Sustainable Steel Production: A Comprehensive LCA Approach for Reducing Environmental Costs and Impacts

Sustainable Steel Production: A Comprehensive LCA Approach for Reducing Environmental Costs and Impacts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sustainable Steel Production: A Comprehensive LCA Approach for Reducing Environmental Costs and Impacts Aref Ahmadian Baghbadarani, Khosro Ashrafi, Abdolreza Karbassi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4930754/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Apr, 2025 Read the published version in Materials Circular Economy → Version 1 posted 9 You are reading this latest preprint version Abstract The life cycle assessment (LCA) is a powerful tool for evaluating environmental impacts and costs. In this study, LCA was applied to steel production, specifically focusing on the electric arc furnace (EAF) and the Midrex direct reduction of iron ore. The functional unit considered is one tonne of molten steel extracted from the EAF. EAF inputs mainly consist of sponge iron with a 90:10 proportion of sponge iron to scrap. The study employs the ReCiPe (H) 2016 V1.1 method for LCA, and environmental cost calculations utilize the Environmental Prices method. The total environmental costs, normalized midpoint impacts, and normalized endpoint impacts amount to 462.72 euros, 8.11 pt and, 0.13 pt, respectively. The analysis of steel production identifies three principal stages: Sponge Iron Consumption, Electricity Consumption, Other Inputs and Outputs Associated with Steel Production. Notably, electricity consumption and sponge iron usage account for approximately 70% and 75% of the impacts on midpoints and endpoints, respectively, as well as 75% of the total environmental costs. Making specific choices—such as using solar power instead of traditional gas-based electricity and scrap instead of sponge iron—can effectively enhance the sustainability of the steel-making process. The scenario VI, when compared to other scenarios, results in the following reductions: Midpoint Impacts: 5.03 pt, Endpoint Impacts: 0.04 pt, Environmental Costs: 167.69 euros. Regarding the ReCiPe method, it was assessed from various perspectives. The egalitarian perspective consistently demonstrated the highest value at the endpoint level, followed by the hierarchist and individualist viewpoints. life cycle assessment environmental cost ReCiPe method electric arc furnace MIDREX iron reduction steel making Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlights Assessing of environmental costs and effects of steel production Presenting two ways to reduce costs and environmental effects and examining these two ways in five different scenarios Measuring and evaluating the effect of different perspectives of the ReCiPe method on the results 1. Introduction In the manufacturing sector, factors like environmental responsibility and sustainable development are becoming crucial increasingly. Steel's extensive use in the building and automobile industries, as well as in home appliances, packaging, and other products, makes more steel production essential for economic growth (Liang et al. 2020 ). Over the past few decades, there has been an increase in global steel output, which can be attributed to both rapid urbanization and industrialization (Sun et al. 2020 ). Over 1950.5 million tons of crude steel were produced globally in 2021; Iran produced 28.5 million tons and was the first in the Middle East (Steel Production by Country 2024). This amount of steel production is continuously rising globally due to the growing need for steel manufacturing. Unquestionably, the steel manufacture adds significantly to energy use and CO 2 emissions, but it also uses a lot of water and causes pollution (Zhu et al. 2020 ). The manufacture of steel was responsible for over 7% of the world's carbon dioxide emissions in 2020 (Steel Production by Country 2024). Conventional steel making techniques, like the blast furnace process, have been linked to severe environmental effects. The method of making steel from scrap is a form of recycling. Consequently, it is especially crucial to lessen the harmful impacts of steel manufacturing on the environment. The percentage of steel produced using electric arc furnace (EAF) technology is predicted to rise from 26% in 2014 to 40% in 2050 (Ramezani Moziraji et al. 2023 ). EAFs in Iran create more than 70% of the country's steel from scrap and direct reduced iron. EAF technology has known as a more sustainable steel production process in recent years. The global warming potential of the biosyngas based direct reduced iron- electric arc furnace (DRI-EAF) system is 75% lower than that of the existing natural gas (NG)-based DRI-EAF course and 85% lower than that of the blast furnace-basic oxygen furnace (BF-BOF) route, according to the findings of Nurdiawati et al (Seidel 2016 ). Liang et al. found that an electric arc furnace had a lower environmental impact than a blast furnace-basic oxygen furnace based on a life cycle assessment (LCA) of steel production (Liang et al. 2020 ). One of the most popular inputs for steel making in EAF is sponge iron (DRI). Depending on resource availability, direct reduction techniques are classified as either gas- or coal-based, but gas-based DRI production is the primary technique (Zhu et al. 2020 ). Because of its plentiful gas resources, Iran uses the direct gas-based reduction technique to make sponge iron. In 2022, the world's production of direct reduced iron (DRI) reached 127.36 million tonnes (Mt), surpassing the previous record of 119.2 Mt established in 2021 by about 6.9%. Over the past half-decade, the global output of DRI has increased by over 55 Mt, or roughly 75% (Steel Production by Country 2024). Suer et al. claimed that using pre-reduced iron ores in a blast furnace and injecting hydrogen might already cut greenhouse gas (GHG) emissions by up to 200 kg CO 2 /t of hot metal (Su et al. 2022 ). The MIDREX method is the most popular kind of direct reduction technique. 3.8% more DRI (73.55 Mt) was generated in 2022 by MIDREX Plants than in 2021 (70.85 Mt). In 2022, MIDREX Technology continued to account for approximately 80% of worldwide DRI production by shaft furnaces (The World Leader in Direct Reduction Technology | Midrex Technologies, 2022). The LCA is a powerful tool for providing an accurate overview of environmental impacts. LCA is defined as a method for examining a product or service's effects on the environment. Therefore, LCA determines environmentally critical points in a process or product's life cycle that has the most signicant influence on the environment (Nicholas et al. 2000 , Yilmaz, Anctil, and Karanfil 2015 ). It is simple to modify the LCA techniques to create public policy (Seidel 2016 ). Numerous studies have examined the environmental impact of the entire life cycle of steel production. According to prior results, in terms of potential eutrophication, cumulative energy consumption, abiotic depletion, human toxicity, and global warming, the BF-BOF route had a higher environmental impact than the EAF route (Liang et al. 2020 , X. Li et al. 2018 ). In a study, the environmental effects of DRI (gas based)-EAF steel production in Iran were assessed using LCA. The results show that the processes associated with the EAF (35%), oxide pellet processes, and DRI (17.1% and 28.9%, respectively) have the highest environmental impacts (Shatokha 2016 ). Lin et al.'s study on materials flow and energy in steel production highlights that the EAF sector consumes a significant amount of resources, resulting in negative impacts on the environment, specifically freshwater eutrophication and human toxicity (Lin et al. 2016 ). A shaft-EAF based on CO 2 -CH 4 dry reforming was assessed by Chen et al. It was discovered that the DRI-EAF process reduced CO 2 emissions by 40% compared to the BF-BOF process, with minimal change in energy consumption. However, because of China's higher natural gas costs, the DRI-EAF process was 34% more expensive than the BF-BOF process (Chen et al. 2018 ). However, Ozdemir et al. used LCA to examine the environmental effects of a factory producing one tonne of steel rebar from start to finish. The CML-IA[1] method was used to conduct an impact assessment. The analysis findings indicated that the production of crude steel and steel rebar contributed, respectively, 670 and 720 kg CO 2 equivalent/tonne to the possibility of global warming (Özdemir et al. 2017 ). Environmental costs associated with the effects of human activity on the environment are an essential part of economic analysis. Comprehending and evaluating environmental costs has become crucial for sustainable development and conscientious decision-making as societies confront the obstacles of pollution, resource depletion, and climate change. Only experts in this field can effectively utilize and comprehend the environmental impact of any activity or service based on LCA results. To ensure that LCA results are comprehensible to policymakers and the general public, it is essential to calculate environmental prices.The environmental prices method is monetizing the harmful effects of human activities on the natural environment. These impacts include pollution, reduced resources, biodiversity loss, climate change, and health impacts. Doing so can help governments and individuals reduce their environmental footprint, improve their ecological performance, and reach to the Sustainable Development Goals [16, 17]. The present investigation introduces a comprehensive (LCA) conducted through the utilization of SIMAPRO software, aimed at examining the environmental costs and impacts associated with steel production utilizing electric arc furnace (EAF) and MIDREX DRI technologies. Additionally, recommendations were proposed and evaluated across five distinct scenarios to mitigate environmental costs and impacts. The LCA study was carried out utilizing the ReCiPe[2] method, focusing on determining the influence of different ReCiPe method perspectives on the study outcomes. 2. Methodology 2.1 Production description Turning raw iron ores into finished, qualified steel products is known as iron and steel production. Figure 1 illustrates two steelmaking methods. The BF-BOF route exhibits a greater environmental impact compared to the EAF route. (Burchart-Korol 2013 ). The EAF route, which is the primary process flow of high-quality special steel smelting, has the advantages of a quick process, low energy consumption, flexible charge and product structure, and lower costs as compared to the conventional BOF route based on metallurgical coke and iron ore (Z. Li and Hanaoka 2020). 2.1.1 Midrex sponge iron (DRI) Utilizing NG to produce sponge iron, which is subsequently used in the EAF, has the potential to reduce CO 2 emissions by 33% compared to the baseline blast furnace and BF–BOF technology (Liang et al. 2020 ). At lower temperatures (900–1000°C), the DR method converts iron oxides in the ore to metallic iron in a solid state instead of the BF process. Coal, or gas, is the reducing agent. Natural gas produces hydrogen and carbon monoxide, which are mainly used for reduction (Johansson et al. 2014). In this paper, LCA was applied to the Midrex DR and EAF steel making method. The midrex method uses a tall vertical reactor to carry out the iron ore reduction operations. Figure 2a illustrates how high-temperature, reformed natural gas is introduced from the bottom of the furnace during the MIDREX process. The consumed gas is then removed from the furnace's top after the iron has been reduced. Sponge iron, the main result of this process, is released when the temperature falls. 2.1.2 EAF steel making: The EAF steel making route is typically consists of four steps: charging, melting, heating, refining and tapping. Scrap and other iron-bearing materials (sponge iron, hot metal, and hot briquetted iron (HBI) are the primary components of the EAF. Before introducing sponge iron into the furnace, a portion of scrap is charged and melted using electrical arcs. To prevent electrode failure during the initial meltdown phase, the arcs operate with less power. High power levels can be employed after the electrodes reach the melt surface. At this time, the scrap protects the furnace's roof and walls from electric arcs (Hay et al. 2021 ). Depending on the qualities of the primary material (scrap, etc.), additional materials are utilized in addition to the primary raw materials that constitute the signicant charging portion of the EAF to modify the final attributes of steel. Figure 2b depicts an EAF schematic. Figure 2. a) Midrex DR b) EAF schematic. 2.2 Life cycle assessment (LCA): LCA allows for the evaluation of resource consumption and pollutant emissions across all life stages of a product, service, or process. Many performance measures and indicators, including those related to ozone depletion, global warming potential, toxicity, land use, ecosystem health, etc., have been proposed during the LCA process (Diwekar and Shastri 2011). In LCA, three primary scales are commonly used: "cradle to grave", "cradle to gate", and "cradle to cradle" (Pedersen and Remmen 2022). The term "cradle-to-grave" refers to the process by which products are made from raw materials, processed, manufactured, used, discarded, and eventually abandoned (Parajuli, Matlock, and Thoma 2021). "Cradle to gate" refers to measuring the effects of product manufacturing, considering the place of raw material origin, raw material delivery, and unit process at the plant site (Liang et al. 2020 ). From the point of manufacture to the end of ultimate deconstruction, recycling, and reuse, "cradle to cradle" design is applied (Kausch and Klosterhaus 2016). In this case, we used a “cradle-to-gate” scope because we looked for the environmental impacts and costs of the mentioned steel production method. Under ISO 14040 (ISO 14040 2006), the LCA framework is displayed in Fig. 3. The LCA can be broken down into four primary stages, as per ISO 14040: goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and interpretation (ISO 14040 2006). Figure 3. LCA Framework 2.2.1 Goal and scope definition The base and essential stage of the LCA is goal and scope definition because, in this stage, the aim of the study, system boundary, hypotheses, and functional unit (FU) must be defined. A FU provides a quantitative reference for all inputs and outputs associated with a product, process, or system (ISO 14040 2006)The objective of this study is to evaluate the LCA of Midrex direct reduction (DR) and EAF steel production from cradle to gate. The FU in this study corresponds to one tonne of molten steel extracted from the EAF. The Sefiddasht Steel Factory produces 800 thousand tonnes of steel annually, serving as the study's basis. This plant produces its sponge iron through the Midrex DR process, which it uses to make steel in the EAF. The majority of the factory's EAF inputs are sponge iron. In this factory, the proportion of sponge iron to scrap is 90:10. In this study, various alternative scenarios were explored to identify an efficient steel production path. These included varying the ratio of sponge iron to scrap and the ratio of using conventional electricity (produced by a gas-based plant) to using photovoltaic electricity (concerning a nearby photovoltaic electricity production factory). Figure 4 illustrates inputs and outputs as well as the system boundaries. Raw material transportation, like iron pellets, is considered, but the manufacturing and transport of factory infrastructure are not. After being created somewhere else, the iron pellets were transported 200 kilometers by trucks from the origin factory to the steel making factory. Therefore, the information found in Simapro's database was applied to iron pellets. Figure 4. system boundary. 2.2.2 Life cycle inventory (LCI) LCI is an important part of a LCA. Getting precise and reliable data is one of the most crucial parts of LCA. Inaccurate data leads to many issues, including incorrect policy and decision-making. The data was sourced from the Sefiddasht steel factory, situated in Charmahal and Bakhtiary province in Iran. This factory comprises two main processes: Midrex DR and EAF steel production. The reference year for the data was 2022. Each process has its inputs, outputs, and emissions; nevertheless, concerning the FU (1 tonne of molten steel), all the inventory is listed in Table 1 . Table 1 Data of LCI. Midrex DR EAF steel making Input Iron pellets (kg/FU) 1491.7500 - Transportation of Iron pellets (tkm/FU) 198.9000 - Natural gas (m 3 /FU) 298.3500 4.8432 Water (m3/FU) 1.2431 4.5000 Electricity (kWh/FU) 136.4454 600 Lime (kg/FU) 0.4973 - Oxygen (kg/FU) 18.6966 56 Iron scrap (kg/FU) - 110.5000 Sponge iron (kg/FU) - 994.5000 Refractory (kg/FU) - 3 Dolomite (kg/FU) - 10.3600 Electrode (kg/FU) - 3 Ferrochromium (kg/FU) - 0.1131 Ferromanganese (kg/FU) - 0.1455 Ferrosilicon (kg/FU) - 1.1390 Coke (kg/FU) - 21 Aluminum (kg/FU) - 0.1627 Quicklime(kg/FU) - 85 Output Sponge iron (kg) 994.5000 - Molten iron (kg) - 1000 Emissions Carbon dioxide (kg/FU) 290.2946 223.1993 Carbon monoxide (kg/FU) 1.1934 9.1966 Nitrogen oxides (kg/FU) 0.0457 0.9238 Particle matter10 (kg/FU) 0.0176 - Sulfur dioxide (kg/FU) 0.0617 0.4237 Waste Waste water (m 3 /FU) 0.3729 0.45 Solid waste (kg/FU) 55.9406 - Dust (kg/FU) - 0.6117 Refractory waste (kg/FU) - 1.562 Sludge - 22.5 2.2.3 Life cycle impact assessment (LCIA) Characterization, Damage Assessment, Normalization, and Single Score are the phases in the impact assessment process. LCIA converts the emissions that affect the environment into different kinds of midpoints (impact categories) and endpoints. Midpoints (impact categories) are problematic fields like land use, global warming, etc. An endpoint is anything valued, like the environment, resources, or human health (Soares, Toffoletto, and Deschênes 2006 ). This step is done by various methods, such as ReCiPe (M. Huijbregts et al. 2016 ), CML (de et al. 2002), IMPACT 2002[3] (Pennington et al. 2005 ), etc. Several methods calculate one single issue, like IPCC[4] (Houghton et al. 2001 ), CED[5] (Frischknecht et al. 2007 ), etc. This study employed the widely used ReCiPe (H) 2016 V1.1 method for LCIA. There are three endpoint categories and seventeen midpoint categories in ReCiPe 2016. Instead of being reflective of the european scale, the characterization elements offered by ReCiPe 2016 are represent of the worldwide scale (Soares, Toffoletto, and Deschênes 2006 ). In addition to the characterized and normalized results, the single scores were also reported. Normalization is an optional component of LCIA, per ISO 14044 (ISO 14040 2006). Normalization in LCA refers to assessing the significance of category indicator results by comparing them to reference information (Hélias and Servien 2021). The normalized values are unitless. For the purpose of comprehending and displaying normalized numbers in charts, the point (pt) unit is used. The entire environmental impact of a process, product, or service can be expressed as a single score. For calculating of the environmental cost of this steel production process, another LCIA method named Environmental Prices was used (Environmental Prices Handbook 2023 ). The social cost of pollution is factored into environmental pricing, which is stated in euros 2021 per kilogram of pollutants. Therefore, environmental prices represent the reduction in economic welfare for every kilogram of the pollutant that enters the ecosystem. This method was based on the ReCiPE(H) 2016 method. 2.2.4 interpretation According to ISO, the final stage of LCA is result interpretation (ISO 14040 2006). During the interpretation phase, which aligns with each step of the LCA, the objective is to assess the data and derive conclusions based on the results obtained from the preceding three phases. 2.3 examined scenarios Six proposed scenarios were utilized to measure environmental impacts and cost reductions (based on the various ratios of sponge iron to scrap and conventional gas power plant electricity to photovoltaic electricity). The basic scenario evaluated the steel plant's on-site production process. According to Table 1 , in scenario I, molten steel comprises 90% sponge iron and 10% scrap. The industrial site sourced all its electricity from a conventional natural gas power plant. ІІ–VI scenarios were evaluated to determine the impact of different inputs on the outcome. The percentage of each input for each scenario is shown in Table 2 . Table 2 Attributes of each scenario. Scenarios number conventional gas power plant electricity (%) photovoltaic electricity (%) sponge iron (%) Scrap (%) І (base scenario) 100 0 90 10 ІІ 100 0 50 50 ІІІ 100 0 0 100 IV 50 50 90 10 V 0 100 90 10 VI 0 100 0 100 2.4 Perspective comparison in environmental impacts: The ReCiPe technique is a LCIA approach that employs three distinct viewpoints to represent various value choices and hypotheses: hierarchist, individualist, and egalitarian (M. A. J. Huijbregts et al. 2017 ). The primary distinction between individualist, egalitarian, and hierarchist approaches is the degree of uncertainty and caution they apply to environmental issues. The individualist viewpoint assumes that most effects can be prevented or managed through technological and human creativity, the egalitarian viewpoint applies the precautionary principle and considers the most extensive time frame and impact kinds that have yet to be completely identified. The hierarchist view is founded on consensus among scientists and accepted policy frameworks (M. A. J. Huijbregts et al. 2017 ). Individualist perspectives tend to produce lower indicator ratings than egalitarian and hierarchist perspectives because they ignore potential long-term and contested consequences. The egalitarian perspective, which contains the most conservative assumptions and the most tremendous variety of impacts, tends to produce higher indicator ratings than the individualist and hierarchist perspectives (M. A. J. Huijbregts et al. 2017 ). The hierarchist perspective is frequently regarded as the default model since it reflects the most prevalent policy concepts and the best accessible science. So, a perspective comparison was applied to the I and VI scenarios to depict the influence of vision on outputs. 3 Results and discussion 3.1 Base scenario: Table 3 outlines the environmental costs and impacts related to the production of one metric tonnes of molten steel, using data from the base scenario. The environmental costs are quantified at the midpoint level, as per the Environmental Prices approach, amounting to 462.72 euros per FU. The environmental impacts are delineated at both the midpoint and endpoint levels. Employing the ReCiPe method, eighteen categories at the midpoint level and three categories at the endpoint level were documented, featuring both characterized and normalized values. The rate of global warming is quantified at 1593.5 kg CO 2 eq / FU, signifying the rise in Earth's temperature due to the greenhouse effect, which is a consequence of greenhouse gas emissions (Joos et al. 2013 ). The potential for ozone depletion, measured in CFC11 equivalents, stands at 0.00048 / FU. Over an endless time horizon, it describes a time-integrated drop in the concentration of stratospheric ozone (NOAA CSL 2010). Ionizing radiation, expressed as 3 kBq Co-60 eq / FU, represents the cumulative dose from radionuclide emissions (M. A. J. Huijbregts et al. 2020 ). The formation of fine particulate matter is recorded at 1.82 kg PM 2.5 eq / FU. This figure denotes the increase in PM 2.5 concentration due to emissions of precursors like NH 3 , NOx, SO 2 , and primary PM 2.5 (van Zelm et al. 2016 ). Ozone formation, which impacts human health and ecosystems, is measured at 3.3 kg NO x eq / FU and 3.4 kg NO x eq / FU, respectively. These values reflect the concentration changes in ozone due to emissions of NO x or non-methane volatile organic compounds (NMVOC) (van Zelm et al. 2016 ). Terrestrial acidification, a significant shift from the ideal acidity for most plant species, is gauged at 3.6 kg SO 2 eq / FU (M. Huijbregts et al. 2016 ). Eutrophication, resulting from nutrient discharge into soils or water bodies, is quantified at 0.02036 kg P eq / FU for freshwater and 0.00644 kg N eq / FU for marine environments (M. Huijbregts et al. 2016 ). The impact of chemical emissions on freshwater, marine, and terrestrial ecotoxicity is characterized using 1,4-dichlorobenzene-equivalents, with toxicity values of 1050.4, 1.4, and 2.4 per FU, respectively (M. A. J. Huijbregts et al. 2020 ). Human toxicity potential, indicating the harm from environmental chemical releases, is noted at 5 kg 1,4-DCB / FU for carcinogens and 59.3 kg 1,4-DCB / FU for non-carcinogens (Joos et al. 2013 ). Land use, in terms of annual crop equivalents, is 2.8 m²a crop eq / FU. This metric assesses species loss due to various land use types (M. A. J. Huijbregts et al. 2020 ). Mineral resource scarcity is 75.3 kg Cu eq / FU, reflecting the future ore production increase due to current mineral extraction (Vieira et al. 2017 ). Fossil resource scarcity, calculated as 606.5 kg oil eq / FU, compares the energy value of fossil resources to crude oil (Frischknecht et al. 2007 ). Water consumption is using water by assimilation into other processes, such as evaporation, product creation, transportation to other watersheds, or marine disposal. The water consumption per cubic meter of recycled water serves as the unit for the water consumption midpoint (Joos et al. 2013 ). The amount of water used is 9.7 m 3 / FU. At the endpoint level, the effects on human health, ecosystems, and resources are 0.00268 Disability Adjusted Life Years (DALY), 5.84E-06 species.yr, and 227.9 USD2013, respectively. Human health endpoint is measured in DALY, a measure of the years lost to illness or premature death (M. Huijbregts et al. 2016 ). The endpoint of ecosystems is the estimated number of species that, as a result of the activities that have been evaluated, should vanish in a given region during a given time (M. Huijbregts et al. 2016 ). The term resources endpoint describes how present resource use is driving up extraction costs and reducing the availability of resources in the future (M. Huijbregts et al. 2016 ). Previous studies have corroborated these findings for the baseline scenario (Ramezani Moziraji et al. 2023 , Chen et al. 2018 , Vieira et al. 2017 ). The aggregate normalized midpoints and endpoints' environmental effect value for producing one tonne of molten steel are quantified at 8.1 pt and 0.12902 pt, respectively. Table 3 environmental costs and impacts of base scenario. Impact category Environmental impact Environmental costs (2021 euros /FU) Midpoint Chraracterized (unit / FU) Normalized (pt / FU) Global warming 1593.521 (kg CO 2 eq) 0.19951 207.15771 Stratospheric ozone depletion 0.00048 (kg CFC11 eq) 0.00806 0.01404 Ionizing radiation 2.98549 (kBq Co-60 eq) 0.00621 0.01260 Ozone formation, Human health 3.30223 (kg NO x eq) 0.16049 7.16584 Fine particulate matter formation 1.81916 (kg PM2.5 eq) 0.07113 180.46066 Ozone formation, Terrestrial ecosystems 3.38131 (kg NO x eq) 0.19037 1.40663 Terrestrial acidification 3.6293 (kg SO 2 eq) 0.08855 19.12639 Freshwater eutrophication 0.02036 (kg P eq) 0.03136 0.07615 Marine eutrophication 0.00644 (kg N eq) 0.00140 0.09181 Terrestrial ecotoxicity 1050.442 (kg 1,4-DCB) 1.01368 0.67228 Freshwater ecotoxicity 1.38159 (kg 1,4-DCB) 1.12600 0.02888 Marine ecotoxicity 2.42557 (kg 1,4-DCB) 2.35038 0.00776 Human carcinogenic toxicity 5.02259 (kg 1,4-DCB) 1.81315 20.04012 Human non-carcinogenic toxicity 59.30704 (kg 1,4-DCB) 0.39795 4.21080 Land use 2.84795 (m 2 a crop eq) 0.00046 0.28195 Mineral resource scarcity 75.25761 (kg Cu eq) 0.00063 1.05361 Fossil resource scarcity 606.4838 (kg oil eq) 0.61861 16.98155 Water consumption 9.65094 (m 3 ) 0.03619 3.92793 Total - 8.11412 462.71669 Endpoint Human health 0.00268 (DALY) 0.11273 - Ecosystem 5.84E-06 (species.yr) 0.00815 - resources 227.88015 (USD2013) 0.00814 - Total - 0.12902 - Figure 5a illustrates the relative importance of each midpoint category, with marine ecotoxicity, human carcinogenic toxicity, terrestrial ecotoxicity, and freshwater ecotoxicity collectively contributing to 78% of the total environmental impact. Figure 5b accentuates the significance at the endpoint level, revealing that the effects on human health possess the highest value at 88%. Subsequently, ecosystems and resources are impacted equally, each bearing 6% of the total environmental effects. Figure 5. Contribution of each a) midpoint and b) endpoint to environmental impacts. In the analysis of steel production, three principal stages were identified: sponge iron (DRI) consumption, electricity consumption, and the assortment of other inputs and outputs associated with steel production. Figure 6a and b delineate the proportional contribution of electricity usage, sponge iron consumption, and other inputs and outputs to liquid steel production within each midpoint and endpoint category, respectively. It was observed that most midpoint categories experienced significant adverse effects attributable to the consumption of sponge iron. Consequently, sponge iron consumption exerts an influence exceeding 50% at the endpoint level across all categories. Figure 6. Contribution of sponge iron, electricity, and other inputs and outputs to a) midpoints and b) endpoints. Figure 7a illustrates the distribution of environmental costs attributed to the consumption of electrical power, sponge iron, and other inputs and outputs in liquid steel production. Sponge iron is a significant contributor, accounting for 57% of the environmental costs. Subsequently, electricity consumption holds secondary importance, with the balance of the proportion not ascribed to any particular consumption or process encompassing all other inputs and outputs. Figure 7b details the percentage contribution of each midpoint category to the environmental costs. Notably, global warming and fine particulate matter formation represent 45% and 39% of the environmental costs, respectively. Figure 7. Contribution of a) sponge iron, electricity, and other inputs and outputs, and b) midpoints in environmental costs. 3.2 Comparing different scenarios Based on the preceding findings presented in Section 3.1 , the LCA of one tonne of molten steel production in the base scenario was calculated. The study revealed that electricity consumption and the use of sponge iron had the most significant impact on environmental factors and costs. To mitigate these issues, two potential solutions were suggested: replacing conventional gas power plants with solar power plants and increasing the utilization of scrap instead of sponge iron. The consequences of these two options were tested in five different scenarios. The last scenario (VI) is mixed since it has the most remarkable shift in the scrap to sponge iron ratio and the source of electricity production. Table 4S displays the characterized and normalized detailed results of a LCA of one tonne of molten steel manufacturing under five scenarios. Across most midpoint impact categories, adjusting the scrap-to-sponge-iron ratio proves more effective in reducing environmental impact and costs compared to altering the electricity production source. However, in specific cases (such as ionizing radiation, terrestrial ecotoxicity, and human carcinogenic toxicity), the opposite holds true. Concerning total normalized midpoints, scenario II has the most signicant environmental impact with 8.3 pt, followed by scenarios IV, V, III, and VI with 7.7 pt, 7.2 pt, 7.1 pt, and 5 pt, respectively. Scenario VI is the most favorable across all midpoint categories, according to the characterized and normalized values. Considering the relationship between midpoint and endpoint impact categories, opting for increased scrap usage over sponge iron proves more effective in reducing impacts at the endpoint level. The ensuing figures represent the hierarchy of environmental effects predicated on normalized data at the endpoint level: Scenario IV stands at 0.11851 pt, Scenario II at 0.11059 pt, Scenario V at 0.10801 pt, Scenario III at 0.07301 pt, and Scenario VI emerges as the most favorable with 0.04037 pt. The environmental prices were successfully reduced for each scenario. In scenario VI, the environmental expenses decreased to 167.7 euros, a substantial reduction. Scenario VI demonstrates superior carbon management, resulting in a reduction of 496.67 kg CO 2 eq / FU in global warming potential. Additionally, it contributes to mitigating harm to the ozone layer, with a reduction of 0.00009 kg CFC11 eq / FU in stratospheric ozone depletion. Air quality improves due to reduced impact, as indicated by 1.37466 kg NO x eq / FU in terrestrial ecosystems and 1.39078 kg NO x eq / FU in human health ozone formation.The formation of fine particulate matter is lower, with 0.59305 kg PM 2.5 equivalent, suggesting decreased emissions of fine particle-increasing pollutants. Soil chemistry, plant life, and biodiversity face less harm due to the 1.41558 kg SO 2 / FU equivalent in terrestrial acidification. Freshwater and marine eutrophication are better managed, as evidenced by 0.01849 kg P eq / FU and 0.00387 kg N eq / FU, indicating more controlled nutrient runoff or emissions. Scenario VI also shows improved management of toxic emissions or effluents, with 637.5 kg 1,4-DCB / FU in ecotoxicity across terrestrial, freshwater, and marine environments. Human carcinogenic and non-carcinogenic toxicity are addressed with lower emissions of harmful chemicals, at 6.52831 kg / FU and 28.7384 kg 1,4-DCB / FU, respectively. Resource use efficiency is highlighted by 1.51507 kg Cu eq / FU in mineral resource use and 64.13712 kg oil eq / FU in fossil resource use, pointing to more effective use or recycling. Ionizing radiation is minimized, with 1.34218 kBq Co-60 eq / FU, suggesting less use of radiative materials or better containment. Lastly, water consumption is optimized, with only 6.48111 m 3 / FU used, reflecting a commitment to conserving this vital resource. Figure 8a shows normalized data related to all scenarios in comparison with each other and the importance of each impact category at the midpoint level. According to Fig. 8a, environmental and human toxicities have the highest impact among all categories. Figure 8b presents the characterized data for all scenarios, comparing them individually at each midpoint level. In Fig. 8b, the highest scenario for each midpoint level is 100, and other scenarios are presented and compared to it. The base scenario exhibits the highest harmful impact across all categories, while the VI scenario emerges as the most favorable option in most midpoint assessments. Figure 8. a) Normalized value comparison of all scenarios at midpoints level, b) comparison of all scenario to the highest scenario in each midpoint level. Figure 9 shows the environmental prices at every midpoint level for all scenarios. According to Fig. 9, the base scenario is not the worst scenario at all midpoint levels, but scenario VI is the best scenario at all midpoints. Among all scenarios, the midpoints of global warming potential and particulate matter formation account for most environmental costs. Figure 9. Environmental price comparison of all scenarios at midpoints level. Figure 10a shows normalized data related to all scenarios in comparison with each other and the importance of each impact category at the endpoint level. Figure 10b presents the characterized data for all scenarios, comparing them individually at each endpoint level. Within each endpoint level, the paramount scenario illustrated in Fig. 10b is assigned a value of 100; the corollary scenarios are depicted in proportion to this benchmark. To Fig. 10a and b, it is discernible that the environmental impacts of the base scenario surpass those of the alternative scenarios, with Scenario VI being the most exemplary. The advancements in human health, ecosystems, and resources from the basic scenario to Scenario VI are quantified at 67.8%, 65.9%, and 91.6%, respectively. Figure 10. a) normalized value comparison of all scenarios at endpoints level, b) comparison of all scenario to the highest scenario in each endpoint level. 3.3 Perspective comparison: In Section 2.4 , LCA using the ReCiPe method was categorized into three perspectives: hierarchist, individualist, and egalitarian. The assumptions behind each of these perspectives impact how the calculations proceed. In this section, a comparison of viewpoints was applied to scenario I and scenario VI. The characterized and normalized data for these circumstances from multiple perspectives are displayed in Table 5S. The results show no significant differences in key categories, including land use, water consumption, freshwater eutrophication, marine eutrophication, terrestrial ecosystems ozone formation, human health ozone formation, fossil resource scarcity, and terrestrial acidification. However, other impact categories exhibit distinct differences. For global warming, the individualist perspective indicates a more harmful impact compared to the egalitarian and hierarchist perspectives. Because many of the pollutants that contributed to global warming were eliminated over a long time horizon, but from an individual standpoint, a short time horizon (20 years) was adopted (M. Huijbregts et al. 2016 ). Regarding mineral resource scarcity, the individualist perspective holds less value than the hierarchist and egalitarian perspectives. In hierarchical and egalitarian views, it is assumed that the estimation should be done based on extracting the total mineral resources available in the Earth’s crust. However, in the individualistic view, the analysis is based on the amount of extraction at the time of calculation and with the available technologies (M. Huijbregts et al. 2016 ). The formation of fine particulate matter is similar to the scarcity of mineral resources. The individualist perspective reported less value than the hierarchist and egalitarian perspectives. While the hierarchist and egalitarian viewpoints acknowledge that secondary aerosols contribute to particle formation, the individualist perspective contends that only primary aerosols impact fine particle formation (M. Huijbregts et al. 2016 ), (Avinal and Ergenekon 2022). In the remaining midpoint impact categories, such as stratospheric ozone depletion, ionizing radiation, freshwater environmental toxicity, marine environmental toxicity, terrestrial environmental toxicity, human carcinogenic toxicity, and human and non-carcinogenic toxicity, the egalitarian perspective calculates more influence than the hierarchical and individualist perspectives. The egalitarian perspective considers a longer time horizon than the hierarchical perspective, and the hierarchical perspective, in turn, has a longer time horizon than the individualistic perspective. In addition, the egalitarian view considers more diseases caused by pollutants compared to the hierarchical and individualistic view, and the egalitarian perspective considers more protection in different cases (M. Huijbregts et al. 2016 ). The hierarchist viewpoint assumes that society prefers effects that are long-lasting, global, and certain. It is predicated on the belief that society will manage environmental problems with caution and is based on an expert consensus (Dong and Ng 2014). The individualist viewpoint assumes that, society favors immediate, focused, and local effects. It is predicated on an market-driven perspective and presumes that society will handle environmental effects following short-term requirements and advantages (Dong and Ng 2014). The egalitarian view assumes that society prefers effects that are broad, ambiguous, and long-term. Its foundation lies in a perspective that prioritizes fairness and equality, assuming that even in the face of uncertainty, society will manage environmental repercussions with caution (Dong and Ng 2014). The egalitarian approach reported the highest value among all endpoint groups, with the hierarchical and individualistic approaches following in second and third place, respectively. Conclusion Beyond its role as a significant driver of economic growth, the steel industry exerts a substantial impact on pollution, energy consumption, CO 2 emissions, and water usage. In response to the environmental consequences associated with traditional steel making methods (such as BF-BOF process), there has been a shift toward more sustainable approaches, including DRI-EAF. Iran is among the world's leaders in the steel production using this technique. Due to Iran's plentiful gas supplies, gas-based DRI production is the primary method for producing this popular feedstock for steel making in EAF. The global production of DRI has been increasing steadily, according to the MIDREX method. A valuable technique for evaluating the environmental effects of steel production processes is LCA. Additionally, the steelmaker might become more ecologically conscious and sustainable due to its findings. To promote sustainable development, governments and individuals can make informed decisions by understanding the costs associated with their environmental impact. The study conducts a LCA using SIMAPRO software to investigate the environmental costs and effects of producing steel in an electric arc furnace and direct reduction of iron (MIDREX). For LCA, the ReCiPe and Environmental Prices methods were applied. The environmental costs of producing steel were assessed using the Environmental Prices Method. ReCiPe evaluated the effects on the environment at three endpoints and eighteen middle levels. The relevance of sustainable practices and environmental responsibility in the manufacturing sector is emphasized as suggestions are given to lower environmental costs and impacts in the steel production process. According to the base scenario LCA, the leadingcauses of the expenses and environmental impacts were energy use and sponge iron consumption. Utilizing sponge iron and using electricity at the base scenario's endpoints contributed almost 70%, 50%, and 57% to resources, ecosystems, and human health, respectively. Using electricity and sponge iron contributed more than 50% and 10%, respectively, to the majority of the base scenario's midpoints. The procedure had an environmental cost of 462.71669 euros. The steel industry must implement more environmentally friendly procedures to reduce pollution and advance long-term sustainability. Making particular rules and choices about the use of electricity (using solar power instead of traditional gas-based electricity) and sponge iron (using scrap instead of sponge iron) is one effective way to manage the steel making process for sustainability. Six scenarios were compared, and the results indicated a considerable decrease in both environmental impacts and costs. The optimal situation showed a reduction of over 70% in all endpoints and over 60% in the majority of midpoints. The prices for the environment were lowered to 167.69458 euros. The LCA results were directly influenced by the chosen perspective within the ReCiPe method. Each perspective has distinct conversion factors for translating inputs and outputs into midpoints and endpoints. At each midpoint, the highest value was provided by one of these perspectives. At the endpoint level, the egalitarian perspective, however, continuously registered the highest value. After that, the individualist and hierarchist viewpoints came in second and third, respectively. Declarations Author Contribution 1. Aref Ahmadian BaghbadaraniExpertise in life cycle assessment (LCA) modeling.Conceptualization, methodology, data analysis, and writing original draft.Design all the tables and figures.2. Dr. Khosro AshrafiContributed to data collection and interpretation.Assisted with literature review and article revision as the supervisor.3. 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Yilmaz, Ozge, Annick Anctil, and Tanju Karanfil. 2015. “LCA as a Decision Support Tool for Evaluation of Best Available Techniques (BATs) for Cleaner Production of Iron Casting.” Journal of Cleaner Production 105: 337–47. van Zelm, Rosalie et al. 2016. “Regionalized Life Cycle Impact Assessment of Air Pollution on the Global Scale: Damage to Human Health and Vegetation.” Atmospheric Environment 134: 129–37. Zhu, Bangzhu et al. 2020. “Exploring the Effect of Carbon Trading Mechanism on China’s Green Development Efficiency: A Novel Integrated Approach.” Energy Economics 85: 104601. Footnotes CML-IA is a LCA methodology developed by the Center of Environmental Science (CML) of Leiden University in The Netherlands. The ReCiPe method is a LCIA method that it was created by RIVM, Radboud University, Norwegian University of Science and Technology and PRé Consultants. The IMPACT 2002 methodology for LCIA introduces a practical approach that combines midpoint and damage assessment. It connects various LCI results (including elementary flows and other interventions) through 14 midpoint categories, ultimately leading to four damage categories. IPCC was developed by the Intergovernmental Panel on Climate Change. It contains the climate change factors of IPCC. The Cumulative Energy Demand (LHV) method was created by PRé Consultants based on data published by ecoinvents for raw materials available in the SimaPro database. Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 12 Apr, 2025 Read the published version in Materials Circular Economy → Version 1 posted Editorial decision: Revision requested 11 Dec, 2024 Reviews received at journal 05 Nov, 2024 Reviews received at journal 31 Oct, 2024 Reviewers agreed at journal 20 Oct, 2024 Reviewers agreed at journal 20 Oct, 2024 Reviewers invited by journal 20 Aug, 2024 Editor assigned by journal 20 Aug, 2024 Submission checks completed at journal 20 Aug, 2024 First submitted to journal 17 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4930754","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345057733,"identity":"127a2c87-2367-420f-9c11-5ad98cc0adac","order_by":0,"name":"Aref Ahmadian 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5","display":"","copyAsset":false,"role":"figure","size":464990,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of each a) midpoint and b) endpoint to environmental impacts.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/6839f9a4a90062f9837aac3e.png"},{"id":64667558,"identity":"f2dcddb6-3603-4a53-af18-ecb9fc96bbd6","added_by":"auto","created_at":"2024-09-17 09:32:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":378634,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of sponge iron, electricity, and other inputs and outputs to a) midpoints and b) endpoints.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/dfec019e470d2a06a2cf3842.png"},{"id":64667034,"identity":"0bbafba0-93fb-496f-bd95-8369843e32c2","added_by":"auto","created_at":"2024-09-17 09:24:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":321437,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of a) sponge iron, electricity, and other inputs and outputs, and b) midpoints in environmental costs.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/4dcaf42eb4fdecd091d428a9.png"},{"id":64667036,"identity":"98453217-f48c-4cc7-bf71-b5bfb40d6290","added_by":"auto","created_at":"2024-09-17 09:24:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":416813,"visible":true,"origin":"","legend":"\u003cp\u003ea) Normalized value comparison of all scenarios at midpoints level, b) comparison of all scenario to the highest scenario in each midpoint level.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/9c77f539d72d082f74adea82.png"},{"id":64667040,"identity":"fd124fe9-8d70-4731-b3ae-b289f05ebbb7","added_by":"auto","created_at":"2024-09-17 09:24:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":204124,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental price comparison of all scenarios at midpoints level.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/9806ea64d9d91a16e0b47c9b.png"},{"id":64667561,"identity":"0b8b0170-27d1-4441-9313-9bfaab9132df","added_by":"auto","created_at":"2024-09-17 09:32:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":124989,"visible":true,"origin":"","legend":"\u003cp\u003ea) normalized value comparison of all scenarios at endpoints level, b) comparison of all scenario to the highest scenario in each endpoint level.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/d820beef1bc520d1634f086a.png"},{"id":80558609,"identity":"fd62c969-86e3-4c2e-b1e2-90ee2e43d017","added_by":"auto","created_at":"2025-04-14 16:14:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4644524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/4eee8a44-96a7-49eb-b348-9084cb7e7f0a.pdf"},{"id":64667042,"identity":"54a06473-7c3a-4023-8773-1c1678dbf74c","added_by":"auto","created_at":"2024-09-17 09:24:44","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":37112,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4930754/v1/aee3d0aa00b01729dee79bb0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainable Steel Production: A Comprehensive LCA Approach for Reducing Environmental Costs and Impacts","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eAssessing of environmental costs and effects of steel production\u003c/li\u003e\n \u003cli\u003ePresenting two ways to reduce costs and environmental effects and examining these two ways in five different scenarios\u003c/li\u003e\n \u003cli\u003eMeasuring and evaluating the effect of different perspectives of the ReCiPe method on the results\u003c/li\u003e\n\u003c/ul\u003e\n"},{"header":"1. Introduction","content":"\u003cp\u003eIn the manufacturing sector, factors like environmental responsibility and sustainable development are becoming crucial increasingly. Steel's extensive use in the building and automobile industries, as well as in home appliances, packaging, and other products, makes more steel production essential for economic growth (Liang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Over the past few decades, there has been an increase in global steel output, which can be attributed to both rapid urbanization and industrialization (Sun et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Over 1950.5\u0026nbsp;million tons of crude steel were produced globally in 2021; Iran produced 28.5\u0026nbsp;million tons and was the first in the Middle East (Steel Production by Country 2024). This amount of steel production is continuously rising globally due to the growing need for steel manufacturing.\u003c/p\u003e \u003cp\u003eUnquestionably, the steel manufacture adds significantly to energy use and CO\u003csub\u003e2\u003c/sub\u003e emissions, but it also uses a lot of water and causes pollution (Zhu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The manufacture of steel was responsible for over 7% of the world's carbon dioxide emissions in 2020 (Steel Production by Country 2024). Conventional steel making techniques, like the blast furnace process, have been linked to severe environmental effects. The method of making steel from scrap is a form of recycling. Consequently, it is especially crucial to lessen the harmful impacts of steel manufacturing on the environment.\u003c/p\u003e \u003cp\u003eThe percentage of steel produced using electric arc furnace (EAF) technology is predicted to rise from 26% in 2014 to 40% in 2050 (Ramezani Moziraji et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). EAFs in Iran create more than 70% of the country's steel from scrap and direct reduced iron. EAF technology has known as a more sustainable steel production process in recent years. The global warming potential of the biosyngas based direct reduced iron- electric arc furnace (DRI-EAF) system is 75% lower than that of the existing natural gas (NG)-based DRI-EAF course and 85% lower than that of the blast furnace-basic oxygen furnace (BF-BOF) route, according to the findings of Nurdiawati et al (Seidel \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Liang et al. found that an electric arc furnace had a lower environmental impact than a blast furnace-basic oxygen furnace based on a life cycle assessment (LCA) of steel production (Liang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most popular inputs for steel making in EAF is sponge iron (DRI). Depending on resource availability, direct reduction techniques are classified as either gas- or coal-based, but gas-based DRI production is the primary technique (Zhu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because of its plentiful gas resources, Iran uses the direct gas-based reduction technique to make sponge iron. In 2022, the world's production of direct reduced iron (DRI) reached 127.36\u0026nbsp;million tonnes (Mt), surpassing the previous record of 119.2 Mt established in 2021 by about 6.9%. Over the past half-decade, the global output of DRI has increased by over 55 Mt, or roughly 75% (Steel Production by Country 2024). Suer et al. claimed that using pre-reduced iron ores in a blast furnace and injecting hydrogen might already cut greenhouse gas (GHG) emissions by up to 200 kg CO\u003csub\u003e2\u003c/sub\u003e/t of hot metal (Su et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MIDREX method is the most popular kind of direct reduction technique. 3.8% more DRI (73.55 Mt) was generated in 2022 by MIDREX Plants than in 2021 (70.85 Mt). In 2022, MIDREX Technology continued to account for approximately 80% of worldwide DRI production by shaft furnaces (The World Leader in Direct Reduction Technology | Midrex Technologies, 2022).\u003c/p\u003e \u003cp\u003eThe LCA is a powerful tool for providing an accurate overview of environmental impacts. LCA is defined as a method for examining a product or service's effects on the environment. Therefore, LCA determines environmentally critical points in a process or product's life cycle that has the most signicant influence on the environment (Nicholas et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Yilmaz, Anctil, and Karanfil \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is simple to modify the LCA techniques to create public policy (Seidel \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have examined the environmental impact of the entire life cycle of steel production. According to prior results, in terms of potential eutrophication, cumulative energy consumption, abiotic depletion, human toxicity, and global warming, the BF-BOF route had a higher environmental impact than the EAF route (Liang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, X. Li et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In a study, the environmental effects of DRI (gas based)-EAF steel production in Iran were assessed using LCA. The results show that the processes associated with the EAF (35%), oxide pellet processes, and DRI (17.1% and 28.9%, respectively) have the highest environmental impacts (Shatokha \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLin et al.'s study on materials flow and energy in steel production highlights that the EAF sector consumes a significant amount of resources, resulting in negative impacts on the environment, specifically freshwater eutrophication and human toxicity (Lin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A shaft-EAF based on CO\u003csub\u003e2\u003c/sub\u003e-CH\u003csub\u003e4\u003c/sub\u003e dry reforming was assessed by Chen et al. It was discovered that the DRI-EAF process reduced CO\u003csub\u003e2\u003c/sub\u003e emissions by 40% compared to the BF-BOF process, with minimal change in energy consumption. However, because of China's higher natural gas costs, the DRI-EAF process was 34% more expensive than the BF-BOF process (Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, Ozdemir et al. used LCA to examine the environmental effects of a factory producing one tonne of steel rebar from start to finish. The CML-IA[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e method was used to conduct an impact assessment. The analysis findings indicated that the production of crude steel and steel rebar contributed, respectively, 670 and 720 kg CO\u003csub\u003e2\u003c/sub\u003e equivalent/tonne to the possibility of global warming (\u0026Ouml;zdemir et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnvironmental costs associated with the effects of human activity on the environment are an essential part of economic analysis. Comprehending and evaluating environmental costs has become crucial for sustainable development and conscientious decision-making as societies confront the obstacles of pollution, resource depletion, and climate change. Only experts in this field can effectively utilize and comprehend the environmental impact of any activity or service based on LCA results. To ensure that LCA results are comprehensible to policymakers and the general public, it is essential to calculate environmental prices.The environmental prices method is monetizing the harmful effects of human activities on the natural environment. These impacts include pollution, reduced resources, biodiversity loss, climate change, and health impacts. Doing so can help governments and individuals reduce their environmental footprint, improve their ecological performance, and reach to the Sustainable Development Goals [16, 17].\u003c/p\u003e \u003cp\u003eThe present investigation introduces a comprehensive (LCA) conducted through the utilization of SIMAPRO software, aimed at examining the environmental costs and impacts associated with steel production utilizing electric arc furnace (EAF) and MIDREX DRI technologies. Additionally, recommendations were proposed and evaluated across five distinct scenarios to mitigate environmental costs and impacts. The LCA study was carried out utilizing the ReCiPe[2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e method, focusing on determining the influence of different ReCiPe method perspectives on the study outcomes.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Production description\u003c/h2\u003e \u003cp\u003eTurning raw iron ores into finished, qualified steel products is known as iron and steel production. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates two steelmaking methods. The BF-BOF route exhibits a greater environmental impact compared to the EAF route. (Burchart-Korol \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The EAF route, which is the primary process flow of high-quality special steel smelting, has the advantages of a quick process, low energy consumption, flexible charge and product structure, and lower costs as compared to the conventional BOF route based on metallurgical coke and iron ore (Z. Li and Hanaoka 2020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Midrex sponge iron (DRI)\u003c/h2\u003e \u003cp\u003eUtilizing NG to produce sponge iron, which is subsequently used in the EAF, has the potential to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions by 33% compared to the baseline blast furnace and BF\u0026ndash;BOF technology (Liang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At lower temperatures (900\u0026ndash;1000\u0026deg;C), the DR method converts iron oxides in the ore to metallic iron in a solid state instead of the BF process. Coal, or gas, is the reducing agent. Natural gas produces hydrogen and carbon monoxide, which are mainly used for reduction (Johansson et al. 2014).\u003c/p\u003e \u003cp\u003eIn this paper, LCA was applied to the Midrex DR and EAF steel making method. The midrex method uses a tall vertical reactor to carry out the iron ore reduction operations. Figure\u0026nbsp;2a illustrates how high-temperature, reformed natural gas is introduced from the bottom of the furnace during the MIDREX process. The consumed gas is then removed from the furnace's top after the iron has been reduced. Sponge iron, the main result of this process, is released when the temperature falls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 EAF steel making:\u003c/h2\u003e \u003cp\u003eThe EAF steel making route is typically consists of four steps: charging, melting, heating, refining and tapping. Scrap and other iron-bearing materials (sponge iron, hot metal, and hot briquetted iron (HBI) are the primary components of the EAF. Before introducing sponge iron into the furnace, a portion of scrap is charged and melted using electrical arcs. To prevent electrode failure during the initial meltdown phase, the arcs operate with less power. High power levels can be employed after the electrodes reach the melt surface. At this time, the scrap protects the furnace's roof and walls from electric arcs (Hay et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Depending on the qualities of the primary material (scrap, etc.), additional materials are utilized in addition to the primary raw materials that constitute the signicant charging portion of the EAF to modify the final attributes of steel. Figure\u0026nbsp;2b depicts an EAF schematic.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;2. a) Midrex DR b) EAF schematic.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Life cycle assessment (LCA):\u003c/h2\u003e \u003cp\u003eLCA allows for the evaluation of resource consumption and pollutant emissions across all life stages of a product, service, or process. Many performance measures and indicators, including those related to ozone depletion, global warming potential, toxicity, land use, ecosystem health, etc., have been proposed during the LCA process (Diwekar and Shastri 2011). In LCA, three primary scales are commonly used: \"cradle to grave\", \"cradle to gate\", and \"cradle to cradle\" (Pedersen and Remmen 2022). The term \"cradle-to-grave\" refers to the process by which products are made from raw materials, processed, manufactured, used, discarded, and eventually abandoned (Parajuli, Matlock, and Thoma 2021). \"Cradle to gate\" refers to measuring the effects of product manufacturing, considering the place of raw material origin, raw material delivery, and unit process at the plant site (Liang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From the point of manufacture to the end of ultimate deconstruction, recycling, and reuse, \"cradle to cradle\" design is applied (Kausch and Klosterhaus 2016). In this case, we used a \u0026ldquo;cradle-to-gate\u0026rdquo; scope because we looked for the environmental impacts and costs of the mentioned steel production method. Under ISO 14040 (ISO 14040 2006), the LCA framework is displayed in Fig.\u0026nbsp;3. The LCA can be broken down into four primary stages, as per ISO 14040: goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and interpretation (ISO 14040 2006).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;3. LCA Framework\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Goal and scope definition\u003c/h2\u003e \u003cp\u003eThe base and essential stage of the LCA is goal and scope definition because, in this stage, the aim of the study, system boundary, hypotheses, and functional unit (FU) must be defined. A FU provides a quantitative reference for all inputs and outputs associated with a product, process, or system (ISO 14040 2006)The objective of this study is to evaluate the LCA of Midrex direct reduction (DR) and EAF steel production from cradle to gate. The FU in this study corresponds to one tonne of molten steel extracted from the EAF.\u003c/p\u003e \u003cp\u003eThe Sefiddasht Steel Factory produces 800 thousand tonnes of steel annually, serving as the study's basis. This plant produces its sponge iron through the Midrex DR process, which it uses to make steel in the EAF. The majority of the factory's EAF inputs are sponge iron. In this factory, the proportion of sponge iron to scrap is 90:10.\u003c/p\u003e \u003cp\u003eIn this study, various alternative scenarios were explored to identify an efficient steel production path. These included varying the ratio of sponge iron to scrap and the ratio of using conventional electricity (produced by a gas-based plant) to using photovoltaic electricity (concerning a nearby photovoltaic electricity production factory).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;4 illustrates inputs and outputs as well as the system boundaries. Raw material transportation, like iron pellets, is considered, but the manufacturing and transport of factory infrastructure are not. After being created somewhere else, the iron pellets were transported 200 kilometers by trucks from the origin factory to the steel making factory. Therefore, the information found in Simapro's database was applied to iron pellets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;4. system boundary.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Life cycle inventory (LCI)\u003c/h2\u003e \u003cp\u003eLCI is an important part of a LCA. Getting precise and reliable data is one of the most crucial parts of LCA. Inaccurate data leads to many issues, including incorrect policy and decision-making. The data was sourced from the Sefiddasht steel factory, situated in Charmahal and Bakhtiary province in Iran. This factory comprises two main processes: Midrex DR and EAF steel production.\u003c/p\u003e \u003cp\u003eThe reference year for the data was 2022. Each process has its inputs, outputs, and emissions; nevertheless, concerning the FU (1 tonne of molten steel), all the inventory is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData of LCI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMidrex DR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAF steel making\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInput\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron pellets (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1491.7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation of\u003c/p\u003e \u003cp\u003eIron pellets (tkm/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198.9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural gas (m\u003csup\u003e3\u003c/sup\u003e/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298.3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater (m3/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity (kWh/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.4454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLime (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron scrap (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.5000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSponge iron (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e994.5000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefractory (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDolomite (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrode (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerrochromium (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerromanganese (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerrosilicon (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoke (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAluminum (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuicklime(kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutput\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSponge iron (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e994.5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolten iron (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmissions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon dioxide (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290.2946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223.1993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon monoxide (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen oxides (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticle matter10 (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur dioxide (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaste\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste water (m\u003csup\u003e3\u003c/sup\u003e/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid waste (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.9406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDust (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefractory waste (kg/FU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSludge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Life cycle impact assessment (LCIA)\u003c/h2\u003e \u003cp\u003eCharacterization, Damage Assessment, Normalization, and Single Score are the phases in the impact assessment process. LCIA converts the emissions that affect the environment into different kinds of midpoints (impact categories) and endpoints. Midpoints (impact categories) are problematic fields like land use, global warming, etc. An endpoint is anything valued, like the environment, resources, or human health (Soares, Toffoletto, and Desch\u0026ecirc;nes \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This step is done by various methods, such as ReCiPe (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), CML (de et al. 2002), IMPACT 2002[3]\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e (Pennington et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), etc. Several methods calculate one single issue, like IPCC[4]\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e (Houghton et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), CED[5]\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e (Frischknecht et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), etc.\u003c/p\u003e \u003cp\u003eThis study employed the widely used ReCiPe (H) 2016 V1.1 method for LCIA. There are three endpoint categories and seventeen midpoint categories in ReCiPe 2016. Instead of being reflective of the european scale, the characterization elements offered by ReCiPe 2016 are represent of the worldwide scale (Soares, Toffoletto, and Desch\u0026ecirc;nes \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In addition to the characterized and normalized results, the single scores were also reported. Normalization is an optional component of LCIA, per ISO 14044 (ISO 14040 2006). Normalization in LCA refers to assessing the significance of category indicator results by comparing them to reference information (H\u0026eacute;lias and Servien 2021). The normalized values are unitless. For the purpose of comprehending and displaying normalized numbers in charts, the point (pt) unit is used. The entire environmental impact of a process, product, or service can be expressed as a single score.\u003c/p\u003e \u003cp\u003eFor calculating of the environmental cost of this steel production process, another LCIA method named Environmental Prices was used (Environmental Prices Handbook \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The social cost of pollution is factored into environmental pricing, which is stated in euros 2021 per kilogram of pollutants. Therefore, environmental prices represent the reduction in economic welfare for every kilogram of the pollutant that enters the ecosystem. This method was based on the ReCiPE(H) 2016 method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 interpretation\u003c/h2\u003e \u003cp\u003eAccording to ISO, the final stage of LCA is result interpretation (ISO 14040 2006). During the interpretation phase, which aligns with each step of the LCA, the objective is to assess the data and derive conclusions based on the results obtained from the preceding three phases.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 examined scenarios\u003c/h2\u003e \u003cp\u003eSix proposed scenarios were utilized to measure environmental impacts and cost reductions (based on the various ratios of sponge iron to scrap and conventional gas power plant electricity to photovoltaic electricity). The basic scenario evaluated the steel plant's on-site production process. According to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, in scenario I, molten steel comprises 90% sponge iron and 10% scrap. The industrial site sourced all its electricity from a conventional natural gas power plant. ІІ\u0026ndash;VI scenarios were evaluated to determine the impact of different inputs on the outcome. The percentage of each input for each scenario is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAttributes of each scenario.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenarios number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003econventional gas power plant electricity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ephotovoltaic electricity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003esponge iron (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScrap (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eІ (base scenario)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eІІ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eІІІ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Perspective comparison in environmental impacts:\u003c/h2\u003e \u003cp\u003eThe ReCiPe technique is a LCIA approach that employs three distinct viewpoints to represent various value choices and hypotheses: hierarchist, individualist, and egalitarian (M. A. J. Huijbregts et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary distinction between individualist, egalitarian, and hierarchist approaches is the degree of uncertainty and caution they apply to environmental issues. The individualist viewpoint assumes that most effects can be prevented or managed through technological and human creativity, the egalitarian viewpoint applies the precautionary principle and considers the most extensive time frame and impact kinds that have yet to be completely identified. The hierarchist view is founded on consensus among scientists and accepted policy frameworks (M. A. J. Huijbregts et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndividualist perspectives tend to produce lower indicator ratings than egalitarian and hierarchist perspectives because they ignore potential long-term and contested consequences. The egalitarian perspective, which contains the most conservative assumptions and the most tremendous variety of impacts, tends to produce higher indicator ratings than the individualist and hierarchist perspectives (M. A. J. Huijbregts et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The hierarchist perspective is frequently regarded as the default model since it reflects the most prevalent policy concepts and the best accessible science. So, a perspective comparison was applied to the I and VI scenarios to depict the influence of vision on outputs.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Base scenario:\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the environmental costs and impacts related to the production of one metric tonnes of molten steel, using data from the base scenario. The environmental costs are quantified at the midpoint level, as per the Environmental Prices approach, amounting to 462.72 euros per FU. The environmental impacts are delineated at both the midpoint and endpoint levels. Employing the ReCiPe method, eighteen categories at the midpoint level and three categories at the endpoint level were documented, featuring both characterized and normalized values.\u003c/p\u003e \u003cp\u003eThe rate of global warming is quantified at 1593.5 kg CO\u003csub\u003e2\u003c/sub\u003e eq / FU, signifying the rise in Earth's temperature due to the greenhouse effect, which is a consequence of greenhouse gas emissions (Joos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The potential for ozone depletion, measured in CFC11 equivalents, stands at 0.00048 / FU. Over an endless time horizon, it describes a time-integrated drop in the concentration of stratospheric ozone (NOAA CSL 2010). Ionizing radiation, expressed as 3 kBq Co-60 eq / FU, represents the cumulative dose from radionuclide emissions (M. A. J. Huijbregts et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The formation of fine particulate matter is recorded at 1.82 kg PM\u003csub\u003e2.5\u003c/sub\u003e eq / FU. This figure denotes the increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentration due to emissions of precursors like NH\u003csub\u003e3\u003c/sub\u003e, NOx, SO\u003csub\u003e2\u003c/sub\u003e, and primary PM\u003csub\u003e2.5\u003c/sub\u003e (van Zelm et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOzone formation, which impacts human health and ecosystems, is measured at 3.3 kg NO\u003csub\u003ex\u003c/sub\u003e eq / FU and 3.4 kg NO\u003csub\u003ex\u003c/sub\u003e eq / FU, respectively. These values reflect the concentration changes in ozone due to emissions of NO\u003csub\u003ex\u003c/sub\u003e or non-methane volatile organic compounds (NMVOC) (van Zelm et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Terrestrial acidification, a significant shift from the ideal acidity for most plant species, is gauged at 3.6 kg SO\u003csub\u003e2\u003c/sub\u003e eq / FU (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Eutrophication, resulting from nutrient discharge into soils or water bodies, is quantified at 0.02036 kg P eq / FU for freshwater and 0.00644 kg N eq / FU for marine environments (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe impact of chemical emissions on freshwater, marine, and terrestrial ecotoxicity is characterized using 1,4-dichlorobenzene-equivalents, with toxicity values of 1050.4, 1.4, and 2.4 per FU, respectively (M. A. J. Huijbregts et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Human toxicity potential, indicating the harm from environmental chemical releases, is noted at 5 kg 1,4-DCB / FU for carcinogens and 59.3 kg 1,4-DCB / FU for non-carcinogens (Joos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Land use, in terms of annual crop equivalents, is 2.8 m²a crop eq / FU. This metric assesses species loss due to various land use types (M. A. J. Huijbregts et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mineral resource scarcity is 75.3 kg Cu eq / FU, reflecting the future ore production increase due to current mineral extraction (Vieira et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFossil resource scarcity, calculated as 606.5 kg oil eq / FU, compares the energy value of fossil resources to crude oil (Frischknecht et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Water consumption is using water by assimilation into other processes, such as evaporation, product creation, transportation to other watersheds, or marine disposal. The water consumption per cubic meter of recycled water serves as the unit for the water consumption midpoint (Joos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The amount of water used is 9.7 m\u003csup\u003e3\u003c/sup\u003e / FU.\u003c/p\u003e \u003cp\u003eAt the endpoint level, the effects on human health, ecosystems, and resources are 0.00268 Disability Adjusted Life Years (DALY), 5.84E-06 species.yr, and 227.9 USD2013, respectively. Human health endpoint is measured in DALY, a measure of the years lost to illness or premature death (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The endpoint of ecosystems is the estimated number of species that, as a result of the activities that have been evaluated, should vanish in a given region during a given time (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The term resources endpoint describes how present resource use is driving up extraction costs and reducing the availability of resources in the future (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous studies have corroborated these findings for the baseline scenario (Ramezani Moziraji et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Vieira et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aggregate normalized midpoints and endpoints' environmental effect value for producing one tonne of molten steel are quantified at 8.1 pt and 0.12902 pt, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eenvironmental costs and impacts of base scenario.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact category\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEnvironmental impact\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironmental costs (2021 euros /FU)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidpoint\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChraracterized (unit / FU)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormalized (pt / FU)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal warming\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1593.521 (kg CO\u003csub\u003e2\u003c/sub\u003e eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19951\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207.15771\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStratospheric ozone depletion\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00048 (kg CFC11 eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00806\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01404\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIonizing radiation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98549 (kBq Co-60 eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00621\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01260\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOzone formation, Human health\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.30223 (kg NO\u003csub\u003ex\u003c/sub\u003e eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16049\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.16584\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFine particulate matter formation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81916 (kg PM2.5 eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07113\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180.46066\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOzone formation, Terrestrial ecosystems\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.38131 (kg NO\u003csub\u003ex\u003c/sub\u003e eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19037\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40663\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerrestrial acidification\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6293 (kg SO\u003csub\u003e2\u003c/sub\u003e eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08855\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.12639\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreshwater eutrophication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02036 (kg P eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03136\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07615\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarine eutrophication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00644 (kg N eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00140\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09181\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerrestrial ecotoxicity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1050.442 (kg 1,4-DCB)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01368\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67228\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreshwater ecotoxicity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38159 (kg 1,4-DCB)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12600\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02888\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarine ecotoxicity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.42557 (kg 1,4-DCB)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35038\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00776\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman carcinogenic toxicity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.02259 (kg 1,4-DCB)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81315\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.04012\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman non-carcinogenic toxicity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.30704 (kg 1,4-DCB)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39795\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21080\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84795 (m\u003csup\u003e2\u003c/sup\u003ea crop eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00046\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28195\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMineral resource scarcity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.25761 (kg Cu eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00063\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05361\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFossil resource scarcity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e606.4838 (kg oil eq)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61861\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.98155\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater consumption\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.65094 (m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03619\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.92793\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.11412\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e462.71669\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndpoint\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman health\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00268 (DALY)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11273\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcosystem\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.84E-06 (species.yr)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00815\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresources\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227.88015 (USD2013)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00814\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;5a illustrates the relative importance of each midpoint category, with marine ecotoxicity, human carcinogenic toxicity, terrestrial ecotoxicity, and freshwater ecotoxicity collectively contributing to 78% of the total environmental impact. Figure\u0026nbsp;5b accentuates the significance at the endpoint level, revealing that the effects on human health possess the highest value at 88%. Subsequently, ecosystems and resources are impacted equally, each bearing 6% of the total environmental effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;5. Contribution of each a) midpoint and b) endpoint to environmental impacts.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn the analysis of steel production, three principal stages were identified: sponge iron (DRI) consumption, electricity consumption, and the assortment of other inputs and outputs associated with steel production. Figure\u0026nbsp;6a and b delineate the proportional contribution of electricity usage, sponge iron consumption, and other inputs and outputs to liquid steel production within each midpoint and endpoint category, respectively. It was observed that most midpoint categories experienced significant adverse effects attributable to the consumption of sponge iron. Consequently, sponge iron consumption exerts an influence exceeding 50% at the endpoint level across all categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;6. Contribution of sponge iron, electricity, and other inputs and outputs to a) midpoints and b) endpoints.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;7a illustrates the distribution of environmental costs attributed to the consumption of electrical power, sponge iron, and other inputs and outputs in liquid steel production. Sponge iron is a significant contributor, accounting for 57% of the environmental costs. Subsequently, electricity consumption holds secondary importance, with the balance of the proportion not ascribed to any particular consumption or process encompassing all other inputs and outputs. Figure\u0026nbsp;7b details the percentage contribution of each midpoint category to the environmental costs. Notably, global warming and fine particulate matter formation represent 45% and 39% of the environmental costs, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;7. Contribution of a) sponge iron, electricity, and other inputs and outputs, and b) midpoints in environmental costs.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparing different scenarios\u003c/h2\u003e \u003cp\u003eBased on the preceding findings presented in Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e, the LCA of one tonne of molten steel production in the base scenario was calculated. The study revealed that electricity consumption and the use of sponge iron had the most significant impact on environmental factors and costs. To mitigate these issues, two potential solutions were suggested: replacing conventional gas power plants with solar power plants and increasing the utilization of scrap instead of sponge iron. The consequences of these two options were tested in five different scenarios. The last scenario (VI) is mixed since it has the most remarkable shift in the scrap to sponge iron ratio and the source of electricity production.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;4S displays the characterized and normalized detailed results of a LCA of one tonne of molten steel manufacturing under five scenarios. Across most midpoint impact categories, adjusting the scrap-to-sponge-iron ratio proves more effective in reducing environmental impact and costs compared to altering the electricity production source. However, in specific cases (such as ionizing radiation, terrestrial ecotoxicity, and human carcinogenic toxicity), the opposite holds true.\u003c/p\u003e \u003cp\u003eConcerning total normalized midpoints, scenario II has the most signicant environmental impact with 8.3 pt, followed by scenarios IV, V, III, and VI with 7.7 pt, 7.2 pt, 7.1 pt, and 5 pt, respectively. Scenario VI is the most favorable across all midpoint categories, according to the characterized and normalized values. Considering the relationship between midpoint and endpoint impact categories, opting for increased scrap usage over sponge iron proves more effective in reducing impacts at the endpoint level. The ensuing figures represent the hierarchy of environmental effects predicated on normalized data at the endpoint level: Scenario IV stands at 0.11851 pt, Scenario II at 0.11059 pt, Scenario V at 0.10801 pt, Scenario III at 0.07301 pt, and Scenario VI emerges as the most favorable with 0.04037 pt. The environmental prices were successfully reduced for each scenario. In scenario VI, the environmental expenses decreased to 167.7 euros, a substantial reduction.\u003c/p\u003e \u003cp\u003eScenario VI demonstrates superior carbon management, resulting in a reduction of 496.67 kg CO\u003csub\u003e2\u003c/sub\u003e eq / FU in global warming potential. Additionally, it contributes to mitigating harm to the ozone layer, with a reduction of 0.00009 kg CFC11 eq / FU in stratospheric ozone depletion. Air quality improves due to reduced impact, as indicated by 1.37466 kg NO\u003csub\u003ex\u003c/sub\u003e eq / FU in terrestrial ecosystems and 1.39078 kg NO\u003csub\u003ex\u003c/sub\u003e eq / FU in human health ozone formation.The formation of fine particulate matter is lower, with 0.59305 kg PM\u003csub\u003e2.5\u003c/sub\u003e equivalent, suggesting decreased emissions of fine particle-increasing pollutants. Soil chemistry, plant life, and biodiversity face less harm due to the 1.41558 kg SO\u003csub\u003e2\u003c/sub\u003e / FU equivalent in terrestrial acidification. Freshwater and marine eutrophication are better managed, as evidenced by 0.01849 kg P\u003csub\u003eeq\u003c/sub\u003e / FU and 0.00387 kg N\u003csub\u003eeq\u003c/sub\u003e / FU, indicating more controlled nutrient runoff or emissions.\u003c/p\u003e \u003cp\u003eScenario VI also shows improved management of toxic emissions or effluents, with 637.5 kg 1,4-DCB / FU in ecotoxicity across terrestrial, freshwater, and marine environments. Human carcinogenic and non-carcinogenic toxicity are addressed with lower emissions of harmful chemicals, at 6.52831 kg / FU and 28.7384 kg 1,4-DCB / FU, respectively. Resource use efficiency is highlighted by 1.51507 kg Cu eq / FU in mineral resource use and 64.13712 kg oil eq / FU in fossil resource use, pointing to more effective use or recycling. Ionizing radiation is minimized, with 1.34218 kBq Co-60 eq / FU, suggesting less use of radiative materials or better containment. Lastly, water consumption is optimized, with only 6.48111 m\u003csup\u003e3\u003c/sup\u003e / FU used, reflecting a commitment to conserving this vital resource.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;8a shows normalized data related to all scenarios in comparison with each other and the importance of each impact category at the midpoint level. According to Fig.\u0026nbsp;8a, environmental and human toxicities have the highest impact among all categories. Figure\u0026nbsp;8b presents the characterized data for all scenarios, comparing them individually at each midpoint level. In Fig.\u0026nbsp;8b, the highest scenario for each midpoint level is 100, and other scenarios are presented and compared to it. The base scenario exhibits the highest harmful impact across all categories, while the VI scenario emerges as the most favorable option in most midpoint assessments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;8. a) Normalized value comparison of all scenarios at midpoints level, b) comparison of all scenario to the highest scenario in each midpoint level.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;9 shows the environmental prices at every midpoint level for all scenarios. According to Fig.\u0026nbsp;9, the base scenario is not the worst scenario at all midpoint levels, but scenario VI is the best scenario at all midpoints. Among all scenarios, the midpoints of global warming potential and particulate matter formation account for most environmental costs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabh\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;9. Environmental price comparison of all scenarios at midpoints level.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;10a shows normalized data related to all scenarios in comparison with each other and the importance of each impact category at the endpoint level. Figure\u0026nbsp;10b presents the characterized data for all scenarios, comparing them individually at each endpoint level. Within each endpoint level, the paramount scenario illustrated in Fig.\u0026nbsp;10b is assigned a value of 100; the corollary scenarios are depicted in proportion to this benchmark. To Fig.\u0026nbsp;10a and b, it is discernible that the environmental impacts of the base scenario surpass those of the alternative scenarios, with Scenario VI being the most exemplary. The advancements in human health, ecosystems, and resources from the basic scenario to Scenario VI are quantified at 67.8%, 65.9%, and 91.6%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabi\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;10. a) normalized value comparison of all scenarios at endpoints level, b) comparison of all scenario to the highest scenario in each endpoint level.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Perspective comparison:\u003c/h2\u003e \u003cp\u003eIn Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e, LCA using the ReCiPe method was categorized into three perspectives: hierarchist, individualist, and egalitarian. The assumptions behind each of these perspectives impact how the calculations proceed. In this section, a comparison of viewpoints was applied to scenario I and scenario VI. The characterized and normalized data for these circumstances from multiple perspectives are displayed in Table\u0026nbsp;5S. The results show no significant differences in key categories, including land use, water consumption, freshwater eutrophication, marine eutrophication, terrestrial ecosystems ozone formation, human health ozone formation, fossil resource scarcity, and terrestrial acidification. However, other impact categories exhibit distinct differences. For global warming, the individualist perspective indicates a more harmful impact compared to the egalitarian and hierarchist perspectives. Because many of the pollutants that contributed to global warming were eliminated over a long time horizon, but from an individual standpoint, a short time horizon (20 years) was adopted (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding mineral resource scarcity, the individualist perspective holds less value than the hierarchist and egalitarian perspectives. In hierarchical and egalitarian views, it is assumed that the estimation should be done based on extracting the total mineral resources available in the Earth’s crust. However, in the individualistic view, the analysis is based on the amount of extraction at the time of calculation and with the available technologies (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The formation of fine particulate matter is similar to the scarcity of mineral resources. The individualist perspective reported less value than the hierarchist and egalitarian perspectives. While the hierarchist and egalitarian viewpoints acknowledge that secondary aerosols contribute to particle formation, the individualist perspective contends that only primary aerosols impact fine particle formation (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), (Avinal and Ergenekon 2022).\u003c/p\u003e \u003cp\u003eIn the remaining midpoint impact categories, such as stratospheric ozone depletion, ionizing radiation, freshwater environmental toxicity, marine environmental toxicity, terrestrial environmental toxicity, human carcinogenic toxicity, and human and non-carcinogenic toxicity, the egalitarian perspective calculates more influence than the hierarchical and individualist perspectives. The egalitarian perspective considers a longer time horizon than the hierarchical perspective, and the hierarchical perspective, in turn, has a longer time horizon than the individualistic perspective. In addition, the egalitarian view considers more diseases caused by pollutants compared to the hierarchical and individualistic view, and the egalitarian perspective considers more protection in different cases (M. Huijbregts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The hierarchist viewpoint assumes that society prefers effects that are long-lasting, global, and certain. It is predicated on the belief that society will manage environmental problems with caution and is based on an expert consensus (Dong and Ng 2014). The individualist viewpoint assumes that, society favors immediate, focused, and local effects. It is predicated on an market-driven perspective and presumes that society will handle environmental effects following short-term requirements and advantages (Dong and Ng 2014). The egalitarian view assumes that society prefers effects that are broad, ambiguous, and long-term. Its foundation lies in a perspective that prioritizes fairness and equality, assuming that even in the face of uncertainty, society will manage environmental repercussions with caution (Dong and Ng 2014). The egalitarian approach reported the highest value among all endpoint groups, with the hierarchical and individualistic approaches following in second and third place, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBeyond its role as a significant driver of economic growth, the steel industry exerts a substantial impact on pollution, energy consumption, CO\u003csub\u003e2\u003c/sub\u003e emissions, and water usage. In response to the environmental consequences associated with traditional steel making methods (such as BF-BOF process), there has been a shift toward more sustainable approaches, including DRI-EAF. Iran is among the world's leaders in the steel production using this technique. Due to Iran's plentiful gas supplies, gas-based DRI production is the primary method for producing this popular feedstock for steel making in EAF. The global production of DRI has been increasing steadily, according to the MIDREX method.\u003c/p\u003e\u003cp\u003eA valuable technique for evaluating the environmental effects of steel production processes is LCA. Additionally, the steelmaker might become more ecologically conscious and sustainable due to its findings. To promote sustainable development, governments and individuals can make informed decisions by understanding the costs associated with their environmental impact. The study conducts a LCA using SIMAPRO software to investigate the environmental costs and effects of producing steel in an electric arc furnace and direct reduction of iron (MIDREX). For LCA, the ReCiPe and Environmental Prices methods were applied. The environmental costs of producing steel were assessed using the Environmental Prices Method. ReCiPe evaluated the effects on the environment at three endpoints and eighteen middle levels.\u003c/p\u003e\u003cp\u003eThe relevance of sustainable practices and environmental responsibility in the manufacturing sector is emphasized as suggestions are given to lower environmental costs and impacts in the steel production process. According to the base scenario LCA, the leadingcauses of the expenses and environmental impacts were energy use and sponge iron consumption. Utilizing sponge iron and using electricity at the base scenario's endpoints contributed almost 70%, 50%, and 57% to resources, ecosystems, and human health, respectively. Using electricity and sponge iron contributed more than 50% and 10%, respectively, to the majority of the base scenario's midpoints. The procedure had an environmental cost of 462.71669 euros.\u003c/p\u003e\u003cp\u003eThe steel industry must implement more environmentally friendly procedures to reduce pollution and advance long-term sustainability. Making particular rules and choices about the use of electricity (using solar power instead of traditional gas-based electricity) and sponge iron (using scrap instead of sponge iron) is one effective way to manage the steel making process for sustainability. Six scenarios were compared, and the results indicated a considerable decrease in both environmental impacts and costs. The optimal situation showed a reduction of over 70% in all endpoints and over 60% in the majority of midpoints. The prices for the environment were lowered to 167.69458 euros.\u003c/p\u003e\u003cp\u003eThe LCA results were directly influenced by the chosen perspective within the ReCiPe method. Each perspective has distinct conversion factors for translating inputs and outputs into midpoints and endpoints. At each midpoint, the highest value was provided by one of these perspectives. At the endpoint level, the egalitarian perspective, however, continuously registered the highest value. After that, the individualist and hierarchist viewpoints came in second and third, respectively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1. Aref Ahmadian BaghbadaraniExpertise in life cycle assessment (LCA) modeling.Conceptualization, methodology, data analysis, and writing original draft.Design all the tables and figures.2. Dr. Khosro AshrafiContributed to data collection and interpretation.Assisted with literature review and article revision as the supervisor.3. Dr. Abdolreza Karbasireviewed the manuscript.Assisted with literature review as an advisor.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAvinal, Aysegul, and Pinar Ergenekon. 2022. \u0026ldquo;Life Cycle Impacts of Induction Furnace Technology for Crude Steel Production: Case Study.\u0026rdquo; \u003cem\u003eEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects\u003c/em\u003e 44(4): 9974\u0026ndash;87. https://www.tandfonline.com/doi/abs/10.1080/15567036.2022.2143946 (April 24, 2024).\u003c/li\u003e\n\u003cli\u003eBurchart-Korol, Dorota. 2013. \u0026ldquo;Life Cycle Assessment of Steel Production in Poland: A Case Study.\u0026rdquo; \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e 54: 235\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eChen, Qianqian et al. 2018. \u0026ldquo;Assessment of Low-Carbon Iron and Steel Production with CO2 Recycling and Utilization Technologies: A Case Study in China.\u0026rdquo; \u003cem\u003eApplied Energy\u003c/em\u003e 220: 192\u0026ndash;207.\u003c/li\u003e\n\u003cli\u003ede, Koning A et al. 2002. \u0026ldquo;Handbook on Life Cycle Assessment Operational Guide to the ISO Standards.\u0026rdquo; \u003cem\u003eThe International Journal of Life Cycle Assessment 2002 7:5\u003c/em\u003e 7(5): 311\u0026ndash;13. https://link.springer.com/article/10.1007/BF02978897 (April 24, 2024).\u003c/li\u003e\n\u003cli\u003eDiwekar, Urmila, and Yogendra Shastri. 2011. \u0026ldquo;Design for Environment: A State-of-the-Art Review.\u0026rdquo; \u003cem\u003eClean Technologies and Environmental Policy\u003c/em\u003e 13(2): 227\u0026ndash;40. https://link.springer.com/article/10.1007/s10098-010-0320-6 (April 24, 2024).\u003c/li\u003e\n\u003cli\u003eDong, Ya Hong, and S. 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Ponsioen, Mark J. Goedkoop, and Mark A.J. Huijbregts. 2017. \u0026ldquo;Surplus Ore Potential as a Scarcity Indicator for Resource Extraction.\u0026rdquo; \u003cem\u003eJournal of Industrial Ecology\u003c/em\u003e 21(2): 381\u0026ndash;90. https://onlinelibrary.wiley.com/doi/full/10.1111/jiec.12444 (April 24, 2024).\u003c/li\u003e\n\u003cli\u003eYilmaz, Ozge, Annick Anctil, and Tanju Karanfil. 2015. \u0026ldquo;LCA as a Decision Support Tool for Evaluation of Best Available Techniques (BATs) for Cleaner Production of Iron Casting.\u0026rdquo; \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e 105: 337\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003evan Zelm, Rosalie et al. 2016. \u0026ldquo;Regionalized Life Cycle Impact Assessment of Air Pollution on the Global Scale: Damage to Human Health and Vegetation.\u0026rdquo; \u003cem\u003eAtmospheric Environment\u003c/em\u003e 134: 129\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eZhu, Bangzhu et al. 2020. \u0026ldquo;Exploring the Effect of Carbon Trading Mechanism on China\u0026rsquo;s Green Development Efficiency: A Novel Integrated Approach.\u0026rdquo; \u003cem\u003eEnergy Economics\u003c/em\u003e 85: 104601.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e CML-IA is a LCA methodology developed by the Center of Environmental Science (CML) of Leiden University in The Netherlands.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The ReCiPe method is a LCIA method that it was created by RIVM, Radboud University, Norwegian University of Science and Technology and PR\u0026eacute; Consultants.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The IMPACT 2002 methodology for LCIA introduces a practical approach that combines midpoint and damage assessment. It connects various LCI results (including elementary flows and other interventions) through 14 midpoint categories, ultimately leading to four damage categories.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e IPCC was developed by the Intergovernmental Panel on Climate Change. It contains the climate change factors of IPCC.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The Cumulative Energy Demand (LHV) method was created by PR\u0026eacute; Consultants based on data published by ecoinvents for raw materials available in the SimaPro database.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"materials-circular-economy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mate","sideBox":"Learn more about [Materials Circular Economy](https://www.springer.com/journal/42824)","snPcode":"42824","submissionUrl":"https://submission.springernature.com/new-submission/42824/3","title":"Materials Circular Economy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"life cycle assessment, environmental cost, ReCiPe method, electric arc furnace, MIDREX iron reduction, steel making","lastPublishedDoi":"10.21203/rs.3.rs-4930754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4930754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe life cycle assessment (LCA) is a powerful tool for evaluating environmental impacts and costs. In this study, LCA was applied to steel production, specifically focusing on the electric arc furnace (EAF) and the Midrex direct reduction of iron ore. The functional unit considered is one tonne of molten steel extracted from the EAF. EAF inputs mainly consist of sponge iron with a 90:10 proportion of sponge iron to scrap. The study employs the ReCiPe (H) 2016 V1.1 method for LCA, and environmental cost calculations utilize the Environmental Prices method. The total environmental costs, normalized midpoint impacts, and normalized endpoint impacts amount to 462.72 euros, 8.11 pt and, 0.13 pt, respectively. The analysis of steel production identifies three principal stages: Sponge Iron Consumption, Electricity Consumption, Other Inputs and Outputs Associated with Steel Production. Notably, electricity consumption and sponge iron usage account for approximately 70% and 75% of the impacts on midpoints and endpoints, respectively, as well as 75% of the total environmental costs. Making specific choices\u0026mdash;such as using solar power instead of traditional gas-based electricity and scrap instead of sponge iron\u0026mdash;can effectively enhance the sustainability of the steel-making process. The scenario VI, when compared to other scenarios, results in the following reductions: Midpoint Impacts: 5.03 pt, Endpoint Impacts: 0.04 pt, Environmental Costs: 167.69 euros. Regarding the ReCiPe method, it was assessed from various perspectives. The egalitarian perspective consistently demonstrated the highest value at the endpoint level, followed by the hierarchist and individualist viewpoints.\u003c/p\u003e","manuscriptTitle":"Sustainable Steel Production: A Comprehensive LCA Approach for Reducing Environmental Costs and Impacts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-17 09:24:39","doi":"10.21203/rs.3.rs-4930754/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-12T00:56:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T17:00:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-31T18:21:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22504938349728298706714692302486804081","date":"2024-10-21T03:16:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116569173895717066724035612380665163215","date":"2024-10-21T02:47:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-20T13:38:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-20T04:25:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-20T04:25:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Materials Circular Economy","date":"2024-08-17T17:05:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"materials-circular-economy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mate","sideBox":"Learn more about [Materials Circular Economy](https://www.springer.com/journal/42824)","snPcode":"42824","submissionUrl":"https://submission.springernature.com/new-submission/42824/3","title":"Materials Circular Economy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f99995d6-04b3-440b-9290-84755c9deb56","owner":[],"postedDate":"September 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:09:18+00:00","versionOfRecord":{"articleIdentity":"rs-4930754","link":"https://doi.org/10.1007/s42824-025-00164-x","journal":{"identity":"materials-circular-economy","isVorOnly":false,"title":"Materials Circular Economy"},"publishedOn":"2025-04-12 16:05:25","publishedOnDateReadable":"April 12th, 2025"},"versionCreatedAt":"2024-09-17 09:24:39","video":"","vorDoi":"10.1007/s42824-025-00164-x","vorDoiUrl":"https://doi.org/10.1007/s42824-025-00164-x","workflowStages":[]},"version":"v1","identity":"rs-4930754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4930754","identity":"rs-4930754","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}