Stock dynamics and emission pathways of the global ...Liu et al. 2011; Liu and Müller 2012). Figure S1 | Schematic diagram of the GlobAlEE model, consisting of the global dynamic

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE1698

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

S1

Stock dynamics and emission pathways of the global aluminium cycle

Gang Liu ★, Colton E. Bangs, Daniel B. Müller ★

Industrial Ecology Programme and Department of Hydraulic and Environmental Engineering,

Norwegian University of Science and Technology, S.P. Andersens vei 5, 7491 Trondheim,

Norway. ★ e-mail: [email protected], [email protected]

This document stands as Supplementary information for the article:

Liu, G.; Bangs, C. E.; Müller, D. B., Stock dynamics and emission pathways of the global

aluminium cycle. Nature Climate Change, 2012.

Outline:

1. The model framework and system definition………………………………………..S2

2. Model details and data sources for historic stocks and flows………………………..S4

3. Model details and data sources for future stocks and flows………………………….S19

4. The energy and emissions layers and mitigation scenarios…………………………..S23

5. Future material demand, scrap availability and other additional results……………...S32

6. References for the Supplementary information……………………………………….S36

Figures:

Figure S1-S23

Tables:

Table S1-S11

© 2012 Macmillan Publishers Limited. All rights reserved.

S2

1. The model framework and system definition

A schematic diagram of the GlobAlEE (global aluminium cycle and associated energy use

and GHG emissions) model is shown in Fig. S1. By integrating the global dynamic

aluminium material flows and their associated energy and emissions layers, the model can

simulate the historic and future aluminium stocks and flows and encompass all production-

related GHG emissions along the cycle (emissions from Manufacturing and Use are excluded

because they are difficult to allocate to a single material and instead reported by other

sectors). Note that all the aluminium life cycle processes simplified in Fig. S1 are actually

further disaggregated into more complicated sub-processes or product categories in the model

for better accuracy. A detailed system definition and life cycle description can be found in

Fig. S2 as well as in our previous studies (Liu et al. 2011; Liu and Müller 2012).

Figure S1 | Schematic diagram of the GlobAlEE model, consisting of the global dynamic

aluminium cycle and its associated energy and emissions layers. Nine types of energy

carriers (ƞ, 1-9): smelting contract mix, grid mix, natural gas, heavy oil, hard coal, propane,

gasoline, kerosene, and diesel & light fuel oil. Further disaggregation of all the processes and

other details are described in Fig. S2 and Table S1.


S3

Figure S2 | System definition of the anthropogenic aluminium cycle. Nine categories of

semi-manufacturing products (α, 1-9): sheet and plate, foil, can sheet, extrusion, wire and

cable, shape casting, other semis, powder and paste, and deoxidization aluminium; twelve

categories of final and obsolete products (β, 1-12) are detailed in Table S1; three types of old/

post-consumer scrap (γ, 1-3): can scrap, cable and wire scrap, and other general scrap; four

types of system stocks: lithosphere/ore stock (S1), in use stock (S8), loss stock (including

both deposited losses, i.e., S2, S4, S5, S6, S7, and S11, and dissipative losses, i.e., S3 and S9),

and hibernating/obsolete stock (S10) (Müller et al. 2006; Liu et al. 2011; Krook et al. 2011).

Table S1 | Final and obsolete product categories, codes, and examples used in the model

Code Product category Product examples BC Building & Construction Roofing, cladding, window and door frames

TAU Transportation: Automobiles & Light Trucks

Engine blocks, suspension components, automobile frames and body panels, wheel rims

TAE Transportation: Aerospace Aircraft frames and decking TOT Transportation: Others Railway cars, marine vessels, motorcycles & bicycles PCA Packaging: Cans Beverage cans, aerosol cans POT Packaging: Others Foil for flexible packaging, semi-rigid food containers ME Machinery & Equipment Irrigation pipe, ladders, office and hospital equipment ECA Electrical: Cables Wire, cables EOT Electrical: Others Transformers and capacitors, electric lamps CD Consumer Durables Air conditioners, refrigerators, dishwashers, cookware

OTN Other Uses: non-Destructive Use Other uses except destructive use

OTD Destructive Uses Metallurgical products for steelmaking


S4

2. Model details and data sources for historic stocks and flows

2.1 Simulation method for historic aluminium cycle

The “production-driven” top-down dynamic material flow approach was used for simulating

the historic aluminum stocks and flows (1900-2009) in selected countries and the world.

Historic apparent aluminium consumption was first calculated as:

*i i i i iAP SP y I E= + − (S1)

where i represents different product categories (as in Table S1), AP is the apparent

consumption of aluminium, SP is the industry net shipment of aluminium products from semi-

manufacturing to manufacturing, y is manufacturing yield ratio, and I and E are imports and

exports of aluminum embedded in different final products (“indirect trade”).

Then the outflows (“obsolete products”) from use and in-use stocks were simulated according

to inflows (AP) by a function of lifetime as shown in equations (2) and (3):

0

( , ) * ( )t

i it

O L t t AP t dt′ ′ ′= ∫ (S2)

0

, ( )t

i t i it

S AP O dt= −∫ (S3)

where ( , )L t t′ is the lifetime distribution of product categories, which is the probability that a

product entering at time t will leave use at time t′ .

From here, we moved both backwards in the cycle to calculate semi-manufacturing output by

semi-manufacturing process, and forward to calculate the use and post-consumer scrap flows.

Pre-consumer scrap is generated at each semi-manufacturing and manufacturing process and

treated in one of three manners: internally recycled, externally remelted or externally refined,

based on the current practice of the European aluminium industry (Boin and Bertram 2005;

EAA 2008). Final products entering use will accumulate as in-use stock and retire as obsolete


S5

product after they reach their end of life. A big share of these obsolete products will be

collected and recycled. Uncollected products from PCA, POT and CD were assumed to enter

the municipal solid waste stream and be discarded in landfills (or incinerated later). All other

uncollected products were assumed to enter other repositories (e.g., the hibernating/obsolete

stock (Müller et al. 2006; Liu et al. 2011; Krook et al. 2011) and other waste streams).

Collected products entering waste management are first transported to processing facilities. It

was assumed that there is no aluminium loss in this process. They then enter one of three pre-

melt processing treatments, can scrap (PCA), wire and cable scrap (ECA), and general scrap

(all others). Yield rates from these processes are high for all end-use categories, as reported by

GARC (GARC 2011). Pre-melt processing output goes to remelting or refining according to

end-use category, based on the current European practice (Boin and Bertram 2005).

The model calculated primary production as the amount needed to satisfy the remainder of

demand not met through the three secondary production processes. The flow of aluminium in

the primary production chain was calculated back from the required primary ingot (aluminium

that is cast into a shape suitable for further processing) production by a series of ratios that

approximate the historical and current proportionality of different production and waste flows

in the chain. The primary ingot casting process flows were built around the 1% dross (a mass

of solid impurities floating on the molten aluminium) generation rate of primary ingot

production (GARC 2011). The electrolysis process was solved from the spent potline (a

contaminated graphite/ceramics cell waste generated in primary smelters) generation rate

(based upon primary ingot output not molten metal output) and its aluminium concentration,

and the historical ratio between calculated electrolysis metal required and reported alumina

production from the USGS (USGS 1932-2011). The refining process was solved through the

red mud (a solid waste product of the Bayer process) generation rate and its aluminium


S6

concentration, along with the ratio between historical bauxite and alumina production. Finally,

bauxite mining was calculated via the mining waste generation rate and its aluminium content.

Bauxite ore extraction for both metal content and total mass were calculated from the mass

balance. The non-aluminium flows for anodes (the electrode used in smelters) were computed

based on average anode consumption from IAI data and anode composition (a mixture of

ground used carbon anodes, calcined petroleum coke and coal tar or petroleum pitch) from the

U.S. Department of Energy (USDOE 2007).

Table S2 | Mathematical equations for calculating the primary production requirement.

Variables: PD = Primary Dross PI = Primary Ingot PM = Primary Molten TDR = Total Dross Recovery IR = Internal Remelting Dross Recovery ER = External Remelting Dross Recovery RR = Refining Dross Recovery TI = Total Ingot Requirement I = Internal Remelting Ingot E = External Remelting Ingot R = Refining Ingot (without dross recovery) Parameters: p = Primary Dross Generation Rate (1%) d = Dross Recovery Rate (50%) Derivation: Dross generation transfer coefficient at primary ingot casting: PD = p*PM (a) PI = (1-p)*PM (b) Mass balance on dross recovered: DR = d*PD + IR + ER + RR = d*p*PM + IR + ER + RR Mass balance on the ingot market: TI = P + I + E + R + TDR = P + I + E + R + IR + ER + RR + d*p*PM TI = (1-p)*PM + I + E + R + IR + ER + RR + d*p*PM PM*(1-p+d*p) = TI – I – E – R – IR – ER – RR PM = (TI – I – E – R – IR – ER – RR)/(1-p+d*p) PD and PI can then be calculated from (a) and (b), respectively


S7

The modeled results for historical primary aluminium production were compared with the

historic statistics (USGS 1932-2011) to validate our model assumptions (Fig. S3). The two

datasets agree quite well in general for most of the years (± 8% difference), although in the

most recent years of economic downturn (especially for the year 2009), the modeled

production dips a bit below reported production. One of the main reasons may be because the

modeled production follows the actual aluminium demand, whereas the reported production

includes the aluminium entering industry stocks. So the jagged difference between model and

reported production is partly the result of industry stock changes not being considered within

the model. The global primary aluminium production is indeed in surplus in recent years. The

industry stock in terms of consumption was about 89 days in 2010 and is projected to remain

around 96 days in the coming three years (Thomas and Eilbeck 2011).

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

0

5

10

15

20

25

30

35

40

45

1900 1920 1940 1960 1980 2000

Primary Aluminium Historic Statistics

Primary Aluminium Model Simulation

% difference

Figure S3 | Model calculations for primary aluminium production compared with

historic statistics, 1900-2009. Units: Megatonnes (Mt)1.

2.2 Data sources for historic aluminium cycle

Historic global SP data were taken from industry statistics compiled by the Global Aluminium

Recycling Committee (GARC) (GARC 2011) of the International Aluminium Institute’s (IAI).

1 Note that “Mt” (Megatonne) and “Gt” (Gigatonne) are used throughout this document and the main text of this paper. While converted to Systeme International (SI) units, 1 Mt = 1Gg, and 1 Gt = 1Tg.


S8

Historic SP data for the 12 selected case countries were compiled from various sources as

shown in Table S3. When historic SP data are not available, historic apparent consumption

data were broken down using the end-use pattern of the closest year. When the reported end-

use product categories from some sources are not the same as shown in Table S1, they were

converted to the twelve categories using assumptions in the GARC model.

Table S3 | Data sources of aluminium domestic end-use shipment for different countries.

Countries Domestic end-use shipment Total consumption Australia 1950-2009 (2) 1900-1949 (1)

Brazil 1950-2009 (2) 1900-1949 (1) China 1950-2009 (2) 1900-1949 (1) France 1962-1997 (1) 1900-1961 (1), 1998-2009 (1,3)

Germany 1954-2006 (1) 1900-1953 (1), 2007-2009 (3) India 1950-2009 (2) 1900-1949 (1) Italy 1962-1994 (1) 1900-1961 (1), 1995-2009 (4) Japan 1950-2009 (2) 1900-1949 (1)

Netherlands 1962-1970 & 1982-1997 (1) 1900-1961 (1), 1971-1981 & 1998-2009 (1,3) Spain 1969-1997 (1) 1900-1968 (1), 1998-2009 (1,3) U.K. 1962-1997 (1) 1900-1961 (1), 1998-2009 (5) U.S. 1950-2009 (2) 1900-1949 (1)

Note and data source: (1): (Metallgesellschaft 1889-2007 (Various Issues)) (The data for 2008

and 2009 were assumed the same as 2007); (2) (GARC 2011); (3) (WBMS various years); (4)

(ASSOMET 2003-2010); (5) (Alfred 2011).

For the country level, the international trade of aluminium containing products was explicitly

considered. The trade data were taken from United Nations Commodity Trade Database (UN

Comtrade 1962-2011) and were calculated based on the reported data of SITC-1 series (the

first Standard International Trade Classification). About 130 aluminum containing products

were selected, and analyzed for each year from 1962 to 2009. The trade prior to 1961 was

very small, and we assumed it to be negligible. The Comtrade data are reported either in

physical (net weight of kilograms) and/or monetary (current dollars) values. For the years

when only monetary values are available, they were converted into mass flows by the


S9

estimation of “price”. Details of historic stock calculation at country level are also shown in

our previous case studies for the U.S. (Liu et al. 2011).

The trade flows between two countries in Comtrade are reported by both importing countries

and exporting countries. The two values should be equal in theory, but they are mostly

inconsistent in practice due to different reporting regulations, capabilities, and many other

reasons. The reported import data are preferred in our previous study (Liu et al. 2011) and

several other trade analyses. Here we have performed an uncertainty analysis for historic

aluminium stocks in major countries by using reported import data, reported export data, and

the mean values of the two. The results are shown in Fig. S4. The stock results using import

data and export data differ from stock results using the mean values (which are used in Figure

3 of the main text) by less than 10%, except for the Netherlands (20%).

0

100

200

300

400

500

600

1950 1960 1970 1980 1990 2000

ExportMeanImport

US[kg/capita]

0

100

200

300

400

500

600

1950 1960 1970 1980 1990 2000

ExportMeanImport

Netherlands[kg/capita]

0

100

200

300

400

1950 1960 1970 1980 1990 2000

ExportMeanImport

Australia[kg/capita]

0

100

200

300

400

1950 1960 1970 1980 1990 2000

ExportMeanImport

Germany[kg/capita]

0

100

200

300

400

1950 1960 1970 1980 1990 2000

ExportMeanImport

Japan[kg/capita]

0

100

200

300

400

1950 1960 1970 1980 1990 2000

ExportMeanImport

UK[kg/capita]

0

100

200

300

400

1950 1960 1970 1980 1990 2000

ExportMeanImport

France[kg/capita]

0

100

200

300

400

1950 1960 1970 1980 1990 2000

ExportMeanImport

Italy[kg/capita]

0

100

200

300

400

1950 1960 1970 1980 1990 2000

ExportMeanImport

Spain[kg/capita]

0

25

50

75

100

1950 1960 1970 1980 1990 2000

ExportMeanImport

China[kg/capita]

0

25

50

75

100

1950 1960 1970 1980 1990 2000

ExportMeanImport

Brazil[kg/capita]

0

25

50

75

100

1950 1960 1970 1980 1990 2000

ExportMeanImport

India[kg/capita]

Figure S4 | Historic aluminum in-use stocks in major countries using reported export

data, reported import data, and the mean values of the two, 1950-2009.


S10

As there is little knowledge about material composition of different final products, the

information on aluminum concentrations was compiled from two intensive Nordic industry

survey databases of manufactured commodities (Lassen et al. 1999; KEMI 2010) and various

literature data (EPD undated; Starke and Staley 1996; Lassen et al. 1999; Dahlström et al.

2004; Amicarelli et al. 2004; Millbank 2004; Ducker 2008; Recalde et al. 2008; Wang and

Graedel 2010; Menzie et al. 2010; Bumby et al. 2010; Mathieux et al. 2006; Mathieux and

Brissaud 2010). The historic change of these concentrations was not considered except for

aluminum use in passenger cars (Ducker 2008). We note that the uncertainty of aluminium

concentration data for individual products may be high and consequently conducted a Monte

Carlo simulation for the aggregated uncertainty of total indirect trade of aluminium in final

products for the case of U.S. (Fig. S5). The differences for the maximum and minimum from

the mean values for most product categories are around 5%-10% except ECA (20%-25%

instead). The aggregated result shows roughly a 20% difference for the maximum and

minimum from the mean, indicating a relatively low uncertainty induced by our estimation of

aluminum concentration data.

0

1

2

3

4

5

6

Total BC TAU TAE TOT ECA EOT ME CD POT OTD

Impo

rt (M

t)

MaximumMinimumMean

0%

5%

10%

15%

20%

25%

30%

Total BC TAU TAE TOT ECA EOT ME CD POT OTD

Stan

dard

Dev

iatio

n/M

ean Import

Export

Figure S5 | Monte Carlo simulation results for the U.S. indirect trade of aluminium in

final products in 2006 with uncertainties of aluminum concentration data. Parameter

uncertainty estimation: low 10%, medium 30%, high 50%; normal distribution assumed; 1000

MC iterations applied.


S11

The lifetime assumptions are crucial for the stock dynamic results but also difficult to be

determined. The previous aluminum dynamic MFA studies used slightly different

distributions and mean values (Melo 1999; Dahlström et al. 2004; Liu et al. 2011). In this

model, we used product lifetimes with normal distributions as shown in Table S4 to calculate

when they will come out of the in-use stock as obsolete products. These mean values were

taken from the International Aluminum Institute’s GARC expert committee (GARC 2011)

and standard deviations were assumed as 30% of the means. In order to assess the sensitivity

of stock results to lifetime assumptions, we created a relatively long lifetime scenario and a

relatively short lifetime scenario (Table S4). The consequent stock results of the short and

long lifetime scenarios differ from that of the medium by about 15% to 30% (Fig. S6).

Table S4 | Life time assumptions of global aluminum end-use sectors.

End-use sectors Lifetime assumption in this model

Standard deviation

Long lifetime scenario

Short lifetime scenario

Building & Construction 50 15 80 30 Transportation - Auto & Lt Truck 20 6 30 10

Transportation - Aerospace 40 12 50 20 Trans - Other 30 9 40 10

Packaging - Cans 1 0.3 1 1 Packaging - Other 1 0.3 1 1

Machinery & Equipment 40 12 60 20 Electrical - Cable 40 12 60 20 Electrical - Other 20 6 30 10

Consumer Durables 12 3.6 20 5 Other (ex Destructive Uses) 20 6 30 10

Destructive Uses 1 0.3 1 1


S12

0

20

40

60

80

100

120

1900 1920 1940 1960 1980 2000

Long lifetime

Medium lifetime (this model)

Short lifetime

0

5

10

15

20

25

30

35

1900 1920 1940 1960 1980 2000

BCTAUECATOTME

Figure S6 | Historic development of the global aluminum in-use stock and the sensitivity

of lifetime assumptions: total (left) and the major product categories (right), megatonnes,

1900-2009.

Table S5 | Semi-manufacturing product category, yield rate, and scrap destination.

Product Category Yield Rate Source and

comment

Semi-Manuf Scrap Destination Source and comment Internal

remelting Internal refining Remelting Refining

Sheet and Plate 72.3% (EAA 2008) 100% (Boin and

Bertram 2005)

Foil 62.7% (EAA 2008) 100% (Boin and Bertram 2005)

Can Sheet 72.0%

(Silva et al. 2010) Process includes 78% sheet for can

body and 22% lid component

100% Assume same as sheet and

plate

Extrusion 75.5% (EAA 2008) 54% 46% (Boin and Bertram 2005)

Wire and Cable 75.5% Assume same as

extrusion 100% (Boin and Bertram 2005)

Shape Casting 45.5% (USDOE 2007) 95% 5%

External percentage from (Tharumarajah

2008)

Deoxidation Al 100%

Assume no extra processing

needed No scrap

Other Semis 75.5%

Mostly forgings and impacts,

assume same as extrusion

54% 46% Assume same as extrusion

Power and Paste 100%

Assume no extra processing

needed

No scrap


S13

All the transfer coefficients for calculating other flows were taken from industry statistics and

expert estimates as shown in Table S5-S7 and Fig. S7.

Table S6 | Manufacturing yield rate and manufacturing scrap collection and destination.

Manufacturing Product

Categories

Manufacturing Yield Rate

(GARC 2011)

Collection rate (GARC

2011)

Collected Scrap Destination (Boin and

Bertram 2005)

Uncollected Scrap Destination (Boin and

Bertram 2005)

Remelting Refining Landfilled/ incinerated

Other repositories

BC 90% 98% 61% 39% 100% TAU 84% 98% 59% 41% 100% TAE 60% 98% 59% 41% 100% TOT 80% 98% 59% 41% 100% PCA 75% 98% 100% 100% POT 75% 98% 100% 100% ME 75% 98% 63% 37% 100%

ECA 90% 98% 100% 100% EOT 80% 98% 100% 100% CD 80% 98% 53% 47% 100%

OTN 80% 98% 53% 47% 100% OTD 80% 98% 53% 47% 100%

Table S7 | End-of-life scrap type, pre-melt yield rate, and scrap destination.

End-Use and Obsolete Product

Categories Pre-Melt Process

Pre-Melt processing Yield Rate (Silva et

al. 2010; USGS 2011)

Scrap destination (Boin and Bertram 2005)

Remelting Refining BC General Scrap 100% 49% 51%

TAU General Scrap 97% 5% 95% TAE General Scrap 100% 49% 51% TOT General Scrap 100% 49% 51% PCA Can Scrap 99% 80% 20% POT General Scrap 97% 100% ME General Scrap 97% 9% 91%

ECA Wire and Cable Scrap 97% 100% EOT General Scrap 97% 100% CD General Scrap 97% 100%

OTN General Scrap 97% 100% OTD


S14

0%

20%

40%

60%

80%

100%

1900 1920 1940 1960 1980 2000

BC TAU TAETOT PCA POTME ECA EOTCD OTN

Figure S7 | Collection rates of different aluminium obsolete products from GARC

experts’ estimation, 1950-2009 from (GARC 2011) and values before 1950 are assumed

as the same in 1950.

2.3 Determining the “semi-manufacturing to manufacturing recipe”

The “recipe” of flows from semi-manufacturing to manufacturing (Fig. S8) was calculated

using manufacturing input data from GARC, market analysis data for the production of

different semi-manufactured goods and the percentage of these goods being shipped to each

industry, and empirical industry data from several countries on the use of semi-products in

manufacturing industries. The data sources and estimation methods are detailed in Table S8.

0% 20% 40% 60% 80% 100%

BCTAUTAETOTPCAPOTME

ECAEOT

CDOTNOTD

Semis-to-Manufacturing "Recipe"

Rolling

Foil

Can Sheet

Extrusion

Wire & Cable

Casting

Deoxidation Al

Other

Powder & Paste

Figure S8 | The aluminium semis-to-manufacturing “recipe”.


S15

Table S8 | Semi-manufacturing to manufacturing flow shares matrix and data sources.

Sheet & Plate2 Foil 2,6 Can

Sheet2,7 Extrusion2 Wire

& Cable2

Shape Casting2

Deoxidation Product2,16 Other2

Powder &

Paste2,17

BC1 18%5 - - 72%8 - 5%11 - 5%13 -

TAU1 15%5,18 2%10,18 - 12%8,18 - 67%9,18 - 4%10,18 -

TAE1 50%5,18 - - 50%8,18 - - - - -

TOT1 15%5,18 2%10,18 - 12%8,18 - 67%9,18 - 4%10,18 -

PCA1 - - 100%7 - - - - - -

POT1 - 100%4 - - - - - - -

ME1 38%5 - - 39%8 - 22%12 - - -

ECA1 - - - - 100%3 - - - -

EOT1 33%5 - - 25%8 22%3 21%14 - - -

CD1 45%5 19%15 - 17%8 - 18%11 - - -

OTN1 17%5 - - 18%8 - 16%11 - - 49%17

OTD1 - - - - - - 100%16 - -

Notes: Rows add to 100% may be slightly off due to rounding.

(1) Manufacturing input data used from GARC for 2006 (GARC 2011), Fig. S8-1

Figure S8-1 | 2006 manufacturing input by end-use (Mt) from GARC.

(2) Semi-manufacturing product output taken from market analysis, Fig. S8-2


S16

Figure S8-2 | Estimation of the global breakdown of semi-manufactured aluminium products

(Roskill Information Services 2009).

(3) “Wire & Cable” from Fig. S8-2 fills ECA product shipments with the remainder going to

EOT

(4) “Foil” assigned to meet all demand for POT

(5) Market analysis on rolled products used to begin calculating “Sheet & Plate”, Fig. S8-3

Figure S8-3 | Consumption of aluminium flat rolled products by end-use (2007) (Roskill

Information Services 2009).

(6) “Foil” calculated as 23% of rolling production from Fig. S8-2 and Fig. S8-3

(7) “Can Sheet” disaggregated from rolled products as the entire demand from PCA

(8) Market analysis on extrusion by end-use used to begin calculating “Extrusion” shipments

for most industries, Table S8-1

Table S8-1 | Global aluminium extrusion consumption by end-use (kt) (CRU 2010).


S17

(9) Breakdown of semi-products for the U.S. transportation industry used to calculate

castings to global transportation, seen in Fig. S8-4 as ingot

Figure S8-4 | Shipments to the U.S. transportation industry by semi-manufactured product

(2007) (Roskill Information Services 2009).

(10) “Other” and “Foil” to transportation calculated from U.S. data in Fig. S8-4

(11) “Shape Casting” to BC, CD and OTN taken from weighted averages calculated from data

from six national aluminium associations as seen in Table S8-2

Table S8-2 | Weighted average calculation for castings to different end-uses based on national

data from the U.S.(AA various years), EU (Dahlström et al. 2004; EAA 2011), Japan (JAA

2010), Brazil (ABAL 2010), Argentina (CAIMA 2010) and South Africa (AFSA 2001).


S18

(12) Rest of ME filled by “Shape Casting”, thus assuming no “Foil” to ME

(13) Rest of BC filled by “Other”, thus assuming no “Foil” in BC

(14) Rest of EOT filled with “Shape Casting”, thus assuming no “Foil” in EOT

(15) CD given the remainder of “Foil”

(16) OTD filled with “Deoxidation Products”, which is disaggregated from “Shape Casting”,

thus assuming all OTD is in the form of deoxidation products for steelmaking, this hypothesis

supported by two facts: (i) “Shape Casting” and “Deoxidation Products” are made at the

same facilities (refiners) and therefore it is reasonable to assume both are considered as

castings in statistics (EAA 2011; EAA/OEA 2006); and (ii) “Shape Casting” production for

2007 from Modern Casting (Modern Casting 2008) subtracted from market analysis estimates

for casting production in 2007 (Fig. S8-2) is close to the GARC data for “Deoxidation

Products” in 2007

(17) Rest of OTN filled with “Powder & Paste”, which is disaggregated from “Other”

(18) Transportation split into the three categories used in GARC: (i) TAE assigned 50%

“Sheet & Plate” and 50% “Extrusion” based on (Roskill Information Services 2009)–

“aluminium alloys used annually in aerospace applications, mainly in sheet and extrusion

form for airframes”; and (ii) TAU and TOT assigned the remainder of semi-product demand

to transport with each semi-product remaining proportional to TAU and TOT total demand.


S19

3. Model details and data sources for future stocks and flows

2.1 Simulation methods for future aluminium cycle

The future aluminium cycle (2010-2100) was calculated from a “stock-driven” approach,

meaning that future flows were computed “backwards” from assumed stock patterns and

product lifetime. The “stocks drive flows” concept has recently been developed inspired by

the stocks and flows analyses in industrial ecology (Müller et al. 2006; Müller et al. 2011) and

since then have been attempted for future aluminium (Hatayama et al. 2009, 2012), steel

(Igarashi et al. 2008; Hatayama et al. 2010; Pauliuk et al. 2012), and copper (Bader et al. 2011)

cycles on various scales. This approach shares a similar hypothesis of potential saturation of

future material use as the flow-based (either production or consumption) ‘intensity of use’

models, but keep essential advantages in two aspects: (i) stock saturation is a better indicator

for societal maturation because stocks reflect the ultimate demand for services and behavior

more robust for long-term scenarios; (ii) with consideration of stocks, the model can explicitly

follow the mass balance principle and include the entire system interactions, especially future

scrap availability and its influence on primary production.

Previous models used a three-parameter logistic growth curve to model future per capita stock

growth (Hatayama et al. 2009, 2010, 2012; Igarashi et al. 2008; Pauliuk et al. 2012). In order

to define saturation level and saturation time independently for the future global per-capita

aluminium stock growth, we used a four-parameter logistic and Gompertz combined model as

equation (S4) for the growth curve of future per-capita stock:

.

*(1 ).

0

1 ( 1)*t

satt

esat

ss s es

βα −=

+ − (S4)


S20

where ts is the per capita in-use stock at time t, .sats represents the per capita in-use stock

saturation level, t represents the number of years after the beginning of in-use stock (t0 =

2009), and α and β are parameters determining the growth patterns of the curve (the stock

reaches 98% of the saturation level at a given time). By using this modified Gompertz curve,

we can model a smooth transition of the global aluminium in-use stock out of the current

bottom due to the recent financial crisis in the following years, as expected by the industry

and also as shown historically, and then an “S” curve to the assumed saturation levels in the

next couple of decades.

Then the manufactured product inflows at year t (denoted as ( )I t ) were calculated as the sum

of net addition to stock and obsolete products as equations (S5) and (S6):

( ) ( ) * ( ) ( 1)* ( 1) ( )I t s t P t s t P t O t= − − − + (S5)

0

' '( ) ( , ) * ( )t

t

O t L t t I t dt= ∫ (S6)

where P represents population, and '( , )L t t is the life time distribution of product categories

(normal distribution in this study).

From there, all other aluminium flows in the system were solved from ( )I t and ( )O t by use of

transfer coefficients and explicit projections for future collection rates of obsolete products.


S21

2.2 Projections of future global aluminium per-capita in-use stock

Considering the fast penetration of aluminium in various products and the urbanization and

industrialization of developing countries, global per-capita aluminium in-use stock is

anticipated to continue growing in the coming decades. In order to explore future pathways of

global aluminium demand and associated emissions, we created a wide range of scenarios for

the future development of global in-use stock up to 2100. These scenarios vary based on stock

saturation level (kg/capita) and approximate time of reaching saturation (reaching 98% of the

saturation level in the model). Three saturation levels were matched with three saturation

times as seen in Table S9. Saturation levels were set to represent a low (200 kg/capita),

medium (400 kg/capita), and high (600 kg/capita) penetration of future global aluminium use

in light of the observed historical patterns in major industrialized countries (the assumed

saturation levels were detailed in Fig. S9). This variation may be due to a variety of factors

including wealth, lifestyle, urbanization model, and aluminium material intensity in products

and infrastructures. The future stock growth curves were modeled using equation S4 explicitly

for different end-use categories, as examples shown in Fig. S10 for BC and TAU. Saturation

times are approximated by the time when the stocks reach 98% of the saturation level and

have been chosen to reflect a fast (2050), medium (2075), and slow (2100) stock growth,

depending on the rate of developing countries’ stock growth and industrialized countries’

stock replacement.

Table S9 | The nine scenarios for future development of global aluminium in-use stock.

Saturation Time Saturation Level 2050 2075 2100

600 kg/capita High 2050 High 2075 High 2100 400 kg/capita Medium 2050 Medium 2075 Medium 2100 200 kg/capita Low 2050 Low 2075 Low 2100


S22

High Medium Low WorldBC 200.0 130.0 60.0 30.8TAU 100.0 65.0 35.0 15.3TAE 4.0 3.0 2.0 0.6TOT 70.0 45.0 20.0 10.3PCA 3.0 2.0 1.0 0.5POT 2.0 1.5 1.0 0.3ME 60.0 40.0 20.0 9.6ECA 85.0 60.0 30.0 13.7EOT 15.0 10.0 5.0 2.5CD 25.0 20.0 15.0 4.2OTN 35.0 23.0 10.8 5.4OTD 1.0 0.5 0.2 0.1Total 600 400 200 93

Figure S9 | Assumed saturation levels of global per-capita aluminium stock for different

end-use categories, shown next to the current world aluminium stock breakdown.

0

40

80

120

160

200

2000 2025 2050 2075 2100

BC [kg/capita]Low-2100

Low-2075

Low-2050

Med-2100

Med-2075

Med-2050

High-2100

High-2075

High-2050

0

20

40

60

80

100

2000 2025 2050 2075 2100

TAU [kg/capita]Low-2100

Low-2075

Low-2050

Med-2100

Med-2075

Med-2050

High-2100

High-2075

High-2050

Figure S10 | Examples of simulated future stock growth curves for BC and TAU.

The population data were taken from the United Nation population forecast until 2100 (Fig.

S11) (United Nations 2010) . We used the medium scenario in our model.

0

2 000

4 000

6 000

8 000

10 000

12 000

14 000

16 000

18 000

1950 2000 2050 2100

Mill

ion

HistoricalHighMediumLow

Figure S11 | World population historical data and future scenarios, 1950-2100.


S23

4. The energy and emissions layers and mitigation scenarios

4.1 The energy and emissions layer and data sources

Material flow requires the transformation of aluminium in various processes within the cycle,

which in turn requires energy use and results in emissions (Fig. S1-S2). The energy layer

consists of nine energy carriers: contract mix, grid mix, natural gas, heavy oil, hard coal,

propane, gasoline, kerosene, and diesel and light fuel oil, and distinguishes direct energy and

indirect energy (which is defined as the energy needed to produce and transport the direct

energy carrier to the process site). The emissions layer consists of direct emissions, indirect

emissions, and process emissions. Direct energy use causes direct emissions at the site of

aluminium processes. Indirect emissions are created off-site as a result of the indirect energy

use as well as from the emissions occurring in the energy chain (e.g., the off-gassing of

hydroelectric reservoirs). Significant process emissions occur only in the electrolysis and

anode production processes. Both processes emit CO2, while electrolysis emits CF4 and C2F6.

Direct energy coefficients were taken from various life cycle inventories (LCIs) in the

literature, and converted to common energy intensity units (MJ/t), as seen in Table S10.

Indirect energy coefficients for fossil fuel energy carriers were taken from the U.S. DOE

(USDOE 2007), while coefficients for the two electricity mixes required the determination of

energy sources in the two mixes. Energy sources for electricity production in the contract mix

are reported annually by the IAI (IAI 2011), while the global grid mix sources are reported by

the U.S. Energy Information Administration (EIA) (EIA 2011), as well as by ObservÉR

(ObservÉR 2010). Indirect energy values of electricity production were calculated from the

tacit energy factors reported by the U.S. DOE (Appendix D of reference (USDOE 2007)).

Electricity transmission losses for the U.S. are reported by the U.S. DOE (USDOE 2007) at


S24

9.5%, which agrees with the world transmission losses reported in reference (Business

Council for Sustainable Energy 2004).

Table S10 | Current direct energy intensity of processes throughout the aluminium cycle.

Unit: MJ/t.

Process Grid Mix

Contract Mix

Heavy Oil

Diesel & LFO Propane Natural

Gas Hard Coal Kerosene Gasoline Data source

Mining 7 10 52 0 (IAI 2007) Bauxite Refining 452 4099 31 4129 1598 (GARC

2011) Electrolysis 54996 (IAI 2007)

Anode Production 487 213 2367 103 (IAI 2007)

Coke Production 130 1211 2826 (Margolis

1997)

Pitch Production 119 296 (Margolis 1997)

Primary Ingot Casting 299 231 60 1098 29 (IAI 2007)

Internal Remelting

Rolling 167 29 3 33 926 (EAA 2008)

Internal Remelting Extrusion

321 12 10 885 (EAA 2008)

Internal Refining Casting

3008 (EAA 2008)

Remelting 478 109 13 125 3116 (EAA 2008)

Refining 219 171 3829 (EAA 2008)

Sheet and Plate 1722 142 1356 (EAA 2008)

Foil 3351 74 11 2639 (EAA 2008)

Can Sheet 1196 34 8 1 2876 9 1 (Silva et al. 2010)

Extrusion 2059 35 1379 (EAA 2008)

Wire & Cable 1246 1352 (Bumby et al. 2010)

Shape Casting 7 1670 (USDOE 2007)

Deoxidation Product

Other Semis (Extrusion) 2059 35 1379 (EAA 2008)

Powder & Paste

Collection 245 (Quinkertz et al. 2001)

General Scrap Preparation 227 85 618 (EAA 2008)

Can Scrap Preparation 31 281 (Silva et al.

2010) Wire & Cable

Scrap Preparation

70 (Bumby et al. 2010)


S25

Direct emission intensities were calculated for fossil fuel energy carriers from the carbon

emission coefficients used by the U.S. DOE (USDOE 2007). Indirect emissions were taken

from Ecoinvent (Dones 2004) for fossil fuel energy carriers and were based on energy sources

in the electricity mix for electricity. Coal, oil, natural gas and nuclear were taken from an

LCA literature survey (Varun et al. 2009). The same study reported a wide range of values for

hydropower, indicating a large source of uncertainty. The final value chosen was from a Pew

Center study (Pew Center on Global Climate Change 2009), as it was more in-line with the

zero-emission assumption used in most studies (McMillan and Keoleian 2009; Schlimbach et

al. 2001). Finally, renewable energy indirect emission intensity was calculated from the

breakdown of renewable energy sources globally (ObservÉR 2010) and the emission intensity

of each from Varun et al (Varun et al. 2009). Process emission intensities were taken from the

IAI (IAI 2007) for global average production. More details about the energy and emission

layer and calculating method can also be found in our previous study (Liu et al. 2011).

4.2 Details of the four “mitigation wedges” and future energy and emission scenarios

In order to assess future emission reduction potentials up to 2100 and the change in emissions

in 2050 below the 2000 baseline levels, nine “No Action” emission pathways were first

simulated using the current energy and emission parameters in combination with all nine in-

use stock scenarios (note this “No Action” scenario is different from a “Business As Usual”

scenario). Then the most potent mitigation technologies and strategies discussed in the

literature for the aluminium industry were additionally implemented. Inspired by the

“stabilisation wedges” concept (Pacala and Socolow 2004), these strategies have been

categorized as four “mitigation wedges” (Table 1 in the main text): Near Perfect Collection

(M1-NPC), Technologies for Yield Improvement (M2-TYI), Technologies for Energy and


S26

Emissions Efficiency Improvement (M3-TEE), and Carbon Capture and Storage and

Electricity Decarbonisation (M4-CCS+).

M1-NPC

Collection rates of BC (current: 88%), TAU (current: 88%), TAE (current: 83%), and TOT

(current: 83%) obsolete products were assumed to gradually reach 95% in 2050; collection

rates of all other obsolete products (current rates for PCA, POT, ME, ECA, EOT, CD, and

OTN are 65%, 11%, 54%, 71%, 42%, 25%, 25%, respectively) were assumed to gradually

reach 90% in 2050 (Ayres 2006). Their future growth curves (Fig. S12) were modeled using

the Gompertz model in equation S4. These assumptions are very optimistic or only

theoretically possible (for example, it would be very difficult to achieve a 90% collection for

aluminium foils) in order to assess the highest emissions reduction potentials from recycling.

0%

20%

40%

60%

80%

100%

2000 2050 2100

BC TAU

TAE TOT

PCA POT

ME ECA

EOT CD

OTN

Figure S12 | Obsolete product collection rates in the “Near Perfect Collection” scenario.

M2-TYI

Here yield ratios are defined as “the efficiency of metal to downstream process relative to the

sum of all process inputs” (Milford et al. 2011; Carruth et al. 2011). Yield ratios of all semi-


S27

manufacturing processes (see current levels in Table S5) were assumed to gradually reach

90% in 2050; yield ratios of all manufacturing processes (see current levels in Table S6) were

assumed to gradually reach 95% in 2050. Their future growth curves (Fig. S13) were modeled

using the Gompertz model in equation S4. Several strategies may help improve the yield

ratios, for example developing new manufacturing processes with higher yields, operating

existing processes more effectively, or designing components with geometries more similar to

those of semi-finished products (Milford et al. 2011). But eventually achieving a 90%-95%

yield ratio would also be very optimistic or only theoretically possible considering various

mechanical, physical, economical barriers.

40%

50%

60%

70%

80%

90%

100%

2000 2050 2100

Sheet & PlateFoilCan SheetExtrusionWire & CableShape CastingDeoxidation AlOther SemisPowder & Paste

60%

70%

80%

90%

100%

2000 2050 2100

BC TAU

TAE TOT

PCA POT

ME ECA

EOT CD

OTN OTD

Figure S13 | Yield ratios of aluminium semi-manufacturing (left) and manufacturing

(right) processes in the “Technologies for Yield Improvement” scenario.

M3-TEE

Four key parameters from primary production were explicitly projected due to their

significant contributions to total systems emissions (Fig.2 in the main text): electrolysis

energy intensity, PFC emission intensity, contract mix emission intensity, and refining

emission intensity.


S28

Historical electrolysis energy intensity was assessed from three sources, as seen in Figure S14.

Energy intensities dating back to 1890 (Kvande and Haupin 2000) were used along with

energy intensity data from the U.S. Department of Energy since the 1960s (USDOE 2007)

and the IAI global weighted average (GARC 2011) for the recent two decades. In order to

achieve the scenario assumption of an electrolysis energy intensity at the Inert Anode and

Wetted Cathode level (i.e., 13.11 kWh/kg) (USDOE 2007) since approximately 2030, a

hyperbolic curve (equation shown in Fig. S14) was used to project future gains in energy

efficiency until 2100. The value in 2100 is already approaching the theoretical minimum

energy requirement (9.03 kWh/kg) (USDOE 2007). As a trade-off of implementing the Inert

Anode technologies, the anode production emissions will increase by a factor of 2.08

(USDOE 2007), which was explicitly considered in our model as well.

10

20

30

40

50

1850 1875 1900 1925 1950 1975 2000 2025 2050

Ele

ctro

lysi

s Ene

rgy

Inte

nsity

(kW

h / k

g) IAI Global Weighted Average

Kvande and Haupin, 2000

U.S. Smelting Energy, DOE, 2007

Hyperbolic Fit

9

10

11

12

13

2050 2060 2070 2080 2090 2100

Elec

troly

sis E

nerg

y In

tens

ity (k

Wh

/ kg)

Hyperbolic Fit (higher resolution 2050-2100)

Figure S14 | Historic data and future scenario for electrolysis energy intensity. The red

five-pointed star indicates the electrolysis energy intensity at the Inert Anode and Wetted

Cathode level (13.11 kWh/kg) (USDOE 2007). The hyperbolic curve fit is shown in higher

resolution between 2050 and 2100.

Contract mix intensity was projected using GARC projections for Chinese and rest of world

(ROW) production and contract mix electricity sources. Beyond the GARC projections,


S29

emission intensity was modeled to stabilize in 2030 and remain constant until 2100. Fig. S15

displays historical energy sources in the contract mix of ROW and China, taken from GARC

(GARC 2011), with emission intensities calculated from the secondary emission factors. The

ROW and China contract mix data was combined into a global production-weighted average

in Fig. S16. Projections for the contract mix out to 2030 were taken from GARC (GARC

2011), which projects all new smelters operating on natural gas following the most recent

trend of new smelting capacity being added to the Middle East. With no other available

information, it was assumed that the contract mix will stabilize after 2030 in the model.

0

200

400

600

800

1000

0%

20%

40%

60%

80%

100%

1980 1985 1990 1995 2000 2005

RO

W C

ontra

ct M

ix In

tens

nity

(g

CD

E / k

Wh)

NuclearNatural GasOilCoalHydroIntensity

0

200

400

600

800

1000

0%

20%

40%

60%

80%

100%

1980 1985 1990 1995 2000 2005

Chi

na C

ontra

ct M

ix In

tens

ity

(g C

DE

/ kW

h)

Figure S15 | Energy sources in the contract mix of ROW and China and their associated

emission intensity, 1980-2009.

-

200

400

600

800

0%

20%

40%

60%

80%

100%

1980 2000 2020 2040 2060 2080 2100

NuclearNatural GasOilCoalHydroEmission Intensity

Figure S16 | Global production-weighted average of the contract mix and the associated

emission intensity, 1980-2100.


S30

PFC emission intensity was modeled using a series of data points out to 2100 (Fig. S17). This

curve was created first with data from the European Database for Global Atmospheric

Research (EDGAR 4.0) for 1970 to 1988, which has global emissions of PFCs from metal

production (EDGAR 2010). The data were normalized by annual primary aluminium

production from GARC (GARC 2011) to get emission intensity, and converted to GWP-100.

Data before 1970 were extrapolated backwards from 1970 with an annual 0.1% increase rate.

IAI data were taken from GARC (GARC 2011) for 1990-2010 and the two datasets were

combined at 1989 using linear extrapolation. The future projection of emission intensity was

created using an exponential curve fit on the IAI data and was fit to the combined curve using

linear extrapolation between it and the IAI data between 2011 and 2013. As we assume the

Inert Anode technologies will be implemented from 2030 (the red five-pointed star), the PFC

emissions will be eliminated then.

0

1

2

3

4

5

6

7

8

9

1950 1975 2000 2025 2050

Ele

ctro

lysi

s PFC

Em

issi

on In

tens

ity(k

g C

DE

/ kg

)

CombinedAssume decline rateEDGARLinear extrapolationIAILinear extrapolationIAI Exponential Fit

Figure S17 | Historical data and future scenarios for PFC emission intensity.

Refining emission intensity was projected again with guidance from GARC using a

China/ROW separation and a 0.25% annual energy efficiency improvement until 2100

(GARC 2011). This resulting combined global curve was shown in Fig. S18. Chinese bauxite

refining has been much higher in emission intensity due to lower ore grades, which require a


S31

more energy-intensive refining process called sintering, though this emission intensity has

rapidly declined as new processes are developing and it is quickly approaching the ROW

average.

0

1

2

3

4

5

1980 2000 2020 2040 2060 2080 2100

Ref

inin

g Em

issi

on In

tens

ity(t

CD

E / t

)ChinaROWGlobal

Figure S18 | Historical data and future scenarios for bauxite refining emission.

Besides all the above-mentioned projections, it was assumed in the “M3 Technologies for

Energy and Emissions Efficiency Improvement” scenario that energy intensities of all semi-

manufacturing processes are reduced by 25%, as demonstrated by continuous strip casting for

rolling (GARC 2011), and a 55% reduction is achieved on all natural gas energy use in the

model, through the implementation of oxy-fuel combustion (IPCC 2007).

M4-CCS+

CCS technology was assumed to be implemented at 85% effectiveness until 2030 (a linear

penetration from 2010) on all coal power supplying electrolysis in the contract mix. The

electricity supply in the contract mix was assumed to be decarbonised by 30% through greater

use of renewables, clean coal and others. These assumptions are also made on purpose the

most optimistic to assess the highest emissions reduction potentials of this strategy.


S32

5. Future material demand, scrap availability and other additional results

0

100

200

300

400

500

600

2000 2050 2100

Low2050

Med2050

High2050

0

100

200

300

400

500

600

2000 2050 2100

Low2075Med2075High2075

0

100

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300

400

500

600

2000 2050 2100

Low2100

Med2100

High2100

0

100

200

300

400

500

600

2000 2050 2100

High 2050

0

100

200

300

400

500

600

2000 2050 2100

High 2075

0

100

200

300

400

500

600

2000 2050 2100

High 2100

0

100

200

300

400

500

600

2000 2050 2100

Med 2050

0

100

200

300

400

500

600

2000 2050 2100

Med 2075

0

100

200

300

400

500

600

2000 2050 2100

Med 2100

0

100

200

300

400

500

600

2000 2050 2100

Primary Ingot

Internal Remelting

Secondary Ingot

Low 2050

0

100

200

300

400

500

600

2000 2050 2100

Primary Ingot

Internal Remelting

Secondary Ingot

Low 2075

0

100

200

300

400

500

600

2000 2050 2100

Primary Ingot

Internal Remelting

Secondary Ingot

Low 2100

Figure S19 | “No Action” material demand pathways across the nine stock dynamic

scenarios (Unit: Mt). The first row shows summarized results for total ingot production,

which are further broken down as primary ingot, internal remelt ingot, and secondary ingot in

the second to fourth rows in different scenarios.


S33

0

50

100

150

200

250

300

350

400

2000 2050 2100

Low2050

Med2050

High2050

0

50

100

150

200

250

300

350

400

2000 2050 2100


0

50

100

150

200

250

300

350

400

2000 2050 2100

Low2100

Med2100

High2100

0

50

100

150

200

250

300

350

400

2000 2050 2100

High 2050

0

50

100

150

200

250

300

350

400

2000 2050 2100

High 2075

0

50

100

150

200

250

300

350

400

2000 2050 2100

High 2100

0

50

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250

300

350

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2000 2050 2100

Med 2050

0

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250

300

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400

2000 2050 2100

Med 2075

0

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350

400

2000 2050 2100

Med 2100

0

50

100

150

200

250

300

350

400

2000 2050 2100

Primary Ingot

Internal Remelting

Secondary Ingot

Low 2050

0

50

100

150

200

250

300

350

400

2000 2050 2100

Primary Ingot

Internal Remelting

Secondary Ingot

Low 2075

0

50

100

150

200

250

300

350

400

2000 2050 2100

Primary Ingot

Internal Remelting

Secondary Ingot

Low 2100

Figure S20 | Material demand pathways across the nine stock dynamic scenarios, after

M1-MPC and M2-TYI implemented (Unit: Mt). The first row shows summarized results

for total ingot production, which are further broken down as primary ingot, internal remelt

ingot, and secondary ingot in the second to fourth rows in different scenarios.


S34

0

50

100

150

200

250

300

2000 2050 2100


0

50

100

150

200

250

300

2000 2050 2100


0

50

100

150

200

250

300

2000 2050 2100


Figure S21 | Obsolete products generation (post-consumer scrap availability) across the

nine stock dynamic scenarios (Unit: Mt).

Fig. S22 shows the increase of aluminium ingot demand from 2006 to 2050 using different

calculation methods. If we exclude internal recycling (because the following mentioned

literature didn’t include it either), this increase varies across different scenarios from a factor

of 1.7 to 5.5 before implementing M1-NPC and M2-TYI; and the Med-2075 scenario result (a

factor of 3.3) is comparable with previous projections of a tripling (IEA 2009) or quadrupling

(Sinden et al. 2011) of demand by 2050.

S-2050 S-2075 S-2100 S-2050 S-2075 S-21005.2 4.9 3.6 5.0 5.5 3.6 S-High3.5 3.4 2.8 3.4 3.3 2.8 S-Med2.0 1.8 1.7 1.9 1.8 1.7 S-Low

S-2050 S-2075 S-2100 S-2050 S-2075 S-21003.1 3.0 2.3 4.0 4.0 3.0 S-High2.1 2.0 1.7 2.8 2.7 2.3 S-Med1.2 1.1 1.0 1.5 1.4 1.4 S-Low

Without N

PC

and TYIW

ith NPC

and TYI

Excluding Internal RecyclingIncluding Internal Recycling

Figure S22 | Ingot production in 2050 relative to production in 2006, depending on if

internal recycling are considered and if M1-NPC and M2-TYI are implemented.


S35

The emission reduction contributions of different reduction wedges across the nine stock

scenarios are shown in Fig. S23. In early saturation scenarios (in 2050), secondary production

starts to take a larger share than primary production approximately a decade earlier than late

saturation scenarios (in 2075 and 2100). The “window of opportunity” for maximum

effectiveness of technology improvements in primary production (M3-TEE and M4-CCS+) is

projected to close in about three to six decades depending on different stock patterns (three to

four decades for an early saturation or a low saturation level).

0

400

800

1200

1600

2000

2010 2030 2050 2070 2090

High2050

0

400

800

1200

1600

2000

2010 2030 2050 2070 2090

High2075

0

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2010 2030 2050 2070 2090

High2100

0

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2010 2030 2050 2070 2090

Med2050

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2010 2030 2050 2070 2090

Med2075

0

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1600

2000

2010 2030 2050 2070 2090

Med2075

0

400

800

1200

1600

2000

2010 2030 2050 2070 2090

M4-CCS+ M3-TEE

M2-TYI M1-NPC

M1+M2 M3+M4

Low 2050

0

400

800

1200

1600

2000

2010 2030 2050 2070 2090

M4-CCS+ M3-TEE

M2-TYI M1-NPC

M1+M2 M3+M4

Low 2075

0

400

800

1200

1600

2000

2010 2030 2050 2070 2090

M4-CCS+ M3-TEE

M2-TYI M1-NPC

M1+M2 M3+M4

Low 2100

Figure S23 | The contribution of different wedges for GHG emissions reduction across

the nine stock scenarios.


S36

Table S11 | Detailed GHG emissions inventory for the global aluminium cycle in 2009,

by process and emission type (data for Fig.2 in the main text).

MiningBauxite Refining

Anode, Coke &

Pitch [1] SmeltingInternal

Recycling

Scrap Remelting &

Refining

Semi-Manufact

uring

Pre-Melt Waste

Management Sum ShareHeavy Oil 0.1 11.6 0.2 0.5 0.0 0.1 0.1 0.0 12.6 2.8%

Diesel and light fuel oil 0.6 4.5 0.7 0.1 0.0 0.1 0.2 0.4 6.5 1.5%Propane 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0%

Natural Gas 0.0 10.7 2.7 1.6 2.1 3.8 6.3 0.3 27.5 6.2%Hard Coal 0.0 28.1 0.1 0.1 0.0 0.0 0.0 0.0 28.3 6.3%Kerosene 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0%Gasoline 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0%

Secondary Emissions 0.3 17.8 2.1 249.4 0.9 2.3 17.5 0.5 290.7 65.2%Process Emissions 0.0 0.0 5.2 74.9 0.0 0.0 0.0 0.0 80.1 18.0%

Total Emissions 1.0 72.7 10.9 326.6 3.1 6.4 24.1 1.1 445.8Share 0.2% 16.3% 2.5% 73.3% 0.7% 1.4% 5.4% 0.3%

Note: [1] Our model for anode production (“Anode” in Fig.2) includes a mixture of ground

used carbon anodes, calcined petroleum coke, and petroleum pitches (see above section 2.1).

6. References for the Supplementary information

AA. various years. Aluminum Statistical Review. Washington, DC: The Aluminium Association.

ABAL. 2010. Brazilian Aluminum Statistical Yearbook 2009. Brazilian Aluminum Association.

AFSA. 2001. Aluminium Statistics Review. Isando, South Africa: Aluminium Federation of South Africa.

Alfred. 2011. Aluminium Federation UK Production Statistics. West Bromwich: Aluminium Federation, UK.

Amicarelli, V., G. Lagioia, and O. de Marco. 2004. Aluminium industrial metabolism: a commodity science contribution. Forum Ware International 1: 1-11.

ASSOMET. 2003-2010. L'industria Dei Metalli Non Ferrosi In Italia (The non-ferrous metals industry in Italy). Milano: Associazione Nazionale Industrie Metalli non Ferrosi.

Ayres, R. U. 2006. Turning point: The end of exponential growth? Technological Forecasting and Social Change 73(9): 1188-1203.

Bader, H. P., R. Scheidegger, D. Wittmer, and T. Lichtensteiger. 2011. Copper flows in buildings, infrastructure and mobiles: a dynamic model and its application to Switzerland. Clean Technologies and Environmental Policy 13(1): 87-101.

Boin, U. and M. Bertram. 2005. Melting standardized aluminum scrap: A mass balance model for Europe. JOM Journal of the Minerals, Metals and Materials Society 57(8): 26-33.

Bumby, S., E. Druzhinina, R. Feraldi, D. Werthmann, R. Geyer, and J. Sahl. 2010. Life Cycle Assessment of Overhead and Underground Primary Power Distribution. Environmental Science & Technology 44(14): 5587-5593.

Business Council for Sustainable Energy. 2004. Increasing Energy Access in Developing Countries: The Role of Distributed Generation.


S37

CAIMA. 2010. Aluminum Industry in Argentina 2010 Statistical Yearbook. Buenos Aires: Cámara Argentina de la Industria del Aluminio y Metales Afines.

Carruth, M. A., J. M. Allwood, and M. C. Moynihan. 2011. The technical potential for reducing metal requirements through lightweight product design. Resources, Conservation and Recycling 57(0): 48-60.

CRU. 2010. The aluminium extrusion industry overview. London and Beijing: CRU Strategies Limited and Sunlight Metal,.

Dahlström, K., P. Ekins, J. He, J. Davis, and R. Clift. 2004. Iron, Steel and Aluminium in the UK: Material Flows and their Economic Dimensions. London: Centre for Environmental Strategy, University of Surrey.

Dones, R. 2004. Life Cycle Inventories of Energy Systems: Results for Current Systems in Switzerland and other UCTE Countries, Ecoinvent report No. 5. Swiss Centre for Life Cycle Inventories. Dubendorf:

Ducker. 2008. Aluminium Association Auto and Light Truck Group, 2009 Update. Troy, Michigan: Ducker Worldwide.

EAA. 2008. Environmental Profile Report for the European Aluminium Industry: Life Cycle Inventory data for aluminium production and transformation processes in Europe. Brussels: European Aluminium Association.

EAA. 2011. European aluminium end-use market. http://www.alueurope.eu/consumption-end-use-markets-for-aluminium-products-2010/. Accessed.

EAA/OEA. 2006. Aluminium Recycling in Europe: The Road to High Quality Products. Brussels: European Aluminium Association and Organization of European Aluminium Remelters and Refiners.

EDGAR. 2010. Emission Database for Global Atmospheric Research (EDGAR), Spreadsheet, release version 4.1. European Commission Joint Research Centre/Netherlands Environmental Assessment Agency.

EIA. 2011. World net electricity generation by fuel. http://www.eia.gov/countries/. Accessed. EPD. undated. The international EPD (environmental product declaration) system.

http://www.environdec.com/. Accessed May 1 2010. GARC. 2011. Global Aluminium Recycling Model. London: Global Aluminium Recycling

Committee, International Aluminium Institute. Hatayama, H., I. Daigo, Y. Matsuno, and Y. Adachi. 2009. Assessment of the Recycling

Potential of Aluminum in Japan, the United States, Europe and China. Materials Transactions 50(3): 650-656.

Hatayama, H., I. Daigo, Y. Matsuno, and Y. Adachi. 2010. Outlook of the World Steel Cycle Based on the Stock and Flow Dynamics. Environmental Science & Technology 44(16): 6457-6463.

Hatayama, H., I. Daigo, Y. Matsuno, and Y. Adachi. 2012. Evolution of aluminum recycling initiated by the introduction of next-generation vehicles and scrap sorting technology. Resources, Conservation and Recycling 66(0): 8-14.

IAI. 2007. Life cycle assessment of aluminium: inventory data for the primary aluminium industry - Year 2005 update. London: International Aluminium Institute.

IAI. 2011. International Aluminium Institute Historical Statistics. https://stats.world-aluminium.org/iai/stats_new/index.asp. Accessed Accessed May 1, 2011.

IEA. 2009. Energy Technology Transitions for Industry: Strategies for the Next Industrial Revolution. Paris, France: The International Energy Agency (IEA).

Igarashi, Y., E. Kakiuchi, I. Daigo, Y. Matsuno, and Y. Adachi. 2008. Estimation of Steel Consumption and Obsolete Scrap Generation in Japan and Asian Countries in the Future. ISIJ International 48(5): 696–704.


http://www.alueurope.eu/consumption-end-use-markets-for-aluminium-products-2010/

http://www.alueurope.eu/consumption-end-use-markets-for-aluminium-products-2010/

http://www.eia.gov/countries/

http://www.environdec.com/

S38

IPCC. 2007. Climate Change 2007: the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

JAA. 2010. Aluminium Statistics in Japan. Tokyo: Japan Aluminium Instuitute. KEMI, Swedish Chemicals Agency. 2010. The Commodity Guide: Swedish Chemicals

Agency. Krook, J., A. Carlsson, M. Eklund, P. Frändegård, and N. Svensson. 2011. Urban mining:

hibernating copper stocks in local power grids. Journal of Cleaner Production 19(9-10): 1052-1056.

Kvande, H. and W. Haupin. 2000. Cell voltage in aluminum electrolysis: A practical approach. JOM Journal of the Minerals, Metals and Materials Society 52(2): 31-37.

Lassen, C., E. Hansen, T. Kaas, and J. Larsen. 1999. Aluminium: Massestrømsnanalyse og vurdering af muligheder for reduktion af tab (Aluminium: Substance flow analysis and loss reduction feasibility study). Copenhagen: Danish Environmental Protection Agency (In Denish).

Liu, G. and D. B. Müller. 2012. Addressing sustainability in the aluminium industry: A critical review of life cycle assessments. Journal of Cleaner Production 35: 108–117.

Liu, G., C. E. Bangs, and D. B. Müller. 2011. Unearthing Potentials for Decarbonizing the U.S. Aluminum Cycle. Environmental Science & Technology 45(22): 9515–9522.

Margolis, N. 1997. Energy and Environmental Profile of the U.S. Aluminum Industry. http://www1.eere.energy.gov/industry/aluminum/pdfs/aluminum.pdf. Accessed July 1 2009.

Mathieux, F. and D. Brissaud. 2010. End-of-life product-specific material flow analysis. Application to aluminum coming from end-of-life commercial vehicles in Europe. Resources, Conservation and Recycling 55(2): 92-105.

Mathieux, F., Y. Bouzidi, M. Outters, A. Tavares, and D. Froelich. 2006. Fate of aluminium contained in commercial vehicles leaving the fleet in Europe: a snapshot of current situation. Brussels, Belgium: European Aluminium Association.

McMillan, C. A. and G. A. Keoleian. 2009. Not All Primary Aluminum Is Created Equal: Life Cycle Greenhouse Gas Emissions from 1990 to 2005. Environmental Science & Technology 43(5): 1571-1577.

Melo, M. T. 1999. Statistical analysis of metal scrap generation: the case of aluminium in Germany. Resources, Conservation and Recycling 26(2): 91-113.

Menzie, W. D., J. J. Barry, D. I. Bleiwas, E. L. Bray, T. G. Goonan, and G. Matos. 2010. Sustainable materials management - Materials Case Study 4: Aluminium. Mechelen, Belgium: OECD global forum on environment, OECD Environment Directorate.

Metallgesellschaft. 1889-2007 (Various Issues). Metallstatistik/Metal Statistics. Frankfurt am Main, Germany and Ware, England: Metallgesellschaft and World Bureau of Metal Statistics.

Milford, R. L., J. M. Allwood, and J. M. Cullen. 2011. Assessing the potential of yield improvements, through process scrap reduction, for energy and CO2 abatement in the steel and aluminium sectors. Resources, Conservation and Recycling 55(12): 1185-1195.

Millbank, P. 2004. Aerospace dominance increasingly challenged: aluminium continues to dominate aircraft structural materials and metal producers work hard to keep it that way--but they are not always successful and composites are achieving some notable successes. Aluminium International Today 16(5): 20-22.

Modern Casting. 2008. The 42nd census of world casting production (2007). Modern Casting.


http://www1.eere.energy.gov/industry/aluminum/pdfs/aluminum.pdf

S39

Müller, D. B., T. Wang, and B. Duval. 2011. Patterns of Iron Use in Societal Evolution. Environmental Science & Technology 45(1): 182-188.

Müller, D. B., T. Wang, B. Duval, and T. E. Graedel. 2006. Exploring the engine of anthropogenic iron cycles. Proceedings of the National Academy of Sciences 103(44): 16111-16116.

ObservÉR. 2010. Worldwide electricity production from renewable energy sources: Stats and figures series. Twelfth inventory.

Pacala, S. and R. Socolow. 2004. Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies. Science 305(5686): 968-972.

Pauliuk, S., T. Wang, and D. Müller. 2012. Moving Toward the Circular Economy: The Role of Stocks in the Chinese Steel Cycle. Environmental Science & Technology 46(1): 148–154.

Pew Center on Global Climate Change. 2009. Climate TechBook: Hydropower. Quinkertz, R., G. Rombach, and D. Liebig. 2001. A scenario to optimise the energy demand

of aluminium production depending on the recycling quota. Resources,Conservation and Recycling 33(3): 217-234.

Recalde, K., J. Wang, and T. E. Graedel. 2008. Aluminium in-use stocks in the state of Connecticut. Resources, Conservation and Recycling 52(11): 1271-1282.

Roskill Information Services. 2009. The Economics of Aluminium (9th Edition). London: Roskill Information Services.

Schlimbach, J., G. Rombach, B. Friedrich, and J. Krüger. 2001. Resource Conservation by Improvements of Primary Aluminium Production. Paper presented at Light Metals 2001 of The Minerals, Metals and Materials Society.

Silva, N. d., N. d’Souza, and M. Binder. 2010. Life Cycle Impact Assessment of Aluminum Beverage Cans. Boston: PE Americas.

Sinden, G. E., G. P. Peters, J. Minx, and C. L. Weber. 2011. International Carbon Flows: Aluminium. London: The Carbon Trust.

Starke, E. A. and J. T. Staley. 1996. Application of modern aluminum alloys to aircraft. Progress in Aerospace Sciences 32(2-3): 131-172.

Tharumarajah, A. 2008. Benchmarking aluminium die casting operations. Resources, Conservation and Recycling 52(10): 1185-1189.

Thomas, A. and C. Eilbeck. 2011. The outlook for aluminium and zinc prices. Die Casting Engineer March.

UN Comtrade. 1962-2011. United Nations Commodity Trade Database, United Nations Statistical Division.

United Nations. 2010. World Population Prospects: The 2010 Revision. Population Division, Department of Economic and Social Affairs, United Nations.

USDOE. 2007. U.S. Energy Requirements for Aluminum Production: Historical Perspective, Theoretical Limits and Current Practices. Washington: U.S. Department of Energy.

USGS. 1932-2011. Minerals Yearbook: Bauxite and Alumina and Minerals Yearbook: Aluminium. Washington, DC: United States Geological Survey.

USGS. 2011. Minerals Yearbook 2010: Aluminium (Advanced Release). Washington, DC: United States Geological Survey.

Varun, I. K. Bhat, and R. Prakash. 2009. LCA of renewable energy for electricity generation systems--A review. Renewable and Sustainable Energy Reviews 13(5): 1067-1073.

Wang, J. and T. Graedel. 2010. Aluminum in-use stocks in China: a bottom-up study. Journal of Material Cycles and Waste Management 12(1): 66-82.

WBMS. various years. World Metal Statistics Yearbook London: World Bereau of Metal Statistics.


Documents

Stock dynamics and emission pathways of the global ...Liu et al. 2011; Liu and Müller 2012). Figure S1 | Schematic diagram of the GlobAlEE model, consisting of the global dynamic