Supporting information A: Extended method, assumptions, and data

Branco W. Schipper a, c, *, Hsiu-Chuan Lin a, c, Marco A. Meloni a, c, Kjell Wansleeben d, Reinout Heijungs a,b, and Ester van der Voet a

a Institute of Environmental Sciences, Leiden University, Einsteinweg 2, 2333 CC, Leiden, The Netherlandsb Department of Econometrics and Operations Research, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlandsc Faculty of Technology, Policy and Management, Technical University Delft, Jaffalaan 5, 2628 BX, Delft, The Netherlandsd Department of Spatial Planning, Municipality of Pijnacker-Nootdorp, Oranjeplein 1, 2641 EZ, Pijnacker, The Netherlands* *E-mail: b.w.schipper@umail.leidenuniv.nl

In supporting information A, we elaborate on assumptions and give a more detailed explanation of the calculation methods, sources and accuracy of the estimations on the level of subcategories in the bottom-up method. Subcategories that were left out are also discussed along with the various reasons for not considering them in our estimations. Further, we provide an overview of basic input data, sources and assumptions at the end of each bottom-up category.

Bottom-up method

1. Infrastructure

The formula used in calculations for all sub-categories under infrastructure can be found in Table A.1. The data used for calculation are shown in Table A.2. The following section describes how the copper demand for all the subcategories under infrastructure is calculated. These subcategories include electricity generation, electricity transmission and distribution, telecommunication and broadcasting networks, water supply and distribution, traffic and streetlights, and rail systems.

1.1 Electricity generation

For the copper demand for electricity generation, instead of calculating a stock and deducing the demand from that, we have used Ecoinvent data to calculate the demands directly. In this database, each production method of electricity has its copper content, lifespan, and possible maintenance, which has all been taken into account to get an amount of copper used per kWh. Note that this is a flow requirement, and not a stock requirement. Using CMLCA software, we can deduce the copper demand for 1 kWh of electricity, sorted by production method (Dones et al., 2007). The data available in EcoInvent v2.2 is data from European electricity plants from 2004, and we assume that this data will remain relevant for the time period until 2100. Likewise for PV solar cells, their copper requirement and generation data dates from 2007. Not all electricity generation technologies are represented in EcoInvent, in which case we assumed them to have similar copper requirement to other technologies. This seems like an absurd assumption to make, however copper demand per kWh do not see large differences, with the exception of solar PV, and thus effects on projections are minimal. Finally, to make up for the lack of data on Carbon Capture and Sequestration (CCS) in EcoInvent, we assumed an increase of copper requirement per kWh of 20% compared to the same technology without CCS. The copper requirement increase should reflect reduced efficiencies of CCS.

To determine the total required electricity and have this change along with our SSP datasets, we assumed electricity demand to be purely driven by GDP. We have found a relation between electricity demand and GDP by means of a

regression with GDP and electricity demand data from GEA (IIASA, 2012). The R2 of the relation is 0.9959 indicating the accuracy of the relation. However it should be said that the relation does not have to hold for higher GDP values than projected by GEA (IIASA, 2012). The electricity mix was modeled after GEA projections, which show strong increases in renewable technologies, up to 100% in 2100. To accommodate for SSP scenarios with little or no investment in renewable technologies, we slowed the increase of renewable electricity to 45% in 2100, with the production mix of GEA (IIASA, 2012).

Since no stock figures are present for generation of electricity the outflow of used copper has been calculated with a different method than used elsewhere in the model. For each generation method the lifespan of a power plant or electricity generator was found, and it was assumed that the copper outflow would be as large as the sum of the copper inflow per generation technology of the year of one lifespan before, up to a maximum of 30 years. For example, if the copper inflow of coal power plants was 1 Mton in the year 2000, the outflow of copper in the same category would be 1 Mton in 2030. The sum of each generation method is taken, with their different lifespans to come to a total copper outflow for the entire category. A maximum lifespan of copper of 30 years was assumed for two reasons. Firstly, copper has to be replaced or maintained even before a power plant is torn down at the end of its lifespan. Copper from cables powering electric trains for example, are replaced completely on average every 30 years. Secondly, reliable data for electricity production only goes back to around 1980. Any data before that sees large gaps for several countries and regions, and is therefore unreliable and incomplete. The data of electricity generation per source from 1980-2004 was obtained from the worldbank database.

1.2 Electricity transmission and distribution

Electricity transmission and distribution copper demand uses a similar calculation method as used for electricity generation. The ecoinvent database contains data on transmission and distribution cables. For example the copper content of a kilometer of cable, but also the average length of cable that needs replacing for supplying a kWh of electricity. Electricity passes through transmission cables, which have a high voltage, and pass over to the distribution network, which consists of medium voltage cables, followed by low voltage cables. Each sort of cable has its own copper content and length that needs replacing. Therefore, a distinction is made between low voltage and medium voltage consumers, based on figures from the European Environment Agency (EEA, 2012). We assume all industry and transport (trains mostly), use medium voltage electricity, whereas agriculture, households and services use low voltage electricity (EEA, 2012).

Since ecoinvent only has data on copper conductor cables for all distribution cables (low and medium voltage), and not aluminium conductor cables, we instead based the copper content per meter on (Krook et al., 2011). Data on the length of cable per kWh passing through used (giving an indication of the lifespan) however remains to be adopted from EcoInvent for these cables.

Since stock figures are not present for electricity transmission and distribution, the outflow of copper from the stock is calculated with the same method as the electricity generation category uses. The copper outflow is assumed as large as the inflow 30 years earlier (Ruhrberg, 2006).

1.3 Rail systems

Combining data from the International Union of Railways and the International Energy Agency, we have made projections of the copper demand by railway systems until 2100. Due to the lack of quality data on a global level, proxy estimations have been made to represent this category. However, since the total copper demand of railways is minimal in comparison to other (sub)categories, the inaccuracy of this subcategory has limited impact on the total results. Nevertheless, the paragraphs below give a detailed description of the calculations and formulas used to determine the copper demand of rail systems. The general formula is similar to other subcategories:

stock increase+stock ¿ the previous year / lifetime (Eq. A.1)

The international Union of Railways provides detailed per country data on the total length of railways tracks, and whether or not these are electrified tracks. The length of track in the year 2007-2013 shows both increases and decreases (UIC, 2014). For our projections we have assumed the general trend of total track length is increasing although at a slow rate. The formula guiding the increase in tracks is given equation A.2:

(Track length¿the previous year)+(maximumestimated tracks−track length¿the previous year )×(growthfactor)(Eq. A.2)

wherein our assumption for the maximum amount of tracks has been set at 1,500,000 track-km, and our growth factor at 2% (UNIFE, 2012) To put it in perspective, currently there are roughly 1,000,000 km of railways. Equation A.2 gives a slowing increase of railways. The total railway length growth rate is 1% at the start of our projections, slowing to 0.13% in the year 2100.

Not all railways use copper however, only the electrified tracks require copper. In 2008, roughly 27% of all in-use railways were electrified, and we have assumed this percentage will grow. To determine the growth curve of electrified tracks we used the same formula as for the total track length, replacing track length figures with percentages of electrification. For our renewable projection we assumed the maximum percentage of electrification of tracks is 80% because of the potential of zero-CO2 of electricity, whereas in the BAU projection this is just 30%. The growth factor in this formula is set equal to the GDP growth rate in both cases, because electrification of railroads is money intensive.

Furthermore, we have included High-Speed rail (HSR) as a separate part of our calculations. HSR uses more copper per km (10,000 kg), however the length of HSR is much lower compared to conventional electrified tracks. In 2015, almost 30,000 km of HSR (2.95% of total railroads) was in use (UIC, 2015), and this will grow due to increases in countries including China and Brazil (Dulac, 2013). We estimate that HSR will constitute 3.81% of all railway tracks in the year 2100. This estimation was made assuming a maximum total length of HSR of 4% of all railways.

The outflow of copper from the rail system stock is calculated using the method from Elshkaki and Graedel (2013). The annual discarded flow is calculated by the initial stock divided by the life time. Since the discarded flow fluctuates with the annual life stock, the stock from the previous year divided by the lifetime of rail system is calculated to represent the outflow for both the electrified tracks and the HSR rail system.

1.4 Traffic and streetlights

Another challenge in the infrastructure category is the copper demand due to traffic and street lighting. Since it is difficult to determine an exact amount of street lamps worldwide, let alone estimate this, we instead derived a figure from the electricity use by street lamps (Brown, 2011).

We then divided these figures by the population to get a per capita reading of street and traffic lights. Similar to the assumptions in our railroads calculation we used the equation A.3 to have the lights per capita slowly grow.

( Number of lights¿ the previous year per capita )+( Maximum estimated number oflights per capita−number of lights¿the previous year per capita )×(growth factor )

(Eq. A.3)

Our current figures of lights per capita read 1.21 traffic and 4.70 street lights per capita, this is expected to grow to maximums of 2.5 and 9, doubling the per capita figures. For the grow factor we use the annual GDP growth from our SSPs. Once again, our figures are rough estimates similar to the railroad subcategory. In this subcategory however, we are dealing with a larger projected demand, of about 1 million tonne of copper annually, which is significant yet still a small figure, certainly for projections in 2100.

The outflow of copper from traffic and street light stock is calculated using the method from Elshkaki and Graedel (2013). The annual discarded flow is calculated by the initial stock divided by the life time. Since the discarded flow fluctuates with the annual life stock, the stock from the previous year divided by the lifetime of each lighting system is calculated to represent the outflow. The replacement stock of the street lamps and traffic lights is multiplied by their corresponding  copper content to obtain the weight of the replacement stock.

1.5 Water supply and distribution

Copper has been used in water supply systems, especially in the latter half of the 20th century. However, the copper was mostly used in domestic supply lines, meaning mostly inside of residential housing and commercial buildings. This copper is mainly taken into account in the buildings section of the research. As for the water supply outside of housing, from water cleaning installations to homes, other materials were and are more common. Due to high price and even laws that limits the concentration of lead and copper, distribution pipes have been replaced with other materials, such as plastics. Because of this, we have not taken the copper use for supply and distribution of water into account as a separate section.

1.6 Telecommunication and broadcasting networks

Telecommunications systems like telephone, TV, internet and other broadcasting methods have all used copper in their networks. But with technologies like optical fiber performing better in quality and price, networks have swapped over to this cheaper and better option. Most networks use a combination of optic fiber and coaxial, the latter of which does contain copper and is used in the ‘last mile’, since this is often expensive to replace. But the majority of the network is composed of optical fiber. On top of that, when part of a network that still is coaxial cable needs replacing, the choice of material easily falls to optical fiber for its price and quality. Therefore, we have not included telecommunication and broadcasting networks in our estimations of copper demand.

Table A.1. Copper demand calculation equations for infrastructure

Category Calculation formula Parameter description Parameter values

Power generation (Dgen ,a)D gen ,a=∑

i=1

14

(E¿¿ gen , i , a ∙ mgen ,i , a¿)¿¿

Egen ,i , a=ai , a ∙ f (GDPa)

Egen ,i , a = Electricity produced with generation type i in year A

mgen , i ,a = Copper demand per produced kWh with generation type i in year Aa i ,a = fraction of energy produced by generation type i in year A

f (GDPa) = regressional relationship found between GDP and electricity demand based on GEA projections

Egen ,a (i = 1,2,3,…,14) a

mgen , a (i = 1,2,3,…,14) b

a i ,a (i = 1, 2, 3, …, 14) a

Distribution & transmission (Ddis ,a) Ddis ,a=∑

i=1

5(E¿¿ pro , a ∙ P sec, i ,a¿∙ msec, i¿¿)¿

¿¿

Epro ,a=∑i=1

14

Egen , i ,a

Epro ,a= Global energy production in year APsec ,i , a= Percentage of global energy used in consuming sector imsec ,i= Copper demand per distributed kWh in sector iEgen ,i , a= Electricity produced with generation type i in year A

Epro ,a (i = 1,2,3,…,5) a

Psec ,i , a( = 1,2,3,…,5) c

msec ,i= 3.12E-4, 3.53E-5 kg b

Traffic & street lights (Dlig, a) Dlig, a=∑

i=1

2

¿¿¿

∙mlig ,i ¿+(N lig ,i , a−1

r lig,i∙ mlig, i)¿

N lig ,i ,a=nlig, i ,a ∙ Pa

N lig ,i ,a= Number of light i usedin year AN lig ,i ,a−1= Number of light i usedin year A-1mlig , i= Copper content per light irlig ,i = Average residence time of copper in light inlig ,i , a= Number of light i used per capita in year APa = Population in year A

N lig ,i ,a (i = 1,2) d

mlig , i= 0.72, 0.15 kgrlig ,i= 30, 30 years

Rail systems ¿)Drai ,a=∑

i=1

2

¿¿¿

∙ mrai,i ¿+(N rai ,i , a−1

rrai, i∙ mrai ,i)¿

N rai ,i ,a = Length of tracks i usedin year AN rai ,i ,a−1 = Length of tracks i used in year A-1mrai, i= Copper content per kilometer of track irrai ,i= Average residence time of copper in tracks i

N rai ,i ,a (i = 1,2) e

mrai, i = 4450, 10000 kgrrai ,i= 30, 30 yearsf

a: IIASA, 2012b: Frischknecht et al., 2005c: EEA, 2012d: Brown, 2011e: UIC, 2014

f: Ruhrberg, 2006

Table A.2. Data used for infrastructure

Sub-category Cu weight (kg/unit) Life span (year) Assumptions

Traffic lights 0.72 kg /light a 30b An average is taken for the traffic lights which assumes that all lights have the same height and length and thus also the same copper content

Street lights 0.15 kg /light c 30 b An average is taken for the street lights which assumes that all lights have the same height and length and thus also the same copper content

Conventional rail system 4450 kg/ km d 30d There is a constant increase in the amount of rail system. This growth rate was determined at 2% e

High speed rail system 10000 kg /km d 30d The percentage of high speed rail increases from 2.95% of the total rail system in 2015 till 3.81% in 2100 for both scenarios

Electricity generation - - Electricity generation is calculated differently, for assumptions see Table A.3.

Electricity distribution - - Electricity distribution is calculated differently, for assumptions see Table A.4.

a: C40, 2015b: BRE Group Enquiries, 2011c: Auckland Transport, 2013d: UIC, 2014e: UNIFE, 2012

Table A.3. Data used for power generation

Sub-category Copper demand(g/kWh)a

% of total electricity demand 2100 - Renewable scenario

% of total electricity demand 2100 - Baseline scenario

Assumptions

Nuclear 0.0106 27.99 9.98

Oil 0.0152 0 0.52

Coal 0.0205 0 10.93

Coal w/ CCS 0.0246 0.08 3.02 No data available; assumed 20% additional copper demand than original technology due to reduced efficiency of CCS.

Gas 0.00735 0.02 20.57

Gas w/ CCS 0.00882 0.08 9.38 No data available; assumed 20% additional copper demand than original technology due to reduced efficiency of CCS.

Hydro 0.000523 6.92 15.94

Biomass/waste 0.0278 0.35 1.07

Biomass w/ CCS 0.03336 0.77 0.07 No data available; assumed 20% additional copper demand than original technology due to reduced efficiency of CCS.

Geothermal See nuclear 1.03 0.88 No data available; assumed similar to copper demand nuclear

Wind, onshore 0.0566 14.33 14.70 No distinction between wind on- and offshore. Assumed copper demand off onshore wind

Wind, offshore 0.0572 See notes Wind, onshore See notes Wind, onshore No distinction between onshore and offshore is made, copper demand is similar between the two, thus the average is taken.

Solar | PV 0.504 23.04 8.55

Solar | CSP See offshore wind 25.40 4.33 No data available; assumed similar to copper demand offshore wind

Other See offshore wind 0.01 0.06Unknown what this category is, assumed to be various ocean generation technologies, and assumed similar copper demand to offshore wind

a: Frischknecht et al., 2005 ; Data only for production methods without CCS

Table A.4. Data used for power distribution

Sector Electricity use of total consumption a

Assumed voltage

Copper demand [g/kWh]b

Agriculture 1,80% Low 3,12E-04

Households 29,70% Low 3,12E-04

Services 29,70% Low 3,12E-04

Industry 36,50% Medium 3,53E-05

Transport 2,40% Medium 3,53E-05

a: EEA, 2012b: Medium voltage electricity uses high and medium voltage lines; low voltage electricity uses low voltage lines in addition to high and medium voltage lines. The replacement of these cables per kWh and copper content per km is available in Frischknecht et al., 2005

2. Building construction

2.1 Residential buildings

Data to estimate the total global residential building stock is scarce. In 1990 the global number of households was estimated to be around 1.3 billion (Ironmonger et al., 2000) and is expected to grow to around 3.7 billion in 2050. To estimate the total residential building stock for medium and long term future, the GEA population projection is used, divided by the expected global household size. In 2015 the average household size was 4.1 persons with a strong declining trend (Ürge-Vorsatz et al., 2015). Household size is assumed to decline to 2.7 persons per household in 2100 (MacKellar et al., 1995).

The stock calculations are then used to estimate demand until 2100. For this the copper content per building is needed. The copper content per building varies greatly by region, type and size. In less developed regions copper content can be lower than 20 kilogram per building (Gerst, 2009; Nie et al., 2012) in developed regions copper content easily exceeds 200 kilogram (Gerst, 2009; van Beers et al., 2007). The global average copper content of a building is calculated based on multiple factors. The percentage of less developed households versus households in developed regions, the expected increase of the copper content of buildings due to increase of electronic use, and the changing ratio less-more developed households because less developed regions are expected to grow faster (UNDESA, 2015).

In addition to copper content, the average lifespan for buildings is needed to estimate the demand for the replacement of copper in old buildings. The residence time is estimated differently by multiple sources (Graedel et al., 2002; Spatari et al., 2002; van Beers et al., 2007). This is mainly due to different applications of copper in a building. Estimations range from 20 to 50 years. As wiring and plumbing are the dominant uses of copper and their residence time is on average 30 years this will be used as an average for all copper in residential buildings.

The annual outflow of copper from residential buildings is calculated using the method from Elshkaki and Graedel (2013). The annual discarded flow is calculated by the initial stock divided by the life time. Since the discarded flow fluctuates with the annual life stock, the stock from the previous year divided by the lifetime of residential buildings is calculated to represent the outflow. The replacement stock of the residential buildings multiplied by the medium range of the copper content of residential buildings to obtain the weight of the replacement stock.

2.2 Service buildings

The second part of the building stock is the combination of commercial and industrial buildings. Copper content of these buildings shows extreme variations. Ranges have been estimated to be from similar to residential buildings (20-250 kg) to over 60,000 kg per building (van Beers et al., 2007). This is mainly due to difference in size and architectural uses of copper in some buildings. In addition to this extreme range there is no data available for the total building stocks of commercial and industrial buildings. Because of this data scarcity it makes no sense to make a distinction between these types of buildings, and therefore, both are grouped in the category called service buildings.

Because of the ranges of copper used in service buildings and absence of data, the copper demand for service buildings has been based on the residential building demand. Copper use per square meter of service buildings is assumed to be lower by a factor of two compared to residential buildings (Nie et al., 2012). To estimate the global service floor area the global in use floor space is used. In 2011, 145 billion m2 was in use (Navigant Research, 2012). This total floor space is expected to grow 2.1-2.2 percent per year to 182 billion square meter ten years later. Of this global floor space around 75 percent is residential floor area and 25 percent service area (Businesswire, 2012). In Europe the percentage service floor area is slightly higher than in less developed countries and therefore the ratios are assumed to be slowly shifting towards more service floor area (UNEP, 2011). Calculations give the amount of service floor space in square meter and the copper content of service buildings per square meter.

The annual outflow of copper from service buildings is calculated using the same method as in residential buildings.

Table A.5. Copper demand equations for building construction

Category Calculation formula Parameter description Parameter values

Residential buildings (Dres ,a)

Dres ,a=¿¿

∙mres, a¿+(Nhh ,a−1

rres∙ mres, a)

N hh , a= Number of householdsin year ANhh,a-1 = Number of householdsin year A-1mres ,a= Average copper content of a residential building in year Arres = Average residence time of copper in residential buildings

Nhh= world population * household size abc

mres = “variable” kg de

rres = 30 years fgh

Service buildings (Dser ,a ¿

Dser ,a=( A ser , a

A res ,a∙ Dℜ snew , a

2 )+(

A ser , a−1

r ser∙mser ,a)

A ser ,a= Global floor area of service buildings in year AAres ,a= Global floor area of residential buildings in year ADℜsnew , a= Demand for new residential buildings in year A

A ser ,a−1= Global floor area of service buildings in year A-1r ser = Average residence time of copper in service buildingsmser ,a= Average copper content per m² of service building in year A

A = world population * floor area /capita i

r ser = 30 years fgh

mser ,a= “variable” kg dj

a: MacKellar et al., 1995 d: IIASA, 2012 g: Graedel et al., 2002 j: Zhang et al., 2014b: UNDESA, 2015 e: Gerst, 2009 h: Spatari et al., 2002c: SSP Database, 2012-2015 f: van Beers et al., 2007 i: Dulac, 2014

Table A.6. Data used for building construction

Sub-category Cu weight (kg/dwelling) ab

Cu weight (kg/m²)

Life span (year) cde

Assumptions

Residential buildings

62-125 0.92 - 3.18 30

● Household size and world population determine required building stock● Household size is assumed to decrease to 2.7 by 2100● Copper content per buildings increases as we expect increase in use of electrical applications● Copper content per building in less developed countries increases faster than in developed

countries

Service buildings

-0.46 - 1.59

(half of residential)

30● Service buildings require half the amount of copper per m² compared to residential buildings b

● Per capita floor area is assumed to increase● Per capita service area is assumed to increase f

a: Gerst, 2009 c: van Beers et al., 2007 e: Spatari et al., 2002b: Nie et al., 2012 d: Graedel et al., 2002 f: Businesswire, 2012

3. Industrial durables

3.1 Agricultural durables

Stock calculations for agricultural durables are based on the hectares of arable land and the average number of machinery per hectare. Arable land is expected to increase slowly (Bruinsma, 2009) as is the number of machines per hectare (Trading economics, 2015). Saturation of machines is assumed to be 500 pieces per 10.000 hectare. This is based on number of machines per hectare in developed regions. In combination with the lifespan of around eight years and an increasing copper content per tractor, with 20 kilogram per tractor (Dyer and Desjardins, 2006) the annual copper demand is estimated.

The annual outflow of copper from agricultural durables is calculated using the same method as in residential buildings.

3.2 Industrial durables

Estimating for the industrial demand has proven to be one of the most difficult ones. Machines are used in a lot of different machines with different sizes, functions and copper contents. For estimations it is assumed most copper uses are related to electricity consumption. The electricity used is conducted by copper. We assume there is a relation between the electricity used by sector and the copper demand. Based on the copper demand for the agricultural sector and the energy used by the agricultural and industrial sector we are able to calculate industrial copper demand. For the ratio agricultural-industrial energy use, data from the European Environment Agency (EEA, 2015) has been used.

The annual outflow of copper from industrial durables is calculated by the annual discarded flow of agricultural durables divided by the ratio of the agricultural-industrial energy use following the assumption that the copper usage is a function of the electricity use.

Table A.7. Copper demand equations industrial durables

Category Calculation formula Parameter description Parameter values

Agricultural machinery (Dagr , a)

Dagr , a=¿¿

∙ magr ,a ¿+(Nagr , a−1

r agr∙ magr , a)

Nagr , a = Number of tractors used in year ANagr , a−1= Number of tractors used in year A-1magr , a= Average copper content of a tractor in year Aragr = Average residence time of copper in tractors

Nagr = arable land * machinery/km² ab

magr , a= 29, … ,42 kg c

ragr = 8 years c

Industrial machinery (Dind ,a)

Dind ,a=Dagr , a

Ragr/ ind

Dagr , a= Total copper demand for agricultural machinery in year ARagr /ind= Ratio energy used in agriculture-industry

Ragr /ind = 0.05 d

Table A.8. Data used for industrial durables

Sub-category Cu weight (kg/tractor) c

Life span (year) c

Energy use (% of total consumption) d

Assumptions

Agriculture >20 8 2,89● Based on arable land and tractors per hectare● Arable land is expected to increase a

● Number of tractors per hectare is expected to increase b

Industry N.A. N.A. 26,94

● Calculations are based on copper demand of agriculture● Assumed is a relation between the electricity use and copper use in each sector● Electricity use is for Europe in 2015, global electricity use is assumed to develop towards these ratios in 2100

Note table A.7 and A.8:a: Bruinsma, 2009 b: Trading economics, 2015 c: Dyer and Desjardins, 2006 d: EEA, 2015

4. Transport

Table A.9. Copper demand equations transportation

Category Calculation formula Parameter description Parameter values

Transportation (Dtra ,a) Dtra ,a=∑

i=1

4

¿¿

∙mtra,i ¿+(N tra ,i , a−1

r tra,i)¿

N tra ,i ,a = Number of transport type i used in year AN tra ,i ,a−1= Number of transport type i used in year A-1mtra, i= Copper content per transport type ir tra ,i= Average residence time of transportation type i

N tra ,i ,a (i = 1,2,3,4) = world population * vehicles/capita a

N tra ,i ,a (i = 2,3,4) = GDP growth * multiplier * vehicles in year A-1N tra ,i ,a−1 (i = 1,2,3,4)mtra, i (i = 1,2,3,4)rtra ,i= (i = 1,2,3,4)

a: WBCSD, 2004

Table A.10. Average copper content for gasoline and electric motorcycles

Type of motorcycle Weight (kg)Copper content (kg/unit)

Assumed stock share (%)Gasoline motorcycles Electric motorcycles

Scooter (50 cc) 80.00 a 1.06 12.52 30%

Scooter (125 cc) 99.88 b 1.33 15.63 50%

Motorcycle (250 cc) 202.00 c 2.69 31.61 10%

Motorcycle (650 cc) 216.24 d 2.88 33.84 5%

Motorcycle (1000 cc) 237.26 e 3.16 37.13 5%

Average copper content (kg/unit) 1.55 18.28 -

a: Meuwissen and Spätjens, 2013b: Genuine Scooters Company: Product name: Buddy 125 (Genuinescooter, 2016)c: Kymco: Product name: Venox 260. (Kymco, 2016)d: Kawasaki: Product name: VERSYS® 650 ABS (Kawasaki, 2015a)e: Kawasaki: Product name: 998 cc Ninja H2TMR (Kawasaki, 2015b)

Table A.11. Data used for motorcycles

Sub-category Copper content (%)

Cu weight (kg/unit) Life spana (year) Assumptions

Gasoline motorcycles b 1.33% 1.5517.5 ( in 2015)17.6 (in 2020)17.7 (in 2025)17.8 (in 2035)17.8 (2040-2100)

● Average copper content is weighed by assumed stock shareBefore 2050, global motorcycle stock is assumed the same as WBCSD IEA/SMP Transport Model a

After 2050, the motorcycle stock is calculated using 5 year growth rate of 2045-2050.For SSP 1, 2, and 4, where a renewable and electrification of transport system is set up, the share of gasoline motorcycle remains the same as in the cars model.For SSP 3 and SSP5, all motorcycles are assumed to be gasoline motorcycles.

Electric motorcycles c 16% 18.28

a: WBCSD, 2004b: Rauch et al., 2007c: Braconi, 2014

4.1 Motorcycle

Two wheel vehicles function as a main transportation method in many parts of the world, especially in developing countries. The amount of copper in motorcycles is calculated by multiplying the total weight of a motorcycle by percentage of copper found in literature. To account for the variety of different products sizes, we assumed a percentage share of different types of motorcycles to come to an average of copper content per unit. In Table A.10, we use products on the market to represent different sizes of motorcycles.

The in-use stock of motorcycles is adopted from WBCSD’s (World Business Council for Sustainable Development) transport model (WBCSD, 2004) until 2050. After 2050, the number of motorcycles is estimated by using the five year growth rates from 2045-2050. For scenarios with transition towards electrification of motor vehicles (SSP 1, 2, and 4), the share of electric motorcycles is assumed to be the same as for electric cars plus hybrid vehicles in the cars estimation.

The annual outflow of copper from motorcycles is calculated using the same method as in residential buildings.

4.2 Trucks

Unlike cars, the type of trucks is assumed to remain the same until 2100. The copper demand is estimated based on the truck stock from the GEA-mix and reference pathways model. In GEA 2012 report, it was estimated that about 375 million and 350 million trucks will be in-use in 2100 for the GEA-mix pathway and reference pathway, respectively. The GEA-mix pathway truck stock estimation is applied to the SSP 1, 2, and 4 scenarios. The reference pathway is applied to SSP 3 and SSP5.

The annual outflow of copper from trucks is calculated using the same method as in residential buildings.

Table A.12. Data used for copper content in trucks

Copper content(kg/unit) b 20

Life spana

(year)

17.5 ( in 2015)17.6 ( in 2020)17.7 (in 2025)17.8 (in 2035)17.8 (from 2040-2100)

AssumptionsAssuming all trucks are powered by fossil fuel until 2100.In-use stocks of trucks are assumed to be the same as the estimations in GEA report c. (From GEA-mix and GEA-baseline pathways.)

a: From 2015-2040, life span uses the assumptions from WBCSD IEA/SMP Transport Model (WBCSD, 2004). From 2040-2100, the life span is the sameb: van Beers and Graedel, 2003c: IIASA, 2012

4.3 Cars

To estimate the copper demand in cars, the in-use stock is first estimated by the car type. The in-use stock estimation of cars made in GEA-mix (for SSP 1,2, and 4) and GEA baseline (for SSP 3 and 5) models (International Institute for Applied System Analysis, 2012) are used as a basis here. Three types of vehicles are studied in this estimation, including conventional cars, hybrid cars, and electric cars. The copper content and life span of cars are shown in table A.13.

The global total car stock is first calculated by summing up the regional ownership rate in WBCSD (2004) weighed by the regional population from 2000 - 2050 for SSP 3 and SSP5. For the GEA baseline model, it consists of mostly conventional cars, with a small portion of hybrid cars. The number of hybrid cars and conventional cars are calculated based on the WBCSD (2004) model’s percentage share of car types from 2000-2050 (see table A.13). From 2050 to 2100, the in-use stock of hybrid and conventional vehicles is calculated by the in-use stock in previous year plus the annual sales, deducted by the replacement stock. With the number of cars by type, the copper content, and the lifespan as shown in table A.14, the copper demand are calculated.

The annual outflow of copper from cars is calculated using the same method as in residential buildings.

Table A.13. Percentage share of car types for SSP3 and 5

Year Car stock (million)a

Conventional cars share (%)a

Hybrid car share (%)a Electric vehicle (%)a

2000 683 99.98% 0.02% 0%

2005 754 99.66% 0.05% 0%

2010 843 99.86% 0.14% 0%

2015 938 99.67% 0.33% 0%

2020 1038 99.47% 0.53% 0%

2025 1152 99.29% 0.71% 0%

2030 1286 99.10% 0.90% 0%

2035 1424 99.00% 1.00% 0%

2040 1587 98.91% 1.09% 0%

2045 1779 98.81% 1.19% 0%

2050 2007 98.71% 1.29% 0%

a: WBCSD, 2004

Table A.14. Data used for copper content in cars

Type of cars Conventional cars Hybrid cars Electric car

Copper content (kg/unit) a 15 35 60

Life span (years)

17.5 ( in 2015)17.6 ( in 2020)17.7 (in 2025)17.8 (in 2035)17.8 (from 2040-2100)

a: Trussel, 2013

The GEA-mix pathways set a future of increased use of biofuels, fuel efficiency, and intensive introduction of hybrid and electric vehicles on the market (IIASA, 2012). In addition, the travel pattern is set to shift towards more trains and public transport, with less personal vehicles. For SSP1, 2, and 4 we adopted the GEA-mix pathway model’s in-use vehicle stock. The total stock of vehicles reaches 1300, 1775, 2025 million cars in 2030, 2050, and

2100, respectively, according to the GEA-mix model (IIASA, 2012). In order to estimate the in-use stock of different car types until 2100, the annual sales estimated by the GEA-mix model is adopted. It is assumed that the fuel cell vehicles and electric vehicles have the same copper content. The annual sales data of electric vehicles by type in the GEA-mix model is summarized as table A.15. Assuming that in 2004 the initial global stock is the same as in the WBCSD model (754 million cars). Also, assuming the hybrid and electric vehicle stock begin with zero, we use the annual sales number in table A.15 to calculate the in-use stock. The in-use stock is calculated by adding up the previous year’s in-use stock and the annual sale, minus the replacement number. Finally, with the copper content and lifespan of products in table A.14, the copper demand is calculated.

Table A.15. Car sales by type estimated by GEA-mix model

Year/Car typeCars

(million)a

Conventional 32 22 18

Hybrid 2 57 25

EV(including fuel cell)

5 42 117

2030 2050 2100

a: IIASA, 2012

4.4 Aircraft

First, the in-use stock of aircrafts is estimated based on historical data. Avolon (Forsberg, 2014) conducted a world fleet forecast and derived a relation of fleet growth rate and the GDP growth rate (fleet growth rate is 1.6 times the GDP growth rate). The relation is used here to estimate the global in-use stock of aircrafts. Historical global in-use stock of aircrafts from 2005-2014 in International Civil Aviation Organization (ICAO) annual report (ICAO, 2014) is used (see table A.16) to develop the in-use stock until 2100. The copper content and the lifespan of aircraft are listed in table A.17. The type of aircraft and the relation between aircraft number growth and GDP growth are assumed to be the same for SSP1-5.

The annual outflow of copper from aircrafts is calculated using the same method as in residential buildings.

Table A.16. In-use stock of global aircraft

Year In-use stock(No.)a

2005 20,356

2006 21,037

2007 21,809

2008 22,552

2009 23,264

2010 23,880

2011 24,552

2012 25,252

2013 25,954

2014 26,653

a: ICAO, 2014

Table A.17. Data used for copper demand estimation for aircraft

Copper content (kg/unit) 664a

Lifetime (year) 27b

Assumption● The aircrafts are assumed to be the same in all scenarios.● Growth of aircraft stock is assumed to be 1.6 times the

growth of GDP c

a: The Copper Development Association, 2016b: Aviation Today, 2015c: Forsberg, 2014

4.5 Train

The global copper demand in trains is estimated based on the formula in table A.9. The global in-use stock beginning from 2002 International railway data (Sato, 2005) is used (as shown in table A.18.) The assumptions are listed as table A.19. Some railcar models for passengers and freight are used to represent these types of transportation. The copper content in each type of rail car models are then averaged for each sub-category as shown in table A.19.

The annual outflow of copper from trains is calculated using the same method as in residential buildings.

Table A.18. Data used for global train stock

Category Types In-use stock (No.)a

Locomotive Locomotives 98,360

Freight railcars, wagon Railcars 47,655

Passenger rolling stock Carriages 292,616

Freight railcars, wagon Wagons 3,951,558

Total 4,390,189

a: Sato, 2005

Table A.19. Data used for trains

Type Sub-typesWeight of Vehicles(kg)

Copper content (%)

Estimated copper weight (kg)

Average copper content (kg) Lifespan (years)h

Locomotive

Fuel cell locomotive (high) 130,999a 2.5%e 3,275

2,960

40

Fuel cell locomotive (low) 124,586a 2.5%e 3,115 40

Electrification locomotive 91,999a 2.5%e 2,300 40

Diesel locomotive 126,000a 2.5%e 3,150

Freight railcars, wagon

Railcar(Hopper Car) 23,000b 0.5%f 115 115 40

Passenger rolling stock

Light rail vehicles 45,000c 3%g 1,3501,299

40

Tram (Helsinki) 41,600d 3%g 1,248 40

Assumptions

Growth rate of passenger rolling stock = 0.87 x GDP growth rate. The relation is derived from Roland Berger (6)Growth rate of freight rolling stocks (locomotive and rail cars, wagon) equals to the GDP growthPassenger train grows largely in 2100 due to travel pattern shift from passenger cars. (for SSP 1, 2, and 4)In SSP1,2, and 4, the stock of passenger rail vehicle is calculated by multiplying the stock in SSP3 and 5 (baseline) with the activity ratio between the baseline and renewable scenarios. Ratio of baseline/renewable scenario is 0.8, 0.7, 0.4 in 2030, 2050, and 2100, respectively.

a: Marin et al., 2010b: Uniwagon, n.d.c: Siemens, 2014d: Transtech, 2012e: European Commission, 2012f: Drakonakis et al., 2007g: Allianz pro Schiene, 2006h: United States Government Accountability Office, 2007

4.6 Buses

The demand of copper is estimated by first calculating the total in-use stock of buses. The WBCSD (2004) model is used as the basis for the number of buses from 2000-2050 (as shown in table A.20). From 2050-2100, the total number of buses is calculated by assuming the five year growth rate of buses to remain the same as 2045-2050 (2.3%). For SSP3 and SSP5, all the buses are assumed to be diesel. For SSP1, 2, and 4, GEA-mix pathways are adopted. This sets the buses to transition towards electric and hybrid buses. In this study, the share of diesel, electric, and hybrid buses are assumed to be the same as in the estimation for cars (SSP1, 2, and 4). The copper content and lifespan of buses are listed in table A.21.

The annual outflow of copper from buses is calculated using the same method as in residential buildings.

Table A.20. In-use stock of buses from WBCSD model (WBCSD, 2004)

YearGlobal buses

(No.)Global mini-

bus (No.)Total(No.)

Growth rate (%) /5 years

2000 6,299 7,248 13,547 -

2005 6,402 7,452 13,854 2.3%

2010 6,506 7,661 14,168 2.3%

2015 6,613 7,877 14,490 2.3%

2020 6,721 8,099 14,820 2.3%

2025 6,831 8,327 15,159 2.3%

2030 6,943 8,563 15,506 2.3%

2035 7,058 8,805 15,862 2.3%

2040 7,174 9,054 16,228 2.3%

2045 7,292 9,311 16,603 2.3%

2050 7,413 9,575 16,987 2.3%

Table A.21. Data for buses

TypeCopper content(kg)

Lifespan (years) Assumptions

Diesel bus - 109a

17.5 (2015)17.6 (2020)17.7 (2025)17.8 (2035)17.8 (2040-2100)

From 2050-2100, the total number of buses are calculated by assuming the five year growth rate of buses to remain the same as 2045-2050The share of diesel, electric, and hybrid buses are assumed to be the same as in the estimation for cars (SSP1, 2, and 4)

Hybrid bus - 212b

Electric bus - 219b

a: Cherry et al., 2009b: Kärnä, 2012

4.7 Vessels

The copper demand in vessels is calculated by first using the historical data from global merchant fleet (Equasis Statistics, 2014) as listed in table A.22. It is assumed that the stock of fleets has the same growth rates as GDP. The GDP growth rate is based on SSP1-5’s individual scenarios. Medium size fleet of 1500 tonnes per vessel is assumed to represent all the fleets because it has the highest percentage among all sizes of fleet (in 2014).

The annual outflow of copper from vessels is calculated using the same method as in residential buildings.

Table A.22. World merchant fleet (Equasis Statistics, 2014)

Year Global fleet

2005 61,227

2006 69,572

2007 71,929

2008 74,814

2009 74,951

2010 77,768

2011 79,074

2012 79,471

2013 81,584

2014 85,094

Table A.23. Copper content in vessels

Vessel weight (tonne) 1500a

Copper content (%) 0.5%b

Copper content(tonne/unit)

7500

Lifespan (year) 27.5c

Assumptions

● The stock growth of vessels are assumed to be the same as the GDP growth rates (2015-2100).

● The type of vessels are assumed to be the same for SSP1-5.● Assuming all the vessels are 1500 tonne medium size fleet (because mediums size

fleet outnumbers other sizes of fleet (44% in 2014).

a: medium size fleet (between 500 and 25000 tonne) in Equasis statistics (2014)b: Rauch et al., 2007c: Shippipedia, n.d

5. Consumer durables

General assumptions

Copper is widely used in consumer durables, including non-electronic items such as art crafts and medical devices. In this study it is assumed that copper contained in non-electronic consumer products are negligible and is therefore not considered in the estimation.

In order to reflect the changes of the annual demand, stock dynamics are taken into account. Historical data of global stock, stock per capita, or stock per household have been obtained during data collection in this study. While we strive to establish a model that can best represent the global stock and demand, we also recognize the fact that this category does not impact the total copper demand significantly. It is decided that the estimation will not take into account the development of technology. As a result, copper content, lifespan, and product weight are assumed constant to maintain the simplicity of the calculation and the data collection feasibility. In addition, it is assumed that the ownership rates of the consumer products remain the same in all the scenarios we study. The following section provides a detailed overview of the copper demand calculation based on different data availability and type of data collected based on data from table A.23.

The annual outflow of copper from all the consumer durables is calculated using the same method as in residential buildings.

Table A.24. Copper demand equations consumer durables

Category Calculation formula Parameter description Parameter values

Consumer durables (Dcon , a)

Dcon , a=∑i=1

11

((N con ,i ,a−Ncon ,i , a−1 ∙ mcon ,i)+(N con ,i , a−1

rcon ,i))

N con , i ,a= Number of consumer durable i used in year AN con , i ,a−1= Number of consumer durable i used in year A-1mcon,i= Copper content per consumer durable ircon , i= Average residence time of copper in consumer durable i

N con , i ,a (i = 1,2,3,…,11) = global households * products/householdN con , i ,a−1 (i = 1,2,3,…,11)mcon ,i (i = 1,2,3,…,11)rcon , i (i = 1,2,3,…,11)

Table A.25. Detailed information of copper application of consumer durables

Sub-category Weight (kg/unit) Cu content (%) Unit Cu weight (kg) Life span (year)

Television 17.14a, c 2.04%b, c 0.52 9a, c

Refrigerator 61a 3.4%b 2.07 12a

Air conditioner 46a 17.8%b 8.19 13a

Washing machine 39a 0.03%b 1 10a

Personal computer (PC) 8.09a,e 0.01b 0.07b 7b

Electric heater 3.4c 0.25% c 0.01 c 8c

Microwave 15a 0.06d 0.96d 13d

Printer 5.6b 3.20%b 0.18b 7b

Cellphone 0.11b 0.30%b 0.0003b 4b

Landline 1b 10.30%b 0.10b 7b

Others - - 7.29 9

a: Average product weight (Oguchi et al., 2008)b: Average copper content (Oguchi et al., 2011)c: Ratio between CRT and LCD (NMR Group Inc., 2012)

d: Copper content of product (UNEP, 2013)e: Ratio between desktop PC and notebook PC (Statista, 2015)

5.1 Television

The copper demand of the television is calculated based on the annual in-use stock. For televisions, data of household stock were collected through marketing research company Nakono (Nakono, n.d.). The available data covers the period between 2000 and 2012. An average global household stock was calculated by weighing the regional population and the average household stock of the region. The regions adopted are in line with the UN grouping (UNDESA, 2015). Elshkaki and Graedel (2013) assume that annual discarded flow can be calculated by initial stock divided by the life time. However, the discarded flow is unlikely to remain constant due to the fluctuation of annual stock. Therefore, in this study it is suggested to use the stock from previous year divided by the lifespan of product to represent the discarded flow.

Table A.26. Copper content of LCD and CRT television

Type Weight (kg/unit)

Cu content (%)

Unit Cu weight (kg) Life span (year)

Television - LCD (Liquid Crystal Display) 7.9 0.8% 0.06 7

Television - CRT 31 3.9% 1.21 12

Average(LCD/CRT = 3:2) 17.14 2.04% 0.52 9

Assumptions

Table A.26 summarizes the calculation of average copper content of TV. Average copper content and product weight are calculated by using a LCD/CRT ratio. The ratio of LCD television and CRT is assumed to be 3:2 (NMR Group Inc., 2012). It was assumed that everyone owns a television in 2050. Between 2012 and 2050, television sets per household is assumed to be linearly increasing. In 2013 and 2014, stock per household is assumed to increase by 0.4 TV set/household annually, using the previous trend.

However, the development of screens is moving towards reducing the thickness. Bulky CRT television sets are being replaced by thin screen plasma TV sets or LCD TV sets. Therefore the share of stock of CRT is expected to decrease, while the share of stock of LCD TV sets is expected to increase. In this study, the ratio of LCD and CRT is expected to remain the same until 2100 for the simplicity of calculation. On one hand, the end result might be overestimated because the copper content in LCD is only 1/20 of CRT. On the other hand, the lifespan of LCD is 5 years shorter, implying that the stock needs to be replaced on a more frequent basis.

In order to be able to estimate copper demand, it is necessary to make assumptions of future ownership rates of consumer products in 2100. There are no figures available that could be used and therefore own assumptions have been made. The main assumption is that wealth in all parts of the world will increase to the current United States level, by 2100. Ownership rates for all different products are available and have been collected. Exact numbers used for each product will be discussed in the remainder of this chapter.

5.2 Refrigerators

Information about the copper content in refrigerators is summarized in table A.27. The stock per household data is obtained by weighing the regional product per household data and regional population. The data we used is based on the appliances saturation study conducted by Letschert and Mcneil (2010). Product per household in developed, less developed, and least developed regions are 1.1, 0.65, 0.12 products, respectively in 2000 (Letschert and Mcneil, 2010).

Table A.27. Refrigerator data

Unit weight (60)(kg/unit)

Cu content (61)(%)

UnitCu weight (kg)

Life span(year)

61 3.4% 2.07 12

It is assumed that every household has one refrigerator in 2100. In this product category data availability is extremely limited. Only household ownership data was obtained for one year (2000). We adopted a global stock number of domestic refrigerators and freezers for 2020 (1822 million refrigerators) (Barthel and Götz, 2012).

According to Letschert and Mcneil (2010), household income, the level of electrification, and urbanization all contribute to the level of ownership of refrigerator. Therefore it is useful to take into account how these factors will develop in the future while interpreting the results.

5.3 Air conditioner

The stock per household data is obtained by weighing the regional product per household data and regional population. Product per household in developed, less developed, and least developed regions are calculated to be 0.29, 0.07, 0.04 products, respectively in 2000 based on the appliance saturation study conducted by Letschert and Mcneil (2010). In 2030, every household is estimated to own 0.45, 0.18, and 0.06 air conditioners in respectively developed, less developed, and least developed region. The global household stock of air conditioner in 2000 and 2030 are 0.09 and 0.22, respectively. In Letschert and Mcneil’s study (2010), both climate and household purchasing power were taken into consideration in their model.

It is assumed that in 2100, AC ownership reaches 0.5 units per household. The development of ownership is assumed to increase faster in the first years compared to years in the future, towards 2100. From 2030 to 2100, there is a total increase from 0.2 to 0.5 units per household.

5.4 Washing machine

Household in-use stock data is obtained by weighing the regional product per household data and regional population. In 2000, product per household in developed, less developed, and least developed regions are calculated to be 0.82, 0.51, 0.03 products, respectively based on the study of Letschert and Mcneil (2010). The study found out that both income and electrification are statistically significant.

In 2100, it is assumed that no more than 70% of all global households own a washing machine. Like for the other products this is the current saturation rate of washing machines in households in the United States. Copper content and lifespan and weight of the product are assumed to remain constant as shown in table A.27.

5.5 Computer

For televisions, data of household stock were collected through marketing research company Nakono (Nakono, n.d.). The available data covers the period between 2000 and 2012. An average global household stock was calculated by weighing the regional population and the average household stock of the region. The regions adopted are in line with the UN grouping (UNDESA, 2015).

Personal computers are divided into two categories, desktop and laptop computers. Based on the shipment data in 2012, it is assumed that the ratio of desktop and laptop is 3:4 (Statista, 2015), as shown in table A.28. The final copper content of PC and average weight of the product is calculated based on this ratio. Tablets are ignored in this

category because it is relatively insignificant in weight comparing with laptop and desktop PC (~600 g/ piece, 20% of laptop PC, 4% of desktop PC). It is assumed that in 2100, everyone owns three PC devices.

Table A.28. Copper in PC

Sub-category Weight

(kg/unit)aCu content (%)b

Unit Cu weight (kg)

Life span (year)

Desktop PC 15 0.9% 0.14 7

Notebook PC 2.9 1% 0.03 7

Average PC(desktop:laptop = 3:4) c 8.09 0.01 0.07 7

a: Average product weight (Oguchi et al., 2008)b: Average copper content (Oguchi et al., 2011)c: Average copper content (Statista, 2015)

5.6 Electric heater

Data for global in-use household stock of electric heaters are highly inaccessible. Table A.29 shows the number of heaters globally being calculated by the global market value from (Deneen et al., 2010). Unit price of 50 USD for electric heaters are applied. It is assumed that the global stock of heater is zero before 2002 and the stock is built up by the estimated market sales from 2002 to 2012. From 2012 to 2100, the stock is estimated by the assumed ownership rates.

Table A.29. Global heater market and calculated heater annual sales (Deneen et al., 2010)

Global market of heaters Sales of heaters globally

year million USD million units

2002 14,780 295

2007 18,580 372

2012 23,650 473

Assume 60% of the households have one heater in 2100. Copper content and lifespan and weight of the product are assumed to remain constant. Climate is not taken into consideration in this estimation.

5.7 Microwave oven

Data for in-use household stock of microwave by income level is obtained from a study conducted by United States Department of Agriculture (2009). The income level by country is averaged according to the level of development to obtain the ownership of microwave ovens as shown in table A.30. The household in-use stock of microwaves is then weighed in accordance with the population for more developed, less developed, and least developed countries from UN population data to obtain the global household in-use stock of microwave oven.

Stable growth between 2002-2005, 2005-2008, and 2008-2100 is assumed in the estimation. The ownership per household is weighed against the population estimation of 2002, 2005, and 2008. It is assumed that the household ownership rate of microwaves in least developed countries is 20% of less developed countries. For 2100, the

household ownership rate is assumed to be one device per household. The copper content and lifespan and weight are all assumed to be constant.

Table A.30. Household in-use stock by region calculated based on USDA survey

Region/year 2002 2005 2008

More developed 0.72 0.78 0.83

Less developed 0.17 0.2 0.29

Least developed 0.03 0.05 0.06

Global(Weighed by regional

population)0.26 0.32 0.36

5.8 Printer

Global household in-use stock for printers are not accessible, however, it was possible to use a ratio between printer and personal computer to estimate the stock of printer. We adopted a PC:printer ratio according to the enterprise printer asset management report from Office of Information and Technology Services, Department of Environmental Protection in 2009. The ratio of computer: printer 8:1 is used in this study. This ratio is applied to obtain the global household in-use stock of printers.

It is assumed that the ratio between printer and computers remain constant until 2100. In addition, it is assumed that copper content and lifespan and weight are all constant.

5.9 Cellphone

Cellphone is a relatively small category both by copper content and by weight. However, it was chosen because of it has been functioning as a substitute of landline telephones. The market report by eMarketer (2014) provides the percentage of cellphone users from 2012 to 2017. Global in-use stock of cellphones is calculated using global population multiplied by the percentage of cellphone users.

It is assumed that the copper content in cell phones remains constant until 2050. Also, it is assumed that the ownership of cellphones will be increasing steadily until 2100. In 2100 it is assumed that everyone has one cell phone.

5.10 Landline

The copper demand of the landline is calculated by the annual in-use stock per household. For landline telephone, data of household stock were collected through marketing research company Nakono (Nakono, n.d.). The available data covers the period between 2000 to 2012. An average global household stock was calculated by weighing the regional population and the average household stock of the region. The regions adopted are in line with the UN grouping (UNDESA, 2015)

In 2100 it is assumed that 20% of the households have a landline. Every year the number of landline per household drops steadily between 2013-2100. The copper content is assumed to remain constant until 2100.

5.11 Other consumer durables

This category is the aggregation of a mix of appliances to account for other appliances not included in the previous categories. It is calculated by multiplying the assumption of the number of appliances per household and the item’s copper content based on the report of UNEP (2013). The aggregated number is the copper content in the “other” category per household.

Assumption

It is assumed that the combination of these appliances is made for one household. Therefore the number of “other” category appliance is assumed to be the same as refrigerator through 2100.

Table A.31. Estimating copper content in the “Other” category

Item Cu content (kg/unit)a unit/household total Cu content

(kg)

large appliances (excluding cooling and heating appliances) 1.74 1 1.736

medium appliances 0.96 1 0.956

small appliances 0.48 2 0.968

consumer electronics (DVD, stereos) 0.42 2 0.846

electric tools 1.08 1 1.075

leisure appliances 0.02 1 0.02358

lighting equipment 0.00 15 0.0414

Total (kg) 5.56

a: Elshkaki and Graedel, 2013

6. Commercial durables

Commercial durables are assumed to be the same as the 11 subcategories of consumer durables. It is assumed that the copper demand in commercial durables is directly proportional to the amount of energy consumed by the appliances. The copper demand of commercial durables is calculated based on the ratio of energy consumption between the residential buildings and the commercial buildings. In 2010, commercial buildings accounted for about 8% of global energy consumption while residential buildings’ energy consumption is responsible for 24% of global energy consumption (Ürge-Vorsatz et al., 2015). Therefore, it was assumed that the copper demand of commercial durables is one-third of the consumers’ durables for the purpose of the estimation. The ratio of copper demand in commercial durables and consumer durables are assumed to remain as constant until 2100.

The annual outflow of copper from trains is calculated by dividing the annual discarded flow of the consumer with the ratio of commercial durables and consumer demands.

Table A.32. Copper demand equations commercial durables

Category Calculation formula Parameter description Parameter values

Commercial durables (Dcom, a) Dcom, a=Dcon , a∙

Ecom

Eres

Dcon , a= Total copper demand for consumer durables in year AEcom = Global commercial energy consumptionEres = Global residential energy consumption

Ecom = 29.29 EJ a

Eres = 85.21 EJ a

a: Ürge-Vorsatz et al., 2015

6. GDP and Population dataThis chapter aims to give more insight in the basic data used in our models, which both used population and GDP as main drivers for copper demand. The data was obtained from the SSP database (2012-2015). For each of the five scenarios, the marker scenario was selected from the available model interpretations. This is the selection made by the groups responsible for the database. Figure 1 and 2 show the population and GDP data for five scenarios. The raw data was obtained in the form of 10 data points, each separated by 10 years. This data was regressed, all with R2 of above 0.999, to obtain the lines given in the figures.

20052008201120142017202020232026202920322035203820412044204720502053205620592062206520682071207420772080208320862089209220952098

0

2000000000

4000000000

6000000000

8000000000

10000000000

12000000000

14000000000

SSP1SSP2SSP3SSP4SSP5

Year

Popu

latio

n

Figure 1. Population data used in model calculations.

20052008201120142017202020232026202920322035203820412044204720502053205620592062206520682071207420772080208320862089209220952098

0

200000

400000

600000

800000

1000000

1200000

SSP1SSP2SSP3SSP4SSP5

Year

GDP

[Bill

ion

2005

$]

Figure 2. GDP data used in model calculations.

Top-down method

Table A.33. Data used in the multivariate regression

YearCopper demand

[Mtonnes]

Population [Millions]

GDP/cap2.13

[2005$]

1950 2.51 2525.15 6.90976E-081951 2.63 2571.87 7.03633E-081952 2.82 2617.94 7.08855E-081953 2.97 2664.03 7.17386E-081954 2.97 2710.68 7.14977E-081955 3.30 2758.31 7.32846E-081956 3.62 2807.25 7.391E-081957 3.67 2857.66 7.38684E-081958 3.58 2909.65 7.33681E-081959 3.80 2963.22 7.38064E-081960 4.57 3018.34 7.46785E-081961 4.64 3075.07 7.42642E-081962 4.86 3133.55 7.47049E-081963 4.97 3194.08 7.48393E-081964 5.25 3256.99 7.69999E-081965 5.54 3322.50 7.76665E-081966 5.56 3390.69 7.84325E-081967 5.39 3461.34 7.78546E-081968 5.49 3533.97 7.8585E-081969 6.01 3607.87 7.93454E-081970 6.12 3682.49 7.98096E-081971 6.11 3757.73 7.96124E-081972 6.92 3833.59 7.99233E-081973 7.24 3909.72 8.17317E-081974 7.59 3985.73 8.02737E-081975 7.14 4061.40 7.82979E-081976 8.24 4136.54 7.89726E-081977 8.54 4211.32 7.90979E-081978 8.79 4286.28 7.95302E-081979 8.94 4362.19 7.93505E-081980 8.97 4439.63 7.79762E-081981 9.32 4518.60 7.65743E-081982 9.00 4599.00 7.45662E-081983 9.25 4681.21 7.3847E-081984 9.14 4765.66 7.43302E-081985 9.40 4852.54 7.39894E-081986 9.50 4942.06 7.36807E-08

1987 9.71 5033.80 7.35662E-081988 10.20 5126.63 7.37936E-081989 10.61 5218.98 7.33109E-081990 10.80 5309.67 7.21526E-081991 10.60 5398.33 7.05764E-081992 11.10 5485.12 6.96659E-081993 11.40 5570.05 6.89026E-081994 11.20 5653.32 6.90957E-081995 11.90 5735.12 6.9821E-081996 12.70 5815.39 7.00806E-081997 13.60 5894.16 7.07647E-081998 14.10 5971.88 6.99801E-081999 14.60 6049.21 7.04933E-082000 15.00 6126.62 7.19245E-082001 15.70 6204.31 7.20257E-082002 15.50 6282.30 7.25168E-082003 15.30 6360.76 7.38614E-082004 15.90 6439.84 7.57166E-082005 16.60 6519.64 7.72465E-082006 17.30 6600.22 7.91962E-082007 17.90 6681.61 8.05301E-082008 18.30 6763.73 8.09434E-082009 18.40 6846.48 7.92216E-082010 19.10 6929.73 8.1656E-08

Table A.33 shows the data used in the multivariate regression. As clarification, a time variable was not used for the results but only represents the timescale of this data.

The copper data was obtained from the USGS online archives. The data corresponds to worldwide copper production, but assuming there is no significant storage of copper, it equals the world copper demand. The population data was obtained from World Population Prospects: The 2015 revision, published by the United Nations Department of Economic and Social Affairs. Finally, the GDP data was obtained from the Maddison-Project (2013). Their data has been published using the United States Dollar (USD) value in the year 1990 as reference. To match the SPP data used to project our estimates, which is given with a USD value referenced to the USD value in the year 2005, we converted the data from the Maddison Project to 2005 USD value, with the help of an online tool (Arpeppim, n.d.)

The dataset in Table A.33 was used as input for a multivariate regression and resulted in the coefficients for the general formula and R2 given in Table A.34.

Table A.34. Results of the multivariate regression

Coefficient Estimated value

Standardised coefficients

T-statistic value

P-value VIF

C (Constant) -8.233 0.00 -8.075 4.58E-11 -

b (Population, P) 1.808 0.989 80,846 2,537E-61 1.0003

c (Affluence,

GDP/capit a2.13)

1,164 0.102 8,352 1,580E-11 1.0003

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