Skip to content

Advertisement

  • Research
  • Open Access

Integrating life cycle analysis into system dynamics: the case of steel in Europe

Environmental Systems Research20198:15

https://doi.org/10.1186/s40068-019-0144-2

  • Received: 14 February 2019
  • Accepted: 30 April 2019
  • Published:

Abstract

Background

Steel is an important material in modern economies but responsible, nevertheless, for substantial environmental impacts throughout its supply chain. During the last couple of decades, this industry has addressed its impacts more incisively with the support of modelling and assessment tools.

Methodology

This article used the European steel industry as a case study to explore the potential benefits of integrating life cycle analysis (LCA) into system dynamics (SD) under the scopes of circular economy and industrial ecology. The goal was to explore if this integration could not only reproduce results generated separately by LCA and SD, but also to provide additional support for decision- and policy-making on the biophysical aspects of long-term materials sourcing. Unlike previous studies focused on methodological exchanges between the two, the entire LCA methodology was brought into the SD modelling environment, following ILCD and ISO guidelines and standards.

Results

The results indicated that integrating LCA into SD is feasible and capable of contributing to both in different levels, supporting discussions on raw material scarcity and self-sufficiency, and resource ownership retention.

Conclusion

Given continued effort is put into supporting the use of environmental impact indicators, this approach has potential to interest policy-makers and industrial decision-makers alike.

Keywords

  • Steel
  • Europe
  • System dynamics
  • Life cycle assessment
  • Industrial ecology
  • Circular economy

Background

Steel is the most commonly used alloy of iron and has historically been one of the most essential materials worldwide. It is present in most aspects of everyday life, from infrastructure to transport, from canned food to electronics (WS 2012a, b, 2017b, c; Beddows 2014). Steel’s cycle through environment and society originates in the ores mined from mountains and underground reserves and most commonly meets its end inside long service life structures or as recyclable scrap (Warrian 2012; Vaclav 2016).

The Second World War was a turning point for steelmaking due to the substantial changes it caused to the geopolitical environment. By the end of the conflict, the European demand for steel decreased significantly, all the while Chinese and Indian steelmakers became competitive. As decision-making became more complex, European steelmakers once focused only on scale and costs to supply the war effort then faced new challenges: over-capacity, the alloy specialization requirements of the private sector, and the growing attention given by society and governments to environmental impacts (Vaclav 2016; WS 2017b; Nuss and Blengini 2018).

Adequately supporting and informing decision-makers rose even further in the list of priorities as the roles and importance of technology critical elements (TCEs) and critical raw material (CRMs) present in steel became more evident. Thus, this industrial sector was among the first to the benefit from the efforts of managerial scientists, engineers and academics as the development of new concepts, tools and methods gained traction, notably after the 1960s (van Berkel et al. 1997; Baas and Boons 2004).

From that period onward, European steelmakers have increased their strategic outreach towards environmental goals, improving their supply chain management to encompass both end-of-life and circularity solutions (D’Costa 1999; Material Economics 2018). Today, steel in Europe is recycled at a 70% rate and most of its byproducts can be reused in other industries (Yellishetty et al. 2012; WS 2017a).

In comparison to the 1980s, the average manufacture now uses 50% less energy, helping vehicles become more fuel efficient with stronger and lighter steel alloys. Steel today can even be environmentally competitive enough to front plastics and aluminum products (Warrian 2012; WS 2013; Vaclav 2016; Material Economics 2018).

Renowned worldwide after the success of the Kalundborg Industrial Park, Industrial Ecology (IE)—one of the drivers of the aforementioned environmental progess—studies, organizes and models industrial activities and their interactions with the environment by approaching them organically. It seeks to accrue benefits from transitioning linear- or open-loop operations—in which outputs end up in sinks—toward closed-loop operations—in which outputs can become inputs (Erkman 1997; Ehrenfeld 1997, 2004; Nielsen 2007; Taddeo 2016; Prosman et al. 2017).

To do so, IE encompasses approaches to multiple aspects of industrial operations, namely (a) material and energy flows—known as industrial metabolism, (b) technological change, (c) eco-design, (d) life-cycle planning, (e) dematerialization, (f) decarbonization, (g) corporate responsibility and stewardship, and (h) industrial parks—also known as industrial symbiosis (Chevalier 1995; Cohen-Rosenthal 2004; Gibbs and Deutz 2007; Despeisse et al. 2012; Leigh and Xiaohong 2015).

Circular economy (CE) complements IE by approaching materials from two perspectives: biological nutrients—which should eventually reintegrate the biosphere without causing any harm, and technical nutrients—which circulate in the economy (Pearce and Turner 1989; Seager and Theis 2002; Korhonen 2004; EMF 2012, 2013, 2014b; Liao et al. 2012; Tukker 2015; Geissdoerfer et al. 2017).

CE suggests that all economic activities should be performed focusing on (a) the use of wastes as inputs, (b) the adoption of renewable and clean energy sources, (c) the accurate biophysical costs of their extraction, transformation, use and reinsertion into either economy or biosphere, and (d) outputs designed from the beginning so as to facilitate collection, recycling, refurbishing, reuse, redistribution, maintenance and sharing throughout their lifespan (Park et al. 2010; EMF 2014a, 2015a, 2016, 2017; Haas et al. 2015).

Due to the commoditization of its products and of its raw materials, the steel industry traditionally pays close attention to factors and productive variables that can affect price and competitiveness just as much as quality. With this in mind, for decades, this industry has been using putting in place environmentally–friendly practices such as recycling and by-product reuse even before Circular Economy and Industrial Ecology became widespread concepts or part of policy-driven efforts (EC 2013b; WS 2016).

In Europe, most policies regarding environmental impacts came into force or were revised close to the turn of the century. Notably examples are the Environmental Assessment Directive 2011/92/EU (EP 2011), the Industrial Emissions Directive 2010/78/EU (EP 2010), the Air Quality Directive 2008/50/EC (EP 2008a), the Water Framework Directive 2000/60/EC (EP 2000), the Packaging Waste Directive 94/62/EC (EP 1994), the Waste and Hazardous Waste Framework Directive 2008/98/EC (EP 2008b), and the Landfill Directive 99/31/EC (EU 1999).

Although these documents address how industries should manage, control and report their undesired or potentially hazardous outputs, minimal attention was given to input alternatives, resource efficiency or circular behaviors. Moreover, no particular or direct attention was given to the steel supply chain (EP 1999, 2000a, 2008a, b, 2010, 2011), with the exception of the Extracting and Mining Waste Directive 2006/21/EC (EP 2006).

In 2012 the European Union and its member states committed to the application of a Ciruclar Economy Package as new driver for its economic model, boosting a transition to resource-efficient practices that eventually lead to a regenerative progress toward nature (Zhijun and Nailing 2007; UNEP 2011; EC 2012; Su et al. 2013; Kahle and Gurel-Atay 2014; EMF 2015b; Gregson et al. 2015). Soon after, the European Commission conceived an action plan focused on the European steel industry, which summarized the situation of the European steel industry as of 2012 and brought to light the difficulties faced by the sector in terms of prices, competitiveness, trading and energy (EC 2013b).

To deal with these obstacles while furthering environmental progress on resource efficiency and climate, the action plan highlighted the need for developing secondary metals markets in order to boost the production of steel from scrap (EC 2013b; EUROFER 2015). From that point on, the European Commission and the European Council created multiple policy-supporting documents, the most noteworthy being the Best Available Techniques (BAT) for Iron and Steel Production (EC 2013a). Along Directive 2006/21/EC and the BAT for the Ferrous Metals Processing Industry (EC 2001). These documents proposed operational techniques capable of directly addressing certain environmental impacts and, when possible and pertinent, suggested potential circular integrations.

Still, the previously mentioned policies and most of their supporting documents either addressed steel indirectly through other sectors or approached different stakeholders/process of the steel supply chain separately (EUROFER 2015). Even alongside the BAT documents, these policies have been deemed insufficient to address climate and resource efficiency issues. Therefore, in order to stop this counterintuitive obstruction of circularity, more attention should be given to end-of-life steel, energy sourcing and systemic/holistic approaches (EC 2013b, 2014; Diener and Tillman 2016; Dunant et al. 2018; EUROFER 2015).

In an attempt to provide the European steel industry with additional support for decision- and policy-making, this article explored the potential benefits on integrating two methodologies used in the context of IE and CE: life cycle assessment (LCA) and system dynamics (SD) (Lewandowski 2016; Pomponi and Moncaster 2017; Winans et al. 2017).

Unlike previous studies, in which LCA and SD models ran in parallel and exchanged intermediary outputs or data exogenous to each other (Yao et al. 2018; Stasinopoulos et al. 2011), the present article brought the entire LCA methodology, in its attributional form, into the SD modelling environment. By doing so, the authors expected to maximize the amount of endogenous dynamics at play.

In the interest of identifying possible barriers or constraints to the integration, available literature was investigated and both LCA and the SD methodology were subjected to SWOT analyses—a strategic assessment technique used to identify the strengths, weaknesses, opportunities and threats faced by a given object of study (USDA 2008). Furthermore, it was deemed important to ensure that both LCA and SD would operate properly despite the integration, task achieved by comparing the results to those in existing literature generated by LCA and SD separately.

LCA and its uses in the European steel industry

As a tool, LCA is used for the accounting of series of static inventory inputs and outputs of the processes that exist in the life cycle of an object of study. These values are then scaled in accordance to a functional unit and characterized into sets of environmental impact indicators. This allows for a clearer understanding of the environmental performance of a series of processes throughout an item’s life cycle and enables detailed analyses and comparisons with similar goods (Palazzo and Geyer 2019; Tietenberg and Lewis 2004; ISO 2006; Ekvall et al. 2016; Koffler et al. 2014).

LCA has gained ground over the years due to its quantitative diagnostic applications, helping companies identify improvement opportunities in their supply chains (Hunt and Franklin 1996; Sonnemann et al. 2004). By individually analyzing the environmental impacts and environmental performance of each stage of a product’s life cycle, LCA enables product designers and decision-makers to better visualize the ramifications of inserting a product into the market (Ferreira 2004). This then allows for the revision and correction of a product’s characteristics or of a supply chain’s operation in order to reduce potential harm to the environment (Daddi et al. 2017).

The life cycle of steel, summarized in Fig. 1, begins with at least one of two main raw materials: iron ore or steel scrap. Iron ore is mined from Hematite (Fe2O3, ~ 70% Fe content), Magnetite (Fe3O4, ~ 72% Fe content), Limonite (2Fe2O3 + 3H2O, ~ 59% Fe content), Goethite (Fe2O3 + H2O ~ 63% Fe content) or Siderite (FeCO3, ~ 48% Fe content) (Stubbles 2017; Jones 2017; Kozak and Dzierzawski 2017).
Fig. 1
Fig. 1

Steel’s life cycle as per the circular economy framework

(adapted from EMF 2017)

Steel scrap, on the other hand, often has over 95% Fe content and, once given the appropriate triage and treatment, goes straight into steelmaking after its collection from manufacturing processes, recycling centers, junkyards or even landfills (Warrian 2012; WS 2012b; Beddows 2014; Stahl 2017).

Steel can leave the manufacturing stage in many forms and with many different chemical and mechanical characteristics, depending on the application to which it was designed (Beddows 2014; Stahl 2017). Once it goes into the use stage, it will be stored, reused and remanufactured until losses in quality demand its recycling (WS 2012b; Vaclav 2016). Throughout this entire sequence of stages, however, energy is consumed, byproducts are created and environmental impacts are generated, all of which can be accounted by LCA.

By following the guidelines of ISO 14040:2006 and using Simapro as a modelling platform to analyze data from Ecoinvent, Burchart-Korol (2013) developed the LCA of the Polish steel industry. In the study, the functional unit was set to one tonne of cast steel produced within Polish cradle-to-gate boundaries, resulting in CO2eq emissions measurements according to IPCC and CED criteria, as well as in ReCiPe Midpoint indicators for 17 different categories of environmental impacts per main productive process. Not only were the authors capable of identifying the human health and environmental risks posed by the raw materials as well as the energy demand of each productive process, but also to suggest changes in energy sourcing that could allow for the Electric Arc Furnace (EAF) method to be less emission-intensive (Burchart-Korol 2013).

A similar study was performed in the Turkish steel industry, in which 14 IMPACT2002 + Midpoint indicators were used instead of ReCiPe’s 17, focusing on five different steel products: billet, slab, hot rolled wire rod, hot rolled coil (Olmez et al. 2015). The main contributions of this study were (a) identifying hot rolled products as the most environmentally hazardous due to their intensive emission of inorganic particles—thus requiring efficient dust collection methods, and (b) highlighting the significant Global Warming Potential of this industry as a whole due to its high consumption of fossil fuels (Olmez et al. 2015).

Another similar example of LCA pertinent to the discussion at hand took place in Italy, additionally considering emissions from logistics while focusing on a functional unit of 1 million tons of steel slab (Renzulli et al. 2016). Unlike previous studies, this one suggested the regional reuse of BOF and BF slag for agriculture or infrastructure purposes as a mean to help reduce the overall environmental impact of the production process, while also suggesting a partnership with nearby power plants in order to improve energy efficiency (Renzulli et al. 2016).

Based on literature and practice just as much as on the examples above, Table 1 summarizes the analysis of strengths, weaknesses, opportunities and threats (SWOT) executed by the authors of this article.
Table 1

SWOT analysis of life cycle assessment.

[Sources: Hunt and Franklin (1996), Huijbregts (2002), Ferreira (2004), Sonnemann et al. (2004), ISO (2006), Finnveden et al. (2009), Curran (2012), Daddi et al. (2017)]

Strengths

Weaknesses

Focus on environmentally friendly product design and its development

Strong diagnostic and planning approach

Clear depiction of stocks and flows of a product along a supply chain

Stakeholder involvement in the supply chain is made visible

Internationally accepted and indicator-friendly

Linear, bottom-up approach

Designed to objectively represent series of processes and to account for the flows therein

Complex inputs and outputs

Limited comparability due to high specificity

High time and effort requirements

Performs best when the object of study is a contained unit or a simple combination

Limited scenario analyses, often requiring One-Factor-at-a-time (OFAT) approach

Unless a time frame or time series is tested, long-term decision-making application can be limited

Disaggregation level can pollute the identification of key issues if not properly managed

Standard application does not consider market dynamics

Opportunities

Threats

Allows for ISO certification

Can spearhead public image efforts regarding a company’s environmental concerns

Standardization allows for cross-cultural exchanges

Interpretation of results can be confusing, misleading or complex for general management or communication purposes

Scarce expertise

Vulnerable to data availability

Data inputs regarding future trends or behaviors depend on exogenous sources

It is from understanding and experiencing some of the limitations above as well as the limited availability of literature on LCA for European steel that the authors of this article considered also exploring how SD can support decision-making in the steel industry.

SD and its uses in the steel industry

While LCA is capable of giving scholars and decision-makers a very insightful snapshot of a supply chain, SD can, in turn, transform that snapshot into a film. Decision-makers gain, thus, the means to analyze a supply chain as it progresses through the effects of multiple feedbacks and loops of which visibility, relevance or scale could only become evident with the passage of time or with their simultaneous interactions (Forrester 1962; Booth and Meadows 1995).

SD is a methodology for studying complex nonlinear behavior within systems, often used for simulating new potential behaviors by adding, removing or changing variables, triggers and delays (Sterman 2000; Ogata 2003). To do so, it deconstructs a system into smaller—often binary—interactions. It then analyzes their behavior not only independently but also as part of the whole, which then generate balancing or reinforcing loops that help determine the system’s overall behavior (Ruth and Hannon 2012).

Instead of pushing data through series of stocks and flows—as LCA commonly does, SD lets the ensemble of interactions between each correlated pair of variables define the behavior of the system (Ogata 2003; Ruth and Hannon 2012). This approach allows for very small-scale problem-solving just as much as it allows for the analysis of large-scale interactions. SD often encompasses market dynamics and relies on endogenous data to create projections and trends, more easily representing circular behaviors when compared to other methodologies (Sterman 2000; Ruth and Hannon 2012).

SD derived from the school of Systems Thinking of the 1950s and 60s, which intended to support and improve productive decision-making (Forrester 1962; Booth and Meadows 1995). Its application begins on the definition of a clear question, then proceeds to conceptualize the system where the problem is located. During this step, its components, the causal relations and the feedbacks therein are mapped, generating a causal loop diagram (CLD) (Forrester 1969; Coyle 1997; Haraldson 2004; Morgan 2012; Capra and Luisi 2014).

Next, the CLD is converted into a Flow Chart (FC), a diagram which allows for data and variable inputs, task usually performed in a modelling software such as Stella or Vensim (Morgan 2012; Ruth and Hannon 2012). Having built a model that represents the system at hand and having added pertinent data to it, results and analyses can be derived from the simulation of scenarios (Randers 1980; Karnopp and Rosenberg 1975; Sterman 2000; Ogata 2003). Regarding the steel industry, and especially in Europe, not many studies and publications have yet made use of SD. Below, the authors present examples of SD studies on steel performed by researchers in China, Iran, Sweden and the United Kingdom.

The first study consisted of a macro-level analysis of the sintering process, one of the raw material preparation steps commonly used in the iron making stage. Both CLDs and FCs were created, resulting in a SD model capable of replicating the known behavior of sintering operations in the Anshan Iron and Steel Corporation (AISC) (Liu et al. 2015). The model was then used to run a multi-variable simulation comparing the AISC’s operation to the Shouqin Corporation’s operation, pointing to the latter as capable of delivering sinter with better compacted ness and higher iron content to the Chinese market (Liu et al. 2015).

The next study focused on reducing the consumption of natural gas and oil in Iranian national steelmaking by simulating the energy requirements through 20 years of subsidies, exports and consumption (Ansari and Seifi 2012). A macroeconomic SD model was created to test the aforementioned variables simultaneously and in face of price variations, resulting in up to 33% reductions in fossil fuel consumption depending on the mix of subsidy reforms, recycling stimuli and EAF deployment scenarios (Ansari and Seifi 2012).

Next, researchers studied how SD can support decision-makers in identifying the main obstacles for extending a product’s lifespan so as to comprise multiple life cycles (Asif et al. 2015). Global and North American data on steel was used to build a simplified global SD model in which resource scarcity and steel consumption were defined as the main drivers (Asif et al. 2015). As a result, the researchers suggested that enterprises and nations should attempt to keep scarce or non-renewable resources within their supply chains for as long as possible during multiple life cycles in order to accrue the most economic and environmental advantage possible (Asif et al. 2015).

The last study brought to the reader’s attention was one of the earliest concerning the steel industry using SD as a methodology. In it, the researchers attempted to create a model capable of reproducing the effects of bottlenecks, breakdowns and other operational constraints in steelmaking supply chains which adopt Minimum Reasonable Inventory (MRI) as a business strategy (Hafeez et al. 1996). After simulating different operational scenarios, the main outcome of the study was a set of strategies to achieve MRI for each individual stock unit according to system-wide operational risks, instead of altogether uniformly, which would tend to require either operational risk insurances or higher levels of working capital binding (Hafeez et al. 1996).

As previously performed for LCA, Table 2 summarizes the SWOT analysis of SD considering the examples above as well as other relevant literature.
Table 2

SWOT analysis of system dynamics.

[Sources: Forrester (1962), Booth and Meadows (1995), Coyle (1997), Hafeez et al. (1996), Ogata (2003), Haraldson (2004), Ansari and Seifi (2012), Capra and Luisi (2014), Asif et al. (2015), Liu et al. (2015), Kunc (2017)]

Strengths

Weaknesses

Focus on circularity, causality and the effects of variables over time

Strong for strategic analyses and problem-solving

Flexible modelling environment facilitates the use subjective or abstract variables if necessary

Multiple independent objects of study can be subject of analysis simultaneously

Model structure is easy to adapt and change if necessary

Non-linear, top-down approach

Can be used for modelling market dynamics

Strategic analyses often do not suffice for effective decision-making

Visualization of stakeholder involvement is highly dependent on how the model is built

Levels of error and uncertainty are harder to determine

Aggregation can hide or ignore important variables if not done carefully

Model structure might not be objectively represent the actual series of processes and flows of the system under study

Limited support for using indicators

Opportunities

Threats

Can be of great use for communication purposes

Can foster the development of multidisciplinary studies

Can generate endogenous trends and projections

Scarce expertise

Analyses can become over-simplistic

Vulnerable to data reliability

After having finished SWOT analyses for both LCA and SD, the authors identified multiple points of divergence but also of convergence. Most importantly, however, is that in situations where one flounders, the other often excels, thus pointing to the potential benefits of a combined approach.

Methodology

Bringing LCA and SD together is a relatively recent idea as of the development of this article, with earliest attempts dating back to 2011. In the scientific studies published so far, systems thinking was used to pursue the same results generated by either LCA (Onat et al. 2016; Halog and Manik 2011; Yao et al. 2018; Stasinopoulos et al. 2011) or Material Flow Analysis (MFA) (Sprecher et al. 2015).

In some of their attempts, previous academics ran both methodologies in parallel and used them as interchangeable sources of endogenous data to each other (e.g. Yao et al. 2018; Stasinopoulos et al. 2011). In other attempts, series of results originated in MFA or LCA were then used in a SD model, simulating a circular environment for the retrieval of dynamic behaviors (e.g. Sprecher et al. 2015; Onat et al. 2016; Halog and Manik 2011). Plevin et al. (2014) and Palazzo and Geyer (2019) also tested different variations of LCA—attributional or consequential, respectively—in order to bring systemic attributes into their LCA results and discussions.

In all cases, authors were capable of broadening and deepening the understanding of the systems under study and their efforts brought significant advancements to the discussion of how approaching LCA and MFA with a SD mindset can be productive and insightful (Onat et al. 2017; Palazzo and Geyer 2019).

Nevertheless, answering questions regarding sustainability’s triple bottom line or regarding different environmental nexi with larger scopes and boundaries still faced two main methodological dilemmas: data aggregation levels had to be altered—often upwards and towards simplification; and data output formats had to be adapted—often seeking the lowest common complexity denominator.

Although none of these modifications were inherently negative, they interfered with each individual methodology enough to justify this article’s different approach: one focused on maximizing endogenous feedback and minimizing data aggregation issues or format adjustments. Having learned from the aforementioned experiments and from the authors’ previous experiences, this article tries something different: to bring the entire LCA methodology into the SD modelling environment.

It is to say that, in addition to using SD to broaden and deepen the achievements of LCA, we have attempted to create a winwin environment in which LCA can provide its own contributions to SD as well. To delve into the details of this endeavor, this section is divided in three parts, namely (a) research design—in which the authors introduce question, case-study and the methodological steps; (b) model description—in which the model itself and its development are explained; and (c) parameterizing and operation—where details regarding data inputs, variable control and operational behaviors are presented.

Research design

Considering that neither SD nor LCA were originally devised to work with each other, as well as the limited number of available attempts of their integration until now, the primary concern was to properly envision where, when and how LCA and SD could supplant each other’s weaknesses while maintaining their own strengths. With that in mind, a methodological question took priority over the originally conceived one, resulting in the following:
  1. 1.

    Can the integration of LCA into SD reproduce the results or behaviors previously observed in studies that used LCA or SD separately?

     
  2. 2.

    What potential benefits derive from this integration toward decision-making on the biophysical aspects of long-term materials sourcing?

     

Keeping in mind the frameworks and concepts of both IE and CE, the main expected result of the study was achieving a favorable answer to the first question, which would hypothetically indicate that the integration was realized adequately and to the extent of not interfering with either SD’s or LCA’s correct implementation. The quantitative criteria for answering both questions, keeping in mind the case study at hand, focused on (a) emissions, (b) biophysical depletion of iron ore, (c) steel scrap generation and consumption, (d) liquid steel output from production, (e) iron circularity, and (f) steel input into the economy, as derived from literature already presented thus far or to be introduced further in this section.

For qualitatively answering them, the SWOT analyses based the search for the following patterns: SD’s broader and more flexible modelling approach contributing to LCA’s (a) circularity, (b) long-term perspective, and (c) the macro analysis potential; while LCA’s objective and empirical representation of an operation improves SD’s (d) stakeholder involvement identification, (e) analysis reliability, and (f) applied/practical usefulness across managerial levels.

The case study used for testing this integration was the European steel industry, chosen by the authors due to (a) its current transition towards more environmentally-oriented decision-making; (b) its importance for the European economy, security and sovereignty; (c) its global contextual concerns regarding the rise of international competitors, and; (d) to the policy limitations regarding its environmental aspects. Therefore, as boundary, the study took into account the EU28 zone, represented by the supply chains of the steelmakers members of the WorldSteel Association that operate within it, which account for 84% of the entire European steel industry.

In order to adequately represent this industrial activity and give focus to the biophysical transformations that take place throughout the supply chain while keeping in mind European average steel production behavior, the study was conducted using the following methodological steps: (1) business process mapping (BPM), carried out with the support of the BizAgi software and aimed at identifying all the core processes of steelmaking in Europe; (2) causal loop diagraming (CLD), made with the support of the OmniGraffle tool so as to represent the steelmaking supply chain in a systematic and holistic manner; (3) flow charting (FC), within the SD modelling environment of the Stella Architect software (ISEE Systems 2016); (4) data collection and scenario building; (5) model parameterizing and testing; and (6) simulation runs and analyses.

Iron was defined as the driving chemical element of steelmaking, while steel scrap and iron ore were defined as the key raw materials. Nevertheless, connections to all other chemical elements and raw materials involved in steelmaking were included, as summarized in Fig. 2.
Fig. 2
Fig. 2

CLD of the system under study, made in OmniGraffle

Furthermore, two different levels of aggregation were adopted: cradle-to-gate processes were disaggregated down to chemical level, while gate-to-cradle processes were aggregated to product level. This choice was made in order to give decision-making granularity for the steelmakers without over encumbering macro-level analyses that could affect policy-making on end-of-life and circularity services.

In order to obtain the desired alloys, the material needs of the furnaces were used to define the amounts of raw materials pulled from their respective sources. This pulling behavior is present in the system until liquid steel becomes an intermediary output, point in which the system then pushes materials through the subsequent processes so as to reproduce the continuous casting operation. Additionally, attention was given to the feedbacks that close the loop (e.g. recycling, repair, refurbishment), so as to enable the system to operate under the definitions of CE and IE.

Model description

In total, twenty modules were created, one for each chemical element involved in the steel supply chain (e.g. iron, carbon, nickel, chromium, zinc, oxygen), all of which used a functional unit (FU) of 1 ton of steel and were built to be structurally identical. Specific flows and stocks were introduced whenever necessary so as to properly represent the typical behaviors of each chemical element throughout the supply chain.

Within each module, the production processes and the stocks of steelmaking were approached modularly and established as individual LCA-based units, capable of being displaced, rearranged or replicated with minimal interference in the overall structure of the model. This allowed for the user interface to be less polluted then traditional SD models and should enable this model to be easily adapted to the reality of different stakeholders in the future, as exemplified in Fig. 3.
Fig. 3
Fig. 3

Iron (Fe) module’s flow chart interface diagram

The productive processes were grouped into macro-processes based on their most common occurrence in the European steel industry, namely: (a) EAF and (b) BFBOF—each encompassing sintering, pelletizing, degassing, alloying, desiliconization, desulfurization, homogenization or dephosphorization, whenever applicable; (c) casting—which encompassed all shape, heat and surface treatments; (d) metallurgy—which encompassed all forming and metalworking processes; (e) economic sectors—divided in construction, automotive, other transportation, tools and machinery, appliances and electronics, and heavy mechanical equipment, as per WorldSteel Association standards; (f) recycling—which fed back into the stock of scrap used as input for “a” and “b”; (g) repair/refurbishment—which fed back into each economic sector according to their share in its demand; and (h) losses and landfills—which configured a process-based sink.

It is important to note, however, that (1) due to the lack of available disaggregated data, emissions from mining, casting and metallurgy were attributed to the EAF and the BFBOF macro-processes accordingly and proportionally; (2) dust and particulate matter generation were incorporated into the mass of emissions; (3) no disaggregated emission data was found for end-of-life and circularity solutions; (4) energy flows were considered only in the form of amount of fossil fuels consumed and not in the form of heat or electricity (directly by BFBOF and indirectly from generation for EAF); and (6) no pricing, costing or speculative variables were included in this attempt—variables these which will be addressed in a subsequent publication.

Finally, a control panel was created in order to facilitate the visualization and management of data inputs and variable control, as well as for the easier identification of issues. It allowed for the (a) adjustment of variables that affect all 20 modules, (b) monitoring of stocks, flows and outputs of the supply chain, and (c) follow-up on operational losses. Moreover, different levels of granularity were made possible for analysis merely by switching on and off the tracking of individual chemical elements.

Parameterizing and operation

Table 3 summarizes the data inputs used in the study, all of which encompassed the interval between 2001 and 2014, and were verified for cohesion, coherence and reliability based on the criteria of the ILCD Handbook (EC 2010) and of ISO14044:2006 (ISO 2006),1 as well as being compared to their equivalent data points in the WorldSteel Association’s Life Cycle Inventory Study for Steel Products (WS 2017c) and EUROSTAT Databases (EUROSTAT 2009, 2017, 2018a, b).
Table 3

Summary of data inputs

Type

Variable

Unit

Sources

EAF inputs

Scrap, oxygen, natural gas, coal, limestone, dolomite, water, ore

kg/kg of steel

Shamsuddin (2016), WS (2012a, b, 2017b, c), EU (2011), Madias (2013), Cullen et al. (2012), Yellishetty et al. (2011a, b), EUROFER (2017a), EUROSTAT (2009, 2018b), Seetharaman (2013)

BFBOF inputs

Ore, hot blast, scrap, water, limestone, coke, dolomite

kg/kg of steel

Typical chemical compositions of the inputs

Scrap, ore, coke, natural gas, coal, dolomite, limestone, hot blast

%

MINDAT (2017)

Webmineral (2017)

Typical compositions of steel alloys, as outputs

UNS S30400, UNS S31600, UNS S43000, UNS S17400, UNS S32205, UNS S40900

%

Bringas (2004)

Typical slag composition ranges

EAF slag, BF slag, BOF slag

%

Yildirim and Prezzi (2011), Adegoloye et al. (2016), EUROSLAG (2017)

Typical composition ranges of emissions to the atmosphere

EAF emissions, BF emissions, BOF emissions

%

Ferreira and Leite (2015), Ramírez-Santos et al. (2018), Uribe-Soto et al. (2017), Schubert and Gottschling (2011), Seetharaman (2013)

Stocks in use

Automotive, construction, tools + machinery, appliances + electronics, heavy mechanical equipment, other transportation

tons

Pauliuk et al. (2013), EC (2017)

Participation of economic sectors in steel demand

%

WS (2017b), EUROSTAT (2009, 2018b)

Typical lifespan of steel per economic sector (as delays)

years

Cooper et al. (2014), EUROSTAT (2009, 2018b), EC (2017)

Recycling/refurbishment rate per economic sector

%

NFDC (2012), EUROSTAT (2018a), Björkman and Samuelsson (2014), BIR (2017), Panasiyk et al. (2016), EUROFER (2017b), Eckelman et al. (2014), Terörde (2006)

Repair/reuse rate per economic sector

%

NFDC (2012), EUROSTAT (2018a), Dindarian and Gibson (2011), Truttmann and Rechberger (2006), Bovea et al. (2016), Kissling et al. (2013), RREUSE (2012), Eckelman et al. (2014)

Distribution and end-of-life losses

%

Pauliuk et al. (2017), Johnson et al. (2008)

Typical cooling water reuse and recycling rates

EAF cooling water, BFBOF cooling water

%

WS (2015a, b), WSSTP (2013), Burchart-Korol and Kruczek (2015)

The model was then parameterized for annual calculations during a period of 200 years, assuming that the demand for steel focused on the 6 most produced types of steel (UNS S30400, UNS S31600, UNS S43000, UNS S17400, UNS S32205, UNS S40900). The yields of the EAF and the BFBOF production macro-processes were set according to their respective capacity and productivity, as well as to their share of participation in the EU28. The parameters can be seen in Table 4.
Table 4

Summary of parameters used to test and run the model

Parameter

Value

Unit

Sources

EAF tap-to-tap timea

0.8

Hours

Shamsuddin (2016), WS (2012a, b, 2017b, c), EU (2011), Madias (2013), Cullen et al. (2012), Yellishetty et al. (2011a, b), EUROFER (2017a), Seetharaman (2013)

EAF furnace capacity

100.000,00

kg

BFBOF cycle capacity

42.000,00

kg/batch

BFBOF productivitya

7

Batches/h

Share of EAF production in the EU28

39.70

%

WS (2017b)

Share of BFBOF production in the EU28

60.30

%

Worldwide recoverable high-grade iron ore

82 billion

Tons

Sverdrup and Ragnarsdottir (2014), UNCTAD (2017)

Worldwide recoverable low-grade iron ore

92 billion

Tons

Worldwide recoverable very-low-grade iron ore

166 billion

Tons

aAs both delay and yield factor

Keeping in mind that all of the steelmakers considered within the boundaries of the study either import iron ore or ship it from their international branches, inherent behaviors of the model structure included (a) the gradual transition from BFBOF production to EAF production as function of steel scrap availability; iron ore quality decrease and iron ore scarcity over time (Waugh 2016); (b) the gradual shift towards consuming steel scrap instead of iron ore as a function of iron content and availability, still respecting alloying and operational requirements; and (c) steel scrap down cycling over time due to alloying quality loss during repeated service lives.

Finally, all circularity and end-of-life behaviors were set to respond in a business-as-usual pattern, with no direct or indirect stimulus of any kind, evolving only in proportion to the demands of the elements present in steel scrap.

Results and discussion

After running the model, the authors proceeded to verify if the integration could reproduce results of studies that used SD and LCA separately. In what regarded SD, the results were favorable and all features of SD remained functional.

As the biophysical depletion of recoverable high-grade iron ore reserves takes place, as seen in Fig. 4, BFBOF production would be forced to migrate to inferior grades of iron ores by 2051. Moreover, its availability would become critical circa 2054, i.e. 53 years after the initial data point of 2001. These results very much reproduced those of Sverdrup and Ragnarsdottir (2014), in which such a condition would take place around the year 2050. Having analyzed and reproduced the means by which their results were achieved, the authors identified that the 4-years difference occurred due to two main factors: Sverdrup and Ragnarsdottir (2014) used (a) longer data series and (b) considered the aggregate demand for all steel types.
Fig. 4
Fig. 4

High-grade iron ore depletion

When analyzed alongside Fig. 5, the decrease in iron ore consumption associated with its loss in iron content had a direct effect on the input of steel into the economy, despite a strong trend of increasing steel scrap generation until around 2060. This happened due to a delayed transition from BFBOF towards EAF, limiting the amount of steel delivered to the economy even with BFBOF eventually operating at maximum capacity during phase-out, corroborating the conclusions of Asif et al. (2015).
Fig. 5
Fig. 5

Results for ore, scrap, steel input and circularity (tons)

As high-grade iron ore becomes scarcer, higher priority should be given to retaining the resources and materials originated from it within a same supply chain, in order to accrue as much environmental and economic benefits from them as possible. The same logic applies to all of the TCEs and CRMs involved in the production of different steel alloys, notably nickel, niobium, titanium, vanadium and molybdenum. To do so in the EU28 while keeping in mind CE would require stakeholders within a supply chain to work on improving and integrating their operations, also an argument brought up by Asif et al. (2015) and Nuss and Blengini (2018).

Figure 5 also points to iron circularity being hardly affected, phenomenon replicated to other elements until biophysical exhaustion, and consequent of a balancing effect in which (a) even though more steel scrap is generated, more of it is consumed, and (b) no additional stimulus is being given to increasing circularity other than by responding to the demand for scrap and the elements within it. If a transition from BFBOF to EAF production occurs as is, steel’s presence in the EU28 economy would be forced to go through a decline not only due to availability restrictions on other alloying elements, but due to iron itself—argument also previously brought forward by Ansari and Seifi (2012) and Sverdrup and Ragnarsdottir (2014).

Figure 6 reinforces this notion, in which by maintaining the status quo, EAF will not be able to cover for the liquid steel output reduction of BFBOF steelmaking: even by using more scrap and less ore, the depletion of ore itself would slow down. One of the main drawbacks of such a situation is the undesired and indirect stimuli potentially given to the market for developing materials alternative to steel, which could add competition detrimental to steelmakers’ margins (Asif et al. 2015). Therefore, if a faster transition towards EAF steelmaking is desired, policy-based initiatives towards the development and strengthening of a secondary raw materials market is necessary, as highlighted before not only by the European Commission (2013b), but also by EUROFER (2015).
Fig. 6
Fig. 6

Steel output and the sources of iron (tons)

Next, regarding LCA, the results were also favorable, but one of its features could not be reproduced. As an example, obtaining the average CO2 eq emissions of 837.41 kg/FU from EAF steelmaking and 2255.39 kg/FU from BFBOF steelmaking was possible as they derived directly from the model’s mass balances—results consistent with those of Burchart-Korol (2013). Due to the need for modeling each individual characterization criteria for each potential indicator, however, it was not possible to determine the impacts of these emissions on specific environmental compartments—as per ReCiPe characterization criteria, for example.

The same occurred for slag generation: while the average results of 459.84 kg/FU from the BFBOF and 121.17 kg/FU from the EAF aligned with those from Renzulli et al. (2016), determining specific impact indicators was, notwithstanding, unachievable at this point. In the cases of both slag and emissions, nevertheless, the integrated model allowed for easier analysis of individual chemical elements, as exemplified in Table 5.
Table 5

Summary of observed slag and emission compositions

 

Emissions

Slag

Comments

BFBOF

EAF

BFBOF

EAF

CO

39.1%

62.7%

From partial oxidation in the furnaces

CO2

20.8%

3.1%

From the combustion of fossil fuels

N

3.4%

30.8%

Mostly in the form of oxides (NOx)

H

32.6%

3.3%

Either as CH4 or as H2

H2O

4.0%

Byproduct

Ca

28.5%

30.6%

As part of CaO and CaS

O

36.3%

32.8%

Present in all oxides

Si

11.4%

7.3%

As part of SiO2

Mg

4.5%

3.8%

As part of MgO

Al

3.9%

2.3%

As part of Al2O3

Cr

*

*

11.8%

1.1%

Free ion or as part of Cr2O3

Mn

*

*

1.5%

3.3%

As part of MnO

Fe

*

*

0.4%

17.6%

As part of FeO and Fe2O3

P

0.4%

0.8%

As part of P2O5

S

1.0%

0.2%

Free ion or as part of CaS

Zn

*

*

0.3%

0.2%

Free ion or as part of ZnO

Ti

*

*

Free ion or as part of TiO2

* Trace amounts, less than 0.1% altogether

The results and analyses derived from the integrated model answered favorably the first question, indicating that the integration did not interfere with the results of either LCA or SD. The use of indicators, however, one of LCA’s features—was rendered impractical. After identifying the flows within the system during the inventory phase of LCA, most LCA softwares provide a solid platform for the characterization of each flow into an impact indicator category. SD, on the other hand, requires each indicator and its characterization factors to be modelled individually, offering no support for the allocation of the flows into impact categories, point in which more extensive research and development would be necessary.

In order to answer the second question, the authors referred back to the criteria listed in “Research design” section. Criterion ‘a’ was perceived by the authors as considerably improved, with the addition of a more detailed understanding of the dynamics of steel in the economy outside of the steelmakers’ gates.

Criterion ‘b’, on the other hand, saw SD give LCA a substantial boost in terms of how many years of steelmaking operation could be simulated or projected using only endogenous data feedback. Whether calculating annually for a period of 200 years—as performed in this study—or even down to hourly calculations for a certain period of interest, SD’s delay and feedback mechanics allowed LCA to have a better grasp on how the gate-to-cradle dynamics loop back into its mostly cradle-to-gate approach.

The contribution to the improvement of LCA’s macro analysis potential, as per criterion ‘c’, derived mostly from the possibility to track many different elements while concurrently simulating changes in more than one variable at a time throughout the entire supply chain, as exemplified in Fig. 7. Moreover, not only did stocks and flows help influence the system’s overall behavior, but so did both feedbacks and delays, features characteristic of SD that broadened LCA’s range of analysis.
Fig. 7
Fig. 7

Presence of iron in the economy, per sector

With respect to criterion ‘d’, bringing LCA into SD did in fact allow for more precisely and objectively visualizing and accounting the stocks and flows of materials through and within the involved stakeholders, notably after steel leaves the industry and cycles through the economy and through end-of-life and circularity services.

The collection and input of case-specific data following the LCA guidelines of ILCD and ISO improved the reliability and especially the granularity of the SD analyses—as per criterion ‘e’—, which were better supported by objective and empirical results such as those exemplified in Table 5.

For these reasons, the practical usefulness of the results across managerial levels-criterion ‘f’—was also perceived as improved, which could allow for different decision-makers to use the same model for variables that ranged from chemical composition all the way to ore scarcity and demand planning. In all cases, nevertheless, further improvements to its managerial applicability could be achieved by linking such a model to real-time operational data inputs.

The authors understand that verifying the feasibility and the potential benefits of integrating SD and LCA very much depends on how the integration itself is performed and, considering the methodological steps and the modelling approach used in this study, the integration was deemed not only feasible, but also capable of better supporting stakeholders that would previously only consider SD or LCA, adding to their individual strengths.

With this in mind, it is important to note that LCA seemed to contribute more for the improvement of SD than the other way around. It is to say that, overall, the distinctive diagnostic and process efficiency features of LCA emerged much more tangibly as a result of the integration process than SD’s problem-solving orientation.

For professionals or academics used to LCA applications, the current obstacles for working with indicators might configure enough of a barrier to avoid either a transition or an integration into SD. Future improvements on this integration could potentially solve such issues and favor its adoption. Nevertheless, the aforementioned strategic gains should suffice to attract attention to the discussion and to entice interested agents to further investigate gate-to-cradle dynamics and their feedbacks into production.

For SD scholars, however, the benefits of integrating LCA expertise into SD modelling were substantial. Enhancing the reliability, the granularity and the stakeholder visibility in the results can compensate for many of the weaknesses identified in the SWOT analysis of standard SD applications, notably helping to mitigate the threat of over-simplistic analyses. SD practitioners and policy-makers could take advantage of this approach to better subside their analyses, adding to the levels of objectivity and representativeness of their studies, especially when process efficiency is a key decision factor.

Additionally, particularly from cradle-to-gate, the integrated model was very reminiscent of what IE calls Industrial Metabolism. Certain similarities to other IE tools such as Material Flow Analysis (MFA) and its dynamic form (dMFA) became evident as well, especially regarding the visibility of flows and stocks. Also, due to the characteristics of the European steel industry, the model posed as another good example of how CE envisions end-of-life processes as suppliers to the earlier stages of the supply chain. Further studies would need to be done, however, in order to add more renewable energy sources into the operation, as well as to better manage how some chemical elements rejoin the biosphere.

Finally, the authors believe that if data in more disaggregated levels were available, even better results would have been achieved. This could lead to significantly better analyses of individual processes such as sintering, pelletizing, mining, forming, metalworking and recycling, especially regarding emissions and the use of energy directly in the form of heat and electricity.

Conclusions and recommendations

This study based itself on SWOT analyses of relevant SD and LCA studies on steel as well as on business process mapping to subside the creation of a model that integrated LCA into SD. The model was built in ISEE Stella Architect using the European steel industry as a case study while following ISO and ILCD standards. As the main result, the integration was deemed feasible and beneficial for both SD and LCA in different levels. Table 6 summarizes the results for both the quantitative and qualitative criteria used in evaluating the performance of the integrated model.
Table 6

Summary of quantitative and qualitative results

Quantitative

Qualitative

Criterion

Reproduced LCA?

Reproduced SD?

Criterion

Integration evaluation

Emissions

Yes

SD improves LCA’s circularity analyses

Considerable/minor improvement: more detailed gate-to-cradle dynamics

Biophysical depletion of iron ore

Yes

SD improves LCA’s long-term perspective

Substantial/major improvement: allows for full timespan flexibility

Steel scrap generation and consumption

Yes

Yes

SD improves LCA’s macro analyses potential

Substantial/major improvement: allows for the tracking of multiple elements while multiple variables are interacting or changing simultaneously, not only OFAT

Liquid steel output

Yes

Yes

LCA improves SD’s stakeholder involvement identification

Substantial/major improvement: more precise depiction of flows, stocks and roles as per LCA requirements

Iron circularity

Yes

LCA improves SD’s analysis reliability

Substantial/major improvement: increased reliability and granularity due to data disaggregation and objectivity

Steel input into economy

Yes

Yes

LCA improves SD’s applicability across managerial levels

Considerable/minor improvement: analyses can range from operational to strategic levels, but depend on how the model is built

By allowing the simulation of longer periods of time, the testing of multiple simultaneously changing variables, endogenous feedbacks, and a clear visualization of gate-to-cradle dynamics, SD added strategic value to LCA. This could potentially interest industrial decision-makers who would like to broaden the understanding of their operations as their goods and products integrate the economy as well as when they leave it.

The benefits that LCA brought to SD were more substantial and revolved around increased granularity, reliability, stakeholder involvement and applicability of the results on different managerial levels, factors that could attract policy-makers in need of a deeper understanding of a specific supply chain.

No interferences to the application of SD were identified while reproducing the results of previous studies. The replicability of LCA results from previous studies suffered no interferences either, however, it could not benefit from the use of indicators derived from ReCiPe’s characterization criteria, for example. Further research on how to better integrate LCA indicators into a SD modeling environment is required in order to improve the integration. Moreover, even when integrated into SD, LCA still calls for complex or disaggregated data to be as effective as possible.

Henceforward, the authors recommend further investigation into the integration of LCA and SD. However well aligned it already was to the concepts and frameworks of both IE and CE, more attention to environmental impact indicators, renewable energy sources and to the reintroduction of substances into the biosphere is desirable.

By giving the model pertinent market data, setting other TCEs or CRMs present in the supply chain as key drivers instead of iron, and by using an industrial case study, researchers should be able to make even more progress towards the implementation of a joint LCA + SD mindset across academia, management and government.

Finally, based on the potential brought forward by the results of this study, the authors will extend the exploration of this methodological integration and its application to the European steel industry. Planned developments include: (a) testing the benefits that different supply chain integration strategies focused on closed loop operations could bring to biophysical circularity; (b) examining the potential effects of different end-of-life and secondary market development policies on supply- and demand-side dynamics; as well as (c) verifying which biophysical dynamics have the most relevant interactions with steel trade and its futures market.

Highlights

  • Compiles relevant SD and LCA studies on steel and presents SWOT analyses of both SD and LCA;

  • Introduces a model integrating LCA into SD and studies its application in the European steel industry;

  • Integration of SD and LCA is deemed feasible and beneficial for both methodologies in different levels;

  • Corroborates discussions on raw material scarcity, transition towards EAF steelmaking and resource ownership retention.

Footnotes
1

The authors also considered adopting product environmental footprint (PEF) standards (JRC 2012), however, in its current state, it presented itself as a less consolidated and less disseminated methodology, with available applications focused mainly in the construction sector.

 

Abbreviations

LCA: 

life cycle assessment

SD: 

system dynamics

TCE: 

technology critical elements

CRM: 

critical raw material

IE: 

industrial ecology

CE: 

circular economy

BAT: 

best available techniques

ISO: 

International Standardization Organization

IPCC: 

International Panel on Climate Change

EAF: 

electric arc furnace

BFBOF: 

blast furnace and basic oxygen furnace

SWOT: 

strengths, weaknesses, opportunities and threats

OFAT: 

one-factor-at-a-time

CLD: 

causal loop diagaram

FC: 

flow chart

MRI: 

minimum reasonable inventory

MFA: 

material flow analysis

BPM: 

business process mapping

FU: 

functional unit

ILCD: 

International Life Cycle Database and Guidelines

PEF: 

product environmental footprint

UNS: 

unified numbering system

Declarations

Acknowledgements

This article is the first in a series of publications aimed at helping to improve environmentally-oriented policy- and decision-making in the European steel industry, with regard to sustainable resource management. It derives from AdaptEconII’s project #4, under the European Commission’s Horizon 2020 Programme. The authors would like to thank Mr. Gregor Wernet, Executive Director of Ecoinvent, for his methodological insights during the development of this study.

Funding

Marie Skłodowska Curie Fellowship Action in Excellent Research (Grant Agreement No. 675153).

Authors’ contributions

Due to the nature of the study, all authors were involved in all steps of development. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The author declares that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
European Commission’s Horizon 2020 Programme, Marie Skłodowska Curie Fellowship Actions in Excellent Research, AdaptEconII Project, Clermont-Ferrand, France
(2)
Department of Industrial Engineering, University of Iceland, Reykjavik, Iceland
(3)
Centre d’Etudes et de Recherches sur le Développement International (CERDI), Université Clermont-Auvergne, 26 Avenue Léon Blum, 63000 Clermont-Ferrand, France

References

  1. Adegoloye G, Beaucour AL, Ortola S, Noumowe A (2016) Mineralogical composition of EAF slag and stabilised AOD slag aggregates and dimensional stability of slag aggregate concretes. Construct Build Mater 115:171–178. https://doi.org/10.1016/j.conbuildmat.2016.04.036 View ArticleGoogle Scholar
  2. Ansari N, Seifi A (2012) A system dynamics analysis of energy consumption and corrective policies in Iranian iron and steel industry. Energy 43–1:334–343. https://doi.org/10.1016/j.energy.2012.04.020 View ArticleGoogle Scholar
  3. Asif FMA, Rashid A, Bianchi C, Nicolescu CM (2015) System dynamics models for decision making in product multiple lifecycles. Resour Conserv Recycling 101:20–33. https://doi.org/10.1016/j.resconrec.2015.05.002 View ArticleGoogle Scholar
  4. Baas LW, Boons FA (2004) An industrial ecology project in practice: exploring the boundaries of decision-making levels in regional industrial systems. J Cleaner Prod 12–8:1073–1085. https://doi.org/10.1016/j.jclepro.2004.02.005 View ArticleGoogle Scholar
  5. Beddows R (2014) Steel 2050—how steel transformed the world and now must transform itself. Devonian, Kingsbridge. ISBN 0993038107Google Scholar
  6. BIR (2017) World steel recycling in figures. Bureau of International Recycling, BrusselsGoogle Scholar
  7. Björkman B, Samuelsson C (2014) Recycling of steel. In: Worrell E, Reuter M. Handbook of recycling. Elsevier, New York. ISBN 9780123965066View ArticleGoogle Scholar
  8. Booth SL, Meadows D (1995) The systems thinking playbook. Chelsea Green Publishing, Hartford. ISBN 9781603582582Google Scholar
  9. Bovea MD, Ibáñez-Forés V, Pérez-Belis V, Quemades-Beltrán P (2016) Potential reuse of small household waste electrical and electronic equipment: methodology and case study. Waste Manag 53:2016. https://doi.org/10.1016/j.wasman.2016.03.038 View ArticleGoogle Scholar
  10. Bringas JE (2004) Handbook of comparative world steel standards. ASTM International, New York. ISBN 0803133626Google Scholar
  11. Burchart-Korol D (2013) Life cycle assessment of steel production in Poland: a case study. J Cleaner Prod 54:235–243. https://doi.org/10.1016/j.jclepro.2013.04.031 View ArticleGoogle Scholar
  12. Burchart-Korol D, Kruczek M (2015) Water scarcity assessment of steel production. Metalurgija 54:276–278Google Scholar
  13. Capra F, Luisi PL (2014) The systems view of life—a unifying vision. Cambridge University Press, Cambridge. ISBN 1107011361Google Scholar
  14. Chevalier J (1995) L’économie industrielle des stratégies d’entreprises. Montchrestien, Paris. ISBN 978-2-7076-1211-3Google Scholar
  15. Cohen-Rosenthal E (2004) Making sense out of industrial ecology: a framework for analysis and action. J Cleaner Prod 12–8:1111–1123. https://doi.org/10.1016/j.jclepro.2004.02.009 View ArticleGoogle Scholar
  16. Cooper DR, Skelton ACH, Moynihan MC, Allwood JM (2014) Component level strategies for exploiting the lifespan of steel in products. Resour Conserv Recycling 84:24–34. https://doi.org/10.1016/j.resconrec.2013.11.014 View ArticleGoogle Scholar
  17. Coyle RG (1997) System dynamics modelling: a practical approach. Chapman and Hall/CRC, Boston. ISBN 978-0412617102Google Scholar
  18. Cullen JM, Allwood JM, Bambach MD (2012) Mapping the global flow of steel: from steelmaking to end-use goods. Environ Sci Technol 46:13048–13055. https://doi.org/10.1021/es302433p View ArticleGoogle Scholar
  19. Curran MA (2012) Life cycle assessment handbook: a guide for environmentally sustainable products. Wiley-Scrivener, Beverly. ISBN 978-1-118-09972-8Google Scholar
  20. Daddi T, Nucci B, Iraldo F (2017) Using Life Cycle Assessment (LCA) to measure the environmental benefits of industrial symbiosis in an industrial cluster of SMEs. J Cleaner Prod 147:157–164. https://doi.org/10.1016/j.jclepro.2017.01.090 View ArticleGoogle Scholar
  21. D’costa AP (1999) The global restructuring of the steel industry—innovations, institutions and industrial change. Routledge, Oxon. ISBN 0415148278Google Scholar
  22. Despeisse M, Ball PD, Levers A, Evans S (2012) Industrial ecology at factory level—a conceptual model. J Cleaner Prod 31:30–39. https://doi.org/10.1016/j.jclepro.2012.02.027 View ArticleGoogle Scholar
  23. Diener DL, Tillman A (2016) Scrapping steel components for recycling, isn’t that good enough? Seeking improvements in automotive component end-of-life. Resour Conserv Recycling 110:48–60. https://doi.org/10.1016/j.resconrec.2016.03.001 View ArticleGoogle Scholar
  24. Dindarian A, Gibson AP (2011) Reuse of EEE/WEEE in UK: review on functionality of EEE/WEEE at the point of disposal. Sustain Syst Technol Conf. https://doi.org/10.1109/issst.2011.5936867 View ArticleGoogle Scholar
  25. Dunant CF, Drewniok MP, Sansom M, Corbey S, Cullen JM, Allwood JM (2018) Options to make steel reuse profitable: an analysis of cost and risk distribution across the UK construction value chain. J Cleaner Prod 183:102–111. https://doi.org/10.1016/j.jclepro.2018.02.141 View ArticleGoogle Scholar
  26. EC (2001) Integrated Pollution Prevention and Control (IPPC) reference document on best available techniques in the ferrous metals processing industry. Joint Research Center, BrusselsGoogle Scholar
  27. EC (2010) ILCD handbook: general guide for life cycle assessment—detailed guidance. Joint Research Centre-Institute for Environment and Sustainability, LuxembourgGoogle Scholar
  28. EC (2012) Manifesto for a resource-efficient Europe. European Commission, BrusselsGoogle Scholar
  29. EC (2013a) Communication from the commission to the parliament, the council, the European economic and social committee and the committee of regions: Action Plan for a competitive and sustainable steel industry in Europe. COM 407. Strasbourg: European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52013DC0407
  30. EC (2013b) Integrated Pollution Prevention and Control (IPPC) reference document on best available techniques for iron and steel production. Joint Research Center, BrusselsGoogle Scholar
  31. EC (2013c) Communication from the commission to the parliament, the council, the European economic and social committee and the committee of regions: action plan for a competitive and sustainable steel industry in Europe. COM 407. Strasbourg: European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52013DC0407
  32. EC (2014) State of play on implementation of the Commission Communication Action Plan for a competitive and sustainable steel industry in Europe. European Commission, BrusselsGoogle Scholar
  33. EC (2017) Study on the review of the list of critical raw materials. Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs, LuxembourgGoogle Scholar
  34. Eckelman MJ, Ciacci L, Kavlak G, Nuss P, Reck BK, Graedel TE (2014) Life cycle carbon benefits of aerospace alloy recycling. J Cleaner Prod 80:38–45. https://doi.org/10.1016/j.jclepro.2014.05.039 View ArticleGoogle Scholar
  35. Economics Material (2018) The circular economy—a powerful force for climate mitigation. Stockholm, Material EconomicsGoogle Scholar
  36. Ehrenfeld JR (1997) Industrial ecology: a framework for product and process design. J Cleaner Prod 5–1:87–95. https://doi.org/10.1016/S0959-6526(97)00015-2 View ArticleGoogle Scholar
  37. Ehrenfeld JR (2004) Industrial ecology: a new field or only a metaphor? J Cleaner Prod 12–8:825–831. https://doi.org/10.1016/j.jclepro.2004.02.003 View ArticleGoogle Scholar
  38. Ekvall T, Azapagic A, Finnveden G, Rydberg T, Weidema BP, Zamagni A (2016) Attributional and consequential LCA in the ILCD handbook. Int J Life Cycle Assess 21:293–296. https://doi.org/10.1007/s11367-015-1026-0 View ArticleGoogle Scholar
  39. EMF (2012) Towards the circular economy—an economic and business rationale for an accelerated transition. Ellen McArthur Foundation, CowesGoogle Scholar
  40. EMF (2013) Towards the circular economy—opportunities for the consumer goods sector. Ellen McArthur Foundation, CowesGoogle Scholar
  41. EMF (2014a) A new dynamic—effective business in a circular economy. Ellen McArthur Foundation, CowesGoogle Scholar
  42. EMF (2014b) Towards the circular economy—accelerating the scale-up across global supply chains. Ellen McArthur Foundation, CowesGoogle Scholar
  43. EMF (2015a) Growth within—a circular economy vision for a competitive Europe. Ellen McArthur Foundation, CowesGoogle Scholar
  44. EMF (2015b) Delivering the circular economy—a toolkit for policymakers. Ellen McArthur Foundation, CowesGoogle Scholar
  45. EMF (2016) Intelligent assets—unlocking the circular economy potential. Ellen McArthur Foundation, CowesGoogle Scholar
  46. EMF (2017) The circular economy—a wealth of flows. Ellen McArthur Foundation, CowesGoogle Scholar
  47. EP (1994) European Parliament and Council Directive 94/62/EC of 20 December 1994 on packaging and packaging waste. Brussels: Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:01994L0062-20150526
  48. EP (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Brussels: Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000L0060
  49. EP (2006) Directive 2006/21/EC of the European Parliament and of the Council of 15 March 2006 on the management of waste from extractive industries and amending Directive 2004/35/EC—Statement by the European Parliament, the Council and the Commission. Brussels: Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32006L0021
  50. EP (2008a) Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Brussels: Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex:32008L0050
  51. EP (2008b) Directive 2008/98/EC of the European Parliament and of the Council of 19 November 2008 on waste and repealing certain Directives. Brussels: Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32008L0098
  52. EP (2010) Directive 2010/75/EU of the European Parliament and of the Council of 24 November 2010 on industrial emissions (integrated pollution prevention and control). Brussels: Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32010L0075
  53. EP (2011) Directive 2011/92/EU of the European Parliament and of the Council of 13 December 2011 on the assessment of the effects of certain public and private projects on the environment. Official Journal of the European Union, Brussels. https://eur-lex.europa.eu/legal-content/en/TXT/?uri=CELEX:32011L0092
  54. Erkman S (1997) Industrial ecology: an historical view. J Cleaner Prod 5–1:1–10. https://doi.org/10.1016/S0959-6526(97)00003-6 View ArticleGoogle Scholar
  55. EU (1999) Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste. Brussels: The Council of The European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A31999L0031
  56. EU (2011) Improved EAF process control using on-line offgas analysis—OFFGAS. European Union, LuxembourgGoogle Scholar
  57. EUROFER (2015) Open letter to the European Commission: circular Economy from 12 EU resource manufacturing industries. The European Steel Association, BrusselsGoogle Scholar
  58. EUROFER (2017a) European steel in figures. The European Steel Association, Brussels, BrusselsGoogle Scholar
  59. EUROFER (2017b) Steel, the backbone of sustainability in Europe. The European Steel Association, BrusselsGoogle Scholar
  60. EUROSLAG (2017) European association of metallurgical slag producers. https://www.euroslag.com. Accessed 5 Sept 2017
  61. EUROSTAT (2009) Iron and steel production and processing statistics, NACE Rev. 1.1. Statistics explained database. https://ec.europa.eu/eurostat/statistics-explained/index.php/Archive:Iron_and_steel_production_and_processing_statistics_-_NACE_Rev._1.1. Accessed 5 Sept 2018
  62. EUROSTAT (2017) Mining and quarrying statistics—NACE Rev. 2. Statistics explained database. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Mining_and_quarrying_statistics_-_NACE_Rev._2. Accessed 5 Sept 2018
  63. EUROSTAT (2018a) Data reporting on quantity, hazardousness and shipments of waste. http://ec.europa.eu/eurostat/web/waste/data. Accessed 5 Sept 2018
  64. EUROSTAT (2018b) Manufacturing statistics—NACE Rev. 2. Statistics explained database. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Manufacturing_statistics_-_NACE_Rev._2. Accessed 5 Sept 2018
  65. Ferreira JV (2004) Análise do ciclo de vida dos produtos. PhD Thesis. Instituto Politécnico de Viseu, LisbonGoogle Scholar
  66. Ferreira H, Leite MPG (2015) A Life Cycle Assessment study of iron ore mining. J Cleaner Prod 108A:1081–1091. https://doi.org/10.1016/j.jclepro.2015.05.140 View ArticleGoogle Scholar
  67. Finnveden G, Hauschild MZ, Ekvall T, Guinée J, Heijungs R, Hellweg S, Koehler A, Pennington D, Suh S (2009) Recent developments in Life Cycle Assessment. J Environ Manag 91–1:1–21. https://doi.org/10.1016/j.jenvman.2009.06.018 View ArticleGoogle Scholar
  68. Forrester JW (1962) Industrial dynamics. MIT Press, Boston. ISBN 1614275335Google Scholar
  69. Forrester JW (1969) Urban dynamics. MIT Press, Boston. ISBN 1883823390Google Scholar
  70. Geissdoerfer M, Savaget P, Bocken NMP, Hultnik EJ (2017) The circular economy—a new sustainability paradigm? J Cleaner Prod 143:757–768. https://doi.org/10.1016/j.jclepro.2016.12.048 View ArticleGoogle Scholar
  71. Gibbs D, Deutz P (2007) Reflections on implementing industrial ecology through eco-industrial park development. J Cleaner Prod 15–17:1683–1695. https://doi.org/10.1016/j.jclepro.2007.02.003 View ArticleGoogle Scholar
  72. Gregson N, Crang N, Fuller S, Holmes H (2015) Interrogating the circular economy: the moral economy of resource recovery in the EU. Econ Soc 44:218–243. https://doi.org/10.1080/03085147.2015.1013353 View ArticleGoogle Scholar
  73. Haas W, Krausmann F, Wiedenhofer D, Heinz M (2015) How circular is the global economy? An assessment of material flows, waste production, and recycling in the European Union and the World in 2005. J Ind Ecol 19(5):765–777. https://doi.org/10.1111/jiec.12244 View ArticleGoogle Scholar
  74. Hafeez K, Griffiths M, Griffiths C, Naim MM (1996) Systems design of a two-echelon steel industry supply chain. Int J Prod Econ 45–1:121–130. https://doi.org/10.1016/0925-5273(96)00052-7 View ArticleGoogle Scholar
  75. Halog A, Manik Y (2011) Advancing integrated systems modelling framework for life cycle sustainability assessment. Sustainability 3:469–499. https://doi.org/10.3390/su3020469 View ArticleGoogle Scholar
  76. Haraldson HV (2004) Introduction to system thinking and casual loop diagram. KFS AB, LundGoogle Scholar
  77. Huijbregts M (2002) Uncertainty and variability in environmental life-cycle assessment. Int J Life Cycle Assess 7:173. https://doi.org/10.1007/BF02994052 View ArticleGoogle Scholar
  78. Hunt R, Franklin E (1996) How it came about—personal reflections on the origin and the development of LCA in the USA. Int J Life Cycle Assess 1–1:4–7View ArticleGoogle Scholar
  79. ISO (2006) ISO 14044—environmental management, life cycle analysis, requirement and orientations. International Standardization Organization, GenevaGoogle Scholar
  80. ISEE Systems (2017) Stella architect. https://www.iseesystems.com/store/products/stella-architect.aspx. Accessed 5 May 2017
  81. Johnson J, Reck BK, Wang T, Graedel TE (2008) The energy benefit of stainless steel recycling. Energy Policy 36:181–192. https://doi.org/10.1016/j.enpol.2007.08.028 View ArticleGoogle Scholar
  82. Jones JAT (2017) Electric arc furnace steelmaking. Steelworks. http://www.steel.org/making-steel/how-its-made/processes/processes-info/electric-arc-furnace-steelmaking.aspx. Accessed 11 Feb 2017
  83. JRC (2012) Joint Research Centre-Institute for Environment and Sustainability—European Commission. Product Environmental Footprint (PEF) Guide, Ispra. http://ec.europa.eu/environment/archives/eussd/pdf/footprint/PEF%20methodology%20final%20draft.pdf
  84. Kahle LR, Gurel-Atay E (2014) Communicating sustainability for the green economy. M.E. Sharpe, New York. ISBN 0765636816Google Scholar
  85. Karnopp DC, Rosenberg RC (1975) System dynamics—a unified approach. Wiley, New York. ISBN 0471459402Google Scholar
  86. Kissling R, Coughlan D, Fitzpatrick C, Böni H, Luepschen C, Andrew S, Dickenson J (2013) Success factors and barriers in re-use of electrical and electronic equipment. Resour Conserv Recycling 80:21–31. https://doi.org/10.1016/j.resconrec.2013.07.009 View ArticleGoogle Scholar
  87. Koffler C, Geyer R, Volz T (2014) “Life cycle inventory. In: Koffler C (ed) Environmental Life Cycle Assessment: measuring the environmental performance of products. American Center for Life Cycle Assessment, Vashon Island, Washington, pp 46–57Google Scholar
  88. Korhonen J (2004) Industrial ecology in the strategic sustainable development model: strategic applications of industrial ecology. J Cleaner Prod 12–8:809–823. https://doi.org/10.1016/j.jclepro.2004.02.026 View ArticleGoogle Scholar
  89. Kozak B, Dzierzawski J (2017) Continuous casting of steel: basic principles. Steelworks. http://www.steel.org/making-steel/how-its-made/processes/processes-info/continuous-casting-of-steel—basic-principles.aspx. Accessed 11 Feb 2017
  90. Kunc M (2017) System dynamics: soft and hard operational research. Palgrave Macmillan, Basingstoke. ISBN 978-1-349-95257-1Google Scholar
  91. Leigh M, Xiaohong L (2015) Industrial ecology, industrial symbiosis and supply chain environmental sustainability: a case study of a large UK distributor. J Cleaner Prod 106:632–643. https://doi.org/10.1016/j.jclepro.2014.09.022 View ArticleGoogle Scholar
  92. Lewandowski M (2016) Designing the business models for circular economy—towards the conceptual framework. Sustainability 8:43–51. https://doi.org/10.3390/su8010043 View ArticleGoogle Scholar
  93. Liao W, Heijungs R, Huppes G (2012) Thermodynamic analysis of human–environment systems: a review focused on industrial ecology. Ecol Model 228:76–88. https://doi.org/10.1016/j.ecolmodel.2012.01.004 View ArticleGoogle Scholar
  94. Liu C, Xie Z, Sun F, Chen L (2015) System dynamics analysis on characteristics of iron-flow in sintering process. Appl Thermal Eng 82:206–211. https://doi.org/10.1016/j.applthermaleng.2015.02.077 View ArticleGoogle Scholar
  95. Madias J (2013) Electric furnace steelmaking. In: Seshadri S (ed) Treatise on process metallurgy. Elsevier, New York. ISBN 9780080969879View ArticleGoogle Scholar
  96. MINDAT (2017) Mineralogy Database. https://www.mindat.org/. Accessed 5 Sept 2017
  97. Morgan MS (2012) The world in the model. Cambridge University Press, Cambridge. ISBN 9781107002975Google Scholar
  98. NFDC (2012) The recycling and reuse survey. National Federation of Demolition Contractors, HertsGoogle Scholar
  99. Nielsen SN (2007) What has modern ecosystem theory to offer to cleaner production, industrial ecology and society? The views of an ecologist. J Cleaner Prod 15–17:1639–1653. https://doi.org/10.1016/j.jclepro.2006.08.008 View ArticleGoogle Scholar
  100. Nuss P, Blengini GA (2018) Towards better monitoring of technology critical elements in Europe: coupling of natural and anthropogenic cycles. Sci Total Environ 613–614:569–578. https://doi.org/10.1016/j.scitotenv.2017.09.117 View ArticleGoogle Scholar
  101. Ogata K (2003) System dynamics. Prentice Hall, New York. ISBN 0131424629Google Scholar
  102. Olmez GM, Dilek FB, Karanfil T, Yetis U (2015) The environmental impacts of iron and steel industry: a life cycle assessment study. J Cleaner Prod 130:195–201. https://doi.org/10.1016/j.jclepro.2015.09.139 View ArticleGoogle Scholar
  103. Onat NC, Kucukvar M, Tatari O (2016) Integration of system dynamics approach toward deeping and broadening the life cycle sustainability assessment framework: a case for electric vehicles. Int J Life Cycle Assess 21:1009. https://doi.org/10.1007/s11367-016-1070-4 View ArticleGoogle Scholar
  104. Onat NC, Kucukvar M, Halog A, Cloutier S (2017) Systems thinking for life cycle sustainability assessment: a review of recent developments, applications, and future perspectives. Sustainability 9:706. https://doi.org/10.3390/su9050706 View ArticleGoogle Scholar
  105. Palazzo J, Geyer R (2019) Consequential life cycle assessment of automotive material substitution: replacing steel with aluminum in production of north American vehicles. Environ Impact Assess Rev 75:47–58. https://doi.org/10.1016/j.eiar.2018.12.001 View ArticleGoogle Scholar
  106. Panasiyk D, Laratte B, Remy S (2016) Steel stock analysis in Europe from 1945 to 2013. Conf Life Cycle Eng 48:348–351. https://doi.org/10.1016/j.procir.2016.04.084 View ArticleGoogle Scholar
  107. Park J, Sarkis J, Wu Z (2010) Creating integrated business and environmental value within the context of China’s circular economy and ecological modernization. J Cleaner Prod 18–15:1494–1501. https://doi.org/10.1016/j.jclepro.2010.06.001 View ArticleGoogle Scholar
  108. Pauliuk S, Wang T, Müller DB (2013) Steel all over the world: estimating in-use stocks of iron for 200 countries. Resour Conserv Recycling 71:22–30. https://doi.org/10.1016/j.resconrec.2012.11.008 View ArticleGoogle Scholar
  109. Pauliuk S, Kondo Y, Nakamura S, Nakajima K (2017) Regional distribution and losses of end-of-life steel throughout multiple product life cycles—insights from the global multiregional MaTrace model. Resour Conserv Recycling 116:84–93. https://doi.org/10.1016/j.resconrec.2016.09.029 View ArticleGoogle Scholar
  110. Pearce DR, Turner KR (1989) Economics of natural resources and the environment. John Hopkins University Press, Baltimore. ISBN 9780801839870Google Scholar
  111. Plevin RJ, Delucchi MA, Creutzig F (2014) Using attributional life cycle assessment to estimate climate-change mitigation benefits misleads policy makers. J Ind Ecol 18–1:73–83. https://doi.org/10.1111/jiec.12074 View ArticleGoogle Scholar
  112. Pomponi F, Moncaster A (2017) Circular economy for the built environment: a research framework. J Cleaner Prod 143:710–718. https://doi.org/10.1016/j.jclepro.2016.12.055 View ArticleGoogle Scholar
  113. Prosman EJ, Waehrens B, Liotta G (2017) Closing global material loops: initial insights into firm-level challenges. J Ind Ecol 21–3:641–650. https://doi.org/10.1111/jiec.12535 View ArticleGoogle Scholar
  114. Ramírez-Santos A, Castel C, Favre E (2018) A review of gas separation technologies within emission reduction programs in the iron and steel sector: current application and development perspectives. Sep Purif Technol 194:425–442. https://doi.org/10.1016/j.seppur.2017.11.063 View ArticleGoogle Scholar
  115. Randers J (1980) Elements of the system dynamics method. MIT Press, Massachussets. ISBN 0915299399Google Scholar
  116. Renzulli PA, Notarnicola B, Tassielli G, Arcese G, Di Capua R (2016) Life Cycle Assessment of steel produced in an italian integrated steel mill. Sustainability 8:719–728. https://doi.org/10.3390/su8080719 View ArticleGoogle Scholar
  117. RREUSE (2012) Challenges to boosting reuse rates in Europe. RREUSE, BrusselsGoogle Scholar
  118. Ruth M, Hannon B (2012) Modeling dynamic economic systems. Springer, New York. ISBN 978-1-4614-2209-9View ArticleGoogle Scholar
  119. Schubert ES, Gottschling R (2011) Co-generation: a challenge for furnace off-gas cleaning systems. Southern African Pyrometallurgy Institute, MarshalltownGoogle Scholar
  120. Seager TP, Theis TL (2002) A uniform definition and quantitative basis for industrial ecology. J Cleaner Prod 10–3:225–235. https://doi.org/10.1016/S0959-6526(01)00040-3 View ArticleGoogle Scholar
  121. Seetharaman S (2013) Treatise on process metallurgy, volumes 1 to 3: process fundamentals, process phenomena and industrial processes. Elsevier Online. ASINs B00GY5XF3E, B00H1YWDGW and B00HEMSS88Google Scholar
  122. Shamsuddin M (2016) Physical chemistry of metallurgical processes. Wiley Kindle Edition, Hoboken. ISBN 978-1-119-07833-3View ArticleGoogle Scholar
  123. Sonnemann G, Castells F, Tsang M, Schumacher M (2004) Integrated life-cycle and risk assessment for industrial processes. CRC Press, Madrid. ISBN 9781566706445Google Scholar
  124. Sprecher B, Daigo I, Murakami S, Kleijn R, Vos M, Kramer GJ (2015) Framework for resilience in material supply chains, with a case study from the 2010 rare earth crisis. Environ Sci Technol 49–11:6740–6750. https://doi.org/10.1021/acs.est.5b00206 View ArticleGoogle Scholar
  125. STAHL (2017) Hot metal and crude steel production. German steel federation. http://en.stahl-online.de/index.php/topics/technology/steelmaking/. Accessed 11 Feb 2017
  126. Stasinopoulos P, Compston P, Newell B, Jones HM (2011) A system dynamics approach in LCA to account for temporal effects—a consequential energy LCI of car body-in-whites. Int J Life Cycle Assess 17–2:199–207. https://doi.org/10.1007/s11367-011-0344-0 View ArticleGoogle Scholar
  127. Sterman JD (2000) Business dynamics—systems thinking and modelling for a complex world. McGraw-Hill, New York. ISBN 9780072389159Google Scholar
  128. Stubbles J (2017) The Basic Oxygen Steelmaking (BOS) Process. Steelworks. http://www.steel.org/making-steel/how-its-made/processes/processes-info/the-basic-oxygen-steelmaking-process.aspx. Accessed 11 Feb 2017
  129. Su B, Heshmati A, Geng Y, Yu X (2013) A review of the circular economy in China: moving from rhetoric to implementation. J Cleaner Prod 42:215–227. https://doi.org/10.1016/j.jclepro.2012.11.020 View ArticleGoogle Scholar
  130. Sverdrup H, Ragnarsdottir KV (2014) Geochemical perspectives: natural resources in a planetary perspective. Toulouse. https://doi.org/10.7185/geochempersp.3.2 View ArticleGoogle Scholar
  131. Taddeo R (2016) Local industrial systems towards the eco-industrial parks: the model of the ecologically equipped industrial areas. J Cleaner Prod 131:189–197. https://doi.org/10.1016/j.jclepro.2016.05.051 View ArticleGoogle Scholar
  132. Terörde F (2006) Stainless steel recycling, data and scenarios, availability of scrap. ISSF, MunichGoogle Scholar
  133. Tietenberg TH, Lewis L (2004) Environmental economics and policy. Pearson Addison Wesley, New York. ISBN 1292026804Google Scholar
  134. Truttmann N, Rechberger H (2006) Contribution to resource conservation by reuse of electrical and electronic household appliances. Resourc Conserv Recycling 48–3:249–262. https://doi.org/10.1016/j.resconrec.2006.02.003 View ArticleGoogle Scholar
  135. Tukker A (2015) Product services for a resource-efficient and circular economy—a review. J Cleaner Prod 97:76–91. https://doi.org/10.1016/j.jclepro.2013.11.049 View ArticleGoogle Scholar
  136. UNCTAD (2017) The iron ore market 2017. In: United Nations Conference on Trade and Development Trust Fund Project in Iron Ore InformationGoogle Scholar
  137. UNEP (2011) Towards a green economy—pathways to sustainable development and poverty erradication. United Nations’ Environmental Programme, GenevaGoogle Scholar
  138. Uribe-Soto W, Portha J, Commenge J, Falk L (2017) A review of thermochemical processes and technologies to use steelworks off-gases. Renew Sustain Energy Rev 74:809–823. https://doi.org/10.1016/j.rser.2017.03.008 View ArticleGoogle Scholar
  139. USDA (2008) SWOT analysis: a tool for making better business decisions. Risk management agency, U.S. Department of Agriculture. ASIN: B07DDBT1H8Google Scholar
  140. Vaclav S (2016) Still the iron age. Butterworth-Heinemann, Elsevier, Oxford. ISBN 9780128042359Google Scholar
  141. Van Berkel R, Willems E, Lafleur M (1997) Development of an industrial ecology toolbox for the introduction of industrial ecology in enterprises. J Cleaner Prod 5–1:11–25. https://doi.org/10.1016/S0959-6526(97)00004-8 View ArticleGoogle Scholar
  142. Warrian P (2012) A profile of the steel industry—global reinvention for a new economy. Business Expert Press, New York. ISBN 1631573845Google Scholar
  143. Waugh R (2016) The end of the blast furnace era? Ironmaking Steelmaking. https://doi.org/10.1018/03019233.2016.1153325 View ArticleGoogle Scholar
  144. Webmineral (2017) Mineralogy database. http://webmineral.com/. Accessed 5 Sept 2017
  145. Winans K, Kendall A, Deng H (2017) The history and current applications of the circular economy concept. Renew Sustain Energy Rev 68–1:825–833. https://doi.org/10.1016/j.rser.2016.09.123 View ArticleGoogle Scholar
  146. WS (2012a) Sustainable steel: at the core of a green economy. World Steel Association, BrusselsGoogle Scholar
  147. WS (2012b) The white book of steel. Worldsteel, BrusselsGoogle Scholar
  148. WS (2013) Sustainable steel—policy and indicators 2013. Worldsteel, BrusselsGoogle Scholar
  149. WS (2015a) Steel in the circular economy—a life cycle perspective. World Steel Association, BrusselsGoogle Scholar
  150. WS (2015b) Water management in the steel industry. World Steel Association, BrusselsGoogle Scholar
  151. WS (2016) Steel, the permanent material in the circular economy. World Steel Association, BrusselsGoogle Scholar
  152. WS (2017a) Methodology report life cycle inventory study for steel products. World Steel Association, BrusselsGoogle Scholar
  153. WS (2017b) Steel markets. World Steel Association. http://www.worldsteel.org/steel-by-topic/steel-markets/Buildings-and-infrastructure.html. Accessed 30 Jan 2017
  154. WS (2017c) World Steel in Figures. World Steel Association, BrusselsGoogle Scholar
  155. WSSTP (2013) Research and technology development needs for water and steel. Water Supply and Sanitation Technology Platform, BrusselsGoogle Scholar
  156. Yao L, Liu T, Chen X, Mahdi M, Ni J (2018) An integrated method of life-cycle assessment and system dynamics for waste mobile phone management and recycling in China. J Cleaner Prod 187:852–862. https://doi.org/10.1016/j.jclepro.2018.03.195 View ArticleGoogle Scholar
  157. Yellishetty M, Mudd G, Ranjith PG, Tharumarajah A (2011a) Environmental life-cycle comparisons of steel production and recycling: sustainability issues, problems and prospects. Environ Sci Technol 14–6:650–663. https://doi.org/10.1016/j.envsci.2011.04.008 View ArticleGoogle Scholar
  158. Yellishetty M, Mudd GM, Ranjith PG (2011b) Availability, addiction and alternatives: three criteria for assessing the impact of peak minerals on society. J Cleaner Prod 19:78–90. https://doi.org/10.1016/j.jclepro.2010.12.006 View ArticleGoogle Scholar
  159. Yellishetty M, Mudd G, Mason L, Mohr S, Prior T, Giurco D (2012) Iron resources and production—technology, sustainability and future prospects. CSIRO and Monash University, Sydney. ISBN 978-1-922173-46-1Google Scholar
  160. Yildirim IZ, Prezzi M (2011) Chemical, mineralogical, and morphological properties of steel slag. Adv Civil Eng 1:1. https://doi.org/10.1155/2011/463638 View ArticleGoogle Scholar
  161. Zhijun F, Nailing Y (2007) Putting a circular economy into practice in China. Sustain Sci 2:95–101View ArticleGoogle Scholar

Copyright

© The Author(s) 2019

Advertisement