Long-term global availability of steel scrap

Resources, Conservation and Recycling 81 (2013) 81–91

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Long-term global availability of steel scrap Junichiro Oda a,∗ , Keigo Akimoto a,b , Toshimasa Tomoda a a b

Systems Analysis Group, Research Institute of Innovative Technology for the Earth (RITE), 9-2 Kizugawadai, Kizugawa-Shi, Kyoto 619-0292, Japan Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan

a r t i c l e

i n f o

Article history: Received 6 April 2013 Received in revised form 3 October 2013 Accepted 6 October 2013 Keywords: Old scrap Primary steelmaking Secondary steelmaking New scrap Home scrap Material flow analysis

a b s t r a c t Primary steelmaking involves CO2 -intensive processes, but the expansion of secondary steel production is limited by the global availability of steel scrap. The present work examines global scrap consumption in the past (1870–2012) and future scrap availability (2013–2050) based on the historical trend. The results reveal that (i) historically, the consumption of old scrap has been insufficient compared with the amounts of discarded steel, and (ii) based on historical scrap consumption, the future availability of scrap will not be sufficient to satisfy the two assumed cases of steel demand. Primary steelmaking is expected to remain the dominant process, at least up until 2050. Under the reference-demand case of 2.19 billion tons in crude steel production by 2050, the total production of pig iron and direct reduced iron could reach 1.35 billion tons. Consumption of old scrap could reach 0.76 billion tons. Because the availability of scrap will be limited in the context of the global total, it is important to research and develop innovative low-carbon technologies for primary steelmaking and to explore their economic viability if we are to aim for achieving large reductions in CO2 emissions from the iron and steel industry. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Long-term scenarios for future global CO2 emissions have been widely discussed, both for domestic energy policies and for international negotiations. The Intergovernmental Panel on Climate Change (2007) presents six categories for total long-term global CO2 emission levels in the fourth assessment reports. Among the six categories, Category I entails the lowest levels of future emissions, with negative total global CO2 emissions by around 2080. International discussions related to the United Nations Framework Convention on Climate Change frequently refer to the so-called 2 degrees target, which would limit global warming to less than 2 ◦ C above pre-industrial levels (UNFCCC, 2012). It is necessary to consider the characteristics of the iron and steel industry when exploring realistic long-term CO2 emission scenarios. The volume of CO2 emissions from the iron and steel sector was 2.6 Gt CO2 in 2010, which accounted for 8.3% of global energyrelated CO2 emissions (International Energy Agency, 2011, 2012a). Secondary steelmaking requires only one-third of the energy used for primary production. The expansion of secondary steel production is, however, limited by the global availability of steel scrap (International Energy Agency, 2007).

∗ Corresponding author. Tel.: +81 774 75 2304; fax: +81 774 75 2317. E-mail addresses: [email protected] (J. Oda), [email protected] (K. Akimoto), [email protected] (T. Tomoda). 0921-3449/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.resconrec.2013.10.002

Several regional material flow analyses have been undertaken to quantify past steel scrap use for the United Kingdom (Michaelis and Jackson, 2000; Davis et al., 2007), the United States (Fenton, 2004), and Japan (Daigo et al., 2007). These regional analyses indicate that there is a substantial difference between the estimated amount of discarded steel and the consumption of old scrap. However, Pauliuk et al. (2012) estimate a relatively small difference between discarded steel and the consumption of old scrap in China. Pauliuk et al. (2012) concluded that old scrap supply would replace up to 80% of iron ore as a resource used for steelmaking in China by 2050. Global-scale material flow analyses have been also conducted (Neelis and Patel, 2006; Hatayama et al., 2010). International Energy Agency (2007) and Neelis and Patel (2006) conducted a global-scale analysis and applied a 70% end-of-life recycling rate, based on their estimated historical trend. Hatayama et al. (2010) conducted a detailed material flow analysis for 42 countries and presented selected categories of steel products for future analysis (due to the limited data availability). Müller et al. (2011) and Cullen et al. (2012) investigated historical iron stock and recent iron flow, respectively, and they discussed in qualitative terms the possibility and necessity of long-term CO2 emission reductions through the increase of secondary steel production. These results from the previous works imply substantial differences in future primary and secondary steel production ratios. There is an overall qualitative common understanding regarding the relationship between steel production trends and secondary steel ratio. The point to note here is the quantitative relationship. In the period from 1970s to 1990s, secondary steel ratio experienced

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a very slow pace of increase under conditions in which steel production was almost stable. In the decade from 2000, the ratio decreased due to the Chinese production hike. The purpose of the present work is to explore the total global future availability of steel scrap up until 2050. The availability of scrap has a significant effect on the role of primary steel. The future scenario presented is quantitatively consistent with the estimated historical parameters, such as the relationship between final products, in-use stock, discarded steel, consumption of old scrap, and the end-of-life recycling rate. 2. Methodology

difficult to quantify the global amount of underground pipes and damaged seawalls, and it is hard to categorize these steel products. This study defines in-use stock and waste as given in Table 1. Existing literature uses different category classifications for stock and waste. Table 1 divides these types into two large groups. The World Steel Association (2009) and Takamatsu et al. (2010a,b) take a descriptive approach based on the observed flow of waste. Hayashi (2012) explicitly notes that defined stock includes the pile foundations of infrastructure and weathered buildings. The World Steel Association (2009) estimates “the (end-of-life) recycling rate was 83% in 2007,” which seems to be based on the implicit definition of Eq. (1). The World Steel Association also refers to 85% as its default value (Broadbent, 2011).

2.1. System boundary within the study Recycling rate [descriptive] = This study has been conducted using the system boundary shown in Fig. 1. The current dominant up-stream flow of iron is from primary steel production through (i) the making of pig iron by blast furnaces; (ii) the making of crude steel using a basic oxygen furnace and continuous casting; (iii) the making of finished steel by hot rolling and finishing; (iv) the manufacturing of final products; and (v) the use of final products by the consumer in society. Home scrap is considered to be usable scrap produced in steel mills and foundries. Note that home scrap excludes circulating iron before crude steel production (Takamatsu et al., 2010a). New scrap is obtained in the fabrication process, typically from the auto industry and packaging-container manufacturing. The volume of new scrap is the difference between finished steel (bars, sheets, coils, pipes, etc.) and final products (vehicles, machines, appliances, containers, etc.). Next in the cycle, after use, obsolete products are gathered together by waste management (WM) and fabricated (cut, shred, and compressed), based on economic incentives and governmental regulations. Old scrap is thus obtained through WM activities, and becomes the main source of iron used for electric arc furnaces (EAF). In some regions, pig iron and direct reduced iron (DRI) are the main iron sources for EAF. Within the iron and steel sector, most liquid steel undergoes continuous casting or is made into ingots to produce slabs, blooms, and billets. Other liquid steel is used in forging steel or ingot molds, which were major products in the early twentieth century (Carr and Taplin, 1962). The present work differentiates between forging steel and ingot molds and hot-rolled products within the iron and steel sector. This study focuses not only on the iron and steel sector, but also on the foundry sector, which is treated as a part of the machine industry in general (Hayashi, 2007). Historically, the pig iron and scrap consumption in the foundry sector are relatively large (Carr and Taplin, 1962; American Foundry Society, 1972–2011). 2.2. Definition of stock and waste Up-stream processes and consumption of old scrap in steel and foundry sectors are relatively well defined. However, there are no common definitions for the terms used in describing the down-stream processes, such as in-use stock, lifetime, discarded steel (obsolete products), hibernation, and waste. Society has widely utilized iron and steel products in various forms, and as a result, the boundary between steel stock and waste is vague. For example, although an abandoned tunnel is not being put to work in the sense that we cannot travel through it, the steel arch support of the tunnel is being put to work in the sense that the steel is supporting the sides of the tunnel and preventing the walls from caving in, which is necessary in order to avoid a sinkhole over the tunnel (Takamatsu et al., 2010a). Underground pipes and damaged seawalls would be also significant amount of steel. It is

old scrap old scrap + waste

(1)

Daigo et al. (2007), however, categorized the gray area as obsolete stock, and distinguished it from in-use stock in Japan. The combination of obsolete stock and in-use stock is called overall stock. The estimated obsolete stock accounted for 23% of overall stock in 2000. UNEP (2011) also refers to a similar framework, except in the categorization of in-use dissipation, which is treated as a part of the gray area in this study. UNEP (2011) covers not only steel but also a wide variety of metals, and tries to be as a consensus document and to alleviate the inconsistencies in what is being used in the literature. Based on this lifetime-model-based approach, a transparent recycling rate can be calculated using Eq. (2). Recycling rate [model-based] =

old scrap old scrap + waste + gray area

(2)

As the scope covered by the gray area is extensive rather than negligible, the model-based recycling rate (Eq. (2)) is substantially lower than the descriptive recycling rate (Eq. (1)). Michaelis and Jackson (2000) revealed a range of between 74% and 41% between 1969 and 1994 in the United Kingdom. The rate in the EU-15 in 2000 was 66% (Moll et al., 2005), and 51% in the United States in 1998 (Fenton, 2004). In this paper, the model-based recycling rate (Eq. (2)) is simply called the end-of-life (EOL) recycling rate or recycling rate. Note that several expressions used in Section 2.1 follow the definitions given in this section. 2.3. Methodology overview for past and future 2.3.1. End-of-life recycling rate This study applies a lifetime-model-based approach in order to explore the future availability of scrap. The consumption of old scrap is essentially derived from a mass balance between final products, primary steel production, loss, and return to blast furnace. The distinction between in-use stock and the gray area is based on an assumed lifetime distribution function. Historical trends of the model-based recycling rate are then calculated. For a future material flow scenario, we refer to estimated historical parameters, such as the model-based recycling rate. The future availability of old scrap is derived from a calculation using discarded steel (obsolete products) and the historical model-based recycling rate. We assumed that the recycling rate in the future would be equal to the historical cumulative recycling rate between 1951 and 2012 (Appendix B). Caution is necessary when employing the model-based recycling rate, because it depends on an assumed life duration function. Even more importantly, however, it is necessary to avoid confusion of Eqs. (1) and (2). If, for example, a recycling rate of 85% is used in Eq. (2), this would lead to an overestimate of future scrap availability. The assumed distinctions between in-use stock

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Fig. 1. System boundary used in this study.

and the gray area, as listed in Table 1, are fully consistent with past estimates and future scenarios.

2.3.2. End-of-life recycling rate In terms of future crude steel production, we refer to the scenarios of crude steel production in the “Alternative Pathways toward Sustainable Development and Climate Stabilization (ALPS)” project conducted by RITE (Akimoto et al., 2012; Oda et al., 2012). Each country’s apparent consumption of crude steel per capita was basically assumed to follow a logistic function of GDP per capita. The assumed logistic functions, however, have a limitation, because apparent consumptions deeply depend on national industrial structure. For example, one-third of steel produced in Japan and Korea has been indirectly exported in the form of final products such as vehicles and machinery (Japan Ferrous Raw Materials Association, 2013). On the other hand, GDP growth of highly industrialized countries (such as the United States and the United Kingdom) has less relationship to their domestic steel productions, since they depend on indirect import of steel. Appendix C provides the logistic equations of the relationship between apparent consumption and GDP per capita, and parameter details in major steel-producing countries.

In the study’s reference-demand case, the global total of crude steel production reaches 2.19 billion tons in 2050, taking into consideration accelerated weight saving such as is possible with high-strength steel and pre-stressed concrete. Since the IEA’s low demand scenario reaches 2.44 billion tons in 2050 (International Energy Agency, 2012b), the reference-demand case would be based on more progressive improvement in material efficiency (steel demand/GDP). Numerical values for crude steel, GDP, and population over the entire study period are given in the Supplementary data (Appendix E).

2.3.3. Market share and lifetime duration In terms of in-use stock, finished steel is fabricated to make final products. In this study, the marketing of final products is distributed into six markets from Category No. 1 to Category No. 6. The world is divided into two mega regions: (i) Europe, CIS, North America, South America, and Africa; and (ii) Asia, Oceania, and the Middle East. The former region has a relatively long lifetime for categories No. 1 and No. 2 and a large market share for No. 4. The typical markets for these six categories are: (i) civil engineering; (ii) buildings; (iii) machinery and appliances; (iv) transport (vehicles, buses, trucks,

Table 1 Overview of categories of stock, waste, and other residues. Lifetime-model-based approach (e.g., Daigo et al., 2007)

Obsolete stock

Definition in this study

Waste/observed waste - Landfill with municipal solid waste - Sunken ships Gray area/unobserved residue - Abandoned/damaged infrastructures (abandoned roads, tunnels, bridges, dams, seawalls, revetments, and harbors) - Dissipation by corrosion and erosion

In-use stock

In-use stock - In-use vehicles, ships, containers, machinery, buildings, and infrastructures

Note: More detailed, specific examples are shown in Table A.1 (Appendix A).

Descriptive approach (e.g., World Steel Association, 2009; Takamatsu et al., 2010a, 2010b) Waste

In-use stock/working stock/stock

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Fig. 2. Results for scrap consumption in steel and foundry sectors.

and trains); (v) shipbuilding; and (vi) others (containers, furniture, and miscellaneous goods), respectively. In this work, we have used the Weibull distribution for simulating product lifetime. The assumed Weibull distribution function and the market share for the two mega-regions are summarized in Table 2. The methodology used in this study uses detailed iron flows, including forging steel, ingot molds, and iron casting. For the purpose of this study, it was more productive to use a regional resolution. Statistics from individual countries for pig iron and crude steel production can be widely obtained (World Steel Association, 1978–2011); however, other parameters such as the indirect steel trade are not widely available. This study divides the world into two mega-regions for analysis of in-use stock, life duration functions, and market shares. In terms of trajectory of lifetime and market share, the parameters of Table 2 are used in time over the entire study period. Müller et al. (2011) expressly indicated time-variable market shares for major six countries in the nineteenth century; however, the amounts of change shown are relatively small compared with global-scale data reliability. World Steel Dynamics (2000–2010) exhibited nearly constant market share on a global scale. In addition to the assumed life-duration distributions and market shares, further parameters such as the yield ratio and process loss are based on Wang et al. (2007) and American Foundry Society (1972–2011). 3. Results 3.1. Results of past iron flow (1870–2012) Past iron flow from 1870 to 2012 is fully estimated using the above methodology. Fig. 2 shows the estimated consumption of past scrap, including that from the foundry sector. Numerical values for home scrap, new scrap, and old scrap in the steel and foundry sectors are given in the Supplementary data (Appendix E). The figures for consumption of total scrap and new scrap are relatively similar to those in existing literature. The statistics of the World Steel Association (1978–2011, 2002–2012) show total scrap consumption from 1965, and such existing literature refers to statistics and a similar new scrap ratio per finished steel (Müller et al., 2011). In contrast, the consumption of home scrap and old scrap differ from each other. Table 3 compares the estimated consumption of old scrap among literature. The estimated amount of old scrap in this study is smaller than that of the IEA (2007) and Neelis and Patel (2006), but larger than that cited in other literature. Since the amounts of historical old scrap are calculated using a mass balance, these amounts depend inevitably on assumed historical loss during steelmaking and iron casting processes. Such loss includes the interfusion of iron in slag, dust, and sludge, which is then transferred into the repository. Blast furnace consumption of scrap is also significant (American Foundry Society, 1972–2011). A

Fig. 3. Results of scrap consumption in steel and foundry sectors.

comparison of home scrap consumption figures among literature is given in Table D.1 (Appendix D). The amount of in-use stock is estimated based on the mass balance of the shipping volume of final products and discarded steel. Tables 4 and 5 compare estimates of discarded steel and in-use stock among literature. Hatayama et al. (2010) indicated an apparently small amount of discarded steel, because they covered the iron and steel sector in 42 countries. This study estimates a similar level of discarded steel as Hatayama et al. (2010) and Neelis and Patel (2006). In terms of in-use stock, this study estimates a lower level than Müller et al. (2011). Müller et al. (2011) referred to a relatively long lifetime (from 50 to 100 years) also for Asian construction sectors. In contrast, Kozawa and Tsukihashi (2009) referred to relatively short lifetime (about 30 years) also for construction sectors in Western countries. Table 6 compares estimates of end-of-life recycling rate among literature. Care must be taken in interpretation, because the definition of in-use stock varies among literature sources, as denoted in the previous section and in Table 5. Takamatsu et al. (2010a) indicate a relatively large amount of in-use stock, because their definition of in-use stock includes the gray area shown in Tables 1 and A.1. Table 5 implies the possibility of the extent of the gray area being considerable. The historical mass balance between final products, old scrap, in-use stock, and the gray area is given in Fig. B.1 (Appendix B). 3.2. Results of future iron flow in reference-demand case (2013–2050) As described in Section 2, figures for old scrap are derived from a combination of discarded steel (obsolete products) using a modelbased recycling rate (Eq. (2)). Fig. 3 shows the results for scrap consumption. The amount of old scrap consumed will steadily increase and reach 764 million tons of iron in 2050, which is four times the amount of consumption of old scrap in the year 2000. Home scrap and new scrap will remain at the same level or gradually decline, due to assumed improvement in yield rates (Neelis and Patel, 2006). Home scrap and new scrap are derived from year-to-year iron flow. In contrast, old scrap is basically derived from steel stock, which means that long-term trends for old scrap are important. Thus, this study simulates long-term (up to the year 2100) iron flow and steel stock, which are given in the Supplementary data (Appendix E). Due to the limitation of data credibility and future uncertainty, we limit the presentation to 2050 in the body text of this paper, including in Fig. 3. Fig. 4 shows the requirements for primary steel production. The amount of old scrap will steadily increase; however, under a moderate increase in crude steel production, primary steel will be the main source of iron. Fig. 5(a) shows the two kinds of primary steel ratios: (i) primary steel in comparison with crude steel, and (ii) primary steel in

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Table 2 Assumed lifetime data and market share of final products. Category no.

Typical market category

Civil engineering Buildings Machinery and appliances Transport (vehicles, buses, trucks, and trains) Shipbuilding Others

1 2 3 4 5 6

Lifetime duration [y]Average (standard deviation) Europe, CIS, America, Africa

Asia, Oceania, the Middle East

67 (29) 67 (29) 20 (9) 17 (7)

34 (14) 31 (13) 17 (7) 15 (6)

30 (13) 20 (9)

30 (13) 15 (5)

Market share [%] Europe, CIS, America, Africa

Asia, Oceania, the Middle East

18% 19% 22% 34%

23% 28% 27% 15%

0.5% 6.5%

1.5% 5.5%

Note: The above data is assumed based on Hatayama et al. (2010), Wang et al. (2007), Müller et al. (2011), World Steel Dynamics (2000–2010), and Neelis and Patel (2006). The assumed lifetime durations include the time lag between discarding by consumers and remelting in steel mills and foundries. Table 3 Comparison of consumption of old scrap in selected years (million tons of iron/y). Sector

Iron and steel

Iron and steel + foundries

Literature

1970

Present study IEA (2007), Takamatsu et al. (2010a) Bureau of International Recycling (2012) Present study Neelis and Patel (2006) Wang et al. (2007), Fig. 7 Kozawa and Tsukihashi (2009)

1990

2000

2003

2008

2010

77 91

146 165

87 100

157 185

215 221 182 229 250

267 242 230 284

263 259 230 278

20

85

178 190 173 189 205 173 125

150

Note: Care must be taken in interpretation, as in some cases it is not clear whether iron weight or gross weight are referred to, and in other cases the value is less accurate as it is based on a reading of figures. Table 4 Comparison of discarded steel. Item

Literature

1990

2000

2003

2005

2008

323 320

412 400 265 305

436 420

454

486

Discarded steel [million tons of iron/y]

Present study Neelis and Patel (2006) Wang et al. (2007), Fig. 7 Hatayama et al. (2010)a

325

340

234

a

Hatayama et al. (2010) covers 42 countries that accounted 85% of the world’s steel consumption in 2005. They cover the iron and steel sector and exclude iron casting products. Table 5 Comparison of in-use stock (billion tons of iron at the end of the year). Sector Iron and steel Iron and steel + foundries a

Definition

Literature

Including gray area Excluding gray area Including gray area

Takamatsu et al. (2010a) Hatayama et al. (2010)a Hayashi (2007) Present study Müller et al. (2011) Kozawa and Tsukihashi (2009)

Excluding gray area

2000

2005

10.8

12.7 20.0 17.0 17.8 10.9

2008 20.8

14.9 9.4

18.9

Hatayama et al. (2010) covers 42 countries that accounted 85% of the world’s steel consumption in 2005.

Table 6 Comparison of end-of-life recycling rate. Definition

Literature

End-of-life recycling rate

Region

Note

Descriptive recycling rate, Eq. (1)

World Steel Association (2009) Takamatsu et al. (2010a,b) Broadbent (2011)

83% 85% 85%

Global Global Global

Lifetime-model-based recycling rate, Eq (2)

Present study

53%

Global

Michaelis and Jackson (2000)

UK

Davis et al. (2007) Moll et al. (2005) Fenton (2004) Kozawa and Tsukihashi (2011)

Range of between 74% and 41% 65% 66% 51% 39%

UK EU-15 US Global

Neelis and Patel (2006)

70%

Global

Estimates in 2007 Estimates in past few decades Assumed default value for life cycle assessment Estimates of cumulative rate in past few decades and more (Appendix B) Estimates between 1969 and 1994 Estimates in 2001 Estimates in 2000 Estimates in 1998 Estimates of cumulative rate between 1870 and 2005 after their initial assumption Assumed rate in the future based on their estimated historical trend

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Fig. 4. Results of availability of old scrap and primary steel production. Note: In this figure, gross weight refers to that of pig iron, direct reduced iron (DRI), and crude steel.

comparison with final products. In terms of long-term trends, these two indicators are steadily decreasing. Between the years 1950 and 2010, primary steel in comparison with crude steel increased, due to the tremendous improvement in yield rates in the processes of crude steel production to final products (American Foundry Society, 1972–2011; Wang et al., 2007; Müller et al., 2011). However, as the future improvement of yield rates becomes relatively slow (Neelis and Patel, 2006), primary steel in comparison with crude steel will also decrease. The ratios on gross weight basis shown in Fig. 5(a) can be directly calculated based on statistics. A ratio on iron weight basis, however, gives us an additional implication including resource efficiency since it reflects the trend of net iron source. Fig. 5(b) shows the result for historical primary and secondary ratios in selected years. From the historical viewpoint shown in Fig. 5(b), primary and secondary

Fig. 6. Results of crude steel production by process.

ratios have been strongly linked with the steel demand trend. A sensitivity analysis for steel demand will be shown in Section 3.3. Fig. 6 shows the results of crude steel production by process. The relationship between scrap consumption and the process share is not straightforward. Pig iron is the dominant iron source for basic oxygen furnaces (BOF), and scrap and DRI are the typical sources for electric arc furnaces (EAF). However, as a global average trend, increasing amounts of scrap have been put into BOF. China currently depends on pig iron for its EAF iron sources. The present work assumed that the global average of pig iron in comparison with BOF steel will gradually decrease from 1.04 in 2010 to 0.95 in 2050. It is assumed that DRI will retain its ratio in comparison with crude steel production (i.e., 5%). Based on Fig. 6, EAF steel figures will rapidly increase and reach 872 million tons of iron for 2050, which are 2.1 times as great as the EAF steel figures for the year 2010. In the same period, BOF steel

(a) Gross weight basis

Secondary steel ratio

Primary steel ratio

(b) Iron weight basis in selected years Fig. 5. Results for primary steel ratios. Note: (a) Gross weight refers to that of pig iron, direct reduced iron (DRI), and crude steel. Final products include foundries. (b) Includes iron resource for foundries.

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figures for 2050 are 1.3 times as great as the BOF steel figures for the year 2010. As a result, the EAF steel ratio will increase from 29% in 2010 to 40% in 2050. Fig. 6 reconfirms the importance of old scrap and electric arc furnaces (EAF). At the same time, Fig. 6 also indicates the importance of blast furnaces and basic oxygen furnaces (BF-BOF). Even by 2050, primary steel production will remain dominant in this reference-demand case. Another demand case will be discussed in the next section. 3.3. Results of future iron flow in high-demand case (2013–2050) This study must refer to numerous assumed parameters for conducting future iron flow analysis, and crude steel production is one of the most uncertain of such parameters. From a historical viewpoint, the period from 1973 to 2000 indicated a certain GDP growth with roughly flat crude steel production. In the next decade, the relationship underwent drastic change due to the Chinese production hike. While the reference-demand case was used before this section, this section introduces a high-demand case, shown in Table 7. In the high-demand case, crude steel production is 1.5 times as great as that in reference-demand case in 2050. Table 8 compares the results of two steel-demand cases. In the high-demand case, in-use stock per capita steadily increases 2.3 times over the next four decades and reaches 6.6 tons of iron per capita. While the level of crude steel production in the highdemand case is very high compared with the reference-demand levels, the level of steel stock is still lower than the observed saturation levels for highly industrialized country; e.g., 12 tons of iron per capita (Hatayama et al., 2010; Müller et al., 2011). Steel demand also has an effect on consumption of old scrap, as well as the activity level of electric arc furnaces (EAF). In the highdemand case, the absolute amount of EAF steelmaking increases as an increase of consumption of old scrap, which is driven by in-use stock; however, the EAF ratio maintains roughly constant levels due to the rapid increase of steel demand. The possibility of a resource-circulating society depends on two factors; (i) the long-term level of steel demand, and (ii) the difference between discarded steel and consumption of old scrap. Since this paper indicates that such difference is substantial, a lower level of steel demand compared with that of the reference-demand case is required if we are to aim for a resource-circulating society. In this paper, each country’s apparent consumption of crude steel per capita was assumed to follow a logistic function of GDP per capita. Further analysis, including with the use of other forms of logic such as the stock-driven model, shape of steel demand trajectory, and short-term fluctuation, remains as future work. Section 4.4 provides some qualitative discussion. 4. Discussion 4.1. Past consumption and future availability of old scrap This study estimates the consumption of past old scrap using a mass balance calculation. The estimation inevitably depends on overall input parameters, such as process losses. The present work explicitly calculates iron flow, not only from the iron and steel industry (i.e., rolled steel, forging steel and ingot molds), but also from foundries. However, ferrous alloys have the potential to affect the estimated iron flow. This study investigated the input parameters and the definition of parameters by comparing results with existing literature. To estimate the future availability of old scrap, the end-of-life recycling rate is of key importance. Definitions such as lifetime, discarded steel (obsolete products), in-use stock, and gray area, are inevitably linked to the recycling rate. The boundaries of in-use

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stock and waste are vague, because society has widely utilized iron and steel products in various forms and for various purposes. Since it is not possible to observe the life duration function for all products on a global scale, this study applies a lifetime-model based approach in order to explore the future availability of scrap. The constructed future old scrap scenario is fully consistent with past iron flow, using the same classification of in-use stock, waste, and gray area. The Supplementary data (Appendix E) shows the numerical values of major parameters and the yearly results. The present parameters and results are valuable basic data for further detailed material flow analysis. 4.2. Definition of end-of-life recycling rate The results indicate that the historical model-based recycling rate (Eq. (2)) is around 53% (Fig. B.1). This could be consistent with a larger descriptive recycling rate (Eq. (1)), e.g., 85% (IEA, 2007; World Steel Association, 2009; Takamatsu et al., 2010b; Broadbent, 2011), which assumes the existence of the gray area to a certain extent (Table 6). The definition of recycling rate that is used has a significant impact on the numerical value of recycling rate. Also, overall definitions such as in-use stock, discarded steel, and waste are required for the definition of recycling rate. The present work clarifies these quantitative relationships. Such clarification is of key importance in order to construct future possible scenarios for primary and secondary steelmaking. 4.3. Policy directions in the context of global CO2 emission mitigations Currently, there is intense social pressure to decrease CO2 emissions in the iron and steel industry. IPCC’s Category I (Intergovernmental Panel on Climate Change, 2007), 450 ppm-CO2 equivalent and 2 degrees target are frequently referred to (United Nations Framework Convention on Climate Change, 2012). Even with more moderate targets such as Category IV, 650 ppm-CO2 equivalent, and 3 degrees target, it is necessary to achieve a large reduction in CO2 emissions from the iron and steel industry (Oda et al., 2007). There are three possible directions for large reduction: (i) enhancement of recycling rate; (ii) decarbonization of primary steelmaking; and (iii) steel demand decrease in comparison with GDP (material efficiency). The gray area components in Tables 1 and A1 imply the difficulty of significant enhancement of the recycling rate. While the World Steel Association (2009) has the target of enhancement of recycling rate from 83% in 2007 (estimate) to 90% in 2050 (target), the enhancement is not significant compared to the large CO2 reduction above. Steel demand decrease in comparison with GDP (material efficiency) theoretically has a significant effect on the volume of global CO2 emissions as shown in the sensitivity analysis of steel demand. WellMett2050 provides a very wide range of material efficiency measures (WellMett2050, 2013), and Allwood et al. (2011) emphasized the importance of policy intervention for material efficiency. Söderholm and Tilton (2012) commented on Allwood et al. (2011) and indicated several difficulties with policy interventions. Jochem (2004) quantified not only ongoing material efficiency as competition among firms but also material efficiency derived from additional policy interventions, which is also not significant compared with the large reduction above. We need further quantitative analysis for three possible directions; however, the conclusion in this study is that decarbonization of primary steelmaking is important if we are to aim for corresponding to social pressure for the large reduction above. Several R&D

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Table 7 Historical trend of crude steel production and future scenario on a global scale.

Crude steel production (Mt/y) [annual change rate (%/y)] GDPPPP (trillion 2005 int $/y) [annual change rate (%/y)] Crude steel production per GDPPPP (kg/thousand 2005 int $) [annual change rate (%/y)]

1950

1973 [1950–]

2000 [1973–]

2010 [2000–]

2050 [2010–] Reference-demand

High-demand

192

703 [5.8%]

849 [0.7%]

1417 [5.3%]

2194 [1.1%]

3291 [2.1%]

178 [2.8%]

178 [2.8%]

7.58 25.3

22.5 [4.9%]

47.6 [2.8%]

59.3 [2.2%]

31.2 [0.9%]

17.9 [−2.0%]

23.9 [3.0%]

12.4 [-1.6%]

18.5 [-0.6%]

Table 8 Summary of results for two steel demand cases on a global scale.

Consumption of old scrap (Mt of iron/y) EAF steel ratio (%) In-use stock per capita (tons of iron per capita)

1950

1973

2000

2010

44 11% 0.9

95 17% 1.8

189 34% 2.4

278 29% 2.9

2050 Reference-demand

High-demand

764 40% 4.8

1007 35% 6.6

Note: DRI is assumed to retain its ratio in comparison with crude steel production (i.e., 5%), in both demand cases.

programs are ongoing, such as the ULCOS program in Europe, the COURSE 50 research programs in Japan, the US program and the POSCO program. 4.4. Steel demand and drivers from a historical viewpoint Possible drivers of steel demand are urbanization as well as industrialization. The demand for steel has rapidly increased in countries that have gone through the primary stage of economic growth; e.g., Japan in the 1960s, the Former Soviet Union in 1960s and 1970s, the Republic of Korea in 1980s and 1990s (Appendix C), and China in 2000s. Another driver of steel demand might be more basic needs, e.g., poverty reduction, basic human needs, modern energy access, and equity as treated in the United Nations Millennium Development Goals. Implementation of responses to these needs would require a certain degree of material input on a global scale. In the two demand cases, global averages of steel stock per capita in 2050 would be 4.8 tons of iron per capita and 6.6 tons of iron per capita, respectively, which are relatively low compared with observed saturation levels in highly industrialized countries (Hatayama et al., 2010; Müller et al., 2011). The steel demand presented by heavily populated regions, such as South Asia and Sub-Saharan Africa, have high potential impact on long-term global steel demand trends. While we conducted sensitivity analysis for steel demand shown in Section 3.3, further qualitative and quantitative analysis of these basic uncertainties remains as future work. Consistent discussion on these basic uncertainties and steel demand decrease in comparison with GDP (material efficiency) are also of key importance.

Cr, and Zn), and high-grade scrap is expensive. Special steel from the EAF route also requires high-grade scrap. The regional segmentation of primary steel and secondary steel also depends on scrap grade, degree of steel demand, and steel market (e.g., construction, vehicles, machinery, and appliances). This paper indicates that primary steel would keep its dominant position on a global scale; however, in the case that scrap-based EAF were to gain the dominant position, the contamination problem would become a more critical issue. 4.6. Regional composition of gray area A detailed analysis, including regionally specific investigation, is required for detailed discussions on the gray area (Daigo et al., 2007). For example, in Japan, land is relatively expensive, and therefore there are negligible numbers of abandoned aboveground facilities (abandoned houses, buildings, and fences) and abandoned in outlying areas of the country. These temporal hibernating stocks can be recovered within the next few decades. The assumed lifetime shown in Table 2 includes this hibernation period between discarding by consumers and remelting in steel mills and foundries. On the other hand, due to the high frequency of natural disasters such as floods, potentially large amounts of structures could be harvested from damaged rivers in Japan. In a vast country that has a low population density, the numbers of abandoned aboveground facilities could affect the availability of old scrap (Hayashi, 2012). Quantitatively significant gray area content can vary by region. 5. Conclusion

4.5. Regional segmentation of primary steel and secondary steel This paper indicates that, even by 2050, primary steel production would remain dominant; however, this is a message in the context of global total volume. Regional segmentation would be significant. The United States and Turkey, for example, have enhanced electric arc furnace (EAF) activities. Turkey imports a large amount of steel scrap, and then exports EAF steel products to the Middle East countries (World Steel Association, 2002–2012). The grade of old scrap also has an impact on the steel quality in EAF steelmaking. While several EAF steel companies have been technically successful in making coil products for vehicles and appliances, it is not easy to maintain the economic viability because low-grade scrap includes contaminations (e.g., Cu, Sn, Ni,

The present work estimates the past iron flow, including that of foundries, and conducts an analysis of the future global availability of old scrap. The future scenario constructed in this study is realistic, because it is fully consistent with past iron flow, using the same classification of in-use stock, waste, and gray area. This study simulates long-term (up to the year 2100) iron flow and steel stock, which are given only in the Supplementary data (Appendix E). In order to conduct an intensive discussion, we limit the presentation to 2050 in the body text of this paper. We reconfirm the importance of old scrap and electric arc furnaces (EAF). In 2050, EAF steelmaking will increase to 2.1 times its extent in 2010. On the other hand, the future availability of old scrap will not be sufficient to overtake the two assumed cases of crude

J. Oda et al. / Resources, Conservation and Recycling 81 (2013) 81–91

steel production in the context of a global total. As a result, even by 2050, primary steel production is expected to remain dominant. While we conducted sensitivity analysis for steel demand, further qualitative and quantitative analysis of these basic uncertainties of steel demand remains as future work. Enhancement of the recycling rate is of key importance for resource conservation and environmental impacts. Gray area content; however, implies the difficulty of significant recycling rate enhancement. Because the availability of future scrap will be limited in the context of the global total, it is important to research and develop innovative low-carbon technologies for primary steelmaking and to explore their economic viability if we are to aim for corresponding to social pressure for large reductions in CO2 emissions.

89

40

Consumption of old scrap, 9.2 (Billion tons of iron)

30

20

Final products, 39.4

Waste + Gray area, 8.4

In-use stock, 21.7

10

Acknowledgments 0

We thank Seiichi Hayashi of Steel Recycling Research Co. Ltd., Nobuhiko Takamatsu, and Naokazu Nakano of Nippon Steel and Sumitomo Metal Co. for their valuable advice in the categorization of in-use stock and waste. We wish to thank all three anonymous reviewers for their constructive comments. This study was conducted as a part of the Alternative Pathways toward Sustainable Development and Climate Stabilization (ALPS) project, funded by the Ministry of Economy, Trade and Industry, Japan. Appendix A. Categories of stock, waste, and other residues See Table A.1. Appendix B. Results of historical cumulative balances See Fig. B.1. Based on the assumed lifetime distribution functions, the amount of in-use stock was 21.7 billion tons of iron at the end of 2012. The amount of cumulative old scrap between 1870 and 2012 was 9.2 billion tons of iron. The total amount of waste and gray area was 8.4 billion tons of iron. This implies that the model-based recycling rate (Eq. (2)) is 52.4%. The recycling rate between 1951 and 2012 was also at a similar level, i.e., 52.7%, which is referred to for this future scenario analysis.

Table A.1 Detailed categories of stock, waste, and other residues. Definition in this study Waste/observed waste - Landfill with municipal solid waste - Landfill with industrial waste - Sunken ships and abandoned submarine cables - Illegal dumping Gray area/unobserved residue - Abandoned aboveground facilities (abandoned houses, buildings, and fences) in outlying areas of the country - Abandoned civil engineering structures (abandoned roads, railroads, tunnels, bridges, dams, seawalls, revetments, harbors, and the pile foundations of these structures) - Abandoned mines and depleted oil/gas wells - Limited utilization of steel property (landfill with dangerous waste, artificial reefs, counterweights with other metals, etc.) - Dissipation by flood and wave - Dissipation by corrosion and erosion - Others (abandoned military bases, used bullets, and other military-related consumptions of steel) In-use stock - In-use vehicles, ships, containers, machinery, buildings, and infrastructures - Secondary use of steel (historical relics, reuse of steel, and miscellaneous use of steel)

Estimates based on statistics

Calculation of lifetime model

Fig. B.1. Results of cumulative mass balance for the years 1870–2012. Note: This figure includes foundries. Waste, gray area, and in-use stock follow the definitions given in Section 2.2 and Table A.1.

Appendix C. Logistic equation for apparent consumption of crude steel Eq. (C.1) shows the assumed logistic equation for apparent consumption of crude steel. This paper referred to the historical apparent consumptions from 1980 to 2010 (World Steel Association, 1978–2011) and constructed future apparent consumptions. Used parameters in several steel-producing countries are shown in Table C.1. As shown in Fig. C.1, the assumed equation in Korea represent the historical trajectory. During the period from 1980 to 2010, the United States and Germany, however, have no relationship between apparent consumptions and GDP per capita. In Eq. (C.1), apparent consumption of crude steel is aggregated by sector (e.g., construction, vehicles, machinery, and appliances). The price of scrap could be an important factor for scrap amount and scrap quality; however, economic fluctuation also affects the scrap amount available. Eq. (C.1) assumed that apparent consumption would be driven by GDP per capita in market exchange rates. Further analysis, including with more detail steel demand function that is disaggregated by sector and the use of other forms of logic such as stock-driven model, remains as future work. Apparent consumptin of crude steel per capita [kg per capita] =

a 1 + b · exp(−c · (GDPMER per capita [thousand 2005 US$ per capita]))

(C.1)

Table C.1 Used equation parameters in the reference-demand case. Country

US Germany Spain, Portugal Japan Korea China India

Used parameters a

b

c

384 491 491 648 1169 478 288

0 0 55.0 77.9 16.9 19.2 16.8

0 0 0.35 0.25 0.31 1.68 1.43

Note: In the case of that the assumed logistic equation is not practical such as the US and Germany, we referred to other downward regression formulas instead of the logistic equation.

90

J. Oda et al. / Resources, Conservation and Recycling 81 (2013) 81–91

1250 (kg per capita)

Apparent consumption of crude steel

1500

1000

750 500 250

Statistics Assumed logistic equation

0 1980 1990 2000 2010 2020 2030 2040 2050 Fig. C.1. Statistics of apparent consumption and assumed logistic equation in the reference-demand case in Korea. Table D.1 Comparison of home scrap consumption in selected years (million tons of iron/y). Sector

Iron and steel

Iron and steel + foundries

Literature

1970

1990

2000

2003

2008

2010

Present study Present study (home scrap + circulating iron before crude steel production) IEA (2007), Takamatsu et al. (2010a) Bureau of International Recycling (2012) Present study Present study (home scrap + circulating iron before crude steel making) Neelis and Patel (2006) Wang et al. (2007), Fig. 7 Kozawa and Tsukihashi (2009) Bureau of International Recycling (2012)

124 196

102 169

66 125

52 114

84 164

86 169

149

119

71

60

68

125

144

195

190

110 190

109 192

223

215

158 230

126 194

86 145

74 136

145

145

115

145

133 155

135

100

105

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