1
Raw Material Equivalents (RME)
of Austrian Trade Flows
„ÖRME 3“
Endbericht an das BMLFUW
zum Vertrag GZ: BMLFUW-UW.1.4.18/0039-V/10/2010
Anke Schaffartzik, Nina Eisenmenger, Fridolin Krausmann
Institut für Soziale Ökologie
IFF - Fakultät für interdisziplinäre Forschung und Fortbildung der Alpen-Adria-Universität Klagenfurt
Schottenfeldgasse 29, A-1070 Wien, Österreich
Wien, November 2011
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Table of Contents
AVAILABLE RME ACCOUNTS AND THE METHODOLOGICAL APPROACHES .............................................................................. 4
CALCULATING RME: THE AUSTRIAN HYBRID METHOD ..................................................................................... 6
THE LCA MODULE ............................................................................................................................................. 8
THE INPUT-OUTPUT MODULE .................................................................................................................................. 10
DATA USED: MATERIAL FLOW DATA ......................................................................................................................... 11
DATA USED: LCA COEFFICIENTS ............................................................................................................................... 11
DATA USED: INPUT-OUTPUT MODULE ...................................................................................................................... 13
RME OF AUSTRIAN TRADE 1995-2007 ............................................................................................................ 13
AUSTRIA’S PHYSICAL TRADE BALANCE IN RME ............................................................................................................ 17
AUSTRIA’S DIRECT MATERIAL INPUTS IN RME ............................................................................................................ 19
AUSTRIA’S DOMESTIC MATERIAL CONSUMPTION IN RME ............................................................................................. 20
ADDING ECONOMIC DETAIL ........................................................................................................................... 22
THE MATERIAL REQUIREMENTS OF THE SECTORS ......................................................................................................... 22
SECTORAL IMPORT INTENSITY ................................................................................................................................... 25
HOW DOES AUSTRIA PERFORM IN COMPARISON TO OTHER COUNTRIES? .................................................... 27
AUSTRIA IN COMPARISON TO THE CZECH REPUBLIC AND GERMANY ................................................................................. 27
COMPARISON WITH RESULTS FROM THE GRAM CALCULATION ....................................................................................... 29
INTERNATIONAL COMPARISON ACROSS WORLD REGIONS AND DEVELOPMENT STATUS .......................................................... 30
OUTSOURCING AND RESOURCE PRODUCTIVITY ............................................................................................. 31
AUSTRIAN RESOURCE PRODUCTIVITY IN COMPARISON TO GERMANY AND THE CZECH REPUBLIC ............................................ 33
REFERENCES: DATA AND LITERATURE ............................................................................................................ 35
ANNEX: DATA TABLES .................................................................................................................................... 37
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International trade grew to an important driver for a nation’s economic performance. At the end of
the 20th century shares of imports and exports in total GDP reached levels between 15% and 70%
(UNDP 2004; the World Bank Group 2007). Besides, trade is also showing high dynamics with growth
rates double as fast as GDP. (The World Bank Group 2007) Hence, economic growth and its
maintenance are increasingly depending on growing markets and international trade. Exports are
used as an important variable in stimulating growth, and imports enable industrialized countries in
particular to access foreign resources and markets. But trade also plays a specific and increasingly
important role when measured in physical terms. Imports currently account for 34% of the direct
material input (DMI) into the Austrian economy, and 23% of all materials entering Austria leave the
country as exports. (Statistik Austria 2011c)
Trade includes goods on very different stages of the production process, i.e. basic commodities such
as wheat or crude oil, intermediate goods such as copper wires, or final products such as bread or
cars. In the production process, raw materials are used and transformed to wastes and emissions.
Considering a traded good, part of the raw materials used in the production process are left behind
and are not included in the physical mass that actually crosses administrative borders. A country’s
resource use is thus significantly shaped by traded goods. Is a country producing basic commodities
for exports a lot of wastes and emissions stay within its boundaries that are related to the exports.
Economies that are specialising on high-end production using a lot of imported goods reduced the
domestic material use. In Austria, fossil fuels and many important metals are not (or no longer)
available for extraction within Austria but have strategically important functions in the economy. This
situation gives rise to a high degree of import-dependency which has been on the rise since 1960.
In order to understand societal resource use and its resource efficiency, upstream material
requirements have to be considered. However, standard MFA methodology (economy-wide MFA)
only considers direct imports and exports, i.e. with their weight while crossing the administrative
borders. Thus far upstream material use is not incorporated.
Recent research activity in the area of MFA thus concentrated on the calculation of RME and the
integration of the intermediate inputs into the production of traded goods into material flow
accounting. Methodological approaches are currently under development. One of these approaches
is the calculation of so-called raw material equivalents (RME) for imported and exported goods. Raw
material equivalents of traded goods consist of the material inputs required to provide the goods for
export and include the weight of the good itself (Eurostat 2001: 22). Raw material equivalents are
calculated for both imports and exports, the assumption being that the RME of imports must be
included in an economy’s consumption while the RME of exports must be deducted in order to arrive
at a complete balance of material inputs and outputs.
Within the framework of research funded by the Austrian Federal Ministry of Agriculture, Forestry,
Environment and Water Management, we have developed a method for calculating the raw material
equivalents of Austria’s foreign trade and established an RME time series covering 1995-2007.
Methodologically, this approach is derived from well-established input-output calculations. The data
required are Austrian MFA data (in tonnes), Austrian supply and use data (in Euros), and coefficients
from life cycle analysis (LCA) (in tonnes/tonne). Our work ties in closely to similar endeavours in
Germany (Buyny et al. 2009) and the Czech Republic (Weinzettel and Kovanda 2009).
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Available RME accounts and the methodological approaches
RME accounts are still scarce; however, the area of research has been growing quickly in recent
years. The major challenge with RME accounts is the calculation of upstream material requirements
of imports because for this one needs foreign MFA data and information on inter-industry relations
or material requirements by product or type of production in order to calculate material flows
related to foreign production. Methods to calculate RME of imports are diverse, but four main
approaches can be identified:
1. “Single region IO” (SRIO): first RME accounts (see discussion of this approach in our first project
report: Weisz et al. 2008) applied domestic inter-industry relations and thus technical coefficients to
the calculation of the RME of imports. This was due to the fact that only one IO table (that of the
importing country) was used. This approach allowed for the calculation of the RME of imports only
insofar as they were also produced domestically and even then made the assumption necessary that
domestic production used the same quantity and mix of inputs as did the economies from which the
product was imported. This assumption may allow for valid results if the domestic production system
covers all production sectors and products that are demanded by final consumption and if the type
of production used does not differ greatly from the production in the exporting economies. But this
approach lead to strong distortions if a country is a smaller economy where some goods are not
produced (or some raw materials are not available for extraction) and thus crucial production
processes are not represented in the domestic IO data.
2. Multi-regional IO (MRIO): MRIO approaches solve the problem faced by SRIO approaches by
integrating the input-output structure of other regions into their calculation thus representing
different types of foreign production in MRIO tables. This approach can be considered as the most
advanced in terms of actually depicting the specific inter-industry relations in the economies (or
economic regions) producing the imported goods. At the same time, the extremely large amount of
data involved can make these accounts very complex and difficult to harmonize (high number of
sectors and products, different underlying assumptions in calculation and allocation of MFA data,
different number of sectors from one region or country to the next and overall need to harmonize IO
tables internationally). In most MRIO approaches, this complexity is reduced somewhat by using
average IO tables for regions (instead of specific IO tables per economy). Unfortunately, this can
sometimes pose an additional problem in the assumptions behind grouping specific countries
together in a region (e.g. if countries are grouped geographically, the Southeast Asian region would
include such different economies as Cambodia and Singapore). These problems can be overcome by
choosing the regions based on the specific imports under investigation and thus ensuring that the
economic structures of the main trading partners are represented as precisely as possible. While this
will improve the validity of the results for the country under investigation, it unfortunately can make
the resulting MRIO model less applicable to other regions and thus pose and obstacle to eventual
standardization and international application of the RME indicators. The latter may also be hindered
by the high complexity of the data involved which means that the calculation usually requires major
processing power and long-term dedication, and a centralized effort and thus cannot be easily
performed by single actors (statistical offices or research institutions).
3. The coefficient approach uses coefficients from life-cycle analysis (LCA) instead of the information
on inter-industry relations contained in IO models. This approach can be considered a more bottom-
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up approach compiling data from case studies on single products or product groups. The challenge
here is again a huge effort in data compilation but most strongly the proper calculation without
double counting1. Eurostat is currently funding a project where a set of coefficients is established.
These coefficients are partly from IO analysis of selected EU countries or from LCA databases. They
are currently being compiled and should then be used for calculating RME and RMC for the EU27.
Results are expected within the next two years. The coefficient approach was also and even for the
first time developed and applied by the Wuppertal Institute (ref MAIA, TMR). In comparison to RME
calculations, the coefficients of the Wuppertal Institute always also include unused extraction. Thus,
results cannot be directly compared to RME accounts. The main indicator derived from WI accounts
is the TMR (Total Material Requirement) (Eurostat 2001).
4. Hybrid approaches combine IO and LCA approaches. They extend an SRIO model for those
products which are produced domestically with an LCA module for those products/sectors where no
or negligible domestic production is given. The project at hand is based on a hybrid approach so that
a more detailed description is available in the methodological section.
Table 1 provides an overview of the above described approaches and available studies.
Table 1: overview on case studies accounting for upstream material requirements
Approach Countries References Data coverage
Coefficient approach
EU Eurostat RME Not yet available
Single-region IO approach (SRIO)
Brazil, Chile, Colombia, Ecuador, Mexico, USA
Munoz et al. 2009 2003 (Chile: 1977, 1986, 1996, 2003)
Denmark Weisz 2006 1990
Multi-regional IO approach (MRIO)
Global data set Giljum et al. 2008 2000
Hybrid approach Germany Buyny et al. 2009, Buyny and Lauber 2010
2000-2005, 2000-2007
Czech Republic Weinzettel and Kovanda 2009 2003
Austria 1995-2007
1 The LCA-based approach poses two major difficulties with regard to simultaneously ensuring as full a
coverage as possible and avoiding double counting. The former has to do with the fact that any given LCA coefficient involves a somewhat arbitrary truncation decision somewhere along the production process. This has to do with to which degree of complexity the production process is considered both on a spatial and on a time scale. The second problem is that of allocation. Different principles exist by which intermediate inputs can be assigned to specific products. This decision must be made whenever one and the same production process produces more than one good (e.g. rape seed oil for nutrition and biofuel production and rape seed cakes as animal fodder from rape agriculture). The allocation can be made based on the share of each of the products in overall economic value or based on physical properties such as share in overall mass or energy or based on the amount of material input that could be avoided by producing something in coupled production rather than as a single-product process. Unfortunately, the results of these different allocation approaches can be completely contrary so that the decision as to which procedure is used has a major impact on the overall results.
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Calculating RME: the Austrian Hybrid Method
Generally speaking, in order to be able to calculate the raw material equivalents of Austrian trade,
we need to know how much material input was required to produce those goods which Austria
imported in a given year. In order to make our approach consistent with the economy-wide MFA
framework, we want to work as much as possible from a top-down and not a bottom-up perspective.
The results of our calculations should therefore reflect the intermediate inputs associated with all
imported goods. Thus far, MFA has – for the most part – treated the socio-economic system as a
black box, focussing on the inputs into and outputs from that black box and not on the material flows
within the socio-economic system. For the purposes of calculating raw material equivalents,
however, we need to open this black box in order to be able to trace in what quantity and quality
materials are required for the production of specific goods.
Table 2: IOT for a Hypothetical 3-Product Economy
Wood Paper Books Total
Wood 85 90 0 175
Paper 10 10 90 110
Books 5 5 20 30
Total 100 105 110 315
These types of inter-industrial relations are documented in the monetary supply and use tables
(SUT) which are part of the United Nations’ System of National Accounts (SNA) and are annually
published by Statistics Austria (Statistik Austria 2011a). While the supply tables indicate how much of
a given product (or group of products) is provided (or supplied) by which sector of the economy, the
use tables indicate how much of a given group of products is used by which sector. The SUT form the
basis from which input-output tables (IOT) for an economy can be constructed. These IOT depict the
inter-industry relations in monetary terms. For each group of products, they indicate which other
products were needed in the production process.
Table 2 shows a very simple IOT for a hypothetical economy which produces only three goods: wood,
paper, and books. The values in the columns show which inputs are required for the production of all
the wood, paper, and books in this economy. In this case, 90 units worth of wood, 10 units worth of
paper and 5 units worth of books are needed to produce the total amount of paper in this economy.
This is, of course, a highly simplified IOT, included here only to introduce the type of information
contained in these tables.
Thus, for the Austrian economy, the IOT provides information about the monetary inputs into the
production of each group of goods. This is exactly the structure of information we need for
calculating the RME of Austria’s exports. For each unit of output of a certain group of goods, the IOT
can be used to calculate which intermediate direct and indirect inputs were necessary to produce
this good. The direct inputs are those which can easily be read out of Table 2 for our hypothetical
economy. The indirect inputs are those inputs which are required if the economy has to increase its
output of a certain product. If, for example, our hypothetical economy wanted to produce more
books, the book production would need more wood, paper, and books. If the wood production
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consequentially were to produce more wood, it too would need more wood, paper, and books.
These reiterations can be accounted for within the framework of a generalized input-output model
which is also the type of model on which we based our calculations.
In monetary terms, information about the input-output structure of the Austrian economy is
available in the IOT. By making some generalized assumptions (homogenous production and prices),
this monetary information can be translated into terms of mass and thus linked to the available MFA
data. This information can be used as the basis for calculating the RME of Austria’s exports. Assuming
the same input-output structure to hold true for Austria’s imports, however, would probably lead to
a misrepresentation. Some of the goods which Austria imports are not produced in the Austrian
economy, some are produced with different inputs. The highest degree of precision could be
attained by combining bilateral trade data with country-by-country input-output data. Unfortunately,
this information is not readily available and the resources required to generate it are
disproportionately large in relation to the degree of precision thus attained. Rather than following
such a route, we aimed to contribute towards the development of an approach that could be applied
to other countries and other points in time with relative ease.
Figure 1: Schematic Representation of the RME Calculation
In calculating the RME of Austria’s imports, we combined a generalized input-output model (Lenzen
2001) with a component based on life cycle analysis (LCA) data in a modular fashion. While the
intermediate inputs required for the production of Austria’s exports were calculated with the help of
monetary input-output data combined with data on domestic extraction, imports and exports could
be based on data in physical terms from material flow accounting. The intermediate inputs required
in the production of those goods imported into Austria and for which no comparable production
exists within Austria where calculated with the help of LCA-based coefficients reflecting the array of
material requirements associated with the production of a specific good. Figure 1 offers a schematic
representation of the interplay between MFA data and the IO and LCA modules in the calculation
process. It must be noted, however, that this figures is the static representation of what is actually a
dynamic calculation: calculating the RME of Austria’s exports, the higher level of imports as
expressed in RME is taken into consideration.
Intermediate Inputs of ImportsIntermediate
Inputs of Exports
MFA Data on Physical Imports
without Comparable
Production in Austria
MFA Data on Physical Imports
with Comparable
Production in Austria
IO Module LCA Module
RME of Imports
MFA Data on
Physical Exports
RME of Exports
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The LCA Module
The LCA module within the model consists of a matrix in the rows of which the intermediate input
coefficients are contained for those (groups of) goods for which no comparable production exists in
Austria (cf. Table 3).
Table 3: MFA data for imports computed in the LCA module (2000)
These coefficients represent the material input required per unit of output in the production of
imported goods and are in the unit of t/t. Some disaggregation from the four main MFA categories
was necessary because the intermediate inputs required differ greatly at different stages of
production, e.g. between metal ores and consumer goods produced from these metals, between
crude oil and gasoline. In order to adapt the coefficients to the level of aggregation at which the
underlying MFA trade data are available, a weighted average of the coefficients for different
production processes / goods at different degrees of manufacturing was calculated. The UN
Comtrade data was disaggregated to the group level (3) of the SITC Rev. 3 classification where 261
different products are distinguished. Based on this data, the share of the different raw materials and
consumer goods under each relevant material category was calculated. For example, in 2000, 61% of
Austria’s iron imports (mass) were iron ore and concentrates while 39% were products of iron and
steel. The LCA coefficients applied to iron were selected and weighted accordingly (cf. Table 3).
Figure 2 shows a selection of the weighted, LCA-based coefficients for the intermediate inputs into
the production of imported goods.
MFA SITC Rev.3 Importe 2000 [t] Anteil
2.1 Iron
281 Iron ore and concentrates 5 426 249 60,62%
282 Ferrous waste and scrap; remelting scrap ingots of iron or steel 700 528 7,83%
Div.67 Iron and steel 2 824 472 31,55%
2.2.1 Copper
283 Copper ores and concentrates; copper mattes, cement copper 182 0,11%
682 Manufactured goods classified chiefly by material: Copper 164 125 99,89%
2.2.7 Aluminium
285 Aluminium ores and concentrates (including alumina) 79 784 16,36%
684 Manufactured goods classified chiefly by material: Aluminium 407 755 83,64%
3.1 Ornamental or building stone
273 Stone, sand and gravel 740 541 33%
661 Lime, cement, and fabricated construction materials (except glass and clay materials) 1 480 113 67%
3.6 Chemical and fertilizer minerals
272 Natural calcium phosphates, natural aluminium calcium phosphates and phosphatic chalk 274 258 29%
278 Minerals, crude, n.e.s. 88 414 9%
562 Fertilizers (other than those of group 272) 596 354 62%
4.2 Hard coal
321 Coal, whether or not pulverized, but not agglomerated 3 412 666
4.3 Petroleum
Div.33 Petroleum, petroleum products and related materials 12 212 435
4.4 Natural gas
342 Liquefied propane and butane 136 870 3%
3432 Natural gas, in the gaseous state 4 548 516 97%
344 Petroleum gases and other gaseous hydrocarbons, n.e.s. 391 0%
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Figure 2: Selection of weighted, LCA-based coefficients of intermediate inputs by MFA categories (Source of LCA coefficients: Öko-Institut 2009)
These coefficients illustrate the high energy input required in the extraction and refining of metallic
resources (copper, aluminium, iron). Within the category of chemical and fertilizer minerals (second
column from the right), the fertilizers and especially the potassic (K) fertilizers are dominant: In order
to produce 1 tonne of potassic fertilizer, 10 tonnes of minerals must be extracted since the average K
content is only around 20%. The other major group of fertilizers, the phosphate (P) fertilizers are a
little less material-intensive at 4 tonnes of mineral extracted (with up to 40% P content) per tonne of
fertilizer obtained.
Figure 3: Weighted, LCA-based coefficients of intermediate inputs for metals, gross ore (Source of LCA coefficients: Öko-Institut 2009)
In order to make the high intermediate inputs of energy in metal production readily visible in Figure
2, the surrounding rock which – together with the metal itself – makes up gross ore has been
excluded. The average metal content of around 1% in the case of copper ore means that the value of
0
50
100
150
200
250
Copper Aluminium Iron
[t/t
]
Fossil Fuels
Non-MetallicMinerals
MetallicMinerals
Biomass
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the intermediate inputs would rise by a factor of approximately 100 when gross ore is considered
dwarfing all other intermediate inputs (cf. Figure 3).
Figure 3 shows that the surrounding rock constitutes the dominant category of intermediate inputs
for copper while for aluminium and iron, the energy input remains important due to the higher metal
contents that prevail for these minerals. For this calculation, standard factors for metal content were
extracted from the data of the US Geological Survey (copper 1%, iron 50%, aluminium 25%).
In accordance with the MFA framework (Eurostat 2001 and 2009), water and air were not considered
as inputs for the sake of consistency. However, their contribution to the intermediate inputs is in no
way irrelevant with approximately 70 tonnes of water required for the production of 1 tonne of
aluminium (Öko-Institut 2009).
The vectors of LCA coefficients (t/t) are multiplied with the import vectors (t) in order to calculate the
intermediate inputs required for the production of these imports:
e2 = ln in (3)
These imports including their intermediate input requirements are then introduced into the IO
module.
The Input-Output Module
The input-output module is based on the work of Wassily Leontief (Leontief 1936, Leontief 1941).
Sectoralized vectors had to be formed from the MFA data on domestic extraction and imports and
exports before it could be introduced into the input-output model (Weisz et al. 2008).
The input-output model which was used to calculate the RME of Austria’s exports and of those
imports which are also produced in the Austrian economy has the following general form (the
derivation of this form is discussed in greater detail in Weisz et al. 2008):
r = f xˆ-1 (I-A)-1 (1)
Vector r is composed of the direct and indirect physical intermediate inputs required by each sector
for the production of one unit of monetary output. The elements contained in this vector thus have
the unit kg/€. The elements in r are calculated using the material intensity (f xˆ-1), i.e. the direct
material inputs per unit of economic output. The elements in f are in physical units, e.g. tonnes or
kilograms. Vector x represents the gross production of the economic sectors and is in monetary units.
A is the matrix of the direct input coefficients and I is the identity matrix with ones along the main
diagonal and zeros elsewhere. The inverse of the difference between these two is the Leontief
inverse (I-A)-1. The latter forms the core of the input-output model. Each cell of the Leontief inverse
contains the information on the amount of direct and indirect inputs required by each sector for the
production of one unit of output. The direct inputs are those which flow directly into the sector, the
indirect inputs are those which are required for the production of the direct inputs along the
economy-wide supply chain.
By multiplying this vector r (kg/€) with the final consumption vector y (€), the intermediate inputs
required for the production of traded goods can be approximated:
e1 = r <y> (2)
By adding the intermediate inputs to the mass of the traded goods themselves, the raw material
equivalents of trade can be calculated.
Data
The intermediate inputs into the production of traded goods also had to be disaggregated in a
manner suitable to the MFA categories in the LCA module. This way it is ensured that the two
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modules are compatible and the results can be totalled. In addition, this is the prerequisite for the
integration of RME into the existing MFA framework. In the IO module, the material flows had to be
assigned to the sectors and product groups of the supply and use tables.
Data Used: Material Flow Data
The material flow data stem from the material flow accounting performed by Statistics Austria for
the 1995 - 2007 (Statistik Austria 2011b). The data were aggregated according to the MFA categories
as published in the current version of the Eurostat standard tables (Eurostat 2009). A special
aggregation of the MFA data was necessary in order to ensure compatibility with the structure of the
input-output data. Initially, the allocation of the MFA data to the sectors was performed manually.
This was only possible due to the limited points in time originally analysed (3). The expansion of the
time series to include all those years for which Austria input-output data are available (1995, 1997,
1999, and 2000-2007) greatly increased the amount of data involved and therefore required
methodological standardization. For the allocation of domestic extraction to the primary production
sectors, this was a very straight-forward procedure. Import-allocation matrices were developed for
all examined points in time. These are based on correspondence tables between the SITC, CPA2 and
MFA classification schemes and on the physical trade data provided by the UN (UN Comtrade 2011).
The latter is available at such a level of disaggregation that the allocation to sectors is unambiguous.
UN Comtrade data was extracted for Austrian imports between 1995 and 2007 in kg, SITC Rev 3 in as
much detail as needed (down to AG5) for allocation to NACE sectors. For the purposes of allocation,
not all data not reported in physical terms were estimated. It was ensured however, that at least 95%
of imports as recorded in material flow accounting were covered. This was seen to be sufficient in
order to achieve a fairly accurate allocation of imports to sectors. Where gap-filling was necessary
(most notably for natural gas (SITC code 343) and iron ore (SITC code 281) between 1997 and 2002),
it was performed with the help of the existing MFA data.
All calculations of RME of Austrian trade were based on the MFA data provided by Statistics Austria.
The UN Comtrade data was only used as auxiliary data in order to allocate the import flows to the
given economic sectors.
Data Used: LCA Coefficients
LCA coefficients stem from a different accounting approach than both the monetary supply and use
data and the physical MFA data. While the latter two are both based on an economy-wide
perspective which seeks to understand the distribution of total amounts (e.g. GDP or DMC) into
different sectors or material groups, LCA is a process based approach which takes a very close look at
one particular (production) process within the economy. In this sense, the approaches are very
complementary because both shed light on aspects of resource use which the respective other
cannot cover. Nonetheless, including both approaches within the same model as we do in this
proposed RME calculation requires consideration of the methodological differences in order to avoid
errors where possible and to correctly interpret the results. With regard to the LCA coefficients used
in this study, two groups of factors are especially important to consider:
2 Statistical Classification of Products by Activity (CPA)
12
1. the suitability of LCA coefficients
The coefficients are usually based on one particular production process, i.e. on a specific use of
technology and of material and energy inputs. These production processes, however, vary in
space and time. The inputs needed can be quite different from one geographic region to the
next: To stick with our example of iron, in terms of tailings, it would make a difference whether
the iron was minded in Brazil with a metal content of over 60% or in Canada where metal
content averages at around 50%. Similarly, the technology used in production processes can
change over time or also differ from one site to the next. Again, for the example of iron, basic
oxygen steelmaking and the use of electric arc furnaces require different inputs when it comes
to making steel from iron.
2. the system boundaries applied
Even though LCA manages to cover a very large fraction of the inputs into any given production
process, there remains a point at which the branching out into upstream input processes is or
has to be terminated so that a complete economy-wide coverage is not possible (Chapman
1974, Wilting 1996). Additionally, the processes involved will often deliver inputs for more than
one downstream process so that a choice must be made as to how these requirements are
allocated to the different processes.
In selecting the database of LCA coefficients used in this model, three criteria were of particular
importance: 1) the production processes relevant for Austria’s imports must be included, 2) the
intermediate inputs must be declared in mass units or physical units convertible to mass, 3) the
system boundaries applied must be transparent. In addition, choosing a database which could be
used free of charge was an additional criterion in order to avoid obstacles to the reproducibility of
our results for other scholars. Based on these deliberations, we decided to use GEMIS (Global
Emissions Model of Integrated Systems). GEMIS provides both an extensive database as well as the
software with which to compile and process the data required for a particular analysis. The software
additionally affords the advantage of allowing the user to define some of the system boundaries
applied, most notably on including transport and on allocating inputs in case of coupled production.
GEMIS is published by the German Institute for Applied Ecology and the current version 4.5 we used
during our research covers over 10.000 processes and 1.440 products (Öko-Institut 2009).
GEMIS allows for the depiction of intermediate inputs of material in units of mass (e.g. t, kg) and of
energy in the according units (J, kWh). In order to render all intermediate inputs comparable and to
enable the formation of totals, the intermediate inputs of energy must therefore be converted to
units of mass. This was achieved by using a set of standard factors for the energy content or the
calorific value respectively of the different energy carriers. The material inputs corresponding to the
energy input vary according to the assumptions made about which energy mix is used in the given
production processes. By using the energy mix reported in GEMIS for each production process and
differentiating between nuclear energy, hard coal, lignite, oil, gas, renewable energies (biomass,
wind, water, geothermal) and waste incineration, a good approximation of the material inputs
corresponding to the energy used in production could be made.
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Data Used: Input-Output Module
The input-output module of the model was built on the basis of the monetary supply and use tables
(SUT) published by Statistics Austria for the years 1995, 1997, and 1999-2007 (Statistik Austria 2011).
These tables are disaggregated into 57 sectors and product groups. The transformation of the SU
tables to the Leontief inverse required for RME calculation was based on the methodological
descriptions also provided by the Statistics Austria (ÖSTAT 1994, Kolleritsch 2004).
RME of Austrian Trade 1995-2007
By developing an automated method for the allocation of imported flows, we were able to calculate
the raw material equivalents of Austria’s trade for all years for which monetary supply and use data
is available from Statistics Austria. Therefore, in the following, we can now present results for the
years 1995, 1997 and 1999 – 2007. As opposed to the three points in time (1995, 2000, 2005) which
we calculated in the previous RME project(s), the time series makes it possible to now analyze the
development and trends of the RME of Austrian trade in greater detail.
In accounting for the RME of Austrian trade, two components must be calculated. The RME of
imports include the direct imported flows as well as the intermediate material requirements that
were used in the production of these goods in other economies. The RME of exports include the
direct exported flows as well as the intermediate material inputs that were dedicated to the
production of these exported goods within the Austrian economy. Based on these two indicators and
the information on domestic extraction as available from the Austrian material flow accounts, we can
then calculate the traditional MFA indicators in their raw material equivalents.
RME of Austrian Imports
In the year 2007, Austria directly imported approximately 91 million tons of material. The production
of these goods was associated with intermediate material inputs in the economies of origin. In the
case of Austria’s imports, these inputs were highly significant. If we add the intermediate inputs to
the direct import flows, we obtain the raw material equivalents (RMEIM) of Austrian imports. In 2007,
they amounted to approximately 318 million tons and thus surpassed the direct imports by a factor
of 3.5 (see Figure 4).
Figure 4: Austria's Direct Imports and RME of Imports in 2007 in Million Tons
14
The major share of Austria’s direct imports was made up of fossil energy carriers (31%), biomass
(25%) and ores (23%). This relationship is noticeably different in the RME of imports where metal
ores make up the largest share (47%) and are followed by fossil energy carriers (20%) and non-
metallic minerals (18%). This shift is especially due to the fact that metals are imported mainly in the
shape of metal concentrates or metals products. From the extraction of metal ores via the
concentration of these metals to the completion of metal products, however, a steady reduction of
the material mass occurs. While this is obviously not included in the direct imports, it does become
visible in the RME of these imports. The second factor making ores a much larger fraction in the RME
of imports than in the direct imports are the metal requirements of infrastructure for the production
of many of the imported goods, especially also of the fossil energy carriers. Overall, this means that
the RME of metal imports surpasses direct metal imports by a factor of 7.1. While fossil energy
carriers are not subject to the reduction of mass along the chain of production to such an extent as
the metals are, they are important intermediate inputs in virtually all other production processes and
are therefore significantly higher (factor 2.3) when measured in RME than as direct imports. A very
noticeable difference between direct imports and RME of imports is also given for non-metallic
minerals. These are imported in very small amounts only and make up 13% of Austria’s direct
imports. However, non-metallic minerals are also required in large amounts in the construction of
infrastructure as required for other production processes so that the RME of Austria’s non-metallic
mineral imports is 4.8 times larger than the direct imports of this category.
Figure 5: Austria's Imports 1995 - 2007 in Million Tons
Across the period of time under investigation, Austria’s direct imports grew by a factor of 1.7 (Figure 5) and played an increasingly important role in meeting the economy’s resource demand. In 1995, imports accounted for 26% of Austria’s direct material input (DMI=DE+Imports). In 2007, this share had already increased to 35%. This growing importance of traded goods underlines how relevant the consideration of intermediate material requirements through the calculation of the raw material equivalents is. The share of biomass in these imports grew slightly from 23% in 1995 to 25% in 2007. The share of metal ores in these imports grew from 19% to 23% across the same period of time. Non-metallic minerals remained fairly constant at 13%. The share of fossil energy carriers decreased from 38% to 31% but still makes up the largest fraction of imports.
15
Figure 6: RME of Austria's Imports, 1995-2007 in Million Tons
During the period of time under investigation, the RME of Austria’s imports also grew by a factor of 1.7 (Figure 6). Since the hybrid approach used for RME calculation is not a coefficient-based approach, the RME of imports does not necessarily have to grow proportionally to the direct imports. In this case, the rate of growth is the aggregate effect of the developments in the 5 material categories. The shares of the material categories in overall RME of imports remained more constant than did the shares in direct imports. The share of biomass increased slightly from 11% to 12%. Metal ores made up 50% in 1995 and 47% in 2007. The share of non-metallic minerals rose slightly from 17% to 18%. Fossil energy carriers remained constant at 20% across the period under investigation. The ratio of RME imports to direct imports remained fairly constant between 1995 and 2007, varying from 3.4 to 3.6.
RME of Austrian Exports
The Austrian economy is a net-importer of resources, i.e. it imports more than it exports. In 2007,
Austria directly exported approximately 59 million tons of material. As an industrialized economy,
Austria mainly exports highly processed goods for the production of which it imports raw materials
or less processed goods. Consequently, a significant amount of the material mobilized within the
Austrian economy or imported from other economies is used for the sake of producing exports. This
amount of material can be made visible by calculating the RME of Austria’s exports. In 2007, the
latter amounted to approximately 203 million tons and were thus about 3.4 times larger than the
direct exports (Figure 7).
16
Figure 7: Austria's Direct Exports and RME of Exports in 2007 in Million Tons
The major share of Austria’s direct exports was made up of biomass (25%) followed by metal ores
(17%) and non-metallic minerals (10%). Other products which cannot be definitely allocated to one
of the material categories due to their heterogeneous composition made up 9% of exports. At 6 %,
fossil fuels were the smallest fraction. Most of the intermediate inputs required for this production
were metal ores for which the RME of exports was 6 times larger than the direct trade flow. For non-
metallic minerals and fossil energy carriers, the factor between direct exports and RME exports was
approximately 5. Biomass only made up 12% of RME exports (as opposed to 25% of direct exports)
with a factor of 1.7 between direct and RME flows. As was the case for imports, there is no direct
correspondence between the direct exports and their RME by material category, i.e. not all
intermediate inputs of ores were required for the production of exported metals. Instead, especially
metals and construction minerals are required by the infrastructure for almost all production
processes. Fossil energy is also an input that feeds into almost all production processes.
Figure 8: Austria's Exports 1995 - 2007 in Million Tons
As was previously illustrated, the importance of trade grew noticeably between 1995 and 2007. This
is true not only for the role of imports in meeting Austria’s resource demand also for exports. Across
17
the 12 year period, direct exports grew by a factor of 2.1 (Figure 8). The strongest growth occurred in
the smallest share of the exports – fossil energy carriers, the exports of which grew by a factor of
almost 5.
The growth in these exports may seem unusual at first considering that Austria only has very few
domestic sources of fossil energy carriers and is dependent on imports to meet its demand.
Petroleum refinery is, however, an important branch within the Austrian economy: It contributes a
significant share to GDP and the mineral oil authority OMV is the biggest Austrian enterprise. The
refinery at Schwechat processes approximately 90% imported and 10% domestic energy carriers of
which approximately 20% are exported (Fachverband der Mineralölindustrie Österreichs 2010).
Direct exports of biomass doubled between 1995 and 2007. The same is true for direct exports of
metal ores. Non-metallic mineral exports grew by a factor of 1.7. Exports of heterogeneous other
products grew by a factor of 2.2.
The RME of Austria’s exports grew even slightly more strongly than the exports themselves by a
factor of 2.3 between 1995 and 2007 (Figure 9). In 1995, the RME of exports amounted to
approximately 88 million tons and increased to 203 million tons in 2007.
Figure 9: RME of Austria's Exports 1995-2007 in Million Tons
As was the case for the RME of imports, the shares of the different material categories in the overall
RME remained fairly constant across the period of time under investigation. The strongest growth
occurred in the RME of exported fossil energy carriers with a factor of 3.3. While fossil energy
carriers contributed 8% to overall RME, this share increased to 12% by 2007. In the remaining
material categories, a little more than a doubling (factor 2.1 to 2.3) can be observed during these 12
years. The ratio of exported RME to direct exports increased from 3.1 in 1995 to 3.6 in 2006 (and 3.4
in 2007). More than 3 times the material contained in the exported goods themselves is used within
the Austrian economy in the production process.
Austria’s Physical Trade Balance in RME
As was outlined above, the Austrian economy is a net importer of goods, i.e. more is imported than
exported. An indicator for this relationship is the physical trade balance (PTB) which corresponds to
the total imports minus the total exports in a given year. If the PTB is positive, that economy is a net
18
importer, if it is negative, a net exporter. In 2007, Austria’s PTB was slightly above 31 million tons to
which fossil energy carriers contributed the major share (23 million tons or 75%), see Figure 10. As
has been discussed above, this reflects that Austria imports fossil energy carriers in significantly
greater amounts than it exports them. The second largest share in the PTB consisted of metal ores
(22%). Again, this is a group of products imported to a large extent and hardly exported. For the
remaining material categories, the PTB is more strongly balanced. Imports of non-metallic minerals
supersede exports by about 3 million tons. For biomass, imports and exports exhibit nearly the same
quantities with a PTB of just 65 thousand tons. The other products category is the only one in which
more is exported than imported so that the PTB takes on a negative value (-2 million tons). As an
industrialized economy, Austria tends to produce these rather heterogeneous goods and export
them rather than import them.
Figure 10: Austria's Direct Physical Trade Balance (PTB) and RTB in Raw Material Equivalents (RTB) in 2007 in Million Tons
By calculating the PTB not from the direct import and export flows but from their raw material
equivalents, we obtain an indicator of the PTB as expressed in RME which shall be referred to as the
RTB. In the year 2007, the RTB has higher than the PTB by a factor of 3.7. For comparison: RME of
imports was higher than direct imports by a factor of 3.5 and for RME of exports and direct exports,
this factor was 3.4. Here, the large amount of intermediate inputs required in the shape of metal
ores accounts for the major part of this difference. The RTB for this material category shows that 65
million tons more were imported than exported. The next largest fraction are the fossil energy
carriers of which approximately 40 million tons more were imported than exported. The balance of
non-metallic minerals also increases from 3 to 13 million tons. Biomass changes from being an
(almost balanced) category of net imports to a category of net exports with an RTB of just under -1
million tons. The other products category remains almost unchanged in its RTB compared to its PTB.
This is due to the fact that a good deal of the intermediate input requirements can usually be
assigned to one of the material categories whereas this is not always possible for the final products.
19
Figure 11: Austria's Physical Trade Balance in Direct Flows (left) and RME (right) in 2007 in Million Tons
In comparing the development of the PTB and the RTB over time, the difference in mass is first
clearly visible difference (Figure 11). Between 1995 and 2007, PTB grew from approximately 25 to 31
million tons by a factor of 1.3. During the same period of time, RTB grew from approximately 100 to
115 million tons by a factor of 1.1. This stagnation in the trade balances in comparison to the growth
that we have seen in other indicators across the 12-year period is due to the fact that direct exports
and imports as well as the RME of these flows grew to roughly the same extent.
Austria’s Direct Material Inputs in RME
Figure 12: Austria's Direct Material Inputs in Direct Flows (DMI) and in RME (RMI) in 2007 in Million Tons
Based on the calculation of the raw material equivalents of imports and exports, the MFA indicators
can now also be presented both in terms of direct flows and in RME. An economy’s direct material
inputs correspond to domestic extraction plus imports. In 2007, Austria required approximately 258
million tons of DMI over half (52%) of which was accounted for by non-metallic minerals (Figure 12).
Out of these 133 million tons of non-metallic mineral DMI, 92% (122 million tons) were domestic
20
extraction and only 8% were imports. The RMI (DMI in raw material equivalents) in 2007 was almost
double the DMI and amounted to 485 million tons. Non-metallic minerals still make up the largest
fraction of RMI but now contribute only 37% and are closely followed by metal ores which make up
32% of RMI (as opposed to just 9% of DMI). This again is due to the high amount of intermediate
inputs required in the production of Austria’s imported goods. Fossil energy carriers contribute 14%
to RMI (and similarly 12% to DMI). Biomass undergoes the least absolute change, contributing 63
million tons to DMI and 78 million tons to RMI. However, this does significantly change biomass’
share from 25% of DMI to 16% of RMI.
Between 1995 and 2007, Austria’s RMI grew from 341 to 485 million tons by a factor of 1.4. In
examining the RMI by its components (Figure 13), it can be seen that domestic extraction remained
fairly constant across the period under investigation, increasing only slightly from 153 to 167 million
tons (factor 1.1). As was discussed previously, imports grew from 53 to 91 million tons (factor 1.7).
The intermediate inputs associated with these imports also increased by a factor of 1.7 from 135 to
227 million tons.
Figure 13: Austria's RMI by Components 1995-2007 in Million Tons
This means that the share of intermediate inputs in Austria’s RMI increased from 40% in 1995 to 47%
in 2007. During the same period of time, direct imports increased from a share of 15% to 19% while
the share of domestic extraction in RMI decreased from 45% to 34%. This development means that in
order to meet its demand for direct material inputs, Austria is increasingly dependent on imports and
on material provided in other economies for the production of these imports.
Austria’s Domestic Material Consumption in RME
In 2007, Austria’s domestic material consumption (i.e. domestic extraction plus imports minus
exports, direct flows) reached 198 million tons (Figure 14) or a total of approximately 24 t/cap. The
major share of the DMC consisted of non-metallic minerals (125 million tons corresponding to 63% of
DMC) followed by biomass (40 million tons, 20% of DMC) and fossil energy carriers (26 million tons,
13% of DMC). RMC in that same year reached a total of approximately 282 million tons,
corresponding to 34 t/cap. This means that Austria’s resource consumption grows by a factor of 1.4
21
(or by 10 t/cap) if we take the intermediate inputs required for the production of imports and
exports into account. The composition of RMC also differs from that of DMC. Non-metallic minerals
continue to make up the major share (48% of RMC compared to 63% of DMC) but are now followed
by the other non-renewable material categories (metal ores 24%, fossil energy carriers 15%) and
biomass (14%). These results mean that each Austrian indirectly consumed an additional 10 tons of
material in 2007: approximately 7 tons of metal ores, 2 tons of fossil energy carriers, and 1 tons of
non-metallic minerals. Only the consumption of biomass decreases very slightly if it is assessed in
terms of RME instead of direct flows.
Figure 14: Austria's Domestic Material Consumption in Direct Flows (DMC) and in RME (RMC) in 2007 in Million Tons
On the one hand, some of this outsourcing of the material requirements associated with Austrian
consumption may help protect the domestic resource base. On the other hand it must be taken into
account that the higher level of consumption may also be associated with a higher contribution to
global environmental impacts. Using the RMC of fossil energy carriers as an example, this could be
understood to mean that the approximately 17 million tons that were additionally consumed in this
material category are also linked to an additional amount of CO2 emissions. Using an average factor
of 9.1 tons of CO2 equivalent per ton based on the Austrian import-mix for the greenhouse gas
emissions resulting from this material consumption (Öko-Institut 2009), this corresponds to an
additional 150 million tons of CO2 emissions.
22
Figure 15: Austria's Material Consumption in Direct Flows (left) and RME (right) in 2007 in Million Tons
Between 1995 and 2007, Austrian DMC almost stagnated, growing only slightly from 177 to 198
million tons (factor 1.1). Austrian RMC grew slightly by about the same factor from 253 to 282 million
tons. In contrast to the developments in the components of DMC and RMC, the composition in the
overall indicators also remained constant with non-metallic minerals contributing over 60% to DMC,
followed by biomass (20%) and fossil energy carriers (13%). In terms of RMC, non-metallic minerals
also consistently contributed the largest share (just under 50%) followed by metal ores (23-24%),
biomass and fossil energy carriers (each around 15%). Again, Austria’s high domestic extraction of
non-metallic minerals remains visible as do the high intermediate material inputs associated with the
given level of metal consumption.
Adding Economic Detail
Through the work on the raw material equivalents of Austria’s trade it has become possible to
directly link the high-quality data on material flows with existing economic accounts. In a sense, this
has added a new dimension to the analyses which can be performed based on MFA data. Due to the
information on inter-industry relations provided by the economic supply and use tables, it was
possible to open up the ‘black box’ that, within the MFA framework, had hitherto been in the
economy to some degree. In the following, we will present some of the new insights gained on the
distribution of material demand throughout the Austrian economy.
The Material Requirements of the Sectors
In order to be able to link the physical MFA data with the monetary information contained in the
supply and use tables, it was necessary to develop a method for the allocation of the material flows
of both domestic extraction and imports to the economic sectors (for a more detailed description of
this procedure, please refer to the section on methodological development).
In terms of domestic extraction, the results showed that the largest share of material within Austria
is extracted by the mining sector (almost 70 million tons in the year 2007), followed by the
construction sector (slightly above 50 million tons), agriculture (approximately 29 million tons),
forestry (14 million tons), and the extraction of crude petroleum and natural gas, services incidental
23
thereto (5 million tons). The ranking of the sectors remained unchanged across the 12-year period
under investigation and the amounts extracted were fairly constant.
Figure 16: Austria's DMI by Sectors, 1995-2007
When the imported flows are also considered, the major share of Austria’s direct material input (90%
in 2007) is accounted for by 8 sectors (see Figure 16): mining and quarrying (dark grey), construction
(light grey), agriculture (green), extraction of crude petroleum and natural gas, services incidental
thereto (orange), forestry (olive), manufacture of chemical products (light red), manufacture of wood
and of wood and cork products (brown), and manufacture of basic metals and fabricated metal
products (blue). The Austrian DMI is dominated by construction minerals extracted by the mining and
the construction sector which account for almost 50% of total DMI. As was outlined above, this is in
large part due to the high domestic extraction of construction minerals. The latter are used in large
quantities in most economies but are of comparatively low economic value. Therefore, the statistical
coverage on the extraction of these materials is often incomplete. Through a concerted effort of the
Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management, the
Austrian Federal Ministry of Economy, Family and Youth, Statistics Austria, and the Institute of Social
Ecology, the data for the Austrian economy could be greatly improved (cf. Milota et al. 2011). The
more complete coverage of construction minerals has highlighted the quantitatively large role they
play in overall resource use.
The picture of sectoral material input shifts if the raw material equivalents of imports are taken into
account (see Figure 17). Austria hardly extracts ores: Imports make up 89% of the country’s metal
DMI. Ores are imported in the shape of metals and especially metal products. This means that the
surrounding rock which the excavated ores contain as well as the other material requirements
associated with mining occur in the exporting countries and not in the domestic economy. Therefore,
when we include the intermediate material requirements associated with Austrian imports, the
manufacture of basic metals and fabricated metal products (blue) becomes the sector claiming the
highest share of RMI. It is followed by mining and quarrying (dark grey), for which imports do not
play a very important role, contributing only 5% to total direct imports and 3% to total raw material
equivalents of imports in 2007. The mining sector plays a dominant role in the RMI due to its high
domestic extraction.
Figure 17: Austria‘s RMI by Sectors (DE and imports in raw material equivalents), 1995-2007
24
Next to metals, fossil energy carrier imports also play a very important role in Austria’s material
inputs. It was the largest material import category in 2007 and imports make up 92% of the country’s
fossil energy carrier DMI. When the intermediate material inputs are included in the sectoral DMI,
the extraction of crude petroleum and natural gas, services incidental thereto (orange) becomes the
sector with the third largest share in RMI. It is followed construction (light grey) – still mainly due to
its high amount of domestic extraction, agriculture (green), manufacture of chemical products (light
red), manufacture of food products (yellow), and forestry (olive).
The aforementioned sectors processing metals and (crude) petroleum merit special attention
because of the particularly high degree to which Austria depends on imports of these resources.
Shown on the dotted lines in Figure 18, the direct imports of these sectors increased by a factor of
more than 2 for the metals and more than 1.5 for the petroleum sector. The full lines in the same
diagram show the imports by each of these sectors in raw material equivalents. While these also
increased noticeably between 1995 and 2007, the dynamics are different than those of the direct
imports. The RME of imports for the metals sector increased by a factor of 1.5; for the petroleum
sector, this factor was slightly higher at 1.8.
25
Figure 18: Indexed Development of Austria's Imports in the Metals (blue) and Petroleum (orange) Sectors, 1995-2007
The explanation for this seemingly inconsistent development lies in the intensity of the imported
goods. The intensity is a measure of the amount of physical input (in kg) required for one monetary
unit of imports (in 1000 €) of a given sector. For both sectors, the material intensity of the goods they
imported decreased between 1995 and 2007. This means that less material was required per unit of
imported goods. In the case of the metals sector, the intensity of imports was decreased by 17%. For
the petroleum sector, the decline was even steeper at 64%. However, the intensity of the imports of
the petroleum sector was much higher to begin with and decreased to 703 kg/k€ while the imports
of the metals sector decreased to 145 kg/k€.
Sectoral Import Intensity
The trend of decreasing material intensities of imports that could be observed for the metals as well
as the petroleum sector is one that has generally been evident across the period under investigation.
Figure 19 and Figure 20 illustrate the material intensity of sectoral imports in 2000 and 2007.
Figure 19: Material Intensity of Austria's Imports in the Mining and Quarrying Sector in 2000 and 2007
The year 2000 (rather than 1995 as the first point in time under investigation) was chosen here due
to a change in the supply and use data reported by Statistics Austria between 1999 and 2000. Prior to
the year 2000, the monetary SU data were reported at a more highly aggregated level with the
fishery and the forestry sectors included in the agricultural sector. In order to provide more detail,
26
we therefore contrast the material intensity in the years 2000 and 2007 here. The most material
intensive imports flowed into the mining and quarrying sector (Figure 19). Per 1000 € worth of
imports, 14.6 tons of material were required in 2000 and 12.4 tons in 2007. While the intensities are
high, it must be kept in mind that most of the stones and earths required by this sector are not
imported but rather extracted domestically. In 2007, the direct imports of this sector – non-metallic
minerals only – made up only 5% of Austria’s total imports. When the intermediate inputs required
for the production of imported goods are taken into account, it becomes apparent that the sector
also indirectly imports fossil energy carriers. The latter, however, make up only 2% of the imported
RME, while non-metallic minerals contribute 98%. The dominance of this fraction is what leads to the
high intensity of these imports because the materials are used in bulk but have a relatively low price.
Figure 20: Material Intensity of Austrian Imports in kg/k€ in 2000 and 2007
The intensities of the imports of other sectors are significantly lower and, in most cases, a decrease
in intensity can be observed between 2000 and 2007. The second highest intensity is exhibited by the
imports of the mining of coal and lignite sector (2.5 t/k€ in 2000 and 1.7 t/k€ in 2007), followed by
agriculture, petroleum, forestry, construction, and manufacture of wood products, manufacture of
coke and refined petroleum products, manufacture of paper and paper products, manufacture of
chemicals and chemical products, manufacture of metals and metal products, fishery, and
manufacture of glass and glass products, ceramic products, other non-metallic mineral products
(from bottom to top in Figure 20).
The decreasing intensities that occur for the imports of almost all of the aforementioned sectors are
due to the fact that the material inputs required by the respective production grew less quickly than
the gross monetary output of the producing sectors. In the case of the coal and lignite mining sector,
for example, material input grew by a factor of 1.3 between 2000 and 2007, while the gross
monetary output thereby generated grew by 1.9. These efficiency gains, however, could not be
translated into an absolute reduction in the sectoral resource demand, as was described in detail in
27
the preceding section. For the nine sectors described above for which the material intensity of
imports decreased, the RME of imported goods increased by a factor between 1.2 for mining and
quarrying and 5.5 for forestry or by a factor of 1.5 on average across these nine sectors. This
phenomenon is commonly referred to as the rebound effect or Jevons’ paradox by which efficiency
gains are offset by higher amounts of total consumption (Weizsäcker et al. 2009).
How does Austria perform in comparison to other countries?
Austria in comparison to the Czech Republic and Germany
In this section, we will compare Austria with the Czech Republic and Germany. For both countries,
the same methodological approach was applied, i.e. the hybrid approach which combines an IO
model with LCA coefficients.
The German study was performed by the German statistics office (DESTATIS) and the underlying
empirical database is highly detailed: the IO matrix differentiates 120 sectors and 3000 products and
the LCA coefficients applied were derived from detailed case studies for 122 production processes
which were particularly conducted for that specific purpose. The Czech study was performed by the
Charles University in Prague and is of a comparable level of detail and derived the LCA coefficients
from the same LCA databases as the Austrian study.
Table 4: DMC and RMC per capita for Austria, CZ, and Germany, 2003
2003 DMC p.cap.
[t/cap]
RMC p.cap.
[t/cap]
RMC / DMC
[factor]
Austria 23 33 *1.4
Czech Republic 18 22 *1.3
Germany 16 23 *1.4
Sources: Buyny et al. 2009, Buyny and Lauber 2010 (DE) , Weinzettel and Kovanda 2009 (CZ)
Table 4 gives DMC and RMC (per capita) for the three countries in 2003. What we see is that RMC is
considerably higher than DMC for all three countries. Interestingly, the difference between direct
and upstream material use is about the same for all countries, i.e. RMC is bigger than DMC by a
factor of 1.3 to 1.4. In terms of total amounts, Germany and the Czech Republic arrive at comparable
quantities of material use. The Czech Republic uses 18 tonnes per capita as domestic consumption
and an additional 4 tonnes per capita in upstream material requirements. Total raw material
consumption thus amounts to 22 tonnes per capita. In Germany, domestic consumption is slightly
lower, i.e. 16 tonnes per capita, but another 7 tonnes per capita add to material use via upstream
material requirements. Total raw material consumption in Germany is 23 tonnes per capita.
Austria’s material use is considerably higher, i.e. 23 tonnes per capita as domestic consumption and
33 tonnes per capita in terms of raw material consumption, adding 10 tonnes per capita of upstream
material requirements. The higher level of material use is especially due to the higher use of non-
metallic minerals. These materials are mainly used for construction purposes (buildings and transport
infrastructure). Austria recently adopted a new method for calculating construction minerals (see
28
Milota et al. 2011) because significant amounts of resource extraction in the construction sector
were previously not reported by standard statistics3. The new calculation method improved data
coverage of physical data and in consequence increased total material use from previously 19 tonnes
per capita to now 23 t/cap in 2003.
Figure 21: per capita RMC in the Czech Republic, Germany, and Austria, 2003
Sources: Buyny et al. 2009, Buyny and Lauber 2010 (DE) , Weinzettel and Kovanda 2009 (CZ)
Figure 21 presents RMC for the three countries disaggregated by the four material categories
(biomass, fossil fuels, metals, non-metallic minerals). In the right diagram, the non-metallic minerals
were not included to illustrate the impact of the difference in calculating construction minerals for
Austria as described above. Among the three material categories, biomass, fossil fuels, and metals,
high similarities can be observed: one quarter of use are biotic materials (Czech Republic uses less,
i.e. 20%), 45% are fossil fuels (Austria’s share is only 30%), and 33-37% of raw material use are
metallic minerals (Austria’s share is 45%).
The higher raw material use of Austria in the category metals can be explained by the high
importance of the domestic steel industry which requires high foreign and domestic raw material
inputs for the domestic production. The lower requirements of fossil fuels might be driven by the
high share of renewable energy (mainly hydro power).
Figure 22: per capita RMC in Germany and Austria, 2000-2005
Sources: Buyny et al. 2009, Buyny and Lauber 2010 (DE)
3 Underestimations were due to (1) confidential data, (2) reporting procedures i.e. enterprises below 20
employees have no reporting obligation, and (3) non-characteristic production in the construction sectors which is not reported as domestic extraction and thus was not included in MF accounts.
29
German RME accounts are available in time series (2000-2005, on the aggregate level until 2007). A
comparison of trends in raw material use shows that Germany decreased its raw material use from
27 t/cap in 2000 to 23 t/cap in 2005 (and 22 t/cap in 2007). Austria on the other hand increased its
raw material use – slightly but still – from 33 t/cap to 35 t/cap in 2005 (as well as 2007). German
decrease is driven by a reduction of use of metals and non-metallic minerals which occurred during
2000 and 2003. Use of biomass and fossil fuels more or less stayed the same.
Comparison with results from the GRAM calculation
SERI published first results from the “Global Resource Accounting Model (GRAM)” where they
conducted a calculation of RME on the global scale. (Giljum et al. 2008). The calculation uses IO
tables for 52 countries and world regions, disaggregated by 48 sectors, and linked via OECD bilateral
trade data. Material flow data (see www.materialflows.net) were then linked to the IO model.
Figure 23 compares the results from the GRAM calculation with the three case studies on the Czech
Republic, Germany and Austria. RMC from national studies are higher for all three cases. The biggest
deviation is given for Austria where RMC from the national study at hand surpasses RMC as
calculated for GRAM by a factor of 1.7. However, this is again driven by the new calculation method
of construction minerals (see above). Czech results for RMC are higher by a factor of 1.4 in the
national study compared to GRAM, and German RMC by a factor of 1.2.
Figure 23: DMC and RMC comparison of GRAM estimates and country studies
Sources: Gi ljum et a l. 2008 (GRAM), Buyny et a l. 2009, Buyny and Lauber 2010 (DE) , Weinzettel and
Kovanda 2009 (CZ)
But more interesting is the relation between DMC and RMC. The publication on the GRAM results
does not report direct trade flows. In order to compare results, we included physical trade data from
the national studies in the GRAM data. The resulting DMC is of more or less of the same magnitude
as RMC (factor 1-1.1, for the Czech Republic RMC is even below DMC). These results have to be
doubted and are most likely due to the mix of data from different sources. However, the results
show the challenges that the research field of RME is still facing and that the theoretically more
sophisticated method does not necessarily lead to more plausible results. A deeper understanding of
advantages but also of problems of the different methodological approaches is still needed.
30
International comparison across world regions and development status
Another publication (Munoz et al. 2009) provides RME accounts for a set of 5 Latin American
countries (Brazil, Chile, Colombia, Ecuador, Mexico) as well as the USA. The authors applied a “single-
region IO” approach which is expected to result in underestimations for countries where the
domestic economy does not cover all production processes. However, for big economies active in all
sectors, the approximation can be expected to be within reasonable margins. The United States can
be perceived as an example for the latter.
In the following, we compare the RME accounts of Munoz et al. (2009) with the national case studies
for Austria, Germany, and the Czech Republic. Figure 24 shows trade flows (direct and RME) in
tonnes per capita, and the share of direct and indirect flows in imports and exports. First of all, the
overall magnitude of RME results does differ greatly between the calculations of Munoz and
colleagues and the national case studies for the Czech Republic, Germany and Austria. We will
therefore not discuss the results in detail, but will focus on general trends.
Figure 24: direct trade flows and RME for selected countries
Legend: imports in orange and exports in blue; light colour: direct trade flows, dark colour: related indirect flows. Both
together form RME of imports and exports.
Sources: Munoz et a l. 2009, Buyny et al. 2009, Buyny and Lauber 2010, Weinzettel and Kovanda 2009
What is interesting is the fact that all industrialized countries (AT, CZ, DE, US) are characterized by
comparable shares of direct and indirect flows for imports and exports. Developing countries on the
other hand do not exhibit a common pattern. Chile’s results show the major masses of waste rock
that are extracted in copper mining but do not form imports in copper processing. In the production
of Ecuador’s exports, the mobilization of materials not included in the exports themselves and
31
remaining in the country as waste or emissions is much smaller. This is most likely due to the fact
that Ecuador exports a lot of fossil fuels in the production of which the required intermediate inputs
are much lower than in the case of metals extraction and production. Mexico shows a similar share
for exports; again, Mexico is a large exporter of fossil fuels.
With regard to the imports, the developing countries have higher shares of RME/imports. This leads
to the assumption that the import basket of developing countries covers all stages of production
(from basic processing to service oriented products) whereas the industrialized countries seem to
import relatively higher processed goods with higher RME.
Figure 25: International comparison of DMC and RMC
Sources: Munoz et a l. 2009, Buyny et al. 2009, Buyny and Lauber 2010, Weinzettel and Kovanda 2009
Figure 25 compares DMC and RMC for the selected 9 countries. For all industrialized countries, RMC
is higher than DMC. This means, they depend on raw material inputs in other economies in order
meet their domestic final demand. Mexico’s and Ecuador’s RMC is also higher than DMC. The other
countries – Brazil, Colombia, and most notably Chile (due to the specific case of copper production) –
exhibit lower material consumption in raw material equivalents than in direct flows. In the case of
Chile, the outstandingly high DMC (48 t/cap) translates into an RMC per capita that is comparable to
that of other Latin American countries.
Outsourcing and Resource Productivity
With the updated calculation of the RME of Austrian trade, domestic material consumption (DMC)
and raw material consumption (RMC) largely follow the same trends but on different levels. RMC is
larger than DMC by a factor of 1.4 (see left diagram of Figure 26) but both indicators, DMC and RMC,
grow at the same rate, i.e. by a factor of 1.1 (see right diagram of Figure 26).
This development shows that Austria is not achieving a different trend in its resource productivity by
outsourcing material intensive production. On the aggregate level, no major shift in production can
be observed. Even though no difference of trend can be observed for resource productivity based on
the RMC instead of the DMC, the level of resource productivity does change significantly. In 2007,
the Austrian economy was able to generate 1316 Euros of GDP per tonne of DMC but only 927 Euros
of GDP per tonne of RMC.
32
Figure 26: Trends in Austrian DMC, RMC, and Resource Productivity, 1995-2007
Resource Productivity is calculated as the economic output per unit of material use (Euro per tonne).
With a growing economy (GDP4 grows by a factor of 1.3) and a growing material use (both in direct
and indirect accounts), the resource productivity is growing (factor 1.2 for RPDMC and RPRMC) but
slower than GDP. Thus, the Austrian economy is increasing its resource productivity and thus
dematerializing. However, only relative dematerialization could be achieved because in absolute
terms material use is still increasing. In order to get on a path of absolute dematerialization, Austria
has to decrease its material use and by that achieve resource productivity rates that are growing
faster than GDP.
Figure 27: Austrian Resource Productivity based on RMC for the four material categories, 1995-2007
Figure 27 shows RPrmc along the four material categories biomass, fossil fuels, metals and non-
metallic minerals. Strongest improvement in resource productivity can be observed for raw material
use of biomass: The use of biotic materials grew by a factor of 1.35, which is slightly higher than GDP
growth. Thus absolute decoupling is reached for biomass. Decoupling of biomass use from economic
growth was even stronger until 2003, but broke down and then stayed at a lower level from 2004
onwards. No single sub-category could be identified that led to the decrease in resource productivity
4 GDP is given in real terms, chain volumes, based on the year 2005 (Havel et al. 2010).
33
of biomass, but increases in biomass use spread across domestic extraction (crops and crop residues)
as well as imports (wood, crops).
RPRMC for metals and non-metallic minerals is growing at an intermediate level (factor 1.2). Finally,
resource productivity of fossil fuels does not show any development towards a more sustainable, i.e.
efficient, use but stays more or less the same over the 12 year period.
Austrian Resource Productivity in comparison to Germany and the Czech
Republic
In comparison to the resource productivity based on RMC (RPRMC) of the Czech Republic and
Germany, Austria ranges right in the middle. In2003, the Czech Republic produced 307 Euro with
each tonne of raw material used, Austria produced 826 Euro per tonne, and Germany was most
productive deriving 1,089 Euro per tonne.
Figure 28: Resource Productivity in the Czech Republic, Germany, and Austria in Euro/Tonne
Sources: Buyny et al. 2009, Buyny and Lauber 2010 (DE) , Weinzettel and Kovanda 2009 (CZ)
Recalling the improvement in RMC (just as in DMC) of the German economy, the stronger
improvements in resource productivity are no surprise. RPRMC of Germany increased by a factor of 1.3
(RPDMC by 1.2) whereas Austrian RPRMC only improved by a factor of 1.1 (and the same for RPDMC).
34
Figure 29: Growth of Resource Productivity in Germany and Austria
Sources: Buyny et al. 2009, Buyny and Lauber 2010 (DE))
35
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37
Annex: Data Tables
RME Compilation of Results
Years: 1995,1997,1999, 2000-2007
Unit: kt
Domestic Extraction 1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 36.881 38.116 38.003 34.296 35.089 36.759 35.138 39.427 40.077 39.487 40.237
Metal Ores 2.307 2.183 2.157 2.276 2.309 2.390 2.576 2.337 2.521 2.492 2.588
Non-Metallic Minerals110.186 118.671 118.690 117.709 111.842 123.106 113.921 119.401 122.039 122.871 121.780
Fossil Energy Carriers 3.562 3.302 3.594 3.765 3.604 3.927 4.031 2.865 2.241 2.405 2.407
Other
Total 152.936 162.271 162.444 158.045 152.844 166.183 155.666 164.031 166.878 167.256 167.012
Imports 1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 11.891 13.032 15.786 17.714 17.577 17.729 18.312 20.087 20.673 22.951 23.010
Metal Ores 10.060 11.843 11.641 13.548 13.973 14.130 14.573 15.904 17.192 19.373 21.258
Non-Metallic Minerals 6.809 8.222 8.077 8.136 8.101 8.465 8.515 8.776 9.621 9.882 11.667
Fossil Energy Carriers 20.212 22.152 21.917 21.685 23.316 24.910 26.667 26.996 28.478 28.883 28.478
Other 3.558 3.854 3.968 4.285 4.472 4.584 4.661 5.093 5.331 5.616 6.113
Total 52.530 59.104 61.389 65.368 67.439 69.819 72.727 76.857 81.296 86.706 90.527
RME Imports 1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 20.579 23.107 26.759 28.724 28.993 29.319 29.750 33.429 34.475 37.513 38.056
Metal Ores 93.354 104.637 99.983 111.376 109.204 109.715 112.707 125.501 128.816 145.378 150.858
Non-Metallic Minerals31.300 38.040 36.999 39.140 38.780 40.756 41.002 44.220 47.065 51.326 55.738
Fossil Energy Carriers 37.972 42.980 42.566 44.872 47.618 50.038 53.501 56.411 60.267 63.223 64.371
Other 4.768 5.283 5.481 5.928 6.263 6.393 6.535 7.304 7.684 8.134 8.887
Total 187.973 214.047 211.788 230.041 230.858 236.221 243.495 266.865 278.307 305.573 317.910
Exports 1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 11.273 12.515 15.077 15.737 16.226 17.402 18.359 19.170 20.347 20.546 22.944
Metal Ores 6.879 7.321 8.420 9.472 9.788 10.200 10.660 11.893 12.067 13.155 14.249
Non-Metallic Minerals 5.105 6.193 5.819 6.153 6.925 7.384 7.134 7.738 7.445 8.357 8.723
Fossil Energy Carriers 1.026 1.491 1.626 1.501 1.999 1.996 2.098 2.758 3.323 3.631 5.077
Other 3.724 4.244 4.888 4.977 5.233 5.634 5.937 6.417 6.664 6.845 8.192
Total 28.006 31.765 35.830 37.840 40.170 42.616 44.187 47.976 49.846 52.534 59.185
RME Exports 1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 18.168 20.469 24.796 26.026 27.110 28.948 30.268 32.935 34.646 35.959 39.189
Metal Ores 36.779 44.703 44.676 52.071 52.356 53.459 54.775 64.918 66.642 79.320 85.940
Non-Metallic Minerals20.575 26.346 25.313 27.290 28.271 31.169 30.459 34.477 35.440 40.400 42.770
Fossil Energy Carriers 7.404 9.600 10.370 11.209 12.927 13.784 14.750 17.768 19.530 21.555 24.454
Other 4.745 5.505 6.238 6.469 6.887 7.391 7.721 8.521 8.916 9.320 10.943
Total 87.671 106.623 111.394 123.066 127.551 134.750 137.974 158.619 165.174 186.553 203.295
38
RME Compilation of Results
Years: 1995,1997,1999, 2000-2007
Unit: kt
Physical Trade Balance (PTB)1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 618 517 709 1.977 1.351 328 47- 918 326 2.405 65
Metal Ores 3.181 4.523 3.221 4.077 4.185 3.930 3.913 4.010 5.126 6.218 7.010
Non-Metallic Minerals 1.704 2.029 2.258 1.983 1.176 1.082 1.381 1.037 2.175 1.525 2.944
Fossil Energy Carriers 19.187 20.661 20.290 20.184 21.317 22.914 24.569 24.238 25.155 25.252 23.402
Other 165- 390- 919- 692- 761- 1.050- 1.276- 1.323- 1.332- 1.229- 2.079-
Total 24.524 27.339 25.559 27.528 27.269 27.203 28.541 28.881 31.450 34.172 31.341
Physical Trade Balance in RME (RTB)1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 2.411 2.638 1.963 2.697 1.883 371 518- 494 171- 1.554 1.133-
Metal Ores 56.575 59.934 55.306 59.306 56.848 56.256 57.932 60.582 62.174 66.058 64.919
Non-Metallic Minerals10.725 11.694 11.686 11.850 10.509 9.587 10.543 9.743 11.626 10.926 12.968
Fossil Energy Carriers 30.568 33.380 32.195 33.663 34.691 36.255 38.751 38.643 40.737 41.668 39.918
Other 23 222- 757- 541- 624- 998- 1.187- 1.217- 1.232- 1.185- 2.056-
Total 100.302 107.424 100.394 106.975 103.307 101.471 105.521 108.246 113.133 119.020 114.615
Domestic Material Consumption (DMC)1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 37.499 38.632 38.712 36.273 36.440 37.086 35.092 40.345 40.403 41.893 40.302
Metal Ores 5.488 6.706 5.378 6.353 6.494 6.320 6.488 6.348 7.647 8.710 9.598
Non-Metallic Minerals111.890 120.700 120.948 119.692 113.018 124.188 115.302 120.439 124.214 124.397 124.724
Fossil Energy Carriers 22.749 23.963 23.884 23.948 24.921 26.841 28.601 27.103 27.396 27.657 25.809
Other 165- 390- 919- 692- 761- 1.050- 1.276- 1.323- 1.332- 1.229- 2.079-
Total 177.460 189.610 188.003 185.574 180.113 193.385 184.207 192.912 198.328 201.428 198.353
Raw Material Consumption (RMC)1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007
Biomass 39.291 40.753 39.966 36.993 36.972 37.129 34.620 39.922 39.906 41.041 39.104
Metal Ores 58.882 62.117 57.463 61.582 59.157 58.646 60.508 62.920 64.695 68.550 67.507
Non-Metallic Minerals120.911 130.365 130.376 129.559 122.351 132.694 124.464 129.145 133.665 133.798 134.748
Fossil Energy Carriers 34.130 36.682 35.789 37.427 38.295 40.182 42.782 41.508 42.978 44.073 42.325
Other 23 222- 757- 541- 624- 998- 1.187- 1.217- 1.232- 1.185- 2.056-
Total 253.238 269.695 262.838 265.020 256.151 267.654 261.187 272.277 280.011 286.276 281.627