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

10

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

References: Data and Literature

Buyny, Šárka, Klink, Steffen, and Lauber, Ursula (2009). Verbesserung von Rohstoffproduktivität und

Ressourcenschonung - Weiterentwicklung des direkten Materialinputindikators. Endbericht.

Wiesbaden, Statistisches Bundesamt.

Buyny, Šárka and Lauber, Ursula (2010). Environmental-Economic Accoutning (EEA). Further

development of the indicator “Raw material productivity” in the National Strategy for

Sustainable Development Calculating imports and exports in raw material equivalents. Federal

Statistical Office Germany, Wiesbaden.

Eurostat (2001). Economy-wide Material Flow Accounts and Derived Indicators. A methodological

guide. ISBN: 92-894-0459-0, pp. 1-92. 2001. Luxembourg, Eurostat, European Commission,

Office for Official Publications of the European Communities. 21-12-2000.

Eurostat (2009): Economy Wide Material Flow Accounts. Compilation Guidelines for Reporting to the

2009 Eurostat Questionnaire (Version 01 – June 2009). Luxembourg: European Statistical Office.

Fachverband der Mineralölindustrie Österreichs (2010). The Austrian Petroleum Industry in 2009.

English Summary. Fachverband der Mineralölindustrie Österreichs, Vienna.

Giljum, Stefan, Christian Lutz, Ariane Jungnitz, Martin Bruckner, Friedrich Hinterberger (2008). Global

dimensions of European natural resource use. First results from the Global Resource Accounting

Model (GRAM). SERI Working Paper No. 7. Vienna

Havel, Ursula et al. (2010). Volkswirtschaftliche Gesamtrechnungen 1978–2009, Hauptergebnisse.

Wien: Statistik Austria

Kolleritsch, Erwin (2004). Input-Output Multiplikatoren 2000. Statistik Austria, Wien.

Lenzen,M. & Peters,G.M. (2010) How City Dwellers Affect Their Resource Hinterland. Journal of

Industrial Ecology, 14, 73-90.

Lenzen,M. (2001) A generalized Input-Output Multiplier Calculus for Australia. Economic Systems

Research, 13, 65-92.

Lenzen,M., Pade,L.-L. & Munksgaard,J. (2004) CO2 Multipliers in Multi-region Input-Output Models.

Economic Systems Research, 16, 391-412.

Leontief,W. (1936) Quantitative input-output relations in the economic system. Review of Economics

and Statistics, 18, 105-125.

Leontief,W. (1941) The Structure of the American Economy. Oxford University Press, New York.

Milota, Eva, Schaffartzik, Anke, and Eisenmenger, Nina (2011). Ressourcendaten - Verbesserung des

statistischen Datenmaterials im Bereich natürliche Ressourcen. Projektbericht. 2011. Vienna,

Statistik Austria.

36

Munoz,P., Giljum,S. & Roca,J. (2009) The Raw Material Equivalents of International Trade. Journal of

Industrial Ecology, 13, 881-898.

Öko-Institut (2009). Globales Emissions-Modell Integrierter Systeme (GEMIS) 4.5. Öko-Institut,

Darmstadt.

ÖSTAT (1994). Input.Output-Tabelle 1983. Band 2: Technologiematrizen. Beiträge zur

österreichischen Statistik. Österreichisches Statistisches Zentralamt, Vienna.

Paul, Alistair, Wiedmann, Thomas, Barrett, John, Minx, Jan, Scott, Kate, Dawkins, Elena, Owen, Anne,

Briggs, Julian and Gray, Ian (2010). Introducing the Resources and Energy Analysis Programme

(REAP). Stockholm Environment Institute, Stockholm.

Statistik Austria 2011a. Input-Output-Statistik: Aufkommens- und Verwendungstabellen. Statistik

Austria, Vienna.

Statistik Austria 2011b. Materialflussrechnung ab 1995: Inländische Entnahme und Importe Gesamt.

Statistik Austria, Vienna.

Statistik Austria (2011c): Materialflussrechnung.

http://www.statistik.at/web_de/statistiken/energie_und_umwelt/umwelt/materialflussrechnun

g/index.html.

The World Bank Group (2007). World Development Indicators 2007. CD-ROM . 2007. The World

Bank.

UN Comtrade (2011). United Nations Commodity Trade Statistics Database. United Nations.

UNDP (2004). Human Development Report 2004, Cultural liberty in today's diverse world. United

Nations Development Programme, New York, NY.

Weinzettel,J. & Kovanda,J. (2009). Assessing Socioeconomic Metabolism Through Hybrid Life Cycle

Assessment - The Case of the Czech Republic. Journal of Industrial Ecology, 13, 607-621.

Weisz, Helga 2006: Accounting for raw material equivalents of traded goods. A comparison of input-

output approaches in physical, monetary, and mixed units. Social Ecology Working Paper 87.

Vienna

Weisz, Helga, Eisenmenger, Nina, and Schaffartzik, Anke (2008). Methodische Weiterentwicklung der

Berechnung des Rohmaterialbedarfes im Außenhandel. Endbericht an das BMLFUW zum Vertrag

GZ: BMLFUW-UW.1.4.18/0028-V/10/2007, September 2008. 2008. Wien, IFF Soziale Ökologie.

Weizsäcker,E.U.v., Hargroves,K., Smith,M.H., Desha,C. & Stasinopoulos,P. (2009) Factor Five.

Transforming the Global Economy through 80% Improvements in Resource Productivity.

Earthscan, London.

Wiedmann, Thomas, Wood, Richard, Minx, Jan C., Lenzen, Manfred, Guan, Dabo, Harris, Rocky

(2010). A carbon footprint time series of the UK–results from a multi-region input–output

model. Economic Systems Research, 22, 1.

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