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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:bodomueller@web.de

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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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