Mater ials Science & Technology
External Costs in the
European Copper Value Chain
A Comparison of Copper Primary Production and Recycling
Master Thesis
MAS Management Technology and Economics MTEC/BWI ETH Zurich / May 2008
Author:
Supervisors:
M.Sc. Martin Eugster - Stäheli / Empa St. Gallen
Prof. Dr. Lucas Bretschger / ETH Zurich
Dr. Christa Brunnschweiler / ETH Zurich
Dr. Patrick Wäger / Empa St. Gallen
MASTER THESIS
MAS MANAGEMENT TECHNOLOGY AND ECONOMICS MTEC/BWI ETH ZURICH
Title:
External Costs in the European Copper Value Chain
A Comparison of Copper Primary Production and Recycling
Date:
May 2008
Author:
M.Sc. Martin Eugster - Stäheli / Empa St. Gallen – Technology and Society Laboratory
martin.eugster@empa.ch
ETH Zurich Student ID: 95-909-875
Supervisors:
Prof. Dr. Lucas Bretschger / ETH Zurich – Center of Economic Research
Dr. Christa Brunnschweiler / ETH Zurich – Center of Economic Research
Dr. Patrick Wäger / Empa St. Gallen – Technology and Society Laboratory
Cover Picture:
Copper smelter, Flin Flon, Manitoba / Canada (Google Earth, 2007)
Abstract
Besides having beneficial effects to society, copper production also induces unwanted side
effects. Emissions of pollutants into air, water and soil can seriously impact human health,
ecosystems, crops and infrastructures. As a consequence, additional 'external' costs incur for
the society, which are not compensated through copper sales by the producers and thus are
not reflected by the copper price.
This study aims to evaluate external costs in the copper value chain and to compare in
particular the primary and the secondary copper production in Europe. The present level of
external costs is estimated based on the contemporary European copper cycle and current
production technology. Future external costs are estimated for the year 2020, each in an
optimistic and pessimistic scenario, where distinguished technology development paths are
evaluated.
In this study, 29 copper smelters and refineries located in Europe were considered. The
copper production in the most important trading partner countries for refined or intermediate
copper products was also taken into account. The underlying methodology for modelling and
estimating external costs associated with environmental pollution is based on the impact
pathway approach. Since air pollution contributes to more than 98% of the total
environmental impacts in the entire copper production chain, emissions to water and soil
were neglected. Annual emissions to air in the copper production were determined based on
a Life Cycle Assessment (LCA) inventory database for each production facility. The stack
emissions from the European production facilities were translated into spatially resolved
ambient concentration increments by atmospheric dispersion modelling. Considering
spatially dependent receptor data such as population density and agricultural land, the
damages or physical impacts from increased ambient concentration levels of various
pollutants were determined and monetarised by applying dose-response relationships.
According to the calculations, in 2005 the average specific external costs of the copper
consumed in Europe amount to €978 per ton. In order to internalize the external costs
incurred in the copper value chain due to air pollution, the average annual market price for
copper in the year 2005 of €2’963 per ton should therefore have been 33% higher. However,
since specific external costs differ significantly between regions as well as primary copper
production generates clearly higher impacts than secondary production; a differentiated
allocation procedure for cost internalisation would be essential, and presumably highly
challenging.
The evaluation of distinguished technology development paths showed that the total external
costs could be substantially reduced (30 - 50%) by consequently implementing environmentally sound technologies, in particular in the European trading partner countries.
However, the scenario analyses also indicate that increasing demand could compensate
technological improvements and overall external cost could remain on a constant level or
could even increase.
1
Acknowledgements
I would like to thank Dr. Christa Brunnschweiler, my supervisor from the Center of Economic
Research at the ETH Zurich, for her guidance and excellent support during the master thesis.
In several fruitful meetings she helped me a lot in focusing the study and she provided many
helpful comments on the report. I’d like to express my gratitude to Prof. Dr. Lucas Bretschger
who provided me the opportunity working in this highly interesting field of resource
economics.
Many thanks also go to Dr. Patrick Wäger from the Technology and Society Laboratory at
Empa St. Gallen for his recommendations and valuable feedbacks on the thesis report. I
would further thank my employer Empa for supporting me in kind during my studies and
thesis work and provided funding for a site visit in Hoboken / Belgium and for the modelling
software tool.
A special thank goes to Dr. Jan Kegels, head of the department environment at Umicore
Precious Metals in Hoboken, for his help in gathering and interpreting emission data of
copper production sites. I would also thank Mr. Jean-Francois Equey from Metallo-Chimique
N.V in Beerse / Belgium for providing emission data of a secondary copper smelter.
I am also very grateful for the support of Mr. Volker Klotz and Mr. Phillip Preiss from the
University of Stuttgart / Germany in the modelling and simulation work with EcoSenseWeb.
Last but not least I would like to thank my family, my wife Barbara and our children Demian,
Elena and Isaak for their patience during the entire thesis and for providing me a wonderful
working environment.
2
Table of Contents
1
INTRODUCTION ........................................................................................................................ 9
2
GENERAL APPROACH .......................................................................................................... 11
3
COPPER VALUE CHAIN ANALYSIS ..................................................................................... 15
4
3.1
SUPPLY AND DEMAND OF COPPER .................................................................................. 15
3.2
THE COPPER CYCLE ...................................................................................................... 19
3.3
AVAILABILITY AND STOCKS OF COPPER ........................................................................... 23
3.4
INTERNATIONAL VALUE CHAIN STRUCTURE ..................................................................... 27
3.5
DEVELOPMENT AND TRENDS OF THE COPPER PRICE ....................................................... 29
MODELLING EXTERNAL COSTS IN THE EUROPEAN COPPER VALUE CHAIN.............. 33
4.1
FRAMEWORK SCENARIOS ............................................................................................... 33
4.2
EMISSIONS OF POLLUTANTS IN THE COPPER PRODUCTION ............................................... 35
4.3
DISPERSION OF POLLUTANTS AND EXPOSURE ................................................................. 38
4.4
DOSE-RESPONSE FUNCTIONS ........................................................................................ 42
4.5
MONETARY VALUATION OF AIRBORNE POLLUTION ........................................................... 44
4.6
MODELLING RESULTS AND COMPARISON......................................................................... 49
5
CONCLUSIONS ....................................................................................................................... 53
6
REFERENCES ......................................................................................................................... 55
ANNEX
A
COPPER PRODUCTION NETWORKS EUROPE AND INDONESIA..................................... 61
B
WORLD COPPER PRODUCTION DATA 2001 – 2005 .......................................................... 65
C
CHARACTERISATION OF THE LOCAL ENVIRONMENT..................................................... 73
D
AIR EMISSION DATA.............................................................................................................. 75
E
DELTA CONCENTRATION MAPS FOR THE FACILITIES HOBOKEN / BELGIUM AND
GLOGOW I / POLAND IN THE BASE SCENARIO 2005 ....................................................... 85
F
EXTERNAL COST ESTIMATES.............................................................................................. 95
3
List of Tables
TABLE 1:
EUROPEAN COPPER SMELTERS IN 2003 ..................................................17
TABLE 2:
PRINCIPAL USES OF COPPER IN 1990 .......................................................18
TABLE 3:
ESTIMATED COPPER RESERVES AND RESERVE BASE IN 2005 ............25
TABLE 4:
WORLD COPPER RESERVES AND ANNUAL MINE PRODUCTION,
1950–2005.......................................................................................................26
TABLE 5:
PRODUCTION VOLUME PER COMPANY IN 2004 .......................................28
TABLE 6:
ACCUMULATED ENVIRONMENTAL IMPACTS FROM THE
PRODUCTION OF PRIMARY COPPER FOR DIFFERENT REGIONS..........37
TABLE 7:
CONCENTRATION RESPONSE FUNCTIONS OF PM2.5, PM10 AND
OZONE AND CORRESPONDING MONETARY VALUES .............................44
TABLE 8:
GENERIC MONETARY VALUES FOR THE ESTIMATION OF EXTERNAL
COSTS FROM EMISSIONS OF GREENHOUSE GASES..............................48
TABLE 9:
GENERIC MONETARY VALUES FOR THE ESTIMATION OF EXTERNAL
COSTS FROM EMISSIONS OF CERTAIN HEAVY METALS AND
ORGANIC COMPOUNDS ...............................................................................49
TABLE 10:
COMPARISON OF SPECIFIC EXTERNAL COSTS OF PRIMARY AND
SECONDARY COPPER PRODUCTION AND COPPER CONCENTRATE
PRODUCTION IN EUROPE, CHILE, PERU, RUSSIA AND INDONESIA ......50
TABLE 11:
COMPARISON OF TOTAL EXTERNAL COSTS OF PRIMARY AND
SECONDARY COPPER PRODUCTION AND COPPER CONCENTRATE
PRODUCTION IN EUROPE, CHILE, PERU, RUSSIA AND INDONESIA ......51
4
List of Figures
FIGURE 1:
THE PRINCIPAL STEPS OF AN IMPACT PATHWAY ANALYSIS FOR
THE EXAMPLE OF AIR POLLUTION .............................................................12
FIGURE 2:
DETERMINATION OF PHYSICAL IMPACTS BASED ON DELTA
CONCENTRATION MAPS, POPULATION DENSITY MAPS AND
CONCENTRATION – RESPONSE FUNCTIONS ...........................................12
FIGURE 3:
GLOBAL PRIMARY AND SECONDARY COPPER PRODUCTION AND
COPPER CONSUMPTION 1966 – 2005 ........................................................15
FIGURE 4:
GLOBAL COPPER MINE PRODUCTION, SMELTER PRODUCTION AND
REFINERY PRODUCTION IN 2005................................................................16
FIGURE 5:
GLOBAL “BEST ESTIMATE” OF THE ANTHROPOGENIC COPPER
CYCLE FOR 1994 ...........................................................................................20
FIGURE 6:
GLOBAL “BEST ESTIMATE” OF THE ANTHROPOGENIC COPPER
CYCLE FOR 2005 (OWN ESTIMATES) .........................................................20
FIGURE 7:
EUROPEAN COPPER CYCLE IN 1994..........................................................21
FIGURE 8:
EUROPEAN COPPER CYCLE IN 2005 (OWN ESTIMATES) ........................22
FIGURE 9:
PORPHYRY COPPER DEPOSITS OF THE WORLD ....................................23
FIGURE 10:
THE ‘‘MCKELVEY DIAGRAM’’ OF MINERAL RESERVES AND
RESOURCES..................................................................................................24
FIGURE 11:
GEOGRAPHIC DISTRIBUTION OF PRIMARY PRODUCTION FOR
VARIOUS METALS. ........................................................................................27
FIGURE 12:
MAIN IMPORT PARTNERS OF THE EU-25 FOR COPPER ORES AND
CONCENTRATES AND REFINED CATHODE COPPER...............................28
FIGURE 13:
NOMINAL COPPER PRICE DEVELOPMENT 1988 – 2007 IN USD..............29
FIGURE 14:
INDEX OF THE US PRODUCER PRICE OF COPPER FROM 1870 TO
2000.................................................................................................................31
FIGURE 15:
WORLD LARGEST OPEN-PIT COPPER MINE IN CHUQUICAMATA /
CHILE ..............................................................................................................35
FIGURE 16:
COPPER SMELTER AND REFINERY IN SKELLEFTEHAMN / SWEDEN,
PLANT RÖNNSKÄR OF THE COMPANY BOLIDEN MINERAL AB...............36
FIGURE 17:
ACCUMULATED ENVIRONMENTAL IMPACTS FROM THE
PRODUCTION OF PRIMARY COPPER AND CONTRIBUTION OF IN
ECO-INDICATOR POINTS (EIP) FOR DIFFERENT REGIONS .....................37
FIGURE 18:
ACCUMULATED EMISSIONS OF SULPHUR DIOXIDE, NITROGEN
OXIDES AND PARTICULATES IN THE PRODUCTION OF REFINED
PRIMARY COPPER FOR DIFFERENT REGIONS.........................................38
5
FIGURE 19:
CHEMICAL REACTIONS OF THE SULPHUR AND NITROGEN SPECIES
INCLUDED IN THE REGIONAL SCALE MODELLING...................................40
FIGURE 20:
SPATIALLY DISTRIBUTED DELTA CONCENTRATION VALUES OF
PM2.5 FOR THE FACILITY GLOGOW I / POLAND IN THE BASE
SCENARIO......................................................................................................41
FIGURE 21:
POSSIBLE BEHAVIOUR OF DOSE-RESPONSE FUNCTIONS AT LOW
DOSE ..............................................................................................................43
FIGURE 22:
LOCAL ENVIRONMENT OF AN “AGGLOMERATION”; ZURICH /
SWITZERLAND...............................................................................................74
FIGURE 23:
LOCAL ENVIRONMENT OF AN “URBAN AREA”; SREDNEURALSK /
RUSSIAN FEDERATION AND OF A “RURAL AREA”; CHUQUICAMATA /
CHILE ..............................................................................................................74
6
List of Abbreviation
BAT
Best available technology
CPI
Consumer price index
CRF
Concentration - response function
CVM
Contingent valuation method
DRF
Dose - response function
EIP
Eco - Indicator points
ELV
End of life vehicles
EU
European Union
GWP
Greenhouse warming potential
IQ
Intelligence quotient
LCA
Life cycle assessment
LME
London metals exchange
LRS
Lower respiratory symptoms
MFA
Material flow analysis
PDF
Potentially disappeared fraction of certain organisms
RT
Residence time
SIA10
Secondary inorganic aerosols < 10 micrometers
SX-EW
Solvent extraction electrowinning process
TBRC
Top blown rotary converter
USGS
U.S. Geological Survey
USSR
Union of Soviet Socialist Republics
VOLY
Value of life years
VPF
Value of prevented fatality
VSL
Value of statistical life
WEEE
Waste electronic and electrical equipment
WTA
Willing to accept
WTP
Willing to pay
YOLL
Years of life lost
7
8
1 Introduction
Copper is one of the most important industrial metals since it has been used over eight
millennia. Today, primary production of copper ranks third in terms of annual global metal
tonnage and annual production value, behind iron and aluminium. Due to its significant role
in many applications, copper is a relevant commodity today and will presumably continue to
be important in the future.
Within the past decade the copper price has quadrupled. Copper has been traded at the
London Metals Exchange (LME) this year between €4’500 and €5’600 per ton. Due to a
continuously increasing global copper demand and at the same time uncertainties about
copper stocks and availability, the copper prices increased substantially. Speculative
activities at the stock markets may also contribute to the increase of the copper price.
However, in these prices social costs such as environmental and human health damages
from copper production are not included, since these impacts are usually not considered in
the copper value chain.
The copper production, aside from its beneficial consequences to society, causes some
unwanted side effects. Emissions of pollutants into air, water and soil can seriously impact
human health, ecosystems, crops and infrastructures. The most important environmental
impact of primary and secondary copper production is airborne pollution, in particular
increased ambient concentration levels of particulates (PM2.5. and PM10) and of ozone (O3).
While particulates are primary pollutants which are directly released by the production
facilities, ozone is a secondary pollutant which is formed in a series of non-linear chemical
reactions from the emitted sulphur and nitrogen species. The most relevant and thoroughly
studied consequence or physical impact of these pollutants is human health degradation.
Empirical studies confirm that, even at current ambient levels, air pollution increases
morbidity (especially respiratory and cardiovascular diseases) and leads to premature
mortality. Since social costs such as increased human health costs due to environmental
pollution incur outside the copper value chain, these costs are not reflected in the copper
market price and therefore not compensated by the generator or namely the copper
producers. These external costs are finally covered by the society, by affected individuals or
groups and the entire population as such.
As a consequence of increasing environmental awareness of the society and growing
pressure for cost transparency and cost internalisation from regulatory bodies, further
knowledge on external costs in the copper value chain is needed. To date, for the mining
industry and in particular for the copper industry no comparable studies are available.
Therefore, in this study the external costs in the European copper value chain are modelled
and estimated for different framework scenarios using best practice approaches.
This study aims to evaluate external costs in the copper value chain and to compare in
particular the primary and the secondary copper production in Europe. The present level of
9
external costs is estimated based on the contemporary European copper cycle and current
production technology. Future external costs are estimated for 2020 in an optimistic and
pessimistic scenario, where distinguished technology development paths are evaluated.
The analysis of pollution levels and its monetarisation are carried out for the European
copper value chain. In this study the 29 copper smelters and refineries located Europe are
considered; the copper production in the most important trading partner countries for refined
or intermediate copper products are also taken into account.
For the interpretation of the results of this study, at least three limitations have to be
considered. First, there are significant uncertainties in future trends of the copper market,
second, there are limitations in assessing environmental and social impacts as such and
third, there are methodological constraints in the estimation of external cost. Nevertheless, in
spite of these uncertainties and constraints, this study seeks to estimate the current and
future levels of external costs in the copper primary production and recycling, since to date
no comparable estimates have been carried out. The results shall contribute to the general
understanding of social costs in the copper value chain and give a hint on its absolute and
relative magnitude differentiated by major production steps and geographical regions.
10
2 General Approach
Industrial and agricultural activities, transport processes and energy conversion can cause
substantial environmental and human health damages, which vary widely depending on
where the activity takes place, and on the type of the activity. The damages caused are for
the most part not integrated into the pricing system and are therefore called externalities or
external costs. According to Perman et al. (Perman, Ma et al. 2003) an externality is said to
occur when the production or consumption decisions of one agent have an impact on the
utility or profit of another agent in an unintended way, and when no compensation / payment
is made by the generator of the impact to the affected party. External effects originate either
in production or consumption and affect producer, consumer or both. These unintended
effects may be beneficial or harmful. There is no payment in respect of a beneficial effect by
the beneficiaries, or no compensation in respect of a harmful effect for the affected party.
While external effects can have the characteristics of private goods, those that are most
relevant for policy analysis are external costs with the characteristics of public goods, which
exhibit characteristics of non-rivalry and non-excludability. This is especially the case with
external effects caused by pollution problems that involve the natural environment and
human health. External costs with the characteristics of public goods lead to market failures,
which is why non-market valuation techniques are necessary to estimate monetary values of
welfare changes.
Since the main focus of this study is to estimate external costs in copper production, only
harmful effects originated in primary and secondary production steps are taken into account and not effects from copper product fabrication, product use, waste management or
transportation processes.
The estimation of external costs can be divided in two major methodological steps:
1. Environmental and social impact analysis: The relevant environmental and social
impacts in the copper value chain are identified, and appropriate indicator variables
are selected and quantified.
2. Monetary valuation: The environmental and social impacts are valuated in monetary
terms and external costs are estimated.
The underlying methodology used for estimating external costs associated with
environmental pollution is based on the impact pathway approach suggested by the ExternE-
11
project1 of the European Commission for quantifying externalities in the energy sector (Berry,
Holland et al. 1995; Bickel and Friedrich 2005). Although the estimation of external costs has
to consider several constraints and uncertainties, there is a wide consensus on the major
methodological issues. In particular for air pollution, the impact pathway analysis is broadly
acknowledged as the preferred approach. The principal steps in this approach are presented
in Figure 1.
SOURCE
DISPERSION
DOSE-RESPONSE
(specification of site and
technology)
(e.g. atmospheric dispersion
model)
(or concentration-response
functions)
⇒ Emission
⇒ Concentration increase
at receptor
(e.g. kg/a of particulates)
(e.g. µ/m of particulates)
3
MONETARY VALUATION
⇒ Impact
⇒ Cost
(e.g. cases of asthma due to
ambient concentration of
particulates)
(e.g. cost of asthma per case)
Figure 1: The principal steps of an impact pathway analysis, for the example of air pollution (Bickel and Friedrich
2005)
Starting point for the analysis is the emission of pollutants at sources such as industrial sites
or transport systems. The annual release of pollutants into air, water or soil needs to be
quantified for each source, and site specific technical data2 have to be specified. In the
subsequent dispersion modelling of air pollutants or fate analysis of water and soil emissions,
the increase of ambient concentration levels at different receptors such as population,
agricultural land, building materials or ecosystems is determined. By applying dose-response
or concentration-response functions, the impacts in terms of life expectancy reduction, work
loss days, cases of chronic bronchitis and other physical impact categories are then
quantified (see Figure 2).
Delta concentration
+
Population
+
CRF
Function
=
IMPACT
Figure 2: Determination of physical impacts based on delta concentration maps, population density maps and
concentration – response functions in the impact pathway analysis
Finally, in the monetary valuation step, these physical impacts are monetarized based on
country specific cost factors such as health care service costs or average employee
1
The ExternE-Project was funded by the European Commission within the scope of the sixth research framework
programme 2002 – 2006. The project contributors were from the University of Stuttgart / Germany, Ecole des
Mines / France, University of Bath / UK, University of Hamburg / Germany, Institute of Occupational Medicine
/ UK, E-CO Tech as / Norway, Paul Scherrer Institute / Switzerland, Vlaamse instelling coor technologisch /
Belgium and the Swedish Corrosion Institute / Sweden (www.externe.info)
2
Required site specific technical data for air pollution in the applied modelling procedure: annual production
quantity, flue gas volume and temperature, stack height and diameter as well as position of emission source
(latitude, longitude)
12
productivity losses. The techniques for valuating the environment can be divided in indirect
and direct expression of preferences. In indirect methods the value is derived from market
prices of other goods. The most prominent indirect methods are the avoidance cost approach
(i.e. cost for end-of-pipe measures are determined), the travel cost approach and the hedonic
price analysis. In direct methods the value for an environmental good is estimated through
surveys on a hypothetical price (e.g. contingent valuation method) or through estimation of
replacement or restoration costs. In the contingent valuation method (CVM) a survey of the
willingness to pay (WTP) for a certain environmental good is determined. For some impact
categories these techniques cannot be fully applied since data on valuation is missing (e.g.
acidification and eutrophication of ecosystems) or because estimation of all physical impacts
is limited (e.g. global warming). In such cases a second best approach has to be chosen.
In this study two framework scenarios are distinguished for the copper production in Europe,
a reference or base scenario 2005 and a future scenario for the year 2020 (see chapter 4.1).
Two technology development paths until 2020 were considered assuming optimistic and
pessimistic implementation of environmentally sound technologies3. In order to define
plausible framework scenarios, a copper value chain analysis is carried out (see chapter 3).
Based on data and information from literature review and expert interviews, the
developments and trends of the global copper market are studied, copper resource depletion
and the level of scarcity is evaluated, and the institutional framework influencing the supply
and demand regime is analysed.
For the quantification of emissions in the European copper value chain, data from the Life
Cycle Assessment (LCA) inventory database ecoinvent (ecoinvent 2008) were used.
According to these data, air pollution contributes for 98% of the total environmental impacts4
in the entire copper production chain (see chapter 4.2). Thus, in this study emissions to water
and soil are not taken into account. The accumulated air emissions in the entire copper
production chain were allocated to the 29 operating smelting and refinery plants in Europe.
Hence, emission levels for each facility are overestimated since the emissions of preprocessing steps (mining of copper ore and concentrate production) occur at other locations
in or even outside Europe, but are included in the European production sites. According to
ecoinvent-data, in Europe copper smelting and refinery processes account for only 23.3% of
the overall environmental impacts in the copper production chain, and copper concentrate
production accounts for the other 86.7%. In Indonesia the impacts in the smelting process
are much higher, and the share of smelting and refinery is 71.2% and for copper concentrate
production 28.8% of the overall environmental impacts. In Annex A detailed production
3
Environmentally sound technologies (ESTs) are technologies that have the potential for significantly improved
environmental performance relative to other technologies (United Nations Environment Programme – UNEP:
www.unep.org)
4
The environmental impacts are assessed and quantified using the Eco-Indicator ’99 points (Hierarchist
perspective) V2.04 (Goedkoop and Spriensma 2000)
13
networks are displayed and the environmental load of sub-processes contributing more than
0.5% to the overall impact is presented.
In this study, the atmospheric dispersion modelling of air pollution in Europe and the
calculation of spatially distributed concentration changes of ambient pollutants, the
determination of physical impacts based on empirically derived concentration-response
functions and the monetary valuation was done with the software tool EcoSenseWeb (Haigis
2008) developed within the ExternE-project (see chapters 4.3 - 4.5). The model covers the
major pollutants sulphur dioxide (SO2), nitrous oxides (NOx), ozone (O3) and particulates
(PM2.5, PM10) and some minor pollutants, namely toxic heavy metals (As, Cd, Cr, Hg, Ni
and Pb) as well as certain organic pollutants (formaldehydes and dioxins). In order to
evaluate impacts from greenhouse gases, emissions of carbon dioxide (CO2) and methane
(CH4) are considered.
The calculations for the global analysis of imported copper were done in EcoSenseLE5, a
simplified tool based on the EcoSenseWeb software. Only two sets of receptor information
(for the ‘EU-15 and new members’ and for ‘Sweden’) were used, since no population density
maps or country specific data are available outside Europe. The population densities in the
selected European region / country are comparable with the relevant countries used in the
global analysis, namely Chile, Peru, Russia and Indonesia.6
5
EcoSenseLE V1.3 (Lookup Edition) is a simplified and parameterised version of the EcoSenseWeb model based
on European data for receptor distribution (population, crops, building materials), background emissions
(amount and spatial distribution), and meteorology. The input required is annual emissions of NOx, SO2,
PM10, NMVOC, CO2, N2O, CH4; the pollutants considered are O3, SO2, PM10, sulfates, nitrates and
greenhouse gases. (http://ecoweb.ier.uni-stuttgart.de/ecosense_web/ecosensele_web/frame.php)
6
Population densities of relevant countries in persons / km2: EU25 = 111, Sweden = 22, Chile = 21, Peru = 21,
Russia = 9, Indonesia = 118
14
3 Copper Value Chain Analysis
3.1
Supply and Demand of Copper
Economic, technological and societal factors influence the supply and demand of copper. As
society's demand for copper increases, new mines and plants are introduced and existing
ones expanded. In times of market surplus, existing operations can be scaled back or closed
down, while planned expansions can be delayed or cancelled. But copper is not only
produced from ores and concentrates (primary copper), copper scrap or recycling also
provide a relevant and continuous supply source (secondary copper).
According to data from the International Copper Study Group (ICSG 2007a) and the U.S.
Geological survey (USGS 2005; USGS 2008) the global copper mine and refinery production
is increasing steadily to reach 15’047 kt and 16’559 kt respectively in 2005 (see Annex B).
For 2008, mine production is expected to increase to 17’000 kt and refined copper production
is estimated to increase to 18’950 kt (ICSG 2007b). In 2005, the 125 operating copper
smelters worldwide had a cumulated capacity of 14’325 kt and produced 13’482 kt blister
copper, i.e. raw copper with an average Cu content of 98%. In Figure 3 the global primary
and secondary production of refined cathode copper (refinery production) and the copper
consumption from 1966 to 2005 is presented.
18'000
16'000
Primary Copper
Secondary Copper
Consumption
14'000
[ kt ]
12'000
10'000
8'000
6'000
4'000
2'000
0
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
Figure 3: Global primary and secondary production of refined cathode copper (refinery production) and copper
consumption 1966 – 2005 (ICSG 2007a)
Traditionally, the mining industry is the most important supplier of the copper smelting
industry. Chile has increased its share of world mine production from 13% in 1978 to 35% or
5’321 kt in 2005. Another 37% or 5’597 kt has been mined by the United States, Indonesia,
15
Peru, China and Russia in 2005. In Europe the most important copper mines are located in
Poland (523 kt), Sweden (98 kt), Bulgaria (97 kt) and Portugal (90 kt). In the following figure,
the mine production, smelter production and refinery production in the most important copper
production countries are presented for the year 2005. The pie chart shows the share of the
country’s refinery production.
6
3%
Mine
5
3%
Peru
3%
3%
26%
Canada
India
3%
Smelter
4%
Korea, Republic of
Poland
Refinery
6%
[ 106 Metric tons ]
4
Germany
Russia
United States
8%
3
Japan
China
17%
8%
2
Chile
Others (< 500'000 t/a)
16%
1
Zambia
Others (< 200'000 t/a)
Sweden
United States
Spain
Russia
Poland
Peru
Philippines
Mexico
Korea, Republic of
Kazakhstan
Japan
Indonesia
India
Germany
Chile
China
Canada
Brazil
Bulgaria
Belgium
Australia
0
Figure 4: Global copper mine production, smelter production and refinery production in 2005 in Million tons and
share of the refinery production of different countries in percent; Data source: U.S. Geological
Survey (USGS 2005)
Primary smelters use mine concentrates as their main source of feed. Secondary copper
smelters use copper scrap (mainly low grade) as their feed. Globally 12.2% of the total
smelter copper production in 2005 originated from secondary copper sources. In Europe
there is in particular a high concentration of secondary smelters: 10 of the 16 specialized
plants for secondary materials worldwide are located in European countries. Accordingly, in
Europe 23.1% of the total smelter copper production are from secondary sources. Similarly,
12.2% of the total refinery copper production in 2005 originated from secondary copper
sources on global scale and 26.2% in Europe.
Recently, the trend to recover copper directly from ores through leaching processes has
been on the increase. In the solvent extraction-electrowinning (SX-EW) process the ores are
first leached and then directly refined. In 2006, the SX-EW technology accounted for 16% of
production (ICSG 2007a). This newer hydrometallurgical process is in a rapid growth stage
and to some extent displacing the older pyrometallurgical approach. The pyrometallurgical
approach is a net producer of by-product sulphuric acid, whereas the hydrometallurgical
process is a net consumer of the acid. Thus, in the long run it is likely that the two will be
increasingly combined and that SO2 gas released by the furnaces will be recovered in a
sulphuric acid plant.
16
In the following table the 29 operating copper smelters in Europe, which have an
accumulated production capacity of 2’839 kt/a, are presented. In 2005, the smelter
production in Europe amounted to 2’227 kt, and the refinery production to 2’560 kt.
Table 1: European copper smelters in 2003 (USGS 2003), accumulated capacity 2’839 kt/a
Country
Albania
Austria
Belgium
Bulgaria
Finland
France
Germany
Hungary
Italy
Poland
Romania
Serbia / Montenegro
Slovakia
Spain
Sweden
Turkey
Location
Kukes (Gjegian)
Lac
Rubik
Brixlegg
Beerse
Hoboken
Eliseina
Pirdop
Harjavalta
Poissy
Hettstedt
Lunen (Huettenwerke Kayser)
Hamburg (Norddeutsche Affinerie)
Csepel
Porto Marghera
Glogow District (Glogow I)
Glogow District (Glogow II)
Wroclaw (Hutmen S.A.)
Legnica
Baia Mare
Zlatna
Zlatna
Zlatna
Bor
Krompachy
Asua-Bilbao
Huelva
Skelleftehamn (Ronnskar)
Samsun
Latitude Longitude
42.08
41.63
41.77
47.42
51.32
51.17
43.08
42.70
61.32
48.93
51.65
51.62
53.55
47.40
45.45
51.67
51.67
51.15
51.21
47.67
46.12
46.12
46.12
44.08
48.92
43.18
37.26
65.00
41.28
20.42
19.71
19.79
11.88
4.87
4.35
23.48
24.18
22.13
2.05
11.50
7.52
10.00
19.23
12.22
16.08
16.08
17.08
16.16
23.58
23.22
23.22
23.22
22.10
20.87
-2.56
-6.95
21.60
36.33
Capacity
Process Type
[ kt ]
(S: secondary smelter)
5
7
5
85
150
50
14
190
160
10
96
170
420
4
24
220
205
9
93
35
10
40
13
170
20
32
320
240
42
Reverberatory
Blast Furnace
Reverberatory
Blast Furnace (S)
TBRC (S)
Isasmelt (S)
Blast Furnace (S)
Outokumpu Flash
Outokumpu Flash
Blast Furnace
Reverberatory/Blast
Isasmelt
Outokumpu, Contimelt, Electric
Reverberatory (S)
Reverberatory (S)
Blast Furnace
Outokumpu Flash
Blast Furnace (S)
Blast Furnace
Outokumpu Flash
Reverberatory (S)
Outokumpu Flash
Reverberatory
Reverberatory
Reverberatory (S)
Rotary Furnace (S)
Outokumpu Flash
Electric (TBRC)
Outokumpu Flash
Copper is used as pure metal and as alloying element with other metals in component
manufacturing, where semi-finished products such as shapes or ingots (known as ‘semis’)
are produced. Copper is used in building construction, power generation and transmission,
electrical and electronic equipments, aircraft parts, automotive parts, industrial applications
and other applications (MMSD 2002). The principal uses of copper shown in Table 2 account
for 79% of the worldwide consumption of copper (Graedel, Bertram et al. 2002). Graedel et
al. further added estimates on residence time (RT) of the application in the use phase and
estimates on the distribution between recoverable copper scrap and waste at the end of use
(S/W). According to data from the German Metal Industry Association (WVM 2007), 40% of
the copper was used in construction, 36% in electrical and electronic equipments, 16% in the
machineries, 6% in automotives and 2% in other sectors.
The temporal development of the global copper use or demand is shown in Figure 3. Since
the beginning of the 20th century, demand for refined copper has increased from 500 kt to
over 16’560 kt in 2005. Prior to the Second World War, demand grew at an annual rate of
17
3.1%. During the post war expansion years (1945 to 1973) demand grew by 4.5% per year.
Since the first oil shock of 1974, demand has grown by 2.4% per year. During the 1990's,
demand for copper has resumed at an above average rate of 2.9%. (ICSG 2007a)
Table 2: Principal uses of copper in 1990; RT: residence time; S/W: scrap and waste at the end of use (Graedel,
Bertram et al. 2002)
Category
Building wire
Tube
Alloy rod
Magnet wire
Telecommunication wire
Power cable
Copper sheet and strip
Alloy sheet and strip
Casting alloys
Motor vehicle wire
Appliance wire
Bare wire
Copper rod
Alloy tube
Wire (other)
Alloy wire
Chemical and powder
Use
RT
S/W
[%]
[ years ]
[%/%]
14
12
11
9
8
8
8
7
6
4
4
3
2
2
1
1
<1
45
60
20
15
50
40
50
25
30
10
20
10
40
35
5
5
1
50/50
45/55
10/90
50/50
25/75
60/40
60/40
20/80
50/50
80/20
50/50
0/100
60/40
95/5
0/100
5/95
5/95
Key drivers for copper consumption today are investments in power generation and
distribution and telephone systems. Ongoing industrialisation in developing and emerging
economies, mainly in China, India and Russia, fuelled the demand of copper during the past
decade. Further improvements of the standard of living and widespread electrification of rural
areas would undoubtedly result in increased copper consumption in the near future. World
copper use in 2008 is projected to grow by 3.8%, or about 690 kt, to 18’700 kt (ICSG 2007b).
According to forecasts of Frondel et al. (Frondel, Grösche et al. 2006), the global copper
demand will increase to 24’100 kt in 2020 and 28’400 in 2025. The copper demand in China
only is estimated to be 5’000 – 9’000 kt in 2020 and 6’000 – 13’000 kt in 2025.
As a consequence of steadily growing copper demand, growing copper prices and
controversial expert discussions on potential supply restrictions, increased attention has
been paid to the issue of substitution in recent years. In economics, one kind of good (or
service) is said to be a substitute good for another kind insofar as the two kinds of goods can
be consumed or used in place of one another in at least some of their possible uses (Wiki
2008). Potential substitutes for copper wires are optical fibres or aluminium wires, in building
applications substitutes are zinc, aluminium or copper composite materials and in some
applications like boilers or plumbing tubes stainless steel and plastics have the potential to
substitute the copper metal. The copper used in electrical and electronic equipment
components and in electrical motors is hardest to substitute.
18
3.2
The Copper Cycle
A large number of comprehensive studies have been carried out in order to characterize and
describe stock and flows of contemporary copper cycles in different regions and countries
(Ayres, Ayres et al. 2002; Bertram, Graedel et al. 2002; Graedel, Bertram et al. 2002;
Rechberger and Graedel 2002; Spatari, Bertram et al. 2002; Bertram, Graedel et al. 2003;
Kapur, Bertram et al. 2003; Graedel, Van Beers et al. 2004; Spatari, Bertram et al. 2005;
Sullivan 2005; Alonso, Gregory et al. 2007; Rauch and Graedel 2007). According to the
scientific literature, the copper cycle can be divided into four main phases:
1. Production: refining of cathode copper (99.99% Cu) including all the pre-processes
such as mining / milling of copper ores and smelting of concentrates and scrap
2. Fabrication & manufacturing: copper and alloy fabrication from cathode copper or
new scrap (production waste), component and final product manufacturing
3. Use: consumption of copper and alloy products in different applications and building
up new stock-in-use
4. Waste management: collection and separation of copper scrap (old scrap) for
recycling and final disposal in incineration or landfills (copper losses)
An important characteristic of metals in a material cycle is that they are rather used than
consumed like other commodities in the use phase. Metals are to a large extent separated in
the waste management phase and sent back to production or fabrication processes as new
or old scrap. Hence in order to meet society’s copper demand the primary copper production
can potentially be reduced and resource extraction diminished. However, increasing demand
still leads to growing extraction rates.
Graedel et al. (Graedel, Van Beers et al. 2004) carried out a material flow analysis (MFA) for
the anthropogenic global copper cycle in 1994 and presented “best estimates” (see Figure
5).
In this study, a global “best estimate” for 2005 has been approximated using respective data
from the U.S. Geological Survey (USGS 2005) and from the International Copper Study
Group (ICSG 2007a) (see Figure 6). Due to lack of present data in the use and end-of life
phases, highest uncertainties are expected in the estimations of the data for these stages.
According the USGS-data, in 2005 global mine production amounted to 15’050 kt and refined
cathode copper production to 16’560 kt, whereas 2’020 kt of (secondary) copper were
produced from copper scrap. The copper stock at production has been reduced by 130 kt in
2005 (ICSG 2007a). For a balanced material balance, the losses in production through
tailings and slag minus reworked tailings must be 640 kt. Presumably, the losses into tailings
and slag have been reduced significantly since 1994 – similar to gold production (Mudd
2007) - and are estimated to be approximately 800 kt. In 1994, 5% of the copper
consumption in the fabrication & manufacturing process was new scrap sent back to copper
refinery (Graedel, Van Beers et al. 2004). Assuming improved manufacturing in terms of
resources losses, the share was reduced in this study to 3% in 2005 and the flows for new
and old scarp are determined. The data from Graedel et al. suggests that approximately 30%
19
of old copper scrap can be directly re-melted in fabrication & manufacturing and the other
70% have to be smelted and refined in copper production processes. These shares are used
to determine copper scrap flow in 2005. The overall recycling rate in 1994 was estimated by
Graedel et al. to be 53%. In this study, a slight increase of the overall recycling rate to 55%
for 2005 is assumed and the increase of the stock in-use, the discard flow and waste flow to
landfill was estimated.
Figure 5: Global “best estimate” of the anthropogenic copper cycle for 1994 according to Graedel et al. (Graedel,
Van Beers et al. 2004); units in thousand tons [ kt ]
15‘050
12‘760
130
16‘560
500
160
800
1‘520
3‘950
16‘710
650
2‘170
1‘780
15‘050
+2‘580
Figure 6: Global “best estimate” of the anthropogenic copper cycle for 2005 (own estimates); units in thousand
tons [ kt ]
Accordingly, in this study the contemporary European copper cycle is approximated for the
year 2005 and derived from the MFA carried out by Graedel et al. (Graedel, Van Beers et al.
2004) (see Figure 7 and Figure 8). Additionally to the production and market data from the
U.S. Geological Survey and the International Copper Study Group, trade data of the United
Nations Commodity Trade Statistics Database (UN 2008) has been used to determine import
20
and export volumes of copper ores and concentrate, blister copper, refined copper, semis
and finished products and copper scrap.
Figure 7: European copper cycle in 1994 according to Graedel et al. (Graedel, Van Beers et al. 2004); units in
thousand tons [ kt ]
According the USGS-data, European mine production in 2005 amounted to 940 kt and
refined cathode copper production to 2’560 kt, whereas 670 kt of (secondary) copper were
produced from copper scrap. Changes in the copper stock at production were not considered
in the approximation for 2005 since no data was available. According to the UN-trade
statistics, 770 kt copper from ores and concentrates7, 260 kt blister copper and other
unrefined copper8, 1’990 kt cathode9 copper and 220 kt copper from old scrap10 were
imported into the EU-2511. The export of blister copper and of refined cathode copper
amounted to 20 kt and 200 kt, respectively. No exports were reported for ores and
concentrates and copper scrap for the EU-25. Data on exports of semis and finished
products from the UN-trade statistics are incomplete and thus not applicable for estimating
export volumes. Hence, in this study the estimates base on unchanged export shares
compared to 1994 and is only a first and rough estimate.
7
Concentration of ore grade 1% and concentrate 30% and an import share of 20% and 80% respectively
8
Concentration of blister and other refined copper 98%
9
Concentration of cathode copper 99.9%
10
Concentration of copper scrap 80%
11
Since Norway, Turkey, Bulgaria, Romania and Serbia and Montenegro are not included in the EU-25 data, but
reported in USGS data, the flows have been corrected accordingly for USGS system boundaries
21
For a balanced material balance in the copper production stage, the losses in production
through tailings and slag minus reworked tailings must be 50 kt. In 1994, 5% of the copper
consumption in fabrication & manufacturing process was new scrap sent back to copper
refinery (Graedel, Van Beers et al. 2004). Assuming improved manufacturing in terms of
resources losses, the share was reduced in this study to 3% in 2005 and the flows for new
and old scarp are determined. The overall recycling rate in 1994 was estimated by Graedel
et al. to be 60%. Assuming in this study a slight increase of the overall recycling rate to 62%,
the increase of the stock in-use, the discard flow and waste flow to landfill was estimated for
2005.
- 2‘390
220
760
240
1‘740
80
570
3‘540
940
3‘810
960
2‘560
160
820
1‘230
220
40
730
510
450
10
-940
+ 500
Figure 8: European copper cycle in 2005 (own estimates); units in thousand tons [ kt ]
The refined copper production in Europe increased by 11.8% from 2’290 kt to 2’560 kt and
copper consumption (cathode copper produced in Europe and abroad, copper scrap)
increased by 16.1% from 4’600 kt in 1994 to 5’340 kt in 2005. The import share increased
from 29.6% in 1994 to 32.6% in 2005. The global copper consumption increased by 41% in
the same period.
On global average, approximately 55% of the copper in discarded products is separated and
recycled as old scrap. In Europe the recycling rate is estimated to be on a higher level
(>60%) due to improved collection systems and infrastructure. However, end-of life flows are
rough estimates since only secondary copper production and old scrap imports are
statistically reported. Data on increase of stock-in use, discards quantities and copper waste
volumes are not available.
22
3.3
Availability and Stocks of Copper
Approximately 99% of the mass of Earth’s crust is made up of eight elements: Oxygen
(47%), Silicon (29%), Aluminium (8%), and Iron (4%), followed by Calcium, Sodium,
Magnesium and Potassium. The remaining 1% contains about 90 elements of natural origin.
Recoverable copper deposits are according Ayres et al. (Ayres, Ayres et al. 2002) mostly
(90-95%) in the form of disseminated sulphide minerals, such as chalcopyrite (CuFeS2),
chalcocite (Cu2S), bornite (Cu5FeS4) and enargite (Cu3AsS4). So called oxide ores, which
account for 5%-10% of global output, are weathered products of sulphide minerals, notably
malachite (CuCO3·Cu(OH)2), azurite (2CuCO3·Cu (OH)2), cuprite (Cu2O) and chrysacolla
(CuSiO3·2H2O). The world’s porphyry copper deposits are mainly located in North- and South
America, South-East Asia, Australia, Russia and South-East Europe. The locations are
shown in the following map prepared by Singer et al. (Singer, Berger et al. 2002):
Figure 9: Porphyry copper deposits of the world (Singer, Berger et al. 2002)
Copper is a finite and thus an exhaustible or non-renewable resource. Providing today’s
developed-country level of services for copper worldwide (as well as for zinc and, perhaps,
platinum) would appear to require conversion of essentially all of the ore in the lithosphere to
stock-in-use plus near-complete recycling of the metals from that point forward (Gordon,
Bertram et al. 2006). According to Gordon et al., the fraction of the stock of recoverable
resources in the lithosphere already placed in use or in wastes from which it will be probably
never be recovered is currently approximately 26% for copper.
Economic geologists divide Earth’s mineral stocks into several categories based on their
geology and economics (Gordon, Bertram et al. 2007; Tilton and Lagos 2007; USGS 2008):
ƒ
Resource base: The resource base contains all the copper in the earth’s crust. It
encompasses reserves and resources, as well as a great deal of other copper not
now considered potentially feasible for future exploitation.
23
ƒ
Resources: A concentration of naturally occurring solid, liquid, or gaseous material in
or on earth’s crust in such form and amount that economic extraction of a commodity
from the concentration is currently or potentially feasible. Resources encompass
reserves plus the copper contained in undiscovered deposits that are currently or
potentially economic and the copper in known deposits that are potentially but not
currently economic.
ƒ
Reserve Base: That part of an identified resource that meets specified physical and
chemical criteria related to current mining and production practices, including those
for grade, quality, thickness, and depth. The reserve base includes some
subeconomic resources that have a reasonable potential for becoming economically
available within the planning horizons beyond those that assume proven technology
and current economics.
ƒ
Reserves: That part of the reserve base, which could be economically extracted or
produced at the time of determination. Reserves measure the copper contained in
deposits that are both known and profitable to exploit given the copper price, state of
technology and other conditions currently existing.
The divisions were initially proposed by McKelvey (McKelvey 1972), a version of whose
explanatory diagram is shown in Figure 10. Since minerals in the resource base are not
considered potentially feasible for future exploitation, the diagram only covers reserves,
reserve base and resources.
Figure 10: The ‘‘McKelvey diagram’’ of mineral reserves and resources (McKelvey 1972)
Geologists estimated the size and grade of ore bodies and determine ore reserves.
According to estimates by Erickson and Kapur, the world’s copper resource base ranges
between 1.5x1015 and 9.4x1015 tons (Erickson 1973; Kapur and Graedel 2006). According to
estimates in 2002 by the U.S. Geological Survey, the world’s land-based copper resources
amount to 1’600x106 tons. In 2006, the USGS revised the estimates and increased the value
to 3’700x106 tons. The rationale behind this sudden increase remains unclear. Gordon et al.
(Gordon, Bertram et al. 2007) criticize that the research leading to this new value has not
been published in peer-reviewed literature nor elsewhere. The world’s copper reserve base
is estimated in 2005 to be 940x106 tons and the copper reserves are 470x106 tons.
24
The estimated copper reserves and reserve base in 2005 is given for the most important
extraction countries in the following Table 3. Chile, the United States and Peru hold together
49% of the world’s copper reserve and 53% of the reserve base.
Table 3: Estimated copper reserves and reserve base in 2005 (USGS 2005)
Reserves
6
United States
Australia
Canada
Chile
China
Indonesia
Kazakhstan
Mexico
Peru
Poland
Russia
Zambia
Other countries
World total
Reserve base
[ 10 t Cu ]
[ 106 t Cu ]
35
24
10
160
26
28
14
27
35
31
20
19
50
470
70
43
23
370
63
38
20
40
60
50
30
35
100
940
Data collected by the US Bureau of Mines on the development of the average copper ore
grade in the United States show steadily decreasing values. In 1905, the average ore grade
was 3.6%, in 1945 1.1% and in 1995 only 0.6%. This implies that in extraction more material
has to be processed and consequently the energy consumption increases if unchanged
technology is applied.
The analysis on the copper content in waste streams shows that for most of the metals that
are currently targeted for recycling, the post disassembly concentrations lie above the metals
specific minimum profitable ore grades (Johnson, Harper et al. 2007). This suggests that
material recycling at the end-of-life can be efficient, not only in terms of resource use but also
in terms of economic profitability. Graedel et al compared seven different waste streams in
1994 and found that copper waste generation from waste electronic and electrical equipment
(WEEE) is approximately 2’200 kt and from end of life vehicles (ELV) approximately 800 kt
(Graedel, Van Beers et al. 2004). In 2005, the total copper production amounted to 16’600 kt,
the share of the secondary copper industry with its 2’000 – 3’000 kt being comparatively
small (less than 20%). An analysis on the developments of the secondary copper industry
showed that while available old scrap has increased by over 35% over the past 10 years,
secondary production has actually stagnated on that low level (Gomez, Guzman et al. 2007).
Hence, potentially secondary copper production could be significantly increased through
improved collection systems and waste management processes for WEEE and ELV and
therefore could contribute substantially or overall resource saving.
Since product lifetimes are getting shorter and shorter the material turnover in its life cycles
increases proportionally and copper demand and scrap volume increases. In a statistical
entropy analysis of the European copper cycle (Rechberger and Graedel 2002) the authors
have concluded that the overall system is rather balanced and of non-dissipative nature. It
has been demonstrated that the stock of copper currently in use has the potential for a future
25
secondary reservoir and potentially a near-sustainable copper management can be
maintained. However, high intrinsic value of copper can only ensure that scrap has some
value unless concentration is too low. Ongoing miniaturisation could lead to highly dispersed
applications and therefore collection and recovery might not be feasible anymore. Kraeuchi
et al. and Waeger et al. showed that broad application of so called smart labels bears some
risk of dissipating both toxic and valuable substances, and even of disrupting established
recycling processes (Kraeuchi, Waeger et al. 2005; Waeger, Eugster et al. 2005). Hence,
due to new miniaturized technologies large volumes of copper or other valuable materials
could potentially be dissipated in nature and most probably never be recovered.
In the scientific literature there is a controversial debate on the scarcity of copper. In the
history of economic thought, scarcity always was a crucial concept and there have
traditionally been a number of differentiated classifications of scarcity. In the actual debate
the authors argue from two main perspectives. Economists base their arguments on
opportunity costs and relative scarcity, environmental scientists on global copper deposits
and supply and demand characteristics. Economists consider opportunity costs for
consumption bundles and think in alternatives and thus in a relative scarcity of goods. The
human actor is seen as a rational decision-maker, who makes choices based on his own
preferences over goods (homo economicus). If a certain good is neither substitutable against
others on the production side nor on the preference side, a relative notion of scarcity will not
capture the scarce nature of this good (Baumgatner, Becker et al. 2006). This aspect of
absolute scarcity is not within the scope of economics. Tilton et al. (Tilton and Lagos 2007)
assess long-run trends in the availability of copper by real prices applying the opportunity cost approach and concludes that copper could conceivably become less scarce by the end
of the century. Tilton et al. argue that copper reserves cannot be considered a fixed stock
since estimates on copper reserves are increasing steadily. The estimates of global reserves
and the time to depletion based on mine production are given in the following Table 4.
Table 4: World copper reserves and annual mine production, 1950–2005 (Tilton and Lagos 2007)
Year
Reserves
6
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
Mine production
6
Time to depletion
[ 10 t Cu ]
[ 10 t Cu ]
[ years ]
91
146
154
195
280
408
350
340
326
348
340
470
2.38
2.9
3.94
4.66
5.9
6.74
7.2
7.99
9.2
10
13.2
14.9
38
50
39
42
47
61
49
43
35
35
26
32
According to the data shown in Table 4, the estimates on copper reserves are increasing
steadily. Since these reserve estimates base on regularly surveys in mining companies, the
values reflect also economic constraints and strategic behaviour of the mining industry.
26
Mining companies usually manage reserves for about 30 years (Alonso, Gregory et al. 2007).
Hence, accumulating stocks beyond that level does not seem to provide sufficient discounted
revenues to offset exploration costs and market uncertainties. As a result, the estimated time
to depletion is rather constant between 26 and 50 years (61 years in 1975 due to clear
overestimates of the reserves) and does not reflect resource scarcity.
Environmental scientists consider copper as a finite resource and see an increasing scarcity
in absolute terms. The approach bases on geological findings, fixed stock assumptions and
contemporary market characteristics. In their paper, Gordon et al. (Gordon, Bertram et al.
2007) conclude that the world is likely to experience a growing scarcity of copper over this
century.
3.4
International Value Chain Structure
In 1975, the three main copper primary producing countries were the United States, the
Union of Soviet Socialist Republics (USSR) and Chile. Together they had a cumulative
fraction of 44% of the total global production volume. In 2004, main copper producing
countries were Chile, the United States and Indonesia. Together they had a cumulative
fraction of more than 50%. Currently, Chile is the main global copper producer and is steadily
increasing its global market share. In Figure 11, the geographic distribution of primary
production for various metals is shown. For most of the metals an increased cumulative
fraction of the three main producing countries can be observed from 1975 to 2004, indicating
an increased country specific market concentration.
Figure 11: Geographic distribution of primary production for various metals. Top three producing countries for
each metal 1975 (left) and 2004 (right) (Alonso, Gregory et al. 2007)
In response to low commodity prices and poor returns, there has been an additionally
increasing company specific market concentration in the last couple of years (MMSD 2002).
Main global copper producers in 2004 and their corresponding market shares are shown in
Table 5. Together, the ten largest copper manufacturers produce 54.4% of the global market
volume.
27
Table 5: Production volume per company in 2004 (Wiki 2008)
Nr
1
2
3
4
5
6
7
8
9
10
Company
Country
Codelco
BHP Billiton
Phelps Dodge
Anglo American
Rio Tinto
Grupo México
KGHM Polska Miedź
MMC Norilsk Nickel
Freeport-McMoRan
Kazakhmys
Production
Market Share
[ kt Cu ]
[%]
1'876
1'055
1'054
744
727
717
529
483
415
377
12.80
7.20
7.19
5.08
4.96
4.89
3.61
3.30
2.83
2.57
Chile
Australia
USA
UK
UK
Mexico
Poland
Russia
USA
UK
In Europe, 2’560 kt of cathode copper were produced from ore (940 kt), from scrap (670 kt) ,
from imported ores and concentrate (760 kt) and from imported blister copper (240 kt) in
2005 (see Figure 8). The refined cathode copper consumption in the fabrication &
manufacturing stage was 5’340 kt, whereas 1’740 kt was imported from outside Europe.
304; 5%
138; 2%
94; 4%
93; 4%
75; 3%
281; 5%
99; 4%
834; 35%
340; 6%
273; 11%
1'148; 20%
305; 13%
3'610; 62%
612; 26%
Chile
Indonesia
Peru
Argentina
Brazil
Papua New Guinea
Canda
Other partners
Chile
Russian Federation
Peru
Kazakhstan
Norway
Other partners
Figure 12: Main import partners of the EU-25 for copper ores and concentrates (left diagram) and refined cathode
copper (right diagram) (UN 2008); import values in Million USD and shares in percent
The main trading partners of the EU-25 in 2005 for copper ores and concentrates and
cathode copper are shown in the flowing Figure 12 based on the UN-trade statistics (UN
2008). The total import values in 2005 for ores and concentrates amount to 2’386 Mio USD
and for cathode copper to 5’821 Mio USD. The import value for blister copper was 875 Mio
USD and for copper scrap imports 550 Mio USD. The three most important trading partners
for ores and concentrates are Chile, Indonesia and Peru, together covering 74% of the
import market. The three most important trading partners for cathode copper are Chile,
Russia and Peru, together covering 88% of the imports.
28
3.5
Development and Trends of the Copper Price
At the London Metal Exchange (LME) copper is traded and world prices determined based
on global supply and demand. LME prices are even used as the basis for upstream products
such as ores and concentrates and for downstream products such as semi-fabricated
products or even copper scrap (MMSD 2002). The development of the annual average
nominal copper prices for the years 1988 - 2007 is shown in Figure 13.
Within the past two decades a tremendous increase of the nominal copper price can be
observed. This year, copper has been traded at the London Metals Exchange (LME)
between USD 7’000 and USD 8’700 or €4’500 and €5’600 per ton. Due to a continuously
increasing global copper demand and at the same time uncertainties about copper supply
shortages, the copper prices increased substantially.
8'000
7'000
6'000
[ USD ]
5'000
4'000
3'000
2'000
1'000
0
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Figure 13: Nominal copper price development 1988 – 2007 in USD
Speculative activities at the stock markets may also contribute to the increase of the copper
price. According to Boutellier et al. (Boutellier and Rohner 2007) the developments of the
natural resource prices - namely the dramatic increase in the recent years - cannot be
explained only due to increasing demand. Speculative elements in metal trading play an
important role in this price increase. Concerns on supply shortages can lead to speculation.
For instance, in 1972, mercury was identified as becoming critically scarce. However,
through changes in economic and regulatory conditions, by 2004, mercury had a static
depletion index (reserve base divided by annual mine production) approaching 200 years
(Alonso, Gregory et al. 2007). Legal restrictions in the use of mercury in final products due to
its toxicity lead to a dramatic decrease of production and consequently to a sharp increase of
the static depletion index12 or to a reduced level of scarcity.
12
Static depletion index (Tstat): Years until depletion, assuming constant use (depletion) of the resource.( Tstat =
stock of resource / resource use)
29
The selling prices of virgin materials are known to vary approximately proportionally with their
degree of concentration in the matrix from which they are extracted. This relationship is
known as the metals-specific Sherwood-plot (Sherwood 1959). Within the last century a
continuously decreasing average copper ore grade can be observed and thus, according
Sherwood, the copper price must increase. At the same time cost-decreasing effects like
new discoveries and technology influence the copper price and furthermore the market
characteristics can influence the copper price significantly.
The formal theory for describing and assessing long-term price trends for non-renewable
commodities dates back to Hotelling (Hotelling 1931). The simplest Hotelling model predicts
that, in the absence of extraction cost, the price of an exhaustible-resource commodity will
rise at the rate of the market interest rate. Thus, the commodity price increase should follow
an exponential growth path. However, most of the empirical analyses for testing the Hotelling
model produced negative results, even if cost-increasing effects of depletion and costdecreasing cost effects like new discoveries and technology are considered (Smith 1978;
Slade and Thille 1997; Livernois, Thille et al. 2006; Gaudet 2007; Lin and Wagner 2007). For
example Smith looked at the stability of the coefficients of estimated price-trend relationships
and decided that the data are too volatile to support definitive conclusions on long-run trends
(Smith 1978; Smith 1979). Slade suggest a U-shaped time path for relative prices
considering exogenous technical change and endogenous change in the grade of ores
mined (Slade 1982). In particular, prices and rents do not increase exponentially as predicted
in the Hotelling model; instead they tend to fall.
Natural resources prices of thirteen commodities deflated by consumer price indices and by
manufacturing wages are analyzed by econometric tests for the period 1870 – 1998 by
Brown et al. (Brown and Wolk 2000). The authors found little evidence of increased natural
resource scarcity.
Similar results were presented in the analysis by Svedberg et al. (Svedberg and Tilton 2006).
Additionally, the authors stated that almost all of the studies analysing natural resources
prices are using US consumer price index or other standard deflators which according recent
research overestimate inflations. Svedberg et al. examined copper prices with adjusted
deflators designed to eliminate this bias and concluded that the trend over time, which is
significantly downward when no adjustment is made to the deflator, displays no tendency in
either direction or is significantly upward depending on the magnitude of the deflator
adjustment employed (see Figure 14). These findings suggest that real resource prices
provide less support than widely assumed for the hypothesis that resources are becoming
more available or less scarce over time.
From the above mentioned data and studies there is no empirical evidence found for copper
scarcity as well as no clear trend of future copper price developments can be derived. In the
short-run copper prices might increase further. Nevertheless, because of uncertainties about
the magnitude of influences from speculation on the copper price as well as due historic price
developments showing similar peaks within the last century, the copper price might not
continue to grow at that rapid pace.
30
Figure 14: Index of the US producer price of copper from 1870 to 2000 with 1950 = 100. Copper price deflated by
the CPI and the (a) CPI minus 0.5%, (b) CPI minus 1.0%, and (c) CPI minus 1.5% (Svedberg and
Tilton 2006)
31
32
4 Modelling External Costs in the European Copper Value
Chain
4.1
Framework Scenarios
New environmental regulations or strengthened enforcement and technological
improvements in the copper production processes significantly influence the level of pollution
and thus the level of external costs. Data on emissions in the copper production clearly show
a tendency towards decreasing values, in particular for heavy metals in the dust, sulphur
dioxide (SO2), non-methane volatile organic compounds (NMVOC) and nitrogen oxide (NOx).
According to the environmental report of the German copper smelter Norddeutsche Affinerie
AG in Hamburg, since 1990 the emissions of the heavy metals, lead and arsenic from stack
have been reduced by 90%, and those of SO2 by 60% (NA 2007). The Belgian precious
metals refinery Umicore in Hoboken reported a reduction of guided emissions by 98% for
lead, 85% for arsenic and 73% for SO2 since 1993 (Umicore 2007).
Chemical transformation of these primary pollutants into secondary pollutants like nitrates,
sulphates and ozone is depending on the background concentrations of these substances in
the environment on the one hand, and meteorological parameters on the other hand.
Presumably, the overall emissions in Europe from sources such as industry, transportation
and housing will decrease with time and the background concentrations will be reduced
(CAFE 2005). The background concentrations or background emission scenario has a great
influence on the chemical transformation of primary and secondary pollutants.
In order to consider these temporal dynamics of technological change and background
pollution levels, two framework scenarios are distinguished:
1. Base scenario 2005: The base scenario reflects emissions in the copper value chain
in the year 2005. For the calculations, average emission factors for all copper
smelters / refineries are taken from the ecoinvent database (ecoinvent 2008).13 The
ecoinvent data for copper production is representative for processes applied in the
years around 2000. Contemporary copper production and consumption volumes of
the year 2005 (see Figure 6 and Figure 7) are used for calculating annual emissions
flows. For the background concentration levels in the European atmospheric
dispersion model, current overall emissions are considered. In the calculation of
13
Ecoinvent provide emission data of primary and secondary copper production differentiated by the five regions
Europe, Latin America, North America, Russia / Asia, Indonesia / Australia. Accumulated and unit process
data are available for concentrate production, blister production and cathode production.
33
external costs from global warming a price of 22 Euro per ton CO2 equivalent is
assumed (see chapter 4.5.3).
2. Scenario 2020 – In the scenario 2020, future external costs in the copper value chain
are estimated assuming a pessimistic and an optimistic technology development
path. For both paths a moderate increase of the European copper demand of 10%
from 2005 until 2020 is assumed, based on growth forecasts for copper demand by
Frondel et. al (Frondel, Grösche et al. 2006). The market structure is supposed to
remain unchanged. For the background concentrations of air-pollutants in Europe, the
future emission scenario proposed by the “Clean Air for Europe Project” (CAFE 2005)
with significantly lower emissions is applied. The two development paths are
characterized as follows:
Optimistic 2020: In the ‘Scenario 2020 - Optimistic’, the average emission factors
from ecoinvent are reduced assuming full deployment of current best available
technologies (BAT) in Europe. As a reference for the emission factors of the facilities
in Europe, actual emission data from state-of-the-art plants in Germany, Belgium and
Spain were considered14. For the future emissions of the production facilities outside
Europe, i.e. in Chile, Peru, Russia and Indonesia, the average European emission
factors in 2000 have been applied. In the calculation of external costs from global
warming, a price of 28 Euro per ton CO2 equivalent is assumed (Anthoff 2007).
Pessimistic 2020: In the ‘Scenario 2020 - Pessimistic’ the technological improvements
are smaller than in the optimistic scenario. The average emission factors from
ecoinvent are reduced only by 5% for European facilities and by 10% for facilities
outside Europe, hypothesizing a small decrease of emissions in the entire value chain
due to technological improvements. In the calculation of external costs from global
warming a price of 70 Euro per ton CO2 equivalent is assumed (Maibach, Schreyer et
al. 2007).
In the base scenario and in the scenario 2020 identical meteorological conditions are
applied. The average values of the relevant parameters such as wind speed, wind direction
or temperature profiles of several years is used (1996, 1997, 1998 and 2000).
In the European model a gridded population density map is incorporated. For the global
model no spatial population data is applied, but per country averages are considered.
Temporal developments of the population densities are not considered.
In both frameworks scenarios, the annual and mass specific external costs are calculated for
primary and secondary copper production in Europe and for primary copper production in the
most important countries exporting copper to the European market.
14
Personal communication with Dr. Jan Kegels from Umicore Precious Metals in Hoboken / Belgium and Mr.
Jean-François Equey from Metallo Chimique N.V. in Beerse / Belgium in April 2008
34
4.2
Emissions of Pollutants in the Copper Production
Primary copper production starts with the extraction of copper-bearing ores. There are three
basic ways of copper mining: surface, underground mining and leaching. Surface or open-pit
mining is the predominant mining method in the world. The following picture shows the world
largest open-pit copper mine Chuquicamata located in the north-east of Chile. The mine is
elliptical in form, with a surface of almost 8 km2, and it is 900 m deep.
Figure 15: World largest open-pit copper mine in Chuquicamata / Chile (source: www.panoramio.com)
After mining, the ore is concentrated through grinding, milling and subsequent gravity
separation processes. After this first concentration step, flotation is carried out to remove the
gangue from the sulphidic minerals. For neutralisation lime is added. In the flotation several
organic chemicals are used as collector, frother, activator, depressor and flocculant;
sometimes, cyanide is used as depressant for pyrite (Classen and Althaus 2007). As a result,
copper concentrates containing around 30% Cu are produced.
In the following smelting process, sometimes preceded by a roasting step, copper is
transformed into a “matte” containing 50-70% copper. The molten matte is processed in a
converter resulting in a so-called blister copper of 98.5-99.5% copper content. In the next
step, the blister copper is fire refined in the traditional process route, or, increasingly, remelted and cast into anodes for electro-refining. The output of electro-refining is refined
copper cathodes, assaying over 99.99% of copper. (ICSG 2007a)
Alternatively, in the hydrometallurgical route, copper is extracted from mainly low grade oxide
ores and also some sulphide ores, through leaching (solvent extraction) and electrowinning
(SX-EW process). The output in the hydrometallurgical route is the same as through the
electro-refining route - refined copper cathodes. (ICSG 2007a)
35
Figure 16: Copper smelter and refinery in Skelleftehamn / Sweden, Plant Rönnskär of the company Boliden
Mineral AB (source: Empa, 2003)
Informal small-scale mining activities can play a crucial role in providing sources of income in
poor areas. However, the informal sector is better known for this high environmental impacts
and poor health and safety record. In this study the informal copper mining, smelting and
refining is not considered due to lack of data.
According to the data from the LCA inventory database ecoinvent (ecoinvent 2008) for
primary copper production, the emissions to air contribute more than 98% to the total
environmental impacts generated by emissions into air, soil and water. The absolute level of
environmental impacts in Eco-Indicator ’99 points (EIP)15 varies substantially between
different geographical regions, since the production technologies applied differ from each
other. In Europe, only 0.55 EIP caused by emissions to air are generated in the production of
one kg primary copper, in the region Russia / Asia 6.07 EIP and in South / Middle America
however 4.16 EIP.
The environmental impacts from resource use (copper mining and land-use) account for 1.63
EIP in Europe, 2.29 EIP in Russia / Asia and 2.07 EIP in South / Middle America. The
impacts from resource use significantly contribute to the total impacts, in particular in Europe,
where it amounts to 74.4%.
The accumulated environmental impacts in the primary copper production for the regions
Europe, Russia / Asia and South / Middle America are given in Table 6.
15
Eco-Indicator ’99 is a state of the art impact assessment method for Life Cycle Assessments ( www.pre.nl )
36
Table 6: Accumulated environmental impacts from the production of 1 kg refined primary copper in Eco-Indicator
points (EIP) for different regions
Europe
Emissions to Air
Emissions to Soil
Emissions to Water
Resource Use
Total
Russia / Asia
South / Middle America
[ EIP ]
[%]
[ EIP ]
[%]
[ EIP ]
[%]
0.55
0.00
0.01
1.63
2.20
25.1
0.0
0.5
74.4
100.0
6.07
0.00
0.02
2.29
8.38
72.4
0.0
0.3
27.3
100.0
4.16
0.00
0.02
2.07
6.24
66.7
0.0
0.2
33.1
100.0
A graphical representation of the different levels of environmental impacts in EIP for
producing one kilogram refined primary copper is shown for the regions Indonesia / Australia,
Russia / Asia, Europe, South / Middle America and North America in Figure 17. It can clearly
be seen that impacts in the categories ‘carcinogens’, ‘respiratory effects due to organics’ and
‘ecotoxicity’ are on a very high level in Indonesia / Australia, Russia / Asia and South / Middle
America. In North America the impacts due to ‘carcinogens’ are low, however, impacts from
‘respiratory effects due to inorganics’ are on a high level.
Europe shows the lowest level of environmental impacts compared to the other regions. This
is due to the fact that strict environmental legislations are implemented and enforced. Hence,
emissions of sulphur dioxide, heavy metals and other pollutants are significantly reduced.
The highest share of environmental impacts results from the extraction and consumption of
the non-renewable resources copper and fossil fuel. Since this category represents the
resource consumption as such, it is generated on an equal level in all five regions.
9
8
7
EI'99 Points / kg Cu
6
5
4
3
2
1
0
Indonesia / Australia
Carcinogens
Radiation
Land use
Russia / Asia
Resp. organics
Ozone layer
Minerals
Europe
South / Middle America
Resp. inorganics
Ecotoxicity
Fossil fuels
North America
Climate change
Acidification/ Eutrophication
Figure 17: Accumulated environmental impacts from the production of 1 kg refined primary copper and
contribution of considered impact categories in Eco-Indicator points (EIP) for different regions
In the case of secondary copper production, the impacts from copper resource consumption
are significantly lower, since copper scrap is the main feed for smelting and refining and only
10% of primary blister copper is used. The total environmental impacts of secondary copper
produced in Europe is only 0.46 EIP, compared to 2.20 EIP for refined primary copper
produced in Europe. The impacts from collection of old copper scrap amount to less than
0.01 EIP and can be neglected. No additional impacts are considered from recycling of new
37
scrap or pure old scrap which can directly be melted in the fabrication & manufacturing
stage, since it is assumed that its quality equals the quality of refined cathode copper.
In ecoinvent (ecoinvent 2008) more than 600 emission factors, which relate the released
mass flow of pollutants into the environment to one kilogram copper produced, are available
for different copper production processes and regions (see Figure 18). For the modelling of
air pollution, the following pollutants with their respective emission factor from ecoinvent are
considered: sulfur dioxide, nitrogen oxides, particulates (PM2.5, PM10), carbon dioxide,
methane, heavy metals (Cd, Hg, As, Pb, Cr, Cr-VI, Ni) and organic compounds (NMVOC,
CH2O, dioxins). The mass flows of these pollutants for each facility in the base scenario and
in the scenario 2020 are given in the Annex D.
1'400
140
1'200
120
1'000
100
800
80
PM10
PM2.5
[ g / kg ]
[ g SO2 / kg ]
NOx
600
60
400
40
200
20
0
Indonesia /
Australia
Russia / Asia
Europe
South /
Middle
America
North
America
0
Indonesia /
Australia
Russia / Asia
Europe
South /
Middle
America
North
America
Figure 18: Accumulated emissions of sulphur dioxide, nitrogen oxides and particulates in the production of one
kilogram refined primary copper for different regions [ g / kg ]
Besides the guided emissions from chimneys, all other emissions are diffuse emissions, such
as dust or gas from buildings, or dust that is blown away during shipping, storage or
transportation of raw materials. In state-of-the-art processes the share of diffuse metals
emissions can amount to a substantial portion of the total emissions. The Norddeutsche
Affinerie AG estimates that the share of diffuse emission is higher than 70% of the total
release (NA 2007). Diffuse emissions are not considered in this study since these emissions
are not included in the ecoinvent data.
4.3
4.3.1
Dispersion of Pollutants and Exposure
Modelling of Atmospheric Transport and Chemical Transformation of Air Pollutants
In the software tool EcoSenseWeb the atmospheric transport and chemical transformation of
air pollutants can be simulated for emission sources in Europe. A reference database allows
to efficiently include site specific data like meteorological and topographical data or
background concentration levels based on emission scenarios. Outside Europe no integrated
tool for modelling air pollution and determining external costs is available.
38
The atmospheric pollutants released by copper smelters and refineries in Europe are
transported by wind and diluted by atmospheric turbulence until they are deposited to the
ground by either dry deposition or precipitation. From primary pollutants16, which are directly
emitted to the environment by the facilities, secondary pollutants17 are formed in a series of
complex and non-linearly related chemical reactions. For simulating the atmospheric
transport and chemical transformation processes, a large number of commercial stand-alone
software tools exist. Most of these tools base their algorithms on the Gaussian plume
dispersion model, on Lagrangian trajectory models or Eulerian models.
In EcoSenseWeb these three different air quality models are used to simulate transport
processes and chemical reactions, and finally to predict the increase of concentration levels
for 14 pollutants. The Gaussian plume model is applied to predict local scale concentration
distribution, the Lagrangian wind rose trajectory model and an Eulerian dispersion model are
used to analyze concentration increments on regional and hemispheric scale respectively.
For local scale modelling of primary pollutants the Industrial Source Complex Model18 is
used. At distances of 10-50 km, chemical reactions have little influence on the concentration
of primary pollutants and are therefore neglected in this model approach. Turbulent diffusion
and vertical mixing of primary pollutants are described using the Gaussian plume dispersion
model. The concentration distribution from a continuous release in to the atmosphere is
assumed to have a Gaussian shape:
c ( x, y , z ) =
⎛
⎛ ( z + h) 2 ⎞ ⎤
y 2 ⎞⎟ ⎡ ⎛ ( z − h) 2 ⎞
⎜
⎟
⎜−
⎟
* exp⎜ −
*
exp
−
+
exp
2 ⎟
2 ⎟⎥
⎜
⎜ 2σ 2 ⎟ ⎢⎢ ⎜
u 2πσ yσ z
2
σ
2
σ
y
z
z
⎝
⎠
⎝
⎠⎥⎦
⎝
⎠ ⎣
Q
where: c(x,y,z)
concentration of pollutant a receptor location (x,y,z)
Q
pollutant emission rate (mass per unit time)
u
mean wind speed at release height
σy
standard deviation of lateral concentration distribution at downwind
distance x
σz
standard deviation of vertical concentration distribution at downwind
distance x
h
plume height above terrain
Equation 1: Concentration distribution according to the Gaussian plume model
The pollution emission rate Q is determined for each facility using emission data as
described in chapter 4.2. The EcoSense model calculates concentration values of SO2, NOx
and particulate matter at each 10 x 10 km2 grid cell in the local scale simulation (100 x 100
16
Typical primary pollutants are Particles (PM10, PM2.5), SO2, CO, NO, CO2, NO2, NH3
17
Typical secondary pollutants are SO3, H2SO4, O3, H2O2, HNO3
18
Industrial Source Complex Short-term Model Version 2 (ISCST2) of the U.S. Environmental Protection Agency
39
km2 around the facility). Meteorological site specific data including mean wind speed at
release height u, wind direction, air temperature and diffusion parameters σ are provided by
the reference database implemented in EcoSenseWeb. The plume height above terrain h
has been estimated to amount to 80 m on average, for the terrain height a topographic map
is implemented in the reference database.
With increasing distance from the copper production facilities, i.e. more than 50 km, it can be
assumed that the pollutants have been vertically mixed throughout the height of the
atmospheric mixing layer. However, on a regional scale chemical transformation and
deposition become relevant and can no longer be neglected. In EcoSenseWeb the
Lagrangian wind rose trajectory model is incorporated and used for the regional scale
modelling of primary pollutants and acid deposition. This model considers air parcels that
move with the direction and velocity of the wind, chemical transformation processes of the
sulphur and nitrogen species (see Figure 19), and deposition.
Figure 19: Chemical reactions of the sulphur and nitrogen species included in the regional scale modelling
(Derwent and Nodop 1986)
Meteorological data is provided by the reference database. During simulation, concentration
values of nitrogen species (NH4, NO3, NOx), sulphate and particulate matter, and dry and wet
deposition of nitrogen and sulphur are determined for each 50 x 50 km2 grid cell19 in the
regional scale model (2’500 km2). For the regional scale modelling of ozone formation, the
Lagrangian Ozone model is incorporated; in addition, background concentrations of
pollutants in the environment are considered.
In a hemispheric scale analysis the intercontinental influence of primary and secondary
airborne pollutants on concentrations is estimated in EcoSenseWeb. The analysis is based
on source-receptor relationships at the hemispheric scale determined by Eulerian dispersion
modelling.
19
The EUROGRID coordinate system is used in EcoSenseWeb
40
The calculation results of the delta concentration values for PM2.5, PM10, Ozone and
secondary inorganic aerosols (SIA10) in the base scenario for the facilities Hoboken /
Belgium and Glogow I / Poland are shown in Annex E. The spatially distributed delta
concentration values for PM2.5 of the Glogow I copper smelter in Poland are exemplarily
presented in the following geographical map in Figure 20.
Figure 20: Spatially distributed delta concentration values of PM2.5 for the facility Glogow I / Poland in the base
2
3
scenario; 50 x 50 km grid cells; maximum value of 0.10 µg /m indicated in red cells
4.3.2
Fate Analysis of Water and Soil Emissions
In order to allow for a bottom-up impact assessment of direct and indirect emissions to water
and soil in line with the impact pathway approach, soil and water need to be modelled in a
spatially-resolved way. For indirect emissions from atmospheric dry and wet deposition, a
multimedia modelling approach is needed, which links the different modelling modules. A
model proposed for Europe called integrated water and soil environmental fate, exposure
and impact assessment of noxious substances (WATSON) is currently being developed
within the ExternE project20 and is planned to be incorporated into EcoSenseWeb. The model
aims to predict and assess the indirect exposure to living organisms and finally to humans
through food and drinking water. Hence, different food chains need be considered and
transportation and transformation processes of pollutants in surface water and groundwater
20
ExternE Project: A project co-funded by the European Commission within the sixth framework programme 2004
- 2008 ( www.externe.info )
41
aquifers. Until now, EcoSenseWeb does not allow for assessing water and soil emissions.
However, acidification of agricultural soils and fertilisation effects from nitrogen deposition
from airborne pollutants are considered in the atmospheric modelling.
4.3.3
Exposure
Impacts of pollutants on human health, material and crops can be characterized as the result
of an exposure of receptors to hazardous substances. In EcoSenseWeb, spatial receptor
data are available for population, production of various crop types, agricultural area, building
material and land use in Europe. In EcoSenseLE average country specific receptor data is
used, and for the local scale analysis three different local environments are distinguished:
agglomeration, urban area and rural areas including small towns. For the classification of
local environments of the copper smelters in Chile, Peru, Russia and Indonesia, the open
source software tool Google Earth21 has been used (see Annex B).
4.4
4.4.1
Dose-Response Functions
Dose-Response Functions (DRF)
The dose – response function (DRF) relates the quantity of a pollutant that affects a receptor
(e.g. population) to the physical impact on this receptor (e.g. incremental number of
hospitalisation) (Bickel and Friedrich 2005). For the classical air pollutants (NOx, SO2, O3,
PM), DRFs are typically used as kind of concentration – response functions (CRF). These
relations are a central element of the impact pathway analysis. However, there are large
uncertainties in the behaviour of the DRF in particular for low doses. The functions can be
linear through the origin or linear with threshold characterizing a zero effect as well as nonlinear through the origin (see Figure 21). Non-linear functions can even result in negative
responses for low doses characterizing for instance a fertilizer effect. A fertilizer effect can be
observed for example in the DRFs of NOx and SO2 on crops. The crop yield may increase
and lead to a negative damage.
21
Google Earth: Google Earth combines satellite imagery, maps, terrain and 3D buildings to put the world's
geographic information (earth.google.com )
42
Figure 21: Possible behaviour of dose-response functions at low dose (Bickel and Friedrich 2005)
The CRFs for air pollution are determined by epidemiological studies using statistical
analysis. However, the uncertainties are very large, since for typical concentration levels in
the environment the health impacts are very small and extremely difficult to measure.
Furthermore, the populations are exposed to a mix of different pollutants that tend to be
highly correlated with each other. Thus, separating out the role of each pollutant is difficult
and problematic. For instance, it is not clear to what extent the apparent effects of PM in
reality are a reflection of effects from NO2 or SO2 or vice versa, or whether the presence of
other pollutants affects the toxicity of PM (Bickel and Friedrich 2005). As a consequence of
these uncertainties, and of the weak empirical evidence of human health impacts from
sulphur and nitrogen oxides, the current position of ExternE is to use CRFs for PM2.5, PM10
and O3, but not for SO2 or NOx. The CRFs used in EcoSenseWeb are assumed to be linear
without threshold.
For other pollutants such as heavy metals, organic compounds and greenhouse gases the
external costs are estimated directly using generic monetary values (see chapter 4.5.4). For
these pollutants, no specific CRFs are defined in the EcoSense model.
4.4.2
CRF of Particle Matters (PM2.5, PM10) and Ozone
Empirical studies confirm that, even at current ambient levels, air pollution increases
morbidity (especially respiratory and cardiovascular diseases) and leads to premature
mortality. Fine particles (PM2.5) and ozone are the most important contributors to these
health impacts. The concentration response functions (CRF) for PM2.5, PM10 and Ozone in
the EcoSense model, which describe the physical impacts per person resulting from an
ambient concentration increase, are given in Table 7. The physical impacts are measured in
incremental number of cases, affected number of days and the years of life lost (YOLL).
The mortality due to air pollution has been calculated in the past (in studies before 1996) by
the number of premature deaths. However, since it does not make sense to add the number
of deaths resulting from different contributing causes (e.g. air pollution, smoking, and lack of
exercise), and since the number of deaths alone does not take into account the magnitude of
43
the loss of life per death, this concept is not universally accepted. There has been a growing
recognition that it is more meaningful to look at loss of life expectancy. The impact categories
‘life expectancy reduction’ and ‘increased mortality risk’ are used in the model for estimating
cost due to mortality.
The morbidity due to air pollution is estimated using the relative risk found in epidemiological
studies. Some damages from health impacts are measured by relating the pollution levels
directly with the number of ‘work loss days’ and ‘restricted activity days’. Other concentration
– response functions relate the pollution levels to the observed health effects. The following
health impact categories are considered: ‘new cases of chronic bronchitis’, ‘respiratory
hospital admissions’, ‘cardiac hospital admissions’, ‘bronchodilator use’, ‘lower respiratory
symptoms’ and ‘cough days’.
Table 7: Concentration response functions of PM2.5, PM10 and Ozone and corresponding monetary values per
impact category (Bickel and Friedrich 2005)
Monetary Value
per case, day or YOLL*
3
Physical Impact per Person per µg / m
Category
PM2.5
PM10
Ozone
Life expectancy reduction / chronic (all)
Work loss days (adult)
netto Restricted activity days (mix)
Minor restricted activity days (adult)
Increased mortality risk (all)
New cases of chronic bronchitis (adult)
Respiratory hospital admissions (all)
Cardiac hospital admissions (all)
Medication use / brochodilator use (adult)
Medication use / brochodilator use (child)
Lower respiratory symptoms (adult)
Lower respiratory symptoms (child)
Increased mortality risk / acute (all)
Respiratory hospital admissions (adult)
Medication use / brochodilator use (adult)
LRS** excluding cough (child)
Cough days (child)
Minor restricted activity days (adult)
3
[ 1 / µg/m ]
Unit
[ €2000 ]
6.51E-04
1.39E-02
9.59E-03
3.69E-02
6.84E-08
1.86E-05
7.03E-06
4.34E-06
3.27E-03
4.03E-04
3.24E-02
2.08E-02
2.23E-06
1.98E-06
2.62E-03
1.79E-03
1.04E-02
7.36E-03
YOLL*
days
days
days
cases
cases
cases
cases
cases
cases
days
days
YOLL*
cases
cases
days
days
days
40'000
295
130
38
3'000'000
200'000
2'000
2'000
1
1
38
38
60'000
2'000
1
38
38
38
* YOLL: Years of life lost
** LRS: Lower respiratory symptoms
4.5
4.5.1
Monetary Valuation of Airborne Pollution
Local, Regional and Hemispheric Air Pollution
The monetary valuation of air pollution aims at translating physical impacts into monetary
terms in order to estimate the level of external costs. External costs are determined for the
impact categories health, buildings and crops. The EcoSense model also takes into account
the impact category noise, but within the scope of this study noise has not been considered
due to lack of data. On the one hand, the valuation of health impacts consists in the
derivation of monetary values for mortality or premature deaths, and on the other hand on
the derivation of values for morbidity or health impacts.
44
The cost of mortality is usually evaluated by estimating the value of statistical life (VSL) or
the value of prevented fatality (VPF)22. There are two general approaches mainly used for
valuating a statistical life. A first approach measures the economic productivity and uses an
individual’s lifetime earning as its measure of value. Another approach evaluates the
willingness to pay (WTP) to reduce risk of premature death. Various empirical studies have
been carried out following the WTP approach and applying three different methods: the
compensating wage method, the avertive behaviour method and the contingent valuation
method. In the compensating wage method labour market data on wage are analysed for
different jobs and their corresponding health risks. The additional wage that workers receive
when they take over a risky position reflects their risk choice or WTP. However, there are
many critics on this method since it assumes that workers are fully aware of job risks and the
labour market is strictly competitive. According empirical studies applying the compensating
wage methods, the conservative mean value of VSL is estimated to be 6.5 Mio € (Bickel and
Friedrich 2005). The avertive behaviour method determines the money individuals spend on
safety equipment for reducing their risk of death (e.g. air bag, smoke detectors, etc.).
According to empirical studies applying this method, the average VSL is 1-1.5 Mio € (Bickel
and Friedrich 2005). The contingent valuation method (CVM) is a direct method in that it
involves asking questions about the WTP or willingness to accept (WTA) to a sample of the
relevant population, based on contingent hypothetical scenarios (Perman, Ma et al. 2003).
The applicability of the CVM in the air pollution context appears to be high since the survey
instrument allows relating the WTP question precisely to the nature of the commodity to be
valued – something that is not so easily done in the market based approaches. However,
due to the fact that the number of deaths from air pollution is not observable since it is not an
instantaneous but cumulative result after years of exposure, the ExternE project team
recommended not to use the VSL or VPF, but the value of life years (VOLY) as the principal
metric to value premature death. Using the VOLY allows to valuate the change of life
expectancy in the context of air pollution. Within the scope of the ExternE project a survey in
9 European countries23 was carried out; according to the contingent valuation method, the
VSL was derived and finally a VOLY of €40’000 was estimated (Bickel and Friedrich 2005).
This value is currently used in the EcoSense model.
The cost of morbidity or health impacts comprises three exclusive cost categories: health
care resource costs (i.e. medical costs for health service), opportunity costs (i.e. lost
productivity and leisure time lost) and disutility costs (i.e. other social or economic cost such
as pain, suffering, anxiety, reduced enjoyment, etc.). Country and symptom specific health
care costs for hospitalisation, hospital admissions, emergency room visits and doctor are
determined and incorporated in EcoSenseWeb. Furthermore, monetary values for work loss
22
Value of prevented fatality (VPF) is synonymously used for value of a statistical life (VSL)
23
France, Spain, UK, Denmark, Germany, Switzerland, Czech Republic, Hungary, Poland; sample size: 1’463
45
days and minor restricted activity days are considered. The values for the considered
physical impact categories are given in Table 7.
Air pollutants can have severe impacts on buildings and can cause significant damages. The
effects include loss of mechanical strength, leakage and failure of protective coatings due to
degradation of materials (Bickel and Friedrich 2005). Air pollutants are transferred from the
atmosphere to the material surface either by dry or wet deposition and a number of chemical,
physical and biological processes can cause damages. Sulphur and nitrogen oxides and
particulates are the most important pollutants acting as corrosive agents, whereby sulphur
dioxide (SO2) is the main contributor. Dose-response functions and corresponding monetary
values for SO2 and particulates on different building materials such as stone materials,
galvanized steel, paint coatings and historic and cultural monuments are considered in
EcoSenseWeb.
Effects from SO2 and ozone concentrations and nitrogen deposition are considered in
EcoSenseWeb for the major crops cultivated: sunflower, wheat, potato, rice, rye, oats,
tobacco, barley and sugar beet. Dose-response functions relate the concentration levels and
deposition flux, respectively, to the crop yield change. In the current version of
EcoSenseWeb, the price per ton of crops is considered.
4.5.2
Biodiversity Losses
The biological diversity refers to the variability of biological resources. The Rio 1992
Convention on Biological Diversity defines biodiversity as “the variability among all living
organisms from all sources, including inter alia, terrestrial, marine and other aquatic
ecosystems and ecological complexes of which they are part; this includes diversity within
species (genetic diversity), between species (species diversity) and of ecosystems
(ecosystem or habitat diversity).”24
The external costs of biodiversity losses due to industrial activities can be assessed
according to the EcoSense model in two ways:
ƒ
Biodiversity losses from land-use change due to production and distribution
infrastructure
ƒ
Biodiversity losses from airborne emissions in production and distribution processes
(eutrophication and acidification)
The change of biodiversity from land use change is estimated applying the PDF-concept
(PDF = potentially disappeared fraction of certain organisms). For a specific land use type a
certain number of species is defined. If the type of land use changes from one type to
another, the difference between the PDF-value of the first and of the second state can be
calculated. The monetary evaluation of disappeared biodiversity is based on restoration
24
Rio 1992 Convention on Biological Diversity, Article 2 (www.cbd.int, retrieved 28. April 2008)
46
costs. However, as a consequence of data limitations and applicability restrictions25,
biodiversity losses from land use change are not considered in this study.
Nevertheless, impacts from biodiversity losses from airborne emissions in production are
taken into account. Acidification is mainly caused by emissions of sulphur oxides (SOx),
nitrogen oxides (NOx) and ammonia (NH3), while eutrophication by airborne pollutants is
mainly caused by NOx and NH3. For the quantification of the effects of acidification and
eutrophication by airborne emissions on biodiversity, the Eco-Indicator 99 method
(Goedkoop and Spriensma 2000) is used in the EcoSense model. Restoration costs per kg
deposition of these substances have been determined per country within the ExternE project
and are incorporated in the EcoSenseWeb tool. The results range from 0.12 €/kg in Hungary
to 1.04 €/kg in Norway for SOx depositions, from 0.67 €/kg in Hungary to 5.96 €/kg in Norway
for NOx depositions and from 1.82 €/kg in Hungary to 15.50 €/kg in Norway for NH3
depositions.
4.5.3
Climate Change
Climate change is a far more complicated negative externality than, for example, local or
regional pollution (such as smog) or congestion (such as traffic jams). The emissions of
greenhouse gases such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) or
hydrofluorocarbons affect the Earth’s climate. The impacts are manifold26, truly global and
long term, hence damages and risk patterns are difficult to anticipate. Climate change is an
international problem, but at the same time also an intergenerational issue. According to the
recently published Stern review (Stern 2007), the key features of the greenhouse-gas
externality are the following:
ƒ
it is a global externality, as the damage from emissions is broadly the same
regardless of where they are emitted, but impacts are likely to fall unevenly around
the world;
ƒ
its impacts are not immediately tangible, but are likely to be felt some way into the
future. There are significant differences in the short-run and long-run implications of
greenhouse gas emissions. It is the stock of carbon in the atmosphere that drives the
climate change, rather than the annual flow of emissions. Once released, carbon
dioxide remains in the atmosphere for up to 100 years;
ƒ
there is uncertainty about the scale and timing of the impacts and about the
irreversible damage from emission concentration will occur;
ƒ
the effects are potentially on a massive scale.
25
The model is designed to be only applicable on a European scale
26
Major impacts from climate change include: sea level rise, increase / decrease of energy use, agricultural
impacts, impacts on water supply, health impacts, ecosystems and biodiversity impacts and extreme water
events (Stern 2007)
47
Because of these characteristics of climate change impacts, the EcoSense method
determines the external costs from emissions of greenhouse gases with two approaches. On
the one hand, models are applied to estimate damage costs of climate change impacts, and
on the other hand, the costs are estimated in order to avoid these impacts (avoidance costs).
Estimates of external cost factors in the short term (2010 - 2020) are based on avoidance
costs since the costs can be related with policy goals of the Kyoto protocol. The uncertainty
range for avoidance costs is smaller than for damage costs. This makes the use of
avoidance costs more acceptable from a political and practical point of view. In the long term
(2030 – 2050), the monetary values are based on damage cost estimates. However, these
values are not applied in this study since the framework scenarios only cover the year 2020.
The external costs from climate change are determined by multiplying the total tons of CO2
equivalent of the greenhouse gas emissions with the external cost factor in €/t. The CO2
equivalents for CH4 and N2O are derived by using their specific greenhouse warming
potentials (GWP)27. Emissions of hydrofluorocarbons are not considered in the EcoSense
model, since compared to the other greenhouse gases they contribute significantly less to
the overall impacts.
In 2005 the average price for tradeable CO2 permits was 21.60 €/t, whereas the prices varied
between 15.95 €/t and 29.10 €/t.28 In the Base Scenario 2005, a central monetary value of 22
€ / t CO2,eq is selected.
Table 8: Generic monetary values for the estimation of external costs from emissions of greenhouse gases;
expressed as single values for a central estimate and lower and upper values (Maibach, Schreyer
et al. 2007)
Monetary Value [ €2000 / t CO2,equivalent]
Year
2010
2020
2030
2040
2050
Lower Value
Central Value
Upper Value
7
17
22
22
20
25
40
55
70
85
45
70
100
135
180
Assuming a development of the prices for tradeable CO2 permits according the EcoSense
method (Anthoff 2007), the central value in 2020 is estimated to be 28.15 €/t. 29 In the
Scenario 2020 – Optimistic a monetary value of 28 € / t CO2,eq is chosen. For the Scenario
2020 – Pessimistic, the recommended upper monetary value of 70 € / t CO2,eq of Maibach et
al. has been used (Maibach, Schreyer et al. 2007) (see Table 8).
27
According to the Intergovernmental Panel on Climate Change (IPCC 2001) the greenhouse warming potential
(GWP) for CH4 is 23 and for N2O is 296 (per definition the GWP for CO2 equals 1)
28
Prices published by the European Climate Exchange (ECX) for Certified Emissions Reductions Futures
Contracts between April and December 2005 (www.europeanclimateexchange.com)
29
Recommended value by EcoSenseWeb: 1% trimmed average and 1% discounting according to Anthoff et al.
(Anthoff 2007)
48
4.5.4
Micro-Pollutants
The category micro-pollutants includes substances which are emitted in small amounts.
Nevertheless, physical impacts or damages of such emissions can be very large. In the
EcoSense model the heavy metals cadmium (Cd), arsenic (As), nickel (Ni), lead (Pb),
mercury (Hg), chromium (Cr) and chromium VI (Cr-VI) are considered. Further, emissions of
formaldehyde (CH2O) and dioxins are taken into account.
For these heavy metals an organic compounds a variety of health impacts are reported, such
as cancer, cognitive impairment in adults and children, hearing impairment in children,
reduced IQ of children, effects on nerve conduction and others. There is considerable
overlap between these impacts, and for most of them no monetary values are available. In
EcoSenseWeb, for Pb and Hg only the impact category ‘IQ loss’ is considered, for the other
micro-pollutants only the impact category ‘cancer’ is taken into account. The corresponding
generic monetary values for the emission of one ton of these substances are given in Table
9.
Table 9: Generic monetary values for the estimation of external costs from emissions of certain heavy metals and
organic compounds (Bickel and Friedrich 2005)
Pollutant
Impacts
Monetary Value
Cadmium (Cd)
Arsenic (As)
Nickel (Ni)
Lead (Pb)
Mercury (Hg)
Chomium (Cr)
Chomium VI (Cr-VI)
Formaldehyde (CH2O)
Cancer
Cancer
Cancer
IQ loss
IQ loss
Cancer
Cancer
Cancer
39'000
80'000
4'000
600'000
8'000'000
31'500
240'000
200
Dioxin
Cancer
37'000'000'000
[ €2000 / t ]
4.6
Modelling Results and Comparison
The average specific external costs from air borne pollution in the ‘Base Scenario 2005’ in
Europe is estimated to be €783 per ton in the copper primary production and €310 per ton in
the copper secondary production (see Table 10). In the ‘Scenario 2020 – Optimistic’ the
values decrease slightly to €737 per ton in the copper primary production and to €304 per ton
in the copper secondary production due to improved technology and thus lower emission
levels. The pessimistic assumption of a significantly higher CO2-price per ton of €70 leads to
an increase of the average specific external costs in the ‘Scenario 2020 – Pessimistic’. The
external cost estimates increase to €886 per ton in the copper primary production and to
€407 per ton in the copper secondary production.
49
Table 10: Comparison of specific external costs of primary and secondary cathode copper production and copper
concentrate production in Europe, Chile, Peru, Russia and Indonesia; Average, minimum and
maximum monetary values given in EURO per ton copper normalized to the year 2000
Region /
Countries
Primary Copper
Secondary Copper
Copper Concentrate
Europe
Chile, Peru
Russia
Europe
Chile, Peru
Indonesia
Base Scenario
2005
Scenario 2020
Optimist
Scenario 2020
Pessimist
[ €2000 / t ]
[ €2000 / t ]
[ €2000 / t ]
783
1'245
2'434
310
144
118
(269 / 1'445)
(647 / 1'843)
(1'202 / 3'665)
(110 / 409)
(63 / 225)
(52 / 184)
737
353
353
304
56
56
(243 / 1'369)
(183 / 523)
(183 / 523)
(97 / 412)
(25 / 86)
(25 / 86)
886
1'267
2'399
407
150
124
(346 / 1'582)
(731 / 1'803)
(1'291 / 3'506)
(188 / 523)
(77 / 222)
(65 / 183)
In the ‘Base Scenario 2005’ of the European copper primary production, 87.7% of the
external costs result from the local, regional and hemispheric scale impacts of air pollution
induced by the major pollutants sulphur dioxide (SO2), nitrous oxides (NOx), ozone (O3) and
particulates (PM2.5, PM10) (see Annex F.1). Biodiversity losses, which are assessed in a
separate module, account for 6.3% of the total external costs, and greenhouse gases for
5.1%. Comparably low impacts (0.9%) are generated from the release of minor pollutants
such as heavy metals and organic compounds. In the copper secondary production, external
costs from local, regional and hemispheric scale impacts account for 81.0%, from biodiversity
losses for 5.0%, from greenhouse gases for 12.8% and from minor pollutants for 1.1%. In the
‘Scenario 2020 – Optimistic’ these distributions of impacts remain rather unchanged for
copper primary and secondary production (see Annex F.2). However, in the ‘Scenario 2020 –
Pessimistic’, the share of impacts from greenhouse gases increases up to 13.9% in the
primary production and up to 29.5% in the secondary production (see Annex F3).
Due to a very low population density in Sweden and a favourable location of the assessed
facility at the coastal area, in all analysed scenarios the lowest external costs incur in the
plant Boliden in Skelleftehamn / Sweden. By contrast, the highest external costs are
estimated for the plant Hüttenwerke Kayser of the Norddeutsche Affinerie AG in the highly
populated area of Lunen, Germany. However, one has to consider that these company
specific results only hold under the assumption that all 29 smelting and refinery plants in
Europe release the same quantity of pollutants per unit copper produced. Since in all plants
different production technologies and in particular different flue gas treatment units are
installed, this assumption is questionable. Due to constraints in terms of time and data
availability within the scope of this study, the calculations were done applying
undifferentiated average emission factors from the LCA inventory database ecoinvent
(ecoinvent 2008) for all facilities under assessment.
In Chile and Peru, the average specific external costs30 in the primary copper production is
estimated to be €1’245 per ton, which is 59% higher than the estimated value for Europe in
30
The average specific external costs for the facilities assessed in Chile, Peru, Russia and Indonesia is
determined by calculating the mean value of the upper and lower results obtained by assuming two
distinguished sets of receptor data (‘EU-15 and new members’ and ‘Sweden’)
50
the ‘Base Scenario 2005’. In Russia, the specific external costs are estimated to be €2’434
per ton; they are more than three times higher than in Europe. However, due to very low
population densities in these countries, presumably the external costs are rather at the lower
end of the range given in Table 10 and thus clearly below the average specific external cost
value. Considering the lower obtained modelling results only, the specific external costs in
Chile and Peru are estimated to be even below the European average cost factors (-13%)
and, in Russia the specific external costs are estimated to be just moderately higher (+54%).
Assuming full deployment of present European technological standard for the countries
Chile, Peru and Russia in 2020, a substantial reduction of external costs to €353 per ton
could potentially be achieved. This very large reduction is potentially possible since all three
countries have very low population densities and therefore low exposure of pollutants to
receptors. However, these optimistic assumptions in the ‘Scenario 2020 – Optimistic’ are
presumably too dreamy and difficult to materialize. In the ‘Scenario 2020 – Pessimistic’ the
level of external costs for Chile, Peru and Russia remain on a rather constant level, since
external cost reductions from lower emissions due to technological progress are
compensated through the higher assumed CO2-price in this scenario.
In the ‘Base Scenario 2005’, the average external costs of the copper consumed in Europe is
estimated to be €978 per ton. In this average, primary and secondary copper production in
Europe and the imported copper are considered and weighted according their volumes. For
comparison, the average annual market price in 2005 has been €2’963 per ton.31
Specific external costs are also calculated for the production of copper concentrate, since
relevant imports to the European markets from Chile, Peru and Indonesia are reported. In the
‘Base Scenario 2005’ the external costs of copper concentrate production is estimated to be
€144 per ton in Chile and Peru and €118 per ton in Indonesia, respectively. Potentially, the
external costs can be more than halved in the ‘Scenario 2020 – Optimistic’, or again, external
cost reductions can be compensated by higher assumed CO2-prices in the ‘Scenario 2020 –
Pessimistic’ leading to a rather constant value.
Table 11: Comparison of total external costs of primary and secondary cathode copper production and copper
concentrate production in Europe, Chile, Peru, Russia and Indonesia; Monetary values given in
Million EURO normalized to the year 2000
Region /
Countries
Primary Copper
Secondary Copper
Total
31
Europe
Chile
Peru
Russia
Europe
Share of
Imports
62%
6%
20%
-
Base Scenario
2005
Scenario 2020
Optimist
Scenario 2020
Pessimist
[ Mio €2000 ]
[ Mio €2000 ]
[ Mio €2000 ]
1'480
698 - 1'988
65 - 186
412 - 1'256
208
2'863 - 5'118
1'501
217 - 619
20 - 58
69 - 197
224
2'031 - 2'599
1'804
866 - 2'136
81 - 200
486 - 1'319
300
3'537 - 5'759
Average LME price per ton in 2005: USD 3’683; converted in EUR with an annual average exchange rate of
0.80453 EUR / USD (www. oanda.com)
51
The total external costs from airborne pollution in the ‘Base Scenario 2005’ in Europe are
estimated to be 1’480 Mio € for the production of primary copper and 208 Mio € for the
production of secondary copper (see Table 11). Considering the imported copper to Europe32
according to the shares of the three most important trading partners, the total external costs
are estimated to be 2.9 – 5.1 billion €. For comparison, the total copper sales in Europe in
2005 have been 15.8 billion € with an annual market price of €2’963 per ton.
The total external cost estimates for the ‘Scenario 2020 – Optimistic’ and the ‘Scenario 2020
– Pessimistic’ are also given in Table 11. In the optimistic scenario, the total external costs
can potentially be reduced by 30 - 50% to 2.0 – 2.6 billion € in spite of increasing production
and consumption volumes. In the pessimistic scenario the total external costs are expected
to increase by 13 – 24% to 3.5 – 5.7 billion €.
32
Chile, Peru and Russia account for 88% of the Cathode copper imports to Europe. The other 12% are not
considered in the total external cost estimations.
52
5 Conclusions
The emissions of pollutants from copper production into air, water and soil affect human
health, ecosystems, crops and infrastructure. Accordingly, damages are generated at
receptors and external costs for the society incur from production processes. These incurred
external costs are not compensated through copper sales by the producers and thus are not
reflected by the copper market price.
This study aims to evaluate external costs in the copper value chain and to compare in
particular the primary and the secondary copper production in Europe. Based on a copper
value chain analysis, two distinguished framework scenarios were analysed; a reference
scenario ‘Base Scenario 2005’ and a future scenario for the year 2020. Two technology
development paths until 2020 were considered assuming optimistic and pessimistic
implementation of environmentally sound technologies.
In 2005, the average specific external costs due to air pollution are estimated to be €978 per
ton of the copper consumed in Europe. In order to internalize the external costs incurred in
the copper value chain due to air pollution, the average annual market price for copper in the
year 2005 of €2’963 per ton should therefore have been 33% higher. Since the copper price
is highly volatile and in particular increased tremendously over the past decade, the relative
share of external cost has been decreasing.
The modelling results show clearly lower specific external cost for the recycling or secondary
copper production compared to the primary production. The energy intensive smelting
processes for the concentrate and in particular for the blister copper production, contribute
substantially to the higher external costs of primary copper. In the analyzed scenarios, the
external costs of primary copper are by a factor 2.2 – 2.5 higher than for secondary copper.
The specific external costs for primary copper production differ also significantly between
regions, namely for Europe, Chile, Peru and Russia. In Europe, the lowest external costs
incurred in 2005 due to more advanced flue gas treatment systems and therefore
significantly lower emissions. In Chile and Peru, the external costs are estimated to be by a
factor 1.6 higher than in Europe. Because of poor implementation of environmentally sound
technologies in Russia, the external costs are estimated to be by a factor 3.1 higher than in
Europe.
The evaluation of distinguished technology development paths showed that potentially the
external costs can be reduced substantially by implementing consequently environmentally
sound technologies, in particular in the European trading partner countries. However, the
scenario analyses also indicated that increasing demand could compensate technological
improvements and overall external cost could remain on a constant level or could even
increase. The total external costs due to air pollution of copper production for the European
market are estimated in the ‘Base Scenario 2005’ to be 2.9 – 5.1 billion €, whereby the total
copper sales have been 15.8 billion €. Assuming an optimistic development path, the total
53
external cost would be in 2020 lower and is estimated to be 2.0 – 2.6 billion €, under
pessimistic assumptions the total external costs are expected to increase to 3.5 – 5.7 billion
€.
The modelling approach chosen in this study is based on the broadly acknowledged impact
pathway analysis, which is the preferred approach for estimating external costs of air
pollution. However, for the interpretation of the results several methodological limitations and
constraints have to be considered:
ƒ
The representation of the real European copper supply chain in the model for
estimating external costs is difficult due to limited data availability and high
complexity. The supply chain is characterized through different processing steps
distributed globally and among these various types of intermediate copper products
such as concentrates, blister, anodes, cathodes and copper scrap is exchanged.
ƒ
To estimate emission flows of the European copper smelters / refineries and of the
facilities in the main copper trading partners of Europe, regional accumulated
emission factors have been used in this study. These emission factors include all
emissions generated in the copper production chain and not only the emissions of the
assessed facility. The emission flows allocated to the individual facilities are therefore
systematically overestimated. Further, it has to be considered that company specific
emission levels can differ substantially from average values and can change over
time.
ƒ
External costs are estimated on the one hand based on dose-response functions and
on the other hand based on generic monetary values. The applied functions and
generic monetary values were determined in several empirical studies and
considered in the external cost model. The monetary valuation of physical impacts
from air pollution considers most of the relevant cost categories. However, there are
uncertainties in the determination of external cost factors because of partly crucial
and cost-relevant assumptions such as the value of statistical life.
Nevertheless, in spite of these uncertainties and constraints, in this study the current and
future levels of external costs in the copper primary and secondary production are
estimated, since to date no comparable estimates have been carried out. The modelling
results give a hint on the absolute and relative magnitude of external costs in the
European copper value chain differentiated by major production steps and geographical
regions. Since specific external costs differ significantly between regions as well as
primary copper production generates clearly higher impacts than secondary production; a
differentiated allocation procedure for cost internalisation would be essential, and
presumably highly challenging.
54
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57
58
ANNEX
59
60
Annex A
Copper production networks Europe and Indonesia
61
1) Production network for refined primary copper produced in Europe
In the following two figures the copper production network is shown for Europe and Indonesia
with the most relevant processes (cut-off 0.5%) for environmental pollution according the
LCA inventory database ecoinvent (ecoinvent 2008). The environmental pollution of
producing one kg refined copper is assessed for each process using the Eco-Indicator ’99
method (Hierarchist perspective) V2.04 (Goedkoop and Spriensma 2000) and is given in
accumulated contribution to the total impacts in percent. Accordingly, in Europe 86.7% of the
generated environmental impacts occur in the production of copper concentrates and in
Indonesia 28.8%.
62
2) Production network for refined primary copper produced in Indonesia
63
64
Annex B
World Copper Production Data 2001 – 2005
65
1) World Mine Production
Data source: (USGS 2005) / Unit: metric tons
Country
Argentina
Armenia
Australia:
Concentrates
Leaching, electrowon
Total
Bolivia
Botswanae
Brazil
Bulgaria
Burma, leaching, electrowon
Canada, concentrates
Chile:4
Concentrates
Leaching, electrowon
Total
China:e
Concentrates
Leaching, electrowon
Total
Colombia
Congo (Kinshasa):e, 5
Concentrates
Leaching, electrowon
Total
Cubae
Cyprus, leaching, electrowon
Ecuadore
Finland
Georgiae
India
Indonesia5
Iran:e
Concentrates
Leaching, electrowon
Total
Japan
Kazakhstane
Korea, Northe
Laos
Macedoniae
Mexico:
Concentrates
Leaching, electrowon
Total
Mongolia
Morocco
Namibia
Pakistan
Papua New Guinea
Peru:
Concentrates
Leaching, electrowon
Total
See footnotes at end of table.
2001
191'667
16'460
2002
204'027
16'641
769'000
102'000
871'000
18
19'200
32'734
88'000
25'800
633'531
787'000
96'000
883'000
3
21'600
32'711
92'800
27'500
603'498
e
r
r
e
e
r
2003
199'020
18'000
763'000
67'000
830'000
182
27'400
26'275
91'700
27'870
557'082
e
r
r
2004
177'143
17'700
795'800
58'300
854'100
596
22'500
103'153
93'000
31'756
566'491
e
r
r, 3
r
r
3'200'800
1'538'200
4'739'000
2'979'000
1'602'000
4'581'000
3'251'100
1'653'100
4'904'200
3'776'200
1'636'300
5'412'500
587'000
18'000
605'000
2'192
568'000
25'000
593'000
1'853
610'000
10'000
620'000
1'578
742'000
10'000
752'000
1'701
r
31'800
41'500
73'300
-1'240
100
15'500
12'000
29'500
840'318
r
150'000
12'000
162'000
-461'000
12'000
---
r
37'800
-37'800
1'000
5'176
100
13'715
8'000
32'400
1'081'040
121'000
12'000
133'000
744
470'100
12'000
-9'000
310'623
60'500
371'123
133'503
5'400
12'393
-218'000
590'896
131'409
722'305
r
r
3
r
r
e
r
r
27'500
6'500
34'000
1'000
3'695
100
14'400
10'000
31'500
1'171'726
121'000
12'000
133'000
-490'000
12'000
-5'600
260'574
69'300
329'874
131'705
5'000
18'012
-211'311
686'748
156'467
843'215
r
r
r
r
r
r
r
r
r
30'300
29'500
59'800
-2'552
100
14'900
12'000
28'500
1'005'831
130'000
12'000
142'000
-485'000
12'000
-700
284'653
71'000
355'653
131'600
4'900
16'175
3'200
190'200
660'025
171'198
831'223
r
r
r
r
r
r
r
r
333'540
72'000
405'540
132'000
4'400
11'174
15'000
173'400
868'574
167'000
1'035'574
2005e
188'000
16'400
876'000
51'000
927'000
714
26'100
131'000
97'000
34'478
566'500
3'735'900
1'584'600
5'320'500
r
r
r
r
r
r
r
3
r
r
r
3
3
3
3
p
3
p
p, 3
p, 3
p, 3
740'000
15'000
755'000
1'700
49'500
56'500
106'000
---15'000
12'000
26'900
1'065'000
3
3
3
3
185'000
12'000
197'000
-402'000
12'000
30'500
22'000
350'000
75'000
425'000
126'547
4'400
10'900
17'700
193'000
844'368
165'530
1'009'898
3
3
3
3
3
3
66
Country
Philippines
Poland
Portugal
Romania6
Russiae
Saudi Arabiae
Serbia and Montenegroe
South Africa
Spain
Sweden
Tanzania, in concentrates and bullion
Turkey6
United States:5
Concentrates
Leaching, electrowon
Total
Uzbekistane
Zambia:
Concentrates
Leaching, electrowon
Total
Zimbabwe, concentrates
Grand total
Of which:
Concentrates
Leaching, electrowon
2001
20'322
474'000
82'900
19'185
600'000
800
31'000
141'865
9'700
74'269
2'645
56'864
2002
18'364
502'800
77'000
18'962
695'000
800
36'900
129'589
-76'200
4'191
48'253
714'000
624'000
1'340'000
78'000
601'000
542'000
1'140'000
80'000
525'000
591'000
1'120'000
80'000
576'000
584'000
1'160'000
95'000
251'100
78'900
330'000
2'502
13'700'000
269'000
80'000
349'000
2'767
13'700'000
344'300
82'600
426'900
2'383
14'700'000
11'000'000
2'620'000
11'000'000
2'720'000
12'000'000
2'700'000
233'000
79'000
312'000
2'057
13'700'000
11'100'000
2'600'000
7
r
r
2003
20'400
495'000
78'000
23'389
675'000
800
26'400
120'800
-96'000
3'715
58'000
r
r
r
r
e
2004
15'984
531'000
96'000
18'767
675'000
500
13'000
120'577
-90'600
4'133
49'000
r
r
r
r
r
r
e
r
r
r
2005e
16'323
523'000
89'500
15'000
700'000
700
25'000
103'907
4'900
97'800
4'200
48'000
586'000
554'000
1'140'000
100'000
3
p
3
3
3
3
3
330'000
106'000
436'000
2'700
15'100'000
12'400'000
2'660'000
e
Estimated. pPreliminary. rRevised. -- Zero.
World totals, U.S. data, and estimated data are rounded to no more than three significant digits; may not add to totals shown.
2
Table represent copper content by analysis of concentrates produced (includes cement copper, if applicable), except where otherwise
noted. Table includes data available through July 22, 2006.
3
Reported figure.
4
Reported by Comision Chilena del Cobre. Includes recoverable copper content of nonduplicative mine and metal products produced
from domestic ores and concentrates and leach production for electrowinning.
5
Recoverable content.
6
Excludes copper content of pyrite.
7
Data are for fiscal years beginning April 1 of year stated.
1
67
2) World Smelter Production
Data source: (USGS 2005) / Unit: metric tons
Country
Armenia, primarye
Australia, primary
Austria, secondary
Belgium, secondary
Bolivia, primary
Botswana, primary4
Brazil, primary
Bulgaria:
Primary
Secondarye
Total
Canada:
Primary
Secondary
Total
Chile, primary
China:e
Primary
Secondary
Total
Congo (Kinshasa), primary, electrowon
Finland:
Primary
Secondarye
Total
Germany:
Primary
Secondary
Total
India:
Primary
Secondarye
Total
Indonesia, undifferentiated
Iran, undifferentiated5
Japan:
Primary
Secondary
Total
Kazakhstan, undifferentiated
Korea, North, primary and secondarye
Korea, Republic of:
Primary
Secondary
Total
Mexico:
Primary
Secondarye
Total
Namibia, primary6, 7
Oman, primarye
Peru, primary
Philippines, primary
See footnotes at end of table.
2001
4'000
455'000
68'642
138'200
-19'209
212'243
157'000
5'000
162'000
2002
6'700
458'000
64'932
125'900
-21'590
189'651
r
r
181'000
15'000
196'000
e
r
r
2003
7'500
435'000
75'000
117'500
-25'292
173'378
215'300
16'000
231'300
r
r
r
r
2004
7'500
443'000
88'000
107'000
-21'195
208'020
227'100
7'000
234'100
r
r
r
r
r
r
601'359
41'640
642'999
1'503'200
513'934
24'761
538'695
1'438'700
430'116
26'789
456'905
1'542'400
446'221
29'962
476'183
1'517'600
1'120'000
190'000
1'310'000
25'000
1'180'000
310'000
1'490'000
10'000
1'380'000
350'000
1'730'000
8'000
1'500'000
440'000
1'940'000
20'000
r
168'600
2'000
170'600
r
r
169'300
2'000
171'300
r
160'900
2'000
162'900
317'700
240'900
558'600
r
293'000
-293'000
217'500
181'526
e
r
r
e
r
1'328'489
139'764
1'468'253
433'600
15'000
295'100
283'100
578'200
385'400
-385'400
211'200
171'591
176'400
2'000
178'400
r
r
r
r
1'317'291
182'069
1'499'360
446'200
15'000
386'200
42'300
428'500
r
305'000
5'000
310'000
27'015
24'200
396'400
165'000
r
r
r
3
r
r
r
288'800
306'600
595'400
278'600
262'600
541'200
391'000
-391'000
247'400
168'613
401'000
-401'000
211'600
184'814
r
1'343'353
172'724
1'516'077
431'930
15'000
380'000
50'000
430'000
r
243'000
5'000
248'000
26'703
25'000
379'600
165'800
r
r
r
r
r
r
r
r
r
r
1'270'495
194'927
1'465'422
445'200
15'000
410'000
50'000
460'000
r
238'000
5'000
243'000
26'036
18'000
396'100
227'900
r
r
r
r
r
380'000
50'000
430'000
r
274'000
5'000
279'000
24'704
25'000
377'800
217'300
r
r
r
r
r
r
r
2005e
9'800
410'000
90'000
99'200
-26'700
210'000
3
3
3
p
240'100
5'000
245'000
3
450'000
30'000
480'000
1'558'100
p
p
3
p, 3
1'700'000
540'000
2'240'000
10'000
170'000
2'000
172'000
257'200
251'400
508'600
3
486'600
38'000
524'600
275'000
185'000
3
1'319'247
198'516
1'517'763
425'000
15'000
3
426'000
50'000
476'000
290'000
5'000
295'000
23'300
25'000
381'600
201'300
3
3
3
3
3
3
3
3
68
Country
Poland:
Primary
Secondarye
Total
Romania:
Primary
Secondarye
Total
Russia:e
Primary
Secondary
Total
Serbia and Montenegro:e
Primary
Secondary
Total
South Africa, primary
Spain:
Primary
Secondarye
Total
Sweden:e
Primary
Secondary
Total
Turkey, undifferentiated8
United States, undifferentiated
Uzbekistan, undifferentiatede
Zambia, primary:
Electrowon
Other
Total
Zimbabwe, primarye, 6
Grand total
Of which:
Primary:
Electrowon
Other
Secondary
Undifferentiated9
2001
2002
485'900
27'900
513'800
511'000
39'400
550'400
9'279
2'000
11'279
2003
560'000
24'100
584'100
r
8'871
2'000
10'871
4'493
500
4'993
r
650'000
245'000
895'000
660'000
200'000
860'000
670'000
170'000
840'000
24'000
14'000
38'000
117'237
36'000
6'700
42'700
116'996
255'200
24'700
279'900
r
r
r
r
r
14'000
3'600
17'600
112'025
r
r
r
545'000
25'000
570'000
r
61
-61
r
662'000
257'000
919'000
r
r
r
12'000
1'100
13'100
89'300
281'300
16'700
298'000
276'300
14'000
290'300
210'200
14'100
224'300
173'000
35'000
208'000
33'504
919'000
90'000
188'000
35'000
223'000
32'550
683'000
75'000
185'000
30'000
215'000
30'400
539'000
75'000
206'000
30'000
236'000
11'500
542'000
105'000
50'000
306'000
356'000
2'160
12'700'000
60'000
311'400
371'400
-12'500'000
50'000
200'000
250'000
-12'700'000
75'000
9'510'000
1'220'000
1'890'000
3
r
r
r
r
r
70'000
9'480'000
1'360'000
1'630'000
r
r
r
r
r
r
58'000
9'750'000
1'360'000
1'510'000
2005e
2004
e
r
r
r
r
r
r
60'000
220'000
280'000
-12'800'000
80'000
9'730'000
1'510'000
1'520'000
r
r
550'000
25'000
575'000
100
-100
r
3
r
r
r
e
686'000
272'000
958'000
16'300
6'000
22'300
100'000
278'600
5'000
283'600
r
r
r
r
r
r
r
r
r
192'000
30'000
222'000
9'000
523'000
115'000
3
3
50'000
220'000
270'000
-13'500'000
60'000
10'200'000
1'650'000
1'550'000
e
Estimated. pPreliminary. rRevised. -- Zero.
World totals, U.S. data, and estimated data are rounded to no more than three significant digits; may not add to totals shown.
2
This table includes total production of smelted copper metal, including low-grade cathode produced by electrowinning methods. The smelter feed maybe derived
from ore, concentrates, copper precipitate or matte (primary), and/or scrap (secondary). To the extent possible, primary and secondary output of each country
is shown separately. In some cases, total smelter production is officially reported, but the distribution between primary and secondary has been estimated. Table
includes data available through July 15, 2006.
3
Reported figure.
4
Copper content of nickel-copper matte exported to Norway for refining.
5
Data are for year beginning March 21 of that stated. Secondary production is estimated to be about 5% of total.
6
Includes impure cathodes produced by electrowinning in nickel processing.
7
Includes 8,000 to 10,000 metric tons per year for 2001-05 produced from imported toll concentrates.
8
Secondary production is estimated to be about one-third of total.
1
69
3) World Refinery Production
Data source: (USGS 2005) / Unit: metric tons
Country
Argentina, secondarye
Australia, primary:
Electrowon
Other
Total
Austria, secondarye
Belgium:e
Primary4
Secondary
Total
Brazil, primary
Bulgaria:e
Primary
Secondary
Total
Burma, electrowon
Canada:
Primary
Secondary
Total
Chile, primary
Electrowon
Other
Total
China, primarye
Primary
Electrowon
Other
Secondary
Total
Cyprus, electrowon
Egypt, secondarye
Finland:e
Primary
Secondary
Total
Germany:
Primary
Secondary
Total
Hungary, secondarye
India:e
Primary, electrolytic
Secondary
Total
Indonesia, primary
Iran, primary5
Electrowone
Other6
Total
Italy, secondary
See footnotes at end of table.
2001
16'000
102'000
456'000
558'000
69'000
2002
16'000
e
e
e
236'000
187'000
423'000
212'243
29'400
5'000
34'400
25'800
3
524'900
42'800
567'700
r
r
96'000
449'000
545'000
65'000
e
e
e
2003
16'000
2004
16'000
2005
16'000
67'400
416'600
484'000
65'100
58'300
431'800
490'100
59'000
50'900
420'200
471'100
52'000
207'000
216'000
423'000
189'651
208'000
215'000
423'000
173'378
38'000
3'000
41'000
27'500
43'000
2'000
45'000
27'900
469'760
24'761
494'521
r
r
428'077
26'789
454'866
r
r
e
r
r
223'000
174'000
397'000
208'020
495'867
31'100
526'967
1'602'000
1'248'100
2'850'100
1'653'100
1'248'800
2'901'900
1'636'300
1'200'400
2'836'700
18'000
1'200'000
300'000
1'518'000
5'176
4'000
20'000
1'280'000
350'000
1'650'000
3'695
4'000
10'000
1'420'000
430'000
1'860'000
2'552
4'000
10'000
1'580'000
620'000
2'210'000
1'240
4'000
112'000
15'000
127'000
120'000
15'000
135'000
105'000
15'000
120'000
303'000
390'773
693'773
10'000
r
310'000
18'000
328'000
212'500
3
12'000
140'000
152'000
35'500
r
r
r
r
327'000
368'791
695'791
10'000
r
r
r
354'000
20'000
374'000
192'400
12'000
131'000
143'000
32'400
r
r
r
52'300
3'000
55'300
31'800
1'538'200
1'344'000
2'882'200
3
r, 3
60'500
3'000
63'500
32'000
r
r
r
r
r
r
r
117'000
16'000
133'000
r
283'686
368'956
652'642
10'000
r
375'000
19'000
394'000
223'300
399'000
20'000
419'000
210'500
r
12'000
134'632
146'632
26'700
12'000
140'000
152'000
34'000
286'653
310'925
597'578
10'000
r
r
r
r
r
e
252'900
130'000
382'900
210'000
r
r
r
r
r
r
r
3
3
p
e
483'500
31'800
515'300
1'584'600
1'239'400
2'824'000
15'000
1'850'000
750'000
2'615'000
-4'000
p
p
p
3
118'000
16'000
134'000
293'800
344'400
638'200
10'000
497'000
20'000
517'000
262'900
3
12'000
163'100
175'100
32'200
70
Country
Japan:
Primary
Secondary
Total
Kazakhstan, primary
Korea, North, primarye
Korea, Republic of, undifferentiated
Laos, electrowon
Mexico, primary:
Electrowon
Other
Secondary
Totale
Mongolia, electrowon
Norway, primary6
Oman, primarye
Peru, primary:
Electrowon
Other
Total
Philippines, primary
Poland:
Primary
Secondary
Total
Romania:
Primary
Secondarye
Total
Russia:
Primary
Secondary
Total
Serbia and Montenegro:
Primary
Secondarye
Total
South Africa, primary6
Spain:
Primary
Secondarye
Total
Sweden:e
Primary
Secondary
Total
Taiwan, secondarye
Thailand, primary
Turkey:e
Primary
Secondary
Total
Ukraine, secondary
See footnotes at end of table.
2001
2002
2003
2004
2005
1'287'165
138'526
1'425'691
425'700
15'000
473'252
--
1'211'111
189'968
1'401'079
453'000
15'000
499'116
--
1'251'728
178'637
1'430'365
432'901
15'000
509'970
--
1'188'491
191'653
1'380'144
445'200
15'000
495'952
--
1'227'528
167'756
1'395'284
418'833
15'000
526'566
30'500
r
r
60'500
332'500
15'000
408'000
1'476
26'700
24'000
r
131'409
342'502
473'911
164'530
r
r
r
r
r
69'300
318'700
35'000
423'000
1'500
30'500
24'000
r
156'467
346'282
502'749
144'315
r
r
r
e
71'000
249'000
35'000
355'000
1'341
35'900
17'000
r
r
r
r
r
72'000
321'000
35'000
428'000
2'376
35'600
24'000
r
r
e
75'000
325'000
35'000
435'000
2'475
38'500
24'000
r
r
r
r
e
e
.
r
r
r
r
171'198
345'848
517'046
171'200
498'451
30'286
528'737
508'674
19'146
527'820
513'600
16'000
529'600
18'500
4'000
22'500
11'453
2'000
13'453
16'739
2'000
18'739
650'000
244'500
894'500
670'000
200'000
870'000
32'365
17'000
49'365
132'000
235'100
55'600
290'700
179'000
25'000
204'000
4'000
-54'400
4'000
58'400
--
r
r
r
3
3
3
35'897
17'000
52'897
119'970
272'000
37'000
309'000
e
e
e
r
r
r
e
e
3
r
r
r
670'000
170'000
840'000
e
14'000
8'000
22'000
111'400
r
259'000
35'000
294'000
e
e
e
r
r
r
e
167'000
338'308
505'308
175'000
531'100
21'000
552'100
165'530
344'862
510'392
172'000
r
r
r
540'300
20'000
560'300
24'383
2'000
26'383
30'000
2'000
32'000
662'000
257'000
919'000
664'000
269'000
933'000
12'000
7'000
19'000
91'495
r
r
r
r
23'000
7'000
30'000
97'000
193'200
35'000
228'200
242'700
26'300
269'000
200'000
22'000
222'000
4'000
26'100
199'000
25'000
224'000
4'000
--
189'000
25'000
214'000
4'000
--
210'000
25'000
235'000
4'000
27'200
39'000
2'000
41'000
10
40'000
5'000
45'000
20
45'000
5'000
50'000
20
r
3
90'000
5'000
95'000
20
71
Country
United States:
Primary:
Electrowon
Other
Secondary
Total
Uzbekistan:e
Primary
Secondary
Total
Zambia, primary:
Electrowon7
Other
Total
Zimbabwe, primary
Grand total
Of which:
Primary:
Electrowon
Other
Secondary
2001
2002
628'000
1'000'000
172'000
1'800'000
601'000
841'000
69'900
1'510'000
80'000
10'000
90'000
79'000
217'500
296'500
5'300
15'700'000
2'600'000
11'300'000
1'810'000
e
r
r
2003
2004
2005
591'000
662'000
53'300
1'310'000
584'000
671'000
50'800
1'310'000
554'000
654'000
47'200
1'260'000
75'000
-75'000
75'000
-75'000
105'000
-105'000
r
83'700
253'100
336'800
2'502
15'500'000
109'000
240'800
349'800
2'767
15'300'000
124'000
286'000
410'000
2'383
15'900'000
r
2'670'000
11'100'000
1'730'000
r
r
r
r
2'720'000
10'900'000
1'670'000
r
r
r
2'700'000
11'200'000
2'000'000
r
r
r
r
r
115'000
-115'000
150'000
244'000
394'000
2'400
16'600'000
e
2'670'000
11'900'000
2'010'000
e
e
e
e
e
e
e
e
Estimated. pPreliminary. rRevised. -- Zero.
World totals, U.S. data, and estimated data are rounded to no more than three significant digits; may not add to totals shown.
2
This table includes total production of refined copper whether produced by pyrometallurgical or electrolytic refining methods and whether derived from
primary unrefined copper or from scrap. Copper cathode derived from electrowinning processing is also included. Table includes data available through
July 22, 2006.
3
Reported figure.
4
Includes reprocessed leach cathode from Congo (Kinshasa).
5
Data are for Iranian years beginning March 21 of that stated.
6
May include secondary.
7
Electrowon covers only high-grade electrowon cathodes reported as "finished production leach cathodes."
1
72
Annex C
Characterisation of the Local Environment
73
Local impacts are determined by the population density close to the emission source, which
is high in urban areas and lower in extra-urban areas. The tool EcoSenseLE V1.3, a
parameterised version of the EcoSenseWeb model, is not considering the spatial distribution
of the population density. However, the following three different local environments are
distinguished in order to determine the impacts of PM10 emissions (see Figure 22 and
Figure 23):
ƒ
Agglomeration: agglomeration, e.g. Paris, London, Athens, etc.
ƒ
Urban area: large cities with one to three Million inhabitants
ƒ
Rural area: other locations surrounded by rural areas including small towns
Figure 22: Local environment of an “Agglomeration”; Zurich / Switzerland, Position 47°22’26.20’’ N 8°33’24.28’’ E,
Picture taken at altitude 12.68 km (Source: Google Earth 2007)
Figure 23: Local environment of an “Urban Area”; Sredneuralsk / Russian Federation (left), Position 56°59’18.14’’
N 60°28’46.89’’ E, Picture taken at altitude 11.65 km and local environment of a “Rural Area”;
Chuquicamata / Chile (right), Position 22°18’20.06’’ S 68°54’30.02’’ W, Picture taken at altitude
11.71 km (Source: Google Earth 2007)
74
Annex D
Air Emission Data
75
Annex D.1
Air Emission in the Base Scenario 2005
Europe: Smelting and Refining of Primary Copper
Country
Austria
Austria
Belgium
Belgium
Belgium
Bulgaria
Bulgaria
Bulgaria
Finland
Finland
Germany
Germany
Germany
Germany
Hungary
Hungary
Italy
Italy
Poland
Poland
Poland
Poland
Poland
Romania
Romania
Romania
Romania
Romania
Serbia and Montenegro
Serbia and Montenegro
Spain
Spain
Spain
Sweden
Sweden
Turkey
Turkey
Location Cu Smelter (Name)
Brixlegg
Primary Cu
t /a
SO2
t/a
NOx
t/a
PM10
t/a
PM2.5
t/a
NH3
t/a
NMVOC
t/a
Cd
kg / a
Hg
kg / a
As
kg / a
Pb
kg / a
Cr
kg / a
Cr-VI
kg / a
Ni
kg / a
CH2O
kg / a
Dioxin
kg / a
CO2
t/a
CH4
t/a
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Beerse
Hoboken
190'000
63'000
7'908.6
2'622.3
3'714.5
1'231.7
4'546.9
1'507.7
2'435.4
807.5
580.0
192.3
670.1
222.2
1'290.9
428.0
49.8
16.5
6'311.6
2'092.8
29'116.7
9'654.5
8'084.8
2'680.7
190.6
63.2
11'712.6
3'883.6
937.3
310.8
0.6
0.2
339'294.4
112'502.9
578.4
191.8
Eliseina
Pirdop
0
61'000
0.0
2'539.1
0.0
1'192.6
0.0
1'459.8
0.0
781.9
0.0
186.2
0.0
215.1
0.0
414.4
0.0
16.0
0.0
2'026.4
0.0
9'348.0
0.0
2'595.6
0.0
61.2
0.0
3'760.4
0.0
300.9
0.0
0.2
0.0
108'931.4
0.0
185.7
Harjavalta
118'000
4'911.7
2'306.9
2'823.9
1'512.5
360.2
416.2
801.7
30.9
3'919.8
18'083.0
5'021.1
118.4
7'274.1
582.1
0.4
210'719.7
359.2
Hettstedt
Lunen (Huettenwerke Kayser)
Hamburg (Norddeutsche Affinerie)
41'000
73'000
180'000
1'706.6
3'038.6
7'492.4
801.6
1'427.2
3'519.0
981.2
1'747.0
4'307.6
525.5
935.7
2'307.2
125.2
222.9
549.5
144.6
257.5
634.8
278.6
496.0
1'222.9
10.7
19.1
47.2
1'362.0
2'425.0
5'979.4
6'283.1
11'186.9
27'584.3
1'744.6
3'106.3
7'659.3
41.1
73.2
180.6
2'527.5
4'500.1
11'096.1
202.3
360.1
888.0
0.1
0.2
0.6
73'216.2
130'360.5
321'436.8
124.8
222.2
548.0
Csepel
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Porto Marghera
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
229'000
214'000
0
97'000
9'531.9
8'907.6
0.0
4'037.5
4'477.0
4'183.7
0.0
1'896.4
5'480.2
5'121.2
0.0
2'321.3
2'935.3
2'743.0
0.0
1'243.3
699.1
653.3
0.0
296.1
807.6
754.7
0.0
342.1
1'555.8
1'453.9
0.0
659.0
60.0
56.1
0.0
25.4
7'607.1
7'108.8
0.0
3'222.2
35'093.3
32'794.6
0.0
14'864.8
9'744.3
9'106.0
0.0
4'127.5
229.7
214.7
0.0
97.3
14'116.7
13'192.1
0.0
5'979.6
1'129.7
1'055.7
0.0
478.5
0.7
0.7
0.0
0.3
408'939.1
382'152.7
0.0
173'218.7
697.2
651.5
0.0
295.3
Baia Mare
Zlatna
Zlatna
Zlatna
12'000
0
14'000
4'000
499.5
0.0
582.7
166.5
234.6
0.0
273.7
78.2
287.2
0.0
335.0
95.7
153.8
0.0
179.4
51.3
36.6
0.0
42.7
12.2
42.3
0.0
49.4
14.1
81.5
0.0
95.1
27.2
3.1
0.0
3.7
1.0
398.6
0.0
465.1
132.9
1'839.0
0.0
2'145.4
613.0
510.6
0.0
595.7
170.2
12.0
0.0
14.0
4.0
739.7
0.0
863.0
246.6
59.2
0.0
69.1
19.7
0.0
0.0
0.0
0.0
21'429.1
0.0
25'000.6
7'143.0
36.5
0.0
42.6
12.2
Bor
Glogow District (Glogow I)
Glogow District (Glogow II)
Wroclaw (Hutmen S.A.)
Legnica
23'000
957.4
449.7
550.4
294.8
70.2
81.1
156.3
6.0
764.0
3'524.7
978.7
23.1
1'417.8
113.5
0.1
41'072.5
70.0
Asua-Bilbao
Huelva
0
243'000
0.0
10'114.7
0.0
4'750.7
0.0
5'815.3
0.0
3'114.7
0.0
741.8
0.0
857.0
0.0
1'651.0
0.0
63.7
0.0
8'072.2
0.0
37'238.7
0.0
10'340.0
0.0
243.8
0.0
14'979.8
0.0
1'198.8
0.0
0.7
0.0
433'939.7
0.0
739.8
Skelleftehamn (Ronnskar)
200'000
8'324.8
3'910.0
4'786.2
2'563.6
610.6
705.4
1'358.8
52.4
6'643.8
30'649.2
8'510.3
200.6
12'329.0
986.7
0.6
357'152.0
608.9
90'000
1'852'000
3'746.2
77'088
1'759.5
36'207
2'153.8
44'320
1'153.6
23'739
274.8
5'654
317.4
6'532
611.5
12'583
23.6
485
2'989.7
61'521
13'792.1
283'811
3'829.6
78'805
90.3
1'858
5'548.1
114'167
444.0
9'137
0.3
6
160'718.4
3'307'228
274.0
5'638
Samsun
76
Annex D.1
Air Emission in the Base Scenario 2005
Europe: Smelting and Refining of Secondary Copper
Country
Austria
Austria
Belgium
Belgium
Belgium
Bulgaria
Bulgaria
Bulgaria
Finland
Finland
Germany
Germany
Germany
Germany
Hungary
Hungary
Italy
Italy
Poland
Poland
Poland
Poland
Poland
Romania
Romania
Romania
Romania
Romania
Serbia and Montenegro
Serbia and Montenegro
Spain
Spain
Spain
Sweden
Sweden
Turkey
Turkey
Location Cu Smelter (Name)
Secondary Cu
t /a
SO2
t/a
NOx
t/a
PM10
t/a
PM2.5
t/a
NH3
t/a
NMVOC
t/a
Cd
kg / a
Hg
kg / a
As
kg / a
Pb
kg / a
Cr
kg / a
Cr-VI
kg / a
Ni
kg / a
CH2O
kg / a
Dioxin
kg / a
CO2
t/a
CH4
t/a
Brixlegg
52'000
764.6
346.6
198.1
113.6
19.8
35.0
204.5
6.8
319.5
5'647.0
331.2
8.6
547.1
198.9
2.6
89'758.6
180.0
Beerse
Hoboken
98'000
33'000
1'441.0
485.2
653.2
219.9
373.3
125.7
214.1
72.1
37.3
12.5
65.9
22.2
385.3
129.8
12.8
4.3
602.2
202.8
10'642.4
3'583.7
624.2
210.2
16.3
5.5
1'031.2
347.2
374.8
126.2
5.0
1.7
169'160.4
56'962.2
339.3
114.3
Eliseina
Pirdop
3'000
0
44.1
0.0
20.0
0.0
11.4
0.0
6.6
0.0
1.1
0.0
2.0
0.0
11.8
0.0
0.4
0.0
18.4
0.0
325.8
0.0
19.1
0.0
0.5
0.0
31.6
0.0
11.5
0.0
0.2
0.0
5'178.4
0.0
10.4
0.0
Harjavalta
Hettstedt
Lunen (Huettenwerke Kayser)
Hamburg (Norddeutsche Affinerie)
16'000
235.3
106.6
60.9
35.0
6.1
10.8
62.9
2.1
98.3
1'737.5
101.9
2.7
168.4
61.2
0.8
27'618.0
55.4
48'000
85'000
211'000
705.8
1'249.8
3'102.5
319.9
566.5
1'406.3
182.8
323.8
803.7
104.9
185.7
461.0
18.2
32.3
80.2
32.3
57.2
141.9
188.7
334.2
829.7
6.3
11.1
27.5
294.9
522.3
1'296.5
5'212.6
9'230.6
22'913.7
305.7
541.4
1'344.0
8.0
14.1
35.1
505.1
894.4
2'220.2
183.6
325.1
806.9
2.4
4.3
10.7
82'854.1
146'720.8
364'212.7
166.2
294.3
730.6
Csepel
10'000
147.0
66.7
38.1
21.8
3.8
6.7
39.3
1.3
61.4
1'086.0
63.7
1.7
105.2
38.2
0.5
17'261.3
34.6
Porto Marghera
32'000
470.5
213.3
121.9
69.9
12.2
21.5
125.8
4.2
196.6
3'475.1
203.8
5.3
336.7
122.4
1.6
55'236.0
110.8
Glogow District (Glogow I)
Glogow District (Glogow II)
Wroclaw (Hutmen S.A.)
Legnica
0
0
20'000
0
0.0
0.0
294.1
0.0
0.0
0.0
133.3
0.0
0.0
0.0
76.2
0.0
0.0
0.0
43.7
0.0
0.0
0.0
7.6
0.0
0.0
0.0
13.5
0.0
0.0
0.0
78.6
0.0
0.0
0.0
2.6
0.0
0.0
0.0
122.9
0.0
0.0
0.0
2'171.9
0.0
0.0
0.0
127.4
0.0
0.0
0.0
3.3
0.0
0.0
0.0
210.4
0.0
0.0
0.0
76.5
0.0
0.0
0.0
1.0
0.0
0.0
0.0
34'522.5
0.0
0.0
0.0
69.2
0.0
0
2'000
0
0
0.0
29.4
0.0
0.0
0.0
13.3
0.0
0.0
0.0
7.6
0.0
0.0
0.0
4.4
0.0
0.0
0.0
0.8
0.0
0.0
0.0
1.3
0.0
0.0
0.0
7.9
0.0
0.0
0.0
0.3
0.0
0.0
0.0
12.3
0.0
0.0
0.0
217.2
0.0
0.0
0.0
12.7
0.0
0.0
0.0
0.3
0.0
0.0
0.0
21.0
0.0
0.0
0.0
7.6
0.0
0.0
0.0
0.1
0.0
0.0
0.0
3'452.3
0.0
0.0
0.0
6.9
0.0
0.0
Baia Mare
Zlatna
Zlatna
Zlatna
Bor
7'000
102.9
46.7
26.7
15.3
2.7
4.7
27.5
0.9
43.0
760.2
44.6
1.2
73.7
26.8
0.4
12'082.9
24.2
Asua-Bilbao
Huelva
26'000
0
382.3
0.0
173.3
0.0
99.0
0.0
56.8
0.0
9.9
0.0
17.5
0.0
102.2
0.0
3.4
0.0
159.8
0.0
2'823.5
0.0
165.6
0.0
4.3
0.0
273.6
0.0
99.4
0.0
1.3
0.0
44'879.3
0.0
90.0
0.0
Skelleftehamn (Ronnskar)
22'000
323.5
146.6
83.8
48.1
8.4
14.8
86.5
2.9
135.2
2'389.1
140.1
3.7
231.5
84.1
1.1
37'974.8
76.2
5'000
670'000
73.5
9'851
33.3
4'466
19.0
2'552
10.9
1'464
1.9
255
3.4
451
19.7
2'634
0.7
87
30.7
4'117
543.0
72'759
31.8
4'268
0.8
111
52.6
7'050
19.1
2'562
0.3
34
8'630.6
1'156'505
17.3
2'320
Samsun
77
Annex D.1
Air Emission in the Base Scenario 2005
Chile, Peru, Russia and Indonesia: Concentrate Production, Smelting and Refining of Primary Copper
Cathode Production
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
t /a
1'078'800
114'471
104'064
346'881
277'505
52'032
79'783
104'064
100'920
79'674
21'246
342'780
19'454
27'625
15'563
38'908
155'632
7'782
35'017
42'799
NOx
t /a
56'470
5'992
5'447
18'157
14'526
2'724
4'176
5'447
5'283
4'171
1'112
31'442
1'784
2'534
1'428
3'569
14'276
714
3'212
3'926
SO2
t /a
365'808
38'816
35'287
117'623
94'098
17'643
27'053
35'287
34'221
27'016
7'204
203'344
11'541
16'388
9'232
23'081
92'324
4'616
20'773
25'389
PM10
t /a
45'685
4'848
4'407
14'690
11'752
2'203
3'379
4'407
4'274
3'374
900
39'844
2'261
3'211
1'809
4'523
18'090
905
4'070
4'975
NMVOC
t /a
10'123
1'074
976
3'255
2'604
488
749
976
947
748
199
5'646
320
455
256
641
2'563
128
577
705
CO2
t /a
3'553'039
377'011
342'737
1'142'456
913'965
171'368
262'765
342'737
332'381
262'406
69'975
1'571'019
89'161
126'609
71'329
178'322
713'289
35'664
160'490
196'154
CH4
t /a
Pimary Cu
t /a
266'000
28'225
25'659
85'531
68'424
12'830
19'672
25'659
98'800
78'000
20'800
197'600
197'600
NOx
t /a
SO2
t /a
PM10
t /a
4'021
427
388
1'293
1'034
194
297
388
1'494
1'179
314
2'416
2'416
NMVOC
t /a
CO2
t /a
117'650
12'484
11'349
37'830
30'264
5'674
8'701
11'349
43'698
34'499
9'200
77'808
77'808
CH4
t /a
6'323
671
610
2'033
1'627
305
468
610
592
467
125
3'136
178
253
142
356
1'424
71
320
392
Concentrate Production
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
3'134
333
302
1'008
806
151
232
302
1'164
919
245
1'894
1'894
419
44
40
135
108
20
31
40
155
123
33
276
276
562
60
54
181
145
27
42
54
209
165
44
339
339
273
29
26
88
70
13
20
26
101
80
21
203
203
78
Annex D.2
Air Emission in the Scenario 2020 - Optimistic
Europe: Smelting and Refining of Primary Copper
Country
Austria
Austria
Belgium
Belgium
Belgium
Bulgaria
Bulgaria
Bulgaria
Finland
Finland
Germany
Germany
Germany
Germany
Hungary
Hungary
Italy
Italy
Poland
Poland
Poland
Poland
Poland
Romania
Romania
Romania
Romania
Romania
Serbia and Montenegro
Serbia and Montenegro
Spain
Spain
Spain
Sweden
Sweden
Turkey
Turkey
Location Cu Smelter (Name)
Brixlegg
Primary Cu
t /a
SO2
t/a
NOx
t/a
PM10
t/a
PM2.5
t/a
NH3
t/a
NMVOC
t/a
Cd
kg / a
Hg
kg / a
As
kg / a
Pb
kg / a
Cr
kg / a
Cr-VI
kg / a
Ni
kg / a
CH2O
kg / a
Dioxin
kg / a
CO2
t/a
CH4
t/a
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Beerse
Hoboken
209'000
70'000
8'090.5
2'709.7
4'086.0
1'368.5
3'851.2
1'289.9
2'062.8
690.9
638.0
213.7
737.1
246.9
1'420.0
475.6
43.8
14.7
4'443.4
1'488.2
23'380.7
7'830.9
7'114.6
2'382.9
167.7
56.2
10'307.1
3'452.1
1'031.1
345.3
0.6
0.2
238'863.3
80'002.1
407.2
136.4
Eliseina
Pirdop
0
67'000
0.0
2'593.6
0.0
1'309.9
0.0
1'234.6
0.0
661.3
0.0
204.5
0.0
236.3
0.0
455.2
0.0
14.0
0.0
1'424.4
0.0
7'495.3
0.0
2'280.8
0.0
53.8
0.0
3'304.2
0.0
330.5
0.0
0.2
0.0
76'573.4
0.0
130.5
Harjavalta
130'000
5'032.4
2'541.5
2'395.5
1'283.1
396.9
458.5
883.2
27.2
2'763.8
14'543.0
4'425.4
104.3
6'411.1
641.3
0.4
148'575.2
253.3
Hettstedt
Lunen (Huettenwerke Kayser)
Hamburg (Norddeutsche Affinerie)
45'000
80'000
198'000
1'742.0
3'096.8
7'664.7
879.8
1'564.0
3'870.9
829.2
1'474.2
3'648.5
444.1
789.6
1'954.2
137.4
244.2
604.5
158.7
282.1
698.3
305.7
543.5
1'345.2
9.4
16.8
41.5
956.7
1'700.8
4'209.5
5'034.1
8'949.6
22'150.2
1'531.9
2'723.3
6'740.2
36.1
64.2
158.9
2'219.2
3'945.3
9'764.6
222.0
394.7
976.8
0.1
0.2
0.6
51'429.9
91'430.9
226'291.5
87.7
155.9
385.8
Csepel
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Porto Marghera
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
252'000
235'000
0
107'000
9'755.0
9'097.0
0.0
4'142.0
4'926.6
4'594.3
0.0
2'091.9
4'643.6
4'330.3
0.0
1'971.7
2'487.2
2'319.4
0.0
1'056.1
769.3
717.4
0.0
326.6
888.7
828.8
0.0
377.4
1'712.1
1'596.6
0.0
727.0
52.8
49.3
0.0
22.4
5'357.5
4'996.1
0.0
2'274.8
28'191.1
26'289.3
0.0
11'970.0
8'578.4
7'999.7
0.0
3'642.4
202.2
188.6
0.0
85.9
12'427.7
11'589.3
0.0
5'276.8
1'243.2
1'159.3
0.0
527.9
0.8
0.7
0.0
0.3
288'007.4
268'578.3
0.0
122'288.9
491.0
457.9
0.0
208.5
Baia Mare
Zlatna
Zlatna
Zlatna
13'000
0
15'000
5'000
503.2
0.0
580.7
193.6
254.2
0.0
293.3
97.8
239.6
0.0
276.4
92.1
128.3
0.0
148.0
49.3
39.7
0.0
45.8
15.3
45.8
0.0
52.9
17.6
88.3
0.0
101.9
34.0
2.7
0.0
3.1
1.0
276.4
0.0
318.9
106.3
1'454.3
0.0
1'678.0
559.3
442.5
0.0
510.6
170.2
10.4
0.0
12.0
4.0
641.1
0.0
739.7
246.6
64.1
0.0
74.0
24.7
0.0
0.0
0.0
0.0
14'857.5
0.0
17'143.3
5'714.4
25.3
0.0
29.2
9.7
Bor
Glogow District (Glogow I)
Glogow District (Glogow II)
Wroclaw (Hutmen S.A.)
Legnica
25'000
967.8
488.8
460.7
246.7
76.3
88.2
169.9
5.2
531.5
2'796.7
851.0
20.1
1'232.9
123.3
0.1
28'572.2
48.7
Asua-Bilbao
Huelva
0
267'000
0.0
10'335.7
0.0
5'219.9
0.0
4'920.0
0.0
2'635.2
0.0
815.1
0.0
941.7
0.0
1'814.0
0.0
56.0
0.0
5'676.4
0.0
29'869.2
0.0
9'089.0
0.0
214.3
0.0
13'167.4
0.0
1'317.2
0.0
0.8
0.0
305'150.7
0.0
520.2
Skelleftehamn (Ronnskar)
220'000
8'516.3
4'301.0
4'053.9
2'171.3
671.6
775.9
1'494.7
46.1
4'677.2
24'611.3
7'489.1
176.5
10'849.5
1'085.3
0.7
251'435.0
428.6
99'000
2'037'000
3'832.3
78'853
1'935.5
39'823
1'824.3
37'536
977.1
20'105
302.2
6'219
349.2
7'184
672.6
13'840
20.7
427
2'104.7
43'307
11'075.1
227'878
3'370.1
69'342
79.4
1'635
4'882.3
100'457
488.4
10'049
0.3
6
113'145.8
2'328'060
192.9
3'969
Samsun
79
Annex D.2
Air Emission in the Scenario 2020 - Optimistic
Europe: Smelting and Refining of Secondary Copper
Country
Austria
Austria
Belgium
Belgium
Belgium
Bulgaria
Bulgaria
Bulgaria
Finland
Finland
Germany
Germany
Germany
Germany
Hungary
Hungary
Italy
Italy
Poland
Poland
Poland
Poland
Poland
Romania
Romania
Romania
Romania
Romania
Serbia and Montenegro
Serbia and Montenegro
Spain
Spain
Spain
Sweden
Sweden
Turkey
Turkey
Location Cu Smelter (Name)
Secondary Cu
t /a
SO2
t/a
NOx
t/a
PM10
t/a
PM2.5
t/a
NH3
t/a
NMVOC
t/a
Cd
kg / a
Hg
kg / a
As
kg / a
Pb
kg / a
Cr
kg / a
Cr-VI
kg / a
Ni
kg / a
CH2O
kg / a
Dioxin
kg / a
CO2
t/a
CH4
t/a
Brixlegg
57'000
779.4
379.9
167.2
95.9
21.7
38.3
224.1
5.9
224.2
4'518.7
290.5
7.6
479.8
218.0
2.9
62'969.1
126.3
Beerse
Hoboken
107'000
36'000
1'463.2
492.3
713.2
239.9
313.8
105.6
180.0
60.6
40.7
13.7
72.0
24.2
420.7
141.6
11.2
3.8
420.8
141.6
8'482.4
2'853.9
545.2
183.4
14.2
4.8
900.7
303.0
409.2
137.7
5.4
1.8
118'205.1
39'770.0
237.1
79.8
3'000
0
41.0
0.0
20.0
0.0
8.8
0.0
5.0
0.0
1.1
0.0
2.0
0.0
11.8
0.0
0.3
0.0
11.8
0.0
237.8
0.0
15.3
0.0
0.4
0.0
25.3
0.0
11.5
0.0
0.2
0.0
3'314.2
0.0
6.6
0.0
Eliseina
Pirdop
Harjavalta
Hettstedt
Lunen (Huettenwerke Kayser)
Hamburg (Norddeutsche Affinerie)
18'000
246.1
120.0
52.8
30.3
6.8
12.1
70.8
1.9
70.8
1'426.9
91.7
2.4
151.5
68.8
0.9
19'885.0
39.9
53'000
94'000
232'000
724.7
1'285.4
3'172.5
353.3
626.5
1'546.3
155.4
275.7
680.4
89.2
158.1
390.3
20.1
35.7
88.2
35.6
63.2
156.0
208.4
369.6
912.2
5.5
9.8
24.2
208.4
369.7
912.3
4'201.6
7'451.8
18'391.7
270.1
479.0
1'182.2
7.1
12.5
30.9
446.1
791.3
1'952.9
202.7
359.5
887.2
2.7
4.8
11.7
58'550.2
103'843.8
256'295.3
117.4
208.3
514.1
Csepel
11'000
150.4
73.3
32.3
18.5
4.2
7.4
43.3
1.1
43.3
872.0
56.1
1.5
92.6
42.1
0.6
12'151.9
24.4
Porto Marghera
35'000
478.6
233.3
102.6
58.9
13.3
23.5
137.6
3.7
137.6
2'774.6
178.3
4.7
294.6
133.8
1.8
38'665.2
77.6
Glogow District (Glogow I)
Glogow District (Glogow II)
Wroclaw (Hutmen S.A.)
Legnica
0
0
22'000
0
0.0
0.0
300.8
0.0
0.0
0.0
146.6
0.0
0.0
0.0
64.5
0.0
0.0
0.0
37.0
0.0
0.0
0.0
8.4
0.0
0.0
0.0
14.8
0.0
0.0
0.0
86.5
0.0
0.0
0.0
2.3
0.0
0.0
0.0
86.5
0.0
0.0
0.0
1'744.0
0.0
0.0
0.0
112.1
0.0
0.0
0.0
2.9
0.0
0.0
0.0
185.2
0.0
0.0
0.0
84.1
0.0
0.0
0.0
1.1
0.0
0.0
0.0
24'303.9
0.0
0.0
0.0
48.8
0.0
0
2'000
0
0
0.0
27.3
0.0
0.0
0.0
13.3
0.0
0.0
0.0
5.9
0.0
0.0
0.0
3.4
0.0
0.0
0.0
0.8
0.0
0.0
0.0
1.3
0.0
0.0
0.0
7.9
0.0
0.0
0.0
0.2
0.0
0.0
0.0
7.9
0.0
0.0
0.0
158.5
0.0
0.0
0.0
10.2
0.0
0.0
0.0
0.3
0.0
0.0
0.0
16.8
0.0
0.0
0.0
7.6
0.0
0.0
0.0
0.1
0.0
0.0
0.0
2'209.4
0.0
0.0
0.0
4.4
0.0
0.0
Baia Mare
Zlatna
Zlatna
Zlatna
Bor
8'000
109.4
53.3
23.5
13.5
3.0
5.4
31.5
0.8
31.5
634.2
40.8
1.1
67.3
30.6
0.4
8'837.8
17.7
Asua-Bilbao
Huelva
29'000
0
396.6
0.0
193.3
0.0
85.1
0.0
48.8
0.0
11.0
0.0
19.5
0.0
114.0
0.0
3.0
0.0
114.0
0.0
2'299.0
0.0
147.8
0.0
3.9
0.0
244.1
0.0
110.9
0.0
1.5
0.0
32'036.9
0.0
64.3
0.0
Skelleftehamn (Ronnskar)
24'000
328.2
160.0
70.4
40.4
9.1
16.1
94.4
2.5
94.4
1'902.6
122.3
3.2
202.0
91.8
1.2
26'513.3
53.2
6'000
737'000
82.0
10'078
40.0
4'912
17.6
2'161
10.1
1'240
2.3
280
4.0
496
23.6
2'898
0.6
77
23.6
2'898
475.6
58'425
30.6
3'756
0.8
98
50.5
6'204
22.9
2'818
0.3
37
6'628.3
814'179
13.3
1'633
Samsun
80
Annex D.2
Air Emission in the Scenario 2020 - Optimistic
Chile, Peru, Russia and Indonesia: Concentrate Production, Smelting and Refining of Primary Copper
Cathode Production
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
t /a
1'184'200
125'655
114'232
380'772
304'617
57'116
87'577
114'232
110'780
87'458
23'322
376'270
21'355
30'324
17'084
42'709
170'838
8'542
38'438
46'980
NOx
t /a
23'151
2'457
2'233
7'444
5'955
1'117
1'712
2'233
2'166
1'710
456
7'356
417
593
334
835
3'340
167
751
918
SO2
t /a
49'291
5'230
4'755
15'849
12'679
2'377
3'645
4'755
4'611
3'640
971
15'662
889
1'262
711
1'778
7'111
356
1'600
1'956
PM10
t /a
28'339
3'007
2'734
9'112
7'290
1'367
2'096
2'734
2'651
2'093
558
9'005
511
726
409
1'022
4'088
204
920
1'124
NMVOC
t /a
4'176
443
403
1'343
1'074
201
309
403
391
308
82
1'327
75
107
60
151
603
30
136
166
CO2
t /a
2'114'697
224'389
203'990
679'967
543'974
101'995
156'392
203'990
197'827
156'179
41'648
671'928
38'134
54'151
30'508
76'269
305'075
15'254
68'642
83'896
CH4
t /a
Pimary Cu
t /a
294'000
31'196
28'360
94'534
75'627
14'180
21'743
28'360
109'200
86'211
22'989
218'400
218'400
NOx
t /a
SO2
t /a
PM10
t /a
1'654
176
160
532
425
80
122
160
614
485
129
1'229
1'229
NMVOC
t /a
CO2
t /a
54'544
5'788
5'261
17'538
14'031
2'631
4'034
5'261
20'259
15'994
4'265
40'518
40'518
CH4
t /a
3'605
383
348
1'159
927
174
267
348
337
266
71
1'146
65
92
52
130
520
26
117
143
Concentrate Production
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
1'292
137
125
415
332
62
96
125
480
379
101
960
960
192
20
19
62
49
9
14
19
71
56
15
143
143
231
24
22
74
59
11
17
22
86
68
18
171
171
124
13
12
40
32
6
9
12
46
36
10
92
92
81
Annex D.3
Air Emission in the Scenario 2020 - Pessimistic
Europe: Smelting and Refining of Primary Copper
Country
Austria
Austria
Belgium
Belgium
Belgium
Bulgaria
Bulgaria
Bulgaria
Finland
Finland
Germany
Germany
Germany
Germany
Hungary
Hungary
Italy
Italy
Poland
Poland
Poland
Poland
Poland
Romania
Romania
Romania
Romania
Romania
Serbia and Montenegro
Serbia and Montenegro
Spain
Spain
Spain
Sweden
Sweden
Turkey
Turkey
Location Cu Smelter (Name)
Brixlegg
Primary Cu
t /a
SO2
t/a
NOx
t/a
PM10
t/a
PM2.5
t/a
NH3
t/a
NMVOC
t/a
Cd
kg / a
Hg
kg / a
As
kg / a
Pb
kg / a
Cr
kg / a
Cr-VI
kg / a
Ni
kg / a
CH2O
kg / a
Dioxin
kg / a
CO2
t/a
CH4
t/a
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Beerse
Hoboken
209'000
70'000
8'264.5
2'768.0
4'086.0
1'368.5
4'751.5
1'591.4
2'545.0
852.4
638.0
213.7
737.1
246.9
1'420.0
475.6
52.0
17.4
6'595.6
2'209.1
30'427.0
10'190.9
8'448.6
2'829.7
199.2
66.7
12'239.6
4'099.4
1'031.1
345.3
0.6
0.2
354'562.7
118'753.0
604.5
202.5
Eliseina
Pirdop
0
67'000
0.0
2'649.4
0.0
1'309.9
0.0
1'523.2
0.0
815.9
0.0
204.5
0.0
236.3
0.0
455.2
0.0
16.7
0.0
2'114.4
0.0
9'754.1
0.0
2'708.4
0.0
63.8
0.0
3'923.7
0.0
330.5
0.0
0.2
0.0
113'663.6
0.0
193.8
Harjavalta
130'000
5'140.6
2'541.5
2'955.5
1'583.0
396.9
458.5
883.2
32.4
4'102.5
18'925.9
5'255.1
123.9
7'613.2
641.3
0.4
220'541.4
376.0
Hettstedt
Lunen (Huettenwerke Kayser)
Hamburg (Norddeutsche Affinerie)
45'000
80'000
198'000
1'779.4
3'163.4
7'829.5
879.8
1'564.0
3'870.9
1'023.1
1'818.8
4'501.4
548.0
974.2
2'411.0
137.4
244.2
604.5
158.7
282.1
698.3
305.7
543.5
1'345.2
11.2
19.9
49.3
1'420.1
2'524.6
6'248.5
6'551.3
11'646.7
28'825.5
1'819.1
3'233.9
8'003.9
42.9
76.2
188.7
2'635.3
4'685.0
11'595.4
222.0
394.7
976.8
0.1
0.2
0.6
76'341.2
135'717.8
335'901.5
130.1
231.4
572.6
Csepel
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Porto Marghera
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
252'000
235'000
0
107'000
9'964.8
9'292.6
0.0
4'231.1
4'926.6
4'594.3
0.0
2'091.9
5'729.1
5'342.6
0.0
2'432.6
3'068.6
2'861.6
0.0
1'302.9
769.3
717.4
0.0
326.6
888.7
828.8
0.0
377.4
1'712.1
1'596.6
0.0
727.0
62.7
58.5
0.0
26.6
7'952.6
7'416.1
0.0
3'376.7
36'687.1
34'212.1
0.0
15'577.4
10'186.8
9'499.6
0.0
4'325.4
240.1
223.9
0.0
102.0
14'757.8
13'762.3
0.0
6'266.2
1'243.2
1'159.3
0.0
527.9
0.8
0.7
0.0
0.3
427'511.0
398'670.9
0.0
181'522.5
728.8
679.7
0.0
309.5
Baia Mare
Zlatna
Zlatna
Zlatna
13'000
0
15'000
5'000
514.1
0.0
593.1
197.7
254.2
0.0
293.3
97.8
295.5
0.0
341.0
113.7
158.3
0.0
182.7
60.9
39.7
0.0
45.8
15.3
45.8
0.0
52.9
17.6
88.3
0.0
101.9
34.0
3.2
0.0
3.7
1.2
410.3
0.0
473.4
157.8
1'892.6
0.0
2'183.8
727.9
525.5
0.0
606.4
202.1
12.4
0.0
14.3
4.8
761.3
0.0
878.4
292.8
64.1
0.0
74.0
24.7
0.0
0.0
0.0
0.0
22'054.1
0.0
25'447.1
8'482.4
37.6
0.0
43.4
14.5
Bor
Glogow District (Glogow I)
Glogow District (Glogow II)
Wroclaw (Hutmen S.A.)
Legnica
25'000
988.6
488.8
568.4
304.4
76.3
88.2
169.9
6.2
788.9
3'639.6
1'010.6
23.8
1'464.1
123.3
0.1
42'411.8
72.3
Asua-Bilbao
Huelva
0
267'000
0.0
10'558.0
0.0
5'219.9
0.0
6'070.1
0.0
3'251.2
0.0
815.1
0.0
941.7
0.0
1'814.0
0.0
66.5
0.0
8'426.0
0.0
38'870.8
0.0
10'793.2
0.0
254.4
0.0
15'636.3
0.0
1'317.2
0.0
0.8
0.0
452'958.0
0.0
772.2
Skelleftehamn (Ronnskar)
220'000
8'699.5
4'301.0
5'001.6
2'678.9
671.6
775.9
1'494.7
54.8
6'942.7
32'028.4
8'893.3
209.7
12'883.8
1'085.3
0.7
373'223.9
636.3
99'000
2'037'000
3'914.8
80'549
1'935.5
39'823
2'250.7
46'310
1'205.5
24'804
302.2
6'219
349.2
7'184
672.6
13'840
24.6
507
3'124.2
64'283
14'412.8
296'554
4'002.0
82'344
94.3
1'941
5'797.7
119'293
488.4
10'049
0.3
6
167'950.7
3'455'714
286.3
5'891
Samsun
82
Annex D.3
Air Emission in the Scenario 2020 - Pessimistic
Europe: Smelting and Refining of Secondary Copper
Country
Austria
Austria
Belgium
Belgium
Belgium
Bulgaria
Bulgaria
Bulgaria
Finland
Finland
Germany
Germany
Germany
Germany
Hungary
Hungary
Italy
Italy
Poland
Poland
Poland
Poland
Poland
Romania
Romania
Romania
Romania
Romania
Serbia and Montenegro
Serbia and Montenegro
Spain
Spain
Spain
Sweden
Sweden
Turkey
Turkey
Location Cu Smelter (Name)
Secondary Cu
t /a
SO2
t/a
NOx
t/a
PM10
t/a
PM2.5
t/a
NH3
t/a
NMVOC
t/a
Cd
kg / a
Hg
kg / a
As
kg / a
Pb
kg / a
Cr
kg / a
Cr-VI
kg / a
Ni
kg / a
CH2O
kg / a
Dioxin
kg / a
CO2
t/a
CH4
t/a
Brixlegg
57'000
796.2
379.9
206.3
118.3
21.7
38.3
224.1
7.1
332.7
5'880.4
344.9
9.0
569.8
218.0
2.9
93'469.8
187.5
Beerse
Hoboken
107'000
36'000
1'494.6
502.9
713.2
239.9
387.2
130.3
222.1
74.7
40.7
13.7
72.0
24.2
420.7
141.6
13.3
4.5
624.6
210.1
11'038.7
3'714.0
647.5
217.8
16.9
5.7
1'069.6
359.9
409.2
137.7
5.4
1.8
175'460.8
59'033.5
352.0
118.4
3'000
0
41.9
0.0
20.0
0.0
10.9
0.0
6.2
0.0
1.1
0.0
2.0
0.0
11.8
0.0
0.4
0.0
17.5
0.0
309.5
0.0
18.2
0.0
0.5
0.0
30.0
0.0
11.5
0.0
0.2
0.0
4'919.5
0.0
9.9
0.0
Eliseina
Pirdop
Harjavalta
Hettstedt
Lunen (Huettenwerke Kayser)
Hamburg (Norddeutsche Affinerie)
18'000
251.4
120.0
65.1
37.4
6.8
12.1
70.8
2.2
105.1
1'857.0
108.9
2.8
179.9
68.8
0.9
29'516.8
59.2
53'000
94'000
232'000
740.3
1'313.0
3'240.7
353.3
626.5
1'546.3
191.8
340.1
839.5
110.0
195.1
481.5
20.1
35.7
88.2
35.6
63.2
156.0
208.4
369.6
912.2
6.6
11.7
28.8
309.4
548.7
1'354.2
5'467.8
9'697.6
23'934.5
320.7
568.8
1'403.9
8.4
14.9
36.7
529.8
939.6
2'319.1
202.7
359.5
887.2
2.7
4.8
11.7
86'910.5
154'143.1
380'438.3
174.3
309.2
763.1
Csepel
11'000
153.7
73.3
39.8
22.8
4.2
7.4
43.3
1.4
64.2
1'134.8
66.6
1.7
110.0
42.1
0.6
18'038.0
36.2
Porto Marghera
35'000
488.9
233.3
126.6
72.6
13.3
23.5
137.6
4.3
204.3
3'610.8
211.8
5.5
349.9
133.8
1.8
57'393.7
115.1
Glogow District (Glogow I)
Glogow District (Glogow II)
Wroclaw (Hutmen S.A.)
Legnica
0
0
22'000
0
0.0
0.0
307.3
0.0
0.0
0.0
146.6
0.0
0.0
0.0
79.6
0.0
0.0
0.0
45.7
0.0
0.0
0.0
8.4
0.0
0.0
0.0
14.8
0.0
0.0
0.0
86.5
0.0
0.0
0.0
2.7
0.0
0.0
0.0
128.4
0.0
0.0
0.0
2'269.6
0.0
0.0
0.0
133.1
0.0
0.0
0.0
3.5
0.0
0.0
0.0
219.9
0.0
0.0
0.0
84.1
0.0
0.0
0.0
1.1
0.0
0.0
0.0
36'076.0
0.0
0.0
0.0
72.4
0.0
0
2'000
0
0
0.0
27.9
0.0
0.0
0.0
13.3
0.0
0.0
0.0
7.2
0.0
0.0
0.0
4.2
0.0
0.0
0.0
0.8
0.0
0.0
0.0
1.3
0.0
0.0
0.0
7.9
0.0
0.0
0.0
0.2
0.0
0.0
0.0
11.7
0.0
0.0
0.0
206.3
0.0
0.0
0.0
12.1
0.0
0.0
0.0
0.3
0.0
0.0
0.0
20.0
0.0
0.0
0.0
7.6
0.0
0.0
0.0
0.1
0.0
0.0
0.0
3'279.6
0.0
0.0
0.0
6.6
0.0
0.0
Baia Mare
Zlatna
Zlatna
Zlatna
Bor
8'000
111.7
53.3
28.9
16.6
3.0
5.4
31.5
1.0
46.7
825.3
48.4
1.3
80.0
30.6
0.4
13'118.6
26.3
Asua-Bilbao
Huelva
29'000
0
405.1
0.0
193.3
0.0
104.9
0.0
60.2
0.0
11.0
0.0
19.5
0.0
114.0
0.0
3.6
0.0
169.3
0.0
2'991.8
0.0
175.5
0.0
4.6
0.0
289.9
0.0
110.9
0.0
1.5
0.0
47'554.8
0.0
95.4
0.0
Skelleftehamn (Ronnskar)
24'000
335.2
160.0
86.8
49.8
9.1
16.1
94.4
3.0
140.1
2'476.0
145.2
3.8
239.9
91.8
1.2
39'355.7
78.9
6'000
737'000
83.8
10'295
40.0
4'912
21.7
2'667
12.5
1'530
2.3
280
4.0
496
23.6
2'898
0.7
91
35.0
4'302
619.0
76'033
36.3
4'460
0.9
116
60.0
7'367
22.9
2'818
0.3
37
9'838.9
1'208'547
19.7
2'424
Samsun
83
Annex D.3
Air Emission in the Scenario 2020 - Pessimistic
Chile, Peru, Russia and Indonesia: Concentrate Production, Smelting and Refining of Primary Copper
Cathode Production
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
t /a
1'184'200
125'655
114'232
380'772
304'617
57'116
87'577
114'232
110'780
87'458
23'322
376'270
21'355
30'324
17'084
42'709
170'838
8'542
38'438
46'980
NOx
t /a
55'788
5'920
5'381
17'938
14'351
2'691
4'126
5'381
5'219
4'120
1'099
31'063
1'763
2'503
1'410
3'526
14'103
705
3'173
3'878
SO2
t /a
361'393
38'347
34'861
116'203
92'963
17'431
26'727
34'861
33'808
26'690
7'117
200'890
11'401
16'190
9'121
22'802
91'210
4'560
20'522
25'083
PM10
t /a
45'133
4'789
4'354
14'512
11'610
2'177
3'338
4'354
4'222
3'333
889
39'363
2'234
3'172
1'787
4'468
17'872
894
4'021
4'915
NMVOC
t /a
10'001
1'061
965
3'216
2'572
482
740
965
936
739
197
5'578
317
450
253
633
2'533
127
570
696
CO2
t /a
3'510'157
372'460
338'600
1'128'668
902'934
169'300
259'594
338'600
328'370
259'239
69'130
1'552'059
88'085
125'081
70'468
176'170
704'680
35'234
158'553
193'787
CH4
t /a
Pimary Cu
t /a
294'000
31'196
28'360
94'534
75'627
14'180
21'743
28'360
109'200
86'211
22'989
218'400
218'400
NOx
t /a
SO2
t /a
PM10
t /a
4'000
424
386
1'286
1'029
193
296
386
1'486
1'173
313
2'403
2'403
NMVOC
t /a
CO2
t /a
117'031
12'418
11'289
37'630
30'104
5'645
8'655
11'289
43'468
34'317
9'151
77'398
77'398
CH4
t /a
6'247
663
603
2'009
1'607
301
462
603
584
461
123
3'099
176
250
141
352
1'407
70
317
387
Concentrate Production
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
3'117
331
301
1'002
802
150
231
301
1'158
914
244
1'884
1'884
416
44
40
134
107
20
31
40
155
122
33
275
275
559
59
54
180
144
27
41
54
208
164
44
337
337
272
29
26
87
70
13
20
26
101
80
21
202
202
84
Annex E
Delta Concentration Maps for the Facilities Hoboken / Belgium
and
Glogow I / Poland in the Base Scenarios
85
Delta Concentration Map: Base Scenario 2005
Location: Hoboken / Belgium
Pollutant: PM2.5 / Unit: µg/m3
0.00
0.06
86
Delta Concentration Map: Base Scenario 2005
Location: Hoboken / Belgium
Pollutant: PM10 / Unit: µg/m3
0.00
0.08
87
Delta Concentration Map: Base Scenario 2005
Location: Hoboken / Belgium
Pollutant: Ozone / Unit: µg/m3
0.00
0.12
88
Delta Concentration Map: Base Scenario 2005
Location: Hoboken / Belgium
Pollutant: SIA10 / Unit: µg/m3
0.00
0.02
89
Delta Concentration Map: Base Scenario 2005
Location: Glogow I / Poland
Pollutant: PM2.5 / Unit: µg/m3
0.00
0.10
90
Delta Concentration Map: Base Scenario 2005
Location: Glogow I / Poland
Pollutant: PM10 / Unit: µg/m3
0.00
0.15
91
Delta Concentration Map: Base Scenario 2005
Location: Glogow I / Poland
Pollutant: Ozone / Unit: µg/m3
0.00
17.38
92
Delta Concentration Map: Base Scenario 2005
Location: Glogow I / Poland
Pollutant: SIA10 / Unit: µg/m3
0.00
0.08
93
94
Annex F
External Cost Estimates
95
Annex F.1 Base Scenario 2005: Primary Copper Production in Europe
Base Scenario 2005
Primary Copper Europe
Baia Mare
Mio €2000 / a
€2000 / t
Beerse
Mio €2000 / a
Bor
Mio €2000 / a
€2000 / t
€2000 / t
Glogow I
Mio €2000 / a
€2000 / t
Glogow II
Mio €2000 / a
€2000 / t
Hamburg
Mio €2000 / a
€2000 / t
Harjavalta
Mio €2000 / a
€2000 / t
Hettstedt
Mio €2000 / a
€2000 / t
Hoboken
Mio €2000 / a
Huelva
Mio €2000 / a
€2000 / t
€2000 / t
Legnica
Mio €2000 / a
€2000 / t
Lunen
Mio €2000 / a
Pridop
Mio €2000 / a
€2000 / t
€2000 / t
Samsun
Mio €2000 / a
€2000 / t
Skellefteham
Mio €2000 / a
€2000 / t
Zlatna I
Mio €2000 / a
Zlatna II
Mio €2000 / a
€2000 / t
€2000 / t
Total
Biodiversity
Losses
Local/Regional/Hemispheric Scale
(human health, material and crops)
Mio €2000 / a
€2000 / t
Local Scale
0.336
Regional Scale
Hemispheric Scale
10.940
0.209
Greenhouse
Gas
Micropollutants
Total
Total
11.485
0.213
0.490
0.086
28
912
17
957
18
41
7
12.274
1'023
5.884
210.799
3.315
219.998
8.496
7.757
1.355
237.606
31
1'109
17
1'157
45
41
7
1'250
0.385
15.840
0.401
16.626
0.344
0.939
0.164
18.072
17
688
17
722
15
41
7
785
1.614
171.651
3.996
177.261
19.960
9.349
1.633
208.203
7
749
17
773
87
41
7
908
1.588
160.414
3.734
165.736
18.653
8.737
1.526
194.652
7
750
17
774
87
41
7
909
7.722
145.672
3.141
156.535
8.637
7.349
1.283
173.804
43
809
17
869
48
41
7
965
0.299
28.024
2.059
30.382
4.780
4.818
0.841
40.821
3
237
17
257
41
41
7
346
0.841
31.969
0.715
33.525
2.633
1.674
0.292
38.124
21
778
17
816
64
41
7
928
4.164
67.431
1.099
72.695
2.817
2.572
0.449
78.533
66
1'070
17
1'153
45
41
7
1'246
0.727
78.143
4.240
83.110
2.146
9.921
1.733
96.909
3
321
17
341
9
41
7
398
1.373
72.621
1.693
75.686
8.455
3.960
0.692
88.792
14
749
17
780
87
41
7
915
6.376
89.548
1.274
97.198
4.916
2.980
0.521
105.614
87
1'226
17
1'330
67
41
7
1'445
0.202
49.636
1.064
50.903
0.581
2.491
0.435
54.409
3
814
17
834
10
41
7
892
0.469
53.122
1.570
55.162
0.333
3.674
0.642
59.811
5
590
17
612
4
41
7
664
0.054
30.990
3.490
34.534
9.356
8.562
1.426
53.878
0
155
17
172
47
43
7
269
0.109
12.754
0.244
13.107
0.248
0.572
0.100
14.026
8
911
17
936
18
41
7
1'002
0.061
3.644
0.070
3.774
0.071
0.163
0.029
4.037
15
32.203
17
911
1'233.196
653
17
32.316
17
943
1'297.715
687
18
92.637
49
41
76.008
40
7
13.205
7
1'009
1'479.565
783
96
Annex F.1 Base Scenario 2005: Secondary Copper Production in Europe
Base Scenario 2005
Secondary Copper Europe
Asua-Bilbao
Mio €2000 / a
€2000 / t
Beerse
Mio €2000 / a
€2000 / t
Bor
Mio €2000 / a
€2000 / t
Brixlegg
Mio €2000 / a
€2000 / t
Csepel
Mio €2000 / a
Eliseina
Mio €2000 / a
€2000 / t
€2000 / t
Hamburg
Mio €2000 / a
€2000 / t
Harjavalta
Mio €2000 / a
€2000 / t
Hettstedt
Mio €2000 / a
€2000 / t
Hoboken
Mio €2000 / a
Lunen
Mio €2000 / a
Porto Marghera
Mio €2000 / a
€2000 / t
€2000 / t
€2000 / t
Samsun
Mio €2000 / a
€2000 / t
Skellefteham
Mio €2000 / a
€2000 / t
Wroclaw
Mio €2000 / a
€2000 / t
Zlatna
Total
Biodiversity
Losses
Local/Regional/Hemispheric Scale
(human health, material and crops)
Local Scale
0.155
Regional Scale
Hemispheric Scale
2.396
0.144
Greenhouse
Gas
Micropollutants
Total
Total
2.695
0.129
1.033
0.092
6
92
6
104
5
40
4
3.950
153
0.744
27.020
0.544
28.309
1.294
3.893
0.348
33.844
8
275
6
289
13
40
4
346
0.040
1.326
0.039
1.405
0.030
0.278
0.025
1.738
6
189
6
201
4
40
4
249
0.082
13.695
0.289
14.066
1.070
2.066
0.185
17.386
2
263
6
271
21
40
4
336
0.339
2.724
0.055
3.119
0.108
0.398
0.036
3.660
34
273
6
313
11
40
4
368
0.014
0.733
0.017
0.763
0.007
0.119
0.011
0.900
5
244
6
255
2
40
4
301
1.293
46.574
1.172
49.039
3.054
8.383
0.750
61.225
6
220
6
232
14
40
4
290
0.019
1.017
0.089
1.125
0.206
0.635
0.057
2.023
1
64
6
71
13
40
4
128
0.154
10.023
0.267
10.443
0.901
1.907
0.171
13.422
3
209
6
218
19
40
4
281
0.501
8.878
0.183
9.562
0.436
1.311
0.117
11.426
15
269
6
290
13
40
4
347
0.839
28.516
0.472
29.827
1.668
3.377
0.302
35.173
10
336
6
352
20
40
4
416
0.253
10.666
0.178
11.097
0.579
1.271
0.114
13.060
8
333
6
347
18
40
4
409
0.027
0.757
0.028
0.812
0.004
0.199
0.017
1.032
5
152
6
163
1
40
3
207
0.004
0.971
0.122
1.097
0.338
0.874
0.078
2.386
0
45
6
51
15
40
4
110
0.132
3.986
0.111
4.229
0.554
0.794
0.071
5.648
7
200
6
213
28
40
4
285
Mio €2000 / a
0.006
0.525
0.011
0.541
0.009
0.080
0.007
0.637
€2000 / t
3
4.601
7
262
159.806
239
6
3.721
6
271
168.128
251
5
10.385
16
40
26.616
40
4
2.381
4
320
207.510
310
Mio €2000 / a
€2000 / t
97
Annex F.1 Base Scenario 2005: Primary Copper and Concentrate Production in Chile, Peru, Russia and Indonesia (low impacts)
Cathode Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
1'078'800
370'373
195'082
114'471
39'300
20'700
104'064
35'727
18'818
346'881
119'091
62'727
277'505
95'273
50'182
52'032
17'864
9'409
79'783
27'391
14'427
104'064
35'727
18'818
100'920
34'580
18'240
79'674
27'300
14'400
21'246
7'280
3'840
342'780
227'318
119'856
19'454
12'900
6'800
27'625
18'318
9'656
15'563
10'320
5'440
38'908
25'800
13'600
155'632
103'200
54'400
7'782
5'160
2'720
35'017
23'220
12'240
42'799
28'400
15'000
Climate Change [k€]
Crops
CO2
Materials
9'028
958
871
2'903
2'322
435
668
871
845
667
178
5'023
285
405
228
570
2'280
114
513
628
42'409
4'500
4'091
13'636
10'909
2'045
3'136
4'091
3'965
3'130
835
23'603
1'340
1'903
1'072
2'680
10'720
536
2'412
2'940
Total [Mio€]
Total [€/t]
698
74
67
225
180
34
52
67
65
52
14
412
23
33
19
47
187
9
42
51
1'175
647
647
647
647
647
647
647
647
646
646
646
1'202
1'202
1'202
1'202
1'202
1'202
1'202
1'202
1'203
Total [Mio€]
Total [€/t]
17
2
2
5
4
1
1
2
6
5
1
10
10
33
63
63
63
63
63
63
63
63
63
63
63
52
52
CH4
78'127
8'290
7'536
25'121
20'097
3'768
5'778
7'536
7'309
5'770
1'539
34'543
1'960
2'783
1'568
3'920
15'680
784
3'528
4'320
3'204
340
309
1'030
824
155
237
309
299
236
63
1'587
90
128
72
180
721
36
162
198
Concentrate Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
266'000
8'576
4'684
28'225
910
497
25'659
827
452
85'531
2'758
1'506
68'424
2'206
1'205
12'830
414
226
19'672
634
346
25'659
827
452
98'800
3'179
1'735
78'000
2'510
1'370
20'800
669
365
197'600
5'180
2'830
197'600
5'180
2'830
Climate Change [k€]
Crops
CO2
Materials
694
74
67
223
178
33
51
67
257
203
54
418
418
144
15
14
46
37
7
11
14
54
43
11
90
90
CH4
2'592
275
250
833
667
125
192
250
961
759
202
1'710
1'710
139
15
13
45
36
7
10
13
51
41
11
103
103
98
Annex F.1 Base Scenario 2005: Primary Copper and Concentrate Production in Chile, Peru, Russia and Indonesia (high impacts)
Cathode Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
1'078'800
1'196'879
610'691
114'471
127'000
64'800
104'064
115'455
58'909
346'881
384'848
196'364
277'505
307'879
157'091
52'032
57'727
29'455
79'783
88'515
45'164
104'064
115'455
58'909
100'920
111'720
57'127
79'674
88'200
45'100
21'246
23'520
12'027
342'780
770'054
394'608
19'454
43'700
22'400
27'625
62'054
31'808
15'563
34'960
17'920
38'908
87'400
44'800
155'632
349'600
179'200
7'782
17'480
8'960
35'017
78'660
40'320
42'799
96'200
49'200
Climate Change [k€]
Crops
CO2
Materials
11'026
1'170
1'064
3'545
2'836
532
815
1'064
1'035
817
218
6'167
350
497
280
700
2'800
140
630
770
88'399
9'380
8'527
28'424
22'739
4'264
6'538
8'527
8'271
6'530
1'741
49'162
2'790
3'962
2'232
5'580
22'320
1'116
5'022
6'140
Total [Mio€]
Total [€/t]
1'988
211
192
639
511
96
147
192
186
147
39
1'256
71
101
57
143
570
29
128
157
3'430
1'843
1'843
1'843
1'843
1'843
1'843
1'843
1'843
1'841
1'841
1'841
3'665
3'665
3'665
3'665
3'665
3'665
3'665
3'665
3'664
Total [Mio€]
Total [€/t]
60
6
6
19
15
3
4
6
22
18
5
36
36
118
225
225
225
225
225
225
225
225
225
225
225
184
184
CH4
78'127
8'290
7'536
25'121
20'097
3'768
5'778
7'536
7'309
5'770
1'539
34'543
1'960
2'783
1'568
3'920
15'680
784
3'528
4'320
3'204
340
309
1'030
824
155
237
309
299
236
63
1'587
90
128
72
180
721
36
162
198
Concentrate Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
266'000
37'132
18'848
28'225
3'940
2'000
25'659
3'582
1'818
85'531
11'939
6'061
68'424
9'552
4'848
12'830
1'791
909
19'672
2'746
1'394
25'659
3'582
1'818
98'800
13'807
7'017
78'000
10'900
5'540
20'800
2'907
1'477
197'600
22'400
11'400
197'600
22'400
11'400
Climate Change [k€]
Crops
CO2
Materials
777
82
75
250
200
37
57
75
288
227
61
467
467
319
34
31
103
82
15
24
31
119
94
25
199
199
CH4
2'592
275
250
833
667
125
192
250
961
759
202
1'710
1'710
139
15
13
45
36
7
10
13
51
41
11
103
103
99
Annex F.2 Scenario 2020 - Optimistic: Primary Copper Production in Europe
Scenario 2020 Optimist
Primary Copper Europe
Baia Mare
Local/Regional/Hemispheric Scale
(human health, material and crops)
Mio €2000 / a
€2000 / t
Beerse
Mio €2000 / a
Bor
Mio €2000 / a
Glogow I
Mio €2000 / a
€2000 / t
€2000 / t
€2000 / t
Glogow II
Mio €2000 / a
€2000 / t
Hamburg
Mio €2000 / a
Harjavalta
Mio €2000 / a
€2000 / t
€2000 / t
Hettstedt
Mio €2000 / a
Hoboken
Mio €2000 / a
Huelva
Mio €2000 / a
€2000 / t
€2000 / t
€2000 / t
Legnica
Mio €2000 / a
Lunen
Mio €2000 / a
Pridop
Mio €2000 / a
€2000 / t
€2000 / t
€2000 / t
Samsun
Mio €2000 / a
€2000 / t
Skellefteham
Mio €2000 / a
€2000 / t
Zlatna I
Mio €2000 / a
€2000 / t
Zlatna II
Mio €2000 / a
€2000 / t
Total
Mio €2000 / a
€2000 / t
Local Scale
0.258
Regional Scale
Hemispheric Scale
9.196
0.210
Biodiversity
Losses
Greenhouse
Gas
Micropollutants
Total
Total
9.664
0.225
0.432
0.065
20
708
16
744
17
33
5
10.386
799
4.688
228.511
3.377
236.577
9.682
6.950
1.051
254.260
22
1'094
16
1'132
46
33
5
1'216
0.308
15.395
0.404
16.107
0.388
0.832
0.126
17.452
12
616
16
644
16
33
5
698
1.273
184.945
4.072
190.290
11.245
8.380
1.267
211.183
5
734
16
755
45
33
5
838
1.249
172.453
3.798
177.500
10.487
7.815
1.182
196.983
5
734
16
755
45
33
5
838
5.982
153.933
3.200
163.115
8.053
6.585
0.996
178.749
30
777
16
823
41
33
5
902
0.240
28.196
2.101
30.537
5.050
4.323
0.654
40.563
2
217
16
235
39
33
5
312
0.681
32.710
0.727
34.118
2.616
1.497
0.226
38.457
15
727
16
758
58
33
5
854
3.355
74.400
1.131
78.886
3.243
2.328
0.352
84.809
48
1'063
16
1'127
46
33
5
1'211
0.569
71.844
4.315
76.727
2.331
10.139
1.343
90.541
2
269
16
287
9
38
5
339
1.103
78.457
1.729
81.290
4.775
3.559
0.538
90.161
10
734
16
760
45
33
5
843
5.123
95.293
1.293
101.709
4.834
2.661
0.402
109.605
64
1'191
16
1'271
60
33
5
1'369
0.161
46.959
1.083
48.203
0.675
2.228
0.337
51.443
2
702
16
720
10
33
5
768
0.371
50.695
1.600
52.666
0.361
3.292
0.498
56.817
4
511
16
531
4
33
5
573
0.042
31.781
3.555
35.378
9.681
7.317
1.106
53.482
0
145
16
161
44
33
5
243
0.086
10.603
0.242
10.932
0.259
0.499
0.075
11.765
6
707
16
729
17
33
5
784
0.056
3.534
0.081
3.671
0.086
0.166
0.025
3.949
11
25.543
13
708
1'288.907
633
16
32.918
16
735
1'347.368
661
17
73.989
36
33
69.002
34
5
10.245
5
790
1'500.604
737
100
Annex F.2 Scenario 2020 - Optimistic: Secondary Copper Production in Europe
Scenario 2020 Optimist
Secondary Copper Europe
Asua-Bilbao
Local/Regional/Hemispheric Scale
(human health, material and crops)
Mio €2000 / a
€2000 / t
Beerse
Mio €2000 / a
€2000 / t
Bor
Mio €2000 / a
€2000 / t
Brixlegg
Mio €2000 / a
€2000 / t
Csepel
Mio €2000 / a
Eliseina
Mio €2000 / a
€2000 / t
€2000 / t
Hamburg
Mio €2000 / a
€2000 / t
Harjavalta
Mio €2000 / a
€2000 / t
Hettstedt
Mio €2000 / a
€2000 / t
Hoboken
Mio €2000 / a
Lunen
Mio €2000 / a
€2000 / t
€2000 / t
Porto Marghera
Mio €2000 / a
€2000 / t
Samsun
Mio €2000 / a
€2000 / t
Skellefteham
Mio €2000 / a
€2000 / t
Wroclaw
Mio €2000 / a
€2000 / t
Zlatna
Total
Local Scale
0.124
Regional Scale
Hemispheric Scale
2.435
0.150
Biodiversity
Losses
Greenhouse
Gas
Micropollutants
Total
Total
2.710
0.143
0.938
0.096
4
84
5
93
5
32
3
3.887
133
0.606
31.717
0.555
32.879
1.469
3.462
0.353
38.163
6
297
5
308
14
32
3
357
0.034
1.416
0.042
1.492
0.036
0.259
0.026
1.813
4
178
5
187
5
32
3
227
0.065
15.252
0.296
15.613
1.105
1.844
0.188
18.750
1
269
5
275
19
32
3
329
0.274
2.800
0.057
3.131
0.119
0.356
0.036
3.642
25
254
5
284
11
32
3
330
0.011
0.644
0.016
0.671
0.008
0.097
0.010
0.785
4
215
5
224
3
32
3
262
1.018
52.319
1.204
54.541
2.737
7.507
0.766
65.551
4
226
5
235
12
32
3
282
0.015
1.105
0.093
1.214
0.220
0.583
0.059
2.076
1
61
5
67
12
32
3
114
0.126
10.967
0.275
11.368
0.870
1.715
0.175
14.127
2
208
5
215
16
32
3
266
0.412
10.478
0.187
11.076
0.494
1.165
0.119
12.855
11
292
5
308
14
32
3
357
0.692
32.586
0.488
33.766
1.598
3.042
0.311
38.717
7
348
5
360
17
32
3
412
0.204
11.424
0.182
11.809
0.617
1.133
0.116
13.675
6
327
5
338
18
32
3
391
0.024
0.826
0.031
0.881
0.005
0.194
0.020
1.100
4
137
5
146
1
32
3
182
0.003
1.030
0.125
1.157
0.344
0.777
0.079
2.357
0
43
5
48
14
32
3
97
0.107
4.653
0.114
4.874
0.276
0.712
0.073
5.935
5
211
5
221
13
32
3
269
Mio €2000 / a
0.004
0.409
0.010
0.424
0.009
0.065
0.007
0.504
€2000 / t
2
3.720
5
205
180.062
244
5
3.823
5
212
187.605
255
4
10.050
14
32
23.848
32
3
2.434
3
251
223.937
304
Mio €2000 / a
€2000 / t
101
Annex F.2 Scenario 2020 - Optimistic: Primary Copper and Concentrate Production in Chile, Peru, Russia and Indonesia (low
impacts)
Cathode Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
1'184'200
94'242
50'514
125'655
10'000
5'360
114'232
9'091
4'873
380'772
30'303
16'242
304'617
24'242
12'994
57'116
4'545
2'436
87'577
6'970
3'736
114'232
9'091
4'873
110'780
8'829
4'725
87'458
6'970
3'730
23'322
1'859
995
376'270
29'954
16'048
21'355
1'700
911
30'324
2'414
1'294
17'084
1'360
729
42'709
3'400
1'822
170'838
13'600
7'288
8'542
680
364
38'438
3'060
1'640
46'980
3'740
2'000
Climate Change [k€]
Crops
CO2
Materials
4'674
496
451
1'503
1'202
225
346
451
437
345
92
1'482
84
119
67
168
673
34
151
185
6'201
658
598
1'994
1'595
299
459
598
580
458
122
1'973
112
159
90
224
896
45
202
246
Total [Mio€]
Total [€/t]
217
23
21
70
56
10
16
21
20
16
4
69
4
6
3
8
31
2
7
9
307
183
183
183
183
183
183
183
183
183
183
183
183
184
184
184
184
184
184
184
183
Total [Mio€]
Total [€/t]
7
1
1
2
2
0
1
1
3
2
1
6
6
16
25
25
25
25
25
25
25
25
25
25
25
25
25
CH4
59'184
6'280
5'709
19'030
15'224
2'855
4'377
5'709
5'535
4'370
1'165
18'849
1'070
1'519
856
2'140
8'560
428
1'926
2'350
2'328
247
225
748
599
112
172
225
217
171
46
738
42
59
34
84
335
17
75
92
Concentrate Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
294'000
3'544
1'941
31'196
376
206
28'360
342
187
94'534
1'139
624
75'627
912
499
14'180
171
94
21'743
262
144
28'360
342
187
109'200
1'317
719
86'211
1'040
568
22'989
277
151
218'400
2'630
1'440
218'400
2'630
1'440
Climate Change [k€]
Crops
CO2
Materials
285
30
27
92
73
14
21
27
106
84
22
212
212
61
7
6
20
16
3
5
6
23
18
5
46
46
CH4
1'527
162
147
491
393
74
113
147
567
448
119
1'130
1'130
79
8
8
25
20
4
6
8
29
23
6
59
59
102
Annex F.2 Scenario 2020 - Optimistic: Primary Copper and Concentrate Production in Chile, Peru, Russia and Indonesia (high
impacts)
Cathode Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
1'184'200
357'179
181'888
125'655
37'900
19'300
114'232
34'455
17'545
380'772
114'848
58'485
304'617
91'879
46'788
57'116
17'227
8'773
87'577
26'415
13'452
114'232
34'455
17'545
110'780
33'440
17'100
87'458
26'400
13'500
23'322
7'040
3'600
376'270
113'473
57'794
21'355
6'440
3'280
30'324
9'145
4'658
17'084
5'152
2'624
42'709
12'880
6'560
170'838
51'520
26'240
8'542
2'576
1'312
38'438
11'592
5'904
46'980
14'168
7'216
Climate Change [k€]
Crops
CO2
Materials
5'362
569
517
1'724
1'379
259
397
517
502
396
106
1'699
96
137
77
193
771
39
174
212
13'005
1'380
1'255
4'182
3'345
627
962
1'255
1'219
962
257
4'141
235
334
188
470
1'880
94
423
517
Total [Mio€]
Total [€/t]
619
66
60
199
159
30
46
60
58
46
12
197
11
16
9
22
89
4
20
25
874
523
523
523
523
523
523
523
523
524
524
524
523
523
523
523
523
523
523
523
523
Total [Mio€]
Total [€/t]
25
3
2
8
6
1
2
2
9
7
2
19
19
53
86
86
86
86
86
86
86
86
86
86
86
86
86
CH4
59'184
6'280
5'709
19'030
15'224
2'855
4'377
5'709
5'535
4'370
1'165
18'853
1'070
1'519
856
2'140
8'560
428
1'926
2'354
2'328
247
225
748
599
112
172
225
217
171
46
738
42
59
34
84
335
17
75
92
Concentrate Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
294'000
15'362
7'803
31'196
1'630
828
28'360
1'482
753
94'534
4'939
2'509
75'627
3'952
2'007
14'180
741
376
21'743
1'136
577
28'360
1'482
753
109'200
5'687
2'888
86'211
4'490
2'280
22'989
1'197
608
218'400
11'400
5'790
218'400
11'400
5'790
Climate Change [k€]
Crops
CO2
Materials
315
33
30
101
81
15
23
30
119
94
25
236
236
136
14
13
44
35
7
10
13
51
40
11
102
102
CH4
1'527
162
147
491
393
74
113
147
567
448
119
1'130
1'130
79
8
8
25
20
4
6
8
29
23
6
59
59
103
Annex F.3 Scenario 2020 - Pessimistic: Primary Copper Production in Europe
Scenario 2020 Pessimist
Primary Copper Europe
Baia Mare
Local/Regional/Hemispheric Scale
(human health, material and crops)
Mio €2000 / a
€2000 / t
Beerse
Mio €2000 / a
Bor
Mio €2000 / a
Glogow I
Mio €2000 / a
€2000 / t
€2000 / t
€2000 / t
Glogow II
Mio €2000 / a
€2000 / t
Hamburg
Mio €2000 / a
Harjavalta
Mio €2000 / a
€2000 / t
€2000 / t
Hettstedt
Mio €2000 / a
Hoboken
Mio €2000 / a
Huelva
Mio €2000 / a
€2000 / t
€2000 / t
€2000 / t
Legnica
Mio €2000 / a
Lunen
Mio €2000 / a
Pridop
Mio €2000 / a
€2000 / t
€2000 / t
€2000 / t
Samsun
Mio €2000 / a
€2000 / t
Skellefteham
Mio €2000 / a
€2000 / t
Zlatna I
Mio €2000 / a
€2000 / t
Zlatna II
Mio €2000 / a
€2000 / t
Total
Mio €2000 / a
€2000 / t
Local Scale
0.317
Regional Scale
Hemispheric Scale
9.945
0.218
Biodiversity
Losses
Greenhouse
Gas
Micropollutants
Total
Total
10.480
0.225
1.605
0.090
24
766
17
807
17
123
7
12.400
954
5.771
250.898
3.503
260.171
9.748
25.794
1.441
297.153
28
1'201
17
1'246
47
123
7
1'423
0.379
16.642
0.419
17.440
0.390
3.085
0.172
21.087
15
666
17
698
16
123
7
844
1.567
199.736
4.224
205.526
11.317
31.100
1.737
249.680
6
793
17
816
45
123
7
991
1.537
186.256
3.939
191.732
10.554
29.002
1.620
232.908
7
792
17
816
45
123
7
991
7.364
166.072
3.319
176.754
8.097
24.436
1.365
210.652
37
838
17
892
41
123
7
1'063
0.295
30.602
2.179
33.077
5.097
16.043
0.896
55.112
2
236
17
255
39
123
7
424
0.838
35.441
0.754
37.033
2.631
5.553
0.310
45.528
19
787
17
823
58
123
7
1'011
3.692
80.034
1.172
84.897
3.265
8.640
0.483
97.284
53
1'144
17
1'214
47
123
7
1'391
0.701
79.460
4.475
84.636
2.343
32.950
1.840
121.769
3
297
17
317
9
123
7
456
1.358
84.715
1.793
87.866
4.805
13.206
0.738
106.615
13
791
17
821
45
123
7
996
6.307
103.651
1.341
111.299
4.861
9.872
0.551
126.584
79
1'295
17
1'391
61
123
7
1'582
0.198
50.004
1.123
51.325
0.675
8.269
0.462
60.731
3
746
17
766
10
123
7
906
0.456
56.236
1.659
58.352
0.361
12.217
0.457
71.386
5
568
17
590
4
123
5
722
0.052
34.032
3.688
37.771
9.790
27.150
1.516
76.227
0
155
17
172
44
123
7
346
0.106
11.466
0.251
11.823
0.260
1.851
0.103
14.037
7
765
17
789
17
123
7
936
0.069
3.822
0.084
3.975
0.087
0.618
0.035
4.714
14
31.006
15
765
1'399.011
687
17
34.142
17
796
1'464.158
719
17
74.505
37
124
251.388
123
7
13.815
7
944
1'803.867
886
104
Annex F.3 Scenario 2020 - Pessimistic: Secondary Copper Production in Europe
Scenario 2020 Pessimist
Secondary Copper Europe
Asua-Bilbao
Local/Regional/Hemispheric Scale
(human health, material and crops)
Mio €2000 / a
€2000 / t
Beerse
Mio €2000 / a
€2000 / t
Bor
Mio €2000 / a
€2000 / t
Brixlegg
Mio €2000 / a
€2000 / t
Csepel
Mio €2000 / a
Eliseina
Mio €2000 / a
€2000 / t
€2000 / t
Hamburg
Mio €2000 / a
€2000 / t
Harjavalta
Mio €2000 / a
€2000 / t
Hettstedt
Mio €2000 / a
€2000 / t
Hoboken
Mio €2000 / a
Lunen
Mio €2000 / a
€2000 / t
€2000 / t
Porto Marghera
Mio €2000 / a
€2000 / t
Samsun
Mio €2000 / a
€2000 / t
Skellefteham
Mio €2000 / a
€2000 / t
Wroclaw
Mio €2000 / a
€2000 / t
Zlatna
Total
Local Scale
0.152
Regional Scale
Hemispheric Scale
2.566
0.155
Biodiversity
Losses
Greenhouse
Gas
Micropollutants
Total
Total
2.872
0.144
3.482
0.102
5
88
5
98
5
120
4
6.600
227
0.742
33.775
0.570
35.087
1.480
12.849
0.376
49.791
7
316
5
328
14
120
4
466
0.042
1.491
0.043
1.575
0.036
0.960
0.028
2.600
5
187
5
197
5
120
4
326
0.080
16.062
0.304
16.446
1.113
6.846
0.200
24.604
1
283
5
289
20
120
4
433
0.335
2.924
0.059
3.317
0.120
1.321
0.039
4.797
30
267
5
302
11
120
4
437
0.013
0.671
0.016
0.700
0.008
0.361
0.011
1.079
4
224
5
233
3
120
4
360
1.246
54.967
1.236
57.449
2.754
27.859
0.815
88.877
5
237
5
247
12
120
4
383
0.019
1.168
0.096
1.282
0.222
2.163
0.063
3.730
1
65
5
71
12
120
4
207
0.154
11.564
0.282
12.000
0.876
6.364
0.186
19.426
3
218
5
226
17
120
4
367
0.503
11.128
0.192
11.823
0.498
4.322
0.127
16.770
14
310
5
329
14
120
4
467
0.847
34.650
0.501
35.997
1.609
11.288
0.330
49.224
9
368
5
382
17
120
4
523
0.249
12.046
0.187
12.482
0.621
4.203
0.123
17.429
7
345
5
357
18
120
4
499
0.029
0.887
0.032
0.948
0.005
0.721
0.021
1.695
5
147
5
157
1
120
4
282
0.004
1.077
0.128
1.209
0.348
2.882
0.084
4.523
0
45
5
50
14
120
4
188
0.131
4.889
0.117
5.137
0.278
2.641
0.077
8.134
6
222
5
233
13
120
4
370
Mio €2000 / a
0.005
0.431
0.011
0.447
0.009
0.241
0.007
0.703
€2000 / t
3
4.549
6
215
190.295
258
5
3.926
5
223
198.771
270
4
10.121
14
120
88.501
120
4
2.589
4
351
299.981
407
Mio €2000 / a
€2000 / t
105
Annex F.3 Scenario 2020 – Pessimistic: Primary Copper and Concentrate Production in Chile, Peru, Russia and Indonesia (low
impacts)
Cathode Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
1'184'200
365'661
193'197
125'655
38'800
20'500
114'232
35'273
18'636
380'772
117'576
62'121
304'617
94'061
49'697
57'116
17'636
9'318
87'577
27'042
14'288
114'232
35'273
18'636
110'780
34'200
17'987
87'458
27'000
14'200
23'322
7'200
3'787
376'270
225'476
118'422
21'355
12'800
6'720
30'324
18'176
9'542
17'084
10'240
5'376
42'709
25'600
13'440
170'838
102'400
53'760
8'542
5'120
2'688
38'438
23'040
12'096
46'980
28'100
14'800
Climate Change [k€]
Crops
CO2
Materials
8'925
947
861
2'870
2'296
430
660
861
835
659
176
4'969
282
400
226
564
2'256
113
508
621
41'844
4'440
4'036
13'455
10'764
2'018
3'095
4'036
3'914
3'090
824
23'264
1'320
1'874
1'056
2'640
10'560
528
2'376
2'910
Total [Mio€]
Total [€/t]
866
92
84
278
223
42
64
84
81
64
17
486
28
39
22
55
221
11
50
61
1'432
731
731
731
731
731
731
731
731
729
729
729
1'291
1'291
1'291
1'291
1'291
1'291
1'291
1'291
1'291
Total [Mio€]
Total [€/t]
23
2
2
7
6
1
2
2
8
7
2
14
14
45
77
77
77
77
77
77
77
77
77
77
77
65
65
CH4
245'973
26'100
23'727
79'091
63'273
11'864
18'191
23'727
22'927
18'100
4'827
108'741
6'170
8'761
4'936
12'340
49'360
2'468
11'106
13'600
10'084
1'070
973
3'242
2'594
486
746
973
940
742
198
4'987
283
402
226
566
2'264
113
509
623
Concentrate Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
294'000
8'520
4'656
31'196
904
494
28'360
822
449
94'534
2'739
1'497
75'627
2'192
1'198
14'180
411
225
21'743
630
344
28'360
822
449
109'200
3'167
1'735
86'211
2'500
1'370
22'989
667
365
218'400
5'150
2'810
218'400
5'150
2'810
Climate Change [k€]
Crops
CO2
Materials
689
73
66
222
177
33
51
66
256
202
54
416
416
144
15
14
46
37
7
11
14
53
42
11
90
90
CH4
8'190
869
790
2'633
2'107
395
606
790
3'040
2'400
640
5'420
5'420
440
47
42
142
113
21
33
42
163
129
34
325
325
106
Annex F.3 Scenario 2020 – Pessimistic: Primary Copper and Concentrate Production in Chile, Peru, Russia and Indonesia (high
impacts)
Cathode Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Karabash
Krasnouralsk ( Svyatogor)
Mednogorsk
Nadezhdinsky (Nadezhda)
Norlisk
Pechenganikel
Monchegorsk (Severonikel)
Sredneuralsk (Revda)
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
1'184'200
1'178'030
603'152
125'655
125'000
64'000
114'232
113'636
58'182
380'772
378'788
193'939
304'617
303'030
155'152
57'116
56'818
29'091
87'577
87'121
44'606
114'232
113'636
58'182
110'780
110'327
56'367
87'458
87'100
44'500
23'322
23'227
11'867
376'270
761'144
389'382
21'355
43'200
22'100
30'324
61'344
31'382
17'084
34'560
17'680
42'709
86'400
44'200
170'838
345'600
176'800
8'542
17'280
8'840
38'438
77'760
39'780
46'980
95'000
48'600
Climate Change [k€]
Crops
CO2
Materials
10'932
1'160
1'055
3'515
2'812
527
808
1'055
1'022
807
215
6'095
346
491
277
692
2'768
138
623
760
87'363
9'270
8'427
28'091
22'473
4'214
6'461
8'427
8'170
6'450
1'720
48'619
2'760
3'919
2'208
5'520
22'080
1'104
4'968
6'060
Total [Mio€]
Total [€/t]
2'136
227
206
687
549
103
158
206
200
158
42
1'319
75
106
60
150
599
30
135
165
3'654
1'803
1'803
1'803
1'803
1'803
1'803
1'803
1'803
1'803
1'803
1'803
3'505
3'506
3'506
3'506
3'506
3'506
3'506
3'506
3'505
Total [Mio€]
Total [€/t]
65
7
6
21
17
3
5
6
24
19
5
40
40
129
222
222
222
222
222
222
222
222
222
222
222
183
183
CH4
245'973
26'100
23'727
79'091
63'273
11'864
18'191
23'727
22'927
18'100
4'827
108'741
6'170
8'761
4'936
12'340
49'360
2'468
11'106
13'600
10'084
1'070
973
3'242
2'594
486
746
973
940
742
198
4'987
283
402
226
566
2'264
113
509
623
Concentrate Production
Air Pollution [k€]
Country
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Chile
Peru
Peru
Peru
Indonesia
Indonesia
Location Cu Smelter (Name)
La Negra (Altonorte)
Chagres
Chuquicamata
El Teniente (Caletones)
Paipote
Las Ventanas
Potrerillos (El Salvador)
IIo
La Oroya
Gresik
Pimary Cu
Human Health
Human Health
t /a
Mortality
Morbidity
294'000
36'849
18'754
31'196
3'910
1'990
28'360
3'555
1'809
94'534
11'848
6'030
75'627
9'479
4'824
14'180
1'777
905
21'743
2'725
1'387
28'360
3'555
1'809
109'200
13'680
6'979
86'211
10'800
5'510
22'989
2'880
1'469
218'400
22'200
11'300
218'400
22'200
11'300
Climate Change [k€]
Crops
CO2
Materials
768
82
74
247
198
37
57
74
286
226
60
464
464
319
34
31
102
82
15
24
31
118
93
25
198
198
CH4
8'190
869
790
2'633
2'107
395
606
790
3'040
2'400
640
5'420
5'420
440
47
42
142
113
21
33
42
163
129
34
325
325
107
108