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First revision for Environmental Science and Technology; December 17th, 2012

The Steel Scrap Age – Supplementary Information

Stefan Pauliuk1,*, Rachel L. Milford2, Daniel B. Müller1, Julian M. Allwood2

1 Industrial Ecology Programme, Department for Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway

2 Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom

* To whom correspondence should be addressed:

Stefan Pauliuk Industrial Ecology Programme NTNU NO-7491 Trondheim telephone: +47-735-95156 email: stefan.pauliuk@ntnu.no

Number of pages: 36. Number of Figures: 18. Number of Tables: 9.

Content:

S1) System definition and model approach

S2) Data sources and data treatment

S3) Calibration and additional results

S4) References

S-2

S1) System definition and model approach

S1.1) System definition

Figure S1: System definition The system definition (Fig. S1) comprises mining, beneficiation, sintering, different

smelting technologies, steel making, remelting, casting, forming, fabrication, use, waste management, different types of landfills and deposits as well as scrap trade and preparation. The in-use stock is split into four product categories and ten regions (Table S1) while the response of the steel industry is modeled on the aggregate global level.

Table S1 contains a list and description of all processes. Table S2 lists the system variables. S1.2) List of processes

Due to changes in the model during the project, the process numbers are not ordered.

Table S1: List of processes

Process number

Process name Description

1 Use phase Contains in use stock of steel split into ten regions and four product categories

3 Fabrication Manufacturing of final products out of finished steel and other materials, divided into four product categories

5 Forming Covers the part of the steel industry after continuous casting: Hot and cold rolling, extrusion of pipes.

6 BOF Basic oxygen furnace, including open hearth furnaces

7a Scrap-only EAF EAFs that are entirely fed with scrap

7b DRI-fed EAF EAFS that are fed with direct reduced iron and scrap

8 Pig iron cross section Balancing cross section for pig iron

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10 Scrap market Global scrap market

11 Blast furnace Blast furnaces

12 Direct reduction Direct reduction of iron ore

14 Beneficiation Beneficiation of iron ore

15 Iron ore mining Iron ore deposits and mining of iron ore

16 Obsolete stocks Stocks that are out of use and not accessible for recycling, e.g., foundations or pipes

17 Waste management Collection, sorting, and shipping of steel scrap of all kinds

18a Tailings Iron oxide in tailing ponds, beneficiation residues

18b Slag and scaling losses Land-filled iron in form of slag and scaling losses

18c Landfills Land-filled post-consumer scrap

19 Scrap diversion Diversion of fabrication scrap back into manufacturing

20 Sintering Sintering of iron ore

21 Smelt reduction Smelt reduction of iron ore

22 Electrolysis Electrolytic production of iron

25 Scrap preparation Preparation of steel scrap for remelting

26 Liquid steel cross section Balancing cross section for liquid steel

S1.3) List of system variables

Table S2: List of system variables

Symbol Name Description Indices (Sets)

t Time Model time (2008-2100)

t’ Cohort Cohort of steel product (1700-2100)

c Product category 1: Transportation, 2: Machinery, 3: Construction, 4: Products (Appliances, packaging, other)

r Region 1: North America (USA and Canada) 2: Latin America (the entire Americas without the US and Canada 3: Western Europe (all European countries which have not been part of the former Soviet Union, not Turkey and Cyprus) 4: Commonwealth of Independent States (CIS) and other states that were part of the Soviet Union 5: Africa 6: Middle East, including Turkey and Cyprus 7: India 8: China 9: Developed Asia and Oceania: Japan, South Korea, Australia, New Zealand 10: Developing Asia and Oceania

f Category of finished steel product

1: Cold rolled coil (CRC) 2: CRC coated 3: CRC galvanized 4: Tin plated steel 5: Electrical strip steel 6: Hot rolled coil galvanized

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7: Hot rolled coil 8: Hot rolled narrow strip 9: Plates 10: Welded pipes 11: Seamless tubes 12: Concrete rebars 13: Wire rods 14: Hot rolled bars 15: Rail sections 16: Light sections 17: Heavy sections 18: Cast steel 19: Cast iron

Stocks

1( , ', , )S t t c r In use stock In use stock broken down on cohorts, categories, and

regions

15 ( )S t Stock of iron in ore Reserve and reserve base of iron in form of iron ore

18 ( )aS t Stock of iron in tailings

18 ( )bS t Stock of iron in slag and scaling deposits

18 ( )cS t Stock of post-consumer iron in landfills

16 ( )S t Obsolete stocks Stocks that are out of use and not accessible for

recycling, e.g., foundations or pipes

10 ( )S t Stocks on the scrap market Accumulation of steel scrap on the scrap market (only

used in scenarios where there is an oversupply of scrap due to low demand)

Flows

15 14 ( )F t− Extracted iron in form of ore

14 18 ( )aF t− Iron in form of iron ore tailings

14 20 ( )F t− Iron in iron ore and concentrates

20 11( )F t− Iron ore sinter to blast furnace

20 12 ( )F t− Iron ore sinter to direct reduction

14 21( )F t− Iron ore sinter to smelt reduction

14 22 ( )F t− Iron ore sinter to electrolysis

11 8 ( )F t− Pig iron (from blast furnace) to pig iron balance

21 8 ( )F t− Pig iron (from smelt reduction) to pig iron balance

22 8 ( )F t− Pig iron (from electrolysis) to pig iron balance

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8 5 ( )F t− Pig iron (from pig iron balance) to foundries

8 6 ( )F t− Pig iron (from pig iron balance) to basic oxygen furnaces

12 7 ( )bF t− Production of direct reduced iron

6 26 ( )F t− Crude steel from basic oxygen furnace

7 26 ( )aF t− Crude steel from scrap-only EAF

7 26 ( )bF t− Crude steel from DRI-fed EAF

26 5 ( )F t− Crude steel to forming

25 5 ( )F t− Scrap consumed by iron foundries

25 6 ( )F t− Scrap consumed by BOFs

25 7 ( )aF t− Scrap consumed by EAFs (scrap only)

25 7 ( )bF t− Scrap consumed by EAFs (DRI-fed)

10 25 ( )F t− Total scrap consumption

5 3 ( , )F t f− Finished steel consumption Broken down onto the 19 categories of

finished steel

3 1( , , )F t r c− Final steel consumption Final demand for steel in all categories and

regions

3 19 ( , )F t c− Fabrication scrap

19 3 ( , )F t c− Re-diverted fabrication scrap Not considered in this paper, but in paper 2

(Milford et al.)

1 17 ( , , )F t r c− End-of-life steel (EOL) All steel leaving use

17 16 ( )F t− (EOL)-steel into obsolete products

17 18 ( )cF t− (EOL)-steel to landfills

17 3 ( , )F t c− Re-use of EOL-scrap Not considered in this paper, but in paper 2

(Milford et al.)

17 10 ( , , )F t r c− EOL-scrap by region and category

19 10 ( )F t− Fabrication scrap

5 10 ( )F t− Forming scrap

Not included in the system drawing but considered in the model

18 ( )X bF t− Slag and scaling losses {5,11,12,21,22,25,20}X ∈

20 ( )YF t− Slag and mill scaling sent to sintering for iron recovery

{5,6,7 ,7 }Y a b∈

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S1.4) Model approach

The model is programmed in Matlab and can run both on a PC and on a MAC (Fig. S2). All data are read from an Excel file while all the computation, evaluation, and plotting is done in Matlab. This section explains the model structure.

The data required are marked brown. NOTE: The dependency of each variable is always made explicit: A flow over time which is

split onto regions and categories is denoted by ( , , )a bF t r c− , and a flow that only depends on

time by ( )a bF t− .

( , , ) ( ) !a b a bF t r c F t− −≠

The first term is a 93x10x4 array, the latter a 93x1 vector. But

r r,c( , ) ( , , ), ( ) ( , , ), etc.a b a b a b a bF t c F t r c F t F t r c− − − −= =∑ ∑

Figure S2: Structure of the entire model.

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S1.4.1.) Use phase The in-use stock is determined from estimates for population ( , )P r t and assumptions on

the per capita in use stock pattern ( , , )CS t r c , identical with our previous work (Pauliuk et al.).

1 ( , ) ( ,, ,) )( , CP r t S cS t tr rc = ⋅

In order to model future availability of old scrap we need to know the distribution of the stock onto different cohorts and their lifetime. According to previous work (Pauliuk et al.) we assume a normally distributed lifetime ( , ) ( , )( , ', , )r c ct rt σλ τ with region- and category

dependent mean ( , )r cτ and standard deviation ( , )r cσ : 2

2( , ) ( 1 ( ' )( , ' , ), , ) exp22

r c rt c t ttλπ

ττ σσσ

− − −= ⋅

Which determines the outflow from previous inflows:

1 17 3 1'

( , )( , , ) ( ', , ) ( , ', , )( , )t t

rF t r c F t r c rt t c cτλ σ− −≤

= ⋅∑

Once all historic inflows 3 1( ', , ), ' 2008F t r c t− ≤ are known, the model performs a year-by-

year calculation to i) determine the outflow from previous cohorts and ii) determine the inflow from mass balance of the stock:

3 1 1 1 1 17( , , ) ( , , ) ( 1, , ) ( , , )F t r c S t r c S t r c F t r c− −= − − +

More intense use, lifetime extension, and light-weighting: We approximate the future service level of steel in-use by the present stock level in industrialized countries where saturation has occurred. More intense use of steel stocks is a major strategy to reduced metal demand and we model it by introducing a parameter 1( , ) IM t c ≥ to reduce future per

capita stock demand:

( , , ) ( , )( , )1 ,C C

I

S t r c S t r cM t c

→ ⋅

Light-weighting ) 1,0 (LM t c≤ ≤ of steel-bearing products affects the inflow:

3 1 3 1( , , ) ( , ,, ) )(LM tF t r c F cc t r− −→ ⋅

This procedure will lead to an effective reduction of the in-use stock which is re-calculated subsequently with the new and lighter inflow. Lifetime extension can affect both past and future cohorts. We introduce the parameter

( , 1)XM t c ≥ which can increase cohort lifetime in two versions

a) Future cohorts only: The cohort mean lifetime becomes time-dependent according to ( ', , ) ( ', ) ( , ), ' 2008Xt r c M t c r c tτ τ= ⋅ ≥

b) Delayed scrapping: For each year the scrap flows are computed as if the current cohort lifetime was ( ', , ) ( ', ) ( , )Xt r c M t c r cτ τ= ⋅ . This is calculated by re-computing the

probability density function for discard during each model year. This procedure is applied to all cohorts.

The three strategies are part of the material efficiency package explored in paper 2 (Milford et al.).

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S1.4.2.) Waste management and landfills Obsolete stocks: A certain fraction of the end-of-life flow is forming obsolete stocks which

is controlled by the parameter ( )O c :

17 16 1 17c,r

( ) ( , , )( )F t FO c t r c− −= ⋅∑

Another fraction of the remaining flow can be re-used or re-manufactured which is controlled by the parameter ) 1,0 (RM t c≤ ≤ . This option is explored in paper 2 (Milford et al.).

17 3 1 17r

( , ) (( , ) ( )1 ) ( , , )RM t cF t c r cc FO t− −= ⋅ − ⋅∑

Finally, only a certain fraction of the remaining flow is made available as old scrap. This is controlled by the end-of-life recycling efficiency ( , )EOL t c :

17 10 1 17

17 18 1 17r,c

( , , ) (1 ) (1 ) ( , , )( )

( , ) ( , ) ( )( , ) (1 ) (1 ) (1 ) ( , , )( , ) ( )

R

Rc

F t r c EOL t c M t c O cEOL t c M t c

F t r cF F t rOt c c

− −

− −

= ⋅ − ⋅ − ⋅

= − ⋅ − ⋅ − ⋅∑

S1.4.3.) Fabrication and scrap diversion Fabrication and manufacturing comprises all industries that turn finished steel into final

products such as cars, electronics, and furniture, but also the construction sector and machine building. We distinguish four different manufacturing sectors according to the four product categories and 19 different types of finished steel. First we introduce the split of products onto finished steel ( , )PFS c f which is assumed to remain constant over time:

3 1 3 1r

( , )( , , ) ( , , )F t c PFf F tS c cf r− −= ⋅∑

The yield loss ( , )Y c f is category and product specific and determines the total amount of

fabrication scrap per unit of throughput. We introduce another material efficiency strategy

1( , ) YM t c ≥ to reduce the yield loss:

( , )( , )( , )Y

Y c fY c fM t c

→

( )3 19 5 3 19 3 17 3

3 1

( , , ) ( , , ) ( , , ) ( , )

( , , )1

( , )( , )

( , )

YF t c f F t c f Fc fY c f

Y

t c f F t c

F t c fc f

− − − −

−

= ⋅ + +

= ⋅−

The scrap diversion ) 1,0 (DM t c≤ ≤ determines the share of scrap that is sent back into

manufacturing:

We assume that the diverted and re-used scrap ( , )SU t c is used in different product

categories according to the final demand:

( ) 3 117 3 19 3

3 1

( , )( , ) ( ) ( )( )

F t cSU t c F t F tF t−

− −−

= + ⋅

And hence

( )3 1 3 15 3 17 3 19 3

f 3 1

( , , ) ( , )( , ) ( ) ( )1 ( )( , )F t c f F t cF t c F

Y c ft F t

F t− −

− − −−

= − + ⋅−∑

19 3 3 19

19 10 3 19c,f

( , , ) ( , , )( )

( , )( , ) (1 ) ( , , )

D

D

M t cM

F t c f F t c fF t F t c ft c

− −

− −

= ⋅

= − ⋅∑

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Finally, we determine the split of fabrication input onto different types of finished steel

5 3 ( , )F t f− by assuming that the share of different steel products in the fabrication input is the

same as in the products:

3 15 3 5-3

c 3 1

( , , )( , ) ( , )( , )

F t c fF t f F t cF t c−

−−

= ⋅∑

This flow will be used to determine the input and losses of the various forming processes. S1.4.4.) Forming

During forming, liquid steel is cast to slabs, blooms, billets, or ingots which in the subsequent process steps are then rolled, plated, coated, cast, tin-plated, welded, extruded, etc., to form the 19 categories of finished steel (Cullen et al. 2012). In addition to liquid steel we consider pig iron and scrap as input to iron foundries (finished steel category no. 19). Each of the 15 sub-processes of forming is characterized by

• The yield rate of the process (output/input) ( )FY process

• The recovery rate of forming yield losses ( )FYR process : Yield losses that are recovered

as scrap are called forming scrap and are fed back into the steelmaking process. The remainder is either collected as mill scale and sent to sintering or is lost as slag.

• The input mix of the process: E.g., the share of crude steel and ingots in hot strip mills

This part of our model is taken from another piece of work (Cullen et al. 2012). Here it is

solved from right (output) to left (input) to determine the required amount of liquid steel and foundry input to supply the finished steel determined by the models for use phase and fabrication. The forming module yields

• The total demand for liquid steel 26 5 ( )F t−

• The demand for pig iron for iron castings 8 5 ( )F t−

• The demand of scrap for iron castings 25 5 ( )F t−

• The total forming scrap 5 10 ( )F t−

• The flow of mill scale sent to sintering 5 20 ( )F t−

• The total slag losses 5 18 ( )bF t−

S1.4.5.) Liquid metal production

The liquid metal module consists of the reduction processes (11, 12, 21, 22), the refining and remelting (6, 7a, and 7b), the cross-sections for pig iron (8) and liquid steel (26) as well as scrap preparation (25) and the scrap market (10). 30 flows enter, leave, or are located within the system. In addition the stock in the scrap market has to be considered which increases the number of system variables in this module to 31.

The six flows 26 5 ( )F t− , 8 5 ( )F t− , 25 5 ( )F t− , 5 10 ( )F t− , 19 10 ( )F t− , and 17 10 ( )F t− are known from previous

calculations. In addition we have 11 mass balances for the processes included which means that another 14 parameters or model assumptions are needed. They are listed below:

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• The slag and loss rates LR for the eight processes 11, 12, 21, 22, 6, 7a, 7b, and 25. (8

equations). The loss rates are defined as follows:

1

L

L

L

Loss Total Inflow

Loss Useful

R

OutR wR

flo

= ⋅

= ⋅−

• The share of direct reduced iron in total liquid steel DRIR : 12 7 26 5( ) ( )Db RIRF t F t− −= ⋅

• The shares of smelt-reduced and electrolyzed iron in the total pig iron production,

SRR and ER :

21 8 11 8 21 8 22 8 22 8 11 8 21 8 22 8( ) ( ( ) ( ) ( )), ( ) ( ( ) ( ) ( )).SR EF t F t F t F t F t F t F t F tR R− − − − − − − −= ⋅ + + = ⋅ + +

• The share of scrap SDRIR in DRI-fed EAF: 12 7 25 7( ) ( )SD bRIb RF t F t− −= ⋅

• The time-dependent share of scrap ( )SBOFR t in BOF: ( )2 56 8 6 2 56( )( ) ( ) ( )SBOFF t F t F tR t− − −= ⋅ +

• For the default case we can process all available scrap and hence the scrap market can be cleared for each year and no stocks accumulate:

10 ( ) 0S t =

The complete system is written in matrix form and solved for all unknowns. For scenarios with very high adaptation of material efficiency a negative BOF output may occur. This happens when large scrap volumes from previously high consumption, e.g., in China, cannot be processed since demand for steel has dropped so much. The model response is to dump the scrap into negative BOF output, which is not physically possible. For these cases, we set the BOF output to zero and build up scrap stocks in the market, where it is stored for later use. The 14 additional parameters are then:

• The flows 8 6 25 6 6 26 6 18( ), ( ), ( ),an d ( )bF t F t F t F t− − − − entering and leaving the BOF are set to

zero (3 independent equations, the fourth follows from mass balance).

• The slag and loss rates LR for the seven processes 11, 12, 21, 22, 7a, 7b, and 25. (7

equations). The loss rates are defined as follows:

1

L

L

L

Loss Total Inflow

Loss Useful

R

OutR wR

flo

= ⋅

= ⋅−

These losses are processed further. Part of the losses from steel making (6, 7a, and 7b) and from forming is sent to sintering, the rest and all other process losses are landfilled.

• The share of direct reduced iron in total liquid steel DRIR : 12 7 26 5( ) ( )Db RIRF t F t− −= ⋅

• The shares of smelt-reduced and electrolyzed iron in the total pig iron production,

SRR and ER :

21 8 11 8 21 8 22 8 22 8 11 8 21 8 22 8( ) ( ( ) ( ) ( )), ( ) ( ( ) ( ) ( )).SR EF t F t F t F t F t F t F t F tR R− − − − − − − −= ⋅ + + = ⋅ + +

• The share of scrap SDRIR in DRI-fed EAF: 12 7 25 7( ) ( )SD bRIb RF t F t− −= ⋅

Again, the complete system is written in matrix form and solved for the unknowns.

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S1.4.6.) Mining, beneficiation, and sintering The iron ore mined often has to be beneficiated and iron oxide which is too finely

dispersed ends up in tailing ponds. The beneficiated ore is either shipped directly or sintered to increase the strength when being charged into the blast furnace. Some losses occur in this process as well. Finally, the mill scale collected is prepared and added to the sinter; some losses occur here as well. The parameter ML indicates the share of mill scale and slag

going back into the blast furnace, and SL denotes the share of iron ore lost during sintering.

With the known total demand for sinter 20 11 20 12( ) ( )F t F t− −+ we then can resolve the sintering

process:

( )

20 11 20 12 20Y

14 20

20 18 20 11 20 12 20 20Y Y

( ) ( ) ( )( ) , {5,6,7 ,7 }

1

( ) ( ) ( ) ( ) 1 ( ), {5,6,7 ,7 }1

M

S

M

Y

b Y YS

MS

LF t F t F tF t Y

La b

F t F t F t F tL L LL

F t Y a b

− − −

−

− − − − −

+ − ⋅= ∈

−

= ⋅ + − ⋅ + − ⋅ ∈ −

∑

∑ ∑

The total demand for iron ore extracted is given from the efficiency of iron ore beneficiation BL :

14 2015 14

14 18 14 20

( )( )

1( ) ( )B

Ba

B

F tF t

F L tL

t

L

F

−−

− −

=

−= ⋅

S1.4.7.) Scrap market, model closure The use phase plays a double role as it determines both final demand and old scrap

supply. Steel production creates a scrap demand and the recycling loop is closed at the scrap market. We assume that as long as demand is high enough, all available scrap from forming, fabrication, and use will be processed to some form of steel. Only in cases where steel demand is so low that not all scrap can be processed, e.g. through full-scale implementation of material efficiency strategies (Milford et al.), we allow for steel scrap stocks to accumulate and be stored for future use.

S1.4.8.) Comparison between our model and the approach taken by Hatayama et al.

(2010): Hatayama et al. (2010) compiled the steel cycles for 42 countries covering 85% of global

steel consumption in 2005. The world is split into eight regions and three end-use categories (buildings, civil engineering (infrastructure), and vehicles) are considered. Future stock saturation is assumed and modeled by a three-parameter logistic curve for buildings and civil engineering, and a GDP-driven Gompertz-model for the vehicle stock. For buildings and civil engineering the saturation levels for different countries were set by correlating population density with the present stocks in the 47 prefectures of Japan.

We extend the work of Hatayama et al. and challenge some of the assumptions therein: (1) Japan has a much higher per capita consumption of steel (World Steel Association

2011) and our previous work (Müller et al. 2011; Pauliuk et al.) showed that steel stocks in Japan have not saturated yet. Therefore we infer saturation levels from a broader analysis of steel stocks in a larger number of developed countries. Since the definition of end use

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categories varies between countries and scrap flows are dominated by the short-lived categories transportation, machinery, and products (Allwood et al. 2012), which are only partly covered by Hatayama et al. (2010), we cover the entire steel consumption and stock.

(2) The three parameter logistic equation does not allow the effect of the saturation time to be explored independently of other parameter choices. We introduce an additional parameter to the future stock pattern to make saturation time an explicit scenario variable.

(3) The consequences of future demand and old scrap supply on the steel industry are not covered by previous work. Forming and fabrication stages need to be included as they determine the yield loss of the material industry, which has a significant impact on liquid metal demand and the split between primary and secondary production capacities. We cover the entire steel cycle including: mining and ore beneficiation; a detailed model of upstream supply chain for more accurate fabrication and forming yield losses; the use phase; waste management; and, secondary production. This extension is also crucial to model the future carbon footprint of the steel sector.

(4) To accommodate the extremely long lifetime of both steel industry assets (SI chapter S2.8.1) and construction and infrastructure (Müller et al. 2007) the time frame of the analysis is extended to cover at least the expected minimum economic life of ca. 60 years of the recently erected primary capacity in China. We extend the time horizon until 2100 to examine whether we can expect global steel production to peak within the 21st century.

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S2) Data sources and data treatment

S2.1) Use phase S2.1.1) Population ( , )P r t

Different long-term population estimates are available and they differ largely in the underlying assumptions (Basten 2012) (Fig. S3). The UN projections (UN Population Division 2011) are based on fixed assumptions of the level of mortality and fertility in a region. E.g., as Africa is currently experiencing a fertility stalling (where the mortality levels have been falling, but the fertility levels are not following, as would be expected), projections based on a continuation of this stalling will inevitably end up with high population projections. We refer to the probabilistic population scenarios of IIASA (Lutz et al. 2007), which are supplied with confidence intervals. To be able to use this projection three adjustments need to be made:

1) With four countries being exceptions, the 13 IIASA regions can be simply aggregated so they match with ours. The four exceptions are Turkey (from Western Europe to Middle East), South Korea (from Pacific Asia to Developed Asia/Oceania), India (from South Asia to separate region), and China (from China and CPA to separate region). The share of the 2010 population of the four countries in their respective region is either very small (Turkey 15%, South Korea 9%) or very large (India 75%, China 90%). Since the regions in the model are assumed to be homogeneous we use the current share to determine the future population of these countries, and transfer the resulting number of people to the regions mentioned above.

2) The yearly estimates obviously have been obtained from a coarser projection, as several peaks and steps are contained. Using these data directly would results in a large number of peaks and discontinuities in the resulting flows. To smoothen the estimates we perform a cubic interpolation to each of the ten regions and re-calculate the estimates from the cubic model.

3) The IIASA estimates date from 1996 and need to be scaled up to the current population reported by the UN. For each region we multiply the curve by a constant factor so that for 2008, the population estimates coincide with the actual population in each region.

The result is shown in Figure S3 with the UN estimates for comparison. The most striking difference is that for the IIASA estimate population in Africa will peak at 2.5 billion around 2100 whereas for the UN estimate the population in Africa will be more than 3 billion and still growing fast.

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Figure S3: Population estimates from UN and IIASA by region.

Figure S4: Apparent final consumption for mean trade, by region.

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S2.1.2) Historic consumption 3 1( ', , ), ' 2008F t r c t− ≤

The time series for historic apparent consumption of steel is taken directly from (Pauliuk et al.), where it has been derived from historic production, loss rates, and trade statistics. It has been aggregated from the country to the regional level (Fig. S4). S2.1.3) Stock patterns ( , , )CS t r c and lifetime ,( , ) ( , )r c r cτ σ

Based on the analysis of historic iron consumption (Pauliuk et al.) we identify a set of saturation levels and saturation times as well as lifetime distributions for the ten regions. As pointed out in the paper we chose the following analytical expression for the stock curve:

( )( )( )00

( ) ,1 1 · · 1 ·( )

SS tS exp cS

exp d t t=

+ − − −

where t is the time, S the saturation level, and S0 the stock at a given t0. c and d are two shape parameters that are chosen numerically so that two further boundary conditions hold: a) the stock curve is tangent to the latest historic stock in t0=2008 and b) the stock reaches 99% of the saturation level at a given time tS. Table 1 in the paper provides an overview on saturation levels and saturation time chosen for the different categories and regions. The lifetime estimates are the same as in the historic analysis (Pauliuk et al.), where they could be calibrated to some extent. This paper provides a sensitivity analysis on this parameter and the follow-up paper (Milford et al.) introduces lifetime extension to change product lifetime. For the baseline we assume all regions to eventually build up a per capita stock of ca. 14 tonnes, which is about the stock level in those countries with the most mature stock (Pauliuk et al.). For the developed regions North America, CIS, and Developed Asia the stock curve would need to change abruptly when assuming a future saturation level of 14 tons, so we release the assumption for those regions and assume slightly different levels of 12.8 to 15.4 tons. We assume four different saturation times according to the present state of development if possible: Stock in the developed world seem to be close to saturation and hence we set the saturation time (the time when the stock will reach 99% of the saturation level) to 2020 or 2030. For China per capita stock is growing with about 250 kg/year (Pauliuk et al. 2012) and hence there is a clear trend that stocks will pass the 10 ton benchmark around 2030. We assume that as the infrastructure is developed and large parts of the people live in modern cities steel stocks will saturate on a similar level as developed regions show today. In accordance with previous work we assume that this will be the case around 2050 (Pauliuk et al. 2012). For the other five developing regions the present growth speed is much slower than in China which may indicate later saturation. A boom in steel consumption similar to what has been observed in China between 1995 and 2010 may happen of course, but what we observe today is that the industrialization process in India, Africa, and developing Asia

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proceeds at a much slower pace than in China. The historic analysis, including China, reveals that for rapid industrialization and with it growing steel stocks to start, a stock level of 1-2 tons per capita is required (Fig. 2a in the paper). We therefore assume that Latin America and the Middle East which already have passed this level will saturate later than China but earlier than India, Africa, and developing Asia. We choose 2100 as saturation time for the former two and 2150 for the latter three. The lifetime assumptions are set to the values in previous work (Müller et al. 2011) except for Developed Asia where the historic analysis (Pauliuk et al.) suggests a significantly shorter lifetime. For all categories in Developed Asia we choose a lifetime that is 1/3 smaller than for the other regions. The historic analysis also reveals that lifetime seems to differ a lot between different countries. For a number of developed countries lifetimes can be both significantly shorter and longer than the base values: for construction, a range of 40 to 100 years has been identified. Still, we have to come up with a single baseline value for each region, and we use the assumptions made in previous work to get comparable results. We do however challenge these assumptions in two ways: 1) by performing a sensitivity analysis with a ±30% change is this paper and 2) the introduction of lifetime extension in Milford et al. (). Little is known about lifetime of especially machinery, buildings, and infrastructure. In line with previous work we assume a normally distributed lifetime with a standard deviation of 0.3 the mean so that 95% of each cohort leaves use between 0.4τ and 1.6τ (2σ confidence interval). S2.2) Material efficiency strategies This paper does not explore material efficiency which is a major pillar in Milford et al. (). Therefore all material efficiency parameters are set to their default values (Table S3). Table S3: Material efficiency strategies

Symbol Name Default Value

) 1,0 (LM t c≤ ≤ Less metal, same service

0

1( , ) IM t c ≥ More intense use

1

( , 1)XM t c ≥ Life extension 1

) 1,0 (DM t c≤ ≤ Fabrication scrap diversion

0

) 1,0 (RM t c≤ ≤ Re-use of end-of-life scrap

0

1( , ) YM t c ≥ Fabrication

yield improvements

1

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S2.3) End-of life process parameters

S2.3.1) Obsolete stock formation by product category ( )O c

We assume that 10% of construction steel leaving use is accumulated as obsolete stock. This fraction refers to the typical amount of steel used in foundation walls or pipes underground. For the other categories we assume no significant formation of obsolete stocks, cf. Table S4:

Table S4: Obsolete stock fraction

Sector Transportation Machinery Construction Products ( )O c 0 0 0.1 0

Source: Assumption Assumption Assume that steel in substructures remains underground, for 10%, (Allwood et al. 2012)(Fig. 3.4), lower limit!

Assumption

S2.3.2) End-of-life recycling over time and by product category ( , )EOL t c

For present and future estimates on end-of-life recovery efficiency we refer to a recent update of the WorldSteel 3R-factsheet (World Steel Association 2010a) where the figures shown in Table 5 are reported. In Pauliuk et al. (), we compare the WorldSteel data to three other datasets, which lie in the range of 0.67 to 0.9. The average of these three datasets almost coincides with the most recent figures from WorldSteel. Since the later ones are the only sector-specific estimate available and the numbers lie well within the range of other estimates, we decided to use the most recent WorldSteel figures.

Table S5: Present and future EOL-recovery rates ( , )EOL t c as estimated by WorldSteel.

Source: (World Steel Association 2010a).

Market 2007 estimate 2050 Target

Construction 0.85 0.9

Automotive 0.85 0.95

Machinery 0.9 0.95

Appliances 0.5 0.75

Containers 0.69 0.75

Total 0.83 0.9

For the time series between 2007 and 2050, we apply a branch of the cosine function

because it has zero slope at both ends and there is a unique branch with no additional extrema that fits between the two end points:

( ) 1 20071 cos( , ) (2007, ) (2050, ) (2007, , 2007 20502 2050 2007

)EOL t c EOL c EOL c t tEOL c π − = + − ⋅ ⋅ − ⋅ ≤ ≤ −

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S2.4) Fabrication S2.4.1) Final products to finished steel products split ( , )PFS c f

A detailed description is part of the work that our colleague Jonathan Cullen has done as part of the WellMet2050 project (Cullen et al. 2012). An overview on the system used and the major flows can be obtained from Table 4.1 in the book that resulted from the WellMet2050 project (Allwood et al. 2012). We assume no changes in the structure of the manufacturing system and the share of different finished steel types in the four product categories.

S2.4.2) Fabrication yield losses ( , )Y c f

A detailed description is part of the work that our colleague Jonathan Cullen has done as part of the WellMet2050 project. (Cullen et al. 2012). An overview on the system used and the major losses can be obtained from Table 4.1 in the book that resulted from the WellMet2050 project (Allwood et al. 2012). We assume no changes in the structure of the manufacturing system and the share of different finished steel types in the four product categories.

S2.5) Forming S2.5.1) Forming yield by forming process ( )FY process

A detailed description is part of the work that our colleague Jonathan Cullen has done as part of the WellMet2050 project. (Cullen et al. 2012). An overview on the system used and the major yield losses can be obtained from Table 4.1 in the book that resulted from the WellMet2050 project (Allwood et al. 2012). We assume no changes in the structure of the manufacturing system and the share of different finished steel types in the four product categories.

S2.5.2) Recovery of forming yield losses by forming process ( )FYR process

A detailed description is part of the work that our colleague Jonathan Cullen has done as part of the WellMet2050 project. (Cullen et al. 2012). An overview on the system used and the major yield losses recovered as forming scrap can be obtained from Table 4.1 in the book that resulted from the WellMet2050 project (Allwood et al. 2012). We assume no changes in the structure of the manufacturing system and the share of different finished steel types in the four product categories.

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S2.6) Liquid metal S2.6.1) Process slag rates LR

Blast furnace

Value: 99.37% (Fe-yield); 1630kg sinter for 1 tonne hot metal (95% iron)

This is the Fe-yield value taken from World Steel Association (2009), which includes absolute losses to the environment and losses recovered outside of the blast furnace. The blast furnace has many different fuel and feedstock flows into the process, and for some inflows, the material acts as both a fuel and a feedstock. Therefore, we do not describe an absolute yield (where all mass flows are included) rather we describe a yield for the mass flows that contain iron. This is illustrated in Figure S5 below:

Figure S5: Iron and non iron inputs and losses.

For our non-Fe yield calculations, we also do not consider the non-Fe-containing inputs as it is not always apparent how much of the inputs are acting as fuels or as feedstocks; also, these flows are generally less well reported.

This approach allows us to determine the mass of sinter required for the process, but the result is that the mass flow for the process losses will not necessarily equal the total process losses. Using data from SteelUniversity (2012) we know that to produce one tonne of hot metal (which has an iron content of ~95%), we need 1630kg of agglomerated iron ore. This gives us the relationship between iron outflows and sinter inflows and the losses are calculated to make up the mass balance.

Direct reduction

Value: 99.37% (Fe-yield); 1490kg sinter for 1 tonne DRI (92% Fe)

This is the Fe-yield value for the blast furnace taken from (World Steel Association 2009), which includes absolute losses to the environment and losses recovered outside of the direct reduction process. As a Fe-yield value specific to direct reduction could not be found, we assume the same value as for the blast furnace.

As for the blast furnace, a relationship between the absolute iron outflows and sinter inflows is defined, here using average data from (International iron and Steel Institute 1998); we need approximately 1490kg of agglomerated iron ore to produce one tonne of

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direct reduced iron (which has an iron content of ~92%). The process losses are calculated to achieve a mass balance, but will not necessarily equal the total process losses.

Electric arc furnace (EAF)

Value: 95.7% (Fe-yield); 88.7% (non-Fe yield)

These values include the yield impact of secondary metallurgy (Fe-yield 100%; non Fe-yield 99.75%, from World Steel Association (2009)), as this is not considered as a separate process in the global steel model. The value for the Fe-yield is calculated using the median value for the EAF yield from World Steel Association (2009), as the average value is not presented.

Basic oxygen furnace (BOF)

Value: 93.8% (Fe-yield); 86.4% (non-Fe yield)

There are three elements to the yield values: pretreatment (which only applies to liquid metal inputs to the BOF), the BOF process itself, and secondary metallurgy. The yield values for pretreatment are taken directly from World Steel Association (2009) and we assume that the proportion of scrap used in the BOF remains constant at ~20%, therefore the pretreatment yield only applies to ~80% of the BOF input. The non Fe-yield for the BOF process is taken from Figure 49 in World Steel Association (2009); the Fe-yield is calculated using the median value for the BOF yield from World Steel Association (2009), as the average value in not presented. The secondary metallurgy yield values are the same as for the EAF.

Smelt reduction Value: 99% (Fe-yield)

Smelt reduction is not yet a mature process and data on its efficiency are not available yet. We assume the Fe-yield to be similar to the other reduction technologies. The non-Fe yield is assumed to be 5%. Electroloysis Value: 99% (Fe-yield)

Smelt reduction is not yet a mature process and data on its efficiency are not available yet. We assume the Fe-yield to be similar to the other reduction technologies. The non-Fe yield is assumed to be 5%. Scrap preparation Estimating losses in scrap preparation is part of the work that our colleague Jonathan Cullen has done as part of the WellMet2050 project. (Cullen et al. 2012). The yield factor for scrap preparation in this work is 0.99.

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S2.6.2) Share of DRI production related to crude steel production DRIR

Value: 5% This reflects the 2008 ratio of DRI production to crude steel production (WorldSteel

Association 2009). S2.6.3) Share of smelt reduction in pig iron production SRR

Value: 0 Smelt reduction is a novel iron making technology aiming at reducing the carbon footprint by moving from coke to coal as reduction agent and thus eliminating the need for coking. It does not affect iron flows and hence it is not considered in this paper. S2.6.4) Share of electrolysis in pig iron production ER

Value: 0 Electrolysis is a novel iron making technology in its infancy aiming at reducing the carbon footprint by moving from coke to electricity as reduction agent and thus allowing low carbon electricity to be used for iron making. It does not affect iron flows and hence it is not considered in this paper. S2.6.5) Ratio of DRI to scrap in DRI melting SDRIR

Value: 50% At present, DRI is used to dilute scrap in EAF furnaces. Since we expect future scrap flows

to rise significantly there will always be much more scrap than DRI. We assume that the share of DRI to scrap in those furnaces that charge DRI is 50%. This assumption does neither alter the overall mass flows nor the losses that occur during melting. S2.6.6) Ratio of scrap in total BOF input ( )SBOFR t

0.1, 2008

20080.1 0.05 1 cos , 2008 205042

0.2, 2050

( )SBOF

ttR

t

t tπ

=

− = + ⋅ − ⋅ < < ≥

In the BOF most of the energy released when oxidizing the carbon in the pig iron remains in the melt. This is necessary to increase the temperature from ca. 1150 °C to ca. 1500°C but in many cases there is still excess heat which can be used to melt scrap. Rates of scrap over total iron input of up to 30% have been reported (Tardy 2008). On a global scale however the ratio is significantly lower, mainly because there is not enough scrap available, especially in China. For 2008, we determine the global average share of scrap in BOFs by calibration: The scrap share in BOFs is chosen so that the model results for BOF output and total steel production equal the reported figures (cf. calibration plot Fig. S9). The result is 10%. For the years 2009-2050 we assume a gradual doubling of this share, as more scrap will be available and using it in BOFs does not require large changes within the steel sector. The

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transition is modelled by the branch of the cosine function, which has zero slope in both 2008 and 2050. S2.7) Mining and beneficiation S2.7.1) Iron sintering loss rate SL and mill scale and slag recovery efficiency ML

Value: 99.83% (Fe-yield SL ); 92.1 % (non-Fe yield)

Comment: The Fe-yield value is taken from World Steel Association (2009), but this yield value considers both losses to the environment and losses that are recovered outside of the process i.e. iron recovered from sintering dust/sludge. The yield cannot be used to calculate directly the flow from mining required as a significant proportion of the input to the sintering process arises from the recycling of iron oxide from downstream processes. The availability of this material is a function of the throughput of these downstream processes. The non-Fe yield is calculated using data from World Steel Association (2009) and the mass flows from the 2008 steel Sankey diagram, as shown in Figure S6 and described in Table S6:

Table S6: Flows and data sources for the sintering process

Flow Value (2008) Data source

a 1,580 Mt From Figure 21 in World Steel Association (2009), flow c is equal to 25.5% of the iron input. The same Figure shows that 67.3% of the iron input is iron from iron ore or other sources. This assumes that all inputs have the same iron concentration.

b 169 Mt From Figure 21 in World Steel Association (2009), flow c is equal to 25.5% of the iron input. The same Figure reports that 7.2% of the iron input is iron recycled from downstream inputs. This assumes that all inputs have the same iron concentration.

c 599 Mt Mass balance: c = d – f

d 2,200 Mt USGS iron ore yearbook, gross weight of iron ore produced in 2008 (Jorgenson 2009)

e 137 Mt Mass balance: e = a + b + c – d

f 1,611 Mt Mass of sinter used to produce 994 Mt iron (blast furnace and DRI from Sankey), assuming 1630 kg sinter and 1490 kg sinter for 1 tonne hot metal from the blast furnace and DRI, respectively (see blast furnace and DRI section)

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Figure S6: Detailed flow diagram for the sintering process

The non-Fe yield is defined excluding the internal recycling loop, i.e. yield = f/(a + b). Comparing the value for flow b (169 Mt) and the iron oxide predicted to be available for recycling from the 2008 Sankey diagram (212 Mt), we can see more scale from downstream process is available than is recycled in the sintering process. This corresponds to a recovery efficiency of 0.8ML = and we will assume that this is a static variable in the model. In the

model, flow a is calculated by considering the sinter output required and the scale available for recycling from downstream processes.

S2.7.2) Iron ore beneficiation efficiency BL

Many deposits contain high grade ores of high strength that can be crushed to lump ore and fed directly to the blast furnace. Ores from lower grade deposits have to be ground, separated, and sintered which involves losses in the grinding process since there is an economic compromise between extracting more ore and grinding to smaller grain sizes. For China, losses in beneficiation have been estimated to be around 25% (Pauliuk et al. 2012). For this work we assume a global average beneficiation efficiency of 85% which is between the extreme values for high grade ores (virtually zero) and low grade ores.

S2.8) Determining future capacities

S2.8.1) Historic capacity

When determining where to build up future capacities in primary production one central factor is to know about the age of the existing capacities and the speed of their turnover. We therefore compiled a list of the running blast furnaces both in the UK and in Germany which have a mature steel sector. Blast furnaces are at the heart of integrated steel plants and their lifetime may serve as indicator on the lifetime of steel plants and the related infrastructure as a whole. In addition we found some information on historic blast furnaces in both countries. Both pieces of information are compiled in Table S7. The majority of the blast furnaces in blast in both countries are older than 50 years, some blast furnaces are even kept in reserve despite their age. We assume therefore 60 years to be the minimum economic lifetime of a blast furnace. The operation age different blast furnaces have reached in both countries varies a lot. Some had to be decommissioned at an early stage of their life during the steel crisis (such as in Duisburg or Burbach), and some reached a

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lifetime of up to 100 years and beyond. From these data we assume 100 years to be the maximum operation time for a blast furnace before the entire steel plant is eventually restructured ore demolished. In addition we need to estimate the ratio of capacity to output for non-crisis periods. We found these data on the global level from (World Steel Dynamics 2008). We infer that in the long term average, capacity is about 8% larger than production volumes. Since iron and steel production assets respond to dropping demand very slowly, we need to assume a waiting time between the first occurrence of overcapacity and the beginning demolition (Table S8). Not all overcapacity will be demolished within one year, and we assume the share of the overcapacity that is demolished after five years to be 30%, which yields and average life of idle capacity of ca. 7.5 years between becoming idle and demolition. Table S7: Overview on the lifetime on selected blast furnaces and the year of first blow-in for the existing capacity in the UK and Germany.

Location

First blow-

in

Out of operation

Status Lifetime Years in

duty Source

Port Talbot (UK) 1903 1961 demolished 58 (Coflein 2008) Port Talbot (UK) 1956

running

56 (Riden and Owen 1995)

Port Talbot (UK) 1959

running

53 (Riden and Owen 1995) Scunthorpe/Frodingham/Appleby (UK) 1960

running

52 (Riden and Owen 1995)

Scunthorpe/Frodingham/Appleby (UK) 1955

running

57 (Riden and Owen 1995) Scunthorpe/Frodingham/Appleby (UK) 1951

running

61 (Riden and Owen 1995)

Scunthorpe/Frodingham/Appleby (UK) 1947

running

65 (Riden and Owen 1995) Brymbo (UK) 1796 1871 demolished 75

(Riden and Owen 1995)

Brymbo (UK) 1805 1893 demolished 88

(Riden and Owen 1995) Redcar (UK) 1979 2010 idle 31 (Riden and Owen 1995)

Duisburg-Hamborn (DE) 1961

running

51 (Metal Bulletin 2009) Duisburg-Hamborn (DE) 2007

running

5 (Metal Bulletin 2009)

Duisburg-Hamborn (DE) 1974

reserve

38 (Metal Bulletin 2009) Duisburg-Hamborn (DE) 1973

running

39 (Metal Bulletin 2009)

Duisburg-Hamborn (DE) 1993

running

19 (Metal Bulletin 2009) Duisburg-Rheinhausen (DE) 1972 1990 demolished 18

(Müller 2000)

Duisburg-Rheinhausen (DE) 1973 1988 demolished 15

(Müller 2000) Duisburg-Rheinhausen (DE) 1993 1997 demolished 4

(Müller 2000)

Henrichshütte Hattingen (DE) 1857 1885 demolished 28

(Anonymous 2012) Henrichshütte Hattingen (DE) 1939 1987 museum 48

(Anonymous 2012)

Henrichshütte Hattingen (DE) 1963 1987 sold to China 24

(Anonymous 2012) Völklingen (DE) 1883 1986 museum 103

(Hell 2012)

Völklingen (DE) 1887 1986 museum 99

(Hell 2012) Völklingen (DE) 1888 1986 museum 98

(Hell 2012)

Völklingen (DE) 1891 1986 museum 95

(Hell 2012) Völklingen (DE) 1893 1986 museum 93

(Hell 2012)

Völklingen (DE) 1905 1986 museum 81

(Hell 2012) Burbach (DE) 1857 1950 demolished 93

(Saarstahl 2012)

Burbach (DE) 1858 1950 demolished 92

(Saarstahl 2012) Burbach (DE) 1953 1977 demolished 24

(Saarstahl 2012)

Burbach (DE) 1953 1977 demolished 24

(Saarstahl 2012) Burbach (DE) 1964 1977 demolished 13

(Saarstahl 2012)

Bremen (DE) 1959

running

53 (ArcelorMittal 2012) Bremen (DE) 1966

running

46 (ArcelorMittal 2012)

Eisenhuettenstadt (DE) 1951

running

61 (Metal Bulletin 2009) Eisenhuettenstadt (DE) 1955

reserve

57 (Metal Bulletin 2009)

Eisenhuettenstadt (DE) 1955

reserve

57 (Metal Bulletin 2009) Eisenhuettenstadt (DE) 1997 running 15 (Metal Bulletin 2009)

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S2.8.2) The capacity model With the historic analysis and the additional model assumptions summarized in Table S8

the following capacity model for a given region is constructed: a) Capacity lifetime, calculations are performed year-by-year:

• At the beginning of each model year, the existing capacity is transferred into the new year and all capacity older than 100 years is demolished

• If demand exceeds the capacity, build new capacity so that is equals 1.08 times demand

• If there has been overcapacity for more than five years in a row, demolish 30% of the current overcapacity; start with the oldest assets (99 years old).

• If all overcapacity has been demolished, reset the waiting time counter b) How the model responds to demand:

• The model starts at 1700 when production flows were virtually zero.

• From 1700 to 2008, the regional capacity is being built up according to the given pig iron production (Pauliuk et al.).

• From 2009 on, demand modeling is described below (section S2.8.3)

Table S8: Parameters of the capacity model

Parameter Value Source

Ratio of capacity to output, long term average 108% (World Steel Dynamics 2008)

Minimum economic lifetime of blast furnaces 60 years Table S7

Maximal technical lifetime of blast furnaces 100 years Table S7

Waiting time until idle capacity is demolished 5 years Assumption

Share of overcapacity demolished each year 30% Assumption

Figure S7: Historic and future capacity development for the global capacity scenario,

North America and China.

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The mature steel industry in the US and Canada has faced several crises during the 20th century (Fig. S7 left). Large capacities were closed as response to the great depression and the steel crisis in the 70ies, most of it before reaching minimum economic lifetime. For China (Fig. S7 right) most of the capacity is less than ten years old and should hence be running for at least 50 more years from now. Below we discuss under which circumstances this may happen.

Figure S8: Historic and future capacity development, World. Global capacity development after 1850 (Fig. S8) shows several periods of large capacity

extension, which however are on a very different scale: The first period between ca. 1870 and World War I, the second period from the end of World War II to the beginning of the steel crisis, and the third period started with the boom in China around 2000.

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S2.8.3) Future capacity scenarios

We consider two extreme cases for future capacity development. The corresponding global capacity development is shown in Fig. 5 in the paper. a) Trade follows capacity (Global model, Fig. 5b in the paper)

• Consider one large global steel production capacity pool:

• Existing assets are used first, irrespective of their location

• New capacity is added regions of unsatisfied demand

• In case of demolition the oldest capacities are decommissioned first, irrespective of their location

b) Capacity follows demand (regional model, Fig. 5c in the paper)

• Consider ten isolated regional capacity pools:

• Capacity is built according to regional demand

• For each region, overcapacities are decommissioned according to the rules described above

The consequences of both approaches are shown in Figure 5 in the paper.

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S3) Additional results

S3.1) Model calibration

Figure S9: Model calibration for major iron flows. Base year: 2008. For 2008, the upstream iron flows are calculated back from the values for apparent final

consumption and the obtained model parameters on yield loss, sector split, and technology mix. In Figure S9 and Table S9, we present the literature data for some major iron flows together with the model results for 2008.

Table S9: Model calibration

Flow name Data source Literature

value (Mt/yr) Model result

Pig iron production (World Steel Association 2010b), Table

41 935.4 982

BOF output (World Steel Association 2010b), Table 6 920.6 920

DRI production (World Steel Association 2010b), Table

42 66.5 68

Total scrap consumption

(WorldSteel Association 2009), page 26 475.5 630

EAF output (World Steel Association 2010b), Table 8 409.7 451

Crude steel production (World Steel Association 2010b), Table 1 1329.0 1370

Finished steel production

(World Steel Association 2010b), Table 39 1208.6 1245

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Except for pig iron production and apparent scrap consumption the model results fit very

well with the data. Our estimate for pig iron production is 5% higher than the reported figure, and our apparent scrap consumption is ca. 25% higher. For the latter we encountered a similar difference in our work on China (Pauliuk et al. 2012), where even under the assumption of very long lifetimes, scrap flows were considerably higher than reported scrap consumption. Our model covers all kinds of scrap, including several types of home scrap, and not all of these scrap types may be included in the scrap consumption statistics.

S3.2) Future stock trajectories

Figure S10: Historic and future per capita stocks, legend.

Figure S11: Historic and future per capita stocks, transportation. Dashed line: global

average.

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Figure S12: Historic and future per capita stocks, machinery. Dashed line: global average.

Figure S13: Historic and future per capita stocks, construction. Dashed line: global

average.

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Figure S14: Historic and future per capita stocks, products. Dashed line: global average. Figures S11 to S14 show the stock model curves for the ten regions, broken down onto

the four categories. The dashed line shows the global average per capita stock for each category. The large gap between the developed regions North America, Western Europe, CIS, and Developed Asia and the developing world shrinks as all regions approach saturation by 2150. Our analysis (Pauliuk et al.) revealed that stocks in transportation and products differ most, mainly due to different car ownership, registration of ships, and stocks of appliances and packaging/containers. Construction stocks which account for the major share are between 8 and 11 tons in the developed regions. Note that there is also a considerable difference in stock within the OECD countries (Pauliuk et al.).

The model tries to fit in the curve given above so that 99% of the saturation level are

reached at the given saturation time, while keeping the model curve tangential to the historic stock curve. In some cases where the stock is already close to 99% of the saturation level or where the stock is falling, such a curve does not exist, and we assume the following curves instead:

i) Stock growing in 2008: We extrapolate the current growth by assuming a linear growth until the saturation level is reached; thereafter, the stock remains constant. Example: Machinery in Developed Asia.

ii) Stock shrinking in 2008: Instead of assuming further decline we keep the stock on the present level. Example: Transportation in CIS.

In some cases the assumed default saturation levels (1.5, 1.6, 10, and 0.6 tonnes) do not fit to the recent development in the industrialized world. We adapt the stock levels accordingly (cf. Table 1 in the paper) to avoid steps and jumps that may seem arbitrary.

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S3.3) Historic and future total in-use stock

Figure S15: Historic and future stock in use, by region. Figure S15 shows how the future in-use stock will be distributed over the different world regions for the baseline scenario. In-use stocks determine the recycling potential and can be referred to as ‘urban mines’. Before 2000, Western Europe, North America, and the CIS disposed of the largest stock share (70-100%). As more regions develop mature stocks, this is going to change. A huge stock and hence also recycling potential is currently growing in China which may account for 1/3 of global in use stock around 2040. By the end of the century, India, Latin America, Africa and Developing Asia, which today account for ca. 10% of the global in-use stock, will control ca. 60% thereof.

S3.4) Historic and future final demand Figure S16 shows the data from Figure 3a in the paper in stacked form. China will continue

to dominate global steel demand for 10-15 more years. Once stock approaches saturation final demand drops drastically while at the same time, other regions, namely India, Latin America, and Developing Asia may take over. The consumption peak in China around 2025 leads to an almost flat global demand for the years between 2025 and 2040, at a high level of ca. 1550 Mt/yr.

Figure S17 shows the total supply of steel in obsolete products (products leaving use), broken down on the ten regions.

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Figure S16: Historic and future final demand, by region.

Figure S17: Historic and future flow of obsolete products (products leaving use), by region.

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S3.5) Distribution of different stocks

Figure S18: Overall mass balance of the system: Temporal development of the stocks in the cycle.

The in use stock is expected to grow from 25 Gt in 2008 to ca. 60 Gt in 2050 and ca. 90 Gt in 2100 (Fig. S18). The presently developed ore resources (80 Gt, thereof ca. 65 Gt available after beneficiation) may be depleted within 60 years from now, but the estimated reserve base of more than 230 Gt (Jorgenson 2012) does not indicate a supply shortage within the foreseeable future. Some iron will be lost to tailings, slag piles, and landfills, but recovery may be unlikely due to low concentration, contamination with other metals, and abundance of iron ore. Further growth of steel throughput is not constrained by resource availability, rather by the challenge of contributing sufficiently to climate change mitigation (Allwood et al. 2010).

S3.6) How to allocate benefits from recycling and efficient capacity utilization

When trading large amounts of steel and scrap for the sake of efficient capacity utilization and ultimately emission reduction the question of how to allocate the related benefits arises. While the modeled global production mix and average emission factors will not change under reallocation of assets and scrap flows, individual manufacturers could be motivated to use scrap for high quality steel products by being granted a benefit for using secondary steel. Against the background of discussing allocation benefits in the new ISO 14067 (International Organization for Standardization 2012) we propose to consider a dual assessment system of using i) global average impact factors to report the average contribution of the product and ii) an additional crediting system on the product or facility level to encourage scrap use through a reduced carbon tax, for example.

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