Mapping Global Steel Scrap Flows (2023 PDF)

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LETTER OPEN ACCESS Mapping the global flows of steel scraps: an alloy elements recovery perspective To cite this article: Wenqiu Cai et al 2023 View the article online for updates and enhancements. This content Environ. Res. LETTER Mapping the global OPEN ACCESS recovery perspective RECEIVED 29 May 2023 Wenqiu Cai1, REVISED 1 12 August 2023 School of 2 School of ACCEPTED FOR PUBLICATION ∗ 22 August 2023 Authors to PUBLISHED E-mail: [email protected] and [email protected] scraps, social network analysis, alloy elements, material flow analysis, governance, resource management material for this article is available online scraps by the use of electric arc furnace is one of the most promising approaches for to achieve net-zero emissions. Due to the uneven distribution of global steel countries are actively involving in the global steel scraps trade. Steel scraps contain a elements, which may be transferred across borders through international trade of However, existing studies have paid little attention to the global flows of steel scraps alloy elements (AEs). This study maps the journey of global steel scraps and the for the period of 2000–2021 for the first time by employing trade-linked material and social network analysis. The results indicate that the global steel scraps trade had the study period, with a few core countries (such as USA, Germany, and Turkey) steel scraps network. Also, critical metals had been transferred across borders in through the trade of steel scraps, especially from global north countries to global The largest AE flows include Chromium (Cr), nickel (Ni), manganese (Mn) and flows. Other AE flows, such as cobalt (Co), vanadium (V), and niobium (Nb) but with high values or being regarded scarce. From a global perspective, steel and recycling can contribute to the decarbonization efforts of the global steel industry shortages in some countries. Therefore, it is urgent to promote the overall of steel scraps and the embodied AEs by various efforts. requires an energy input of only 8.7%–25.0% of that in the primary steel production route (Harvey 2021). Agreement established an ambi- As such, the carbon footprint per ton of recycled steel target of achieving net-zero by 2050 (van is only 10%–20% of that of the primary steel (Fan and 2018, van Soest et al 2021). As the largest Friedmann 2021). However, only 40.3% of the global source (Li and Hanaoka 2022), steel production comes from steel scraps (Wang et al industry alone accounted for 25% of the 2021a). The availability of steel scraps is substantially industrial carbon emissions (Ren et al limited by the historical production and the life spans an urgent need to decarbonize this of various steel-containing products (Pauliuk et al al 2019). As the most promising mater- 2013b), especially in countries with low industrializ- virgin iron ore production, steel scraps ation levels (Pauliuk et al 2013a). Several countries repeatedly without losing its prop- rely heavily on importing steel scraps to meet their energy consumption and carbon emis- domestic demand (Neşer et al 2008). But these coun- reduced (International Energy Agency tries are facing a risk of supply chain disruptions, steel scraps in electric arc furnaces which may be induced by geopolitical conflicts, price Published by IOP Publishing Ltd W Cai et al theory (Wasserman and Faust 1994). It is an effect- ive method to identify patterns of relations among components within a system (An et al 2014), and can objectively assess a country’s market dominance through metrics such as trade volume, number of trade relations, and trade intensity (Liu et al 2022, Zhou et al 2022). This method has been widely used to study the characteristics of international trade networks for various commodities, such as metal resources (Ge et al 2016, Tokito et al 2016, Zhong et al 2018, Wang et al 2019, Shao et al 2021, Zheng et al 2022, Gao et al 2022a), energy products (Du et al 2017, Chen et al 2018, Wang et al 2019) and solid waste (Wang et al 2020b, Ma et al 2021). However, although several scholars highlighted the importance of studying the trade patterns of steel scraps (Lee and Sohn 2015, Zhong et al 2018), little attention has been paid on the trade linkages between different countries or regions from an AEs transfer perspective. In summary, in order to address the above con- cerns, this study investigates the global flows of steel scraps and its embodied AEs by integrating trade- linked MFA with social network analysis so that the potential risks in the global steel scraps trade can be identified. We expect to recognize the dilemma of embodied AEs in global steel scraps recycling so that valuable insights can be obtained for preparing appropriate resource management policies. 2. Methods and data the We measure the global trade flows of steel scraps and its embodied AEs between different countries by applying a trade-linked MFA and then investigate the trade patterns of steel scraps and individual AEs by conducting social network analysis. The global steel scraps trade network (GSSTN) includes more than 200 countries and regions, and the temporal bound- ary of this study is from 2000 to 2021. The investig- ated AEs include manganese (Mn), nickel (Ni), zinc (Zn), stannum (Sn), plumbum (Pb), copper (Cu), aluminum (Al), chromium (Cr), cobalt (Co), vana- dium (V), niobium (Nb), and molybdenum (Mo). All the detailed explanations of data processing are presented in the Supporting Information. metal ele- 2.1. Trade-linked MFA The global anthropogenic iron & steel cycle usually includes primary production, fabrication and man- ufacturing, final use, and waste management stages (Zhong et al 2018). In this study, we focus on the waste management stage. We trace all AE flows in AE metallic equivalents by compiling extensive data from various sources, including reported statistics, published literature and expert interviews. Bilateral trade data of steel scraps were obtained from the UN Comtrade database (UN Comtrade 2023). Table S1 lists different steel scraps products covered in this study, along with their W Cai et al Table 1. Indicators used in social network analysis. Explanation The in-degree of a node refers to the number of countries importing steel scraps products from a specified country. The out-degree of a node refers to the number of countries exporting steel scraps products to a specified country. In-strength refers to the imported trade volume of the edges connected to a node. Out-strength refers to the exported trade volume of the edges connected to a node. The betweenness centrality of one node refers to the number of these shortest paths that pass through this node. The modularity index refers to the density of links inside communities. out-strength, betweenness centrality, and modularity indicators (table 1) (Jackson 2010, Liu et al 2016, Jiang et al 2019, Ma et al 2021, Pacini et al 2021, Gao et al 2022a). The associated calculation methods are detailed in the supporting information. 2.3. Uncertainty analysis All AEs flows are calculated based on the actual (1) contents of such elements in different steel scraps products under different HS codes (Wang et al 2021b). The uncertainty of this study is mainly caused by the AEs contents (Gao et al 2022b). Simplified ranges are determined to estimate the uncertainties (Laner et al 2014). According to a previous study, the aggregate uncertainty for element concentration data may be lower than 20% (Liu et al 2013). Relevant studies on steel AEs present that such uncertainty ranges from 2% to 10% (Gao et al 2022b, Su et al 2023). Therefore, we assume the uncertainty ranges of AEs contents directly collected from databases and literature are at a low level (2%), while the uncer- tainty ranges derived from estimated parameters are assumed to be high (10%). The calculated results are illustrated in figures S54 and S55 of the Supporting Information. 3. Results 3.1. Features of the GSSTN By considering the top 80% of the global steel scraps trade volume during 2000–2021, we identified a total of 44 key nodes within GSSTN (figures S1, S2 and table S3). Figure 1 shows the trade patterns of key  nodes within GSSTN from 2000 to 2021, in which the top three exporters were the United Kingdom (153.89 · · · ω2,n   Mt), Japan (143.31 Mt), the Russian Federation (131.46 Mt), while the top three importers were Turkey (344.75 Mt), Republic of Korea (139.26 Mt), Spain (113.73 Mt). During the study period, sev- eral countries emerged as key players in the global steel scraps trade, including the USA, Canada, and Denmark, which are key nodes in the export trade network, while Pakistan and Viet Nam emerged as key nodes in the import trade network. In contrast, 3 W Cai et al of key nodes within GSSTN from 2000 to 2021. unit: kilotons (kt)]. import amount from left to right, with positive values indicating imports and negative of steel scraps trade volume among these key nodes is detailed in the Supporting S4–S47. 3. The red line represents the net-import of each key node. those low and middle-income countries in Africa, South America, and Asia. However, being influenced by COVID-19, the number of participating coun- tries in GSSTN decreased significantly after 2020 (figure S50). Also, different regions have different trade patterns. The main export destinations of those European countries include the Mediterranean and Arabian countries, while the main export destinations of Asian countries include those East and Southeast Asian countries. Moreover, the destinations for those American countries are relatively diversified, with South American and Asian countries as the main des- tinations. The number of communities in GSSTN fluctu- ated considerably between 2000 and 2021, ranging from 5 to 10. Overall, the modularity of GSSTN has remained high, with values between 0.32 and 0.52 (figure S51), indicating a good quality of com- munity division. However, a fluctuating decline in the modularity index can be observed during this period although it has become stabilized since 2018. This phenomenon shows that the steel scraps trade has become increasingly globalized in recent years, and the division of each community has become less signi- ficant. Also, GSSTN remained a hierarchical structure during the study period, which has been dominated by a few core countries. The community structure of GSSTN experienced a dynamic evolution trend 4 W Cai et al scraps trade network in 2000 (a), 2007 (b), 2014 (c), and 2021 (d). scraps trade relation, flowing from an export country to an import country with a node represents the total trade volume of a country; 3. The width of each edge represents 4. Different colors represent different communities. Nodes within GSSTN are in the same community being more densely connected to each other than to the represents the ranking of each community, with community 1 representing the while community 10 representing the community with the least trade relations. 5. Since smaller trade volumes than those in the top five communities, we do not include relations within communities 6–10 in this figure. 3.2. Global flow patterns of AEs embodied in steel scraps trade With the increasing steel scraps trade, the total amount of each AE embodied in global steel scraps trade increased from 1076.12 kt in 2000 to 2413.25 kt in 2021 (figure 3(a)). In particular, a surge of embodied AEs occurred in 2004. Typical AEs, such as V, Nb, and Ni, had grown faster than other AEs, increasing by more than 1.5 times. Sn had the slow- est growth rate of 60.67%. The most embodied AEs include Cr (21 304.79 kt), Ni (8997.67 kt), and Mn (6759.82 kt) due to their universal and vital functions in various types of steel products. These elements together accounted for more than 80% of all the AEs (figure 3(b)). Other key embodied AEs include Cu, Mo, Zn, Sn, and Pb, with a total amount ranging from 300 kt to 2500 kt. The amount of each of other remaining AEs is below 210 kt. However, although the contents of these AEs in the steel products are relat- ively less, their market values are high due to their scarcity and irreplaceability, such as Nb, Co, and V. We compared the structure of embodied AEs within GSSTN (figure 4 and supporting information figure S53). International steel scraps flows may not necessarily result in significant losses/acquisitions of embodied AEs. For example, although Ukraine was the third largest steel scraps exporter in 2000, its total 5 W Cai et al in global steel scraps from 2000 to 2021. (a) Historical evolution of global exports of over 2000–2021 (unit: kt); (b) Shares of AEs embodied in steel scraps over 2000–2021. each alloy element embodied in steel scraps from 2000 to 2021 (unit: kt). (d) Top ten element embodied in steel scraps from 2000 to 2021 (unit: kt). in key countries accounting for 80% of the global steel scraps exports and imports in (unit: kt). In addition, regional disparities exist in terms of the trade structure of AEs. Countries mainly involving in stainless steel scraps trade, such as Germany, Belgium, and India, had much higher Cr, Ni, and Mo contents embodied in their traded steel scraps. In contrast, countries mainly involved in cast iron scraps and steel ingots for remelting, such as Malaysia, Belarus, and 6 W Cai et al of alloy elements embodied in steel scraps from 2000 to 2021 (unit: kt). flow of each AE embodied in steel scraps. 2. The size of each node represents the total of each edge represents the trade flow between two trading partners. 4. Different colors GSSTN are grouped by community detection, with nodes in the same community than to the rest of the network. The community number represents the ranking of the community with the largest trade relations, while Community 10 the community with the least trade relations. USA, from Mexico to the USA, and from the USA to China were always the top three trade flows, sev- eral characteristics are observed. For example, India and the United Kingdom dominated the first com- munity (the community with the most trade rela- tions) in both embodied Ni and Nb networks. In con- trast, American and Asian countries dominated the first community in the embodied V network, such as the Republic of Korea and Canada. Similarly, India, Spain, and the United Kingdom dominated the first community in the embodied Mo network. And, sev- eral European and Asian countries dominated the first community in the embodied Sn trade network. Finally, Spain, India, United Kingdom, and Thailand dominated the first community in the embodied Cr trade network, while Germany, Finland, and Italy 7 W Cai et al products. Additionally, since several core countries dominated this GSSTN, their policies on steel scraps trade can greatly influence the global steel scraps trade structure. For instance, with the implementa- tion of Solid Waste Import Ban policy, China gradu- ally reduced its core role in the global steel scraps trade (figure 2). Consequently, as China’s original trade partner, Japan began to sell steel scraps to other Asian countries, such as Viet Nam and Bangladesh. Moreover, our social network analysis results reflect that only a few countries have the most trade relations within GSSTN. Most countries tend to keep their original trade partners. However, due to the clear emission reduction feature of recycled steel, now more countries have determined to sup- port steel recycling within their territories and begun to restrict the export of their steel scraps, such as Argentina (Lee 2023), the United Arab Emirates (Gerber Group 2022), and Kenya (The Scrap Metal Council 2015). The EU has also restricted the export of such steel scraps to non-OECD coun- tries (European Commission 2021). Similarly, sev- eral countries have restricted the export of their steel scraps by charging higher export taxes (Price and Nance 2009). For instance, the export tariff rate on steel scraps is 40% in China (Recycling Today 2017), while the export tariff on steel scraps is €290/ton in Russia if the exported amount exceeds the official quota (International Information Group 2022). These policy changes may disrupt current steel scraps supply chain. In particular, trade restrictions on steel scraps by those more developed countries may lower production efficiency in emerging eco- nomics, especially advanced developing countries, since it is difficult for them to access cheap steel scraps (Yamaguchi 2018). For example, several coun- tries (such as Turkey) heavily rely on importing steel scraps to support their steel industry (Özdemir et al 2018). These countries may suffer from get- ting steel scraps with unreasonable prices (Wübbeke and Heroth 2014). In addition, such export restric- tion policy may increase the domestic steel scraps supply in developed economies, which will lower the local steel scraps prices and reduce the incentives of local recyclers to collect more steel scraps (Ohno et al 2015). 4.2. The dilemma of embodied AEs in the global steel scraps recycling The amount of AEs embodied in the global steel scraps trade has increased rapidly, with the total volume in 2021 exceeding 1.5 times that of 2000. It indicates that more countries involved in the global steel scraps trade. The recovery rate for some special steel scraps, such as stainless-steel scraps, is normally high. A previous study shows that the recovery rates of Cr and Ni from stainless-steel scraps are nearly 95% (Team Stainless 2023). However, basic recovery technologies, such as shredding, crushing, magnetic W Cai et al 4.3. Policy implications Based on the main findings of this study, we propose two policy recommendations, including the optimiz- ation of steel scraps trade structure and technological cooperation. Our findings suggest that it makes sense for all the involved countries to develop their steel scraps trade policies to avoid potential risks. Those highly industrialized economies, such as the USA, the EU and Japan, have significant influences on the global steel scraps supply chain. Therefore, key steel scraps import countries, such as Turkey and Republic of Korea, should seek to diversify their import part- ners. Another consideration for these import coun- tries is how to avoid steel scraps price volatility or supply disruptions. Stockpiling is one solution, but its effective implementation relies on careful national plan and international cooperation. Key sec- ondary steel production countries (such as Turkey and India) should have more trade cooperation with high-media countries (such as the United Kingdom, Germany, Sweden, and the USA) so that they can bet- ter express their trade opinions in the global steel scraps market. In addition, some countries impor- ted cleaner steel scraps to produce high value-added steel products, but exported low grade steel scraps (Wang et al 2020a). For example, the copper contam- ination in the exported steel scraps from the USA is 14% higher than that in its domestic recycling source (Cooper et al 2020). To date, more countries start to restrict the import of low-grade steel scraps, which means that steel scraps export countries should fur- ther improve the quality of their steel scraps through pre-treatments. Additionally, we recommend that all countries involved pay closer attention to those AEs embod- ied in steel scraps. Although the development of advanced recycling technologies for embodied AEs that are economically feasible still faces a number of technical difficulties, it is promising to seek poten- tial solutions through joint research and develop- ment efforts on laser, near-infrared, or x-ray sort- ing technologies by all trade countries (Reck and Graedel 2012). The establishment of a formalized international research body, including key stakehold- ers such as scientists, engineers, entrepreneurs and policymakers, is encouraged so that they can work together to seek more innovative technologies on AE recovery. In order to make it effective, it is necessary to host regular meetings to help different stakehold- ers identify key technological problems, discuss how to collect adequate research funds, allocate research missions, and prepare a road map. For instance, laser-induced breakdown spectroscopy is an emer- ging technology for sorting steel scraps (Björkman and Samuelsson 2014). Similarly, x-ray fluorescence sorter can help further improve the sorting efficiency of steel scraps (Brooks et al 2019). Both technolo- gies should be further promoted so that they can W Cai et al promote the overall resource efficiency. Finally, global north countries should pay more attention on the corresponding environmental impacts and technical challenges associated with steel scraps trade, espe- cially in those global south countries. 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Mapping Global Steel Scrap Flows (2023 PDF)

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