)
where
zu = cumulative emissions of airborne substance u (u =
CO2, CH4, etc.) for a given final demand;
A = direct requirements coefficients matrix;
Bu = environmental intervention matrix (emissions of air-
borne substance u in physical units per euro []
output);
I = identity matrix; and
y = final demand vector.
MatrixAcontains the direct requirement coefficients taken
from the German IO tables for 71 sectors. Matrix B is the
quotient for the same 71 sectors, derived by dividing the direct
airborne emissions for the seven gases CO2, CH4, N2O, SO2,
NH3, NOx, and NMVOC by the total amount of the goods
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0%
5%
10%
15%
20%
25%
30%
35%
40%
0 0.0625 0.125 0.25 0.5 1 2 4 8 more
up to up to up to up to up to up to up to up to up to than
0.0625 0.125 0.25 0.5 1 2 4 8 16 16
Relativef
requency
Emission quotients
0%
5%
10%
15%
20%
25%
30%
35%
40%
0 0.0625 0.125 0.25 0.5 1 2 4 8 more
up to up to up to up to up to up to up to up to up to than
0.0625 0.125 0.25 0.5 1 2 4 8 16 16
Relativefrequency
Emission quotients
0%
5%
10%
15%
20%
25%
30%
35%
40%
0 0.0625 0.125 0.25 0.5 1 2 4 8 more
up to up to up to up to up to up to up to up to up to than
0.0625 0.125 0.25 0.5 1 2 4 8 16 16
R
elativefrequency
Emission quotients
(a)
(b)
(c)
Figure 1 Emission quotients relative frequency of (a) carbon dioxide (CO2) emissions; (b) methane (CH4) emissions; (c) ammonia (NH3)
emissions; (d) nitrous oxide (N2O) emissions; (e) nitrogen oxide (NOx) emissions; (f) sulfur dioxide (SO2) emissions; and (g) nonmethane
volatile organic compound (NMVOC) emissions. Subfigure (h) shows the under/overestimation of emissions by input-output (IO)-based life
cycle inventory (LCI) data sets.
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0%
5%
10%
15%
20%
25%
30%
35%
40%
0 0.063 0.125 0.25 0.5 1 2 4 8 more
up to up to up to up to up to up to up to up to up to than
0.063 0.125 0.25 0.5 1 2 4 8 16 16
Relativefrequency
Emission quotients
(g)
(h) Underestimation by
IO-based LCI data sets
Overestimation by
IO-based LCI data sets
Figure 1 Continued.
The frequency distributions of NH3, SO2, and NOxshow a shifttoward lower quotients, which means that these emissions in
most of the IO-LCI data sets are underestimated compared to
those of the system processes. In contrast, the N2O emissions
are drifting toward higher quotients and are therefore overesti-
mated by the IO-based LCI. This means that in most cases the
calculated N2O emissions for a building product in the IO-LCI
data sets are greater than in the corresponding system processes
and that the NH3emissions are lower.
The reasons that system processes are underestimated by
the IO data sets are assumed to be at least partially systematic
in nature: (a) Not all economic transactions are gathered in
Germany. For example, companies with fewer than 20 employ-
ees do not have to report to the Federal Statistical Office. Inthe case of the building sector, 30% of all companies do have
less than 20 employees (Gromling2011). (b) Emissions are not
completely available for all processes due to the specific aims
of national inventory reporting (NIR; balancing of the green-
house gases according to certain grouped sources of emissions)
and the corresponding requirements. In contrast, there are ob-
viously different reasons for the overestimates by data sets and
for the specific shape of the frequency distribution, which have
to be investigated in more detail.
Reasons for Deviations BetweenProcess-based and Input-Output-basedLife Cycle Inventory Data Sets
Generic Differences Between Process-Based and
Input-Output-Based Life Cycle Inventories
Due to their different methodological approaches, the
generic differences between process-based and IO-based LCIs
may contribute to the deviations in the results. Some of these
differences were eliminated by the calculation methodology
that we used. This applies specifically to the issue of system
boundaries. As mentioned before, the system boundaries of
IO-based LCI are much broader than those of process-based
LCI. The reason for this is that statistical data comprises all theactivities in a national economy. In contrast to most process-
based LCI, in IO-based LCI the corresponding emissions for
services such as trade, consultancy, advertising, and finance
are enclosed within the system boundaries. Although this
may be considered a generic advantage of IO-based LCI, we
excluded these sectors in the IO tables in order to match the
system boundaries to those of process-based LCI and thus to
be able to compare them (see equation(2)). Still some generic
differences remain in our approach.
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Differences in the Allocation of Emissions
IO-based LCI is grounded on monetary IO tables and related
models. The physical flows of goods between the sectors are
accounted for in monetary units. Cumulative emissions for a
single product are calculated from the final demand of a whole
sector by allocating the share of emissions in relation to the
share of the monetary value of this product as related to the
monetary value of thewholesector. In contrast, in process-basedLCI all processes are connected by physical flows measured
in physical units. Emissions are calculated by determining the
sums of the amounts contributed by all the processes within
the process chain in relation to the demand for the respective
process in physical units. It is only in the special case of multi-
output processes that allocation at the level of that process may
be done either according to monetary values or according to
physical values.
Geographical Boundaries
Figures for direct emissions in a national IO model are taken
from the reported national data. The same emission factors are
applied to imported goods. Consequently, imports are treatedas if they were produced domestically and as if production pro-
cesses were performed under the same conditions and at the
same level of technology as the domestic state of the art (Moll
et al.2004). This may lead to an underestimation of the emis-
sions fromintermediateinputsproducedin lessdeveloped coun-
tries in IO-based LCI. In contrast, the country-specific produc-
tion conditions and corresponding emissions are usually taken
into consideration in process-based LCI data sets (Ecoinvent
2011).
Some other methodological reasons may be important with
regard to their contribution to deviations. This may be true
specifically for the sector affiliation of building products, which
is done top-down in IO-based LCI, in contrast to the calcula-tion of an individual production system in process-based LCI.
Furthermore, the quality of the underlying primary data from
NIR is of course decisive for the resulting IO-based LCI data
sets. The results of a systematic evaluation of the extent to
which these reasons contribute to deviations are presented in
the following.
Systematic Errors
Deviations between IO-LCI data sets and those of system
processes could be the consequence of a systematic error inthe calculation procedure, such as the monetary allocation of
emissions at a sector level to single products in IO-based LCI.
To evaluate the occurrence of systematic errors, the respective
correlations of therelative deviations between pairs of the seven
airborne emissions were investigated by statistical regression
analysis. For example, the correlation of CH4emissions to CO2emissions is shown in figure 2, where a high correlation of
relative deviations is observed. In the case of a systematic error,
correlations between all emissions should be expected to be in
the same range of magnitude.
The results for all seven airborne emissions are shown in
table 1. The coefficients of determination R2 as well as the
correlation coefficientsR show the relationship between two
variables (CO2 as an independent variable on thex-axis, the
other gases as dependent variables on the y-axis). The result-
ing magnitude of the relationship ranges between a very high
correlation and a weak correlation according to benchmarks
recommended in the work of Brosius (1999). This shows that
a systematic error (i.e., one single factor such as the allocation
of emissions according to monetary units) cannot be the reason
for the deviations observed. Systematic error can consequently
be excluded as the general reason for a deviation.
Geographic Boundaries
Data sets in the Swiss Ecoinvent database often refer toa specific geographic region, whereas German IO-LCI data
sets refer exclusively to Germany. This made it possible to
y = 1.65x - 0.28
R2 = 0.79
1E-04
1E-03
1E-02
1E-01
1E+00
1E+01
1E+02
1E-04 1E-03 1E-02 1E-01 1E+00 1E+01 1E+02
quoentCH4
quoent CO2
Figure 2 Scatter plot of the deviations of methane (CH4) emissions relative to carbon dioxide (CO2) emissions on the product level.
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Table 1 Coefficients of determination and correlation coefficients
Magnitude of the
Variables relationship
(quotients of gases) R2 R (Brosius 1999)
x: CO2;y: CH4 0.79 0.89 Very high correlation
(R: 0.81)
x: CO2
;y: SO2
0.42 0.64 High correlation
(R: 0.60.8)
x: CO2;y: NMVOC 0.22 0.47 Average correlation
(R: 0.40.6)
x: CO2;y: NOx 0.21 0.45 Average correlation
(R: 0.40.6)
x: CO2;y: NH3 0.08 0.28 Weak correlation
(R: 0.20.4)
x: CO2;y: N2O 0.05 0.23 Weak correlation
(R: 0.20.4)
Notes: CO2 = carbon dioxide; CH4 = methane; SO2 = sulfur dioxide;
NMVOC= nonmethane volatile organic compounds; NOx = nitrogen
oxides; NH3 = ammonia; N2O= nitrous oxide;R2 =coefficient of deter-
mination;R = correlation coefficient.
Table 2 Analysis of geographic relations
Number of Number of
IO-LCI data IO-LCI data
sets with sets with
Ecoinvent data Number of deviations > deviations