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ifeu - Institut für Energie- und Umweltforschung Heidelberg GmbH

CONVERSION OF EUROPEAN PRODUCT FLOWS INTO RAW MATERIAL EQUIVALENTS

Final report of the project: Assistance in the development and maintenance of Raw Material Equivalents conversion factors and calculation of RMC time se-ries

Karl Schoer, Jürgen Giegrich, Jan Kovanda, Christoph Lauwigi,

Axel Liebich, Sarka Buyny, Josefine Matthias

commissioned by Statistical Office of the European Communities – Eurostat; Directorate E – Agriculture and Environmental Statis-tics; Statistical Cooperation Unit E3: Environment statistics Contract no. 50902.2010.001.-2010.612

ifeu - Institut für Energie- und Umweltforschung,

Heidelberg

in cooperation with:

Sustainable Solutions Germany – Consultants,

Wiesbaden (SSG)

Charles University in Prague, Environment Centre

CUEC

Heidelberg, May 2012

Assistance in the development and maintenance of Raw Material Equivalents

conversion factors and calculation of RMC time series

Content

Summary _______________________________________________________________ 1

1. Introduction _________________________________________________________ 4

1.1. Background ___________________________________________________________ 4

1.2. The concept of raw material equivalents ____________________________________ 6

2. The RME calculation model ____________________________________________ 10

2.1. General considerations _________________________________________________ 10

2.2. Overview calculation model _____________________________________________ 12

2.3. Disaggregated monetary IOT ____________________________________________ 15 2.3.1. The concept and rationale for disaggregating the IOT _____________________________ 15 2.3.2. Calculation method and data sources __________________________________________ 17

2.3.2.1. Structural information based on German expanded IOT _________________________ 18 2.3.2.2. EU level balance column and line total _______________________________________ 20 2.3.2.3. Iterative adjustment approach _____________________________________________ 21 2.3.2.4. Data issues _____________________________________________________________ 25

2.4. Disaggregated hybrid IOT _______________________________________________ 27 2.4.1. The concept and rationale for a hybrid IOT ______________________________________ 27 2.4.2. Calculation method and data sources __________________________________________ 30

2.4.2.1. Price adjustment of monetary use structure___________________________________ 30 2.4.2.2. Physical use structure for biomass and other minerals __________________________ 31 2.4.2.3. Physical use structure for metals ____________________________________________ 32 2.4.2.4. Physical use structures for energy carriers ____________________________________ 34

2.5. Annual environmental extensions ________________________________________ 35 2.5.1. Domestic extraction used ____________________________________________________ 35 2.5.2. External approach for estimating RME of imported LCA products ____________________ 35

2.6. Metal model for estimating RME coefficients _______________________________ 38 2.6.1. Introduction to the metal model ______________________________________________ 38

2.6.1.1. Representative RME coefficients ____________________________________________ 39 2.6.1.2. Methodology for polymetallic ores __________________________________________ 43 2.6.1.3. Auxiliary materials _______________________________________________________ 47 2.6.1.4. Metal content of alloys ___________________________________________________ 48

2.6.2. Metal content of imported metal concentrates __________________________________ 49 2.6.3. Treatment of secondary metals _______________________________________________ 50 2.6.4. RME coefficients for imported metal products ___________________________________ 53

2.7. A mixed Leontief / LCA approach _________________________________________ 54

2.8. Data quality considerations _____________________________________________ 56 2.8.1. Comparison to standard approach _____________________________________________ 56 2.8.2. Comparison to other calculation models ________________________________________ 58

2.8.2.1. Czech approach _________________________________________________________ 59 2.8.2.2. German approach ________________________________________________________ 60 2.8.2.3. EXIOPOL approach _______________________________________________________ 61

3. Analytical properties of RME accounting and selected results_________________ 66

Assistance in the development and maintenance of Raw Material Equivalents


3.1. Economy wide indicators _______________________________________________ 67

3.2. Ecological link ________________________________________________________ 68 3.2.1. Disaggregation by raw material categories ______________________________________ 68 3.2.2. Pressure profiles of raw materials _____________________________________________ 71

3.3. Economic link _________________________________________________________ 75 3.3.1. RME by products and categories of final uses ____________________________________ 75 3.3.2. Structural decomposition of RME _____________________________________________ 78

4. Outlook RME model __________________________________________________ 81

4.1. Migration from CPA 2002 to CPA 2008 _____________________________________ 81

4.2. Options for regionalization of RME calculation ______________________________ 82

4.3. Internalized capital formation ___________________________________________ 89 4.3.1. Concept __________________________________________________________________ 89 4.3.2. Calculation approach _______________________________________________________ 91 4.3.3. Results ___________________________________________________________________ 92

5. Acknowledgements __________________________________________________ 95

List of Abbreviations _____________________________________________________ 96

Bibliography ____________________________________________________________ 97

ANNEX 1 Product classification for expHIOT _________________________________ 101

ANNEX 2 Data sources ___________________________________________________ 106

ANNEX 3 Conversion factors imported metal to RME __________________________ 107

ANNEX 4 Data documentation metal model _________________________________ 108

Assistance in the development and maintenance of Raw Material Equivalents page 1


Summary

English:

This report presents an approach for converting product flows into raw material equiva-

lents (RME). The RME concept takes the perspective of raw materials embodied in prod-

ucts. The RME of a product indicates how much extraction of material was necessary over

the whole production chain for manufacturing a specific product, irrespective whether

those raw materials where extracted from the domestic or the rest of the world environ-

ment. The weight of the consumed raw materials is measured at the point of extraction

from the environment.

It was the objective of the project to improve the current European material flow indicator

“Domestic Material Consumption” (DMC) by overcoming the asymmetry of measuring at

the one hand the mass of domestic extraction of raw materials from the environment at

the point of extraction, but on the other to measure the weight of material which is import-

ed or exported at the time of crossing the border. By expressing all flows in RME it be-

comes possible to make up a mass balance of domestic uses, imports and exports by a

coherent unit of measurement. It is intended to use the indicator “Domestic Raw Material

Consumption” (RMC), which is derived from the RME calculation system, as a central in-

dicator of the European Resource Strategy.

The estimation of RME is based on the Leontief approach, which is a well-established

method for environmental economic analysis. That approach applies Input-Output analy-

sis for assigning direct environmental pressures – measured in physical units – by the

individual production activities to the products of final use and of imports.

As far as the application of the “standard version” of the Leontief approach – which is

based on the standard European monetary IOT of the size 60x60 - for the estimating RME

is concerned, the following major deficits where identified:

a) The resolution of the European monetary standard Input-Output table (IOT) with

the format 60x60 is not sufficient for depicting the flow of raw materials through the

economy with an acceptable degree of accuracy. Therefore an approach was de-

veloped for estimating an expanded IOT of the format 166x166 at an annual basis.



b) IOT use structures in physical units are able to provide much more accurate re-

sults for calculation of RME than monetary use structures, as far as raw products

and some primary processed raw products are concerned. Therefore a hybrid IOT

was established, i.e. monetary structures of the monetary IOT where replaced by

use structures in physical units for selected product groups

c) The assumption that imported products are produced with domestic production re-

lationships, which is inherent to the standard Leontief approach, is especially not

maintainable in case of calculation of RME for metals. Therefore the RME of se-

lected imported products where estimated by an external approach. That external

approach is based on annual preparation of business reports of mining companies

for 160 mines worldwide and of results from Life Cycle Assessment.

A comparison of the outcome of the project approach and of the “standard approach”

demonstrates that the new method yields calculation results which are significantly more

accurate.

An automated calculation procedure furnishes annual results for Raw Material Equivalents

(RME) in a detailed disaggregation by categories of final use and imports, product groups

and type of raw material. From that data the central economy wide material flow indicator

of the European Sustainable Development Strategy “Domestic Raw material Consump-

tion” (RMC) is derived by aggregation. At the same time that data set provides the basis

for analyzing the RMC in an environmental economic context by linking it to the underlying

economic driving forces (economic link) and to the environmental impacts (ecological link)

which are intended to be addressed by that general indicator.

Deutsch:

Dieser Bericht stellt ein Verfahren zur Konvertierung von Güterströmen in Rohstoffäquiva-

lente vor (Raw Material Equivalents, RME). RME beschreiben die Menge an Rohstoffen in

Gewichtseinheiten, die über die gesamte Produktionskette hinweg für die Herstellung ei-

nes Gutes im In- oder Ausland aufgewendet wurden (embodied raw materials). Das Ge-

wicht der eingesetzten Rohstoffe wird einheitlich zum Zeitpunkt der Entnahme aus der

Natur gemessen.

Zielsetzung des Projektes war es, den bisherigen Europäischen Rohstoffindikator

"Domestic Material Consumption" (DMC) zu verbessern durch Überwindung der Asym-



metrie von Messung der inländischen Rohstoffentnahme als Rohstoffgewicht bei der Ent-

nahme aus der Natur und der Import- und Exportströme mit dem Gewicht beim Grenz-

übergang. Durch die Darstellung der entsprechenden Ströme in RME wird es möglich,

inländische Verwendung sowie die Importe und Exporte von Gütern in Gewichtseinheiten

nach einem einheitlichen Maßstab zu bilanzieren. Es ist vorgesehen, den aus dem Be-

rechnungssystem abgeleiteten Indikator „Domestic Raw Material Consumption“ (RMC) als

zentralen Rohstoffindikator für die europäische Ressourcenstrategie zu verwenden.

Die Berechnung von RME beruht auf dem in der umweltökonomischen Analyse etablier-

ten Verfahren des Leontief Ansatzes. Dabei handelt es um ein Verfahren der Input-Output

Analyse, das direkte Umweltbelastungen - gemessen in physischen Einheiten - durch die

einzelnen Produktionsaktivitäten den Gütern der letzten Verwendung und dem Import

zuordnet.

Bezüglich der Verwendung dieses Verfahrens in der "Standardvariante" für die Schätzung

von RME wurden allerdings folgende grundlegende Defizite identifiziert:

a) Der Detaillierungsgrad der Europäischen monetären Standard Input-Output Tabel-

le (IOT) mit dem Format 60x60 reicht nicht aus, um den Fluss von Rohstoffen

durch die Wirtschaft mit hinreichender Genauigkeit abzubilden. Deshalb wurde ein

Verfahren zur jährlichen Schätzung einer erweiterten IOT im Format 166x166 ent-

wickelt.

b) Für Rohstoffgütergruppen und einige rohstoffnahe Gütergruppen führen Verwen-

dungsstrukturen in physischen Einheiten zu deutlich genaueren Ergebnissen bei

der RME Berechnung, als monetäre Strukturen. Deshalb wurden Verfahrenswei-

sen zur Schätzung einer hybriden IOT entwickelt, bei der monetäre Verwendungs-

strukturen selektiv durch physische ersetzt wurden.

c) Die dem Leontief Verfahren inhärente Annahme, dass importierte Güter mit inlän-

discher Produktionstechnologie produziert wurden, ist insbesondere mit Hinblick

auf die Berechnungen für Metalle nicht haltbar. Aus diesem Grund wurden die

RME ausgewählter Importgüter mit Hilfe eines externen Ansatzes geschätzt, der

sich vor allem auf die Auswertung der Geschäftsberichte für weltweit etwa 160 Mi-

nen und auf Ergebnisse von sogenannten Lebenszyklusanalysen (Life Cycle

Assessment) stützt.



Ein Vergleich der Projektergebnisse mit den Ergebnissen nach dem Standardverfah-

ren verdeutlicht, dass das im Projekt entwickelte Verfahren zu wesentlich genaueren

Ergebnissen führt.

In einem automatisierten Berechnungsverfahren werden jährliche Ergebnisse in Roh-

stoffäquivalenten (RME) in tiefer Untergliederung nach Kategorien der letzten Ver-

wendung und Importen, Gütergruppen und Rohstoffarten bereitgestellt. Aus diesen

Daten wird der zentrale gesamtwirtschaftliche Rohstoffindikator der Europäischen

Ressourcenstrategie "Domestic Raw Material Consumption" (RMC) durch Aggregation

abgeleitet. Der Datensatz bildet aber zugleich die Grundlage, um diesen Indikator in

einem umweltökonomischen Kontext zu analysieren und dabei den Indikator sowohl

mit den dahinter stehenden ökonomischen Triebkräften (economic link) als auch mit

den durch den Indikator indizierten Umweltauswirkungen (ecological link) zu verknüp-

fen.

1. Introduction

1.1. Background1

Based on the Thematic Strategy on the Sustainable Use of Natural Resources [EC 2005]

and the 6th Environmental Action Programme [EC 2002] the EU Commission decided to

provide policy makers and other stakeholders with a framework of information about the

use of resources and products. Therefore, Environmental Data Centres had been estab-

lished to fulfil this purpose. Within an agreement between other Directorates of the EU the

responsibility for the Environmental Data Centres for natural resources, products, and

waste had been assigned to Eurostat.

The main task of the Environmental Data Centres on resources and products is to im-

prove knowledge about the relationship between resource use, economic growth and en-

vironmental impacts. The combination of economic information and environmental input

and output information – both handled at Eurostat – is an important basis for fulfilling the

needs of such an Environmental Data Centre. Resource productivity is considered to be

an important indicator of Eurostat’s resource strategy.

1 First results of this project are reported in the Journal Environmental Science and Technology.

See: Schoer, et al. (submitted)



For the measurement of the resource productivity the domestic material consumption

(DMC) is related to the gross domestic product (GDP). Therefore DMC is a key indicator

for measuring the use of natural resources in an economy. This indicator is derived from

economy-wide material flow accounts (EW-MFA) which covers all material inputs into na-

tional economies, the changes of stocks and their respective outputs.

Domestic material consumption (DMC) is defined as the total amount of material directly

used in a given economy. DMC is calculated by subtracting the exports from the direct

material input (DMI) of an economy.

Now a couple of research projects and expert groups pointed out that the value of DMI as

a basis for the DMC depends strongly from where the input is coming from. If e.g. metal

ore is extracted domestically the total amount of ore is accounted but if metals are im-

ported only their imported mass is used. This asymmetry led to the proposal to express all

imported goods (and also exported goods) in terms of raw material. Consequently all im-

ported semi-finished and finished goods have to be be expressed in raw material equiva-

lents (RME).

Eurostat aims to convert the current asymmetric DMC indicator into an indicator which is

expressed in raw materials equivalents (RMC). Conversion factors are needed to translate

the masses of any imported good and exported good into their mass expressed in RME. A

methodology had been developed in a past project2 how to calculate these RME with a

reasonable effort. The methodology is based on an integration of IOT data and life cycle

based data. That former project was conducted by the same consortium as this project.

In the former project a first set of RME conversion factors were developed and applied for

selected situations of the EU-27 and Germany as a pilot country. The set of RME factors

where regarded as a first suggestion. It was the aim of this project to consolidate the

method and the results and to develop an approach for calculating time series of RME by

an automated procedure. A major issue for methodological consolidation was to develop

an empirically broader founded "metal model" for estimating raw materials coefficients for

imported metals.

2 Conceptual framework for measuring the environmental impact of the use of natural resources

and products; Eurostat Contract 50304.2008.008-2008.715



The approach for calculating RME presented in this report is fully integrated into the sys-

tem of Integrated Environmental and Economic Accounting (SEEA) [UN (2003)] which is

designed for depicting the interaction between the economy and the environment in a sys-

tematic and coherent manner. The economic system at European level is described by

the European economic accounting system of ESA [ESTAT (1996)]. Input output tables

(IOT) are an integral part of the ESA. Applying an IOT based approach for estimating

RME supports the objective of embedding those indicators into the accounting system.

Behind that background a specific IOT based calculation model was developed which is

called mixed Leontief / Life Cycle Assessment (LCA) approach. This type of approach is

sometimes referred to as a hybrid LCA in the literature [Joshi (2000)].

This report presents an annual automated calculation model for converting product

flows into raw material equivalents (RME). The calculation approach provides detailed

annual results on product flows in RME in a breakdown by the following dimensions:

1. Categories of final uses and imports

2. 166 product groups

3. 52 raw material categories

1.2. The concept of raw material equivalents

The main purpose of establishing estimates in RME is to improve the existing environ-

mental mass indicator DMC regarding meaningfulness and interpretability. The improve-

ments refer to three main aspects:

a) Improvement of the mass flow indicator DMC by converting product flows into

flows of raw material equivalents.

b) Establishing an ecological link by detailed disaggregation of RME by 52 mate-

rial flow categories. A detailed disaggregation by material flow categories takes

account of the fact that the different raw material categories can have extremely

different pressure characteristics (environmental impact). That disaggregation is

therefore a precondition of a meaningful ecological analysis and interpretation of

material flows.



c) Establishing an economic link by relating the direct and indirect demand for

raw materials by 52 material flow categories to the final use of products by 166

product groups. Further, the underlying IOT establishes a link between the final

use of products and the direct economic production and consumption activities.

That link is essential for embedding the material flow indicator into an environ-

mental-economic context which is able to relate the demand for raw materials to

the economic driving forces.

The term raw materials equivalent (RME) corresponds conceptually to the term domes-

tic extraction used (DEU) of the EW-MFA system. The RME is supposed to "convert"

trade flows expressed in simple product weight into their equivalent of domestic extraction

used (DEU). The RME concept takes the perspective of raw materials embodied in prod-

ucts. The RME of a product indicates how much extraction of material was necessary over

the whole production chain for manufacturing that specific product, irrespective whether

those raw materials were extracted from the domestic or the rest of the world environ-

ment.

One important aim of physical flow accounting of the EW-MFA system is to derive indica-

tors – one of the most important indicators being DMC - which are used for the purpose of

the European Resource Strategy.

However, the DMC indicator inherently bears two major asymmetries:

a) The imports and export are measured in a kind of different "unit" than the

domestic extraction used (DEU). The latter is measured in virgin raw mate-

rial extraction (e.g. tonnes of gross iron ore which is much heavier than the

resulting steel). Trade is measured only in simple product weight (e.g. ton-

nes of steel).

b) The composition of imports and exports can differ leading to a further

asymmetry. For the EU one knows that the imports are dominated by prod-

ucts with a rather low degree of processing, like metal ores or primary en-

ergy carriers. These raw or semi-manufactured products are usually much

heavier than more finished products and have a rather low RME per tonne

product. EU exports are rather dominated by finished products, like cars,

machinery etc. The weight of the finished products usually represents only



a fraction of the weight of raw materials which were originally extracted for

their manufacturing. This means that 1 million tonnes of imported products

– measured in simple product weight – have a significant lower RME than

1 million tonnes exported products.

The RME concept overcomes these asymmetries in considering the imports and exports

measured in raw material equivalents which have the same "unit" as the domestic extrac-

tion used (DEU).

Figure 1 illustrates the differences between original EW-MFA figures and the correspond-

ing results which are converted into RME

Figure 1: Comparison EW-MFA and RME accounts

The figure shows that imports expressed in RME are double as high as the figures from

EW-MFA accounts. The exports in RME are even more than three times higher than the

original EW-MFA results.

Eurostat aims to establish economy wide indicators where the trade components are

"converted" from product weight into RME. The following accounting rule shows how the

indicators – following the RME concept – are derived:

6,711

1,681

513

7,932

6,711

3,401

1,676

8,435

DEU Imports Exports DMC / RMC

2005Mill tonnes

EW-MFA RME accounts



+ Domestic extraction used (DEU)

+ Imports in raw material equivalents (IMPRME)

= Raw material input (RMI)

– Exports in raw material equivalents (EXPRME)

= Raw material consumption (RMC)

It has to be pointed out that it is an important property of the RME accounting system that

the above accounting identity is not only valid for total materials, but also at the level of

individual raw material categories. Compared to that, the DMC can only be disaggregated

to the level of aggregated raw material categories (biomass, metal ores, non-metallic min-

erals and fossil energy resources), as semi-finished and finished imported and exported

products can only be lumped together into rather broad material categories like “products

mainly from biomass” or “products mainly from metals”.

Compared to the RME concept the TMR concept includes in addition also unused extrac-

tion (e.g. mining overburden). Ideally TMR should be designed as a methodical extension

of the RME accounts, i.e. RME supplemented by unused flows. Technically that would

mean, that the RME including unused extraction are derived from RME by multiplying the

RME of the individual raw material categories by a factor which expressed the average

relationship between total (used plus unused) and used extraction.

However in practice it is rather questionable whether it would be worth to estimate total

extraction for data and methodical reasons.

As described below in section 2.6, the statistical coverage of unused extraction is rather

poor for metals. Unused extraction of metal mining is a major source of unused materials.

Therefore, including unused extraction is likely to increase inaccuracy of the accounting

system significantly, as it is necessarily burdened by applying quite rough estimates. Sec-

ondly, in a general manner it is rather disputable that the specific element of unused ex-

traction correlates with environmental pressures or impacts. However establishing mass

flow indicators like DMC, RMC or TMR is not an end in itself in the sense that all materials

which are activated by economic activities should be counted in the most comprehensive

manner, but the ultimate purpose is to indicate the development of environmental pres-

sures or impacts in an aggregated manner.



The structure of the report can be described as follows:

Chapter 2 of the report presents the RME calculation model. It starts with some general

considerations and a short overview over the steps and the data sources of the calculation

model. In the following subsections the principal steps of the calculation model are de-

scribed in a more detailed manner. One central issue is the “metal model” which provides

coefficients for converting metal flows into RME. In a final section of that chapter it is tried

to assess the quality of the final results.

In chapter 3 the analytical properties of the RME accounting system are discussed and

illustrated by analysing selected empirical results of the project.

In chapter 4 issues for further methodological development of the RME accounting system

are considered, as migration of the calculation system to new classification, regionaliza-

tion of calculation of RME and the alternative approach of using an IOT matrix with “inter-

nalized capital formation”.

2. The RME calculation model

2.1. General considerations

This project has developed an annual automated calculation model converting product

flows into raw material equivalents (RME). The RME are estimated by a specific Leontief

type calculation procedure.

Generally, the Leontief method is an approach of IOT analysis which is based on an IOT

matrix and an environmental extension which carries the information on generation of

pressures (direct pressures) by economic activities (homogenous branches and final use

categories). By that approach, which is based on the so called Leontief inverses, the di-

rect pressures are assigned to the products of final uses, as exports, consumption expen-

diture and capital formation (embodied pressures). The embodied pressures of imports

are generally estimated under the assumption that the imported products are manufac-

tured under domestic production conditions ("domestic technology assumption"). That

general Leontief type approach can also be applied to raw material consumption by treat-

ing direct raw material consumption by production activities as type of environmental ex-

tension.



The Leontief approach for estimating embodied pressure is a well-established approach in

environmental-economic analysis. For example, the environmental-economic accounting

units of the Federal Statistical Office Germany and of EUROSTAT are regularly calculat-

ing and publishing results on embodied air emissions [Statistisches Bundesamt (2011)]

[Schoer et al (2007)]3.

The "standard Leontief approach" is based on the monetary IOT as it is published by the

National Accounts and the pressure flow tables (environmental extension) which are pro-

vided by the Environmental Accounts. The standard monetary input output table (MIOT) at

European level has the format 60 product groups by 60 homogeneous branches

(MIOT60).

That standard Leontief approach was taken as a conceptual starting point for designing a

method for converting product flows into RME at European level. For that purpose annual

data from Eurostat are available for MIOT60 and data of the material flow accounts (EW-

MFA) on domestic extraction used (DEU) of materials from the environment by 42 mate-

rial categories.

But the standard approach had to be considerably modified in order to cope with a num-

ber of deficiencies which had been identified with respect to applying it for calculation of

RME.

Figure 2 shows the major deficits of the standard Leontief approach and the solutions

which were adopted for establishing a specific approach for the purpose of calculation of

RME.

Further the following issue had to be regarded, which is not a specific feature of the stan-

dard Leontief approach. The degree of detail of the breakdown of the domestic extraction

used (DEU) of the Economy Wide Material Flow Accounts by 42 raw material categories

was considered not to be adequate for metals. Therefore the standard breakdown was

disaggregated for metals (expanded material classification of EW-MFA).

3 See Eurostat's reference database:

http://epp.eurostat.ec.europa.eu/portal/page/portal/environment/data/database

http://epp.eurostat.ec.europa.eu/portal/page/portal/environment/data/database



Figure 2: Leontief standard approach versus project approach for RME calculation

The description of the calculation model below follows those principal steps

2.2. Overview calculation model

The core of the calculation model is a specific annual expanded hybrid input output

table of the format 166 product groups by 166 homogeneous branches (expHIOT166).

That table is applied together with physical information of raw material inputs from domes-

tic extraction and embodied RME of selected imported products into the economy in a

type of Leontief approach (mixed Leontief / LCA approach). Figure 3 illustrates the steps

and the data sources of the RME calculation.

Problem Solution

The degree of resolution of the

standard 60x60 IOT matrix is not

sufficient for tracking the flows of

individual raw materials.

Disaggregated monetary IOT of to the

size of 166x166 (expMIOT166)

Monetary use structures are in some

cases not appropriate for depicting

flows of raw material through the

economy

Establishing a hybrid IOT

(expHIOT166), i.e. monetary

structures of the expMIOT166 are

replaced by use structures in physical

units for selected product groups

The "domestic technology

assumption" is not suitable for

representing the raw material content

of a number of imported products in a

sufficient manner

External approach for estimating RME

of selected imported products (mixed

Leontief / LCA approach) was

introduced to the model.



Figure 3: Calculation steps and data sources for RME calculation

(3) DE 2000 expMIOT166Expanded monetary IOT for Germany 166x166

(Structural information)

(7) EU expMIOT166Annual expanded monetary IOT for EU-27 166x166(iterative adjustment approach for integrating German structural information into existing

EU-level framework)

(11) EU expHIOT166Annual expanded hybrid IOT for EU-27 166x166(replacement of selected monetary use structures by physical use structures

(5) EU MIOT60 Annual monetary IOT for EU-27 60x60

(6) Balance column for total domestic uses and total line for inputs by 166 product groups / omogeneous branches for EU-27

(9) Physical flow tablesbv 166 branches- Biotic raw materials- Energy carriers- Metal ores and basic

metals- Other minerals

(2) DE 2000 MIOT72Monetary IOT for Germany 72x72

(1) Auxiliary information forDE 2000

- Supply and use table by about3000 product groups and 120

branches (unpublished.- MIOT 120x120 (unpublished)- Agricultural IOT by 46

agricultural product groups andbranches

- External trade statistics,structural business statistics,

and other sources

(4) Annual auxiliary monetary information for EU-27- COMEXT- Balance of payment

statistics- Structural business

statistics- Agriculural accounts- other sources

(10) EW-MFAAnnual domestic extraction used in tons by material categoriesl

(8) Annual auxiliary physical information for EU-27- COMEXT (tons)- Energy balance

statistics- Metal flow information from USGS and BGS (mine production of ores, prices, recycling, typical uses)- Agricultural statistics- other sources

(13) Metal modelAnnual conversion coefficients (tonnes traded weigh to tonnes RME, total metal to primary metal) for imported metals by 60 metal categories

(12) Auxiliary information for metal model for EU-27- annual business reports for

about 160 mines - Information from life

cycle assessment (LCA)

- other sources

(14) Auxiliary information for environmental extension for EU-27- COMEXT: imports in tons by 60

metal categories)

(15) EU ENVEXT166Annual environmental extension (52 raw material categories) by 166 branches for domestic extraction and imports (selected product groups)

(16) EU Raw Material Equivalents (RME)Annual output, imports exports and 6 final domestic use categories in RME by 52 raw material categories and 166 product groups(calculation by mixed Leontief / LCA approach)

Annual calculation of Raw Material Equivalents (RME)

Calculation for Germany 2000

Annual calculation for EU-27



The EU level calculation procedure can be subdivided into the following principal steps:

a) Annual expansion of the monetary EU-level standard IOT 60x60 to the size of

166x166 (expMIOT166).

b) Converting the expMIOT166 into an annual hybrid IOT (expHIOT166) by insert-

ing physical use structures for selected product groups

c) Estimation of annual environmental extensions (ENVEXT166), i.e. input of raw

materials into the economy

d) Converting product flows into RME by a specific Leontief approach

Ad (a): The expMIOT166 can be regarded as a necessary interim step for estimating

expHIOT166. However, data of that IOT are also required for appoaches of analysing the

the "economic link".

The expMIOT166 at EU-level is estimated by fitting structural information from the

German expMIOT166 into the EU-level data framework of MIOT60 and monetary vectors

by 166 product groups / homogeneous branches for outputs, imports, exports and inputs.

Major data sources for estimating the German expMIOT166 are the national German

MIOT72 and detailed information from internal supply and use tables of the format about

3000 product groups x 120 homogeneous branches and agricultural IOTs by 46

agricultural production processes.

Ad (b): The expHIOT166 is obtained by replacing selected monetary use structurs of the

expMIOT166 by physical use structurs. The physical use structures are obtained from flow

tables in different physical units for biomass products (excluding animal production), metal

ores and basic metals, non-metallic minerals and energy carriers. It was considered that

for those selected products the underlying raw material flow are more closely represented

by physical than by monetary information.

Ad (c): Eenvironmental extensions in a breakdown by 166 homogenous branches

(ENVEXT166) are required as an input to the Leontief calculation together with the ex-

pHIOT. In the context of calculation of RME the ENVEXT denotes the input of raw mate-

rial into the economy in physical units. Following the specific approach of Leontief calcula-

tion of this project two types of ENVEXT166 are required for each raw material category:

1. domestic extraction used of raw materials,

2. raw materials embodied in selected imported products (called "LCA products").



The RME of LCA products are estimated by an external approach which is based on the

"metal model". The metal model provides RME coefficients (tonne RME per tonne traded

weight) which are combined with data from external trade statistics (COMEXT) on imports

in tonnes traded weight.

Ad (d): In the final step RME of product flows are estimated by a "mixed Leontief / LCA

approach". Whereas the standard Leontief approach estimates RME by assuming that

the imported products are produced under the same conditions than the domestic prod-

ucts, the mixed Leontief / LCA approach avoids the domestic technology assumption for

"LCA products" by using external information which enters the calculation system as an

additional environmental extension for imported products.

The calculation approach provides detailed results on product flows in RME in a break-

down by the following dimensions:

1. Categories of final uses (consumption expenditure, capital formation, exports)

and imports

2. 166 product groups

3. 52 raw material categories

2.3. Disaggregated monetary IOT

2.3.1. The concept and rationale for disaggregating the IOT

The standard 60x60 IOT is primarily designed for analysing economic relationships. With

respect to the relationship between the environment and economy it has to be regarded

that – depending on the environmental problem under consideration – the focus may

change. Some product flows which are not important in terms of income generation or

other economic aspects can be highly relevant under an environmental perspective. In

those cases the standard IOT may not able to provide the necessary degree of detail for

tracking those flows.

A high level of aggregation may seriously impair the accuracy of RME estimation for a

number of reasons:



a) Flows of a raw material are usually related to rather specific production activi-

ties, as far as the first steps in the production chain are concerned

b) The degree of inhomogeneity of a product group usually increases with the level

of aggregation

Ad (a): With respect to estimating embodied raw material an important feature of raw ma-

terial flows compared to many other pressure flows has to be taken into consideration.

Other pressure flows, as for example CO2 emissions, usually originate from a wide range

of production and consumption activities. Compared to that, each raw material enters the

economy by a specific production activity (extraction). And also the following step of trans-

formation is usually confined to one or a rather limited number of specific production ac-

tivities (primary processing). Therefore it is very crucial to depict those first steps in a suf-

ficient degree of detail for tracking the flow of a raw material through to economy. How-

ever, in the standard IOT those flows are presented only in a rather aggregated manner.

The production of all agricultural crops is grouped together with animal production in one

product group / homogenous branch "agriculture". A substantial part of primary processing

of agricultural crops as input to animal production takes place within agriculture. And the

food production which is the major primary processing branch for agricultural crops and

animal products outside agriculture is again grouped to only one branch.

A similar situation can be stated for metal ores and other mining and quarrying products

with one branch each for extraction and one (in case of metals) or two (in case of other

mining) primary processing branches.

Ad (b): It has to be pointed out that the degree of inhomogeneity – in terms of monetary

value per unit of raw material equivalent or some other weight unit – is likely to increase

with the level of aggregation. That is the composition of imports, exports or intermediate

consumption of such a rather aggregated product group may differ. For example, the ex-

ports and the imports of the product group "other mining and quarrying" (CPA 14) are in

monetary terms highly dominated by "Other mining and quarrying products n.e.c" (CPA

14.5) whereas the domestic use is prevailed by stone, sand and clay which have a much

lower price than other mining products. For reference see annex 1 Product classification

for expHIOT. RME calculation which is based on monetary relationships assumes that

the average price for exported products of a product groups is identical with the prices for

domestic uses of that product groups. That is not the case, if at the same time the struc-



ture of the exports within a product group differs from final domestic uses and the prices of

the components are not nearly at the same level. In that case an estimation of RME which

is based on monetary relationships for a product group is not able to provide a realistic

result.

As a solution for overcoming those deficits of the standard IOT an expanded IOT matrix of

the size 166 product groups by 166 homogeneous branches (expMIOT166) was devel-

oped as a first step.

Raw materials are reported by the calculation system in a breakdown by 52 raw material

categories. In order to meet the requirements for calculation of RME for each of those raw

material categories a corresponding product group was established for the purpose of

expMIOT166. Furthermore, also the branches of primary processing of raw materials, like

agricultural animal production, food production, basic metal production, other non-metallic

mineral products and energy transformation were disaggregated. In addition some further

branches which are considered to be important under the perspective of raw material con-

sumption (e.g. chemical industry, metalworking industry) are also presented in a disag-

gregated manner. See annex 1.

Beyond improving the accuracy of the calculation results for RME, the disaggregated

IOT has a further important advantage. The breakdown of the calculation results by type

of product follows the classification of the underlying IOT. Therefore also the analytical

relevance of the RME indicators is strengthened by establishing an “economic link” at a

much more detailed level.

2.3.2. Calculation method and data sources

The expansion of the MIOT60 to the format of MIOT166 means that individual cells of

the MIOT60 have to be disaggregated. That disaggregation requires two principal types of

auxiliary information:

a) Structural information for those arrays which have to be disaggregated and

b) Information for subdividing the IOT balance column for total domestic use and the

total line for inputs to the size of 166 product groups / homogeneous branches.

A systematic overview of all important data sources is presented in annex 2 data sources.



2.3.2.1. Structural information based on German expanded IOT

At European level only few auxiliary data are available establishing the required struc-

tural information. Exceptions are energy (energy balance data in physical terms and

some information from SBS on purchases of energy carriers) and agriculture (data on use

of fodder crops from the agricultural statistics).

Compared to that the data situation for Germany is much more favourable. The German

economy accounts for roughly 20 per cent of total EU economy. Therefore, in a first step

an expanded German IOT for the year 2000 was established with the objective to utilize

the structural information of that IOT matrix for disaggregating the European IOT.

The standard IOT of the German Statistical Office has the format 72 x 72. The expansion

of that IOT was predominantly based on detailed supply and use tables of the size 3000

product groups by 120 homogeneous branches. Those detailed tables are not published.

They are designed as an internal tool of the German national accounting department for

tuning different parts of the national accounting system and for generating the published

IOT. Those parts of that table which carry significant information for disaggregating the

standard IOT to the size of 166x166 where made available to this project by the German

Federal Statistical Office.

The breakdown by products of the detailed supply and use table widely covers the re-

quirement for estimating the breakdown of the expMIOT by 166 product groups, with the

exception of disaggregation for other metals (CPA 2745, excluding nickel). For further

subdividing that item the output, the imports and the exports information from COMEXT

and a special preparation of the German production statistics for metal was used. Such

original disaggregated information was available for total supply (imports plus output) and

for exports. As far as the domestic use is concerned, it was generally assumed that the

disaggregated rows have the same use structure as the aggregated row. However some

information from life cycle inventories on typical composition of steel alloys (e.g. nickel,

manganese, chromium. tungsten, titanium, molybdenum) was applied for modifying the

use structures of those metals.



For the disaggregation by homogeneous branches further information was required for

disaggregating agriculture (18 branches), metal ores (17 branches) and non-ferrous basic

metals (16 branches).

For subdividing agriculture a detailed agricultural IOT by 46 agricultural production

processes for the year 1999 could be used. The agricultural IOT was estimated in a joint

project of the Johann Heinrich von Thünen Institut and the German Federal Statistical

Office [Schmidt, Osterburg (2009)]. The agricultural IOT provides detailed information on

intra agricultural flows. But other inputs to the agricultural production processes are only

reported in a summary manner. However some additional auxiliary information from the

German Ministry of Agriculture on the allocation of important non-agricultural inputs (e.g.

animal fodder from food industry, fertilizers, pesticides, energy and medical services)

could be used for filling the most important gaps.

Regarding disaggregation of agriculture it has to be paid some attention to the issue of so

called ancillary activities. It is a special feature of agricultural production that quite fre-

quently different crops and animal products are produced in the same holding. Above all

crop and animal production are strongly intermingled. Animals consume fodder crops,

crop residues like straw and gazed biomass on the one hand and on the other hand farm

manure which can be regarded to be a by-product of animal production is used as input

into crop production. Also within animal production there are interrelationships, like milk

consumption by animals.

Generally, the agricultural accounts treat the outcomes of activities which are usually mar-

keted as products. That is, they are regarded also as output and as input, if consumed in

the same holding. But the results of other activities which are usually not marketed, like

grass which is directly taken up by the animals (grazed biomass) or farm manure, are re-

garded as ancillary activities which are not treated as output but only as an interim stage

within a production process. For example milk is the output of a specific production proc-

ess. The milk producing animals may consume grazed biomass. Input which are neces-

sary for producing that grazed biomass are assigned to production of milk, but the grazed

biomass does not appear as a separate output. And the other way round also the produc-

tion and use of farm manure is only regarded as an internal flow.



However if total agriculture is subdivided into a branches production of grazed biomass

and of milk the flows between both activities (e.g. grazed biomass and farm manure) have

to be made explicit by treating those flows as outputs and inputs. Doing so would mean

that the total outputs from and the total inputs to agriculture are higher for the disaggre-

gated IOT than for the standard IOT. That effect has also to be regarded accordingly for

disaggregating the EU level IOT.

Regarding the disaggregation of metal branches it has to be noted that iron and casting

services are shown separately in the German 72x72 IOT. The ore input into basic non-

ferrous metal production was disaggregated by the general assumption that the ore of a

metal almost fully enters basic metal production of the corresponding metal. Some rather

insignificant corrections where made for alloys. The other inputs into metal production

were widely allocated according to output relationship. Energy inputs into extraction and

primary processing of metals were cross checked and adjusted by some information from

life cycle inventories on typical energy consumption.

2.3.2.2. EU level balance column and line total

The second element for expanding the MIOT60 is monetary balance columns and line

totals by 166 product groups / homogeneous branches for total domestic use and for in-

puts.

The total domestic use is obtained by the following calculation:

Outputs + imports = total supply = total use - exports = total domestic use

Annual European information is widely available from Eurostat’s online reference data

base4 for disaggregating the above items to the size of 166 product groups or homogene-

ous branches.

Monetary data from the European external trade statistics (COMEXT) are applied for dis-

aggregating the import and export vector for goods. The product groups for services are

4 Eurostat. Eurostat’s reference database:

http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/themes

http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/themes



disaggregated by information from Balance of Payment Statistic (BoP). Main source for

the subdivision of the output and the input vector is the Structural Business Statistic

(SBS). The SBS data set had to be gap filled in order to cope for missing values, mainly

due to suppressing of data for confidentiality reasons. For agriculture information from

agricultural accounts on output structure can be utilized. For full disaggregation of metal

mining and basic metal production it has also to be referred to data from the Production

Statistics (PRODCOM) and information from USGS and BGS on physical production and

on metal prices.

2.3.2.3. Iterative adjustment approach

The EU level expMIOT166 is estimated by fitting in the structural information from German

expMIOT166 to the European data framework of MIOT60 and the disaggregated balance

column and line total by a RAS-type approach5. The disaggregation of individual cells of

the European MIOT60 is conducted by three principal steps:

a) Estimation of raw values by disaggregating individual cells of the European IOT

by structural information from the German IOT,

b) Adjustment of the raw values of step a) to the vector of total domestic uses and

to the corresponding cells of the MIOT60 in an iterative adjustment approach,

c) Adjustment of the results of step b) to the vector of total inputs and the corre-

sponding cells of the MIOT60 in an iterative adjustment approach.

The principal steps of the calculation approach are schematically illustrated by Figure 4 to

Figure 6.

The green cells represent the European data framework and the red cells denote the dis-

aggregated values. In the first calculation step raw values are estimated by subdividing

individual cells (indicated by the arrows) of the EuropeaMIOT60 IOT (light green) by verti-

cal, horizontal or matrix type disaggregation. Those raw values sum up to the disaggre-

gated cell but they do not match to the vectors for total inputs and total final domestic

uses.

5 The general RAS method is an approach to reconcile inconsistencies in data that should match or

sum up to the same amount. “It is a bi-proportional adjustment algorithm that balances matri-

ces in a mechanical way. See: [Bouwmeester (2007)]



Figure 4: Scheme for the disaggregation of 60X60 IOT to the format of 166X166 (Step 1)

Disaggregation of 60x60 IOT to the format of 166x166

Step (1) raw values

Schematic description

(1) Adjustment of German structural information to the cells of the EU-level MIOT60

To

tal

do

mesti

c u

ses b

y 1

66 p

rod

uct

gro

up

s

Total inputs / final uses by 166 homogeneous branches / final use categories

EU-level framew ork Totals by 166 product groups /homogeneous branches

Cells of 60x60 IOT not disaggregated

Cells of 60x60 IOT disaggregated

German structural information f it

into the EU-level framew ork by an

iterative adjustment approach

Cells of 166x166 IOT estimated by horizontal disaggregation

Cells of 166x166 IOT estimated by vertical disaggregation

Cells of 166x166 IOT estimated by matrix shape disaggregation




Step two shows the adjustment of the raw values of step one to the total domestic use

vector in an iterative approach by observing the coherence to the cells of the MIOT60.

The arrows indicate the adjustment steps.


Step (2)


(2) Adjustment of (1) to total domestic uses and MIOT60 by an iterative approach

To

tal

do

mesti

c u

ses b

y 1

66 p

rod

uct

gro

up

s


EU-level framew ork Totals by 166 product groups /homogeneous branches





iterative adjustment approach


Cells of 166x166 IOT estimated by vertical disaggregation





The result of step two is adjusted in a final iterative approach to the vector of total inputs

into intermediate consumption and of total final uses for the individual final use categories

by observing the coherence to the cells of the MIOT60.


Step (3)


(3) Adjustment of (2) to total inputs / final uses and MIOT60 by an iterative approach

To

tal

do

mesti

c u

ses b

y 1

66 p

rod

uct

gro

up

s


EU-level framew ork



Totals by 166 product groups /homogeneous branches



iterative adjustment approach Cells of 166x166 IOT estimated by vertical disaggregation





As a result a fully coherent monetary IOT matrix of the format 166x166 is obtained. It has

to be pointed out that that IOT to a large extent is an approximation to real European rela-

tionship and not the German conditions, as the German structural information was not

simply adjusted to the European balance column and total line. The disaggregated IOT

fully carries the information on European average production technology as it is repre-

sented in the European MIOT60. Only below that level German technological relations

have been used. But also for that level it has to be considered that the structural informa-

tion from the German IOT was further adjusted to European demand and input totals.

2.3.2.4. Data issues

Structural Business Statistics

Regarding Structural Business Statistics (SBS) data two major data issues have to be

mentioned, filling of data gaps and transition to new classification beginning in 2008.

Gap filling: At the EU-27 level there are many data gaps at the required level of detail,

mainly due to confidentiality cases or non-reporting of individual countries for some years.

Those gaps were filled by the following general approach: It was referred to country level

data. At that level most gaps could be filled by referring to corresponding results of previ-

ous or following years. In some cases further auxiliary information had to be consulted,

like information from COMEXT, BGS, USG and even annual business reports.

Change of classification: Data of the SBS are reported in NACE Rev. 1.1 until the year

2008. For 2008 data are reported also according to the revised classification NACE Rev. 2

and after 2008 data are only available in NACE Rev. 2. The calculation model is based on

CPA 2002, which fully corresponds to NACE Rev. 1.1, except that the branches according

to CPA are reported as homogeneous branches whereas branches according to NACE

are demarcated as local kind of activity unit (establishments). From 2009 on SBS data

have to be recoded from new to old classification. For that purpose a conversion key was

developed which is based on two elements:

a) A correspondence table which conceptually relates the individual items of both

versions of the classification to each other at the most detailed level.



b) An empirical conversion key which indicates what proportion of an item of the

one classification has to be assigned to what item of the other classification.

The conceptual correspondence tables are provided by Eurostat6. The empirical conver-

sion tables are based on the conceptual correspondence tables and on double prepara-

tions according to both versions of the classification for one year. The empirical conver-

sion matrix for SBS shows what proportions of the items of the NACE Rev. 2 are assigned

to the items of NACE Rev. 1.1. The disaggregation of NACE Rev. 2 follows the 4 digit

level. NACE Rev. 1.1 is allocated according to the “166 level breakdown” of the expHIOT,

which is a mix of 2 digit, 3 digit, 4 digit and some special disaggregation of 4 digit level

items (metals). That matrix was estimated for production values.

Beginning in 2009 the original data for production values and purchases of goods and

services of SBS are converted by that matrix into the project specific 166 level breakdown

of NACE Rev. 1.1.

COMEXT

In some cases original COMEXT data had to be corrected. The corrections refer to both,

results in EUR as well as in metric tonnes. The corrections go back to two major reasons,

apparent assignment errors and non-plausibility.

A number of apparent assignment errors were especially detected for gold and some oth-

er high priced metals.

Cross checking with external trade data from BGS lead to corrections of import and export

figures in case of some high priced metals.

In case of tin applying the original COMEXT data lead to inconsistent IOT relationships

(negative domestic uses) for some years.

Therefore it has to be noted that especially the calculation of separate results for gold and

tin should be used with care.

6 See: RAMON Eurostat’s metadata server:

http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELC

http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELC



2.4. Disaggregated hybrid IOT

2.4.1. The concept and rationale for a hybrid IOT

For the specific purpose of calculation of RME the environmental IOT of the type of

expMIOT166 was further developed by converting it into a hybrid IOT (expHIOT166)

where monetary use structures were replaced by use structures in physical units for some

selected product groups7.

The aim of that modification is to arrive at an improved presentation of flows of raw mate-

rials through the economy by the use structures of the IOT.

For analysing raw material flows it would be crucial that the use structures for the ex-

tracted raw materials (raw material products) represent the physical flows as much as

possible. This may hold to a certain extent also for products of primary processing of raw

materials, like basic metals or refinery products which still largely contain the original raw

materials8.

In principal the aim of depicting physical flows of raw material products and of primary

processed raw materials could also be achieved by applying monetary use structures.

However monetary units can only work well if there are no significant price effects, i.e. if

the unit value is the same for all uses. However, it can be shown that this is sometimes

not the case in practice.

Physical use structures for raw materials (raw products) and primary processed raw mate-

rials are required for the following reasons:

a) Price differentiation: different users pay different prices for the same product

b) Structural effects: the composition within disaggregated product groups differs by

users

7 An early approach for a hybrid IOT was developed by Beutel and Stahmer. See: [Beutel (1982)]

8 There were some attempts in the past by statistical offices also to estimate full physical input out-

put tables (PIOT) where all use structures are expressed in mass units. One example is the PIOT of the German Statistical Office. However to establish such a table is extremely data demanding. In case of Germany the calculation were predominantly based on detailed mone-tary supply and use tables by 3000 product groups. For German PIOT see: [Waldmüller. (2001)]



Case a) is especially relevant for energy carriers. Examples for case b) are: Significant

structural differences for exports and domestic uses for other minerals n.e.c; differences in

the share of secondary metal for exports and domestic uses; prepared animal feed (CPA

156.7) for pets and farm animals has considerably different prices.

Table 1 compares the prices as reported by COMEXT of imports with the prices of exports

for raw materials and some products which represent the stage of primary processing of

raw materials.

That comparison reveals that the prices for imports and exports differ for almost all prod-

uct groups9 which are presented in the table. Moreover the differences change over time

in many cases. In some cases the differences may simply reflect statistical inconsisten-

cies. But more generally it can be assumed that different prices rather reflect structural

effects.

It has to be regarded that especially the quantitative relationship between the imports and

exports in RME is most crucial for estimating the central indicator RMC. Therefore, it can

be concluded that applying physical instead of monetary use structures for raw materials

and some products of primary processing is likely to improve the accuracy of the calcula-

tion results for RME.

It has to be pointed out that for none of the products under consideration full physical in-

formation is available for converting monetary into physical use structures. Therefore, the

estimated physical use structures have to be always based on a mixture of physical and

monetary information. In most cases only imports, exports and output are available in

physical terms and monetary relationships have to be applied for disaggregating of do-

mestic uses in physical terms. Only for energy carriers the energy balance provides rather

detailed physical information on the consumption of energies by economic activities.

9 As a matter of fact unit prices for imports and exports prices may not only differ for raw materials,

but also for semi-finished and finished products. However, for those more complex products physical use structures are not likely to be advantageous compared to monetary structures for estimating the raw material content.



Table 1: Relationship unit value of imports to unit value of exports

Selected product groups

Excluding scrap

Classification for expHIOT 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

01.11.1 Cereals 1.4 1.2 1.1 1.2 1.2 1.3 1.2 1.0 1.0 1.3

01.11.21 Potatoes 1.4 1.3 1.1 1.3 1.1 1.2 0.9 0.9 1.1 1.1

01.11.22 Dried leguminous vegetables, shelled 0.9 1.3 1.8 2.0 1.3 1.1 1.4 1.6 1.7 2.1

01.11.23

Edible roots and tubers with high starch or

inulin content 0.2 0.3 0.1 0.1 0.1 0.2 0.3 0.2 0.4 0.9

01.11.3 Oil seeds and oleaginous fruits 1.1 0.9 0.8 0.8 0.9 1.0 0.9 0.7 0.6 0.9

01.11.4 Unmanufactured tobacco 1.7 1.7 1.8 1.4 1.3 1.2 1.3 1.2 1.1 1.0

01.11.5 Plants used for sugar manufacturing 2.7 3.0 24.7 47.3 23.6 92.9 21.8 41.7 29.2 23.2

01.11.7 Raw vegetable materials used in textiles 1.2 1.4 1.2 1.0 1.2 1.1 1.2 1.1 1.1 1.2

01.12

Vegetables, horticultural specialities and

nursery products 1.0 0.9 0.8 0.8 0.8 0.9 0.8 0.8 0.9 0.9

01.13 Fruit, nuts, beverage and spice crops 1.5 1.4 1.3 1.2 1.3 1.4 1.4 1.3 1.2 1.5

01.11.8, 01.11.9, 01.19Other crop products 0.7 0.8 0.8 1.0 0.9 1.0 1.3 1.0 1.0 1.1

02

PRODUCTS OF FORESTRY, LOGGING

AND RELATED SERVICES 0.7 0.7 0.8 0.8 0.8 0.8 0.7 0.8 1.1 0.8

05

FISH AND OTHER FISHING PRODUCTS;

SERVICES INCIDENTAL TO FISHING

0.8 0.8 0.9 0.7 0.7 0.7 0.8 0.8 0.7 0.9

10.1, excl 10.10.12 Coal, not agglomerated 1.0 1.1 0.9 1.0 0.8 0.9 0.9 0.9 0.9 0.8

10.2.a Lignite, not agglomerated 0.8 1.1 0.3 0.8 0.4 0.6 0.5 0.5 0.7 0.8

10.3 Peat 0.7 0.7 0.6 0.6 0.5 0.5 0.6 0.6 0.5 0.5

11.10.1

Petroleum oils and oils obtained from

bituminous minerals, crude 1.0 1.0 1.0 1.0 1.0 1.0 0.9 1.0 1.0 1.0

11.10.2

Natural gas, liquefied or in gaseous state

0.8 0.9 0.9 0.9 0.8 0.8 0.8 0.9 0.8 0.7

13.1 Iron ores 0.9 0.8 0.8 0.8 0.9 0.7 0.6 0.8 0.7 1.0

13.20.11 Copper ores and concentrates 1.3 1.8 1.1 1.3 1.2 1.4 1.4 1.3 1.1 1.2

13.20.12 Nickel ores and concentrates 1.4 1.0 1.0 0.7 0.7 1.9 2.9 2.1 3.6 3.1

13.20.13 Aluminium ores and concentrates 1.3 1.2 1.0 1.0 0.9 1.2 0.9 0.7 0.9 0.5

13.20.14.a Gold 601.9 43.2 48.1 59.2 80.3 679.7 24.5 48.7 28.4 236.2

13.20.14.b Silver 3.6 39.9 11.5 2.3 8.7 49.9 1.1 81.5 18.7 0.3

13.20.15.a Lead 2.9 2.9 3.2 1.8 1.7 1.8 2.5 1.8 2.2 2.3

13.20.15.b Zinc 1.0 1.1 0.7 0.7 0.9 0.9 0.9 0.9 1.0 1.2

13.20.15.c Tin 1.2 0.6 0.0 0.3 0.4 1.1 1.5 0.1

13.20.16.a Tungsten ores and concentrates 0.6 0.9 0.4 12.6 0.6 0.8 0.6 0.7 0.8 2.0

13.20.16.d

Titanium ores (Ilmenite) and concentrates

0.2 0.3 0.4 0.4 0.6 0.1 0.1 0.3 0.4 1.4

13.20.16.e Manganese ores and concentrates 0.8 0.9 1.2 0.8 0.9 1.1 1.1 1.0 1.0 1.0

13.20.16.f Chromium ores and concentrates 0.5 0.5 0.3 0.3 0.4 0.3 0.4 0.4 0.6 1.1

13.20.16.g Other ores and concentrates 0.7 1.0 0.8 0.8 1.3 1.0 1.0 1.7 2.2 1.6

14.1 Stone 1.6 1.3 1.3 1.2 1.2 1.3 1.7 1.6 1.4 1.4

14.2 Sand and clay 1.3 1.3 1.1 1.0 1.0 1.0 0.9 0.9 1.0 0.9

14.3 Chemical and fertilizer minerals 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.7 0.9

14.4 Salt 0.6 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.6 0.4

14.5

Other mining and quarrying products

n.e.c. 0.3 0.2 0.2 0.2 0.3 0.3 0.3 0.2 0.2 0.3

23.1 Coke oven products 0.7 0.8 0.7 0.8 0.9 0.9 0.9 1.0 1.1 1.5

23.2 Refined petroleum products 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.0

23.3 Nuclear fuel 1.2 1.0 0.8 0.7 0.5 0.7 0.7 1.2 1.0 0.5

27.1-3

Basic iron and steel and ferro-alloys,

tubes and other first processed iron and

steel 0.7 0.7 0.7 0.7 0.8 0.7 0.6 0.6 0.7 0.7

27.41.1, 27.41.5, 27.41.62.a Silver and silver products 1.8 1.6 1.9 1.2 1.1 1.3 0.8 1.1 1.4 1.3

27.41.2, 27.41.4Gold and gold products 1.2 1.5 3.0 4.4 1.7 1.1 1.1 1.2 1.4 1.2

27.41.3, 27.41.62.b Platinum and platinum products 1.2 1.3 1.0 1.2 1.2 1.1 1.0 1.0 1.0 1.0

27.42 Aluminium and aluminium products 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.5 0.4

27.43.11, 27.43.21-3 Lead and lead products 1.0 1.0 0.8 1.6 2.1 1.2 1.7 1.9 1.4 1.4

27.43.12, 27.43.24-6 Zinc and zinc products 0.8 0.7 0.7 0.8 0.9 0.6 0.8 0.7 0.7 0.8

27.43.13, 27.43.27-9 Tin and tin products 1.0 1.1 0.9 1.0 1.0 0.7 0.5 0.6 1.2 1.1

27.44 Copper products 0.7 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7

27.45.1, 27.45.2, 27.45.42Nickel products 0.7 0.6 0.5 0.7 0.7 0.7 0.8 0.8 0.7 0.7

27.45.a Tungsten products 0.9 0.7 1.1 0.9 0.8 0.8 0.9 0.6 0.4 0.6

27.45.b Tantalum products 2.1 1.6 1.9 1.2 1.6 1.8 1.3 1.6 1.5 1.4

27.45.c Magnesium products 0.5 0.4 0.6 0.5 0.6 0.4 0.4 0.5 0.6 0.5

27.45.d Titanium products 1.0 0.9 0.9 0.8 0.6 0.8 0.7 0.8 0.6 0.6

27.45.e Manganese products 0.5 0.5 0.5 0.6 0.7 0.7 0.6 0.8 0.8 0.7

27.45.f Chromium products 0.9 0.8 0.7 0.7 0.9 0.9 0.8 1.1 1.0 0.9

27.45.g Other non-ferrous metal products 0.5 0.4 0.4 0.4 0.7 0.3 0.2 0.3 0.4 0.3

Source: COMEXT



But notwithstanding, introducing physical information for imports, exports and outputs

alone is a crucial step for improving the quality of the calculation result for RME.

In a first step the monetary use structures for some product groups were corrected in or-

der to cope for some identifiable price effects. In a second step, by integrating elements of

that corrected monetary use structures, a set of different physical flow table in a break-

down by 166 homogeneous branches was developed for the following product categories:

a) biotic raw materials (agricultural crop production, wood, fish)

b) energy carriers

c) metals (ores and basic metals)

d) other minerals

For establishing those physical flow tables different methodical approaches are applied

and different physical units are used. Those flow tables are inserted to the expMIOT166 in

order to convert it to an expHIOT166.

2.4.2. Calculation method and data sources

2.4.2.1. Price adjustment of monetary use structure

In a first step the monetary use structures for selected product groups were corrected in

order to cope for some identifiable price effects. Those corrections are based on plausibil-

ity checks which compare the inputs and the outputs of individual product groups in physi-

cal units.

For example, for checking the plausibility of the input-output relationship of swine pro-

duction (CPA 01.23) the physical output was compared to the amount of relevant physi-

cal inputs. For that comparison the output of the product under review and the relevant

inputs have to be converted into tonnes. With regard to swine production cereals (CPA

01.11.1) and prepared animal feed (CPA 15.7) were identified as the relevant inputs to be

considered. The monetary use structures of both input product groups were converted into

mass units by using information in tonnes for imports, exports and output and the relation-



ships of the monetary use structure for domestic uses. As a result the inputs of cereals

and prepared animal feed into swine production were obtained in mass units.

It turned out that the calculated relationship fodder input to meat output was far below the

usual ratio of pig fattening as it is known from literature. In particular the assumption of

equal prices for cereals as well as for prepared fodder for all users, which was inherent to

the IOT based calculation approach, was identified as a reason for that discrepancy. That

assumption neglects that the cereals which are used for animal fodder and human con-

sumption represent different qualities which have different prices. The same holds for pre-

pared animal feeds. The price for pet feed is much higher than for farm animal feed.

Therefore, under the "equal price assumption" the amount of cereals and of prepared

animal feed in tonnes which was allocated to swine production was too low. In order to

cope for that effect of "price differentiation" the implicit calculative prices were adjusted in

a manner that the physical input-output relationship for swine production arrived at a plau-

sible order of magnitude.

Following that consideration the original monetary use structure for cereals and prepared

animal feed were corrected accordingly in order to represent rather the physical and not

the monetary use structures.

Similar considerations and corrections were made for the use structures of fish, stone,

sand and clay and salt.

2.4.2.2. Physical use structure for biomass and other minerals

For agricultural crop products, products from forestry and fishery and for other mining and

quarrying products physical use structures were estimated in the unit tonnes traded

weight. Physical information on imports and exports are taken from COMEXT10 and the

information on output is obtained from EW-MFA (domestic extraction)11. The physical use

structures for domestic uses were estimated by applying the monetary relationship after

correction for price effects (see section 2.3.2.1).

10

It can be assumed that the RME of imported and exported raw products of biomass and other

minerals are reflected quite well by the traded weight. However, diamonds are an exception.

The special case of diamonds has not been explicitly regarded so far in the calculation model.

But it is planned to take account of that issue during the next round of updating. 11

The MFA compilation manual of Eurostat recommends estimating the RME of domestic extrac-tion of those materials by referring to the traded weight. See [ESTAT (2011)]



Those physical use structures take into account effects of differences in the structural

composition of imports, exports and domestic uses. Moreover, also within domestic use

activities also some important price or structural effects are regarded.

2.4.2.3. Physical use structure for metals

Similar to the approach for biomass and other minerals the benchmark values for outputs,

imports and exports of metals are estimated in physical units and for the disaggregation of

the domestic uses the monetary relationships are applied.

However, compared to those materials there are some peculiarities for metals which re-

quire a more complex estimation approach for the benchmark values. One rather out-

standing feature is that the differences between tonnes traded weight and tonnes RME

are extremely high for most metals. Also the trade flows of ores are usually not reported in

gross ore but for concentrates. Another issue is that the external trade figures for metals

have to be adjusted for scrap and secondary metal (which is made of recycled metal).

Finally the lack of reliable direct information on output of basic metals has to be regarded.

In a first step of the calculation approach the benchmark values are expressed in metal

content. That step is necessary for adjusting exports for secondary metal content. In a

final step the benchmark values are converted into the unit tonne RME.

The model is based on a number of sources as:

a) Imports and exports of ores and basic metals (excluding scrap) in tonne traded

weight (COMEXT)

b) Mine production of metal ores in tonne metal content (BGS)

c) DEU of metal ores in tonne RME (EW-MFA)

d) Conversion coefficients tonne ore concentrate to tonne metal content

(metal model)

e) Conversion coefficients tonne metal content to tonne RME for ores and basic

metal (metal model)

f) Conversion coefficients tonne total metal to tonne primary metal for imports

(metal model)



The RME concept refers to consumption of material that was extracted from the environ-

ment, i.e. in the case of metal the amount of embodied ore. Metal scrap and secondary

metal is not generated from new ore but goes back to ore which was already counted

earlier. Therefore, it has to be excluded for the purpose of RME calculation12.

The monetary IOT includes scrap and secondary metal which is a further reason that

monetary relationships are not a suitable basis for estimating RME for metals.

In a first step the benchmark values for imports, exports and outputs are estimated in

tonne of metal content.

The imports and exports of ores in tonnes traded weight are converted by coefficients

from the metal model into metal content. The outputs of ores (mine production in tonne

metal content) are reported by BGS.

For converting the flows of basic metal into metal content of primary metal, scrap and

secondary metal has to be excluded. As far as the data source COMEXT for imports and

exports of basic metals is concerned, scrap can be excluded easily, as it is reported sepa-

rately. But that is not the case for primary and secondary metal, except for aluminium.

Therefore, average recycling coefficients for imported metal from the "metal model" (see

section 2.6) are applied for estimating imports of basic metals in metal content. The coef-

ficients represent the world average.

As no direct reliable and complete information on domestic production of basis metals

is available in tonne of metal content that item was estimated by assuming that the output

of basic metal is equal to the input of ore, which of course is a simplification, as some of

the metal which is contained in the ore maybe gets lost. The input of ore into the same

metal – which is usually quite near 100% – was estimated by the relationships of the

monetary use structure.

The secondary metal content for exports cannot be simply estimated by applying an ex-

ternal coefficient as it was used for imports. Exports of secondary metals can be ap-

12

In principal this issue is also of relevance for other materials, like wood as also a rather high share of paper, which is the main use category for wood, originates from recycling. However due to the lesser quantitative importance for the overall results that issue was neglected so far for non-metal materials.


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